Razvan27/ML4SE-Python · Datasets at Hugging Face

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26,200	__init__.py	numenta_nupic-legacy/tests/swarming/nupic/swarming/__init__.py	# ---------------------------------------------------------------------- # Numenta Platform for Intelligent Computing (NuPIC) # Copyright (C) 2013, Numenta, Inc. Unless you have an agreement # with Numenta, Inc., for a separate license for this software code, the # following terms and conditions apply: # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU Affero Public License version 3 as # published by the Free Software Foundation. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. # See the GNU Affero Public License for more details. # # You should have received a copy of the GNU Affero Public License # along with this program. If not, see http://www.gnu.org/licenses. # # http://numenta.org/licenses/ # ----------------------------------------------------------------------	976	Python	.py	20	47.8	72	0.665272	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,201	swarming_test.py	numenta_nupic-legacy/tests/swarming/nupic/swarming/swarming_test.py	# ---------------------------------------------------------------------- # Numenta Platform for Intelligent Computing (NuPIC) # Copyright (C) 2013, Numenta, Inc. Unless you have an agreement # with Numenta, Inc., for a separate license for this software code, the # following terms and conditions apply: # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU Affero Public License version 3 as # published by the Free Software Foundation. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. # See the GNU Affero Public License for more details. # # You should have received a copy of the GNU Affero Public License # along with this program. If not, see http://www.gnu.org/licenses. # # http://numenta.org/licenses/ # ---------------------------------------------------------------------- import sys import os import imp import subprocess import re import json import pprint import shutil import copy import StringIO import logging import itertools import numpy import time import math import uuid import tempfile from pkg_resources import resource_filename from optparse import OptionParser from nupic.database.client_jobs_dao import ClientJobsDAO from nupic.support import configuration, initLogging from nupic.support.unittesthelpers.testcasebase import (unittest, TestCaseBase as HelperTestCaseBase) from nupic.swarming import hypersearch_worker from nupic.swarming.api import getSwarmModelParams, createAndStartSwarm from nupic.swarming.utils import generatePersistentJobGUID from nupic.swarming.dummy_model_runner import OPFDummyModelRunner DEFAULT_JOB_TIMEOUT_SEC = 60 * 2 # Filters _debugOut messages g_debug = True # Our setUpModule entry block sets this to an instance of MyTestEnvironment() g_myEnv = None # These are the args after using the optparse # This value for the swarm maturity window gives more repeatable results for # unit tests that use multiple workers g_repeatableSwarmMaturityWindow = 5 class MyTestEnvironment(object): # ======================================================================= def __init__(self): # Save all command line options self.options = _ArgParser.parseArgs() # Create the path to our source experiments thisFile = __file__ testDir = os.path.split(os.path.abspath(thisFile))[0] self.testSrcExpDir = os.path.join(testDir, 'experiments') self.testSrcDataDir = os.path.join(testDir, 'data') return class ExperimentTestBaseClass(HelperTestCaseBase): def setUp(self): """ Method called to prepare the test fixture. This is called by the unittest framework immediately before calling the test method; any exception raised by this method will be considered an error rather than a test failure. The default implementation does nothing. """ pass def tearDown(self): """ Method called immediately after the test method has been called and the result recorded. This is called even if the test method raised an exception, so the implementation in subclasses may need to be particularly careful about checking internal state. Any exception raised by this method will be considered an error rather than a test failure. This method will only be called if the setUp() succeeds, regardless of the outcome of the test method. The default implementation does nothing. """ # Reset our log items self.resetExtraLogItems() def shortDescription(self): """ Override to force unittest framework to use test method names instead of docstrings in the report. """ return None def _printTestHeader(self): """ Print out what test we are running """ print "###############################################################" print "Running test: %s.%s..." % (self.__class__, self._testMethodName) def _setDataPath(self, env): """ Put the path to our datasets int the NTA_DATA_PATH variable which will be used to set the environment for each of the workers Parameters: --------------------------------------------------------------------- env: The current environment dict """ assert env is not None # If already have a path, concatenate to it if "NTA_DATA_PATH" in env: newPath = "%s%s%s" % (env["NTA_DATA_PATH"], os.pathsep, g_myEnv.testSrcDataDir) else: newPath = g_myEnv.testSrcDataDir env["NTA_DATA_PATH"] = newPath def _launchWorkers(self, cmdLine, numWorkers): """ Launch worker processes to execute the given command line Parameters: ----------------------------------------------- cmdLine: The command line for each worker numWorkers: number of workers to launch retval: list of workers """ workers = [] for i in range(numWorkers): stdout = tempfile.TemporaryFile() stderr = tempfile.TemporaryFile() p = subprocess.Popen(cmdLine, bufsize=1, env=os.environ, shell=True, stdin=None, stdout=stdout, stderr=stderr) workers.append(p) return workers def _getJobInfo(self, cjDAO, workers, jobID): """ Return the job info for a job Parameters: ----------------------------------------------- cjDAO: client jobs database instance workers: list of workers for this job jobID: which job ID retval: job info """ # Get the job info jobInfo = cjDAO.jobInfo(jobID) # Since we're running outside of the Nupic engine, we launched the workers # ourself, so see how many are still running and jam the correct status # into the job info. When using the Nupic engine, it would do this # for us. runningCount = 0 for worker in workers: retCode = worker.poll() if retCode is None: runningCount += 1 if runningCount > 0: status = ClientJobsDAO.STATUS_RUNNING else: status = ClientJobsDAO.STATUS_COMPLETED jobInfo = jobInfo._replace(status=status) if status == ClientJobsDAO.STATUS_COMPLETED: jobInfo = jobInfo._replace( completionReason=ClientJobsDAO.CMPL_REASON_SUCCESS) return jobInfo def _generateHSJobParams(self, expDirectory=None, hsImp='v2', maxModels=2, predictionCacheMaxRecords=None, dataPath=None, maxRecords=10): """ This method generates a canned Hypersearch Job Params structure based on some high level options Parameters: --------------------------------------------------------------------- predictionCacheMaxRecords: If specified, determine the maximum number of records in the prediction cache. dataPath: When expDirectory is not specified, this is the data file to be used for the operation. If this value is not specified, it will use the /extra/qa/hotgym/qa_hotgym.csv. """ if expDirectory is not None: descriptionPyPath = os.path.join(expDirectory, "description.py") permutationsPyPath = os.path.join(expDirectory, "permutations.py") permutationsPyContents = open(permutationsPyPath, 'r').read() descriptionPyContents = open(descriptionPyPath, 'r').read() jobParams = {'persistentJobGUID' : generatePersistentJobGUID(), 'permutationsPyContents': permutationsPyContents, 'descriptionPyContents': descriptionPyContents, 'maxModels': maxModels, 'hsVersion': hsImp} if predictionCacheMaxRecords is not None: jobParams['predictionCacheMaxRecords'] = predictionCacheMaxRecords else: # Form the stream definition if dataPath is None: dataPath = resource_filename("nupic.data", os.path.join("extra", "qa", "hotgym", "qa_hotgym.csv")) streamDef = dict( version = 1, info = "TestHypersearch", streams = [ dict(source="file://%s" % (dataPath), info=dataPath, columns=[""], first_record=0, last_record=maxRecords), ], ) # Generate the experiment description expDesc = { "predictionField": "consumption", "streamDef": streamDef, "includedFields": [ { "fieldName": "gym", "fieldType": "string" }, { "fieldName": "consumption", "fieldType": "float", "minValue": 0, "maxValue": 200, }, ], "iterationCount": maxRecords, "resetPeriod": { 'weeks': 0, 'days': 0, 'hours': 8, 'minutes': 0, 'seconds': 0, 'milliseconds': 0, 'microseconds': 0, }, } jobParams = { "persistentJobGUID": generatePersistentJobGUID(), "description":expDesc, "maxModels": maxModels, "hsVersion": hsImp, } if predictionCacheMaxRecords is not None: jobParams['predictionCacheMaxRecords'] = predictionCacheMaxRecords return jobParams def _runPermutationsLocal(self, jobParams, loggingLevel=logging.INFO, env=None, waitForCompletion=True, continueJobId=None, ignoreErrModels=False): """ This runs permutations on the given experiment using just 1 worker in the current process Parameters: ------------------------------------------------------------------- jobParams: filled in job params for a hypersearch loggingLevel: logging level to use in the Hypersearch worker env: if not None, this is a dict of environment variables that should be sent to each worker process. These can aid in re-using the same description/permutations file for different tests. waitForCompletion: If True, wait for job to complete before returning If False, then return resultsInfoForAllModels and metricResults will be None continueJobId: If not None, then this is the JobId of a job we want to continue working on with another worker. ignoreErrModels: If true, ignore erred models retval: (jobId, jobInfo, resultsInfoForAllModels, metricResults) """ print print "==================================================================" print "Running Hypersearch job using 1 worker in current process" print "==================================================================" # Plug in modified environment variables if env is not None: saveEnvState = copy.deepcopy(os.environ) os.environ.update(env) # Insert the job entry into the database in the pre-running state cjDAO = ClientJobsDAO.get() if continueJobId is None: jobID = cjDAO.jobInsert(client='test', cmdLine='<started manually>', params=json.dumps(jobParams), alreadyRunning=True, minimumWorkers=1, maximumWorkers=1, jobType = cjDAO.JOB_TYPE_HS) else: jobID = continueJobId # Command line args. args = ['ignoreThis', '--jobID=%d' % (jobID), '--logLevel=%d' % (loggingLevel)] if continueJobId is None: args.append('--clearModels') # Run it in the current process try: hypersearch_worker.main(args) # The dummy model runner will call sys.exit(0) when # NTA_TEST_sysExitAfterNIterations is set except SystemExit: pass except: raise # Restore environment if env is not None: os.environ = saveEnvState # ---------------------------------------------------------------------- # Make sure all models completed successfully models = cjDAO.modelsGetUpdateCounters(jobID) modelIDs = [model.modelId for model in models] if len(modelIDs) > 0: results = cjDAO.modelsGetResultAndStatus(modelIDs) else: results = [] metricResults = [] for result in results: if result.results is not None: metricResults.append(json.loads(result.results)[1].values()[0]) else: metricResults.append(None) if not ignoreErrModels: self.assertNotEqual(result.completionReason, cjDAO.CMPL_REASON_ERROR, "Model did not complete successfully:\n%s" % (result.completionMsg)) # Print worker completion message jobInfo = cjDAO.jobInfo(jobID) return (jobID, jobInfo, results, metricResults) def _runPermutationsCluster(self, jobParams, loggingLevel=logging.INFO, maxNumWorkers=4, env=None, waitForCompletion=True, ignoreErrModels=False, timeoutSec=DEFAULT_JOB_TIMEOUT_SEC): """ Given a prepared, filled in jobParams for a hypersearch, this starts the job, waits for it to complete, and returns the results for all models. Parameters: ------------------------------------------------------------------- jobParams: filled in job params for a hypersearch loggingLevel: logging level to use in the Hypersearch worker maxNumWorkers: max # of worker processes to use env: if not None, this is a dict of environment variables that should be sent to each worker process. These can aid in re-using the same description/permutations file for different tests. waitForCompletion: If True, wait for job to complete before returning If False, then return resultsInfoForAllModels and metricResults will be None ignoreErrModels: If true, ignore erred models retval: (jobID, jobInfo, resultsInfoForAllModels, metricResults) """ print print "==================================================================" print "Running Hypersearch job on cluster" print "==================================================================" # -------------------------------------------------------------------- # Submit the job if env is not None and len(env) > 0: envItems = [] for (key, value) in env.iteritems(): if (sys.platform.startswith('win')): envItems.append("set \"%s=%s\"" % (key, value)) else: envItems.append("export %s=%s" % (key, value)) if (sys.platform.startswith('win')): envStr = "%s &" % (' & '.join(envItems)) else: envStr = "%s;" % (';'.join(envItems)) else: envStr = '' cmdLine = '%s python -m nupic.swarming.hypersearch_worker ' \ '--jobID={JOBID} --logLevel=%d' \ % (envStr, loggingLevel) cjDAO = ClientJobsDAO.get() jobID = cjDAO.jobInsert(client='test', cmdLine=cmdLine, params=json.dumps(jobParams), minimumWorkers=1, maximumWorkers=maxNumWorkers, jobType = cjDAO.JOB_TYPE_HS) # Launch the workers ourself if necessary (no nupic engine running). workerCmdLine = '%s python -m nupic.swarming.hypersearch_worker ' \ '--jobID=%d --logLevel=%d' \ % (envStr, jobID, loggingLevel) workers = self._launchWorkers(cmdLine=workerCmdLine, numWorkers=maxNumWorkers) print "Successfully submitted new test job, jobID=%d" % (jobID) print "Each of %d workers executing the command line: " % (maxNumWorkers), \ cmdLine if not waitForCompletion: return (jobID, None, None) if timeoutSec is None: timeout=DEFAULT_JOB_TIMEOUT_SEC else: timeout=timeoutSec # -------------------------------------------------------------------- # Wait for it to complete startTime = time.time() lastUpdate = time.time() lastCompleted = 0 lastCompletedWithError = 0 lastCompletedAsOrphan = 0 lastStarted = 0 lastJobStatus = "NA" lastJobResults = None lastActiveSwarms = None lastEngStatus = None modelIDs = [] print "\n%-15s %-15s %-15s %-15s %-15s" % ("jobStatus", "modelsStarted", "modelsCompleted", "modelErrs", "modelOrphans") print "-------------------------------------------------------------------" while (lastJobStatus != ClientJobsDAO.STATUS_COMPLETED) \ and (time.time() - lastUpdate < timeout): printUpdate = False if g_myEnv.options.verbosity == 0: time.sleep(0.5) # -------------------------------------------------------------------- # Get the job status jobInfo = self._getJobInfo(cjDAO, workers, jobID) if jobInfo.status != lastJobStatus: if jobInfo.status == ClientJobsDAO.STATUS_RUNNING \ and lastJobStatus != ClientJobsDAO.STATUS_RUNNING: print "# Swarm job now running. jobID=%s" \ % (jobInfo.jobId) lastJobStatus = jobInfo.status printUpdate = True if g_myEnv.options.verbosity >= 1: if jobInfo.engWorkerState is not None: activeSwarms = json.loads(jobInfo.engWorkerState)['activeSwarms'] if activeSwarms != lastActiveSwarms: #print "-------------------------------------------------------" print ">> Active swarms:\n ", '\n '.join(activeSwarms) lastActiveSwarms = activeSwarms print if jobInfo.results != lastJobResults: #print "-------------------------------------------------------" print ">> New best:", jobInfo.results, "###" lastJobResults = jobInfo.results if jobInfo.engStatus != lastEngStatus: print '>> Status: "%s"' % jobInfo.engStatus print lastEngStatus = jobInfo.engStatus # -------------------------------------------------------------------- # Get the list of models created for this job modelCounters = cjDAO.modelsGetUpdateCounters(jobID) if len(modelCounters) != lastStarted: modelIDs = [x.modelId for x in modelCounters] lastStarted = len(modelCounters) printUpdate = True # -------------------------------------------------------------------- # See how many have finished if len(modelIDs) > 0: completed = 0 completedWithError = 0 completedAsOrphan = 0 infos = cjDAO.modelsGetResultAndStatus(modelIDs) for info in infos: if info.status == ClientJobsDAO.STATUS_COMPLETED: completed += 1 if info.completionReason == ClientJobsDAO.CMPL_REASON_ERROR: completedWithError += 1 if info.completionReason == ClientJobsDAO.CMPL_REASON_ORPHAN: completedAsOrphan += 1 if completed != lastCompleted \ or completedWithError != lastCompletedWithError \ or completedAsOrphan != lastCompletedAsOrphan: lastCompleted = completed lastCompletedWithError = completedWithError lastCompletedAsOrphan = completedAsOrphan printUpdate = True # -------------------------------------------------------------------- # Print update? if printUpdate: lastUpdate = time.time() if g_myEnv.options.verbosity >= 1: print ">>", print "%-15s %-15d %-15d %-15d %-15d" % (lastJobStatus, lastStarted, lastCompleted, lastCompletedWithError, lastCompletedAsOrphan) # ======================================================================== # Final total print "\n<< %-15s %-15d %-15d %-15d %-15d" % (lastJobStatus, lastStarted, lastCompleted, lastCompletedWithError, lastCompletedAsOrphan) # Success? jobInfo = self._getJobInfo(cjDAO, workers, jobID) if not ignoreErrModels: self.assertEqual (jobInfo.completionReason, ClientJobsDAO.CMPL_REASON_SUCCESS) # Get final model results models = cjDAO.modelsGetUpdateCounters(jobID) modelIDs = [model.modelId for model in models] if len(modelIDs) > 0: results = cjDAO.modelsGetResultAndStatus(modelIDs) else: results = [] metricResults = [] for result in results: if result.results is not None: metricResults.append(json.loads(result.results)[1].values()[0]) else: metricResults.append(None) if not ignoreErrModels: self.assertNotEqual(result.completionReason, cjDAO.CMPL_REASON_ERROR, "Model did not complete successfully:\n%s" % (result.completionMsg)) return (jobID, jobInfo, results, metricResults) def runPermutations(self, expDirectory, hsImp='v2', maxModels=2, maxNumWorkers=4, loggingLevel=logging.INFO, onCluster=False, env=None, waitForCompletion=True, continueJobId=None, dataPath=None, maxRecords=None, timeoutSec=None, ignoreErrModels=False, predictionCacheMaxRecords=None, kwargs): """ This runs permutations on the given experiment using just 1 worker Parameters: ------------------------------------------------------------------- expDirectory: directory containing the description.py and permutations.py hsImp: which implementation of Hypersearch to use maxModels: max # of models to generate maxNumWorkers: max # of workers to use, N/A if onCluster is False loggingLevel: logging level to use in the Hypersearch worker onCluster: if True, run on the Hadoop cluster env: if not None, this is a dict of environment variables that should be sent to each worker process. These can aid in re-using the same description/permutations file for different tests. waitForCompletion: If True, wait for job to complete before returning If False, then return resultsInfoForAllModels and metricResults will be None continueJobId: If not None, then this is the JobId of a job we want to continue working on with another worker. ignoreErrModels: If true, ignore erred models maxRecords: This value is passed to the function, _generateHSJobParams(), to represent the maximum number of records to generate for the operation. dataPath: This value is passed to the function, _generateHSJobParams(), which points to the data file for the operation. predictionCacheMaxRecords: If specified, determine the maximum number of records in the prediction cache. retval: (jobID, jobInfo, resultsInfoForAllModels, metricResults, minErrScore) """ # Put in the path to our datasets if env is None: env = dict() self._setDataPath(env) # ---------------------------------------------------------------- # Prepare the jobParams jobParams = self._generateHSJobParams(expDirectory=expDirectory, hsImp=hsImp, maxModels=maxModels, maxRecords=maxRecords, dataPath=dataPath, predictionCacheMaxRecords=predictionCacheMaxRecords) jobParams.update(kwargs) if onCluster: (jobID, jobInfo, resultInfos, metricResults) \ = self._runPermutationsCluster(jobParams=jobParams, loggingLevel=loggingLevel, maxNumWorkers=maxNumWorkers, env=env, waitForCompletion=waitForCompletion, ignoreErrModels=ignoreErrModels, timeoutSec=timeoutSec) else: (jobID, jobInfo, resultInfos, metricResults) \ = self._runPermutationsLocal(jobParams=jobParams, loggingLevel=loggingLevel, env=env, waitForCompletion=waitForCompletion, continueJobId=continueJobId, ignoreErrModels=ignoreErrModels) if not waitForCompletion: return (jobID, jobInfo, resultInfos, metricResults, None) # Print job status print "\n------------------------------------------------------------------" print "Hadoop completion reason: %s" % (jobInfo.completionReason) print "Worker completion reason: %s" % (jobInfo.workerCompletionReason) print "Worker completion msg: %s" % (jobInfo.workerCompletionMsg) if jobInfo.engWorkerState is not None: print "\nEngine worker state:" print "---------------------------------------------------------------" pprint.pprint(json.loads(jobInfo.engWorkerState)) # Print out best results minErrScore=None metricAmts = [] for result in metricResults: if result is None: metricAmts.append(numpy.inf) else: metricAmts.append(result) metricAmts = numpy.array(metricAmts) if len(metricAmts) > 0: minErrScore = metricAmts.min() minModelID = resultInfos[metricAmts.argmin()].modelId # Get model info cjDAO = ClientJobsDAO.get() modelParams = cjDAO.modelsGetParams([minModelID])[0].params print "Model params for best model: \n%s" \ % (pprint.pformat(json.loads(modelParams))) print "Best model result: %f" % (minErrScore) else: print "No models finished" return (jobID, jobInfo, resultInfos, metricResults, minErrScore) class OneNodeTests(ExperimentTestBaseClass): """ """ # AWS tests attribute required for tagging via automatic test discovery via # nosetests engineAWSClusterTest=True def setUp(self): super(OneNodeTests, self).setUp() if not g_myEnv.options.runInProc: self.skipTest("Skipping One Node test since runInProc is not specified") def testSimpleV2(self, onCluster=False, env=None, kwargs): """ Try running simple permutations """ self._printTestHeader() expDir = os.path.join(g_myEnv.testSrcExpDir, 'simpleV2') # Test it out if env is None: env = dict() env["NTA_TEST_numIterations"] = '99' env["NTA_CONF_PROP_nupic_hypersearch_swarmMaturityWindow"] = \ '%d' % (g_repeatableSwarmMaturityWindow) (jobID, jobInfo, resultInfos, metricResults, minErrScore) \ = self.runPermutations(expDir, hsImp='v2', loggingLevel=g_myEnv.options.logLevel, onCluster=onCluster, env=env, maxModels=None, kwargs) self.assertEqual(minErrScore, 20) self.assertLess(len(resultInfos), 350) return def testDeltaV2(self, onCluster=False, env=None, kwargs): """ Try running a simple permutations with delta encoder Test which tests the delta encoder. Runs a swarm of the sawtooth dataset With a functioning delta encoder this should give a perfect result DEBUG: disabled temporarily because this test takes too long!!! """ self._printTestHeader() expDir = os.path.join(g_myEnv.testSrcExpDir, 'delta') # Test it out if env is None: env = dict() env["NTA_CONF_PROP_nupic_hypersearch_swarmMaturityWindow"] = \ '%d' % (g_repeatableSwarmMaturityWindow) env["NTA_TEST_exitAfterNModels"] = str(20) (jobID, jobInfo, resultInfos, metricResults, minErrScore) \ = self.runPermutations(expDir, hsImp='v2', loggingLevel=g_myEnv.options.logLevel, onCluster=onCluster, env=env, maxModels=None, kwargs) self.assertLess(minErrScore, 0.002) return def testSimpleV2NoSpeculation(self, onCluster=False, env=None, kwargs): """ Try running a simple permutations """ self._printTestHeader() expDir = os.path.join(g_myEnv.testSrcExpDir, 'simpleV2') # Test it out if env is None: env = dict() env["NTA_TEST_numIterations"] = '99' env["NTA_CONF_PROP_nupic_hypersearch_swarmMaturityWindow"] = \ '%d' % (g_repeatableSwarmMaturityWindow) (jobID, jobInfo, resultInfos, metricResults, minErrScore) \ = self.runPermutations(expDir, hsImp='v2', loggingLevel=g_myEnv.options.logLevel, onCluster=onCluster, env=env, maxModels=None, speculativeParticles=False, kwargs) self.assertEqual(minErrScore, 20) self.assertGreater(len(resultInfos), 1) self.assertLess(len(resultInfos), 350) return def testHTMPredictionModelV2(self, onCluster=False, env=None, maxModels=2, kwargs): """ Try running a simple permutations using an actual CLA model, not a dummy """ self._printTestHeader() expDir = os.path.join(g_myEnv.testSrcExpDir, 'dummyV2') # Test it out if env is None: env = dict() (jobID, jobInfo, resultInfos, metricResults, minErrScore) \ = self.runPermutations(expDir, hsImp='v2', loggingLevel=g_myEnv.options.logLevel, onCluster=onCluster, env=env, maxModels=maxModels, kwargs) self.assertEqual(len(resultInfos), maxModels) return def testCLAMultistepModel(self, onCluster=False, env=None, maxModels=2, kwargs): """ Try running a simple permutations using an actual CLA model, not a dummy """ self._printTestHeader() expDir = os.path.join(g_myEnv.testSrcExpDir, 'simple_cla_multistep') # Test it out if env is None: env = dict() (jobID, jobInfo, resultInfos, metricResults, minErrScore) \ = self.runPermutations(expDir, hsImp='v2', loggingLevel=g_myEnv.options.logLevel, onCluster=onCluster, env=env, maxModels=maxModels, kwargs) self.assertEqual(len(resultInfos), maxModels) return def testLegacyCLAMultistepModel(self, onCluster=False, env=None, maxModels=2, kwargs): """ Try running a simple permutations using an actual CLA model, not a dummy. This is a legacy CLA multi-step model that doesn't declare a separate 'classifierOnly' encoder for the predicted field. """ self._printTestHeader() expDir = os.path.join(g_myEnv.testSrcExpDir, 'legacy_cla_multistep') # Test it out if env is None: env = dict() (jobID, jobInfo, resultInfos, metricResults, minErrScore) \ = self.runPermutations(expDir, hsImp='v2', loggingLevel=g_myEnv.options.logLevel, onCluster=onCluster, env=env, maxModels=maxModels, kwargs) self.assertEqual(len(resultInfos), maxModels) return def testFilterV2(self, onCluster=False): """ Try running a simple permutations """ self._printTestHeader() expDir = os.path.join(g_myEnv.testSrcExpDir, 'simpleV2') # Don't allow the consumption encoder maxval to get to it's optimum # value (which is 250). This increases our errScore by +25. env = dict() env["NTA_TEST_maxvalFilter"] = '225' env["NTA_CONF_PROP_nupic_hypersearch_swarmMaturityWindow"] = '6' (jobID, jobInfo, resultInfos, metricResults, minErrScore) \ = self.runPermutations(expDir, hsImp='v2', loggingLevel=g_myEnv.options.logLevel, onCluster=onCluster, env=env, maxModels=None) self.assertEqual(minErrScore, 45) self.assertLess(len(resultInfos), 400) return def testLateWorker(self, onCluster=False): """ Try running a simple permutations where a worker comes in late, after the some models have already been evaluated """ self._printTestHeader() expDir = os.path.join(g_myEnv.testSrcExpDir, 'simpleV2') env = dict() env["NTA_CONF_PROP_nupic_hypersearch_swarmMaturityWindow"] = \ '%d' % (g_repeatableSwarmMaturityWindow) env["NTA_TEST_exitAfterNModels"] = '100' (jobID, jobInfo, resultInfos, metricResults, minErrScore) \ = self.runPermutations(expDir, hsImp='v2', loggingLevel=g_myEnv.options.logLevel, maxModels=None, onCluster=onCluster, env=env, waitForCompletion=True, ) self.assertEqual(len(resultInfos), 100) # Run another worker the rest of the way env.pop("NTA_TEST_exitAfterNModels") (jobID, jobInfo, resultInfos, metricResults, minErrScore) \ = self.runPermutations(expDir, hsImp='v2', loggingLevel=g_myEnv.options.logLevel, maxModels=None, onCluster=onCluster, env=env, waitForCompletion=True, continueJobId = jobID, ) self.assertEqual(minErrScore, 20) self.assertLess(len(resultInfos), 350) return def testOrphanedModel(self, onCluster=False, modelRange=(0,1)): """ Run a worker on a model for a while, then have it exit before the model finishes. Then, run another worker, which should detect the orphaned model. """ self._printTestHeader() expDir = os.path.join(g_myEnv.testSrcExpDir, 'simpleV2') # NTA_TEST_numIterations is watched by the dummyModelParams() method of # the permutations file. # NTA_TEST_sysExitModelRange is watched by the dummyModelParams() method of # the permutations file. It tells it to do a sys.exit() after so many # iterations. # We increase the swarm maturity window to make our unit tests more # repeatable. There is an element of randomness as to which model # parameter combinations get evaluated first when running with # multiple workers, so this insures that we can find the "best" model # that we expect to see in our unit tests. env = dict() env["NTA_TEST_numIterations"] = '2' env["NTA_TEST_sysExitModelRange"] = '%d,%d' % (modelRange[0], modelRange[1]) env["NTA_CONF_PROP_nupic_hypersearch_swarmMaturityWindow"] \ = '%d' % (g_repeatableSwarmMaturityWindow) (jobID, jobInfo, resultInfos, metricResults, minErrScore) \ = self.runPermutations(expDir, hsImp='v2', loggingLevel=g_myEnv.options.logLevel, maxModels=300, onCluster=onCluster, env=env, waitForCompletion=False, ) # At this point, we should have 1 model, still running (beg, end) = modelRange self.assertEqual(len(resultInfos), end) numRunning = 0 for res in resultInfos: if res.status == ClientJobsDAO.STATUS_RUNNING: numRunning += 1 self.assertEqual(numRunning, 1) # Run another worker the rest of the way, after delaying enough time to # generate an orphaned model env["NTA_CONF_PROP_nupic_hypersearch_modelOrphanIntervalSecs"] = '1' time.sleep(2) # Here we launch another worker to finish up the job. We set the maxModels # to 300 (200 something should be enough) in case the orphan detection is # not working, it will make sure we don't loop for excessively long. # With orphan detection working, we should detect that the first model # would never complete, orphan it, and create a new one in the 1st sprint. # Without orphan detection working, we will wait forever for the 1st sprint # to finish, and will create a bunch of gen 1, then gen2, then gen 3, etc. # and gen 0 will never finish, so the swarm will never mature. (jobID, jobInfo, resultInfos, metricResults, minErrScore) \ = self.runPermutations(expDir, hsImp='v2', loggingLevel=g_myEnv.options.logLevel, maxModels=300, onCluster=onCluster, env=env, waitForCompletion=True, continueJobId = jobID, ) self.assertEqual(minErrScore, 20) self.assertLess(len(resultInfos), 350) return def testOrphanedModelGen1(self): """ Run a worker on a model for a while, then have it exit before a model finishes in gen index 2. Then, run another worker, which should detect the orphaned model. """ self._printTestHeader() inst = OneNodeTests(self._testMethodName) return inst.testOrphanedModel(modelRange=(10,11)) def testErredModel(self, onCluster=False, modelRange=(6,7)): """ Run with 1 or more models generating errors """ self._printTestHeader() expDir = os.path.join(g_myEnv.testSrcExpDir, 'simpleV2') # We increase the swarm maturity window to make our unit tests more # repeatable. There is an element of randomness as to which model # parameter combinations get evaluated first when running with # multiple workers, so this insures that we can find the "best" model # that we expect to see in our unit tests. env = dict() env["NTA_TEST_errModelRange"] = '%d,%d' % (modelRange[0], modelRange[1]) env["NTA_CONF_PROP_nupic_hypersearch_swarmMaturityWindow"] \ = '%d' % (g_repeatableSwarmMaturityWindow) (jobID, jobInfo, resultInfos, metricResults, minErrScore) \ = self.runPermutations(expDir, hsImp='v2', loggingLevel=g_myEnv.options.logLevel, onCluster=onCluster, env=env, maxModels=None, ignoreErrModels=True ) self.assertEqual(minErrScore, 20) self.assertLess(len(resultInfos), 350) return def testJobFailModel(self, onCluster=False, modelRange=(6,7)): """ Run with 1 or more models generating jobFail exception """ self._printTestHeader() expDir = os.path.join(g_myEnv.testSrcExpDir, 'simpleV2') # We increase the swarm maturity window to make our unit tests more # repeatable. There is an element of randomness as to which model # parameter combinations get evaluated first when running with # multiple workers, so this insures that we can find the "best" model # that we expect to see in our unit tests. env = dict() env["NTA_TEST_jobFailErr"] = 'True' maxNumWorkers = 4 (jobID, jobInfo, resultInfos, metricResults, minErrScore) \ = self.runPermutations(expDir, hsImp='v2', loggingLevel=g_myEnv.options.logLevel, onCluster=onCluster, env=env, maxModels=None, maxNumWorkers=maxNumWorkers, ignoreErrModels=True ) # Make sure workerCompletionReason was error self.assertEqual (jobInfo.workerCompletionReason, ClientJobsDAO.CMPL_REASON_ERROR) self.assertLess (len(resultInfos), maxNumWorkers+1) return def testTooManyErredModels(self, onCluster=False, modelRange=(5,10)): """ Run with too many models generating errors """ self._printTestHeader() expDir = os.path.join(g_myEnv.testSrcExpDir, 'simpleV2') # We increase the swarm maturity window to make our unit tests more # repeatable. There is an element of randomness as to which model # parameter combinations get evaluated first when running with # multiple workers, so this insures that we can find the "best" model # that we expect to see in our unit tests. env = dict() env["NTA_TEST_errModelRange"] = '%d,%d' % (modelRange[0], modelRange[1]) env["NTA_CONF_PROP_nupic_hypersearch_swarmMaturityWindow"] \ = '%d' % (g_repeatableSwarmMaturityWindow) (jobID, jobInfo, resultInfos, metricResults, minErrScore) \ = self.runPermutations(expDir, hsImp='v2', loggingLevel=g_myEnv.options.logLevel, onCluster=onCluster, env=env, maxModels=None, ignoreErrModels=True ) self.assertEqual (jobInfo.workerCompletionReason, ClientJobsDAO.CMPL_REASON_ERROR) return def testFieldThreshold(self, onCluster=False, env=None, kwargs): """ Test minimum field contribution threshold for a field to be included in further sprints """ self._printTestHeader() expDir = os.path.join(g_myEnv.testSrcExpDir, 'field_threshold_temporal') # Test it out if env is None: env = dict() env["NTA_TEST_numIterations"] = '99' env["NTA_CONF_PROP_nupic_hypersearch_swarmMaturityWindow"] = \ '%d' % (g_repeatableSwarmMaturityWindow) env["NTA_CONF_PROP_nupic_hypersearch_max_field_branching"] = \ '%d' % (0) env["NTA_CONF_PROP_nupic_hypersearch_minParticlesPerSwarm"] = \ '%d' % (2) env["NTA_CONF_PROP_nupic_hypersearch_min_field_contribution"] = \ '%f' % (100) (jobID, jobInfo, resultInfos, metricResults, minErrScore) \ = self.runPermutations(expDir, hsImp='v2', loggingLevel=g_myEnv.options.logLevel, onCluster=onCluster, env=env, maxModels=None, dummyModel={'iterations':200}, kwargs) # Get the field contributions from the hypersearch results dict cjDAO = ClientJobsDAO.get() jobResultsStr = cjDAO.jobGetFields(jobID, ['results'])[0] jobResults = json.loads(jobResultsStr) bestModel = cjDAO.modelsInfo([jobResults["bestModel"]])[0] params = json.loads(bestModel.params) prefix = 'modelParams\|sensorParams\|encoders\|' expectedSwarmId = prefix + ('.' + prefix).join([ 'attendance', 'visitor_winloss']) self.assertEqual(params["particleState"]["swarmId"], expectedSwarmId, "Actual swarm id = %s\nExpcted swarm id = %s" \ % (params["particleState"]["swarmId"], expectedSwarmId)) self.assertEqual( bestModel.optimizedMetric, 75) #========================================================================== env["NTA_CONF_PROP_nupic_hypersearch_min_field_contribution"] = \ '%f' % (20) (jobID, jobInfo, resultInfos, metricResults, minErrScore) \ = self.runPermutations(expDir, hsImp='v2', loggingLevel=g_myEnv.options.logLevel, onCluster=onCluster, env=env, maxModels=None, dummyModel={'iterations':200}, kwargs) # Get the field contributions from the hypersearch results dict cjDAO = ClientJobsDAO.get() jobResultsStr = cjDAO.jobGetFields(jobID, ['results'])[0] jobResults = json.loads(jobResultsStr) bestModel = cjDAO.modelsInfo([jobResults["bestModel"]])[0] params = json.loads(bestModel.params) prefix = 'modelParams\|sensorParams\|encoders\|' expectedSwarmId = prefix + ('.' + prefix).join([ 'attendance', 'home_winloss', 'visitor_winloss']) self.assertEqual(params["particleState"]["swarmId"], expectedSwarmId, "Actual swarm id = %s\nExpcted swarm id = %s" \ % (params["particleState"]["swarmId"], expectedSwarmId)) assert bestModel.optimizedMetric == 55, bestModel.optimizedMetric #========================================================================== # Find best combo possible env["NTA_CONF_PROP_nupic_hypersearch_min_field_contribution"] = \ '%f' % (0.0) (jobID, jobInfo, resultInfos, metricResults, minErrScore) \ = self.runPermutations(expDir, hsImp='v2', loggingLevel=g_myEnv.options.logLevel, onCluster=onCluster, env=env, maxModels=None, dummyModel={'iterations':200}, kwargs) # Get the field contributions from the hypersearch results dict cjDAO = ClientJobsDAO.get() jobResultsStr = cjDAO.jobGetFields(jobID, ['results'])[0] jobResults = json.loads(jobResultsStr) bestModel = cjDAO.modelsInfo([jobResults["bestModel"]])[0] params = json.loads(bestModel.params) prefix = 'modelParams\|sensorParams\|encoders\|' expectedSwarmId = prefix + ('.' + prefix).join([ 'attendance', 'home_winloss', 'precip', 'timestamp_dayOfWeek', 'timestamp_timeOfDay', 'visitor_winloss']) self.assertEqual(params["particleState"]["swarmId"], expectedSwarmId, "Actual swarm id = %s\nExpcted swarm id = %s" \ % (params["particleState"]["swarmId"], expectedSwarmId)) assert bestModel.optimizedMetric == 25, bestModel.optimizedMetric def testSpatialClassification(self, onCluster=False, env=None, kwargs): """ Try running a spatial classification swarm """ self._printTestHeader() expDir = os.path.join(g_myEnv.testSrcExpDir, 'spatial_classification') # Test it out if env is None: env = dict() env["NTA_TEST_numIterations"] = '99' env["NTA_CONF_PROP_nupic_hypersearch_swarmMaturityWindow"] = \ '%d' % (g_repeatableSwarmMaturityWindow) (jobID, jobInfo, resultInfos, metricResults, minErrScore) \ = self.runPermutations(expDir, hsImp='v2', loggingLevel=g_myEnv.options.logLevel, onCluster=onCluster, env=env, maxModels=None, kwargs) self.assertEqual(minErrScore, 20) self.assertLess(len(resultInfos), 350) # Check the expected field contributions cjDAO = ClientJobsDAO.get() jobResultsStr = cjDAO.jobGetFields(jobID, ['results'])[0] jobResults = json.loads(jobResultsStr) bestModel = cjDAO.modelsInfo([jobResults["bestModel"]])[0] params = json.loads(bestModel.params) actualFieldContributions = jobResults['fieldContributions'] print "Actual field contributions:", \ pprint.pformat(actualFieldContributions) expectedFieldContributions = { 'address': 100 (90.0-30)/90.0, 'gym': 100 * (90.0-40)/90.0, 'timestamp_dayOfWeek': 100 * (90.0-80.0)/90.0, 'timestamp_timeOfDay': 100 * (90.0-90.0)/90.0, } for key, value in expectedFieldContributions.items(): self.assertEqual(actualFieldContributions[key], value, "actual field contribution from field '%s' does not " "match the expected value of %f" % (key, value)) # Check the expected best encoder combination prefix = 'modelParams\|sensorParams\|encoders\|' expectedSwarmId = prefix + ('.' + prefix).join([ 'address', 'gym']) self.assertEqual(params["particleState"]["swarmId"], expectedSwarmId, "Actual swarm id = %s\nExpcted swarm id = %s" \ % (params["particleState"]["swarmId"], expectedSwarmId)) return def testAlwaysInputPredictedField(self, onCluster=False, env=None, kwargs): """ Run a swarm where 'inputPredictedField' is set in the permutations file. The dummy model for this swarm is designed to give the lowest error when the predicted field is INCLUDED, so make sure we don't get this low error """ self._printTestHeader() expDir = os.path.join(g_myEnv.testSrcExpDir, 'input_predicted_field') # Test it out not requiring the predicted field. This should yield a # low error score if env is None: env = dict() env["NTA_TEST_inputPredictedField"] = "auto" env["NTA_TEST_numIterations"] = '99' env["NTA_CONF_PROP_nupic_hypersearch_minParticlesPerSwarm"] = \ '%d' % (2) env["NTA_CONF_PROP_nupic_hypersearch_swarmMaturityWindow"] = \ '%d' % (g_repeatableSwarmMaturityWindow) (jobID, jobInfo, resultInfos, metricResults, minErrScore) \ = self.runPermutations(expDir, hsImp='v2', loggingLevel=g_myEnv.options.logLevel, onCluster=onCluster, env=env, maxModels=None, kwargs) self.assertEqual(minErrScore, -50) self.assertLess(len(resultInfos), 350) # Now, require the predicted field. This should yield a high error score if env is None: env = dict() env["NTA_TEST_inputPredictedField"] = "yes" env["NTA_TEST_numIterations"] = '99' env["NTA_CONF_PROP_nupic_hypersearch_minParticlesPerSwarm"] = \ '%d' % (2) env["NTA_CONF_PROP_nupic_hypersearch_swarmMaturityWindow"] = \ '%d' % (g_repeatableSwarmMaturityWindow) (jobID, jobInfo, resultInfos, metricResults, minErrScore) \ = self.runPermutations(expDir, hsImp='v2', loggingLevel=g_myEnv.options.logLevel, onCluster=onCluster, env=env, maxModels=None, kwargs) self.assertEqual(minErrScore, -40) self.assertLess(len(resultInfos), 350) return def testFieldThresholdNoPredField(self, onCluster=False, env=None, kwargs): """ Test minimum field contribution threshold for a field to be included in further sprints when doing a temporal search that does not require the predicted field. """ self._printTestHeader() expDir = os.path.join(g_myEnv.testSrcExpDir, 'input_predicted_field') # Test it out without any max field branching in effect if env is None: env = dict() env["NTA_TEST_numIterations"] = '99' env["NTA_TEST_inputPredictedField"] = "auto" env["NTA_CONF_PROP_nupic_hypersearch_swarmMaturityWindow"] = \ '%d' % (g_repeatableSwarmMaturityWindow) env["NTA_CONF_PROP_nupic_hypersearch_max_field_branching"] = \ '%d' % (0) env["NTA_CONF_PROP_nupic_hypersearch_minParticlesPerSwarm"] = \ '%d' % (2) env["NTA_CONF_PROP_nupic_hypersearch_min_field_contribution"] = \ '%f' % (0) if True: (jobID, jobInfo, resultInfos, metricResults, minErrScore) \ = self.runPermutations(expDir, hsImp='v2', loggingLevel=g_myEnv.options.logLevel, onCluster=onCluster, env=env, maxModels=None, dummyModel={'iterations':200}, *kwargs) # Verify the best model and check the field contributions. cjDAO = ClientJobsDAO.get() jobResultsStr = cjDAO.jobGetFields(jobID, ['results'])[0] jobResults = json.loads(jobResultsStr) bestModel = cjDAO.modelsInfo([jobResults["bestModel"]])[0] params = json.loads(bestModel.params) prefix = 'modelParams\|sensorParams\|encoders\|' expectedSwarmId = prefix + ('.' + prefix).join([ 'address', 'gym', 'timestamp_dayOfWeek', 'timestamp_timeOfDay']) self.assertEqual(params["particleState"]["swarmId"], expectedSwarmId, "Actual swarm id = %s\nExpcted swarm id = %s" \ % (params["particleState"]["swarmId"], expectedSwarmId)) self.assertEqual( bestModel.optimizedMetric, -50) # Check the field contributions actualFieldContributions = jobResults['fieldContributions'] print "Actual field contributions:", \ pprint.pformat(actualFieldContributions) expectedFieldContributions = { 'consumption': 0.0, 'address': 100 (60.0-40.0)/60.0, 'timestamp_timeOfDay': 100 * (60.0-20.0)/60.0, 'timestamp_dayOfWeek': 100 * (60.0-10.0)/60.0, 'gym': 100 * (60.0-30.0)/60.0} for key, value in expectedFieldContributions.items(): self.assertEqual(actualFieldContributions[key], value, "actual field contribution from field '%s' does not " "match the expected value of %f" % (key, value)) if True: #========================================================================== # Now test ignoring all fields that contribute less than 55% to the # error score. This means we can only use the timestamp_timeOfDay and # timestamp_dayOfWeek fields. # This should bring our best error score up to 50-30-40 = -20 env["NTA_CONF_PROP_nupic_hypersearch_min_field_contribution"] = \ '%f' % (55) env["NTA_CONF_PROP_nupic_hypersearch_max_field_branching"] = \ '%d' % (5) (jobID, jobInfo, resultInfos, metricResults, minErrScore) \ = self.runPermutations(expDir, hsImp='v2', loggingLevel=g_myEnv.options.logLevel, onCluster=onCluster, env=env, maxModels=None, dummyModel={'iterations':200}, *kwargs) # Get the best model cjDAO = ClientJobsDAO.get() jobResultsStr = cjDAO.jobGetFields(jobID, ['results'])[0] jobResults = json.loads(jobResultsStr) bestModel = cjDAO.modelsInfo([jobResults["bestModel"]])[0] params = json.loads(bestModel.params) prefix = 'modelParams\|sensorParams\|encoders\|' expectedSwarmId = prefix + ('.' + prefix).join([ 'timestamp_dayOfWeek', 'timestamp_timeOfDay']) self.assertEqual(params["particleState"]["swarmId"], expectedSwarmId, "Actual swarm id = %s\nExpcted swarm id = %s" \ % (params["particleState"]["swarmId"], expectedSwarmId)) self.assertEqual( bestModel.optimizedMetric, -20) # Check field contributions returned actualFieldContributions = jobResults['fieldContributions'] print "Actual field contributions:", \ pprint.pformat(actualFieldContributions) expectedFieldContributions = { 'consumption': 0.0, 'address': 100 (60.0-40.0)/60.0, 'timestamp_timeOfDay': 100 * (60.0-20.0)/60.0, 'timestamp_dayOfWeek': 100 * (60.0-10.0)/60.0, 'gym': 100 * (60.0-30.0)/60.0} for key, value in expectedFieldContributions.items(): self.assertEqual(actualFieldContributions[key], value, "actual field contribution from field '%s' does not " "match the expected value of %f" % (key, value)) if True: #========================================================================== # Now, test using maxFieldBranching to limit the max number of fields to # 3. This means we can only use the timestamp_timeOfDay, timestamp_dayOfWeek, # gym fields. # This should bring our error score to 50-30-40-20 = -40 env["NTA_CONF_PROP_nupic_hypersearch_min_field_contribution"] = \ '%f' % (0) env["NTA_CONF_PROP_nupic_hypersearch_max_field_branching"] = \ '%d' % (3) (jobID, jobInfo, resultInfos, metricResults, minErrScore) \ = self.runPermutations(expDir, hsImp='v2', loggingLevel=g_myEnv.options.logLevel, onCluster=onCluster, env=env, maxModels=None, dummyModel={'iterations':200}, kwargs) # Get the best model cjDAO = ClientJobsDAO.get() jobResultsStr = cjDAO.jobGetFields(jobID, ['results'])[0] jobResults = json.loads(jobResultsStr) bestModel = cjDAO.modelsInfo([jobResults["bestModel"]])[0] params = json.loads(bestModel.params) prefix = 'modelParams\|sensorParams\|encoders\|' expectedSwarmId = prefix + ('.' + prefix).join([ 'gym', 'timestamp_dayOfWeek', 'timestamp_timeOfDay']) self.assertEqual(params["particleState"]["swarmId"], expectedSwarmId, "Actual swarm id = %s\nExpcted swarm id = %s" \ % (params["particleState"]["swarmId"], expectedSwarmId)) self.assertEqual( bestModel.optimizedMetric, -40) if True: #========================================================================== # Now, test setting max models so that no swarm can finish completely. # Make sure we get the expected field contributions env["NTA_CONF_PROP_nupic_hypersearch_swarmMaturityWindow"] = \ '%d' % (g_repeatableSwarmMaturityWindow) env["NTA_CONF_PROP_nupic_hypersearch_max_field_branching"] = \ '%d' % (0) env["NTA_CONF_PROP_nupic_hypersearch_minParticlesPerSwarm"] = \ '%d' % (5) env["NTA_CONF_PROP_nupic_hypersearch_min_field_contribution"] = \ '%f' % (0) (jobID, jobInfo, resultInfos, metricResults, minErrScore) \ = self.runPermutations(expDir, hsImp='v2', loggingLevel=g_myEnv.options.logLevel, onCluster=onCluster, env=env, maxModels=10, dummyModel={'iterations':200}, kwargs) # Get the best model cjDAO = ClientJobsDAO.get() jobResultsStr = cjDAO.jobGetFields(jobID, ['results'])[0] jobResults = json.loads(jobResultsStr) bestModel = cjDAO.modelsInfo([jobResults["bestModel"]])[0] params = json.loads(bestModel.params) prefix = 'modelParams\|sensorParams\|encoders\|' expectedSwarmId = prefix + ('.' + prefix).join([ 'timestamp_dayOfWeek']) self.assertEqual(params["particleState"]["swarmId"], expectedSwarmId, "Actual swarm id = %s\nExpcted swarm id = %s" \ % (params["particleState"]["swarmId"], expectedSwarmId)) self.assertEqual( bestModel.optimizedMetric, 10) # Check field contributions returned actualFieldContributions = jobResults['fieldContributions'] print "Actual field contributions:", \ pprint.pformat(actualFieldContributions) expectedFieldContributions = { 'consumption': 0.0, 'address': 100 * (60.0-40.0)/60.0, 'timestamp_timeOfDay': 100 * (60.0-20.0)/60.0, 'timestamp_dayOfWeek': 100 * (60.0-10.0)/60.0, 'gym': 100 * (60.0-30.0)/60.0} class MultiNodeTests(ExperimentTestBaseClass): """ Test hypersearch on multiple nodes """ # AWS tests attribute required for tagging via automatic test discovery via # nosetests engineAWSClusterTest=True def testSimpleV2(self): """ Try running a simple permutations """ self._printTestHeader() inst = OneNodeTests(self._testMethodName) return inst.testSimpleV2(onCluster=True) #, maxNumWorkers=7) def testDeltaV2(self): """ Try running a simple permutations """ self._printTestHeader() inst = OneNodeTests(self._testMethodName) return inst.testDeltaV2(onCluster=True) #, maxNumWorkers=7) def testSmartSpeculation(self, onCluster=True, env=None, kwargs): """ Try running a simple permutations """ self._printTestHeader() expDir = os.path.join(g_myEnv.testSrcExpDir, 'smart_speculation_temporal') # Test it out if env is None: env = dict() env["NTA_TEST_numIterations"] = '99' env["NTA_CONF_PROP_nupic_hypersearch_swarmMaturityWindow"] = \ '%d' % (g_repeatableSwarmMaturityWindow) env["NTA_CONF_PROP_nupic_hypersearch_minParticlesPerSwarm"] = \ '%d' % (1) (jobID, jobInfo, resultInfos, metricResults, minErrScore) \ = self.runPermutations(expDir, hsImp='v2', loggingLevel=g_myEnv.options.logLevel, onCluster=onCluster, env=env, maxModels=None, dummyModel={'iterations':200}, kwargs) # Get the field contributions from the hypersearch results dict cjDAO = ClientJobsDAO.get() jobInfoStr = cjDAO.jobGetFields(jobID, ['results','engWorkerState']) jobResultsStr = jobInfoStr[0] engState = jobInfoStr[1] engState = json.loads(engState) swarms = engState["swarms"] jobResults = json.loads(jobResultsStr) bestModel = cjDAO.modelsInfo([jobResults["bestModel"]])[0] params = json.loads(bestModel.params) # Make sure that the only nonkilled models are the ones that would have been # run without speculation prefix = 'modelParams\|sensorParams\|encoders\|' correctOrder = ["A","B","C","D","E","F","G","Pred"] correctOrder = [prefix + x for x in correctOrder] for swarm in swarms: if swarms[swarm]["status"] == 'killed': swarmId = swarm.split(".") if(len(swarmId)>1): # Make sure that something before the last two encoders is in the # wrong sprint progression, hence why it was killed # The last encoder is the predicted field and the second to last is # the current new addition wrong=0 for i in range(len(swarmId)-2): if correctOrder[i] != swarmId[i]: wrong=1 assert wrong==1, "Some of the killed swarms should not have been " \ + "killed as they are a legal combination." if swarms[swarm]["status"] == 'completed': swarmId = swarm.split(".") if(len(swarmId)>3): # Make sure that the completed swarms are all swarms that should # have been run. # The last encoder is the predicted field and the second to last is # the current new addition for i in range(len(swarmId)-3): if correctOrder[i] != swarmId[i]: assert False , "Some of the completed swarms should not have " \ "finished as they are illegal combinations" if swarms[swarm]["status"] == 'active': assert False , "Some swarms are still active at the end of hypersearch" pass def testSmartSpeculationSpatialClassification(self, onCluster=True, env=None, kwargs): """ Test that smart speculation does the right thing with spatial classification models. This also applies to temporal models where the predicted field is optional (or excluded) since Hypersearch treats them the same. """ self._printTestHeader() expDir = os.path.join(g_myEnv.testSrcExpDir, 'smart_speculation_spatial_classification') # Test it out if env is None: env = dict() env["NTA_TEST_numIterations"] = '99' env["NTA_CONF_PROP_nupic_hypersearch_swarmMaturityWindow"] = \ '%d' % (g_repeatableSwarmMaturityWindow) env["NTA_CONF_PROP_nupic_hypersearch_minParticlesPerSwarm"] = \ '%d' % (1) (jobID, jobInfo, resultInfos, metricResults, minErrScore) \ = self.runPermutations(expDir, hsImp='v2', loggingLevel=g_myEnv.options.logLevel, onCluster=onCluster, env=env, maxModels=None, maxNumWorkers=5, dummyModel={'iterations':200}, kwargs) # Get the worker state cjDAO = ClientJobsDAO.get() jobInfoStr = cjDAO.jobGetFields(jobID, ['results','engWorkerState']) jobResultsStr = jobInfoStr[0] engState = jobInfoStr[1] engState = json.loads(engState) swarms = engState["swarms"] jobResults = json.loads(jobResultsStr) bestModel = cjDAO.modelsInfo([jobResults["bestModel"]])[0] params = json.loads(bestModel.params) # Make sure that the only non-killed models are the ones that would have been # run without speculation prefix = 'modelParams\|sensorParams\|encoders\|' correctOrder = ["A","B","C"] correctOrder = [prefix + x for x in correctOrder] for swarm in swarms: if swarms[swarm]["status"] == 'killed': swarmId = swarm.split(".") if(len(swarmId) > 1): # Make sure that the best encoder is not in this swarm if correctOrder[0] in swarmId: raise RuntimeError("Some of the killed swarms should not have been " "killed as they are a legal combination.") elif swarms[swarm]["status"] == 'completed': swarmId = swarm.split(".") if(len(swarmId) >= 2): # Make sure that the completed swarms are all swarms that should # have been run. for i in range(len(swarmId)-1): if correctOrder[i] != swarmId[i]: raise RuntimeError("Some of the completed swarms should not have " "finished as they are illegal combinations") elif swarms[swarm]["status"] == 'active': raise RuntimeError("Some swarms are still active at the end of " "hypersearch") def testFieldBranching(self, onCluster=True, env=None, kwargs): """ Try running a simple permutations """ self._printTestHeader() expDir = os.path.join(g_myEnv.testSrcExpDir, 'max_branching_temporal') # Test it out if env is None: env = dict() env["NTA_TEST_numIterations"] = '99' env["NTA_CONF_PROP_nupic_hypersearch_swarmMaturityWindow"] = \ '%d' % (g_repeatableSwarmMaturityWindow) env["NTA_CONF_PROP_nupic_hypersearch_max_field_branching"] = \ '%d' % (4) env["NTA_CONF_PROP_nupic_hypersearch_min_field_contribution"] = \ '%f' % (-20.0) env["NTA_CONF_PROP_nupic_hypersearch_minParticlesPerSwarm"] = \ '%d' % (2) (jobID, jobInfo, resultInfos, metricResults, minErrScore) \ = self.runPermutations(expDir, hsImp='v2', loggingLevel=g_myEnv.options.logLevel, onCluster=onCluster, env=env, maxModels=None, dummyModel={'iterations':200}, kwargs) # Get the field contributions from the hypersearch results dict cjDAO = ClientJobsDAO.get() jobResultsStr = cjDAO.jobGetFields(jobID, ['results'])[0] jobResults = json.loads(jobResultsStr) bestModel = cjDAO.modelsInfo([jobResults["bestModel"]])[0] params = json.loads(bestModel.params) prefix = 'modelParams\|sensorParams\|encoders\|' expectedSwarmId = prefix + ('.' + prefix).join([ 'attendance', 'home_winloss', 'timestamp_dayOfWeek', 'timestamp_timeOfDay', 'visitor_winloss']) assert params["particleState"]["swarmId"] == expectedSwarmId, \ params["particleState"]["swarmId"] assert bestModel.optimizedMetric == 432, bestModel.optimizedMetric env["NTA_CONF_PROP_nupic_hypersearch_max_field_branching"] = \ '%d' % (3) (jobID, jobInfo, resultInfos, metricResults, minErrScore) \ = self.runPermutations(expDir, hsImp='v2', loggingLevel=g_myEnv.options.logLevel, onCluster=onCluster, env=env, maxModels=None, dummyModel={'iterations':200}, kwargs) # Get the field contributions from the hypersearch results dict cjDAO = ClientJobsDAO.get() jobResultsStr = cjDAO.jobGetFields(jobID, ['results'])[0] jobResults = json.loads(jobResultsStr) bestModel = cjDAO.modelsInfo([jobResults["bestModel"]])[0] params = json.loads(bestModel.params) prefix = 'modelParams\|sensorParams\|encoders\|' expectedSwarmId = prefix + ('.' + prefix).join([ 'attendance', 'home_winloss', 'timestamp_timeOfDay', 'visitor_winloss']) assert params["particleState"]["swarmId"] == expectedSwarmId, \ params["particleState"]["swarmId"] assert bestModel.optimizedMetric == 465, bestModel.optimizedMetric env["NTA_CONF_PROP_nupic_hypersearch_max_field_branching"] = \ '%d' % (5) (jobID, jobInfo, resultInfos, metricResults, minErrScore) \ = self.runPermutations(expDir, hsImp='v2', loggingLevel=g_myEnv.options.logLevel, onCluster=onCluster, env=env, maxModels=None, dummyModel={'iterations':200}, kwargs) # Get the field contributions from the hypersearch results dict cjDAO = ClientJobsDAO.get() jobResultsStr = cjDAO.jobGetFields(jobID, ['results'])[0] jobResults = json.loads(jobResultsStr) bestModel = cjDAO.modelsInfo([jobResults["bestModel"]])[0] params = json.loads(bestModel.params) prefix = 'modelParams\|sensorParams\|encoders\|' expectedSwarmId = prefix + ('.' + prefix).join([ 'attendance', 'home_winloss', 'precip', 'timestamp_dayOfWeek', 'timestamp_timeOfDay', 'visitor_winloss']) assert params["particleState"]["swarmId"] == expectedSwarmId, \ params["particleState"]["swarmId"] assert bestModel.optimizedMetric == 390, bestModel.optimizedMetric #Find best combo with 3 fields env["NTA_CONF_PROP_nupic_hypersearch_max_field_branching"] = \ '%d' % (0) (jobID, jobInfo, resultInfos, metricResults, minErrScore) \ = self.runPermutations(expDir, hsImp='v2', loggingLevel=g_myEnv.options.logLevel, onCluster=onCluster, env=env, maxModels=100, dummyModel={'iterations':200}, kwargs) # Get the field contributions from the hypersearch results dict cjDAO = ClientJobsDAO.get() jobResultsStr = cjDAO.jobGetFields(jobID, ['results'])[0] jobResults = json.loads(jobResultsStr) bestModel = cjDAO.modelsInfo([jobResults["bestModel"]])[0] params = json.loads(bestModel.params) prefix = 'modelParams\|sensorParams\|encoders\|' expectedSwarmId = prefix + ('.' + prefix).join([ 'attendance', 'daynight', 'visitor_winloss']) assert params["particleState"]["swarmId"] == expectedSwarmId, \ params["particleState"]["swarmId"] assert bestModel.optimizedMetric == 406, bestModel.optimizedMetric return def testFieldThreshold(self, onCluster=True, env=None, kwargs): """ Test minimum field contribution threshold for a field to be included in further sprints """ self._printTestHeader() inst = OneNodeTests(self._testMethodName) return inst.testFieldThreshold(onCluster=True) def testFieldContributions(self, onCluster=True, env=None, kwargs): """ Try running a simple permutations """ self._printTestHeader() expDir = os.path.join(g_myEnv.testSrcExpDir, 'field_contrib_temporal') # Test it out if env is None: env = dict() env["NTA_TEST_numIterations"] = '99' env["NTA_CONF_PROP_nupic_hypersearch_swarmMaturityWindow"] = \ '%d' % (g_repeatableSwarmMaturityWindow) (jobID, jobInfo, resultInfos, metricResults, minErrScore) \ = self.runPermutations(expDir, hsImp='v2', loggingLevel=g_myEnv.options.logLevel, onCluster=onCluster, env=env, maxModels=None, kwargs) # Get the field contributions from the hypersearch results dict cjDAO = ClientJobsDAO.get() jobResultsStr = cjDAO.jobGetFields(jobID, ['results'])[0] jobResults = json.loads(jobResultsStr) actualFieldContributions = jobResults['fieldContributions'] print "Actual field contributions:", actualFieldContributions expectedFieldContributions = {'consumption': 0.0, 'address': 0.0, 'timestamp_timeOfDay': 20.0, 'timestamp_dayOfWeek': 50.0, 'gym': 10.0} for key, value in expectedFieldContributions.items(): self.assertEqual(actualFieldContributions[key], value, "actual field contribution from field '%s' does not " "match the expected value of %f" % (key, value)) return def testHTMPredictionModelV2(self): """ Try running a simple permutations through a real CLA model """ self._printTestHeader() inst = OneNodeTests(self._testMethodName) return inst.testHTMPredictionModelV2(onCluster=True, maxModels=4) def testCLAMultistepModel(self): """ Try running a simple permutations through a real CLA model that uses multistep """ self._printTestHeader() inst = OneNodeTests(self._testMethodName) return inst.testCLAMultistepModel(onCluster=True, maxModels=4) def testLegacyCLAMultistepModel(self): """ Try running a simple permutations through a real CLA model that uses multistep """ self._printTestHeader() inst = OneNodeTests(self._testMethodName) return inst.testLegacyCLAMultistepModel(onCluster=True, maxModels=4) def testSimpleV2VariableWaits(self): """ Try running a simple permutations where certain field combinations take longer to complete, this lets us test that we successfully kill models in bad swarms that are still running. """ self._printTestHeader() # NTA_TEST_variableWaits and NTA_TEST_numIterations are watched by the # dummyModelParams() method of the permutations.py file # NTA_TEST_numIterations env = dict() env["NTA_TEST_variableWaits"] ='True' env["NTA_TEST_numIterations"] = '100' inst = OneNodeTests('testSimpleV2') return inst.testSimpleV2(onCluster=True, env=env) def testOrphanedModel(self, modelRange=(0,2)): """ Run a worker on a model for a while, then have it exit before the model finishes. Then, run another worker, which should detect the orphaned model. """ self._printTestHeader() expDir = os.path.join(g_myEnv.testSrcExpDir, 'simpleV2') # NTA_TEST_numIterations is watched by the dummyModelParams() method of # the permutations file. # NTA_TEST_sysExitModelRange is watched by the dummyModelParams() method of # the permutations file. It tells it to do a sys.exit() after so many # iterations. env = dict() env["NTA_TEST_numIterations"] = '99' env["NTA_TEST_sysExitModelRange"] = '%d,%d' % (modelRange[0], modelRange[1]) env["NTA_CONF_PROP_nupic_hypersearch_modelOrphanIntervalSecs"] = '1' env["NTA_CONF_PROP_nupic_hypersearch_swarmMaturityWindow"] \ = '%d' % (g_repeatableSwarmMaturityWindow) (jobID, jobInfo, resultInfos, metricResults, minErrScore) \ = self.runPermutations(expDir, hsImp='v2', loggingLevel=g_myEnv.options.logLevel, maxModels=500, onCluster=True, env=env, waitForCompletion=True, maxNumWorkers=4, ) self.assertEqual(minErrScore, 20) self.assertLess(len(resultInfos), 500) return def testTwoOrphanedModels(self, modelRange=(0,2)): """ Test behavior when a worker marks 2 models orphaned at the same time. """ self._printTestHeader() expDir = os.path.join(g_myEnv.testSrcExpDir, 'oneField') # NTA_TEST_numIterations is watched by the dummyModelParams() method of # the permutations file. # NTA_TEST_sysExitModelRange is watched by the dummyModelParams() method of # the permutations file. It tells it to do a sys.exit() after so many # iterations. env = dict() env["NTA_TEST_numIterations"] = '99' env["NTA_TEST_delayModelRange"] = '%d,%d' % (modelRange[0], modelRange[1]) env["NTA_CONF_PROP_nupic_hypersearch_modelOrphanIntervalSecs"] = '1' env["NTA_CONF_PROP_nupic_hypersearch_swarmMaturityWindow"] \ = '%d' % (g_repeatableSwarmMaturityWindow) (jobID, jobInfo, resultInfos, metricResults, minErrScore) \ = self.runPermutations(expDir, hsImp='v2', loggingLevel=g_myEnv.options.logLevel, maxModels=100, onCluster=True, env=env, waitForCompletion=True, maxNumWorkers=4, ) self.assertEqual(minErrScore, 50) self.assertLess(len(resultInfos), 100) return def testOrphanedModelGen1(self): """ Run a worker on a model for a while, then have it exit before the model finishes. Then, run another worker, which should detect the orphaned model. """ self._printTestHeader() inst = MultiNodeTests(self._testMethodName) return inst.testOrphanedModel(modelRange=(10,11)) def testOrphanedModelMaxModels(self): """ Test to make sure that the maxModels parameter doesn't include orphaned models. Run a test with maxModels set to 2, where one becomes orphaned. At the end, there should be 3 models in the models table, one of which will be the new model that adopted the orphaned model """ self._printTestHeader() expDir = os.path.join(g_myEnv.testSrcExpDir, 'dummyV2') numModels = 5 env = dict() env["NTA_CONF_PROP_nupic_hypersearch_modelOrphanIntervalSecs"] = '3' env['NTA_TEST_max_num_models']=str(numModels) (jobID, jobInfo, resultInfos, metricResults, minErrScore) \ = self.runPermutations(expDir, hsImp='v2', loggingLevel=g_myEnv.options.logLevel, maxModels=numModels, env=env, onCluster=True, waitForCompletion=True, dummyModel={'metricValue': ['25','50'], 'sysExitModelRange': '0, 1', 'iterations': 20, } ) cjDB = ClientJobsDAO.get() self.assertGreaterEqual(len(resultInfos), numModels+1) completionReasons = [x.completionReason for x in resultInfos] self.assertGreaterEqual(completionReasons.count(cjDB.CMPL_REASON_EOF), numModels) self.assertGreaterEqual(completionReasons.count(cjDB.CMPL_REASON_ORPHAN), 1) def testOrphanedModelConnection(self): """Test for the correct behavior when a model uses a different connection id than what is stored in the db. The correct behavior is for the worker to log this as a warning and move on to a new model""" self._printTestHeader() # ----------------------------------------------------------------------- # Trigger "Using connection from another worker" exception inside # ModelRunner # ----------------------------------------------------------------------- expDir = os.path.join(g_myEnv.testSrcExpDir, 'dummy_multi_v2') numModels = 2 env = dict() env["NTA_CONF_PROP_nupic_hypersearch_modelOrphanIntervalSecs"] = '1' (jobID, jobInfo, resultInfos, metricResults, minErrScore) \ = self.runPermutations(expDir, hsImp='v2', loggingLevel=g_myEnv.options.logLevel, maxModels=numModels, env=env, onCluster=True, waitForCompletion=True, dummyModel={'metricValue': ['25','50'], 'sleepModelRange': '0, 1:5', 'iterations': 20, } ) cjDB = ClientJobsDAO.get() self.assertGreaterEqual(len(resultInfos), numModels, "%d were run. Expecting %s"%(len(resultInfos), numModels+1)) completionReasons = [x.completionReason for x in resultInfos] self.assertGreaterEqual(completionReasons.count(cjDB.CMPL_REASON_EOF), numModels) self.assertGreaterEqual(completionReasons.count(cjDB.CMPL_REASON_ORPHAN), 1) def testErredModel(self, modelRange=(6,7)): """ Run a worker on a model for a while, then have it exit before the model finishes. Then, run another worker, which should detect the orphaned model. """ self._printTestHeader() inst = OneNodeTests(self._testMethodName) return inst.testErredModel(onCluster=True) def testJobFailModel(self): """ Run a worker on a model for a while, then have it exit before the model finishes. Then, run another worker, which should detect the orphaned model. """ self._printTestHeader() inst = OneNodeTests(self._testMethodName) return inst.testJobFailModel(onCluster=True) def testTooManyErredModels(self, modelRange=(5,10)): """ Run a worker on a model for a while, then have it exit before the model finishes. Then, run another worker, which should detect the orphaned model. """ self._printTestHeader() inst = OneNodeTests(self._testMethodName) return inst.testTooManyErredModels(onCluster=True) def testSpatialClassification(self): """ Try running a simple permutations """ self._printTestHeader() inst = OneNodeTests(self._testMethodName) return inst.testSpatialClassification(onCluster=True) #, maxNumWorkers=7) def testAlwaysInputPredictedField(self): self._printTestHeader() inst = OneNodeTests(self._testMethodName) return inst.testAlwaysInputPredictedField(onCluster=True) def testFieldThresholdNoPredField(self): self._printTestHeader() inst = OneNodeTests(self._testMethodName) return inst.testFieldThresholdNoPredField(onCluster=True) class ModelMaturityTests(ExperimentTestBaseClass): """ """ # AWS tests attribute required for tagging via automatic test discovery via # nosetests engineAWSClusterTest=True def setUp(self): # Ignore the global hypersearch version setting. Always test hypersearch v2 hsVersion = 2 self.expDir = os.path.join(g_myEnv.testSrcExpDir, 'dummyV%d' %hsVersion) self.hsImp = "v%d" % hsVersion self.env = {'NTA_CONF_PROP_nupic_hypersearch_enableModelTermination':'0', 'NTA_CONF_PROP_nupic_hypersearch_enableModelMaturity':'1', 'NTA_CONF_PROP_nupic_hypersearch_maturityMaxSlope':'0.1', 'NTA_CONF_PROP_nupic_hypersearch_enableSwarmTermination':'0', 'NTA_CONF_PROP_nupic_hypersearch_bestModelMinRecords':'0'} def testMatureInterleaved(self): """ Test to make sure that the best model continues running even when it has matured. The 2nd model (constant) will be marked as mature first and will continue to run till the end. The 2nd model reaches maturity and should stop before all the records are consumed, and should be the best model because it has a lower error """ self._printTestHeader() self.expDir = os.path.join(g_myEnv.testSrcExpDir, 'dummy_multi_v%d' % 2) self.env['NTA_TEST_max_num_models'] = '2' jobID,_,_,_,_ = self.runPermutations(self.expDir, hsImp=self.hsImp, maxModels=2, loggingLevel = g_myEnv.options.logLevel, env = self.env, onCluster = True, dummyModel={'metricFunctions': ['lambda x: -10math.log10(x+1) +100', 'lambda x: 100.0'], 'delay': [2.0, 0.0 ], 'waitTime':[0.05, 0.01], 'iterations':500, 'experimentDirectory':self.expDir, }) cjDB = ClientJobsDAO.get() modelIDs, records, completionReasons, matured = \ zip(self.getModelFields( jobID, ['numRecords', 'completionReason', 'engMatured'])) results = cjDB.jobGetFields(jobID, ['results'])[0] results = json.loads(results) self.assertEqual(results['bestModel'], modelIDs[0]) self.assertEqual(records[1], 500) self.assertTrue(records[0] > 100 and records[0] < 500, "Model 2 num records: 100 < %d < 500 " % records[1]) self.assertEqual(completionReasons[1], cjDB.CMPL_REASON_EOF) self.assertEqual(completionReasons[0], cjDB.CMPL_REASON_STOPPED) self.assertTrue(matured[0], True) def testConstant(self): """ Sanity check to make sure that when only 1 model is running, it continues to run even when it has reached maturity """ self._printTestHeader() jobID,_,_,_,_ = self.runPermutations(self.expDir, hsImp=self.hsImp, maxModels=1, loggingLevel = g_myEnv.options.logLevel, env = self.env, dummyModel={'metricFunctions': ['lambda x: 100'], 'iterations':350, 'experimentDirectory':self.expDir, }) cjDB = ClientJobsDAO.get() modelIDs = cjDB.jobGetModelIDs(jobID) dbResults = cjDB.modelsGetFields(modelIDs, ['numRecords', 'completionReason', 'engMatured']) modelIDs = [x[0] for x in dbResults] records = [x[1][0] for x in dbResults] completionReasons = [x[1][1] for x in dbResults] matured = [x[1][2] for x in dbResults] results = cjDB.jobGetFields(jobID, ['results'])[0] results = json.loads(results) self.assertEqual(results['bestModel'], min(modelIDs)) self.assertEqual(records[0], 350) self.assertEqual(completionReasons[0], cjDB.CMPL_REASON_EOF) self.assertEqual(matured[0], True) def getModelFields(self, jobID, fields): cjDB = ClientJobsDAO.get() modelIDs = cjDB.jobGetModelIDs(jobID) modelParams = cjDB.modelsGetFields(modelIDs, ['params']+fields) modelIDs = [e[0] for e in modelParams] modelOrders = [json.loads(e[1][0])['structuredParams']['__model_num'] for e in modelParams] modelFields = [] for f in xrange(len(fields)): modelFields.append([e[1][f+1] for e in modelParams]) modelInfo = zip(modelOrders, modelIDs, *tuple(modelFields)) modelInfo.sort(key=lambda info:info[0]) return [e[1:] for e in sorted(modelInfo, key=lambda info:info[0])] class SwarmTerminatorTests(ExperimentTestBaseClass): """ """ # AWS tests attribute required for tagging via automatic test discovery via # nosetests engineAWSClusterTest=True def setUp(self): self.env = {'NTA_CONF_PROP_nupic_hypersearch_enableModelMaturity':'0', 'NTA_CONF_PROP_nupic_hypersearch_enableModelTermination':'0', 'NTA_CONF_PROP_nupic_hypersearch_enableSwarmTermination':'1', 'NTA_TEST_recordSwarmTerminations':'1'} def testSimple(self, useCluster=False): """Run with one really bad swarm to see if terminator picks it up correctly""" if not g_myEnv.options.runInProc: self.skipTest("Skipping One Node test since runInProc is not specified") self._printTestHeader() expDir = os.path.join(g_myEnv.testSrcExpDir, 'swarm_v2') (jobID, jobInfo, resultInfos, metricResults, minErrScore) \ = self.runPermutations(expDir, hsImp='v2', loggingLevel=g_myEnv.options.logLevel, maxModels=None, onCluster=useCluster, env=self.env, dummyModel={'iterations':200}) cjDB = ClientJobsDAO.get() jobResultsStr = cjDB.jobGetFields(jobID, ['results'])[0] jobResults = json.loads(jobResultsStr) terminatedSwarms = jobResults['terminatedSwarms'] swarmMaturityWindow = int(configuration.Configuration.get( 'nupic.hypersearch.swarmMaturityWindow')) prefix = 'modelParams\|sensorParams\|encoders\|' for swarm, (generation, scores) in terminatedSwarms.iteritems(): if prefix + 'gym' in swarm.split('.'): self.assertEqual(generation, swarmMaturityWindow-1) else: self.assertEqual(generation, swarmMaturityWindow-1+4) def testMaturity(self, useCluster=False): if not g_myEnv.options.runInProc: self.skipTest("Skipping One Node test since runInProc is not specified") self._printTestHeader() self.env['NTA_CONF_PROP_enableSwarmTermination'] = '0' expDir = os.path.join(g_myEnv.testSrcExpDir, 'swarm_maturity_v2') (jobID, jobInfo, resultInfos, metricResults, minErrScore) \ = self.runPermutations(expDir, hsImp='v2', loggingLevel=g_myEnv.options.logLevel, maxModels=None, onCluster=useCluster, env=self.env, dummyModel={'iterations':200}) cjDB = ClientJobsDAO.get() jobResultsStr = cjDB.jobGetFields(jobID, ['results'])[0] jobResults = json.loads(jobResultsStr) terminatedSwarms = jobResults['terminatedSwarms'] swarmMaturityWindow = int(configuration.Configuration.get( 'nupic.hypersearch.swarmMaturityWindow')) prefix = 'modelParams\|sensorParams\|encoders\|' for swarm, (generation, scores) in terminatedSwarms.iteritems(): encoders = swarm.split('.') if prefix + 'gym' in encoders: self.assertEqual(generation, swarmMaturityWindow-1 + 3) elif prefix + 'address' in encoders: self.assertEqual(generation, swarmMaturityWindow-1) else: self.assertEqual(generation, swarmMaturityWindow-1 + 7) def testSimpleMN(self): self.testSimple(useCluster=True) def testMaturityMN(self): self.testMaturity(useCluster=True) def getHypersearchWinningModelID(jobID): """ Parameters: ------------------------------------------------------------------- jobID: jobID of successfully-completed Hypersearch job retval: modelID of the winning model """ cjDAO = ClientJobsDAO.get() jobResults = cjDAO.jobGetFields(jobID, ['results'])[0] print "Hypersearch job results: %r" % (jobResults,) jobResults = json.loads(jobResults) return jobResults['bestModel'] def _executeExternalCmdAndReapStdout(args): """ args: Args list as defined for the args parameter in subprocess.Popen() Returns: result dicionary: { 'exitStatus':<exit-status-of-external-command>, 'stdoutData':"string", 'stderrData':"string" } """ _debugOut(("_executeExternalCmdAndReapStdout: Starting...\n<%s>") % \ (args,)) p = subprocess.Popen(args, env=os.environ, stdout=subprocess.PIPE, stderr=subprocess.PIPE) _debugOut(("Process started for <%s>") % (args,)) (stdoutData, stderrData) = p.communicate() _debugOut(("Process completed for <%s>: exit status=%s, stdoutDataType=%s, " + \ "stdoutData=<%s>, stderrData=<%s>") % \ (args, p.returncode, type(stdoutData), stdoutData, stderrData)) result = dict( exitStatus = p.returncode, stdoutData = stdoutData, stderrData = stderrData, ) _debugOut(("_executeExternalCmdAndReapStdout for <%s>: result=\n%s") % \ (args, pprint.pformat(result, indent=4))) return result def _debugOut(text): global g_debug if g_debug: print text sys.stdout.flush() return def _getTestList(): """ Get the list of tests that can be run from this module""" suiteNames = [ 'OneNodeTests', 'MultiNodeTests', 'ModelMaturityTests', 'SwarmTerminatorTests', ] testNames = [] for suite in suiteNames: for f in dir(eval(suite)): if f.startswith('test'): testNames.append('%s.%s' % (suite, f)) return testNames class _ArgParser(object): """Class which handles command line arguments and arguments passed to the test """ args = [] @classmethod def _processArgs(cls): """ Parse our command-line args/options and strip them from sys.argv Returns the tuple (parsedOptions, remainingArgs) """ helpString = \ """%prog [options...] [-- unittestoptions...] [suitename.testname \| suitename] Run the Hypersearch unit tests. To see unit test framework options, enter: python %prog -- --help Example usages: python %prog MultiNodeTests python %prog MultiNodeTests.testOrphanedModel python %prog -- MultiNodeTests.testOrphanedModel python %prog -- --failfast python %prog -- --failfast OneNodeTests.testOrphanedModel Available suitename.testnames: """ # Update help string allTests = _getTestList() for test in allTests: helpString += "\n %s" % (test) # ============================================================================ # Process command line arguments parser = OptionParser(helpString,conflict_handler="resolve") parser.add_option("--verbosity", default=0, type="int", help="Verbosity level, either 0, 1, 2, or 3 [default: %default].") parser.add_option("--runInProc", action="store_true", default=False, help="Run inProc tests, currently inProc are not being run by default " " running. [default: %default].") parser.add_option("--logLevel", action="store", type="int", default=logging.INFO, help="override default log level. Pass in an integer value that " "represents the desired logging level (10=logging.DEBUG, " "20=logging.INFO, etc.) [default: %default].") parser.add_option("--hs", dest="hsVersion", default=2, type='int', help=("Hypersearch version (only 2 supported; 1 was " "deprecated) [default: %default].")) return parser.parse_args(args=cls.args) @classmethod def parseArgs(cls): """ Returns the test arguments after parsing """ return cls._processArgs()[0] @classmethod def consumeArgs(cls): """ Consumes the test arguments and returns the remaining arguments meant for unittest.man """ return cls._processArgs()[1] def setUpModule(): print "\nCURRENT DIRECTORY:", os.getcwd() initLogging(verbose=True) global g_myEnv # Setup our environment g_myEnv = MyTestEnvironment() if __name__ == '__main__': # Form the command line for the unit test framework # Consume test specific arguments and pass remaining to unittest.main _ArgParser.args = sys.argv[1:] args = [sys.argv[0]] + _ArgParser.consumeArgs() # Run the tests if called using python unittest.main(argv=args)	101,464	Python	.py	2,125	35.606588	95	0.581319	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,202	description.py	numenta_nupic-legacy/tests/swarming/nupic/swarming/experiments/delta/description.py	# ---------------------------------------------------------------------- # Numenta Platform for Intelligent Computing (NuPIC) # Copyright (C) 2013, Numenta, Inc. Unless you have an agreement # with Numenta, Inc., for a separate license for this software code, the # following terms and conditions apply: # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU Affero Public License version 3 as # published by the Free Software Foundation. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. # See the GNU Affero Public License for more details. # # You should have received a copy of the GNU Affero Public License # along with this program. If not, see http://www.gnu.org/licenses. # # http://numenta.org/licenses/ # ---------------------------------------------------------------------- """ Template file used by the OPF Experiment Generator to generate the actual description.py file by replacing $XXXXXXXX tokens with desired values. This description.py file was generated by: '/Users/ronmarianetti/nupic/eng/lib/python2.6/site-packages/nupic/frameworks/opf/expGenerator/experiment_generator.py' """ from nupic.frameworks.opf.exp_description_api import ExperimentDescriptionAPI from nupic.frameworks.opf.exp_description_helpers import ( updateConfigFromSubConfig, applyValueGettersToContainer, DeferredDictLookup) from nupic.frameworks.opf.htm_prediction_model_callbacks import * from nupic.frameworks.opf.metrics import MetricSpec from nupic.frameworks.opf.opf_utils import (InferenceType, InferenceElement) from nupic.support import aggregationDivide from nupic.frameworks.opf.opf_task_driver import ( IterationPhaseSpecLearnOnly, IterationPhaseSpecInferOnly, IterationPhaseSpecLearnAndInfer) # Model Configuration Dictionary: # # Define the model parameters and adjust for any modifications if imported # from a sub-experiment. # # These fields might be modified by a sub-experiment; this dict is passed # between the sub-experiment and base experiment # # # NOTE: Use of DEFERRED VALUE-GETTERs: dictionary fields and list elements # within the config dictionary may be assigned futures derived from the # ValueGetterBase class, such as DeferredDictLookup. # This facility is particularly handy for enabling substitution of values in # the config dictionary from other values in the config dictionary, which is # needed by permutation.py-based experiments. These values will be resolved # during the call to applyValueGettersToContainer(), # which we call after the base experiment's config dictionary is updated from # the sub-experiment. See ValueGetterBase and # DeferredDictLookup for more details about value-getters. # # For each custom encoder parameter to be exposed to the sub-experiment/ # permutation overrides, define a variable in this section, using key names # beginning with a single underscore character to avoid collisions with # pre-defined keys (e.g., _dsEncoderFieldName2_N). # # Example: # config = dict( # _dsEncoderFieldName2_N = 70, # _dsEncoderFieldName2_W = 5, # dsEncoderSchema = [ # base=dict( # fieldname='Name2', type='ScalarEncoder', # name='Name2', minval=0, maxval=270, clipInput=True, # n=DeferredDictLookup('_dsEncoderFieldName2_N'), # w=DeferredDictLookup('_dsEncoderFieldName2_W')), # ], # ) # updateConfigFromSubConfig(config) # applyValueGettersToContainer(config) config = { # Type of model that the rest of these parameters apply to. 'model': "HTMPrediction", # Version that specifies the format of the config. 'version': 1, # Intermediate variables used to compute fields in modelParams and also # referenced from the control section. 'aggregationInfo': { 'days': 0, 'fields': [], 'hours': 0, 'microseconds': 0, 'milliseconds': 0, 'minutes': 0, 'months': 0, 'seconds': 0, 'weeks': 0, 'years': 0}, 'predictAheadTime': None, # Model parameter dictionary. 'modelParams': { # The type of inference that this model will perform 'inferenceType': 'TemporalMultiStep', 'sensorParams': { # Sensor diagnostic output verbosity control; # if > 0: sensor region will print out on screen what it's sensing # at each step 0: silent; >=1: some info; >=2: more info; # >=3: even more info (see compute() in py/regions/RecordSensor.py) 'verbosity' : 0, # Example: # dsEncoderSchema = [ # DeferredDictLookup('__field_name_encoder'), # ], # # (value generated from DS_ENCODER_SCHEMA) 'encoders': { 'value': { 'clipInput': True, 'fieldname': u'value', 'n': 100, 'name': u'value', 'type': 'ScalarSpaceEncoder', 'w': 21}, '_classifierInput': { 'name': u'_classifierInput', 'fieldname': u'value', 'classifierOnly': True, 'type': 'ScalarSpaceEncoder', 'n': 100, 'w': 21}, }, # A dictionary specifying the period for automatically-generated # resets from a RecordSensor; # # None = disable automatically-generated resets (also disabled if # all of the specified values evaluate to 0). # Valid keys is the desired combination of the following: # days, hours, minutes, seconds, milliseconds, microseconds, weeks # # Example for 1.5 days: sensorAutoReset = dict(days=1,hours=12), # # (value generated from SENSOR_AUTO_RESET) 'sensorAutoReset' : None, }, 'spEnable': True, 'spParams': { # SP diagnostic output verbosity control; # 0: silent; >=1: some info; >=2: more info; 'spVerbosity' : 0, 'globalInhibition': 1, # Number of cell columns in the cortical region (same number for # SP and TM) # (see also tpNCellsPerCol) 'columnCount': 2048, 'inputWidth': 0, # SP inhibition control (absolute value); # Maximum number of active columns in the SP region's output (when # there are more, the weaker ones are suppressed) 'numActiveColumnsPerInhArea': 40, 'seed': 1956, # potentialPct # What percent of the columns's receptive field is available # for potential synapses. At initialization time, we will # choose potentialPct * (2potentialRadius+1)^2 'potentialPct': 0.5, # The default connected threshold. Any synapse whose # permanence value is above the connected threshold is # a "connected synapse", meaning it can contribute to the # cell's firing. Typical value is 0.10. Cells whose activity # level before inhibition falls below minDutyCycleBeforeInh # will have their own internal synPermConnectedCell # threshold set below this default value. # (This concept applies to both SP and TM and so 'cells' # is correct here as opposed to 'columns') 'synPermConnected': 0.1, 'synPermActiveInc': 0.1, 'synPermInactiveDec': 0.01, }, # Controls whether TM is enabled or disabled; # TM is necessary for making temporal predictions, such as predicting # the next inputs. Without TM, the model is only capable of # reconstructing missing sensor inputs (via SP). 'tmEnable' : True, 'tmParams': { # TM diagnostic output verbosity control; # 0: silent; [1..6]: increasing levels of verbosity # (see verbosity in nupic/trunk/py/nupic/research/backtracking_tm.py and backtracking_tm_cpp.py) 'verbosity': 0, # Number of cell columns in the cortical region (same number for # SP and TM) # (see also tpNCellsPerCol) 'columnCount': 2048, # The number of cells (i.e., states), allocated per column. 'cellsPerColumn': 32, 'inputWidth': 2048, 'seed': 1960, # Temporal Pooler implementation selector (see _getTPClass in # CLARegion.py). 'temporalImp': 'cpp', # New Synapse formation count # NOTE: If None, use spNumActivePerInhArea # # TODO: need better explanation 'newSynapseCount': 20, # Maximum number of synapses per segment # > 0 for fixed-size CLA # -1 for non-fixed-size CLA # # TODO: for Ron: once the appropriate value is placed in TM # constructor, see if we should eliminate this parameter from # description.py. 'maxSynapsesPerSegment': 32, # Maximum number of segments per cell # > 0 for fixed-size CLA # -1 for non-fixed-size CLA # # TODO: for Ron: once the appropriate value is placed in TM # constructor, see if we should eliminate this parameter from # description.py. 'maxSegmentsPerCell': 128, # Initial Permanence # TODO: need better explanation 'initialPerm': 0.21, # Permanence Increment 'permanenceInc': 0.1, # Permanence Decrement # If set to None, will automatically default to tpPermanenceInc # value. 'permanenceDec' : 0.1, 'globalDecay': 0.0, 'maxAge': 0, # Minimum number of active synapses for a segment to be considered # during search for the best-matching segments. # None=use default # Replaces: tpMinThreshold 'minThreshold': 12, # Segment activation threshold. # A segment is active if it has >= tpSegmentActivationThreshold # connected synapses that are active due to infActiveState # None=use default # Replaces: tpActivationThreshold 'activationThreshold': 16, 'outputType': 'normal', # "Pay Attention Mode" length. This tells the TM how many new # elements to append to the end of a learned sequence at a time. # Smaller values are better for datasets with short sequences, # higher values are better for datasets with long sequences. 'pamLength': 1, }, 'clParams': { 'regionName' : 'SDRClassifierRegion', # Classifier diagnostic output verbosity control; # 0: silent; [1..6]: increasing levels of verbosity 'verbosity' : 0, # This controls how fast the classifier learns/forgets. Higher values # make it adapt faster and forget older patterns faster. 'alpha': 0.001, # This is set after the call to updateConfigFromSubConfig and is # computed from the aggregationInfo and predictAheadTime. 'steps': '1,5', }, 'trainSPNetOnlyIfRequested': False, }, } # end of config dictionary # Adjust base config dictionary for any modifications if imported from a # sub-experiment updateConfigFromSubConfig(config) # Compute predictionSteps based on the predictAheadTime and the aggregation # period, which may be permuted over. if config['predictAheadTime'] is not None: predictionSteps = int(round(aggregationDivide( config['predictAheadTime'], config['aggregationInfo']))) assert (predictionSteps >= 1) config['modelParams']['clParams']['steps'] = str(predictionSteps) # Adjust config by applying ValueGetterBase-derived # futures. NOTE: this MUST be called after updateConfigFromSubConfig() in order # to support value-getter-based substitutions from the sub-experiment (if any) applyValueGettersToContainer(config) control = { # The environment that the current model is being run in "environment": 'nupic', # Input stream specification per py/nupicengine/cluster/database/StreamDef.json. # 'dataset' : { u'info': u'sawtooth test', u'streams': [ { u'columns': [u'value'], u'info': u'sawtooth', u'source': u'file://extra/sawtooth/sawtooth.csv'}], u'version': 1}, # Iteration count: maximum number of iterations. Each iteration corresponds # to one record from the (possibly aggregated) dataset. The task is # terminated when either number of iterations reaches iterationCount or # all records in the (possibly aggregated) database have been processed, # whichever occurs first. # # iterationCount of -1 = iterate over the entire dataset 'iterationCount' : 20, # A dictionary containing all the supplementary parameters for inference "inferenceArgs":{u'predictedField': u'value', u'predictionSteps': [1, 5]}, # Metrics: A list of MetricSpecs that instantiate the metrics that are # computed for this experiment 'metrics':[ MetricSpec(field=u'value', metric='multiStep', inferenceElement='multiStepBestPredictions', params={'window': 10, 'steps': 1, 'errorMetric': 'aae'}), MetricSpec(field=u'value', metric='multiStep', inferenceElement='multiStepBestPredictions', params={'window': 10, 'steps': 5, 'errorMetric': 'aae'}), ], # Logged Metrics: A sequence of regular expressions that specify which of # the metrics from the Inference Specifications section MUST be logged for # every prediction. The regex's correspond to the automatically generated # metric labels. This is similar to the way the optimization metric is # specified in permutations.py. 'loggedMetrics': ['.nupicScore.*'], } descriptionInterface = ExperimentDescriptionAPI(modelConfig=config, control=control)	14,643	Python	.py	312	37.967949	153	0.635114	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,203	permutations.py	numenta_nupic-legacy/tests/swarming/nupic/swarming/experiments/delta/permutations.py	# ---------------------------------------------------------------------- # Numenta Platform for Intelligent Computing (NuPIC) # Copyright (C) 2013, Numenta, Inc. Unless you have an agreement # with Numenta, Inc., for a separate license for this software code, the # following terms and conditions apply: # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU Affero Public License version 3 as # published by the Free Software Foundation. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. # See the GNU Affero Public License for more details. # # You should have received a copy of the GNU Affero Public License # along with this program. If not, see http://www.gnu.org/licenses. # # http://numenta.org/licenses/ # ---------------------------------------------------------------------- """ Template file used by ExpGenerator to generate the actual permutations.py file by replacing $XXXXXXXX tokens with desired values. This permutations.py file was generated by: '/Users/ronmarianetti/nupic/eng/lib/python2.6/site-packages/nupic/frameworks/opf/expGenerator/experiment_generator.py' """ import os from nupic.swarming.permutation_helpers import * # The name of the field being predicted. Any allowed permutation MUST contain # the prediction field. # (generated from PREDICTION_FIELD) predictedField = 'value' permutations = { 'modelParams': { 'sensorParams': { 'encoders': { 'value': PermuteEncoder(fieldName='value', encoderClass='ScalarSpaceEncoder', space=PermuteChoices(['delta', 'absolute']), clipInput=True, w=21, n=PermuteInt(28, 521)), '_classifierInput': dict(fieldname='value', type='ScalarSpaceEncoder', classifierOnly=True, space=PermuteChoices(['delta', 'absolute']), clipInput=True, w=21, n=PermuteInt(28, 521)), }, }, 'tmParams': { 'minThreshold': PermuteInt(9, 12), 'activationThreshold': PermuteInt(12, 16), 'pamLength': PermuteInt(1, 5), }, 'clParams': { 'alpha': PermuteFloat(0.000100, 0.100000), }, } } # Fields selected for final hypersearch report; # NOTE: These values are used as regular expressions by RunPermutations.py's # report generator # (fieldname values generated from PERM_PREDICTED_FIELD_NAME) report = [ '.value.', ] # Permutation optimization setting: either minimize or maximize metric # used by RunPermutations. # NOTE: The value is used as a regular expressions by RunPermutations.py's # report generator # (generated from minimize = "multiStepBestPredictions:multiStep:errorMetric='aae':steps=1:window=1000:field=value") minimize = "multiStepBestPredictions:multiStep:errorMetric='aae':steps=1:window=10:field=value" minParticlesPerSwarm = None def permutationFilter(perm): """ This function can be used to selectively filter out specific permutation combinations. It is called by RunPermutations for every possible permutation of the variables in the permutations dict. It should return True for valid a combination of permutation values and False for an invalid one. Parameters: --------------------------------------------------------- perm: dict of one possible combination of name:value pairs chosen from permutations. """ # An example of how to use this #if perm['__consumption_encoder']['maxval'] > 300: # return False; # return True	3,895	Python	.py	90	36.644444	118	0.645707	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,204	description.py	numenta_nupic-legacy/tests/swarming/nupic/swarming/experiments/spatial_classification/description.py	# ---------------------------------------------------------------------- # Numenta Platform for Intelligent Computing (NuPIC) # Copyright (C) 2013, Numenta, Inc. Unless you have an agreement # with Numenta, Inc., for a separate license for this software code, the # following terms and conditions apply: # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU Affero Public License version 3 as # published by the Free Software Foundation. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. # See the GNU Affero Public License for more details. # # You should have received a copy of the GNU Affero Public License # along with this program. If not, see http://www.gnu.org/licenses. # # http://numenta.org/licenses/ # ---------------------------------------------------------------------- """ Template file used by the OPF Experiment Generator to generate the actual description.py file by replacing $XXXXXXXX tokens with desired values. This description.py file was generated by: '/Users/ronmarianetti/nupic/eng/lib/python2.6/site-packages/nupicengine/frameworks/opf/expGenerator/ExpGenerator.pyc' """ from nupic.frameworks.opf.exp_description_api import ExperimentDescriptionAPI from nupic.frameworks.opf.exp_description_helpers import ( updateConfigFromSubConfig, applyValueGettersToContainer, DeferredDictLookup) from nupic.frameworks.opf.htm_prediction_model_callbacks import * from nupic.frameworks.opf.metrics import MetricSpec from nupic.frameworks.opf.opf_utils import (InferenceType, InferenceElement) from nupic.support import aggregationDivide from nupic.frameworks.opf.opf_task_driver import ( IterationPhaseSpecLearnOnly, IterationPhaseSpecInferOnly, IterationPhaseSpecLearnAndInfer) # Model Configuration Dictionary: # # Define the model parameters and adjust for any modifications if imported # from a sub-experiment. # # These fields might be modified by a sub-experiment; this dict is passed # between the sub-experiment and base experiment # # # NOTE: Use of DEFERRED VALUE-GETTERs: dictionary fields and list elements # within the config dictionary may be assigned futures derived from the # ValueGetterBase class, such as DeferredDictLookup. # This facility is particularly handy for enabling substitution of values in # the config dictionary from other values in the config dictionary, which is # needed by permutation.py-based experiments. These values will be resolved # during the call to applyValueGettersToContainer(), # which we call after the base experiment's config dictionary is updated from # the sub-experiment. See ValueGetterBase and # DeferredDictLookup for more details about value-getters. # # For each custom encoder parameter to be exposed to the sub-experiment/ # permutation overrides, define a variable in this section, using key names # beginning with a single underscore character to avoid collisions with # pre-defined keys (e.g., _dsEncoderFieldName2_N). # # Example: # config = dict( # _dsEncoderFieldName2_N = 70, # _dsEncoderFieldName2_W = 5, # dsEncoderSchema = [ # base=dict( # fieldname='Name2', type='ScalarEncoder', # name='Name2', minval=0, maxval=270, clipInput=True, # n=DeferredDictLookup('_dsEncoderFieldName2_N'), # w=DeferredDictLookup('_dsEncoderFieldName2_W')), # ], # ) # updateConfigFromSubConfig(config) # applyValueGettersToContainer(config) config = { # Type of model that the rest of these parameters apply to. 'model': "HTMPrediction", # Version that specifies the format of the config. 'version': 1, # Intermediate variables used to compute fields in modelParams and also # referenced from the control section. 'aggregationInfo': { 'fields': [], 'days': 0, 'hours': 0, 'microseconds': 0, 'milliseconds': 0, 'minutes': 0, 'months': 0, 'seconds': 0, 'weeks': 0, 'years': 0 }, 'predictAheadTime': None, # Model parameter dictionary. 'modelParams': { # The type of inference that this model will perform 'inferenceType': 'NontemporalClassification', 'sensorParams': { # Sensor diagnostic output verbosity control; # if > 0: sensor region will print out on screen what it's sensing # at each step 0: silent; >=1: some info; >=2: more info; # >=3: even more info (see compute() in py/regions/RecordSensor.py) 'verbosity' : 0, # Example: # dsEncoderSchema = [ # DeferredDictLookup('__field_name_encoder'), # ], # # (value generated from DS_ENCODER_SCHEMA) 'encoders': { 'address': { 'fieldname': u'address', 'n': 300, 'name': u'address', 'type': 'SDRCategoryEncoder', 'w': 21 }, '_classifierInput': { 'name': u'_classifierInput', 'fieldname': u'consumption', 'classifierOnly': True, 'clipInput': True, 'maxval': 200, 'minval': 0, 'n': 1500, 'type': 'ScalarEncoder', 'w': 21 }, 'gym': { 'fieldname': u'gym', 'n': 300, 'name': u'gym', 'type': 'SDRCategoryEncoder', 'w': 21 }, 'timestamp_dayOfWeek': { 'dayOfWeek': (7, 3), 'fieldname': u'timestamp', 'name': u'timestamp_dayOfWeek', 'type': 'DateEncoder' }, 'timestamp_timeOfDay': { 'fieldname': u'timestamp', 'name': u'timestamp_timeOfDay', 'timeOfDay': (7, 8), 'type': 'DateEncoder' } }, # A dictionary specifying the period for automatically-generated # resets from a RecordSensor; # # None = disable automatically-generated resets (also disabled if # all of the specified values evaluate to 0). # Valid keys is the desired combination of the following: # days, hours, minutes, seconds, milliseconds, microseconds, weeks # # Example for 1.5 days: sensorAutoReset = dict(days=1,hours=12), # # (value generated from SENSOR_AUTO_RESET) 'sensorAutoReset' : None, }, 'spEnable': False, 'spParams': { # SP diagnostic output verbosity control; # 0: silent; >=1: some info; >=2: more info; 'spVerbosity' : 0, 'globalInhibition': 1, # Number of cell columns in the cortical region (same number for # SP and TM) # (see also tpNCellsPerCol) 'columnCount': 2048, 'inputWidth': 0, # SP inhibition control (absolute value); # Maximum number of active columns in the SP region's output (when # there are more, the weaker ones are suppressed) 'numActiveColumnsPerInhArea': 40, 'seed': 1956, # potentialPct # What percent of the columns's receptive field is available # for potential synapses. At initialization time, we will # choose potentialPct * (2potentialRadius+1)^2 'potentialPct': 0.5, # The default connected threshold. Any synapse whose # permanence value is above the connected threshold is # a "connected synapse", meaning it can contribute to the # cell's firing. Typical value is 0.10. Cells whose activity # level before inhibition falls below minDutyCycleBeforeInh # will have their own internal synPermConnectedCell # threshold set below this default value. # (This concept applies to both SP and TM and so 'cells' # is correct here as opposed to 'columns') 'synPermConnected': 0.1, 'synPermActiveInc': 0.1, 'synPermInactiveDec': 0.01, }, # Controls whether TM is enabled or disabled; # TM is necessary for making temporal predictions, such as predicting # the next inputs. Without TM, the model is only capable of # reconstructing missing sensor inputs (via SP). 'tmEnable' : False, 'tmParams': { # TM diagnostic output verbosity control; # 0: silent; [1..6]: increasing levels of verbosity # (see verbosity in nupic/trunk/py/nupic/research/backtracking_tm.py and backtracking_tm_cpp.py) 'verbosity': 0, # Number of cell columns in the cortical region (same number for # SP and TM) # (see also tpNCellsPerCol) 'columnCount': 2048, # The number of cells (i.e., states), allocated per column. 'cellsPerColumn': 32, 'inputWidth': 2048, 'seed': 1960, # Temporal Pooler implementation selector (see _getTPClass in # CLARegion.py). 'temporalImp': 'cpp', # New Synapse formation count # NOTE: If None, use spNumActivePerInhArea # # TODO: need better explanation 'newSynapseCount': 20, # Maximum number of synapses per segment # > 0 for fixed-size CLA # -1 for non-fixed-size CLA # # TODO: for Ron: once the appropriate value is placed in TM # constructor, see if we should eliminate this parameter from # description.py. 'maxSynapsesPerSegment': 32, # Maximum number of segments per cell # > 0 for fixed-size CLA # -1 for non-fixed-size CLA # # TODO: for Ron: once the appropriate value is placed in TM # constructor, see if we should eliminate this parameter from # description.py. 'maxSegmentsPerCell': 128, # Initial Permanence # TODO: need better explanation 'initialPerm': 0.21, # Permanence Increment 'permanenceInc': 0.1, # Permanence Decrement # If set to None, will automatically default to tpPermanenceInc # value. 'permanenceDec' : 0.1, 'globalDecay': 0.0, 'maxAge': 0, # Minimum number of active synapses for a segment to be considered # during search for the best-matching segments. # None=use default # Replaces: tpMinThreshold 'minThreshold': 12, # Segment activation threshold. # A segment is active if it has >= tpSegmentActivationThreshold # connected synapses that are active due to infActiveState # None=use default # Replaces: tpActivationThreshold 'activationThreshold': 16, 'outputType': 'normal', # "Pay Attention Mode" length. This tells the TM how many new # elements to append to the end of a learned sequence at a time. # Smaller values are better for datasets with short sequences, # higher values are better for datasets with long sequences. 'pamLength': 1, }, 'clParams': { 'regionName' : 'SDRClassifierRegion', 'implementation': 'py', # Classifier diagnostic output verbosity control; # 0: silent; [1..6]: increasing levels of verbosity 'verbosity' : 0, # This controls how fast the classifier learns/forgets. Higher values # make it adapt faster and forget older patterns faster. 'alpha': 0.001, # This is set after the call to updateConfigFromSubConfig and is # computed from the aggregationInfo and predictAheadTime. 'steps': '0', }, 'anomalyParams': { u'anomalyCacheRecords': None, u'autoDetectThreshold': None, u'autoDetectWaitRecords': None}, 'trainSPNetOnlyIfRequested': False, }, } # end of config dictionary # Adjust base config dictionary for any modifications if imported from a # sub-experiment updateConfigFromSubConfig(config) # Compute predictionSteps based on the predictAheadTime and the aggregation # period, which may be permuted over. if config['predictAheadTime'] is not None: predictionSteps = int(round(aggregationDivide( config['predictAheadTime'], config['aggregationInfo']))) assert (predictionSteps >= 1) config['modelParams']['clParams']['steps'] = str(predictionSteps) # Adjust config by applying ValueGetterBase-derived # futures. NOTE: this MUST be called after updateConfigFromSubConfig() in order # to support value-getter-based substitutions from the sub-experiment (if any) applyValueGettersToContainer(config) control = { # The environment that the current model is being run in "environment": 'nupic', # Input stream specification per py/nupic/frameworks/opf/jsonschema/stream_def.json. # 'dataset' : { u'info': u'testSpatialClassification', u'streams': [ { u'columns': [u''], u'info': u'test data', u'source': u'file://swarming/test_data.csv'}], u'version': 1}, # Iteration count: maximum number of iterations. Each iteration corresponds # to one record from the (possibly aggregated) dataset. The task is # terminated when either number of iterations reaches iterationCount or # all records in the (possibly aggregated) database have been processed, # whichever occurs first. # # iterationCount of -1 = iterate over the entire dataset #'iterationCount' : ITERATION_COUNT, # A dictionary containing all the supplementary parameters for inference "inferenceArgs":{u'predictedField': u'consumption', u'predictionSteps': [0]}, # Metrics: A list of MetricSpecs that instantiate the metrics that are # computed for this experiment 'metrics':[ MetricSpec(field=u'consumption', metric='multiStep', inferenceElement='multiStepBestPredictions', params={'window': 1000, 'steps': [0], 'errorMetric': 'avg_err'}) ], # Logged Metrics: A sequence of regular expressions that specify which of # the metrics from the Inference Specifications section MUST be logged for # every prediction. The regex's correspond to the automatically generated # metric labels. This is similar to the way the optimization metric is # specified in permutations.py. 'loggedMetrics': ['.*'], } descriptionInterface = ExperimentDescriptionAPI(modelConfig=config, control=control)	15,453	Python	.py	342	35.748538	117	0.620437	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,205	permutations.py	numenta_nupic-legacy/tests/swarming/nupic/swarming/experiments/spatial_classification/permutations.py	# ---------------------------------------------------------------------- # Numenta Platform for Intelligent Computing (NuPIC) # Copyright (C) 2013, Numenta, Inc. Unless you have an agreement # with Numenta, Inc., for a separate license for this software code, the # following terms and conditions apply: # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU Affero Public License version 3 as # published by the Free Software Foundation. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. # See the GNU Affero Public License for more details. # # You should have received a copy of the GNU Affero Public License # along with this program. If not, see http://www.gnu.org/licenses. # # http://numenta.org/licenses/ # ---------------------------------------------------------------------- """ Template file used by ExpGenerator to generate the actual permutations.py file by replacing $XXXXXXXX tokens with desired values. This permutations.py file was generated by: '/Users/ronmarianetti/nupic/eng/lib/python2.6/site-packages/nupicengine/frameworks/opf/expGenerator/ExpGenerator.pyc' """ import os from nupic.swarming.permutation_helpers import * # The name of the field being predicted. Any allowed permutation MUST contain # the prediction field. # (generated from PREDICTION_FIELD) predictedField = 'consumption' permutations = { 'modelParams': { 'sensorParams': { 'encoders': { 'gym': PermuteEncoder(fieldName='gym', encoderClass='SDRCategoryEncoder', w=7, n=100), 'timestamp_dayOfWeek': PermuteEncoder(fieldName='timestamp', encoderClass='DateEncoder.dayOfWeek', radius=PermuteChoices([1, 3]), w=7), 'timestamp_timeOfDay': PermuteEncoder(fieldName='timestamp', encoderClass='DateEncoder.timeOfDay', radius=PermuteChoices([1, 8]), w=7), '_classifierInput': dict(fieldname='consumption', classifierOnly=True, type='ScalarEncoder', maxval=PermuteInt(100, 300, 25), n=PermuteInt(13, 500, 20), w=7, minval=0), 'address': PermuteEncoder(fieldName='address', encoderClass='SDRCategoryEncoder', w=7, n=100), }, }, 'spParams': { }, 'tmParams': { }, 'clParams': { 'alpha': PermuteFloat(0.0001, 0.1), }, } } # Fields selected for final hypersearch report; # NOTE: These values are used as regular expressions by RunPermutations.py's # report generator # (fieldname values generated from PERM_PREDICTED_FIELD_NAME) report = [ '.consumption.', ] # Permutation optimization setting: either minimize or maximize metric # used by RunPermutations. # NOTE: The value is used as a regular expressions by RunPermutations.py's # report generator minimize = "multiStepBestPredictions:multiStep:errorMetric='avg_err':steps=\[0\]:window=1000:field=consumption" minParticlesPerSwarm = None def dummyModelParams(perm): """ This function can be used for Hypersearch algorithm development. When present, Hypersearch doesn't actually run the CLA model in the OPF, but instead runs a dummy model. This function returns the dummy model params that will be used. See the OPFDummyModelRunner class source code (in nupic.swarming.ModelRunner) for a description of the schema for the dummy model params. """ # We are trying to get Hyperseach to find a model with: # consumption encoder maxval=250 # consumption encoder n=53 # any address encoder # any gym encoder # no other fields errScore = 50 if perm['modelParams']['sensorParams']['encoders']['address'] is not None: errScore -= 20 if perm['modelParams']['sensorParams']['encoders']['gym'] is not None: errScore -= 10 if perm['modelParams']['sensorParams']['encoders']['timestamp_dayOfWeek'] \ is not None: errScore += 30 if perm['modelParams']['sensorParams']['encoders']['timestamp_timeOfDay'] \ is not None: errScore += 40 dummyModelParams = dict( metricValue = errScore, iterations = int(os.environ.get('NTA_TEST_numIterations', '1')), waitTime = None, sysExitModelRange = os.environ.get('NTA_TEST_sysExitModelRange', None), errModelRange = os.environ.get('NTA_TEST_errModelRange', None), jobFailErr = bool(os.environ.get('NTA_TEST_jobFailErr', False)) ) return dummyModelParams def permutationFilter(perm): """ This function can be used to selectively filter out specific permutation combinations. It is called by RunPermutations for every possible permutation of the variables in the permutations dict. It should return True for valid a combination of permutation values and False for an invalid one. Parameters: --------------------------------------------------------- perm: dict of one possible combination of name:value pairs chosen from permutations. """ # An example of how to use this: #limit = int(os.environ.get('NTA_TEST_maxvalFilter', 300)) #if perm['modelParams']['sensorParams']['encoders']['consumption']['maxval'] > limit: # return False; return True	5,554	Python	.py	125	38.28	117	0.664505	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,206	description.py	numenta_nupic-legacy/tests/swarming/nupic/swarming/experiments/input_predicted_field/description.py	# ---------------------------------------------------------------------- # Numenta Platform for Intelligent Computing (NuPIC) # Copyright (C) 2013, Numenta, Inc. Unless you have an agreement # with Numenta, Inc., for a separate license for this software code, the # following terms and conditions apply: # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU Affero Public License version 3 as # published by the Free Software Foundation. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. # See the GNU Affero Public License for more details. # # You should have received a copy of the GNU Affero Public License # along with this program. If not, see http://www.gnu.org/licenses. # # http://numenta.org/licenses/ # ---------------------------------------------------------------------- """ Template file used by the OPF Experiment Generator to generate the actual description.py file by replacing $XXXXXXXX tokens with desired values. This description.py file was generated by: '/Users/ronmarianetti/nupic/eng/lib/python2.6/site-packages/nupicengine/frameworks/opf/expGenerator/ExpGenerator.pyc' """ from nupic.frameworks.opf.exp_description_api import ExperimentDescriptionAPI from nupic.frameworks.opf.exp_description_helpers import ( updateConfigFromSubConfig, applyValueGettersToContainer ) from nupic.frameworks.opf.htm_prediction_model_callbacks import * from nupic.frameworks.opf.metrics import MetricSpec from nupic.frameworks.opf.opf_utils import (InferenceType, InferenceElement) from nupic.support import aggregationDivide from nupic.frameworks.opf.opf_task_driver import ( IterationPhaseSpecLearnOnly, IterationPhaseSpecInferOnly, IterationPhaseSpecLearnAndInfer) # Model Configuration Dictionary: # # Define the model parameters and adjust for any modifications if imported # from a sub-experiment. # # These fields might be modified by a sub-experiment; this dict is passed # between the sub-experiment and base experiment # # config = { # Type of model that the rest of these parameters apply to. 'model': "HTMPrediction", # Version that specifies the format of the config. 'version': 1, # Intermediate variables used to compute fields in modelParams and also # referenced from the control section. 'aggregationInfo': { 'days': 0, 'fields': [ (u'timestamp', 'first'), (u'consumption', 'sum'), ], 'hours': 0, 'microseconds': 0, 'milliseconds': 0, 'minutes': 0, 'months': 0, 'seconds': 0, 'weeks': 0, 'years': 0}, 'predictAheadTime': None, # Model parameter dictionary. 'modelParams': { # The type of inference that this model will perform 'inferenceType': 'TemporalMultiStep', 'sensorParams': { # Sensor diagnostic output verbosity control; # if > 0: sensor region will print out on screen what it's sensing # at each step 0: silent; >=1: some info; >=2: more info; # >=3: even more info (see compute() in py/regions/RecordSensor.py) 'verbosity' : 0, # Example: # 'encoders': {'field1': {'fieldname': 'field1', 'n':100, # 'name': 'field1', 'type': 'AdaptiveScalarEncoder', # 'w': 21}} # 'encoders': { 'consumption': { 'clipInput': True, 'fieldname': u'consumption', 'n': 100, 'name': u'consumption', 'type': 'AdaptiveScalarEncoder', 'w': 21}, 'address': { 'fieldname': u'address', 'n': 300, 'name': u'address', 'type': 'SDRCategoryEncoder', 'w': 21}, 'gym': { 'fieldname': u'gym', 'n': 100, 'name': u'gym', 'type': 'SDRCategoryEncoder', 'w': 21}, 'timestamp_dayOfWeek': { 'dayOfWeek': (7, 3), 'fieldname': u'timestamp', 'name': u'timestamp_dayOfWeek', 'type': 'DateEncoder'}, 'timestamp_timeOfDay': { 'fieldname': u'timestamp', 'name': u'timestamp_timeOfDay', 'timeOfDay': (7, 8), 'type': 'DateEncoder'}, '_classifierInput': { 'name': u'_classifierInput', 'fieldname': u'consumption', 'classifierOnly': True, 'type': 'AdaptiveScalarEncoder', 'clipInput': True, 'n': 100, 'w': 21}, }, # A dictionary specifying the period for automatically-generated # resets from a RecordSensor; # # None = disable automatically-generated resets (also disabled if # all of the specified values evaluate to 0). # Valid keys is the desired combination of the following: # days, hours, minutes, seconds, milliseconds, microseconds, weeks # # Example for 1.5 days: sensorAutoReset = dict(days=1,hours=12), # # (value generated from SENSOR_AUTO_RESET) 'sensorAutoReset' : { u'days': 0, u'hours': 0}, }, 'spEnable': True, 'spParams': { # SP diagnostic output verbosity control; # 0: silent; >=1: some info; >=2: more info; 'spVerbosity' : 0, 'globalInhibition': 1, # Number of cell columns in the cortical region (same number for # SP and TM) # (see also tpNCellsPerCol) 'columnCount': 2048, 'inputWidth': 0, # SP inhibition control (absolute value); # Maximum number of active columns in the SP region's output (when # there are more, the weaker ones are suppressed) 'numActiveColumnsPerInhArea': 40, 'seed': 1956, # potentialPct # What percent of the columns's receptive field is available # for potential synapses. At initialization time, we will # choose potentialPct * (2potentialRadius+1)^2 'potentialPct': 0.5, # The default connected threshold. Any synapse whose # permanence value is above the connected threshold is # a "connected synapse", meaning it can contribute to the # cell's firing. Typical value is 0.10. Cells whose activity # level before inhibition falls below minDutyCycleBeforeInh # will have their own internal synPermConnectedCell # threshold set below this default value. # (This concept applies to both SP and TM and so 'cells' # is correct here as opposed to 'columns') 'synPermConnected': 0.1, 'synPermActiveInc': 0.1, 'synPermInactiveDec': 0.01, }, # Controls whether TM is enabled or disabled; # TM is necessary for making temporal predictions, such as predicting # the next inputs. Without TM, the model is only capable of # reconstructing missing sensor inputs (via SP). 'tmEnable' : True, 'tmParams': { # TM diagnostic output verbosity control; # 0: silent; [1..6]: increasing levels of verbosity # (see verbosity in nupic/trunk/py/nupic/research/backtracking_tm.py and backtracking_tm_cpp.py) 'verbosity': 0, # Number of cell columns in the cortical region (same number for # SP and TM) # (see also tpNCellsPerCol) 'columnCount': 2048, # The number of cells (i.e., states), allocated per column. 'cellsPerColumn': 32, 'inputWidth': 2048, 'seed': 1960, # Temporal Pooler implementation selector (see _getTPClass in # CLARegion.py). 'temporalImp': 'cpp', # New Synapse formation count # NOTE: If None, use spNumActivePerInhArea # # TODO: need better explanation 'newSynapseCount': 20, # Maximum number of synapses per segment # > 0 for fixed-size CLA # -1 for non-fixed-size CLA # # TODO: for Ron: once the appropriate value is placed in TM # constructor, see if we should eliminate this parameter from # description.py. 'maxSynapsesPerSegment': 32, # Maximum number of segments per cell # > 0 for fixed-size CLA # -1 for non-fixed-size CLA # # TODO: for Ron: once the appropriate value is placed in TM # constructor, see if we should eliminate this parameter from # description.py. 'maxSegmentsPerCell': 128, # Initial Permanence # TODO: need better explanation 'initialPerm': 0.21, # Permanence Increment 'permanenceInc': 0.1, # Permanence Decrement # If set to None, will automatically default to tpPermanenceInc # value. 'permanenceDec' : 0.1, 'globalDecay': 0.0, 'maxAge': 0, # Minimum number of active synapses for a segment to be considered # during search for the best-matching segments. # None=use default # Replaces: tpMinThreshold 'minThreshold': 12, # Segment activation threshold. # A segment is active if it has >= tpSegmentActivationThreshold # connected synapses that are active due to infActiveState # None=use default # Replaces: tpActivationThreshold 'activationThreshold': 16, 'outputType': 'normal', # "Pay Attention Mode" length. This tells the TM how many new # elements to append to the end of a learned sequence at a time. # Smaller values are better for datasets with short sequences, # higher values are better for datasets with long sequences. 'pamLength': 1, }, 'clParams': { 'regionName' : 'SDRClassifierRegion', # Classifier diagnostic output verbosity control; # 0: silent; [1..6]: increasing levels of verbosity 'verbosity' : 0, # This controls how fast the classifier learns/forgets. Higher values # make it adapt faster and forget older patterns faster. 'alpha': 0.001, # This is set after the call to updateConfigFromSubConfig and is # computed from the aggregationInfo and predictAheadTime. 'steps': '1', }, 'anomalyParams': { u'anomalyCacheRecords': None, u'autoDetectThreshold': None, u'autoDetectWaitRecords': None}, 'trainSPNetOnlyIfRequested': False, }, } # end of config dictionary # Adjust base config dictionary for any modifications if imported from a # sub-experiment updateConfigFromSubConfig(config) # Compute predictionSteps based on the predictAheadTime and the aggregation # period, which may be permuted over. if config['predictAheadTime'] is not None: predictionSteps = int(round(aggregationDivide( config['predictAheadTime'], config['aggregationInfo']))) assert (predictionSteps >= 1) config['modelParams']['clParams']['steps'] = str(predictionSteps) # Adjust config by applying ValueGetterBase-derived # futures. NOTE: this MUST be called after updateConfigFromSubConfig() in order # to support value-getter-based substitutions from the sub-experiment (if any) applyValueGettersToContainer(config) control = { # The environment that the current model is being run in "environment": 'nupic', # Input stream specification per py/nupic/frameworks/opf/jsonschema/stream_def.json. # 'dataset' : { u'info': u'test_hotgym', u'streams': [ { u'columns': [u''], u'info': u'test data', u'source': u'file://swarming/test_data.csv'}], u'version': 1}, # Iteration count: maximum number of iterations. Each iteration corresponds # to one record from the (possibly aggregated) dataset. The task is # terminated when either number of iterations reaches iterationCount or # all records in the (possibly aggregated) database have been processed, # whichever occurs first. # # iterationCount of -1 = iterate over the entire dataset 'iterationCount' : -1, # A dictionary containing all the supplementary parameters for inference "inferenceArgs":{u'predictedField': u'consumption', u'predictionSteps': [1]}, # Metrics: A list of MetricSpecs that instantiate the metrics that are # computed for this experiment 'metrics':[ MetricSpec(field=u'consumption', metric='multiStep', inferenceElement='multiStepBestPredictions', params={'window': 1000, 'steps': [1], 'errorMetric': 'altMAPE'}), ], # Logged Metrics: A sequence of regular expressions that specify which of # the metrics from the Inference Specifications section MUST be logged for # every prediction. The regex's correspond to the automatically generated # metric labels. This is similar to the way the optimization metric is # specified in permutations.py. 'loggedMetrics': ['.*'], } descriptionInterface = ExperimentDescriptionAPI(modelConfig=config, control=control)	14,113	Python	.py	311	35.163987	117	0.605796	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,207	permutations.py	numenta_nupic-legacy/tests/swarming/nupic/swarming/experiments/input_predicted_field/permutations.py	# ---------------------------------------------------------------------- # Numenta Platform for Intelligent Computing (NuPIC) # Copyright (C) 2013, Numenta, Inc. Unless you have an agreement # with Numenta, Inc., for a separate license for this software code, the # following terms and conditions apply: # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU Affero Public License version 3 as # published by the Free Software Foundation. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. # See the GNU Affero Public License for more details. # # You should have received a copy of the GNU Affero Public License # along with this program. If not, see http://www.gnu.org/licenses. # # http://numenta.org/licenses/ # ---------------------------------------------------------------------- """ Template file used by ExpGenerator to generate the actual permutations.py file by replacing $XXXXXXXX tokens with desired values. This permutations.py file was generated by: '/Users/ronmarianetti/nupic/eng/lib/python2.6/site-packages/nupicengine/frameworks/opf/expGenerator/ExpGenerator.pyc' """ import os from nupic.swarming.permutation_helpers import * # The name of the field being predicted. Any allowed permutation MUST contain # the prediction field. # (generated from PREDICTION_FIELD) predictedField = 'consumption' permutations = { 'modelParams': { 'sensorParams': { 'encoders': { 'consumption': PermuteEncoder(fieldName='consumption', encoderClass='ScalarEncoder', maxval=PermuteInt(100, 300, 25), n=PermuteInt(13, 500, 20), w=7, minval=0), 'gym': PermuteEncoder(fieldName='gym', encoderClass='SDRCategoryEncoder', w=7, n=100), 'timestamp_dayOfWeek': PermuteEncoder(fieldName='timestamp', encoderClass='DateEncoder.dayOfWeek', radius=PermuteChoices([1, 3]), w=7), 'timestamp_timeOfDay': PermuteEncoder(fieldName='timestamp', encoderClass='DateEncoder.timeOfDay', radius=PermuteChoices([1, 8]), w=7), 'address': PermuteEncoder(fieldName='address', encoderClass='SDRCategoryEncoder', w=7, n=100), '_classifierInput': dict(fieldname='consumption', classifierOnly=True, encoderClass='ScalarEncoder', maxval=PermuteInt(100, 300, 25), n=PermuteInt(13, 500, 20), w=7, minval=0), }, }, 'spParams': { }, 'tmParams': { }, 'clParams': { 'alpha': PermuteFloat(0.0001, 0.1), }, } } # Fields selected for final hypersearch report; # NOTE: These values are used as regular expressions by RunPermutations.py's # report generator # (fieldname values generated from PERM_PREDICTED_FIELD_NAME) report = [ '.consumption.', ] # Permutation optimization setting: either minimize or maximize metric # used by RunPermutations. # NOTE: The value is used as a regular expressions by RunPermutations.py's # report generator # (generated from minimize = "multiStepBestPredictions:multiStep:errorMetric='altMAPE':steps=\[1\]:window=1000:field=consumption") minimize = "multiStepBestPredictions:multiStep:errorMetric='altMAPE':steps=\[1\]:window=1000:field=consumption" minParticlesPerSwarm = None inputPredictedField = os.environ.get("NTA_TEST_inputPredictedField", "auto") def dummyModelParams(perm): """ This function can be used for Hypersearch algorithm development. When present, Hypersearch doesn't actually run the CLA model in the OPF, but instead runs a dummy model. This function returns the dummy model params that will be used. See the OPFDummyModelRunner class source code (in nupic.swarming.ModelRunner) for a description of the schema for the dummy model params. """ # We are trying to get Hypersearch to find a model with: # no consumption encoder # any address encoder # any gym encoder # no other fields errScore = 50 if perm['modelParams']['sensorParams']['encoders']['consumption'] is not None: errScore += 10 if perm['modelParams']['sensorParams']['encoders']['address'] is not None: errScore -= 10 if perm['modelParams']['sensorParams']['encoders']['gym'] is not None: errScore -= 20 if perm['modelParams']['sensorParams']['encoders']['timestamp_timeOfDay'] \ is not None: errScore -= 30 if perm['modelParams']['sensorParams']['encoders']['timestamp_dayOfWeek'] \ is not None: errScore -= 40 dummyModelParams = dict( metricValue = errScore, iterations = int(os.environ.get('NTA_TEST_numIterations', '1')), waitTime = None, sysExitModelRange = os.environ.get('NTA_TEST_sysExitModelRange', None), errModelRange = os.environ.get('NTA_TEST_errModelRange', None), jobFailErr = bool(os.environ.get('NTA_TEST_jobFailErr', False)) ) return dummyModelParams def permutationFilter(perm): """ This function can be used to selectively filter out specific permutation combinations. It is called by RunPermutations for every possible permutation of the variables in the permutations dict. It should return True for valid a combination of permutation values and False for an invalid one. Parameters: --------------------------------------------------------- perm: dict of one possible combination of name:value pairs chosen from permutations. """ # An example of how to use this: #limit = int(os.environ.get('NTA_TEST_maxvalFilter', 300)) #if perm['modelParams']['sensorParams']['encoders']['consumption']['maxval'] > limit: # return False; return True	6,042	Python	.py	132	39.492424	130	0.667916	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,208	description.py	numenta_nupic-legacy/tests/swarming/nupic/swarming/experiments/smart_speculation_temporal/description.py	# ---------------------------------------------------------------------- # Numenta Platform for Intelligent Computing (NuPIC) # Copyright (C) 2013, Numenta, Inc. Unless you have an agreement # with Numenta, Inc., for a separate license for this software code, the # following terms and conditions apply: # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU Affero Public License version 3 as # published by the Free Software Foundation. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. # See the GNU Affero Public License for more details. # # You should have received a copy of the GNU Affero Public License # along with this program. If not, see http://www.gnu.org/licenses. # # http://numenta.org/licenses/ # ---------------------------------------------------------------------- """ Template file used by the OPF Experiment Generator to generate the actual description.py file by replacing $XXXXXXXX tokens with desired values. This description.py file was generated by: '/Users/ronmarianetti/nupic/eng/lib/python2.6/site-packages/nupic/frameworks/opf/expGenerator/experiment_generator.py' """ from nupic.frameworks.opf.exp_description_api import ExperimentDescriptionAPI from nupic.frameworks.opf.exp_description_helpers import ( updateConfigFromSubConfig, applyValueGettersToContainer, DeferredDictLookup) from nupic.frameworks.opf.htm_prediction_model_callbacks import * from nupic.frameworks.opf.metrics import MetricSpec from nupic.frameworks.opf.opf_utils import (InferenceType, InferenceElement) from nupic.support import aggregationDivide from nupic.frameworks.opf.opf_task_driver import ( IterationPhaseSpecLearnOnly, IterationPhaseSpecInferOnly, IterationPhaseSpecLearnAndInfer) # Model Configuration Dictionary: # # Define the model parameters and adjust for any modifications if imported # from a sub-experiment. # # These fields might be modified by a sub-experiment; this dict is passed # between the sub-experiment and base experiment # # # NOTE: Use of DEFERRED VALUE-GETTERs: dictionary fields and list elements # within the config dictionary may be assigned futures derived from the # ValueGetterBase class, such as DeferredDictLookup. # This facility is particularly handy for enabling substitution of values in # the config dictionary from other values in the config dictionary, which is # needed by permutation.py-based experiments. These values will be resolved # during the call to applyValueGettersToContainer(), # which we call after the base experiment's config dictionary is updated from # the sub-experiment. See ValueGetterBase and # DeferredDictLookup for more details about value-getters. # # For each custom encoder parameter to be exposed to the sub-experiment/ # permutation overrides, define a variable in this section, using key names # beginning with a single underscore character to avoid collisions with # pre-defined keys (e.g., _dsEncoderFieldName2_N). # # Example: # config = dict( # _dsEncoderFieldName2_N = 70, # _dsEncoderFieldName2_W = 5, # dsEncoderSchema = [ # base=dict( # fieldname='Name2', type='ScalarEncoder', # name='Name2', minval=0, maxval=270, clipInput=True, # n=DeferredDictLookup('_dsEncoderFieldName2_N'), # w=DeferredDictLookup('_dsEncoderFieldName2_W')), # ], # ) # updateConfigFromSubConfig(config) # applyValueGettersToContainer(config) config = { # Type of model that the rest of these parameters apply to. 'model': "HTMPrediction", # Version that specifies the format of the config. 'version': 1, # Intermediate variables used to compute fields in modelParams and also # referenced from the control section. 'aggregationInfo': { 'days': 0, 'fields': [], 'hours': 0, 'microseconds': 0, 'milliseconds': 0, 'minutes': 0, 'months': 0, 'seconds': 0, 'weeks': 0, 'years': 0}, 'predictAheadTime': None, # Model parameter dictionary. 'modelParams': { # The type of inference that this model will perform 'inferenceType': 'TemporalNextStep', 'sensorParams': { # Sensor diagnostic output verbosity control; # if > 0: sensor region will print out on screen what it's sensing # at each step 0: silent; >=1: some info; >=2: more info; # >=3: even more info (see compute() in py/regions/RecordSensor.py) 'verbosity' : 0, # Example: # dsEncoderSchema = [ # DeferredDictLookup('__field_name_encoder'), # ], # # (value generated from DS_ENCODER_SCHEMA) 'encoders': { u'A': { 'fieldname': u'daynight', 'n': 300, 'name': u'daynight', 'type': 'SDRCategoryEncoder', 'w': 21}, u'B': { 'fieldname': u'daynight', 'n': 300, 'name': u'daynight', 'type': 'SDRCategoryEncoder', 'w': 21}, u'C': { 'fieldname': u'precip', 'n': 300, 'name': u'precip', 'type': 'SDRCategoryEncoder', 'w': 21}, u'D': { 'clipInput': True, 'fieldname': u'visitor_winloss', 'maxval': 0.78600000000000003, 'minval': 0.0, 'n': 150, 'name': u'visitor_winloss', 'type': 'AdaptiveScalarEncoder', 'w': 21}, u'E': { 'clipInput': True, 'fieldname': u'home_winloss', 'maxval': 0.69999999999999996, 'minval': 0.0, 'n': 150, 'name': u'home_winloss', 'type': 'AdaptiveScalarEncoder', 'w': 21}, u'F': { 'dayOfWeek': (7, 1), 'fieldname': u'timestamp', 'name': u'timestamp_dayOfWeek', 'type': 'DateEncoder'}, u'G': { 'fieldname': u'timestamp', 'name': u'timestamp_timeOfDay', 'timeOfDay': (7, 1), 'type': 'DateEncoder'}, u'pred': { 'clipInput': True, 'fieldname': u'attendance', 'maxval': 36067, 'minval': 0, 'n': 150, 'name': u'attendance', 'type': 'AdaptiveScalarEncoder', 'w': 21}}, # A dictionary specifying the period for automatically-generated # resets from a RecordSensor; # # None = disable automatically-generated resets (also disabled if # all of the specified values evaluate to 0). # Valid keys is the desired combination of the following: # days, hours, minutes, seconds, milliseconds, microseconds, weeks # # Example for 1.5 days: sensorAutoReset = dict(days=1,hours=12), # # (value generated from SENSOR_AUTO_RESET) 'sensorAutoReset' : None, }, 'spEnable': True, 'spParams': { # SP diagnostic output verbosity control; # 0: silent; >=1: some info; >=2: more info; 'spVerbosity' : 0, 'globalInhibition': 1, # Number of cell columns in the cortical region (same number for # SP and TM) # (see also tpNCellsPerCol) 'columnCount': 2048, 'inputWidth': 0, # SP inhibition control (absolute value); # Maximum number of active columns in the SP region's output (when # there are more, the weaker ones are suppressed) 'numActiveColumnsPerInhArea': 40, 'seed': 1956, # potentialPct # What percent of the columns's receptive field is available # for potential synapses. At initialization time, we will # choose potentialPct * (2potentialRadius+1)^2 'potentialPct': 1.0, # The default connected threshold. Any synapse whose # permanence value is above the connected threshold is # a "connected synapse", meaning it can contribute to the # cell's firing. Typical value is 0.10. Cells whose activity # level before inhibition falls below minDutyCycleBeforeInh # will have their own internal synPermConnectedCell # threshold set below this default value. # (This concept applies to both SP and TM and so 'cells' # is correct here as opposed to 'columns') 'synPermConnected': 0.1, 'synPermActiveInc': 0.1, 'synPermInactiveDec': 0.01, }, # Controls whether TM is enabled or disabled; # TM is necessary for making temporal predictions, such as predicting # the next inputs. Without TM, the model is only capable of # reconstructing missing sensor inputs (via SP). 'tmEnable' : True, 'tmParams': { # TM diagnostic output verbosity control; # 0: silent; [1..6]: increasing levels of verbosity # (see verbosity in nupic/trunk/py/nupic/research/backtracking_tm.py and backtracking_tm_cpp.py) 'verbosity': 0, # Number of cell columns in the cortical region (same number for # SP and TM) # (see also tpNCellsPerCol) 'columnCount': 2048, # The number of cells (i.e., states), allocated per column. 'cellsPerColumn': 32, 'inputWidth': 2048, 'seed': 1960, # Temporal Pooler implementation selector (see _getTPClass in # CLARegion.py). 'temporalImp': 'cpp', # New Synapse formation count # NOTE: If None, use spNumActivePerInhArea # # TODO: need better explanation 'newSynapseCount': 15, # Maximum number of synapses per segment # > 0 for fixed-size CLA # -1 for non-fixed-size CLA # # TODO: for Ron: once the appropriate value is placed in TM # constructor, see if we should eliminate this parameter from # description.py. 'maxSynapsesPerSegment': 32, # Maximum number of segments per cell # > 0 for fixed-size CLA # -1 for non-fixed-size CLA # # TODO: for Ron: once the appropriate value is placed in TM # constructor, see if we should eliminate this parameter from # description.py. 'maxSegmentsPerCell': 128, # Initial Permanence # TODO: need better explanation 'initialPerm': 0.21, # Permanence Increment 'permanenceInc': 0.1, # Permanence Decrement # If set to None, will automatically default to tpPermanenceInc # value. 'permanenceDec' : 0.1, 'globalDecay': 0.0, 'maxAge': 0, # Minimum number of active synapses for a segment to be considered # during search for the best-matching segments. # None=use default # Replaces: tpMinThreshold 'minThreshold': 12, # Segment activation threshold. # A segment is active if it has >= tpSegmentActivationThreshold # connected synapses that are active due to infActiveState # None=use default # Replaces: tpActivationThreshold 'activationThreshold': 16, 'outputType': 'normal', # "Pay Attention Mode" length. This tells the TM how many new # elements to append to the end of a learned sequence at a time. # Smaller values are better for datasets with short sequences, # higher values are better for datasets with long sequences. 'pamLength': 1, }, 'clParams': { 'regionName' : 'SDRClassifierRegion', # Classifier diagnostic output verbosity control; # 0: silent; [1..6]: increasing levels of verbosity 'verbosity' : 0, # This controls how fast the classifier learns/forgets. Higher values # make it adapt faster and forget older patterns faster. 'alpha': 0.001, # This is set after the call to updateConfigFromSubConfig and is # computed from the aggregationInfo and predictAheadTime. 'steps': '1', }, 'trainSPNetOnlyIfRequested': False, }, } # end of config dictionary # Adjust base config dictionary for any modifications if imported from a # sub-experiment updateConfigFromSubConfig(config) # Compute predictionSteps based on the predictAheadTime and the aggregation # period, which may be permuted over. if config['predictAheadTime'] is not None: predictionSteps = int(round(aggregationDivide( config['predictAheadTime'], config['aggregationInfo']))) assert (predictionSteps >= 1) config['modelParams']['clParams']['steps'] = str(predictionSteps) # Adjust config by applying ValueGetterBase-derived # futures. NOTE: this MUST be called after updateConfigFromSubConfig() in order # to support value-getter-based substitutions from the sub-experiment (if any) applyValueGettersToContainer(config) control = { # The environment that the current model is being run in "environment": 'nupic', # Input stream specification per py/nupicengine/cluster/database/StreamDef.json. # 'dataset' : { u'info': u'baseball benchmark test', u'streams': [ { u'columns': [ u'daynight', u'precip', u'home_winloss', u'visitor_winloss', u'attendance', u'timestamp'], u'info': u'OAK01.csv', u'source': u'file://extra/baseball_stadium/OAK01reformatted.csv'}], u'version': 1}, # Iteration count: maximum number of iterations. Each iteration corresponds # to one record from the (possibly aggregated) dataset. The task is # terminated when either number of iterations reaches iterationCount or # all records in the (possibly aggregated) database have been processed, # whichever occurs first. # # iterationCount of -1 = iterate over the entire dataset #'iterationCount' : ITERATION_COUNT, # Metrics: A list of MetricSpecs that instantiate the metrics that are # computed for this experiment 'metrics':[ MetricSpec(field=u'attendance', inferenceElement=InferenceElement.prediction, metric='aae', params={'window': 1000}), MetricSpec(field=u'attendance', inferenceElement=InferenceElement.prediction, metric='trivial_aae', params={'window': 1000}), MetricSpec(field=u'attendance', inferenceElement=InferenceElement.prediction, metric='nupicScore_scalar', params={'frequencyWindow': 1000, 'movingAverageWindow': 1000}), MetricSpec(field=u'attendance', inferenceElement=InferenceElement.prediction, metric='nupicScore_scalar', params={'frequencyWindow': 1000}) ], # Logged Metrics: A sequence of regular expressions that specify which of # the metrics from the Inference Specifications section MUST be logged for # every prediction. The regex's correspond to the automatically generated # metric labels. This is similar to the way the optimization metric is # specified in permutations.py. 'loggedMetrics': ['.nupicScore.*'], } descriptionInterface = ExperimentDescriptionAPI(modelConfig=config, control=control)	16,850	Python	.py	352	36.485795	118	0.593864	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,209	permutations.py	numenta_nupic-legacy/tests/swarming/nupic/swarming/experiments/smart_speculation_temporal/permutations.py	# ---------------------------------------------------------------------- # Numenta Platform for Intelligent Computing (NuPIC) # Copyright (C) 2013, Numenta, Inc. Unless you have an agreement # with Numenta, Inc., for a separate license for this software code, the # following terms and conditions apply: # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU Affero Public License version 3 as # published by the Free Software Foundation. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. # See the GNU Affero Public License for more details. # # You should have received a copy of the GNU Affero Public License # along with this program. If not, see http://www.gnu.org/licenses. # # http://numenta.org/licenses/ # ---------------------------------------------------------------------- """ Template file used by ExpGenerator to generate the actual permutations.py file by replacing $XXXXXXXX tokens with desired values. This permutations.py file was generated by: '/Users/ronmarianetti/nupic/eng/lib/python2.6/site-packages/nupic/frameworks/opf/expGenerator/experiment_generator.py' """ import os from nupic.swarming.permutation_helpers import * # The name of the field being predicted. Any allowed permutation MUST contain # the prediction field. # (generated from PREDICTION_FIELD) predictedField = 'attendance' permutations = { 'modelParams': { 'sensorParams': { 'encoders': { 'A': PermuteEncoder(fieldName='daynight', encoderClass='SDRCategoryEncoder', w=7, n=100), 'C': PermuteEncoder(fieldName='precip', encoderClass='SDRCategoryEncoder', w=7, n=100), 'B': PermuteEncoder(fieldName='daynight', encoderClass='SDRCategoryEncoder', w=7, n=100), 'E': PermuteEncoder(fieldName='home_winloss', encoderClass='AdaptiveScalarEncoder', maxval=0.7, n=PermuteInt(13, 500, 25), clipInput=True, w=7, minval=0), 'D': PermuteEncoder(fieldName='visitor_winloss', encoderClass='AdaptiveScalarEncoder', maxval=0.786, n=PermuteInt(13, 500, 25), clipInput=True, w=7, minval=0), 'G': PermuteEncoder(fieldName='timestamp', encoderClass='DateEncoder.timeOfDay', radius=PermuteChoices([1, 8]), w=7), 'F': PermuteEncoder(fieldName='timestamp', encoderClass='DateEncoder.dayOfWeek', radius=PermuteChoices([1, 3]), w=7), 'Pred': PermuteEncoder(fieldName='attendance', encoderClass='AdaptiveScalarEncoder', maxval=36067, n=PermuteInt(13, 500, 25), clipInput=True, w=7, minval=0), }, }, 'tmParams': { 'minThreshold': PermuteInt(9, 12), 'activationThreshold': PermuteInt(12, 16), }, } } # Fields selected for final hypersearch report; # NOTE: These values are used as regular expressions by RunPermutations.py's # report generator # (fieldname values generated from PERM_PREDICTED_FIELD_NAME) report = [ '.attendance.', ] # Permutation optimization setting: either minimize or maximize metric # used by RunPermutations. # NOTE: The value is used as a regular expressions by RunPermutations.py's # report generator # (generated from minimize = 'nonprediction:aae:window=1000:field=attendance') minimize = 'prediction:aae:window=1000:field=attendance' def dummyModelParams(perm): """ This function can be used for Hypersearch algorithm development. When present, we don't actually run the CLA model in the OPF, but instead run a dummy model. This function returns the dummy model params that will be used. See the OPFDummyModelRunner class source code (in nupic.swarming.ModelRunner) for a description of the schema for the dummy model params. """ errScore = 500 if not perm['modelParams']['sensorParams']['encoders']['A'] is None: errScore -= 50 if not perm['modelParams']['sensorParams']['encoders']['B'] is None: errScore -= 40 if not perm['modelParams']['sensorParams']['encoders']['C'] is None: errScore -= 30 if not perm['modelParams']['sensorParams']['encoders']['D'] is None: errScore -= 20 if not perm['modelParams']['sensorParams']['encoders']['E'] is None: errScore -= 15 if not perm['modelParams']['sensorParams']['encoders']['F'] is None: errScore -= 10 if not perm['modelParams']['sensorParams']['encoders']['G'] is None: errScore -= 5 delay = 0 #If the model only has the A field have it run slowly to simulate speculation. encoderCount = 0 for key in perm.keys(): if 'encoder' in key and not perm[key] is None: encoderCount+=1 delay=encoderCountencoderCount.1 dummyModelParams = dict( metricValue = errScore, metricFunctions = None, delay=delay, ) return dummyModelParams def permutationFilter(perm): """ This function can be used to selectively filter out specific permutation combinations. It is called by RunPermutations for every possible permutation of the variables in the permutations dict. It should return True for valid a combination of permutation values and False for an invalid one. Parameters: --------------------------------------------------------- perm: dict of one possible combination of name:value pairs chosen from permutations. """ # An example of how to use this #if perm['__consumption_encoder']['maxval'] > 300: # return False; # return True	5,491	Python	.py	116	43.525862	167	0.704452	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,210	description.py	numenta_nupic-legacy/tests/swarming/nupic/swarming/experiments/oneField/description.py	# ---------------------------------------------------------------------- # Numenta Platform for Intelligent Computing (NuPIC) # Copyright (C) 2013, Numenta, Inc. Unless you have an agreement # with Numenta, Inc., for a separate license for this software code, the # following terms and conditions apply: # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU Affero Public License version 3 as # published by the Free Software Foundation. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. # See the GNU Affero Public License for more details. # # You should have received a copy of the GNU Affero Public License # along with this program. If not, see http://www.gnu.org/licenses. # # http://numenta.org/licenses/ # ---------------------------------------------------------------------- """ Template file used by the OPF Experiment Generator to generate the actual description.py file by replacing $XXXXXXXX tokens with desired values. This description.py file was generated by: '/Users/ronmarianetti/nupic/eng/lib/python2.6/site-packages/nupic/frameworks/opf/expGenerator/experiment_generator.py' """ from nupic.frameworks.opf.exp_description_api import ExperimentDescriptionAPI from nupic.frameworks.opf.exp_description_helpers import ( updateConfigFromSubConfig, applyValueGettersToContainer, DeferredDictLookup) from nupic.frameworks.opf.htm_prediction_model_callbacks import * from nupic.frameworks.opf.metrics import MetricSpec from nupic.frameworks.opf.opf_utils import (InferenceType, InferenceElement) from nupic.support import aggregationDivide from nupic.frameworks.opf.opf_task_driver import ( IterationPhaseSpecLearnOnly, IterationPhaseSpecInferOnly, IterationPhaseSpecLearnAndInfer) # Model Configuration Dictionary: # # Define the model parameters and adjust for any modifications if imported # from a sub-experiment. # # These fields might be modified by a sub-experiment; this dict is passed # between the sub-experiment and base experiment # # # NOTE: Use of DEFERRED VALUE-GETTERs: dictionary fields and list elements # within the config dictionary may be assigned futures derived from the # ValueGetterBase class, such as DeferredDictLookup. # This facility is particularly handy for enabling substitution of values in # the config dictionary from other values in the config dictionary, which is # needed by permutation.py-based experiments. These values will be resolved # during the call to applyValueGettersToContainer(), # which we call after the base experiment's config dictionary is updated from # the sub-experiment. See ValueGetterBase and # DeferredDictLookup for more details about value-getters. # # For each custom encoder parameter to be exposed to the sub-experiment/ # permutation overrides, define a variable in this section, using key names # beginning with a single underscore character to avoid collisions with # pre-defined keys (e.g., _dsEncoderFieldName2_N). # # Example: # config = dict( # _dsEncoderFieldName2_N = 70, # _dsEncoderFieldName2_W = 5, # dsEncoderSchema = [ # base=dict( # fieldname='Name2', type='ScalarEncoder', # name='Name2', minval=0, maxval=270, clipInput=True, # n=DeferredDictLookup('_dsEncoderFieldName2_N'), # w=DeferredDictLookup('_dsEncoderFieldName2_W')), # ], # ) # updateConfigFromSubConfig(config) # applyValueGettersToContainer(config) config = { # Type of model that the rest of these parameters apply to. 'model': "HTMPrediction", # Version that specifies the format of the config. 'version': 1, # Intermediate variables used to compute fields in modelParams and also # referenced from the control section. 'aggregationInfo': { 'days': 0, 'fields': [ (u'timestamp', 'first'), (u'gym', 'first'), (u'consumption', 'mean'), (u'address', 'first')], 'hours': 0, 'microseconds': 0, 'milliseconds': 0, 'minutes': 0, 'months': 0, 'seconds': 0, 'weeks': 0, 'years': 0}, 'predictAheadTime': None, # Model parameter dictionary. 'modelParams': { # The type of inference that this model will perform 'inferenceType': 'TemporalNextStep', 'sensorParams': { # Sensor diagnostic output verbosity control; # if > 0: sensor region will print out on screen what it's sensing # at each step 0: silent; >=1: some info; >=2: more info; # >=3: even more info (see compute() in py/regions/RecordSensor.py) 'verbosity' : 0, # Example: # dsEncoderSchema = [ # DeferredDictLookup('__field_name_encoder'), # ], # # (value generated from DS_ENCODER_SCHEMA) 'encoders': { 'consumption': { 'clipInput': True, 'fieldname': u'consumption', 'maxval': 200, 'minval': 0, 'n': 1500, 'name': u'consumption', 'type': 'ScalarEncoder', 'w': 21 }, }, # A dictionary specifying the period for automatically-generated # resets from a RecordSensor; # # None = disable automatically-generated resets (also disabled if # all of the specified values evaluate to 0). # Valid keys is the desired combination of the following: # days, hours, minutes, seconds, milliseconds, microseconds, weeks # # Example for 1.5 days: sensorAutoReset = dict(days=1,hours=12), # # (value generated from SENSOR_AUTO_RESET) 'sensorAutoReset' : None, }, 'spEnable': True, 'spParams': { # SP diagnostic output verbosity control; # 0: silent; >=1: some info; >=2: more info; 'spVerbosity' : 0, 'globalInhibition': 1, # Number of cell columns in the cortical region (same number for # SP and TM) # (see also tpNCellsPerCol) 'columnCount': 2048, 'inputWidth': 0, # SP inhibition control (absolute value); # Maximum number of active columns in the SP region's output (when # there are more, the weaker ones are suppressed) 'numActiveColumnsPerInhArea': 40, 'seed': 1956, # potentialPct # What percent of the columns's receptive field is available # for potential synapses. At initialization time, we will # choose potentialPct * (2potentialRadius+1)^2 'potentialPct': 0.5, # The default connected threshold. Any synapse whose # permanence value is above the connected threshold is # a "connected synapse", meaning it can contribute to the # cell's firing. Typical value is 0.10. Cells whose activity # level before inhibition falls below minDutyCycleBeforeInh # will have their own internal synPermConnectedCell # threshold set below this default value. # (This concept applies to both SP and TM and so 'cells' # is correct here as opposed to 'columns') 'synPermConnected': 0.1, 'synPermActiveInc': 0.1, 'synPermInactiveDec': 0.01, }, # Controls whether TM is enabled or disabled; # TM is necessary for making temporal predictions, such as predicting # the next inputs. Without TM, the model is only capable of # reconstructing missing sensor inputs (via SP). 'tmEnable' : True, 'tmParams': { # TM diagnostic output verbosity control; # 0: silent; [1..6]: increasing levels of verbosity # (see verbosity in nupic/trunk/py/nupic/research/backtracking_tm.py and backtracking_tm_cpp.py) 'verbosity': 0, # Number of cell columns in the cortical region (same number for # SP and TM) # (see also tpNCellsPerCol) 'columnCount': 2048, # The number of cells (i.e., states), allocated per column. 'cellsPerColumn': 32, 'inputWidth': 2048, 'seed': 1960, # Temporal Pooler implementation selector (see _getTPClass in # CLARegion.py). 'temporalImp': 'cpp', # New Synapse formation count # NOTE: If None, use spNumActivePerInhArea # # TODO: need better explanation 'newSynapseCount': 15, # Maximum number of synapses per segment # > 0 for fixed-size CLA # -1 for non-fixed-size CLA # # TODO: for Ron: once the appropriate value is placed in TM # constructor, see if we should eliminate this parameter from # description.py. 'maxSynapsesPerSegment': 32, # Maximum number of segments per cell # > 0 for fixed-size CLA # -1 for non-fixed-size CLA # # TODO: for Ron: once the appropriate value is placed in TM # constructor, see if we should eliminate this parameter from # description.py. 'maxSegmentsPerCell': 128, # Initial Permanence # TODO: need better explanation 'initialPerm': 0.21, # Permanence Increment 'permanenceInc': 0.1, # Permanence Decrement # If set to None, will automatically default to tpPermanenceInc # value. 'permanenceDec' : 0.1, 'globalDecay': 0.0, 'maxAge': 0, # Minimum number of active synapses for a segment to be considered # during search for the best-matching segments. # None=use default # Replaces: tpMinThreshold 'minThreshold': 12, # Segment activation threshold. # A segment is active if it has >= tpSegmentActivationThreshold # connected synapses that are active due to infActiveState # None=use default # Replaces: tpActivationThreshold 'activationThreshold': 16, 'outputType': 'normal', # "Pay Attention Mode" length. This tells the TM how many new # elements to append to the end of a learned sequence at a time. # Smaller values are better for datasets with short sequences, # higher values are better for datasets with long sequences. 'pamLength': 1, }, 'clParams': { 'regionName' : 'SDRClassifierRegion', # Classifier diagnostic output verbosity control; # 0: silent; [1..6]: increasing levels of verbosity 'verbosity' : 0, # This controls how fast the classifier learns/forgets. Higher values # make it adapt faster and forget older patterns faster. 'alpha': 0.001, # This is set after the call to updateConfigFromSubConfig and is # computed from the aggregationInfo and predictAheadTime. 'steps': '1', }, 'trainSPNetOnlyIfRequested': False, }, } # end of config dictionary # Adjust base config dictionary for any modifications if imported from a # sub-experiment updateConfigFromSubConfig(config) # Compute predictionSteps based on the predictAheadTime and the aggregation # period, which may be permuted over. if config['predictAheadTime'] is not None: predictionSteps = int(round(aggregationDivide( config['predictAheadTime'], config['aggregationInfo']))) assert (predictionSteps >= 1) config['modelParams']['clParams']['steps'] = str(predictionSteps) # Adjust config by applying ValueGetterBase-derived # futures. NOTE: this MUST be called after updateConfigFromSubConfig() in order # to support value-getter-based substitutions from the sub-experiment (if any) applyValueGettersToContainer(config) control = { # The environment that the current model is being run in "environment": 'nupic', # Input stream specification per py/nupicengine/cluster/database/StreamDef.json. # 'dataset' : { u'info': u'test_NoProviders', u'streams': [ { u'columns': [u''], u'info': u'test data', u'source': u'file://swarming/test_data.csv'}], u'version': 1}, # Iteration count: maximum number of iterations. Each iteration corresponds # to one record from the (possibly aggregated) dataset. The task is # terminated when either number of iterations reaches iterationCount or # all records in the (possibly aggregated) database have been processed, # whichever occurs first. # # iterationCount of -1 = iterate over the entire dataset #'iterationCount' : ITERATION_COUNT, # Metrics: A list of MetricSpecs that instantiate the metrics that are # computed for this experiment 'metrics':[ MetricSpec(field=u'consumption', inferenceElement=InferenceElement.prediction, metric='rmse'), ], # Logged Metrics: A sequence of regular expressions that specify which of # the metrics from the Inference Specifications section MUST be logged for # every prediction. The regex's correspond to the automatically generated # metric labels. This is similar to the way the optimization metric is # specified in permutations.py. 'loggedMetrics': ['.nupicScore.'], } descriptionInterface = ExperimentDescriptionAPI(modelConfig=config, control=control)	14,410	Python	.py	309	37.18123	118	0.626657	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,211	permutations.py	numenta_nupic-legacy/tests/swarming/nupic/swarming/experiments/oneField/permutations.py	# ---------------------------------------------------------------------- # Numenta Platform for Intelligent Computing (NuPIC) # Copyright (C) 2013, Numenta, Inc. Unless you have an agreement # with Numenta, Inc., for a separate license for this software code, the # following terms and conditions apply: # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU Affero Public License version 3 as # published by the Free Software Foundation. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. # See the GNU Affero Public License for more details. # # You should have received a copy of the GNU Affero Public License # along with this program. If not, see http://www.gnu.org/licenses. # # http://numenta.org/licenses/ # ---------------------------------------------------------------------- """ Template file used by ExpGenerator to generate the actual permutations.py file by replacing $XXXXXXXX tokens with desired values. This permutations.py file was generated by: '/Users/ronmarianetti/nupic/eng/lib/python2.6/site-packages/nupic/frameworks/opf/expGenerator/experiment_generator.py' """ import os from nupic.swarming.permutation_helpers import * # The name of the field being predicted. Any allowed permutation MUST contain # the prediction field. # (generated from PREDICTION_FIELD) predictedField = 'consumption' permutations = { 'modelParams': { 'sensorParams': { 'encoders': { 'consumption': PermuteEncoder(fieldName='consumption', encoderClass='ScalarEncoder', maxval=PermuteInt(100, 300, 1), n=PermuteInt(13, 500, 1), w=7, minval=0), }, }, 'tmParams': { 'minThreshold': PermuteInt(9, 12), 'activationThreshold': PermuteInt(12, 16), }, } } # Fields selected for final hypersearch report; # NOTE: These values are used as regular expressions by RunPermutations.py's # report generator # (fieldname values generated from PERM_PREDICTED_FIELD_NAME) report = [ '.consumption.', ] # Permutation optimization setting: either minimize or maximize metric # used by RunPermutations. # NOTE: The value is used as a regular expressions by RunPermutations.py's # report generator # (generated from minimize = 'prediction:rmse:field=consumption') minimize = 'prediction:rmse:field=consumption' def dummyModelParams(perm): """ This function can be used for Hypersearch algorithm development. When present, Hypersearch doesn't actually run the CLA model in the OPF, but instead run a dummy model. This function returns the dummy model params that will be used. See the OPFDummyModelRunner class source code (in nupic.swarming.ModelRunner) for a description of the schema for the dummy model params. """ errScore = 50 #errScore += abs(perm['modelParams']['sensorParams']['encoders']\ # ['consumption']['maxval'] - 250) #errScore += abs(perm['modelParams']['sensorParams']['encoders']\ # ['consumption']['n'] - 53) # Make models that contain the __timestamp_timeOfDay encoder run a bit # slower so we can test that we successfully kill running models waitTime = 0.01 dummyModelParams = dict( metricValue = errScore, iterations = int(os.environ.get('NTA_TEST_numIterations', '5')), waitTime = waitTime, sysExitModelRange = os.environ.get('NTA_TEST_sysExitModelRange', None), delayModelRange = os.environ.get('NTA_TEST_delayModelRange', None), errModelRange = os.environ.get('NTA_TEST_errModelRange', None), jobFailErr = bool(os.environ.get('NTA_TEST_jobFailErr', False)) ) return dummyModelParams def permutationFilter(perm): """ This function can be used to selectively filter out specific permutation combinations. It is called by RunPermutations for every possible permutation of the variables in the permutations dict. It should return True for valid a combination of permutation values and False for an invalid one. Parameters: --------------------------------------------------------- perm: dict of one possible combination of name:value pairs chosen from permutations. """ # An example of how to use this limit = int(os.environ.get('NTA_TEST_maxvalFilter', 300)) if perm['modelParams']['sensorParams']['encoders']['consumption']['maxval'] > limit: return False; return True	4,947	Python	.py	107	39.224299	118	0.64893	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,212	description.py	numenta_nupic-legacy/tests/swarming/nupic/swarming/experiments/legacy_cla_multistep/description.py	# ---------------------------------------------------------------------- # Numenta Platform for Intelligent Computing (NuPIC) # Copyright (C) 2013, Numenta, Inc. Unless you have an agreement # with Numenta, Inc., for a separate license for this software code, the # following terms and conditions apply: # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU Affero Public License version 3 as # published by the Free Software Foundation. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. # See the GNU Affero Public License for more details. # # You should have received a copy of the GNU Affero Public License # along with this program. If not, see http://www.gnu.org/licenses. # # http://numenta.org/licenses/ # ---------------------------------------------------------------------- """ Template file used by the OPF Experiment Generator to generate the actual description.py file by replacing $XXXXXXXX tokens with desired values. This description.py file was generated by: '/Users/ronmarianetti/nupic/eng/lib/python2.6/site-packages/nupicengine/frameworks/opf/expGenerator/ExpGenerator.pyc' """ from nupic.frameworks.opf.exp_description_api import ExperimentDescriptionAPI from nupic.frameworks.opf.exp_description_helpers import ( updateConfigFromSubConfig, applyValueGettersToContainer ) from nupic.frameworks.opf.htm_prediction_model_callbacks import * from nupic.frameworks.opf.metrics import MetricSpec from nupic.frameworks.opf.opf_utils import (InferenceType, InferenceElement) from nupic.support import aggregationDivide from nupic.frameworks.opf.opf_task_driver import ( IterationPhaseSpecLearnOnly, IterationPhaseSpecInferOnly, IterationPhaseSpecLearnAndInfer) # Model Configuration Dictionary: # # Define the model parameters and adjust for any modifications if imported # from a sub-experiment. # # These fields might be modified by a sub-experiment; this dict is passed # between the sub-experiment and base experiment # # config = { # Type of model that the rest of these parameters apply to. 'model': "HTMPrediction", # Version that specifies the format of the config. 'version': 1, # Intermediate variables used to compute fields in modelParams and also # referenced from the control section. 'aggregationInfo': { 'days': 0, 'fields': [ (u'timestamp', 'first'), (u'consumption', 'sum'), ], 'hours': 0, 'microseconds': 0, 'milliseconds': 0, 'minutes': 0, 'months': 0, 'seconds': 0, 'weeks': 0, 'years': 0}, 'predictAheadTime': None, # Model parameter dictionary. 'modelParams': { # The type of inference that this model will perform 'inferenceType': 'TemporalMultiStep', 'sensorParams': { # Sensor diagnostic output verbosity control; # if > 0: sensor region will print out on screen what it's sensing # at each step 0: silent; >=1: some info; >=2: more info; # >=3: even more info (see compute() in py/regions/RecordSensor.py) 'verbosity' : 0, # Example: # 'encoders': {'field1': {'fieldname': 'field1', 'n':100, # 'name': 'field1', 'type': 'AdaptiveScalarEncoder', # 'w': 21}} # 'encoders': { 'consumption': { 'clipInput': True, 'fieldname': u'consumption', 'n': 100, 'name': u'consumption', 'type': 'AdaptiveScalarEncoder', 'w': 21}, 'timestamp_timeOfDay': { 'fieldname': u'timestamp', 'name': u'timestamp_timeOfDay', 'type': 'DateEncoder', 'timeOfDay': (21, 1)}, 'timestamp_dayOfWeek': { 'fieldname': u'timestamp', 'name': u'timestamp_dayOfWeek', 'type': 'DateEncoder', 'dayOfWeek': (21, 1)}, }, # A dictionary specifying the period for automatically-generated # resets from a RecordSensor; # # None = disable automatically-generated resets (also disabled if # all of the specified values evaluate to 0). # Valid keys is the desired combination of the following: # days, hours, minutes, seconds, milliseconds, microseconds, weeks # # Example for 1.5 days: sensorAutoReset = dict(days=1,hours=12), # # (value generated from SENSOR_AUTO_RESET) 'sensorAutoReset' : { u'days': 0, u'hours': 0}, }, 'spEnable': True, 'spParams': { # SP diagnostic output verbosity control; # 0: silent; >=1: some info; >=2: more info; 'spVerbosity' : 0, 'globalInhibition': 1, # Number of cell columns in the cortical region (same number for # SP and TM) # (see also tpNCellsPerCol) 'columnCount': 2048, 'inputWidth': 0, # SP inhibition control (absolute value); # Maximum number of active columns in the SP region's output (when # there are more, the weaker ones are suppressed) 'numActiveColumnsPerInhArea': 40, 'seed': 1956, # potentialPct # What percent of the columns's receptive field is available # for potential synapses. At initialization time, we will # choose potentialPct * (2potentialRadius+1)^2 'potentialPct': 0.5, # The default connected threshold. Any synapse whose # permanence value is above the connected threshold is # a "connected synapse", meaning it can contribute to the # cell's firing. Typical value is 0.10. Cells whose activity # level before inhibition falls below minDutyCycleBeforeInh # will have their own internal synPermConnectedCell # threshold set below this default value. # (This concept applies to both SP and TM and so 'cells' # is correct here as opposed to 'columns') 'synPermConnected': 0.1, 'synPermActiveInc': 0.1, 'synPermInactiveDec': 0.01, }, # Controls whether TM is enabled or disabled; # TM is necessary for making temporal predictions, such as predicting # the next inputs. Without TM, the model is only capable of # reconstructing missing sensor inputs (via SP). 'tmEnable' : True, 'tmParams': { # TM diagnostic output verbosity control; # 0: silent; [1..6]: increasing levels of verbosity # (see verbosity in nupic/trunk/py/nupic/research/backtracking_tm.py and backtracking_tm_cpp.py) 'verbosity': 0, # Number of cell columns in the cortical region (same number for # SP and TM) # (see also tpNCellsPerCol) 'columnCount': 2048, # The number of cells (i.e., states), allocated per column. 'cellsPerColumn': 32, 'inputWidth': 2048, 'seed': 1960, # Temporal Pooler implementation selector (see _getTPClass in # CLARegion.py). 'temporalImp': 'cpp', # New Synapse formation count # NOTE: If None, use spNumActivePerInhArea # # TODO: need better explanation 'newSynapseCount': 20, # Maximum number of synapses per segment # > 0 for fixed-size CLA # -1 for non-fixed-size CLA # # TODO: for Ron: once the appropriate value is placed in TM # constructor, see if we should eliminate this parameter from # description.py. 'maxSynapsesPerSegment': 32, # Maximum number of segments per cell # > 0 for fixed-size CLA # -1 for non-fixed-size CLA # # TODO: for Ron: once the appropriate value is placed in TM # constructor, see if we should eliminate this parameter from # description.py. 'maxSegmentsPerCell': 128, # Initial Permanence # TODO: need better explanation 'initialPerm': 0.21, # Permanence Increment 'permanenceInc': 0.1, # Permanence Decrement # If set to None, will automatically default to tpPermanenceInc # value. 'permanenceDec' : 0.1, 'globalDecay': 0.0, 'maxAge': 0, # Minimum number of active synapses for a segment to be considered # during search for the best-matching segments. # None=use default # Replaces: tpMinThreshold 'minThreshold': 12, # Segment activation threshold. # A segment is active if it has >= tpSegmentActivationThreshold # connected synapses that are active due to infActiveState # None=use default # Replaces: tpActivationThreshold 'activationThreshold': 16, 'outputType': 'normal', # "Pay Attention Mode" length. This tells the TM how many new # elements to append to the end of a learned sequence at a time. # Smaller values are better for datasets with short sequences, # higher values are better for datasets with long sequences. 'pamLength': 1, }, 'clParams': { 'regionName' : 'SDRClassifierRegion', # Classifier diagnostic output verbosity control; # 0: silent; [1..6]: increasing levels of verbosity 'verbosity' : 0, # This controls how fast the classifier learns/forgets. Higher values # make it adapt faster and forget older patterns faster. 'alpha': 0.001, # This is set after the call to updateConfigFromSubConfig and is # computed from the aggregationInfo and predictAheadTime. 'steps': '1', }, 'anomalyParams': { u'anomalyCacheRecords': None, u'autoDetectThreshold': None, u'autoDetectWaitRecords': None}, 'trainSPNetOnlyIfRequested': False, }, } # end of config dictionary # Adjust base config dictionary for any modifications if imported from a # sub-experiment updateConfigFromSubConfig(config) # Compute predictionSteps based on the predictAheadTime and the aggregation # period, which may be permuted over. if config['predictAheadTime'] is not None: predictionSteps = int(round(aggregationDivide( config['predictAheadTime'], config['aggregationInfo']))) assert (predictionSteps >= 1) config['modelParams']['clParams']['steps'] = str(predictionSteps) # Adjust config by applying ValueGetterBase-derived # futures. NOTE: this MUST be called after updateConfigFromSubConfig() in order # to support value-getter-based substitutions from the sub-experiment (if any) applyValueGettersToContainer(config) control = { # The environment that the current model is being run in "environment": 'nupic', # Input stream specification per py/nupic/frameworks/opf/jsonschema/stream_def.json. # 'dataset' : { u'info': u'test_hotgym', u'streams': [ { u'columns': [u''], u'info': u'test data', u'source': u'file://swarming/test_data.csv'}], u'version': 1}, # Iteration count: maximum number of iterations. Each iteration corresponds # to one record from the (possibly aggregated) dataset. The task is # terminated when either number of iterations reaches iterationCount or # all records in the (possibly aggregated) database have been processed, # whichever occurs first. # # iterationCount of -1 = iterate over the entire dataset 'iterationCount' : -1, # A dictionary containing all the supplementary parameters for inference "inferenceArgs":{u'predictedField': u'consumption', u'predictionSteps': [1]}, # Metrics: A list of MetricSpecs that instantiate the metrics that are # computed for this experiment 'metrics':[ MetricSpec(field=u'consumption', metric='multiStep', inferenceElement='multiStepBestPredictions', params={'window': 1000, 'steps': [1], 'errorMetric': 'altMAPE'}), ], # Logged Metrics: A sequence of regular expressions that specify which of # the metrics from the Inference Specifications section MUST be logged for # every prediction. The regex's correspond to the automatically generated # metric labels. This is similar to the way the optimization metric is # specified in permutations.py. 'loggedMetrics': ['.*'], } descriptionInterface = ExperimentDescriptionAPI(modelConfig=config, control=control)	13,421	Python	.py	291	36.350515	117	0.618971	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,213	permutations.py	numenta_nupic-legacy/tests/swarming/nupic/swarming/experiments/legacy_cla_multistep/permutations.py	# ---------------------------------------------------------------------- # Numenta Platform for Intelligent Computing (NuPIC) # Copyright (C) 2013, Numenta, Inc. Unless you have an agreement # with Numenta, Inc., for a separate license for this software code, the # following terms and conditions apply: # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU Affero Public License version 3 as # published by the Free Software Foundation. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. # See the GNU Affero Public License for more details. # # You should have received a copy of the GNU Affero Public License # along with this program. If not, see http://www.gnu.org/licenses. # # http://numenta.org/licenses/ # ---------------------------------------------------------------------- """ Template file used by ExpGenerator to generate the actual permutations.py file by replacing $XXXXXXXX tokens with desired values. This permutations.py file was generated by: '/Users/ronmarianetti/nupic/eng/lib/python2.6/site-packages/nupicengine/frameworks/opf/expGenerator/ExpGenerator.pyc' """ import os from nupic.swarming.permutation_helpers import * # The name of the field being predicted. Any allowed permutation MUST contain # the prediction field. # (generated from PREDICTION_FIELD) predictedField = 'consumption' permutations = { 'aggregationInfo': { 'days': 0, 'fields': [ (u'timestamp', 'first'), (u'gym', 'first'), (u'consumption', 'sum')], 'hours': 1, 'microseconds': 0, 'milliseconds': 0, 'minutes': 0, 'months': 0, 'seconds': 0, 'weeks': 0, 'years': 0}, 'modelParams': { 'inferenceType': PermuteChoices(['NontemporalMultiStep', 'TemporalMultiStep']), 'sensorParams': { 'encoders': { u'timestamp_timeOfDay': PermuteEncoder( fieldName='timestamp', encoderClass='DateEncoder.timeOfDay', w=21, radius=PermuteFloat(0.5, 12)), u'timestamp_dayOfWeek': PermuteEncoder( fieldName='timestamp', encoderClass='DateEncoder.dayOfWeek', w=21, radius=PermuteFloat(1, 6)), u'timestamp_weekend': PermuteEncoder( fieldName='timestamp', encoderClass='DateEncoder.weekend', w=21, radius=PermuteChoices([1])), u'consumption': PermuteEncoder( fieldName='consumption', encoderClass='AdaptiveScalarEncoder', w=21, n=PermuteInt(28, 521), clipInput=True), }, }, 'spParams': { 'synPermInactiveDec': PermuteFloat(0.005, 0.1), }, 'tmParams': { 'activationThreshold': PermuteInt(12, 16), 'minThreshold': PermuteInt(9, 12), 'pamLength': PermuteInt(1, 5), }, 'clParams': { 'alpha': PermuteFloat(0.0001, 0.1), }, } } # Fields selected for final hypersearch report; # NOTE: These values are used as regular expressions by RunPermutations.py's # report generator # (fieldname values generated from PERM_PREDICTED_FIELD_NAME) report = [ '.consumption.', ] # Permutation optimization setting: either minimize or maximize metric # used by RunPermutations. # NOTE: The value is used as a regular expressions by RunPermutations.py's # report generator # (generated from minimize = "multiStepBestPredictions:multiStep:errorMetric='altMAPE':steps=\[1\]:window=1000:field=consumption") minimize = "multiStepBestPredictions:multiStep:errorMetric='altMAPE':steps=\[1\]:window=1000:field=consumption" minParticlesPerSwarm = None def permutationFilter(perm): """ This function can be used to selectively filter out specific permutation combinations. It is called by RunPermutations for every possible permutation of the variables in the permutations dict. It should return True for valid a combination of permutation values and False for an invalid one. Parameters: --------------------------------------------------------- perm: dict of one possible combination of name:value pairs chosen from permutations. """ # An example of how to use this #if perm['__consumption_encoder']['maxval'] > 300: # return False; # return True	4,770	Python	.py	114	34.04386	130	0.620682	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,214	description.py	numenta_nupic-legacy/tests/swarming/nupic/swarming/experiments/simple_cla_multistep/description.py	# ---------------------------------------------------------------------- # Numenta Platform for Intelligent Computing (NuPIC) # Copyright (C) 2013, Numenta, Inc. Unless you have an agreement # with Numenta, Inc., for a separate license for this software code, the # following terms and conditions apply: # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU Affero Public License version 3 as # published by the Free Software Foundation. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. # See the GNU Affero Public License for more details. # # You should have received a copy of the GNU Affero Public License # along with this program. If not, see http://www.gnu.org/licenses. # # http://numenta.org/licenses/ # ---------------------------------------------------------------------- """ Template file used by the OPF Experiment Generator to generate the actual description.py file by replacing $XXXXXXXX tokens with desired values. This description.py file was generated by: '/Users/ronmarianetti/nupic/eng/lib/python2.6/site-packages/nupicengine/frameworks/opf/expGenerator/ExpGenerator.pyc' """ from nupic.frameworks.opf.exp_description_api import ExperimentDescriptionAPI from nupic.frameworks.opf.exp_description_helpers import ( updateConfigFromSubConfig, applyValueGettersToContainer ) from nupic.frameworks.opf.htm_prediction_model_callbacks import * from nupic.frameworks.opf.metrics import MetricSpec from nupic.frameworks.opf.opf_utils import (InferenceType, InferenceElement) from nupic.support import aggregationDivide from nupic.frameworks.opf.opf_task_driver import ( IterationPhaseSpecLearnOnly, IterationPhaseSpecInferOnly, IterationPhaseSpecLearnAndInfer) # Model Configuration Dictionary: # # Define the model parameters and adjust for any modifications if imported # from a sub-experiment. # # These fields might be modified by a sub-experiment; this dict is passed # between the sub-experiment and base experiment # # config = { # Type of model that the rest of these parameters apply to. 'model': "HTMPrediction", # Version that specifies the format of the config. 'version': 1, # Intermediate variables used to compute fields in modelParams and also # referenced from the control section. 'aggregationInfo': { 'days': 0, 'fields': [ (u'timestamp', 'first'), (u'consumption', 'sum'), ], 'hours': 0, 'microseconds': 0, 'milliseconds': 0, 'minutes': 0, 'months': 0, 'seconds': 0, 'weeks': 0, 'years': 0}, 'predictAheadTime': None, # Model parameter dictionary. 'modelParams': { # The type of inference that this model will perform 'inferenceType': 'TemporalMultiStep', 'sensorParams': { # Sensor diagnostic output verbosity control; # if > 0: sensor region will print out on screen what it's sensing # at each step 0: silent; >=1: some info; >=2: more info; # >=3: even more info (see compute() in py/regions/RecordSensor.py) 'verbosity' : 0, # Example: # 'encoders': {'field1': {'fieldname': 'field1', 'n':100, # 'name': 'field1', 'type': 'AdaptiveScalarEncoder', # 'w': 21}} # 'encoders': { 'consumption': { 'clipInput': True, 'fieldname': u'consumption', 'n': 100, 'name': u'consumption', 'type': 'AdaptiveScalarEncoder', 'w': 21}, 'timestamp_timeOfDay': { 'fieldname': u'timestamp', 'name': u'timestamp_timeOfDay', 'type': 'DateEncoder', 'timeOfDay': (21, 1)}, 'timestamp_dayOfWeek': { 'fieldname': u'timestamp', 'name': u'timestamp_dayOfWeek', 'type': 'DateEncoder', 'dayOfWeek': (21, 1)}, '_classifierInput': { 'name': u'_classifierInput', 'fieldname': u'consumption', 'classifierOnly': True, 'type': 'AdaptiveScalarEncoder', 'clipInput': True, 'n': 100, 'w': 21}, }, # A dictionary specifying the period for automatically-generated # resets from a RecordSensor; # # None = disable automatically-generated resets (also disabled if # all of the specified values evaluate to 0). # Valid keys is the desired combination of the following: # days, hours, minutes, seconds, milliseconds, microseconds, weeks # # Example for 1.5 days: sensorAutoReset = dict(days=1,hours=12), # # (value generated from SENSOR_AUTO_RESET) 'sensorAutoReset' : { u'days': 0, u'hours': 0}, }, 'spEnable': True, 'spParams': { # SP diagnostic output verbosity control; # 0: silent; >=1: some info; >=2: more info; 'spVerbosity' : 0, 'globalInhibition': 1, # Number of cell columns in the cortical region (same number for # SP and TM) # (see also tpNCellsPerCol) 'columnCount': 2048, 'inputWidth': 0, # SP inhibition control (absolute value); # Maximum number of active columns in the SP region's output (when # there are more, the weaker ones are suppressed) 'numActiveColumnsPerInhArea': 40, 'seed': 1956, # potentialPct # What percent of the columns's receptive field is available # for potential synapses. At initialization time, we will # choose potentialPct * (2potentialRadius+1)^2 'potentialPct': 0.5, # The default connected threshold. Any synapse whose # permanence value is above the connected threshold is # a "connected synapse", meaning it can contribute to the # cell's firing. Typical value is 0.10. Cells whose activity # level before inhibition falls below minDutyCycleBeforeInh # will have their own internal synPermConnectedCell # threshold set below this default value. # (This concept applies to both SP and TM and so 'cells' # is correct here as opposed to 'columns') 'synPermConnected': 0.1, 'synPermActiveInc': 0.1, 'synPermInactiveDec': 0.01, }, # Controls whether TM is enabled or disabled; # TM is necessary for making temporal predictions, such as predicting # the next inputs. Without TM, the model is only capable of # reconstructing missing sensor inputs (via SP). 'tmEnable' : True, 'tmParams': { # TM diagnostic output verbosity control; # 0: silent; [1..6]: increasing levels of verbosity # (see verbosity in nupic/trunk/py/nupic/research/backtracking_tm.py and backtracking_tm_cpp.py) 'verbosity': 0, # Number of cell columns in the cortical region (same number for # SP and TM) # (see also tpNCellsPerCol) 'columnCount': 2048, # The number of cells (i.e., states), allocated per column. 'cellsPerColumn': 32, 'inputWidth': 2048, 'seed': 1960, # Temporal Pooler implementation selector (see _getTPClass in # CLARegion.py). 'temporalImp': 'cpp', # New Synapse formation count # NOTE: If None, use spNumActivePerInhArea # # TODO: need better explanation 'newSynapseCount': 20, # Maximum number of synapses per segment # > 0 for fixed-size CLA # -1 for non-fixed-size CLA # # TODO: for Ron: once the appropriate value is placed in TM # constructor, see if we should eliminate this parameter from # description.py. 'maxSynapsesPerSegment': 32, # Maximum number of segments per cell # > 0 for fixed-size CLA # -1 for non-fixed-size CLA # # TODO: for Ron: once the appropriate value is placed in TM # constructor, see if we should eliminate this parameter from # description.py. 'maxSegmentsPerCell': 128, # Initial Permanence # TODO: need better explanation 'initialPerm': 0.21, # Permanence Increment 'permanenceInc': 0.1, # Permanence Decrement # If set to None, will automatically default to tpPermanenceInc # value. 'permanenceDec' : 0.1, 'globalDecay': 0.0, 'maxAge': 0, # Minimum number of active synapses for a segment to be considered # during search for the best-matching segments. # None=use default # Replaces: tpMinThreshold 'minThreshold': 12, # Segment activation threshold. # A segment is active if it has >= tpSegmentActivationThreshold # connected synapses that are active due to infActiveState # None=use default # Replaces: tpActivationThreshold 'activationThreshold': 16, 'outputType': 'normal', # "Pay Attention Mode" length. This tells the TM how many new # elements to append to the end of a learned sequence at a time. # Smaller values are better for datasets with short sequences, # higher values are better for datasets with long sequences. 'pamLength': 1, }, 'clParams': { 'regionName' : 'SDRClassifierRegion', # Classifier diagnostic output verbosity control; # 0: silent; [1..6]: increasing levels of verbosity 'verbosity' : 0, # This controls how fast the classifier learns/forgets. Higher values # make it adapt faster and forget older patterns faster. 'alpha': 0.001, # This is set after the call to updateConfigFromSubConfig and is # computed from the aggregationInfo and predictAheadTime. 'steps': '1', }, 'anomalyParams': { u'anomalyCacheRecords': None, u'autoDetectThreshold': None, u'autoDetectWaitRecords': None}, 'trainSPNetOnlyIfRequested': False, }, } # end of config dictionary # Adjust base config dictionary for any modifications if imported from a # sub-experiment updateConfigFromSubConfig(config) # Compute predictionSteps based on the predictAheadTime and the aggregation # period, which may be permuted over. if config['predictAheadTime'] is not None: predictionSteps = int(round(aggregationDivide( config['predictAheadTime'], config['aggregationInfo']))) assert (predictionSteps >= 1) config['modelParams']['clParams']['steps'] = str(predictionSteps) # Adjust config by applying ValueGetterBase-derived # futures. NOTE: this MUST be called after updateConfigFromSubConfig() in order # to support value-getter-based substitutions from the sub-experiment (if any) applyValueGettersToContainer(config) control = { # The environment that the current model is being run in "environment": 'nupic', # Input stream specification per py/nupic/frameworks/opf/jsonschema/stream_def.json. # 'dataset' : { u'info': u'test_hotgym', u'streams': [ { u'columns': [u''], u'info': u'test data', u'source': u'file://swarming/test_data.csv'}], u'version': 1}, # Iteration count: maximum number of iterations. Each iteration corresponds # to one record from the (possibly aggregated) dataset. The task is # terminated when either number of iterations reaches iterationCount or # all records in the (possibly aggregated) database have been processed, # whichever occurs first. # # iterationCount of -1 = iterate over the entire dataset 'iterationCount' : -1, # A dictionary containing all the supplementary parameters for inference "inferenceArgs":{u'predictedField': u'consumption', u'predictionSteps': [1]}, # Metrics: A list of MetricSpecs that instantiate the metrics that are # computed for this experiment 'metrics':[ MetricSpec(field=u'consumption', metric='multiStep', inferenceElement='multiStepBestPredictions', params={'window': 1000, 'steps': [1], 'errorMetric': 'altMAPE'}), ], # Logged Metrics: A sequence of regular expressions that specify which of # the metrics from the Inference Specifications section MUST be logged for # every prediction. The regex's correspond to the automatically generated # metric labels. This is similar to the way the optimization metric is # specified in permutations.py. 'loggedMetrics': ['.*'], } descriptionInterface = ExperimentDescriptionAPI(modelConfig=config, control=control)	13,727	Python	.py	299	35.953177	117	0.614072	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,215	permutations.py	numenta_nupic-legacy/tests/swarming/nupic/swarming/experiments/simple_cla_multistep/permutations.py	# ---------------------------------------------------------------------- # Numenta Platform for Intelligent Computing (NuPIC) # Copyright (C) 2013, Numenta, Inc. Unless you have an agreement # with Numenta, Inc., for a separate license for this software code, the # following terms and conditions apply: # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU Affero Public License version 3 as # published by the Free Software Foundation. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. # See the GNU Affero Public License for more details. # # You should have received a copy of the GNU Affero Public License # along with this program. If not, see http://www.gnu.org/licenses. # # http://numenta.org/licenses/ # ---------------------------------------------------------------------- """ Template file used by ExpGenerator to generate the actual permutations.py file by replacing $XXXXXXXX tokens with desired values. This permutations.py file was generated by: '/Users/ronmarianetti/nupic/eng/lib/python2.6/site-packages/nupicengine/frameworks/opf/expGenerator/ExpGenerator.pyc' """ import os from nupic.swarming.permutation_helpers import * # The name of the field being predicted. Any allowed permutation MUST contain # the prediction field. # (generated from PREDICTION_FIELD) predictedField = 'consumption' permutations = { 'aggregationInfo': { 'days': 0, 'fields': [ (u'timestamp', 'first'), (u'gym', 'first'), (u'consumption', 'sum')], 'hours': 1, 'microseconds': 0, 'milliseconds': 0, 'minutes': 0, 'months': 0, 'seconds': 0, 'weeks': 0, 'years': 0}, 'modelParams': { 'inferenceType': PermuteChoices(['NontemporalMultiStep', 'TemporalMultiStep']), 'sensorParams': { 'encoders': { u'timestamp_timeOfDay': PermuteEncoder( fieldName='timestamp', encoderClass='DateEncoder.timeOfDay', w=21, radius=PermuteFloat(0.5, 12)), u'timestamp_dayOfWeek': PermuteEncoder( fieldName='timestamp', encoderClass='DateEncoder.dayOfWeek', w=21, radius=PermuteFloat(1, 6)), u'timestamp_weekend': PermuteEncoder( fieldName='timestamp', encoderClass='DateEncoder.weekend', w=21, radius=PermuteChoices([1])), u'consumption': PermuteEncoder( fieldName='consumption', encoderClass='AdaptiveScalarEncoder', w=21, n=PermuteInt(28, 521), clipInput=True), u'_classifierInput': dict( fieldname='consumption', classifierOnly=True, type='AdaptiveScalarEncoder', w=21, n=PermuteInt(28, 521), clipInput=True), }, }, 'spParams': { 'synPermInactiveDec': PermuteFloat(0.005, 0.1), }, 'tmParams': { 'activationThreshold': PermuteInt(12, 16), 'minThreshold': PermuteInt(9, 12), 'pamLength': PermuteInt(1, 5), }, 'clParams': { 'alpha': PermuteFloat(0.0001, 0.1), }, } } # Fields selected for final hypersearch report; # NOTE: These values are used as regular expressions by RunPermutations.py's # report generator # (fieldname values generated from PERM_PREDICTED_FIELD_NAME) report = [ '.consumption.', ] # Permutation optimization setting: either minimize or maximize metric # used by RunPermutations. # NOTE: The value is used as a regular expressions by RunPermutations.py's # report generator # (generated from minimize = "multiStepBestPredictions:multiStep:errorMetric='altMAPE':steps=\[1\]:window=1000:field=consumption") minimize = "multiStepBestPredictions:multiStep:errorMetric='altMAPE':steps=\[1\]:window=1000:field=consumption" minParticlesPerSwarm = None def permutationFilter(perm): """ This function can be used to selectively filter out specific permutation combinations. It is called by RunPermutations for every possible permutation of the variables in the permutations dict. It should return True for valid a combination of permutation values and False for an invalid one. Parameters: --------------------------------------------------------- perm: dict of one possible combination of name:value pairs chosen from permutations. """ # An example of how to use this #if perm['__consumption_encoder']['maxval'] > 300: # return False; # return True	5,107	Python	.py	121	33.247934	130	0.60258	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,216	description.py	numenta_nupic-legacy/tests/swarming/nupic/swarming/experiments/simpleV2/description.py	# ---------------------------------------------------------------------- # Numenta Platform for Intelligent Computing (NuPIC) # Copyright (C) 2013, Numenta, Inc. Unless you have an agreement # with Numenta, Inc., for a separate license for this software code, the # following terms and conditions apply: # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU Affero Public License version 3 as # published by the Free Software Foundation. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. # See the GNU Affero Public License for more details. # # You should have received a copy of the GNU Affero Public License # along with this program. If not, see http://www.gnu.org/licenses. # # http://numenta.org/licenses/ # ---------------------------------------------------------------------- """ Template file used by the OPF Experiment Generator to generate the actual description.py file by replacing $XXXXXXXX tokens with desired values. This description.py file was generated by: '/Users/ronmarianetti/nupic/eng/lib/python2.6/site-packages/nupic/frameworks/opf/expGenerator/experiment_generator.py' """ from nupic.frameworks.opf.exp_description_api import ExperimentDescriptionAPI from nupic.frameworks.opf.exp_description_helpers import ( updateConfigFromSubConfig, applyValueGettersToContainer, DeferredDictLookup) from nupic.frameworks.opf.htm_prediction_model_callbacks import * from nupic.frameworks.opf.metrics import MetricSpec from nupic.frameworks.opf.opf_utils import (InferenceType, InferenceElement) from nupic.support import aggregationDivide from nupic.frameworks.opf.opf_task_driver import ( IterationPhaseSpecLearnOnly, IterationPhaseSpecInferOnly, IterationPhaseSpecLearnAndInfer) # Model Configuration Dictionary: # # Define the model parameters and adjust for any modifications if imported # from a sub-experiment. # # These fields might be modified by a sub-experiment; this dict is passed # between the sub-experiment and base experiment # # # NOTE: Use of DEFERRED VALUE-GETTERs: dictionary fields and list elements # within the config dictionary may be assigned futures derived from the # ValueGetterBase class, such as DeferredDictLookup. # This facility is particularly handy for enabling substitution of values in # the config dictionary from other values in the config dictionary, which is # needed by permutation.py-based experiments. These values will be resolved # during the call to applyValueGettersToContainer(), # which we call after the base experiment's config dictionary is updated from # the sub-experiment. See ValueGetterBase and # DeferredDictLookup for more details about value-getters. # # For each custom encoder parameter to be exposed to the sub-experiment/ # permutation overrides, define a variable in this section, using key names # beginning with a single underscore character to avoid collisions with # pre-defined keys (e.g., _dsEncoderFieldName2_N). # # Example: # config = dict( # _dsEncoderFieldName2_N = 70, # _dsEncoderFieldName2_W = 5, # dsEncoderSchema = [ # base=dict( # fieldname='Name2', type='ScalarEncoder', # name='Name2', minval=0, maxval=270, clipInput=True, # n=DeferredDictLookup('_dsEncoderFieldName2_N'), # w=DeferredDictLookup('_dsEncoderFieldName2_W')), # ], # ) # updateConfigFromSubConfig(config) # applyValueGettersToContainer(config) config = { # Type of model that the rest of these parameters apply to. 'model': "HTMPrediction", # Version that specifies the format of the config. 'version': 1, # Intermediate variables used to compute fields in modelParams and also # referenced from the control section. 'aggregationInfo': { 'days': 0, 'fields': [ (u'timestamp', 'first'), (u'gym', 'first'), (u'consumption', 'mean'), (u'address', 'first')], 'hours': 0, 'microseconds': 0, 'milliseconds': 0, 'minutes': 0, 'months': 0, 'seconds': 0, 'weeks': 0, 'years': 0}, 'predictAheadTime': None, # Model parameter dictionary. 'modelParams': { # The type of inference that this model will perform 'inferenceType': 'TemporalNextStep', 'sensorParams': { # Sensor diagnostic output verbosity control; # if > 0: sensor region will print out on screen what it's sensing # at each step 0: silent; >=1: some info; >=2: more info; # >=3: even more info (see compute() in py/regions/RecordSensor.py) 'verbosity' : 0, # Example: # dsEncoderSchema = [ # DeferredDictLookup('__field_name_encoder'), # ], # # (value generated from DS_ENCODER_SCHEMA) 'encoders': { 'address': { 'fieldname': u'address', 'n': 300, 'name': u'address', 'type': 'SDRCategoryEncoder', 'w': 21}, 'consumption': { 'clipInput': True, 'fieldname': u'consumption', 'maxval': 200, 'minval': 0, 'n': 1500, 'name': u'consumption', 'type': 'ScalarEncoder', 'w': 21}, 'gym': { 'fieldname': u'gym', 'n': 300, 'name': u'gym', 'type': 'SDRCategoryEncoder', 'w': 21}, 'timestamp_dayOfWeek': { 'dayOfWeek': (7, 3), 'fieldname': u'timestamp', 'name': u'timestamp_dayOfWeek', 'type': 'DateEncoder'}, 'timestamp_timeOfDay': { 'fieldname': u'timestamp', 'name': u'timestamp_timeOfDay', 'timeOfDay': (7, 8), 'type': 'DateEncoder'}}, # A dictionary specifying the period for automatically-generated # resets from a RecordSensor; # # None = disable automatically-generated resets (also disabled if # all of the specified values evaluate to 0). # Valid keys is the desired combination of the following: # days, hours, minutes, seconds, milliseconds, microseconds, weeks # # Example for 1.5 days: sensorAutoReset = dict(days=1,hours=12), # # (value generated from SENSOR_AUTO_RESET) 'sensorAutoReset' : None, }, 'spEnable': True, 'spParams': { # SP diagnostic output verbosity control; # 0: silent; >=1: some info; >=2: more info; 'spVerbosity' : 0, 'globalInhibition': 1, # Number of cell columns in the cortical region (same number for # SP and TM) # (see also tpNCellsPerCol) 'columnCount': 2048, 'inputWidth': 0, # SP inhibition control (absolute value); # Maximum number of active columns in the SP region's output (when # there are more, the weaker ones are suppressed) 'numActiveColumnsPerInhArea': 40, 'seed': 1956, # potentialPct # What percent of the columns's receptive field is available # for potential synapses. At initialization time, we will # choose potentialPct * (2potentialRadius+1)^2 'potentialPct': 0.5, # The default connected threshold. Any synapse whose # permanence value is above the connected threshold is # a "connected synapse", meaning it can contribute to the # cell's firing. Typical value is 0.10. Cells whose activity # level before inhibition falls below minDutyCycleBeforeInh # will have their own internal synPermConnectedCell # threshold set below this default value. # (This concept applies to both SP and TM and so 'cells' # is correct here as opposed to 'columns') 'synPermConnected': 0.1, 'synPermActiveInc': 0.1, 'synPermInactiveDec': 0.01, }, # Controls whether TM is enabled or disabled; # TM is necessary for making temporal predictions, such as predicting # the next inputs. Without TM, the model is only capable of # reconstructing missing sensor inputs (via SP). 'tmEnable' : True, 'tmParams': { # TM diagnostic output verbosity control; # 0: silent; [1..6]: increasing levels of verbosity # (see verbosity in nupic/trunk/py/nupic/research/backtracking_tm.py and backtracking_tm_cpp.py) 'verbosity': 0, # Number of cell columns in the cortical region (same number for # SP and TM) # (see also tpNCellsPerCol) 'columnCount': 2048, # The number of cells (i.e., states), allocated per column. 'cellsPerColumn': 32, 'inputWidth': 2048, 'seed': 1960, # Temporal Pooler implementation selector (see _getTPClass in # CLARegion.py). 'temporalImp': 'cpp', # New Synapse formation count # NOTE: If None, use spNumActivePerInhArea # # TODO: need better explanation 'newSynapseCount': 15, # Maximum number of synapses per segment # > 0 for fixed-size CLA # -1 for non-fixed-size CLA # # TODO: for Ron: once the appropriate value is placed in TM # constructor, see if we should eliminate this parameter from # description.py. 'maxSynapsesPerSegment': 32, # Maximum number of segments per cell # > 0 for fixed-size CLA # -1 for non-fixed-size CLA # # TODO: for Ron: once the appropriate value is placed in TM # constructor, see if we should eliminate this parameter from # description.py. 'maxSegmentsPerCell': 128, # Initial Permanence # TODO: need better explanation 'initialPerm': 0.21, # Permanence Increment 'permanenceInc': 0.1, # Permanence Decrement # If set to None, will automatically default to tpPermanenceInc # value. 'permanenceDec' : 0.1, 'globalDecay': 0.0, 'maxAge': 0, # Minimum number of active synapses for a segment to be considered # during search for the best-matching segments. # None=use default # Replaces: tpMinThreshold 'minThreshold': 12, # Segment activation threshold. # A segment is active if it has >= tpSegmentActivationThreshold # connected synapses that are active due to infActiveState # None=use default # Replaces: tpActivationThreshold 'activationThreshold': 16, 'outputType': 'normal', # "Pay Attention Mode" length. This tells the TM how many new # elements to append to the end of a learned sequence at a time. # Smaller values are better for datasets with short sequences, # higher values are better for datasets with long sequences. 'pamLength': 1, }, 'clParams': { 'regionName' : 'SDRClassifierRegion', # Classifier diagnostic output verbosity control; # 0: silent; [1..6]: increasing levels of verbosity 'verbosity' : 0, # This controls how fast the classifier learns/forgets. Higher values # make it adapt faster and forget older patterns faster. 'alpha': 0.001, # This is set after the call to updateConfigFromSubConfig and is # computed from the aggregationInfo and predictAheadTime. 'steps': '1', }, 'trainSPNetOnlyIfRequested': False, }, } # end of config dictionary # Adjust base config dictionary for any modifications if imported from a # sub-experiment updateConfigFromSubConfig(config) # Compute predictionSteps based on the predictAheadTime and the aggregation # period, which may be permuted over. if config['predictAheadTime'] is not None: predictionSteps = int(round(aggregationDivide( config['predictAheadTime'], config['aggregationInfo']))) assert (predictionSteps >= 1) config['modelParams']['clParams']['steps'] = str(predictionSteps) # Adjust config by applying ValueGetterBase-derived # futures. NOTE: this MUST be called after updateConfigFromSubConfig() in order # to support value-getter-based substitutions from the sub-experiment (if any) applyValueGettersToContainer(config) control = { # The environment that the current model is being run in "environment": 'nupic', # Input stream specification per py/nupicengine/cluster/database/StreamDef.json. # 'dataset' : { u'info': u'test_NoProviders', u'streams': [ { u'columns': [u''], u'info': u'test data', u'source': u'file://swarming/test_data.csv'}], u'version': 1}, # Iteration count: maximum number of iterations. Each iteration corresponds # to one record from the (possibly aggregated) dataset. The task is # terminated when either number of iterations reaches iterationCount or # all records in the (possibly aggregated) database have been processed, # whichever occurs first. # # iterationCount of -1 = iterate over the entire dataset #'iterationCount' : ITERATION_COUNT, # Metrics: A list of MetricSpecs that instantiate the metrics that are # computed for this experiment 'metrics':[ MetricSpec(field=u'consumption', inferenceElement=InferenceElement.prediction, metric='rmse'), ], # Logged Metrics: A sequence of regular expressions that specify which of # the metrics from the Inference Specifications section MUST be logged for # every prediction. The regex's correspond to the automatically generated # metric labels. This is similar to the way the optimization metric is # specified in permutations.py. 'loggedMetrics': ['.nupicScore.'], } descriptionInterface = ExperimentDescriptionAPI(modelConfig=config, control=control)	15,403	Python	.py	325	36.738462	118	0.605184	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,217	permutations.py	numenta_nupic-legacy/tests/swarming/nupic/swarming/experiments/simpleV2/permutations.py	# ---------------------------------------------------------------------- # Numenta Platform for Intelligent Computing (NuPIC) # Copyright (C) 2013, Numenta, Inc. Unless you have an agreement # with Numenta, Inc., for a separate license for this software code, the # following terms and conditions apply: # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU Affero Public License version 3 as # published by the Free Software Foundation. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. # See the GNU Affero Public License for more details. # # You should have received a copy of the GNU Affero Public License # along with this program. If not, see http://www.gnu.org/licenses. # # http://numenta.org/licenses/ # ---------------------------------------------------------------------- """ Template file used by ExpGenerator to generate the actual permutations.py file by replacing $XXXXXXXX tokens with desired values. This permutations.py file was generated by: '/Users/ronmarianetti/nupic/eng/lib/python2.6/site-packages/nupic/frameworks/opf/expGenerator/experiment_generator.py' """ import os from nupic.swarming.permutation_helpers import * # The name of the field being predicted. Any allowed permutation MUST contain # the prediction field. # (generated from PREDICTION_FIELD) predictedField = 'consumption' permutations = { 'modelParams': { 'sensorParams': { 'encoders': { 'gym': PermuteEncoder(fieldName='gym', encoderClass='SDRCategoryEncoder', w=7, n=100), 'timestamp_dayOfWeek': PermuteEncoder(fieldName='timestamp', encoderClass='DateEncoder.dayOfWeek', radius=PermuteChoices([1, 3]), w=7), 'timestamp_timeOfDay': PermuteEncoder(fieldName='timestamp', encoderClass='DateEncoder.timeOfDay', radius=PermuteChoices([1, 8]), w=7), 'consumption': PermuteEncoder(fieldName='consumption', encoderClass='ScalarEncoder', maxval=PermuteInt(100, 300, 25), n=PermuteInt(13, 500, 20), w=7, minval=0), 'address': PermuteEncoder(fieldName='address', encoderClass='SDRCategoryEncoder', w=7, n=100), }, }, 'tmParams': { 'minThreshold': PermuteInt(9, 12), 'activationThreshold': PermuteInt(12, 16), }, } } # Fields selected for final hypersearch report; # NOTE: These values are used as regular expressions by RunPermutations.py's # report generator # (fieldname values generated from PERM_PREDICTED_FIELD_NAME) report = [ '.consumption.', ] # Permutation optimization setting: either minimize or maximize metric # used by RunPermutations. # NOTE: The value is used as a regular expressions by RunPermutations.py's # report generator # (generated from minimize = 'prediction:rmse:field=consumption') minimize = 'prediction:rmse:field=consumption' def dummyModelParams(perm): """ This function can be used for Hypersearch algorithm development. When present, Hypersearch doesn't actually run the CLA model in the OPF, but instead runs a dummy model. This function returns the dummy model params that will be used. See the OPFDummyModelRunner class source code (in nupic.swarming.ModelRunner) for a description of the schema for the dummy model params. """ errScore = 50 errScore += abs(perm['modelParams']['sensorParams']['encoders']\ ['consumption']['maxval'] - 250) errScore += abs(perm['modelParams']['sensorParams']['encoders']\ ['consumption']['n'] - 53) if perm['modelParams']['sensorParams']['encoders']['address'] is not None: errScore -= 20 if perm['modelParams']['sensorParams']['encoders']['gym'] is not None: errScore -= 10 # Make models that contain the __timestamp_timeOfDay encoder run a bit # slower so we can test that we successfully kill running models waitTime = None if eval(os.environ.get('NTA_TEST_variableWaits', 'False')): if perm['modelParams']['sensorParams']['encoders']\ ['timestamp_timeOfDay'] is not None: waitTime = 0.01 dummyModelParams = dict( metricValue = errScore, iterations = int(os.environ.get('NTA_TEST_numIterations', '1')), waitTime = waitTime, sysExitModelRange = os.environ.get('NTA_TEST_sysExitModelRange', None), errModelRange = os.environ.get('NTA_TEST_errModelRange', None), jobFailErr = bool(os.environ.get('NTA_TEST_jobFailErr', False)) ) return dummyModelParams def permutationFilter(perm): """ This function can be used to selectively filter out specific permutation combinations. It is called by RunPermutations for every possible permutation of the variables in the permutations dict. It should return True for valid a combination of permutation values and False for an invalid one. Parameters: --------------------------------------------------------- perm: dict of one possible combination of name:value pairs chosen from permutations. """ # An example of how to use this limit = int(os.environ.get('NTA_TEST_maxvalFilter', 300)) if perm['modelParams']['sensorParams']['encoders']['consumption']['maxval'] > limit: return False; return True	5,477	Python	.py	112	43.625	168	0.686341	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,218	description.py	numenta_nupic-legacy/tests/swarming/nupic/swarming/experiments/dummy_multi_v2/description.py	# ---------------------------------------------------------------------- # Numenta Platform for Intelligent Computing (NuPIC) # Copyright (C) 2013, Numenta, Inc. Unless you have an agreement # with Numenta, Inc., for a separate license for this software code, the # following terms and conditions apply: # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU Affero Public License version 3 as # published by the Free Software Foundation. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. # See the GNU Affero Public License for more details. # # You should have received a copy of the GNU Affero Public License # along with this program. If not, see http://www.gnu.org/licenses. # # http://numenta.org/licenses/ # ---------------------------------------------------------------------- """ Template file used by the OPF Experiment Generator to generate the actual description.py file by replacing $XXXXXXXX tokens with desired values. This description.py file was generated by: '/Users/ronmarianetti/nupic/eng/lib/python2.6/site-packages/nupic/frameworks/opf/expGenerator/experiment_generator.py' """ from nupic.frameworks.opf.exp_description_api import ExperimentDescriptionAPI from nupic.frameworks.opf.exp_description_helpers import ( updateConfigFromSubConfig, applyValueGettersToContainer, DeferredDictLookup) from nupic.frameworks.opf.htm_prediction_model_callbacks import * from nupic.frameworks.opf.metrics import MetricSpec from nupic.frameworks.opf.opf_utils import (InferenceType, InferenceElement) from nupic.support import aggregationDivide from nupic.frameworks.opf.opf_task_driver import ( IterationPhaseSpecLearnOnly, IterationPhaseSpecInferOnly, IterationPhaseSpecLearnAndInfer) # Model Configuration Dictionary: # # Define the model parameters and adjust for any modifications if imported # from a sub-experiment. # # These fields might be modified by a sub-experiment; this dict is passed # between the sub-experiment and base experiment # # # NOTE: Use of DEFERRED VALUE-GETTERs: dictionary fields and list elements # within the config dictionary may be assigned futures derived from the # ValueGetterBase class, such as DeferredDictLookup. # This facility is particularly handy for enabling substitution of values in # the config dictionary from other values in the config dictionary, which is # needed by permutation.py-based experiments. These values will be resolved # during the call to applyValueGettersToContainer(), # which we call after the base experiment's config dictionary is updated from # the sub-experiment. See ValueGetterBase and # DeferredDictLookup for more details about value-getters. # # For each custom encoder parameter to be exposed to the sub-experiment/ # permutation overrides, define a variable in this section, using key names # beginning with a single underscore character to avoid collisions with # pre-defined keys (e.g., _dsEncoderFieldName2_N). # # Example: # config = dict( # _dsEncoderFieldName2_N = 70, # _dsEncoderFieldName2_W = 5, # dsEncoderSchema = [ # base=dict( # fieldname='Name2', type='ScalarEncoder', # name='Name2', minval=0, maxval=270, clipInput=True, # n=DeferredDictLookup('_dsEncoderFieldName2_N'), # w=DeferredDictLookup('_dsEncoderFieldName2_W')), # ], # ) # updateConfigFromSubConfig(config) # applyValueGettersToContainer(config) config = { # Type of model that the rest of these parameters apply to. 'model': "HTMPrediction", # Version that specifies the format of the config. 'version': 1, # Intermediate variables used to compute fields in modelParams and also # referenced from the control section. 'aggregationInfo': { 'days': 0, 'fields': [ (u'timestamp', 'first'), (u'gym', 'first'), (u'consumption', 'mean'), (u'address', 'first')], 'hours': 0, 'microseconds': 0, 'milliseconds': 0, 'minutes': 0, 'months': 0, 'seconds': 0, 'weeks': 0, 'years': 0}, 'predictAheadTime': None, # Model parameter dictionary. 'modelParams': { # The type of inference that this model will perform 'inferenceType': 'TemporalNextStep', 'sensorParams': { # Sensor diagnostic output verbosity control; # if > 0: sensor region will print out on screen what it's sensing # at each step 0: silent; >=1: some info; >=2: more info; # >=3: even more info (see compute() in py/regions/RecordSensor.py) 'verbosity' : 0, # Example: # dsEncoderSchema = [ # DeferredDictLookup('__field_name_encoder'), # ], # # (value generated from DS_ENCODER_SCHEMA) 'encoders': { 'address': { 'fieldname': u'address', 'n': 300, 'name': u'address', 'type': 'SDRCategoryEncoder', 'w': 21}, 'consumption': { 'clipInput': True, 'fieldname': u'consumption', 'maxval': 200, 'minval': 0, 'n': 1500, 'name': u'consumption', 'type': 'ScalarEncoder', 'w': 21}, 'gym': { 'fieldname': u'gym', 'n': 300, 'name': u'gym', 'type': 'SDRCategoryEncoder', 'w': 21}, 'timestamp_dayOfWeek': { 'dayOfWeek': (7, 3), 'fieldname': u'timestamp', 'name': u'timestamp_dayOfWeek', 'type': 'DateEncoder'}, 'timestamp_timeOfDay': { 'fieldname': u'timestamp', 'name': u'timestamp_timeOfDay', 'timeOfDay': (7, 8), 'type': 'DateEncoder'}}, # A dictionary specifying the period for automatically-generated # resets from a RecordSensor; # # None = disable automatically-generated resets (also disabled if # all of the specified values evaluate to 0). # Valid keys is the desired combination of the following: # days, hours, minutes, seconds, milliseconds, microseconds, weeks # # Example for 1.5 days: sensorAutoReset = dict(days=1,hours=12), # # (value generated from SENSOR_AUTO_RESET) 'sensorAutoReset' : None, }, 'spEnable': True, 'spParams': { # SP diagnostic output verbosity control; # 0: silent; >=1: some info; >=2: more info; 'spVerbosity' : 0, 'globalInhibition': 1, # Number of cell columns in the cortical region (same number for # SP and TM) # (see also tpNCellsPerCol) 'columnCount': 2048, 'inputWidth': 0, # SP inhibition control (absolute value); # Maximum number of active columns in the SP region's output (when # there are more, the weaker ones are suppressed) 'numActiveColumnsPerInhArea': 40, 'seed': 1956, # potentialPct # What percent of the columns's receptive field is available # for potential synapses. At initialization time, we will # choose potentialPct * (2potentialRadius+1)^2 'potentialPct': 0.5, # The default connected threshold. Any synapse whose # permanence value is above the connected threshold is # a "connected synapse", meaning it can contribute to the # cell's firing. Typical value is 0.10. Cells whose activity # level before inhibition falls below minDutyCycleBeforeInh # will have their own internal synPermConnectedCell # threshold set below this default value. # (This concept applies to both SP and TM and so 'cells' # is correct here as opposed to 'columns') 'synPermConnected': 0.1, 'synPermActiveInc': 0.1, 'synPermInactiveDec': 0.01, }, # Controls whether TM is enabled or disabled; # TM is necessary for making temporal predictions, such as predicting # the next inputs. Without TM, the model is only capable of # reconstructing missing sensor inputs (via SP). 'tmEnable' : True, 'tmParams': { # TM diagnostic output verbosity control; # 0: silent; [1..6]: increasing levels of verbosity # (see verbosity in nupic/trunk/py/nupic/research/backtracking_tm.py and backtracking_tm_cpp.py) 'verbosity': 0, # Number of cell columns in the cortical region (same number for # SP and TM) # (see also tpNCellsPerCol) 'columnCount': 2048, # The number of cells (i.e., states), allocated per column. 'cellsPerColumn': 32, 'inputWidth': 2048, 'seed': 1960, # Temporal Pooler implementation selector (see _getTPClass in # CLARegion.py). 'temporalImp': 'cpp', # New Synapse formation count # NOTE: If None, use spNumActivePerInhArea # # TODO: need better explanation 'newSynapseCount': 15, # Maximum number of synapses per segment # > 0 for fixed-size CLA # -1 for non-fixed-size CLA # # TODO: for Ron: once the appropriate value is placed in TM # constructor, see if we should eliminate this parameter from # description.py. 'maxSynapsesPerSegment': 32, # Maximum number of segments per cell # > 0 for fixed-size CLA # -1 for non-fixed-size CLA # # TODO: for Ron: once the appropriate value is placed in TM # constructor, see if we should eliminate this parameter from # description.py. 'maxSegmentsPerCell': 128, # Initial Permanence # TODO: need better explanation 'initialPerm': 0.21, # Permanence Increment 'permanenceInc': 0.1, # Permanence Decrement # If set to None, will automatically default to tpPermanenceInc # value. 'permanenceDec' : 0.1, 'globalDecay': 0.0, 'maxAge': 0, # Minimum number of active synapses for a segment to be considered # during search for the best-matching segments. # None=use default # Replaces: tpMinThreshold 'minThreshold': 12, # Segment activation threshold. # A segment is active if it has >= tpSegmentActivationThreshold # connected synapses that are active due to infActiveState # None=use default # Replaces: tpActivationThreshold 'activationThreshold': 16, 'outputType': 'normal', # "Pay Attention Mode" length. This tells the TM how many new # elements to append to the end of a learned sequence at a time. # Smaller values are better for datasets with short sequences, # higher values are better for datasets with long sequences. 'pamLength': 1, }, 'clParams': { 'regionName' : 'SDRClassifierRegion', # Classifier diagnostic output verbosity control; # 0: silent; [1..6]: increasing levels of verbosity 'verbosity' : 0, # This controls how fast the classifier learns/forgets. Higher values # make it adapt faster and forget older patterns faster. 'alpha': 0.001, # This is set after the call to updateConfigFromSubConfig and is # computed from the aggregationInfo and predictAheadTime. 'steps': '1', }, 'trainSPNetOnlyIfRequested': False, }, } # end of config dictionary # Adjust base config dictionary for any modifications if imported from a # sub-experiment updateConfigFromSubConfig(config) # Compute predictionSteps based on the predictAheadTime and the aggregation # period, which may be permuted over. if config['predictAheadTime'] is not None: predictionSteps = int(round(aggregationDivide( config['predictAheadTime'], config['aggregationInfo']))) assert (predictionSteps >= 1) config['modelParams']['clParams']['steps'] = str(predictionSteps) # Adjust config by applying ValueGetterBase-derived # futures. NOTE: this MUST be called after updateConfigFromSubConfig() in order # to support value-getter-based substitutions from the sub-experiment (if any) applyValueGettersToContainer(config) control = { # The environment that the current model is being run in "environment": 'nupic', # Input stream specification per py/nupicengine/cluster/database/StreamDef.json. # 'dataset' : {u'info': u'test_NoProviders', u'streams': [ { u'columns': [u''], u'info': u'test data', u'source': u'file://swarming/test_data.csv'}], u'version': 1}, # Iteration count: maximum number of iterations. Each iteration corresponds # to one record from the (possibly aggregated) dataset. The task is # terminated when either number of iterations reaches iterationCount or # all records in the (possibly aggregated) database have been processed, # whichever occurs first. # # iterationCount of -1 = iterate over the entire dataset #'iterationCount' : ITERATION_COUNT, # Metrics: A list of MetricSpecs that instantiate the metrics that are # computed for this experiment 'metrics':[ MetricSpec(field=u'consumption', inferenceElement=InferenceElement.prediction, metric='rmse'), ], # Logged Metrics: A sequence of regular expressions that specify which of # the metrics from the Inference Specifications section MUST be logged for # every prediction. The regex's correspond to the automatically generated # metric labels. This is similar to the way the optimization metric is # specified in permutations.py. 'loggedMetrics': [".nupicScore."], } descriptionInterface = ExperimentDescriptionAPI(modelConfig=config, control=control)	15,387	Python	.py	324	36.858025	118	0.605749	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,219	permutations.py	numenta_nupic-legacy/tests/swarming/nupic/swarming/experiments/dummy_multi_v2/permutations.py	# ---------------------------------------------------------------------- # Numenta Platform for Intelligent Computing (NuPIC) # Copyright (C) 2013, Numenta, Inc. Unless you have an agreement # with Numenta, Inc., for a separate license for this software code, the # following terms and conditions apply: # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU Affero Public License version 3 as # published by the Free Software Foundation. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. # See the GNU Affero Public License for more details. # # You should have received a copy of the GNU Affero Public License # along with this program. If not, see http://www.gnu.org/licenses. # # http://numenta.org/licenses/ # ---------------------------------------------------------------------- """ Template file used by ExpGenerator to generate the actual permutations.py file by replacing $XXXXXXXX tokens with desired values. This permutations.py file was generated by: '/Users/ronmarianetti/nupic/eng/lib/python2.6/site-packages/nupic/frameworks/opf/expGenerator/experiment_generator.py' """ import os from nupic.swarming.permutation_helpers import * # The name of the field being predicted. Any allowed permutation MUST contain # the prediction field. # (generated from PREDICTION_FIELD) predictedField = 'consumption' numModels = int(os.environ.get('NTA_TEST_max_num_models',10)) permutations = { '__model_num' : PermuteInt(0, numModels-1, 1), 'modelParams': { 'sensorParams': { 'encoders': { 'gym': PermuteEncoder(fieldName='gym', encoderClass='SDRCategoryEncoder', w=7, n=100), 'timestamp_dayOfWeek': PermuteEncoder(fieldName='timestamp', encoderClass='DateEncoder.dayOfWeek', radius=PermuteChoices([1, 3]), w=7), 'timestamp_timeOfDay': PermuteEncoder(fieldName='timestamp', encoderClass='DateEncoder.timeOfDay', radius=PermuteChoices([1, 8]), w=7), 'consumption': PermuteEncoder(fieldName='consumption', encoderClass='ScalarEncoder', maxval=PermuteInt(100, 300, 25), n=PermuteInt(13, 500, 20), w=7, minval=0), 'address': PermuteEncoder(fieldName='address', encoderClass='SDRCategoryEncoder', w=7, n=100), }, }, 'tmParams': { 'minThreshold': PermuteInt(9, 12), 'activationThreshold': PermuteInt(12, 16), }, } } # Fields selected for final hypersearch report; # NOTE: These values are used as regular expressions by RunPermutations.py's # report generator # (fieldname values generated from PERM_PREDICTED_FIELD_NAME) report = [ '.consumption.', ] # Permutation optimization setting: either minimize or maximize metric # used by RunPermutations. # NOTE: The value is used as a regular expressions by RunPermutations.py's # report generator # (generated from minimize = 'prediction:rmse:field=consumption') minimize = 'prediction:rmse:field=consumption' def permutationFilter(perm): """ This function can be used to selectively filter out specific permutation combinations. It is called by RunPermutations for every possible permutation of the variables in the permutations dict. It should return True for valid a combination of permutation values and False for an invalid one. Parameters: --------------------------------------------------------- perm: dict of one possible combination of name:value pairs chosen from permutations. """ # An example of how to use this limit = int(os.environ.get('NTA_TEST_maxvalFilter', 300)) if perm['modelParams']['sensorParams']['encoders']['consumption']['maxval'] > limit: return False; return True	3,803	Python	.py	79	45.075949	168	0.712203	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,220	description.py	numenta_nupic-legacy/tests/swarming/nupic/swarming/experiments/field_contrib_temporal/description.py	# ---------------------------------------------------------------------- # Numenta Platform for Intelligent Computing (NuPIC) # Copyright (C) 2013, Numenta, Inc. Unless you have an agreement # with Numenta, Inc., for a separate license for this software code, the # following terms and conditions apply: # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU Affero Public License version 3 as # published by the Free Software Foundation. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. # See the GNU Affero Public License for more details. # # You should have received a copy of the GNU Affero Public License # along with this program. If not, see http://www.gnu.org/licenses. # # http://numenta.org/licenses/ # ---------------------------------------------------------------------- """ Template file used by the OPF Experiment Generator to generate the actual description.py file by replacing $XXXXXXXX tokens with desired values. This description.py file was generated by: '/Users/ronmarianetti/nupic/eng/lib/python2.6/site-packages/nupic/frameworks/opf/expGenerator/experiment_generator.py' """ from nupic.frameworks.opf.exp_description_api import ExperimentDescriptionAPI from nupic.frameworks.opf.exp_description_helpers import ( updateConfigFromSubConfig, applyValueGettersToContainer, DeferredDictLookup) from nupic.frameworks.opf.htm_prediction_model_callbacks import * from nupic.frameworks.opf.metrics import MetricSpec from nupic.frameworks.opf.opf_utils import (InferenceType, InferenceElement) from nupic.support import aggregationDivide from nupic.frameworks.opf.opf_task_driver import ( IterationPhaseSpecLearnOnly, IterationPhaseSpecInferOnly, IterationPhaseSpecLearnAndInfer) # Model Configuration Dictionary: # # Define the model parameters and adjust for any modifications if imported # from a sub-experiment. # # These fields might be modified by a sub-experiment; this dict is passed # between the sub-experiment and base experiment # # # NOTE: Use of DEFERRED VALUE-GETTERs: dictionary fields and list elements # within the config dictionary may be assigned futures derived from the # ValueGetterBase class, such as DeferredDictLookup. # This facility is particularly handy for enabling substitution of values in # the config dictionary from other values in the config dictionary, which is # needed by permutation.py-based experiments. These values will be resolved # during the call to applyValueGettersToContainer(), # which we call after the base experiment's config dictionary is updated from # the sub-experiment. See ValueGetterBase and # DeferredDictLookup for more details about value-getters. # # For each custom encoder parameter to be exposed to the sub-experiment/ # permutation overrides, define a variable in this section, using key names # beginning with a single underscore character to avoid collisions with # pre-defined keys (e.g., _dsEncoderFieldName2_N). # # Example: # config = dict( # _dsEncoderFieldName2_N = 70, # _dsEncoderFieldName2_W = 5, # dsEncoderSchema = [ # base=dict( # fieldname='Name2', type='ScalarEncoder', # name='Name2', minval=0, maxval=270, clipInput=True, # n=DeferredDictLookup('_dsEncoderFieldName2_N'), # w=DeferredDictLookup('_dsEncoderFieldName2_W')), # ], # ) # updateConfigFromSubConfig(config) # applyValueGettersToContainer(config) config = { # Type of model that the rest of these parameters apply to. 'model': "HTMPrediction", # Version that specifies the format of the config. 'version': 1, # Intermediate variables used to compute fields in modelParams and also # referenced from the control section. 'aggregationInfo': { 'days': 0, 'fields': [ (u'timestamp', 'first'), (u'gym', 'first'), (u'consumption', 'mean'), (u'address', 'first')], 'hours': 0, 'microseconds': 0, 'milliseconds': 0, 'minutes': 0, 'months': 0, 'seconds': 0, 'weeks': 0, 'years': 0}, 'predictAheadTime': None, # Model parameter dictionary. 'modelParams': { # The type of inference that this model will perform 'inferenceType': 'TemporalNextStep', 'sensorParams': { # Sensor diagnostic output verbosity control; # if > 0: sensor region will print out on screen what it's sensing # at each step 0: silent; >=1: some info; >=2: more info; # >=3: even more info (see compute() in py/regions/RecordSensor.py) 'verbosity' : 0, # Example: # dsEncoderSchema = [ # DeferredDictLookup('__field_name_encoder'), # ], # # (value generated from DS_ENCODER_SCHEMA) 'encoders': { 'address': { 'fieldname': u'address', 'n': 300, 'name': u'address', 'type': 'SDRCategoryEncoder', 'w': 21}, 'consumption': { 'clipInput': True, 'fieldname': u'consumption', 'maxval': 200, 'minval': 0, 'n': 1500, 'name': u'consumption', 'type': 'ScalarEncoder', 'w': 21}, 'gym': { 'fieldname': u'gym', 'n': 300, 'name': u'gym', 'type': 'SDRCategoryEncoder', 'w': 21}, 'timestamp_dayOfWeek': { 'dayOfWeek': (7, 3), 'fieldname': u'timestamp', 'name': u'timestamp_dayOfWeek', 'type': 'DateEncoder'}, 'timestamp_timeOfDay': { 'fieldname': u'timestamp', 'name': u'timestamp_timeOfDay', 'timeOfDay': (7, 8), 'type': 'DateEncoder'}}, # A dictionary specifying the period for automatically-generated # resets from a RecordSensor; # # None = disable automatically-generated resets (also disabled if # all of the specified values evaluate to 0). # Valid keys is the desired combination of the following: # days, hours, minutes, seconds, milliseconds, microseconds, weeks # # Example for 1.5 days: sensorAutoReset = dict(days=1,hours=12), # # (value generated from SENSOR_AUTO_RESET) 'sensorAutoReset' : None, }, 'spEnable': True, 'spParams': { # SP diagnostic output verbosity control; # 0: silent; >=1: some info; >=2: more info; 'spVerbosity' : 0, 'globalInhibition': 1, # Number of cell columns in the cortical region (same number for # SP and TM) # (see also tpNCellsPerCol) 'columnCount': 2048, 'inputWidth': 0, # SP inhibition control (absolute value); # Maximum number of active columns in the SP region's output (when # there are more, the weaker ones are suppressed) 'numActiveColumnsPerInhArea': 40, 'seed': 1956, # potentialPct # What percent of the columns's receptive field is available # for potential synapses. At initialization time, we will # choose potentialPct * (2potentialRadius+1)^2 'potentialPct': 0.5, # The default connected threshold. Any synapse whose # permanence value is above the connected threshold is # a "connected synapse", meaning it can contribute to the # cell's firing. Typical value is 0.10. Cells whose activity # level before inhibition falls below minDutyCycleBeforeInh # will have their own internal synPermConnectedCell # threshold set below this default value. # (This concept applies to both SP and TM and so 'cells' # is correct here as opposed to 'columns') 'synPermConnected': 0.1, 'synPermActiveInc': 0.1, 'synPermInactiveDec': 0.01, }, # Controls whether TM is enabled or disabled; # TM is necessary for making temporal predictions, such as predicting # the next inputs. Without TM, the model is only capable of # reconstructing missing sensor inputs (via SP). 'tmEnable' : True, 'tmParams': { # TM diagnostic output verbosity control; # 0: silent; [1..6]: increasing levels of verbosity # (see verbosity in nupic/trunk/py/nupic/research/backtracking_tm.py and backtracking_tm_cpp.py) 'verbosity': 0, # Number of cell columns in the cortical region (same number for # SP and TM) # (see also tpNCellsPerCol) 'columnCount': 2048, # The number of cells (i.e., states), allocated per column. 'cellsPerColumn': 32, 'inputWidth': 2048, 'seed': 1960, # Temporal Pooler implementation selector (see _getTPClass in # CLARegion.py). 'temporalImp': 'cpp', # New Synapse formation count # NOTE: If None, use spNumActivePerInhArea # # TODO: need better explanation 'newSynapseCount': 15, # Maximum number of synapses per segment # > 0 for fixed-size CLA # -1 for non-fixed-size CLA # # TODO: for Ron: once the appropriate value is placed in TM # constructor, see if we should eliminate this parameter from # description.py. 'maxSynapsesPerSegment': 32, # Maximum number of segments per cell # > 0 for fixed-size CLA # -1 for non-fixed-size CLA # # TODO: for Ron: once the appropriate value is placed in TM # constructor, see if we should eliminate this parameter from # description.py. 'maxSegmentsPerCell': 128, # Initial Permanence # TODO: need better explanation 'initialPerm': 0.21, # Permanence Increment 'permanenceInc': 0.1, # Permanence Decrement # If set to None, will automatically default to tpPermanenceInc # value. 'permanenceDec' : 0.1, 'globalDecay': 0.0, 'maxAge': 0, # Minimum number of active synapses for a segment to be considered # during search for the best-matching segments. # None=use default # Replaces: tpMinThreshold 'minThreshold': 12, # Segment activation threshold. # A segment is active if it has >= tpSegmentActivationThreshold # connected synapses that are active due to infActiveState # None=use default # Replaces: tpActivationThreshold 'activationThreshold': 16, 'outputType': 'normal', # "Pay Attention Mode" length. This tells the TM how many new # elements to append to the end of a learned sequence at a time. # Smaller values are better for datasets with short sequences, # higher values are better for datasets with long sequences. 'pamLength': 1, }, 'clParams': { 'regionName' : 'SDRClassifierRegion', # Classifier diagnostic output verbosity control; # 0: silent; [1..6]: increasing levels of verbosity 'verbosity' : 0, # This controls how fast the classifier learns/forgets. Higher values # make it adapt faster and forget older patterns faster. 'alpha': 0.001, # This is set after the call to updateConfigFromSubConfig and is # computed from the aggregationInfo and predictAheadTime. 'steps': '1', }, 'trainSPNetOnlyIfRequested': False, }, } # end of config dictionary # Adjust base config dictionary for any modifications if imported from a # sub-experiment updateConfigFromSubConfig(config) # Compute predictionSteps based on the predictAheadTime and the aggregation # period, which may be permuted over. if config['predictAheadTime'] is not None: predictionSteps = int(round(aggregationDivide( config['predictAheadTime'], config['aggregationInfo']))) assert (predictionSteps >= 1) config['modelParams']['clParams']['steps'] = str(predictionSteps) # Adjust config by applying ValueGetterBase-derived # futures. NOTE: this MUST be called after updateConfigFromSubConfig() in order # to support value-getter-based substitutions from the sub-experiment (if any) applyValueGettersToContainer(config) control = { # The environment that the current model is being run in "environment": 'nupic', # Input stream specification per py/nupicengine/cluster/database/StreamDef.json. # 'dataset' : { u'info': u'test_NoProviders', u'streams': [ { u'columns': [u''], u'info': u'test data', u'source': u'file://swarming/test_data.csv'}], u'version': 1}, # Iteration count: maximum number of iterations. Each iteration corresponds # to one record from the (possibly aggregated) dataset. The task is # terminated when either number of iterations reaches iterationCount or # all records in the (possibly aggregated) database have been processed, # whichever occurs first. # # iterationCount of -1 = iterate over the entire dataset #'iterationCount' : ITERATION_COUNT, # Metrics: A list of MetricSpecs that instantiate the metrics that are # computed for this experiment 'metrics':[ MetricSpec(field=u'consumption', inferenceElement=InferenceElement.prediction, metric='rmse'), ], # Logged Metrics: A sequence of regular expressions that specify which of # the metrics from the Inference Specifications section MUST be logged for # every prediction. The regex's correspond to the automatically generated # metric labels. This is similar to the way the optimization metric is # specified in permutations.py. 'loggedMetrics': ['.nupicScore.'], } descriptionInterface = ExperimentDescriptionAPI(modelConfig=config, control=control)	15,389	Python	.py	324	36.861111	118	0.605709	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,221	permutations.py	numenta_nupic-legacy/tests/swarming/nupic/swarming/experiments/field_contrib_temporal/permutations.py	# ---------------------------------------------------------------------- # Numenta Platform for Intelligent Computing (NuPIC) # Copyright (C) 2013, Numenta, Inc. Unless you have an agreement # with Numenta, Inc., for a separate license for this software code, the # following terms and conditions apply: # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU Affero Public License version 3 as # published by the Free Software Foundation. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. # See the GNU Affero Public License for more details. # # You should have received a copy of the GNU Affero Public License # along with this program. If not, see http://www.gnu.org/licenses. # # http://numenta.org/licenses/ # ---------------------------------------------------------------------- """ Template file used by ExpGenerator to generate the actual permutations.py file by replacing $XXXXXXXX tokens with desired values. This permutations.py file was generated by: '/Users/ronmarianetti/nupic/eng/lib/python2.6/site-packages/nupic/frameworks/opf/expGenerator/experiment_generator.py' """ import os from nupic.swarming.permutation_helpers import * # The name of the field being predicted. Any allowed permutation MUST contain # the prediction field. # (generated from PREDICTION_FIELD) predictedField = 'consumption' permutations = { 'modelParams': { 'sensorParams': { 'encoders': { 'gym': PermuteEncoder(fieldName='gym', encoderClass='SDRCategoryEncoder', w=7, n=100), 'timestamp_dayOfWeek': PermuteEncoder(fieldName='timestamp', encoderClass='DateEncoder.dayOfWeek', radius=PermuteChoices([1, 3]), w=7), 'timestamp_timeOfDay': PermuteEncoder(fieldName='timestamp', encoderClass='DateEncoder.timeOfDay', radius=PermuteChoices([1, 8]), w=7), 'consumption': PermuteEncoder(fieldName='consumption', encoderClass='ScalarEncoder', maxval=PermuteInt(100, 300, 25), n=PermuteInt(13, 500, 20), w=7, minval=0), 'address': PermuteEncoder(fieldName='address', encoderClass='SDRCategoryEncoder', w=7, n=100), }, }, 'tmParams': { 'minThreshold': PermuteInt(9, 12), 'activationThreshold': PermuteInt(12, 16), }, } } # Fields selected for final hypersearch report; # NOTE: These values are used as regular expressions by RunPermutations.py's # report generator # (fieldname values generated from PERM_PREDICTED_FIELD_NAME) report = [ '.consumption.', ] # Permutation optimization setting: either minimize or maximize metric # used by RunPermutations. # NOTE: The value is used as a regular expressions by RunPermutations.py's # report generator # (generated from minimize = 'prediction:rmse:field=consumption') minimize = 'prediction:rmse:field=consumption' def dummyModelParams(perm): """ This function can be used for Hypersearch algorithm development. When present, Hypersearch doesn't actually run the CLA model in the OPF, but instead run a dummy model. This function returns the dummy model params that will be used. See the OPFDummyModelRunner class source code (in nupic.swarming.ModelRunner) for a description of the schema for the dummy model params. """ errScore = 100 if perm['modelParams']['sensorParams']['encoders']['address'] is not None: errScore -= 0 if perm['modelParams']['sensorParams']['encoders']['gym'] is not None: errScore -= 10 if perm['modelParams']['sensorParams']['encoders']['timestamp_timeOfDay'] \ is not None: errScore -= 20 if perm['modelParams']['sensorParams']['encoders']['timestamp_dayOfWeek'] \ is not None: errScore -= 50 dummyModelParams = dict( metricValue = errScore, metricFunctions = None, ) return dummyModelParams def permutationFilter(perm): """ This function can be used to selectively filter out specific permutation combinations. It is called by RunPermutations for every possible permutation of the variables in the permutations dict. It should return True for valid a combination of permutation values and False for an invalid one. Parameters: --------------------------------------------------------- perm: dict of one possible combination of name:value pairs chosen from permutations. """ # An example of how to use this limit = int(os.environ.get('NTA_TEST_maxvalFilter', 300)) if perm['modelParams']['sensorParams']['encoders']['consumption']['maxval'] > limit: return False; return True	4,711	Python	.py	101	42.910891	168	0.708924	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,222	description.py	numenta_nupic-legacy/tests/swarming/nupic/swarming/experiments/smart_speculation_spatial_classification/description.py	# ---------------------------------------------------------------------- # Numenta Platform for Intelligent Computing (NuPIC) # Copyright (C) 2013, Numenta, Inc. Unless you have an agreement # with Numenta, Inc., for a separate license for this software code, the # following terms and conditions apply: # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU Affero Public License version 3 as # published by the Free Software Foundation. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. # See the GNU Affero Public License for more details. # # You should have received a copy of the GNU Affero Public License # along with this program. If not, see http://www.gnu.org/licenses. # # http://numenta.org/licenses/ # ---------------------------------------------------------------------- """ Template file used by the OPF Experiment Generator to generate the actual description.py file by replacing $XXXXXXXX tokens with desired values. This description.py file was generated by: '/Users/ronmarianetti/nupic/eng/lib/python2.6/site-packages/nupic/frameworks/opf/expGenerator/experiment_generator.py' """ from nupic.frameworks.opf.exp_description_api import ExperimentDescriptionAPI from nupic.frameworks.opf.exp_description_helpers import ( updateConfigFromSubConfig, applyValueGettersToContainer, DeferredDictLookup) from nupic.frameworks.opf.htm_prediction_model_callbacks import * from nupic.frameworks.opf.metrics import MetricSpec from nupic.frameworks.opf.opf_utils import (InferenceType, InferenceElement) from nupic.support import aggregationDivide from nupic.frameworks.opf.opf_task_driver import ( IterationPhaseSpecLearnOnly, IterationPhaseSpecInferOnly, IterationPhaseSpecLearnAndInfer) # Model Configuration Dictionary: # # Define the model parameters and adjust for any modifications if imported # from a sub-experiment. # # These fields might be modified by a sub-experiment; this dict is passed # between the sub-experiment and base experiment # # # NOTE: Use of DEFERRED VALUE-GETTERs: dictionary fields and list elements # within the config dictionary may be assigned futures derived from the # ValueGetterBase class, such as DeferredDictLookup. # This facility is particularly handy for enabling substitution of values in # the config dictionary from other values in the config dictionary, which is # needed by permutation.py-based experiments. These values will be resolved # during the call to applyValueGettersToContainer(), # which we call after the base experiment's config dictionary is updated from # the sub-experiment. See ValueGetterBase and # DeferredDictLookup for more details about value-getters. # # For each custom encoder parameter to be exposed to the sub-experiment/ # permutation overrides, define a variable in this section, using key names # beginning with a single underscore character to avoid collisions with # pre-defined keys (e.g., _dsEncoderFieldName2_N). # # Example: # config = dict( # _dsEncoderFieldName2_N = 70, # _dsEncoderFieldName2_W = 5, # dsEncoderSchema = [ # base=dict( # fieldname='Name2', type='ScalarEncoder', # name='Name2', minval=0, maxval=270, clipInput=True, # n=DeferredDictLookup('_dsEncoderFieldName2_N'), # w=DeferredDictLookup('_dsEncoderFieldName2_W')), # ], # ) # updateConfigFromSubConfig(config) # applyValueGettersToContainer(config) config = { # Type of model that the rest of these parameters apply to. 'model': "HTMPrediction", # Version that specifies the format of the config. 'version': 1, # Intermediate variables used to compute fields in modelParams and also # referenced from the control section. 'aggregationInfo': { 'days': 0, 'fields': [], 'hours': 0, 'microseconds': 0, 'milliseconds': 0, 'minutes': 0, 'months': 0, 'seconds': 0, 'weeks': 0, 'years': 0}, 'predictAheadTime': None, # Model parameter dictionary. 'modelParams': { # The type of inference that this model will perform 'inferenceType': 'NontemporalClassification', 'sensorParams': { # Sensor diagnostic output verbosity control; # if > 0: sensor region will print out on screen what it's sensing # at each step 0: silent; >=1: some info; >=2: more info; # >=3: even more info (see compute() in py/regions/RecordSensor.py) 'verbosity' : 0, # Example: # dsEncoderSchema = [ # DeferredDictLookup('__field_name_encoder'), # ], # # (value generated from DS_ENCODER_SCHEMA) 'encoders': { u'A': { 'fieldname': u'daynight', 'n': 300, 'name': u'daynight', 'type': 'SDRCategoryEncoder', 'w': 21}, u'B': { 'fieldname': u'daynight', 'n': 300, 'name': u'daynight', 'type': 'SDRCategoryEncoder', 'w': 21}, u'C': { 'fieldname': u'precip', 'n': 300, 'name': u'precip', 'type': 'SDRCategoryEncoder', 'w': 21}, u'D': { 'clipInput': True, 'fieldname': u'visitor_winloss', 'maxval': 0.78600000000000003, 'minval': 0.0, 'n': 150, 'name': u'visitor_winloss', 'type': 'AdaptiveScalarEncoder', 'w': 21}, u'E': { 'clipInput': True, 'fieldname': u'home_winloss', 'maxval': 0.69999999999999996, 'minval': 0.0, 'n': 150, 'name': u'home_winloss', 'type': 'AdaptiveScalarEncoder', 'w': 21}, u'F': { 'dayOfWeek': (7, 1), 'fieldname': u'timestamp', 'name': u'timestamp_dayOfWeek', 'type': 'DateEncoder'}, u'G': { 'fieldname': u'timestamp', 'name': u'timestamp_timeOfDay', 'timeOfDay': (7, 1), 'type': 'DateEncoder'}, u'_classifierInput': { 'clipInput': True, 'fieldname': u'attendance', 'classifierOnly': True, 'maxval': 36067, 'minval': 0, 'n': 150, 'name': u'attendance', 'type': 'AdaptiveScalarEncoder', 'w': 21}}, # A dictionary specifying the period for automatically-generated # resets from a RecordSensor; # # None = disable automatically-generated resets (also disabled if # all of the specified values evaluate to 0). # Valid keys is the desired combination of the following: # days, hours, minutes, seconds, milliseconds, microseconds, weeks # # Example for 1.5 days: sensorAutoReset = dict(days=1,hours=12), # # (value generated from SENSOR_AUTO_RESET) 'sensorAutoReset' : None, }, 'spEnable': True, 'spParams': { # SP diagnostic output verbosity control; # 0: silent; >=1: some info; >=2: more info; 'spVerbosity' : 0, 'globalInhibition': 1, # Number of cell columns in the cortical region (same number for # SP and TM) # (see also tpNCellsPerCol) 'columnCount': 2048, 'inputWidth': 0, # SP inhibition control (absolute value); # Maximum number of active columns in the SP region's output (when # there are more, the weaker ones are suppressed) 'numActiveColumnsPerInhArea': 40, 'seed': 1956, # potentialPct # What percent of the columns's receptive field is available # for potential synapses. At initialization time, we will # choose potentialPct * (2potentialRadius+1)^2 'potentialPct': 1.0, # The default connected threshold. Any synapse whose # permanence value is above the connected threshold is # a "connected synapse", meaning it can contribute to the # cell's firing. Typical value is 0.10. Cells whose activity # level before inhibition falls below minDutyCycleBeforeInh # will have their own internal synPermConnectedCell # threshold set below this default value. # (This concept applies to both SP and TM and so 'cells' # is correct here as opposed to 'columns') 'synPermConnected': 0.1, 'synPermActiveInc': 0.1, 'synPermInactiveDec': 0.01, }, # Controls whether TM is enabled or disabled; # TM is necessary for making temporal predictions, such as predicting # the next inputs. Without TM, the model is only capable of # reconstructing missing sensor inputs (via SP). 'tmEnable' : True, 'tmParams': { # TM diagnostic output verbosity control; # 0: silent; [1..6]: increasing levels of verbosity # (see verbosity in nupic/trunk/py/nupic/research/backtracking_tm.py and backtracking_tm_cpp.py) 'verbosity': 0, # Number of cell columns in the cortical region (same number for # SP and TM) # (see also tpNCellsPerCol) 'columnCount': 2048, # The number of cells (i.e., states), allocated per column. 'cellsPerColumn': 32, 'inputWidth': 2048, 'seed': 1960, # Temporal Pooler implementation selector (see _getTPClass in # CLARegion.py). 'temporalImp': 'cpp', # New Synapse formation count # NOTE: If None, use spNumActivePerInhArea # # TODO: need better explanation 'newSynapseCount': 15, # Maximum number of synapses per segment # > 0 for fixed-size CLA # -1 for non-fixed-size CLA # # TODO: for Ron: once the appropriate value is placed in TM # constructor, see if we should eliminate this parameter from # description.py. 'maxSynapsesPerSegment': 32, # Maximum number of segments per cell # > 0 for fixed-size CLA # -1 for non-fixed-size CLA # # TODO: for Ron: once the appropriate value is placed in TM # constructor, see if we should eliminate this parameter from # description.py. 'maxSegmentsPerCell': 128, # Initial Permanence # TODO: need better explanation 'initialPerm': 0.21, # Permanence Increment 'permanenceInc': 0.1, # Permanence Decrement # If set to None, will automatically default to tpPermanenceInc # value. 'permanenceDec' : 0.1, 'globalDecay': 0.0, 'maxAge': 0, # Minimum number of active synapses for a segment to be considered # during search for the best-matching segments. # None=use default # Replaces: tpMinThreshold 'minThreshold': 12, # Segment activation threshold. # A segment is active if it has >= tpSegmentActivationThreshold # connected synapses that are active due to infActiveState # None=use default # Replaces: tpActivationThreshold 'activationThreshold': 16, 'outputType': 'normal', # "Pay Attention Mode" length. This tells the TM how many new # elements to append to the end of a learned sequence at a time. # Smaller values are better for datasets with short sequences, # higher values are better for datasets with long sequences. 'pamLength': 1, }, 'clParams': { 'regionName' : 'SDRClassifierRegion', # Classifier diagnostic output verbosity control; # 0: silent; [1..6]: increasing levels of verbosity 'verbosity' : 0, # This controls how fast the classifier learns/forgets. Higher values # make it adapt faster and forget older patterns faster. 'alpha': 0.001, # This is set after the call to updateConfigFromSubConfig and is # computed from the aggregationInfo and predictAheadTime. 'steps': '1', }, 'trainSPNetOnlyIfRequested': False, }, } # end of config dictionary # Adjust base config dictionary for any modifications if imported from a # sub-experiment updateConfigFromSubConfig(config) # Compute predictionSteps based on the predictAheadTime and the aggregation # period, which may be permuted over. if config['predictAheadTime'] is not None: predictionSteps = int(round(aggregationDivide( config['predictAheadTime'], config['aggregationInfo']))) assert (predictionSteps >= 1) config['modelParams']['clParams']['steps'] = str(predictionSteps) # Adjust config by applying ValueGetterBase-derived # futures. NOTE: this MUST be called after updateConfigFromSubConfig() in order # to support value-getter-based substitutions from the sub-experiment (if any) applyValueGettersToContainer(config) control = { # The environment that the current model is being run in "environment": 'nupic', # Input stream specification per py/nupicengine/cluster/database/StreamDef.json. # 'dataset' : { u'info': u'baseball benchmark test', u'streams': [ { u'columns': [ u'daynight', u'precip', u'home_winloss', u'visitor_winloss', u'attendance', u'timestamp'], u'info': u'OAK01.csv', u'source': u'file://extra/baseball_stadium/OAK01reformatted.csv'}], u'version': 1}, # Iteration count: maximum number of iterations. Each iteration corresponds # to one record from the (possibly aggregated) dataset. The task is # terminated when either number of iterations reaches iterationCount or # all records in the (possibly aggregated) database have been processed, # whichever occurs first. # # iterationCount of -1 = iterate over the entire dataset #'iterationCount' : ITERATION_COUNT, # Metrics: A list of MetricSpecs that instantiate the metrics that are # computed for this experiment 'metrics':[ MetricSpec(field=u'attendance', metric='multiStep', inferenceElement='multiStepBestPredictions', params={'window': 1000, 'steps': [0], 'errorMetric': 'aae'}), ], # Logged Metrics: A sequence of regular expressions that specify which of # the metrics from the Inference Specifications section MUST be logged for # every prediction. The regex's correspond to the automatically generated # metric labels. This is similar to the way the optimization metric is # specified in permutations.py. 'loggedMetrics': ['.'], } descriptionInterface = ExperimentDescriptionAPI(modelConfig=config, control=control)	16,475	Python	.py	348	35.876437	118	0.58884	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,223	permutations.py	numenta_nupic-legacy/tests/swarming/nupic/swarming/experiments/smart_speculation_spatial_classification/permutations.py	# ---------------------------------------------------------------------- # Numenta Platform for Intelligent Computing (NuPIC) # Copyright (C) 2013, Numenta, Inc. Unless you have an agreement # with Numenta, Inc., for a separate license for this software code, the # following terms and conditions apply: # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU Affero Public License version 3 as # published by the Free Software Foundation. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. # See the GNU Affero Public License for more details. # # You should have received a copy of the GNU Affero Public License # along with this program. If not, see http://www.gnu.org/licenses. # # http://numenta.org/licenses/ # ---------------------------------------------------------------------- """ Template file used by ExpGenerator to generate the actual permutations.py file by replacing $XXXXXXXX tokens with desired values. This permutations.py file was generated by: '/Users/ronmarianetti/nupic/eng/lib/python2.6/site-packages/nupic/frameworks/opf/expGenerator/experiment_generator.py' """ import os from nupic.swarming.permutation_helpers import * # The name of the field being predicted. Any allowed permutation MUST contain # the prediction field. # (generated from PREDICTION_FIELD) predictedField = 'attendance' permutations = { 'modelParams': { 'sensorParams': { 'encoders': { 'A': PermuteEncoder(fieldName='daynight', encoderClass='SDRCategoryEncoder', w=7, n=100), 'B': PermuteEncoder(fieldName='daynight', encoderClass='SDRCategoryEncoder', w=7, n=100), 'C': PermuteEncoder(fieldName='precip', encoderClass='SDRCategoryEncoder', w=7, n=100), '_classifierInput': dict(fieldNname='attendance', classifierOnly=True, type='AdaptiveScalarEncoder', maxval=36067, n=PermuteInt(13, 500, 25), clipInput=True, w=7, minval=0), }, }, } } # Fields selected for final hypersearch report; # NOTE: These values are used as regular expressions by RunPermutations.py's # report generator # (fieldname values generated from PERM_PREDICTED_FIELD_NAME) report = [ '.attendance.', ] # Permutation optimization setting: either minimize or maximize metric # used by RunPermutations. # NOTE: The value is used as a regular expressions by RunPermutations.py's # report generator # (generated from minimize = 'nonprediction:aae:window=1000:field=attendance') minimize = "multiStepBestPredictions:multiStep:errorMetric='aae':steps=\[0\]:window=1000:field=attendance" def dummyModelParams(perm): """ This function can be used for Hypersearch algorithm development. When present, we don't actually run the CLA model in the OPF, but instead run a dummy model. This function returns the dummy model params that will be used. See the OPFDummyModelRunner class source code (in nupic.swarming.ModelRunner) for a description of the schema for the dummy model params. """ errScore = 500 if not perm['modelParams']['sensorParams']['encoders']['A'] is None: errScore -= 40 if not perm['modelParams']['sensorParams']['encoders']['B'] is None: errScore -= 30 if not perm['modelParams']['sensorParams']['encoders']['C'] is None: errScore -= 20 delay = 0 # Make the best model in sprint 0 run slower so that we create more # speculative models encoderCount = 0 encoders = perm['modelParams']['sensorParams']['encoders'] for field,encoder in encoders.items(): if encoder is not None: encoderCount += 1 # NOTE: The _classifierInput is always present. It seems that speculation # favors fields that are not done yet, so make the worse fields take # longer so that the speculative particles choose them in the second # sprint. if encoderCount == 2: delay = 0.1 # Make speculative swarms that should be killed take longer... elif encoderCount == 3 \ and perm['modelParams']['sensorParams']['encoders']["A"] is None: delay = 0.2 # Make the best possible combination take the longest so that we have # an opportunity to kill other swarms before we finish elif encoderCount == 3: delay = 0.3 dummyModelParams = dict( metricValue = errScore, metricFunctions = None, delay=delay, ) return dummyModelParams def permutationFilter(perm): """ This function can be used to selectively filter out specific permutation combinations. It is called by RunPermutations for every possible permutation of the variables in the permutations dict. It should return True for valid a combination of permutation values and False for an invalid one. Parameters: --------------------------------------------------------- perm: dict of one possible combination of name:value pairs chosen from permutations. """ # An example of how to use this #if perm['__consumption_encoder']['maxval'] > 300: # return False; # return True	5,330	Python	.py	120	39.55	118	0.688211	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,224	description.py	numenta_nupic-legacy/tests/swarming/nupic/swarming/experiments/max_branching_temporal/description.py	# ---------------------------------------------------------------------- # Numenta Platform for Intelligent Computing (NuPIC) # Copyright (C) 2013, Numenta, Inc. Unless you have an agreement # with Numenta, Inc., for a separate license for this software code, the # following terms and conditions apply: # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU Affero Public License version 3 as # published by the Free Software Foundation. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. # See the GNU Affero Public License for more details. # # You should have received a copy of the GNU Affero Public License # along with this program. If not, see http://www.gnu.org/licenses. # # http://numenta.org/licenses/ # ---------------------------------------------------------------------- """ Template file used by the OPF Experiment Generator to generate the actual description.py file by replacing $XXXXXXXX tokens with desired values. This description.py file was generated by: '/Users/ronmarianetti/nupic/eng/lib/python2.6/site-packages/nupic/frameworks/opf/expGenerator/experiment_generator.py' """ from nupic.frameworks.opf.exp_description_api import ExperimentDescriptionAPI from nupic.frameworks.opf.exp_description_helpers import ( updateConfigFromSubConfig, applyValueGettersToContainer, DeferredDictLookup) from nupic.frameworks.opf.htm_prediction_model_callbacks import * from nupic.frameworks.opf.metrics import MetricSpec from nupic.frameworks.opf.opf_utils import (InferenceType, InferenceElement) from nupic.support import aggregationDivide from nupic.frameworks.opf.opf_task_driver import ( IterationPhaseSpecLearnOnly, IterationPhaseSpecInferOnly, IterationPhaseSpecLearnAndInfer) # Model Configuration Dictionary: # # Define the model parameters and adjust for any modifications if imported # from a sub-experiment. # # These fields might be modified by a sub-experiment; this dict is passed # between the sub-experiment and base experiment # # # NOTE: Use of DEFERRED VALUE-GETTERs: dictionary fields and list elements # within the config dictionary may be assigned futures derived from the # ValueGetterBase class, such as DeferredDictLookup. # This facility is particularly handy for enabling substitution of values in # the config dictionary from other values in the config dictionary, which is # needed by permutation.py-based experiments. These values will be resolved # during the call to applyValueGettersToContainer(), # which we call after the base experiment's config dictionary is updated from # the sub-experiment. See ValueGetterBase and # DeferredDictLookup for more details about value-getters. # # For each custom encoder parameter to be exposed to the sub-experiment/ # permutation overrides, define a variable in this section, using key names # beginning with a single underscore character to avoid collisions with # pre-defined keys (e.g., _dsEncoderFieldName2_N). # # Example: # config = dict( # _dsEncoderFieldName2_N = 70, # _dsEncoderFieldName2_W = 5, # dsEncoderSchema = [ # base=dict( # fieldname='Name2', type='ScalarEncoder', # name='Name2', minval=0, maxval=270, clipInput=True, # n=DeferredDictLookup('_dsEncoderFieldName2_N'), # w=DeferredDictLookup('_dsEncoderFieldName2_W')), # ], # ) # updateConfigFromSubConfig(config) # applyValueGettersToContainer(config) config = { # Type of model that the rest of these parameters apply to. 'model': "HTMPrediction", # Version that specifies the format of the config. 'version': 1, # Intermediate variables used to compute fields in modelParams and also # referenced from the control section. 'aggregationInfo': { 'days': 0, 'fields': [], 'hours': 0, 'microseconds': 0, 'milliseconds': 0, 'minutes': 0, 'months': 0, 'seconds': 0, 'weeks': 0, 'years': 0}, 'predictAheadTime': None, # Model parameter dictionary. 'modelParams': { # The type of inference that this model will perform 'inferenceType': 'TemporalNextStep', 'sensorParams': { # Sensor diagnostic output verbosity control; # if > 0: sensor region will print out on screen what it's sensing # at each step 0: silent; >=1: some info; >=2: more info; # >=3: even more info (see compute() in py/regions/RecordSensor.py) 'verbosity' : 0, # Example: # dsEncoderSchema = [ # DeferredDictLookup('__field_name_encoder'), # ], # # (value generated from DS_ENCODER_SCHEMA) 'encoders': { u'attendance': { 'clipInput': True, 'fieldname': u'attendance', 'maxval': 36067, 'minval': 0, 'n': 150, 'name': u'attendance', 'type': 'AdaptiveScalarEncoder', 'w': 21}, u'daynight': { 'fieldname': u'daynight', 'n': 300, 'name': u'daynight', 'type': 'SDRCategoryEncoder', 'w': 21}, u'home_winloss': { 'clipInput': True, 'fieldname': u'home_winloss', 'maxval': 0.69999999999999996, 'minval': 0.0, 'n': 150, 'name': u'home_winloss', 'type': 'AdaptiveScalarEncoder', 'w': 21}, u'precip': { 'fieldname': u'precip', 'n': 300, 'name': u'precip', 'type': 'SDRCategoryEncoder', 'w': 21}, u'timestamp_dayOfWeek': { 'dayOfWeek': (7, 1), 'fieldname': u'timestamp', 'name': u'timestamp_dayOfWeek', 'type': 'DateEncoder'}, u'timestamp_timeOfDay': { 'fieldname': u'timestamp', 'name': u'timestamp_timeOfDay', 'timeOfDay': (7, 1), 'type': 'DateEncoder'}, u'visitor_winloss': { 'clipInput': True, 'fieldname': u'visitor_winloss', 'maxval': 0.78600000000000003, 'minval': 0.0, 'n': 150, 'name': u'visitor_winloss', 'type': 'AdaptiveScalarEncoder', 'w': 21}}, # A dictionary specifying the period for automatically-generated # resets from a RecordSensor; # # None = disable automatically-generated resets (also disabled if # all of the specified values evaluate to 0). # Valid keys is the desired combination of the following: # days, hours, minutes, seconds, milliseconds, microseconds, weeks # # Example for 1.5 days: sensorAutoReset = dict(days=1,hours=12), # # (value generated from SENSOR_AUTO_RESET) 'sensorAutoReset' : None, }, 'spEnable': True, 'spParams': { # SP diagnostic output verbosity control; # 0: silent; >=1: some info; >=2: more info; 'spVerbosity' : 0, 'globalInhibition': 1, # Number of cell columns in the cortical region (same number for # SP and TM) # (see also tpNCellsPerCol) 'columnCount': 2048, 'inputWidth': 0, # SP inhibition control (absolute value); # Maximum number of active columns in the SP region's output (when # there are more, the weaker ones are suppressed) 'numActiveColumnsPerInhArea': 40, 'seed': 1956, # potentialPct # What percent of the columns's receptive field is available # for potential synapses. At initialization time, we will # choose potentialPct * (2potentialRadius+1)^2 'potentialPct': 1.0, # The default connected threshold. Any synapse whose # permanence value is above the connected threshold is # a "connected synapse", meaning it can contribute to the # cell's firing. Typical value is 0.10. Cells whose activity # level before inhibition falls below minDutyCycleBeforeInh # will have their own internal synPermConnectedCell # threshold set below this default value. # (This concept applies to both SP and TM and so 'cells' # is correct here as opposed to 'columns') 'synPermConnected': 0.1, 'synPermActiveInc': 0.1, 'synPermInactiveDec': 0.01, }, # Controls whether TM is enabled or disabled; # TM is necessary for making temporal predictions, such as predicting # the next inputs. Without TM, the model is only capable of # reconstructing missing sensor inputs (via SP). 'tmEnable' : True, 'tmParams': { # TM diagnostic output verbosity control; # 0: silent; [1..6]: increasing levels of verbosity # (see verbosity in nupic/trunk/py/nupic/research/backtracking_tm.py and backtracking_tm_cpp.py) 'verbosity': 0, # Number of cell columns in the cortical region (same number for # SP and TM) # (see also tpNCellsPerCol) 'columnCount': 2048, # The number of cells (i.e., states), allocated per column. 'cellsPerColumn': 32, 'inputWidth': 2048, 'seed': 1960, # Temporal Pooler implementation selector (see _getTPClass in # CLARegion.py). 'temporalImp': 'cpp', # New Synapse formation count # NOTE: If None, use spNumActivePerInhArea # # TODO: need better explanation 'newSynapseCount': 15, # Maximum number of synapses per segment # > 0 for fixed-size CLA # -1 for non-fixed-size CLA # # TODO: for Ron: once the appropriate value is placed in TM # constructor, see if we should eliminate this parameter from # description.py. 'maxSynapsesPerSegment': 32, # Maximum number of segments per cell # > 0 for fixed-size CLA # -1 for non-fixed-size CLA # # TODO: for Ron: once the appropriate value is placed in TM # constructor, see if we should eliminate this parameter from # description.py. 'maxSegmentsPerCell': 128, # Initial Permanence # TODO: need better explanation 'initialPerm': 0.21, # Permanence Increment 'permanenceInc': 0.1, # Permanence Decrement # If set to None, will automatically default to tpPermanenceInc # value. 'permanenceDec' : 0.1, 'globalDecay': 0.0, 'maxAge': 0, # Minimum number of active synapses for a segment to be considered # during search for the best-matching segments. # None=use default # Replaces: tpMinThreshold 'minThreshold': 12, # Segment activation threshold. # A segment is active if it has >= tpSegmentActivationThreshold # connected synapses that are active due to infActiveState # None=use default # Replaces: tpActivationThreshold 'activationThreshold': 16, 'outputType': 'normal', # "Pay Attention Mode" length. This tells the TM how many new # elements to append to the end of a learned sequence at a time. # Smaller values are better for datasets with short sequences, # higher values are better for datasets with long sequences. 'pamLength': 1, }, 'clParams': { 'regionName' : 'SDRClassifierRegion', # Classifier diagnostic output verbosity control; # 0: silent; [1..6]: increasing levels of verbosity 'verbosity' : 0, # This controls how fast the classifier learns/forgets. Higher values # make it adapt faster and forget older patterns faster. 'alpha': 0.001, # This is set after the call to updateConfigFromSubConfig and is # computed from the aggregationInfo and predictAheadTime. 'steps': '1', }, 'trainSPNetOnlyIfRequested': False, }, } # end of config dictionary # Adjust base config dictionary for any modifications if imported from a # sub-experiment updateConfigFromSubConfig(config) # Compute predictionSteps based on the predictAheadTime and the aggregation # period, which may be permuted over. if config['predictAheadTime'] is not None: predictionSteps = int(round(aggregationDivide( config['predictAheadTime'], config['aggregationInfo']))) assert (predictionSteps >= 1) config['modelParams']['clParams']['steps'] = str(predictionSteps) # Adjust config by applying ValueGetterBase-derived # futures. NOTE: this MUST be called after updateConfigFromSubConfig() in order # to support value-getter-based substitutions from the sub-experiment (if any) applyValueGettersToContainer(config) control = { # The environment that the current model is being run in "environment": 'nupic', # Input stream specification per py/nupicengine/cluster/database/StreamDef.json. # 'dataset' : { u'info': u'baseball benchmark test', u'streams': [ { u'columns': [ u'daynight', u'precip', u'home_winloss', u'visitor_winloss', u'attendance', u'timestamp'], u'info': u'OAK01.csv', u'source': u'file://extra/baseball_stadium/OAK01reformatted.csv'}], u'version': 1}, # Iteration count: maximum number of iterations. Each iteration corresponds # to one record from the (possibly aggregated) dataset. The task is # terminated when either number of iterations reaches iterationCount or # all records in the (possibly aggregated) database have been processed, # whichever occurs first. # # iterationCount of -1 = iterate over the entire dataset #'iterationCount' : ITERATION_COUNT, # Metrics: A list of MetricSpecs that instantiate the metrics that are # computed for this experiment 'metrics':[ MetricSpec(field=u'attendance', inferenceElement=InferenceElement.prediction, metric='aae', params={'window': 1000}), MetricSpec(field=u'attendance', inferenceElement=InferenceElement.prediction, metric='trivial_aae', params={'window': 1000}), MetricSpec(field=u'attendance', inferenceElement=InferenceElement.encodings, metric='nupicScore_scalar', params={'frequencyWindow': 1000, 'movingAverageWindow': 1000}), MetricSpec(field=u'attendance', inferenceElement=InferenceElement.encodings, metric='nupicScore_scalar', params={'frequencyWindow': 1000}) ], 'inferenceArgs':dict(testFields=['attendance']), # Logged Metrics: A sequence of regular expressions that specify which of # the metrics from the Inference Specifications section MUST be logged for # every prediction. The regex's correspond to the automatically generated # metric labels. This is similar to the way the optimization metric is # specified in permutations.py. 'loggedMetrics': ['.nupicScore.*'], } descriptionInterface = ExperimentDescriptionAPI(modelConfig=config, control=control)	17,281	Python	.py	353	36.433428	118	0.581633	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,225	permutations.py	numenta_nupic-legacy/tests/swarming/nupic/swarming/experiments/max_branching_temporal/permutations.py	# ---------------------------------------------------------------------- # Numenta Platform for Intelligent Computing (NuPIC) # Copyright (C) 2013, Numenta, Inc. Unless you have an agreement # with Numenta, Inc., for a separate license for this software code, the # following terms and conditions apply: # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU Affero Public License version 3 as # published by the Free Software Foundation. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. # See the GNU Affero Public License for more details. # # You should have received a copy of the GNU Affero Public License # along with this program. If not, see http://www.gnu.org/licenses. # # http://numenta.org/licenses/ # ---------------------------------------------------------------------- """ Template file used by ExpGenerator to generate the actual permutations.py file by replacing $XXXXXXXX tokens with desired values. This permutations.py file was generated by: '/Users/ronmarianetti/nupic/eng/lib/python2.6/site-packages/nupic/frameworks/opf/expGenerator/experiment_generator.py' """ import os from nupic.swarming.permutation_helpers import * # The name of the field being predicted. Any allowed permutation MUST contain # the prediction field. # (generated from PREDICTION_FIELD) predictedField = 'attendance' permutations = { 'modelParams': { 'sensorParams': { 'encoders': { 'attendance': PermuteEncoder(fieldName='attendance', encoderClass='AdaptiveScalarEncoder', maxval=36067, n=PermuteInt(13, 500, 25), clipInput=True, w=7, minval=0), 'visitor_winloss': PermuteEncoder(fieldName='visitor_winloss', encoderClass='AdaptiveScalarEncoder', maxval=0.786, n=PermuteInt(13, 500, 25), clipInput=True, w=7, minval=0), 'timestamp_dayOfWeek': PermuteEncoder(fieldName='timestamp', encoderClass='DateEncoder.dayOfWeek', radius=PermuteChoices([1, 3]), w=7), 'precip': PermuteEncoder(fieldName='precip', encoderClass='SDRCategoryEncoder', w=7, n=100), 'timestamp_timeOfDay': PermuteEncoder(fieldName='timestamp', encoderClass='DateEncoder.timeOfDay', radius=PermuteChoices([1, 8]), w=7), 'daynight': PermuteEncoder(fieldName='daynight', encoderClass='SDRCategoryEncoder', w=7, n=100), 'home_winloss': PermuteEncoder(fieldName='home_winloss', encoderClass='AdaptiveScalarEncoder', maxval=0.7, n=PermuteInt(13, 500, 25), clipInput=True, w=7, minval=0), }, }, 'tmParams': { 'minThreshold': PermuteInt(9, 12), 'activationThreshold': PermuteInt(12, 16), }, } } # Fields selected for final hypersearch report; # NOTE: These values are used as regular expressions by RunPermutations.py's # report generator # (fieldname values generated from PERM_PREDICTED_FIELD_NAME) report = [ '.attendance.', ] # Permutation optimization setting: either minimize or maximize metric # used by RunPermutations. # NOTE: The value is used as a regular expressions by RunPermutations.py's # report generator # (generated from minimize = 'nonprediction:aae:window=1000:field=attendance') minimize = 'prediction:aae:window=1000:field=attendance' def dummyModelParams(perm): """ This function can be used for Hypersearch algorithm development. When present, we don't actually run the CLA model in the OPF, but instead run a dummy model. This function returns the dummy model params that will be used. See the OPFDummyModelRunner class source code (in nupic.swarming.ModelRunner) for a description of the schema for the dummy model params. """ #By contribution order from most to least A,B,C,D,E,F #Want A,E combo to give the most contribution followed by A,D then A,C errScore = 500 #A if not perm['modelParams']['sensorParams']['encoders']['visitor_winloss'] \ is None: errScore -= 6 if not perm['modelParams']['sensorParams']['encoders']['daynight'] \ is None: errScore -= 90 if not perm['modelParams']['sensorParams']['encoders']['precip'] \ is None: errScore -= 40 if not perm['modelParams']['sensorParams']['encoders']\ ['timestamp_dayOfWeek'] is None: errScore -= 30 if not perm['modelParams']['sensorParams']['encoders']\ ['timestamp_timeOfDay'] is None: errScore -= 20 #B if not perm['modelParams']['sensorParams']['encoders']['home_winloss'] \ is None: errScore -= 5 #C if not perm['modelParams']['sensorParams']['encoders']\ ['timestamp_timeOfDay'] is None: errScore -= 4 #D if not perm['modelParams']['sensorParams']['encoders']\ ['timestamp_dayOfWeek'] is None: errScore -= 3 #E if not perm['modelParams']['sensorParams']['encoders']['precip'] is None: errScore -= 2 #F if not perm['modelParams']['sensorParams']['encoders']['daynight'] is None: errScore += 2 dummyModelParams = dict( metricValue = errScore, metricFunctions = None, ) return dummyModelParams def permutationFilter(perm): """ This function can be used to selectively filter out specific permutation combinations. It is called by RunPermutations for every possible permutation of the variables in the permutations dict. It should return True for valid a combination of permutation values and False for an invalid one. Parameters: --------------------------------------------------------- perm: dict of one possible combination of name:value pairs chosen from permutations. """ # An example of how to use this #if perm['__consumption_encoder']['maxval'] > 300: # return False; # return True	5,856	Python	.py	129	41.170543	181	0.696422	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,226	description.py	numenta_nupic-legacy/tests/swarming/nupic/swarming/experiments/field_threshold_temporal/description.py	# ---------------------------------------------------------------------- # Numenta Platform for Intelligent Computing (NuPIC) # Copyright (C) 2013, Numenta, Inc. Unless you have an agreement # with Numenta, Inc., for a separate license for this software code, the # following terms and conditions apply: # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU Affero Public License version 3 as # published by the Free Software Foundation. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. # See the GNU Affero Public License for more details. # # You should have received a copy of the GNU Affero Public License # along with this program. If not, see http://www.gnu.org/licenses. # # http://numenta.org/licenses/ # ---------------------------------------------------------------------- """ Template file used by the OPF Experiment Generator to generate the actual description.py file by replacing $XXXXXXXX tokens with desired values. This description.py file was generated by: '/Users/ronmarianetti/nupic/eng/lib/python2.6/site-packages/nupic/frameworks/opf/expGenerator/experiment_generator.py' """ from nupic.frameworks.opf.exp_description_api import ExperimentDescriptionAPI from nupic.frameworks.opf.exp_description_helpers import ( updateConfigFromSubConfig, applyValueGettersToContainer, DeferredDictLookup) from nupic.frameworks.opf.htm_prediction_model_callbacks import * from nupic.frameworks.opf.metrics import MetricSpec from nupic.frameworks.opf.opf_utils import (InferenceType, InferenceElement) from nupic.support import aggregationDivide from nupic.frameworks.opf.opf_task_driver import ( IterationPhaseSpecLearnOnly, IterationPhaseSpecInferOnly, IterationPhaseSpecLearnAndInfer) # Model Configuration Dictionary: # # Define the model parameters and adjust for any modifications if imported # from a sub-experiment. # # These fields might be modified by a sub-experiment; this dict is passed # between the sub-experiment and base experiment # # # NOTE: Use of DEFERRED VALUE-GETTERs: dictionary fields and list elements # within the config dictionary may be assigned futures derived from the # ValueGetterBase class, such as DeferredDictLookup. # This facility is particularly handy for enabling substitution of values in # the config dictionary from other values in the config dictionary, which is # needed by permutation.py-based experiments. These values will be resolved # during the call to applyValueGettersToContainer(), # which we call after the base experiment's config dictionary is updated from # the sub-experiment. See ValueGetterBase and # DeferredDictLookup for more details about value-getters. # # For each custom encoder parameter to be exposed to the sub-experiment/ # permutation overrides, define a variable in this section, using key names # beginning with a single underscore character to avoid collisions with # pre-defined keys (e.g., _dsEncoderFieldName2_N). # # Example: # config = dict( # _dsEncoderFieldName2_N = 70, # _dsEncoderFieldName2_W = 5, # dsEncoderSchema = [ # base=dict( # fieldname='Name2', type='ScalarEncoder', # name='Name2', minval=0, maxval=270, clipInput=True, # n=DeferredDictLookup('_dsEncoderFieldName2_N'), # w=DeferredDictLookup('_dsEncoderFieldName2_W')), # ], # ) # updateConfigFromSubConfig(config) # applyValueGettersToContainer(config) config = { # Type of model that the rest of these parameters apply to. 'model': "HTMPrediction", # Version that specifies the format of the config. 'version': 1, # Intermediate variables used to compute fields in modelParams and also # referenced from the control section. 'aggregationInfo': { 'days': 0, 'fields': [], 'hours': 0, 'microseconds': 0, 'milliseconds': 0, 'minutes': 0, 'months': 0, 'seconds': 0, 'weeks': 0, 'years': 0}, 'predictAheadTime': None, # Model parameter dictionary. 'modelParams': { # The type of inference that this model will perform 'inferenceType': 'TemporalNextStep', 'sensorParams': { # Sensor diagnostic output verbosity control; # if > 0: sensor region will print out on screen what it's sensing # at each step 0: silent; >=1: some info; >=2: more info; # >=3: even more info (see compute() in py/regions/RecordSensor.py) 'verbosity' : 0, # Example: # dsEncoderSchema = [ # DeferredDictLookup('__field_name_encoder'), # ], # # (value generated from DS_ENCODER_SCHEMA) 'encoders': { u'attendance': { 'clipInput': True, 'fieldname': u'attendance', 'maxval': 36067, 'minval': 0, 'n': 150, 'name': u'attendance', 'type': 'AdaptiveScalarEncoder', 'w': 21}, u'daynight': { 'fieldname': u'daynight', 'n': 300, 'name': u'daynight', 'type': 'SDRCategoryEncoder', 'w': 21}, u'home_winloss': { 'clipInput': True, 'fieldname': u'home_winloss', 'maxval': 0.69999999999999996, 'minval': 0.0, 'n': 150, 'name': u'home_winloss', 'type': 'AdaptiveScalarEncoder', 'w': 21}, u'precip': { 'fieldname': u'precip', 'n': 300, 'name': u'precip', 'type': 'SDRCategoryEncoder', 'w': 21}, u'timestamp_dayOfWeek': { 'dayOfWeek': (7, 1), 'fieldname': u'timestamp', 'name': u'timestamp_dayOfWeek', 'type': 'DateEncoder'}, u'timestamp_timeOfDay': { 'fieldname': u'timestamp', 'name': u'timestamp_timeOfDay', 'timeOfDay': (7, 1), 'type': 'DateEncoder'}, u'visitor_winloss': { 'clipInput': True, 'fieldname': u'visitor_winloss', 'maxval': 0.78600000000000003, 'minval': 0.0, 'n': 150, 'name': u'visitor_winloss', 'type': 'AdaptiveScalarEncoder', 'w': 21}}, # A dictionary specifying the period for automatically-generated # resets from a RecordSensor; # # None = disable automatically-generated resets (also disabled if # all of the specified values evaluate to 0). # Valid keys is the desired combination of the following: # days, hours, minutes, seconds, milliseconds, microseconds, weeks # # Example for 1.5 days: sensorAutoReset = dict(days=1,hours=12), # # (value generated from SENSOR_AUTO_RESET) 'sensorAutoReset' : None, }, 'spEnable': True, 'spParams': { # SP diagnostic output verbosity control; # 0: silent; >=1: some info; >=2: more info; 'spVerbosity' : 0, 'globalInhibition': 1, # Number of cell columns in the cortical region (same number for # SP and TM) # (see also tpNCellsPerCol) 'columnCount': 2048, 'inputWidth': 0, # SP inhibition control (absolute value); # Maximum number of active columns in the SP region's output (when # there are more, the weaker ones are suppressed) 'numActiveColumnsPerInhArea': 40, 'seed': 1956, # potentialPct # What percent of the columns's receptive field is available # for potential synapses. At initialization time, we will # choose potentialPct * (2potentialRadius+1)^2 'potentialPct': 1.0, # The default connected threshold. Any synapse whose # permanence value is above the connected threshold is # a "connected synapse", meaning it can contribute to the # cell's firing. Typical value is 0.10. Cells whose activity # level before inhibition falls below minDutyCycleBeforeInh # will have their own internal synPermConnectedCell # threshold set below this default value. # (This concept applies to both SP and TM and so 'cells' # is correct here as opposed to 'columns') 'synPermConnected': 0.1, 'synPermActiveInc': 0.1, 'synPermInactiveDec': 0.01, }, # Controls whether TM is enabled or disabled; # TM is necessary for making temporal predictions, such as predicting # the next inputs. Without TM, the model is only capable of # reconstructing missing sensor inputs (via SP). 'tmEnable' : True, 'tmParams': { # TM diagnostic output verbosity control; # 0: silent; [1..6]: increasing levels of verbosity # (see verbosity in nupic/trunk/py/nupic/research/backtracking_tm.py and backtracking_tm_cpp.py) 'verbosity': 0, # Number of cell columns in the cortical region (same number for # SP and TM) # (see also tpNCellsPerCol) 'columnCount': 2048, # The number of cells (i.e., states), allocated per column. 'cellsPerColumn': 32, 'inputWidth': 2048, 'seed': 1960, # Temporal Pooler implementation selector (see _getTPClass in # CLARegion.py). 'temporalImp': 'cpp', # New Synapse formation count # NOTE: If None, use spNumActivePerInhArea # # TODO: need better explanation 'newSynapseCount': 15, # Maximum number of synapses per segment # > 0 for fixed-size CLA # -1 for non-fixed-size CLA # # TODO: for Ron: once the appropriate value is placed in TM # constructor, see if we should eliminate this parameter from # description.py. 'maxSynapsesPerSegment': 32, # Maximum number of segments per cell # > 0 for fixed-size CLA # -1 for non-fixed-size CLA # # TODO: for Ron: once the appropriate value is placed in TM # constructor, see if we should eliminate this parameter from # description.py. 'maxSegmentsPerCell': 128, # Initial Permanence # TODO: need better explanation 'initialPerm': 0.21, # Permanence Increment 'permanenceInc': 0.1, # Permanence Decrement # If set to None, will automatically default to tpPermanenceInc # value. 'permanenceDec' : 0.1, 'globalDecay': 0.0, 'maxAge': 0, # Minimum number of active synapses for a segment to be considered # during search for the best-matching segments. # None=use default # Replaces: tpMinThreshold 'minThreshold': 12, # Segment activation threshold. # A segment is active if it has >= tpSegmentActivationThreshold # connected synapses that are active due to infActiveState # None=use default # Replaces: tpActivationThreshold 'activationThreshold': 16, 'outputType': 'normal', # "Pay Attention Mode" length. This tells the TM how many new # elements to append to the end of a learned sequence at a time. # Smaller values are better for datasets with short sequences, # higher values are better for datasets with long sequences. 'pamLength': 1, }, 'clParams': { 'regionName' : 'SDRClassifierRegion', # Classifier diagnostic output verbosity control; # 0: silent; [1..6]: increasing levels of verbosity 'verbosity' : 0, # This controls how fast the classifier learns/forgets. Higher values # make it adapt faster and forget older patterns faster. 'alpha': 0.001, # This is set after the call to updateConfigFromSubConfig and is # computed from the aggregationInfo and predictAheadTime. 'steps': '1', }, 'trainSPNetOnlyIfRequested': False, }, } # end of config dictionary # Adjust base config dictionary for any modifications if imported from a # sub-experiment updateConfigFromSubConfig(config) # Compute predictionSteps based on the predictAheadTime and the aggregation # period, which may be permuted over. if config['predictAheadTime'] is not None: predictionSteps = int(round(aggregationDivide( config['predictAheadTime'], config['aggregationInfo']))) assert (predictionSteps >= 1) config['modelParams']['clParams']['steps'] = str(predictionSteps) # Adjust config by applying ValueGetterBase-derived # futures. NOTE: this MUST be called after updateConfigFromSubConfig() in order # to support value-getter-based substitutions from the sub-experiment (if any) applyValueGettersToContainer(config) control = { # The environment that the current model is being run in "environment": 'nupic', # Input stream specification per py/nupicengine/cluster/database/StreamDef.json. # 'dataset' : { u'info': u'baseball benchmark test', u'streams': [ { u'columns': [ u'daynight', u'precip', u'home_winloss', u'visitor_winloss', u'attendance', u'timestamp'], u'info': u'OAK01.csv', u'source': u'file://extra/baseball_stadium/OAK01reformatted.csv'}], u'version': 1}, # Iteration count: maximum number of iterations. Each iteration corresponds # to one record from the (possibly aggregated) dataset. The task is # terminated when either number of iterations reaches iterationCount or # all records in the (possibly aggregated) database have been processed, # whichever occurs first. # # iterationCount of -1 = iterate over the entire dataset #'iterationCount' : ITERATION_COUNT, # Metrics: A list of MetricSpecs that instantiate the metrics that are # computed for this experiment 'metrics':[ MetricSpec(field=u'attendance', inferenceElement=InferenceElement.prediction, metric='aae', params={'window': 1000}), MetricSpec(field=u'attendance', inferenceElement=InferenceElement.prediction, metric='trivial_aae', params={'window': 1000}), MetricSpec(field=u'attendance', inferenceElement=InferenceElement.encodings, metric='nupicScore_scalar', params={'frequencyWindow': 1000, 'movingAverageWindow': 1000}), MetricSpec(field=u'attendance', inferenceElement=InferenceElement.encodings, metric='nupicScore_scalar', params={'frequencyWindow': 1000}) ], # Logged Metrics: A sequence of regular expressions that specify which of # the metrics from the Inference Specifications section MUST be logged for # every prediction. The regex's correspond to the automatically generated # metric labels. This is similar to the way the optimization metric is # specified in permutations.py. 'loggedMetrics': ['.nupicScore.*'], } descriptionInterface = ExperimentDescriptionAPI(modelConfig=config, control=control)	17,229	Python	.py	352	36.400568	118	0.581161	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,227	permutations.py	numenta_nupic-legacy/tests/swarming/nupic/swarming/experiments/field_threshold_temporal/permutations.py	# ---------------------------------------------------------------------- # Numenta Platform for Intelligent Computing (NuPIC) # Copyright (C) 2013, Numenta, Inc. Unless you have an agreement # with Numenta, Inc., for a separate license for this software code, the # following terms and conditions apply: # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU Affero Public License version 3 as # published by the Free Software Foundation. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. # See the GNU Affero Public License for more details. # # You should have received a copy of the GNU Affero Public License # along with this program. If not, see http://www.gnu.org/licenses. # # http://numenta.org/licenses/ # ---------------------------------------------------------------------- """ Template file used by ExpGenerator to generate the actual permutations.py file by replacing $XXXXXXXX tokens with desired values. This permutations.py file was generated by: '/Users/ronmarianetti/nupic/eng/lib/python2.6/site-packages/nupic/frameworks/opf/expGenerator/experiment_generator.py' """ import os from nupic.swarming.permutation_helpers import * # The name of the field being predicted. Any allowed permutation MUST contain # the prediction field. # (generated from PREDICTION_FIELD) predictedField = 'attendance' permutations = { 'modelParams': { 'sensorParams': { 'encoders': { 'attendance': PermuteEncoder(fieldName='attendance', encoderClass='AdaptiveScalarEncoder', maxval=36067, n=PermuteInt(13, 500, 25), clipInput=True, w=7, minval=0), 'visitor_winloss': PermuteEncoder(fieldName='visitor_winloss', encoderClass='AdaptiveScalarEncoder', maxval=0.786, n=PermuteInt(13, 500, 25), clipInput=True, w=7, minval=0), 'timestamp_dayOfWeek': PermuteEncoder(fieldName='timestamp', encoderClass='DateEncoder.dayOfWeek', radius=PermuteChoices([1, 3]), w=7), 'precip': PermuteEncoder(fieldName='precip', encoderClass='SDRCategoryEncoder', w=7, n=100), 'timestamp_timeOfDay': PermuteEncoder(fieldName='timestamp', encoderClass='DateEncoder.timeOfDay', radius=PermuteChoices([1, 8]), w=7), 'daynight': PermuteEncoder(fieldName='daynight', encoderClass='SDRCategoryEncoder', w=7, n=100), 'home_winloss': PermuteEncoder(fieldName='home_winloss', encoderClass='AdaptiveScalarEncoder', maxval=0.7, n=PermuteInt(13, 500, 25), clipInput=True, w=7, minval=0), }, }, 'tmParams': { 'minThreshold': PermuteInt(9, 12), 'activationThreshold': PermuteInt(12, 16), }, } } # Fields selected for final hypersearch report; # NOTE: These values are used as regular expressions by RunPermutations.py's # report generator # (fieldname values generated from PERM_PREDICTED_FIELD_NAME) report = [ '.attendance.', ] # Permutation optimization setting: either minimize or maximize metric # used by RunPermutations. # NOTE: The value is used as a regular expressions by RunPermutations.py's # report generator # (generated from minimize = 'nonprediction:aae:window=1000:field=attendance') minimize = 'prediction:aae:window=1000:field=attendance' def dummyModelParams(perm): """ This function can be used for Hypersearch algorithm development. When present, we don't actually run the CLA model in the OPF, but instead run a dummy model. This function returns the dummy model params that will be used. See the OPFDummyModelRunner class source code (in nupic.swarming.ModelRunner) for a description of the schema for the dummy model params. """ #By contribution order from most to least A,B,C,D,E,F #Want A,E combo to give the most contribution followed by A,D then A,C errScore = 100 #A if not perm['modelParams']['sensorParams']['encoders']['visitor_winloss'] \ is None: errScore -= 25 #B if not perm['modelParams']['sensorParams']['encoders']['home_winloss'] \ is None: errScore -= 20 #C if not perm['modelParams']['sensorParams']['encoders']\ ['timestamp_timeOfDay'] is None: errScore -= 15 #D if not perm['modelParams']['sensorParams']['encoders']\ ['timestamp_dayOfWeek'] is None: errScore -= 10 #E if not perm['modelParams']['sensorParams']['encoders']['precip'] is None: errScore -= 5 #F if not perm['modelParams']['sensorParams']['encoders']['daynight'] is None: errScore += 10 dummyModelParams = dict( metricValue = errScore, metricFunctions = None, ) return dummyModelParams def permutationFilter(perm): """ This function can be used to selectively filter out specific permutation combinations. It is called by RunPermutations for every possible permutation of the variables in the permutations dict. It should return True for valid a combination of permutation values and False for an invalid one. Parameters: --------------------------------------------------------- perm: dict of one possible combination of name:value pairs chosen from permutations. """ # An example of how to use this #if perm['__consumption_encoder']['maxval'] > 300: # return False; # return True	5,393	Python	.py	117	42.188034	181	0.705009	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,228	description.py	numenta_nupic-legacy/tests/swarming/nupic/swarming/experiments/dummyV2/description.py	# ---------------------------------------------------------------------- # Numenta Platform for Intelligent Computing (NuPIC) # Copyright (C) 2013, Numenta, Inc. Unless you have an agreement # with Numenta, Inc., for a separate license for this software code, the # following terms and conditions apply: # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU Affero Public License version 3 as # published by the Free Software Foundation. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. # See the GNU Affero Public License for more details. # # You should have received a copy of the GNU Affero Public License # along with this program. If not, see http://www.gnu.org/licenses. # # http://numenta.org/licenses/ # ---------------------------------------------------------------------- """ Template file used by the OPF Experiment Generator to generate the actual description.py file by replacing $XXXXXXXX tokens with desired values. This description.py file was generated by: '/Users/ronmarianetti/nupic/eng/lib/python2.6/site-packages/nupic/frameworks/opf/expGenerator/experiment_generator.py' """ from nupic.frameworks.opf.exp_description_api import ExperimentDescriptionAPI from nupic.frameworks.opf.exp_description_helpers import ( updateConfigFromSubConfig, applyValueGettersToContainer, DeferredDictLookup) from nupic.frameworks.opf.htm_prediction_model_callbacks import * from nupic.frameworks.opf.metrics import MetricSpec from nupic.frameworks.opf.opf_utils import (InferenceType, InferenceElement) from nupic.support import aggregationDivide from nupic.frameworks.opf.opf_task_driver import ( IterationPhaseSpecLearnOnly, IterationPhaseSpecInferOnly, IterationPhaseSpecLearnAndInfer) # Model Configuration Dictionary: # # Define the model parameters and adjust for any modifications if imported # from a sub-experiment. # # These fields might be modified by a sub-experiment; this dict is passed # between the sub-experiment and base experiment # # # NOTE: Use of DEFERRED VALUE-GETTERs: dictionary fields and list elements # within the config dictionary may be assigned futures derived from the # ValueGetterBase class, such as DeferredDictLookup. # This facility is particularly handy for enabling substitution of values in # the config dictionary from other values in the config dictionary, which is # needed by permutation.py-based experiments. These values will be resolved # during the call to applyValueGettersToContainer(), # which we call after the base experiment's config dictionary is updated from # the sub-experiment. See ValueGetterBase and # DeferredDictLookup for more details about value-getters. # # For each custom encoder parameter to be exposed to the sub-experiment/ # permutation overrides, define a variable in this section, using key names # beginning with a single underscore character to avoid collisions with # pre-defined keys (e.g., _dsEncoderFieldName2_N). # # Example: # config = dict( # _dsEncoderFieldName2_N = 70, # _dsEncoderFieldName2_W = 5, # dsEncoderSchema = [ # base=dict( # fieldname='Name2', type='ScalarEncoder', # name='Name2', minval=0, maxval=270, clipInput=True, # n=DeferredDictLookup('_dsEncoderFieldName2_N'), # w=DeferredDictLookup('_dsEncoderFieldName2_W')), # ], # ) # updateConfigFromSubConfig(config) # applyValueGettersToContainer(config) config = { # Type of model that the rest of these parameters apply to. 'model': "HTMPrediction", # Version that specifies the format of the config. 'version': 1, # Intermediate variables used to compute fields in modelParams and also # referenced from the control section. 'aggregationInfo': { 'days': 0, 'fields': [ (u'timestamp', 'first'), (u'gym', 'first'), (u'consumption', 'mean'), (u'address', 'first')], 'hours': 0, 'microseconds': 0, 'milliseconds': 0, 'minutes': 0, 'months': 0, 'seconds': 0, 'weeks': 0, 'years': 0}, 'predictAheadTime': None, # Model parameter dictionary. 'modelParams': { # The type of inference that this model will perform 'inferenceType': 'TemporalNextStep', 'sensorParams': { # Sensor diagnostic output verbosity control; # if > 0: sensor region will print out on screen what it's sensing # at each step 0: silent; >=1: some info; >=2: more info; # >=3: even more info (see compute() in py/regions/RecordSensor.py) 'verbosity' : 0, # Example: # dsEncoderSchema = [ # DeferredDictLookup('__field_name_encoder'), # ], # # (value generated from DS_ENCODER_SCHEMA) 'encoders': { 'address': { 'fieldname': u'address', 'n': 300, 'name': u'address', 'type': 'SDRCategoryEncoder', 'w': 21}, 'consumption': { 'clipInput': True, 'fieldname': u'consumption', 'maxval': 200, 'minval': 0, 'n': 1500, 'name': u'consumption', 'type': 'ScalarEncoder', 'w': 21}, 'gym': { 'fieldname': u'gym', 'n': 600, 'name': u'gym', 'type': 'SDRCategoryEncoder', 'w': 21}, 'timestamp_dayOfWeek': { 'dayOfWeek': (7, 3), 'fieldname': u'timestamp', 'name': u'timestamp_dayOfWeek', 'type': 'DateEncoder'}, 'timestamp_timeOfDay': { 'fieldname': u'timestamp', 'name': u'timestamp_timeOfDay', 'timeOfDay': (7, 8), 'type': 'DateEncoder'}}, # A dictionary specifying the period for automatically-generated # resets from a RecordSensor; # # None = disable automatically-generated resets (also disabled if # all of the specified values evaluate to 0). # Valid keys is the desired combination of the following: # days, hours, minutes, seconds, milliseconds, microseconds, weeks # # Example for 1.5 days: sensorAutoReset = dict(days=1,hours=12), # # (value generated from SENSOR_AUTO_RESET) 'sensorAutoReset' : None, }, 'spEnable': True, 'spParams': { # SP diagnostic output verbosity control; # 0: silent; >=1: some info; >=2: more info; 'spVerbosity' : 0, 'globalInhibition': 1, # Number of cell columns in the cortical region (same number for # SP and TM) # (see also tpNCellsPerCol) 'columnCount': 2048, 'inputWidth': 0, # SP inhibition control (absolute value); # Maximum number of active columns in the SP region's output (when # there are more, the weaker ones are suppressed) 'numActiveColumnsPerInhArea': 40, 'seed': 1956, # potentialPct # What percent of the columns's receptive field is available # for potential synapses. At initialization time, we will # choose potentialPct * (2potentialRadius+1)^2 'potentialPct': 0.5, # The default connected threshold. Any synapse whose # permanence value is above the connected threshold is # a "connected synapse", meaning it can contribute to the # cell's firing. Typical value is 0.10. Cells whose activity # level before inhibition falls below minDutyCycleBeforeInh # will have their own internal synPermConnectedCell # threshold set below this default value. # (This concept applies to both SP and TM and so 'cells' # is correct here as opposed to 'columns') 'synPermConnected': 0.1, 'synPermActiveInc': 0.1, 'synPermInactiveDec': 0.01, }, # Controls whether TM is enabled or disabled; # TM is necessary for making temporal predictions, such as predicting # the next inputs. Without TM, the model is only capable of # reconstructing missing sensor inputs (via SP). 'tmEnable' : True, 'tmParams': { # TM diagnostic output verbosity control; # 0: silent; [1..6]: increasing levels of verbosity # (see verbosity in nupic/trunk/py/nupic/research/backtracking_tm.py and backtracking_tm_cpp.py) 'verbosity': 0, # Number of cell columns in the cortical region (same number for # SP and TM) # (see also tpNCellsPerCol) 'columnCount': 2048, # The number of cells (i.e., states), allocated per column. 'cellsPerColumn': 32, 'inputWidth': 2048, 'seed': 1960, # Temporal Pooler implementation selector (see _getTPClass in # CLARegion.py). 'temporalImp': 'cpp', # New Synapse formation count # NOTE: If None, use spNumActivePerInhArea # # TODO: need better explanation 'newSynapseCount': 15, # Maximum number of synapses per segment # > 0 for fixed-size CLA # -1 for non-fixed-size CLA # # TODO: for Ron: once the appropriate value is placed in TM # constructor, see if we should eliminate this parameter from # description.py. 'maxSynapsesPerSegment': 32, # Maximum number of segments per cell # > 0 for fixed-size CLA # -1 for non-fixed-size CLA # # TODO: for Ron: once the appropriate value is placed in TM # constructor, see if we should eliminate this parameter from # description.py. 'maxSegmentsPerCell': 128, # Initial Permanence # TODO: need better explanation 'initialPerm': 0.21, # Permanence Increment 'permanenceInc': 0.1, # Permanence Decrement # If set to None, will automatically default to tpPermanenceInc # value. 'permanenceDec' : 0.1, 'globalDecay': 0.0, 'maxAge': 0, # Minimum number of active synapses for a segment to be considered # during search for the best-matching segments. # None=use default # Replaces: tpMinThreshold 'minThreshold': 12, # Segment activation threshold. # A segment is active if it has >= tpSegmentActivationThreshold # connected synapses that are active due to infActiveState # None=use default # Replaces: tpActivationThreshold 'activationThreshold': 16, 'outputType': 'normal', # "Pay Attention Mode" length. This tells the TM how many new # elements to append to the end of a learned sequence at a time. # Smaller values are better for datasets with short sequences, # higher values are better for datasets with long sequences. 'pamLength': 1, }, 'clParams': { 'regionName' : 'SDRClassifierRegion', # Classifier diagnostic output verbosity control; # 0: silent; [1..6]: increasing levels of verbosity 'verbosity' : 0, # This controls how fast the classifier learns/forgets. Higher values # make it adapt faster and forget older patterns faster. 'alpha': 0.001, # This is set after the call to updateConfigFromSubConfig and is # computed from the aggregationInfo and predictAheadTime. 'steps': '1', }, 'trainSPNetOnlyIfRequested': False, }, } # end of config dictionary # Adjust base config dictionary for any modifications if imported from a # sub-experiment updateConfigFromSubConfig(config) # Compute predictionSteps based on the predictAheadTime and the aggregation # period, which may be permuted over. if config['predictAheadTime'] is not None: predictionSteps = int(round(aggregationDivide( config['predictAheadTime'], config['aggregationInfo']))) assert (predictionSteps >= 1) config['modelParams']['clParams']['steps'] = str(predictionSteps) # Adjust config by applying ValueGetterBase-derived # futures. NOTE: this MUST be called after updateConfigFromSubConfig() in order # to support value-getter-based substitutions from the sub-experiment (if any) applyValueGettersToContainer(config) control = { # The environment that the current model is being run in "environment": 'nupic', # Input stream specification per py/nupicengine/cluster/database/StreamDef.json. # 'dataset' : {u'info': u'test_NoProviders', u'streams': [ { u'columns': [u''], u'info': "test data", u'source': "file://swarming/test_data.csv"}], u'version': 1}, # Iteration count: maximum number of iterations. Each iteration corresponds # to one record from the (possibly aggregated) dataset. The task is # terminated when either number of iterations reaches iterationCount or # all records in the (possibly aggregated) database have been processed, # whichever occurs first. # # iterationCount of -1 = iterate over the entire dataset #'iterationCount' : ITERATION_COUNT, # Metrics: A list of MetricSpecs that instantiate the metrics that are # computed for this experiment 'metrics':[ MetricSpec(field=u'consumption',inferenceElement=InferenceElement.prediction, metric='rmse'), ], # Logged Metrics: A sequence of regular expressions that specify which of # the metrics from the Inference Specifications section MUST be logged for # every prediction. The regex's correspond to the automatically generated # metric labels. This is similar to the way the optimization metric is # specified in permutations.py. } descriptionInterface = ExperimentDescriptionAPI(modelConfig=config, control=control)	15,344	Python	.py	323	36.845201	118	0.605779	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,229	permutations.py	numenta_nupic-legacy/tests/swarming/nupic/swarming/experiments/dummyV2/permutations.py	# ---------------------------------------------------------------------- # Numenta Platform for Intelligent Computing (NuPIC) # Copyright (C) 2013, Numenta, Inc. Unless you have an agreement # with Numenta, Inc., for a separate license for this software code, the # following terms and conditions apply: # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU Affero Public License version 3 as # published by the Free Software Foundation. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. # See the GNU Affero Public License for more details. # # You should have received a copy of the GNU Affero Public License # along with this program. If not, see http://www.gnu.org/licenses. # # http://numenta.org/licenses/ # ---------------------------------------------------------------------- """ Template file used by ExpGenerator to generate the actual permutations.py file by replacing $XXXXXXXX tokens with desired values. This permutations.py file was generated by: '/Users/ronmarianetti/nupic/eng/lib/python2.6/site-packages/nupic/frameworks/opf/expGenerator/experiment_generator.py' """ import os from nupic.swarming.permutation_helpers import * # The name of the field being predicted. Any allowed permutation MUST contain # the prediction field. # (generated from PREDICTION_FIELD) predictedField = 'consumption' permutations = { 'modelParams': { 'sensorParams': { 'encoders': { 'gym': PermuteEncoder(fieldName='gym', encoderClass='SDRCategoryEncoder', w=21, n=300), 'timestamp_dayOfWeek': PermuteEncoder(fieldName='timestamp', encoderClass='DateEncoder.dayOfWeek', radius=PermuteChoices([1, 3]), w=7), 'timestamp_timeOfDay': PermuteEncoder(fieldName='timestamp', encoderClass='DateEncoder.timeOfDay', radius=PermuteChoices([1, 8]), w=7), 'consumption': PermuteEncoder(fieldName='consumption', encoderClass='ScalarEncoder', maxval=PermuteInt(100, 300, 25), n=PermuteInt(39, 1500, 60), w=21, minval=0), 'address': PermuteEncoder(fieldName='address', encoderClass='SDRCategoryEncoder', w=21, n=300), }, }, 'tmParams': { 'minThreshold': PermuteInt(9, 12), 'activationThreshold': PermuteInt(12, 16), }, } } # Fields selected for final hypersearch report; # NOTE: These values are used as regular expressions by RunPermutations.py's # report generator # (fieldname values generated from PERM_PREDICTED_FIELD_NAME) report = [ '.consumption.', ] # Permutation optimization setting: either minimize or maximize metric # used by RunPermutations. # NOTE: The value is used as a regular expressions by RunPermutations.py's # report generator # (generated from minimize = 'prediction:rmse:field=consumption') minimize = 'prediction:rmse:field=consumption' def permutationFilter(perm): """ This function can be used to selectively filter out specific permutation combinations. It is called by RunPermutations for every possible permutation of the variables in the permutations dict. It should return True for valid a combination of permutation values and False for an invalid one. Parameters: --------------------------------------------------------- perm: dict of one possible combination of name:value pairs chosen from permutations. """ # An example of how to use this if perm['modelParams']['sensorParams']['encoders']['consumption']['maxval'] > 250: return False; return True	3,634	Python	.py	76	44.723684	170	0.713115	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,230	LICENSE.tweepy-2.1.txt	numenta_nupic-legacy/external/licenses/LICENSE.tweepy-2.1.txt	MIT License Copyright (c) 2009-2010 Joshua Roesslein Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.	1,077	Python	.py	17	62.176471	77	0.822138	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,231	pkg_resources.py	numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/pkg_resources.py	"""Package resource API -------------------- A resource is a logical file contained within a package, or a logical subdirectory thereof. The package resource API expects resource names to have their path parts separated with ``/``, not whatever the local path separator is. Do not use os.path operations to manipulate resource names being passed into the API. The package resource API is designed to work with normal filesystem packages, .egg files, and unpacked .egg files. It can also work in a limited way with .zip files and with custom PEP 302 loaders that support the ``get_data()`` method. """ import sys, os, zipimport, time, re, imp, new try: frozenset except NameError: from sets import ImmutableSet as frozenset from os import utime, rename, unlink # capture these to bypass sandboxing from os import open as os_open def get_supported_platform(): """Return this platform's maximum compatible version. distutils.util.get_platform() normally reports the minimum version of Mac OS X that would be required to use extensions produced by distutils. But what we want when checking compatibility is to know the version of Mac OS X that we are running. To allow usage of packages that explicitly require a newer version of Mac OS X, we must also know the current version of the OS. If this condition occurs for any other platform with a version in its platform strings, this function should be extended accordingly. """ plat = get_build_platform(); m = macosVersionString.match(plat) if m is not None and sys.platform == "darwin": try: plat = 'macosx-%s-%s' % ('.'.join(_macosx_vers()[:2]), m.group(3)) except ValueError: pass # not Mac OS X return plat __all__ = [ # Basic resource access and distribution/entry point discovery 'require', 'run_script', 'get_provider', 'get_distribution', 'load_entry_point', 'get_entry_map', 'get_entry_info', 'iter_entry_points', 'resource_string', 'resource_stream', 'resource_filename', 'resource_listdir', 'resource_exists', 'resource_isdir', # Environmental control 'declare_namespace', 'working_set', 'add_activation_listener', 'find_distributions', 'set_extraction_path', 'cleanup_resources', 'get_default_cache', # Primary implementation classes 'Environment', 'WorkingSet', 'ResourceManager', 'Distribution', 'Requirement', 'EntryPoint', # Exceptions 'ResolutionError','VersionConflict','DistributionNotFound','UnknownExtra', 'ExtractionError', # Parsing functions and string utilities 'parse_requirements', 'parse_version', 'safe_name', 'safe_version', 'get_platform', 'compatible_platforms', 'yield_lines', 'split_sections', 'safe_extra', 'to_filename', # filesystem utilities 'ensure_directory', 'normalize_path', # Distribution "precedence" constants 'EGG_DIST', 'BINARY_DIST', 'SOURCE_DIST', 'CHECKOUT_DIST', 'DEVELOP_DIST', # "Provider" interfaces, implementations, and registration/lookup APIs 'IMetadataProvider', 'IResourceProvider', 'FileMetadata', 'PathMetadata', 'EggMetadata', 'EmptyProvider', 'empty_provider', 'NullProvider', 'EggProvider', 'DefaultProvider', 'ZipProvider', 'register_finder', 'register_namespace_handler', 'register_loader_type', 'fixup_namespace_packages', 'get_importer', # Deprecated/backward compatibility only 'run_main', 'AvailableDistributions', ] class ResolutionError(Exception): """Abstract base for dependency resolution errors""" def __repr__(self): return self.__class__.__name__+repr(self.args) class VersionConflict(ResolutionError): """An already-installed version conflicts with the requested version""" class DistributionNotFound(ResolutionError): """A requested distribution was not found""" class UnknownExtra(ResolutionError): """Distribution doesn't have an "extra feature" of the given name""" _provider_factories = {} PY_MAJOR = sys.version[:3] EGG_DIST = 3 BINARY_DIST = 2 SOURCE_DIST = 1 CHECKOUT_DIST = 0 DEVELOP_DIST = -1 def register_loader_type(loader_type, provider_factory): """Register `provider_factory` to make providers for `loader_type` `loader_type` is the type or class of a PEP 302 ``module.__loader__``, and `provider_factory` is a function that, passed a module object, returns an ``IResourceProvider`` for that module. """ _provider_factories[loader_type] = provider_factory def get_provider(moduleOrReq): """Return an IResourceProvider for the named module or requirement""" if isinstance(moduleOrReq,Requirement): return working_set.find(moduleOrReq) or require(str(moduleOrReq))[0] try: module = sys.modules[moduleOrReq] except KeyError: __import__(moduleOrReq) module = sys.modules[moduleOrReq] loader = getattr(module, '__loader__', None) return _find_adapter(_provider_factories, loader)(module) def _macosx_vers(_cache=[]): if not _cache: info = os.popen('/usr/bin/sw_vers').read().splitlines() for line in info: key, value = line.split(None, 1) if key == 'ProductVersion:': _cache.append(value.strip().split(".")) break else: raise ValueError, "What?!" return _cache[0] def _macosx_arch(machine): return {'PowerPC':'ppc', 'Power_Macintosh':'ppc'}.get(machine,machine) def get_build_platform(): """Return this platform's string for platform-specific distributions XXX Currently this is the same as ``distutils.util.get_platform()``, but it needs some hacks for Linux and Mac OS X. """ from distutils.util import get_platform plat = get_platform() if sys.platform == "darwin" and not plat.startswith('macosx-'): try: version = _macosx_vers() machine = os.uname()[4].replace(" ", "_") return "macosx-%d.%d-%s" % (int(version[0]), int(version[1]), _macosx_arch(machine)) except ValueError: # if someone is running a non-Mac darwin system, this will fall # through to the default implementation pass return plat macosVersionString = re.compile(r"macosx-(\d+)\.(\d+)-(.)") darwinVersionString = re.compile(r"darwin-(\d+)\.(\d+)\.(\d+)-(.)") get_platform = get_build_platform # XXX backward compat def compatible_platforms(provided,required): """Can code for the `provided` platform run on the `required` platform? Returns true if either platform is ``None``, or the platforms are equal. XXX Needs compatibility checks for Linux and other unixy OSes. """ if provided is None or required is None or provided==required: return True # easy case # Mac OS X special cases reqMac = macosVersionString.match(required) if reqMac: provMac = macosVersionString.match(provided) # is this a Mac package? if not provMac: # this is backwards compatibility for packages built before # setuptools 0.6. All packages built after this point will # use the new macosx designation. provDarwin = darwinVersionString.match(provided) if provDarwin: dversion = int(provDarwin.group(1)) macosversion = "%s.%s" % (reqMac.group(1), reqMac.group(2)) if dversion == 7 and macosversion >= "10.3" or \ dversion == 8 and macosversion >= "10.4": #import warnings #warnings.warn("Mac eggs should be rebuilt to " # "use the macosx designation instead of darwin.", # category=DeprecationWarning) return True return False # egg isn't macosx or legacy darwin # are they the same major version and machine type? if provMac.group(1) != reqMac.group(1) or \ provMac.group(3) != reqMac.group(3): return False # is the required OS major update >= the provided one? if int(provMac.group(2)) > int(reqMac.group(2)): return False return True # XXX Linux and other platforms' special cases should go here return False def run_script(dist_spec, script_name): """Locate distribution `dist_spec` and run its `script_name` script""" ns = sys._getframe(1).f_globals name = ns['__name__'] ns.clear() ns['__name__'] = name require(dist_spec)[0].run_script(script_name, ns) run_main = run_script # backward compatibility def get_distribution(dist): """Return a current distribution object for a Requirement or string""" if isinstance(dist,basestring): dist = Requirement.parse(dist) if isinstance(dist,Requirement): dist = get_provider(dist) if not isinstance(dist,Distribution): raise TypeError("Expected string, Requirement, or Distribution", dist) return dist def load_entry_point(dist, group, name): """Return `name` entry point of `group` for `dist` or raise ImportError""" return get_distribution(dist).load_entry_point(group, name) def get_entry_map(dist, group=None): """Return the entry point map for `group`, or the full entry map""" return get_distribution(dist).get_entry_map(group) def get_entry_info(dist, group, name): """Return the EntryPoint object for `group`+`name`, or ``None``""" return get_distribution(dist).get_entry_info(group, name) class IMetadataProvider: def has_metadata(name): """Does the package's distribution contain the named metadata?""" def get_metadata(name): """The named metadata resource as a string""" def get_metadata_lines(name): """Yield named metadata resource as list of non-blank non-comment lines Leading and trailing whitespace is stripped from each line, and lines with ``#`` as the first non-blank character are omitted.""" def metadata_isdir(name): """Is the named metadata a directory? (like ``os.path.isdir()``)""" def metadata_listdir(name): """List of metadata names in the directory (like ``os.listdir()``)""" def run_script(script_name, namespace): """Execute the named script in the supplied namespace dictionary""" class IResourceProvider(IMetadataProvider): """An object that provides access to package resources""" def get_resource_filename(manager, resource_name): """Return a true filesystem path for `resource_name` `manager` must be an ``IResourceManager``""" def get_resource_stream(manager, resource_name): """Return a readable file-like object for `resource_name` `manager` must be an ``IResourceManager``""" def get_resource_string(manager, resource_name): """Return a string containing the contents of `resource_name` `manager` must be an ``IResourceManager``""" def has_resource(resource_name): """Does the package contain the named resource?""" def resource_isdir(resource_name): """Is the named resource a directory? (like ``os.path.isdir()``)""" def resource_listdir(resource_name): """List of resource names in the directory (like ``os.listdir()``)""" class WorkingSet(object): """A collection of active distributions on sys.path (or a similar list)""" def __init__(self, entries=None): """Create working set from list of path entries (default=sys.path)""" self.entries = [] self.entry_keys = {} self.by_key = {} self.callbacks = [] if entries is None: entries = sys.path for entry in entries: self.add_entry(entry) def add_entry(self, entry): """Add a path item to ``.entries``, finding any distributions on it ``find_distributions(entry,False)`` is used to find distributions corresponding to the path entry, and they are added. `entry` is always appended to ``.entries``, even if it is already present. (This is because ``sys.path`` can contain the same value more than once, and the ``.entries`` of the ``sys.path`` WorkingSet should always equal ``sys.path``.) """ self.entry_keys.setdefault(entry, []) self.entries.append(entry) for dist in find_distributions(entry, True): self.add(dist, entry, False) def __contains__(self,dist): """True if `dist` is the active distribution for its project""" return self.by_key.get(dist.key) == dist def find(self, req): """Find a distribution matching requirement `req` If there is an active distribution for the requested project, this returns it as long as it meets the version requirement specified by `req`. But, if there is an active distribution for the project and it does not meet the `req` requirement, ``VersionConflict`` is raised. If there is no active distribution for the requested project, ``None`` is returned. """ dist = self.by_key.get(req.key) if dist is not None and dist not in req: raise VersionConflict(dist,req) # XXX add more info else: return dist def iter_entry_points(self, group, name=None): """Yield entry point objects from `group` matching `name` If `name` is None, yields all entry points in `group` from all distributions in the working set, otherwise only ones matching both `group` and `name` are yielded (in distribution order). """ for dist in self: entries = dist.get_entry_map(group) if name is None: for ep in entries.values(): yield ep elif name in entries: yield entries[name] def run_script(self, requires, script_name): """Locate distribution for `requires` and run `script_name` script""" ns = sys._getframe(1).f_globals name = ns['__name__'] ns.clear() ns['__name__'] = name self.require(requires)[0].run_script(script_name, ns) def __iter__(self): """Yield distributions for non-duplicate projects in the working set The yield order is the order in which the items' path entries were added to the working set. """ seen = {} for item in self.entries: for key in self.entry_keys[item]: if key not in seen: seen[key]=1 yield self.by_key[key] def add(self, dist, entry=None, insert=True): """Add `dist` to working set, associated with `entry` If `entry` is unspecified, it defaults to the ``.location`` of `dist`. On exit from this routine, `entry` is added to the end of the working set's ``.entries`` (if it wasn't already present). `dist` is only added to the working set if it's for a project that doesn't already have a distribution in the set. If it's added, any callbacks registered with the ``subscribe()`` method will be called. """ if insert: dist.insert_on(self.entries, entry) if entry is None: entry = dist.location keys = self.entry_keys.setdefault(entry,[]) keys2 = self.entry_keys.setdefault(dist.location,[]) if dist.key in self.by_key: return # ignore hidden distros self.by_key[dist.key] = dist if dist.key not in keys: keys.append(dist.key) if dist.key not in keys2: keys2.append(dist.key) self._added_new(dist) def resolve(self, requirements, env=None, installer=None): """List all distributions needed to (recursively) meet `requirements` `requirements` must be a sequence of ``Requirement`` objects. `env`, if supplied, should be an ``Environment`` instance. If not supplied, it defaults to all distributions available within any entry or distribution in the working set. `installer`, if supplied, will be invoked with each requirement that cannot be met by an already-installed distribution; it should return a ``Distribution`` or ``None``. """ requirements = list(requirements)[::-1] # set up the stack processed = {} # set of processed requirements best = {} # key -> dist to_activate = [] while requirements: req = requirements.pop(0) # process dependencies breadth-first if req in processed: # Ignore cyclic or redundant dependencies continue dist = best.get(req.key) if dist is None: # Find the best distribution and add it to the map dist = self.by_key.get(req.key) if dist is None: if env is None: env = Environment(self.entries) dist = best[req.key] = env.best_match(req, self, installer) if dist is None: raise DistributionNotFound(req) # XXX put more info here to_activate.append(dist) if dist not in req: # Oops, the "best" so far conflicts with a dependency raise VersionConflict(dist,req) # XXX put more info here requirements.extend(dist.requires(req.extras)[::-1]) processed[req] = True return to_activate # return list of distros to activate def find_plugins(self, plugin_env, full_env=None, installer=None, fallback=True ): """Find all activatable distributions in `plugin_env` Example usage:: distributions, errors = working_set.find_plugins( Environment(plugin_dirlist) ) map(working_set.add, distributions) # add plugins+libs to sys.path print "Couldn't load", errors # display errors The `plugin_env` should be an ``Environment`` instance that contains only distributions that are in the project's "plugin directory" or directories. The `full_env`, if supplied, should be an ``Environment`` contains all currently-available distributions. If `full_env` is not supplied, one is created automatically from the ``WorkingSet`` this method is called on, which will typically mean that every directory on ``sys.path`` will be scanned for distributions. `installer` is a standard installer callback as used by the ``resolve()`` method. The `fallback` flag indicates whether we should attempt to resolve older versions of a plugin if the newest version cannot be resolved. This method returns a 2-tuple: (`distributions`, `error_info`), where `distributions` is a list of the distributions found in `plugin_env` that were loadable, along with any other distributions that are needed to resolve their dependencies. `error_info` is a dictionary mapping unloadable plugin distributions to an exception instance describing the error that occurred. Usually this will be a ``DistributionNotFound`` or ``VersionConflict`` instance. """ plugin_projects = list(plugin_env) plugin_projects.sort() # scan project names in alphabetic order error_info = {} distributions = {} if full_env is None: env = Environment(self.entries) env += plugin_env else: env = full_env + plugin_env shadow_set = self.__class__([]) map(shadow_set.add, self) # put all our entries in shadow_set for project_name in plugin_projects: for dist in plugin_env[project_name]: req = [dist.as_requirement()] try: resolvees = shadow_set.resolve(req, env, installer) except ResolutionError,v: error_info[dist] = v # save error info if fallback: continue # try the next older version of project else: break # give up on this project, keep going else: map(shadow_set.add, resolvees) distributions.update(dict.fromkeys(resolvees)) # success, no need to try any more versions of this project break distributions = list(distributions) distributions.sort() return distributions, error_info def require(self, requirements): """Ensure that distributions matching `requirements` are activated `requirements` must be a string or a (possibly-nested) sequence thereof, specifying the distributions and versions required. The return value is a sequence of the distributions that needed to be activated to fulfill the requirements; all relevant distributions are included, even if they were already activated in this working set. """ needed = self.resolve(parse_requirements(requirements)) for dist in needed: self.add(dist) return needed def subscribe(self, callback): """Invoke `callback` for all distributions (including existing ones)""" if callback in self.callbacks: return self.callbacks.append(callback) for dist in self: callback(dist) def _added_new(self, dist): for callback in self.callbacks: callback(dist) class Environment(object): """Searchable snapshot of distributions on a search path""" def __init__(self, search_path=None, platform=get_supported_platform(), python=PY_MAJOR): """Snapshot distributions available on a search path Any distributions found on `search_path` are added to the environment. `search_path` should be a sequence of ``sys.path`` items. If not supplied, ``sys.path`` is used. `platform` is an optional string specifying the name of the platform that platform-specific distributions must be compatible with. If unspecified, it defaults to the current platform. `python` is an optional string naming the desired version of Python (e.g. ``'2.4'``); it defaults to the current version. You may explicitly set `platform` (and/or `python`) to ``None`` if you wish to map all* distributions, not just those compatible with the running platform or Python version. """ self._distmap = {} self._cache = {} self.platform = platform self.python = python self.scan(search_path) def can_add(self, dist): """Is distribution `dist` acceptable for this environment? The distribution must match the platform and python version requirements specified when this environment was created, or False is returned. """ return (self.python is None or dist.py_version is None or dist.py_version==self.python) \ and compatible_platforms(dist.platform,self.platform) def remove(self, dist): """Remove `dist` from the environment""" self._distmap[dist.key].remove(dist) def scan(self, search_path=None): """Scan `search_path` for distributions usable in this environment Any distributions found are added to the environment. `search_path` should be a sequence of ``sys.path`` items. If not supplied, ``sys.path`` is used. Only distributions conforming to the platform/python version defined at initialization are added. """ if search_path is None: search_path = sys.path for item in search_path: for dist in find_distributions(item): self.add(dist) def __getitem__(self,project_name): """Return a newest-to-oldest list of distributions for `project_name` """ try: return self._cache[project_name] except KeyError: project_name = project_name.lower() if project_name not in self._distmap: return [] if project_name not in self._cache: dists = self._cache[project_name] = self._distmap[project_name] _sort_dists(dists) return self._cache[project_name] def add(self,dist): """Add `dist` if we ``can_add()`` it and it isn't already added""" if self.can_add(dist) and dist.has_version(): dists = self._distmap.setdefault(dist.key,[]) if dist not in dists: dists.append(dist) if dist.key in self._cache: _sort_dists(self._cache[dist.key]) def best_match(self, req, working_set, installer=None): """Find distribution best matching `req` and usable on `working_set` This calls the ``find(req)`` method of the `working_set` to see if a suitable distribution is already active. (This may raise ``VersionConflict`` if an unsuitable version of the project is already active in the specified `working_set`.) If a suitable distribution isn't active, this method returns the newest distribution in the environment that meets the ``Requirement`` in `req`. If no suitable distribution is found, and `installer` is supplied, then the result of calling the environment's ``obtain(req, installer)`` method will be returned. """ dist = working_set.find(req) if dist is not None: return dist for dist in self[req.key]: if dist in req: return dist return self.obtain(req, installer) # try and download/install def obtain(self, requirement, installer=None): """Obtain a distribution matching `requirement` (e.g. via download) Obtain a distro that matches requirement (e.g. via download). In the base ``Environment`` class, this routine just returns ``installer(requirement)``, unless `installer` is None, in which case None is returned instead. This method is a hook that allows subclasses to attempt other ways of obtaining a distribution before falling back to the `installer` argument.""" if installer is not None: return installer(requirement) def __iter__(self): """Yield the unique project names of the available distributions""" for key in self._distmap.keys(): if self[key]: yield key def __iadd__(self, other): """In-place addition of a distribution or environment""" if isinstance(other,Distribution): self.add(other) elif isinstance(other,Environment): for project in other: for dist in other[project]: self.add(dist) else: raise TypeError("Can't add %r to environment" % (other,)) return self def __add__(self, other): """Add an environment or distribution to an environment""" new = self.__class__([], platform=None, python=None) for env in self, other: new += env return new AvailableDistributions = Environment # XXX backward compatibility class ExtractionError(RuntimeError): """An error occurred extracting a resource The following attributes are available from instances of this exception: manager The resource manager that raised this exception cache_path The base directory for resource extraction original_error The exception instance that caused extraction to fail """ class ResourceManager: """Manage resource extraction and packages""" extraction_path = None def __init__(self): self.cached_files = {} def resource_exists(self, package_or_requirement, resource_name): """Does the named resource exist?""" return get_provider(package_or_requirement).has_resource(resource_name) def resource_isdir(self, package_or_requirement, resource_name): """Is the named resource an existing directory?""" return get_provider(package_or_requirement).resource_isdir( resource_name ) def resource_filename(self, package_or_requirement, resource_name): """Return a true filesystem path for specified resource""" return get_provider(package_or_requirement).get_resource_filename( self, resource_name ) def resource_stream(self, package_or_requirement, resource_name): """Return a readable file-like object for specified resource""" return get_provider(package_or_requirement).get_resource_stream( self, resource_name ) def resource_string(self, package_or_requirement, resource_name): """Return specified resource as a string""" return get_provider(package_or_requirement).get_resource_string( self, resource_name ) def resource_listdir(self, package_or_requirement, resource_name): """List the contents of the named resource directory""" return get_provider(package_or_requirement).resource_listdir( resource_name ) def extraction_error(self): """Give an error message for problems extracting file(s)""" old_exc = sys.exc_info()[1] cache_path = self.extraction_path or get_default_cache() err = ExtractionError("""Can't extract file(s) to egg cache The following error occurred while trying to extract file(s) to the Python egg cache: %s The Python egg cache directory is currently set to: %s Perhaps your account does not have write access to this directory? You can change the cache directory by setting the PYTHON_EGG_CACHE environment variable to point to an accessible directory. """ % (old_exc, cache_path) ) err.manager = self err.cache_path = cache_path err.original_error = old_exc raise err def get_cache_path(self, archive_name, names=()): """Return absolute location in cache for `archive_name` and `names` The parent directory of the resulting path will be created if it does not already exist. `archive_name` should be the base filename of the enclosing egg (which may not be the name of the enclosing zipfile!), including its ".egg" extension. `names`, if provided, should be a sequence of path name parts "under" the egg's extraction location. This method should only be called by resource providers that need to obtain an extraction location, and only for names they intend to extract, as it tracks the generated names for possible cleanup later. """ extract_path = self.extraction_path or get_default_cache() target_path = os.path.join(extract_path, archive_name+'-tmp', names) try: ensure_directory(target_path) except: self.extraction_error() self.cached_files[target_path] = 1 return target_path def postprocess(self, tempname, filename): """Perform any platform-specific postprocessing of `tempname` This is where Mac header rewrites should be done; other platforms don't have anything special they should do. Resource providers should call this method ONLY after successfully extracting a compressed resource. They must NOT call it on resources that are already in the filesystem. `tempname` is the current (temporary) name of the file, and `filename` is the name it will be renamed to by the caller after this routine returns. """ if os.name == 'posix': # Make the resource executable mode = ((os.stat(tempname).st_mode) \| 0555) & 07777 os.chmod(tempname, mode) def set_extraction_path(self, path): """Set the base path where resources will be extracted to, if needed. If you do not call this routine before any extractions take place, the path defaults to the return value of ``get_default_cache()``. (Which is based on the ``PYTHON_EGG_CACHE`` environment variable, with various platform-specific fallbacks. See that routine's documentation for more details.) Resources are extracted to subdirectories of this path based upon information given by the ``IResourceProvider``. You may set this to a temporary directory, but then you must call ``cleanup_resources()`` to delete the extracted files when done. There is no guarantee that ``cleanup_resources()`` will be able to remove all extracted files. (Note: you may not change the extraction path for a given resource manager once resources have been extracted, unless you first call ``cleanup_resources()``.) """ if self.cached_files: raise ValueError( "Can't change extraction path, files already extracted" ) self.extraction_path = path def cleanup_resources(self, force=False): """ Delete all extracted resource files and directories, returning a list of the file and directory names that could not be successfully removed. This function does not have any concurrency protection, so it should generally only be called when the extraction path is a temporary directory exclusive to a single process. This method is not automatically called; you must call it explicitly or register it as an ``atexit`` function if you wish to ensure cleanup of a temporary directory used for extractions. """ # XXX def get_default_cache(): """Determine the default cache location This returns the ``PYTHON_EGG_CACHE`` environment variable, if set. Otherwise, on Windows, it returns a "Python-Eggs" subdirectory of the "Application Data" directory. On all other systems, it's "~/.python-eggs". """ try: return os.environ['PYTHON_EGG_CACHE'] except KeyError: pass if os.name!='nt': return os.path.expanduser('~/.python-eggs') app_data = 'Application Data' # XXX this may be locale-specific! app_homes = [ (('APPDATA',), None), # best option, should be locale-safe (('USERPROFILE',), app_data), (('HOMEDRIVE','HOMEPATH'), app_data), (('HOMEPATH',), app_data), (('HOME',), None), (('WINDIR',), app_data), # 95/98/ME ] for keys, subdir in app_homes: dirname = '' for key in keys: if key in os.environ: dirname = os.path.join(dirname, os.environ[key]) else: break else: if subdir: dirname = os.path.join(dirname,subdir) return os.path.join(dirname, 'Python-Eggs') else: raise RuntimeError( "Please set the PYTHON_EGG_CACHE enviroment variable" ) def safe_name(name): """Convert an arbitrary string to a standard distribution name Any runs of non-alphanumeric/. characters are replaced with a single '-'. """ return re.sub('[^A-Za-z0-9.]+', '-', name) def safe_version(version): """Convert an arbitrary string to a standard version string Spaces become dots, and all other non-alphanumeric characters become dashes, with runs of multiple dashes condensed to a single dash. """ version = version.replace(' ','.') return re.sub('[^A-Za-z0-9.]+', '-', version) def safe_extra(extra): """Convert an arbitrary string to a standard 'extra' name Any runs of non-alphanumeric characters are replaced with a single '_', and the result is always lowercased. """ return re.sub('[^A-Za-z0-9.]+', '_', extra).lower() def to_filename(name): """Convert a project or version name to its filename-escaped form Any '-' characters are currently replaced with '_'. """ return name.replace('-','_') class NullProvider: """Try to implement resources and metadata for arbitrary PEP 302 loaders""" egg_name = None egg_info = None loader = None def __init__(self, module): self.loader = getattr(module, '__loader__', None) self.module_path = os.path.dirname(getattr(module, '__file__', '')) def get_resource_filename(self, manager, resource_name): return self._fn(self.module_path, resource_name) def get_resource_stream(self, manager, resource_name): return StringIO(self.get_resource_string(manager, resource_name)) def get_resource_string(self, manager, resource_name): return self._get(self._fn(self.module_path, resource_name)) def has_resource(self, resource_name): return self._has(self._fn(self.module_path, resource_name)) def has_metadata(self, name): return self.egg_info and self._has(self._fn(self.egg_info,name)) def get_metadata(self, name): if not self.egg_info: return "" return self._get(self._fn(self.egg_info,name)) def get_metadata_lines(self, name): return yield_lines(self.get_metadata(name)) def resource_isdir(self,resource_name): return self._isdir(self._fn(self.module_path, resource_name)) def metadata_isdir(self,name): return self.egg_info and self._isdir(self._fn(self.egg_info,name)) def resource_listdir(self,resource_name): return self._listdir(self._fn(self.module_path,resource_name)) def metadata_listdir(self,name): if self.egg_info: return self._listdir(self._fn(self.egg_info,name)) return [] def run_script(self,script_name,namespace): script = 'scripts/'+script_name if not self.has_metadata(script): raise ResolutionError("No script named %r" % script_name) script_text = self.get_metadata(script).replace('\r\n','\n') script_text = script_text.replace('\r','\n') script_filename = self._fn(self.egg_info,script) namespace['__file__'] = script_filename if os.path.exists(script_filename): execfile(script_filename, namespace, namespace) else: from linecache import cache cache[script_filename] = ( len(script_text), 0, script_text.split('\n'), script_filename ) script_code = compile(script_text,script_filename,'exec') exec script_code in namespace, namespace def _has(self, path): raise NotImplementedError( "Can't perform this operation for unregistered loader type" ) def _isdir(self, path): raise NotImplementedError( "Can't perform this operation for unregistered loader type" ) def _listdir(self, path): raise NotImplementedError( "Can't perform this operation for unregistered loader type" ) def _fn(self, base, resource_name): return os.path.join(base, resource_name.split('/')) def _get(self, path): if hasattr(self.loader, 'get_data'): return self.loader.get_data(path) raise NotImplementedError( "Can't perform this operation for loaders without 'get_data()'" ) register_loader_type(object, NullProvider) class EggProvider(NullProvider): """Provider based on a virtual filesystem""" def __init__(self,module): NullProvider.__init__(self,module) self._setup_prefix() def _setup_prefix(self): # we assume here that our metadata may be nested inside a "basket" # of multiple eggs; that's why we use module_path instead of .archive path = self.module_path old = None while path!=old: if path.lower().endswith('.egg'): self.egg_name = os.path.basename(path) self.egg_info = os.path.join(path, 'EGG-INFO') self.egg_root = path break old = path path, base = os.path.split(path) class DefaultProvider(EggProvider): """Provides access to package resources in the filesystem""" def _has(self, path): return os.path.exists(path) def _isdir(self,path): return os.path.isdir(path) def _listdir(self,path): return os.listdir(path) def get_resource_stream(self, manager, resource_name): return open(self._fn(self.module_path, resource_name), 'rb') def _get(self, path): stream = open(path, 'rb') try: return stream.read() finally: stream.close() register_loader_type(type(None), DefaultProvider) class EmptyProvider(NullProvider): """Provider that returns nothing for all requests""" _isdir = _has = lambda self,path: False _get = lambda self,path: '' _listdir = lambda self,path: [] module_path = None def __init__(self): pass empty_provider = EmptyProvider() class ZipProvider(EggProvider): """Resource support for zips and eggs""" eagers = None def __init__(self, module): EggProvider.__init__(self,module) self.zipinfo = zipimport._zip_directory_cache[self.loader.archive] self.zip_pre = self.loader.archive+os.sep def _zipinfo_name(self, fspath): # Convert a virtual filename (full path to file) into a zipfile subpath # usable with the zipimport directory cache for our target archive if fspath.startswith(self.zip_pre): return fspath[len(self.zip_pre):] raise AssertionError( "%s is not a subpath of %s" % (fspath,self.zip_pre) ) def _parts(self,zip_path): # Convert a zipfile subpath into an egg-relative path part list fspath = self.zip_pre+zip_path # pseudo-fs path if fspath.startswith(self.egg_root+os.sep): return fspath[len(self.egg_root)+1:].split(os.sep) raise AssertionError( "%s is not a subpath of %s" % (fspath,self.egg_root) ) def get_resource_filename(self, manager, resource_name): if not self.egg_name: raise NotImplementedError( "resource_filename() only supported for .egg, not .zip" ) # no need to lock for extraction, since we use temp names zip_path = self._resource_to_zip(resource_name) eagers = self._get_eager_resources() if '/'.join(self._parts(zip_path)) in eagers: for name in eagers: self._extract_resource(manager, self._eager_to_zip(name)) return self._extract_resource(manager, zip_path) def _extract_resource(self, manager, zip_path): if zip_path in self._index(): for name in self._index()[zip_path]: last = self._extract_resource( manager, os.path.join(zip_path, name) ) return os.path.dirname(last) # return the extracted directory name zip_stat = self.zipinfo[zip_path] t,d,size = zip_stat[5], zip_stat[6], zip_stat[3] date_time = ( (d>>9)+1980, (d>>5)&0xF, d&0x1F, # ymd (t&0xFFFF)>>11, (t>>5)&0x3F, (t&0x1F) * 2, 0, 0, -1 # hms, etc. ) timestamp = time.mktime(date_time) try: real_path = manager.get_cache_path( self.egg_name, self._parts(zip_path) ) if os.path.isfile(real_path): stat = os.stat(real_path) if stat.st_size==size and stat.st_mtime==timestamp: # size and stamp match, don't bother extracting return real_path outf, tmpnam = _mkstemp(".$extract", dir=os.path.dirname(real_path)) os.write(outf, self.loader.get_data(zip_path)) os.close(outf) utime(tmpnam, (timestamp,timestamp)) manager.postprocess(tmpnam, real_path) try: rename(tmpnam, real_path) except os.error: if os.path.isfile(real_path): stat = os.stat(real_path) if stat.st_size==size and stat.st_mtime==timestamp: # size and stamp match, somebody did it just ahead of # us, so we're done return real_path elif os.name=='nt': # Windows, del old file and retry unlink(real_path) rename(tmpnam, real_path) return real_path raise except os.error: manager.extraction_error() # report a user-friendly error return real_path def _get_eager_resources(self): if self.eagers is None: eagers = [] for name in ('native_libs.txt', 'eager_resources.txt'): if self.has_metadata(name): eagers.extend(self.get_metadata_lines(name)) self.eagers = eagers return self.eagers def _index(self): try: return self._dirindex except AttributeError: ind = {} for path in self.zipinfo: parts = path.split(os.sep) while parts: parent = os.sep.join(parts[:-1]) if parent in ind: ind[parent].append(parts[-1]) break else: ind[parent] = [parts.pop()] self._dirindex = ind return ind def _has(self, fspath): zip_path = self._zipinfo_name(fspath) return zip_path in self.zipinfo or zip_path in self._index() def _isdir(self,fspath): return self._zipinfo_name(fspath) in self._index() def _listdir(self,fspath): return list(self._index().get(self._zipinfo_name(fspath), ())) def _eager_to_zip(self,resource_name): return self._zipinfo_name(self._fn(self.egg_root,resource_name)) def _resource_to_zip(self,resource_name): return self._zipinfo_name(self._fn(self.module_path,resource_name)) register_loader_type(zipimport.zipimporter, ZipProvider) class FileMetadata(EmptyProvider): """Metadata handler for standalone PKG-INFO files Usage:: metadata = FileMetadata("/path/to/PKG-INFO") This provider rejects all data and metadata requests except for PKG-INFO, which is treated as existing, and will be the contents of the file at the provided location. """ def __init__(self,path): self.path = path def has_metadata(self,name): return name=='PKG-INFO' def get_metadata(self,name): if name=='PKG-INFO': return open(self.path,'rU').read() raise KeyError("No metadata except PKG-INFO is available") def get_metadata_lines(self,name): return yield_lines(self.get_metadata(name)) class PathMetadata(DefaultProvider): """Metadata provider for egg directories Usage:: # Development eggs: egg_info = "/path/to/PackageName.egg-info" base_dir = os.path.dirname(egg_info) metadata = PathMetadata(base_dir, egg_info) dist_name = os.path.splitext(os.path.basename(egg_info))[0] dist = Distribution(basedir,project_name=dist_name,metadata=metadata) # Unpacked egg directories: egg_path = "/path/to/PackageName-ver-pyver-etc.egg" metadata = PathMetadata(egg_path, os.path.join(egg_path,'EGG-INFO')) dist = Distribution.from_filename(egg_path, metadata=metadata) """ def __init__(self, path, egg_info): self.module_path = path self.egg_info = egg_info class EggMetadata(ZipProvider): """Metadata provider for .egg files""" def __init__(self, importer): """Create a metadata provider from a zipimporter""" self.zipinfo = zipimport._zip_directory_cache[importer.archive] self.zip_pre = importer.archive+os.sep self.loader = importer if importer.prefix: self.module_path = os.path.join(importer.archive, importer.prefix) else: self.module_path = importer.archive self._setup_prefix() class ImpWrapper: """PEP 302 Importer that wraps Python's "normal" import algorithm""" def __init__(self, path=None): self.path = path def find_module(self, fullname, path=None): subname = fullname.split(".")[-1] if subname != fullname and self.path is None: return None if self.path is None: path = None else: path = [self.path] try: file, filename, etc = imp.find_module(subname, path) except ImportError: return None return ImpLoader(file, filename, etc) class ImpLoader: """PEP 302 Loader that wraps Python's "normal" import algorithm""" def __init__(self, file, filename, etc): self.file = file self.filename = filename self.etc = etc def load_module(self, fullname): try: mod = imp.load_module(fullname, self.file, self.filename, self.etc) finally: if self.file: self.file.close() # Note: we don't set __loader__ because we want the module to look # normal; i.e. this is just a wrapper for standard import machinery return mod def get_importer(path_item): """Retrieve a PEP 302 "importer" for the given path item If there is no importer, this returns a wrapper around the builtin import machinery. The returned importer is only cached if it was created by a path hook. """ try: importer = sys.path_importer_cache[path_item] except KeyError: for hook in sys.path_hooks: try: importer = hook(path_item) except ImportError: pass else: break else: importer = None sys.path_importer_cache.setdefault(path_item,importer) if importer is None: try: importer = ImpWrapper(path_item) except ImportError: pass return importer try: from pkgutil import get_importer, ImpImporter except ImportError: pass # Python 2.3 or 2.4, use our own implementation else: ImpWrapper = ImpImporter # Python 2.5, use pkgutil's implementation del ImpLoader, ImpImporter _distribution_finders = {} def register_finder(importer_type, distribution_finder): """Register `distribution_finder` to find distributions in sys.path items `importer_type` is the type or class of a PEP 302 "Importer" (sys.path item handler), and `distribution_finder` is a callable that, passed a path item and the importer instance, yields ``Distribution`` instances found on that path item. See ``pkg_resources.find_on_path`` for an example.""" _distribution_finders[importer_type] = distribution_finder def find_distributions(path_item, only=False): """Yield distributions accessible via `path_item`""" importer = get_importer(path_item) finder = _find_adapter(_distribution_finders, importer) return finder(importer, path_item, only) def find_in_zip(importer, path_item, only=False): metadata = EggMetadata(importer) if metadata.has_metadata('PKG-INFO'): yield Distribution.from_filename(path_item, metadata=metadata) if only: return # don't yield nested distros for subitem in metadata.resource_listdir('/'): if subitem.endswith('.egg'): subpath = os.path.join(path_item, subitem) for dist in find_in_zip(zipimport.zipimporter(subpath), subpath): yield dist register_finder(zipimport.zipimporter, find_in_zip) def StringIO(args, kw): """Thunk to load the real StringIO on demand""" global StringIO try: from cStringIO import StringIO except ImportError: from StringIO import StringIO return StringIO(args,*kw) def find_nothing(importer, path_item, only=False): return () register_finder(object,find_nothing) def find_on_path(importer, path_item, only=False): """Yield distributions accessible on a sys.path directory""" path_item = _normalize_cached(path_item) if os.path.isdir(path_item): if path_item.lower().endswith('.egg'): # unpacked egg yield Distribution.from_filename( path_item, metadata=PathMetadata( path_item, os.path.join(path_item,'EGG-INFO') ) ) else: # scan for .egg and .egg-info in directory for entry in os.listdir(path_item): lower = entry.lower() if lower.endswith('.egg-info'): fullpath = os.path.join(path_item, entry) if os.path.isdir(fullpath): # egg-info directory, allow getting metadata metadata = PathMetadata(path_item, fullpath) else: metadata = FileMetadata(fullpath) yield Distribution.from_location( path_item,entry,metadata,precedence=DEVELOP_DIST ) elif not only and lower.endswith('.egg'): for dist in find_distributions(os.path.join(path_item, entry)): yield dist elif not only and lower.endswith('.egg-link'): for line in file(os.path.join(path_item, entry)): if not line.strip(): continue for item in find_distributions(os.path.join(path_item,line.rstrip())): yield item break register_finder(ImpWrapper,find_on_path) _namespace_handlers = {} _namespace_packages = {} def register_namespace_handler(importer_type, namespace_handler): """Register `namespace_handler` to declare namespace packages `importer_type` is the type or class of a PEP 302 "Importer" (sys.path item handler), and `namespace_handler` is a callable like this:: def namespace_handler(importer,path_entry,moduleName,module): # return a path_entry to use for child packages Namespace handlers are only called if the importer object has already agreed that it can handle the relevant path item, and they should only return a subpath if the module __path__ does not already contain an equivalent subpath. For an example namespace handler, see ``pkg_resources.file_ns_handler``. """ _namespace_handlers[importer_type] = namespace_handler def _handle_ns(packageName, path_item): """Ensure that named package includes a subpath of path_item (if needed)""" importer = get_importer(path_item) if importer is None: return None loader = importer.find_module(packageName) if loader is None: return None module = sys.modules.get(packageName) if module is None: module = sys.modules[packageName] = new.module(packageName) module.__path__ = []; _set_parent_ns(packageName) elif not hasattr(module,'__path__'): raise TypeError("Not a package:", packageName) handler = _find_adapter(_namespace_handlers, importer) subpath = handler(importer,path_item,packageName,module) if subpath is not None: path = module.__path__; path.append(subpath) loader.load_module(packageName); module.__path__ = path return subpath def declare_namespace(packageName): """Declare that package 'packageName' is a namespace package""" imp.acquire_lock() try: if packageName in _namespace_packages: return path, parent = sys.path, None if '.' in packageName: parent = '.'.join(packageName.split('.')[:-1]) declare_namespace(parent) __import__(parent) try: path = sys.modules[parent].__path__ except AttributeError: raise TypeError("Not a package:", parent) # Track what packages are namespaces, so when new path items are added, # they can be updated _namespace_packages.setdefault(parent,[]).append(packageName) _namespace_packages.setdefault(packageName,[]) for path_item in path: # Ensure all the parent's path items are reflected in the child, # if they apply _handle_ns(packageName, path_item) finally: imp.release_lock() def fixup_namespace_packages(path_item, parent=None): """Ensure that previously-declared namespace packages include path_item""" imp.acquire_lock() try: for package in _namespace_packages.get(parent,()): subpath = _handle_ns(package, path_item) if subpath: fixup_namespace_packages(subpath,package) finally: imp.release_lock() def file_ns_handler(importer, path_item, packageName, module): """Compute an ns-package subpath for a filesystem or zipfile importer""" subpath = os.path.join(path_item, packageName.split('.')[-1]) normalized = _normalize_cached(subpath) for item in module.__path__: if _normalize_cached(item)==normalized: break else: # Only return the path if it's not already there return subpath register_namespace_handler(ImpWrapper,file_ns_handler) register_namespace_handler(zipimport.zipimporter,file_ns_handler) def null_ns_handler(importer, path_item, packageName, module): return None register_namespace_handler(object,null_ns_handler) def normalize_path(filename): """Normalize a file/dir name for comparison purposes""" return os.path.normcase(os.path.realpath(filename)) def _normalize_cached(filename,_cache={}): try: return _cache[filename] except KeyError: _cache[filename] = result = normalize_path(filename) return result def _set_parent_ns(packageName): parts = packageName.split('.') name = parts.pop() if parts: parent = '.'.join(parts) setattr(sys.modules[parent], name, sys.modules[packageName]) def yield_lines(strs): """Yield non-empty/non-comment lines of a ``basestring`` or sequence""" if isinstance(strs,basestring): for s in strs.splitlines(): s = s.strip() if s and not s.startswith('#'): # skip blank lines/comments yield s else: for ss in strs: for s in yield_lines(ss): yield s LINE_END = re.compile(r"\s(#.)?$").match # whitespace and comment CONTINUE = re.compile(r"\s\\\s(#.)?$").match # line continuation DISTRO = re.compile(r"\s((\w\|[-.])+)").match # Distribution or extra VERSION = re.compile(r"\s(<=?\|>=?\|==\|!=)\s((\w\|[-.])+)").match # ver. info COMMA = re.compile(r"\s,").match # comma between items OBRACKET = re.compile(r"\s\[").match CBRACKET = re.compile(r"\s\]").match MODULE = re.compile(r"\w+(\.\w+)$").match EGG_NAME = re.compile( r"(?P<name>[^-]+)" r"( -(?P<ver>[^-]+) (-py(?P<pyver>[^-]+) (-(?P<plat>.+))? )? )?", re.VERBOSE \| re.IGNORECASE ).match component_re = re.compile(r'(\d+ \| [a-z]+ \| \.\| -)', re.VERBOSE) replace = {'pre':'c', 'preview':'c','-':'final-','rc':'c','dev':'@'}.get def _parse_version_parts(s): for part in component_re.split(s): part = replace(part,part) if not part or part=='.': continue if part[:1] in '0123456789': yield part.zfill(8) # pad for numeric comparison else: yield ''+part yield 'final' # ensure that alpha/beta/candidate are before final def parse_version(s): """Convert a version string to a chronologically-sortable key This is a rough cross between distutils' StrictVersion and LooseVersion; if you give it versions that would work with StrictVersion, then it behaves the same; otherwise it acts like a slightly-smarter LooseVersion. It is possible* to create pathological version coding schemes that will fool this parser, but they should be very rare in practice. The returned value will be a tuple of strings. Numeric portions of the version are padded to 8 digits so they will compare numerically, but without relying on how numbers compare relative to strings. Dots are dropped, but dashes are retained. Trailing zeros between alpha segments or dashes are suppressed, so that e.g. "2.4.0" is considered the same as "2.4". Alphanumeric parts are lower-cased. The algorithm assumes that strings like "-" and any alpha string that alphabetically follows "final" represents a "patch level". So, "2.4-1" is assumed to be a branch or patch of "2.4", and therefore "2.4.1" is considered newer than "2.4-1", whic in turn is newer than "2.4". Strings like "a", "b", "c", "alpha", "beta", "candidate" and so on (that come before "final" alphabetically) are assumed to be pre-release versions, so that the version "2.4" is considered newer than "2.4a1". Finally, to handle miscellaneous cases, the strings "pre", "preview", and "rc" are treated as if they were "c", i.e. as though they were release candidates, and therefore are not as new as a version string that does not contain them. """ parts = [] for part in _parse_version_parts(s.lower()): if part.startswith(''): if part<'final': # remove '-' before a prerelease tag while parts and parts[-1]=='final-': parts.pop() # remove trailing zeros from each series of numeric parts while parts and parts[-1]=='00000000': parts.pop() parts.append(part) return tuple(parts) class EntryPoint(object): """Object representing an advertised importable object""" def __init__(self, name, module_name, attrs=(), extras=(), dist=None): if not MODULE(module_name): raise ValueError("Invalid module name", module_name) self.name = name self.module_name = module_name self.attrs = tuple(attrs) self.extras = Requirement.parse(("x[%s]" % ','.join(extras))).extras self.dist = dist def __str__(self): s = "%s = %s" % (self.name, self.module_name) if self.attrs: s += ':' + '.'.join(self.attrs) if self.extras: s += ' [%s]' % ','.join(self.extras) return s def __repr__(self): return "EntryPoint.parse(%r)" % str(self) def load(self, require=True, env=None, installer=None): if require: self.require(env, installer) entry = __import__(self.module_name, globals(),globals(), ['__name__']) for attr in self.attrs: try: entry = getattr(entry,attr) except AttributeError: raise ImportError("%r has no %r attribute" % (entry,attr)) return entry def require(self, env=None, installer=None): if self.extras and not self.dist: raise UnknownExtra("Can't require() without a distribution", self) map(working_set.add, working_set.resolve(self.dist.requires(self.extras),env,installer)) #@classmethod def parse(cls, src, dist=None): """Parse a single entry point from string `src` Entry point syntax follows the form:: name = some.module:some.attr [extra1,extra2] The entry name and module name are required, but the ``:attrs`` and ``[extras]`` parts are optional """ try: attrs = extras = () name,value = src.split('=',1) if '[' in value: value,extras = value.split('[',1) req = Requirement.parse("x["+extras) if req.specs: raise ValueError extras = req.extras if ':' in value: value,attrs = value.split(':',1) if not MODULE(attrs.rstrip()): raise ValueError attrs = attrs.rstrip().split('.') except ValueError: raise ValueError( "EntryPoint must be in 'name=module:attrs [extras]' format", src ) else: return cls(name.strip(), value.strip(), attrs, extras, dist) parse = classmethod(parse) #@classmethod def parse_group(cls, group, lines, dist=None): """Parse an entry point group""" if not MODULE(group): raise ValueError("Invalid group name", group) this = {} for line in yield_lines(lines): ep = cls.parse(line, dist) if ep.name in this: raise ValueError("Duplicate entry point", group, ep.name) this[ep.name]=ep return this parse_group = classmethod(parse_group) #@classmethod def parse_map(cls, data, dist=None): """Parse a map of entry point groups""" if isinstance(data,dict): data = data.items() else: data = split_sections(data) maps = {} for group, lines in data: if group is None: if not lines: continue raise ValueError("Entry points must be listed in groups") group = group.strip() if group in maps: raise ValueError("Duplicate group name", group) maps[group] = cls.parse_group(group, lines, dist) return maps parse_map = classmethod(parse_map) class Distribution(object): """Wrap an actual or potential sys.path entry w/metadata""" def __init__(self, location=None, metadata=None, project_name=None, version=None, py_version=PY_MAJOR, platform=None, precedence = EGG_DIST ): self.project_name = safe_name(project_name or 'Unknown') if version is not None: self._version = safe_version(version) self.py_version = py_version self.platform = platform self.location = location self.precedence = precedence self._provider = metadata or empty_provider #@classmethod def from_location(cls,location,basename,metadata=None,kw): project_name, version, py_version, platform = [None]4 basename, ext = os.path.splitext(basename) if ext.lower() in (".egg",".egg-info"): match = EGG_NAME(basename) if match: project_name, version, py_version, platform = match.group( 'name','ver','pyver','plat' ) return cls( location, metadata, project_name=project_name, version=version, py_version=py_version, platform=platform, kw ) from_location = classmethod(from_location) hashcmp = property( lambda self: ( getattr(self,'parsed_version',()), self.precedence, self.key, -len(self.location or ''), self.location, self.py_version, self.platform ) ) def __cmp__(self, other): return cmp(self.hashcmp, other) def __hash__(self): return hash(self.hashcmp) # These properties have to be lazy so that we don't have to load any # metadata until/unless it's actually needed. (i.e., some distributions # may not know their name or version without loading PKG-INFO) #@property def key(self): try: return self._key except AttributeError: self._key = key = self.project_name.lower() return key key = property(key) #@property def parsed_version(self): try: return self._parsed_version except AttributeError: self._parsed_version = pv = parse_version(self.version) return pv parsed_version = property(parsed_version) #@property def version(self): try: return self._version except AttributeError: for line in self._get_metadata('PKG-INFO'): if line.lower().startswith('version:'): self._version = safe_version(line.split(':',1)[1].strip()) return self._version else: raise ValueError( "Missing 'Version:' header and/or PKG-INFO file", self ) version = property(version) #@property def _dep_map(self): try: return self.__dep_map except AttributeError: dm = self.__dep_map = {None: []} for name in 'requires.txt', 'depends.txt': for extra,reqs in split_sections(self._get_metadata(name)): if extra: extra = safe_extra(extra) dm.setdefault(extra,[]).extend(parse_requirements(reqs)) return dm _dep_map = property(_dep_map) def requires(self,extras=()): """List of Requirements needed for this distro if `extras` are used""" dm = self._dep_map deps = [] deps.extend(dm.get(None,())) for ext in extras: try: deps.extend(dm[safe_extra(ext)]) except KeyError: raise UnknownExtra( "%s has no such extra feature %r" % (self, ext) ) return deps def _get_metadata(self,name): if self.has_metadata(name): for line in self.get_metadata_lines(name): yield line def activate(self,path=None): """Ensure distribution is importable on `path` (default=sys.path)""" if path is None: path = sys.path self.insert_on(path) if path is sys.path: fixup_namespace_packages(self.location) map(declare_namespace, self._get_metadata('namespace_packages.txt')) def egg_name(self): """Return what this distribution's standard .egg filename should be""" filename = "%s-%s-py%s" % ( to_filename(self.project_name), to_filename(self.version), self.py_version or PY_MAJOR ) if self.platform: filename += '-'+self.platform return filename def __repr__(self): if self.location: return "%s (%s)" % (self,self.location) else: return str(self) def __str__(self): try: version = getattr(self,'version',None) except ValueError: version = None version = version or "[unknown version]" return "%s %s" % (self.project_name,version) def __getattr__(self,attr): """Delegate all unrecognized public attributes to .metadata provider""" if attr.startswith('_'): raise AttributeError,attr return getattr(self._provider, attr) #@classmethod def from_filename(cls,filename,metadata=None, kw): return cls.from_location( _normalize_cached(filename), os.path.basename(filename), metadata, kw ) from_filename = classmethod(from_filename) def as_requirement(self): """Return a ``Requirement`` that matches this distribution exactly""" return Requirement.parse('%s==%s' % (self.project_name, self.version)) def load_entry_point(self, group, name): """Return the `name` entry point of `group` or raise ImportError""" ep = self.get_entry_info(group,name) if ep is None: raise ImportError("Entry point %r not found" % ((group,name),)) return ep.load() def get_entry_map(self, group=None): """Return the entry point map for `group`, or the full entry map""" try: ep_map = self._ep_map except AttributeError: ep_map = self._ep_map = EntryPoint.parse_map( self._get_metadata('entry_points.txt'), self ) if group is not None: return ep_map.get(group,{}) return ep_map def get_entry_info(self, group, name): """Return the EntryPoint object for `group`+`name`, or ``None``""" return self.get_entry_map(group).get(name) def insert_on(self, path, loc = None): """Insert self.location in path before its nearest parent directory""" loc = loc or self.location if not loc: return if path is sys.path: self.check_version_conflict() nloc = _normalize_cached(loc) bdir = os.path.dirname(nloc) npath= map(_normalize_cached, path) bp = None for p, item in enumerate(npath): if item==nloc: break elif item==bdir and self.precedence==EGG_DIST: # if it's an .egg, give it precedence over its directory path.insert(p, loc) npath.insert(p, nloc) break else: path.append(loc) return # p is the spot where we found or inserted loc; now remove duplicates while 1: try: np = npath.index(nloc, p+1) except ValueError: break else: del npath[np], path[np] p = np # ha! return def check_version_conflict(self): if self.key=='setuptools': return # ignore the inevitable setuptools self-conflicts :( nsp = dict.fromkeys(self._get_metadata('namespace_packages.txt')) loc = normalize_path(self.location) for modname in self._get_metadata('top_level.txt'): if (modname not in sys.modules or modname in nsp or modname in _namespace_packages ): continue fn = getattr(sys.modules[modname], '__file__', None) if fn and normalize_path(fn).startswith(loc): continue issue_warning( "Module %s was already imported from %s, but %s is being added" " to sys.path" % (modname, fn, self.location), ) def has_version(self): try: self.version except ValueError: issue_warning("Unbuilt egg for "+repr(self)) return False return True def clone(self,kw): """Copy this distribution, substituting in any changed keyword args""" for attr in ( 'project_name', 'version', 'py_version', 'platform', 'location', 'precedence' ): kw.setdefault(attr, getattr(self,attr,None)) kw.setdefault('metadata', self._provider) return self.__class__(*kw) #@property def extras(self): return [dep for dep in self._dep_map if dep] extras = property(extras) def issue_warning(args,*kw): level = 1 g = globals() try: # find the first stack frame that is not* code in # the pkg_resources module, to use for the warning while sys._getframe(level).f_globals is g: level += 1 except ValueError: pass from warnings import warn warn(stacklevel = level+1, args, kw) def parse_requirements(strs): """Yield ``Requirement`` objects for each specification in `strs` `strs` must be an instance of ``basestring``, or a (possibly-nested) iterable thereof. """ # create a steppable iterator, so we can handle \-continuations lines = iter(yield_lines(strs)) def scan_list(ITEM,TERMINATOR,line,p,groups,item_name): items = [] while not TERMINATOR(line,p): if CONTINUE(line,p): try: line = lines.next(); p = 0 except StopIteration: raise ValueError( "\\ must not appear on the last nonblank line" ) match = ITEM(line,p) if not match: raise ValueError("Expected "+item_name+" in",line,"at",line[p:]) items.append(match.group(groups)) p = match.end() match = COMMA(line,p) if match: p = match.end() # skip the comma elif not TERMINATOR(line,p): raise ValueError( "Expected ',' or end-of-list in",line,"at",line[p:] ) match = TERMINATOR(line,p) if match: p = match.end() # skip the terminator, if any return line, p, items for line in lines: match = DISTRO(line) if not match: raise ValueError("Missing distribution spec", line) project_name = match.group(1) p = match.end() extras = [] match = OBRACKET(line,p) if match: p = match.end() line, p, extras = scan_list( DISTRO, CBRACKET, line, p, (1,), "'extra' name" ) line, p, specs = scan_list(VERSION,LINE_END,line,p,(1,2),"version spec") specs = [(op,safe_version(val)) for op,val in specs] yield Requirement(project_name, specs, extras) def _sort_dists(dists): tmp = [(dist.hashcmp,dist) for dist in dists] tmp.sort() dists[::-1] = [d for hc,d in tmp] class Requirement: def __init__(self, project_name, specs, extras): """DO NOT CALL THIS UNDOCUMENTED METHOD; use Requirement.parse()!""" self.unsafe_name, project_name = project_name, safe_name(project_name) self.project_name, self.key = project_name, project_name.lower() index = [(parse_version(v),state_machine[op],op,v) for op,v in specs] index.sort() self.specs = [(op,ver) for parsed,trans,op,ver in index] self.index, self.extras = index, tuple(map(safe_extra,extras)) self.hashCmp = ( self.key, tuple([(op,parsed) for parsed,trans,op,ver in index]), frozenset(self.extras) ) self.__hash = hash(self.hashCmp) def __str__(self): specs = ','.join([''.join(s) for s in self.specs]) extras = ','.join(self.extras) if extras: extras = '[%s]' % extras return '%s%s%s' % (self.project_name, extras, specs) def __eq__(self,other): return isinstance(other,Requirement) and self.hashCmp==other.hashCmp def __contains__(self,item): if isinstance(item,Distribution): if item.key <> self.key: return False if self.index: item = item.parsed_version # only get if we need it elif isinstance(item,basestring): item = parse_version(item) last = None for parsed,trans,op,ver in self.index: action = trans[cmp(item,parsed)] if action=='F': return False elif action=='T': return True elif action=='+': last = True elif action=='-' or last is None: last = False if last is None: last = True # no rules encountered return last def __hash__(self): return self.__hash def __repr__(self): return "Requirement.parse(%r)" % str(self) #@staticmethod def parse(s): reqs = list(parse_requirements(s)) if reqs: if len(reqs)==1: return reqs[0] raise ValueError("Expected only one requirement", s) raise ValueError("No requirements found", s) parse = staticmethod(parse) state_machine = { # =>< '<' : '--T', '<=': 'T-T', '>' : 'F+F', '>=': 'T+F', '==': 'T..', '!=': 'F++', } def _get_mro(cls): """Get an mro for a type or classic class""" if not isinstance(cls,type): class cls(cls,object): pass return cls.__mro__[1:] return cls.__mro__ def _find_adapter(registry, ob): """Return an adapter factory for `ob` from `registry`""" for t in _get_mro(getattr(ob, '__class__', type(ob))): if t in registry: return registry[t] def ensure_directory(path): """Ensure that the parent directory of `path` exists""" dirname = os.path.dirname(path) if not os.path.isdir(dirname): os.makedirs(dirname) def split_sections(s): """Split a string or iterable thereof into (section,content) pairs Each ``section`` is a stripped version of the section header ("[section]") and each ``content`` is a list of stripped lines excluding blank lines and comment-only lines. If there are any such lines before the first section header, they're returned in a first ``section`` of ``None``. """ section = None content = [] for line in yield_lines(s): if line.startswith("["): if line.endswith("]"): if section or content: yield section, content section = line[1:-1].strip() content = [] else: raise ValueError("Invalid section heading", line) else: content.append(line) # wrap up last segment yield section, content def _mkstemp(args,kw): from tempfile import mkstemp old_open = os.open try: os.open = os_open # temporarily bypass sandboxing return mkstemp(args,**kw) finally: os.open = old_open # and then put it back # Set up global resource manager _manager = ResourceManager() def _initialize(g): for name in dir(_manager): if not name.startswith('_'): g[name] = getattr(_manager, name) _initialize(globals()) # Prepare the master working set and make the ``require()`` API available working_set = WorkingSet() try: # Does the main program list any requirements? from __main__ import __requires__ except ImportError: pass # No: just use the default working set based on sys.path else: # Yes: ensure the requirements are met, by prefixing sys.path if necessary try: working_set.require(__requires__) except VersionConflict: # try it without defaults already on sys.path working_set = WorkingSet([]) # by starting with an empty path for dist in working_set.resolve( parse_requirements(__requires__), Environment() ): working_set.add(dist) for entry in sys.path: # add any missing entries from sys.path if entry not in working_set.entries: working_set.add_entry(entry) sys.path[:] = working_set.entries # then copy back to sys.path require = working_set.require iter_entry_points = working_set.iter_entry_points add_activation_listener = working_set.subscribe run_script = working_set.run_script run_main = run_script # backward compatibility # Activate all distributions already on sys.path, and ensure that # all distributions added to the working set in the future (e.g. by # calling ``require()``) will get activated as well. add_activation_listener(lambda dist: dist.activate()) working_set.entries=[]; map(working_set.add_entry,sys.path) # match order	83,809	Python	.py	1,862	35.66971	94	0.622621	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,232	tzfile.py	numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/pytz/tzfile.py	''' $Id: tzfile.py,v 1.8 2004/06/03 00:15:24 zenzen Exp $ ''' from cStringIO import StringIO from datetime import datetime, timedelta from struct import unpack, calcsize from pytz.tzinfo import StaticTzInfo, DstTzInfo, memorized_ttinfo from pytz.tzinfo import memorized_datetime, memorized_timedelta def build_tzinfo(zone, fp): head_fmt = '>4s 16x 6l' head_size = calcsize(head_fmt) (magic,ttisgmtcnt,ttisstdcnt,leapcnt, timecnt,typecnt,charcnt) = unpack(head_fmt, fp.read(head_size)) # Make sure it is a tzinfo(5) file assert magic == 'TZif' # Read out the transition times, localtime indices and ttinfo structures. data_fmt = '>%(timecnt)dl %(timecnt)dB %(ttinfo)s %(charcnt)ds' % dict( timecnt=timecnt, ttinfo='lBB'typecnt, charcnt=charcnt) data_size = calcsize(data_fmt) data = unpack(data_fmt, fp.read(data_size)) # make sure we unpacked the right number of values assert len(data) == 2 timecnt + 3 * typecnt + 1 transitions = [memorized_datetime(trans) for trans in data[:timecnt]] lindexes = list(data[timecnt:2 * timecnt]) ttinfo_raw = data[2 * timecnt:-1] tznames_raw = data[-1] del data # Process ttinfo into separate structs ttinfo = [] tznames = {} i = 0 while i < len(ttinfo_raw): # have we looked up this timezone name yet? tzname_offset = ttinfo_raw[i+2] if tzname_offset not in tznames: nul = tznames_raw.find('\0', tzname_offset) if nul < 0: nul = len(tznames_raw) tznames[tzname_offset] = tznames_raw[tzname_offset:nul] ttinfo.append((ttinfo_raw[i], bool(ttinfo_raw[i+1]), tznames[tzname_offset])) i += 3 # Now build the timezone object if len(transitions) == 0: ttinfo[0][0], ttinfo[0][2] cls = type(zone, (StaticTzInfo,), dict( zone=zone, _utcoffset=memorized_timedelta(ttinfo[0][0]), _tzname=ttinfo[0][2])) else: # Early dates use the first standard time ttinfo i = 0 while ttinfo[i][1]: i += 1 if ttinfo[i] == ttinfo[lindexes[0]]: transitions[0] = datetime.min else: transitions.insert(0, datetime.min) lindexes.insert(0, i) # calculate transition info transition_info = [] for i in range(len(transitions)): inf = ttinfo[lindexes[i]] utcoffset = inf[0] if not inf[1]: dst = 0 else: for j in range(i-1, -1, -1): prev_inf = ttinfo[lindexes[j]] if not prev_inf[1]: break dst = inf[0] - prev_inf[0] # dst offset tzname = inf[2] # Round utcoffset and dst to the nearest minute or the # datetime library will complain. Conversions to these timezones # might be up to plus or minus 30 seconds out, but it is # the best we can do. utcoffset = int((utcoffset + 30) / 60) * 60 dst = int((dst + 30) / 60) * 60 transition_info.append(memorized_ttinfo(utcoffset, dst, tzname)) cls = type(zone, (DstTzInfo,), dict( zone=zone, _utc_transition_times=transitions, _transition_info=transition_info)) return cls() if __name__ == '__main__': import os.path from pprint import pprint base = os.path.join(os.path.dirname(__file__), 'zoneinfo') tz = build_tzinfo('Australia/Melbourne', open(os.path.join(base,'Australia','Melbourne'), 'rb')) tz = build_tzinfo('US/Eastern', open(os.path.join(base,'US','Eastern'), 'rb')) pprint(tz._utc_transition_times) #print tz.asPython(4) #print tz.transitions_mapping	3,937	Python	.py	98	30.72449	77	0.583617	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,233	__init__.py	numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/pytz/__init__.py	''' datetime.tzinfo timezone definitions generated from the Olson timezone database: ftp://elsie.nci.nih.gov/pub/tz.tar.gz See the datetime section of the Python Library Reference for information on how to use these modules. ''' # The Olson database has historically been updated about 4 times a year OLSON_VERSION = '2008c' VERSION = OLSON_VERSION #VERSION = OLSON_VERSION + '.2' __version__ = OLSON_VERSION OLSEN_VERSION = OLSON_VERSION # Old releases had this misspelling __all__ = [ 'timezone', 'utc', 'country_timezones', 'AmbiguousTimeError', 'UnknownTimeZoneError', 'all_timezones', 'all_timezones_set', 'common_timezones', 'common_timezones_set', ] import sys, datetime, os.path, gettext try: from pkg_resources import resource_stream except ImportError: resource_stream = None from tzinfo import AmbiguousTimeError, unpickler from tzfile import build_tzinfo # Use 2.3 sets module implementation if set builtin is not available try: set except NameError: from sets import Set as set def open_resource(name): """Open a resource from the zoneinfo subdir for reading. Uses the pkg_resources module if available. """ if resource_stream is not None: return resource_stream(__name__, 'zoneinfo/' + name) else: name_parts = name.lstrip('/').split('/') for part in name_parts: if part == os.path.pardir or os.path.sep in part: raise ValueError('Bad path segment: %r' % part) filename = os.path.join(os.path.dirname(__file__), 'zoneinfo', name_parts) return open(filename, 'rb') # Enable this when we get some translations? # We want an i18n API that is useful to programs using Python's gettext # module, as well as the Zope3 i18n package. Perhaps we should just provide # the POT file and translations, and leave it up to callers to make use # of them. # # t = gettext.translation( # 'pytz', os.path.join(os.path.dirname(__file__), 'locales'), # fallback=True # ) # def _(timezone_name): # """Translate a timezone name using the current locale, returning Unicode""" # return t.ugettext(timezone_name) class UnknownTimeZoneError(KeyError): '''Exception raised when pytz is passed an unknown timezone. >>> isinstance(UnknownTimeZoneError(), LookupError) True This class is actually a subclass of KeyError to provide backwards compatibility with code relying on the undocumented behavior of earlier pytz releases. >>> isinstance(UnknownTimeZoneError(), KeyError) True ''' pass _tzinfo_cache = {} def timezone(zone): r''' Return a datetime.tzinfo implementation for the given timezone >>> from datetime import datetime, timedelta >>> utc = timezone('UTC') >>> eastern = timezone('US/Eastern') >>> eastern.zone 'US/Eastern' >>> timezone(u'US/Eastern') is eastern True >>> utc_dt = datetime(2002, 10, 27, 6, 0, 0, tzinfo=utc) >>> loc_dt = utc_dt.astimezone(eastern) >>> fmt = '%Y-%m-%d %H:%M:%S %Z (%z)' >>> loc_dt.strftime(fmt) '2002-10-27 01:00:00 EST (-0500)' >>> (loc_dt - timedelta(minutes=10)).strftime(fmt) '2002-10-27 00:50:00 EST (-0500)' >>> eastern.normalize(loc_dt - timedelta(minutes=10)).strftime(fmt) '2002-10-27 01:50:00 EDT (-0400)' >>> (loc_dt + timedelta(minutes=10)).strftime(fmt) '2002-10-27 01:10:00 EST (-0500)' Raises UnknownTimeZoneError if passed an unknown zone. >>> timezone('Asia/Shangri-La') Traceback (most recent call last): ... UnknownTimeZoneError: 'Asia/Shangri-La' >>> timezone(u'\N{TRADE MARK SIGN}') Traceback (most recent call last): ... UnknownTimeZoneError: u'\u2122' ''' if zone.upper() == 'UTC': return utc try: zone = zone.encode('US-ASCII') except UnicodeEncodeError: # All valid timezones are ASCII raise UnknownTimeZoneError(zone) zone = _unmunge_zone(zone) if zone not in _tzinfo_cache: if zone in all_timezones_set: _tzinfo_cache[zone] = build_tzinfo(zone, open_resource(zone)) else: raise UnknownTimeZoneError(zone) return _tzinfo_cache[zone] def _unmunge_zone(zone): """Undo the time zone name munging done by older versions of pytz.""" return zone.replace('_plus_', '+').replace('_minus_', '-') ZERO = datetime.timedelta(0) HOUR = datetime.timedelta(hours=1) class UTC(datetime.tzinfo): """UTC Identical to the reference UTC implementation given in Python docs except that it unpickles using the single module global instance defined beneath this class declaration. Also contains extra attributes and methods to match other pytz tzinfo instances. """ zone = "UTC" def utcoffset(self, dt): return ZERO def tzname(self, dt): return "UTC" def dst(self, dt): return ZERO def __reduce__(self): return _UTC, () def localize(self, dt, is_dst=False): '''Convert naive time to local time''' if dt.tzinfo is not None: raise ValueError, 'Not naive datetime (tzinfo is already set)' return dt.replace(tzinfo=self) def normalize(self, dt, is_dst=False): '''Correct the timezone information on the given datetime''' if dt.tzinfo is None: raise ValueError, 'Naive time - no tzinfo set' return dt.replace(tzinfo=self) def __repr__(self): return "<UTC>" def __str__(self): return "UTC" UTC = utc = UTC() # UTC is a singleton def _UTC(): """Factory function for utc unpickling. Makes sure that unpickling a utc instance always returns the same module global. These examples belong in the UTC class above, but it is obscured; or in the README.txt, but we are not depending on Python 2.4 so integrating the README.txt examples with the unit tests is not trivial. >>> import datetime, pickle >>> dt = datetime.datetime(2005, 3, 1, 14, 13, 21, tzinfo=utc) >>> naive = dt.replace(tzinfo=None) >>> p = pickle.dumps(dt, 1) >>> naive_p = pickle.dumps(naive, 1) >>> len(p), len(naive_p), len(p) - len(naive_p) (60, 43, 17) >>> new = pickle.loads(p) >>> new == dt True >>> new is dt False >>> new.tzinfo is dt.tzinfo True >>> utc is UTC is timezone('UTC') True >>> utc is timezone('GMT') False """ return utc _UTC.__safe_for_unpickling__ = True def _p(args): """Factory function for unpickling pytz tzinfo instances. Just a wrapper around tzinfo.unpickler to save a few bytes in each pickle by shortening the path. """ return unpickler(args) _p.__safe_for_unpickling__ = True _country_timezones_cache = {} def country_timezones(iso3166_code): """Return a list of timezones used in a particular country. iso3166_code is the two letter code used to identify the country. >>> country_timezones('ch') ['Europe/Zurich'] >>> country_timezones('CH') ['Europe/Zurich'] >>> country_timezones(u'ch') ['Europe/Zurich'] >>> country_timezones('XXX') Traceback (most recent call last): ... KeyError: 'XXX' """ iso3166_code = iso3166_code.upper() if not _country_timezones_cache: zone_tab = open_resource('zone.tab') for line in zone_tab: if line.startswith('#'): continue code, coordinates, zone = line.split(None, 4)[:3] try: _country_timezones_cache[code].append(zone) except KeyError: _country_timezones_cache[code] = [zone] return _country_timezones_cache[iso3166_code] # Time-zone info based solely on fixed offsets class _FixedOffset(datetime.tzinfo): zone = None # to match the standard pytz API def __init__(self, minutes): if abs(minutes) >= 1440: raise ValueError("absolute offset is too large", minutes) self._minutes = minutes self._offset = datetime.timedelta(minutes=minutes) def utcoffset(self, dt): return self._offset def __reduce__(self): return FixedOffset, (self._minutes, ) def dst(self, dt): return None def tzname(self, dt): return None def __repr__(self): return 'pytz.FixedOffset(%d)' % self._minutes def localize(self, dt, is_dst=False): '''Convert naive time to local time''' if dt.tzinfo is not None: raise ValueError, 'Not naive datetime (tzinfo is already set)' return dt.replace(tzinfo=self) def normalize(self, dt, is_dst=False): '''Correct the timezone information on the given datetime''' if dt.tzinfo is None: raise ValueError, 'Naive time - no tzinfo set' return dt.replace(tzinfo=self) def FixedOffset(offset, _tzinfos = {}): """return a fixed-offset timezone based off a number of minutes. >>> one = FixedOffset(-330) >>> one pytz.FixedOffset(-330) >>> one.utcoffset(datetime.datetime.now()) datetime.timedelta(-1, 66600) >>> two = FixedOffset(1380) >>> two pytz.FixedOffset(1380) >>> two.utcoffset(datetime.datetime.now()) datetime.timedelta(0, 82800) The datetime.timedelta must be between the range of -1 and 1 day, non-inclusive. >>> FixedOffset(1440) Traceback (most recent call last): ... ValueError: ('absolute offset is too large', 1440) >>> FixedOffset(-1440) Traceback (most recent call last): ... ValueError: ('absolute offset is too large', -1440) An offset of 0 is special-cased to return UTC. >>> FixedOffset(0) is UTC True There should always be only one instance of a FixedOffset per timedelta. This should be true for multiple creation calls. >>> FixedOffset(-330) is one True >>> FixedOffset(1380) is two True It should also be true for pickling. >>> import pickle >>> pickle.loads(pickle.dumps(one)) is one True >>> pickle.loads(pickle.dumps(two)) is two True """ if offset == 0: return UTC info = _tzinfos.get(offset) if info is None: # We haven't seen this one before. we need to save it. # Use setdefault to avoid a race condition and make sure we have # only one info = _tzinfos.setdefault(offset, _FixedOffset(offset)) return info FixedOffset.__safe_for_unpickling__ = True def _test(): import doctest, os, sys sys.path.insert(0, os.pardir) import pytz return doctest.testmod(pytz) if __name__ == '__main__': _test() common_timezones = \ ['Africa/Abidjan', 'Africa/Accra', 'Africa/Addis_Ababa', 'Africa/Algiers', 'Africa/Asmara', 'Africa/Asmera', 'Africa/Bamako', 'Africa/Bangui', 'Africa/Banjul', 'Africa/Bissau', 'Africa/Blantyre', 'Africa/Brazzaville', 'Africa/Bujumbura', 'Africa/Cairo', 'Africa/Casablanca', 'Africa/Ceuta', 'Africa/Conakry', 'Africa/Dakar', 'Africa/Dar_es_Salaam', 'Africa/Djibouti', 'Africa/Douala', 'Africa/El_Aaiun', 'Africa/Freetown', 'Africa/Gaborone', 'Africa/Harare', 'Africa/Johannesburg', 'Africa/Kampala', 'Africa/Khartoum', 'Africa/Kigali', 'Africa/Kinshasa', 'Africa/Lagos', 'Africa/Libreville', 'Africa/Lome', 'Africa/Luanda', 'Africa/Lubumbashi', 'Africa/Lusaka', 'Africa/Malabo', 'Africa/Maputo', 'Africa/Maseru', 'Africa/Mbabane', 'Africa/Mogadishu', 'Africa/Monrovia', 'Africa/Nairobi', 'Africa/Ndjamena', 'Africa/Niamey', 'Africa/Nouakchott', 'Africa/Ouagadougou', 'Africa/Porto-Novo', 'Africa/Sao_Tome', 'Africa/Timbuktu', 'Africa/Tripoli', 'Africa/Tunis', 'Africa/Windhoek', 'America/Adak', 'America/Anchorage', 'America/Anguilla', 'America/Antigua', 'America/Araguaina', 'America/Aruba', 'America/Asuncion', 'America/Atikokan', 'America/Atka', 'America/Bahia', 'America/Barbados', 'America/Belem', 'America/Belize', 'America/Blanc-Sablon', 'America/Boa_Vista', 'America/Bogota', 'America/Boise', 'America/Buenos_Aires', 'America/Cambridge_Bay', 'America/Campo_Grande', 'America/Cancun', 'America/Caracas', 'America/Catamarca', 'America/Cayenne', 'America/Cayman', 'America/Chicago', 'America/Chihuahua', 'America/Coral_Harbour', 'America/Cordoba', 'America/Costa_Rica', 'America/Cuiaba', 'America/Curacao', 'America/Danmarkshavn', 'America/Dawson', 'America/Dawson_Creek', 'America/Denver', 'America/Detroit', 'America/Dominica', 'America/Edmonton', 'America/Eirunepe', 'America/El_Salvador', 'America/Ensenada', 'America/Fort_Wayne', 'America/Fortaleza', 'America/Glace_Bay', 'America/Godthab', 'America/Goose_Bay', 'America/Grand_Turk', 'America/Grenada', 'America/Guadeloupe', 'America/Guatemala', 'America/Guayaquil', 'America/Guyana', 'America/Halifax', 'America/Havana', 'America/Hermosillo', 'America/Indianapolis', 'America/Inuvik', 'America/Iqaluit', 'America/Jamaica', 'America/Jujuy', 'America/Juneau', 'America/Knox_IN', 'America/La_Paz', 'America/Lima', 'America/Los_Angeles', 'America/Louisville', 'America/Maceio', 'America/Managua', 'America/Manaus', 'America/Marigot', 'America/Martinique', 'America/Mazatlan', 'America/Mendoza', 'America/Menominee', 'America/Merida', 'America/Mexico_City', 'America/Miquelon', 'America/Moncton', 'America/Monterrey', 'America/Montevideo', 'America/Montreal', 'America/Montserrat', 'America/Nassau', 'America/New_York', 'America/Nipigon', 'America/Nome', 'America/Noronha', 'America/Panama', 'America/Pangnirtung', 'America/Paramaribo', 'America/Phoenix', 'America/Port-au-Prince', 'America/Port_of_Spain', 'America/Porto_Acre', 'America/Porto_Velho', 'America/Puerto_Rico', 'America/Rainy_River', 'America/Rankin_Inlet', 'America/Recife', 'America/Regina', 'America/Resolute', 'America/Rio_Branco', 'America/Rosario', 'America/Santiago', 'America/Santo_Domingo', 'America/Sao_Paulo', 'America/Scoresbysund', 'America/Shiprock', 'America/St_Barthelemy', 'America/St_Johns', 'America/St_Kitts', 'America/St_Lucia', 'America/St_Thomas', 'America/St_Vincent', 'America/Swift_Current', 'America/Tegucigalpa', 'America/Thule', 'America/Thunder_Bay', 'America/Tijuana', 'America/Toronto', 'America/Tortola', 'America/Vancouver', 'America/Virgin', 'America/Whitehorse', 'America/Winnipeg', 'America/Yakutat', 'America/Yellowknife', 'Antarctica/Casey', 'Antarctica/Davis', 'Antarctica/DumontDUrville', 'Antarctica/Mawson', 'Antarctica/McMurdo', 'Antarctica/Palmer', 'Antarctica/Rothera', 'Antarctica/South_Pole', 'Antarctica/Syowa', 'Antarctica/Vostok', 'Arctic/Longyearbyen', 'Asia/Aden', 'Asia/Almaty', 'Asia/Amman', 'Asia/Anadyr', 'Asia/Aqtau', 'Asia/Aqtobe', 'Asia/Ashgabat', 'Asia/Ashkhabad', 'Asia/Baghdad', 'Asia/Bahrain', 'Asia/Baku', 'Asia/Bangkok', 'Asia/Beirut', 'Asia/Bishkek', 'Asia/Brunei', 'Asia/Calcutta', 'Asia/Choibalsan', 'Asia/Chongqing', 'Asia/Chungking', 'Asia/Colombo', 'Asia/Dacca', 'Asia/Damascus', 'Asia/Dhaka', 'Asia/Dili', 'Asia/Dubai', 'Asia/Dushanbe', 'Asia/Gaza', 'Asia/Harbin', 'Asia/Ho_Chi_Minh', 'Asia/Hong_Kong', 'Asia/Hovd', 'Asia/Irkutsk', 'Asia/Istanbul', 'Asia/Jakarta', 'Asia/Jayapura', 'Asia/Jerusalem', 'Asia/Kabul', 'Asia/Kamchatka', 'Asia/Karachi', 'Asia/Kashgar', 'Asia/Katmandu', 'Asia/Kolkata', 'Asia/Krasnoyarsk', 'Asia/Kuala_Lumpur', 'Asia/Kuching', 'Asia/Kuwait', 'Asia/Macao', 'Asia/Macau', 'Asia/Magadan', 'Asia/Makassar', 'Asia/Manila', 'Asia/Muscat', 'Asia/Nicosia', 'Asia/Novosibirsk', 'Asia/Omsk', 'Asia/Oral', 'Asia/Phnom_Penh', 'Asia/Pontianak', 'Asia/Pyongyang', 'Asia/Qatar', 'Asia/Qyzylorda', 'Asia/Rangoon', 'Asia/Riyadh', 'Asia/Saigon', 'Asia/Sakhalin', 'Asia/Samarkand', 'Asia/Seoul', 'Asia/Shanghai', 'Asia/Singapore', 'Asia/Taipei', 'Asia/Tashkent', 'Asia/Tbilisi', 'Asia/Tehran', 'Asia/Tel_Aviv', 'Asia/Thimbu', 'Asia/Thimphu', 'Asia/Tokyo', 'Asia/Ujung_Pandang', 'Asia/Ulaanbaatar', 'Asia/Ulan_Bator', 'Asia/Urumqi', 'Asia/Vientiane', 'Asia/Vladivostok', 'Asia/Yakutsk', 'Asia/Yekaterinburg', 'Asia/Yerevan', 'Atlantic/Azores', 'Atlantic/Bermuda', 'Atlantic/Canary', 'Atlantic/Cape_Verde', 'Atlantic/Faeroe', 'Atlantic/Faroe', 'Atlantic/Jan_Mayen', 'Atlantic/Madeira', 'Atlantic/Reykjavik', 'Atlantic/South_Georgia', 'Atlantic/St_Helena', 'Atlantic/Stanley', 'Australia/ACT', 'Australia/Adelaide', 'Australia/Brisbane', 'Australia/Broken_Hill', 'Australia/Canberra', 'Australia/Currie', 'Australia/Darwin', 'Australia/Eucla', 'Australia/Hobart', 'Australia/LHI', 'Australia/Lindeman', 'Australia/Lord_Howe', 'Australia/Melbourne', 'Australia/NSW', 'Australia/North', 'Australia/Perth', 'Australia/Queensland', 'Australia/South', 'Australia/Sydney', 'Australia/Tasmania', 'Australia/Victoria', 'Australia/West', 'Australia/Yancowinna', 'Brazil/Acre', 'Brazil/DeNoronha', 'Brazil/East', 'Brazil/West', 'Canada/Atlantic', 'Canada/Central', 'Canada/East-Saskatchewan', 'Canada/Eastern', 'Canada/Mountain', 'Canada/Newfoundland', 'Canada/Pacific', 'Canada/Saskatchewan', 'Canada/Yukon', 'Chile/Continental', 'Chile/EasterIsland', 'Europe/Amsterdam', 'Europe/Andorra', 'Europe/Athens', 'Europe/Belfast', 'Europe/Belgrade', 'Europe/Berlin', 'Europe/Bratislava', 'Europe/Brussels', 'Europe/Bucharest', 'Europe/Budapest', 'Europe/Chisinau', 'Europe/Copenhagen', 'Europe/Dublin', 'Europe/Gibraltar', 'Europe/Guernsey', 'Europe/Helsinki', 'Europe/Isle_of_Man', 'Europe/Istanbul', 'Europe/Jersey', 'Europe/Kaliningrad', 'Europe/Kiev', 'Europe/Lisbon', 'Europe/Ljubljana', 'Europe/London', 'Europe/Luxembourg', 'Europe/Madrid', 'Europe/Malta', 'Europe/Mariehamn', 'Europe/Minsk', 'Europe/Monaco', 'Europe/Moscow', 'Europe/Nicosia', 'Europe/Oslo', 'Europe/Paris', 'Europe/Podgorica', 'Europe/Prague', 'Europe/Riga', 'Europe/Rome', 'Europe/Samara', 'Europe/San_Marino', 'Europe/Sarajevo', 'Europe/Simferopol', 'Europe/Skopje', 'Europe/Sofia', 'Europe/Stockholm', 'Europe/Tallinn', 'Europe/Tirane', 'Europe/Tiraspol', 'Europe/Uzhgorod', 'Europe/Vaduz', 'Europe/Vatican', 'Europe/Vienna', 'Europe/Vilnius', 'Europe/Volgograd', 'Europe/Warsaw', 'Europe/Zagreb', 'Europe/Zaporozhye', 'Europe/Zurich', 'GMT', 'Indian/Antananarivo', 'Indian/Chagos', 'Indian/Christmas', 'Indian/Cocos', 'Indian/Comoro', 'Indian/Kerguelen', 'Indian/Mahe', 'Indian/Maldives', 'Indian/Mauritius', 'Indian/Mayotte', 'Indian/Reunion', 'Mexico/BajaNorte', 'Mexico/BajaSur', 'Mexico/General', 'Pacific/Apia', 'Pacific/Auckland', 'Pacific/Chatham', 'Pacific/Easter', 'Pacific/Efate', 'Pacific/Enderbury', 'Pacific/Fakaofo', 'Pacific/Fiji', 'Pacific/Funafuti', 'Pacific/Galapagos', 'Pacific/Gambier', 'Pacific/Guadalcanal', 'Pacific/Guam', 'Pacific/Honolulu', 'Pacific/Johnston', 'Pacific/Kiritimati', 'Pacific/Kosrae', 'Pacific/Kwajalein', 'Pacific/Majuro', 'Pacific/Marquesas', 'Pacific/Midway', 'Pacific/Nauru', 'Pacific/Niue', 'Pacific/Norfolk', 'Pacific/Noumea', 'Pacific/Pago_Pago', 'Pacific/Palau', 'Pacific/Pitcairn', 'Pacific/Ponape', 'Pacific/Port_Moresby', 'Pacific/Rarotonga', 'Pacific/Saipan', 'Pacific/Samoa', 'Pacific/Tahiti', 'Pacific/Tarawa', 'Pacific/Tongatapu', 'Pacific/Truk', 'Pacific/Wake', 'Pacific/Wallis', 'Pacific/Yap', 'US/Alaska', 'US/Aleutian', 'US/Arizona', 'US/Central', 'US/East-Indiana', 'US/Eastern', 'US/Hawaii', 'US/Indiana-Starke', 'US/Michigan', 'US/Mountain', 'US/Pacific', 'US/Pacific-New', 'US/Samoa', 'UTC'] common_timezones_set = set(common_timezones) all_timezones = \ ['Africa/Abidjan', 'Africa/Accra', 'Africa/Addis_Ababa', 'Africa/Algiers', 'Africa/Asmara', 'Africa/Asmera', 'Africa/Bamako', 'Africa/Bangui', 'Africa/Banjul', 'Africa/Bissau', 'Africa/Blantyre', 'Africa/Brazzaville', 'Africa/Bujumbura', 'Africa/Cairo', 'Africa/Casablanca', 'Africa/Ceuta', 'Africa/Conakry', 'Africa/Dakar', 'Africa/Dar_es_Salaam', 'Africa/Djibouti', 'Africa/Douala', 'Africa/El_Aaiun', 'Africa/Freetown', 'Africa/Gaborone', 'Africa/Harare', 'Africa/Johannesburg', 'Africa/Kampala', 'Africa/Khartoum', 'Africa/Kigali', 'Africa/Kinshasa', 'Africa/Lagos', 'Africa/Libreville', 'Africa/Lome', 'Africa/Luanda', 'Africa/Lubumbashi', 'Africa/Lusaka', 'Africa/Malabo', 'Africa/Maputo', 'Africa/Maseru', 'Africa/Mbabane', 'Africa/Mogadishu', 'Africa/Monrovia', 'Africa/Nairobi', 'Africa/Ndjamena', 'Africa/Niamey', 'Africa/Nouakchott', 'Africa/Ouagadougou', 'Africa/Porto-Novo', 'Africa/Sao_Tome', 'Africa/Timbuktu', 'Africa/Tripoli', 'Africa/Tunis', 'Africa/Windhoek', 'America/Adak', 'America/Anchorage', 'America/Anguilla', 'America/Antigua', 'America/Araguaina', 'America/Argentina/Buenos_Aires', 'America/Argentina/Catamarca', 'America/Argentina/ComodRivadavia', 'America/Argentina/Cordoba', 'America/Argentina/Jujuy', 'America/Argentina/La_Rioja', 'America/Argentina/Mendoza', 'America/Argentina/Rio_Gallegos', 'America/Argentina/San_Juan', 'America/Argentina/San_Luis', 'America/Argentina/Tucuman', 'America/Argentina/Ushuaia', 'America/Aruba', 'America/Asuncion', 'America/Atikokan', 'America/Atka', 'America/Bahia', 'America/Barbados', 'America/Belem', 'America/Belize', 'America/Blanc-Sablon', 'America/Boa_Vista', 'America/Bogota', 'America/Boise', 'America/Buenos_Aires', 'America/Cambridge_Bay', 'America/Campo_Grande', 'America/Cancun', 'America/Caracas', 'America/Catamarca', 'America/Cayenne', 'America/Cayman', 'America/Chicago', 'America/Chihuahua', 'America/Coral_Harbour', 'America/Cordoba', 'America/Costa_Rica', 'America/Cuiaba', 'America/Curacao', 'America/Danmarkshavn', 'America/Dawson', 'America/Dawson_Creek', 'America/Denver', 'America/Detroit', 'America/Dominica', 'America/Edmonton', 'America/Eirunepe', 'America/El_Salvador', 'America/Ensenada', 'America/Fort_Wayne', 'America/Fortaleza', 'America/Glace_Bay', 'America/Godthab', 'America/Goose_Bay', 'America/Grand_Turk', 'America/Grenada', 'America/Guadeloupe', 'America/Guatemala', 'America/Guayaquil', 'America/Guyana', 'America/Halifax', 'America/Havana', 'America/Hermosillo', 'America/Indiana/Indianapolis', 'America/Indiana/Knox', 'America/Indiana/Marengo', 'America/Indiana/Petersburg', 'America/Indiana/Tell_City', 'America/Indiana/Vevay', 'America/Indiana/Vincennes', 'America/Indiana/Winamac', 'America/Indianapolis', 'America/Inuvik', 'America/Iqaluit', 'America/Jamaica', 'America/Jujuy', 'America/Juneau', 'America/Kentucky/Louisville', 'America/Kentucky/Monticello', 'America/Knox_IN', 'America/La_Paz', 'America/Lima', 'America/Los_Angeles', 'America/Louisville', 'America/Maceio', 'America/Managua', 'America/Manaus', 'America/Marigot', 'America/Martinique', 'America/Mazatlan', 'America/Mendoza', 'America/Menominee', 'America/Merida', 'America/Mexico_City', 'America/Miquelon', 'America/Moncton', 'America/Monterrey', 'America/Montevideo', 'America/Montreal', 'America/Montserrat', 'America/Nassau', 'America/New_York', 'America/Nipigon', 'America/Nome', 'America/Noronha', 'America/North_Dakota/Center', 'America/North_Dakota/New_Salem', 'America/Panama', 'America/Pangnirtung', 'America/Paramaribo', 'America/Phoenix', 'America/Port-au-Prince', 'America/Port_of_Spain', 'America/Porto_Acre', 'America/Porto_Velho', 'America/Puerto_Rico', 'America/Rainy_River', 'America/Rankin_Inlet', 'America/Recife', 'America/Regina', 'America/Resolute', 'America/Rio_Branco', 'America/Rosario', 'America/Santiago', 'America/Santo_Domingo', 'America/Sao_Paulo', 'America/Scoresbysund', 'America/Shiprock', 'America/St_Barthelemy', 'America/St_Johns', 'America/St_Kitts', 'America/St_Lucia', 'America/St_Thomas', 'America/St_Vincent', 'America/Swift_Current', 'America/Tegucigalpa', 'America/Thule', 'America/Thunder_Bay', 'America/Tijuana', 'America/Toronto', 'America/Tortola', 'America/Vancouver', 'America/Virgin', 'America/Whitehorse', 'America/Winnipeg', 'America/Yakutat', 'America/Yellowknife', 'Antarctica/Casey', 'Antarctica/Davis', 'Antarctica/DumontDUrville', 'Antarctica/Mawson', 'Antarctica/McMurdo', 'Antarctica/Palmer', 'Antarctica/Rothera', 'Antarctica/South_Pole', 'Antarctica/Syowa', 'Antarctica/Vostok', 'Arctic/Longyearbyen', 'Asia/Aden', 'Asia/Almaty', 'Asia/Amman', 'Asia/Anadyr', 'Asia/Aqtau', 'Asia/Aqtobe', 'Asia/Ashgabat', 'Asia/Ashkhabad', 'Asia/Baghdad', 'Asia/Bahrain', 'Asia/Baku', 'Asia/Bangkok', 'Asia/Beirut', 'Asia/Bishkek', 'Asia/Brunei', 'Asia/Calcutta', 'Asia/Choibalsan', 'Asia/Chongqing', 'Asia/Chungking', 'Asia/Colombo', 'Asia/Dacca', 'Asia/Damascus', 'Asia/Dhaka', 'Asia/Dili', 'Asia/Dubai', 'Asia/Dushanbe', 'Asia/Gaza', 'Asia/Harbin', 'Asia/Ho_Chi_Minh', 'Asia/Hong_Kong', 'Asia/Hovd', 'Asia/Irkutsk', 'Asia/Istanbul', 'Asia/Jakarta', 'Asia/Jayapura', 'Asia/Jerusalem', 'Asia/Kabul', 'Asia/Kamchatka', 'Asia/Karachi', 'Asia/Kashgar', 'Asia/Katmandu', 'Asia/Kolkata', 'Asia/Krasnoyarsk', 'Asia/Kuala_Lumpur', 'Asia/Kuching', 'Asia/Kuwait', 'Asia/Macao', 'Asia/Macau', 'Asia/Magadan', 'Asia/Makassar', 'Asia/Manila', 'Asia/Muscat', 'Asia/Nicosia', 'Asia/Novosibirsk', 'Asia/Omsk', 'Asia/Oral', 'Asia/Phnom_Penh', 'Asia/Pontianak', 'Asia/Pyongyang', 'Asia/Qatar', 'Asia/Qyzylorda', 'Asia/Rangoon', 'Asia/Riyadh', 'Asia/Saigon', 'Asia/Sakhalin', 'Asia/Samarkand', 'Asia/Seoul', 'Asia/Shanghai', 'Asia/Singapore', 'Asia/Taipei', 'Asia/Tashkent', 'Asia/Tbilisi', 'Asia/Tehran', 'Asia/Tel_Aviv', 'Asia/Thimbu', 'Asia/Thimphu', 'Asia/Tokyo', 'Asia/Ujung_Pandang', 'Asia/Ulaanbaatar', 'Asia/Ulan_Bator', 'Asia/Urumqi', 'Asia/Vientiane', 'Asia/Vladivostok', 'Asia/Yakutsk', 'Asia/Yekaterinburg', 'Asia/Yerevan', 'Atlantic/Azores', 'Atlantic/Bermuda', 'Atlantic/Canary', 'Atlantic/Cape_Verde', 'Atlantic/Faeroe', 'Atlantic/Faroe', 'Atlantic/Jan_Mayen', 'Atlantic/Madeira', 'Atlantic/Reykjavik', 'Atlantic/South_Georgia', 'Atlantic/St_Helena', 'Atlantic/Stanley', 'Australia/ACT', 'Australia/Adelaide', 'Australia/Brisbane', 'Australia/Broken_Hill', 'Australia/Canberra', 'Australia/Currie', 'Australia/Darwin', 'Australia/Eucla', 'Australia/Hobart', 'Australia/LHI', 'Australia/Lindeman', 'Australia/Lord_Howe', 'Australia/Melbourne', 'Australia/NSW', 'Australia/North', 'Australia/Perth', 'Australia/Queensland', 'Australia/South', 'Australia/Sydney', 'Australia/Tasmania', 'Australia/Victoria', 'Australia/West', 'Australia/Yancowinna', 'Brazil/Acre', 'Brazil/DeNoronha', 'Brazil/East', 'Brazil/West', 'CET', 'CST6CDT', 'Canada/Atlantic', 'Canada/Central', 'Canada/East-Saskatchewan', 'Canada/Eastern', 'Canada/Mountain', 'Canada/Newfoundland', 'Canada/Pacific', 'Canada/Saskatchewan', 'Canada/Yukon', 'Chile/Continental', 'Chile/EasterIsland', 'Cuba', 'EET', 'EST', 'EST5EDT', 'Egypt', 'Eire', 'Etc/GMT', 'Etc/GMT+0', 'Etc/GMT+1', 'Etc/GMT+10', 'Etc/GMT+11', 'Etc/GMT+12', 'Etc/GMT+2', 'Etc/GMT+3', 'Etc/GMT+4', 'Etc/GMT+5', 'Etc/GMT+6', 'Etc/GMT+7', 'Etc/GMT+8', 'Etc/GMT+9', 'Etc/GMT-0', 'Etc/GMT-1', 'Etc/GMT-10', 'Etc/GMT-11', 'Etc/GMT-12', 'Etc/GMT-13', 'Etc/GMT-14', 'Etc/GMT-2', 'Etc/GMT-3', 'Etc/GMT-4', 'Etc/GMT-5', 'Etc/GMT-6', 'Etc/GMT-7', 'Etc/GMT-8', 'Etc/GMT-9', 'Etc/GMT0', 'Etc/Greenwich', 'Etc/UCT', 'Etc/UTC', 'Etc/Universal', 'Etc/Zulu', 'Europe/Amsterdam', 'Europe/Andorra', 'Europe/Athens', 'Europe/Belfast', 'Europe/Belgrade', 'Europe/Berlin', 'Europe/Bratislava', 'Europe/Brussels', 'Europe/Bucharest', 'Europe/Budapest', 'Europe/Chisinau', 'Europe/Copenhagen', 'Europe/Dublin', 'Europe/Gibraltar', 'Europe/Guernsey', 'Europe/Helsinki', 'Europe/Isle_of_Man', 'Europe/Istanbul', 'Europe/Jersey', 'Europe/Kaliningrad', 'Europe/Kiev', 'Europe/Lisbon', 'Europe/Ljubljana', 'Europe/London', 'Europe/Luxembourg', 'Europe/Madrid', 'Europe/Malta', 'Europe/Mariehamn', 'Europe/Minsk', 'Europe/Monaco', 'Europe/Moscow', 'Europe/Nicosia', 'Europe/Oslo', 'Europe/Paris', 'Europe/Podgorica', 'Europe/Prague', 'Europe/Riga', 'Europe/Rome', 'Europe/Samara', 'Europe/San_Marino', 'Europe/Sarajevo', 'Europe/Simferopol', 'Europe/Skopje', 'Europe/Sofia', 'Europe/Stockholm', 'Europe/Tallinn', 'Europe/Tirane', 'Europe/Tiraspol', 'Europe/Uzhgorod', 'Europe/Vaduz', 'Europe/Vatican', 'Europe/Vienna', 'Europe/Vilnius', 'Europe/Volgograd', 'Europe/Warsaw', 'Europe/Zagreb', 'Europe/Zaporozhye', 'Europe/Zurich', 'GB', 'GB-Eire', 'GMT', 'GMT+0', 'GMT-0', 'GMT0', 'Greenwich', 'HST', 'Hongkong', 'Iceland', 'Indian/Antananarivo', 'Indian/Chagos', 'Indian/Christmas', 'Indian/Cocos', 'Indian/Comoro', 'Indian/Kerguelen', 'Indian/Mahe', 'Indian/Maldives', 'Indian/Mauritius', 'Indian/Mayotte', 'Indian/Reunion', 'Iran', 'Israel', 'Jamaica', 'Japan', 'Kwajalein', 'Libya', 'MET', 'MST', 'MST7MDT', 'Mexico/BajaNorte', 'Mexico/BajaSur', 'Mexico/General', 'NZ', 'NZ-CHAT', 'Navajo', 'PRC', 'PST8PDT', 'Pacific/Apia', 'Pacific/Auckland', 'Pacific/Chatham', 'Pacific/Easter', 'Pacific/Efate', 'Pacific/Enderbury', 'Pacific/Fakaofo', 'Pacific/Fiji', 'Pacific/Funafuti', 'Pacific/Galapagos', 'Pacific/Gambier', 'Pacific/Guadalcanal', 'Pacific/Guam', 'Pacific/Honolulu', 'Pacific/Johnston', 'Pacific/Kiritimati', 'Pacific/Kosrae', 'Pacific/Kwajalein', 'Pacific/Majuro', 'Pacific/Marquesas', 'Pacific/Midway', 'Pacific/Nauru', 'Pacific/Niue', 'Pacific/Norfolk', 'Pacific/Noumea', 'Pacific/Pago_Pago', 'Pacific/Palau', 'Pacific/Pitcairn', 'Pacific/Ponape', 'Pacific/Port_Moresby', 'Pacific/Rarotonga', 'Pacific/Saipan', 'Pacific/Samoa', 'Pacific/Tahiti', 'Pacific/Tarawa', 'Pacific/Tongatapu', 'Pacific/Truk', 'Pacific/Wake', 'Pacific/Wallis', 'Pacific/Yap', 'Poland', 'Portugal', 'ROC', 'ROK', 'Singapore', 'Turkey', 'UCT', 'US/Alaska', 'US/Aleutian', 'US/Arizona', 'US/Central', 'US/East-Indiana', 'US/Eastern', 'US/Hawaii', 'US/Indiana-Starke', 'US/Michigan', 'US/Mountain', 'US/Pacific', 'US/Pacific-New', 'US/Samoa', 'UTC', 'Universal', 'W-SU', 'WET', 'Zulu', 'posixrules'] all_timezones_set = set(all_timezones)	30,648	Python	.py	1,312	20.38872	81	0.694118	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,234	tzinfo.py	numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/pytz/tzinfo.py	'''Base classes and helpers for building zone specific tzinfo classes''' from datetime import datetime, timedelta, tzinfo from bisect import bisect_right try: set except NameError: from sets import Set as set import pytz __all__ = [] _timedelta_cache = {} def memorized_timedelta(seconds): '''Create only one instance of each distinct timedelta''' try: return _timedelta_cache[seconds] except KeyError: delta = timedelta(seconds=seconds) _timedelta_cache[seconds] = delta return delta _epoch = datetime.utcfromtimestamp(0) _datetime_cache = {0: _epoch} def memorized_datetime(seconds): '''Create only one instance of each distinct datetime''' try: return _datetime_cache[seconds] except KeyError: # NB. We can't just do datetime.utcfromtimestamp(seconds) as this # fails with negative values under Windows (Bug #90096) dt = _epoch + timedelta(seconds=seconds) _datetime_cache[seconds] = dt return dt _ttinfo_cache = {} def memorized_ttinfo(args): '''Create only one instance of each distinct tuple''' try: return _ttinfo_cache[args] except KeyError: ttinfo = ( memorized_timedelta(args[0]), memorized_timedelta(args[1]), args[2] ) _ttinfo_cache[args] = ttinfo return ttinfo _notime = memorized_timedelta(0) def _to_seconds(td): '''Convert a timedelta to seconds''' return td.seconds + td.days 24 * 60 * 60 class BaseTzInfo(tzinfo): # Overridden in subclass _utcoffset = None _tzname = None zone = None def __str__(self): return self.zone class StaticTzInfo(BaseTzInfo): '''A timezone that has a constant offset from UTC These timezones are rare, as most regions have changed their offset from UTC at some point in their history ''' def fromutc(self, dt): '''See datetime.tzinfo.fromutc''' return (dt + self._utcoffset).replace(tzinfo=self) def utcoffset(self,dt): '''See datetime.tzinfo.utcoffset''' return self._utcoffset def dst(self,dt): '''See datetime.tzinfo.dst''' return _notime def tzname(self,dt): '''See datetime.tzinfo.tzname''' return self._tzname def localize(self, dt, is_dst=False): '''Convert naive time to local time''' if dt.tzinfo is not None: raise ValueError, 'Not naive datetime (tzinfo is already set)' return dt.replace(tzinfo=self) def normalize(self, dt, is_dst=False): '''Correct the timezone information on the given datetime''' if dt.tzinfo is None: raise ValueError, 'Naive time - no tzinfo set' return dt.replace(tzinfo=self) def __repr__(self): return '<StaticTzInfo %r>' % (self.zone,) def __reduce__(self): # Special pickle to zone remains a singleton and to cope with # database changes. return pytz._p, (self.zone,) class DstTzInfo(BaseTzInfo): '''A timezone that has a variable offset from UTC The offset might change if daylight savings time comes into effect, or at a point in history when the region decides to change their timezone definition. ''' # Overridden in subclass _utc_transition_times = None # Sorted list of DST transition times in UTC _transition_info = None # [(utcoffset, dstoffset, tzname)] corresponding # to _utc_transition_times entries zone = None # Set in __init__ _tzinfos = None _dst = None # DST offset def __init__(self, _inf=None, _tzinfos=None): if _inf: self._tzinfos = _tzinfos self._utcoffset, self._dst, self._tzname = _inf else: _tzinfos = {} self._tzinfos = _tzinfos self._utcoffset, self._dst, self._tzname = self._transition_info[0] _tzinfos[self._transition_info[0]] = self for inf in self._transition_info[1:]: if inf not in _tzinfos: _tzinfos[inf] = self.__class__(inf, _tzinfos) def fromutc(self, dt): '''See datetime.tzinfo.fromutc''' dt = dt.replace(tzinfo=None) idx = max(0, bisect_right(self._utc_transition_times, dt) - 1) inf = self._transition_info[idx] return (dt + inf[0]).replace(tzinfo=self._tzinfos[inf]) def normalize(self, dt): '''Correct the timezone information on the given datetime If date arithmetic crosses DST boundaries, the tzinfo is not magically adjusted. This method normalizes the tzinfo to the correct one. To test, first we need to do some setup >>> from pytz import timezone >>> utc = timezone('UTC') >>> eastern = timezone('US/Eastern') >>> fmt = '%Y-%m-%d %H:%M:%S %Z (%z)' We next create a datetime right on an end-of-DST transition point, the instant when the wallclocks are wound back one hour. >>> utc_dt = datetime(2002, 10, 27, 6, 0, 0, tzinfo=utc) >>> loc_dt = utc_dt.astimezone(eastern) >>> loc_dt.strftime(fmt) '2002-10-27 01:00:00 EST (-0500)' Now, if we subtract a few minutes from it, note that the timezone information has not changed. >>> before = loc_dt - timedelta(minutes=10) >>> before.strftime(fmt) '2002-10-27 00:50:00 EST (-0500)' But we can fix that by calling the normalize method >>> before = eastern.normalize(before) >>> before.strftime(fmt) '2002-10-27 01:50:00 EDT (-0400)' ''' if dt.tzinfo is None: raise ValueError, 'Naive time - no tzinfo set' # Convert dt in localtime to UTC offset = dt.tzinfo._utcoffset dt = dt.replace(tzinfo=None) dt = dt - offset # convert it back, and return it return self.fromutc(dt) def localize(self, dt, is_dst=False): '''Convert naive time to local time. This method should be used to construct localtimes, rather than passing a tzinfo argument to a datetime constructor. is_dst is used to determine the correct timezone in the ambigous period at the end of daylight savings time. >>> from pytz import timezone >>> fmt = '%Y-%m-%d %H:%M:%S %Z (%z)' >>> amdam = timezone('Europe/Amsterdam') >>> dt = datetime(2004, 10, 31, 2, 0, 0) >>> loc_dt1 = amdam.localize(dt, is_dst=True) >>> loc_dt2 = amdam.localize(dt, is_dst=False) >>> loc_dt1.strftime(fmt) '2004-10-31 02:00:00 CEST (+0200)' >>> loc_dt2.strftime(fmt) '2004-10-31 02:00:00 CET (+0100)' >>> str(loc_dt2 - loc_dt1) '1:00:00' Use is_dst=None to raise an AmbiguousTimeError for ambiguous times at the end of daylight savings >>> try: ... loc_dt1 = amdam.localize(dt, is_dst=None) ... except AmbiguousTimeError: ... print 'Oops' Oops >>> loc_dt1 = amdam.localize(dt, is_dst=None) Traceback (most recent call last): [...] AmbiguousTimeError: 2004-10-31 02:00:00 is_dst defaults to False >>> amdam.localize(dt) == amdam.localize(dt, False) True ''' if dt.tzinfo is not None: raise ValueError, 'Not naive datetime (tzinfo is already set)' # Find the possibly correct timezones. We probably just have one, # but we might end up with two if we are in the end-of-DST # transition period. Or possibly more in some particularly confused # location... possible_loc_dt = set() for tzinfo in self._tzinfos.values(): loc_dt = tzinfo.normalize(dt.replace(tzinfo=tzinfo)) if loc_dt.replace(tzinfo=None) == dt: possible_loc_dt.add(loc_dt) if len(possible_loc_dt) == 1: return possible_loc_dt.pop() # If told to be strict, raise an exception since we have an # ambiguous case if is_dst is None: raise AmbiguousTimeError(dt) # Filter out the possiblilities that don't match the requested # is_dst filtered_possible_loc_dt = [ p for p in possible_loc_dt if bool(p.tzinfo._dst) == is_dst ] # Hopefully we only have one possibility left. Return it. if len(filtered_possible_loc_dt) == 1: return filtered_possible_loc_dt[0] if len(filtered_possible_loc_dt) == 0: filtered_possible_loc_dt = list(possible_loc_dt) # If we get this far, we have in a wierd timezone transition # where the clocks have been wound back but is_dst is the same # in both (eg. Europe/Warsaw 1915 when they switched to CET). # At this point, we just have to guess unless we allow more # hints to be passed in (such as the UTC offset or abbreviation), # but that is just getting silly. # # Choose the earliest (by UTC) applicable timezone. def mycmp(a,b): return cmp( a.replace(tzinfo=None) - a.tzinfo._utcoffset, b.replace(tzinfo=None) - b.tzinfo._utcoffset, ) filtered_possible_loc_dt.sort(mycmp) return filtered_possible_loc_dt[0] def utcoffset(self, dt): '''See datetime.tzinfo.utcoffset''' return self._utcoffset def dst(self, dt): '''See datetime.tzinfo.dst''' return self._dst def tzname(self, dt): '''See datetime.tzinfo.tzname''' return self._tzname def __repr__(self): if self._dst: dst = 'DST' else: dst = 'STD' if self._utcoffset > _notime: return '<DstTzInfo %r %s+%s %s>' % ( self.zone, self._tzname, self._utcoffset, dst ) else: return '<DstTzInfo %r %s%s %s>' % ( self.zone, self._tzname, self._utcoffset, dst ) def __reduce__(self): # Special pickle to zone remains a singleton and to cope with # database changes. return pytz._p, ( self.zone, _to_seconds(self._utcoffset), _to_seconds(self._dst), self._tzname ) class AmbiguousTimeError(Exception): '''Exception raised when attempting to create an ambiguous wallclock time. At the end of a DST transition period, a particular wallclock time will occur twice (once before the clocks are set back, once after). Both possibilities may be correct, unless further information is supplied. See DstTzInfo.normalize() for more info ''' def unpickler(zone, utcoffset=None, dstoffset=None, tzname=None): """Factory function for unpickling pytz tzinfo instances. This is shared for both StaticTzInfo and DstTzInfo instances, because database changes could cause a zones implementation to switch between these two base classes and we can't break pickles on a pytz version upgrade. """ # Raises a KeyError if zone no longer exists, which should never happen # and would be a bug. tz = pytz.timezone(zone) # A StaticTzInfo - just return it if utcoffset is None: return tz # This pickle was created from a DstTzInfo. We need to # determine which of the list of tzinfo instances for this zone # to use in order to restore the state of any datetime instances using # it correctly. utcoffset = memorized_timedelta(utcoffset) dstoffset = memorized_timedelta(dstoffset) try: return tz._tzinfos[(utcoffset, dstoffset, tzname)] except KeyError: # The particular state requested in this timezone no longer exists. # This indicates a corrupt pickle, or the timezone database has been # corrected violently enough to make this particular # (utcoffset,dstoffset) no longer exist in the zone, or the # abbreviation has been changed. pass # See if we can find an entry differing only by tzname. Abbreviations # get changed from the initial guess by the database maintainers to # match reality when this information is discovered. for localized_tz in tz._tzinfos.values(): if (localized_tz._utcoffset == utcoffset and localized_tz._dst == dstoffset): return localized_tz # This (utcoffset, dstoffset) information has been removed from the # zone. Add it back. This might occur when the database maintainers have # corrected incorrect information. datetime instances using this # incorrect information will continue to do so, exactly as they were # before being pickled. This is purely an overly paranoid safety net - I # doubt this will ever been needed in real life. inf = (utcoffset, dstoffset, tzname) tz._tzinfos[inf] = tz.__class__(inf, tz._tzinfos) return tz._tzinfos[inf]	13,161	Python	.py	311	33.749196	79	0.624139	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,235	reference.py	numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/pytz/reference.py	''' Reference tzinfo implementations from the Python docs. Used for testing against as they are only correct for the years 1987 to 2006. Do not use these for real code. ''' from datetime import tzinfo, timedelta, datetime from pytz import utc, UTC, HOUR, ZERO # A class building tzinfo objects for fixed-offset time zones. # Note that FixedOffset(0, "UTC") is a different way to build a # UTC tzinfo object. class FixedOffset(tzinfo): """Fixed offset in minutes east from UTC.""" def __init__(self, offset, name): self.__offset = timedelta(minutes = offset) self.__name = name def utcoffset(self, dt): return self.__offset def tzname(self, dt): return self.__name def dst(self, dt): return ZERO # A class capturing the platform's idea of local time. import time as _time STDOFFSET = timedelta(seconds = -_time.timezone) if _time.daylight: DSTOFFSET = timedelta(seconds = -_time.altzone) else: DSTOFFSET = STDOFFSET DSTDIFF = DSTOFFSET - STDOFFSET class LocalTimezone(tzinfo): def utcoffset(self, dt): if self._isdst(dt): return DSTOFFSET else: return STDOFFSET def dst(self, dt): if self._isdst(dt): return DSTDIFF else: return ZERO def tzname(self, dt): return _time.tzname[self._isdst(dt)] def _isdst(self, dt): tt = (dt.year, dt.month, dt.day, dt.hour, dt.minute, dt.second, dt.weekday(), 0, -1) stamp = _time.mktime(tt) tt = _time.localtime(stamp) return tt.tm_isdst > 0 Local = LocalTimezone() # A complete implementation of current DST rules for major US time zones. def first_sunday_on_or_after(dt): days_to_go = 6 - dt.weekday() if days_to_go: dt += timedelta(days_to_go) return dt # In the US, DST starts at 2am (standard time) on the first Sunday in April. DSTSTART = datetime(1, 4, 1, 2) # and ends at 2am (DST time; 1am standard time) on the last Sunday of Oct. # which is the first Sunday on or after Oct 25. DSTEND = datetime(1, 10, 25, 1) class USTimeZone(tzinfo): def __init__(self, hours, reprname, stdname, dstname): self.stdoffset = timedelta(hours=hours) self.reprname = reprname self.stdname = stdname self.dstname = dstname def __repr__(self): return self.reprname def tzname(self, dt): if self.dst(dt): return self.dstname else: return self.stdname def utcoffset(self, dt): return self.stdoffset + self.dst(dt) def dst(self, dt): if dt is None or dt.tzinfo is None: # An exception may be sensible here, in one or both cases. # It depends on how you want to treat them. The default # fromutc() implementation (called by the default astimezone() # implementation) passes a datetime with dt.tzinfo is self. return ZERO assert dt.tzinfo is self # Find first Sunday in April & the last in October. start = first_sunday_on_or_after(DSTSTART.replace(year=dt.year)) end = first_sunday_on_or_after(DSTEND.replace(year=dt.year)) # Can't compare naive to aware objects, so strip the timezone from # dt first. if start <= dt.replace(tzinfo=None) < end: return HOUR else: return ZERO Eastern = USTimeZone(-5, "Eastern", "EST", "EDT") Central = USTimeZone(-6, "Central", "CST", "CDT") Mountain = USTimeZone(-7, "Mountain", "MST", "MDT") Pacific = USTimeZone(-8, "Pacific", "PST", "PDT")	3,649	Python	.py	97	30.989691	76	0.641965	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,236	colors.py	numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/colors.py	""" A module for converting numbers or color arguments to RGB or RGBA RGB and RGBA are sequences of, respectively, 3 or 4 floats in the range 0-1. This module includes functions and classes for color specification conversions, and for mapping numbers to colors in a 1-D array of colors called a colormap. Colormapping typically involves two steps: a data array is first mapped onto the range 0-1 using an instance of :class:`Normalize` or of a subclass; then this number in the 0-1 range is mapped to a color using an instance of a subclass of :class:`Colormap`. Two are provided here: :class:`LinearSegmentedColormap`, which is used to generate all the built-in colormap instances, but is also useful for making custom colormaps, and :class:`ListedColormap`, which is used for generating a custom colormap from a list of color specifications. The module also provides a single instance, colorConverter, of the :class:`ColorConverter` class providing methods for converting single color specifications or sequences of them to RGB or RGBA. Commands which take color arguments can use several formats to specify the colors. For the basic builtin colors, you can use a single letter - b : blue - g : green - r : red - c : cyan - m : magenta - y : yellow - k : black - w : white Gray shades can be given as a string encoding a float in the 0-1 range, e.g.:: color = '0.75' For a greater range of colors, you have two options. You can specify the color using an html hex string, as in:: color = '#eeefff' or you can pass an R , G , B tuple, where each of R , G , B are in the range [0,1]. Finally, legal html names for colors, like 'red', 'burlywood' and 'chartreuse' are supported. """ import re import numpy as np from numpy import ma import matplotlib.cbook as cbook parts = np.__version__.split('.') NP_MAJOR, NP_MINOR = map(int, parts[:2]) # true if clip supports the out kwarg NP_CLIP_OUT = NP_MAJOR>=1 and NP_MINOR>=2 cnames = { 'aliceblue' : '#F0F8FF', 'antiquewhite' : '#FAEBD7', 'aqua' : '#00FFFF', 'aquamarine' : '#7FFFD4', 'azure' : '#F0FFFF', 'beige' : '#F5F5DC', 'bisque' : '#FFE4C4', 'black' : '#000000', 'blanchedalmond' : '#FFEBCD', 'blue' : '#0000FF', 'blueviolet' : '#8A2BE2', 'brown' : '#A52A2A', 'burlywood' : '#DEB887', 'cadetblue' : '#5F9EA0', 'chartreuse' : '#7FFF00', 'chocolate' : '#D2691E', 'coral' : '#FF7F50', 'cornflowerblue' : '#6495ED', 'cornsilk' : '#FFF8DC', 'crimson' : '#DC143C', 'cyan' : '#00FFFF', 'darkblue' : '#00008B', 'darkcyan' : '#008B8B', 'darkgoldenrod' : '#B8860B', 'darkgray' : '#A9A9A9', 'darkgreen' : '#006400', 'darkkhaki' : '#BDB76B', 'darkmagenta' : '#8B008B', 'darkolivegreen' : '#556B2F', 'darkorange' : '#FF8C00', 'darkorchid' : '#9932CC', 'darkred' : '#8B0000', 'darksalmon' : '#E9967A', 'darkseagreen' : '#8FBC8F', 'darkslateblue' : '#483D8B', 'darkslategray' : '#2F4F4F', 'darkturquoise' : '#00CED1', 'darkviolet' : '#9400D3', 'deeppink' : '#FF1493', 'deepskyblue' : '#00BFFF', 'dimgray' : '#696969', 'dodgerblue' : '#1E90FF', 'firebrick' : '#B22222', 'floralwhite' : '#FFFAF0', 'forestgreen' : '#228B22', 'fuchsia' : '#FF00FF', 'gainsboro' : '#DCDCDC', 'ghostwhite' : '#F8F8FF', 'gold' : '#FFD700', 'goldenrod' : '#DAA520', 'gray' : '#808080', 'green' : '#008000', 'greenyellow' : '#ADFF2F', 'honeydew' : '#F0FFF0', 'hotpink' : '#FF69B4', 'indianred' : '#CD5C5C', 'indigo' : '#4B0082', 'ivory' : '#FFFFF0', 'khaki' : '#F0E68C', 'lavender' : '#E6E6FA', 'lavenderblush' : '#FFF0F5', 'lawngreen' : '#7CFC00', 'lemonchiffon' : '#FFFACD', 'lightblue' : '#ADD8E6', 'lightcoral' : '#F08080', 'lightcyan' : '#E0FFFF', 'lightgoldenrodyellow' : '#FAFAD2', 'lightgreen' : '#90EE90', 'lightgrey' : '#D3D3D3', 'lightpink' : '#FFB6C1', 'lightsalmon' : '#FFA07A', 'lightseagreen' : '#20B2AA', 'lightskyblue' : '#87CEFA', 'lightslategray' : '#778899', 'lightsteelblue' : '#B0C4DE', 'lightyellow' : '#FFFFE0', 'lime' : '#00FF00', 'limegreen' : '#32CD32', 'linen' : '#FAF0E6', 'magenta' : '#FF00FF', 'maroon' : '#800000', 'mediumaquamarine' : '#66CDAA', 'mediumblue' : '#0000CD', 'mediumorchid' : '#BA55D3', 'mediumpurple' : '#9370DB', 'mediumseagreen' : '#3CB371', 'mediumslateblue' : '#7B68EE', 'mediumspringgreen' : '#00FA9A', 'mediumturquoise' : '#48D1CC', 'mediumvioletred' : '#C71585', 'midnightblue' : '#191970', 'mintcream' : '#F5FFFA', 'mistyrose' : '#FFE4E1', 'moccasin' : '#FFE4B5', 'navajowhite' : '#FFDEAD', 'navy' : '#000080', 'oldlace' : '#FDF5E6', 'olive' : '#808000', 'olivedrab' : '#6B8E23', 'orange' : '#FFA500', 'orangered' : '#FF4500', 'orchid' : '#DA70D6', 'palegoldenrod' : '#EEE8AA', 'palegreen' : '#98FB98', 'palevioletred' : '#AFEEEE', 'papayawhip' : '#FFEFD5', 'peachpuff' : '#FFDAB9', 'peru' : '#CD853F', 'pink' : '#FFC0CB', 'plum' : '#DDA0DD', 'powderblue' : '#B0E0E6', 'purple' : '#800080', 'red' : '#FF0000', 'rosybrown' : '#BC8F8F', 'royalblue' : '#4169E1', 'saddlebrown' : '#8B4513', 'salmon' : '#FA8072', 'sandybrown' : '#FAA460', 'seagreen' : '#2E8B57', 'seashell' : '#FFF5EE', 'sienna' : '#A0522D', 'silver' : '#C0C0C0', 'skyblue' : '#87CEEB', 'slateblue' : '#6A5ACD', 'slategray' : '#708090', 'snow' : '#FFFAFA', 'springgreen' : '#00FF7F', 'steelblue' : '#4682B4', 'tan' : '#D2B48C', 'teal' : '#008080', 'thistle' : '#D8BFD8', 'tomato' : '#FF6347', 'turquoise' : '#40E0D0', 'violet' : '#EE82EE', 'wheat' : '#F5DEB3', 'white' : '#FFFFFF', 'whitesmoke' : '#F5F5F5', 'yellow' : '#FFFF00', 'yellowgreen' : '#9ACD32', } # add british equivs for k, v in cnames.items(): if k.find('gray')>=0: k = k.replace('gray', 'grey') cnames[k] = v def is_color_like(c): 'Return True if c can be converted to RGB' try: colorConverter.to_rgb(c) return True except ValueError: return False def rgb2hex(rgb): 'Given a len 3 rgb tuple of 0-1 floats, return the hex string' return '#%02x%02x%02x' % tuple([round(val255) for val in rgb]) hexColorPattern = re.compile("\A#[a-fA-F0-9]{6}\Z") def hex2color(s): """ Take a hex string s* and return the corresponding rgb 3-tuple Example: #efefef -> (0.93725, 0.93725, 0.93725) """ if not isinstance(s, basestring): raise TypeError('hex2color requires a string argument') if hexColorPattern.match(s) is None: raise ValueError('invalid hex color string "%s"' % s) return tuple([int(n, 16)/255.0 for n in (s[1:3], s[3:5], s[5:7])]) class ColorConverter: """ Provides methods for converting color specifications to RGB or RGBA Caching is used for more efficient conversion upon repeated calls with the same argument. Ordinarily only the single instance instantiated in this module, colorConverter, is needed. """ colors = { 'b' : (0.0, 0.0, 1.0), 'g' : (0.0, 0.5, 0.0), 'r' : (1.0, 0.0, 0.0), 'c' : (0.0, 0.75, 0.75), 'm' : (0.75, 0, 0.75), 'y' : (0.75, 0.75, 0), 'k' : (0.0, 0.0, 0.0), 'w' : (1.0, 1.0, 1.0), } cache = {} def to_rgb(self, arg): """ Returns an RGB tuple of three floats from 0-1. arg can be an RGB or RGBA sequence or a string in any of several forms: 1) a letter from the set 'rgbcmykw' 2) a hex color string, like '#00FFFF' 3) a standard name, like 'aqua' 4) a float, like '0.4', indicating gray on a 0-1 scale if arg is RGBA, the A will simply be discarded. """ try: return self.cache[arg] except KeyError: pass except TypeError: # could be unhashable rgb seq arg = tuple(arg) try: return self.cache[arg] except KeyError: pass except TypeError: raise ValueError( 'to_rgb: arg "%s" is unhashable even inside a tuple' % (str(arg),)) try: if cbook.is_string_like(arg): color = self.colors.get(arg, None) if color is None: str1 = cnames.get(arg, arg) if str1.startswith('#'): color = hex2color(str1) else: fl = float(arg) if fl < 0 or fl > 1: raise ValueError( 'gray (string) must be in range 0-1') color = tuple([fl]3) elif cbook.iterable(arg): if len(arg) > 4 or len(arg) < 3: raise ValueError( 'sequence length is %d; must be 3 or 4'%len(arg)) color = tuple(arg[:3]) if [x for x in color if (float(x) < 0) or (x > 1)]: # This will raise TypeError if x is not a number. raise ValueError('number in rbg sequence outside 0-1 range') else: raise ValueError('cannot convert argument to rgb sequence') self.cache[arg] = color except (KeyError, ValueError, TypeError), exc: raise ValueError('to_rgb: Invalid rgb arg "%s"\n%s' % (str(arg), exc)) # Error messages could be improved by handling TypeError # separately; but this should be rare and not too hard # for the user to figure out as-is. return color def to_rgba(self, arg, alpha=None): """ Returns an RGBA* tuple of four floats from 0-1. For acceptable values of arg, see :meth:`to_rgb`. If arg is an RGBA sequence and alpha is not None, alpha will replace the original A. """ try: if not cbook.is_string_like(arg) and cbook.iterable(arg): if len(arg) == 4: if [x for x in arg if (float(x) < 0) or (x > 1)]: # This will raise TypeError if x is not a number. raise ValueError('number in rbga sequence outside 0-1 range') if alpha is None: return tuple(arg) if alpha < 0.0 or alpha > 1.0: raise ValueError("alpha must be in range 0-1") return arg[0], arg[1], arg[2], arg[3] * alpha r,g,b = arg[:3] if [x for x in (r,g,b) if (float(x) < 0) or (x > 1)]: raise ValueError('number in rbg sequence outside 0-1 range') else: r,g,b = self.to_rgb(arg) if alpha is None: alpha = 1.0 return r,g,b,alpha except (TypeError, ValueError), exc: raise ValueError('to_rgba: Invalid rgba arg "%s"\n%s' % (str(arg), exc)) def to_rgba_array(self, c, alpha=None): """ Returns a numpy array of RGBA tuples. Accepts a single mpl color spec or a sequence of specs. Special case to handle "no color": if c is "none" (case-insensitive), then an empty array will be returned. Same for an empty list. """ try: if c.lower() == 'none': return np.zeros((0,4), dtype=np.float_) except AttributeError: pass if len(c) == 0: return np.zeros((0,4), dtype=np.float_) try: result = np.array([self.to_rgba(c, alpha)], dtype=np.float_) except ValueError: if isinstance(c, np.ndarray): if c.ndim != 2 and c.dtype.kind not in 'SU': raise ValueError("Color array must be two-dimensional") result = np.zeros((len(c), 4)) for i, cc in enumerate(c): result[i] = self.to_rgba(cc, alpha) # change in place return np.asarray(result, np.float_) colorConverter = ColorConverter() def makeMappingArray(N, data): """Create an N -element 1-d lookup table data represented by a list of x,y0,y1 mapping correspondences. Each element in this list represents how a value between 0 and 1 (inclusive) represented by x is mapped to a corresponding value between 0 and 1 (inclusive). The two values of y are to allow for discontinuous mapping functions (say as might be found in a sawtooth) where y0 represents the value of y for values of x <= to that given, and y1 is the value to be used for x > than that given). The list must start with x=0, end with x=1, and all values of x must be in increasing order. Values between the given mapping points are determined by simple linear interpolation. The function returns an array "result" where ``result[x(N-1)]`` gives the closest value for values of x between 0 and 1. """ try: adata = np.array(data) except: raise TypeError("data must be convertable to an array") shape = adata.shape if len(shape) != 2 and shape[1] != 3: raise ValueError("data must be nx3 format") x = adata[:,0] y0 = adata[:,1] y1 = adata[:,2] if x[0] != 0. or x[-1] != 1.0: raise ValueError( "data mapping points must start with x=0. and end with x=1") if np.sometrue(np.sort(x)-x): raise ValueError( "data mapping points must have x in increasing order") # begin generation of lookup table x = x (N-1) lut = np.zeros((N,), np.float) xind = np.arange(float(N)) ind = np.searchsorted(x, xind)[1:-1] lut[1:-1] = ( ((xind[1:-1] - x[ind-1]) / (x[ind] - x[ind-1])) * (y0[ind] - y1[ind-1]) + y1[ind-1]) lut[0] = y1[0] lut[-1] = y0[-1] # ensure that the lut is confined to values between 0 and 1 by clipping it np.clip(lut, 0.0, 1.0) #lut = where(lut > 1., 1., lut) #lut = where(lut < 0., 0., lut) return lut class Colormap: """Base class for all scalar to rgb mappings Important methods: * :meth:`set_bad` * :meth:`set_under` * :meth:`set_over` """ def __init__(self, name, N=256): """ Public class attributes: :attr:`N` : number of rgb quantization levels :attr:`name` : name of colormap """ self.name = name self.N = N self._rgba_bad = (0.0, 0.0, 0.0, 0.0) # If bad, don't paint anything. self._rgba_under = None self._rgba_over = None self._i_under = N self._i_over = N+1 self._i_bad = N+2 self._isinit = False def __call__(self, X, alpha=1.0, bytes=False): """ X is either a scalar or an array (of any dimension). If scalar, a tuple of rgba values is returned, otherwise an array with the new shape = oldshape+(4,). If the X-values are integers, then they are used as indices into the array. If they are floating point, then they must be in the interval (0.0, 1.0). Alpha must be a scalar. If bytes is False, the rgba values will be floats on a 0-1 scale; if True, they will be uint8, 0-255. """ if not self._isinit: self._init() alpha = min(alpha, 1.0) # alpha must be between 0 and 1 alpha = max(alpha, 0.0) self._lut[:-3, -1] = alpha mask_bad = None if not cbook.iterable(X): vtype = 'scalar' xa = np.array([X]) else: vtype = 'array' xma = ma.asarray(X) xa = xma.filled(0) mask_bad = ma.getmask(xma) if xa.dtype.char in np.typecodes['Float']: np.putmask(xa, xa==1.0, 0.9999999) #Treat 1.0 as slightly less than 1. # The following clip is fast, and prevents possible # conversion of large positive values to negative integers. if NP_CLIP_OUT: np.clip(xa * self.N, -1, self.N, out=xa) else: xa = np.clip(xa * self.N, -1, self.N) xa = xa.astype(int) # Set the over-range indices before the under-range; # otherwise the under-range values get converted to over-range. np.putmask(xa, xa>self.N-1, self._i_over) np.putmask(xa, xa<0, self._i_under) if mask_bad is not None and mask_bad.shape == xa.shape: np.putmask(xa, mask_bad, self._i_bad) if bytes: lut = (self._lut * 255).astype(np.uint8) else: lut = self._lut rgba = np.empty(shape=xa.shape+(4,), dtype=lut.dtype) lut.take(xa, axis=0, mode='clip', out=rgba) # twice as fast as lut[xa]; # using the clip or wrap mode and providing an # output array speeds it up a little more. if vtype == 'scalar': rgba = tuple(rgba[0,:]) return rgba def set_bad(self, color = 'k', alpha = 1.0): '''Set color to be used for masked values. ''' self._rgba_bad = colorConverter.to_rgba(color, alpha) if self._isinit: self._set_extremes() def set_under(self, color = 'k', alpha = 1.0): '''Set color to be used for low out-of-range values. Requires norm.clip = False ''' self._rgba_under = colorConverter.to_rgba(color, alpha) if self._isinit: self._set_extremes() def set_over(self, color = 'k', alpha = 1.0): '''Set color to be used for high out-of-range values. Requires norm.clip = False ''' self._rgba_over = colorConverter.to_rgba(color, alpha) if self._isinit: self._set_extremes() def _set_extremes(self): if self._rgba_under: self._lut[self._i_under] = self._rgba_under else: self._lut[self._i_under] = self._lut[0] if self._rgba_over: self._lut[self._i_over] = self._rgba_over else: self._lut[self._i_over] = self._lut[self.N-1] self._lut[self._i_bad] = self._rgba_bad def _init(): '''Generate the lookup table, self._lut''' raise NotImplementedError("Abstract class only") def is_gray(self): if not self._isinit: self._init() return (np.alltrue(self._lut[:,0] == self._lut[:,1]) and np.alltrue(self._lut[:,0] == self._lut[:,2])) class LinearSegmentedColormap(Colormap): """Colormap objects based on lookup tables using linear segments. The lookup table is generated using linear interpolation for each primary color, with the 0-1 domain divided into any number of segments. """ def __init__(self, name, segmentdata, N=256): """Create color map from linear mapping segments segmentdata argument is a dictionary with a red, green and blue entries. Each entry should be a list of x, y0, y1 tuples, forming rows in a table. Example: suppose you want red to increase from 0 to 1 over the bottom half, green to do the same over the middle half, and blue over the top half. Then you would use:: cdict = {'red': [(0.0, 0.0, 0.0), (0.5, 1.0, 1.0), (1.0, 1.0, 1.0)], 'green': [(0.0, 0.0, 0.0), (0.25, 0.0, 0.0), (0.75, 1.0, 1.0), (1.0, 1.0, 1.0)], 'blue': [(0.0, 0.0, 0.0), (0.5, 0.0, 0.0), (1.0, 1.0, 1.0)]} Each row in the table for a given color is a sequence of x, y0, y1 tuples. In each sequence, x must increase monotonically from 0 to 1. For any input value z falling between x[i] and x[i+1], the output value of a given color will be linearly interpolated between y1[i] and y0[i+1]:: row i: x y0 y1 / / row i+1: x y0 y1 Hence y0 in the first row and y1 in the last row are never used. .. seealso:: :func:`makeMappingArray` """ self.monochrome = False # True only if all colors in map are identical; # needed for contouring. Colormap.__init__(self, name, N) self._segmentdata = segmentdata def _init(self): self._lut = np.ones((self.N + 3, 4), np.float) self._lut[:-3, 0] = makeMappingArray(self.N, self._segmentdata['red']) self._lut[:-3, 1] = makeMappingArray(self.N, self._segmentdata['green']) self._lut[:-3, 2] = makeMappingArray(self.N, self._segmentdata['blue']) self._isinit = True self._set_extremes() class ListedColormap(Colormap): """Colormap object generated from a list of colors. This may be most useful when indexing directly into a colormap, but it can also be used to generate special colormaps for ordinary mapping. """ def __init__(self, colors, name = 'from_list', N = None): """ Make a colormap from a list of colors. colors a list of matplotlib color specifications, or an equivalent Nx3 floating point array (N rgb values) name a string to identify the colormap N the number of entries in the map. The default is None, in which case there is one colormap entry for each element in the list of colors. If:: N < len(colors) the list will be truncated at N. If:: N > len(colors) the list will be extended by repetition. """ self.colors = colors self.monochrome = False # True only if all colors in map are identical; # needed for contouring. if N is None: N = len(self.colors) else: if cbook.is_string_like(self.colors): self.colors = [self.colors] * N self.monochrome = True elif cbook.iterable(self.colors): self.colors = list(self.colors) # in case it was a tuple if len(self.colors) == 1: self.monochrome = True if len(self.colors) < N: self.colors = list(self.colors) * N del(self.colors[N:]) else: try: gray = float(self.colors) except TypeError: pass else: self.colors = [gray] * N self.monochrome = True Colormap.__init__(self, name, N) def _init(self): rgb = np.array([colorConverter.to_rgb(c) for c in self.colors], np.float) self._lut = np.zeros((self.N + 3, 4), np.float) self._lut[:-3, :-1] = rgb self._lut[:-3, -1] = 1 self._isinit = True self._set_extremes() class Normalize: """ Normalize a given value to the 0-1 range """ def __init__(self, vmin=None, vmax=None, clip=False): """ If vmin or vmax is not given, they are taken from the input's minimum and maximum value respectively. If clip is True and the given value falls outside the range, the returned value will be 0 or 1, whichever is closer. Returns 0 if:: vmin==vmax Works with scalars or arrays, including masked arrays. If clip is True, masked values are set to 1; otherwise they remain masked. Clipping silently defeats the purpose of setting the over, under, and masked colors in the colormap, so it is likely to lead to surprises; therefore the default is clip = False. """ self.vmin = vmin self.vmax = vmax self.clip = clip def __call__(self, value, clip=None): if clip is None: clip = self.clip if cbook.iterable(value): vtype = 'array' val = ma.asarray(value).astype(np.float) else: vtype = 'scalar' val = ma.array([value]).astype(np.float) self.autoscale_None(val) vmin, vmax = self.vmin, self.vmax if vmin > vmax: raise ValueError("minvalue must be less than or equal to maxvalue") elif vmin==vmax: return 0.0 * val else: if clip: mask = ma.getmask(val) val = ma.array(np.clip(val.filled(vmax), vmin, vmax), mask=mask) result = (val-vmin) * (1.0/(vmax-vmin)) if vtype == 'scalar': result = result[0] return result def inverse(self, value): if not self.scaled(): raise ValueError("Not invertible until scaled") vmin, vmax = self.vmin, self.vmax if cbook.iterable(value): val = ma.asarray(value) return vmin + val * (vmax - vmin) else: return vmin + value * (vmax - vmin) def autoscale(self, A): ''' Set vmin, vmax to min, max of A. ''' self.vmin = ma.minimum(A) self.vmax = ma.maximum(A) def autoscale_None(self, A): ' autoscale only None-valued vmin or vmax' if self.vmin is None: self.vmin = ma.minimum(A) if self.vmax is None: self.vmax = ma.maximum(A) def scaled(self): 'return true if vmin and vmax set' return (self.vmin is not None and self.vmax is not None) class LogNorm(Normalize): """ Normalize a given value to the 0-1 range on a log scale """ def __call__(self, value, clip=None): if clip is None: clip = self.clip if cbook.iterable(value): vtype = 'array' val = ma.asarray(value).astype(np.float) else: vtype = 'scalar' val = ma.array([value]).astype(np.float) self.autoscale_None(val) vmin, vmax = self.vmin, self.vmax if vmin > vmax: raise ValueError("minvalue must be less than or equal to maxvalue") elif vmin<=0: raise ValueError("values must all be positive") elif vmin==vmax: return 0.0 * val else: if clip: mask = ma.getmask(val) val = ma.array(np.clip(val.filled(vmax), vmin, vmax), mask=mask) result = (ma.log(val)-np.log(vmin))/(np.log(vmax)-np.log(vmin)) if vtype == 'scalar': result = result[0] return result def inverse(self, value): if not self.scaled(): raise ValueError("Not invertible until scaled") vmin, vmax = self.vmin, self.vmax if cbook.iterable(value): val = ma.asarray(value) return vmin * ma.power((vmax/vmin), val) else: return vmin * pow((vmax/vmin), value) class BoundaryNorm(Normalize): ''' Generate a colormap index based on discrete intervals. Unlike :class:`Normalize` or :class:`LogNorm`, :class:`BoundaryNorm` maps values to integers instead of to the interval 0-1. Mapping to the 0-1 interval could have been done via piece-wise linear interpolation, but using integers seems simpler, and reduces the number of conversions back and forth between integer and floating point. ''' def __init__(self, boundaries, ncolors, clip=False): ''' boundaries a monotonically increasing sequence ncolors number of colors in the colormap to be used If:: b[i] <= v < b[i+1] then v is mapped to color j; as i varies from 0 to len(boundaries)-2, j goes from 0 to ncolors-1. Out-of-range values are mapped to -1 if low and ncolors if high; these are converted to valid indices by :meth:`Colormap.__call__` . ''' self.clip = clip self.vmin = boundaries[0] self.vmax = boundaries[-1] self.boundaries = np.asarray(boundaries) self.N = len(self.boundaries) self.Ncmap = ncolors if self.N-1 == self.Ncmap: self._interp = False else: self._interp = True def __call__(self, x, clip=None): if clip is None: clip = self.clip x = ma.asarray(x) mask = ma.getmaskarray(x) xx = x.filled(self.vmax+1) if clip: np.clip(xx, self.vmin, self.vmax) iret = np.zeros(x.shape, dtype=np.int16) for i, b in enumerate(self.boundaries): iret[xx>=b] = i if self._interp: iret = (iret * (float(self.Ncmap-1)/(self.N-2))).astype(np.int16) iret[xx<self.vmin] = -1 iret[xx>=self.vmax] = self.Ncmap ret = ma.array(iret, mask=mask) if ret.shape == () and not mask: ret = int(ret) # assume python scalar return ret def inverse(self, value): return ValueError("BoundaryNorm is not invertible") class NoNorm(Normalize): ''' Dummy replacement for Normalize, for the case where we want to use indices directly in a :class:`~matplotlib.cm.ScalarMappable` . ''' def __call__(self, value, clip=None): return value def inverse(self, value): return value # compatibility with earlier class names that violated convention: normalize = Normalize no_norm = NoNorm	31,676	Python	.py	767	32.179922	85	0.531855	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,237	transforms.py	numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/transforms.py	""" matplotlib includes a framework for arbitrary geometric transformations that is used determine the final position of all elements drawn on the canvas. Transforms are composed into trees of :class:`TransformNode` objects whose actual value depends on their children. When the contents of children change, their parents are automatically invalidated. The next time an invalidated transform is accessed, it is recomputed to reflect those changes. This invalidation/caching approach prevents unnecessary recomputations of transforms, and contributes to better interactive performance. For example, here is a graph of the transform tree used to plot data to the graph: .. image:: ../_static/transforms.png The framework can be used for both affine and non-affine transformations. However, for speed, we want use the backend renderers to perform affine transformations whenever possible. Therefore, it is possible to perform just the affine or non-affine part of a transformation on a set of data. The affine is always assumed to occur after the non-affine. For any transform:: full transform == non-affine part + affine part The backends are not expected to handle non-affine transformations themselves. """ import numpy as np from numpy import ma from matplotlib._path import affine_transform from numpy.linalg import inv from weakref import WeakKeyDictionary import warnings try: set except NameError: from sets import Set as set import cbook from path import Path from _path import count_bboxes_overlapping_bbox, update_path_extents DEBUG = False if DEBUG: import warnings MaskedArray = ma.MaskedArray class TransformNode(object): """ :class:`TransformNode` is the base class for anything that participates in the transform tree and needs to invalidate its parents or be invalidated. This includes classes that are not really transforms, such as bounding boxes, since some transforms depend on bounding boxes to compute their values. """ _gid = 0 # Invalidation may affect only the affine part. If the # invalidation was "affine-only", the _invalid member is set to # INVALID_AFFINE_ONLY INVALID_NON_AFFINE = 1 INVALID_AFFINE = 2 INVALID = INVALID_NON_AFFINE \| INVALID_AFFINE # Some metadata about the transform, used to determine whether an # invalidation is affine-only is_affine = False is_bbox = False # If pass_through is True, all ancestors will always be # invalidated, even if 'self' is already invalid. pass_through = False def __init__(self): """ Creates a new :class:`TransformNode`. """ # Parents are stored in a WeakKeyDictionary, so that if the # parents are deleted, references from the children won't keep # them alive. self._parents = WeakKeyDictionary() # TransformNodes start out as invalid until their values are # computed for the first time. self._invalid = 1 def __copy__(self, args): raise NotImplementedError( "TransformNode instances can not be copied. " + "Consider using frozen() instead.") __deepcopy__ = __copy__ def invalidate(self): """ Invalidate this :class:`TransformNode` and all of its ancestors. Should be called any time the transform changes. """ # If we are an affine transform being changed, we can set the # flag to INVALID_AFFINE_ONLY value = (self.is_affine) and self.INVALID_AFFINE or self.INVALID # Shortcut: If self is already invalid, that means its parents # are as well, so we don't need to do anything. if self._invalid == value: return if not len(self._parents): self._invalid = value return # Invalidate all ancestors of self using pseudo-recursion. stack = [self] while len(stack): root = stack.pop() # Stop at subtrees that have already been invalidated if root._invalid != value or root.pass_through: root._invalid = self.INVALID stack.extend(root._parents.keys()) def set_children(self, children): """ Set the children of the transform, to let the invalidation system know which transforms can invalidate this transform. Should be called from the constructor of any transforms that depend on other transforms. """ for child in children: child._parents[self] = None if DEBUG: _set_children = set_children def set_children(self, children): self._set_children(children) self._children = children set_children.__doc__ = _set_children.__doc__ def frozen(self): """ Returns a frozen copy of this transform node. The frozen copy will not update when its children change. Useful for storing a previously known state of a transform where ``copy.deepcopy()`` might normally be used. """ return self if DEBUG: def write_graphviz(self, fobj, highlight=[]): """ For debugging purposes. Writes the transform tree rooted at 'self' to a graphviz "dot" format file. This file can be run through the "dot" utility to produce a graph of the transform tree. Affine transforms are marked in blue. Bounding boxes are marked in yellow. fobj: A Python file-like object """ seen = set() def recurse(root): if root in seen: return seen.add(root) props = {} label = root.__class__.__name__ if root._invalid: label = '[%s]' % label if root in highlight: props['style'] = 'bold' props['shape'] = 'box' props['label'] = '"%s"' % label props = ' '.join(['%s=%s' % (key, val) for key, val in props.items()]) fobj.write('%s [%s];\n' % (hash(root), props)) if hasattr(root, '_children'): for child in root._children: name = '?' for key, val in root.__dict__.items(): if val is child: name = key break fobj.write('%s -> %s [label="%s", fontsize=10];\n' % ( hash(root), hash(child), name)) recurse(child) fobj.write("digraph G {\n") recurse(self) fobj.write("}\n") else: def write_graphviz(self, fobj, highlight=[]): return class BboxBase(TransformNode): """ This is the base class of all bounding boxes, and provides read-only access to its data. A mutable bounding box is provided by the :class:`Bbox` class. The canonical representation is as two points, with no restrictions on their ordering. Convenience properties are provided to get the left, bottom, right and top edges and width and height, but these are not stored explicity. """ is_bbox = True is_affine = True #* Redundant: Removed for performance # # def __init__(self): # TransformNode.__init__(self) if DEBUG: def _check(points): if ma.isMaskedArray(points): warnings.warn("Bbox bounds are a masked array.") points = np.asarray(points) if (points[1,0] - points[0,0] == 0 or points[1,1] - points[0,1] == 0): warnings.warn("Singular Bbox.") _check = staticmethod(_check) def frozen(self): return Bbox(self.get_points().copy()) frozen.__doc__ = TransformNode.__doc__ def __array__(self, args, kwargs): return self.get_points() def is_unit(self): """ Returns True if the :class:`Bbox` is the unit bounding box from (0, 0) to (1, 1). """ return list(self.get_points().flatten()) == [0., 0., 1., 1.] def _get_x0(self): return self.get_points()[0, 0] x0 = property(_get_x0, None, None, """ (property) :attr:`x0` is the first of the pair of x* coordinates that define the bounding box. :attr:`x0` is not guaranteed to be less than :attr:`x1`. If you require that, use :attr:`xmin`.""") def _get_y0(self): return self.get_points()[0, 1] y0 = property(_get_y0, None, None, """ (property) :attr:`y0` is the first of the pair of y coordinates that define the bounding box. :attr:`y0` is not guaranteed to be less than :attr:`y1`. If you require that, use :attr:`ymin`.""") def _get_x1(self): return self.get_points()[1, 0] x1 = property(_get_x1, None, None, """ (property) :attr:`x1` is the second of the pair of x coordinates that define the bounding box. :attr:`x1` is not guaranteed to be greater than :attr:`x0`. If you require that, use :attr:`xmax`.""") def _get_y1(self): return self.get_points()[1, 1] y1 = property(_get_y1, None, None, """ (property) :attr:`y1` is the second of the pair of y coordinates that define the bounding box. :attr:`y1` is not guaranteed to be greater than :attr:`y0`. If you require that, use :attr:`ymax`.""") def _get_p0(self): return self.get_points()[0] p0 = property(_get_p0, None, None, """ (property) :attr:`p0` is the first pair of (x, y) coordinates that define the bounding box. It is not guaranteed to be the bottom-left corner. For that, use :attr:`min`.""") def _get_p1(self): return self.get_points()[1] p1 = property(_get_p1, None, None, """ (property) :attr:`p1` is the second pair of (x, y) coordinates that define the bounding box. It is not guaranteed to be the top-right corner. For that, use :attr:`max`.""") def _get_xmin(self): return min(self.get_points()[:, 0]) xmin = property(_get_xmin, None, None, """ (property) :attr:`xmin` is the left edge of the bounding box.""") def _get_ymin(self): return min(self.get_points()[:, 1]) ymin = property(_get_ymin, None, None, """ (property) :attr:`ymin` is the bottom edge of the bounding box.""") def _get_xmax(self): return max(self.get_points()[:, 0]) xmax = property(_get_xmax, None, None, """ (property) :attr:`xmax` is the right edge of the bounding box.""") def _get_ymax(self): return max(self.get_points()[:, 1]) ymax = property(_get_ymax, None, None, """ (property) :attr:`ymax` is the top edge of the bounding box.""") def _get_min(self): return [min(self.get_points()[:, 0]), min(self.get_points()[:, 1])] min = property(_get_min, None, None, """ (property) :attr:`min` is the bottom-left corner of the bounding box.""") def _get_max(self): return [max(self.get_points()[:, 0]), max(self.get_points()[:, 1])] max = property(_get_max, None, None, """ (property) :attr:`max` is the top-right corner of the bounding box.""") def _get_intervalx(self): return self.get_points()[:, 0] intervalx = property(_get_intervalx, None, None, """ (property) :attr:`intervalx` is the pair of x coordinates that define the bounding box. It is not guaranteed to be sorted from left to right.""") def _get_intervaly(self): return self.get_points()[:, 1] intervaly = property(_get_intervaly, None, None, """ (property) :attr:`intervaly` is the pair of y coordinates that define the bounding box. It is not guaranteed to be sorted from bottom to top.""") def _get_width(self): points = self.get_points() return points[1, 0] - points[0, 0] width = property(_get_width, None, None, """ (property) The width of the bounding box. It may be negative if :attr:`x1` < :attr:`x0`.""") def _get_height(self): points = self.get_points() return points[1, 1] - points[0, 1] height = property(_get_height, None, None, """ (property) The height of the bounding box. It may be negative if :attr:`y1` < :attr:`y0`.""") def _get_size(self): points = self.get_points() return points[1] - points[0] size = property(_get_size, None, None, """ (property) The width and height of the bounding box. May be negative, in the same way as :attr:`width` and :attr:`height`.""") def _get_bounds(self): x0, y0, x1, y1 = self.get_points().flatten() return (x0, y0, x1 - x0, y1 - y0) bounds = property(_get_bounds, None, None, """ (property) Returns (:attr:`x0`, :attr:`y0`, :attr:`width`, :attr:`height`).""") def _get_extents(self): return self.get_points().flatten().copy() extents = property(_get_extents, None, None, """ (property) Returns (:attr:`x0`, :attr:`y0`, :attr:`x1`, :attr:`y1`).""") def get_points(self): return NotImplementedError() def containsx(self, x): """ Returns True if x is between or equal to :attr:`x0` and :attr:`x1`. """ x0, x1 = self.intervalx return ((x0 < x1 and (x >= x0 and x <= x1)) or (x >= x1 and x <= x0)) def containsy(self, y): """ Returns True if y is between or equal to :attr:`y0` and :attr:`y1`. """ y0, y1 = self.intervaly return ((y0 < y1 and (y >= y0 and y <= y1)) or (y >= y1 and y <= y0)) def contains(self, x, y): """ Returns True if (x, y) is a coordinate inside the bounding box or on its edge. """ return self.containsx(x) and self.containsy(y) def overlaps(self, other): """ Returns True if this bounding box overlaps with the given bounding box other. """ ax1, ay1, ax2, ay2 = self._get_extents() bx1, by1, bx2, by2 = other._get_extents() if ax2 < ax1: ax2, ax1 = ax1, ax2 if ay2 < ay1: ay2, ay1 = ay1, ay2 if bx2 < bx1: bx2, bx1 = bx1, bx2 if by2 < by1: by2, by1 = by1, by2 return not ((bx2 < ax1) or (by2 < ay1) or (bx1 > ax2) or (by1 > ay2)) def fully_containsx(self, x): """ Returns True if x is between but not equal to :attr:`x0` and :attr:`x1`. """ x0, x1 = self.intervalx return ((x0 < x1 and (x > x0 and x < x1)) or (x > x1 and x < x0)) def fully_containsy(self, y): """ Returns True if y is between but not equal to :attr:`y0` and :attr:`y1`. """ y0, y1 = self.intervaly return ((y0 < y1 and (x > y0 and x < y1)) or (x > y1 and x < y0)) def fully_contains(self, x, y): """ Returns True if (x, y) is a coordinate inside the bounding box, but not on its edge. """ return self.fully_containsx(x) \ and self.fully_containsy(y) def fully_overlaps(self, other): """ Returns True if this bounding box overlaps with the given bounding box other, but not on its edge alone. """ ax1, ay1, ax2, ay2 = self._get_extents() bx1, by1, bx2, by2 = other._get_extents() if ax2 < ax1: ax2, ax1 = ax1, ax2 if ay2 < ay1: ay2, ay1 = ay1, ay2 if bx2 < bx1: bx2, bx1 = bx1, bx2 if by2 < by1: by2, by1 = by1, by2 return not ((bx2 <= ax1) or (by2 <= ay1) or (bx1 >= ax2) or (by1 >= ay2)) def transformed(self, transform): """ Return a new :class:`Bbox` object, statically transformed by the given transform. """ return Bbox(transform.transform(self.get_points())) def inverse_transformed(self, transform): """ Return a new :class:`Bbox` object, statically transformed by the inverse of the given transform. """ return Bbox(transform.inverted().transform(self.get_points())) coefs = {'C': (0.5, 0.5), 'SW': (0,0), 'S': (0.5, 0), 'SE': (1.0, 0), 'E': (1.0, 0.5), 'NE': (1.0, 1.0), 'N': (0.5, 1.0), 'NW': (0, 1.0), 'W': (0, 0.5)} def anchored(self, c, container = None): """ Return a copy of the :class:`Bbox`, shifted to position c within a container. c: may be either: * a sequence (cx, cy) where cx and cy range from 0 to 1, where 0 is left or bottom and 1 is right or top * a string: - 'C' for centered - 'S' for bottom-center - 'SE' for bottom-left - 'E' for left - etc. Optional argument container is the box within which the :class:`Bbox` is positioned; it defaults to the initial :class:`Bbox`. """ if container is None: container = self l, b, w, h = container.bounds if isinstance(c, str): cx, cy = self.coefs[c] else: cx, cy = c L, B, W, H = self.bounds return Bbox(self._points + [(l + cx * (w-W)) - L, (b + cy * (h-H)) - B]) def shrunk(self, mx, my): """ Return a copy of the :class:`Bbox`, shrunk by the factor mx in the x direction and the factor my in the y direction. The lower left corner of the box remains unchanged. Normally mx and my will be less than 1, but this is not enforced. """ w, h = self.size return Bbox([self._points[0], self._points[0] + [mx * w, my * h]]) def shrunk_to_aspect(self, box_aspect, container = None, fig_aspect = 1.0): """ Return a copy of the :class:`Bbox`, shrunk so that it is as large as it can be while having the desired aspect ratio, box_aspect. If the box coordinates are relative---that is, fractions of a larger box such as a figure---then the physical aspect ratio of that figure is specified with fig_aspect, so that box_aspect can also be given as a ratio of the absolute dimensions, not the relative dimensions. """ assert box_aspect > 0 and fig_aspect > 0 if container is None: container = self w, h = container.size H = w * box_aspect/fig_aspect if H <= h: W = w else: W = h * fig_aspect/box_aspect H = h return Bbox([self._points[0], self._points[0] + (W, H)]) def splitx(self, args): """ e.g., ``bbox.splitx(f1, f2, ...)`` Returns a list of new :class:`Bbox` objects formed by splitting the original one with vertical lines at fractional positions f1, f2, ... """ boxes = [] xf = [0] + list(args) + [1] x0, y0, x1, y1 = self._get_extents() w = x1 - x0 for xf0, xf1 in zip(xf[:-1], xf[1:]): boxes.append(Bbox([[x0 + xf0 w, y0], [x0 + xf1 * w, y1]])) return boxes def splity(self, args): """ e.g., ``bbox.splitx(f1, f2, ...)`` Returns a list of new :class:`Bbox` objects formed by splitting the original one with horizontal lines at fractional positions f1, f2, ... """ boxes = [] yf = [0] + list(args) + [1] x0, y0, x1, y1 = self._get_extents() h = y1 - y0 for yf0, yf1 in zip(yf[:-1], yf[1:]): boxes.append(Bbox([[x0, y0 + yf0 h], [x1, y0 + yf1 * h]])) return boxes def count_contains(self, vertices): """ Count the number of vertices contained in the :class:`Bbox`. vertices is a Nx2 Numpy array. """ if len(vertices) == 0: return 0 vertices = np.asarray(vertices) x0, y0, x1, y1 = self._get_extents() dx0 = np.sign(vertices[:, 0] - x0) dy0 = np.sign(vertices[:, 1] - y0) dx1 = np.sign(vertices[:, 0] - x1) dy1 = np.sign(vertices[:, 1] - y1) inside = (abs(dx0 + dx1) + abs(dy0 + dy1)) <= 2 return np.sum(inside) def count_overlaps(self, bboxes): """ Count the number of bounding boxes that overlap this one. bboxes is a sequence of :class:`BboxBase` objects """ return count_bboxes_overlapping_bbox(self, bboxes) def expanded(self, sw, sh): """ Return a new :class:`Bbox` which is this :class:`Bbox` expanded around its center by the given factors sw and sh. """ width = self.width height = self.height deltaw = (sw * width - width) / 2.0 deltah = (sh * height - height) / 2.0 a = np.array([[-deltaw, -deltah], [deltaw, deltah]]) return Bbox(self._points + a) def padded(self, p): """ Return a new :class:`Bbox` that is padded on all four sides by the given value. """ points = self._points return Bbox(points + [[-p, -p], [p, p]]) def translated(self, tx, ty): """ Return a copy of the :class:`Bbox`, statically translated by tx and ty. """ return Bbox(self._points + (tx, ty)) def corners(self): """ Return an array of points which are the four corners of this rectangle. For example, if this :class:`Bbox` is defined by the points (a, b) and (c, d), :meth:`corners` returns (a, b), (a, d), (c, b) and (c, d). """ l, b, r, t = self.get_points().flatten() return np.array([[l, b], [l, t], [r, b], [r, t]]) def rotated(self, radians): """ Return a new bounding box that bounds a rotated version of this bounding box by the given radians. The new bounding box is still aligned with the axes, of course. """ corners = self.corners() corners_rotated = Affine2D().rotate(radians).transform(corners) bbox = Bbox.unit() bbox.update_from_data_xy(corners_rotated, ignore=True) return bbox #@staticmethod def union(bboxes): """ Return a :class:`Bbox` that contains all of the given bboxes. """ assert(len(bboxes)) if len(bboxes) == 1: return bboxes[0] x0 = np.inf y0 = np.inf x1 = -np.inf y1 = -np.inf for bbox in bboxes: points = bbox.get_points() xs = points[:, 0] ys = points[:, 1] x0 = min(x0, np.min(xs)) y0 = min(y0, np.min(ys)) x1 = max(x1, np.max(xs)) y1 = max(y1, np.max(ys)) return Bbox.from_extents(x0, y0, x1, y1) union = staticmethod(union) class Bbox(BboxBase): """ A mutable bounding box. """ def __init__(self, points): """ points: a 2x2 numpy array of the form [[x0, y0], [x1, y1]] If you need to create a :class:`Bbox` object from another form of data, consider the static methods :meth:`unit`, :meth:`from_bounds` and :meth:`from_extents`. """ BboxBase.__init__(self) self._points = np.asarray(points, np.float_) self._minpos = np.array([0.0000001, 0.0000001]) self._ignore = True if DEBUG: ___init__ = __init__ def __init__(self, points): self._check(points) self.___init__(points) def invalidate(self): self._check(self._points) TransformNode.invalidate(self) _unit_values = np.array([[0.0, 0.0], [1.0, 1.0]], np.float_) #@staticmethod def unit(): """ (staticmethod) Create a new unit :class:`Bbox` from (0, 0) to (1, 1). """ return Bbox(Bbox._unit_values.copy()) unit = staticmethod(unit) #@staticmethod def from_bounds(x0, y0, width, height): """ (staticmethod) Create a new :class:`Bbox` from x0, y0, width and height. width and height may be negative. """ return Bbox.from_extents(x0, y0, x0 + width, y0 + height) from_bounds = staticmethod(from_bounds) #@staticmethod def from_extents(args): """ (staticmethod) Create a new Bbox from left, bottom, right* and top. The y-axis increases upwards. """ points = np.array(args, dtype=np.float_).reshape(2, 2) return Bbox(points) from_extents = staticmethod(from_extents) def __repr__(self): return 'Bbox(%s)' % repr(self._points) __str__ = __repr__ def ignore(self, value): """ Set whether the existing bounds of the box should be ignored by subsequent calls to :meth:`update_from_data` or :meth:`update_from_data_xy`. value: - When True, subsequent calls to :meth:`update_from_data` will ignore the existing bounds of the :class:`Bbox`. - When False, subsequent calls to :meth:`update_from_data` will include the existing bounds of the :class:`Bbox`. """ self._ignore = value def update_from_data(self, x, y, ignore=None): """ Update the bounds of the :class:`Bbox` based on the passed in data. After updating, the bounds will have positive width and height; x0 and y0 will be the minimal values. x: a numpy array of x-values y: a numpy array of y-values ignore: - when True, ignore the existing bounds of the :class:`Bbox`. - when False, include the existing bounds of the :class:`Bbox`. - when None, use the last value passed to :meth:`ignore`. """ warnings.warn( "update_from_data requires a memory copy -- please replace with update_from_data_xy") xy = np.hstack((x.reshape((len(x), 1)), y.reshape((len(y), 1)))) return self.update_from_data_xy(xy, ignore) def update_from_path(self, path, ignore=None, updatex=True, updatey=True): """ Update the bounds of the :class:`Bbox` based on the passed in data. After updating, the bounds will have positive width and height; x0 and y0 will be the minimal values. path: a :class:`~matplotlib.path.Path` instance ignore: - when True, ignore the existing bounds of the :class:`Bbox`. - when False, include the existing bounds of the :class:`Bbox`. - when None, use the last value passed to :meth:`ignore`. updatex: when True, update the x values updatey: when True, update the y values """ if ignore is None: ignore = self._ignore if path.vertices.size == 0: return points, minpos, changed = update_path_extents( path, None, self._points, self._minpos, ignore) if changed: self.invalidate() if updatex: self._points[:,0] = points[:,0] self._minpos[0] = minpos[0] if updatey: self._points[:,1] = points[:,1] self._minpos[1] = minpos[1] def update_from_data_xy(self, xy, ignore=None, updatex=True, updatey=True): """ Update the bounds of the :class:`Bbox` based on the passed in data. After updating, the bounds will have positive width and height; x0 and y0 will be the minimal values. xy: a numpy array of 2D points ignore: - when True, ignore the existing bounds of the :class:`Bbox`. - when False, include the existing bounds of the :class:`Bbox`. - when None, use the last value passed to :meth:`ignore`. updatex: when True, update the x values updatey: when True, update the y values """ if len(xy) == 0: return path = Path(xy) self.update_from_path(path, ignore=ignore, updatex=updatex, updatey=updatey) def _set_x0(self, val): self._points[0, 0] = val self.invalidate() x0 = property(BboxBase._get_x0, _set_x0) def _set_y0(self, val): self._points[0, 1] = val self.invalidate() y0 = property(BboxBase._get_y0, _set_y0) def _set_x1(self, val): self._points[1, 0] = val self.invalidate() x1 = property(BboxBase._get_x1, _set_x1) def _set_y1(self, val): self._points[1, 1] = val self.invalidate() y1 = property(BboxBase._get_y1, _set_y1) def _set_p0(self, val): self._points[0] = val self.invalidate() p0 = property(BboxBase._get_p0, _set_p0) def _set_p1(self, val): self._points[1] = val self.invalidate() p1 = property(BboxBase._get_p1, _set_p1) def _set_intervalx(self, interval): self._points[:, 0] = interval self.invalidate() intervalx = property(BboxBase._get_intervalx, _set_intervalx) def _set_intervaly(self, interval): self._points[:, 1] = interval self.invalidate() intervaly = property(BboxBase._get_intervaly, _set_intervaly) def _set_bounds(self, bounds): l, b, w, h = bounds points = np.array([[l, b], [l+w, b+h]], np.float_) if np.any(self._points != points): self._points = points self.invalidate() bounds = property(BboxBase._get_bounds, _set_bounds) def _get_minpos(self): return self._minpos minpos = property(_get_minpos) def _get_minposx(self): return self._minpos[0] minposx = property(_get_minposx) def _get_minposy(self): return self._minpos[1] minposy = property(_get_minposy) def get_points(self): """ Get the points of the bounding box directly as a numpy array of the form: [[x0, y0], [x1, y1]]. """ self._invalid = 0 return self._points def set_points(self, points): """ Set the points of the bounding box directly from a numpy array of the form: [[x0, y0], [x1, y1]]. No error checking is performed, as this method is mainly for internal use. """ if np.any(self._points != points): self._points = points self.invalidate() def set(self, other): """ Set this bounding box from the "frozen" bounds of another :class:`Bbox`. """ if np.any(self._points != other.get_points()): self._points = other.get_points() self.invalidate() class TransformedBbox(BboxBase): """ A :class:`Bbox` that is automatically transformed by a given transform. When either the child bounding box or transform changes, the bounds of this bbox will update accordingly. """ def __init__(self, bbox, transform): """ bbox: a child :class:`Bbox` transform: a 2D :class:`Transform` """ assert bbox.is_bbox assert isinstance(transform, Transform) assert transform.input_dims == 2 assert transform.output_dims == 2 BboxBase.__init__(self) self._bbox = bbox self._transform = transform self.set_children(bbox, transform) self._points = None def __repr__(self): return "TransformedBbox(%s, %s)" % (self._bbox, self._transform) __str__ = __repr__ def get_points(self): if self._invalid: points = self._transform.transform(self._bbox.get_points()) if ma.isMaskedArray(points): points.putmask(0.0) points = np.asarray(points) self._points = points self._invalid = 0 return self._points get_points.__doc__ = Bbox.get_points.__doc__ if DEBUG: _get_points = get_points def get_points(self): points = self._get_points() self._check(points) return points class Transform(TransformNode): """ The base class of all :class:`TransformNode` instances that actually perform a transformation. All non-affine transformations should be subclasses of this class. New affine transformations should be subclasses of :class:`Affine2D`. Subclasses of this class should override the following members (at minimum): - :attr:`input_dims` - :attr:`output_dims` - :meth:`transform` - :attr:`is_separable` - :attr:`has_inverse` - :meth:`inverted` (if :meth:`has_inverse` can return True) If the transform needs to do something non-standard with :class:`mathplotlib.path.Path` objects, such as adding curves where there were once line segments, it should override: - :meth:`transform_path` """ # The number of input and output dimensions for this transform. # These must be overridden (with integers) in the subclass. input_dims = None output_dims = None # True if this transform as a corresponding inverse transform. has_inverse = False # True if this transform is separable in the x- and y- dimensions. is_separable = False #* Redundant: Removed for performance # # def __init__(self): # TransformNode.__init__(self) def __add__(self, other): """ Composes two transforms together such that self is followed by other. """ if isinstance(other, Transform): return composite_transform_factory(self, other) raise TypeError( "Can not add Transform to object of type '%s'" % type(other)) def __radd__(self, other): """ Composes two transforms together such that self is followed by other. """ if isinstance(other, Transform): return composite_transform_factory(other, self) raise TypeError( "Can not add Transform to object of type '%s'" % type(other)) def __array__(self, args, kwargs): """ Used by C/C++ -based backends to get at the array matrix data. """ return self.frozen().__array__() def transform(self, values): """ Performs the transformation on the given array of values. Accepts a numpy array of shape (N x :attr:`input_dims`) and returns a numpy array of shape (N x :attr:`output_dims`). """ raise NotImplementedError() def transform_affine(self, values): """ Performs only the affine part of this transformation on the given array of values. ``transform(values)`` is always equivalent to ``transform_affine(transform_non_affine(values))``. In non-affine transformations, this is generally a no-op. In affine transformations, this is equivalent to ``transform(values)``. Accepts a numpy array of shape (N x :attr:`input_dims`) and returns a numpy array of shape (N x :attr:`output_dims`). """ return values def transform_non_affine(self, values): """ Performs only the non-affine part of the transformation. ``transform(values)`` is always equivalent to ``transform_affine(transform_non_affine(values))``. In non-affine transformations, this is generally equivalent to ``transform(values)``. In affine transformations, this is always a no-op. Accepts a numpy array of shape (N x :attr:`input_dims`) and returns a numpy array of shape (N x :attr:`output_dims`). """ return self.transform(values) def get_affine(self): """ Get the affine part of this transform. """ return IdentityTransform() def transform_point(self, point): """ A convenience function that returns the transformed copy of a single point. The point is given as a sequence of length :attr:`input_dims`. The transformed point is returned as a sequence of length :attr:`output_dims`. """ assert len(point) == self.input_dims return self.transform(np.asarray([point]))[0] def transform_path(self, path): """ Returns a transformed copy of path. path: a :class:`~matplotlib.path.Path` instance. In some cases, this transform may insert curves into the path that began as line segments. """ return Path(self.transform(path.vertices), path.codes) def transform_path_affine(self, path): """ Returns a copy of path, transformed only by the affine part of this transform. path: a :class:`~matplotlib.path.Path` instance. ``transform_path(path)`` is equivalent to ``transform_path_affine(transform_path_non_affine(values))``. """ return path def transform_path_non_affine(self, path): """ Returns a copy of path, transformed only by the non-affine part of this transform. path: a :class:`~matplotlib.path.Path` instance. ``transform_path(path)`` is equivalent to ``transform_path_affine(transform_path_non_affine(values))``. """ return Path(self.transform_non_affine(path.vertices), path.codes) def transform_angles(self, angles, pts, radians=False, pushoff=1e-5): """ Performs transformation on a set of angles anchored at specific locations. The angles* must be a column vector (i.e., numpy array). The pts must be a two-column numpy array of x,y positions (angle transforms currently only work in 2D). This array must have the same number of rows as angles. radians indicates whether or not input angles are given in radians (True) or degrees (False; the default). pushoff is the distance to move away from pts for determining transformed angles (see discussion of method below). The transformed angles are returned in an array with the same size as angles. The generic version of this method uses a very generic algorithm that transforms pts, as well as locations very close to pts, to find the angle in the transformed system. """ # Must be 2D if self.input_dims <> 2 or self.output_dims <> 2: raise NotImplementedError('Only defined in 2D') # pts must be array with 2 columns for x,y assert pts.shape[1] == 2 # angles must be a column vector and have same number of # rows as pts assert np.prod(angles.shape) == angles.shape[0] == pts.shape[0] # Convert to radians if desired if not radians: angles = angles / 180.0 * np.pi # Move a short distance away pts2 = pts + pushoff * np.c_[ np.cos(angles), np.sin(angles) ] # Transform both sets of points tpts = self.transform( pts ) tpts2 = self.transform( pts2 ) # Calculate transformed angles d = tpts2 - tpts a = np.arctan2( d[:,1], d[:,0] ) # Convert back to degrees if desired if not radians: a = a * 180.0 / np.pi return a def inverted(self): """ Return the corresponding inverse transformation. The return value of this method should be treated as temporary. An update to self does not cause a corresponding update to its inverted copy. ``x === self.inverted().transform(self.transform(x))`` """ raise NotImplementedError() class TransformWrapper(Transform): """ A helper class that holds a single child transform and acts equivalently to it. This is useful if a node of the transform tree must be replaced at run time with a transform of a different type. This class allows that replacement to correctly trigger invalidation. Note that :class:`TransformWrapper` instances must have the same input and output dimensions during their entire lifetime, so the child transform may only be replaced with another child transform of the same dimensions. """ pass_through = True is_affine = False def __init__(self, child): """ child: A class:`Transform` instance. This child may later be replaced with :meth:`set`. """ assert isinstance(child, Transform) Transform.__init__(self) self.input_dims = child.input_dims self.output_dims = child.output_dims self._set(child) self._invalid = 0 def __repr__(self): return "TransformWrapper(%r)" % self._child __str__ = __repr__ def frozen(self): return self._child.frozen() frozen.__doc__ = Transform.frozen.__doc__ def _set(self, child): self._child = child self.set_children(child) self.transform = child.transform self.transform_affine = child.transform_affine self.transform_non_affine = child.transform_non_affine self.transform_path = child.transform_path self.transform_path_affine = child.transform_path_affine self.transform_path_non_affine = child.transform_path_non_affine self.get_affine = child.get_affine self.inverted = child.inverted def set(self, child): """ Replace the current child of this transform with another one. The new child must have the same number of input and output dimensions as the current child. """ assert child.input_dims == self.input_dims assert child.output_dims == self.output_dims self._set(child) self._invalid = 0 self.invalidate() self._invalid = 0 def _get_is_separable(self): return self._child.is_separable is_separable = property(_get_is_separable) def _get_has_inverse(self): return self._child.has_inverse has_inverse = property(_get_has_inverse) class AffineBase(Transform): """ The base class of all affine transformations of any number of dimensions. """ is_affine = True def __init__(self): Transform.__init__(self) self._inverted = None def __array__(self, args, kwargs): return self.get_matrix() #@staticmethod def _concat(a, b): """ Concatenates two transformation matrices (represented as numpy arrays) together. """ return np.dot(b, a) _concat = staticmethod(_concat) def get_matrix(self): """ Get the underlying transformation matrix as a numpy array. """ raise NotImplementedError() def transform_non_affine(self, points): return points transform_non_affine.__doc__ = Transform.transform_non_affine.__doc__ def transform_path_affine(self, path): return self.transform_path(path) transform_path_affine.__doc__ = Transform.transform_path_affine.__doc__ def transform_path_non_affine(self, path): return path transform_path_non_affine.__doc__ = Transform.transform_path_non_affine.__doc__ def get_affine(self): return self get_affine.__doc__ = Transform.get_affine.__doc__ class Affine2DBase(AffineBase): """ The base class of all 2D affine transformations. 2D affine transformations are performed using a 3x3 numpy array:: a c e b d f 0 0 1 This class provides the read-only interface. For a mutable 2D affine transformation, use :class:`Affine2D`. Subclasses of this class will generally only need to override a constructor and :meth:`get_matrix` that generates a custom 3x3 matrix. """ input_dims = 2 output_dims = 2 # Redundant: Removed for performance # # def __init__(self): # Affine2DBase.__init__(self) def frozen(self): return Affine2D(self.get_matrix().copy()) frozen.__doc__ = AffineBase.frozen.__doc__ def _get_is_separable(self): mtx = self.get_matrix() return mtx[0, 1] == 0.0 and mtx[1, 0] == 0.0 is_separable = property(_get_is_separable) def __array__(self, args, kwargs): return self.get_matrix() def to_values(self): """ Return the values of the matrix as a sequence (a,b,c,d,e,f) """ mtx = self.get_matrix() return tuple(mtx[:2].swapaxes(0, 1).flatten()) #@staticmethod def matrix_from_values(a, b, c, d, e, f): """ (staticmethod) Create a new transformation matrix as a 3x3 numpy array of the form:: a c e b d f 0 0 1 """ return np.array([[a, c, e], [b, d, f], [0.0, 0.0, 1.0]], np.float_) matrix_from_values = staticmethod(matrix_from_values) def transform(self, points): mtx = self.get_matrix() if isinstance(points, MaskedArray): tpoints = affine_transform(points.data, mtx) return ma.MaskedArray(tpoints, mask=ma.getmask(points)) return affine_transform(points, mtx) def transform_point(self, point): mtx = self.get_matrix() return affine_transform(point, mtx) transform_point.__doc__ = AffineBase.transform_point.__doc__ if DEBUG: _transform = transform def transform(self, points): # The major speed trap here is just converting to the # points to an array in the first place. If we can use # more arrays upstream, that should help here. if (not ma.isMaskedArray(points) and not isinstance(points, np.ndarray)): warnings.warn( ('A non-numpy array of type %s was passed in for ' + 'transformation. Please correct this.') % type(values)) return self._transform(points) transform.__doc__ = AffineBase.transform.__doc__ transform_affine = transform transform_affine.__doc__ = AffineBase.transform_affine.__doc__ def inverted(self): if self._inverted is None or self._invalid: mtx = self.get_matrix() self._inverted = Affine2D(inv(mtx)) self._invalid = 0 return self._inverted inverted.__doc__ = AffineBase.inverted.__doc__ class Affine2D(Affine2DBase): """ A mutable 2D affine transformation. """ def __init__(self, matrix = None): """ Initialize an Affine transform from a 3x3 numpy float array:: a c e b d f 0 0 1 If matrix* is None, initialize with the identity transform. """ Affine2DBase.__init__(self) if matrix is None: matrix = np.identity(3) elif DEBUG: matrix = np.asarray(matrix, np.float_) assert matrix.shape == (3, 3) self._mtx = matrix self._invalid = 0 def __repr__(self): return "Affine2D(%s)" % repr(self._mtx) __str__ = __repr__ def __cmp__(self, other): if (isinstance(other, Affine2D) and (self.get_matrix() == other.get_matrix()).all()): return 0 return -1 #@staticmethod def from_values(a, b, c, d, e, f): """ (staticmethod) Create a new Affine2D instance from the given values:: a c e b d f 0 0 1 """ return Affine2D( np.array([a, c, e, b, d, f, 0.0, 0.0, 1.0], np.float_) .reshape((3,3))) from_values = staticmethod(from_values) def get_matrix(self): """ Get the underlying transformation matrix as a 3x3 numpy array:: a c e b d f 0 0 1 """ self._invalid = 0 return self._mtx def set_matrix(self, mtx): """ Set the underlying transformation matrix from a 3x3 numpy array:: a c e b d f 0 0 1 """ self._mtx = mtx self.invalidate() def set(self, other): """ Set this transformation from the frozen copy of another :class:`Affine2DBase` object. """ assert isinstance(other, Affine2DBase) self._mtx = other.get_matrix() self.invalidate() #@staticmethod def identity(): """ (staticmethod) Return a new :class:`Affine2D` object that is the identity transform. Unless this transform will be mutated later on, consider using the faster :class:`IdentityTransform` class instead. """ return Affine2D(np.identity(3)) identity = staticmethod(identity) def clear(self): """ Reset the underlying matrix to the identity transform. """ self._mtx = np.identity(3) self.invalidate() return self def rotate(self, theta): """ Add a rotation (in radians) to this transform in place. Returns self, so this method can easily be chained with more calls to :meth:`rotate`, :meth:`rotate_deg`, :meth:`translate` and :meth:`scale`. """ a = np.cos(theta) b = np.sin(theta) rotate_mtx = np.array( [[a, -b, 0.0], [b, a, 0.0], [0.0, 0.0, 1.0]], np.float_) self._mtx = np.dot(rotate_mtx, self._mtx) self.invalidate() return self def rotate_deg(self, degrees): """ Add a rotation (in degrees) to this transform in place. Returns self, so this method can easily be chained with more calls to :meth:`rotate`, :meth:`rotate_deg`, :meth:`translate` and :meth:`scale`. """ return self.rotate(degreesnp.pi/180.) def rotate_around(self, x, y, theta): """ Add a rotation (in radians) around the point (x, y) in place. Returns self, so this method can easily be chained with more calls to :meth:`rotate`, :meth:`rotate_deg`, :meth:`translate` and :meth:`scale`. """ return self.translate(-x, -y).rotate(theta).translate(x, y) def rotate_deg_around(self, x, y, degrees): """ Add a rotation (in degrees) around the point (x, y) in place. Returns self, so this method can easily be chained with more calls to :meth:`rotate`, :meth:`rotate_deg`, :meth:`translate` and :meth:`scale`. """ return self.translate(-x, -y).rotate_deg(degrees).translate(x, y) def translate(self, tx, ty): """ Adds a translation in place. Returns self, so this method can easily be chained with more calls to :meth:`rotate`, :meth:`rotate_deg`, :meth:`translate` and :meth:`scale`. """ translate_mtx = np.array( [[1.0, 0.0, tx], [0.0, 1.0, ty], [0.0, 0.0, 1.0]], np.float_) self._mtx = np.dot(translate_mtx, self._mtx) self.invalidate() return self def scale(self, sx, sy=None): """ Adds a scale in place. If sy* is None, the same scale is applied in both the x- and y-directions. Returns self, so this method can easily be chained with more calls to :meth:`rotate`, :meth:`rotate_deg`, :meth:`translate` and :meth:`scale`. """ if sy is None: sy = sx scale_mtx = np.array( [[sx, 0.0, 0.0], [0.0, sy, 0.0], [0.0, 0.0, 1.0]], np.float_) self._mtx = np.dot(scale_mtx, self._mtx) self.invalidate() return self def _get_is_separable(self): mtx = self.get_matrix() return mtx[0, 1] == 0.0 and mtx[1, 0] == 0.0 is_separable = property(_get_is_separable) class IdentityTransform(Affine2DBase): """ A special class that does on thing, the identity transform, in a fast way. """ _mtx = np.identity(3) def frozen(self): return self frozen.__doc__ = Affine2DBase.frozen.__doc__ def __repr__(self): return "IdentityTransform()" __str__ = __repr__ def get_matrix(self): return self._mtx get_matrix.__doc__ = Affine2DBase.get_matrix.__doc__ def transform(self, points): return points transform.__doc__ = Affine2DBase.transform.__doc__ transform_affine = transform transform_affine.__doc__ = Affine2DBase.transform_affine.__doc__ transform_non_affine = transform transform_non_affine.__doc__ = Affine2DBase.transform_non_affine.__doc__ def transform_path(self, path): return path transform_path.__doc__ = Affine2DBase.transform_path.__doc__ transform_path_affine = transform_path transform_path_affine.__doc__ = Affine2DBase.transform_path_affine.__doc__ transform_path_non_affine = transform_path transform_path_non_affine.__doc__ = Affine2DBase.transform_path_non_affine.__doc__ def get_affine(self): return self get_affine.__doc__ = Affine2DBase.get_affine.__doc__ inverted = get_affine inverted.__doc__ = Affine2DBase.inverted.__doc__ class BlendedGenericTransform(Transform): """ A "blended" transform uses one transform for the x-direction, and another transform for the y-direction. This "generic" version can handle any given child transform in the x- and y-directions. """ input_dims = 2 output_dims = 2 is_separable = True pass_through = True def __init__(self, x_transform, y_transform): """ Create a new "blended" transform using x_transform to transform the x-axis and y_transform to transform the y-axis. You will generally not call this constructor directly but use the :func:`blended_transform_factory` function instead, which can determine automatically which kind of blended transform to create. """ # Here we ask: "Does it blend?" Transform.__init__(self) self._x = x_transform self._y = y_transform self.set_children(x_transform, y_transform) self._affine = None def _get_is_affine(self): return self._x.is_affine and self._y.is_affine is_affine = property(_get_is_affine) def frozen(self): return blended_transform_factory(self._x.frozen(), self._y.frozen()) frozen.__doc__ = Transform.frozen.__doc__ def __repr__(self): return "BlendedGenericTransform(%s,%s)" % (self._x, self._y) __str__ = __repr__ def transform(self, points): x = self._x y = self._y if x is y and x.input_dims == 2: return x.transform(points) if x.input_dims == 2: x_points = x.transform(points)[:, 0:1] else: x_points = x.transform(points[:, 0]) x_points = x_points.reshape((len(x_points), 1)) if y.input_dims == 2: y_points = y.transform(points)[:, 1:] else: y_points = y.transform(points[:, 1]) y_points = y_points.reshape((len(y_points), 1)) if isinstance(x_points, MaskedArray) or isinstance(y_points, MaskedArray): return ma.concatenate((x_points, y_points), 1) else: return np.concatenate((x_points, y_points), 1) transform.__doc__ = Transform.transform.__doc__ def transform_affine(self, points): return self.get_affine().transform(points) transform_affine.__doc__ = Transform.transform_affine.__doc__ def transform_non_affine(self, points): if self._x.is_affine and self._y.is_affine: return points return self.transform(points) transform_non_affine.__doc__ = Transform.transform_non_affine.__doc__ def inverted(self): return BlendedGenericTransform(self._x.inverted(), self._y.inverted()) inverted.__doc__ = Transform.inverted.__doc__ def get_affine(self): if self._invalid or self._affine is None: if self._x.is_affine and self._y.is_affine: if self._x == self._y: self._affine = self._x.get_affine() else: x_mtx = self._x.get_affine().get_matrix() y_mtx = self._y.get_affine().get_matrix() # This works because we already know the transforms are # separable, though normally one would want to set b and # c to zero. mtx = np.vstack((x_mtx[0], y_mtx[1], [0.0, 0.0, 1.0])) self._affine = Affine2D(mtx) else: self._affine = IdentityTransform() self._invalid = 0 return self._affine get_affine.__doc__ = Transform.get_affine.__doc__ class BlendedAffine2D(Affine2DBase): """ A "blended" transform uses one transform for the x-direction, and another transform for the y-direction. This version is an optimization for the case where both child transforms are of type :class:`Affine2DBase`. """ is_separable = True def __init__(self, x_transform, y_transform): """ Create a new "blended" transform using x_transform to transform the x-axis and y_transform to transform the y-axis. Both x_transform and y_transform must be 2D affine transforms. You will generally not call this constructor directly but use the :func:`blended_transform_factory` function instead, which can determine automatically which kind of blended transform to create. """ assert x_transform.is_affine assert y_transform.is_affine assert x_transform.is_separable assert y_transform.is_separable Transform.__init__(self) self._x = x_transform self._y = y_transform self.set_children(x_transform, y_transform) Affine2DBase.__init__(self) self._mtx = None def __repr__(self): return "BlendedAffine2D(%s,%s)" % (self._x, self._y) __str__ = __repr__ def get_matrix(self): if self._invalid: if self._x == self._y: self._mtx = self._x.get_matrix() else: x_mtx = self._x.get_matrix() y_mtx = self._y.get_matrix() # This works because we already know the transforms are # separable, though normally one would want to set b and # c to zero. self._mtx = np.vstack((x_mtx[0], y_mtx[1], [0.0, 0.0, 1.0])) self._inverted = None self._invalid = 0 return self._mtx get_matrix.__doc__ = Affine2DBase.get_matrix.__doc__ def blended_transform_factory(x_transform, y_transform): """ Create a new "blended" transform using x_transform to transform the x-axis and y_transform to transform the y-axis. A faster version of the blended transform is returned for the case where both child transforms are affine. """ if (isinstance(x_transform, Affine2DBase) and isinstance(y_transform, Affine2DBase)): return BlendedAffine2D(x_transform, y_transform) return BlendedGenericTransform(x_transform, y_transform) class CompositeGenericTransform(Transform): """ A composite transform formed by applying transform a then transform b. This "generic" version can handle any two arbitrary transformations. """ pass_through = True def __init__(self, a, b): """ Create a new composite transform that is the result of applying transform a then transform b. You will generally not call this constructor directly but use the :func:`composite_transform_factory` function instead, which can automatically choose the best kind of composite transform instance to create. """ assert a.output_dims == b.input_dims self.input_dims = a.input_dims self.output_dims = b.output_dims Transform.__init__(self) self._a = a self._b = b self.set_children(a, b) def frozen(self): self._invalid = 0 frozen = composite_transform_factory(self._a.frozen(), self._b.frozen()) if not isinstance(frozen, CompositeGenericTransform): return frozen.frozen() return frozen frozen.__doc__ = Transform.frozen.__doc__ def _get_is_affine(self): return self._a.is_affine and self._b.is_affine is_affine = property(_get_is_affine) def _get_is_separable(self): return self._a.is_separable and self._b.is_separable is_separable = property(_get_is_separable) def __repr__(self): return "CompositeGenericTransform(%s, %s)" % (self._a, self._b) __str__ = __repr__ def transform(self, points): return self._b.transform( self._a.transform(points)) transform.__doc__ = Transform.transform.__doc__ def transform_affine(self, points): return self.get_affine().transform(points) transform_affine.__doc__ = Transform.transform_affine.__doc__ def transform_non_affine(self, points): if self._a.is_affine and self._b.is_affine: return points return self._b.transform_non_affine( self._a.transform(points)) transform_non_affine.__doc__ = Transform.transform_non_affine.__doc__ def transform_path(self, path): return self._b.transform_path( self._a.transform_path(path)) transform_path.__doc__ = Transform.transform_path.__doc__ def transform_path_affine(self, path): return self._b.transform_path_affine( self._a.transform_path(path)) transform_path_affine.__doc__ = Transform.transform_path_affine.__doc__ def transform_path_non_affine(self, path): if self._a.is_affine and self._b.is_affine: return path return self._b.transform_path_non_affine( self._a.transform_path(path)) transform_path_non_affine.__doc__ = Transform.transform_path_non_affine.__doc__ def get_affine(self): if self._a.is_affine and self._b.is_affine: return Affine2D(np.dot(self._b.get_affine().get_matrix(), self._a.get_affine().get_matrix())) else: return self._b.get_affine() get_affine.__doc__ = Transform.get_affine.__doc__ def inverted(self): return CompositeGenericTransform(self._b.inverted(), self._a.inverted()) inverted.__doc__ = Transform.inverted.__doc__ class CompositeAffine2D(Affine2DBase): """ A composite transform formed by applying transform a then transform b. This version is an optimization that handles the case where both a and b are 2D affines. """ def __init__(self, a, b): """ Create a new composite transform that is the result of applying transform a then transform b. Both a and b must be instances of :class:`Affine2DBase`. You will generally not call this constructor directly but use the :func:`composite_transform_factory` function instead, which can automatically choose the best kind of composite transform instance to create. """ assert a.output_dims == b.input_dims self.input_dims = a.input_dims self.output_dims = b.output_dims assert a.is_affine assert b.is_affine Affine2DBase.__init__(self) self._a = a self._b = b self.set_children(a, b) self._mtx = None def __repr__(self): return "CompositeAffine2D(%s, %s)" % (self._a, self._b) __str__ = __repr__ def get_matrix(self): if self._invalid: self._mtx = np.dot( self._b.get_matrix(), self._a.get_matrix()) self._inverted = None self._invalid = 0 return self._mtx get_matrix.__doc__ = Affine2DBase.get_matrix.__doc__ def composite_transform_factory(a, b): """ Create a new composite transform that is the result of applying transform a then transform b. Shortcut versions of the blended transform are provided for the case where both child transforms are affine, or one or the other is the identity transform. Composite transforms may also be created using the '+' operator, e.g.:: c = a + b """ if isinstance(a, IdentityTransform): return b elif isinstance(b, IdentityTransform): return a elif isinstance(a, AffineBase) and isinstance(b, AffineBase): return CompositeAffine2D(a, b) return CompositeGenericTransform(a, b) class BboxTransform(Affine2DBase): """ :class:`BboxTransform` linearly transforms points from one :class:`Bbox` to another :class:`Bbox`. """ is_separable = True def __init__(self, boxin, boxout): """ Create a new :class:`BboxTransform` that linearly transforms points from boxin to boxout. """ assert boxin.is_bbox assert boxout.is_bbox Affine2DBase.__init__(self) self._boxin = boxin self._boxout = boxout self.set_children(boxin, boxout) self._mtx = None self._inverted = None def __repr__(self): return "BboxTransform(%s, %s)" % (self._boxin, self._boxout) __str__ = __repr__ def get_matrix(self): if self._invalid: inl, inb, inw, inh = self._boxin.bounds outl, outb, outw, outh = self._boxout.bounds x_scale = outw / inw y_scale = outh / inh if DEBUG and (x_scale == 0 or y_scale == 0): raise ValueError("Transforming from or to a singular bounding box.") self._mtx = np.array([[x_scale, 0.0 , (-inlx_scale+outl)], [0.0 , y_scale, (-inby_scale+outb)], [0.0 , 0.0 , 1.0 ]], np.float_) self._inverted = None self._invalid = 0 return self._mtx get_matrix.__doc__ = Affine2DBase.get_matrix.__doc__ class BboxTransformTo(Affine2DBase): """ :class:`BboxTransformTo` is a transformation that linearly transforms points from the unit bounding box to a given :class:`Bbox`. """ is_separable = True def __init__(self, boxout): """ Create a new :class:`BboxTransformTo` that linearly transforms points from the unit bounding box to boxout. """ assert boxout.is_bbox Affine2DBase.__init__(self) self._boxout = boxout self.set_children(boxout) self._mtx = None self._inverted = None def __repr__(self): return "BboxTransformTo(%s)" % (self._boxout) __str__ = __repr__ def get_matrix(self): if self._invalid: outl, outb, outw, outh = self._boxout.bounds if DEBUG and (outw == 0 or outh == 0): raise ValueError("Transforming to a singular bounding box.") self._mtx = np.array([[outw, 0.0, outl], [ 0.0, outh, outb], [ 0.0, 0.0, 1.0]], np.float_) self._inverted = None self._invalid = 0 return self._mtx get_matrix.__doc__ = Affine2DBase.get_matrix.__doc__ class BboxTransformFrom(Affine2DBase): """ :class:`BboxTransformFrom` linearly transforms points from a given :class:`Bbox` to the unit bounding box. """ is_separable = True def __init__(self, boxin): assert boxin.is_bbox Affine2DBase.__init__(self) self._boxin = boxin self.set_children(boxin) self._mtx = None self._inverted = None def __repr__(self): return "BboxTransformFrom(%s)" % (self._boxin) __str__ = __repr__ def get_matrix(self): if self._invalid: inl, inb, inw, inh = self._boxin.bounds if DEBUG and (inw == 0 or inh == 0): raise ValueError("Transforming from a singular bounding box.") x_scale = 1.0 / inw y_scale = 1.0 / inh self._mtx = np.array([[x_scale, 0.0 , (-inlx_scale)], [0.0 , y_scale, (-inby_scale)], [0.0 , 0.0 , 1.0 ]], np.float_) self._inverted = None self._invalid = 0 return self._mtx get_matrix.__doc__ = Affine2DBase.get_matrix.__doc__ class ScaledTranslation(Affine2DBase): """ A transformation that translates by xt and yt, after xt and yt have been transformad by the given transform scale_trans. """ def __init__(self, xt, yt, scale_trans): Affine2DBase.__init__(self) self._t = (xt, yt) self._scale_trans = scale_trans self.set_children(scale_trans) self._mtx = None self._inverted = None def __repr__(self): return "ScaledTranslation(%s)" % (self._t,) __str__ = __repr__ def get_matrix(self): if self._invalid: xt, yt = self._scale_trans.transform_point(self._t) self._mtx = np.array([[1.0, 0.0, xt], [0.0, 1.0, yt], [0.0, 0.0, 1.0]], np.float_) self._invalid = 0 self._inverted = None return self._mtx get_matrix.__doc__ = Affine2DBase.get_matrix.__doc__ class TransformedPath(TransformNode): """ A :class:`TransformedPath` caches a non-affine transformed copy of the :class:`~matplotlib.path.Path`. This cached copy is automatically updated when the non-affine part of the transform changes. """ def __init__(self, path, transform): """ Create a new :class:`TransformedPath` from the given :class:`~matplotlib.path.Path` and :class:`Transform`. """ assert isinstance(transform, Transform) TransformNode.__init__(self) self._path = path self._transform = transform self.set_children(transform) self._transformed_path = None self._transformed_points = None def _revalidate(self): if ((self._invalid & self.INVALID_NON_AFFINE == self.INVALID_NON_AFFINE) or self._transformed_path is None): self._transformed_path = \ self._transform.transform_path_non_affine(self._path) self._transformed_points = \ Path(self._transform.transform_non_affine(self._path.vertices)) self._invalid = 0 def get_transformed_points_and_affine(self): """ Return a copy of the child path, with the non-affine part of the transform already applied, along with the affine part of the path necessary to complete the transformation. Unlike :meth:`get_transformed_path_and_affine`, no interpolation will be performed. """ self._revalidate() return self._transformed_points, self.get_affine() def get_transformed_path_and_affine(self): """ Return a copy of the child path, with the non-affine part of the transform already applied, along with the affine part of the path necessary to complete the transformation. """ self._revalidate() return self._transformed_path, self.get_affine() def get_fully_transformed_path(self): """ Return a fully-transformed copy of the child path. """ if ((self._invalid & self.INVALID_NON_AFFINE == self.INVALID_NON_AFFINE) or self._transformed_path is None): self._transformed_path = \ self._transform.transform_path_non_affine(self._path) self._invalid = 0 return self._transform.transform_path_affine(self._transformed_path) def get_affine(self): return self._transform.get_affine() def nonsingular(vmin, vmax, expander=0.001, tiny=1e-15, increasing=True): ''' Ensure the endpoints of a range are finite and not too close together. "too close" means the interval is smaller than 'tiny' times the maximum absolute value. If they are too close, each will be moved by the 'expander'. If 'increasing' is True and vmin > vmax, they will be swapped, regardless of whether they are too close. If either is inf or -inf or nan, return - expander, expander. ''' if (not np.isfinite(vmin)) or (not np.isfinite(vmax)): return -expander, expander swapped = False if vmax < vmin: vmin, vmax = vmax, vmin swapped = True if vmax - vmin <= max(abs(vmin), abs(vmax)) * tiny: if vmin == 0.0: vmin = -expander vmax = expander else: vmin -= expanderabs(vmin) vmax += expanderabs(vmax) if swapped and not increasing: vmin, vmax = vmax, vmin return vmin, vmax def interval_contains(interval, val): a, b = interval return ( ((a < b) and (a <= val and b >= val)) or (b <= val and a >= val)) def interval_contains_open(interval, val): a, b = interval return ( ((a < b) and (a < val and b > val)) or (b < val and a > val)) def offset_copy(trans, fig, x=0.0, y=0.0, units='inches'): ''' Return a new transform with an added offset. args: trans is any transform kwargs: fig is the current figure; it can be None if units are 'dots' x, y give the offset units is 'inches', 'points' or 'dots' ''' if units == 'dots': return trans + Affine2D().translate(x, y) if fig is None: raise ValueError('For units of inches or points a fig kwarg is needed') if units == 'points': x /= 72.0 y /= 72.0 elif not units == 'inches': raise ValueError('units must be dots, points, or inches') return trans + ScaledTranslation(x, y, fig.dpi_scale_trans)	75,638	Python	.py	1,895	30.928232	97	0.584582	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,238	legend.py	numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/legend.py	""" Place a legend on the axes at location loc. Labels are a sequence of strings and loc can be a string or an integer specifying the legend location The location codes are 'best' : 0, (only implemented for axis legends) 'upper right' : 1, 'upper left' : 2, 'lower left' : 3, 'lower right' : 4, 'right' : 5, 'center left' : 6, 'center right' : 7, 'lower center' : 8, 'upper center' : 9, 'center' : 10, Return value is a sequence of text, line instances that make up the legend """ from __future__ import division import warnings import numpy as np from matplotlib import rcParams from matplotlib.artist import Artist from matplotlib.cbook import is_string_like, iterable, silent_list, safezip from matplotlib.font_manager import FontProperties from matplotlib.lines import Line2D from matplotlib.patches import Patch, Rectangle, Shadow, FancyBboxPatch from matplotlib.collections import LineCollection, RegularPolyCollection from matplotlib.transforms import Bbox from matplotlib.offsetbox import HPacker, VPacker, PackerBase, TextArea, DrawingArea class Legend(Artist): """ Place a legend on the axes at location loc. Labels are a sequence of strings and loc can be a string or an integer specifying the legend location The location codes are:: 'best' : 0, (only implemented for axis legends) 'upper right' : 1, 'upper left' : 2, 'lower left' : 3, 'lower right' : 4, 'right' : 5, 'center left' : 6, 'center right' : 7, 'lower center' : 8, 'upper center' : 9, 'center' : 10, loc can be a tuple of the noramilzed coordinate values with respect its parent. Return value is a sequence of text, line instances that make up the legend """ codes = {'best' : 0, # only implemented for axis legends 'upper right' : 1, 'upper left' : 2, 'lower left' : 3, 'lower right' : 4, 'right' : 5, 'center left' : 6, 'center right' : 7, 'lower center' : 8, 'upper center' : 9, 'center' : 10, } zorder = 5 def __str__(self): return "Legend" def __init__(self, parent, handles, labels, loc = None, numpoints = None, # the number of points in the legend line markerscale = None, # the relative size of legend markers vs. original scatterpoints = 3, # TODO: may be an rcParam scatteryoffsets=None, prop = None, # properties for the legend texts # the following dimensions are in axes coords pad = None, # deprecated; use borderpad labelsep = None, # deprecated; use labelspacing handlelen = None, # deprecated; use handlelength handletextsep = None, # deprecated; use handletextpad axespad = None, # deprecated; use borderaxespad # spacing & pad defined as a fractionof the font-size borderpad = None, # the whitespace inside the legend border labelspacing=None, #the vertical space between the legend entries handlelength=None, # the length of the legend handles handletextpad=None, # the pad between the legend handle and text borderaxespad=None, # the pad between the axes and legend border columnspacing=None, # spacing between columns ncol=1, # number of columns mode=None, # mode for horizontal distribution of columns. None, "expand" fancybox=None, # True use a fancy box, false use a rounded box, none use rc shadow = None, ): """ - parent : the artist that contains the legend - handles : a list of artists (lines, patches) to add to the legend - labels : a list of strings to label the legend Optional keyword arguments: ================ ================================================================== Keyword Description ================ ================================================================== loc a location code or a tuple of coordinates numpoints the number of points in the legend line prop the font property markerscale the relative size of legend markers vs. original fancybox if True, draw a frame with a round fancybox. If None, use rc shadow if True, draw a shadow behind legend scatteryoffsets a list of yoffsets for scatter symbols in legend borderpad the fractional whitespace inside the legend border labelspacing the vertical space between the legend entries handlelength the length of the legend handles handletextpad the pad between the legend handle and text borderaxespad the pad between the axes and legend border columnspacing the spacing between columns ================ ================================================================== The dimensions of pad and spacing are given as a fraction of the fontsize. Values from rcParams will be used if None. """ from matplotlib.axes import Axes # local import only to avoid circularity from matplotlib.figure import Figure # local import only to avoid circularity Artist.__init__(self) if prop is None: self.prop=FontProperties(size=rcParams["legend.fontsize"]) else: self.prop=prop self.fontsize = self.prop.get_size_in_points() propnames=['numpoints', 'markerscale', 'shadow', "columnspacing", "scatterpoints"] localdict = locals() for name in propnames: if localdict[name] is None: value = rcParams["legend."+name] else: value = localdict[name] setattr(self, name, value) # Take care the deprecated keywords deprecated_kwds = {"pad":"borderpad", "labelsep":"labelspacing", "handlelen":"handlelength", "handletextsep":"handletextpad", "axespad":"borderaxespad"} # convert values of deprecated keywords (ginve in axes coords) # to new vaules in a fraction of the font size # conversion factor bbox = parent.bbox axessize_fontsize = min(bbox.width, bbox.height)/self.fontsize for k, v in deprecated_kwds.items(): # use deprecated value if not None and if their newer # counter part is None. if localdict[k] is not None and localdict[v] is None: warnings.warn("Use '%s' instead of '%s'." % (v, k), DeprecationWarning) setattr(self, v, localdict[k]axessize_fontsize) continue # Otherwise, use new keywords if localdict[v] is None: setattr(self, v, rcParams["legend."+v]) else: setattr(self, v, localdict[v]) del localdict self._ncol = ncol if self.numpoints <= 0: raise ValueError("numpoints must be >= 0; it was %d"% numpoints) # introduce y-offset for handles of the scatter plot if scatteryoffsets is None: self._scatteryoffsets = np.array([3./8., 4./8., 2.5/8.]) else: self._scatteryoffsets = np.asarray(scatteryoffsets) reps = int(self.numpoints / len(self._scatteryoffsets)) + 1 self._scatteryoffsets = np.tile(self._scatteryoffsets, reps)[:self.scatterpoints] # _legend_box is an OffsetBox instance that contains all # legend items and will be initialized from _init_legend_box() # method. self._legend_box = None if isinstance(parent,Axes): self.isaxes = True self.set_figure(parent.figure) elif isinstance(parent,Figure): self.isaxes = False self.set_figure(parent) else: raise TypeError("Legend needs either Axes or Figure as parent") self.parent = parent if loc is None: loc = rcParams["legend.loc"] if not self.isaxes and loc in [0,'best']: loc = 'upper right' if is_string_like(loc): if loc not in self.codes: if self.isaxes: warnings.warn('Unrecognized location "%s". Falling back on "best"; ' 'valid locations are\n\t%s\n' % (loc, '\n\t'.join(self.codes.keys()))) loc = 0 else: warnings.warn('Unrecognized location "%s". Falling back on "upper right"; ' 'valid locations are\n\t%s\n' % (loc, '\n\t'.join(self.codes.keys()))) loc = 1 else: loc = self.codes[loc] if not self.isaxes and loc == 0: warnings.warn('Automatic legend placement (loc="best") not implemented for figure legend. ' 'Falling back on "upper right".') loc = 1 self._loc = loc self._mode = mode # We use FancyBboxPatch to draw a legend frame. The location # and size of the box will be updated during the drawing time. self.legendPatch = FancyBboxPatch( xy=(0.0, 0.0), width=1., height=1., facecolor='w', edgecolor='k', mutation_scale=self.fontsize, snap=True ) # The width and height of the legendPatch will be set (in the # draw()) to the length that includes the padding. Thus we set # pad=0 here. if fancybox is None: fancybox = rcParams["legend.fancybox"] if fancybox == True: self.legendPatch.set_boxstyle("round",pad=0, rounding_size=0.2) else: self.legendPatch.set_boxstyle("square",pad=0) self._set_artist_props(self.legendPatch) self._drawFrame = True # init with null renderer self._init_legend_box(handles, labels) self._last_fontsize_points = self.fontsize def _set_artist_props(self, a): """ set the boilerplate props for artists added to axes """ a.set_figure(self.figure) for c in self.get_children(): c.set_figure(self.figure) a.set_transform(self.get_transform()) def _findoffset_best(self, width, height, xdescent, ydescent, renderer): "Heper function to locate the legend at its best position" ox, oy = self._find_best_position(width, height, renderer) return ox+xdescent, oy+ydescent def _findoffset_loc(self, width, height, xdescent, ydescent, renderer): "Heper function to locate the legend using the location code" if iterable(self._loc) and len(self._loc)==2: # when loc is a tuple of axes(or figure) coordinates. fx, fy = self._loc bbox = self.parent.bbox x, y = bbox.x0 + bbox.width fx, bbox.y0 + bbox.height * fy else: bbox = Bbox.from_bounds(0, 0, width, height) x, y = self._get_anchored_bbox(self._loc, bbox, self.parent.bbox, renderer) return x+xdescent, y+ydescent def draw(self, renderer): "Draw everything that belongs to the legend" if not self.get_visible(): return self._update_legend_box(renderer) renderer.open_group('legend') # find_offset function will be provided to _legend_box and # _legend_box will draw itself at the location of the return # value of the find_offset. if self._loc == 0: _findoffset = self._findoffset_best else: _findoffset = self._findoffset_loc def findoffset(width, height, xdescent, ydescent): return _findoffset(width, height, xdescent, ydescent, renderer) self._legend_box.set_offset(findoffset) fontsize = renderer.points_to_pixels(self.fontsize) # if mode == fill, set the width of the legend_box to the # width of the paret (minus pads) if self._mode in ["expand"]: pad = 2(self.borderaxespad+self.borderpad)fontsize self._legend_box.set_width(self.parent.bbox.width-pad) if self._drawFrame: # update the location and size of the legend bbox = self._legend_box.get_window_extent(renderer) self.legendPatch.set_bounds(bbox.x0, bbox.y0, bbox.width, bbox.height) self.legendPatch.set_mutation_scale(fontsize) if self.shadow: shadow = Shadow(self.legendPatch, 2, -2) shadow.draw(renderer) self.legendPatch.draw(renderer) self._legend_box.draw(renderer) renderer.close_group('legend') def _approx_text_height(self, renderer=None): """ Return the approximate height of the text. This is used to place the legend handle. """ if renderer is None: return self.fontsize else: return renderer.points_to_pixels(self.fontsize) def _init_legend_box(self, handles, labels): """ Initiallize the legend_box. The legend_box is an instance of the OffsetBox, which is packed with legend handles and texts. Once packed, their location is calculated during the drawing time. """ fontsize = self.fontsize # legend_box is a HPacker, horizontally packed with # columns. Each column is a VPacker, vertically packed with # legend items. Each legend item is HPacker packed with # legend handleBox and labelBox. handleBox is an instance of # offsetbox.DrawingArea which contains legend handle. labelBox # is an instance of offsetbox.TextArea which contains legend # text. text_list = [] # the list of text instances handle_list = [] # the list of text instances label_prop = dict(verticalalignment='baseline', horizontalalignment='left', fontproperties=self.prop, ) labelboxes = [] for l in labels: textbox = TextArea(l, textprops=label_prop, multilinebaseline=True, minimumdescent=True) text_list.append(textbox._text) labelboxes.append(textbox) handleboxes = [] # The approximate height and descent of text. These values are # only used for plotting the legend handle. height = self._approx_text_height() * 0.7 descent = 0. # each handle needs to be drawn inside a box of (x, y, w, h) = # (0, -descent, width, height). And their corrdinates should # be given in the display coordinates. # NOTE : the coordinates will be updated again in # _update_legend_box() method. # The transformation of each handle will be automatically set # to self.get_trasnform(). If the artist does not uses its # default trasnform (eg, Collections), you need to # manually set their transform to the self.get_transform(). for handle in handles: if isinstance(handle, RegularPolyCollection): npoints = self.scatterpoints else: npoints = self.numpoints if npoints > 1: # we put some pad here to compensate the size of the # marker xdata = np.linspace(0.3fontsize, (self.handlelength-0.3)fontsize, npoints) xdata_marker = xdata elif npoints == 1: xdata = np.linspace(0, self.handlelengthfontsize, 2) xdata_marker = [0.5self.handlelengthfontsize] if isinstance(handle, Line2D): ydata = ((height-descent)/2.)np.ones(xdata.shape, float) legline = Line2D(xdata, ydata) legline.update_from(handle) self._set_artist_props(legline) # after update legline.set_clip_box(None) legline.set_clip_path(None) legline.set_drawstyle('default') legline.set_marker('None') handle_list.append(legline) legline_marker = Line2D(xdata_marker, ydata[:len(xdata_marker)]) legline_marker.update_from(handle) self._set_artist_props(legline_marker) legline_marker.set_clip_box(None) legline_marker.set_clip_path(None) legline_marker.set_linestyle('None') # we don't want to add this to the return list because # the texts and handles are assumed to be in one-to-one # correpondence. legline._legmarker = legline_marker elif isinstance(handle, Patch): p = Rectangle(xy=(0., 0.), width = self.handlelengthfontsize, height=(height-descent), ) p.update_from(handle) self._set_artist_props(p) p.set_clip_box(None) p.set_clip_path(None) handle_list.append(p) elif isinstance(handle, LineCollection): ydata = ((height-descent)/2.)np.ones(xdata.shape, float) legline = Line2D(xdata, ydata) self._set_artist_props(legline) legline.set_clip_box(None) legline.set_clip_path(None) lw = handle.get_linewidth()[0] dashes = handle.get_dashes()[0] color = handle.get_colors()[0] legline.set_color(color) legline.set_linewidth(lw) legline.set_dashes(dashes) handle_list.append(legline) elif isinstance(handle, RegularPolyCollection): #ydata = self._scatteryoffsets ydata = heightself._scatteryoffsets size_max, size_min = max(handle.get_sizes()),\ min(handle.get_sizes()) # we may need to scale these sizes by "markerscale" # attribute. But other handle types does not seem # to care about this attribute and it is currently ignored. if self.scatterpoints < 4: sizes = [.5(size_max+size_min), size_max, size_min] else: sizes = (size_max-size_min)np.linspace(0,1,self.scatterpoints)+size_min p = type(handle)(handle.get_numsides(), rotation=handle.get_rotation(), sizes=sizes, offsets=zip(xdata_marker,ydata), transOffset=self.get_transform(), ) p.update_from(handle) p.set_figure(self.figure) p.set_clip_box(None) p.set_clip_path(None) handle_list.append(p) else: handle_list.append(None) handlebox = DrawingArea(width=self.handlelengthfontsize, height=height, xdescent=0., ydescent=descent) handle = handle_list[-1] handlebox.add_artist(handle) if hasattr(handle, "_legmarker"): handlebox.add_artist(handle._legmarker) handleboxes.append(handlebox) # We calculate number of lows in each column. The first # (num_largecol) columns will have (nrows+1) rows, and remaing # (num_smallcol) columns will have (nrows) rows. nrows, num_largecol = divmod(len(handleboxes), self._ncol) num_smallcol = self._ncol-num_largecol # starting index of each column and number of rows in it. largecol = safezip(range(0, num_largecol(nrows+1), (nrows+1)), [nrows+1] num_largecol) smallcol = safezip(range(num_largecol(nrows+1), len(handleboxes), nrows), [nrows] num_smallcol) handle_label = safezip(handleboxes, labelboxes) columnbox = [] for i0, di in largecol+smallcol: # pack handleBox and labelBox into itemBox itemBoxes = [HPacker(pad=0, sep=self.handletextpadfontsize, children=[h, t], align="baseline") for h, t in handle_label[i0:i0+di]] # minimumdescent=False for the text of the last row of the column itemBoxes[-1].get_children()[1].set_minimumdescent(False) # pack columnBox columnbox.append(VPacker(pad=0, sep=self.labelspacingfontsize, align="baseline", children=itemBoxes)) if self._mode == "expand": mode = "expand" else: mode = "fixed" sep = self.columnspacingfontsize self._legend_box = HPacker(pad=self.borderpadfontsize, sep=sep, align="baseline", mode=mode, children=columnbox) self._legend_box.set_figure(self.figure) self.texts = text_list self.legendHandles = handle_list def _update_legend_box(self, renderer): """ Update the dimension of the legend_box. This is required becuase the paddings, the hadle size etc. depends on the dpi of the renderer. """ # fontsize in points. fontsize = renderer.points_to_pixels(self.fontsize) if self._last_fontsize_points == fontsize: # no update is needed return # each handle needs to be drawn inside a box of # (x, y, w, h) = (0, -descent, width, height). # And their corrdinates should be given in the display coordinates. # The approximate height and descent of text. These values are # only used for plotting the legend handle. height = self._approx_text_height(renderer) * 0.7 descent = 0. for handle in self.legendHandles: if isinstance(handle, RegularPolyCollection): npoints = self.scatterpoints else: npoints = self.numpoints if npoints > 1: # we put some pad here to compensate the size of the # marker xdata = np.linspace(0.3fontsize, (self.handlelength-0.3)fontsize, npoints) xdata_marker = xdata elif npoints == 1: xdata = np.linspace(0, self.handlelengthfontsize, 2) xdata_marker = [0.5self.handlelengthfontsize] if isinstance(handle, Line2D): legline = handle ydata = ((height-descent)/2.)np.ones(xdata.shape, float) legline.set_data(xdata, ydata) legline_marker = legline._legmarker legline_marker.set_data(xdata_marker, ydata[:len(xdata_marker)]) elif isinstance(handle, Patch): p = handle p.set_bounds(0., 0., self.handlelengthfontsize, (height-descent), ) elif isinstance(handle, RegularPolyCollection): p = handle ydata = heightself._scatteryoffsets p.set_offsets(zip(xdata_marker,ydata)) # correction factor cor = fontsize / self._last_fontsize_points # helper function to iterate over all children def all_children(parent): yield parent for c in parent.get_children(): for cc in all_children(c): yield cc #now update paddings for box in all_children(self._legend_box): if isinstance(box, PackerBase): box.pad = box.pad * cor box.sep = box.sep * cor elif isinstance(box, DrawingArea): box.width = self.handlelengthfontsize box.height = height box.xdescent = 0. box.ydescent=descent self._last_fontsize_points = fontsize def _auto_legend_data(self): """ Returns list of vertices and extents covered by the plot. Returns a two long list. First element is a list of (x, y) vertices (in display-coordinates) covered by all the lines and line collections, in the legend's handles. Second element is a list of bounding boxes for all the patches in the legend's handles. """ assert self.isaxes # should always hold because function is only called internally ax = self.parent vertices = [] bboxes = [] lines = [] for handle in ax.lines: assert isinstance(handle, Line2D) path = handle.get_path() trans = handle.get_transform() tpath = trans.transform_path(path) lines.append(tpath) for handle in ax.patches: assert isinstance(handle, Patch) if isinstance(handle, Rectangle): transform = handle.get_data_transform() bboxes.append(handle.get_bbox().transformed(transform)) else: transform = handle.get_transform() bboxes.append(handle.get_path().get_extents(transform)) return [vertices, bboxes, lines] def draw_frame(self, b): 'b is a boolean. Set draw frame to b' self._drawFrame = b def get_children(self): 'return a list of child artists' children = [] if self._legend_box: children.append(self._legend_box) return children def get_frame(self): 'return the Rectangle instance used to frame the legend' return self.legendPatch def get_lines(self): 'return a list of lines.Line2D instances in the legend' return [h for h in self.legendHandles if isinstance(h, Line2D)] def get_patches(self): 'return a list of patch instances in the legend' return silent_list('Patch', [h for h in self.legendHandles if isinstance(h, Patch)]) def get_texts(self): 'return a list of text.Text instance in the legend' return silent_list('Text', self.texts) def get_window_extent(self): 'return a extent of the the legend' return self.legendPatch.get_window_extent() def _get_anchored_bbox(self, loc, bbox, parentbbox, renderer): """ Place the bbox* inside the parentbbox according to a given location code. Return the (x,y) coordinate of the bbox. - loc: a location code in range(1, 11). This corresponds to the possible values for self._loc, excluding "best". - bbox: bbox to be placed, display coodinate units. - parentbbox: a parent box which will contain the bbox. In display coordinates. """ assert loc in range(1,11) # called only internally BEST, UR, UL, LL, LR, R, CL, CR, LC, UC, C = range(11) anchor_coefs={UR:"NE", UL:"NW", LL:"SW", LR:"SE", R:"E", CL:"W", CR:"E", LC:"S", UC:"N", C:"C"} c = anchor_coefs[loc] fontsize = renderer.points_to_pixels(self.fontsize) container = parentbbox.padded(-(self.borderaxespad) * fontsize) anchored_box = bbox.anchored(c, container=container) return anchored_box.x0, anchored_box.y0 def _find_best_position(self, width, height, renderer, consider=None): """ Determine the best location to place the legend. `consider` is a list of (x, y) pairs to consider as a potential lower-left corner of the legend. All are display coords. """ assert self.isaxes # should always hold because function is only called internally verts, bboxes, lines = self._auto_legend_data() bbox = Bbox.from_bounds(0, 0, width, height) consider = [self._get_anchored_bbox(x, bbox, self.parent.bbox, renderer) for x in range(1, len(self.codes))] #tx, ty = self.legendPatch.get_x(), self.legendPatch.get_y() candidates = [] for l, b in consider: legendBox = Bbox.from_bounds(l, b, width, height) badness = 0 badness = legendBox.count_contains(verts) badness += legendBox.count_overlaps(bboxes) for line in lines: if line.intersects_bbox(legendBox): badness += 1 ox, oy = l, b if badness == 0: return ox, oy candidates.append((badness, (l, b))) # rather than use min() or list.sort(), do this so that we are assured # that in the case of two equal badnesses, the one first considered is # returned. # NOTE: list.sort() is stable.But leave as it is for now. -JJL minCandidate = candidates[0] for candidate in candidates: if candidate[0] < minCandidate[0]: minCandidate = candidate ox, oy = minCandidate[1] return ox, oy	30,705	Python	.py	647	34.256569	116	0.56115	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,239	mlab.py	numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/mlab.py	""" Numerical python functions written for compatability with matlab(TM) commands with the same names. Matlab(TM) compatible functions ------------------------------- :func:`cohere` Coherence (normalized cross spectral density) :func:`csd` Cross spectral density uing Welch's average periodogram :func:`detrend` Remove the mean or best fit line from an array :func:`find` Return the indices where some condition is true; numpy.nonzero is similar but more general. :func:`griddata` interpolate irregularly distributed data to a regular grid. :func:`prctile` find the percentiles of a sequence :func:`prepca` Principal Component Analysis :func:`psd` Power spectral density uing Welch's average periodogram :func:`rk4` A 4th order runge kutta integrator for 1D or ND systems :func:`specgram` Spectrogram (power spectral density over segments of time) Miscellaneous functions ------------------------- Functions that don't exist in matlab(TM), but are useful anyway: :meth:`cohere_pairs` Coherence over all pairs. This is not a matlab function, but we compute coherence a lot in my lab, and we compute it for a lot of pairs. This function is optimized to do this efficiently by caching the direct FFTs. :meth:`rk4` A 4th order Runge-Kutta ODE integrator in case you ever find yourself stranded without scipy (and the far superior scipy.integrate tools) record array helper functions ------------------------------- A collection of helper methods for numpyrecord arrays .. _htmlonly:: See :ref:`misc-examples-index` :meth:`rec2txt` pretty print a record array :meth:`rec2csv` store record array in CSV file :meth:`csv2rec` import record array from CSV file with type inspection :meth:`rec_append_fields` adds field(s)/array(s) to record array :meth:`rec_drop_fields` drop fields from record array :meth:`rec_join` join two record arrays on sequence of fields :meth:`rec_groupby` summarize data by groups (similar to SQL GROUP BY) :meth:`rec_summarize` helper code to filter rec array fields into new fields For the rec viewer functions(e rec2csv), there are a bunch of Format objects you can pass into the functions that will do things like color negative values red, set percent formatting and scaling, etc. Example usage:: r = csv2rec('somefile.csv', checkrows=0) formatd = dict( weight = FormatFloat(2), change = FormatPercent(2), cost = FormatThousands(2), ) rec2excel(r, 'test.xls', formatd=formatd) rec2csv(r, 'test.csv', formatd=formatd) scroll = rec2gtk(r, formatd=formatd) win = gtk.Window() win.set_size_request(600,800) win.add(scroll) win.show_all() gtk.main() Deprecated functions --------------------- The following are deprecated; please import directly from numpy (with care--function signatures may differ): :meth:`conv` convolution (numpy.convolve) :meth:`corrcoef` The matrix of correlation coefficients :meth:`hist` Histogram (numpy.histogram) :meth:`linspace` Linear spaced array from min to max :meth:`load` load ASCII file - use numpy.loadtxt :meth:`meshgrid` Make a 2D grid from 2 1 arrays (numpy.meshgrid) :meth:`polyfit` least squares best polynomial fit of x to y (numpy.polyfit) :meth:`polyval` evaluate a vector for a vector of polynomial coeffs (numpy.polyval) :meth:`save` save ASCII file - use numpy.savetxt :meth:`trapz` trapeziodal integration (trapz(x,y) -> numpy.trapz(y,x)) :meth:`vander` the Vandermonde matrix (numpy.vander) """ from __future__ import division import csv, warnings, copy, os import numpy as np ma = np.ma from matplotlib import verbose import matplotlib.nxutils as nxutils import matplotlib.cbook as cbook # set is a new builtin function in 2.4; delete the following when # support for 2.3 is dropped. try: set except NameError: from sets import Set as set def linspace(args, kw): warnings.warn("use numpy.linspace", DeprecationWarning) return np.linspace(args, *kw) def meshgrid(x,y): warnings.warn("use numpy.meshgrid", DeprecationWarning) return np.meshgrid(x,y) def mean(x, dim=None): warnings.warn("Use numpy.mean(x) or x.mean()", DeprecationWarning) if len(x)==0: return None return np.mean(x, axis=dim) def logspace(xmin,xmax,N): return np.exp(np.linspace(np.log(xmin), np.log(xmax), N)) def _norm(x): "return sqrt(x dot x)" return np.sqrt(np.dot(x,x)) def window_hanning(x): "return x times the hanning window of len(x)" return np.hanning(len(x))x def window_none(x): "No window function; simply return x" return x #from numpy import convolve as conv def conv(x, y, mode=2): 'convolve x with y' warnings.warn("Use numpy.convolve(x, y, mode='full')", DeprecationWarning) return np.convolve(x,y,mode) def detrend(x, key=None): if key is None or key=='constant': return detrend_mean(x) elif key=='linear': return detrend_linear(x) def demean(x, axis=0): "Return x minus its mean along the specified axis" x = np.asarray(x) if axis: ind = [slice(None)] * axis ind.append(np.newaxis) return x - x.mean(axis)[ind] return x - x.mean(axis) def detrend_mean(x): "Return x minus the mean(x)" return x - x.mean() def detrend_none(x): "Return x: no detrending" return x def detrend_linear(y): "Return y minus best fit line; 'linear' detrending " # This is faster than an algorithm based on linalg.lstsq. x = np.arange(len(y), dtype=np.float_) C = np.cov(x, y, bias=1) b = C[0,1]/C[0,0] a = y.mean() - bx.mean() return y - (bx + a) #This is a helper function that implements the commonality between the #psd, csd, and spectrogram. It is NOT meant to be used outside of mlab def _spectral_helper(x, y, NFFT=256, Fs=2, detrend=detrend_none, window=window_hanning, noverlap=0, pad_to=None, sides='default', scale_by_freq=None): #The checks for if y is x are so that we can use the same function to #implement the core of psd(), csd(), and spectrogram() without doing #extra calculations. We return the unaveraged Pxy, freqs, and t. same_data = y is x #Make sure we're dealing with a numpy array. If y and x were the same #object to start with, keep them that way x = np.asarray(x) if not same_data: y = np.asarray(y) # zero pad x and y up to NFFT if they are shorter than NFFT if len(x)<NFFT: n = len(x) x = np.resize(x, (NFFT,)) x[n:] = 0 if not same_data and len(y)<NFFT: n = len(y) y = np.resize(y, (NFFT,)) y[n:] = 0 if pad_to is None: pad_to = NFFT if scale_by_freq is None: warnings.warn("psd, csd, and specgram have changed to scale their " "densities by the sampling frequency for better MatLab " "compatibility. You can pass scale_by_freq=False to disable " "this behavior. Also, one-sided densities are scaled by a " "factor of 2.") scale_by_freq = True # For real x, ignore the negative frequencies unless told otherwise if (sides == 'default' and np.iscomplexobj(x)) or sides == 'twosided': numFreqs = pad_to scaling_factor = 1. elif sides in ('default', 'onesided'): numFreqs = pad_to//2 + 1 scaling_factor = 2. else: raise ValueError("sides must be one of: 'default', 'onesided', or " "'twosided'") # Matlab divides by the sampling frequency so that density function # has units of dB/Hz and can be integrated by the plotted frequency # values. Perform the same scaling here. if scale_by_freq: scaling_factor /= Fs if cbook.iterable(window): assert(len(window) == NFFT) windowVals = window else: windowVals = window(np.ones((NFFT,), x.dtype)) step = NFFT - noverlap ind = np.arange(0, len(x) - NFFT + 1, step) n = len(ind) Pxy = np.zeros((numFreqs,n), np.complex_) # do the ffts of the slices for i in range(n): thisX = x[ind[i]:ind[i]+NFFT] thisX = windowVals * detrend(thisX) fx = np.fft.fft(thisX, n=pad_to) if same_data: fy = fx else: thisY = y[ind[i]:ind[i]+NFFT] thisY = windowVals * detrend(thisY) fy = np.fft.fft(thisY, n=pad_to) Pxy[:,i] = np.conjugate(fx[:numFreqs]) * fy[:numFreqs] # Scale the spectrum by the norm of the window to compensate for # windowing loss; see Bendat & Piersol Sec 11.5.2. Also include # scaling factors for one-sided densities and dividing by the sampling # frequency, if desired. Pxy = scaling_factor / (np.abs(windowVals)2).sum() t = 1./Fs (ind + NFFT / 2.) freqs = float(Fs) / pad_to * np.arange(numFreqs) return Pxy, freqs, t #Split out these keyword docs so that they can be used elsewhere kwdocd = dict() kwdocd['PSD'] =""" Keyword arguments: NFFT: integer The number of data points used in each block for the FFT. Must be even; a power 2 is most efficient. The default value is 256. Fs: scalar The sampling frequency (samples per time unit). It is used to calculate the Fourier frequencies, freqs, in cycles per time unit. The default value is 2. detrend: callable The function applied to each segment before fft-ing, designed to remove the mean or linear trend. Unlike in matlab, where the detrend parameter is a vector, in matplotlib is it a function. The :mod:`~matplotlib.pylab` module defines :func:`~matplotlib.pylab.detrend_none`, :func:`~matplotlib.pylab.detrend_mean`, and :func:`~matplotlib.pylab.detrend_linear`, but you can use a custom function as well. window: callable or ndarray A function or a vector of length NFFT. To create window vectors see :func:`window_hanning`, :func:`window_none`, :func:`numpy.blackman`, :func:`numpy.hamming`, :func:`numpy.bartlett`, :func:`scipy.signal`, :func:`scipy.signal.get_window`, etc. The default is :func:`window_hanning`. If a function is passed as the argument, it must take a data segment as an argument and return the windowed version of the segment. noverlap: integer The number of points of overlap between blocks. The default value is 0 (no overlap). pad_to: integer The number of points to which the data segment is padded when performing the FFT. This can be different from NFFT, which specifies the number of data points used. While not increasing the actual resolution of the psd (the minimum distance between resolvable peaks), this can give more points in the plot, allowing for more detail. This corresponds to the n parameter in the call to fft(). The default is None, which sets pad_to equal to NFFT sides: [ 'default' \| 'onesided' \| 'twosided' ] Specifies which sides of the PSD to return. Default gives the default behavior, which returns one-sided for real data and both for complex data. 'onesided' forces the return of a one-sided PSD, while 'twosided' forces two-sided. scale_by_freq: boolean Specifies whether the resulting density values should be scaled by the scaling frequency, which gives density in units of Hz^-1. This allows for integration over the returned frequency values. The default is True for MatLab compatibility. """ def psd(x, NFFT=256, Fs=2, detrend=detrend_none, window=window_hanning, noverlap=0, pad_to=None, sides='default', scale_by_freq=None): """ The power spectral density by Welch's average periodogram method. The vector x is divided into NFFT length blocks. Each block is detrended by the function detrend and windowed by the function window. noverlap gives the length of the overlap between blocks. The absolute(fft(block))*2 of each segment are averaged to compute Pxx, with a scaling to correct for power loss due to windowing. If len(x) < NFFT, it will be zero padded to NFFT. x* Array or sequence containing the data %(PSD)s Returns the tuple (Pxx, freqs). Refs: Bendat & Piersol -- Random Data: Analysis and Measurement Procedures, John Wiley & Sons (1986) """ Pxx,freqs = csd(x, x, NFFT, Fs, detrend, window, noverlap, pad_to, sides, scale_by_freq) return Pxx.real,freqs psd.__doc__ = psd.__doc__ % kwdocd def csd(x, y, NFFT=256, Fs=2, detrend=detrend_none, window=window_hanning, noverlap=0, pad_to=None, sides='default', scale_by_freq=None): """ The cross power spectral density by Welch's average periodogram method. The vectors x and y are divided into NFFT length blocks. Each block is detrended by the function detrend and windowed by the function window. noverlap gives the length of the overlap between blocks. The product of the direct FFTs of x and y are averaged over each segment to compute Pxy, with a scaling to correct for power loss due to windowing. If len(x) < NFFT or len(y) < NFFT, they will be zero padded to NFFT. x, y Array or sequence containing the data %(PSD)s Returns the tuple (Pxy, freqs). Refs: Bendat & Piersol -- Random Data: Analysis and Measurement Procedures, John Wiley & Sons (1986) """ Pxy, freqs, t = _spectral_helper(x, y, NFFT, Fs, detrend, window, noverlap, pad_to, sides, scale_by_freq) if len(Pxy.shape) == 2 and Pxy.shape[1]>1: Pxy = Pxy.mean(axis=1) return Pxy, freqs csd.__doc__ = csd.__doc__ % kwdocd def specgram(x, NFFT=256, Fs=2, detrend=detrend_none, window=window_hanning, noverlap=128, pad_to=None, sides='default', scale_by_freq=None): """ Compute a spectrogram of data in x. Data are split into NFFT length segements and the PSD of each section is computed. The windowing function window is applied to each segment, and the amount of overlap of each segment is specified with noverlap. If x is real (i.e. non-complex) only the spectrum of the positive frequencie is returned. If x is complex then the complete spectrum is returned. %(PSD)s Returns a tuple (Pxx, freqs, t): - Pxx: 2-D array, columns are the periodograms of successive segments - freqs: 1-D array of frequencies corresponding to the rows in Pxx - t: 1-D array of times corresponding to midpoints of segments. .. seealso:: :func:`psd`: :func:`psd` differs in the default overlap; in returning the mean of the segment periodograms; and in not returning times. """ assert(NFFT > noverlap) Pxx, freqs, t = _spectral_helper(x, x, NFFT, Fs, detrend, window, noverlap, pad_to, sides, scale_by_freq) Pxx = Pxx.real #Needed since helper implements generically if (np.iscomplexobj(x) and sides == 'default') or sides == 'twosided': # center the frequency range at zero freqs = np.concatenate((freqs[NFFT/2:]-Fs,freqs[:NFFT/2])) Pxx = np.concatenate((Pxx[NFFT/2:,:],Pxx[:NFFT/2,:]),0) return Pxx, freqs, t specgram.__doc__ = specgram.__doc__ % kwdocd _coh_error = """Coherence is calculated by averaging over NFFT length segments. Your signal is too short for your choice of NFFT. """ def cohere(x, y, NFFT=256, Fs=2, detrend=detrend_none, window=window_hanning, noverlap=0, pad_to=None, sides='default', scale_by_freq=None): """ The coherence between x and y. Coherence is the normalized cross spectral density: .. math:: C_{xy} = \\frac{\|P_{xy}\|^2}{P_{xx}P_{yy}} x, y Array or sequence containing the data %(PSD)s The return value is the tuple (Cxy, f), where f are the frequencies of the coherence vector. For cohere, scaling the individual densities by the sampling frequency has no effect, since the factors cancel out. .. seealso:: :func:`psd` and :func:`csd`: For information about the methods used to compute :math:`P_{xy}`, :math:`P_{xx}` and :math:`P_{yy}`. """ if len(x)<2NFFT: raise ValueError(_coh_error) Pxx, f = psd(x, NFFT, Fs, detrend, window, noverlap, pad_to, sides, scale_by_freq) Pyy, f = psd(y, NFFT, Fs, detrend, window, noverlap, pad_to, sides, scale_by_freq) Pxy, f = csd(x, y, NFFT, Fs, detrend, window, noverlap, pad_to, sides, scale_by_freq) Cxy = np.divide(np.absolute(Pxy)2, PxxPyy) Cxy.shape = (len(f),) return Cxy, f cohere.__doc__ = cohere.__doc__ % kwdocd def corrcoef(args): """ corrcoef(X) where X* is a matrix returns a matrix of correlation coefficients for the columns of X corrcoef(x, y) where x and y are vectors returns the matrix of correlation coefficients for x and y. Numpy arrays can be real or complex. The correlation matrix is defined from the covariance matrix C as .. math:: r_{ij} = \\frac{C_{ij}}{\\sqrt{C_{ii}C_{jj}}} """ warnings.warn("Use numpy.corrcoef", DeprecationWarning) kw = dict(rowvar=False) return np.corrcoef(args, kw) def polyfit(args, *kwargs): u""" polyfit(x, y, N) Do a best fit polynomial of order N* of y to x. Return value is a vector of polynomial coefficients [pk ... p1 p0]. Eg, for N=2:: p2x0^2 + p1x0 + p0 = y1 p2x1^2 + p1x1 + p0 = y1 p2x2^2 + p1x2 + p0 = y2 ..... p2xk^2 + p1xk + p0 = yk Method: if X is a the Vandermonde Matrix computed from x (see `vandermonds <http://mathworld.wolfram.com/VandermondeMatrix.html>`_), then the polynomial least squares solution is given by the 'p' in Xp = y where X* is a (len(x) \N{MULTIPLICATION SIGN} N + 1) matrix, p is a N+1 length vector, and y is a (len(x) \N{MULTIPLICATION SIGN} 1) vector. This equation can be solved as .. math:: p = (X_t X)^-1 X_t y where :math:`X_t` is the transpose of X and -1 denotes the inverse. Numerically, however, this is not a good method, so we use :func:`numpy.linalg.lstsq`. For more info, see `least squares fitting <http://mathworld.wolfram.com/LeastSquaresFittingPolynomial.html>`_, but note that the k's and n's in the superscripts and subscripts on that page. The linear algebra is correct, however. .. seealso:: :func:`polyval` """ warnings.warn("use numpy.poyfit", DeprecationWarning) return np.polyfit(args, kwargs) def polyval(args, *kwargs): """ y* = polyval(p, x) p is a vector of polynomial coeffients and y is the polynomial evaluated at x. Example code to remove a polynomial (quadratic) trend from y:: p = polyfit(x, y, 2) trend = polyval(p, x) resid = y - trend .. seealso:: :func:`polyfit` """ warnings.warn("use numpy.polyval", DeprecationWarning) return np.polyval(args, kwargs) def vander(args, *kwargs): """ X* = vander(x, N = None) The Vandermonde matrix of vector x. The i-th column of X is the the i-th power of x. N is the maximum power to compute; if N is None it defaults to len(x). """ warnings.warn("Use numpy.vander()", DeprecationWarning) return np.vander(args, kwargs) def donothing_callback(args): pass def cohere_pairs( X, ij, NFFT=256, Fs=2, detrend=detrend_none, window=window_hanning, noverlap=0, preferSpeedOverMemory=True, progressCallback=donothing_callback, returnPxx=False): u""" Cxy, Phase, freqs = cohere_pairs(X, ij, ...) Compute the coherence for all pairs in ij. X is a (numSamples, numCols) numpy array. ij is a list of tuples (i, j). Each tuple is a pair of indexes into the columns of X for which you want to compute coherence. For example, if X has 64 columns, and you want to compute all nonredundant pairs, define ij as:: ij = [] for i in range(64): for j in range(i+1,64): ij.append( (i, j) ) The other function arguments, except for preferSpeedOverMemory (see below), are explained in the help string of :func:`psd`. Return value is a tuple (Cxy, Phase, freqs). - Cxy: a dictionary of (i, j) tuples -> coherence vector for that pair. I.e., ``Cxy[(i,j)] = cohere(X[:,i], X[:,j])``. Number of dictionary keys is ``len(ij)``. - Phase: a dictionary of phases of the cross spectral density at each frequency for each pair. The keys are ``(i,j)``. - freqs: a vector of frequencies, equal in length to either the coherence or phase vectors for any (i, j) key.. Eg, to make a coherence Bode plot:: subplot(211) plot( freqs, Cxy[(12,19)]) subplot(212) plot( freqs, Phase[(12,19)]) For a large number of pairs, :func:`cohere_pairs` can be much more efficient than just calling :func:`cohere` for each pair, because it caches most of the intensive computations. If N is the number of pairs, this function is O(N) for most of the heavy lifting, whereas calling cohere for each pair is O(N\N{SUPERSCRIPT TWO}). However, because of the caching, it is also more memory intensive, making 2 additional complex arrays with approximately the same number of elements as X. The parameter preferSpeedOverMemory, if False, limits the caching by only making one, rather than two, complex cache arrays. This is useful if memory becomes critical. Even when preferSpeedOverMemory is False, :func:`cohere_pairs` will still give significant performace gains over calling :func:`cohere` for each pair, and will use subtantially less memory than if preferSpeedOverMemory is True. In my tests with a (43000, 64) array over all non-redundant pairs, preferSpeedOverMemory = True delivered a 33% performace boost on a 1.7GHZ Athlon with 512MB RAM compared with preferSpeedOverMemory = False. But both solutions were more than 10x faster than naievly crunching all possible pairs through cohere. .. seealso:: :file:`test/cohere_pairs_test.py` in the src tree: For an example script that shows that this :func:`cohere_pairs` and :func:`cohere` give the same results for a given pair. """ numRows, numCols = X.shape # zero pad if X is too short if numRows < NFFT: tmp = X X = np.zeros( (NFFT, numCols), X.dtype) X[:numRows,:] = tmp del tmp numRows, numCols = X.shape # get all the columns of X that we are interested in by checking # the ij tuples seen = {} for i,j in ij: seen[i]=1; seen[j] = 1 allColumns = seen.keys() Ncols = len(allColumns) del seen # for real X, ignore the negative frequencies if np.iscomplexobj(X): numFreqs = NFFT else: numFreqs = NFFT//2+1 # cache the FFT of every windowed, detrended NFFT length segement # of every channel. If preferSpeedOverMemory, cache the conjugate # as well if cbook.iterable(window): assert(len(window) == NFFT) windowVals = window else: windowVals = window(np.ones((NFFT,), typecode(X))) ind = range(0, numRows-NFFT+1, NFFT-noverlap) numSlices = len(ind) FFTSlices = {} FFTConjSlices = {} Pxx = {} slices = range(numSlices) normVal = norm(windowVals)*2 for iCol in allColumns: progressCallback(i/Ncols, 'Cacheing FFTs') Slices = np.zeros( (numSlices,numFreqs), dtype=np.complex_) for iSlice in slices: thisSlice = X[ind[iSlice]:ind[iSlice]+NFFT, iCol] thisSlice = windowValsdetrend(thisSlice) Slices[iSlice,:] = fft(thisSlice)[:numFreqs] FFTSlices[iCol] = Slices if preferSpeedOverMemory: FFTConjSlices[iCol] = conjugate(Slices) Pxx[iCol] = np.divide(np.mean(absolute(Slices)*2), normVal) del Slices, ind, windowVals # compute the coherences and phases for all pairs using the # cached FFTs Cxy = {} Phase = {} count = 0 N = len(ij) for i,j in ij: count +=1 if count%10==0: progressCallback(count/N, 'Computing coherences') if preferSpeedOverMemory: Pxy = FFTSlices[i] FFTConjSlices[j] else: Pxy = FFTSlices[i] * np.conjugate(FFTSlices[j]) if numSlices>1: Pxy = np.mean(Pxy) Pxy = np.divide(Pxy, normVal) Cxy[(i,j)] = np.divide(np.absolute(Pxy)*2, Pxx[i]Pxx[j]) Phase[(i,j)] = np.arctan2(Pxy.imag, Pxy.real) freqs = Fs/NFFTnp.arange(numFreqs) if returnPxx: return Cxy, Phase, freqs, Pxx else: return Cxy, Phase, freqs def entropy(y, bins): r""" Return the entropy of the data in y. .. math:: \sum p_i \log_2(p_i) where :math:`p_i` is the probability of observing y* in the :math:`i^{th}` bin of bins. bins can be a number of bins or a range of bins; see :func:`numpy.histogram`. Compare S with analytic calculation for a Gaussian:: x = mu + sigma * randn(200000) Sanalytic = 0.5 * ( 1.0 + log(2pisigma*2.0) ) """ n,bins = np.histogram(y, bins) n = n.astype(np.float_) n = np.take(n, np.nonzero(n)[0]) # get the positive p = np.divide(n, len(y)) delta = bins[1]-bins[0] S = -1.0np.sum(plog(p)) + log(delta) #S = -1.0np.sum(plog(p)) return S def hist(y, bins=10, normed=0): """ Return the histogram of y* with bins equally sized bins. If bins is an array, use those bins. Return value is (n, x) where n is the count for each bin in x. If normed is False, return the counts in the first element of the returned tuple. If normed is True, return the probability density :math:`\\frac{n}{(len(y)\mathrm{dbin}}`. If y has rank > 1, it will be raveled. If y is masked, only the unmasked values will be used. Credits: the Numeric 22 documentation """ warnings.warn("Use numpy.histogram()", DeprecationWarning) return np.histogram(y, bins=bins, range=None, normed=normed) def normpdf(x, args): "Return the normal pdf evaluated at x; args provides mu, sigma" mu, sigma = args return 1./(np.sqrt(2np.pi)sigma)np.exp(-0.5 * (1./sigma(x - mu))2) def levypdf(x, gamma, alpha): "Returm the levy pdf evaluated at x* for params gamma, alpha" N = len(x) if N%2 != 0: raise ValueError, 'x must be an event length array; try\n' + \ 'x = np.linspace(minx, maxx, N), where N is even' dx = x[1]-x[0] f = 1/(Ndx)np.arange(-N/2, N/2, np.float_) ind = np.concatenate([np.arange(N/2, N, int), np.arange(0, N/2, int)]) df = f[1]-f[0] cfl = exp(-gammanp.absolute(2pif)alpha) px = np.fft.fft(np.take(cfl,ind)df).astype(np.float_) return np.take(px, ind) def find(condition): "Return the indices where ravel(condition) is true" res, = np.nonzero(np.ravel(condition)) return res def trapz(x, y): """ Trapezoidal integral of y(x). """ warnings.warn("Use numpy.trapz(y,x) instead of trapz(x,y)", DeprecationWarning) return np.trapz(y, x) #if len(x)!=len(y): # raise ValueError, 'x and y must have the same length' #if len(x)<2: # raise ValueError, 'x and y must have > 1 element' #return np.sum(0.5np.diff(x)(y[1:]+y[:-1])) def longest_contiguous_ones(x): """ Return the indices of the longest stretch of contiguous ones in x, assuming x is a vector of zeros and ones. If there are two equally long stretches, pick the first. """ x = np.ravel(x) if len(x)==0: return np.array([]) ind = (x==0).nonzero()[0] if len(ind)==0: return np.arange(len(x)) if len(ind)==len(x): return np.array([]) y = np.zeros( (len(x)+2,), x.dtype) y[1:-1] = x dif = np.diff(y) up = (dif == 1).nonzero()[0]; dn = (dif == -1).nonzero()[0]; i = (dn-up == max(dn - up)).nonzero()[0][0] ind = np.arange(up[i], dn[i]) return ind def longest_ones(x): '''alias for longest_contiguous_ones''' return longest_contiguous_ones(x) def prepca(P, frac=0): """ Compute the principal components of P. P is a (numVars, numObs) array. frac is the minimum fraction of variance that a component must contain to be included. Return value is a tuple of the form (Pcomponents, Trans, fracVar) where: - Pcomponents : a (numVars, numObs) array - Trans : the weights matrix, ie, Pcomponents = Trans * P - fracVar : the fraction of the variance accounted for by each component returned A similar function of the same name was in the Matlab (TM) R13 Neural Network Toolbox but is not found in later versions; its successor seems to be called "processpcs". """ U,s,v = np.linalg.svd(P) varEach = s*2/P.shape[1] totVar = varEach.sum() fracVar = varEach/totVar ind = slice((fracVar>=frac).sum()) # select the components that are greater Trans = U[:,ind].transpose() # The transformed data Pcomponents = np.dot(Trans,P) return Pcomponents, Trans, fracVar[ind] def prctile(x, p = (0.0, 25.0, 50.0, 75.0, 100.0)): """ Return the percentiles of x. p* can either be a sequence of percentile values or a scalar. If p is a sequence, the ith element of the return sequence is the p(i)-th percentile of x. If p is a scalar, the largest value of x less than or equal to the p percentage point in the sequence is returned. """ x = np.array(x).ravel() # we need a copy x.sort() Nx = len(x) if not cbook.iterable(p): return x[int(pNx/100.0)] p = np.asarray(p) Nx/100.0 ind = p.astype(int) ind = np.where(ind>=Nx, Nx-1, ind) return x.take(ind) def prctile_rank(x, p): """ Return the rank for each element in x, return the rank 0..len(p). Eg if p = (25, 50, 75), the return value will be a len(x) array with values in [0,1,2,3] where 0 indicates the value is less than the 25th percentile, 1 indicates the value is >= the 25th and < 50th percentile, ... and 3 indicates the value is above the 75th percentile cutoff. p is either an array of percentiles in [0..100] or a scalar which indicates how many quantiles of data you want ranked. """ if not cbook.iterable(p): p = np.arange(100.0/p, 100.0, 100.0/p) else: p = np.asarray(p) if p.max()<=1 or p.min()<0 or p.max()>100: raise ValueError('percentiles should be in range 0..100, not 0..1') ptiles = prctile(x, p) return np.searchsorted(ptiles, x) def center_matrix(M, dim=0): """ Return the matrix M with each row having zero mean and unit std. If dim = 1 operate on columns instead of rows. (dim is opposite to the numpy axis kwarg.) """ M = np.asarray(M, np.float_) if dim: M = (M - M.mean(axis=0)) / M.std(axis=0) else: M = (M - M.mean(axis=1)[:,np.newaxis]) M = M / M.std(axis=1)[:,np.newaxis] return M def rk4(derivs, y0, t): """ Integrate 1D or ND system of ODEs using 4-th order Runge-Kutta. This is a toy implementation which may be useful if you find yourself stranded on a system w/o scipy. Otherwise use :func:`scipy.integrate`. y0 initial state vector t sample times derivs returns the derivative of the system and has the signature ``dy = derivs(yi, ti)`` Example 1 :: ## 2D system def derivs6(x,t): d1 = x[0] + 2x[1] d2 = -3x[0] + 4x[1] return (d1, d2) dt = 0.0005 t = arange(0.0, 2.0, dt) y0 = (1,2) yout = rk4(derivs6, y0, t) Example 2:: ## 1D system alpha = 2 def derivs(x,t): return -alphax + exp(-t) y0 = 1 yout = rk4(derivs, y0, t) If you have access to scipy, you should probably be using the scipy.integrate tools rather than this function. """ try: Ny = len(y0) except TypeError: yout = np.zeros( (len(t),), np.float_) else: yout = np.zeros( (len(t), Ny), np.float_) yout[0] = y0 i = 0 for i in np.arange(len(t)-1): thist = t[i] dt = t[i+1] - thist dt2 = dt/2.0 y0 = yout[i] k1 = np.asarray(derivs(y0, thist)) k2 = np.asarray(derivs(y0 + dt2k1, thist+dt2)) k3 = np.asarray(derivs(y0 + dt2k2, thist+dt2)) k4 = np.asarray(derivs(y0 + dtk3, thist+dt)) yout[i+1] = y0 + dt/6.0(k1 + 2k2 + 2k3 + k4) return yout def bivariate_normal(X, Y, sigmax=1.0, sigmay=1.0, mux=0.0, muy=0.0, sigmaxy=0.0): """ Bivariate Gaussian distribution for equal shape X, Y. See `bivariate normal <http://mathworld.wolfram.com/BivariateNormalDistribution.html>`_ at mathworld. """ Xmu = X-mux Ymu = Y-muy rho = sigmaxy/(sigmaxsigmay) z = Xmu2/sigmax2 + Ymu2/sigmay2 - 2rhoXmuYmu/(sigmaxsigmay) denom = 2np.pisigmaxsigmaynp.sqrt(1-rho2) return np.exp( -z/(2(1-rho*2))) / denom def get_xyz_where(Z, Cond): """ Z* and Cond are M x N matrices. Z are data and Cond is a boolean matrix where some condition is satisfied. Return value is (x, y, z) where x and y are the indices into Z and z are the values of Z at those indices. x, y, and z are 1D arrays. """ X,Y = np.indices(Z.shape) return X[Cond], Y[Cond], Z[Cond] def get_sparse_matrix(M,N,frac=0.1): """ Return a M x N sparse matrix with frac elements randomly filled. """ data = np.zeros((M,N))0. for i in range(int(MNfrac)): x = np.random.randint(0,M-1) y = np.random.randint(0,N-1) data[x,y] = np.random.rand() return data def dist(x,y): """ Return the distance between two points. """ d = x-y return np.sqrt(np.dot(d,d)) def dist_point_to_segment(p, s0, s1): """ Get the distance of a point to a segment. p, s0, s1* are xy sequences This algorithm from http://softsurfer.com/Archive/algorithm_0102/algorithm_0102.htm#Distance%20to%20Ray%20or%20Segment """ p = np.asarray(p, np.float_) s0 = np.asarray(s0, np.float_) s1 = np.asarray(s1, np.float_) v = s1 - s0 w = p - s0 c1 = np.dot(w,v); if ( c1 <= 0 ): return dist(p, s0); c2 = np.dot(v,v) if ( c2 <= c1 ): return dist(p, s1); b = c1 / c2 pb = s0 + b * v; return dist(p, pb) def segments_intersect(s1, s2): """ Return True if s1 and s2 intersect. s1 and s2 are defined as:: s1: (x1, y1), (x2, y2) s2: (x3, y3), (x4, y4) """ (x1, y1), (x2, y2) = s1 (x3, y3), (x4, y4) = s2 den = ((y4-y3) * (x2-x1)) - ((x4-x3)(y2-y1)) n1 = ((x4-x3) (y1-y3)) - ((y4-y3)(x1-x3)) n2 = ((x2-x1) (y1-y3)) - ((y2-y1)(x1-x3)) if den == 0: # lines parallel return False u1 = n1/den u2 = n2/den return 0.0 <= u1 <= 1.0 and 0.0 <= u2 <= 1.0 def fftsurr(x, detrend=detrend_none, window=window_none): """ Compute an FFT phase randomized surrogate of x. """ if cbook.iterable(window): x=windowdetrend(x) else: x = window(detrend(x)) z = np.fft.fft(x) a = 2.np.pi1j phase = a * np.random.rand(len(x)) z = znp.exp(phase) return np.fft.ifft(z).real def liaupunov(x, fprime): """ x* is a very long trajectory from a map, and fprime returns the derivative of x. Returns : .. math:: \lambda = \\frac{1}{n}\\sum \\ln\|f^'(x_i)\| .. seealso:: Sec 10.5 Strogatz (1994) "Nonlinear Dynamics and Chaos". `Wikipedia article on Lyapunov Exponent <http://en.wikipedia.org/wiki/Lyapunov_exponent>`_. .. note:: What the function here calculates may not be what you really want; caveat emptor. It also seems that this function's name is badly misspelled. """ return np.mean(np.log(np.absolute(fprime(x)))) class FIFOBuffer: """ A FIFO queue to hold incoming x, y data in a rotating buffer using numpy arrays under the hood. It is assumed that you will call asarrays much less frequently than you add data to the queue -- otherwise another data structure will be faster. This can be used to support plots where data is added from a real time feed and the plot object wants to grab data from the buffer and plot it to screen less freqeuently than the incoming. If you set the dataLim attr to :class:`~matplotlib.transforms.BBox` (eg :attr:`matplotlib.Axes.dataLim`), the dataLim will be updated as new data come in. TODO: add a grow method that will extend nmax .. note:: mlab seems like the wrong place for this class. """ def __init__(self, nmax): """ Buffer up to nmax points. """ self._xa = np.zeros((nmax,), np.float_) self._ya = np.zeros((nmax,), np.float_) self._xs = np.zeros((nmax,), np.float_) self._ys = np.zeros((nmax,), np.float_) self._ind = 0 self._nmax = nmax self.dataLim = None self.callbackd = {} def register(self, func, N): """ Call func every time N events are passed; func signature is ``func(fifo)``. """ self.callbackd.setdefault(N, []).append(func) def add(self, x, y): """ Add scalar x and y to the queue. """ if self.dataLim is not None: xys = ((x,y),) self.dataLim.update(xys, -1) #-1 means use the default ignore setting ind = self._ind % self._nmax #print 'adding to fifo:', ind, x, y self._xs[ind] = x self._ys[ind] = y for N,funcs in self.callbackd.items(): if (self._ind%N)==0: for func in funcs: func(self) self._ind += 1 def last(self): """ Get the last x, y or None. None if no data set. """ if self._ind==0: return None, None ind = (self._ind-1) % self._nmax return self._xs[ind], self._ys[ind] def asarrays(self): """ Return x and y as arrays; their length will be the len of data added or nmax. """ if self._ind<self._nmax: return self._xs[:self._ind], self._ys[:self._ind] ind = self._ind % self._nmax self._xa[:self._nmax-ind] = self._xs[ind:] self._xa[self._nmax-ind:] = self._xs[:ind] self._ya[:self._nmax-ind] = self._ys[ind:] self._ya[self._nmax-ind:] = self._ys[:ind] return self._xa, self._ya def update_datalim_to_current(self): """ Update the datalim in the current data in the fifo. """ if self.dataLim is None: raise ValueError('You must first set the dataLim attr') x, y = self.asarrays() self.dataLim.update_numerix(x, y, True) def movavg(x,n): """ Compute the len(n) moving average of x. """ w = np.empty((n,), dtype=np.float_) w[:] = 1.0/n return np.convolve(x, w, mode='valid') def save(fname, X, fmt='%.18e',delimiter=' '): """ Save the data in X to file fname using fmt string to convert the data to strings. fname can be a filename or a file handle. If the filename ends in '.gz', the file is automatically saved in compressed gzip format. The :func:`load` function understands gzipped files transparently. Example usage:: save('test.out', X) # X is an array save('test1.out', (x,y,z)) # x,y,z equal sized 1D arrays save('test2.out', x) # x is 1D save('test3.out', x, fmt='%1.4e') # use exponential notation delimiter is used to separate the fields, eg. delimiter ',' for comma-separated values. """ if cbook.is_string_like(fname): if fname.endswith('.gz'): import gzip fh = gzip.open(fname,'wb') else: fh = file(fname,'w') elif hasattr(fname, 'seek'): fh = fname else: raise ValueError('fname must be a string or file handle') X = np.asarray(X) origShape = None if X.ndim == 1: origShape = X.shape X.shape = len(X), 1 for row in X: fh.write(delimiter.join([fmt%val for val in row]) + '\n') if origShape is not None: X.shape = origShape def load(fname,comments='#',delimiter=None, converters=None,skiprows=0, usecols=None, unpack=False, dtype=np.float_): """ Load ASCII data from fname into an array and return the array. The data must be regular, same number of values in every row fname can be a filename or a file handle. Support for gzipped files is automatic, if the filename ends in '.gz'. matfile data is not supported; for that, use :mod:`scipy.io.mio` module. Example usage:: X = load('test.dat') # data in two columns t = X[:,0] y = X[:,1] Alternatively, you can do the same with "unpack"; see below:: X = load('test.dat') # a matrix of data x = load('test.dat') # a single column of data - comments: the character used to indicate the start of a comment in the file - delimiter is a string-like character used to seperate values in the file. If delimiter is unspecified or None, any whitespace string is a separator. - converters, if not None, is a dictionary mapping column number to a function that will convert that column to a float (or the optional dtype if specified). Eg, if column 0 is a date string:: converters = {0:datestr2num} - skiprows is the number of rows from the top to skip. - usecols, if not None, is a sequence of integer column indexes to extract where 0 is the first column, eg ``usecols=[1,4,5]`` to extract just the 2nd, 5th and 6th columns - unpack, if True, will transpose the matrix allowing you to unpack into named arguments on the left hand side:: t,y = load('test.dat', unpack=True) # for two column data x,y,z = load('somefile.dat', usecols=[3,5,7], unpack=True) - dtype: the array will have this dtype. default: ``numpy.float_`` .. seealso:: See :file:`examples/pylab_examples/load_converter.py` in the source tree: Exercises many of these options. """ if converters is None: converters = {} fh = cbook.to_filehandle(fname) X = [] if delimiter==' ': # space splitting is a special case since x.split() is what # you want, not x.split(' ') def splitfunc(x): return x.split() else: def splitfunc(x): return x.split(delimiter) converterseq = None for i,line in enumerate(fh): if i<skiprows: continue line = line.split(comments, 1)[0].strip() if not len(line): continue if converterseq is None: converterseq = [converters.get(j,float) for j,val in enumerate(splitfunc(line))] if usecols is not None: vals = splitfunc(line) row = [converterseq[j](vals[j]) for j in usecols] else: row = [converterseq[j](val) for j,val in enumerate(splitfunc(line))] thisLen = len(row) X.append(row) X = np.array(X, dtype) r,c = X.shape if r==1 or c==1: X.shape = max(r,c), if unpack: return X.transpose() else: return X def slopes(x,y): """ SLOPES calculate the slope y'(x) Given data vectors X and Y SLOPES calculates Y'(X), i.e the slope of a curve Y(X). The slope is estimated using the slope obtained from that of a parabola through any three consecutive points. This method should be superior to that described in the appendix of A CONSISTENTLY WELL BEHAVED METHOD OF INTERPOLATION by Russel W. Stineman (Creative Computing July 1980) in at least one aspect: Circles for interpolation demand a known aspect ratio between x- and y-values. For many functions, however, the abscissa are given in different dimensions, so an aspect ratio is completely arbitrary. The parabola method gives very similar results to the circle method for most regular cases but behaves much better in special cases Norbert Nemec, Institute of Theoretical Physics, University or Regensburg, April 2006 Norbert.Nemec at physik.uni-regensburg.de (inspired by a original implementation by Halldor Bjornsson, Icelandic Meteorological Office, March 2006 halldor at vedur.is) """ # Cast key variables as float. x=np.asarray(x, np.float_) y=np.asarray(y, np.float_) yp=np.zeros(y.shape, np.float_) dx=x[1:] - x[:-1] dy=y[1:] - y[:-1] dydx = dy/dx yp[1:-1] = (dydx[:-1] * dx[1:] + dydx[1:] * dx[:-1])/(dx[1:] + dx[:-1]) yp[0] = 2.0 * dy[0]/dx[0] - yp[1] yp[-1] = 2.0 * dy[-1]/dx[-1] - yp[-2] return yp def stineman_interp(xi,x,y,yp=None): """ STINEMAN_INTERP Well behaved data interpolation. Given data vectors X and Y, the slope vector YP and a new abscissa vector XI the function stineman_interp(xi,x,y,yp) uses Stineman interpolation to calculate a vector YI corresponding to XI. Here's an example that generates a coarse sine curve, then interpolates over a finer abscissa: x = linspace(0,2pi,20); y = sin(x); yp = cos(x) xi = linspace(0,2pi,40); yi = stineman_interp(xi,x,y,yp); plot(x,y,'o',xi,yi) The interpolation method is described in the article A CONSISTENTLY WELL BEHAVED METHOD OF INTERPOLATION by Russell W. Stineman. The article appeared in the July 1980 issue of Creative Computing with a note from the editor stating that while they were not an academic journal but once in a while something serious and original comes in adding that this was "apparently a real solution" to a well known problem. For yp=None, the routine automatically determines the slopes using the "slopes" routine. X is assumed to be sorted in increasing order For values xi[j] < x[0] or xi[j] > x[-1], the routine tries a extrapolation. The relevance of the data obtained from this, of course, questionable... original implementation by Halldor Bjornsson, Icelandic Meteorolocial Office, March 2006 halldor at vedur.is completely reworked and optimized for Python by Norbert Nemec, Institute of Theoretical Physics, University or Regensburg, April 2006 Norbert.Nemec at physik.uni-regensburg.de """ # Cast key variables as float. x=np.asarray(x, np.float_) y=np.asarray(y, np.float_) assert x.shape == y.shape N=len(y) if yp is None: yp = slopes(x,y) else: yp=np.asarray(yp, np.float_) xi=np.asarray(xi, np.float_) yi=np.zeros(xi.shape, np.float_) # calculate linear slopes dx = x[1:] - x[:-1] dy = y[1:] - y[:-1] s = dy/dx #note length of s is N-1 so last element is #N-2 # find the segment each xi is in # this line actually is the key to the efficiency of this implementation idx = np.searchsorted(x[1:-1], xi) # now we have generally: x[idx[j]] <= xi[j] <= x[idx[j]+1] # except at the boundaries, where it may be that xi[j] < x[0] or xi[j] > x[-1] # the y-values that would come out from a linear interpolation: sidx = s.take(idx) xidx = x.take(idx) yidx = y.take(idx) xidxp1 = x.take(idx+1) yo = yidx + sidx * (xi - xidx) # the difference that comes when using the slopes given in yp dy1 = (yp.take(idx)- sidx) * (xi - xidx) # using the yp slope of the left point dy2 = (yp.take(idx+1)-sidx) * (xi - xidxp1) # using the yp slope of the right point dy1dy2 = dy1dy2 # The following is optimized for Python. The solution actually # does more calculations than necessary but exploiting the power # of numpy, this is far more efficient than coding a loop by hand # in Python yi = yo + dy1dy2 np.choose(np.array(np.sign(dy1dy2), np.int32)+1, ((2xi-xidx-xidxp1)/((dy1-dy2)(xidxp1-xidx)), 0.0, 1/(dy1+dy2),)) return yi def inside_poly(points, verts): """ points is a sequence of x,y points verts is a sequence of x,y vertices of a poygon return value is a sequence of indices into points for the points that are inside the polygon """ res, = np.nonzero(nxutils.points_inside_poly(points, verts)) return res def poly_below(ymin, xs, ys): """ given a arrays xs and ys, return the vertices of a polygon that has a scalar lower bound ymin and an upper bound at the ys. intended for use with Axes.fill, eg:: xv, yv = poly_below(0, x, y) ax.fill(xv, yv) """ return poly_between(xs, ys, xmin) def poly_between(x, ylower, yupper): """ given a sequence of x, ylower and yupper, return the polygon that fills the regions between them. ylower or yupper can be scalar or iterable. If they are iterable, they must be equal in length to x return value is x, y arrays for use with Axes.fill """ Nx = len(x) if not cbook.iterable(ylower): ylower = ylowernp.ones(Nx) if not cbook.iterable(yupper): yupper = yuppernp.ones(Nx) x = np.concatenate( (x, x[::-1]) ) y = np.concatenate( (yupper, ylower[::-1]) ) return x,y ### the following code was written and submitted by Fernando Perez ### from the ipython numutils package under a BSD license # begin fperez functions """ A set of convenient utilities for numerical work. Most of this module requires numpy or is meant to be used with it. Copyright (c) 2001-2004, Fernando Perez. <Fernando.Perez@colorado.edu> All rights reserved. This license was generated from the BSD license template as found in: http://www.opensource.org/licenses/bsd-license.php Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the IPython project nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. """ import operator import math #*************************************************************************** # Globals #************************************************************************** # function definitions exp_safe_MIN = math.log(2.2250738585072014e-308) exp_safe_MAX = 1.7976931348623157e+308 def exp_safe(x): """ Compute exponentials which safely underflow to zero. Slow, but convenient to use. Note that numpy provides proper floating point exception handling with access to the underlying hardware. """ if type(x) is np.ndarray: return exp(np.clip(x,exp_safe_MIN,exp_safe_MAX)) else: return math.exp(x) def amap(fn,args): """ amap(function, sequence[, sequence, ...]) -> array. Works like :func:`map`, but it returns an array. This is just a convenient shorthand for ``numpy.array(map(...))``. """ return np.array(map(fn,args)) #from numpy import zeros_like def zeros_like(a): """ Return an array of zeros of the shape and typecode of a. """ warnings.warn("Use numpy.zeros_like(a)", DeprecationWarning) return np.zeros_like(a) #from numpy import sum as sum_flat def sum_flat(a): """ Return the sum of all the elements of a, flattened out. It uses ``a.flat``, and if a is not contiguous, a call to ``ravel(a)`` is made. """ warnings.warn("Use numpy.sum(a) or a.sum()", DeprecationWarning) return np.sum(a) #from numpy import mean as mean_flat def mean_flat(a): """ Return the mean of all the elements of a, flattened out. """ warnings.warn("Use numpy.mean(a) or a.mean()", DeprecationWarning) return np.mean(a) def rms_flat(a): """ Return the root mean square of all the elements of a, flattened out. """ return np.sqrt(np.mean(np.absolute(a)*2)) def l1norm(a): """ Return the l1* norm of a, flattened out. Implemented as a separate function (not a call to :func:`norm` for speed). """ return np.sum(np.absolute(a)) def l2norm(a): """ Return the l2 norm of a, flattened out. Implemented as a separate function (not a call to :func:`norm` for speed). """ return np.sqrt(np.sum(np.absolute(a)*2)) def norm_flat(a,p=2): """ norm(a,p=2) -> l-p norm of a.flat Return the l-p norm of a, considered as a flat array. This is NOT a true matrix norm, since arrays of arbitrary rank are always flattened. p* can be a number or the string 'Infinity' to get the L-infinity norm. """ # This function was being masked by a more general norm later in # the file. We may want to simply delete it. if p=='Infinity': return np.amax(np.absolute(a)) else: return (np.sum(np.absolute(a)p))(1.0/p) def frange(xini,xfin=None,delta=None,*kw): """ frange([start,] stop[, step, keywords]) -> array of floats Return a numpy ndarray containing a progression of floats. Similar to :func:`numpy.arange`, but defaults to a closed interval. ``frange(x0, x1)`` returns ``[x0, x0+1, x0+2, ..., x1]``; start* defaults to 0, and the endpoint is included. This behavior is different from that of :func:`range` and :func:`numpy.arange`. This is deliberate, since :func:`frange` will probably be more useful for generating lists of points for function evaluation, and endpoints are often desired in this use. The usual behavior of :func:`range` can be obtained by setting the keyword closed = 0, in this case, :func:`frange` basically becomes :func:numpy.arange`. When step is given, it specifies the increment (or decrement). All arguments can be floating point numbers. ``frange(x0,x1,d)`` returns ``[x0,x0+d,x0+2d,...,xfin]`` where xfin <= x1. :func:`frange` can also be called with the keyword npts. This sets the number of points the list should contain (and overrides the value step might have been given). :func:`numpy.arange` doesn't offer this option. Examples:: >>> frange(3) array([ 0., 1., 2., 3.]) >>> frange(3,closed=0) array([ 0., 1., 2.]) >>> frange(1,6,2) array([1, 3, 5]) or 1,3,5,7, depending on floating point vagueries >>> frange(1,6.5,npts=5) array([ 1. , 2.375, 3.75 , 5.125, 6.5 ]) """ #defaults kw.setdefault('closed',1) endpoint = kw['closed'] != 0 # funny logic to allow the first argument to be optional (like range()) # This was modified with a simpler version from a similar frange() found # at http://aspn.activestate.com/ASPN/Cookbook/Python/Recipe/66472 if xfin == None: xfin = xini + 0.0 xini = 0.0 if delta == None: delta = 1.0 # compute # of points, spacing and return final list try: npts=kw['npts'] delta=(xfin-xini)/float(npts-endpoint) except KeyError: npts = int(round((xfin-xini)/delta)) + endpoint #npts = int(floor((xfin-xini)/delta)(1.0+1e-10)) + endpoint # round finds the nearest, so the endpoint can be up to # delta/2 larger than xfin. return np.arange(npts)delta+xini # end frange() #import numpy.diag as diagonal_matrix def diagonal_matrix(diag): """ Return square diagonal matrix whose non-zero elements are given by the input array. """ warnings.warn("Use numpy.diag(d)", DeprecationWarning) return np.diag(diag) def identity(n, rank=2, dtype='l', typecode=None): """ Returns the identity matrix of shape (n, n, ..., n) (rank r). For ranks higher than 2, this object is simply a multi-index Kronecker delta:: / 1 if i0=i1=...=iR, id[i0,i1,...,iR] = -\| \ 0 otherwise. Optionally a dtype (or typecode) may be given (it defaults to 'l'). Since rank defaults to 2, this function behaves in the default case (when only n is given) like ``numpy.identity(n)`` -- but surprisingly, it is much faster. """ if typecode is not None: warnings.warn("Use dtype kwarg instead of typecode", DeprecationWarning) dtype = typecode iden = np.zeros((n,)rank, dtype) for i in range(n): idx = (i,)rank iden[idx] = 1 return iden def base_repr (number, base = 2, padding = 0): """ Return the representation of a number in any given base. """ chars = '0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZ' if number < base: \ return (padding - 1) * chars [0] + chars [int (number)] max_exponent = int (math.log (number)/math.log (base)) max_power = long (base) ** max_exponent lead_digit = int (number/max_power) return chars [lead_digit] + \ base_repr (number - max_power * lead_digit, base, \ max (padding - 1, max_exponent)) def binary_repr(number, max_length = 1025): """ Return the binary representation of the input number as a string. This is more efficient than using :func:`base_repr` with base 2. Increase the value of max_length for very large numbers. Note that on 32-bit machines, 2*1023 is the largest integer power of 2 which can be converted to a Python float. """ #assert number < 2L << max_length shifts = map (operator.rshift, max_length [number], \ range (max_length - 1, -1, -1)) digits = map (operator.mod, shifts, max_length * [2]) if not digits.count (1): return 0 digits = digits [digits.index (1):] return ''.join (map (repr, digits)).replace('L','') def log2(x,ln2 = math.log(2.0)): """ Return the log(x) in base 2. This is a _slow_ function but which is guaranteed to return the correct integer value if the input is an integer exact power of 2. """ try: bin_n = binary_repr(x)[1:] except (AssertionError,TypeError): return math.log(x)/ln2 else: if '1' in bin_n: return math.log(x)/ln2 else: return len(bin_n) def ispower2(n): """ Returns the log base 2 of n if n is a power of 2, zero otherwise. Note the potential ambiguity if n == 1: 2*0 == 1, interpret accordingly. """ bin_n = binary_repr(n)[1:] if '1' in bin_n: return 0 else: return len(bin_n) def isvector(X): """ Like the Matlab (TM) function with the same name, returns True* if the supplied numpy array or matrix X looks like a vector, meaning it has a one non-singleton axis (i.e., it can have multiple axes, but all must have length 1, except for one of them). If you just want to see if the array has 1 axis, use X.ndim == 1. """ return np.prod(X.shape)==np.max(X.shape) #from numpy import fromfunction as fromfunction_kw def fromfunction_kw(function, dimensions, kwargs): """ Drop-in replacement for :func:`numpy.fromfunction`. Allows passing keyword arguments to the desired function. Call it as (keywords are optional):: fromfunction_kw(MyFunction, dimensions, keywords) The function ``MyFunction`` is responsible for handling the dictionary of keywords it will receive. """ warnings.warn("Use numpy.fromfunction()", DeprecationWarning) return np.fromfunction(function, dimensions, kwargs) ### end fperez numutils code def rem(x,y): """ Deprecated - see :func:`numpy.remainder` """ raise NotImplementedError('Deprecated - see numpy.remainder') def norm(x,y=2): """ Deprecated - see :func:`numpy.linalg.norm` """ raise NotImplementedError('Deprecated - see numpy.linalg.norm') def orth(A): """ Deprecated - needs clean room implementation """ raise NotImplementedError('Deprecated - needs clean room implementation') def rank(x): """ Deprecated - see :func:`numpy.rank` """ raise NotImplementedError('Deprecated - see numpy.rank') def sqrtm(x): """ Deprecated - needs clean room implementation """ raise NotImplementedError('Deprecated - see scipy.linalg.sqrtm') def mfuncC(f, x): """ Deprecated """ raise NotImplementedError('Deprecated - needs clean room implementation') def approx_real(x): """ Deprecated - needs clean room implementation """ raise NotImplementedError('Deprecated - needs clean room implementation') #helpers for loading, saving, manipulating and viewing numpy record arrays def safe_isnan(x): ':func:`numpy.isnan` for arbitrary types' if cbook.is_string_like(x): return False try: b = np.isnan(x) except NotImplementedError: return False except TypeError: return False else: return b def safe_isinf(x): ':func:`numpy.isinf` for arbitrary types' if cbook.is_string_like(x): return False try: b = np.isinf(x) except NotImplementedError: return False except TypeError: return False else: return b def rec_view(rec): """ Return a view of an ndarray as a recarray .. seealso:: http://projects.scipy.org/pipermail/numpy-discussion/2008-August/036429.html """ return rec.view(np.recarray) #return rec.view(dtype=(np.record, rec.dtype), type=np.recarray) def rec_append_field(rec, name, arr, dtype=None): """ Return a new record array with field name populated with data from array arr. This function is Deprecated. Please use :func:`rec_append_fields`. """ warnings.warn("use rec_append_fields", DeprecationWarning) return rec_append_fields(rec, name, arr, dtype) def rec_append_fields(rec, names, arrs, dtypes=None): """ Return a new record array with field names populated with data from arrays in arrs. If appending a single field, then names, arrs and dtypes do not have to be lists. They can just be the values themselves. """ if (not cbook.is_string_like(names) and cbook.iterable(names) \ and len(names) and cbook.is_string_like(names[0])): if len(names) != len(arrs): raise ValueError, "number of arrays do not match number of names" else: # we have only 1 name and 1 array names = [names] arrs = [arrs] arrs = map(np.asarray, arrs) if dtypes is None: dtypes = [a.dtype for a in arrs] elif not cbook.iterable(dtypes): dtypes = [dtypes] if len(arrs) != len(dtypes): if len(dtypes) == 1: dtypes = dtypes * len(arrs) else: raise ValueError, "dtypes must be None, a single dtype or a list" newdtype = np.dtype(rec.dtype.descr + zip(names, dtypes)) newrec = np.empty(rec.shape, dtype=newdtype) for field in rec.dtype.fields: newrec[field] = rec[field] for name, arr in zip(names, arrs): newrec[name] = arr return rec_view(newrec) def rec_drop_fields(rec, names): """ Return a new numpy record array with fields in names dropped. """ names = set(names) Nr = len(rec) newdtype = np.dtype([(name, rec.dtype[name]) for name in rec.dtype.names if name not in names]) newrec = np.empty(Nr, dtype=newdtype) for field in newdtype.names: newrec[field] = rec[field] return rec_view(newrec) def rec_groupby(r, groupby, stats): """ r is a numpy record array groupby is a sequence of record array attribute names that together form the grouping key. eg ('date', 'productcode') stats is a sequence of (attr, func, outname) tuples which will call ``x = func(attr)`` and assign x to the record array output with attribute outname. For example:: stats = ( ('sales', len, 'numsales'), ('sales', np.mean, 'avgsale') ) Return record array has dtype names for each attribute name in the the groupby argument, with the associated group values, and for each outname name in the stats argument, with the associated stat summary output. """ # build a dictionary from groupby keys-> list of indices into r with # those keys rowd = dict() for i, row in enumerate(r): key = tuple([row[attr] for attr in groupby]) rowd.setdefault(key, []).append(i) # sort the output by groupby keys keys = rowd.keys() keys.sort() rows = [] for key in keys: row = list(key) # get the indices for this groupby key ind = rowd[key] thisr = r[ind] # call each stat function for this groupby slice row.extend([func(thisr[attr]) for attr, func, outname in stats]) rows.append(row) # build the output record array with groupby and outname attributes attrs, funcs, outnames = zip(stats) names = list(groupby) names.extend(outnames) return np.rec.fromrecords(rows, names=names) def rec_summarize(r, summaryfuncs): """ r* is a numpy record array summaryfuncs is a list of (attr, func, outname) tuples which will apply func to the the array r[attr] and assign the output to a new attribute name outname. The returned record array is identical to r, with extra arrays for each element in summaryfuncs. """ names = list(r.dtype.names) arrays = [r[name] for name in names] for attr, func, outname in summaryfuncs: names.append(outname) arrays.append(np.asarray(func(r[attr]))) return np.rec.fromarrays(arrays, names=names) def rec_join(key, r1, r2, jointype='inner', defaults=None, r1postfix='1', r2postfix='2'): """ Join record arrays r1 and r2 on key; key is a tuple of field names -- if key is a string it is assumed to be a single attribute name. If r1 and r2 have equal values on all the keys in the key tuple, then their fields will be merged into a new record array containing the intersection of the fields of r1 and r2. r1 (also r2) must not have any duplicate keys. The jointype keyword can be 'inner', 'outer', 'leftouter'. To do a rightouter join just reverse r1 and r2. The defaults keyword is a dictionary filled with ``{column_name:default_value}`` pairs. The keywords r1postfix and r2postfix are postfixed to column names (other than keys) that are both in r1 and r2. """ if cbook.is_string_like(key): key = (key, ) for name in key: if name not in r1.dtype.names: raise ValueError('r1 does not have key field %s'%name) if name not in r2.dtype.names: raise ValueError('r2 does not have key field %s'%name) def makekey(row): return tuple([row[name] for name in key]) r1d = dict([(makekey(row),i) for i,row in enumerate(r1)]) r2d = dict([(makekey(row),i) for i,row in enumerate(r2)]) r1keys = set(r1d.keys()) r2keys = set(r2d.keys()) common_keys = r1keys & r2keys r1ind = np.array([r1d[k] for k in common_keys]) r2ind = np.array([r2d[k] for k in common_keys]) common_len = len(common_keys) left_len = right_len = 0 if jointype == "outer" or jointype == "leftouter": left_keys = r1keys.difference(r2keys) left_ind = np.array([r1d[k] for k in left_keys]) left_len = len(left_ind) if jointype == "outer": right_keys = r2keys.difference(r1keys) right_ind = np.array([r2d[k] for k in right_keys]) right_len = len(right_ind) def key_desc(name): 'if name is a string key, use the larger size of r1 or r2 before merging' dt1 = r1.dtype[name] if dt1.type != np.string_: return (name, dt1.descr[0][1]) dt2 = r1.dtype[name] assert dt2==dt1 if dt1.num>dt2.num: return (name, dt1.descr[0][1]) else: return (name, dt2.descr[0][1]) keydesc = [key_desc(name) for name in key] def mapped_r1field(name): """ The column name in newrec that corresponds to the column in r1. """ if name in key or name not in r2.dtype.names: return name else: return name + r1postfix def mapped_r2field(name): """ The column name in newrec that corresponds to the column in r2. """ if name in key or name not in r1.dtype.names: return name else: return name + r2postfix r1desc = [(mapped_r1field(desc[0]), desc[1]) for desc in r1.dtype.descr if desc[0] not in key] r2desc = [(mapped_r2field(desc[0]), desc[1]) for desc in r2.dtype.descr if desc[0] not in key] newdtype = np.dtype(keydesc + r1desc + r2desc) newrec = np.empty(common_len + left_len + right_len, dtype=newdtype) if jointype != 'inner' and defaults is not None: # fill in the defaults enmasse newrec_fields = newrec.dtype.fields.keys() for k, v in defaults.items(): if k in newrec_fields: newrec[k] = v for field in r1.dtype.names: newfield = mapped_r1field(field) if common_len: newrec[newfield][:common_len] = r1[field][r1ind] if (jointype == "outer" or jointype == "leftouter") and left_len: newrec[newfield][common_len:(common_len+left_len)] = r1[field][left_ind] for field in r2.dtype.names: newfield = mapped_r2field(field) if field not in key and common_len: newrec[newfield][:common_len] = r2[field][r2ind] if jointype == "outer" and right_len: newrec[newfield][-right_len:] = r2[field][right_ind] newrec.sort(order=key) return rec_view(newrec) def csv2rec(fname, comments='#', skiprows=0, checkrows=0, delimiter=',', converterd=None, names=None, missing='', missingd=None, use_mrecords=True): """ Load data from comma/space/tab delimited file in fname into a numpy record array and return the record array. If names is None, a header row is required to automatically assign the recarray names. The headers will be lower cased, spaces will be converted to underscores, and illegal attribute name characters removed. If names is not None, it is a sequence of names to use for the column names. In this case, it is assumed there is no header row. - fname: can be a filename or a file handle. Support for gzipped files is automatic, if the filename ends in '.gz' - comments: the character used to indicate the start of a comment in the file - skiprows: is the number of rows from the top to skip - checkrows: is the number of rows to check to validate the column data type. When set to zero all rows are validated. - converted: if not None, is a dictionary mapping column number or munged column name to a converter function. - names: if not None, is a list of header names. In this case, no header will be read from the file - missingd is a dictionary mapping munged column names to field values which signify that the field does not contain actual data and should be masked, e.g. '0000-00-00' or 'unused' - missing: a string whose value signals a missing field regardless of the column it appears in - use_mrecords: if True, return an mrecords.fromrecords record array if any of the data are missing If no rows are found, None is returned -- see :file:`examples/loadrec.py` """ if converterd is None: converterd = dict() if missingd is None: missingd = {} import dateutil.parser import datetime parsedate = dateutil.parser.parse fh = cbook.to_filehandle(fname) class FH: """ For space-delimited files, we want different behavior than comma or tab. Generally, we want multiple spaces to be treated as a single separator, whereas with comma and tab we want multiple commas to return multiple (empty) fields. The join/strip trick below effects this. """ def __init__(self, fh): self.fh = fh def close(self): self.fh.close() def seek(self, arg): self.fh.seek(arg) def fix(self, s): return ' '.join(s.split()) def next(self): return self.fix(self.fh.next()) def __iter__(self): for line in self.fh: yield self.fix(line) if delimiter==' ': fh = FH(fh) reader = csv.reader(fh, delimiter=delimiter) def process_skiprows(reader): if skiprows: for i, row in enumerate(reader): if i>=(skiprows-1): break return fh, reader process_skiprows(reader) def ismissing(name, val): "Should the value val in column name be masked?" if val == missing or val == missingd.get(name) or val == '': return True else: return False def with_default_value(func, default): def newfunc(name, val): if ismissing(name, val): return default else: return func(val) return newfunc def mybool(x): if x=='True': return True elif x=='False': return False else: raise ValueError('invalid bool') dateparser = dateutil.parser.parse mydateparser = with_default_value(dateparser, datetime.date(1,1,1)) myfloat = with_default_value(float, np.nan) myint = with_default_value(int, -1) mystr = with_default_value(str, '') mybool = with_default_value(mybool, None) def mydate(x): # try and return a date object d = dateparser(x) if d.hour>0 or d.minute>0 or d.second>0: raise ValueError('not a date') return d.date() mydate = with_default_value(mydate, datetime.date(1,1,1)) def get_func(name, item, func): # promote functions in this order funcmap = {mybool:myint,myint:myfloat, myfloat:mydate, mydate:mydateparser, mydateparser:mystr} try: func(name, item) except: if func==mystr: raise ValueError('Could not find a working conversion function') else: return get_func(name, item, funcmap[func]) # recurse else: return func # map column names that clash with builtins -- TODO - extend this list itemd = { 'return' : 'return_', 'file' : 'file_', 'print' : 'print_', } def get_converters(reader): converters = None for i, row in enumerate(reader): if i==0: converters = [mybool]len(row) if checkrows and i>checkrows: break #print i, len(names), len(row) #print 'converters', zip(converters, row) for j, (name, item) in enumerate(zip(names, row)): func = converterd.get(j) if func is None: func = converterd.get(name) if func is None: #if not item.strip(): continue func = converters[j] if len(item.strip()): func = get_func(name, item, func) else: # how should we handle custom converters and defaults? func = with_default_value(func, None) converters[j] = func return converters # Get header and remove invalid characters needheader = names is None if needheader: for row in reader: #print 'csv2rec', row if len(row) and row[0].startswith(comments): continue headers = row break # remove these chars delete = set("""~!@#$%^&()-=+~\\|]}[{';: /?.>,<""") delete.add('"') names = [] seen = dict() for i, item in enumerate(headers): item = item.strip().lower().replace(' ', '_') item = ''.join([c for c in item if c not in delete]) if not len(item): item = 'column%d'%i item = itemd.get(item, item) cnt = seen.get(item, 0) if cnt>0: names.append(item + '_%d'%cnt) else: names.append(item) seen[item] = cnt+1 else: if cbook.is_string_like(names): names = [n.strip() for n in names.split(',')] # get the converter functions by inspecting checkrows converters = get_converters(reader) if converters is None: raise ValueError('Could not find any valid data in CSV file') # reset the reader and start over fh.seek(0) reader = csv.reader(fh, delimiter=delimiter) process_skiprows(reader) if needheader: skipheader = reader.next() # iterate over the remaining rows and convert the data to date # objects, ints, or floats as approriate rows = [] rowmasks = [] for i, row in enumerate(reader): if not len(row): continue if row[0].startswith(comments): continue rows.append([func(name, val) for func, name, val in zip(converters, names, row)]) rowmasks.append([ismissing(name, val) for name, val in zip(names, row)]) fh.close() if not len(rows): return None if use_mrecords and np.any(rowmasks): try: from numpy.ma import mrecords except ImportError: raise RuntimeError('numpy 1.05 or later is required for masked array support') else: r = mrecords.fromrecords(rows, names=names, mask=rowmasks) else: r = np.rec.fromrecords(rows, names=names) return r # a series of classes for describing the format intentions of various rec views class FormatObj: def tostr(self, x): return self.toval(x) def toval(self, x): return str(x) def fromstr(self, s): return s class FormatString(FormatObj): def tostr(self, x): val = repr(x) return val[1:-1] #class FormatString(FormatObj): # def tostr(self, x): # return '"%r"'%self.toval(x) class FormatFormatStr(FormatObj): def __init__(self, fmt): self.fmt = fmt def tostr(self, x): if x is None: return 'None' return self.fmt%self.toval(x) class FormatFloat(FormatFormatStr): def __init__(self, precision=4, scale=1.): FormatFormatStr.__init__(self, '%%1.%df'%precision) self.precision = precision self.scale = scale def toval(self, x): if x is not None: x = x * self.scale return x def fromstr(self, s): return float(s)/self.scale class FormatInt(FormatObj): def tostr(self, x): return '%d'%int(x) def toval(self, x): return int(x) def fromstr(self, s): return int(s) class FormatBool(FormatObj): def toval(self, x): return str(x) def fromstr(self, s): return bool(s) class FormatPercent(FormatFloat): def __init__(self, precision=4): FormatFloat.__init__(self, precision, scale=100.) class FormatThousands(FormatFloat): def __init__(self, precision=4): FormatFloat.__init__(self, precision, scale=1e-3) class FormatMillions(FormatFloat): def __init__(self, precision=4): FormatFloat.__init__(self, precision, scale=1e-6) class FormatDate(FormatObj): def __init__(self, fmt): self.fmt = fmt def toval(self, x): if x is None: return 'None' return x.strftime(self.fmt) def fromstr(self, x): import dateutil.parser return dateutil.parser.parse(x).date() class FormatDatetime(FormatDate): def __init__(self, fmt='%Y-%m-%d %H:%M:%S'): FormatDate.__init__(self, fmt) def fromstr(self, x): import dateutil.parser return dateutil.parser.parse(x) defaultformatd = { np.bool_ : FormatBool(), np.int16 : FormatInt(), np.int32 : FormatInt(), np.int64 : FormatInt(), np.float32 : FormatFloat(), np.float64 : FormatFloat(), np.object_ : FormatObj(), np.string_ : FormatString(), } def get_formatd(r, formatd=None): 'build a formatd guaranteed to have a key for every dtype name' if formatd is None: formatd = dict() for i, name in enumerate(r.dtype.names): dt = r.dtype[name] format = formatd.get(name) if format is None: format = defaultformatd.get(dt.type, FormatObj()) formatd[name] = format return formatd def csvformat_factory(format): format = copy.deepcopy(format) if isinstance(format, FormatFloat): format.scale = 1. # override scaling for storage format.fmt = '%r' return format def rec2txt(r, header=None, padding=3, precision=3): """ Returns a textual representation of a record array. r: numpy recarray header: list of column headers padding: space between each column precision: number of decimal places to use for floats. Set to an integer to apply to all floats. Set to a list of integers to apply precision individually. Precision for non-floats is simply ignored. Example:: precision=[0,2,3] Output:: ID Price Return ABC 12.54 0.234 XYZ 6.32 -0.076 """ if cbook.is_numlike(precision): precision = [precision]len(r.dtype) def get_type(item,atype=int): tdict = {None:int, int:float, float:str} try: atype(str(item)) except: return get_type(item,tdict[atype]) return atype def get_justify(colname, column, precision): ntype = type(column[0]) if ntype==np.str or ntype==np.str_ or ntype==np.string0 or ntype==np.string_: length = max(len(colname),column.itemsize) return 0, length+padding, "%s" # left justify if ntype==np.int or ntype==np.int16 or ntype==np.int32 or ntype==np.int64 or ntype==np.int8 or ntype==np.int_: length = max(len(colname),np.max(map(len,map(str,column)))) return 1, length+padding, "%d" # right justify # JDH: my powerbook does not have np.float96 using np 1.3.0 """ In [2]: np.__version__ Out[2]: '1.3.0.dev5948' In [3]: !uname -a Darwin Macintosh-5.local 9.4.0 Darwin Kernel Version 9.4.0: Mon Jun 9 19:30:53 PDT 2008; root:xnu-1228.5.20~1/RELEASE_I386 i386 i386 In [4]: np.float96 --------------------------------------------------------------------------- AttributeError Traceback (most recent call la """ if ntype==np.float or ntype==np.float32 or ntype==np.float64 or (hasattr(np, 'float96') and (ntype==np.float96)) or ntype==np.float_: fmt = "%." + str(precision) + "f" length = max(len(colname),np.max(map(len,map(lambda x:fmt%x,column)))) return 1, length+padding, fmt # right justify return 0, max(len(colname),np.max(map(len,map(str,column))))+padding, "%s" if header is None: header = r.dtype.names justify_pad_prec = [get_justify(header[i],r.__getitem__(colname),precision[i]) for i, colname in enumerate(r.dtype.names)] justify_pad_prec_spacer = [] for i in range(len(justify_pad_prec)): just,pad,prec = justify_pad_prec[i] if i == 0: justify_pad_prec_spacer.append((just,pad,prec,0)) else: pjust,ppad,pprec = justify_pad_prec[i-1] if pjust == 0 and just == 1: justify_pad_prec_spacer.append((just,pad-padding,prec,0)) elif pjust == 1 and just == 0: justify_pad_prec_spacer.append((just,pad,prec,padding)) else: justify_pad_prec_spacer.append((just,pad,prec,0)) def format(item, just_pad_prec_spacer): just, pad, prec, spacer = just_pad_prec_spacer if just == 0: return spacer' ' + str(item).ljust(pad) else: if get_type(item) == float: item = (prec%float(item)) elif get_type(item) == int: item = (prec%int(item)) return item.rjust(pad) textl = [] textl.append(''.join([format(colitem,justify_pad_prec_spacer[j]) for j, colitem in enumerate(header)])) for i, row in enumerate(r): textl.append(''.join([format(colitem,justify_pad_prec_spacer[j]) for j, colitem in enumerate(row)])) if i==0: textl[0] = textl[0].rstrip() text = os.linesep.join(textl) return text def rec2csv(r, fname, delimiter=',', formatd=None, missing='', missingd=None): """ Save the data from numpy recarray r into a comma-/space-/tab-delimited file. The record array dtype names will be used for column headers. fname: can be a filename or a file handle. Support for gzipped files is automatic, if the filename ends in '.gz' .. seealso:: :func:`csv2rec`: For information about missing and missingd, which can be used to fill in masked values into your CSV file. """ if missingd is None: missingd = dict() def with_mask(func): def newfunc(val, mask, mval): if mask: return mval else: return func(val) return newfunc formatd = get_formatd(r, formatd) funcs = [] for i, name in enumerate(r.dtype.names): funcs.append(with_mask(csvformat_factory(formatd[name]).tostr)) fh, opened = cbook.to_filehandle(fname, 'w', return_opened=True) writer = csv.writer(fh, delimiter=delimiter) header = r.dtype.names writer.writerow(header) # Our list of specials for missing values mvals = [] for name in header: mvals.append(missingd.get(name, missing)) ismasked = False if len(r): row = r[0] ismasked = hasattr(row, '_fieldmask') for row in r: if ismasked: row, rowmask = row.item(), row._fieldmask.item() else: rowmask = [False] * len(row) writer.writerow([func(val, mask, mval) for func, val, mask, mval in zip(funcs, row, rowmask, mvals)]) if opened: fh.close() def griddata(x,y,z,xi,yi): """ ``zi = griddata(x,y,z,xi,yi)`` fits a surface of the form z = f(x, y) to the data in the (usually) nonuniformly spaced vectors (x, y, z). :func:`griddata` interpolates this surface at the points specified by (xi, yi) to produce zi. xi and yi must describe a regular grid, can be either 1D or 2D, but must be monotonically increasing. A masked array is returned if any grid points are outside convex hull defined by input data (no extrapolation is done). Uses natural neighbor interpolation based on Delaunay triangulation. By default, this algorithm is provided by the :mod:`matplotlib.delaunay` package, written by Robert Kern. The triangulation algorithm in this package is known to fail on some nearly pathological cases. For this reason, a separate toolkit (:mod:`mpl_tookits.natgrid`) has been created that provides a more robust algorithm fof triangulation and interpolation. This toolkit is based on the NCAR natgrid library, which contains code that is not redistributable under a BSD-compatible license. When installed, this function will use the :mod:`mpl_toolkits.natgrid` algorithm, otherwise it will use the built-in :mod:`matplotlib.delaunay` package. The natgrid matplotlib toolkit can be downloaded from http://sourceforge.net/project/showfiles.php?group_id=80706&package_id=142792 """ try: from mpl_toolkits.natgrid import _natgrid, __version__ _use_natgrid = True except ImportError: import matplotlib.delaunay as delaunay from matplotlib.delaunay import __version__ _use_natgrid = False if not griddata._reported: if _use_natgrid: verbose.report('using natgrid version %s' % __version__) else: verbose.report('using delaunay version %s' % __version__) griddata._reported = True if xi.ndim != yi.ndim: raise TypeError("inputs xi and yi must have same number of dimensions (1 or 2)") if xi.ndim != 1 and xi.ndim != 2: raise TypeError("inputs xi and yi must be 1D or 2D.") if not len(x)==len(y)==len(z): raise TypeError("inputs x,y,z must all be 1D arrays of the same length") # remove masked points. if hasattr(z,'mask'): x = x.compress(z.mask == False) y = y.compress(z.mask == False) z = z.compressed() if _use_natgrid: # use natgrid toolkit if available. if xi.ndim == 2: xi = xi[0,:] yi = yi[:,0] # override default natgrid internal parameters. _natgrid.seti('ext',0) _natgrid.setr('nul',np.nan) # cast input arrays to doubles (this makes a copy) x = x.astype(np.float) y = y.astype(np.float) z = z.astype(np.float) xo = xi.astype(np.float) yo = yi.astype(np.float) if min(xo[1:]-xo[0:-1]) < 0 or min(yo[1:]-yo[0:-1]) < 0: raise ValueError, 'output grid defined by xi,yi must be monotone increasing' # allocate array for output (buffer will be overwritten by nagridd) zo = np.empty((yo.shape[0],xo.shape[0]), np.float) _natgrid.natgridd(x,y,z,xo,yo,zo) else: # use Robert Kern's delaunay package from scikits (default) if xi.ndim != yi.ndim: raise TypeError("inputs xi and yi must have same number of dimensions (1 or 2)") if xi.ndim != 1 and xi.ndim != 2: raise TypeError("inputs xi and yi must be 1D or 2D.") if xi.ndim == 1: xi,yi = np.meshgrid(xi,yi) # triangulate data tri = delaunay.Triangulation(x,y) # interpolate data interp = tri.nn_interpolator(z) zo = interp(xi,yi) # mask points on grid outside convex hull of input data. if np.any(np.isnan(zo)): zo = np.ma.masked_where(np.isnan(zo),zo) return zo griddata._reported = False ################################################## # Linear interpolation algorithms ################################################## def less_simple_linear_interpolation( x, y, xi, extrap=False ): """ This function provides simple (but somewhat less so than :func:`cbook.simple_linear_interpolation`) linear interpolation. :func:`simple_linear_interpolation` will give a list of point between a start and an end, while this does true linear interpolation at an arbitrary set of points. This is very inefficient linear interpolation meant to be used only for a small number of points in relatively non-intensive use cases. For real linear interpolation, use scipy. """ if cbook.is_scalar(xi): xi = [xi] x = np.asarray(x) y = np.asarray(y) xi = np.asarray(xi) s = list(y.shape) s[0] = len(xi) yi = np.tile( np.nan, s ) for ii,xx in enumerate(xi): bb = x == xx if np.any(bb): jj, = np.nonzero(bb) yi[ii] = y[jj[0]] elif xx<x[0]: if extrap: yi[ii] = y[0] elif xx>x[-1]: if extrap: yi[ii] = y[-1] else: jj, = np.nonzero(x<xx) jj = max(jj) yi[ii] = y[jj] + (xx-x[jj])/(x[jj+1]-x[jj]) * (y[jj+1]-y[jj]) return yi def slopes(x,y): """ :func:`slopes` calculates the slope y'(x) The slope is estimated using the slope obtained from that of a parabola through any three consecutive points. This method should be superior to that described in the appendix of A CONSISTENTLY WELL BEHAVED METHOD OF INTERPOLATION by Russel W. Stineman (Creative Computing July 1980) in at least one aspect: Circles for interpolation demand a known aspect ratio between x- and y-values. For many functions, however, the abscissa are given in different dimensions, so an aspect ratio is completely arbitrary. The parabola method gives very similar results to the circle method for most regular cases but behaves much better in special cases. Norbert Nemec, Institute of Theoretical Physics, University or Regensburg, April 2006 Norbert.Nemec at physik.uni-regensburg.de (inspired by a original implementation by Halldor Bjornsson, Icelandic Meteorological Office, March 2006 halldor at vedur.is) """ # Cast key variables as float. x=np.asarray(x, np.float_) y=np.asarray(y, np.float_) yp=np.zeros(y.shape, np.float_) dx=x[1:] - x[:-1] dy=y[1:] - y[:-1] dydx = dy/dx yp[1:-1] = (dydx[:-1] * dx[1:] + dydx[1:] * dx[:-1])/(dx[1:] + dx[:-1]) yp[0] = 2.0 * dy[0]/dx[0] - yp[1] yp[-1] = 2.0 * dy[-1]/dx[-1] - yp[-2] return yp def stineman_interp(xi,x,y,yp=None): """ Given data vectors x and y, the slope vector yp and a new abscissa vector xi, the function :func:`stineman_interp` uses Stineman interpolation to calculate a vector yi corresponding to xi. Here's an example that generates a coarse sine curve, then interpolates over a finer abscissa:: x = linspace(0,2pi,20); y = sin(x); yp = cos(x) xi = linspace(0,2pi,40); yi = stineman_interp(xi,x,y,yp); plot(x,y,'o',xi,yi) The interpolation method is described in the article A CONSISTENTLY WELL BEHAVED METHOD OF INTERPOLATION by Russell W. Stineman. The article appeared in the July 1980 issue of Creative Computing with a note from the editor stating that while they were: not an academic journal but once in a while something serious and original comes in adding that this was "apparently a real solution" to a well known problem. For yp = None, the routine automatically determines the slopes using the :func:`slopes` routine. x is assumed to be sorted in increasing order. For values ``xi[j] < x[0]`` or ``xi[j] > x[-1]``, the routine tries an extrapolation. The relevance of the data obtained from this, of course, is questionable... Original implementation by Halldor Bjornsson, Icelandic Meteorolocial Office, March 2006 halldor at vedur.is Completely reworked and optimized for Python by Norbert Nemec, Institute of Theoretical Physics, University or Regensburg, April 2006 Norbert.Nemec at physik.uni-regensburg.de """ # Cast key variables as float. x=np.asarray(x, np.float_) y=np.asarray(y, np.float_) assert x.shape == y.shape N=len(y) if yp is None: yp = slopes(x,y) else: yp=np.asarray(yp, np.float_) xi=np.asarray(xi, np.float_) yi=np.zeros(xi.shape, np.float_) # calculate linear slopes dx = x[1:] - x[:-1] dy = y[1:] - y[:-1] s = dy/dx #note length of s is N-1 so last element is #N-2 # find the segment each xi is in # this line actually is the key to the efficiency of this implementation idx = np.searchsorted(x[1:-1], xi) # now we have generally: x[idx[j]] <= xi[j] <= x[idx[j]+1] # except at the boundaries, where it may be that xi[j] < x[0] or xi[j] > x[-1] # the y-values that would come out from a linear interpolation: sidx = s.take(idx) xidx = x.take(idx) yidx = y.take(idx) xidxp1 = x.take(idx+1) yo = yidx + sidx * (xi - xidx) # the difference that comes when using the slopes given in yp dy1 = (yp.take(idx)- sidx) * (xi - xidx) # using the yp slope of the left point dy2 = (yp.take(idx+1)-sidx) * (xi - xidxp1) # using the yp slope of the right point dy1dy2 = dy1dy2 # The following is optimized for Python. The solution actually # does more calculations than necessary but exploiting the power # of numpy, this is far more efficient than coding a loop by hand # in Python yi = yo + dy1dy2 np.choose(np.array(np.sign(dy1dy2), np.int32)+1, ((2xi-xidx-xidxp1)/((dy1-dy2)(xidxp1-xidx)), 0.0, 1/(dy1+dy2),)) return yi ################################################## # Code related to things in and around polygons ################################################## def inside_poly(points, verts): """ points is a sequence of x, y points. verts is a sequence of x, y vertices of a polygon. Return value is a sequence of indices into points for the points that are inside the polygon. """ res, = np.nonzero(nxutils.points_inside_poly(points, verts)) return res def poly_below(xmin, xs, ys): """ Given a sequence of xs and ys, return the vertices of a polygon that has a horizontal base at xmin and an upper bound at the ys. xmin is a scalar. Intended for use with :meth:`matplotlib.axes.Axes.fill`, eg:: xv, yv = poly_below(0, x, y) ax.fill(xv, yv) """ if ma.isMaskedArray(xs) or ma.isMaskedArray(ys): nx = ma else: nx = np xs = nx.asarray(xs) ys = nx.asarray(ys) Nx = len(xs) Ny = len(ys) assert(Nx==Ny) x = xminnx.ones(2Nx) y = nx.ones(2Nx) x[:Nx] = xs y[:Nx] = ys y[Nx:] = ys[::-1] return x, y def poly_between(x, ylower, yupper): """ Given a sequence of x, ylower* and yupper, return the polygon that fills the regions between them. ylower or yupper can be scalar or iterable. If they are iterable, they must be equal in length to x. Return value is x, y arrays for use with :meth:`matplotlib.axes.Axes.fill`. """ if ma.isMaskedArray(ylower) or ma.isMaskedArray(yupper) or ma.isMaskedArray(x): nx = ma else: nx = np Nx = len(x) if not cbook.iterable(ylower): ylower = ylowernx.ones(Nx) if not cbook.iterable(yupper): yupper = yuppernx.ones(Nx) x = nx.concatenate( (x, x[::-1]) ) y = nx.concatenate( (yupper, ylower[::-1]) ) return x,y def is_closed_polygon(X): """ Tests whether first and last object in a sequence are the same. These are presumably coordinates on a polygonal curve, in which case this function tests if that curve is closed. """ return np.all(X[0] == X[-1]) def contiguous_regions(mask): """ return a list of (ind0, ind1) such that mask[ind0:ind1].all() is True and we cover all such regions TODO: this is a pure python implementation which probably has a much faster numpy impl """ in_region = None boundaries = [] for i, val in enumerate(mask): if in_region is None and val: in_region = i elif in_region is not None and not val: boundaries.append((in_region, i)) in_region = None if in_region is not None: boundaries.append((in_region, i+1)) return boundaries ################################################## # Vector and path length geometry calculations ################################################## def vector_lengths( X, P=2., axis=None ): """ Finds the length of a set of vectors in n dimensions. This is like the :func:`numpy.norm` function for vectors, but has the ability to work over a particular axis of the supplied array or matrix. Computes ``(sum((x_i)^P))^(1/P)`` for each ``{x_i}`` being the elements of X along the given axis. If axis is None, compute over all elements of X. """ X = np.asarray(X) return (np.sum(X(P),axis=axis))(1./P) def distances_along_curve( X ): """ Computes the distance between a set of successive points in N dimensions. Where X is an M x N array or matrix. The distances between successive rows is computed. Distance is the standard Euclidean distance. """ X = np.diff( X, axis=0 ) return vector_lengths(X,axis=1) def path_length(X): """ Computes the distance travelled along a polygonal curve in N dimensions. Where X is an M x N array or matrix. Returns an array of length M consisting of the distance along the curve at each point (i.e., the rows of X). """ X = distances_along_curve(X) return np.concatenate( (np.zeros(1), np.cumsum(X)) ) def quad2cubic(q0x, q0y, q1x, q1y, q2x, q2y): """ Converts a quadratic Bezier curve to a cubic approximation. The inputs are the x and y coordinates of the three control points of a quadratic curve, and the output is a tuple of x and y coordinates of the four control points of the cubic curve. """ # c0x, c0y = q0x, q0y c1x, c1y = q0x + 2./3. * (q1x - q0x), q0y + 2./3. * (q1y - q0y) c2x, c2y = c1x + 1./3. * (q2x - q0x), c1y + 1./3. * (q2y - q0y) # c3x, c3y = q2x, q2y return q0x, q0y, c1x, c1y, c2x, c2y, q2x, q2y	104,273	Python	.py	2,564	33.9922	141	0.629487	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,240	fontconfig_pattern.py	numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/fontconfig_pattern.py	""" A module for parsing and generating fontconfig patterns. See the `fontconfig pattern specification <http://www.fontconfig.org/fontconfig-user.html>`_ for more information. """ # Author : Michael Droettboom <mdroe@stsci.edu> # License : matplotlib license (PSF compatible) # This class is defined here because it must be available in: # - The old-style config framework (:file:`rcsetup.py`) # - The traits-based config framework (:file:`mpltraits.py`) # - The font manager (:file:`font_manager.py`) # It probably logically belongs in :file:`font_manager.py`, but # placing it in any of these places would have created cyclical # dependency problems, or an undesired dependency on traits even # when the traits-based config framework is not used. import re from matplotlib.pyparsing import Literal, ZeroOrMore, \ Optional, Regex, StringEnd, ParseException, Suppress family_punc = r'\\\-:,' family_unescape = re.compile(r'\\([%s])' % family_punc).sub family_escape = re.compile(r'([%s])' % family_punc).sub value_punc = r'\\=_:,' value_unescape = re.compile(r'\\([%s])' % value_punc).sub value_escape = re.compile(r'([%s])' % value_punc).sub class FontconfigPatternParser: """A simple pyparsing-based parser for fontconfig-style patterns. See the `fontconfig pattern specification <http://www.fontconfig.org/fontconfig-user.html>`_ for more information. """ _constants = { 'thin' : ('weight', 'light'), 'extralight' : ('weight', 'light'), 'ultralight' : ('weight', 'light'), 'light' : ('weight', 'light'), 'book' : ('weight', 'book'), 'regular' : ('weight', 'regular'), 'normal' : ('weight', 'normal'), 'medium' : ('weight', 'medium'), 'demibold' : ('weight', 'demibold'), 'semibold' : ('weight', 'semibold'), 'bold' : ('weight', 'bold'), 'extrabold' : ('weight', 'extra bold'), 'black' : ('weight', 'black'), 'heavy' : ('weight', 'heavy'), 'roman' : ('slant', 'normal'), 'italic' : ('slant', 'italic'), 'oblique' : ('slant', 'oblique'), 'ultracondensed' : ('width', 'ultra-condensed'), 'extracondensed' : ('width', 'extra-condensed'), 'condensed' : ('width', 'condensed'), 'semicondensed' : ('width', 'semi-condensed'), 'expanded' : ('width', 'expanded'), 'extraexpanded' : ('width', 'extra-expanded'), 'ultraexpanded' : ('width', 'ultra-expanded') } def __init__(self): family = Regex(r'([^%s]\|(\\[%s]))' % (family_punc, family_punc)) \ .setParseAction(self._family) size = Regex(r"([0-9]+\.?[0-9]\|\.[0-9]+)") \ .setParseAction(self._size) name = Regex(r'[a-z]+') \ .setParseAction(self._name) value = Regex(r'([^%s]\|(\\[%s]))' % (value_punc, value_punc)) \ .setParseAction(self._value) families =(family + ZeroOrMore( Literal(',') + family) ).setParseAction(self._families) point_sizes =(size + ZeroOrMore( Literal(',') + size) ).setParseAction(self._point_sizes) property =( (name + Suppress(Literal('=')) + value + ZeroOrMore( Suppress(Literal(',')) + value) ) \| name ).setParseAction(self._property) pattern =(Optional( families) + Optional( Literal('-') + point_sizes) + ZeroOrMore( Literal(':') + property) + StringEnd() ) self._parser = pattern self.ParseException = ParseException def parse(self, pattern): """ Parse the given fontconfig pattern* and return a dictionary of key/value pairs useful for initializing a :class:`font_manager.FontProperties` object. """ props = self._properties = {} try: self._parser.parseString(pattern) except self.ParseException, e: raise ValueError("Could not parse font string: '%s'\n%s" % (pattern, e)) self._properties = None return props def _family(self, s, loc, tokens): return [family_unescape(r'\1', str(tokens[0]))] def _size(self, s, loc, tokens): return [float(tokens[0])] def _name(self, s, loc, tokens): return [str(tokens[0])] def _value(self, s, loc, tokens): return [value_unescape(r'\1', str(tokens[0]))] def _families(self, s, loc, tokens): self._properties['family'] = [str(x) for x in tokens] return [] def _point_sizes(self, s, loc, tokens): self._properties['size'] = [str(x) for x in tokens] return [] def _property(self, s, loc, tokens): if len(tokens) == 1: if tokens[0] in self._constants: key, val = self._constants[tokens[0]] self._properties.setdefault(key, []).append(val) else: key = tokens[0] val = tokens[1:] self._properties.setdefault(key, []).extend(val) return [] parse_fontconfig_pattern = FontconfigPatternParser().parse def generate_fontconfig_pattern(d): """ Given a dictionary of key/value pairs, generates a fontconfig pattern string. """ props = [] families = '' size = '' for key in 'family style variant weight stretch file size'.split(): val = getattr(d, 'get_' + key)() if val is not None and val != []: if type(val) == list: val = [value_escape(r'\\\1', str(x)) for x in val if x is not None] if val != []: val = ','.join(val) props.append(":%s=%s" % (key, val)) return ''.join(props)	6,429	Python	.py	154	31.253247	84	0.516005	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,241	mpl.py	numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/mpl.py	from matplotlib import artist from matplotlib import axis from matplotlib import axes from matplotlib import cbook from matplotlib import collections from matplotlib import colors from matplotlib import colorbar from matplotlib import contour from matplotlib import dates from matplotlib import figure from matplotlib import finance from matplotlib import font_manager from matplotlib import image from matplotlib import legend from matplotlib import lines from matplotlib import mlab from matplotlib import cm from matplotlib import patches from matplotlib import quiver from matplotlib import rcParams from matplotlib import table from matplotlib import text from matplotlib import ticker from matplotlib import transforms from matplotlib import units from matplotlib import widgets	785	Python	.py	26	29.192308	35	0.895916	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,242	pyplot.py	numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/pyplot.py	import sys import matplotlib from matplotlib import _pylab_helpers, interactive from matplotlib.cbook import dedent, silent_list, is_string_like, is_numlike from matplotlib.figure import Figure, figaspect from matplotlib.backend_bases import FigureCanvasBase from matplotlib.image import imread as _imread from matplotlib import rcParams, rcParamsDefault, get_backend from matplotlib.rcsetup import interactive_bk as _interactive_bk from matplotlib.artist import getp, get, Artist from matplotlib.artist import setp as _setp from matplotlib.axes import Axes from matplotlib.projections import PolarAxes from matplotlib import mlab # for csv2rec in plotfile from matplotlib.scale import get_scale_docs, get_scale_names from matplotlib import cm from matplotlib.cm import get_cmap # We may not need the following imports here: from matplotlib.colors import Normalize, normalize # latter for backwards compat. from matplotlib.lines import Line2D from matplotlib.text import Text, Annotation from matplotlib.patches import Polygon, Rectangle, Circle, Arrow from matplotlib.widgets import SubplotTool, Button, Slider, Widget from ticker import TickHelper, Formatter, FixedFormatter, NullFormatter,\ FuncFormatter, FormatStrFormatter, ScalarFormatter,\ LogFormatter, LogFormatterExponent, LogFormatterMathtext,\ Locator, IndexLocator, FixedLocator, NullLocator,\ LinearLocator, LogLocator, AutoLocator, MultipleLocator,\ MaxNLocator ## Backend detection ## def _backend_selection(): """ If rcParams['backend_fallback'] is true, check to see if the current backend is compatible with the current running event loop, and if not switches to a compatible one. """ backend = rcParams['backend'] if not rcParams['backend_fallback'] or \ backend not in _interactive_bk: return is_agg_backend = rcParams['backend'].endswith('Agg') if 'wx' in sys.modules and not backend in ('WX', 'WXAgg'): import wx if wx.App.IsMainLoopRunning(): rcParams['backend'] = 'wx' + 'Agg' * is_agg_backend elif 'qt' in sys.modules and not backend == 'QtAgg': import qt if not qt.qApp.startingUp(): # The mainloop is running. rcParams['backend'] = 'qtAgg' elif 'PyQt4.QtCore' in sys.modules and not backend == 'Qt4Agg': import PyQt4.QtGui if not PyQt4.QtGui.qApp.startingUp(): # The mainloop is running. rcParams['backend'] = 'qt4Agg' elif 'gtk' in sys.modules and not backend in ('GTK', 'GTKAgg', 'GTKCairo'): import gobject if gobject.MainLoop().is_running(): rcParams['backend'] = 'gtk' + 'Agg' * is_agg_backend elif 'Tkinter' in sys.modules and not backend == 'TkAgg': #import Tkinter pass #what if anything do we need to do for tkinter? _backend_selection() ## Global ## from matplotlib.backends import pylab_setup new_figure_manager, draw_if_interactive, show = pylab_setup() def findobj(o=None, match=None): if o is None: o = gcf() return o.findobj(match) findobj.__doc__ = Artist.findobj.__doc__ def switch_backend(newbackend): """ Switch the default backend to newbackend. This feature is experimental, and is only expected to work switching to an image backend. Eg, if you have a bunch of PostScript scripts that you want to run from an interactive ipython session, you may want to switch to the PS backend before running them to avoid having a bunch of GUI windows popup. If you try to interactively switch from one GUI backend to another, you will explode. Calling this command will close all open windows. """ close('all') global new_figure_manager, draw_if_interactive, show matplotlib.use(newbackend, warn=False) reload(matplotlib.backends) from matplotlib.backends import pylab_setup new_figure_manager, draw_if_interactive, show = pylab_setup() def isinteractive(): """ Return the interactive status """ return matplotlib.is_interactive() def ioff(): 'Turn interactive mode off.' matplotlib.interactive(False) def ion(): 'Turn interactive mode on.' matplotlib.interactive(True) def rc(args, kwargs): matplotlib.rc(args, *kwargs) if matplotlib.rc.__doc__ is not None: rc.__doc__ = dedent(matplotlib.rc.__doc__) def rcdefaults(): matplotlib.rcdefaults() draw_if_interactive() if matplotlib.rcdefaults.__doc__ is not None: rcdefaults.__doc__ = dedent(matplotlib.rcdefaults.__doc__) # The current "image" (ScalarMappable) is tracked here on a # per-pylab-session basis: def gci(): """ Get the current :class:`~matplotlib.cm.ScalarMappable` instance (image or patch collection), or None* if no images or patch collections have been defined. The commands :func:`~matplotlib.pyplot.imshow` and :func:`~matplotlib.pyplot.figimage` create :class:`~matplotlib.image.Image` instances, and the commands :func:`~matplotlib.pyplot.pcolor` and :func:`~matplotlib.pyplot.scatter` create :class:`~matplotlib.collections.Collection` instances. """ return gci._current gci._current = None def sci(im): """ Set the current image (target of colormap commands like :func:`~matplotlib.pyplot.jet`, :func:`~matplotlib.pyplot.hot` or :func:`~matplotlib.pyplot.clim`). """ gci._current = im ## Any Artist ## # (getp is simply imported) def setp(args, kwargs): ret = _setp(args, kwargs) draw_if_interactive() return ret if _setp.__doc__ is not None: setp.__doc__ = _setp.__doc__ ## Figures ## def figure(num=None, # autoincrement if None, else integer from 1-N figsize = None, # defaults to rc figure.figsize dpi = None, # defaults to rc figure.dpi facecolor = None, # defaults to rc figure.facecolor edgecolor = None, # defaults to rc figure.edgecolor frameon = True, FigureClass = Figure, kwargs ): """ call signature:: figure(num=None, figsize=(8, 6), dpi=80, facecolor='w', edgecolor='k') Create a new figure and return a :class:`matplotlib.figure.Figure` instance. If num = None, the figure number will be incremented and a new figure will be created. The returned figure objects have a number attribute holding this number. If num is an integer, and ``figure(num)`` already exists, make it active and return the handle to it. If ``figure(num)`` does not exist it will be created. Numbering starts at 1, matlab style:: figure(1) If you are creating many figures, make sure you explicitly call "close" on the figures you are not using, because this will enable pylab to properly clean up the memory. Optional keyword arguments: ========= ======================================================= Keyword Description ========= ======================================================= figsize width x height in inches; defaults to rc figure.figsize dpi resolution; defaults to rc figure.dpi facecolor the background color; defaults to rc figure.facecolor edgecolor the border color; defaults to rc figure.edgecolor ========= ======================================================= rcParams defines the default values, which can be modified in the matplotlibrc file FigureClass is a :class:`~matplotlib.figure.Figure` or derived class that will be passed on to :meth:`new_figure_manager` in the backends which allows you to hook custom Figure classes into the pylab interface. Additional kwargs will be passed on to your figure init function. """ if figsize is None : figsize = rcParams['figure.figsize'] if dpi is None : dpi = rcParams['figure.dpi'] if facecolor is None : facecolor = rcParams['figure.facecolor'] if edgecolor is None : edgecolor = rcParams['figure.edgecolor'] if num is None: allnums = [f.num for f in _pylab_helpers.Gcf.get_all_fig_managers()] if allnums: num = max(allnums) + 1 else: num = 1 else: num = int(num) # crude validation of num argument figManager = _pylab_helpers.Gcf.get_fig_manager(num) if figManager is None: if get_backend().lower() == 'ps': dpi = 72 figManager = new_figure_manager(num, figsize=figsize, dpi=dpi, facecolor=facecolor, edgecolor=edgecolor, frameon=frameon, FigureClass=FigureClass, *kwargs) # make this figure current on button press event def make_active(event): _pylab_helpers.Gcf.set_active(figManager) cid = figManager.canvas.mpl_connect('button_press_event', make_active) figManager._cidgcf = cid _pylab_helpers.Gcf.set_active(figManager) figManager.canvas.figure.number = num draw_if_interactive() return figManager.canvas.figure def gcf(): "Return a handle to the current figure." figManager = _pylab_helpers.Gcf.get_active() if figManager is not None: return figManager.canvas.figure else: return figure() def get_current_fig_manager(): figManager = _pylab_helpers.Gcf.get_active() if figManager is None: gcf() # creates an active figure as a side effect figManager = _pylab_helpers.Gcf.get_active() return figManager # note we check for __doc__ is not None since py2exe optimize removes # the docstrings def connect(s, func): return get_current_fig_manager().canvas.mpl_connect(s, func) if FigureCanvasBase.mpl_connect.__doc__ is not None: connect.__doc__ = dedent(FigureCanvasBase.mpl_connect.__doc__) def disconnect(cid): return get_current_fig_manager().canvas.mpl_disconnect(cid) if FigureCanvasBase.mpl_disconnect.__doc__ is not None: disconnect.__doc__ = dedent(FigureCanvasBase.mpl_disconnect.__doc__) def close(args): """ Close a figure window ``close()`` by itself closes the current figure ``close(num)`` closes figure number num ``close(h)`` where h is a :class:`Figure` instance, closes that figure ``close('all')`` closes all the figure windows """ if len(args)==0: figManager = _pylab_helpers.Gcf.get_active() if figManager is None: return else: figManager.canvas.mpl_disconnect(figManager._cidgcf) _pylab_helpers.Gcf.destroy(figManager.num) elif len(args)==1: arg = args[0] if arg=='all': for manager in _pylab_helpers.Gcf.get_all_fig_managers(): manager.canvas.mpl_disconnect(manager._cidgcf) _pylab_helpers.Gcf.destroy(manager.num) elif isinstance(arg, int): _pylab_helpers.Gcf.destroy(arg) elif isinstance(arg, Figure): for manager in _pylab_helpers.Gcf.get_all_fig_managers(): if manager.canvas.figure==arg: manager.canvas.mpl_disconnect(manager._cidgcf) _pylab_helpers.Gcf.destroy(manager.num) else: raise TypeError('Unrecognized argument type %s to close'%type(arg)) else: raise TypeError('close takes 0 or 1 arguments') def clf(): """ Clear the current figure """ gcf().clf() draw_if_interactive() def draw(): 'redraw the current figure' get_current_fig_manager().canvas.draw() def savefig(args, kwargs): fig = gcf() return fig.savefig(args, *kwargs) if Figure.savefig.__doc__ is not None: savefig.__doc__ = dedent(Figure.savefig.__doc__) def ginput(args, *kwargs): """ Blocking call to interact with the figure. This will wait for n* clicks from the user and return a list of the coordinates of each click. If timeout is negative, does not timeout. """ return gcf().ginput(args, kwargs) if Figure.ginput.__doc__ is not None: ginput.__doc__ = dedent(Figure.ginput.__doc__) def waitforbuttonpress(args, *kwargs): """ Blocking call to interact with the figure. This will wait for n* key or mouse clicks from the user and return a list containing True's for keyboard clicks and False's for mouse clicks. If timeout is negative, does not timeout. """ return gcf().waitforbuttonpress(args, kwargs) if Figure.waitforbuttonpress.__doc__ is not None: waitforbuttonpress.__doc__ = dedent(Figure.waitforbuttonpress.__doc__) # Putting things in figures def figtext(args, *kwargs): ret = gcf().text(args, *kwargs) draw_if_interactive() return ret if Figure.text.__doc__ is not None: figtext.__doc__ = dedent(Figure.text.__doc__) def suptitle(args, *kwargs): ret = gcf().suptitle(args, *kwargs) draw_if_interactive() return ret if Figure.suptitle.__doc__ is not None: suptitle.__doc__ = dedent(Figure.suptitle.__doc__) def figimage(args, *kwargs): # allow callers to override the hold state by passing hold=True\|False ret = gcf().figimage(args, kwargs) draw_if_interactive() gci._current = ret return ret if Figure.figimage.__doc__ is not None: figimage.__doc__ = dedent(Figure.figimage.__doc__) + """ Addition kwargs: hold = [True\|False] overrides default hold state""" def figlegend(handles, labels, loc, kwargs): """ Place a legend in the figure. labels a sequence of strings handles a sequence of :class:`~matplotlib.lines.Line2D` or :class:`~matplotlib.patches.Patch` instances loc can be a string or an integer specifying the legend location A :class:`matplotlib.legend.Legend` instance is returned. Example:: figlegend( (line1, line2, line3), ('label1', 'label2', 'label3'), 'upper right' ) .. seealso:: :func:`~matplotlib.pyplot.legend`: For information about the location codes """ l = gcf().legend(handles, labels, loc, *kwargs) draw_if_interactive() return l ## Figure and Axes hybrid ## def hold(b=None): """ Set the hold state. If b* is None (default), toggle the hold state, else set the hold state to boolean value b:: hold() # toggle hold hold(True) # hold is on hold(False) # hold is off When hold is True, subsequent plot commands will be added to the current axes. When hold is False, the current axes and figure will be cleared on the next plot command. """ fig = gcf() ax = fig.gca() fig.hold(b) ax.hold(b) # b=None toggles the hold state, so let's get get the current hold # state; but should pyplot hold toggle the rc setting - me thinks # not b = ax.ishold() rc('axes', hold=b) def ishold(): """ Return the hold status of the current axes """ return gca().ishold() def over(func, args, kwargs): """ over calls:: func(args, *kwargs) with ``hold(True)`` and then restores the hold state. """ h = ishold() hold(True) func(args, *kwargs) hold(h) ## Axes ## def axes(args, *kwargs): """ Add an axes at position rect specified by: - ``axes()`` by itself creates a default full ``subplot(111)`` window axis. - ``axes(rect, axisbg='w')`` where rect* = [left, bottom, width, height] in normalized (0, 1) units. axisbg is the background color for the axis, default white. - ``axes(h)`` where h is an axes instance makes h the current axis. An :class:`~matplotlib.axes.Axes` instance is returned. ======= ============ ================================================ kwarg Accepts Desctiption ======= ============ ================================================ axisbg color the axes background color frameon [True\|False] display the frame? sharex otherax current axes shares xaxis attribute with otherax sharey otherax current axes shares yaxis attribute with otherax polar [True\|False] use a polar axes? ======= ============ ================================================ Examples: * :file:`examples/pylab_examples/axes_demo.py` places custom axes. * :file:`examples/pylab_examples/shared_axis_demo.py` uses sharex and sharey. """ nargs = len(args) if len(args)==0: return subplot(111, kwargs) if nargs>1: raise TypeError('Only one non keyword arg to axes allowed') arg = args[0] if isinstance(arg, Axes): a = gcf().sca(arg) else: rect = arg a = gcf().add_axes(rect, kwargs) draw_if_interactive() return a def delaxes(args): """ ``delaxes(ax)``: remove ax* from the current figure. If ax doesn't exist, an error will be raised. ``delaxes()``: delete the current axes """ if not len(args): ax = gca() else: ax = args[0] ret = gcf().delaxes(ax) draw_if_interactive() return ret def gca(kwargs): """ Return the current axis instance. This can be used to control axis properties either using set or the :class:`~matplotlib.axes.Axes` methods, for example, setting the xaxis range:: plot(t,s) set(gca(), 'xlim', [0,10]) or:: plot(t,s) a = gca() a.set_xlim([0,10]) """ ax = gcf().gca(kwargs) return ax # More ways of creating axes: def subplot(args, kwargs): """ Create a subplot command, creating axes with:: subplot(numRows, numCols, plotNum) where plotNum* = 1 is the first plot number and increasing plotNums fill rows first. max(plotNum) == numRows * numCols You can leave out the commas if numRows <= numCols <= plotNum < 10, as in:: subplot(211) # 2 rows, 1 column, first (upper) plot ``subplot(111)`` is the default axis. New subplots that overlap old will delete the old axes. If you do not want this behavior, use :meth:`matplotlib.figure.Figure.add_subplot` or the :func:`~matplotlib.pyplot.axes` command. Eg.:: from pylab import * plot([1,2,3]) # implicitly creates subplot(111) subplot(211) # overlaps, subplot(111) is killed plot(rand(12), rand(12)) subplot(212, axisbg='y') # creates 2nd subplot with yellow background Keyword arguments: axisbg: The background color of the subplot, which can be any valid color specifier. See :mod:`matplotlib.colors` for more information. polar: A boolean flag indicating whether the subplot plot should be a polar projection. Defaults to False. projection: A string giving the name of a custom projection to be used for the subplot. This projection must have been previously registered. See :func:`matplotlib.projections.register_projection` .. seealso:: :func:`~matplotlib.pyplot.axes`: For additional information on :func:`axes` and :func:`subplot` keyword arguments. :file:`examples/pylab_examples/polar_scatter.py` Example: .. plot:: mpl_examples/pylab_examples/subplot_demo.py """ fig = gcf() a = fig.add_subplot(args, kwargs) bbox = a.bbox byebye = [] for other in fig.axes: if other==a: continue if bbox.fully_overlaps(other.bbox): byebye.append(other) for ax in byebye: delaxes(ax) draw_if_interactive() return a def twinx(ax=None): """ Make a second axes overlay ax* (or the current axes if ax is None) sharing the xaxis. The ticks for ax2 will be placed on the right, and the ax2 instance is returned. .. seealso:: :file:`examples/api_examples/two_scales.py` """ if ax is None: ax=gca() ax1 = ax.twinx() draw_if_interactive() return ax1 def twiny(ax=None): """ Make a second axes overlay ax (or the current axes if ax is None) sharing the yaxis. The ticks for ax2 will be placed on the top, and the ax2 instance is returned. """ if ax is None: ax=gca() ax1 = ax.twiny() draw_if_interactive() return ax1 def subplots_adjust(args, kwargs): """ call signature:: subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=None, hspace=None) Tune the subplot layout via the :class:`matplotlib.figure.SubplotParams` mechanism. The parameter meanings (and suggested defaults) are:: left = 0.125 # the left side of the subplots of the figure right = 0.9 # the right side of the subplots of the figure bottom = 0.1 # the bottom of the subplots of the figure top = 0.9 # the top of the subplots of the figure wspace = 0.2 # the amount of width reserved for blank space between subplots hspace = 0.2 # the amount of height reserved for white space between subplots The actual defaults are controlled by the rc file """ fig = gcf() fig.subplots_adjust(args, *kwargs) draw_if_interactive() def subplot_tool(targetfig=None): """ Launch a subplot tool window for targetfig* (default gcf). A :class:`matplotlib.widgets.SubplotTool` instance is returned. """ tbar = rcParams['toolbar'] # turn off the navigation toolbar for the toolfig rcParams['toolbar'] = 'None' if targetfig is None: manager = get_current_fig_manager() targetfig = manager.canvas.figure else: # find the manager for this figure for manager in _pylab_helpers.Gcf._activeQue: if manager.canvas.figure==targetfig: break else: raise RuntimeError('Could not find manager for targetfig') toolfig = figure(figsize=(6,3)) toolfig.subplots_adjust(top=0.9) ret = SubplotTool(targetfig, toolfig) rcParams['toolbar'] = tbar _pylab_helpers.Gcf.set_active(manager) # restore the current figure return ret def box(on=None): """ Turn the axes box on or off according to on. If on is None, toggle state. """ ax = gca() if on is None: on = not ax.get_frame_on() ax.set_frame_on(on) draw_if_interactive() def title(s, args, kwargs): """ Set the title of the current axis to s. Default font override is:: override = {'fontsize': 'medium', 'verticalalignment': 'bottom', 'horizontalalignment': 'center'} .. seealso:: :func:`~matplotlib.pyplot.text`: for information on how override and the optional args work. """ l = gca().set_title(s, args, *kwargs) draw_if_interactive() return l ## Axis ## def axis(v, *kwargs): """ Set/Get the axis properties: >>> axis() returns the current axes limits ``[xmin, xmax, ymin, ymax]``. >>> axis(v) sets the min and max of the x and y axes, with ``v = [xmin, xmax, ymin, ymax]``. >>> axis('off') turns off the axis lines and labels. >>> axis('equal') changes limits of x* or y axis so that equal increments of x and y have the same length; a circle is circular. >>> axis('scaled') achieves the same result by changing the dimensions of the plot box instead of the axis data limits. >>> axis('tight') changes x and y axis limits such that all data is shown. If all data is already shown, it will move it to the center of the figure without modifying (xmax - xmin) or (ymax - ymin). Note this is slightly different than in matlab. >>> axis('image') is 'scaled' with the axis limits equal to the data limits. >>> axis('auto') and >>> axis('normal') are deprecated. They restore default behavior; axis limits are automatically scaled to make the data fit comfortably within the plot box. if ``len(v)==0``, you can pass in xmin, xmax, ymin, ymax* as kwargs selectively to alter just those limits without changing the others. The xmin, xmax, ymin, ymax tuple is returned .. seealso:: :func:`xlim`, :func:`ylim` """ ax = gca() v = ax.axis(v, kwargs) draw_if_interactive() return v def xlabel(s, args, *kwargs): """ Set the x* axis label of the current axis to s Default override is:: override = { 'fontsize' : 'small', 'verticalalignment' : 'top', 'horizontalalignment' : 'center' } .. seealso:: :func:`~matplotlib.pyplot.text`: For information on how override and the optional args work """ l = gca().set_xlabel(s, args, kwargs) draw_if_interactive() return l def ylabel(s, args, *kwargs): """ Set the y* axis label of the current axis to s. Defaults override is:: override = { 'fontsize' : 'small', 'verticalalignment' : 'center', 'horizontalalignment' : 'right', 'rotation'='vertical' : } .. seealso:: :func:`~matplotlib.pyplot.text`: For information on how override and the optional args work. """ l = gca().set_ylabel(s, args, kwargs) draw_if_interactive() return l def xlim(args, *kwargs): """ Set/Get the xlimits of the current axes:: xmin, xmax = xlim() # return the current xlim xlim( (xmin, xmax) ) # set the xlim to xmin, xmax xlim( xmin, xmax ) # set the xlim to xmin, xmax If you do not specify args, you can pass the xmin and xmax as kwargs, eg.:: xlim(xmax=3) # adjust the max leaving min unchanged xlim(xmin=1) # adjust the min leaving max unchanged The new axis limits are returned as a length 2 tuple. """ ax = gca() ret = ax.set_xlim(args, *kwargs) draw_if_interactive() return ret def ylim(args, *kwargs): """ Set/Get the ylimits of the current axes:: ymin, ymax = ylim() # return the current ylim ylim( (ymin, ymax) ) # set the ylim to ymin, ymax ylim( ymin, ymax ) # set the ylim to ymin, ymax If you do not specify args, you can pass the ymin* and ymax as kwargs, eg.:: ylim(ymax=3) # adjust the max leaving min unchanged ylim(ymin=1) # adjust the min leaving max unchanged The new axis limits are returned as a length 2 tuple. """ ax = gca() ret = ax.set_ylim(args, kwargs) draw_if_interactive() return ret def xscale(args, kwargs): """ call signature:: xscale(scale, kwargs) Set the scaling for the x-axis: %(scale)s Different keywords may be accepted, depending on the scale: %(scale_docs)s """ ax = gca() ret = ax.set_xscale(args, kwargs) draw_if_interactive() return ret xscale.__doc__ = dedent(xscale.__doc__) % { 'scale': ' \| '.join([repr(_x) for _x in get_scale_names()]), 'scale_docs': get_scale_docs()} def yscale(args, kwargs): """ call signature:: xscale(scale, kwargs) Set the scaling for the y-axis: %(scale)s Different keywords may be accepted, depending on the scale: %(scale_docs)s """ ax = gca() ret = ax.set_yscale(args, kwargs) draw_if_interactive() return ret yscale.__doc__ = dedent(yscale.__doc__) % { 'scale': ' \| '.join([repr(_x) for _x in get_scale_names()]), 'scale_docs': get_scale_docs()} def xticks(args, kwargs): """ Set/Get the xlimits of the current ticklocs and labels:: # return locs, labels where locs is an array of tick locations and # labels is an array of tick labels. locs, labels = xticks() # set the locations of the xticks xticks( arange(6) ) # set the locations and labels of the xticks xticks( arange(5), ('Tom', 'Dick', 'Harry', 'Sally', 'Sue') ) The keyword args, if any, are :class:`~matplotlib.text.Text` properties. """ ax = gca() if len(args)==0: locs = ax.get_xticks() labels = ax.get_xticklabels() elif len(args)==1: locs = ax.set_xticks(args[0]) labels = ax.get_xticklabels() elif len(args)==2: locs = ax.set_xticks(args[0]) labels = ax.set_xticklabels(args[1], kwargs) else: raise TypeError('Illegal number of arguments to xticks') if len(kwargs): for l in labels: l.update(kwargs) draw_if_interactive() return locs, silent_list('Text xticklabel', labels) def yticks(args, kwargs): """ Set/Get the ylimits of the current ticklocs and labels:: # return locs, labels where locs is an array of tick locations and # labels is an array of tick labels. locs, labels = yticks() # set the locations of the yticks yticks( arange(6) ) # set the locations and labels of the yticks yticks( arange(5), ('Tom', 'Dick', 'Harry', 'Sally', 'Sue') ) The keyword args, if any, are :class:`~matplotlib.text.Text` properties. """ ax = gca() if len(args)==0: locs = ax.get_yticks() labels = ax.get_yticklabels() elif len(args)==1: locs = ax.set_yticks(args[0]) labels = ax.get_yticklabels() elif len(args)==2: locs = ax.set_yticks(args[0]) labels = ax.set_yticklabels(args[1], kwargs) else: raise TypeError('Illegal number of arguments to yticks') if len(kwargs): for l in labels: l.update(kwargs) draw_if_interactive() return ( locs, silent_list('Text yticklabel', labels) ) def rgrids(args, kwargs): """ Set/Get the radial locations of the gridlines and ticklabels on a polar plot. call signatures:: lines, labels = rgrids() lines, labels = rgrids(radii, labels=None, angle=22.5, kwargs) When called with no arguments, :func:`rgrid` simply returns the tuple (lines, labels), where lines is an array of radial gridlines (:class:`~matplotlib.lines.Line2D` instances) and labels is an array of tick labels (:class:`~matplotlib.text.Text` instances). When called with arguments, the labels will appear at the specified radial distances and angles. labels, if not None, is a len(radii) list of strings of the labels to use at each angle. If labels is None, the rformatter will be used Examples:: # set the locations of the radial gridlines and labels lines, labels = rgrids( (0.25, 0.5, 1.0) ) # set the locations and labels of the radial gridlines and labels lines, labels = rgrids( (0.25, 0.5, 1.0), ('Tom', 'Dick', 'Harry' ) """ ax = gca() if not isinstance(ax, PolarAxes): raise RuntimeError('rgrids only defined for polar axes') if len(args)==0: lines = ax.yaxis.get_ticklines() labels = ax.yaxis.get_ticklabels() else: lines, labels = ax.set_rgrids(args, kwargs) draw_if_interactive() return ( silent_list('Line2D rgridline', lines), silent_list('Text rgridlabel', labels) ) def thetagrids(args, *kwargs): """ Set/Get the theta locations of the gridlines and ticklabels. If no arguments are passed, return a tuple (lines, labels) where lines* is an array of radial gridlines (:class:`~matplotlib.lines.Line2D` instances) and labels is an array of tick labels (:class:`~matplotlib.text.Text` instances):: lines, labels = thetagrids() Otherwise the syntax is:: lines, labels = thetagrids(angles, labels=None, fmt='%d', frac = 1.1) set the angles at which to place the theta grids (these gridlines are equal along the theta dimension). angles is in degrees. labels, if not None, is a len(angles) list of strings of the labels to use at each angle. If labels is None, the labels will be ``fmt%angle``. frac is the fraction of the polar axes radius at which to place the label (1 is the edge). Eg. 1.05 is outside the axes and 0.95 is inside the axes. Return value is a list of tuples (lines, labels): - lines are :class:`~matplotlib.lines.Line2D` instances - labels are :class:`~matplotlib.text.Text` instances. Note that on input, the labels argument is a list of strings, and on output it is a list of :class:`~matplotlib.text.Text` instances. Examples:: # set the locations of the radial gridlines and labels lines, labels = thetagrids( range(45,360,90) ) # set the locations and labels of the radial gridlines and labels lines, labels = thetagrids( range(45,360,90), ('NE', 'NW', 'SW','SE') ) """ ax = gca() if not isinstance(ax, PolarAxes): raise RuntimeError('rgrids only defined for polar axes') if len(args)==0: lines = ax.xaxis.get_ticklines() labels = ax.xaxis.get_ticklabels() else: lines, labels = ax.set_thetagrids(args, kwargs) draw_if_interactive() return (silent_list('Line2D thetagridline', lines), silent_list('Text thetagridlabel', labels) ) ## Plotting Info ## def plotting(): """ Plotting commands =============== ========================================================= Command Description =============== ========================================================= axes Create a new axes axis Set or return the current axis limits bar make a bar chart boxplot make a box and whiskers chart cla clear current axes clabel label a contour plot clf clear a figure window close close a figure window colorbar add a colorbar to the current figure cohere make a plot of coherence contour make a contour plot contourf make a filled contour plot csd make a plot of cross spectral density draw force a redraw of the current figure errorbar make an errorbar graph figlegend add a legend to the figure figimage add an image to the figure, w/o resampling figtext add text in figure coords figure create or change active figure fill make filled polygons fill_between make filled polygons gca return the current axes gcf return the current figure gci get the current image, or None getp get a handle graphics property hist make a histogram hold set the hold state on current axes legend add a legend to the axes loglog a log log plot imread load image file into array imshow plot image data matshow display a matrix in a new figure preserving aspect pcolor make a pseudocolor plot plot make a line plot plotfile plot data from a flat file psd make a plot of power spectral density quiver make a direction field (arrows) plot rc control the default params savefig save the current figure scatter make a scatter plot setp set a handle graphics property semilogx log x axis semilogy log y axis show show the figures specgram a spectrogram plot stem make a stem plot subplot make a subplot (numrows, numcols, axesnum) table add a table to the axes text add some text at location x,y to the current axes title add a title to the current axes xlabel add an xlabel to the current axes ylabel add a ylabel to the current axes =============== ========================================================= The following commands will set the default colormap accordingly: autumn * bone * cool * copper * flag * gray * hot * hsv * jet * pink * prism * spring * summer * winter * spectral """ pass def get_plot_commands(): return ( 'axes', 'axis', 'bar', 'boxplot', 'cla', 'clf', 'close', 'colorbar', 'cohere', 'csd', 'draw', 'errorbar', 'figlegend', 'figtext', 'figimage', 'figure', 'fill', 'gca', 'gcf', 'gci', 'get', 'gray', 'barh', 'jet', 'hist', 'hold', 'imread', 'imshow', 'legend', 'loglog', 'quiver', 'rc', 'pcolor', 'pcolormesh', 'plot', 'psd', 'savefig', 'scatter', 'set', 'semilogx', 'semilogy', 'show', 'specgram', 'stem', 'subplot', 'table', 'text', 'title', 'xlabel', 'ylabel', 'pie', 'polar') def colors(): """ This is a do nothing function to provide you with help on how matplotlib handles colors. Commands which take color arguments can use several formats to specify the colors. For the basic builtin colors, you can use a single letter ===== ======= Alias Color ===== ======= 'b' blue 'g' green 'r' red 'c' cyan 'm' magenta 'y' yellow 'k' black 'w' white ===== ======= For a greater range of colors, you have two options. You can specify the color using an html hex string, as in:: color = '#eeefff' or you can pass an R,G,B tuple, where each of R,G,B are in the range [0,1]. You can also use any legal html name for a color, for example:: color = 'red', color = 'burlywood' color = 'chartreuse' The example below creates a subplot with a dark slate gray background subplot(111, axisbg=(0.1843, 0.3098, 0.3098)) Here is an example that creates a pale turqoise title:: title('Is this the best color?', color='#afeeee') """ pass def colormaps(): """ matplotlib provides the following colormaps. * autumn * bone * cool * copper * flag * gray * hot * hsv * jet * pink * prism * spring * summer * winter * spectral You can set the colormap for an image, pcolor, scatter, etc, either as a keyword argument:: imshow(X, cmap=cm.hot) or post-hoc using the corresponding pylab interface function:: imshow(X) hot() jet() In interactive mode, this will update the colormap allowing you to see which one works best for your data. """ pass ## Plotting part 1: manually generated functions and wrappers ## from matplotlib.colorbar import colorbar_doc def colorbar(mappable=None, cax=None, ax=None, kw): if mappable is None: mappable = gci() if ax is None: ax = gca() ret = gcf().colorbar(mappable, cax = cax, ax=ax, kw) draw_if_interactive() return ret colorbar.__doc__ = colorbar_doc def clim(vmin=None, vmax=None): """ Set the color limits of the current image To apply clim to all axes images do:: clim(0, 0.5) If either vmin or vmax is None, the image min/max respectively will be used for color scaling. If you want to set the clim of multiple images, use, for example:: for im in gca().get_images(): im.set_clim(0, 0.05) """ im = gci() if im is None: raise RuntimeError('You must first define an image, eg with imshow') im.set_clim(vmin, vmax) draw_if_interactive() def imread(args, kwargs): return _imread(args, kwargs) if _imread.__doc__ is not None: imread.__doc__ = dedent(_imread.__doc__) def matshow(A, fignum=None, kw): """ Display an array as a matrix in a new figure window. The origin is set at the upper left hand corner and rows (first dimension of the array) are displayed horizontally. The aspect ratio of the figure window is that of the array, unless this would make an excessively short or narrow figure. Tick labels for the xaxis are placed on top. With the exception of fignum, keyword arguments are passed to :func:`~matplotlib.pyplot.imshow`. fignum: [ None \| integer \| False ] By default, :func:`matshow` creates a new figure window with automatic numbering. If fignum is given as an integer, the created figure will use this figure number. Because of how :func:`matshow` tries to set the figure aspect ratio to be the one of the array, if you provide the number of an already existing figure, strange things may happen. If fignum is False or 0, a new figure window will NOT be created. """ if fignum is False or fignum is 0: ax = gca() else: # Extract actual aspect ratio of array and make appropriately sized figure fig = figure(fignum, figsize=figaspect(A)) ax = fig.add_axes([0.15, 0.09, 0.775, 0.775]) im = ax.matshow(A, *kw) gci._current = im draw_if_interactive() return im def polar(args, kwargs): """ call signature:: polar(theta, r, kwargs) Make a polar plot. Multiple theta, r arguments are supported, with format strings, as in :func:`~matplotlib.pyplot.plot`. """ ax = gca(polar=True) ret = ax.plot(args, kwargs) draw_if_interactive() return ret def plotfile(fname, cols=(0,), plotfuncs=None, comments='#', skiprows=0, checkrows=5, delimiter=',', kwargs): """ Plot the data in fname* cols is a sequence of column identifiers to plot. An identifier is either an int or a string. If it is an int, it indicates the column number. If it is a string, it indicates the column header. matplotlib will make column headers lower case, replace spaces with underscores, and remove all illegal characters; so ``'Adj Close'`` will have name ``'adj_close'``. - If len(cols) == 1, only that column will be plotted on the y* axis. - If len(cols) > 1, the first element will be an identifier for data for the x axis and the remaining elements will be the column indexes for multiple subplots plotfuncs, if not None, is a dictionary mapping identifier to an :class:`~matplotlib.axes.Axes` plotting function as a string. Default is 'plot', other choices are 'semilogy', 'fill', 'bar', etc. You must use the same type of identifier in the cols vector as you use in the plotfuncs dictionary, eg., integer column numbers in both or column names in both. comments, skiprows, checkrows, and delimiter are all passed on to :func:`matplotlib.pylab.csv2rec` to load the data into a record array. kwargs are passed on to plotting functions. Example usage:: # plot the 2nd and 4th column against the 1st in two subplots plotfile(fname, (0,1,3)) # plot using column names; specify an alternate plot type for volume plotfile(fname, ('date', 'volume', 'adj_close'), plotfuncs={'volume': 'semilogy'}) """ fig = figure() if len(cols)<1: raise ValueError('must have at least one column of data') if plotfuncs is None: plotfuncs = dict() r = mlab.csv2rec(fname, comments=comments, skiprows=skiprows, checkrows=checkrows, delimiter=delimiter) def getname_val(identifier): 'return the name and column data for identifier' if is_string_like(identifier): return identifier, r[identifier] elif is_numlike(identifier): name = r.dtype.names[int(identifier)] return name, r[name] else: raise TypeError('identifier must be a string or integer') xname, x = getname_val(cols[0]) if len(cols)==1: ax1 = fig.add_subplot(1,1,1) funcname = plotfuncs.get(cols[0], 'plot') func = getattr(ax1, funcname) func(x, kwargs) ax1.set_xlabel(xname) else: N = len(cols) for i in range(1,N): if i==1: ax = ax1 = fig.add_subplot(N-1,1,i) ax.grid(True) else: ax = fig.add_subplot(N-1,1,i, sharex=ax1) ax.grid(True) yname, y = getname_val(cols[i]) funcname = plotfuncs.get(cols[i], 'plot') func = getattr(ax, funcname) func(x, y, kwargs) ax.set_ylabel(yname) if ax.is_last_row(): ax.set_xlabel(xname) else: ax.set_xlabel('') if xname=='date': fig.autofmt_xdate() draw_if_interactive() ## Plotting part 2: autogenerated wrappers for axes methods ## # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def acorr(args, kwargs): # allow callers to override the hold state by passing hold=True\|False b = ishold() h = kwargs.pop('hold', None) if h is not None: hold(h) try: ret = gca().acorr(args, *kwargs) draw_if_interactive() except: hold(b) raise hold(b) return ret if Axes.acorr.__doc__ is not None: acorr.__doc__ = dedent(Axes.acorr.__doc__) + """ Additional kwargs: hold = [True\|False] overrides default hold state""" # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def arrow(args, *kwargs): # allow callers to override the hold state by passing hold=True\|False b = ishold() h = kwargs.pop('hold', None) if h is not None: hold(h) try: ret = gca().arrow(args, *kwargs) draw_if_interactive() except: hold(b) raise hold(b) return ret if Axes.arrow.__doc__ is not None: arrow.__doc__ = dedent(Axes.arrow.__doc__) + """ Additional kwargs: hold = [True\|False] overrides default hold state""" # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def axhline(args, *kwargs): # allow callers to override the hold state by passing hold=True\|False b = ishold() h = kwargs.pop('hold', None) if h is not None: hold(h) try: ret = gca().axhline(args, *kwargs) draw_if_interactive() except: hold(b) raise hold(b) return ret if Axes.axhline.__doc__ is not None: axhline.__doc__ = dedent(Axes.axhline.__doc__) + """ Additional kwargs: hold = [True\|False] overrides default hold state""" # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def axhspan(args, *kwargs): # allow callers to override the hold state by passing hold=True\|False b = ishold() h = kwargs.pop('hold', None) if h is not None: hold(h) try: ret = gca().axhspan(args, *kwargs) draw_if_interactive() except: hold(b) raise hold(b) return ret if Axes.axhspan.__doc__ is not None: axhspan.__doc__ = dedent(Axes.axhspan.__doc__) + """ Additional kwargs: hold = [True\|False] overrides default hold state""" # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def axvline(args, *kwargs): # allow callers to override the hold state by passing hold=True\|False b = ishold() h = kwargs.pop('hold', None) if h is not None: hold(h) try: ret = gca().axvline(args, *kwargs) draw_if_interactive() except: hold(b) raise hold(b) return ret if Axes.axvline.__doc__ is not None: axvline.__doc__ = dedent(Axes.axvline.__doc__) + """ Additional kwargs: hold = [True\|False] overrides default hold state""" # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def axvspan(args, *kwargs): # allow callers to override the hold state by passing hold=True\|False b = ishold() h = kwargs.pop('hold', None) if h is not None: hold(h) try: ret = gca().axvspan(args, *kwargs) draw_if_interactive() except: hold(b) raise hold(b) return ret if Axes.axvspan.__doc__ is not None: axvspan.__doc__ = dedent(Axes.axvspan.__doc__) + """ Additional kwargs: hold = [True\|False] overrides default hold state""" # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def bar(args, *kwargs): # allow callers to override the hold state by passing hold=True\|False b = ishold() h = kwargs.pop('hold', None) if h is not None: hold(h) try: ret = gca().bar(args, *kwargs) draw_if_interactive() except: hold(b) raise hold(b) return ret if Axes.bar.__doc__ is not None: bar.__doc__ = dedent(Axes.bar.__doc__) + """ Additional kwargs: hold = [True\|False] overrides default hold state""" # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def barh(args, *kwargs): # allow callers to override the hold state by passing hold=True\|False b = ishold() h = kwargs.pop('hold', None) if h is not None: hold(h) try: ret = gca().barh(args, *kwargs) draw_if_interactive() except: hold(b) raise hold(b) return ret if Axes.barh.__doc__ is not None: barh.__doc__ = dedent(Axes.barh.__doc__) + """ Additional kwargs: hold = [True\|False] overrides default hold state""" # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def broken_barh(args, *kwargs): # allow callers to override the hold state by passing hold=True\|False b = ishold() h = kwargs.pop('hold', None) if h is not None: hold(h) try: ret = gca().broken_barh(args, *kwargs) draw_if_interactive() except: hold(b) raise hold(b) return ret if Axes.broken_barh.__doc__ is not None: broken_barh.__doc__ = dedent(Axes.broken_barh.__doc__) + """ Additional kwargs: hold = [True\|False] overrides default hold state""" # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def boxplot(args, *kwargs): # allow callers to override the hold state by passing hold=True\|False b = ishold() h = kwargs.pop('hold', None) if h is not None: hold(h) try: ret = gca().boxplot(args, *kwargs) draw_if_interactive() except: hold(b) raise hold(b) return ret if Axes.boxplot.__doc__ is not None: boxplot.__doc__ = dedent(Axes.boxplot.__doc__) + """ Additional kwargs: hold = [True\|False] overrides default hold state""" # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def cohere(args, *kwargs): # allow callers to override the hold state by passing hold=True\|False b = ishold() h = kwargs.pop('hold', None) if h is not None: hold(h) try: ret = gca().cohere(args, *kwargs) draw_if_interactive() except: hold(b) raise hold(b) return ret if Axes.cohere.__doc__ is not None: cohere.__doc__ = dedent(Axes.cohere.__doc__) + """ Additional kwargs: hold = [True\|False] overrides default hold state""" # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def clabel(args, *kwargs): # allow callers to override the hold state by passing hold=True\|False b = ishold() h = kwargs.pop('hold', None) if h is not None: hold(h) try: ret = gca().clabel(args, *kwargs) draw_if_interactive() except: hold(b) raise hold(b) return ret if Axes.clabel.__doc__ is not None: clabel.__doc__ = dedent(Axes.clabel.__doc__) + """ Additional kwargs: hold = [True\|False] overrides default hold state""" # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def contour(args, *kwargs): # allow callers to override the hold state by passing hold=True\|False b = ishold() h = kwargs.pop('hold', None) if h is not None: hold(h) try: ret = gca().contour(args, *kwargs) draw_if_interactive() except: hold(b) raise if ret._A is not None: gci._current = ret hold(b) return ret if Axes.contour.__doc__ is not None: contour.__doc__ = dedent(Axes.contour.__doc__) + """ Additional kwargs: hold = [True\|False] overrides default hold state""" # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def contourf(args, *kwargs): # allow callers to override the hold state by passing hold=True\|False b = ishold() h = kwargs.pop('hold', None) if h is not None: hold(h) try: ret = gca().contourf(args, *kwargs) draw_if_interactive() except: hold(b) raise if ret._A is not None: gci._current = ret hold(b) return ret if Axes.contourf.__doc__ is not None: contourf.__doc__ = dedent(Axes.contourf.__doc__) + """ Additional kwargs: hold = [True\|False] overrides default hold state""" # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def csd(args, *kwargs): # allow callers to override the hold state by passing hold=True\|False b = ishold() h = kwargs.pop('hold', None) if h is not None: hold(h) try: ret = gca().csd(args, *kwargs) draw_if_interactive() except: hold(b) raise hold(b) return ret if Axes.csd.__doc__ is not None: csd.__doc__ = dedent(Axes.csd.__doc__) + """ Additional kwargs: hold = [True\|False] overrides default hold state""" # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def errorbar(args, *kwargs): # allow callers to override the hold state by passing hold=True\|False b = ishold() h = kwargs.pop('hold', None) if h is not None: hold(h) try: ret = gca().errorbar(args, *kwargs) draw_if_interactive() except: hold(b) raise hold(b) return ret if Axes.errorbar.__doc__ is not None: errorbar.__doc__ = dedent(Axes.errorbar.__doc__) + """ Additional kwargs: hold = [True\|False] overrides default hold state""" # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def fill(args, *kwargs): # allow callers to override the hold state by passing hold=True\|False b = ishold() h = kwargs.pop('hold', None) if h is not None: hold(h) try: ret = gca().fill(args, *kwargs) draw_if_interactive() except: hold(b) raise hold(b) return ret if Axes.fill.__doc__ is not None: fill.__doc__ = dedent(Axes.fill.__doc__) + """ Additional kwargs: hold = [True\|False] overrides default hold state""" # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def fill_between(args, *kwargs): # allow callers to override the hold state by passing hold=True\|False b = ishold() h = kwargs.pop('hold', None) if h is not None: hold(h) try: ret = gca().fill_between(args, *kwargs) draw_if_interactive() except: hold(b) raise hold(b) return ret if Axes.fill_between.__doc__ is not None: fill_between.__doc__ = dedent(Axes.fill_between.__doc__) + """ Additional kwargs: hold = [True\|False] overrides default hold state""" # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def hexbin(args, *kwargs): # allow callers to override the hold state by passing hold=True\|False b = ishold() h = kwargs.pop('hold', None) if h is not None: hold(h) try: ret = gca().hexbin(args, *kwargs) draw_if_interactive() except: hold(b) raise gci._current = ret hold(b) return ret if Axes.hexbin.__doc__ is not None: hexbin.__doc__ = dedent(Axes.hexbin.__doc__) + """ Additional kwargs: hold = [True\|False] overrides default hold state""" # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def hist(args, *kwargs): # allow callers to override the hold state by passing hold=True\|False b = ishold() h = kwargs.pop('hold', None) if h is not None: hold(h) try: ret = gca().hist(args, *kwargs) draw_if_interactive() except: hold(b) raise hold(b) return ret if Axes.hist.__doc__ is not None: hist.__doc__ = dedent(Axes.hist.__doc__) + """ Additional kwargs: hold = [True\|False] overrides default hold state""" # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def hlines(args, *kwargs): # allow callers to override the hold state by passing hold=True\|False b = ishold() h = kwargs.pop('hold', None) if h is not None: hold(h) try: ret = gca().hlines(args, *kwargs) draw_if_interactive() except: hold(b) raise hold(b) return ret if Axes.hlines.__doc__ is not None: hlines.__doc__ = dedent(Axes.hlines.__doc__) + """ Additional kwargs: hold = [True\|False] overrides default hold state""" # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def imshow(args, *kwargs): # allow callers to override the hold state by passing hold=True\|False b = ishold() h = kwargs.pop('hold', None) if h is not None: hold(h) try: ret = gca().imshow(args, *kwargs) draw_if_interactive() except: hold(b) raise gci._current = ret hold(b) return ret if Axes.imshow.__doc__ is not None: imshow.__doc__ = dedent(Axes.imshow.__doc__) + """ Additional kwargs: hold = [True\|False] overrides default hold state""" # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def loglog(args, *kwargs): # allow callers to override the hold state by passing hold=True\|False b = ishold() h = kwargs.pop('hold', None) if h is not None: hold(h) try: ret = gca().loglog(args, *kwargs) draw_if_interactive() except: hold(b) raise hold(b) return ret if Axes.loglog.__doc__ is not None: loglog.__doc__ = dedent(Axes.loglog.__doc__) + """ Additional kwargs: hold = [True\|False] overrides default hold state""" # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def pcolor(args, *kwargs): # allow callers to override the hold state by passing hold=True\|False b = ishold() h = kwargs.pop('hold', None) if h is not None: hold(h) try: ret = gca().pcolor(args, *kwargs) draw_if_interactive() except: hold(b) raise gci._current = ret hold(b) return ret if Axes.pcolor.__doc__ is not None: pcolor.__doc__ = dedent(Axes.pcolor.__doc__) + """ Additional kwargs: hold = [True\|False] overrides default hold state""" # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def pcolormesh(args, *kwargs): # allow callers to override the hold state by passing hold=True\|False b = ishold() h = kwargs.pop('hold', None) if h is not None: hold(h) try: ret = gca().pcolormesh(args, *kwargs) draw_if_interactive() except: hold(b) raise gci._current = ret hold(b) return ret if Axes.pcolormesh.__doc__ is not None: pcolormesh.__doc__ = dedent(Axes.pcolormesh.__doc__) + """ Additional kwargs: hold = [True\|False] overrides default hold state""" # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def pie(args, *kwargs): # allow callers to override the hold state by passing hold=True\|False b = ishold() h = kwargs.pop('hold', None) if h is not None: hold(h) try: ret = gca().pie(args, *kwargs) draw_if_interactive() except: hold(b) raise hold(b) return ret if Axes.pie.__doc__ is not None: pie.__doc__ = dedent(Axes.pie.__doc__) + """ Additional kwargs: hold = [True\|False] overrides default hold state""" # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def plot(args, *kwargs): # allow callers to override the hold state by passing hold=True\|False b = ishold() h = kwargs.pop('hold', None) if h is not None: hold(h) try: ret = gca().plot(args, *kwargs) draw_if_interactive() except: hold(b) raise hold(b) return ret if Axes.plot.__doc__ is not None: plot.__doc__ = dedent(Axes.plot.__doc__) + """ Additional kwargs: hold = [True\|False] overrides default hold state""" # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def plot_date(args, *kwargs): # allow callers to override the hold state by passing hold=True\|False b = ishold() h = kwargs.pop('hold', None) if h is not None: hold(h) try: ret = gca().plot_date(args, *kwargs) draw_if_interactive() except: hold(b) raise hold(b) return ret if Axes.plot_date.__doc__ is not None: plot_date.__doc__ = dedent(Axes.plot_date.__doc__) + """ Additional kwargs: hold = [True\|False] overrides default hold state""" # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def psd(args, *kwargs): # allow callers to override the hold state by passing hold=True\|False b = ishold() h = kwargs.pop('hold', None) if h is not None: hold(h) try: ret = gca().psd(args, *kwargs) draw_if_interactive() except: hold(b) raise hold(b) return ret if Axes.psd.__doc__ is not None: psd.__doc__ = dedent(Axes.psd.__doc__) + """ Additional kwargs: hold = [True\|False] overrides default hold state""" # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def quiver(args, *kwargs): # allow callers to override the hold state by passing hold=True\|False b = ishold() h = kwargs.pop('hold', None) if h is not None: hold(h) try: ret = gca().quiver(args, *kwargs) draw_if_interactive() except: hold(b) raise gci._current = ret hold(b) return ret if Axes.quiver.__doc__ is not None: quiver.__doc__ = dedent(Axes.quiver.__doc__) + """ Additional kwargs: hold = [True\|False] overrides default hold state""" # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def quiverkey(args, *kwargs): # allow callers to override the hold state by passing hold=True\|False b = ishold() h = kwargs.pop('hold', None) if h is not None: hold(h) try: ret = gca().quiverkey(args, *kwargs) draw_if_interactive() except: hold(b) raise hold(b) return ret if Axes.quiverkey.__doc__ is not None: quiverkey.__doc__ = dedent(Axes.quiverkey.__doc__) + """ Additional kwargs: hold = [True\|False] overrides default hold state""" # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def scatter(args, *kwargs): # allow callers to override the hold state by passing hold=True\|False b = ishold() h = kwargs.pop('hold', None) if h is not None: hold(h) try: ret = gca().scatter(args, *kwargs) draw_if_interactive() except: hold(b) raise gci._current = ret hold(b) return ret if Axes.scatter.__doc__ is not None: scatter.__doc__ = dedent(Axes.scatter.__doc__) + """ Additional kwargs: hold = [True\|False] overrides default hold state""" # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def semilogx(args, *kwargs): # allow callers to override the hold state by passing hold=True\|False b = ishold() h = kwargs.pop('hold', None) if h is not None: hold(h) try: ret = gca().semilogx(args, *kwargs) draw_if_interactive() except: hold(b) raise hold(b) return ret if Axes.semilogx.__doc__ is not None: semilogx.__doc__ = dedent(Axes.semilogx.__doc__) + """ Additional kwargs: hold = [True\|False] overrides default hold state""" # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def semilogy(args, *kwargs): # allow callers to override the hold state by passing hold=True\|False b = ishold() h = kwargs.pop('hold', None) if h is not None: hold(h) try: ret = gca().semilogy(args, *kwargs) draw_if_interactive() except: hold(b) raise hold(b) return ret if Axes.semilogy.__doc__ is not None: semilogy.__doc__ = dedent(Axes.semilogy.__doc__) + """ Additional kwargs: hold = [True\|False] overrides default hold state""" # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def specgram(args, *kwargs): # allow callers to override the hold state by passing hold=True\|False b = ishold() h = kwargs.pop('hold', None) if h is not None: hold(h) try: ret = gca().specgram(args, *kwargs) draw_if_interactive() except: hold(b) raise gci._current = ret[-1] hold(b) return ret if Axes.specgram.__doc__ is not None: specgram.__doc__ = dedent(Axes.specgram.__doc__) + """ Additional kwargs: hold = [True\|False] overrides default hold state""" # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def spy(args, *kwargs): # allow callers to override the hold state by passing hold=True\|False b = ishold() h = kwargs.pop('hold', None) if h is not None: hold(h) try: ret = gca().spy(args, *kwargs) draw_if_interactive() except: hold(b) raise gci._current = ret hold(b) return ret if Axes.spy.__doc__ is not None: spy.__doc__ = dedent(Axes.spy.__doc__) + """ Additional kwargs: hold = [True\|False] overrides default hold state""" # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def stem(args, *kwargs): # allow callers to override the hold state by passing hold=True\|False b = ishold() h = kwargs.pop('hold', None) if h is not None: hold(h) try: ret = gca().stem(args, *kwargs) draw_if_interactive() except: hold(b) raise hold(b) return ret if Axes.stem.__doc__ is not None: stem.__doc__ = dedent(Axes.stem.__doc__) + """ Additional kwargs: hold = [True\|False] overrides default hold state""" # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def step(args, *kwargs): # allow callers to override the hold state by passing hold=True\|False b = ishold() h = kwargs.pop('hold', None) if h is not None: hold(h) try: ret = gca().step(args, *kwargs) draw_if_interactive() except: hold(b) raise hold(b) return ret if Axes.step.__doc__ is not None: step.__doc__ = dedent(Axes.step.__doc__) + """ Additional kwargs: hold = [True\|False] overrides default hold state""" # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def vlines(args, *kwargs): # allow callers to override the hold state by passing hold=True\|False b = ishold() h = kwargs.pop('hold', None) if h is not None: hold(h) try: ret = gca().vlines(args, *kwargs) draw_if_interactive() except: hold(b) raise hold(b) return ret if Axes.vlines.__doc__ is not None: vlines.__doc__ = dedent(Axes.vlines.__doc__) + """ Additional kwargs: hold = [True\|False] overrides default hold state""" # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def xcorr(args, *kwargs): # allow callers to override the hold state by passing hold=True\|False b = ishold() h = kwargs.pop('hold', None) if h is not None: hold(h) try: ret = gca().xcorr(args, *kwargs) draw_if_interactive() except: hold(b) raise hold(b) return ret if Axes.xcorr.__doc__ is not None: xcorr.__doc__ = dedent(Axes.xcorr.__doc__) + """ Additional kwargs: hold = [True\|False] overrides default hold state""" # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def barbs(args, *kwargs): # allow callers to override the hold state by passing hold=True\|False b = ishold() h = kwargs.pop('hold', None) if h is not None: hold(h) try: ret = gca().barbs(args, *kwargs) draw_if_interactive() except: hold(b) raise hold(b) return ret if Axes.barbs.__doc__ is not None: barbs.__doc__ = dedent(Axes.barbs.__doc__) + """ Additional kwargs: hold = [True\|False] overrides default hold state""" # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def cla(args, *kwargs): ret = gca().cla(args, *kwargs) draw_if_interactive() return ret if Axes.cla.__doc__ is not None: cla.__doc__ = dedent(Axes.cla.__doc__) # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def grid(args, *kwargs): ret = gca().grid(args, *kwargs) draw_if_interactive() return ret if Axes.grid.__doc__ is not None: grid.__doc__ = dedent(Axes.grid.__doc__) # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def legend(args, *kwargs): ret = gca().legend(args, *kwargs) draw_if_interactive() return ret if Axes.legend.__doc__ is not None: legend.__doc__ = dedent(Axes.legend.__doc__) # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def table(args, *kwargs): ret = gca().table(args, *kwargs) draw_if_interactive() return ret if Axes.table.__doc__ is not None: table.__doc__ = dedent(Axes.table.__doc__) # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def text(args, *kwargs): ret = gca().text(args, *kwargs) draw_if_interactive() return ret if Axes.text.__doc__ is not None: text.__doc__ = dedent(Axes.text.__doc__) # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def annotate(args, *kwargs): ret = gca().annotate(args, **kwargs) draw_if_interactive() return ret if Axes.annotate.__doc__ is not None: annotate.__doc__ = dedent(Axes.annotate.__doc__) # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def autumn(): ''' set the default colormap to autumn and apply to current image if any. See help(colormaps) for more information ''' rc('image', cmap='autumn') im = gci() if im is not None: im.set_cmap(cm.autumn) draw_if_interactive() # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def bone(): ''' set the default colormap to bone and apply to current image if any. See help(colormaps) for more information ''' rc('image', cmap='bone') im = gci() if im is not None: im.set_cmap(cm.bone) draw_if_interactive() # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def cool(): ''' set the default colormap to cool and apply to current image if any. See help(colormaps) for more information ''' rc('image', cmap='cool') im = gci() if im is not None: im.set_cmap(cm.cool) draw_if_interactive() # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def copper(): ''' set the default colormap to copper and apply to current image if any. See help(colormaps) for more information ''' rc('image', cmap='copper') im = gci() if im is not None: im.set_cmap(cm.copper) draw_if_interactive() # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def flag(): ''' set the default colormap to flag and apply to current image if any. See help(colormaps) for more information ''' rc('image', cmap='flag') im = gci() if im is not None: im.set_cmap(cm.flag) draw_if_interactive() # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def gray(): ''' set the default colormap to gray and apply to current image if any. See help(colormaps) for more information ''' rc('image', cmap='gray') im = gci() if im is not None: im.set_cmap(cm.gray) draw_if_interactive() # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def hot(): ''' set the default colormap to hot and apply to current image if any. See help(colormaps) for more information ''' rc('image', cmap='hot') im = gci() if im is not None: im.set_cmap(cm.hot) draw_if_interactive() # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def hsv(): ''' set the default colormap to hsv and apply to current image if any. See help(colormaps) for more information ''' rc('image', cmap='hsv') im = gci() if im is not None: im.set_cmap(cm.hsv) draw_if_interactive() # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def jet(): ''' set the default colormap to jet and apply to current image if any. See help(colormaps) for more information ''' rc('image', cmap='jet') im = gci() if im is not None: im.set_cmap(cm.jet) draw_if_interactive() # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def pink(): ''' set the default colormap to pink and apply to current image if any. See help(colormaps) for more information ''' rc('image', cmap='pink') im = gci() if im is not None: im.set_cmap(cm.pink) draw_if_interactive() # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def prism(): ''' set the default colormap to prism and apply to current image if any. See help(colormaps) for more information ''' rc('image', cmap='prism') im = gci() if im is not None: im.set_cmap(cm.prism) draw_if_interactive() # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def spring(): ''' set the default colormap to spring and apply to current image if any. See help(colormaps) for more information ''' rc('image', cmap='spring') im = gci() if im is not None: im.set_cmap(cm.spring) draw_if_interactive() # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def summer(): ''' set the default colormap to summer and apply to current image if any. See help(colormaps) for more information ''' rc('image', cmap='summer') im = gci() if im is not None: im.set_cmap(cm.summer) draw_if_interactive() # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def winter(): ''' set the default colormap to winter and apply to current image if any. See help(colormaps) for more information ''' rc('image', cmap='winter') im = gci() if im is not None: im.set_cmap(cm.winter) draw_if_interactive() # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def spectral(): ''' set the default colormap to spectral and apply to current image if any. See help(colormaps) for more information ''' rc('image', cmap='spectral') im = gci() if im is not None: im.set_cmap(cm.spectral) draw_if_interactive()	77,521	Python	.py	2,170	29.981106	88	0.632719	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,243	quiver.py	numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/quiver.py	""" Support for plotting vector fields. Presently this contains Quiver and Barb. Quiver plots an arrow in the direction of the vector, with the size of the arrow related to the magnitude of the vector. Barbs are like quiver in that they point along a vector, but the magnitude of the vector is given schematically by the presence of barbs or flags on the barb. This will also become a home for things such as standard deviation ellipses, which can and will be derived very easily from the Quiver code. """ import numpy as np from numpy import ma import matplotlib.collections as collections import matplotlib.transforms as transforms import matplotlib.text as mtext import matplotlib.artist as martist import matplotlib.font_manager as font_manager from matplotlib.cbook import delete_masked_points from matplotlib.patches import CirclePolygon import math _quiver_doc = """ Plot a 2-D field of arrows. call signatures:: quiver(U, V, kw) quiver(U, V, C, kw) quiver(X, Y, U, V, kw) quiver(X, Y, U, V, C, kw) Arguments: X, Y: The x and y coordinates of the arrow locations (default is tail of arrow; see pivot kwarg) U, V: give the x and y components of the arrow vectors C: an optional array used to map colors to the arrows All arguments may be 1-D or 2-D arrays or sequences. If X and Y are absent, they will be generated as a uniform grid. If U and V are 2-D arrays but X and Y are 1-D, and if len(X) and len(Y) match the column and row dimensions of U, then X and Y will be expanded with :func:`numpy.meshgrid`. U, V, C may be masked arrays, but masked X, Y are not supported at present. Keyword arguments: units: ['width' \| 'height' \| 'dots' \| 'inches' \| 'x' \| 'y' ] arrow units; the arrow dimensions except for length are in multiples of this unit. * 'width' or 'height': the width or height of the axes * 'dots' or 'inches': pixels or inches, based on the figure dpi * 'x' or 'y': X or Y data units The arrows scale differently depending on the units. For 'x' or 'y', the arrows get larger as one zooms in; for other units, the arrow size is independent of the zoom state. For 'width or 'height', the arrow size increases with the width and height of the axes, respectively, when the the window is resized; for 'dots' or 'inches', resizing does not change the arrows. angles: ['uv' \| 'xy' \| array] With the default 'uv', the arrow aspect ratio is 1, so that if U==V the angle of the arrow on the plot is 45 degrees CCW from the x-axis. With 'xy', the arrow points from (x,y) to (x+u, y+v). Alternatively, arbitrary angles may be specified as an array of values in degrees, CCW from the x-axis. scale: [ None \| float ] data units per arrow unit, e.g. m/s per plot width; a smaller scale parameter makes the arrow longer. If None, a simple autoscaling algorithm is used, based on the average vector length and the number of vectors. width: shaft width in arrow units; default depends on choice of units, above, and number of vectors; a typical starting value is about 0.005 times the width of the plot. headwidth: scalar head width as multiple of shaft width, default is 3 headlength: scalar head length as multiple of shaft width, default is 5 headaxislength: scalar head length at shaft intersection, default is 4.5 minshaft: scalar length below which arrow scales, in units of head length. Do not set this to less than 1, or small arrows will look terrible! Default is 1 minlength: scalar minimum length as a multiple of shaft width; if an arrow length is less than this, plot a dot (hexagon) of this diameter instead. Default is 1. pivot: [ 'tail' \| 'middle' \| 'tip' ] The part of the arrow that is at the grid point; the arrow rotates about this point, hence the name pivot. color: [ color \| color sequence ] This is a synonym for the :class:`~matplotlib.collections.PolyCollection` facecolor kwarg. If C has been set, color has no effect. The defaults give a slightly swept-back arrow; to make the head a triangle, make headaxislength the same as headlength. To make the arrow more pointed, reduce headwidth or increase headlength and headaxislength. To make the head smaller relative to the shaft, scale down all the head parameters. You will probably do best to leave minshaft alone. linewidths and edgecolors can be used to customize the arrow outlines. Additional :class:`~matplotlib.collections.PolyCollection` keyword arguments: %(PolyCollection)s """ % martist.kwdocd _quiverkey_doc = """ Add a key to a quiver plot. call signature:: quiverkey(Q, X, Y, U, label, *kw) Arguments: Q: The Quiver instance returned by a call to quiver. X, Y: The location of the key; additional explanation follows. U: The length of the key label: a string with the length and units of the key Keyword arguments: coordinates* = [ 'axes' \| 'figure' \| 'data' \| 'inches' ] Coordinate system and units for X, Y: 'axes' and 'figure' are normalized coordinate systems with 0,0 in the lower left and 1,1 in the upper right; 'data' are the axes data coordinates (used for the locations of the vectors in the quiver plot itself); 'inches' is position in the figure in inches, with 0,0 at the lower left corner. color: overrides face and edge colors from Q. labelpos = [ 'N' \| 'S' \| 'E' \| 'W' ] Position the label above, below, to the right, to the left of the arrow, respectively. labelsep: Distance in inches between the arrow and the label. Default is 0.1 labelcolor: defaults to default :class:`~matplotlib.text.Text` color. fontproperties: A dictionary with keyword arguments accepted by the :class:`~matplotlib.font_manager.FontProperties` initializer: family, style, variant, size, weight Any additional keyword arguments are used to override vector properties taken from Q. The positioning of the key depends on X, Y, coordinates, and labelpos. If labelpos is 'N' or 'S', X, Y give the position of the middle of the key arrow. If labelpos is 'E', X, Y positions the head, and if labelpos is 'W', X, Y positions the tail; in either of these two cases, X, Y is somewhere in the middle of the arrow+label key object. """ class QuiverKey(martist.Artist): """ Labelled arrow for use as a quiver plot scale key. """ halign = {'N': 'center', 'S': 'center', 'E': 'left', 'W': 'right'} valign = {'N': 'bottom', 'S': 'top', 'E': 'center', 'W': 'center'} pivot = {'N': 'mid', 'S': 'mid', 'E': 'tip', 'W': 'tail'} def __init__(self, Q, X, Y, U, label, *kw): martist.Artist.__init__(self) self.Q = Q self.X = X self.Y = Y self.U = U self.coord = kw.pop('coordinates', 'axes') self.color = kw.pop('color', None) self.label = label self._labelsep_inches = kw.pop('labelsep', 0.1) self.labelsep = (self._labelsep_inches Q.ax.figure.dpi) def on_dpi_change(fig): self.labelsep = (self._labelsep_inches * fig.dpi) self._initialized = False # simple brute force update # works because _init is called # at the start of draw. Q.ax.figure.callbacks.connect('dpi_changed', on_dpi_change) self.labelpos = kw.pop('labelpos', 'N') self.labelcolor = kw.pop('labelcolor', None) self.fontproperties = kw.pop('fontproperties', dict()) self.kw = kw _fp = self.fontproperties #boxprops = dict(facecolor='red') self.text = mtext.Text(text=label, # bbox=boxprops, horizontalalignment=self.halign[self.labelpos], verticalalignment=self.valign[self.labelpos], fontproperties=font_manager.FontProperties(_fp)) if self.labelcolor is not None: self.text.set_color(self.labelcolor) self._initialized = False self.zorder = Q.zorder + 0.1 __init__.__doc__ = _quiverkey_doc def _init(self): if True: ##not self._initialized: self._set_transform() _pivot = self.Q.pivot self.Q.pivot = self.pivot[self.labelpos] self.verts = self.Q._make_verts(np.array([self.U]), np.zeros((1,))) self.Q.pivot = _pivot kw = self.Q.polykw kw.update(self.kw) self.vector = collections.PolyCollection(self.verts, offsets=[(self.X,self.Y)], transOffset=self.get_transform(), kw) if self.color is not None: self.vector.set_color(self.color) self.vector.set_transform(self.Q.get_transform()) self._initialized = True def _text_x(self, x): if self.labelpos == 'E': return x + self.labelsep elif self.labelpos == 'W': return x - self.labelsep else: return x def _text_y(self, y): if self.labelpos == 'N': return y + self.labelsep elif self.labelpos == 'S': return y - self.labelsep else: return y def draw(self, renderer): self._init() self.vector.draw(renderer) x, y = self.get_transform().transform_point((self.X, self.Y)) self.text.set_x(self._text_x(x)) self.text.set_y(self._text_y(y)) self.text.draw(renderer) def _set_transform(self): if self.coord == 'data': self.set_transform(self.Q.ax.transData) elif self.coord == 'axes': self.set_transform(self.Q.ax.transAxes) elif self.coord == 'figure': self.set_transform(self.Q.ax.figure.transFigure) elif self.coord == 'inches': self.set_transform(self.Q.ax.figure.dpi_scale_trans) else: raise ValueError('unrecognized coordinates') def set_figure(self, fig): martist.Artist.set_figure(self, fig) self.text.set_figure(fig) def contains(self, mouseevent): # Maybe the dictionary should allow one to # distinguish between a text hit and a vector hit. if (self.text.contains(mouseevent)[0] or self.vector.contains(mouseevent)[0]): return True, {} return False, {} quiverkey_doc = _quiverkey_doc class Quiver(collections.PolyCollection): """ Specialized PolyCollection for arrows. The only API method is set_UVC(), which can be used to change the size, orientation, and color of the arrows; their locations are fixed when the class is instantiated. Possibly this method will be useful in animations. Much of the work in this class is done in the draw() method so that as much information as possible is available about the plot. In subsequent draw() calls, recalculation is limited to things that might have changed, so there should be no performance penalty from putting the calculations in the draw() method. """ def __init__(self, ax, args, kw): self.ax = ax X, Y, U, V, C = self._parse_args(args) self.X = X self.Y = Y self.XY = np.hstack((X[:,np.newaxis], Y[:,np.newaxis])) self.N = len(X) self.scale = kw.pop('scale', None) self.headwidth = kw.pop('headwidth', 3) self.headlength = float(kw.pop('headlength', 5)) self.headaxislength = kw.pop('headaxislength', 4.5) self.minshaft = kw.pop('minshaft', 1) self.minlength = kw.pop('minlength', 1) self.units = kw.pop('units', 'width') self.angles = kw.pop('angles', 'uv') self.width = kw.pop('width', None) self.color = kw.pop('color', 'k') self.pivot = kw.pop('pivot', 'tail') kw.setdefault('facecolors', self.color) kw.setdefault('linewidths', (0,)) collections.PolyCollection.__init__(self, [], offsets=self.XY, transOffset=ax.transData, closed=False, *kw) self.polykw = kw self.set_UVC(U, V, C) self._initialized = False self.keyvec = None self.keytext = None def on_dpi_change(fig): self._new_UV = True # vertices depend on width, span # which in turn depend on dpi self._initialized = False # simple brute force update # works because _init is called # at the start of draw. self.ax.figure.callbacks.connect('dpi_changed', on_dpi_change) __init__.__doc__ = """ The constructor takes one required argument, an Axes instance, followed by the args and kwargs described by the following pylab interface documentation: %s""" % _quiver_doc def _parse_args(self, args): X, Y, U, V, C = [None]5 args = list(args) if len(args) == 3 or len(args) == 5: C = ma.asarray(args.pop(-1)).ravel() V = ma.asarray(args.pop(-1)) U = ma.asarray(args.pop(-1)) nn = np.shape(U) nc = nn[0] nr = 1 if len(nn) > 1: nr = nn[1] if len(args) == 2: # remaining after removing U,V,C X, Y = [np.array(a).ravel() for a in args] if len(X) == nc and len(Y) == nr: X, Y = [a.ravel() for a in np.meshgrid(X, Y)] else: indexgrid = np.meshgrid(np.arange(nc), np.arange(nr)) X, Y = [np.ravel(a) for a in indexgrid] return X, Y, U, V, C def _init(self): """initialization delayed until first draw; allow time for axes setup. """ # It seems that there are not enough event notifications # available to have this work on an as-needed basis at present. if True: ##not self._initialized: trans = self._set_transform() ax = self.ax sx, sy = trans.inverted().transform_point( (ax.bbox.width, ax.bbox.height)) self.span = sx sn = max(8, min(25, math.sqrt(self.N))) if self.width is None: self.width = 0.06 self.span / sn def draw(self, renderer): self._init() if self._new_UV or self.angles == 'xy': verts = self._make_verts(self.U, self.V) self.set_verts(verts, closed=False) self._new_UV = False collections.PolyCollection.draw(self, renderer) def set_UVC(self, U, V, C=None): self.U = U.ravel() self.V = V.ravel() if C is not None: self.set_array(C.ravel()) self._new_UV = True def _set_transform(self): ax = self.ax if self.units in ('x', 'y'): if self.units == 'x': dx0 = ax.viewLim.width dx1 = ax.bbox.width else: dx0 = ax.viewLim.height dx1 = ax.bbox.height dx = dx1/dx0 else: if self.units == 'width': dx = ax.bbox.width elif self.units == 'height': dx = ax.bbox.height elif self.units == 'dots': dx = 1.0 elif self.units == 'inches': dx = ax.figure.dpi else: raise ValueError('unrecognized units') trans = transforms.Affine2D().scale(dx) self.set_transform(trans) return trans def _angles(self, U, V, eps=0.001): xy = self.ax.transData.transform(self.XY) uv = ma.hstack((U[:,np.newaxis], V[:,np.newaxis])).filled(0) xyp = self.ax.transData.transform(self.XY + eps * uv) dxy = xyp - xy ang = ma.arctan2(dxy[:,1], dxy[:,0]) return ang def _make_verts(self, U, V): uv = ma.asarray(U+V1j) a = ma.absolute(uv) if self.scale is None: sn = max(10, math.sqrt(self.N)) scale = 1.8 a.mean() * sn / self.span # crude auto-scaling self.scale = scale length = a/(self.scaleself.width) X, Y = self._h_arrows(length) if self.angles == 'xy': theta = self._angles(U, V).filled(0)[:,np.newaxis] elif self.angles == 'uv': theta = np.angle(ma.asarray(uv[..., np.newaxis]).filled(0)) else: theta = ma.asarray(self.anglesnp.pi/180.0).filled(0) xy = (X+Y1j) np.exp(1jtheta)self.width xy = xy[:,:,np.newaxis] XY = ma.concatenate((xy.real, xy.imag), axis=2) return XY def _h_arrows(self, length): """ length is in arrow width units """ # It might be possible to streamline the code # and speed it up a bit by using complex (x,y) # instead of separate arrays; but any gain would be slight. minsh = self.minshaft * self.headlength N = len(length) length = length.reshape(N, 1) # x, y: normal horizontal arrow x = np.array([0, -self.headaxislength, -self.headlength, 0], np.float64) x = x + np.array([0,1,1,1]) * length y = 0.5 * np.array([1, 1, self.headwidth, 0], np.float64) y = np.repeat(y[np.newaxis,:], N, axis=0) # x0, y0: arrow without shaft, for short vectors x0 = np.array([0, minsh-self.headaxislength, minsh-self.headlength, minsh], np.float64) y0 = 0.5 * np.array([1, 1, self.headwidth, 0], np.float64) ii = [0,1,2,3,2,1,0] X = x.take(ii, 1) Y = y.take(ii, 1) Y[:, 3:] = -1 X0 = x0.take(ii) Y0 = y0.take(ii) Y0[3:] = -1 shrink = length/minsh X0 = shrink * X0[np.newaxis,:] Y0 = shrink * Y0[np.newaxis,:] short = np.repeat(length < minsh, 7, axis=1) #print 'short', length < minsh # Now select X0, Y0 if short, otherwise X, Y X = ma.where(short, X0, X) Y = ma.where(short, Y0, Y) if self.pivot[:3] == 'mid': X -= 0.5 * X[:,3, np.newaxis] elif self.pivot[:3] == 'tip': X = X - X[:,3, np.newaxis] #numpy bug? using -= does not # work here unless we multiply # by a float first, as with 'mid'. tooshort = length < self.minlength if tooshort.any(): # Use a heptagonal dot: th = np.arange(0,7,1, np.float64) * (np.pi/3.0) x1 = np.cos(th) * self.minlength * 0.5 y1 = np.sin(th) * self.minlength * 0.5 X1 = np.repeat(x1[np.newaxis, :], N, axis=0) Y1 = np.repeat(y1[np.newaxis, :], N, axis=0) tooshort = ma.repeat(tooshort, 7, 1) X = ma.where(tooshort, X1, X) Y = ma.where(tooshort, Y1, Y) return X, Y quiver_doc = _quiver_doc _barbs_doc = """ Plot a 2-D field of barbs. call signatures:: barb(U, V, kw) barb(U, V, C, kw) barb(X, Y, U, V, kw) barb(X, Y, U, V, C, kw) Arguments: X, Y: The x and y coordinates of the barb locations (default is head of barb; see pivot kwarg) U, V: give the x and y components of the barb shaft C: an optional array used to map colors to the barbs All arguments may be 1-D or 2-D arrays or sequences. If X and Y are absent, they will be generated as a uniform grid. If U and V are 2-D arrays but X and Y are 1-D, and if len(X) and len(Y) match the column and row dimensions of U, then X and Y will be expanded with :func:`numpy.meshgrid`. U, V, C may be masked arrays, but masked X, Y are not supported at present. Keyword arguments: length: Length of the barb in points; the other parts of the barb are scaled against this. Default is 9 pivot: [ 'tip' \| 'middle' ] The part of the arrow that is at the grid point; the arrow rotates about this point, hence the name pivot. Default is 'tip' barbcolor: [ color \| color sequence ] Specifies the color all parts of the barb except any flags. This parameter is analagous to the edgecolor parameter for polygons, which can be used instead. However this parameter will override facecolor. flagcolor: [ color \| color sequence ] Specifies the color of any flags on the barb. This parameter is analagous to the facecolor parameter for polygons, which can be used instead. However this parameter will override facecolor. If this is not set (and C has not either) then flagcolor will be set to match barbcolor so that the barb has a uniform color. If C has been set, flagcolor has no effect. sizes: A dictionary of coefficients specifying the ratio of a given feature to the length of the barb. Only those values one wishes to override need to be included. These features include: - 'spacing' - space between features (flags, full/half barbs) - 'height' - height (distance from shaft to top) of a flag or full barb - 'width' - width of a flag, twice the width of a full barb - 'emptybarb' - radius of the circle used for low magnitudes fill_empty: A flag on whether the empty barbs (circles) that are drawn should be filled with the flag color. If they are not filled, they will be drawn such that no color is applied to the center. Default is False rounding: A flag to indicate whether the vector magnitude should be rounded when allocating barb components. If True, the magnitude is rounded to the nearest multiple of the half-barb increment. If False, the magnitude is simply truncated to the next lowest multiple. Default is True barb_increments: A dictionary of increments specifying values to associate with different parts of the barb. Only those values one wishes to override need to be included. - 'half' - half barbs (Default is 5) - 'full' - full barbs (Default is 10) - 'flag' - flags (default is 50) flip_barb: Either a single boolean flag or an array of booleans. Single boolean indicates whether the lines and flags should point opposite to normal for all barbs. An array (which should be the same size as the other data arrays) indicates whether to flip for each individual barb. Normal behavior is for the barbs and lines to point right (comes from wind barbs having these features point towards low pressure in the Northern Hemisphere.) Default is False Barbs are traditionally used in meteorology as a way to plot the speed and direction of wind observations, but can technically be used to plot any two dimensional vector quantity. As opposed to arrows, which give vector magnitude by the length of the arrow, the barbs give more quantitative information about the vector magnitude by putting slanted lines or a triangle for various increments in magnitude, as show schematically below:: : /\ \\ : / \ \\ : / \ \ \\ : / \ \ \\ : ------------------------------ .. note the double \\ at the end of each line to make the figure .. render correctly The largest increment is given by a triangle (or "flag"). After those come full lines (barbs). The smallest increment is a half line. There is only, of course, ever at most 1 half line. If the magnitude is small and only needs a single half-line and no full lines or triangles, the half-line is offset from the end of the barb so that it can be easily distinguished from barbs with a single full line. The magnitude for the barb shown above would nominally be 65, using the standard increments of 50, 10, and 5. linewidths and edgecolors can be used to customize the barb. Additional :class:`~matplotlib.collections.PolyCollection` keyword arguments: %(PolyCollection)s """ % martist.kwdocd class Barbs(collections.PolyCollection): ''' Specialized PolyCollection for barbs. The only API method is :meth:`set_UVC`, which can be used to change the size, orientation, and color of the arrows. Locations are changed using the :meth:`set_offsets` collection method. Possibly this method will be useful in animations. There is one internal function :meth:`_find_tails` which finds exactly what should be put on the barb given the vector magnitude. From there :meth:`_make_barbs` is used to find the vertices of the polygon to represent the barb based on this information. ''' #This may be an abuse of polygons here to render what is essentially maybe #1 triangle and a series of lines. It works fine as far as I can tell #however. def __init__(self, ax, args, kw): self._pivot = kw.pop('pivot', 'tip') self._length = kw.pop('length', 7) barbcolor = kw.pop('barbcolor', None) flagcolor = kw.pop('flagcolor', None) self.sizes = kw.pop('sizes', dict()) self.fill_empty = kw.pop('fill_empty', False) self.barb_increments = kw.pop('barb_increments', dict()) self.rounding = kw.pop('rounding', True) self.flip = kw.pop('flip_barb', False) #Flagcolor and and barbcolor provide convenience parameters for setting #the facecolor and edgecolor, respectively, of the barb polygon. We #also work here to make the flag the same color as the rest of the barb #by default if None in (barbcolor, flagcolor): kw['edgecolors'] = 'face' if flagcolor: kw['facecolors'] = flagcolor elif barbcolor: kw['facecolors'] = barbcolor else: #Set to facecolor passed in or default to black kw.setdefault('facecolors', 'k') else: kw['edgecolors'] = barbcolor kw['facecolors'] = flagcolor #Parse out the data arrays from the various configurations supported x, y, u, v, c = self._parse_args(args) self.x = x self.y = y xy = np.hstack((x[:,np.newaxis], y[:,np.newaxis])) #Make a collection barb_size = self._length2 / 4 #Empirically determined collections.PolyCollection.__init__(self, [], (barb_size,), offsets=xy, transOffset=ax.transData, kw) self.set_transform(transforms.IdentityTransform()) self.set_UVC(u, v, c) __init__.__doc__ = """ The constructor takes one required argument, an Axes instance, followed by the args and kwargs described by the following pylab interface documentation: %s""" % _barbs_doc def _find_tails(self, mag, rounding=True, half=5, full=10, flag=50): ''' Find how many of each of the tail pieces is necessary. Flag specifies the increment for a flag, barb for a full barb, and half for half a barb. Mag should be the magnitude of a vector (ie. >= 0). This returns a tuple of: (number of flags, number of barbs, half_flag, empty_flag) half_flag is a boolean whether half of a barb is needed, since there should only ever be one half on a given barb. empty_flag flag is an array of flags to easily tell if a barb is empty (too low to plot any barbs/flags. ''' #If rounding, round to the nearest multiple of half, the smallest #increment if rounding: mag = half * (mag / half + 0.5).astype(np.int) num_flags = np.floor(mag / flag).astype(np.int) mag = np.mod(mag, flag) num_barb = np.floor(mag / full).astype(np.int) mag = np.mod(mag, full) half_flag = mag >= half empty_flag = ~(half_flag \| (num_flags > 0) \| (num_barb > 0)) return num_flags, num_barb, half_flag, empty_flag def _make_barbs(self, u, v, nflags, nbarbs, half_barb, empty_flag, length, pivot, sizes, fill_empty, flip): ''' This function actually creates the wind barbs. u and v are components of the vector in the x and y directions, respectively. nflags, nbarbs, and half_barb, empty_flag* are, respectively, the number of flags, number of barbs, flag for half a barb, and flag for empty barb, ostensibly obtained from :meth:`_find_tails`. length* is the length of the barb staff in points. pivot specifies the point on the barb around which the entire barb should be rotated. Right now, valid options are 'head' and 'middle'. sizes is a dictionary of coefficients specifying the ratio of a given feature to the length of the barb. These features include: - spacing: space between features (flags, full/half barbs) - height: distance from shaft of top of a flag or full barb - width - width of a flag, twice the width of a full barb - emptybarb - radius of the circle used for low magnitudes fill_empty specifies whether the circle representing an empty barb should be filled or not (this changes the drawing of the polygon). flip is a flag indicating whether the features should be flipped to the other side of the barb (useful for winds in the southern hemisphere. This function returns list of arrays of vertices, defining a polygon for each of the wind barbs. These polygons have been rotated to properly align with the vector direction. ''' #These control the spacing and size of barb elements relative to the #length of the shaft spacing = length * sizes.get('spacing', 0.125) full_height = length * sizes.get('height', 0.4) full_width = length * sizes.get('width', 0.25) empty_rad = length * sizes.get('emptybarb', 0.15) #Controls y point where to pivot the barb. pivot_points = dict(tip=0.0, middle=-length/2.) #Check for flip if flip: full_height = -full_height endx = 0.0 endy = pivot_points[pivot.lower()] #Get the appropriate angle for the vector components. The offset is due #to the way the barb is initially drawn, going down the y-axis. This #makes sense in a meteorological mode of thinking since there 0 degrees #corresponds to north (the y-axis traditionally) angles = -(ma.arctan2(v, u) + np.pi/2) #Used for low magnitude. We just get the vertices, so if we make it #out here, it can be reused. The center set here should put the #center of the circle at the location(offset), rather than at the #same point as the barb pivot; this seems more sensible. circ = CirclePolygon((0,0), radius=empty_rad).get_verts() if fill_empty: empty_barb = circ else: #If we don't want the empty one filled, we make a degenerate polygon #that wraps back over itself empty_barb = np.concatenate((circ, circ[::-1])) barb_list = [] for index, angle in np.ndenumerate(angles): #If the vector magnitude is too weak to draw anything, plot an #empty circle instead if empty_flag[index]: #We can skip the transform since the circle has no preferred #orientation barb_list.append(empty_barb) continue poly_verts = [(endx, endy)] offset = length #Add vertices for each flag for i in range(nflags[index]): #The spacing that works for the barbs is a little to much for #the flags, but this only occurs when we have more than 1 flag. if offset != length: offset += spacing / 2. poly_verts.extend([[endx, endy + offset], [endx + full_height, endy - full_width/2 + offset], [endx, endy - full_width + offset]]) offset -= full_width + spacing #Add vertices for each barb. These really are lines, but works #great adding 3 vertices that basically pull the polygon out and #back down the line for i in range(nbarbs[index]): poly_verts.extend([(endx, endy + offset), (endx + full_height, endy + offset + full_width/2), (endx, endy + offset)]) offset -= spacing #Add the vertices for half a barb, if needed if half_barb[index]: #If the half barb is the first on the staff, traditionally it is #offset from the end to make it easy to distinguish from a barb #with a full one if offset == length: poly_verts.append((endx, endy + offset)) offset -= 1.5 * spacing poly_verts.extend([(endx, endy + offset), (endx + full_height/2, endy + offset + full_width/4), (endx, endy + offset)]) #Rotate the barb according the angle. Making the barb first and then #rotating it made the math for drawing the barb really easy. Also, #the transform framework makes doing the rotation simple. poly_verts = transforms.Affine2D().rotate(-angle).transform( poly_verts) barb_list.append(poly_verts) return barb_list #Taken shamelessly from Quiver def _parse_args(self, args): X, Y, U, V, C = [None]5 args = list(args) if len(args) == 3 or len(args) == 5: C = ma.asarray(args.pop(-1)).ravel() V = ma.asarray(args.pop(-1)) U = ma.asarray(args.pop(-1)) nn = np.shape(U) nc = nn[0] nr = 1 if len(nn) > 1: nr = nn[1] if len(args) == 2: # remaining after removing U,V,C X, Y = [np.array(a).ravel() for a in args] if len(X) == nc and len(Y) == nr: X, Y = [a.ravel() for a in np.meshgrid(X, Y)] else: indexgrid = np.meshgrid(np.arange(nc), np.arange(nr)) X, Y = [np.ravel(a) for a in indexgrid] return X, Y, U, V, C def set_UVC(self, U, V, C=None): self.u = ma.asarray(U).ravel() self.v = ma.asarray(V).ravel() if C is not None: c = ma.asarray(C).ravel() x,y,u,v,c = delete_masked_points(self.x.ravel(), self.y.ravel(), self.u, self.v, c) else: x,y,u,v = delete_masked_points(self.x.ravel(), self.y.ravel(), self.u, self.v) magnitude = np.sqrt(uu + vv) flags, barbs, halves, empty = self._find_tails(magnitude, self.rounding, *self.barb_increments) #Get the vertices for each of the barbs plot_barbs = self._make_barbs(u, v, flags, barbs, halves, empty, self._length, self._pivot, self.sizes, self.fill_empty, self.flip) self.set_verts(plot_barbs) #Set the color array if C is not None: self.set_array(c) #Update the offsets in case the masked data changed xy = np.hstack((x[:,np.newaxis], y[:,np.newaxis])) self._offsets = xy def set_offsets(self, xy): ''' Set the offsets for the barb polygons. This saves the offets passed in and actually sets version masked as appropriate for the existing U/V data. offsets* should be a sequence. ACCEPTS: sequence of pairs of floats ''' self.x = xy[:,0] self.y = xy[:,1] x,y,u,v = delete_masked_points(self.x.ravel(), self.y.ravel(), self.u, self.v) xy = np.hstack((x[:,np.newaxis], y[:,np.newaxis])) collections.PolyCollection.set_offsets(self, xy) set_offsets.__doc__ = collections.PolyCollection.set_offsets.__doc__ barbs_doc = _barbs_doc	36,790	Python	.py	806	36.774194	80	0.608087	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,244	path.py	numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/path.py	""" Contains a class for managing paths (polylines). """ import math from weakref import WeakValueDictionary import numpy as np from numpy import ma from matplotlib._path import point_in_path, get_path_extents, \ point_in_path_collection, get_path_collection_extents, \ path_in_path, path_intersects_path, convert_path_to_polygons from matplotlib.cbook import simple_linear_interpolation class Path(object): """ :class:`Path` represents a series of possibly disconnected, possibly closed, line and curve segments. The underlying storage is made up of two parallel numpy arrays: - vertices: an Nx2 float array of vertices - codes: an N-length uint8 array of vertex types These two arrays always have the same length in the first dimension. For example, to represent a cubic curve, you must provide three vertices as well as three codes ``CURVE3``. The code types are: - ``STOP`` : 1 vertex (ignored) A marker for the end of the entire path (currently not required and ignored) - ``MOVETO`` : 1 vertex Pick up the pen and move to the given vertex. - ``LINETO`` : 1 vertex Draw a line from the current position to the given vertex. - ``CURVE3`` : 1 control point, 1 endpoint Draw a quadratic Bezier curve from the current position, with the given control point, to the given end point. - ``CURVE4`` : 2 control points, 1 endpoint Draw a cubic Bezier curve from the current position, with the given control points, to the given end point. - ``CLOSEPOLY`` : 1 vertex (ignored) Draw a line segment to the start point of the current polyline. Users of Path objects should not access the vertices and codes arrays directly. Instead, they should use :meth:`iter_segments` to get the vertex/code pairs. This is important, since many :class:`Path` objects, as an optimization, do not store a codes at all, but have a default one provided for them by :meth:`iter_segments`. Note also that the vertices and codes arrays should be treated as immutable -- there are a number of optimizations and assumptions made up front in the constructor that will not change when the data changes. """ # Path codes STOP = 0 # 1 vertex MOVETO = 1 # 1 vertex LINETO = 2 # 1 vertex CURVE3 = 3 # 2 vertices CURVE4 = 4 # 3 vertices CLOSEPOLY = 5 # 1 vertex NUM_VERTICES = [1, 1, 1, 2, 3, 1] code_type = np.uint8 def __init__(self, vertices, codes=None): """ Create a new path with the given vertices and codes. vertices is an Nx2 numpy float array, masked array or Python sequence. codes is an N-length numpy array or Python sequence of type :attr:`matplotlib.path.Path.code_type`. These two arrays must have the same length in the first dimension. If codes is None, vertices will be treated as a series of line segments. If vertices contains masked values, they will be converted to NaNs which are then handled correctly by the Agg PathIterator and other consumers of path data, such as :meth:`iter_segments`. """ if ma.isMaskedArray(vertices): vertices = vertices.astype(np.float_).filled(np.nan) else: vertices = np.asarray(vertices, np.float_) if codes is not None: codes = np.asarray(codes, self.code_type) assert codes.ndim == 1 assert len(codes) == len(vertices) assert vertices.ndim == 2 assert vertices.shape[1] == 2 self.should_simplify = (len(vertices) >= 128 and (codes is None or np.all(codes <= Path.LINETO))) self.has_nonfinite = not np.isfinite(vertices).all() self.codes = codes self.vertices = vertices #@staticmethod def make_compound_path(args): """ (staticmethod) Make a compound path from a list of Path objects. Only polygons (not curves) are supported. """ for p in args: assert p.codes is None lengths = [len(x) for x in args] total_length = sum(lengths) vertices = np.vstack([x.vertices for x in args]) vertices.reshape((total_length, 2)) codes = Path.LINETO np.ones(total_length) i = 0 for length in lengths: codes[i] = Path.MOVETO i += length return Path(vertices, codes) make_compound_path = staticmethod(make_compound_path) def __repr__(self): return "Path(%s, %s)" % (self.vertices, self.codes) def __len__(self): return len(self.vertices) def iter_segments(self, simplify=None): """ Iterates over all of the curve segments in the path. Each iteration returns a 2-tuple (vertices, code), where vertices is a sequence of 1 - 3 coordinate pairs, and code is one of the :class:`Path` codes. If simplify is provided, it must be a tuple (width, height) defining the size of the figure, in native units (e.g. pixels or points). Simplification implies both removing adjacent line segments that are very close to parallel, and removing line segments outside of the figure. The path will be simplified only if :attr:`should_simplify` is True, which is determined in the constructor by this criteria: - No curves - More than 128 vertices """ vertices = self.vertices if not len(vertices): return codes = self.codes len_vertices = len(vertices) isfinite = np.isfinite NUM_VERTICES = self.NUM_VERTICES MOVETO = self.MOVETO LINETO = self.LINETO CLOSEPOLY = self.CLOSEPOLY STOP = self.STOP if simplify is not None and self.should_simplify: polygons = self.to_polygons(None, simplify) for vertices in polygons: yield vertices[0], MOVETO for v in vertices[1:]: yield v, LINETO elif codes is None: if self.has_nonfinite: next_code = MOVETO for v in vertices: if np.isfinite(v).all(): yield v, next_code next_code = LINETO else: next_code = MOVETO else: yield vertices[0], MOVETO for v in vertices[1:]: yield v, LINETO else: i = 0 was_nan = False while i < len_vertices: code = codes[i] if code == CLOSEPOLY: yield [], code i += 1 elif code == STOP: return else: num_vertices = NUM_VERTICES[int(code)] curr_vertices = vertices[i:i+num_vertices].flatten() if not isfinite(curr_vertices).all(): was_nan = True elif was_nan: yield curr_vertices[-2:], MOVETO was_nan = False else: yield curr_vertices, code i += num_vertices def transformed(self, transform): """ Return a transformed copy of the path. .. seealso:: :class:`matplotlib.transforms.TransformedPath`: A specialized path class that will cache the transformed result and automatically update when the transform changes. """ return Path(transform.transform(self.vertices), self.codes) def contains_point(self, point, transform=None): """ Returns True* if the path contains the given point. If transform is not None, the path will be transformed before performing the test. """ if transform is not None: transform = transform.frozen() return point_in_path(point[0], point[1], self, transform) def contains_path(self, path, transform=None): """ Returns True if this path completely contains the given path. If transform is not None, the path will be transformed before performing the test. """ if transform is not None: transform = transform.frozen() return path_in_path(self, None, path, transform) def get_extents(self, transform=None): """ Returns the extents (xmin, ymin, xmax, ymax) of the path. Unlike computing the extents on the vertices alone, this algorithm will take into account the curves and deal with control points appropriately. """ from transforms import Bbox if transform is not None: transform = transform.frozen() return Bbox(get_path_extents(self, transform)) def intersects_path(self, other, filled=True): """ Returns True if this path intersects another given path. filled, when True, treats the paths as if they were filled. That is, if one path completely encloses the other, :meth:`intersects_path` will return True. """ return path_intersects_path(self, other, filled) def intersects_bbox(self, bbox, filled=True): """ Returns True if this path intersects a given :class:`~matplotlib.transforms.Bbox`. filled, when True, treats the path as if it was filled. That is, if one path completely encloses the other, :meth:`intersects_path` will return True. """ from transforms import BboxTransformTo rectangle = self.unit_rectangle().transformed( BboxTransformTo(bbox)) result = self.intersects_path(rectangle, filled) return result def interpolated(self, steps): """ Returns a new path resampled to length N x steps. Does not currently handle interpolating curves. """ vertices = simple_linear_interpolation(self.vertices, steps) codes = self.codes if codes is not None: new_codes = Path.LINETO * np.ones(((len(codes) - 1) * steps + 1, )) new_codes[0::steps] = codes else: new_codes = None return Path(vertices, new_codes) def to_polygons(self, transform=None, width=0, height=0): """ Convert this path to a list of polygons. Each polygon is an Nx2 array of vertices. In other words, each polygon has no ``MOVETO`` instructions or curves. This is useful for displaying in backends that do not support compound paths or Bezier curves, such as GDK. If width and height are both non-zero then the lines will be simplified so that vertices outside of (0, 0), (width, height) will be clipped. """ if len(self.vertices) == 0: return [] if transform is not None: transform = transform.frozen() if self.codes is None and (width == 0 or height == 0): if transform is None: return [self.vertices] else: return [transform.transform(self.vertices)] # Deal with the case where there are curves and/or multiple # subpaths (using extension code) return convert_path_to_polygons(self, transform, width, height) _unit_rectangle = None #@classmethod def unit_rectangle(cls): """ (staticmethod) Returns a :class:`Path` of the unit rectangle from (0, 0) to (1, 1). """ if cls._unit_rectangle is None: cls._unit_rectangle = \ Path([[0.0, 0.0], [1.0, 0.0], [1.0, 1.0], [0.0, 1.0], [0.0, 0.0]]) return cls._unit_rectangle unit_rectangle = classmethod(unit_rectangle) _unit_regular_polygons = WeakValueDictionary() #@classmethod def unit_regular_polygon(cls, numVertices): """ (staticmethod) Returns a :class:`Path` for a unit regular polygon with the given numVertices and radius of 1.0, centered at (0, 0). """ if numVertices <= 16: path = cls._unit_regular_polygons.get(numVertices) else: path = None if path is None: theta = (2np.pi/numVertices np.arange(numVertices + 1).reshape((numVertices + 1, 1))) # This initial rotation is to make sure the polygon always # "points-up" theta += np.pi / 2.0 verts = np.concatenate((np.cos(theta), np.sin(theta)), 1) path = Path(verts) cls._unit_regular_polygons[numVertices] = path return path unit_regular_polygon = classmethod(unit_regular_polygon) _unit_regular_stars = WeakValueDictionary() #@classmethod def unit_regular_star(cls, numVertices, innerCircle=0.5): """ (staticmethod) Returns a :class:`Path` for a unit regular star with the given numVertices and radius of 1.0, centered at (0, 0). """ if numVertices <= 16: path = cls._unit_regular_stars.get((numVertices, innerCircle)) else: path = None if path is None: ns2 = numVertices * 2 theta = (2np.pi/ns2 np.arange(ns2 + 1)) # This initial rotation is to make sure the polygon always # "points-up" theta += np.pi / 2.0 r = np.ones(ns2 + 1) r[1::2] = innerCircle verts = np.vstack((rnp.cos(theta), rnp.sin(theta))).transpose() path = Path(verts) cls._unit_regular_polygons[(numVertices, innerCircle)] = path return path unit_regular_star = classmethod(unit_regular_star) #@classmethod def unit_regular_asterisk(cls, numVertices): """ (staticmethod) Returns a :class:`Path` for a unit regular asterisk with the given numVertices and radius of 1.0, centered at (0, 0). """ return cls.unit_regular_star(numVertices, 0.0) unit_regular_asterisk = classmethod(unit_regular_asterisk) _unit_circle = None #@classmethod def unit_circle(cls): """ (staticmethod) Returns a :class:`Path` of the unit circle. The circle is approximated using cubic Bezier curves. This uses 8 splines around the circle using the approach presented here: Lancaster, Don. `Approximating a Circle or an Ellipse Using Four Bezier Cubic Splines <http://www.tinaja.com/glib/ellipse4.pdf>`_. """ if cls._unit_circle is None: MAGIC = 0.2652031 SQRTHALF = np.sqrt(0.5) MAGIC45 = np.sqrt((MAGICMAGIC) / 2.0) vertices = np.array( [[0.0, -1.0], [MAGIC, -1.0], [SQRTHALF-MAGIC45, -SQRTHALF-MAGIC45], [SQRTHALF, -SQRTHALF], [SQRTHALF+MAGIC45, -SQRTHALF+MAGIC45], [1.0, -MAGIC], [1.0, 0.0], [1.0, MAGIC], [SQRTHALF+MAGIC45, SQRTHALF-MAGIC45], [SQRTHALF, SQRTHALF], [SQRTHALF-MAGIC45, SQRTHALF+MAGIC45], [MAGIC, 1.0], [0.0, 1.0], [-MAGIC, 1.0], [-SQRTHALF+MAGIC45, SQRTHALF+MAGIC45], [-SQRTHALF, SQRTHALF], [-SQRTHALF-MAGIC45, SQRTHALF-MAGIC45], [-1.0, MAGIC], [-1.0, 0.0], [-1.0, -MAGIC], [-SQRTHALF-MAGIC45, -SQRTHALF+MAGIC45], [-SQRTHALF, -SQRTHALF], [-SQRTHALF+MAGIC45, -SQRTHALF-MAGIC45], [-MAGIC, -1.0], [0.0, -1.0], [0.0, -1.0]], np.float_) codes = cls.CURVE4 np.ones(26) codes[0] = cls.MOVETO codes[-1] = cls.CLOSEPOLY cls._unit_circle = Path(vertices, codes) return cls._unit_circle unit_circle = classmethod(unit_circle) #@classmethod def arc(cls, theta1, theta2, n=None, is_wedge=False): """ (staticmethod) Returns an arc on the unit circle from angle theta1 to angle theta2 (in degrees). If n is provided, it is the number of spline segments to make. If n is not provided, the number of spline segments is determined based on the delta between theta1 and theta2. Masionobe, L. 2003. `Drawing an elliptical arc using polylines, quadratic or cubic Bezier curves <http://www.spaceroots.org/documents/ellipse/index.html>`_. """ # degrees to radians theta1 = np.pi / 180.0 theta2 = np.pi / 180.0 twopi = np.pi * 2.0 halfpi = np.pi * 0.5 eta1 = np.arctan2(np.sin(theta1), np.cos(theta1)) eta2 = np.arctan2(np.sin(theta2), np.cos(theta2)) eta2 -= twopi * np.floor((eta2 - eta1) / twopi) if (theta2 - theta1 > np.pi) and (eta2 - eta1 < np.pi): eta2 += twopi # number of curve segments to make if n is None: n = int(2 ** np.ceil((eta2 - eta1) / halfpi)) if n < 1: raise ValueError("n must be >= 1 or None") deta = (eta2 - eta1) / n t = np.tan(0.5 * deta) alpha = np.sin(deta) * (np.sqrt(4.0 + 3.0 * t * t) - 1) / 3.0 steps = np.linspace(eta1, eta2, n + 1, True) cos_eta = np.cos(steps) sin_eta = np.sin(steps) xA = cos_eta[:-1] yA = sin_eta[:-1] xA_dot = -yA yA_dot = xA xB = cos_eta[1:] yB = sin_eta[1:] xB_dot = -yB yB_dot = xB if is_wedge: length = n * 3 + 4 vertices = np.zeros((length, 2), np.float_) codes = Path.CURVE4 * np.ones((length, ), Path.code_type) vertices[1] = [xA[0], yA[0]] codes[0:2] = [Path.MOVETO, Path.LINETO] codes[-2:] = [Path.LINETO, Path.CLOSEPOLY] vertex_offset = 2 end = length - 2 else: length = n * 3 + 1 vertices = np.zeros((length, 2), np.float_) codes = Path.CURVE4 * np.ones((length, ), Path.code_type) vertices[0] = [xA[0], yA[0]] codes[0] = Path.MOVETO vertex_offset = 1 end = length vertices[vertex_offset :end:3, 0] = xA + alpha * xA_dot vertices[vertex_offset :end:3, 1] = yA + alpha * yA_dot vertices[vertex_offset+1:end:3, 0] = xB - alpha * xB_dot vertices[vertex_offset+1:end:3, 1] = yB - alpha * yB_dot vertices[vertex_offset+2:end:3, 0] = xB vertices[vertex_offset+2:end:3, 1] = yB return Path(vertices, codes) arc = classmethod(arc) #@classmethod def wedge(cls, theta1, theta2, n=None): """ (staticmethod) Returns a wedge of the unit circle from angle theta1 to angle theta2 (in degrees). If n is provided, it is the number of spline segments to make. If n is not provided, the number of spline segments is determined based on the delta between theta1 and theta2. """ return cls.arc(theta1, theta2, n, True) wedge = classmethod(wedge) _get_path_collection_extents = get_path_collection_extents def get_path_collection_extents(args): """ Given a sequence of :class:`Path` objects, returns the bounding box that encapsulates all of them. """ from transforms import Bbox if len(args[1]) == 0: raise ValueError("No paths provided") return Bbox.from_extents(_get_path_collection_extents(*args))	20,263	Python	.py	481	31.669439	82	0.577806	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,245	patches.py	numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/patches.py	# -- coding: utf-8 -- from __future__ import division import math import matplotlib as mpl import numpy as np import matplotlib.cbook as cbook import matplotlib.artist as artist import matplotlib.colors as colors import matplotlib.transforms as transforms from matplotlib.path import Path # these are not available for the object inspector until after the # class is built so we define an initial set here for the init # function and they will be overridden after object definition artist.kwdocd['Patch'] = """ ================= ============================================== Property Description ================= ============================================== alpha float animated [True \| False] antialiased or aa [True \| False] clip_box a matplotlib.transform.Bbox instance clip_on [True \| False] edgecolor or ec any matplotlib color facecolor or fc any matplotlib color figure a matplotlib.figure.Figure instance fill [True \| False] hatch unknown label any string linewidth or lw float lod [True \| False] transform a matplotlib.transform transformation instance visible [True \| False] zorder any number ================= ============================================== """ class Patch(artist.Artist): """ A patch is a 2D thingy with a face color and an edge color. If any of edgecolor, facecolor, linewidth, or antialiased are None, they default to their rc params setting. """ zorder = 1 def __str__(self): return str(self.__class__).split('.')[-1] def get_verts(self): """ Return a copy of the vertices used in this patch If the patch contains Bézier curves, the curves will be interpolated by line segments. To access the curves as curves, use :meth:`get_path`. """ trans = self.get_transform() path = self.get_path() polygons = path.to_polygons(trans) if len(polygons): return polygons[0] return [] def contains(self, mouseevent): """Test whether the mouse event occurred in the patch. Returns T/F, {} """ # This is a general version of contains that should work on any # patch with a path. However, patches that have a faster # algebraic solution to hit-testing should override this # method. if callable(self._contains): return self._contains(self,mouseevent) inside = self.get_path().contains_point( (mouseevent.x, mouseevent.y), self.get_transform()) return inside, {} def update_from(self, other): """ Updates this :class:`Patch` from the properties of other. """ artist.Artist.update_from(self, other) self.set_edgecolor(other.get_edgecolor()) self.set_facecolor(other.get_facecolor()) self.set_fill(other.get_fill()) self.set_hatch(other.get_hatch()) self.set_linewidth(other.get_linewidth()) self.set_linestyle(other.get_linestyle()) self.set_transform(other.get_data_transform()) self.set_figure(other.get_figure()) self.set_alpha(other.get_alpha()) def get_extents(self): """ Return a :class:`~matplotlib.transforms.Bbox` object defining the axis-aligned extents of the :class:`Patch`. """ return self.get_path().get_extents(self.get_transform()) def get_transform(self): """ Return the :class:`~matplotlib.transforms.Transform` applied to the :class:`Patch`. """ return self.get_patch_transform() + artist.Artist.get_transform(self) def get_data_transform(self): return artist.Artist.get_transform(self) def get_patch_transform(self): return transforms.IdentityTransform() def get_antialiased(self): """ Returns True if the :class:`Patch` is to be drawn with antialiasing. """ return self._antialiased get_aa = get_antialiased def get_edgecolor(self): """ Return the edge color of the :class:`Patch`. """ return self._edgecolor get_ec = get_edgecolor def get_facecolor(self): """ Return the face color of the :class:`Patch`. """ return self._facecolor get_fc = get_facecolor def get_linewidth(self): """ Return the line width in points. """ return self._linewidth get_lw = get_linewidth def get_linestyle(self): """ Return the linestyle. Will be one of ['solid' \| 'dashed' \| 'dashdot' \| 'dotted'] """ return self._linestyle get_ls = get_linestyle def set_antialiased(self, aa): """ Set whether to use antialiased rendering ACCEPTS: [True \| False] or None for default """ if aa is None: aa = mpl.rcParams['patch.antialiased'] self._antialiased = aa def set_aa(self, aa): """alias for set_antialiased""" return self.set_antialiased(aa) def set_edgecolor(self, color): """ Set the patch edge color ACCEPTS: mpl color spec, or None for default, or 'none' for no color """ if color is None: color = mpl.rcParams['patch.edgecolor'] self._edgecolor = color def set_ec(self, color): """alias for set_edgecolor""" return self.set_edgecolor(color) def set_facecolor(self, color): """ Set the patch face color ACCEPTS: mpl color spec, or None for default, or 'none' for no color """ if color is None: color = mpl.rcParams['patch.facecolor'] self._facecolor = color def set_fc(self, color): """alias for set_facecolor""" return self.set_facecolor(color) def set_linewidth(self, w): """ Set the patch linewidth in points ACCEPTS: float or None for default """ if w is None: w = mpl.rcParams['patch.linewidth'] self._linewidth = w def set_lw(self, lw): """alias for set_linewidth""" return self.set_linewidth(lw) def set_linestyle(self, ls): """ Set the patch linestyle ACCEPTS: ['solid' \| 'dashed' \| 'dashdot' \| 'dotted'] """ if ls is None: ls = "solid" self._linestyle = ls def set_ls(self, ls): """alias for set_linestyle""" return self.set_linestyle(ls) def set_fill(self, b): """ Set whether to fill the patch ACCEPTS: [True \| False] """ self.fill = b def get_fill(self): 'return whether fill is set' return self.fill def set_hatch(self, h): """ Set the hatching pattern hatch can be one of:: / - diagonal hatching \ - back diagonal \| - vertical - - horizontal # - crossed x - crossed diagonal Letters can be combined, in which case all the specified hatchings are done. If same letter repeats, it increases the density of hatching in that direction. CURRENT LIMITATIONS: 1. Hatching is supported in the PostScript backend only. 2. Hatching is done with solid black lines of width 0. ACCEPTS: [ '/' \| '\\' \| '\|' \| '-' \| '#' \| 'x' ] """ self._hatch = h def get_hatch(self): 'Return the current hatching pattern' return self._hatch def draw(self, renderer): 'Draw the :class:`Patch` to the given renderer.' if not self.get_visible(): return #renderer.open_group('patch') gc = renderer.new_gc() if cbook.is_string_like(self._edgecolor) and self._edgecolor.lower()=='none': gc.set_linewidth(0) else: gc.set_foreground(self._edgecolor) gc.set_linewidth(self._linewidth) gc.set_linestyle(self._linestyle) gc.set_antialiased(self._antialiased) self._set_gc_clip(gc) gc.set_capstyle('projecting') gc.set_url(self._url) gc.set_snap(self._snap) if (not self.fill or self._facecolor is None or (cbook.is_string_like(self._facecolor) and self._facecolor.lower()=='none')): rgbFace = None gc.set_alpha(1.0) else: r, g, b, a = colors.colorConverter.to_rgba(self._facecolor, self._alpha) rgbFace = (r, g, b) gc.set_alpha(a) if self._hatch: gc.set_hatch(self._hatch ) path = self.get_path() transform = self.get_transform() tpath = transform.transform_path_non_affine(path) affine = transform.get_affine() renderer.draw_path(gc, tpath, affine, rgbFace) #renderer.close_group('patch') def get_path(self): """ Return the path of this patch """ raise NotImplementedError('Derived must override') def get_window_extent(self, renderer=None): return self.get_path().get_extents(self.get_transform()) artist.kwdocd['Patch'] = patchdoc = artist.kwdoc(Patch) for k in ('Rectangle', 'Circle', 'RegularPolygon', 'Polygon', 'Wedge', 'Arrow', 'FancyArrow', 'YAArrow', 'CirclePolygon', 'Ellipse', 'Arc', 'FancyBboxPatch'): artist.kwdocd[k] = patchdoc # define Patch.__init__ after the class so that the docstring can be # auto-generated. def __patch__init__(self, edgecolor=None, facecolor=None, linewidth=None, linestyle=None, antialiased = None, hatch = None, fill=True, kwargs ): """ The following kwarg properties are supported %(Patch)s """ artist.Artist.__init__(self) if linewidth is None: linewidth = mpl.rcParams['patch.linewidth'] if linestyle is None: linestyle = "solid" if antialiased is None: antialiased = mpl.rcParams['patch.antialiased'] self.set_edgecolor(edgecolor) self.set_facecolor(facecolor) self.set_linewidth(linewidth) self.set_linestyle(linestyle) self.set_antialiased(antialiased) self.set_hatch(hatch) self.fill = fill self._combined_transform = transforms.IdentityTransform() if len(kwargs): artist.setp(self, kwargs) __patch__init__.__doc__ = cbook.dedent(__patch__init__.__doc__) % artist.kwdocd Patch.__init__ = __patch__init__ class Shadow(Patch): def __str__(self): return "Shadow(%s)"%(str(self.patch)) def __init__(self, patch, ox, oy, props=None, *kwargs): """ Create a shadow of the given patch* offset by ox, oy. props, if not None, is a patch property update dictionary. If None, the shadow will have have the same color as the face, but darkened. kwargs are %(Patch)s """ Patch.__init__(self) self.patch = patch self.props = props self._ox, self._oy = ox, oy self._update_transform() self._update() __init__.__doc__ = cbook.dedent(__init__.__doc__) % artist.kwdocd def _update(self): self.update_from(self.patch) if self.props is not None: self.update(self.props) else: r,g,b,a = colors.colorConverter.to_rgba(self.patch.get_facecolor()) rho = 0.3 r = rhor g = rhog b = rhob self.set_facecolor((r,g,b,0.5)) self.set_edgecolor((r,g,b,0.5)) def _update_transform(self): self._shadow_transform = transforms.Affine2D().translate(self._ox, self._oy) def _get_ox(self): return self._ox def _set_ox(self, ox): self._ox = ox self._update_transform() def _get_oy(self): return self._oy def _set_oy(self, oy): self._oy = oy self._update_transform() def get_path(self): return self.patch.get_path() def get_patch_transform(self): return self.patch.get_patch_transform() + self._shadow_transform class Rectangle(Patch): """ Draw a rectangle with lower left at xy* = (x, y) with specified width and height. """ def __str__(self): return self.__class__.__name__ \ + "(%g,%g;%gx%g)" % (self._x, self._y, self._width, self._height) def __init__(self, xy, width, height, *kwargs): """ fill* is a boolean indicating whether to fill the rectangle Valid kwargs are: %(Patch)s """ Patch.__init__(self, *kwargs) self._x = xy[0] self._y = xy[1] self._width = width self._height = height # Note: This cannot be calculated until this is added to an Axes self._rect_transform = transforms.IdentityTransform() __init__.__doc__ = cbook.dedent(__init__.__doc__) % artist.kwdocd def get_path(self): """ Return the vertices of the rectangle """ return Path.unit_rectangle() def _update_patch_transform(self): """NOTE: This cannot be called until after this has been added to an Axes, otherwise unit conversion will fail. This maxes it very important to call the accessor method and not directly access the transformation member variable. """ x = self.convert_xunits(self._x) y = self.convert_yunits(self._y) width = self.convert_xunits(self._width) height = self.convert_yunits(self._height) bbox = transforms.Bbox.from_bounds(x, y, width, height) self._rect_transform = transforms.BboxTransformTo(bbox) def get_patch_transform(self): self._update_patch_transform() return self._rect_transform def contains(self, mouseevent): # special case the degenerate rectangle if self._width==0 or self._height==0: return False, {} x, y = self.get_transform().inverted().transform_point( (mouseevent.x, mouseevent.y)) return (x >= 0.0 and x <= 1.0 and y >= 0.0 and y <= 1.0), {} def get_x(self): "Return the left coord of the rectangle" return self._x def get_y(self): "Return the bottom coord of the rectangle" return self._y def get_xy(self): "Return the left and bottom coords of the rectangle" return self._x, self._y def get_width(self): "Return the width of the rectangle" return self._width def get_height(self): "Return the height of the rectangle" return self._height def set_x(self, x): """ Set the left coord of the rectangle ACCEPTS: float """ self._x = x def set_y(self, y): """ Set the bottom coord of the rectangle ACCEPTS: float """ self._y = y def set_xy(self, xy): """ Set the left and bottom coords of the rectangle ACCEPTS: 2-item sequence """ self._x, self._y = xy def set_width(self, w): """ Set the width rectangle ACCEPTS: float """ self._width = w def set_height(self, h): """ Set the width rectangle ACCEPTS: float """ self._height = h def set_bounds(self, args): """ Set the bounds of the rectangle: l,b,w,h ACCEPTS: (left, bottom, width, height) """ if len(args)==0: l,b,w,h = args[0] else: l,b,w,h = args self._x = l self._y = b self._width = w self._height = h def get_bbox(self): return transforms.Bbox.from_bounds(self._x, self._y, self._width, self._height) xy = property(get_xy, set_xy) class RegularPolygon(Patch): """ A regular polygon patch. """ def __str__(self): return "Poly%d(%g,%g)"%(self._numVertices,self._xy[0],self._xy[1]) def __init__(self, xy, numVertices, radius=5, orientation=0, *kwargs): """ Constructor arguments: xy* A length 2 tuple (x, y) of the center. numVertices the number of vertices. radius The distance from the center to each of the vertices. orientation rotates the polygon (in radians). Valid kwargs are: %(Patch)s """ self._xy = xy self._numVertices = numVertices self._orientation = orientation self._radius = radius self._path = Path.unit_regular_polygon(numVertices) self._poly_transform = transforms.Affine2D() self._update_transform() Patch.__init__(self, *kwargs) __init__.__doc__ = cbook.dedent(__init__.__doc__) % artist.kwdocd def _update_transform(self): self._poly_transform.clear() \ .scale(self.radius) \ .rotate(self.orientation) \ .translate(self.xy) def _get_xy(self): return self._xy def _set_xy(self, xy): self._update_transform() xy = property(_get_xy, _set_xy) def _get_orientation(self): return self._orientation def _set_orientation(self, xy): self._orientation = xy orientation = property(_get_orientation, _set_orientation) def _get_radius(self): return self._radius def _set_radius(self, xy): self._radius = xy radius = property(_get_radius, _set_radius) def _get_numvertices(self): return self._numVertices def _set_numvertices(self, numVertices): self._numVertices = numVertices numvertices = property(_get_numvertices, _set_numvertices) def get_path(self): return self._path def get_patch_transform(self): self._update_transform() return self._poly_transform class PathPatch(Patch): """ A general polycurve path patch. """ def __str__(self): return "Poly((%g, %g) ...)" % tuple(self._path.vertices[0]) def __init__(self, path, *kwargs): """ path* is a :class:`matplotlib.path.Path` object. Valid kwargs are: %(Patch)s .. seealso:: :class:`Patch`: For additional kwargs """ Patch.__init__(self, kwargs) self._path = path __init__.__doc__ = cbook.dedent(__init__.__doc__) % artist.kwdocd def get_path(self): return self._path class Polygon(Patch): """ A general polygon patch. """ def __str__(self): return "Poly((%g, %g) ...)" % tuple(self._path.vertices[0]) def __init__(self, xy, closed=True, kwargs): """ xy is a numpy array with shape Nx2. If closed is True, the polygon will be closed so the starting and ending points are the same. Valid kwargs are: %(Patch)s .. seealso:: :class:`Patch`: For additional kwargs """ Patch.__init__(self, kwargs) xy = np.asarray(xy, np.float_) self._path = Path(xy) self.set_closed(closed) __init__.__doc__ = cbook.dedent(__init__.__doc__) % artist.kwdocd def get_path(self): return self._path def get_closed(self): return self._closed def set_closed(self, closed): self._closed = closed xy = self._get_xy() if closed: if len(xy) and (xy[0] != xy[-1]).any(): xy = np.concatenate([xy, [xy[0]]]) else: if len(xy)>2 and (xy[0]==xy[-1]).all(): xy = xy[0:-1] self._set_xy(xy) def get_xy(self): return self._path.vertices def set_xy(self, vertices): self._path = Path(vertices) _get_xy = get_xy _set_xy = set_xy xy = property( get_xy, set_xy, None, """Set/get the vertices of the polygon. This property is provided for backward compatibility with matplotlib 0.91.x only. New code should use :meth:`~matplotlib.patches.Polygon.get_xy` and :meth:`~matplotlib.patches.Polygon.set_xy` instead.""") class Wedge(Patch): """ Wedge shaped patch. """ def __str__(self): return "Wedge(%g,%g)"%(self.theta1,self.theta2) def __init__(self, center, r, theta1, theta2, width=None, kwargs): """ Draw a wedge centered at x, y center with radius r that sweeps theta1 to theta2 (in degrees). If width is given, then a partial wedge is drawn from inner radius r - width to outer radius r. Valid kwargs are: %(Patch)s """ Patch.__init__(self, *kwargs) self.center = center self.r,self.width = r,width self.theta1,self.theta2 = theta1,theta2 # Inner and outer rings are connected unless the annulus is complete delta=theta2-theta1 if abs((theta2-theta1) - 360) <= 1e-12: theta1,theta2 = 0,360 connector = Path.MOVETO else: connector = Path.LINETO # Form the outer ring arc = Path.arc(theta1,theta2) if width is not None: # Partial annulus needs to draw the outter ring # followed by a reversed and scaled inner ring v1 = arc.vertices v2 = arc.vertices[::-1]float(r-width)/r v = np.vstack([v1,v2,v1[0,:],(0,0)]) c = np.hstack([arc.codes,arc.codes,connector,Path.CLOSEPOLY]) c[len(arc.codes)]=connector else: # Wedge doesn't need an inner ring v = np.vstack([arc.vertices,[(0,0),arc.vertices[0,:],(0,0)]]) c = np.hstack([arc.codes,[connector,connector,Path.CLOSEPOLY]]) # Shift and scale the wedge to the final location. v = r v += np.asarray(center) self._path = Path(v,c) self._patch_transform = transforms.IdentityTransform() __init__.__doc__ = cbook.dedent(__init__.__doc__) % artist.kwdocd def get_path(self): return self._path # COVERAGE NOTE: Not used internally or from examples class Arrow(Patch): """ An arrow patch. """ def __str__(self): return "Arrow()" _path = Path( [ [ 0.0, 0.1 ], [ 0.0, -0.1], [ 0.8, -0.1 ], [ 0.8, -0.3], [ 1.0, 0.0 ], [ 0.8, 0.3], [ 0.8, 0.1 ], [ 0.0, 0.1] ] ) def __init__( self, x, y, dx, dy, width=1.0, kwargs ): """ Draws an arrow, starting at (x, y), direction and length given by (dx, dy) the width of the arrow is scaled by width. Valid kwargs are: %(Patch)s """ Patch.__init__(self, kwargs) L = np.sqrt(dx2+dy2) or 1 # account for div by zero cx = float(dx)/L sx = float(dy)/L trans1 = transforms.Affine2D().scale(L, width) trans2 = transforms.Affine2D.from_values(cx, sx, -sx, cx, 0.0, 0.0) trans3 = transforms.Affine2D().translate(x, y) trans = trans1 + trans2 + trans3 self._patch_transform = trans.frozen() __init__.__doc__ = cbook.dedent(__init__.__doc__) % artist.kwdocd def get_path(self): return self._path def get_patch_transform(self): return self._patch_transform class FancyArrow(Polygon): """ Like Arrow, but lets you set head width and head height independently. """ def __str__(self): return "FancyArrow()" def __init__(self, x, y, dx, dy, width=0.001, length_includes_head=False, \ head_width=None, head_length=None, shape='full', overhang=0, \ head_starts_at_zero=False,kwargs): """ Constructor arguments length_includes_head: True* if head is counted in calculating the length. shape: ['full', 'left', 'right'] overhang: distance that the arrow is swept back (0 overhang means triangular shape). head_starts_at_zero: If True, the head starts being drawn at coordinate 0 instead of ending at coordinate 0. Valid kwargs are: %(Patch)s """ if head_width is None: head_width = 3 * width if head_length is None: head_length = 1.5 * head_width distance = np.sqrt(dx2 + dy2) if length_includes_head: length=distance else: length=distance+head_length if not length: verts = [] #display nothing if empty else: #start by drawing horizontal arrow, point at (0,0) hw, hl, hs, lw = head_width, head_length, overhang, width left_half_arrow = np.array([ [0.0,0.0], #tip [-hl, -hw/2.0], #leftmost [-hl(1-hs), -lw/2.0], #meets stem [-length, -lw/2.0], #bottom left [-length, 0], ]) #if we're not including the head, shift up by head length if not length_includes_head: left_half_arrow += [head_length, 0] #if the head starts at 0, shift up by another head length if head_starts_at_zero: left_half_arrow += [head_length/2.0, 0] #figure out the shape, and complete accordingly if shape == 'left': coords = left_half_arrow else: right_half_arrow = left_half_arrow[1,-1] if shape == 'right': coords = right_half_arrow elif shape == 'full': # The half-arrows contain the midpoint of the stem, # which we can omit from the full arrow. Including it # twice caused a problem with xpdf. coords=np.concatenate([left_half_arrow[:-1], right_half_arrow[-2::-1]]) else: raise ValueError, "Got unknown shape: %s" % shape cx = float(dx)/distance sx = float(dy)/distance M = np.array([[cx, sx],[-sx,cx]]) verts = np.dot(coords, M) + (x+dx, y+dy) Polygon.__init__(self, map(tuple, verts), *kwargs) __init__.__doc__ = cbook.dedent(__init__.__doc__) % artist.kwdocd class YAArrow(Patch): """ Yet another arrow class. This is an arrow that is defined in display space and has a tip at x1, y1* and a base at x2, y2. """ def __str__(self): return "YAArrow()" def __init__(self, figure, xytip, xybase, width=4, frac=0.1, headwidth=12, *kwargs): """ Constructor arguments: xytip* (x, y) location of arrow tip xybase (x, y) location the arrow base mid point figure The :class:`~matplotlib.figure.Figure` instance (fig.dpi) width The width of the arrow in points frac The fraction of the arrow length occupied by the head headwidth The width of the base of the arrow head in points Valid kwargs are: %(Patch)s """ self.figure = figure self.xytip = xytip self.xybase = xybase self.width = width self.frac = frac self.headwidth = headwidth Patch.__init__(self, *kwargs) __init__.__doc__ = cbook.dedent(__init__.__doc__) % artist.kwdocd def get_path(self): # Since this is dpi dependent, we need to recompute the path # every time. # the base vertices x1, y1 = self.xytip x2, y2 = self.xybase k1 = self.widthself.figure.dpi/72./2. k2 = self.headwidthself.figure.dpi/72./2. xb1, yb1, xb2, yb2 = self.getpoints(x1, y1, x2, y2, k1) # a point on the segment 20% of the distance from the tip to the base theta = math.atan2(y2-y1, x2-x1) r = math.sqrt((y2-y1)2. + (x2-x1)2.) xm = x1 + self.frac r * math.cos(theta) ym = y1 + self.frac * r * math.sin(theta) xc1, yc1, xc2, yc2 = self.getpoints(x1, y1, xm, ym, k1) xd1, yd1, xd2, yd2 = self.getpoints(x1, y1, xm, ym, k2) xs = self.convert_xunits([xb1, xb2, xc2, xd2, x1, xd1, xc1, xb1]) ys = self.convert_yunits([yb1, yb2, yc2, yd2, y1, yd1, yc1, yb1]) return Path(zip(xs, ys)) def get_patch_transform(self): return transforms.IdentityTransform() def getpoints(self, x1,y1,x2,y2, k): """ For line segment defined by (x1, y1) and (x2, y2) return the points on the line that is perpendicular to the line and intersects (x2, y2) and the distance from (x2, y2) of the returned points is k. """ x1,y1,x2,y2,k = map(float, (x1,y1,x2,y2,k)) if y2-y1 == 0: return x2, y2+k, x2, y2-k elif x2-x1 == 0: return x2+k, y2, x2-k, y2 m = (y2-y1)/(x2-x1) pm = -1./m a = 1 b = -2y2 c = y22. - k2.pm2./(1. + pm2.) y3a = (-b + math.sqrt(b*2.-4ac))/(2.a) x3a = (y3a - y2)/pm + x2 y3b = (-b - math.sqrt(b*2.-4ac))/(2.a) x3b = (y3b - y2)/pm + x2 return x3a, y3a, x3b, y3b class CirclePolygon(RegularPolygon): """ A polygon-approximation of a circle patch. """ def __str__(self): return "CirclePolygon(%d,%d)"%self.center def __init__(self, xy, radius=5, resolution=20, # the number of vertices *kwargs): """ Create a circle at xy* = (x, y) with given radius. This circle is approximated by a regular polygon with resolution sides. For a smoother circle drawn with splines, see :class:`~matplotlib.patches.Circle`. Valid kwargs are: %(Patch)s """ RegularPolygon.__init__(self, xy, resolution, radius, orientation=0, kwargs) __init__.__doc__ = cbook.dedent(__init__.__doc__) % artist.kwdocd class Ellipse(Patch): """ A scale-free ellipse. """ def __str__(self): return "Ellipse(%s,%s;%sx%s)"%(self.center[0],self.center[1],self.width,self.height) def __init__(self, xy, width, height, angle=0.0, kwargs): """ xy center of ellipse width length of horizontal axis height length of vertical axis angle rotation in degrees (anti-clockwise) Valid kwargs are: %(Patch)s """ Patch.__init__(self, *kwargs) self.center = xy self.width, self.height = width, height self.angle = angle self._path = Path.unit_circle() # Note: This cannot be calculated until this is added to an Axes self._patch_transform = transforms.IdentityTransform() __init__.__doc__ = cbook.dedent(__init__.__doc__) % artist.kwdocd def _recompute_transform(self): """NOTE: This cannot be called until after this has been added to an Axes, otherwise unit conversion will fail. This maxes it very important to call the accessor method and not directly access the transformation member variable. """ center = (self.convert_xunits(self.center[0]), self.convert_yunits(self.center[1])) width = self.convert_xunits(self.width) height = self.convert_yunits(self.height) self._patch_transform = transforms.Affine2D() \ .scale(width 0.5, height * 0.5) \ .rotate_deg(self.angle) \ .translate(center) def get_path(self): """ Return the vertices of the rectangle """ return self._path def get_patch_transform(self): self._recompute_transform() return self._patch_transform def contains(self,ev): if ev.x is None or ev.y is None: return False,{} x, y = self.get_transform().inverted().transform_point((ev.x, ev.y)) return (xx + yy) <= 1.0, {} class Circle(Ellipse): """ A circle patch. """ def __str__(self): return "Circle((%g,%g),r=%g)"%(self.center[0],self.center[1],self.radius) def __init__(self, xy, radius=5, kwargs): """ Create true circle at center xy* = (x, y) with given radius. Unlike :class:`~matplotlib.patches.CirclePolygon` which is a polygonal approximation, this uses Bézier splines and is much closer to a scale-free circle. Valid kwargs are: %(Patch)s """ if 'resolution' in kwargs: import warnings warnings.warn('Circle is now scale free. Use CirclePolygon instead!', DeprecationWarning) kwargs.pop('resolution') self.radius = radius Ellipse.__init__(self, xy, radius2, radius2, kwargs) __init__.__doc__ = cbook.dedent(__init__.__doc__) % artist.kwdocd class Arc(Ellipse): """ An elliptical arc. Because it performs various optimizations, it can not be filled. The arc must be used in an :class:`~matplotlib.axes.Axes` instance---it can not be added directly to a :class:`~matplotlib.figure.Figure`---because it is optimized to only render the segments that are inside the axes bounding box with high resolution. """ def __str__(self): return "Arc(%s,%s;%sx%s)"%(self.center[0],self.center[1],self.width,self.height) def __init__(self, xy, width, height, angle=0.0, theta1=0.0, theta2=360.0, kwargs): """ The following args are supported: xy center of ellipse width length of horizontal axis height length of vertical axis angle rotation in degrees (anti-clockwise) theta1 starting angle of the arc in degrees theta2 ending angle of the arc in degrees If theta1 and theta2 are not provided, the arc will form a complete ellipse. Valid kwargs are: %(Patch)s """ fill = kwargs.pop('fill') if fill: raise ValueError("Arc objects can not be filled") kwargs['fill'] = False Ellipse.__init__(self, xy, width, height, angle, *kwargs) self.theta1 = theta1 self.theta2 = theta2 __init__.__doc__ = cbook.dedent(__init__.__doc__) % artist.kwdocd def draw(self, renderer): """ Ellipses are normally drawn using an approximation that uses eight cubic bezier splines. The error of this approximation is 1.89818e-6, according to this unverified source: Lancaster, Don. Approximating a Circle or an Ellipse Using Four Bezier Cubic Splines. http://www.tinaja.com/glib/ellipse4.pdf There is a use case where very large ellipses must be drawn with very high accuracy, and it is too expensive to render the entire ellipse with enough segments (either splines or line segments). Therefore, in the case where either radius of the ellipse is large enough that the error of the spline approximation will be visible (greater than one pixel offset from the ideal), a different technique is used. In that case, only the visible parts of the ellipse are drawn, with each visible arc using a fixed number of spline segments (8). The algorithm proceeds as follows: 1. The points where the ellipse intersects the axes bounding box are located. (This is done be performing an inverse transformation on the axes bbox such that it is relative to the unit circle -- this makes the intersection calculation much easier than doing rotated ellipse intersection directly). This uses the "line intersecting a circle" algorithm from: Vince, John. Geometry for Computer Graphics: Formulae, Examples & Proofs. London: Springer-Verlag, 2005. 2. The angles of each of the intersection points are calculated. 3. Proceeding counterclockwise starting in the positive x-direction, each of the visible arc-segments between the pairs of vertices are drawn using the bezier arc approximation technique implemented in :meth:`matplotlib.path.Path.arc`. """ if not hasattr(self, 'axes'): raise RuntimeError('Arcs can only be used in Axes instances') self._recompute_transform() # Get the width and height in pixels width = self.convert_xunits(self.width) height = self.convert_yunits(self.height) width, height = self.get_transform().transform_point( (width, height)) inv_error = (1.0 / 1.89818e-6) 0.5 if width < inv_error and height < inv_error: self._path = Path.arc(self.theta1, self.theta2) return Patch.draw(self, renderer) def iter_circle_intersect_on_line(x0, y0, x1, y1): dx = x1 - x0 dy = y1 - y0 dr2 = dxdx + dydy D = x0y1 - x1y0 D2 = DD discrim = dr2 - D2 # Single (tangential) intersection if discrim == 0.0: x = (Ddy) / dr2 y = (-Ddx) / dr2 yield x, y elif discrim > 0.0: # The definition of "sign" here is different from # np.sign: we never want to get 0.0 if dy < 0.0: sign_dy = -1.0 else: sign_dy = 1.0 sqrt_discrim = np.sqrt(discrim) for sign in (1., -1.): x = (Ddy + sign * sign_dy * dx * sqrt_discrim) / dr2 y = (-Ddx + sign np.abs(dy) * sqrt_discrim) / dr2 yield x, y def iter_circle_intersect_on_line_seg(x0, y0, x1, y1): epsilon = 1e-9 if x1 < x0: x0e, x1e = x1, x0 else: x0e, x1e = x0, x1 if y1 < y0: y0e, y1e = y1, y0 else: y0e, y1e = y0, y1 x0e -= epsilon y0e -= epsilon x1e += epsilon y1e += epsilon for x, y in iter_circle_intersect_on_line(x0, y0, x1, y1): if x >= x0e and x <= x1e and y >= y0e and y <= y1e: yield x, y # Transforms the axes box_path so that it is relative to the unit # circle in the same way that it is relative to the desired # ellipse. box_path = Path.unit_rectangle() box_path_transform = transforms.BboxTransformTo(self.axes.bbox) + \ self.get_transform().inverted() box_path = box_path.transformed(box_path_transform) PI = np.pi TWOPI = PI * 2.0 RAD2DEG = 180.0 / PI DEG2RAD = PI / 180.0 theta1 = self.theta1 theta2 = self.theta2 thetas = {} # For each of the point pairs, there is a line segment for p0, p1 in zip(box_path.vertices[:-1], box_path.vertices[1:]): x0, y0 = p0 x1, y1 = p1 for x, y in iter_circle_intersect_on_line_seg(x0, y0, x1, y1): theta = np.arccos(x) if y < 0: theta = TWOPI - theta # Convert radians to angles theta = RAD2DEG if theta > theta1 and theta < theta2: thetas[theta] = None thetas = thetas.keys() thetas.sort() thetas.append(theta2) last_theta = theta1 theta1_rad = theta1 DEG2RAD inside = box_path.contains_point((np.cos(theta1_rad), np.sin(theta1_rad))) for theta in thetas: if inside: self._path = Path.arc(last_theta, theta, 8) Patch.draw(self, renderer) inside = False else: inside = True last_theta = theta def bbox_artist(artist, renderer, props=None, fill=True): """ This is a debug function to draw a rectangle around the bounding box returned by :meth:`~matplotlib.artist.Artist.get_window_extent` of an artist, to test whether the artist is returning the correct bbox. props is a dict of rectangle props with the additional property 'pad' that sets the padding around the bbox in points. """ if props is None: props = {} props = props.copy() # don't want to alter the pad externally pad = props.pop('pad', 4) pad = renderer.points_to_pixels(pad) bbox = artist.get_window_extent(renderer) l,b,w,h = bbox.bounds l-=pad/2. b-=pad/2. w+=pad h+=pad r = Rectangle(xy=(l,b), width=w, height=h, fill=fill, ) r.set_transform(transforms.IdentityTransform()) r.set_clip_on( False ) r.update(props) r.draw(renderer) def draw_bbox(bbox, renderer, color='k', trans=None): """ This is a debug function to draw a rectangle around the bounding box returned by :meth:`~matplotlib.artist.Artist.get_window_extent` of an artist, to test whether the artist is returning the correct bbox. """ l,b,w,h = bbox.get_bounds() r = Rectangle(xy=(l,b), width=w, height=h, edgecolor=color, fill=False, ) if trans is not None: r.set_transform(trans) r.set_clip_on( False ) r.draw(renderer) def _pprint_table(_table, leadingspace=2): """ Given the list of list of strings, return a string of REST table format. """ if leadingspace: pad = ' 'leadingspace else: pad = '' columns = [[] for cell in _table[0]] for row in _table: for column, cell in zip(columns, row): column.append(cell) col_len = [max([len(cell) for cell in column]) for column in columns] lines = [] table_formatstr = pad + ' '.join([('=' cl) for cl in col_len]) lines.append('') lines.append(table_formatstr) lines.append(pad + ' '.join([cell.ljust(cl) for cell, cl in zip(_table[0], col_len)])) lines.append(table_formatstr) lines.extend([(pad + ' '.join([cell.ljust(cl) for cell, cl in zip(row, col_len)])) for row in _table[1:]]) lines.append(table_formatstr) lines.append('') return "\n".join(lines) def _pprint_styles(_styles, leadingspace=2): """ A helper function for the _Style class. Given the dictionary of (stylename : styleclass), return a formatted string listing all the styles. Used to update the documentation. """ if leadingspace: pad = ' 'leadingspace else: pad = '' names, attrss, clss = [], [], [] import inspect _table = [["Class", "Name", "Attrs"]] for name, cls in sorted(_styles.items()): args, varargs, varkw, defaults = inspect.getargspec(cls.__init__) if defaults: args = [(argname, argdefault) \ for argname, argdefault in zip(args[1:], defaults)] else: args = None if args is None: argstr = 'None' else: argstr = ",".join([("%s=%s" % (an, av)) for an, av in args]) #adding quotes for now to work around tex bug treating '-' as itemize _table.append([cls.__name__, "'%s'"%name, argstr]) return _pprint_table(_table) class _Style(object): """ A base class for the Styles. It is meant to be a container class, where actual styles are declared as subclass of it, and it provides some helper functions. """ def __new__(self, stylename, kw): """ return the instance of the subclass with the given style name. """ # the "class" should have the _style_list attribute, which is # a dictionary of stylname, style class paie. _list = stylename.replace(" ","").split(",") _name = _list[0].lower() try: _cls = self._style_list[_name] except KeyError: raise ValueError("Unknown style : %s" % stylename) try: _args_pair = [cs.split("=") for cs in _list[1:]] _args = dict([(k, float(v)) for k, v in _args_pair]) except ValueError: raise ValueError("Incorrect style argument : %s" % stylename) _args.update(kw) return _cls(_args) @classmethod def get_styles(klass): """ A class method which returns a dictionary of available styles. """ return klass._style_list @classmethod def pprint_styles(klass): """ A class method which returns a string of the available styles. """ return _pprint_styles(klass._style_list) class BoxStyle(_Style): """ :class:`BoxStyle` is a container class which defines several boxstyle classes, which are used for :class:`FancyBoxPatch`. A style object can be created as:: BoxStyle.Round(pad=0.2) or:: BoxStyle("Round", pad=0.2) or:: BoxStyle("Round, pad=0.2") Following boxstyle classes are defined. %(AvailableBoxstyles)s An instance of any boxstyle class is an callable object, whose call signature is:: __call__(self, x0, y0, width, height, mutation_size, aspect_ratio=1.) and returns a :class:`Path` instance. x0, y0, width* and height specify the location and size of the box to be drawn. mutation_scale determines the overall size of the mutation (by which I mean the transformation of the rectangle to the fancy box). mutation_aspect determines the aspect-ratio of the mutation. .. plot:: mpl_examples/pylab_examples/fancybox_demo2.py """ _style_list = {} class _Base(object): """ :class:`BBoxTransmuterBase` and its derivatives are used to make a fancy box around a given rectangle. The :meth:`__call__` method returns the :class:`~matplotlib.path.Path` of the fancy box. This class is not an artist and actual drawing of the fancy box is done by the :class:`FancyBboxPatch` class. """ # The derived classes are required to be able to be initialized # w/o arguments, i.e., all its argument (except self) must have # the default values. def __init__(self): """ initializtion. """ super(BoxStyle._Base, self).__init__() def transmute(self, x0, y0, width, height, mutation_size): """ The transmute method is a very core of the :class:`BboxTransmuter` class and must be overriden in the subclasses. It receives the location and size of the rectangle, and the mutation_size, with which the amount of padding and etc. will be scaled. It returns a :class:`~matplotlib.path.Path` instance. """ raise NotImplementedError('Derived must override') def __call__(self, x0, y0, width, height, mutation_size, aspect_ratio=1.): """ Given the location and size of the box, return the path of the box around it. - x0, y0, width, height : location and size of the box - mutation_size : a reference scale for the mutation. - aspect_ratio : aspect-ration for the mutation. """ # The __call__ method is a thin wrapper around the transmute method # and take care of the aspect. if aspect_ratio is not None: # Squeeze the given height by the aspect_ratio y0, height = y0/aspect_ratio, height/aspect_ratio # call transmute method with squeezed height. path = self.transmute(x0, y0, width, height, mutation_size) vertices, codes = path.vertices, path.codes # Restore the height vertices[:,1] = vertices[:,1] * aspect_ratio return Path(vertices, codes) else: return self.transmute(x0, y0, width, height, mutation_size) class Square(_Base): """ A simple square box. """ def __init__(self, pad=0.3): """ pad amount of padding """ self.pad = pad super(BoxStyle.Square, self).__init__() def transmute(self, x0, y0, width, height, mutation_size): # padding pad = mutation_size * self.pad # width and height with padding added. width, height = width + 2.pad, \ height + 2.pad, # boundary of the padded box x0, y0 = x0-pad, y0-pad, x1, y1 = x0+width, y0 + height cp = [(x0, y0), (x1, y0), (x1, y1), (x0, y1), (x0, y0), (x0, y0)] com = [Path.MOVETO, Path.LINETO, Path.LINETO, Path.LINETO, Path.LINETO, Path.CLOSEPOLY] path = Path(cp, com) return path _style_list["square"] = Square class LArrow(_Base): """ (left) Arrow Box """ def __init__(self, pad=0.3): self.pad = pad super(BoxStyle.LArrow, self).__init__() def transmute(self, x0, y0, width, height, mutation_size): # padding pad = mutation_size * self.pad # width and height with padding added. width, height = width + 2.pad, \ height + 2.pad, # boundary of the padded box x0, y0 = x0-pad, y0-pad, x1, y1 = x0+width, y0 + height dx = (y1-y0)/2. dxx = dx.5 # adjust x0. 1.4 <- sqrt(2) x0 = x0 + pad / 1.4 cp = [(x0+dxx, y0), (x1, y0), (x1, y1), (x0+dxx, y1), (x0+dxx, y1+dxx), (x0-dx, y0+dx), (x0+dxx, y0-dxx), # arrow (x0+dxx, y0), (x0+dxx, y0)] com = [Path.MOVETO, Path.LINETO, Path.LINETO, Path.LINETO, Path.LINETO, Path.LINETO, Path.LINETO, Path.LINETO, Path.CLOSEPOLY] path = Path(cp, com) return path _style_list["larrow"] = LArrow class RArrow(LArrow): """ (right) Arrow Box """ def __init__(self, pad=0.3): self.pad = pad super(BoxStyle.RArrow, self).__init__() def transmute(self, x0, y0, width, height, mutation_size): p = BoxStyle.LArrow.transmute(self, x0, y0, width, height, mutation_size) p.vertices[:,0] = 2x0 + width - p.vertices[:,0] return p _style_list["rarrow"] = RArrow class Round(_Base): """ A box with round corners. """ def __init__(self, pad=0.3, rounding_size=None): """ pad amount of padding rounding_size rounding radius of corners. pad if None """ self.pad = pad self.rounding_size = rounding_size super(BoxStyle.Round, self).__init__() def transmute(self, x0, y0, width, height, mutation_size): # padding pad = mutation_size * self.pad # size of the roudning corner if self.rounding_size: dr = mutation_size * self.rounding_size else: dr = pad width, height = width + 2.pad, \ height + 2.pad, x0, y0 = x0-pad, y0-pad, x1, y1 = x0+width, y0 + height # Round corners are implemented as quadratic bezier. eg. # [(x0, y0-dr), (x0, y0), (x0+dr, y0)] for lower left corner. cp = [(x0+dr, y0), (x1-dr, y0), (x1, y0), (x1, y0+dr), (x1, y1-dr), (x1, y1), (x1-dr, y1), (x0+dr, y1), (x0, y1), (x0, y1-dr), (x0, y0+dr), (x0, y0), (x0+dr, y0), (x0+dr, y0)] com = [Path.MOVETO, Path.LINETO, Path.CURVE3, Path.CURVE3, Path.LINETO, Path.CURVE3, Path.CURVE3, Path.LINETO, Path.CURVE3, Path.CURVE3, Path.LINETO, Path.CURVE3, Path.CURVE3, Path.CLOSEPOLY] path = Path(cp, com) return path _style_list["round"] = Round class Round4(_Base): """ Another box with round edges. """ def __init__(self, pad=0.3, rounding_size=None): """ pad amount of padding rounding_size rounding size of edges. pad if None """ self.pad = pad self.rounding_size = rounding_size super(BoxStyle.Round4, self).__init__() def transmute(self, x0, y0, width, height, mutation_size): # padding pad = mutation_size * self.pad # roudning size. Use a half of the pad if not set. if self.rounding_size: dr = mutation_size * self.rounding_size else: dr = pad / 2. width, height = width + 2.pad - 2dr, \ height + 2.pad - 2dr, x0, y0 = x0-pad+dr, y0-pad+dr, x1, y1 = x0+width, y0 + height cp = [(x0, y0), (x0+dr, y0-dr), (x1-dr, y0-dr), (x1, y0), (x1+dr, y0+dr), (x1+dr, y1-dr), (x1, y1), (x1-dr, y1+dr), (x0+dr, y1+dr), (x0, y1), (x0-dr, y1-dr), (x0-dr, y0+dr), (x0, y0), (x0, y0)] com = [Path.MOVETO, Path.CURVE4, Path.CURVE4, Path.CURVE4, Path.CURVE4, Path.CURVE4, Path.CURVE4, Path.CURVE4, Path.CURVE4, Path.CURVE4, Path.CURVE4, Path.CURVE4, Path.CURVE4, Path.CLOSEPOLY] path = Path(cp, com) return path _style_list["round4"] = Round4 class Sawtooth(_Base): """ A sawtooth box. """ def __init__(self, pad=0.3, tooth_size=None): """ pad amount of padding tooth_size size of the sawtooth. pad* if None """ self.pad = pad self.tooth_size = tooth_size super(BoxStyle.Sawtooth, self).__init__() def _get_sawtooth_vertices(self, x0, y0, width, height, mutation_size): # padding pad = mutation_size * self.pad # size of sawtooth if self.tooth_size is None: tooth_size = self.pad * .5 * mutation_size else: tooth_size = self.tooth_size * mutation_size tooth_size2 = tooth_size / 2. width, height = width + 2.pad - tooth_size, \ height + 2.pad - tooth_size, # the sizes of the vertical and horizontal sawtooth are # separately adjusted to fit the given box size. dsx_n = int(round((width - tooth_size) / (tooth_size * 2))) * 2 dsx = (width - tooth_size) / dsx_n dsy_n = int(round((height - tooth_size) / (tooth_size * 2))) * 2 dsy = (height - tooth_size) / dsy_n x0, y0 = x0-pad+tooth_size2, y0-pad+tooth_size2 x1, y1 = x0+width, y0 + height bottom_saw_x = [x0] + \ [x0 + tooth_size2 + dsx.5 i for i in range(dsx_n2)] + \ [x1 - tooth_size2] bottom_saw_y = [y0] + \ [y0 - tooth_size2, y0, y0 + tooth_size2, y0] dsx_n + \ [y0 - tooth_size2] right_saw_x = [x1] + \ [x1 + tooth_size2, x1, x1 - tooth_size2, x1] * dsx_n + \ [x1 + tooth_size2] right_saw_y = [y0] + \ [y0 + tooth_size2 + dsy.5 i for i in range(dsy_n2)] + \ [y1 - tooth_size2] top_saw_x = [x1] + \ [x1 - tooth_size2 - dsx.5* i for i in range(dsx_n2)] + \ [x0 + tooth_size2] top_saw_y = [y1] + \ [y1 + tooth_size2, y1, y1 - tooth_size2, y1] dsx_n + \ [y1 + tooth_size2] left_saw_x = [x0] + \ [x0 - tooth_size2, x0, x0 + tooth_size2, x0] * dsy_n + \ [x0 - tooth_size2] left_saw_y = [y1] + \ [y1 - tooth_size2 - dsy.5 i for i in range(dsy_n2)] + \ [y0 + tooth_size2] saw_vertices = zip(bottom_saw_x, bottom_saw_y) + \ zip(right_saw_x, right_saw_y) + \ zip(top_saw_x, top_saw_y) + \ zip(left_saw_x, left_saw_y) + \ [(bottom_saw_x[0], bottom_saw_y[0])] return saw_vertices def transmute(self, x0, y0, width, height, mutation_size): saw_vertices = self._get_sawtooth_vertices(x0, y0, width, height, mutation_size) path = Path(saw_vertices) return path _style_list["sawtooth"] = Sawtooth class Roundtooth(Sawtooth): """ A roundtooth(?) box. """ def __init__(self, pad=0.3, tooth_size=None): """ pad* amount of padding tooth_size size of the sawtooth. pad* if None """ super(BoxStyle.Roundtooth, self).__init__(pad, tooth_size) def transmute(self, x0, y0, width, height, mutation_size): saw_vertices = self._get_sawtooth_vertices(x0, y0, width, height, mutation_size) cp = [Path.MOVETO] + ([Path.CURVE3, Path.CURVE3] * ((len(saw_vertices)-1)//2)) path = Path(saw_vertices, cp) return path _style_list["roundtooth"] = Roundtooth __doc__ = cbook.dedent(__doc__) % \ {"AvailableBoxstyles": _pprint_styles(_style_list)} class FancyBboxPatch(Patch): """ Draw a fancy box around a rectangle with lower left at xy=(x, y) with specified width and height. :class:`FancyBboxPatch` class is similar to :class:`Rectangle` class, but it draws a fancy box around the rectangle. The transformation of the rectangle box to the fancy box is delegated to the :class:`BoxTransmuterBase` and its derived classes. """ def __str__(self): return self.__class__.__name__ \ + "FancyBboxPatch(%g,%g;%gx%g)" % (self._x, self._y, self._width, self._height) def __init__(self, xy, width, height, boxstyle="round", bbox_transmuter=None, mutation_scale=1., mutation_aspect=None, *kwargs): """ xy* = lower left corner width, height boxstyle determines what kind of fancy box will be drawn. It can be a string of the style name with a comma separated attribute, or an instance of :class:`BoxStyle`. Following box styles are available. %(AvailableBoxstyles)s mutation_scale : a value with which attributes of boxstyle (e.g., pad) will be scaled. default=1. mutation_aspect : The height of the rectangle will be squeezed by this value before the mutation and the mutated box will be stretched by the inverse of it. default=None. Valid kwargs are: %(Patch)s """ Patch.__init__(self, kwargs) self._x = xy[0] self._y = xy[1] self._width = width self._height = height if boxstyle == "custom": if bbox_transmuter is None: raise ValueError("bbox_transmuter argument is needed with custom boxstyle") self._bbox_transmuter = bbox_transmuter else: self.set_boxstyle(boxstyle) self._mutation_scale=mutation_scale self._mutation_aspect=mutation_aspect kwdoc = dict() kwdoc["AvailableBoxstyles"]=_pprint_styles(BoxStyle._style_list) kwdoc.update(artist.kwdocd) __init__.__doc__ = cbook.dedent(__init__.__doc__) % kwdoc del kwdoc def set_boxstyle(self, boxstyle=None, kw): """ Set the box style. boxstyle can be a string with boxstyle name with optional comma-separated attributes. Alternatively, the attrs can be provided as keywords:: set_boxstyle("round,pad=0.2") set_boxstyle("round", pad=0.2) Old attrs simply are forgotten. Without argument (or with boxstyle = None), it returns available box styles. ACCEPTS: [ %(AvailableBoxstyles)s ] """ if boxstyle==None: return BoxStyle.pprint_styles() if isinstance(boxstyle, BoxStyle._Base): self._bbox_transmuter = boxstyle elif callable(boxstyle): self._bbox_transmuter = boxstyle else: self._bbox_transmuter = BoxStyle(boxstyle, *kw) kwdoc = dict() kwdoc["AvailableBoxstyles"]=_pprint_styles(BoxStyle._style_list) kwdoc.update(artist.kwdocd) set_boxstyle.__doc__ = cbook.dedent(set_boxstyle.__doc__) % kwdoc del kwdoc def set_mutation_scale(self, scale): """ Set the mutation scale. ACCEPTS: float """ self._mutation_scale=scale def get_mutation_scale(self): """ Return the mutation scale. """ return self._mutation_scale def set_mutation_aspect(self, aspect): """ Set the aspect ratio of the bbox mutation. ACCEPTS: float """ self._mutation_aspect=aspect def get_mutation_aspect(self): """ Return the aspect ratio of the bbox mutation. """ return self._mutation_aspect def get_boxstyle(self): "Return the boxstyle object" return self._bbox_transmuter def get_path(self): """ Return the mutated path of the rectangle """ _path = self.get_boxstyle()(self._x, self._y, self._width, self._height, self.get_mutation_scale(), self.get_mutation_aspect()) return _path # Following methods are borrowed from the Rectangle class. def get_x(self): "Return the left coord of the rectangle" return self._x def get_y(self): "Return the bottom coord of the rectangle" return self._y def get_width(self): "Return the width of the rectangle" return self._width def get_height(self): "Return the height of the rectangle" return self._height def set_x(self, x): """ Set the left coord of the rectangle ACCEPTS: float """ self._x = x def set_y(self, y): """ Set the bottom coord of the rectangle ACCEPTS: float """ self._y = y def set_width(self, w): """ Set the width rectangle ACCEPTS: float """ self._width = w def set_height(self, h): """ Set the width rectangle ACCEPTS: float """ self._height = h def set_bounds(self, args): """ Set the bounds of the rectangle: l,b,w,h ACCEPTS: (left, bottom, width, height) """ if len(args)==0: l,b,w,h = args[0] else: l,b,w,h = args self._x = l self._y = b self._width = w self._height = h def get_bbox(self): return transforms.Bbox.from_bounds(self._x, self._y, self._width, self._height) from matplotlib.bezier import split_bezier_intersecting_with_closedpath from matplotlib.bezier import get_intersection, inside_circle, get_parallels from matplotlib.bezier import make_wedged_bezier2 from matplotlib.bezier import split_path_inout, get_cos_sin class ConnectionStyle(_Style): """ :class:`ConnectionStyle` is a container class which defines several connectionstyle classes, which is used to create a path between two points. These are mainly used with :class:`FancyArrowPatch`. A connectionstyle object can be either created as:: ConnectionStyle.Arc3(rad=0.2) or:: ConnectionStyle("Arc3", rad=0.2) or:: ConnectionStyle("Arc3, rad=0.2") The following classes are defined %(AvailableConnectorstyles)s An instance of any connection style class is an callable object, whose call signature is:: __call__(self, posA, posB, patchA=None, patchB=None, shrinkA=2., shrinkB=2.) and it returns a :class:`Path` instance. posA and posB are tuples of x,y coordinates of the two points to be connected. patchA (or patchB) is given, the returned path is clipped so that it start (or end) from the boundary of the patch. The path is further shrunk by shrinkA (or shrinkB) which is given in points. """ _style_list = {} class _Base(object): """ A base class for connectionstyle classes. The dervided needs to implement a connect methods whose call signature is:: connect(posA, posB) where posA and posB are tuples of x, y coordinates to be connected. The methods needs to return a path connecting two points. This base class defines a __call__ method, and few helper methods. """ class SimpleEvent: def __init__(self, xy): self.x, self.y = xy def _clip(self, path, patchA, patchB): """ Clip the path to the boundary of the patchA and patchB. The starting point of the path needed to be inside of the patchA and the end point inside the patch B. The contains methods of each patch object is utilized to test if the point is inside the path. """ if patchA: def insideA(xy_display): #xy_display = patchA.get_data_transform().transform_point(xy_data) xy_event = ConnectionStyle._Base.SimpleEvent(xy_display) return patchA.contains(xy_event)[0] try: left, right = split_path_inout(path, insideA) except ValueError: right = path path = right if patchB: def insideB(xy_display): #xy_display = patchB.get_data_transform().transform_point(xy_data) xy_event = ConnectionStyle._Base.SimpleEvent(xy_display) return patchB.contains(xy_event)[0] try: left, right = split_path_inout(path, insideB) except ValueError: left = path path = left return path def _shrink(self, path, shrinkA, shrinkB): """ Shrink the path by fixed size (in points) with shrinkA and shrinkB """ if shrinkA: x, y = path.vertices[0] insideA = inside_circle(x, y, shrinkA) left, right = split_path_inout(path, insideA) path = right if shrinkB: x, y = path.vertices[-1] insideB = inside_circle(x, y, shrinkB) left, right = split_path_inout(path, insideB) path = left return path def __call__(self, posA, posB, shrinkA=2., shrinkB=2., patchA=None, patchB=None): """ Calls the connect method to create a path between posA and posB. The path is clipped and shrinked. """ path = self.connect(posA, posB) clipped_path = self._clip(path, patchA, patchB) shrinked_path = self._shrink(clipped_path, shrinkA, shrinkB) return shrinked_path class Arc3(_Base): """ Creates a simple quadratic bezier curve between two points. The curve is created so that the middle contol points (C1) is located at the same distance from the start (C0) and end points(C2) and the distance of the C1 to the line connecting C0-C2 is rad times the distance of C0-C2. """ def __init__(self, rad=0.): """ rad curvature of the curve. """ self.rad = rad def connect(self, posA, posB): x1, y1 = posA x2, y2 = posB x12, y12 = (x1 + x2)/2., (y1 + y2)/2. dx, dy = x2 - x1, y2 - y1 f = self.rad cx, cy = x12 + fdy, y12 - fdx vertices = [(x1, y1), (cx, cy), (x2, y2)] codes = [Path.MOVETO, Path.CURVE3, Path.CURVE3] return Path(vertices, codes) _style_list["arc3"] = Arc3 class Angle3(_Base): """ Creates a simple quadratic bezier curve between two points. The middle control points is placed at the intersecting point of two lines which crosses the start (or end) point and has a angle of angleA (or angleB). """ def __init__(self, angleA=90, angleB=0): """ angleA starting angle of the path angleB ending angle of the path """ self.angleA = angleA self.angleB = angleB def connect(self, posA, posB): x1, y1 = posA x2, y2 = posB cosA, sinA = math.cos(self.angleA/180.math.pi),\ math.sin(self.angleA/180.math.pi), cosB, sinB = math.cos(self.angleB/180.math.pi),\ math.sin(self.angleB/180.math.pi), cx, cy = get_intersection(x1, y1, cosA, sinA, x2, y2, cosB, sinB) vertices = [(x1, y1), (cx, cy), (x2, y2)] codes = [Path.MOVETO, Path.CURVE3, Path.CURVE3] return Path(vertices, codes) _style_list["angle3"] = Angle3 class Angle(_Base): """ Creates a picewise continuous quadratic bezier path between two points. The path has a one passing-through point placed at the intersecting point of two lines which crosses the start (or end) point and has a angle of angleA (or angleB). The connecting edges are rounded with rad. """ def __init__(self, angleA=90, angleB=0, rad=0.): """ angleA starting angle of the path angleB ending angle of the path rad rounding radius of the edge """ self.angleA = angleA self.angleB = angleB self.rad = rad def connect(self, posA, posB): x1, y1 = posA x2, y2 = posB cosA, sinA = math.cos(self.angleA/180.math.pi),\ math.sin(self.angleA/180.math.pi), cosB, sinB = math.cos(self.angleB/180.math.pi),\ -math.sin(self.angleB/180.math.pi), cx, cy = get_intersection(x1, y1, cosA, sinA, x2, y2, cosB, sinB) vertices = [(x1, y1)] codes = [Path.MOVETO] if self.rad == 0.: vertices.append((cx, cy)) codes.append(Path.LINETO) else: vertices.extend([(cx - self.rad * cosA, cy - self.rad * sinA), (cx, cy), (cx + self.rad * cosB, cy + self.rad * sinB)]) codes.extend([Path.LINETO, Path.CURVE3, Path.CURVE3]) vertices.append((x2, y2)) codes.append(Path.LINETO) return Path(vertices, codes) _style_list["angle"] = Angle class Arc(_Base): """ Creates a picewise continuous quadratic bezier path between two points. The path can have two passing-through points, a point placed at the distance of armA and angle of angleA from point A, another point with respect to point B. The edges are rounded with rad. """ def __init__(self, angleA=0, angleB=0, armA=None, armB=None, rad=0.): """ angleA : starting angle of the path angleB : ending angle of the path armA : length of the starting arm armB : length of the ending arm rad : rounding radius of the edges """ self.angleA = angleA self.angleB = angleB self.armA = armA self.armB = armB self.rad = rad def connect(self, posA, posB): x1, y1 = posA x2, y2 = posB vertices = [(x1, y1)] rounded = [] codes = [Path.MOVETO] if self.armA: cosA = math.cos(self.angleA/180.math.pi) sinA = math.sin(self.angleA/180.math.pi) #x_armA, y_armB d = self.armA - self.rad rounded.append((x1 + dcosA, y1 + dsinA)) d = self.armA rounded.append((x1 + dcosA, y1 + dsinA)) if self.armB: cosB = math.cos(self.angleB/180.math.pi) sinB = math.sin(self.angleB/180.math.pi) x_armB, y_armB = x2 + self.armBcosB, y2 + self.armBsinB if rounded: xp, yp = rounded[-1] dx, dy = x_armB - xp, y_armB - yp dd = (dxdx + dydy)*.5 rounded.append((xp + self.raddx/dd, yp + self.raddy/dd)) vertices.extend(rounded) codes.extend([Path.LINETO, Path.CURVE3, Path.CURVE3]) else: xp, yp = vertices[-1] dx, dy = x_armB - xp, y_armB - yp dd = (dxdx + dydy).5 d = dd - self.rad rounded = [(xp + ddx/dd, yp + ddy/dd), (x_armB, y_armB)] if rounded: xp, yp = rounded[-1] dx, dy = x2 - xp, y2 - yp dd = (dxdx + dydy).5 rounded.append((xp + self.raddx/dd, yp + self.raddy/dd)) vertices.extend(rounded) codes.extend([Path.LINETO, Path.CURVE3, Path.CURVE3]) vertices.append((x2, y2)) codes.append(Path.LINETO) return Path(vertices, codes) _style_list["arc"] = Arc __doc__ = cbook.dedent(__doc__) % \ {"AvailableConnectorstyles": _pprint_styles(_style_list)} class ArrowStyle(_Style): """ :class:`ArrowStyle` is a container class which defines several arrowstyle classes, which is used to create an arrow path along a given path. These are mainly used with :class:`FancyArrowPatch`. A arrowstyle object can be either created as:: ArrowStyle.Fancy(head_length=.4, head_width=.4, tail_width=.4) or:: ArrowStyle("Fancy", head_length=.4, head_width=.4, tail_width=.4) or:: ArrowStyle("Fancy, head_length=.4, head_width=.4, tail_width=.4") The following classes are defined %(AvailableArrowstyles)s An instance of any arrow style class is an callable object, whose call signature is:: __call__(self, path, mutation_size, linewidth, aspect_ratio=1.) and it returns a tuple of a :class:`Path` instance and a boolean value. path* is a :class:`Path` instance along witch the arrow will be drawn. mutation_size and aspect_ratio has a same meaning as in :class:`BoxStyle`. linewidth is a line width to be stroked. This is meant to be used to correct the location of the head so that it does not overshoot the destination point, but not all classes support it. .. plot:: mpl_examples/pylab_examples/fancyarrow_demo.py """ _style_list = {} class _Base(object): """ Arrow Transmuter Base class ArrowTransmuterBase and its derivatives are used to make a fancy arrow around a given path. The __call__ method returns a path (which will be used to create a PathPatch instance) and a boolean value indicating the path is open therefore is not fillable. This class is not an artist and actual drawing of the fancy arrow is done by the FancyArrowPatch class. """ # The derived classes are required to be able to be initialized # w/o arguments, i.e., all its argument (except self) must have # the default values. def __init__(self): super(ArrowStyle._Base, self).__init__() @staticmethod def ensure_quadratic_bezier(path): """ Some ArrowStyle class only wokrs with a simple quaratic bezier curve (created with Arc3Connetion or Angle3Connector). This static method is to check if the provided path is a simple quadratic bezier curve and returns its control points if true. """ segments = list(path.iter_segments()) assert len(segments) == 2 assert segments[0][1] == Path.MOVETO assert segments[1][1] == Path.CURVE3 return list(segments[0][0]) + list(segments[1][0]) def transmute(self, path, mutation_size, linewidth): """ The transmute method is a very core of the ArrowStyle class and must be overriden in the subclasses. It receives the path object along which the arrow will be drawn, and the mutation_size, with which the amount arrow head and etc. will be scaled. It returns a Path instance. The linewidth may be used to adjust the the path so that it does not pass beyond the given points. """ raise NotImplementedError('Derived must override') def __call__(self, path, mutation_size, linewidth, aspect_ratio=1.): """ The __call__ method is a thin wrapper around the transmute method and take care of the aspect ratio. """ if aspect_ratio is not None: # Squeeze the given height by the aspect_ratio vertices, codes = path.vertices[:], path.codes[:] # Squeeze the height vertices[:,1] = vertices[:,1] / aspect_ratio path_shrinked = Path(vertices, codes) # call transmute method with squeezed height. path_mutated, closed = self.transmute(path_shrinked, linewidth, mutation_size) vertices, codes = path_mutated.vertices, path_mutated.codes # Restore the height vertices[:,1] = vertices[:,1] * aspect_ratio return Path(vertices, codes), closed else: return self.transmute(path, mutation_size, linewidth) class _Curve(_Base): """ A simple arrow which will work with any path instance. The returned path is simply concatenation of the original path + at most two paths representing the arrow at the begin point and the at the end point. The returned path is not closed and only meant to be stroked. """ def __init__(self, beginarrow=None, endarrow=None, head_length=.2, head_width=.1): """ The arrows are drawn if beginarrow and/or endarrow are true. head_length and head_width determines the size of the arrow relative to the mutation scale. """ self.beginarrow, self.endarrow = beginarrow, endarrow self.head_length, self.head_width = \ head_length, head_width super(ArrowStyle._Curve, self).__init__() def _get_pad_projected(self, x0, y0, x1, y1, linewidth): # when no arrow head is drawn dx, dy = x0 - x1, y0 - y1 cp_distance = math.sqrt(dx2 + dy2) # padx_projected, pady_projected : amount of pad to account # projection of the wedge padx_projected = (.5linewidth) pady_projected = (.5linewidth) # apply pad for projected edge ddx = padx_projected * dx / cp_distance ddy = pady_projected * dy / cp_distance return ddx, ddy def _get_arrow_wedge(self, x0, y0, x1, y1, head_dist, cos_t, sin_t, linewidth ): """ Return the paths for arrow heads. Since arrow lines are drawn with capstyle=projected, The arrow is goes beyond the desired point. This method also returns the amount of the path to be shrinked so that it does not overshoot. """ # arrow from x0, y0 to x1, y1 dx, dy = x0 - x1, y0 - y1 cp_distance = math.sqrt(dx2 + dy2) # padx_projected, pady_projected : amount of pad for account # the overshooting of the projection of the wedge padx_projected = (.5linewidth / cos_t) pady_projected = (.5linewidth / sin_t) # apply pad for projected edge ddx = padx_projected * dx / cp_distance ddy = pady_projected * dy / cp_distance # offset for arrow wedge dx, dy = dx / cp_distance * head_dist, dy / cp_distance * head_dist dx1, dy1 = cos_t * dx + sin_t * dy, -sin_t * dx + cos_t * dy dx2, dy2 = cos_t * dx - sin_t * dy, sin_t * dx + cos_t * dy vertices_arrow = [(x1+ddx+dx1, y1+ddy+dy1), (x1+ddx, y1++ddy), (x1+ddx+dx2, y1+ddy+dy2)] codes_arrow = [Path.MOVETO, Path.LINETO, Path.LINETO] return vertices_arrow, codes_arrow, ddx, ddy def transmute(self, path, mutation_size, linewidth): head_length, head_width = self.head_length * mutation_size, \ self.head_width * mutation_size head_dist = math.sqrt(head_length2 + head_width2) cos_t, sin_t = head_length / head_dist, head_width / head_dist # begin arrow x0, y0 = path.vertices[0] x1, y1 = path.vertices[1] if self.beginarrow: verticesA, codesA, ddxA, ddyA = \ self._get_arrow_wedge(x1, y1, x0, y0, head_dist, cos_t, sin_t, linewidth) else: verticesA, codesA = [], [] #ddxA, ddyA = self._get_pad_projected(x1, y1, x0, y0, linewidth) ddxA, ddyA = 0., 0., #self._get_pad_projected(x1, y1, x0, y0, linewidth) # end arrow x2, y2 = path.vertices[-2] x3, y3 = path.vertices[-1] if self.endarrow: verticesB, codesB, ddxB, ddyB = \ self._get_arrow_wedge(x2, y2, x3, y3, head_dist, cos_t, sin_t, linewidth) else: verticesB, codesB = [], [] ddxB, ddyB = 0., 0. #self._get_pad_projected(x2, y2, x3, y3, linewidth) # this simple code will not work if ddx, ddy is greater than # separation bettern vertices. vertices = np.concatenate([verticesA + [(x0+ddxA, y0+ddyA)], path.vertices[1:-1], [(x3+ddxB, y3+ddyB)] + verticesB]) codes = np.concatenate([codesA, path.codes, codesB]) p = Path(vertices, codes) return p, False class Curve(_Curve): """ A simple curve without any arrow head. """ def __init__(self): super(ArrowStyle.Curve, self).__init__( \ beginarrow=False, endarrow=False) _style_list["-"] = Curve class CurveA(_Curve): """ An arrow with a head at its begin point. """ def __init__(self, head_length=.4, head_width=.2): """ head_length length of the arrow head head_width width of the arrow head """ super(ArrowStyle.CurveA, self).__init__( \ beginarrow=True, endarrow=False, head_length=head_length, head_width=head_width ) _style_list["<-"] = CurveA class CurveB(_Curve): """ An arrow with a head at its end point. """ def __init__(self, head_length=.4, head_width=.2): """ head_length length of the arrow head head_width width of the arrow head """ super(ArrowStyle.CurveB, self).__init__( \ beginarrow=False, endarrow=True, head_length=head_length, head_width=head_width ) #_style_list["->"] = CurveB _style_list["->"] = CurveB class CurveAB(_Curve): """ An arrow with heads both at the begin and the end point. """ def __init__(self, head_length=.4, head_width=.2): """ head_length length of the arrow head head_width width of the arrow head """ super(ArrowStyle.CurveAB, self).__init__( \ beginarrow=True, endarrow=True, head_length=head_length, head_width=head_width ) #_style_list["<->"] = CurveAB _style_list["<->"] = CurveAB class _Bracket(_Base): def __init__(self, bracketA=None, bracketB=None, widthA=1., widthB=1., lengthA=0.2, lengthB=0.2, angleA=None, angleB=None, scaleA=None, scaleB=None ): self.bracketA, self.bracketB = bracketA, bracketB self.widthA, self.widthB = widthA, widthB self.lengthA, self.lengthB = lengthA, lengthB self.angleA, self.angleB = angleA, angleB self.scaleA, self.scaleB= scaleA, scaleB def _get_bracket(self, x0, y0, cos_t, sin_t, width, length, ): # arrow from x0, y0 to x1, y1 from matplotlib.bezier import get_normal_points x1, y1, x2, y2 = get_normal_points(x0, y0, cos_t, sin_t, width) dx, dy = length * cos_t, length * sin_t vertices_arrow = [(x1+dx, y1+dy), (x1, y1), (x2, y2), (x2+dx, y2+dy)] codes_arrow = [Path.MOVETO, Path.LINETO, Path.LINETO, Path.LINETO] return vertices_arrow, codes_arrow def transmute(self, path, mutation_size, linewidth): if self.scaleA is None: scaleA = mutation_size else: scaleA = self.scaleA if self.scaleB is None: scaleB = mutation_size else: scaleB = self.scaleB vertices_list, codes_list = [], [] if self.bracketA: x0, y0 = path.vertices[0] x1, y1 = path.vertices[1] cos_t, sin_t = get_cos_sin(x1, y1, x0, y0) verticesA, codesA = self._get_bracket(x0, y0, cos_t, sin_t, self.widthAscaleA, self.legnthAscaleA) vertices_list.append(verticesA) codes_list.append(codesA) vertices_list.append(path.vertices) codes_list.append(path.codes) if self.bracketB: x0, y0 = path.vertices[-1] x1, y1 = path.vertices[-2] cos_t, sin_t = get_cos_sin(x1, y1, x0, y0) verticesB, codesB = self._get_bracket(x0, y0, cos_t, sin_t, self.widthBscaleB, self.lengthBscaleB) vertices_list.append(verticesB) codes_list.append(codesB) vertices = np.concatenate(vertices_list) codes = np.concatenate(codes_list) p = Path(vertices, codes) return p, False class BracketB(_Bracket): """ An arrow with a bracket([) at its end. """ def __init__(self, widthB=1., lengthB=0.2, angleB=None): """ widthB width of the bracket lengthB length of the bracket angleB angle between the bracket and the line """ super(ArrowStyle.BracketB, self).__init__(None, True, widthB=widthB, lengthB=lengthB, angleB=None ) #_style_list["-["] = BracketB _style_list["-["] = BracketB class Simple(_Base): """ A simple arrow. Only works with a quadratic bezier curve. """ def __init__(self, head_length=.5, head_width=.5, tail_width=.2): """ head_length length of the arrow head head_with width of the arrow head tail_width width of the arrow tail """ self.head_length, self.head_width, self.tail_width = \ head_length, head_width, tail_width super(ArrowStyle.Simple, self).__init__() def transmute(self, path, mutation_size, linewidth): x0, y0, x1, y1, x2, y2 = self.ensure_quadratic_bezier(path) # divide the path into a head and a tail head_length = self.head_length * mutation_size in_f = inside_circle(x2, y2, head_length) arrow_path = [(x0, y0), (x1, y1), (x2, y2)] arrow_out, arrow_in = \ split_bezier_intersecting_with_closedpath(arrow_path, in_f, tolerence=0.01) # head head_width = self.head_width * mutation_size head_l, head_r = make_wedged_bezier2(arrow_in, head_width/2., wm=.5) # tail tail_width = self.tail_width * mutation_size tail_left, tail_right = get_parallels(arrow_out, tail_width/2.) head_right, head_left = head_r, head_l patch_path = [(Path.MOVETO, tail_right[0]), (Path.CURVE3, tail_right[1]), (Path.CURVE3, tail_right[2]), (Path.LINETO, head_right[0]), (Path.CURVE3, head_right[1]), (Path.CURVE3, head_right[2]), (Path.CURVE3, head_left[1]), (Path.CURVE3, head_left[0]), (Path.LINETO, tail_left[2]), (Path.CURVE3, tail_left[1]), (Path.CURVE3, tail_left[0]), (Path.LINETO, tail_right[0]), (Path.CLOSEPOLY, tail_right[0]), ] path = Path([p for c, p in patch_path], [c for c, p in patch_path]) return path, True _style_list["simple"] = Simple class Fancy(_Base): """ A fancy arrow. Only works with a quadratic bezier curve. """ def __init__(self, head_length=.4, head_width=.4, tail_width=.4): """ head_length length of the arrow head head_with width of the arrow head tail_width width of the arrow tail """ self.head_length, self.head_width, self.tail_width = \ head_length, head_width, tail_width super(ArrowStyle.Fancy, self).__init__() def transmute(self, path, mutation_size, linewidth): x0, y0, x1, y1, x2, y2 = self.ensure_quadratic_bezier(path) # divide the path into a head and a tail head_length = self.head_length * mutation_size arrow_path = [(x0, y0), (x1, y1), (x2, y2)] # path for head in_f = inside_circle(x2, y2, head_length) path_out, path_in = \ split_bezier_intersecting_with_closedpath(arrow_path, in_f, tolerence=0.01) path_head = path_in # path for head in_f = inside_circle(x2, y2, head_length.8) path_out, path_in = \ split_bezier_intersecting_with_closedpath(arrow_path, in_f, tolerence=0.01) path_tail = path_out # head head_width = self.head_width mutation_size head_l, head_r = make_wedged_bezier2(path_head, head_width/2., wm=.6) # tail tail_width = self.tail_width * mutation_size tail_left, tail_right = make_wedged_bezier2(path_tail, tail_width.5, w1=1., wm=0.6, w2=0.3) # path for head in_f = inside_circle(x0, y0, tail_width.3) path_in, path_out = \ split_bezier_intersecting_with_closedpath(arrow_path, in_f, tolerence=0.01) tail_start = path_in[-1] head_right, head_left = head_r, head_l patch_path = [(Path.MOVETO, tail_start), (Path.LINETO, tail_right[0]), (Path.CURVE3, tail_right[1]), (Path.CURVE3, tail_right[2]), (Path.LINETO, head_right[0]), (Path.CURVE3, head_right[1]), (Path.CURVE3, head_right[2]), (Path.CURVE3, head_left[1]), (Path.CURVE3, head_left[0]), (Path.LINETO, tail_left[2]), (Path.CURVE3, tail_left[1]), (Path.CURVE3, tail_left[0]), (Path.LINETO, tail_start), (Path.CLOSEPOLY, tail_start), ] patch_path2 = [(Path.MOVETO, tail_right[0]), (Path.CURVE3, tail_right[1]), (Path.CURVE3, tail_right[2]), (Path.LINETO, head_right[0]), (Path.CURVE3, head_right[1]), (Path.CURVE3, head_right[2]), (Path.CURVE3, head_left[1]), (Path.CURVE3, head_left[0]), (Path.LINETO, tail_left[2]), (Path.CURVE3, tail_left[1]), (Path.CURVE3, tail_left[0]), (Path.CURVE3, tail_start), (Path.CURVE3, tail_right[0]), (Path.CLOSEPOLY, tail_right[0]), ] path = Path([p for c, p in patch_path], [c for c, p in patch_path]) return path, True _style_list["fancy"] = Fancy class Wedge(_Base): """ Wedge(?) shape. Only wokrs with a quadratic bezier curve. The begin point has a width of the tail_width and the end point has a width of 0. At the middle, the width is shrink_factortail_width. """ def __init__(self, tail_width=.3, shrink_factor=0.5): """ tail_width* width of the tail shrink_factor fraction of the arrow width at the middle point """ self.tail_width = tail_width self.shrink_factor = shrink_factor super(ArrowStyle.Wedge, self).__init__() def transmute(self, path, mutation_size, linewidth): x0, y0, x1, y1, x2, y2 = self.ensure_quadratic_bezier(path) arrow_path = [(x0, y0), (x1, y1), (x2, y2)] b_plus, b_minus = make_wedged_bezier2(arrow_path, self.tail_width * mutation_size / 2., wm=self.shrink_factor) patch_path = [(Path.MOVETO, b_plus[0]), (Path.CURVE3, b_plus[1]), (Path.CURVE3, b_plus[2]), (Path.LINETO, b_minus[2]), (Path.CURVE3, b_minus[1]), (Path.CURVE3, b_minus[0]), (Path.CLOSEPOLY, b_minus[0]), ] path = Path([p for c, p in patch_path], [c for c, p in patch_path]) return path, True _style_list["wedge"] = Wedge __doc__ = cbook.dedent(__doc__) % \ {"AvailableArrowstyles": _pprint_styles(_style_list)} class FancyArrowPatch(Patch): """ A fancy arrow patch. It draws an arrow using the :class:ArrowStyle. """ def __str__(self): return self.__class__.__name__ \ + "FancyArrowPatch(%g,%g,%g,%g,%g,%g)" % tuple(self._q_bezier) def __init__(self, posA=None, posB=None, path=None, arrowstyle="simple", arrow_transmuter=None, connectionstyle="arc3", connector=None, patchA=None, patchB=None, shrinkA=2., shrinkB=2., mutation_scale=1., mutation_aspect=None, *kwargs): """ If posA* and posB is given, a path connecting two point are created according to the connectionstyle. The path will be clipped with patchA and patchB and further shirnked by shrinkA and shrinkB. An arrow is drawn along this resulting path using the arrowstyle parameter. If path provided, an arrow is drawn along this path and patchA, patchB, shrinkA, and shrinkB are ignored. The connectionstyle describes how posA and posB are connected. It can be an instance of the ConnectionStyle class (matplotlib.patches.ConnectionStlye) or a string of the connectionstyle name, with optional comma-separated attributes. The following connection styles are available. %(AvailableConnectorstyles)s The arrowstyle describes how the fancy arrow will be drawn. It can be string of the available arrowstyle names, with optional comma-separated attributes, or one of the ArrowStyle instance. The optional attributes are meant to be scaled with the mutation_scale. The following arrow styles are available. %(AvailableArrowstyles)s mutation_scale : a value with which attributes of arrowstyle (e.g., head_length) will be scaled. default=1. mutation_aspect : The height of the rectangle will be squeezed by this value before the mutation and the mutated box will be stretched by the inverse of it. default=None. Valid kwargs are: %(Patch)s """ if posA is not None and posB is not None and path is None: self._posA_posB = [posA, posB] if connectionstyle is None: connectionstyle = "arc3" self.set_connectionstyle(connectionstyle) elif posA is None and posB is None and path is not None: self._posA_posB = None self._connetors = None else: raise ValueError("either posA and posB, or path need to provided") self.patchA = patchA self.patchB = patchB self.shrinkA = shrinkA self.shrinkB = shrinkB Patch.__init__(self, kwargs) self._path_original = path self.set_arrowstyle(arrowstyle) self._mutation_scale=mutation_scale self._mutation_aspect=mutation_aspect #self._draw_in_display_coordinate = True kwdoc = dict() kwdoc["AvailableArrowstyles"]=_pprint_styles(ArrowStyle._style_list) kwdoc["AvailableConnectorstyles"]=_pprint_styles(ConnectionStyle._style_list) kwdoc.update(artist.kwdocd) __init__.__doc__ = cbook.dedent(__init__.__doc__) % kwdoc del kwdoc def set_positions(self, posA, posB): """ set the begin end end positions of the connecting path. Use current vlaue if None. """ if posA is not None: self._posA_posB[0] = posA if posB is not None: self._posA_posB[1] = posB def set_patchA(self, patchA): """ set the begin patch. """ self.patchA = patchA def set_patchB(self, patchB): """ set the begin patch """ self.patchB = patchB def set_connectionstyle(self, connectionstyle, kw): """ Set the connection style. connectionstyle can be a string with connectionstyle name with optional comma-separated attributes. Alternatively, the attrs can be probided as keywords. set_connectionstyle("arc,angleA=0,armA=30,rad=10") set_connectionstyle("arc", angleA=0,armA=30,rad=10) Old attrs simply are forgotten. Without argument (or with connectionstyle=None), return available styles as a list of strings. """ if connectionstyle==None: return ConnectionStyle.pprint_styles() if isinstance(connectionstyle, ConnectionStyle._Base): self._connector = connectionstyle elif callable(connectionstyle): # we may need check the calling convention of the given function self._connector = connectionstyle else: self._connector = ConnectionStyle(connectionstyle, kw) def get_connectionstyle(self): """ Return the ConnectionStyle instance """ return self._connector def set_arrowstyle(self, arrowstyle=None, kw): """ Set the arrow style. arrowstyle can be a string with arrowstyle name with optional comma-separated attributes. Alternatively, the attrs can be provided as keywords. set_arrowstyle("Fancy,head_length=0.2") set_arrowstyle("fancy", head_length=0.2) Old attrs simply are forgotten. Without argument (or with arrowstyle=None), return available box styles as a list of strings. """ if arrowstyle==None: return ArrowStyle.pprint_styles() if isinstance(arrowstyle, ConnectionStyle._Base): self._arrow_transmuter = arrowstyle else: self._arrow_transmuter = ArrowStyle(arrowstyle, **kw) def get_arrowstyle(self): """ Return the arrowstyle object """ return self._arrow_transmuter def set_mutation_scale(self, scale): """ Set the mutation scale. ACCEPTS: float """ self._mutation_scale=scale def get_mutation_scale(self): """ Return the mutation scale. """ return self._mutation_scale def set_mutation_aspect(self, aspect): """ Set the aspect ratio of the bbox mutation. ACCEPTS: float """ self._mutation_aspect=aspect def get_mutation_aspect(self): """ Return the aspect ratio of the bbox mutation. """ return self._mutation_aspect def get_path(self): """ return the path of the arrow in the data coordinate. Use get_path_in_displaycoord() medthod to retrieve the arrow path in the disaply coord. """ _path = self.get_path_in_displaycoord() return self.get_transform().inverted().transform_path(_path) def get_path_in_displaycoord(self): """ Return the mutated path of the arrow in the display coord """ if self._posA_posB is not None: posA = self.get_transform().transform_point(self._posA_posB[0]) posB = self.get_transform().transform_point(self._posA_posB[1]) _path = self.get_connectionstyle()(posA, posB, patchA=self.patchA, patchB=self.patchB, shrinkA=self.shrinkA, shrinkB=self.shrinkB ) else: _path = self.get_transform().transform_path(self._path_original) _path, closed = self.get_arrowstyle()(_path, self.get_mutation_scale(), self.get_linewidth(), self.get_mutation_aspect() ) if not closed: self.fill = False return _path def draw(self, renderer): if not self.get_visible(): return #renderer.open_group('patch') gc = renderer.new_gc() fill_orig = self.fill path = self.get_path_in_displaycoord() affine = transforms.IdentityTransform() if cbook.is_string_like(self._edgecolor) and self._edgecolor.lower()=='none': gc.set_linewidth(0) else: gc.set_foreground(self._edgecolor) gc.set_linewidth(self._linewidth) gc.set_linestyle(self._linestyle) gc.set_antialiased(self._antialiased) self._set_gc_clip(gc) gc.set_capstyle('round') if (not self.fill or self._facecolor is None or (cbook.is_string_like(self._facecolor) and self._facecolor.lower()=='none')): rgbFace = None gc.set_alpha(1.0) else: r, g, b, a = colors.colorConverter.to_rgba(self._facecolor, self._alpha) rgbFace = (r, g, b) gc.set_alpha(a) if self._hatch: gc.set_hatch(self._hatch ) renderer.draw_path(gc, path, affine, rgbFace) self.fill = fill_orig #renderer.close_group('patch')	110,325	Python	.py	2,673	29.265619	102	0.533629	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,246	cbook.py	numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/cbook.py	""" A collection of utility functions and classes. Many (but not all) from the Python Cookbook -- hence the name cbook """ from __future__ import generators import re, os, errno, sys, StringIO, traceback, locale, threading, types import time, datetime import warnings import numpy as np import numpy.ma as ma from weakref import ref major, minor1, minor2, s, tmp = sys.version_info # on some systems, locale.getpreferredencoding returns None, which can break unicode preferredencoding = locale.getpreferredencoding() def unicode_safe(s): if preferredencoding is None: return unicode(s) else: return unicode(s, preferredencoding) class converter: """ Base class for handling string -> python type with support for missing values """ def __init__(self, missing='Null', missingval=None): self.missing = missing self.missingval = missingval def __call__(self, s): if s==self.missing: return self.missingval return s def is_missing(self, s): return not s.strip() or s==self.missing class tostr(converter): 'convert to string or None' def __init__(self, missing='Null', missingval=''): converter.__init__(self, missing=missing, missingval=missingval) class todatetime(converter): 'convert to a datetime or None' def __init__(self, fmt='%Y-%m-%d', missing='Null', missingval=None): 'use a :func:`time.strptime` format string for conversion' converter.__init__(self, missing, missingval) self.fmt = fmt def __call__(self, s): if self.is_missing(s): return self.missingval tup = time.strptime(s, self.fmt) return datetime.datetime(tup[:6]) class todate(converter): 'convert to a date or None' def __init__(self, fmt='%Y-%m-%d', missing='Null', missingval=None): 'use a :func:`time.strptime` format string for conversion' converter.__init__(self, missing, missingval) self.fmt = fmt def __call__(self, s): if self.is_missing(s): return self.missingval tup = time.strptime(s, self.fmt) return datetime.date(tup[:3]) class tofloat(converter): 'convert to a float or None' def __init__(self, missing='Null', missingval=None): converter.__init__(self, missing) self.missingval = missingval def __call__(self, s): if self.is_missing(s): return self.missingval return float(s) class toint(converter): 'convert to an int or None' def __init__(self, missing='Null', missingval=None): converter.__init__(self, missing) def __call__(self, s): if self.is_missing(s): return self.missingval return int(s) class CallbackRegistry: """ Handle registering and disconnecting for a set of signals and callbacks:: signals = 'eat', 'drink', 'be merry' def oneat(x): print 'eat', x def ondrink(x): print 'drink', x callbacks = CallbackRegistry(signals) ideat = callbacks.connect('eat', oneat) iddrink = callbacks.connect('drink', ondrink) #tmp = callbacks.connect('drunk', ondrink) # this will raise a ValueError callbacks.process('drink', 123) # will call oneat callbacks.process('eat', 456) # will call ondrink callbacks.process('be merry', 456) # nothing will be called callbacks.disconnect(ideat) # disconnect oneat callbacks.process('eat', 456) # nothing will be called """ def __init__(self, signals): 'signals is a sequence of valid signals' self.signals = set(signals) # callbacks is a dict mapping the signal to a dictionary # mapping callback id to the callback function self.callbacks = dict([(s, dict()) for s in signals]) self._cid = 0 def _check_signal(self, s): 'make sure s is a valid signal or raise a ValueError' if s not in self.signals: signals = list(self.signals) signals.sort() raise ValueError('Unknown signal "%s"; valid signals are %s'%(s, signals)) def connect(self, s, func): """ register func to be called when a signal s is generated func will be called """ self._check_signal(s) self._cid +=1 self.callbacks[s][self._cid] = func return self._cid def disconnect(self, cid): """ disconnect the callback registered with callback id cid """ for eventname, callbackd in self.callbacks.items(): try: del callbackd[cid] except KeyError: continue else: return def process(self, s, args, kwargs): """ process signal s. All of the functions registered to receive callbacks on s* will be called with \args* and \\kwargs """ self._check_signal(s) for func in self.callbacks[s].values(): func(args, kwargs) class Scheduler(threading.Thread): """ Base class for timeout and idle scheduling """ idlelock = threading.Lock() id = 0 def __init__(self): threading.Thread.__init__(self) self.id = Scheduler.id self._stopped = False Scheduler.id += 1 self._stopevent = threading.Event() def stop(self): if self._stopped: return self._stopevent.set() self.join() self._stopped = True class Timeout(Scheduler): """ Schedule recurring events with a wait time in seconds """ def __init__(self, wait, func): Scheduler.__init__(self) self.wait = wait self.func = func def run(self): while not self._stopevent.isSet(): self._stopevent.wait(self.wait) Scheduler.idlelock.acquire() b = self.func(self) Scheduler.idlelock.release() if not b: break class Idle(Scheduler): """ Schedule callbacks when scheduler is idle """ # the prototype impl is a bit of a poor man's idle handler. It # just implements a short wait time. But it will provide a # placeholder for a proper impl ater waittime = 0.05 def __init__(self, func): Scheduler.__init__(self) self.func = func def run(self): while not self._stopevent.isSet(): self._stopevent.wait(Idle.waittime) Scheduler.idlelock.acquire() b = self.func(self) Scheduler.idlelock.release() if not b: break class silent_list(list): """ override repr when returning a list of matplotlib artists to prevent long, meaningless output. This is meant to be used for a homogeneous list of a give type """ def __init__(self, type, seq=None): self.type = type if seq is not None: self.extend(seq) def __repr__(self): return '<a list of %d %s objects>' % (len(self), self.type) def __str__(self): return '<a list of %d %s objects>' % (len(self), self.type) def strip_math(s): 'remove latex formatting from mathtext' remove = (r'\mathdefault', r'\rm', r'\cal', r'\tt', r'\it', '\\', '{', '}') s = s[1:-1] for r in remove: s = s.replace(r,'') return s class Bunch: """ Often we want to just collect a bunch of stuff together, naming each item of the bunch; a dictionary's OK for that, but a small do- nothing class is even handier, and prettier to use. Whenever you want to group a few variables: >>> point = Bunch(datum=2, squared=4, coord=12) >>> point.datum By: Alex Martelli From: http://aspn.activestate.com/ASPN/Cookbook/Python/Recipe/52308 """ def __init__(self, kwds): self.__dict__.update(kwds) def unique(x): 'Return a list of unique elements of x' return dict([ (val, 1) for val in x]).keys() def iterable(obj): 'return true if obj* is iterable' try: len(obj) except: return False return True def is_string_like(obj): 'Return True if obj looks like a string' if isinstance(obj, (str, unicode)): return True # numpy strings are subclass of str, ma strings are not if ma.isMaskedArray(obj): if obj.ndim == 0 and obj.dtype.kind in 'SU': return True else: return False try: obj + '' except (TypeError, ValueError): return False return True def is_sequence_of_strings(obj): """ Returns true if obj is iterable and contains strings """ if not iterable(obj): return False if is_string_like(obj): return False for o in obj: if not is_string_like(o): return False return True def is_writable_file_like(obj): 'return true if obj looks like a file object with a write method' return hasattr(obj, 'write') and callable(obj.write) def is_scalar(obj): 'return true if obj is not string like and is not iterable' return not is_string_like(obj) and not iterable(obj) def is_numlike(obj): 'return true if obj looks like a number' try: obj+1 except TypeError: return False else: return True def to_filehandle(fname, flag='r', return_opened=False): """ fname can be a filename or a file handle. Support for gzipped files is automatic, if the filename ends in .gz. flag is a read/write flag for :func:`file` """ if is_string_like(fname): if fname.endswith('.gz'): import gzip fh = gzip.open(fname, flag) else: fh = file(fname, flag) opened = True elif hasattr(fname, 'seek'): fh = fname opened = False else: raise ValueError('fname must be a string or file handle') if return_opened: return fh, opened return fh def is_scalar_or_string(val): return is_string_like(val) or not iterable(val) def flatten(seq, scalarp=is_scalar_or_string): """ this generator flattens nested containers such as >>> l=( ('John', 'Hunter'), (1,23), [[[[42,(5,23)]]]]) so that >>> for i in flatten(l): print i, John Hunter 1 23 42 5 23 By: Composite of Holger Krekel and Luther Blissett From: http://aspn.activestate.com/ASPN/Cookbook/Python/Recipe/121294 and Recipe 1.12 in cookbook """ for item in seq: if scalarp(item): yield item else: for subitem in flatten(item, scalarp): yield subitem class Sorter: """ Sort by attribute or item Example usage:: sort = Sorter() list = [(1, 2), (4, 8), (0, 3)] dict = [{'a': 3, 'b': 4}, {'a': 5, 'b': 2}, {'a': 0, 'b': 0}, {'a': 9, 'b': 9}] sort(list) # default sort sort(list, 1) # sort by index 1 sort(dict, 'a') # sort a list of dicts by key 'a' """ def _helper(self, data, aux, inplace): aux.sort() result = [data[i] for junk, i in aux] if inplace: data[:] = result return result def byItem(self, data, itemindex=None, inplace=1): if itemindex is None: if inplace: data.sort() result = data else: result = data[:] result.sort() return result else: aux = [(data[i][itemindex], i) for i in range(len(data))] return self._helper(data, aux, inplace) def byAttribute(self, data, attributename, inplace=1): aux = [(getattr(data[i],attributename),i) for i in range(len(data))] return self._helper(data, aux, inplace) # a couple of handy synonyms sort = byItem __call__ = byItem class Xlator(dict): """ All-in-one multiple-string-substitution class Example usage:: text = "Larry Wall is the creator of Perl" adict = { "Larry Wall" : "Guido van Rossum", "creator" : "Benevolent Dictator for Life", "Perl" : "Python", } print multiple_replace(adict, text) xlat = Xlator(adict) print xlat.xlat(text) """ def _make_regex(self): """ Build re object based on the keys of the current dictionary """ return re.compile("\|".join(map(re.escape, self.keys()))) def __call__(self, match): """ Handler invoked for each regex match """ return self[match.group(0)] def xlat(self, text): """ Translate text, returns the modified text. """ return self._make_regex().sub(self, text) def soundex(name, len=4): """ soundex module conforming to Odell-Russell algorithm """ # digits holds the soundex values for the alphabet soundex_digits = '01230120022455012623010202' sndx = '' fc = '' # Translate letters in name to soundex digits for c in name.upper(): if c.isalpha(): if not fc: fc = c # Remember first letter d = soundex_digits[ord(c)-ord('A')] # Duplicate consecutive soundex digits are skipped if not sndx or (d != sndx[-1]): sndx += d # Replace first digit with first letter sndx = fc + sndx[1:] # Remove all 0s from the soundex code sndx = sndx.replace('0', '') # Return soundex code truncated or 0-padded to len characters return (sndx + (len * '0'))[:len] class Null: """ Null objects always and reliably "do nothing." """ def __init__(self, args, kwargs): pass def __call__(self, args, *kwargs): return self def __str__(self): return "Null()" def __repr__(self): return "Null()" def __nonzero__(self): return 0 def __getattr__(self, name): return self def __setattr__(self, name, value): return self def __delattr__(self, name): return self def mkdirs(newdir, mode=0777): """ make directory newdir* recursively, and set mode. Equivalent to :: > mkdir -p NEWDIR > chmod MODE NEWDIR """ try: if not os.path.exists(newdir): parts = os.path.split(newdir) for i in range(1, len(parts)+1): thispart = os.path.join(parts[:i]) if not os.path.exists(thispart): os.makedirs(thispart, mode) except OSError, err: # Reraise the error unless it's about an already existing directory if err.errno != errno.EEXIST or not os.path.isdir(newdir): raise class GetRealpathAndStat: def __init__(self): self._cache = {} def __call__(self, path): result = self._cache.get(path) if result is None: realpath = os.path.realpath(path) if sys.platform == 'win32': stat_key = realpath else: stat = os.stat(realpath) stat_key = (stat.st_ino, stat.st_dev) result = realpath, stat_key self._cache[path] = result return result get_realpath_and_stat = GetRealpathAndStat() def dict_delall(d, keys): 'delete all of the keys* from the :class:`dict` d' for key in keys: try: del d[key] except KeyError: pass class RingBuffer: """ class that implements a not-yet-full buffer """ def __init__(self,size_max): self.max = size_max self.data = [] class __Full: """ class that implements a full buffer """ def append(self, x): """ Append an element overwriting the oldest one. """ self.data[self.cur] = x self.cur = (self.cur+1) % self.max def get(self): """ return list of elements in correct order """ return self.data[self.cur:]+self.data[:self.cur] def append(self,x): """append an element at the end of the buffer""" self.data.append(x) if len(self.data) == self.max: self.cur = 0 # Permanently change self's class from non-full to full self.__class__ = __Full def get(self): """ Return a list of elements from the oldest to the newest. """ return self.data def __get_item__(self, i): return self.data[i % len(self.data)] def get_split_ind(seq, N): """ seq is a list of words. Return the index into seq such that:: len(' '.join(seq[:ind])<=N """ sLen = 0 # todo: use Alex's xrange pattern from the cbook for efficiency for (word, ind) in zip(seq, range(len(seq))): sLen += len(word) + 1 # +1 to account for the len(' ') if sLen>=N: return ind return len(seq) def wrap(prefix, text, cols): 'wrap text with prefix at length cols' pad = ' 'len(prefix.expandtabs()) available = cols - len(pad) seq = text.split(' ') Nseq = len(seq) ind = 0 lines = [] while ind<Nseq: lastInd = ind ind += get_split_ind(seq[ind:], available) lines.append(seq[lastInd:ind]) # add the prefix to the first line, pad with spaces otherwise ret = prefix + ' '.join(lines[0]) + '\n' for line in lines[1:]: ret += pad + ' '.join(line) + '\n' return ret # A regular expression used to determine the amount of space to # remove. It looks for the first sequence of spaces immediately # following the first newline, or at the beginning of the string. _find_dedent_regex = re.compile("(?:(?:\n\r?)\|^)( )\S") # A cache to hold the regexs that actually remove the indent. _dedent_regex = {} def dedent(s): """ Remove excess indentation from docstring s. Discards any leading blank lines, then removes up to n whitespace characters from each line, where n is the number of leading whitespace characters in the first line. It differs from textwrap.dedent in its deletion of leading blank lines and its use of the first non-blank line to determine the indentation. It is also faster in most cases. """ # This implementation has a somewhat obtuse use of regular # expressions. However, this function accounted for almost 30% of # matplotlib startup time, so it is worthy of optimization at all # costs. if not s: # includes case of s is None return '' match = _find_dedent_regex.match(s) if match is None: return s # This is the number of spaces to remove from the left-hand side. nshift = match.end(1) - match.start(1) if nshift == 0: return s # Get a regex that will remove up to nshift spaces from the # beginning of each line. If it isn't in the cache, generate it. unindent = _dedent_regex.get(nshift, None) if unindent is None: unindent = re.compile("\n\r? {0,%d}" % nshift) _dedent_regex[nshift] = unindent result = unindent.sub("\n", s).strip() return result def listFiles(root, patterns='', recurse=1, return_folders=0): """ Recursively list files from Parmar and Martelli in the Python Cookbook """ import os.path, fnmatch # Expand patterns from semicolon-separated string to list pattern_list = patterns.split(';') # Collect input and output arguments into one bunch class Bunch: def __init__(self, kwds): self.__dict__.update(kwds) arg = Bunch(recurse=recurse, pattern_list=pattern_list, return_folders=return_folders, results=[]) def visit(arg, dirname, files): # Append to arg.results all relevant files (and perhaps folders) for name in files: fullname = os.path.normpath(os.path.join(dirname, name)) if arg.return_folders or os.path.isfile(fullname): for pattern in arg.pattern_list: if fnmatch.fnmatch(name, pattern): arg.results.append(fullname) break # Block recursion if recursion was disallowed if not arg.recurse: files[:]=[] os.path.walk(root, visit, arg) return arg.results def get_recursive_filelist(args): """ Recurs all the files and dirs in args* ignoring symbolic links and return the files as a list of strings """ files = [] for arg in args: if os.path.isfile(arg): files.append(arg) continue if os.path.isdir(arg): newfiles = listFiles(arg, recurse=1, return_folders=1) files.extend(newfiles) return [f for f in files if not os.path.islink(f)] def pieces(seq, num=2): "Break up the seq into num tuples" start = 0 while 1: item = seq[start:start+num] if not len(item): break yield item start += num def exception_to_str(s = None): sh = StringIO.StringIO() if s is not None: print >>sh, s traceback.print_exc(file=sh) return sh.getvalue() def allequal(seq): """ Return True if all elements of seq compare equal. If seq is 0 or 1 length, return True """ if len(seq)<2: return True val = seq[0] for i in xrange(1, len(seq)): thisval = seq[i] if thisval != val: return False return True def alltrue(seq): """ Return True if all elements of seq evaluate to True. If seq is empty, return False. """ if not len(seq): return False for val in seq: if not val: return False return True def onetrue(seq): """ Return True if one element of seq is True. It seq is empty, return False. """ if not len(seq): return False for val in seq: if val: return True return False def allpairs(x): """ return all possible pairs in sequence x Condensed by Alex Martelli from this thread_ on c.l.python .. _thread: http://groups.google.com/groups?q=all+pairs+group:python&hl=en&lr=&ie=UTF-8&selm=mailman.4028.1096403649.5135.python-list%40python.org&rnum=1 """ return [ (s, f) for i, f in enumerate(x) for s in x[i+1:] ] # python 2.2 dicts don't have pop--but we don't support 2.2 any more def popd(d, args): """ Should behave like python2.3 :meth:`dict.pop` method; d* is a :class:`dict`:: # returns value for key and deletes item; raises a KeyError if key # is not in dict val = popd(d, key) # returns value for key if key exists, else default. Delete key, # val item if it exists. Will not raise a KeyError val = popd(d, key, default) """ warnings.warn("Use native python dict.pop method", DeprecationWarning) # warning added 2008/07/22 if len(args)==1: key = args[0] val = d[key] del d[key] elif len(args)==2: key, default = args val = d.get(key, default) try: del d[key] except KeyError: pass return val class maxdict(dict): """ A dictionary with a maximum size; this doesn't override all the relevant methods to contrain size, just setitem, so use with caution """ def __init__(self, maxsize): dict.__init__(self) self.maxsize = maxsize self._killkeys = [] def __setitem__(self, k, v): if len(self)>=self.maxsize: del self[self._killkeys[0]] del self._killkeys[0] dict.__setitem__(self, k, v) self._killkeys.append(k) class Stack: """ Implement a stack where elements can be pushed on and you can move back and forth. But no pop. Should mimic home / back / forward in a browser """ def __init__(self, default=None): self.clear() self._default = default def __call__(self): 'return the current element, or None' if not len(self._elements): return self._default else: return self._elements[self._pos] def forward(self): 'move the position forward and return the current element' N = len(self._elements) if self._pos<N-1: self._pos += 1 return self() def back(self): 'move the position back and return the current element' if self._pos>0: self._pos -= 1 return self() def push(self, o): """ push object onto stack at current position - all elements occurring later than the current position are discarded """ self._elements = self._elements[:self._pos+1] self._elements.append(o) self._pos = len(self._elements)-1 return self() def home(self): 'push the first element onto the top of the stack' if not len(self._elements): return self.push(self._elements[0]) return self() def empty(self): return len(self._elements)==0 def clear(self): 'empty the stack' self._pos = -1 self._elements = [] def bubble(self, o): """ raise o to the top of the stack and return o. o must be in the stack """ if o not in self._elements: raise ValueError('Unknown element o') old = self._elements[:] self.clear() bubbles = [] for thiso in old: if thiso==o: bubbles.append(thiso) else: self.push(thiso) for thiso in bubbles: self.push(o) return o def remove(self, o): 'remove element o from the stack' if o not in self._elements: raise ValueError('Unknown element o') old = self._elements[:] self.clear() for thiso in old: if thiso==o: continue else: self.push(thiso) def popall(seq): 'empty a list' for i in xrange(len(seq)): seq.pop() def finddir(o, match, case=False): """ return all attributes of o which match string in match. if case is True require an exact case match. """ if case: names = [(name,name) for name in dir(o) if is_string_like(name)] else: names = [(name.lower(), name) for name in dir(o) if is_string_like(name)] match = match.lower() return [orig for name, orig in names if name.find(match)>=0] def reverse_dict(d): 'reverse the dictionary -- may lose data if values are not unique!' return dict([(v,k) for k,v in d.items()]) def report_memory(i=0): # argument may go away 'return the memory consumed by process' pid = os.getpid() if sys.platform=='sunos5': a2 = os.popen('ps -p %d -o osz' % pid).readlines() mem = int(a2[-1].strip()) elif sys.platform.startswith('linux'): a2 = os.popen('ps -p %d -o rss,sz' % pid).readlines() mem = int(a2[1].split()[1]) elif sys.platform.startswith('darwin'): a2 = os.popen('ps -p %d -o rss,vsz' % pid).readlines() mem = int(a2[1].split()[0]) return mem _safezip_msg = 'In safezip, len(args[0])=%d but len(args[%d])=%d' def safezip(args): 'make sure args* are equal len before zipping' Nx = len(args[0]) for i, arg in enumerate(args[1:]): if len(arg) != Nx: raise ValueError(_safezip_msg % (Nx, i+1, len(arg))) return zip(args) def issubclass_safe(x, klass): 'return issubclass(x, klass) and return False on a TypeError' try: return issubclass(x, klass) except TypeError: return False class MemoryMonitor: def __init__(self, nmax=20000): self._nmax = nmax self._mem = np.zeros((self._nmax,), np.int32) self.clear() def clear(self): self._n = 0 self._overflow = False def __call__(self): mem = report_memory() if self._n < self._nmax: self._mem[self._n] = mem self._n += 1 else: self._overflow = True return mem def report(self, segments=4): n = self._n segments = min(n, segments) dn = int(n/segments) ii = range(0, n, dn) ii[-1] = n-1 print print 'memory report: i, mem, dmem, dmem/nloops' print 0, self._mem[0] for i in range(1, len(ii)): di = ii[i] - ii[i-1] if di == 0: continue dm = self._mem[ii[i]] - self._mem[ii[i-1]] print '%5d %5d %3d %8.3f' % (ii[i], self._mem[ii[i]], dm, dm / float(di)) if self._overflow: print "Warning: array size was too small for the number of calls." def xy(self, i0=0, isub=1): x = np.arange(i0, self._n, isub) return x, self._mem[i0:self._n:isub] def plot(self, i0=0, isub=1, fig=None): if fig is None: from pylab import figure, show fig = figure() ax = fig.add_subplot(111) ax.plot(self.xy(i0, isub)) fig.canvas.draw() def print_cycles(objects, outstream=sys.stdout, show_progress=False): """ objects A list of objects to find cycles in. It is often useful to pass in gc.garbage to find the cycles that are preventing some objects from being garbage collected. outstream The stream for output. show_progress If True, print the number of objects reached as they are found. """ import gc from types import FrameType def print_path(path): for i, step in enumerate(path): # next "wraps around" next = path[(i + 1) % len(path)] outstream.write(" %s -- " % str(type(step))) if isinstance(step, dict): for key, val in step.items(): if val is next: outstream.write("[%s]" % repr(key)) break if key is next: outstream.write("[key] = %s" % repr(val)) break elif isinstance(step, list): outstream.write("[%d]" % step.index(next)) elif isinstance(step, tuple): outstream.write("( tuple )") else: outstream.write(repr(step)) outstream.write(" ->\n") outstream.write("\n") def recurse(obj, start, all, current_path): if show_progress: outstream.write("%d\r" % len(all)) all[id(obj)] = None referents = gc.get_referents(obj) for referent in referents: # If we've found our way back to the start, this is # a cycle, so print it out if referent is start: print_path(current_path) # Don't go back through the original list of objects, or # through temporary references to the object, since those # are just an artifact of the cycle detector itself. elif referent is objects or isinstance(referent, FrameType): continue # We haven't seen this object before, so recurse elif id(referent) not in all: recurse(referent, start, all, current_path + [obj]) for obj in objects: outstream.write("Examining: %r\n" % (obj,)) recurse(obj, obj, { }, []) class Grouper(object): """ This class provides a lightweight way to group arbitrary objects together into disjoint sets when a full-blown graph data structure would be overkill. Objects can be joined using :meth:`join`, tested for connectedness using :meth:`joined`, and all disjoint sets can be retreived by using the object as an iterator. The objects being joined must be hashable. For example: >>> g = grouper.Grouper() >>> g.join('a', 'b') >>> g.join('b', 'c') >>> g.join('d', 'e') >>> list(g) [['a', 'b', 'c'], ['d', 'e']] >>> g.joined('a', 'b') True >>> g.joined('a', 'c') True >>> g.joined('a', 'd') False """ def __init__(self, init=[]): mapping = self._mapping = {} for x in init: mapping[ref(x)] = [ref(x)] def __contains__(self, item): return ref(item) in self._mapping def clean(self): """ Clean dead weak references from the dictionary """ mapping = self._mapping for key, val in mapping.items(): if key() is None: del mapping[key] val.remove(key) def join(self, a, args): """ Join given arguments into the same set. Accepts one or more arguments. """ mapping = self._mapping set_a = mapping.setdefault(ref(a), [ref(a)]) for arg in args: set_b = mapping.get(ref(arg)) if set_b is None: set_a.append(ref(arg)) mapping[ref(arg)] = set_a elif set_b is not set_a: if len(set_b) > len(set_a): set_a, set_b = set_b, set_a set_a.extend(set_b) for elem in set_b: mapping[elem] = set_a self.clean() def joined(self, a, b): """ Returns True if a* and b are members of the same set. """ self.clean() mapping = self._mapping try: return mapping[ref(a)] is mapping[ref(b)] except KeyError: return False def __iter__(self): """ Iterate over each of the disjoint sets as a list. The iterator is invalid if interleaved with calls to join(). """ self.clean() class Token: pass token = Token() # Mark each group as we come across if by appending a token, # and don't yield it twice for group in self._mapping.itervalues(): if not group[-1] is token: yield [x() for x in group] group.append(token) # Cleanup the tokens for group in self._mapping.itervalues(): if group[-1] is token: del group[-1] def get_siblings(self, a): """ Returns all of the items joined with a, including itself. """ self.clean() siblings = self._mapping.get(ref(a), [ref(a)]) return [x() for x in siblings] def simple_linear_interpolation(a, steps): steps = np.floor(steps) new_length = ((len(a) - 1) * steps) + 1 new_shape = list(a.shape) new_shape[0] = new_length result = np.zeros(new_shape, a.dtype) result[0] = a[0] a0 = a[0:-1] a1 = a[1: ] delta = ((a1 - a0) / steps) for i in range(1, int(steps)): result[i::steps] = delta * i + a0 result[steps::steps] = a1 return result def recursive_remove(path): if os.path.isdir(path): for fname in glob.glob(os.path.join(path, '')) + glob.glob(os.path.join(path, '.')): if os.path.isdir(fname): recursive_remove(fname) os.removedirs(fname) else: os.remove(fname) #os.removedirs(path) else: os.remove(path) def delete_masked_points(args): """ Find all masked and/or non-finite points in a set of arguments, and return the arguments with only the unmasked points remaining. Arguments can be in any of 5 categories: 1) 1-D masked arrays 2) 1-D ndarrays 3) ndarrays with more than one dimension 4) other non-string iterables 5) anything else The first argument must be in one of the first four categories; any argument with a length differing from that of the first argument (and hence anything in category 5) then will be passed through unchanged. Masks are obtained from all arguments of the correct length in categories 1, 2, and 4; a point is bad if masked in a masked array or if it is a nan or inf. No attempt is made to extract a mask from categories 2, 3, and 4 if :meth:`np.isfinite` does not yield a Boolean array. All input arguments that are not passed unchanged are returned as ndarrays after removing the points or rows corresponding to masks in any of the arguments. A vastly simpler version of this function was originally written as a helper for Axes.scatter(). """ if not len(args): return () if (is_string_like(args[0]) or not iterable(args[0])): raise ValueError("First argument must be a sequence") nrecs = len(args[0]) margs = [] seqlist = [False] len(args) for i, x in enumerate(args): if (not is_string_like(x)) and iterable(x) and len(x) == nrecs: seqlist[i] = True if ma.isMA(x): if x.ndim > 1: raise ValueError("Masked arrays must be 1-D") else: x = np.asarray(x) margs.append(x) masks = [] # list of masks that are True where good for i, x in enumerate(margs): if seqlist[i]: if x.ndim > 1: continue # Don't try to get nan locations unless 1-D. if ma.isMA(x): masks.append(~ma.getmaskarray(x)) # invert the mask xd = x.data else: xd = x try: mask = np.isfinite(xd) if isinstance(mask, np.ndarray): masks.append(mask) except: #Fixme: put in tuple of possible exceptions? pass if len(masks): mask = reduce(np.logical_and, masks) igood = mask.nonzero()[0] if len(igood) < nrecs: for i, x in enumerate(margs): if seqlist[i]: margs[i] = x.take(igood, axis=0) for i, x in enumerate(margs): if seqlist[i] and ma.isMA(x): margs[i] = x.filled() return margs def unmasked_index_ranges(mask, compressed = True): ''' Find index ranges where mask is False. mask will be flattened if it is not already 1-D. Returns Nx2 :class:`numpy.ndarray` with each row the start and stop indices for slices of the compressed :class:`numpy.ndarray` corresponding to each of N uninterrupted runs of unmasked values. If optional argument compressed is False, it returns the start and stop indices into the original :class:`numpy.ndarray`, not the compressed :class:`numpy.ndarray`. Returns None if there are no unmasked values. Example:: y = ma.array(np.arange(5), mask = [0,0,1,0,0]) ii = unmasked_index_ranges(ma.getmaskarray(y)) # returns array [[0,2,] [2,4,]] y.compressed()[ii[1,0]:ii[1,1]] # returns array [3,4,] ii = unmasked_index_ranges(ma.getmaskarray(y), compressed=False) # returns array [[0, 2], [3, 5]] y.filled()[ii[1,0]:ii[1,1]] # returns array [3,4,] Prior to the transforms refactoring, this was used to support masked arrays in Line2D. ''' mask = mask.reshape(mask.size) m = np.concatenate(((1,), mask, (1,))) indices = np.arange(len(mask) + 1) mdif = m[1:] - m[:-1] i0 = np.compress(mdif == -1, indices) i1 = np.compress(mdif == 1, indices) assert len(i0) == len(i1) if len(i1) == 0: return None # Maybe this should be np.zeros((0,2), dtype=int) if not compressed: return np.concatenate((i0[:, np.newaxis], i1[:, np.newaxis]), axis=1) seglengths = i1 - i0 breakpoints = np.cumsum(seglengths) ic0 = np.concatenate(((0,), breakpoints[:-1])) ic1 = breakpoints return np.concatenate((ic0[:, np.newaxis], ic1[:, np.newaxis]), axis=1) # a dict to cross-map linestyle arguments _linestyles = [('-', 'solid'), ('--', 'dashed'), ('-.', 'dashdot'), (':', 'dotted')] ls_mapper = dict(_linestyles) ls_mapper.update([(ls[1], ls[0]) for ls in _linestyles]) def less_simple_linear_interpolation( x, y, xi, extrap=False ): """ This function has been moved to matplotlib.mlab -- please import it from there """ # deprecated from cbook in 0.98.4 warnings.warn('less_simple_linear_interpolation has been moved to matplotlib.mlab -- please import it from there', DeprecationWarning) import matplotlib.mlab as mlab return mlab.less_simple_linear_interpolation( x, y, xi, extrap=extrap ) def isvector(X): """ This function has been moved to matplotlib.mlab -- please import it from there """ # deprecated from cbook in 0.98.4 warnings.warn('isvector has been moved to matplotlib.mlab -- please import it from there', DeprecationWarning) import matplotlib.mlab as mlab return mlab.isvector( x, y, xi, extrap=extrap ) def vector_lengths( X, P=2., axis=None ): """ This function has been moved to matplotlib.mlab -- please import it from there """ # deprecated from cbook in 0.98.4 warnings.warn('vector_lengths has been moved to matplotlib.mlab -- please import it from there', DeprecationWarning) import matplotlib.mlab as mlab return mlab.vector_lengths( X, P=2., axis=axis ) def distances_along_curve( X ): """ This function has been moved to matplotlib.mlab -- please import it from there """ # deprecated from cbook in 0.98.4 warnings.warn('distances_along_curve has been moved to matplotlib.mlab -- please import it from there', DeprecationWarning) import matplotlib.mlab as mlab return mlab.distances_along_curve( X ) def path_length(X): """ This function has been moved to matplotlib.mlab -- please import it from there """ # deprecated from cbook in 0.98.4 warnings.warn('path_length has been moved to matplotlib.mlab -- please import it from there', DeprecationWarning) import matplotlib.mlab as mlab return mlab.path_length(X) def is_closed_polygon(X): """ This function has been moved to matplotlib.mlab -- please import it from there """ # deprecated from cbook in 0.98.4 warnings.warn('is_closed_polygon has been moved to matplotlib.mlab -- please import it from there', DeprecationWarning) import matplotlib.mlab as mlab return mlab.is_closed_polygon(X) def quad2cubic(q0x, q0y, q1x, q1y, q2x, q2y): """ This function has been moved to matplotlib.mlab -- please import it from there """ # deprecated from cbook in 0.98.4 warnings.warn('quad2cubic has been moved to matplotlib.mlab -- please import it from there', DeprecationWarning) import matplotlib.mlab as mlab return mlab.quad2cubic(q0x, q0y, q1x, q1y, q2x, q2y) if __name__=='__main__': assert( allequal([1,1,1]) ) assert(not allequal([1,1,0]) ) assert( allequal([]) ) assert( allequal(('a', 'a'))) assert( not allequal(('a', 'b')))	42,525	Python	.py	1,144	29.562937	159	0.601435	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,247	axes.py	numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/axes.py	from __future__ import division, generators import math, sys, warnings, datetime, new import numpy as np from numpy import ma import matplotlib rcParams = matplotlib.rcParams import matplotlib.artist as martist import matplotlib.axis as maxis import matplotlib.cbook as cbook import matplotlib.collections as mcoll import matplotlib.colors as mcolors import matplotlib.contour as mcontour import matplotlib.dates as mdates import matplotlib.font_manager as font_manager import matplotlib.image as mimage import matplotlib.legend as mlegend import matplotlib.lines as mlines import matplotlib.mlab as mlab import matplotlib.patches as mpatches import matplotlib.quiver as mquiver import matplotlib.scale as mscale import matplotlib.table as mtable import matplotlib.text as mtext import matplotlib.ticker as mticker import matplotlib.transforms as mtransforms iterable = cbook.iterable is_string_like = cbook.is_string_like def _process_plot_format(fmt): """ Process a matlab(TM) style color/line style format string. Return a (linestyle, color) tuple as a result of the processing. Default values are ('-', 'b'). Example format strings include: * 'ko': black circles * '.b': blue dots * 'r--': red dashed lines .. seealso:: :func:`~matplotlib.Line2D.lineStyles` and :func:`~matplotlib.pyplot.colors`: for all possible styles and color format string. """ linestyle = None marker = None color = None # Is fmt just a colorspec? try: color = mcolors.colorConverter.to_rgb(fmt) return linestyle, marker, color # Yes. except ValueError: pass # No, not just a color. # handle the multi char special cases and strip them from the # string if fmt.find('--')>=0: linestyle = '--' fmt = fmt.replace('--', '') if fmt.find('-.')>=0: linestyle = '-.' fmt = fmt.replace('-.', '') if fmt.find(' ')>=0: linestyle = 'None' fmt = fmt.replace(' ', '') chars = [c for c in fmt] for c in chars: if c in mlines.lineStyles: if linestyle is not None: raise ValueError( 'Illegal format string "%s"; two linestyle symbols' % fmt) linestyle = c elif c in mlines.lineMarkers: if marker is not None: raise ValueError( 'Illegal format string "%s"; two marker symbols' % fmt) marker = c elif c in mcolors.colorConverter.colors: if color is not None: raise ValueError( 'Illegal format string "%s"; two color symbols' % fmt) color = c else: raise ValueError( 'Unrecognized character %c in format string' % c) if linestyle is None and marker is None: linestyle = rcParams['lines.linestyle'] if linestyle is None: linestyle = 'None' if marker is None: marker = 'None' return linestyle, marker, color def set_default_color_cycle(clist): """ Change the default cycle of colors that will be used by the plot command. This must be called before creating the :class:`Axes` to which it will apply; it will apply to all future axes. clist is a sequence of mpl color specifiers """ _process_plot_var_args.defaultColors = clist[:] rcParams['lines.color'] = clist[0] class _process_plot_var_args: """ Process variable length arguments to the plot command, so that plot commands like the following are supported:: plot(t, s) plot(t1, s1, t2, s2) plot(t1, s1, 'ko', t2, s2) plot(t1, s1, 'ko', t2, s2, 'r--', t3, e3) an arbitrary number of x, y, fmt are allowed """ defaultColors = ['b','g','r','c','m','y','k'] def __init__(self, axes, command='plot'): self.axes = axes self.command = command self._clear_color_cycle() def _clear_color_cycle(self): self.colors = _process_plot_var_args.defaultColors[:] # if the default line color is a color format string, move it up # in the que try: ind = self.colors.index(rcParams['lines.color']) except ValueError: self.firstColor = rcParams['lines.color'] else: self.colors[0], self.colors[ind] = self.colors[ind], self.colors[0] self.firstColor = self.colors[0] self.Ncolors = len(self.colors) self.count = 0 def set_color_cycle(self, clist): self.colors = clist[:] self.firstColor = self.colors[0] self.Ncolors = len(self.colors) self.count = 0 def _get_next_cycle_color(self): if self.count==0: color = self.firstColor else: color = self.colors[int(self.count % self.Ncolors)] self.count += 1 return color def __call__(self, args, kwargs): if self.axes.xaxis is not None and self.axes.yaxis is not None: xunits = kwargs.pop( 'xunits', self.axes.xaxis.units) yunits = kwargs.pop( 'yunits', self.axes.yaxis.units) if xunits!=self.axes.xaxis.units: self.axes.xaxis.set_units(xunits) if yunits!=self.axes.yaxis.units: self.axes.yaxis.set_units(yunits) ret = self._grab_next_args(args, kwargs) return ret def set_lineprops(self, line, kwargs): assert self.command == 'plot', 'set_lineprops only works with "plot"' for key, val in kwargs.items(): funcName = "set_%s"%key if not hasattr(line,funcName): raise TypeError, 'There is no line property "%s"'%key func = getattr(line,funcName) func(val) def set_patchprops(self, fill_poly, kwargs): assert self.command == 'fill', 'set_patchprops only works with "fill"' for key, val in kwargs.items(): funcName = "set_%s"%key if not hasattr(fill_poly,funcName): raise TypeError, 'There is no patch property "%s"'%key func = getattr(fill_poly,funcName) func(val) def _xy_from_y(self, y): if self.axes.yaxis is not None: b = self.axes.yaxis.update_units(y) if b: return np.arange(len(y)), y, False if not ma.isMaskedArray(y): y = np.asarray(y) if len(y.shape) == 1: y = y[:,np.newaxis] nr, nc = y.shape x = np.arange(nr) if len(x.shape) == 1: x = x[:,np.newaxis] return x,y, True def _xy_from_xy(self, x, y): if self.axes.xaxis is not None and self.axes.yaxis is not None: bx = self.axes.xaxis.update_units(x) by = self.axes.yaxis.update_units(y) # right now multicol is not supported if either x or y are # unit enabled but this can be fixed.. if bx or by: return x, y, False x = ma.asarray(x) y = ma.asarray(y) if len(x.shape) == 1: x = x[:,np.newaxis] if len(y.shape) == 1: y = y[:,np.newaxis] nrx, ncx = x.shape nry, ncy = y.shape assert nrx == nry, 'Dimensions of x and y are incompatible' if ncx == ncy: return x, y, True if ncx == 1: x = np.repeat(x, ncy, axis=1) if ncy == 1: y = np.repeat(y, ncx, axis=1) assert x.shape == y.shape, 'Dimensions of x and y are incompatible' return x, y, True def _plot_1_arg(self, y, kwargs): assert self.command == 'plot', 'fill needs at least 2 arguments' ret = [] x, y, multicol = self._xy_from_y(y) if multicol: for j in xrange(y.shape[1]): color = self._get_next_cycle_color() seg = mlines.Line2D(x, y[:,j], color = color, axes=self.axes, ) self.set_lineprops(seg, kwargs) ret.append(seg) else: color = self._get_next_cycle_color() seg = mlines.Line2D(x, y, color = color, axes=self.axes, ) self.set_lineprops(seg, kwargs) ret.append(seg) return ret def _plot_2_args(self, tup2, kwargs): ret = [] if is_string_like(tup2[1]): assert self.command == 'plot', ('fill needs at least 2 non-string ' 'arguments') y, fmt = tup2 x, y, multicol = self._xy_from_y(y) linestyle, marker, color = _process_plot_format(fmt) def makeline(x, y): _color = color if _color is None: _color = self._get_next_cycle_color() seg = mlines.Line2D(x, y, color=_color, linestyle=linestyle, marker=marker, axes=self.axes, ) self.set_lineprops(seg, kwargs) ret.append(seg) if multicol: for j in xrange(y.shape[1]): makeline(x[:,j], y[:,j]) else: makeline(x, y) return ret else: x, y = tup2 x, y, multicol = self._xy_from_xy(x, y) def makeline(x, y): color = self._get_next_cycle_color() seg = mlines.Line2D(x, y, color=color, axes=self.axes, ) self.set_lineprops(seg, kwargs) ret.append(seg) def makefill(x, y): x = self.axes.convert_xunits(x) y = self.axes.convert_yunits(y) facecolor = self._get_next_cycle_color() seg = mpatches.Polygon(np.hstack( (x[:,np.newaxis],y[:,np.newaxis])), facecolor = facecolor, fill=True, closed=closed ) self.set_patchprops(seg, kwargs) ret.append(seg) if self.command == 'plot': func = makeline else: closed = kwargs.get('closed', True) func = makefill if multicol: for j in xrange(y.shape[1]): func(x[:,j], y[:,j]) else: func(x, y) return ret def _plot_3_args(self, tup3, kwargs): ret = [] x, y, fmt = tup3 x, y, multicol = self._xy_from_xy(x, y) linestyle, marker, color = _process_plot_format(fmt) def makeline(x, y): _color = color if _color is None: _color = self._get_next_cycle_color() seg = mlines.Line2D(x, y, color=_color, linestyle=linestyle, marker=marker, axes=self.axes, ) self.set_lineprops(seg, kwargs) ret.append(seg) def makefill(x, y): facecolor = color x = self.axes.convert_xunits(x) y = self.axes.convert_yunits(y) seg = mpatches.Polygon(np.hstack( (x[:,np.newaxis],y[:,np.newaxis])), facecolor = facecolor, fill=True, closed=closed ) self.set_patchprops(seg, *kwargs) ret.append(seg) if self.command == 'plot': func = makeline else: closed = kwargs.get('closed', True) func = makefill if multicol: for j in xrange(y.shape[1]): func(x[:,j], y[:,j]) else: func(x, y) return ret def _grab_next_args(self, args, kwargs): remaining = args while 1: if len(remaining)==0: return if len(remaining)==1: for seg in self._plot_1_arg(remaining[0], kwargs): yield seg remaining = [] continue if len(remaining)==2: for seg in self._plot_2_args(remaining, kwargs): yield seg remaining = [] continue if len(remaining)==3: if not is_string_like(remaining[2]): raise ValueError, 'third arg must be a format string' for seg in self._plot_3_args(remaining, kwargs): yield seg remaining=[] continue if is_string_like(remaining[2]): for seg in self._plot_3_args(remaining[:3], kwargs): yield seg remaining=remaining[3:] else: for seg in self._plot_2_args(remaining[:2], kwargs): yield seg remaining=remaining[2:] class Axes(martist.Artist): """ The :class:`Axes` contains most of the figure elements: :class:`~matplotlib.axis.Axis`, :class:`~matplotlib.axis.Tick`, :class:`~matplotlib.lines.Line2D`, :class:`~matplotlib.text.Text`, :class:`~matplotlib.patches.Polygon`, etc., and sets the coordinate system. The :class:`Axes` instance supports callbacks through a callbacks attribute which is a :class:`~matplotlib.cbook.CallbackRegistry` instance. The events you can connect to are 'xlim_changed' and 'ylim_changed' and the callback will be called with func(ax) where ax is the :class:`Axes` instance. """ name = "rectilinear" _shared_x_axes = cbook.Grouper() _shared_y_axes = cbook.Grouper() def __str__(self): return "Axes(%g,%g;%gx%g)" % tuple(self._position.bounds) def __init__(self, fig, rect, axisbg = None, # defaults to rc axes.facecolor frameon = True, sharex=None, # use Axes instance's xaxis info sharey=None, # use Axes instance's yaxis info label='', *kwargs ): """ Build an :class:`Axes` instance in :class:`~matplotlib.figure.Figure` fig* with rect=[left, bottom, width, height] in :class:`~matplotlib.figure.Figure` coordinates Optional keyword arguments: ================ ========================================= Keyword Description ================ ========================================= adjustable [ 'box' \| 'datalim' ] alpha float: the alpha transparency anchor [ 'C', 'SW', 'S', 'SE', 'E', 'NE', 'N', 'NW', 'W' ] aspect [ 'auto' \| 'equal' \| aspect_ratio ] autoscale_on [ True \| False ] whether or not to autoscale the viewlim axis_bgcolor any matplotlib color, see :func:`~matplotlib.pyplot.colors` axisbelow draw the grids and ticks below the other artists cursor_props a (float, color) tuple figure a :class:`~matplotlib.figure.Figure` instance frame_on a boolean - draw the axes frame label the axes label navigate [ True \| False ] navigate_mode [ 'PAN' \| 'ZOOM' \| None ] the navigation toolbar button status position [left, bottom, width, height] in class:`~matplotlib.figure.Figure` coords sharex an class:`~matplotlib.axes.Axes` instance to share the x-axis with sharey an class:`~matplotlib.axes.Axes` instance to share the y-axis with title the title string visible [ True \| False ] whether the axes is visible xlabel the xlabel xlim (xmin, xmax) view limits xscale [%(scale)s] xticklabels sequence of strings xticks sequence of floats ylabel the ylabel strings ylim (ymin, ymax) view limits yscale [%(scale)s] yticklabels sequence of strings yticks sequence of floats ================ ========================================= """ % {'scale': ' \| '.join([repr(x) for x in mscale.get_scale_names()])} martist.Artist.__init__(self) if isinstance(rect, mtransforms.Bbox): self._position = rect else: self._position = mtransforms.Bbox.from_bounds(rect) self._originalPosition = self._position.frozen() self.set_axes(self) self.set_aspect('auto') self._adjustable = 'box' self.set_anchor('C') self._sharex = sharex self._sharey = sharey if sharex is not None: self._shared_x_axes.join(self, sharex) if sharex._adjustable == 'box': sharex._adjustable = 'datalim' #warnings.warn( # 'shared axes: "adjustable" is being changed to "datalim"') self._adjustable = 'datalim' if sharey is not None: self._shared_y_axes.join(self, sharey) if sharey._adjustable == 'box': sharey._adjustable = 'datalim' #warnings.warn( # 'shared axes: "adjustable" is being changed to "datalim"') self._adjustable = 'datalim' self.set_label(label) self.set_figure(fig) # this call may differ for non-sep axes, eg polar self._init_axis() if axisbg is None: axisbg = rcParams['axes.facecolor'] self._axisbg = axisbg self._frameon = frameon self._axisbelow = rcParams['axes.axisbelow'] self._hold = rcParams['axes.hold'] self._connected = {} # a dict from events to (id, func) self.cla() # funcs used to format x and y - fall back on major formatters self.fmt_xdata = None self.fmt_ydata = None self.set_cursor_props((1,'k')) # set the cursor properties for axes self._cachedRenderer = None self.set_navigate(True) self.set_navigate_mode(None) if len(kwargs): martist.setp(self, kwargs) if self.xaxis is not None: self._xcid = self.xaxis.callbacks.connect('units finalize', self.relim) if self.yaxis is not None: self._ycid = self.yaxis.callbacks.connect('units finalize', self.relim) def get_window_extent(self, args, *kwargs): ''' get the axes bounding box in display space; args* and kwargs are empty ''' return self.bbox def _init_axis(self): "move this out of __init__ because non-separable axes don't use it" self.xaxis = maxis.XAxis(self) self.yaxis = maxis.YAxis(self) self._update_transScale() def set_figure(self, fig): """ Set the class:`~matplotlib.axes.Axes` figure accepts a class:`~matplotlib.figure.Figure` instance """ martist.Artist.set_figure(self, fig) self.bbox = mtransforms.TransformedBbox(self._position, fig.transFigure) #these will be updated later as data is added self.dataLim = mtransforms.Bbox.unit() self.viewLim = mtransforms.Bbox.unit() self.transScale = mtransforms.TransformWrapper( mtransforms.IdentityTransform()) self._set_lim_and_transforms() def _set_lim_and_transforms(self): """ set the dataLim and viewLim :class:`~matplotlib.transforms.Bbox` attributes and the transScale, transData, transLimits and transAxes transformations. """ self.transAxes = mtransforms.BboxTransformTo(self.bbox) # Transforms the x and y axis separately by a scale factor # It is assumed that this part will have non-linear components self.transScale = mtransforms.TransformWrapper( mtransforms.IdentityTransform()) # An affine transformation on the data, generally to limit the # range of the axes self.transLimits = mtransforms.BboxTransformFrom( mtransforms.TransformedBbox(self.viewLim, self.transScale)) # The parentheses are important for efficiency here -- they # group the last two (which are usually affines) separately # from the first (which, with log-scaling can be non-affine). self.transData = self.transScale + (self.transLimits + self.transAxes) self._xaxis_transform = mtransforms.blended_transform_factory( self.axes.transData, self.axes.transAxes) self._yaxis_transform = mtransforms.blended_transform_factory( self.axes.transAxes, self.axes.transData) def get_xaxis_transform(self): """ Get the transformation used for drawing x-axis labels, ticks and gridlines. The x-direction is in data coordinates and the y-direction is in axis coordinates. .. note:: This transformation is primarily used by the :class:`~matplotlib.axis.Axis` class, and is meant to be overridden by new kinds of projections that may need to place axis elements in different locations. """ return self._xaxis_transform def get_xaxis_text1_transform(self, pad_points): """ Get the transformation used for drawing x-axis labels, which will add the given amount of padding (in points) between the axes and the label. The x-direction is in data coordinates and the y-direction is in axis coordinates. Returns a 3-tuple of the form:: (transform, valign, halign) where valign and halign are requested alignments for the text. .. note:: This transformation is primarily used by the :class:`~matplotlib.axis.Axis` class, and is meant to be overridden by new kinds of projections that may need to place axis elements in different locations. """ return (self._xaxis_transform + mtransforms.ScaledTranslation(0, -1 * pad_points / 72.0, self.figure.dpi_scale_trans), "top", "center") def get_xaxis_text2_transform(self, pad_points): """ Get the transformation used for drawing the secondary x-axis labels, which will add the given amount of padding (in points) between the axes and the label. The x-direction is in data coordinates and the y-direction is in axis coordinates. Returns a 3-tuple of the form:: (transform, valign, halign) where valign and halign are requested alignments for the text. .. note:: This transformation is primarily used by the :class:`~matplotlib.axis.Axis` class, and is meant to be overridden by new kinds of projections that may need to place axis elements in different locations. """ return (self._xaxis_transform + mtransforms.ScaledTranslation(0, pad_points / 72.0, self.figure.dpi_scale_trans), "bottom", "center") def get_yaxis_transform(self): """ Get the transformation used for drawing y-axis labels, ticks and gridlines. The x-direction is in axis coordinates and the y-direction is in data coordinates. .. note:: This transformation is primarily used by the :class:`~matplotlib.axis.Axis` class, and is meant to be overridden by new kinds of projections that may need to place axis elements in different locations. """ return self._yaxis_transform def get_yaxis_text1_transform(self, pad_points): """ Get the transformation used for drawing y-axis labels, which will add the given amount of padding (in points) between the axes and the label. The x-direction is in axis coordinates and the y-direction is in data coordinates. Returns a 3-tuple of the form:: (transform, valign, halign) where valign and halign are requested alignments for the text. .. note:: This transformation is primarily used by the :class:`~matplotlib.axis.Axis` class, and is meant to be overridden by new kinds of projections that may need to place axis elements in different locations. """ return (self._yaxis_transform + mtransforms.ScaledTranslation(-1 * pad_points / 72.0, 0, self.figure.dpi_scale_trans), "center", "right") def get_yaxis_text2_transform(self, pad_points): """ Get the transformation used for drawing the secondary y-axis labels, which will add the given amount of padding (in points) between the axes and the label. The x-direction is in axis coordinates and the y-direction is in data coordinates. Returns a 3-tuple of the form:: (transform, valign, halign) where valign and halign are requested alignments for the text. .. note:: This transformation is primarily used by the :class:`~matplotlib.axis.Axis` class, and is meant to be overridden by new kinds of projections that may need to place axis elements in different locations. """ return (self._yaxis_transform + mtransforms.ScaledTranslation(pad_points / 72.0, 0, self.figure.dpi_scale_trans), "center", "left") def _update_transScale(self): self.transScale.set( mtransforms.blended_transform_factory( self.xaxis.get_transform(), self.yaxis.get_transform())) if hasattr(self, "lines"): for line in self.lines: line._transformed_path.invalidate() def get_position(self, original=False): 'Return the a copy of the axes rectangle as a Bbox' if original: return self._originalPosition.frozen() else: return self._position.frozen() def set_position(self, pos, which='both'): """ Set the axes position with:: pos = [left, bottom, width, height] in relative 0,1 coords, or pos can be a :class:`~matplotlib.transforms.Bbox` There are two position variables: one which is ultimately used, but which may be modified by :meth:`apply_aspect`, and a second which is the starting point for :meth:`apply_aspect`. Optional keyword arguments: which ========== ==================== value description ========== ==================== 'active' to change the first 'original' to change the second 'both' to change both ========== ==================== """ if not isinstance(pos, mtransforms.BboxBase): pos = mtransforms.Bbox.from_bounds(pos) if which in ('both', 'active'): self._position.set(pos) if which in ('both', 'original'): self._originalPosition.set(pos) def reset_position(self): 'Make the original position the active position' pos = self.get_position(original=True) self.set_position(pos, which='active') def _set_artist_props(self, a): 'set the boilerplate props for artists added to axes' a.set_figure(self.figure) if not a.is_transform_set(): a.set_transform(self.transData) a.set_axes(self) def _gen_axes_patch(self): """ Returns the patch used to draw the background of the axes. It is also used as the clipping path for any data elements on the axes. In the standard axes, this is a rectangle, but in other projections it may not be. .. note:: Intended to be overridden by new projection types. """ return mpatches.Rectangle((0.0, 0.0), 1.0, 1.0) def cla(self): 'Clear the current axes' # Note: this is called by Axes.__init__() self.xaxis.cla() self.yaxis.cla() self.ignore_existing_data_limits = True self.callbacks = cbook.CallbackRegistry(('xlim_changed', 'ylim_changed')) if self._sharex is not None: # major and minor are class instances with # locator and formatter attributes self.xaxis.major = self._sharex.xaxis.major self.xaxis.minor = self._sharex.xaxis.minor x0, x1 = self._sharex.get_xlim() self.set_xlim(x0, x1, emit=False) self.xaxis.set_scale(self._sharex.xaxis.get_scale()) else: self.xaxis.set_scale('linear') if self._sharey is not None: self.yaxis.major = self._sharey.yaxis.major self.yaxis.minor = self._sharey.yaxis.minor y0, y1 = self._sharey.get_ylim() self.set_ylim(y0, y1, emit=False) self.yaxis.set_scale(self._sharey.yaxis.get_scale()) else: self.yaxis.set_scale('linear') self._autoscaleon = True self._update_transScale() # needed? self._get_lines = _process_plot_var_args(self) self._get_patches_for_fill = _process_plot_var_args(self, 'fill') self._gridOn = rcParams['axes.grid'] self.lines = [] self.patches = [] self.texts = [] self.tables = [] self.artists = [] self.images = [] self.legend_ = None self.collections = [] # collection.Collection instances self.grid(self._gridOn) props = font_manager.FontProperties(size=rcParams['axes.titlesize']) self.titleOffsetTrans = mtransforms.ScaledTranslation( 0.0, 5.0 / 72.0, self.figure.dpi_scale_trans) self.title = mtext.Text( x=0.5, y=1.0, text='', fontproperties=props, verticalalignment='bottom', horizontalalignment='center', ) self.title.set_transform(self.transAxes + self.titleOffsetTrans) self.title.set_clip_box(None) self._set_artist_props(self.title) # the patch draws the background of the axes. we want this to # be below the other artists; the axesPatch name is # deprecated. We use the frame to draw the edges so we are # setting the edgecolor to None self.patch = self.axesPatch = self._gen_axes_patch() self.patch.set_figure(self.figure) self.patch.set_facecolor(self._axisbg) self.patch.set_edgecolor('None') self.patch.set_linewidth(0) self.patch.set_transform(self.transAxes) # the frame draws the border around the axes and we want this # above. this is a place holder for a more sophisticated # artist that might just draw a left, bottom frame, or a # centered frame, etc the axesFrame name is deprecated self.frame = self.axesFrame = self._gen_axes_patch() self.frame.set_figure(self.figure) self.frame.set_facecolor('none') self.frame.set_edgecolor(rcParams['axes.edgecolor']) self.frame.set_linewidth(rcParams['axes.linewidth']) self.frame.set_transform(self.transAxes) self.frame.set_zorder(2.5) self.axison = True self.xaxis.set_clip_path(self.patch) self.yaxis.set_clip_path(self.patch) self._shared_x_axes.clean() self._shared_y_axes.clean() def clear(self): 'clear the axes' self.cla() def set_color_cycle(self, clist): """ Set the color cycle for any future plot commands on this Axes. clist is a list of mpl color specifiers. """ self._get_lines.set_color_cycle(clist) def ishold(self): 'return the HOLD status of the axes' return self._hold def hold(self, b=None): """ call signature:: hold(b=None) Set the hold state. If hold* is None (default), toggle the hold state. Else set the hold state to boolean value b. Examples: * toggle hold: >>> hold() * turn hold on: >>> hold(True) * turn hold off >>> hold(False) When hold is True, subsequent plot commands will be added to the current axes. When hold is False, the current axes and figure will be cleared on the next plot command """ if b is None: self._hold = not self._hold else: self._hold = b def get_aspect(self): return self._aspect def set_aspect(self, aspect, adjustable=None, anchor=None): """ aspect ======== ================================================ value description ======== ================================================ 'auto' automatic; fill position rectangle with data 'normal' same as 'auto'; deprecated 'equal' same scaling from data to plot units for x and y num a circle will be stretched such that the height is num times the width. aspect=1 is the same as aspect='equal'. ======== ================================================ adjustable ========= ============================ value description ========= ============================ 'box' change physical size of axes 'datalim' change xlim or ylim ========= ============================ anchor ===== ===================== value description ===== ===================== 'C' centered 'SW' lower left corner 'S' middle of bottom edge 'SE' lower right corner etc. ===== ===================== """ if aspect in ('normal', 'auto'): self._aspect = 'auto' elif aspect == 'equal': self._aspect = 'equal' else: self._aspect = float(aspect) # raise ValueError if necessary if adjustable is not None: self.set_adjustable(adjustable) if anchor is not None: self.set_anchor(anchor) def get_adjustable(self): return self._adjustable def set_adjustable(self, adjustable): """ ACCEPTS: [ 'box' \| 'datalim' ] """ if adjustable in ('box', 'datalim'): if self in self._shared_x_axes or self in self._shared_y_axes: if adjustable == 'box': raise ValueError( 'adjustable must be "datalim" for shared axes') self._adjustable = adjustable else: raise ValueError('argument must be "box", or "datalim"') def get_anchor(self): return self._anchor def set_anchor(self, anchor): """ anchor ===== ============ value description ===== ============ 'C' Center 'SW' bottom left 'S' bottom 'SE' bottom right 'E' right 'NE' top right 'N' top 'NW' top left 'W' left ===== ============ """ if anchor in mtransforms.Bbox.coefs.keys() or len(anchor) == 2: self._anchor = anchor else: raise ValueError('argument must be among %s' % ', '.join(mtransforms.BBox.coefs.keys())) def get_data_ratio(self): """ Returns the aspect ratio of the raw data. This method is intended to be overridden by new projection types. """ xmin,xmax = self.get_xbound() xsize = max(math.fabs(xmax-xmin), 1e-30) ymin,ymax = self.get_ybound() ysize = max(math.fabs(ymax-ymin), 1e-30) return ysize/xsize def apply_aspect(self, position=None): ''' Use :meth:`_aspect` and :meth:`_adjustable` to modify the axes box or the view limits. ''' if position is None: position = self.get_position(original=True) aspect = self.get_aspect() if aspect == 'auto': self.set_position( position , which='active') return if aspect == 'equal': A = 1 else: A = aspect #Ensure at drawing time that any Axes involved in axis-sharing # does not have its position changed. if self in self._shared_x_axes or self in self._shared_y_axes: if self._adjustable == 'box': self._adjustable = 'datalim' warnings.warn( 'shared axes: "adjustable" is being changed to "datalim"') figW,figH = self.get_figure().get_size_inches() fig_aspect = figH/figW if self._adjustable == 'box': box_aspect = A * self.get_data_ratio() pb = position.frozen() pb1 = pb.shrunk_to_aspect(box_aspect, pb, fig_aspect) self.set_position(pb1.anchored(self.get_anchor(), pb), 'active') return # reset active to original in case it had been changed # by prior use of 'box' self.set_position(position, which='active') xmin,xmax = self.get_xbound() xsize = max(math.fabs(xmax-xmin), 1e-30) ymin,ymax = self.get_ybound() ysize = max(math.fabs(ymax-ymin), 1e-30) l,b,w,h = position.bounds box_aspect = fig_aspect * (h/w) data_ratio = box_aspect / A y_expander = (data_ratioxsize/ysize - 1.0) #print 'y_expander', y_expander # If y_expander > 0, the dy/dx viewLim ratio needs to increase if abs(y_expander) < 0.005: #print 'good enough already' return dL = self.dataLim xr = 1.05 dL.width yr = 1.05 * dL.height xmarg = xsize - xr ymarg = ysize - yr Ysize = data_ratio * xsize Xsize = ysize / data_ratio Xmarg = Xsize - xr Ymarg = Ysize - yr xm = 0 # Setting these targets to, e.g., 0.05xr does not seem to help. ym = 0 #print 'xmin, xmax, ymin, ymax', xmin, xmax, ymin, ymax #print 'xsize, Xsize, ysize, Ysize', xsize, Xsize, ysize, Ysize changex = (self in self._shared_y_axes and self not in self._shared_x_axes) changey = (self in self._shared_x_axes and self not in self._shared_y_axes) if changex and changey: warnings.warn("adjustable='datalim' cannot work with shared " "x and y axes") return if changex: adjust_y = False else: #print 'xmarg, ymarg, Xmarg, Ymarg', xmarg, ymarg, Xmarg, Ymarg if xmarg > xm and ymarg > ym: adjy = ((Ymarg > 0 and y_expander < 0) or (Xmarg < 0 and y_expander > 0)) else: adjy = y_expander > 0 #print 'y_expander, adjy', y_expander, adjy adjust_y = changey or adjy #(Ymarg > xmarg) if adjust_y: yc = 0.5(ymin+ymax) y0 = yc - Ysize/2.0 y1 = yc + Ysize/2.0 self.set_ybound((y0, y1)) #print 'New y0, y1:', y0, y1 #print 'New ysize, ysize/xsize', y1-y0, (y1-y0)/xsize else: xc = 0.5(xmin+xmax) x0 = xc - Xsize/2.0 x1 = xc + Xsize/2.0 self.set_xbound((x0, x1)) #print 'New x0, x1:', x0, x1 #print 'New xsize, ysize/xsize', x1-x0, ysize/(x1-x0) def axis(self, v, *kwargs): ''' Convenience method for manipulating the x and y view limits and the aspect ratio of the plot. kwargs* are passed on to :meth:`set_xlim` and :meth:`set_ylim` ''' if len(v)==1 and is_string_like(v[0]): s = v[0].lower() if s=='on': self.set_axis_on() elif s=='off': self.set_axis_off() elif s in ('equal', 'tight', 'scaled', 'normal', 'auto', 'image'): self.set_autoscale_on(True) self.set_aspect('auto') self.autoscale_view() # self.apply_aspect() if s=='equal': self.set_aspect('equal', adjustable='datalim') elif s == 'scaled': self.set_aspect('equal', adjustable='box', anchor='C') self.set_autoscale_on(False) # Req. by Mark Bakker elif s=='tight': self.autoscale_view(tight=True) self.set_autoscale_on(False) elif s == 'image': self.autoscale_view(tight=True) self.set_autoscale_on(False) self.set_aspect('equal', adjustable='box', anchor='C') else: raise ValueError('Unrecognized string %s to axis; ' 'try on or off' % s) xmin, xmax = self.get_xlim() ymin, ymax = self.get_ylim() return xmin, xmax, ymin, ymax try: v[0] except IndexError: emit = kwargs.get('emit', True) xmin = kwargs.get('xmin', None) xmax = kwargs.get('xmax', None) xmin, xmax = self.set_xlim(xmin, xmax, emit) ymin = kwargs.get('ymin', None) ymax = kwargs.get('ymax', None) ymin, ymax = self.set_ylim(ymin, ymax, emit) return xmin, xmax, ymin, ymax v = v[0] if len(v) != 4: raise ValueError('v must contain [xmin xmax ymin ymax]') self.set_xlim([v[0], v[1]]) self.set_ylim([v[2], v[3]]) return v def get_child_artists(self): """ Return a list of artists the axes contains. .. deprecated:: 0.98 """ raise DeprecationWarning('Use get_children instead') def get_frame(self): 'Return the axes Rectangle frame' warnings.warn('use ax.patch instead', DeprecationWarning) return self.patch def get_legend(self): 'Return the legend.Legend instance, or None if no legend is defined' return self.legend_ def get_images(self): 'return a list of Axes images contained by the Axes' return cbook.silent_list('AxesImage', self.images) def get_lines(self): 'Return a list of lines contained by the Axes' return cbook.silent_list('Line2D', self.lines) def get_xaxis(self): 'Return the XAxis instance' return self.xaxis def get_xgridlines(self): 'Get the x grid lines as a list of Line2D instances' return cbook.silent_list('Line2D xgridline', self.xaxis.get_gridlines()) def get_xticklines(self): 'Get the xtick lines as a list of Line2D instances' return cbook.silent_list('Text xtickline', self.xaxis.get_ticklines()) def get_yaxis(self): 'Return the YAxis instance' return self.yaxis def get_ygridlines(self): 'Get the y grid lines as a list of Line2D instances' return cbook.silent_list('Line2D ygridline', self.yaxis.get_gridlines()) def get_yticklines(self): 'Get the ytick lines as a list of Line2D instances' return cbook.silent_list('Line2D ytickline', self.yaxis.get_ticklines()) #### Adding and tracking artists def has_data(self): '''Return True if any artists have been added to axes. This should not be used to determine whether the dataLim need to be updated, and may not actually be useful for anything. ''' return ( len(self.collections) + len(self.images) + len(self.lines) + len(self.patches))>0 def add_artist(self, a): 'Add any :class:`~matplotlib.artist.Artist` to the axes' a.set_axes(self) self.artists.append(a) self._set_artist_props(a) a.set_clip_path(self.patch) a._remove_method = lambda h: self.artists.remove(h) def add_collection(self, collection, autolim=True): ''' add a :class:`~matplotlib.collections.Collection` instance to the axes ''' label = collection.get_label() if not label: collection.set_label('collection%d'%len(self.collections)) self.collections.append(collection) self._set_artist_props(collection) collection.set_clip_path(self.patch) if autolim: if collection._paths and len(collection._paths): self.update_datalim(collection.get_datalim(self.transData)) collection._remove_method = lambda h: self.collections.remove(h) def add_line(self, line): ''' Add a :class:`~matplotlib.lines.Line2D` to the list of plot lines ''' self._set_artist_props(line) line.set_clip_path(self.patch) self._update_line_limits(line) if not line.get_label(): line.set_label('_line%d'%len(self.lines)) self.lines.append(line) line._remove_method = lambda h: self.lines.remove(h) def _update_line_limits(self, line): p = line.get_path() if p.vertices.size > 0: self.dataLim.update_from_path(p, self.ignore_existing_data_limits, updatex=line.x_isdata, updatey=line.y_isdata) self.ignore_existing_data_limits = False def add_patch(self, p): """ Add a :class:`~matplotlib.patches.Patch` p to the list of axes patches; the clipbox will be set to the Axes clipping box. If the transform is not set, it will be set to :attr:`transData`. """ self._set_artist_props(p) p.set_clip_path(self.patch) self._update_patch_limits(p) self.patches.append(p) p._remove_method = lambda h: self.patches.remove(h) def _update_patch_limits(self, patch): 'update the data limits for patch p' # hist can add zero height Rectangles, which is useful to keep # the bins, counts and patches lined up, but it throws off log # scaling. We'll ignore rects with zero height or width in # the auto-scaling if (isinstance(patch, mpatches.Rectangle) and (patch.get_width()==0 or patch.get_height()==0)): return vertices = patch.get_path().vertices if vertices.size > 0: xys = patch.get_patch_transform().transform(vertices) if patch.get_data_transform() != self.transData: transform = (patch.get_data_transform() + self.transData.inverted()) xys = transform.transform(xys) self.update_datalim(xys, updatex=patch.x_isdata, updatey=patch.y_isdata) def add_table(self, tab): ''' Add a :class:`~matplotlib.tables.Table` instance to the list of axes tables ''' self._set_artist_props(tab) self.tables.append(tab) tab.set_clip_path(self.patch) tab._remove_method = lambda h: self.tables.remove(h) def relim(self): 'recompute the data limits based on current artists' # Collections are deliberately not supported (yet); see # the TODO note in artists.py. self.dataLim.ignore(True) self.ignore_existing_data_limits = True for line in self.lines: self._update_line_limits(line) for p in self.patches: self._update_patch_limits(p) def update_datalim(self, xys, updatex=True, updatey=True): 'Update the data lim bbox with seq of xy tups or equiv. 2-D array' # if no data is set currently, the bbox will ignore its # limits and set the bound to be the bounds of the xydata. # Otherwise, it will compute the bounds of it's current data # and the data in xydata if iterable(xys) and not len(xys): return if not ma.isMaskedArray(xys): xys = np.asarray(xys) self.dataLim.update_from_data_xy(xys, self.ignore_existing_data_limits, updatex=updatex, updatey=updatey) self.ignore_existing_data_limits = False def update_datalim_numerix(self, x, y): 'Update the data lim bbox with seq of xy tups' # if no data is set currently, the bbox will ignore it's # limits and set the bound to be the bounds of the xydata. # Otherwise, it will compute the bounds of it's current data # and the data in xydata if iterable(x) and not len(x): return self.dataLim.update_from_data(x, y, self.ignore_existing_data_limits) self.ignore_existing_data_limits = False def update_datalim_bounds(self, bounds): ''' Update the datalim to include the given :class:`~matplotlib.transforms.Bbox` bounds ''' self.dataLim.set(mtransforms.Bbox.union([self.dataLim, bounds])) def _process_unit_info(self, xdata=None, ydata=None, kwargs=None): 'look for unit kwargs and update the axis instances as necessary' if self.xaxis is None or self.yaxis is None: return #print 'processing', self.get_geometry() if xdata is not None: # we only need to update if there is nothing set yet. if not self.xaxis.have_units(): self.xaxis.update_units(xdata) #print '\tset from xdata', self.xaxis.units if ydata is not None: # we only need to update if there is nothing set yet. if not self.yaxis.have_units(): self.yaxis.update_units(ydata) #print '\tset from ydata', self.yaxis.units # process kwargs 2nd since these will override default units if kwargs is not None: xunits = kwargs.pop( 'xunits', self.xaxis.units) if xunits!=self.xaxis.units: #print '\tkw setting xunits', xunits self.xaxis.set_units(xunits) # If the units being set imply a different converter, # we need to update. if xdata is not None: self.xaxis.update_units(xdata) yunits = kwargs.pop('yunits', self.yaxis.units) if yunits!=self.yaxis.units: #print '\tkw setting yunits', yunits self.yaxis.set_units(yunits) # If the units being set imply a different converter, # we need to update. if ydata is not None: self.yaxis.update_units(ydata) def in_axes(self, mouseevent): ''' return True if the given mouseevent (in display coords) is in the Axes ''' return self.patch.contains(mouseevent)[0] def get_autoscale_on(self): """ Get whether autoscaling is applied on plot commands """ return self._autoscaleon def set_autoscale_on(self, b): """ Set whether autoscaling is applied on plot commands accepts: [ True \| False ] """ self._autoscaleon = b def autoscale_view(self, tight=False, scalex=True, scaley=True): """ autoscale the view limits using the data limits. You can selectively autoscale only a single axis, eg, the xaxis by setting scaley to False. The autoscaling preserves any axis direction reversal that has already been done. """ # if image data only just use the datalim if not self._autoscaleon: return if scalex: xshared = self._shared_x_axes.get_siblings(self) dl = [ax.dataLim for ax in xshared] bb = mtransforms.BboxBase.union(dl) x0, x1 = bb.intervalx if scaley: yshared = self._shared_y_axes.get_siblings(self) dl = [ax.dataLim for ax in yshared] bb = mtransforms.BboxBase.union(dl) y0, y1 = bb.intervaly if (tight or (len(self.images)>0 and len(self.lines)==0 and len(self.patches)==0)): if scalex: self.set_xbound(x0, x1) if scaley: self.set_ybound(y0, y1) return if scalex: XL = self.xaxis.get_major_locator().view_limits(x0, x1) self.set_xbound(XL) if scaley: YL = self.yaxis.get_major_locator().view_limits(y0, y1) self.set_ybound(YL) #### Drawing def draw(self, renderer=None, inframe=False): "Draw everything (plot lines, axes, labels)" if renderer is None: renderer = self._cachedRenderer if renderer is None: raise RuntimeError('No renderer defined') if not self.get_visible(): return renderer.open_group('axes') self.apply_aspect() # the patch draws the background rectangle -- the frame below # will draw the edges if self.axison and self._frameon: self.patch.draw(renderer) artists = [] if len(self.images)<=1 or renderer.option_image_nocomposite(): for im in self.images: im.draw(renderer) else: # make a composite image blending alpha # list of (mimage.Image, ox, oy) mag = renderer.get_image_magnification() ims = [(im.make_image(mag),0,0) for im in self.images if im.get_visible()] l, b, r, t = self.bbox.extents width = mag((round(r) + 0.5) - (round(l) - 0.5)) height = mag((round(t) + 0.5) - (round(b) - 0.5)) im = mimage.from_images(height, width, ims) im.is_grayscale = False l, b, w, h = self.bbox.bounds # composite images need special args so they will not # respect z-order for now renderer.draw_image( round(l), round(b), im, self.bbox, self.patch.get_path(), self.patch.get_transform()) artists.extend(self.collections) artists.extend(self.patches) artists.extend(self.lines) artists.extend(self.texts) artists.extend(self.artists) if self.axison and not inframe: if self._axisbelow: self.xaxis.set_zorder(0.5) self.yaxis.set_zorder(0.5) else: self.xaxis.set_zorder(2.5) self.yaxis.set_zorder(2.5) artists.extend([self.xaxis, self.yaxis]) if not inframe: artists.append(self.title) artists.extend(self.tables) if self.legend_ is not None: artists.append(self.legend_) # the frame draws the edges around the axes patch -- we # decouple these so the patch can be in the background and the # frame in the foreground. if self.axison and self._frameon: artists.append(self.frame) dsu = [ (a.zorder, i, a) for i, a in enumerate(artists) if not a.get_animated() ] dsu.sort() for zorder, i, a in dsu: a.draw(renderer) renderer.close_group('axes') self._cachedRenderer = renderer def draw_artist(self, a): """ This method can only be used after an initial draw which caches the renderer. It is used to efficiently update Axes data (axis ticks, labels, etc are not updated) """ assert self._cachedRenderer is not None a.draw(self._cachedRenderer) def redraw_in_frame(self): """ This method can only be used after an initial draw which caches the renderer. It is used to efficiently update Axes data (axis ticks, labels, etc are not updated) """ assert self._cachedRenderer is not None self.draw(self._cachedRenderer, inframe=True) def get_renderer_cache(self): return self._cachedRenderer def __draw_animate(self): # ignore for now; broken if self._lastRenderer is None: raise RuntimeError('You must first call ax.draw()') dsu = [(a.zorder, a) for a in self.animated.keys()] dsu.sort() renderer = self._lastRenderer renderer.blit() for tmp, a in dsu: a.draw(renderer) #### Axes rectangle characteristics def get_frame_on(self): """ Get whether the axes rectangle patch is drawn """ return self._frameon def set_frame_on(self, b): """ Set whether the axes rectangle patch is drawn ACCEPTS: [ True \| False ] """ self._frameon = b def get_axisbelow(self): """ Get whether axis below is true or not """ return self._axisbelow def set_axisbelow(self, b): """ Set whether the axis ticks and gridlines are above or below most artists ACCEPTS: [ True \| False ] """ self._axisbelow = b def grid(self, b=None, kwargs): """ call signature:: grid(self, b=None, kwargs) Set the axes grids on or off; b is a boolean If b is None and ``len(kwargs)==0``, toggle the grid state. If kwargs are supplied, it is assumed that you want a grid and b is thus set to True kawrgs are used to set the grid line properties, eg:: ax.grid(color='r', linestyle='-', linewidth=2) Valid :class:`~matplotlib.lines.Line2D` kwargs are %(Line2D)s """ if len(kwargs): b = True self.xaxis.grid(b, kwargs) self.yaxis.grid(b, kwargs) grid.__doc__ = cbook.dedent(grid.__doc__) % martist.kwdocd def ticklabel_format(self, *kwargs): """ Convenience method for manipulating the ScalarFormatter used by default for linear axes. Optional keyword arguments: ============ ===================================== Keyword Description ============ ===================================== style* [ 'sci' (or 'scientific') \| 'plain' ] plain turns off scientific notation scilimits (m, n), pair of integers; if style is 'sci', scientific notation will be used for numbers outside the range 10`-m`:sup: to 10`n`:sup:. Use (0,0) to include all numbers. axis [ 'x' \| 'y' \| 'both' ] ============ ===================================== Only the major ticks are affected. If the method is called when the :class:`~matplotlib.ticker.ScalarFormatter` is not the :class:`~matplotlib.ticker.Formatter` being used, an :exc:`AttributeError` will be raised. """ style = kwargs.pop('style', '').lower() scilimits = kwargs.pop('scilimits', None) if scilimits is not None: try: m, n = scilimits m+n+1 # check that both are numbers except (ValueError, TypeError): raise ValueError("scilimits must be a sequence of 2 integers") axis = kwargs.pop('axis', 'both').lower() if style[:3] == 'sci': sb = True elif style in ['plain', 'comma']: sb = False if style == 'plain': cb = False else: cb = True raise NotImplementedError, "comma style remains to be added" elif style == '': sb = None else: raise ValueError, "%s is not a valid style value" try: if sb is not None: if axis == 'both' or axis == 'x': self.xaxis.major.formatter.set_scientific(sb) if axis == 'both' or axis == 'y': self.yaxis.major.formatter.set_scientific(sb) if scilimits is not None: if axis == 'both' or axis == 'x': self.xaxis.major.formatter.set_powerlimits(scilimits) if axis == 'both' or axis == 'y': self.yaxis.major.formatter.set_powerlimits(scilimits) except AttributeError: raise AttributeError( "This method only works with the ScalarFormatter.") def set_axis_off(self): """turn off the axis""" self.axison = False def set_axis_on(self): """turn on the axis""" self.axison = True def get_axis_bgcolor(self): 'Return the axis background color' return self._axisbg def set_axis_bgcolor(self, color): """ set the axes background color ACCEPTS: any matplotlib color - see :func:`~matplotlib.pyplot.colors` """ self._axisbg = color self.patch.set_facecolor(color) ### data limits, ticks, tick labels, and formatting def invert_xaxis(self): "Invert the x-axis." left, right = self.get_xlim() self.set_xlim(right, left) def xaxis_inverted(self): 'Returns True if the x-axis is inverted.' left, right = self.get_xlim() return right < left def get_xbound(self): """ Returns the x-axis numerical bounds where:: lowerBound < upperBound """ left, right = self.get_xlim() if left < right: return left, right else: return right, left def set_xbound(self, lower=None, upper=None): """ Set the lower and upper numerical bounds of the x-axis. This method will honor axes inversion regardless of parameter order. """ if upper is None and iterable(lower): lower,upper = lower old_lower,old_upper = self.get_xbound() if lower is None: lower = old_lower if upper is None: upper = old_upper if self.xaxis_inverted(): if lower < upper: self.set_xlim(upper, lower) else: self.set_xlim(lower, upper) else: if lower < upper: self.set_xlim(lower, upper) else: self.set_xlim(upper, lower) def get_xlim(self): """ Get the x-axis range [xmin, xmax] """ return tuple(self.viewLim.intervalx) def set_xlim(self, xmin=None, xmax=None, emit=True, *kwargs): """ call signature:: set_xlim(self, args, *kwargs) Set the limits for the xaxis Returns the current xlimits as a length 2 tuple: [xmin, xmax] Examples:: set_xlim((valmin, valmax)) set_xlim(valmin, valmax) set_xlim(xmin=1) # xmax unchanged set_xlim(xmax=1) # xmin unchanged Keyword arguments: ymin: scalar the min of the ylim ymax: scalar the max of the ylim emit: [ True \| False ] notify observers of lim change ACCEPTS: len(2) sequence of floats """ if xmax is None and iterable(xmin): xmin,xmax = xmin self._process_unit_info(xdata=(xmin, xmax)) if xmin is not None: xmin = self.convert_xunits(xmin) if xmax is not None: xmax = self.convert_xunits(xmax) old_xmin,old_xmax = self.get_xlim() if xmin is None: xmin = old_xmin if xmax is None: xmax = old_xmax xmin, xmax = mtransforms.nonsingular(xmin, xmax, increasing=False) xmin, xmax = self.xaxis.limit_range_for_scale(xmin, xmax) self.viewLim.intervalx = (xmin, xmax) if emit: self.callbacks.process('xlim_changed', self) # Call all of the other x-axes that are shared with this one for other in self._shared_x_axes.get_siblings(self): if other is not self: other.set_xlim(self.viewLim.intervalx, emit=False) if (other.figure != self.figure and other.figure.canvas is not None): other.figure.canvas.draw_idle() return xmin, xmax def get_xscale(self): 'return the xaxis scale string: %s' % ( ", ".join(mscale.get_scale_names())) return self.xaxis.get_scale() def set_xscale(self, value, kwargs): """ call signature:: set_xscale(value) Set the scaling of the x-axis: %(scale)s ACCEPTS: [%(scale)s] Different kwargs are accepted, depending on the scale: %(scale_docs)s """ self.xaxis.set_scale(value, kwargs) self.autoscale_view() self._update_transScale() set_xscale.__doc__ = cbook.dedent(set_xscale.__doc__) % { 'scale': ' \| '.join([repr(x) for x in mscale.get_scale_names()]), 'scale_docs': mscale.get_scale_docs().strip()} def get_xticks(self, minor=False): 'Return the x ticks as a list of locations' return self.xaxis.get_ticklocs(minor=minor) def set_xticks(self, ticks, minor=False): """ Set the x ticks with list of ticks* ACCEPTS: sequence of floats """ return self.xaxis.set_ticks(ticks, minor=minor) def get_xmajorticklabels(self): 'Get the xtick labels as a list of Text instances' return cbook.silent_list('Text xticklabel', self.xaxis.get_majorticklabels()) def get_xminorticklabels(self): 'Get the xtick labels as a list of Text instances' return cbook.silent_list('Text xticklabel', self.xaxis.get_minorticklabels()) def get_xticklabels(self, minor=False): 'Get the xtick labels as a list of Text instances' return cbook.silent_list('Text xticklabel', self.xaxis.get_ticklabels(minor=minor)) def set_xticklabels(self, labels, fontdict=None, minor=False, kwargs): """ call signature:: set_xticklabels(labels, fontdict=None, minor=False, kwargs) Set the xtick labels with list of strings labels. Return a list of axis text instances. kwargs set the :class:`~matplotlib.text.Text` properties. Valid properties are %(Text)s ACCEPTS: sequence of strings """ return self.xaxis.set_ticklabels(labels, fontdict, minor=minor, *kwargs) set_xticklabels.__doc__ = cbook.dedent( set_xticklabels.__doc__) % martist.kwdocd def invert_yaxis(self): "Invert the y-axis." left, right = self.get_ylim() self.set_ylim(right, left) def yaxis_inverted(self): 'Returns True if the y-axis is inverted.' left, right = self.get_ylim() return right < left def get_ybound(self): "Return y-axis numerical bounds in the form of lowerBound < upperBound" left, right = self.get_ylim() if left < right: return left, right else: return right, left def set_ybound(self, lower=None, upper=None): """Set the lower and upper numerical bounds of the y-axis. This method will honor axes inversion regardless of parameter order. """ if upper is None and iterable(lower): lower,upper = lower old_lower,old_upper = self.get_ybound() if lower is None: lower = old_lower if upper is None: upper = old_upper if self.yaxis_inverted(): if lower < upper: self.set_ylim(upper, lower) else: self.set_ylim(lower, upper) else: if lower < upper: self.set_ylim(lower, upper) else: self.set_ylim(upper, lower) def get_ylim(self): """ Get the y-axis range [ymin, ymax] """ return tuple(self.viewLim.intervaly) def set_ylim(self, ymin=None, ymax=None, emit=True, kwargs): """ call signature:: set_ylim(self, args, *kwargs): Set the limits for the yaxis; v = [ymin, ymax]:: set_ylim((valmin, valmax)) set_ylim(valmin, valmax) set_ylim(ymin=1) # ymax unchanged set_ylim(ymax=1) # ymin unchanged Keyword arguments: ymin: scalar the min of the ylim ymax: scalar the max of the ylim emit: [ True \| False ] notify observers of lim change Returns the current ylimits as a length 2 tuple ACCEPTS: len(2) sequence of floats """ if ymax is None and iterable(ymin): ymin,ymax = ymin if ymin is not None: ymin = self.convert_yunits(ymin) if ymax is not None: ymax = self.convert_yunits(ymax) old_ymin,old_ymax = self.get_ylim() if ymin is None: ymin = old_ymin if ymax is None: ymax = old_ymax ymin, ymax = mtransforms.nonsingular(ymin, ymax, increasing=False) ymin, ymax = self.yaxis.limit_range_for_scale(ymin, ymax) self.viewLim.intervaly = (ymin, ymax) if emit: self.callbacks.process('ylim_changed', self) # Call all of the other y-axes that are shared with this one for other in self._shared_y_axes.get_siblings(self): if other is not self: other.set_ylim(self.viewLim.intervaly, emit=False) if (other.figure != self.figure and other.figure.canvas is not None): other.figure.canvas.draw_idle() return ymin, ymax def get_yscale(self): 'return the xaxis scale string: %s' % ( ", ".join(mscale.get_scale_names())) return self.yaxis.get_scale() def set_yscale(self, value, kwargs): """ call signature:: set_yscale(value) Set the scaling of the y-axis: %(scale)s ACCEPTS: [%(scale)s] Different kwargs are accepted, depending on the scale: %(scale_docs)s """ self.yaxis.set_scale(value, kwargs) self.autoscale_view() self._update_transScale() set_yscale.__doc__ = cbook.dedent(set_yscale.__doc__) % { 'scale': ' \| '.join([repr(x) for x in mscale.get_scale_names()]), 'scale_docs': mscale.get_scale_docs().strip()} def get_yticks(self, minor=False): 'Return the y ticks as a list of locations' return self.yaxis.get_ticklocs(minor=minor) def set_yticks(self, ticks, minor=False): """ Set the y ticks with list of ticks* ACCEPTS: sequence of floats Keyword arguments: minor: [ False \| True ] Sets the minor ticks if True """ return self.yaxis.set_ticks(ticks, minor=minor) def get_ymajorticklabels(self): 'Get the xtick labels as a list of Text instances' return cbook.silent_list('Text yticklabel', self.yaxis.get_majorticklabels()) def get_yminorticklabels(self): 'Get the xtick labels as a list of Text instances' return cbook.silent_list('Text yticklabel', self.yaxis.get_minorticklabels()) def get_yticklabels(self, minor=False): 'Get the xtick labels as a list of Text instances' return cbook.silent_list('Text yticklabel', self.yaxis.get_ticklabels(minor=minor)) def set_yticklabels(self, labels, fontdict=None, minor=False, kwargs): """ call signature:: set_yticklabels(labels, fontdict=None, minor=False, kwargs) Set the ytick labels with list of strings labels. Return a list of :class:`~matplotlib.text.Text` instances. kwargs set :class:`~matplotlib.text.Text` properties for the labels. Valid properties are %(Text)s ACCEPTS: sequence of strings """ return self.yaxis.set_ticklabels(labels, fontdict, minor=minor, *kwargs) set_yticklabels.__doc__ = cbook.dedent( set_yticklabels.__doc__) % martist.kwdocd def xaxis_date(self, tz=None): """Sets up x-axis ticks and labels that treat the x data as dates. tz* is the time zone to use in labeling dates. Defaults to rc value. """ xmin, xmax = self.dataLim.intervalx if xmin==0.: # no data has been added - let's set the default datalim. # We should probably use a better proxy for the datalim # have been updated than the ignore setting dmax = today = datetime.date.today() dmin = today-datetime.timedelta(days=10) self._process_unit_info(xdata=(dmin, dmax)) dmin, dmax = self.convert_xunits([dmin, dmax]) self.viewLim.intervalx = dmin, dmax self.dataLim.intervalx = dmin, dmax locator = self.xaxis.get_major_locator() if not isinstance(locator, mdates.DateLocator): locator = mdates.AutoDateLocator(tz) self.xaxis.set_major_locator(locator) # the autolocator uses the viewlim to pick the right date # locator, but it may not have correct viewlim before an # autoscale. If the viewlim is still zero..1, set it to the # datalim and the autoscaler will update it on request if self.viewLim.intervalx[0]==0.: self.viewLim.intervalx = tuple(self.dataLim.intervalx) locator.refresh() formatter = self.xaxis.get_major_formatter() if not isinstance(formatter, mdates.DateFormatter): formatter = mdates.AutoDateFormatter(locator, tz) self.xaxis.set_major_formatter(formatter) def yaxis_date(self, tz=None): """Sets up y-axis ticks and labels that treat the y data as dates. tz is the time zone to use in labeling dates. Defaults to rc value. """ ymin, ymax = self.dataLim.intervaly if ymin==0.: # no data has been added - let's set the default datalim. # We should probably use a better proxy for the datalim # have been updated than the ignore setting dmax = today = datetime.date.today() dmin = today-datetime.timedelta(days=10) self._process_unit_info(ydata=(dmin, dmax)) dmin, dmax = self.convert_yunits([dmin, dmax]) self.viewLim.intervaly = dmin, dmax self.dataLim.intervaly = dmin, dmax locator = self.yaxis.get_major_locator() if not isinstance(locator, mdates.DateLocator): locator = mdates.AutoDateLocator(tz) self.yaxis.set_major_locator(locator) # the autolocator uses the viewlim to pick the right date # locator, but it may not have correct viewlim before an # autoscale. If the viewlim is still zero..1, set it to the # datalim and the autoscaler will update it on request if self.viewLim.intervaly[0]==0.: self.viewLim.intervaly = tuple(self.dataLim.intervaly) locator.refresh() formatter = self.xaxis.get_major_formatter() if not isinstance(formatter, mdates.DateFormatter): formatter = mdates.AutoDateFormatter(locator, tz) self.yaxis.set_major_formatter(formatter) def format_xdata(self, x): """ Return x string formatted. This function will use the attribute self.fmt_xdata if it is callable, else will fall back on the xaxis major formatter """ try: return self.fmt_xdata(x) except TypeError: func = self.xaxis.get_major_formatter().format_data_short val = func(x) return val def format_ydata(self, y): """ Return y string formatted. This function will use the :attr:`fmt_ydata` attribute if it is callable, else will fall back on the yaxis major formatter """ try: return self.fmt_ydata(y) except TypeError: func = self.yaxis.get_major_formatter().format_data_short val = func(y) return val def format_coord(self, x, y): 'return a format string formatting the x, y coord' if x is None: x = '???' if y is None: y = '???' xs = self.format_xdata(x) ys = self.format_ydata(y) return 'x=%s, y=%s'%(xs,ys) #### Interactive manipulation def can_zoom(self): """ Return True if this axes support the zoom box """ return True def get_navigate(self): """ Get whether the axes responds to navigation commands """ return self._navigate def set_navigate(self, b): """ Set whether the axes responds to navigation toolbar commands ACCEPTS: [ True \| False ] """ self._navigate = b def get_navigate_mode(self): """ Get the navigation toolbar button status: 'PAN', 'ZOOM', or None """ return self._navigate_mode def set_navigate_mode(self, b): """ Set the navigation toolbar button status; .. warning:: this is not a user-API function. """ self._navigate_mode = b def start_pan(self, x, y, button): """ Called when a pan operation has started. x, y are the mouse coordinates in display coords. button is the mouse button number: * 1: LEFT * 2: MIDDLE * 3: RIGHT .. note:: Intended to be overridden by new projection types. """ self._pan_start = cbook.Bunch( lim = self.viewLim.frozen(), trans = self.transData.frozen(), trans_inverse = self.transData.inverted().frozen(), bbox = self.bbox.frozen(), x = x, y = y ) def end_pan(self): """ Called when a pan operation completes (when the mouse button is up.) .. note:: Intended to be overridden by new projection types. """ del self._pan_start def drag_pan(self, button, key, x, y): """ Called when the mouse moves during a pan operation. button is the mouse button number: * 1: LEFT * 2: MIDDLE * 3: RIGHT key is a "shift" key x, y are the mouse coordinates in display coords. .. note:: Intended to be overridden by new projection types. """ def format_deltas(key, dx, dy): if key=='control': if(abs(dx)>abs(dy)): dy = dx else: dx = dy elif key=='x': dy = 0 elif key=='y': dx = 0 elif key=='shift': if 2abs(dx) < abs(dy): dx=0 elif 2abs(dy) < abs(dx): dy=0 elif(abs(dx)>abs(dy)): dy=dy/abs(dy)abs(dx) else: dx=dx/abs(dx)abs(dy) return (dx,dy) p = self._pan_start dx = x - p.x dy = y - p.y if dx == 0 and dy == 0: return if button == 1: dx, dy = format_deltas(key, dx, dy) result = p.bbox.translated(-dx, -dy) \ .transformed(p.trans_inverse) elif button == 3: try: dx = -dx / float(self.bbox.width) dy = -dy / float(self.bbox.height) dx, dy = format_deltas(key, dx, dy) if self.get_aspect() != 'auto': dx = 0.5 * (dx + dy) dy = dx alpha = np.power(10.0, (dx, dy)) start = p.trans_inverse.transform_point((p.x, p.y)) lim_points = p.lim.get_points() result = start + alpha * (lim_points - start) result = mtransforms.Bbox(result) except OverflowError: warnings.warn('Overflow while panning') return self.set_xlim(result.intervalx) self.set_ylim(result.intervaly) def get_cursor_props(self): """ return the cursor propertiess as a (linewidth, color) tuple, where linewidth is a float and color is an RGBA tuple """ return self._cursorProps def set_cursor_props(self, args): """ Set the cursor property as:: ax.set_cursor_props(linewidth, color) or:: ax.set_cursor_props((linewidth, color)) ACCEPTS: a (float, color) tuple """ if len(args)==1: lw, c = args[0] elif len(args)==2: lw, c = args else: raise ValueError('args must be a (linewidth, color) tuple') c =mcolors.colorConverter.to_rgba(c) self._cursorProps = lw, c def connect(self, s, func): """ Register observers to be notified when certain events occur. Register with callback functions with the following signatures. The function has the following signature:: func(ax) # where ax is the instance making the callback. The following events can be connected to: 'xlim_changed','ylim_changed' The connection id is is returned - you can use this with disconnect to disconnect from the axes event """ raise DeprecationWarning('use the callbacks CallbackRegistry instance ' 'instead') def disconnect(self, cid): 'disconnect from the Axes event.' raise DeprecationWarning('use the callbacks CallbackRegistry instance ' 'instead') def get_children(self): 'return a list of child artists' children = [] children.append(self.xaxis) children.append(self.yaxis) children.extend(self.lines) children.extend(self.patches) children.extend(self.texts) children.extend(self.tables) children.extend(self.artists) children.extend(self.images) if self.legend_ is not None: children.append(self.legend_) children.extend(self.collections) children.append(self.title) children.append(self.patch) children.append(self.frame) return children def contains(self,mouseevent): """Test whether the mouse event occured in the axes. Returns T/F, {} """ if callable(self._contains): return self._contains(self,mouseevent) return self.patch.contains(mouseevent) def pick(self, args): """ call signature:: pick(mouseevent) each child artist will fire a pick event if mouseevent is over the artist and the artist has picker set """ if len(args)>1: raise DeprecationWarning('New pick API implemented -- ' 'see API_CHANGES in the src distribution') martist.Artist.pick(self,args[0]) def __pick(self, x, y, trans=None, among=None): """ Return the artist under point that is closest to the x, y. If trans is None, x, and y are in window coords, (0,0 = lower left). Otherwise, trans is a :class:`~matplotlib.transforms.Transform` that specifies the coordinate system of x, y. The selection of artists from amongst which the pick function finds an artist can be narrowed using the optional keyword argument among. If provided, this should be either a sequence of permitted artists or a function taking an artist as its argument and returning a true value if and only if that artist can be selected. Note this algorithm calculates distance to the vertices of the polygon, so if you want to pick a patch, click on the edge! """ # MGDTODO: Needs updating if trans is not None: xywin = trans.transform_point((x,y)) else: xywin = x,y def dist_points(p1, p2): 'return the distance between two points' x1, y1 = p1 x2, y2 = p2 return math.sqrt((x1-x2)2+(y1-y2)2) def dist_x_y(p1, x, y): 'x and y are arrays; return the distance to the closest point' x1, y1 = p1 return min(np.sqrt((x-x1)2+(y-y1)2)) def dist(a): if isinstance(a, Text): bbox = a.get_window_extent() l,b,w,h = bbox.bounds verts = (l,b), (l,b+h), (l+w,b+h), (l+w, b) xt, yt = zip(verts) elif isinstance(a, Patch): path = a.get_path() tverts = a.get_transform().transform_path(path) xt, yt = zip(tverts) elif isinstance(a, mlines.Line2D): xdata = a.get_xdata(orig=False) ydata = a.get_ydata(orig=False) xt, yt = a.get_transform().numerix_x_y(xdata, ydata) return dist_x_y(xywin, np.asarray(xt), np.asarray(yt)) artists = self.lines + self.patches + self.texts if callable(among): artists = filter(test, artists) elif iterable(among): amongd = dict([(k,1) for k in among]) artists = [a for a in artists if a in amongd] elif among is None: pass else: raise ValueError('among must be callable or iterable') if not len(artists): return None ds = [ (dist(a),a) for a in artists] ds.sort() return ds[0][1] #### Labelling def get_title(self): """ Get the title text string. """ return self.title.get_text() def set_title(self, label, fontdict=None, kwargs): """ call signature:: set_title(label, fontdict=None, kwargs): Set the title for the axes. kwargs are Text properties: %(Text)s ACCEPTS: str .. seealso:: :meth:`text`: for information on how override and the optional args work """ default = { 'fontsize':rcParams['axes.titlesize'], 'verticalalignment' : 'bottom', 'horizontalalignment' : 'center' } self.title.set_text(label) self.title.update(default) if fontdict is not None: self.title.update(fontdict) self.title.update(kwargs) return self.title set_title.__doc__ = cbook.dedent(set_title.__doc__) % martist.kwdocd def get_xlabel(self): """ Get the xlabel text string. """ label = self.xaxis.get_label() return label.get_text() def set_xlabel(self, xlabel, fontdict=None, kwargs): """ call signature:: set_xlabel(xlabel, fontdict=None, kwargs) Set the label for the xaxis. Valid kwargs are Text properties: %(Text)s ACCEPTS: str .. seealso:: :meth:`text`: for information on how override and the optional args work """ label = self.xaxis.get_label() label.set_text(xlabel) if fontdict is not None: label.update(fontdict) label.update(kwargs) return label set_xlabel.__doc__ = cbook.dedent(set_xlabel.__doc__) % martist.kwdocd def get_ylabel(self): """ Get the ylabel text string. """ label = self.yaxis.get_label() return label.get_text() def set_ylabel(self, ylabel, fontdict=None, kwargs): """ call signature:: set_ylabel(ylabel, fontdict=None, kwargs) Set the label for the yaxis Valid kwargs are Text properties: %(Text)s ACCEPTS: str .. seealso:: :meth:`text`: for information on how override and the optional args work """ label = self.yaxis.get_label() label.set_text(ylabel) if fontdict is not None: label.update(fontdict) label.update(kwargs) return label set_ylabel.__doc__ = cbook.dedent(set_ylabel.__doc__) % martist.kwdocd def text(self, x, y, s, fontdict=None, withdash=False, kwargs): """ call signature:: text(x, y, s, fontdict=None, kwargs) Add text in string s to axis at location x, y, data coordinates. Keyword arguments: fontdict: A dictionary to override the default text properties. If fontdict is None, the defaults are determined by your rc parameters. withdash: [ False \| True ] Creates a :class:`~matplotlib.text.TextWithDash` instance instead of a :class:`~matplotlib.text.Text` instance. Individual keyword arguments can be used to override any given parameter:: text(x, y, s, fontsize=12) The default transform specifies that text is in data coords, alternatively, you can specify text in axis coords (0,0 is lower-left and 1,1 is upper-right). The example below places text in the center of the axes:: text(0.5, 0.5,'matplotlib', horizontalalignment='center', verticalalignment='center', transform = ax.transAxes) You can put a rectangular box around the text instance (eg. to set a background color) by using the keyword bbox. bbox is a dictionary of :class:`matplotlib.patches.Rectangle` properties. For example:: text(x, y, s, bbox=dict(facecolor='red', alpha=0.5)) Valid kwargs are :class:`matplotlib.text.Text` properties: %(Text)s """ default = { 'verticalalignment' : 'bottom', 'horizontalalignment' : 'left', #'verticalalignment' : 'top', 'transform' : self.transData, } # At some point if we feel confident that TextWithDash # is robust as a drop-in replacement for Text and that # the performance impact of the heavier-weight class # isn't too significant, it may make sense to eliminate # the withdash kwarg and simply delegate whether there's # a dash to TextWithDash and dashlength. if withdash: t = mtext.TextWithDash( x=x, y=y, text=s, ) else: t = mtext.Text( x=x, y=y, text=s, ) self._set_artist_props(t) t.update(default) if fontdict is not None: t.update(fontdict) t.update(kwargs) self.texts.append(t) t._remove_method = lambda h: self.texts.remove(h) #if t.get_clip_on(): t.set_clip_box(self.bbox) if 'clip_on' in kwargs: t.set_clip_box(self.bbox) return t text.__doc__ = cbook.dedent(text.__doc__) % martist.kwdocd def annotate(self, args, kwargs): """ call signature:: annotate(s, xy, xytext=None, xycoords='data', textcoords='data', arrowprops=None, kwargs) Keyword arguments: %(Annotation)s .. plot:: mpl_examples/pylab_examples/annotation_demo2.py """ a = mtext.Annotation(args, kwargs) a.set_transform(mtransforms.IdentityTransform()) self._set_artist_props(a) if kwargs.has_key('clip_on'): a.set_clip_path(self.patch) self.texts.append(a) return a annotate.__doc__ = cbook.dedent(annotate.__doc__) % martist.kwdocd #### Lines and spans def axhline(self, y=0, xmin=0, xmax=1, kwargs): """ call signature:: axhline(y=0, xmin=0, xmax=1, *kwargs) Axis Horizontal Line Draw a horizontal line at y* from xmin to xmax. With the default values of xmin = 0 and xmax = 1, this line will always span the horizontal extent of the axes, regardless of the xlim settings, even if you change them, eg. with the :meth:`set_xlim` command. That is, the horizontal extent is in axes coords: 0=left, 0.5=middle, 1.0=right but the y location is in data coordinates. Return value is the :class:`~matplotlib.lines.Line2D` instance. kwargs are the same as kwargs to plot, and can be used to control the line properties. Eg., * draw a thick red hline at y = 0 that spans the xrange >>> axhline(linewidth=4, color='r') * draw a default hline at y = 1 that spans the xrange >>> axhline(y=1) * draw a default hline at y = .5 that spans the the middle half of the xrange >>> axhline(y=.5, xmin=0.25, xmax=0.75) Valid kwargs are :class:`~matplotlib.lines.Line2D` properties: %(Line2D)s .. seealso:: :meth:`axhspan`: for example plot and source code """ ymin, ymax = self.get_ybound() # We need to strip away the units for comparison with # non-unitized bounds yy = self.convert_yunits( y ) scaley = (yy<ymin) or (yy>ymax) trans = mtransforms.blended_transform_factory( self.transAxes, self.transData) l = mlines.Line2D([xmin,xmax], [y,y], transform=trans, kwargs) l.x_isdata = False self.add_line(l) self.autoscale_view(scalex=False, scaley=scaley) return l axhline.__doc__ = cbook.dedent(axhline.__doc__) % martist.kwdocd def axvline(self, x=0, ymin=0, ymax=1, kwargs): """ call signature:: axvline(x=0, ymin=0, ymax=1, *kwargs) Axis Vertical Line Draw a vertical line at x* from ymin to ymax. With the default values of ymin = 0 and ymax = 1, this line will always span the vertical extent of the axes, regardless of the xlim settings, even if you change them, eg. with the :meth:`set_xlim` command. That is, the vertical extent is in axes coords: 0=bottom, 0.5=middle, 1.0=top but the x location is in data coordinates. Return value is the :class:`~matplotlib.lines.Line2D` instance. kwargs are the same as kwargs to plot, and can be used to control the line properties. Eg., * draw a thick red vline at x = 0 that spans the yrange >>> axvline(linewidth=4, color='r') * draw a default vline at x = 1 that spans the yrange >>> axvline(x=1) * draw a default vline at x = .5 that spans the the middle half of the yrange >>> axvline(x=.5, ymin=0.25, ymax=0.75) Valid kwargs are :class:`~matplotlib.lines.Line2D` properties: %(Line2D)s .. seealso:: :meth:`axhspan`: for example plot and source code """ xmin, xmax = self.get_xbound() # We need to strip away the units for comparison with # non-unitized bounds xx = self.convert_xunits( x ) scalex = (xx<xmin) or (xx>xmax) trans = mtransforms.blended_transform_factory( self.transData, self.transAxes) l = mlines.Line2D([x,x], [ymin,ymax] , transform=trans, kwargs) l.y_isdata = False self.add_line(l) self.autoscale_view(scalex=scalex, scaley=False) return l axvline.__doc__ = cbook.dedent(axvline.__doc__) % martist.kwdocd def axhspan(self, ymin, ymax, xmin=0, xmax=1, kwargs): """ call signature:: axhspan(ymin, ymax, xmin=0, xmax=1, *kwargs) Axis Horizontal Span. y* coords are in data units and x coords are in axes (relative 0-1) units. Draw a horizontal span (rectangle) from ymin to ymax. With the default values of xmin = 0 and xmax = 1, this always spans the xrange, regardless of the xlim settings, even if you change them, eg. with the :meth:`set_xlim` command. That is, the horizontal extent is in axes coords: 0=left, 0.5=middle, 1.0=right but the y location is in data coordinates. Return value is a :class:`matplotlib.patches.Polygon` instance. Examples: * draw a gray rectangle from y = 0.25-0.75 that spans the horizontal extent of the axes >>> axhspan(0.25, 0.75, facecolor='0.5', alpha=0.5) Valid kwargs are :class:`~matplotlib.patches.Polygon` properties: %(Polygon)s Example: .. plot:: mpl_examples/pylab_examples/axhspan_demo.py """ trans = mtransforms.blended_transform_factory( self.transAxes, self.transData) # process the unit information self._process_unit_info( [xmin, xmax], [ymin, ymax], kwargs=kwargs ) # first we need to strip away the units xmin, xmax = self.convert_xunits( [xmin, xmax] ) ymin, ymax = self.convert_yunits( [ymin, ymax] ) verts = (xmin, ymin), (xmin, ymax), (xmax, ymax), (xmax, ymin) p = mpatches.Polygon(verts, kwargs) p.set_transform(trans) p.x_isdata = False self.add_patch(p) return p axhspan.__doc__ = cbook.dedent(axhspan.__doc__) % martist.kwdocd def axvspan(self, xmin, xmax, ymin=0, ymax=1, kwargs): """ call signature:: axvspan(xmin, xmax, ymin=0, ymax=1, *kwargs) Axis Vertical Span. x* coords are in data units and y coords are in axes (relative 0-1) units. Draw a vertical span (rectangle) from xmin to xmax. With the default values of ymin = 0 and ymax = 1, this always spans the yrange, regardless of the ylim settings, even if you change them, eg. with the :meth:`set_ylim` command. That is, the vertical extent is in axes coords: 0=bottom, 0.5=middle, 1.0=top but the y location is in data coordinates. Return value is the :class:`matplotlib.patches.Polygon` instance. Examples: * draw a vertical green translucent rectangle from x=1.25 to 1.55 that spans the yrange of the axes >>> axvspan(1.25, 1.55, facecolor='g', alpha=0.5) Valid kwargs are :class:`~matplotlib.patches.Polygon` properties: %(Polygon)s .. seealso:: :meth:`axhspan`: for example plot and source code """ trans = mtransforms.blended_transform_factory( self.transData, self.transAxes) # process the unit information self._process_unit_info( [xmin, xmax], [ymin, ymax], kwargs=kwargs ) # first we need to strip away the units xmin, xmax = self.convert_xunits( [xmin, xmax] ) ymin, ymax = self.convert_yunits( [ymin, ymax] ) verts = [(xmin, ymin), (xmin, ymax), (xmax, ymax), (xmax, ymin)] p = mpatches.Polygon(verts, kwargs) p.set_transform(trans) p.y_isdata = False self.add_patch(p) return p axvspan.__doc__ = cbook.dedent(axvspan.__doc__) % martist.kwdocd def hlines(self, y, xmin, xmax, colors='k', linestyles='solid', label='', kwargs): """ call signature:: hlines(y, xmin, xmax, colors='k', linestyles='solid', *kwargs) Plot horizontal lines at each y* from xmin to xmax. Returns the :class:`~matplotlib.collections.LineCollection` that was added. Required arguments: y: a 1-D numpy array or iterable. xmin and xmax: can be scalars or ``len(x)`` numpy arrays. If they are scalars, then the respective values are constant, else the widths of the lines are determined by xmin and xmax. Optional keyword arguments: colors: a line collections color argument, either a single color or a ``len(y)`` list of colors linestyles: [ 'solid' \| 'dashed' \| 'dashdot' \| 'dotted' ] Example: .. plot:: mpl_examples/pylab_examples/hline_demo.py """ if kwargs.get('fmt') is not None: raise DeprecationWarning('hlines now uses a ' 'collections.LineCollection and not a ' 'list of Line2D to draw; see API_CHANGES') # We do the conversion first since not all unitized data is uniform y = self.convert_yunits( y ) xmin = self.convert_xunits( xmin ) xmax = self.convert_xunits( xmax ) if not iterable(y): y = [y] if not iterable(xmin): xmin = [xmin] if not iterable(xmax): xmax = [xmax] y = np.asarray(y) xmin = np.asarray(xmin) xmax = np.asarray(xmax) if len(xmin)==1: xmin = np.resize( xmin, y.shape ) if len(xmax)==1: xmax = np.resize( xmax, y.shape ) if len(xmin)!=len(y): raise ValueError, 'xmin and y are unequal sized sequences' if len(xmax)!=len(y): raise ValueError, 'xmax and y are unequal sized sequences' verts = [ ((thisxmin, thisy), (thisxmax, thisy)) for thisxmin, thisxmax, thisy in zip(xmin, xmax, y)] coll = mcoll.LineCollection(verts, colors=colors, linestyles=linestyles, label=label) self.add_collection(coll) coll.update(kwargs) minx = min(xmin.min(), xmax.min()) maxx = max(xmin.max(), xmax.max()) miny = y.min() maxy = y.max() corners = (minx, miny), (maxx, maxy) self.update_datalim(corners) self.autoscale_view() return coll hlines.__doc__ = cbook.dedent(hlines.__doc__) def vlines(self, x, ymin, ymax, colors='k', linestyles='solid', label='', *kwargs): """ call signature:: vlines(x, ymin, ymax, color='k', linestyles='solid') Plot vertical lines at each x* from ymin to ymax. ymin or ymax can be scalars or len(x) numpy arrays. If they are scalars, then the respective values are constant, else the heights of the lines are determined by ymin and ymax. colors a line collections color args, either a single color or a len(x) list of colors linestyles one of [ 'solid' \| 'dashed' \| 'dashdot' \| 'dotted' ] Returns the :class:`matplotlib.collections.LineCollection` that was added. kwargs are :class:`~matplotlib.collections.LineCollection` properties: %(LineCollection)s """ if kwargs.get('fmt') is not None: raise DeprecationWarning('vlines now uses a ' 'collections.LineCollection and not a ' 'list of Line2D to draw; see API_CHANGES') self._process_unit_info(xdata=x, ydata=ymin, kwargs=kwargs) # We do the conversion first since not all unitized data is uniform x = self.convert_xunits( x ) ymin = self.convert_yunits( ymin ) ymax = self.convert_yunits( ymax ) if not iterable(x): x = [x] if not iterable(ymin): ymin = [ymin] if not iterable(ymax): ymax = [ymax] x = np.asarray(x) ymin = np.asarray(ymin) ymax = np.asarray(ymax) if len(ymin)==1: ymin = np.resize( ymin, x.shape ) if len(ymax)==1: ymax = np.resize( ymax, x.shape ) if len(ymin)!=len(x): raise ValueError, 'ymin and x are unequal sized sequences' if len(ymax)!=len(x): raise ValueError, 'ymax and x are unequal sized sequences' Y = np.array([ymin, ymax]).T verts = [ ((thisx, thisymin), (thisx, thisymax)) for thisx, (thisymin, thisymax) in zip(x,Y)] #print 'creating line collection' coll = mcoll.LineCollection(verts, colors=colors, linestyles=linestyles, label=label) self.add_collection(coll) coll.update(kwargs) minx = min( x ) maxx = max( x ) miny = min( min(ymin), min(ymax) ) maxy = max( max(ymin), max(ymax) ) corners = (minx, miny), (maxx, maxy) self.update_datalim(corners) self.autoscale_view() return coll vlines.__doc__ = cbook.dedent(vlines.__doc__) % martist.kwdocd #### Basic plotting def plot(self, args, kwargs): """ Plot lines and/or markers to the :class:`~matplotlib.axes.Axes`. args* is a variable length argument, allowing for multiple x, y pairs with an optional format string. For example, each of the following is legal:: plot(x, y) # plot x and y using default line style and color plot(x, y, 'bo') # plot x and y using blue circle markers plot(y) # plot y using x as index array 0..N-1 plot(y, 'r+') # ditto, but with red plusses If x and/or y is 2-dimensional, then the corresponding columns will be plotted. An arbitrary number of x, y, fmt groups can be specified, as in:: a.plot(x1, y1, 'g^', x2, y2, 'g-') Return value is a list of lines that were added. The following format string characters are accepted to control the line style or marker: ================ =============================== character description ================ =============================== '-' solid line style '--' dashed line style '-.' dash-dot line style ':' dotted line style '.' point marker ',' pixel marker 'o' circle marker 'v' triangle_down marker '^' triangle_up marker '<' triangle_left marker '>' triangle_right marker '1' tri_down marker '2' tri_up marker '3' tri_left marker '4' tri_right marker 's' square marker 'p' pentagon marker '' star marker 'h' hexagon1 marker 'H' hexagon2 marker '+' plus marker 'x' x marker 'D' diamond marker 'd' thin_diamond marker '\|' vline marker '_' hline marker ================ =============================== The following color abbreviations are supported: ========== ======== character color ========== ======== 'b' blue 'g' green 'r' red 'c' cyan 'm' magenta 'y' yellow 'k' black 'w' white ========== ======== In addition, you can specify colors in many weird and wonderful ways, including full names (``'green'``), hex strings (``'#008000'``), RGB or RGBA tuples (``(0,1,0,1)``) or grayscale intensities as a string (``'0.8'``). Of these, the string specifications can be used in place of a ``fmt`` group, but the tuple forms can be used only as ``kwargs``. Line styles and colors are combined in a single format string, as in ``'bo'`` for blue circles. The kwargs* can be used to set line properties (any property that has a ``set_`` method). You can use this to set a line label (for auto legends), linewidth, anitialising, marker face color, etc. Here is an example:: plot([1,2,3], [1,2,3], 'go-', label='line 1', linewidth=2) plot([1,2,3], [1,4,9], 'rs', label='line 2') axis([0, 4, 0, 10]) legend() If you make multiple lines with one plot command, the kwargs apply to all those lines, e.g.:: plot(x1, y1, x2, y2, antialised=False) Neither line will be antialiased. You do not need to use format strings, which are just abbreviations. All of the line properties can be controlled by keyword arguments. For example, you can set the color, marker, linestyle, and markercolor with:: plot(x, y, color='green', linestyle='dashed', marker='o', markerfacecolor='blue', markersize=12). See :class:`~matplotlib.lines.Line2D` for details. The kwargs are :class:`~matplotlib.lines.Line2D` properties: %(Line2D)s kwargs scalex* and scaley, if defined, are passed on to :meth:`~matplotlib.axes.Axes.autoscale_view` to determine whether the x and y axes are autoscaled; the default is True. """ scalex = kwargs.pop( 'scalex', True) scaley = kwargs.pop( 'scaley', True) if not self._hold: self.cla() lines = [] for line in self._get_lines(args, kwargs): self.add_line(line) lines.append(line) self.autoscale_view(scalex=scalex, scaley=scaley) return lines plot.__doc__ = cbook.dedent(plot.__doc__) % martist.kwdocd def plot_date(self, x, y, fmt='bo', tz=None, xdate=True, ydate=False, kwargs): """ call signature:: plot_date(x, y, fmt='bo', tz=None, xdate=True, ydate=False, kwargs) Similar to the :func:`~matplotlib.pyplot.plot` command, except the x* or y (or both) data is considered to be dates, and the axis is labeled accordingly. x and/or y can be a sequence of dates represented as float days since 0001-01-01 UTC. Keyword arguments: fmt: string The plot format string. tz: [ None \| timezone string ] The time zone to use in labeling dates. If None, defaults to rc value. xdate: [ True \| False ] If True, the x-axis will be labeled with dates. ydate: [ False \| True ] If True, the y-axis will be labeled with dates. Note if you are using custom date tickers and formatters, it may be necessary to set the formatters/locators after the call to :meth:`plot_date` since :meth:`plot_date` will set the default tick locator to :class:`matplotlib.ticker.AutoDateLocator` (if the tick locator is not already set to a :class:`matplotlib.ticker.DateLocator` instance) and the default tick formatter to :class:`matplotlib.ticker.AutoDateFormatter` (if the tick formatter is not already set to a :class:`matplotlib.ticker.DateFormatter` instance). Valid kwargs are :class:`~matplotlib.lines.Line2D` properties: %(Line2D)s .. seealso:: :mod:`~matplotlib.dates`: for helper functions :func:`~matplotlib.dates.date2num`, :func:`~matplotlib.dates.num2date` and :func:`~matplotlib.dates.drange`: for help on creating the required floating point dates. """ if not self._hold: self.cla() ret = self.plot(x, y, fmt, *kwargs) if xdate: self.xaxis_date(tz) if ydate: self.yaxis_date(tz) self.autoscale_view() return ret plot_date.__doc__ = cbook.dedent(plot_date.__doc__) % martist.kwdocd def loglog(self, args, *kwargs): """ call signature:: loglog(args, *kwargs) Make a plot with log scaling on the x* and y axis. :func:`~matplotlib.pyplot.loglog` supports all the keyword arguments of :func:`~matplotlib.pyplot.plot` and :meth:`matplotlib.axes.Axes.set_xscale` / :meth:`matplotlib.axes.Axes.set_yscale`. Notable keyword arguments: basex/basey: scalar > 1 base of the x/y logarithm subsx/subsy: [ None \| sequence ] the location of the minor x/y ticks; None defaults to autosubs, which depend on the number of decades in the plot; see :meth:`matplotlib.axes.Axes.set_xscale` / :meth:`matplotlib.axes.Axes.set_yscale` for details The remaining valid kwargs are :class:`~matplotlib.lines.Line2D` properties: %(Line2D)s Example: .. plot:: mpl_examples/pylab_examples/log_demo.py """ if not self._hold: self.cla() dx = {'basex': kwargs.pop('basex', 10), 'subsx': kwargs.pop('subsx', None), } dy = {'basey': kwargs.pop('basey', 10), 'subsy': kwargs.pop('subsy', None), } self.set_xscale('log', dx) self.set_yscale('log', dy) b = self._hold self._hold = True # we've already processed the hold l = self.plot(args, kwargs) self._hold = b # restore the hold return l loglog.__doc__ = cbook.dedent(loglog.__doc__) % martist.kwdocd def semilogx(self, args, *kwargs): """ call signature:: semilogx(args, *kwargs) Make a plot with log scaling on the x* axis. :func:`semilogx` supports all the keyword arguments of :func:`~matplotlib.pyplot.plot` and :meth:`matplotlib.axes.Axes.set_xscale`. Notable keyword arguments: basex: scalar > 1 base of the x logarithm subsx: [ None \| sequence ] The location of the minor xticks; None defaults to autosubs, which depend on the number of decades in the plot; see :meth:`~matplotlib.axes.Axes.set_xscale` for details. The remaining valid kwargs are :class:`~matplotlib.lines.Line2D` properties: %(Line2D)s .. seealso:: :meth:`loglog`: For example code and figure """ if not self._hold: self.cla() d = {'basex': kwargs.pop( 'basex', 10), 'subsx': kwargs.pop( 'subsx', None), } self.set_xscale('log', *d) b = self._hold self._hold = True # we've already processed the hold l = self.plot(args, *kwargs) self._hold = b # restore the hold return l semilogx.__doc__ = cbook.dedent(semilogx.__doc__) % martist.kwdocd def semilogy(self, args, *kwargs): """ call signature:: semilogy(args, *kwargs) Make a plot with log scaling on the y* axis. :func:`semilogy` supports all the keyword arguments of :func:`~matplotlib.pylab.plot` and :meth:`matplotlib.axes.Axes.set_yscale`. Notable keyword arguments: basey: scalar > 1 Base of the y logarithm subsy: [ None \| sequence ] The location of the minor yticks; None defaults to autosubs, which depend on the number of decades in the plot; see :meth:`~matplotlib.axes.Axes.set_yscale` for details. The remaining valid kwargs are :class:`~matplotlib.lines.Line2D` properties: %(Line2D)s .. seealso:: :meth:`loglog`: For example code and figure """ if not self._hold: self.cla() d = {'basey': kwargs.pop('basey', 10), 'subsy': kwargs.pop('subsy', None), } self.set_yscale('log', *d) b = self._hold self._hold = True # we've already processed the hold l = self.plot(args, kwargs) self._hold = b # restore the hold return l semilogy.__doc__ = cbook.dedent(semilogy.__doc__) % martist.kwdocd def acorr(self, x, kwargs): """ call signature:: acorr(x, normed=False, detrend=mlab.detrend_none, usevlines=False, maxlags=None, *kwargs) Plot the autocorrelation of x. If normed* = True, normalize the data by the autocorrelation at 0-th lag. x is detrended by the detrend callable (default no normalization). Data are plotted as ``plot(lags, c, *kwargs)`` Return value is a tuple (lags, c, line) where: - lags* are a length 2maxlags+1 lag vector - c* is the 2maxlags+1 auto correlation vector - line* is a :class:`~matplotlib.lines.Line2D` instance returned by :meth:`plot` The default linestyle is None and the default marker is ``'o'``, though these can be overridden with keyword args. The cross correlation is performed with :func:`numpy.correlate` with mode = 2. If usevlines is True, :meth:`~matplotlib.axes.Axes.vlines` rather than :meth:`~matplotlib.axes.Axes.plot` is used to draw vertical lines from the origin to the acorr. Otherwise, the plot style is determined by the kwargs, which are :class:`~matplotlib.lines.Line2D` properties. maxlags is a positive integer detailing the number of lags to show. The default value of None will return all :math:`2 \mathrm{len}(x) - 1` lags. The return value is a tuple (lags, c, linecol, b) where - linecol is the :class:`~matplotlib.collections.LineCollection` - b is the x-axis. .. seealso:: :meth:`~matplotlib.axes.Axes.plot` or :meth:`~matplotlib.axes.Axes.vlines`: For documentation on valid kwargs. Example: :func:`~matplotlib.pyplot.xcorr` above, and :func:`~matplotlib.pyplot.acorr` below. Example: .. plot:: mpl_examples/pylab_examples/xcorr_demo.py """ return self.xcorr(x, x, kwargs) acorr.__doc__ = cbook.dedent(acorr.__doc__) % martist.kwdocd def xcorr(self, x, y, normed=False, detrend=mlab.detrend_none, usevlines=False, maxlags=None, kwargs): """ call signature:: xcorr(x, y, normed=False, detrend=mlab.detrend_none, usevlines=False, *kwargs): Plot the cross correlation between x* and y. If normed = True, normalize the data by the cross correlation at 0-th lag. x and y are detrended by the detrend callable (default no normalization). x and y must be equal length. Data are plotted as ``plot(lags, c, *kwargs)`` Return value is a tuple (lags, c, line) where: - lags* are a length ``2maxlags+1`` lag vector - c* is the ``2maxlags+1`` auto correlation vector - line* is a :class:`~matplotlib.lines.Line2D` instance returned by :func:`~matplotlib.pyplot.plot`. The default linestyle is None and the default marker is 'o', though these can be overridden with keyword args. The cross correlation is performed with :func:`numpy.correlate` with mode = 2. If usevlines is True: :func:`~matplotlib.pyplot.vlines` rather than :func:`~matplotlib.pyplot.plot` is used to draw vertical lines from the origin to the xcorr. Otherwise the plotstyle is determined by the kwargs, which are :class:`~matplotlib.lines.Line2D` properties. The return value is a tuple (lags, c, linecol, b) where linecol is the :class:`matplotlib.collections.LineCollection` instance and b is the x-axis. maxlags is a positive integer detailing the number of lags to show. The default value of None will return all ``(2len(x)-1)`` lags. Example:* :func:`~matplotlib.pyplot.xcorr` above, and :func:`~matplotlib.pyplot.acorr` below. Example: .. plot:: mpl_examples/pylab_examples/xcorr_demo.py """ Nx = len(x) if Nx!=len(y): raise ValueError('x and y must be equal length') x = detrend(np.asarray(x)) y = detrend(np.asarray(y)) c = np.correlate(x, y, mode=2) if normed: c/= np.sqrt(np.dot(x,x) * np.dot(y,y)) if maxlags is None: maxlags = Nx - 1 if maxlags >= Nx or maxlags < 1: raise ValueError('maglags must be None or strictly ' 'positive < %d'%Nx) lags = np.arange(-maxlags,maxlags+1) c = c[Nx-1-maxlags:Nx+maxlags] if usevlines: a = self.vlines(lags, [0], c, kwargs) b = self.axhline(kwargs) else: kwargs.setdefault('marker', 'o') kwargs.setdefault('linestyle', 'None') a, = self.plot(lags, c, *kwargs) b = None return lags, c, a, b xcorr.__doc__ = cbook.dedent(xcorr.__doc__) % martist.kwdocd def legend(self, args, *kwargs): """ call signature:: legend(args, *kwargs) Place a legend on the current axes at location loc. Labels are a sequence of strings and loc* can be a string or an integer specifying the legend location. To make a legend with existing lines:: legend() :meth:`legend` by itself will try and build a legend using the label property of the lines/patches/collections. You can set the label of a line by doing:: plot(x, y, label='my data') or:: line.set_label('my data'). If label is set to '_nolegend_', the item will not be shown in legend. To automatically generate the legend from labels:: legend( ('label1', 'label2', 'label3') ) To make a legend for a list of lines and labels:: legend( (line1, line2, line3), ('label1', 'label2', 'label3') ) To make a legend at a given location, using a location argument:: legend( ('label1', 'label2', 'label3'), loc='upper left') or:: legend( (line1, line2, line3), ('label1', 'label2', 'label3'), loc=2) The location codes are =============== ============= Location String Location Code =============== ============= 'best' 0 'upper right' 1 'upper left' 2 'lower left' 3 'lower right' 4 'right' 5 'center left' 6 'center right' 7 'lower center' 8 'upper center' 9 'center' 10 =============== ============= If none of these are locations are suitable, loc can be a 2-tuple giving x,y in axes coords, ie:: loc = 0, 1 # left top loc = 0.5, 0.5 # center Keyword arguments: isaxes: [ True \| False ] Indicates that this is an axes legend numpoints: integer The number of points in the legend line, default is 4 prop: [ None \| FontProperties ] A :class:`matplotlib.font_manager.FontProperties` instance, or None to use rc settings. pad: [ None \| scalar ] The fractional whitespace inside the legend border, between 0 and 1. If None, use rc settings. markerscale: [ None \| scalar ] The relative size of legend markers vs. original. If None, use rc settings. shadow: [ None \| False \| True ] If True, draw a shadow behind legend. If None, use rc settings. labelsep: [ None \| scalar ] The vertical space between the legend entries. If None, use rc settings. handlelen: [ None \| scalar ] The length of the legend lines. If None, use rc settings. handletextsep: [ None \| scalar ] The space between the legend line and legend text. If None, use rc settings. axespad: [ None \| scalar ] The border between the axes and legend edge. If None, use rc settings. Example: .. plot:: mpl_examples/api/legend_demo.py """ def get_handles(): handles = self.lines[:] handles.extend(self.patches) handles.extend([c for c in self.collections if isinstance(c, mcoll.LineCollection)]) handles.extend([c for c in self.collections if isinstance(c, mcoll.RegularPolyCollection)]) return handles if len(args)==0: handles = [] labels = [] for handle in get_handles(): label = handle.get_label() if (label is not None and label != '' and not label.startswith('_')): handles.append(handle) labels.append(label) if len(handles) == 0: warnings.warn("No labeled objects found. " "Use label='...' kwarg on individual plots.") return None elif len(args)==1: # LABELS labels = args[0] handles = [h for h, label in zip(get_handles(), labels)] elif len(args)==2: if is_string_like(args[1]) or isinstance(args[1], int): # LABELS, LOC labels, loc = args handles = [h for h, label in zip(get_handles(), labels)] kwargs['loc'] = loc else: # LINES, LABELS handles, labels = args elif len(args)==3: # LINES, LABELS, LOC handles, labels, loc = args kwargs['loc'] = loc else: raise TypeError('Invalid arguments to legend') handles = cbook.flatten(handles) self.legend_ = mlegend.Legend(self, handles, labels, *kwargs) return self.legend_ #### Specialized plotting def step(self, x, y, args, *kwargs): ''' call signature:: step(x, y, args, *kwargs) Make a step plot. Additional keyword args to :func:`step` are the same as those for :func:`~matplotlib.pyplot.plot`. x* and y must be 1-D sequences, and it is assumed, but not checked, that x is uniformly increasing. Keyword arguments: where: [ 'pre' \| 'post' \| 'mid' ] If 'pre', the interval from x[i] to x[i+1] has level y[i] If 'post', that interval has level y[i+1] If 'mid', the jumps in y occur half-way between the x-values. ''' where = kwargs.pop('where', 'pre') if where not in ('pre', 'post', 'mid'): raise ValueError("'where' argument to step must be " "'pre', 'post' or 'mid'") kwargs['linestyle'] = 'steps-' + where return self.plot(x, y, args, kwargs) def bar(self, left, height, width=0.8, bottom=None, color=None, edgecolor=None, linewidth=None, yerr=None, xerr=None, ecolor=None, capsize=3, align='edge', orientation='vertical', log=False, kwargs ): """ call signature:: bar(left, height, width=0.8, bottom=0, color=None, edgecolor=None, linewidth=None, yerr=None, xerr=None, ecolor=None, capsize=3, align='edge', orientation='vertical', log=False) Make a bar plot with rectangles bounded by: left, left* + width, bottom, bottom + height (left, right, bottom and top edges) left, height, width, and bottom can be either scalars or sequences Return value is a list of :class:`matplotlib.patches.Rectangle` instances. Required arguments: ======== =============================================== Argument Description ======== =============================================== left the x coordinates of the left sides of the bars height the heights of the bars ======== =============================================== Optional keyword arguments: =============== ========================================== Keyword Description =============== ========================================== width the widths of the bars bottom the y coordinates of the bottom edges of the bars color the colors of the bars edgecolor the colors of the bar edges linewidth width of bar edges; None means use default linewidth; 0 means don't draw edges. xerr if not None, will be used to generate errorbars on the bar chart yerr if not None, will be used to generate errorbars on the bar chart ecolor specifies the color of any errorbar capsize (default 3) determines the length in points of the error bar caps align 'edge' (default) \| 'center' orientation 'vertical' \| 'horizontal' log [False\|True] False (default) leaves the orientation axis as-is; True sets it to log scale =============== ========================================== For vertical bars, align = 'edge' aligns bars by their left edges in left, while align = 'center' interprets these values as the x coordinates of the bar centers. For horizontal bars, align = 'edge' aligns bars by their bottom edges in bottom, while align = 'center' interprets these values as the y coordinates of the bar centers. The optional arguments color, edgecolor, linewidth, xerr, and yerr can be either scalars or sequences of length equal to the number of bars. This enables you to use bar as the basis for stacked bar charts, or candlestick plots. Other optional kwargs: %(Rectangle)s Example: A stacked bar chart. .. plot:: mpl_examples/pylab_examples/bar_stacked.py """ if not self._hold: self.cla() label = kwargs.pop('label', '') def make_iterable(x): if not iterable(x): return [x] else: return x # make them safe to take len() of _left = left left = make_iterable(left) height = make_iterable(height) width = make_iterable(width) _bottom = bottom bottom = make_iterable(bottom) linewidth = make_iterable(linewidth) adjust_ylim = False adjust_xlim = False if orientation == 'vertical': self._process_unit_info(xdata=left, ydata=height, kwargs=kwargs) if log: self.set_yscale('log') # size width and bottom according to length of left if _bottom is None: if self.get_yscale() == 'log': bottom = [1e-100] adjust_ylim = True else: bottom = [0] nbars = len(left) if len(width) == 1: width = nbars if len(bottom) == 1: bottom = nbars elif orientation == 'horizontal': self._process_unit_info(xdata=width, ydata=bottom, kwargs=kwargs) if log: self.set_xscale('log') # size left and height according to length of bottom if _left is None: if self.get_xscale() == 'log': left = [1e-100] adjust_xlim = True else: left = [0] nbars = len(bottom) if len(left) == 1: left = nbars if len(height) == 1: height = nbars else: raise ValueError, 'invalid orientation: %s' % orientation # do not convert to array here as unit info is lost #left = np.asarray(left) #height = np.asarray(height) #width = np.asarray(width) #bottom = np.asarray(bottom) if len(linewidth) < nbars: linewidth = nbars if color is None: color = [None] nbars else: color = list(mcolors.colorConverter.to_rgba_array(color)) if len(color) < nbars: color = nbars if edgecolor is None: edgecolor = [None] nbars else: edgecolor = list(mcolors.colorConverter.to_rgba_array(edgecolor)) if len(edgecolor) < nbars: edgecolor = nbars if yerr is not None: if not iterable(yerr): yerr = [yerr]nbars if xerr is not None: if not iterable(xerr): xerr = [xerr]nbars # FIXME: convert the following to proper input validation # raising ValueError; don't use assert for this. assert len(left)==nbars, "argument 'left' must be %d or scalar" % nbars assert len(height)==nbars, ("argument 'height' must be %d or scalar" % nbars) assert len(width)==nbars, ("argument 'width' must be %d or scalar" % nbars) assert len(bottom)==nbars, ("argument 'bottom' must be %d or scalar" % nbars) if yerr is not None and len(yerr)!=nbars: raise ValueError( "bar() argument 'yerr' must be len(%s) or scalar" % nbars) if xerr is not None and len(xerr)!=nbars: raise ValueError( "bar() argument 'xerr' must be len(%s) or scalar" % nbars) patches = [] # lets do some conversions now since some types cannot be # subtracted uniformly if self.xaxis is not None: xconv = self.xaxis.converter if xconv is not None: units = self.xaxis.get_units() left = xconv.convert( left, units ) width = xconv.convert( width, units ) if self.yaxis is not None: yconv = self.yaxis.converter if yconv is not None : units = self.yaxis.get_units() bottom = yconv.convert( bottom, units ) height = yconv.convert( height, units ) if align == 'edge': pass elif align == 'center': if orientation == 'vertical': left = [left[i] - width[i]/2. for i in xrange(len(left))] elif orientation == 'horizontal': bottom = [bottom[i] - height[i]/2. for i in xrange(len(bottom))] else: raise ValueError, 'invalid alignment: %s' % align args = zip(left, bottom, width, height, color, edgecolor, linewidth) for l, b, w, h, c, e, lw in args: if h<0: b += h h = abs(h) if w<0: l += w w = abs(w) r = mpatches.Rectangle( xy=(l, b), width=w, height=h, facecolor=c, edgecolor=e, linewidth=lw, label=label ) label = '_nolegend_' r.update(kwargs) #print r.get_label(), label, 'label' in kwargs self.add_patch(r) patches.append(r) holdstate = self._hold self.hold(True) # ensure hold is on before plotting errorbars if xerr is not None or yerr is not None: if orientation == 'vertical': # using list comps rather than arrays to preserve unit info x = [l+0.5w for l, w in zip(left, width)] y = [b+h for b,h in zip(bottom, height)] elif orientation == 'horizontal': # using list comps rather than arrays to preserve unit info x = [l+w for l,w in zip(left, width)] y = [b+0.5h for b,h in zip(bottom, height)] self.errorbar( x, y, yerr=yerr, xerr=xerr, fmt=None, ecolor=ecolor, capsize=capsize) self.hold(holdstate) # restore previous hold state if adjust_xlim: xmin, xmax = self.dataLim.intervalx xmin = np.amin(width[width!=0]) # filter out the 0 width rects if xerr is not None: xmin = xmin - np.amax(xerr) xmin = max(xmin0.9, 1e-100) self.dataLim.intervalx = (xmin, xmax) if adjust_ylim: ymin, ymax = self.dataLim.intervaly ymin = np.amin(height[height!=0]) # filter out the 0 height rects if yerr is not None: ymin = ymin - np.amax(yerr) ymin = max(ymin0.9, 1e-100) self.dataLim.intervaly = (ymin, ymax) self.autoscale_view() return patches bar.__doc__ = cbook.dedent(bar.__doc__) % martist.kwdocd def barh(self, bottom, width, height=0.8, left=None, kwargs): """ call signature:: barh(bottom, width, height=0.8, left=0, kwargs) Make a horizontal bar plot with rectangles bounded by: left, left* + width, bottom, bottom + height (left, right, bottom and top edges) bottom, width, height, and left can be either scalars or sequences Return value is a list of :class:`matplotlib.patches.Rectangle` instances. Required arguments: ======== ====================================================== Argument Description ======== ====================================================== bottom the vertical positions of the bottom edges of the bars width the lengths of the bars ======== ====================================================== Optional keyword arguments: =============== ========================================== Keyword Description =============== ========================================== height the heights (thicknesses) of the bars left the x coordinates of the left edges of the bars color the colors of the bars edgecolor the colors of the bar edges linewidth width of bar edges; None means use default linewidth; 0 means don't draw edges. xerr if not None, will be used to generate errorbars on the bar chart yerr if not None, will be used to generate errorbars on the bar chart ecolor specifies the color of any errorbar capsize (default 3) determines the length in points of the error bar caps align 'edge' (default) \| 'center' log [False\|True] False (default) leaves the horizontal axis as-is; True sets it to log scale =============== ========================================== Setting align = 'edge' aligns bars by their bottom edges in bottom, while align = 'center' interprets these values as the y coordinates of the bar centers. The optional arguments color, edgecolor, linewidth, xerr, and yerr can be either scalars or sequences of length equal to the number of bars. This enables you to use barh as the basis for stacked bar charts, or candlestick plots. other optional kwargs: %(Rectangle)s """ patches = self.bar(left=left, height=height, width=width, bottom=bottom, orientation='horizontal', kwargs) return patches barh.__doc__ = cbook.dedent(barh.__doc__) % martist.kwdocd def broken_barh(self, xranges, yrange, kwargs): """ call signature:: broken_barh(self, xranges, yrange, *kwargs) A collection of horizontal bars spanning yrange* with a sequence of xranges. Required arguments: ========= ============================== Argument Description ========= ============================== xranges sequence of (xmin, xwidth) yrange sequence of (ymin, ywidth) ========= ============================== kwargs are :class:`matplotlib.collections.BrokenBarHCollection` properties: %(BrokenBarHCollection)s these can either be a single argument, ie:: facecolors = 'black' or a sequence of arguments for the various bars, ie:: facecolors = ('black', 'red', 'green') Example: .. plot:: mpl_examples/pylab_examples/broken_barh.py """ col = mcoll.BrokenBarHCollection(xranges, yrange, *kwargs) self.add_collection(col, autolim=True) self.autoscale_view() return col broken_barh.__doc__ = cbook.dedent(broken_barh.__doc__) % martist.kwdocd def stem(self, x, y, linefmt='b-', markerfmt='bo', basefmt='r-'): """ call signature:: stem(x, y, linefmt='b-', markerfmt='bo', basefmt='r-') A stem plot plots vertical lines (using linefmt) at each x* location from the baseline to y, and places a marker there using markerfmt. A horizontal line at 0 is is plotted using basefmt. Return value is a tuple (markerline, stemlines, baseline). .. seealso:: `this document`__ for details :file:`examples/pylab_examples/stem_plot.py`: for a demo __ http://www.mathworks.com/access/helpdesk/help/techdoc/ref/stem.html """ remember_hold=self._hold if not self._hold: self.cla() self.hold(True) markerline, = self.plot(x, y, markerfmt) stemlines = [] for thisx, thisy in zip(x, y): l, = self.plot([thisx,thisx], [0, thisy], linefmt) stemlines.append(l) baseline, = self.plot([np.amin(x), np.amax(x)], [0,0], basefmt) self.hold(remember_hold) return markerline, stemlines, baseline def pie(self, x, explode=None, labels=None, colors=None, autopct=None, pctdistance=0.6, shadow=False, labeldistance=1.1): r""" call signature:: pie(x, explode=None, labels=None, colors=('b', 'g', 'r', 'c', 'm', 'y', 'k', 'w'), autopct=None, pctdistance=0.6, labeldistance=1.1, shadow=False) Make a pie chart of array x. The fractional area of each wedge is given by x/sum(x). If sum(x) <= 1, then the values of x give the fractional area directly and the array will not be normalized. Keyword arguments: explode: [ None \| len(x) sequence ] If not None, is a len(x) array which specifies the fraction of the radius with which to offset each wedge. colors: [ None \| color sequence ] A sequence of matplotlib color args through which the pie chart will cycle. labels: [ None \| len(x) sequence of strings ] A sequence of strings providing the labels for each wedge autopct: [ None \| format string \| format function ] If not None, is a string or function used to label the wedges with their numeric value. The label will be placed inside the wedge. If it is a format string, the label will be ``fmt%pct``. If it is a function, it will be called. pctdistance: scalar The ratio between the center of each pie slice and the start of the text generated by autopct. Ignored if autopct is None; default is 0.6. labeldistance: scalar The radial distance at which the pie labels are drawn shadow: [ False \| True ] Draw a shadow beneath the pie. The pie chart will probably look best if the figure and axes are square. Eg.:: figure(figsize=(8,8)) ax = axes([0.1, 0.1, 0.8, 0.8]) Return value: If autopct is None, return the tuple (patches, texts): - patches is a sequence of :class:`matplotlib.patches.Wedge` instances - texts is a list of the label :class:`matplotlib.text.Text` instances. If autopct is not None, return the tuple (patches, texts, autotexts), where patches and texts are as above, and autotexts is a list of :class:`~matplotlib.text.Text` instances for the numeric labels. """ self.set_frame_on(False) x = np.asarray(x).astype(np.float32) sx = float(x.sum()) if sx>1: x = np.divide(x,sx) if labels is None: labels = ['']len(x) if explode is None: explode = [0]len(x) assert(len(x)==len(labels)) assert(len(x)==len(explode)) if colors is None: colors = ('b', 'g', 'r', 'c', 'm', 'y', 'k', 'w') center = 0,0 radius = 1 theta1 = 0 i = 0 texts = [] slices = [] autotexts = [] for frac, label, expl in cbook.safezip(x,labels, explode): x, y = center theta2 = theta1 + frac thetam = 2math.pi0.5(theta1+theta2) x += explmath.cos(thetam) y += explmath.sin(thetam) w = mpatches.Wedge((x,y), radius, 360.theta1, 360.theta2, facecolor=colors[i%len(colors)]) slices.append(w) self.add_patch(w) w.set_label(label) if shadow: # make sure to add a shadow after the call to # add_patch so the figure and transform props will be # set shad = mpatches.Shadow(w, -0.02, -0.02, #props={'facecolor':w.get_facecolor()} ) shad.set_zorder(0.9w.get_zorder()) self.add_patch(shad) xt = x + labeldistanceradiusmath.cos(thetam) yt = y + labeldistanceradiusmath.sin(thetam) label_alignment = xt > 0 and 'left' or 'right' t = self.text(xt, yt, label, size=rcParams['xtick.labelsize'], horizontalalignment=label_alignment, verticalalignment='center') texts.append(t) if autopct is not None: xt = x + pctdistanceradiusmath.cos(thetam) yt = y + pctdistanceradiusmath.sin(thetam) if is_string_like(autopct): s = autopct%(100.frac) elif callable(autopct): s = autopct(100.frac) else: raise TypeError( 'autopct must be callable or a format string') t = self.text(xt, yt, s, horizontalalignment='center', verticalalignment='center') autotexts.append(t) theta1 = theta2 i += 1 self.set_xlim((-1.25, 1.25)) self.set_ylim((-1.25, 1.25)) self.set_xticks([]) self.set_yticks([]) if autopct is None: return slices, texts else: return slices, texts, autotexts def errorbar(self, x, y, yerr=None, xerr=None, fmt='-', ecolor=None, elinewidth=None, capsize=3, barsabove=False, lolims=False, uplims=False, xlolims=False, xuplims=False, *kwargs): """ call signature:: errorbar(x, y, yerr=None, xerr=None, fmt='-', ecolor=None, elinewidth=None, capsize=3, barsabove=False, lolims=False, uplims=False, xlolims=False, xuplims=False) Plot x* versus y with error deltas in yerr and xerr. Vertical errorbars are plotted if yerr is not None. Horizontal errorbars are plotted if xerr is not None. x, y, xerr, and yerr can all be scalars, which plots a single error bar at x, y. Optional keyword arguments: xerr/yerr: [ scalar \| N, Nx1, Nx2 array-like ] If a scalar number, len(N) array-like object, or an Nx1 array-like object, errorbars are drawn +/- value. If a rank-1, Nx2 Numpy array, errorbars are drawn at -column1 and +column2 fmt: '-' The plot format symbol for y. If fmt is None, just plot the errorbars with no line symbols. This can be useful for creating a bar plot with errorbars. ecolor: [ None \| mpl color ] a matplotlib color arg which gives the color the errorbar lines; if None, use the marker color. elinewidth: scalar the linewidth of the errorbar lines. If None, use the linewidth. capsize: scalar the size of the error bar caps in points barsabove: [ True \| False ] if True, will plot the errorbars above the plot symbols. Default is below. lolims/uplims/xlolims/xuplims: [ False \| True ] These arguments can be used to indicate that a value gives only upper/lower limits. In that case a caret symbol is used to indicate this. lims-arguments may be of the same type as xerr and yerr. All other keyword arguments are passed on to the plot command for the markers, so you can add additional key=value pairs to control the errorbar markers. For example, this code makes big red squares with thick green edges:: x,y,yerr = rand(3,10) errorbar(x, y, yerr, marker='s', mfc='red', mec='green', ms=20, mew=4) where mfc, mec, ms and mew are aliases for the longer property names, markerfacecolor, markeredgecolor, markersize and markeredgewith. valid kwargs for the marker properties are %(Line2D)s Return value is a length 3 tuple. The first element is the :class:`~matplotlib.lines.Line2D` instance for the y symbol lines. The second element is a list of error bar cap lines, the third element is a list of :class:`~matplotlib.collections.LineCollection` instances for the horizontal and vertical error ranges. Example: .. plot:: mpl_examples/pylab_examples/errorbar_demo.py """ self._process_unit_info(xdata=x, ydata=y, kwargs=kwargs) if not self._hold: self.cla() # make sure all the args are iterable; use lists not arrays to # preserve units if not iterable(x): x = [x] if not iterable(y): y = [y] if xerr is not None: if not iterable(xerr): xerr = [xerr]len(x) if yerr is not None: if not iterable(yerr): yerr = [yerr]len(y) l0 = None if barsabove and fmt is not None: l0, = self.plot(x,y,fmt,*kwargs) barcols = [] caplines = [] lines_kw = {'label':'_nolegend_'} if elinewidth: lines_kw['linewidth'] = elinewidth else: if 'linewidth' in kwargs: lines_kw['linewidth']=kwargs['linewidth'] if 'lw' in kwargs: lines_kw['lw']=kwargs['lw'] if 'transform' in kwargs: lines_kw['transform'] = kwargs['transform'] # arrays fine here, they are booleans and hence not units if not iterable(lolims): lolims = np.asarray([lolims]len(x), bool) else: lolims = np.asarray(lolims, bool) if not iterable(uplims): uplims = np.array([uplims]len(x), bool) else: uplims = np.asarray(uplims, bool) if not iterable(xlolims): xlolims = np.array([xlolims]len(x), bool) else: xlolims = np.asarray(xlolims, bool) if not iterable(xuplims): xuplims = np.array([xuplims]len(x), bool) else: xuplims = np.asarray(xuplims, bool) def xywhere(xs, ys, mask): """ return xs[mask], ys[mask] where mask is True but xs and ys are not arrays """ assert len(xs)==len(ys) assert len(xs)==len(mask) xs = [thisx for thisx, b in zip(xs, mask) if b] ys = [thisy for thisy, b in zip(ys, mask) if b] return xs, ys if capsize > 0: plot_kw = { 'ms':2capsize, 'label':'_nolegend_'} if 'markeredgewidth' in kwargs: plot_kw['markeredgewidth']=kwargs['markeredgewidth'] if 'mew' in kwargs: plot_kw['mew']=kwargs['mew'] if 'transform' in kwargs: plot_kw['transform'] = kwargs['transform'] if xerr is not None: if (iterable(xerr) and len(xerr)==2 and iterable(xerr[0]) and iterable(xerr[1])): # using list comps rather than arrays to preserve units left = [thisx-thiserr for (thisx, thiserr) in cbook.safezip(x,xerr[0])] right = [thisx+thiserr for (thisx, thiserr) in cbook.safezip(x,xerr[1])] else: # using list comps rather than arrays to preserve units left = [thisx-thiserr for (thisx, thiserr) in cbook.safezip(x,xerr)] right = [thisx+thiserr for (thisx, thiserr) in cbook.safezip(x,xerr)] barcols.append( self.hlines(y, left, right, lines_kw ) ) if capsize > 0: if xlolims.any(): # can't use numpy logical indexing since left and # y are lists leftlo, ylo = xywhere(left, y, xlolims) caplines.extend( self.plot(leftlo, ylo, ls='None', marker=mlines.CARETLEFT, plot_kw) ) xlolims = ~xlolims leftlo, ylo = xywhere(left, y, xlolims) caplines.extend( self.plot(leftlo, ylo, 'k\|', plot_kw) ) else: caplines.extend( self.plot(left, y, 'k\|', plot_kw) ) if xuplims.any(): rightup, yup = xywhere(right, y, xuplims) caplines.extend( self.plot(rightup, yup, ls='None', marker=mlines.CARETRIGHT, plot_kw) ) xuplims = ~xuplims rightup, yup = xywhere(right, y, xuplims) caplines.extend( self.plot(rightup, yup, 'k\|', plot_kw) ) else: caplines.extend( self.plot(right, y, 'k\|', plot_kw) ) if yerr is not None: if (iterable(yerr) and len(yerr)==2 and iterable(yerr[0]) and iterable(yerr[1])): # using list comps rather than arrays to preserve units lower = [thisy-thiserr for (thisy, thiserr) in cbook.safezip(y,yerr[0])] upper = [thisy+thiserr for (thisy, thiserr) in cbook.safezip(y,yerr[1])] else: # using list comps rather than arrays to preserve units lower = [thisy-thiserr for (thisy, thiserr) in cbook.safezip(y,yerr)] upper = [thisy+thiserr for (thisy, thiserr) in cbook.safezip(y,yerr)] barcols.append( self.vlines(x, lower, upper, lines_kw) ) if capsize > 0: if lolims.any(): xlo, lowerlo = xywhere(x, lower, lolims) caplines.extend( self.plot(xlo, lowerlo, ls='None', marker=mlines.CARETDOWN, plot_kw) ) lolims = ~lolims xlo, lowerlo = xywhere(x, lower, lolims) caplines.extend( self.plot(xlo, lowerlo, 'k_', plot_kw) ) else: caplines.extend( self.plot(x, lower, 'k_', plot_kw) ) if uplims.any(): xup, upperup = xywhere(x, upper, uplims) caplines.extend( self.plot(xup, upperup, ls='None', marker=mlines.CARETUP, plot_kw) ) uplims = ~uplims xup, upperup = xywhere(x, upper, uplims) caplines.extend( self.plot(xup, upperup, 'k_', plot_kw) ) else: caplines.extend( self.plot(x, upper, 'k_', plot_kw) ) if not barsabove and fmt is not None: l0, = self.plot(x,y,fmt,*kwargs) if ecolor is None: if l0 is None: ecolor = self._get_lines._get_next_cycle_color() else: ecolor = l0.get_color() for l in barcols: l.set_color(ecolor) for l in caplines: l.set_color(ecolor) self.autoscale_view() return (l0, caplines, barcols) errorbar.__doc__ = cbook.dedent(errorbar.__doc__) % martist.kwdocd def boxplot(self, x, notch=0, sym='b+', vert=1, whis=1.5, positions=None, widths=None): """ call signature:: boxplot(x, notch=0, sym='+', vert=1, whis=1.5, positions=None, widths=None) Make a box and whisker plot for each column of x* or each vector in sequence x. The box extends from the lower to upper quartile values of the data, with a line at the median. The whiskers extend from the box to show the range of the data. Flier points are those past the end of the whiskers. - notch = 0 (default) produces a rectangular box plot. - notch = 1 will produce a notched box plot sym (default 'b+') is the default symbol for flier points. Enter an empty string ('') if you don't want to show fliers. - vert = 1 (default) makes the boxes vertical. - vert = 0 makes horizontal boxes. This seems goofy, but that's how Matlab did it. whis (default 1.5) defines the length of the whiskers as a function of the inner quartile range. They extend to the most extreme data point within ( ``whis(75%-25%)`` ) data range. positions* (default 1,2,...,n) sets the horizontal positions of the boxes. The ticks and limits are automatically set to match the positions. widths is either a scalar or a vector and sets the width of each box. The default is 0.5, or ``0.15(distance between extreme positions)`` if that is smaller. x* is an array or a sequence of vectors. Returns a dictionary mapping each component of the boxplot to a list of the :class:`matplotlib.lines.Line2D` instances created. Example: .. plot:: pyplots/boxplot_demo.py """ if not self._hold: self.cla() holdStatus = self._hold whiskers, caps, boxes, medians, fliers = [], [], [], [], [] # convert x to a list of vectors if hasattr(x, 'shape'): if len(x.shape) == 1: if hasattr(x[0], 'shape'): x = list(x) else: x = [x,] elif len(x.shape) == 2: nr, nc = x.shape if nr == 1: x = [x] elif nc == 1: x = [x.ravel()] else: x = [x[:,i] for i in xrange(nc)] else: raise ValueError, "input x can have no more than 2 dimensions" if not hasattr(x[0], '__len__'): x = [x] col = len(x) # get some plot info if positions is None: positions = range(1, col + 1) if widths is None: distance = max(positions) - min(positions) widths = min(0.15max(distance,1.0), 0.5) if isinstance(widths, float) or isinstance(widths, int): widths = np.ones((col,), float) widths # loop through columns, adding each to plot self.hold(True) for i,pos in enumerate(positions): d = np.ravel(x[i]) row = len(d) # get median and quartiles q1, med, q3 = mlab.prctile(d,[25,50,75]) # get high extreme iq = q3 - q1 hi_val = q3 + whisiq wisk_hi = np.compress( d <= hi_val , d ) if len(wisk_hi) == 0: wisk_hi = q3 else: wisk_hi = max(wisk_hi) # get low extreme lo_val = q1 - whisiq wisk_lo = np.compress( d >= lo_val, d ) if len(wisk_lo) == 0: wisk_lo = q1 else: wisk_lo = min(wisk_lo) # get fliers - if we are showing them flier_hi = [] flier_lo = [] flier_hi_x = [] flier_lo_x = [] if len(sym) != 0: flier_hi = np.compress( d > wisk_hi, d ) flier_lo = np.compress( d < wisk_lo, d ) flier_hi_x = np.ones(flier_hi.shape[0]) * pos flier_lo_x = np.ones(flier_lo.shape[0]) * pos # get x locations for fliers, whisker, whisker cap and box sides box_x_min = pos - widths[i] * 0.5 box_x_max = pos + widths[i] * 0.5 wisk_x = np.ones(2) * pos cap_x_min = pos - widths[i] * 0.25 cap_x_max = pos + widths[i] * 0.25 cap_x = [cap_x_min, cap_x_max] # get y location for median med_y = [med, med] # calculate 'regular' plot if notch == 0: # make our box vectors box_x = [box_x_min, box_x_max, box_x_max, box_x_min, box_x_min ] box_y = [q1, q1, q3, q3, q1 ] # make our median line vectors med_x = [box_x_min, box_x_max] # calculate 'notch' plot else: notch_max = med + 1.57iq/np.sqrt(row) notch_min = med - 1.57iq/np.sqrt(row) if notch_max > q3: notch_max = q3 if notch_min < q1: notch_min = q1 # make our notched box vectors box_x = [box_x_min, box_x_max, box_x_max, cap_x_max, box_x_max, box_x_max, box_x_min, box_x_min, cap_x_min, box_x_min, box_x_min ] box_y = [q1, q1, notch_min, med, notch_max, q3, q3, notch_max, med, notch_min, q1] # make our median line vectors med_x = [cap_x_min, cap_x_max] med_y = [med, med] # vertical or horizontal plot? if vert: def doplot(args): return self.plot(args) else: def doplot(args): shuffled = [] for i in xrange(0, len(args), 3): shuffled.extend([args[i+1], args[i], args[i+2]]) return self.plot(shuffled) whiskers.extend(doplot(wisk_x, [q1, wisk_lo], 'b--', wisk_x, [q3, wisk_hi], 'b--')) caps.extend(doplot(cap_x, [wisk_hi, wisk_hi], 'k-', cap_x, [wisk_lo, wisk_lo], 'k-')) boxes.extend(doplot(box_x, box_y, 'b-')) medians.extend(doplot(med_x, med_y, 'r-')) fliers.extend(doplot(flier_hi_x, flier_hi, sym, flier_lo_x, flier_lo, sym)) # fix our axes/ticks up a little if 1 == vert: setticks, setlim = self.set_xticks, self.set_xlim else: setticks, setlim = self.set_yticks, self.set_ylim newlimits = min(positions)-0.5, max(positions)+0.5 setlim(newlimits) setticks(positions) # reset hold status self.hold(holdStatus) return dict(whiskers=whiskers, caps=caps, boxes=boxes, medians=medians, fliers=fliers) def scatter(self, x, y, s=20, c='b', marker='o', cmap=None, norm=None, vmin=None, vmax=None, alpha=1.0, linewidths=None, faceted=True, verts=None, kwargs): """ call signatures:: scatter(x, y, s=20, c='b', marker='o', cmap=None, norm=None, vmin=None, vmax=None, alpha=1.0, linewidths=None, verts=None, kwargs) Make a scatter plot of x versus y, where x, y are 1-D sequences of the same length, N. Keyword arguments: s: size in points^2. It is a scalar or an array of the same length as x and y. c: a color. c can be a single color format string, or a sequence of color specifications of length N, or a sequence of N numbers to be mapped to colors using the cmap and norm specified via kwargs (see below). Note that c should not be a single numeric RGB or RGBA sequence because that is indistinguishable from an array of values to be colormapped. c can be a 2-D array in which the rows are RGB or RGBA, however. marker: can be one of: ===== ============== Value Description ===== ============== 's' square 'o' circle '^' triangle up '>' triangle right 'v' triangle down '<' triangle left 'd' diamond 'p' pentagram 'h' hexagon '8' octagon '+' plus 'x' cross ===== ============== The marker can also be a tuple (numsides, style, angle), which will create a custom, regular symbol. numsides: the number of sides style: the style of the regular symbol: ===== ============================================= Value Description ===== ============================================= 0 a regular polygon 1 a star-like symbol 2 an asterisk 3 a circle (numsides and angle is ignored) ===== ============================================= angle: the angle of rotation of the symbol Finally, marker can be (verts, 0): verts is a sequence of (x, y) vertices for a custom scatter symbol. Alternatively, use the kwarg combination marker = None, verts = verts. Any or all of x, y, s, and c may be masked arrays, in which case all masks will be combined and only unmasked points will be plotted. Other keyword arguments: the color mapping and normalization arguments will be used only if c is an array of floats. cmap: [ None \| Colormap ] A :class:`matplotlib.colors.Colormap` instance. If None, defaults to rc ``image.cmap``. cmap is only used if c is an array of floats. norm: [ None \| Normalize ] A :class:`matplotlib.colors.Normalize` instance is used to scale luminance data to 0, 1. If None, use the default :func:`normalize`. norm is only used if c is an array of floats. vmin/vmax: vmin and vmax are used in conjunction with norm to normalize luminance data. If either are None, the min and max of the color array C is used. Note if you pass a norm instance, your settings for vmin and vmax will be ignored. alpha: 0 <= scalar <= 1 The alpha value for the patches linewidths: [ None \| scalar \| sequence ] If None, defaults to (lines.linewidth,). Note that this is a tuple, and if you set the linewidths argument you must set it as a sequence of floats, as required by :class:`~matplotlib.collections.RegularPolyCollection`. Optional kwargs control the :class:`~matplotlib.collections.Collection` properties; in particular: edgecolors: 'none' to plot faces with no outlines facecolors: 'none' to plot unfilled outlines Here are the standard descriptions of all the :class:`~matplotlib.collections.Collection` kwargs: %(Collection)s A :class:`~matplotlib.collections.Collection` instance is returned. """ if not self._hold: self.cla() syms = { # a dict from symbol to (numsides, angle) 's' : (4,math.pi/4.0,0), # square 'o' : (20,3,0), # circle '^' : (3,0,0), # triangle up '>' : (3,math.pi/2.0,0), # triangle right 'v' : (3,math.pi,0), # triangle down '<' : (3,3math.pi/2.0,0), # triangle left 'd' : (4,0,0), # diamond 'p' : (5,0,0), # pentagram 'h' : (6,0,0), # hexagon '8' : (8,0,0), # octagon '+' : (4,0,2), # plus 'x' : (4,math.pi/4.0,2) # cross } self._process_unit_info(xdata=x, ydata=y, kwargs=kwargs) x, y, s, c = cbook.delete_masked_points(x, y, s, c) if is_string_like(c) or cbook.is_sequence_of_strings(c): colors = mcolors.colorConverter.to_rgba_array(c, alpha) else: sh = np.shape(c) # The inherent ambiguity is resolved in favor of color # mapping, not interpretation as rgb or rgba: if len(sh) == 1 and sh[0] == len(x): colors = None # use cmap, norm after collection is created else: colors = mcolors.colorConverter.to_rgba_array(c, alpha) if not iterable(s): scales = (s,) else: scales = s if faceted: edgecolors = None else: edgecolors = 'none' warnings.warn( '''replace "faceted=False" with "edgecolors='none'"''', DeprecationWarning) #2008/04/18 sym = None symstyle = 0 # to be API compatible if marker is None and not (verts is None): marker = (verts, 0) verts = None if is_string_like(marker): # the standard way to define symbols using a string character sym = syms.get(marker) if sym is None and verts is None: raise ValueError('Unknown marker symbol to scatter') numsides, rotation, symstyle = syms[marker] elif iterable(marker): # accept marker to be: # (numsides, style, [angle]) # or # (verts[], style, [angle]) if len(marker)<2 or len(marker)>3: raise ValueError('Cannot create markersymbol from marker') if cbook.is_numlike(marker[0]): # (numsides, style, [angle]) if len(marker)==2: numsides, rotation = marker[0], 0. elif len(marker)==3: numsides, rotation = marker[0], marker[2] sym = True if marker[1] in (1,2): symstyle = marker[1] else: verts = np.asarray(marker[0]) if sym is not None: if symstyle==0: collection = mcoll.RegularPolyCollection( numsides, rotation, scales, facecolors = colors, edgecolors = edgecolors, linewidths = linewidths, offsets = zip(x,y), transOffset = self.transData, ) elif symstyle==1: collection = mcoll.StarPolygonCollection( numsides, rotation, scales, facecolors = colors, edgecolors = edgecolors, linewidths = linewidths, offsets = zip(x,y), transOffset = self.transData, ) elif symstyle==2: collection = mcoll.AsteriskPolygonCollection( numsides, rotation, scales, facecolors = colors, edgecolors = edgecolors, linewidths = linewidths, offsets = zip(x,y), transOffset = self.transData, ) elif symstyle==3: collection = mcoll.CircleCollection( scales, facecolors = colors, edgecolors = edgecolors, linewidths = linewidths, offsets = zip(x,y), transOffset = self.transData, ) else: rescale = np.sqrt(max(verts[:,0]2+verts[:,1]2)) verts /= rescale collection = mcoll.PolyCollection( (verts,), scales, facecolors = colors, edgecolors = edgecolors, linewidths = linewidths, offsets = zip(x,y), transOffset = self.transData, ) collection.set_transform(mtransforms.IdentityTransform()) collection.set_alpha(alpha) collection.update(kwargs) if colors is None: if norm is not None: assert(isinstance(norm, mcolors.Normalize)) if cmap is not None: assert(isinstance(cmap, mcolors.Colormap)) collection.set_array(np.asarray(c)) collection.set_cmap(cmap) collection.set_norm(norm) if vmin is not None or vmax is not None: collection.set_clim(vmin, vmax) else: collection.autoscale_None() temp_x = x temp_y = y minx = np.amin(temp_x) maxx = np.amax(temp_x) miny = np.amin(temp_y) maxy = np.amax(temp_y) w = maxx-minx h = maxy-miny # the pad is a little hack to deal with the fact that we don't # want to transform all the symbols whose scales are in points # to data coords to get the exact bounding box for efficiency # reasons. It can be done right if this is deemed important padx, pady = 0.05w, 0.05h corners = (minx-padx, miny-pady), (maxx+padx, maxy+pady) self.update_datalim( corners) self.autoscale_view() # add the collection last self.add_collection(collection) return collection scatter.__doc__ = cbook.dedent(scatter.__doc__) % martist.kwdocd def hexbin(self, x, y, C = None, gridsize = 100, bins = None, xscale = 'linear', yscale = 'linear', cmap=None, norm=None, vmin=None, vmax=None, alpha=1.0, linewidths=None, edgecolors='none', reduce_C_function = np.mean, kwargs): """ call signature:: hexbin(x, y, C = None, gridsize = 100, bins = None, xscale = 'linear', yscale = 'linear', cmap=None, norm=None, vmin=None, vmax=None, alpha=1.0, linewidths=None, edgecolors='none' reduce_C_function = np.mean, kwargs) Make a hexagonal binning plot of x* versus y, where x, y are 1-D sequences of the same length, N. If C is None (the default), this is a histogram of the number of occurences of the observations at (x[i],y[i]). If C is specified, it specifies values at the coordinate (x[i],y[i]). These values are accumulated for each hexagonal bin and then reduced according to reduce_C_function, which defaults to numpy's mean function (np.mean). (If C is specified, it must also be a 1-D sequence of the same length as x and y.) x, y and/or C may be masked arrays, in which case only unmasked points will be plotted. Optional keyword arguments: gridsize: [ 100 \| integer ] The number of hexagons in the x-direction, default is 100. The corresponding number of hexagons in the y-direction is chosen such that the hexagons are approximately regular. Alternatively, gridsize can be a tuple with two elements specifying the number of hexagons in the x-direction and the y-direction. bins: [ None \| 'log' \| integer \| sequence ] If None, no binning is applied; the color of each hexagon directly corresponds to its count value. If 'log', use a logarithmic scale for the color map. Internally, :math:`log_{10}(i+1)` is used to determine the hexagon color. If an integer, divide the counts in the specified number of bins, and color the hexagons accordingly. If a sequence of values, the values of the lower bound of the bins to be used. xscale: [ 'linear' \| 'log' ] Use a linear or log10 scale on the horizontal axis. scale: [ 'linear' \| 'log' ] Use a linear or log10 scale on the vertical axis. Other keyword arguments controlling color mapping and normalization arguments: cmap: [ None \| Colormap ] a :class:`matplotlib.cm.Colormap` instance. If None, defaults to rc ``image.cmap``. norm: [ None \| Normalize ] :class:`matplotlib.colors.Normalize` instance is used to scale luminance data to 0,1. vmin/vmax: scalar vmin and vmax are used in conjunction with norm to normalize luminance data. If either are None, the min and max of the color array C is used. Note if you pass a norm instance, your settings for vmin and vmax will be ignored. alpha: scalar the alpha value for the patches linewidths: [ None \| scalar ] If None, defaults to rc lines.linewidth. Note that this is a tuple, and if you set the linewidths argument you must set it as a sequence of floats, as required by :class:`~matplotlib.collections.RegularPolyCollection`. Other keyword arguments controlling the Collection properties: edgecolors: [ None \| mpl color \| color sequence ] If 'none', draws the edges in the same color as the fill color. This is the default, as it avoids unsightly unpainted pixels between the hexagons. If None, draws the outlines in the default color. If a matplotlib color arg or sequence of rgba tuples, draws the outlines in the specified color. Here are the standard descriptions of all the :class:`~matplotlib.collections.Collection` kwargs: %(Collection)s The return value is a :class:`~matplotlib.collections.PolyCollection` instance; use :meth:`~matplotlib.collection.PolyCollection.get_array` on this :class:`~matplotlib.collections.PolyCollection` to get the counts in each hexagon. Example: .. plot:: mpl_examples/pylab_examples/hexbin_demo.py """ if not self._hold: self.cla() self._process_unit_info(xdata=x, ydata=y, kwargs=kwargs) x, y, C = cbook.delete_masked_points(x, y, C) # Set the size of the hexagon grid if iterable(gridsize): nx, ny = gridsize else: nx = gridsize ny = int(nx/math.sqrt(3)) # Count the number of data in each hexagon x = np.array(x, float) y = np.array(y, float) if xscale=='log': x = np.log10(x) if yscale=='log': y = np.log10(y) xmin = np.amin(x) xmax = np.amax(x) ymin = np.amin(y) ymax = np.amax(y) # In the x-direction, the hexagons exactly cover the region from # xmin to xmax. Need some padding to avoid roundoff errors. padding = 1.e-9 * (xmax - xmin) xmin -= padding xmax += padding sx = (xmax-xmin) / nx sy = (ymax-ymin) / ny x = (x-xmin)/sx y = (y-ymin)/sy ix1 = np.round(x).astype(int) iy1 = np.round(y).astype(int) ix2 = np.floor(x).astype(int) iy2 = np.floor(y).astype(int) nx1 = nx + 1 ny1 = ny + 1 nx2 = nx ny2 = ny n = nx1ny1+nx2ny2 d1 = (x-ix1)*2 + 3.0 (y-iy1)2 d2 = (x-ix2-0.5)2 + 3.0 * (y-iy2-0.5)*2 bdist = (d1<d2) if C is None: accum = np.zeros(n) # Create appropriate views into "accum" array. lattice1 = accum[:nx1ny1] lattice2 = accum[nx1ny1:] lattice1.shape = (nx1,ny1) lattice2.shape = (nx2,ny2) for i in xrange(len(x)): if bdist[i]: lattice1[ix1[i], iy1[i]]+=1 else: lattice2[ix2[i], iy2[i]]+=1 else: # create accumulation arrays lattice1 = np.empty((nx1,ny1),dtype=object) for i in xrange(nx1): for j in xrange(ny1): lattice1[i,j] = [] lattice2 = np.empty((nx2,ny2),dtype=object) for i in xrange(nx2): for j in xrange(ny2): lattice2[i,j] = [] for i in xrange(len(x)): if bdist[i]: lattice1[ix1[i], iy1[i]].append( C[i] ) else: lattice2[ix2[i], iy2[i]].append( C[i] ) for i in xrange(nx1): for j in xrange(ny1): vals = lattice1[i,j] if len(vals): lattice1[i,j] = reduce_C_function( vals ) else: lattice1[i,j] = np.nan for i in xrange(nx2): for j in xrange(ny2): vals = lattice2[i,j] if len(vals): lattice2[i,j] = reduce_C_function( vals ) else: lattice2[i,j] = np.nan accum = np.hstack(( lattice1.astype(float).ravel(), lattice2.astype(float).ravel())) good_idxs = ~np.isnan(accum) px = xmin + sx np.array([ 0.5, 0.5, 0.0, -0.5, -0.5, 0.0]) py = ymin + sy * np.array([-0.5, 0.5, 1.0, 0.5, -0.5, -1.0]) / 3.0 polygons = np.zeros((6, n, 2), float) polygons[:,:nx1ny1,0] = np.repeat(np.arange(nx1), ny1) polygons[:,:nx1ny1,1] = np.tile(np.arange(ny1), nx1) polygons[:,nx1ny1:,0] = np.repeat(np.arange(nx2) + 0.5, ny2) polygons[:,nx1ny1:,1] = np.tile(np.arange(ny2), nx2) + 0.5 if C is not None: # remove accumulation bins with no data polygons = polygons[:,good_idxs,:] accum = accum[good_idxs] polygons = np.transpose(polygons, axes=[1,0,2]) polygons[:,:,0] = sx polygons[:,:,1] = sy polygons[:,:,0] += px polygons[:,:,1] += py if xscale=='log': polygons[:,:,0] = 10(polygons[:,:,0]) xmin = 10xmin xmax = 10xmax self.set_xscale('log') if yscale=='log': polygons[:,:,1] = 10(polygons[:,:,1]) ymin = 10ymin ymax = 10ymax self.set_yscale('log') if edgecolors=='none': edgecolors = 'face' collection = mcoll.PolyCollection( polygons, edgecolors = edgecolors, linewidths = linewidths, transOffset = self.transData, ) # Transform accum if needed if bins=='log': accum = np.log10(accum+1) elif bins!=None: if not iterable(bins): minimum, maximum = min(accum), max(accum) bins-=1 # one less edge than bins bins = minimum + (maximum-minimum)np.arange(bins)/bins bins = np.sort(bins) accum = bins.searchsorted(accum) if norm is not None: assert(isinstance(norm, mcolors.Normalize)) if cmap is not None: assert(isinstance(cmap, mcolors.Colormap)) collection.set_array(accum) collection.set_cmap(cmap) collection.set_norm(norm) collection.set_alpha(alpha) collection.update(kwargs) if vmin is not None or vmax is not None: collection.set_clim(vmin, vmax) else: collection.autoscale_None() corners = ((xmin, ymin), (xmax, ymax)) self.update_datalim( corners) self.autoscale_view() # add the collection last self.add_collection(collection) return collection hexbin.__doc__ = cbook.dedent(hexbin.__doc__) % martist.kwdocd def arrow(self, x, y, dx, dy, kwargs): """ call signature:: arrow(x, y, dx, dy, kwargs) Draws arrow on specified axis from (x, y) to (x* + dx, y + dy). Optional kwargs control the arrow properties: %(FancyArrow)s Example: .. plot:: mpl_examples/pylab_examples/arrow_demo.py """ a = mpatches.FancyArrow(x, y, dx, dy, *kwargs) self.add_artist(a) return a arrow.__doc__ = cbook.dedent(arrow.__doc__) % martist.kwdocd def quiverkey(self, args, *kw): qk = mquiver.QuiverKey(args, *kw) self.add_artist(qk) return qk quiverkey.__doc__ = mquiver.QuiverKey.quiverkey_doc def quiver(self, args, *kw): if not self._hold: self.cla() q = mquiver.Quiver(self, args, *kw) self.add_collection(q, False) self.update_datalim(q.XY) self.autoscale_view() return q quiver.__doc__ = mquiver.Quiver.quiver_doc def barbs(self, args, kw): """ %(barbs_doc)s Example:** .. plot:: mpl_examples/pylab_examples/barb_demo.py """ if not self._hold: self.cla() b = mquiver.Barbs(self, args, kw) self.add_collection(b) self.update_datalim(b.get_offsets()) self.autoscale_view() return b barbs.__doc__ = cbook.dedent(barbs.__doc__) % { 'barbs_doc': mquiver.Barbs.barbs_doc} def fill(self, args, *kwargs): """ call signature:: fill(args, *kwargs) Plot filled polygons. args* is a variable length argument, allowing for multiple x, y pairs with an optional color format string; see :func:`~matplotlib.pyplot.plot` for details on the argument parsing. For example, to plot a polygon with vertices at x, y in blue.:: ax.fill(x,y, 'b' ) An arbitrary number of x, y, color groups can be specified:: ax.fill(x1, y1, 'g', x2, y2, 'r') Return value is a list of :class:`~matplotlib.patches.Patch` instances that were added. The same color strings that :func:`~matplotlib.pyplot.plot` supports are supported by the fill format string. If you would like to fill below a curve, eg. shade a region between 0 and y along x, use :meth:`fill_between` The closed kwarg will close the polygon when True (default). kwargs control the Polygon properties: %(Polygon)s Example: .. plot:: mpl_examples/pylab_examples/fill_demo.py """ if not self._hold: self.cla() patches = [] for poly in self._get_patches_for_fill(args, kwargs): self.add_patch( poly ) patches.append( poly ) self.autoscale_view() return patches fill.__doc__ = cbook.dedent(fill.__doc__) % martist.kwdocd def fill_between(self, x, y1, y2=0, where=None, kwargs): """ call signature:: fill_between(x, y1, y2=0, where=None, kwargs) Create a :class:`~matplotlib.collections.PolyCollection` filling the regions between y1* and y2 where ``where==True`` x an N length np array of the x data y1 an N length scalar or np array of the x data y2 an N length scalar or np array of the x data where if None, default to fill between everywhere. If not None, it is a a N length numpy boolean array and the fill will only happen over the regions where ``where==True`` kwargs keyword args passed on to the :class:`PolyCollection` kwargs control the Polygon properties: %(PolyCollection)s .. plot:: mpl_examples/pylab_examples/fill_between.py """ # Handle united data, such as dates self._process_unit_info(xdata=x, ydata=y1, kwargs=kwargs) self._process_unit_info(ydata=y2) # Convert the arrays so we can work with them x = np.asarray(self.convert_xunits(x)) y1 = np.asarray(self.convert_yunits(y1)) y2 = np.asarray(self.convert_yunits(y2)) if not cbook.iterable(y1): y1 = np.ones_like(x)y1 if not cbook.iterable(y2): y2 = np.ones_like(x)y2 if where is None: where = np.ones(len(x), np.bool) where = np.asarray(where) assert( (len(x)==len(y1)) and (len(x)==len(y2)) and len(x)==len(where)) polys = [] for ind0, ind1 in mlab.contiguous_regions(where): theseverts = [] xslice = x[ind0:ind1] y1slice = y1[ind0:ind1] y2slice = y2[ind0:ind1] if not len(xslice): continue N = len(xslice) X = np.zeros((2N+2, 2), np.float) # the purpose of the next two lines is for when y2 is a # scalar like 0 and we want the fill to go all the way # down to 0 even if none of the y1 sample points do X[0] = xslice[0], y2slice[0] X[N+1] = xslice[-1], y2slice[-1] X[1:N+1,0] = xslice X[1:N+1,1] = y1slice X[N+2:,0] = xslice[::-1] X[N+2:,1] = y2slice[::-1] polys.append(X) collection = mcoll.PolyCollection(polys, kwargs) # now update the datalim and autoscale XY1 = np.array([x[where], y1[where]]).T XY2 = np.array([x[where], y2[where]]).T self.dataLim.update_from_data_xy(XY1, self.ignore_existing_data_limits, updatex=True, updatey=True) self.dataLim.update_from_data_xy(XY2, self.ignore_existing_data_limits, updatex=False, updatey=True) self.add_collection(collection) self.autoscale_view() return collection fill_between.__doc__ = cbook.dedent(fill_between.__doc__) % martist.kwdocd #### plotting z(x,y): imshow, pcolor and relatives, contour def imshow(self, X, cmap=None, norm=None, aspect=None, interpolation=None, alpha=1.0, vmin=None, vmax=None, origin=None, extent=None, shape=None, filternorm=1, filterrad=4.0, imlim=None, resample=None, url=None, kwargs): """ call signature:: imshow(X, cmap=None, norm=None, aspect=None, interpolation=None, alpha=1.0, vmin=None, vmax=None, origin=None, extent=None, kwargs) Display the image in X* to current axes. X may be a float array, a uint8 array or a PIL image. If X is an array, X can have the following shapes: * MxN -- luminance (grayscale, float array only) * MxNx3 -- RGB (float or uint8 array) * MxNx4 -- RGBA (float or uint8 array) The value for each component of MxNx3 and MxNx4 float arrays should be in the range 0.0 to 1.0; MxN float arrays may be normalised. An :class:`matplotlib.image.AxesImage` instance is returned. Keyword arguments: cmap: [ None \| Colormap ] A :class:`matplotlib.cm.Colormap` instance, eg. cm.jet. If None, default to rc ``image.cmap`` value. cmap is ignored when X has RGB(A) information aspect: [ None \| 'auto' \| 'equal' \| scalar ] If 'auto', changes the image aspect ratio to match that of the axes If 'equal', and extent is None, changes the axes aspect ratio to match that of the image. If extent is not None, the axes aspect ratio is changed to match that of the extent. If None, default to rc ``image.aspect`` value. interpolation: Acceptable values are None, 'nearest', 'bilinear', 'bicubic', 'spline16', 'spline36', 'hanning', 'hamming', 'hermite', 'kaiser', 'quadric', 'catrom', 'gaussian', 'bessel', 'mitchell', 'sinc', 'lanczos', If interpolation is None, default to rc ``image.interpolation``. See also the filternorm and filterrad parameters norm: [ None \| Normalize ] An :class:`matplotlib.colors.Normalize` instance; if None, default is ``normalization()``. This scales luminance -> 0-1 norm is only used for an MxN float array. vmin/vmax: [ None \| scalar ] Used to scale a luminance image to 0-1. If either is None, the min and max of the luminance values will be used. Note if norm is not None, the settings for vmin and vmax will be ignored. alpha: scalar The alpha blending value, between 0 (transparent) and 1 (opaque) origin: [ None \| 'upper' \| 'lower' ] Place the [0,0] index of the array in the upper left or lower left corner of the axes. If None, default to rc ``image.origin``. extent: [ None \| scalars (left, right, bottom, top) ] Eata values of the axes. The default assigns zero-based row, column indices to the x, y centers of the pixels. shape: [ None \| scalars (columns, rows) ] For raw buffer images filternorm: A parameter for the antigrain image resize filter. From the antigrain documentation, if filternorm = 1, the filter normalizes integer values and corrects the rounding errors. It doesn't do anything with the source floating point values, it corrects only integers according to the rule of 1.0 which means that any sum of pixel weights must be equal to 1.0. So, the filter function must produce a graph of the proper shape. filterrad: The filter radius for filters that have a radius parameter, i.e. when interpolation is one of: 'sinc', 'lanczos' or 'blackman' Additional kwargs are :class:`~matplotlib.artist.Artist` properties: %(Artist)s Example: .. plot:: mpl_examples/pylab_examples/image_demo.py """ if not self._hold: self.cla() if norm is not None: assert(isinstance(norm, mcolors.Normalize)) if cmap is not None: assert(isinstance(cmap, mcolors.Colormap)) if aspect is None: aspect = rcParams['image.aspect'] self.set_aspect(aspect) im = mimage.AxesImage(self, cmap, norm, interpolation, origin, extent, filternorm=filternorm, filterrad=filterrad, resample=resample, *kwargs) im.set_data(X) im.set_alpha(alpha) self._set_artist_props(im) im.set_clip_path(self.patch) #if norm is None and shape is None: # im.set_clim(vmin, vmax) if vmin is not None or vmax is not None: im.set_clim(vmin, vmax) else: im.autoscale_None() im.set_url(url) xmin, xmax, ymin, ymax = im.get_extent() corners = (xmin, ymin), (xmax, ymax) self.update_datalim(corners) if self._autoscaleon: self.set_xlim((xmin, xmax)) self.set_ylim((ymin, ymax)) self.images.append(im) return im imshow.__doc__ = cbook.dedent(imshow.__doc__) % martist.kwdocd def _pcolorargs(self, funcname, args): if len(args)==1: C = args[0] numRows, numCols = C.shape X, Y = np.meshgrid(np.arange(numCols+1), np.arange(numRows+1) ) elif len(args)==3: X, Y, C = args else: raise TypeError( 'Illegal arguments to %s; see help(%s)' % (funcname, funcname)) Nx = X.shape[-1] Ny = Y.shape[0] if len(X.shape) <> 2 or X.shape[0] == 1: x = X.reshape(1,Nx) X = x.repeat(Ny, axis=0) if len(Y.shape) <> 2 or Y.shape[1] == 1: y = Y.reshape(Ny, 1) Y = y.repeat(Nx, axis=1) if X.shape != Y.shape: raise TypeError( 'Incompatible X, Y inputs to %s; see help(%s)' % ( funcname, funcname)) return X, Y, C def pcolor(self, args, kwargs): """ call signatures:: pcolor(C, kwargs) pcolor(X, Y, C, kwargs) Create a pseudocolor plot of a 2-D array. C* is the array of color values. X and Y, if given, specify the (x, y) coordinates of the colored quadrilaterals; the quadrilateral for C[i,j] has corners at:: (X[i, j], Y[i, j]), (X[i, j+1], Y[i, j+1]), (X[i+1, j], Y[i+1, j]), (X[i+1, j+1], Y[i+1, j+1]). Ideally the dimensions of X and Y should be one greater than those of C; if the dimensions are the same, then the last row and column of C will be ignored. Note that the the column index corresponds to the x-coordinate, and the row index corresponds to y; for details, see the :ref:`Grid Orientation <axes-pcolor-grid-orientation>` section below. If either or both of X and Y are 1-D arrays or column vectors, they will be expanded as needed into the appropriate 2-D arrays, making a rectangular grid. X, Y and C may be masked arrays. If either C[i, j], or one of the vertices surrounding C[i,j] (X or Y at [i, j], [i+1, j], [i, j+1],[i+1, j+1]) is masked, nothing is plotted. Keyword arguments: cmap: [ None \| Colormap ] A :class:`matplotlib.cm.Colormap` instance. If None, use rc settings. norm: [ None \| Normalize ] An :class:`matplotlib.colors.Normalize` instance is used to scale luminance data to 0,1. If None, defaults to :func:`normalize`. vmin/vmax: [ None \| scalar ] vmin and vmax are used in conjunction with norm to normalize luminance data. If either are None, the min and max of the color array C is used. If you pass a norm instance, vmin and vmax will be ignored. shading: [ 'flat' \| 'faceted' ] If 'faceted', a black grid is drawn around each rectangle; if 'flat', edges are not drawn. Default is 'flat', contrary to Matlab(TM). This kwarg is deprecated; please use 'edgecolors' instead: * shading='flat' -- edgecolors='None' * shading='faceted -- edgecolors='k' edgecolors: [ None \| 'None' \| color \| color sequence] If None, the rc setting is used by default. If 'None', edges will not be visible. An mpl color or sequence of colors will set the edge color alpha: 0 <= scalar <= 1 the alpha blending value Return value is a :class:`matplotlib.collection.Collection` instance. .. _axes-pcolor-grid-orientation: The grid orientation follows the Matlab(TM) convention: an array C with shape (nrows, ncolumns) is plotted with the column number as X and the row number as Y, increasing up; hence it is plotted the way the array would be printed, except that the Y axis is reversed. That is, C is taken as C(y, x). Similarly for :func:`~matplotlib.pyplot.meshgrid`:: x = np.arange(5) y = np.arange(3) X, Y = meshgrid(x,y) is equivalent to: X = array([[0, 1, 2, 3, 4], [0, 1, 2, 3, 4], [0, 1, 2, 3, 4]]) Y = array([[0, 0, 0, 0, 0], [1, 1, 1, 1, 1], [2, 2, 2, 2, 2]]) so if you have:: C = rand( len(x), len(y)) then you need:: pcolor(X, Y, C.T) or:: pcolor(C.T) Matlab :func:`pcolor` always discards the last row and column of C, but matplotlib displays the last row and column if X and Y are not specified, or if X and Y have one more row and column than C. kwargs can be used to control the :class:`~matplotlib.collection.PolyCollection` properties: %(PolyCollection)s """ if not self._hold: self.cla() alpha = kwargs.pop('alpha', 1.0) norm = kwargs.pop('norm', None) cmap = kwargs.pop('cmap', None) vmin = kwargs.pop('vmin', None) vmax = kwargs.pop('vmax', None) shading = kwargs.pop('shading', 'flat') X, Y, C = self._pcolorargs('pcolor', args) Ny, Nx = X.shape # convert to MA, if necessary. C = ma.asarray(C) X = ma.asarray(X) Y = ma.asarray(Y) mask = ma.getmaskarray(X)+ma.getmaskarray(Y) xymask = mask[0:-1,0:-1]+mask[1:,1:]+mask[0:-1,1:]+mask[1:,0:-1] # don't plot if C or any of the surrounding vertices are masked. mask = ma.getmaskarray(C)[0:Ny-1,0:Nx-1]+xymask newaxis = np.newaxis compress = np.compress ravelmask = (mask==0).ravel() X1 = compress(ravelmask, ma.filled(X[0:-1,0:-1]).ravel()) Y1 = compress(ravelmask, ma.filled(Y[0:-1,0:-1]).ravel()) X2 = compress(ravelmask, ma.filled(X[1:,0:-1]).ravel()) Y2 = compress(ravelmask, ma.filled(Y[1:,0:-1]).ravel()) X3 = compress(ravelmask, ma.filled(X[1:,1:]).ravel()) Y3 = compress(ravelmask, ma.filled(Y[1:,1:]).ravel()) X4 = compress(ravelmask, ma.filled(X[0:-1,1:]).ravel()) Y4 = compress(ravelmask, ma.filled(Y[0:-1,1:]).ravel()) npoly = len(X1) xy = np.concatenate((X1[:,newaxis], Y1[:,newaxis], X2[:,newaxis], Y2[:,newaxis], X3[:,newaxis], Y3[:,newaxis], X4[:,newaxis], Y4[:,newaxis], X1[:,newaxis], Y1[:,newaxis]), axis=1) verts = xy.reshape((npoly, 5, 2)) #verts = zip(zip(X1,Y1),zip(X2,Y2),zip(X3,Y3),zip(X4,Y4)) C = compress(ravelmask, ma.filled(C[0:Ny-1,0:Nx-1]).ravel()) if shading == 'faceted': edgecolors = (0,0,0,1), linewidths = (0.25,) else: edgecolors = 'face' linewidths = (1.0,) kwargs.setdefault('edgecolors', edgecolors) kwargs.setdefault('antialiaseds', (0,)) kwargs.setdefault('linewidths', linewidths) collection = mcoll.PolyCollection(verts, kwargs) collection.set_alpha(alpha) collection.set_array(C) if norm is not None: assert(isinstance(norm, mcolors.Normalize)) if cmap is not None: assert(isinstance(cmap, mcolors.Colormap)) collection.set_cmap(cmap) collection.set_norm(norm) if vmin is not None or vmax is not None: collection.set_clim(vmin, vmax) else: collection.autoscale_None() self.grid(False) x = X.compressed() y = Y.compressed() minx = np.amin(x) maxx = np.amax(x) miny = np.amin(y) maxy = np.amax(y) corners = (minx, miny), (maxx, maxy) self.update_datalim( corners) self.autoscale_view() self.add_collection(collection) return collection pcolor.__doc__ = cbook.dedent(pcolor.__doc__) % martist.kwdocd def pcolormesh(self, args, kwargs): """ call signatures:: pcolormesh(C) pcolormesh(X, Y, C) pcolormesh(C, kwargs) C may be a masked array, but X and Y may not. Masked array support is implemented via cmap and norm; in contrast, :func:`~matplotlib.pyplot.pcolor` simply does not draw quadrilaterals with masked colors or vertices. Keyword arguments: cmap: [ None \| Colormap ] A :class:`matplotlib.cm.Colormap` instance. If None, use rc settings. norm: [ None \| Normalize ] A :class:`matplotlib.colors.Normalize` instance is used to scale luminance data to 0,1. If None, defaults to :func:`normalize`. vmin/vmax: [ None \| scalar ] vmin and vmax are used in conjunction with norm to normalize luminance data. If either are None, the min and max of the color array C is used. If you pass a norm instance, vmin and vmax will be ignored. shading: [ 'flat' \| 'faceted' ] If 'faceted', a black grid is drawn around each rectangle; if 'flat', edges are not drawn. Default is 'flat', contrary to Matlab(TM). This kwarg is deprecated; please use 'edgecolors' instead: * shading='flat' -- edgecolors='None' * shading='faceted -- edgecolors='k' edgecolors: [ None \| 'None' \| color \| color sequence] If None, the rc setting is used by default. If 'None', edges will not be visible. An mpl color or sequence of colors will set the edge color alpha: 0 <= scalar <= 1 the alpha blending value Return value is a :class:`matplotlib.collection.QuadMesh` object. kwargs can be used to control the :class:`matplotlib.collections.QuadMesh` properties: %(QuadMesh)s .. seealso:: :func:`~matplotlib.pyplot.pcolor`: For an explanation of the grid orientation and the expansion of 1-D X and/or Y to 2-D arrays. """ if not self._hold: self.cla() alpha = kwargs.pop('alpha', 1.0) norm = kwargs.pop('norm', None) cmap = kwargs.pop('cmap', None) vmin = kwargs.pop('vmin', None) vmax = kwargs.pop('vmax', None) shading = kwargs.pop('shading', 'flat') edgecolors = kwargs.pop('edgecolors', 'None') antialiased = kwargs.pop('antialiased', False) X, Y, C = self._pcolorargs('pcolormesh', args) Ny, Nx = X.shape # convert to one dimensional arrays C = ma.ravel(C[0:Ny-1, 0:Nx-1]) # data point in each cell is value at # lower left corner X = X.ravel() Y = Y.ravel() coords = np.zeros(((Nx Ny), 2), dtype=float) coords[:, 0] = X coords[:, 1] = Y if shading == 'faceted' or edgecolors != 'None': showedges = 1 else: showedges = 0 collection = mcoll.QuadMesh( Nx - 1, Ny - 1, coords, showedges, antialiased=antialiased) # kwargs are not used collection.set_alpha(alpha) collection.set_array(C) if norm is not None: assert(isinstance(norm, mcolors.Normalize)) if cmap is not None: assert(isinstance(cmap, mcolors.Colormap)) collection.set_cmap(cmap) collection.set_norm(norm) if vmin is not None or vmax is not None: collection.set_clim(vmin, vmax) else: collection.autoscale_None() self.grid(False) minx = np.amin(X) maxx = np.amax(X) miny = np.amin(Y) maxy = np.amax(Y) corners = (minx, miny), (maxx, maxy) self.update_datalim( corners) self.autoscale_view() self.add_collection(collection) return collection pcolormesh.__doc__ = cbook.dedent(pcolormesh.__doc__) % martist.kwdocd def pcolorfast(self, args, kwargs): """ pseudocolor plot of a 2-D array Experimental; this is a version of pcolor that does not draw lines, that provides the fastest possible rendering with the Agg backend, and that can handle any quadrilateral grid. Call signatures:: pcolor(C, kwargs) pcolor(xr, yr, C, kwargs) pcolor(x, y, C, kwargs) pcolor(X, Y, C, kwargs) C is the 2D array of color values corresponding to quadrilateral cells. Let (nr, nc) be its shape. C may be a masked array. ``pcolor(C, kwargs)`` is equivalent to ``pcolor([0,nc], [0,nr], C, kwargs)`` xr, yr* specify the ranges of x and y corresponding to the rectangular region bounding C. If:: xr = [x0, x1] and:: yr = [y0,y1] then x goes from x0 to x1 as the second index of C goes from 0 to nc, etc. (x0, y0) is the outermost corner of cell (0,0), and (x1, y1) is the outermost corner of cell (nr-1, nc-1). All cells are rectangles of the same size. This is the fastest version. x, y are 1D arrays of length nc +1 and nr +1, respectively, giving the x and y boundaries of the cells. Hence the cells are rectangular but the grid may be nonuniform. The speed is intermediate. (The grid is checked, and if found to be uniform the fast version is used.) X and Y are 2D arrays with shape (nr +1, nc +1) that specify the (x,y) coordinates of the corners of the colored quadrilaterals; the quadrilateral for C[i,j] has corners at (X[i,j],Y[i,j]), (X[i,j+1],Y[i,j+1]), (X[i+1,j],Y[i+1,j]), (X[i+1,j+1],Y[i+1,j+1]). The cells need not be rectangular. This is the most general, but the slowest to render. It may produce faster and more compact output using ps, pdf, and svg backends, however. Note that the the column index corresponds to the x-coordinate, and the row index corresponds to y; for details, see the "Grid Orientation" section below. Optional keyword arguments: cmap: [ None \| Colormap ] A cm Colormap instance from cm. If None, use rc settings. norm: [ None \| Normalize ] An mcolors.Normalize instance is used to scale luminance data to 0,1. If None, defaults to normalize() vmin/vmax: [ None \| scalar ] vmin and vmax are used in conjunction with norm to normalize luminance data. If either are None, the min and max of the color array C is used. If you pass a norm instance, vmin and vmax will be None. alpha: 0 <= scalar <= 1 the alpha blending value Return value is an image if a regular or rectangular grid is specified, and a QuadMesh collection in the general quadrilateral case. """ if not self._hold: self.cla() alpha = kwargs.pop('alpha', 1.0) norm = kwargs.pop('norm', None) cmap = kwargs.pop('cmap', None) vmin = kwargs.pop('vmin', None) vmax = kwargs.pop('vmax', None) if norm is not None: assert(isinstance(norm, mcolors.Normalize)) if cmap is not None: assert(isinstance(cmap, mcolors.Colormap)) C = args[-1] nr, nc = C.shape if len(args) == 1: style = "image" x = [0, nc] y = [0, nr] elif len(args) == 3: x, y = args[:2] x = np.asarray(x) y = np.asarray(y) if x.ndim == 1 and y.ndim == 1: if x.size == 2 and y.size == 2: style = "image" else: dx = np.diff(x) dy = np.diff(y) if (np.ptp(dx) < 0.01np.abs(dx.mean()) and np.ptp(dy) < 0.01np.abs(dy.mean())): style = "image" else: style = "pcolorimage" elif x.ndim == 2 and y.ndim == 2: style = "quadmesh" else: raise TypeError("arguments do not match valid signatures") else: raise TypeError("need 1 argument or 3 arguments") if style == "quadmesh": # convert to one dimensional arrays # This should also be moved to the QuadMesh class C = ma.ravel(C) # data point in each cell is value # at lower left corner X = x.ravel() Y = y.ravel() Nx = nc+1 Ny = nr+1 # The following needs to be cleaned up; the renderer # requires separate contiguous arrays for X and Y, # but the QuadMesh class requires the 2D array. coords = np.empty(((Nx * Ny), 2), np.float64) coords[:, 0] = X coords[:, 1] = Y # The QuadMesh class can also be changed to # handle relevant superclass kwargs; the initializer # should do much more than it does now. collection = mcoll.QuadMesh(nc, nr, coords, 0) collection.set_alpha(alpha) collection.set_array(C) collection.set_cmap(cmap) collection.set_norm(norm) self.add_collection(collection) xl, xr, yb, yt = X.min(), X.max(), Y.min(), Y.max() ret = collection else: # One of the image styles: xl, xr, yb, yt = x[0], x[-1], y[0], y[-1] if style == "image": im = mimage.AxesImage(self, cmap, norm, interpolation='nearest', origin='lower', extent=(xl, xr, yb, yt), kwargs) im.set_data(C) im.set_alpha(alpha) self.images.append(im) ret = im if style == "pcolorimage": im = mimage.PcolorImage(self, x, y, C, cmap=cmap, norm=norm, alpha=alpha, kwargs) self.images.append(im) ret = im self._set_artist_props(ret) if vmin is not None or vmax is not None: ret.set_clim(vmin, vmax) else: ret.autoscale_None() self.update_datalim(np.array([[xl, yb], [xr, yt]])) self.autoscale_view(tight=True) return ret def contour(self, args, kwargs): if not self._hold: self.cla() kwargs['filled'] = False return mcontour.ContourSet(self, args, *kwargs) contour.__doc__ = mcontour.ContourSet.contour_doc def contourf(self, args, *kwargs): if not self._hold: self.cla() kwargs['filled'] = True return mcontour.ContourSet(self, args, *kwargs) contourf.__doc__ = mcontour.ContourSet.contour_doc def clabel(self, CS, args, *kwargs): return CS.clabel(args, kwargs) clabel.__doc__ = mcontour.ContourSet.clabel.__doc__ def table(self, kwargs): """ call signature:: table(cellText=None, cellColours=None, cellLoc='right', colWidths=None, rowLabels=None, rowColours=None, rowLoc='left', colLabels=None, colColours=None, colLoc='center', loc='bottom', bbox=None): Add a table to the current axes. Returns a :class:`matplotlib.table.Table` instance. For finer grained control over tables, use the :class:`~matplotlib.table.Table` class and add it to the axes with :meth:`~matplotlib.axes.Axes.add_table`. Thanks to John Gill for providing the class and table. kwargs control the :class:`~matplotlib.table.Table` properties: %(Table)s """ return mtable.table(self, kwargs) table.__doc__ = cbook.dedent(table.__doc__) % martist.kwdocd def twinx(self): """ call signature:: ax = twinx() create a twin of Axes for generating a plot with a sharex x-axis but independent y axis. The y-axis of self will have ticks on left and the returned axes will have ticks on the right """ ax2 = self.figure.add_axes(self.get_position(True), sharex=self, frameon=False) ax2.yaxis.tick_right() ax2.yaxis.set_label_position('right') self.yaxis.tick_left() return ax2 def twiny(self): """ call signature:: ax = twiny() create a twin of Axes for generating a plot with a shared y-axis but independent x axis. The x-axis of self will have ticks on bottom and the returned axes will have ticks on the top """ ax2 = self.figure.add_axes(self.get_position(True), sharey=self, frameon=False) ax2.xaxis.tick_top() ax2.xaxis.set_label_position('top') self.xaxis.tick_bottom() return ax2 def get_shared_x_axes(self): 'Return a copy of the shared axes Grouper object for x axes' return self._shared_x_axes def get_shared_y_axes(self): 'Return a copy of the shared axes Grouper object for y axes' return self._shared_y_axes #### Data analysis def hist(self, x, bins=10, range=None, normed=False, cumulative=False, bottom=None, histtype='bar', align='mid', orientation='vertical', rwidth=None, log=False, kwargs): """ call signature:: hist(x, bins=10, range=None, normed=False, cumulative=False, bottom=None, histtype='bar', align='mid', orientation='vertical', rwidth=None, log=False, *kwargs) Compute and draw the histogram of x. The return value is a tuple (n, bins, patches) or ([n0, n1, ...], bins, [patches0, patches1,...]) if the input contains multiple data. Keyword arguments: bins: Either an integer number of bins or a sequence giving the bins. x* are the data to be binned. x can be an array, a 2D array with multiple data in its columns, or a list of arrays with data of different length. Note, if bins is an integer input argument=numbins, bins + 1 bin edges will be returned, compatible with the semantics of :func:`numpy.histogram` with the new = True argument. Unequally spaced bins are supported if bins is a sequence. range: The lower and upper range of the bins. Lower and upper outliers are ignored. If not provided, range is (x.min(), x.max()). Range has no effect if bins is a sequence. If bins is a sequence or range is specified, autoscaling is set off (autoscale_on is set to False) and the xaxis limits are set to encompass the full specified bin range. normed: If True, the first element of the return tuple will be the counts normalized to form a probability density, i.e., ``n/(len(x)dbin)``. In a probability density, the integral of the histogram should be 1; you can verify that with a trapezoidal integration of the probability density function:: pdf, bins, patches = ax.hist(...) print np.sum(pdf np.diff(bins)) cumulative: If True, then a histogram is computed where each bin gives the counts in that bin plus all bins for smaller values. The last bin gives the total number of datapoints. If normed is also True then the histogram is normalized such that the last bin equals 1. If cumulative evaluates to less than 0 (e.g. -1), the direction of accumulation is reversed. In this case, if normed is also True, then the histogram is normalized such that the first bin equals 1. histtype: [ 'bar' \| 'barstacked' \| 'step' \| 'stepfilled' ] The type of histogram to draw. - 'bar' is a traditional bar-type histogram. If multiple data are given the bars are aranged side by side. - 'barstacked' is a bar-type histogram where multiple data are stacked on top of each other. - 'step' generates a lineplot that is by default unfilled. - 'stepfilled' generates a lineplot that is by default filled. align: ['left' \| 'mid' \| 'right' ] Controls how the histogram is plotted. - 'left': bars are centered on the left bin edges. - 'mid': bars are centered between the bin edges. - 'right': bars are centered on the right bin edges. orientation: [ 'horizontal' \| 'vertical' ] If 'horizontal', :func:`~matplotlib.pyplot.barh` will be used for bar-type histograms and the bottom kwarg will be the left edges. rwidth: The relative width of the bars as a fraction of the bin width. If None, automatically compute the width. Ignored if histtype = 'step' or 'stepfilled'. log: If True, the histogram axis will be set to a log scale. If log is True and x is a 1D array, empty bins will be filtered out and only the non-empty (n, bins, patches) will be returned. kwargs are used to update the properties of the hist :class:`~matplotlib.patches.Rectangle` instances: %(Rectangle)s You can use labels for your histogram, and only the first :class:`~matplotlib.patches.Rectangle` gets the label (the others get the magic string '_nolegend_'. This will make the histograms work in the intuitive way for bar charts:: ax.hist(10+2np.random.randn(1000), label='men') ax.hist(12+3np.random.randn(1000), label='women', alpha=0.5) ax.legend() Example: .. plot:: mpl_examples/pylab_examples/histogram_demo.py """ if not self._hold: self.cla() # NOTE: the range keyword overwrites the built-in func range !!! # needs to be fixed in with numpy !!! if kwargs.get('width') is not None: raise DeprecationWarning( 'hist now uses the rwidth to give relative width ' 'and not absolute width') try: # make sure a copy is created: don't use asarray x = np.transpose(np.array(x)) if len(x.shape)==1: x.shape = (1,x.shape[0]) elif len(x.shape)==2 and x.shape[1]<x.shape[0]: warnings.warn('2D hist should be nsamples x nvariables; ' 'this looks transposed') except ValueError: # multiple hist with data of different length if iterable(x[0]) and not is_string_like(x[0]): tx = [] for i in xrange(len(x)): tx.append( np.array(x[i]) ) x = tx else: raise ValueError, 'Can not use providet data to create a histogram' # Check whether bins or range are given explicitly. In that # case do not autoscale axes. binsgiven = (cbook.iterable(bins) or range != None) # check the version of the numpy if np.__version__ < "1.3": # version 1.1 and 1.2 hist_kwargs = dict(range=range, normed=bool(normed), new=True) else: # version 1.3 and later, drop new=True hist_kwargs = dict(range=range, normed=bool(normed)) n = [] for i in xrange(len(x)): # this will automatically overwrite bins, # so that each histogram uses the same bins m, bins = np.histogram(x[i], bins, *hist_kwargs) n.append(m) if cumulative: slc = slice(None) if cbook.is_numlike(cumulative) and cumulative < 0: slc = slice(None,None,-1) if normed: n = [(m np.diff(bins))[slc].cumsum()[slc] for m in n] else: n = [m[slc].cumsum()[slc] for m in n] patches = [] if histtype.startswith('bar'): totwidth = np.diff(bins) stacked = False if rwidth is not None: dr = min(1., max(0., rwidth)) elif len(n)>1: dr = 0.8 else: dr = 1.0 if histtype=='bar': width = drtotwidth/len(n) dw = width if len(n)>1: boffset = -0.5drtotwidth(1.-1./len(n)) else: boffset = 0.0 elif histtype=='barstacked': width = drtotwidth boffset, dw = 0.0, 0.0 stacked = True else: raise ValueError, 'invalid histtype: %s' % histtype if align == 'mid' or align == 'edge': boffset += 0.5totwidth elif align == 'right': boffset += totwidth elif align != 'left' and align != 'center': raise ValueError, 'invalid align: %s' % align if orientation == 'horizontal': for m in n: color = self._get_lines._get_next_cycle_color() patch = self.barh(bins[:-1]+boffset, m, height=width, left=bottom, align='center', log=log, color=color) patches.append(patch) if stacked: if bottom is None: bottom = 0.0 bottom += m boffset += dw elif orientation == 'vertical': for m in n: color = self._get_lines._get_next_cycle_color() patch = self.bar(bins[:-1]+boffset, m, width=width, bottom=bottom, align='center', log=log, color=color) patches.append(patch) if stacked: if bottom is None: bottom = 0.0 bottom += m boffset += dw else: raise ValueError, 'invalid orientation: %s' % orientation elif histtype.startswith('step'): x = np.zeros( 2len(bins), np.float ) y = np.zeros( 2len(bins), np.float ) x[0::2], x[1::2] = bins, bins if align == 'left' or align == 'center': x -= 0.5(bins[1]-bins[0]) elif align == 'right': x += 0.5(bins[1]-bins[0]) elif align != 'mid' and align != 'edge': raise ValueError, 'invalid align: %s' % align if log: y[0],y[-1] = 1e-100, 1e-100 if orientation == 'horizontal': self.set_xscale('log') elif orientation == 'vertical': self.set_yscale('log') fill = False if histtype == 'stepfilled': fill = True elif histtype != 'step': raise ValueError, 'invalid histtype: %s' % histtype for m in n: y[1:-1:2], y[2::2] = m, m if orientation == 'horizontal': x,y = y,x elif orientation != 'vertical': raise ValueError, 'invalid orientation: %s' % orientation color = self._get_lines._get_next_cycle_color() if fill: patches.append( self.fill(x, y, closed=False, facecolor=color) ) else: patches.append( self.fill(x, y, closed=False, edgecolor=color, fill=False) ) # adopted from adjust_x/ylim part of the bar method if orientation == 'horizontal': xmin, xmax = 0, self.dataLim.intervalx[1] for m in n: xmin = np.amin(m[m!=0]) # filter out the 0 height bins xmin = max(xmin0.9, 1e-100) self.dataLim.intervalx = (xmin, xmax) elif orientation == 'vertical': ymin, ymax = 0, self.dataLim.intervaly[1] for m in n: ymin = np.amin(m[m!=0]) # filter out the 0 height bins ymin = max(ymin0.9, 1e-100) self.dataLim.intervaly = (ymin, ymax) self.autoscale_view() else: raise ValueError, 'invalid histtype: %s' % histtype label = kwargs.pop('label', '') for patch in patches: for p in patch: p.update(kwargs) p.set_label(label) label = '_nolegend_' if binsgiven: self.set_autoscale_on(False) if orientation == 'vertical': self.autoscale_view(scalex=False, scaley=True) XL = self.xaxis.get_major_locator().view_limits(bins[0], bins[-1]) self.set_xbound(XL) else: self.autoscale_view(scalex=True, scaley=False) YL = self.yaxis.get_major_locator().view_limits(bins[0], bins[-1]) self.set_ybound(YL) if len(n)==1: return n[0], bins, cbook.silent_list('Patch', patches[0]) else: return n, bins, cbook.silent_list('Lists of Patches', patches) hist.__doc__ = cbook.dedent(hist.__doc__) % martist.kwdocd def psd(self, x, NFFT=256, Fs=2, Fc=0, detrend=mlab.detrend_none, window=mlab.window_hanning, noverlap=0, pad_to=None, sides='default', scale_by_freq=None, kwargs): """ call signature:: psd(x, NFFT=256, Fs=2, Fc=0, detrend=mlab.detrend_none, window=mlab.window_hanning, noverlap=0, pad_to=None, sides='default', scale_by_freq=None, kwargs) The power spectral density by Welch's average periodogram method. The vector x is divided into NFFT length segments. Each segment is detrended by function detrend and windowed by function window. noverlap gives the length of the overlap between segments. The :math:`\|\mathrm{fft}(i)\|^2` of each segment :math:`i` are averaged to compute Pxx, with a scaling to correct for power loss due to windowing. Fs is the sampling frequency. %(PSD)s Fc: integer The center frequency of x (defaults to 0), which offsets the x extents of the plot to reflect the frequency range used when a signal is acquired and then filtered and downsampled to baseband. Returns the tuple (Pxx, freqs). For plotting, the power is plotted as :math:`10\log_{10}(P_{xx})` for decibels, though Pxx itself is returned. References: Bendat & Piersol -- Random Data: Analysis and Measurement Procedures, John Wiley & Sons (1986) kwargs control the :class:`~matplotlib.lines.Line2D` properties: %(Line2D)s Example: .. plot:: mpl_examples/pylab_examples/psd_demo.py """ if not self._hold: self.cla() pxx, freqs = mlab.psd(x, NFFT, Fs, detrend, window, noverlap, pad_to, sides, scale_by_freq) pxx.shape = len(freqs), freqs += Fc if scale_by_freq in (None, True): psd_units = 'dB/Hz' else: psd_units = 'dB' self.plot(freqs, 10np.log10(pxx), kwargs) self.set_xlabel('Frequency') self.set_ylabel('Power Spectral Density (%s)' % psd_units) self.grid(True) vmin, vmax = self.viewLim.intervaly intv = vmax-vmin logi = int(np.log10(intv)) if logi==0: logi=.1 step = 10logi #print vmin, vmax, step, intv, math.floor(vmin), math.ceil(vmax)+1 ticks = np.arange(math.floor(vmin), math.ceil(vmax)+1, step) self.set_yticks(ticks) return pxx, freqs psd_doc_dict = dict() psd_doc_dict.update(martist.kwdocd) psd_doc_dict.update(mlab.kwdocd) psd_doc_dict['PSD'] = cbook.dedent(psd_doc_dict['PSD']) psd.__doc__ = cbook.dedent(psd.__doc__) % psd_doc_dict def csd(self, x, y, NFFT=256, Fs=2, Fc=0, detrend=mlab.detrend_none, window=mlab.window_hanning, noverlap=0, pad_to=None, sides='default', scale_by_freq=None, kwargs): """ call signature:: csd(x, y, NFFT=256, Fs=2, Fc=0, detrend=mlab.detrend_none, window=mlab.window_hanning, noverlap=0, pad_to=None, sides='default', scale_by_freq=None, kwargs) The cross spectral density :math:`P_{xy}` by Welch's average periodogram method. The vectors x and y are divided into NFFT length segments. Each segment is detrended by function detrend and windowed by function window. The product of the direct FFTs of x and y are averaged over each segment to compute :math:`P_{xy}`, with a scaling to correct for power loss due to windowing. Returns the tuple (Pxy, freqs). P is the cross spectrum (complex valued), and :math:`10\log_{10}\|P_{xy}\|` is plotted. %(PSD)s Fc: integer The center frequency of x (defaults to 0), which offsets the x extents of the plot to reflect the frequency range used when a signal is acquired and then filtered and downsampled to baseband. References: Bendat & Piersol -- Random Data: Analysis and Measurement Procedures, John Wiley & Sons (1986) kwargs control the Line2D properties: %(Line2D)s Example: .. plot:: mpl_examples/pylab_examples/csd_demo.py .. seealso: :meth:`psd` For a description of the optional parameters. """ if not self._hold: self.cla() pxy, freqs = mlab.csd(x, y, NFFT, Fs, detrend, window, noverlap, pad_to, sides, scale_by_freq) pxy.shape = len(freqs), # pxy is complex freqs += Fc self.plot(freqs, 10np.log10(np.absolute(pxy)), kwargs) self.set_xlabel('Frequency') self.set_ylabel('Cross Spectrum Magnitude (dB)') self.grid(True) vmin, vmax = self.viewLim.intervaly intv = vmax-vmin step = 10int(np.log10(intv)) ticks = np.arange(math.floor(vmin), math.ceil(vmax)+1, step) self.set_yticks(ticks) return pxy, freqs csd.__doc__ = cbook.dedent(csd.__doc__) % psd_doc_dict def cohere(self, x, y, NFFT=256, Fs=2, Fc=0, detrend=mlab.detrend_none, window=mlab.window_hanning, noverlap=0, pad_to=None, sides='default', scale_by_freq=None, kwargs): """ call signature:: cohere(x, y, NFFT=256, Fs=2, Fc=0, detrend = mlab.detrend_none, window = mlab.window_hanning, noverlap=0, pad_to=None, sides='default', scale_by_freq=None, kwargs) cohere the coherence between x and y. Coherence is the normalized cross spectral density: .. math:: C_{xy} = \\frac{\|P_{xy}\|^2}{P_{xx}P_{yy}} %(PSD)s Fc: integer The center frequency of x (defaults to 0), which offsets the x extents of the plot to reflect the frequency range used when a signal is acquired and then filtered and downsampled to baseband. The return value is a tuple (Cxy, f), where f are the frequencies of the coherence vector. kwargs are applied to the lines. References: * Bendat & Piersol -- Random Data: Analysis and Measurement Procedures, John Wiley & Sons (1986) kwargs control the :class:`~matplotlib.lines.Line2D` properties of the coherence plot: %(Line2D)s Example: .. plot:: mpl_examples/pylab_examples/cohere_demo.py """ if not self._hold: self.cla() cxy, freqs = mlab.cohere(x, y, NFFT, Fs, detrend, window, noverlap, scale_by_freq) freqs += Fc self.plot(freqs, cxy, *kwargs) self.set_xlabel('Frequency') self.set_ylabel('Coherence') self.grid(True) return cxy, freqs cohere.__doc__ = cbook.dedent(cohere.__doc__) % psd_doc_dict def specgram(self, x, NFFT=256, Fs=2, Fc=0, detrend=mlab.detrend_none, window=mlab.window_hanning, noverlap=128, cmap=None, xextent=None, pad_to=None, sides='default', scale_by_freq=None): """ call signature:: specgram(x, NFFT=256, Fs=2, Fc=0, detrend=mlab.detrend_none, window=mlab.window_hanning, noverlap=128, cmap=None, xextent=None, pad_to=None, sides='default', scale_by_freq=None) Compute a spectrogram of data in x. Data are split into NFFT* length segments and the PSD of each section is computed. The windowing function window is applied to each segment, and the amount of overlap of each segment is specified with noverlap. %(PSD)s Fc: integer The center frequency of x (defaults to 0), which offsets the y extents of the plot to reflect the frequency range used when a signal is acquired and then filtered and downsampled to baseband. cmap: A :class:`matplotlib.cm.Colormap` instance; if None use default determined by rc xextent: The image extent along the x-axis. xextent = (xmin,xmax) The default is (0,max(bins)), where bins is the return value from :func:`mlab.specgram` Return value is (Pxx, freqs, bins, im): - bins are the time points the spectrogram is calculated over - freqs is an array of frequencies - Pxx is a len(times) x len(freqs) array of power - im is a :class:`matplotlib.image.AxesImage` instance Note: If x is real (i.e. non-complex), only the positive spectrum is shown. If x is complex, both positive and negative parts of the spectrum are shown. This can be overridden using the sides keyword argument. Example: .. plot:: mpl_examples/pylab_examples/specgram_demo.py """ if not self._hold: self.cla() Pxx, freqs, bins = mlab.specgram(x, NFFT, Fs, detrend, window, noverlap, pad_to, sides, scale_by_freq) Z = 10. * np.log10(Pxx) Z = np.flipud(Z) if xextent is None: xextent = 0, np.amax(bins) xmin, xmax = xextent freqs += Fc extent = xmin, xmax, freqs[0], freqs[-1] im = self.imshow(Z, cmap, extent=extent) self.axis('auto') return Pxx, freqs, bins, im specgram.__doc__ = cbook.dedent(specgram.__doc__) % psd_doc_dict del psd_doc_dict #So that this does not become an Axes attribute def spy(self, Z, precision=0, marker=None, markersize=None, aspect='equal', kwargs): """ call signature:: spy(Z, precision=0, marker=None, markersize=None, aspect='equal', kwargs) ``spy(Z)`` plots the sparsity pattern of the 2-D array Z. If precision is 0, any non-zero value will be plotted; else, values of :math:`\|Z\| > precision` will be plotted. For :class:`scipy.sparse.spmatrix` instances, there is a special case: if precision is 'present', any value present in the array will be plotted, even if it is identically zero. The array will be plotted as it would be printed, with the first index (row) increasing down and the second index (column) increasing to the right. By default aspect is 'equal', so that each array element occupies a square space; set the aspect kwarg to 'auto' to allow the plot to fill the plot box, or to any scalar number to specify the aspect ratio of an array element directly. Two plotting styles are available: image or marker. Both are available for full arrays, but only the marker style works for :class:`scipy.sparse.spmatrix` instances. If marker and markersize are None, an image will be returned and any remaining kwargs are passed to :func:`~matplotlib.pyplot.imshow`; else, a :class:`~matplotlib.lines.Line2D` object will be returned with the value of marker determining the marker type, and any remaining kwargs passed to the :meth:`~matplotlib.axes.Axes.plot` method. If marker and markersize are None, useful kwargs include: * cmap * alpha .. seealso:: :func:`~matplotlib.pyplot.imshow` For controlling colors, e.g. cyan background and red marks, use:: cmap = mcolors.ListedColormap(['c','r']) If marker or markersize is not None, useful kwargs include: * marker * markersize * color Useful values for marker include: * 's' square (default) * 'o' circle * '.' point * ',' pixel .. seealso:: :func:`~matplotlib.pyplot.plot` """ if precision is None: precision = 0 warnings.DeprecationWarning("Use precision=0 instead of None") # 2008/10/03 if marker is None and markersize is None and hasattr(Z, 'tocoo'): marker = 's' if marker is None and markersize is None: Z = np.asarray(Z) mask = np.absolute(Z)>precision if 'cmap' not in kwargs: kwargs['cmap'] = mcolors.ListedColormap(['w', 'k'], name='binary') nr, nc = Z.shape extent = [-0.5, nc-0.5, nr-0.5, -0.5] ret = self.imshow(mask, interpolation='nearest', aspect=aspect, extent=extent, origin='upper', kwargs) else: if hasattr(Z, 'tocoo'): c = Z.tocoo() if precision == 'present': y = c.row x = c.col else: nonzero = np.absolute(c.data) > precision y = c.row[nonzero] x = c.col[nonzero] else: Z = np.asarray(Z) nonzero = np.absolute(Z)>precision y, x = np.nonzero(nonzero) if marker is None: marker = 's' if markersize is None: markersize = 10 marks = mlines.Line2D(x, y, linestyle='None', marker=marker, markersize=markersize, kwargs) self.add_line(marks) nr, nc = Z.shape self.set_xlim(xmin=-0.5, xmax=nc-0.5) self.set_ylim(ymin=nr-0.5, ymax=-0.5) self.set_aspect(aspect) ret = marks self.title.set_y(1.05) self.xaxis.tick_top() self.xaxis.set_ticks_position('both') self.xaxis.set_major_locator(mticker.MaxNLocator(nbins=9, steps=[1, 2, 5, 10], integer=True)) self.yaxis.set_major_locator(mticker.MaxNLocator(nbins=9, steps=[1, 2, 5, 10], integer=True)) return ret def matshow(self, Z, *kwargs): ''' Plot a matrix or array as an image. The matrix will be shown the way it would be printed, with the first row at the top. Row and column numbering is zero-based. Argument: Z* anything that can be interpreted as a 2-D array kwargs all are passed to :meth:`~matplotlib.axes.Axes.imshow`. :meth:`matshow` sets defaults for extent, origin, interpolation, and aspect; use care in overriding the extent and origin kwargs, because they interact. (Also, if you want to change them, you probably should be using imshow directly in your own version of matshow.) Returns: an :class:`matplotlib.image.AxesImage` instance. ''' Z = np.asarray(Z) nr, nc = Z.shape extent = [-0.5, nc-0.5, nr-0.5, -0.5] kw = {'extent': extent, 'origin': 'upper', 'interpolation': 'nearest', 'aspect': 'equal'} # (already the imshow default) kw.update(kwargs) im = self.imshow(Z, *kw) self.title.set_y(1.05) self.xaxis.tick_top() self.xaxis.set_ticks_position('both') self.xaxis.set_major_locator(mticker.MaxNLocator(nbins=9, steps=[1, 2, 5, 10], integer=True)) self.yaxis.set_major_locator(mticker.MaxNLocator(nbins=9, steps=[1, 2, 5, 10], integer=True)) return im class SubplotBase: """ Base class for subplots, which are :class:`Axes` instances with additional methods to facilitate generating and manipulating a set of :class:`Axes` within a figure. """ def __init__(self, fig, args, *kwargs): """ fig* is a :class:`matplotlib.figure.Figure` instance. args is the tuple (numRows, numCols, plotNum), where the array of subplots in the figure has dimensions numRows, numCols, and where plotNum is the number of the subplot being created. plotNum starts at 1 in the upper left corner and increases to the right. If numRows <= numCols <= plotNum < 10, args can be the decimal integer numRows * 100 + numCols * 10 + plotNum. """ self.figure = fig if len(args)==1: s = str(args[0]) if len(s) != 3: raise ValueError('Argument to subplot must be a 3 digits long') rows, cols, num = map(int, s) elif len(args)==3: rows, cols, num = args else: raise ValueError( 'Illegal argument to subplot') total = rowscols num -= 1 # convert from matlab to python indexing # ie num in range(0,total) if num >= total: raise ValueError( 'Subplot number exceeds total subplots') self._rows = rows self._cols = cols self._num = num self.update_params() # _axes_class is set in the subplot_class_factory self._axes_class.__init__(self, fig, self.figbox, kwargs) def get_geometry(self): 'get the subplot geometry, eg 2,2,3' return self._rows, self._cols, self._num+1 # COVERAGE NOTE: Never used internally or from examples def change_geometry(self, numrows, numcols, num): 'change subplot geometry, eg. from 1,1,1 to 2,2,3' self._rows = numrows self._cols = numcols self._num = num-1 self.update_params() self.set_position(self.figbox) def update_params(self): 'update the subplot position from fig.subplotpars' rows = self._rows cols = self._cols num = self._num pars = self.figure.subplotpars left = pars.left right = pars.right bottom = pars.bottom top = pars.top wspace = pars.wspace hspace = pars.hspace totWidth = right-left totHeight = top-bottom figH = totHeight/(rows + hspace(rows-1)) sepH = hspacefigH figW = totWidth/(cols + wspace(cols-1)) sepW = wspacefigW rowNum, colNum = divmod(num, cols) figBottom = top - (rowNum+1)figH - rowNumsepH figLeft = left + colNum(figW + sepW) self.figbox = mtransforms.Bbox.from_bounds(figLeft, figBottom, figW, figH) self.rowNum = rowNum self.colNum = colNum self.numRows = rows self.numCols = cols if 0: print 'rcn', rows, cols, num print 'lbrt', left, bottom, right, top print 'self.figBottom', self.figBottom print 'self.figLeft', self.figLeft print 'self.figW', self.figW print 'self.figH', self.figH print 'self.rowNum', self.rowNum print 'self.colNum', self.colNum print 'self.numRows', self.numRows print 'self.numCols', self.numCols def is_first_col(self): return self.colNum==0 def is_first_row(self): return self.rowNum==0 def is_last_row(self): return self.rowNum==self.numRows-1 def is_last_col(self): return self.colNum==self.numCols-1 # COVERAGE NOTE: Never used internally or from examples def label_outer(self): """ set the visible property on ticklabels so xticklabels are visible only if the subplot is in the last row and yticklabels are visible only if the subplot is in the first column """ lastrow = self.is_last_row() firstcol = self.is_first_col() for label in self.get_xticklabels(): label.set_visible(lastrow) for label in self.get_yticklabels(): label.set_visible(firstcol) _subplot_classes = {} def subplot_class_factory(axes_class=None): # This makes a new class that inherits from SubclassBase and the # given axes_class (which is assumed to be a subclass of Axes). # This is perhaps a little bit roundabout to make a new class on # the fly like this, but it means that a new Subplot class does # not have to be created for every type of Axes. if axes_class is None: axes_class = Axes new_class = _subplot_classes.get(axes_class) if new_class is None: new_class = new.classobj("%sSubplot" % (axes_class.__name__), (SubplotBase, axes_class), {'_axes_class': axes_class}) _subplot_classes[axes_class] = new_class return new_class # This is provided for backward compatibility Subplot = subplot_class_factory() martist.kwdocd['Axes'] = martist.kwdocd['Subplot'] = martist.kwdoc(Axes) """ # this is some discarded code I was using to find the minimum positive # data point for some log scaling fixes. I realized there was a # cleaner way to do it, but am keeping this around as an example for # how to get the data out of the axes. Might want to make something # like this a method one day, or better yet make get_verts an Artist # method minx, maxx = self.get_xlim() if minx<=0 or maxx<=0: # find the min pos value in the data xs = [] for line in self.lines: xs.extend(line.get_xdata(orig=False)) for patch in self.patches: xs.extend([x for x,y in patch.get_verts()]) for collection in self.collections: xs.extend([x for x,y in collection.get_verts()]) posx = [x for x in xs if x>0] if len(posx): minx = min(posx) maxx = max(posx) # warning, probably breaks inverted axis self.set_xlim((0.1*minx, maxx)) """	259,904	Python	.py	5,861	32.439515	83	0.546114	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,248	dviread.py	numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/dviread.py	""" An experimental module for reading dvi files output by TeX. Several limitations make this not (currently) useful as a general-purpose dvi preprocessor. Interface:: dvi = Dvi(filename, 72) for page in dvi: # iterate over pages w, h, d = page.width, page.height, page.descent for x,y,font,glyph,width in page.text: fontname = font.texname pointsize = font.size ... for x,y,height,width in page.boxes: ... """ import errno import matplotlib import matplotlib.cbook as mpl_cbook import numpy as np import struct import subprocess _dvistate = mpl_cbook.Bunch(pre=0, outer=1, inpage=2, post_post=3, finale=4) class Dvi(object): """ A dvi ("device-independent") file, as produced by TeX. The current implementation only reads the first page and does not even attempt to verify the postamble. """ def __init__(self, filename, dpi): """ Initialize the object. This takes the filename as input and opens the file; actually reading the file happens when iterating through the pages of the file. """ matplotlib.verbose.report('Dvi: ' + filename, 'debug') self.file = open(filename, 'rb') self.dpi = dpi self.fonts = {} self.state = _dvistate.pre def __iter__(self): """ Iterate through the pages of the file. Returns (text, pages) pairs, where: text is a list of (x, y, fontnum, glyphnum, width) tuples boxes is a list of (x, y, height, width) tuples The coordinates are transformed into a standard Cartesian coordinate system at the dpi value given when initializing. The coordinates are floating point numbers, but otherwise precision is not lost and coordinate values are not clipped to integers. """ while True: have_page = self._read() if have_page: yield self._output() else: break def close(self): """ Close the underlying file if it is open. """ if not self.file.closed: self.file.close() def _output(self): """ Output the text and boxes belonging to the most recent page. page = dvi._output() """ minx, miny, maxx, maxy = np.inf, np.inf, -np.inf, -np.inf maxy_pure = -np.inf for elt in self.text + self.boxes: if len(elt) == 4: # box x,y,h,w = elt e = 0 # zero depth else: # glyph x,y,font,g,w = elt h = _mul2012(font._scale, font._tfm.height[g]) e = _mul2012(font._scale, font._tfm.depth[g]) minx = min(minx, x) miny = min(miny, y - h) maxx = max(maxx, x + w) maxy = max(maxy, y + e) maxy_pure = max(maxy_pure, y) if self.dpi is None: # special case for ease of debugging: output raw dvi coordinates return mpl_cbook.Bunch(text=self.text, boxes=self.boxes, width=maxx-minx, height=maxy_pure-miny, descent=maxy-maxy_pure) d = self.dpi / (72.27 * 2*16) # from TeX's "scaled points" to dpi units text = [ ((x-minx)d, (maxy-y)d, f, g, wd) for (x,y,f,g,w) in self.text ] boxes = [ ((x-minx)d, (maxy-y)d, hd, wd) for (x,y,h,w) in self.boxes ] return mpl_cbook.Bunch(text=text, boxes=boxes, width=(maxx-minx)d, height=(maxy_pure-miny)d, descent=(maxy-maxy_pure)d) def _read(self): """ Read one page from the file. Return True if successful, False if there were no more pages. """ while True: byte = ord(self.file.read(1)) self._dispatch(byte) # if self.state == _dvistate.inpage: # matplotlib.verbose.report( # 'Dvi._read: after %d at %f,%f' % # (byte, self.h, self.v), # 'debug-annoying') if byte == 140: # end of page return True if self.state == _dvistate.post_post: # end of file self.close() return False def _arg(self, nbytes, signed=False): """ Read and return an integer argument "nbytes" long. Signedness is determined by the "signed" keyword. """ str = self.file.read(nbytes) value = ord(str[0]) if signed and value >= 0x80: value = value - 0x100 for i in range(1, nbytes): value = 0x100value + ord(str[i]) return value def _dispatch(self, byte): """ Based on the opcode "byte", read the correct kinds of arguments from the dvi file and call the method implementing that opcode with those arguments. """ if 0 <= byte <= 127: self._set_char(byte) elif byte == 128: self._set_char(self._arg(1)) elif byte == 129: self._set_char(self._arg(2)) elif byte == 130: self._set_char(self._arg(3)) elif byte == 131: self._set_char(self._arg(4, True)) elif byte == 132: self._set_rule(self._arg(4, True), self._arg(4, True)) elif byte == 133: self._put_char(self._arg(1)) elif byte == 134: self._put_char(self._arg(2)) elif byte == 135: self._put_char(self._arg(3)) elif byte == 136: self._put_char(self._arg(4, True)) elif byte == 137: self._put_rule(self._arg(4, True), self._arg(4, True)) elif byte == 138: self._nop() elif byte == 139: self._bop([self._arg(4, True) for i in range(11)]) elif byte == 140: self._eop() elif byte == 141: self._push() elif byte == 142: self._pop() elif byte == 143: self._right(self._arg(1, True)) elif byte == 144: self._right(self._arg(2, True)) elif byte == 145: self._right(self._arg(3, True)) elif byte == 146: self._right(self._arg(4, True)) elif byte == 147: self._right_w(None) elif byte == 148: self._right_w(self._arg(1, True)) elif byte == 149: self._right_w(self._arg(2, True)) elif byte == 150: self._right_w(self._arg(3, True)) elif byte == 151: self._right_w(self._arg(4, True)) elif byte == 152: self._right_x(None) elif byte == 153: self._right_x(self._arg(1, True)) elif byte == 154: self._right_x(self._arg(2, True)) elif byte == 155: self._right_x(self._arg(3, True)) elif byte == 156: self._right_x(self._arg(4, True)) elif byte == 157: self._down(self._arg(1, True)) elif byte == 158: self._down(self._arg(2, True)) elif byte == 159: self._down(self._arg(3, True)) elif byte == 160: self._down(self._arg(4, True)) elif byte == 161: self._down_y(None) elif byte == 162: self._down_y(self._arg(1, True)) elif byte == 163: self._down_y(self._arg(2, True)) elif byte == 164: self._down_y(self._arg(3, True)) elif byte == 165: self._down_y(self._arg(4, True)) elif byte == 166: self._down_z(None) elif byte == 167: self._down_z(self._arg(1, True)) elif byte == 168: self._down_z(self._arg(2, True)) elif byte == 169: self._down_z(self._arg(3, True)) elif byte == 170: self._down_z(self._arg(4, True)) elif 171 <= byte <= 234: self._fnt_num(byte-171) elif byte == 235: self._fnt_num(self._arg(1)) elif byte == 236: self._fnt_num(self._arg(2)) elif byte == 237: self._fnt_num(self._arg(3)) elif byte == 238: self._fnt_num(self._arg(4, True)) elif 239 <= byte <= 242: len = self._arg(byte-238) special = self.file.read(len) self._xxx(special) elif 243 <= byte <= 246: k = self._arg(byte-242, byte==246) c, s, d, a, l = [ self._arg(x) for x in (4, 4, 4, 1, 1) ] n = self.file.read(a+l) self._fnt_def(k, c, s, d, a, l, n) elif byte == 247: i, num, den, mag, k = [ self._arg(x) for x in (1, 4, 4, 4, 1) ] x = self.file.read(k) self._pre(i, num, den, mag, x) elif byte == 248: self._post() elif byte == 249: self._post_post() else: raise ValueError, "unknown command: byte %d"%byte def _pre(self, i, num, den, mag, comment): if self.state != _dvistate.pre: raise ValueError, "pre command in middle of dvi file" if i != 2: raise ValueError, "Unknown dvi format %d"%i if num != 25400000 or den != 7227 2*16: raise ValueError, "nonstandard units in dvi file" # meaning: TeX always uses those exact values, so it # should be enough for us to support those # (There are 72.27 pt to an inch so 7227 pt = # 7227 216 sp to 100 in. The numerator is multiplied # by 10^5 to get units of 10-7 meters.) if mag != 1000: raise ValueError, "nonstandard magnification in dvi file" # meaning: LaTeX seems to frown on setting \mag, so # I think we can assume this is constant self.state = _dvistate.outer def _set_char(self, char): if self.state != _dvistate.inpage: raise ValueError, "misplaced set_char in dvi file" self._put_char(char) self.h += self.fonts[self.f]._width_of(char) def _set_rule(self, a, b): if self.state != _dvistate.inpage: raise ValueError, "misplaced set_rule in dvi file" self._put_rule(a, b) self.h += b def _put_char(self, char): if self.state != _dvistate.inpage: raise ValueError, "misplaced put_char in dvi file" font = self.fonts[self.f] if font._vf is None: self.text.append((self.h, self.v, font, char, font._width_of(char))) # matplotlib.verbose.report( # 'Dvi._put_char: %d,%d %d' %(self.h, self.v, char), # 'debug-annoying') else: scale = font._scale for x, y, f, g, w in font._vf[char].text: newf = DviFont(scale=_mul2012(scale, f._scale), tfm=f._tfm, texname=f.texname, vf=f._vf) self.text.append((self.h + _mul2012(x, scale), self.v + _mul2012(y, scale), newf, g, newf._width_of(g))) self.boxes.extend([(self.h + _mul2012(x, scale), self.v + _mul2012(y, scale), _mul2012(a, scale), _mul2012(b, scale)) for x, y, a, b in font._vf[char].boxes]) def _put_rule(self, a, b): if self.state != _dvistate.inpage: raise ValueError, "misplaced put_rule in dvi file" if a > 0 and b > 0: self.boxes.append((self.h, self.v, a, b)) # matplotlib.verbose.report( # 'Dvi._put_rule: %d,%d %d,%d' % (self.h, self.v, a, b), # 'debug-annoying') def _nop(self): pass def _bop(self, c0, c1, c2, c3, c4, c5, c6, c7, c8, c9, p): if self.state != _dvistate.outer: raise ValueError, \ "misplaced bop in dvi file (state %d)" % self.state self.state = _dvistate.inpage self.h, self.v, self.w, self.x, self.y, self.z = 0, 0, 0, 0, 0, 0 self.stack = [] self.text = [] # list of (x,y,fontnum,glyphnum) self.boxes = [] # list of (x,y,width,height) def _eop(self): if self.state != _dvistate.inpage: raise ValueError, "misplaced eop in dvi file" self.state = _dvistate.outer del self.h, self.v, self.w, self.x, self.y, self.z, self.stack def _push(self): if self.state != _dvistate.inpage: raise ValueError, "misplaced push in dvi file" self.stack.append((self.h, self.v, self.w, self.x, self.y, self.z)) def _pop(self): if self.state != _dvistate.inpage: raise ValueError, "misplaced pop in dvi file" self.h, self.v, self.w, self.x, self.y, self.z = self.stack.pop() def _right(self, b): if self.state != _dvistate.inpage: raise ValueError, "misplaced right in dvi file" self.h += b def _right_w(self, new_w): if self.state != _dvistate.inpage: raise ValueError, "misplaced w in dvi file" if new_w is not None: self.w = new_w self.h += self.w def _right_x(self, new_x): if self.state != _dvistate.inpage: raise ValueError, "misplaced x in dvi file" if new_x is not None: self.x = new_x self.h += self.x def _down(self, a): if self.state != _dvistate.inpage: raise ValueError, "misplaced down in dvi file" self.v += a def _down_y(self, new_y): if self.state != _dvistate.inpage: raise ValueError, "misplaced y in dvi file" if new_y is not None: self.y = new_y self.v += self.y def _down_z(self, new_z): if self.state != _dvistate.inpage: raise ValueError, "misplaced z in dvi file" if new_z is not None: self.z = new_z self.v += self.z def _fnt_num(self, k): if self.state != _dvistate.inpage: raise ValueError, "misplaced fnt_num in dvi file" self.f = k def _xxx(self, special): matplotlib.verbose.report( 'Dvi._xxx: encountered special: %s' % ''.join([(32 <= ord(ch) < 127) and ch or '<%02x>' % ord(ch) for ch in special]), 'debug') def _fnt_def(self, k, c, s, d, a, l, n): tfm = _tfmfile(n[-l:]) if c != 0 and tfm.checksum != 0 and c != tfm.checksum: raise ValueError, 'tfm checksum mismatch: %s'%n # It seems that the assumption behind the following check is incorrect: #if d != tfm.design_size: # raise ValueError, 'tfm design size mismatch: %d in dvi, %d in %s'%\ # (d, tfm.design_size, n) vf = _vffile(n[-l:]) self.fonts[k] = DviFont(scale=s, tfm=tfm, texname=n, vf=vf) def _post(self): if self.state != _dvistate.outer: raise ValueError, "misplaced post in dvi file" self.state = _dvistate.post_post # TODO: actually read the postamble and finale? # currently post_post just triggers closing the file def _post_post(self): raise NotImplementedError class DviFont(object): """ Object that holds a font's texname and size, supports comparison, and knows the widths of glyphs in the same units as the AFM file. There are also internal attributes (for use by dviread.py) that are _not_ used for comparison. The size is in Adobe points (converted from TeX points). """ __slots__ = ('texname', 'size', 'widths', '_scale', '_vf', '_tfm') def __init__(self, scale, tfm, texname, vf): self._scale, self._tfm, self.texname, self._vf = \ scale, tfm, texname, vf self.size = scale * (72.0 / (72.27 * 2*16)) try: nchars = max(tfm.width.iterkeys()) except ValueError: nchars = 0 self.widths = [ (1000tfm.width.get(char, 0)) >> 20 for char in range(nchars) ] def __eq__(self, other): return self.__class__ == other.__class__ and \ self.texname == other.texname and self.size == other.size def __ne__(self, other): return not self.__eq__(other) def _width_of(self, char): """ Width of char in dvi units. For internal use by dviread.py. """ width = self._tfm.width.get(char, None) if width is not None: return _mul2012(width, self._scale) matplotlib.verbose.report( 'No width for char %d in font %s' % (char, self.texname), 'debug') return 0 class Vf(Dvi): """ A virtual font (\.vf file) containing subroutines for dvi files. Usage:: vf = Vf(filename) glyph = vf[code] glyph.text, glyph.boxes, glyph.width """ def __init__(self, filename): Dvi.__init__(self, filename, 0) self._first_font = None self._chars = {} self._packet_ends = None self._read() self.close() def __getitem__(self, code): return self._chars[code] def _dispatch(self, byte): # If we are in a packet, execute the dvi instructions if self.state == _dvistate.inpage: byte_at = self.file.tell()-1 if byte_at == self._packet_ends: self._finalize_packet() # fall through elif byte_at > self._packet_ends: raise ValueError, "Packet length mismatch in vf file" else: if byte in (139, 140) or byte >= 243: raise ValueError, "Inappropriate opcode %d in vf file" % byte Dvi._dispatch(self, byte) return # We are outside a packet if byte < 242: # a short packet (length given by byte) cc, tfm = self._arg(1), self._arg(3) self._init_packet(byte, cc, tfm) elif byte == 242: # a long packet pl, cc, tfm = [ self._arg(x) for x in (4, 4, 4) ] self._init_packet(pl, cc, tfm) elif 243 <= byte <= 246: Dvi._dispatch(self, byte) elif byte == 247: # preamble i, k = self._arg(1), self._arg(1) x = self.file.read(k) cs, ds = self._arg(4), self._arg(4) self._pre(i, x, cs, ds) elif byte == 248: # postamble (just some number of 248s) self.state = _dvistate.post_post else: raise ValueError, "unknown vf opcode %d" % byte def _init_packet(self, pl, cc, tfm): if self.state != _dvistate.outer: raise ValueError, "Misplaced packet in vf file" self.state = _dvistate.inpage self._packet_ends = self.file.tell() + pl self._packet_char = cc self._packet_width = tfm self.h, self.v, self.w, self.x, self.y, self.z = 0, 0, 0, 0, 0, 0 self.stack, self.text, self.boxes = [], [], [] self.f = self._first_font def _finalize_packet(self): self._chars[self._packet_char] = mpl_cbook.Bunch( text=self.text, boxes=self.boxes, width = self._packet_width) self.state = _dvistate.outer def _pre(self, i, x, cs, ds): if self.state != _dvistate.pre: raise ValueError, "pre command in middle of vf file" if i != 202: raise ValueError, "Unknown vf format %d" % i if len(x): matplotlib.verbose.report('vf file comment: ' + x, 'debug') self.state = _dvistate.outer # cs = checksum, ds = design size def _fnt_def(self, k, args): Dvi._fnt_def(self, k, args) if self._first_font is None: self._first_font = k def _fix2comp(num): """ Convert from two's complement to negative. """ assert 0 <= num < 232 if num & 231: return num - 232 else: return num def _mul2012(num1, num2): """ Multiply two numbers in 20.12 fixed point format. """ # Separated into a function because >> has surprising precedence return (num1num2) >> 20 class Tfm(object): """ A TeX Font Metric file. This implementation covers only the bare minimum needed by the Dvi class. Attributes: checksum: for verifying against dvi file design_size: design size of the font (in what units?) width[i]: width of character \#i, needs to be scaled by the factor specified in the dvi file (this is a dict because indexing may not start from 0) height[i], depth[i]: height and depth of character \#i """ __slots__ = ('checksum', 'design_size', 'width', 'height', 'depth') def __init__(self, filename): matplotlib.verbose.report('opening tfm file ' + filename, 'debug') file = open(filename, 'rb') try: header1 = file.read(24) lh, bc, ec, nw, nh, nd = \ struct.unpack('!6H', header1[2:14]) matplotlib.verbose.report( 'lh=%d, bc=%d, ec=%d, nw=%d, nh=%d, nd=%d' % ( lh, bc, ec, nw, nh, nd), 'debug') header2 = file.read(4lh) self.checksum, self.design_size = \ struct.unpack('!2I', header2[:8]) # there is also encoding information etc. char_info = file.read(4(ec-bc+1)) widths = file.read(4nw) heights = file.read(4nh) depths = file.read(4nd) finally: file.close() self.width, self.height, self.depth = {}, {}, {} widths, heights, depths = \ [ struct.unpack('!%dI' % (len(x)/4), x) for x in (widths, heights, depths) ] for i in range(ec-bc): self.width[bc+i] = _fix2comp(widths[ord(char_info[4i])]) self.height[bc+i] = _fix2comp(heights[ord(char_info[4i+1]) >> 4]) self.depth[bc+i] = _fix2comp(depths[ord(char_info[4i+1]) & 0xf]) class PsfontsMap(object): """ A psfonts.map formatted file, mapping TeX fonts to PS fonts. Usage: map = PsfontsMap('.../psfonts.map'); map['cmr10'] For historical reasons, TeX knows many Type-1 fonts by different names than the outside world. (For one thing, the names have to fit in eight characters.) Also, TeX's native fonts are not Type-1 but Metafont, which is nontrivial to convert to PostScript except as a bitmap. While high-quality conversions to Type-1 format exist and are shipped with modern TeX distributions, we need to know which Type-1 fonts are the counterparts of which native fonts. For these reasons a mapping is needed from internal font names to font file names. A texmf tree typically includes mapping files called e.g. psfonts.map, pdftex.map, dvipdfm.map. psfonts.map is used by dvips, pdftex.map by pdfTeX, and dvipdfm.map by dvipdfm. psfonts.map might avoid embedding the 35 PostScript fonts, while the pdf-related files perhaps only avoid the "Base 14" pdf fonts. But the user may have configured these files differently. """ __slots__ = ('_font',) def __init__(self, filename): self._font = {} file = open(filename, 'rt') try: self._parse(file) finally: file.close() def __getitem__(self, texname): result = self._font[texname] fn, enc = result.filename, result.encoding if fn is not None and not fn.startswith('/'): result.filename = find_tex_file(fn) if enc is not None and not enc.startswith('/'): result.encoding = find_tex_file(result.encoding) return result def _parse(self, file): """Parse each line into words.""" for line in file: line = line.strip() if line == '' or line.startswith('%'): continue words, pos = [], 0 while pos < len(line): if line[pos] == '"': # double quoted word pos += 1 end = line.index('"', pos) words.append(line[pos:end]) pos = end + 1 else: # ordinary word end = line.find(' ', pos+1) if end == -1: end = len(line) words.append(line[pos:end]) pos = end while pos < len(line) and line[pos] == ' ': pos += 1 self._register(words) def _register(self, words): """Register a font described by "words". The format is, AFAIK: texname fontname [effects and filenames] Effects are PostScript snippets like ".177 SlantFont", filenames begin with one or two less-than signs. A filename ending in enc is an encoding file, other filenames are font files. This can be overridden with a left bracket: <[foobar indicates an encoding file named foobar. There is some difference between <foo.pfb and <<bar.pfb in subsetting, but I have no example of << in my TeX installation. """ texname, psname = words[:2] effects, encoding, filename = [], None, None for word in words[2:]: if not word.startswith('<'): effects.append(word) else: word = word.lstrip('<') if word.startswith('['): assert encoding is None encoding = word[1:] elif word.endswith('.enc'): assert encoding is None encoding = word else: assert filename is None filename = word self._font[texname] = mpl_cbook.Bunch( texname=texname, psname=psname, effects=effects, encoding=encoding, filename=filename) class Encoding(object): """ Parses a \*.enc file referenced from a psfonts.map style file. The format this class understands is a very limited subset of PostScript. Usage (subject to change):: for name in Encoding(filename): whatever(name) """ __slots__ = ('encoding',) def __init__(self, filename): file = open(filename, 'rt') try: matplotlib.verbose.report('Parsing TeX encoding ' + filename, 'debug-annoying') self.encoding = self._parse(file) matplotlib.verbose.report('Result: ' + `self.encoding`, 'debug-annoying') finally: file.close() def __iter__(self): for name in self.encoding: yield name def _parse(self, file): result = [] state = 0 for line in file: comment_start = line.find('%') if comment_start > -1: line = line[:comment_start] line = line.strip() if state == 0: # Expecting something like /FooEncoding [ if '[' in line: state = 1 line = line[line.index('[')+1:].strip() if state == 1: if ']' in line: # ] def line = line[:line.index(']')] state = 2 words = line.split() for w in words: if w.startswith('/'): # Allow for /abc/def/ghi subwords = w.split('/') result.extend(subwords[1:]) else: raise ValueError, "Broken name in encoding file: " + w return result def find_tex_file(filename, format=None): """ Call kpsewhich to find a file in the texmf tree. If format is not None, it is used as the value for the --format option. See the kpathsea documentation for more information. Apparently most existing TeX distributions on Unix-like systems use kpathsea. I hear MikTeX (a popular distribution on Windows) doesn't use kpathsea, so what do we do? (TODO) """ cmd = ['kpsewhich'] if format is not None: cmd += ['--format=' + format] cmd += [filename] matplotlib.verbose.report('find_tex_file(%s): %s' \ % (filename,cmd), 'debug') pipe = subprocess.Popen(cmd, stdout=subprocess.PIPE) result = pipe.communicate()[0].rstrip() matplotlib.verbose.report('find_tex_file result: %s' % result, 'debug') return result def _read_nointr(pipe, bufsize=-1): while True: try: return pipe.read(bufsize) except OSError, e: if e.errno == errno.EINTR: continue else: raise # With multiple text objects per figure (e.g. tick labels) we may end # up reading the same tfm and vf files many times, so we implement a # simple cache. TODO: is this worth making persistent? _tfmcache = {} _vfcache = {} def _fontfile(texname, class_, suffix, cache): try: return cache[texname] except KeyError: pass filename = find_tex_file(texname + suffix) if filename: result = class_(filename) else: result = None cache[texname] = result return result def _tfmfile(texname): return _fontfile(texname, Tfm, '.tfm', _tfmcache) def _vffile(texname): return _fontfile(texname, Vf, '.vf', _vfcache) if __name__ == '__main__': import sys matplotlib.verbose.set_level('debug-annoying') fname = sys.argv[1] try: dpi = float(sys.argv[2]) except IndexError: dpi = None dvi = Dvi(fname, dpi) fontmap = PsfontsMap(find_tex_file('pdftex.map')) for page in dvi: print '=== new page ===' fPrev = None for x,y,f,c,w in page.text: if f != fPrev: print 'font', f.texname, 'scaled', f._scale/pow(2.0,20) fPrev = f print x,y,c, 32 <= c < 128 and chr(c) or '.', w for x,y,w,h in page.boxes: print x,y,'BOX',w,h	29,920	Python	.py	709	32.038082	91	0.546954	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,249	afm.py	numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/afm.py	""" This is a python interface to Adobe Font Metrics Files. Although a number of other python implementations exist (and may be more complete than mine) I decided not to go with them because either they were either 1) copyrighted or used a non-BSD compatible license 2) had too many dependencies and I wanted a free standing lib 3) Did more than I needed and it was easier to write my own than figure out how to just get what I needed from theirs It is pretty easy to use, and requires only built-in python libs:: >>> from afm import AFM >>> fh = file('ptmr8a.afm') >>> afm = AFM(fh) >>> afm.string_width_height('What the heck?') (6220.0, 683) >>> afm.get_fontname() 'Times-Roman' >>> afm.get_kern_dist('A', 'f') 0 >>> afm.get_kern_dist('A', 'y') -92.0 >>> afm.get_bbox_char('!') [130, -9, 238, 676] >>> afm.get_bbox_font() [-168, -218, 1000, 898] AUTHOR: John D. Hunter <jdh2358@gmail.com> """ import sys, os, re from _mathtext_data import uni2type1 #Convert string the a python type _to_int = int _to_float = float _to_str = str def _to_list_of_ints(s): s = s.replace(',', ' ') return [_to_int(val) for val in s.split()] def _to_list_of_floats(s): return [_to_float(val) for val in s.split()] def _to_bool(s): if s.lower().strip() in ('false', '0', 'no'): return False else: return True def _sanity_check(fh): """ Check if the file at least looks like AFM. If not, raise :exc:`RuntimeError`. """ # Remember the file position in case the caller wants to # do something else with the file. pos = fh.tell() try: line = fh.readline() finally: fh.seek(pos, 0) # AFM spec, Section 4: The StartFontMetrics keyword [followed by a # version number] must be the first line in the file, and the # EndFontMetrics keyword must be the last non-empty line in the # file. We just check the first line. if not line.startswith('StartFontMetrics'): raise RuntimeError('Not an AFM file') def _parse_header(fh): """ Reads the font metrics header (up to the char metrics) and returns a dictionary mapping key to val. val will be converted to the appropriate python type as necessary; eg: * 'False'->False * '0'->0 * '-168 -218 1000 898'-> [-168, -218, 1000, 898] Dictionary keys are StartFontMetrics, FontName, FullName, FamilyName, Weight, ItalicAngle, IsFixedPitch, FontBBox, UnderlinePosition, UnderlineThickness, Version, Notice, EncodingScheme, CapHeight, XHeight, Ascender, Descender, StartCharMetrics """ headerConverters = { 'StartFontMetrics': _to_float, 'FontName': _to_str, 'FullName': _to_str, 'FamilyName': _to_str, 'Weight': _to_str, 'ItalicAngle': _to_float, 'IsFixedPitch': _to_bool, 'FontBBox': _to_list_of_ints, 'UnderlinePosition': _to_int, 'UnderlineThickness': _to_int, 'Version': _to_str, 'Notice': _to_str, 'EncodingScheme': _to_str, 'CapHeight': _to_float, # Is the second version a mistake, or 'Capheight': _to_float, # do some AFM files contain 'Capheight'? -JKS 'XHeight': _to_float, 'Ascender': _to_float, 'Descender': _to_float, 'StdHW': _to_float, 'StdVW': _to_float, 'StartCharMetrics': _to_int, 'CharacterSet': _to_str, 'Characters': _to_int, } d = {} while 1: line = fh.readline() if not line: break line = line.rstrip() if line.startswith('Comment'): continue lst = line.split( ' ', 1 ) #print '%-s\t%-d line :: %-s' % ( fh.name, len(lst), line ) key = lst[0] if len( lst ) == 2: val = lst[1] else: val = '' #key, val = line.split(' ', 1) try: d[key] = headerConverters[key](val) except ValueError: print >>sys.stderr, 'Value error parsing header in AFM:', key, val continue except KeyError: print >>sys.stderr, 'Found an unknown keyword in AFM header (was %s)' % key continue if key=='StartCharMetrics': return d raise RuntimeError('Bad parse') def _parse_char_metrics(fh): """ Return a character metric dictionary. Keys are the ASCII num of the character, values are a (wx, name, bbox) tuple, where wx is the character width, name is the postscript language name, and bbox is a (llx, lly, urx, ury) tuple. This function is incomplete per the standard, but thus far parses all the sample afm files tried. """ ascii_d = {} name_d = {} while 1: line = fh.readline() if not line: break line = line.rstrip() if line.startswith('EndCharMetrics'): return ascii_d, name_d vals = line.split(';')[:4] if len(vals) !=4 : raise RuntimeError('Bad char metrics line: %s' % line) num = _to_int(vals[0].split()[1]) wx = _to_float(vals[1].split()[1]) name = vals[2].split()[1] bbox = _to_list_of_ints(vals[3][2:]) # Workaround: If the character name is 'Euro', give it the corresponding # character code, according to WinAnsiEncoding (see PDF Reference). if name == 'Euro': num = 128 if num != -1: ascii_d[num] = (wx, name, bbox) name_d[name] = (wx, bbox) raise RuntimeError('Bad parse') def _parse_kern_pairs(fh): """ Return a kern pairs dictionary; keys are (char1, char2) tuples and values are the kern pair value. For example, a kern pairs line like ``KPX A y -50`` will be represented as:: d[ ('A', 'y') ] = -50 """ line = fh.readline() if not line.startswith('StartKernPairs'): raise RuntimeError('Bad start of kern pairs data: %s'%line) d = {} while 1: line = fh.readline() if not line: break line = line.rstrip() if len(line)==0: continue if line.startswith('EndKernPairs'): fh.readline() # EndKernData return d vals = line.split() if len(vals)!=4 or vals[0]!='KPX': raise RuntimeError('Bad kern pairs line: %s'%line) c1, c2, val = vals[1], vals[2], _to_float(vals[3]) d[(c1,c2)] = val raise RuntimeError('Bad kern pairs parse') def _parse_composites(fh): """ Return a composites dictionary. Keys are the names of the composites. Values are a num parts list of composite information, with each element being a (name, dx, dy) tuple. Thus a composites line reading: CC Aacute 2 ; PCC A 0 0 ; PCC acute 160 170 ; will be represented as:: d['Aacute'] = [ ('A', 0, 0), ('acute', 160, 170) ] """ d = {} while 1: line = fh.readline() if not line: break line = line.rstrip() if len(line)==0: continue if line.startswith('EndComposites'): return d vals = line.split(';') cc = vals[0].split() name, numParts = cc[1], _to_int(cc[2]) pccParts = [] for s in vals[1:-1]: pcc = s.split() name, dx, dy = pcc[1], _to_float(pcc[2]), _to_float(pcc[3]) pccParts.append( (name, dx, dy) ) d[name] = pccParts raise RuntimeError('Bad composites parse') def _parse_optional(fh): """ Parse the optional fields for kern pair data and composites return value is a (kernDict, compositeDict) which are the return values from :func:`_parse_kern_pairs`, and :func:`_parse_composites` if the data exists, or empty dicts otherwise """ optional = { 'StartKernData' : _parse_kern_pairs, 'StartComposites' : _parse_composites, } d = {'StartKernData':{}, 'StartComposites':{}} while 1: line = fh.readline() if not line: break line = line.rstrip() if len(line)==0: continue key = line.split()[0] if key in optional: d[key] = optional[key](fh) l = ( d['StartKernData'], d['StartComposites'] ) return l def parse_afm(fh): """ Parse the Adobe Font Metics file in file handle fh. Return value is a (dhead, dcmetrics, dkernpairs, dcomposite) tuple where dhead is a :func:`_parse_header` dict, dcmetrics is a :func:`_parse_composites` dict, dkernpairs is a :func:`_parse_kern_pairs` dict (possibly {}), and dcomposite is a :func:`_parse_composites` dict (possibly {}) """ _sanity_check(fh) dhead = _parse_header(fh) dcmetrics_ascii, dcmetrics_name = _parse_char_metrics(fh) doptional = _parse_optional(fh) return dhead, dcmetrics_ascii, dcmetrics_name, doptional[0], doptional[1] class AFM: def __init__(self, fh): """ Parse the AFM file in file object fh """ (dhead, dcmetrics_ascii, dcmetrics_name, dkernpairs, dcomposite) = \ parse_afm(fh) self._header = dhead self._kern = dkernpairs self._metrics = dcmetrics_ascii self._metrics_by_name = dcmetrics_name self._composite = dcomposite def get_bbox_char(self, c, isord=False): if not isord: c=ord(c) wx, name, bbox = self._metrics[c] return bbox def string_width_height(self, s): """ Return the string width (including kerning) and string height as a (w, h) tuple. """ if not len(s): return 0,0 totalw = 0 namelast = None miny = 1e9 maxy = 0 for c in s: if c == '\n': continue wx, name, bbox = self._metrics[ord(c)] l,b,w,h = bbox # find the width with kerning try: kp = self._kern[ (namelast, name) ] except KeyError: kp = 0 totalw += wx + kp # find the max y thismax = b+h if thismax>maxy: maxy = thismax # find the min y thismin = b if thismin<miny: miny = thismin return totalw, maxy-miny def get_str_bbox_and_descent(self, s): """ Return the string bounding box """ if not len(s): return 0,0,0,0 totalw = 0 namelast = None miny = 1e9 maxy = 0 left = 0 if not isinstance(s, unicode): s = s.decode() for c in s: if c == '\n': continue name = uni2type1.get(ord(c), 'question') try: wx, bbox = self._metrics_by_name[name] except KeyError: name = 'question' wx, bbox = self._metrics_by_name[name] l,b,w,h = bbox if l<left: left = l # find the width with kerning try: kp = self._kern[ (namelast, name) ] except KeyError: kp = 0 totalw += wx + kp # find the max y thismax = b+h if thismax>maxy: maxy = thismax # find the min y thismin = b if thismin<miny: miny = thismin return left, miny, totalw, maxy-miny, -miny def get_str_bbox(self, s): """ Return the string bounding box """ return self.get_str_bbox_and_descent(s)[:4] def get_name_char(self, c, isord=False): """ Get the name of the character, ie, ';' is 'semicolon' """ if not isord: c=ord(c) wx, name, bbox = self._metrics[c] return name def get_width_char(self, c, isord=False): """ Get the width of the character from the character metric WX field """ if not isord: c=ord(c) wx, name, bbox = self._metrics[c] return wx def get_width_from_char_name(self, name): """ Get the width of the character from a type1 character name """ wx, bbox = self._metrics_by_name[name] return wx def get_height_char(self, c, isord=False): """ Get the height of character c from the bounding box. This is the ink height (space is 0) """ if not isord: c=ord(c) wx, name, bbox = self._metrics[c] return bbox[-1] def get_kern_dist(self, c1, c2): """ Return the kerning pair distance (possibly 0) for chars c1 and c2 """ name1, name2 = self.get_name_char(c1), self.get_name_char(c2) return self.get_kern_dist_from_name(name1, name2) def get_kern_dist_from_name(self, name1, name2): """ Return the kerning pair distance (possibly 0) for chars name1 and name2 """ try: return self._kern[ (name1, name2) ] except: return 0 def get_fontname(self): "Return the font name, eg, 'Times-Roman'" return self._header['FontName'] def get_fullname(self): "Return the font full name, eg, 'Times-Roman'" name = self._header.get('FullName') if name is None: # use FontName as a substitute name = self._header['FontName'] return name def get_familyname(self): "Return the font family name, eg, 'Times'" name = self._header.get('FamilyName') if name is not None: return name # FamilyName not specified so we'll make a guess name = self.get_fullname() extras = r'(?i)([ -](regular\|plain\|italic\|oblique\|bold\|semibold\|light\|ultralight\|extra\|condensed))+$' return re.sub(extras, '', name) def get_weight(self): "Return the font weight, eg, 'Bold' or 'Roman'" return self._header['Weight'] def get_angle(self): "Return the fontangle as float" return self._header['ItalicAngle'] def get_capheight(self): "Return the cap height as float" return self._header['CapHeight'] def get_xheight(self): "Return the xheight as float" return self._header['XHeight'] def get_underline_thickness(self): "Return the underline thickness as float" return self._header['UnderlineThickness'] def get_horizontal_stem_width(self): """ Return the standard horizontal stem width as float, or None if not specified in AFM file. """ return self._header.get('StdHW', None) def get_vertical_stem_width(self): """ Return the standard vertical stem width as float, or None if not specified in AFM file. """ return self._header.get('StdVW', None) if __name__=='__main__': #pathname = '/usr/local/lib/R/afm/' pathname = '/usr/local/share/fonts/afms/adobe' for fname in os.listdir(pathname): fh = file(os.path.join(pathname,fname)) afm = AFM(fh) w,h = afm.string_width_height('John Hunter is the Man!')	15,057	Python	.py	416	28.298077	109	0.581062	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,250	figure.py	numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/figure.py	""" The figure module provides the top-level :class:`~matplotlib.artist.Artist`, the :class:`Figure`, which contains all the plot elements. The following classes are defined :class:`SubplotParams` control the default spacing of the subplots :class:`Figure` top level container for all plot elements """ import numpy as np import time import artist from artist import Artist from axes import Axes, SubplotBase, subplot_class_factory from cbook import flatten, allequal, Stack, iterable, dedent import _image import colorbar as cbar from image import FigureImage from matplotlib import rcParams from patches import Rectangle from text import Text, _process_text_args from legend import Legend from transforms import Affine2D, Bbox, BboxTransformTo, TransformedBbox from projections import projection_factory, get_projection_names, \ get_projection_class from matplotlib.blocking_input import BlockingMouseInput, BlockingKeyMouseInput import matplotlib.cbook as cbook class SubplotParams: """ A class to hold the parameters for a subplot """ def __init__(self, left=None, bottom=None, right=None, top=None, wspace=None, hspace=None): """ All dimensions are fraction of the figure width or height. All values default to their rc params The following attributes are available left = 0.125 the left side of the subplots of the figure right = 0.9 the right side of the subplots of the figure bottom = 0.1 the bottom of the subplots of the figure top = 0.9 the top of the subplots of the figure wspace = 0.2 the amount of width reserved for blank space between subplots hspace = 0.2 the amount of height reserved for white space between subplots validate make sure the params are in a legal state (left<right, etc) """ self.validate = True self.update(left, bottom, right, top, wspace, hspace) def update(self,left=None, bottom=None, right=None, top=None, wspace=None, hspace=None): """ Update the current values. If any kwarg is None, default to the current value, if set, otherwise to rc """ thisleft = getattr(self, 'left', None) thisright = getattr(self, 'right', None) thistop = getattr(self, 'top', None) thisbottom = getattr(self, 'bottom', None) thiswspace = getattr(self, 'wspace', None) thishspace = getattr(self, 'hspace', None) self._update_this('left', left) self._update_this('right', right) self._update_this('bottom', bottom) self._update_this('top', top) self._update_this('wspace', wspace) self._update_this('hspace', hspace) def reset(): self.left = thisleft self.right = thisright self.top = thistop self.bottom = thisbottom self.wspace = thiswspace self.hspace = thishspace if self.validate: if self.left>=self.right: reset() raise ValueError('left cannot be >= right') if self.bottom>=self.top: reset() raise ValueError('bottom cannot be >= top') def _update_this(self, s, val): if val is None: val = getattr(self, s, None) if val is None: key = 'figure.subplot.' + s val = rcParams[key] setattr(self, s, val) class Figure(Artist): """ The Figure instance supports callbacks through a callbacks attribute which is a :class:`matplotlib.cbook.CallbackRegistry` instance. The events you can connect to are 'dpi_changed', and the callback will be called with ``func(fig)`` where fig is the :class:`Figure` instance. The figure patch is drawn by a the attribute patch a :class:`matplotlib.patches.Rectangle` instance suppressComposite for multiple figure images, the figure will make composite images depending on the renderer option_image_nocomposite function. If suppressComposite is True\|False, this will override the renderer """ def __str__(self): return "Figure(%gx%g)" % tuple(self.bbox.size) def __init__(self, figsize = None, # defaults to rc figure.figsize dpi = None, # defaults to rc figure.dpi facecolor = None, # defaults to rc figure.facecolor edgecolor = None, # defaults to rc figure.edgecolor linewidth = 1.0, # the default linewidth of the frame frameon = True, # whether or not to draw the figure frame subplotpars = None, # default to rc ): """ figsize w,h tuple in inches dpi dots per inch facecolor the figure patch facecolor; defaults to rc ``figure.facecolor`` edgecolor the figure patch edge color; defaults to rc ``figure.edgecolor`` linewidth the figure patch edge linewidth; the default linewidth of the frame frameon if False, suppress drawing the figure frame subplotpars a :class:`SubplotParams` instance, defaults to rc """ Artist.__init__(self) self.callbacks = cbook.CallbackRegistry(('dpi_changed', )) if figsize is None : figsize = rcParams['figure.figsize'] if dpi is None : dpi = rcParams['figure.dpi'] if facecolor is None: facecolor = rcParams['figure.facecolor'] if edgecolor is None: edgecolor = rcParams['figure.edgecolor'] self.dpi_scale_trans = Affine2D() self.dpi = dpi self.bbox_inches = Bbox.from_bounds(0, 0, figsize) self.bbox = TransformedBbox(self.bbox_inches, self.dpi_scale_trans) self.frameon = frameon self.transFigure = BboxTransformTo(self.bbox) # the figurePatch name is deprecated self.patch = self.figurePatch = Rectangle( xy=(0,0), width=1, height=1, facecolor=facecolor, edgecolor=edgecolor, linewidth=linewidth, ) self._set_artist_props(self.patch) self._hold = rcParams['axes.hold'] self.canvas = None if subplotpars is None: subplotpars = SubplotParams() self.subplotpars = subplotpars self._axstack = Stack() # maintain the current axes self.axes = [] self.clf() self._cachedRenderer = None def _get_dpi(self): return self._dpi def _set_dpi(self, dpi): self._dpi = dpi self.dpi_scale_trans.clear().scale(dpi, dpi) self.callbacks.process('dpi_changed', self) dpi = property(_get_dpi, _set_dpi) def autofmt_xdate(self, bottom=0.2, rotation=30, ha='right'): """ Date ticklabels often overlap, so it is useful to rotate them and right align them. Also, a common use case is a number of subplots with shared xaxes where the x-axis is date data. The ticklabels are often long, and it helps to rotate them on the bottom subplot and turn them off on other subplots, as well as turn off xlabels. bottom* the bottom of the subplots for :meth:`subplots_adjust` rotation the rotation of the xtick labels ha the horizontal alignment of the xticklabels """ allsubplots = np.alltrue([hasattr(ax, 'is_last_row') for ax in self.axes]) if len(self.axes)==1: for label in ax.get_xticklabels(): label.set_ha(ha) label.set_rotation(rotation) else: if allsubplots: for ax in self.get_axes(): if ax.is_last_row(): for label in ax.get_xticklabels(): label.set_ha(ha) label.set_rotation(rotation) else: for label in ax.get_xticklabels(): label.set_visible(False) ax.set_xlabel('') if allsubplots: self.subplots_adjust(bottom=bottom) def get_children(self): 'get a list of artists contained in the figure' children = [self.patch] children.extend(self.artists) children.extend(self.axes) children.extend(self.lines) children.extend(self.patches) children.extend(self.texts) children.extend(self.images) children.extend(self.legends) return children def contains(self, mouseevent): """ Test whether the mouse event occurred on the figure. Returns True,{} """ if callable(self._contains): return self._contains(self,mouseevent) #inside = mouseevent.x >= 0 and mouseevent.y >= 0 inside = self.bbox.contains(mouseevent.x,mouseevent.y) return inside,{} def get_window_extent(self, args, kwargs): 'get the figure bounding box in display space; kwargs are void' return self.bbox def suptitle(self, t, kwargs): """ Add a centered title to the figure. kwargs are :class:`matplotlib.text.Text` properties. Using figure coordinates, the defaults are: - x* = 0.5 the x location of text in figure coords - y = 0.98 the y location of the text in figure coords - horizontalalignment = 'center' the horizontal alignment of the text - verticalalignment = 'top' the vertical alignment of the text A :class:`matplotlib.text.Text` instance is returned. Example:: fig.subtitle('this is the figure title', fontsize=12) """ x = kwargs.pop('x', 0.5) y = kwargs.pop('y', 0.98) if ('horizontalalignment' not in kwargs) and ('ha' not in kwargs): kwargs['horizontalalignment'] = 'center' if ('verticalalignment' not in kwargs) and ('va' not in kwargs): kwargs['verticalalignment'] = 'top' t = self.text(x, y, t, kwargs) return t def set_canvas(self, canvas): """ Set the canvas the contains the figure ACCEPTS: a FigureCanvas instance """ self.canvas = canvas def hold(self, b=None): """ Set the hold state. If hold is None (default), toggle the hold state. Else set the hold state to boolean value b. Eg:: hold() # toggle hold hold(True) # hold is on hold(False) # hold is off """ if b is None: self._hold = not self._hold else: self._hold = b def figimage(self, X, xo=0, yo=0, alpha=1.0, norm=None, cmap=None, vmin=None, vmax=None, origin=None): """ call signatures:: figimage(X, kwargs) adds a non-resampled array X to the figure. :: figimage(X, xo, yo) with pixel offsets xo, yo, X must be a float array: * If X is MxN, assume luminance (grayscale) * If X is MxNx3, assume RGB * If X is MxNx4, assume RGBA Optional keyword arguments: ========= ========================================================== Keyword Description ========= ========================================================== xo or yo An integer, the x and y image offset in pixels cmap a :class:`matplotlib.cm.ColorMap` instance, eg cm.jet. If None, default to the rc ``image.cmap`` value norm a :class:`matplotlib.colors.Normalize` instance. The default is normalization(). This scales luminance -> 0-1 vmin\|vmax are used to scale a luminance image to 0-1. If either is None, the min and max of the luminance values will be used. Note if you pass a norm instance, the settings for vmin and vmax will be ignored. alpha the alpha blending value, default is 1.0 origin [ 'upper' \| 'lower' ] Indicates where the [0,0] index of the array is in the upper left or lower left corner of the axes. Defaults to the rc image.origin value ========= ========================================================== figimage complements the axes image (:meth:`~matplotlib.axes.Axes.imshow`) which will be resampled to fit the current axes. If you want a resampled image to fill the entire figure, you can define an :class:`~matplotlib.axes.Axes` with size [0,1,0,1]. An :class:`matplotlib.image.FigureImage` instance is returned. .. plot:: mpl_examples/pylab_examples/figimage_demo.py """ if not self._hold: self.clf() im = FigureImage(self, cmap, norm, xo, yo, origin) im.set_array(X) im.set_alpha(alpha) if norm is None: im.set_clim(vmin, vmax) self.images.append(im) return im def set_figsize_inches(self, args, kwargs): import warnings warnings.warn('Use set_size_inches instead!', DeprecationWarning) self.set_size_inches(args, *kwargs) def set_size_inches(self, args, *kwargs): """ set_size_inches(w,h, forward=False) Set the figure size in inches Usage:: fig.set_size_inches(w,h) # OR fig.set_size_inches((w,h) ) optional kwarg forward=True* will cause the canvas size to be automatically updated; eg you can resize the figure window from the shell WARNING: forward=True is broken on all backends except GTK* and WX* ACCEPTS: a w,h tuple with w,h in inches """ forward = kwargs.get('forward', False) if len(args)==1: w,h = args[0] else: w,h = args dpival = self.dpi self.bbox_inches.p1 = w, h if forward: dpival = self.dpi canvasw = wdpival canvash = hdpival manager = getattr(self.canvas, 'manager', None) if manager is not None: manager.resize(int(canvasw), int(canvash)) def get_size_inches(self): return self.bbox_inches.p1 def get_edgecolor(self): 'Get the edge color of the Figure rectangle' return self.patch.get_edgecolor() def get_facecolor(self): 'Get the face color of the Figure rectangle' return self.patch.get_facecolor() def get_figwidth(self): 'Return the figwidth as a float' return self.bbox_inches.width def get_figheight(self): 'Return the figheight as a float' return self.bbox_inches.height def get_dpi(self): 'Return the dpi as a float' return self.dpi def get_frameon(self): 'get the boolean indicating frameon' return self.frameon def set_edgecolor(self, color): """ Set the edge color of the Figure rectangle ACCEPTS: any matplotlib color - see help(colors) """ self.patch.set_edgecolor(color) def set_facecolor(self, color): """ Set the face color of the Figure rectangle ACCEPTS: any matplotlib color - see help(colors) """ self.patch.set_facecolor(color) def set_dpi(self, val): """ Set the dots-per-inch of the figure ACCEPTS: float """ self.dpi = val def set_figwidth(self, val): """ Set the width of the figure in inches ACCEPTS: float """ self.bbox_inches.x1 = val def set_figheight(self, val): """ Set the height of the figure in inches ACCEPTS: float """ self.bbox_inches.y1 = val def set_frameon(self, b): """ Set whether the figure frame (background) is displayed or invisible ACCEPTS: boolean """ self.frameon = b def delaxes(self, a): 'remove a from the figure and update the current axes' self.axes.remove(a) self._axstack.remove(a) keys = [] for key, thisax in self._seen.items(): if a==thisax: del self._seen[key] for func in self._axobservers: func(self) def _make_key(self, args, kwargs): 'make a hashable key out of args and kwargs' def fixitems(items): #items may have arrays and lists in them, so convert them # to tuples for the key ret = [] for k, v in items: if iterable(v): v = tuple(v) ret.append((k,v)) return tuple(ret) def fixlist(args): ret = [] for a in args: if iterable(a): a = tuple(a) ret.append(a) return tuple(ret) key = fixlist(args), fixitems(kwargs.items()) return key def add_axes(self, args, *kwargs): """ Add an a axes with axes rect [left, bottom, width, height] where all quantities are in fractions of figure width and height. kwargs are legal :class:`~matplotlib.axes.Axes` kwargs plus projection* which sets the projection type of the axes. (For backward compatibility, ``polar=True`` may also be provided, which is equivalent to ``projection='polar'``). Valid values for projection are: %(list)s. Some of these projections support additional kwargs, which may be provided to :meth:`add_axes`:: rect = l,b,w,h fig.add_axes(rect) fig.add_axes(rect, frameon=False, axisbg='g') fig.add_axes(rect, polar=True) fig.add_axes(rect, projection='polar') fig.add_axes(ax) # add an Axes instance If the figure already has an axes with the same parameters, then it will simply make that axes current and return it. If you do not want this behavior, eg. you want to force the creation of a new axes, you must use a unique set of args and kwargs. The axes :attr:`~matplotlib.axes.Axes.label` attribute has been exposed for this purpose. Eg., if you want two axes that are otherwise identical to be added to the figure, make sure you give them unique labels:: fig.add_axes(rect, label='axes1') fig.add_axes(rect, label='axes2') The :class:`~matplotlib.axes.Axes` instance will be returned. The following kwargs are supported: %(Axes)s """ key = self._make_key(args, kwargs) if key in self._seen: ax = self._seen[key] self.sca(ax) return ax if not len(args): return if isinstance(args[0], Axes): a = args[0] assert(a.get_figure() is self) else: rect = args[0] ispolar = kwargs.pop('polar', False) projection = kwargs.pop('projection', None) if ispolar: if projection is not None and projection != 'polar': raise ValueError( "polar=True, yet projection='%s'. " + "Only one of these arguments should be supplied." % projection) projection = 'polar' a = projection_factory(projection, self, rect, kwargs) self.axes.append(a) self._axstack.push(a) self.sca(a) self._seen[key] = a return a add_axes.__doc__ = dedent(add_axes.__doc__) % \ {'list': (", ".join(get_projection_names())), 'Axes': artist.kwdocd['Axes']} def add_subplot(self, args, *kwargs): """ Add a subplot. Examples: fig.add_subplot(111) fig.add_subplot(1,1,1) # equivalent but more general fig.add_subplot(212, axisbg='r') # add subplot with red background fig.add_subplot(111, polar=True) # add a polar subplot fig.add_subplot(sub) # add Subplot instance sub kwargs* are legal :class:`!matplotlib.axes.Axes` kwargs plus projection, which chooses a projection type for the axes. (For backward compatibility, polar=True may also be provided, which is equivalent to projection='polar'). Valid values for projection are: %(list)s. Some of these projections support additional kwargs, which may be provided to :meth:`add_axes`. The :class:`~matplotlib.axes.Axes` instance will be returned. If the figure already has a subplot with key (args, kwargs) then it will simply make that subplot current and return it. The following kwargs are supported: %(Axes)s """ kwargs = kwargs.copy() if not len(args): return if isinstance(args[0], SubplotBase): a = args[0] assert(a.get_figure() is self) else: ispolar = kwargs.pop('polar', False) projection = kwargs.pop('projection', None) if ispolar: if projection is not None and projection != 'polar': raise ValueError( "polar=True, yet projection='%s'. " + "Only one of these arguments should be supplied." % projection) projection = 'polar' projection_class = get_projection_class(projection) key = self._make_key(args, kwargs) if key in self._seen: ax = self._seen[key] if isinstance(ax, projection_class): self.sca(ax) return ax else: self.axes.remove(ax) self._axstack.remove(ax) a = subplot_class_factory(projection_class)(self, args, *kwargs) self._seen[key] = a self.axes.append(a) self._axstack.push(a) self.sca(a) return a add_subplot.__doc__ = dedent(add_subplot.__doc__) % { 'list': ", ".join(get_projection_names()), 'Axes': artist.kwdocd['Axes']} def clf(self): """ Clear the figure """ self.suppressComposite = None self.callbacks = cbook.CallbackRegistry(('dpi_changed', )) for ax in tuple(self.axes): # Iterate over the copy. ax.cla() self.delaxes(ax) # removes ax from self.axes toolbar = getattr(self.canvas, 'toolbar', None) if toolbar is not None: toolbar.update() self._axstack.clear() self._seen = {} self.artists = [] self.lines = [] self.patches = [] self.texts=[] self.images = [] self.legends = [] self._axobservers = [] def clear(self): """ Clear the figure -- synonym for fig.clf """ self.clf() def draw(self, renderer): """ Render the figure using :class:`matplotlib.backend_bases.RendererBase` instance renderer """ # draw the figure bounding box, perhaps none for white figure #print 'figure draw' if not self.get_visible(): return renderer.open_group('figure') if self.frameon: self.patch.draw(renderer) # todo: respect zorder for p in self.patches: p.draw(renderer) for l in self.lines: l.draw(renderer) for a in self.artists: a.draw(renderer) # override the renderer default if self.suppressComposite # is not None composite = renderer.option_image_nocomposite() if self.suppressComposite is not None: composite = self.suppressComposite if len(self.images)<=1 or composite or not allequal([im.origin for im in self.images]): for im in self.images: im.draw(renderer) else: # make a composite image blending alpha # list of (_image.Image, ox, oy) mag = renderer.get_image_magnification() ims = [(im.make_image(mag), im.ox, im.oy) for im in self.images] im = _image.from_images(self.bbox.height mag, self.bbox.width * mag, ims) im.is_grayscale = False l, b, w, h = self.bbox.bounds clippath, affine = self.get_transformed_clip_path_and_affine() renderer.draw_image(l, b, im, self.bbox, clippath, affine) # render the axes for a in self.axes: a.draw(renderer) # render the figure text for t in self.texts: t.draw(renderer) for legend in self.legends: legend.draw(renderer) renderer.close_group('figure') self._cachedRenderer = renderer self.canvas.draw_event(renderer) def draw_artist(self, a): """ draw :class:`matplotlib.artist.Artist` instance a only -- this is available only after the figure is drawn """ assert self._cachedRenderer is not None a.draw(self._cachedRenderer) def get_axes(self): return self.axes def legend(self, handles, labels, args, kwargs): """ Place a legend in the figure. Labels are a sequence of strings, handles is a sequence of :class:`~matplotlib.lines.Line2D` or :class:`~matplotlib.patches.Patch` instances, and loc can be a string or an integer specifying the legend location USAGE:: legend( (line1, line2, line3), ('label1', 'label2', 'label3'), 'upper right') The loc* location codes are:: 'best' : 0, (currently not supported for figure legends) 'upper right' : 1, 'upper left' : 2, 'lower left' : 3, 'lower right' : 4, 'right' : 5, 'center left' : 6, 'center right' : 7, 'lower center' : 8, 'upper center' : 9, 'center' : 10, loc can also be an (x,y) tuple in figure coords, which specifies the lower left of the legend box. figure coords are (0,0) is the left, bottom of the figure and 1,1 is the right, top. The legend instance is returned. The following kwargs are supported loc the location of the legend numpoints the number of points in the legend line prop a :class:`matplotlib.font_manager.FontProperties` instance pad the fractional whitespace inside the legend border markerscale the relative size of legend markers vs. original shadow if True, draw a shadow behind legend labelsep the vertical space between the legend entries handlelen the length of the legend lines handletextsep the space between the legend line and legend text axespad the border between the axes and legend edge .. plot:: mpl_examples/pylab_examples/figlegend_demo.py """ handles = flatten(handles) l = Legend(self, handles, labels, args, kwargs) self.legends.append(l) return l def text(self, x, y, s, args, kwargs): """ Call signature:: figtext(x, y, s, fontdict=None, kwargs) Add text to figure at location x, y (relative 0-1 coords). See :func:`~matplotlib.pyplot.text` for the meaning of the other arguments. kwargs control the :class:`~matplotlib.text.Text` properties: %(Text)s """ override = _process_text_args({}, args, kwargs) t = Text( x=x, y=y, text=s, ) t.update(override) self._set_artist_props(t) self.texts.append(t) return t text.__doc__ = dedent(text.__doc__) % artist.kwdocd def _set_artist_props(self, a): if a!= self: a.set_figure(self) a.set_transform(self.transFigure) def gca(self, kwargs): """ Return the current axes, creating one if necessary The following kwargs are supported %(Axes)s """ ax = self._axstack() if ax is not None: ispolar = kwargs.get('polar', False) projection = kwargs.get('projection', None) if ispolar: if projection is not None and projection != 'polar': raise ValueError( "polar=True, yet projection='%s'. " + "Only one of these arguments should be supplied." % projection) projection = 'polar' projection_class = get_projection_class(projection) if isinstance(ax, projection_class): return ax return self.add_subplot(111, kwargs) gca.__doc__ = dedent(gca.__doc__) % artist.kwdocd def sca(self, a): 'Set the current axes to be a and return a' self._axstack.bubble(a) for func in self._axobservers: func(self) return a def add_axobserver(self, func): 'whenever the axes state change, func(self) will be called' self._axobservers.append(func) def savefig(self, args, *kwargs): """ call signature:: savefig(fname, dpi=None, facecolor='w', edgecolor='w', orientation='portrait', papertype=None, format=None, transparent=False): Save the current figure. The output formats available depend on the backend being used. Arguments: fname: A string containing a path to a filename, or a Python file-like object. If format* is None and fname is a string, the output format is deduced from the extension of the filename. Keyword arguments: dpi: [ None \| scalar > 0 ] The resolution in dots per inch. If None it will default to the value ``savefig.dpi`` in the matplotlibrc file. facecolor, edgecolor: the colors of the figure rectangle orientation: [ 'landscape' \| 'portrait' ] not supported on all backends; currently only on postscript output papertype: One of 'letter', 'legal', 'executive', 'ledger', 'a0' through 'a10', 'b0' through 'b10'. Only supported for postscript output. format: One of the file extensions supported by the active backend. Most backends support png, pdf, ps, eps and svg. transparent: If True, the figure patch and axes patches will all be transparent. This is useful, for example, for displaying a plot on top of a colored background on a web page. The transparency of these patches will be restored to their original values upon exit of this function. """ for key in ('dpi', 'facecolor', 'edgecolor'): if key not in kwargs: kwargs[key] = rcParams['savefig.%s'%key] transparent = kwargs.pop('transparent', False) if transparent: original_figure_alpha = self.patch.get_alpha() self.patch.set_alpha(0.0) original_axes_alpha = [] for ax in self.axes: patch = ax.patch original_axes_alpha.append(patch.get_alpha()) patch.set_alpha(0.0) self.canvas.print_figure(args, kwargs) if transparent: self.patch.set_alpha(original_figure_alpha) for ax, alpha in zip(self.axes, original_axes_alpha): ax.patch.set_alpha(alpha) def colorbar(self, mappable, cax=None, ax=None, kw): if ax is None: ax = self.gca() if cax is None: cax, kw = cbar.make_axes(ax, kw) cax.hold(True) cb = cbar.Colorbar(cax, mappable, kw) def on_changed(m): #print 'calling on changed', m.get_cmap().name cb.set_cmap(m.get_cmap()) cb.set_clim(m.get_clim()) cb.update_bruteforce(m) self.cbid = mappable.callbacksSM.connect('changed', on_changed) mappable.set_colorbar(cb, cax) self.sca(ax) return cb colorbar.__doc__ = ''' Create a colorbar for a ScalarMappable instance. Documentation for the pylab thin wrapper: %s '''% cbar.colorbar_doc def subplots_adjust(self, args, *kwargs): """ fig.subplots_adjust(left=None, bottom=None, right=None, wspace=None, hspace=None) Update the :class:`SubplotParams` with kwargs* (defaulting to rc where None) and update the subplot locations """ self.subplotpars.update(args, kwargs) import matplotlib.axes for ax in self.axes: if not isinstance(ax, matplotlib.axes.SubplotBase): # Check if sharing a subplots axis if ax._sharex is not None and isinstance(ax._sharex, matplotlib.axes.SubplotBase): ax._sharex.update_params() ax.set_position(ax._sharex.figbox) elif ax._sharey is not None and isinstance(ax._sharey, matplotlib.axes.SubplotBase): ax._sharey.update_params() ax.set_position(ax._sharey.figbox) else: ax.update_params() ax.set_position(ax.figbox) def ginput(self, n=1, timeout=30, show_clicks=True): """ call signature:: ginput(self, n=1, timeout=30, show_clicks=True) Blocking call to interact with the figure. This will wait for n* clicks from the user and return a list of the coordinates of each click. If timeout is zero or negative, does not timeout. If n is zero or negative, accumulate clicks until a middle click (or potentially both mouse buttons at once) terminates the input. Right clicking cancels last input. The keyboard can also be used to select points in case your mouse does not have one or more of the buttons. The delete and backspace keys act like right clicking (i.e., remove last point), the enter key terminates input and any other key (not already used by the window manager) selects a point. """ blocking_mouse_input = BlockingMouseInput(self) return blocking_mouse_input(n=n, timeout=timeout, show_clicks=show_clicks) def waitforbuttonpress(self, timeout=-1): """ call signature:: waitforbuttonpress(self, timeout=-1) Blocking call to interact with the figure. This will return True is a key was pressed, False if a mouse button was pressed and None if timeout was reached without either being pressed. If timeout is negative, does not timeout. """ blocking_input = BlockingKeyMouseInput(self) return blocking_input(timeout=timeout) def figaspect(arg): """ Create a figure with specified aspect ratio. If arg is a number, use that aspect ratio. If arg is an array, figaspect will determine the width and height for a figure that would fit array preserving aspect ratio. The figure width, height in inches are returned. Be sure to create an axes with equal with and height, eg Example usage:: # make a figure twice as tall as it is wide w, h = figaspect(2.) fig = Figure(figsize=(w,h)) ax = fig.add_axes([0.1, 0.1, 0.8, 0.8]) ax.imshow(A, kwargs) # make a figure with the proper aspect for an array A = rand(5,3) w, h = figaspect(A) fig = Figure(figsize=(w,h)) ax = fig.add_axes([0.1, 0.1, 0.8, 0.8]) ax.imshow(A, kwargs) Thanks to Fernando Perez for this function """ isarray = hasattr(arg, 'shape') # min/max sizes to respect when autoscaling. If John likes the idea, they # could become rc parameters, for now they're hardwired. figsize_min = np.array((4.0,2.0)) # min length for width/height figsize_max = np.array((16.0,16.0)) # max length for width/height #figsize_min = rcParams['figure.figsize_min'] #figsize_max = rcParams['figure.figsize_max'] # Extract the aspect ratio of the array if isarray: nr,nc = arg.shape[:2] arr_ratio = float(nr)/nc else: arr_ratio = float(arg) # Height of user figure defaults fig_height = rcParams['figure.figsize'][1] # New size for the figure, keeping the aspect ratio of the caller newsize = np.array((fig_height/arr_ratio,fig_height)) # Sanity checks, don't drop either dimension below figsize_min newsize /= min(1.0,(newsize/figsize_min)) # Avoid humongous windows as well newsize /= max(1.0,(newsize/figsize_max)) # Finally, if we have a really funky aspect ratio, break it but respect # the min/max dimensions (we don't want figures 10 feet tall!) newsize = np.clip(newsize,figsize_min,figsize_max) return newsize artist.kwdocd['Figure'] = artist.kwdoc(Figure)	38,331	Python	.py	912	31.475877	100	0.581762	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,251	rcsetup.py	numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/rcsetup.py	""" The rcsetup module contains the default values and the validation code for customization using matplotlib's rc settings. Each rc setting is assigned a default value and a function used to validate any attempted changes to that setting. The default values and validation functions are defined in the rcsetup module, and are used to construct the rcParams global object which stores the settings and is referenced throughout matplotlib. These default values should be consistent with the default matplotlibrc file that actually reflects the values given here. Any additions or deletions to the parameter set listed here should also be visited to the :file:`matplotlibrc.template` in matplotlib's root source directory. """ import os import warnings from matplotlib.fontconfig_pattern import parse_fontconfig_pattern from matplotlib.colors import is_color_like #interactive_bk = ['gtk', 'gtkagg', 'gtkcairo', 'fltkagg', 'qtagg', 'qt4agg', # 'tkagg', 'wx', 'wxagg', 'cocoaagg'] # The capitalized forms are needed for ipython at present; this may # change for later versions. interactive_bk = ['GTK', 'GTKAgg', 'GTKCairo', 'FltkAgg', 'MacOSX', 'QtAgg', 'Qt4Agg', 'TkAgg', 'WX', 'WXAgg', 'CocoaAgg'] non_interactive_bk = ['agg', 'cairo', 'emf', 'gdk', 'pdf', 'ps', 'svg', 'template'] all_backends = interactive_bk + non_interactive_bk class ValidateInStrings: def __init__(self, key, valid, ignorecase=False): 'valid is a list of legal strings' self.key = key self.ignorecase = ignorecase def func(s): if ignorecase: return s.lower() else: return s self.valid = dict([(func(k),k) for k in valid]) def __call__(self, s): if self.ignorecase: s = s.lower() if s in self.valid: return self.valid[s] raise ValueError('Unrecognized %s string "%s": valid strings are %s' % (self.key, s, self.valid.values())) def validate_path_exists(s): 'If s is a path, return s, else False' if os.path.exists(s): return s else: raise RuntimeError('"%s" should be a path but it does not exist'%s) def validate_bool(b): 'Convert b to a boolean or raise' if type(b) is str: b = b.lower() if b in ('t', 'y', 'yes', 'on', 'true', '1', 1, True): return True elif b in ('f', 'n', 'no', 'off', 'false', '0', 0, False): return False else: raise ValueError('Could not convert "%s" to boolean' % b) def validate_bool_maybe_none(b): 'Convert b to a boolean or raise' if type(b) is str: b = b.lower() if b=='none': return None if b in ('t', 'y', 'yes', 'on', 'true', '1', 1, True): return True elif b in ('f', 'n', 'no', 'off', 'false', '0', 0, False): return False else: raise ValueError('Could not convert "%s" to boolean' % b) def validate_float(s): 'convert s to float or raise' try: return float(s) except ValueError: raise ValueError('Could not convert "%s" to float' % s) def validate_int(s): 'convert s to int or raise' try: return int(s) except ValueError: raise ValueError('Could not convert "%s" to int' % s) def validate_fonttype(s): 'confirm that this is a Postscript of PDF font type that we know how to convert to' fonttypes = { 'type3': 3, 'truetype': 42 } try: fonttype = validate_int(s) except ValueError: if s.lower() in fonttypes.keys(): return fonttypes[s.lower()] raise ValueError('Supported Postscript/PDF font types are %s' % fonttypes.keys()) else: if fonttype not in fonttypes.values(): raise ValueError('Supported Postscript/PDF font types are %s' % fonttypes.values()) return fonttype #validate_backend = ValidateInStrings('backend', all_backends, ignorecase=True) _validate_standard_backends = ValidateInStrings('backend', all_backends, ignorecase=True) def validate_backend(s): if s.startswith('module://'): return s else: return _validate_standard_backends(s) validate_numerix = ValidateInStrings('numerix',[ 'Numeric','numarray','numpy', ], ignorecase=True) validate_toolbar = ValidateInStrings('toolbar',[ 'None','classic','toolbar2', ], ignorecase=True) def validate_autolayout(v): if v: warnings.warn("figure.autolayout is not currently supported") class validate_nseq_float: def __init__(self, n): self.n = n def __call__(self, s): 'return a seq of n floats or raise' if type(s) is str: ss = s.split(',') if len(ss) != self.n: raise ValueError('You must supply exactly %d comma separated values'%self.n) try: return [float(val) for val in ss] except ValueError: raise ValueError('Could not convert all entries to floats') else: assert type(s) in (list,tuple) if len(s) != self.n: raise ValueError('You must supply exactly %d values'%self.n) return [float(val) for val in s] class validate_nseq_int: def __init__(self, n): self.n = n def __call__(self, s): 'return a seq of n ints or raise' if type(s) is str: ss = s.split(',') if len(ss) != self.n: raise ValueError('You must supply exactly %d comma separated values'%self.n) try: return [int(val) for val in ss] except ValueError: raise ValueError('Could not convert all entries to ints') else: assert type(s) in (list,tuple) if len(s) != self.n: raise ValueError('You must supply exactly %d values'%self.n) return [int(val) for val in s] def validate_color(s): 'return a valid color arg' if s.lower() == 'none': return 'None' if is_color_like(s): return s stmp = '#' + s if is_color_like(stmp): return stmp # If it is still valid, it must be a tuple. colorarg = s msg = '' if s.find(',')>=0: # get rid of grouping symbols stmp = ''.join([ c for c in s if c.isdigit() or c=='.' or c==',']) vals = stmp.split(',') if len(vals)!=3: msg = '\nColor tuples must be length 3' else: try: colorarg = [float(val) for val in vals] except ValueError: msg = '\nCould not convert all entries to floats' if not msg and is_color_like(colorarg): return colorarg raise ValueError('%s does not look like a color arg%s'%(s, msg)) def validate_stringlist(s): 'return a list' if type(s) is str: return [ v.strip() for v in s.split(',') ] else: assert type(s) in [list,tuple] return [ str(v) for v in s ] validate_orientation = ValidateInStrings('orientation',[ 'landscape', 'portrait', ]) def validate_aspect(s): if s in ('auto', 'equal'): return s try: return float(s) except ValueError: raise ValueError('not a valid aspect specification') def validate_fontsize(s): if type(s) is str: s = s.lower() if s in ['xx-small', 'x-small', 'small', 'medium', 'large', 'x-large', 'xx-large', 'smaller', 'larger']: return s try: return float(s) except ValueError: raise ValueError('not a valid font size') def validate_font_properties(s): parse_fontconfig_pattern(s) return s validate_fontset = ValidateInStrings('fontset', ['cm', 'stix', 'stixsans', 'custom']) validate_verbose = ValidateInStrings('verbose',[ 'silent', 'helpful', 'debug', 'debug-annoying', ]) validate_cairo_format = ValidateInStrings('cairo_format', ['png', 'ps', 'pdf', 'svg'], ignorecase=True) validate_ps_papersize = ValidateInStrings('ps_papersize',[ 'auto', 'letter', 'legal', 'ledger', 'a0', 'a1', 'a2','a3', 'a4', 'a5', 'a6', 'a7', 'a8', 'a9', 'a10', 'b0', 'b1', 'b2', 'b3', 'b4', 'b5', 'b6', 'b7', 'b8', 'b9', 'b10', ], ignorecase=True) def validate_ps_distiller(s): if type(s) is str: s = s.lower() if s in ('none',None): return None elif s in ('false', False): return False elif s in ('ghostscript', 'xpdf'): return s else: raise ValueError('matplotlibrc ps.usedistiller must either be none, ghostscript or xpdf') validate_joinstyle = ValidateInStrings('joinstyle',['miter', 'round', 'bevel'], ignorecase=True) validate_capstyle = ValidateInStrings('capstyle',['butt', 'round', 'projecting'], ignorecase=True) validate_negative_linestyle = ValidateInStrings('negative_linestyle',['solid', 'dashed'], ignorecase=True) def validate_negative_linestyle_legacy(s): try: res = validate_negative_linestyle(s) return res except ValueError: dashes = validate_nseq_float(2)(s) warnings.warn("Deprecated negative_linestyle specification; use 'solid' or 'dashed'") return (0, dashes) # (offset, (solid, blank)) validate_legend_loc = ValidateInStrings('legend_loc',[ 'best', 'upper right', 'upper left', 'lower left', 'lower right', 'right', 'center left', 'center right', 'lower center', 'upper center', 'center', ], ignorecase=True) class ValidateInterval: """ Value must be in interval """ def __init__(self, vmin, vmax, closedmin=True, closedmax=True): self.vmin = vmin self.vmax = vmax self.cmin = closedmin self.cmax = closedmax def __call__(self, s): try: s = float(s) except: raise RuntimeError('Value must be a float; found "%s"'%s) if self.cmin and s<self.vmin: raise RuntimeError('Value must be >= %f; found "%f"'%(self.vmin, s)) elif not self.cmin and s<=self.vmin: raise RuntimeError('Value must be > %f; found "%f"'%(self.vmin, s)) if self.cmax and s>self.vmax: raise RuntimeError('Value must be <= %f; found "%f"'%(self.vmax, s)) elif not self.cmax and s>=self.vmax: raise RuntimeError('Value must be < %f; found "%f"'%(self.vmax, s)) return s # a map from key -> value, converter defaultParams = { 'backend' : ['Agg', validate_backend], # agg is certainly present 'backend_fallback' : [True, validate_bool], # agg is certainly present 'numerix' : ['numpy', validate_numerix], 'maskedarray' : [False, validate_bool], 'toolbar' : ['toolbar2', validate_toolbar], 'datapath' : [None, validate_path_exists], # handled by _get_data_path_cached 'units' : [False, validate_bool], 'interactive' : [False, validate_bool], 'timezone' : ['UTC', str], # the verbosity setting 'verbose.level' : ['silent', validate_verbose], 'verbose.fileo' : ['sys.stdout', str], # line props 'lines.linewidth' : [1.0, validate_float], # line width in points 'lines.linestyle' : ['-', str], # solid line 'lines.color' : ['b', validate_color], # blue 'lines.marker' : ['None', str], # black 'lines.markeredgewidth' : [0.5, validate_float], 'lines.markersize' : [6, validate_float], # markersize, in points 'lines.antialiased' : [True, validate_bool], # antialised (no jaggies) 'lines.dash_joinstyle' : ['miter', validate_joinstyle], 'lines.solid_joinstyle' : ['miter', validate_joinstyle], 'lines.dash_capstyle' : ['butt', validate_capstyle], 'lines.solid_capstyle' : ['projecting', validate_capstyle], # patch props 'patch.linewidth' : [1.0, validate_float], # line width in points 'patch.edgecolor' : ['k', validate_color], # black 'patch.facecolor' : ['b', validate_color], # blue 'patch.antialiased' : [True, validate_bool], # antialised (no jaggies) # font props 'font.family' : ['sans-serif', str], # used by text object 'font.style' : ['normal', str], # 'font.variant' : ['normal', str], # 'font.stretch' : ['normal', str], # 'font.weight' : ['normal', str], # 'font.size' : [12.0, validate_float], # 'font.serif' : [['Bitstream Vera Serif', 'DejaVu Serif', 'New Century Schoolbook', 'Century Schoolbook L', 'Utopia', 'ITC Bookman', 'Bookman', 'Nimbus Roman No9 L','Times New Roman', 'Times','Palatino','Charter','serif'], validate_stringlist], 'font.sans-serif' : [['Bitstream Vera Sans', 'DejaVu Sans', 'Lucida Grande', 'Verdana', 'Geneva', 'Lucid', 'Arial', 'Helvetica', 'Avant Garde', 'sans-serif'], validate_stringlist], 'font.cursive' : [['Apple Chancery','Textile','Zapf Chancery', 'Sand','cursive'], validate_stringlist], 'font.fantasy' : [['Comic Sans MS','Chicago','Charcoal','Impact' 'Western','fantasy'], validate_stringlist], 'font.monospace' : [['Bitstream Vera Sans Mono', 'DejaVu Sans Mono', 'Andale Mono', 'Nimbus Mono L', 'Courier New', 'Courier','Fixed', 'Terminal','monospace'], validate_stringlist], # text props 'text.color' : ['k', validate_color], # black 'text.usetex' : [False, validate_bool], 'text.latex.unicode' : [False, validate_bool], 'text.latex.preamble' : [[''], validate_stringlist], 'text.dvipnghack' : [None, validate_bool_maybe_none], 'text.fontstyle' : ['normal', str], 'text.fontangle' : ['normal', str], 'text.fontvariant' : ['normal', str], 'text.fontweight' : ['normal', str], 'text.fontsize' : ['medium', validate_fontsize], 'mathtext.cal' : ['cursive', validate_font_properties], 'mathtext.rm' : ['serif', validate_font_properties], 'mathtext.tt' : ['monospace', validate_font_properties], 'mathtext.it' : ['serif:italic', validate_font_properties], 'mathtext.bf' : ['serif:bold', validate_font_properties], 'mathtext.sf' : ['sans\-serif', validate_font_properties], 'mathtext.fontset' : ['cm', validate_fontset], 'mathtext.fallback_to_cm' : [True, validate_bool], 'image.aspect' : ['equal', validate_aspect], # equal, auto, a number 'image.interpolation' : ['bilinear', str], 'image.cmap' : ['jet', str], # one of gray, jet, etc 'image.lut' : [256, validate_int], # lookup table 'image.origin' : ['upper', str], # lookup table 'image.resample' : [False, validate_bool], 'contour.negative_linestyle' : ['dashed', validate_negative_linestyle_legacy], # axes props 'axes.axisbelow' : [False, validate_bool], 'axes.hold' : [True, validate_bool], 'axes.facecolor' : ['w', validate_color], # background color; white 'axes.edgecolor' : ['k', validate_color], # edge color; black 'axes.linewidth' : [1.0, validate_float], # edge linewidth 'axes.titlesize' : ['large', validate_fontsize], # fontsize of the axes title 'axes.grid' : [False, validate_bool], # display grid or not 'axes.labelsize' : ['medium', validate_fontsize], # fontsize of the x any y labels 'axes.labelcolor' : ['k', validate_color], # color of axis label 'axes.formatter.limits' : [[-7, 7], validate_nseq_int(2)], # use scientific notation if log10 # of the axis range is smaller than the # first or larger than the second 'axes.unicode_minus' : [True, validate_bool], 'polaraxes.grid' : [True, validate_bool], # display polar grid or not #legend properties 'legend.fancybox' : [False,validate_bool], 'legend.loc' : ['upper right',validate_legend_loc], # at some point, this should be changed to 'best' 'legend.isaxes' : [True,validate_bool], # this option is internally ignored - it never served any useful purpose 'legend.numpoints' : [2, validate_int], # the number of points in the legend line 'legend.fontsize' : ['large', validate_fontsize], 'legend.pad' : [0, validate_float], # was 0.2, deprecated; the fractional whitespace inside the legend border 'legend.borderpad' : [0.4, validate_float], # units are fontsize 'legend.markerscale' : [1.0, validate_float], # the relative size of legend markers vs. original # the following dimensions are in axes coords 'legend.labelsep' : [0.010, validate_float], # the vertical space between the legend entries 'legend.handlelen' : [0.05, validate_float], # the length of the legend lines 'legend.handletextsep' : [0.02, validate_float], # the space between the legend line and legend text 'legend.axespad' : [0.02, validate_float], # the border between the axes and legend edge 'legend.shadow' : [False, validate_bool], 'legend.labelspacing' : [0.5, validate_float], # the vertical space between the legend entries 'legend.handlelength' : [2., validate_float], # the length of the legend lines 'legend.handletextpad' : [.8, validate_float], # the space between the legend line and legend text 'legend.borderaxespad' : [0.5, validate_float], # the border between the axes and legend edge 'legend.columnspacing' : [2., validate_float], # the border between the axes and legend edge 'legend.markerscale' : [1.0, validate_float], # the relative size of legend markers vs. original # the following dimensions are in axes coords 'legend.labelsep' : [0.010, validate_float], # the vertical space between the legend entries 'legend.handlelen' : [0.05, validate_float], # the length of the legend lines 'legend.handletextsep' : [0.02, validate_float], # the space between the legend line and legend text 'legend.axespad' : [0.5, validate_float], # the border between the axes and legend edge 'legend.shadow' : [False, validate_bool], # tick properties 'xtick.major.size' : [4, validate_float], # major xtick size in points 'xtick.minor.size' : [2, validate_float], # minor xtick size in points 'xtick.major.pad' : [4, validate_float], # distance to label in points 'xtick.minor.pad' : [4, validate_float], # distance to label in points 'xtick.color' : ['k', validate_color], # color of the xtick labels 'xtick.labelsize' : ['medium', validate_fontsize], # fontsize of the xtick labels 'xtick.direction' : ['in', str], # direction of xticks 'ytick.major.size' : [4, validate_float], # major ytick size in points 'ytick.minor.size' : [2, validate_float], # minor ytick size in points 'ytick.major.pad' : [4, validate_float], # distance to label in points 'ytick.minor.pad' : [4, validate_float], # distance to label in points 'ytick.color' : ['k', validate_color], # color of the ytick labels 'ytick.labelsize' : ['medium', validate_fontsize], # fontsize of the ytick labels 'ytick.direction' : ['in', str], # direction of yticks 'grid.color' : ['k', validate_color], # grid color 'grid.linestyle' : [':', str], # dotted 'grid.linewidth' : [0.5, validate_float], # in points # figure props # figure size in inches: width by height 'figure.figsize' : [ [8.0,6.0], validate_nseq_float(2)], 'figure.dpi' : [ 80, validate_float], # DPI 'figure.facecolor' : [ '0.75', validate_color], # facecolor; scalar gray 'figure.edgecolor' : [ 'w', validate_color], # edgecolor; white 'figure.autolayout' : [ False, validate_autolayout], 'figure.subplot.left' : [0.125, ValidateInterval(0, 1, closedmin=True, closedmax=True)], 'figure.subplot.right' : [0.9, ValidateInterval(0, 1, closedmin=True, closedmax=True)], 'figure.subplot.bottom' : [0.1, ValidateInterval(0, 1, closedmin=True, closedmax=True)], 'figure.subplot.top' : [0.9, ValidateInterval(0, 1, closedmin=True, closedmax=True)], 'figure.subplot.wspace' : [0.2, ValidateInterval(0, 1, closedmin=True, closedmax=False)], 'figure.subplot.hspace' : [0.2, ValidateInterval(0, 1, closedmin=True, closedmax=False)], 'savefig.dpi' : [100, validate_float], # DPI 'savefig.facecolor' : ['w', validate_color], # facecolor; white 'savefig.edgecolor' : ['w', validate_color], # edgecolor; white 'savefig.orientation' : ['portrait', validate_orientation], # edgecolor; white 'cairo.format' : ['png', validate_cairo_format], 'tk.window_focus' : [False, validate_bool], # Maintain shell focus for TkAgg 'tk.pythoninspect' : [False, validate_bool], # Set PYTHONINSPECT 'ps.papersize' : ['letter', validate_ps_papersize], # Set the papersize/type 'ps.useafm' : [False, validate_bool], # Set PYTHONINSPECT 'ps.usedistiller' : [False, validate_ps_distiller], # use ghostscript or xpdf to distill ps output 'ps.distiller.res' : [6000, validate_int], # dpi 'ps.fonttype' : [3, validate_fonttype], # 3 (Type3) or 42 (Truetype) 'pdf.compression' : [6, validate_int], # compression level from 0 to 9; 0 to disable 'pdf.inheritcolor' : [False, validate_bool], # ignore any color-setting commands from the frontend 'pdf.use14corefonts' : [False, validate_bool], # use only the 14 PDF core fonts # embedded in every PDF viewing application 'pdf.fonttype' : [3, validate_fonttype], # 3 (Type3) or 42 (Truetype) 'svg.image_inline' : [True, validate_bool], # write raster image data directly into the svg file 'svg.image_noscale' : [False, validate_bool], # suppress scaling of raster data embedded in SVG 'svg.embed_char_paths' : [True, validate_bool], # True to save all characters as paths in the SVG 'docstring.hardcopy' : [False, validate_bool], # set this when you want to generate hardcopy docstring 'plugins.directory' : ['.matplotlib_plugins', str], # where plugin directory is locate 'path.simplify' : [False, validate_bool], 'agg.path.chunksize' : [0, validate_int] # 0 to disable chunking; # recommend about 20000 to # enable. Experimental. } if __name__ == '__main__': rc = defaultParams rc['datapath'][0] = '/' for key in rc: if not rc[key][1](rc[key][0]) == rc[key][0]: print "%s: %s != %s"%(key, rc[key][1](rc[key][0]), rc[key][0])	23,344	Python	.py	448	44.694196	123	0.602805	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,252	dates.py	numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/dates.py	""" Matplotlib provides sophisticated date plotting capabilities, standing on the shoulders of python :mod:`datetime`, the add-on modules :mod:`pytz` and :mod:`dateutils`. :class:`datetime` objects are converted to floating point numbers which represent the number of days since 0001-01-01 UTC. The helper functions :func:`date2num`, :func:`num2date` and :func:`drange` are used to facilitate easy conversion to and from :mod:`datetime` and numeric ranges. A wide range of specific and general purpose date tick locators and formatters are provided in this module. See :mod:`matplotlib.ticker` for general information on tick locators and formatters. These are described below. All the matplotlib date converters, tickers and formatters are timezone aware, and the default timezone is given by the timezone parameter in your :file:`matplotlibrc` file. If you leave out a :class:`tz` timezone instance, the default from your rc file will be assumed. If you want to use a custom time zone, pass a :class:`pytz.timezone` instance with the tz keyword argument to :func:`num2date`, :func:`plot_date`, and any custom date tickers or locators you create. See `pytz <http://pytz.sourceforge.net>`_ for information on :mod:`pytz` and timezone handling. The `dateutil module <http://labix.org/python-dateutil>`_ provides additional code to handle date ticking, making it easy to place ticks on any kinds of dates. See examples below. Date tickers ------------ Most of the date tickers can locate single or multiple values. For example:: # tick on mondays every week loc = WeekdayLocator(byweekday=MO, tz=tz) # tick on mondays and saturdays loc = WeekdayLocator(byweekday=(MO, SA)) In addition, most of the constructors take an interval argument:: # tick on mondays every second week loc = WeekdayLocator(byweekday=MO, interval=2) The rrule locator allows completely general date ticking:: # tick every 5th easter rule = rrulewrapper(YEARLY, byeaster=1, interval=5) loc = RRuleLocator(rule) Here are all the date tickers: * :class:`MinuteLocator`: locate minutes * :class:`HourLocator`: locate hours * :class:`DayLocator`: locate specifed days of the month * :class:`WeekdayLocator`: Locate days of the week, eg MO, TU * :class:`MonthLocator`: locate months, eg 7 for july * :class:`YearLocator`: locate years that are multiples of base * :class:`RRuleLocator`: locate using a :class:`matplotlib.dates.rrulewrapper`. The :class:`rrulewrapper` is a simple wrapper around a :class:`dateutils.rrule` (`dateutil <https://moin.conectiva.com.br/DateUtil>`_) which allow almost arbitrary date tick specifications. See `rrule example <../examples/pylab_examples/date_demo_rrule.html>`_. Date formatters --------------- Here all all the date formatters: * :class:`DateFormatter`: use :func:`strftime` format strings * :class:`IndexDateFormatter`: date plots with implicit x indexing. """ import re, time, math, datetime import pytz # compatability for 2008c and older versions try: import pytz.zoneinfo except ImportError: pytz.zoneinfo = pytz.tzinfo pytz.zoneinfo.UTC = pytz.UTC import matplotlib import numpy as np import matplotlib.units as units import matplotlib.cbook as cbook import matplotlib.ticker as ticker from pytz import timezone from dateutil.rrule import rrule, MO, TU, WE, TH, FR, SA, SU, YEARLY, \ MONTHLY, WEEKLY, DAILY, HOURLY, MINUTELY, SECONDLY from dateutil.relativedelta import relativedelta import dateutil.parser __all__ = ( 'date2num', 'num2date', 'drange', 'epoch2num', 'num2epoch', 'mx2num', 'DateFormatter', 'IndexDateFormatter', 'DateLocator', 'RRuleLocator', 'YearLocator', 'MonthLocator', 'WeekdayLocator', 'DayLocator', 'HourLocator', 'MinuteLocator', 'SecondLocator', 'rrule', 'MO', 'TU', 'WE', 'TH', 'FR', 'SA', 'SU', 'YEARLY', 'MONTHLY', 'WEEKLY', 'DAILY', 'HOURLY', 'MINUTELY', 'SECONDLY', 'relativedelta', 'seconds', 'minutes', 'hours', 'weeks') UTC = pytz.timezone('UTC') def _get_rc_timezone(): s = matplotlib.rcParams['timezone'] return pytz.timezone(s) HOURS_PER_DAY = 24. MINUTES_PER_DAY = 60.HOURS_PER_DAY SECONDS_PER_DAY = 60.MINUTES_PER_DAY MUSECONDS_PER_DAY = 1e6SECONDS_PER_DAY SEC_PER_MIN = 60 SEC_PER_HOUR = 3600 SEC_PER_DAY = SEC_PER_HOUR 24 SEC_PER_WEEK = SEC_PER_DAY * 7 MONDAY, TUESDAY, WEDNESDAY, THURSDAY, FRIDAY, SATURDAY, SUNDAY = ( MO, TU, WE, TH, FR, SA, SU) WEEKDAYS = (MONDAY, TUESDAY, WEDNESDAY, THURSDAY, FRIDAY, SATURDAY, SUNDAY) def _to_ordinalf(dt): """ Convert :mod:`datetime` to the Gregorian date as UTC float days, preserving hours, minutes, seconds and microseconds. Return value is a :func:`float`. """ if hasattr(dt, 'tzinfo') and dt.tzinfo is not None: delta = dt.tzinfo.utcoffset(dt) if delta is not None: dt -= delta base = float(dt.toordinal()) if hasattr(dt, 'hour'): base += (dt.hour/HOURS_PER_DAY + dt.minute/MINUTES_PER_DAY + dt.second/SECONDS_PER_DAY + dt.microsecond/MUSECONDS_PER_DAY ) return base def _from_ordinalf(x, tz=None): """ Convert Gregorian float of the date, preserving hours, minutes, seconds and microseconds. Return value is a :class:`datetime`. """ if tz is None: tz = _get_rc_timezone() ix = int(x) dt = datetime.datetime.fromordinal(ix) remainder = float(x) - ix hour, remainder = divmod(24remainder, 1) minute, remainder = divmod(60remainder, 1) second, remainder = divmod(60remainder, 1) microsecond = int(1e6remainder) if microsecond<10: microsecond=0 # compensate for rounding errors dt = datetime.datetime( dt.year, dt.month, dt.day, int(hour), int(minute), int(second), microsecond, tzinfo=UTC).astimezone(tz) if microsecond>999990: # compensate for rounding errors dt += datetime.timedelta(microseconds=1e6-microsecond) return dt class strpdate2num: """ Use this class to parse date strings to matplotlib datenums when you know the date format string of the date you are parsing. See :file:`examples/load_demo.py`. """ def __init__(self, fmt): """ fmt: any valid strptime format is supported """ self.fmt = fmt def __call__(self, s): """s : string to be converted return value: a date2num float """ return date2num(datetime.datetime(time.strptime(s, self.fmt)[:6])) def datestr2num(d): """ Convert a date string to a datenum using :func:`dateutil.parser.parse`. d* can be a single string or a sequence of strings. """ if cbook.is_string_like(d): dt = dateutil.parser.parse(d) return date2num(dt) else: return date2num([dateutil.parser.parse(s) for s in d]) def date2num(d): """ d is either a :class:`datetime` instance or a sequence of datetimes. Return value is a floating point number (or sequence of floats) which gives number of days (fraction part represents hours, minutes, seconds) since 0001-01-01 00:00:00 UTC. """ if not cbook.iterable(d): return _to_ordinalf(d) else: return np.asarray([_to_ordinalf(val) for val in d]) def julian2num(j): 'Convert a Julian date (or sequence) to a matplotlib date (or sequence).' if cbook.iterable(j): j = np.asarray(j) return j + 1721425.5 def num2julian(n): 'Convert a matplotlib date (or sequence) to a Julian date (or sequence).' if cbook.iterable(n): n = np.asarray(n) return n - 1721425.5 def num2date(x, tz=None): """ x is a float value which gives number of days (fraction part represents hours, minutes, seconds) since 0001-01-01 00:00:00 UTC. Return value is a :class:`datetime` instance in timezone tz (default to rcparams TZ value). If x is a sequence, a sequence of :class:`datetime` objects will be returned. """ if tz is None: tz = _get_rc_timezone() if not cbook.iterable(x): return _from_ordinalf(x, tz) else: return [_from_ordinalf(val, tz) for val in x] def drange(dstart, dend, delta): """ Return a date range as float Gregorian ordinals. dstart and dend are :class:`datetime` instances. delta is a :class:`datetime.timedelta` instance. """ step = (delta.days + delta.seconds/SECONDS_PER_DAY + delta.microseconds/MUSECONDS_PER_DAY) f1 = _to_ordinalf(dstart) f2 = _to_ordinalf(dend) return np.arange(f1, f2, step) ### date tickers and formatters ### class DateFormatter(ticker.Formatter): """ Tick location is seconds since the epoch. Use a :func:`strftime` format string. Python only supports :mod:`datetime` :func:`strftime` formatting for years greater than 1900. Thanks to Andrew Dalke, Dalke Scientific Software who contributed the :func:`strftime` code below to include dates earlier than this year. """ illegal_s = re.compile(r"((^\|[^%])(%%)%s)") def __init__(self, fmt, tz=None): """ fmt* is an :func:`strftime` format string; tz is the :class:`tzinfo` instance. """ if tz is None: tz = _get_rc_timezone() self.fmt = fmt self.tz = tz def __call__(self, x, pos=0): dt = num2date(x, self.tz) return self.strftime(dt, self.fmt) def set_tzinfo(self, tz): self.tz = tz def _findall(self, text, substr): # Also finds overlaps sites = [] i = 0 while 1: j = text.find(substr, i) if j == -1: break sites.append(j) i=j+1 return sites # Dalke: I hope I did this math right. Every 28 years the # calendar repeats, except through century leap years excepting # the 400 year leap years. But only if you're using the Gregorian # calendar. def strftime(self, dt, fmt): fmt = self.illegal_s.sub(r"\1", fmt) fmt = fmt.replace("%s", "s") if dt.year > 1900: return cbook.unicode_safe(dt.strftime(fmt)) year = dt.year # For every non-leap year century, advance by # 6 years to get into the 28-year repeat cycle delta = 2000 - year off = 6(delta // 100 + delta // 400) year = year + off # Move to around the year 2000 year = year + ((2000 - year)//28)28 timetuple = dt.timetuple() s1 = time.strftime(fmt, (year,) + timetuple[1:]) sites1 = self._findall(s1, str(year)) s2 = time.strftime(fmt, (year+28,) + timetuple[1:]) sites2 = self._findall(s2, str(year+28)) sites = [] for site in sites1: if site in sites2: sites.append(site) s = s1 syear = "%4d" % (dt.year,) for site in sites: s = s[:site] + syear + s[site+4:] return cbook.unicode_safe(s) class IndexDateFormatter(ticker.Formatter): """ Use with :class:`~matplotlib.ticker.IndexLocator` to cycle format strings by index. """ def __init__(self, t, fmt, tz=None): """ t is a sequence of dates (floating point days). fmt is a :func:`strftime` format string. """ if tz is None: tz = _get_rc_timezone() self.t = t self.fmt = fmt self.tz = tz def __call__(self, x, pos=0): 'Return the label for time x at position pos' ind = int(round(x)) if ind>=len(self.t) or ind<=0: return '' dt = num2date(self.t[ind], self.tz) return cbook.unicode_safe(dt.strftime(self.fmt)) class AutoDateFormatter(ticker.Formatter): """ This class attempts to figure out the best format to use. This is most useful when used with the :class:`AutoDateLocator`. """ # This can be improved by providing some user-level direction on # how to choose the best format (precedence, etc...) # Perhaps a 'struct' that has a field for each time-type where a # zero would indicate "don't show" and a number would indicate # "show" with some sort of priority. Same priorities could mean # show all with the same priority. # Or more simply, perhaps just a format string for each # possibility... def __init__(self, locator, tz=None): self._locator = locator self._formatter = DateFormatter("%b %d %Y %H:%M:%S %Z", tz) self._tz = tz def __call__(self, x, pos=0): scale = float( self._locator._get_unit() ) if ( scale == 365.0 ): self._formatter = DateFormatter("%Y", self._tz) elif ( scale == 30.0 ): self._formatter = DateFormatter("%b %Y", self._tz) elif ( (scale == 1.0) or (scale == 7.0) ): self._formatter = DateFormatter("%b %d %Y", self._tz) elif ( scale == (1.0/24.0) ): self._formatter = DateFormatter("%H:%M:%S %Z", self._tz) elif ( scale == (1.0/(2460)) ): self._formatter = DateFormatter("%H:%M:%S %Z", self._tz) elif ( scale == (1.0/(243600)) ): self._formatter = DateFormatter("%H:%M:%S %Z", self._tz) else: self._formatter = DateFormatter("%b %d %Y %H:%M:%S %Z", self._tz) return self._formatter(x, pos) class rrulewrapper: def __init__(self, freq, kwargs): self._construct = kwargs.copy() self._construct["freq"] = freq self._rrule = rrule(self._construct) def set(self, kwargs): self._construct.update(kwargs) self._rrule = rrule(self._construct) def __getattr__(self, name): if name in self.__dict__: return self.__dict__[name] return getattr(self._rrule, name) class DateLocator(ticker.Locator): hms0d = {'byhour':0, 'byminute':0,'bysecond':0} def __init__(self, tz=None): """ tz is a :class:`tzinfo` instance. """ if tz is None: tz = _get_rc_timezone() self.tz = tz def set_tzinfo(self, tz): self.tz = tz def datalim_to_dt(self): dmin, dmax = self.axis.get_data_interval() return num2date(dmin, self.tz), num2date(dmax, self.tz) def viewlim_to_dt(self): vmin, vmax = self.axis.get_view_interval() return num2date(vmin, self.tz), num2date(vmax, self.tz) def _get_unit(self): """ Return how many days a unit of the locator is; used for intelligent autoscaling. """ return 1 def nonsingular(self, vmin, vmax): unit = self._get_unit() vmin -= 2unit vmax += 2unit return vmin, vmax class RRuleLocator(DateLocator): # use the dateutil rrule instance def __init__(self, o, tz=None): DateLocator.__init__(self, tz) self.rule = o def __call__(self): # if no data have been set, this will tank with a ValueError try: dmin, dmax = self.viewlim_to_dt() except ValueError: return [] if dmin>dmax: dmax, dmin = dmin, dmax delta = relativedelta(dmax, dmin) self.rule.set(dtstart=dmin-delta, until=dmax+delta) dates = self.rule.between(dmin, dmax, True) return date2num(dates) def _get_unit(self): """ Return how many days a unit of the locator is; used for intelligent autoscaling. """ freq = self.rule._rrule._freq if ( freq == YEARLY ): return 365 elif ( freq == MONTHLY ): return 30 elif ( freq == WEEKLY ): return 7 elif ( freq == DAILY ): return 1 elif ( freq == HOURLY ): return (1.0/24.0) elif ( freq == MINUTELY ): return (1.0/(2460)) elif ( freq == SECONDLY ): return (1.0/(243600)) else: # error return -1 #or should this just return '1'? def autoscale(self): """ Set the view limits to include the data range. """ dmin, dmax = self.datalim_to_dt() if dmin>dmax: dmax, dmin = dmin, dmax delta = relativedelta(dmax, dmin) self.rule.set(dtstart=dmin-delta, until=dmax+delta) dmin, dmax = self.datalim_to_dt() vmin = self.rule.before(dmin, True) if not vmin: vmin=dmin vmax = self.rule.after(dmax, True) if not vmax: vmax=dmax vmin = date2num(vmin) vmax = date2num(vmax) return self.nonsingular(vmin, vmax) class AutoDateLocator(DateLocator): """ On autoscale, this class picks the best :class:`MultipleDateLocator` to set the view limits and the tick locations. """ def __init__(self, tz=None): DateLocator.__init__(self, tz) self._locator = YearLocator() self._freq = YEARLY def __call__(self): 'Return the locations of the ticks' self.refresh() return self._locator() def set_axis(self, axis): DateLocator.set_axis(self, axis) self._locator.set_axis(axis) def refresh(self): 'Refresh internal information based on current limits.' dmin, dmax = self.viewlim_to_dt() self._locator = self.get_locator(dmin, dmax) def _get_unit(self): if ( self._freq == YEARLY ): return 365.0 elif ( self._freq == MONTHLY ): return 30.0 elif ( self._freq == WEEKLY ): return 7.0 elif ( self._freq == DAILY ): return 1.0 elif ( self._freq == HOURLY ): return 1.0/24 elif ( self._freq == MINUTELY ): return 1.0/(2460) elif ( self._freq == SECONDLY ): return 1.0/(243600) else: # error return -1 def autoscale(self): 'Try to choose the view limits intelligently.' dmin, dmax = self.datalim_to_dt() self._locator = self.get_locator(dmin, dmax) return self._locator.autoscale() def get_locator(self, dmin, dmax): 'Pick the best locator based on a distance.' delta = relativedelta(dmax, dmin) numYears = (delta.years * 1.0) numMonths = (numYears * 12.0) + delta.months numDays = (numMonths * 31.0) + delta.days numHours = (numDays * 24.0) + delta.hours numMinutes = (numHours * 60.0) + delta.minutes numSeconds = (numMinutes * 60.0) + delta.seconds numticks = 5 # self._freq = YEARLY interval = 1 bymonth = 1 bymonthday = 1 byhour = 0 byminute = 0 bysecond = 0 if ( numYears >= numticks ): self._freq = YEARLY elif ( numMonths >= numticks ): self._freq = MONTHLY bymonth = range(1, 13) if ( (0 <= numMonths) and (numMonths <= 14) ): interval = 1 # show every month elif ( (15 <= numMonths) and (numMonths <= 29) ): interval = 3 # show every 3 months elif ( (30 <= numMonths) and (numMonths <= 44) ): interval = 4 # show every 4 months else: # 45 <= numMonths <= 59 interval = 6 # show every 6 months elif ( numDays >= numticks ): self._freq = DAILY bymonth = None bymonthday = range(1, 32) if ( (0 <= numDays) and (numDays <= 9) ): interval = 1 # show every day elif ( (10 <= numDays) and (numDays <= 19) ): interval = 2 # show every 2 days elif ( (20 <= numDays) and (numDays <= 49) ): interval = 3 # show every 3 days elif ( (50 <= numDays) and (numDays <= 99) ): interval = 7 # show every 1 week else: # 100 <= numDays <= ~150 interval = 14 # show every 2 weeks elif ( numHours >= numticks ): self._freq = HOURLY bymonth = None bymonthday = None byhour = range(0, 24) # show every hour if ( (0 <= numHours) and (numHours <= 14) ): interval = 1 # show every hour elif ( (15 <= numHours) and (numHours <= 30) ): interval = 2 # show every 2 hours elif ( (30 <= numHours) and (numHours <= 45) ): interval = 3 # show every 3 hours elif ( (45 <= numHours) and (numHours <= 68) ): interval = 4 # show every 4 hours elif ( (68 <= numHours) and (numHours <= 90) ): interval = 6 # show every 6 hours else: # 90 <= numHours <= 120 interval = 12 # show every 12 hours elif ( numMinutes >= numticks ): self._freq = MINUTELY bymonth = None bymonthday = None byhour = None byminute = range(0, 60) if ( numMinutes > (10.0 * numticks) ): interval = 10 # end if elif ( numSeconds >= numticks ): self._freq = SECONDLY bymonth = None bymonthday = None byhour = None byminute = None bysecond = range(0, 60) if ( numSeconds > (10.0 * numticks) ): interval = 10 # end if else: # do what? # microseconds as floats, but floats from what reference point? pass rrule = rrulewrapper( self._freq, interval=interval, \ dtstart=dmin, until=dmax, \ bymonth=bymonth, bymonthday=bymonthday, \ byhour=byhour, byminute = byminute, \ bysecond=bysecond ) locator = RRuleLocator(rrule, self.tz) locator.set_axis(self.axis) locator.set_view_interval(self.axis.get_view_interval()) locator.set_data_interval(self.axis.get_data_interval()) return locator class YearLocator(DateLocator): """ Make ticks on a given day of each year that is a multiple of base. Examples:: # Tick every year on Jan 1st locator = YearLocator() # Tick every 5 years on July 4th locator = YearLocator(5, month=7, day=4) """ def __init__(self, base=1, month=1, day=1, tz=None): """ Mark years that are multiple of base on a given month and day (default jan 1). """ DateLocator.__init__(self, tz) self.base = ticker.Base(base) self.replaced = { 'month' : month, 'day' : day, 'hour' : 0, 'minute' : 0, 'second' : 0, 'tzinfo' : tz } def _get_unit(self): """ Return how many days a unit of the locator is; used for intelligent autoscaling. """ return 365 def __call__(self): dmin, dmax = self.viewlim_to_dt() ymin = self.base.le(dmin.year) ymax = self.base.ge(dmax.year) ticks = [dmin.replace(year=ymin, self.replaced)] while 1: dt = ticks[-1] if dt.year>=ymax: return date2num(ticks) year = dt.year + self.base.get_base() ticks.append(dt.replace(year=year, self.replaced)) def autoscale(self): """ Set the view limits to include the data range. """ dmin, dmax = self.datalim_to_dt() ymin = self.base.le(dmin.year) ymax = self.base.ge(dmax.year) vmin = dmin.replace(year=ymin, self.replaced) vmax = dmax.replace(year=ymax, self.replaced) vmin = date2num(vmin) vmax = date2num(vmax) return self.nonsingular(vmin, vmax) class MonthLocator(RRuleLocator): """ Make ticks on occurances of each month month, eg 1, 3, 12. """ def __init__(self, bymonth=None, bymonthday=1, interval=1, tz=None): """ Mark every month in bymonth; bymonth can be an int or sequence. Default is ``range(1,13)``, i.e. every month. interval is the interval between each iteration. For example, if ``interval=2``, mark every second occurance. """ if bymonth is None: bymonth=range(1,13) o = rrulewrapper(MONTHLY, bymonth=bymonth, bymonthday=bymonthday, interval=interval, *self.hms0d) RRuleLocator.__init__(self, o, tz) def _get_unit(self): """ Return how many days a unit of the locator is; used for intelligent autoscaling. """ return 30 class WeekdayLocator(RRuleLocator): """ Make ticks on occurances of each weekday. """ def __init__(self, byweekday=1, interval=1, tz=None): """ Mark every weekday in byweekday; byweekday* can be a number or sequence. Elements of byweekday must be one of MO, TU, WE, TH, FR, SA, SU, the constants from :mod:`dateutils.rrule`. interval specifies the number of weeks to skip. For example, ``interval=2`` plots every second week. """ o = rrulewrapper(DAILY, byweekday=byweekday, interval=interval, *self.hms0d) RRuleLocator.__init__(self, o, tz) def _get_unit(self): """ return how many days a unit of the locator is; used for intelligent autoscaling. """ return 7 class DayLocator(RRuleLocator): """ Make ticks on occurances of each day of the month. For example, 1, 15, 30. """ def __init__(self, bymonthday=None, interval=1, tz=None): """ Mark every day in bymonthday; bymonthday* can be an int or sequence. Default is to tick every day of the month: ``bymonthday=range(1,32)`` """ if bymonthday is None: bymonthday=range(1,32) o = rrulewrapper(DAILY, bymonthday=bymonthday, interval=interval, *self.hms0d) RRuleLocator.__init__(self, o, tz) def _get_unit(self): """ Return how many days a unit of the locator is; used for intelligent autoscaling. """ return 1 class HourLocator(RRuleLocator): """ Make ticks on occurances of each hour. """ def __init__(self, byhour=None, interval=1, tz=None): """ Mark every hour in byhour; byhour* can be an int or sequence. Default is to tick every hour: ``byhour=range(24)`` interval is the interval between each iteration. For example, if ``interval=2``, mark every second occurrence. """ if byhour is None: byhour=range(24) rule = rrulewrapper(HOURLY, byhour=byhour, interval=interval, byminute=0, bysecond=0) RRuleLocator.__init__(self, rule, tz) def _get_unit(self): """ return how many days a unit of the locator is; use for intelligent autoscaling """ return 1/24. class MinuteLocator(RRuleLocator): """ Make ticks on occurances of each minute. """ def __init__(self, byminute=None, interval=1, tz=None): """ Mark every minute in byminute; byminute can be an int or sequence. Default is to tick every minute: ``byminute=range(60)`` interval is the interval between each iteration. For example, if ``interval=2``, mark every second occurrence. """ if byminute is None: byminute=range(60) rule = rrulewrapper(MINUTELY, byminute=byminute, interval=interval, bysecond=0) RRuleLocator.__init__(self, rule, tz) def _get_unit(self): """ Return how many days a unit of the locator is; used for intelligent autoscaling. """ return 1./(2460) class SecondLocator(RRuleLocator): """ Make ticks on occurances of each second. """ def __init__(self, bysecond=None, interval=1, tz=None): """ Mark every second in bysecond; bysecond* can be an int or sequence. Default is to tick every second: ``bysecond = range(60)`` interval is the interval between each iteration. For example, if ``interval=2``, mark every second occurrence. """ if bysecond is None: bysecond=range(60) rule = rrulewrapper(SECONDLY, bysecond=bysecond, interval=interval) RRuleLocator.__init__(self, rule, tz) def _get_unit(self): """ Return how many days a unit of the locator is; used for intelligent autoscaling. """ return 1./(246060) def _close_to_dt(d1, d2, epsilon=5): 'Assert that datetimes d1 and d2 are within epsilon microseconds.' delta = d2-d1 mus = abs(delta.daysMUSECONDS_PER_DAY + delta.seconds1e6 + delta.microseconds) assert(mus<epsilon) def _close_to_num(o1, o2, epsilon=5): 'Assert that float ordinals o1 and o2 are within epsilon microseconds.' delta = abs((o2-o1)MUSECONDS_PER_DAY) assert(delta<epsilon) def epoch2num(e): """ Convert an epoch or sequence of epochs to the new date format, that is days since 0001. """ spd = 24.3600. return 719163 + np.asarray(e)/spd def num2epoch(d): """ Convert days since 0001 to epoch. d can be a number or sequence. """ spd = 24.3600. return (np.asarray(d)-719163)spd def mx2num(mxdates): """ Convert mx :class:`datetime` instance (or sequence of mx instances) to the new date format. """ scalar = False if not cbook.iterable(mxdates): scalar = True mxdates = [mxdates] ret = epoch2num([m.ticks() for m in mxdates]) if scalar: return ret[0] else: return ret def date_ticker_factory(span, tz=None, numticks=5): """ Create a date locator with numticks (approx) and a date formatter for span in days. Return value is (locator, formatter). """ if span==0: span = 1/24. minutes = span2460 hours = span24 days = span weeks = span/7. months = span/31. # approx years = span/365. if years>numticks: locator = YearLocator(int(years/numticks), tz=tz) # define fmt = '%Y' elif months>numticks: locator = MonthLocator(tz=tz) fmt = '%b %Y' elif weeks>numticks: locator = WeekdayLocator(tz=tz) fmt = '%a, %b %d' elif days>numticks: locator = DayLocator(interval=int(math.ceil(days/numticks)), tz=tz) fmt = '%b %d' elif hours>numticks: locator = HourLocator(interval=int(math.ceil(hours/numticks)), tz=tz) fmt = '%H:%M\n%b %d' elif minutes>numticks: locator = MinuteLocator(interval=int(math.ceil(minutes/numticks)), tz=tz) fmt = '%H:%M:%S' else: locator = MinuteLocator(tz=tz) fmt = '%H:%M:%S' formatter = DateFormatter(fmt, tz=tz) return locator, formatter def seconds(s): 'Return seconds as days.' return float(s)/SEC_PER_DAY def minutes(m): 'Return minutes as days.' return float(m)/MINUTES_PER_DAY def hours(h): 'Return hours as days.' return h/24. def weeks(w): 'Return weeks as days.' return w7. class DateConverter(units.ConversionInterface): def axisinfo(unit): 'return the unit AxisInfo' if unit=='date': majloc = AutoDateLocator() majfmt = AutoDateFormatter(majloc) return units.AxisInfo( majloc = majloc, majfmt = majfmt, label='', ) else: return None axisinfo = staticmethod(axisinfo) def convert(value, unit): if units.ConversionInterface.is_numlike(value): return value return date2num(value) convert = staticmethod(convert) def default_units(x): 'Return the default unit for x or None' return 'date' default_units = staticmethod(default_units) units.registry[datetime.date] = DateConverter() units.registry[datetime.datetime] = DateConverter() if __name__=='__main__': #tz = None tz = pytz.timezone('US/Pacific') #tz = UTC dt = datetime.datetime(1011, 10, 9, 13, 44, 22, 101010, tzinfo=tz) x = date2num(dt) _close_to_dt(dt, num2date(x, tz)) #tz = _get_rc_timezone() d1 = datetime.datetime( 2000, 3, 1, tzinfo=tz) d2 = datetime.datetime( 2000, 3, 5, tzinfo=tz) #d1 = datetime.datetime( 2002, 1, 5, tzinfo=tz) #d2 = datetime.datetime( 2003, 12, 1, tzinfo=tz) delta = datetime.timedelta(hours=6) dates = drange(d1, d2, delta) # MGDTODO: Broken on transforms branch #print 'orig', d1 #print 'd2n and back', num2date(date2num(d1), tz) from _transforms import Value, Interval v1 = Value(date2num(d1)) v2 = Value(date2num(d2)) dlim = Interval(v1,v2) vlim = Interval(v1,v2) #locator = HourLocator(byhour=(3,15), tz=tz) #locator = MinuteLocator(byminute=(15,30,45), tz=tz) #locator = YearLocator(base=5, month=7, day=4, tz=tz) #locator = MonthLocator(bymonthday=15) locator = DayLocator(tz=tz) locator.set_data_interval(dlim) locator.set_view_interval(vlim) dmin, dmax = locator.autoscale() vlim.set_bounds(dmin, dmax) ticks = locator() fmt = '%Y-%m-%d %H:%M:%S %Z' formatter = DateFormatter(fmt, tz) #for t in ticks: print formatter(t) for t in dates: print formatter(t)	33,969	Python	.py	873	30.972509	81	0.600438	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,253	offsetbox.py	numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/offsetbox.py	""" The OffsetBox is a simple container artist. The child artist are meant to be drawn at a relative position to its parent. The [VH]Packer, DrawingArea and TextArea are derived from the OffsetBox. The [VH]Packer automatically adjust the relative postisions of their children, which should be instances of the OffsetBox. This is used to align similar artists together, e.g., in legend. The DrawingArea can contain any Artist as a child. The DrawingArea has a fixed width and height. The position of children relative to the parent is fixed. The TextArea is contains a single Text instance. The width and height of the TextArea instance is the width and height of the its child text. """ import matplotlib.transforms as mtransforms import matplotlib.artist as martist import matplotlib.text as mtext import numpy as np from matplotlib.patches import bbox_artist as mbbox_artist DEBUG=False # for debuging use def bbox_artist(args, kwargs): if DEBUG: mbbox_artist(args, *kwargs) # _get_packed_offsets() and _get_aligned_offsets() are coded assuming # that we are packing boxes horizontally. But same function will be # used with vertical packing. def _get_packed_offsets(wd_list, total, sep, mode="fixed"): """ Geiven a list of (width, xdescent) of each boxes, calculate the total width and the x-offset positions of each items according to mode. xdescent is analagous to the usual descent, but along the x-direction. xdescent values are currently ignored. wd_list* : list of (width, xdescent) of boxes to be packed. sep : spacing between boxes total : Intended total length. None if not used. mode : packing mode. 'fixed', 'expand', or 'equal'. """ w_list, d_list = zip(wd_list) # d_list is currently not used. if mode == "fixed": offsets_ = np.add.accumulate([0]+[w + sep for w in w_list]) offsets = offsets_[:-1] if total is None: total = offsets_[-1] - sep return total, offsets elif mode == "expand": sep = (total - sum(w_list))/(len(w_list)-1.) offsets_ = np.add.accumulate([0]+[w + sep for w in w_list]) offsets = offsets_[:-1] return total, offsets elif mode == "equal": maxh = max(w_list) if total is None: total = (maxh+sep)len(w_list) else: sep = float(total)/(len(w_list)) - maxh offsets = np.array([(maxh+sep)i for i in range(len(w_list))]) return total, offsets else: raise ValueError("Unknown mode : %s" % (mode,)) def _get_aligned_offsets(hd_list, height, align="baseline"): """ Geiven a list of (height, descent) of each boxes, align the boxes with align* and calculate the y-offsets of each boxes. total width and the offset positions of each items according to mode. xdescent is analagous to the usual descent, but along the x-direction. xdescent values are currently ignored. hd_list : list of (width, xdescent) of boxes to be aligned. sep : spacing between boxes height : Intended total length. None if not used. align : align mode. 'baseline', 'top', 'bottom', or 'center'. """ if height is None: height = max([h for h, d in hd_list]) if align == "baseline": height_descent = max([h-d for h, d in hd_list]) descent = max([d for h, d in hd_list]) height = height_descent + descent offsets = [0. for h, d in hd_list] elif align in ["left","top"]: descent=0. offsets = [d for h, d in hd_list] elif align in ["right","bottom"]: descent=0. offsets = [height-h+d for h, d in hd_list] elif align == "center": descent=0. offsets = [(height-h).5+d for h, d in hd_list] else: raise ValueError("Unknown Align mode : %s" % (align,)) return height, descent, offsets class OffsetBox(martist.Artist): """ The OffsetBox is a simple container artist. The child artist are meant to be drawn at a relative position to its parent. """ def __init__(self, args, *kwargs): super(OffsetBox, self).__init__(args, *kwargs) self._children = [] self._offset = (0, 0) def set_figure(self, fig): """ Set the figure accepts a class:`~matplotlib.figure.Figure` instance """ martist.Artist.set_figure(self, fig) for c in self.get_children(): c.set_figure(fig) def set_offset(self, xy): """ Set the offset accepts x, y, tuple, or a callable object. """ self._offset = xy def get_offset(self, width, height, xdescent, ydescent): """ Get the offset accepts extent of the box """ if callable(self._offset): return self._offset(width, height, xdescent, ydescent) else: return self._offset def set_width(self, width): """ Set the width accepts float """ self.width = width def set_height(self, height): """ Set the height accepts float """ self.height = height def get_children(self): """ Return a list of artists it contains. """ return self._children def get_extent_offsets(self, renderer): raise Exception("") def get_extent(self, renderer): """ Return with, height, xdescent, ydescent of box """ w, h, xd, yd, offsets = self.get_extent_offsets(renderer) return w, h, xd, yd def get_window_extent(self, renderer): ''' get the bounding box in display space. ''' w, h, xd, yd, offsets = self.get_extent_offsets(renderer) px, py = self.get_offset(w, h, xd, yd) return mtransforms.Bbox.from_bounds(px-xd, py-yd, w, h) def draw(self, renderer): """ Update the location of children if necessary and draw them to the given renderer. """ width, height, xdescent, ydescent, offsets = self.get_extent_offsets(renderer) px, py = self.get_offset(width, height, xdescent, ydescent) for c, (ox, oy) in zip(self.get_children(), offsets): c.set_offset((px+ox, py+oy)) c.draw(renderer) bbox_artist(self, renderer, fill=False, props=dict(pad=0.)) class PackerBase(OffsetBox): def __init__(self, pad=None, sep=None, width=None, height=None, align=None, mode=None, children=None): """ pad* : boundary pad sep : spacing between items width, height : width and height of the container box. calculated if None. align : alignment of boxes mode : packing mode """ super(PackerBase, self).__init__() self.height = height self.width = width self.sep = sep self.pad = pad self.mode = mode self.align = align self._children = children class VPacker(PackerBase): """ The VPacker has its children packed vertically. It automatically adjust the relative postisions of children in the drawing time. """ def __init__(self, pad=None, sep=None, width=None, height=None, align="baseline", mode="fixed", children=None): """ pad : boundary pad sep : spacing between items width, height : width and height of the container box. calculated if None. align : alignment of boxes mode : packing mode """ super(VPacker, self).__init__(pad, sep, width, height, align, mode, children) def get_extent_offsets(self, renderer): """ update offset of childrens and return the extents of the box """ whd_list = [c.get_extent(renderer) for c in self.get_children()] whd_list = [(w, h, xd, (h-yd)) for w, h, xd, yd in whd_list] wd_list = [(w, xd) for w, h, xd, yd in whd_list] width, xdescent, xoffsets = _get_aligned_offsets(wd_list, self.width, self.align) pack_list = [(h, yd) for w,h,xd,yd in whd_list] height, yoffsets_ = _get_packed_offsets(pack_list, self.height, self.sep, self.mode) yoffsets = yoffsets_ + [yd for w,h,xd,yd in whd_list] ydescent = height - yoffsets[0] yoffsets = height - yoffsets #w, h, xd, h_yd = whd_list[-1] yoffsets = yoffsets - ydescent return width + 2self.pad, height + 2self.pad, \ xdescent+self.pad, ydescent+self.pad, \ zip(xoffsets, yoffsets) class HPacker(PackerBase): """ The HPacker has its children packed horizontally. It automatically adjust the relative postisions of children in the drawing time. """ def __init__(self, pad=None, sep=None, width=None, height=None, align="baseline", mode="fixed", children=None): """ pad : boundary pad sep : spacing between items width, height : width and height of the container box. calculated if None. align : alignment of boxes mode : packing mode """ super(HPacker, self).__init__(pad, sep, width, height, align, mode, children) def get_extent_offsets(self, renderer): """ update offset of childrens and return the extents of the box """ whd_list = [c.get_extent(renderer) for c in self.get_children()] if self.height is None: height_descent = max([h-yd for w,h,xd,yd in whd_list]) ydescent = max([yd for w,h,xd,yd in whd_list]) height = height_descent + ydescent else: height = self.height - 2self._pad # width w/o pad hd_list = [(h, yd) for w, h, xd, yd in whd_list] height, ydescent, yoffsets = _get_aligned_offsets(hd_list, self.height, self.align) pack_list = [(w, xd) for w,h,xd,yd in whd_list] width, xoffsets_ = _get_packed_offsets(pack_list, self.width, self.sep, self.mode) xoffsets = xoffsets_ + [xd for w,h,xd,yd in whd_list] xdescent=whd_list[0][2] xoffsets = xoffsets - xdescent return width + 2self.pad, height + 2self.pad, \ xdescent + self.pad, ydescent + self.pad, \ zip(xoffsets, yoffsets) class DrawingArea(OffsetBox): """ The DrawingArea can contain any Artist as a child. The DrawingArea has a fixed width and height. The position of children relative to the parent is fixed. """ def __init__(self, width, height, xdescent=0., ydescent=0., clip=True): """ width, height* : width and height of the container box. xdescent, ydescent : descent of the box in x- and y-direction. """ super(DrawingArea, self).__init__() self.width = width self.height = height self.xdescent = xdescent self.ydescent = ydescent self.offset_transform = mtransforms.Affine2D() self.offset_transform.clear() self.offset_transform.translate(0, 0) def get_transform(self): """ Return the :class:`~matplotlib.transforms.Transform` applied to the children """ return self.offset_transform def set_transform(self, t): """ set_transform is ignored. """ pass def set_offset(self, xy): """ set offset of the container. Accept : tuple of x,y cooridnate in disokay units. """ self._offset = xy self.offset_transform.clear() self.offset_transform.translate(xy[0], xy[1]) def get_offset(self): """ return offset of the container. """ return self._offset def get_window_extent(self, renderer): ''' get the bounding box in display space. ''' w, h, xd, yd = self.get_extent(renderer) ox, oy = self.get_offset() #w, h, xd, yd) return mtransforms.Bbox.from_bounds(ox-xd, oy-yd, w, h) def get_extent(self, renderer): """ Return with, height, xdescent, ydescent of box """ return self.width, self.height, self.xdescent, self.ydescent def add_artist(self, a): 'Add any :class:`~matplotlib.artist.Artist` to the container box' self._children.append(a) a.set_transform(self.get_transform()) def draw(self, renderer): """ Draw the children """ for c in self._children: c.draw(renderer) bbox_artist(self, renderer, fill=False, props=dict(pad=0.)) class TextArea(OffsetBox): """ The TextArea is contains a single Text instance. The text is placed at (0,0) with baseline+left alignment. The width and height of the TextArea instance is the width and height of the its child text. """ def __init__(self, s, textprops=None, multilinebaseline=None, minimumdescent=True, ): """ s : a string to be displayed. textprops : property dictionary for the text multilinebaseline : If True, baseline for multiline text is adjusted so that it is (approximatedly) center-aligned with singleline text. minimumdescent : If True, the box has a minimum descent of "p". """ if textprops is None: textprops = {} if not textprops.has_key("va"): textprops["va"]="baseline" self._text = mtext.Text(0, 0, s, *textprops) OffsetBox.__init__(self) self._children = [self._text] self.offset_transform = mtransforms.Affine2D() self.offset_transform.clear() self.offset_transform.translate(0, 0) self._baseline_transform = mtransforms.Affine2D() self._text.set_transform(self.offset_transform+self._baseline_transform) self._multilinebaseline = multilinebaseline self._minimumdescent = minimumdescent def set_multilinebaseline(self, t): """ Set multilinebaseline . If True, baseline for multiline text is adjusted so that it is (approximatedly) center-aligned with singleline text. """ self._multilinebaseline = t def get_multilinebaseline(self): """ get multilinebaseline . """ return self._multilinebaseline def set_minimumdescent(self, t): """ Set minimumdescent . If True, extent of the single line text is adjusted so that it has minimum descent of "p" """ self._minimumdescent = t def get_minimumdescent(self): """ get minimumdescent. """ return self._minimumdescent def set_transform(self, t): """ set_transform is ignored. """ pass def set_offset(self, xy): """ set offset of the container. Accept : tuple of x,y cooridnate in disokay units. """ self._offset = xy self.offset_transform.clear() self.offset_transform.translate(xy[0], xy[1]) def get_offset(self): """ return offset of the container. """ return self._offset def get_window_extent(self, renderer): ''' get the bounding box in display space. ''' w, h, xd, yd = self.get_extent(renderer) ox, oy = self.get_offset() #w, h, xd, yd) return mtransforms.Bbox.from_bounds(ox-xd, oy-yd, w, h) def get_extent(self, renderer): clean_line, ismath = self._text.is_math_text(self._text._text) _, h_, d_ = renderer.get_text_width_height_descent( "lp", self._text._fontproperties, ismath=False) bbox, info = self._text._get_layout(renderer) w, h = bbox.width, bbox.height line = info[0][0] # first line _, hh, dd = renderer.get_text_width_height_descent( clean_line, self._text._fontproperties, ismath=ismath) self._baseline_transform.clear() if len(info) > 1 and self._multilinebaseline: # multi line d = h-(hh-dd) # the baseline of the first line d_new = 0.5 h - 0.5 * (h_ - d_) self._baseline_transform.translate(0, d - d_new) d = d_new else: # single line h_d = max(h_ - d_, h-dd) if self.get_minimumdescent(): ## to have a minimum descent, #i.e., "l" and "p" have same ## descents. d = max(dd, d_) else: d = dd h = h_d + d return w, h, 0., d def draw(self, renderer): """ Draw the children """ self._text.draw(renderer) bbox_artist(self, renderer, fill=False, props=dict(pad=0.))	17,728	Python	.py	450	29.648889	86	0.581216	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,254	_pylab_helpers.py	numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/_pylab_helpers.py	import sys, gc def error_msg(msg): print >>sys.stderr, msgs class Gcf(object): _activeQue = [] figs = {} def get_fig_manager(num): figManager = Gcf.figs.get(num, None) if figManager is not None: Gcf.set_active(figManager) return figManager get_fig_manager = staticmethod(get_fig_manager) def destroy(num): if not Gcf.has_fignum(num): return figManager = Gcf.figs[num] oldQue = Gcf._activeQue[:] Gcf._activeQue = [] for f in oldQue: if f != figManager: Gcf._activeQue.append(f) del Gcf.figs[num] #print len(Gcf.figs.keys()), len(Gcf._activeQue) figManager.destroy() gc.collect() destroy = staticmethod(destroy) def has_fignum(num): return num in Gcf.figs has_fignum = staticmethod(has_fignum) def get_all_fig_managers(): return Gcf.figs.values() get_all_fig_managers = staticmethod(get_all_fig_managers) def get_num_fig_managers(): return len(Gcf.figs.values()) get_num_fig_managers = staticmethod(get_num_fig_managers) def get_active(): if len(Gcf._activeQue)==0: return None else: return Gcf._activeQue[-1] get_active = staticmethod(get_active) def set_active(manager): oldQue = Gcf._activeQue[:] Gcf._activeQue = [] for m in oldQue: if m != manager: Gcf._activeQue.append(m) Gcf._activeQue.append(manager) Gcf.figs[manager.num] = manager set_active = staticmethod(set_active)	1,572	Python	.py	45	27.4	61	0.623265	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,255	collections.py	numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/collections.py	""" Classes for the efficient drawing of large collections of objects that share most properties, e.g. a large number of line segments or polygons. The classes are not meant to be as flexible as their single element counterparts (e.g. you may not be able to select all line styles) but they are meant to be fast for common use cases (e.g. a bunch of solid line segemnts) """ import copy, math, warnings import numpy as np from numpy import ma import matplotlib as mpl import matplotlib.cbook as cbook import matplotlib.colors as _colors # avoid conflict with kwarg import matplotlib.cm as cm import matplotlib.transforms as transforms import matplotlib.artist as artist import matplotlib.backend_bases as backend_bases import matplotlib.path as mpath import matplotlib.mlab as mlab class Collection(artist.Artist, cm.ScalarMappable): """ Base class for Collections. Must be subclassed to be usable. All properties in a collection must be sequences or scalars; if scalars, they will be converted to sequences. The property of the ith element of the collection is:: prop[i % len(props)] Keyword arguments and default values: * edgecolors: None * facecolors: None * linewidths: None * antialiaseds: None * offsets: None * transOffset: transforms.IdentityTransform() * norm: None (optional for :class:`matplotlib.cm.ScalarMappable`) * cmap: None (optional for :class:`matplotlib.cm.ScalarMappable`) offsets and transOffset are used to translate the patch after rendering (default no offsets). If any of edgecolors, facecolors, linewidths, antialiaseds are None, they default to their :data:`matplotlib.rcParams` patch setting, in sequence form. The use of :class:`~matplotlib.cm.ScalarMappable` is optional. If the :class:`~matplotlib.cm.ScalarMappable` matrix _A is not None (ie a call to set_array has been made), at draw time a call to scalar mappable will be made to set the face colors. """ _offsets = np.array([], np.float_) _transOffset = transforms.IdentityTransform() _transforms = [] zorder = 1 def __init__(self, edgecolors=None, facecolors=None, linewidths=None, linestyles='solid', antialiaseds = None, offsets = None, transOffset = None, norm = None, # optional for ScalarMappable cmap = None, # ditto pickradius = 5.0, urls = None, *kwargs ): """ Create a Collection %(Collection)s """ artist.Artist.__init__(self) cm.ScalarMappable.__init__(self, norm, cmap) self.set_edgecolor(edgecolors) self.set_facecolor(facecolors) self.set_linewidth(linewidths) self.set_linestyle(linestyles) self.set_antialiased(antialiaseds) self.set_urls(urls) self._uniform_offsets = None self._offsets = np.array([], np.float_) if offsets is not None: offsets = np.asarray(offsets) if len(offsets.shape) == 1: offsets = offsets[np.newaxis,:] # Make it Nx2. if transOffset is not None: self._offsets = offsets self._transOffset = transOffset else: self._uniform_offsets = offsets self._pickradius = pickradius self.update(kwargs) def _get_value(self, val): try: return (float(val), ) except TypeError: if cbook.iterable(val) and len(val): try: float(val[0]) except TypeError: pass # raise below else: return val raise TypeError('val must be a float or nonzero sequence of floats') def _get_bool(self, val): try: return (bool(val), ) except TypeError: if cbook.iterable(val) and len(val): try: bool(val[0]) except TypeError: pass # raise below else: return val raise TypeError('val must be a bool or nonzero sequence of them') def get_paths(self): raise NotImplementedError def get_transforms(self): return self._transforms def get_datalim(self, transData): transform = self.get_transform() transOffset = self._transOffset offsets = self._offsets paths = self.get_paths() if not transform.is_affine: paths = [transform.transform_path_non_affine(p) for p in paths] transform = transform.get_affine() if not transOffset.is_affine: offsets = transOffset.transform_non_affine(offsets) transOffset = transOffset.get_affine() offsets = np.asarray(offsets, np.float_) result = mpath.get_path_collection_extents( transform.frozen(), paths, self.get_transforms(), offsets, transOffset.frozen()) result = result.inverse_transformed(transData) return result def get_window_extent(self, renderer): bbox = self.get_datalim(transforms.IdentityTransform()) #TODO:check to ensure that this does not fail for #cases other than scatter plot legend return bbox def _prepare_points(self): """Point prep for drawing and hit testing""" transform = self.get_transform() transOffset = self._transOffset offsets = self._offsets paths = self.get_paths() if self.have_units(): paths = [] for path in self.get_paths(): vertices = path.vertices xs, ys = vertices[:, 0], vertices[:, 1] xs = self.convert_xunits(xs) ys = self.convert_yunits(ys) paths.append(mpath.Path(zip(xs, ys), path.codes)) if len(self._offsets): xs = self.convert_xunits(self._offsets[:0]) ys = self.convert_yunits(self._offsets[:1]) offsets = zip(xs, ys) offsets = np.asarray(offsets, np.float_) if not transform.is_affine: paths = [transform.transform_path_non_affine(path) for path in paths] transform = transform.get_affine() if not transOffset.is_affine: offsets = transOffset.transform_non_affine(offsets) transOffset = transOffset.get_affine() return transform, transOffset, offsets, paths def draw(self, renderer): if not self.get_visible(): return renderer.open_group(self.__class__.__name__) self.update_scalarmappable() clippath, clippath_trans = self.get_transformed_clip_path_and_affine() if clippath_trans is not None: clippath_trans = clippath_trans.frozen() transform, transOffset, offsets, paths = self._prepare_points() renderer.draw_path_collection( transform.frozen(), self.clipbox, clippath, clippath_trans, paths, self.get_transforms(), offsets, transOffset, self.get_facecolor(), self.get_edgecolor(), self._linewidths, self._linestyles, self._antialiaseds, self._urls) renderer.close_group(self.__class__.__name__) def contains(self, mouseevent): """ Test whether the mouse event occurred in the collection. Returns True \| False, ``dict(ind=itemlist)``, where every item in itemlist contains the event. """ if callable(self._contains): return self._contains(self,mouseevent) if not self.get_visible(): return False,{} transform, transOffset, offsets, paths = self._prepare_points() ind = mpath.point_in_path_collection( mouseevent.x, mouseevent.y, self._pickradius, transform.frozen(), paths, self.get_transforms(), offsets, transOffset, len(self._facecolors)>0) return len(ind)>0,dict(ind=ind) def set_pickradius(self,pickradius): self.pickradius = 5 def get_pickradius(self): return self.pickradius def set_urls(self, urls): if urls is None: self._urls = [None,] else: self._urls = urls def get_urls(self): return self._urls def set_offsets(self, offsets): """ Set the offsets for the collection. offsets* can be a scalar or a sequence. ACCEPTS: float or sequence of floats """ offsets = np.asarray(offsets, np.float_) if len(offsets.shape) == 1: offsets = offsets[np.newaxis,:] # Make it Nx2. #This decision is based on how they are initialized above if self._uniform_offsets is None: self._offsets = offsets else: self._uniform_offsets = offsets def get_offsets(self): """ Return the offsets for the collection. """ #This decision is based on how they are initialized above in __init__() if self._uniform_offsets is None: return self._offsets else: return self._uniform_offsets def set_linewidth(self, lw): """ Set the linewidth(s) for the collection. lw can be a scalar or a sequence; if it is a sequence the patches will cycle through the sequence ACCEPTS: float or sequence of floats """ if lw is None: lw = mpl.rcParams['patch.linewidth'] self._linewidths = self._get_value(lw) def set_linewidths(self, lw): """alias for set_linewidth""" return self.set_linewidth(lw) def set_lw(self, lw): """alias for set_linewidth""" return self.set_linewidth(lw) def set_linestyle(self, ls): """ Set the linestyle(s) for the collection. ACCEPTS: ['solid' \| 'dashed', 'dashdot', 'dotted' \| (offset, on-off-dash-seq) ] """ try: dashd = backend_bases.GraphicsContextBase.dashd if cbook.is_string_like(ls): if ls in dashd: dashes = [dashd[ls]] elif ls in cbook.ls_mapper: dashes = [dashd[cbook.ls_mapper[ls]]] else: raise ValueError() elif cbook.iterable(ls): try: dashes = [] for x in ls: if cbook.is_string_like(x): if x in dashd: dashes.append(dashd[x]) elif x in cbook.ls_mapper: dashes.append(dashd[cbook.ls_mapper[x]]) else: raise ValueError() elif cbook.iterable(x) and len(x) == 2: dashes.append(x) else: raise ValueError() except ValueError: if len(ls)==2: dashes = ls else: raise ValueError() else: raise ValueError() except ValueError: raise ValueError('Do not know how to convert %s to dashes'%ls) self._linestyles = dashes def set_linestyles(self, ls): """alias for set_linestyle""" return self.set_linestyle(ls) def set_dashes(self, ls): """alias for set_linestyle""" return self.set_linestyle(ls) def set_antialiased(self, aa): """ Set the antialiasing state for rendering. ACCEPTS: Boolean or sequence of booleans """ if aa is None: aa = mpl.rcParams['patch.antialiased'] self._antialiaseds = self._get_bool(aa) def set_antialiaseds(self, aa): """alias for set_antialiased""" return self.set_antialiased(aa) def set_color(self, c): """ Set both the edgecolor and the facecolor. ACCEPTS: matplotlib color arg or sequence of rgba tuples .. seealso:: :meth:`set_facecolor`, :meth:`set_edgecolor` """ self.set_facecolor(c) self.set_edgecolor(c) def set_facecolor(self, c): """ Set the facecolor(s) of the collection. c can be a matplotlib color arg (all patches have same color), or a sequence or rgba tuples; if it is a sequence the patches will cycle through the sequence ACCEPTS: matplotlib color arg or sequence of rgba tuples """ if c is None: c = mpl.rcParams['patch.facecolor'] self._facecolors_original = c self._facecolors = _colors.colorConverter.to_rgba_array(c, self._alpha) def set_facecolors(self, c): """alias for set_facecolor""" return self.set_facecolor(c) def get_facecolor(self): return self._facecolors get_facecolors = get_facecolor def get_edgecolor(self): if self._edgecolors == 'face': return self.get_facecolors() else: return self._edgecolors get_edgecolors = get_edgecolor def set_edgecolor(self, c): """ Set the edgecolor(s) of the collection. c can be a matplotlib color arg (all patches have same color), or a sequence or rgba tuples; if it is a sequence the patches will cycle through the sequence. If c is 'face', the edge color will always be the same as the face color. ACCEPTS: matplotlib color arg or sequence of rgba tuples """ if c == 'face': self._edgecolors = 'face' self._edgecolors_original = 'face' else: if c is None: c = mpl.rcParams['patch.edgecolor'] self._edgecolors_original = c self._edgecolors = _colors.colorConverter.to_rgba_array(c, self._alpha) def set_edgecolors(self, c): """alias for set_edgecolor""" return self.set_edgecolor(c) def set_alpha(self, alpha): """ Set the alpha tranparencies of the collection. alpha must be a float. ACCEPTS: float """ try: float(alpha) except TypeError: raise TypeError('alpha must be a float') else: artist.Artist.set_alpha(self, alpha) try: self._facecolors = _colors.colorConverter.to_rgba_array( self._facecolors_original, self._alpha) except (AttributeError, TypeError, IndexError): pass try: if self._edgecolors_original != 'face': self._edgecolors = _colors.colorConverter.to_rgba_array( self._edgecolors_original, self._alpha) except (AttributeError, TypeError, IndexError): pass def get_linewidths(self): return self._linewidths get_linewidth = get_linewidths def get_linestyles(self): return self._linestyles get_dashes = get_linestyle = get_linestyles def update_scalarmappable(self): """ If the scalar mappable array is not none, update colors from scalar data """ if self._A is None: return if self._A.ndim > 1: raise ValueError('Collections can only map rank 1 arrays') if len(self._facecolors): self._facecolors = self.to_rgba(self._A, self._alpha) else: self._edgecolors = self.to_rgba(self._A, self._alpha) def update_from(self, other): 'copy properties from other to self' artist.Artist.update_from(self, other) self._antialiaseds = other._antialiaseds self._edgecolors_original = other._edgecolors_original self._edgecolors = other._edgecolors self._facecolors_original = other._facecolors_original self._facecolors = other._facecolors self._linewidths = other._linewidths self._linestyles = other._linestyles self._pickradius = other._pickradius # these are not available for the object inspector until after the # class is built so we define an initial set here for the init # function and they will be overridden after object defn artist.kwdocd['Collection'] = """\ Valid Collection keyword arguments: * edgecolors: None * facecolors: None * linewidths: None * antialiaseds: None * offsets: None * transOffset: transforms.IdentityTransform() * norm: None (optional for :class:`matplotlib.cm.ScalarMappable`) * cmap: None (optional for :class:`matplotlib.cm.ScalarMappable`) offsets and transOffset are used to translate the patch after rendering (default no offsets) If any of edgecolors, facecolors, linewidths, antialiaseds are None, they default to their :data:`matplotlib.rcParams` patch setting, in sequence form. """ class QuadMesh(Collection): """ Class for the efficient drawing of a quadrilateral mesh. A quadrilateral mesh consists of a grid of vertices. The dimensions of this array are (meshWidth + 1, meshHeight + 1). Each vertex in the mesh has a different set of "mesh coordinates" representing its position in the topology of the mesh. For any values (m, n) such that 0 <= m <= meshWidth and 0 <= n <= meshHeight, the vertices at mesh coordinates (m, n), (m, n + 1), (m + 1, n + 1), and (m + 1, n) form one of the quadrilaterals in the mesh. There are thus (meshWidth * meshHeight) quadrilaterals in the mesh. The mesh need not be regular and the polygons need not be convex. A quadrilateral mesh is represented by a (2 x ((meshWidth + 1) * (meshHeight + 1))) numpy array coordinates, where each row is the x and y coordinates of one of the vertices. To define the function that maps from a data point to its corresponding color, use the :meth:`set_cmap` method. Each of these arrays is indexed in row-major order by the mesh coordinates of the vertex (or the mesh coordinates of the lower left vertex, in the case of the colors). For example, the first entry in coordinates is the coordinates of the vertex at mesh coordinates (0, 0), then the one at (0, 1), then at (0, 2) .. (0, meshWidth), (1, 0), (1, 1), and so on. """ def __init__(self, meshWidth, meshHeight, coordinates, showedges, antialiased=True): Collection.__init__(self) self._meshWidth = meshWidth self._meshHeight = meshHeight self._coordinates = coordinates self._showedges = showedges self._antialiased = antialiased self._paths = None self._bbox = transforms.Bbox.unit() self._bbox.update_from_data_xy(coordinates.reshape( ((meshWidth + 1) * (meshHeight + 1), 2))) # By converting to floats now, we can avoid that on every draw. self._coordinates = self._coordinates.reshape((meshHeight + 1, meshWidth + 1, 2)) self._coordinates = np.array(self._coordinates, np.float_) def get_paths(self, dataTrans=None): if self._paths is None: self._paths = self.convert_mesh_to_paths( self._meshWidth, self._meshHeight, self._coordinates) return self._paths #@staticmethod def convert_mesh_to_paths(meshWidth, meshHeight, coordinates): """ Converts a given mesh into a sequence of :class:`matplotlib.path.Path` objects for easier rendering by backends that do not directly support quadmeshes. This function is primarily of use to backend implementers. """ Path = mpath.Path if ma.isMaskedArray(coordinates): c = coordinates.data else: c = coordinates points = np.concatenate(( c[0:-1, 0:-1], c[0:-1, 1: ], c[1: , 1: ], c[1: , 0:-1], c[0:-1, 0:-1] ), axis=2) points = points.reshape((meshWidth * meshHeight, 5, 2)) return [Path(x) for x in points] convert_mesh_to_paths = staticmethod(convert_mesh_to_paths) def get_datalim(self, transData): return self._bbox def draw(self, renderer): if not self.get_visible(): return renderer.open_group(self.__class__.__name__) transform = self.get_transform() transOffset = self._transOffset offsets = self._offsets if self.have_units(): if len(self._offsets): xs = self.convert_xunits(self._offsets[:0]) ys = self.convert_yunits(self._offsets[:1]) offsets = zip(xs, ys) offsets = np.asarray(offsets, np.float_) if self.check_update('array'): self.update_scalarmappable() clippath, clippath_trans = self.get_transformed_clip_path_and_affine() if clippath_trans is not None: clippath_trans = clippath_trans.frozen() if not transform.is_affine: coordinates = self._coordinates.reshape( (self._coordinates.shape[0] * self._coordinates.shape[1], 2)) coordinates = transform.transform(coordinates) coordinates = coordinates.reshape(self._coordinates.shape) transform = transforms.IdentityTransform() else: coordinates = self._coordinates if not transOffset.is_affine: offsets = transOffset.transform_non_affine(offsets) transOffset = transOffset.get_affine() renderer.draw_quad_mesh( transform.frozen(), self.clipbox, clippath, clippath_trans, self._meshWidth, self._meshHeight, coordinates, offsets, transOffset, self.get_facecolor(), self._antialiased, self._showedges) renderer.close_group(self.__class__.__name__) class PolyCollection(Collection): def __init__(self, verts, sizes = None, closed = True, *kwargs): """ verts* is a sequence of ( verts0, verts1, ...) where verts_i is a sequence of xy tuples of vertices, or an equivalent :mod:`numpy` array of shape (nv, 2). sizes is None (default) or a sequence of floats that scale the corresponding verts_i. The scaling is applied before the Artist master transform; if the latter is an identity transform, then the overall scaling is such that if verts_i specify a unit square, then sizes_i is the area of that square in points^2. If len(sizes) < nv, the additional values will be taken cyclically from the array. closed, when True, will explicitly close the polygon. %(Collection)s """ Collection.__init__(self,*kwargs) self._sizes = sizes self.set_verts(verts, closed) __init__.__doc__ = cbook.dedent(__init__.__doc__) % artist.kwdocd def set_verts(self, verts, closed=True): '''This allows one to delay initialization of the vertices.''' if closed: self._paths = [] for xy in verts: if np.ma.isMaskedArray(xy): if len(xy) and (xy[0] != xy[-1]).any(): xy = np.ma.concatenate([xy, [xy[0]]]) else: xy = np.asarray(xy) if len(xy) and (xy[0] != xy[-1]).any(): xy = np.concatenate([xy, [xy[0]]]) self._paths.append(mpath.Path(xy)) else: self._paths = [mpath.Path(xy) for xy in verts] def get_paths(self): return self._paths def draw(self, renderer): if self._sizes is not None: self._transforms = [ transforms.Affine2D().scale( (np.sqrt(x) self.figure.dpi / 72.0)) for x in self._sizes] return Collection.draw(self, renderer) class BrokenBarHCollection(PolyCollection): """ A collection of horizontal bars spanning yrange with a sequence of xranges. """ def __init__(self, xranges, yrange, *kwargs): """ xranges* sequence of (xmin, xwidth) yrange ymin, ywidth %(Collection)s """ ymin, ywidth = yrange ymax = ymin + ywidth verts = [ [(xmin, ymin), (xmin, ymax), (xmin+xwidth, ymax), (xmin+xwidth, ymin), (xmin, ymin)] for xmin, xwidth in xranges] PolyCollection.__init__(self, verts, kwargs) __init__.__doc__ = cbook.dedent(__init__.__doc__) % artist.kwdocd @staticmethod def span_where(x, ymin, ymax, where, kwargs): """ Create a BrokenBarHCollection to plot horizontal bars from over the regions in x where where is True. The bars range on the y-axis from ymin to ymax A :class:`BrokenBarHCollection` is returned. kwargs are passed on to the collection """ xranges = [] for ind0, ind1 in mlab.contiguous_regions(where): xslice = x[ind0:ind1] if not len(xslice): continue xranges.append((xslice[0], xslice[-1]-xslice[0])) collection = BrokenBarHCollection(xranges, [ymin, ymax-ymin], *kwargs) return collection class RegularPolyCollection(Collection): """Draw a collection of regular polygons with numsides.""" _path_generator = mpath.Path.unit_regular_polygon def __init__(self, numsides, rotation = 0 , sizes = (1,), kwargs): """ numsides* the number of sides of the polygon rotation the rotation of the polygon in radians sizes gives the area of the circle circumscribing the regular polygon in points^2 %(Collection)s Example: see :file:`examples/dynamic_collection.py` for complete example:: offsets = np.random.rand(20,2) facecolors = [cm.jet(x) for x in np.random.rand(20)] black = (0,0,0,1) collection = RegularPolyCollection( numsides=5, # a pentagon rotation=0, sizes=(50,), facecolors = facecolors, edgecolors = (black,), linewidths = (1,), offsets = offsets, transOffset = ax.transData, ) """ Collection.__init__(self,*kwargs) self._sizes = sizes self._numsides = numsides self._paths = [self._path_generator(numsides)] self._rotation = rotation self.set_transform(transforms.IdentityTransform()) __init__.__doc__ = cbook.dedent(__init__.__doc__) % artist.kwdocd def draw(self, renderer): self._transforms = [ transforms.Affine2D().rotate(-self._rotation).scale( (np.sqrt(x) self.figure.dpi / 72.0) / np.sqrt(np.pi)) for x in self._sizes] return Collection.draw(self, renderer) def get_paths(self): return self._paths def get_numsides(self): return self._numsides def get_rotation(self): return self._rotation def get_sizes(self): return self._sizes class StarPolygonCollection(RegularPolyCollection): """ Draw a collection of regular stars with numsides points.""" _path_generator = mpath.Path.unit_regular_star class AsteriskPolygonCollection(RegularPolyCollection): """ Draw a collection of regular asterisks with numsides points.""" _path_generator = mpath.Path.unit_regular_asterisk class LineCollection(Collection): """ All parameters must be sequences or scalars; if scalars, they will be converted to sequences. The property of the ith line segment is:: prop[i % len(props)] i.e., the properties cycle if the ``len`` of props is less than the number of segments. """ zorder = 2 def __init__(self, segments, # Can be None. linewidths = None, colors = None, antialiaseds = None, linestyles = 'solid', offsets = None, transOffset = None, norm = None, cmap = None, pickradius = 5, *kwargs ): """ segments* a sequence of (line0, line1, line2), where:: linen = (x0, y0), (x1, y1), ... (xm, ym) or the equivalent numpy array with two columns. Each line can be a different length. colors must be a sequence of RGBA tuples (eg arbitrary color strings, etc, not allowed). antialiaseds must be a sequence of ones or zeros linestyles [ 'solid' \| 'dashed' \| 'dashdot' \| 'dotted' ] a string or dash tuple. The dash tuple is:: (offset, onoffseq), where onoffseq is an even length tuple of on and off ink in points. If linewidths, colors, or antialiaseds is None, they default to their rcParams setting, in sequence form. If offsets and transOffset are not None, then offsets are transformed by transOffset and applied after the segments have been transformed to display coordinates. If offsets is not None but transOffset is None, then the offsets are added to the segments before any transformation. In this case, a single offset can be specified as:: offsets=(xo,yo) and this value will be added cumulatively to each successive segment, so as to produce a set of successively offset curves. norm None (optional for :class:`matplotlib.cm.ScalarMappable`) cmap None (optional for :class:`matplotlib.cm.ScalarMappable`) pickradius is the tolerance for mouse clicks picking a line. The default is 5 pt. The use of :class:`~matplotlib.cm.ScalarMappable` is optional. If the :class:`~matplotlib.cm.ScalarMappable` matrix :attr:`~matplotlib.cm.ScalarMappable._A` is not None (ie a call to :meth:`~matplotlib.cm.ScalarMappable.set_array` has been made), at draw time a call to scalar mappable will be made to set the colors. """ if colors is None: colors = mpl.rcParams['lines.color'] if linewidths is None: linewidths = (mpl.rcParams['lines.linewidth'],) if antialiaseds is None: antialiaseds = (mpl.rcParams['lines.antialiased'],) self.set_linestyles(linestyles) colors = _colors.colorConverter.to_rgba_array(colors) Collection.__init__( self, edgecolors=colors, linewidths=linewidths, linestyles=linestyles, antialiaseds=antialiaseds, offsets=offsets, transOffset=transOffset, norm=norm, cmap=cmap, pickradius=pickradius, *kwargs) self.set_facecolors([]) self.set_segments(segments) def get_paths(self): return self._paths def set_segments(self, segments): if segments is None: return _segments = [] for seg in segments: if not np.ma.isMaskedArray(seg): seg = np.asarray(seg, np.float_) _segments.append(seg) if self._uniform_offsets is not None: _segments = self._add_offsets(_segments) self._paths = [mpath.Path(seg) for seg in _segments] set_verts = set_segments # for compatibility with PolyCollection def _add_offsets(self, segs): offsets = self._uniform_offsets Nsegs = len(segs) Noffs = offsets.shape[0] if Noffs == 1: for i in range(Nsegs): segs[i] = segs[i] + i offsets else: for i in range(Nsegs): io = i%Noffs segs[i] = segs[i] + offsets[io:io+1] return segs def set_color(self, c): """ Set the color(s) of the line collection. c can be a matplotlib color arg (all patches have same color), or a sequence or rgba tuples; if it is a sequence the patches will cycle through the sequence ACCEPTS: matplotlib color arg or sequence of rgba tuples """ self._edgecolors = _colors.colorConverter.to_rgba_array(c) def color(self, c): """ Set the color(s) of the line collection. c can be a matplotlib color arg (all patches have same color), or a sequence or rgba tuples; if it is a sequence the patches will cycle through the sequence ACCEPTS: matplotlib color arg or sequence of rgba tuples """ warnings.warn('LineCollection.color deprecated; use set_color instead') return self.set_color(c) def get_color(self): return self._edgecolors get_colors = get_color # for compatibility with old versions class CircleCollection(Collection): """ A collection of circles, drawn using splines. """ def __init__(self, sizes, *kwargs): """ sizes* Gives the area of the circle in points^2 %(Collection)s """ Collection.__init__(self,*kwargs) self._sizes = sizes self.set_transform(transforms.IdentityTransform()) self._paths = [mpath.Path.unit_circle()] __init__.__doc__ = cbook.dedent(__init__.__doc__) % artist.kwdocd def draw(self, renderer): # sizes is the area of the circle circumscribing the polygon # in points^2 self._transforms = [ transforms.Affine2D().scale( (np.sqrt(x) self.figure.dpi / 72.0) / np.sqrt(np.pi)) for x in self._sizes] return Collection.draw(self, renderer) def get_paths(self): return self._paths class EllipseCollection(Collection): """ A collection of ellipses, drawn using splines. """ def __init__(self, widths, heights, angles, units='points', *kwargs): """ widths: sequence half-lengths of first axes (e.g., semi-major axis lengths) heights: sequence half-lengths of second axes angles: sequence angles of first axes, degrees CCW from the X-axis units: ['points' \| 'inches' \| 'dots' \| 'width' \| 'height' \| 'x' \| 'y'] units in which majors and minors are given; 'width' and 'height' refer to the dimensions of the axes, while 'x' and 'y' refer to the offsets* data units. Additional kwargs inherited from the base :class:`Collection`: %(Collection)s """ Collection.__init__(self,*kwargs) self._widths = np.asarray(widths).ravel() self._heights = np.asarray(heights).ravel() self._angles = np.asarray(angles).ravel() (np.pi/180.0) self._units = units self.set_transform(transforms.IdentityTransform()) self._transforms = [] self._paths = [mpath.Path.unit_circle()] self._initialized = False __init__.__doc__ = cbook.dedent(__init__.__doc__) % artist.kwdocd def _init(self): def on_dpi_change(fig): self._transforms = [] self.figure.callbacks.connect('dpi_changed', on_dpi_change) self._initialized = True def set_transforms(self): if not self._initialized: self._init() self._transforms = [] ax = self.axes fig = self.figure if self._units in ('x', 'y'): if self._units == 'x': dx0 = ax.viewLim.width dx1 = ax.bbox.width else: dx0 = ax.viewLim.height dx1 = ax.bbox.height sc = dx1/dx0 else: if self._units == 'inches': sc = fig.dpi elif self._units == 'points': sc = fig.dpi / 72.0 elif self._units == 'width': sc = ax.bbox.width elif self._units == 'height': sc = ax.bbox.height elif self._units == 'dots': sc = 1.0 else: raise ValueError('unrecognized units: %s' % self._units) _affine = transforms.Affine2D for x, y, a in zip(self._widths, self._heights, self._angles): trans = _affine().scale(x * sc, y * sc).rotate(a) self._transforms.append(trans) def draw(self, renderer): if True: ###not self._transforms: self.set_transforms() return Collection.draw(self, renderer) def get_paths(self): return self._paths class PatchCollection(Collection): """ A generic collection of patches. This makes it easier to assign a color map to a heterogeneous collection of patches. This also may improve plotting speed, since PatchCollection will draw faster than a large number of patches. """ def __init__(self, patches, match_original=False, *kwargs): """ patches* a sequence of Patch objects. This list may include a heterogeneous assortment of different patch types. match_original If True, use the colors and linewidths of the original patches. If False, new colors may be assigned by providing the standard collection arguments, facecolor, edgecolor, linewidths, norm or cmap. If any of edgecolors, facecolors, linewidths, antialiaseds are None, they default to their :data:`matplotlib.rcParams` patch setting, in sequence form. The use of :class:`~matplotlib.cm.ScalarMappable` is optional. If the :class:`~matplotlib.cm.ScalarMappable` matrix _A is not None (ie a call to set_array has been made), at draw time a call to scalar mappable will be made to set the face colors. """ if match_original: def determine_facecolor(patch): if patch.fill: return patch.get_facecolor() return [0, 0, 0, 0] facecolors = [determine_facecolor(p) for p in patches] edgecolors = [p.get_edgecolor() for p in patches] linewidths = [p.get_linewidths() for p in patches] antialiaseds = [p.get_antialiased() for p in patches] Collection.__init__( self, edgecolors=edgecolors, facecolors=facecolors, linewidths=linewidths, linestyles='solid', antialiaseds = antialiaseds) else: Collection.__init__(self, **kwargs) paths = [p.get_transform().transform_path(p.get_path()) for p in patches] self._paths = paths def get_paths(self): return self._paths artist.kwdocd['Collection'] = patchstr = artist.kwdoc(Collection) for k in ('QuadMesh', 'PolyCollection', 'BrokenBarHCollection', 'RegularPolyCollection', 'StarPolygonCollection', 'PatchCollection', 'CircleCollection'): artist.kwdocd[k] = patchstr artist.kwdocd['LineCollection'] = artist.kwdoc(LineCollection)	39,876	Python	.py	939	32.13738	131	0.592839	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,256	finance.py	numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/finance.py	""" A collection of modules for collecting, analyzing and plotting financial data. User contributions welcome! """ #from __future__ import division import os, time, warnings from urllib import urlopen try: from hashlib import md5 except ImportError: from md5 import md5 #Deprecated in 2.5 try: import datetime except ImportError: raise ImportError('The finance module requires datetime support (python2.3)') import numpy as np from matplotlib import verbose, get_configdir from dates import date2num from matplotlib.cbook import Bunch from matplotlib.collections import LineCollection, PolyCollection from matplotlib.colors import colorConverter from lines import Line2D, TICKLEFT, TICKRIGHT from patches import Rectangle from matplotlib.transforms import Affine2D configdir = get_configdir() cachedir = os.path.join(configdir, 'finance.cache') def parse_yahoo_historical(fh, asobject=False, adjusted=True): """ Parse the historical data in file handle fh from yahoo finance and return results as a list of d, open, close, high, low, volume where d is a floating poing representation of date, as returned by date2num if adjust=True, use adjusted prices """ results = [] lines = fh.readlines() datefmt = None for line in lines[1:]: vals = line.split(',') if len(vals)!=7: continue datestr = vals[0] if datefmt is None: try: datefmt = '%Y-%m-%d' dt = datetime.date(time.strptime(datestr, datefmt)[:3]) except ValueError: datefmt = '%d-%b-%y' # Old Yahoo--cached file? dt = datetime.date(time.strptime(datestr, datefmt)[:3]) d = date2num(dt) open, high, low, close = [float(val) for val in vals[1:5]] volume = int(vals[5]) if adjusted: aclose = float(vals[6]) m = aclose/close open = m high = m low = m close = aclose results.append((d, open, close, high, low, volume)) results.reverse() if asobject: if len(results)==0: return None else: date, open, close, high, low, volume = map(np.asarray, zip(results)) return Bunch(date=date, open=open, close=close, high=high, low=low, volume=volume) else: return results def fetch_historical_yahoo(ticker, date1, date2, cachename=None): """ Fetch historical data for ticker between date1 and date2. date1 and date2 are datetime instances Ex: fh = fetch_historical_yahoo('^GSPC', d1, d2) cachename is the name of the local file cache. If None, will default to the md5 hash or the url (which incorporates the ticker and date range) a file handle is returned """ ticker = ticker.upper() d1 = (date1.month-1, date1.day, date1.year) d2 = (date2.month-1, date2.day, date2.year) urlFmt = 'http://table.finance.yahoo.com/table.csv?a=%d&b=%d&c=%d&d=%d&e=%d&f=%d&s=%s&y=0&g=d&ignore=.csv' url = urlFmt % (d1[0], d1[1], d1[2], d2[0], d2[1], d2[2], ticker) if cachename is None: cachename = os.path.join(cachedir, md5(url).hexdigest()) if os.path.exists(cachename): fh = file(cachename) verbose.report('Using cachefile %s for %s'%(cachename, ticker)) else: if not os.path.isdir(cachedir): os.mkdir(cachedir) fh = file(cachename, 'w') fh.write(urlopen(url).read()) fh.close() verbose.report('Saved %s data to cache file %s'%(ticker, cachename)) fh = file(cachename, 'r') return fh def quotes_historical_yahoo(ticker, date1, date2, asobject=False, adjusted=True, cachename=None): """ Get historical data for ticker between date1 and date2. date1 and date2 are datetime instances results are a list of tuples (d, open, close, high, low, volume) where d is a floating poing representation of date, as returned by date2num if asobject is True, the return val is an object with attrs date, open, close, high, low, volume, which are equal length arrays if adjust=True, use adjusted prices Ex: sp = f.quotes_historical_yahoo('^GSPC', d1, d2, asobject=True, adjusted=True) returns = (sp.open[1:] - sp.open[:-1])/sp.open[1:] [n,bins,patches] = hist(returns, 100) mu = mean(returns) sigma = std(returns) x = normpdf(bins, mu, sigma) plot(bins, x, color='red', lw=2) cachename is the name of the local file cache. If None, will default to the md5 hash or the url (which incorporates the ticker and date range) """ fh = fetch_historical_yahoo(ticker, date1, date2, cachename) try: ret = parse_yahoo_historical(fh, asobject, adjusted) except IOError, exc: warnings.warn('urlopen() failure\n' + url + '\n' + exc.strerror[1]) return None return ret def plot_day_summary(ax, quotes, ticksize=3, colorup='k', colordown='r', ): """ quotes is a list of (time, open, close, high, low, ...) tuples Represent the time, open, close, high, low as a vertical line ranging from low to high. The left tick is the open and the right tick is the close. time must be in float date format - see date2num ax : an Axes instance to plot to ticksize : open/close tick marker in points colorup : the color of the lines where close >= open colordown : the color of the lines where close < open return value is a list of lines added """ lines = [] for q in quotes: t, open, close, high, low = q[:5] if close>=open : color = colorup else : color = colordown vline = Line2D( xdata=(t, t), ydata=(low, high), color=color, antialiased=False, # no need to antialias vert lines ) oline = Line2D( xdata=(t, t), ydata=(open, open), color=color, antialiased=False, marker=TICKLEFT, markersize=ticksize, ) cline = Line2D( xdata=(t, t), ydata=(close, close), color=color, antialiased=False, markersize=ticksize, marker=TICKRIGHT) lines.extend((vline, oline, cline)) ax.add_line(vline) ax.add_line(oline) ax.add_line(cline) ax.autoscale_view() return lines def candlestick(ax, quotes, width=0.2, colorup='k', colordown='r', alpha=1.0): """ quotes is a list of (time, open, close, high, low, ...) tuples. As long as the first 5 elements of the tuples are these values, the tuple can be as long as you want (eg it may store volume). time must be in float days format - see date2num Plot the time, open, close, high, low as a vertical line ranging from low to high. Use a rectangular bar to represent the open-close span. If close >= open, use colorup to color the bar, otherwise use colordown ax : an Axes instance to plot to width : fraction of a day for the rectangle width colorup : the color of the rectangle where close >= open colordown : the color of the rectangle where close < open alpha : the rectangle alpha level return value is lines, patches where lines is a list of lines added and patches is a list of the rectangle patches added """ OFFSET = width/2.0 lines = [] patches = [] for q in quotes: t, open, close, high, low = q[:5] if close>=open : color = colorup lower = open height = close-open else : color = colordown lower = close height = open-close vline = Line2D( xdata=(t, t), ydata=(low, high), color='k', linewidth=0.5, antialiased=True, ) rect = Rectangle( xy = (t-OFFSET, lower), width = width, height = height, facecolor = color, edgecolor = color, ) rect.set_alpha(alpha) lines.append(vline) patches.append(rect) ax.add_line(vline) ax.add_patch(rect) ax.autoscale_view() return lines, patches def plot_day_summary2(ax, opens, closes, highs, lows, ticksize=4, colorup='k', colordown='r', ): """ Represent the time, open, close, high, low as a vertical line ranging from low to high. The left tick is the open and the right tick is the close. ax : an Axes instance to plot to ticksize : size of open and close ticks in points colorup : the color of the lines where close >= open colordown : the color of the lines where close < open return value is a list of lines added """ # note this code assumes if any value open, close, low, high is # missing they all are missing rangeSegments = [ ((i, low), (i, high)) for i, low, high in zip(xrange(len(lows)), lows, highs) if low != -1 ] # the ticks will be from ticksize to 0 in points at the origin and # we'll translate these to the i, close location openSegments = [ ((-ticksize, 0), (0, 0)) ] # the ticks will be from 0 to ticksize in points at the origin and # we'll translate these to the i, close location closeSegments = [ ((0, 0), (ticksize, 0)) ] offsetsOpen = [ (i, open) for i, open in zip(xrange(len(opens)), opens) if open != -1 ] offsetsClose = [ (i, close) for i, close in zip(xrange(len(closes)), closes) if close != -1 ] scale = ax.figure.dpi * (1.0/72.0) tickTransform = Affine2D().scale(scale, 0.0) r,g,b = colorConverter.to_rgb(colorup) colorup = r,g,b,1 r,g,b = colorConverter.to_rgb(colordown) colordown = r,g,b,1 colord = { True : colorup, False : colordown, } colors = [colord[open<close] for open, close in zip(opens, closes) if open!=-1 and close !=-1] assert(len(rangeSegments)==len(offsetsOpen)) assert(len(offsetsOpen)==len(offsetsClose)) assert(len(offsetsClose)==len(colors)) useAA = 0, # use tuple here lw = 1, # and here rangeCollection = LineCollection(rangeSegments, colors = colors, linewidths = lw, antialiaseds = useAA, ) openCollection = LineCollection(openSegments, colors = colors, antialiaseds = useAA, linewidths = lw, offsets = offsetsOpen, transOffset = ax.transData, ) openCollection.set_transform(tickTransform) closeCollection = LineCollection(closeSegments, colors = colors, antialiaseds = useAA, linewidths = lw, offsets = offsetsClose, transOffset = ax.transData, ) closeCollection.set_transform(tickTransform) minpy, maxx = (0, len(rangeSegments)) miny = min([low for low in lows if low !=-1]) maxy = max([high for high in highs if high != -1]) corners = (minpy, miny), (maxx, maxy) ax.update_datalim(corners) ax.autoscale_view() # add these last ax.add_collection(rangeCollection) ax.add_collection(openCollection) ax.add_collection(closeCollection) return rangeCollection, openCollection, closeCollection def candlestick2(ax, opens, closes, highs, lows, width=4, colorup='k', colordown='r', alpha=0.75, ): """ Represent the open, close as a bar line and high low range as a vertical line. ax : an Axes instance to plot to width : the bar width in points colorup : the color of the lines where close >= open colordown : the color of the lines where close < open alpha : bar transparency return value is lineCollection, barCollection """ # note this code assumes if any value open, close, low, high is # missing they all are missing delta = width/2. barVerts = [ ( (i-delta, open), (i-delta, close), (i+delta, close), (i+delta, open) ) for i, open, close in zip(xrange(len(opens)), opens, closes) if open != -1 and close!=-1 ] rangeSegments = [ ((i, low), (i, high)) for i, low, high in zip(xrange(len(lows)), lows, highs) if low != -1 ] r,g,b = colorConverter.to_rgb(colorup) colorup = r,g,b,alpha r,g,b = colorConverter.to_rgb(colordown) colordown = r,g,b,alpha colord = { True : colorup, False : colordown, } colors = [colord[open<close] for open, close in zip(opens, closes) if open!=-1 and close !=-1] assert(len(barVerts)==len(rangeSegments)) useAA = 0, # use tuple here lw = 0.5, # and here rangeCollection = LineCollection(rangeSegments, colors = ( (0,0,0,1), ), linewidths = lw, antialiaseds = useAA, ) barCollection = PolyCollection(barVerts, facecolors = colors, edgecolors = ( (0,0,0,1), ), antialiaseds = useAA, linewidths = lw, ) minx, maxx = 0, len(rangeSegments) miny = min([low for low in lows if low !=-1]) maxy = max([high for high in highs if high != -1]) corners = (minx, miny), (maxx, maxy) ax.update_datalim(corners) ax.autoscale_view() # add these last ax.add_collection(barCollection) ax.add_collection(rangeCollection) return rangeCollection, barCollection def volume_overlay(ax, opens, closes, volumes, colorup='k', colordown='r', width=4, alpha=1.0): """ Add a volume overlay to the current axes. The opens and closes are used to determine the color of the bar. -1 is missing. If a value is missing on one it must be missing on all ax : an Axes instance to plot to width : the bar width in points colorup : the color of the lines where close >= open colordown : the color of the lines where close < open alpha : bar transparency """ r,g,b = colorConverter.to_rgb(colorup) colorup = r,g,b,alpha r,g,b = colorConverter.to_rgb(colordown) colordown = r,g,b,alpha colord = { True : colorup, False : colordown, } colors = [colord[open<close] for open, close in zip(opens, closes) if open!=-1 and close !=-1] delta = width/2. bars = [ ( (i-delta, 0), (i-delta, v), (i+delta, v), (i+delta, 0)) for i, v in enumerate(volumes) if v != -1 ] barCollection = PolyCollection(bars, facecolors = colors, edgecolors = ( (0,0,0,1), ), antialiaseds = (0,), linewidths = (0.5,), ) corners = (0, 0), (len(bars), max(volumes)) ax.update_datalim(corners) ax.autoscale_view() # add these last return barCollection def volume_overlay2(ax, closes, volumes, colorup='k', colordown='r', width=4, alpha=1.0): """ Add a volume overlay to the current axes. The closes are used to determine the color of the bar. -1 is missing. If a value is missing on one it must be missing on all ax : an Axes instance to plot to width : the bar width in points colorup : the color of the lines where close >= open colordown : the color of the lines where close < open alpha : bar transparency nb: first point is not displayed - it is used only for choosing the right color """ return volume_overlay(ax,closes[:-1],closes[1:],volumes[1:],colorup,colordown,width,alpha) def volume_overlay3(ax, quotes, colorup='k', colordown='r', width=4, alpha=1.0): """ Add a volume overlay to the current axes. quotes is a list of (d, open, close, high, low, volume) and close-open is used to determine the color of the bar kwarg width : the bar width in points colorup : the color of the lines where close1 >= close0 colordown : the color of the lines where close1 < close0 alpha : bar transparency """ r,g,b = colorConverter.to_rgb(colorup) colorup = r,g,b,alpha r,g,b = colorConverter.to_rgb(colordown) colordown = r,g,b,alpha colord = { True : colorup, False : colordown, } dates, opens, closes, highs, lows, volumes = zip(quotes) colors = [colord[close1>=close0] for close0, close1 in zip(closes[:-1], closes[1:]) if close0!=-1 and close1 !=-1] colors.insert(0,colord[closes[0]>=opens[0]]) right = width/2.0 left = -width/2.0 bars = [ ( (left, 0), (left, volume), (right, volume), (right, 0)) for d, open, close, high, low, volume in quotes] sx = ax.figure.dpi (1.0/72.0) # scale for points sy = ax.bbox.height / ax.viewLim.height barTransform = Affine2D().scale(sx,sy) dates = [d for d, open, close, high, low, volume in quotes] offsetsBars = [(d, 0) for d in dates] useAA = 0, # use tuple here lw = 0.5, # and here barCollection = PolyCollection(bars, facecolors = colors, edgecolors = ( (0,0,0,1), ), antialiaseds = useAA, linewidths = lw, offsets = offsetsBars, transOffset = ax.transData, ) barCollection.set_transform(barTransform) minpy, maxx = (min(dates), max(dates)) miny = 0 maxy = max([volume for d, open, close, high, low, volume in quotes]) corners = (minpy, miny), (maxx, maxy) ax.update_datalim(corners) #print 'datalim', ax.dataLim.get_bounds() #print 'viewlim', ax.viewLim.get_bounds() ax.add_collection(barCollection) ax.autoscale_view() return barCollection def index_bar(ax, vals, facecolor='b', edgecolor='l', width=4, alpha=1.0, ): """ Add a bar collection graph with height vals (-1 is missing). ax : an Axes instance to plot to width : the bar width in points alpha : bar transparency """ facecolors = (colorConverter.to_rgba(facecolor, alpha),) edgecolors = (colorConverter.to_rgba(edgecolor, alpha),) right = width/2.0 left = -width/2.0 bars = [ ( (left, 0), (left, v), (right, v), (right, 0)) for v in vals if v != -1 ] sx = ax.figure.dpi * (1.0/72.0) # scale for points sy = ax.bbox.height / ax.viewLim.height barTransform = Affine2D().scale(sx,sy) offsetsBars = [ (i, 0) for i,v in enumerate(vals) if v != -1 ] barCollection = PolyCollection(bars, facecolors = facecolors, edgecolors = edgecolors, antialiaseds = (0,), linewidths = (0.5,), offsets = offsetsBars, transOffset = ax.transData, ) barCollection.set_transform(barTransform) minpy, maxx = (0, len(offsetsBars)) miny = 0 maxy = max([v for v in vals if v!=-1]) corners = (minpy, miny), (maxx, maxy) ax.update_datalim(corners) ax.autoscale_view() # add these last ax.add_collection(barCollection) return barCollection	20,558	Python	.py	480	32.9875	180	0.578723	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,257	scale.py	numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/scale.py	import textwrap import numpy as np from numpy import ma MaskedArray = ma.MaskedArray from cbook import dedent from ticker import NullFormatter, ScalarFormatter, LogFormatterMathtext, Formatter from ticker import NullLocator, LogLocator, AutoLocator, SymmetricalLogLocator, FixedLocator from transforms import Transform, IdentityTransform class ScaleBase(object): """ The base class for all scales. Scales are separable transformations, working on a single dimension. Any subclasses will want to override: - :attr:`name` - :meth:`get_transform` And optionally: - :meth:`set_default_locators_and_formatters` - :meth:`limit_range_for_scale` """ def get_transform(self): """ Return the :class:`~matplotlib.transforms.Transform` object associated with this scale. """ raise NotImplementedError def set_default_locators_and_formatters(self, axis): """ Set the :class:`~matplotlib.ticker.Locator` and :class:`~matplotlib.ticker.Formatter` objects on the given axis to match this scale. """ raise NotImplementedError def limit_range_for_scale(self, vmin, vmax, minpos): """ Returns the range vmin, vmax, possibly limited to the domain supported by this scale. minpos should be the minimum positive value in the data. This is used by log scales to determine a minimum value. """ return vmin, vmax class LinearScale(ScaleBase): """ The default linear scale. """ name = 'linear' def __init__(self, axis, *kwargs): pass def set_default_locators_and_formatters(self, axis): """ Set the locators and formatters to reasonable defaults for linear scaling. """ axis.set_major_locator(AutoLocator()) axis.set_major_formatter(ScalarFormatter()) axis.set_minor_locator(NullLocator()) axis.set_minor_formatter(NullFormatter()) def get_transform(self): """ The transform for linear scaling is just the :class:`~matplotlib.transforms.IdentityTransform`. """ return IdentityTransform() def _mask_non_positives(a): """ Return a Numpy masked array where all non-positive values are masked. If there are no non-positive values, the original array is returned. """ mask = a <= 0.0 if mask.any(): return ma.MaskedArray(a, mask=mask) return a class LogScale(ScaleBase): """ A standard logarithmic scale. Care is taken so non-positive values are not plotted. For computational efficiency (to push as much as possible to Numpy C code in the common cases), this scale provides different transforms depending on the base of the logarithm: - base 10 (:class:`Log10Transform`) - base 2 (:class:`Log2Transform`) - base e (:class:`NaturalLogTransform`) - arbitrary base (:class:`LogTransform`) """ name = 'log' class Log10Transform(Transform): input_dims = 1 output_dims = 1 is_separable = True base = 10.0 def transform(self, a): a = _mask_non_positives(a 10.0) if isinstance(a, MaskedArray): return ma.log10(a) return np.log10(a) def inverted(self): return LogScale.InvertedLog10Transform() class InvertedLog10Transform(Transform): input_dims = 1 output_dims = 1 is_separable = True base = 10.0 def transform(self, a): return ma.power(10.0, a) / 10.0 def inverted(self): return LogScale.Log10Transform() class Log2Transform(Transform): input_dims = 1 output_dims = 1 is_separable = True base = 2.0 def transform(self, a): a = _mask_non_positives(a * 2.0) if isinstance(a, MaskedArray): return ma.log(a) / np.log(2) return np.log2(a) def inverted(self): return LogScale.InvertedLog2Transform() class InvertedLog2Transform(Transform): input_dims = 1 output_dims = 1 is_separable = True base = 2.0 def transform(self, a): return ma.power(2.0, a) / 2.0 def inverted(self): return LogScale.Log2Transform() class NaturalLogTransform(Transform): input_dims = 1 output_dims = 1 is_separable = True base = np.e def transform(self, a): a = _mask_non_positives(a * np.e) if isinstance(a, MaskedArray): return ma.log(a) return np.log(a) def inverted(self): return LogScale.InvertedNaturalLogTransform() class InvertedNaturalLogTransform(Transform): input_dims = 1 output_dims = 1 is_separable = True base = np.e def transform(self, a): return ma.power(np.e, a) / np.e def inverted(self): return LogScale.NaturalLogTransform() class LogTransform(Transform): input_dims = 1 output_dims = 1 is_separable = True def __init__(self, base): Transform.__init__(self) self.base = base def transform(self, a): a = _mask_non_positives(a * self.base) if isinstance(a, MaskedArray): return ma.log(a) / np.log(self.base) return np.log(a) / np.log(self.base) def inverted(self): return LogScale.InvertedLogTransform(self.base) class InvertedLogTransform(Transform): input_dims = 1 output_dims = 1 is_separable = True def __init__(self, base): Transform.__init__(self) self.base = base def transform(self, a): return ma.power(self.base, a) / self.base def inverted(self): return LogScale.LogTransform(self.base) def __init__(self, axis, *kwargs): """ basex/basey: The base of the logarithm subsx/subsy: Where to place the subticks between each major tick. Should be a sequence of integers. For example, in a log10 scale: ``[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]`` will place 10 logarithmically spaced minor ticks between each major tick. """ if axis.axis_name == 'x': base = kwargs.pop('basex', 10.0) subs = kwargs.pop('subsx', None) else: base = kwargs.pop('basey', 10.0) subs = kwargs.pop('subsy', None) if base == 10.0: self._transform = self.Log10Transform() elif base == 2.0: self._transform = self.Log2Transform() elif base == np.e: self._transform = self.NaturalLogTransform() else: self._transform = self.LogTransform(base) self.base = base self.subs = subs def set_default_locators_and_formatters(self, axis): """ Set the locators and formatters to specialized versions for log scaling. """ axis.set_major_locator(LogLocator(self.base)) axis.set_major_formatter(LogFormatterMathtext(self.base)) axis.set_minor_locator(LogLocator(self.base, self.subs)) axis.set_minor_formatter(NullFormatter()) def get_transform(self): """ Return a :class:`~matplotlib.transforms.Transform` instance appropriate for the given logarithm base. """ return self._transform def limit_range_for_scale(self, vmin, vmax, minpos): """ Limit the domain to positive values. """ return (vmin <= 0.0 and minpos or vmin, vmax <= 0.0 and minpos or vmax) class SymmetricalLogScale(ScaleBase): """ The symmetrical logarithmic scale is logarithmic in both the positive and negative directions from the origin. Since the values close to zero tend toward infinity, there is a need to have a range around zero that is linear. The parameter linthresh* allows the user to specify the size of this range (-linthresh, linthresh). """ name = 'symlog' class SymmetricalLogTransform(Transform): input_dims = 1 output_dims = 1 is_separable = True def __init__(self, base, linthresh): Transform.__init__(self) self.base = base self.linthresh = linthresh self._log_base = np.log(base) self._linadjust = (np.log(linthresh) / self._log_base) / linthresh def transform(self, a): a = np.asarray(a) sign = np.sign(a) masked = ma.masked_inside(a, -self.linthresh, self.linthresh, copy=False) log = sign * ma.log(np.abs(masked)) / self._log_base if masked.mask.any(): return np.asarray(ma.where(masked.mask, a * self._linadjust, log)) else: return np.asarray(log) def inverted(self): return SymmetricalLogScale.InvertedSymmetricalLogTransform(self.base, self.linthresh) class InvertedSymmetricalLogTransform(Transform): input_dims = 1 output_dims = 1 is_separable = True def __init__(self, base, linthresh): Transform.__init__(self) self.base = base self.linthresh = linthresh self._log_base = np.log(base) self._log_linthresh = np.log(linthresh) / self._log_base self._linadjust = linthresh / (np.log(linthresh) / self._log_base) def transform(self, a): a = np.asarray(a) return np.where(a <= self._log_linthresh, np.where(a >= -self._log_linthresh, a * self._linadjust, -(np.power(self.base, -a))), np.power(self.base, a)) def inverted(self): return SymmetricalLogScale.SymmetricalLogTransform(self.base) def __init__(self, axis, *kwargs): """ basex/basey: The base of the logarithm linthreshx/linthreshy: The range (-x, x) within which the plot is linear (to avoid having the plot go to infinity around zero). subsx/subsy: Where to place the subticks between each major tick. Should be a sequence of integers. For example, in a log10 scale: ``[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]`` will place 10 logarithmically spaced minor ticks between each major tick. """ if axis.axis_name == 'x': base = kwargs.pop('basex', 10.0) linthresh = kwargs.pop('linthreshx', 2.0) subs = kwargs.pop('subsx', None) else: base = kwargs.pop('basey', 10.0) linthresh = kwargs.pop('linthreshy', 2.0) subs = kwargs.pop('subsy', None) self._transform = self.SymmetricalLogTransform(base, linthresh) self.base = base self.linthresh = linthresh self.subs = subs def set_default_locators_and_formatters(self, axis): """ Set the locators and formatters to specialized versions for symmetrical log scaling. """ axis.set_major_locator(SymmetricalLogLocator(self.get_transform())) axis.set_major_formatter(LogFormatterMathtext(self.base)) axis.set_minor_locator(SymmetricalLogLocator(self.get_transform(), self.subs)) axis.set_minor_formatter(NullFormatter()) def get_transform(self): """ Return a :class:`SymmetricalLogTransform` instance. """ return self._transform _scale_mapping = { 'linear' : LinearScale, 'log' : LogScale, 'symlog' : SymmetricalLogScale } def get_scale_names(): names = _scale_mapping.keys() names.sort() return names def scale_factory(scale, axis, kwargs): """ Return a scale class by name. ACCEPTS: [ %(names)s ] """ scale = scale.lower() if scale is None: scale = 'linear' if scale not in _scale_mapping: raise ValueError("Unknown scale type '%s'" % scale) return _scale_mapping[scale](axis, kwargs) scale_factory.__doc__ = dedent(scale_factory.__doc__) % \ {'names': " \| ".join(get_scale_names())} def register_scale(scale_class): """ Register a new kind of scale. scale_class* must be a subclass of :class:`ScaleBase`. """ _scale_mapping[scale_class.name] = scale_class def get_scale_docs(): """ Helper function for generating docstrings related to scales. """ docs = [] for name in get_scale_names(): scale_class = _scale_mapping[name] docs.append(" '%s'" % name) docs.append("") class_docs = dedent(scale_class.__init__.__doc__) class_docs = "".join([" %s\n" % x for x in class_docs.split("\n")]) docs.append(class_docs) docs.append("") return "\n".join(docs)	13,414	Python	.py	357	28.218487	97	0.590688	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,258	ticker.py	numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/ticker.py	""" Tick locating and formatting ============================ This module contains classes to support completely configurable tick locating and formatting. Although the locators know nothing about major or minor ticks, they are used by the Axis class to support major and minor tick locating and formatting. Generic tick locators and formatters are provided, as well as domain specific custom ones.. Tick locating ------------- The Locator class is the base class for all tick locators. The locators handle autoscaling of the view limits based on the data limits, and the choosing of tick locations. A useful semi-automatic tick locator is MultipleLocator. You initialize this with a base, eg 10, and it picks axis limits and ticks that are multiples of your base. The Locator subclasses defined here are :class:`NullLocator` No ticks :class:`FixedLocator` Tick locations are fixed :class:`IndexLocator` locator for index plots (eg. where x = range(len(y))) :class:`LinearLocator` evenly spaced ticks from min to max :class:`LogLocator` logarithmically ticks from min to max :class:`MultipleLocator` ticks and range are a multiple of base; either integer or float :class:`OldAutoLocator` choose a MultipleLocator and dyamically reassign it for intelligent ticking during navigation :class:`MaxNLocator` finds up to a max number of ticks at nice locations :class:`AutoLocator` :class:`MaxNLocator` with simple defaults. This is the default tick locator for most plotting. There are a number of locators specialized for date locations - see the dates module You can define your own locator by deriving from Locator. You must override the __call__ method, which returns a sequence of locations, and you will probably want to override the autoscale method to set the view limits from the data limits. If you want to override the default locator, use one of the above or a custom locator and pass it to the x or y axis instance. The relevant methods are:: ax.xaxis.set_major_locator( xmajorLocator ) ax.xaxis.set_minor_locator( xminorLocator ) ax.yaxis.set_major_locator( ymajorLocator ) ax.yaxis.set_minor_locator( yminorLocator ) The default minor locator is the NullLocator, eg no minor ticks on by default. Tick formatting --------------- Tick formatting is controlled by classes derived from Formatter. The formatter operates on a single tick value and returns a string to the axis. :class:`NullFormatter` no labels on the ticks :class:`FixedFormatter` set the strings manually for the labels :class:`FuncFormatter` user defined function sets the labels :class:`FormatStrFormatter` use a sprintf format string :class:`ScalarFormatter` default formatter for scalars; autopick the fmt string :class:`LogFormatter` formatter for log axes You can derive your own formatter from the Formatter base class by simply overriding the ``__call__`` method. The formatter class has access to the axis view and data limits. To control the major and minor tick label formats, use one of the following methods:: ax.xaxis.set_major_formatter( xmajorFormatter ) ax.xaxis.set_minor_formatter( xminorFormatter ) ax.yaxis.set_major_formatter( ymajorFormatter ) ax.yaxis.set_minor_formatter( yminorFormatter ) See :ref:`pylab_examples-major_minor_demo1` for an example of setting major an minor ticks. See the :mod:`matplotlib.dates` module for more information and examples of using date locators and formatters. """ from __future__ import division import math import numpy as np from matplotlib import rcParams from matplotlib import cbook from matplotlib import transforms as mtransforms class TickHelper: axis = None class DummyAxis: def __init__(self): self.dataLim = mtransforms.Bbox.unit() self.viewLim = mtransforms.Bbox.unit() def get_view_interval(self): return self.viewLim.intervalx def set_view_interval(self, vmin, vmax): self.viewLim.intervalx = vmin, vmax def get_data_interval(self): return self.dataLim.intervalx def set_data_interval(self, vmin, vmax): self.dataLim.intervalx = vmin, vmax def set_axis(self, axis): self.axis = axis def create_dummy_axis(self): if self.axis is None: self.axis = self.DummyAxis() def set_view_interval(self, vmin, vmax): self.axis.set_view_interval(vmin, vmax) def set_data_interval(self, vmin, vmax): self.axis.set_data_interval(vmin, vmax) def set_bounds(self, vmin, vmax): self.set_view_interval(vmin, vmax) self.set_data_interval(vmin, vmax) class Formatter(TickHelper): """ Convert the tick location to a string """ # some classes want to see all the locs to help format # individual ones locs = [] def __call__(self, x, pos=None): 'Return the format for tick val x at position pos; pos=None indicated unspecified' raise NotImplementedError('Derived must overide') def format_data(self,value): return self.__call__(value) def format_data_short(self,value): 'return a short string version' return self.format_data(value) def get_offset(self): return '' def set_locs(self, locs): self.locs = locs def fix_minus(self, s): """ some classes may want to replace a hyphen for minus with the proper unicode symbol as described `here <http://sourceforge.net/tracker/index.php?func=detail&aid=1962574&group_id=80706&atid=560720>`_. The default is to do nothing Note, if you use this method, eg in :meth`format_data` or call, you probably don't want to use it for :meth:`format_data_short` since the toolbar uses this for interative coord reporting and I doubt we can expect GUIs across platforms will handle the unicode correctly. So for now the classes that override :meth:`fix_minus` should have an explicit :meth:`format_data_short` method """ return s class NullFormatter(Formatter): 'Always return the empty string' def __call__(self, x, pos=None): 'Return the format for tick val x at position pos' return '' class FixedFormatter(Formatter): 'Return fixed strings for tick labels' def __init__(self, seq): """ seq is a sequence of strings. For positions `i<len(seq)` return seq[i] regardless of x. Otherwise return '' """ self.seq = seq self.offset_string = '' def __call__(self, x, pos=None): 'Return the format for tick val x at position pos' if pos is None or pos>=len(self.seq): return '' else: return self.seq[pos] def get_offset(self): return self.offset_string def set_offset_string(self, ofs): self.offset_string = ofs class FuncFormatter(Formatter): """ User defined function for formatting """ def __init__(self, func): self.func = func def __call__(self, x, pos=None): 'Return the format for tick val x at position pos' return self.func(x, pos) class FormatStrFormatter(Formatter): """ Use a format string to format the tick """ def __init__(self, fmt): self.fmt = fmt def __call__(self, x, pos=None): 'Return the format for tick val x at position pos' return self.fmt % x class OldScalarFormatter(Formatter): """ Tick location is a plain old number. """ def __call__(self, x, pos=None): 'Return the format for tick val x at position pos' xmin, xmax = self.axis.get_view_interval() d = abs(xmax - xmin) return self.pprint_val(x,d) def pprint_val(self, x, d): #if the number is not too big and it's an int, format it as an #int if abs(x)<1e4 and x==int(x): return '%d' % x if d < 1e-2: fmt = '%1.3e' elif d < 1e-1: fmt = '%1.3f' elif d > 1e5: fmt = '%1.1e' elif d > 10 : fmt = '%1.1f' elif d > 1 : fmt = '%1.2f' else: fmt = '%1.3f' s = fmt % x #print d, x, fmt, s tup = s.split('e') if len(tup)==2: mantissa = tup[0].rstrip('0').rstrip('.') sign = tup[1][0].replace('+', '') exponent = tup[1][1:].lstrip('0') s = '%se%s%s' %(mantissa, sign, exponent) else: s = s.rstrip('0').rstrip('.') return s class ScalarFormatter(Formatter): """ Tick location is a plain old number. If useOffset==True and the data range is much smaller than the data average, then an offset will be determined such that the tick labels are meaningful. Scientific notation is used for data < 1e-3 or data >= 1e4. """ def __init__(self, useOffset=True, useMathText=False): # useOffset allows plotting small data ranges with large offsets: # for example: [1+1e-9,1+2e-9,1+3e-9] # useMathText will render the offset and scientific notation in mathtext self._useOffset = useOffset self._usetex = rcParams['text.usetex'] self._useMathText = useMathText self.offset = 0 self.orderOfMagnitude = 0 self.format = '' self._scientific = True self._powerlimits = rcParams['axes.formatter.limits'] def fix_minus(self, s): 'use a unicode minus rather than hyphen' if rcParams['text.usetex'] or not rcParams['axes.unicode_minus']: return s else: return s.replace('-', u'\u2212') def __call__(self, x, pos=None): 'Return the format for tick val x at position pos' if len(self.locs)==0: return '' else: s = self.pprint_val(x) return self.fix_minus(s) def set_scientific(self, b): '''True or False to turn scientific notation on or off see also :meth:`set_powerlimits` ''' self._scientific = bool(b) def set_powerlimits(self, lims): ''' Sets size thresholds for scientific notation. e.g. ``xaxis.set_powerlimits((-3, 4))`` sets the pre-2007 default in which scientific notation is used for numbers less than 1e-3 or greater than 1e4. See also :meth:`set_scientific`. ''' assert len(lims) == 2, "argument must be a sequence of length 2" self._powerlimits = lims def format_data_short(self,value): 'return a short formatted string representation of a number' return '%1.3g'%value def format_data(self,value): 'return a formatted string representation of a number' s = self._formatSciNotation('%1.10e'% value) return self.fix_minus(s) def get_offset(self): """Return scientific notation, plus offset""" if len(self.locs)==0: return '' s = '' if self.orderOfMagnitude or self.offset: offsetStr = '' sciNotStr = '' if self.offset: offsetStr = self.format_data(self.offset) if self.offset > 0: offsetStr = '+' + offsetStr if self.orderOfMagnitude: if self._usetex or self._useMathText: sciNotStr = self.format_data(10self.orderOfMagnitude) else: sciNotStr = '1e%d'% self.orderOfMagnitude if self._useMathText: if sciNotStr != '': sciNotStr = r'\times\mathdefault{%s}' % sciNotStr s = ''.join(('$',sciNotStr,r'\mathdefault{',offsetStr,'}$')) elif self._usetex: if sciNotStr != '': sciNotStr = r'\times%s' % sciNotStr s = ''.join(('$',sciNotStr,offsetStr,'$')) else: s = ''.join((sciNotStr,offsetStr)) return self.fix_minus(s) def set_locs(self, locs): 'set the locations of the ticks' self.locs = locs if len(self.locs) > 0: vmin, vmax = self.axis.get_view_interval() d = abs(vmax-vmin) if self._useOffset: self._set_offset(d) self._set_orderOfMagnitude(d) self._set_format() def _set_offset(self, range): # offset of 20,001 is 20,000, for example locs = self.locs if locs is None or not len(locs) or range == 0: self.offset = 0 return ave_loc = np.mean(locs) if ave_loc: # dont want to take log10(0) ave_oom = math.floor(math.log10(np.mean(np.absolute(locs)))) range_oom = math.floor(math.log10(range)) if np.absolute(ave_oom-range_oom) >= 3: # four sig-figs if ave_loc < 0: self.offset = math.ceil(np.max(locs)/10range_oom)10range_oom else: self.offset = math.floor(np.min(locs)/10(range_oom))10(range_oom) else: self.offset = 0 def _set_orderOfMagnitude(self,range): # if scientific notation is to be used, find the appropriate exponent # if using an numerical offset, find the exponent after applying the offset if not self._scientific: self.orderOfMagnitude = 0 return locs = np.absolute(self.locs) if self.offset: oom = math.floor(math.log10(range)) else: if locs[0] > locs[-1]: val = locs[0] else: val = locs[-1] if val == 0: oom = 0 else: oom = math.floor(math.log10(val)) if oom <= self._powerlimits[0]: self.orderOfMagnitude = oom elif oom >= self._powerlimits[1]: self.orderOfMagnitude = oom else: self.orderOfMagnitude = 0 def _set_format(self): # set the format string to format all the ticklabels # The floating point black magic (adding 1e-15 and formatting # to 8 digits) may warrant review and cleanup. locs = (np.asarray(self.locs)-self.offset) / 10self.orderOfMagnitude+1e-15 sigfigs = [len(str('%1.8f'% loc).split('.')[1].rstrip('0')) \ for loc in locs] sigfigs.sort() self.format = '%1.' + str(sigfigs[-1]) + 'f' if self._usetex: self.format = '$%s$' % self.format elif self._useMathText: self.format = '$\mathdefault{%s}$' % self.format def pprint_val(self, x): xp = (x-self.offset)/10*self.orderOfMagnitude if np.absolute(xp) < 1e-8: xp = 0 return self.format % xp def _formatSciNotation(self, s): # transform 1e+004 into 1e4, for example tup = s.split('e') try: significand = tup[0].rstrip('0').rstrip('.') sign = tup[1][0].replace('+', '') exponent = tup[1][1:].lstrip('0') if self._useMathText or self._usetex: if significand == '1': # reformat 1x10^y as 10^y significand = '' if exponent: exponent = '10^{%s%s}'%(sign, exponent) if significand and exponent: return r'%s{\times}%s'%(significand, exponent) else: return r'%s%s'%(significand, exponent) else: s = ('%se%s%s' %(significand, sign, exponent)).rstrip('e') return s except IndexError, msg: return s class LogFormatter(Formatter): """ Format values for log axis; if attribute decadeOnly* is True, only the decades will be labelled. """ def __init__(self, base=10.0, labelOnlyBase = True): """ base is used to locate the decade tick, which will be the only one to be labeled if labelOnlyBase is ``False`` """ self._base = base+0.0 self.labelOnlyBase=labelOnlyBase self.decadeOnly = True def base(self,base): 'change the base for labeling - warning: should always match the base used for :class:`LogLocator`' self._base=base def label_minor(self,labelOnlyBase): 'switch on/off minor ticks labeling' self.labelOnlyBase=labelOnlyBase def __call__(self, x, pos=None): 'Return the format for tick val x at position pos' vmin, vmax = self.axis.get_view_interval() d = abs(vmax - vmin) b=self._base if x == 0.0: return '0' sign = np.sign(x) # only label the decades fx = math.log(abs(x))/math.log(b) isDecade = self.is_decade(fx) if not isDecade and self.labelOnlyBase: s = '' elif x>10000: s= '%1.0e'%x elif x<1: s = '%1.0e'%x else : s = self.pprint_val(x,d) if sign == -1: s = '-%s' % s return self.fix_minus(s) def format_data(self,value): self.labelOnlyBase = False value = cbook.strip_math(self.__call__(value)) self.labelOnlyBase = True return value def format_data_short(self,value): 'return a short formatted string representation of a number' return '%1.3g'%value def is_decade(self, x): n = self.nearest_long(x) return abs(x-n)<1e-10 def nearest_long(self, x): if x==0: return 0L elif x>0: return long(x+0.5) else: return long(x-0.5) def pprint_val(self, x, d): #if the number is not too big and it's an int, format it as an #int if abs(x)<1e4 and x==int(x): return '%d' % x if d < 1e-2: fmt = '%1.3e' elif d < 1e-1: fmt = '%1.3f' elif d > 1e5: fmt = '%1.1e' elif d > 10 : fmt = '%1.1f' elif d > 1 : fmt = '%1.2f' else: fmt = '%1.3f' s = fmt % x #print d, x, fmt, s tup = s.split('e') if len(tup)==2: mantissa = tup[0].rstrip('0').rstrip('.') sign = tup[1][0].replace('+', '') exponent = tup[1][1:].lstrip('0') s = '%se%s%s' %(mantissa, sign, exponent) else: s = s.rstrip('0').rstrip('.') return s class LogFormatterExponent(LogFormatter): """ Format values for log axis; using ``exponent = log_base(value)`` """ def __call__(self, x, pos=None): 'Return the format for tick val x at position pos' vmin, vmax = self.axis.get_view_interval() vmin, vmax = mtransforms.nonsingular(vmin, vmax, expander = 0.05) d = abs(vmax-vmin) b=self._base if x == 0: return '0' sign = np.sign(x) # only label the decades fx = math.log(abs(x))/math.log(b) isDecade = self.is_decade(fx) if not isDecade and self.labelOnlyBase: s = '' #if 0: pass elif fx>10000: s= '%1.0e'%fx #elif x<1: s = '$10^{%d}$'%fx #elif x<1: s = '10^%d'%fx elif fx<1: s = '%1.0e'%fx else : s = self.pprint_val(fx,d) if sign == -1: s = '-%s' % s return self.fix_minus(s) class LogFormatterMathtext(LogFormatter): """ Format values for log axis; using ``exponent = log_base(value)`` """ def __call__(self, x, pos=None): 'Return the format for tick val x at position pos' b = self._base # only label the decades if x == 0: return '$0$' sign = np.sign(x) fx = math.log(abs(x))/math.log(b) isDecade = self.is_decade(fx) usetex = rcParams['text.usetex'] if sign == -1: sign_string = '-' else: sign_string = '' if not isDecade and self.labelOnlyBase: s = '' elif not isDecade: if usetex: s = r'$%s%d^{%.2f}$'% (sign_string, b, fx) else: s = '$\mathdefault{%s%d^{%.2f}}$'% (sign_string, b, fx) else: if usetex: s = r'$%s%d^{%d}$'% (sign_string, b, self.nearest_long(fx)) else: s = r'$\mathdefault{%s%d^{%d}}$'% (sign_string, b, self.nearest_long(fx)) return s class Locator(TickHelper): """ Determine the tick locations; Note, you should not use the same locator between different :class:`~matplotlib.axis.Axis` because the locator stores references to the Axis data and view limits """ def __call__(self): 'Return the locations of the ticks' raise NotImplementedError('Derived must override') def view_limits(self, vmin, vmax): """ select a scale for the range from vmin to vmax Normally This will be overridden. """ return mtransforms.nonsingular(vmin, vmax) def autoscale(self): 'autoscale the view limits' return self.view_limits(self.axis.get_view_interval()) def pan(self, numsteps): 'Pan numticks (can be positive or negative)' ticks = self() numticks = len(ticks) vmin, vmax = self.axis.get_view_interval() vmin, vmax = mtransforms.nonsingular(vmin, vmax, expander = 0.05) if numticks>2: step = numstepsabs(ticks[0]-ticks[1]) else: d = abs(vmax-vmin) step = numstepsd/6. vmin += step vmax += step self.axis.set_view_interval(vmin, vmax, ignore=True) def zoom(self, direction): "Zoom in/out on axis; if direction is >0 zoom in, else zoom out" vmin, vmax = self.axis.get_view_interval() vmin, vmax = mtransforms.nonsingular(vmin, vmax, expander = 0.05) interval = abs(vmax-vmin) step = 0.1intervaldirection self.axis.set_view_interval(vmin + step, vmax - step, ignore=True) def refresh(self): 'refresh internal information based on current lim' pass class IndexLocator(Locator): """ Place a tick on every multiple of some base number of points plotted, eg on every 5th point. It is assumed that you are doing index plotting; ie the axis is 0, len(data). This is mainly useful for x ticks. """ def __init__(self, base, offset): 'place ticks on the i-th data points where (i-offset)%base==0' self._base = base self.offset = offset def __call__(self): 'Return the locations of the ticks' dmin, dmax = self.axis.get_data_interval() return np.arange(dmin + self.offset, dmax+1, self._base) class FixedLocator(Locator): """ Tick locations are fixed. If nbins is not None, the array of possible positions will be subsampled to keep the number of ticks <= nbins +1. """ def __init__(self, locs, nbins=None): self.locs = locs self.nbins = nbins if self.nbins is not None: self.nbins = max(self.nbins, 2) def __call__(self): 'Return the locations of the ticks' if self.nbins is None: return self.locs step = max(int(0.99 + len(self.locs) / float(self.nbins)), 1) return self.locs[::step] class NullLocator(Locator): """ No ticks """ def __call__(self): 'Return the locations of the ticks' return [] class LinearLocator(Locator): """ Determine the tick locations The first time this function is called it will try to set the number of ticks to make a nice tick partitioning. Thereafter the number of ticks will be fixed so that interactive navigation will be nice """ def __init__(self, numticks = None, presets=None): """ Use presets to set locs based on lom. A dict mapping vmin, vmax->locs """ self.numticks = numticks if presets is None: self.presets = {} else: self.presets = presets def __call__(self): 'Return the locations of the ticks' vmin, vmax = self.axis.get_view_interval() vmin, vmax = mtransforms.nonsingular(vmin, vmax, expander = 0.05) if vmax<vmin: vmin, vmax = vmax, vmin if (vmin, vmax) in self.presets: return self.presets[(vmin, vmax)] if self.numticks is None: self._set_numticks() if self.numticks==0: return [] ticklocs = np.linspace(vmin, vmax, self.numticks) return ticklocs def _set_numticks(self): self.numticks = 11 # todo; be smart here; this is just for dev def view_limits(self, vmin, vmax): 'Try to choose the view limits intelligently' if vmax<vmin: vmin, vmax = vmax, vmin if vmin==vmax: vmin-=1 vmax+=1 exponent, remainder = divmod(math.log10(vmax - vmin), 1) if remainder < 0.5: exponent -= 1 scale = 10(-exponent) vmin = math.floor(scalevmin)/scale vmax = math.ceil(scalevmax)/scale return mtransforms.nonsingular(vmin, vmax) def closeto(x,y): if abs(x-y)<1e-10: return True else: return False class Base: 'this solution has some hacks to deal with floating point inaccuracies' def __init__(self, base): assert(base>0) self._base = base def lt(self, x): 'return the largest multiple of base < x' d,m = divmod(x, self._base) if closeto(m,0) and not closeto(m/self._base,1): return (d-1)self._base return dself._base def le(self, x): 'return the largest multiple of base <= x' d,m = divmod(x, self._base) if closeto(m/self._base,1): # was closeto(m, self._base) #looks like floating point error return (d+1)self._base return dself._base def gt(self, x): 'return the smallest multiple of base > x' d,m = divmod(x, self._base) if closeto(m/self._base,1): #looks like floating point error return (d+2)self._base return (d+1)self._base def ge(self, x): 'return the smallest multiple of base >= x' d,m = divmod(x, self._base) if closeto(m,0) and not closeto(m/self._base,1): return dself._base return (d+1)self._base def get_base(self): return self._base class MultipleLocator(Locator): """ Set a tick on every integer that is multiple of base in the view interval """ def __init__(self, base=1.0): self._base = Base(base) def __call__(self): 'Return the locations of the ticks' vmin, vmax = self.axis.get_view_interval() if vmax<vmin: vmin, vmax = vmax, vmin vmin = self._base.ge(vmin) base = self._base.get_base() n = (vmax - vmin + 0.001base)//base locs = vmin + np.arange(n+1) * base return locs def view_limits(self, dmin, dmax): """ Set the view limits to the nearest multiples of base that contain the data """ vmin = self._base.le(dmin) vmax = self._base.ge(dmax) if vmin==vmax: vmin -=1 vmax +=1 return mtransforms.nonsingular(vmin, vmax) def scale_range(vmin, vmax, n = 1, threshold=100): dv = abs(vmax - vmin) maxabsv = max(abs(vmin), abs(vmax)) if maxabsv == 0 or dv/maxabsv < 1e-12: return 1.0, 0.0 meanv = 0.5(vmax+vmin) if abs(meanv)/dv < threshold: offset = 0 elif meanv > 0: ex = divmod(math.log10(meanv), 1)[0] offset = 10ex else: ex = divmod(math.log10(-meanv), 1)[0] offset = -10ex ex = divmod(math.log10(dv/n), 1)[0] scale = 10ex return scale, offset class MaxNLocator(Locator): """ Select no more than N intervals at nice locations. """ def __init__(self, nbins = 10, steps = None, trim = True, integer=False, symmetric=False): self._nbins = int(nbins) self._trim = trim self._integer = integer self._symmetric = symmetric if steps is None: self._steps = [1, 1.5, 2, 2.5, 3, 4, 5, 6, 8, 10] else: if int(steps[-1]) != 10: steps = list(steps) steps.append(10) self._steps = steps if integer: self._steps = [n for n in self._steps if divmod(n,1)[1] < 0.001] def bin_boundaries(self, vmin, vmax): nbins = self._nbins scale, offset = scale_range(vmin, vmax, nbins) if self._integer: scale = max(1, scale) vmin -= offset vmax -= offset raw_step = (vmax-vmin)/nbins scaled_raw_step = raw_step/scale for step in self._steps: if step < scaled_raw_step: continue step = scale best_vmin = stepdivmod(vmin, step)[0] best_vmax = best_vmin + stepnbins if (best_vmax >= vmax): break if self._trim: extra_bins = int(divmod((best_vmax - vmax), step)[0]) nbins -= extra_bins return (np.arange(nbins+1) * step + best_vmin + offset) def __call__(self): vmin, vmax = self.axis.get_view_interval() vmin, vmax = mtransforms.nonsingular(vmin, vmax, expander = 0.05) return self.bin_boundaries(vmin, vmax) def view_limits(self, dmin, dmax): if self._symmetric: maxabs = max(abs(dmin), abs(dmax)) dmin = -maxabs dmax = maxabs dmin, dmax = mtransforms.nonsingular(dmin, dmax, expander = 0.05) return np.take(self.bin_boundaries(dmin, dmax), [0,-1]) def decade_down(x, base=10): 'floor x to the nearest lower decade' lx = math.floor(math.log(x)/math.log(base)) return baselx def decade_up(x, base=10): 'ceil x to the nearest higher decade' lx = math.ceil(math.log(x)/math.log(base)) return baselx def is_decade(x,base=10): lx = math.log(x)/math.log(base) return lx==int(lx) class LogLocator(Locator): """ Determine the tick locations for log axes """ def __init__(self, base=10.0, subs=[1.0]): """ place ticks on the location= base*isubs[j] """ self.base(base) self.subs(subs) self.numticks = 15 def base(self,base): """ set the base of the log scaling (major tick every basei, i interger) """ self._base=base+0.0 def subs(self,subs): """ set the minor ticks the log scaling every baseisubs[j] """ if subs is None: self._subs = None # autosub else: self._subs = np.asarray(subs)+0.0 def _set_numticks(self): self.numticks = 15 # todo; be smart here; this is just for dev def __call__(self): 'Return the locations of the ticks' b=self._base vmin, vmax = self.axis.get_view_interval() if vmin <= 0.0: vmin = self.axis.get_minpos() if vmin <= 0.0: raise ValueError( "Data has no positive values, and therefore can not be log-scaled.") vmin = math.log(vmin)/math.log(b) vmax = math.log(vmax)/math.log(b) if vmax<vmin: vmin, vmax = vmax, vmin numdec = math.floor(vmax)-math.ceil(vmin) if self._subs is None: # autosub if numdec>10: subs = np.array([1.0]) elif numdec>6: subs = np.arange(2.0, b, 2.0) else: subs = np.arange(2.0, b) else: subs = self._subs stride = 1 while numdec/stride+1 > self.numticks: stride += 1 decades = np.arange(math.floor(vmin), math.ceil(vmax)+stride, stride) if len(subs) > 1 or (len(subs == 1) and subs[0] != 1.0): ticklocs = [] for decadeStart in bdecades: ticklocs.extend( subsdecadeStart ) else: ticklocs = bdecades return np.array(ticklocs) def view_limits(self, vmin, vmax): 'Try to choose the view limits intelligently' if vmax<vmin: vmin, vmax = vmax, vmin minpos = self.axis.get_minpos() if minpos<=0: raise ValueError( "Data has no positive values, and therefore can not be log-scaled.") if vmin <= minpos: vmin = minpos if not is_decade(vmin,self._base): vmin = decade_down(vmin,self._base) if not is_decade(vmax,self._base): vmax = decade_up(vmax,self._base) if vmin==vmax: vmin = decade_down(vmin,self._base) vmax = decade_up(vmax,self._base) result = mtransforms.nonsingular(vmin, vmax) return result class SymmetricalLogLocator(Locator): """ Determine the tick locations for log axes """ def __init__(self, transform, subs=[1.0]): """ place ticks on the location= baseisubs[j] """ self._transform = transform self._subs = subs self.numticks = 15 def _set_numticks(self): self.numticks = 15 # todo; be smart here; this is just for dev def __call__(self): 'Return the locations of the ticks' b = self._transform.base vmin, vmax = self.axis.get_view_interval() vmin, vmax = self._transform.transform((vmin, vmax)) if vmax<vmin: vmin, vmax = vmax, vmin numdec = math.floor(vmax)-math.ceil(vmin) if self._subs is None: if numdec>10: subs = np.array([1.0]) elif numdec>6: subs = np.arange(2.0, b, 2.0) else: subs = np.arange(2.0, b) else: subs = np.asarray(self._subs) stride = 1 while numdec/stride+1 > self.numticks: stride += 1 decades = np.arange(math.floor(vmin), math.ceil(vmax)+stride, stride) if len(subs) > 1 or subs[0] != 1.0: ticklocs = [] for decade in decades: ticklocs.extend(subs (np.sign(decade) * b ** np.abs(decade))) else: ticklocs = np.sign(decades) * b np.abs(decades) return np.array(ticklocs) def view_limits(self, vmin, vmax): 'Try to choose the view limits intelligently' b = self._transform.base if vmax<vmin: vmin, vmax = vmax, vmin if not is_decade(abs(vmin), b): if vmin < 0: vmin = -decade_up(-vmin, b) else: vmin = decade_down(vmin, b) if not is_decade(abs(vmax), b): if vmax < 0: vmax = -decade_down(-vmax, b) else: vmax = decade_up(vmax, b) if vmin == vmax: if vmin < 0: vmin = -decade_up(-vmin, b) vmax = -decade_down(-vmax, b) else: vmin = decade_down(vmin, b) vmax = decade_up(vmax, b) result = mtransforms.nonsingular(vmin, vmax) return result class AutoLocator(MaxNLocator): def __init__(self): MaxNLocator.__init__(self, nbins=9, steps=[1, 2, 5, 10]) class OldAutoLocator(Locator): """ On autoscale this class picks the best MultipleLocator to set the view limits and the tick locs. """ def __init__(self): self._locator = LinearLocator() def __call__(self): 'Return the locations of the ticks' self.refresh() return self._locator() def refresh(self): 'refresh internal information based on current lim' vmin, vmax = self.axis.get_view_interval() vmin, vmax = mtransforms.nonsingular(vmin, vmax, expander = 0.05) d = abs(vmax-vmin) self._locator = self.get_locator(d) def view_limits(self, vmin, vmax): 'Try to choose the view limits intelligently' d = abs(vmax-vmin) self._locator = self.get_locator(d) return self._locator.view_limits(vmin, vmax) def get_locator(self, d): 'pick the best locator based on a distance' d = abs(d) if d<=0: locator = MultipleLocator(0.2) else: try: ld = math.log10(d) except OverflowError: raise RuntimeError('AutoLocator illegal data interval range') fld = math.floor(ld) base = 10fld #if ld==fld: base = 10(fld-1) #else: base = 10fld if d >= 5base : ticksize = base elif d >= 2base : ticksize = base/2.0 else : ticksize = base/5.0 locator = MultipleLocator(ticksize) return locator __all__ = ('TickHelper', 'Formatter', 'FixedFormatter', 'NullFormatter', 'FuncFormatter', 'FormatStrFormatter', 'ScalarFormatter', 'LogFormatter', 'LogFormatterExponent', 'LogFormatterMathtext', 'Locator', 'IndexLocator', 'FixedLocator', 'NullLocator', 'LinearLocator', 'LogLocator', 'AutoLocator', 'MultipleLocator', 'MaxNLocator', )	37,420	Python	.py	970	29.780412	109	0.586351	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,259	artist.py	numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/artist.py	from __future__ import division import re, warnings import matplotlib import matplotlib.cbook as cbook from transforms import Bbox, IdentityTransform, TransformedBbox, TransformedPath from path import Path ## Note, matplotlib artists use the doc strings for set and get # methods to enable the introspection methods of setp and getp. Every # set_* method should have a docstring containing the line # # ACCEPTS: [ legal \| values ] # # and aliases for setters and getters should have a docstring that # starts with 'alias for ', as in 'alias for set_somemethod' # # You may wonder why we use so much boiler-plate manually defining the # set_alias and get_alias functions, rather than using some clever # python trick. The answer is that I need to be able to manipulate # the docstring, and there is no clever way to do that in python 2.2, # as far as I can see - see # http://groups.google.com/groups?hl=en&lr=&threadm=mailman.5090.1098044946.5135.python-list%40python.org&rnum=1&prev=/groups%3Fq%3D__doc__%2Bauthor%253Ajdhunter%2540ace.bsd.uchicago.edu%26hl%3Den%26btnG%3DGoogle%2BSearch class Artist(object): """ Abstract base class for someone who renders into a :class:`FigureCanvas`. """ aname = 'Artist' zorder = 0 def __init__(self): self.figure = None self._transform = None self._transformSet = False self._visible = True self._animated = False self._alpha = 1.0 self.clipbox = None self._clippath = None self._clipon = True self._lod = False self._label = '' self._picker = None self._contains = None self.eventson = False # fire events only if eventson self._oid = 0 # an observer id self._propobservers = {} # a dict from oids to funcs self.axes = None self._remove_method = None self._url = None self.x_isdata = True # False to avoid updating Axes.dataLim with x self.y_isdata = True # with y self._snap = None def remove(self): """ Remove the artist from the figure if possible. The effect will not be visible until the figure is redrawn, e.g., with :meth:`matplotlib.axes.Axes.draw_idle`. Call :meth:`matplotlib.axes.Axes.relim` to update the axes limits if desired. Note: :meth:`~matplotlib.axes.Axes.relim` will not see collections even if the collection was added to axes with autolim = True. Note: there is no support for removing the artist's legend entry. """ # There is no method to set the callback. Instead the parent should set # the _remove_method attribute directly. This would be a protected # attribute if Python supported that sort of thing. The callback # has one parameter, which is the child to be removed. if self._remove_method != None: self._remove_method(self) else: raise NotImplementedError('cannot remove artist') # TODO: the fix for the collections relim problem is to move the # limits calculation into the artist itself, including the property # of whether or not the artist should affect the limits. Then there # will be no distinction between axes.add_line, axes.add_patch, etc. # TODO: add legend support def have_units(self): 'Return True if units are set on the x or y axes' ax = self.axes if ax is None or ax.xaxis is None: return False return ax.xaxis.have_units() or ax.yaxis.have_units() def convert_xunits(self, x): """For artists in an axes, if the xaxis has units support, convert x using xaxis unit type """ ax = getattr(self, 'axes', None) if ax is None or ax.xaxis is None: #print 'artist.convert_xunits no conversion: ax=%s'%ax return x return ax.xaxis.convert_units(x) def convert_yunits(self, y): """For artists in an axes, if the yaxis has units support, convert y using yaxis unit type """ ax = getattr(self, 'axes', None) if ax is None or ax.yaxis is None: return y return ax.yaxis.convert_units(y) def set_axes(self, axes): """ Set the :class:`~matplotlib.axes.Axes` instance in which the artist resides, if any. ACCEPTS: an :class:`~matplotlib.axes.Axes` instance """ self.axes = axes def get_axes(self): """ Return the :class:`~matplotlib.axes.Axes` instance the artist resides in, or None """ return self.axes def add_callback(self, func): """ Adds a callback function that will be called whenever one of the :class:`Artist`'s properties changes. Returns an id that is useful for removing the callback with :meth:`remove_callback` later. """ oid = self._oid self._propobservers[oid] = func self._oid += 1 return oid def remove_callback(self, oid): """ Remove a callback based on its id. .. seealso:: :meth:`add_callback` """ try: del self._propobservers[oid] except KeyError: pass def pchanged(self): """ Fire an event when property changed, calling all of the registered callbacks. """ for oid, func in self._propobservers.items(): func(self) def is_transform_set(self): """ Returns True if :class:`Artist` has a transform explicitly set. """ return self._transformSet def set_transform(self, t): """ Set the :class:`~matplotlib.transforms.Transform` instance used by this artist. ACCEPTS: :class:`~matplotlib.transforms.Transform` instance """ self._transform = t self._transformSet = True self.pchanged() def get_transform(self): """ Return the :class:`~matplotlib.transforms.Transform` instance used by this artist. """ if self._transform is None: self._transform = IdentityTransform() return self._transform def hitlist(self, event): """ List the children of the artist which contain the mouse event event. """ import traceback L = [] try: hascursor,info = self.contains(event) if hascursor: L.append(self) except: traceback.print_exc() print "while checking",self.__class__ for a in self.get_children(): L.extend(a.hitlist(event)) return L def get_children(self): """ Return a list of the child :class:`Artist`s this :class:`Artist` contains. """ return [] def contains(self, mouseevent): """Test whether the artist contains the mouse event. Returns the truth value and a dictionary of artist specific details of selection, such as which points are contained in the pick radius. See individual artists for details. """ if callable(self._contains): return self._contains(self,mouseevent) #raise NotImplementedError,str(self.__class__)+" needs 'contains' method" warnings.warn("'%s' needs 'contains' method" % self.__class__.__name__) return False,{} def set_contains(self,picker): """ Replace the contains test used by this artist. The new picker should be a callable function which determines whether the artist is hit by the mouse event:: hit, props = picker(artist, mouseevent) If the mouse event is over the artist, return hit = True and props is a dictionary of properties you want returned with the contains test. ACCEPTS: a callable function """ self._contains = picker def get_contains(self): """ Return the _contains test used by the artist, or None for default. """ return self._contains def pickable(self): 'Return True if :class:`Artist` is pickable.' return (self.figure is not None and self.figure.canvas is not None and self._picker is not None) def pick(self, mouseevent): """ call signature:: pick(mouseevent) each child artist will fire a pick event if mouseevent is over the artist and the artist has picker set """ # Pick self if self.pickable(): picker = self.get_picker() if callable(picker): inside,prop = picker(self,mouseevent) else: inside,prop = self.contains(mouseevent) if inside: self.figure.canvas.pick_event(mouseevent, self, *prop) # Pick children for a in self.get_children(): a.pick(mouseevent) def set_picker(self, picker): """ Set the epsilon for picking used by this artist picker* can be one of the following: * None: picking is disabled for this artist (default) * A boolean: if True then picking will be enabled and the artist will fire a pick event if the mouse event is over the artist * A float: if picker is a number it is interpreted as an epsilon tolerance in points and the artist will fire off an event if it's data is within epsilon of the mouse event. For some artists like lines and patch collections, the artist may provide additional data to the pick event that is generated, e.g. the indices of the data within epsilon of the pick event * A function: if picker is callable, it is a user supplied function which determines whether the artist is hit by the mouse event:: hit, props = picker(artist, mouseevent) to determine the hit test. if the mouse event is over the artist, return hit=True and props is a dictionary of properties you want added to the PickEvent attributes. ACCEPTS: [None\|float\|boolean\|callable] """ self._picker = picker def get_picker(self): 'Return the picker object used by this artist' return self._picker def is_figure_set(self): """ Returns True if the artist is assigned to a :class:`~matplotlib.figure.Figure`. """ return self.figure is not None def get_url(self): """ Returns the url """ return self._url def set_url(self, url): """ Sets the url for the artist """ self._url = url def get_snap(self): """ Returns the snap setting which may be: * True: snap vertices to the nearest pixel center * False: leave vertices as-is * None: (auto) If the path contains only rectilinear line segments, round to the nearest pixel center Only supported by the Agg backends. """ return self._snap def set_snap(self, snap): """ Sets the snap setting which may be: * True: snap vertices to the nearest pixel center * False: leave vertices as-is * None: (auto) If the path contains only rectilinear line segments, round to the nearest pixel center Only supported by the Agg backends. """ self._snap = snap def get_figure(self): """ Return the :class:`~matplotlib.figure.Figure` instance the artist belongs to. """ return self.figure def set_figure(self, fig): """ Set the :class:`~matplotlib.figure.Figure` instance the artist belongs to. ACCEPTS: a :class:`matplotlib.figure.Figure` instance """ self.figure = fig self.pchanged() def set_clip_box(self, clipbox): """ Set the artist's clip :class:`~matplotlib.transforms.Bbox`. ACCEPTS: a :class:`matplotlib.transforms.Bbox` instance """ self.clipbox = clipbox self.pchanged() def set_clip_path(self, path, transform=None): """ Set the artist's clip path, which may be: * a :class:`~matplotlib.patches.Patch` (or subclass) instance * a :class:`~matplotlib.path.Path` instance, in which case an optional :class:`~matplotlib.transforms.Transform` instance may be provided, which will be applied to the path before using it for clipping. * None, to remove the clipping path For efficiency, if the path happens to be an axis-aligned rectangle, this method will set the clipping box to the corresponding rectangle and set the clipping path to None. ACCEPTS: [ (:class:`~matplotlib.path.Path`, :class:`~matplotlib.transforms.Transform`) \| :class:`~matplotlib.patches.Patch` \| None ] """ from patches import Patch, Rectangle success = False if transform is None: if isinstance(path, Rectangle): self.clipbox = TransformedBbox(Bbox.unit(), path.get_transform()) self._clippath = None success = True elif isinstance(path, Patch): self._clippath = TransformedPath( path.get_path(), path.get_transform()) success = True if path is None: self._clippath = None success = True elif isinstance(path, Path): self._clippath = TransformedPath(path, transform) success = True if not success: print type(path), type(transform) raise TypeError("Invalid arguments to set_clip_path") self.pchanged() def get_alpha(self): """ Return the alpha value used for blending - not supported on all backends """ return self._alpha def get_visible(self): "Return the artist's visiblity" return self._visible def get_animated(self): "Return the artist's animated state" return self._animated def get_clip_on(self): 'Return whether artist uses clipping' return self._clipon def get_clip_box(self): 'Return artist clipbox' return self.clipbox def get_clip_path(self): 'Return artist clip path' return self._clippath def get_transformed_clip_path_and_affine(self): ''' Return the clip path with the non-affine part of its transformation applied, and the remaining affine part of its transformation. ''' if self._clippath is not None: return self._clippath.get_transformed_path_and_affine() return None, None def set_clip_on(self, b): """ Set whether artist uses clipping. ACCEPTS: [True \| False] """ self._clipon = b self.pchanged() def _set_gc_clip(self, gc): 'Set the clip properly for the gc' if self._clipon: if self.clipbox is not None: gc.set_clip_rectangle(self.clipbox) gc.set_clip_path(self._clippath) else: gc.set_clip_rectangle(None) gc.set_clip_path(None) def draw(self, renderer, args, kwargs): 'Derived classes drawing method' if not self.get_visible(): return def set_alpha(self, alpha): """ Set the alpha value used for blending - not supported on all backends ACCEPTS: float (0.0 transparent through 1.0 opaque) """ self._alpha = alpha self.pchanged() def set_lod(self, on): """ Set Level of Detail on or off. If on, the artists may examine things like the pixel width of the axes and draw a subset of their contents accordingly ACCEPTS: [True \| False] """ self._lod = on self.pchanged() def set_visible(self, b): """ Set the artist's visiblity. ACCEPTS: [True \| False] """ self._visible = b self.pchanged() def set_animated(self, b): """ Set the artist's animation state. ACCEPTS: [True \| False] """ self._animated = b self.pchanged() def update(self, props): """ Update the properties of this :class:`Artist` from the dictionary prop. """ store = self.eventson self.eventson = False changed = False for k,v in props.items(): func = getattr(self, 'set_'+k, None) if func is None or not callable(func): raise AttributeError('Unknown property %s'%k) func(v) changed = True self.eventson = store if changed: self.pchanged() def get_label(self): """ Get the label used for this artist in the legend. """ return self._label def set_label(self, s): """ Set the label to s* for auto legend. ACCEPTS: any string """ self._label = s self.pchanged() def get_zorder(self): """ Return the :class:`Artist`'s zorder. """ return self.zorder def set_zorder(self, level): """ Set the zorder for the artist. Artists with lower zorder values are drawn first. ACCEPTS: any number """ self.zorder = level self.pchanged() def update_from(self, other): 'Copy properties from other to self.' self._transform = other._transform self._transformSet = other._transformSet self._visible = other._visible self._alpha = other._alpha self.clipbox = other.clipbox self._clipon = other._clipon self._clippath = other._clippath self._lod = other._lod self._label = other._label self.pchanged() def set(self, *kwargs): """ A tkstyle set command, pass kwargs* to set properties """ ret = [] for k,v in kwargs.items(): k = k.lower() funcName = "set_%s"%k func = getattr(self,funcName) ret.extend( [func(v)] ) return ret def findobj(self, match=None): """ pyplot signature: findobj(o=gcf(), match=None) Recursively find all :class:matplotlib.artist.Artist instances contained in self. match can be - None: return all objects contained in artist (including artist) - function with signature ``boolean = match(artist)`` used to filter matches - class instance: eg Line2D. Only return artists of class type .. plot:: mpl_examples/pylab_examples/findobj_demo.py """ if match is None: # always return True def matchfunc(x): return True elif cbook.issubclass_safe(match, Artist): def matchfunc(x): return isinstance(x, match) elif callable(match): matchfunc = match else: raise ValueError('match must be None, an matplotlib.artist.Artist subclass, or a callable') artists = [] for c in self.get_children(): if matchfunc(c): artists.append(c) artists.extend([thisc for thisc in c.findobj(matchfunc) if matchfunc(thisc)]) if matchfunc(self): artists.append(self) return artists class ArtistInspector: """ A helper class to inspect an :class:`~matplotlib.artist.Artist` and return information about it's settable properties and their current values. """ def __init__(self, o): """ Initialize the artist inspector with an :class:`~matplotlib.artist.Artist` or sequence of :class:`Artists`. If a sequence is used, we assume it is a homogeneous sequence (all :class:`Artists` are of the same type) and it is your responsibility to make sure this is so. """ if cbook.iterable(o) and len(o): o = o[0] self.oorig = o if not isinstance(o, type): o = type(o) self.o = o self.aliasd = self.get_aliases() def get_aliases(self): """ Get a dict mapping fullname -> alias for each alias in the :class:`~matplotlib.artist.ArtistInspector`. Eg., for lines:: {'markerfacecolor': 'mfc', 'linewidth' : 'lw', } """ names = [name for name in dir(self.o) if (name.startswith('set_') or name.startswith('get_')) and callable(getattr(self.o,name))] aliases = {} for name in names: func = getattr(self.o, name) if not self.is_alias(func): continue docstring = func.__doc__ fullname = docstring[10:] aliases.setdefault(fullname[4:], {})[name[4:]] = None return aliases _get_valid_values_regex = re.compile(r"\n\sACCEPTS:\s((?:.\|\n)?)(?:$\|(?:\n\n))") def get_valid_values(self, attr): """ Get the legal arguments for the setter associated with attr. This is done by querying the docstring of the function set_attr* for a line that begins with ACCEPTS: Eg., for a line linestyle, return [ '-' \| '--' \| '-.' \| ':' \| 'steps' \| 'None' ] """ name = 'set_%s'%attr if not hasattr(self.o, name): raise AttributeError('%s has no function %s'%(self.o,name)) func = getattr(self.o, name) docstring = func.__doc__ if docstring is None: return 'unknown' if docstring.startswith('alias for '): return None match = self._get_valid_values_regex.search(docstring) if match is not None: return match.group(1).replace('\n', ' ') return 'unknown' def _get_setters_and_targets(self): """ Get the attribute strings and a full path to where the setter is defined for all setters in an object. """ setters = [] for name in dir(self.o): if not name.startswith('set_'): continue o = getattr(self.o, name) if not callable(o): continue func = o if self.is_alias(func): continue source_class = self.o.__module__ + "." + self.o.__name__ for cls in self.o.mro(): if name in cls.__dict__: source_class = cls.__module__ + "." + cls.__name__ break setters.append((name[4:], source_class + "." + name)) return setters def get_setters(self): """ Get the attribute strings with setters for object. Eg., for a line, return ``['markerfacecolor', 'linewidth', ....]``. """ return [prop for prop, target in self._get_setters_and_targets()] def is_alias(self, o): """ Return True if method object o is an alias for another function. """ ds = o.__doc__ if ds is None: return False return ds.startswith('alias for ') def aliased_name(self, s): """ return 'PROPNAME or alias' if s has an alias, else return PROPNAME. E.g. for the line markerfacecolor property, which has an alias, return 'markerfacecolor or mfc' and for the transform property, which does not, return 'transform' """ if s in self.aliasd: return s + ''.join([' or %s' % x for x in self.aliasd[s].keys()]) else: return s def aliased_name_rest(self, s, target): """ return 'PROPNAME or alias' if s has an alias, else return PROPNAME formatted for ReST E.g. for the line markerfacecolor property, which has an alias, return 'markerfacecolor or mfc' and for the transform property, which does not, return 'transform' """ if s in self.aliasd: aliases = ''.join([' or %s' % x for x in self.aliasd[s].keys()]) else: aliases = '' return ':meth:`%s <%s>`%s' % (s, target, aliases) def pprint_setters(self, prop=None, leadingspace=2): """ If prop is None, return a list of strings of all settable properies and their valid values. If prop is not None, it is a valid property name and that property will be returned as a string of property : valid values. """ if leadingspace: pad = ' 'leadingspace else: pad = '' if prop is not None: accepts = self.get_valid_values(prop) return '%s%s: %s' %(pad, prop, accepts) attrs = self._get_setters_and_targets() attrs.sort() lines = [] for prop, path in attrs: accepts = self.get_valid_values(prop) name = self.aliased_name(prop) lines.append('%s%s: %s' %(pad, name, accepts)) return lines def pprint_setters_rest(self, prop=None, leadingspace=2): """ If prop* is None, return a list of strings of all settable properies and their valid values. Format the output for ReST If prop is not None, it is a valid property name and that property will be returned as a string of property : valid values. """ if leadingspace: pad = ' 'leadingspace else: pad = '' if prop is not None: accepts = self.get_valid_values(prop) return '%s%s: %s' %(pad, prop, accepts) attrs = self._get_setters_and_targets() attrs.sort() lines = [] ######## names = [self.aliased_name_rest(prop, target) for prop, target in attrs] accepts = [self.get_valid_values(prop) for prop, target in attrs] col0_len = max([len(n) for n in names]) col1_len = max([len(a) for a in accepts]) table_formatstr = pad + '='col0_len + ' ' + '='col1_len lines.append('') lines.append(table_formatstr) lines.append(pad + 'Property'.ljust(col0_len+3) + \ 'Description'.ljust(col1_len)) lines.append(table_formatstr) lines.extend([pad + n.ljust(col0_len+3) + a.ljust(col1_len) for n, a in zip(names, accepts)]) lines.append(table_formatstr) lines.append('') return lines ######## for prop, path in attrs: accepts = self.get_valid_values(prop) name = self.aliased_name_rest(prop, path) lines.append('%s%s: %s' %(pad, name, accepts)) return lines def pprint_getters(self): """ Return the getters and actual values as list of strings. """ o = self.oorig getters = [name for name in dir(o) if name.startswith('get_') and callable(getattr(o, name))] #print getters getters.sort() lines = [] for name in getters: func = getattr(o, name) if self.is_alias(func): continue try: val = func() except: continue if getattr(val, 'shape', ()) != () and len(val)>6: s = str(val[:6]) + '...' else: s = str(val) s = s.replace('\n', ' ') if len(s)>50: s = s[:50] + '...' name = self.aliased_name(name[4:]) lines.append(' %s = %s' %(name, s)) return lines def findobj(self, match=None): """ Recursively find all :class:`matplotlib.artist.Artist` instances contained in self. If match* is not None, it can be - function with signature ``boolean = match(artist)`` - class instance: eg :class:`~matplotlib.lines.Line2D` used to filter matches. """ if match is None: # always return True def matchfunc(x): return True elif issubclass(match, Artist): def matchfunc(x): return isinstance(x, match) elif callable(match): matchfunc = func else: raise ValueError('match must be None, an matplotlib.artist.Artist subclass, or a callable') artists = [] for c in self.get_children(): if matchfunc(c): artists.append(c) artists.extend([thisc for thisc in c.findobj(matchfunc) if matchfunc(thisc)]) if matchfunc(self): artists.append(self) return artists def getp(o, property=None): """ Return the value of handle property. property is an optional string for the property you want to return Example usage:: getp(o) # get all the object properties getp(o, 'linestyle') # get the linestyle property o is a :class:`Artist` instance, eg :class:`~matplotllib.lines.Line2D` or an instance of a :class:`~matplotlib.axes.Axes` or :class:`matplotlib.text.Text`. If the property is 'somename', this function returns o.get_somename() :func:`getp` can be used to query all the gettable properties with ``getp(o)``. Many properties have aliases for shorter typing, e.g. 'lw' is an alias for 'linewidth'. In the output, aliases and full property names will be listed as: property or alias = value e.g.: linewidth or lw = 2 """ insp = ArtistInspector(o) if property is None: ret = insp.pprint_getters() print '\n'.join(ret) return func = getattr(o, 'get_' + property) return func() # alias get = getp def setp(h, args, kwargs): """ matplotlib supports the use of :func:`setp` ("set property") and :func:`getp` to set and get object properties, as well as to do introspection on the object. For example, to set the linestyle of a line to be dashed, you can do:: >>> line, = plot([1,2,3]) >>> setp(line, linestyle='--') If you want to know the valid types of arguments, you can provide the name of the property you want to set without a value:: >>> setp(line, 'linestyle') linestyle: [ '-' \| '--' \| '-.' \| ':' \| 'steps' \| 'None' ] If you want to see all the properties that can be set, and their possible values, you can do:: >>> setp(line) ... long output listing omitted :func:`setp` operates on a single instance or a list of instances. If you are in query mode introspecting the possible values, only the first instance in the sequence is used. When actually setting values, all the instances will be set. E.g., suppose you have a list of two lines, the following will make both lines thicker and red:: >>> x = arange(0,1.0,0.01) >>> y1 = sin(2pix) >>> y2 = sin(4pi*x) >>> lines = plot(x, y1, x, y2) >>> setp(lines, linewidth=2, color='r') :func:`setp` works with the matlab(TM) style string/value pairs or with python kwargs. For example, the following are equivalent:: >>> setp(lines, 'linewidth', 2, 'color', r') # matlab style >>> setp(lines, linewidth=2, color='r') # python style """ insp = ArtistInspector(h) if len(kwargs)==0 and len(args)==0: print '\n'.join(insp.pprint_setters()) return if len(kwargs)==0 and len(args)==1: print insp.pprint_setters(prop=args[0]) return if not cbook.iterable(h): h = [h] else: h = cbook.flatten(h) if len(args)%2: raise ValueError('The set args must be string, value pairs') funcvals = [] for i in range(0, len(args)-1, 2): funcvals.append((args[i], args[i+1])) funcvals.extend(kwargs.items()) ret = [] for o in h: for s, val in funcvals: s = s.lower() funcName = "set_%s"%s func = getattr(o,funcName) ret.extend( [func(val)] ) return [x for x in cbook.flatten(ret)] def kwdoc(a): hardcopy = matplotlib.rcParams['docstring.hardcopy'] if hardcopy: return '\n'.join(ArtistInspector(a).pprint_setters_rest(leadingspace=2)) else: return '\n'.join(ArtistInspector(a).pprint_setters(leadingspace=2)) kwdocd = dict() kwdocd['Artist'] = kwdoc(Artist)	33,042	Python	.py	855	29.326316	221	0.58659	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,260	text.py	numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/text.py	""" Classes for including text in a figure. """ from __future__ import division import math import numpy as np from matplotlib import cbook from matplotlib import rcParams import artist from artist import Artist from cbook import is_string_like, maxdict from font_manager import FontProperties from patches import bbox_artist, YAArrow, FancyBboxPatch, \ FancyArrowPatch, Rectangle import transforms as mtransforms from transforms import Affine2D, Bbox from lines import Line2D import matplotlib.nxutils as nxutils def _process_text_args(override, fontdict=None, *kwargs): "Return an override dict. See :func:`~pyplot.text' docstring for info" if fontdict is not None: override.update(fontdict) override.update(kwargs) return override # Extracted from Text's method to serve as a function def get_rotation(rotation): """ Return the text angle as float. rotation* may be 'horizontal', 'vertical', or a numeric value in degrees. """ if rotation in ('horizontal', None): angle = 0. elif rotation == 'vertical': angle = 90. else: angle = float(rotation) return angle%360 # these are not available for the object inspector until after the # class is build so we define an initial set here for the init # function and they will be overridden after object defn artist.kwdocd['Text'] = """ ========================== ========================================================================= Property Value ========================== ========================================================================= alpha float animated [True \| False] backgroundcolor any matplotlib color bbox rectangle prop dict plus key 'pad' which is a pad in points clip_box a matplotlib.transform.Bbox instance clip_on [True \| False] color any matplotlib color family [ 'serif' \| 'sans-serif' \| 'cursive' \| 'fantasy' \| 'monospace' ] figure a matplotlib.figure.Figure instance fontproperties a matplotlib.font_manager.FontProperties instance horizontalalignment or ha [ 'center' \| 'right' \| 'left' ] label any string linespacing float lod [True \| False] multialignment ['left' \| 'right' \| 'center' ] name or fontname string eg, ['Sans' \| 'Courier' \| 'Helvetica' ...] position (x,y) rotation [ angle in degrees 'vertical' \| 'horizontal' size or fontsize [ size in points \| relative size eg 'smaller', 'x-large' ] style or fontstyle [ 'normal' \| 'italic' \| 'oblique'] text string transform a matplotlib.transform transformation instance variant [ 'normal' \| 'small-caps' ] verticalalignment or va [ 'center' \| 'top' \| 'bottom' \| 'baseline' ] visible [True \| False] weight or fontweight [ 'normal' \| 'bold' \| 'heavy' \| 'light' \| 'ultrabold' \| 'ultralight'] x float y float zorder any number ========================== ========================================================================= """ # TODO : This function may move into the Text class as a method. As a # matter of fact, The information from the _get_textbox function # should be available during the Text._get_layout() call, which is # called within the _get_textbox. So, it would better to move this # function as a method with some refactoring of _get_layout method. def _get_textbox(text, renderer): """ Calculate the bounding box of the text. Unlike :meth:`matplotlib.text.Text.get_extents` method, The bbox size of the text before the rotation is calculated. """ projected_xs = [] projected_ys = [] theta = text.get_rotation()/180.math.pi tr = mtransforms.Affine2D().rotate(-theta) for t, wh, x, y in text._get_layout(renderer)[1]: w, h = wh xt1, yt1 = tr.transform_point((x, y)) xt2, yt2 = xt1+w, yt1+h projected_xs.extend([xt1, xt2]) projected_ys.extend([yt1, yt2]) xt_box, yt_box = min(projected_xs), min(projected_ys) w_box, h_box = max(projected_xs) - xt_box, max(projected_ys) - yt_box tr = mtransforms.Affine2D().rotate(theta) x_box, y_box = tr.transform_point((xt_box, yt_box)) return x_box, y_box, w_box, h_box class Text(Artist): """ Handle storing and drawing of text in window or data coordinates. """ zorder = 3 def __str__(self): return "Text(%g,%g,%s)"%(self._y,self._y,repr(self._text)) def __init__(self, x=0, y=0, text='', color=None, # defaults to rc params verticalalignment='bottom', horizontalalignment='left', multialignment=None, fontproperties=None, # defaults to FontProperties() rotation=None, linespacing=None, kwargs ): """ Create a :class:`~matplotlib.text.Text` instance at x, y* with string text. Valid kwargs are %(Text)s """ Artist.__init__(self) self.cached = maxdict(5) self._x, self._y = x, y if color is None: color = rcParams['text.color'] if fontproperties is None: fontproperties=FontProperties() elif is_string_like(fontproperties): fontproperties=FontProperties(fontproperties) self.set_text(text) self.set_color(color) self._verticalalignment = verticalalignment self._horizontalalignment = horizontalalignment self._multialignment = multialignment self._rotation = rotation self._fontproperties = fontproperties self._bbox = None self._bbox_patch = None # a FancyBboxPatch instance self._renderer = None if linespacing is None: linespacing = 1.2 # Maybe use rcParam later. self._linespacing = linespacing self.update(kwargs) #self.set_bbox(dict(pad=0)) def contains(self,mouseevent): """Test whether the mouse event occurred in the patch. In the case of text, a hit is true anywhere in the axis-aligned bounding-box containing the text. Returns True or False. """ if callable(self._contains): return self._contains(self,mouseevent) if not self.get_visible() or self._renderer is None: return False,{} l,b,w,h = self.get_window_extent().bounds r = l+w t = b+h xyverts = (l,b), (l, t), (r, t), (r, b) x, y = mouseevent.x, mouseevent.y inside = nxutils.pnpoly(x, y, xyverts) return inside,{} def _get_xy_display(self): 'get the (possibly unit converted) transformed x, y in display coords' x, y = self.get_position() return self.get_transform().transform_point((x,y)) def _get_multialignment(self): if self._multialignment is not None: return self._multialignment else: return self._horizontalalignment def get_rotation(self): 'return the text angle as float in degrees' return get_rotation(self._rotation) # string_or_number -> number def update_from(self, other): 'Copy properties from other to self' Artist.update_from(self, other) self._color = other._color self._multialignment = other._multialignment self._verticalalignment = other._verticalalignment self._horizontalalignment = other._horizontalalignment self._fontproperties = other._fontproperties.copy() self._rotation = other._rotation self._picker = other._picker self._linespacing = other._linespacing def _get_layout(self, renderer): key = self.get_prop_tup() if key in self.cached: return self.cached[key] horizLayout = [] thisx, thisy = 0.0, 0.0 xmin, ymin = 0.0, 0.0 width, height = 0.0, 0.0 lines = self._text.split('\n') whs = np.zeros((len(lines), 2)) horizLayout = np.zeros((len(lines), 4)) # Find full vertical extent of font, # including ascenders and descenders: tmp, heightt, bl = renderer.get_text_width_height_descent( 'lp', self._fontproperties, ismath=False) offsety = heightt * self._linespacing baseline = None for i, line in enumerate(lines): clean_line, ismath = self.is_math_text(line) w, h, d = renderer.get_text_width_height_descent( clean_line, self._fontproperties, ismath=ismath) if baseline is None: baseline = h - d whs[i] = w, h horizLayout[i] = thisx, thisy, w, h thisy -= offsety width = max(width, w) ymin = horizLayout[-1][1] ymax = horizLayout[0][1] + horizLayout[0][3] height = ymax-ymin xmax = xmin + width # get the rotation matrix M = Affine2D().rotate_deg(self.get_rotation()) offsetLayout = np.zeros((len(lines), 2)) offsetLayout[:] = horizLayout[:, 0:2] # now offset the individual text lines within the box if len(lines)>1: # do the multiline aligment malign = self._get_multialignment() if malign == 'center': offsetLayout[:, 0] += width/2.0 - horizLayout[:, 2] / 2.0 elif malign == 'right': offsetLayout[:, 0] += width - horizLayout[:, 2] # the corners of the unrotated bounding box cornersHoriz = np.array( [(xmin, ymin), (xmin, ymax), (xmax, ymax), (xmax, ymin)], np.float_) # now rotate the bbox cornersRotated = M.transform(cornersHoriz) txs = cornersRotated[:, 0] tys = cornersRotated[:, 1] # compute the bounds of the rotated box xmin, xmax = txs.min(), txs.max() ymin, ymax = tys.min(), tys.max() width = xmax - xmin height = ymax - ymin # Now move the box to the targe position offset the display bbox by alignment halign = self._horizontalalignment valign = self._verticalalignment # compute the text location in display coords and the offsets # necessary to align the bbox with that location if halign=='center': offsetx = (xmin + width/2.0) elif halign=='right': offsetx = (xmin + width) else: offsetx = xmin if valign=='center': offsety = (ymin + height/2.0) elif valign=='top': offsety = (ymin + height) elif valign=='baseline': offsety = (ymin + height) - baseline else: offsety = ymin xmin -= offsetx ymin -= offsety bbox = Bbox.from_bounds(xmin, ymin, width, height) # now rotate the positions around the first x,y position xys = M.transform(offsetLayout) xys -= (offsetx, offsety) xs, ys = xys[:, 0], xys[:, 1] ret = bbox, zip(lines, whs, xs, ys) self.cached[key] = ret return ret def set_bbox(self, rectprops): """ Draw a bounding box around self. rectprops are any settable properties for a rectangle, eg facecolor='red', alpha=0.5. t.set_bbox(dict(facecolor='red', alpha=0.5)) If rectprops has "boxstyle" key. A FancyBboxPatch is initialized with rectprops and will be drawn. The mutation scale of the FancyBboxPath is set to the fontsize. ACCEPTS: rectangle prop dict """ # The self._bbox_patch object is created only if rectprops has # boxstyle key. Otherwise, self._bbox will be set to the # rectprops and the bbox will be drawn using bbox_artist # function. This is to keep the backward compatibility. if rectprops is not None and "boxstyle" in rectprops: props = rectprops.copy() boxstyle = props.pop("boxstyle") bbox_transmuter = props.pop("bbox_transmuter", None) self._bbox_patch = FancyBboxPatch((0., 0.), 1., 1., boxstyle=boxstyle, bbox_transmuter=bbox_transmuter, transform=mtransforms.IdentityTransform(), *props) self._bbox = None else: self._bbox_patch = None self._bbox = rectprops def get_bbox_patch(self): """ Return the bbox Patch object. Returns None if the the FancyBboxPatch is not made. """ return self._bbox_patch def update_bbox_position_size(self, renderer): """ Update the location and the size of the bbox. This method should be used when the position and size of the bbox needs to be updated before actually drawing the bbox. """ # For arrow_patch, use textbox as patchA by default. if not isinstance(self.arrow_patch, FancyArrowPatch): return if self._bbox_patch: trans = self.get_transform() # don't use self.get_position here, which refers to text position # in Text, and dash position in TextWithDash: posx = float(self.convert_xunits(self._x)) posy = float(self.convert_yunits(self._y)) posx, posy = trans.transform_point((posx, posy)) x_box, y_box, w_box, h_box = _get_textbox(self, renderer) self._bbox_patch.set_bounds(0., 0., w_box, h_box) theta = self.get_rotation()/180.math.pi tr = mtransforms.Affine2D().rotate(theta) tr = tr.translate(posx+x_box, posy+y_box) self._bbox_patch.set_transform(tr) fontsize_in_pixel = renderer.points_to_pixels(self.get_size()) self._bbox_patch.set_mutation_scale(fontsize_in_pixel) #self._bbox_patch.draw(renderer) else: props = self._bbox if props is None: props = {} props = props.copy() # don't want to alter the pad externally pad = props.pop('pad', 4) pad = renderer.points_to_pixels(pad) bbox = self.get_window_extent(renderer) l,b,w,h = bbox.bounds l-=pad/2. b-=pad/2. w+=pad h+=pad r = Rectangle(xy=(l,b), width=w, height=h, ) r.set_transform(mtransforms.IdentityTransform()) r.set_clip_on( False ) r.update(props) self.arrow_patch.set_patchA(r) def _draw_bbox(self, renderer, posx, posy): """ Update the location and the size of the bbox (FancyBoxPatch), and draw """ x_box, y_box, w_box, h_box = _get_textbox(self, renderer) self._bbox_patch.set_bounds(0., 0., w_box, h_box) theta = self.get_rotation()/180.math.pi tr = mtransforms.Affine2D().rotate(theta) tr = tr.translate(posx+x_box, posy+y_box) self._bbox_patch.set_transform(tr) fontsize_in_pixel = renderer.points_to_pixels(self.get_size()) self._bbox_patch.set_mutation_scale(fontsize_in_pixel) self._bbox_patch.draw(renderer) def draw(self, renderer): """ Draws the :class:`Text` object to the given renderer. """ if renderer is not None: self._renderer = renderer if not self.get_visible(): return if self._text=='': return bbox, info = self._get_layout(renderer) trans = self.get_transform() # don't use self.get_position here, which refers to text position # in Text, and dash position in TextWithDash: posx = float(self.convert_xunits(self._x)) posy = float(self.convert_yunits(self._y)) posx, posy = trans.transform_point((posx, posy)) canvasw, canvash = renderer.get_canvas_width_height() # draw the FancyBboxPatch if self._bbox_patch: self._draw_bbox(renderer, posx, posy) gc = renderer.new_gc() gc.set_foreground(self._color) gc.set_alpha(self._alpha) gc.set_url(self._url) if self.get_clip_on(): gc.set_clip_rectangle(self.clipbox) if self._bbox: bbox_artist(self, renderer, self._bbox) angle = self.get_rotation() if rcParams['text.usetex']: for line, wh, x, y in info: x = x + posx y = y + posy if renderer.flipy(): y = canvash-y clean_line, ismath = self.is_math_text(line) renderer.draw_tex(gc, x, y, clean_line, self._fontproperties, angle) return for line, wh, x, y in info: x = x + posx y = y + posy if renderer.flipy(): y = canvash-y clean_line, ismath = self.is_math_text(line) renderer.draw_text(gc, x, y, clean_line, self._fontproperties, angle, ismath=ismath) def get_color(self): "Return the color of the text" return self._color def get_fontproperties(self): "Return the :class:`~font_manager.FontProperties` object" return self._fontproperties def get_font_properties(self): 'alias for get_fontproperties' return self.get_fontproperties def get_family(self): "Return the list of font families used for font lookup" return self._fontproperties.get_family() def get_fontfamily(self): 'alias for get_family' return self.get_family() def get_name(self): "Return the font name as string" return self._fontproperties.get_name() def get_style(self): "Return the font style as string" return self._fontproperties.get_style() def get_size(self): "Return the font size as integer" return self._fontproperties.get_size_in_points() def get_variant(self): "Return the font variant as a string" return self._fontproperties.get_variant() def get_fontvariant(self): 'alias for get_variant' return self.get_variant() def get_weight(self): "Get the font weight as string or number" return self._fontproperties.get_weight() def get_fontname(self): 'alias for get_name' return self.get_name() def get_fontstyle(self): 'alias for get_style' return self.get_style() def get_fontsize(self): 'alias for get_size' return self.get_size() def get_fontweight(self): 'alias for get_weight' return self.get_weight() def get_stretch(self): 'Get the font stretch as a string or number' return self._fontproperties.get_stretch() def get_fontstretch(self): 'alias for get_stretch' return self.get_stretch() def get_ha(self): 'alias for get_horizontalalignment' return self.get_horizontalalignment() def get_horizontalalignment(self): """ Return the horizontal alignment as string. Will be one of 'left', 'center' or 'right'. """ return self._horizontalalignment def get_position(self): "Return the position of the text as a tuple (x, y)" x = float(self.convert_xunits(self._x)) y = float(self.convert_yunits(self._y)) return x, y def get_prop_tup(self): """ Return a hashable tuple of properties. Not intended to be human readable, but useful for backends who want to cache derived information about text (eg layouts) and need to know if the text has changed. """ x, y = self.get_position() return (x, y, self._text, self._color, self._verticalalignment, self._horizontalalignment, hash(self._fontproperties), self._rotation, self.figure.dpi, id(self._renderer), ) def get_text(self): "Get the text as string" return self._text def get_va(self): 'alias for :meth:`getverticalalignment`' return self.get_verticalalignment() def get_verticalalignment(self): """ Return the vertical alignment as string. Will be one of 'top', 'center', 'bottom' or 'baseline'. """ return self._verticalalignment def get_window_extent(self, renderer=None, dpi=None): ''' Return a :class:`~matplotlib.transforms.Bbox` object bounding the text, in display units. In addition to being used internally, this is useful for specifying clickable regions in a png file on a web page. renderer* defaults to the _renderer attribute of the text object. This is not assigned until the first execution of :meth:`draw`, so you must use this kwarg if you want to call :meth:`get_window_extent` prior to the first :meth:`draw`. For getting web page regions, it is simpler to call the method after saving the figure. dpi defaults to self.figure.dpi; the renderer dpi is irrelevant. For the web application, if figure.dpi is not the value used when saving the figure, then the value that was used must be specified as the dpi argument. ''' #return _unit_box if not self.get_visible(): return Bbox.unit() if dpi is not None: dpi_orig = self.figure.dpi self.figure.dpi = dpi if self._text == '': tx, ty = self._get_xy_display() return Bbox.from_bounds(tx,ty,0,0) if renderer is not None: self._renderer = renderer if self._renderer is None: raise RuntimeError('Cannot get window extent w/o renderer') bbox, info = self._get_layout(self._renderer) x, y = self.get_position() x, y = self.get_transform().transform_point((x, y)) bbox = bbox.translated(x, y) if dpi is not None: self.figure.dpi = dpi_orig return bbox def set_backgroundcolor(self, color): """ Set the background color of the text by updating the bbox. .. seealso:: :meth:`set_bbox` ACCEPTS: any matplotlib color """ if self._bbox is None: self._bbox = dict(facecolor=color, edgecolor=color) else: self._bbox.update(dict(facecolor=color)) def set_color(self, color): """ Set the foreground color of the text ACCEPTS: any matplotlib color """ # Make sure it is hashable, or get_prop_tup will fail. try: hash(color) except TypeError: color = tuple(color) self._color = color def set_ha(self, align): 'alias for set_horizontalalignment' self.set_horizontalalignment(align) def set_horizontalalignment(self, align): """ Set the horizontal alignment to one of ACCEPTS: [ 'center' \| 'right' \| 'left' ] """ legal = ('center', 'right', 'left') if align not in legal: raise ValueError('Horizontal alignment must be one of %s' % str(legal)) self._horizontalalignment = align def set_ma(self, align): 'alias for set_verticalalignment' self.set_multialignment(align) def set_multialignment(self, align): """ Set the alignment for multiple lines layout. The layout of the bounding box of all the lines is determined bu the horizontalalignment and verticalalignment properties, but the multiline text within that box can be ACCEPTS: ['left' \| 'right' \| 'center' ] """ legal = ('center', 'right', 'left') if align not in legal: raise ValueError('Horizontal alignment must be one of %s' % str(legal)) self._multialignment = align def set_linespacing(self, spacing): """ Set the line spacing as a multiple of the font size. Default is 1.2. ACCEPTS: float (multiple of font size) """ self._linespacing = spacing def set_family(self, fontname): """ Set the font family. May be either a single string, or a list of strings in decreasing priority. Each string may be either a real font name or a generic font class name. If the latter, the specific font names will be looked up in the :file:`matplotlibrc` file. ACCEPTS: [ FONTNAME \| 'serif' \| 'sans-serif' \| 'cursive' \| 'fantasy' \| 'monospace' ] """ self._fontproperties.set_family(fontname) def set_variant(self, variant): """ Set the font variant, either 'normal' or 'small-caps'. ACCEPTS: [ 'normal' \| 'small-caps' ] """ self._fontproperties.set_variant(variant) def set_fontvariant(self, variant): 'alias for set_variant' return self.set_variant(variant) def set_name(self, fontname): """alias for set_family""" return self.set_family(fontname) def set_fontname(self, fontname): """alias for set_family""" self.set_family(fontname) def set_style(self, fontstyle): """ Set the font style. ACCEPTS: [ 'normal' \| 'italic' \| 'oblique'] """ self._fontproperties.set_style(fontstyle) def set_fontstyle(self, fontstyle): 'alias for set_style' return self.set_style(fontstyle) def set_size(self, fontsize): """ Set the font size. May be either a size string, relative to the default font size, or an absolute font size in points. ACCEPTS: [ size in points \| 'xx-small' \| 'x-small' \| 'small' \| 'medium' \| 'large' \| 'x-large' \| 'xx-large' ] """ self._fontproperties.set_size(fontsize) def set_fontsize(self, fontsize): 'alias for set_size' return self.set_size(fontsize) def set_weight(self, weight): """ Set the font weight. ACCEPTS: [ a numeric value in range 0-1000 \| 'ultralight' \| 'light' \| 'normal' \| 'regular' \| 'book' \| 'medium' \| 'roman' \| 'semibold' \| 'demibold' \| 'demi' \| 'bold' \| 'heavy' \| 'extra bold' \| 'black' ] """ self._fontproperties.set_weight(weight) def set_fontweight(self, weight): 'alias for set_weight' return self.set_weight(weight) def set_stretch(self, stretch): """ Set the font stretch (horizontal condensation or expansion). ACCEPTS: [ a numeric value in range 0-1000 \| 'ultra-condensed' \| 'extra-condensed' \| 'condensed' \| 'semi-condensed' \| 'normal' \| 'semi-expanded' \| 'expanded' \| 'extra-expanded' \| 'ultra-expanded' ] """ self._fontproperties.set_stretch(stretch) def set_fontstretch(self, stretch): 'alias for set_stretch' return self.set_stretch(stretch) def set_position(self, xy): """ Set the (x, y) position of the text ACCEPTS: (x,y) """ self.set_x(xy[0]) self.set_y(xy[1]) def set_x(self, x): """ Set the x position of the text ACCEPTS: float """ self._x = x def set_y(self, y): """ Set the y position of the text ACCEPTS: float """ self._y = y def set_rotation(self, s): """ Set the rotation of the text ACCEPTS: [ angle in degrees \| 'vertical' \| 'horizontal' ] """ self._rotation = s def set_va(self, align): 'alias for set_verticalalignment' self.set_verticalalignment(align) def set_verticalalignment(self, align): """ Set the vertical alignment ACCEPTS: [ 'center' \| 'top' \| 'bottom' \| 'baseline' ] """ legal = ('top', 'bottom', 'center', 'baseline') if align not in legal: raise ValueError('Vertical alignment must be one of %s' % str(legal)) self._verticalalignment = align def set_text(self, s): """ Set the text string s It may contain newlines (``\\n``) or math in LaTeX syntax. ACCEPTS: string or anything printable with '%s' conversion. """ self._text = '%s' % (s,) def is_math_text(self, s): """ Returns True if the given string s contains any mathtext. """ # Did we find an even number of non-escaped dollar signs? # If so, treat is as math text. dollar_count = s.count(r'$') - s.count(r'\$') even_dollars = (dollar_count > 0 and dollar_count % 2 == 0) if rcParams['text.usetex']: return s, 'TeX' if even_dollars: return s, True else: return s.replace(r'\$', '$'), False def set_fontproperties(self, fp): """ Set the font properties that control the text. fp must be a :class:`matplotlib.font_manager.FontProperties` object. ACCEPTS: a :class:`matplotlib.font_manager.FontProperties` instance """ if is_string_like(fp): fp = FontProperties(fp) self._fontproperties = fp.copy() def set_font_properties(self, fp): 'alias for set_fontproperties' self.set_fontproperties(fp) artist.kwdocd['Text'] = artist.kwdoc(Text) Text.__init__.im_func.__doc__ = cbook.dedent(Text.__init__.__doc__) % artist.kwdocd class TextWithDash(Text): """ This is basically a :class:`~matplotlib.text.Text` with a dash (drawn with a :class:`~matplotlib.lines.Line2D`) before/after it. It is intended to be a drop-in replacement for :class:`~matplotlib.text.Text`, and should behave identically to it when dashlength = 0.0. The dash always comes between the point specified by :meth:`~matplotlib.text.Text.set_position` and the text. When a dash exists, the text alignment arguments (horizontalalignment, verticalalignment) are ignored. dashlength is the length of the dash in canvas units. (default = 0.0). dashdirection is one of 0 or 1, where 0 draws the dash after the text and 1 before. (default = 0). dashrotation specifies the rotation of the dash, and should generally stay None. In this case :meth:`~matplotlib.text.TextWithDash.get_dashrotation` returns :meth:`~matplotlib.text.Text.get_rotation`. (I.e., the dash takes its rotation from the text's rotation). Because the text center is projected onto the dash, major deviations in the rotation cause what may be considered visually unappealing results. (default = None) dashpad is a padding length to add (or subtract) space between the text and the dash, in canvas units. (default = 3) dashpush "pushes" the dash and text away from the point specified by :meth:`~matplotlib.text.Text.set_position` by the amount in canvas units. (default = 0) .. note:: The alignment of the two objects is based on the bounding box of the :class:`~matplotlib.text.Text`, as obtained by :meth:`~matplotlib.artist.Artist.get_window_extent`. This, in turn, appears to depend on the font metrics as given by the rendering backend. Hence the quality of the "centering" of the label text with respect to the dash varies depending on the backend used. .. note:: I'm not sure that I got the :meth:`~matplotlib.text.TextWithDash.get_window_extent` right, or whether that's sufficient for providing the object bounding box. """ __name__ = 'textwithdash' def __str__(self): return "TextWithDash(%g,%g,%s)"%(self._x,self._y,repr(self._text)) def __init__(self, x=0, y=0, text='', color=None, # defaults to rc params verticalalignment='center', horizontalalignment='center', multialignment=None, fontproperties=None, # defaults to FontProperties() rotation=None, linespacing=None, dashlength=0.0, dashdirection=0, dashrotation=None, dashpad=3, dashpush=0, ): Text.__init__(self, x=x, y=y, text=text, color=color, verticalalignment=verticalalignment, horizontalalignment=horizontalalignment, multialignment=multialignment, fontproperties=fontproperties, rotation=rotation, linespacing=linespacing) # The position (x,y) values for text and dashline # are bogus as given in the instantiation; they will # be set correctly by update_coords() in draw() self.dashline = Line2D(xdata=(x, x), ydata=(y, y), color='k', linestyle='-') self._dashx = float(x) self._dashy = float(y) self._dashlength = dashlength self._dashdirection = dashdirection self._dashrotation = dashrotation self._dashpad = dashpad self._dashpush = dashpush #self.set_bbox(dict(pad=0)) def get_position(self): "Return the position of the text as a tuple (x, y)" x = float(self.convert_xunits(self._dashx)) y = float(self.convert_yunits(self._dashy)) return x, y def get_prop_tup(self): """ Return a hashable tuple of properties. Not intended to be human readable, but useful for backends who want to cache derived information about text (eg layouts) and need to know if the text has changed. """ props = [p for p in Text.get_prop_tup(self)] props.extend([self._x, self._y, self._dashlength, self._dashdirection, self._dashrotation, self._dashpad, self._dashpush]) return tuple(props) def draw(self, renderer): """ Draw the :class:`TextWithDash` object to the given renderer. """ self.update_coords(renderer) Text.draw(self, renderer) if self.get_dashlength() > 0.0: self.dashline.draw(renderer) def update_coords(self, renderer): """ Computes the actual x, y coordinates for text based on the input x, y and the dashlength. Since the rotation is with respect to the actual canvas's coordinates we need to map back and forth. """ dashx, dashy = self.get_position() dashlength = self.get_dashlength() # Shortcircuit this process if we don't have a dash if dashlength == 0.0: self._x, self._y = dashx, dashy return dashrotation = self.get_dashrotation() dashdirection = self.get_dashdirection() dashpad = self.get_dashpad() dashpush = self.get_dashpush() angle = get_rotation(dashrotation) theta = np.pi(angle/180.0+dashdirection-1) cos_theta, sin_theta = np.cos(theta), np.sin(theta) transform = self.get_transform() # Compute the dash end points # The 'c' prefix is for canvas coordinates cxy = transform.transform_point((dashx, dashy)) cd = np.array([cos_theta, sin_theta]) c1 = cxy+dashpushcd c2 = cxy+(dashpush+dashlength)cd inverse = transform.inverted() (x1, y1) = inverse.transform_point(tuple(c1)) (x2, y2) = inverse.transform_point(tuple(c2)) self.dashline.set_data((x1, x2), (y1, y2)) # We now need to extend this vector out to # the center of the text area. # The basic problem here is that we're "rotating" # two separate objects but want it to appear as # if they're rotated together. # This is made non-trivial because of the # interaction between text rotation and alignment - # text alignment is based on the bbox after rotation. # We reset/force both alignments to 'center' # so we can do something relatively reasonable. # There's probably a better way to do this by # embedding all this in the object's transformations, # but I don't grok the transformation stuff # well enough yet. we = Text.get_window_extent(self, renderer=renderer) w, h = we.width, we.height # Watch for zeros if sin_theta == 0.0: dx = w dy = 0.0 elif cos_theta == 0.0: dx = 0.0 dy = h else: tan_theta = sin_theta/cos_theta dx = w dy = wtan_theta if dy > h or dy < -h: dy = h dx = h/tan_theta cwd = np.array([dx, dy])/2 cwd = 1+dashpad/np.sqrt(np.dot(cwd,cwd)) cw = c2+(dashdirection2-1)cwd newx, newy = inverse.transform_point(tuple(cw)) self._x, self._y = newx, newy # Now set the window extent # I'm not at all sure this is the right way to do this. we = Text.get_window_extent(self, renderer=renderer) self._twd_window_extent = we.frozen() self._twd_window_extent.update_from_data_xy(np.array([c1]), False) # Finally, make text align center Text.set_horizontalalignment(self, 'center') Text.set_verticalalignment(self, 'center') def get_window_extent(self, renderer=None): ''' Return a :class:`~matplotlib.transforms.Bbox` object bounding the text, in display units. In addition to being used internally, this is useful for specifying clickable regions in a png file on a web page. renderer* defaults to the _renderer attribute of the text object. This is not assigned until the first execution of :meth:`draw`, so you must use this kwarg if you want to call :meth:`get_window_extent` prior to the first :meth:`draw`. For getting web page regions, it is simpler to call the method after saving the figure. ''' self.update_coords(renderer) if self.get_dashlength() == 0.0: return Text.get_window_extent(self, renderer=renderer) else: return self._twd_window_extent def get_dashlength(self): """ Get the length of the dash. """ return self._dashlength def set_dashlength(self, dl): """ Set the length of the dash. ACCEPTS: float (canvas units) """ self._dashlength = dl def get_dashdirection(self): """ Get the direction dash. 1 is before the text and 0 is after. """ return self._dashdirection def set_dashdirection(self, dd): """ Set the direction of the dash following the text. 1 is before the text and 0 is after. The default is 0, which is what you'd want for the typical case of ticks below and on the left of the figure. ACCEPTS: int (1 is before, 0 is after) """ self._dashdirection = dd def get_dashrotation(self): """ Get the rotation of the dash in degrees. """ if self._dashrotation == None: return self.get_rotation() else: return self._dashrotation def set_dashrotation(self, dr): """ Set the rotation of the dash, in degrees ACCEPTS: float (degrees) """ self._dashrotation = dr def get_dashpad(self): """ Get the extra spacing between the dash and the text, in canvas units. """ return self._dashpad def set_dashpad(self, dp): """ Set the "pad" of the TextWithDash, which is the extra spacing between the dash and the text, in canvas units. ACCEPTS: float (canvas units) """ self._dashpad = dp def get_dashpush(self): """ Get the extra spacing between the dash and the specified text position, in canvas units. """ return self._dashpush def set_dashpush(self, dp): """ Set the "push" of the TextWithDash, which is the extra spacing between the beginning of the dash and the specified position. ACCEPTS: float (canvas units) """ self._dashpush = dp def set_position(self, xy): """ Set the (x, y) position of the :class:`TextWithDash`. ACCEPTS: (x, y) """ self.set_x(xy[0]) self.set_y(xy[1]) def set_x(self, x): """ Set the x position of the :class:`TextWithDash`. ACCEPTS: float """ self._dashx = float(x) def set_y(self, y): """ Set the y position of the :class:`TextWithDash`. ACCEPTS: float """ self._dashy = float(y) def set_transform(self, t): """ Set the :class:`matplotlib.transforms.Transform` instance used by this artist. ACCEPTS: a :class:`matplotlib.transforms.Transform` instance """ Text.set_transform(self, t) self.dashline.set_transform(t) def get_figure(self): 'return the figure instance the artist belongs to' return self.figure def set_figure(self, fig): """ Set the figure instance the artist belong to. ACCEPTS: a :class:`matplotlib.figure.Figure` instance """ Text.set_figure(self, fig) self.dashline.set_figure(fig) artist.kwdocd['TextWithDash'] = artist.kwdoc(TextWithDash) class Annotation(Text): """ A :class:`~matplotlib.text.Text` class to make annotating things in the figure, such as :class:`~matplotlib.figure.Figure`, :class:`~matplotlib.axes.Axes`, :class:`~matplotlib.patches.Rectangle`, etc., easier. """ def __str__(self): return "Annotation(%g,%g,%s)"%(self.xy[0],self.xy[1],repr(self._text)) def __init__(self, s, xy, xytext=None, xycoords='data', textcoords=None, arrowprops=None, *kwargs): """ Annotate the x, y* point xy with text s at x, y location xytext. (If xytext = None, defaults to xy, and if textcoords = None, defaults to xycoords). arrowprops, if not None, is a dictionary of line properties (see :class:`matplotlib.lines.Line2D`) for the arrow that connects annotation to the point. If the dictionary has a key arrowstyle, a FancyArrowPatch instance is created with the given dictionary and is drawn. Otherwise, a YAArow patch instance is created and drawn. Valid keys for YAArow are ========= ============================================================= Key Description ========= ============================================================= width the width of the arrow in points frac the fraction of the arrow length occupied by the head headwidth the width of the base of the arrow head in points shrink oftentimes it is convenient to have the arrowtip and base a bit away from the text and point being annotated. If d is the distance between the text and annotated point, shrink will shorten the arrow so the tip and base are shink percent of the distance d away from the endpoints. ie, ``shrink=0.05 is 5%%`` ? any key for :class:`matplotlib.patches.polygon` ========= ============================================================= Valid keys for FancyArrowPatch are =============== ====================================================== Key Description =============== ====================================================== arrowstyle the arrow style connectionstyle the connection style relpos default is (0.5, 0.5) patchA default is bounding box of the text patchB default is None shrinkA default is 2 points shrinkB default is 2 points mutation_scale default is text size (in points) mutation_aspect default is 1. ? any key for :class:`matplotlib.patches.PathPatch` =============== ====================================================== xycoords and textcoords are strings that indicate the coordinates of xy and xytext. ================= =================================================== Property Description ================= =================================================== 'figure points' points from the lower left corner of the figure 'figure pixels' pixels from the lower left corner of the figure 'figure fraction' 0,0 is lower left of figure and 1,1 is upper, right 'axes points' points from lower left corner of axes 'axes pixels' pixels from lower left corner of axes 'axes fraction' 0,1 is lower left of axes and 1,1 is upper right 'data' use the coordinate system of the object being annotated (default) 'offset points' Specify an offset (in points) from the xy value 'polar' you can specify theta, r for the annotation, even in cartesian plots. Note that if you are using a polar axes, you do not need to specify polar for the coordinate system since that is the native "data" coordinate system. ================= =================================================== If a 'points' or 'pixels' option is specified, values will be added to the bottom-left and if negative, values will be subtracted from the top-right. Eg:: # 10 points to the right of the left border of the axes and # 5 points below the top border xy=(10,-5), xycoords='axes points' Additional kwargs are Text properties: %(Text)s """ if xytext is None: xytext = xy if textcoords is None: textcoords = xycoords # we'll draw ourself after the artist we annotate by default x,y = self.xytext = xytext Text.__init__(self, x, y, s, kwargs) self.xy = xy self.xycoords = xycoords self.textcoords = textcoords self.arrowprops = arrowprops self.arrow = None if arrowprops and arrowprops.has_key("arrowstyle"): self._arrow_relpos = arrowprops.pop("relpos", (0.5, 0.5)) self.arrow_patch = FancyArrowPatch((0, 0), (1,1), arrowprops) else: self.arrow_patch = None __init__.__doc__ = cbook.dedent(__init__.__doc__) % artist.kwdocd def contains(self,event): t,tinfo = Text.contains(self,event) if self.arrow is not None: a,ainfo=self.arrow.contains(event) t = t or a # self.arrow_patch is currently not checked as this can be a line - JJ return t,tinfo def set_figure(self, fig): if self.arrow is not None: self.arrow.set_figure(fig) if self.arrow_patch is not None: self.arrow_patch.set_figure(fig) Artist.set_figure(self, fig) def _get_xy(self, x, y, s): if s=='data': trans = self.axes.transData x = float(self.convert_xunits(x)) y = float(self.convert_yunits(y)) return trans.transform_point((x, y)) elif s=='offset points': # convert the data point dx, dy = self.xy # prevent recursion if self.xycoords == 'offset points': return self._get_xy(dx, dy, 'data') dx, dy = self._get_xy(dx, dy, self.xycoords) # convert the offset dpi = self.figure.get_dpi() x = dpi/72. y = dpi/72. # add the offset to the data point x += dx y += dy return x, y elif s=='polar': theta, r = x, y x = rnp.cos(theta) y = rnp.sin(theta) trans = self.axes.transData return trans.transform_point((x,y)) elif s=='figure points': #points from the lower left corner of the figure dpi = self.figure.dpi l,b,w,h = self.figure.bbox.bounds r = l+w t = b+h x = dpi/72. y = dpi/72. if x<0: x = r + x if y<0: y = t + y return x,y elif s=='figure pixels': #pixels from the lower left corner of the figure l,b,w,h = self.figure.bbox.bounds r = l+w t = b+h if x<0: x = r + x if y<0: y = t + y return x, y elif s=='figure fraction': #(0,0) is lower left, (1,1) is upper right of figure trans = self.figure.transFigure return trans.transform_point((x,y)) elif s=='axes points': #points from the lower left corner of the axes dpi = self.figure.dpi l,b,w,h = self.axes.bbox.bounds r = l+w t = b+h if x<0: x = r + xdpi/72. else: x = l + xdpi/72. if y<0: y = t + ydpi/72. else: y = b + ydpi/72. return x, y elif s=='axes pixels': #pixels from the lower left corner of the axes l,b,w,h = self.axes.bbox.bounds r = l+w t = b+h if x<0: x = r + x else: x = l + x if y<0: y = t + y else: y = b + y return x, y elif s=='axes fraction': #(0,0) is lower left, (1,1) is upper right of axes trans = self.axes.transAxes return trans.transform_point((x, y)) def update_positions(self, renderer): x, y = self.xytext self._x, self._y = self._get_xy(x, y, self.textcoords) x, y = self.xy x, y = self._get_xy(x, y, self.xycoords) ox0, oy0 = self._x, self._y ox1, oy1 = x, y if self.arrowprops: x0, y0 = x, y l,b,w,h = self.get_window_extent(renderer).bounds r = l+w t = b+h xc = 0.5(l+r) yc = 0.5(b+t) d = self.arrowprops.copy() # Use FancyArrowPatch if self.arrowprops has "arrowstyle" key. # Otherwise, fallback to YAArrow. #if d.has_key("arrowstyle"): if self.arrow_patch: # adjust the starting point of the arrow relative to # the textbox. # TODO : Rotation needs to be accounted. relpos = self._arrow_relpos bbox = self.get_window_extent(renderer) ox0 = bbox.x0 + bbox.width * relpos[0] oy0 = bbox.y0 + bbox.height * relpos[1] # The arrow will be drawn from (ox0, oy0) to (ox1, # oy1). It will be first clipped by patchA and patchB. # Then it will be shrinked by shirnkA and shrinkB # (in points). If patch A is not set, self.bbox_patch # is used. self.arrow_patch.set_positions((ox0, oy0), (ox1,oy1)) mutation_scale = d.pop("mutation_scale", self.get_size()) mutation_scale = renderer.points_to_pixels(mutation_scale) self.arrow_patch.set_mutation_scale(mutation_scale) if self._bbox_patch: patchA = d.pop("patchA", self._bbox_patch) self.arrow_patch.set_patchA(patchA) else: patchA = d.pop("patchA", self._bbox) self.arrow_patch.set_patchA(patchA) else: # pick the x,y corner of the text bbox closest to point # annotated dsu = [(abs(val-x0), val) for val in l, r, xc] dsu.sort() _, x = dsu[0] dsu = [(abs(val-y0), val) for val in b, t, yc] dsu.sort() _, y = dsu[0] shrink = d.pop('shrink', 0.0) theta = math.atan2(y-y0, x-x0) r = math.sqrt((y-y0)2. + (x-x0)2.) dx = shrinkrmath.cos(theta) dy = shrinkrmath.sin(theta) width = d.pop('width', 4) headwidth = d.pop('headwidth', 12) frac = d.pop('frac', 0.1) self.arrow = YAArrow(self.figure, (x0+dx,y0+dy), (x-dx, y-dy), width=width, headwidth=headwidth, frac=frac, *d) self.arrow.set_clip_box(self.get_clip_box()) def draw(self, renderer): """ Draw the :class:`Annotation` object to the given renderer*. """ self.update_positions(renderer) self.update_bbox_position_size(renderer) if self.arrow is not None: if self.arrow.figure is None and self.figure is not None: self.arrow.figure = self.figure self.arrow.draw(renderer) if self.arrow_patch is not None: if self.arrow_patch.figure is None and self.figure is not None: self.arrow_patch.figure = self.figure self.arrow_patch.draw(renderer) Text.draw(self, renderer) artist.kwdocd['Annotation'] = Annotation.__init__.__doc__	55,366	Python	.py	1,299	32.357968	209	0.564406	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,261	__init__.py	numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/__init__.py	""" This is an object-orient plotting library. A procedural interface is provided by the companion pylab module, which may be imported directly, e.g:: from pylab import * or using ipython:: ipython -pylab For the most part, direct use of the object-oriented library is encouraged when programming rather than working interactively. The exceptions are the pylab commands :func:`~matplotlib.pyplot.figure`, :func:`~matplotlib.pyplot.subplot`, :func:`~matplotlib.backends.backend_qt4agg.show`, and :func:`~pyplot.savefig`, which can greatly simplify scripting. Modules include: :mod:`matplotlib.axes` defines the :class:`~matplotlib.axes.Axes` class. Most pylab commands are wrappers for :class:`~matplotlib.axes.Axes` methods. The axes module is the highest level of OO access to the library. :mod:`matplotlib.figure` defines the :class:`~matplotlib.figure.Figure` class. :mod:`matplotlib.artist` defines the :class:`~matplotlib.artist.Artist` base class for all classes that draw things. :mod:`matplotlib.lines` defines the :class:`~matplotlib.lines.Line2D` class for drawing lines and markers :mod`matplotlib.patches` defines classes for drawing polygons :mod:`matplotlib.text` defines the :class:`~matplotlib.text.Text`, :class:`~matplotlib.text.TextWithDash`, and :class:`~matplotlib.text.Annotate` classes :mod:`matplotlib.image` defines the :class:`~matplotlib.image.AxesImage` and :class:`~matplotlib.image.FigureImage` classes :mod:`matplotlib.collections` classes for efficient drawing of groups of lines or polygons :mod:`matplotlib.colors` classes for interpreting color specifications and for making colormaps :mod:`matplotlib.cm` colormaps and the :class:`~matplotlib.image.ScalarMappable` mixin class for providing color mapping functionality to other classes :mod:`matplotlib.ticker` classes for calculating tick mark locations and for formatting tick labels :mod:`matplotlib.backends` a subpackage with modules for various gui libraries and output formats The base matplotlib namespace includes: :data:`~matplotlib.rcParams` a global dictionary of default configuration settings. It is initialized by code which may be overridded by a matplotlibrc file. :func:`~matplotlib.rc` a function for setting groups of rcParams values :func:`~matplotlib.use` a function for setting the matplotlib backend. If used, this function must be called immediately after importing matplotlib for the first time. In particular, it must be called before importing pylab (if pylab is imported). matplotlib is written by John D. Hunter (jdh2358 at gmail.com) and a host of others. """ from __future__ import generators __version__ = '0.98.5.2' __revision__ = '$Revision: 6660 $' __date__ = '$Date: 2008-12-18 06:10:51 -0600 (Thu, 18 Dec 2008) $' import os, re, shutil, subprocess, sys, warnings import distutils.sysconfig import distutils.version NEWCONFIG = False # Needed for toolkit setuptools support if 0: try: __import__('pkg_resources').declare_namespace(__name__) except ImportError: pass # must not have setuptools if not hasattr(sys, 'argv'): # for modpython sys.argv = ['modpython'] """ Manage user customizations through a rc file. The default file location is given in the following order - environment variable MATPLOTLIBRC - HOME/.matplotlib/matplotlibrc if HOME is defined - PATH/matplotlibrc where PATH is the return value of get_data_path() """ import sys, os, tempfile from rcsetup import defaultParams, validate_backend, validate_toolbar from rcsetup import validate_cairo_format major, minor1, minor2, s, tmp = sys.version_info _python24 = major>=2 and minor1>=4 # the havedate check was a legacy from old matplotlib which preceeded # datetime support _havedate = True #try: # import pkg_resources # pkg_resources is part of setuptools #except ImportError: _have_pkg_resources = False #else: _have_pkg_resources = True if not _python24: raise ImportError('matplotlib requires Python 2.4 or later') import numpy nn = numpy.__version__.split('.') if not (int(nn[0]) >= 1 and int(nn[1]) >= 1): raise ImportError( 'numpy 1.1 or later is required; you have %s' % numpy.__version__) def is_string_like(obj): if hasattr(obj, 'shape'): return 0 try: obj + '' except (TypeError, ValueError): return 0 return 1 def _is_writable_dir(p): """ p is a string pointing to a putative writable dir -- return True p is such a string, else False """ try: p + '' # test is string like except TypeError: return False try: t = tempfile.TemporaryFile(dir=p) t.write('1') t.close() except OSError: return False else: return True class Verbose: """ A class to handle reporting. Set the fileo attribute to any file instance to handle the output. Default is sys.stdout """ levels = ('silent', 'helpful', 'debug', 'debug-annoying') vald = dict( [(level, i) for i,level in enumerate(levels)]) # parse the verbosity from the command line; flags look like # --verbose-silent or --verbose-helpful _commandLineVerbose = None for arg in sys.argv[1:]: if not arg.startswith('--verbose-'): continue _commandLineVerbose = arg[10:] def __init__(self): self.set_level('silent') self.fileo = sys.stdout def set_level(self, level): 'set the verbosity to one of the Verbose.levels strings' if self._commandLineVerbose is not None: level = self._commandLineVerbose if level not in self.levels: raise ValueError('Illegal verbose string "%s". Legal values are %s'%(level, self.levels)) self.level = level def set_fileo(self, fname): std = { 'sys.stdout': sys.stdout, 'sys.stderr': sys.stderr, } if fname in std: self.fileo = std[fname] else: try: fileo = file(fname, 'w') except IOError: raise ValueError('Verbose object could not open log file "%s" for writing.\nCheck your matplotlibrc verbose.fileo setting'%fname) else: self.fileo = fileo def report(self, s, level='helpful'): """ print message s to self.fileo if self.level>=level. Return value indicates whether a message was issued """ if self.ge(level): print >>self.fileo, s return True return False def wrap(self, fmt, func, level='helpful', always=True): """ return a callable function that wraps func and reports it output through the verbose handler if current verbosity level is higher than level if always is True, the report will occur on every function call; otherwise only on the first time the function is called """ assert callable(func) def wrapper(args, kwargs): ret = func(args, *kwargs) if (always or not wrapper._spoke): spoke = self.report(fmt%ret, level) if not wrapper._spoke: wrapper._spoke = spoke return ret wrapper._spoke = False wrapper.__doc__ = func.__doc__ return wrapper def ge(self, level): 'return true if self.level is >= level' return self.vald[self.level]>=self.vald[level] verbose=Verbose() def checkdep_dvipng(): try: s = subprocess.Popen(['dvipng','-version'], stdout=subprocess.PIPE, stderr=subprocess.PIPE) line = s.stdout.readlines()[1] v = line.split()[-1] return v except (IndexError, ValueError, OSError): return None def checkdep_ghostscript(): try: if sys.platform == 'win32': command_args = ['gswin32c', '--version'] else: command_args = ['gs', '--version'] s = subprocess.Popen(command_args, stdout=subprocess.PIPE, stderr=subprocess.PIPE) v = s.stdout.read()[:-1] return v except (IndexError, ValueError, OSError): return None def checkdep_tex(): try: s = subprocess.Popen(['tex','-version'], stdout=subprocess.PIPE, stderr=subprocess.PIPE) line = s.stdout.readlines()[0] pattern = '3\.1\d+' match = re.search(pattern, line) v = match.group(0) return v except (IndexError, ValueError, AttributeError, OSError): return None def checkdep_pdftops(): try: s = subprocess.Popen(['pdftops','-v'], stdout=subprocess.PIPE, stderr=subprocess.PIPE) for line in s.stderr: if 'version' in line: v = line.split()[-1] return v except (IndexError, ValueError, UnboundLocalError, OSError): return None def compare_versions(a, b): "return True if a is greater than or equal to b" if a: a = distutils.version.LooseVersion(a) b = distutils.version.LooseVersion(b) if a>=b: return True else: return False else: return False def checkdep_ps_distiller(s): if not s: return False flag = True gs_req = '7.07' gs_sugg = '7.07' gs_v = checkdep_ghostscript() if compare_versions(gs_v, gs_sugg): pass elif compare_versions(gs_v, gs_req): verbose.report(('ghostscript-%s found. ghostscript-%s or later ' 'is recommended to use the ps.usedistiller option.') % (gs_v, gs_sugg)) else: flag = False warnings.warn(('matplotlibrc ps.usedistiller option can not be used ' 'unless ghostscript-%s or later is installed on your system') % gs_req) if s == 'xpdf': pdftops_req = '3.0' pdftops_req_alt = '0.9' # poppler version numbers, ugh pdftops_v = checkdep_pdftops() if compare_versions(pdftops_v, pdftops_req): pass elif compare_versions(pdftops_v, pdftops_req_alt) and not \ compare_versions(pdftops_v, '1.0'): pass else: flag = False warnings.warn(('matplotlibrc ps.usedistiller can not be set to ' 'xpdf unless xpdf-%s or later is installed on your system') % pdftops_req) if flag: return s else: return False def checkdep_usetex(s): if not s: return False tex_req = '3.1415' gs_req = '7.07' gs_sugg = '7.07' dvipng_req = '1.5' flag = True tex_v = checkdep_tex() if compare_versions(tex_v, tex_req): pass else: flag = False warnings.warn(('matplotlibrc text.usetex option can not be used ' 'unless TeX-%s or later is ' 'installed on your system') % tex_req) dvipng_v = checkdep_dvipng() if compare_versions(dvipng_v, dvipng_req): pass else: flag = False warnings.warn( 'matplotlibrc text.usetex can not be used with Agg ' 'backend unless dvipng-1.5 or later is ' 'installed on your system') gs_v = checkdep_ghostscript() if compare_versions(gs_v, gs_sugg): pass elif compare_versions(gs_v, gs_req): verbose.report(('ghostscript-%s found. ghostscript-%s or later is ' 'recommended for use with the text.usetex ' 'option.') % (gs_v, gs_sugg)) else: flag = False warnings.warn(('matplotlibrc text.usetex can not be used ' 'unless ghostscript-%s or later is ' 'installed on your system') % gs_req) return flag def _get_home(): """Find user's home directory if possible. Otherwise raise error. :see: http://mail.python.org/pipermail/python-list/2005-February/263921.html """ path='' try: path=os.path.expanduser("~") except: pass if not os.path.isdir(path): for evar in ('HOME', 'USERPROFILE', 'TMP'): try: path = os.environ[evar] if os.path.isdir(path): break except: pass if path: return path else: raise RuntimeError('please define environment variable $HOME') get_home = verbose.wrap('$HOME=%s', _get_home, always=False) def _get_configdir(): """ Return the string representing the configuration dir. default is HOME/.matplotlib. you can override this with the MPLCONFIGDIR environment variable """ configdir = os.environ.get('MPLCONFIGDIR') if configdir is not None: if not _is_writable_dir(configdir): raise RuntimeError('Could not write to MPLCONFIGDIR="%s"'%configdir) return configdir h = get_home() p = os.path.join(get_home(), '.matplotlib') if os.path.exists(p): if not _is_writable_dir(p): raise RuntimeError("'%s' is not a writable dir; you must set %s/.matplotlib to be a writable dir. You can also set environment variable MPLCONFIGDIR to any writable directory where you want matplotlib data stored "% (h, h)) else: if not _is_writable_dir(h): raise RuntimeError("Failed to create %s/.matplotlib; consider setting MPLCONFIGDIR to a writable directory for matplotlib configuration data"%h) os.mkdir(p) return p get_configdir = verbose.wrap('CONFIGDIR=%s', _get_configdir, always=False) def _get_data_path(): 'get the path to matplotlib data' if 'MATPLOTLIBDATA' in os.environ: path = os.environ['MATPLOTLIBDATA'] if not os.path.isdir(path): raise RuntimeError('Path in environment MATPLOTLIBDATA not a directory') return path path = os.sep.join([os.path.dirname(__file__), 'mpl-data']) if os.path.isdir(path): return path # setuptools' namespace_packages may highjack this init file # so need to try something known to be in matplotlib, not basemap import matplotlib.afm path = os.sep.join([os.path.dirname(matplotlib.afm.__file__), 'mpl-data']) if os.path.isdir(path): return path # py2exe zips pure python, so still need special check if getattr(sys,'frozen',None): path = os.path.join(os.path.split(sys.path[0])[0], 'mpl-data') if os.path.isdir(path): return path else: # Try again assuming we need to step up one more directory path = os.path.join(os.path.split(os.path.split(sys.path[0])[0])[0], 'mpl-data') if os.path.isdir(path): return path else: # Try again assuming sys.path[0] is a dir not a exe path = os.path.join(sys.path[0], 'mpl-data') if os.path.isdir(path): return path raise RuntimeError('Could not find the matplotlib data files') def _get_data_path_cached(): if defaultParams['datapath'][0] is None: defaultParams['datapath'][0] = _get_data_path() return defaultParams['datapath'][0] get_data_path = verbose.wrap('matplotlib data path %s', _get_data_path_cached, always=False) def get_example_data(fname): """ return a filehandle to one of the example files in mpl-data/example fname the name of one of the files in mpl-data/example """ datadir = os.path.join(get_data_path(), 'example') fullpath = os.path.join(datadir, fname) if not os.path.exists(fullpath): raise IOError('could not find matplotlib example file "%s" in data directory "%s"'%( fname, datadir)) return file(fullpath, 'rb') def get_py2exe_datafiles(): datapath = get_data_path() head, tail = os.path.split(datapath) d = {} for root, dirs, files in os.walk(datapath): # Need to explicitly remove cocoa_agg files or py2exe complains # NOTE I dont know why, but do as previous version if 'Matplotlib.nib' in files: files.remove('Matplotlib.nib') files = [os.path.join(root, filename) for filename in files] root = root.replace(tail, 'mpl-data') root = root[root.index('mpl-data'):] d[root] = files return d.items() def matplotlib_fname(): """ Return the path to the rc file Search order: * current working dir * environ var MATPLOTLIBRC * HOME/.matplotlib/matplotlibrc * MATPLOTLIBDATA/matplotlibrc """ oldname = os.path.join( os.getcwd(), '.matplotlibrc') if os.path.exists(oldname): print >> sys.stderr, """\ WARNING: Old rc filename ".matplotlibrc" found in working dir and and renamed to new default rc file name "matplotlibrc" (no leading"dot"). """ shutil.move('.matplotlibrc', 'matplotlibrc') home = get_home() oldname = os.path.join( home, '.matplotlibrc') if os.path.exists(oldname): configdir = get_configdir() newname = os.path.join(configdir, 'matplotlibrc') print >> sys.stderr, """\ WARNING: Old rc filename "%s" found and renamed to new default rc file name "%s"."""%(oldname, newname) shutil.move(oldname, newname) fname = os.path.join( os.getcwd(), 'matplotlibrc') if os.path.exists(fname): return fname if 'MATPLOTLIBRC' in os.environ: path = os.environ['MATPLOTLIBRC'] if os.path.exists(path): fname = os.path.join(path, 'matplotlibrc') if os.path.exists(fname): return fname fname = os.path.join(get_configdir(), 'matplotlibrc') if os.path.exists(fname): return fname path = get_data_path() # guaranteed to exist or raise fname = os.path.join(path, 'matplotlibrc') if not os.path.exists(fname): warnings.warn('Could not find matplotlibrc; using defaults') return fname _deprecated_map = { 'text.fontstyle': 'font.style', 'text.fontangle': 'font.style', 'text.fontvariant': 'font.variant', 'text.fontweight': 'font.weight', 'text.fontsize': 'font.size', 'tick.size' : 'tick.major.size', } class RcParams(dict): """ A dictionary object including validation validating functions are defined and associated with rc parameters in :mod:`matplotlib.rcsetup` """ validate = dict([ (key, converter) for key, (default, converter) in \ defaultParams.iteritems() ]) def __setitem__(self, key, val): try: if key in _deprecated_map.keys(): alt = _deprecated_map[key] warnings.warn('%s is deprecated in matplotlibrc. Use %s \ instead.'% (key, alt)) key = alt cval = self.validate[key](val) dict.__setitem__(self, key, cval) except KeyError: raise KeyError('%s is not a valid rc parameter.\ See rcParams.keys() for a list of valid parameters.'%key) def rc_params(fail_on_error=False): 'Return the default params updated from the values in the rc file' fname = matplotlib_fname() if not os.path.exists(fname): # this should never happen, default in mpl-data should always be found message = 'could not find rc file; returning defaults' ret = RcParams([ (key, default) for key, (default, converter) in \ defaultParams.iteritems() ]) warnings.warn(message) return ret cnt = 0 rc_temp = {} for line in file(fname): cnt += 1 strippedline = line.split('#',1)[0].strip() if not strippedline: continue tup = strippedline.split(':',1) if len(tup) !=2: warnings.warn('Illegal line #%d\n\t%s\n\tin file "%s"'%\ (cnt, line, fname)) continue key, val = tup key = key.strip() val = val.strip() if key in rc_temp: warnings.warn('Duplicate key in file "%s", line #%d'%(fname,cnt)) rc_temp[key] = (val, line, cnt) ret = RcParams([ (key, default) for key, (default, converter) in \ defaultParams.iteritems() ]) for key in ('verbose.level', 'verbose.fileo'): if key in rc_temp: val, line, cnt = rc_temp.pop(key) if fail_on_error: ret[key] = val # try to convert to proper type or raise else: try: ret[key] = val # try to convert to proper type or skip except Exception, msg: warnings.warn('Bad val "%s" on line #%d\n\t"%s"\n\tin file \ "%s"\n\t%s' % (val, cnt, line, fname, msg)) verbose.set_level(ret['verbose.level']) verbose.set_fileo(ret['verbose.fileo']) for key, (val, line, cnt) in rc_temp.iteritems(): if key in defaultParams: if fail_on_error: ret[key] = val # try to convert to proper type or raise else: try: ret[key] = val # try to convert to proper type or skip except Exception, msg: warnings.warn('Bad val "%s" on line #%d\n\t"%s"\n\tin file \ "%s"\n\t%s' % (val, cnt, line, fname, msg)) else: print >> sys.stderr, """ Bad key "%s" on line %d in %s. You probably need to get an updated matplotlibrc file from http://matplotlib.sf.net/_static/matplotlibrc or from the matplotlib source distribution""" % (key, cnt, fname) if ret['datapath'] is None: ret['datapath'] = get_data_path() if not ret['text.latex.preamble'] == ['']: verbose.report(""" *************************************************************** You have the following UNSUPPORTED LaTeX preamble customizations: %s Please do not ask for support with these customizations active. ************************************************************* """% '\n'.join(ret['text.latex.preamble']), 'helpful') verbose.report('loaded rc file %s'%fname) return ret # this is the instance used by the matplotlib classes rcParams = rc_params() rcParamsDefault = RcParams([ (key, default) for key, (default, converter) in \ defaultParams.iteritems() ]) rcParams['ps.usedistiller'] = checkdep_ps_distiller(rcParams['ps.usedistiller']) rcParams['text.usetex'] = checkdep_usetex(rcParams['text.usetex']) def rc(group, kwargs): """ Set the current rc params. Group is the grouping for the rc, eg. for ``lines.linewidth`` the group is ``lines``, for ``axes.facecolor``, the group is ``axes``, and so on. Group may also be a list or tuple of group names, eg. (xtick, ytick). kwargs is a dictionary attribute name/value pairs, eg:: rc('lines', linewidth=2, color='r') sets the current rc params and is equivalent to:: rcParams['lines.linewidth'] = 2 rcParams['lines.color'] = 'r' The following aliases are available to save typing for interactive users: ===== ================= Alias Property ===== ================= 'lw' 'linewidth' 'ls' 'linestyle' 'c' 'color' 'fc' 'facecolor' 'ec' 'edgecolor' 'mew' 'markeredgewidth' 'aa' 'antialiased' ===== ================= Thus you could abbreviate the above rc command as:: rc('lines', lw=2, c='r') Note you can use python's kwargs dictionary facility to store dictionaries of default parameters. Eg, you can customize the font rc as follows:: font = {'family' : 'monospace', 'weight' : 'bold', 'size' : 'larger'} rc('font', *font) # pass in the font dict as kwargs This enables you to easily switch between several configurations. Use :func:`~matplotlib.pyplot.rcdefaults` to restore the default rc params after changes. """ aliases = { 'lw' : 'linewidth', 'ls' : 'linestyle', 'c' : 'color', 'fc' : 'facecolor', 'ec' : 'edgecolor', 'mew' : 'markeredgewidth', 'aa' : 'antialiased', } if is_string_like(group): group = (group,) for g in group: for k,v in kwargs.items(): name = aliases.get(k) or k key = '%s.%s' % (g, name) if key not in rcParams: raise KeyError('Unrecognized key "%s" for group "%s" and name "%s"' % (key, g, name)) rcParams[key] = v def rcdefaults(): """ Restore the default rc params - the ones that were created at matplotlib load time. """ rcParams.update(rcParamsDefault) if NEWCONFIG: #print "importing from reorganized config system!" try: from config import rcParams, rcdefaults, mplConfig, save_config verbose.set_level(rcParams['verbose.level']) verbose.set_fileo(rcParams['verbose.fileo']) except: from config import rcParams, rcdefaults _use_error_msg = """ This call to matplotlib.use() has no effect because the the backend has already been chosen; matplotlib.use() must be called before* pylab, matplotlib.pyplot, or matplotlib.backends is imported for the first time. """ def use(arg, warn=True): """ Set the matplotlib backend to one of the known backends. The argument is case-insensitive. For the Cairo backend, the argument can have an extension to indicate the type of output. Example: use('cairo.pdf') will specify a default of pdf output generated by Cairo. Note: this function must be called before importing pylab for the first time; or, if you are not using pylab, it must be called before importing matplotlib.backends. If warn is True, a warning is issued if you try and callthis after pylab or pyplot have been loaded. In certain black magic use cases, eg pyplot.switch_backends, we are doing the reloading necessary to make the backend switch work (in some cases, eg pure image backends) so one can set warn=False to supporess the warnings """ if 'matplotlib.backends' in sys.modules: if warn: warnings.warn(_use_error_msg) return arg = arg.lower() if arg.startswith('module://'): name = arg else: be_parts = arg.split('.') name = validate_backend(be_parts[0]) rcParams['backend'] = name if name == 'cairo' and len(be_parts) > 1: rcParams['cairo.format'] = validate_cairo_format(be_parts[1]) def get_backend(): "Returns the current backend" return rcParams['backend'] def interactive(b): """ Set interactive mode to boolean b. If b is True, then draw after every plotting command, eg, after xlabel """ rcParams['interactive'] = b def is_interactive(): 'Return true if plot mode is interactive' b = rcParams['interactive'] return b def tk_window_focus(): """Return true if focus maintenance under TkAgg on win32 is on. This currently works only for python.exe and IPython.exe. Both IDLE and Pythonwin.exe fail badly when tk_window_focus is on.""" if rcParams['backend'] != 'TkAgg': return False return rcParams['tk.window_focus'] # Now allow command line to override # Allow command line access to the backend with -d (matlab compatible # flag) for s in sys.argv[1:]: if s.startswith('-d') and len(s) > 2: # look for a -d flag try: use(s[2:]) except (KeyError, ValueError): pass # we don't want to assume all -d flags are backends, eg -debug verbose.report('matplotlib version %s'%__version__) verbose.report('verbose.level %s'%verbose.level) verbose.report('interactive is %s'%rcParams['interactive']) verbose.report('units is %s'%rcParams['units']) verbose.report('platform is %s'%sys.platform) verbose.report('loaded modules: %s'%sys.modules.keys(), 'debug')	28,184	Python	.py	698	32.866762	236	0.628524	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,262	axes3d.py	numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/axes3d.py	raise NotImplementedError('axes3d is not supported in matplotlib-0.98. You may want to try the 0.91.x maintenance branch')	124	Python	.py	1	123	123	0.796748	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,263	type1font.py	numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/type1font.py	""" A class representing a Type 1 font. This version merely reads pfa and pfb files and splits them for embedding in pdf files. There is no support yet for subsetting or anything like that. Usage (subject to change): font = Type1Font(filename) clear_part, encrypted_part, finale = font.parts Source: Adobe Technical Note #5040, Supporting Downloadable PostScript Language Fonts. If extending this class, see also: Adobe Type 1 Font Format, Adobe Systems Incorporated, third printing, v1.1, 1993. ISBN 0-201-57044-0. """ import re import struct class Type1Font(object): def __init__(self, filename): file = open(filename, 'rb') try: data = self._read(file) finally: file.close() self.parts = self._split(data) #self._parse() def _read(self, file): rawdata = file.read() if not rawdata.startswith(chr(128)): return rawdata data = '' while len(rawdata) > 0: if not rawdata.startswith(chr(128)): raise RuntimeError, \ 'Broken pfb file (expected byte 128, got %d)' % \ ord(rawdata[0]) type = ord(rawdata[1]) if type in (1,2): length, = struct.unpack('<i', rawdata[2:6]) segment = rawdata[6:6+length] rawdata = rawdata[6+length:] if type == 1: # ASCII text: include verbatim data += segment elif type == 2: # binary data: encode in hexadecimal data += ''.join(['%02x' % ord(char) for char in segment]) elif type == 3: # end of file break else: raise RuntimeError, \ 'Unknown segment type %d in pfb file' % type return data def _split(self, data): """ Split the Type 1 font into its three main parts. The three parts are: (1) the cleartext part, which ends in a eexec operator; (2) the encrypted part; (3) the fixed part, which contains 512 ASCII zeros possibly divided on various lines, a cleartomark operator, and possibly something else. """ # Cleartext part: just find the eexec and skip whitespace idx = data.index('eexec') idx += len('eexec') while data[idx] in ' \t\r\n': idx += 1 len1 = idx # Encrypted part: find the cleartomark operator and count # zeros backward idx = data.rindex('cleartomark') - 1 zeros = 512 while zeros and data[idx] in ('0', '\n', '\r'): if data[idx] == '0': zeros -= 1 idx -= 1 if zeros: raise RuntimeError, 'Insufficiently many zeros in Type 1 font' # Convert encrypted part to binary (if we read a pfb file, we # may end up converting binary to hexadecimal to binary again; # but if we read a pfa file, this part is already in hex, and # I am not quite sure if even the pfb format guarantees that # it will be in binary). binary = ''.join([chr(int(data[i:i+2], 16)) for i in range(len1, idx, 2)]) return data[:len1], binary, data[idx:] _whitespace = re.compile(r'[\0\t\r\014\n ]+') _delim = re.compile(r'[()<>[]{}/%]') _token = re.compile(r'/{0,2}[^]\0\t\r\v\n ()<>{}/%[]+') _comment = re.compile(r'%[^\r\n\v]*') _instring = re.compile(r'[()\\]') def _parse(self): """ A very limited kind of parsing to find the Encoding of the font. """ def tokens(text): """ Yield pairs (position, token), ignoring comments and whitespace. Numbers count as tokens. """ pos = 0 while pos < len(text): match = self._comment.match(text[pos:]) or self._whitespace.match(text[pos:]) if match: pos += match.end() elif text[pos] == '(': start = pos pos += 1 depth = 1 while depth: match = self._instring.search(text[pos:]) if match is None: return if match.group() == '(': depth += 1 pos += 1 elif match.group() == ')': depth -= 1 pos += 1 else: pos += 2 yield (start, text[start:pos]) elif text[pos:pos+2] in ('<<', '>>'): yield (pos, text[pos:pos+2]) pos += 2 elif text[pos] == '<': start = pos pos += text[pos:].index('>') yield (start, text[start:pos]) else: match = self._token.match(text[pos:]) if match: yield (pos, match.group()) pos += match.end() else: yield (pos, text[pos]) pos += 1 enc_starts, enc_ends = None, None state = 0 # State transitions: # 0 -> /Encoding -> 1 # 1 -> StandardEncoding -> 2 -> def -> (ends) # 1 -> dup -> 4 -> put -> 5 # 5 -> dup -> 4 -> put -> 5 # 5 -> def -> (ends) for pos,token in tokens(self.parts[0]): if state == 0 and token == '/Encoding': enc_starts = pos state = 1 elif state == 1 and token == 'StandardEncoding': state = 2 elif state in (2,5) and token == 'def': enc_ends = pos+3 break elif state in (1,5) and token == 'dup': state = 4 elif state == 4 and token == 'put': state = 5 self.enc_starts, self.enc_ends = enc_starts, enc_ends if __name__ == '__main__': import sys font = Type1Font(sys.argv[1]) parts = font.parts print len(parts[0]), len(parts[1]), len(parts[2]) #print parts[0][font.enc_starts:font.enc_ends]	6,406	Python	.py	161	26.881988	93	0.48436	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,264	_cm.py	numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/_cm.py	""" Color data and pre-defined cmap objects. This is a helper for cm.py, originally part of that file. Separating the data (this file) from cm.py makes both easier to deal with. Objects visible in cm.py are the individual cmap objects ('autumn', etc.) and a dictionary, 'datad', including all of these objects. """ import matplotlib as mpl import matplotlib.colors as colors LUTSIZE = mpl.rcParams['image.lut'] _binary_data = { 'red' : ((0., 1., 1.), (1., 0., 0.)), 'green': ((0., 1., 1.), (1., 0., 0.)), 'blue' : ((0., 1., 1.), (1., 0., 0.)) } _bone_data = {'red': ((0., 0., 0.),(1.0, 1.0, 1.0)), 'green': ((0., 0., 0.),(1.0, 1.0, 1.0)), 'blue': ((0., 0., 0.),(1.0, 1.0, 1.0))} _autumn_data = {'red': ((0., 1.0, 1.0),(1.0, 1.0, 1.0)), 'green': ((0., 0., 0.),(1.0, 1.0, 1.0)), 'blue': ((0., 0., 0.),(1.0, 0., 0.))} _bone_data = {'red': ((0., 0., 0.),(0.746032, 0.652778, 0.652778),(1.0, 1.0, 1.0)), 'green': ((0., 0., 0.),(0.365079, 0.319444, 0.319444), (0.746032, 0.777778, 0.777778),(1.0, 1.0, 1.0)), 'blue': ((0., 0., 0.),(0.365079, 0.444444, 0.444444),(1.0, 1.0, 1.0))} _cool_data = {'red': ((0., 0., 0.), (1.0, 1.0, 1.0)), 'green': ((0., 1., 1.), (1.0, 0., 0.)), 'blue': ((0., 1., 1.), (1.0, 1., 1.))} _copper_data = {'red': ((0., 0., 0.),(0.809524, 1.000000, 1.000000),(1.0, 1.0, 1.0)), 'green': ((0., 0., 0.),(1.0, 0.7812, 0.7812)), 'blue': ((0., 0., 0.),(1.0, 0.4975, 0.4975))} _flag_data = {'red': ((0., 1., 1.),(0.015873, 1.000000, 1.000000), (0.031746, 0.000000, 0.000000),(0.047619, 0.000000, 0.000000), (0.063492, 1.000000, 1.000000),(0.079365, 1.000000, 1.000000), (0.095238, 0.000000, 0.000000),(0.111111, 0.000000, 0.000000), (0.126984, 1.000000, 1.000000),(0.142857, 1.000000, 1.000000), (0.158730, 0.000000, 0.000000),(0.174603, 0.000000, 0.000000), (0.190476, 1.000000, 1.000000),(0.206349, 1.000000, 1.000000), (0.222222, 0.000000, 0.000000),(0.238095, 0.000000, 0.000000), (0.253968, 1.000000, 1.000000),(0.269841, 1.000000, 1.000000), (0.285714, 0.000000, 0.000000),(0.301587, 0.000000, 0.000000), (0.317460, 1.000000, 1.000000),(0.333333, 1.000000, 1.000000), (0.349206, 0.000000, 0.000000),(0.365079, 0.000000, 0.000000), (0.380952, 1.000000, 1.000000),(0.396825, 1.000000, 1.000000), (0.412698, 0.000000, 0.000000),(0.428571, 0.000000, 0.000000), (0.444444, 1.000000, 1.000000),(0.460317, 1.000000, 1.000000), (0.476190, 0.000000, 0.000000),(0.492063, 0.000000, 0.000000), (0.507937, 1.000000, 1.000000),(0.523810, 1.000000, 1.000000), (0.539683, 0.000000, 0.000000),(0.555556, 0.000000, 0.000000), (0.571429, 1.000000, 1.000000),(0.587302, 1.000000, 1.000000), (0.603175, 0.000000, 0.000000),(0.619048, 0.000000, 0.000000), (0.634921, 1.000000, 1.000000),(0.650794, 1.000000, 1.000000), (0.666667, 0.000000, 0.000000),(0.682540, 0.000000, 0.000000), (0.698413, 1.000000, 1.000000),(0.714286, 1.000000, 1.000000), (0.730159, 0.000000, 0.000000),(0.746032, 0.000000, 0.000000), (0.761905, 1.000000, 1.000000),(0.777778, 1.000000, 1.000000), (0.793651, 0.000000, 0.000000),(0.809524, 0.000000, 0.000000), (0.825397, 1.000000, 1.000000),(0.841270, 1.000000, 1.000000), (0.857143, 0.000000, 0.000000),(0.873016, 0.000000, 0.000000), (0.888889, 1.000000, 1.000000),(0.904762, 1.000000, 1.000000), (0.920635, 0.000000, 0.000000),(0.936508, 0.000000, 0.000000), (0.952381, 1.000000, 1.000000),(0.968254, 1.000000, 1.000000), (0.984127, 0.000000, 0.000000),(1.0, 0., 0.)), 'green': ((0., 0., 0.),(0.015873, 1.000000, 1.000000), (0.031746, 0.000000, 0.000000),(0.063492, 0.000000, 0.000000), (0.079365, 1.000000, 1.000000),(0.095238, 0.000000, 0.000000), (0.126984, 0.000000, 0.000000),(0.142857, 1.000000, 1.000000), (0.158730, 0.000000, 0.000000),(0.190476, 0.000000, 0.000000), (0.206349, 1.000000, 1.000000),(0.222222, 0.000000, 0.000000), (0.253968, 0.000000, 0.000000),(0.269841, 1.000000, 1.000000), (0.285714, 0.000000, 0.000000),(0.317460, 0.000000, 0.000000), (0.333333, 1.000000, 1.000000),(0.349206, 0.000000, 0.000000), (0.380952, 0.000000, 0.000000),(0.396825, 1.000000, 1.000000), (0.412698, 0.000000, 0.000000),(0.444444, 0.000000, 0.000000), (0.460317, 1.000000, 1.000000),(0.476190, 0.000000, 0.000000), (0.507937, 0.000000, 0.000000),(0.523810, 1.000000, 1.000000), (0.539683, 0.000000, 0.000000),(0.571429, 0.000000, 0.000000), (0.587302, 1.000000, 1.000000),(0.603175, 0.000000, 0.000000), (0.634921, 0.000000, 0.000000),(0.650794, 1.000000, 1.000000), (0.666667, 0.000000, 0.000000),(0.698413, 0.000000, 0.000000), (0.714286, 1.000000, 1.000000),(0.730159, 0.000000, 0.000000), (0.761905, 0.000000, 0.000000),(0.777778, 1.000000, 1.000000), (0.793651, 0.000000, 0.000000),(0.825397, 0.000000, 0.000000), (0.841270, 1.000000, 1.000000),(0.857143, 0.000000, 0.000000), (0.888889, 0.000000, 0.000000),(0.904762, 1.000000, 1.000000), (0.920635, 0.000000, 0.000000),(0.952381, 0.000000, 0.000000), (0.968254, 1.000000, 1.000000),(0.984127, 0.000000, 0.000000), (1.0, 0., 0.)), 'blue': ((0., 0., 0.),(0.015873, 1.000000, 1.000000), (0.031746, 1.000000, 1.000000),(0.047619, 0.000000, 0.000000), (0.063492, 0.000000, 0.000000),(0.079365, 1.000000, 1.000000), (0.095238, 1.000000, 1.000000),(0.111111, 0.000000, 0.000000), (0.126984, 0.000000, 0.000000),(0.142857, 1.000000, 1.000000), (0.158730, 1.000000, 1.000000),(0.174603, 0.000000, 0.000000), (0.190476, 0.000000, 0.000000),(0.206349, 1.000000, 1.000000), (0.222222, 1.000000, 1.000000),(0.238095, 0.000000, 0.000000), (0.253968, 0.000000, 0.000000),(0.269841, 1.000000, 1.000000), (0.285714, 1.000000, 1.000000),(0.301587, 0.000000, 0.000000), (0.317460, 0.000000, 0.000000),(0.333333, 1.000000, 1.000000), (0.349206, 1.000000, 1.000000),(0.365079, 0.000000, 0.000000), (0.380952, 0.000000, 0.000000),(0.396825, 1.000000, 1.000000), (0.412698, 1.000000, 1.000000),(0.428571, 0.000000, 0.000000), (0.444444, 0.000000, 0.000000),(0.460317, 1.000000, 1.000000), (0.476190, 1.000000, 1.000000),(0.492063, 0.000000, 0.000000), (0.507937, 0.000000, 0.000000),(0.523810, 1.000000, 1.000000), (0.539683, 1.000000, 1.000000),(0.555556, 0.000000, 0.000000), (0.571429, 0.000000, 0.000000),(0.587302, 1.000000, 1.000000), (0.603175, 1.000000, 1.000000),(0.619048, 0.000000, 0.000000), (0.634921, 0.000000, 0.000000),(0.650794, 1.000000, 1.000000), (0.666667, 1.000000, 1.000000),(0.682540, 0.000000, 0.000000), (0.698413, 0.000000, 0.000000),(0.714286, 1.000000, 1.000000), (0.730159, 1.000000, 1.000000),(0.746032, 0.000000, 0.000000), (0.761905, 0.000000, 0.000000),(0.777778, 1.000000, 1.000000), (0.793651, 1.000000, 1.000000),(0.809524, 0.000000, 0.000000), (0.825397, 0.000000, 0.000000),(0.841270, 1.000000, 1.000000), (0.857143, 1.000000, 1.000000),(0.873016, 0.000000, 0.000000), (0.888889, 0.000000, 0.000000),(0.904762, 1.000000, 1.000000), (0.920635, 1.000000, 1.000000),(0.936508, 0.000000, 0.000000), (0.952381, 0.000000, 0.000000),(0.968254, 1.000000, 1.000000), (0.984127, 1.000000, 1.000000),(1.0, 0., 0.))} _gray_data = {'red': ((0., 0, 0), (1., 1, 1)), 'green': ((0., 0, 0), (1., 1, 1)), 'blue': ((0., 0, 0), (1., 1, 1))} _hot_data = {'red': ((0., 0.0416, 0.0416),(0.365079, 1.000000, 1.000000),(1.0, 1.0, 1.0)), 'green': ((0., 0., 0.),(0.365079, 0.000000, 0.000000), (0.746032, 1.000000, 1.000000),(1.0, 1.0, 1.0)), 'blue': ((0., 0., 0.),(0.746032, 0.000000, 0.000000),(1.0, 1.0, 1.0))} _hsv_data = {'red': ((0., 1., 1.),(0.158730, 1.000000, 1.000000), (0.174603, 0.968750, 0.968750),(0.333333, 0.031250, 0.031250), (0.349206, 0.000000, 0.000000),(0.666667, 0.000000, 0.000000), (0.682540, 0.031250, 0.031250),(0.841270, 0.968750, 0.968750), (0.857143, 1.000000, 1.000000),(1.0, 1.0, 1.0)), 'green': ((0., 0., 0.),(0.158730, 0.937500, 0.937500), (0.174603, 1.000000, 1.000000),(0.507937, 1.000000, 1.000000), (0.666667, 0.062500, 0.062500),(0.682540, 0.000000, 0.000000), (1.0, 0., 0.)), 'blue': ((0., 0., 0.),(0.333333, 0.000000, 0.000000), (0.349206, 0.062500, 0.062500),(0.507937, 1.000000, 1.000000), (0.841270, 1.000000, 1.000000),(0.857143, 0.937500, 0.937500), (1.0, 0.09375, 0.09375))} _jet_data = {'red': ((0., 0, 0), (0.35, 0, 0), (0.66, 1, 1), (0.89,1, 1), (1, 0.5, 0.5)), 'green': ((0., 0, 0), (0.125,0, 0), (0.375,1, 1), (0.64,1, 1), (0.91,0,0), (1, 0, 0)), 'blue': ((0., 0.5, 0.5), (0.11, 1, 1), (0.34, 1, 1), (0.65,0, 0), (1, 0, 0))} _pink_data = {'red': ((0., 0.1178, 0.1178),(0.015873, 0.195857, 0.195857), (0.031746, 0.250661, 0.250661),(0.047619, 0.295468, 0.295468), (0.063492, 0.334324, 0.334324),(0.079365, 0.369112, 0.369112), (0.095238, 0.400892, 0.400892),(0.111111, 0.430331, 0.430331), (0.126984, 0.457882, 0.457882),(0.142857, 0.483867, 0.483867), (0.158730, 0.508525, 0.508525),(0.174603, 0.532042, 0.532042), (0.190476, 0.554563, 0.554563),(0.206349, 0.576204, 0.576204), (0.222222, 0.597061, 0.597061),(0.238095, 0.617213, 0.617213), (0.253968, 0.636729, 0.636729),(0.269841, 0.655663, 0.655663), (0.285714, 0.674066, 0.674066),(0.301587, 0.691980, 0.691980), (0.317460, 0.709441, 0.709441),(0.333333, 0.726483, 0.726483), (0.349206, 0.743134, 0.743134),(0.365079, 0.759421, 0.759421), (0.380952, 0.766356, 0.766356),(0.396825, 0.773229, 0.773229), (0.412698, 0.780042, 0.780042),(0.428571, 0.786796, 0.786796), (0.444444, 0.793492, 0.793492),(0.460317, 0.800132, 0.800132), (0.476190, 0.806718, 0.806718),(0.492063, 0.813250, 0.813250), (0.507937, 0.819730, 0.819730),(0.523810, 0.826160, 0.826160), (0.539683, 0.832539, 0.832539),(0.555556, 0.838870, 0.838870), (0.571429, 0.845154, 0.845154),(0.587302, 0.851392, 0.851392), (0.603175, 0.857584, 0.857584),(0.619048, 0.863731, 0.863731), (0.634921, 0.869835, 0.869835),(0.650794, 0.875897, 0.875897), (0.666667, 0.881917, 0.881917),(0.682540, 0.887896, 0.887896), (0.698413, 0.893835, 0.893835),(0.714286, 0.899735, 0.899735), (0.730159, 0.905597, 0.905597),(0.746032, 0.911421, 0.911421), (0.761905, 0.917208, 0.917208),(0.777778, 0.922958, 0.922958), (0.793651, 0.928673, 0.928673),(0.809524, 0.934353, 0.934353), (0.825397, 0.939999, 0.939999),(0.841270, 0.945611, 0.945611), (0.857143, 0.951190, 0.951190),(0.873016, 0.956736, 0.956736), (0.888889, 0.962250, 0.962250),(0.904762, 0.967733, 0.967733), (0.920635, 0.973185, 0.973185),(0.936508, 0.978607, 0.978607), (0.952381, 0.983999, 0.983999),(0.968254, 0.989361, 0.989361), (0.984127, 0.994695, 0.994695),(1.0, 1.0, 1.0)), 'green': ((0., 0., 0.),(0.015873, 0.102869, 0.102869), (0.031746, 0.145479, 0.145479),(0.047619, 0.178174, 0.178174), (0.063492, 0.205738, 0.205738),(0.079365, 0.230022, 0.230022), (0.095238, 0.251976, 0.251976),(0.111111, 0.272166, 0.272166), (0.126984, 0.290957, 0.290957),(0.142857, 0.308607, 0.308607), (0.158730, 0.325300, 0.325300),(0.174603, 0.341178, 0.341178), (0.190476, 0.356348, 0.356348),(0.206349, 0.370899, 0.370899), (0.222222, 0.384900, 0.384900),(0.238095, 0.398410, 0.398410), (0.253968, 0.411476, 0.411476),(0.269841, 0.424139, 0.424139), (0.285714, 0.436436, 0.436436),(0.301587, 0.448395, 0.448395), (0.317460, 0.460044, 0.460044),(0.333333, 0.471405, 0.471405), (0.349206, 0.482498, 0.482498),(0.365079, 0.493342, 0.493342), (0.380952, 0.517549, 0.517549),(0.396825, 0.540674, 0.540674), (0.412698, 0.562849, 0.562849),(0.428571, 0.584183, 0.584183), (0.444444, 0.604765, 0.604765),(0.460317, 0.624669, 0.624669), (0.476190, 0.643958, 0.643958),(0.492063, 0.662687, 0.662687), (0.507937, 0.680900, 0.680900),(0.523810, 0.698638, 0.698638), (0.539683, 0.715937, 0.715937),(0.555556, 0.732828, 0.732828), (0.571429, 0.749338, 0.749338),(0.587302, 0.765493, 0.765493), (0.603175, 0.781313, 0.781313),(0.619048, 0.796819, 0.796819), (0.634921, 0.812029, 0.812029),(0.650794, 0.826960, 0.826960), (0.666667, 0.841625, 0.841625),(0.682540, 0.856040, 0.856040), (0.698413, 0.870216, 0.870216),(0.714286, 0.884164, 0.884164), (0.730159, 0.897896, 0.897896),(0.746032, 0.911421, 0.911421), (0.761905, 0.917208, 0.917208),(0.777778, 0.922958, 0.922958), (0.793651, 0.928673, 0.928673),(0.809524, 0.934353, 0.934353), (0.825397, 0.939999, 0.939999),(0.841270, 0.945611, 0.945611), (0.857143, 0.951190, 0.951190),(0.873016, 0.956736, 0.956736), (0.888889, 0.962250, 0.962250),(0.904762, 0.967733, 0.967733), (0.920635, 0.973185, 0.973185),(0.936508, 0.978607, 0.978607), (0.952381, 0.983999, 0.983999),(0.968254, 0.989361, 0.989361), (0.984127, 0.994695, 0.994695),(1.0, 1.0, 1.0)), 'blue': ((0., 0., 0.),(0.015873, 0.102869, 0.102869), (0.031746, 0.145479, 0.145479),(0.047619, 0.178174, 0.178174), (0.063492, 0.205738, 0.205738),(0.079365, 0.230022, 0.230022), (0.095238, 0.251976, 0.251976),(0.111111, 0.272166, 0.272166), (0.126984, 0.290957, 0.290957),(0.142857, 0.308607, 0.308607), (0.158730, 0.325300, 0.325300),(0.174603, 0.341178, 0.341178), (0.190476, 0.356348, 0.356348),(0.206349, 0.370899, 0.370899), (0.222222, 0.384900, 0.384900),(0.238095, 0.398410, 0.398410), (0.253968, 0.411476, 0.411476),(0.269841, 0.424139, 0.424139), (0.285714, 0.436436, 0.436436),(0.301587, 0.448395, 0.448395), (0.317460, 0.460044, 0.460044),(0.333333, 0.471405, 0.471405), (0.349206, 0.482498, 0.482498),(0.365079, 0.493342, 0.493342), (0.380952, 0.503953, 0.503953),(0.396825, 0.514344, 0.514344), (0.412698, 0.524531, 0.524531),(0.428571, 0.534522, 0.534522), (0.444444, 0.544331, 0.544331),(0.460317, 0.553966, 0.553966), (0.476190, 0.563436, 0.563436),(0.492063, 0.572750, 0.572750), (0.507937, 0.581914, 0.581914),(0.523810, 0.590937, 0.590937), (0.539683, 0.599824, 0.599824),(0.555556, 0.608581, 0.608581), (0.571429, 0.617213, 0.617213),(0.587302, 0.625727, 0.625727), (0.603175, 0.634126, 0.634126),(0.619048, 0.642416, 0.642416), (0.634921, 0.650600, 0.650600),(0.650794, 0.658682, 0.658682), (0.666667, 0.666667, 0.666667),(0.682540, 0.674556, 0.674556), (0.698413, 0.682355, 0.682355),(0.714286, 0.690066, 0.690066), (0.730159, 0.697691, 0.697691),(0.746032, 0.705234, 0.705234), (0.761905, 0.727166, 0.727166),(0.777778, 0.748455, 0.748455), (0.793651, 0.769156, 0.769156),(0.809524, 0.789314, 0.789314), (0.825397, 0.808969, 0.808969),(0.841270, 0.828159, 0.828159), (0.857143, 0.846913, 0.846913),(0.873016, 0.865261, 0.865261), (0.888889, 0.883229, 0.883229),(0.904762, 0.900837, 0.900837), (0.920635, 0.918109, 0.918109),(0.936508, 0.935061, 0.935061), (0.952381, 0.951711, 0.951711),(0.968254, 0.968075, 0.968075), (0.984127, 0.984167, 0.984167),(1.0, 1.0, 1.0))} _prism_data = {'red': ((0., 1., 1.),(0.031746, 1.000000, 1.000000), (0.047619, 0.000000, 0.000000),(0.063492, 0.000000, 0.000000), (0.079365, 0.666667, 0.666667),(0.095238, 1.000000, 1.000000), (0.126984, 1.000000, 1.000000),(0.142857, 0.000000, 0.000000), (0.158730, 0.000000, 0.000000),(0.174603, 0.666667, 0.666667), (0.190476, 1.000000, 1.000000),(0.222222, 1.000000, 1.000000), (0.238095, 0.000000, 0.000000),(0.253968, 0.000000, 0.000000), (0.269841, 0.666667, 0.666667),(0.285714, 1.000000, 1.000000), (0.317460, 1.000000, 1.000000),(0.333333, 0.000000, 0.000000), (0.349206, 0.000000, 0.000000),(0.365079, 0.666667, 0.666667), (0.380952, 1.000000, 1.000000),(0.412698, 1.000000, 1.000000), (0.428571, 0.000000, 0.000000),(0.444444, 0.000000, 0.000000), (0.460317, 0.666667, 0.666667),(0.476190, 1.000000, 1.000000), (0.507937, 1.000000, 1.000000),(0.523810, 0.000000, 0.000000), (0.539683, 0.000000, 0.000000),(0.555556, 0.666667, 0.666667), (0.571429, 1.000000, 1.000000),(0.603175, 1.000000, 1.000000), (0.619048, 0.000000, 0.000000),(0.634921, 0.000000, 0.000000), (0.650794, 0.666667, 0.666667),(0.666667, 1.000000, 1.000000), (0.698413, 1.000000, 1.000000),(0.714286, 0.000000, 0.000000), (0.730159, 0.000000, 0.000000),(0.746032, 0.666667, 0.666667), (0.761905, 1.000000, 1.000000),(0.793651, 1.000000, 1.000000), (0.809524, 0.000000, 0.000000),(0.825397, 0.000000, 0.000000), (0.841270, 0.666667, 0.666667),(0.857143, 1.000000, 1.000000), (0.888889, 1.000000, 1.000000),(0.904762, 0.000000, 0.000000), (0.920635, 0.000000, 0.000000),(0.936508, 0.666667, 0.666667), (0.952381, 1.000000, 1.000000),(0.984127, 1.000000, 1.000000), (1.0, 0.0, 0.0)), 'green': ((0., 0., 0.),(0.031746, 1.000000, 1.000000), (0.047619, 1.000000, 1.000000),(0.063492, 0.000000, 0.000000), (0.095238, 0.000000, 0.000000),(0.126984, 1.000000, 1.000000), (0.142857, 1.000000, 1.000000),(0.158730, 0.000000, 0.000000), (0.190476, 0.000000, 0.000000),(0.222222, 1.000000, 1.000000), (0.238095, 1.000000, 1.000000),(0.253968, 0.000000, 0.000000), (0.285714, 0.000000, 0.000000),(0.317460, 1.000000, 1.000000), (0.333333, 1.000000, 1.000000),(0.349206, 0.000000, 0.000000), (0.380952, 0.000000, 0.000000),(0.412698, 1.000000, 1.000000), (0.428571, 1.000000, 1.000000),(0.444444, 0.000000, 0.000000), (0.476190, 0.000000, 0.000000),(0.507937, 1.000000, 1.000000), (0.523810, 1.000000, 1.000000),(0.539683, 0.000000, 0.000000), (0.571429, 0.000000, 0.000000),(0.603175, 1.000000, 1.000000), (0.619048, 1.000000, 1.000000),(0.634921, 0.000000, 0.000000), (0.666667, 0.000000, 0.000000),(0.698413, 1.000000, 1.000000), (0.714286, 1.000000, 1.000000),(0.730159, 0.000000, 0.000000), (0.761905, 0.000000, 0.000000),(0.793651, 1.000000, 1.000000), (0.809524, 1.000000, 1.000000),(0.825397, 0.000000, 0.000000), (0.857143, 0.000000, 0.000000),(0.888889, 1.000000, 1.000000), (0.904762, 1.000000, 1.000000),(0.920635, 0.000000, 0.000000), (0.952381, 0.000000, 0.000000),(0.984127, 1.000000, 1.000000), (1.0, 1.0, 1.0)), 'blue': ((0., 0., 0.),(0.047619, 0.000000, 0.000000), (0.063492, 1.000000, 1.000000),(0.079365, 1.000000, 1.000000), (0.095238, 0.000000, 0.000000),(0.142857, 0.000000, 0.000000), (0.158730, 1.000000, 1.000000),(0.174603, 1.000000, 1.000000), (0.190476, 0.000000, 0.000000),(0.238095, 0.000000, 0.000000), (0.253968, 1.000000, 1.000000),(0.269841, 1.000000, 1.000000), (0.285714, 0.000000, 0.000000),(0.333333, 0.000000, 0.000000), (0.349206, 1.000000, 1.000000),(0.365079, 1.000000, 1.000000), (0.380952, 0.000000, 0.000000),(0.428571, 0.000000, 0.000000), (0.444444, 1.000000, 1.000000),(0.460317, 1.000000, 1.000000), (0.476190, 0.000000, 0.000000),(0.523810, 0.000000, 0.000000), (0.539683, 1.000000, 1.000000),(0.555556, 1.000000, 1.000000), (0.571429, 0.000000, 0.000000),(0.619048, 0.000000, 0.000000), (0.634921, 1.000000, 1.000000),(0.650794, 1.000000, 1.000000), (0.666667, 0.000000, 0.000000),(0.714286, 0.000000, 0.000000), (0.730159, 1.000000, 1.000000),(0.746032, 1.000000, 1.000000), (0.761905, 0.000000, 0.000000),(0.809524, 0.000000, 0.000000), (0.825397, 1.000000, 1.000000),(0.841270, 1.000000, 1.000000), (0.857143, 0.000000, 0.000000),(0.904762, 0.000000, 0.000000), (0.920635, 1.000000, 1.000000),(0.936508, 1.000000, 1.000000), (0.952381, 0.000000, 0.000000),(1.0, 0.0, 0.0))} _spring_data = {'red': ((0., 1., 1.),(1.0, 1.0, 1.0)), 'green': ((0., 0., 0.),(1.0, 1.0, 1.0)), 'blue': ((0., 1., 1.),(1.0, 0.0, 0.0))} _summer_data = {'red': ((0., 0., 0.),(1.0, 1.0, 1.0)), 'green': ((0., 0.5, 0.5),(1.0, 1.0, 1.0)), 'blue': ((0., 0.4, 0.4),(1.0, 0.4, 0.4))} _winter_data = {'red': ((0., 0., 0.),(1.0, 0.0, 0.0)), 'green': ((0., 0., 0.),(1.0, 1.0, 1.0)), 'blue': ((0., 1., 1.),(1.0, 0.5, 0.5))} _spectral_data = {'red': [(0.0, 0.0, 0.0), (0.05, 0.4667, 0.4667), (0.10, 0.5333, 0.5333), (0.15, 0.0, 0.0), (0.20, 0.0, 0.0), (0.25, 0.0, 0.0), (0.30, 0.0, 0.0), (0.35, 0.0, 0.0), (0.40, 0.0, 0.0), (0.45, 0.0, 0.0), (0.50, 0.0, 0.0), (0.55, 0.0, 0.0), (0.60, 0.0, 0.0), (0.65, 0.7333, 0.7333), (0.70, 0.9333, 0.9333), (0.75, 1.0, 1.0), (0.80, 1.0, 1.0), (0.85, 1.0, 1.0), (0.90, 0.8667, 0.8667), (0.95, 0.80, 0.80), (1.0, 0.80, 0.80)], 'green': [(0.0, 0.0, 0.0), (0.05, 0.0, 0.0), (0.10, 0.0, 0.0), (0.15, 0.0, 0.0), (0.20, 0.0, 0.0), (0.25, 0.4667, 0.4667), (0.30, 0.6000, 0.6000), (0.35, 0.6667, 0.6667), (0.40, 0.6667, 0.6667), (0.45, 0.6000, 0.6000), (0.50, 0.7333, 0.7333), (0.55, 0.8667, 0.8667), (0.60, 1.0, 1.0), (0.65, 1.0, 1.0), (0.70, 0.9333, 0.9333), (0.75, 0.8000, 0.8000), (0.80, 0.6000, 0.6000), (0.85, 0.0, 0.0), (0.90, 0.0, 0.0), (0.95, 0.0, 0.0), (1.0, 0.80, 0.80)], 'blue': [(0.0, 0.0, 0.0), (0.05, 0.5333, 0.5333), (0.10, 0.6000, 0.6000), (0.15, 0.6667, 0.6667), (0.20, 0.8667, 0.8667), (0.25, 0.8667, 0.8667), (0.30, 0.8667, 0.8667), (0.35, 0.6667, 0.6667), (0.40, 0.5333, 0.5333), (0.45, 0.0, 0.0), (0.5, 0.0, 0.0), (0.55, 0.0, 0.0), (0.60, 0.0, 0.0), (0.65, 0.0, 0.0), (0.70, 0.0, 0.0), (0.75, 0.0, 0.0), (0.80, 0.0, 0.0), (0.85, 0.0, 0.0), (0.90, 0.0, 0.0), (0.95, 0.0, 0.0), (1.0, 0.80, 0.80)]} autumn = colors.LinearSegmentedColormap('autumn', _autumn_data, LUTSIZE) bone = colors.LinearSegmentedColormap('bone ', _bone_data, LUTSIZE) binary = colors.LinearSegmentedColormap('binary ', _binary_data, LUTSIZE) cool = colors.LinearSegmentedColormap('cool', _cool_data, LUTSIZE) copper = colors.LinearSegmentedColormap('copper', _copper_data, LUTSIZE) flag = colors.LinearSegmentedColormap('flag', _flag_data, LUTSIZE) gray = colors.LinearSegmentedColormap('gray', _gray_data, LUTSIZE) hot = colors.LinearSegmentedColormap('hot', _hot_data, LUTSIZE) hsv = colors.LinearSegmentedColormap('hsv', _hsv_data, LUTSIZE) jet = colors.LinearSegmentedColormap('jet', _jet_data, LUTSIZE) pink = colors.LinearSegmentedColormap('pink', _pink_data, LUTSIZE) prism = colors.LinearSegmentedColormap('prism', _prism_data, LUTSIZE) spring = colors.LinearSegmentedColormap('spring', _spring_data, LUTSIZE) summer = colors.LinearSegmentedColormap('summer', _summer_data, LUTSIZE) winter = colors.LinearSegmentedColormap('winter', _winter_data, LUTSIZE) spectral = colors.LinearSegmentedColormap('spectral', _spectral_data, LUTSIZE) datad = { 'autumn': _autumn_data, 'bone': _bone_data, 'binary': _binary_data, 'cool': _cool_data, 'copper': _copper_data, 'flag': _flag_data, 'gray' : _gray_data, 'hot': _hot_data, 'hsv': _hsv_data, 'jet' : _jet_data, 'pink': _pink_data, 'prism': _prism_data, 'spring': _spring_data, 'summer': _summer_data, 'winter': _winter_data, 'spectral': _spectral_data } # 34 colormaps based on color specifications and designs # developed by Cynthia Brewer (http://colorbrewer.org). # The ColorBrewer palettes have been included under the terms # of an Apache-stype license (for details, see the file # LICENSE_COLORBREWER in the license directory of the matplotlib # source distribution). _Accent_data = {'blue': [(0.0, 0.49803921580314636, 0.49803921580314636), (0.14285714285714285, 0.83137255907058716, 0.83137255907058716), (0.2857142857142857, 0.52549022436141968, 0.52549022436141968), (0.42857142857142855, 0.60000002384185791, 0.60000002384185791), (0.5714285714285714, 0.69019609689712524, 0.69019609689712524), (0.7142857142857143, 0.49803921580314636, 0.49803921580314636), (0.8571428571428571, 0.090196080505847931, 0.090196080505847931), (1.0, 0.40000000596046448, 0.40000000596046448)], 'green': [(0.0, 0.78823530673980713, 0.78823530673980713), (0.14285714285714285, 0.68235296010971069, 0.68235296010971069), (0.2857142857142857, 0.75294119119644165, 0.75294119119644165), (0.42857142857142855, 1.0, 1.0), (0.5714285714285714, 0.42352941632270813, 0.42352941632270813), (0.7142857142857143, 0.0078431377187371254, 0.0078431377187371254), (0.8571428571428571, 0.35686275362968445, 0.35686275362968445), (1.0, 0.40000000596046448, 0.40000000596046448)], 'red': [(0.0, 0.49803921580314636, 0.49803921580314636), (0.14285714285714285, 0.7450980544090271, 0.7450980544090271), (0.2857142857142857, 0.99215686321258545, 0.99215686321258545), (0.42857142857142855, 1.0, 1.0), (0.5714285714285714, 0.21960784494876862, 0.21960784494876862), (0.7142857142857143, 0.94117647409439087, 0.94117647409439087), (0.8571428571428571, 0.74901962280273438, 0.74901962280273438), (1.0, 0.40000000596046448, 0.40000000596046448)]} _Blues_data = {'blue': [(0.0, 1.0, 1.0), (0.125, 0.9686274528503418, 0.9686274528503418), (0.25, 0.93725490570068359, 0.93725490570068359), (0.375, 0.88235294818878174, 0.88235294818878174), (0.5, 0.83921569585800171, 0.83921569585800171), (0.625, 0.7764706015586853, 0.7764706015586853), (0.75, 0.70980393886566162, 0.70980393886566162), (0.875, 0.61176472902297974, 0.61176472902297974), (1.0, 0.41960784792900085, 0.41960784792900085)], 'green': [(0.0, 0.9843137264251709, 0.9843137264251709), (0.125, 0.92156863212585449, 0.92156863212585449), (0.25, 0.85882353782653809, 0.85882353782653809), (0.375, 0.7921568751335144, 0.7921568751335144), (0.5, 0.68235296010971069, 0.68235296010971069), (0.625, 0.57254904508590698, 0.57254904508590698), (0.75, 0.44313725829124451, 0.44313725829124451), (0.875, 0.31764706969261169, 0.31764706969261169), (1.0, 0.18823529779911041, 0.18823529779911041)], 'red': [(0.0, 0.9686274528503418, 0.9686274528503418), (0.125, 0.87058824300765991, 0.87058824300765991), (0.25, 0.7764706015586853, 0.7764706015586853), (0.375, 0.61960786581039429, 0.61960786581039429), (0.5, 0.41960784792900085, 0.41960784792900085), (0.625, 0.25882354378700256, 0.25882354378700256), (0.75, 0.12941177189350128, 0.12941177189350128), (0.875, 0.031372550874948502, 0.031372550874948502), (1.0, 0.031372550874948502, 0.031372550874948502)]} _BrBG_data = {'blue': [(0.0, 0.019607843831181526, 0.019607843831181526), (0.10000000000000001, 0.039215687662363052, 0.039215687662363052), (0.20000000000000001, 0.17647059261798859, 0.17647059261798859), (0.29999999999999999, 0.49019607901573181, 0.49019607901573181), (0.40000000000000002, 0.76470589637756348, 0.76470589637756348), (0.5, 0.96078431606292725, 0.96078431606292725), (0.59999999999999998, 0.89803922176361084, 0.89803922176361084), (0.69999999999999996, 0.75686275959014893, 0.75686275959014893), (0.80000000000000004, 0.56078433990478516, 0.56078433990478516), (0.90000000000000002, 0.36862745881080627, 0.36862745881080627), (1.0, 0.18823529779911041, 0.18823529779911041)], 'green': [(0.0, 0.18823529779911041, 0.18823529779911041), (0.10000000000000001, 0.31764706969261169, 0.31764706969261169), (0.20000000000000001, 0.5058823823928833, 0.5058823823928833), (0.29999999999999999, 0.7607843279838562, 0.7607843279838562), (0.40000000000000002, 0.90980392694473267, 0.90980392694473267), (0.5, 0.96078431606292725, 0.96078431606292725), (0.59999999999999998, 0.91764706373214722, 0.91764706373214722), (0.69999999999999996, 0.80392158031463623, 0.80392158031463623), (0.80000000000000004, 0.59215688705444336, 0.59215688705444336), (0.90000000000000002, 0.40000000596046448, 0.40000000596046448), (1.0, 0.23529411852359772, 0.23529411852359772)], 'red': [(0.0, 0.32941177487373352, 0.32941177487373352), (0.10000000000000001, 0.54901963472366333, 0.54901963472366333), (0.20000000000000001, 0.74901962280273438, 0.74901962280273438), (0.29999999999999999, 0.87450981140136719, 0.87450981140136719), (0.40000000000000002, 0.96470588445663452, 0.96470588445663452), (0.5, 0.96078431606292725, 0.96078431606292725), (0.59999999999999998, 0.78039216995239258, 0.78039216995239258), (0.69999999999999996, 0.50196081399917603, 0.50196081399917603), (0.80000000000000004, 0.20784313976764679, 0.20784313976764679), (0.90000000000000002, 0.0039215688593685627, 0.0039215688593685627), (1.0, 0.0, 0.0)]} _BuGn_data = {'blue': [(0.0, 0.99215686321258545, 0.99215686321258545), (0.125, 0.97647058963775635, 0.97647058963775635), (0.25, 0.90196079015731812, 0.90196079015731812), (0.375, 0.78823530673980713, 0.78823530673980713), (0.5, 0.64313727617263794, 0.64313727617263794), (0.625, 0.46274510025978088, 0.46274510025978088), (0.75, 0.27058824896812439, 0.27058824896812439), (0.875, 0.17254902422428131, 0.17254902422428131), (1.0, 0.10588235408067703, 0.10588235408067703)], 'green': [(0.0, 0.98823529481887817, 0.98823529481887817), (0.125, 0.96078431606292725, 0.96078431606292725), (0.25, 0.92549020051956177, 0.92549020051956177), (0.375, 0.84705883264541626, 0.84705883264541626), (0.5, 0.7607843279838562, 0.7607843279838562), (0.625, 0.68235296010971069, 0.68235296010971069), (0.75, 0.54509806632995605, 0.54509806632995605), (0.875, 0.42745098471641541, 0.42745098471641541), (1.0, 0.26666668057441711, 0.26666668057441711)], 'red': [(0.0, 0.9686274528503418, 0.9686274528503418), (0.125, 0.89803922176361084, 0.89803922176361084), (0.25, 0.80000001192092896, 0.80000001192092896), (0.375, 0.60000002384185791, 0.60000002384185791), (0.5, 0.40000000596046448, 0.40000000596046448), (0.625, 0.25490197539329529, 0.25490197539329529), (0.75, 0.13725490868091583, 0.13725490868091583), (0.875, 0.0, 0.0), (1.0, 0.0, 0.0)]} _BuPu_data = {'blue': [(0.0, 0.99215686321258545, 0.99215686321258545), (0.125, 0.95686274766921997, 0.95686274766921997), (0.25, 0.90196079015731812, 0.90196079015731812), (0.375, 0.85490196943283081, 0.85490196943283081), (0.5, 0.7764706015586853, 0.7764706015586853), (0.625, 0.69411766529083252, 0.69411766529083252), (0.75, 0.61568629741668701, 0.61568629741668701), (0.875, 0.48627451062202454, 0.48627451062202454), (1.0, 0.29411765933036804, 0.29411765933036804)], 'green': [(0.0, 0.98823529481887817, 0.98823529481887817), (0.125, 0.92549020051956177, 0.92549020051956177), (0.25, 0.82745099067687988, 0.82745099067687988), (0.375, 0.73725491762161255, 0.73725491762161255), (0.5, 0.58823531866073608, 0.58823531866073608), (0.625, 0.41960784792900085, 0.41960784792900085), (0.75, 0.25490197539329529, 0.25490197539329529), (0.875, 0.058823529630899429, 0.058823529630899429), (1.0, 0.0, 0.0)], 'red': [(0.0, 0.9686274528503418, 0.9686274528503418), (0.125, 0.87843137979507446, 0.87843137979507446), (0.25, 0.74901962280273438, 0.74901962280273438), (0.375, 0.61960786581039429, 0.61960786581039429), (0.5, 0.54901963472366333, 0.54901963472366333), (0.625, 0.54901963472366333, 0.54901963472366333), (0.75, 0.53333336114883423, 0.53333336114883423), (0.875, 0.5058823823928833, 0.5058823823928833), (1.0, 0.30196079611778259, 0.30196079611778259)]} _Dark2_data = {'blue': [(0.0, 0.46666666865348816, 0.46666666865348816), (0.14285714285714285, 0.0078431377187371254, 0.0078431377187371254), (0.2857142857142857, 0.70196080207824707, 0.70196080207824707), (0.42857142857142855, 0.54117649793624878, 0.54117649793624878), (0.5714285714285714, 0.11764705926179886, 0.11764705926179886), (0.7142857142857143, 0.0078431377187371254, 0.0078431377187371254), (0.8571428571428571, 0.11372549086809158, 0.11372549086809158), (1.0, 0.40000000596046448, 0.40000000596046448)], 'green': [(0.0, 0.61960786581039429, 0.61960786581039429), (0.14285714285714285, 0.37254902720451355, 0.37254902720451355), (0.2857142857142857, 0.43921568989753723, 0.43921568989753723), (0.42857142857142855, 0.16078431904315948, 0.16078431904315948), (0.5714285714285714, 0.65098041296005249, 0.65098041296005249), (0.7142857142857143, 0.67058825492858887, 0.67058825492858887), (0.8571428571428571, 0.46274510025978088, 0.46274510025978088), (1.0, 0.40000000596046448, 0.40000000596046448)], 'red': [(0.0, 0.10588235408067703, 0.10588235408067703), (0.14285714285714285, 0.85098040103912354, 0.85098040103912354), (0.2857142857142857, 0.45882353186607361, 0.45882353186607361), (0.42857142857142855, 0.90588235855102539, 0.90588235855102539), (0.5714285714285714, 0.40000000596046448, 0.40000000596046448), (0.7142857142857143, 0.90196079015731812, 0.90196079015731812), (0.8571428571428571, 0.65098041296005249, 0.65098041296005249), (1.0, 0.40000000596046448, 0.40000000596046448)]} _GnBu_data = {'blue': [(0.0, 0.94117647409439087, 0.94117647409439087), (0.125, 0.85882353782653809, 0.85882353782653809), (0.25, 0.77254903316497803, 0.77254903316497803), (0.375, 0.70980393886566162, 0.70980393886566162), (0.5, 0.76862746477127075, 0.76862746477127075), (0.625, 0.82745099067687988, 0.82745099067687988), (0.75, 0.7450980544090271, 0.7450980544090271), (0.875, 0.67450982332229614, 0.67450982332229614), (1.0, 0.5058823823928833, 0.5058823823928833)], 'green': [(0.0, 0.98823529481887817, 0.98823529481887817), (0.125, 0.9529411792755127, 0.9529411792755127), (0.25, 0.92156863212585449, 0.92156863212585449), (0.375, 0.86666667461395264, 0.86666667461395264), (0.5, 0.80000001192092896, 0.80000001192092896), (0.625, 0.70196080207824707, 0.70196080207824707), (0.75, 0.54901963472366333, 0.54901963472366333), (0.875, 0.40784314274787903, 0.40784314274787903), (1.0, 0.25098040699958801, 0.25098040699958801)], 'red': [(0.0, 0.9686274528503418, 0.9686274528503418), (0.125, 0.87843137979507446, 0.87843137979507446), (0.25, 0.80000001192092896, 0.80000001192092896), (0.375, 0.65882354974746704, 0.65882354974746704), (0.5, 0.48235294222831726, 0.48235294222831726), (0.625, 0.30588236451148987, 0.30588236451148987), (0.75, 0.16862745583057404, 0.16862745583057404), (0.875, 0.031372550874948502, 0.031372550874948502), (1.0, 0.031372550874948502, 0.031372550874948502)]} _Greens_data = {'blue': [(0.0, 0.96078431606292725, 0.96078431606292725), (0.125, 0.87843137979507446, 0.87843137979507446), (0.25, 0.75294119119644165, 0.75294119119644165), (0.375, 0.60784316062927246, 0.60784316062927246), (0.5, 0.46274510025978088, 0.46274510025978088), (0.625, 0.364705890417099, 0.364705890417099), (0.75, 0.27058824896812439, 0.27058824896812439), (0.875, 0.17254902422428131, 0.17254902422428131), (1.0, 0.10588235408067703, 0.10588235408067703)], 'green': [(0.0, 0.98823529481887817, 0.98823529481887817), (0.125, 0.96078431606292725, 0.96078431606292725), (0.25, 0.91372549533843994, 0.91372549533843994), (0.375, 0.85098040103912354, 0.85098040103912354), (0.5, 0.76862746477127075, 0.76862746477127075), (0.625, 0.67058825492858887, 0.67058825492858887), (0.75, 0.54509806632995605, 0.54509806632995605), (0.875, 0.42745098471641541, 0.42745098471641541), (1.0, 0.26666668057441711, 0.26666668057441711)], 'red': [(0.0, 0.9686274528503418, 0.9686274528503418), (0.125, 0.89803922176361084, 0.89803922176361084), (0.25, 0.78039216995239258, 0.78039216995239258), (0.375, 0.63137257099151611, 0.63137257099151611), (0.5, 0.45490196347236633, 0.45490196347236633), (0.625, 0.25490197539329529, 0.25490197539329529), (0.75, 0.13725490868091583, 0.13725490868091583), (0.875, 0.0, 0.0), (1.0, 0.0, 0.0)]} _Greys_data = {'blue': [(0.0, 1.0, 1.0), (0.125, 0.94117647409439087, 0.94117647409439087), (0.25, 0.85098040103912354, 0.85098040103912354), (0.375, 0.74117648601531982, 0.74117648601531982), (0.5, 0.58823531866073608, 0.58823531866073608), (0.625, 0.45098039507865906, 0.45098039507865906), (0.75, 0.32156863808631897, 0.32156863808631897), (0.875, 0.14509804546833038, 0.14509804546833038), (1.0, 0.0, 0.0)], 'green': [(0.0, 1.0, 1.0), (0.125, 0.94117647409439087, 0.94117647409439087), (0.25, 0.85098040103912354, 0.85098040103912354), (0.375, 0.74117648601531982, 0.74117648601531982), (0.5, 0.58823531866073608, 0.58823531866073608), (0.625, 0.45098039507865906, 0.45098039507865906), (0.75, 0.32156863808631897, 0.32156863808631897), (0.875, 0.14509804546833038, 0.14509804546833038), (1.0, 0.0, 0.0)], 'red': [(0.0, 1.0, 1.0), (0.125, 0.94117647409439087, 0.94117647409439087), (0.25, 0.85098040103912354, 0.85098040103912354), (0.375, 0.74117648601531982, 0.74117648601531982), (0.5, 0.58823531866073608, 0.58823531866073608), (0.625, 0.45098039507865906, 0.45098039507865906), (0.75, 0.32156863808631897, 0.32156863808631897), (0.875, 0.14509804546833038, 0.14509804546833038), (1.0, 0.0, 0.0)]} _Oranges_data = {'blue': [(0.0, 0.92156863212585449, 0.92156863212585449), (0.125, 0.80784314870834351, 0.80784314870834351), (0.25, 0.63529413938522339, 0.63529413938522339), (0.375, 0.41960784792900085, 0.41960784792900085), (0.5, 0.23529411852359772, 0.23529411852359772), (0.625, 0.074509806931018829, 0.074509806931018829), (0.75, 0.0039215688593685627, 0.0039215688593685627), (0.875, 0.011764706112444401, 0.011764706112444401), (1.0, 0.015686275437474251, 0.015686275437474251)], 'green': [(0.0, 0.96078431606292725, 0.96078431606292725), (0.125, 0.90196079015731812, 0.90196079015731812), (0.25, 0.81568628549575806, 0.81568628549575806), (0.375, 0.68235296010971069, 0.68235296010971069), (0.5, 0.55294120311737061, 0.55294120311737061), (0.625, 0.4117647111415863, 0.4117647111415863), (0.75, 0.28235295414924622, 0.28235295414924622), (0.875, 0.21176470816135406, 0.21176470816135406), (1.0, 0.15294118225574493, 0.15294118225574493)], 'red': [(0.0, 1.0, 1.0), (0.125, 0.99607843160629272, 0.99607843160629272), (0.25, 0.99215686321258545, 0.99215686321258545), (0.375, 0.99215686321258545, 0.99215686321258545), (0.5, 0.99215686321258545, 0.99215686321258545), (0.625, 0.94509804248809814, 0.94509804248809814), (0.75, 0.85098040103912354, 0.85098040103912354), (0.875, 0.65098041296005249, 0.65098041296005249), (1.0, 0.49803921580314636, 0.49803921580314636)]} _OrRd_data = {'blue': [(0.0, 0.92549020051956177, 0.92549020051956177), (0.125, 0.78431373834609985, 0.78431373834609985), (0.25, 0.61960786581039429, 0.61960786581039429), (0.375, 0.51764708757400513, 0.51764708757400513), (0.5, 0.3490196168422699, 0.3490196168422699), (0.625, 0.28235295414924622, 0.28235295414924622), (0.75, 0.12156862765550613, 0.12156862765550613), (0.875, 0.0, 0.0), (1.0, 0.0, 0.0)], 'green': [(0.0, 0.9686274528503418, 0.9686274528503418), (0.125, 0.90980392694473267, 0.90980392694473267), (0.25, 0.83137255907058716, 0.83137255907058716), (0.375, 0.73333334922790527, 0.73333334922790527), (0.5, 0.55294120311737061, 0.55294120311737061), (0.625, 0.3960784375667572, 0.3960784375667572), (0.75, 0.18823529779911041, 0.18823529779911041), (0.875, 0.0, 0.0), (1.0, 0.0, 0.0)], 'red': [(0.0, 1.0, 1.0), (0.125, 0.99607843160629272, 0.99607843160629272), (0.25, 0.99215686321258545, 0.99215686321258545), (0.375, 0.99215686321258545, 0.99215686321258545), (0.5, 0.98823529481887817, 0.98823529481887817), (0.625, 0.93725490570068359, 0.93725490570068359), (0.75, 0.84313726425170898, 0.84313726425170898), (0.875, 0.70196080207824707, 0.70196080207824707), (1.0, 0.49803921580314636, 0.49803921580314636)]} _Paired_data = {'blue': [(0.0, 0.89019608497619629, 0.89019608497619629), (0.090909090909090912, 0.70588237047195435, 0.70588237047195435), (0.18181818181818182, 0.54117649793624878, 0.54117649793624878), (0.27272727272727271, 0.17254902422428131, 0.17254902422428131), (0.36363636363636365, 0.60000002384185791, 0.60000002384185791), (0.45454545454545453, 0.10980392247438431, 0.10980392247438431), (0.54545454545454541, 0.43529412150382996, 0.43529412150382996), (0.63636363636363635, 0.0, 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0.80000001192092896), (0.75, 0.74117648601531982, 0.74117648601531982), (0.875, 0.92549020051956177, 0.92549020051956177), (1.0, 0.94901961088180542, 0.94901961088180542)], 'green': [(0.0, 0.70588237047195435, 0.70588237047195435), (0.125, 0.80392158031463623, 0.80392158031463623), (0.25, 0.92156863212585449, 0.92156863212585449), (0.375, 0.79607844352722168, 0.79607844352722168), (0.5, 0.85098040103912354, 0.85098040103912354), (0.625, 1.0, 1.0), (0.75, 0.84705883264541626, 0.84705883264541626), (0.875, 0.85490196943283081, 0.85490196943283081), (1.0, 0.94901961088180542, 0.94901961088180542)], 'red': [(0.0, 0.9843137264251709, 0.9843137264251709), (0.125, 0.70196080207824707, 0.70196080207824707), (0.25, 0.80000001192092896, 0.80000001192092896), (0.375, 0.87058824300765991, 0.87058824300765991), (0.5, 0.99607843160629272, 0.99607843160629272), (0.625, 1.0, 1.0), (0.75, 0.89803922176361084, 0.89803922176361084), (0.875, 0.99215686321258545, 0.99215686321258545), (1.0, 0.94901961088180542, 0.94901961088180542)]} _Pastel2_data = {'blue': [(0.0, 0.80392158031463623, 0.80392158031463623), (0.14285714285714285, 0.67450982332229614, 0.67450982332229614), (0.2857142857142857, 0.90980392694473267, 0.90980392694473267), (0.42857142857142855, 0.89411765336990356, 0.89411765336990356), (0.5714285714285714, 0.78823530673980713, 0.78823530673980713), (0.7142857142857143, 0.68235296010971069, 0.68235296010971069), (0.8571428571428571, 0.80000001192092896, 0.80000001192092896), (1.0, 0.80000001192092896, 0.80000001192092896)], 'green': [(0.0, 0.88627451658248901, 0.88627451658248901), (0.14285714285714285, 0.80392158031463623, 0.80392158031463623), (0.2857142857142857, 0.83529412746429443, 0.83529412746429443), (0.42857142857142855, 0.7921568751335144, 0.7921568751335144), (0.5714285714285714, 0.96078431606292725, 0.96078431606292725), (0.7142857142857143, 0.94901961088180542, 0.94901961088180542), (0.8571428571428571, 0.88627451658248901, 0.88627451658248901), (1.0, 0.80000001192092896, 0.80000001192092896)], 'red': [(0.0, 0.70196080207824707, 0.70196080207824707), (0.14285714285714285, 0.99215686321258545, 0.99215686321258545), (0.2857142857142857, 0.79607844352722168, 0.79607844352722168), (0.42857142857142855, 0.95686274766921997, 0.95686274766921997), (0.5714285714285714, 0.90196079015731812, 0.90196079015731812), (0.7142857142857143, 1.0, 1.0), (0.8571428571428571, 0.94509804248809814, 0.94509804248809814), (1.0, 0.80000001192092896, 0.80000001192092896)]} _PiYG_data = {'blue': [(0.0, 0.32156863808631897, 0.32156863808631897), (0.10000000000000001, 0.49019607901573181, 0.49019607901573181), (0.20000000000000001, 0.68235296010971069, 0.68235296010971069), (0.29999999999999999, 0.85490196943283081, 0.85490196943283081), (0.40000000000000002, 0.93725490570068359, 0.93725490570068359), (0.5, 0.9686274528503418, 0.9686274528503418), (0.59999999999999998, 0.81568628549575806, 0.81568628549575806), (0.69999999999999996, 0.52549022436141968, 0.52549022436141968), (0.80000000000000004, 0.25490197539329529, 0.25490197539329529), (0.90000000000000002, 0.12941177189350128, 0.12941177189350128), (1.0, 0.098039217293262482, 0.098039217293262482)], 'green': [(0.0, 0.0039215688593685627, 0.0039215688593685627), (0.10000000000000001, 0.10588235408067703, 0.10588235408067703), (0.20000000000000001, 0.46666666865348816, 0.46666666865348816), (0.29999999999999999, 0.7137255072593689, 0.7137255072593689), (0.40000000000000002, 0.87843137979507446, 0.87843137979507446), (0.5, 0.9686274528503418, 0.9686274528503418), (0.59999999999999998, 0.96078431606292725, 0.96078431606292725), (0.69999999999999996, 0.88235294818878174, 0.88235294818878174), (0.80000000000000004, 0.73725491762161255, 0.73725491762161255), (0.90000000000000002, 0.57254904508590698, 0.57254904508590698), (1.0, 0.39215686917304993, 0.39215686917304993)], 'red': [(0.0, 0.55686277151107788, 0.55686277151107788), (0.10000000000000001, 0.77254903316497803, 0.77254903316497803), (0.20000000000000001, 0.87058824300765991, 0.87058824300765991), (0.29999999999999999, 0.94509804248809814, 0.94509804248809814), (0.40000000000000002, 0.99215686321258545, 0.99215686321258545), (0.5, 0.9686274528503418, 0.9686274528503418), (0.59999999999999998, 0.90196079015731812, 0.90196079015731812), (0.69999999999999996, 0.72156864404678345, 0.72156864404678345), (0.80000000000000004, 0.49803921580314636, 0.49803921580314636), (0.90000000000000002, 0.30196079611778259, 0.30196079611778259), (1.0, 0.15294118225574493, 0.15294118225574493)]} _PRGn_data = {'blue': [(0.0, 0.29411765933036804, 0.29411765933036804), (0.10000000000000001, 0.51372551918029785, 0.51372551918029785), (0.20000000000000001, 0.67058825492858887, 0.67058825492858887), (0.29999999999999999, 0.81176471710205078, 0.81176471710205078), (0.40000000000000002, 0.90980392694473267, 0.90980392694473267), (0.5, 0.9686274528503418, 0.9686274528503418), (0.59999999999999998, 0.82745099067687988, 0.82745099067687988), (0.69999999999999996, 0.62745100259780884, 0.62745100259780884), (0.80000000000000004, 0.3803921639919281, 0.3803921639919281), (0.90000000000000002, 0.21568627655506134, 0.21568627655506134), (1.0, 0.10588235408067703, 0.10588235408067703)], 'green': [(0.0, 0.0, 0.0), (0.10000000000000001, 0.16470588743686676, 0.16470588743686676), (0.20000000000000001, 0.43921568989753723, 0.43921568989753723), (0.29999999999999999, 0.64705884456634521, 0.64705884456634521), (0.40000000000000002, 0.83137255907058716, 0.83137255907058716), (0.5, 0.9686274528503418, 0.9686274528503418), (0.59999999999999998, 0.94117647409439087, 0.94117647409439087), (0.69999999999999996, 0.85882353782653809, 0.85882353782653809), (0.80000000000000004, 0.68235296010971069, 0.68235296010971069), (0.90000000000000002, 0.47058823704719543, 0.47058823704719543), (1.0, 0.26666668057441711, 0.26666668057441711)], 'red': [(0.0, 0.25098040699958801, 0.25098040699958801), (0.10000000000000001, 0.46274510025978088, 0.46274510025978088), (0.20000000000000001, 0.60000002384185791, 0.60000002384185791), (0.29999999999999999, 0.7607843279838562, 0.7607843279838562), (0.40000000000000002, 0.90588235855102539, 0.90588235855102539), (0.5, 0.9686274528503418, 0.9686274528503418), (0.59999999999999998, 0.85098040103912354, 0.85098040103912354), (0.69999999999999996, 0.65098041296005249, 0.65098041296005249), (0.80000000000000004, 0.35294118523597717, 0.35294118523597717), (0.90000000000000002, 0.10588235408067703, 0.10588235408067703), (1.0, 0.0, 0.0)]} _PuBu_data = {'blue': [(0.0, 0.9843137264251709, 0.9843137264251709), (0.125, 0.94901961088180542, 0.94901961088180542), (0.25, 0.90196079015731812, 0.90196079015731812), (0.375, 0.85882353782653809, 0.85882353782653809), (0.5, 0.81176471710205078, 0.81176471710205078), (0.625, 0.75294119119644165, 0.75294119119644165), (0.75, 0.69019609689712524, 0.69019609689712524), (0.875, 0.55294120311737061, 0.55294120311737061), (1.0, 0.34509804844856262, 0.34509804844856262)], 'green': [(0.0, 0.9686274528503418, 0.9686274528503418), (0.125, 0.90588235855102539, 0.90588235855102539), (0.25, 0.81960785388946533, 0.81960785388946533), (0.375, 0.74117648601531982, 0.74117648601531982), (0.5, 0.66274511814117432, 0.66274511814117432), (0.625, 0.56470590829849243, 0.56470590829849243), (0.75, 0.43921568989753723, 0.43921568989753723), (0.875, 0.35294118523597717, 0.35294118523597717), (1.0, 0.21960784494876862, 0.21960784494876862)], 'red': [(0.0, 1.0, 1.0), (0.125, 0.92549020051956177, 0.92549020051956177), (0.25, 0.81568628549575806, 0.81568628549575806), (0.375, 0.65098041296005249, 0.65098041296005249), (0.5, 0.45490196347236633, 0.45490196347236633), (0.625, 0.21176470816135406, 0.21176470816135406), (0.75, 0.019607843831181526, 0.019607843831181526), (0.875, 0.015686275437474251, 0.015686275437474251), (1.0, 0.0078431377187371254, 0.0078431377187371254)]} _PuBuGn_data = {'blue': [(0.0, 0.9843137264251709, 0.9843137264251709), (0.125, 0.94117647409439087, 0.94117647409439087), (0.25, 0.90196079015731812, 0.90196079015731812), (0.375, 0.85882353782653809, 0.85882353782653809), (0.5, 0.81176471710205078, 0.81176471710205078), (0.625, 0.75294119119644165, 0.75294119119644165), (0.75, 0.54117649793624878, 0.54117649793624878), (0.875, 0.3490196168422699, 0.3490196168422699), (1.0, 0.21176470816135406, 0.21176470816135406)], 'green': [(0.0, 0.9686274528503418, 0.9686274528503418), (0.125, 0.88627451658248901, 0.88627451658248901), (0.25, 0.81960785388946533, 0.81960785388946533), (0.375, 0.74117648601531982, 0.74117648601531982), (0.5, 0.66274511814117432, 0.66274511814117432), (0.625, 0.56470590829849243, 0.56470590829849243), (0.75, 0.5058823823928833, 0.5058823823928833), (0.875, 0.42352941632270813, 0.42352941632270813), (1.0, 0.27450981736183167, 0.27450981736183167)], 'red': [(0.0, 1.0, 1.0), (0.125, 0.92549020051956177, 0.92549020051956177), (0.25, 0.81568628549575806, 0.81568628549575806), (0.375, 0.65098041296005249, 0.65098041296005249), (0.5, 0.40392157435417175, 0.40392157435417175), (0.625, 0.21176470816135406, 0.21176470816135406), (0.75, 0.0078431377187371254, 0.0078431377187371254), (0.875, 0.0039215688593685627, 0.0039215688593685627), (1.0, 0.0039215688593685627, 0.0039215688593685627)]} _PuOr_data = {'blue': [(0.0, 0.031372550874948502, 0.031372550874948502), (0.10000000000000001, 0.023529412224888802, 0.023529412224888802), (0.20000000000000001, 0.078431375324726105, 0.078431375324726105), (0.29999999999999999, 0.38823530077934265, 0.38823530077934265), (0.40000000000000002, 0.7137255072593689, 0.7137255072593689), (0.5, 0.9686274528503418, 0.9686274528503418), (0.59999999999999998, 0.92156863212585449, 0.92156863212585449), (0.69999999999999996, 0.82352942228317261, 0.82352942228317261), (0.80000000000000004, 0.67450982332229614, 0.67450982332229614), (0.90000000000000002, 0.53333336114883423, 0.53333336114883423), (1.0, 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0.99607843160629272), (0.5, 0.9686274528503418, 0.9686274528503418), (0.59999999999999998, 0.84705883264541626, 0.84705883264541626), (0.69999999999999996, 0.69803923368453979, 0.69803923368453979), (0.80000000000000004, 0.50196081399917603, 0.50196081399917603), (0.90000000000000002, 0.32941177487373352, 0.32941177487373352), (1.0, 0.17647059261798859, 0.17647059261798859)]} _PuRd_data = {'blue': [(0.0, 0.97647058963775635, 0.97647058963775635), (0.125, 0.93725490570068359, 0.93725490570068359), (0.25, 0.85490196943283081, 0.85490196943283081), (0.375, 0.78039216995239258, 0.78039216995239258), (0.5, 0.69019609689712524, 0.69019609689712524), (0.625, 0.54117649793624878, 0.54117649793624878), (0.75, 0.33725491166114807, 0.33725491166114807), (0.875, 0.26274511218070984, 0.26274511218070984), (1.0, 0.12156862765550613, 0.12156862765550613)], 'green': [(0.0, 0.95686274766921997, 0.95686274766921997), (0.125, 0.88235294818878174, 0.88235294818878174), (0.25, 0.72549021244049072, 0.72549021244049072), (0.375, 0.58039218187332153, 0.58039218187332153), (0.5, 0.3960784375667572, 0.3960784375667572), (0.625, 0.16078431904315948, 0.16078431904315948), (0.75, 0.070588238537311554, 0.070588238537311554), (0.875, 0.0, 0.0), (1.0, 0.0, 0.0)], 'red': [(0.0, 0.9686274528503418, 0.9686274528503418), (0.125, 0.90588235855102539, 0.90588235855102539), (0.25, 0.83137255907058716, 0.83137255907058716), (0.375, 0.78823530673980713, 0.78823530673980713), (0.5, 0.87450981140136719, 0.87450981140136719), (0.625, 0.90588235855102539, 0.90588235855102539), (0.75, 0.80784314870834351, 0.80784314870834351), (0.875, 0.59607845544815063, 0.59607845544815063), (1.0, 0.40392157435417175, 0.40392157435417175)]} _Purples_data = {'blue': [(0.0, 0.99215686321258545, 0.99215686321258545), (0.125, 0.96078431606292725, 0.96078431606292725), (0.25, 0.92156863212585449, 0.92156863212585449), (0.375, 0.86274510622024536, 0.86274510622024536), (0.5, 0.78431373834609985, 0.78431373834609985), (0.625, 0.729411780834198, 0.729411780834198), (0.75, 0.63921570777893066, 0.63921570777893066), (0.875, 0.56078433990478516, 0.56078433990478516), (1.0, 0.49019607901573181, 0.49019607901573181)], 'green': [(0.0, 0.9843137264251709, 0.9843137264251709), (0.125, 0.92941176891326904, 0.92941176891326904), (0.25, 0.85490196943283081, 0.85490196943283081), (0.375, 0.74117648601531982, 0.74117648601531982), (0.5, 0.60392159223556519, 0.60392159223556519), (0.625, 0.49019607901573181, 0.49019607901573181), (0.75, 0.31764706969261169, 0.31764706969261169), (0.875, 0.15294118225574493, 0.15294118225574493), (1.0, 0.0, 0.0)], 'red': [(0.0, 0.98823529481887817, 0.98823529481887817), (0.125, 0.93725490570068359, 0.93725490570068359), (0.25, 0.85490196943283081, 0.85490196943283081), (0.375, 0.73725491762161255, 0.73725491762161255), (0.5, 0.61960786581039429, 0.61960786581039429), (0.625, 0.50196081399917603, 0.50196081399917603), (0.75, 0.41568627953529358, 0.41568627953529358), (0.875, 0.32941177487373352, 0.32941177487373352), (1.0, 0.24705882370471954, 0.24705882370471954)]} _RdBu_data = {'blue': [(0.0, 0.12156862765550613, 0.12156862765550613), (0.10000000000000001, 0.16862745583057404, 0.16862745583057404), (0.20000000000000001, 0.30196079611778259, 0.30196079611778259), (0.29999999999999999, 0.50980395078659058, 0.50980395078659058), (0.40000000000000002, 0.78039216995239258, 0.78039216995239258), (0.5, 0.9686274528503418, 0.9686274528503418), (0.59999999999999998, 0.94117647409439087, 0.94117647409439087), (0.69999999999999996, 0.87058824300765991, 0.87058824300765991), (0.80000000000000004, 0.76470589637756348, 0.76470589637756348), (0.90000000000000002, 0.67450982332229614, 0.67450982332229614), (1.0, 0.3803921639919281, 0.3803921639919281)], 'green': [(0.0, 0.0, 0.0), (0.10000000000000001, 0.094117648899555206, 0.094117648899555206), (0.20000000000000001, 0.37647059559822083, 0.37647059559822083), (0.29999999999999999, 0.64705884456634521, 0.64705884456634521), (0.40000000000000002, 0.85882353782653809, 0.85882353782653809), (0.5, 0.9686274528503418, 0.9686274528503418), (0.59999999999999998, 0.89803922176361084, 0.89803922176361084), (0.69999999999999996, 0.77254903316497803, 0.77254903316497803), (0.80000000000000004, 0.57647061347961426, 0.57647061347961426), (0.90000000000000002, 0.40000000596046448, 0.40000000596046448), (1.0, 0.18823529779911041, 0.18823529779911041)], 'red': [(0.0, 0.40392157435417175, 0.40392157435417175), (0.10000000000000001, 0.69803923368453979, 0.69803923368453979), (0.20000000000000001, 0.83921569585800171, 0.83921569585800171), (0.29999999999999999, 0.95686274766921997, 0.95686274766921997), (0.40000000000000002, 0.99215686321258545, 0.99215686321258545), (0.5, 0.9686274528503418, 0.9686274528503418), (0.59999999999999998, 0.81960785388946533, 0.81960785388946533), (0.69999999999999996, 0.57254904508590698, 0.57254904508590698), (0.80000000000000004, 0.26274511218070984, 0.26274511218070984), (0.90000000000000002, 0.12941177189350128, 0.12941177189350128), (1.0, 0.019607843831181526, 0.019607843831181526)]} _RdGy_data = {'blue': [(0.0, 0.12156862765550613, 0.12156862765550613), (0.10000000000000001, 0.16862745583057404, 0.16862745583057404), (0.20000000000000001, 0.30196079611778259, 0.30196079611778259), (0.29999999999999999, 0.50980395078659058, 0.50980395078659058), (0.40000000000000002, 0.78039216995239258, 0.78039216995239258), (0.5, 1.0, 1.0), (0.59999999999999998, 0.87843137979507446, 0.87843137979507446), (0.69999999999999996, 0.729411780834198, 0.729411780834198), (0.80000000000000004, 0.52941179275512695, 0.52941179275512695), (0.90000000000000002, 0.30196079611778259, 0.30196079611778259), (1.0, 0.10196078568696976, 0.10196078568696976)], 'green': [(0.0, 0.0, 0.0), (0.10000000000000001, 0.094117648899555206, 0.094117648899555206), (0.20000000000000001, 0.37647059559822083, 0.37647059559822083), (0.29999999999999999, 0.64705884456634521, 0.64705884456634521), (0.40000000000000002, 0.85882353782653809, 0.85882353782653809), (0.5, 1.0, 1.0), (0.59999999999999998, 0.87843137979507446, 0.87843137979507446), (0.69999999999999996, 0.729411780834198, 0.729411780834198), (0.80000000000000004, 0.52941179275512695, 0.52941179275512695), (0.90000000000000002, 0.30196079611778259, 0.30196079611778259), (1.0, 0.10196078568696976, 0.10196078568696976)], 'red': [(0.0, 0.40392157435417175, 0.40392157435417175), (0.10000000000000001, 0.69803923368453979, 0.69803923368453979), (0.20000000000000001, 0.83921569585800171, 0.83921569585800171), (0.29999999999999999, 0.95686274766921997, 0.95686274766921997), (0.40000000000000002, 0.99215686321258545, 0.99215686321258545), (0.5, 1.0, 1.0), (0.59999999999999998, 0.87843137979507446, 0.87843137979507446), (0.69999999999999996, 0.729411780834198, 0.729411780834198), (0.80000000000000004, 0.52941179275512695, 0.52941179275512695), (0.90000000000000002, 0.30196079611778259, 0.30196079611778259), (1.0, 0.10196078568696976, 0.10196078568696976)]} _RdPu_data = {'blue': [(0.0, 0.9529411792755127, 0.9529411792755127), (0.125, 0.86666667461395264, 0.86666667461395264), (0.25, 0.75294119119644165, 0.75294119119644165), (0.375, 0.70980393886566162, 0.70980393886566162), (0.5, 0.63137257099151611, 0.63137257099151611), (0.625, 0.59215688705444336, 0.59215688705444336), (0.75, 0.49411764740943909, 0.49411764740943909), (0.875, 0.46666666865348816, 0.46666666865348816), (1.0, 0.41568627953529358, 0.41568627953529358)], 'green': [(0.0, 0.9686274528503418, 0.9686274528503418), (0.125, 0.87843137979507446, 0.87843137979507446), (0.25, 0.77254903316497803, 0.77254903316497803), (0.375, 0.62352943420410156, 0.62352943420410156), (0.5, 0.40784314274787903, 0.40784314274787903), (0.625, 0.20392157137393951, 0.20392157137393951), (0.75, 0.0039215688593685627, 0.0039215688593685627), (0.875, 0.0039215688593685627, 0.0039215688593685627), (1.0, 0.0, 0.0)], 'red': [(0.0, 1.0, 1.0), (0.125, 0.99215686321258545, 0.99215686321258545), (0.25, 0.98823529481887817, 0.98823529481887817), (0.375, 0.98039215803146362, 0.98039215803146362), (0.5, 0.9686274528503418, 0.9686274528503418), (0.625, 0.86666667461395264, 0.86666667461395264), (0.75, 0.68235296010971069, 0.68235296010971069), (0.875, 0.47843137383460999, 0.47843137383460999), (1.0, 0.28627452254295349, 0.28627452254295349)]} _RdYlBu_data = {'blue': [(0.0, 0.14901961386203766, 0.14901961386203766), (0.10000000149011612, 0.15294118225574493, 0.15294118225574493), (0.20000000298023224, 0.26274511218070984, 0.26274511218070984), (0.30000001192092896, 0.3803921639919281, 0.3803921639919281), (0.40000000596046448, 0.56470590829849243, 0.56470590829849243), (0.5, 0.74901962280273438, 0.74901962280273438), (0.60000002384185791, 0.97254902124404907, 0.97254902124404907), (0.69999998807907104, 0.91372549533843994, 0.91372549533843994), (0.80000001192092896, 0.81960785388946533, 0.81960785388946533), (0.89999997615814209, 0.70588237047195435, 0.70588237047195435), (1.0, 0.58431375026702881, 0.58431375026702881)], 'green': [(0.0, 0.0, 0.0), (0.10000000149011612, 0.18823529779911041, 0.18823529779911041), (0.20000000298023224, 0.42745098471641541, 0.42745098471641541), (0.30000001192092896, 0.68235296010971069, 0.68235296010971069), (0.40000000596046448, 0.87843137979507446, 0.87843137979507446), (0.5, 1.0, 1.0), (0.60000002384185791, 0.9529411792755127, 0.9529411792755127), (0.69999998807907104, 0.85098040103912354, 0.85098040103912354), (0.80000001192092896, 0.67843139171600342, 0.67843139171600342), (0.89999997615814209, 0.45882353186607361, 0.45882353186607361), (1.0, 0.21176470816135406, 0.21176470816135406)], 'red': [(0.0, 0.64705884456634521, 0.64705884456634521), (0.10000000149011612, 0.84313726425170898, 0.84313726425170898), (0.20000000298023224, 0.95686274766921997, 0.95686274766921997), (0.30000001192092896, 0.99215686321258545, 0.99215686321258545), (0.40000000596046448, 0.99607843160629272, 0.99607843160629272), (0.5, 1.0, 1.0), (0.60000002384185791, 0.87843137979507446, 0.87843137979507446), (0.69999998807907104, 0.67058825492858887, 0.67058825492858887), (0.80000001192092896, 0.45490196347236633, 0.45490196347236633), (0.89999997615814209, 0.27058824896812439, 0.27058824896812439), (1.0, 0.19215686619281769, 0.19215686619281769)]} _RdYlGn_data = {'blue': [(0.0, 0.14901961386203766, 0.14901961386203766), (0.10000000000000001, 0.15294118225574493, 0.15294118225574493), (0.20000000000000001, 0.26274511218070984, 0.26274511218070984), (0.29999999999999999, 0.3803921639919281, 0.3803921639919281), (0.40000000000000002, 0.54509806632995605, 0.54509806632995605), (0.5, 0.74901962280273438, 0.74901962280273438), (0.59999999999999998, 0.54509806632995605, 0.54509806632995605), (0.69999999999999996, 0.41568627953529358, 0.41568627953529358), (0.80000000000000004, 0.38823530077934265, 0.38823530077934265), (0.90000000000000002, 0.31372550129890442, 0.31372550129890442), (1.0, 0.21568627655506134, 0.21568627655506134)], 'green': [(0.0, 0.0, 0.0), (0.10000000000000001, 0.18823529779911041, 0.18823529779911041), (0.20000000000000001, 0.42745098471641541, 0.42745098471641541), (0.29999999999999999, 0.68235296010971069, 0.68235296010971069), (0.40000000000000002, 0.87843137979507446, 0.87843137979507446), (0.5, 1.0, 1.0), (0.59999999999999998, 0.93725490570068359, 0.93725490570068359), (0.69999999999999996, 0.85098040103912354, 0.85098040103912354), (0.80000000000000004, 0.74117648601531982, 0.74117648601531982), (0.90000000000000002, 0.59607845544815063, 0.59607845544815063), (1.0, 0.40784314274787903, 0.40784314274787903)], 'red': [(0.0, 0.64705884456634521, 0.64705884456634521), (0.10000000000000001, 0.84313726425170898, 0.84313726425170898), (0.20000000000000001, 0.95686274766921997, 0.95686274766921997), (0.29999999999999999, 0.99215686321258545, 0.99215686321258545), (0.40000000000000002, 0.99607843160629272, 0.99607843160629272), (0.5, 1.0, 1.0), (0.59999999999999998, 0.85098040103912354, 0.85098040103912354), (0.69999999999999996, 0.65098041296005249, 0.65098041296005249), (0.80000000000000004, 0.40000000596046448, 0.40000000596046448), (0.90000000000000002, 0.10196078568696976, 0.10196078568696976), (1.0, 0.0, 0.0)]} _Reds_data = {'blue': [(0.0, 0.94117647409439087, 0.94117647409439087), (0.125, 0.82352942228317261, 0.82352942228317261), (0.25, 0.63137257099151611, 0.63137257099151611), (0.375, 0.44705882668495178, 0.44705882668495178), (0.5, 0.29019609093666077, 0.29019609093666077), (0.625, 0.17254902422428131, 0.17254902422428131), (0.75, 0.11372549086809158, 0.11372549086809158), (0.875, 0.08235294371843338, 0.08235294371843338), (1.0, 0.050980392843484879, 0.050980392843484879)], 'green': [(0.0, 0.96078431606292725, 0.96078431606292725), (0.125, 0.87843137979507446, 0.87843137979507446), (0.25, 0.73333334922790527, 0.73333334922790527), (0.375, 0.57254904508590698, 0.57254904508590698), (0.5, 0.41568627953529358, 0.41568627953529358), (0.625, 0.23137255012989044, 0.23137255012989044), (0.75, 0.094117648899555206, 0.094117648899555206), (0.875, 0.058823529630899429, 0.058823529630899429), (1.0, 0.0, 0.0)], 'red': [(0.0, 1.0, 1.0), (0.125, 0.99607843160629272, 0.99607843160629272), (0.25, 0.98823529481887817, 0.98823529481887817), (0.375, 0.98823529481887817, 0.98823529481887817), (0.5, 0.9843137264251709, 0.9843137264251709), (0.625, 0.93725490570068359, 0.93725490570068359), (0.75, 0.79607844352722168, 0.79607844352722168), (0.875, 0.64705884456634521, 0.64705884456634521), (1.0, 0.40392157435417175, 0.40392157435417175)]} _Set1_data = {'blue': [(0.0, 0.10980392247438431, 0.10980392247438431), (0.125, 0.72156864404678345, 0.72156864404678345), (0.25, 0.29019609093666077, 0.29019609093666077), (0.375, 0.63921570777893066, 0.63921570777893066), (0.5, 0.0, 0.0), (0.625, 0.20000000298023224, 0.20000000298023224), (0.75, 0.15686275064945221, 0.15686275064945221), (0.875, 0.74901962280273438, 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0.38431373238563538, 0.38431373238563538), (0.2857142857142857, 0.79607844352722168, 0.79607844352722168), (0.42857142857142855, 0.76470589637756348, 0.76470589637756348), (0.5714285714285714, 0.32941177487373352, 0.32941177487373352), (0.7142857142857143, 0.18431372940540314, 0.18431372940540314), (0.8571428571428571, 0.58039218187332153, 0.58039218187332153), (1.0, 0.70196080207824707, 0.70196080207824707)], 'green': [(0.0, 0.7607843279838562, 0.7607843279838562), (0.14285714285714285, 0.55294120311737061, 0.55294120311737061), (0.2857142857142857, 0.62745100259780884, 0.62745100259780884), (0.42857142857142855, 0.54117649793624878, 0.54117649793624878), (0.5714285714285714, 0.84705883264541626, 0.84705883264541626), (0.7142857142857143, 0.85098040103912354, 0.85098040103912354), (0.8571428571428571, 0.76862746477127075, 0.76862746477127075), (1.0, 0.70196080207824707, 0.70196080207824707)], 'red': [(0.0, 0.40000000596046448, 0.40000000596046448), (0.14285714285714285, 0.98823529481887817, 0.98823529481887817), (0.2857142857142857, 0.55294120311737061, 0.55294120311737061), (0.42857142857142855, 0.90588235855102539, 0.90588235855102539), (0.5714285714285714, 0.65098041296005249, 0.65098041296005249), (0.7142857142857143, 1.0, 1.0), (0.8571428571428571, 0.89803922176361084, 0.89803922176361084), (1.0, 0.70196080207824707, 0.70196080207824707)]} _Set3_data = {'blue': [(0.0, 0.78039216995239258, 0.78039216995239258), (0.090909090909090912, 0.70196080207824707, 0.70196080207824707), (0.18181818181818182, 0.85490196943283081, 0.85490196943283081), (0.27272727272727271, 0.44705882668495178, 0.44705882668495178), (0.36363636363636365, 0.82745099067687988, 0.82745099067687988), (0.45454545454545453, 0.38431373238563538, 0.38431373238563538), (0.54545454545454541, 0.4117647111415863, 0.4117647111415863), (0.63636363636363635, 0.89803922176361084, 0.89803922176361084), (0.72727272727272729, 0.85098040103912354, 0.85098040103912354), (0.81818181818181823, 0.74117648601531982, 0.74117648601531982), (0.90909090909090906, 0.77254903316497803, 0.77254903316497803), (1.0, 0.43529412150382996, 0.43529412150382996)], 'green': [(0.0, 0.82745099067687988, 0.82745099067687988), (0.090909090909090912, 1.0, 1.0), (0.18181818181818182, 0.729411780834198, 0.729411780834198), (0.27272727272727271, 0.50196081399917603, 0.50196081399917603), (0.36363636363636365, 0.69411766529083252, 0.69411766529083252), (0.45454545454545453, 0.70588237047195435, 0.70588237047195435), (0.54545454545454541, 0.87058824300765991, 0.87058824300765991), (0.63636363636363635, 0.80392158031463623, 0.80392158031463623), (0.72727272727272729, 0.85098040103912354, 0.85098040103912354), (0.81818181818181823, 0.50196081399917603, 0.50196081399917603), (0.90909090909090906, 0.92156863212585449, 0.92156863212585449), (1.0, 0.92941176891326904, 0.92941176891326904)], 'red': [(0.0, 0.55294120311737061, 0.55294120311737061), (0.090909090909090912, 1.0, 1.0), (0.18181818181818182, 0.7450980544090271, 0.7450980544090271), (0.27272727272727271, 0.9843137264251709, 0.9843137264251709), (0.36363636363636365, 0.50196081399917603, 0.50196081399917603), (0.45454545454545453, 0.99215686321258545, 0.99215686321258545), (0.54545454545454541, 0.70196080207824707, 0.70196080207824707), (0.63636363636363635, 0.98823529481887817, 0.98823529481887817), (0.72727272727272729, 0.85098040103912354, 0.85098040103912354), (0.81818181818181823, 0.73725491762161255, 0.73725491762161255), (0.90909090909090906, 0.80000001192092896, 0.80000001192092896), (1.0, 1.0, 1.0)]} _Spectral_data = {'blue': [(0.0, 0.25882354378700256, 0.25882354378700256), (0.10000000000000001, 0.30980393290519714, 0.30980393290519714), (0.20000000000000001, 0.26274511218070984, 0.26274511218070984), (0.29999999999999999, 0.3803921639919281, 0.3803921639919281), (0.40000000000000002, 0.54509806632995605, 0.54509806632995605), (0.5, 0.74901962280273438, 0.74901962280273438), (0.59999999999999998, 0.59607845544815063, 0.59607845544815063), (0.69999999999999996, 0.64313727617263794, 0.64313727617263794), (0.80000000000000004, 0.64705884456634521, 0.64705884456634521), (0.90000000000000002, 0.74117648601531982, 0.74117648601531982), (1.0, 0.63529413938522339, 0.63529413938522339)], 'green': [(0.0, 0.0039215688593685627, 0.0039215688593685627), (0.10000000000000001, 0.24313725531101227, 0.24313725531101227), (0.20000000000000001, 0.42745098471641541, 0.42745098471641541), (0.29999999999999999, 0.68235296010971069, 0.68235296010971069), (0.40000000000000002, 0.87843137979507446, 0.87843137979507446), (0.5, 1.0, 1.0), (0.59999999999999998, 0.96078431606292725, 0.96078431606292725), (0.69999999999999996, 0.86666667461395264, 0.86666667461395264), (0.80000000000000004, 0.7607843279838562, 0.7607843279838562), (0.90000000000000002, 0.53333336114883423, 0.53333336114883423), (1.0, 0.30980393290519714, 0.30980393290519714)], 'red': [(0.0, 0.61960786581039429, 0.61960786581039429), (0.10000000000000001, 0.83529412746429443, 0.83529412746429443), (0.20000000000000001, 0.95686274766921997, 0.95686274766921997), (0.29999999999999999, 0.99215686321258545, 0.99215686321258545), (0.40000000000000002, 0.99607843160629272, 0.99607843160629272), (0.5, 1.0, 1.0), (0.59999999999999998, 0.90196079015731812, 0.90196079015731812), (0.69999999999999996, 0.67058825492858887, 0.67058825492858887), (0.80000000000000004, 0.40000000596046448, 0.40000000596046448), (0.90000000000000002, 0.19607843458652496, 0.19607843458652496), (1.0, 0.36862745881080627, 0.36862745881080627)]} _YlGn_data = {'blue': [(0.0, 0.89803922176361084, 0.89803922176361084), (0.125, 0.72549021244049072, 0.72549021244049072), (0.25, 0.63921570777893066, 0.63921570777893066), (0.375, 0.55686277151107788, 0.55686277151107788), (0.5, 0.47450980544090271, 0.47450980544090271), (0.625, 0.364705890417099, 0.364705890417099), (0.75, 0.26274511218070984, 0.26274511218070984), (0.875, 0.21568627655506134, 0.21568627655506134), (1.0, 0.16078431904315948, 0.16078431904315948)], 'green': [(0.0, 1.0, 1.0), (0.125, 0.98823529481887817, 0.98823529481887817), (0.25, 0.94117647409439087, 0.94117647409439087), (0.375, 0.86666667461395264, 0.86666667461395264), (0.5, 0.7764706015586853, 0.7764706015586853), (0.625, 0.67058825492858887, 0.67058825492858887), (0.75, 0.51764708757400513, 0.51764708757400513), (0.875, 0.40784314274787903, 0.40784314274787903), (1.0, 0.27058824896812439, 0.27058824896812439)], 'red': [(0.0, 1.0, 1.0), (0.125, 0.9686274528503418, 0.9686274528503418), (0.25, 0.85098040103912354, 0.85098040103912354), (0.375, 0.67843139171600342, 0.67843139171600342), (0.5, 0.47058823704719543, 0.47058823704719543), (0.625, 0.25490197539329529, 0.25490197539329529), (0.75, 0.13725490868091583, 0.13725490868091583), (0.875, 0.0, 0.0), (1.0, 0.0, 0.0)]} _YlGnBu_data = {'blue': [(0.0, 0.85098040103912354, 0.85098040103912354), (0.125, 0.69411766529083252, 0.69411766529083252), (0.25, 0.70588237047195435, 0.70588237047195435), (0.375, 0.73333334922790527, 0.73333334922790527), (0.5, 0.76862746477127075, 0.76862746477127075), (0.625, 0.75294119119644165, 0.75294119119644165), (0.75, 0.65882354974746704, 0.65882354974746704), (0.875, 0.58039218187332153, 0.58039218187332153), (1.0, 0.34509804844856262, 0.34509804844856262)], 'green': [(0.0, 1.0, 1.0), (0.125, 0.97254902124404907, 0.97254902124404907), (0.25, 0.91372549533843994, 0.91372549533843994), (0.375, 0.80392158031463623, 0.80392158031463623), (0.5, 0.7137255072593689, 0.7137255072593689), (0.625, 0.56862747669219971, 0.56862747669219971), (0.75, 0.36862745881080627, 0.36862745881080627), (0.875, 0.20392157137393951, 0.20392157137393951), (1.0, 0.11372549086809158, 0.11372549086809158)], 'red': [(0.0, 1.0, 1.0), (0.125, 0.92941176891326904, 0.92941176891326904), (0.25, 0.78039216995239258, 0.78039216995239258), (0.375, 0.49803921580314636, 0.49803921580314636), (0.5, 0.25490197539329529, 0.25490197539329529), (0.625, 0.11372549086809158, 0.11372549086809158), (0.75, 0.13333334028720856, 0.13333334028720856), (0.875, 0.14509804546833038, 0.14509804546833038), (1.0, 0.031372550874948502, 0.031372550874948502)]} _YlOrBr_data = {'blue': [(0.0, 0.89803922176361084, 0.89803922176361084), (0.125, 0.73725491762161255, 0.73725491762161255), (0.25, 0.56862747669219971, 0.56862747669219971), (0.375, 0.30980393290519714, 0.30980393290519714), (0.5, 0.16078431904315948, 0.16078431904315948), (0.625, 0.078431375324726105, 0.078431375324726105), (0.75, 0.0078431377187371254, 0.0078431377187371254), (0.875, 0.015686275437474251, 0.015686275437474251), (1.0, 0.023529412224888802, 0.023529412224888802)], 'green': [(0.0, 1.0, 1.0), (0.125, 0.9686274528503418, 0.9686274528503418), (0.25, 0.89019608497619629, 0.89019608497619629), (0.375, 0.76862746477127075, 0.76862746477127075), (0.5, 0.60000002384185791, 0.60000002384185791), (0.625, 0.43921568989753723, 0.43921568989753723), (0.75, 0.29803922772407532, 0.29803922772407532), (0.875, 0.20392157137393951, 0.20392157137393951), (1.0, 0.14509804546833038, 0.14509804546833038)], 'red': [(0.0, 1.0, 1.0), (0.125, 1.0, 1.0), (0.25, 0.99607843160629272, 0.99607843160629272), (0.375, 0.99607843160629272, 0.99607843160629272), (0.5, 0.99607843160629272, 0.99607843160629272), (0.625, 0.92549020051956177, 0.92549020051956177), (0.75, 0.80000001192092896, 0.80000001192092896), (0.875, 0.60000002384185791, 0.60000002384185791), (1.0, 0.40000000596046448, 0.40000000596046448)]} _YlOrRd_data = {'blue': [(0.0, 0.80000001192092896, 0.80000001192092896), (0.125, 0.62745100259780884, 0.62745100259780884), (0.25, 0.46274510025978088, 0.46274510025978088), (0.375, 0.29803922772407532, 0.29803922772407532), (0.5, 0.23529411852359772, 0.23529411852359772), (0.625, 0.16470588743686676, 0.16470588743686676), (0.75, 0.10980392247438431, 0.10980392247438431), (0.875, 0.14901961386203766, 0.14901961386203766), (1.0, 0.14901961386203766, 0.14901961386203766)], 'green': [(0.0, 1.0, 1.0), (0.125, 0.92941176891326904, 0.92941176891326904), (0.25, 0.85098040103912354, 0.85098040103912354), (0.375, 0.69803923368453979, 0.69803923368453979), (0.5, 0.55294120311737061, 0.55294120311737061), (0.625, 0.30588236451148987, 0.30588236451148987), (0.75, 0.10196078568696976, 0.10196078568696976), (0.875, 0.0, 0.0), (1.0, 0.0, 0.0)], 'red': [(0.0, 1.0, 1.0), (0.125, 1.0, 1.0), (0.25, 0.99607843160629272, 0.99607843160629272), (0.375, 0.99607843160629272, 0.99607843160629272), (0.5, 0.99215686321258545, 0.99215686321258545), (0.625, 0.98823529481887817, 0.98823529481887817), (0.75, 0.89019608497619629, 0.89019608497619629), (0.875, 0.74117648601531982, 0.74117648601531982), (1.0, 0.50196081399917603, 0.50196081399917603)]} # The next 7 palettes are from the Yorick scientific visalisation package, # an evolution of the GIST package, both by David H. Munro. # They are released under a BSD-like license (see LICENSE_YORICK in # the license directory of the matplotlib source distribution). _gist_earth_data = {'blue': [(0.0, 0.0, 0.0), (0.0042016808874905109, 0.18039216101169586, 0.18039216101169586), (0.0084033617749810219, 0.22745098173618317, 0.22745098173618317), (0.012605042196810246, 0.27058824896812439, 0.27058824896812439), (0.016806723549962044, 0.31764706969261169, 0.31764706969261169), (0.021008403971791267, 0.36078432202339172, 0.36078432202339172), (0.025210084393620491, 0.40784314274787903, 0.40784314274787903), (0.029411764815449715, 0.45490196347236633, 0.45490196347236633), (0.033613447099924088, 0.45490196347236633, 0.45490196347236633), (0.037815127521753311, 0.45490196347236633, 0.45490196347236633), (0.042016807943582535, 0.45490196347236633, 0.45490196347236633), (0.046218488365411758, 0.45490196347236633, 0.45490196347236633), (0.050420168787240982, 0.45882353186607361, 0.45882353186607361), (0.054621849209070206, 0.45882353186607361, 0.45882353186607361), (0.058823529630899429, 0.45882353186607361, 0.45882353186607361), (0.063025213778018951, 0.45882353186607361, 0.45882353186607361), (0.067226894199848175, 0.45882353186607361, 0.45882353186607361), (0.071428574621677399, 0.46274510025978088, 0.46274510025978088), (0.075630255043506622, 0.46274510025978088, 0.46274510025978088), (0.079831935465335846, 0.46274510025978088, 0.46274510025978088), (0.08403361588716507, 0.46274510025978088, 0.46274510025978088), (0.088235296308994293, 0.46274510025978088, 0.46274510025978088), (0.092436976730823517, 0.46666666865348816, 0.46666666865348816), (0.09663865715265274, 0.46666666865348816, 0.46666666865348816), (0.10084033757448196, 0.46666666865348816, 0.46666666865348816), (0.10504201799631119, 0.46666666865348816, 0.46666666865348816), (0.10924369841814041, 0.46666666865348816, 0.46666666865348816), (0.11344537883996964, 0.47058823704719543, 0.47058823704719543), (0.11764705926179886, 0.47058823704719543, 0.47058823704719543), (0.12184873968362808, 0.47058823704719543, 0.47058823704719543), (0.1260504275560379, 0.47058823704719543, 0.47058823704719543), (0.13025210797786713, 0.47058823704719543, 0.47058823704719543), (0.13445378839969635, 0.47450980544090271, 0.47450980544090271), (0.13865546882152557, 0.47450980544090271, 0.47450980544090271), (0.1428571492433548, 0.47450980544090271, 0.47450980544090271), (0.14705882966518402, 0.47450980544090271, 0.47450980544090271), (0.15126051008701324, 0.47450980544090271, 0.47450980544090271), (0.15546219050884247, 0.47843137383460999, 0.47843137383460999), (0.15966387093067169, 0.47843137383460999, 0.47843137383460999), (0.16386555135250092, 0.47843137383460999, 0.47843137383460999), (0.16806723177433014, 0.47843137383460999, 0.47843137383460999), (0.17226891219615936, 0.47843137383460999, 0.47843137383460999), (0.17647059261798859, 0.48235294222831726, 0.48235294222831726), (0.18067227303981781, 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(0.67226892709732056, 0.32941177487373352, 0.32941177487373352), (0.67647057771682739, 0.32549020648002625, 0.32549020648002625), (0.680672287940979, 0.32156863808631897, 0.32156863808631897), (0.68487393856048584, 0.31764706969261169, 0.31764706969261169), (0.68907564878463745, 0.31372550129890442, 0.31372550129890442), (0.69327729940414429, 0.30980393290519714, 0.30980393290519714), (0.6974790096282959, 0.30588236451148987, 0.30588236451148987), (0.70168066024780273, 0.30196079611778259, 0.30196079611778259), (0.70588237047195435, 0.29803922772407532, 0.29803922772407532), (0.71008402109146118, 0.29411765933036804, 0.29411765933036804), (0.71428573131561279, 0.29019609093666077, 0.29019609093666077), (0.71848738193511963, 0.28627452254295349, 0.28627452254295349), (0.72268909215927124, 0.28235295414924622, 0.28235295414924622), (0.72689074277877808, 0.27450981736183167, 0.27450981736183167), (0.73109245300292969, 0.27058824896812439, 0.27058824896812439), (0.73529410362243652, 0.26666668057441711, 0.26666668057441711), (0.73949581384658813, 0.26274511218070984, 0.26274511218070984), (0.74369746446609497, 0.25882354378700256, 0.25882354378700256), (0.74789917469024658, 0.25490197539329529, 0.25490197539329529), (0.75210082530975342, 0.25098040699958801, 0.25098040699958801), (0.75630253553390503, 0.24705882370471954, 0.24705882370471954), (0.76050418615341187, 0.24313725531101227, 0.24313725531101227), (0.76470589637756348, 0.23921568691730499, 0.23921568691730499), (0.76890754699707031, 0.23529411852359772, 0.23529411852359772), (0.77310925722122192, 0.23137255012989044, 0.23137255012989044), (0.77731090784072876, 0.22745098173618317, 0.22745098173618317), (0.78151261806488037, 0.22352941334247589, 0.22352941334247589), (0.78571426868438721, 0.21960784494876862, 0.21960784494876862), (0.78991597890853882, 0.21176470816135406, 0.21176470816135406), (0.79411762952804565, 0.20784313976764679, 0.20784313976764679), (0.79831933975219727, 0.20392157137393951, 0.20392157137393951), (0.8025209903717041, 0.20000000298023224, 0.20000000298023224), (0.80672270059585571, 0.19607843458652496, 0.19607843458652496), (0.81092435121536255, 0.19215686619281769, 0.19215686619281769), (0.81512606143951416, 0.18823529779911041, 0.18823529779911041), (0.819327712059021, 0.18431372940540314, 0.18431372940540314), (0.82352942228317261, 0.18039216101169586, 0.18039216101169586), (0.82773107290267944, 0.17647059261798859, 0.17647059261798859), (0.83193278312683105, 0.17254902422428131, 0.17254902422428131), (0.83613443374633789, 0.16862745583057404, 0.16862745583057404), (0.8403361439704895, 0.16470588743686676, 0.16470588743686676), (0.84453779458999634, 0.16078431904315948, 0.16078431904315948), (0.84873950481414795, 0.15686275064945221, 0.15686275064945221), (0.85294115543365479, 0.14901961386203766, 0.14901961386203766), (0.8571428656578064, 0.14509804546833038, 0.14509804546833038), (0.86134451627731323, 0.14117647707462311, 0.14117647707462311), (0.86554622650146484, 0.13725490868091583, 0.13725490868091583), (0.86974787712097168, 0.13333334028720856, 0.13333334028720856), (0.87394958734512329, 0.12941177189350128, 0.12941177189350128), (0.87815123796463013, 0.12549020349979401, 0.12549020349979401), (0.88235294818878174, 0.12156862765550613, 0.12156862765550613), (0.88655459880828857, 0.11764705926179886, 0.11764705926179886), (0.89075630903244019, 0.11372549086809158, 0.11372549086809158), (0.89495795965194702, 0.10980392247438431, 0.10980392247438431), (0.89915966987609863, 0.10588235408067703, 0.10588235408067703), (0.90336132049560547, 0.10196078568696976, 0.10196078568696976), (0.90756303071975708, 0.098039217293262482, 0.098039217293262482), (0.91176468133926392, 0.094117648899555206, 0.094117648899555206), (0.91596639156341553, 0.086274512112140656, 0.086274512112140656), (0.92016804218292236, 0.08235294371843338, 0.08235294371843338), (0.92436975240707397, 0.078431375324726105, 0.078431375324726105), (0.92857140302658081, 0.074509806931018829, 0.074509806931018829), (0.93277311325073242, 0.070588238537311554, 0.070588238537311554), (0.93697476387023926, 0.066666670143604279, 0.066666670143604279), (0.94117647409439087, 0.062745101749897003, 0.062745101749897003), (0.94537812471389771, 0.058823529630899429, 0.058823529630899429), (0.94957983493804932, 0.054901961237192154, 0.054901961237192154), (0.95378148555755615, 0.050980392843484879, 0.050980392843484879), (0.95798319578170776, 0.047058824449777603, 0.047058824449777603), (0.9621848464012146, 0.043137256056070328, 0.043137256056070328), (0.96638655662536621, 0.039215687662363052, 0.039215687662363052), (0.97058820724487305, 0.035294119268655777, 0.035294119268655777), (0.97478991746902466, 0.031372550874948502, 0.031372550874948502), (0.97899156808853149, 0.023529412224888802, 0.023529412224888802), (0.98319327831268311, 0.019607843831181526, 0.019607843831181526), (0.98739492893218994, 0.015686275437474251, 0.015686275437474251), (0.99159663915634155, 0.011764706112444401, 0.011764706112444401), (0.99579828977584839, 0.0078431377187371254, 0.0078431377187371254), (1.0, 0.0039215688593685627, 0.0039215688593685627)]} Accent = colors.LinearSegmentedColormap('Accent', _Accent_data, LUTSIZE) Blues = colors.LinearSegmentedColormap('Blues', _Blues_data, LUTSIZE) BrBG = colors.LinearSegmentedColormap('BrBG', _BrBG_data, LUTSIZE) BuGn = colors.LinearSegmentedColormap('BuGn', _BuGn_data, LUTSIZE) BuPu = colors.LinearSegmentedColormap('BuPu', _BuPu_data, LUTSIZE) Dark2 = colors.LinearSegmentedColormap('Dark2', _Dark2_data, LUTSIZE) GnBu = colors.LinearSegmentedColormap('GnBu', _GnBu_data, LUTSIZE) Greens = colors.LinearSegmentedColormap('Greens', _Greens_data, LUTSIZE) Greys = colors.LinearSegmentedColormap('Greys', _Greys_data, LUTSIZE) Oranges = colors.LinearSegmentedColormap('Oranges', _Oranges_data, LUTSIZE) OrRd = colors.LinearSegmentedColormap('OrRd', _OrRd_data, LUTSIZE) Paired = colors.LinearSegmentedColormap('Paired', _Paired_data, LUTSIZE) Pastel1 = colors.LinearSegmentedColormap('Pastel1', _Pastel1_data, LUTSIZE) Pastel2 = colors.LinearSegmentedColormap('Pastel2', _Pastel2_data, LUTSIZE) PiYG = colors.LinearSegmentedColormap('PiYG', _PiYG_data, LUTSIZE) PRGn = colors.LinearSegmentedColormap('PRGn', _PRGn_data, LUTSIZE) PuBu = colors.LinearSegmentedColormap('PuBu', _PuBu_data, LUTSIZE) PuBuGn = colors.LinearSegmentedColormap('PuBuGn', _PuBuGn_data, LUTSIZE) PuOr = colors.LinearSegmentedColormap('PuOr', _PuOr_data, LUTSIZE) PuRd = colors.LinearSegmentedColormap('PuRd', _PuRd_data, LUTSIZE) Purples = colors.LinearSegmentedColormap('Purples', _Purples_data, LUTSIZE) RdBu = colors.LinearSegmentedColormap('RdBu', _RdBu_data, LUTSIZE) RdGy = colors.LinearSegmentedColormap('RdGy', _RdGy_data, LUTSIZE) RdPu = colors.LinearSegmentedColormap('RdPu', _RdPu_data, LUTSIZE) RdYlBu = colors.LinearSegmentedColormap('RdYlBu', _RdYlBu_data, LUTSIZE) RdYlGn = colors.LinearSegmentedColormap('RdYlGn', _RdYlGn_data, LUTSIZE) Reds = colors.LinearSegmentedColormap('Reds', _Reds_data, LUTSIZE) Set1 = colors.LinearSegmentedColormap('Set1', _Set1_data, LUTSIZE) Set2 = colors.LinearSegmentedColormap('Set2', _Set2_data, LUTSIZE) Set3 = colors.LinearSegmentedColormap('Set3', _Set3_data, LUTSIZE) Spectral = colors.LinearSegmentedColormap('Spectral', _Spectral_data, LUTSIZE) YlGn = colors.LinearSegmentedColormap('YlGn', _YlGn_data, LUTSIZE) YlGnBu = colors.LinearSegmentedColormap('YlGnBu', _YlGnBu_data, LUTSIZE) YlOrBr = colors.LinearSegmentedColormap('YlOrBr', _YlOrBr_data, LUTSIZE) YlOrRd = colors.LinearSegmentedColormap('YlOrRd', _YlOrRd_data, LUTSIZE) gist_earth = colors.LinearSegmentedColormap('gist_earth', _gist_earth_data, LUTSIZE) gist_gray = colors.LinearSegmentedColormap('gist_gray', _gist_gray_data, LUTSIZE) gist_heat = colors.LinearSegmentedColormap('gist_heat', _gist_heat_data, LUTSIZE) gist_ncar = colors.LinearSegmentedColormap('gist_ncar', _gist_ncar_data, LUTSIZE) gist_rainbow = colors.LinearSegmentedColormap('gist_rainbow', _gist_rainbow_data, LUTSIZE) gist_stern = colors.LinearSegmentedColormap('gist_stern', _gist_stern_data, LUTSIZE) gist_yarg = colors.LinearSegmentedColormap('gist_yarg', _gist_yarg_data, LUTSIZE) datad['Accent']=_Accent_data datad['Blues']=_Blues_data datad['BrBG']=_BrBG_data datad['BuGn']=_BuGn_data datad['BuPu']=_BuPu_data datad['Dark2']=_Dark2_data datad['GnBu']=_GnBu_data datad['Greens']=_Greens_data datad['Greys']=_Greys_data datad['Oranges']=_Oranges_data datad['OrRd']=_OrRd_data datad['Paired']=_Paired_data datad['Pastel1']=_Pastel1_data datad['Pastel2']=_Pastel2_data datad['PiYG']=_PiYG_data datad['PRGn']=_PRGn_data datad['PuBu']=_PuBu_data datad['PuBuGn']=_PuBuGn_data datad['PuOr']=_PuOr_data datad['PuRd']=_PuRd_data datad['Purples']=_Purples_data datad['RdBu']=_RdBu_data datad['RdGy']=_RdGy_data datad['RdPu']=_RdPu_data datad['RdYlBu']=_RdYlBu_data datad['RdYlGn']=_RdYlGn_data datad['Reds']=_Reds_data datad['Set1']=_Set1_data datad['Set2']=_Set2_data datad['Set3']=_Set3_data datad['Spectral']=_Spectral_data datad['YlGn']=_YlGn_data datad['YlGnBu']=_YlGnBu_data datad['YlOrBr']=_YlOrBr_data datad['YlOrRd']=_YlOrRd_data datad['gist_earth']=_gist_earth_data datad['gist_gray']=_gist_gray_data datad['gist_heat']=_gist_heat_data datad['gist_ncar']=_gist_ncar_data datad['gist_rainbow']=_gist_rainbow_data datad['gist_stern']=_gist_stern_data datad['gist_yarg']=_gist_yarg_data # reverse all the colormaps. # reversed colormaps have '_r' appended to the name. def revcmap(data): data_r = {} for key, val in data.iteritems(): valnew = [(1.-a, b, c) for a, b, c in reversed(val)] data_r[key] = valnew return data_r cmapnames = datad.keys() for cmapname in cmapnames: cmapname_r = cmapname+'_r' cmapdat_r = revcmap(datad[cmapname]) datad[cmapname_r] = cmapdat_r locals()[cmapname_r] = colors.LinearSegmentedColormap(cmapname_r, cmapdat_r, LUTSIZE)	375,423	Python	.py	5,825	61.546609	92	0.782012	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,265	table.py	numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/table.py	""" Place a table below the x-axis at location loc. The table consists of a grid of cells. The grid need not be rectangular and can have holes. Cells are added by specifying their row and column. For the purposes of positioning the cell at (0, 0) is assumed to be at the top left and the cell at (max_row, max_col) is assumed to be at bottom right. You can add additional cells outside this range to have convenient ways of positioning more interesting grids. Author : John Gill <jng@europe.renre.com> Copyright : 2004 John Gill and John Hunter License : matplotlib license """ from __future__ import division import warnings import artist from artist import Artist from patches import Rectangle from cbook import is_string_like from text import Text from transforms import Bbox class Cell(Rectangle): """ A cell is a Rectangle with some associated text. """ PAD = 0.1 # padding between text and rectangle def __init__(self, xy, width, height, edgecolor='k', facecolor='w', fill=True, text='', loc=None, fontproperties=None ): # Call base Rectangle.__init__(self, xy, width=width, height=height, edgecolor=edgecolor, facecolor=facecolor, ) self.set_clip_on(False) # Create text object if loc is None: loc = 'right' self._loc = loc self._text = Text(x=xy[0], y=xy[1], text=text, fontproperties=fontproperties) self._text.set_clip_on(False) def set_transform(self, trans): Rectangle.set_transform(self, trans) # the text does not get the transform! def set_figure(self, fig): Rectangle.set_figure(self, fig) self._text.set_figure(fig) def get_text(self): 'Return the cell Text intance' return self._text def set_fontsize(self, size): self._text.set_fontsize(size) def get_fontsize(self): 'Return the cell fontsize' return self._text.get_fontsize() def auto_set_font_size(self, renderer): """ Shrink font size until text fits. """ fontsize = self.get_fontsize() required = self.get_required_width(renderer) while fontsize > 1 and required > self.get_width(): fontsize -= 1 self.set_fontsize(fontsize) required = self.get_required_width(renderer) return fontsize def draw(self, renderer): if not self.get_visible(): return # draw the rectangle Rectangle.draw(self, renderer) # position the text self._set_text_position(renderer) self._text.draw(renderer) def _set_text_position(self, renderer): """ Set text up so it draws in the right place. Currently support 'left', 'center' and 'right' """ bbox = self.get_window_extent(renderer) l, b, w, h = bbox.bounds # draw in center vertically self._text.set_verticalalignment('center') y = b + (h / 2.0) # now position horizontally if self._loc == 'center': self._text.set_horizontalalignment('center') x = l + (w / 2.0) elif self._loc == 'left': self._text.set_horizontalalignment('left') x = l + (w * self.PAD) else: self._text.set_horizontalalignment('right') x = l + (w * (1.0 - self.PAD)) self._text.set_position((x, y)) def get_text_bounds(self, renderer): """ Get text bounds in axes co-ordinates. """ bbox = self._text.get_window_extent(renderer) bboxa = bbox.inverse_transformed(self.get_data_transform()) return bboxa.bounds def get_required_width(self, renderer): """ Get width required for this cell. """ l,b,w,h = self.get_text_bounds(renderer) return w * (1.0 + (2.0 * self.PAD)) def set_text_props(self, *kwargs): 'update the text properties with kwargs' self._text.update(kwargs) class Table(Artist): """ Create a table of cells. Table can have (optional) row and column headers. Each entry in the table can be either text or patches. Column widths and row heights for the table can be specifified. Return value is a sequence of text, line and patch instances that make up the table """ codes = {'best' : 0, 'upper right' : 1, # default 'upper left' : 2, 'lower left' : 3, 'lower right' : 4, 'center left' : 5, 'center right' : 6, 'lower center' : 7, 'upper center' : 8, 'center' : 9, 'top right' : 10, 'top left' : 11, 'bottom left' : 12, 'bottom right' : 13, 'right' : 14, 'left' : 15, 'top' : 16, 'bottom' : 17, } FONTSIZE = 10 AXESPAD = 0.02 # the border between the axes and table edge def __init__(self, ax, loc=None, bbox=None): Artist.__init__(self) if is_string_like(loc) and loc not in self.codes: warnings.warn('Unrecognized location %s. Falling back on bottom; valid locations are\n%s\t' %(loc, '\n\t'.join(self.codes.keys()))) loc = 'bottom' if is_string_like(loc): loc = self.codes.get(loc, 1) self.set_figure(ax.figure) self._axes = ax self._loc = loc self._bbox = bbox # use axes coords self.set_transform(ax.transAxes) self._texts = [] self._cells = {} self._autoRows = [] self._autoColumns = [] self._autoFontsize = True self._cachedRenderer = None def add_cell(self, row, col, args, *kwargs): """ Add a cell to the table. """ xy = (0,0) cell = Cell(xy, args, *kwargs) cell.set_figure(self.figure) cell.set_transform(self.get_transform()) cell.set_clip_on(False) self._cells[(row, col)] = cell def _approx_text_height(self): return self.FONTSIZE/72.0self.figure.dpi/self._axes.bbox.height * 1.2 def draw(self, renderer): # Need a renderer to do hit tests on mouseevent; assume the last one will do if renderer is None: renderer = self._cachedRenderer if renderer is None: raise RuntimeError('No renderer defined') self._cachedRenderer = renderer if not self.get_visible(): return renderer.open_group('table') self._update_positions(renderer) keys = self._cells.keys() keys.sort() for key in keys: self._cells[key].draw(renderer) #for c in self._cells.itervalues(): # c.draw(renderer) renderer.close_group('table') def _get_grid_bbox(self, renderer): """Get a bbox, in axes co-ordinates for the cells. Only include those in the range (0,0) to (maxRow, maxCol)""" boxes = [self._cells[pos].get_window_extent(renderer) for pos in self._cells.keys() if pos[0] >= 0 and pos[1] >= 0] bbox = Bbox.union(boxes) return bbox.inverse_transformed(self.get_transform()) def contains(self,mouseevent): """Test whether the mouse event occurred in the table. Returns T/F, {} """ if callable(self._contains): return self._contains(self,mouseevent) # TODO: Return index of the cell containing the cursor so that the user # doesn't have to bind to each one individually. if self._cachedRenderer is not None: boxes = [self._cells[pos].get_window_extent(self._cachedRenderer) for pos in self._cells.keys() if pos[0] >= 0 and pos[1] >= 0] bbox = bbox_all(boxes) return bbox.contains(mouseevent.x,mouseevent.y),{} else: return False,{} def get_children(self): 'Return the Artists contained by the table' return self._cells.values() get_child_artists = get_children # backward compatibility def get_window_extent(self, renderer): 'Return the bounding box of the table in window coords' boxes = [c.get_window_extent(renderer) for c in self._cells] return bbox_all(boxes) def _do_cell_alignment(self): """ Calculate row heights and column widths. Position cells accordingly. """ # Calculate row/column widths widths = {} heights = {} for (row, col), cell in self._cells.iteritems(): height = heights.setdefault(row, 0.0) heights[row] = max(height, cell.get_height()) width = widths.setdefault(col, 0.0) widths[col] = max(width, cell.get_width()) # work out left position for each column xpos = 0 lefts = {} cols = widths.keys() cols.sort() for col in cols: lefts[col] = xpos xpos += widths[col] ypos = 0 bottoms = {} rows = heights.keys() rows.sort() rows.reverse() for row in rows: bottoms[row] = ypos ypos += heights[row] # set cell positions for (row, col), cell in self._cells.iteritems(): cell.set_x(lefts[col]) cell.set_y(bottoms[row]) def auto_set_column_width(self, col): self._autoColumns.append(col) def _auto_set_column_width(self, col, renderer): """ Automagically set width for column. """ cells = [key for key in self._cells if key[1] == col] # find max width width = 0 for cell in cells: c = self._cells[cell] width = max(c.get_required_width(renderer), width) # Now set the widths for cell in cells: self._cells[cell].set_width(width) def auto_set_font_size(self, value=True): """ Automatically set font size. """ self._autoFontsize = value def _auto_set_font_size(self, renderer): if len(self._cells) == 0: return fontsize = self._cells.values()[0].get_fontsize() cells = [] for key, cell in self._cells.iteritems(): # ignore auto-sized columns if key[1] in self._autoColumns: continue size = cell.auto_set_font_size(renderer) fontsize = min(fontsize, size) cells.append(cell) # now set all fontsizes equal for cell in self._cells.itervalues(): cell.set_fontsize(fontsize) def scale(self, xscale, yscale): """ Scale column widths by xscale and row heights by yscale. """ for c in self._cells.itervalues(): c.set_width(c.get_width() * xscale) c.set_height(c.get_height() * yscale) def set_fontsize(self, size): """ Set the fontsize of the cell text ACCEPTS: a float in points """ for cell in self._cells.itervalues(): cell.set_fontsize(size) def _offset(self, ox, oy): 'Move all the artists by ox,oy (axes coords)' for c in self._cells.itervalues(): x, y = c.get_x(), c.get_y() c.set_x(x+ox) c.set_y(y+oy) def _update_positions(self, renderer): # called from renderer to allow more precise estimates of # widths and heights with get_window_extent # Do any auto width setting for col in self._autoColumns: self._auto_set_column_width(col, renderer) if self._autoFontsize: self._auto_set_font_size(renderer) # Align all the cells self._do_cell_alignment() bbox = self._get_grid_bbox(renderer) l,b,w,h = bbox.bounds if self._bbox is not None: # Position according to bbox rl, rb, rw, rh = self._bbox self.scale(rw/w, rh/h) ox = rl - l oy = rb - b self._do_cell_alignment() else: # Position using loc (BEST, UR, UL, LL, LR, CL, CR, LC, UC, C, TR, TL, BL, BR, R, L, T, B) = range(len(self.codes)) # defaults for center ox = (0.5-w/2)-l oy = (0.5-h/2)-b if self._loc in (UL, LL, CL): # left ox = self.AXESPAD - l if self._loc in (BEST, UR, LR, R, CR): # right ox = 1 - (l + w + self.AXESPAD) if self._loc in (BEST, UR, UL, UC): # upper oy = 1 - (b + h + self.AXESPAD) if self._loc in (LL, LR, LC): # lower oy = self.AXESPAD - b if self._loc in (LC, UC, C): # center x ox = (0.5-w/2)-l if self._loc in (CL, CR, C): # center y oy = (0.5-h/2)-b if self._loc in (TL, BL, L): # out left ox = - (l + w) if self._loc in (TR, BR, R): # out right ox = 1.0 - l if self._loc in (TR, TL, T): # out top oy = 1.0 - b if self._loc in (BL, BR, B): # out bottom oy = - (b + h) self._offset(ox, oy) def get_celld(self): 'return a dict of cells in the table' return self._cells def table(ax, cellText=None, cellColours=None, cellLoc='right', colWidths=None, rowLabels=None, rowColours=None, rowLoc='left', colLabels=None, colColours=None, colLoc='center', loc='bottom', bbox=None): """ TABLE(cellText=None, cellColours=None, cellLoc='right', colWidths=None, rowLabels=None, rowColours=None, rowLoc='left', colLabels=None, colColours=None, colLoc='center', loc='bottom', bbox=None) Factory function to generate a Table instance. Thanks to John Gill for providing the class and table. """ # Check we have some cellText if cellText is None: # assume just colours are needed rows = len(cellColours) cols = len(cellColours[0]) cellText = [[''] * rows] * cols rows = len(cellText) cols = len(cellText[0]) for row in cellText: assert len(row) == cols if cellColours is not None: assert len(cellColours) == rows for row in cellColours: assert len(row) == cols else: cellColours = ['w' * cols] * rows # Set colwidths if not given if colWidths is None: colWidths = [1.0/cols] * cols # Check row and column labels rowLabelWidth = 0 if rowLabels is None: if rowColours is not None: rowLabels = [''] * cols rowLabelWidth = colWidths[0] elif rowColours is None: rowColours = 'w' * rows if rowLabels is not None: assert len(rowLabels) == rows offset = 0 if colLabels is None: if colColours is not None: colLabels = [''] * rows offset = 1 elif colColours is None: colColours = 'w' * cols offset = 1 if rowLabels is not None: assert len(rowLabels) == rows # Set up cell colours if not given if cellColours is None: cellColours = ['w' * cols] * rows # Now create the table table = Table(ax, loc, bbox) height = table._approx_text_height() # Add the cells for row in xrange(rows): for col in xrange(cols): table.add_cell(row+offset, col, width=colWidths[col], height=height, text=cellText[row][col], facecolor=cellColours[row][col], loc=cellLoc) # Do column labels if colLabels is not None: for col in xrange(cols): table.add_cell(0, col, width=colWidths[col], height=height, text=colLabels[col], facecolor=colColours[col], loc=colLoc) # Do row labels if rowLabels is not None: for row in xrange(rows): table.add_cell(row+offset, -1, width=rowLabelWidth or 1e-15, height=height, text=rowLabels[row], facecolor=rowColours[row], loc=rowLoc) if rowLabelWidth == 0: table.auto_set_column_width(-1) ax.add_table(table) return table artist.kwdocd['Table'] = artist.kwdoc(Table)	16,757	Python	.py	431	29.053364	143	0.561941	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,266	lines.py	numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/lines.py	""" This module contains all the 2D line class which can draw with a variety of line styles, markers and colors. """ # TODO: expose cap and join style attrs from __future__ import division import numpy as np from numpy import ma from matplotlib import verbose import artist from artist import Artist from cbook import iterable, is_string_like, is_numlike, ls_mapper, dedent,\ flatten from colors import colorConverter from path import Path from transforms import Affine2D, Bbox, TransformedPath, IdentityTransform from matplotlib import rcParams # special-purpose marker identifiers: (TICKLEFT, TICKRIGHT, TICKUP, TICKDOWN, CARETLEFT, CARETRIGHT, CARETUP, CARETDOWN) = range(8) # COVERAGE NOTE: Never called internally or from examples def unmasked_index_ranges(mask, compressed = True): warnings.warn("Import this directly from matplotlib.cbook", DeprecationWarning) # Warning added 2008/07/22 from matplotlib.cbook import unmasked_index_ranges as _unmasked_index_ranges return _unmasked_index_ranges(mask, compressed=compressed) def segment_hits(cx, cy, x, y, radius): """ Determine if any line segments are within radius of a point. Returns the list of line segments that are within that radius. """ # Process single points specially if len(x) < 2: res, = np.nonzero( (cx - x)2 + (cy - y)2 <= radius2 ) return res # We need to lop the last element off a lot. xr,yr = x[:-1],y[:-1] # Only look at line segments whose nearest point to C on the line # lies within the segment. dx,dy = x[1:]-xr, y[1:]-yr Lnorm_sq = dx2+dy*2 # Possibly want to eliminate Lnorm==0 u = ( (cx-xr)dx + (cy-yr)dy )/Lnorm_sq candidates = (u>=0) & (u<=1) #if any(candidates): print "candidates",xr[candidates] # Note that there is a little area near one side of each point # which will be near neither segment, and another which will # be near both, depending on the angle of the lines. The # following radius test eliminates these ambiguities. point_hits = (cx - x)2 + (cy - y)2 <= radius2 #if any(point_hits): print "points",xr[candidates] candidates = candidates & ~(point_hits[:-1] \| point_hits[1:]) # For those candidates which remain, determine how far they lie away # from the line. px,py = xr+udx,yr+udy line_hits = (cx-px)2 + (cy-py)2 <= radius2 #if any(line_hits): print "lines",xr[candidates] line_hits = line_hits & candidates points, = point_hits.ravel().nonzero() lines, = line_hits.ravel().nonzero() #print points,lines return np.concatenate((points,lines)) class Line2D(Artist): """ A line - the line can have both a solid linestyle connecting all the vertices, and a marker at each vertex. Additionally, the drawing of the solid line is influenced by the drawstyle, eg one can create "stepped" lines in various styles. """ lineStyles = _lineStyles = { # hidden names deprecated '-' : '_draw_solid', '--' : '_draw_dashed', '-.' : '_draw_dash_dot', ':' : '_draw_dotted', 'None' : '_draw_nothing', ' ' : '_draw_nothing', '' : '_draw_nothing', } _drawStyles_l = { 'default' : '_draw_lines', 'steps-mid' : '_draw_steps_mid', 'steps-pre' : '_draw_steps_pre', 'steps-post' : '_draw_steps_post', } _drawStyles_s = { 'steps' : '_draw_steps_pre', } drawStyles = {} drawStyles.update(_drawStyles_l) drawStyles.update(_drawStyles_s) markers = _markers = { # hidden names deprecated '.' : '_draw_point', ',' : '_draw_pixel', 'o' : '_draw_circle', 'v' : '_draw_triangle_down', '^' : '_draw_triangle_up', '<' : '_draw_triangle_left', '>' : '_draw_triangle_right', '1' : '_draw_tri_down', '2' : '_draw_tri_up', '3' : '_draw_tri_left', '4' : '_draw_tri_right', 's' : '_draw_square', 'p' : '_draw_pentagon', '' : '_draw_star', 'h' : '_draw_hexagon1', 'H' : '_draw_hexagon2', '+' : '_draw_plus', 'x' : '_draw_x', 'D' : '_draw_diamond', 'd' : '_draw_thin_diamond', '\|' : '_draw_vline', '_' : '_draw_hline', TICKLEFT : '_draw_tickleft', TICKRIGHT : '_draw_tickright', TICKUP : '_draw_tickup', TICKDOWN : '_draw_tickdown', CARETLEFT : '_draw_caretleft', CARETRIGHT : '_draw_caretright', CARETUP : '_draw_caretup', CARETDOWN : '_draw_caretdown', 'None' : '_draw_nothing', ' ' : '_draw_nothing', '' : '_draw_nothing', } filled_markers = ('o', '^', 'v', '<', '>', 's', 'd', 'D', 'h', 'H', 'p', '') zorder = 2 validCap = ('butt', 'round', 'projecting') validJoin = ('miter', 'round', 'bevel') def __str__(self): if self._label != "": return "Line2D(%s)"%(self._label) elif hasattr(self, '_x') and len(self._x) > 3: return "Line2D((%g,%g),(%g,%g),...,(%g,%g))"\ %(self._x[0],self._y[0],self._x[0],self._y[0],self._x[-1],self._y[-1]) elif hasattr(self, '_x'): return "Line2D(%s)"\ %(",".join(["(%g,%g)"%(x,y) for x,y in zip(self._x,self._y)])) else: return "Line2D()" def __init__(self, xdata, ydata, linewidth = None, # all Nones default to rc linestyle = None, color = None, marker = None, markersize = None, markeredgewidth = None, markeredgecolor = None, markerfacecolor = None, antialiased = None, dash_capstyle = None, solid_capstyle = None, dash_joinstyle = None, solid_joinstyle = None, pickradius = 5, drawstyle = None, kwargs ): """ Create a :class:`~matplotlib.lines.Line2D` instance with x* and y data in sequences xdata, ydata. The kwargs are :class:`~matplotlib.lines.Line2D` properties: %(Line2D)s See :meth:`set_linestyle` for a decription of the line styles, :meth:`set_marker` for a description of the markers, and :meth:`set_drawstyle` for a description of the draw styles. """ Artist.__init__(self) #convert sequences to numpy arrays if not iterable(xdata): raise RuntimeError('xdata must be a sequence') if not iterable(ydata): raise RuntimeError('ydata must be a sequence') if linewidth is None : linewidth=rcParams['lines.linewidth'] if linestyle is None : linestyle=rcParams['lines.linestyle'] if marker is None : marker=rcParams['lines.marker'] if color is None : color=rcParams['lines.color'] if markersize is None : markersize=rcParams['lines.markersize'] if antialiased is None : antialiased=rcParams['lines.antialiased'] if dash_capstyle is None : dash_capstyle=rcParams['lines.dash_capstyle'] if dash_joinstyle is None : dash_joinstyle=rcParams['lines.dash_joinstyle'] if solid_capstyle is None : solid_capstyle=rcParams['lines.solid_capstyle'] if solid_joinstyle is None : solid_joinstyle=rcParams['lines.solid_joinstyle'] if drawstyle is None : drawstyle='default' self.set_dash_capstyle(dash_capstyle) self.set_dash_joinstyle(dash_joinstyle) self.set_solid_capstyle(solid_capstyle) self.set_solid_joinstyle(solid_joinstyle) self.set_linestyle(linestyle) self.set_drawstyle(drawstyle) self.set_linewidth(linewidth) self.set_color(color) self.set_marker(marker) self.set_antialiased(antialiased) self.set_markersize(markersize) self._dashSeq = None self.set_markerfacecolor(markerfacecolor) self.set_markeredgecolor(markeredgecolor) self.set_markeredgewidth(markeredgewidth) self._point_size_reduction = 0.5 self.verticalOffset = None # update kwargs before updating data to give the caller a # chance to init axes (and hence unit support) self.update(kwargs) self.pickradius = pickradius if is_numlike(self._picker): self.pickradius = self._picker self._xorig = np.asarray([]) self._yorig = np.asarray([]) self._invalid = True self.set_data(xdata, ydata) def contains(self, mouseevent): """ Test whether the mouse event occurred on the line. The pick radius determines the precision of the location test (usually within five points of the value). Use :meth:`~matplotlib.lines.Line2D.get_pickradius` or :meth:`~matplotlib.lines.Line2D.set_pickradius` to view or modify it. Returns True if any values are within the radius along with ``{'ind': pointlist}``, where pointlist is the set of points within the radius. TODO: sort returned indices by distance """ if callable(self._contains): return self._contains(self,mouseevent) if not is_numlike(self.pickradius): raise ValueError,"pick radius should be a distance" # Make sure we have data to plot if self._invalid: self.recache() if len(self._xy)==0: return False,{} # Convert points to pixels path, affine = self._transformed_path.get_transformed_path_and_affine() path = affine.transform_path(path) xy = path.vertices xt = xy[:, 0] yt = xy[:, 1] # Convert pick radius from points to pixels if self.figure == None: warning.warn('no figure set when check if mouse is on line') pixels = self.pickradius else: pixels = self.figure.dpi/72. * self.pickradius # Check for collision if self._linestyle in ['None',None]: # If no line, return the nearby point(s) d = (xt-mouseevent.x)2 + (yt-mouseevent.y)2 ind, = np.nonzero(np.less_equal(d, pixels2)) else: # If line, return the nearby segment(s) ind = segment_hits(mouseevent.x,mouseevent.y,xt,yt,pixels) # Debugging message if False and self._label != u'': print "Checking line",self._label,"at",mouseevent.x,mouseevent.y print 'xt', xt print 'yt', yt #print 'dx,dy', (xt-mouseevent.x)2., (yt-mouseevent.y)*2. print 'ind',ind # Return the point(s) within radius return len(ind)>0,dict(ind=ind) def get_pickradius(self): 'return the pick radius used for containment tests' return self.pickradius def setpickradius(self,d): """Sets the pick radius used for containment tests ACCEPTS: float distance in points """ self.pickradius = d def set_picker(self,p): """Sets the event picker details for the line. ACCEPTS: float distance in points or callable pick function ``fn(artist, event)`` """ if callable(p): self._contains = p else: self.pickradius = p self._picker = p def get_window_extent(self, renderer): bbox = Bbox.unit() bbox.update_from_data_xy(self.get_transform().transform(self.get_xydata()), ignore=True) # correct for marker size, if any if self._marker is not None: ms = (self._markersize / 72.0 self.figure.dpi) * 0.5 bbox = bbox.padded(ms) return bbox def set_axes(self, ax): Artist.set_axes(self, ax) if ax.xaxis is not None: self._xcid = ax.xaxis.callbacks.connect('units', self.recache) if ax.yaxis is not None: self._ycid = ax.yaxis.callbacks.connect('units', self.recache) set_axes.__doc__ = Artist.set_axes.__doc__ def set_data(self, args): """ Set the x and y data ACCEPTS: 2D array """ if len(args)==1: x, y = args[0] else: x, y = args not_masked = 0 if not ma.isMaskedArray(x): x = np.asarray(x) not_masked += 1 if not ma.isMaskedArray(y): y = np.asarray(y) not_masked += 1 if (not_masked < 2 or (x is not self._xorig and (x.shape != self._xorig.shape or np.any(x != self._xorig))) or (y is not self._yorig and (y.shape != self._yorig.shape or np.any(y != self._yorig)))): self._xorig = x self._yorig = y self._invalid = True def recache(self): #if self.axes is None: print 'recache no axes' #else: print 'recache units', self.axes.xaxis.units, self.axes.yaxis.units if ma.isMaskedArray(self._xorig) or ma.isMaskedArray(self._yorig): x = ma.asarray(self.convert_xunits(self._xorig), float) y = ma.asarray(self.convert_yunits(self._yorig), float) x = ma.ravel(x) y = ma.ravel(y) else: x = np.asarray(self.convert_xunits(self._xorig), float) y = np.asarray(self.convert_yunits(self._yorig), float) x = np.ravel(x) y = np.ravel(y) if len(x)==1 and len(y)>1: x = x np.ones(y.shape, float) if len(y)==1 and len(x)>1: y = y * np.ones(x.shape, float) if len(x) != len(y): raise RuntimeError('xdata and ydata must be the same length') x = x.reshape((len(x), 1)) y = y.reshape((len(y), 1)) if ma.isMaskedArray(x) or ma.isMaskedArray(y): self._xy = ma.concatenate((x, y), 1) else: self._xy = np.concatenate((x, y), 1) self._x = self._xy[:, 0] # just a view self._y = self._xy[:, 1] # just a view # Masked arrays are now handled by the Path class itself self._path = Path(self._xy) self._transformed_path = TransformedPath(self._path, self.get_transform()) self._invalid = False def set_transform(self, t): """ set the Transformation instance used by this artist ACCEPTS: a :class:`matplotlib.transforms.Transform` instance """ Artist.set_transform(self, t) self._invalid = True # self._transformed_path = TransformedPath(self._path, self.get_transform()) def _is_sorted(self, x): "return true if x is sorted" if len(x)<2: return 1 return np.alltrue(x[1:]-x[0:-1]>=0) def draw(self, renderer): if self._invalid: self.recache() renderer.open_group('line2d') if not self._visible: return gc = renderer.new_gc() self._set_gc_clip(gc) gc.set_foreground(self._color) gc.set_antialiased(self._antialiased) gc.set_linewidth(self._linewidth) gc.set_alpha(self._alpha) if self.is_dashed(): cap = self._dashcapstyle join = self._dashjoinstyle else: cap = self._solidcapstyle join = self._solidjoinstyle gc.set_joinstyle(join) gc.set_capstyle(cap) gc.set_snap(self.get_snap()) funcname = self._lineStyles.get(self._linestyle, '_draw_nothing') if funcname != '_draw_nothing': tpath, affine = self._transformed_path.get_transformed_path_and_affine() self._lineFunc = getattr(self, funcname) funcname = self.drawStyles.get(self._drawstyle, '_draw_lines') drawFunc = getattr(self, funcname) drawFunc(renderer, gc, tpath, affine.frozen()) if self._marker is not None: gc = renderer.new_gc() self._set_gc_clip(gc) gc.set_foreground(self.get_markeredgecolor()) gc.set_linewidth(self._markeredgewidth) gc.set_alpha(self._alpha) funcname = self._markers.get(self._marker, '_draw_nothing') if funcname != '_draw_nothing': tpath, affine = self._transformed_path.get_transformed_points_and_affine() markerFunc = getattr(self, funcname) markerFunc(renderer, gc, tpath, affine.frozen()) renderer.close_group('line2d') def get_antialiased(self): return self._antialiased def get_color(self): return self._color def get_drawstyle(self): return self._drawstyle def get_linestyle(self): return self._linestyle def get_linewidth(self): return self._linewidth def get_marker(self): return self._marker def get_markeredgecolor(self): if (is_string_like(self._markeredgecolor) and self._markeredgecolor == 'auto'): if self._marker in self.filled_markers: return 'k' else: return self._color else: return self._markeredgecolor return self._markeredgecolor def get_markeredgewidth(self): return self._markeredgewidth def get_markerfacecolor(self): if (self._markerfacecolor is None or (is_string_like(self._markerfacecolor) and self._markerfacecolor.lower()=='none') ): return self._markerfacecolor elif (is_string_like(self._markerfacecolor) and self._markerfacecolor.lower() == 'auto'): return self._color else: return self._markerfacecolor def get_markersize(self): return self._markersize def get_data(self, orig=True): """ Return the xdata, ydata. If orig is True, return the original data """ return self.get_xdata(orig=orig), self.get_ydata(orig=orig) def get_xdata(self, orig=True): """ Return the xdata. If orig is True, return the original data, else the processed data. """ if orig: return self._xorig if self._invalid: self.recache() return self._x def get_ydata(self, orig=True): """ Return the ydata. If orig is True, return the original data, else the processed data. """ if orig: return self._yorig if self._invalid: self.recache() return self._y def get_path(self): """ Return the :class:`~matplotlib.path.Path` object associated with this line. """ if self._invalid: self.recache() return self._path def get_xydata(self): """ Return the xy data as a Nx2 numpy array. """ if self._invalid: self.recache() return self._xy def set_antialiased(self, b): """ True if line should be drawin with antialiased rendering ACCEPTS: [True \| False] """ self._antialiased = b def set_color(self, color): """ Set the color of the line ACCEPTS: any matplotlib color """ self._color = color def set_drawstyle(self, drawstyle): """ Set the drawstyle of the plot 'default' connects the points with lines. The steps variants produce step-plots. 'steps' is equivalent to 'steps-pre' and is maintained for backward-compatibility. ACCEPTS: [ 'default' \| 'steps' \| 'steps-pre' \| 'steps-mid' \| 'steps-post' ] """ self._drawstyle = drawstyle def set_linewidth(self, w): """ Set the line width in points ACCEPTS: float value in points """ self._linewidth = w def set_linestyle(self, linestyle): """ Set the linestyle of the line (also accepts drawstyles) ================ ================= linestyle description ================ ================= '-' solid '--' dashed '-.' dash_dot ':' dotted 'None' draw nothing ' ' draw nothing '' draw nothing ================ ================= 'steps' is equivalent to 'steps-pre' and is maintained for backward-compatibility. .. seealso:: :meth:`set_drawstyle` ACCEPTS: [ '-' \| '--' \| '-.' \| ':' \| 'None' \| ' ' \| '' ] and any drawstyle in combination with a linestyle, e.g. 'steps--'. """ # handle long drawstyle names before short ones ! for ds in flatten([k.keys() for k in (self._drawStyles_l, self._drawStyles_s)], is_string_like): if linestyle.startswith(ds): self.set_drawstyle(ds) if len(linestyle) > len(ds): linestyle = linestyle[len(ds):] else: linestyle = '-' if linestyle not in self._lineStyles: if linestyle in ls_mapper: linestyle = ls_mapper[linestyle] else: verbose.report('Unrecognized line style %s, %s' % (linestyle, type(linestyle))) if linestyle in [' ','']: linestyle = 'None' self._linestyle = linestyle def set_marker(self, marker): """ Set the line marker ========== ========================== marker description ========== ========================== '.' point ',' pixel 'o' circle 'v' triangle_down '^' triangle_up '<' triangle_left '>' triangle_right '1' tri_down '2' tri_up '3' tri_left '4' tri_right 's' square 'p' pentagon '' star 'h' hexagon1 'H' hexagon2 '+' plus 'x' x 'D' diamond 'd' thin_diamond '\|' vline '_' hline TICKLEFT tickleft TICKRIGHT tickright TICKUP tickup TICKDOWN tickdown CARETLEFT caretleft CARETRIGHT caretright CARETUP caretup CARETDOWN caretdown 'None' nothing ' ' nothing '' nothing ========== ========================== ACCEPTS: [ '+' \| '' \| ',' \| '.' \| '1' \| '2' \| '3' \| '4' \| '<' \| '>' \| 'D' \| 'H' \| '^' \| '_' \| 'd' \| 'h' \| 'o' \| 'p' \| 's' \| 'v' \| 'x' \| '\|' \| TICKUP \| TICKDOWN \| TICKLEFT \| TICKRIGHT \| 'None' \| ' ' \| '' ] """ if marker not in self._markers: verbose.report('Unrecognized marker style %s, %s' % (marker, type(marker))) if marker in [' ','']: marker = 'None' self._marker = marker self._markerFunc = self._markers[marker] def set_markeredgecolor(self, ec): """ Set the marker edge color ACCEPTS: any matplotlib color """ if ec is None : ec = 'auto' self._markeredgecolor = ec def set_markeredgewidth(self, ew): """ Set the marker edge width in points ACCEPTS: float value in points """ if ew is None : ew = rcParams['lines.markeredgewidth'] self._markeredgewidth = ew def set_markerfacecolor(self, fc): """ Set the marker face color ACCEPTS: any matplotlib color """ if fc is None : fc = 'auto' self._markerfacecolor = fc def set_markersize(self, sz): """ Set the marker size in points ACCEPTS: float """ self._markersize = sz def set_xdata(self, x): """ Set the data np.array for x ACCEPTS: 1D array """ x = np.asarray(x) self.set_data(x, self._yorig) def set_ydata(self, y): """ Set the data np.array for y ACCEPTS: 1D array """ y = np.asarray(y) self.set_data(self._xorig, y) def set_dashes(self, seq): """ Set the dash sequence, sequence of dashes with on off ink in points. If seq is empty or if seq = (None, None), the linestyle will be set to solid. ACCEPTS: sequence of on/off ink in points """ if seq == (None, None) or len(seq)==0: self.set_linestyle('-') else: self.set_linestyle('--') self._dashSeq = seq # TODO: offset ignored for now def _draw_lines(self, renderer, gc, path, trans): self._lineFunc(renderer, gc, path, trans) def _draw_steps_pre(self, renderer, gc, path, trans): vertices = self._xy steps = ma.zeros((2len(vertices)-1, 2), np.float_) steps[0::2, 0], steps[1::2, 0] = vertices[:, 0], vertices[:-1, 0] steps[0::2, 1], steps[1:-1:2, 1] = vertices[:, 1], vertices[1:, 1] path = Path(steps) path = path.transformed(self.get_transform()) self._lineFunc(renderer, gc, path, IdentityTransform()) def _draw_steps_post(self, renderer, gc, path, trans): vertices = self._xy steps = ma.zeros((2len(vertices)-1, 2), np.float_) steps[::2, 0], steps[1:-1:2, 0] = vertices[:, 0], vertices[1:, 0] steps[0::2, 1], steps[1::2, 1] = vertices[:, 1], vertices[:-1, 1] path = Path(steps) path = path.transformed(self.get_transform()) self._lineFunc(renderer, gc, path, IdentityTransform()) def _draw_steps_mid(self, renderer, gc, path, trans): vertices = self._xy steps = ma.zeros((2len(vertices), 2), np.float_) steps[1:-1:2, 0] = 0.5 (vertices[:-1, 0] + vertices[1:, 0]) steps[2::2, 0] = 0.5 * (vertices[:-1, 0] + vertices[1:, 0]) steps[0, 0] = vertices[0, 0] steps[-1, 0] = vertices[-1, 0] steps[0::2, 1], steps[1::2, 1] = vertices[:, 1], vertices[:, 1] path = Path(steps) path = path.transformed(self.get_transform()) self._lineFunc(renderer, gc, path, IdentityTransform()) def _draw_nothing(self, args, kwargs): pass def _draw_solid(self, renderer, gc, path, trans): gc.set_linestyle('solid') renderer.draw_path(gc, path, trans) def _draw_dashed(self, renderer, gc, path, trans): gc.set_linestyle('dashed') if self._dashSeq is not None: gc.set_dashes(0, self._dashSeq) renderer.draw_path(gc, path, trans) def _draw_dash_dot(self, renderer, gc, path, trans): gc.set_linestyle('dashdot') renderer.draw_path(gc, path, trans) def _draw_dotted(self, renderer, gc, path, trans): gc.set_linestyle('dotted') renderer.draw_path(gc, path, trans) def _draw_point(self, renderer, gc, path, path_trans): w = renderer.points_to_pixels(self._markersize) \ self._point_size_reduction * 0.5 gc.set_snap(renderer.points_to_pixels(self._markersize) > 3.0) rgbFace = self._get_rgb_face() transform = Affine2D().scale(w) renderer.draw_markers( gc, Path.unit_circle(), transform, path, path_trans, rgbFace) _draw_pixel_transform = Affine2D().translate(-0.5, -0.5) def _draw_pixel(self, renderer, gc, path, path_trans): rgbFace = self._get_rgb_face() gc.set_snap(False) renderer.draw_markers(gc, Path.unit_rectangle(), self._draw_pixel_transform, path, path_trans, rgbFace) def _draw_circle(self, renderer, gc, path, path_trans): w = renderer.points_to_pixels(self._markersize) * 0.5 gc.set_snap(renderer.points_to_pixels(self._markersize) > 3.0) rgbFace = self._get_rgb_face() transform = Affine2D().scale(w, w) renderer.draw_markers( gc, Path.unit_circle(), transform, path, path_trans, rgbFace) _triangle_path = Path([[0.0, 1.0], [-1.0, -1.0], [1.0, -1.0], [0.0, 1.0]]) def _draw_triangle_up(self, renderer, gc, path, path_trans): gc.set_snap(renderer.points_to_pixels(self._markersize) >= 5.0) offset = 0.5renderer.points_to_pixels(self._markersize) transform = Affine2D().scale(offset, offset) rgbFace = self._get_rgb_face() renderer.draw_markers(gc, self._triangle_path, transform, path, path_trans, rgbFace) def _draw_triangle_down(self, renderer, gc, path, path_trans): gc.set_snap(renderer.points_to_pixels(self._markersize) >= 5.0) offset = 0.5renderer.points_to_pixels(self._markersize) transform = Affine2D().scale(offset, -offset) rgbFace = self._get_rgb_face() renderer.draw_markers(gc, self._triangle_path, transform, path, path_trans, rgbFace) def _draw_triangle_left(self, renderer, gc, path, path_trans): gc.set_snap(renderer.points_to_pixels(self._markersize) >= 5.0) offset = 0.5renderer.points_to_pixels(self._markersize) transform = Affine2D().scale(offset, offset).rotate_deg(90) rgbFace = self._get_rgb_face() renderer.draw_markers(gc, self._triangle_path, transform, path, path_trans, rgbFace) def _draw_triangle_right(self, renderer, gc, path, path_trans): gc.set_snap(renderer.points_to_pixels(self._markersize) >= 5.0) offset = 0.5renderer.points_to_pixels(self._markersize) transform = Affine2D().scale(offset, offset).rotate_deg(-90) rgbFace = self._get_rgb_face() renderer.draw_markers(gc, self._triangle_path, transform, path, path_trans, rgbFace) def _draw_square(self, renderer, gc, path, path_trans): gc.set_snap(renderer.points_to_pixels(self._markersize) >= 2.0) side = renderer.points_to_pixels(self._markersize) transform = Affine2D().translate(-0.5, -0.5).scale(side) rgbFace = self._get_rgb_face() renderer.draw_markers(gc, Path.unit_rectangle(), transform, path, path_trans, rgbFace) def _draw_diamond(self, renderer, gc, path, path_trans): gc.set_snap(renderer.points_to_pixels(self._markersize) >= 5.0) side = renderer.points_to_pixels(self._markersize) transform = Affine2D().translate(-0.5, -0.5).rotate_deg(45).scale(side) rgbFace = self._get_rgb_face() renderer.draw_markers(gc, Path.unit_rectangle(), transform, path, path_trans, rgbFace) def _draw_thin_diamond(self, renderer, gc, path, path_trans): gc.set_snap(renderer.points_to_pixels(self._markersize) >= 3.0) offset = renderer.points_to_pixels(self._markersize) transform = Affine2D().translate(-0.5, -0.5) \ .rotate_deg(45).scale(offset * 0.6, offset) rgbFace = self._get_rgb_face() renderer.draw_markers(gc, Path.unit_rectangle(), transform, path, path_trans, rgbFace) def _draw_pentagon(self, renderer, gc, path, path_trans): gc.set_snap(renderer.points_to_pixels(self._markersize) >= 5.0) offset = 0.5 * renderer.points_to_pixels(self._markersize) transform = Affine2D().scale(offset) rgbFace = self._get_rgb_face() renderer.draw_markers(gc, Path.unit_regular_polygon(5), transform, path, path_trans, rgbFace) def _draw_star(self, renderer, gc, path, path_trans): gc.set_snap(renderer.points_to_pixels(self._markersize) >= 5.0) offset = 0.5 * renderer.points_to_pixels(self._markersize) transform = Affine2D().scale(offset) rgbFace = self._get_rgb_face() _starpath = Path.unit_regular_star(5, innerCircle=0.381966) renderer.draw_markers(gc, _starpath, transform, path, path_trans, rgbFace) def _draw_hexagon1(self, renderer, gc, path, path_trans): gc.set_snap(renderer.points_to_pixels(self._markersize) >= 5.0) offset = 0.5 * renderer.points_to_pixels(self._markersize) transform = Affine2D().scale(offset) rgbFace = self._get_rgb_face() renderer.draw_markers(gc, Path.unit_regular_polygon(6), transform, path, path_trans, rgbFace) def _draw_hexagon2(self, renderer, gc, path, path_trans): gc.set_snap(renderer.points_to_pixels(self._markersize) >= 5.0) offset = 0.5 * renderer.points_to_pixels(self._markersize) transform = Affine2D().scale(offset).rotate_deg(30) rgbFace = self._get_rgb_face() renderer.draw_markers(gc, Path.unit_regular_polygon(6), transform, path, path_trans, rgbFace) _line_marker_path = Path([[0.0, -1.0], [0.0, 1.0]]) def _draw_vline(self, renderer, gc, path, path_trans): gc.set_snap(renderer.points_to_pixels(self._markersize) >= 1.0) offset = 0.5renderer.points_to_pixels(self._markersize) transform = Affine2D().scale(offset) renderer.draw_markers(gc, self._line_marker_path, transform, path, path_trans) def _draw_hline(self, renderer, gc, path, path_trans): gc.set_snap(renderer.points_to_pixels(self._markersize) >= 1.0) offset = 0.5renderer.points_to_pixels(self._markersize) transform = Affine2D().scale(offset).rotate_deg(90) renderer.draw_markers(gc, self._line_marker_path, transform, path, path_trans) _tickhoriz_path = Path([[0.0, 0.0], [1.0, 0.0]]) def _draw_tickleft(self, renderer, gc, path, path_trans): gc.set_snap(renderer.points_to_pixels(self._markersize) >= 1.0) offset = renderer.points_to_pixels(self._markersize) marker_transform = Affine2D().scale(-offset, 1.0) renderer.draw_markers(gc, self._tickhoriz_path, marker_transform, path, path_trans) def _draw_tickright(self, renderer, gc, path, path_trans): gc.set_snap(renderer.points_to_pixels(self._markersize) >= 1.0) offset = renderer.points_to_pixels(self._markersize) marker_transform = Affine2D().scale(offset, 1.0) renderer.draw_markers(gc, self._tickhoriz_path, marker_transform, path, path_trans) _tickvert_path = Path([[-0.0, 0.0], [-0.0, 1.0]]) def _draw_tickup(self, renderer, gc, path, path_trans): gc.set_snap(renderer.points_to_pixels(self._markersize) >= 1.0) offset = renderer.points_to_pixels(self._markersize) marker_transform = Affine2D().scale(1.0, offset) renderer.draw_markers(gc, self._tickvert_path, marker_transform, path, path_trans) def _draw_tickdown(self, renderer, gc, path, path_trans): gc.set_snap(renderer.points_to_pixels(self._markersize) >= 1.0) offset = renderer.points_to_pixels(self._markersize) marker_transform = Affine2D().scale(1.0, -offset) renderer.draw_markers(gc, self._tickvert_path, marker_transform, path, path_trans) _plus_path = Path([[-1.0, 0.0], [1.0, 0.0], [0.0, -1.0], [0.0, 1.0]], [Path.MOVETO, Path.LINETO, Path.MOVETO, Path.LINETO]) def _draw_plus(self, renderer, gc, path, path_trans): gc.set_snap(renderer.points_to_pixels(self._markersize) >= 3.0) offset = 0.5renderer.points_to_pixels(self._markersize) transform = Affine2D().scale(offset) renderer.draw_markers(gc, self._plus_path, transform, path, path_trans) _tri_path = Path([[0.0, 0.0], [0.0, -1.0], [0.0, 0.0], [0.8, 0.5], [0.0, 0.0], [-0.8, 0.5]], [Path.MOVETO, Path.LINETO, Path.MOVETO, Path.LINETO, Path.MOVETO, Path.LINETO]) def _draw_tri_down(self, renderer, gc, path, path_trans): gc.set_snap(renderer.points_to_pixels(self._markersize) >= 5.0) offset = 0.5renderer.points_to_pixels(self._markersize) transform = Affine2D().scale(offset) renderer.draw_markers(gc, self._tri_path, transform, path, path_trans) def _draw_tri_up(self, renderer, gc, path, path_trans): gc.set_snap(renderer.points_to_pixels(self._markersize) >= 5.0) offset = 0.5renderer.points_to_pixels(self._markersize) transform = Affine2D().scale(offset).rotate_deg(180) renderer.draw_markers(gc, self._tri_path, transform, path, path_trans) def _draw_tri_left(self, renderer, gc, path, path_trans): gc.set_snap(renderer.points_to_pixels(self._markersize) >= 5.0) offset = 0.5renderer.points_to_pixels(self._markersize) transform = Affine2D().scale(offset).rotate_deg(90) renderer.draw_markers(gc, self._tri_path, transform, path, path_trans) def _draw_tri_right(self, renderer, gc, path, path_trans): gc.set_snap(renderer.points_to_pixels(self._markersize) >= 5.0) offset = 0.5renderer.points_to_pixels(self._markersize) transform = Affine2D().scale(offset).rotate_deg(270) renderer.draw_markers(gc, self._tri_path, transform, path, path_trans) _caret_path = Path([[-1.0, 1.5], [0.0, 0.0], [1.0, 1.5]]) def _draw_caretdown(self, renderer, gc, path, path_trans): gc.set_snap(renderer.points_to_pixels(self._markersize) >= 3.0) offset = 0.5renderer.points_to_pixels(self._markersize) transform = Affine2D().scale(offset) renderer.draw_markers(gc, self._caret_path, transform, path, path_trans) def _draw_caretup(self, renderer, gc, path, path_trans): gc.set_snap(renderer.points_to_pixels(self._markersize) >= 3.0) offset = 0.5renderer.points_to_pixels(self._markersize) transform = Affine2D().scale(offset).rotate_deg(180) renderer.draw_markers(gc, self._caret_path, transform, path, path_trans) def _draw_caretleft(self, renderer, gc, path, path_trans): gc.set_snap(renderer.points_to_pixels(self._markersize) >= 3.0) offset = 0.5renderer.points_to_pixels(self._markersize) transform = Affine2D().scale(offset).rotate_deg(270) renderer.draw_markers(gc, self._caret_path, transform, path, path_trans) def _draw_caretright(self, renderer, gc, path, path_trans): gc.set_snap(renderer.points_to_pixels(self._markersize) >= 3.0) offset = 0.5renderer.points_to_pixels(self._markersize) transform = Affine2D().scale(offset).rotate_deg(90) renderer.draw_markers(gc, self._caret_path, transform, path, path_trans) _x_path = Path([[-1.0, -1.0], [1.0, 1.0], [-1.0, 1.0], [1.0, -1.0]], [Path.MOVETO, Path.LINETO, Path.MOVETO, Path.LINETO]) def _draw_x(self, renderer, gc, path, path_trans): gc.set_snap(renderer.points_to_pixels(self._markersize) >= 3.0) offset = 0.5renderer.points_to_pixels(self._markersize) transform = Affine2D().scale(offset) renderer.draw_markers(gc, self._x_path, transform, path, path_trans) def update_from(self, other): 'copy properties from other to self' Artist.update_from(self, other) self._linestyle = other._linestyle self._linewidth = other._linewidth self._color = other._color self._markersize = other._markersize self._markerfacecolor = other._markerfacecolor self._markeredgecolor = other._markeredgecolor self._markeredgewidth = other._markeredgewidth self._dashSeq = other._dashSeq self._dashcapstyle = other._dashcapstyle self._dashjoinstyle = other._dashjoinstyle self._solidcapstyle = other._solidcapstyle self._solidjoinstyle = other._solidjoinstyle self._linestyle = other._linestyle self._marker = other._marker self._drawstyle = other._drawstyle def _get_rgb_face(self): facecolor = self.get_markerfacecolor() if is_string_like(facecolor) and facecolor.lower()=='none': rgbFace = None else: rgbFace = colorConverter.to_rgb(facecolor) return rgbFace # some aliases.... def set_aa(self, val): 'alias for set_antialiased' self.set_antialiased(val) def set_c(self, val): 'alias for set_color' self.set_color(val) def set_ls(self, val): 'alias for set_linestyle' self.set_linestyle(val) def set_lw(self, val): 'alias for set_linewidth' self.set_linewidth(val) def set_mec(self, val): 'alias for set_markeredgecolor' self.set_markeredgecolor(val) def set_mew(self, val): 'alias for set_markeredgewidth' self.set_markeredgewidth(val) def set_mfc(self, val): 'alias for set_markerfacecolor' self.set_markerfacecolor(val) def set_ms(self, val): 'alias for set_markersize' self.set_markersize(val) def get_aa(self): 'alias for get_antialiased' return self.get_antialiased() def get_c(self): 'alias for get_color' return self.get_color() def get_ls(self): 'alias for get_linestyle' return self.get_linestyle() def get_lw(self): 'alias for get_linewidth' return self.get_linewidth() def get_mec(self): 'alias for get_markeredgecolor' return self.get_markeredgecolor() def get_mew(self): 'alias for get_markeredgewidth' return self.get_markeredgewidth() def get_mfc(self): 'alias for get_markerfacecolor' return self.get_markerfacecolor() def get_ms(self): 'alias for get_markersize' return self.get_markersize() def set_dash_joinstyle(self, s): """ Set the join style for dashed linestyles ACCEPTS: ['miter' \| 'round' \| 'bevel'] """ s = s.lower() if s not in self.validJoin: raise ValueError('set_dash_joinstyle passed "%s";\n' % (s,) + 'valid joinstyles are %s' % (self.validJoin,)) self._dashjoinstyle = s def set_solid_joinstyle(self, s): """ Set the join style for solid linestyles ACCEPTS: ['miter' \| 'round' \| 'bevel'] """ s = s.lower() if s not in self.validJoin: raise ValueError('set_solid_joinstyle passed "%s";\n' % (s,) + 'valid joinstyles are %s' % (self.validJoin,)) self._solidjoinstyle = s def get_dash_joinstyle(self): """ Get the join style for dashed linestyles """ return self._dashjoinstyle def get_solid_joinstyle(self): """ Get the join style for solid linestyles """ return self._solidjoinstyle def set_dash_capstyle(self, s): """ Set the cap style for dashed linestyles ACCEPTS: ['butt' \| 'round' \| 'projecting'] """ s = s.lower() if s not in self.validCap: raise ValueError('set_dash_capstyle passed "%s";\n' % (s,) + 'valid capstyles are %s' % (self.validCap,)) self._dashcapstyle = s def set_solid_capstyle(self, s): """ Set the cap style for solid linestyles ACCEPTS: ['butt' \| 'round' \| 'projecting'] """ s = s.lower() if s not in self.validCap: raise ValueError('set_solid_capstyle passed "%s";\n' % (s,) + 'valid capstyles are %s' % (self.validCap,)) self._solidcapstyle = s def get_dash_capstyle(self): """ Get the cap style for dashed linestyles """ return self._dashcapstyle def get_solid_capstyle(self): """ Get the cap style for solid linestyles """ return self._solidcapstyle def is_dashed(self): 'return True if line is dashstyle' return self._linestyle in ('--', '-.', ':') class VertexSelector: """ Manage the callbacks to maintain a list of selected vertices for :class:`matplotlib.lines.Line2D`. Derived classes should override :meth:`~matplotlib.lines.VertexSelector.process_selected` to do something with the picks. Here is an example which highlights the selected verts with red circles:: import numpy as np import matplotlib.pyplot as plt import matplotlib.lines as lines class HighlightSelected(lines.VertexSelector): def __init__(self, line, fmt='ro', kwargs): lines.VertexSelector.__init__(self, line) self.markers, = self.axes.plot([], [], fmt, kwargs) def process_selected(self, ind, xs, ys): self.markers.set_data(xs, ys) self.canvas.draw() fig = plt.figure() ax = fig.add_subplot(111) x, y = np.random.rand(2, 30) line, = ax.plot(x, y, 'bs-', picker=5) selector = HighlightSelected(line) plt.show() """ def __init__(self, line): """ Initialize the class with a :class:`matplotlib.lines.Line2D` instance. The line should already be added to some :class:`matplotlib.axes.Axes` instance and should have the picker property set. """ if not hasattr(line, 'axes'): raise RuntimeError('You must first add the line to the Axes') if line.get_picker() is None: raise RuntimeError('You must first set the picker property of the line') self.axes = line.axes self.line = line self.canvas = self.axes.figure.canvas self.cid = self.canvas.mpl_connect('pick_event', self.onpick) self.ind = set() def process_selected(self, ind, xs, ys): """ Default "do nothing" implementation of the :meth:`process_selected` method. ind are the indices of the selected vertices. xs and ys are the coordinates of the selected vertices. """ pass def onpick(self, event): 'When the line is picked, update the set of selected indicies.' if event.artist is not self.line: return for i in event.ind: if i in self.ind: self.ind.remove(i) else: self.ind.add(i) ind = list(self.ind) ind.sort() xdata, ydata = self.line.get_data() self.process_selected(ind, xdata[ind], ydata[ind]) lineStyles = Line2D._lineStyles lineMarkers = Line2D._markers drawStyles = Line2D.drawStyles artist.kwdocd['Line2D'] = artist.kwdoc(Line2D) # You can not set the docstring of an instancemethod, # but you can on the underlying function. Go figure. Line2D.__init__.im_func.__doc__ = dedent(Line2D.__init__.__doc__) % artist.kwdocd	48,233	Python	.py	1,116	33.258065	90	0.572304	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,267	backend_bases.py	numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/backend_bases.py	""" Abstract base classes define the primitives that renderers and graphics contexts must implement to serve as a matplotlib backend :class:`RendererBase` An abstract base class to handle drawing/rendering operations. :class:`FigureCanvasBase` The abstraction layer that separates the :class:`matplotlib.figure.Figure` from the backend specific details like a user interface drawing area :class:`GraphicsContextBase` An abstract base class that provides color, line styles, etc... :class:`Event` The base class for all of the matplotlib event handling. Derived classes suh as :class:`KeyEvent` and :class:`MouseEvent` store the meta data like keys and buttons pressed, x and y locations in pixel and :class:`~matplotlib.axes.Axes` coordinates. """ from __future__ import division import os, warnings, time import numpy as np import matplotlib.cbook as cbook import matplotlib.colors as colors import matplotlib.transforms as transforms import matplotlib.widgets as widgets from matplotlib import rcParams class RendererBase: """An abstract base class to handle drawing/rendering operations. The following methods must be implemented in the backend: * :meth:`draw_path` * :meth:`draw_image` * :meth:`draw_text` * :meth:`get_text_width_height_descent` The following methods should be implemented in the backend for optimization reasons: * :meth:`draw_markers` * :meth:`draw_path_collection` * :meth:`draw_quad_mesh` """ def __init__(self): self._texmanager = None def open_group(self, s): """ Open a grouping element with label s. Is only currently used by :mod:`~matplotlib.backends.backend_svg` """ pass def close_group(self, s): """ Close a grouping element with label s Is only currently used by :mod:`~matplotlib.backends.backend_svg` """ pass def draw_path(self, gc, path, transform, rgbFace=None): """ Draws a :class:`~matplotlib.path.Path` instance using the given affine transform. """ raise NotImplementedError def draw_markers(self, gc, marker_path, marker_trans, path, trans, rgbFace=None): """ Draws a marker at each of the vertices in path. This includes all vertices, including control points on curves. To avoid that behavior, those vertices should be removed before calling this function. gc the :class:`GraphicsContextBase` instance marker_trans is an affine transform applied to the marker. trans is an affine transform applied to the path. This provides a fallback implementation of draw_markers that makes multiple calls to :meth:`draw_path`. Some backends may want to override this method in order to draw the marker only once and reuse it multiple times. """ tpath = trans.transform_path(path) for vertices, codes in tpath.iter_segments(): if len(vertices): x,y = vertices[-2:] self.draw_path(gc, marker_path, marker_trans + transforms.Affine2D().translate(x, y), rgbFace) def draw_path_collection(self, master_transform, cliprect, clippath, clippath_trans, paths, all_transforms, offsets, offsetTrans, facecolors, edgecolors, linewidths, linestyles, antialiaseds, urls): """ Draws a collection of paths, selecting drawing properties from the lists facecolors, edgecolors, linewidths, linestyles and antialiaseds. offsets is a list of offsets to apply to each of the paths. The offsets in offsets are first transformed by offsetTrans before being applied. This provides a fallback implementation of :meth:`draw_path_collection` that makes multiple calls to draw_path. Some backends may want to override this in order to render each set of path data only once, and then reference that path multiple times with the different offsets, colors, styles etc. The generator methods :meth:`_iter_collection_raw_paths` and :meth:`_iter_collection` are provided to help with (and standardize) the implementation across backends. It is highly recommended to use those generators, so that changes to the behavior of :meth:`draw_path_collection` can be made globally. """ path_ids = [] for path, transform in self._iter_collection_raw_paths( master_transform, paths, all_transforms): path_ids.append((path, transform)) for xo, yo, path_id, gc, rgbFace in self._iter_collection( path_ids, cliprect, clippath, clippath_trans, offsets, offsetTrans, facecolors, edgecolors, linewidths, linestyles, antialiaseds, urls): path, transform = path_id transform = transforms.Affine2D(transform.get_matrix()).translate(xo, yo) self.draw_path(gc, path, transform, rgbFace) def draw_quad_mesh(self, master_transform, cliprect, clippath, clippath_trans, meshWidth, meshHeight, coordinates, offsets, offsetTrans, facecolors, antialiased, showedges): """ This provides a fallback implementation of :meth:`draw_quad_mesh` that generates paths and then calls :meth:`draw_path_collection`. """ from matplotlib.collections import QuadMesh paths = QuadMesh.convert_mesh_to_paths( meshWidth, meshHeight, coordinates) if showedges: edgecolors = np.array([[0.0, 0.0, 0.0, 1.0]], np.float_) linewidths = np.array([1.0], np.float_) else: edgecolors = facecolors linewidths = np.array([0.0], np.float_) return self.draw_path_collection( master_transform, cliprect, clippath, clippath_trans, paths, [], offsets, offsetTrans, facecolors, edgecolors, linewidths, [], [antialiased], [None]) def _iter_collection_raw_paths(self, master_transform, paths, all_transforms): """ This is a helper method (along with :meth:`_iter_collection`) to make it easier to write a space-efficent :meth:`draw_path_collection` implementation in a backend. This method yields all of the base path/transform combinations, given a master transform, a list of paths and list of transforms. The arguments should be exactly what is passed in to :meth:`draw_path_collection`. The backend should take each yielded path and transform and create an object that can be referenced (reused) later. """ Npaths = len(paths) Ntransforms = len(all_transforms) N = max(Npaths, Ntransforms) if Npaths == 0: return transform = transforms.IdentityTransform() for i in xrange(N): path = paths[i % Npaths] if Ntransforms: transform = all_transforms[i % Ntransforms] yield path, transform + master_transform def _iter_collection(self, path_ids, cliprect, clippath, clippath_trans, offsets, offsetTrans, facecolors, edgecolors, linewidths, linestyles, antialiaseds, urls): """ This is a helper method (along with :meth:`_iter_collection_raw_paths`) to make it easier to write a space-efficent :meth:`draw_path_collection` implementation in a backend. This method yields all of the path, offset and graphics context combinations to draw the path collection. The caller should already have looped over the results of :meth:`_iter_collection_raw_paths` to draw this collection. The arguments should be the same as that passed into :meth:`draw_path_collection`, with the exception of path_ids, which is a list of arbitrary objects that the backend will use to reference one of the paths created in the :meth:`_iter_collection_raw_paths` stage. Each yielded result is of the form:: xo, yo, path_id, gc, rgbFace where xo, yo is an offset; path_id is one of the elements of path_ids; gc is a graphics context and rgbFace is a color to use for filling the path. """ Npaths = len(path_ids) Noffsets = len(offsets) N = max(Npaths, Noffsets) Nfacecolors = len(facecolors) Nedgecolors = len(edgecolors) Nlinewidths = len(linewidths) Nlinestyles = len(linestyles) Naa = len(antialiaseds) Nurls = len(urls) if (Nfacecolors == 0 and Nedgecolors == 0) or Npaths == 0: return if Noffsets: toffsets = offsetTrans.transform(offsets) gc = self.new_gc() gc.set_clip_rectangle(cliprect) if clippath is not None: clippath = transforms.TransformedPath(clippath, clippath_trans) gc.set_clip_path(clippath) if Nfacecolors == 0: rgbFace = None if Nedgecolors == 0: gc.set_linewidth(0.0) xo, yo = 0, 0 for i in xrange(N): path_id = path_ids[i % Npaths] if Noffsets: xo, yo = toffsets[i % Noffsets] if Nfacecolors: rgbFace = facecolors[i % Nfacecolors] if Nedgecolors: gc.set_foreground(edgecolors[i % Nedgecolors]) if Nlinewidths: gc.set_linewidth(linewidths[i % Nlinewidths]) if Nlinestyles: gc.set_dashes(linestyles[i % Nlinestyles]) if rgbFace is not None and len(rgbFace)==4: gc.set_alpha(rgbFace[-1]) rgbFace = rgbFace[:3] gc.set_antialiased(antialiaseds[i % Naa]) if Nurls: gc.set_url(urls[i % Nurls]) yield xo, yo, path_id, gc, rgbFace def get_image_magnification(self): """ Get the factor by which to magnify images passed to :meth:`draw_image`. Allows a backend to have images at a different resolution to other artists. """ return 1.0 def draw_image(self, x, y, im, bbox, clippath=None, clippath_trans=None): """ Draw the image instance into the current axes; x* is the distance in pixels from the left hand side of the canvas. y the distance from the origin. That is, if origin is upper, y is the distance from top. If origin is lower, y is the distance from bottom im the :class:`matplotlib._image.Image` instance bbox a :class:`matplotlib.transforms.Bbox` instance for clipping, or None """ raise NotImplementedError def option_image_nocomposite(self): """ overwrite this method for renderers that do not necessarily want to rescale and composite raster images. (like SVG) """ return False def draw_tex(self, gc, x, y, s, prop, angle, ismath='TeX!'): raise NotImplementedError def draw_text(self, gc, x, y, s, prop, angle, ismath=False): """ Draw the text instance gc the :class:`GraphicsContextBase` instance x the x location of the text in display coords y the y location of the text in display coords s a :class:`matplotlib.text.Text` instance prop a :class:`matplotlib.font_manager.FontProperties` instance angle the rotation angle in degrees backend implementers note When you are trying to determine if you have gotten your bounding box right (which is what enables the text layout/alignment to work properly), it helps to change the line in text.py:: if 0: bbox_artist(self, renderer) to if 1, and then the actual bounding box will be blotted along with your text. """ raise NotImplementedError def flipy(self): """ Return true if y small numbers are top for renderer Is used for drawing text (:mod:`matplotlib.text`) and images (:mod:`matplotlib.image`) only """ return True def get_canvas_width_height(self): 'return the canvas width and height in display coords' return 1, 1 def get_texmanager(self): """ return the :class:`matplotlib.texmanager.TexManager` instance """ if self._texmanager is None: from matplotlib.texmanager import TexManager self._texmanager = TexManager() return self._texmanager def get_text_width_height_descent(self, s, prop, ismath): """ get the width and height, and the offset from the bottom to the baseline (descent), in display coords of the string s with :class:`~matplotlib.font_manager.FontProperties` prop """ raise NotImplementedError def new_gc(self): """ Return an instance of a :class:`GraphicsContextBase` """ return GraphicsContextBase() def points_to_pixels(self, points): """ Convert points to display units points a float or a numpy array of float return points converted to pixels You need to override this function (unless your backend doesn't have a dpi, eg, postscript or svg). Some imaging systems assume some value for pixels per inch:: points to pixels = points * pixels_per_inch/72.0 * dpi/72.0 """ return points def strip_math(self, s): return cbook.strip_math(s) def start_rasterizing(self): pass def stop_rasterizing(self): pass class GraphicsContextBase: """ An abstract base class that provides color, line styles, etc... """ # a mapping from dash styles to suggested offset, dash pairs dashd = { 'solid' : (None, None), 'dashed' : (0, (6.0, 6.0)), 'dashdot' : (0, (3.0, 5.0, 1.0, 5.0)), 'dotted' : (0, (1.0, 3.0)), } def __init__(self): self._alpha = 1.0 self._antialiased = 1 # use 0,1 not True, False for extension code self._capstyle = 'butt' self._cliprect = None self._clippath = None self._dashes = None, None self._joinstyle = 'miter' self._linestyle = 'solid' self._linewidth = 1 self._rgb = (0.0, 0.0, 0.0) self._hatch = None self._url = None self._snap = None def copy_properties(self, gc): 'Copy properties from gc to self' self._alpha = gc._alpha self._antialiased = gc._antialiased self._capstyle = gc._capstyle self._cliprect = gc._cliprect self._clippath = gc._clippath self._dashes = gc._dashes self._joinstyle = gc._joinstyle self._linestyle = gc._linestyle self._linewidth = gc._linewidth self._rgb = gc._rgb self._hatch = gc._hatch self._url = gc._url self._snap = gc._snap def get_alpha(self): """ Return the alpha value used for blending - not supported on all backends """ return self._alpha def get_antialiased(self): "Return true if the object should try to do antialiased rendering" return self._antialiased def get_capstyle(self): """ Return the capstyle as a string in ('butt', 'round', 'projecting') """ return self._capstyle def get_clip_rectangle(self): """ Return the clip rectangle as a :class:`~matplotlib.transforms.Bbox` instance """ return self._cliprect def get_clip_path(self): """ Return the clip path in the form (path, transform), where path is a :class:`~matplotlib.path.Path` instance, and transform is an affine transform to apply to the path before clipping. """ if self._clippath is not None: return self._clippath.get_transformed_path_and_affine() return None, None def get_dashes(self): """ Return the dash information as an offset dashlist tuple. The dash list is a even size list that gives the ink on, ink off in pixels. See p107 of to PostScript `BLUEBOOK <http://www-cdf.fnal.gov/offline/PostScript/BLUEBOOK.PDF>`_ for more info. Default value is None """ return self._dashes def get_joinstyle(self): """ Return the line join style as one of ('miter', 'round', 'bevel') """ return self._joinstyle def get_linestyle(self, style): """ Return the linestyle: one of ('solid', 'dashed', 'dashdot', 'dotted'). """ return self._linestyle def get_linewidth(self): """ Return the line width in points as a scalar """ return self._linewidth def get_rgb(self): """ returns a tuple of three floats from 0-1. color can be a matlab format string, a html hex color string, or a rgb tuple """ return self._rgb def get_url(self): """ returns a url if one is set, None otherwise """ return self._url def get_snap(self): """ returns the snap setting which may be: * True: snap vertices to the nearest pixel center * False: leave vertices as-is * None: (auto) If the path contains only rectilinear line segments, round to the nearest pixel center """ return self._snap def set_alpha(self, alpha): """ Set the alpha value used for blending - not supported on all backends """ self._alpha = alpha def set_antialiased(self, b): """ True if object should be drawn with antialiased rendering """ # use 0, 1 to make life easier on extension code trying to read the gc if b: self._antialiased = 1 else: self._antialiased = 0 def set_capstyle(self, cs): """ Set the capstyle as a string in ('butt', 'round', 'projecting') """ if cs in ('butt', 'round', 'projecting'): self._capstyle = cs else: raise ValueError('Unrecognized cap style. Found %s' % cs) def set_clip_rectangle(self, rectangle): """ Set the clip rectangle with sequence (left, bottom, width, height) """ self._cliprect = rectangle def set_clip_path(self, path): """ Set the clip path and transformation. Path should be a :class:`~matplotlib.transforms.TransformedPath` instance. """ assert path is None or isinstance(path, transforms.TransformedPath) self._clippath = path def set_dashes(self, dash_offset, dash_list): """ Set the dash style for the gc. dash_offset is the offset (usually 0). dash_list specifies the on-off sequence as points. ``(None, None)`` specifies a solid line """ self._dashes = dash_offset, dash_list def set_foreground(self, fg, isRGB=False): """ Set the foreground color. fg can be a matlab format string, a html hex color string, an rgb unit tuple, or a float between 0 and 1. In the latter case, grayscale is used. The :class:`GraphicsContextBase` converts colors to rgb internally. If you know the color is rgb already, you can set ``isRGB=True`` to avoid the performace hit of the conversion """ if isRGB: self._rgb = fg else: self._rgb = colors.colorConverter.to_rgba(fg) def set_graylevel(self, frac): """ Set the foreground color to be a gray level with frac """ self._rgb = (frac, frac, frac) def set_joinstyle(self, js): """ Set the join style to be one of ('miter', 'round', 'bevel') """ if js in ('miter', 'round', 'bevel'): self._joinstyle = js else: raise ValueError('Unrecognized join style. Found %s' % js) def set_linewidth(self, w): """ Set the linewidth in points """ self._linewidth = w def set_linestyle(self, style): """ Set the linestyle to be one of ('solid', 'dashed', 'dashdot', 'dotted'). """ try: offset, dashes = self.dashd[style] except: raise ValueError('Unrecognized linestyle: %s' % style) self._linestyle = style self.set_dashes(offset, dashes) def set_url(self, url): """ Sets the url for links in compatible backends """ self._url = url def set_snap(self, snap): """ Sets the snap setting which may be: * True: snap vertices to the nearest pixel center * False: leave vertices as-is * None: (auto) If the path contains only rectilinear line segments, round to the nearest pixel center """ self._snap = snap def set_hatch(self, hatch): """ Sets the hatch style for filling """ self._hatch = hatch def get_hatch(self): """ Gets the current hatch style """ return self._hatch class Event: """ A matplotlib event. Attach additional attributes as defined in :meth:`FigureCanvasBase.mpl_connect`. The following attributes are defined and shown with their default values name the event name canvas the FigureCanvas instance generating the event guiEvent the GUI event that triggered the matplotlib event """ def __init__(self, name, canvas,guiEvent=None): self.name = name self.canvas = canvas self.guiEvent = guiEvent class IdleEvent(Event): """ An event triggered by the GUI backend when it is idle -- useful for passive animation """ pass class DrawEvent(Event): """ An event triggered by a draw operation on the canvas In addition to the :class:`Event` attributes, the following event attributes are defined: renderer the :class:`RendererBase` instance for the draw event """ def __init__(self, name, canvas, renderer): Event.__init__(self, name, canvas) self.renderer = renderer class ResizeEvent(Event): """ An event triggered by a canvas resize In addition to the :class:`Event` attributes, the following event attributes are defined: width width of the canvas in pixels height height of the canvas in pixels """ def __init__(self, name, canvas): Event.__init__(self, name, canvas) self.width, self.height = canvas.get_width_height() class LocationEvent(Event): """ A event that has a screen location The following additional attributes are defined and shown with their default values In addition to the :class:`Event` attributes, the following event attributes are defined: x x position - pixels from left of canvas y y position - pixels from bottom of canvas inaxes the :class:`~matplotlib.axes.Axes` instance if mouse is over axes xdata x coord of mouse in data coords ydata y coord of mouse in data coords """ x = None # x position - pixels from left of canvas y = None # y position - pixels from right of canvas inaxes = None # the Axes instance if mouse us over axes xdata = None # x coord of mouse in data coords ydata = None # y coord of mouse in data coords # the last event that was triggered before this one lastevent = None def __init__(self, name, canvas, x, y,guiEvent=None): """ x, y in figure coords, 0,0 = bottom, left """ Event.__init__(self, name, canvas,guiEvent=guiEvent) self.x = x self.y = y if x is None or y is None: # cannot check if event was in axes if no x,y info self.inaxes = None self._update_enter_leave() return # Find all axes containing the mouse axes_list = [a for a in self.canvas.figure.get_axes() if a.in_axes(self)] if len(axes_list) == 0: # None found self.inaxes = None self._update_enter_leave() return elif (len(axes_list) > 1): # Overlap, get the highest zorder axCmp = lambda _x,_y: cmp(_x.zorder, _y.zorder) axes_list.sort(axCmp) self.inaxes = axes_list[-1] # Use the highest zorder else: # Just found one hit self.inaxes = axes_list[0] try: xdata, ydata = self.inaxes.transData.inverted().transform_point((x, y)) except ValueError: self.xdata = None self.ydata = None else: self.xdata = xdata self.ydata = ydata self._update_enter_leave() def _update_enter_leave(self): 'process the figure/axes enter leave events' if LocationEvent.lastevent is not None: last = LocationEvent.lastevent if last.inaxes!=self.inaxes: # process axes enter/leave events if last.inaxes is not None: last.canvas.callbacks.process('axes_leave_event', last) if self.inaxes is not None: self.canvas.callbacks.process('axes_enter_event', self) else: # process a figure enter event if self.inaxes is not None: self.canvas.callbacks.process('axes_enter_event', self) LocationEvent.lastevent = self class MouseEvent(LocationEvent): """ A mouse event ('button_press_event', 'button_release_event', 'scroll_event', 'motion_notify_event'). In addition to the :class:`Event` and :class:`LocationEvent` attributes, the following attributes are defined: button button pressed None, 1, 2, 3, 'up', 'down' (up and down are used for scroll events) key the key pressed: None, chr(range(255), 'shift', 'win', or 'control' step number of scroll steps (positive for 'up', negative for 'down') Example usage:: def on_press(event): print 'you pressed', event.button, event.xdata, event.ydata cid = fig.canvas.mpl_connect('button_press_event', on_press) """ x = None # x position - pixels from left of canvas y = None # y position - pixels from right of canvas button = None # button pressed None, 1, 2, 3 inaxes = None # the Axes instance if mouse us over axes xdata = None # x coord of mouse in data coords ydata = None # y coord of mouse in data coords step = None # scroll steps for scroll events def __init__(self, name, canvas, x, y, button=None, key=None, step=0, guiEvent=None): """ x, y in figure coords, 0,0 = bottom, left button pressed None, 1, 2, 3, 'up', 'down' """ LocationEvent.__init__(self, name, canvas, x, y, guiEvent=guiEvent) self.button = button self.key = key self.step = step class PickEvent(Event): """ a pick event, fired when the user picks a location on the canvas sufficiently close to an artist. Attrs: all the :class:`Event` attributes plus mouseevent the :class:`MouseEvent` that generated the pick artist the :class:`~matplotlib.artist.Artist` picked other extra class dependent attrs -- eg a :class:`~matplotlib.lines.Line2D` pick may define different extra attributes than a :class:`~matplotlib.collections.PatchCollection` pick event Example usage:: line, = ax.plot(rand(100), 'o', picker=5) # 5 points tolerance def on_pick(event): thisline = event.artist xdata, ydata = thisline.get_data() ind = event.ind print 'on pick line:', zip(xdata[ind], ydata[ind]) cid = fig.canvas.mpl_connect('pick_event', on_pick) """ def __init__(self, name, canvas, mouseevent, artist, guiEvent=None, *kwargs): Event.__init__(self, name, canvas, guiEvent) self.mouseevent = mouseevent self.artist = artist self.__dict__.update(kwargs) class KeyEvent(LocationEvent): """ A key event (key press, key release). Attach additional attributes as defined in :meth:`FigureCanvasBase.mpl_connect`. In addition to the :class:`Event` and :class:`LocationEvent` attributes, the following attributes are defined: key* the key pressed: None, chr(range(255), shift, win, or control This interface may change slightly when better support for modifier keys is included. Example usage:: def on_key(event): print 'you pressed', event.key, event.xdata, event.ydata cid = fig.canvas.mpl_connect('key_press_event', on_key) """ def __init__(self, name, canvas, key, x=0, y=0, guiEvent=None): LocationEvent.__init__(self, name, canvas, x, y, guiEvent=guiEvent) self.key = key class FigureCanvasBase: """ The canvas the figure renders into. Public attributes figure A :class:`matplotlib.figure.Figure` instance """ events = [ 'resize_event', 'draw_event', 'key_press_event', 'key_release_event', 'button_press_event', 'button_release_event', 'scroll_event', 'motion_notify_event', 'pick_event', 'idle_event', 'figure_enter_event', 'figure_leave_event', 'axes_enter_event', 'axes_leave_event' ] def __init__(self, figure): figure.set_canvas(self) self.figure = figure # a dictionary from event name to a dictionary that maps cid->func self.callbacks = cbook.CallbackRegistry(self.events) self.widgetlock = widgets.LockDraw() self._button = None # the button pressed self._key = None # the key pressed self._lastx, self._lasty = None, None self.button_pick_id = self.mpl_connect('button_press_event',self.pick) self.scroll_pick_id = self.mpl_connect('scroll_event',self.pick) if False: ## highlight the artists that are hit self.mpl_connect('motion_notify_event',self.onHilite) ## delete the artists that are clicked on #self.mpl_disconnect(self.button_pick_id) #self.mpl_connect('button_press_event',self.onRemove) def onRemove(self, ev): """ Mouse event processor which removes the top artist under the cursor. Connect this to the 'mouse_press_event' using:: canvas.mpl_connect('mouse_press_event',canvas.onRemove) """ def sort_artists(artists): # This depends on stable sort and artists returned # from get_children in z order. L = [ (h.zorder, h) for h in artists ] L.sort() return [ h for zorder, h in L ] # Find the top artist under the cursor under = sort_artists(self.figure.hitlist(ev)) h = None if under: h = under[-1] # Try deleting that artist, or its parent if you # can't delete the artist while h: print "Removing",h if h.remove(): self.draw_idle() break parent = None for p in under: if h in p.get_children(): parent = p break h = parent def onHilite(self, ev): """ Mouse event processor which highlights the artists under the cursor. Connect this to the 'motion_notify_event' using:: canvas.mpl_connect('motion_notify_event',canvas.onHilite) """ if not hasattr(self,'_active'): self._active = dict() under = self.figure.hitlist(ev) enter = [a for a in under if a not in self._active] leave = [a for a in self._active if a not in under] print "within:"," ".join([str(x) for x in under]) #print "entering:",[str(a) for a in enter] #print "leaving:",[str(a) for a in leave] # On leave restore the captured colour for a in leave: if hasattr(a,'get_color'): a.set_color(self._active[a]) elif hasattr(a,'get_edgecolor'): a.set_edgecolor(self._active[a][0]) a.set_facecolor(self._active[a][1]) del self._active[a] # On enter, capture the color and repaint the artist # with the highlight colour. Capturing colour has to # be done first in case the parent recolouring affects # the child. for a in enter: if hasattr(a,'get_color'): self._active[a] = a.get_color() elif hasattr(a,'get_edgecolor'): self._active[a] = (a.get_edgecolor(),a.get_facecolor()) else: self._active[a] = None for a in enter: if hasattr(a,'get_color'): a.set_color('red') elif hasattr(a,'get_edgecolor'): a.set_edgecolor('red') a.set_facecolor('lightblue') else: self._active[a] = None self.draw_idle() def pick(self, mouseevent): if not self.widgetlock.locked(): self.figure.pick(mouseevent) def blit(self, bbox=None): """ blit the canvas in bbox (default entire canvas) """ pass def resize(self, w, h): """ set the canvas size in pixels """ pass def draw_event(self, renderer): """ This method will be call all functions connected to the 'draw_event' with a :class:`DrawEvent` """ s = 'draw_event' event = DrawEvent(s, self, renderer) self.callbacks.process(s, event) def resize_event(self): """ This method will be call all functions connected to the 'resize_event' with a :class:`ResizeEvent` """ s = 'resize_event' event = ResizeEvent(s, self) self.callbacks.process(s, event) def key_press_event(self, key, guiEvent=None): """ This method will be call all functions connected to the 'key_press_event' with a :class:`KeyEvent` """ self._key = key s = 'key_press_event' event = KeyEvent(s, self, key, self._lastx, self._lasty, guiEvent=guiEvent) self.callbacks.process(s, event) def key_release_event(self, key, guiEvent=None): """ This method will be call all functions connected to the 'key_release_event' with a :class:`KeyEvent` """ s = 'key_release_event' event = KeyEvent(s, self, key, self._lastx, self._lasty, guiEvent=guiEvent) self.callbacks.process(s, event) self._key = None def pick_event(self, mouseevent, artist, kwargs): """ This method will be called by artists who are picked and will fire off :class:`PickEvent` callbacks registered listeners """ s = 'pick_event' event = PickEvent(s, self, mouseevent, artist, kwargs) self.callbacks.process(s, event) def scroll_event(self, x, y, step, guiEvent=None): """ Backend derived classes should call this function on any scroll wheel event. x,y are the canvas coords: 0,0 is lower, left. button and key are as defined in MouseEvent. This method will be call all functions connected to the 'scroll_event' with a :class:`MouseEvent` instance. """ if step >= 0: self._button = 'up' else: self._button = 'down' s = 'scroll_event' mouseevent = MouseEvent(s, self, x, y, self._button, self._key, step=step, guiEvent=guiEvent) self.callbacks.process(s, mouseevent) def button_press_event(self, x, y, button, guiEvent=None): """ Backend derived classes should call this function on any mouse button press. x,y are the canvas coords: 0,0 is lower, left. button and key are as defined in :class:`MouseEvent`. This method will be call all functions connected to the 'button_press_event' with a :class:`MouseEvent` instance. """ self._button = button s = 'button_press_event' mouseevent = MouseEvent(s, self, x, y, button, self._key, guiEvent=guiEvent) self.callbacks.process(s, mouseevent) def button_release_event(self, x, y, button, guiEvent=None): """ Backend derived classes should call this function on any mouse button release. x the canvas coordinates where 0=left y the canvas coordinates where 0=bottom guiEvent the native UI event that generated the mpl event This method will be call all functions connected to the 'button_release_event' with a :class:`MouseEvent` instance. """ s = 'button_release_event' event = MouseEvent(s, self, x, y, button, self._key, guiEvent=guiEvent) self.callbacks.process(s, event) self._button = None def motion_notify_event(self, x, y, guiEvent=None): """ Backend derived classes should call this function on any motion-notify-event. x the canvas coordinates where 0=left y the canvas coordinates where 0=bottom guiEvent the native UI event that generated the mpl event This method will be call all functions connected to the 'motion_notify_event' with a :class:`MouseEvent` instance. """ self._lastx, self._lasty = x, y s = 'motion_notify_event' event = MouseEvent(s, self, x, y, self._button, self._key, guiEvent=guiEvent) self.callbacks.process(s, event) def leave_notify_event(self, guiEvent=None): """ Backend derived classes should call this function when leaving canvas guiEvent the native UI event that generated the mpl event """ self.callbacks.process('figure_leave_event', LocationEvent.lastevent) LocationEvent.lastevent = None def enter_notify_event(self, guiEvent=None): """ Backend derived classes should call this function when entering canvas guiEvent the native UI event that generated the mpl event """ event = Event('figure_enter_event', self, guiEvent) self.callbacks.process('figure_enter_event', event) def idle_event(self, guiEvent=None): 'call when GUI is idle' s = 'idle_event' event = IdleEvent(s, self, guiEvent=guiEvent) self.callbacks.process(s, event) def draw(self, args, kwargs): """ Render the :class:`~matplotlib.figure.Figure` """ pass def draw_idle(self, args, *kwargs): """ :meth:`draw` only if idle; defaults to draw but backends can overrride """ self.draw(args, *kwargs) def draw_cursor(self, event): """ Draw a cursor in the event.axes if inaxes is not None. Use native GUI drawing for efficiency if possible """ pass def get_width_height(self): """ return the figure width and height in points or pixels (depending on the backend), truncated to integers """ return int(self.figure.bbox.width), int(self.figure.bbox.height) filetypes = { 'emf': 'Enhanced Metafile', 'eps': 'Encapsulated Postscript', 'pdf': 'Portable Document Format', 'png': 'Portable Network Graphics', 'ps' : 'Postscript', 'raw': 'Raw RGBA bitmap', 'rgba': 'Raw RGBA bitmap', 'svg': 'Scalable Vector Graphics', 'svgz': 'Scalable Vector Graphics' } # All of these print_ functions do a lazy import because # a) otherwise we'd have cyclical imports, since all of these # classes inherit from FigureCanvasBase # b) so we don't import a bunch of stuff the user may never use def print_emf(self, args, kwargs): from backends.backend_emf import FigureCanvasEMF # lazy import emf = self.switch_backends(FigureCanvasEMF) return emf.print_emf(args, *kwargs) def print_eps(self, args, *kwargs): from backends.backend_ps import FigureCanvasPS # lazy import ps = self.switch_backends(FigureCanvasPS) return ps.print_eps(args, *kwargs) def print_pdf(self, args, *kwargs): from backends.backend_pdf import FigureCanvasPdf # lazy import pdf = self.switch_backends(FigureCanvasPdf) return pdf.print_pdf(args, *kwargs) def print_png(self, args, *kwargs): from backends.backend_agg import FigureCanvasAgg # lazy import agg = self.switch_backends(FigureCanvasAgg) return agg.print_png(args, *kwargs) def print_ps(self, args, *kwargs): from backends.backend_ps import FigureCanvasPS # lazy import ps = self.switch_backends(FigureCanvasPS) return ps.print_ps(args, *kwargs) def print_raw(self, args, *kwargs): from backends.backend_agg import FigureCanvasAgg # lazy import agg = self.switch_backends(FigureCanvasAgg) return agg.print_raw(args, *kwargs) print_bmp = print_rgb = print_raw def print_svg(self, args, *kwargs): from backends.backend_svg import FigureCanvasSVG # lazy import svg = self.switch_backends(FigureCanvasSVG) return svg.print_svg(args, *kwargs) def print_svgz(self, args, *kwargs): from backends.backend_svg import FigureCanvasSVG # lazy import svg = self.switch_backends(FigureCanvasSVG) return svg.print_svgz(args, kwargs) def get_supported_filetypes(self): return self.filetypes def get_supported_filetypes_grouped(self): groupings = {} for ext, name in self.filetypes.items(): groupings.setdefault(name, []).append(ext) groupings[name].sort() return groupings def print_figure(self, filename, dpi=None, facecolor='w', edgecolor='w', orientation='portrait', format=None, kwargs): """ Render the figure to hardcopy. Set the figure patch face and edge colors. This is useful because some of the GUIs have a gray figure face color background and you'll probably want to override this on hardcopy. Arguments are: filename can also be a file object on image backends orientation only currently applies to PostScript printing. dpi the dots per inch to save the figure in; if None, use savefig.dpi facecolor the facecolor of the figure edgecolor the edgecolor of the figure orientation ' landscape' \| 'portrait' (not supported on all backends) format when set, forcibly set the file format to save to """ if format is None: if cbook.is_string_like(filename): format = os.path.splitext(filename)[1][1:] if format is None or format == '': format = self.get_default_filetype() if cbook.is_string_like(filename): filename = filename.rstrip('.') + '.' + format format = format.lower() method_name = 'print_%s' % format if (format not in self.filetypes or not hasattr(self, method_name)): formats = self.filetypes.keys() formats.sort() raise ValueError( 'Format "%s" is not supported.\n' 'Supported formats: ' '%s.' % (format, ', '.join(formats))) if dpi is None: dpi = rcParams['savefig.dpi'] origDPI = self.figure.dpi origfacecolor = self.figure.get_facecolor() origedgecolor = self.figure.get_edgecolor() self.figure.dpi = dpi self.figure.set_facecolor(facecolor) self.figure.set_edgecolor(edgecolor) try: result = getattr(self, method_name)( filename, dpi=dpi, facecolor=facecolor, edgecolor=edgecolor, orientation=orientation, *kwargs) finally: self.figure.dpi = origDPI self.figure.set_facecolor(origfacecolor) self.figure.set_edgecolor(origedgecolor) self.figure.set_canvas(self) #self.figure.canvas.draw() ## seems superfluous return result def get_default_filetype(self): raise NotImplementedError def set_window_title(self, title): """ Set the title text of the window containing the figure. Note that this has no effect if there is no window (eg, a PS backend). """ if hasattr(self, "manager"): self.manager.set_window_title(title) def switch_backends(self, FigureCanvasClass): """ instantiate an instance of FigureCanvasClass This is used for backend switching, eg, to instantiate a FigureCanvasPS from a FigureCanvasGTK. Note, deep copying is not done, so any changes to one of the instances (eg, setting figure size or line props), will be reflected in the other """ newCanvas = FigureCanvasClass(self.figure) return newCanvas def mpl_connect(self, s, func): """ Connect event with string s* to func. The signature of func is:: def func(event) where event is a :class:`matplotlib.backend_bases.Event`. The following events are recognized - 'button_press_event' - 'button_release_event' - 'draw_event' - 'key_press_event' - 'key_release_event' - 'motion_notify_event' - 'pick_event' - 'resize_event' - 'scroll_event' For the location events (button and key press/release), if the mouse is over the axes, the variable ``event.inaxes`` will be set to the :class:`~matplotlib.axes.Axes` the event occurs is over, and additionally, the variables ``event.xdata`` and ``event.ydata`` will be defined. This is the mouse location in data coords. See :class:`~matplotlib.backend_bases.KeyEvent` and :class:`~matplotlib.backend_bases.MouseEvent` for more info. Return value is a connection id that can be used with :meth:`~matplotlib.backend_bases.Event.mpl_disconnect`. Example usage:: def on_press(event): print 'you pressed', event.button, event.xdata, event.ydata cid = canvas.mpl_connect('button_press_event', on_press) """ return self.callbacks.connect(s, func) def mpl_disconnect(self, cid): """ disconnect callback id cid Example usage:: cid = canvas.mpl_connect('button_press_event', on_press) #...later canvas.mpl_disconnect(cid) """ return self.callbacks.disconnect(cid) def flush_events(self): """ Flush the GUI events for the figure. Implemented only for backends with GUIs. """ raise NotImplementedError def start_event_loop(self,timeout): """ Start an event loop. This is used to start a blocking event loop so that interactive functions, such as ginput and waitforbuttonpress, can wait for events. This should not be confused with the main GUI event loop, which is always running and has nothing to do with this. This is implemented only for backends with GUIs. """ raise NotImplementedError def stop_event_loop(self): """ Stop an event loop. This is used to stop a blocking event loop so that interactive functions, such as ginput and waitforbuttonpress, can wait for events. This is implemented only for backends with GUIs. """ raise NotImplementedError def start_event_loop_default(self,timeout=0): """ Start an event loop. This is used to start a blocking event loop so that interactive functions, such as ginput and waitforbuttonpress, can wait for events. This should not be confused with the main GUI event loop, which is always running and has nothing to do with this. This function provides default event loop functionality based on time.sleep that is meant to be used until event loop functions for each of the GUI backends can be written. As such, it throws a deprecated warning. Call signature:: start_event_loop_default(self,timeout=0) This call blocks until a callback function triggers stop_event_loop() or timeout is reached. If timeout is <=0, never timeout. """ str = "Using default event loop until function specific" str += " to this GUI is implemented" warnings.warn(str,DeprecationWarning) if timeout <= 0: timeout = np.inf timestep = 0.01 counter = 0 self._looping = True while self._looping and countertimestep < timeout: self.flush_events() time.sleep(timestep) counter += 1 def stop_event_loop_default(self): """ Stop an event loop. This is used to stop a blocking event loop so that interactive functions, such as ginput and waitforbuttonpress, can wait for events. Call signature:: stop_event_loop_default(self) """ self._looping = False class FigureManagerBase: """ Helper class for matlab mode, wraps everything up into a neat bundle Public attibutes: canvas* A :class:`FigureCanvasBase` instance num The figure nuamber """ def __init__(self, canvas, num): self.canvas = canvas canvas.manager = self # store a pointer to parent self.num = num self.canvas.mpl_connect('key_press_event', self.key_press) def destroy(self): pass def full_screen_toggle (self): pass def resize(self, w, h): 'For gui backends: resize window in pixels' pass def key_press(self, event): # these bindings happen whether you are over an axes or not #if event.key == 'q': # self.destroy() # how cruel to have to destroy oneself! # return if event.key == 'f': self.full_screen_toggle() # home or reset mnemonic elif event.key == 'h' or event.key == 'r' or event.key == "home": self.canvas.toolbar.home() # c and v to enable left handed quick navigation elif event.key == 'left' or event.key == 'c' or event.key == 'backspace': self.canvas.toolbar.back() elif event.key == 'right' or event.key == 'v': self.canvas.toolbar.forward() # pan mnemonic elif event.key == 'p': self.canvas.toolbar.pan() # zoom mnemonic elif event.key == 'o': self.canvas.toolbar.zoom() elif event.key == 's': self.canvas.toolbar.save_figure(self.canvas.toolbar) if event.inaxes is None: return # the mouse has to be over an axes to trigger these if event.key == 'g': event.inaxes.grid() self.canvas.draw() elif event.key == 'l': ax = event.inaxes scale = ax.get_yscale() if scale=='log': ax.set_yscale('linear') ax.figure.canvas.draw() elif scale=='linear': ax.set_yscale('log') ax.figure.canvas.draw() elif event.key is not None and (event.key.isdigit() and event.key!='0') or event.key=='a': # 'a' enables all axes if event.key!='a': n=int(event.key)-1 for i, a in enumerate(self.canvas.figure.get_axes()): if event.x is not None and event.y is not None and a.in_axes(event): if event.key=='a': a.set_navigate(True) else: a.set_navigate(i==n) def show_popup(self, msg): """ Display message in a popup -- GUI only """ pass def set_window_title(self, title): """ Set the title text of the window containing the figure. Note that this has no effect if there is no window (eg, a PS backend). """ pass # cursors class Cursors: #namespace HAND, POINTER, SELECT_REGION, MOVE = range(4) cursors = Cursors() class NavigationToolbar2: """ Base class for the navigation cursor, version 2 backends must implement a canvas that handles connections for 'button_press_event' and 'button_release_event'. See :meth:`FigureCanvasBase.mpl_connect` for more information They must also define :meth:`save_figure` save the current figure :meth:`set_cursor` if you want the pointer icon to change :meth:`_init_toolbar` create your toolbar widget :meth:`draw_rubberband` (optional) draw the zoom to rect "rubberband" rectangle :meth:`press` (optional) whenever a mouse button is pressed, you'll be notified with the event :meth:`release` (optional) whenever a mouse button is released, you'll be notified with the event :meth:`dynamic_update` (optional) dynamically update the window while navigating :meth:`set_message` (optional) display message :meth:`set_history_buttons` (optional) you can change the history back / forward buttons to indicate disabled / enabled state. That's it, we'll do the rest! """ def __init__(self, canvas): self.canvas = canvas canvas.toolbar = self # a dict from axes index to a list of view limits self._views = cbook.Stack() self._positions = cbook.Stack() # stack of subplot positions self._xypress = None # the location and axis info at the time of the press self._idPress = None self._idRelease = None self._active = None self._lastCursor = None self._init_toolbar() self._idDrag=self.canvas.mpl_connect('motion_notify_event', self.mouse_move) self._button_pressed = None # determined by the button pressed at start self.mode = '' # a mode string for the status bar self.set_history_buttons() def set_message(self, s): 'display a message on toolbar or in status bar' pass def back(self, args): 'move back up the view lim stack' self._views.back() self._positions.back() self.set_history_buttons() self._update_view() def dynamic_update(self): pass def draw_rubberband(self, event, x0, y0, x1, y1): 'draw a rectangle rubberband to indicate zoom limits' pass def forward(self, args): 'move forward in the view lim stack' self._views.forward() self._positions.forward() self.set_history_buttons() self._update_view() def home(self, args): 'restore the original view' self._views.home() self._positions.home() self.set_history_buttons() self._update_view() def _init_toolbar(self): """ This is where you actually build the GUI widgets (called by __init__). The icons ``home.xpm``, ``back.xpm``, ``forward.xpm``, ``hand.xpm``, ``zoom_to_rect.xpm`` and ``filesave.xpm`` are standard across backends (there are ppm versions in CVS also). You just need to set the callbacks home : self.home back : self.back forward : self.forward hand : self.pan zoom_to_rect : self.zoom filesave : self.save_figure You only need to define the last one - the others are in the base class implementation. """ raise NotImplementedError def mouse_move(self, event): #print 'mouse_move', event.button if not event.inaxes or not self._active: if self._lastCursor != cursors.POINTER: self.set_cursor(cursors.POINTER) self._lastCursor = cursors.POINTER else: if self._active=='ZOOM': if self._lastCursor != cursors.SELECT_REGION: self.set_cursor(cursors.SELECT_REGION) self._lastCursor = cursors.SELECT_REGION if self._xypress: x, y = event.x, event.y lastx, lasty, a, ind, lim, trans = self._xypress[0] self.draw_rubberband(event, x, y, lastx, lasty) elif (self._active=='PAN' and self._lastCursor != cursors.MOVE): self.set_cursor(cursors.MOVE) self._lastCursor = cursors.MOVE if event.inaxes and event.inaxes.get_navigate(): try: s = event.inaxes.format_coord(event.xdata, event.ydata) except ValueError: pass except OverflowError: pass else: if len(self.mode): self.set_message('%s : %s' % (self.mode, s)) else: self.set_message(s) else: self.set_message(self.mode) def pan(self,args): 'Activate the pan/zoom tool. pan with left button, zoom with right' # set the pointer icon and button press funcs to the # appropriate callbacks if self._active == 'PAN': self._active = None else: self._active = 'PAN' if self._idPress is not None: self._idPress = self.canvas.mpl_disconnect(self._idPress) self.mode = '' if self._idRelease is not None: self._idRelease = self.canvas.mpl_disconnect(self._idRelease) self.mode = '' if self._active: self._idPress = self.canvas.mpl_connect( 'button_press_event', self.press_pan) self._idRelease = self.canvas.mpl_connect( 'button_release_event', self.release_pan) self.mode = 'pan/zoom mode' self.canvas.widgetlock(self) else: self.canvas.widgetlock.release(self) for a in self.canvas.figure.get_axes(): a.set_navigate_mode(self._active) self.set_message(self.mode) def press(self, event): 'this will be called whenver a mouse button is pressed' pass def press_pan(self, event): 'the press mouse button in pan/zoom mode callback' if event.button == 1: self._button_pressed=1 elif event.button == 3: self._button_pressed=3 else: self._button_pressed=None return x, y = event.x, event.y # push the current view to define home if stack is empty if self._views.empty(): self.push_current() self._xypress=[] for i, a in enumerate(self.canvas.figure.get_axes()): if x is not None and y is not None and a.in_axes(event) and a.get_navigate(): a.start_pan(x, y, event.button) self._xypress.append((a, i)) self.canvas.mpl_disconnect(self._idDrag) self._idDrag=self.canvas.mpl_connect('motion_notify_event', self.drag_pan) self.press(event) def press_zoom(self, event): 'the press mouse button in zoom to rect mode callback' if event.button == 1: self._button_pressed=1 elif event.button == 3: self._button_pressed=3 else: self._button_pressed=None return x, y = event.x, event.y # push the current view to define home if stack is empty if self._views.empty(): self.push_current() self._xypress=[] for i, a in enumerate(self.canvas.figure.get_axes()): if x is not None and y is not None and a.in_axes(event) \ and a.get_navigate() and a.can_zoom(): self._xypress.append(( x, y, a, i, a.viewLim.frozen(), a.transData.frozen())) self.press(event) def push_current(self): 'push the current view limits and position onto the stack' lims = []; pos = [] for a in self.canvas.figure.get_axes(): xmin, xmax = a.get_xlim() ymin, ymax = a.get_ylim() lims.append( (xmin, xmax, ymin, ymax) ) # Store both the original and modified positions pos.append( ( a.get_position(True).frozen(), a.get_position().frozen() ) ) self._views.push(lims) self._positions.push(pos) self.set_history_buttons() def release(self, event): 'this will be called whenever mouse button is released' pass def release_pan(self, event): 'the release mouse button callback in pan/zoom mode' self.canvas.mpl_disconnect(self._idDrag) self._idDrag=self.canvas.mpl_connect('motion_notify_event', self.mouse_move) for a, ind in self._xypress: a.end_pan() if not self._xypress: return self._xypress = [] self._button_pressed=None self.push_current() self.release(event) self.draw() def drag_pan(self, event): 'the drag callback in pan/zoom mode' for a, ind in self._xypress: #safer to use the recorded button at the press than current button: #multiple button can get pressed during motion... a.drag_pan(self._button_pressed, event.key, event.x, event.y) self.dynamic_update() def release_zoom(self, event): 'the release mouse button callback in zoom to rect mode' if not self._xypress: return last_a = [] for cur_xypress in self._xypress: x, y = event.x, event.y lastx, lasty, a, ind, lim, trans = cur_xypress # ignore singular clicks - 5 pixels is a threshold if abs(x-lastx)<5 or abs(y-lasty)<5: self._xypress = None self.release(event) self.draw() return x0, y0, x1, y1 = lim.extents # zoom to rect inverse = a.transData.inverted() lastx, lasty = inverse.transform_point( (lastx, lasty) ) x, y = inverse.transform_point( (x, y) ) Xmin,Xmax=a.get_xlim() Ymin,Ymax=a.get_ylim() # detect twinx,y axes and avoid double zooming twinx, twiny = False, False if last_a: for la in last_a: if a.get_shared_x_axes().joined(a,la): twinx=True if a.get_shared_y_axes().joined(a,la): twiny=True last_a.append(a) if twinx: x0, x1 = Xmin, Xmax else: if Xmin < Xmax: if x<lastx: x0, x1 = x, lastx else: x0, x1 = lastx, x if x0 < Xmin: x0=Xmin if x1 > Xmax: x1=Xmax else: if x>lastx: x0, x1 = x, lastx else: x0, x1 = lastx, x if x0 > Xmin: x0=Xmin if x1 < Xmax: x1=Xmax if twiny: y0, y1 = Ymin, Ymax else: if Ymin < Ymax: if y<lasty: y0, y1 = y, lasty else: y0, y1 = lasty, y if y0 < Ymin: y0=Ymin if y1 > Ymax: y1=Ymax else: if y>lasty: y0, y1 = y, lasty else: y0, y1 = lasty, y if y0 > Ymin: y0=Ymin if y1 < Ymax: y1=Ymax if self._button_pressed == 1: a.set_xlim((x0, x1)) a.set_ylim((y0, y1)) elif self._button_pressed == 3: if a.get_xscale()=='log': alpha=np.log(Xmax/Xmin)/np.log(x1/x0) rx1=pow(Xmin/x0,alpha)Xmin rx2=pow(Xmax/x0,alpha)Xmin else: alpha=(Xmax-Xmin)/(x1-x0) rx1=alpha(Xmin-x0)+Xmin rx2=alpha(Xmax-x0)+Xmin if a.get_yscale()=='log': alpha=np.log(Ymax/Ymin)/np.log(y1/y0) ry1=pow(Ymin/y0,alpha)Ymin ry2=pow(Ymax/y0,alpha)Ymin else: alpha=(Ymax-Ymin)/(y1-y0) ry1=alpha(Ymin-y0)+Ymin ry2=alpha(Ymax-y0)+Ymin a.set_xlim((rx1, rx2)) a.set_ylim((ry1, ry2)) self.draw() self._xypress = None self._button_pressed = None self.push_current() self.release(event) def draw(self): 'redraw the canvases, update the locators' for a in self.canvas.figure.get_axes(): xaxis = getattr(a, 'xaxis', None) yaxis = getattr(a, 'yaxis', None) locators = [] if xaxis is not None: locators.append(xaxis.get_major_locator()) locators.append(xaxis.get_minor_locator()) if yaxis is not None: locators.append(yaxis.get_major_locator()) locators.append(yaxis.get_minor_locator()) for loc in locators: loc.refresh() self.canvas.draw() def _update_view(self): '''update the viewlim and position from the view and position stack for each axes ''' lims = self._views() if lims is None: return pos = self._positions() if pos is None: return for i, a in enumerate(self.canvas.figure.get_axes()): xmin, xmax, ymin, ymax = lims[i] a.set_xlim((xmin, xmax)) a.set_ylim((ymin, ymax)) # Restore both the original and modified positions a.set_position( pos[i][0], 'original' ) a.set_position( pos[i][1], 'active' ) self.draw() def save_figure(self, args): 'save the current figure' raise NotImplementedError def set_cursor(self, cursor): """ Set the current cursor to one of the :class:`Cursors` enums values """ pass def update(self): 'reset the axes stack' self._views.clear() self._positions.clear() self.set_history_buttons() def zoom(self, args): 'activate zoom to rect mode' if self._active == 'ZOOM': self._active = None else: self._active = 'ZOOM' if self._idPress is not None: self._idPress=self.canvas.mpl_disconnect(self._idPress) self.mode = '' if self._idRelease is not None: self._idRelease=self.canvas.mpl_disconnect(self._idRelease) self.mode = '' if self._active: self._idPress = self.canvas.mpl_connect('button_press_event', self.press_zoom) self._idRelease = self.canvas.mpl_connect('button_release_event', self.release_zoom) self.mode = 'Zoom to rect mode' self.canvas.widgetlock(self) else: self.canvas.widgetlock.release(self) for a in self.canvas.figure.get_axes(): a.set_navigate_mode(self._active) self.set_message(self.mode) def set_history_buttons(self): 'enable or disable back/forward button' pass	69,740	Python	.py	1,718	30.583236	98	0.59163	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,268	_mathtext_data.py	numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/_mathtext_data.py	""" font data tables for truetype and afm computer modern fonts """ # this dict maps symbol names to fontnames, glyphindex. To get the # glyph index from the character code, you have to use get_charmap """ from matplotlib.ft2font import FT2Font font = FT2Font('/usr/local/share/matplotlib/cmr10.ttf') items = font.get_charmap().items() items.sort() for charcode, glyphind in items: print charcode, glyphind """ latex_to_bakoma = { r'\oint' : ('cmex10', 45), r'\bigodot' : ('cmex10', 50), r'\bigoplus' : ('cmex10', 55), r'\bigotimes' : ('cmex10', 59), r'\sum' : ('cmex10', 51), r'\prod' : ('cmex10', 24), r'\int' : ('cmex10', 56), r'\bigcup' : ('cmex10', 28), r'\bigcap' : ('cmex10', 60), r'\biguplus' : ('cmex10', 32), r'\bigwedge' : ('cmex10', 4), r'\bigvee' : ('cmex10', 37), r'\coprod' : ('cmex10', 42), r'\__sqrt__' : ('cmex10', 48), r'\leftbrace' : ('cmex10', 92), r'{' : ('cmex10', 92), r'\{' : ('cmex10', 92), r'\rightbrace' : ('cmex10', 130), r'}' : ('cmex10', 130), r'\}' : ('cmex10', 130), r'\leftangle' : ('cmex10', 97), r'\rightangle' : ('cmex10', 64), r'\langle' : ('cmex10', 97), r'\rangle' : ('cmex10', 64), r'\widehat' : ('cmex10', 15), r'\widetilde' : ('cmex10', 52), r'\omega' : ('cmmi10', 29), r'\varepsilon' : ('cmmi10', 20), r'\vartheta' : ('cmmi10', 22), r'\varrho' : ('cmmi10', 61), r'\varsigma' : ('cmmi10', 41), r'\varphi' : ('cmmi10', 6), r'\leftharpoonup' : ('cmmi10', 108), r'\leftharpoondown' : ('cmmi10', 68), r'\rightharpoonup' : ('cmmi10', 117), r'\rightharpoondown' : ('cmmi10', 77), r'\triangleright' : ('cmmi10', 130), r'\triangleleft' : ('cmmi10', 89), r'.' : ('cmmi10', 51), r',' : ('cmmi10', 44), r'<' : ('cmmi10', 99), r'/' : ('cmmi10', 98), r'>' : ('cmmi10', 107), r'\flat' : ('cmmi10', 131), r'\natural' : ('cmmi10', 90), r'\sharp' : ('cmmi10', 50), r'\smile' : ('cmmi10', 97), r'\frown' : ('cmmi10', 58), r'\ell' : ('cmmi10', 102), r'\imath' : ('cmmi10', 8), r'\jmath' : ('cmmi10', 65), r'\wp' : ('cmmi10', 14), r'\alpha' : ('cmmi10', 13), r'\beta' : ('cmmi10', 35), r'\gamma' : ('cmmi10', 24), r'\delta' : ('cmmi10', 38), r'\epsilon' : ('cmmi10', 54), r'\zeta' : ('cmmi10', 10), r'\eta' : ('cmmi10', 5), r'\theta' : ('cmmi10', 18), r'\iota' : ('cmmi10', 28), r'\lambda' : ('cmmi10', 9), r'\mu' : ('cmmi10', 32), r'\nu' : ('cmmi10', 34), r'\xi' : ('cmmi10', 7), r'\pi' : ('cmmi10', 36), r'\kappa' : ('cmmi10', 30), r'\rho' : ('cmmi10', 39), r'\sigma' : ('cmmi10', 21), r'\tau' : ('cmmi10', 43), r'\upsilon' : ('cmmi10', 25), r'\phi' : ('cmmi10', 42), r'\chi' : ('cmmi10', 17), r'\psi' : ('cmmi10', 31), r'\|' : ('cmsy10', 47), r'\\|' : ('cmsy10', 47), r'(' : ('cmr10', 119), r'\leftparen' : ('cmr10', 119), r'\rightparen' : ('cmr10', 68), r')' : ('cmr10', 68), r'+' : ('cmr10', 76), r'0' : ('cmr10', 40), r'1' : ('cmr10', 100), r'2' : ('cmr10', 49), r'3' : ('cmr10', 110), r'4' : ('cmr10', 59), r'5' : ('cmr10', 120), r'6' : ('cmr10', 69), r'7' : ('cmr10', 127), r'8' : ('cmr10', 77), r'9' : ('cmr10', 22), r':' : ('cmr10', 85), r';' : ('cmr10', 31), r'=' : ('cmr10', 41), r'\leftbracket' : ('cmr10', 62), r'[' : ('cmr10', 62), r'\rightbracket' : ('cmr10', 72), r']' : ('cmr10', 72), r'\%' : ('cmr10', 48), r'%' : ('cmr10', 48), r'\$' : ('cmr10', 99), r'@' : ('cmr10', 111), r'\_' : ('cmtt10', 79), r'\Gamma' : ('cmr10', 19), r'\Delta' : ('cmr10', 6), r'\Theta' : ('cmr10', 7), r'\Lambda' : ('cmr10', 14), r'\Xi' : ('cmr10', 3), r'\Pi' : ('cmr10', 17), r'\Sigma' : ('cmr10', 10), r'\Upsilon' : ('cmr10', 11), r'\Phi' : ('cmr10', 9), r'\Psi' : ('cmr10', 15), r'\Omega' : ('cmr10', 12), # these are mathml names, I think. I'm just using them for the # tex methods noted r'\circumflexaccent' : ('cmr10', 124), # for \hat r'\combiningbreve' : ('cmr10', 81), # for \breve r'\combiningoverline' : ('cmr10', 131), # for \bar r'\combininggraveaccent' : ('cmr10', 114), # for \grave r'\combiningacuteaccent' : ('cmr10', 63), # for \accute r'\combiningdiaeresis' : ('cmr10', 91), # for \ddot r'\combiningtilde' : ('cmr10', 75), # for \tilde r'\combiningrightarrowabove' : ('cmmi10', 110), # for \vec r'\combiningdotabove' : ('cmr10', 26), # for \dot r'\leftarrow' : ('cmsy10', 10), r'\uparrow' : ('cmsy10', 25), r'\downarrow' : ('cmsy10', 28), r'\leftrightarrow' : ('cmsy10', 24), r'\nearrow' : ('cmsy10', 99), r'\searrow' : ('cmsy10', 57), r'\simeq' : ('cmsy10', 108), r'\Leftarrow' : ('cmsy10', 104), r'\Rightarrow' : ('cmsy10', 112), r'\Uparrow' : ('cmsy10', 60), r'\Downarrow' : ('cmsy10', 68), r'\Leftrightarrow' : ('cmsy10', 51), r'\nwarrow' : ('cmsy10', 65), r'\swarrow' : ('cmsy10', 116), r'\propto' : ('cmsy10', 15), r'\prime' : ('cmsy10', 73), r"'" : ('cmsy10', 73), r'\infty' : ('cmsy10', 32), r'\in' : ('cmsy10', 59), r'\ni' : ('cmsy10', 122), r'\bigtriangleup' : ('cmsy10', 80), r'\bigtriangledown' : ('cmsy10', 132), r'\slash' : ('cmsy10', 87), r'\forall' : ('cmsy10', 21), r'\exists' : ('cmsy10', 5), r'\neg' : ('cmsy10', 20), r'\emptyset' : ('cmsy10', 33), r'\Re' : ('cmsy10', 95), r'\Im' : ('cmsy10', 52), r'\top' : ('cmsy10', 100), r'\bot' : ('cmsy10', 11), r'\aleph' : ('cmsy10', 26), r'\cup' : ('cmsy10', 6), r'\cap' : ('cmsy10', 19), r'\uplus' : ('cmsy10', 58), r'\wedge' : ('cmsy10', 43), r'\vee' : ('cmsy10', 96), r'\vdash' : ('cmsy10', 109), r'\dashv' : ('cmsy10', 66), r'\lfloor' : ('cmsy10', 117), r'\rfloor' : ('cmsy10', 74), r'\lceil' : ('cmsy10', 123), r'\rceil' : ('cmsy10', 81), r'\lbrace' : ('cmsy10', 92), r'\rbrace' : ('cmsy10', 105), r'\mid' : ('cmsy10', 47), r'\vert' : ('cmsy10', 47), r'\Vert' : ('cmsy10', 44), r'\updownarrow' : ('cmsy10', 94), r'\Updownarrow' : ('cmsy10', 53), r'\backslash' : ('cmsy10', 126), r'\wr' : ('cmsy10', 101), r'\nabla' : ('cmsy10', 110), r'\sqcup' : ('cmsy10', 67), r'\sqcap' : ('cmsy10', 118), r'\sqsubseteq' : ('cmsy10', 75), r'\sqsupseteq' : ('cmsy10', 124), r'\S' : ('cmsy10', 129), r'\dag' : ('cmsy10', 71), r'\ddag' : ('cmsy10', 127), r'\P' : ('cmsy10', 130), r'\clubsuit' : ('cmsy10', 18), r'\diamondsuit' : ('cmsy10', 34), r'\heartsuit' : ('cmsy10', 22), r'-' : ('cmsy10', 17), r'\cdot' : ('cmsy10', 78), r'\times' : ('cmsy10', 13), r'*' : ('cmsy10', 9), r'\ast' : ('cmsy10', 9), r'\div' : ('cmsy10', 31), r'\diamond' : ('cmsy10', 48), r'\pm' : ('cmsy10', 8), r'\mp' : ('cmsy10', 98), r'\oplus' : ('cmsy10', 16), r'\ominus' : ('cmsy10', 56), r'\otimes' : ('cmsy10', 30), r'\oslash' : ('cmsy10', 107), r'\odot' : ('cmsy10', 64), r'\bigcirc' : ('cmsy10', 115), r'\circ' : ('cmsy10', 72), r'\bullet' : ('cmsy10', 84), r'\asymp' : ('cmsy10', 121), r'\equiv' : ('cmsy10', 35), r'\subseteq' : ('cmsy10', 103), r'\supseteq' : ('cmsy10', 42), r'\leq' : ('cmsy10', 14), r'\geq' : ('cmsy10', 29), r'\preceq' : ('cmsy10', 79), r'\succeq' : ('cmsy10', 131), r'\sim' : ('cmsy10', 27), r'\approx' : ('cmsy10', 23), r'\subset' : ('cmsy10', 50), r'\supset' : ('cmsy10', 86), r'\ll' : ('cmsy10', 85), r'\gg' : ('cmsy10', 40), r'\prec' : ('cmsy10', 93), r'\succ' : ('cmsy10', 49), r'\rightarrow' : ('cmsy10', 12), r'\to' : ('cmsy10', 12), r'\spadesuit' : ('cmsy10', 7), } latex_to_cmex = { r'\__sqrt__' : 112, r'\bigcap' : 92, r'\bigcup' : 91, r'\bigodot' : 75, r'\bigoplus' : 77, r'\bigotimes' : 79, r'\biguplus' : 93, r'\bigvee' : 95, r'\bigwedge' : 94, r'\coprod' : 97, r'\int' : 90, r'\leftangle' : 173, r'\leftbrace' : 169, r'\oint' : 73, r'\prod' : 89, r'\rightangle' : 174, r'\rightbrace' : 170, r'\sum' : 88, r'\widehat' : 98, r'\widetilde' : 101, } latex_to_standard = { r'\cong' : ('psyr', 64), r'\Delta' : ('psyr', 68), r'\Phi' : ('psyr', 70), r'\Gamma' : ('psyr', 89), r'\alpha' : ('psyr', 97), r'\beta' : ('psyr', 98), r'\chi' : ('psyr', 99), r'\delta' : ('psyr', 100), r'\varepsilon' : ('psyr', 101), r'\phi' : ('psyr', 102), r'\gamma' : ('psyr', 103), r'\eta' : ('psyr', 104), r'\iota' : ('psyr', 105), r'\varpsi' : ('psyr', 106), r'\kappa' : ('psyr', 108), r'\nu' : ('psyr', 110), r'\pi' : ('psyr', 112), r'\theta' : ('psyr', 113), r'\rho' : ('psyr', 114), r'\sigma' : ('psyr', 115), r'\tau' : ('psyr', 116), r'\upsilon' : ('psyr', 117), r'\varpi' : ('psyr', 118), r'\omega' : ('psyr', 119), r'\xi' : ('psyr', 120), r'\psi' : ('psyr', 121), r'\zeta' : ('psyr', 122), r'\sim' : ('psyr', 126), r'\leq' : ('psyr', 163), r'\infty' : ('psyr', 165), r'\clubsuit' : ('psyr', 167), r'\diamondsuit' : ('psyr', 168), r'\heartsuit' : ('psyr', 169), r'\spadesuit' : ('psyr', 170), r'\leftrightarrow' : ('psyr', 171), r'\leftarrow' : ('psyr', 172), r'\uparrow' : ('psyr', 173), r'\rightarrow' : ('psyr', 174), r'\downarrow' : ('psyr', 175), r'\pm' : ('psyr', 176), r'\geq' : ('psyr', 179), r'\times' : ('psyr', 180), r'\propto' : ('psyr', 181), r'\partial' : ('psyr', 182), r'\bullet' : ('psyr', 183), r'\div' : ('psyr', 184), r'\neq' : ('psyr', 185), r'\equiv' : ('psyr', 186), r'\approx' : ('psyr', 187), r'\ldots' : ('psyr', 188), r'\aleph' : ('psyr', 192), r'\Im' : ('psyr', 193), r'\Re' : ('psyr', 194), r'\wp' : ('psyr', 195), r'\otimes' : ('psyr', 196), r'\oplus' : ('psyr', 197), r'\oslash' : ('psyr', 198), r'\cap' : ('psyr', 199), r'\cup' : ('psyr', 200), r'\supset' : ('psyr', 201), r'\supseteq' : ('psyr', 202), r'\subset' : ('psyr', 204), r'\subseteq' : ('psyr', 205), r'\in' : ('psyr', 206), r'\notin' : ('psyr', 207), r'\angle' : ('psyr', 208), r'\nabla' : ('psyr', 209), r'\textregistered' : ('psyr', 210), r'\copyright' : ('psyr', 211), r'\texttrademark' : ('psyr', 212), r'\Pi' : ('psyr', 213), r'\prod' : ('psyr', 213), r'\surd' : ('psyr', 214), r'\__sqrt__' : ('psyr', 214), r'\cdot' : ('psyr', 215), r'\urcorner' : ('psyr', 216), r'\vee' : ('psyr', 217), r'\wedge' : ('psyr', 218), r'\Leftrightarrow' : ('psyr', 219), r'\Leftarrow' : ('psyr', 220), r'\Uparrow' : ('psyr', 221), r'\Rightarrow' : ('psyr', 222), r'\Downarrow' : ('psyr', 223), r'\Diamond' : ('psyr', 224), r'\langle' : ('psyr', 225), r'\Sigma' : ('psyr', 229), r'\sum' : ('psyr', 229), r'\forall' : ('psyr', 34), r'\exists' : ('psyr', 36), r'\lceil' : ('psyr', 233), r'\lbrace' : ('psyr', 123), r'\Psi' : ('psyr', 89), r'\bot' : ('psyr', 0136), r'\Omega' : ('psyr', 0127), r'\leftbracket' : ('psyr', 0133), r'\rightbracket' : ('psyr', 0135), r'\leftbrace' : ('psyr', 123), r'\leftparen' : ('psyr', 050), r'\prime' : ('psyr', 0242), r'\sharp' : ('psyr', 043), r'\slash' : ('psyr', 057), r'\Lamda' : ('psyr', 0114), r'\neg' : ('psyr', 0330), r'\Upsilon' : ('psyr', 0241), r'\rightbrace' : ('psyr', 0175), r'\rfloor' : ('psyr', 0373), r'\lambda' : ('psyr', 0154), r'\to' : ('psyr', 0256), r'\Xi' : ('psyr', 0130), r'\emptyset' : ('psyr', 0306), r'\lfloor' : ('psyr', 0353), r'\rightparen' : ('psyr', 051), r'\rceil' : ('psyr', 0371), r'\ni' : ('psyr', 047), r'\epsilon' : ('psyr', 0145), r'\Theta' : ('psyr', 0121), r'\langle' : ('psyr', 0341), r'\leftangle' : ('psyr', 0341), r'\rangle' : ('psyr', 0361), r'\rightangle' : ('psyr', 0361), r'\rbrace' : ('psyr', 0175), r'\circ' : ('psyr', 0260), r'\diamond' : ('psyr', 0340), r'\mu' : ('psyr', 0155), r'\mid' : ('psyr', 0352), r'\imath' : ('pncri8a', 105), r'\%' : ('pncr8a', 37), r'\$' : ('pncr8a', 36), r'\{' : ('pncr8a', 123), r'\}' : ('pncr8a', 125), r'\backslash' : ('pncr8a', 92), r'\ast' : ('pncr8a', 42), r'\circumflexaccent' : ('pncri8a', 124), # for \hat r'\combiningbreve' : ('pncri8a', 81), # for \breve r'\combininggraveaccent' : ('pncri8a', 114), # for \grave r'\combiningacuteaccent' : ('pncri8a', 63), # for \accute r'\combiningdiaeresis' : ('pncri8a', 91), # for \ddot r'\combiningtilde' : ('pncri8a', 75), # for \tilde r'\combiningrightarrowabove' : ('pncri8a', 110), # for \vec r'\combiningdotabove' : ('pncri8a', 26), # for \dot } # Automatically generated. type12uni = {'uni24C8': 9416, 'aring': 229, 'uni22A0': 8864, 'uni2292': 8850, 'quotedblright': 8221, 'uni03D2': 978, 'uni2215': 8725, 'uni03D0': 976, 'V': 86, 'dollar': 36, 'uni301E': 12318, 'uni03D5': 981, 'four': 52, 'uni25A0': 9632, 'uni013C': 316, 'uni013B': 315, 'uni013E': 318, 'Yacute': 221, 'uni25DE': 9694, 'uni013F': 319, 'uni255A': 9562, 'uni2606': 9734, 'uni0180': 384, 'uni22B7': 8887, 'uni044F': 1103, 'uni22B5': 8885, 'uni22B4': 8884, 'uni22AE': 8878, 'uni22B2': 8882, 'uni22B1': 8881, 'uni22B0': 8880, 'uni25CD': 9677, 'uni03CE': 974, 'uni03CD': 973, 'uni03CC': 972, 'uni03CB': 971, 'uni03CA': 970, 'uni22B8': 8888, 'uni22C9': 8905, 'uni0449': 1097, 'uni20DD': 8413, 'uni20DC': 8412, 'uni20DB': 8411, 'uni2231': 8753, 'uni25CF': 9679, 'uni306E': 12398, 'uni03D1': 977, 'uni01A1': 417, 'uni20D7': 8407, 'uni03D6': 982, 'uni2233': 8755, 'uni20D2': 8402, 'uni20D1': 8401, 'uni20D0': 8400, 'P': 80, 'uni22BE': 8894, 'uni22BD': 8893, 'uni22BC': 8892, 'uni22BB': 8891, 'underscore': 95, 'uni03C8': 968, 'uni03C7': 967, 'uni0328': 808, 'uni03C5': 965, 'uni03C4': 964, 'uni03C3': 963, 'uni03C2': 962, 'uni03C1': 961, 'uni03C0': 960, 'uni2010': 8208, 'uni0130': 304, 'uni0133': 307, 'uni0132': 306, 'uni0135': 309, 'uni0134': 308, 'uni0137': 311, 'uni0136': 310, 'uni0139': 313, 'uni0138': 312, 'uni2244': 8772, 'uni229A': 8858, 'uni2571': 9585, 'uni0278': 632, 'uni2239': 8761, 'p': 112, 'uni3019': 12313, 'uni25CB': 9675, 'uni03DB': 987, 'uni03DC': 988, 'uni03DA': 986, 'uni03DF': 991, 'uni03DD': 989, 'uni013D': 317, 'uni220A': 8714, 'uni220C': 8716, 'uni220B': 8715, 'uni220E': 8718, 'uni220D': 8717, 'uni220F': 8719, 'uni22CC': 8908, 'Otilde': 213, 'uni25E5': 9701, 'uni2736': 10038, 'perthousand': 8240, 'zero': 48, 'uni279B': 10139, 'dotlessi': 305, 'uni2279': 8825, 'Scaron': 352, 'zcaron': 382, 'uni21D8': 8664, 'egrave': 232, 'uni0271': 625, 'uni01AA': 426, 'uni2332': 9010, 'section': 167, 'uni25E4': 9700, 'Icircumflex': 206, 'ntilde': 241, 'uni041E': 1054, 'ampersand': 38, 'uni041C': 1052, 'uni041A': 1050, 'uni22AB': 8875, 'uni21DB': 8667, 'dotaccent': 729, 'uni0416': 1046, 'uni0417': 1047, 'uni0414': 1044, 'uni0415': 1045, 'uni0412': 1042, 'uni0413': 1043, 'degree': 176, 'uni0411': 1041, 'K': 75, 'uni25EB': 9707, 'uni25EF': 9711, 'uni0418': 1048, 'uni0419': 1049, 'uni2263': 8803, 'uni226E': 8814, 'uni2251': 8785, 'uni02C8': 712, 'uni2262': 8802, 'acircumflex': 226, 'uni22B3': 8883, 'uni2261': 8801, 'uni2394': 9108, 'Aring': 197, 'uni2260': 8800, 'uni2254': 8788, 'uni0436': 1078, 'uni2267': 8807, 'k': 107, 'uni22C8': 8904, 'uni226A': 8810, 'uni231F': 8991, 'smalltilde': 732, 'uni2201': 8705, 'uni2200': 8704, 'uni2203': 8707, 'uni02BD': 701, 'uni2205': 8709, 'uni2204': 8708, 'Agrave': 192, 'uni2206': 8710, 'uni2209': 8713, 'uni2208': 8712, 'uni226D': 8813, 'uni2264': 8804, 'uni263D': 9789, 'uni2258': 8792, 'uni02D3': 723, 'uni02D2': 722, 'uni02D1': 721, 'uni02D0': 720, 'uni25E1': 9697, 'divide': 247, 'uni02D5': 725, 'uni02D4': 724, 'ocircumflex': 244, 'uni2524': 9508, 'uni043A': 1082, 'uni24CC': 9420, 'asciitilde': 126, 'uni22B9': 8889, 'uni24D2': 9426, 'uni211E': 8478, 'uni211D': 8477, 'uni24DD': 9437, 'uni211A': 8474, 'uni211C': 8476, 'uni211B': 8475, 'uni25C6': 9670, 'uni017F': 383, 'uni017A': 378, 'uni017C': 380, 'uni017B': 379, 'uni0346': 838, 'uni22F1': 8945, 'uni22F0': 8944, 'two': 50, 'uni2298': 8856, 'uni24D1': 9425, 'E': 69, 'uni025D': 605, 'scaron': 353, 'uni2322': 8994, 'uni25E3': 9699, 'uni22BF': 8895, 'F': 70, 'uni0440': 1088, 'uni255E': 9566, 'uni22BA': 8890, 'uni0175': 373, 'uni0174': 372, 'uni0177': 375, 'uni0176': 374, 'bracketleft': 91, 'uni0170': 368, 'uni0173': 371, 'uni0172': 370, 'asciicircum': 94, 'uni0179': 377, 'uni2590': 9616, 'uni25E2': 9698, 'uni2119': 8473, 'uni2118': 8472, 'uni25CC': 9676, 'f': 102, 'ordmasculine': 186, 'uni229B': 8859, 'uni22A1': 8865, 'uni2111': 8465, 'uni2110': 8464, 'uni2113': 8467, 'uni2112': 8466, 'mu': 181, 'uni2281': 8833, 'paragraph': 182, 'nine': 57, 'uni25EC': 9708, 'v': 118, 'uni040C': 1036, 'uni0113': 275, 'uni22D0': 8912, 'uni21CC': 8652, 'uni21CB': 8651, 'uni21CA': 8650, 'uni22A5': 8869, 'uni21CF': 8655, 'uni21CE': 8654, 'uni21CD': 8653, 'guilsinglleft': 8249, 'backslash': 92, 'uni2284': 8836, 'uni224E': 8782, 'uni224D': 8781, 'uni224F': 8783, 'uni224A': 8778, 'uni2287': 8839, 'uni224C': 8780, 'uni224B': 8779, 'uni21BD': 8637, 'uni2286': 8838, 'uni030F': 783, 'uni030D': 781, 'uni030E': 782, 'uni030B': 779, 'uni030C': 780, 'uni030A': 778, 'uni026E': 622, 'uni026D': 621, 'six': 54, 'uni026A': 618, 'uni026C': 620, 'uni25C1': 9665, 'uni20D6': 8406, 'uni045B': 1115, 'uni045C': 1116, 'uni256B': 9579, 'uni045A': 1114, 'uni045F': 1119, 'uni045E': 1118, 'A': 65, 'uni2569': 9577, 'uni0458': 1112, 'uni0459': 1113, 'uni0452': 1106, 'uni0453': 1107, 'uni2562': 9570, 'uni0451': 1105, 'uni0456': 1110, 'uni0457': 1111, 'uni0454': 1108, 'uni0455': 1109, 'icircumflex': 238, 'uni0307': 775, 'uni0304': 772, 'uni0305': 773, 'uni0269': 617, 'uni0268': 616, 'uni0300': 768, 'uni0301': 769, 'uni0265': 613, 'uni0264': 612, 'uni0267': 615, 'uni0266': 614, 'uni0261': 609, 'uni0260': 608, 'uni0263': 611, 'uni0262': 610, 'a': 97, 'uni2207': 8711, 'uni2247': 8775, 'uni2246': 8774, 'uni2241': 8769, 'uni2240': 8768, 'uni2243': 8771, 'uni2242': 8770, 'uni2312': 8978, 'ogonek': 731, 'uni2249': 8777, 'uni2248': 8776, 'uni3030': 12336, 'q': 113, 'uni21C2': 8642, 'uni21C1': 8641, 'uni21C0': 8640, 'uni21C7': 8647, 'uni21C6': 8646, 'uni21C5': 8645, 'uni21C4': 8644, 'uni225F': 8799, 'uni212C': 8492, 'uni21C8': 8648, 'uni2467': 9319, 'oacute': 243, 'uni028F': 655, 'uni028E': 654, 'uni026F': 623, 'uni028C': 652, 'uni028B': 651, 'uni028A': 650, 'uni2510': 9488, 'ograve': 242, 'edieresis': 235, 'uni22CE': 8910, 'uni22CF': 8911, 'uni219F': 8607, 'comma': 44, 'uni22CA': 8906, 'uni0429': 1065, 'uni03C6': 966, 'uni0427': 1063, 'uni0426': 1062, 'uni0425': 1061, 'uni0424': 1060, 'uni0423': 1059, 'uni0422': 1058, 'uni0421': 1057, 'uni0420': 1056, 'uni2465': 9317, 'uni24D0': 9424, 'uni2464': 9316, 'uni0430': 1072, 'otilde': 245, 'uni2661': 9825, 'uni24D6': 9430, 'uni2466': 9318, 'uni24D5': 9429, 'uni219A': 8602, 'uni2518': 9496, 'uni22B6': 8886, 'uni2461': 9313, 'uni24D4': 9428, 'uni2460': 9312, 'uni24EA': 9450, 'guillemotright': 187, 'ecircumflex': 234, 'greater': 62, 'uni2011': 8209, 'uacute': 250, 'uni2462': 9314, 'L': 76, 'bullet': 8226, 'uni02A4': 676, 'uni02A7': 679, 'cedilla': 184, 'uni02A2': 674, 'uni2015': 8213, 'uni22C4': 8900, 'uni22C5': 8901, 'uni22AD': 8877, 'uni22C7': 8903, 'uni22C0': 8896, 'uni2016': 8214, 'uni22C2': 8898, 'uni22C3': 8899, 'uni24CF': 9423, 'uni042F': 1071, 'uni042E': 1070, 'uni042D': 1069, 'ydieresis': 255, 'l': 108, 'logicalnot': 172, 'uni24CA': 9418, 'uni0287': 647, 'uni0286': 646, 'uni0285': 645, 'uni0284': 644, 'uni0283': 643, 'uni0282': 642, 'uni0281': 641, 'uni027C': 636, 'uni2664': 9828, 'exclamdown': 161, 'uni25C4': 9668, 'uni0289': 649, 'uni0288': 648, 'uni039A': 922, 'endash': 8211, 'uni2640': 9792, 'uni20E4': 8420, 'uni0473': 1139, 'uni20E1': 8417, 'uni2642': 9794, 'uni03B8': 952, 'uni03B9': 953, 'agrave': 224, 'uni03B4': 948, 'uni03B5': 949, 'uni03B6': 950, 'uni03B7': 951, 'uni03B0': 944, 'uni03B1': 945, 'uni03B2': 946, 'uni03B3': 947, 'uni2555': 9557, 'Adieresis': 196, 'germandbls': 223, 'Odieresis': 214, 'space': 32, 'uni0126': 294, 'uni0127': 295, 'uni0124': 292, 'uni0125': 293, 'uni0122': 290, 'uni0123': 291, 'uni0120': 288, 'uni0121': 289, 'quoteright': 8217, 'uni2560': 9568, 'uni2556': 9558, 'ucircumflex': 251, 'uni2561': 9569, 'uni2551': 9553, 'uni25B2': 9650, 'uni2550': 9552, 'uni2563': 9571, 'uni2553': 9555, 'G': 71, 'uni2564': 9572, 'uni2552': 9554, 'quoteleft': 8216, 'uni2565': 9573, 'uni2572': 9586, 'uni2568': 9576, 'uni2566': 9574, 'W': 87, 'uni214A': 8522, 'uni012F': 303, 'uni012D': 301, 'uni012E': 302, 'uni012B': 299, 'uni012C': 300, 'uni255C': 9564, 'uni012A': 298, 'uni2289': 8841, 'Q': 81, 'uni2320': 8992, 'uni2321': 8993, 'g': 103, 'uni03BD': 957, 'uni03BE': 958, 'uni03BF': 959, 'uni2282': 8834, 'uni2285': 8837, 'uni03BA': 954, 'uni03BB': 955, 'uni03BC': 956, 'uni2128': 8488, 'uni25B7': 9655, 'w': 119, 'uni0302': 770, 'uni03DE': 990, 'uni25DA': 9690, 'uni0303': 771, 'uni0463': 1123, 'uni0462': 1122, 'uni3018': 12312, 'uni2514': 9492, 'question': 63, 'uni25B3': 9651, 'uni24E1': 9441, 'one': 49, 'uni200A': 8202, 'uni2278': 8824, 'ring': 730, 'uni0195': 405, 'figuredash': 8210, 'uni22EC': 8940, 'uni0339': 825, 'uni0338': 824, 'uni0337': 823, 'uni0336': 822, 'uni0335': 821, 'uni0333': 819, 'uni0332': 818, 'uni0331': 817, 'uni0330': 816, 'uni01C1': 449, 'uni01C0': 448, 'uni01C3': 451, 'uni01C2': 450, 'uni2353': 9043, 'uni0308': 776, 'uni2218': 8728, 'uni2219': 8729, 'uni2216': 8726, 'uni2217': 8727, 'uni2214': 8724, 'uni0309': 777, 'uni2609': 9737, 'uni2213': 8723, 'uni2210': 8720, 'uni2211': 8721, 'uni2245': 8773, 'B': 66, 'uni25D6': 9686, 'iacute': 237, 'uni02E6': 742, 'uni02E7': 743, 'uni02E8': 744, 'uni02E9': 745, 'uni221D': 8733, 'uni221E': 8734, 'Ydieresis': 376, 'uni221C': 8732, 'uni22D7': 8919, 'uni221A': 8730, 'R': 82, 'uni24DC': 9436, 'uni033F': 831, 'uni033E': 830, 'uni033C': 828, 'uni033B': 827, 'uni033A': 826, 'b': 98, 'uni228A': 8842, 'uni22DB': 8923, 'uni2554': 9556, 'uni046B': 1131, 'uni046A': 1130, 'r': 114, 'uni24DB': 9435, 'Ccedilla': 199, 'minus': 8722, 'uni24DA': 9434, 'uni03F0': 1008, 'uni03F1': 1009, 'uni20AC': 8364, 'uni2276': 8822, 'uni24C0': 9408, 'uni0162': 354, 'uni0163': 355, 'uni011E': 286, 'uni011D': 285, 'uni011C': 284, 'uni011B': 283, 'uni0164': 356, 'uni0165': 357, 'Lslash': 321, 'uni0168': 360, 'uni0169': 361, 'uni25C9': 9673, 'uni02E5': 741, 'uni21C3': 8643, 'uni24C4': 9412, 'uni24E2': 9442, 'uni2277': 8823, 'uni013A': 314, 'uni2102': 8450, 'Uacute': 218, 'uni2317': 8983, 'uni2107': 8455, 'uni221F': 8735, 'yacute': 253, 'uni3012': 12306, 'Ucircumflex': 219, 'uni015D': 349, 'quotedbl': 34, 'uni25D9': 9689, 'uni2280': 8832, 'uni22AF': 8879, 'onehalf': 189, 'uni221B': 8731, 'Thorn': 222, 'uni2226': 8742, 'M': 77, 'uni25BA': 9658, 'uni2463': 9315, 'uni2336': 9014, 'eight': 56, 'uni2236': 8758, 'multiply': 215, 'uni210C': 8460, 'uni210A': 8458, 'uni21C9': 8649, 'grave': 96, 'uni210E': 8462, 'uni0117': 279, 'uni016C': 364, 'uni0115': 277, 'uni016A': 362, 'uni016F': 367, 'uni0112': 274, 'uni016D': 365, 'uni016E': 366, 'Ocircumflex': 212, 'uni2305': 8965, 'm': 109, 'uni24DF': 9439, 'uni0119': 281, 'uni0118': 280, 'uni20A3': 8355, 'uni20A4': 8356, 'uni20A7': 8359, 'uni2288': 8840, 'uni24C3': 9411, 'uni251C': 9500, 'uni228D': 8845, 'uni222F': 8751, 'uni222E': 8750, 'uni222D': 8749, 'uni222C': 8748, 'uni222B': 8747, 'uni222A': 8746, 'uni255B': 9563, 'Ugrave': 217, 'uni24DE': 9438, 'guilsinglright': 8250, 'uni250A': 9482, 'Ntilde': 209, 'uni0279': 633, 'questiondown': 191, 'uni256C': 9580, 'Atilde': 195, 'uni0272': 626, 'uni0273': 627, 'uni0270': 624, 'ccedilla': 231, 'uni0276': 630, 'uni0277': 631, 'uni0274': 628, 'uni0275': 629, 'uni2252': 8786, 'uni041F': 1055, 'uni2250': 8784, 'Z': 90, 'uni2256': 8790, 'uni2257': 8791, 'copyright': 169, 'uni2255': 8789, 'uni043D': 1085, 'uni043E': 1086, 'uni043F': 1087, 'yen': 165, 'uni041D': 1053, 'uni043B': 1083, 'uni043C': 1084, 'uni21B0': 8624, 'uni21B1': 8625, 'uni21B2': 8626, 'uni21B3': 8627, 'uni21B4': 8628, 'uni21B5': 8629, 'uni21B6': 8630, 'uni21B7': 8631, 'uni21B8': 8632, 'Eacute': 201, 'uni2311': 8977, 'uni2310': 8976, 'uni228F': 8847, 'uni25DB': 9691, 'uni21BA': 8634, 'uni21BB': 8635, 'uni21BC': 8636, 'uni2017': 8215, 'uni21BE': 8638, 'uni21BF': 8639, 'uni231C': 8988, 'H': 72, 'uni0293': 659, 'uni2202': 8706, 'uni22A4': 8868, 'uni231E': 8990, 'uni2232': 8754, 'uni225B': 8795, 'uni225C': 8796, 'uni24D9': 9433, 'uni225A': 8794, 'uni0438': 1080, 'uni0439': 1081, 'uni225D': 8797, 'uni225E': 8798, 'uni0434': 1076, 'X': 88, 'uni007F': 127, 'uni0437': 1079, 'Idieresis': 207, 'uni0431': 1073, 'uni0432': 1074, 'uni0433': 1075, 'uni22AC': 8876, 'uni22CD': 8909, 'uni25A3': 9635, 'bar': 124, 'uni24BB': 9403, 'uni037E': 894, 'uni027B': 635, 'h': 104, 'uni027A': 634, 'uni027F': 639, 'uni027D': 637, 'uni027E': 638, 'uni2227': 8743, 'uni2004': 8196, 'uni2225': 8741, 'uni2224': 8740, 'uni2223': 8739, 'uni2222': 8738, 'uni2221': 8737, 'uni2220': 8736, 'x': 120, 'uni2323': 8995, 'uni2559': 9561, 'uni2558': 9560, 'uni2229': 8745, 'uni2228': 8744, 'udieresis': 252, 'uni029D': 669, 'ordfeminine': 170, 'uni22CB': 8907, 'uni233D': 9021, 'uni0428': 1064, 'uni24C6': 9414, 'uni22DD': 8925, 'uni24C7': 9415, 'uni015C': 348, 'uni015B': 347, 'uni015A': 346, 'uni22AA': 8874, 'uni015F': 351, 'uni015E': 350, 'braceleft': 123, 'uni24C5': 9413, 'uni0410': 1040, 'uni03AA': 938, 'uni24C2': 9410, 'uni03AC': 940, 'uni03AB': 939, 'macron': 175, 'uni03AD': 941, 'uni03AF': 943, 'uni0294': 660, 'uni0295': 661, 'uni0296': 662, 'uni0297': 663, 'uni0290': 656, 'uni0291': 657, 'uni0292': 658, 'atilde': 227, 'Acircumflex': 194, 'uni2370': 9072, 'uni24C1': 9409, 'uni0298': 664, 'uni0299': 665, 'Oslash': 216, 'uni029E': 670, 'C': 67, 'quotedblleft': 8220, 'uni029B': 667, 'uni029C': 668, 'uni03A9': 937, 'uni03A8': 936, 'S': 83, 'uni24C9': 9417, 'uni03A1': 929, 'uni03A0': 928, 'exclam': 33, 'uni03A5': 933, 'uni03A4': 932, 'uni03A7': 935, 'Zcaron': 381, 'uni2133': 8499, 'uni2132': 8498, 'uni0159': 345, 'uni0158': 344, 'uni2137': 8503, 'uni2005': 8197, 'uni2135': 8501, 'uni2134': 8500, 'uni02BA': 698, 'uni2033': 8243, 'uni0151': 337, 'uni0150': 336, 'uni0157': 343, 'equal': 61, 'uni0155': 341, 'uni0154': 340, 's': 115, 'uni233F': 9023, 'eth': 240, 'uni24BE': 9406, 'uni21E9': 8681, 'uni2060': 8288, 'Egrave': 200, 'uni255D': 9565, 'uni24CD': 9421, 'uni21E1': 8673, 'uni21B9': 8633, 'hyphen': 45, 'uni01BE': 446, 'uni01BB': 443, 'period': 46, 'igrave': 236, 'uni01BA': 442, 'uni2296': 8854, 'uni2297': 8855, 'uni2294': 8852, 'uni2295': 8853, 'colon': 58, 'uni2293': 8851, 'uni2290': 8848, 'uni2291': 8849, 'uni032D': 813, 'uni032E': 814, 'uni032F': 815, 'uni032A': 810, 'uni032B': 811, 'uni032C': 812, 'uni231D': 8989, 'Ecircumflex': 202, 'uni24D7': 9431, 'uni25DD': 9693, 'trademark': 8482, 'Aacute': 193, 'cent': 162, 'uni0445': 1093, 'uni266E': 9838, 'uni266D': 9837, 'uni266B': 9835, 'uni03C9': 969, 'uni2003': 8195, 'uni2047': 8263, 'lslash': 322, 'uni03A6': 934, 'uni2043': 8259, 'uni250C': 9484, 'uni2040': 8256, 'uni255F': 9567, 'uni24CB': 9419, 'uni0472': 1138, 'uni0446': 1094, 'uni0474': 1140, 'uni0475': 1141, 'uni2508': 9480, 'uni2660': 9824, 'uni2506': 9478, 'uni2502': 9474, 'c': 99, 'uni2500': 9472, 'N': 78, 'uni22A6': 8870, 'uni21E7': 8679, 'uni2130': 8496, 'uni2002': 8194, 'breve': 728, 'uni0442': 1090, 'Oacute': 211, 'uni229F': 8863, 'uni25C7': 9671, 'uni229D': 8861, 'uni229E': 8862, 'guillemotleft': 171, 'uni0329': 809, 'uni24E5': 9445, 'uni011F': 287, 'uni0324': 804, 'uni0325': 805, 'uni0326': 806, 'uni0327': 807, 'uni0321': 801, 'uni0322': 802, 'n': 110, 'uni2032': 8242, 'uni2269': 8809, 'uni2268': 8808, 'uni0306': 774, 'uni226B': 8811, 'uni21EA': 8682, 'uni0166': 358, 'uni203B': 8251, 'uni01B5': 437, 'idieresis': 239, 'uni02BC': 700, 'uni01B0': 432, 'braceright': 125, 'seven': 55, 'uni02BB': 699, 'uni011A': 282, 'uni29FB': 10747, 'brokenbar': 166, 'uni2036': 8246, 'uni25C0': 9664, 'uni0156': 342, 'uni22D5': 8917, 'uni0258': 600, 'ugrave': 249, 'uni22D6': 8918, 'uni22D1': 8913, 'uni2034': 8244, 'uni22D3': 8915, 'uni22D2': 8914, 'uni203C': 8252, 'uni223E': 8766, 'uni02BF': 703, 'uni22D9': 8921, 'uni22D8': 8920, 'uni25BD': 9661, 'uni25BE': 9662, 'uni25BF': 9663, 'uni041B': 1051, 'periodcentered': 183, 'uni25BC': 9660, 'uni019E': 414, 'uni019B': 411, 'uni019A': 410, 'uni2007': 8199, 'uni0391': 913, 'uni0390': 912, 'uni0393': 915, 'uni0392': 914, 'uni0395': 917, 'uni0394': 916, 'uni0397': 919, 'uni0396': 918, 'uni0399': 921, 'uni0398': 920, 'uni25C8': 9672, 'uni2468': 9320, 'sterling': 163, 'uni22EB': 8939, 'uni039C': 924, 'uni039B': 923, 'uni039E': 926, 'uni039D': 925, 'uni039F': 927, 'I': 73, 'uni03E1': 993, 'uni03E0': 992, 'uni2319': 8985, 'uni228B': 8843, 'uni25B5': 9653, 'uni25B6': 9654, 'uni22EA': 8938, 'uni24B9': 9401, 'uni044E': 1102, 'uni0199': 409, 'uni2266': 8806, 'Y': 89, 'uni22A2': 8866, 'Eth': 208, 'uni266F': 9839, 'emdash': 8212, 'uni263B': 9787, 'uni24BD': 9405, 'uni22DE': 8926, 'uni0360': 864, 'uni2557': 9559, 'uni22DF': 8927, 'uni22DA': 8922, 'uni22DC': 8924, 'uni0361': 865, 'i': 105, 'uni24BF': 9407, 'uni0362': 866, 'uni263E': 9790, 'uni028D': 653, 'uni2259': 8793, 'uni0323': 803, 'uni2265': 8805, 'daggerdbl': 8225, 'y': 121, 'uni010A': 266, 'plusminus': 177, 'less': 60, 'uni21AE': 8622, 'uni0315': 789, 'uni230B': 8971, 'uni21AF': 8623, 'uni21AA': 8618, 'uni21AC': 8620, 'uni21AB': 8619, 'uni01FB': 507, 'uni01FC': 508, 'uni223A': 8762, 'uni01FA': 506, 'uni01FF': 511, 'uni01FD': 509, 'uni01FE': 510, 'uni2567': 9575, 'uni25E0': 9696, 'uni0104': 260, 'uni0105': 261, 'uni0106': 262, 'uni0107': 263, 'uni0100': 256, 'uni0101': 257, 'uni0102': 258, 'uni0103': 259, 'uni2038': 8248, 'uni2009': 8201, 'uni2008': 8200, 'uni0108': 264, 'uni0109': 265, 'uni02A1': 673, 'uni223B': 8763, 'uni226C': 8812, 'uni25AC': 9644, 'uni24D3': 9427, 'uni21E0': 8672, 'uni21E3': 8675, 'Udieresis': 220, 'uni21E2': 8674, 'D': 68, 'uni21E5': 8677, 'uni2621': 9761, 'uni21D1': 8657, 'uni203E': 8254, 'uni22C6': 8902, 'uni21E4': 8676, 'uni010D': 269, 'uni010E': 270, 'uni010F': 271, 'five': 53, 'T': 84, 'uni010B': 267, 'uni010C': 268, 'uni2605': 9733, 'uni2663': 9827, 'uni21E6': 8678, 'uni24B6': 9398, 'uni22C1': 8897, 'oslash': 248, 'acute': 180, 'uni01F0': 496, 'd': 100, 'OE': 338, 'uni22E3': 8931, 'Igrave': 204, 'uni2308': 8968, 'uni2309': 8969, 'uni21A9': 8617, 't': 116, 'uni2313': 8979, 'uni03A3': 931, 'uni21A4': 8612, 'uni21A7': 8615, 'uni21A6': 8614, 'uni21A1': 8609, 'uni21A0': 8608, 'uni21A3': 8611, 'uni21A2': 8610, 'parenright': 41, 'uni256A': 9578, 'uni25DC': 9692, 'uni24CE': 9422, 'uni042C': 1068, 'uni24E0': 9440, 'uni042B': 1067, 'uni0409': 1033, 'uni0408': 1032, 'uni24E7': 9447, 'uni25B4': 9652, 'uni042A': 1066, 'uni228E': 8846, 'uni0401': 1025, 'adieresis': 228, 'uni0403': 1027, 'quotesingle': 39, 'uni0405': 1029, 'uni0404': 1028, 'uni0407': 1031, 'uni0406': 1030, 'uni229C': 8860, 'uni2306': 8966, 'uni2253': 8787, 'twodotenleader': 8229, 'uni2131': 8497, 'uni21DA': 8666, 'uni2234': 8756, 'uni2235': 8757, 'uni01A5': 421, 'uni2237': 8759, 'uni2230': 8752, 'uni02CC': 716, 'slash': 47, 'uni01A0': 416, 'ellipsis': 8230, 'uni2299': 8857, 'uni2238': 8760, 'numbersign': 35, 'uni21A8': 8616, 'uni223D': 8765, 'uni01AF': 431, 'uni223F': 8767, 'uni01AD': 429, 'uni01AB': 427, 'odieresis': 246, 'uni223C': 8764, 'uni227D': 8829, 'uni0280': 640, 'O': 79, 'uni227E': 8830, 'uni21A5': 8613, 'uni22D4': 8916, 'uni25D4': 9684, 'uni227F': 8831, 'uni0435': 1077, 'uni2302': 8962, 'uni2669': 9833, 'uni24E3': 9443, 'uni2720': 10016, 'uni22A8': 8872, 'uni22A9': 8873, 'uni040A': 1034, 'uni22A7': 8871, 'oe': 339, 'uni040B': 1035, 'uni040E': 1038, 'uni22A3': 8867, 'o': 111, 'uni040F': 1039, 'Edieresis': 203, 'uni25D5': 9685, 'plus': 43, 'uni044D': 1101, 'uni263C': 9788, 'uni22E6': 8934, 'uni2283': 8835, 'uni258C': 9612, 'uni219E': 8606, 'uni24E4': 9444, 'uni2136': 8502, 'dagger': 8224, 'uni24B7': 9399, 'uni219B': 8603, 'uni22E5': 8933, 'three': 51, 'uni210B': 8459, 'uni2534': 9524, 'uni24B8': 9400, 'uni230A': 8970, 'hungarumlaut': 733, 'parenleft': 40, 'uni0148': 328, 'uni0149': 329, 'uni2124': 8484, 'uni2125': 8485, 'uni2126': 8486, 'uni2127': 8487, 'uni0140': 320, 'uni2129': 8489, 'uni25C5': 9669, 'uni0143': 323, 'uni0144': 324, 'uni0145': 325, 'uni0146': 326, 'uni0147': 327, 'uni210D': 8461, 'fraction': 8260, 'uni2031': 8241, 'uni2196': 8598, 'uni2035': 8245, 'uni24E6': 9446, 'uni016B': 363, 'uni24BA': 9402, 'uni266A': 9834, 'uni0116': 278, 'uni2115': 8469, 'registered': 174, 'J': 74, 'uni25DF': 9695, 'uni25CE': 9678, 'uni273D': 10045, 'dieresis': 168, 'uni212B': 8491, 'uni0114': 276, 'uni212D': 8493, 'uni212E': 8494, 'uni212F': 8495, 'uni014A': 330, 'uni014B': 331, 'uni014C': 332, 'uni014D': 333, 'uni014E': 334, 'uni014F': 335, 'uni025E': 606, 'uni24E8': 9448, 'uni0111': 273, 'uni24E9': 9449, 'Ograve': 210, 'j': 106, 'uni2195': 8597, 'uni2194': 8596, 'uni2197': 8599, 'uni2037': 8247, 'uni2191': 8593, 'uni2190': 8592, 'uni2193': 8595, 'uni2192': 8594, 'uni29FA': 10746, 'uni2713': 10003, 'z': 122, 'uni2199': 8601, 'uni2198': 8600, 'uni2667': 9831, 'ae': 230, 'uni0448': 1096, 'semicolon': 59, 'uni2666': 9830, 'uni038F': 911, 'uni0444': 1092, 'uni0447': 1095, 'uni038E': 910, 'uni0441': 1089, 'uni038C': 908, 'uni0443': 1091, 'uni038A': 906, 'uni0250': 592, 'uni0251': 593, 'uni0252': 594, 'uni0253': 595, 'uni0254': 596, 'at': 64, 'uni0256': 598, 'uni0257': 599, 'uni0167': 359, 'uni0259': 601, 'uni228C': 8844, 'uni2662': 9826, 'uni0319': 793, 'uni0318': 792, 'uni24BC': 9404, 'uni0402': 1026, 'uni22EF': 8943, 'Iacute': 205, 'uni22ED': 8941, 'uni22EE': 8942, 'uni0311': 785, 'uni0310': 784, 'uni21E8': 8680, 'uni0312': 786, 'percent': 37, 'uni0317': 791, 'uni0316': 790, 'uni21D6': 8662, 'uni21D7': 8663, 'uni21D4': 8660, 'uni21D5': 8661, 'uni21D2': 8658, 'uni21D3': 8659, 'uni21D0': 8656, 'uni2138': 8504, 'uni2270': 8816, 'uni2271': 8817, 'uni2272': 8818, 'uni2273': 8819, 'uni2274': 8820, 'uni2275': 8821, 'bracketright': 93, 'uni21D9': 8665, 'uni21DF': 8671, 'uni21DD': 8669, 'uni21DE': 8670, 'AE': 198, 'uni03AE': 942, 'uni227A': 8826, 'uni227B': 8827, 'uni227C': 8828, 'asterisk': 42, 'aacute': 225, 'uni226F': 8815, 'uni22E2': 8930, 'uni0386': 902, 'uni22E0': 8928, 'uni22E1': 8929, 'U': 85, 'uni22E7': 8935, 'uni22E4': 8932, 'uni0387': 903, 'uni031A': 794, 'eacute': 233, 'uni22E8': 8936, 'uni22E9': 8937, 'uni24D8': 9432, 'uni025A': 602, 'uni025B': 603, 'uni025C': 604, 'e': 101, 'uni0128': 296, 'uni025F': 607, 'uni2665': 9829, 'thorn': 254, 'uni0129': 297, 'uni253C': 9532, 'uni25D7': 9687, 'u': 117, 'uni0388': 904, 'uni0389': 905, 'uni0255': 597, 'uni0171': 369, 'uni0384': 900, 'uni0385': 901, 'uni044A': 1098, 'uni252C': 9516, 'uni044C': 1100, 'uni044B': 1099} uni2type1 = dict([(v,k) for k,v in type12uni.items()]) tex2uni = { 'widehat': 0x0302, 'widetilde': 0x0303, 'langle': 0x27e8, 'rangle': 0x27e9, 'perp': 0x27c2, 'neq': 0x2260, 'Join': 0x2a1d, 'leqslant': 0x2a7d, 'geqslant': 0x2a7e, 'lessapprox': 0x2a85, 'gtrapprox': 0x2a86, 'lesseqqgtr': 0x2a8b, 'gtreqqless': 0x2a8c, 'triangleeq': 0x225c, 'eqslantless': 0x2a95, 'eqslantgtr': 0x2a96, 'backepsilon': 0x03f6, 'precapprox': 0x2ab7, 'succapprox': 0x2ab8, 'fallingdotseq': 0x2252, 'subseteqq': 0x2ac5, 'supseteqq': 0x2ac6, 'varpropto': 0x221d, 'precnapprox': 0x2ab9, 'succnapprox': 0x2aba, 'subsetneqq': 0x2acb, 'supsetneqq': 0x2acc, 'lnapprox': 0x2ab9, 'gnapprox': 0x2aba, 'longleftarrow': 0x27f5, 'longrightarrow': 0x27f6, 'longleftrightarrow': 0x27f7, 'Longleftarrow': 0x27f8, 'Longrightarrow': 0x27f9, 'Longleftrightarrow': 0x27fa, 'longmapsto': 0x27fc, 'leadsto': 0x21dd, 'dashleftarrow': 0x290e, 'dashrightarrow': 0x290f, 'circlearrowleft': 0x21ba, 'circlearrowright': 0x21bb, 'leftrightsquigarrow': 0x21ad, 'leftsquigarrow': 0x219c, 'rightsquigarrow': 0x219d, 'Game': 0x2141, 'hbar': 0x0127, 'hslash': 0x210f, 'ldots': 0x22ef, 'vdots': 0x22ee, 'doteqdot': 0x2251, 'doteq': 8784, 'partial': 8706, 'gg': 8811, 'asymp': 8781, 'blacktriangledown': 9662, 'otimes': 8855, 'nearrow': 8599, 'varpi': 982, 'vee': 8744, 'vec': 8407, 'smile': 8995, 'succnsim': 8937, 'gimel': 8503, 'vert': 124, '\|': 124, 'varrho': 1009, 'P': 182, 'approxident': 8779, 'Swarrow': 8665, 'textasciicircum': 94, 'imageof': 8887, 'ntriangleleft': 8938, 'nleq': 8816, 'div': 247, 'nparallel': 8742, 'Leftarrow': 8656, 'lll': 8920, 'oiint': 8751, 'ngeq': 8817, 'Theta': 920, 'origof': 8886, 'blacksquare': 9632, 'solbar': 9023, 'neg': 172, 'sum': 8721, 'Vdash': 8873, 'coloneq': 8788, 'degree': 176, 'bowtie': 8904, 'blacktriangleright': 9654, 'varsigma': 962, 'leq': 8804, 'ggg': 8921, 'lneqq': 8808, 'scurel': 8881, 'stareq': 8795, 'BbbN': 8469, 'nLeftarrow': 8653, 'nLeftrightarrow': 8654, 'k': 808, 'bot': 8869, 'BbbC': 8450, 'Lsh': 8624, 'leftleftarrows': 8647, 'BbbZ': 8484, 'digamma': 989, 'BbbR': 8477, 'BbbP': 8473, 'BbbQ': 8474, 'vartriangleright': 8883, 'succsim': 8831, 'wedge': 8743, 'lessgtr': 8822, 'veebar': 8891, 'mapsdown': 8615, 'Rsh': 8625, 'chi': 967, 'prec': 8826, 'nsubseteq': 8840, 'therefore': 8756, 'eqcirc': 8790, 'textexclamdown': 161, 'nRightarrow': 8655, 'flat': 9837, 'notin': 8713, 'llcorner': 8990, 'varepsilon': 949, 'bigtriangleup': 9651, 'aleph': 8501, 'dotminus': 8760, 'upsilon': 965, 'Lambda': 923, 'cap': 8745, 'barleftarrow': 8676, 'mu': 956, 'boxplus': 8862, 'mp': 8723, 'circledast': 8859, 'tau': 964, 'in': 8712, 'backslash': 92, 'varnothing': 8709, 'sharp': 9839, 'eqsim': 8770, 'gnsim': 8935, 'Searrow': 8664, 'updownarrows': 8645, 'heartsuit': 9825, 'trianglelefteq': 8884, 'ddag': 8225, 'sqsubseteq': 8849, 'mapsfrom': 8612, 'boxbar': 9707, 'sim': 8764, 'Nwarrow': 8662, 'nequiv': 8802, 'succ': 8827, 'vdash': 8866, 'Leftrightarrow': 8660, 'parallel': 8741, 'invnot': 8976, 'natural': 9838, 'ss': 223, 'uparrow': 8593, 'nsim': 8769, 'hookrightarrow': 8618, 'Equiv': 8803, 'approx': 8776, 'Vvdash': 8874, 'nsucc': 8833, 'leftrightharpoons': 8651, 'Re': 8476, 'boxminus': 8863, 'equiv': 8801, 'Lleftarrow': 8666, 'thinspace': 8201, 'll': 8810, 'Cup': 8915, 'measeq': 8798, 'upharpoonleft': 8639, 'lq': 8216, 'Upsilon': 933, 'subsetneq': 8842, 'greater': 62, 'supsetneq': 8843, 'Cap': 8914, 'L': 321, 'spadesuit': 9824, 'lrcorner': 8991, 'not': 824, 'bar': 772, 'rightharpoonaccent': 8401, 'boxdot': 8865, 'l': 322, 'leftharpoondown': 8637, 'bigcup': 8899, 'iint': 8748, 'bigwedge': 8896, 'downharpoonleft': 8643, 'textasciitilde': 126, 'subset': 8834, 'leqq': 8806, 'mapsup': 8613, 'nvDash': 8877, 'looparrowleft': 8619, 'nless': 8814, 'rightarrowbar': 8677, 'Vert': 8214, 'downdownarrows': 8650, 'uplus': 8846, 'simeq': 8771, 'napprox': 8777, 'ast': 8727, 'twoheaduparrow': 8607, 'doublebarwedge': 8966, 'Sigma': 931, 'leftharpoonaccent': 8400, 'ntrianglelefteq': 8940, 'nexists': 8708, 'times': 215, 'measuredangle': 8737, 'bumpeq': 8783, 'carriagereturn': 8629, 'adots': 8944, 'checkmark': 10003, 'lambda': 955, 'xi': 958, 'rbrace': 125, 'rbrack': 93, 'Nearrow': 8663, 'maltese': 10016, 'clubsuit': 9827, 'top': 8868, 'overarc': 785, 'varphi': 966, 'Delta': 916, 'iota': 953, 'nleftarrow': 8602, 'candra': 784, 'supset': 8835, 'triangleleft': 9665, 'gtreqless': 8923, 'ntrianglerighteq': 8941, 'quad': 8195, 'Xi': 926, 'gtrdot': 8919, 'leftthreetimes': 8907, 'minus': 8722, 'preccurlyeq': 8828, 'nleftrightarrow': 8622, 'lambdabar': 411, 'blacktriangle': 9652, 'kernelcontraction': 8763, 'Phi': 934, 'angle': 8736, 'spadesuitopen': 9828, 'eqless': 8924, 'mid': 8739, 'varkappa': 1008, 'Ldsh': 8626, 'updownarrow': 8597, 'beta': 946, 'textquotedblleft': 8220, 'rho': 961, 'alpha': 945, 'intercal': 8890, 'beth': 8502, 'grave': 768, 'acwopencirclearrow': 8634, 'nmid': 8740, 'nsupset': 8837, 'sigma': 963, 'dot': 775, 'Rightarrow': 8658, 'turnednot': 8985, 'backsimeq': 8909, 'leftarrowtail': 8610, 'approxeq': 8778, 'curlyeqsucc': 8927, 'rightarrowtail': 8611, 'Psi': 936, 'copyright': 169, 'yen': 165, 'vartriangleleft': 8882, 'rasp': 700, 'triangleright': 9655, 'precsim': 8830, 'infty': 8734, 'geq': 8805, 'updownarrowbar': 8616, 'precnsim': 8936, 'H': 779, 'ulcorner': 8988, 'looparrowright': 8620, 'ncong': 8775, 'downarrow': 8595, 'circeq': 8791, 'subseteq': 8838, 'bigstar': 9733, 'prime': 8242, 'lceil': 8968, 'Rrightarrow': 8667, 'oiiint': 8752, 'curlywedge': 8911, 'vDash': 8872, 'lfloor': 8970, 'ddots': 8945, 'exists': 8707, 'underbar': 817, 'Pi': 928, 'leftrightarrows': 8646, 'sphericalangle': 8738, 'coprod': 8720, 'circledcirc': 8858, 'gtrsim': 8819, 'gneqq': 8809, 'between': 8812, 'theta': 952, 'complement': 8705, 'arceq': 8792, 'nVdash': 8878, 'S': 167, 'wr': 8768, 'wp': 8472, 'backcong': 8780, 'lasp': 701, 'c': 807, 'nabla': 8711, 'dotplus': 8724, 'eta': 951, 'forall': 8704, 'eth': 240, 'colon': 58, 'sqcup': 8852, 'rightrightarrows': 8649, 'sqsupset': 8848, 'mapsto': 8614, 'bigtriangledown': 9661, 'sqsupseteq': 8850, 'propto': 8733, 'pi': 960, 'pm': 177, 'dots': 8230, 'nrightarrow': 8603, 'textasciiacute': 180, 'Doteq': 8785, 'breve': 774, 'sqcap': 8851, 'twoheadrightarrow': 8608, 'kappa': 954, 'vartriangle': 9653, 'diamondsuit': 9826, 'pitchfork': 8916, 'blacktriangleleft': 9664, 'nprec': 8832, 'vdots': 8942, 'curvearrowright': 8631, 'barwedge': 8892, 'multimap': 8888, 'textquestiondown': 191, 'cong': 8773, 'rtimes': 8906, 'rightzigzagarrow': 8669, 'rightarrow': 8594, 'leftarrow': 8592, '__sqrt__': 8730, 'twoheaddownarrow': 8609, 'oint': 8750, 'bigvee': 8897, 'eqdef': 8797, 'sterling': 163, 'phi': 981, 'Updownarrow': 8661, 'backprime': 8245, 'emdash': 8212, 'Gamma': 915, 'i': 305, 'rceil': 8969, 'leftharpoonup': 8636, 'Im': 8465, 'curvearrowleft': 8630, 'wedgeq': 8793, 'fallingdotseq': 8786, 'curlyeqprec': 8926, 'questeq': 8799, 'less': 60, 'upuparrows': 8648, 'tilde': 771, 'textasciigrave': 96, 'smallsetminus': 8726, 'ell': 8467, 'cup': 8746, 'danger': 9761, 'nVDash': 8879, 'cdotp': 183, 'cdots': 8943, 'hat': 770, 'eqgtr': 8925, 'enspace': 8194, 'psi': 968, 'frown': 8994, 'acute': 769, 'downzigzagarrow': 8623, 'ntriangleright': 8939, 'cupdot': 8845, 'circleddash': 8861, 'oslash': 8856, 'mho': 8487, 'd': 803, 'sqsubset': 8847, 'cdot': 8901, 'Omega': 937, 'OE': 338, 'veeeq': 8794, 'Finv': 8498, 't': 865, 'leftrightarrow': 8596, 'swarrow': 8601, 'rightthreetimes': 8908, 'rightleftharpoons': 8652, 'lesssim': 8818, 'searrow': 8600, 'because': 8757, 'gtrless': 8823, 'star': 8902, 'nsubset': 8836, 'zeta': 950, 'dddot': 8411, 'bigcirc': 9675, 'Supset': 8913, 'circ': 8728, 'slash': 8725, 'ocirc': 778, 'prod': 8719, 'twoheadleftarrow': 8606, 'daleth': 8504, 'upharpoonright': 8638, 'odot': 8857, 'Uparrow': 8657, 'O': 216, 'hookleftarrow': 8617, 'trianglerighteq': 8885, 'nsime': 8772, 'oe': 339, 'nwarrow': 8598, 'o': 248, 'ddddot': 8412, 'downharpoonright': 8642, 'succcurlyeq': 8829, 'gamma': 947, 'scrR': 8475, 'dag': 8224, 'thickspace': 8197, 'frakZ': 8488, 'lessdot': 8918, 'triangledown': 9663, 'ltimes': 8905, 'scrB': 8492, 'endash': 8211, 'scrE': 8496, 'scrF': 8497, 'scrH': 8459, 'scrI': 8464, 'rightharpoondown': 8641, 'scrL': 8466, 'scrM': 8499, 'frakC': 8493, 'nsupseteq': 8841, 'circledR': 174, 'circledS': 9416, 'ngtr': 8815, 'bigcap': 8898, 'scre': 8495, 'Downarrow': 8659, 'scrg': 8458, 'overleftrightarrow': 8417, 'scro': 8500, 'lnsim': 8934, 'eqcolon': 8789, 'curlyvee': 8910, 'urcorner': 8989, 'lbrace': 123, 'Bumpeq': 8782, 'delta': 948, 'boxtimes': 8864, 'overleftarrow': 8406, 'prurel': 8880, 'clubsuitopen': 9831, 'cwopencirclearrow': 8635, 'geqq': 8807, 'rightleftarrows': 8644, 'ac': 8766, 'ae': 230, 'int': 8747, 'rfloor': 8971, 'risingdotseq': 8787, 'nvdash': 8876, 'diamond': 8900, 'ddot': 776, 'backsim': 8765, 'oplus': 8853, 'triangleq': 8796, 'check': 780, 'ni': 8715, 'iiint': 8749, 'ne': 8800, 'lesseqgtr': 8922, 'obar': 9021, 'supseteq': 8839, 'nu': 957, 'AA': 8491, 'AE': 198, 'models': 8871, 'ominus': 8854, 'dashv': 8867, 'omega': 969, 'rq': 8217, 'Subset': 8912, 'rightharpoonup': 8640, 'Rdsh': 8627, 'bullet': 8729, 'divideontimes': 8903, 'lbrack': 91, 'textquotedblright': 8221, 'Colon': 8759, '%': 37, '$': 36, '{': 123, '}': 125, '_': 95, 'imath': 0x131, 'circumflexaccent' : 770, 'combiningbreve' : 774, 'combiningoverline' : 772, 'combininggraveaccent' : 768, 'combiningacuteaccent' : 769, 'combiningdiaeresis' : 776, 'combiningtilde' : 771, 'combiningrightarrowabove' : 8407, 'combiningdotabove' : 775, 'to': 8594, 'succeq': 8829, 'emptyset': 8709, 'leftparen': 40, 'rightparen': 41, 'bigoplus': 10753, 'leftangle': 10216, 'rightangle': 10217, 'leftbrace': 124, 'rightbrace': 125, 'jmath': 567, 'bigodot': 10752, 'preceq': 8828, 'biguplus': 10756, 'epsilon': 949, 'vartheta': 977, 'bigotimes': 10754 } # Each element is a 4-tuple of the form: # src_start, src_end, dst_font, dst_start # stix_virtual_fonts = { 'bb': { 'rm': [ (0x0030, 0x0039, 'rm', 0x1d7d8), # 0-9 (0x0041, 0x0042, 'rm', 0x1d538), # A-B (0x0043, 0x0043, 'rm', 0x2102), # C (0x0044, 0x0047, 'rm', 0x1d53b), # D-G (0x0048, 0x0048, 'rm', 0x210d), # H (0x0049, 0x004d, 'rm', 0x1d540), # I-M (0x004e, 0x004e, 'rm', 0x2115), # N (0x004f, 0x004f, 'rm', 0x1d546), # O (0x0050, 0x0051, 'rm', 0x2119), # P-Q (0x0052, 0x0052, 'rm', 0x211d), # R (0x0053, 0x0059, 'rm', 0x1d54a), # S-Y (0x005a, 0x005a, 'rm', 0x2124), # Z (0x0061, 0x007a, 'rm', 0x1d552), # a-z (0x0393, 0x0393, 'rm', 0x213e), # \Gamma (0x03a0, 0x03a0, 'rm', 0x213f), # \Pi (0x03a3, 0x03a3, 'rm', 0x2140), # \Sigma (0x03b3, 0x03b3, 'rm', 0x213d), # \gamma (0x03c0, 0x03c0, 'rm', 0x213c), # \pi ], 'it': [ (0x0030, 0x0039, 'rm', 0x1d7d8), # 0-9 (0x0041, 0x0042, 'it', 0xe154), # A-B (0x0043, 0x0043, 'it', 0x2102), # C (missing in beta STIX fonts) (0x0044, 0x0044, 'it', 0x2145), # D (0x0045, 0x0047, 'it', 0xe156), # E-G (0x0048, 0x0048, 'it', 0x210d), # H (missing in beta STIX fonts) (0x0049, 0x004d, 'it', 0xe159), # I-M (0x004e, 0x004e, 'it', 0x2115), # N (missing in beta STIX fonts) (0x004f, 0x004f, 'it', 0xe15e), # O (0x0050, 0x0051, 'it', 0x2119), # P-Q (missing in beta STIX fonts) (0x0052, 0x0052, 'it', 0x211d), # R (missing in beta STIX fonts) (0x0053, 0x0059, 'it', 0xe15f), # S-Y (0x005a, 0x005a, 'it', 0x2124), # Z (missing in beta STIX fonts) (0x0061, 0x0063, 'it', 0xe166), # a-c (0x0064, 0x0065, 'it', 0x2146), # d-e (0x0066, 0x0068, 'it', 0xe169), # f-h (0x0069, 0x006a, 'it', 0x2148), # i-j (0x006b, 0x007a, 'it', 0xe16c), # k-z (0x0393, 0x0393, 'it', 0x213e), # \Gamma (missing in beta STIX fonts) (0x03a0, 0x03a0, 'it', 0x213f), # \Pi (0x03a3, 0x03a3, 'it', 0x2140), # \Sigma (missing in beta STIX fonts) (0x03b3, 0x03b3, 'it', 0x213d), # \gamma (missing in beta STIX fonts) (0x03c0, 0x03c0, 'it', 0x213c), # \pi ], 'bf': [ (0x0030, 0x0039, 'rm', 0x1d7d8), # 0-9 (0x0041, 0x005a, 'bf', 0xe38a), # A-Z (0x0061, 0x007a, 'bf', 0xe39d), # a-z (0x0393, 0x0393, 'bf', 0x213e), # \Gamma (0x03a0, 0x03a0, 'bf', 0x213f), # \Pi (0x03a3, 0x03a3, 'bf', 0x2140), # \Sigma (0x03b3, 0x03b3, 'bf', 0x213d), # \gamma (0x03c0, 0x03c0, 'bf', 0x213c), # \pi ], }, 'cal': [ (0x0041, 0x005a, 'it', 0xe22d), # A-Z ], 'circled': { 'rm': [ (0x0030, 0x0030, 'rm', 0x24ea), # 0 (0x0031, 0x0039, 'rm', 0x2460), # 1-9 (0x0041, 0x005a, 'rm', 0x24b6), # A-Z (0x0061, 0x007a, 'rm', 0x24d0) # a-z ], 'it': [ (0x0030, 0x0030, 'rm', 0x24ea), # 0 (0x0031, 0x0039, 'rm', 0x2460), # 1-9 (0x0041, 0x005a, 'it', 0x24b6), # A-Z (0x0061, 0x007a, 'it', 0x24d0) # a-z ], 'bf': [ (0x0030, 0x0030, 'bf', 0x24ea), # 0 (0x0031, 0x0039, 'bf', 0x2460), # 1-9 (0x0041, 0x005a, 'bf', 0x24b6), # A-Z (0x0061, 0x007a, 'bf', 0x24d0) # a-z ], }, 'frak': { 'rm': [ (0x0041, 0x0042, 'rm', 0x1d504), # A-B (0x0043, 0x0043, 'rm', 0x212d), # C (0x0044, 0x0047, 'rm', 0x1d507), # D-G (0x0048, 0x0048, 'rm', 0x210c), # H (0x0049, 0x0049, 'rm', 0x2111), # I (0x004a, 0x0051, 'rm', 0x1d50d), # J-Q (0x0052, 0x0052, 'rm', 0x211c), # R (0x0053, 0x0059, 'rm', 0x1d516), # S-Y (0x005a, 0x005a, 'rm', 0x2128), # Z (0x0061, 0x007a, 'rm', 0x1d51e), # a-z ], 'it': [ (0x0041, 0x0042, 'rm', 0x1d504), # A-B (0x0043, 0x0043, 'rm', 0x212d), # C (0x0044, 0x0047, 'rm', 0x1d507), # D-G (0x0048, 0x0048, 'rm', 0x210c), # H (0x0049, 0x0049, 'rm', 0x2111), # I (0x004a, 0x0051, 'rm', 0x1d50d), # J-Q (0x0052, 0x0052, 'rm', 0x211c), # R (0x0053, 0x0059, 'rm', 0x1d516), # S-Y (0x005a, 0x005a, 'rm', 0x2128), # Z (0x0061, 0x007a, 'rm', 0x1d51e), # a-z ], 'bf': [ (0x0041, 0x005a, 'bf', 0x1d56c), # A-Z (0x0061, 0x007a, 'bf', 0x1d586), # a-z ], }, 'scr': [ (0x0041, 0x0041, 'it', 0x1d49c), # A (0x0042, 0x0042, 'it', 0x212c), # B (0x0043, 0x0044, 'it', 0x1d49e), # C-D (0x0045, 0x0046, 'it', 0x2130), # E-F (0x0047, 0x0047, 'it', 0x1d4a2), # G (0x0048, 0x0048, 'it', 0x210b), # H (0x0049, 0x0049, 'it', 0x2110), # I (0x004a, 0x004b, 'it', 0x1d4a5), # J-K (0x004c, 0x004c, 'it', 0x2112), # L (0x004d, 0x003d, 'it', 0x2113), # M (0x004e, 0x0051, 'it', 0x1d4a9), # N-Q (0x0052, 0x0052, 'it', 0x211b), # R (0x0053, 0x005a, 'it', 0x1d4ae), # S-Z (0x0061, 0x0064, 'it', 0x1d4b6), # a-d (0x0065, 0x0065, 'it', 0x212f), # e (0x0066, 0x0066, 'it', 0x1d4bb), # f (0x0067, 0x0067, 'it', 0x210a), # g (0x0068, 0x006e, 'it', 0x1d4bd), # h-n (0x006f, 0x006f, 'it', 0x2134), # o (0x0070, 0x007a, 'it', 0x1d4c5), # p-z ], 'sf': { 'rm': [ (0x0030, 0x0039, 'rm', 0x1d7e2), # 0-9 (0x0041, 0x005a, 'rm', 0x1d5a0), # A-Z (0x0061, 0x007a, 'rm', 0x1d5ba), # a-z (0x0391, 0x03a9, 'rm', 0xe17d), # \Alpha-\Omega (0x03b1, 0x03c9, 'rm', 0xe196), # \alpha-\omega (0x03d1, 0x03d1, 'rm', 0xe1b0), # theta variant (0x03d5, 0x03d5, 'rm', 0xe1b1), # phi variant (0x03d6, 0x03d6, 'rm', 0xe1b3), # pi variant (0x03f1, 0x03f1, 'rm', 0xe1b2), # rho variant (0x03f5, 0x03f5, 'rm', 0xe1af), # lunate epsilon (0x2202, 0x2202, 'rm', 0xe17c), # partial differential ], 'it': [ # These numerals are actually upright. We don't actually # want italic numerals ever. (0x0030, 0x0039, 'rm', 0x1d7e2), # 0-9 (0x0041, 0x005a, 'it', 0x1d608), # A-Z (0x0061, 0x007a, 'it', 0x1d622), # a-z (0x0391, 0x03a9, 'rm', 0xe17d), # \Alpha-\Omega (0x03b1, 0x03c9, 'it', 0xe1d8), # \alpha-\omega (0x03d1, 0x03d1, 'it', 0xe1f2), # theta variant (0x03d5, 0x03d5, 'it', 0xe1f3), # phi variant (0x03d6, 0x03d6, 'it', 0xe1f5), # pi variant (0x03f1, 0x03f1, 'it', 0xe1f4), # rho variant (0x03f5, 0x03f5, 'it', 0xe1f1), # lunate epsilon ], 'bf': [ (0x0030, 0x0039, 'bf', 0x1d7ec), # 0-9 (0x0041, 0x005a, 'bf', 0x1d5d4), # A-Z (0x0061, 0x007a, 'bf', 0x1d5ee), # a-z (0x0391, 0x03a9, 'bf', 0x1d756), # \Alpha-\Omega (0x03b1, 0x03c9, 'bf', 0x1d770), # \alpha-\omega (0x03d1, 0x03d1, 'bf', 0x1d78b), # theta variant (0x03d5, 0x03d5, 'bf', 0x1d78d), # phi variant (0x03d6, 0x03d6, 'bf', 0x1d78f), # pi variant (0x03f0, 0x03f0, 'bf', 0x1d78c), # kappa variant (0x03f1, 0x03f1, 'bf', 0x1d78e), # rho variant (0x03f5, 0x03f5, 'bf', 0x1d78a), # lunate epsilon (0x2202, 0x2202, 'bf', 0x1d789), # partial differential (0x2207, 0x2207, 'bf', 0x1d76f), # \Nabla ], }, 'tt': [ (0x0030, 0x0039, 'rm', 0x1d7f6), # 0-9 (0x0041, 0x005a, 'rm', 0x1d670), # A-Z (0x0061, 0x007a, 'rm', 0x1d68a) # a-z ], }	57,988	Python	.py	2,510	20.624701	82	0.541793	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,269	units.py	numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/units.py	""" The classes here provide support for using custom classes with matplotlib, eg those that do not expose the array interface but know how to converter themselves to arrays. It also supoprts classes with units and units conversion. Use cases include converters for custom objects, eg a list of datetime objects, as well as for objects that are unit aware. We don't assume any particular units implementation, rather a units implementation must provide a ConversionInterface, and the register with the Registry converter dictionary. For example, here is a complete implementation which support plotting with native datetime objects import matplotlib.units as units import matplotlib.dates as dates import matplotlib.ticker as ticker import datetime class DateConverter(units.ConversionInterface): def convert(value, unit): 'convert value to a scalar or array' return dates.date2num(value) convert = staticmethod(convert) def axisinfo(unit): 'return major and minor tick locators and formatters' if unit!='date': return None majloc = dates.AutoDateLocator() majfmt = dates.AutoDateFormatter(majloc) return AxisInfo(majloc=majloc, majfmt=majfmt, label='date') axisinfo = staticmethod(axisinfo) def default_units(x): 'return the default unit for x or None' return 'date' default_units = staticmethod(default_units) # finally we register our object type with a converter units.registry[datetime.date] = DateConverter() """ import numpy as np from matplotlib.cbook import iterable, is_numlike class AxisInfo: 'information to support default axis labeling and tick labeling' def __init__(self, majloc=None, minloc=None, majfmt=None, minfmt=None, label=None): """ majloc and minloc: TickLocators for the major and minor ticks majfmt and minfmt: TickFormatters for the major and minor ticks label: the default axis label If any of the above are None, the axis will simply use the default """ self.majloc = majloc self.minloc = minloc self.majfmt = majfmt self.minfmt = minfmt self.label = label class ConversionInterface: """ The minimal interface for a converter to take custom instances (or sequences) and convert them to values mpl can use """ def axisinfo(unit): 'return an units.AxisInfo instance for unit' return None axisinfo = staticmethod(axisinfo) def default_units(x): 'return the default unit for x or None' return None default_units = staticmethod(default_units) def convert(obj, unit): """ convert obj using unit. If obj is a sequence, return the converted sequence. The ouput must be a sequence of scalars that can be used by the numpy array layer """ return obj convert = staticmethod(convert) def is_numlike(x): """ The matplotlib datalim, autoscaling, locators etc work with scalars which are the units converted to floats given the current unit. The converter may be passed these floats, or arrays of them, even when units are set. Derived conversion interfaces may opt to pass plain-ol unitless numbers through the conversion interface and this is a helper function for them. """ if iterable(x): for thisx in x: return is_numlike(thisx) else: return is_numlike(x) is_numlike = staticmethod(is_numlike) class Registry(dict): """ register types with conversion interface """ def __init__(self): dict.__init__(self) self._cached = {} def get_converter(self, x): 'get the converter interface instance for x, or None' if not len(self): return None # nothing registered #DISABLED idx = id(x) #DISABLED cached = self._cached.get(idx) #DISABLED if cached is not None: return cached converter = None classx = getattr(x, '__class__', None) if classx is not None: converter = self.get(classx) if converter is None and iterable(x): # if this is anything but an object array, we'll assume # there are no custom units if isinstance(x, np.ndarray) and x.dtype != np.object: return None for thisx in x: converter = self.get_converter( thisx ) return converter #DISABLED self._cached[idx] = converter return converter registry = Registry()	4,810	Python	.py	118	32.415254	74	0.658165	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,270	contour.py	numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/contour.py	""" These are classes to support contour plotting and labelling for the axes class """ from __future__ import division import warnings import matplotlib as mpl import numpy as np from numpy import ma import matplotlib._cntr as _cntr import matplotlib.path as path import matplotlib.ticker as ticker import matplotlib.cm as cm import matplotlib.colors as colors import matplotlib.collections as collections import matplotlib.font_manager as font_manager import matplotlib.text as text import matplotlib.cbook as cbook import matplotlib.mlab as mlab # Import needed for adding manual selection capability to clabel from matplotlib.blocking_input import BlockingContourLabeler # We can't use a single line collection for contour because a line # collection can have only a single line style, and we want to be able to have # dashed negative contours, for example, and solid positive contours. # We could use a single polygon collection for filled contours, but it # seems better to keep line and filled contours similar, with one collection # per level. class ContourLabeler: '''Mixin to provide labelling capability to ContourSet''' def clabel(self, args, kwargs): """ call signature:: clabel(cs, kwargs) adds labels to line contours in cs, where cs* is a :class:`~matplotlib.contour.ContourSet` object returned by contour. :: clabel(cs, v, *kwargs) only labels contours listed in v. Optional keyword arguments: fontsize: See http://matplotlib.sf.net/fonts.html colors: - if None, the color of each label matches the color of the corresponding contour - if one string color, e.g. colors* = 'r' or colors = 'red', all labels will be plotted in this color - if a tuple of matplotlib color args (string, float, rgb, etc), different labels will be plotted in different colors in the order specified inline: controls whether the underlying contour is removed or not. Default is True. inline_spacing: space in pixels to leave on each side of label when placing inline. Defaults to 5. This spacing will be exact for labels at locations where the contour is straight, less so for labels on curved contours. fmt: a format string for the label. Default is '%1.3f' Alternatively, this can be a dictionary matching contour levels with arbitrary strings to use for each contour level (i.e., fmt[level]=string) manual: if True, contour labels will be placed manually using mouse clicks. Click the first button near a contour to add a label, click the second button (or potentially both mouse buttons at once) to finish adding labels. The third button can be used to remove the last label added, but only if labels are not inline. Alternatively, the keyboard can be used to select label locations (enter to end label placement, delete or backspace act like the third mouse button, and any other key will select a label location). .. plot:: mpl_examples/pylab_examples/contour_demo.py """ """ NOTES on how this all works: clabel basically takes the input arguments and uses them to add a list of "label specific" attributes to the ContourSet object. These attributes are all of the form label* and names should be fairly self explanatory. Once these attributes are set, clabel passes control to the labels method (case of automatic label placement) or BlockingContourLabeler (case of manual label placement). """ fontsize = kwargs.get('fontsize', None) inline = kwargs.get('inline', 1) inline_spacing = kwargs.get('inline_spacing', 5) self.labelFmt = kwargs.get('fmt', '%1.3f') _colors = kwargs.get('colors', None) # Detect if manual selection is desired and remove from argument list self.labelManual=kwargs.get('manual',False) if len(args) == 0: levels = self.levels indices = range(len(self.levels)) elif len(args) == 1: levlabs = list(args[0]) indices, levels = [], [] for i, lev in enumerate(self.levels): if lev in levlabs: indices.append(i) levels.append(lev) if len(levels) < len(levlabs): msg = "Specified levels " + str(levlabs) msg += "\n don't match available levels " msg += str(self.levels) raise ValueError(msg) else: raise TypeError("Illegal arguments to clabel, see help(clabel)") self.labelLevelList = levels self.labelIndiceList = indices self.labelFontProps = font_manager.FontProperties() if fontsize == None: font_size = int(self.labelFontProps.get_size_in_points()) else: if type(fontsize) not in [int, float, str]: raise TypeError("Font size must be an integer number.") # Can't it be floating point, as indicated in line above? else: if type(fontsize) == str: font_size = int(self.labelFontProps.get_size_in_points()) else: self.labelFontProps.set_size(fontsize) font_size = fontsize self.labelFontSizeList = [font_size] * len(levels) if _colors == None: self.labelMappable = self self.labelCValueList = np.take(self.cvalues, self.labelIndiceList) else: cmap = colors.ListedColormap(_colors, N=len(self.labelLevelList)) self.labelCValueList = range(len(self.labelLevelList)) self.labelMappable = cm.ScalarMappable(cmap = cmap, norm = colors.NoNorm()) #self.labelTexts = [] # Initialized in ContourSet.__init__ #self.labelCValues = [] # same self.labelXYs = [] if self.labelManual: print 'Select label locations manually using first mouse button.' print 'End manual selection with second mouse button.' if not inline: print 'Remove last label by clicking third mouse button.' blocking_contour_labeler = BlockingContourLabeler(self) blocking_contour_labeler(inline,inline_spacing) else: self.labels(inline,inline_spacing) # Hold on to some old attribute names. These are depricated and will # be removed in the near future (sometime after 2008-08-01), but keeping # for now for backwards compatibility self.cl = self.labelTexts self.cl_xy = self.labelXYs self.cl_cvalues = self.labelCValues self.labelTextsList = cbook.silent_list('text.Text', self.labelTexts) return self.labelTextsList def print_label(self, linecontour,labelwidth): "if contours are too short, don't plot a label" lcsize = len(linecontour) if lcsize > 10 * labelwidth: return 1 xmax = np.amax(linecontour[:,0]) xmin = np.amin(linecontour[:,0]) ymax = np.amax(linecontour[:,1]) ymin = np.amin(linecontour[:,1]) lw = labelwidth if (xmax - xmin) > 1.2* lw or (ymax - ymin) > 1.2 * lw: return 1 else: return 0 def too_close(self, x,y, lw): "if there's a label already nearby, find a better place" if self.labelXYs != []: dist = [np.sqrt((x-loc[0]) 2 + (y-loc[1]) 2) for loc in self.labelXYs] for d in dist: if d < 1.2lw: return 1 else: return 0 else: return 0 def get_label_coords(self, distances, XX, YY, ysize, lw): """ labels are ploted at a location with the smallest dispersion of the contour from a straight line unless there's another label nearby, in which case the second best place on the contour is picked up if there's no good place a label isplotted at the beginning of the contour """ hysize = int(ysize/2) adist = np.argsort(distances) for ind in adist: x, y = XX[ind][hysize], YY[ind][hysize] if self.too_close(x,y, lw): continue else: return x,y, ind ind = adist[0] x, y = XX[ind][hysize], YY[ind][hysize] return x,y, ind def get_label_width(self, lev, fmt, fsize): "get the width of the label in points" if cbook.is_string_like(lev): lw = (len(lev)) fsize else: lw = (len(self.get_text(lev,fmt))) * fsize return lw def get_real_label_width( self, lev, fmt, fsize ): """ This computes actual onscreen label width. This uses some black magic to determine onscreen extent of non-drawn label. This magic may not be very robust. """ # Find middle of axes xx = np.mean( np.asarray(self.ax.axis()).reshape(2,2), axis=1 ) # Temporarily create text object t = text.Text( xx[0], xx[1] ) self.set_label_props( t, self.get_text(lev,fmt), 'k' ) # Some black magic to get onscreen extent # NOTE: This will only work for already drawn figures, as the canvas # does not have a renderer otherwise. This is the reason this function # can't be integrated into the rest of the code. bbox = t.get_window_extent(renderer=self.ax.figure.canvas.renderer) # difference in pixel extent of image lw = np.diff(bbox.corners()[0::2,0])[0] return lw def set_label_props(self, label, text, color): "set the label properties - color, fontsize, text" label.set_text(text) label.set_color(color) label.set_fontproperties(self.labelFontProps) label.set_clip_box(self.ax.bbox) def get_text(self, lev, fmt): "get the text of the label" if cbook.is_string_like(lev): return lev else: if isinstance(fmt,dict): return fmt[lev] else: return fmt%lev def locate_label(self, linecontour, labelwidth): """find a good place to plot a label (relatively flat part of the contour) and the angle of rotation for the text object """ nsize= len(linecontour) if labelwidth > 1: xsize = int(np.ceil(nsize/labelwidth)) else: xsize = 1 if xsize == 1: ysize = nsize else: ysize = labelwidth XX = np.resize(linecontour[:,0],(xsize, ysize)) YY = np.resize(linecontour[:,1],(xsize, ysize)) #I might have fouled up the following: yfirst = YY[:,0].reshape(xsize, 1) ylast = YY[:,-1].reshape(xsize, 1) xfirst = XX[:,0].reshape(xsize, 1) xlast = XX[:,-1].reshape(xsize, 1) s = (yfirst-YY) * (xlast-xfirst) - (xfirst-XX) * (ylast-yfirst) L = np.sqrt((xlast-xfirst)2+(ylast-yfirst)2).ravel() dist = np.add.reduce(([(abs(s)[i]/L[i]) for i in range(xsize)]),-1) x,y,ind = self.get_label_coords(dist, XX, YY, ysize, labelwidth) #print 'ind, x, y', ind, x, y # There must be a more efficient way... lc = [tuple(l) for l in linecontour] dind = lc.index((x,y)) #print 'dind', dind #dind = list(linecontour).index((x,y)) return x, y, dind def calc_label_rot_and_inline( self, slc, ind, lw, lc=None, spacing=5 ): """ This function calculates the appropriate label rotation given the linecontour coordinates in screen units, the index of the label location and the label width. It will also break contour and calculate inlining if lc is not empty (lc defaults to the empty list if None). spacing is the space around the label in pixels to leave empty. Do both of these tasks at once to avoid calling mlab.path_length multiple times, which is relatively costly. The method used here involves calculating the path length along the contour in pixel coordinates and then looking approximately label width / 2 away from central point to determine rotation and then to break contour if desired. """ if lc is None: lc = [] # Half the label width hlw = lw/2.0 # Check if closed and, if so, rotate contour so label is at edge closed = mlab.is_closed_polygon(slc) if closed: slc = np.r_[ slc[ind:-1], slc[:ind+1] ] if len(lc): # Rotate lc also if not empty lc = np.r_[ lc[ind:-1], lc[:ind+1] ] ind = 0 # Path length in pixel space pl = mlab.path_length(slc) pl = pl-pl[ind] # Use linear interpolation to get points around label xi = np.array( [ -hlw, hlw ] ) if closed: # Look at end also for closed contours dp = np.array([pl[-1],0]) else: dp = np.zeros_like(xi) ll = mlab.less_simple_linear_interpolation( pl, slc, dp+xi, extrap=True ) # get vector in pixel space coordinates from one point to other dd = np.diff( ll, axis=0 ).ravel() # Get angle of vector - must be calculated in pixel space for # text rotation to work correctly if np.all(dd==0): # Must deal with case of zero length label rotation = 0.0 else: rotation = np.arctan2(dd[1], dd[0]) * 180.0 / np.pi # Fix angle so text is never upside-down if rotation > 90: rotation = rotation - 180.0 if rotation < -90: rotation = 180.0 + rotation # Break contour if desired nlc = [] if len(lc): # Expand range by spacing xi = dp + xi + np.array([-spacing,spacing]) # Get indices near points of interest I = mlab.less_simple_linear_interpolation( pl, np.arange(len(pl)), xi, extrap=False ) # If those indices aren't beyond contour edge, find x,y if (not np.isnan(I[0])) and int(I[0])<>I[0]: xy1 = mlab.less_simple_linear_interpolation( pl, lc, [ xi[0] ] ) if (not np.isnan(I[1])) and int(I[1])<>I[1]: xy2 = mlab.less_simple_linear_interpolation( pl, lc, [ xi[1] ] ) # Make integer I = [ np.floor(I[0]), np.ceil(I[1]) ] # Actually break contours if closed: # This will remove contour if shorter than label if np.all(~np.isnan(I)): nlc.append( np.r_[ xy2, lc[I[1]:I[0]+1], xy1 ] ) else: # These will remove pieces of contour if they have length zero if not np.isnan(I[0]): nlc.append( np.r_[ lc[:I[0]+1], xy1 ] ) if not np.isnan(I[1]): nlc.append( np.r_[ xy2, lc[I[1]:] ] ) # The current implementation removes contours completely # covered by labels. Uncomment line below to keep # original contour if this is the preferred behavoir. #if not len(nlc): nlc = [ lc ] return (rotation,nlc) def add_label(self,x,y,rotation,lev,cvalue): dx,dy = self.ax.transData.inverted().transform_point((x,y)) t = text.Text(dx, dy, rotation = rotation, horizontalalignment='center', verticalalignment='center') color = self.labelMappable.to_rgba(cvalue,alpha=self.alpha) _text = self.get_text(lev,self.labelFmt) self.set_label_props(t, _text, color) self.labelTexts.append(t) self.labelCValues.append(cvalue) self.labelXYs.append((x,y)) # Add label to plot here - useful for manual mode label selection self.ax.add_artist(t) def pop_label(self,index=-1): '''Defaults to removing last label, but any index can be supplied''' self.labelCValues.pop(index) t = self.labelTexts.pop(index) t.remove() def labels(self, inline, inline_spacing): trans = self.ax.transData # A bit of shorthand for icon, lev, fsize, cvalue in zip( self.labelIndiceList, self.labelLevelList, self.labelFontSizeList, self.labelCValueList ): con = self.collections[icon] lw = self.get_label_width(lev, self.labelFmt, fsize) additions = [] paths = con.get_paths() for segNum, linepath in enumerate(paths): lc = linepath.vertices # Line contour slc0 = trans.transform(lc) # Line contour in screen coords # For closed polygons, add extra point to avoid division by # zero in print_label and locate_label. Other than these # functions, this is not necessary and should probably be # eventually removed. if mlab.is_closed_polygon( lc ): slc = np.r_[ slc0, slc0[1:2,:] ] else: slc = slc0 if self.print_label(slc,lw): # Check if long enough for a label x,y,ind = self.locate_label(slc, lw) if inline: lcarg = lc else: lcarg = None rotation,new=self.calc_label_rot_and_inline( slc0, ind, lw, lcarg, inline_spacing ) # Actually add the label self.add_label(x,y,rotation,lev,cvalue) # If inline, add new contours if inline: for n in new: # Add path if not empty or single point if len(n)>1: additions.append( path.Path(n) ) else: # If not adding label, keep old path additions.append(linepath) # After looping over all segments on a contour, remove old # paths and add new ones if inlining if inline: del paths[:] paths.extend(additions) class ContourSet(cm.ScalarMappable, ContourLabeler): """ Create and store a set of contour lines or filled regions. User-callable method: clabel Useful attributes: ax: the axes object in which the contours are drawn collections: a silent_list of LineCollections or PolyCollections levels: contour levels layers: same as levels for line contours; half-way between levels for filled contours. See _process_colors method. """ def __init__(self, ax, args, kwargs): """ Draw contour lines or filled regions, depending on whether keyword arg 'filled' is False (default) or True. The first argument of the initializer must be an axes object. The remaining arguments and keyword arguments are described in ContourSet.contour_doc. """ self.ax = ax self.levels = kwargs.get('levels', None) self.filled = kwargs.get('filled', False) self.linewidths = kwargs.get('linewidths', None) self.linestyles = kwargs.get('linestyles', 'solid') self.alpha = kwargs.get('alpha', 1.0) self.origin = kwargs.get('origin', None) self.extent = kwargs.get('extent', None) cmap = kwargs.get('cmap', None) self.colors = kwargs.get('colors', None) norm = kwargs.get('norm', None) self.extend = kwargs.get('extend', 'neither') self.antialiased = kwargs.get('antialiased', True) self.nchunk = kwargs.get('nchunk', 0) self.locator = kwargs.get('locator', None) if (isinstance(norm, colors.LogNorm) or isinstance(self.locator, ticker.LogLocator)): self.logscale = True if norm is None: norm = colors.LogNorm() if self.extend is not 'neither': raise ValueError('extend kwarg does not work yet with log scale') else: self.logscale = False if self.origin is not None: assert(self.origin in ['lower', 'upper', 'image']) if self.extent is not None: assert(len(self.extent) == 4) if cmap is not None: assert(isinstance(cmap, colors.Colormap)) if self.colors is not None and cmap is not None: raise ValueError('Either colors or cmap must be None') if self.origin == 'image': self.origin = mpl.rcParams['image.origin'] x, y, z = self._contour_args(args) # also sets self.levels, # self.layers if self.colors is not None: cmap = colors.ListedColormap(self.colors, N=len(self.layers)) if self.filled: self.collections = cbook.silent_list('collections.PolyCollection') else: self.collections = cbook.silent_list('collections.LineCollection') # label lists must be initialized here self.labelTexts = [] self.labelCValues = [] kw = {'cmap': cmap} if norm is not None: kw['norm'] = norm cm.ScalarMappable.__init__(self, *kw) # sets self.cmap; self._process_colors() _mask = ma.getmask(z) if _mask is ma.nomask: _mask = None if self.filled: if self.linewidths is not None: warnings.warn('linewidths is ignored by contourf') C = _cntr.Cntr(x, y, z.filled(), _mask) lowers = self._levels[:-1] uppers = self._levels[1:] for level, level_upper in zip(lowers, uppers): nlist = C.trace(level, level_upper, points = 0, nchunk = self.nchunk) col = collections.PolyCollection(nlist, antialiaseds = (self.antialiased,), edgecolors= 'none', alpha=self.alpha) self.ax.add_collection(col) self.collections.append(col) else: tlinewidths = self._process_linewidths() self.tlinewidths = tlinewidths tlinestyles = self._process_linestyles() C = _cntr.Cntr(x, y, z.filled(), _mask) for level, width, lstyle in zip(self.levels, tlinewidths, tlinestyles): nlist = C.trace(level, points = 0) col = collections.LineCollection(nlist, linewidths = width, linestyle = lstyle, alpha=self.alpha) if level < 0.0 and self.monochrome: ls = mpl.rcParams['contour.negative_linestyle'] col.set_linestyle(ls) col.set_label('_nolegend_') self.ax.add_collection(col, False) self.collections.append(col) self.changed() # set the colors x0 = ma.minimum(x) x1 = ma.maximum(x) y0 = ma.minimum(y) y1 = ma.maximum(y) self.ax.update_datalim([(x0,y0), (x1,y1)]) self.ax.autoscale_view() def changed(self): tcolors = [ (tuple(rgba),) for rgba in self.to_rgba(self.cvalues, alpha=self.alpha)] self.tcolors = tcolors for color, collection in zip(tcolors, self.collections): collection.set_alpha(self.alpha) collection.set_color(color) for label, cv in zip(self.labelTexts, self.labelCValues): label.set_alpha(self.alpha) label.set_color(self.labelMappable.to_rgba(cv)) # add label colors cm.ScalarMappable.changed(self) def _autolev(self, z, N): ''' Select contour levels to span the data. We need two more levels for filled contours than for line contours, because for the latter we need to specify the lower and upper boundary of each range. For example, a single contour boundary, say at z = 0, requires only one contour line, but two filled regions, and therefore three levels to provide boundaries for both regions. ''' if self.locator is None: if self.logscale: self.locator = ticker.LogLocator() else: self.locator = ticker.MaxNLocator(N+1) self.locator.create_dummy_axis() zmax = self.zmax zmin = self.zmin self.locator.set_bounds(zmin, zmax) lev = self.locator() zmargin = (zmax - zmin) 0.000001 # so z < (zmax + zmargin) if zmax >= lev[-1]: lev[-1] += zmargin if zmin <= lev[0]: if self.logscale: lev[0] = 0.99 * zmin else: lev[0] -= zmargin self._auto = True if self.filled: return lev return lev[1:-1] def _initialize_x_y(self, z): ''' Return X, Y arrays such that contour(Z) will match imshow(Z) if origin is not None. The center of pixel Z[i,j] depends on origin: if origin is None, x = j, y = i; if origin is 'lower', x = j + 0.5, y = i + 0.5; if origin is 'upper', x = j + 0.5, y = Nrows - i - 0.5 If extent is not None, x and y will be scaled to match, as in imshow. If origin is None and extent is not None, then extent will give the minimum and maximum values of x and y. ''' if z.ndim != 2: raise TypeError("Input must be a 2D array.") else: Ny, Nx = z.shape if self.origin is None: # Not for image-matching. if self.extent is None: return np.meshgrid(np.arange(Nx), np.arange(Ny)) else: x0,x1,y0,y1 = self.extent x = np.linspace(x0, x1, Nx) y = np.linspace(y0, y1, Ny) return np.meshgrid(x, y) # Match image behavior: if self.extent is None: x0,x1,y0,y1 = (0, Nx, 0, Ny) else: x0,x1,y0,y1 = self.extent dx = float(x1 - x0)/Nx dy = float(y1 - y0)/Ny x = x0 + (np.arange(Nx) + 0.5) * dx y = y0 + (np.arange(Ny) + 0.5) * dy if self.origin == 'upper': y = y[::-1] return np.meshgrid(x,y) def _check_xyz(self, args): ''' For functions like contour, check that the dimensions of the input arrays match; if x and y are 1D, convert them to 2D using meshgrid. Possible change: I think we should make and use an ArgumentError Exception class (here and elsewhere). ''' # We can strip away the x and y units x = self.ax.convert_xunits( args[0] ) y = self.ax.convert_yunits( args[1] ) x = np.asarray(x, dtype=np.float64) y = np.asarray(y, dtype=np.float64) z = ma.asarray(args[2], dtype=np.float64) if z.ndim != 2: raise TypeError("Input z must be a 2D array.") else: Ny, Nx = z.shape if x.shape == z.shape and y.shape == z.shape: return x,y,z if x.ndim != 1 or y.ndim != 1: raise TypeError("Inputs x and y must be 1D or 2D.") nx, = x.shape ny, = y.shape if nx != Nx or ny != Ny: raise TypeError("Length of x must be number of columns in z,\n" + "and length of y must be number of rows.") x,y = np.meshgrid(x,y) return x,y,z def _contour_args(self, args): if self.filled: fn = 'contourf' else: fn = 'contour' Nargs = len(args) if Nargs <= 2: z = ma.asarray(args[0], dtype=np.float64) x, y = self._initialize_x_y(z) elif Nargs <=4: x,y,z = self._check_xyz(args[:3]) else: raise TypeError("Too many arguments to %s; see help(%s)" % (fn,fn)) self.zmax = ma.maximum(z) self.zmin = ma.minimum(z) if self.logscale and self.zmin <= 0: z = ma.masked_where(z <= 0, z) warnings.warn('Log scale: values of z <=0 have been masked') self.zmin = z.min() self._auto = False if self.levels is None: if Nargs == 1 or Nargs == 3: lev = self._autolev(z, 7) else: # 2 or 4 args level_arg = args[-1] try: if type(level_arg) == int: lev = self._autolev(z, level_arg) else: lev = np.asarray(level_arg).astype(np.float64) except: raise TypeError( "Last %s arg must give levels; see help(%s)" % (fn,fn)) if self.filled and len(lev) < 2: raise ValueError("Filled contours require at least 2 levels.") # Workaround for cntr.c bug wrt masked interior regions: #if filled: # z = ma.masked_array(z.filled(-1e38)) # It's not clear this is any better than the original bug. self.levels = lev #if self._auto and self.extend in ('both', 'min', 'max'): # raise TypeError("Auto level selection is inconsistent " # + "with use of 'extend' kwarg") self._levels = list(self.levels) if self.extend in ('both', 'min'): self._levels.insert(0, min(self.levels[0],self.zmin) - 1) if self.extend in ('both', 'max'): self._levels.append(max(self.levels[-1],self.zmax) + 1) self._levels = np.asarray(self._levels) self.vmin = np.amin(self.levels) # alternative would be self.layers self.vmax = np.amax(self.levels) if self.extend in ('both', 'min'): self.vmin = 2 self.levels[0] - self.levels[1] if self.extend in ('both', 'max'): self.vmax = 2 * self.levels[-1] - self.levels[-2] self.layers = self._levels # contour: a line is a thin layer if self.filled: self.layers = 0.5 * (self._levels[:-1] + self._levels[1:]) if self.extend in ('both', 'min'): self.layers[0] = 0.5 * (self.vmin + self._levels[1]) if self.extend in ('both', 'max'): self.layers[-1] = 0.5 * (self.vmax + self._levels[-2]) return (x, y, z) def _process_colors(self): """ Color argument processing for contouring. Note that we base the color mapping on the contour levels, not on the actual range of the Z values. This means we don't have to worry about bad values in Z, and we always have the full dynamic range available for the selected levels. The color is based on the midpoint of the layer, except for an extended end layers. """ self.monochrome = self.cmap.monochrome if self.colors is not None: i0, i1 = 0, len(self.layers) if self.extend in ('both', 'min'): i0 = -1 if self.extend in ('both', 'max'): i1 = i1 + 1 self.cvalues = range(i0, i1) self.set_norm(colors.NoNorm()) else: self.cvalues = self.layers if not self.norm.scaled(): self.set_clim(self.vmin, self.vmax) if self.extend in ('both', 'max', 'min'): self.norm.clip = False self.set_array(self.layers) # self.tcolors are set by the "changed" method def _process_linewidths(self): linewidths = self.linewidths Nlev = len(self.levels) if linewidths is None: tlinewidths = [(mpl.rcParams['lines.linewidth'],)] Nlev else: if cbook.iterable(linewidths) and len(linewidths) < Nlev: linewidths = list(linewidths) int(np.ceil(Nlev/len(linewidths))) elif not cbook.iterable(linewidths) and type(linewidths) in [int, float]: linewidths = [linewidths] * Nlev tlinewidths = [(w,) for w in linewidths] return tlinewidths def _process_linestyles(self): linestyles = self.linestyles Nlev = len(self.levels) if linestyles is None: tlinestyles = ['solid'] * Nlev else: if cbook.is_string_like(linestyles): tlinestyles = [linestyles] * Nlev elif cbook.iterable(linestyles) and len(linestyles) <= Nlev: tlinestyles = list(linestyles) * int(np.ceil(Nlev/len(linestyles))) return tlinestyles def get_alpha(self): '''returns alpha to be applied to all ContourSet artists''' return self.alpha def set_alpha(self, alpha): '''sets alpha for all ContourSet artists''' self.alpha = alpha self.changed() contour_doc = """ :func:`~matplotlib.pyplot.contour` and :func:`~matplotlib.pyplot.contourf` draw contour lines and filled contours, respectively. Except as noted, function signatures and return values are the same for both versions. :func:`~matplotlib.pyplot.contourf` differs from the Matlab (TM) version in that it does not draw the polygon edges, because the contouring engine yields simply connected regions with branch cuts. To draw the edges, add line contours with calls to :func:`~matplotlib.pyplot.contour`. call signatures:: contour(Z) make a contour plot of an array Z. The level values are chosen automatically. :: contour(X,Y,Z) X, Y specify the (x, y) coordinates of the surface :: contour(Z,N) contour(X,Y,Z,N) contour N automatically-chosen levels. :: contour(Z,V) contour(X,Y,Z,V) draw contour lines at the values specified in sequence V :: contourf(..., V) fill the (len(V)-1) regions between the values in V :: contour(Z, *kwargs) Use keyword args to control colors, linewidth, origin, cmap ... see below for more details. X, Y, and Z* must be arrays with the same dimensions. Z may be a masked array, but filled contouring may not handle internal masked regions correctly. ``C = contour(...)`` returns a :class:`~matplotlib.contour.ContourSet` object. Optional keyword arguments: colors: [ None \| string \| (mpl_colors) ] If None, the colormap specified by cmap will be used. If a string, like 'r' or 'red', all levels will be plotted in this color. If a tuple of matplotlib color args (string, float, rgb, etc), different levels will be plotted in different colors in the order specified. alpha: float The alpha blending value cmap: [ None \| Colormap ] A cm :class:`~matplotlib.cm.Colormap` instance or None. If cmap is None and colors is None, a default Colormap is used. norm: [ None \| Normalize ] A :class:`matplotlib.colors.Normalize` instance for scaling data values to colors. If norm is None and colors is None, the default linear scaling is used. origin: [ None \| 'upper' \| 'lower' \| 'image' ] If None, the first value of Z will correspond to the lower left corner, location (0,0). If 'image', the rc value for ``image.origin`` will be used. This keyword is not active if X and Y are specified in the call to contour. extent: [ None \| (x0,x1,y0,y1) ] If origin is not None, then extent is interpreted as in :func:`matplotlib.pyplot.imshow`: it gives the outer pixel boundaries. In this case, the position of Z[0,0] is the center of the pixel, not a corner. If origin is None, then (x0, y0) is the position of Z[0,0], and (x1, y1) is the position of Z[-1,-1]. This keyword is not active if X and Y are specified in the call to contour. locator: [ None \| ticker.Locator subclass ] If locator is None, the default :class:`~matplotlib.ticker.MaxNLocator` is used. The locator is used to determine the contour levels if they are not given explicitly via the V argument. extend: [ 'neither' \| 'both' \| 'min' \| 'max' ] Unless this is 'neither', contour levels are automatically added to one or both ends of the range so that all data are included. These added ranges are then mapped to the special colormap values which default to the ends of the colormap range, but can be set via :meth:`matplotlib.cm.Colormap.set_under` and :meth:`matplotlib.cm.Colormap.set_over` methods. contour-only keyword arguments: linewidths: [ None \| number \| tuple of numbers ] If linewidths is None, the default width in ``lines.linewidth`` in ``matplotlibrc`` is used. If a number, all levels will be plotted with this linewidth. If a tuple, different levels will be plotted with different linewidths in the order specified linestyles: [None \| 'solid' \| 'dashed' \| 'dashdot' \| 'dotted' ] If linestyles is None, the 'solid' is used. linestyles can also be an iterable of the above strings specifying a set of linestyles to be used. If this iterable is shorter than the number of contour levels it will be repeated as necessary. If contour is using a monochrome colormap and the contour level is less than 0, then the linestyle specified in ``contour.negative_linestyle`` in ``matplotlibrc`` will be used. contourf-only keyword arguments: antialiased: [ True \| False ] enable antialiasing nchunk: [ 0 \| integer ] If 0, no subdivision of the domain. Specify a positive integer to divide the domain into subdomains of roughly nchunk by nchunk points. This may never actually be advantageous, so this option may be removed. Chunking introduces artifacts at the chunk boundaries unless antialiased is False. Example: .. plot:: mpl_examples/pylab_examples/contour_demo.py """ def find_nearest_contour( self, x, y, indices=None, pixel=True ): """ Finds contour that is closest to a point. Defaults to measuring distance in pixels (screen space - useful for manual contour labeling), but this can be controlled via a keyword argument. Returns a tuple containing the contour, segment, index of segment, x & y of segment point and distance to minimum point. Call signature:: conmin,segmin,imin,xmin,ymin,dmin = find_nearest_contour( self, x, y, indices=None, pixel=True ) Optional keyword arguments:: indices: Indexes of contour levels to consider when looking for nearest point. Defaults to using all levels. pixel: If True, measure distance in pixel space, if not, measure distance in axes space. Defaults to True. """ # This function uses a method that is probably quite # inefficient based on converting each contour segment to # pixel coordinates and then comparing the given point to # those coordinates for each contour. This will probably be # quite slow for complex contours, but for normal use it works # sufficiently well that the time is not noticeable. # Nonetheless, improvements could probably be made. if indices==None: indices = range(len(self.levels)) dmin = 1e10 conmin = None segmin = None xmin = None ymin = None for icon in indices: con = self.collections[icon] paths = con.get_paths() for segNum, linepath in enumerate(paths): lc = linepath.vertices # transfer all data points to screen coordinates if desired if pixel: lc = self.ax.transData.transform(lc) ds = (lc[:,0]-x)2 + (lc[:,1]-y)2 d = min( ds ) if d < dmin: dmin = d conmin = icon segmin = segNum imin = mpl.mlab.find( ds == d )[0] xmin = lc[imin,0] ymin = lc[imin,1] return (conmin,segmin,imin,xmin,ymin,dmin)	42,063	Python	.py	912	34.394737	85	0.573364	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,271	widgets.py	numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/widgets.py	""" GUI Neutral widgets All of these widgets require you to predefine an Axes instance and pass that as the first arg. matplotlib doesn't try to be too smart in layout -- you have to figure out how wide and tall you want your Axes to be to accommodate your widget. """ import numpy as np from mlab import dist from patches import Circle, Rectangle from lines import Line2D from transforms import blended_transform_factory class LockDraw: """ some widgets, like the cursor, draw onto the canvas, and this is not desirable under all circumstaces, like when the toolbar is in zoom-to-rect mode and drawing a rectangle. The module level "lock" allows someone to grab the lock and prevent other widgets from drawing. Use matplotlib.widgets.lock(someobj) to pr """ def __init__(self): self._owner = None def __call__(self, o): 'reserve the lock for o' if not self.available(o): raise ValueError('already locked') self._owner = o def release(self, o): 'release the lock' if not self.available(o): raise ValueError('you do not own this lock') self._owner = None def available(self, o): 'drawing is available to o' return not self.locked() or self.isowner(o) def isowner(self, o): 'o owns the lock' return self._owner is o def locked(self): 'the lock is held' return self._owner is not None class Widget: """ OK, I couldn't resist; abstract base class for mpl GUI neutral widgets """ drawon = True eventson = True class Button(Widget): """ A GUI neutral button The following attributes are accesible ax - the Axes the button renders into label - a text.Text instance color - the color of the button when not hovering hovercolor - the color of the button when hovering Call "on_clicked" to connect to the button """ def __init__(self, ax, label, image=None, color='0.85', hovercolor='0.95'): """ ax is the Axes instance the button will be placed into label is a string which is the button text image if not None, is an image to place in the button -- can be any legal arg to imshow (numpy array, matplotlib Image instance, or PIL image) color is the color of the button when not activated hovercolor is the color of the button when the mouse is over it """ if image is not None: ax.imshow(image) self.label = ax.text(0.5, 0.5, label, verticalalignment='center', horizontalalignment='center', transform=ax.transAxes) self.cnt = 0 self.observers = {} self.ax = ax ax.figure.canvas.mpl_connect('button_press_event', self._click) ax.figure.canvas.mpl_connect('motion_notify_event', self._motion) ax.set_navigate(False) ax.set_axis_bgcolor(color) ax.set_xticks([]) ax.set_yticks([]) self.color = color self.hovercolor = hovercolor self._lastcolor = color def _click(self, event): if event.inaxes != self.ax: return if not self.eventson: return for cid, func in self.observers.items(): func(event) def _motion(self, event): if event.inaxes==self.ax: c = self.hovercolor else: c = self.color if c != self._lastcolor: self.ax.set_axis_bgcolor(c) self._lastcolor = c if self.drawon: self.ax.figure.canvas.draw() def on_clicked(self, func): """ When the button is clicked, call this func with event A connection id is returned which can be used to disconnect """ cid = self.cnt self.observers[cid] = func self.cnt += 1 return cid def disconnect(self, cid): 'remove the observer with connection id cid' try: del self.observers[cid] except KeyError: pass class Slider(Widget): """ A slider representing a floating point range The following attributes are defined ax : the slider axes.Axes instance val : the current slider value vline : a Line2D instance representing the initial value poly : A patch.Polygon instance which is the slider valfmt : the format string for formatting the slider text label : a text.Text instance, the slider label closedmin : whether the slider is closed on the minimum closedmax : whether the slider is closed on the maximum slidermin : another slider - if not None, this slider must be > slidermin slidermax : another slider - if not None, this slider must be < slidermax dragging : allow for mouse dragging on slider Call on_changed to connect to the slider event """ def __init__(self, ax, label, valmin, valmax, valinit=0.5, valfmt='%1.2f', closedmin=True, closedmax=True, slidermin=None, slidermax=None, dragging=True, kwargs): """ Create a slider from valmin to valmax in axes ax; valinit - the slider initial position label - the slider label valfmt - used to format the slider value closedmin and closedmax - indicate whether the slider interval is closed slidermin and slidermax - be used to contrain the value of this slider to the values of other sliders. additional kwargs are passed on to self.poly which is the matplotlib.patches.Rectangle which draws the slider. See the matplotlib.patches.Rectangle documentation for legal property names (eg facecolor, edgecolor, alpha, ...) """ self.ax = ax self.valmin = valmin self.valmax = valmax self.val = valinit self.valinit = valinit self.poly = ax.axvspan(valmin,valinit,0,1, kwargs) self.vline = ax.axvline(valinit,0,1, color='r', lw=1) self.valfmt=valfmt ax.set_yticks([]) ax.set_xlim((valmin, valmax)) ax.set_xticks([]) ax.set_navigate(False) ax.figure.canvas.mpl_connect('button_press_event', self._update) if dragging: ax.figure.canvas.mpl_connect('motion_notify_event', self._update) self.label = ax.text(-0.02, 0.5, label, transform=ax.transAxes, verticalalignment='center', horizontalalignment='right') self.valtext = ax.text(1.02, 0.5, valfmt%valinit, transform=ax.transAxes, verticalalignment='center', horizontalalignment='left') self.cnt = 0 self.observers = {} self.closedmin = closedmin self.closedmax = closedmax self.slidermin = slidermin self.slidermax = slidermax def _update(self, event): 'update the slider position' if event.button !=1: return if event.inaxes != self.ax: return val = event.xdata if not self.closedmin and val<=self.valmin: return if not self.closedmax and val>=self.valmax: return if self.slidermin is not None: if val<=self.slidermin.val: return if self.slidermax is not None: if val>=self.slidermax.val: return self.set_val(val) def set_val(self, val): xy = self.poly.xy xy[-1] = val, 0 xy[-2] = val, 1 self.poly.xy = xy self.valtext.set_text(self.valfmt%val) if self.drawon: self.ax.figure.canvas.draw() self.val = val if not self.eventson: return for cid, func in self.observers.items(): func(val) def on_changed(self, func): """ When the slider valud is changed, call this func with the new slider position A connection id is returned which can be used to disconnect """ cid = self.cnt self.observers[cid] = func self.cnt += 1 return cid def disconnect(self, cid): 'remove the observer with connection id cid' try: del self.observers[cid] except KeyError: pass def reset(self): "reset the slider to the initial value if needed" if (self.val != self.valinit): self.set_val(self.valinit) class CheckButtons(Widget): """ A GUI neutral radio button The following attributes are exposed ax - the Axes instance the buttons are in labels - a list of text.Text instances lines - a list of (line1, line2) tuples for the x's in the check boxes. These lines exist for each box, but have set_visible(False) when box is not checked rectangles - a list of patch.Rectangle instances Connect to the CheckButtons with the on_clicked method """ def __init__(self, ax, labels, actives): """ Add check buttons to axes.Axes instance ax labels is a len(buttons) list of labels as strings actives is a len(buttons) list of booleans indicating whether the button is active """ ax.set_xticks([]) ax.set_yticks([]) ax.set_navigate(False) if len(labels)>1: dy = 1./(len(labels)+1) ys = np.linspace(1-dy, dy, len(labels)) else: dy = 0.25 ys = [0.5] cnt = 0 axcolor = ax.get_axis_bgcolor() self.labels = [] self.lines = [] self.rectangles = [] lineparams = {'color':'k', 'linewidth':1.25, 'transform':ax.transAxes, 'solid_capstyle':'butt'} for y, label in zip(ys, labels): t = ax.text(0.25, y, label, transform=ax.transAxes, horizontalalignment='left', verticalalignment='center') w, h = dy/2., dy/2. x, y = 0.05, y-h/2. p = Rectangle(xy=(x,y), width=w, height=h, facecolor=axcolor, transform=ax.transAxes) l1 = Line2D([x, x+w], [y+h, y], lineparams) l2 = Line2D([x, x+w], [y, y+h], lineparams) l1.set_visible(actives[cnt]) l2.set_visible(actives[cnt]) self.labels.append(t) self.rectangles.append(p) self.lines.append((l1,l2)) ax.add_patch(p) ax.add_line(l1) ax.add_line(l2) cnt += 1 ax.figure.canvas.mpl_connect('button_press_event', self._clicked) self.ax = ax self.cnt = 0 self.observers = {} def _clicked(self, event): if event.button !=1 : return if event.inaxes != self.ax: return for p,t,lines in zip(self.rectangles, self.labels, self.lines): if (t.get_window_extent().contains(event.x, event.y) or p.get_window_extent().contains(event.x, event.y) ): l1, l2 = lines l1.set_visible(not l1.get_visible()) l2.set_visible(not l2.get_visible()) thist = t break else: return if self.drawon: self.ax.figure.canvas.draw() if not self.eventson: return for cid, func in self.observers.items(): func(thist.get_text()) def on_clicked(self, func): """ When the button is clicked, call this func with button label A connection id is returned which can be used to disconnect """ cid = self.cnt self.observers[cid] = func self.cnt += 1 return cid def disconnect(self, cid): 'remove the observer with connection id cid' try: del self.observers[cid] except KeyError: pass class RadioButtons(Widget): """ A GUI neutral radio button The following attributes are exposed ax - the Axes instance the buttons are in activecolor - the color of the button when clicked labels - a list of text.Text instances circles - a list of patch.Circle instances Connect to the RadioButtons with the on_clicked method """ def __init__(self, ax, labels, active=0, activecolor='blue'): """ Add radio buttons to axes.Axes instance ax labels is a len(buttons) list of labels as strings active is the index into labels for the button that is active activecolor is the color of the button when clicked """ self.activecolor = activecolor ax.set_xticks([]) ax.set_yticks([]) ax.set_navigate(False) dy = 1./(len(labels)+1) ys = np.linspace(1-dy, dy, len(labels)) cnt = 0 axcolor = ax.get_axis_bgcolor() self.labels = [] self.circles = [] for y, label in zip(ys, labels): t = ax.text(0.25, y, label, transform=ax.transAxes, horizontalalignment='left', verticalalignment='center') if cnt==active: facecolor = activecolor else: facecolor = axcolor p = Circle(xy=(0.15, y), radius=0.05, facecolor=facecolor, transform=ax.transAxes) self.labels.append(t) self.circles.append(p) ax.add_patch(p) cnt += 1 ax.figure.canvas.mpl_connect('button_press_event', self._clicked) self.ax = ax self.cnt = 0 self.observers = {} def _clicked(self, event): if event.button !=1 : return if event.inaxes != self.ax: return xy = self.ax.transAxes.inverted().transform_point((event.x, event.y)) pclicked = np.array([xy[0], xy[1]]) def inside(p): pcirc = np.array([p.center[0], p.center[1]]) return dist(pclicked, pcirc) < p.radius for p,t in zip(self.circles, self.labels): if t.get_window_extent().contains(event.x, event.y) or inside(p): inp = p thist = t break else: return for p in self.circles: if p==inp: color = self.activecolor else: color = self.ax.get_axis_bgcolor() p.set_facecolor(color) if self.drawon: self.ax.figure.canvas.draw() if not self.eventson: return for cid, func in self.observers.items(): func(thist.get_text()) def on_clicked(self, func): """ When the button is clicked, call this func with button label A connection id is returned which can be used to disconnect """ cid = self.cnt self.observers[cid] = func self.cnt += 1 return cid def disconnect(self, cid): 'remove the observer with connection id cid' try: del self.observers[cid] except KeyError: pass class SubplotTool(Widget): """ A tool to adjust to subplot params of fig """ def __init__(self, targetfig, toolfig): """ targetfig is the figure to adjust toolfig is the figure to embed the the subplot tool into. If None, a default pylab figure will be created. If you are using this from the GUI """ self.targetfig = targetfig toolfig.subplots_adjust(left=0.2, right=0.9) class toolbarfmt: def __init__(self, slider): self.slider = slider def __call__(self, x, y): fmt = '%s=%s'%(self.slider.label.get_text(), self.slider.valfmt) return fmt%x self.axleft = toolfig.add_subplot(711) self.axleft.set_title('Click on slider to adjust subplot param') self.axleft.set_navigate(False) self.sliderleft = Slider(self.axleft, 'left', 0, 1, targetfig.subplotpars.left, closedmax=False) self.sliderleft.on_changed(self.funcleft) self.axbottom = toolfig.add_subplot(712) self.axbottom.set_navigate(False) self.sliderbottom = Slider(self.axbottom, 'bottom', 0, 1, targetfig.subplotpars.bottom, closedmax=False) self.sliderbottom.on_changed(self.funcbottom) self.axright = toolfig.add_subplot(713) self.axright.set_navigate(False) self.sliderright = Slider(self.axright, 'right', 0, 1, targetfig.subplotpars.right, closedmin=False) self.sliderright.on_changed(self.funcright) self.axtop = toolfig.add_subplot(714) self.axtop.set_navigate(False) self.slidertop = Slider(self.axtop, 'top', 0, 1, targetfig.subplotpars.top, closedmin=False) self.slidertop.on_changed(self.functop) self.axwspace = toolfig.add_subplot(715) self.axwspace.set_navigate(False) self.sliderwspace = Slider(self.axwspace, 'wspace', 0, 1, targetfig.subplotpars.wspace, closedmax=False) self.sliderwspace.on_changed(self.funcwspace) self.axhspace = toolfig.add_subplot(716) self.axhspace.set_navigate(False) self.sliderhspace = Slider(self.axhspace, 'hspace', 0, 1, targetfig.subplotpars.hspace, closedmax=False) self.sliderhspace.on_changed(self.funchspace) # constraints self.sliderleft.slidermax = self.sliderright self.sliderright.slidermin = self.sliderleft self.sliderbottom.slidermax = self.slidertop self.slidertop.slidermin = self.sliderbottom bax = toolfig.add_axes([0.8, 0.05, 0.15, 0.075]) self.buttonreset = Button(bax, 'Reset') sliders = (self.sliderleft, self.sliderbottom, self.sliderright, self.slidertop, self.sliderwspace, self.sliderhspace, ) def func(event): thisdrawon = self.drawon self.drawon = False # store the drawon state of each slider bs = [] for slider in sliders: bs.append(slider.drawon) slider.drawon = False # reset the slider to the initial position for slider in sliders: slider.reset() # reset drawon for slider, b in zip(sliders, bs): slider.drawon = b # draw the canvas self.drawon = thisdrawon if self.drawon: toolfig.canvas.draw() self.targetfig.canvas.draw() # during reset there can be a temporary invalid state # depending on the order of the reset so we turn off # validation for the resetting validate = toolfig.subplotpars.validate toolfig.subplotpars.validate = False self.buttonreset.on_clicked(func) toolfig.subplotpars.validate = validate def funcleft(self, val): self.targetfig.subplots_adjust(left=val) if self.drawon: self.targetfig.canvas.draw() def funcright(self, val): self.targetfig.subplots_adjust(right=val) if self.drawon: self.targetfig.canvas.draw() def funcbottom(self, val): self.targetfig.subplots_adjust(bottom=val) if self.drawon: self.targetfig.canvas.draw() def functop(self, val): self.targetfig.subplots_adjust(top=val) if self.drawon: self.targetfig.canvas.draw() def funcwspace(self, val): self.targetfig.subplots_adjust(wspace=val) if self.drawon: self.targetfig.canvas.draw() def funchspace(self, val): self.targetfig.subplots_adjust(hspace=val) if self.drawon: self.targetfig.canvas.draw() class Cursor: """ A horizontal and vertical line span the axes that and move with the pointer. You can turn off the hline or vline spectively with the attributes horizOn =True\|False: controls visibility of the horizontal line vertOn =True\|False: controls visibility of the horizontal line And the visibility of the cursor itself with visible attribute """ def __init__(self, ax, useblit=False, lineprops): """ Add a cursor to ax. If useblit=True, use the backend dependent blitting features for faster updates (GTKAgg only now). lineprops is a dictionary of line properties. See examples/widgets/cursor.py. """ self.ax = ax self.canvas = ax.figure.canvas self.canvas.mpl_connect('motion_notify_event', self.onmove) self.canvas.mpl_connect('draw_event', self.clear) self.visible = True self.horizOn = True self.vertOn = True self.useblit = useblit self.lineh = ax.axhline(ax.get_ybound()[0], visible=False, lineprops) self.linev = ax.axvline(ax.get_xbound()[0], visible=False, *lineprops) self.background = None self.needclear = False def clear(self, event): 'clear the cursor' if self.useblit: self.background = self.canvas.copy_from_bbox(self.ax.bbox) self.linev.set_visible(False) self.lineh.set_visible(False) def onmove(self, event): 'on mouse motion draw the cursor if visible' if event.inaxes != self.ax: self.linev.set_visible(False) self.lineh.set_visible(False) if self.needclear: self.canvas.draw() self.needclear = False return self.needclear = True if not self.visible: return self.linev.set_xdata((event.xdata, event.xdata)) self.lineh.set_ydata((event.ydata, event.ydata)) self.linev.set_visible(self.visible and self.vertOn) self.lineh.set_visible(self.visible and self.horizOn) self._update() def _update(self): if self.useblit: if self.background is not None: self.canvas.restore_region(self.background) self.ax.draw_artist(self.linev) self.ax.draw_artist(self.lineh) self.canvas.blit(self.ax.bbox) else: self.canvas.draw_idle() return False class MultiCursor: """ Provide a vertical line cursor shared between multiple axes from matplotlib.widgets import MultiCursor from pylab import figure, show, nx t = nx.arange(0.0, 2.0, 0.01) s1 = nx.sin(2nx.pit) s2 = nx.sin(4nx.pit) fig = figure() ax1 = fig.add_subplot(211) ax1.plot(t, s1) ax2 = fig.add_subplot(212, sharex=ax1) ax2.plot(t, s2) multi = MultiCursor(fig.canvas, (ax1, ax2), color='r', lw=1) show() """ def __init__(self, canvas, axes, useblit=True, lineprops): self.canvas = canvas self.axes = axes xmin, xmax = axes[-1].get_xlim() xmid = 0.5(xmin+xmax) self.lines = [ax.axvline(xmid, visible=False, lineprops) for ax in axes] self.visible = True self.useblit = useblit self.background = None self.needclear = False self.canvas.mpl_connect('motion_notify_event', self.onmove) self.canvas.mpl_connect('draw_event', self.clear) def clear(self, event): 'clear the cursor' if self.useblit: self.background = self.canvas.copy_from_bbox(self.canvas.figure.bbox) for line in self.lines: line.set_visible(False) def onmove(self, event): if event.inaxes is None: return if not self.canvas.widgetlock.available(self): return self.needclear = True if not self.visible: return for line in self.lines: line.set_xdata((event.xdata, event.xdata)) line.set_visible(self.visible) self._update() def _update(self): if self.useblit: if self.background is not None: self.canvas.restore_region(self.background) for ax, line in zip(self.axes, self.lines): ax.draw_artist(line) self.canvas.blit(self.canvas.figure.bbox) else: self.canvas.draw_idle() class SpanSelector: """ Select a min/max range of the x or y axes for a matplotlib Axes Example usage: ax = subplot(111) ax.plot(x,y) def onselect(vmin, vmax): print vmin, vmax span = SpanSelector(ax, onselect, 'horizontal') onmove_callback is an optional callback that will be called on mouse move with the span range """ def __init__(self, ax, onselect, direction, minspan=None, useblit=False, rectprops=None, onmove_callback=None): """ Create a span selector in ax. When a selection is made, clear the span and call onselect with onselect(vmin, vmax) and clear the span. direction must be 'horizontal' or 'vertical' If minspan is not None, ignore events smaller than minspan The span rect is drawn with rectprops; default rectprops = dict(facecolor='red', alpha=0.5) set the visible attribute to False if you want to turn off the functionality of the span selector """ if rectprops is None: rectprops = dict(facecolor='red', alpha=0.5) assert direction in ['horizontal', 'vertical'], 'Must choose horizontal or vertical for direction' self.direction = direction self.ax = None self.canvas = None self.visible = True self.cids=[] self.rect = None self.background = None self.pressv = None self.rectprops = rectprops self.onselect = onselect self.onmove_callback = onmove_callback self.useblit = useblit self.minspan = minspan # Needed when dragging out of axes self.buttonDown = False self.prev = (0, 0) self.new_axes(ax) def new_axes(self,ax): self.ax = ax if self.canvas is not ax.figure.canvas: for cid in self.cids: self.canvas.mpl_disconnect(cid) self.canvas = ax.figure.canvas self.cids.append(self.canvas.mpl_connect('motion_notify_event', self.onmove)) self.cids.append(self.canvas.mpl_connect('button_press_event', self.press)) self.cids.append(self.canvas.mpl_connect('button_release_event', self.release)) self.cids.append(self.canvas.mpl_connect('draw_event', self.update_background)) if self.direction == 'horizontal': trans = blended_transform_factory(self.ax.transData, self.ax.transAxes) w,h = 0,1 else: trans = blended_transform_factory(self.ax.transAxes, self.ax.transData) w,h = 1,0 self.rect = Rectangle( (0,0), w, h, transform=trans, visible=False, self.rectprops ) if not self.useblit: self.ax.add_patch(self.rect) def update_background(self, event): 'force an update of the background' if self.useblit: self.background = self.canvas.copy_from_bbox(self.ax.bbox) def ignore(self, event): 'return True if event should be ignored' return event.inaxes!=self.ax or not self.visible or event.button !=1 def press(self, event): 'on button press event' if self.ignore(event): return self.buttonDown = True self.rect.set_visible(self.visible) if self.direction == 'horizontal': self.pressv = event.xdata else: self.pressv = event.ydata return False def release(self, event): 'on button release event' if self.pressv is None or (self.ignore(event) and not self.buttonDown): return self.buttonDown = False self.rect.set_visible(False) self.canvas.draw() vmin = self.pressv if self.direction == 'horizontal': vmax = event.xdata or self.prev[0] else: vmax = event.ydata or self.prev[1] if vmin>vmax: vmin, vmax = vmax, vmin span = vmax - vmin if self.minspan is not None and span<self.minspan: return self.onselect(vmin, vmax) self.pressv = None return False def update(self): 'draw using newfangled blit or oldfangled draw depending on useblit' if self.useblit: if self.background is not None: self.canvas.restore_region(self.background) self.ax.draw_artist(self.rect) self.canvas.blit(self.ax.bbox) else: self.canvas.draw_idle() return False def onmove(self, event): 'on motion notify event' if self.pressv is None or self.ignore(event): return x, y = event.xdata, event.ydata self.prev = x, y if self.direction == 'horizontal': v = x else: v = y minv, maxv = v, self.pressv if minv>maxv: minv, maxv = maxv, minv if self.direction == 'horizontal': self.rect.set_x(minv) self.rect.set_width(maxv-minv) else: self.rect.set_y(minv) self.rect.set_height(maxv-minv) if self.onmove_callback is not None: vmin = self.pressv if self.direction == 'horizontal': vmax = event.xdata or self.prev[0] else: vmax = event.ydata or self.prev[1] if vmin>vmax: vmin, vmax = vmax, vmin self.onmove_callback(vmin, vmax) self.update() return False # For backwards compatibility only! class HorizontalSpanSelector(SpanSelector): def __init__(self, ax, onselect, kwargs): import warnings warnings.warn('Use SpanSelector instead!', DeprecationWarning) SpanSelector.__init__(self, ax, onselect, 'horizontal', kwargs) class RectangleSelector: """ Select a min/max range of the x axes for a matplotlib Axes Example usage:: from matplotlib.widgets import RectangleSelector from pylab import * def onselect(eclick, erelease): 'eclick and erelease are matplotlib events at press and release' print ' startposition : (%f, %f)' % (eclick.xdata, eclick.ydata) print ' endposition : (%f, %f)' % (erelease.xdata, erelease.ydata) print ' used button : ', eclick.button def toggle_selector(event): print ' Key pressed.' if event.key in ['Q', 'q'] and toggle_selector.RS.active: print ' RectangleSelector deactivated.' toggle_selector.RS.set_active(False) if event.key in ['A', 'a'] and not toggle_selector.RS.active: print ' RectangleSelector activated.' toggle_selector.RS.set_active(True) x = arange(100)/(99.0) y = sin(x) fig = figure ax = subplot(111) ax.plot(x,y) toggle_selector.RS = RectangleSelector(ax, onselect, drawtype='line') connect('key_press_event', toggle_selector) show() """ def __init__(self, ax, onselect, drawtype='box', minspanx=None, minspany=None, useblit=False, lineprops=None, rectprops=None, spancoords='data'): """ Create a selector in ax. When a selection is made, clear the span and call onselect with onselect(pos_1, pos_2) and clear the drawn box/line. There pos_i are arrays of length 2 containing the x- and y-coordinate. If minspanx is not None then events smaller than minspanx in x direction are ignored(it's the same for y). The rect is drawn with rectprops; default rectprops = dict(facecolor='red', edgecolor = 'black', alpha=0.5, fill=False) The line is drawn with lineprops; default lineprops = dict(color='black', linestyle='-', linewidth = 2, alpha=0.5) Use type if you want the mouse to draw a line, a box or nothing between click and actual position ny setting drawtype = 'line', drawtype='box' or drawtype = 'none'. spancoords is one of 'data' or 'pixels'. If 'data', minspanx and minspanx will be interpreted in the same coordinates as the x and ya axis, if 'pixels', they are in pixels """ self.ax = ax self.visible = True self.canvas = ax.figure.canvas self.canvas.mpl_connect('motion_notify_event', self.onmove) self.canvas.mpl_connect('button_press_event', self.press) self.canvas.mpl_connect('button_release_event', self.release) self.canvas.mpl_connect('draw_event', self.update_background) self.active = True # for activation / deactivation self.to_draw = None self.background = None if drawtype == 'none': drawtype = 'line' # draw a line but make it self.visible = False # invisible if drawtype == 'box': if rectprops is None: rectprops = dict(facecolor='white', edgecolor = 'black', alpha=0.5, fill=False) self.rectprops = rectprops self.to_draw = Rectangle((0,0), 0, 1,visible=False,self.rectprops) self.ax.add_patch(self.to_draw) if drawtype == 'line': if lineprops is None: lineprops = dict(color='black', linestyle='-', linewidth = 2, alpha=0.5) self.lineprops = lineprops self.to_draw = Line2D([0,0],[0,0],visible=False,self.lineprops) self.ax.add_line(self.to_draw) self.onselect = onselect self.useblit = useblit self.minspanx = minspanx self.minspany = minspany assert(spancoords in ('data', 'pixels')) self.spancoords = spancoords self.drawtype = drawtype # will save the data (position at mouseclick) self.eventpress = None # will save the data (pos. at mouserelease) self.eventrelease = None def update_background(self, event): 'force an update of the background' if self.useblit: self.background = self.canvas.copy_from_bbox(self.ax.bbox) def ignore(self, event): 'return True if event should be ignored' # If RectangleSelector is not active : if not self.active: return True # If canvas was locked if not self.canvas.widgetlock.available(self): return True # If no button was pressed yet ignore the event if it was out # of the axes if self.eventpress == None: return event.inaxes!= self.ax # If a button was pressed, check if the release-button is the # same. return (event.inaxes!=self.ax or event.button != self.eventpress.button) def press(self, event): 'on button press event' # Is the correct button pressed within the correct axes? if self.ignore(event): return # make the drawed box/line visible get the click-coordinates, # button, ... self.to_draw.set_visible(self.visible) self.eventpress = event return False def release(self, event): 'on button release event' if self.eventpress is None or self.ignore(event): return # make the box/line invisible again self.to_draw.set_visible(False) self.canvas.draw() # release coordinates, button, ... self.eventrelease = event if self.spancoords=='data': xmin, ymin = self.eventpress.xdata, self.eventpress.ydata xmax, ymax = self.eventrelease.xdata, self.eventrelease.ydata # calculate dimensions of box or line get values in the right # order elif self.spancoords=='pixels': xmin, ymin = self.eventpress.x, self.eventpress.y xmax, ymax = self.eventrelease.x, self.eventrelease.y else: raise ValueError('spancoords must be "data" or "pixels"') if xmin>xmax: xmin, xmax = xmax, xmin if ymin>ymax: ymin, ymax = ymax, ymin spanx = xmax - xmin spany = ymax - ymin xproblems = self.minspanx is not None and spanx<self.minspanx yproblems = self.minspany is not None and spany<self.minspany if (self.drawtype=='box') and (xproblems or yproblems): """Box to small""" # check if drawed distance (if it exists) is return # not to small in neither x nor y-direction if (self.drawtype=='line') and (xproblems and yproblems): """Line to small""" # check if drawed distance (if it exists) is return # not to small in neither x nor y-direction self.onselect(self.eventpress, self.eventrelease) # call desired function self.eventpress = None # reset the variables to their self.eventrelease = None # inital values return False def update(self): 'draw using newfangled blit or oldfangled draw depending on useblit' if self.useblit: if self.background is not None: self.canvas.restore_region(self.background) self.ax.draw_artist(self.to_draw) self.canvas.blit(self.ax.bbox) else: self.canvas.draw_idle() return False def onmove(self, event): 'on motion notify event if box/line is wanted' if self.eventpress is None or self.ignore(event): return x,y = event.xdata, event.ydata # actual position (with # (button still pressed) if self.drawtype == 'box': minx, maxx = self.eventpress.xdata, x # click-x and actual mouse-x miny, maxy = self.eventpress.ydata, y # click-y and actual mouse-y if minx>maxx: minx, maxx = maxx, minx # get them in the right order if miny>maxy: miny, maxy = maxy, miny self.to_draw.set_x(minx) # set lower left of box self.to_draw.set_y(miny) self.to_draw.set_width(maxx-minx) # set width and height of box self.to_draw.set_height(maxy-miny) self.update() return False if self.drawtype == 'line': self.to_draw.set_data([self.eventpress.xdata, x], [self.eventpress.ydata, y]) self.update() return False def set_active(self, active): """ Use this to activate / deactivate the RectangleSelector from your program with an boolean variable 'active'. """ self.active = active def get_active(self): """ to get status of active mode (boolean variable)""" return self.active class Lasso(Widget): def __init__(self, ax, xy, callback=None, useblit=True): self.axes = ax self.figure = ax.figure self.canvas = self.figure.canvas self.useblit = useblit if useblit: self.background = self.canvas.copy_from_bbox(self.axes.bbox) x, y = xy self.verts = [(x,y)] self.line = Line2D([x], [y], linestyle='-', color='black', lw=2) self.axes.add_line(self.line) self.callback = callback self.cids = [] self.cids.append(self.canvas.mpl_connect('button_release_event', self.onrelease)) self.cids.append(self.canvas.mpl_connect('motion_notify_event', self.onmove)) def onrelease(self, event): if self.verts is not None: self.verts.append((event.xdata, event.ydata)) if len(self.verts)>2: self.callback(self.verts) self.axes.lines.remove(self.line) self.verts = None for cid in self.cids: self.canvas.mpl_disconnect(cid) def onmove(self, event): if self.verts is None: return if event.inaxes != self.axes: return if event.button!=1: return self.verts.append((event.xdata, event.ydata)) self.line.set_data(zip(*self.verts)) if self.useblit: self.canvas.restore_region(self.background) self.axes.draw_artist(self.line) self.canvas.blit(self.axes.bbox) else: self.canvas.draw_idle()	40,833	Python	.py	968	31.97624	115	0.601723	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,272	cm.py	numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/cm.py	""" This module contains the instantiations of color mapping classes """ import numpy as np from numpy import ma import matplotlib as mpl import matplotlib.colors as colors import matplotlib.cbook as cbook from matplotlib._cm import * def get_cmap(name=None, lut=None): """ Get a colormap instance, defaulting to rc values if name is None """ if name is None: name = mpl.rcParams['image.cmap'] if lut is None: lut = mpl.rcParams['image.lut'] assert(name in datad.keys()) return colors.LinearSegmentedColormap(name, datad[name], lut) class ScalarMappable: """ This is a mixin class to support scalar -> RGBA mapping. Handles normalization and colormapping """ def __init__(self, norm=None, cmap=None): """ norm is an instance of :class:`colors.Normalize` or one of its subclasses, used to map luminance to 0-1. cmap is a :mod:`cm` colormap instance, for example :data:`cm.jet` """ self.callbacksSM = cbook.CallbackRegistry(( 'changed',)) if cmap is None: cmap = get_cmap() if norm is None: norm = colors.Normalize() self._A = None self.norm = norm self.cmap = cmap self.colorbar = None self.update_dict = {'array':False} def set_colorbar(self, im, ax): 'set the colorbar image and axes associated with mappable' self.colorbar = im, ax def to_rgba(self, x, alpha=1.0, bytes=False): '''Return a normalized rgba array corresponding to x. If x is already an rgb array, insert alpha; if it is already rgba, return it unchanged. If bytes is True, return rgba as 4 uint8s instead of 4 floats. ''' try: if x.ndim == 3: if x.shape[2] == 3: if x.dtype == np.uint8: alpha = np.array(alpha255, np.uint8) m, n = x.shape[:2] xx = np.empty(shape=(m,n,4), dtype = x.dtype) xx[:,:,:3] = x xx[:,:,3] = alpha elif x.shape[2] == 4: xx = x else: raise ValueError("third dimension must be 3 or 4") if bytes and xx.dtype != np.uint8: xx = (xx 255).astype(np.uint8) return xx except AttributeError: pass x = ma.asarray(x) x = self.norm(x) x = self.cmap(x, alpha=alpha, bytes=bytes) return x def set_array(self, A): 'Set the image array from numpy array A' self._A = A self.update_dict['array'] = True def get_array(self): 'Return the array' return self._A def get_cmap(self): 'return the colormap' return self.cmap def get_clim(self): 'return the min, max of the color limits for image scaling' return self.norm.vmin, self.norm.vmax def set_clim(self, vmin=None, vmax=None): """ set the norm limits for image scaling; if vmin is a length2 sequence, interpret it as ``(vmin, vmax)`` which is used to support setp ACCEPTS: a length 2 sequence of floats """ if (vmin is not None and vmax is None and cbook.iterable(vmin) and len(vmin)==2): vmin, vmax = vmin if vmin is not None: self.norm.vmin = vmin if vmax is not None: self.norm.vmax = vmax self.changed() def set_cmap(self, cmap): """ set the colormap for luminance data ACCEPTS: a colormap """ if cmap is None: cmap = get_cmap() self.cmap = cmap self.changed() def set_norm(self, norm): 'set the normalization instance' if norm is None: norm = colors.Normalize() self.norm = norm self.changed() def autoscale(self): """ Autoscale the scalar limits on the norm instance using the current array """ if self._A is None: raise TypeError('You must first set_array for mappable') self.norm.autoscale(self._A) self.changed() def autoscale_None(self): """ Autoscale the scalar limits on the norm instance using the current array, changing only limits that are None """ if self._A is None: raise TypeError('You must first set_array for mappable') self.norm.autoscale_None(self._A) self.changed() def add_checker(self, checker): """ Add an entry to a dictionary of boolean flags that are set to True when the mappable is changed. """ self.update_dict[checker] = False def check_update(self, checker): """ If mappable has changed since the last check, return True; else return False """ if self.update_dict[checker]: self.update_dict[checker] = False return True return False def changed(self): """ Call this whenever the mappable is changed to notify all the callbackSM listeners to the 'changed' signal """ self.callbacksSM.process('changed', self) for key in self.update_dict: self.update_dict[key] = True	5,385	Python	.py	148	27.027027	71	0.576997	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,273	pylab.py	numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/pylab.py	""" This is a procedural interface to the matplotlib object-oriented plotting library. The following plotting commands are provided; the majority have Matlab(TM) analogs and similar argument. _Plotting commands acorr - plot the autocorrelation function annotate - annotate something in the figure arrow - add an arrow to the axes axes - Create a new axes axhline - draw a horizontal line across axes axvline - draw a vertical line across axes axhspan - draw a horizontal bar across axes axvspan - draw a vertical bar across axes axis - Set or return the current axis limits bar - make a bar chart barh - a horizontal bar chart broken_barh - a set of horizontal bars with gaps box - set the axes frame on/off state boxplot - make a box and whisker plot cla - clear current axes clabel - label a contour plot clf - clear a figure window clim - adjust the color limits of the current image close - close a figure window colorbar - add a colorbar to the current figure cohere - make a plot of coherence contour - make a contour plot contourf - make a filled contour plot csd - make a plot of cross spectral density delaxes - delete an axes from the current figure draw - Force a redraw of the current figure errorbar - make an errorbar graph figlegend - make legend on the figure rather than the axes figimage - make a figure image figtext - add text in figure coords figure - create or change active figure fill - make filled polygons findobj - recursively find all objects matching some criteria gca - return the current axes gcf - return the current figure gci - get the current image, or None getp - get a handle graphics property grid - set whether gridding is on hist - make a histogram hold - set the axes hold state ioff - turn interaction mode off ion - turn interaction mode on isinteractive - return True if interaction mode is on imread - load image file into array imshow - plot image data ishold - return the hold state of the current axes legend - make an axes legend loglog - a log log plot matshow - display a matrix in a new figure preserving aspect pcolor - make a pseudocolor plot pcolormesh - make a pseudocolor plot using a quadrilateral mesh pie - make a pie chart plot - make a line plot plot_date - plot dates plotfile - plot column data from an ASCII tab/space/comma delimited file pie - pie charts polar - make a polar plot on a PolarAxes psd - make a plot of power spectral density quiver - make a direction field (arrows) plot rc - control the default params rgrids - customize the radial grids and labels for polar savefig - save the current figure scatter - make a scatter plot setp - set a handle graphics property semilogx - log x axis semilogy - log y axis show - show the figures specgram - a spectrogram plot spy - plot sparsity pattern using markers or image stem - make a stem plot subplot - make a subplot (numrows, numcols, axesnum) subplots_adjust - change the params controlling the subplot positions of current figure subplot_tool - launch the subplot configuration tool suptitle - add a figure title table - add a table to the plot text - add some text at location x,y to the current axes thetagrids - customize the radial theta grids and labels for polar title - add a title to the current axes xcorr - plot the autocorrelation function of x and y xlim - set/get the xlimits ylim - set/get the ylimits xticks - set/get the xticks yticks - set/get the yticks xlabel - add an xlabel to the current axes ylabel - add a ylabel to the current axes autumn - set the default colormap to autumn bone - set the default colormap to bone cool - set the default colormap to cool copper - set the default colormap to copper flag - set the default colormap to flag gray - set the default colormap to gray hot - set the default colormap to hot hsv - set the default colormap to hsv jet - set the default colormap to jet pink - set the default colormap to pink prism - set the default colormap to prism spring - set the default colormap to spring summer - set the default colormap to summer winter - set the default colormap to winter spectral - set the default colormap to spectral _Event handling connect - register an event handler disconnect - remove a connected event handler _Matrix commands cumprod - the cumulative product along a dimension cumsum - the cumulative sum along a dimension detrend - remove the mean or besdt fit line from an array diag - the k-th diagonal of matrix diff - the n-th differnce of an array eig - the eigenvalues and eigen vectors of v eye - a matrix where the k-th diagonal is ones, else zero find - return the indices where a condition is nonzero fliplr - flip the rows of a matrix up/down flipud - flip the columns of a matrix left/right linspace - a linear spaced vector of N values from min to max inclusive logspace - a log spaced vector of N values from min to max inclusive meshgrid - repeat x and y to make regular matrices ones - an array of ones rand - an array from the uniform distribution [0,1] randn - an array from the normal distribution rot90 - rotate matrix k90 degress counterclockwise squeeze - squeeze an array removing any dimensions of length 1 tri - a triangular matrix tril - a lower triangular matrix triu - an upper triangular matrix vander - the Vandermonde matrix of vector x svd - singular value decomposition zeros - a matrix of zeros _Probability levypdf - The levy probability density function from the char. func. normpdf - The Gaussian probability density function rand - random numbers from the uniform distribution randn - random numbers from the normal distribution _Statistics corrcoef - correlation coefficient cov - covariance matrix amax - the maximum along dimension m mean - the mean along dimension m median - the median along dimension m amin - the minimum along dimension m norm - the norm of vector x prod - the product along dimension m ptp - the max-min along dimension m std - the standard deviation along dimension m asum - the sum along dimension m _Time series analysis bartlett - M-point Bartlett window blackman - M-point Blackman window cohere - the coherence using average periodiogram csd - the cross spectral density using average periodiogram fft - the fast Fourier transform of vector x hamming - M-point Hamming window hanning - M-point Hanning window hist - compute the histogram of x kaiser - M length Kaiser window psd - the power spectral density using average periodiogram sinc - the sinc function of array x _Dates date2num - convert python datetimes to numeric representation drange - create an array of numbers for date plots num2date - convert numeric type (float days since 0001) to datetime _Other angle - the angle of a complex array griddata - interpolate irregularly distributed data to a regular grid load - load ASCII data into array polyfit - fit x, y to an n-th order polynomial polyval - evaluate an n-th order polynomial roots - the roots of the polynomial coefficients in p save - save an array to an ASCII file trapz - trapezoidal integration __end """ import sys, warnings from cbook import flatten, is_string_like, exception_to_str, popd, \ silent_list, iterable, dedent import numpy as np from numpy import ma from matplotlib import mpl # pulls in most modules from matplotlib.dates import date2num, num2date,\ datestr2num, strpdate2num, drange,\ epoch2num, num2epoch, mx2num,\ DateFormatter, IndexDateFormatter, DateLocator,\ RRuleLocator, YearLocator, MonthLocator, WeekdayLocator,\ DayLocator, HourLocator, MinuteLocator, SecondLocator,\ rrule, MO, TU, WE, TH, FR, SA, SU, YEARLY, MONTHLY,\ WEEKLY, DAILY, HOURLY, MINUTELY, SECONDLY, relativedelta import matplotlib.dates # bring all the symbols in so folks can import them from # pylab in one fell swoop from matplotlib.mlab import window_hanning, window_none,\ conv, detrend, detrend_mean, detrend_none, detrend_linear,\ polyfit, polyval, entropy, normpdf, griddata,\ levypdf, find, trapz, prepca, rem, norm, orth, rank,\ sqrtm, prctile, center_matrix, rk4, exp_safe, amap,\ sum_flat, mean_flat, rms_flat, l1norm, l2norm, norm, frange,\ diagonal_matrix, base_repr, binary_repr, log2, ispower2,\ bivariate_normal, load, save from matplotlib.mlab import stineman_interp, slopes, \ stineman_interp, inside_poly, poly_below, poly_between, \ is_closed_polygon, path_length, distances_along_curve, vector_lengths from numpy import from numpy.fft import * from numpy.random import * from numpy.linalg import * from matplotlib.mlab import window_hanning, window_none, conv, detrend, demean, \ detrend_mean, detrend_none, detrend_linear, entropy, normpdf, levypdf, \ find, longest_contiguous_ones, longest_ones, prepca, prctile, prctile_rank, \ center_matrix, rk4, bivariate_normal, get_xyz_where, get_sparse_matrix, dist, \ dist_point_to_segment, segments_intersect, fftsurr, liaupunov, movavg, \ save, load, exp_safe, \ amap, rms_flat, l1norm, l2norm, norm_flat, frange, diagonal_matrix, identity, \ base_repr, binary_repr, log2, ispower2, fromfunction_kw, rem, norm, orth, rank, sqrtm,\ mfuncC, approx_real, rec_append_field, rec_drop_fields, rec_join, csv2rec, rec2csv, isvector from matplotlib.pyplot import * # provide the recommended module abbrevs in the pylab namespace import matplotlib.pyplot as plt import numpy as np	10,245	Python	.py	223	42.587444	97	0.722867	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,274	bezier.py	numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/bezier.py	""" A module providing some utility functions regarding bezier path manipulation. """ import numpy as np from math import sqrt from matplotlib.path import Path from operator import xor # some functions def get_intersection(cx1, cy1, cos_t1, sin_t1, cx2, cy2, cos_t2, sin_t2): """ return a intersecting point between a line through (cx1, cy1) and having angle t1 and a line through (cx2, cy2) and angle t2. """ # line1 => sin_t1 * (x - cx1) - cos_t1 * (y - cy1) = 0. # line1 => sin_t1 * x + cos_t1 * y = sin_t1cx1 - cos_t1cy1 line1_rhs = sin_t1 * cx1 - cos_t1 * cy1 line2_rhs = sin_t2 * cx2 - cos_t2 * cy2 # rhs matrix a, b = sin_t1, -cos_t1 c, d = sin_t2, -cos_t2 ad_bc = ad-bc if ad_bc == 0.: raise ValueError("Given lines do not intersect") #rhs_inverse a_, b_ = d, -b c_, d_ = -c, a a_, b_, c_, d_ = [k / ad_bc for k in [a_, b_, c_, d_]] x = a_* line1_rhs + b_ * line2_rhs y = c_* line1_rhs + d_ * line2_rhs return x, y def get_normal_points(cx, cy, cos_t, sin_t, length): """ For a line passing through (cx, cy) and having a angle t, return locations of the two points located along its perpendicular line at the distance of length. """ if length == 0.: return cx, cy, cx, cy cos_t1, sin_t1 = sin_t, -cos_t cos_t2, sin_t2 = -sin_t, cos_t x1, y1 = lengthcos_t1 + cx, lengthsin_t1 + cy x2, y2 = lengthcos_t2 + cx, lengthsin_t2 + cy return x1, y1, x2, y2 ## BEZIER routines # subdividing bezier curve # http://www.cs.mtu.edu/~shene/COURSES/cs3621/NOTES/spline/Bezier/bezier-sub.html def _de_casteljau1(beta, t): next_beta = beta[:-1] * (1-t) + beta[1:] * t return next_beta def split_de_casteljau(beta, t): """split a bezier segment defined by its controlpoints beta into two separate segment divided at t and return their control points. """ beta = np.asarray(beta) beta_list = [beta] while True: beta = _de_casteljau1(beta, t) beta_list.append(beta) if len(beta) == 1: break left_beta = [beta[0] for beta in beta_list] right_beta = [beta[-1] for beta in reversed(beta_list)] return left_beta, right_beta def find_bezier_t_intersecting_with_closedpath(bezier_point_at_t, inside_closedpath, t0=0., t1=1., tolerence=0.01): """ Find a parameter t0 and t1 of the given bezier path which bounds the intersecting points with a provided closed path(inside_closedpath). Search starts from t0 and t1 and it uses a simple bisecting algorithm therefore one of the end point must be inside the path while the orther doesn't. The search stop when \|t0-t1\| gets smaller than the given tolerence. value for - bezier_point_at_t : a function which returns x, y coordinates at t - inside_closedpath : return True if the point is insed the path """ # inside_closedpath : function start = bezier_point_at_t(t0) end = bezier_point_at_t(t1) start_inside = inside_closedpath(start) end_inside = inside_closedpath(end) if not xor(start_inside, end_inside): raise ValueError("the segment does not seemed to intersect with the path") while 1: # return if the distance is smaller than the tolerence if (start[0]-end[0])2 + (start[1]-end[1])2 < tolerence*2: return t0, t1 # calculate the middle point middle_t = 0.5(t0+t1) middle = bezier_point_at_t(middle_t) middle_inside = inside_closedpath(middle) if xor(start_inside, middle_inside): t1 = middle_t end = middle end_inside = middle_inside else: t0 = middle_t start = middle start_inside = middle_inside class BezierSegment: """ A simple class of a 2-dimensional bezier segment """ # Highrt order bezier lines can be supported by simplying adding # correcponding values. _binom_coeff = {1:np.array([1., 1.]), 2:np.array([1., 2., 1.]), 3:np.array([1., 3., 3., 1.])} def __init__(self, control_points): """ control_points : location of contol points. It needs have a shpae of n * 2, where n is the order of the bezier line. 1<= n <= 3 is supported. """ _o = len(control_points) self._orders = np.arange(_o) _coeff = BezierSegment._binom_coeff[_o - 1] _control_points = np.asarray(control_points) xx = _control_points[:,0] yy = _control_points[:,1] self._px = xx * _coeff self._py = yy * _coeff def point_at_t(self, t): "evaluate a point at t" one_minus_t_powers = np.power(1.-t, self._orders)[::-1] t_powers = np.power(t, self._orders) tt = one_minus_t_powers * t_powers _x = sum(tt * self._px) _y = sum(tt * self._py) return _x, _y def split_bezier_intersecting_with_closedpath(bezier, inside_closedpath, tolerence=0.01): """ bezier : control points of the bezier segment inside_closedpath : a function which returns true if the point is inside the path """ bz = BezierSegment(bezier) bezier_point_at_t = bz.point_at_t t0, t1 = find_bezier_t_intersecting_with_closedpath(bezier_point_at_t, inside_closedpath, tolerence=tolerence) _left, _right = split_de_casteljau(bezier, (t0+t1)/2.) return _left, _right def find_r_to_boundary_of_closedpath(inside_closedpath, xy, cos_t, sin_t, rmin=0., rmax=1., tolerence=0.01): """ Find a radius r (centered at xy) between rmin and rmax at which it intersect with the path. inside_closedpath : function cx, cy : center cos_t, sin_t : cosine and sine for the angle rmin, rmax : """ cx, cy = xy def _f(r): return cos_tr + cx, sin_tr + cy find_bezier_t_intersecting_with_closedpath(_f, inside_closedpath, t0=rmin, t1=rmax, tolerence=tolerence) ## matplotlib specific def split_path_inout(path, inside, tolerence=0.01, reorder_inout=False): """ divide a path into two segment at the point where inside(x, y) becomes False. """ path_iter = path.iter_segments() ctl_points, command = path_iter.next() begin_inside = inside(ctl_points[-2:]) # true if begin point is inside bezier_path = None ctl_points_old = ctl_points concat = np.concatenate iold=0 i = 1 for ctl_points, command in path_iter: iold=i i += len(ctl_points)/2 if inside(ctl_points[-2:]) != begin_inside: bezier_path = concat([ctl_points_old[-2:], ctl_points]) break ctl_points_old = ctl_points if bezier_path is None: raise ValueError("The path does not seem to intersect with the patch") bp = zip(bezier_path[::2], bezier_path[1::2]) left, right = split_bezier_intersecting_with_closedpath(bp, inside, tolerence) if len(left) == 2: codes_left = [Path.LINETO] codes_right = [Path.MOVETO, Path.LINETO] elif len(left) == 3: codes_left = [Path.CURVE3, Path.CURVE3] codes_right = [Path.MOVETO, Path.CURVE3, Path.CURVE3] elif len(left) == 4: codes_left = [Path.CURVE4, Path.CURVE4, Path.CURVE4] codes_right = [Path.MOVETO, Path.CURVE4, Path.CURVE4, Path.CURVE4] else: raise ValueError() verts_left = left[1:] verts_right = right[:] #i += 1 if path.codes is None: path_in = Path(concat([path.vertices[:i], verts_left])) path_out = Path(concat([verts_right, path.vertices[i:]])) else: path_in = Path(concat([path.vertices[:iold], verts_left]), concat([path.codes[:iold], codes_left])) path_out = Path(concat([verts_right, path.vertices[i:]]), concat([codes_right, path.codes[i:]])) if reorder_inout and begin_inside == False: path_in, path_out = path_out, path_in return path_in, path_out def inside_circle(cx, cy, r): r2 = r2 def _f(xy): x, y = xy return (x-cx)2 + (y-cy)*2 < r2 return _f # quadratic bezier lines def get_cos_sin(x0, y0, x1, y1): dx, dy = x1-x0, y1-y0 d = (dxdx + dydy).5 return dx/d, dy/d def get_parallels(bezier2, width): """ Given the quadraitc bezier control points bezier2, returns control points of quadrativ bezier lines roughly parralel to given one separated by width. """ # The parallel bezier lines constructed by following ways. # c1 and c2 are contol points representing the begin and end of the bezier line. # cm is the middle point c1x, c1y = bezier2[0] cmx, cmy = bezier2[1] c2x, c2y = bezier2[2] # t1 and t2 is the anlge between c1 and cm, cm, c2. # They are also a angle of the tangential line of the path at c1 and c2 cos_t1, sin_t1 = get_cos_sin(c1x, c1y, cmx, cmy) cos_t2, sin_t2 = get_cos_sin(cmx, cmy, c2x, c2y) # find c1_left, c1_right which are located along the lines # throught c1 and perpendicular to the tangential lines of the # bezier path at a distance of width. Same thing for c2_left and # c2_right with respect to c2. c1x_left, c1y_left, c1x_right, c1y_right = \ get_normal_points(c1x, c1y, cos_t1, sin_t1, width) c2x_left, c2y_left, c2x_right, c2y_right = \ get_normal_points(c2x, c2y, cos_t2, sin_t2, width) # find cm_left which is the intersectng point of a line through # c1_left with angle t1 and a line throught c2_left with angle # t2. Same with cm_right. cmx_left, cmy_left = get_intersection(c1x_left, c1y_left, cos_t1, sin_t1, c2x_left, c2y_left, cos_t2, sin_t2) cmx_right, cmy_right = get_intersection(c1x_right, c1y_right, cos_t1, sin_t1, c2x_right, c2y_right, cos_t2, sin_t2) # the parralel bezier lines are created with control points of # [c1_left, cm_left, c2_left] and [c1_right, cm_right, c2_right] path_left = [(c1x_left, c1y_left), (cmx_left, cmy_left), (c2x_left, c2y_left)] path_right = [(c1x_right, c1y_right), (cmx_right, cmy_right), (c2x_right, c2y_right)] return path_left, path_right def make_wedged_bezier2(bezier2, length, shrink_factor=0.5): """ Being similar to get_parallels, returns control points of two quadrativ bezier lines having a width roughly parralel to given one separated by width. """ xx1, yy1 = bezier2[2] xx2, yy2 = bezier2[1] xx3, yy3 = bezier2[0] cx, cy = xx3, yy3 x0, y0 = xx2, yy2 dist = sqrt((x0-cx)2 + (y0-cy)2) cos_t, sin_t = (x0-cx)/dist, (y0-cy)/dist, x1, y1, x2, y2 = get_normal_points(cx, cy, cos_t, sin_t, length) xx12, yy12 = (xx1+xx2)/2., (yy1+yy2)/2., xx23, yy23 = (xx2+xx3)/2., (yy2+yy3)/2., dist = sqrt((xx12-xx23)2 + (yy12-yy23)2) cos_t, sin_t = (xx12-xx23)/dist, (yy12-yy23)/dist, xm1, ym1, xm2, ym2 = get_normal_points(xx2, yy2, cos_t, sin_t, lengthshrink_factor) l_plus = [(x1, y1), (xm1, ym1), (xx1, yy1)] l_minus = [(x2, y2), (xm2, ym2), (xx1, yy1)] return l_plus, l_minus def find_control_points(c1x, c1y, mmx, mmy, c2x, c2y): """ Find control points of the bezier line throught c1, mm, c2. We simply assume that c1, mm, c2 which have parameteric value 0, 0.5, and 1. """ cmx = .5 * (4mmx - (c1x + c2x)) cmy = .5 (4mmy - (c1y + c2y)) return [(c1x, c1y), (cmx, cmy), (c2x, c2y)] def make_wedged_bezier2(bezier2, width, w1=1., wm=0.5, w2=0.): """ Being similar to get_parallels, returns control points of two quadrativ bezier lines having a width roughly parralel to given one separated by width. """ # c1, cm, c2 c1x, c1y = bezier2[0] cmx, cmy = bezier2[1] c3x, c3y = bezier2[2] # t1 and t2 is the anlge between c1 and cm, cm, c3. # They are also a angle of the tangential line of the path at c1 and c3 cos_t1, sin_t1 = get_cos_sin(c1x, c1y, cmx, cmy) cos_t2, sin_t2 = get_cos_sin(cmx, cmy, c3x, c3y) # find c1_left, c1_right which are located along the lines # throught c1 and perpendicular to the tangential lines of the # bezier path at a distance of width. Same thing for c3_left and # c3_right with respect to c3. c1x_left, c1y_left, c1x_right, c1y_right = \ get_normal_points(c1x, c1y, cos_t1, sin_t1, widthw1) c3x_left, c3y_left, c3x_right, c3y_right = \ get_normal_points(c3x, c3y, cos_t2, sin_t2, widthw2) # find c12, c23 and c123 which are middle points of c1-cm, cm-c3 and c12-c23 c12x, c12y = (c1x+cmx).5, (c1y+cmy).5 c23x, c23y = (cmx+c3x).5, (cmy+c3y).5 c123x, c123y = (c12x+c23x).5, (c12y+c23y).5 # tangential angle of c123 (angle between c12 and c23) cos_t123, sin_t123 = get_cos_sin(c12x, c12y, c23x, c23y) c123x_left, c123y_left, c123x_right, c123y_right = \ get_normal_points(c123x, c123y, cos_t123, sin_t123, widthwm) path_left = find_control_points(c1x_left, c1y_left, c123x_left, c123y_left, c3x_left, c3y_left) path_right = find_control_points(c1x_right, c1y_right, c123x_right, c123y_right, c3x_right, c3y_right) return path_left, path_right if 0: path = Path([(0, 0), (1, 0), (2, 2)], [Path.MOVETO, Path.CURVE3, Path.CURVE3]) left, right = divide_path_inout(path, inside) clf() ax = gca()	14,387	Python	.py	328	35.088415	104	0.593996	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,275	colorbar.py	numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/colorbar.py	''' Colorbar toolkit with two classes and a function: :class:`ColorbarBase` the base class with full colorbar drawing functionality. It can be used as-is to make a colorbar for a given colormap; a mappable object (e.g., image) is not needed. :class:`Colorbar` the derived class for use with images or contour plots. :func:`make_axes` a function for resizing an axes and adding a second axes suitable for a colorbar The :meth:`~matplotlib.figure.Figure.colorbar` method uses :func:`make_axes` and :class:`Colorbar`; the :func:`~matplotlib.pyplot.colorbar` function is a thin wrapper over :meth:`~matplotlib.figure.Figure.colorbar`. ''' import numpy as np import matplotlib as mpl import matplotlib.colors as colors import matplotlib.cm as cm import matplotlib.ticker as ticker import matplotlib.cbook as cbook import matplotlib.lines as lines import matplotlib.patches as patches import matplotlib.collections as collections import matplotlib.contour as contour make_axes_kw_doc = ''' ========== ==================================================== Property Description ========== ==================================================== fraction 0.15; fraction of original axes to use for colorbar pad 0.05 if vertical, 0.15 if horizontal; fraction of original axes between colorbar and new image axes shrink 1.0; fraction by which to shrink the colorbar aspect 20; ratio of long to short dimensions ========== ==================================================== ''' colormap_kw_doc = ''' =========== ==================================================== Property Description =========== ==================================================== extend [ 'neither' \| 'both' \| 'min' \| 'max' ] If not 'neither', make pointed end(s) for out-of- range values. These are set for a given colormap using the colormap set_under and set_over methods. spacing [ 'uniform' \| 'proportional' ] Uniform spacing gives each discrete color the same space; proportional makes the space proportional to the data interval. ticks [ None \| list of ticks \| Locator object ] If None, ticks are determined automatically from the input. format [ None \| format string \| Formatter object ] If None, the :class:`~matplotlib.ticker.ScalarFormatter` is used. If a format string is given, e.g. '%.3f', that is used. An alternative :class:`~matplotlib.ticker.Formatter` object may be given instead. drawedges [ False \| True ] If true, draw lines at color boundaries. =========== ==================================================== The following will probably be useful only in the context of indexed colors (that is, when the mappable has norm=NoNorm()), or other unusual circumstances. ============ =================================================== Property Description ============ =================================================== boundaries None or a sequence values None or a sequence which must be of length 1 less than the sequence of boundaries. For each region delimited by adjacent entries in boundaries, the color mapped to the corresponding value in values will be used. ============ =================================================== ''' colorbar_doc = ''' Add a colorbar to a plot. Function signatures for the :mod:`~matplotlib.pyplot` interface; all but the first are also method signatures for the :meth:`~matplotlib.figure.Figure.colorbar` method:: colorbar(kwargs) colorbar(mappable, kwargs) colorbar(mappable, cax=cax, kwargs) colorbar(mappable, ax=ax, kwargs) arguments: mappable the :class:`~matplotlib.image.Image`, :class:`~matplotlib.contour.ContourSet`, etc. to which the colorbar applies; this argument is mandatory for the :meth:`~matplotlib.figure.Figure.colorbar` method but optional for the :func:`~matplotlib.pyplot.colorbar` function, which sets the default to the current image. keyword arguments: cax None \| axes object into which the colorbar will be drawn ax None \| parent axes object from which space for a new colorbar axes will be stolen Additional keyword arguments are of two kinds: axes properties: %s colorbar properties: %s If mappable is a :class:`~matplotlib.contours.ContourSet`, its extend kwarg is included automatically. Note that the shrink kwarg provides a simple way to keep a vertical colorbar, for example, from being taller than the axes of the mappable to which the colorbar is attached; but it is a manual method requiring some trial and error. If the colorbar is too tall (or a horizontal colorbar is too wide) use a smaller value of shrink. For more precise control, you can manually specify the positions of the axes objects in which the mappable and the colorbar are drawn. In this case, do not use any of the axes properties kwargs. returns: :class:`~matplotlib.colorbar.Colorbar` instance; see also its base class, :class:`~matplotlib.colorbar.ColorbarBase`. Call the :meth:`~matplotlib.colorbar.ColorbarBase.set_label` method to label the colorbar. ''' % (make_axes_kw_doc, colormap_kw_doc) class ColorbarBase(cm.ScalarMappable): ''' Draw a colorbar in an existing axes. This is a base class for the :class:`Colorbar` class, which is the basis for the :func:`~matplotlib.pyplot.colorbar` method and pylab function. It is also useful by itself for showing a colormap. If the cmap kwarg is given but boundaries and values are left as None, then the colormap will be displayed on a 0-1 scale. To show the under- and over-value colors, specify the norm as:: colors.Normalize(clip=False) To show the colors versus index instead of on the 0-1 scale, use:: norm=colors.NoNorm. Useful attributes: :attr:`ax` the Axes instance in which the colorbar is drawn :attr:`lines` a LineCollection if lines were drawn, otherwise None :attr:`dividers` a LineCollection if drawedges is True, otherwise None Useful public methods are :meth:`set_label` and :meth:`add_lines`. ''' _slice_dict = {'neither': slice(0,1000000), 'both': slice(1,-1), 'min': slice(1,1000000), 'max': slice(0,-1)} def __init__(self, ax, cmap=None, norm=None, alpha=1.0, values=None, boundaries=None, orientation='vertical', extend='neither', spacing='uniform', # uniform or proportional ticks=None, format=None, drawedges=False, filled=True, ): self.ax = ax if cmap is None: cmap = cm.get_cmap() if norm is None: norm = colors.Normalize() self.alpha = alpha cm.ScalarMappable.__init__(self, cmap=cmap, norm=norm) self.values = values self.boundaries = boundaries self.extend = extend self._inside = self._slice_dict[extend] self.spacing = spacing self.orientation = orientation self.drawedges = drawedges self.filled = filled self.solids = None self.lines = None self.dividers = None self.set_label('') if cbook.iterable(ticks): self.locator = ticker.FixedLocator(ticks, nbins=len(ticks)) else: self.locator = ticks # Handle default in _ticker() if format is None: if isinstance(self.norm, colors.LogNorm): self.formatter = ticker.LogFormatter() else: self.formatter = ticker.ScalarFormatter() elif cbook.is_string_like(format): self.formatter = ticker.FormatStrFormatter(format) else: self.formatter = format # Assume it is a Formatter # The rest is in a method so we can recalculate when clim changes. self.draw_all() def draw_all(self): ''' Calculate any free parameters based on the current cmap and norm, and do all the drawing. ''' self._process_values() self._find_range() X, Y = self._mesh() C = self._values[:,np.newaxis] self._config_axes(X, Y) if self.filled: self._add_solids(X, Y, C) self._set_label() def _config_axes(self, X, Y): ''' Make an axes patch and outline. ''' ax = self.ax ax.set_frame_on(False) ax.set_navigate(False) xy = self._outline(X, Y) ax.update_datalim(xy) ax.set_xlim(ax.dataLim.intervalx) ax.set_ylim(ax.dataLim.intervaly) self.outline = lines.Line2D(xy[:, 0], xy[:, 1], color=mpl.rcParams['axes.edgecolor'], linewidth=mpl.rcParams['axes.linewidth']) ax.add_artist(self.outline) self.outline.set_clip_box(None) self.outline.set_clip_path(None) c = mpl.rcParams['axes.facecolor'] self.patch = patches.Polygon(xy, edgecolor=c, facecolor=c, linewidth=0.01, zorder=-1) ax.add_artist(self.patch) ticks, ticklabels, offset_string = self._ticker() if self.orientation == 'vertical': ax.set_xticks([]) ax.yaxis.set_label_position('right') ax.yaxis.set_ticks_position('right') ax.set_yticks(ticks) ax.set_yticklabels(ticklabels) ax.yaxis.get_major_formatter().set_offset_string(offset_string) else: ax.set_yticks([]) ax.xaxis.set_label_position('bottom') ax.set_xticks(ticks) ax.set_xticklabels(ticklabels) ax.xaxis.get_major_formatter().set_offset_string(offset_string) def _set_label(self): if self.orientation == 'vertical': self.ax.set_ylabel(self._label, self._labelkw) else: self.ax.set_xlabel(self._label, self._labelkw) def set_label(self, label, *kw): ''' Label the long axis of the colorbar ''' self._label = label self._labelkw = kw self._set_label() def _outline(self, X, Y): ''' Return x, y* arrays of colorbar bounding polygon, taking orientation into account. ''' N = X.shape[0] ii = [0, 1, N-2, N-1, 2N-1, 2N-2, N+1, N, 0] x = np.take(np.ravel(np.transpose(X)), ii) y = np.take(np.ravel(np.transpose(Y)), ii) x = x.reshape((len(x), 1)) y = y.reshape((len(y), 1)) if self.orientation == 'horizontal': return np.hstack((y, x)) return np.hstack((x, y)) def _edges(self, X, Y): ''' Return the separator line segments; helper for _add_solids. ''' N = X.shape[0] # Using the non-array form of these line segments is much # simpler than making them into arrays. if self.orientation == 'vertical': return [zip(X[i], Y[i]) for i in range(1, N-1)] else: return [zip(Y[i], X[i]) for i in range(1, N-1)] def _add_solids(self, X, Y, C): ''' Draw the colors using :meth:`~matplotlib.axes.Axes.pcolor`; optionally add separators. ''' ## Change to pcolorfast after fixing bugs in some backends... if self.orientation == 'vertical': args = (X, Y, C) else: args = (np.transpose(Y), np.transpose(X), np.transpose(C)) kw = {'cmap':self.cmap, 'norm':self.norm, 'shading':'flat', 'alpha':self.alpha} # Save, set, and restore hold state to keep pcolor from # clearing the axes. Ordinarily this will not be needed, # since the axes object should already have hold set. _hold = self.ax.ishold() self.ax.hold(True) col = self.ax.pcolor(args, kw) self.ax.hold(_hold) #self.add_observer(col) # We should observe, not be observed... self.solids = col if self.drawedges: self.dividers = collections.LineCollection(self._edges(X,Y), colors=(mpl.rcParams['axes.edgecolor'],), linewidths=(0.5mpl.rcParams['axes.linewidth'],) ) self.ax.add_collection(self.dividers) def add_lines(self, levels, colors, linewidths): ''' Draw lines on the colorbar. ''' N = len(levels) dummy, y = self._locate(levels) if len(y) <> N: raise ValueError("levels are outside colorbar range") x = np.array([0.0, 1.0]) X, Y = np.meshgrid(x,y) if self.orientation == 'vertical': xy = [zip(X[i], Y[i]) for i in range(N)] else: xy = [zip(Y[i], X[i]) for i in range(N)] col = collections.LineCollection(xy, linewidths=linewidths) self.lines = col col.set_color(colors) self.ax.add_collection(col) def _ticker(self): ''' Return two sequences: ticks (colorbar data locations) and ticklabels (strings). ''' locator = self.locator formatter = self.formatter if locator is None: if self.boundaries is None: if isinstance(self.norm, colors.NoNorm): nv = len(self._values) base = 1 + int(nv/10) locator = ticker.IndexLocator(base=base, offset=0) elif isinstance(self.norm, colors.BoundaryNorm): b = self.norm.boundaries locator = ticker.FixedLocator(b, nbins=10) elif isinstance(self.norm, colors.LogNorm): locator = ticker.LogLocator() else: locator = ticker.MaxNLocator() else: b = self._boundaries[self._inside] locator = ticker.FixedLocator(b, nbins=10) if isinstance(self.norm, colors.NoNorm): intv = self._values[0], self._values[-1] else: intv = self.vmin, self.vmax locator.create_dummy_axis() formatter.create_dummy_axis() locator.set_view_interval(intv) locator.set_data_interval(intv) formatter.set_view_interval(intv) formatter.set_data_interval(intv) b = np.array(locator()) b, ticks = self._locate(b) formatter.set_locs(b) ticklabels = [formatter(t, i) for i, t in enumerate(b)] offset_string = formatter.get_offset() return ticks, ticklabels, offset_string def _process_values(self, b=None): ''' Set the :attr:`_boundaries` and :attr:`_values` attributes based on the input boundaries and values. Input boundaries can be self.boundaries or the argument b. ''' if b is None: b = self.boundaries if b is not None: self._boundaries = np.asarray(b, dtype=float) if self.values is None: self._values = 0.5(self._boundaries[:-1] + self._boundaries[1:]) if isinstance(self.norm, colors.NoNorm): self._values = (self._values + 0.00001).astype(np.int16) return self._values = np.array(self.values) return if self.values is not None: self._values = np.array(self.values) if self.boundaries is None: b = np.zeros(len(self.values)+1, 'd') b[1:-1] = 0.5(self._values[:-1] - self._values[1:]) b[0] = 2.0b[1] - b[2] b[-1] = 2.0b[-2] - b[-3] self._boundaries = b return self._boundaries = np.array(self.boundaries) return # Neither boundaries nor values are specified; # make reasonable ones based on cmap and norm. if isinstance(self.norm, colors.NoNorm): b = self._uniform_y(self.cmap.N+1) * self.cmap.N - 0.5 v = np.zeros((len(b)-1,), dtype=np.int16) v[self._inside] = np.arange(self.cmap.N, dtype=np.int16) if self.extend in ('both', 'min'): v[0] = -1 if self.extend in ('both', 'max'): v[-1] = self.cmap.N self._boundaries = b self._values = v return elif isinstance(self.norm, colors.BoundaryNorm): b = list(self.norm.boundaries) if self.extend in ('both', 'min'): b = [b[0]-1] + b if self.extend in ('both', 'max'): b = b + [b[-1] + 1] b = np.array(b) v = np.zeros((len(b)-1,), dtype=float) bi = self.norm.boundaries v[self._inside] = 0.5(bi[:-1] + bi[1:]) if self.extend in ('both', 'min'): v[0] = b[0] - 1 if self.extend in ('both', 'max'): v[-1] = b[-1] + 1 self._boundaries = b self._values = v return else: if not self.norm.scaled(): self.norm.vmin = 0 self.norm.vmax = 1 b = self.norm.inverse(self._uniform_y(self.cmap.N+1)) if self.extend in ('both', 'min'): b[0] = b[0] - 1 if self.extend in ('both', 'max'): b[-1] = b[-1] + 1 self._process_values(b) def _find_range(self): ''' Set :attr:`vmin` and :attr:`vmax` attributes to the first and last boundary excluding extended end boundaries. ''' b = self._boundaries[self._inside] self.vmin = b[0] self.vmax = b[-1] def _central_N(self): '''number of boundaries before* extension of ends''' nb = len(self._boundaries) if self.extend == 'both': nb -= 2 elif self.extend in ('min', 'max'): nb -= 1 return nb def _extended_N(self): ''' Based on the colormap and extend variable, return the number of boundaries. ''' N = self.cmap.N + 1 if self.extend == 'both': N += 2 elif self.extend in ('min', 'max'): N += 1 return N def _uniform_y(self, N): ''' Return colorbar data coordinates for N uniformly spaced boundaries, plus ends if required. ''' if self.extend == 'neither': y = np.linspace(0, 1, N) else: if self.extend == 'both': y = np.zeros(N + 2, 'd') y[0] = -0.05 y[-1] = 1.05 elif self.extend == 'min': y = np.zeros(N + 1, 'd') y[0] = -0.05 else: y = np.zeros(N + 1, 'd') y[-1] = 1.05 y[self._inside] = np.linspace(0, 1, N) return y def _proportional_y(self): ''' Return colorbar data coordinates for the boundaries of a proportional colorbar. ''' if isinstance(self.norm, colors.BoundaryNorm): b = self._boundaries[self._inside] y = (self._boundaries - self._boundaries[0]) y = y / (self._boundaries[-1] - self._boundaries[0]) else: y = self.norm(self._boundaries.copy()) if self.extend in ('both', 'min'): y[0] = -0.05 if self.extend in ('both', 'max'): y[-1] = 1.05 yi = y[self._inside] norm = colors.Normalize(yi[0], yi[-1]) y[self._inside] = norm(yi) return y def _mesh(self): ''' Return X,Y, the coordinate arrays for the colorbar pcolormesh. These are suitable for a vertical colorbar; swapping and transposition for a horizontal colorbar are done outside this function. ''' x = np.array([0.0, 1.0]) if self.spacing == 'uniform': y = self._uniform_y(self._central_N()) else: y = self._proportional_y() self._y = y X, Y = np.meshgrid(x,y) if self.extend in ('min', 'both'): X[0,:] = 0.5 if self.extend in ('max', 'both'): X[-1,:] = 0.5 return X, Y def _locate(self, x): ''' Given a possible set of color data values, return the ones within range, together with their corresponding colorbar data coordinates. ''' if isinstance(self.norm, (colors.NoNorm, colors.BoundaryNorm)): b = self._boundaries xn = x xout = x else: # Do calculations using normalized coordinates so # as to make the interpolation more accurate. b = self.norm(self._boundaries, clip=False).filled() # We do our own clipping so that we can allow a tiny # bit of slop in the end point ticks to allow for # floating point errors. xn = self.norm(x, clip=False).filled() in_cond = (xn > -0.001) & (xn < 1.001) xn = np.compress(in_cond, xn) xout = np.compress(in_cond, x) # The rest is linear interpolation with clipping. y = self._y N = len(b) ii = np.minimum(np.searchsorted(b, xn), N-1) i0 = np.maximum(ii - 1, 0) #db = b[ii] - b[i0] db = np.take(b, ii) - np.take(b, i0) db = np.where(i0==ii, 1.0, db) #dy = y[ii] - y[i0] dy = np.take(y, ii) - np.take(y, i0) z = np.take(y, i0) + (xn-np.take(b,i0))dy/db return xout, z def set_alpha(self, alpha): self.alpha = alpha class Colorbar(ColorbarBase): def __init__(self, ax, mappable, kw): mappable.autoscale_None() # Ensure mappable.norm.vmin, vmax # are set when colorbar is called, # even if mappable.draw has not yet # been called. This will not change # vmin, vmax if they are already set. self.mappable = mappable kw['cmap'] = mappable.cmap kw['norm'] = mappable.norm kw['alpha'] = mappable.get_alpha() if isinstance(mappable, contour.ContourSet): CS = mappable kw['boundaries'] = CS._levels kw['values'] = CS.cvalues kw['extend'] = CS.extend #kw['ticks'] = CS._levels kw.setdefault('ticks', ticker.FixedLocator(CS.levels, nbins=10)) kw['filled'] = CS.filled ColorbarBase.__init__(self, ax, kw) if not CS.filled: self.add_lines(CS) else: ColorbarBase.__init__(self, ax, kw) def add_lines(self, CS): ''' Add the lines from a non-filled :class:`~matplotlib.contour.ContourSet` to the colorbar. ''' if not isinstance(CS, contour.ContourSet) or CS.filled: raise ValueError('add_lines is only for a ContourSet of lines') tcolors = [c[0] for c in CS.tcolors] tlinewidths = [t[0] for t in CS.tlinewidths] # The following was an attempt to get the colorbar lines # to follow subsequent changes in the contour lines, # but more work is needed: specifically, a careful # look at event sequences, and at how # to make one object track another automatically. #tcolors = [col.get_colors()[0] for col in CS.collections] #tlinewidths = [col.get_linewidth()[0] for lw in CS.collections] #print 'tlinewidths:', tlinewidths ColorbarBase.add_lines(self, CS.levels, tcolors, tlinewidths) def update_bruteforce(self, mappable): ''' Manually change any contour line colors. This is called when the image or contour plot to which this colorbar belongs is changed. ''' # We are using an ugly brute-force method: clearing and # redrawing the whole thing. The problem is that if any # properties have been changed by methods other than the # colorbar methods, those changes will be lost. self.ax.cla() self.draw_all() #if self.vmin != self.norm.vmin or self.vmax != self.norm.vmax: # self.ax.cla() # self.draw_all() if isinstance(self.mappable, contour.ContourSet): CS = self.mappable if not CS.filled: self.add_lines(CS) #if self.lines is not None: # tcolors = [c[0] for c in CS.tcolors] # self.lines.set_color(tcolors) #Fixme? Recalculate boundaries, ticks if vmin, vmax have changed. #Fixme: Some refactoring may be needed; we should not # be recalculating everything if there was a simple alpha # change. def make_axes(parent, kw): orientation = kw.setdefault('orientation', 'vertical') fraction = kw.pop('fraction', 0.15) shrink = kw.pop('shrink', 1.0) aspect = kw.pop('aspect', 20) #pb = transforms.PBox(parent.get_position()) pb = parent.get_position(original=True).frozen() if orientation == 'vertical': pad = kw.pop('pad', 0.05) x1 = 1.0-fraction pb1, pbx, pbcb = pb.splitx(x1-pad, x1) pbcb = pbcb.shrunk(1.0, shrink).anchored('C', pbcb) anchor = (0.0, 0.5) panchor = (1.0, 0.5) else: pad = kw.pop('pad', 0.15) pbcb, pbx, pb1 = pb.splity(fraction, fraction+pad) pbcb = pbcb.shrunk(shrink, 1.0).anchored('C', pbcb) aspect = 1.0/aspect anchor = (0.5, 1.0) panchor = (0.5, 0.0) parent.set_position(pb1) parent.set_anchor(panchor) fig = parent.get_figure() cax = fig.add_axes(pbcb) cax.set_aspect(aspect, anchor=anchor, adjustable='box') return cax, kw make_axes.__doc__ =''' Resize and reposition a parent axes, and return a child axes suitable for a colorbar:: cax, kw = make_axes(parent, kw) Keyword arguments may include the following (with defaults): orientation* 'vertical' or 'horizontal' %s All but the first of these are stripped from the input kw set. Returns (cax, kw), the child axes and the reduced kw dictionary. ''' % make_axes_kw_doc	27,260	Python	.py	656	31.289634	93	0.554571	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,276	texmanager.py	numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/texmanager.py	""" This module supports embedded TeX expressions in matplotlib via dvipng and dvips for the raster and postscript backends. The tex and dvipng/dvips information is cached in ~/.matplotlib/tex.cache for reuse between sessions Requirements: * latex * \Agg backends: dvipng PS backend: latex w/ psfrag, dvips, and Ghostscript 8.51 (older versions do not work properly) Backends: * \Agg PS * PDF For raster output, you can get RGBA numpy arrays from TeX expressions as follows:: texmanager = TexManager() s = '\\TeX\\ is Number $\\displaystyle\\sum_{n=1}^\\infty\\frac{-e^{i\pi}}{2^n}$!' Z = self.texmanager.get_rgba(s, size=12, dpi=80, rgb=(1,0,0)) To enable tex rendering of all text in your matplotlib figure, set text.usetex in your matplotlibrc file (http://matplotlib.sf.net/matplotlibrc) or include these two lines in your script:: from matplotlib import rc rc('text', usetex=True) """ import copy, glob, os, shutil, sys, warnings try: from hashlib import md5 except ImportError: from md5 import md5 #Deprecated in 2.5 import distutils.version import numpy as np import matplotlib as mpl from matplotlib import rcParams from matplotlib._png import read_png DEBUG = False if sys.platform.startswith('win'): cmd_split = '&' else: cmd_split = ';' def dvipng_hack_alpha(): stdin, stdout = os.popen4('dvipng -version') for line in stdout: if line.startswith('dvipng '): version = line.split()[-1] mpl.verbose.report('Found dvipng version %s'% version, 'helpful') version = distutils.version.LooseVersion(version) return version < distutils.version.LooseVersion('1.6') raise RuntimeError('Could not obtain dvipng version') class TexManager: """ Convert strings to dvi files using TeX, caching the results to a working dir """ oldpath = mpl.get_home() if oldpath is None: oldpath = mpl.get_data_path() oldcache = os.path.join(oldpath, '.tex.cache') configdir = mpl.get_configdir() texcache = os.path.join(configdir, 'tex.cache') if os.path.exists(oldcache): print >> sys.stderr, """\ WARNING: found a TeX cache dir in the deprecated location "%s". Moving it to the new default location "%s"."""%(oldcache, texcache) shutil.move(oldcache, texcache) if not os.path.exists(texcache): os.mkdir(texcache) _dvipng_hack_alpha = dvipng_hack_alpha() # mappable cache of rgba_arrayd = {} grey_arrayd = {} postscriptd = {} pscnt = 0 serif = ('cmr', '') sans_serif = ('cmss', '') monospace = ('cmtt', '') cursive = ('pzc', r'\usepackage{chancery}') font_family = 'serif' font_families = ('serif', 'sans-serif', 'cursive', 'monospace') font_info = {'new century schoolbook': ('pnc', r'\renewcommand{\rmdefault}{pnc}'), 'bookman': ('pbk', r'\renewcommand{\rmdefault}{pbk}'), 'times': ('ptm', r'\usepackage{mathptmx}'), 'palatino': ('ppl', r'\usepackage{mathpazo}'), 'zapf chancery': ('pzc', r'\usepackage{chancery}'), 'cursive': ('pzc', r'\usepackage{chancery}'), 'charter': ('pch', r'\usepackage{charter}'), 'serif': ('cmr', ''), 'sans-serif': ('cmss', ''), 'helvetica': ('phv', r'\usepackage{helvet}'), 'avant garde': ('pag', r'\usepackage{avant}'), 'courier': ('pcr', r'\usepackage{courier}'), 'monospace': ('cmtt', ''), 'computer modern roman': ('cmr', ''), 'computer modern sans serif': ('cmss', ''), 'computer modern typewriter': ('cmtt', '')} _rc_cache = None _rc_cache_keys = ('text.latex.preamble', )\ + tuple(['font.'+n for n in ('family', ) + font_families]) def __init__(self): if not os.path.isdir(self.texcache): os.mkdir(self.texcache) ff = rcParams['font.family'].lower() if ff in self.font_families: self.font_family = ff else: mpl.verbose.report('The %s font family is not compatible with LaTeX. serif will be used by default.' % ff, 'helpful') self.font_family = 'serif' fontconfig = [self.font_family] for font_family, font_family_attr in \ [(ff, ff.replace('-', '_')) for ff in self.font_families]: for font in rcParams['font.'+font_family]: if font.lower() in self.font_info: found_font = self.font_info[font.lower()] setattr(self, font_family_attr, self.font_info[font.lower()]) if DEBUG: print 'family: %s, font: %s, info: %s'%(font_family, font, self.font_info[font.lower()]) break else: if DEBUG: print '$s font is not compatible with usetex' else: mpl.verbose.report('No LaTeX-compatible font found for the %s font family in rcParams. Using default.' % ff, 'helpful') setattr(self, font_family_attr, self.font_info[font_family]) fontconfig.append(getattr(self, font_family_attr)[0]) self._fontconfig = ''.join(fontconfig) # The following packages and commands need to be included in the latex # file's preamble: cmd = [self.serif[1], self.sans_serif[1], self.monospace[1]] if self.font_family == 'cursive': cmd.append(self.cursive[1]) while r'\usepackage{type1cm}' in cmd: cmd.remove(r'\usepackage{type1cm}') cmd = '\n'.join(cmd) self._font_preamble = '\n'.join([r'\usepackage{type1cm}', cmd, r'\usepackage{textcomp}']) def get_basefile(self, tex, fontsize, dpi=None): """ returns a filename based on a hash of the string, fontsize, and dpi """ s = ''.join([tex, self.get_font_config(), '%f'%fontsize, self.get_custom_preamble(), str(dpi or '')]) # make sure hash is consistent for all strings, regardless of encoding: bytes = unicode(s).encode('utf-8') return os.path.join(self.texcache, md5(bytes).hexdigest()) def get_font_config(self): """Reinitializes self if relevant rcParams on have changed.""" if self._rc_cache is None: self._rc_cache = dict([(k,None) for k in self._rc_cache_keys]) changed = [par for par in self._rc_cache_keys if rcParams[par] != \ self._rc_cache[par]] if changed: if DEBUG: print 'DEBUG following keys changed:', changed for k in changed: if DEBUG: print 'DEBUG %-20s: %-10s -> %-10s' % \ (k, self._rc_cache[k], rcParams[k]) # deepcopy may not be necessary, but feels more future-proof self._rc_cache[k] = copy.deepcopy(rcParams[k]) if DEBUG: print 'DEBUG RE-INIT\nold fontconfig:', self._fontconfig self.__init__() if DEBUG: print 'DEBUG fontconfig:', self._fontconfig return self._fontconfig def get_font_preamble(self): """ returns a string containing font configuration for the tex preamble """ return self._font_preamble def get_custom_preamble(self): """returns a string containing user additions to the tex preamble""" return '\n'.join(rcParams['text.latex.preamble']) def _get_shell_cmd(self, args): """ On windows, changing directories can be complicated by the presence of multiple drives. get_shell_cmd deals with this issue. """ if sys.platform == 'win32': command = ['%s'% os.path.splitdrive(self.texcache)[0]] else: command = [] command.extend(args) return ' && '.join(command) def make_tex(self, tex, fontsize): """ Generate a tex file to render the tex string at a specific font size returns the file name """ basefile = self.get_basefile(tex, fontsize) texfile = '%s.tex'%basefile fh = file(texfile, 'w') custom_preamble = self.get_custom_preamble() fontcmd = {'sans-serif' : r'{\sffamily %s}', 'monospace' : r'{\ttfamily %s}'}.get(self.font_family, r'{\rmfamily %s}') tex = fontcmd % tex if rcParams['text.latex.unicode']: unicode_preamble = """\usepackage{ucs} \usepackage[utf8x]{inputenc}""" else: unicode_preamble = '' s = r"""\documentclass{article} %s %s %s \usepackage[papersize={72in,72in}, body={70in,70in}, margin={1in,1in}]{geometry} \pagestyle{empty} \begin{document} \fontsize{%f}{%f}%s \end{document} """ % (self._font_preamble, unicode_preamble, custom_preamble, fontsize, fontsize1.25, tex) if rcParams['text.latex.unicode']: fh.write(s.encode('utf8')) else: try: fh.write(s) except UnicodeEncodeError, err: mpl.verbose.report("You are using unicode and latex, but have " "not enabled the matplotlib 'text.latex.unicode' " "rcParam.", 'helpful') raise fh.close() return texfile def make_dvi(self, tex, fontsize): """ generates a dvi file containing latex's layout of tex string returns the file name """ basefile = self.get_basefile(tex, fontsize) dvifile = '%s.dvi'% basefile if DEBUG or not os.path.exists(dvifile): texfile = self.make_tex(tex, fontsize) outfile = basefile+'.output' command = self._get_shell_cmd('cd "%s"'% self.texcache, 'latex -interaction=nonstopmode %s > "%s"'\ %(os.path.split(texfile)[-1], outfile)) mpl.verbose.report(command, 'debug') exit_status = os.system(command) try: fh = file(outfile) report = fh.read() fh.close() except IOError: report = 'No latex error report available.' if exit_status: raise RuntimeError(('LaTeX was not able to process the following \ string:\n%s\nHere is the full report generated by LaTeX: \n\n'% repr(tex)) + report) else: mpl.verbose.report(report, 'debug') for fname in glob.glob(basefile+''): if fname.endswith('dvi'): pass elif fname.endswith('tex'): pass else: try: os.remove(fname) except OSError: pass return dvifile def make_png(self, tex, fontsize, dpi): """ generates a png file containing latex's rendering of tex string returns the filename """ basefile = self.get_basefile(tex, fontsize, dpi) pngfile = '%s.png'% basefile # see get_rgba for a discussion of the background if DEBUG or not os.path.exists(pngfile): dvifile = self.make_dvi(tex, fontsize) outfile = basefile+'.output' command = self._get_shell_cmd('cd "%s"' % self.texcache, 'dvipng -bg Transparent -D %s -T tight -o \ "%s" "%s" > "%s"'%(dpi, os.path.split(pngfile)[-1], os.path.split(dvifile)[-1], outfile)) mpl.verbose.report(command, 'debug') exit_status = os.system(command) try: fh = file(outfile) report = fh.read() fh.close() except IOError: report = 'No dvipng error report available.' if exit_status: raise RuntimeError('dvipng was not able to \ process the flowing file:\n%s\nHere is the full report generated by dvipng: \ \n\n'% dvifile + report) else: mpl.verbose.report(report, 'debug') try: os.remove(outfile) except OSError: pass return pngfile def make_ps(self, tex, fontsize): """ generates a postscript file containing latex's rendering of tex string returns the file name """ basefile = self.get_basefile(tex, fontsize) psfile = '%s.epsf'% basefile if DEBUG or not os.path.exists(psfile): dvifile = self.make_dvi(tex, fontsize) outfile = basefile+'.output' command = self._get_shell_cmd('cd "%s"'% self.texcache, 'dvips -q -E -o "%s" "%s" > "%s"'\ %(os.path.split(psfile)[-1], os.path.split(dvifile)[-1], outfile)) mpl.verbose.report(command, 'debug') exit_status = os.system(command) fh = file(outfile) if exit_status: raise RuntimeError('dvipng was not able to \ process the flowing file:\n%s\nHere is the full report generated by dvipng: \ \n\n'% dvifile + fh.read()) else: mpl.verbose.report(fh.read(), 'debug') fh.close() os.remove(outfile) return psfile def get_ps_bbox(self, tex, fontsize): """ returns a list containing the postscript bounding box for latex's rendering of the tex string """ psfile = self.make_ps(tex, fontsize) ps = file(psfile) for line in ps: if line.startswith('%%BoundingBox:'): return [int(val) for val in line.split()[1:]] raise RuntimeError('Could not parse %s'%psfile) def get_grey(self, tex, fontsize=None, dpi=None): """returns the alpha channel""" key = tex, self.get_font_config(), fontsize, dpi alpha = self.grey_arrayd.get(key) if alpha is None: pngfile = self.make_png(tex, fontsize, dpi) X = read_png(os.path.join(self.texcache, pngfile)) if rcParams['text.dvipnghack'] is not None: hack = rcParams['text.dvipnghack'] else: hack = self._dvipng_hack_alpha if hack: # hack the alpha channel # dvipng assumed a constant background, whereas we want to # overlay these rasters with antialiasing over arbitrary # backgrounds that may have other figure elements under them. # When you set dvipng -bg Transparent, it actually makes the # alpha channel 1 and does the background compositing and # antialiasing itself and puts the blended data in the rgb # channels. So what we do is extract the alpha information # from the red channel, which is a blend of the default dvipng # background (white) and foreground (black). So the amount of # red (or green or blue for that matter since white and black # blend to a grayscale) is the alpha intensity. Once we # extract the correct alpha information, we assign it to the # alpha channel properly and let the users pick their rgb. In # this way, we can overlay tex strings on arbitrary # backgrounds with antialiasing # # red = alphared_foreground + (1-alpha)*red_background # # Since the foreground is black (0) and the background is # white (1) this reduces to red = 1-alpha or alpha = 1-red #alpha = npy.sqrt(1-X[:,:,0]) # should this be sqrt here? alpha = 1-X[:,:,0] else: alpha = X[:,:,-1] self.grey_arrayd[key] = alpha return alpha def get_rgba(self, tex, fontsize=None, dpi=None, rgb=(0,0,0)): """ Returns latex's rendering of the tex string as an rgba array """ if not fontsize: fontsize = rcParams['font.size'] if not dpi: dpi = rcParams['savefig.dpi'] r,g,b = rgb key = tex, self.get_font_config(), fontsize, dpi, tuple(rgb) Z = self.rgba_arrayd.get(key) if Z is None: alpha = self.get_grey(tex, fontsize, dpi) Z = np.zeros((alpha.shape[0], alpha.shape[1], 4), np.float) Z[:,:,0] = r Z[:,:,1] = g Z[:,:,2] = b Z[:,:,3] = alpha self.rgba_arrayd[key] = Z return Z	16,818	Python	.py	377	33.469496	135	0.565069	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,277	axis.py	numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/axis.py	""" Classes for the ticks and x and y axis """ from __future__ import division from matplotlib import rcParams import matplotlib.artist as artist import matplotlib.cbook as cbook import matplotlib.font_manager as font_manager import matplotlib.lines as mlines import matplotlib.patches as mpatches import matplotlib.scale as mscale import matplotlib.text as mtext import matplotlib.ticker as mticker import matplotlib.transforms as mtransforms import matplotlib.units as munits class Tick(artist.Artist): """ Abstract base class for the axis ticks, grid lines and labels 1 refers to the bottom of the plot for xticks and the left for yticks 2 refers to the top of the plot for xticks and the right for yticks Publicly accessible attributes: :attr:`tick1line` a Line2D instance :attr:`tick2line` a Line2D instance :attr:`gridline` a Line2D instance :attr:`label1` a Text instance :attr:`label2` a Text instance :attr:`gridOn` a boolean which determines whether to draw the tickline :attr:`tick1On` a boolean which determines whether to draw the 1st tickline :attr:`tick2On` a boolean which determines whether to draw the 2nd tickline :attr:`label1On` a boolean which determines whether to draw tick label :attr:`label2On` a boolean which determines whether to draw tick label """ def __init__(self, axes, loc, label, size = None, # points gridOn = None, # defaults to axes.grid tick1On = True, tick2On = True, label1On = True, label2On = False, major = True, ): """ bbox is the Bound2D bounding box in display coords of the Axes loc is the tick location in data coords size is the tick size in relative, axes coords """ artist.Artist.__init__(self) if gridOn is None: gridOn = rcParams['axes.grid'] self.set_figure(axes.figure) self.axes = axes name = self.__name__.lower() if size is None: if major: size = rcParams['%s.major.size'%name] pad = rcParams['%s.major.pad'%name] else: size = rcParams['%s.minor.size'%name] pad = rcParams['%s.minor.pad'%name] self._tickdir = rcParams['%s.direction'%name] if self._tickdir == 'in': self._xtickmarkers = (mlines.TICKUP, mlines.TICKDOWN) self._ytickmarkers = (mlines.TICKRIGHT, mlines.TICKLEFT) self._pad = pad else: self._xtickmarkers = (mlines.TICKDOWN, mlines.TICKUP) self._ytickmarkers = (mlines.TICKLEFT, mlines.TICKRIGHT) self._pad = pad + size self._loc = loc self._size = size self.tick1line = self._get_tick1line() self.tick2line = self._get_tick2line() self.gridline = self._get_gridline() self.label1 = self._get_text1() self.label = self.label1 # legacy name self.label2 = self._get_text2() self.gridOn = gridOn self.tick1On = tick1On self.tick2On = tick2On self.label1On = label1On self.label2On = label2On self.update_position(loc) def get_children(self): children = [self.tick1line, self.tick2line, self.gridline, self.label1, self.label2] return children def set_clip_path(self, clippath, transform=None): artist.Artist.set_clip_path(self, clippath, transform) #self.tick1line.set_clip_path(clippath, transform) #self.tick2line.set_clip_path(clippath, transform) self.gridline.set_clip_path(clippath, transform) set_clip_path.__doc__ = artist.Artist.set_clip_path.__doc__ def get_pad_pixels(self): return self.figure.dpi * self._pad / 72.0 def contains(self, mouseevent): """ Test whether the mouse event occured in the Tick marks. This function always returns false. It is more useful to test if the axis as a whole contains the mouse rather than the set of tick marks. """ if callable(self._contains): return self._contains(self,mouseevent) return False,{} def set_pad(self, val): """ Set the tick label pad in points ACCEPTS: float """ self._pad = val def get_pad(self): 'Get the value of the tick label pad in points' return self._pad def _get_text1(self): 'Get the default Text 1 instance' pass def _get_text2(self): 'Get the default Text 2 instance' pass def _get_tick1line(self): 'Get the default line2D instance for tick1' pass def _get_tick2line(self): 'Get the default line2D instance for tick2' pass def _get_gridline(self): 'Get the default grid Line2d instance for this tick' pass def get_loc(self): 'Return the tick location (data coords) as a scalar' return self._loc def draw(self, renderer): if not self.get_visible(): return renderer.open_group(self.__name__) midPoint = mtransforms.interval_contains(self.get_view_interval(), self.get_loc()) if midPoint: if self.gridOn: self.gridline.draw(renderer) if self.tick1On: self.tick1line.draw(renderer) if self.tick2On: self.tick2line.draw(renderer) if self.label1On: self.label1.draw(renderer) if self.label2On: self.label2.draw(renderer) renderer.close_group(self.__name__) def set_label1(self, s): """ Set the text of ticklabel ACCEPTS: str """ self.label1.set_text(s) set_label = set_label1 def set_label2(self, s): """ Set the text of ticklabel2 ACCEPTS: str """ self.label2.set_text(s) def _set_artist_props(self, a): a.set_figure(self.figure) #if isinstance(a, mlines.Line2D): a.set_clip_box(self.axes.bbox) def get_view_interval(self): 'return the view Interval instance for the axis this tick is ticking' raise NotImplementedError('Derived must override') def set_view_interval(self, vmin, vmax, ignore=False): raise NotImplementedError('Derived must override') class XTick(Tick): """ Contains all the Artists needed to make an x tick - the tick line, the label text and the grid line """ __name__ = 'xtick' def _get_text1(self): 'Get the default Text instance' # the y loc is 3 points below the min of y axis # get the affine as an a,b,c,d,tx,ty list # x in data coords, y in axes coords #t = mtext.Text( trans, vert, horiz = self.axes.get_xaxis_text1_transform(self._pad) size = rcParams['xtick.labelsize'] t = mtext.Text( x=0, y=0, fontproperties=font_manager.FontProperties(size=size), color=rcParams['xtick.color'], verticalalignment=vert, horizontalalignment=horiz, ) t.set_transform(trans) self._set_artist_props(t) return t def _get_text2(self): 'Get the default Text 2 instance' # x in data coords, y in axes coords #t = mtext.Text( trans, vert, horiz = self.axes.get_xaxis_text2_transform(self._pad) t = mtext.Text( x=0, y=1, fontproperties=font_manager.FontProperties(size=rcParams['xtick.labelsize']), color=rcParams['xtick.color'], verticalalignment=vert, horizontalalignment=horiz, ) t.set_transform(trans) self._set_artist_props(t) return t def _get_tick1line(self): 'Get the default line2D instance' # x in data coords, y in axes coords l = mlines.Line2D(xdata=(0,), ydata=(0,), color='k', linestyle = 'None', marker = self._xtickmarkers[0], markersize=self._size, ) l.set_transform(self.axes.get_xaxis_transform()) self._set_artist_props(l) return l def _get_tick2line(self): 'Get the default line2D instance' # x in data coords, y in axes coords l = mlines.Line2D( xdata=(0,), ydata=(1,), color='k', linestyle = 'None', marker = self._xtickmarkers[1], markersize=self._size, ) l.set_transform(self.axes.get_xaxis_transform()) self._set_artist_props(l) return l def _get_gridline(self): 'Get the default line2D instance' # x in data coords, y in axes coords l = mlines.Line2D(xdata=(0.0, 0.0), ydata=(0, 1.0), color=rcParams['grid.color'], linestyle=rcParams['grid.linestyle'], linewidth=rcParams['grid.linewidth'], ) l.set_transform(self.axes.get_xaxis_transform()) self._set_artist_props(l) return l def update_position(self, loc): 'Set the location of tick in data coords with scalar loc' x = loc nonlinear = (hasattr(self.axes, 'yaxis') and self.axes.yaxis.get_scale() != 'linear' or hasattr(self.axes, 'xaxis') and self.axes.xaxis.get_scale() != 'linear') if self.tick1On: self.tick1line.set_xdata((x,)) if self.tick2On: self.tick2line.set_xdata((x,)) if self.gridOn: self.gridline.set_xdata((x,)) if self.label1On: self.label1.set_x(x) if self.label2On: self.label2.set_x(x) if nonlinear: self.tick1line._invalid = True self.tick2line._invalid = True self.gridline._invalid = True self._loc = loc def get_view_interval(self): 'return the Interval instance for this axis view limits' return self.axes.viewLim.intervalx def set_view_interval(self, vmin, vmax, ignore = False): if ignore: self.axes.viewLim.intervalx = vmin, vmax else: Vmin, Vmax = self.get_view_interval() self.axes.viewLim.intervalx = min(vmin, Vmin), max(vmax, Vmax) def get_minpos(self): return self.axes.dataLim.minposx def get_data_interval(self): 'return the Interval instance for this axis data limits' return self.axes.dataLim.intervalx class YTick(Tick): """ Contains all the Artists needed to make a Y tick - the tick line, the label text and the grid line """ __name__ = 'ytick' # how far from the y axis line the right of the ticklabel are def _get_text1(self): 'Get the default Text instance' # x in axes coords, y in data coords #t = mtext.Text( trans, vert, horiz = self.axes.get_yaxis_text1_transform(self._pad) t = mtext.Text( x=0, y=0, fontproperties=font_manager.FontProperties(size=rcParams['ytick.labelsize']), color=rcParams['ytick.color'], verticalalignment=vert, horizontalalignment=horiz, ) t.set_transform(trans) #t.set_transform( self.axes.transData ) self._set_artist_props(t) return t def _get_text2(self): 'Get the default Text instance' # x in axes coords, y in data coords #t = mtext.Text( trans, vert, horiz = self.axes.get_yaxis_text2_transform(self._pad) t = mtext.Text( x=1, y=0, fontproperties=font_manager.FontProperties(size=rcParams['ytick.labelsize']), color=rcParams['ytick.color'], verticalalignment=vert, horizontalalignment=horiz, ) t.set_transform(trans) self._set_artist_props(t) return t def _get_tick1line(self): 'Get the default line2D instance' # x in axes coords, y in data coords l = mlines.Line2D( (0,), (0,), color='k', marker = self._ytickmarkers[0], linestyle = 'None', markersize=self._size, ) l.set_transform(self.axes.get_yaxis_transform()) self._set_artist_props(l) return l def _get_tick2line(self): 'Get the default line2D instance' # x in axes coords, y in data coords l = mlines.Line2D( (1,), (0,), color='k', marker = self._ytickmarkers[1], linestyle = 'None', markersize=self._size, ) l.set_transform(self.axes.get_yaxis_transform()) self._set_artist_props(l) return l def _get_gridline(self): 'Get the default line2D instance' # x in axes coords, y in data coords l = mlines.Line2D( xdata=(0,1), ydata=(0, 0), color=rcParams['grid.color'], linestyle=rcParams['grid.linestyle'], linewidth=rcParams['grid.linewidth'], ) l.set_transform(self.axes.get_yaxis_transform()) self._set_artist_props(l) return l def update_position(self, loc): 'Set the location of tick in data coords with scalar loc' y = loc nonlinear = (hasattr(self.axes, 'yaxis') and self.axes.yaxis.get_scale() != 'linear' or hasattr(self.axes, 'xaxis') and self.axes.xaxis.get_scale() != 'linear') if self.tick1On: self.tick1line.set_ydata((y,)) if self.tick2On: self.tick2line.set_ydata((y,)) if self.gridOn: self.gridline.set_ydata((y, )) if self.label1On: self.label1.set_y( y ) if self.label2On: self.label2.set_y( y ) if nonlinear: self.tick1line._invalid = True self.tick2line._invalid = True self.gridline._invalid = True self._loc = loc def get_view_interval(self): 'return the Interval instance for this axis view limits' return self.axes.viewLim.intervaly def set_view_interval(self, vmin, vmax, ignore = False): if ignore: self.axes.viewLim.intervaly = vmin, vmax else: Vmin, Vmax = self.get_view_interval() self.axes.viewLim.intervaly = min(vmin, Vmin), max(vmax, Vmax) def get_minpos(self): return self.axes.dataLim.minposy def get_data_interval(self): 'return the Interval instance for this axis data limits' return self.axes.dataLim.intervaly class Ticker: locator = None formatter = None class Axis(artist.Artist): """ Public attributes * :attr:`transData` - transform data coords to display coords * :attr:`transAxis` - transform axis coords to display coords """ LABELPAD = 5 OFFSETTEXTPAD = 3 def __str__(self): return self.__class__.__name__ \ + "(%f,%f)"%tuple(self.axes.transAxes.transform_point((0,0))) def __init__(self, axes, pickradius=15): """ Init the axis with the parent Axes instance """ artist.Artist.__init__(self) self.set_figure(axes.figure) self.axes = axes self.major = Ticker() self.minor = Ticker() self.callbacks = cbook.CallbackRegistry(('units', 'units finalize')) #class dummy: # locator = None # formatter = None #self.major = dummy() #self.minor = dummy() self._autolabelpos = True self.label = self._get_label() self.offsetText = self._get_offset_text() self.majorTicks = [] self.minorTicks = [] self.pickradius = pickradius self.cla() self.set_scale('linear') def set_label_coords(self, x, y, transform=None): """ Set the coordinates of the label. By default, the x coordinate of the y label is determined by the tick label bounding boxes, but this can lead to poor alignment of multiple ylabels if there are multiple axes. Ditto for the y coodinate of the x label. You can also specify the coordinate system of the label with the transform. If None, the default coordinate system will be the axes coordinate system (0,0) is (left,bottom), (0.5, 0.5) is middle, etc """ self._autolabelpos = False if transform is None: transform = self.axes.transAxes self.label.set_transform(transform) self.label.set_position((x, y)) def get_transform(self): return self._scale.get_transform() def get_scale(self): return self._scale.name def set_scale(self, value, kwargs): self._scale = mscale.scale_factory(value, self, kwargs) self._scale.set_default_locators_and_formatters(self) def limit_range_for_scale(self, vmin, vmax): return self._scale.limit_range_for_scale(vmin, vmax, self.get_minpos()) def get_children(self): children = [self.label] majorticks = self.get_major_ticks() minorticks = self.get_minor_ticks() children.extend(majorticks) children.extend(minorticks) return children def cla(self): 'clear the current axis' self.set_major_locator(mticker.AutoLocator()) self.set_major_formatter(mticker.ScalarFormatter()) self.set_minor_locator(mticker.NullLocator()) self.set_minor_formatter(mticker.NullFormatter()) # Clear the callback registry for this axis, or it may "leak" self.callbacks = cbook.CallbackRegistry(('units', 'units finalize')) # whether the grids are on self._gridOnMajor = rcParams['axes.grid'] self._gridOnMinor = False self.label.set_text('') self._set_artist_props(self.label) # build a few default ticks; grow as necessary later; only # define 1 so properties set on ticks will be copied as they # grow cbook.popall(self.majorTicks) cbook.popall(self.minorTicks) self.majorTicks.extend([self._get_tick(major=True)]) self.minorTicks.extend([self._get_tick(major=False)]) self._lastNumMajorTicks = 1 self._lastNumMinorTicks = 1 self.converter = None self.units = None self.set_units(None) def set_clip_path(self, clippath, transform=None): artist.Artist.set_clip_path(self, clippath, transform) majorticks = self.get_major_ticks() minorticks = self.get_minor_ticks() for child in self.majorTicks + self.minorTicks: child.set_clip_path(clippath, transform) def get_view_interval(self): 'return the Interval instance for this axis view limits' raise NotImplementedError('Derived must override') def set_view_interval(self, vmin, vmax, ignore=False): raise NotImplementedError('Derived must override') def get_data_interval(self): 'return the Interval instance for this axis data limits' raise NotImplementedError('Derived must override') def set_data_interval(self): 'Set the axis data limits' raise NotImplementedError('Derived must override') def _set_artist_props(self, a): if a is None: return a.set_figure(self.figure) def iter_ticks(self): """ Iterate through all of the major and minor ticks. """ majorLocs = self.major.locator() majorTicks = self.get_major_ticks(len(majorLocs)) self.major.formatter.set_locs(majorLocs) majorLabels = [self.major.formatter(val, i) for i, val in enumerate(majorLocs)] minorLocs = self.minor.locator() minorTicks = self.get_minor_ticks(len(minorLocs)) self.minor.formatter.set_locs(minorLocs) minorLabels = [self.minor.formatter(val, i) for i, val in enumerate(minorLocs)] major_minor = [ (majorTicks, majorLocs, majorLabels), (minorTicks, minorLocs, minorLabels)] for group in major_minor: for tick in zip(group): yield tick def get_ticklabel_extents(self, renderer): """ Get the extents of the tick labels on either side of the axes. """ ticklabelBoxes = [] ticklabelBoxes2 = [] interval = self.get_view_interval() for tick, loc, label in self.iter_ticks(): if tick is None: continue if not mtransforms.interval_contains(interval, loc): continue tick.update_position(loc) tick.set_label1(label) tick.set_label2(label) if tick.label1On and tick.label1.get_visible(): extent = tick.label1.get_window_extent(renderer) ticklabelBoxes.append(extent) if tick.label2On and tick.label2.get_visible(): extent = tick.label2.get_window_extent(renderer) ticklabelBoxes2.append(extent) if len(ticklabelBoxes): bbox = mtransforms.Bbox.union(ticklabelBoxes) else: bbox = mtransforms.Bbox.from_extents(0, 0, 0, 0) if len(ticklabelBoxes2): bbox2 = mtransforms.Bbox.union(ticklabelBoxes2) else: bbox2 = mtransforms.Bbox.from_extents(0, 0, 0, 0) return bbox, bbox2 def draw(self, renderer, args, *kwargs): 'Draw the axis lines, grid lines, tick lines and labels' ticklabelBoxes = [] ticklabelBoxes2 = [] if not self.get_visible(): return renderer.open_group(__name__) interval = self.get_view_interval() for tick, loc, label in self.iter_ticks(): if tick is None: continue if not mtransforms.interval_contains(interval, loc): continue tick.update_position(loc) tick.set_label1(label) tick.set_label2(label) tick.draw(renderer) if tick.label1On and tick.label1.get_visible(): extent = tick.label1.get_window_extent(renderer) ticklabelBoxes.append(extent) if tick.label2On and tick.label2.get_visible(): extent = tick.label2.get_window_extent(renderer) ticklabelBoxes2.append(extent) # scale up the axis label box to also find the neighbors, not # just the tick labels that actually overlap note we need a # copy* of the axis label box because we don't wan't to scale # the actual bbox self._update_label_position(ticklabelBoxes, ticklabelBoxes2) self.label.draw(renderer) self._update_offset_text_position(ticklabelBoxes, ticklabelBoxes2) self.offsetText.set_text( self.major.formatter.get_offset() ) self.offsetText.draw(renderer) if 0: # draw the bounding boxes around the text for debug for tick in majorTicks: label = tick.label1 mpatches.bbox_artist(label, renderer) mpatches.bbox_artist(self.label, renderer) renderer.close_group(__name__) def _get_label(self): raise NotImplementedError('Derived must override') def _get_offset_text(self): raise NotImplementedError('Derived must override') def get_gridlines(self): 'Return the grid lines as a list of Line2D instance' ticks = self.get_major_ticks() return cbook.silent_list('Line2D gridline', [tick.gridline for tick in ticks]) def get_label(self): 'Return the axis label as a Text instance' return self.label def get_offset_text(self): 'Return the axis offsetText as a Text instance' return self.offsetText def get_pickradius(self): 'Return the depth of the axis used by the picker' return self.pickradius def get_majorticklabels(self): 'Return a list of Text instances for the major ticklabels' ticks = self.get_major_ticks() labels1 = [tick.label1 for tick in ticks if tick.label1On] labels2 = [tick.label2 for tick in ticks if tick.label2On] return cbook.silent_list('Text major ticklabel', labels1+labels2) def get_minorticklabels(self): 'Return a list of Text instances for the minor ticklabels' ticks = self.get_minor_ticks() labels1 = [tick.label1 for tick in ticks if tick.label1On] labels2 = [tick.label2 for tick in ticks if tick.label2On] return cbook.silent_list('Text minor ticklabel', labels1+labels2) def get_ticklabels(self, minor=False): 'Return a list of Text instances for ticklabels' if minor: return self.get_minorticklabels() return self.get_majorticklabels() def get_majorticklines(self): 'Return the major tick lines as a list of Line2D instances' lines = [] ticks = self.get_major_ticks() for tick in ticks: lines.append(tick.tick1line) lines.append(tick.tick2line) return cbook.silent_list('Line2D ticklines', lines) def get_minorticklines(self): 'Return the minor tick lines as a list of Line2D instances' lines = [] ticks = self.get_minor_ticks() for tick in ticks: lines.append(tick.tick1line) lines.append(tick.tick2line) return cbook.silent_list('Line2D ticklines', lines) def get_ticklines(self, minor=False): 'Return the tick lines as a list of Line2D instances' if minor: return self.get_minorticklines() return self.get_majorticklines() def get_majorticklocs(self): "Get the major tick locations in data coordinates as a numpy array" return self.major.locator() def get_minorticklocs(self): "Get the minor tick locations in data coordinates as a numpy array" return self.minor.locator() def get_ticklocs(self, minor=False): "Get the tick locations in data coordinates as a numpy array" if minor: return self.minor.locator() return self.major.locator() def _get_tick(self, major): 'return the default tick intsance' raise NotImplementedError('derived must override') def _copy_tick_props(self, src, dest): 'Copy the props from src tick to dest tick' if src is None or dest is None: return dest.label1.update_from(src.label1) dest.label2.update_from(src.label2) dest.tick1line.update_from(src.tick1line) dest.tick2line.update_from(src.tick2line) dest.gridline.update_from(src.gridline) dest.tick1On = src.tick1On dest.tick2On = src.tick2On dest.label1On = src.label1On dest.label2On = src.label2On def get_major_locator(self): 'Get the locator of the major ticker' return self.major.locator def get_minor_locator(self): 'Get the locator of the minor ticker' return self.minor.locator def get_major_formatter(self): 'Get the formatter of the major ticker' return self.major.formatter def get_minor_formatter(self): 'Get the formatter of the minor ticker' return self.minor.formatter def get_major_ticks(self, numticks=None): 'get the tick instances; grow as necessary' if numticks is None: numticks = len(self.get_major_locator()()) if len(self.majorTicks) < numticks: # update the new tick label properties from the old for i in range(numticks - len(self.majorTicks)): tick = self._get_tick(major=True) self.majorTicks.append(tick) if self._lastNumMajorTicks < numticks: protoTick = self.majorTicks[0] for i in range(self._lastNumMajorTicks, len(self.majorTicks)): tick = self.majorTicks[i] if self._gridOnMajor: tick.gridOn = True self._copy_tick_props(protoTick, tick) self._lastNumMajorTicks = numticks ticks = self.majorTicks[:numticks] return ticks def get_minor_ticks(self, numticks=None): 'get the minor tick instances; grow as necessary' if numticks is None: numticks = len(self.get_minor_locator()()) if len(self.minorTicks) < numticks: # update the new tick label properties from the old for i in range(numticks - len(self.minorTicks)): tick = self._get_tick(major=False) self.minorTicks.append(tick) if self._lastNumMinorTicks < numticks: protoTick = self.minorTicks[0] for i in range(self._lastNumMinorTicks, len(self.minorTicks)): tick = self.minorTicks[i] if self._gridOnMinor: tick.gridOn = True self._copy_tick_props(protoTick, tick) self._lastNumMinorTicks = numticks ticks = self.minorTicks[:numticks] return ticks def grid(self, b=None, which='major', *kwargs): """ Set the axis grid on or off; b is a boolean use which* = 'major' \| 'minor' to set the grid for major or minor ticks if b is None and len(kwargs)==0, toggle the grid state. If kwargs are supplied, it is assumed you want the grid on and b will be set to True kwargs are used to set the line properties of the grids, eg, xax.grid(color='r', linestyle='-', linewidth=2) """ if len(kwargs): b = True if which.lower().find('minor')>=0: if b is None: self._gridOnMinor = not self._gridOnMinor else: self._gridOnMinor = b for tick in self.minorTicks: # don't use get_ticks here! if tick is None: continue tick.gridOn = self._gridOnMinor if len(kwargs): artist.setp(tick.gridline,kwargs) else: if b is None: self._gridOnMajor = not self._gridOnMajor else: self._gridOnMajor = b for tick in self.majorTicks: # don't use get_ticks here! if tick is None: continue tick.gridOn = self._gridOnMajor if len(kwargs): artist.setp(tick.gridline,kwargs) def update_units(self, data): """ introspect data for units converter and update the axis.converter instance if necessary. Return True is data is registered for unit conversion """ converter = munits.registry.get_converter(data) if converter is None: return False self.converter = converter default = self.converter.default_units(data) #print 'update units: default="%s", units=%s"'%(default, self.units) if default is not None and self.units is None: self.set_units(default) self._update_axisinfo() return True def _update_axisinfo(self): """ check the axis converter for the stored units to see if the axis info needs to be updated """ if self.converter is None: return info = self.converter.axisinfo(self.units) if info is None: return if info.majloc is not None and self.major.locator!=info.majloc: self.set_major_locator(info.majloc) if info.minloc is not None and self.minor.locator!=info.minloc: self.set_minor_locator(info.minloc) if info.majfmt is not None and self.major.formatter!=info.majfmt: self.set_major_formatter(info.majfmt) if info.minfmt is not None and self.minor.formatter!=info.minfmt: self.set_minor_formatter(info.minfmt) if info.label is not None: label = self.get_label() label.set_text(info.label) def have_units(self): return self.converter is not None or self.units is not None def convert_units(self, x): if self.converter is None: self.converter = munits.registry.get_converter(x) if self.converter is None: #print 'convert_units returning identity: units=%s, converter=%s'%(self.units, self.converter) return x ret = self.converter.convert(x, self.units) #print 'convert_units converting: axis=%s, units=%s, converter=%s, in=%s, out=%s'%(self, self.units, self.converter, x, ret) return ret def set_units(self, u): """ set the units for axis ACCEPTS: a units tag """ pchanged = False if u is None: self.units = None pchanged = True else: if u!=self.units: self.units = u #print 'setting units', self.converter, u, munits.registry.get_converter(u) pchanged = True if pchanged: self._update_axisinfo() self.callbacks.process('units') self.callbacks.process('units finalize') def get_units(self): 'return the units for axis' return self.units def set_major_formatter(self, formatter): """ Set the formatter of the major ticker ACCEPTS: A :class:`~matplotlib.ticker.Formatter` instance """ self.major.formatter = formatter formatter.set_axis(self) def set_minor_formatter(self, formatter): """ Set the formatter of the minor ticker ACCEPTS: A :class:`~matplotlib.ticker.Formatter` instance """ self.minor.formatter = formatter formatter.set_axis(self) def set_major_locator(self, locator): """ Set the locator of the major ticker ACCEPTS: a :class:`~matplotlib.ticker.Locator` instance """ self.major.locator = locator locator.set_axis(self) def set_minor_locator(self, locator): """ Set the locator of the minor ticker ACCEPTS: a :class:`~matplotlib.ticker.Locator` instance """ self.minor.locator = locator locator.set_axis(self) def set_pickradius(self, pickradius): """ Set the depth of the axis used by the picker ACCEPTS: a distance in points """ self.pickradius = pickradius def set_ticklabels(self, ticklabels, args, kwargs): """ Set the text values of the tick labels. Return a list of Text instances. Use kwarg* minor=True to select minor ticks. ACCEPTS: sequence of strings """ #ticklabels = [str(l) for l in ticklabels] minor = kwargs.pop('minor', False) if minor: self.set_minor_formatter(mticker.FixedFormatter(ticklabels)) ticks = self.get_minor_ticks() else: self.set_major_formatter( mticker.FixedFormatter(ticklabels) ) ticks = self.get_major_ticks() self.set_major_formatter( mticker.FixedFormatter(ticklabels) ) ret = [] for i, tick in enumerate(ticks): if i<len(ticklabels): tick.label1.set_text(ticklabels[i]) ret.append(tick.label1) tick.label1.update(kwargs) return ret def set_ticks(self, ticks, minor=False): """ Set the locations of the tick marks from sequence ticks ACCEPTS: sequence of floats """ ### XXX if the user changes units, the information will be lost here ticks = self.convert_units(ticks) if len(ticks) > 1: xleft, xright = self.get_view_interval() if xright > xleft: self.set_view_interval(min(ticks), max(ticks)) else: self.set_view_interval(max(ticks), min(ticks)) if minor: self.set_minor_locator(mticker.FixedLocator(ticks)) return self.get_minor_ticks(len(ticks)) else: self.set_major_locator( mticker.FixedLocator(ticks) ) return self.get_major_ticks(len(ticks)) def _update_label_position(self, bboxes, bboxes2): """ Update the label position based on the sequence of bounding boxes of all the ticklabels """ raise NotImplementedError('Derived must override') def _update_offset_text_postion(self, bboxes, bboxes2): """ Update the label position based on the sequence of bounding boxes of all the ticklabels """ raise NotImplementedError('Derived must override') def pan(self, numsteps): 'Pan numsteps (can be positive or negative)' self.major.locator.pan(numsteps) def zoom(self, direction): "Zoom in/out on axis; if direction is >0 zoom in, else zoom out" self.major.locator.zoom(direction) class XAxis(Axis): __name__ = 'xaxis' axis_name = 'x' def contains(self,mouseevent): """Test whether the mouse event occured in the x axis. """ if callable(self._contains): return self._contains(self,mouseevent) x,y = mouseevent.x,mouseevent.y try: trans = self.axes.transAxes.inverted() xaxes,yaxes = trans.transform_point((x,y)) except ValueError: return False, {} l,b = self.axes.transAxes.transform_point((0,0)) r,t = self.axes.transAxes.transform_point((1,1)) inaxis = xaxes>=0 and xaxes<=1 and ( (y<b and y>b-self.pickradius) or (y>t and y<t+self.pickradius)) return inaxis, {} def _get_tick(self, major): return XTick(self.axes, 0, '', major=major) def _get_label(self): # x in axes coords, y in display coords (to be updated at draw # time by _update_label_positions) label = mtext.Text(x=0.5, y=0, fontproperties = font_manager.FontProperties(size=rcParams['axes.labelsize']), color = rcParams['axes.labelcolor'], verticalalignment='top', horizontalalignment='center', ) label.set_transform( mtransforms.blended_transform_factory( self.axes.transAxes, mtransforms.IdentityTransform() )) self._set_artist_props(label) self.label_position='bottom' return label def _get_offset_text(self): # x in axes coords, y in display coords (to be updated at draw time) offsetText = mtext.Text(x=1, y=0, fontproperties = font_manager.FontProperties(size=rcParams['xtick.labelsize']), color = rcParams['xtick.color'], verticalalignment='top', horizontalalignment='right', ) offsetText.set_transform( mtransforms.blended_transform_factory( self.axes.transAxes, mtransforms.IdentityTransform() )) self._set_artist_props(offsetText) self.offset_text_position='bottom' return offsetText def get_label_position(self): """ Return the label position (top or bottom) """ return self.label_position def set_label_position(self, position): """ Set the label position (top or bottom) ACCEPTS: [ 'top' \| 'bottom' ] """ assert position == 'top' or position == 'bottom' if position == 'top': self.label.set_verticalalignment('bottom') else: self.label.set_verticalalignment('top') self.label_position=position def _update_label_position(self, bboxes, bboxes2): """ Update the label position based on the sequence of bounding boxes of all the ticklabels """ if not self._autolabelpos: return x,y = self.label.get_position() if self.label_position == 'bottom': if not len(bboxes): bottom = self.axes.bbox.ymin else: bbox = mtransforms.Bbox.union(bboxes) bottom = bbox.y0 self.label.set_position( (x, bottom - self.LABELPADself.figure.dpi / 72.0)) else: if not len(bboxes2): top = self.axes.bbox.ymax else: bbox = mtransforms.Bbox.union(bboxes2) top = bbox.y1 self.label.set_position( (x, top+self.LABELPADself.figure.dpi / 72.0)) def _update_offset_text_position(self, bboxes, bboxes2): """ Update the offset_text position based on the sequence of bounding boxes of all the ticklabels """ x,y = self.offsetText.get_position() if not len(bboxes): bottom = self.axes.bbox.ymin else: bbox = mtransforms.Bbox.union(bboxes) bottom = bbox.y0 self.offsetText.set_position((x, bottom-self.OFFSETTEXTPADself.figure.dpi/72.0)) def get_text_heights(self, renderer): """ Returns the amount of space one should reserve for text above and below the axes. Returns a tuple (above, below) """ bbox, bbox2 = self.get_ticklabel_extents(renderer) # MGDTODO: Need a better way to get the pad padPixels = self.majorTicks[0].get_pad_pixels() above = 0.0 if bbox2.height: above += bbox2.height + padPixels below = 0.0 if bbox.height: below += bbox.height + padPixels if self.get_label_position() == 'top': above += self.label.get_window_extent(renderer).height + padPixels else: below += self.label.get_window_extent(renderer).height + padPixels return above, below def set_ticks_position(self, position): """ Set the ticks position (top, bottom, both, default or none) both sets the ticks to appear on both positions, but does not change the tick labels. default resets the tick positions to the default: ticks on both positions, labels at bottom. none can be used if you don't want any ticks. ACCEPTS: [ 'top' \| 'bottom' \| 'both' \| 'default' \| 'none' ] """ assert position in ('top', 'bottom', 'both', 'default', 'none') ticks = list( self.get_major_ticks() ) # a copy ticks.extend( self.get_minor_ticks() ) if position == 'top': for t in ticks: t.tick1On = False t.tick2On = True t.label1On = False t.label2On = True elif position == 'bottom': for t in ticks: t.tick1On = True t.tick2On = False t.label1On = True t.label2On = False elif position == 'default': for t in ticks: t.tick1On = True t.tick2On = True t.label1On = True t.label2On = False elif position == 'none': for t in ticks: t.tick1On = False t.tick2On = False else: for t in ticks: t.tick1On = True t.tick2On = True for t in ticks: t.update_position(t._loc) def tick_top(self): 'use ticks only on top' self.set_ticks_position('top') def tick_bottom(self): 'use ticks only on bottom' self.set_ticks_position('bottom') def get_ticks_position(self): """ Return the ticks position (top, bottom, default or unknown) """ majt=self.majorTicks[0] mT=self.minorTicks[0] majorTop=(not majt.tick1On) and majt.tick2On and (not majt.label1On) and majt.label2On minorTop=(not mT.tick1On) and mT.tick2On and (not mT.label1On) and mT.label2On if majorTop and minorTop: return 'top' MajorBottom=majt.tick1On and (not majt.tick2On) and majt.label1On and (not majt.label2On) MinorBottom=mT.tick1On and (not mT.tick2On) and mT.label1On and (not mT.label2On) if MajorBottom and MinorBottom: return 'bottom' majorDefault=majt.tick1On and majt.tick2On and majt.label1On and (not majt.label2On) minorDefault=mT.tick1On and mT.tick2On and mT.label1On and (not mT.label2On) if majorDefault and minorDefault: return 'default' return 'unknown' def get_view_interval(self): 'return the Interval instance for this axis view limits' return self.axes.viewLim.intervalx def set_view_interval(self, vmin, vmax, ignore=False): if ignore: self.axes.viewLim.intervalx = vmin, vmax else: Vmin, Vmax = self.get_view_interval() self.axes.viewLim.intervalx = min(vmin, Vmin), max(vmax, Vmax) def get_minpos(self): return self.axes.dataLim.minposx def get_data_interval(self): 'return the Interval instance for this axis data limits' return self.axes.dataLim.intervalx def set_data_interval(self, vmin, vmax, ignore=False): 'return the Interval instance for this axis data limits' if ignore: self.axes.dataLim.intervalx = vmin, vmax else: Vmin, Vmax = self.get_data_interval() self.axes.dataLim.intervalx = min(vmin, Vmin), max(vmax, Vmax) class YAxis(Axis): __name__ = 'yaxis' axis_name = 'y' def contains(self,mouseevent): """Test whether the mouse event occurred in the y axis. Returns True* \| False """ if callable(self._contains): return self._contains(self,mouseevent) x,y = mouseevent.x,mouseevent.y try: trans = self.axes.transAxes.inverted() xaxes,yaxes = trans.transform_point((x,y)) except ValueError: return False, {} l,b = self.axes.transAxes.transform_point((0,0)) r,t = self.axes.transAxes.transform_point((1,1)) inaxis = yaxes>=0 and yaxes<=1 and ( (x<l and x>l-self.pickradius) or (x>r and x<r+self.pickradius)) return inaxis, {} def _get_tick(self, major): return YTick(self.axes, 0, '', major=major) def _get_label(self): # x in display coords (updated by _update_label_position) # y in axes coords label = mtext.Text(x=0, y=0.5, # todo: get the label position fontproperties=font_manager.FontProperties(size=rcParams['axes.labelsize']), color = rcParams['axes.labelcolor'], verticalalignment='center', horizontalalignment='right', rotation='vertical', ) label.set_transform( mtransforms.blended_transform_factory( mtransforms.IdentityTransform(), self.axes.transAxes) ) self._set_artist_props(label) self.label_position='left' return label def _get_offset_text(self): # x in display coords, y in axes coords (to be updated at draw time) offsetText = mtext.Text(x=0, y=0.5, fontproperties = font_manager.FontProperties(size=rcParams['ytick.labelsize']), color = rcParams['ytick.color'], verticalalignment = 'bottom', horizontalalignment = 'left', ) offsetText.set_transform(mtransforms.blended_transform_factory( self.axes.transAxes, mtransforms.IdentityTransform()) ) self._set_artist_props(offsetText) self.offset_text_position='left' return offsetText def get_label_position(self): """ Return the label position (left or right) """ return self.label_position def set_label_position(self, position): """ Set the label position (left or right) ACCEPTS: [ 'left' \| 'right' ] """ assert position == 'left' or position == 'right' if position == 'right': self.label.set_horizontalalignment('left') else: self.label.set_horizontalalignment('right') self.label_position=position def _update_label_position(self, bboxes, bboxes2): """ Update the label position based on the sequence of bounding boxes of all the ticklabels """ if not self._autolabelpos: return x,y = self.label.get_position() if self.label_position == 'left': if not len(bboxes): left = self.axes.bbox.xmin else: bbox = mtransforms.Bbox.union(bboxes) left = bbox.x0 self.label.set_position( (left-self.LABELPADself.figure.dpi/72.0, y)) else: if not len(bboxes2): right = self.axes.bbox.xmax else: bbox = mtransforms.Bbox.union(bboxes2) right = bbox.x1 self.label.set_position( (right+self.LABELPADself.figure.dpi/72.0, y)) def _update_offset_text_position(self, bboxes, bboxes2): """ Update the offset_text position based on the sequence of bounding boxes of all the ticklabels """ x,y = self.offsetText.get_position() top = self.axes.bbox.ymax self.offsetText.set_position((x, top+self.OFFSETTEXTPAD*self.figure.dpi/72.0)) def set_offset_position(self, position): assert position == 'left' or position == 'right' x,y = self.offsetText.get_position() if position == 'left': x = 0 else: x = 1 self.offsetText.set_ha(position) self.offsetText.set_position((x,y)) def get_text_widths(self, renderer): bbox, bbox2 = self.get_ticklabel_extents(renderer) # MGDTODO: Need a better way to get the pad padPixels = self.majorTicks[0].get_pad_pixels() left = 0.0 if bbox.width: left += bbox.width + padPixels right = 0.0 if bbox2.width: right += bbox2.width + padPixels if self.get_label_position() == 'left': left += self.label.get_window_extent(renderer).width + padPixels else: right += self.label.get_window_extent(renderer).width + padPixels return left, right def set_ticks_position(self, position): """ Set the ticks position (left, right, both or default) both sets the ticks to appear on both positions, but does not change the tick labels. default resets the tick positions to the default: ticks on both positions, labels on the left. ACCEPTS: [ 'left' \| 'right' \| 'both' \| 'default' \| 'none' ] """ assert position in ('left', 'right', 'both', 'default', 'none') ticks = list( self.get_major_ticks() ) # a copy ticks.extend( self.get_minor_ticks() ) if position == 'right': self.set_offset_position('right') for t in ticks: t.tick1On = False t.tick2On = True t.label1On = False t.label2On = True elif position == 'left': self.set_offset_position('left') for t in ticks: t.tick1On = True t.tick2On = False t.label1On = True t.label2On = False elif position == 'default': self.set_offset_position('left') for t in ticks: t.tick1On = True t.tick2On = True t.label1On = True t.label2On = False elif position == 'none': for t in ticks: t.tick1On = False t.tick2On = False else: self.set_offset_position('left') for t in ticks: t.tick1On = True t.tick2On = True def tick_right(self): 'use ticks only on right' self.set_ticks_position('right') def tick_left(self): 'use ticks only on left' self.set_ticks_position('left') def get_ticks_position(self): """ Return the ticks position (left, right, both or unknown) """ majt=self.majorTicks[0] mT=self.minorTicks[0] majorRight=(not majt.tick1On) and majt.tick2On and (not majt.label1On) and majt.label2On minorRight=(not mT.tick1On) and mT.tick2On and (not mT.label1On) and mT.label2On if majorRight and minorRight: return 'right' majorLeft=majt.tick1On and (not majt.tick2On) and majt.label1On and (not majt.label2On) minorLeft=mT.tick1On and (not mT.tick2On) and mT.label1On and (not mT.label2On) if majorLeft and minorLeft: return 'left' majorDefault=majt.tick1On and majt.tick2On and majt.label1On and (not majt.label2On) minorDefault=mT.tick1On and mT.tick2On and mT.label1On and (not mT.label2On) if majorDefault and minorDefault: return 'default' return 'unknown' def get_view_interval(self): 'return the Interval instance for this axis view limits' return self.axes.viewLim.intervaly def set_view_interval(self, vmin, vmax, ignore=False): if ignore: self.axes.viewLim.intervaly = vmin, vmax else: Vmin, Vmax = self.get_view_interval() self.axes.viewLim.intervaly = min(vmin, Vmin), max(vmax, Vmax) def get_minpos(self): return self.axes.dataLim.minposy def get_data_interval(self): 'return the Interval instance for this axis data limits' return self.axes.dataLim.intervaly def set_data_interval(self, vmin, vmax, ignore=False): 'return the Interval instance for this axis data limits' if ignore: self.axes.dataLim.intervaly = vmin, vmax else: Vmin, Vmax = self.get_data_interval() self.axes.dataLim.intervaly = min(vmin, Vmin), max(vmax, Vmax)	54,453	Python	.py	1,305	31.527969	132	0.604136	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,278	windowing.py	numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/windowing.py	from matplotlib import rcParams try: if not rcParams['tk.window_focus']: raise ImportError from _windowing import GetForegroundWindow, SetForegroundWindow except ImportError: def GetForegroundWindow(): return 0 def SetForegroundWindow(hwnd): pass class FocusManager: def __init__(self): self._shellWindow = GetForegroundWindow() def __del__(self): SetForegroundWindow(self._shellWindow)	454	Python	.py	15	24.8	67	0.715596	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,279	font_manager.py	numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/font_manager.py	""" A module for finding, managing, and using fonts across platforms. This module provides a single :class:`FontManager` instance that can be shared across backends and platforms. The :func:`findfont` function returns the best TrueType (TTF) font file in the local or system font path that matches the specified :class:`FontProperties` instance. The :class:`FontManager` also handles Adobe Font Metrics (AFM) font files for use by the PostScript backend. The design is based on the `W3C Cascading Style Sheet, Level 1 (CSS1) font specification <http://www.w3.org/TR/1998/REC-CSS2-19980512/>`_. Future versions may implement the Level 2 or 2.1 specifications. Experimental support is included for using `fontconfig <http://www.fontconfig.org>`_ on Unix variant plaforms (Linux, OS X, Solaris). To enable it, set the constant ``USE_FONTCONFIG`` in this file to ``True``. Fontconfig has the advantage that it is the standard way to look up fonts on X11 platforms, so if a font is installed, it is much more likely to be found. """ """ KNOWN ISSUES - documentation - font variant is untested - font stretch is incomplete - font size is incomplete - font size_adjust is incomplete - default font algorithm needs improvement and testing - setWeights function needs improvement - 'light' is an invalid weight value, remove it. - update_fonts not implemented Authors : John Hunter <jdhunter@ace.bsd.uchicago.edu> Paul Barrett <Barrett@STScI.Edu> Michael Droettboom <mdroe@STScI.edu> Copyright : John Hunter (2004,2005), Paul Barrett (2004,2005) License : matplotlib license (PSF compatible) The font directory code is from ttfquery, see license/LICENSE_TTFQUERY. """ import os, sys, glob try: set except NameError: from sets import Set as set import matplotlib from matplotlib import afm from matplotlib import ft2font from matplotlib import rcParams, get_configdir from matplotlib.cbook import is_string_like from matplotlib.fontconfig_pattern import \ parse_fontconfig_pattern, generate_fontconfig_pattern try: import cPickle as pickle except ImportError: import pickle USE_FONTCONFIG = False verbose = matplotlib.verbose font_scalings = { 'xx-small' : 0.579, 'x-small' : 0.694, 'small' : 0.833, 'medium' : 1.0, 'large' : 1.200, 'x-large' : 1.440, 'xx-large' : 1.728, 'larger' : 1.2, 'smaller' : 0.833, None : 1.0} stretch_dict = { 'ultra-condensed' : 100, 'extra-condensed' : 200, 'condensed' : 300, 'semi-condensed' : 400, 'normal' : 500, 'semi-expanded' : 600, 'expanded' : 700, 'extra-expanded' : 800, 'ultra-expanded' : 900} weight_dict = { 'ultralight' : 100, 'light' : 200, 'normal' : 400, 'regular' : 400, 'book' : 400, 'medium' : 500, 'roman' : 500, 'semibold' : 600, 'demibold' : 600, 'demi' : 600, 'bold' : 700, 'heavy' : 800, 'extra bold' : 800, 'black' : 900} font_family_aliases = set([ 'serif', 'sans-serif', 'cursive', 'fantasy', 'monospace', 'sans']) # OS Font paths MSFolders = \ r'Software\Microsoft\Windows\CurrentVersion\Explorer\Shell Folders' MSFontDirectories = [ r'SOFTWARE\Microsoft\Windows NT\CurrentVersion\Fonts', r'SOFTWARE\Microsoft\Windows\CurrentVersion\Fonts'] X11FontDirectories = [ # an old standard installation point "/usr/X11R6/lib/X11/fonts/TTF/", # here is the new standard location for fonts "/usr/share/fonts/", # documented as a good place to install new fonts "/usr/local/share/fonts/", # common application, not really useful "/usr/lib/openoffice/share/fonts/truetype/", ] OSXFontDirectories = [ "/Library/Fonts/", "/Network/Library/Fonts/", "/System/Library/Fonts/" ] if not USE_FONTCONFIG: home = os.environ.get('HOME') if home is not None: # user fonts on OSX path = os.path.join(home, 'Library', 'Fonts') OSXFontDirectories.append(path) path = os.path.join(home, '.fonts') X11FontDirectories.append(path) def get_fontext_synonyms(fontext): """ Return a list of file extensions extensions that are synonyms for the given file extension fileext. """ return {'ttf': ('ttf', 'otf'), 'otf': ('ttf', 'otf'), 'afm': ('afm',)}[fontext] def win32FontDirectory(): """ Return the user-specified font directory for Win32. This is looked up from the registry key:: \\HKEY_CURRENT_USER\Software\Microsoft\Windows\CurrentVersion\Explorer\Shell Folders\Fonts If the key is not found, $WINDIR/Fonts will be returned. """ try: import _winreg except ImportError: pass # Fall through to default else: try: user = _winreg.OpenKey(_winreg.HKEY_CURRENT_USER, MSFolders) try: try: return _winreg.QueryValueEx(user, 'Fonts')[0] except OSError: pass # Fall through to default finally: _winreg.CloseKey(user) except OSError: pass # Fall through to default return os.path.join(os.environ['WINDIR'], 'Fonts') def win32InstalledFonts(directory=None, fontext='ttf'): """ Search for fonts in the specified font directory, or use the system directories if none given. A list of TrueType font filenames are returned by default, or AFM fonts if fontext == 'afm'. """ import _winreg if directory is None: directory = win32FontDirectory() fontext = get_fontext_synonyms(fontext) key, items = None, {} for fontdir in MSFontDirectories: try: local = _winreg.OpenKey(_winreg.HKEY_LOCAL_MACHINE, fontdir) except OSError: continue if not local: files = [] for ext in fontext: files.extend(glob.glob(os.path.join(directory, '.'+ext))) return files try: for j in range(_winreg.QueryInfoKey(local)[1]): try: key, direc, any = _winreg.EnumValue( local, j) if not os.path.dirname(direc): direc = os.path.join(directory, direc) direc = os.path.abspath(direc).lower() if os.path.splitext(direc)[1][1:] in fontext: items[direc] = 1 except EnvironmentError: continue except WindowsError: continue return items.keys() finally: _winreg.CloseKey(local) return None def OSXFontDirectory(): """ Return the system font directories for OS X. This is done by starting at the list of hardcoded paths in :attr:`OSXFontDirectories` and returning all nested directories within them. """ fontpaths = [] def add(arg,directory,files): fontpaths.append(directory) for fontdir in OSXFontDirectories: try: if os.path.isdir(fontdir): os.path.walk(fontdir, add, None) except (IOError, OSError, TypeError, ValueError): pass return fontpaths def OSXInstalledFonts(directory=None, fontext='ttf'): """ Get list of font files on OS X - ignores font suffix by default. """ if directory is None: directory = OSXFontDirectory() fontext = get_fontext_synonyms(fontext) files = [] for path in directory: if fontext is None: files.extend(glob.glob(os.path.join(path,''))) else: for ext in fontext: files.extend(glob.glob(os.path.join(path, '.'+ext))) files.extend(glob.glob(os.path.join(path, '.'+ext.upper()))) return files def x11FontDirectory(): """ Return the system font directories for X11. This is done by starting at the list of hardcoded paths in :attr:`X11FontDirectories` and returning all nested directories within them. """ fontpaths = [] def add(arg,directory,files): fontpaths.append(directory) for fontdir in X11FontDirectories: try: if os.path.isdir(fontdir): os.path.walk(fontdir, add, None) except (IOError, OSError, TypeError, ValueError): pass return fontpaths def get_fontconfig_fonts(fontext='ttf'): """ Grab a list of all the fonts that are being tracked by fontconfig by making a system call to ``fc-list``. This is an easy way to grab all of the fonts the user wants to be made available to applications, without needing knowing where all of them reside. """ try: import commands except ImportError: return {} fontext = get_fontext_synonyms(fontext) fontfiles = {} status, output = commands.getstatusoutput("fc-list file") if status == 0: for line in output.split('\n'): fname = line.split(':')[0] if (os.path.splitext(fname)[1][1:] in fontext and os.path.exists(fname)): fontfiles[fname] = 1 return fontfiles def findSystemFonts(fontpaths=None, fontext='ttf'): """ Search for fonts in the specified font paths. If no paths are given, will use a standard set of system paths, as well as the list of fonts tracked by fontconfig if fontconfig is installed and available. A list of TrueType fonts are returned by default with AFM fonts as an option. """ fontfiles = {} fontexts = get_fontext_synonyms(fontext) if fontpaths is None: if sys.platform == 'win32': fontdir = win32FontDirectory() fontpaths = [fontdir] # now get all installed fonts directly... for f in win32InstalledFonts(fontdir): base, ext = os.path.splitext(f) if len(ext)>1 and ext[1:].lower() in fontexts: fontfiles[f] = 1 else: fontpaths = x11FontDirectory() # check for OS X & load its fonts if present if sys.platform == 'darwin': for f in OSXInstalledFonts(fontext=fontext): fontfiles[f] = 1 for f in get_fontconfig_fonts(fontext): fontfiles[f] = 1 elif isinstance(fontpaths, (str, unicode)): fontpaths = [fontpaths] for path in fontpaths: files = [] for ext in fontexts: files.extend(glob.glob(os.path.join(path, '.'+ext))) files.extend(glob.glob(os.path.join(path, '.'+ext.upper()))) for fname in files: fontfiles[os.path.abspath(fname)] = 1 return [fname for fname in fontfiles.keys() if os.path.exists(fname)] def weight_as_number(weight): """ Return the weight property as a numeric value. String values are converted to their corresponding numeric value. """ if isinstance(weight, str): try: weight = weight_dict[weight.lower()] except KeyError: weight = 400 elif weight in range(100, 1000, 100): pass else: raise ValueError, 'weight not a valid integer' return weight class FontEntry(object): """ A class for storing Font properties. It is used when populating the font lookup dictionary. """ def __init__(self, fname ='', name ='', style ='normal', variant='normal', weight ='normal', stretch='normal', size ='medium', ): self.fname = fname self.name = name self.style = style self.variant = variant self.weight = weight self.stretch = stretch try: self.size = str(float(size)) except ValueError: self.size = size def ttfFontProperty(font): """ A function for populating the :class:`FontKey` by extracting information from the TrueType font file. font is a :class:`FT2Font` instance. """ name = font.family_name # Styles are: italic, oblique, and normal (default) sfnt = font.get_sfnt() sfnt2 = sfnt.get((1,0,0,2)) sfnt4 = sfnt.get((1,0,0,4)) if sfnt2: sfnt2 = sfnt2.lower() else: sfnt2 = '' if sfnt4: sfnt4 = sfnt4.lower() else: sfnt4 = '' if sfnt4.find('oblique') >= 0: style = 'oblique' elif sfnt4.find('italic') >= 0: style = 'italic' elif sfnt2.find('regular') >= 0: style = 'normal' elif font.style_flags & ft2font.ITALIC: style = 'italic' else: style = 'normal' # Variants are: small-caps and normal (default) # !!!! Untested if name.lower() in ['capitals', 'small-caps']: variant = 'small-caps' else: variant = 'normal' # Weights are: 100, 200, 300, 400 (normal: default), 500 (medium), # 600 (semibold, demibold), 700 (bold), 800 (heavy), 900 (black) # lighter and bolder are also allowed. weight = None for w in weight_dict.keys(): if sfnt4.find(w) >= 0: weight = w break if not weight: if font.style_flags & ft2font.BOLD: weight = 700 else: weight = 400 weight = weight_as_number(weight) # Stretch can be absolute and relative # Absolute stretches are: ultra-condensed, extra-condensed, condensed, # semi-condensed, normal, semi-expanded, expanded, extra-expanded, # and ultra-expanded. # Relative stretches are: wider, narrower # Child value is: inherit # !!!! Incomplete if sfnt4.find('narrow') >= 0 or sfnt4.find('condensed') >= 0 or \ sfnt4.find('cond') >= 0: stretch = 'condensed' elif sfnt4.find('demi cond') >= 0: stretch = 'semi-condensed' elif sfnt4.find('wide') >= 0 or sfnt4.find('expanded') >= 0: stretch = 'expanded' else: stretch = 'normal' # Sizes can be absolute and relative. # Absolute sizes are: xx-small, x-small, small, medium, large, x-large, # and xx-large. # Relative sizes are: larger, smaller # Length value is an absolute font size, e.g. 12pt # Percentage values are in 'em's. Most robust specification. # !!!! Incomplete if font.scalable: size = 'scalable' else: size = str(float(font.get_fontsize())) # !!!! Incomplete size_adjust = None return FontEntry(font.fname, name, style, variant, weight, stretch, size) def afmFontProperty(fontpath, font): """ A function for populating a :class:`FontKey` instance by extracting information from the AFM font file. font is a class:`AFM` instance. """ name = font.get_familyname() # Styles are: italic, oblique, and normal (default) if font.get_angle() != 0 or name.lower().find('italic') >= 0: style = 'italic' elif name.lower().find('oblique') >= 0: style = 'oblique' else: style = 'normal' # Variants are: small-caps and normal (default) # !!!! Untested if name.lower() in ['capitals', 'small-caps']: variant = 'small-caps' else: variant = 'normal' # Weights are: 100, 200, 300, 400 (normal: default), 500 (medium), # 600 (semibold, demibold), 700 (bold), 800 (heavy), 900 (black) # lighter and bolder are also allowed. weight = weight_as_number(font.get_weight().lower()) # Stretch can be absolute and relative # Absolute stretches are: ultra-condensed, extra-condensed, condensed, # semi-condensed, normal, semi-expanded, expanded, extra-expanded, # and ultra-expanded. # Relative stretches are: wider, narrower # Child value is: inherit # !!!! Incomplete stretch = 'normal' # Sizes can be absolute and relative. # Absolute sizes are: xx-small, x-small, small, medium, large, x-large, # and xx-large. # Relative sizes are: larger, smaller # Length value is an absolute font size, e.g. 12pt # Percentage values are in 'em's. Most robust specification. # All AFM fonts are apparently scalable. size = 'scalable' # !!!! Incomplete size_adjust = None return FontEntry(fontpath, name, style, variant, weight, stretch, size) def createFontList(fontfiles, fontext='ttf'): """ A function to create a font lookup list. The default is to create a list of TrueType fonts. An AFM font list can optionally be created. """ fontlist = [] # Add fonts from list of known font files. seen = {} for fpath in fontfiles: verbose.report('createFontDict: %s' % (fpath), 'debug') fname = os.path.split(fpath)[1] if fname in seen: continue else: seen[fname] = 1 if fontext == 'afm': try: fh = open(fpath, 'r') except: verbose.report("Could not open font file %s" % fpath) continue try: try: font = afm.AFM(fh) finally: fh.close() except RuntimeError: verbose.report("Could not parse font file %s"%fpath) continue prop = afmFontProperty(fpath, font) else: try: font = ft2font.FT2Font(str(fpath)) except RuntimeError: verbose.report("Could not open font file %s"%fpath) continue except UnicodeError: verbose.report("Cannot handle unicode filenames") #print >> sys.stderr, 'Bad file is', fpath continue try: prop = ttfFontProperty(font) except: continue fontlist.append(prop) return fontlist class FontProperties(object): """ A class for storing and manipulating font properties. The font properties are those described in the `W3C Cascading Style Sheet, Level 1 <http://www.w3.org/TR/1998/REC-CSS2-19980512/>`_ font specification. The six properties are: - family: A list of font names in decreasing order of priority. The items may include a generic font family name, either 'serif', 'sans-serif', 'cursive', 'fantasy', or 'monospace'. In that case, the actual font to be used will be looked up from the associated rcParam in :file:`matplotlibrc`. - style: Either 'normal', 'italic' or 'oblique'. - variant: Either 'normal' or 'small-caps'. - stretch: A numeric value in the range 0-1000 or one of 'ultra-condensed', 'extra-condensed', 'condensed', 'semi-condensed', 'normal', 'semi-expanded', 'expanded', 'extra-expanded' or 'ultra-expanded' - weight: A numeric value in the range 0-1000 or one of 'ultralight', 'light', 'normal', 'regular', 'book', 'medium', 'roman', 'semibold', 'demibold', 'demi', 'bold', 'heavy', 'extra bold', 'black' - size: Either an relative value of 'xx-small', 'x-small', 'small', 'medium', 'large', 'x-large', 'xx-large' or an absolute font size, e.g. 12 The default font property for TrueType fonts (as specified in the default :file:`matplotlibrc` file) is:: sans-serif, normal, normal, normal, normal, scalable. Alternatively, a font may be specified using an absolute path to a .ttf file, by using the fname kwarg. The preferred usage of font sizes is to use the relative values, e.g. 'large', instead of absolute font sizes, e.g. 12. This approach allows all text sizes to be made larger or smaller based on the font manager's default font size, i.e. by using the :meth:`FontManager.set_default_size` method. This class will also accept a `fontconfig <http://www.fontconfig.org/>`_ pattern, if it is the only argument provided. See the documentation on `fontconfig patterns <http://www.fontconfig.org/fontconfig-user.html>`_. This support does not require fontconfig to be installed. We are merely borrowing its pattern syntax for use here. Note that matplotlib's internal font manager and fontconfig use a different algorithm to lookup fonts, so the results of the same pattern may be different in matplotlib than in other applications that use fontconfig. """ def __init__(self, family = None, style = None, variant= None, weight = None, stretch= None, size = None, fname = None, # if this is set, it's a hardcoded filename to use _init = None # used only by copy() ): self._family = None self._slant = None self._variant = None self._weight = None self._stretch = None self._size = None self._file = None # This is used only by copy() if _init is not None: self.__dict__.update(_init.__dict__) return if is_string_like(family): # Treat family as a fontconfig pattern if it is the only # parameter provided. if (style is None and variant is None and weight is None and stretch is None and size is None and fname is None): self.set_fontconfig_pattern(family) return self.set_family(family) self.set_style(style) self.set_variant(variant) self.set_weight(weight) self.set_stretch(stretch) self.set_file(fname) self.set_size(size) def _parse_fontconfig_pattern(self, pattern): return parse_fontconfig_pattern(pattern) def __hash__(self): l = self.__dict__.items() l.sort() return hash(repr(l)) def __str__(self): return self.get_fontconfig_pattern() def get_family(self): """ Return a list of font names that comprise the font family. """ if self._family is None: family = rcParams['font.family'] if is_string_like(family): return [family] return family return self._family def get_name(self): """ Return the name of the font that best matches the font properties. """ return ft2font.FT2Font(str(findfont(self))).family_name def get_style(self): """ Return the font style. Values are: 'normal', 'italic' or 'oblique'. """ if self._slant is None: return rcParams['font.style'] return self._slant get_slant = get_style def get_variant(self): """ Return the font variant. Values are: 'normal' or 'small-caps'. """ if self._variant is None: return rcParams['font.variant'] return self._variant def get_weight(self): """ Set the font weight. Options are: A numeric value in the range 0-1000 or one of 'light', 'normal', 'regular', 'book', 'medium', 'roman', 'semibold', 'demibold', 'demi', 'bold', 'heavy', 'extra bold', 'black' """ if self._weight is None: return rcParams['font.weight'] return self._weight def get_stretch(self): """ Return the font stretch or width. Options are: 'ultra-condensed', 'extra-condensed', 'condensed', 'semi-condensed', 'normal', 'semi-expanded', 'expanded', 'extra-expanded', 'ultra-expanded'. """ if self._stretch is None: return rcParams['font.stretch'] return self._stretch def get_size(self): """ Return the font size. """ if self._size is None: return rcParams['font.size'] return self._size def get_size_in_points(self): if self._size is not None: try: return float(self._size) except ValueError: pass default_size = fontManager.get_default_size() return default_size * font_scalings.get(self._size) def get_file(self): """ Return the filename of the associated font. """ return self._file def get_fontconfig_pattern(self): """ Get a fontconfig pattern suitable for looking up the font as specified with fontconfig's ``fc-match`` utility. See the documentation on `fontconfig patterns <http://www.fontconfig.org/fontconfig-user.html>`_. This support does not require fontconfig to be installed or support for it to be enabled. We are merely borrowing its pattern syntax for use here. """ return generate_fontconfig_pattern(self) def set_family(self, family): """ Change the font family. May be either an alias (generic name is CSS parlance), such as: 'serif', 'sans-serif', 'cursive', 'fantasy', or 'monospace', or a real font name. """ if family is None: self._family = None else: if is_string_like(family): family = [family] self._family = family set_name = set_family def set_style(self, style): """ Set the font style. Values are: 'normal', 'italic' or 'oblique'. """ if style not in ('normal', 'italic', 'oblique', None): raise ValueError("style must be normal, italic or oblique") self._slant = style set_slant = set_style def set_variant(self, variant): """ Set the font variant. Values are: 'normal' or 'small-caps'. """ if variant not in ('normal', 'small-caps', None): raise ValueError("variant must be normal or small-caps") self._variant = variant def set_weight(self, weight): """ Set the font weight. May be either a numeric value in the range 0-1000 or one of 'ultralight', 'light', 'normal', 'regular', 'book', 'medium', 'roman', 'semibold', 'demibold', 'demi', 'bold', 'heavy', 'extra bold', 'black' """ if weight is not None: try: weight = int(weight) if weight < 0 or weight > 1000: raise ValueError() except ValueError: if weight not in weight_dict: raise ValueError("weight is invalid") self._weight = weight def set_stretch(self, stretch): """ Set the font stretch or width. Options are: 'ultra-condensed', 'extra-condensed', 'condensed', 'semi-condensed', 'normal', 'semi-expanded', 'expanded', 'extra-expanded' or 'ultra-expanded', or a numeric value in the range 0-1000. """ if stretch is not None: try: stretch = int(stretch) if stretch < 0 or stretch > 1000: raise ValueError() except ValueError: if stretch not in stretch_dict: raise ValueError("stretch is invalid") self._stretch = stretch def set_size(self, size): """ Set the font size. Either an relative value of 'xx-small', 'x-small', 'small', 'medium', 'large', 'x-large', 'xx-large' or an absolute font size, e.g. 12. """ if size is not None: try: size = float(size) except ValueError: if size is not None and size not in font_scalings: raise ValueError("size is invalid") self._size = size def set_file(self, file): """ Set the filename of the fontfile to use. In this case, all other properties will be ignored. """ self._file = file def set_fontconfig_pattern(self, pattern): """ Set the properties by parsing a fontconfig pattern. See the documentation on `fontconfig patterns <http://www.fontconfig.org/fontconfig-user.html>`_. This support does not require fontconfig to be installed or support for it to be enabled. We are merely borrowing its pattern syntax for use here. """ for key, val in self._parse_fontconfig_pattern(pattern).items(): if type(val) == list: getattr(self, "set_" + key)(val[0]) else: getattr(self, "set_" + key)(val) def copy(self): """Return a deep copy of self""" return FontProperties(_init = self) def ttfdict_to_fnames(d): """ flatten a ttfdict to all the filenames it contains """ fnames = [] for named in d.values(): for styled in named.values(): for variantd in styled.values(): for weightd in variantd.values(): for stretchd in weightd.values(): for fname in stretchd.values(): fnames.append(fname) return fnames def pickle_dump(data, filename): """ Equivalent to pickle.dump(data, open(filename, 'w')) but closes the file to prevent filehandle leakage. """ fh = open(filename, 'w') try: pickle.dump(data, fh) finally: fh.close() def pickle_load(filename): """ Equivalent to pickle.load(open(filename, 'r')) but closes the file to prevent filehandle leakage. """ fh = open(filename, 'r') try: data = pickle.load(fh) finally: fh.close() return data class FontManager: """ On import, the :class:`FontManager` singleton instance creates a list of TrueType fonts based on the font properties: name, style, variant, weight, stretch, and size. The :meth:`findfont` method does a nearest neighbor search to find the font that most closely matches the specification. If no good enough match is found, a default font is returned. """ def __init__(self, size=None, weight='normal'): self.__default_weight = weight self.default_size = size paths = [os.path.join(rcParams['datapath'], 'fonts', 'ttf'), os.path.join(rcParams['datapath'], 'fonts', 'afm')] # Create list of font paths for pathname in ['TTFPATH', 'AFMPATH']: if pathname in os.environ: ttfpath = os.environ[pathname] if ttfpath.find(';') >= 0: #win32 style paths.extend(ttfpath.split(';')) elif ttfpath.find(':') >= 0: # unix style paths.extend(ttfpath.split(':')) else: paths.append(ttfpath) verbose.report('font search path %s'%(str(paths))) # Load TrueType fonts and create font dictionary. self.ttffiles = findSystemFonts(paths) + findSystemFonts() for fname in self.ttffiles: verbose.report('trying fontname %s' % fname, 'debug') if fname.lower().find('vera.ttf')>=0: self.defaultFont = fname break else: # use anything self.defaultFont = self.ttffiles[0] self.ttflist = createFontList(self.ttffiles) if rcParams['pdf.use14corefonts']: # Load only the 14 PDF core fonts. These fonts do not need to be # embedded; every PDF viewing application is required to have them: # Helvetica, Helvetica-Bold, Helvetica-Oblique, Helvetica-BoldOblique, # Courier, Courier-Bold, Courier-Oblique, Courier-BoldOblique, # Times-Roman, Times-Bold, Times-Italic, Times-BoldItalic, Symbol, # ZapfDingbats. afmpath = os.path.join(rcParams['datapath'],'fonts','pdfcorefonts') afmfiles = findSystemFonts(afmpath, fontext='afm') self.afmlist = createFontList(afmfiles, fontext='afm') else: self.afmfiles = findSystemFonts(paths, fontext='afm') + \ findSystemFonts(fontext='afm') self.afmlist = createFontList(self.afmfiles, fontext='afm') self.ttf_lookup_cache = {} self.afm_lookup_cache = {} def get_default_weight(self): """ Return the default font weight. """ return self.__default_weight def get_default_size(self): """ Return the default font size. """ if self.default_size is None: return rcParams['font.size'] return self.default_size def set_default_weight(self, weight): """ Set the default font weight. The initial value is 'normal'. """ self.__default_weight = weight def set_default_size(self, size): """ Set the default font size in points. The initial value is set by ``font.size`` in rc. """ self.default_size = size def update_fonts(self, filenames): """ Update the font dictionary with new font files. Currently not implemented. """ # !!!! Needs implementing raise NotImplementedError # Each of the scoring functions below should return a value between # 0.0 (perfect match) and 1.0 (terrible match) def score_family(self, families, family2): """ Returns a match score between the list of font families in families and the font family name family2. An exact match anywhere in the list returns 0.0. A match by generic font name will return 0.1. No match will return 1.0. """ for i, family1 in enumerate(families): if family1.lower() in font_family_aliases: if family1 == 'sans': family1 == 'sans-serif' options = rcParams['font.' + family1] if family2 in options: idx = options.index(family2) return 0.1 * (float(idx) / len(options)) elif family1.lower() == family2.lower(): return 0.0 return 1.0 def score_style(self, style1, style2): """ Returns a match score between style1 and style2. An exact match returns 0.0. A match between 'italic' and 'oblique' returns 0.1. No match returns 1.0. """ if style1 == style2: return 0.0 elif style1 in ('italic', 'oblique') and \ style2 in ('italic', 'oblique'): return 0.1 return 1.0 def score_variant(self, variant1, variant2): """ Returns a match score between variant1 and variant2. An exact match returns 0.0, otherwise 1.0. """ if variant1 == variant2: return 0.0 else: return 1.0 def score_stretch(self, stretch1, stretch2): """ Returns a match score between stretch1 and stretch2. The result is the absolute value of the difference between the CSS numeric values of stretch1 and stretch2, normalized between 0.0 and 1.0. """ try: stretchval1 = int(stretch1) except ValueError: stretchval1 = stretch_dict.get(stretch1, 500) try: stretchval2 = int(stretch2) except ValueError: stretchval2 = stretch_dict.get(stretch2, 500) return abs(stretchval1 - stretchval2) / 1000.0 def score_weight(self, weight1, weight2): """ Returns a match score between weight1 and weight2. The result is the absolute value of the difference between the CSS numeric values of weight1 and weight2, normalized between 0.0 and 1.0. """ try: weightval1 = int(weight1) except ValueError: weightval1 = weight_dict.get(weight1, 500) try: weightval2 = int(weight2) except ValueError: weightval2 = weight_dict.get(weight2, 500) return abs(weightval1 - weightval2) / 1000.0 def score_size(self, size1, size2): """ Returns a match score between size1 and size2. If size2 (the size specified in the font file) is 'scalable', this function always returns 0.0, since any font size can be generated. Otherwise, the result is the absolute distance between size1 and size2, normalized so that the usual range of font sizes (6pt - 72pt) will lie between 0.0 and 1.0. """ if size2 == 'scalable': return 0.0 # Size value should have already been try: sizeval1 = float(size1) except ValueError: sizeval1 = self.default_size * font_scalings(size1) try: sizeval2 = float(size2) except ValueError: return 1.0 return abs(sizeval1 - sizeval2) / 72.0 def findfont(self, prop, fontext='ttf'): """ Search the font list for the font that most closely matches the :class:`FontProperties` prop. :meth:`findfont` performs a nearest neighbor search. Each font is given a similarity score to the target font properties. The first font with the highest score is returned. If no matches below a certain threshold are found, the default font (usually Vera Sans) is returned. The result is cached, so subsequent lookups don't have to perform the O(n) nearest neighbor search. See the `W3C Cascading Style Sheet, Level 1 <http://www.w3.org/TR/1998/REC-CSS2-19980512/>`_ documentation for a description of the font finding algorithm. """ debug = False if prop is None: return self.defaultFont if is_string_like(prop): prop = FontProperties(prop) fname = prop.get_file() if fname is not None: verbose.report('findfont returning %s'%fname, 'debug') return fname if fontext == 'afm': font_cache = self.afm_lookup_cache fontlist = self.afmlist else: font_cache = self.ttf_lookup_cache fontlist = self.ttflist cached = font_cache.get(hash(prop)) if cached: return cached best_score = 1e64 best_font = None for font in fontlist: # Matching family should have highest priority, so it is multiplied # by 10.0 score = \ self.score_family(prop.get_family(), font.name) * 10.0 + \ self.score_style(prop.get_style(), font.style) + \ self.score_variant(prop.get_variant(), font.variant) + \ self.score_weight(prop.get_weight(), font.weight) + \ self.score_stretch(prop.get_stretch(), font.stretch) + \ self.score_size(prop.get_size(), font.size) if score < best_score: best_score = score best_font = font if score == 0: break if best_font is None or best_score >= 10.0: verbose.report('findfont: Could not match %s. Returning %s' % (prop, self.defaultFont)) result = self.defaultFont else: verbose.report('findfont: Matching %s to %s (%s) with score of %f' % (prop, best_font.name, best_font.fname, best_score)) result = best_font.fname font_cache[hash(prop)] = result return result _is_opentype_cff_font_cache = {} def is_opentype_cff_font(filename): """ Returns True if the given font is a Postscript Compact Font Format Font embedded in an OpenType wrapper. Used by the PostScript and PDF backends that can not subset these fonts. """ if os.path.splitext(filename)[1].lower() == '.otf': result = _is_opentype_cff_font_cache.get(filename) if result is None: fd = open(filename, 'rb') tag = fd.read(4) fd.close() result = (tag == 'OTTO') _is_opentype_cff_font_cache[filename] = result return result return False # The experimental fontconfig-based backend. if USE_FONTCONFIG and sys.platform != 'win32': import re def fc_match(pattern, fontext): import commands fontexts = get_fontext_synonyms(fontext) ext = "." + fontext status, output = commands.getstatusoutput('fc-match -sv "%s"' % pattern) if status == 0: for match in _fc_match_regex.finditer(output): file = match.group(1) if os.path.splitext(file)[1][1:] in fontexts: return file return None _fc_match_regex = re.compile(r'\sfile:\s+"([^"])"') _fc_match_cache = {} def findfont(prop, fontext='ttf'): if not is_string_like(prop): prop = prop.get_fontconfig_pattern() cached = _fc_match_cache.get(prop) if cached is not None: return cached result = fc_match(prop, fontext) if result is None: result = fc_match(':', fontext) _fc_match_cache[prop] = result return result else: _fmcache = os.path.join(get_configdir(), 'fontList.cache') fontManager = None def _rebuild(): global fontManager fontManager = FontManager() pickle_dump(fontManager, _fmcache) verbose.report("generated new fontManager") try: fontManager = pickle_load(_fmcache) fontManager.default_size = None verbose.report("Using fontManager instance from %s" % _fmcache) except: _rebuild() def findfont(prop, kw): global fontManager font = fontManager.findfont(prop, kw) if not os.path.exists(font): verbose.report("%s returned by pickled fontManager does not exist" % font) _rebuild() font = fontManager.findfont(prop, *kw) return font	42,655	Python	.py	1,121	29.15165	96	0.595298	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,280	image.py	numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/image.py	""" The image module supports basic image loading, rescaling and display operations. """ from __future__ import division import os, warnings import numpy as np from numpy import ma from matplotlib import rcParams from matplotlib import artist as martist from matplotlib import colors as mcolors from matplotlib import cm # For clarity, names from _image are given explicitly in this module: from matplotlib import _image from matplotlib import _png # For user convenience, the names from _image are also imported into # the image namespace: from matplotlib._image import * class AxesImage(martist.Artist, cm.ScalarMappable): zorder = 1 # map interpolation strings to module constants _interpd = { 'nearest' : _image.NEAREST, 'bilinear' : _image.BILINEAR, 'bicubic' : _image.BICUBIC, 'spline16' : _image.SPLINE16, 'spline36' : _image.SPLINE36, 'hanning' : _image.HANNING, 'hamming' : _image.HAMMING, 'hermite' : _image.HERMITE, 'kaiser' : _image.KAISER, 'quadric' : _image.QUADRIC, 'catrom' : _image.CATROM, 'gaussian' : _image.GAUSSIAN, 'bessel' : _image.BESSEL, 'mitchell' : _image.MITCHELL, 'sinc' : _image.SINC, 'lanczos' : _image.LANCZOS, 'blackman' : _image.BLACKMAN, } # reverse interp dict _interpdr = dict([ (v,k) for k,v in _interpd.items()]) interpnames = _interpd.keys() def __str__(self): return "AxesImage(%g,%g;%gx%g)" % tuple(self.axes.bbox.bounds) def __init__(self, ax, cmap = None, norm = None, interpolation=None, origin=None, extent=None, filternorm=1, filterrad=4.0, resample = False, *kwargs ): """ interpolation and cmap default to their rc settings cmap is a colors.Colormap instance norm is a colors.Normalize instance to map luminance to 0-1 extent is data axes (left, right, bottom, top) for making image plots registered with data plots. Default is to label the pixel centers with the zero-based row and column indices. Additional kwargs are matplotlib.artist properties """ martist.Artist.__init__(self) cm.ScalarMappable.__init__(self, norm, cmap) if origin is None: origin = rcParams['image.origin'] self.origin = origin self._extent = extent self.set_filternorm(filternorm) self.set_filterrad(filterrad) self._filterrad = filterrad self.set_interpolation(interpolation) self.set_resample(resample) self.axes = ax self._imcache = None self.update(kwargs) def get_size(self): 'Get the numrows, numcols of the input image' if self._A is None: raise RuntimeError('You must first set the image array') return self._A.shape[:2] def set_alpha(self, alpha): """ Set the alpha value used for blending - not supported on all backends ACCEPTS: float """ martist.Artist.set_alpha(self, alpha) self._imcache = None def changed(self): """ Call this whenever the mappable is changed so observers can update state """ self._imcache = None self._rgbacache = None cm.ScalarMappable.changed(self) def make_image(self, magnification=1.0): if self._A is None: raise RuntimeError('You must first set the image array or the image attribute') xmin, xmax, ymin, ymax = self.get_extent() dxintv = xmax-xmin dyintv = ymax-ymin # the viewport scale factor sx = dxintv/self.axes.viewLim.width sy = dyintv/self.axes.viewLim.height numrows, numcols = self._A.shape[:2] if sx > 2: x0 = (self.axes.viewLim.x0-xmin)/dxintv numcols ix0 = max(0, int(x0 - self._filterrad)) x1 = (self.axes.viewLim.x1-xmin)/dxintv * numcols ix1 = min(numcols, int(x1 + self._filterrad)) xslice = slice(ix0, ix1) xmin_old = xmin xmin = xmin_old + ix0dxintv/numcols xmax = xmin_old + ix1dxintv/numcols dxintv = xmax - xmin sx = dxintv/self.axes.viewLim.width else: xslice = slice(0, numcols) if sy > 2: y0 = (self.axes.viewLim.y0-ymin)/dyintv * numrows iy0 = max(0, int(y0 - self._filterrad)) y1 = (self.axes.viewLim.y1-ymin)/dyintv * numrows iy1 = min(numrows, int(y1 + self._filterrad)) if self.origin == 'upper': yslice = slice(numrows-iy1, numrows-iy0) else: yslice = slice(iy0, iy1) ymin_old = ymin ymin = ymin_old + iy0dyintv/numrows ymax = ymin_old + iy1dyintv/numrows dyintv = ymax - ymin sy = dyintv/self.axes.viewLim.height else: yslice = slice(0, numrows) if xslice != self._oldxslice or yslice != self._oldyslice: self._imcache = None self._oldxslice = xslice self._oldyslice = yslice if self._imcache is None: if self._A.dtype == np.uint8 and len(self._A.shape) == 3: im = _image.frombyte(self._A[yslice,xslice,:], 0) im.is_grayscale = False else: if self._rgbacache is None: x = self.to_rgba(self._A, self._alpha) self._rgbacache = x else: x = self._rgbacache im = _image.fromarray(x[yslice,xslice], 0) if len(self._A.shape) == 2: im.is_grayscale = self.cmap.is_gray() else: im.is_grayscale = False self._imcache = im if self.origin=='upper': im.flipud_in() else: im = self._imcache fc = self.axes.patch.get_facecolor() bg = mcolors.colorConverter.to_rgba(fc, 0) im.set_bg( bg) # image input dimensions im.reset_matrix() numrows, numcols = im.get_size() im.set_interpolation(self._interpd[self._interpolation]) im.set_resample(self._resample) # the viewport translation tx = (xmin-self.axes.viewLim.x0)/dxintv numcols ty = (ymin-self.axes.viewLim.y0)/dyintv * numrows l, b, r, t = self.axes.bbox.extents widthDisplay = (round(r) + 0.5) - (round(l) - 0.5) heightDisplay = (round(t) + 0.5) - (round(b) - 0.5) widthDisplay = magnification heightDisplay = magnification im.apply_translation(tx, ty) # resize viewport to display rx = widthDisplay / numcols ry = heightDisplay / numrows im.apply_scaling(rxsx, rysy) im.resize(int(widthDisplay+0.5), int(heightDisplay+0.5), norm=self._filternorm, radius=self._filterrad) return im def draw(self, renderer, args, kwargs): if not self.get_visible(): return if (self.axes.get_xscale() != 'linear' or self.axes.get_yscale() != 'linear'): warnings.warn("Images are not supported on non-linear axes.") im = self.make_image(renderer.get_image_magnification()) im._url = self.get_url() l, b, widthDisplay, heightDisplay = self.axes.bbox.bounds clippath, affine = self.get_transformed_clip_path_and_affine() renderer.draw_image(round(l), round(b), im, self.axes.bbox.frozen(), clippath, affine) def contains(self, mouseevent): """Test whether the mouse event occured within the image. """ if callable(self._contains): return self._contains(self,mouseevent) # TODO: make sure this is consistent with patch and patch # collection on nonlinear transformed coordinates. # TODO: consider returning image coordinates (shouldn't # be too difficult given that the image is rectilinear x, y = mouseevent.xdata, mouseevent.ydata xmin, xmax, ymin, ymax = self.get_extent() if xmin > xmax: xmin,xmax = xmax,xmin if ymin > ymax: ymin,ymax = ymax,ymin #print x, y, xmin, xmax, ymin, ymax if x is not None and y is not None: inside = x>=xmin and x<=xmax and y>=ymin and y<=ymax else: inside = False return inside,{} def write_png(self, fname, noscale=False): """Write the image to png file with fname""" im = self.make_image() if noscale: numrows, numcols = im.get_size() im.reset_matrix() im.set_interpolation(0) im.resize(numcols, numrows) im.flipud_out() rows, cols, buffer = im.as_rgba_str() _png.write_png(buffer, cols, rows, fname) def set_data(self, A, shape=None): """ Set the image array ACCEPTS: numpy/PIL Image A""" # check if data is PIL Image without importing Image if hasattr(A,'getpixel'): self._A = pil_to_array(A) elif ma.isMA(A): self._A = A else: self._A = np.asarray(A) # assume array if self._A.dtype != np.uint8 and not np.can_cast(self._A.dtype, np.float): raise TypeError("Image data can not convert to float") if (self._A.ndim not in (2, 3) or (self._A.ndim == 3 and self._A.shape[-1] not in (3, 4))): raise TypeError("Invalid dimensions for image data") self._imcache =None self._rgbacache = None self._oldxslice = None self._oldyslice = None def set_array(self, A): """ retained for backwards compatibility - use set_data instead ACCEPTS: numpy array A or PIL Image""" # This also needs to be here to override the inherited # cm.ScalarMappable.set_array method so it is not invoked # by mistake. self.set_data(A) def set_extent(self, extent): """extent is data axes (left, right, bottom, top) for making image plots """ self._extent = extent xmin, xmax, ymin, ymax = extent corners = (xmin, ymin), (xmax, ymax) self.axes.update_datalim(corners) if self.axes._autoscaleon: self.axes.set_xlim((xmin, xmax)) self.axes.set_ylim((ymin, ymax)) def get_interpolation(self): """ Return the interpolation method the image uses when resizing. One of 'nearest', 'bilinear', 'bicubic', 'spline16', 'spline36', 'hanning', 'hamming', 'hermite', 'kaiser', 'quadric', 'catrom', 'gaussian', 'bessel', 'mitchell', 'sinc', 'lanczos', """ return self._interpolation def set_interpolation(self, s): """ Set the interpolation method the image uses when resizing. ACCEPTS: ['nearest' \| 'bilinear' \| 'bicubic' \| 'spline16' \| 'spline36' \| 'hanning' \| 'hamming' \| 'hermite' \| 'kaiser' \| 'quadric' \| 'catrom' \| 'gaussian' \| 'bessel' \| 'mitchell' \| 'sinc' \| 'lanczos' \| ] """ if s is None: s = rcParams['image.interpolation'] s = s.lower() if s not in self._interpd: raise ValueError('Illegal interpolation string') self._interpolation = s def set_resample(self, v): if v is None: v = rcParams['image.resample'] self._resample = v def get_interpolation(self): return self._resample def get_extent(self): 'get the image extent: left, right, bottom, top' if self._extent is not None: return self._extent else: sz = self.get_size() #print 'sz', sz numrows, numcols = sz if self.origin == 'upper': return (-0.5, numcols-0.5, numrows-0.5, -0.5) else: return (-0.5, numcols-0.5, -0.5, numrows-0.5) def set_filternorm(self, filternorm): """Set whether the resize filter norms the weights -- see help for imshow ACCEPTS: 0 or 1 """ if filternorm: self._filternorm = 1 else: self._filternorm = 0 def get_filternorm(self): 'return the filternorm setting' return self._filternorm def set_filterrad(self, filterrad): """Set the resize filter radius only applicable to some interpolation schemes -- see help for imshow ACCEPTS: positive float """ r = float(filterrad) assert(r>0) self._filterrad = r def get_filterrad(self): 'return the filterrad setting' return self._filterrad class NonUniformImage(AxesImage): def __init__(self, ax, kwargs ): interp = kwargs.pop('interpolation', 'nearest') AxesImage.__init__(self, ax, kwargs) AxesImage.set_interpolation(self, interp) def make_image(self, magnification=1.0): if self._A is None: raise RuntimeError('You must first set the image array') x0, y0, v_width, v_height = self.axes.viewLim.bounds l, b, r, t = self.axes.bbox.extents width = (round(r) + 0.5) - (round(l) - 0.5) height = (round(t) + 0.5) - (round(b) - 0.5) width = magnification height = magnification im = _image.pcolor(self._Ax, self._Ay, self._A, height, width, (x0, x0+v_width, y0, y0+v_height), self._interpd[self._interpolation]) fc = self.axes.patch.get_facecolor() bg = mcolors.colorConverter.to_rgba(fc, 0) im.set_bg(bg) im.is_grayscale = self.is_grayscale return im def set_data(self, x, y, A): x = np.asarray(x,np.float32) y = np.asarray(y,np.float32) if not ma.isMA(A): A = np.asarray(A) if len(x.shape) != 1 or len(y.shape) != 1\ or A.shape[0:2] != (y.shape[0], x.shape[0]): raise TypeError("Axes don't match array shape") if len(A.shape) not in [2, 3]: raise TypeError("Can only plot 2D or 3D data") if len(A.shape) == 3 and A.shape[2] not in [1, 3, 4]: raise TypeError("3D arrays must have three (RGB) or four (RGBA) color components") if len(A.shape) == 3 and A.shape[2] == 1: A.shape = A.shape[0:2] if len(A.shape) == 2: if A.dtype != np.uint8: A = (self.cmap(self.norm(A))255).astype(np.uint8) self.is_grayscale = self.cmap.is_gray() else: A = np.repeat(A[:,:,np.newaxis], 4, 2) A[:,:,3] = 255 self.is_grayscale = True else: if A.dtype != np.uint8: A = (255A).astype(np.uint8) if A.shape[2] == 3: B = zeros(tuple(list(A.shape[0:2]) + [4]), np.uint8) B[:,:,0:3] = A B[:,:,3] = 255 A = B self.is_grayscale = False self._A = A self._Ax = x self._Ay = y self._imcache = None def set_array(self, args): raise NotImplementedError('Method not supported') def set_interpolation(self, s): if s != None and not s in ('nearest','bilinear'): raise NotImplementedError('Only nearest neighbor and bilinear interpolations are supported') AxesImage.set_interpolation(self, s) def get_extent(self): if self._A is None: raise RuntimeError('Must set data first') return self._Ax[0], self._Ax[-1], self._Ay[0], self._Ay[-1] def set_filternorm(self, s): pass def set_filterrad(self, s): pass def set_norm(self, norm): if self._A is not None: raise RuntimeError('Cannot change colors after loading data') cm.ScalarMappable.set_norm(self, norm) def set_cmap(self, cmap): if self._A is not None: raise RuntimeError('Cannot change colors after loading data') cm.ScalarMappable.set_cmap(self, norm) class PcolorImage(martist.Artist, cm.ScalarMappable): ''' Make a pcolor-style plot with an irregular rectangular grid. This uses a variation of the original irregular image code, and it is used by pcolorfast for the corresponding grid type. ''' def __init__(self, ax, x=None, y=None, A=None, cmap = None, norm = None, kwargs ): """ cmap defaults to its rc setting cmap is a colors.Colormap instance norm is a colors.Normalize instance to map luminance to 0-1 Additional kwargs are matplotlib.artist properties """ martist.Artist.__init__(self) cm.ScalarMappable.__init__(self, norm, cmap) self.axes = ax self._rgbacache = None self.update(kwargs) self.set_data(x, y, A) def make_image(self, magnification=1.0): if self._A is None: raise RuntimeError('You must first set the image array') fc = self.axes.patch.get_facecolor() bg = mcolors.colorConverter.to_rgba(fc, 0) bg = (np.array(bg)255).astype(np.uint8) l, b, r, t = self.axes.bbox.extents width = (round(r) + 0.5) - (round(l) - 0.5) height = (round(t) + 0.5) - (round(b) - 0.5) width = width * magnification height = height * magnification if self.check_update('array'): A = self.to_rgba(self._A, alpha=self._alpha, bytes=True) self._rgbacache = A if self._A.ndim == 2: self.is_grayscale = self.cmap.is_gray() else: A = self._rgbacache vl = self.axes.viewLim im = _image.pcolor2(self._Ax, self._Ay, A, height, width, (vl.x0, vl.x1, vl.y0, vl.y1), bg) im.is_grayscale = self.is_grayscale return im def draw(self, renderer, args, kwargs): if not self.get_visible(): return im = self.make_image(renderer.get_image_magnification()) renderer.draw_image(round(self.axes.bbox.xmin), round(self.axes.bbox.ymin), im, self.axes.bbox.frozen(), self.get_transformed_clip_path_and_affine()) def set_data(self, x, y, A): if not ma.isMA(A): A = np.asarray(A) if x is None: x = np.arange(0, A.shape[1]+1, dtype=np.float64) else: x = np.asarray(x, np.float64).ravel() if y is None: y = np.arange(0, A.shape[0]+1, dtype=np.float64) else: y = np.asarray(y, np.float64).ravel() if A.shape[:2] != (y.size-1, x.size-1): print A.shape print y.size print x.size raise ValueError("Axes don't match array shape") if A.ndim not in [2, 3]: raise ValueError("A must be 2D or 3D") if A.ndim == 3 and A.shape[2] == 1: A.shape = A.shape[:2] self.is_grayscale = False if A.ndim == 3: if A.shape[2] in [3, 4]: if (A[:,:,0] == A[:,:,1]).all() and (A[:,:,0] == A[:,:,2]).all(): self.is_grayscale = True else: raise ValueError("3D arrays must have RGB or RGBA as last dim") self._A = A self._Ax = x self._Ay = y self.update_dict['array'] = True def set_array(self, args): raise NotImplementedError('Method not supported') def set_alpha(self, alpha): """ Set the alpha value used for blending - not supported on all backends ACCEPTS: float """ martist.Artist.set_alpha(self, alpha) self.update_dict['array'] = True class FigureImage(martist.Artist, cm.ScalarMappable): zorder = 1 def __init__(self, fig, cmap = None, norm = None, offsetx = 0, offsety = 0, origin=None, kwargs ): """ cmap is a colors.Colormap instance norm is a colors.Normalize instance to map luminance to 0-1 kwargs are an optional list of Artist keyword args """ martist.Artist.__init__(self) cm.ScalarMappable.__init__(self, norm, cmap) if origin is None: origin = rcParams['image.origin'] self.origin = origin self.figure = fig self.ox = offsetx self.oy = offsety self.update(kwargs) self.magnification = 1.0 def contains(self, mouseevent): """Test whether the mouse event occured within the image. """ if callable(self._contains): return self._contains(self,mouseevent) xmin, xmax, ymin, ymax = self.get_extent() xdata, ydata = mouseevent.x, mouseevent.y #print xdata, ydata, xmin, xmax, ymin, ymax if xdata is not None and ydata is not None: inside = xdata>=xmin and xdata<=xmax and ydata>=ymin and ydata<=ymax else: inside = False return inside,{} def get_size(self): 'Get the numrows, numcols of the input image' if self._A is None: raise RuntimeError('You must first set the image array') return self._A.shape[:2] def get_extent(self): 'get the image extent: left, right, bottom, top' numrows, numcols = self.get_size() return (-0.5+self.ox, numcols-0.5+self.ox, -0.5+self.oy, numrows-0.5+self.oy) def make_image(self, magnification=1.0): if self._A is None: raise RuntimeError('You must first set the image array') x = self.to_rgba(self._A, self._alpha) self.magnification = magnification # if magnification is not one, we need to resize ismag = magnification!=1 #if ismag: raise RuntimeError if ismag: isoutput = 0 else: isoutput = 1 im = _image.fromarray(x, isoutput) fc = self.figure.get_facecolor() im.set_bg( mcolors.colorConverter.to_rgba(fc, 0) ) im.is_grayscale = (self.cmap.name == "gray" and len(self._A.shape) == 2) if ismag: numrows, numcols = self.get_size() numrows = magnification numcols = magnification im.set_interpolation(_image.NEAREST) im.resize(numcols, numrows) if self.origin=='upper': im.flipud_out() return im def draw(self, renderer, args, kwargs): if not self.get_visible(): return # todo: we should be able to do some cacheing here im = self.make_image(renderer.get_image_magnification()) renderer.draw_image(round(self.ox), round(self.oy), im, self.figure.bbox, self.get_transformed_clip_path_and_affine()) def write_png(self, fname): """Write the image to png file with fname""" im = self.make_image() rows, cols, buffer = im.as_rgba_str() _png.write_png(buffer, cols, rows, fname) def imread(fname): """ Return image file in fname as :class:`numpy.array`. Return value is a :class:`numpy.array`. For grayscale images, the return array is MxN. For RGB images, the return value is MxNx3. For RGBA images the return value is MxNx4. matplotlib can only read PNGs natively, but if `PIL <http://www.pythonware.com/products/pil/>`_ is installed, it will use it to load the image and return an array (if possible) which can be used with :func:`~matplotlib.pyplot.imshow`. TODO: support RGB and grayscale return values in _image.readpng """ def pilread(): 'try to load the image with PIL or return None' try: import Image except ImportError: return None image = Image.open( fname ) return pil_to_array(image) handlers = {'png' :_png.read_png, } basename, ext = os.path.splitext(fname) ext = ext.lower()[1:] if ext not in handlers.keys(): im = pilread() if im is None: raise ValueError('Only know how to handle extensions: %s; with PIL installed matplotlib can handle more images' % handlers.keys()) return im handler = handlers[ext] return handler(fname) def pil_to_array( pilImage ): """ load a PIL image and return it as a numpy array of uint8. For grayscale images, the return array is MxN. For RGB images, the return value is MxNx3. For RGBA images the return value is MxNx4 """ def toarray(im): 'return a 1D array of floats' x_str = im.tostring('raw',im.mode,0,-1) x = np.fromstring(x_str,np.uint8) return x if pilImage.mode in ('RGBA', 'RGBX'): im = pilImage # no need to convert images elif pilImage.mode=='L': im = pilImage # no need to luminance images # return MxN luminance array x = toarray(im) x.shape = im.size[1], im.size[0] return x elif pilImage.mode=='RGB': #return MxNx3 RGB array im = pilImage # no need to RGB images x = toarray(im) x.shape = im.size[1], im.size[0], 3 return x else: # try to convert to an rgba image try: im = pilImage.convert('RGBA') except ValueError: raise RuntimeError('Unknown image mode') # return MxNx4 RGBA array x = toarray(im) x.shape = im.size[1], im.size[0], 4 return x def thumbnail(infile, thumbfile, scale=0.1, interpolation='bilinear', preview=False): """ make a thumbnail of image in infile with output filename thumbfile. infile the image file -- must be PNG or PIL readable if you have `PIL <http://www.pythonware.com/products/pil/>`_ installed thumbfile the thumbnail filename scale the scale factor for the thumbnail interpolation the interpolation scheme used in the resampling preview if True, the default backend (presumably a user interface backend) will be used which will cause a figure to be raised if :func:`~matplotlib.pyplot.show` is called. If it is False, a pure image backend will be used depending on the extension, 'png'->FigureCanvasAgg, 'pdf'->FigureCanvasPDF, 'svg'->FigureCanvasSVG See examples/misc/image_thumbnail.py. .. htmlonly:: :ref:`misc-image_thumbnail` Return value is the figure instance containing the thumbnail """ basedir, basename = os.path.split(infile) baseout, extout = os.path.splitext(thumbfile) im = imread(infile) rows, cols, depth = im.shape # this doesn't really matter, it will cancel in the end, but we # need it for the mpl API dpi = 100 height = float(rows)/dpiscale width = float(cols)/dpiscale extension = extout.lower() if preview: # let the UI backend do everything import matplotlib.pyplot as plt fig = plt.figure(figsize=(width, height), dpi=dpi) else: if extension=='.png': from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas elif extension=='.pdf': from matplotlib.backends.backend_pdf import FigureCanvasPDF as FigureCanvas elif extension=='.svg': from matplotlib.backends.backend_svg import FigureCanvasSVG as FigureCanvas else: raise ValueError("Can only handle extensions 'png', 'svg' or 'pdf'") from matplotlib.figure import Figure fig = Figure(figsize=(width, height), dpi=dpi) canvas = FigureCanvas(fig) ax = fig.add_axes([0,0,1,1], aspect='auto', frameon=False, xticks=[], yticks=[]) basename, ext = os.path.splitext(basename) ax.imshow(im, aspect='auto', resample=True, interpolation='bilinear') fig.savefig(thumbfile, dpi=dpi) return fig	28,764	Python	.py	713	30.262272	142	0.574613	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,281	mathtext.py	numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/mathtext.py	r""" :mod:`~matplotlib.mathtext` is a module for parsing a subset of the TeX math syntax and drawing them to a matplotlib backend. For a tutorial of its usage see :ref:`mathtext-tutorial`. This document is primarily concerned with implementation details. The module uses pyparsing_ to parse the TeX expression. .. _pyparsing: http://pyparsing.wikispaces.com/ The Bakoma distribution of the TeX Computer Modern fonts, and STIX fonts are supported. There is experimental support for using arbitrary fonts, but results may vary without proper tweaking and metrics for those fonts. If you find TeX expressions that don't parse or render properly, please email mdroe@stsci.edu, but please check KNOWN ISSUES below first. """ from __future__ import division import os from cStringIO import StringIO from math import ceil try: set except NameError: from sets import Set as set import unicodedata from warnings import warn from numpy import inf, isinf import numpy as np from matplotlib.pyparsing import Combine, Group, Optional, Forward, \ Literal, OneOrMore, ZeroOrMore, ParseException, Empty, \ ParseResults, Suppress, oneOf, StringEnd, ParseFatalException, \ FollowedBy, Regex, ParserElement # Enable packrat parsing ParserElement.enablePackrat() from matplotlib.afm import AFM from matplotlib.cbook import Bunch, get_realpath_and_stat, \ is_string_like, maxdict from matplotlib.ft2font import FT2Font, FT2Image, KERNING_DEFAULT, LOAD_FORCE_AUTOHINT, LOAD_NO_HINTING from matplotlib.font_manager import findfont, FontProperties from matplotlib._mathtext_data import latex_to_bakoma, \ latex_to_standard, tex2uni, latex_to_cmex, stix_virtual_fonts from matplotlib import get_data_path, rcParams import matplotlib.colors as mcolors import matplotlib._png as _png #################### ############################################################################## # FONTS def get_unicode_index(symbol): """get_unicode_index(symbol) -> integer Return the integer index (from the Unicode table) of symbol. symbol can be a single unicode character, a TeX command (i.e. r'\pi'), or a Type1 symbol name (i.e. 'phi'). """ # From UTF #25: U+2212 minus sign is the preferred # representation of the unary and binary minus sign rather than # the ASCII-derived U+002D hyphen-minus, because minus sign is # unambiguous and because it is rendered with a more desirable # length, usually longer than a hyphen. if symbol == '-': return 0x2212 try:# This will succeed if symbol is a single unicode char return ord(symbol) except TypeError: pass try:# Is symbol a TeX symbol (i.e. \alpha) return tex2uni[symbol.strip("\\")] except KeyError: message = """'%(symbol)s' is not a valid Unicode character or TeX/Type1 symbol"""%locals() raise ValueError, message class MathtextBackend(object): """ The base class for the mathtext backend-specific code. The purpose of :class:`MathtextBackend` subclasses is to interface between mathtext and a specific matplotlib graphics backend. Subclasses need to override the following: - :meth:`render_glyph` - :meth:`render_filled_rect` - :meth:`get_results` And optionally, if you need to use a Freetype hinting style: - :meth:`get_hinting_type` """ def __init__(self): self.fonts_object = None def set_canvas_size(self, w, h, d): 'Dimension the drawing canvas' self.width = w self.height = h self.depth = d def render_glyph(self, ox, oy, info): """ Draw a glyph described by info to the reference point (ox, oy). """ raise NotImplementedError() def render_filled_rect(self, x1, y1, x2, y2): """ Draw a filled black rectangle from (x1, y1) to (x2, y2). """ raise NotImplementedError() def get_results(self, box): """ Return a backend-specific tuple to return to the backend after all processing is done. """ raise NotImplementedError() def get_hinting_type(self): """ Get the Freetype hinting type to use with this particular backend. """ return LOAD_NO_HINTING class MathtextBackendBbox(MathtextBackend): """ A backend whose only purpose is to get a precise bounding box. Only required for the Agg backend. """ def __init__(self, real_backend): MathtextBackend.__init__(self) self.bbox = [0, 0, 0, 0] self.real_backend = real_backend def _update_bbox(self, x1, y1, x2, y2): self.bbox = [min(self.bbox[0], x1), min(self.bbox[1], y1), max(self.bbox[2], x2), max(self.bbox[3], y2)] def render_glyph(self, ox, oy, info): self._update_bbox(ox + info.metrics.xmin, oy - info.metrics.ymax, ox + info.metrics.xmax, oy - info.metrics.ymin) def render_rect_filled(self, x1, y1, x2, y2): self._update_bbox(x1, y1, x2, y2) def get_results(self, box): orig_height = box.height orig_depth = box.depth ship(0, 0, box) bbox = self.bbox bbox = [bbox[0] - 1, bbox[1] - 1, bbox[2] + 1, bbox[3] + 1] self._switch_to_real_backend() self.fonts_object.set_canvas_size( bbox[2] - bbox[0], (bbox[3] - bbox[1]) - orig_depth, (bbox[3] - bbox[1]) - orig_height) ship(-bbox[0], -bbox[1], box) return self.fonts_object.get_results(box) def get_hinting_type(self): return self.real_backend.get_hinting_type() def _switch_to_real_backend(self): self.fonts_object.mathtext_backend = self.real_backend self.real_backend.fonts_object = self.fonts_object self.real_backend.ox = self.bbox[0] self.real_backend.oy = self.bbox[1] class MathtextBackendAggRender(MathtextBackend): """ Render glyphs and rectangles to an FTImage buffer, which is later transferred to the Agg image by the Agg backend. """ def __init__(self): self.ox = 0 self.oy = 0 self.image = None MathtextBackend.__init__(self) def set_canvas_size(self, w, h, d): MathtextBackend.set_canvas_size(self, w, h, d) self.image = FT2Image(ceil(w), ceil(h + d)) def render_glyph(self, ox, oy, info): info.font.draw_glyph_to_bitmap( self.image, ox, oy - info.metrics.ymax, info.glyph) def render_rect_filled(self, x1, y1, x2, y2): height = max(int(y2 - y1) - 1, 0) if height == 0: center = (y2 + y1) / 2.0 y = int(center - (height + 1) / 2.0) else: y = int(y1) self.image.draw_rect_filled(int(x1), y, ceil(x2), y + height) def get_results(self, box): return (self.ox, self.oy, self.width, self.height + self.depth, self.depth, self.image, self.fonts_object.get_used_characters()) def get_hinting_type(self): return LOAD_FORCE_AUTOHINT def MathtextBackendAgg(): return MathtextBackendBbox(MathtextBackendAggRender()) class MathtextBackendBitmapRender(MathtextBackendAggRender): def get_results(self, box): return self.image, self.depth def MathtextBackendBitmap(): """ A backend to generate standalone mathtext images. No additional matplotlib backend is required. """ return MathtextBackendBbox(MathtextBackendBitmapRender()) class MathtextBackendPs(MathtextBackend): """ Store information to write a mathtext rendering to the PostScript backend. """ def __init__(self): self.pswriter = StringIO() self.lastfont = None def render_glyph(self, ox, oy, info): oy = self.height - oy + info.offset postscript_name = info.postscript_name fontsize = info.fontsize symbol_name = info.symbol_name if (postscript_name, fontsize) != self.lastfont: ps = """/%(postscript_name)s findfont %(fontsize)s scalefont setfont """ % locals() self.lastfont = postscript_name, fontsize self.pswriter.write(ps) ps = """%(ox)f %(oy)f moveto /%(symbol_name)s glyphshow\n """ % locals() self.pswriter.write(ps) def render_rect_filled(self, x1, y1, x2, y2): ps = "%f %f %f %f rectfill\n" % (x1, self.height - y2, x2 - x1, y2 - y1) self.pswriter.write(ps) def get_results(self, box): ship(0, -self.depth, box) #print self.depth return (self.width, self.height + self.depth, self.depth, self.pswriter, self.fonts_object.get_used_characters()) class MathtextBackendPdf(MathtextBackend): """ Store information to write a mathtext rendering to the PDF backend. """ def __init__(self): self.glyphs = [] self.rects = [] def render_glyph(self, ox, oy, info): filename = info.font.fname oy = self.height - oy + info.offset self.glyphs.append( (ox, oy, filename, info.fontsize, info.num, info.symbol_name)) def render_rect_filled(self, x1, y1, x2, y2): self.rects.append((x1, self.height - y2, x2 - x1, y2 - y1)) def get_results(self, box): ship(0, -self.depth, box) return (self.width, self.height + self.depth, self.depth, self.glyphs, self.rects, self.fonts_object.get_used_characters()) class MathtextBackendSvg(MathtextBackend): """ Store information to write a mathtext rendering to the SVG backend. """ def __init__(self): self.svg_glyphs = [] self.svg_rects = [] def render_glyph(self, ox, oy, info): oy = self.height - oy + info.offset thetext = unichr(info.num) self.svg_glyphs.append( (info.font, info.fontsize, thetext, ox, oy, info.metrics)) def render_rect_filled(self, x1, y1, x2, y2): self.svg_rects.append( (x1, self.height - y1 + 1, x2 - x1, y2 - y1)) def get_results(self, box): ship(0, -self.depth, box) svg_elements = Bunch(svg_glyphs = self.svg_glyphs, svg_rects = self.svg_rects) return (self.width, self.height + self.depth, self.depth, svg_elements, self.fonts_object.get_used_characters()) class MathtextBackendCairo(MathtextBackend): """ Store information to write a mathtext rendering to the Cairo backend. """ def __init__(self): self.glyphs = [] self.rects = [] def render_glyph(self, ox, oy, info): oy = oy - info.offset - self.height thetext = unichr(info.num) self.glyphs.append( (info.font, info.fontsize, thetext, ox, oy)) def render_rect_filled(self, x1, y1, x2, y2): self.rects.append( (x1, y1 - self.height, x2 - x1, y2 - y1)) def get_results(self, box): ship(0, -self.depth, box) return (self.width, self.height + self.depth, self.depth, self.glyphs, self.rects) class Fonts(object): """ An abstract base class for a system of fonts to use for mathtext. The class must be able to take symbol keys and font file names and return the character metrics. It also delegates to a backend class to do the actual drawing. """ def __init__(self, default_font_prop, mathtext_backend): """ default_font_prop: A :class:`~matplotlib.font_manager.FontProperties` object to use for the default non-math font, or the base font for Unicode (generic) font rendering. mathtext_backend: A subclass of :class:`MathTextBackend` used to delegate the actual rendering. """ self.default_font_prop = default_font_prop self.mathtext_backend = mathtext_backend # Make these classes doubly-linked self.mathtext_backend.fonts_object = self self.used_characters = {} def destroy(self): """ Fix any cyclical references before the object is about to be destroyed. """ self.used_characters = None def get_kern(self, font1, fontclass1, sym1, fontsize1, font2, fontclass2, sym2, fontsize2, dpi): """ Get the kerning distance for font between sym1 and sym2. fontX: one of the TeX font names:: tt, it, rm, cal, sf, bf or default (non-math) fontclassX: TODO symX: a symbol in raw TeX form. e.g. '1', 'x' or '\sigma' fontsizeX: the fontsize in points dpi: the current dots-per-inch """ return 0. def get_metrics(self, font, font_class, sym, fontsize, dpi): """ font: one of the TeX font names:: tt, it, rm, cal, sf, bf or default (non-math) font_class: TODO sym: a symbol in raw TeX form. e.g. '1', 'x' or '\sigma' fontsize: font size in points dpi: current dots-per-inch Returns an object with the following attributes: - advance: The advance distance (in points) of the glyph. - height: The height of the glyph in points. - width: The width of the glyph in points. - xmin, xmax, ymin, ymax - the ink rectangle of the glyph - iceberg - the distance from the baseline to the top of the glyph. This corresponds to TeX's definition of "height". """ info = self._get_info(font, font_class, sym, fontsize, dpi) return info.metrics def set_canvas_size(self, w, h, d): """ Set the size of the buffer used to render the math expression. Only really necessary for the bitmap backends. """ self.width, self.height, self.depth = ceil(w), ceil(h), ceil(d) self.mathtext_backend.set_canvas_size(self.width, self.height, self.depth) def render_glyph(self, ox, oy, facename, font_class, sym, fontsize, dpi): """ Draw a glyph at - ox, oy: position - facename: One of the TeX face names - font_class: - sym: TeX symbol name or single character - fontsize: fontsize in points - dpi: The dpi to draw at. """ info = self._get_info(facename, font_class, sym, fontsize, dpi) realpath, stat_key = get_realpath_and_stat(info.font.fname) used_characters = self.used_characters.setdefault( stat_key, (realpath, set())) used_characters[1].add(info.num) self.mathtext_backend.render_glyph(ox, oy, info) def render_rect_filled(self, x1, y1, x2, y2): """ Draw a filled rectangle from (x1, y1) to (x2, y2). """ self.mathtext_backend.render_rect_filled(x1, y1, x2, y2) def get_xheight(self, font, fontsize, dpi): """ Get the xheight for the given font and fontsize. """ raise NotImplementedError() def get_underline_thickness(self, font, fontsize, dpi): """ Get the line thickness that matches the given font. Used as a base unit for drawing lines such as in a fraction or radical. """ raise NotImplementedError() def get_used_characters(self): """ Get the set of characters that were used in the math expression. Used by backends that need to subset fonts so they know which glyphs to include. """ return self.used_characters def get_results(self, box): """ Get the data needed by the backend to render the math expression. The return value is backend-specific. """ return self.mathtext_backend.get_results(box) def get_sized_alternatives_for_symbol(self, fontname, sym): """ Override if your font provides multiple sizes of the same symbol. Should return a list of symbols matching sym in various sizes. The expression renderer will select the most appropriate size for a given situation from this list. """ return [(fontname, sym)] class TruetypeFonts(Fonts): """ A generic base class for all font setups that use Truetype fonts (through FT2Font). """ class CachedFont: def __init__(self, font): self.font = font self.charmap = font.get_charmap() self.glyphmap = dict( [(glyphind, ccode) for ccode, glyphind in self.charmap.iteritems()]) def __repr__(self): return repr(self.font) def __init__(self, default_font_prop, mathtext_backend): Fonts.__init__(self, default_font_prop, mathtext_backend) self.glyphd = {} self._fonts = {} filename = findfont(default_font_prop) default_font = self.CachedFont(FT2Font(str(filename))) self._fonts['default'] = default_font def destroy(self): self.glyphd = None Fonts.destroy(self) def _get_font(self, font): if font in self.fontmap: basename = self.fontmap[font] else: basename = font cached_font = self._fonts.get(basename) if cached_font is None: font = FT2Font(basename) cached_font = self.CachedFont(font) self._fonts[basename] = cached_font self._fonts[font.postscript_name] = cached_font self._fonts[font.postscript_name.lower()] = cached_font return cached_font def _get_offset(self, cached_font, glyph, fontsize, dpi): if cached_font.font.postscript_name == 'Cmex10': return glyph.height/64.0/2.0 + 256.0/64.0 * dpi/72.0 return 0. def _get_info(self, fontname, font_class, sym, fontsize, dpi): key = fontname, font_class, sym, fontsize, dpi bunch = self.glyphd.get(key) if bunch is not None: return bunch cached_font, num, symbol_name, fontsize, slanted = \ self._get_glyph(fontname, font_class, sym, fontsize) font = cached_font.font font.set_size(fontsize, dpi) glyph = font.load_char( num, flags=self.mathtext_backend.get_hinting_type()) xmin, ymin, xmax, ymax = [val/64.0 for val in glyph.bbox] offset = self._get_offset(cached_font, glyph, fontsize, dpi) metrics = Bunch( advance = glyph.linearHoriAdvance/65536.0, height = glyph.height/64.0, width = glyph.width/64.0, xmin = xmin, xmax = xmax, ymin = ymin+offset, ymax = ymax+offset, # iceberg is the equivalent of TeX's "height" iceberg = glyph.horiBearingY/64.0 + offset, slanted = slanted ) result = self.glyphd[key] = Bunch( font = font, fontsize = fontsize, postscript_name = font.postscript_name, metrics = metrics, symbol_name = symbol_name, num = num, glyph = glyph, offset = offset ) return result def get_xheight(self, font, fontsize, dpi): cached_font = self._get_font(font) cached_font.font.set_size(fontsize, dpi) pclt = cached_font.font.get_sfnt_table('pclt') if pclt is None: # Some fonts don't store the xHeight, so we do a poor man's xHeight metrics = self.get_metrics(font, 'it', 'x', fontsize, dpi) return metrics.iceberg xHeight = (pclt['xHeight'] / 64.0) * (fontsize / 12.0) * (dpi / 100.0) return xHeight def get_underline_thickness(self, font, fontsize, dpi): # This function used to grab underline thickness from the font # metrics, but that information is just too un-reliable, so it # is now hardcoded. return ((0.75 / 12.0) * fontsize * dpi) / 72.0 def get_kern(self, font1, fontclass1, sym1, fontsize1, font2, fontclass2, sym2, fontsize2, dpi): if font1 == font2 and fontsize1 == fontsize2: info1 = self._get_info(font1, fontclass1, sym1, fontsize1, dpi) info2 = self._get_info(font2, fontclass2, sym2, fontsize2, dpi) font = info1.font return font.get_kerning(info1.num, info2.num, KERNING_DEFAULT) / 64.0 return Fonts.get_kern(self, font1, fontclass1, sym1, fontsize1, font2, fontclass2, sym2, fontsize2, dpi) class BakomaFonts(TruetypeFonts): """ Use the Bakoma TrueType fonts for rendering. Symbols are strewn about a number of font files, each of which has its own proprietary 8-bit encoding. """ _fontmap = { 'cal' : 'cmsy10', 'rm' : 'cmr10', 'tt' : 'cmtt10', 'it' : 'cmmi10', 'bf' : 'cmb10', 'sf' : 'cmss10', 'ex' : 'cmex10' } fontmap = {} def __init__(self, args, kwargs): self._stix_fallback = StixFonts(args, *kwargs) TruetypeFonts.__init__(self, args, *kwargs) if not len(self.fontmap): for key, val in self._fontmap.iteritems(): fullpath = findfont(val) self.fontmap[key] = fullpath self.fontmap[val] = fullpath _slanted_symbols = set(r"\int \oint".split()) def _get_glyph(self, fontname, font_class, sym, fontsize): symbol_name = None if fontname in self.fontmap and sym in latex_to_bakoma: basename, num = latex_to_bakoma[sym] slanted = (basename == "cmmi10") or sym in self._slanted_symbols try: cached_font = self._get_font(basename) except RuntimeError: pass else: symbol_name = cached_font.font.get_glyph_name(num) num = cached_font.glyphmap[num] elif len(sym) == 1: slanted = (fontname == "it") try: cached_font = self._get_font(fontname) except RuntimeError: pass else: num = ord(sym) gid = cached_font.charmap.get(num) if gid is not None: symbol_name = cached_font.font.get_glyph_name( cached_font.charmap[num]) if symbol_name is None: return self._stix_fallback._get_glyph( fontname, font_class, sym, fontsize) return cached_font, num, symbol_name, fontsize, slanted # The Bakoma fonts contain many pre-sized alternatives for the # delimiters. The AutoSizedChar class will use these alternatives # and select the best (closest sized) glyph. _size_alternatives = { '(' : [('rm', '('), ('ex', '\xa1'), ('ex', '\xb3'), ('ex', '\xb5'), ('ex', '\xc3')], ')' : [('rm', ')'), ('ex', '\xa2'), ('ex', '\xb4'), ('ex', '\xb6'), ('ex', '\x21')], '{' : [('cal', '{'), ('ex', '\xa9'), ('ex', '\x6e'), ('ex', '\xbd'), ('ex', '\x28')], '}' : [('cal', '}'), ('ex', '\xaa'), ('ex', '\x6f'), ('ex', '\xbe'), ('ex', '\x29')], # The fourth size of '[' is mysteriously missing from the BaKoMa # font, so I've ommitted it for both '[' and ']' '[' : [('rm', '['), ('ex', '\xa3'), ('ex', '\x68'), ('ex', '\x22')], ']' : [('rm', ']'), ('ex', '\xa4'), ('ex', '\x69'), ('ex', '\x23')], r'\lfloor' : [('ex', '\xa5'), ('ex', '\x6a'), ('ex', '\xb9'), ('ex', '\x24')], r'\rfloor' : [('ex', '\xa6'), ('ex', '\x6b'), ('ex', '\xba'), ('ex', '\x25')], r'\lceil' : [('ex', '\xa7'), ('ex', '\x6c'), ('ex', '\xbb'), ('ex', '\x26')], r'\rceil' : [('ex', '\xa8'), ('ex', '\x6d'), ('ex', '\xbc'), ('ex', '\x27')], r'\langle' : [('ex', '\xad'), ('ex', '\x44'), ('ex', '\xbf'), ('ex', '\x2a')], r'\rangle' : [('ex', '\xae'), ('ex', '\x45'), ('ex', '\xc0'), ('ex', '\x2b')], r'\__sqrt__' : [('ex', '\x70'), ('ex', '\x71'), ('ex', '\x72'), ('ex', '\x73')], r'\backslash': [('ex', '\xb2'), ('ex', '\x2f'), ('ex', '\xc2'), ('ex', '\x2d')], r'/' : [('rm', '/'), ('ex', '\xb1'), ('ex', '\x2e'), ('ex', '\xcb'), ('ex', '\x2c')], r'\widehat' : [('rm', '\x5e'), ('ex', '\x62'), ('ex', '\x63'), ('ex', '\x64')], r'\widetilde': [('rm', '\x7e'), ('ex', '\x65'), ('ex', '\x66'), ('ex', '\x67')], r'<' : [('cal', 'h'), ('ex', 'D')], r'>' : [('cal', 'i'), ('ex', 'E')] } for alias, target in [('\leftparen', '('), ('\rightparent', ')'), ('\leftbrace', '{'), ('\rightbrace', '}'), ('\leftbracket', '['), ('\rightbracket', ']')]: _size_alternatives[alias] = _size_alternatives[target] def get_sized_alternatives_for_symbol(self, fontname, sym): return self._size_alternatives.get(sym, [(fontname, sym)]) class UnicodeFonts(TruetypeFonts): """ An abstract base class for handling Unicode fonts. While some reasonably complete Unicode fonts (such as DejaVu) may work in some situations, the only Unicode font I'm aware of with a complete set of math symbols is STIX. This class will "fallback" on the Bakoma fonts when a required symbol can not be found in the font. """ fontmap = {} use_cmex = True def __init__(self, args, *kwargs): # This must come first so the backend's owner is set correctly if rcParams['mathtext.fallback_to_cm']: self.cm_fallback = BakomaFonts(args, *kwargs) else: self.cm_fallback = None TruetypeFonts.__init__(self, args, *kwargs) if not len(self.fontmap): for texfont in "cal rm tt it bf sf".split(): prop = rcParams['mathtext.' + texfont] font = findfont(prop) self.fontmap[texfont] = font prop = FontProperties('cmex10') font = findfont(prop) self.fontmap['ex'] = font _slanted_symbols = set(r"\int \oint".split()) def _map_virtual_font(self, fontname, font_class, uniindex): return fontname, uniindex def _get_glyph(self, fontname, font_class, sym, fontsize): found_symbol = False if self.use_cmex: uniindex = latex_to_cmex.get(sym) if uniindex is not None: fontname = 'ex' found_symbol = True if not found_symbol: try: uniindex = get_unicode_index(sym) found_symbol = True except ValueError: uniindex = ord('?') warn("No TeX to unicode mapping for '%s'" % sym.encode('ascii', 'backslashreplace'), MathTextWarning) fontname, uniindex = self._map_virtual_font( fontname, font_class, uniindex) # Only characters in the "Letter" class should be italicized in 'it' # mode. Greek capital letters should be Roman. if found_symbol: new_fontname = fontname if fontname == 'it': if uniindex < 0x10000: unistring = unichr(uniindex) if (not unicodedata.category(unistring)[0] == "L" or unicodedata.name(unistring).startswith("GREEK CAPITAL")): new_fontname = 'rm' slanted = (new_fontname == 'it') or sym in self._slanted_symbols found_symbol = False try: cached_font = self._get_font(new_fontname) except RuntimeError: pass else: try: glyphindex = cached_font.charmap[uniindex] found_symbol = True except KeyError: pass if not found_symbol: if self.cm_fallback: warn("Substituting with a symbol from Computer Modern.", MathTextWarning) return self.cm_fallback._get_glyph( fontname, 'it', sym, fontsize) else: if fontname == 'it' and isinstance(self, StixFonts): return self._get_glyph('rm', font_class, sym, fontsize) warn("Font '%s' does not have a glyph for '%s'" % (fontname, sym.encode('ascii', 'backslashreplace')), MathTextWarning) warn("Substituting with a dummy symbol.", MathTextWarning) fontname = 'rm' new_fontname = fontname cached_font = self._get_font(fontname) uniindex = 0xA4 # currency character, for lack of anything better glyphindex = cached_font.charmap[uniindex] slanted = False symbol_name = cached_font.font.get_glyph_name(glyphindex) return cached_font, uniindex, symbol_name, fontsize, slanted def get_sized_alternatives_for_symbol(self, fontname, sym): if self.cm_fallback: return self.cm_fallback.get_sized_alternatives_for_symbol( fontname, sym) return [(fontname, sym)] class StixFonts(UnicodeFonts): """ A font handling class for the STIX fonts. In addition to what UnicodeFonts provides, this class: - supports "virtual fonts" which are complete alpha numeric character sets with different font styles at special Unicode code points, such as "Blackboard". - handles sized alternative characters for the STIXSizeX fonts. """ _fontmap = { 'rm' : 'STIXGeneral', 'it' : 'STIXGeneral:italic', 'bf' : 'STIXGeneral:weight=bold', 'nonunirm' : 'STIXNonUnicode', 'nonuniit' : 'STIXNonUnicode:italic', 'nonunibf' : 'STIXNonUnicode:weight=bold', 0 : 'STIXGeneral', 1 : 'STIXSize1', 2 : 'STIXSize2', 3 : 'STIXSize3', 4 : 'STIXSize4', 5 : 'STIXSize5' } fontmap = {} use_cmex = False cm_fallback = False _sans = False def __init__(self, args, *kwargs): TruetypeFonts.__init__(self, args, *kwargs) if not len(self.fontmap): for key, name in self._fontmap.iteritems(): fullpath = findfont(name) self.fontmap[key] = fullpath self.fontmap[name] = fullpath def _map_virtual_font(self, fontname, font_class, uniindex): # Handle these "fonts" that are actually embedded in # other fonts. mapping = stix_virtual_fonts.get(fontname) if self._sans and mapping is None: mapping = stix_virtual_fonts['sf'] doing_sans_conversion = True else: doing_sans_conversion = False if mapping is not None: if isinstance(mapping, dict): mapping = mapping[font_class] # Binary search for the source glyph lo = 0 hi = len(mapping) while lo < hi: mid = (lo+hi)//2 range = mapping[mid] if uniindex < range[0]: hi = mid elif uniindex <= range[1]: break else: lo = mid + 1 if uniindex >= range[0] and uniindex <= range[1]: uniindex = uniindex - range[0] + range[3] fontname = range[2] elif not doing_sans_conversion: # This will generate a dummy character uniindex = 0x1 fontname = 'it' # Handle private use area glyphs if (fontname in ('it', 'rm', 'bf') and uniindex >= 0xe000 and uniindex <= 0xf8ff): fontname = 'nonuni' + fontname return fontname, uniindex _size_alternatives = {} def get_sized_alternatives_for_symbol(self, fontname, sym): alternatives = self._size_alternatives.get(sym) if alternatives: return alternatives alternatives = [] try: uniindex = get_unicode_index(sym) except ValueError: return [(fontname, sym)] fix_ups = { ord('<'): 0x27e8, ord('>'): 0x27e9 } uniindex = fix_ups.get(uniindex, uniindex) for i in range(6): cached_font = self._get_font(i) glyphindex = cached_font.charmap.get(uniindex) if glyphindex is not None: alternatives.append((i, unichr(uniindex))) self._size_alternatives[sym] = alternatives return alternatives class StixSansFonts(StixFonts): """ A font handling class for the STIX fonts (that uses sans-serif characters by default). """ _sans = True class StandardPsFonts(Fonts): """ Use the standard postscript fonts for rendering to backend_ps Unlike the other font classes, BakomaFont and UnicodeFont, this one requires the Ps backend. """ basepath = os.path.join( get_data_path(), 'fonts', 'afm' ) fontmap = { 'cal' : 'pzcmi8a', # Zapf Chancery 'rm' : 'pncr8a', # New Century Schoolbook 'tt' : 'pcrr8a', # Courier 'it' : 'pncri8a', # New Century Schoolbook Italic 'sf' : 'phvr8a', # Helvetica 'bf' : 'pncb8a', # New Century Schoolbook Bold None : 'psyr' # Symbol } def __init__(self, default_font_prop): Fonts.__init__(self, default_font_prop, MathtextBackendPs()) self.glyphd = {} self.fonts = {} filename = findfont(default_font_prop, fontext='afm') default_font = AFM(file(filename, 'r')) default_font.fname = filename self.fonts['default'] = default_font self.pswriter = StringIO() def _get_font(self, font): if font in self.fontmap: basename = self.fontmap[font] else: basename = font cached_font = self.fonts.get(basename) if cached_font is None: fname = os.path.join(self.basepath, basename + ".afm") cached_font = AFM(file(fname, 'r')) cached_font.fname = fname self.fonts[basename] = cached_font self.fonts[cached_font.get_fontname()] = cached_font return cached_font def _get_info (self, fontname, font_class, sym, fontsize, dpi): 'load the cmfont, metrics and glyph with caching' key = fontname, sym, fontsize, dpi tup = self.glyphd.get(key) if tup is not None: return tup # Only characters in the "Letter" class should really be italicized. # This class includes greek letters, so we're ok if (fontname == 'it' and (len(sym) > 1 or not unicodedata.category(unicode(sym)).startswith("L"))): fontname = 'rm' found_symbol = False if sym in latex_to_standard: fontname, num = latex_to_standard[sym] glyph = chr(num) found_symbol = True elif len(sym) == 1: glyph = sym num = ord(glyph) found_symbol = True else: warn("No TeX to built-in Postscript mapping for '%s'" % sym, MathTextWarning) slanted = (fontname == 'it') font = self._get_font(fontname) if found_symbol: try: symbol_name = font.get_name_char(glyph) except KeyError: warn("No glyph in standard Postscript font '%s' for '%s'" % (font.postscript_name, sym), MathTextWarning) found_symbol = False if not found_symbol: glyph = sym = '?' num = ord(glyph) symbol_name = font.get_name_char(glyph) offset = 0 scale = 0.001 fontsize xmin, ymin, xmax, ymax = [val * scale for val in font.get_bbox_char(glyph)] metrics = Bunch( advance = font.get_width_char(glyph) * scale, width = font.get_width_char(glyph) * scale, height = font.get_height_char(glyph) * scale, xmin = xmin, xmax = xmax, ymin = ymin+offset, ymax = ymax+offset, # iceberg is the equivalent of TeX's "height" iceberg = ymax + offset, slanted = slanted ) self.glyphd[key] = Bunch( font = font, fontsize = fontsize, postscript_name = font.get_fontname(), metrics = metrics, symbol_name = symbol_name, num = num, glyph = glyph, offset = offset ) return self.glyphd[key] def get_kern(self, font1, fontclass1, sym1, fontsize1, font2, fontclass2, sym2, fontsize2, dpi): if font1 == font2 and fontsize1 == fontsize2: info1 = self._get_info(font1, fontclass1, sym1, fontsize1, dpi) info2 = self._get_info(font2, fontclass2, sym2, fontsize2, dpi) font = info1.font return (font.get_kern_dist(info1.glyph, info2.glyph) * 0.001 * fontsize1) return Fonts.get_kern(self, font1, fontclass1, sym1, fontsize1, font2, fontclass2, sym2, fontsize2, dpi) def get_xheight(self, font, fontsize, dpi): cached_font = self._get_font(font) return cached_font.get_xheight() * 0.001 * fontsize def get_underline_thickness(self, font, fontsize, dpi): cached_font = self._get_font(font) return cached_font.get_underline_thickness() * 0.001 * fontsize ############################################################################## # TeX-LIKE BOX MODEL # The following is based directly on the document 'woven' from the # TeX82 source code. This information is also available in printed # form: # # Knuth, Donald E.. 1986. Computers and Typesetting, Volume B: # TeX: The Program. Addison-Wesley Professional. # # The most relevant "chapters" are: # Data structures for boxes and their friends # Shipping pages out (Ship class) # Packaging (hpack and vpack) # Data structures for math mode # Subroutines for math mode # Typesetting math formulas # # Many of the docstrings below refer to a numbered "node" in that # book, e.g. node123 # # Note that (as TeX) y increases downward, unlike many other parts of # matplotlib. # How much text shrinks when going to the next-smallest level. GROW_FACTOR # must be the inverse of SHRINK_FACTOR. SHRINK_FACTOR = 0.7 GROW_FACTOR = 1.0 / SHRINK_FACTOR # The number of different sizes of chars to use, beyond which they will not # get any smaller NUM_SIZE_LEVELS = 4 # Percentage of x-height of additional horiz. space after sub/superscripts SCRIPT_SPACE = 0.2 # Percentage of x-height that sub/superscripts drop below the baseline SUBDROP = 0.3 # Percentage of x-height that superscripts drop below the baseline SUP1 = 0.5 # Percentage of x-height that subscripts drop below the baseline SUB1 = 0.0 # Percentage of x-height that superscripts are offset relative to the subscript DELTA = 0.18 class MathTextWarning(Warning): pass class Node(object): """ A node in the TeX box model """ def __init__(self): self.size = 0 def __repr__(self): return self.__internal_repr__() def __internal_repr__(self): return self.__class__.__name__ def get_kerning(self, next): return 0.0 def shrink(self): """ Shrinks one level smaller. There are only three levels of sizes, after which things will no longer get smaller. """ self.size += 1 def grow(self): """ Grows one level larger. There is no limit to how big something can get. """ self.size -= 1 def render(self, x, y): pass class Box(Node): """ Represents any node with a physical location. """ def __init__(self, width, height, depth): Node.__init__(self) self.width = width self.height = height self.depth = depth def shrink(self): Node.shrink(self) if self.size < NUM_SIZE_LEVELS: self.width = SHRINK_FACTOR self.height = SHRINK_FACTOR self.depth = SHRINK_FACTOR def grow(self): Node.grow(self) self.width = GROW_FACTOR self.height = GROW_FACTOR self.depth = GROW_FACTOR def render(self, x1, y1, x2, y2): pass class Vbox(Box): """ A box with only height (zero width). """ def __init__(self, height, depth): Box.__init__(self, 0., height, depth) class Hbox(Box): """ A box with only width (zero height and depth). """ def __init__(self, width): Box.__init__(self, width, 0., 0.) class Char(Node): """ Represents a single character. Unlike TeX, the font information and metrics are stored with each :class:`Char` to make it easier to lookup the font metrics when needed. Note that TeX boxes have a width, height, and depth, unlike Type1 and Truetype which use a full bounding box and an advance in the x-direction. The metrics must be converted to the TeX way, and the advance (if different from width) must be converted into a :class:`Kern` node when the :class:`Char` is added to its parent :class:`Hlist`. """ def __init__(self, c, state): Node.__init__(self) self.c = c self.font_output = state.font_output assert isinstance(state.font, (str, unicode, int)) self.font = state.font self.font_class = state.font_class self.fontsize = state.fontsize self.dpi = state.dpi # The real width, height and depth will be set during the # pack phase, after we know the real fontsize self._update_metrics() def __internal_repr__(self): return '`%s`' % self.c def _update_metrics(self): metrics = self._metrics = self.font_output.get_metrics( self.font, self.font_class, self.c, self.fontsize, self.dpi) if self.c == ' ': self.width = metrics.advance else: self.width = metrics.width self.height = metrics.iceberg self.depth = -(metrics.iceberg - metrics.height) def is_slanted(self): return self._metrics.slanted def get_kerning(self, next): """ Return the amount of kerning between this and the given character. Called when characters are strung together into :class:`Hlist` to create :class:`Kern` nodes. """ advance = self._metrics.advance - self.width kern = 0. if isinstance(next, Char): kern = self.font_output.get_kern( self.font, self.font_class, self.c, self.fontsize, next.font, next.font_class, next.c, next.fontsize, self.dpi) return advance + kern def render(self, x, y): """ Render the character to the canvas """ self.font_output.render_glyph( x, y, self.font, self.font_class, self.c, self.fontsize, self.dpi) def shrink(self): Node.shrink(self) if self.size < NUM_SIZE_LEVELS: self.fontsize = SHRINK_FACTOR self.width = SHRINK_FACTOR self.height = SHRINK_FACTOR self.depth = SHRINK_FACTOR def grow(self): Node.grow(self) self.fontsize = GROW_FACTOR self.width = GROW_FACTOR self.height = GROW_FACTOR self.depth = GROW_FACTOR class Accent(Char): """ The font metrics need to be dealt with differently for accents, since they are already offset correctly from the baseline in TrueType fonts. """ def _update_metrics(self): metrics = self._metrics = self.font_output.get_metrics( self.font, self.font_class, self.c, self.fontsize, self.dpi) self.width = metrics.xmax - metrics.xmin self.height = metrics.ymax - metrics.ymin self.depth = 0 def shrink(self): Char.shrink(self) self._update_metrics() def grow(self): Char.grow(self) self._update_metrics() def render(self, x, y): """ Render the character to the canvas. """ self.font_output.render_glyph( x - self._metrics.xmin, y + self._metrics.ymin, self.font, self.font_class, self.c, self.fontsize, self.dpi) class List(Box): """ A list of nodes (either horizontal or vertical). """ def __init__(self, elements): Box.__init__(self, 0., 0., 0.) self.shift_amount = 0. # An arbitrary offset self.children = elements # The child nodes of this list # The following parameters are set in the vpack and hpack functions self.glue_set = 0. # The glue setting of this list self.glue_sign = 0 # 0: normal, -1: shrinking, 1: stretching self.glue_order = 0 # The order of infinity (0 - 3) for the glue def __repr__(self): return '[%s <%.02f %.02f %.02f %.02f> %s]' % ( self.__internal_repr__(), self.width, self.height, self.depth, self.shift_amount, ' '.join([repr(x) for x in self.children])) def _determine_order(self, totals): """ A helper function to determine the highest order of glue used by the members of this list. Used by vpack and hpack. """ o = 0 for i in range(len(totals) - 1, 0, -1): if totals[i] != 0.0: o = i break return o def _set_glue(self, x, sign, totals, error_type): o = self._determine_order(totals) self.glue_order = o self.glue_sign = sign if totals[o] != 0.: self.glue_set = x / totals[o] else: self.glue_sign = 0 self.glue_ratio = 0. if o == 0: if len(self.children): warn("%s %s: %r" % (error_type, self.__class__.__name__, self), MathTextWarning) def shrink(self): for child in self.children: child.shrink() Box.shrink(self) if self.size < NUM_SIZE_LEVELS: self.shift_amount = SHRINK_FACTOR self.glue_set = SHRINK_FACTOR def grow(self): for child in self.children: child.grow() Box.grow(self) self.shift_amount = GROW_FACTOR self.glue_set = GROW_FACTOR class Hlist(List): """ A horizontal list of boxes. """ def __init__(self, elements, w=0., m='additional', do_kern=True): List.__init__(self, elements) if do_kern: self.kern() self.hpack() def kern(self): """ Insert :class:`Kern` nodes between :class:`Char` nodes to set kerning. The :class:`Char` nodes themselves determine the amount of kerning they need (in :meth:`~Char.get_kerning`), and this function just creates the linked list in the correct way. """ new_children = [] num_children = len(self.children) if num_children: for i in range(num_children): elem = self.children[i] if i < num_children - 1: next = self.children[i + 1] else: next = None new_children.append(elem) kerning_distance = elem.get_kerning(next) if kerning_distance != 0.: kern = Kern(kerning_distance) new_children.append(kern) self.children = new_children # This is a failed experiment to fake cross-font kerning. # def get_kerning(self, next): # if len(self.children) >= 2 and isinstance(self.children[-2], Char): # if isinstance(next, Char): # print "CASE A" # return self.children[-2].get_kerning(next) # elif isinstance(next, Hlist) and len(next.children) and isinstance(next.children[0], Char): # print "CASE B" # result = self.children[-2].get_kerning(next.children[0]) # print result # return result # return 0.0 def hpack(self, w=0., m='additional'): """ The main duty of :meth:`hpack` is to compute the dimensions of the resulting boxes, and to adjust the glue if one of those dimensions is pre-specified. The computed sizes normally enclose all of the material inside the new box; but some items may stick out if negative glue is used, if the box is overfull, or if a ``\\vbox`` includes other boxes that have been shifted left. - w: specifies a width - m: is either 'exactly' or 'additional'. Thus, ``hpack(w, 'exactly')`` produces a box whose width is exactly w, while ``hpack(w, 'additional')`` yields a box whose width is the natural width plus w. The default values produce a box with the natural width. """ # I don't know why these get reset in TeX. Shift_amount is pretty # much useless if we do. #self.shift_amount = 0. h = 0. d = 0. x = 0. total_stretch = [0.] * 4 total_shrink = [0.] * 4 for p in self.children: if isinstance(p, Char): x += p.width h = max(h, p.height) d = max(d, p.depth) elif isinstance(p, Box): x += p.width if not isinf(p.height) and not isinf(p.depth): s = getattr(p, 'shift_amount', 0.) h = max(h, p.height - s) d = max(d, p.depth + s) elif isinstance(p, Glue): glue_spec = p.glue_spec x += glue_spec.width total_stretch[glue_spec.stretch_order] += glue_spec.stretch total_shrink[glue_spec.shrink_order] += glue_spec.shrink elif isinstance(p, Kern): x += p.width self.height = h self.depth = d if m == 'additional': w += x self.width = w x = w - x if x == 0.: self.glue_sign = 0 self.glue_order = 0 self.glue_ratio = 0. return if x > 0.: self._set_glue(x, 1, total_stretch, "Overfull") else: self._set_glue(x, -1, total_shrink, "Underfull") class Vlist(List): """ A vertical list of boxes. """ def __init__(self, elements, h=0., m='additional'): List.__init__(self, elements) self.vpack() def vpack(self, h=0., m='additional', l=float(inf)): """ The main duty of :meth:`vpack` is to compute the dimensions of the resulting boxes, and to adjust the glue if one of those dimensions is pre-specified. - h: specifies a height - m: is either 'exactly' or 'additional'. - l: a maximum height Thus, ``vpack(h, 'exactly')`` produces a box whose height is exactly h, while ``vpack(h, 'additional')`` yields a box whose height is the natural height plus h. The default values produce a box with the natural width. """ # I don't know why these get reset in TeX. Shift_amount is pretty # much useless if we do. # self.shift_amount = 0. w = 0. d = 0. x = 0. total_stretch = [0.] * 4 total_shrink = [0.] * 4 for p in self.children: if isinstance(p, Box): x += d + p.height d = p.depth if not isinf(p.width): s = getattr(p, 'shift_amount', 0.) w = max(w, p.width + s) elif isinstance(p, Glue): x += d d = 0. glue_spec = p.glue_spec x += glue_spec.width total_stretch[glue_spec.stretch_order] += glue_spec.stretch total_shrink[glue_spec.shrink_order] += glue_spec.shrink elif isinstance(p, Kern): x += d + p.width d = 0. elif isinstance(p, Char): raise RuntimeError("Internal mathtext error: Char node found in Vlist.") self.width = w if d > l: x += d - l self.depth = l else: self.depth = d if m == 'additional': h += x self.height = h x = h - x if x == 0: self.glue_sign = 0 self.glue_order = 0 self.glue_ratio = 0. return if x > 0.: self._set_glue(x, 1, total_stretch, "Overfull") else: self._set_glue(x, -1, total_shrink, "Underfull") class Rule(Box): """ A :class:`Rule` node stands for a solid black rectangle; it has width, depth, and height fields just as in an :class:`Hlist`. However, if any of these dimensions is inf, the actual value will be determined by running the rule up to the boundary of the innermost enclosing box. This is called a "running dimension." The width is never running in an :class:`Hlist`; the height and depth are never running in a :class:`Vlist`. """ def __init__(self, width, height, depth, state): Box.__init__(self, width, height, depth) self.font_output = state.font_output def render(self, x, y, w, h): self.font_output.render_rect_filled(x, y, x + w, y + h) class Hrule(Rule): """ Convenience class to create a horizontal rule. """ def __init__(self, state): thickness = state.font_output.get_underline_thickness( state.font, state.fontsize, state.dpi) height = depth = thickness * 0.5 Rule.__init__(self, inf, height, depth, state) class Vrule(Rule): """ Convenience class to create a vertical rule. """ def __init__(self, state): thickness = state.font_output.get_underline_thickness( state.font, state.fontsize, state.dpi) Rule.__init__(self, thickness, inf, inf, state) class Glue(Node): """ Most of the information in this object is stored in the underlying :class:`GlueSpec` class, which is shared between multiple glue objects. (This is a memory optimization which probably doesn't matter anymore, but it's easier to stick to what TeX does.) """ def __init__(self, glue_type, copy=False): Node.__init__(self) self.glue_subtype = 'normal' if is_string_like(glue_type): glue_spec = GlueSpec.factory(glue_type) elif isinstance(glue_type, GlueSpec): glue_spec = glue_type else: raise ArgumentError("glue_type must be a glue spec name or instance.") if copy: glue_spec = glue_spec.copy() self.glue_spec = glue_spec def shrink(self): Node.shrink(self) if self.size < NUM_SIZE_LEVELS: if self.glue_spec.width != 0.: self.glue_spec = self.glue_spec.copy() self.glue_spec.width = SHRINK_FACTOR def grow(self): Node.grow(self) if self.glue_spec.width != 0.: self.glue_spec = self.glue_spec.copy() self.glue_spec.width = GROW_FACTOR class GlueSpec(object): """ See :class:`Glue`. """ def __init__(self, width=0., stretch=0., stretch_order=0, shrink=0., shrink_order=0): self.width = width self.stretch = stretch self.stretch_order = stretch_order self.shrink = shrink self.shrink_order = shrink_order def copy(self): return GlueSpec( self.width, self.stretch, self.stretch_order, self.shrink, self.shrink_order) def factory(cls, glue_type): return cls._types[glue_type] factory = classmethod(factory) GlueSpec._types = { 'fil': GlueSpec(0., 1., 1, 0., 0), 'fill': GlueSpec(0., 1., 2, 0., 0), 'filll': GlueSpec(0., 1., 3, 0., 0), 'neg_fil': GlueSpec(0., 0., 0, 1., 1), 'neg_fill': GlueSpec(0., 0., 0, 1., 2), 'neg_filll': GlueSpec(0., 0., 0, 1., 3), 'empty': GlueSpec(0., 0., 0, 0., 0), 'ss': GlueSpec(0., 1., 1, -1., 1) } # Some convenient ways to get common kinds of glue class Fil(Glue): def __init__(self): Glue.__init__(self, 'fil') class Fill(Glue): def __init__(self): Glue.__init__(self, 'fill') class Filll(Glue): def __init__(self): Glue.__init__(self, 'filll') class NegFil(Glue): def __init__(self): Glue.__init__(self, 'neg_fil') class NegFill(Glue): def __init__(self): Glue.__init__(self, 'neg_fill') class NegFilll(Glue): def __init__(self): Glue.__init__(self, 'neg_filll') class SsGlue(Glue): def __init__(self): Glue.__init__(self, 'ss') class HCentered(Hlist): """ A convenience class to create an :class:`Hlist` whose contents are centered within its enclosing box. """ def __init__(self, elements): Hlist.__init__(self, [SsGlue()] + elements + [SsGlue()], do_kern=False) class VCentered(Hlist): """ A convenience class to create a :class:`Vlist` whose contents are centered within its enclosing box. """ def __init__(self, elements): Vlist.__init__(self, [SsGlue()] + elements + [SsGlue()]) class Kern(Node): """ A :class:`Kern` node has a width field to specify a (normally negative) amount of spacing. This spacing correction appears in horizontal lists between letters like A and V when the font designer said that it looks better to move them closer together or further apart. A kern node can also appear in a vertical list, when its width denotes additional spacing in the vertical direction. """ def __init__(self, width): Node.__init__(self) self.width = width def __repr__(self): return "k%.02f" % self.width def shrink(self): Node.shrink(self) if self.size < NUM_SIZE_LEVELS: self.width = SHRINK_FACTOR def grow(self): Node.grow(self) self.width = GROW_FACTOR class SubSuperCluster(Hlist): """ :class:`SubSuperCluster` is a sort of hack to get around that fact that this code do a two-pass parse like TeX. This lets us store enough information in the hlist itself, namely the nucleus, sub- and super-script, such that if another script follows that needs to be attached, it can be reconfigured on the fly. """ def __init__(self): self.nucleus = None self.sub = None self.super = None Hlist.__init__(self, []) class AutoHeightChar(Hlist): """ :class:`AutoHeightChar` will create a character as close to the given height and depth as possible. When using a font with multiple height versions of some characters (such as the BaKoMa fonts), the correct glyph will be selected, otherwise this will always just return a scaled version of the glyph. """ def __init__(self, c, height, depth, state, always=False): alternatives = state.font_output.get_sized_alternatives_for_symbol( state.font, c) state = state.copy() target_total = height + depth for fontname, sym in alternatives: state.font = fontname char = Char(sym, state) if char.height + char.depth >= target_total: break factor = target_total / (char.height + char.depth) state.fontsize = factor char = Char(sym, state) shift = (depth - char.depth) Hlist.__init__(self, [char]) self.shift_amount = shift class AutoWidthChar(Hlist): """ :class:`AutoWidthChar` will create a character as close to the given width as possible. When using a font with multiple width versions of some characters (such as the BaKoMa fonts), the correct glyph will be selected, otherwise this will always just return a scaled version of the glyph. """ def __init__(self, c, width, state, always=False, char_class=Char): alternatives = state.font_output.get_sized_alternatives_for_symbol( state.font, c) state = state.copy() for fontname, sym in alternatives: state.font = fontname char = char_class(sym, state) if char.width >= width: break factor = width / char.width state.fontsize = factor char = char_class(sym, state) Hlist.__init__(self, [char]) self.width = char.width class Ship(object): """ Once the boxes have been set up, this sends them to output. Since boxes can be inside of boxes inside of boxes, the main work of :class:`Ship` is done by two mutually recursive routines, :meth:`hlist_out` and :meth:`vlist_out`, which traverse the :class:`Hlist` nodes and :class:`Vlist` nodes inside of horizontal and vertical boxes. The global variables used in TeX to store state as it processes have become member variables here. """ def __call__(self, ox, oy, box): self.max_push = 0 # Deepest nesting of push commands so far self.cur_s = 0 self.cur_v = 0. self.cur_h = 0. self.off_h = ox self.off_v = oy + box.height self.hlist_out(box) def clamp(value): if value < -1000000000.: return -1000000000. if value > 1000000000.: return 1000000000. return value clamp = staticmethod(clamp) def hlist_out(self, box): cur_g = 0 cur_glue = 0. glue_order = box.glue_order glue_sign = box.glue_sign base_line = self.cur_v left_edge = self.cur_h self.cur_s += 1 self.max_push = max(self.cur_s, self.max_push) clamp = self.clamp for p in box.children: if isinstance(p, Char): p.render(self.cur_h + self.off_h, self.cur_v + self.off_v) self.cur_h += p.width elif isinstance(p, Kern): self.cur_h += p.width elif isinstance(p, List): # node623 if len(p.children) == 0: self.cur_h += p.width else: edge = self.cur_h self.cur_v = base_line + p.shift_amount if isinstance(p, Hlist): self.hlist_out(p) else: # p.vpack(box.height + box.depth, 'exactly') self.vlist_out(p) self.cur_h = edge + p.width self.cur_v = base_line elif isinstance(p, Box): # node624 rule_height = p.height rule_depth = p.depth rule_width = p.width if isinf(rule_height): rule_height = box.height if isinf(rule_depth): rule_depth = box.depth if rule_height > 0 and rule_width > 0: self.cur_v = baseline + rule_depth p.render(self.cur_h + self.off_h, self.cur_v + self.off_v, rule_width, rule_height) self.cur_v = baseline self.cur_h += rule_width elif isinstance(p, Glue): # node625 glue_spec = p.glue_spec rule_width = glue_spec.width - cur_g if glue_sign != 0: # normal if glue_sign == 1: # stretching if glue_spec.stretch_order == glue_order: cur_glue += glue_spec.stretch cur_g = round(clamp(float(box.glue_set) * cur_glue)) elif glue_spec.shrink_order == glue_order: cur_glue += glue_spec.shrink cur_g = round(clamp(float(box.glue_set) * cur_glue)) rule_width += cur_g self.cur_h += rule_width self.cur_s -= 1 def vlist_out(self, box): cur_g = 0 cur_glue = 0. glue_order = box.glue_order glue_sign = box.glue_sign self.cur_s += 1 self.max_push = max(self.max_push, self.cur_s) left_edge = self.cur_h self.cur_v -= box.height top_edge = self.cur_v clamp = self.clamp for p in box.children: if isinstance(p, Kern): self.cur_v += p.width elif isinstance(p, List): if len(p.children) == 0: self.cur_v += p.height + p.depth else: self.cur_v += p.height self.cur_h = left_edge + p.shift_amount save_v = self.cur_v p.width = box.width if isinstance(p, Hlist): self.hlist_out(p) else: self.vlist_out(p) self.cur_v = save_v + p.depth self.cur_h = left_edge elif isinstance(p, Box): rule_height = p.height rule_depth = p.depth rule_width = p.width if isinf(rule_width): rule_width = box.width rule_height += rule_depth if rule_height > 0 and rule_depth > 0: self.cur_v += rule_height p.render(self.cur_h + self.off_h, self.cur_v + self.off_v, rule_width, rule_height) elif isinstance(p, Glue): glue_spec = p.glue_spec rule_height = glue_spec.width - cur_g if glue_sign != 0: # normal if glue_sign == 1: # stretching if glue_spec.stretch_order == glue_order: cur_glue += glue_spec.stretch cur_g = round(clamp(float(box.glue_set) * cur_glue)) elif glue_spec.shrink_order == glue_order: # shrinking cur_glue += glue_spec.shrink cur_g = round(clamp(float(box.glue_set) * cur_glue)) rule_height += cur_g self.cur_v += rule_height elif isinstance(p, Char): raise RuntimeError("Internal mathtext error: Char node found in vlist") self.cur_s -= 1 ship = Ship() ############################################################################## # PARSER def Error(msg): """ Helper class to raise parser errors. """ def raise_error(s, loc, toks): raise ParseFatalException(msg + "\n" + s) empty = Empty() empty.setParseAction(raise_error) return empty class Parser(object): """ This is the pyparsing-based parser for math expressions. It actually parses full strings containing math expressions, in that raw text may also appear outside of pairs of ``$``. The grammar is based directly on that in TeX, though it cuts a few corners. """ _binary_operators = set(r''' + * \pm \sqcap \rhd \mp \sqcup \unlhd \times \vee \unrhd \div \wedge \oplus \ast \setminus \ominus \star \wr \otimes \circ \diamond \oslash \bullet \bigtriangleup \odot \cdot \bigtriangledown \bigcirc \cap \triangleleft \dagger \cup \triangleright \ddagger \uplus \lhd \amalg'''.split()) _relation_symbols = set(r''' = < > : \leq \geq \equiv \models \prec \succ \sim \perp \preceq \succeq \simeq \mid \ll \gg \asymp \parallel \subset \supset \approx \bowtie \subseteq \supseteq \cong \Join \sqsubset \sqsupset \neq \smile \sqsubseteq \sqsupseteq \doteq \frown \in \ni \propto \vdash \dashv'''.split()) _arrow_symbols = set(r''' \leftarrow \longleftarrow \uparrow \Leftarrow \Longleftarrow \Uparrow \rightarrow \longrightarrow \downarrow \Rightarrow \Longrightarrow \Downarrow \leftrightarrow \longleftrightarrow \updownarrow \Leftrightarrow \Longleftrightarrow \Updownarrow \mapsto \longmapsto \nearrow \hookleftarrow \hookrightarrow \searrow \leftharpoonup \rightharpoonup \swarrow \leftharpoondown \rightharpoondown \nwarrow \rightleftharpoons \leadsto'''.split()) _spaced_symbols = _binary_operators \| _relation_symbols \| _arrow_symbols _punctuation_symbols = set(r', ; . ! \ldotp \cdotp'.split()) _overunder_symbols = set(r''' \sum \prod \coprod \bigcap \bigcup \bigsqcup \bigvee \bigwedge \bigodot \bigotimes \bigoplus \biguplus '''.split()) _overunder_functions = set( r"lim liminf limsup sup max min".split()) _dropsub_symbols = set(r'''\int \oint'''.split()) _fontnames = set("rm cal it tt sf bf default bb frak circled scr".split()) _function_names = set(""" arccos csc ker min arcsin deg lg Pr arctan det lim sec arg dim liminf sin cos exp limsup sinh cosh gcd ln sup cot hom log tan coth inf max tanh""".split()) _ambiDelim = set(r""" \| \\| / \backslash \uparrow \downarrow \updownarrow \Uparrow \Downarrow \Updownarrow .""".split()) _leftDelim = set(r"( [ { < \lfloor \langle \lceil".split()) _rightDelim = set(r") ] } > \rfloor \rangle \rceil".split()) def __init__(self): # All forward declarations are here font = Forward().setParseAction(self.font).setName("font") latexfont = Forward() subsuper = Forward().setParseAction(self.subsuperscript).setName("subsuper") placeable = Forward().setName("placeable") simple = Forward().setName("simple") autoDelim = Forward().setParseAction(self.auto_sized_delimiter) self._expression = Forward().setParseAction(self.finish).setName("finish") float = Regex(r"[-+]?([0-9]+\.?[0-9]\|\.[0-9]+)") lbrace = Literal('{').suppress() rbrace = Literal('}').suppress() start_group = (Optional(latexfont) - lbrace) start_group.setParseAction(self.start_group) end_group = rbrace.copy() end_group.setParseAction(self.end_group) bslash = Literal('\\') accent = oneOf(self._accent_map.keys() + list(self._wide_accents)) function = oneOf(list(self._function_names)) fontname = oneOf(list(self._fontnames)) latex2efont = oneOf(['math' + x for x in self._fontnames]) space =(FollowedBy(bslash) + oneOf([r'\ ', r'\/', r'\,', r'\;', r'\quad', r'\qquad', r'\!']) ).setParseAction(self.space).setName('space') customspace =(Literal(r'\hspace') - (( lbrace - float - rbrace ) \| Error(r"Expected \hspace{n}")) ).setParseAction(self.customspace).setName('customspace') unicode_range = u"\U00000080-\U0001ffff" symbol =(Regex(UR"([a-zA-Z0-9 +\-/<>=:,.;!'@()\[\]\|%s])\|(\\[%%${}\[\]_\|])" % unicode_range) \| (Combine( bslash + oneOf(tex2uni.keys()) ) + FollowedBy(Regex("[^a-zA-Z]"))) ).setParseAction(self.symbol).leaveWhitespace() c_over_c =(Suppress(bslash) + oneOf(self._char_over_chars.keys()) ).setParseAction(self.char_over_chars) accent = Group( Suppress(bslash) + accent - placeable ).setParseAction(self.accent).setName("accent") function =(Suppress(bslash) + function ).setParseAction(self.function).setName("function") group = Group( start_group + ZeroOrMore( autoDelim ^ simple) - end_group ).setParseAction(self.group).setName("group") font <<(Suppress(bslash) + fontname) latexfont <<(Suppress(bslash) + latex2efont) frac = Group( Suppress(Literal(r"\frac")) + ((group + group) \| Error(r"Expected \frac{num}{den}")) ).setParseAction(self.frac).setName("frac") sqrt = Group( Suppress(Literal(r"\sqrt")) + Optional( Suppress(Literal("[")) - Regex("[0-9]+") - Suppress(Literal("]")), default = None ) + (group \| Error("Expected \sqrt{value}")) ).setParseAction(self.sqrt).setName("sqrt") placeable <<(accent ^ function ^ (c_over_c \| symbol) ^ group ^ frac ^ sqrt ) simple <<(space \| customspace \| font \| subsuper ) subsuperop = oneOf(["_", "^"]) subsuper << Group( ( Optional(placeable) + OneOrMore( subsuperop - placeable ) ) \| placeable ) ambiDelim = oneOf(list(self._ambiDelim)) leftDelim = oneOf(list(self._leftDelim)) rightDelim = oneOf(list(self._rightDelim)) autoDelim <<(Suppress(Literal(r"\left")) + ((leftDelim \| ambiDelim) \| Error("Expected a delimiter")) + Group( autoDelim ^ OneOrMore(simple)) + Suppress(Literal(r"\right")) + ((rightDelim \| ambiDelim) \| Error("Expected a delimiter")) ) math = OneOrMore( autoDelim ^ simple ).setParseAction(self.math).setName("math") math_delim = ~bslash + Literal('$') non_math = Regex(r"(?:(?:\\[$])\|[^$])" ).setParseAction(self.non_math).setName("non_math").leaveWhitespace() self._expression << ( non_math + ZeroOrMore( Suppress(math_delim) + Optional(math) + (Suppress(math_delim) \| Error("Expected end of math '$'")) + non_math ) ) + StringEnd() self.clear() def clear(self): """ Clear any state before parsing. """ self._expr = None self._state_stack = None self._em_width_cache = {} def parse(self, s, fonts_object, fontsize, dpi): """ Parse expression s* using the given fonts_object for output, at the given fontsize and dpi. Returns the parse tree of :class:`Node` instances. """ self._state_stack = [self.State(fonts_object, 'default', 'rm', fontsize, dpi)] try: self._expression.parseString(s) except ParseException, err: raise ValueError("\n".join([ "", err.line, " " * (err.column - 1) + "^", str(err)])) return self._expr # The state of the parser is maintained in a stack. Upon # entering and leaving a group { } or math/non-math, the stack # is pushed and popped accordingly. The current state always # exists in the top element of the stack. class State(object): """ Stores the state of the parser. States are pushed and popped from a stack as necessary, and the "current" state is always at the top of the stack. """ def __init__(self, font_output, font, font_class, fontsize, dpi): self.font_output = font_output self._font = font self.font_class = font_class self.fontsize = fontsize self.dpi = dpi def copy(self): return Parser.State( self.font_output, self.font, self.font_class, self.fontsize, self.dpi) def _get_font(self): return self._font def _set_font(self, name): if name in ('it', 'rm', 'bf'): self.font_class = name self._font = name font = property(_get_font, _set_font) def get_state(self): """ Get the current :class:`State` of the parser. """ return self._state_stack[-1] def pop_state(self): """ Pop a :class:`State` off of the stack. """ self._state_stack.pop() def push_state(self): """ Push a new :class:`State` onto the stack which is just a copy of the current state. """ self._state_stack.append(self.get_state().copy()) def finish(self, s, loc, toks): #~ print "finish", toks self._expr = Hlist(toks) return [self._expr] def math(self, s, loc, toks): #~ print "math", toks hlist = Hlist(toks) self.pop_state() return [hlist] def non_math(self, s, loc, toks): #~ print "non_math", toks s = toks[0].replace(r'\$', '$') symbols = [Char(c, self.get_state()) for c in s] hlist = Hlist(symbols) # We're going into math now, so set font to 'it' self.push_state() self.get_state().font = 'it' return [hlist] def _make_space(self, percentage): # All spaces are relative to em width state = self.get_state() key = (state.font, state.fontsize, state.dpi) width = self._em_width_cache.get(key) if width is None: metrics = state.font_output.get_metrics( state.font, 'it', 'm', state.fontsize, state.dpi) width = metrics.advance self._em_width_cache[key] = width return Kern(width * percentage) _space_widths = { r'\ ' : 0.3, r'\,' : 0.4, r'\;' : 0.8, r'\quad' : 1.6, r'\qquad' : 3.2, r'\!' : -0.4, r'\/' : 0.4 } def space(self, s, loc, toks): assert(len(toks)==1) num = self._space_widths[toks[0]] box = self._make_space(num) return [box] def customspace(self, s, loc, toks): return [self._make_space(float(toks[1]))] def symbol(self, s, loc, toks): # print "symbol", toks c = toks[0] try: char = Char(c, self.get_state()) except ValueError: raise ParseFatalException("Unknown symbol: %s" % c) if c in self._spaced_symbols: return [Hlist( [self._make_space(0.2), char, self._make_space(0.2)] , do_kern = False)] elif c in self._punctuation_symbols: return [Hlist( [char, self._make_space(0.2)] , do_kern = False)] return [char] _char_over_chars = { # The first 2 entires in the tuple are (font, char, sizescale) for # the two symbols under and over. The third element is the space # (in multiples of underline height) r'AA' : ( ('rm', 'A', 1.0), (None, '\circ', 0.5), 0.0), } def char_over_chars(self, s, loc, toks): sym = toks[0] state = self.get_state() thickness = state.font_output.get_underline_thickness( state.font, state.fontsize, state.dpi) under_desc, over_desc, space = \ self._char_over_chars.get(sym, (None, None, 0.0)) if under_desc is None: raise ParseFatalException("Error parsing symbol") over_state = state.copy() if over_desc[0] is not None: over_state.font = over_desc[0] over_state.fontsize = over_desc[2] over = Accent(over_desc[1], over_state) under_state = state.copy() if under_desc[0] is not None: under_state.font = under_desc[0] under_state.fontsize = under_desc[2] under = Char(under_desc[1], under_state) width = max(over.width, under.width) over_centered = HCentered([over]) over_centered.hpack(width, 'exactly') under_centered = HCentered([under]) under_centered.hpack(width, 'exactly') return Vlist([ over_centered, Vbox(0., thickness * space), under_centered ]) _accent_map = { r'hat' : r'\circumflexaccent', r'breve' : r'\combiningbreve', r'bar' : r'\combiningoverline', r'grave' : r'\combininggraveaccent', r'acute' : r'\combiningacuteaccent', r'ddot' : r'\combiningdiaeresis', r'tilde' : r'\combiningtilde', r'dot' : r'\combiningdotabove', r'vec' : r'\combiningrightarrowabove', r'"' : r'\combiningdiaeresis', r"`" : r'\combininggraveaccent', r"'" : r'\combiningacuteaccent', r'~' : r'\combiningtilde', r'.' : r'\combiningdotabove', r'^' : r'\circumflexaccent' } _wide_accents = set(r"widehat widetilde".split()) def accent(self, s, loc, toks): assert(len(toks)==1) state = self.get_state() thickness = state.font_output.get_underline_thickness( state.font, state.fontsize, state.dpi) if len(toks[0]) != 2: raise ParseFatalException("Error parsing accent") accent, sym = toks[0] if accent in self._wide_accents: accent = AutoWidthChar( '\\' + accent, sym.width, state, char_class=Accent) else: accent = Accent(self._accent_map[accent], state) centered = HCentered([accent]) centered.hpack(sym.width, 'exactly') return Vlist([ centered, Vbox(0., thickness * 2.0), Hlist([sym]) ]) def function(self, s, loc, toks): #~ print "function", toks self.push_state() state = self.get_state() state.font = 'rm' hlist = Hlist([Char(c, state) for c in toks[0]]) self.pop_state() hlist.function_name = toks[0] return hlist def start_group(self, s, loc, toks): self.push_state() # Deal with LaTeX-style font tokens if len(toks): self.get_state().font = toks[0][4:] return [] def group(self, s, loc, toks): grp = Hlist(toks[0]) return [grp] def end_group(self, s, loc, toks): self.pop_state() return [] def font(self, s, loc, toks): assert(len(toks)==1) name = toks[0] self.get_state().font = name return [] def is_overunder(self, nucleus): if isinstance(nucleus, Char): return nucleus.c in self._overunder_symbols elif isinstance(nucleus, Hlist) and hasattr(nucleus, 'function_name'): return nucleus.function_name in self._overunder_functions return False def is_dropsub(self, nucleus): if isinstance(nucleus, Char): return nucleus.c in self._dropsub_symbols return False def is_slanted(self, nucleus): if isinstance(nucleus, Char): return nucleus.is_slanted() return False def subsuperscript(self, s, loc, toks): assert(len(toks)==1) # print 'subsuperscript', toks nucleus = None sub = None super = None if len(toks[0]) == 1: return toks[0].asList() elif len(toks[0]) == 2: op, next = toks[0] nucleus = Hbox(0.0) if op == '_': sub = next else: super = next elif len(toks[0]) == 3: nucleus, op, next = toks[0] if op == '_': sub = next else: super = next elif len(toks[0]) == 5: nucleus, op1, next1, op2, next2 = toks[0] if op1 == op2: if op1 == '_': raise ParseFatalException("Double subscript") else: raise ParseFatalException("Double superscript") if op1 == '_': sub = next1 super = next2 else: super = next1 sub = next2 else: raise ParseFatalException( "Subscript/superscript sequence is too long. " "Use braces { } to remove ambiguity.") state = self.get_state() rule_thickness = state.font_output.get_underline_thickness( state.font, state.fontsize, state.dpi) xHeight = state.font_output.get_xheight( state.font, state.fontsize, state.dpi) # Handle over/under symbols, such as sum or integral if self.is_overunder(nucleus): vlist = [] shift = 0. width = nucleus.width if super is not None: super.shrink() width = max(width, super.width) if sub is not None: sub.shrink() width = max(width, sub.width) if super is not None: hlist = HCentered([super]) hlist.hpack(width, 'exactly') vlist.extend([hlist, Kern(rule_thickness * 3.0)]) hlist = HCentered([nucleus]) hlist.hpack(width, 'exactly') vlist.append(hlist) if sub is not None: hlist = HCentered([sub]) hlist.hpack(width, 'exactly') vlist.extend([Kern(rule_thickness * 3.0), hlist]) shift = hlist.height + hlist.depth + rule_thickness * 2.0 vlist = Vlist(vlist) vlist.shift_amount = shift + nucleus.depth * 0.5 result = Hlist([vlist]) return [result] # Handle regular sub/superscripts shift_up = nucleus.height - SUBDROP * xHeight if self.is_dropsub(nucleus): shift_down = nucleus.depth + SUBDROP * xHeight else: shift_down = SUBDROP * xHeight if super is None: # node757 sub.shrink() x = Hlist([sub]) # x.width += SCRIPT_SPACE * xHeight shift_down = max(shift_down, SUB1) clr = x.height - (abs(xHeight * 4.0) / 5.0) shift_down = max(shift_down, clr) x.shift_amount = shift_down else: super.shrink() x = Hlist([super, Kern(SCRIPT_SPACE * xHeight)]) # x.width += SCRIPT_SPACE * xHeight clr = SUP1 * xHeight shift_up = max(shift_up, clr) clr = x.depth + (abs(xHeight) / 4.0) shift_up = max(shift_up, clr) if sub is None: x.shift_amount = -shift_up else: # Both sub and superscript sub.shrink() y = Hlist([sub]) # y.width += SCRIPT_SPACE * xHeight shift_down = max(shift_down, SUB1 * xHeight) clr = (2.0 * rule_thickness - ((shift_up - x.depth) - (y.height - shift_down))) if clr > 0.: shift_up += clr shift_down += clr if self.is_slanted(nucleus): x.shift_amount = DELTA * (shift_up + shift_down) x = Vlist([x, Kern((shift_up - x.depth) - (y.height - shift_down)), y]) x.shift_amount = shift_down result = Hlist([nucleus, x]) return [result] def frac(self, s, loc, toks): assert(len(toks)==1) assert(len(toks[0])==2) state = self.get_state() thickness = state.font_output.get_underline_thickness( state.font, state.fontsize, state.dpi) num, den = toks[0] num.shrink() den.shrink() cnum = HCentered([num]) cden = HCentered([den]) width = max(num.width, den.width) + thickness * 10. cnum.hpack(width, 'exactly') cden.hpack(width, 'exactly') vlist = Vlist([cnum, # numerator Vbox(0, thickness * 2.0), # space Hrule(state), # rule Vbox(0, thickness * 4.0), # space cden # denominator ]) # Shift so the fraction line sits in the middle of the # equals sign metrics = state.font_output.get_metrics( state.font, 'it', '=', state.fontsize, state.dpi) shift = (cden.height - ((metrics.ymax + metrics.ymin) / 2 - thickness * 3.0)) vlist.shift_amount = shift hlist = Hlist([vlist, Hbox(thickness * 2.)]) return [hlist] def sqrt(self, s, loc, toks): #~ print "sqrt", toks root, body = toks[0] state = self.get_state() thickness = state.font_output.get_underline_thickness( state.font, state.fontsize, state.dpi) # Determine the height of the body, and add a little extra to # the height so it doesn't seem cramped height = body.height - body.shift_amount + thickness * 5.0 depth = body.depth + body.shift_amount check = AutoHeightChar(r'\__sqrt__', height, depth, state, always=True) height = check.height - check.shift_amount depth = check.depth + check.shift_amount # Put a little extra space to the left and right of the body padded_body = Hlist([Hbox(thickness * 2.0), body, Hbox(thickness * 2.0)]) rightside = Vlist([Hrule(state), Fill(), padded_body]) # Stretch the glue between the hrule and the body rightside.vpack(height + (state.fontsize * state.dpi) / (100.0 * 12.0), depth, 'exactly') # Add the root and shift it upward so it is above the tick. # The value of 0.6 is a hard-coded hack ;) if root is None: root = Box(check.width * 0.5, 0., 0.) else: root = Hlist([Char(x, state) for x in root]) root.shrink() root.shrink() root_vlist = Vlist([Hlist([root])]) root_vlist.shift_amount = -height * 0.6 hlist = Hlist([root_vlist, # Root # Negative kerning to put root over tick Kern(-check.width * 0.5), check, # Check rightside]) # Body return [hlist] def auto_sized_delimiter(self, s, loc, toks): #~ print "auto_sized_delimiter", toks front, middle, back = toks state = self.get_state() height = max([x.height for x in middle]) depth = max([x.depth for x in middle]) parts = [] # \left. and \right. aren't supposed to produce any symbols if front != '.': parts.append(AutoHeightChar(front, height, depth, state)) parts.extend(middle.asList()) if back != '.': parts.append(AutoHeightChar(back, height, depth, state)) hlist = Hlist(parts) return hlist ### ############################################################################## # MAIN class MathTextParser(object): _parser = None _backend_mapping = { 'bitmap': MathtextBackendBitmap, 'agg' : MathtextBackendAgg, 'ps' : MathtextBackendPs, 'pdf' : MathtextBackendPdf, 'svg' : MathtextBackendSvg, 'cairo' : MathtextBackendCairo, 'macosx': MathtextBackendAgg, } _font_type_mapping = { 'cm' : BakomaFonts, 'stix' : StixFonts, 'stixsans' : StixSansFonts, 'custom' : UnicodeFonts } def __init__(self, output): """ Create a MathTextParser for the given backend output. """ self._output = output.lower() self._cache = maxdict(50) def parse(self, s, dpi = 72, prop = None): """ Parse the given math expression s at the given dpi. If prop is provided, it is a :class:`~matplotlib.font_manager.FontProperties` object specifying the "default" font to use in the math expression, used for all non-math text. The results are cached, so multiple calls to :meth:`parse` with the same expression should be fast. """ if prop is None: prop = FontProperties() cacheKey = (s, dpi, hash(prop)) result = self._cache.get(cacheKey) if result is not None: return result if self._output == 'ps' and rcParams['ps.useafm']: font_output = StandardPsFonts(prop) else: backend = self._backend_mapping[self._output]() fontset = rcParams['mathtext.fontset'] fontset_class = self._font_type_mapping.get(fontset.lower()) if fontset_class is not None: font_output = fontset_class(prop, backend) else: raise ValueError( "mathtext.fontset must be either 'cm', 'stix', " "'stixsans', or 'custom'") fontsize = prop.get_size_in_points() # This is a class variable so we don't rebuild the parser # with each request. if self._parser is None: self.__class__._parser = Parser() box = self._parser.parse(s, font_output, fontsize, dpi) font_output.set_canvas_size(box.width, box.height, box.depth) result = font_output.get_results(box) self._cache[cacheKey] = result # Free up the transient data structures self._parser.clear() # Fix cyclical references font_output.destroy() font_output.mathtext_backend.fonts_object = None font_output.mathtext_backend = None return result def to_mask(self, texstr, dpi=120, fontsize=14): """ texstr A valid mathtext string, eg r'IQ: $\sigma_i=15$' dpi The dots-per-inch to render the text fontsize The font size in points Returns a tuple (array, depth) - array is an NxM uint8 alpha ubyte mask array of rasterized tex. - depth is the offset of the baseline from the bottom of the image in pixels. """ assert(self._output=="bitmap") prop = FontProperties(size=fontsize) ftimage, depth = self.parse(texstr, dpi=dpi, prop=prop) x = ftimage.as_array() return x, depth def to_rgba(self, texstr, color='black', dpi=120, fontsize=14): """ texstr A valid mathtext string, eg r'IQ: $\sigma_i=15$' color Any matplotlib color argument dpi The dots-per-inch to render the text fontsize The font size in points Returns a tuple (array, depth) - array is an NxM uint8 alpha ubyte mask array of rasterized tex. - depth is the offset of the baseline from the bottom of the image in pixels. """ x, depth = self.to_mask(texstr, dpi=dpi, fontsize=fontsize) r, g, b = mcolors.colorConverter.to_rgb(color) RGBA = np.zeros((x.shape[0], x.shape[1], 4), dtype=np.uint8) RGBA[:,:,0] = int(255r) RGBA[:,:,1] = int(255g) RGBA[:,:,2] = int(255b) RGBA[:,:,3] = x return RGBA, depth def to_png(self, filename, texstr, color='black', dpi=120, fontsize=14): """ Writes a tex expression to a PNG file. Returns the offset of the baseline from the bottom of the image in pixels. filename* A writable filename or fileobject texstr A valid mathtext string, eg r'IQ: $\sigma_i=15$' color A valid matplotlib color argument dpi The dots-per-inch to render the text fontsize The font size in points Returns the offset of the baseline from the bottom of the image in pixels. """ rgba, depth = self.to_rgba(texstr, color=color, dpi=dpi, fontsize=fontsize) numrows, numcols, tmp = rgba.shape _png.write_png(rgba.tostring(), numcols, numrows, filename) return depth def get_depth(self, texstr, dpi=120, fontsize=14): """ Returns the offset of the baseline from the bottom of the image in pixels. texstr A valid mathtext string, eg r'IQ: $\sigma_i=15$' dpi The dots-per-inch to render the text fontsize The font size in points """ assert(self._output=="bitmap") prop = FontProperties(size=fontsize) ftimage, depth = self.parse(texstr, dpi=dpi, prop=prop) return depth	101,723	Python	.py	2,503	29.952457	106	0.53857	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,282	blocking_input.py	numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/blocking_input.py	""" This provides several classes used for blocking interaction with figure windows: :class:`BlockingInput` creates a callable object to retrieve events in a blocking way for interactive sessions :class:`BlockingKeyMouseInput` creates a callable object to retrieve key or mouse clicks in a blocking way for interactive sessions. Note: Subclass of BlockingInput. Used by waitforbuttonpress :class:`BlockingMouseInput` creates a callable object to retrieve mouse clicks in a blocking way for interactive sessions. Note: Subclass of BlockingInput. Used by ginput :class:`BlockingContourLabeler` creates a callable object to retrieve mouse clicks in a blocking way that will then be used to place labels on a ContourSet Note: Subclass of BlockingMouseInput. Used by clabel """ import time import numpy as np from matplotlib import path, verbose from matplotlib.cbook import is_sequence_of_strings class BlockingInput(object): """ Class that creates a callable object to retrieve events in a blocking way. """ def __init__(self, fig, eventslist=()): self.fig = fig assert is_sequence_of_strings(eventslist), "Requires a sequence of event name strings" self.eventslist = eventslist def on_event(self, event): """ Event handler that will be passed to the current figure to retrieve events. """ # Add a new event to list - using a separate function is # overkill for the base class, but this is consistent with # subclasses self.add_event(event) verbose.report("Event %i" % len(self.events)) # This will extract info from events self.post_event() # Check if we have enough events already if len(self.events) >= self.n and self.n > 0: self.fig.canvas.stop_event_loop() def post_event(self): """For baseclass, do nothing but collect events""" pass def cleanup(self): """Disconnect all callbacks""" for cb in self.callbacks: self.fig.canvas.mpl_disconnect(cb) self.callbacks=[] def add_event(self,event): """For base class, this just appends an event to events.""" self.events.append(event) def pop_event(self,index=-1): """ This removes an event from the event list. Defaults to removing last event, but an index can be supplied. Note that this does not check that there are events, much like the normal pop method. If not events exist, this will throw an exception. """ self.events.pop(index) def pop(self,index=-1): self.pop_event(index) pop.__doc__=pop_event.__doc__ def __call__(self, n=1, timeout=30 ): """ Blocking call to retrieve n events """ assert isinstance(n, int), "Requires an integer argument" self.n = n self.events = [] self.callbacks = [] # Ensure that the figure is shown self.fig.show() # connect the events to the on_event function call for n in self.eventslist: self.callbacks.append( self.fig.canvas.mpl_connect(n, self.on_event) ) try: # Start event loop self.fig.canvas.start_event_loop(timeout=timeout) finally: # Run even on exception like ctrl-c # Disconnect the callbacks self.cleanup() # Return the events in this case return self.events class BlockingMouseInput(BlockingInput): """ Class that creates a callable object to retrieve mouse clicks in a blocking way. This class will also retrieve keyboard clicks and treat them like appropriate mouse clicks (delete and backspace are like mouse button 3, enter is like mouse button 2 and all others are like mouse button 1). """ def __init__(self, fig): BlockingInput.__init__(self, fig=fig, eventslist=('button_press_event', 'key_press_event') ) def post_event(self): """ This will be called to process events """ assert len(self.events)>0, "No events yet" if self.events[-1].name == 'key_press_event': self.key_event() else: self.mouse_event() def mouse_event(self): '''Process a mouse click event''' event = self.events[-1] button = event.button if button == 3: self.button3(event) elif button == 2: self.button2(event) else: self.button1(event) def key_event(self): ''' Process a key click event. This maps certain keys to appropriate mouse click events. ''' event = self.events[-1] key = event.key if key == 'backspace' or key == 'delete': self.button3(event) elif key == 'enter': self.button2(event) else: self.button1(event) def button1( self, event ): """ Will be called for any event involving a button other than button 2 or 3. This will add a click if it is inside axes. """ if event.inaxes: self.add_click(event) else: # If not a valid click, remove from event list BlockingInput.pop(self) def button2( self, event ): """ Will be called for any event involving button 2. Button 2 ends blocking input. """ # Remove last event just for cleanliness BlockingInput.pop(self) # This will exit even if not in infinite mode. This is # consistent with matlab and sometimes quite useful, but will # require the user to test how many points were actually # returned before using data. self.fig.canvas.stop_event_loop() def button3( self, event ): """ Will be called for any event involving button 3. Button 3 removes the last click. """ # Remove this last event BlockingInput.pop(self) # Now remove any existing clicks if possible if len(self.events)>0: self.pop() def add_click(self,event): """ This add the coordinates of an event to the list of clicks """ self.clicks.append((event.xdata,event.ydata)) verbose.report("input %i: %f,%f" % (len(self.clicks),event.xdata, event.ydata)) # If desired plot up click if self.show_clicks: self.marks.extend( event.inaxes.plot([event.xdata,], [event.ydata,], 'r+') ) self.fig.canvas.draw() def pop_click(self,index=-1): """ This removes a click from the list of clicks. Defaults to removing the last click. """ self.clicks.pop(index) if self.show_clicks: mark = self.marks.pop(index) mark.remove() self.fig.canvas.draw() def pop(self,index=-1): """ This removes a click and the associated event from the object. Defaults to removing the last click, but any index can be supplied. """ self.pop_click(index) BlockingInput.pop(self,index) def cleanup(self): # clean the figure if self.show_clicks: for mark in self.marks: mark.remove() self.marks = [] self.fig.canvas.draw() # Call base class to remove callbacks BlockingInput.cleanup(self) def __call__(self, n=1, timeout=30, show_clicks=True): """ Blocking call to retrieve n coordinate pairs through mouse clicks. """ self.show_clicks = show_clicks self.clicks = [] self.marks = [] BlockingInput.__call__(self,n=n,timeout=timeout) return self.clicks class BlockingContourLabeler( BlockingMouseInput ): """ Class that creates a callable object that uses mouse clicks or key clicks on a figure window to place contour labels. """ def __init__(self,cs): self.cs = cs BlockingMouseInput.__init__(self, fig=cs.ax.figure ) def button1(self,event): """ This will be called if an event involving a button other than 2 or 3 occcurs. This will add a label to a contour. """ # Shorthand cs = self.cs if event.inaxes == cs.ax: conmin,segmin,imin,xmin,ymin = cs.find_nearest_contour( event.x, event.y, cs.labelIndiceList)[:5] # Get index of nearest level in subset of levels used for labeling lmin = cs.labelIndiceList.index(conmin) # Coordinates of contour paths = cs.collections[conmin].get_paths() lc = paths[segmin].vertices # In pixel/screen space slc = cs.ax.transData.transform(lc) # Get label width for rotating labels and breaking contours lw = cs.get_label_width(cs.labelLevelList[lmin], cs.labelFmt, cs.labelFontSizeList[lmin]) """ # requires python 2.5 # Figure out label rotation. rotation,nlc = cs.calc_label_rot_and_inline( slc, imin, lw, lc if self.inline else [], self.inline_spacing ) """ # Figure out label rotation. if self.inline: lcarg = lc else: lcarg = None rotation,nlc = cs.calc_label_rot_and_inline( slc, imin, lw, lcarg, self.inline_spacing ) cs.add_label(xmin,ymin,rotation,cs.labelLevelList[lmin], cs.labelCValueList[lmin]) if self.inline: # Remove old, not looping over paths so we can do this up front paths.pop(segmin) # Add paths if not empty or single point for n in nlc: if len(n)>1: paths.append( path.Path(n) ) self.fig.canvas.draw() else: # Remove event if not valid BlockingInput.pop(self) def button3(self,event): """ This will be called if button 3 is clicked. This will remove a label if not in inline mode. Unfortunately, if one is doing inline labels, then there is currently no way to fix the broken contour - once humpty-dumpty is broken, he can't be put back together. In inline mode, this does nothing. """ # Remove this last event - not too important for clabel use # since clabel normally doesn't have a maximum number of # events, but best for cleanliness sake. BlockingInput.pop(self) if self.inline: pass else: self.cs.pop_label() self.cs.ax.figure.canvas.draw() def __call__(self,inline,inline_spacing=5,n=-1,timeout=-1): self.inline=inline self.inline_spacing=inline_spacing BlockingMouseInput.__call__(self,n=n,timeout=timeout, show_clicks=False) class BlockingKeyMouseInput(BlockingInput): """ Class that creates a callable object to retrieve a single mouse or keyboard click """ def __init__(self, fig): BlockingInput.__init__(self, fig=fig, eventslist=('button_press_event','key_press_event') ) def post_event(self): """ Determines if it is a key event """ assert len(self.events)>0, "No events yet" self.keyormouse = self.events[-1].name == 'key_press_event' def __call__(self, timeout=30): """ Blocking call to retrieve a single mouse or key click Returns True if key click, False if mouse, or None if timeout """ self.keyormouse = None BlockingInput.__call__(self,n=1,timeout=timeout) return self.keyormouse	12,119	Python	.py	306	29.859477	127	0.601877	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,283	interpolate.py	numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/delaunay/interpolate.py	import numpy as np from matplotlib._delaunay import compute_planes, linear_interpolate_grid, nn_interpolate_grid from matplotlib._delaunay import nn_interpolate_unstructured __all__ = ['LinearInterpolator', 'NNInterpolator'] def slice2gridspec(key): """Convert a 2-tuple of slices to start,stop,steps for x and y. key -- (slice(ystart,ystop,ystep), slice(xtart, xstop, xstep)) For now, the only accepted step values are imaginary integers (interpreted in the same way numpy.mgrid, etc. do). """ if ((len(key) != 2) or (not isinstance(key[0], slice)) or (not isinstance(key[1], slice))): raise ValueError("only 2-D slices, please") x0 = key[1].start x1 = key[1].stop xstep = key[1].step if not isinstance(xstep, complex) or int(xstep.real) != xstep.real: raise ValueError("only the [start:stop:numsteps1j] form supported") xstep = int(xstep.imag) y0 = key[0].start y1 = key[0].stop ystep = key[0].step if not isinstance(ystep, complex) or int(ystep.real) != ystep.real: raise ValueError("only the [start:stop:numsteps1j] form supported") ystep = int(ystep.imag) return x0, x1, xstep, y0, y1, ystep class LinearInterpolator(object): """Interpolate a function defined on the nodes of a triangulation by using the planes defined by the three function values at each corner of the triangles. LinearInterpolator(triangulation, z, default_value=numpy.nan) triangulation -- Triangulation instance z -- the function values at each node of the triangulation default_value -- a float giving the default value should the interpolating point happen to fall outside of the convex hull of the triangulation At the moment, the only regular rectangular grids are supported for interpolation. vals = interp[ystart:ystop:ysteps1j, xstart:xstop:xsteps1j] vals would then be a (ysteps, xsteps) array containing the interpolated values. These arguments are interpreted the same way as numpy.mgrid. Attributes: planes -- (ntriangles, 3) array of floats specifying the plane for each triangle. Linear Interpolation -------------------- Given the Delauany triangulation (or indeed any complete triangulation) we can interpolate values inside the convex hull by locating the enclosing triangle of the interpolation point and returning the value at that point of the plane defined by the three node values. f = planes[tri,0]x + planes[tri,1]y + planes[tri,2] The interpolated function is C0 continuous across the convex hull of the input points. It is C1 continuous across the convex hull except for the nodes and the edges of the triangulation. """ def __init__(self, triangulation, z, default_value=np.nan): self.triangulation = triangulation self.z = np.asarray(z, dtype=np.float64) self.default_value = default_value self.planes = compute_planes(triangulation.x, triangulation.y, self.z, triangulation.triangle_nodes) def __getitem__(self, key): x0, x1, xstep, y0, y1, ystep = slice2gridspec(key) grid = linear_interpolate_grid(x0, x1, xstep, y0, y1, ystep, self.default_value, self.planes, self.triangulation.x, self.triangulation.y, self.triangulation.triangle_nodes, self.triangulation.triangle_neighbors) return grid class NNInterpolator(object): """Interpolate a function defined on the nodes of a triangulation by the natural neighbors method. NNInterpolator(triangulation, z, default_value=numpy.nan) triangulation -- Triangulation instance z -- the function values at each node of the triangulation default_value -- a float giving the default value should the interpolating point happen to fall outside of the convex hull of the triangulation At the moment, the only regular rectangular grids are supported for interpolation. vals = interp[ystart:ystop:ysteps1j, xstart:xstop:xsteps1j] vals would then be a (ysteps, xsteps) array containing the interpolated values. These arguments are interpreted the same way as numpy.mgrid. Natural Neighbors Interpolation ------------------------------- One feature of the Delaunay triangulation is that for each triangle, its circumcircle contains no other point (although in degenerate cases, like squares, other points may be on the circumcircle). One can also construct what is called the Voronoi diagram from a Delaunay triangulation by connecting the circumcenters of the triangles to those of their neighbors to form a tesselation of irregular polygons covering the plane and containing only one node from the triangulation. Each point in one node's Voronoi polygon is closer to that node than any other node. To compute the Natural Neighbors interpolant, we consider adding the interpolation point to the triangulation. We define the natural neighbors of this point as the set of nodes participating in Delaunay triangles whose circumcircles contain the point. To restore the Delaunay-ness of the triangulation, one would only have to alter those triangles and Voronoi polygons. The new Voronooi diagram would have a polygon around the inserted point. This polygon would "steal" area from the original Voronoi polygons. For each node i in the natural neighbors set, we compute the area stolen from its original Voronoi polygon, stolen[i]. We define the natural neighbors coordinates phi[i] = stolen[i] / sum(stolen,axis=0) We then use these phi[i] to weight the corresponding function values from the input data z to compute the interpolated value. The interpolated surface is C1-continuous except at the nodes themselves across the convex hull of the input points. One can find the set of points that a given node will affect by computing the union of the areas covered by the circumcircles of each Delaunay triangle that node participates in. """ def __init__(self, triangulation, z, default_value=np.nan): self.triangulation = triangulation self.z = np.asarray(z, dtype=np.float64) self.default_value = default_value def __getitem__(self, key): x0, x1, xstep, y0, y1, ystep = slice2gridspec(key) grid = nn_interpolate_grid(x0, x1, xstep, y0, y1, ystep, self.default_value, self.triangulation.x, self.triangulation.y, self.z, self.triangulation.circumcenters, self.triangulation.triangle_nodes, self.triangulation.triangle_neighbors) return grid def __call__(self, intx, inty): intz = nn_interpolate_unstructured(intx, inty, self.default_value, self.triangulation.x, self.triangulation.y, self.z, self.triangulation.circumcenters, self.triangulation.triangle_nodes, self.triangulation.triangle_neighbors) return intz	7,068	Python	.py	127	49.086614	93	0.721338	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,284	testfuncs.py	numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/delaunay/testfuncs.py	"""Some test functions for bivariate interpolation. Most of these have been yoinked from ACM TOMS 792. http://netlib.org/toms/792 """ import numpy as np from triangulate import Triangulation class TestData(dict): def __init__(self, args, kwds): dict.__init__(self, args, kwds) self.__dict__ = self class TestDataSet(object): def __init__(self, kwds): self.__dict__.update(kwds) data = TestData( franke100=TestDataSet( x=np.array([ 0.0227035, 0.0539888, 0.0217008, 0.0175129, 0.0019029, -0.0509685, 0.0395408, -0.0487061, 0.0315828, -0.0418785, 0.1324189, 0.1090271, 0.1254439, 0.093454 , 0.0767578, 0.1451874, 0.0626494, 0.1452734, 0.0958668, 0.0695559, 0.2645602, 0.2391645, 0.208899 , 0.2767329, 0.1714726, 0.2266781, 0.1909212, 0.1867647, 0.2304634, 0.2426219, 0.3663168, 0.3857662, 0.3832392, 0.3179087, 0.3466321, 0.3776591, 0.3873159, 0.3812917, 0.3795364, 0.2803515, 0.4149771, 0.4277679, 0.420001 , 0.4663631, 0.4855658, 0.4092026, 0.4792578, 0.4812279, 0.3977761, 0.4027321, 0.5848691, 0.5730076, 0.6063893, 0.5013894, 0.5741311, 0.6106955, 0.5990105, 0.5380621, 0.6096967, 0.5026188, 0.6616928, 0.6427836, 0.6396475, 0.6703963, 0.7001181, 0.633359 , 0.6908947, 0.6895638, 0.6718889, 0.6837675, 0.7736939, 0.7635332, 0.7410424, 0.8258981, 0.7306034, 0.8086609, 0.8214531, 0.729064 , 0.8076643, 0.8170951, 0.8424572, 0.8684053, 0.8366923, 0.9418461, 0.8478122, 0.8599583, 0.91757 , 0.8596328, 0.9279871, 0.8512805, 1.044982 , 0.9670631, 0.9857884, 0.9676313, 1.0129299, 0.965704 , 1.0019855, 1.0359297, 1.0414677, 0.9471506]), y=np.array([-0.0310206, 0.1586742, 0.2576924, 0.3414014, 0.4943596, 0.5782854, 0.6993418, 0.7470194, 0.9107649, 0.996289 , 0.050133 , 0.0918555, 0.2592973, 0.3381592, 0.4171125, 0.5615563, 0.6552235, 0.7524066, 0.9146523, 0.9632421, 0.0292939, 0.0602303, 0.2668783, 0.3696044, 0.4801738, 0.5940595, 0.6878797, 0.8185576, 0.9046507, 0.9805412, 0.0396955, 0.0684484, 0.2389548, 0.3124129, 0.4902989, 0.5199303, 0.6445227, 0.8203789, 0.8938079, 0.9711719, -0.0284618, 0.1560965, 0.2262471, 0.3175094, 0.3891417, 0.5084949, 0.6324247, 0.7511007, 0.8489712, 0.9978728, -0.0271948, 0.127243 , 0.2709269, 0.3477728, 0.4259422, 0.6084711, 0.6733781, 0.7235242, 0.9242411, 1.0308762, 0.0255959, 0.0707835, 0.2008336, 0.3259843, 0.4890704, 0.5096324, 0.669788 , 0.7759569, 0.9366096, 1.0064516, 0.0285374, 0.1021403, 0.1936581, 0.3235775, 0.4714228, 0.6091595, 0.6685053, 0.8022808, 0.847679 , 1.0512371, 0.0380499, 0.0902048, 0.2083092, 0.3318491, 0.4335632, 0.5910139, 0.6307383, 0.8144841, 0.904231 , 0.969603 , -0.01209 , 0.1334114, 0.2695844, 0.3795281, 0.4396054, 0.5044425, 0.6941519, 0.7459923, 0.8682081, 0.9801409])), franke33=TestDataSet( x=np.array([ 5.00000000e-02, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.00000000e-01, 1.00000000e-01, 1.50000000e-01, 2.00000000e-01, 2.50000000e-01, 3.00000000e-01, 3.50000000e-01, 5.00000000e-01, 5.00000000e-01, 5.50000000e-01, 6.00000000e-01, 6.00000000e-01, 6.00000000e-01, 6.50000000e-01, 7.00000000e-01, 7.00000000e-01, 7.00000000e-01, 7.50000000e-01, 7.50000000e-01, 7.50000000e-01, 8.00000000e-01, 8.00000000e-01, 8.50000000e-01, 9.00000000e-01, 9.00000000e-01, 9.50000000e-01, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00]), y=np.array([ 4.50000000e-01, 5.00000000e-01, 1.00000000e+00, 0.00000000e+00, 1.50000000e-01, 7.50000000e-01, 3.00000000e-01, 1.00000000e-01, 2.00000000e-01, 3.50000000e-01, 8.50000000e-01, 0.00000000e+00, 1.00000000e+00, 9.50000000e-01, 2.50000000e-01, 6.50000000e-01, 8.50000000e-01, 7.00000000e-01, 2.00000000e-01, 6.50000000e-01, 9.00000000e-01, 1.00000000e-01, 3.50000000e-01, 8.50000000e-01, 4.00000000e-01, 6.50000000e-01, 2.50000000e-01, 3.50000000e-01, 8.00000000e-01, 9.00000000e-01, 0.00000000e+00, 5.00000000e-01, 1.00000000e+00])), lawson25=TestDataSet( x=np.array([ 0.1375, 0.9125, 0.7125, 0.225 , -0.05 , 0.475 , 0.05 , 0.45 , 1.0875, 0.5375, -0.0375, 0.1875, 0.7125, 0.85 , 0.7 , 0.275 , 0.45 , 0.8125, 0.45 , 1. , 0.5 , 0.1875, 0.5875, 1.05 , 0.1 ]), y=np.array([ 0.975 , 0.9875 , 0.7625 , 0.8375 , 0.4125 , 0.6375 , -0.05 , 1.0375 , 0.55 , 0.8 , 0.75 , 0.575 , 0.55 , 0.4375 , 0.3125 , 0.425 , 0.2875 , 0.1875 , -0.0375 , 0.2625 , 0.4625 , 0.2625 , 0.125 , -0.06125, 0.1125 ])), random100=TestDataSet( x=np.array([ 0.0096326, 0.0216348, 0.029836 , 0.0417447, 0.0470462, 0.0562965, 0.0646857, 0.0740377, 0.0873907, 0.0934832, 0.1032216, 0.1110176, 0.1181193, 0.1251704, 0.132733 , 0.1439536, 0.1564861, 0.1651043, 0.1786039, 0.1886405, 0.2016706, 0.2099886, 0.2147003, 0.2204141, 0.2343715, 0.240966 , 0.252774 , 0.2570839, 0.2733365, 0.2853833, 0.2901755, 0.2964854, 0.3019725, 0.3125695, 0.3307163, 0.3378504, 0.3439061, 0.3529922, 0.3635507, 0.3766172, 0.3822429, 0.3869838, 0.3973137, 0.4170708, 0.4255588, 0.4299218, 0.4372839, 0.4705033, 0.4736655, 0.4879299, 0.494026 , 0.5055324, 0.5162593, 0.5219219, 0.5348529, 0.5483213, 0.5569571, 0.5638611, 0.5784908, 0.586395 , 0.5929148, 0.5987839, 0.6117561, 0.6252296, 0.6331381, 0.6399048, 0.6488972, 0.6558537, 0.6677405, 0.6814074, 0.6887812, 0.6940896, 0.7061687, 0.7160957, 0.7317445, 0.7370798, 0.746203 , 0.7566957, 0.7699998, 0.7879347, 0.7944014, 0.8164468, 0.8192794, 0.8368405, 0.8500993, 0.8588255, 0.8646496, 0.8792329, 0.8837536, 0.8900077, 0.8969894, 0.9044917, 0.9083947, 0.9203972, 0.9347906, 0.9434519, 0.9490328, 0.9569571, 0.9772067, 0.9983493]), y=np.array([ 0.3083158, 0.2450434, 0.8613847, 0.0977864, 0.3648355, 0.7156339, 0.5311312, 0.9755672, 0.1781117, 0.5452797, 0.1603881, 0.7837139, 0.9982015, 0.6910589, 0.104958 , 0.8184662, 0.7086405, 0.4456593, 0.1178342, 0.3189021, 0.9668446, 0.7571834, 0.2016598, 0.3232444, 0.4368583, 0.8907869, 0.064726 , 0.5692618, 0.2947027, 0.4332426, 0.3347464, 0.7436284, 0.1066265, 0.8845357, 0.515873 , 0.9425637, 0.4799701, 0.1783069, 0.114676 , 0.8225797, 0.2270688, 0.4073598, 0.887508 , 0.7631616, 0.9972804, 0.4959884, 0.3410421, 0.249812 , 0.6409007, 0.105869 , 0.5411969, 0.0089792, 0.8784268, 0.5515874, 0.4038952, 0.1654023, 0.2965158, 0.3660356, 0.0366554, 0.950242 , 0.2638101, 0.9277386, 0.5377694, 0.7374676, 0.4674627, 0.9186109, 0.0416884, 0.1291029, 0.6763676, 0.8444238, 0.3273328, 0.1893879, 0.0645923, 0.0180147, 0.8904992, 0.4160648, 0.4688995, 0.2174508, 0.5734231, 0.8853319, 0.8018436, 0.6388941, 0.8931002, 0.1000558, 0.2789506, 0.9082948, 0.3259159, 0.8318747, 0.0508513, 0.970845 , 0.5120548, 0.2859716, 0.9581641, 0.6183429, 0.3779934, 0.4010423, 0.9478657, 0.7425486, 0.8883287, 0.549675 ])), uniform9=TestDataSet( x=np.array([ 1.25000000e-01, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.25000000e-01, 1.25000000e-01, 1.25000000e-01, 1.25000000e-01, 1.25000000e-01, 1.25000000e-01, 1.25000000e-01, 1.25000000e-01, 2.50000000e-01, 2.50000000e-01, 2.50000000e-01, 2.50000000e-01, 2.50000000e-01, 2.50000000e-01, 2.50000000e-01, 2.50000000e-01, 2.50000000e-01, 3.75000000e-01, 3.75000000e-01, 3.75000000e-01, 3.75000000e-01, 3.75000000e-01, 3.75000000e-01, 3.75000000e-01, 3.75000000e-01, 3.75000000e-01, 5.00000000e-01, 5.00000000e-01, 5.00000000e-01, 5.00000000e-01, 5.00000000e-01, 5.00000000e-01, 5.00000000e-01, 5.00000000e-01, 5.00000000e-01, 6.25000000e-01, 6.25000000e-01, 6.25000000e-01, 6.25000000e-01, 6.25000000e-01, 6.25000000e-01, 6.25000000e-01, 6.25000000e-01, 6.25000000e-01, 7.50000000e-01, 7.50000000e-01, 7.50000000e-01, 7.50000000e-01, 7.50000000e-01, 7.50000000e-01, 7.50000000e-01, 7.50000000e-01, 7.50000000e-01, 8.75000000e-01, 8.75000000e-01, 8.75000000e-01, 8.75000000e-01, 8.75000000e-01, 8.75000000e-01, 8.75000000e-01, 8.75000000e-01, 8.75000000e-01, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00]), y=np.array([ 0.00000000e+00, 1.25000000e-01, 2.50000000e-01, 3.75000000e-01, 5.00000000e-01, 6.25000000e-01, 7.50000000e-01, 8.75000000e-01, 1.00000000e+00, 0.00000000e+00, 1.25000000e-01, 2.50000000e-01, 3.75000000e-01, 5.00000000e-01, 6.25000000e-01, 7.50000000e-01, 8.75000000e-01, 1.00000000e+00, 0.00000000e+00, 1.25000000e-01, 2.50000000e-01, 3.75000000e-01, 5.00000000e-01, 6.25000000e-01, 7.50000000e-01, 8.75000000e-01, 1.00000000e+00, 0.00000000e+00, 1.25000000e-01, 2.50000000e-01, 3.75000000e-01, 5.00000000e-01, 6.25000000e-01, 7.50000000e-01, 8.75000000e-01, 1.00000000e+00, 0.00000000e+00, 1.25000000e-01, 2.50000000e-01, 3.75000000e-01, 5.00000000e-01, 6.25000000e-01, 7.50000000e-01, 8.75000000e-01, 1.00000000e+00, 0.00000000e+00, 1.25000000e-01, 2.50000000e-01, 3.75000000e-01, 5.00000000e-01, 6.25000000e-01, 7.50000000e-01, 8.75000000e-01, 1.00000000e+00, 0.00000000e+00, 1.25000000e-01, 2.50000000e-01, 3.75000000e-01, 5.00000000e-01, 6.25000000e-01, 7.50000000e-01, 8.75000000e-01, 1.00000000e+00, 0.00000000e+00, 1.25000000e-01, 2.50000000e-01, 3.75000000e-01, 5.00000000e-01, 6.25000000e-01, 7.50000000e-01, 8.75000000e-01, 1.00000000e+00, 0.00000000e+00, 1.25000000e-01, 2.50000000e-01, 3.75000000e-01, 5.00000000e-01, 6.25000000e-01, 7.50000000e-01, 8.75000000e-01, 1.00000000e+00])), ) def constant(x, y): return np.ones(x.shape, x.dtype) constant.title = 'Constant' def xramp(x, y): return x xramp.title = 'X Ramp' def yramp(x, y): return y yramp.title = 'Y Ramp' def exponential(x, y): x = x9 y = y9 x1 = x+1.0 x2 = x-2.0 x4 = x-4.0 x7 = x-7.0 y1 = x+1.0 y2 = y-2.0 y3 = y-3.0 y7 = y-7.0 f = (0.75 * np.exp(-(x2x2+y2y2)/4.0) + 0.75 * np.exp(-x1x1/49.0 - y1/10.0) + 0.5 np.exp(-(x7x7 + y3y3)/4.0) - 0.2 * np.exp(-x4x4 -y7y7)) return f exponential.title = 'Exponential and Some Gaussians' def cliff(x, y): f = np.tanh(9.0(y-x) + 1.0)/9.0 return f cliff.title = 'Cliff' def saddle(x, y): f = (1.25 + np.cos(5.4y))/(6.0 + 6.0(3x-1.0)*2) return f saddle.title = 'Saddle' def gentle(x, y): f = np.exp(-5.0625((x-0.5)2+(y-0.5)2))/3.0 return f gentle.title = 'Gentle Peak' def steep(x, y): f = np.exp(-20.25((x-0.5)2+(y-0.5)2))/3.0 return f steep.title = 'Steep Peak' def sphere(x, y): circle = 64-81((x-0.5)2 + (y-0.5)2) f = np.where(circle >= 0, np.sqrt(np.clip(circle,0,100)) - 0.5, 0.0) return f sphere.title = 'Sphere' def trig(x, y): f = 2.0np.cos(10.0x)np.sin(10.0y) + np.sin(10.0xy) return f trig.title = 'Cosines and Sines' def gauss(x, y): x = 5.0-10.0x y = 5.0-10.0y g1 = np.exp(-xx/2) g2 = np.exp(-yy/2) f = g1 + 0.75g2(1 + g1) return f gauss.title = 'Gaussian Peak and Gaussian Ridges' def cloverleaf(x, y): ex = np.exp((10.0-20.0x)/3.0) ey = np.exp((10.0-20.0y)/3.0) logitx = 1.0/(1.0+ex) logity = 1.0/(1.0+ey) f = (((20.0/3.0)*3 exey)2 (logitxlogity)5 (ex-2.0logitx)(ey-2.0logity)) return f cloverleaf.title = 'Cloverleaf' def cosine_peak(x, y): circle = np.hypot(80x-40.0, 90y-45.) f = np.exp(-0.04circle) * np.cos(0.15circle) return f cosine_peak.title = 'Cosine Peak' allfuncs = [exponential, cliff, saddle, gentle, steep, sphere, trig, gauss, cloverleaf, cosine_peak] class LinearTester(object): name = 'Linear' def __init__(self, xrange=(0.0, 1.0), yrange=(0.0, 1.0), nrange=101, npoints=250): self.xrange = xrange self.yrange = yrange self.nrange = nrange self.npoints = npoints rng = np.random.RandomState(1234567890) self.x = rng.uniform(xrange[0], xrange[1], size=npoints) self.y = rng.uniform(yrange[0], yrange[1], size=npoints) self.tri = Triangulation(self.x, self.y) def replace_data(self, dataset): self.x = dataset.x self.y = dataset.y self.tri = Triangulation(self.x, self.y) def interpolator(self, func): z = func(self.x, self.y) return self.tri.linear_extrapolator(z, bbox=self.xrange+self.yrange) def plot(self, func, interp=True, plotter='imshow'): import matplotlib as mpl from matplotlib import pylab as pl if interp: lpi = self.interpolator(func) z = lpi[self.yrange[0]:self.yrange[1]:complex(0,self.nrange), self.xrange[0]:self.xrange[1]:complex(0,self.nrange)] else: y, x = np.mgrid[self.yrange[0]:self.yrange[1]:complex(0,self.nrange), self.xrange[0]:self.xrange[1]:complex(0,self.nrange)] z = func(x, y) z = np.where(np.isinf(z), 0.0, z) extent = (self.xrange[0], self.xrange[1], self.yrange[0], self.yrange[1]) pl.ioff() pl.clf() pl.hot() # Some like it hot if plotter == 'imshow': pl.imshow(np.nan_to_num(z), interpolation='nearest', extent=extent, origin='lower') elif plotter == 'contour': Y, X = np.ogrid[self.yrange[0]:self.yrange[1]:complex(0,self.nrange), self.xrange[0]:self.xrange[1]:complex(0,self.nrange)] pl.contour(np.ravel(X), np.ravel(Y), z, 20) x = self.x y = self.y lc = mpl.collections.LineCollection(np.array([((x[i], y[i]), (x[j], y[j])) for i, j in self.tri.edge_db]), colors=[(0,0,0,0.2)]) ax = pl.gca() ax.add_collection(lc) if interp: title = '%s Interpolant' % self.name else: title = 'Reference' if hasattr(func, 'title'): pl.title('%s: %s' % (func.title, title)) else: pl.title(title) pl.show() pl.ion() class NNTester(LinearTester): name = 'Natural Neighbors' def interpolator(self, func): z = func(self.x, self.y) return self.tri.nn_extrapolator(z, bbox=self.xrange+self.yrange) def plotallfuncs(allfuncs=allfuncs): from matplotlib import pylab as pl pl.ioff() nnt = NNTester(npoints=1000) lpt = LinearTester(npoints=1000) for func in allfuncs: print func.title nnt.plot(func, interp=False, plotter='imshow') pl.savefig('%s-ref-img.png' % func.func_name) nnt.plot(func, interp=True, plotter='imshow') pl.savefig('%s-nn-img.png' % func.func_name) lpt.plot(func, interp=True, plotter='imshow') pl.savefig('%s-lin-img.png' % func.func_name) nnt.plot(func, interp=False, plotter='contour') pl.savefig('%s-ref-con.png' % func.func_name) nnt.plot(func, interp=True, plotter='contour') pl.savefig('%s-nn-con.png' % func.func_name) lpt.plot(func, interp=True, plotter='contour') pl.savefig('%s-lin-con.png' % func.func_name) pl.ion() def plot_dt(tri, colors=None): import matplotlib as mpl from matplotlib import pylab as pl if colors is None: colors = [(0,0,0,0.2)] lc = mpl.collections.LineCollection(np.array([((tri.x[i], tri.y[i]), (tri.x[j], tri.y[j])) for i, j in tri.edge_db]), colors=colors) ax = pl.gca() ax.add_collection(lc) pl.draw_if_interactive() def plot_vo(tri, colors=None): import matplotlib as mpl from matplotlib import pylab as pl if colors is None: colors = [(0,1,0,0.2)] lc = mpl.collections.LineCollection(np.array( [(tri.circumcenters[i], tri.circumcenters[j]) for i in xrange(len(tri.circumcenters)) for j in tri.triangle_neighbors[i] if j != -1]), colors=colors) ax = pl.gca() ax.add_collection(lc) pl.draw_if_interactive() def plot_cc(tri, edgecolor=None): import matplotlib as mpl from matplotlib import pylab as pl if edgecolor is None: edgecolor = (0,0,1,0.2) dxy = (np.array([(tri.x[i], tri.y[i]) for i,j,k in tri.triangle_nodes]) - tri.circumcenters) r = np.hypot(dxy[:,0], dxy[:,1]) ax = pl.gca() for i in xrange(len(r)): p = mpl.patches.Circle(tri.circumcenters[i], r[i], resolution=100, edgecolor=edgecolor, facecolor=(1,1,1,0), linewidth=0.2) ax.add_patch(p) pl.draw_if_interactive() def quality(func, mesh, interpolator='nn', n=33): """Compute a quality factor (the quantity r2 from TOMS792). interpolator must be in ('linear', 'nn'). """ fz = func(mesh.x, mesh.y) tri = Triangulation(mesh.x, mesh.y) intp = getattr(tri, interpolator+'_extrapolator')(fz, bbox=(0.,1.,0.,1.)) Y, X = np.mgrid[0:1:complex(0,n),0:1:complex(0,n)] Z = func(X, Y) iz = intp[0:1:complex(0,n),0:1:complex(0,n)] #nans = np.isnan(iz) #numgood = nn - np.sum(np.array(nans.flat, np.int32)) numgood = nn SE = (Z - iz)2 SSE = np.sum(SE.flat) meanZ = np.sum(Z.flat) / numgood SM = (Z - meanZ)*2 SSM = np.sum(SM.flat) r2 = 1.0 - SSE/SSM print func.func_name, r2, SSE, SSM, numgood return r2 def allquality(interpolator='nn', allfuncs=allfuncs, data=data, n=33): results = {} kv = data.items() kv.sort() for name, mesh in kv: reslist = results.setdefault(name, []) for func in allfuncs: reslist.append(quality(func, mesh, interpolator, n)) return results def funky(): x0 = np.array([0.25, 0.3, 0.5, 0.6, 0.6]) y0 = np.array([0.2, 0.35, 0.0, 0.25, 0.65]) tx = 0.46 ty = 0.23 t0 = Triangulation(x0, y0) t1 = Triangulation(np.hstack((x0, [tx])), np.hstack((y0, [ty]))) return t0, t1	20,890	Python	.py	415	39.6	100	0.551422	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,285	__init__.py	numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/delaunay/__init__.py	"""Delaunay triangulation and interpolation tools. :Author: Robert Kern <robert.kern@gmail.com> :Copyright: Copyright 2005 Robert Kern. :License: BSD-style license. See LICENSE.txt in the scipy source directory. """ from matplotlib._delaunay import delaunay from triangulate import * from interpolate import * __version__ = "0.1"	332	Python	.py	9	35.666667	75	0.794393	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,286	triangulate.py	numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/delaunay/triangulate.py	import warnings try: set except NameError: from sets import Set as set import numpy as np from matplotlib._delaunay import delaunay from interpolate import LinearInterpolator, NNInterpolator __all__ = ['Triangulation', 'DuplicatePointWarning'] class DuplicatePointWarning(RuntimeWarning): """Duplicate points were passed in to the triangulation routine. """ class Triangulation(object): """A Delaunay triangulation of points in a plane. Triangulation(x, y) x, y -- the coordinates of the points as 1-D arrays of floats Let us make the following definitions: npoints = number of points input nedges = number of edges in the triangulation ntriangles = number of triangles in the triangulation point_id = an integer identifying a particular point (specifically, an index into x and y), range(0, npoints) edge_id = an integer identifying a particular edge, range(0, nedges) triangle_id = an integer identifying a particular triangle range(0, ntriangles) Attributes: (all should be treated as read-only to maintain consistency) x, y -- the coordinates of the points as 1-D arrays of floats. circumcenters -- (ntriangles, 2) array of floats giving the (x,y) coordinates of the circumcenters of each triangle (indexed by a triangle_id). edge_db -- (nedges, 2) array of point_id's giving the points forming each edge in no particular order; indexed by an edge_id. triangle_nodes -- (ntriangles, 3) array of point_id's giving the points forming each triangle in counter-clockwise order; indexed by a triangle_id. triangle_neighbors -- (ntriangles, 3) array of triangle_id's giving the neighboring triangle; indexed by a triangle_id. The value can also be -1 meaning that that edge is on the convex hull of the points and there is no neighbor on that edge. The values are ordered such that triangle_neighbors[tri, i] corresponds with the edge opposite triangle_nodes[tri, i]. As such, these neighbors are also in counter-clockwise order. hull -- list of point_id's giving the nodes which form the convex hull of the point set. This list is sorted in counter-clockwise order. """ def __init__(self, x, y): self.x = np.asarray(x, dtype=np.float64) self.y = np.asarray(y, dtype=np.float64) if self.x.shape != self.y.shape or len(self.x.shape) != 1: raise ValueError("x,y must be equal-length 1-D arrays") self.old_shape = self.x.shape j_unique = self._collapse_duplicate_points() if j_unique.shape != self.x.shape: warnings.warn( "Input data contains duplicate x,y points; some values are ignored.", DuplicatePointWarning, ) self.j_unique = j_unique self.x = self.x[self.j_unique] self.y = self.y[self.j_unique] else: self.j_unique = None self.circumcenters, self.edge_db, self.triangle_nodes, \ self.triangle_neighbors = delaunay(self.x, self.y) self.hull = self._compute_convex_hull() def _collapse_duplicate_points(self): """Generate index array that picks out unique x,y points. This appears to be required by the underlying delaunay triangulation code. """ # Find the indices of the unique entries j_sorted = np.lexsort(keys=(self.x, self.y)) mask_unique = np.hstack([ True, (np.diff(self.x[j_sorted]) != 0) \| (np.diff(self.y[j_sorted]) != 0), ]) return j_sorted[mask_unique] def _compute_convex_hull(self): """Extract the convex hull from the triangulation information. The output will be a list of point_id's in counter-clockwise order forming the convex hull of the data set. """ border = (self.triangle_neighbors == -1) edges = {} edges.update(dict(zip(self.triangle_nodes[border[:,0]][:,1], self.triangle_nodes[border[:,0]][:,2]))) edges.update(dict(zip(self.triangle_nodes[border[:,1]][:,2], self.triangle_nodes[border[:,1]][:,0]))) edges.update(dict(zip(self.triangle_nodes[border[:,2]][:,0], self.triangle_nodes[border[:,2]][:,1]))) # Take an arbitrary starting point and its subsequent node hull = list(edges.popitem()) while edges: hull.append(edges.pop(hull[-1])) # hull[-1] == hull[0], so remove hull[-1] hull.pop() return hull def linear_interpolator(self, z, default_value=np.nan): """Get an object which can interpolate within the convex hull by assigning a plane to each triangle. z -- an array of floats giving the known function values at each point in the triangulation. """ z = np.asarray(z, dtype=np.float64) if z.shape != self.old_shape: raise ValueError("z must be the same shape as x and y") if self.j_unique is not None: z = z[self.j_unique] return LinearInterpolator(self, z, default_value) def nn_interpolator(self, z, default_value=np.nan): """Get an object which can interpolate within the convex hull by the natural neighbors method. z -- an array of floats giving the known function values at each point in the triangulation. """ z = np.asarray(z, dtype=np.float64) if z.shape != self.old_shape: raise ValueError("z must be the same shape as x and y") if self.j_unique is not None: z = z[self.j_unique] return NNInterpolator(self, z, default_value) def prep_extrapolator(self, z, bbox=None): if bbox is None: bbox = (self.x[0], self.x[0], self.y[0], self.y[0]) minx, maxx, miny, maxy = np.asarray(bbox, np.float64) minx = min(minx, np.minimum.reduce(self.x)) miny = min(miny, np.minimum.reduce(self.y)) maxx = max(maxx, np.maximum.reduce(self.x)) maxy = max(maxy, np.maximum.reduce(self.y)) M = max((maxx-minx)/2, (maxy-miny)/2) midx = (minx + maxx)/2.0 midy = (miny + maxy)/2.0 xp, yp= np.array([[midx+3M, midx, midx-3M], [midy, midy+3M, midy-3M]]) x1 = np.hstack((self.x, xp)) y1 = np.hstack((self.y, yp)) newtri = self.__class__(x1, y1) # do a least-squares fit to a plane to make pseudo-data xy1 = np.ones((len(self.x), 3), np.float64) xy1[:,0] = self.x xy1[:,1] = self.y from numpy.dual import lstsq c, res, rank, s = lstsq(xy1, z) zp = np.hstack((z, xpc[0] + ypc[1] + c[2])) return newtri, zp def nn_extrapolator(self, z, bbox=None, default_value=np.nan): newtri, zp = self.prep_extrapolator(z, bbox) return newtri.nn_interpolator(zp, default_value) def linear_extrapolator(self, z, bbox=None, default_value=np.nan): newtri, zp = self.prep_extrapolator(z, bbox) return newtri.linear_interpolator(zp, default_value) def node_graph(self): """Return a graph of node_id's pointing to node_id's. The arcs of the graph correspond to the edges in the triangulation. {node_id: set([node_id, ...]), ...} """ g = {} for i, j in self.edge_db: s = g.setdefault(i, set()) s.add(j) s = g.setdefault(j, set()) s.add(i) return g	7,732	Python	.py	163	37.957055	85	0.616111	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,287	polar.py	numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/projections/polar.py	import math import numpy as npy import matplotlib rcParams = matplotlib.rcParams from matplotlib.artist import kwdocd from matplotlib.axes import Axes from matplotlib import cbook from matplotlib.patches import Circle from matplotlib.path import Path from matplotlib.ticker import Formatter, Locator from matplotlib.transforms import Affine2D, Affine2DBase, Bbox, \ BboxTransformTo, IdentityTransform, Transform, TransformWrapper class PolarAxes(Axes): """ A polar graph projection, where the input dimensions are theta, r. Theta starts pointing east and goes anti-clockwise. """ name = 'polar' class PolarTransform(Transform): """ The base polar transform. This handles projection theta and r into Cartesian coordinate space x and y, but does not perform the ultimate affine transformation into the correct position. """ input_dims = 2 output_dims = 2 is_separable = False def __init__(self, resolution): """ Create a new polar transform. Resolution is the number of steps to interpolate between each input line segment to approximate its path in curved polar space. """ Transform.__init__(self) self._resolution = resolution def transform(self, tr): xy = npy.zeros(tr.shape, npy.float_) t = tr[:, 0:1] r = tr[:, 1:2] x = xy[:, 0:1] y = xy[:, 1:2] x[:] = r * npy.cos(t) y[:] = r * npy.sin(t) return xy transform.__doc__ = Transform.transform.__doc__ transform_non_affine = transform transform_non_affine.__doc__ = Transform.transform_non_affine.__doc__ def transform_path(self, path): vertices = path.vertices t = vertices[:, 0:1] t[t != (npy.pi * 2.0)] %= (npy.pi * 2.0) if len(vertices) == 2 and vertices[0, 0] == vertices[1, 0]: return Path(self.transform(vertices), path.codes) ipath = path.interpolated(self._resolution) return Path(self.transform(ipath.vertices), ipath.codes) transform_path.__doc__ = Transform.transform_path.__doc__ transform_path_non_affine = transform_path transform_path_non_affine.__doc__ = Transform.transform_path_non_affine.__doc__ def inverted(self): return PolarAxes.InvertedPolarTransform(self._resolution) inverted.__doc__ = Transform.inverted.__doc__ class PolarAffine(Affine2DBase): """ The affine part of the polar projection. Scales the output so that maximum radius rests on the edge of the axes circle. """ def __init__(self, scale_transform, limits): u""" limits is the view limit of the data. The only part of its bounds that is used is ymax (for the radius maximum). The theta range is always fixed to (0, 2\u03c0). """ Affine2DBase.__init__(self) self._scale_transform = scale_transform self._limits = limits self.set_children(scale_transform, limits) self._mtx = None def get_matrix(self): if self._invalid: limits_scaled = self._limits.transformed(self._scale_transform) ymax = limits_scaled.ymax affine = Affine2D() \ .scale(0.5 / ymax) \ .translate(0.5, 0.5) self._mtx = affine.get_matrix() self._inverted = None self._invalid = 0 return self._mtx get_matrix.__doc__ = Affine2DBase.get_matrix.__doc__ class InvertedPolarTransform(Transform): """ The inverse of the polar transform, mapping Cartesian coordinate space x and y back to theta and r. """ input_dims = 2 output_dims = 2 is_separable = False def __init__(self, resolution): Transform.__init__(self) self._resolution = resolution def transform(self, xy): x = xy[:, 0:1] y = xy[:, 1:] r = npy.sqrt(xx + yy) theta = npy.arccos(x / r) theta = npy.where(y < 0, 2 * npy.pi - theta, theta) return npy.concatenate((theta, r), 1) transform.__doc__ = Transform.transform.__doc__ def inverted(self): return PolarAxes.PolarTransform(self._resolution) inverted.__doc__ = Transform.inverted.__doc__ class ThetaFormatter(Formatter): u""" Used to format the theta tick labels. Converts the native unit of radians into degrees and adds a degree symbol (\u00b0). """ def __call__(self, x, pos=None): # \u00b0 : degree symbol if rcParams['text.usetex'] and not rcParams['text.latex.unicode']: return r"$%0.0f^\circ$" % ((x / npy.pi) * 180.0) else: # we use unicode, rather than mathtext with \circ, so # that it will work correctly with any arbitrary font # (assuming it has a degree sign), whereas $5\circ$ # will only work correctly with one of the supported # math fonts (Computer Modern and STIX) return u"%0.0f\u00b0" % ((x / npy.pi) * 180.0) class RadialLocator(Locator): """ Used to locate radius ticks. Ensures that all ticks are strictly positive. For all other tasks, it delegates to the base :class:`~matplotlib.ticker.Locator` (which may be different depending on the scale of the r-axis. """ def __init__(self, base): self.base = base def __call__(self): ticks = self.base() return [x for x in ticks if x > 0] def autoscale(self): return self.base.autoscale() def pan(self, numsteps): return self.base.pan(numsteps) def zoom(self, direction): return self.base.zoom(direction) def refresh(self): return self.base.refresh() RESOLUTION = 75 def __init__(self, args, kwargs): """ Create a new Polar Axes for a polar plot. """ self._rpad = 0.05 self.resolution = kwargs.pop('resolution', self.RESOLUTION) Axes.__init__(self, args, *kwargs) self.set_aspect('equal', adjustable='box', anchor='C') self.cla() __init__.__doc__ = Axes.__init__.__doc__ def cla(self): Axes.cla(self) self.title.set_y(1.05) self.xaxis.set_major_formatter(self.ThetaFormatter()) angles = npy.arange(0.0, 360.0, 45.0) self.set_thetagrids(angles) self.yaxis.set_major_locator(self.RadialLocator(self.yaxis.get_major_locator())) self.grid(rcParams['polaraxes.grid']) self.xaxis.set_ticks_position('none') self.yaxis.set_ticks_position('none') def _set_lim_and_transforms(self): self.transAxes = BboxTransformTo(self.bbox) # Transforms the x and y axis separately by a scale factor # It is assumed that this part will have non-linear components self.transScale = TransformWrapper(IdentityTransform()) # A (possibly non-linear) projection on the (already scaled) data self.transProjection = self.PolarTransform(self.resolution) # An affine transformation on the data, generally to limit the # range of the axes self.transProjectionAffine = self.PolarAffine(self.transScale, self.viewLim) # The complete data transformation stack -- from data all the # way to display coordinates self.transData = self.transScale + self.transProjection + \ (self.transProjectionAffine + self.transAxes) # This is the transform for theta-axis ticks. It is # equivalent to transData, except it always puts r == 1.0 at # the edge of the axis circle. self._xaxis_transform = ( self.transProjection + self.PolarAffine(IdentityTransform(), Bbox.unit()) + self.transAxes) # The theta labels are moved from radius == 0.0 to radius == 1.1 self._theta_label1_position = Affine2D().translate(0.0, 1.1) self._xaxis_text1_transform = ( self._theta_label1_position + self._xaxis_transform) self._theta_label2_position = Affine2D().translate(0.0, 1.0 / 1.1) self._xaxis_text2_transform = ( self._theta_label2_position + self._xaxis_transform) # This is the transform for r-axis ticks. It scales the theta # axis so the gridlines from 0.0 to 1.0, now go from 0.0 to # 2pi. self._yaxis_transform = ( Affine2D().scale(npy.pi 2.0, 1.0) + self.transData) # The r-axis labels are put at an angle and padded in the r-direction self._r_label1_position = Affine2D().translate(22.5, self._rpad) self._yaxis_text1_transform = ( self._r_label1_position + Affine2D().scale(1.0 / 360.0, 1.0) + self._yaxis_transform ) self._r_label2_position = Affine2D().translate(22.5, self._rpad) self._yaxis_text2_transform = ( self._r_label2_position + Affine2D().scale(1.0 / 360.0, 1.0) + self._yaxis_transform ) def get_xaxis_transform(self): return self._xaxis_transform def get_xaxis_text1_transform(self, pad): return self._xaxis_text1_transform, 'center', 'center' def get_xaxis_text2_transform(self, pad): return self._xaxis_text2_transform, 'center', 'center' def get_yaxis_transform(self): return self._yaxis_transform def get_yaxis_text1_transform(self, pad): return self._yaxis_text1_transform, 'center', 'center' def get_yaxis_text2_transform(self, pad): return self._yaxis_text2_transform, 'center', 'center' def _gen_axes_patch(self): return Circle((0.5, 0.5), 0.5) def set_rmax(self, rmax): self.viewLim.y1 = rmax angle = self._r_label1_position.to_values()[4] self._r_label1_position.clear().translate( angle, rmax * self._rpad) self._r_label2_position.clear().translate( angle, -rmax * self._rpad) def get_rmax(self): return self.viewLim.ymax def set_yscale(self, args, kwargs): Axes.set_yscale(self, args, kwargs) self.yaxis.set_major_locator( self.RadialLocator(self.yaxis.get_major_locator())) set_rscale = Axes.set_yscale set_rticks = Axes.set_yticks def set_thetagrids(self, angles, labels=None, frac=None, kwargs): """ Set the angles at which to place the theta grids (these gridlines are equal along the theta dimension). angles is in degrees. labels, if not None, is a ``len(angles)`` list of strings of the labels to use at each angle. If labels is None, the labels will be ``fmt %% angle`` frac is the fraction of the polar axes radius at which to place the label (1 is the edge). Eg. 1.05 is outside the axes and 0.95 is inside the axes. Return value is a list of tuples (line, label), where line is :class:`~matplotlib.lines.Line2D` instances and the label is :class:`~matplotlib.text.Text` instances. kwargs are optional text properties for the labels: %(Text)s ACCEPTS: sequence of floats """ angles = npy.asarray(angles, npy.float_) self.set_xticks(angles * (npy.pi / 180.0)) if labels is not None: self.set_xticklabels(labels) if frac is not None: self._theta_label1_position.clear().translate(0.0, frac) self._theta_label2_position.clear().translate(0.0, 1.0 / frac) for t in self.xaxis.get_ticklabels(): t.update(kwargs) return self.xaxis.get_ticklines(), self.xaxis.get_ticklabels() set_thetagrids.__doc__ = cbook.dedent(set_thetagrids.__doc__) % kwdocd def set_rgrids(self, radii, labels=None, angle=None, rpad=None, *kwargs): """ Set the radial locations and labels of the r* grids. The labels will appear at radial distances radii at the given angle in degrees. labels, if not None, is a ``len(radii)`` list of strings of the labels to use at each radius. If labels is None, the built-in formatter will be used. rpad is a fraction of the max of radii which will pad each of the radial labels in the radial direction. Return value is a list of tuples (line, label), where line is :class:`~matplotlib.lines.Line2D` instances and the label is :class:`~matplotlib.text.Text` instances. kwargs are optional text properties for the labels: %(Text)s ACCEPTS: sequence of floats """ radii = npy.asarray(radii) rmin = radii.min() if rmin <= 0: raise ValueError('radial grids must be strictly positive') self.set_yticks(radii) if labels is not None: self.set_yticklabels(labels) if angle is None: angle = self._r_label1_position.to_values()[4] if rpad is not None: self._rpad = rpad rmax = self.get_rmax() self._r_label1_position.clear().translate(angle, self._rpad * rmax) self._r_label2_position.clear().translate(angle, -self._rpad * rmax) for t in self.yaxis.get_ticklabels(): t.update(kwargs) return self.yaxis.get_ticklines(), self.yaxis.get_ticklabels() set_rgrids.__doc__ = cbook.dedent(set_rgrids.__doc__) % kwdocd def set_xscale(self, scale, args, kwargs): if scale != 'linear': raise NotImplementedError("You can not set the xscale on a polar plot.") def set_xlim(self, args, *kargs): # The xlim is fixed, no matter what you do self.viewLim.intervalx = (0.0, npy.pi 2.0) def format_coord(self, theta, r): """ Return a format string formatting the coordinate using Unicode characters. """ theta /= math.pi # \u03b8: lower-case theta # \u03c0: lower-case pi # \u00b0: degree symbol return u'\u03b8=%0.3f\u03c0 (%0.3f\u00b0), r=%0.3f' % (theta, theta * 180.0, r) def get_data_ratio(self): ''' Return the aspect ratio of the data itself. For a polar plot, this should always be 1.0 ''' return 1.0 ### Interactive panning def can_zoom(self): """ Return True if this axes support the zoom box """ return False def start_pan(self, x, y, button): angle = self._r_label1_position.to_values()[4] / 180.0 * npy.pi mode = '' if button == 1: epsilon = npy.pi / 45.0 t, r = self.transData.inverted().transform_point((x, y)) if t >= angle - epsilon and t <= angle + epsilon: mode = 'drag_r_labels' elif button == 3: mode = 'zoom' self._pan_start = cbook.Bunch( rmax = self.get_rmax(), trans = self.transData.frozen(), trans_inverse = self.transData.inverted().frozen(), r_label_angle = self._r_label1_position.to_values()[4], x = x, y = y, mode = mode ) def end_pan(self): del self._pan_start def drag_pan(self, button, key, x, y): p = self._pan_start if p.mode == 'drag_r_labels': startt, startr = p.trans_inverse.transform_point((p.x, p.y)) t, r = p.trans_inverse.transform_point((x, y)) # Deal with theta dt0 = t - startt dt1 = startt - t if abs(dt1) < abs(dt0): dt = abs(dt1) * sign(dt0) * -1.0 else: dt = dt0 * -1.0 dt = (dt / npy.pi) * 180.0 rpad = self._r_label1_position.to_values()[5] self._r_label1_position.clear().translate( p.r_label_angle - dt, rpad) self._r_label2_position.clear().translate( p.r_label_angle - dt, -rpad) elif p.mode == 'zoom': startt, startr = p.trans_inverse.transform_point((p.x, p.y)) t, r = p.trans_inverse.transform_point((x, y)) dr = r - startr # Deal with r scale = r / startr self.set_rmax(p.rmax / scale) # These are a couple of aborted attempts to project a polar plot using # cubic bezier curves. # def transform_path(self, path): # twopi = 2.0 * npy.pi # halfpi = 0.5 * npy.pi # vertices = path.vertices # t0 = vertices[0:-1, 0] # t1 = vertices[1: , 0] # td = npy.where(t1 > t0, t1 - t0, twopi - (t0 - t1)) # maxtd = td.max() # interpolate = npy.ceil(maxtd / halfpi) # if interpolate > 1.0: # vertices = self.interpolate(vertices, interpolate) # vertices = self.transform(vertices) # result = npy.zeros((len(vertices) * 3 - 2, 2), npy.float_) # codes = mpath.Path.CURVE4 * npy.ones((len(vertices) * 3 - 2, ), mpath.Path.code_type) # result[0] = vertices[0] # codes[0] = mpath.Path.MOVETO # kappa = 4.0 * ((npy.sqrt(2.0) - 1.0) / 3.0) # kappa = 0.5 # p0 = vertices[0:-1] # p1 = vertices[1: ] # x0 = p0[:, 0:1] # y0 = p0[:, 1: ] # b0 = ((y0 - x0) - y0) / ((x0 + y0) - x0) # a0 = y0 - b0x0 # x1 = p1[:, 0:1] # y1 = p1[:, 1: ] # b1 = ((y1 - x1) - y1) / ((x1 + y1) - x1) # a1 = y1 - b1x1 # x = -(a0-a1) / (b0-b1) # y = a0 + b0x # xk = (x - x0) kappa + x0 # yk = (y - y0) * kappa + y0 # result[1::3, 0:1] = xk # result[1::3, 1: ] = yk # xk = (x - x1) * kappa + x1 # yk = (y - y1) * kappa + y1 # result[2::3, 0:1] = xk # result[2::3, 1: ] = yk # result[3::3] = p1 # print vertices[-2:] # print result[-2:] # return mpath.Path(result, codes) # twopi = 2.0 * npy.pi # halfpi = 0.5 * npy.pi # vertices = path.vertices # t0 = vertices[0:-1, 0] # t1 = vertices[1: , 0] # td = npy.where(t1 > t0, t1 - t0, twopi - (t0 - t1)) # maxtd = td.max() # interpolate = npy.ceil(maxtd / halfpi) # print "interpolate", interpolate # if interpolate > 1.0: # vertices = self.interpolate(vertices, interpolate) # result = npy.zeros((len(vertices) * 3 - 2, 2), npy.float_) # codes = mpath.Path.CURVE4 * npy.ones((len(vertices) * 3 - 2, ), mpath.Path.code_type) # result[0] = vertices[0] # codes[0] = mpath.Path.MOVETO # kappa = 4.0 * ((npy.sqrt(2.0) - 1.0) / 3.0) # tkappa = npy.arctan(kappa) # hyp_kappa = npy.sqrt(kappakappa + 1.0) # t0 = vertices[0:-1, 0] # t1 = vertices[1: , 0] # r0 = vertices[0:-1, 1] # r1 = vertices[1: , 1] # td = npy.where(t1 > t0, t1 - t0, twopi - (t0 - t1)) # td_scaled = td / (npy.pi 0.5) # rd = r1 - r0 # r0kappa = r0 * kappa * td_scaled # r1kappa = r1 * kappa * td_scaled # ravg_kappa = ((r1 + r0) / 2.0) * kappa * td_scaled # result[1::3, 0] = t0 + (tkappa * td_scaled) # result[1::3, 1] = r0hyp_kappa # # result[1::3, 1] = r0 / npy.cos(tkappa td_scaled) # npy.sqrt(r0r0 + ravg_kapparavg_kappa) # result[2::3, 0] = t1 - (tkappa * td_scaled) # result[2::3, 1] = r1hyp_kappa # # result[2::3, 1] = r1 / npy.cos(tkappa td_scaled) # npy.sqrt(r1r1 + ravg_kapparavg_kappa) # result[3::3, 0] = t1 # result[3::3, 1] = r1 # print vertices[:6], result[:6], t0[:6], t1[:6], td[:6], td_scaled[:6], tkappa # result = self.transform(result) # return mpath.Path(result, codes) # transform_path_non_affine = transform_path	20,981	Python	.py	472	35.819915	108	0.555801	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,288	__init__.py	numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/projections/__init__.py	from geo import AitoffAxes, HammerAxes, LambertAxes from polar import PolarAxes from matplotlib import axes class ProjectionRegistry(object): """ Manages the set of projections available to the system. """ def __init__(self): self._all_projection_types = {} def register(self, projections): """ Register a new set of projection(s). """ for projection in projections: name = projection.name self._all_projection_types[name] = projection def get_projection_class(self, name): """ Get a projection class from its name. """ return self._all_projection_types[name] def get_projection_names(self): """ Get a list of the names of all projections currently registered. """ names = self._all_projection_types.keys() names.sort() return names projection_registry = ProjectionRegistry() projection_registry.register( axes.Axes, PolarAxes, AitoffAxes, HammerAxes, LambertAxes) def register_projection(cls): projection_registry.register(cls) def get_projection_class(projection=None): """ Get a projection class from its name. If projection* is None, a standard rectilinear projection is returned. """ if projection is None: projection = 'rectilinear' try: return projection_registry.get_projection_class(projection) except KeyError: raise ValueError("Unknown projection '%s'" % projection) def projection_factory(projection, figure, rect, *kwargs): """ Get a new projection instance. projection* is a projection name. figure is a figure to add the axes to. rect is a :class:`~matplotlib.transforms.Bbox` object specifying the location of the axes within the figure. Any other kwargs are passed along to the specific projection constructor being used. """ return get_projection_class(projection)(figure, rect, **kwargs) def get_projection_names(): """ Get a list of acceptable projection names. """ return projection_registry.get_projection_names()	2,179	Python	.py	66	26.969697	70	0.679389	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,289	geo.py	numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/projections/geo.py	import math import numpy as np import numpy.ma as ma import matplotlib rcParams = matplotlib.rcParams from matplotlib.artist import kwdocd from matplotlib.axes import Axes from matplotlib import cbook from matplotlib.patches import Circle from matplotlib.path import Path from matplotlib.ticker import Formatter, Locator, NullLocator, FixedLocator, NullFormatter from matplotlib.transforms import Affine2D, Affine2DBase, Bbox, \ BboxTransformTo, IdentityTransform, Transform, TransformWrapper class GeoAxes(Axes): """ An abstract base class for geographic projections """ class ThetaFormatter(Formatter): """ Used to format the theta tick labels. Converts the native unit of radians into degrees and adds a degree symbol. """ def __init__(self, round_to=1.0): self._round_to = round_to def __call__(self, x, pos=None): degrees = (x / np.pi) * 180.0 degrees = round(degrees / self._round_to) * self._round_to if rcParams['text.usetex'] and not rcParams['text.latex.unicode']: return r"$%0.0f^\circ$" % degrees else: return u"%0.0f\u00b0" % degrees RESOLUTION = 75 def cla(self): Axes.cla(self) self.set_longitude_grid(30) self.set_latitude_grid(15) self.set_longitude_grid_ends(75) self.xaxis.set_minor_locator(NullLocator()) self.yaxis.set_minor_locator(NullLocator()) self.xaxis.set_ticks_position('none') self.yaxis.set_ticks_position('none') self.grid(rcParams['axes.grid']) Axes.set_xlim(self, -np.pi, np.pi) Axes.set_ylim(self, -np.pi / 2.0, np.pi / 2.0) def _set_lim_and_transforms(self): # A (possibly non-linear) projection on the (already scaled) data self.transProjection = self._get_core_transform(self.RESOLUTION) self.transAffine = self._get_affine_transform() self.transAxes = BboxTransformTo(self.bbox) # The complete data transformation stack -- from data all the # way to display coordinates self.transData = \ self.transProjection + \ self.transAffine + \ self.transAxes # This is the transform for longitude ticks. self._xaxis_pretransform = \ Affine2D() \ .scale(1.0, self._longitude_cap * 2.0) \ .translate(0.0, -self._longitude_cap) self._xaxis_transform = \ self._xaxis_pretransform + \ self.transData self._xaxis_text1_transform = \ Affine2D().scale(1.0, 0.0) + \ self.transData + \ Affine2D().translate(0.0, 4.0) self._xaxis_text2_transform = \ Affine2D().scale(1.0, 0.0) + \ self.transData + \ Affine2D().translate(0.0, -4.0) # This is the transform for latitude ticks. yaxis_stretch = Affine2D().scale(np.pi * 2.0, 1.0).translate(-np.pi, 0.0) yaxis_space = Affine2D().scale(1.0, 1.1) self._yaxis_transform = \ yaxis_stretch + \ self.transData yaxis_text_base = \ yaxis_stretch + \ self.transProjection + \ (yaxis_space + \ self.transAffine + \ self.transAxes) self._yaxis_text1_transform = \ yaxis_text_base + \ Affine2D().translate(-8.0, 0.0) self._yaxis_text2_transform = \ yaxis_text_base + \ Affine2D().translate(8.0, 0.0) def _get_affine_transform(self): transform = self._get_core_transform(1) xscale, _ = transform.transform_point((np.pi, 0)) _, yscale = transform.transform_point((0, np.pi / 2.0)) return Affine2D() \ .scale(0.5 / xscale, 0.5 / yscale) \ .translate(0.5, 0.5) def get_xaxis_transform(self): return self._xaxis_transform def get_xaxis_text1_transform(self, pad): return self._xaxis_text1_transform, 'bottom', 'center' def get_xaxis_text2_transform(self, pad): return self._xaxis_text2_transform, 'top', 'center' def get_yaxis_transform(self): return self._yaxis_transform def get_yaxis_text1_transform(self, pad): return self._yaxis_text1_transform, 'center', 'right' def get_yaxis_text2_transform(self, pad): return self._yaxis_text2_transform, 'center', 'left' def _gen_axes_patch(self): return Circle((0.5, 0.5), 0.5) def set_yscale(self, args, kwargs): if args[0] != 'linear': raise NotImplementedError set_xscale = set_yscale def set_xlim(self, args, *kwargs): Axes.set_xlim(self, -np.pi, np.pi) Axes.set_ylim(self, -np.pi / 2.0, np.pi / 2.0) set_ylim = set_xlim def format_coord(self, long, lat): 'return a format string formatting the coordinate' long = long (180.0 / np.pi) lat = lat * (180.0 / np.pi) if lat >= 0.0: ns = 'N' else: ns = 'S' if long >= 0.0: ew = 'E' else: ew = 'W' return u'%f\u00b0%s, %f\u00b0%s' % (abs(lat), ns, abs(long), ew) def set_longitude_grid(self, degrees): """ Set the number of degrees between each longitude grid. """ number = (360.0 / degrees) + 1 self.xaxis.set_major_locator( FixedLocator( np.linspace(-np.pi, np.pi, number, True)[1:-1])) self._logitude_degrees = degrees self.xaxis.set_major_formatter(self.ThetaFormatter(degrees)) def set_latitude_grid(self, degrees): """ Set the number of degrees between each longitude grid. """ number = (180.0 / degrees) + 1 self.yaxis.set_major_locator( FixedLocator( np.linspace(-np.pi / 2.0, np.pi / 2.0, number, True)[1:-1])) self._latitude_degrees = degrees self.yaxis.set_major_formatter(self.ThetaFormatter(degrees)) def set_longitude_grid_ends(self, degrees): """ Set the latitude(s) at which to stop drawing the longitude grids. """ self._longitude_cap = degrees * (np.pi / 180.0) self._xaxis_pretransform \ .clear() \ .scale(1.0, self._longitude_cap * 2.0) \ .translate(0.0, -self._longitude_cap) def get_data_ratio(self): ''' Return the aspect ratio of the data itself. ''' return 1.0 ### Interactive panning def can_zoom(self): """ Return True if this axes support the zoom box """ return False def start_pan(self, x, y, button): pass def end_pan(self): pass def drag_pan(self, button, key, x, y): pass class AitoffAxes(GeoAxes): name = 'aitoff' class AitoffTransform(Transform): """ The base Aitoff transform. """ input_dims = 2 output_dims = 2 is_separable = False def __init__(self, resolution): """ Create a new Aitoff transform. Resolution is the number of steps to interpolate between each input line segment to approximate its path in curved Aitoff space. """ Transform.__init__(self) self._resolution = resolution def transform(self, ll): longitude = ll[:, 0:1] latitude = ll[:, 1:2] # Pre-compute some values half_long = longitude / 2.0 cos_latitude = np.cos(latitude) alpha = np.arccos(cos_latitude * np.cos(half_long)) # Mask this array, or we'll get divide-by-zero errors alpha = ma.masked_where(alpha == 0.0, alpha) # We want unnormalized sinc. numpy.sinc gives us normalized sinc_alpha = ma.sin(alpha) / alpha x = (cos_latitude * np.sin(half_long)) / sinc_alpha y = (np.sin(latitude) / sinc_alpha) x.set_fill_value(0.0) y.set_fill_value(0.0) return np.concatenate((x.filled(), y.filled()), 1) transform.__doc__ = Transform.transform.__doc__ transform_non_affine = transform transform_non_affine.__doc__ = Transform.transform_non_affine.__doc__ def transform_path(self, path): vertices = path.vertices ipath = path.interpolated(self._resolution) return Path(self.transform(ipath.vertices), ipath.codes) transform_path.__doc__ = Transform.transform_path.__doc__ transform_path_non_affine = transform_path transform_path_non_affine.__doc__ = Transform.transform_path_non_affine.__doc__ def inverted(self): return AitoffAxes.InvertedAitoffTransform(self._resolution) inverted.__doc__ = Transform.inverted.__doc__ class InvertedAitoffTransform(Transform): input_dims = 2 output_dims = 2 is_separable = False def __init__(self, resolution): Transform.__init__(self) self._resolution = resolution def transform(self, xy): # MGDTODO: Math is hard ;( return xy transform.__doc__ = Transform.transform.__doc__ def inverted(self): return AitoffAxes.AitoffTransform(self._resolution) inverted.__doc__ = Transform.inverted.__doc__ def __init__(self, args, kwargs): self._longitude_cap = np.pi / 2.0 GeoAxes.__init__(self, args, *kwargs) self.set_aspect(0.5, adjustable='box', anchor='C') self.cla() def _get_core_transform(self, resolution): return self.AitoffTransform(resolution) class HammerAxes(GeoAxes): name = 'hammer' class HammerTransform(Transform): """ The base Hammer transform. """ input_dims = 2 output_dims = 2 is_separable = False def __init__(self, resolution): """ Create a new Hammer transform. Resolution is the number of steps to interpolate between each input line segment to approximate its path in curved Hammer space. """ Transform.__init__(self) self._resolution = resolution def transform(self, ll): longitude = ll[:, 0:1] latitude = ll[:, 1:2] # Pre-compute some values half_long = longitude / 2.0 cos_latitude = np.cos(latitude) sqrt2 = np.sqrt(2.0) alpha = 1.0 + cos_latitude np.cos(half_long) x = (2.0 * sqrt2) * (cos_latitude * np.sin(half_long)) / alpha y = (sqrt2 * np.sin(latitude)) / alpha return np.concatenate((x, y), 1) transform.__doc__ = Transform.transform.__doc__ transform_non_affine = transform transform_non_affine.__doc__ = Transform.transform_non_affine.__doc__ def transform_path(self, path): vertices = path.vertices ipath = path.interpolated(self._resolution) return Path(self.transform(ipath.vertices), ipath.codes) transform_path.__doc__ = Transform.transform_path.__doc__ transform_path_non_affine = transform_path transform_path_non_affine.__doc__ = Transform.transform_path_non_affine.__doc__ def inverted(self): return HammerAxes.InvertedHammerTransform(self._resolution) inverted.__doc__ = Transform.inverted.__doc__ class InvertedHammerTransform(Transform): input_dims = 2 output_dims = 2 is_separable = False def __init__(self, resolution): Transform.__init__(self) self._resolution = resolution def transform(self, xy): x = xy[:, 0:1] y = xy[:, 1:2] quarter_x = 0.25 * x half_y = 0.5 * y z = np.sqrt(1.0 - quarter_xquarter_x - half_yhalf_y) longitude = 2 * np.arctan((zx) / (2.0 (2.0zz - 1.0))) latitude = np.arcsin(yz) return np.concatenate((longitude, latitude), 1) transform.__doc__ = Transform.transform.__doc__ def inverted(self): return HammerAxes.HammerTransform(self._resolution) inverted.__doc__ = Transform.inverted.__doc__ def __init__(self, args, *kwargs): self._longitude_cap = np.pi / 2.0 GeoAxes.__init__(self, args, *kwargs) self.set_aspect(0.5, adjustable='box', anchor='C') self.cla() def _get_core_transform(self, resolution): return self.HammerTransform(resolution) class MollweideAxes(GeoAxes): name = 'mollweide' class MollweideTransform(Transform): """ The base Mollweide transform. """ input_dims = 2 output_dims = 2 is_separable = False def __init__(self, resolution): """ Create a new Mollweide transform. Resolution is the number of steps to interpolate between each input line segment to approximate its path in curved Mollweide space. """ Transform.__init__(self) self._resolution = resolution def transform(self, ll): longitude = ll[:, 0:1] latitude = ll[:, 1:2] aux = 2.0 np.arcsin((2.0 * latitude) / np.pi) x = (2.0 * np.sqrt(2.0) * longitude * np.cos(aux)) / np.pi y = (np.sqrt(2.0) * np.sin(aux)) return np.concatenate((x, y), 1) transform.__doc__ = Transform.transform.__doc__ transform_non_affine = transform transform_non_affine.__doc__ = Transform.transform_non_affine.__doc__ def transform_path(self, path): vertices = path.vertices ipath = path.interpolated(self._resolution) return Path(self.transform(ipath.vertices), ipath.codes) transform_path.__doc__ = Transform.transform_path.__doc__ transform_path_non_affine = transform_path transform_path_non_affine.__doc__ = Transform.transform_path_non_affine.__doc__ def inverted(self): return MollweideAxes.InvertedMollweideTransform(self._resolution) inverted.__doc__ = Transform.inverted.__doc__ class InvertedMollweideTransform(Transform): input_dims = 2 output_dims = 2 is_separable = False def __init__(self, resolution): Transform.__init__(self) self._resolution = resolution def transform(self, xy): # MGDTODO: Math is hard ;( return xy transform.__doc__ = Transform.transform.__doc__ def inverted(self): return MollweideAxes.MollweideTransform(self._resolution) inverted.__doc__ = Transform.inverted.__doc__ def __init__(self, args, kwargs): self._longitude_cap = np.pi / 2.0 GeoAxes.__init__(self, args, *kwargs) self.set_aspect(0.5, adjustable='box', anchor='C') self.cla() def _get_core_transform(self, resolution): return self.MollweideTransform(resolution) class LambertAxes(GeoAxes): name = 'lambert' class LambertTransform(Transform): """ The base Lambert transform. """ input_dims = 2 output_dims = 2 is_separable = False def __init__(self, center_longitude, center_latitude, resolution): """ Create a new Lambert transform. Resolution is the number of steps to interpolate between each input line segment to approximate its path in curved Lambert space. """ Transform.__init__(self) self._resolution = resolution self._center_longitude = center_longitude self._center_latitude = center_latitude def transform(self, ll): longitude = ll[:, 0:1] latitude = ll[:, 1:2] clong = self._center_longitude clat = self._center_latitude cos_lat = np.cos(latitude) sin_lat = np.sin(latitude) diff_long = longitude - clong cos_diff_long = np.cos(diff_long) inner_k = (1.0 + np.sin(clat)sin_lat + np.cos(clat)cos_latcos_diff_long) # Prevent divide-by-zero problems inner_k = np.where(inner_k == 0.0, 1e-15, inner_k) k = np.sqrt(2.0 / inner_k) x = kcos_latnp.sin(diff_long) y = k(np.cos(clat)sin_lat - np.sin(clat)cos_latcos_diff_long) return np.concatenate((x, y), 1) transform.__doc__ = Transform.transform.__doc__ transform_non_affine = transform transform_non_affine.__doc__ = Transform.transform_non_affine.__doc__ def transform_path(self, path): vertices = path.vertices ipath = path.interpolated(self._resolution) return Path(self.transform(ipath.vertices), ipath.codes) transform_path.__doc__ = Transform.transform_path.__doc__ transform_path_non_affine = transform_path transform_path_non_affine.__doc__ = Transform.transform_path_non_affine.__doc__ def inverted(self): return LambertAxes.InvertedLambertTransform( self._center_longitude, self._center_latitude, self._resolution) inverted.__doc__ = Transform.inverted.__doc__ class InvertedLambertTransform(Transform): input_dims = 2 output_dims = 2 is_separable = False def __init__(self, center_longitude, center_latitude, resolution): Transform.__init__(self) self._resolution = resolution self._center_longitude = center_longitude self._center_latitude = center_latitude def transform(self, xy): x = xy[:, 0:1] y = xy[:, 1:2] clong = self._center_longitude clat = self._center_latitude p = np.sqrt(xx + yy) p = np.where(p == 0.0, 1e-9, p) c = 2.0 * np.arcsin(0.5 * p) sin_c = np.sin(c) cos_c = np.cos(c) lat = np.arcsin(cos_cnp.sin(clat) + ((ysin_cnp.cos(clat)) / p)) long = clong + np.arctan( (xsin_c) / (pnp.cos(clat)cos_c - ynp.sin(clat)sin_c)) return np.concatenate((long, lat), 1) transform.__doc__ = Transform.transform.__doc__ def inverted(self): return LambertAxes.LambertTransform( self._center_longitude, self._center_latitude, self._resolution) inverted.__doc__ = Transform.inverted.__doc__ def __init__(self, args, kwargs): self._longitude_cap = np.pi / 2.0 self._center_longitude = kwargs.pop("center_longitude", 0.0) self._center_latitude = kwargs.pop("center_latitude", 0.0) GeoAxes.__init__(self, args, **kwargs) self.set_aspect('equal', adjustable='box', anchor='C') self.cla() def cla(self): GeoAxes.cla(self) self.yaxis.set_major_formatter(NullFormatter()) def _get_core_transform(self, resolution): return self.LambertTransform( self._center_longitude, self._center_latitude, resolution) def _get_affine_transform(self): return Affine2D() \ .scale(0.25) \ .translate(0.5, 0.5)	19,738	Python	.py	474	31.318565	90	0.576359	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,290	_nc_imports.py	numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/numerix/_nc_imports.py	from Numeric import array, ravel, reshape, shape, alltrue, sometrue from Numeric import Int8, UInt8, Int16, UInt16, Int32, UInt32, \ Float32, Float64, Complex32, Complex64, Float, Int, Complex from numpy import isnan as _isnan class _TypeNamespace: """Numeric compatible type aliases for use with extension functions.""" Int8 = Int8 UInt8 = UInt8 Int16 = Int16 UInt16 = UInt16 Int32 = Int32 UInt32 = UInt32 Float32 = Float32 Float64 = Float64 Complex32 = Complex32 Complex64 = Complex64 nx = _TypeNamespace() def isnan(a): """y = isnan(x) returns True where x is Not-A-Number""" return reshape(array([_isnan(i) for i in ravel(a)],'b'), shape(a)) def all(a, axis=None): '''Numpy-compatible version of all()''' if axis is None: return alltrue(ravel(a)) else: return alltrue(a, axis) def any(a, axis=None): if axis is None: return sometrue(ravel(a)) else: return sometrue(a, axis) # inf is useful for testing infinities in results of array divisions # (which don't raise exceptions) inf = infty = infinity = Infinity = (array([1])/0.0)[0]	1,220	Python	.py	34	31.441176	75	0.642615	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,291	_na_imports.py	numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/numerix/_na_imports.py	"""Imports from numarray for numerix, the numarray/Numeric interchangeability module. These array functions are used when numarray is chosen. """ from numarray import Int8, UInt8, Int16, UInt16, Int32, UInt32, \ Float32, Float64, Complex32, Complex64, Float, Int, Complex,\ typecode import numarray.ieeespecial as _ieee inf = infinity = infty = Infinity = _ieee.inf isnan = _ieee.isnan class _TypeNamespace: """Numeric compatible type aliases for use with extension functions.""" Int8 = typecode[Int8] UInt8 = typecode[UInt8] Int16 = typecode[Int16] UInt16 = typecode[UInt16] Int32 = typecode[Int32] #UInt32 = typecode[UInt32] # Todd: this appears broken Float32 = typecode[Float32] Float64 = typecode[Float64] Complex32 = typecode[Complex32] Complex64 = typecode[Complex64] nx = _TypeNamespace() from numarray import asarray, dot, fromlist, NumArray, shape, alltrue from numarray import all as _all def all(a, axis=None): '''Numpy-compatible version of all()''' if axis is None: return _all(a) return alltrue(a, axis) class _Matrix(NumArray): """_Matrix is a ported, stripped down version of the Numeric Matrix class which supplies only matrix multiplication. """ def _rc(self, a): if len(shape(a)) == 0: return a else: return Matrix(a) def __mul__(self, other): aother = asarray(other) #if len(aother.shape) == 0: # return self._rc(selfaother) #else: # return self._rc(dot(self, aother)) #return self._rc(dot(self, aother)) return dot(self, aother) def __rmul__(self, other): aother = asarray(other) if len(aother.shape) == 0: return self._rc(aotherself) else: return self._rc(dot(aother, self)) def __imul__(self,other): aother = asarray(other) self[:] = dot(self, aother) return self def Matrix(data, typecode=None, copy=1, savespace=0): """Matrix constructs new matrices from 2D nested lists of numbers""" if isinstance(data, type("")): raise TypeError("numerix Matrix does not support Numeric matrix string notation. Use nested lists.") a = fromlist(data, type=typecode) if a.rank == 0: a.shape = (1,1) elif a.rank == 1: a.shape = (1,) + a.shape a.__class__ = _Matrix return a	2,492	Python	.py	67	31.253731	109	0.629967	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,292	__init__.py	numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/numerix/__init__.py	""" numerix imports either Numeric or numarray based on various selectors. 0. If the value "--numpy","--numarray" or "--Numeric" is specified on the command line, then numerix imports the specified array package. 1. The value of numerix in matplotlibrc: either Numeric or numarray 2. If none of the above is done, the default array package is Numeric. Because the matplotlibrc always provides some value for numerix (it has it's own system of default values), this default is most likely never used. To summarize: the commandline is examined first, the rc file second, and the default array package is Numeric. """ import sys, os, struct from matplotlib import rcParams, verbose which = None, None use_maskedarray = None # First, see if --numarray or --Numeric was specified on the command # line: for a in sys.argv: if a in ["--Numeric", "--numeric", "--NUMERIC", "--Numarray", "--numarray", "--NUMARRAY", "--NumPy", "--numpy", "--NUMPY", "--Numpy", ]: which = a[2:], "command line" if a == "--maskedarray": use_maskedarray = True if a == "--ma": use_maskedarray = False try: del a except NameError: pass if which[0] is None: try: # In theory, rcParams always has some value for numerix. which = rcParams['numerix'], "rc" except KeyError: pass if use_maskedarray is None: try: use_maskedarray = rcParams['maskedarray'] except KeyError: use_maskedarray = False # If all the above fail, default to Numeric. Most likely not used. if which[0] is None: which = "numeric", "defaulted" which = which[0].strip().lower(), which[1] if which[0] not in ["numeric", "numarray", "numpy"]: raise ValueError("numerix selector must be either 'Numeric', 'numarray', or 'numpy' but the value obtained from the %s was '%s'." % (which[1], which[0])) if which[0] == "numarray": import warnings warnings.warn("numarray use as a numerix backed for matplotlib is deprecated", DeprecationWarning, stacklevel=1) #from na_imports import * from numarray import * from _na_imports import nx, inf, infinity, Infinity, Matrix, isnan, all from numarray.numeric import nonzero from numarray.convolve import cross_correlate, convolve import numarray version = 'numarray %s'%numarray.__version__ nan = struct.unpack('d', struct.pack('Q', 0x7ff8000000000000))[0] elif which[0] == "numeric": import warnings warnings.warn("Numeric use as a numerix backed for matplotlib is deprecated", DeprecationWarning, stacklevel=1) #from nc_imports import * from Numeric import * from _nc_imports import nx, inf, infinity, Infinity, isnan, all, any from Matrix import Matrix import Numeric version = 'Numeric %s'%Numeric.__version__ nan = struct.unpack('d', struct.pack('Q', 0x7ff8000000000000))[0] elif which[0] == "numpy": try: import numpy.oldnumeric as numpy from numpy.oldnumeric import * except ImportError: import numpy from numpy import * print 'except asarray', asarray from _sp_imports import nx, infinity, rand, randn, isnan, all, any from _sp_imports import UInt8, UInt16, UInt32, Infinity try: from numpy.oldnumeric.matrix import Matrix except ImportError: Matrix = matrix version = 'numpy %s' % numpy.__version__ from numpy import nan else: raise RuntimeError("invalid numerix selector") # Some changes are only applicable to the new numpy: if (which[0] == 'numarray' or which[0] == 'numeric'): from mlab import amin, amax newaxis = NewAxis def typecode(a): return a.typecode() def iscontiguous(a): return a.iscontiguous() def byteswapped(a): return a.byteswapped() def itemsize(a): return a.itemsize() def angle(a): return arctan2(a.imag, a.real) else: # We've already checked for a valid numerix selector, # so assume numpy. from mlab import amin, amax newaxis = NewAxis from numpy import angle def typecode(a): return a.dtype.char def iscontiguous(a): return a.flags.contiguous def byteswapped(a): return a.byteswap() def itemsize(a): return a.itemsize verbose.report('numerix %s'%version) # a bug fix for blas numeric suggested by Fernando Perez matrixmultiply=dot asum = sum def _import_fail_message(module, version): """Prints a message when the array package specific version of an extension fails to import correctly. """ _dict = { "which" : which[0], "module" : module, "specific" : version + module } print """ The import of the %(which)s version of the %(module)s module, %(specific)s, failed. This is is either because %(which)s was unavailable when matplotlib was compiled, because a dependency of %(specific)s could not be satisfied, or because the build flag for this module was turned off in setup.py. If it appears that %(specific)s was not built, make sure you have a working copy of %(which)s and then re-install matplotlib. Otherwise, the following traceback gives more details:\n""" % _dict g = globals() l = locals() __import__('ma', g, l) __import__('fft', g, l) __import__('linear_algebra', g, l) __import__('random_array', g, l) __import__('mlab', g, l) la = linear_algebra ra = random_array	5,473	Python	.py	147	32.285714	157	0.678113	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,293	_sp_imports.py	numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/numerix/_sp_imports.py	try: from numpy.oldnumeric import Int8, UInt8, \ Int16, UInt16, \ Int32, UInt32, \ Float32, Float64, \ Complex32, Complex64, \ Float, Int, Complex except ImportError: from numpy import Int8, UInt8, \ Int16, UInt16, \ Int32, UInt32, \ Float32, Float64, \ Complex32, Complex64, \ Float, Int, Complex class _TypeNamespace: """Numeric compatible type aliases for use with extension functions.""" Int8 = Int8 UInt8 = UInt8 Int16 = Int16 UInt16 = UInt16 Int32 = Int32 UInt32 = UInt32 Float32 = Float32 Float64 = Float64 Complex32 = Complex32 Complex64 = Complex64 nx = _TypeNamespace() from numpy import inf, infty, Infinity from numpy.random import rand, randn infinity = Infinity from numpy import all, isnan, any	922	Python	.py	31	24.064516	75	0.601351	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,294	__init__.py	numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/numerix/linear_algebra/__init__.py	from matplotlib.numerix import which if which[0] == "numarray": from numarray.linear_algebra import * elif which[0] == "numeric": from LinearAlgebra import * elif which[0] == "numpy": try: from numpy.oldnumeric.linear_algebra import * except ImportError: from numpy.linalg.old import * else: raise RuntimeError("invalid numerix selector")	376	Python	.py	12	27.25	53	0.713499	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,295	__init__.py	numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/numerix/random_array/__init__.py	from matplotlib.numerix import which if which[0] == "numarray": from numarray.random_array import * elif which[0] == "numeric": from RandomArray import * elif which[0] == "numpy": try: from numpy.oldnumeric.random_array import * except ImportError: from numpy.random import * else: raise RuntimeError("invalid numerix selector")	366	Python	.py	12	26.416667	51	0.708215	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,296	__init__.py	numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/numerix/npyma/__init__.py	from matplotlib.numerix import use_maskedarray if use_maskedarray: from maskedarray import * print "using maskedarray" else: try: from numpy.ma import * # numpy 1.05 and later except ImportError: from numpy.core.ma import * # earlier #print "using ma"	298	Python	.py	10	25.1	60	0.682927	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,297	__init__.py	numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/numerix/mlab/__init__.py	from matplotlib.numerix import which if which[0] == "numarray": from numarray.linear_algebra.mlab import * elif which[0] == "numeric": from MLab import * elif which[0] == "numpy": try: from numpy.oldnumeric.mlab import * except ImportError: from numpy.lib.mlab import * else: raise RuntimeError("invalid numerix selector") amin = min amax = max	381	Python	.py	14	23.642857	50	0.70411	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,298	__init__.py	numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/numerix/ma/__init__.py	from matplotlib.numerix import which, use_maskedarray if which[0] == "numarray": from numarray.ma import * nomask = None getmaskorNone = getmask elif which[0] == "numeric": from MA import * nomask = None getmaskorNone = getmask elif which[0] == "numpy": if use_maskedarray: from maskedarray import * print "using maskedarray" else: try: from numpy.ma import * # numpy 1.05 and later except ImportError: from numpy.core.ma import * # earlier #print "using ma" def getmaskorNone(obj): _msk = getmask(obj) if _msk is nomask: return None return _msk else: raise RuntimeError("invalid numerix selector")	749	Python	.py	26	22.384615	64	0.620499	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)
26,299	__init__.py	numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/numerix/fft/__init__.py	from matplotlib.numerix import which if which[0] == "numarray": from numarray.fft import * elif which[0] == "numeric": from FFT import * elif which[0] == "numpy": try: from numpy.oldnumeric.fft import * except ImportError: from numpy.dft.old import * else: raise RuntimeError("invalid numerix selector")	341	Python	.py	12	24.333333	50	0.689024	numenta/nupic-legacy	6,330	1,556	464	AGPL-3.0	9/5/2024, 5:13:42 PM (Europe/Amsterdam)

26,200

__init__.py

numenta_nupic-legacy/tests/swarming/nupic/swarming/__init__.py

# ---------------------------------------------------------------------- # Numenta Platform for Intelligent Computing (NuPIC) # Copyright (C) 2013, Numenta, Inc. Unless you have an agreement # with Numenta, Inc., for a separate license for this software code, the # following terms and conditions apply: # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU Affero Public License version 3 as # published by the Free Software Foundation. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. # See the GNU Affero Public License for more details. # # You should have received a copy of the GNU Affero Public License # along with this program. If not, see http://www.gnu.org/licenses. # # http://numenta.org/licenses/ # ----------------------------------------------------------------------

976

Python

.py

20

47.8

72

0.665272

numenta/nupic-legacy

6,330

1,556

464

AGPL-3.0

9/5/2024, 5:13:42 PM (Europe/Amsterdam)

26,201

swarming_test.py

numenta_nupic-legacy/tests/swarming/nupic/swarming/swarming_test.py

# ---------------------------------------------------------------------- # Numenta Platform for Intelligent Computing (NuPIC) # Copyright (C) 2013, Numenta, Inc. Unless you have an agreement # with Numenta, Inc., for a separate license for this software code, the # following terms and conditions apply: # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU Affero Public License version 3 as # published by the Free Software Foundation. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. # See the GNU Affero Public License for more details. # # You should have received a copy of the GNU Affero Public License # along with this program. If not, see http://www.gnu.org/licenses. # # http://numenta.org/licenses/ # ---------------------------------------------------------------------- import sys import os import imp import subprocess import re import json import pprint import shutil import copy import StringIO import logging import itertools import numpy import time import math import uuid import tempfile from pkg_resources import resource_filename from optparse import OptionParser from nupic.database.client_jobs_dao import ClientJobsDAO from nupic.support import configuration, initLogging from nupic.support.unittesthelpers.testcasebase import (unittest, TestCaseBase as HelperTestCaseBase) from nupic.swarming import hypersearch_worker from nupic.swarming.api import getSwarmModelParams, createAndStartSwarm from nupic.swarming.utils import generatePersistentJobGUID from nupic.swarming.dummy_model_runner import OPFDummyModelRunner DEFAULT_JOB_TIMEOUT_SEC = 60 * 2 # Filters _debugOut messages g_debug = True # Our setUpModule entry block sets this to an instance of MyTestEnvironment() g_myEnv = None # These are the args after using the optparse # This value for the swarm maturity window gives more repeatable results for # unit tests that use multiple workers g_repeatableSwarmMaturityWindow = 5 class MyTestEnvironment(object): # ======================================================================= def __init__(self): # Save all command line options self.options = _ArgParser.parseArgs() # Create the path to our source experiments thisFile = __file__ testDir = os.path.split(os.path.abspath(thisFile))[0] self.testSrcExpDir = os.path.join(testDir, 'experiments') self.testSrcDataDir = os.path.join(testDir, 'data') return class ExperimentTestBaseClass(HelperTestCaseBase): def setUp(self): """ Method called to prepare the test fixture. This is called by the unittest framework immediately before calling the test method; any exception raised by this method will be considered an error rather than a test failure. The default implementation does nothing. """ pass def tearDown(self): """ Method called immediately after the test method has been called and the result recorded. This is called even if the test method raised an exception, so the implementation in subclasses may need to be particularly careful about checking internal state. Any exception raised by this method will be considered an error rather than a test failure. This method will only be called if the setUp() succeeds, regardless of the outcome of the test method. The default implementation does nothing. """ # Reset our log items self.resetExtraLogItems() def shortDescription(self): """ Override to force unittest framework to use test method names instead of docstrings in the report. """ return None def _printTestHeader(self): """ Print out what test we are running """ print "###############################################################" print "Running test: %s.%s..." % (self.__class__, self._testMethodName) def _setDataPath(self, env): """ Put the path to our datasets int the NTA_DATA_PATH variable which will be used to set the environment for each of the workers Parameters: --------------------------------------------------------------------- env: The current environment dict """ assert env is not None # If already have a path, concatenate to it if "NTA_DATA_PATH" in env: newPath = "%s%s%s" % (env["NTA_DATA_PATH"], os.pathsep, g_myEnv.testSrcDataDir) else: newPath = g_myEnv.testSrcDataDir env["NTA_DATA_PATH"] = newPath def _launchWorkers(self, cmdLine, numWorkers): """ Launch worker processes to execute the given command line Parameters: ----------------------------------------------- cmdLine: The command line for each worker numWorkers: number of workers to launch retval: list of workers """ workers = [] for i in range(numWorkers): stdout = tempfile.TemporaryFile() stderr = tempfile.TemporaryFile() p = subprocess.Popen(cmdLine, bufsize=1, env=os.environ, shell=True, stdin=None, stdout=stdout, stderr=stderr) workers.append(p) return workers def _getJobInfo(self, cjDAO, workers, jobID): """ Return the job info for a job Parameters: ----------------------------------------------- cjDAO: client jobs database instance workers: list of workers for this job jobID: which job ID retval: job info """ # Get the job info jobInfo = cjDAO.jobInfo(jobID) # Since we're running outside of the Nupic engine, we launched the workers # ourself, so see how many are still running and jam the correct status # into the job info. When using the Nupic engine, it would do this # for us. runningCount = 0 for worker in workers: retCode = worker.poll() if retCode is None: runningCount += 1 if runningCount > 0: status = ClientJobsDAO.STATUS_RUNNING else: status = ClientJobsDAO.STATUS_COMPLETED jobInfo = jobInfo._replace(status=status) if status == ClientJobsDAO.STATUS_COMPLETED: jobInfo = jobInfo._replace( completionReason=ClientJobsDAO.CMPL_REASON_SUCCESS) return jobInfo def _generateHSJobParams(self, expDirectory=None, hsImp='v2', maxModels=2, predictionCacheMaxRecords=None, dataPath=None, maxRecords=10): """ This method generates a canned Hypersearch Job Params structure based on some high level options Parameters: --------------------------------------------------------------------- predictionCacheMaxRecords: If specified, determine the maximum number of records in the prediction cache. dataPath: When expDirectory is not specified, this is the data file to be used for the operation. If this value is not specified, it will use the /extra/qa/hotgym/qa_hotgym.csv. """ if expDirectory is not None: descriptionPyPath = os.path.join(expDirectory, "description.py") permutationsPyPath = os.path.join(expDirectory, "permutations.py") permutationsPyContents = open(permutationsPyPath, 'r').read() descriptionPyContents = open(descriptionPyPath, 'r').read() jobParams = {'persistentJobGUID' : generatePersistentJobGUID(), 'permutationsPyContents': permutationsPyContents, 'descriptionPyContents': descriptionPyContents, 'maxModels': maxModels, 'hsVersion': hsImp} if predictionCacheMaxRecords is not None: jobParams['predictionCacheMaxRecords'] = predictionCacheMaxRecords else: # Form the stream definition if dataPath is None: dataPath = resource_filename("nupic.data", os.path.join("extra", "qa", "hotgym", "qa_hotgym.csv")) streamDef = dict( version = 1, info = "TestHypersearch", streams = [ dict(source="file://%s" % (dataPath), info=dataPath, columns=["*"], first_record=0, last_record=maxRecords), ], ) # Generate the experiment description expDesc = { "predictionField": "consumption", "streamDef": streamDef, "includedFields": [ { "fieldName": "gym", "fieldType": "string" }, { "fieldName": "consumption", "fieldType": "float", "minValue": 0, "maxValue": 200, }, ], "iterationCount": maxRecords, "resetPeriod": { 'weeks': 0, 'days': 0, 'hours': 8, 'minutes': 0, 'seconds': 0, 'milliseconds': 0, 'microseconds': 0, }, } jobParams = { "persistentJobGUID": generatePersistentJobGUID(), "description":expDesc, "maxModels": maxModels, "hsVersion": hsImp, } if predictionCacheMaxRecords is not None: jobParams['predictionCacheMaxRecords'] = predictionCacheMaxRecords return jobParams def _runPermutationsLocal(self, jobParams, loggingLevel=logging.INFO, env=None, waitForCompletion=True, continueJobId=None, ignoreErrModels=False): """ This runs permutations on the given experiment using just 1 worker in the current process Parameters: ------------------------------------------------------------------- jobParams: filled in job params for a hypersearch loggingLevel: logging level to use in the Hypersearch worker env: if not None, this is a dict of environment variables that should be sent to each worker process. These can aid in re-using the same description/permutations file for different tests. waitForCompletion: If True, wait for job to complete before returning If False, then return resultsInfoForAllModels and metricResults will be None continueJobId: If not None, then this is the JobId of a job we want to continue working on with another worker. ignoreErrModels: If true, ignore erred models retval: (jobId, jobInfo, resultsInfoForAllModels, metricResults) """ print print "==================================================================" print "Running Hypersearch job using 1 worker in current process" print "==================================================================" # Plug in modified environment variables if env is not None: saveEnvState = copy.deepcopy(os.environ) os.environ.update(env) # Insert the job entry into the database in the pre-running state cjDAO = ClientJobsDAO.get() if continueJobId is None: jobID = cjDAO.jobInsert(client='test', cmdLine='<started manually>', params=json.dumps(jobParams), alreadyRunning=True, minimumWorkers=1, maximumWorkers=1, jobType = cjDAO.JOB_TYPE_HS) else: jobID = continueJobId # Command line args. args = ['ignoreThis', '--jobID=%d' % (jobID), '--logLevel=%d' % (loggingLevel)] if continueJobId is None: args.append('--clearModels') # Run it in the current process try: hypersearch_worker.main(args) # The dummy model runner will call sys.exit(0) when # NTA_TEST_sysExitAfterNIterations is set except SystemExit: pass except: raise # Restore environment if env is not None: os.environ = saveEnvState # ---------------------------------------------------------------------- # Make sure all models completed successfully models = cjDAO.modelsGetUpdateCounters(jobID) modelIDs = [model.modelId for model in models] if len(modelIDs) > 0: results = cjDAO.modelsGetResultAndStatus(modelIDs) else: results = [] metricResults = [] for result in results: if result.results is not None: metricResults.append(json.loads(result.results)[1].values()[0]) else: metricResults.append(None) if not ignoreErrModels: self.assertNotEqual(result.completionReason, cjDAO.CMPL_REASON_ERROR, "Model did not complete successfully:\n%s" % (result.completionMsg)) # Print worker completion message jobInfo = cjDAO.jobInfo(jobID) return (jobID, jobInfo, results, metricResults) def _runPermutationsCluster(self, jobParams, loggingLevel=logging.INFO, maxNumWorkers=4, env=None, waitForCompletion=True, ignoreErrModels=False, timeoutSec=DEFAULT_JOB_TIMEOUT_SEC): """ Given a prepared, filled in jobParams for a hypersearch, this starts the job, waits for it to complete, and returns the results for all models. Parameters: ------------------------------------------------------------------- jobParams: filled in job params for a hypersearch loggingLevel: logging level to use in the Hypersearch worker maxNumWorkers: max # of worker processes to use env: if not None, this is a dict of environment variables that should be sent to each worker process. These can aid in re-using the same description/permutations file for different tests. waitForCompletion: If True, wait for job to complete before returning If False, then return resultsInfoForAllModels and metricResults will be None ignoreErrModels: If true, ignore erred models retval: (jobID, jobInfo, resultsInfoForAllModels, metricResults) """ print print "==================================================================" print "Running Hypersearch job on cluster" print "==================================================================" # -------------------------------------------------------------------- # Submit the job if env is not None and len(env) > 0: envItems = [] for (key, value) in env.iteritems(): if (sys.platform.startswith('win')): envItems.append("set \"%s=%s\"" % (key, value)) else: envItems.append("export %s=%s" % (key, value)) if (sys.platform.startswith('win')): envStr = "%s &" % (' & '.join(envItems)) else: envStr = "%s;" % (';'.join(envItems)) else: envStr = '' cmdLine = '%s python -m nupic.swarming.hypersearch_worker ' \ '--jobID={JOBID} --logLevel=%d' \ % (envStr, loggingLevel) cjDAO = ClientJobsDAO.get() jobID = cjDAO.jobInsert(client='test', cmdLine=cmdLine, params=json.dumps(jobParams), minimumWorkers=1, maximumWorkers=maxNumWorkers, jobType = cjDAO.JOB_TYPE_HS) # Launch the workers ourself if necessary (no nupic engine running). workerCmdLine = '%s python -m nupic.swarming.hypersearch_worker ' \ '--jobID=%d --logLevel=%d' \ % (envStr, jobID, loggingLevel) workers = self._launchWorkers(cmdLine=workerCmdLine, numWorkers=maxNumWorkers) print "Successfully submitted new test job, jobID=%d" % (jobID) print "Each of %d workers executing the command line: " % (maxNumWorkers), \ cmdLine if not waitForCompletion: return (jobID, None, None) if timeoutSec is None: timeout=DEFAULT_JOB_TIMEOUT_SEC else: timeout=timeoutSec # -------------------------------------------------------------------- # Wait for it to complete startTime = time.time() lastUpdate = time.time() lastCompleted = 0 lastCompletedWithError = 0 lastCompletedAsOrphan = 0 lastStarted = 0 lastJobStatus = "NA" lastJobResults = None lastActiveSwarms = None lastEngStatus = None modelIDs = [] print "\n%-15s %-15s %-15s %-15s %-15s" % ("jobStatus", "modelsStarted", "modelsCompleted", "modelErrs", "modelOrphans") print "-------------------------------------------------------------------" while (lastJobStatus != ClientJobsDAO.STATUS_COMPLETED) \ and (time.time() - lastUpdate < timeout): printUpdate = False if g_myEnv.options.verbosity == 0: time.sleep(0.5) # -------------------------------------------------------------------- # Get the job status jobInfo = self._getJobInfo(cjDAO, workers, jobID) if jobInfo.status != lastJobStatus: if jobInfo.status == ClientJobsDAO.STATUS_RUNNING \ and lastJobStatus != ClientJobsDAO.STATUS_RUNNING: print "# Swarm job now running. jobID=%s" \ % (jobInfo.jobId) lastJobStatus = jobInfo.status printUpdate = True if g_myEnv.options.verbosity >= 1: if jobInfo.engWorkerState is not None: activeSwarms = json.loads(jobInfo.engWorkerState)['activeSwarms'] if activeSwarms != lastActiveSwarms: #print "-------------------------------------------------------" print ">> Active swarms:\n ", '\n '.join(activeSwarms) lastActiveSwarms = activeSwarms print if jobInfo.results != lastJobResults: #print "-------------------------------------------------------" print ">> New best:", jobInfo.results, "###" lastJobResults = jobInfo.results if jobInfo.engStatus != lastEngStatus: print '>> Status: "%s"' % jobInfo.engStatus print lastEngStatus = jobInfo.engStatus # -------------------------------------------------------------------- # Get the list of models created for this job modelCounters = cjDAO.modelsGetUpdateCounters(jobID) if len(modelCounters) != lastStarted: modelIDs = [x.modelId for x in modelCounters] lastStarted = len(modelCounters) printUpdate = True # -------------------------------------------------------------------- # See how many have finished if len(modelIDs) > 0: completed = 0 completedWithError = 0 completedAsOrphan = 0 infos = cjDAO.modelsGetResultAndStatus(modelIDs) for info in infos: if info.status == ClientJobsDAO.STATUS_COMPLETED: completed += 1 if info.completionReason == ClientJobsDAO.CMPL_REASON_ERROR: completedWithError += 1 if info.completionReason == ClientJobsDAO.CMPL_REASON_ORPHAN: completedAsOrphan += 1 if completed != lastCompleted \ or completedWithError != lastCompletedWithError \ or completedAsOrphan != lastCompletedAsOrphan: lastCompleted = completed lastCompletedWithError = completedWithError lastCompletedAsOrphan = completedAsOrphan printUpdate = True # -------------------------------------------------------------------- # Print update? if printUpdate: lastUpdate = time.time() if g_myEnv.options.verbosity >= 1: print ">>", print "%-15s %-15d %-15d %-15d %-15d" % (lastJobStatus, lastStarted, lastCompleted, lastCompletedWithError, lastCompletedAsOrphan) # ======================================================================== # Final total print "\n<< %-15s %-15d %-15d %-15d %-15d" % (lastJobStatus, lastStarted, lastCompleted, lastCompletedWithError, lastCompletedAsOrphan) # Success? jobInfo = self._getJobInfo(cjDAO, workers, jobID) if not ignoreErrModels: self.assertEqual (jobInfo.completionReason, ClientJobsDAO.CMPL_REASON_SUCCESS) # Get final model results models = cjDAO.modelsGetUpdateCounters(jobID) modelIDs = [model.modelId for model in models] if len(modelIDs) > 0: results = cjDAO.modelsGetResultAndStatus(modelIDs) else: results = [] metricResults = [] for result in results: if result.results is not None: metricResults.append(json.loads(result.results)[1].values()[0]) else: metricResults.append(None) if not ignoreErrModels: self.assertNotEqual(result.completionReason, cjDAO.CMPL_REASON_ERROR, "Model did not complete successfully:\n%s" % (result.completionMsg)) return (jobID, jobInfo, results, metricResults) def runPermutations(self, expDirectory, hsImp='v2', maxModels=2, maxNumWorkers=4, loggingLevel=logging.INFO, onCluster=False, env=None, waitForCompletion=True, continueJobId=None, dataPath=None, maxRecords=None, timeoutSec=None, ignoreErrModels=False, predictionCacheMaxRecords=None, **kwargs): """ This runs permutations on the given experiment using just 1 worker Parameters: ------------------------------------------------------------------- expDirectory: directory containing the description.py and permutations.py hsImp: which implementation of Hypersearch to use maxModels: max # of models to generate maxNumWorkers: max # of workers to use, N/A if onCluster is False loggingLevel: logging level to use in the Hypersearch worker onCluster: if True, run on the Hadoop cluster env: if not None, this is a dict of environment variables that should be sent to each worker process. These can aid in re-using the same description/permutations file for different tests. waitForCompletion: If True, wait for job to complete before returning If False, then return resultsInfoForAllModels and metricResults will be None continueJobId: If not None, then this is the JobId of a job we want to continue working on with another worker. ignoreErrModels: If true, ignore erred models maxRecords: This value is passed to the function, _generateHSJobParams(), to represent the maximum number of records to generate for the operation. dataPath: This value is passed to the function, _generateHSJobParams(), which points to the data file for the operation. predictionCacheMaxRecords: If specified, determine the maximum number of records in the prediction cache. retval: (jobID, jobInfo, resultsInfoForAllModels, metricResults, minErrScore) """ # Put in the path to our datasets if env is None: env = dict() self._setDataPath(env) # ---------------------------------------------------------------- # Prepare the jobParams jobParams = self._generateHSJobParams(expDirectory=expDirectory, hsImp=hsImp, maxModels=maxModels, maxRecords=maxRecords, dataPath=dataPath, predictionCacheMaxRecords=predictionCacheMaxRecords) jobParams.update(kwargs) if onCluster: (jobID, jobInfo, resultInfos, metricResults) \ = self._runPermutationsCluster(jobParams=jobParams, loggingLevel=loggingLevel, maxNumWorkers=maxNumWorkers, env=env, waitForCompletion=waitForCompletion, ignoreErrModels=ignoreErrModels, timeoutSec=timeoutSec) else: (jobID, jobInfo, resultInfos, metricResults) \ = self._runPermutationsLocal(jobParams=jobParams, loggingLevel=loggingLevel, env=env, waitForCompletion=waitForCompletion, continueJobId=continueJobId, ignoreErrModels=ignoreErrModels) if not waitForCompletion: return (jobID, jobInfo, resultInfos, metricResults, None) # Print job status print "\n------------------------------------------------------------------" print "Hadoop completion reason: %s" % (jobInfo.completionReason) print "Worker completion reason: %s" % (jobInfo.workerCompletionReason) print "Worker completion msg: %s" % (jobInfo.workerCompletionMsg) if jobInfo.engWorkerState is not None: print "\nEngine worker state:" print "---------------------------------------------------------------" pprint.pprint(json.loads(jobInfo.engWorkerState)) # Print out best results minErrScore=None metricAmts = [] for result in metricResults: if result is None: metricAmts.append(numpy.inf) else: metricAmts.append(result) metricAmts = numpy.array(metricAmts) if len(metricAmts) > 0: minErrScore = metricAmts.min() minModelID = resultInfos[metricAmts.argmin()].modelId # Get model info cjDAO = ClientJobsDAO.get() modelParams = cjDAO.modelsGetParams([minModelID])[0].params print "Model params for best model: \n%s" \ % (pprint.pformat(json.loads(modelParams))) print "Best model result: %f" % (minErrScore) else: print "No models finished" return (jobID, jobInfo, resultInfos, metricResults, minErrScore) class OneNodeTests(ExperimentTestBaseClass): """ """ # AWS tests attribute required for tagging via automatic test discovery via # nosetests engineAWSClusterTest=True def setUp(self): super(OneNodeTests, self).setUp() if not g_myEnv.options.runInProc: self.skipTest("Skipping One Node test since runInProc is not specified") def testSimpleV2(self, onCluster=False, env=None, **kwargs): """ Try running simple permutations """ self._printTestHeader() expDir = os.path.join(g_myEnv.testSrcExpDir, 'simpleV2') # Test it out if env is None: env = dict() env["NTA_TEST_numIterations"] = '99' env["NTA_CONF_PROP_nupic_hypersearch_swarmMaturityWindow"] = \ '%d' % (g_repeatableSwarmMaturityWindow) (jobID, jobInfo, resultInfos, metricResults, minErrScore) \ = self.runPermutations(expDir, hsImp='v2', loggingLevel=g_myEnv.options.logLevel, onCluster=onCluster, env=env, maxModels=None, **kwargs) self.assertEqual(minErrScore, 20) self.assertLess(len(resultInfos), 350) return def testDeltaV2(self, onCluster=False, env=None, **kwargs): """ Try running a simple permutations with delta encoder Test which tests the delta encoder. Runs a swarm of the sawtooth dataset With a functioning delta encoder this should give a perfect result DEBUG: disabled temporarily because this test takes too long!!! """ self._printTestHeader() expDir = os.path.join(g_myEnv.testSrcExpDir, 'delta') # Test it out if env is None: env = dict() env["NTA_CONF_PROP_nupic_hypersearch_swarmMaturityWindow"] = \ '%d' % (g_repeatableSwarmMaturityWindow) env["NTA_TEST_exitAfterNModels"] = str(20) (jobID, jobInfo, resultInfos, metricResults, minErrScore) \ = self.runPermutations(expDir, hsImp='v2', loggingLevel=g_myEnv.options.logLevel, onCluster=onCluster, env=env, maxModels=None, **kwargs) self.assertLess(minErrScore, 0.002) return def testSimpleV2NoSpeculation(self, onCluster=False, env=None, **kwargs): """ Try running a simple permutations """ self._printTestHeader() expDir = os.path.join(g_myEnv.testSrcExpDir, 'simpleV2') # Test it out if env is None: env = dict() env["NTA_TEST_numIterations"] = '99' env["NTA_CONF_PROP_nupic_hypersearch_swarmMaturityWindow"] = \ '%d' % (g_repeatableSwarmMaturityWindow) (jobID, jobInfo, resultInfos, metricResults, minErrScore) \ = self.runPermutations(expDir, hsImp='v2', loggingLevel=g_myEnv.options.logLevel, onCluster=onCluster, env=env, maxModels=None, speculativeParticles=False, **kwargs) self.assertEqual(minErrScore, 20) self.assertGreater(len(resultInfos), 1) self.assertLess(len(resultInfos), 350) return def testHTMPredictionModelV2(self, onCluster=False, env=None, maxModels=2, **kwargs): """ Try running a simple permutations using an actual CLA model, not a dummy """ self._printTestHeader() expDir = os.path.join(g_myEnv.testSrcExpDir, 'dummyV2') # Test it out if env is None: env = dict() (jobID, jobInfo, resultInfos, metricResults, minErrScore) \ = self.runPermutations(expDir, hsImp='v2', loggingLevel=g_myEnv.options.logLevel, onCluster=onCluster, env=env, maxModels=maxModels, **kwargs) self.assertEqual(len(resultInfos), maxModels) return def testCLAMultistepModel(self, onCluster=False, env=None, maxModels=2, **kwargs): """ Try running a simple permutations using an actual CLA model, not a dummy """ self._printTestHeader() expDir = os.path.join(g_myEnv.testSrcExpDir, 'simple_cla_multistep') # Test it out if env is None: env = dict() (jobID, jobInfo, resultInfos, metricResults, minErrScore) \ = self.runPermutations(expDir, hsImp='v2', loggingLevel=g_myEnv.options.logLevel, onCluster=onCluster, env=env, maxModels=maxModels, **kwargs) self.assertEqual(len(resultInfos), maxModels) return def testLegacyCLAMultistepModel(self, onCluster=False, env=None, maxModels=2, **kwargs): """ Try running a simple permutations using an actual CLA model, not a dummy. This is a legacy CLA multi-step model that doesn't declare a separate 'classifierOnly' encoder for the predicted field. """ self._printTestHeader() expDir = os.path.join(g_myEnv.testSrcExpDir, 'legacy_cla_multistep') # Test it out if env is None: env = dict() (jobID, jobInfo, resultInfos, metricResults, minErrScore) \ = self.runPermutations(expDir, hsImp='v2', loggingLevel=g_myEnv.options.logLevel, onCluster=onCluster, env=env, maxModels=maxModels, **kwargs) self.assertEqual(len(resultInfos), maxModels) return def testFilterV2(self, onCluster=False): """ Try running a simple permutations """ self._printTestHeader() expDir = os.path.join(g_myEnv.testSrcExpDir, 'simpleV2') # Don't allow the consumption encoder maxval to get to it's optimum # value (which is 250). This increases our errScore by +25. env = dict() env["NTA_TEST_maxvalFilter"] = '225' env["NTA_CONF_PROP_nupic_hypersearch_swarmMaturityWindow"] = '6' (jobID, jobInfo, resultInfos, metricResults, minErrScore) \ = self.runPermutations(expDir, hsImp='v2', loggingLevel=g_myEnv.options.logLevel, onCluster=onCluster, env=env, maxModels=None) self.assertEqual(minErrScore, 45) self.assertLess(len(resultInfos), 400) return def testLateWorker(self, onCluster=False): """ Try running a simple permutations where a worker comes in late, after the some models have already been evaluated """ self._printTestHeader() expDir = os.path.join(g_myEnv.testSrcExpDir, 'simpleV2') env = dict() env["NTA_CONF_PROP_nupic_hypersearch_swarmMaturityWindow"] = \ '%d' % (g_repeatableSwarmMaturityWindow) env["NTA_TEST_exitAfterNModels"] = '100' (jobID, jobInfo, resultInfos, metricResults, minErrScore) \ = self.runPermutations(expDir, hsImp='v2', loggingLevel=g_myEnv.options.logLevel, maxModels=None, onCluster=onCluster, env=env, waitForCompletion=True, ) self.assertEqual(len(resultInfos), 100) # Run another worker the rest of the way env.pop("NTA_TEST_exitAfterNModels") (jobID, jobInfo, resultInfos, metricResults, minErrScore) \ = self.runPermutations(expDir, hsImp='v2', loggingLevel=g_myEnv.options.logLevel, maxModels=None, onCluster=onCluster, env=env, waitForCompletion=True, continueJobId = jobID, ) self.assertEqual(minErrScore, 20) self.assertLess(len(resultInfos), 350) return def testOrphanedModel(self, onCluster=False, modelRange=(0,1)): """ Run a worker on a model for a while, then have it exit before the model finishes. Then, run another worker, which should detect the orphaned model. """ self._printTestHeader() expDir = os.path.join(g_myEnv.testSrcExpDir, 'simpleV2') # NTA_TEST_numIterations is watched by the dummyModelParams() method of # the permutations file. # NTA_TEST_sysExitModelRange is watched by the dummyModelParams() method of # the permutations file. It tells it to do a sys.exit() after so many # iterations. # We increase the swarm maturity window to make our unit tests more # repeatable. There is an element of randomness as to which model # parameter combinations get evaluated first when running with # multiple workers, so this insures that we can find the "best" model # that we expect to see in our unit tests. env = dict() env["NTA_TEST_numIterations"] = '2' env["NTA_TEST_sysExitModelRange"] = '%d,%d' % (modelRange[0], modelRange[1]) env["NTA_CONF_PROP_nupic_hypersearch_swarmMaturityWindow"] \ = '%d' % (g_repeatableSwarmMaturityWindow) (jobID, jobInfo, resultInfos, metricResults, minErrScore) \ = self.runPermutations(expDir, hsImp='v2', loggingLevel=g_myEnv.options.logLevel, maxModels=300, onCluster=onCluster, env=env, waitForCompletion=False, ) # At this point, we should have 1 model, still running (beg, end) = modelRange self.assertEqual(len(resultInfos), end) numRunning = 0 for res in resultInfos: if res.status == ClientJobsDAO.STATUS_RUNNING: numRunning += 1 self.assertEqual(numRunning, 1) # Run another worker the rest of the way, after delaying enough time to # generate an orphaned model env["NTA_CONF_PROP_nupic_hypersearch_modelOrphanIntervalSecs"] = '1' time.sleep(2) # Here we launch another worker to finish up the job. We set the maxModels # to 300 (200 something should be enough) in case the orphan detection is # not working, it will make sure we don't loop for excessively long. # With orphan detection working, we should detect that the first model # would never complete, orphan it, and create a new one in the 1st sprint. # Without orphan detection working, we will wait forever for the 1st sprint # to finish, and will create a bunch of gen 1, then gen2, then gen 3, etc. # and gen 0 will never finish, so the swarm will never mature. (jobID, jobInfo, resultInfos, metricResults, minErrScore) \ = self.runPermutations(expDir, hsImp='v2', loggingLevel=g_myEnv.options.logLevel, maxModels=300, onCluster=onCluster, env=env, waitForCompletion=True, continueJobId = jobID, ) self.assertEqual(minErrScore, 20) self.assertLess(len(resultInfos), 350) return def testOrphanedModelGen1(self): """ Run a worker on a model for a while, then have it exit before a model finishes in gen index 2. Then, run another worker, which should detect the orphaned model. """ self._printTestHeader() inst = OneNodeTests(self._testMethodName) return inst.testOrphanedModel(modelRange=(10,11)) def testErredModel(self, onCluster=False, modelRange=(6,7)): """ Run with 1 or more models generating errors """ self._printTestHeader() expDir = os.path.join(g_myEnv.testSrcExpDir, 'simpleV2') # We increase the swarm maturity window to make our unit tests more # repeatable. There is an element of randomness as to which model # parameter combinations get evaluated first when running with # multiple workers, so this insures that we can find the "best" model # that we expect to see in our unit tests. env = dict() env["NTA_TEST_errModelRange"] = '%d,%d' % (modelRange[0], modelRange[1]) env["NTA_CONF_PROP_nupic_hypersearch_swarmMaturityWindow"] \ = '%d' % (g_repeatableSwarmMaturityWindow) (jobID, jobInfo, resultInfos, metricResults, minErrScore) \ = self.runPermutations(expDir, hsImp='v2', loggingLevel=g_myEnv.options.logLevel, onCluster=onCluster, env=env, maxModels=None, ignoreErrModels=True ) self.assertEqual(minErrScore, 20) self.assertLess(len(resultInfos), 350) return def testJobFailModel(self, onCluster=False, modelRange=(6,7)): """ Run with 1 or more models generating jobFail exception """ self._printTestHeader() expDir = os.path.join(g_myEnv.testSrcExpDir, 'simpleV2') # We increase the swarm maturity window to make our unit tests more # repeatable. There is an element of randomness as to which model # parameter combinations get evaluated first when running with # multiple workers, so this insures that we can find the "best" model # that we expect to see in our unit tests. env = dict() env["NTA_TEST_jobFailErr"] = 'True' maxNumWorkers = 4 (jobID, jobInfo, resultInfos, metricResults, minErrScore) \ = self.runPermutations(expDir, hsImp='v2', loggingLevel=g_myEnv.options.logLevel, onCluster=onCluster, env=env, maxModels=None, maxNumWorkers=maxNumWorkers, ignoreErrModels=True ) # Make sure workerCompletionReason was error self.assertEqual (jobInfo.workerCompletionReason, ClientJobsDAO.CMPL_REASON_ERROR) self.assertLess (len(resultInfos), maxNumWorkers+1) return def testTooManyErredModels(self, onCluster=False, modelRange=(5,10)): """ Run with too many models generating errors """ self._printTestHeader() expDir = os.path.join(g_myEnv.testSrcExpDir, 'simpleV2') # We increase the swarm maturity window to make our unit tests more # repeatable. There is an element of randomness as to which model # parameter combinations get evaluated first when running with # multiple workers, so this insures that we can find the "best" model # that we expect to see in our unit tests. env = dict() env["NTA_TEST_errModelRange"] = '%d,%d' % (modelRange[0], modelRange[1]) env["NTA_CONF_PROP_nupic_hypersearch_swarmMaturityWindow"] \ = '%d' % (g_repeatableSwarmMaturityWindow) (jobID, jobInfo, resultInfos, metricResults, minErrScore) \ = self.runPermutations(expDir, hsImp='v2', loggingLevel=g_myEnv.options.logLevel, onCluster=onCluster, env=env, maxModels=None, ignoreErrModels=True ) self.assertEqual (jobInfo.workerCompletionReason, ClientJobsDAO.CMPL_REASON_ERROR) return def testFieldThreshold(self, onCluster=False, env=None, **kwargs): """ Test minimum field contribution threshold for a field to be included in further sprints """ self._printTestHeader() expDir = os.path.join(g_myEnv.testSrcExpDir, 'field_threshold_temporal') # Test it out if env is None: env = dict() env["NTA_TEST_numIterations"] = '99' env["NTA_CONF_PROP_nupic_hypersearch_swarmMaturityWindow"] = \ '%d' % (g_repeatableSwarmMaturityWindow) env["NTA_CONF_PROP_nupic_hypersearch_max_field_branching"] = \ '%d' % (0) env["NTA_CONF_PROP_nupic_hypersearch_minParticlesPerSwarm"] = \ '%d' % (2) env["NTA_CONF_PROP_nupic_hypersearch_min_field_contribution"] = \ '%f' % (100) (jobID, jobInfo, resultInfos, metricResults, minErrScore) \ = self.runPermutations(expDir, hsImp='v2', loggingLevel=g_myEnv.options.logLevel, onCluster=onCluster, env=env, maxModels=None, dummyModel={'iterations':200}, **kwargs) # Get the field contributions from the hypersearch results dict cjDAO = ClientJobsDAO.get() jobResultsStr = cjDAO.jobGetFields(jobID, ['results'])[0] jobResults = json.loads(jobResultsStr) bestModel = cjDAO.modelsInfo([jobResults["bestModel"]])[0] params = json.loads(bestModel.params) prefix = 'modelParams|sensorParams|encoders|' expectedSwarmId = prefix + ('.' + prefix).join([ 'attendance', 'visitor_winloss']) self.assertEqual(params["particleState"]["swarmId"], expectedSwarmId, "Actual swarm id = %s\nExpcted swarm id = %s" \ % (params["particleState"]["swarmId"], expectedSwarmId)) self.assertEqual( bestModel.optimizedMetric, 75) #========================================================================== env["NTA_CONF_PROP_nupic_hypersearch_min_field_contribution"] = \ '%f' % (20) (jobID, jobInfo, resultInfos, metricResults, minErrScore) \ = self.runPermutations(expDir, hsImp='v2', loggingLevel=g_myEnv.options.logLevel, onCluster=onCluster, env=env, maxModels=None, dummyModel={'iterations':200}, **kwargs) # Get the field contributions from the hypersearch results dict cjDAO = ClientJobsDAO.get() jobResultsStr = cjDAO.jobGetFields(jobID, ['results'])[0] jobResults = json.loads(jobResultsStr) bestModel = cjDAO.modelsInfo([jobResults["bestModel"]])[0] params = json.loads(bestModel.params) prefix = 'modelParams|sensorParams|encoders|' expectedSwarmId = prefix + ('.' + prefix).join([ 'attendance', 'home_winloss', 'visitor_winloss']) self.assertEqual(params["particleState"]["swarmId"], expectedSwarmId, "Actual swarm id = %s\nExpcted swarm id = %s" \ % (params["particleState"]["swarmId"], expectedSwarmId)) assert bestModel.optimizedMetric == 55, bestModel.optimizedMetric #========================================================================== # Find best combo possible env["NTA_CONF_PROP_nupic_hypersearch_min_field_contribution"] = \ '%f' % (0.0) (jobID, jobInfo, resultInfos, metricResults, minErrScore) \ = self.runPermutations(expDir, hsImp='v2', loggingLevel=g_myEnv.options.logLevel, onCluster=onCluster, env=env, maxModels=None, dummyModel={'iterations':200}, **kwargs) # Get the field contributions from the hypersearch results dict cjDAO = ClientJobsDAO.get() jobResultsStr = cjDAO.jobGetFields(jobID, ['results'])[0] jobResults = json.loads(jobResultsStr) bestModel = cjDAO.modelsInfo([jobResults["bestModel"]])[0] params = json.loads(bestModel.params) prefix = 'modelParams|sensorParams|encoders|' expectedSwarmId = prefix + ('.' + prefix).join([ 'attendance', 'home_winloss', 'precip', 'timestamp_dayOfWeek', 'timestamp_timeOfDay', 'visitor_winloss']) self.assertEqual(params["particleState"]["swarmId"], expectedSwarmId, "Actual swarm id = %s\nExpcted swarm id = %s" \ % (params["particleState"]["swarmId"], expectedSwarmId)) assert bestModel.optimizedMetric == 25, bestModel.optimizedMetric def testSpatialClassification(self, onCluster=False, env=None, **kwargs): """ Try running a spatial classification swarm """ self._printTestHeader() expDir = os.path.join(g_myEnv.testSrcExpDir, 'spatial_classification') # Test it out if env is None: env = dict() env["NTA_TEST_numIterations"] = '99' env["NTA_CONF_PROP_nupic_hypersearch_swarmMaturityWindow"] = \ '%d' % (g_repeatableSwarmMaturityWindow) (jobID, jobInfo, resultInfos, metricResults, minErrScore) \ = self.runPermutations(expDir, hsImp='v2', loggingLevel=g_myEnv.options.logLevel, onCluster=onCluster, env=env, maxModels=None, **kwargs) self.assertEqual(minErrScore, 20) self.assertLess(len(resultInfos), 350) # Check the expected field contributions cjDAO = ClientJobsDAO.get() jobResultsStr = cjDAO.jobGetFields(jobID, ['results'])[0] jobResults = json.loads(jobResultsStr) bestModel = cjDAO.modelsInfo([jobResults["bestModel"]])[0] params = json.loads(bestModel.params) actualFieldContributions = jobResults['fieldContributions'] print "Actual field contributions:", \ pprint.pformat(actualFieldContributions) expectedFieldContributions = { 'address': 100 * (90.0-30)/90.0, 'gym': 100 * (90.0-40)/90.0, 'timestamp_dayOfWeek': 100 * (90.0-80.0)/90.0, 'timestamp_timeOfDay': 100 * (90.0-90.0)/90.0, } for key, value in expectedFieldContributions.items(): self.assertEqual(actualFieldContributions[key], value, "actual field contribution from field '%s' does not " "match the expected value of %f" % (key, value)) # Check the expected best encoder combination prefix = 'modelParams|sensorParams|encoders|' expectedSwarmId = prefix + ('.' + prefix).join([ 'address', 'gym']) self.assertEqual(params["particleState"]["swarmId"], expectedSwarmId, "Actual swarm id = %s\nExpcted swarm id = %s" \ % (params["particleState"]["swarmId"], expectedSwarmId)) return def testAlwaysInputPredictedField(self, onCluster=False, env=None, **kwargs): """ Run a swarm where 'inputPredictedField' is set in the permutations file. The dummy model for this swarm is designed to give the lowest error when the predicted field is INCLUDED, so make sure we don't get this low error """ self._printTestHeader() expDir = os.path.join(g_myEnv.testSrcExpDir, 'input_predicted_field') # Test it out not requiring the predicted field. This should yield a # low error score if env is None: env = dict() env["NTA_TEST_inputPredictedField"] = "auto" env["NTA_TEST_numIterations"] = '99' env["NTA_CONF_PROP_nupic_hypersearch_minParticlesPerSwarm"] = \ '%d' % (2) env["NTA_CONF_PROP_nupic_hypersearch_swarmMaturityWindow"] = \ '%d' % (g_repeatableSwarmMaturityWindow) (jobID, jobInfo, resultInfos, metricResults, minErrScore) \ = self.runPermutations(expDir, hsImp='v2', loggingLevel=g_myEnv.options.logLevel, onCluster=onCluster, env=env, maxModels=None, **kwargs) self.assertEqual(minErrScore, -50) self.assertLess(len(resultInfos), 350) # Now, require the predicted field. This should yield a high error score if env is None: env = dict() env["NTA_TEST_inputPredictedField"] = "yes" env["NTA_TEST_numIterations"] = '99' env["NTA_CONF_PROP_nupic_hypersearch_minParticlesPerSwarm"] = \ '%d' % (2) env["NTA_CONF_PROP_nupic_hypersearch_swarmMaturityWindow"] = \ '%d' % (g_repeatableSwarmMaturityWindow) (jobID, jobInfo, resultInfos, metricResults, minErrScore) \ = self.runPermutations(expDir, hsImp='v2', loggingLevel=g_myEnv.options.logLevel, onCluster=onCluster, env=env, maxModels=None, **kwargs) self.assertEqual(minErrScore, -40) self.assertLess(len(resultInfos), 350) return def testFieldThresholdNoPredField(self, onCluster=False, env=None, **kwargs): """ Test minimum field contribution threshold for a field to be included in further sprints when doing a temporal search that does not require the predicted field. """ self._printTestHeader() expDir = os.path.join(g_myEnv.testSrcExpDir, 'input_predicted_field') # Test it out without any max field branching in effect if env is None: env = dict() env["NTA_TEST_numIterations"] = '99' env["NTA_TEST_inputPredictedField"] = "auto" env["NTA_CONF_PROP_nupic_hypersearch_swarmMaturityWindow"] = \ '%d' % (g_repeatableSwarmMaturityWindow) env["NTA_CONF_PROP_nupic_hypersearch_max_field_branching"] = \ '%d' % (0) env["NTA_CONF_PROP_nupic_hypersearch_minParticlesPerSwarm"] = \ '%d' % (2) env["NTA_CONF_PROP_nupic_hypersearch_min_field_contribution"] = \ '%f' % (0) if True: (jobID, jobInfo, resultInfos, metricResults, minErrScore) \ = self.runPermutations(expDir, hsImp='v2', loggingLevel=g_myEnv.options.logLevel, onCluster=onCluster, env=env, maxModels=None, dummyModel={'iterations':200}, **kwargs) # Verify the best model and check the field contributions. cjDAO = ClientJobsDAO.get() jobResultsStr = cjDAO.jobGetFields(jobID, ['results'])[0] jobResults = json.loads(jobResultsStr) bestModel = cjDAO.modelsInfo([jobResults["bestModel"]])[0] params = json.loads(bestModel.params) prefix = 'modelParams|sensorParams|encoders|' expectedSwarmId = prefix + ('.' + prefix).join([ 'address', 'gym', 'timestamp_dayOfWeek', 'timestamp_timeOfDay']) self.assertEqual(params["particleState"]["swarmId"], expectedSwarmId, "Actual swarm id = %s\nExpcted swarm id = %s" \ % (params["particleState"]["swarmId"], expectedSwarmId)) self.assertEqual( bestModel.optimizedMetric, -50) # Check the field contributions actualFieldContributions = jobResults['fieldContributions'] print "Actual field contributions:", \ pprint.pformat(actualFieldContributions) expectedFieldContributions = { 'consumption': 0.0, 'address': 100 * (60.0-40.0)/60.0, 'timestamp_timeOfDay': 100 * (60.0-20.0)/60.0, 'timestamp_dayOfWeek': 100 * (60.0-10.0)/60.0, 'gym': 100 * (60.0-30.0)/60.0} for key, value in expectedFieldContributions.items(): self.assertEqual(actualFieldContributions[key], value, "actual field contribution from field '%s' does not " "match the expected value of %f" % (key, value)) if True: #========================================================================== # Now test ignoring all fields that contribute less than 55% to the # error score. This means we can only use the timestamp_timeOfDay and # timestamp_dayOfWeek fields. # This should bring our best error score up to 50-30-40 = -20 env["NTA_CONF_PROP_nupic_hypersearch_min_field_contribution"] = \ '%f' % (55) env["NTA_CONF_PROP_nupic_hypersearch_max_field_branching"] = \ '%d' % (5) (jobID, jobInfo, resultInfos, metricResults, minErrScore) \ = self.runPermutations(expDir, hsImp='v2', loggingLevel=g_myEnv.options.logLevel, onCluster=onCluster, env=env, maxModels=None, dummyModel={'iterations':200}, **kwargs) # Get the best model cjDAO = ClientJobsDAO.get() jobResultsStr = cjDAO.jobGetFields(jobID, ['results'])[0] jobResults = json.loads(jobResultsStr) bestModel = cjDAO.modelsInfo([jobResults["bestModel"]])[0] params = json.loads(bestModel.params) prefix = 'modelParams|sensorParams|encoders|' expectedSwarmId = prefix + ('.' + prefix).join([ 'timestamp_dayOfWeek', 'timestamp_timeOfDay']) self.assertEqual(params["particleState"]["swarmId"], expectedSwarmId, "Actual swarm id = %s\nExpcted swarm id = %s" \ % (params["particleState"]["swarmId"], expectedSwarmId)) self.assertEqual( bestModel.optimizedMetric, -20) # Check field contributions returned actualFieldContributions = jobResults['fieldContributions'] print "Actual field contributions:", \ pprint.pformat(actualFieldContributions) expectedFieldContributions = { 'consumption': 0.0, 'address': 100 * (60.0-40.0)/60.0, 'timestamp_timeOfDay': 100 * (60.0-20.0)/60.0, 'timestamp_dayOfWeek': 100 * (60.0-10.0)/60.0, 'gym': 100 * (60.0-30.0)/60.0} for key, value in expectedFieldContributions.items(): self.assertEqual(actualFieldContributions[key], value, "actual field contribution from field '%s' does not " "match the expected value of %f" % (key, value)) if True: #========================================================================== # Now, test using maxFieldBranching to limit the max number of fields to # 3. This means we can only use the timestamp_timeOfDay, timestamp_dayOfWeek, # gym fields. # This should bring our error score to 50-30-40-20 = -40 env["NTA_CONF_PROP_nupic_hypersearch_min_field_contribution"] = \ '%f' % (0) env["NTA_CONF_PROP_nupic_hypersearch_max_field_branching"] = \ '%d' % (3) (jobID, jobInfo, resultInfos, metricResults, minErrScore) \ = self.runPermutations(expDir, hsImp='v2', loggingLevel=g_myEnv.options.logLevel, onCluster=onCluster, env=env, maxModels=None, dummyModel={'iterations':200}, **kwargs) # Get the best model cjDAO = ClientJobsDAO.get() jobResultsStr = cjDAO.jobGetFields(jobID, ['results'])[0] jobResults = json.loads(jobResultsStr) bestModel = cjDAO.modelsInfo([jobResults["bestModel"]])[0] params = json.loads(bestModel.params) prefix = 'modelParams|sensorParams|encoders|' expectedSwarmId = prefix + ('.' + prefix).join([ 'gym', 'timestamp_dayOfWeek', 'timestamp_timeOfDay']) self.assertEqual(params["particleState"]["swarmId"], expectedSwarmId, "Actual swarm id = %s\nExpcted swarm id = %s" \ % (params["particleState"]["swarmId"], expectedSwarmId)) self.assertEqual( bestModel.optimizedMetric, -40) if True: #========================================================================== # Now, test setting max models so that no swarm can finish completely. # Make sure we get the expected field contributions env["NTA_CONF_PROP_nupic_hypersearch_swarmMaturityWindow"] = \ '%d' % (g_repeatableSwarmMaturityWindow) env["NTA_CONF_PROP_nupic_hypersearch_max_field_branching"] = \ '%d' % (0) env["NTA_CONF_PROP_nupic_hypersearch_minParticlesPerSwarm"] = \ '%d' % (5) env["NTA_CONF_PROP_nupic_hypersearch_min_field_contribution"] = \ '%f' % (0) (jobID, jobInfo, resultInfos, metricResults, minErrScore) \ = self.runPermutations(expDir, hsImp='v2', loggingLevel=g_myEnv.options.logLevel, onCluster=onCluster, env=env, maxModels=10, dummyModel={'iterations':200}, **kwargs) # Get the best model cjDAO = ClientJobsDAO.get() jobResultsStr = cjDAO.jobGetFields(jobID, ['results'])[0] jobResults = json.loads(jobResultsStr) bestModel = cjDAO.modelsInfo([jobResults["bestModel"]])[0] params = json.loads(bestModel.params) prefix = 'modelParams|sensorParams|encoders|' expectedSwarmId = prefix + ('.' + prefix).join([ 'timestamp_dayOfWeek']) self.assertEqual(params["particleState"]["swarmId"], expectedSwarmId, "Actual swarm id = %s\nExpcted swarm id = %s" \ % (params["particleState"]["swarmId"], expectedSwarmId)) self.assertEqual( bestModel.optimizedMetric, 10) # Check field contributions returned actualFieldContributions = jobResults['fieldContributions'] print "Actual field contributions:", \ pprint.pformat(actualFieldContributions) expectedFieldContributions = { 'consumption': 0.0, 'address': 100 * (60.0-40.0)/60.0, 'timestamp_timeOfDay': 100 * (60.0-20.0)/60.0, 'timestamp_dayOfWeek': 100 * (60.0-10.0)/60.0, 'gym': 100 * (60.0-30.0)/60.0} class MultiNodeTests(ExperimentTestBaseClass): """ Test hypersearch on multiple nodes """ # AWS tests attribute required for tagging via automatic test discovery via # nosetests engineAWSClusterTest=True def testSimpleV2(self): """ Try running a simple permutations """ self._printTestHeader() inst = OneNodeTests(self._testMethodName) return inst.testSimpleV2(onCluster=True) #, maxNumWorkers=7) def testDeltaV2(self): """ Try running a simple permutations """ self._printTestHeader() inst = OneNodeTests(self._testMethodName) return inst.testDeltaV2(onCluster=True) #, maxNumWorkers=7) def testSmartSpeculation(self, onCluster=True, env=None, **kwargs): """ Try running a simple permutations """ self._printTestHeader() expDir = os.path.join(g_myEnv.testSrcExpDir, 'smart_speculation_temporal') # Test it out if env is None: env = dict() env["NTA_TEST_numIterations"] = '99' env["NTA_CONF_PROP_nupic_hypersearch_swarmMaturityWindow"] = \ '%d' % (g_repeatableSwarmMaturityWindow) env["NTA_CONF_PROP_nupic_hypersearch_minParticlesPerSwarm"] = \ '%d' % (1) (jobID, jobInfo, resultInfos, metricResults, minErrScore) \ = self.runPermutations(expDir, hsImp='v2', loggingLevel=g_myEnv.options.logLevel, onCluster=onCluster, env=env, maxModels=None, dummyModel={'iterations':200}, **kwargs) # Get the field contributions from the hypersearch results dict cjDAO = ClientJobsDAO.get() jobInfoStr = cjDAO.jobGetFields(jobID, ['results','engWorkerState']) jobResultsStr = jobInfoStr[0] engState = jobInfoStr[1] engState = json.loads(engState) swarms = engState["swarms"] jobResults = json.loads(jobResultsStr) bestModel = cjDAO.modelsInfo([jobResults["bestModel"]])[0] params = json.loads(bestModel.params) # Make sure that the only nonkilled models are the ones that would have been # run without speculation prefix = 'modelParams|sensorParams|encoders|' correctOrder = ["A","B","C","D","E","F","G","Pred"] correctOrder = [prefix + x for x in correctOrder] for swarm in swarms: if swarms[swarm]["status"] == 'killed': swarmId = swarm.split(".") if(len(swarmId)>1): # Make sure that something before the last two encoders is in the # wrong sprint progression, hence why it was killed # The last encoder is the predicted field and the second to last is # the current new addition wrong=0 for i in range(len(swarmId)-2): if correctOrder[i] != swarmId[i]: wrong=1 assert wrong==1, "Some of the killed swarms should not have been " \ + "killed as they are a legal combination." if swarms[swarm]["status"] == 'completed': swarmId = swarm.split(".") if(len(swarmId)>3): # Make sure that the completed swarms are all swarms that should # have been run. # The last encoder is the predicted field and the second to last is # the current new addition for i in range(len(swarmId)-3): if correctOrder[i] != swarmId[i]: assert False , "Some of the completed swarms should not have " \ "finished as they are illegal combinations" if swarms[swarm]["status"] == 'active': assert False , "Some swarms are still active at the end of hypersearch" pass def testSmartSpeculationSpatialClassification(self, onCluster=True, env=None, **kwargs): """ Test that smart speculation does the right thing with spatial classification models. This also applies to temporal models where the predicted field is optional (or excluded) since Hypersearch treats them the same. """ self._printTestHeader() expDir = os.path.join(g_myEnv.testSrcExpDir, 'smart_speculation_spatial_classification') # Test it out if env is None: env = dict() env["NTA_TEST_numIterations"] = '99' env["NTA_CONF_PROP_nupic_hypersearch_swarmMaturityWindow"] = \ '%d' % (g_repeatableSwarmMaturityWindow) env["NTA_CONF_PROP_nupic_hypersearch_minParticlesPerSwarm"] = \ '%d' % (1) (jobID, jobInfo, resultInfos, metricResults, minErrScore) \ = self.runPermutations(expDir, hsImp='v2', loggingLevel=g_myEnv.options.logLevel, onCluster=onCluster, env=env, maxModels=None, maxNumWorkers=5, dummyModel={'iterations':200}, **kwargs) # Get the worker state cjDAO = ClientJobsDAO.get() jobInfoStr = cjDAO.jobGetFields(jobID, ['results','engWorkerState']) jobResultsStr = jobInfoStr[0] engState = jobInfoStr[1] engState = json.loads(engState) swarms = engState["swarms"] jobResults = json.loads(jobResultsStr) bestModel = cjDAO.modelsInfo([jobResults["bestModel"]])[0] params = json.loads(bestModel.params) # Make sure that the only non-killed models are the ones that would have been # run without speculation prefix = 'modelParams|sensorParams|encoders|' correctOrder = ["A","B","C"] correctOrder = [prefix + x for x in correctOrder] for swarm in swarms: if swarms[swarm]["status"] == 'killed': swarmId = swarm.split(".") if(len(swarmId) > 1): # Make sure that the best encoder is not in this swarm if correctOrder[0] in swarmId: raise RuntimeError("Some of the killed swarms should not have been " "killed as they are a legal combination.") elif swarms[swarm]["status"] == 'completed': swarmId = swarm.split(".") if(len(swarmId) >= 2): # Make sure that the completed swarms are all swarms that should # have been run. for i in range(len(swarmId)-1): if correctOrder[i] != swarmId[i]: raise RuntimeError("Some of the completed swarms should not have " "finished as they are illegal combinations") elif swarms[swarm]["status"] == 'active': raise RuntimeError("Some swarms are still active at the end of " "hypersearch") def testFieldBranching(self, onCluster=True, env=None, **kwargs): """ Try running a simple permutations """ self._printTestHeader() expDir = os.path.join(g_myEnv.testSrcExpDir, 'max_branching_temporal') # Test it out if env is None: env = dict() env["NTA_TEST_numIterations"] = '99' env["NTA_CONF_PROP_nupic_hypersearch_swarmMaturityWindow"] = \ '%d' % (g_repeatableSwarmMaturityWindow) env["NTA_CONF_PROP_nupic_hypersearch_max_field_branching"] = \ '%d' % (4) env["NTA_CONF_PROP_nupic_hypersearch_min_field_contribution"] = \ '%f' % (-20.0) env["NTA_CONF_PROP_nupic_hypersearch_minParticlesPerSwarm"] = \ '%d' % (2) (jobID, jobInfo, resultInfos, metricResults, minErrScore) \ = self.runPermutations(expDir, hsImp='v2', loggingLevel=g_myEnv.options.logLevel, onCluster=onCluster, env=env, maxModels=None, dummyModel={'iterations':200}, **kwargs) # Get the field contributions from the hypersearch results dict cjDAO = ClientJobsDAO.get() jobResultsStr = cjDAO.jobGetFields(jobID, ['results'])[0] jobResults = json.loads(jobResultsStr) bestModel = cjDAO.modelsInfo([jobResults["bestModel"]])[0] params = json.loads(bestModel.params) prefix = 'modelParams|sensorParams|encoders|' expectedSwarmId = prefix + ('.' + prefix).join([ 'attendance', 'home_winloss', 'timestamp_dayOfWeek', 'timestamp_timeOfDay', 'visitor_winloss']) assert params["particleState"]["swarmId"] == expectedSwarmId, \ params["particleState"]["swarmId"] assert bestModel.optimizedMetric == 432, bestModel.optimizedMetric env["NTA_CONF_PROP_nupic_hypersearch_max_field_branching"] = \ '%d' % (3) (jobID, jobInfo, resultInfos, metricResults, minErrScore) \ = self.runPermutations(expDir, hsImp='v2', loggingLevel=g_myEnv.options.logLevel, onCluster=onCluster, env=env, maxModels=None, dummyModel={'iterations':200}, **kwargs) # Get the field contributions from the hypersearch results dict cjDAO = ClientJobsDAO.get() jobResultsStr = cjDAO.jobGetFields(jobID, ['results'])[0] jobResults = json.loads(jobResultsStr) bestModel = cjDAO.modelsInfo([jobResults["bestModel"]])[0] params = json.loads(bestModel.params) prefix = 'modelParams|sensorParams|encoders|' expectedSwarmId = prefix + ('.' + prefix).join([ 'attendance', 'home_winloss', 'timestamp_timeOfDay', 'visitor_winloss']) assert params["particleState"]["swarmId"] == expectedSwarmId, \ params["particleState"]["swarmId"] assert bestModel.optimizedMetric == 465, bestModel.optimizedMetric env["NTA_CONF_PROP_nupic_hypersearch_max_field_branching"] = \ '%d' % (5) (jobID, jobInfo, resultInfos, metricResults, minErrScore) \ = self.runPermutations(expDir, hsImp='v2', loggingLevel=g_myEnv.options.logLevel, onCluster=onCluster, env=env, maxModels=None, dummyModel={'iterations':200}, **kwargs) # Get the field contributions from the hypersearch results dict cjDAO = ClientJobsDAO.get() jobResultsStr = cjDAO.jobGetFields(jobID, ['results'])[0] jobResults = json.loads(jobResultsStr) bestModel = cjDAO.modelsInfo([jobResults["bestModel"]])[0] params = json.loads(bestModel.params) prefix = 'modelParams|sensorParams|encoders|' expectedSwarmId = prefix + ('.' + prefix).join([ 'attendance', 'home_winloss', 'precip', 'timestamp_dayOfWeek', 'timestamp_timeOfDay', 'visitor_winloss']) assert params["particleState"]["swarmId"] == expectedSwarmId, \ params["particleState"]["swarmId"] assert bestModel.optimizedMetric == 390, bestModel.optimizedMetric #Find best combo with 3 fields env["NTA_CONF_PROP_nupic_hypersearch_max_field_branching"] = \ '%d' % (0) (jobID, jobInfo, resultInfos, metricResults, minErrScore) \ = self.runPermutations(expDir, hsImp='v2', loggingLevel=g_myEnv.options.logLevel, onCluster=onCluster, env=env, maxModels=100, dummyModel={'iterations':200}, **kwargs) # Get the field contributions from the hypersearch results dict cjDAO = ClientJobsDAO.get() jobResultsStr = cjDAO.jobGetFields(jobID, ['results'])[0] jobResults = json.loads(jobResultsStr) bestModel = cjDAO.modelsInfo([jobResults["bestModel"]])[0] params = json.loads(bestModel.params) prefix = 'modelParams|sensorParams|encoders|' expectedSwarmId = prefix + ('.' + prefix).join([ 'attendance', 'daynight', 'visitor_winloss']) assert params["particleState"]["swarmId"] == expectedSwarmId, \ params["particleState"]["swarmId"] assert bestModel.optimizedMetric == 406, bestModel.optimizedMetric return def testFieldThreshold(self, onCluster=True, env=None, **kwargs): """ Test minimum field contribution threshold for a field to be included in further sprints """ self._printTestHeader() inst = OneNodeTests(self._testMethodName) return inst.testFieldThreshold(onCluster=True) def testFieldContributions(self, onCluster=True, env=None, **kwargs): """ Try running a simple permutations """ self._printTestHeader() expDir = os.path.join(g_myEnv.testSrcExpDir, 'field_contrib_temporal') # Test it out if env is None: env = dict() env["NTA_TEST_numIterations"] = '99' env["NTA_CONF_PROP_nupic_hypersearch_swarmMaturityWindow"] = \ '%d' % (g_repeatableSwarmMaturityWindow) (jobID, jobInfo, resultInfos, metricResults, minErrScore) \ = self.runPermutations(expDir, hsImp='v2', loggingLevel=g_myEnv.options.logLevel, onCluster=onCluster, env=env, maxModels=None, **kwargs) # Get the field contributions from the hypersearch results dict cjDAO = ClientJobsDAO.get() jobResultsStr = cjDAO.jobGetFields(jobID, ['results'])[0] jobResults = json.loads(jobResultsStr) actualFieldContributions = jobResults['fieldContributions'] print "Actual field contributions:", actualFieldContributions expectedFieldContributions = {'consumption': 0.0, 'address': 0.0, 'timestamp_timeOfDay': 20.0, 'timestamp_dayOfWeek': 50.0, 'gym': 10.0} for key, value in expectedFieldContributions.items(): self.assertEqual(actualFieldContributions[key], value, "actual field contribution from field '%s' does not " "match the expected value of %f" % (key, value)) return def testHTMPredictionModelV2(self): """ Try running a simple permutations through a real CLA model """ self._printTestHeader() inst = OneNodeTests(self._testMethodName) return inst.testHTMPredictionModelV2(onCluster=True, maxModels=4) def testCLAMultistepModel(self): """ Try running a simple permutations through a real CLA model that uses multistep """ self._printTestHeader() inst = OneNodeTests(self._testMethodName) return inst.testCLAMultistepModel(onCluster=True, maxModels=4) def testLegacyCLAMultistepModel(self): """ Try running a simple permutations through a real CLA model that uses multistep """ self._printTestHeader() inst = OneNodeTests(self._testMethodName) return inst.testLegacyCLAMultistepModel(onCluster=True, maxModels=4) def testSimpleV2VariableWaits(self): """ Try running a simple permutations where certain field combinations take longer to complete, this lets us test that we successfully kill models in bad swarms that are still running. """ self._printTestHeader() # NTA_TEST_variableWaits and NTA_TEST_numIterations are watched by the # dummyModelParams() method of the permutations.py file # NTA_TEST_numIterations env = dict() env["NTA_TEST_variableWaits"] ='True' env["NTA_TEST_numIterations"] = '100' inst = OneNodeTests('testSimpleV2') return inst.testSimpleV2(onCluster=True, env=env) def testOrphanedModel(self, modelRange=(0,2)): """ Run a worker on a model for a while, then have it exit before the model finishes. Then, run another worker, which should detect the orphaned model. """ self._printTestHeader() expDir = os.path.join(g_myEnv.testSrcExpDir, 'simpleV2') # NTA_TEST_numIterations is watched by the dummyModelParams() method of # the permutations file. # NTA_TEST_sysExitModelRange is watched by the dummyModelParams() method of # the permutations file. It tells it to do a sys.exit() after so many # iterations. env = dict() env["NTA_TEST_numIterations"] = '99' env["NTA_TEST_sysExitModelRange"] = '%d,%d' % (modelRange[0], modelRange[1]) env["NTA_CONF_PROP_nupic_hypersearch_modelOrphanIntervalSecs"] = '1' env["NTA_CONF_PROP_nupic_hypersearch_swarmMaturityWindow"] \ = '%d' % (g_repeatableSwarmMaturityWindow) (jobID, jobInfo, resultInfos, metricResults, minErrScore) \ = self.runPermutations(expDir, hsImp='v2', loggingLevel=g_myEnv.options.logLevel, maxModels=500, onCluster=True, env=env, waitForCompletion=True, maxNumWorkers=4, ) self.assertEqual(minErrScore, 20) self.assertLess(len(resultInfos), 500) return def testTwoOrphanedModels(self, modelRange=(0,2)): """ Test behavior when a worker marks 2 models orphaned at the same time. """ self._printTestHeader() expDir = os.path.join(g_myEnv.testSrcExpDir, 'oneField') # NTA_TEST_numIterations is watched by the dummyModelParams() method of # the permutations file. # NTA_TEST_sysExitModelRange is watched by the dummyModelParams() method of # the permutations file. It tells it to do a sys.exit() after so many # iterations. env = dict() env["NTA_TEST_numIterations"] = '99' env["NTA_TEST_delayModelRange"] = '%d,%d' % (modelRange[0], modelRange[1]) env["NTA_CONF_PROP_nupic_hypersearch_modelOrphanIntervalSecs"] = '1' env["NTA_CONF_PROP_nupic_hypersearch_swarmMaturityWindow"] \ = '%d' % (g_repeatableSwarmMaturityWindow) (jobID, jobInfo, resultInfos, metricResults, minErrScore) \ = self.runPermutations(expDir, hsImp='v2', loggingLevel=g_myEnv.options.logLevel, maxModels=100, onCluster=True, env=env, waitForCompletion=True, maxNumWorkers=4, ) self.assertEqual(minErrScore, 50) self.assertLess(len(resultInfos), 100) return def testOrphanedModelGen1(self): """ Run a worker on a model for a while, then have it exit before the model finishes. Then, run another worker, which should detect the orphaned model. """ self._printTestHeader() inst = MultiNodeTests(self._testMethodName) return inst.testOrphanedModel(modelRange=(10,11)) def testOrphanedModelMaxModels(self): """ Test to make sure that the maxModels parameter doesn't include orphaned models. Run a test with maxModels set to 2, where one becomes orphaned. At the end, there should be 3 models in the models table, one of which will be the new model that adopted the orphaned model """ self._printTestHeader() expDir = os.path.join(g_myEnv.testSrcExpDir, 'dummyV2') numModels = 5 env = dict() env["NTA_CONF_PROP_nupic_hypersearch_modelOrphanIntervalSecs"] = '3' env['NTA_TEST_max_num_models']=str(numModels) (jobID, jobInfo, resultInfos, metricResults, minErrScore) \ = self.runPermutations(expDir, hsImp='v2', loggingLevel=g_myEnv.options.logLevel, maxModels=numModels, env=env, onCluster=True, waitForCompletion=True, dummyModel={'metricValue': ['25','50'], 'sysExitModelRange': '0, 1', 'iterations': 20, } ) cjDB = ClientJobsDAO.get() self.assertGreaterEqual(len(resultInfos), numModels+1) completionReasons = [x.completionReason for x in resultInfos] self.assertGreaterEqual(completionReasons.count(cjDB.CMPL_REASON_EOF), numModels) self.assertGreaterEqual(completionReasons.count(cjDB.CMPL_REASON_ORPHAN), 1) def testOrphanedModelConnection(self): """Test for the correct behavior when a model uses a different connection id than what is stored in the db. The correct behavior is for the worker to log this as a warning and move on to a new model""" self._printTestHeader() # ----------------------------------------------------------------------- # Trigger "Using connection from another worker" exception inside # ModelRunner # ----------------------------------------------------------------------- expDir = os.path.join(g_myEnv.testSrcExpDir, 'dummy_multi_v2') numModels = 2 env = dict() env["NTA_CONF_PROP_nupic_hypersearch_modelOrphanIntervalSecs"] = '1' (jobID, jobInfo, resultInfos, metricResults, minErrScore) \ = self.runPermutations(expDir, hsImp='v2', loggingLevel=g_myEnv.options.logLevel, maxModels=numModels, env=env, onCluster=True, waitForCompletion=True, dummyModel={'metricValue': ['25','50'], 'sleepModelRange': '0, 1:5', 'iterations': 20, } ) cjDB = ClientJobsDAO.get() self.assertGreaterEqual(len(resultInfos), numModels, "%d were run. Expecting %s"%(len(resultInfos), numModels+1)) completionReasons = [x.completionReason for x in resultInfos] self.assertGreaterEqual(completionReasons.count(cjDB.CMPL_REASON_EOF), numModels) self.assertGreaterEqual(completionReasons.count(cjDB.CMPL_REASON_ORPHAN), 1) def testErredModel(self, modelRange=(6,7)): """ Run a worker on a model for a while, then have it exit before the model finishes. Then, run another worker, which should detect the orphaned model. """ self._printTestHeader() inst = OneNodeTests(self._testMethodName) return inst.testErredModel(onCluster=True) def testJobFailModel(self): """ Run a worker on a model for a while, then have it exit before the model finishes. Then, run another worker, which should detect the orphaned model. """ self._printTestHeader() inst = OneNodeTests(self._testMethodName) return inst.testJobFailModel(onCluster=True) def testTooManyErredModels(self, modelRange=(5,10)): """ Run a worker on a model for a while, then have it exit before the model finishes. Then, run another worker, which should detect the orphaned model. """ self._printTestHeader() inst = OneNodeTests(self._testMethodName) return inst.testTooManyErredModels(onCluster=True) def testSpatialClassification(self): """ Try running a simple permutations """ self._printTestHeader() inst = OneNodeTests(self._testMethodName) return inst.testSpatialClassification(onCluster=True) #, maxNumWorkers=7) def testAlwaysInputPredictedField(self): self._printTestHeader() inst = OneNodeTests(self._testMethodName) return inst.testAlwaysInputPredictedField(onCluster=True) def testFieldThresholdNoPredField(self): self._printTestHeader() inst = OneNodeTests(self._testMethodName) return inst.testFieldThresholdNoPredField(onCluster=True) class ModelMaturityTests(ExperimentTestBaseClass): """ """ # AWS tests attribute required for tagging via automatic test discovery via # nosetests engineAWSClusterTest=True def setUp(self): # Ignore the global hypersearch version setting. Always test hypersearch v2 hsVersion = 2 self.expDir = os.path.join(g_myEnv.testSrcExpDir, 'dummyV%d' %hsVersion) self.hsImp = "v%d" % hsVersion self.env = {'NTA_CONF_PROP_nupic_hypersearch_enableModelTermination':'0', 'NTA_CONF_PROP_nupic_hypersearch_enableModelMaturity':'1', 'NTA_CONF_PROP_nupic_hypersearch_maturityMaxSlope':'0.1', 'NTA_CONF_PROP_nupic_hypersearch_enableSwarmTermination':'0', 'NTA_CONF_PROP_nupic_hypersearch_bestModelMinRecords':'0'} def testMatureInterleaved(self): """ Test to make sure that the best model continues running even when it has matured. The 2nd model (constant) will be marked as mature first and will continue to run till the end. The 2nd model reaches maturity and should stop before all the records are consumed, and should be the best model because it has a lower error """ self._printTestHeader() self.expDir = os.path.join(g_myEnv.testSrcExpDir, 'dummy_multi_v%d' % 2) self.env['NTA_TEST_max_num_models'] = '2' jobID,_,_,_,_ = self.runPermutations(self.expDir, hsImp=self.hsImp, maxModels=2, loggingLevel = g_myEnv.options.logLevel, env = self.env, onCluster = True, dummyModel={'metricFunctions': ['lambda x: -10*math.log10(x+1) +100', 'lambda x: 100.0'], 'delay': [2.0, 0.0 ], 'waitTime':[0.05, 0.01], 'iterations':500, 'experimentDirectory':self.expDir, }) cjDB = ClientJobsDAO.get() modelIDs, records, completionReasons, matured = \ zip(*self.getModelFields( jobID, ['numRecords', 'completionReason', 'engMatured'])) results = cjDB.jobGetFields(jobID, ['results'])[0] results = json.loads(results) self.assertEqual(results['bestModel'], modelIDs[0]) self.assertEqual(records[1], 500) self.assertTrue(records[0] > 100 and records[0] < 500, "Model 2 num records: 100 < %d < 500 " % records[1]) self.assertEqual(completionReasons[1], cjDB.CMPL_REASON_EOF) self.assertEqual(completionReasons[0], cjDB.CMPL_REASON_STOPPED) self.assertTrue(matured[0], True) def testConstant(self): """ Sanity check to make sure that when only 1 model is running, it continues to run even when it has reached maturity """ self._printTestHeader() jobID,_,_,_,_ = self.runPermutations(self.expDir, hsImp=self.hsImp, maxModels=1, loggingLevel = g_myEnv.options.logLevel, env = self.env, dummyModel={'metricFunctions': ['lambda x: 100'], 'iterations':350, 'experimentDirectory':self.expDir, }) cjDB = ClientJobsDAO.get() modelIDs = cjDB.jobGetModelIDs(jobID) dbResults = cjDB.modelsGetFields(modelIDs, ['numRecords', 'completionReason', 'engMatured']) modelIDs = [x[0] for x in dbResults] records = [x[1][0] for x in dbResults] completionReasons = [x[1][1] for x in dbResults] matured = [x[1][2] for x in dbResults] results = cjDB.jobGetFields(jobID, ['results'])[0] results = json.loads(results) self.assertEqual(results['bestModel'], min(modelIDs)) self.assertEqual(records[0], 350) self.assertEqual(completionReasons[0], cjDB.CMPL_REASON_EOF) self.assertEqual(matured[0], True) def getModelFields(self, jobID, fields): cjDB = ClientJobsDAO.get() modelIDs = cjDB.jobGetModelIDs(jobID) modelParams = cjDB.modelsGetFields(modelIDs, ['params']+fields) modelIDs = [e[0] for e in modelParams] modelOrders = [json.loads(e[1][0])['structuredParams']['__model_num'] for e in modelParams] modelFields = [] for f in xrange(len(fields)): modelFields.append([e[1][f+1] for e in modelParams]) modelInfo = zip(modelOrders, modelIDs, *tuple(modelFields)) modelInfo.sort(key=lambda info:info[0]) return [e[1:] for e in sorted(modelInfo, key=lambda info:info[0])] class SwarmTerminatorTests(ExperimentTestBaseClass): """ """ # AWS tests attribute required for tagging via automatic test discovery via # nosetests engineAWSClusterTest=True def setUp(self): self.env = {'NTA_CONF_PROP_nupic_hypersearch_enableModelMaturity':'0', 'NTA_CONF_PROP_nupic_hypersearch_enableModelTermination':'0', 'NTA_CONF_PROP_nupic_hypersearch_enableSwarmTermination':'1', 'NTA_TEST_recordSwarmTerminations':'1'} def testSimple(self, useCluster=False): """Run with one really bad swarm to see if terminator picks it up correctly""" if not g_myEnv.options.runInProc: self.skipTest("Skipping One Node test since runInProc is not specified") self._printTestHeader() expDir = os.path.join(g_myEnv.testSrcExpDir, 'swarm_v2') (jobID, jobInfo, resultInfos, metricResults, minErrScore) \ = self.runPermutations(expDir, hsImp='v2', loggingLevel=g_myEnv.options.logLevel, maxModels=None, onCluster=useCluster, env=self.env, dummyModel={'iterations':200}) cjDB = ClientJobsDAO.get() jobResultsStr = cjDB.jobGetFields(jobID, ['results'])[0] jobResults = json.loads(jobResultsStr) terminatedSwarms = jobResults['terminatedSwarms'] swarmMaturityWindow = int(configuration.Configuration.get( 'nupic.hypersearch.swarmMaturityWindow')) prefix = 'modelParams|sensorParams|encoders|' for swarm, (generation, scores) in terminatedSwarms.iteritems(): if prefix + 'gym' in swarm.split('.'): self.assertEqual(generation, swarmMaturityWindow-1) else: self.assertEqual(generation, swarmMaturityWindow-1+4) def testMaturity(self, useCluster=False): if not g_myEnv.options.runInProc: self.skipTest("Skipping One Node test since runInProc is not specified") self._printTestHeader() self.env['NTA_CONF_PROP_enableSwarmTermination'] = '0' expDir = os.path.join(g_myEnv.testSrcExpDir, 'swarm_maturity_v2') (jobID, jobInfo, resultInfos, metricResults, minErrScore) \ = self.runPermutations(expDir, hsImp='v2', loggingLevel=g_myEnv.options.logLevel, maxModels=None, onCluster=useCluster, env=self.env, dummyModel={'iterations':200}) cjDB = ClientJobsDAO.get() jobResultsStr = cjDB.jobGetFields(jobID, ['results'])[0] jobResults = json.loads(jobResultsStr) terminatedSwarms = jobResults['terminatedSwarms'] swarmMaturityWindow = int(configuration.Configuration.get( 'nupic.hypersearch.swarmMaturityWindow')) prefix = 'modelParams|sensorParams|encoders|' for swarm, (generation, scores) in terminatedSwarms.iteritems(): encoders = swarm.split('.') if prefix + 'gym' in encoders: self.assertEqual(generation, swarmMaturityWindow-1 + 3) elif prefix + 'address' in encoders: self.assertEqual(generation, swarmMaturityWindow-1) else: self.assertEqual(generation, swarmMaturityWindow-1 + 7) def testSimpleMN(self): self.testSimple(useCluster=True) def testMaturityMN(self): self.testMaturity(useCluster=True) def getHypersearchWinningModelID(jobID): """ Parameters: ------------------------------------------------------------------- jobID: jobID of successfully-completed Hypersearch job retval: modelID of the winning model """ cjDAO = ClientJobsDAO.get() jobResults = cjDAO.jobGetFields(jobID, ['results'])[0] print "Hypersearch job results: %r" % (jobResults,) jobResults = json.loads(jobResults) return jobResults['bestModel'] def _executeExternalCmdAndReapStdout(args): """ args: Args list as defined for the args parameter in subprocess.Popen() Returns: result dicionary: { 'exitStatus':<exit-status-of-external-command>, 'stdoutData':"string", 'stderrData':"string" } """ _debugOut(("_executeExternalCmdAndReapStdout: Starting...\n<%s>") % \ (args,)) p = subprocess.Popen(args, env=os.environ, stdout=subprocess.PIPE, stderr=subprocess.PIPE) _debugOut(("Process started for <%s>") % (args,)) (stdoutData, stderrData) = p.communicate() _debugOut(("Process completed for <%s>: exit status=%s, stdoutDataType=%s, " + \ "stdoutData=<%s>, stderrData=<%s>") % \ (args, p.returncode, type(stdoutData), stdoutData, stderrData)) result = dict( exitStatus = p.returncode, stdoutData = stdoutData, stderrData = stderrData, ) _debugOut(("_executeExternalCmdAndReapStdout for <%s>: result=\n%s") % \ (args, pprint.pformat(result, indent=4))) return result def _debugOut(text): global g_debug if g_debug: print text sys.stdout.flush() return def _getTestList(): """ Get the list of tests that can be run from this module""" suiteNames = [ 'OneNodeTests', 'MultiNodeTests', 'ModelMaturityTests', 'SwarmTerminatorTests', ] testNames = [] for suite in suiteNames: for f in dir(eval(suite)): if f.startswith('test'): testNames.append('%s.%s' % (suite, f)) return testNames class _ArgParser(object): """Class which handles command line arguments and arguments passed to the test """ args = [] @classmethod def _processArgs(cls): """ Parse our command-line args/options and strip them from sys.argv Returns the tuple (parsedOptions, remainingArgs) """ helpString = \ """%prog [options...] [-- unittestoptions...] [suitename.testname | suitename] Run the Hypersearch unit tests. To see unit test framework options, enter: python %prog -- --help Example usages: python %prog MultiNodeTests python %prog MultiNodeTests.testOrphanedModel python %prog -- MultiNodeTests.testOrphanedModel python %prog -- --failfast python %prog -- --failfast OneNodeTests.testOrphanedModel Available suitename.testnames: """ # Update help string allTests = _getTestList() for test in allTests: helpString += "\n %s" % (test) # ============================================================================ # Process command line arguments parser = OptionParser(helpString,conflict_handler="resolve") parser.add_option("--verbosity", default=0, type="int", help="Verbosity level, either 0, 1, 2, or 3 [default: %default].") parser.add_option("--runInProc", action="store_true", default=False, help="Run inProc tests, currently inProc are not being run by default " " running. [default: %default].") parser.add_option("--logLevel", action="store", type="int", default=logging.INFO, help="override default log level. Pass in an integer value that " "represents the desired logging level (10=logging.DEBUG, " "20=logging.INFO, etc.) [default: %default].") parser.add_option("--hs", dest="hsVersion", default=2, type='int', help=("Hypersearch version (only 2 supported; 1 was " "deprecated) [default: %default].")) return parser.parse_args(args=cls.args) @classmethod def parseArgs(cls): """ Returns the test arguments after parsing """ return cls._processArgs()[0] @classmethod def consumeArgs(cls): """ Consumes the test arguments and returns the remaining arguments meant for unittest.man """ return cls._processArgs()[1] def setUpModule(): print "\nCURRENT DIRECTORY:", os.getcwd() initLogging(verbose=True) global g_myEnv # Setup our environment g_myEnv = MyTestEnvironment() if __name__ == '__main__': # Form the command line for the unit test framework # Consume test specific arguments and pass remaining to unittest.main _ArgParser.args = sys.argv[1:] args = [sys.argv[0]] + _ArgParser.consumeArgs() # Run the tests if called using python unittest.main(argv=args)

101,464

Python

.py

2,125

35.606588

95

0.581319

numenta/nupic-legacy

6,330

1,556

464

AGPL-3.0

9/5/2024, 5:13:42 PM (Europe/Amsterdam)

26,202

description.py

numenta_nupic-legacy/tests/swarming/nupic/swarming/experiments/delta/description.py

# ---------------------------------------------------------------------- # Numenta Platform for Intelligent Computing (NuPIC) # Copyright (C) 2013, Numenta, Inc. Unless you have an agreement # with Numenta, Inc., for a separate license for this software code, the # following terms and conditions apply: # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU Affero Public License version 3 as # published by the Free Software Foundation. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. # See the GNU Affero Public License for more details. # # You should have received a copy of the GNU Affero Public License # along with this program. If not, see http://www.gnu.org/licenses. # # http://numenta.org/licenses/ # ---------------------------------------------------------------------- """ Template file used by the OPF Experiment Generator to generate the actual description.py file by replacing $XXXXXXXX tokens with desired values. This description.py file was generated by: '/Users/ronmarianetti/nupic/eng/lib/python2.6/site-packages/nupic/frameworks/opf/expGenerator/experiment_generator.py' """ from nupic.frameworks.opf.exp_description_api import ExperimentDescriptionAPI from nupic.frameworks.opf.exp_description_helpers import ( updateConfigFromSubConfig, applyValueGettersToContainer, DeferredDictLookup) from nupic.frameworks.opf.htm_prediction_model_callbacks import * from nupic.frameworks.opf.metrics import MetricSpec from nupic.frameworks.opf.opf_utils import (InferenceType, InferenceElement) from nupic.support import aggregationDivide from nupic.frameworks.opf.opf_task_driver import ( IterationPhaseSpecLearnOnly, IterationPhaseSpecInferOnly, IterationPhaseSpecLearnAndInfer) # Model Configuration Dictionary: # # Define the model parameters and adjust for any modifications if imported # from a sub-experiment. # # These fields might be modified by a sub-experiment; this dict is passed # between the sub-experiment and base experiment # # # NOTE: Use of DEFERRED VALUE-GETTERs: dictionary fields and list elements # within the config dictionary may be assigned futures derived from the # ValueGetterBase class, such as DeferredDictLookup. # This facility is particularly handy for enabling substitution of values in # the config dictionary from other values in the config dictionary, which is # needed by permutation.py-based experiments. These values will be resolved # during the call to applyValueGettersToContainer(), # which we call after the base experiment's config dictionary is updated from # the sub-experiment. See ValueGetterBase and # DeferredDictLookup for more details about value-getters. # # For each custom encoder parameter to be exposed to the sub-experiment/ # permutation overrides, define a variable in this section, using key names # beginning with a single underscore character to avoid collisions with # pre-defined keys (e.g., _dsEncoderFieldName2_N). # # Example: # config = dict( # _dsEncoderFieldName2_N = 70, # _dsEncoderFieldName2_W = 5, # dsEncoderSchema = [ # base=dict( # fieldname='Name2', type='ScalarEncoder', # name='Name2', minval=0, maxval=270, clipInput=True, # n=DeferredDictLookup('_dsEncoderFieldName2_N'), # w=DeferredDictLookup('_dsEncoderFieldName2_W')), # ], # ) # updateConfigFromSubConfig(config) # applyValueGettersToContainer(config) config = { # Type of model that the rest of these parameters apply to. 'model': "HTMPrediction", # Version that specifies the format of the config. 'version': 1, # Intermediate variables used to compute fields in modelParams and also # referenced from the control section. 'aggregationInfo': { 'days': 0, 'fields': [], 'hours': 0, 'microseconds': 0, 'milliseconds': 0, 'minutes': 0, 'months': 0, 'seconds': 0, 'weeks': 0, 'years': 0}, 'predictAheadTime': None, # Model parameter dictionary. 'modelParams': { # The type of inference that this model will perform 'inferenceType': 'TemporalMultiStep', 'sensorParams': { # Sensor diagnostic output verbosity control; # if > 0: sensor region will print out on screen what it's sensing # at each step 0: silent; >=1: some info; >=2: more info; # >=3: even more info (see compute() in py/regions/RecordSensor.py) 'verbosity' : 0, # Example: # dsEncoderSchema = [ # DeferredDictLookup('__field_name_encoder'), # ], # # (value generated from DS_ENCODER_SCHEMA) 'encoders': { 'value': { 'clipInput': True, 'fieldname': u'value', 'n': 100, 'name': u'value', 'type': 'ScalarSpaceEncoder', 'w': 21}, '_classifierInput': { 'name': u'_classifierInput', 'fieldname': u'value', 'classifierOnly': True, 'type': 'ScalarSpaceEncoder', 'n': 100, 'w': 21}, }, # A dictionary specifying the period for automatically-generated # resets from a RecordSensor; # # None = disable automatically-generated resets (also disabled if # all of the specified values evaluate to 0). # Valid keys is the desired combination of the following: # days, hours, minutes, seconds, milliseconds, microseconds, weeks # # Example for 1.5 days: sensorAutoReset = dict(days=1,hours=12), # # (value generated from SENSOR_AUTO_RESET) 'sensorAutoReset' : None, }, 'spEnable': True, 'spParams': { # SP diagnostic output verbosity control; # 0: silent; >=1: some info; >=2: more info; 'spVerbosity' : 0, 'globalInhibition': 1, # Number of cell columns in the cortical region (same number for # SP and TM) # (see also tpNCellsPerCol) 'columnCount': 2048, 'inputWidth': 0, # SP inhibition control (absolute value); # Maximum number of active columns in the SP region's output (when # there are more, the weaker ones are suppressed) 'numActiveColumnsPerInhArea': 40, 'seed': 1956, # potentialPct # What percent of the columns's receptive field is available # for potential synapses. At initialization time, we will # choose potentialPct * (2*potentialRadius+1)^2 'potentialPct': 0.5, # The default connected threshold. Any synapse whose # permanence value is above the connected threshold is # a "connected synapse", meaning it can contribute to the # cell's firing. Typical value is 0.10. Cells whose activity # level before inhibition falls below minDutyCycleBeforeInh # will have their own internal synPermConnectedCell # threshold set below this default value. # (This concept applies to both SP and TM and so 'cells' # is correct here as opposed to 'columns') 'synPermConnected': 0.1, 'synPermActiveInc': 0.1, 'synPermInactiveDec': 0.01, }, # Controls whether TM is enabled or disabled; # TM is necessary for making temporal predictions, such as predicting # the next inputs. Without TM, the model is only capable of # reconstructing missing sensor inputs (via SP). 'tmEnable' : True, 'tmParams': { # TM diagnostic output verbosity control; # 0: silent; [1..6]: increasing levels of verbosity # (see verbosity in nupic/trunk/py/nupic/research/backtracking_tm.py and backtracking_tm_cpp.py) 'verbosity': 0, # Number of cell columns in the cortical region (same number for # SP and TM) # (see also tpNCellsPerCol) 'columnCount': 2048, # The number of cells (i.e., states), allocated per column. 'cellsPerColumn': 32, 'inputWidth': 2048, 'seed': 1960, # Temporal Pooler implementation selector (see _getTPClass in # CLARegion.py). 'temporalImp': 'cpp', # New Synapse formation count # NOTE: If None, use spNumActivePerInhArea # # TODO: need better explanation 'newSynapseCount': 20, # Maximum number of synapses per segment # > 0 for fixed-size CLA # -1 for non-fixed-size CLA # # TODO: for Ron: once the appropriate value is placed in TM # constructor, see if we should eliminate this parameter from # description.py. 'maxSynapsesPerSegment': 32, # Maximum number of segments per cell # > 0 for fixed-size CLA # -1 for non-fixed-size CLA # # TODO: for Ron: once the appropriate value is placed in TM # constructor, see if we should eliminate this parameter from # description.py. 'maxSegmentsPerCell': 128, # Initial Permanence # TODO: need better explanation 'initialPerm': 0.21, # Permanence Increment 'permanenceInc': 0.1, # Permanence Decrement # If set to None, will automatically default to tpPermanenceInc # value. 'permanenceDec' : 0.1, 'globalDecay': 0.0, 'maxAge': 0, # Minimum number of active synapses for a segment to be considered # during search for the best-matching segments. # None=use default # Replaces: tpMinThreshold 'minThreshold': 12, # Segment activation threshold. # A segment is active if it has >= tpSegmentActivationThreshold # connected synapses that are active due to infActiveState # None=use default # Replaces: tpActivationThreshold 'activationThreshold': 16, 'outputType': 'normal', # "Pay Attention Mode" length. This tells the TM how many new # elements to append to the end of a learned sequence at a time. # Smaller values are better for datasets with short sequences, # higher values are better for datasets with long sequences. 'pamLength': 1, }, 'clParams': { 'regionName' : 'SDRClassifierRegion', # Classifier diagnostic output verbosity control; # 0: silent; [1..6]: increasing levels of verbosity 'verbosity' : 0, # This controls how fast the classifier learns/forgets. Higher values # make it adapt faster and forget older patterns faster. 'alpha': 0.001, # This is set after the call to updateConfigFromSubConfig and is # computed from the aggregationInfo and predictAheadTime. 'steps': '1,5', }, 'trainSPNetOnlyIfRequested': False, }, } # end of config dictionary # Adjust base config dictionary for any modifications if imported from a # sub-experiment updateConfigFromSubConfig(config) # Compute predictionSteps based on the predictAheadTime and the aggregation # period, which may be permuted over. if config['predictAheadTime'] is not None: predictionSteps = int(round(aggregationDivide( config['predictAheadTime'], config['aggregationInfo']))) assert (predictionSteps >= 1) config['modelParams']['clParams']['steps'] = str(predictionSteps) # Adjust config by applying ValueGetterBase-derived # futures. NOTE: this MUST be called after updateConfigFromSubConfig() in order # to support value-getter-based substitutions from the sub-experiment (if any) applyValueGettersToContainer(config) control = { # The environment that the current model is being run in "environment": 'nupic', # Input stream specification per py/nupicengine/cluster/database/StreamDef.json. # 'dataset' : { u'info': u'sawtooth test', u'streams': [ { u'columns': [u'value'], u'info': u'sawtooth', u'source': u'file://extra/sawtooth/sawtooth.csv'}], u'version': 1}, # Iteration count: maximum number of iterations. Each iteration corresponds # to one record from the (possibly aggregated) dataset. The task is # terminated when either number of iterations reaches iterationCount or # all records in the (possibly aggregated) database have been processed, # whichever occurs first. # # iterationCount of -1 = iterate over the entire dataset 'iterationCount' : 20, # A dictionary containing all the supplementary parameters for inference "inferenceArgs":{u'predictedField': u'value', u'predictionSteps': [1, 5]}, # Metrics: A list of MetricSpecs that instantiate the metrics that are # computed for this experiment 'metrics':[ MetricSpec(field=u'value', metric='multiStep', inferenceElement='multiStepBestPredictions', params={'window': 10, 'steps': 1, 'errorMetric': 'aae'}), MetricSpec(field=u'value', metric='multiStep', inferenceElement='multiStepBestPredictions', params={'window': 10, 'steps': 5, 'errorMetric': 'aae'}), ], # Logged Metrics: A sequence of regular expressions that specify which of # the metrics from the Inference Specifications section MUST be logged for # every prediction. The regex's correspond to the automatically generated # metric labels. This is similar to the way the optimization metric is # specified in permutations.py. 'loggedMetrics': ['.*nupicScore.*'], } descriptionInterface = ExperimentDescriptionAPI(modelConfig=config, control=control)

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26,203

permutations.py

numenta_nupic-legacy/tests/swarming/nupic/swarming/experiments/delta/permutations.py

# ---------------------------------------------------------------------- # Numenta Platform for Intelligent Computing (NuPIC) # Copyright (C) 2013, Numenta, Inc. Unless you have an agreement # with Numenta, Inc., for a separate license for this software code, the # following terms and conditions apply: # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU Affero Public License version 3 as # published by the Free Software Foundation. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. # See the GNU Affero Public License for more details. # # You should have received a copy of the GNU Affero Public License # along with this program. If not, see http://www.gnu.org/licenses. # # http://numenta.org/licenses/ # ---------------------------------------------------------------------- """ Template file used by ExpGenerator to generate the actual permutations.py file by replacing $XXXXXXXX tokens with desired values. This permutations.py file was generated by: '/Users/ronmarianetti/nupic/eng/lib/python2.6/site-packages/nupic/frameworks/opf/expGenerator/experiment_generator.py' """ import os from nupic.swarming.permutation_helpers import * # The name of the field being predicted. Any allowed permutation MUST contain # the prediction field. # (generated from PREDICTION_FIELD) predictedField = 'value' permutations = { 'modelParams': { 'sensorParams': { 'encoders': { 'value': PermuteEncoder(fieldName='value', encoderClass='ScalarSpaceEncoder', space=PermuteChoices(['delta', 'absolute']), clipInput=True, w=21, n=PermuteInt(28, 521)), '_classifierInput': dict(fieldname='value', type='ScalarSpaceEncoder', classifierOnly=True, space=PermuteChoices(['delta', 'absolute']), clipInput=True, w=21, n=PermuteInt(28, 521)), }, }, 'tmParams': { 'minThreshold': PermuteInt(9, 12), 'activationThreshold': PermuteInt(12, 16), 'pamLength': PermuteInt(1, 5), }, 'clParams': { 'alpha': PermuteFloat(0.000100, 0.100000), }, } } # Fields selected for final hypersearch report; # NOTE: These values are used as regular expressions by RunPermutations.py's # report generator # (fieldname values generated from PERM_PREDICTED_FIELD_NAME) report = [ '.*value.*', ] # Permutation optimization setting: either minimize or maximize metric # used by RunPermutations. # NOTE: The value is used as a regular expressions by RunPermutations.py's # report generator # (generated from minimize = "multiStepBestPredictions:multiStep:errorMetric='aae':steps=1:window=1000:field=value") minimize = "multiStepBestPredictions:multiStep:errorMetric='aae':steps=1:window=10:field=value" minParticlesPerSwarm = None def permutationFilter(perm): """ This function can be used to selectively filter out specific permutation combinations. It is called by RunPermutations for every possible permutation of the variables in the permutations dict. It should return True for valid a combination of permutation values and False for an invalid one. Parameters: --------------------------------------------------------- perm: dict of one possible combination of name:value pairs chosen from permutations. """ # An example of how to use this #if perm['__consumption_encoder']['maxval'] > 300: # return False; # return True

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0.645707

numenta/nupic-legacy

6,330

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AGPL-3.0

9/5/2024, 5:13:42 PM (Europe/Amsterdam)

26,204

description.py

numenta_nupic-legacy/tests/swarming/nupic/swarming/experiments/spatial_classification/description.py

# ---------------------------------------------------------------------- # Numenta Platform for Intelligent Computing (NuPIC) # Copyright (C) 2013, Numenta, Inc. Unless you have an agreement # with Numenta, Inc., for a separate license for this software code, the # following terms and conditions apply: # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU Affero Public License version 3 as # published by the Free Software Foundation. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. # See the GNU Affero Public License for more details. # # You should have received a copy of the GNU Affero Public License # along with this program. If not, see http://www.gnu.org/licenses. # # http://numenta.org/licenses/ # ---------------------------------------------------------------------- """ Template file used by the OPF Experiment Generator to generate the actual description.py file by replacing $XXXXXXXX tokens with desired values. This description.py file was generated by: '/Users/ronmarianetti/nupic/eng/lib/python2.6/site-packages/nupicengine/frameworks/opf/expGenerator/ExpGenerator.pyc' """ from nupic.frameworks.opf.exp_description_api import ExperimentDescriptionAPI from nupic.frameworks.opf.exp_description_helpers import ( updateConfigFromSubConfig, applyValueGettersToContainer, DeferredDictLookup) from nupic.frameworks.opf.htm_prediction_model_callbacks import * from nupic.frameworks.opf.metrics import MetricSpec from nupic.frameworks.opf.opf_utils import (InferenceType, InferenceElement) from nupic.support import aggregationDivide from nupic.frameworks.opf.opf_task_driver import ( IterationPhaseSpecLearnOnly, IterationPhaseSpecInferOnly, IterationPhaseSpecLearnAndInfer) # Model Configuration Dictionary: # # Define the model parameters and adjust for any modifications if imported # from a sub-experiment. # # These fields might be modified by a sub-experiment; this dict is passed # between the sub-experiment and base experiment # # # NOTE: Use of DEFERRED VALUE-GETTERs: dictionary fields and list elements # within the config dictionary may be assigned futures derived from the # ValueGetterBase class, such as DeferredDictLookup. # This facility is particularly handy for enabling substitution of values in # the config dictionary from other values in the config dictionary, which is # needed by permutation.py-based experiments. These values will be resolved # during the call to applyValueGettersToContainer(), # which we call after the base experiment's config dictionary is updated from # the sub-experiment. See ValueGetterBase and # DeferredDictLookup for more details about value-getters. # # For each custom encoder parameter to be exposed to the sub-experiment/ # permutation overrides, define a variable in this section, using key names # beginning with a single underscore character to avoid collisions with # pre-defined keys (e.g., _dsEncoderFieldName2_N). # # Example: # config = dict( # _dsEncoderFieldName2_N = 70, # _dsEncoderFieldName2_W = 5, # dsEncoderSchema = [ # base=dict( # fieldname='Name2', type='ScalarEncoder', # name='Name2', minval=0, maxval=270, clipInput=True, # n=DeferredDictLookup('_dsEncoderFieldName2_N'), # w=DeferredDictLookup('_dsEncoderFieldName2_W')), # ], # ) # updateConfigFromSubConfig(config) # applyValueGettersToContainer(config) config = { # Type of model that the rest of these parameters apply to. 'model': "HTMPrediction", # Version that specifies the format of the config. 'version': 1, # Intermediate variables used to compute fields in modelParams and also # referenced from the control section. 'aggregationInfo': { 'fields': [], 'days': 0, 'hours': 0, 'microseconds': 0, 'milliseconds': 0, 'minutes': 0, 'months': 0, 'seconds': 0, 'weeks': 0, 'years': 0 }, 'predictAheadTime': None, # Model parameter dictionary. 'modelParams': { # The type of inference that this model will perform 'inferenceType': 'NontemporalClassification', 'sensorParams': { # Sensor diagnostic output verbosity control; # if > 0: sensor region will print out on screen what it's sensing # at each step 0: silent; >=1: some info; >=2: more info; # >=3: even more info (see compute() in py/regions/RecordSensor.py) 'verbosity' : 0, # Example: # dsEncoderSchema = [ # DeferredDictLookup('__field_name_encoder'), # ], # # (value generated from DS_ENCODER_SCHEMA) 'encoders': { 'address': { 'fieldname': u'address', 'n': 300, 'name': u'address', 'type': 'SDRCategoryEncoder', 'w': 21 }, '_classifierInput': { 'name': u'_classifierInput', 'fieldname': u'consumption', 'classifierOnly': True, 'clipInput': True, 'maxval': 200, 'minval': 0, 'n': 1500, 'type': 'ScalarEncoder', 'w': 21 }, 'gym': { 'fieldname': u'gym', 'n': 300, 'name': u'gym', 'type': 'SDRCategoryEncoder', 'w': 21 }, 'timestamp_dayOfWeek': { 'dayOfWeek': (7, 3), 'fieldname': u'timestamp', 'name': u'timestamp_dayOfWeek', 'type': 'DateEncoder' }, 'timestamp_timeOfDay': { 'fieldname': u'timestamp', 'name': u'timestamp_timeOfDay', 'timeOfDay': (7, 8), 'type': 'DateEncoder' } }, # A dictionary specifying the period for automatically-generated # resets from a RecordSensor; # # None = disable automatically-generated resets (also disabled if # all of the specified values evaluate to 0). # Valid keys is the desired combination of the following: # days, hours, minutes, seconds, milliseconds, microseconds, weeks # # Example for 1.5 days: sensorAutoReset = dict(days=1,hours=12), # # (value generated from SENSOR_AUTO_RESET) 'sensorAutoReset' : None, }, 'spEnable': False, 'spParams': { # SP diagnostic output verbosity control; # 0: silent; >=1: some info; >=2: more info; 'spVerbosity' : 0, 'globalInhibition': 1, # Number of cell columns in the cortical region (same number for # SP and TM) # (see also tpNCellsPerCol) 'columnCount': 2048, 'inputWidth': 0, # SP inhibition control (absolute value); # Maximum number of active columns in the SP region's output (when # there are more, the weaker ones are suppressed) 'numActiveColumnsPerInhArea': 40, 'seed': 1956, # potentialPct # What percent of the columns's receptive field is available # for potential synapses. At initialization time, we will # choose potentialPct * (2*potentialRadius+1)^2 'potentialPct': 0.5, # The default connected threshold. Any synapse whose # permanence value is above the connected threshold is # a "connected synapse", meaning it can contribute to the # cell's firing. Typical value is 0.10. Cells whose activity # level before inhibition falls below minDutyCycleBeforeInh # will have their own internal synPermConnectedCell # threshold set below this default value. # (This concept applies to both SP and TM and so 'cells' # is correct here as opposed to 'columns') 'synPermConnected': 0.1, 'synPermActiveInc': 0.1, 'synPermInactiveDec': 0.01, }, # Controls whether TM is enabled or disabled; # TM is necessary for making temporal predictions, such as predicting # the next inputs. Without TM, the model is only capable of # reconstructing missing sensor inputs (via SP). 'tmEnable' : False, 'tmParams': { # TM diagnostic output verbosity control; # 0: silent; [1..6]: increasing levels of verbosity # (see verbosity in nupic/trunk/py/nupic/research/backtracking_tm.py and backtracking_tm_cpp.py) 'verbosity': 0, # Number of cell columns in the cortical region (same number for # SP and TM) # (see also tpNCellsPerCol) 'columnCount': 2048, # The number of cells (i.e., states), allocated per column. 'cellsPerColumn': 32, 'inputWidth': 2048, 'seed': 1960, # Temporal Pooler implementation selector (see _getTPClass in # CLARegion.py). 'temporalImp': 'cpp', # New Synapse formation count # NOTE: If None, use spNumActivePerInhArea # # TODO: need better explanation 'newSynapseCount': 20, # Maximum number of synapses per segment # > 0 for fixed-size CLA # -1 for non-fixed-size CLA # # TODO: for Ron: once the appropriate value is placed in TM # constructor, see if we should eliminate this parameter from # description.py. 'maxSynapsesPerSegment': 32, # Maximum number of segments per cell # > 0 for fixed-size CLA # -1 for non-fixed-size CLA # # TODO: for Ron: once the appropriate value is placed in TM # constructor, see if we should eliminate this parameter from # description.py. 'maxSegmentsPerCell': 128, # Initial Permanence # TODO: need better explanation 'initialPerm': 0.21, # Permanence Increment 'permanenceInc': 0.1, # Permanence Decrement # If set to None, will automatically default to tpPermanenceInc # value. 'permanenceDec' : 0.1, 'globalDecay': 0.0, 'maxAge': 0, # Minimum number of active synapses for a segment to be considered # during search for the best-matching segments. # None=use default # Replaces: tpMinThreshold 'minThreshold': 12, # Segment activation threshold. # A segment is active if it has >= tpSegmentActivationThreshold # connected synapses that are active due to infActiveState # None=use default # Replaces: tpActivationThreshold 'activationThreshold': 16, 'outputType': 'normal', # "Pay Attention Mode" length. This tells the TM how many new # elements to append to the end of a learned sequence at a time. # Smaller values are better for datasets with short sequences, # higher values are better for datasets with long sequences. 'pamLength': 1, }, 'clParams': { 'regionName' : 'SDRClassifierRegion', 'implementation': 'py', # Classifier diagnostic output verbosity control; # 0: silent; [1..6]: increasing levels of verbosity 'verbosity' : 0, # This controls how fast the classifier learns/forgets. Higher values # make it adapt faster and forget older patterns faster. 'alpha': 0.001, # This is set after the call to updateConfigFromSubConfig and is # computed from the aggregationInfo and predictAheadTime. 'steps': '0', }, 'anomalyParams': { u'anomalyCacheRecords': None, u'autoDetectThreshold': None, u'autoDetectWaitRecords': None}, 'trainSPNetOnlyIfRequested': False, }, } # end of config dictionary # Adjust base config dictionary for any modifications if imported from a # sub-experiment updateConfigFromSubConfig(config) # Compute predictionSteps based on the predictAheadTime and the aggregation # period, which may be permuted over. if config['predictAheadTime'] is not None: predictionSteps = int(round(aggregationDivide( config['predictAheadTime'], config['aggregationInfo']))) assert (predictionSteps >= 1) config['modelParams']['clParams']['steps'] = str(predictionSteps) # Adjust config by applying ValueGetterBase-derived # futures. NOTE: this MUST be called after updateConfigFromSubConfig() in order # to support value-getter-based substitutions from the sub-experiment (if any) applyValueGettersToContainer(config) control = { # The environment that the current model is being run in "environment": 'nupic', # Input stream specification per py/nupic/frameworks/opf/jsonschema/stream_def.json. # 'dataset' : { u'info': u'testSpatialClassification', u'streams': [ { u'columns': [u'*'], u'info': u'test data', u'source': u'file://swarming/test_data.csv'}], u'version': 1}, # Iteration count: maximum number of iterations. Each iteration corresponds # to one record from the (possibly aggregated) dataset. The task is # terminated when either number of iterations reaches iterationCount or # all records in the (possibly aggregated) database have been processed, # whichever occurs first. # # iterationCount of -1 = iterate over the entire dataset #'iterationCount' : ITERATION_COUNT, # A dictionary containing all the supplementary parameters for inference "inferenceArgs":{u'predictedField': u'consumption', u'predictionSteps': [0]}, # Metrics: A list of MetricSpecs that instantiate the metrics that are # computed for this experiment 'metrics':[ MetricSpec(field=u'consumption', metric='multiStep', inferenceElement='multiStepBestPredictions', params={'window': 1000, 'steps': [0], 'errorMetric': 'avg_err'}) ], # Logged Metrics: A sequence of regular expressions that specify which of # the metrics from the Inference Specifications section MUST be logged for # every prediction. The regex's correspond to the automatically generated # metric labels. This is similar to the way the optimization metric is # specified in permutations.py. 'loggedMetrics': ['.*'], } descriptionInterface = ExperimentDescriptionAPI(modelConfig=config, control=control)

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permutations.py

numenta_nupic-legacy/tests/swarming/nupic/swarming/experiments/spatial_classification/permutations.py

# ---------------------------------------------------------------------- # Numenta Platform for Intelligent Computing (NuPIC) # Copyright (C) 2013, Numenta, Inc. Unless you have an agreement # with Numenta, Inc., for a separate license for this software code, the # following terms and conditions apply: # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU Affero Public License version 3 as # published by the Free Software Foundation. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. # See the GNU Affero Public License for more details. # # You should have received a copy of the GNU Affero Public License # along with this program. If not, see http://www.gnu.org/licenses. # # http://numenta.org/licenses/ # ---------------------------------------------------------------------- """ Template file used by ExpGenerator to generate the actual permutations.py file by replacing $XXXXXXXX tokens with desired values. This permutations.py file was generated by: '/Users/ronmarianetti/nupic/eng/lib/python2.6/site-packages/nupicengine/frameworks/opf/expGenerator/ExpGenerator.pyc' """ import os from nupic.swarming.permutation_helpers import * # The name of the field being predicted. Any allowed permutation MUST contain # the prediction field. # (generated from PREDICTION_FIELD) predictedField = 'consumption' permutations = { 'modelParams': { 'sensorParams': { 'encoders': { 'gym': PermuteEncoder(fieldName='gym', encoderClass='SDRCategoryEncoder', w=7, n=100), 'timestamp_dayOfWeek': PermuteEncoder(fieldName='timestamp', encoderClass='DateEncoder.dayOfWeek', radius=PermuteChoices([1, 3]), w=7), 'timestamp_timeOfDay': PermuteEncoder(fieldName='timestamp', encoderClass='DateEncoder.timeOfDay', radius=PermuteChoices([1, 8]), w=7), '_classifierInput': dict(fieldname='consumption', classifierOnly=True, type='ScalarEncoder', maxval=PermuteInt(100, 300, 25), n=PermuteInt(13, 500, 20), w=7, minval=0), 'address': PermuteEncoder(fieldName='address', encoderClass='SDRCategoryEncoder', w=7, n=100), }, }, 'spParams': { }, 'tmParams': { }, 'clParams': { 'alpha': PermuteFloat(0.0001, 0.1), }, } } # Fields selected for final hypersearch report; # NOTE: These values are used as regular expressions by RunPermutations.py's # report generator # (fieldname values generated from PERM_PREDICTED_FIELD_NAME) report = [ '.*consumption.*', ] # Permutation optimization setting: either minimize or maximize metric # used by RunPermutations. # NOTE: The value is used as a regular expressions by RunPermutations.py's # report generator minimize = "multiStepBestPredictions:multiStep:errorMetric='avg_err':steps=\[0\]:window=1000:field=consumption" minParticlesPerSwarm = None def dummyModelParams(perm): """ This function can be used for Hypersearch algorithm development. When present, Hypersearch doesn't actually run the CLA model in the OPF, but instead runs a dummy model. This function returns the dummy model params that will be used. See the OPFDummyModelRunner class source code (in nupic.swarming.ModelRunner) for a description of the schema for the dummy model params. """ # We are trying to get Hyperseach to find a model with: # consumption encoder maxval=250 # consumption encoder n=53 # any address encoder # any gym encoder # no other fields errScore = 50 if perm['modelParams']['sensorParams']['encoders']['address'] is not None: errScore -= 20 if perm['modelParams']['sensorParams']['encoders']['gym'] is not None: errScore -= 10 if perm['modelParams']['sensorParams']['encoders']['timestamp_dayOfWeek'] \ is not None: errScore += 30 if perm['modelParams']['sensorParams']['encoders']['timestamp_timeOfDay'] \ is not None: errScore += 40 dummyModelParams = dict( metricValue = errScore, iterations = int(os.environ.get('NTA_TEST_numIterations', '1')), waitTime = None, sysExitModelRange = os.environ.get('NTA_TEST_sysExitModelRange', None), errModelRange = os.environ.get('NTA_TEST_errModelRange', None), jobFailErr = bool(os.environ.get('NTA_TEST_jobFailErr', False)) ) return dummyModelParams def permutationFilter(perm): """ This function can be used to selectively filter out specific permutation combinations. It is called by RunPermutations for every possible permutation of the variables in the permutations dict. It should return True for valid a combination of permutation values and False for an invalid one. Parameters: --------------------------------------------------------- perm: dict of one possible combination of name:value pairs chosen from permutations. """ # An example of how to use this: #limit = int(os.environ.get('NTA_TEST_maxvalFilter', 300)) #if perm['modelParams']['sensorParams']['encoders']['consumption']['maxval'] > limit: # return False; return True

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26,206

description.py

numenta_nupic-legacy/tests/swarming/nupic/swarming/experiments/input_predicted_field/description.py

# ---------------------------------------------------------------------- # Numenta Platform for Intelligent Computing (NuPIC) # Copyright (C) 2013, Numenta, Inc. Unless you have an agreement # with Numenta, Inc., for a separate license for this software code, the # following terms and conditions apply: # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU Affero Public License version 3 as # published by the Free Software Foundation. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. # See the GNU Affero Public License for more details. # # You should have received a copy of the GNU Affero Public License # along with this program. If not, see http://www.gnu.org/licenses. # # http://numenta.org/licenses/ # ---------------------------------------------------------------------- """ Template file used by the OPF Experiment Generator to generate the actual description.py file by replacing $XXXXXXXX tokens with desired values. This description.py file was generated by: '/Users/ronmarianetti/nupic/eng/lib/python2.6/site-packages/nupicengine/frameworks/opf/expGenerator/ExpGenerator.pyc' """ from nupic.frameworks.opf.exp_description_api import ExperimentDescriptionAPI from nupic.frameworks.opf.exp_description_helpers import ( updateConfigFromSubConfig, applyValueGettersToContainer ) from nupic.frameworks.opf.htm_prediction_model_callbacks import * from nupic.frameworks.opf.metrics import MetricSpec from nupic.frameworks.opf.opf_utils import (InferenceType, InferenceElement) from nupic.support import aggregationDivide from nupic.frameworks.opf.opf_task_driver import ( IterationPhaseSpecLearnOnly, IterationPhaseSpecInferOnly, IterationPhaseSpecLearnAndInfer) # Model Configuration Dictionary: # # Define the model parameters and adjust for any modifications if imported # from a sub-experiment. # # These fields might be modified by a sub-experiment; this dict is passed # between the sub-experiment and base experiment # # config = { # Type of model that the rest of these parameters apply to. 'model': "HTMPrediction", # Version that specifies the format of the config. 'version': 1, # Intermediate variables used to compute fields in modelParams and also # referenced from the control section. 'aggregationInfo': { 'days': 0, 'fields': [ (u'timestamp', 'first'), (u'consumption', 'sum'), ], 'hours': 0, 'microseconds': 0, 'milliseconds': 0, 'minutes': 0, 'months': 0, 'seconds': 0, 'weeks': 0, 'years': 0}, 'predictAheadTime': None, # Model parameter dictionary. 'modelParams': { # The type of inference that this model will perform 'inferenceType': 'TemporalMultiStep', 'sensorParams': { # Sensor diagnostic output verbosity control; # if > 0: sensor region will print out on screen what it's sensing # at each step 0: silent; >=1: some info; >=2: more info; # >=3: even more info (see compute() in py/regions/RecordSensor.py) 'verbosity' : 0, # Example: # 'encoders': {'field1': {'fieldname': 'field1', 'n':100, # 'name': 'field1', 'type': 'AdaptiveScalarEncoder', # 'w': 21}} # 'encoders': { 'consumption': { 'clipInput': True, 'fieldname': u'consumption', 'n': 100, 'name': u'consumption', 'type': 'AdaptiveScalarEncoder', 'w': 21}, 'address': { 'fieldname': u'address', 'n': 300, 'name': u'address', 'type': 'SDRCategoryEncoder', 'w': 21}, 'gym': { 'fieldname': u'gym', 'n': 100, 'name': u'gym', 'type': 'SDRCategoryEncoder', 'w': 21}, 'timestamp_dayOfWeek': { 'dayOfWeek': (7, 3), 'fieldname': u'timestamp', 'name': u'timestamp_dayOfWeek', 'type': 'DateEncoder'}, 'timestamp_timeOfDay': { 'fieldname': u'timestamp', 'name': u'timestamp_timeOfDay', 'timeOfDay': (7, 8), 'type': 'DateEncoder'}, '_classifierInput': { 'name': u'_classifierInput', 'fieldname': u'consumption', 'classifierOnly': True, 'type': 'AdaptiveScalarEncoder', 'clipInput': True, 'n': 100, 'w': 21}, }, # A dictionary specifying the period for automatically-generated # resets from a RecordSensor; # # None = disable automatically-generated resets (also disabled if # all of the specified values evaluate to 0). # Valid keys is the desired combination of the following: # days, hours, minutes, seconds, milliseconds, microseconds, weeks # # Example for 1.5 days: sensorAutoReset = dict(days=1,hours=12), # # (value generated from SENSOR_AUTO_RESET) 'sensorAutoReset' : { u'days': 0, u'hours': 0}, }, 'spEnable': True, 'spParams': { # SP diagnostic output verbosity control; # 0: silent; >=1: some info; >=2: more info; 'spVerbosity' : 0, 'globalInhibition': 1, # Number of cell columns in the cortical region (same number for # SP and TM) # (see also tpNCellsPerCol) 'columnCount': 2048, 'inputWidth': 0, # SP inhibition control (absolute value); # Maximum number of active columns in the SP region's output (when # there are more, the weaker ones are suppressed) 'numActiveColumnsPerInhArea': 40, 'seed': 1956, # potentialPct # What percent of the columns's receptive field is available # for potential synapses. At initialization time, we will # choose potentialPct * (2*potentialRadius+1)^2 'potentialPct': 0.5, # The default connected threshold. Any synapse whose # permanence value is above the connected threshold is # a "connected synapse", meaning it can contribute to the # cell's firing. Typical value is 0.10. Cells whose activity # level before inhibition falls below minDutyCycleBeforeInh # will have their own internal synPermConnectedCell # threshold set below this default value. # (This concept applies to both SP and TM and so 'cells' # is correct here as opposed to 'columns') 'synPermConnected': 0.1, 'synPermActiveInc': 0.1, 'synPermInactiveDec': 0.01, }, # Controls whether TM is enabled or disabled; # TM is necessary for making temporal predictions, such as predicting # the next inputs. Without TM, the model is only capable of # reconstructing missing sensor inputs (via SP). 'tmEnable' : True, 'tmParams': { # TM diagnostic output verbosity control; # 0: silent; [1..6]: increasing levels of verbosity # (see verbosity in nupic/trunk/py/nupic/research/backtracking_tm.py and backtracking_tm_cpp.py) 'verbosity': 0, # Number of cell columns in the cortical region (same number for # SP and TM) # (see also tpNCellsPerCol) 'columnCount': 2048, # The number of cells (i.e., states), allocated per column. 'cellsPerColumn': 32, 'inputWidth': 2048, 'seed': 1960, # Temporal Pooler implementation selector (see _getTPClass in # CLARegion.py). 'temporalImp': 'cpp', # New Synapse formation count # NOTE: If None, use spNumActivePerInhArea # # TODO: need better explanation 'newSynapseCount': 20, # Maximum number of synapses per segment # > 0 for fixed-size CLA # -1 for non-fixed-size CLA # # TODO: for Ron: once the appropriate value is placed in TM # constructor, see if we should eliminate this parameter from # description.py. 'maxSynapsesPerSegment': 32, # Maximum number of segments per cell # > 0 for fixed-size CLA # -1 for non-fixed-size CLA # # TODO: for Ron: once the appropriate value is placed in TM # constructor, see if we should eliminate this parameter from # description.py. 'maxSegmentsPerCell': 128, # Initial Permanence # TODO: need better explanation 'initialPerm': 0.21, # Permanence Increment 'permanenceInc': 0.1, # Permanence Decrement # If set to None, will automatically default to tpPermanenceInc # value. 'permanenceDec' : 0.1, 'globalDecay': 0.0, 'maxAge': 0, # Minimum number of active synapses for a segment to be considered # during search for the best-matching segments. # None=use default # Replaces: tpMinThreshold 'minThreshold': 12, # Segment activation threshold. # A segment is active if it has >= tpSegmentActivationThreshold # connected synapses that are active due to infActiveState # None=use default # Replaces: tpActivationThreshold 'activationThreshold': 16, 'outputType': 'normal', # "Pay Attention Mode" length. This tells the TM how many new # elements to append to the end of a learned sequence at a time. # Smaller values are better for datasets with short sequences, # higher values are better for datasets with long sequences. 'pamLength': 1, }, 'clParams': { 'regionName' : 'SDRClassifierRegion', # Classifier diagnostic output verbosity control; # 0: silent; [1..6]: increasing levels of verbosity 'verbosity' : 0, # This controls how fast the classifier learns/forgets. Higher values # make it adapt faster and forget older patterns faster. 'alpha': 0.001, # This is set after the call to updateConfigFromSubConfig and is # computed from the aggregationInfo and predictAheadTime. 'steps': '1', }, 'anomalyParams': { u'anomalyCacheRecords': None, u'autoDetectThreshold': None, u'autoDetectWaitRecords': None}, 'trainSPNetOnlyIfRequested': False, }, } # end of config dictionary # Adjust base config dictionary for any modifications if imported from a # sub-experiment updateConfigFromSubConfig(config) # Compute predictionSteps based on the predictAheadTime and the aggregation # period, which may be permuted over. if config['predictAheadTime'] is not None: predictionSteps = int(round(aggregationDivide( config['predictAheadTime'], config['aggregationInfo']))) assert (predictionSteps >= 1) config['modelParams']['clParams']['steps'] = str(predictionSteps) # Adjust config by applying ValueGetterBase-derived # futures. NOTE: this MUST be called after updateConfigFromSubConfig() in order # to support value-getter-based substitutions from the sub-experiment (if any) applyValueGettersToContainer(config) control = { # The environment that the current model is being run in "environment": 'nupic', # Input stream specification per py/nupic/frameworks/opf/jsonschema/stream_def.json. # 'dataset' : { u'info': u'test_hotgym', u'streams': [ { u'columns': [u'*'], u'info': u'test data', u'source': u'file://swarming/test_data.csv'}], u'version': 1}, # Iteration count: maximum number of iterations. Each iteration corresponds # to one record from the (possibly aggregated) dataset. The task is # terminated when either number of iterations reaches iterationCount or # all records in the (possibly aggregated) database have been processed, # whichever occurs first. # # iterationCount of -1 = iterate over the entire dataset 'iterationCount' : -1, # A dictionary containing all the supplementary parameters for inference "inferenceArgs":{u'predictedField': u'consumption', u'predictionSteps': [1]}, # Metrics: A list of MetricSpecs that instantiate the metrics that are # computed for this experiment 'metrics':[ MetricSpec(field=u'consumption', metric='multiStep', inferenceElement='multiStepBestPredictions', params={'window': 1000, 'steps': [1], 'errorMetric': 'altMAPE'}), ], # Logged Metrics: A sequence of regular expressions that specify which of # the metrics from the Inference Specifications section MUST be logged for # every prediction. The regex's correspond to the automatically generated # metric labels. This is similar to the way the optimization metric is # specified in permutations.py. 'loggedMetrics': ['.*'], } descriptionInterface = ExperimentDescriptionAPI(modelConfig=config, control=control)

14,113

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35.163987

117

0.605796

numenta/nupic-legacy

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26,207

permutations.py

numenta_nupic-legacy/tests/swarming/nupic/swarming/experiments/input_predicted_field/permutations.py

# ---------------------------------------------------------------------- # Numenta Platform for Intelligent Computing (NuPIC) # Copyright (C) 2013, Numenta, Inc. Unless you have an agreement # with Numenta, Inc., for a separate license for this software code, the # following terms and conditions apply: # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU Affero Public License version 3 as # published by the Free Software Foundation. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. # See the GNU Affero Public License for more details. # # You should have received a copy of the GNU Affero Public License # along with this program. If not, see http://www.gnu.org/licenses. # # http://numenta.org/licenses/ # ---------------------------------------------------------------------- """ Template file used by ExpGenerator to generate the actual permutations.py file by replacing $XXXXXXXX tokens with desired values. This permutations.py file was generated by: '/Users/ronmarianetti/nupic/eng/lib/python2.6/site-packages/nupicengine/frameworks/opf/expGenerator/ExpGenerator.pyc' """ import os from nupic.swarming.permutation_helpers import * # The name of the field being predicted. Any allowed permutation MUST contain # the prediction field. # (generated from PREDICTION_FIELD) predictedField = 'consumption' permutations = { 'modelParams': { 'sensorParams': { 'encoders': { 'consumption': PermuteEncoder(fieldName='consumption', encoderClass='ScalarEncoder', maxval=PermuteInt(100, 300, 25), n=PermuteInt(13, 500, 20), w=7, minval=0), 'gym': PermuteEncoder(fieldName='gym', encoderClass='SDRCategoryEncoder', w=7, n=100), 'timestamp_dayOfWeek': PermuteEncoder(fieldName='timestamp', encoderClass='DateEncoder.dayOfWeek', radius=PermuteChoices([1, 3]), w=7), 'timestamp_timeOfDay': PermuteEncoder(fieldName='timestamp', encoderClass='DateEncoder.timeOfDay', radius=PermuteChoices([1, 8]), w=7), 'address': PermuteEncoder(fieldName='address', encoderClass='SDRCategoryEncoder', w=7, n=100), '_classifierInput': dict(fieldname='consumption', classifierOnly=True, encoderClass='ScalarEncoder', maxval=PermuteInt(100, 300, 25), n=PermuteInt(13, 500, 20), w=7, minval=0), }, }, 'spParams': { }, 'tmParams': { }, 'clParams': { 'alpha': PermuteFloat(0.0001, 0.1), }, } } # Fields selected for final hypersearch report; # NOTE: These values are used as regular expressions by RunPermutations.py's # report generator # (fieldname values generated from PERM_PREDICTED_FIELD_NAME) report = [ '.*consumption.*', ] # Permutation optimization setting: either minimize or maximize metric # used by RunPermutations. # NOTE: The value is used as a regular expressions by RunPermutations.py's # report generator # (generated from minimize = "multiStepBestPredictions:multiStep:errorMetric='altMAPE':steps=\[1\]:window=1000:field=consumption") minimize = "multiStepBestPredictions:multiStep:errorMetric='altMAPE':steps=\[1\]:window=1000:field=consumption" minParticlesPerSwarm = None inputPredictedField = os.environ.get("NTA_TEST_inputPredictedField", "auto") def dummyModelParams(perm): """ This function can be used for Hypersearch algorithm development. When present, Hypersearch doesn't actually run the CLA model in the OPF, but instead runs a dummy model. This function returns the dummy model params that will be used. See the OPFDummyModelRunner class source code (in nupic.swarming.ModelRunner) for a description of the schema for the dummy model params. """ # We are trying to get Hypersearch to find a model with: # no consumption encoder # any address encoder # any gym encoder # no other fields errScore = 50 if perm['modelParams']['sensorParams']['encoders']['consumption'] is not None: errScore += 10 if perm['modelParams']['sensorParams']['encoders']['address'] is not None: errScore -= 10 if perm['modelParams']['sensorParams']['encoders']['gym'] is not None: errScore -= 20 if perm['modelParams']['sensorParams']['encoders']['timestamp_timeOfDay'] \ is not None: errScore -= 30 if perm['modelParams']['sensorParams']['encoders']['timestamp_dayOfWeek'] \ is not None: errScore -= 40 dummyModelParams = dict( metricValue = errScore, iterations = int(os.environ.get('NTA_TEST_numIterations', '1')), waitTime = None, sysExitModelRange = os.environ.get('NTA_TEST_sysExitModelRange', None), errModelRange = os.environ.get('NTA_TEST_errModelRange', None), jobFailErr = bool(os.environ.get('NTA_TEST_jobFailErr', False)) ) return dummyModelParams def permutationFilter(perm): """ This function can be used to selectively filter out specific permutation combinations. It is called by RunPermutations for every possible permutation of the variables in the permutations dict. It should return True for valid a combination of permutation values and False for an invalid one. Parameters: --------------------------------------------------------- perm: dict of one possible combination of name:value pairs chosen from permutations. """ # An example of how to use this: #limit = int(os.environ.get('NTA_TEST_maxvalFilter', 300)) #if perm['modelParams']['sensorParams']['encoders']['consumption']['maxval'] > limit: # return False; return True

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Python

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39.492424

130

0.667916

numenta/nupic-legacy

6,330

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AGPL-3.0

9/5/2024, 5:13:42 PM (Europe/Amsterdam)

26,208

description.py

numenta_nupic-legacy/tests/swarming/nupic/swarming/experiments/smart_speculation_temporal/description.py

# ---------------------------------------------------------------------- # Numenta Platform for Intelligent Computing (NuPIC) # Copyright (C) 2013, Numenta, Inc. Unless you have an agreement # with Numenta, Inc., for a separate license for this software code, the # following terms and conditions apply: # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU Affero Public License version 3 as # published by the Free Software Foundation. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. # See the GNU Affero Public License for more details. # # You should have received a copy of the GNU Affero Public License # along with this program. If not, see http://www.gnu.org/licenses. # # http://numenta.org/licenses/ # ---------------------------------------------------------------------- """ Template file used by the OPF Experiment Generator to generate the actual description.py file by replacing $XXXXXXXX tokens with desired values. This description.py file was generated by: '/Users/ronmarianetti/nupic/eng/lib/python2.6/site-packages/nupic/frameworks/opf/expGenerator/experiment_generator.py' """ from nupic.frameworks.opf.exp_description_api import ExperimentDescriptionAPI from nupic.frameworks.opf.exp_description_helpers import ( updateConfigFromSubConfig, applyValueGettersToContainer, DeferredDictLookup) from nupic.frameworks.opf.htm_prediction_model_callbacks import * from nupic.frameworks.opf.metrics import MetricSpec from nupic.frameworks.opf.opf_utils import (InferenceType, InferenceElement) from nupic.support import aggregationDivide from nupic.frameworks.opf.opf_task_driver import ( IterationPhaseSpecLearnOnly, IterationPhaseSpecInferOnly, IterationPhaseSpecLearnAndInfer) # Model Configuration Dictionary: # # Define the model parameters and adjust for any modifications if imported # from a sub-experiment. # # These fields might be modified by a sub-experiment; this dict is passed # between the sub-experiment and base experiment # # # NOTE: Use of DEFERRED VALUE-GETTERs: dictionary fields and list elements # within the config dictionary may be assigned futures derived from the # ValueGetterBase class, such as DeferredDictLookup. # This facility is particularly handy for enabling substitution of values in # the config dictionary from other values in the config dictionary, which is # needed by permutation.py-based experiments. These values will be resolved # during the call to applyValueGettersToContainer(), # which we call after the base experiment's config dictionary is updated from # the sub-experiment. See ValueGetterBase and # DeferredDictLookup for more details about value-getters. # # For each custom encoder parameter to be exposed to the sub-experiment/ # permutation overrides, define a variable in this section, using key names # beginning with a single underscore character to avoid collisions with # pre-defined keys (e.g., _dsEncoderFieldName2_N). # # Example: # config = dict( # _dsEncoderFieldName2_N = 70, # _dsEncoderFieldName2_W = 5, # dsEncoderSchema = [ # base=dict( # fieldname='Name2', type='ScalarEncoder', # name='Name2', minval=0, maxval=270, clipInput=True, # n=DeferredDictLookup('_dsEncoderFieldName2_N'), # w=DeferredDictLookup('_dsEncoderFieldName2_W')), # ], # ) # updateConfigFromSubConfig(config) # applyValueGettersToContainer(config) config = { # Type of model that the rest of these parameters apply to. 'model': "HTMPrediction", # Version that specifies the format of the config. 'version': 1, # Intermediate variables used to compute fields in modelParams and also # referenced from the control section. 'aggregationInfo': { 'days': 0, 'fields': [], 'hours': 0, 'microseconds': 0, 'milliseconds': 0, 'minutes': 0, 'months': 0, 'seconds': 0, 'weeks': 0, 'years': 0}, 'predictAheadTime': None, # Model parameter dictionary. 'modelParams': { # The type of inference that this model will perform 'inferenceType': 'TemporalNextStep', 'sensorParams': { # Sensor diagnostic output verbosity control; # if > 0: sensor region will print out on screen what it's sensing # at each step 0: silent; >=1: some info; >=2: more info; # >=3: even more info (see compute() in py/regions/RecordSensor.py) 'verbosity' : 0, # Example: # dsEncoderSchema = [ # DeferredDictLookup('__field_name_encoder'), # ], # # (value generated from DS_ENCODER_SCHEMA) 'encoders': { u'A': { 'fieldname': u'daynight', 'n': 300, 'name': u'daynight', 'type': 'SDRCategoryEncoder', 'w': 21}, u'B': { 'fieldname': u'daynight', 'n': 300, 'name': u'daynight', 'type': 'SDRCategoryEncoder', 'w': 21}, u'C': { 'fieldname': u'precip', 'n': 300, 'name': u'precip', 'type': 'SDRCategoryEncoder', 'w': 21}, u'D': { 'clipInput': True, 'fieldname': u'visitor_winloss', 'maxval': 0.78600000000000003, 'minval': 0.0, 'n': 150, 'name': u'visitor_winloss', 'type': 'AdaptiveScalarEncoder', 'w': 21}, u'E': { 'clipInput': True, 'fieldname': u'home_winloss', 'maxval': 0.69999999999999996, 'minval': 0.0, 'n': 150, 'name': u'home_winloss', 'type': 'AdaptiveScalarEncoder', 'w': 21}, u'F': { 'dayOfWeek': (7, 1), 'fieldname': u'timestamp', 'name': u'timestamp_dayOfWeek', 'type': 'DateEncoder'}, u'G': { 'fieldname': u'timestamp', 'name': u'timestamp_timeOfDay', 'timeOfDay': (7, 1), 'type': 'DateEncoder'}, u'pred': { 'clipInput': True, 'fieldname': u'attendance', 'maxval': 36067, 'minval': 0, 'n': 150, 'name': u'attendance', 'type': 'AdaptiveScalarEncoder', 'w': 21}}, # A dictionary specifying the period for automatically-generated # resets from a RecordSensor; # # None = disable automatically-generated resets (also disabled if # all of the specified values evaluate to 0). # Valid keys is the desired combination of the following: # days, hours, minutes, seconds, milliseconds, microseconds, weeks # # Example for 1.5 days: sensorAutoReset = dict(days=1,hours=12), # # (value generated from SENSOR_AUTO_RESET) 'sensorAutoReset' : None, }, 'spEnable': True, 'spParams': { # SP diagnostic output verbosity control; # 0: silent; >=1: some info; >=2: more info; 'spVerbosity' : 0, 'globalInhibition': 1, # Number of cell columns in the cortical region (same number for # SP and TM) # (see also tpNCellsPerCol) 'columnCount': 2048, 'inputWidth': 0, # SP inhibition control (absolute value); # Maximum number of active columns in the SP region's output (when # there are more, the weaker ones are suppressed) 'numActiveColumnsPerInhArea': 40, 'seed': 1956, # potentialPct # What percent of the columns's receptive field is available # for potential synapses. At initialization time, we will # choose potentialPct * (2*potentialRadius+1)^2 'potentialPct': 1.0, # The default connected threshold. Any synapse whose # permanence value is above the connected threshold is # a "connected synapse", meaning it can contribute to the # cell's firing. Typical value is 0.10. Cells whose activity # level before inhibition falls below minDutyCycleBeforeInh # will have their own internal synPermConnectedCell # threshold set below this default value. # (This concept applies to both SP and TM and so 'cells' # is correct here as opposed to 'columns') 'synPermConnected': 0.1, 'synPermActiveInc': 0.1, 'synPermInactiveDec': 0.01, }, # Controls whether TM is enabled or disabled; # TM is necessary for making temporal predictions, such as predicting # the next inputs. Without TM, the model is only capable of # reconstructing missing sensor inputs (via SP). 'tmEnable' : True, 'tmParams': { # TM diagnostic output verbosity control; # 0: silent; [1..6]: increasing levels of verbosity # (see verbosity in nupic/trunk/py/nupic/research/backtracking_tm.py and backtracking_tm_cpp.py) 'verbosity': 0, # Number of cell columns in the cortical region (same number for # SP and TM) # (see also tpNCellsPerCol) 'columnCount': 2048, # The number of cells (i.e., states), allocated per column. 'cellsPerColumn': 32, 'inputWidth': 2048, 'seed': 1960, # Temporal Pooler implementation selector (see _getTPClass in # CLARegion.py). 'temporalImp': 'cpp', # New Synapse formation count # NOTE: If None, use spNumActivePerInhArea # # TODO: need better explanation 'newSynapseCount': 15, # Maximum number of synapses per segment # > 0 for fixed-size CLA # -1 for non-fixed-size CLA # # TODO: for Ron: once the appropriate value is placed in TM # constructor, see if we should eliminate this parameter from # description.py. 'maxSynapsesPerSegment': 32, # Maximum number of segments per cell # > 0 for fixed-size CLA # -1 for non-fixed-size CLA # # TODO: for Ron: once the appropriate value is placed in TM # constructor, see if we should eliminate this parameter from # description.py. 'maxSegmentsPerCell': 128, # Initial Permanence # TODO: need better explanation 'initialPerm': 0.21, # Permanence Increment 'permanenceInc': 0.1, # Permanence Decrement # If set to None, will automatically default to tpPermanenceInc # value. 'permanenceDec' : 0.1, 'globalDecay': 0.0, 'maxAge': 0, # Minimum number of active synapses for a segment to be considered # during search for the best-matching segments. # None=use default # Replaces: tpMinThreshold 'minThreshold': 12, # Segment activation threshold. # A segment is active if it has >= tpSegmentActivationThreshold # connected synapses that are active due to infActiveState # None=use default # Replaces: tpActivationThreshold 'activationThreshold': 16, 'outputType': 'normal', # "Pay Attention Mode" length. This tells the TM how many new # elements to append to the end of a learned sequence at a time. # Smaller values are better for datasets with short sequences, # higher values are better for datasets with long sequences. 'pamLength': 1, }, 'clParams': { 'regionName' : 'SDRClassifierRegion', # Classifier diagnostic output verbosity control; # 0: silent; [1..6]: increasing levels of verbosity 'verbosity' : 0, # This controls how fast the classifier learns/forgets. Higher values # make it adapt faster and forget older patterns faster. 'alpha': 0.001, # This is set after the call to updateConfigFromSubConfig and is # computed from the aggregationInfo and predictAheadTime. 'steps': '1', }, 'trainSPNetOnlyIfRequested': False, }, } # end of config dictionary # Adjust base config dictionary for any modifications if imported from a # sub-experiment updateConfigFromSubConfig(config) # Compute predictionSteps based on the predictAheadTime and the aggregation # period, which may be permuted over. if config['predictAheadTime'] is not None: predictionSteps = int(round(aggregationDivide( config['predictAheadTime'], config['aggregationInfo']))) assert (predictionSteps >= 1) config['modelParams']['clParams']['steps'] = str(predictionSteps) # Adjust config by applying ValueGetterBase-derived # futures. NOTE: this MUST be called after updateConfigFromSubConfig() in order # to support value-getter-based substitutions from the sub-experiment (if any) applyValueGettersToContainer(config) control = { # The environment that the current model is being run in "environment": 'nupic', # Input stream specification per py/nupicengine/cluster/database/StreamDef.json. # 'dataset' : { u'info': u'baseball benchmark test', u'streams': [ { u'columns': [ u'daynight', u'precip', u'home_winloss', u'visitor_winloss', u'attendance', u'timestamp'], u'info': u'OAK01.csv', u'source': u'file://extra/baseball_stadium/OAK01reformatted.csv'}], u'version': 1}, # Iteration count: maximum number of iterations. Each iteration corresponds # to one record from the (possibly aggregated) dataset. The task is # terminated when either number of iterations reaches iterationCount or # all records in the (possibly aggregated) database have been processed, # whichever occurs first. # # iterationCount of -1 = iterate over the entire dataset #'iterationCount' : ITERATION_COUNT, # Metrics: A list of MetricSpecs that instantiate the metrics that are # computed for this experiment 'metrics':[ MetricSpec(field=u'attendance', inferenceElement=InferenceElement.prediction, metric='aae', params={'window': 1000}), MetricSpec(field=u'attendance', inferenceElement=InferenceElement.prediction, metric='trivial_aae', params={'window': 1000}), MetricSpec(field=u'attendance', inferenceElement=InferenceElement.prediction, metric='nupicScore_scalar', params={'frequencyWindow': 1000, 'movingAverageWindow': 1000}), MetricSpec(field=u'attendance', inferenceElement=InferenceElement.prediction, metric='nupicScore_scalar', params={'frequencyWindow': 1000}) ], # Logged Metrics: A sequence of regular expressions that specify which of # the metrics from the Inference Specifications section MUST be logged for # every prediction. The regex's correspond to the automatically generated # metric labels. This is similar to the way the optimization metric is # specified in permutations.py. 'loggedMetrics': ['.*nupicScore.*'], } descriptionInterface = ExperimentDescriptionAPI(modelConfig=config, control=control)

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permutations.py

numenta_nupic-legacy/tests/swarming/nupic/swarming/experiments/smart_speculation_temporal/permutations.py

# ---------------------------------------------------------------------- # Numenta Platform for Intelligent Computing (NuPIC) # Copyright (C) 2013, Numenta, Inc. Unless you have an agreement # with Numenta, Inc., for a separate license for this software code, the # following terms and conditions apply: # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU Affero Public License version 3 as # published by the Free Software Foundation. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. # See the GNU Affero Public License for more details. # # You should have received a copy of the GNU Affero Public License # along with this program. If not, see http://www.gnu.org/licenses. # # http://numenta.org/licenses/ # ---------------------------------------------------------------------- """ Template file used by ExpGenerator to generate the actual permutations.py file by replacing $XXXXXXXX tokens with desired values. This permutations.py file was generated by: '/Users/ronmarianetti/nupic/eng/lib/python2.6/site-packages/nupic/frameworks/opf/expGenerator/experiment_generator.py' """ import os from nupic.swarming.permutation_helpers import * # The name of the field being predicted. Any allowed permutation MUST contain # the prediction field. # (generated from PREDICTION_FIELD) predictedField = 'attendance' permutations = { 'modelParams': { 'sensorParams': { 'encoders': { 'A': PermuteEncoder(fieldName='daynight', encoderClass='SDRCategoryEncoder', w=7, n=100), 'C': PermuteEncoder(fieldName='precip', encoderClass='SDRCategoryEncoder', w=7, n=100), 'B': PermuteEncoder(fieldName='daynight', encoderClass='SDRCategoryEncoder', w=7, n=100), 'E': PermuteEncoder(fieldName='home_winloss', encoderClass='AdaptiveScalarEncoder', maxval=0.7, n=PermuteInt(13, 500, 25), clipInput=True, w=7, minval=0), 'D': PermuteEncoder(fieldName='visitor_winloss', encoderClass='AdaptiveScalarEncoder', maxval=0.786, n=PermuteInt(13, 500, 25), clipInput=True, w=7, minval=0), 'G': PermuteEncoder(fieldName='timestamp', encoderClass='DateEncoder.timeOfDay', radius=PermuteChoices([1, 8]), w=7), 'F': PermuteEncoder(fieldName='timestamp', encoderClass='DateEncoder.dayOfWeek', radius=PermuteChoices([1, 3]), w=7), 'Pred': PermuteEncoder(fieldName='attendance', encoderClass='AdaptiveScalarEncoder', maxval=36067, n=PermuteInt(13, 500, 25), clipInput=True, w=7, minval=0), }, }, 'tmParams': { 'minThreshold': PermuteInt(9, 12), 'activationThreshold': PermuteInt(12, 16), }, } } # Fields selected for final hypersearch report; # NOTE: These values are used as regular expressions by RunPermutations.py's # report generator # (fieldname values generated from PERM_PREDICTED_FIELD_NAME) report = [ '.*attendance.*', ] # Permutation optimization setting: either minimize or maximize metric # used by RunPermutations. # NOTE: The value is used as a regular expressions by RunPermutations.py's # report generator # (generated from minimize = 'nonprediction:aae:window=1000:field=attendance') minimize = 'prediction:aae:window=1000:field=attendance' def dummyModelParams(perm): """ This function can be used for Hypersearch algorithm development. When present, we don't actually run the CLA model in the OPF, but instead run a dummy model. This function returns the dummy model params that will be used. See the OPFDummyModelRunner class source code (in nupic.swarming.ModelRunner) for a description of the schema for the dummy model params. """ errScore = 500 if not perm['modelParams']['sensorParams']['encoders']['A'] is None: errScore -= 50 if not perm['modelParams']['sensorParams']['encoders']['B'] is None: errScore -= 40 if not perm['modelParams']['sensorParams']['encoders']['C'] is None: errScore -= 30 if not perm['modelParams']['sensorParams']['encoders']['D'] is None: errScore -= 20 if not perm['modelParams']['sensorParams']['encoders']['E'] is None: errScore -= 15 if not perm['modelParams']['sensorParams']['encoders']['F'] is None: errScore -= 10 if not perm['modelParams']['sensorParams']['encoders']['G'] is None: errScore -= 5 delay = 0 #If the model only has the A field have it run slowly to simulate speculation. encoderCount = 0 for key in perm.keys(): if 'encoder' in key and not perm[key] is None: encoderCount+=1 delay=encoderCount*encoderCount*.1 dummyModelParams = dict( metricValue = errScore, metricFunctions = None, delay=delay, ) return dummyModelParams def permutationFilter(perm): """ This function can be used to selectively filter out specific permutation combinations. It is called by RunPermutations for every possible permutation of the variables in the permutations dict. It should return True for valid a combination of permutation values and False for an invalid one. Parameters: --------------------------------------------------------- perm: dict of one possible combination of name:value pairs chosen from permutations. """ # An example of how to use this #if perm['__consumption_encoder']['maxval'] > 300: # return False; # return True

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description.py

numenta_nupic-legacy/tests/swarming/nupic/swarming/experiments/oneField/description.py

# ---------------------------------------------------------------------- # Numenta Platform for Intelligent Computing (NuPIC) # Copyright (C) 2013, Numenta, Inc. Unless you have an agreement # with Numenta, Inc., for a separate license for this software code, the # following terms and conditions apply: # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU Affero Public License version 3 as # published by the Free Software Foundation. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. # See the GNU Affero Public License for more details. # # You should have received a copy of the GNU Affero Public License # along with this program. If not, see http://www.gnu.org/licenses. # # http://numenta.org/licenses/ # ---------------------------------------------------------------------- """ Template file used by the OPF Experiment Generator to generate the actual description.py file by replacing $XXXXXXXX tokens with desired values. This description.py file was generated by: '/Users/ronmarianetti/nupic/eng/lib/python2.6/site-packages/nupic/frameworks/opf/expGenerator/experiment_generator.py' """ from nupic.frameworks.opf.exp_description_api import ExperimentDescriptionAPI from nupic.frameworks.opf.exp_description_helpers import ( updateConfigFromSubConfig, applyValueGettersToContainer, DeferredDictLookup) from nupic.frameworks.opf.htm_prediction_model_callbacks import * from nupic.frameworks.opf.metrics import MetricSpec from nupic.frameworks.opf.opf_utils import (InferenceType, InferenceElement) from nupic.support import aggregationDivide from nupic.frameworks.opf.opf_task_driver import ( IterationPhaseSpecLearnOnly, IterationPhaseSpecInferOnly, IterationPhaseSpecLearnAndInfer) # Model Configuration Dictionary: # # Define the model parameters and adjust for any modifications if imported # from a sub-experiment. # # These fields might be modified by a sub-experiment; this dict is passed # between the sub-experiment and base experiment # # # NOTE: Use of DEFERRED VALUE-GETTERs: dictionary fields and list elements # within the config dictionary may be assigned futures derived from the # ValueGetterBase class, such as DeferredDictLookup. # This facility is particularly handy for enabling substitution of values in # the config dictionary from other values in the config dictionary, which is # needed by permutation.py-based experiments. These values will be resolved # during the call to applyValueGettersToContainer(), # which we call after the base experiment's config dictionary is updated from # the sub-experiment. See ValueGetterBase and # DeferredDictLookup for more details about value-getters. # # For each custom encoder parameter to be exposed to the sub-experiment/ # permutation overrides, define a variable in this section, using key names # beginning with a single underscore character to avoid collisions with # pre-defined keys (e.g., _dsEncoderFieldName2_N). # # Example: # config = dict( # _dsEncoderFieldName2_N = 70, # _dsEncoderFieldName2_W = 5, # dsEncoderSchema = [ # base=dict( # fieldname='Name2', type='ScalarEncoder', # name='Name2', minval=0, maxval=270, clipInput=True, # n=DeferredDictLookup('_dsEncoderFieldName2_N'), # w=DeferredDictLookup('_dsEncoderFieldName2_W')), # ], # ) # updateConfigFromSubConfig(config) # applyValueGettersToContainer(config) config = { # Type of model that the rest of these parameters apply to. 'model': "HTMPrediction", # Version that specifies the format of the config. 'version': 1, # Intermediate variables used to compute fields in modelParams and also # referenced from the control section. 'aggregationInfo': { 'days': 0, 'fields': [ (u'timestamp', 'first'), (u'gym', 'first'), (u'consumption', 'mean'), (u'address', 'first')], 'hours': 0, 'microseconds': 0, 'milliseconds': 0, 'minutes': 0, 'months': 0, 'seconds': 0, 'weeks': 0, 'years': 0}, 'predictAheadTime': None, # Model parameter dictionary. 'modelParams': { # The type of inference that this model will perform 'inferenceType': 'TemporalNextStep', 'sensorParams': { # Sensor diagnostic output verbosity control; # if > 0: sensor region will print out on screen what it's sensing # at each step 0: silent; >=1: some info; >=2: more info; # >=3: even more info (see compute() in py/regions/RecordSensor.py) 'verbosity' : 0, # Example: # dsEncoderSchema = [ # DeferredDictLookup('__field_name_encoder'), # ], # # (value generated from DS_ENCODER_SCHEMA) 'encoders': { 'consumption': { 'clipInput': True, 'fieldname': u'consumption', 'maxval': 200, 'minval': 0, 'n': 1500, 'name': u'consumption', 'type': 'ScalarEncoder', 'w': 21 }, }, # A dictionary specifying the period for automatically-generated # resets from a RecordSensor; # # None = disable automatically-generated resets (also disabled if # all of the specified values evaluate to 0). # Valid keys is the desired combination of the following: # days, hours, minutes, seconds, milliseconds, microseconds, weeks # # Example for 1.5 days: sensorAutoReset = dict(days=1,hours=12), # # (value generated from SENSOR_AUTO_RESET) 'sensorAutoReset' : None, }, 'spEnable': True, 'spParams': { # SP diagnostic output verbosity control; # 0: silent; >=1: some info; >=2: more info; 'spVerbosity' : 0, 'globalInhibition': 1, # Number of cell columns in the cortical region (same number for # SP and TM) # (see also tpNCellsPerCol) 'columnCount': 2048, 'inputWidth': 0, # SP inhibition control (absolute value); # Maximum number of active columns in the SP region's output (when # there are more, the weaker ones are suppressed) 'numActiveColumnsPerInhArea': 40, 'seed': 1956, # potentialPct # What percent of the columns's receptive field is available # for potential synapses. At initialization time, we will # choose potentialPct * (2*potentialRadius+1)^2 'potentialPct': 0.5, # The default connected threshold. Any synapse whose # permanence value is above the connected threshold is # a "connected synapse", meaning it can contribute to the # cell's firing. Typical value is 0.10. Cells whose activity # level before inhibition falls below minDutyCycleBeforeInh # will have their own internal synPermConnectedCell # threshold set below this default value. # (This concept applies to both SP and TM and so 'cells' # is correct here as opposed to 'columns') 'synPermConnected': 0.1, 'synPermActiveInc': 0.1, 'synPermInactiveDec': 0.01, }, # Controls whether TM is enabled or disabled; # TM is necessary for making temporal predictions, such as predicting # the next inputs. Without TM, the model is only capable of # reconstructing missing sensor inputs (via SP). 'tmEnable' : True, 'tmParams': { # TM diagnostic output verbosity control; # 0: silent; [1..6]: increasing levels of verbosity # (see verbosity in nupic/trunk/py/nupic/research/backtracking_tm.py and backtracking_tm_cpp.py) 'verbosity': 0, # Number of cell columns in the cortical region (same number for # SP and TM) # (see also tpNCellsPerCol) 'columnCount': 2048, # The number of cells (i.e., states), allocated per column. 'cellsPerColumn': 32, 'inputWidth': 2048, 'seed': 1960, # Temporal Pooler implementation selector (see _getTPClass in # CLARegion.py). 'temporalImp': 'cpp', # New Synapse formation count # NOTE: If None, use spNumActivePerInhArea # # TODO: need better explanation 'newSynapseCount': 15, # Maximum number of synapses per segment # > 0 for fixed-size CLA # -1 for non-fixed-size CLA # # TODO: for Ron: once the appropriate value is placed in TM # constructor, see if we should eliminate this parameter from # description.py. 'maxSynapsesPerSegment': 32, # Maximum number of segments per cell # > 0 for fixed-size CLA # -1 for non-fixed-size CLA # # TODO: for Ron: once the appropriate value is placed in TM # constructor, see if we should eliminate this parameter from # description.py. 'maxSegmentsPerCell': 128, # Initial Permanence # TODO: need better explanation 'initialPerm': 0.21, # Permanence Increment 'permanenceInc': 0.1, # Permanence Decrement # If set to None, will automatically default to tpPermanenceInc # value. 'permanenceDec' : 0.1, 'globalDecay': 0.0, 'maxAge': 0, # Minimum number of active synapses for a segment to be considered # during search for the best-matching segments. # None=use default # Replaces: tpMinThreshold 'minThreshold': 12, # Segment activation threshold. # A segment is active if it has >= tpSegmentActivationThreshold # connected synapses that are active due to infActiveState # None=use default # Replaces: tpActivationThreshold 'activationThreshold': 16, 'outputType': 'normal', # "Pay Attention Mode" length. This tells the TM how many new # elements to append to the end of a learned sequence at a time. # Smaller values are better for datasets with short sequences, # higher values are better for datasets with long sequences. 'pamLength': 1, }, 'clParams': { 'regionName' : 'SDRClassifierRegion', # Classifier diagnostic output verbosity control; # 0: silent; [1..6]: increasing levels of verbosity 'verbosity' : 0, # This controls how fast the classifier learns/forgets. Higher values # make it adapt faster and forget older patterns faster. 'alpha': 0.001, # This is set after the call to updateConfigFromSubConfig and is # computed from the aggregationInfo and predictAheadTime. 'steps': '1', }, 'trainSPNetOnlyIfRequested': False, }, } # end of config dictionary # Adjust base config dictionary for any modifications if imported from a # sub-experiment updateConfigFromSubConfig(config) # Compute predictionSteps based on the predictAheadTime and the aggregation # period, which may be permuted over. if config['predictAheadTime'] is not None: predictionSteps = int(round(aggregationDivide( config['predictAheadTime'], config['aggregationInfo']))) assert (predictionSteps >= 1) config['modelParams']['clParams']['steps'] = str(predictionSteps) # Adjust config by applying ValueGetterBase-derived # futures. NOTE: this MUST be called after updateConfigFromSubConfig() in order # to support value-getter-based substitutions from the sub-experiment (if any) applyValueGettersToContainer(config) control = { # The environment that the current model is being run in "environment": 'nupic', # Input stream specification per py/nupicengine/cluster/database/StreamDef.json. # 'dataset' : { u'info': u'test_NoProviders', u'streams': [ { u'columns': [u'*'], u'info': u'test data', u'source': u'file://swarming/test_data.csv'}], u'version': 1}, # Iteration count: maximum number of iterations. Each iteration corresponds # to one record from the (possibly aggregated) dataset. The task is # terminated when either number of iterations reaches iterationCount or # all records in the (possibly aggregated) database have been processed, # whichever occurs first. # # iterationCount of -1 = iterate over the entire dataset #'iterationCount' : ITERATION_COUNT, # Metrics: A list of MetricSpecs that instantiate the metrics that are # computed for this experiment 'metrics':[ MetricSpec(field=u'consumption', inferenceElement=InferenceElement.prediction, metric='rmse'), ], # Logged Metrics: A sequence of regular expressions that specify which of # the metrics from the Inference Specifications section MUST be logged for # every prediction. The regex's correspond to the automatically generated # metric labels. This is similar to the way the optimization metric is # specified in permutations.py. 'loggedMetrics': ['.*nupicScore.*'], } descriptionInterface = ExperimentDescriptionAPI(modelConfig=config, control=control)

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26,211

permutations.py

numenta_nupic-legacy/tests/swarming/nupic/swarming/experiments/oneField/permutations.py

# ---------------------------------------------------------------------- # Numenta Platform for Intelligent Computing (NuPIC) # Copyright (C) 2013, Numenta, Inc. Unless you have an agreement # with Numenta, Inc., for a separate license for this software code, the # following terms and conditions apply: # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU Affero Public License version 3 as # published by the Free Software Foundation. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. # See the GNU Affero Public License for more details. # # You should have received a copy of the GNU Affero Public License # along with this program. If not, see http://www.gnu.org/licenses. # # http://numenta.org/licenses/ # ---------------------------------------------------------------------- """ Template file used by ExpGenerator to generate the actual permutations.py file by replacing $XXXXXXXX tokens with desired values. This permutations.py file was generated by: '/Users/ronmarianetti/nupic/eng/lib/python2.6/site-packages/nupic/frameworks/opf/expGenerator/experiment_generator.py' """ import os from nupic.swarming.permutation_helpers import * # The name of the field being predicted. Any allowed permutation MUST contain # the prediction field. # (generated from PREDICTION_FIELD) predictedField = 'consumption' permutations = { 'modelParams': { 'sensorParams': { 'encoders': { 'consumption': PermuteEncoder(fieldName='consumption', encoderClass='ScalarEncoder', maxval=PermuteInt(100, 300, 1), n=PermuteInt(13, 500, 1), w=7, minval=0), }, }, 'tmParams': { 'minThreshold': PermuteInt(9, 12), 'activationThreshold': PermuteInt(12, 16), }, } } # Fields selected for final hypersearch report; # NOTE: These values are used as regular expressions by RunPermutations.py's # report generator # (fieldname values generated from PERM_PREDICTED_FIELD_NAME) report = [ '.*consumption.*', ] # Permutation optimization setting: either minimize or maximize metric # used by RunPermutations. # NOTE: The value is used as a regular expressions by RunPermutations.py's # report generator # (generated from minimize = 'prediction:rmse:field=consumption') minimize = 'prediction:rmse:field=consumption' def dummyModelParams(perm): """ This function can be used for Hypersearch algorithm development. When present, Hypersearch doesn't actually run the CLA model in the OPF, but instead run a dummy model. This function returns the dummy model params that will be used. See the OPFDummyModelRunner class source code (in nupic.swarming.ModelRunner) for a description of the schema for the dummy model params. """ errScore = 50 #errScore += abs(perm['modelParams']['sensorParams']['encoders']\ # ['consumption']['maxval'] - 250) #errScore += abs(perm['modelParams']['sensorParams']['encoders']\ # ['consumption']['n'] - 53) # Make models that contain the __timestamp_timeOfDay encoder run a bit # slower so we can test that we successfully kill running models waitTime = 0.01 dummyModelParams = dict( metricValue = errScore, iterations = int(os.environ.get('NTA_TEST_numIterations', '5')), waitTime = waitTime, sysExitModelRange = os.environ.get('NTA_TEST_sysExitModelRange', None), delayModelRange = os.environ.get('NTA_TEST_delayModelRange', None), errModelRange = os.environ.get('NTA_TEST_errModelRange', None), jobFailErr = bool(os.environ.get('NTA_TEST_jobFailErr', False)) ) return dummyModelParams def permutationFilter(perm): """ This function can be used to selectively filter out specific permutation combinations. It is called by RunPermutations for every possible permutation of the variables in the permutations dict. It should return True for valid a combination of permutation values and False for an invalid one. Parameters: --------------------------------------------------------- perm: dict of one possible combination of name:value pairs chosen from permutations. """ # An example of how to use this limit = int(os.environ.get('NTA_TEST_maxvalFilter', 300)) if perm['modelParams']['sensorParams']['encoders']['consumption']['maxval'] > limit: return False; return True

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AGPL-3.0

9/5/2024, 5:13:42 PM (Europe/Amsterdam)

26,212

description.py

numenta_nupic-legacy/tests/swarming/nupic/swarming/experiments/legacy_cla_multistep/description.py

# ---------------------------------------------------------------------- # Numenta Platform for Intelligent Computing (NuPIC) # Copyright (C) 2013, Numenta, Inc. Unless you have an agreement # with Numenta, Inc., for a separate license for this software code, the # following terms and conditions apply: # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU Affero Public License version 3 as # published by the Free Software Foundation. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. # See the GNU Affero Public License for more details. # # You should have received a copy of the GNU Affero Public License # along with this program. If not, see http://www.gnu.org/licenses. # # http://numenta.org/licenses/ # ---------------------------------------------------------------------- """ Template file used by the OPF Experiment Generator to generate the actual description.py file by replacing $XXXXXXXX tokens with desired values. This description.py file was generated by: '/Users/ronmarianetti/nupic/eng/lib/python2.6/site-packages/nupicengine/frameworks/opf/expGenerator/ExpGenerator.pyc' """ from nupic.frameworks.opf.exp_description_api import ExperimentDescriptionAPI from nupic.frameworks.opf.exp_description_helpers import ( updateConfigFromSubConfig, applyValueGettersToContainer ) from nupic.frameworks.opf.htm_prediction_model_callbacks import * from nupic.frameworks.opf.metrics import MetricSpec from nupic.frameworks.opf.opf_utils import (InferenceType, InferenceElement) from nupic.support import aggregationDivide from nupic.frameworks.opf.opf_task_driver import ( IterationPhaseSpecLearnOnly, IterationPhaseSpecInferOnly, IterationPhaseSpecLearnAndInfer) # Model Configuration Dictionary: # # Define the model parameters and adjust for any modifications if imported # from a sub-experiment. # # These fields might be modified by a sub-experiment; this dict is passed # between the sub-experiment and base experiment # # config = { # Type of model that the rest of these parameters apply to. 'model': "HTMPrediction", # Version that specifies the format of the config. 'version': 1, # Intermediate variables used to compute fields in modelParams and also # referenced from the control section. 'aggregationInfo': { 'days': 0, 'fields': [ (u'timestamp', 'first'), (u'consumption', 'sum'), ], 'hours': 0, 'microseconds': 0, 'milliseconds': 0, 'minutes': 0, 'months': 0, 'seconds': 0, 'weeks': 0, 'years': 0}, 'predictAheadTime': None, # Model parameter dictionary. 'modelParams': { # The type of inference that this model will perform 'inferenceType': 'TemporalMultiStep', 'sensorParams': { # Sensor diagnostic output verbosity control; # if > 0: sensor region will print out on screen what it's sensing # at each step 0: silent; >=1: some info; >=2: more info; # >=3: even more info (see compute() in py/regions/RecordSensor.py) 'verbosity' : 0, # Example: # 'encoders': {'field1': {'fieldname': 'field1', 'n':100, # 'name': 'field1', 'type': 'AdaptiveScalarEncoder', # 'w': 21}} # 'encoders': { 'consumption': { 'clipInput': True, 'fieldname': u'consumption', 'n': 100, 'name': u'consumption', 'type': 'AdaptiveScalarEncoder', 'w': 21}, 'timestamp_timeOfDay': { 'fieldname': u'timestamp', 'name': u'timestamp_timeOfDay', 'type': 'DateEncoder', 'timeOfDay': (21, 1)}, 'timestamp_dayOfWeek': { 'fieldname': u'timestamp', 'name': u'timestamp_dayOfWeek', 'type': 'DateEncoder', 'dayOfWeek': (21, 1)}, }, # A dictionary specifying the period for automatically-generated # resets from a RecordSensor; # # None = disable automatically-generated resets (also disabled if # all of the specified values evaluate to 0). # Valid keys is the desired combination of the following: # days, hours, minutes, seconds, milliseconds, microseconds, weeks # # Example for 1.5 days: sensorAutoReset = dict(days=1,hours=12), # # (value generated from SENSOR_AUTO_RESET) 'sensorAutoReset' : { u'days': 0, u'hours': 0}, }, 'spEnable': True, 'spParams': { # SP diagnostic output verbosity control; # 0: silent; >=1: some info; >=2: more info; 'spVerbosity' : 0, 'globalInhibition': 1, # Number of cell columns in the cortical region (same number for # SP and TM) # (see also tpNCellsPerCol) 'columnCount': 2048, 'inputWidth': 0, # SP inhibition control (absolute value); # Maximum number of active columns in the SP region's output (when # there are more, the weaker ones are suppressed) 'numActiveColumnsPerInhArea': 40, 'seed': 1956, # potentialPct # What percent of the columns's receptive field is available # for potential synapses. At initialization time, we will # choose potentialPct * (2*potentialRadius+1)^2 'potentialPct': 0.5, # The default connected threshold. Any synapse whose # permanence value is above the connected threshold is # a "connected synapse", meaning it can contribute to the # cell's firing. Typical value is 0.10. Cells whose activity # level before inhibition falls below minDutyCycleBeforeInh # will have their own internal synPermConnectedCell # threshold set below this default value. # (This concept applies to both SP and TM and so 'cells' # is correct here as opposed to 'columns') 'synPermConnected': 0.1, 'synPermActiveInc': 0.1, 'synPermInactiveDec': 0.01, }, # Controls whether TM is enabled or disabled; # TM is necessary for making temporal predictions, such as predicting # the next inputs. Without TM, the model is only capable of # reconstructing missing sensor inputs (via SP). 'tmEnable' : True, 'tmParams': { # TM diagnostic output verbosity control; # 0: silent; [1..6]: increasing levels of verbosity # (see verbosity in nupic/trunk/py/nupic/research/backtracking_tm.py and backtracking_tm_cpp.py) 'verbosity': 0, # Number of cell columns in the cortical region (same number for # SP and TM) # (see also tpNCellsPerCol) 'columnCount': 2048, # The number of cells (i.e., states), allocated per column. 'cellsPerColumn': 32, 'inputWidth': 2048, 'seed': 1960, # Temporal Pooler implementation selector (see _getTPClass in # CLARegion.py). 'temporalImp': 'cpp', # New Synapse formation count # NOTE: If None, use spNumActivePerInhArea # # TODO: need better explanation 'newSynapseCount': 20, # Maximum number of synapses per segment # > 0 for fixed-size CLA # -1 for non-fixed-size CLA # # TODO: for Ron: once the appropriate value is placed in TM # constructor, see if we should eliminate this parameter from # description.py. 'maxSynapsesPerSegment': 32, # Maximum number of segments per cell # > 0 for fixed-size CLA # -1 for non-fixed-size CLA # # TODO: for Ron: once the appropriate value is placed in TM # constructor, see if we should eliminate this parameter from # description.py. 'maxSegmentsPerCell': 128, # Initial Permanence # TODO: need better explanation 'initialPerm': 0.21, # Permanence Increment 'permanenceInc': 0.1, # Permanence Decrement # If set to None, will automatically default to tpPermanenceInc # value. 'permanenceDec' : 0.1, 'globalDecay': 0.0, 'maxAge': 0, # Minimum number of active synapses for a segment to be considered # during search for the best-matching segments. # None=use default # Replaces: tpMinThreshold 'minThreshold': 12, # Segment activation threshold. # A segment is active if it has >= tpSegmentActivationThreshold # connected synapses that are active due to infActiveState # None=use default # Replaces: tpActivationThreshold 'activationThreshold': 16, 'outputType': 'normal', # "Pay Attention Mode" length. This tells the TM how many new # elements to append to the end of a learned sequence at a time. # Smaller values are better for datasets with short sequences, # higher values are better for datasets with long sequences. 'pamLength': 1, }, 'clParams': { 'regionName' : 'SDRClassifierRegion', # Classifier diagnostic output verbosity control; # 0: silent; [1..6]: increasing levels of verbosity 'verbosity' : 0, # This controls how fast the classifier learns/forgets. Higher values # make it adapt faster and forget older patterns faster. 'alpha': 0.001, # This is set after the call to updateConfigFromSubConfig and is # computed from the aggregationInfo and predictAheadTime. 'steps': '1', }, 'anomalyParams': { u'anomalyCacheRecords': None, u'autoDetectThreshold': None, u'autoDetectWaitRecords': None}, 'trainSPNetOnlyIfRequested': False, }, } # end of config dictionary # Adjust base config dictionary for any modifications if imported from a # sub-experiment updateConfigFromSubConfig(config) # Compute predictionSteps based on the predictAheadTime and the aggregation # period, which may be permuted over. if config['predictAheadTime'] is not None: predictionSteps = int(round(aggregationDivide( config['predictAheadTime'], config['aggregationInfo']))) assert (predictionSteps >= 1) config['modelParams']['clParams']['steps'] = str(predictionSteps) # Adjust config by applying ValueGetterBase-derived # futures. NOTE: this MUST be called after updateConfigFromSubConfig() in order # to support value-getter-based substitutions from the sub-experiment (if any) applyValueGettersToContainer(config) control = { # The environment that the current model is being run in "environment": 'nupic', # Input stream specification per py/nupic/frameworks/opf/jsonschema/stream_def.json. # 'dataset' : { u'info': u'test_hotgym', u'streams': [ { u'columns': [u'*'], u'info': u'test data', u'source': u'file://swarming/test_data.csv'}], u'version': 1}, # Iteration count: maximum number of iterations. Each iteration corresponds # to one record from the (possibly aggregated) dataset. The task is # terminated when either number of iterations reaches iterationCount or # all records in the (possibly aggregated) database have been processed, # whichever occurs first. # # iterationCount of -1 = iterate over the entire dataset 'iterationCount' : -1, # A dictionary containing all the supplementary parameters for inference "inferenceArgs":{u'predictedField': u'consumption', u'predictionSteps': [1]}, # Metrics: A list of MetricSpecs that instantiate the metrics that are # computed for this experiment 'metrics':[ MetricSpec(field=u'consumption', metric='multiStep', inferenceElement='multiStepBestPredictions', params={'window': 1000, 'steps': [1], 'errorMetric': 'altMAPE'}), ], # Logged Metrics: A sequence of regular expressions that specify which of # the metrics from the Inference Specifications section MUST be logged for # every prediction. The regex's correspond to the automatically generated # metric labels. This is similar to the way the optimization metric is # specified in permutations.py. 'loggedMetrics': ['.*'], } descriptionInterface = ExperimentDescriptionAPI(modelConfig=config, control=control)

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26,213

permutations.py

numenta_nupic-legacy/tests/swarming/nupic/swarming/experiments/legacy_cla_multistep/permutations.py

# ---------------------------------------------------------------------- # Numenta Platform for Intelligent Computing (NuPIC) # Copyright (C) 2013, Numenta, Inc. Unless you have an agreement # with Numenta, Inc., for a separate license for this software code, the # following terms and conditions apply: # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU Affero Public License version 3 as # published by the Free Software Foundation. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. # See the GNU Affero Public License for more details. # # You should have received a copy of the GNU Affero Public License # along with this program. If not, see http://www.gnu.org/licenses. # # http://numenta.org/licenses/ # ---------------------------------------------------------------------- """ Template file used by ExpGenerator to generate the actual permutations.py file by replacing $XXXXXXXX tokens with desired values. This permutations.py file was generated by: '/Users/ronmarianetti/nupic/eng/lib/python2.6/site-packages/nupicengine/frameworks/opf/expGenerator/ExpGenerator.pyc' """ import os from nupic.swarming.permutation_helpers import * # The name of the field being predicted. Any allowed permutation MUST contain # the prediction field. # (generated from PREDICTION_FIELD) predictedField = 'consumption' permutations = { 'aggregationInfo': { 'days': 0, 'fields': [ (u'timestamp', 'first'), (u'gym', 'first'), (u'consumption', 'sum')], 'hours': 1, 'microseconds': 0, 'milliseconds': 0, 'minutes': 0, 'months': 0, 'seconds': 0, 'weeks': 0, 'years': 0}, 'modelParams': { 'inferenceType': PermuteChoices(['NontemporalMultiStep', 'TemporalMultiStep']), 'sensorParams': { 'encoders': { u'timestamp_timeOfDay': PermuteEncoder( fieldName='timestamp', encoderClass='DateEncoder.timeOfDay', w=21, radius=PermuteFloat(0.5, 12)), u'timestamp_dayOfWeek': PermuteEncoder( fieldName='timestamp', encoderClass='DateEncoder.dayOfWeek', w=21, radius=PermuteFloat(1, 6)), u'timestamp_weekend': PermuteEncoder( fieldName='timestamp', encoderClass='DateEncoder.weekend', w=21, radius=PermuteChoices([1])), u'consumption': PermuteEncoder( fieldName='consumption', encoderClass='AdaptiveScalarEncoder', w=21, n=PermuteInt(28, 521), clipInput=True), }, }, 'spParams': { 'synPermInactiveDec': PermuteFloat(0.005, 0.1), }, 'tmParams': { 'activationThreshold': PermuteInt(12, 16), 'minThreshold': PermuteInt(9, 12), 'pamLength': PermuteInt(1, 5), }, 'clParams': { 'alpha': PermuteFloat(0.0001, 0.1), }, } } # Fields selected for final hypersearch report; # NOTE: These values are used as regular expressions by RunPermutations.py's # report generator # (fieldname values generated from PERM_PREDICTED_FIELD_NAME) report = [ '.*consumption.*', ] # Permutation optimization setting: either minimize or maximize metric # used by RunPermutations. # NOTE: The value is used as a regular expressions by RunPermutations.py's # report generator # (generated from minimize = "multiStepBestPredictions:multiStep:errorMetric='altMAPE':steps=\[1\]:window=1000:field=consumption") minimize = "multiStepBestPredictions:multiStep:errorMetric='altMAPE':steps=\[1\]:window=1000:field=consumption" minParticlesPerSwarm = None def permutationFilter(perm): """ This function can be used to selectively filter out specific permutation combinations. It is called by RunPermutations for every possible permutation of the variables in the permutations dict. It should return True for valid a combination of permutation values and False for an invalid one. Parameters: --------------------------------------------------------- perm: dict of one possible combination of name:value pairs chosen from permutations. """ # An example of how to use this #if perm['__consumption_encoder']['maxval'] > 300: # return False; # return True

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numenta/nupic-legacy

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9/5/2024, 5:13:42 PM (Europe/Amsterdam)

26,214

description.py

numenta_nupic-legacy/tests/swarming/nupic/swarming/experiments/simple_cla_multistep/description.py

# ---------------------------------------------------------------------- # Numenta Platform for Intelligent Computing (NuPIC) # Copyright (C) 2013, Numenta, Inc. Unless you have an agreement # with Numenta, Inc., for a separate license for this software code, the # following terms and conditions apply: # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU Affero Public License version 3 as # published by the Free Software Foundation. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. # See the GNU Affero Public License for more details. # # You should have received a copy of the GNU Affero Public License # along with this program. If not, see http://www.gnu.org/licenses. # # http://numenta.org/licenses/ # ---------------------------------------------------------------------- """ Template file used by the OPF Experiment Generator to generate the actual description.py file by replacing $XXXXXXXX tokens with desired values. This description.py file was generated by: '/Users/ronmarianetti/nupic/eng/lib/python2.6/site-packages/nupicengine/frameworks/opf/expGenerator/ExpGenerator.pyc' """ from nupic.frameworks.opf.exp_description_api import ExperimentDescriptionAPI from nupic.frameworks.opf.exp_description_helpers import ( updateConfigFromSubConfig, applyValueGettersToContainer ) from nupic.frameworks.opf.htm_prediction_model_callbacks import * from nupic.frameworks.opf.metrics import MetricSpec from nupic.frameworks.opf.opf_utils import (InferenceType, InferenceElement) from nupic.support import aggregationDivide from nupic.frameworks.opf.opf_task_driver import ( IterationPhaseSpecLearnOnly, IterationPhaseSpecInferOnly, IterationPhaseSpecLearnAndInfer) # Model Configuration Dictionary: # # Define the model parameters and adjust for any modifications if imported # from a sub-experiment. # # These fields might be modified by a sub-experiment; this dict is passed # between the sub-experiment and base experiment # # config = { # Type of model that the rest of these parameters apply to. 'model': "HTMPrediction", # Version that specifies the format of the config. 'version': 1, # Intermediate variables used to compute fields in modelParams and also # referenced from the control section. 'aggregationInfo': { 'days': 0, 'fields': [ (u'timestamp', 'first'), (u'consumption', 'sum'), ], 'hours': 0, 'microseconds': 0, 'milliseconds': 0, 'minutes': 0, 'months': 0, 'seconds': 0, 'weeks': 0, 'years': 0}, 'predictAheadTime': None, # Model parameter dictionary. 'modelParams': { # The type of inference that this model will perform 'inferenceType': 'TemporalMultiStep', 'sensorParams': { # Sensor diagnostic output verbosity control; # if > 0: sensor region will print out on screen what it's sensing # at each step 0: silent; >=1: some info; >=2: more info; # >=3: even more info (see compute() in py/regions/RecordSensor.py) 'verbosity' : 0, # Example: # 'encoders': {'field1': {'fieldname': 'field1', 'n':100, # 'name': 'field1', 'type': 'AdaptiveScalarEncoder', # 'w': 21}} # 'encoders': { 'consumption': { 'clipInput': True, 'fieldname': u'consumption', 'n': 100, 'name': u'consumption', 'type': 'AdaptiveScalarEncoder', 'w': 21}, 'timestamp_timeOfDay': { 'fieldname': u'timestamp', 'name': u'timestamp_timeOfDay', 'type': 'DateEncoder', 'timeOfDay': (21, 1)}, 'timestamp_dayOfWeek': { 'fieldname': u'timestamp', 'name': u'timestamp_dayOfWeek', 'type': 'DateEncoder', 'dayOfWeek': (21, 1)}, '_classifierInput': { 'name': u'_classifierInput', 'fieldname': u'consumption', 'classifierOnly': True, 'type': 'AdaptiveScalarEncoder', 'clipInput': True, 'n': 100, 'w': 21}, }, # A dictionary specifying the period for automatically-generated # resets from a RecordSensor; # # None = disable automatically-generated resets (also disabled if # all of the specified values evaluate to 0). # Valid keys is the desired combination of the following: # days, hours, minutes, seconds, milliseconds, microseconds, weeks # # Example for 1.5 days: sensorAutoReset = dict(days=1,hours=12), # # (value generated from SENSOR_AUTO_RESET) 'sensorAutoReset' : { u'days': 0, u'hours': 0}, }, 'spEnable': True, 'spParams': { # SP diagnostic output verbosity control; # 0: silent; >=1: some info; >=2: more info; 'spVerbosity' : 0, 'globalInhibition': 1, # Number of cell columns in the cortical region (same number for # SP and TM) # (see also tpNCellsPerCol) 'columnCount': 2048, 'inputWidth': 0, # SP inhibition control (absolute value); # Maximum number of active columns in the SP region's output (when # there are more, the weaker ones are suppressed) 'numActiveColumnsPerInhArea': 40, 'seed': 1956, # potentialPct # What percent of the columns's receptive field is available # for potential synapses. At initialization time, we will # choose potentialPct * (2*potentialRadius+1)^2 'potentialPct': 0.5, # The default connected threshold. Any synapse whose # permanence value is above the connected threshold is # a "connected synapse", meaning it can contribute to the # cell's firing. Typical value is 0.10. Cells whose activity # level before inhibition falls below minDutyCycleBeforeInh # will have their own internal synPermConnectedCell # threshold set below this default value. # (This concept applies to both SP and TM and so 'cells' # is correct here as opposed to 'columns') 'synPermConnected': 0.1, 'synPermActiveInc': 0.1, 'synPermInactiveDec': 0.01, }, # Controls whether TM is enabled or disabled; # TM is necessary for making temporal predictions, such as predicting # the next inputs. Without TM, the model is only capable of # reconstructing missing sensor inputs (via SP). 'tmEnable' : True, 'tmParams': { # TM diagnostic output verbosity control; # 0: silent; [1..6]: increasing levels of verbosity # (see verbosity in nupic/trunk/py/nupic/research/backtracking_tm.py and backtracking_tm_cpp.py) 'verbosity': 0, # Number of cell columns in the cortical region (same number for # SP and TM) # (see also tpNCellsPerCol) 'columnCount': 2048, # The number of cells (i.e., states), allocated per column. 'cellsPerColumn': 32, 'inputWidth': 2048, 'seed': 1960, # Temporal Pooler implementation selector (see _getTPClass in # CLARegion.py). 'temporalImp': 'cpp', # New Synapse formation count # NOTE: If None, use spNumActivePerInhArea # # TODO: need better explanation 'newSynapseCount': 20, # Maximum number of synapses per segment # > 0 for fixed-size CLA # -1 for non-fixed-size CLA # # TODO: for Ron: once the appropriate value is placed in TM # constructor, see if we should eliminate this parameter from # description.py. 'maxSynapsesPerSegment': 32, # Maximum number of segments per cell # > 0 for fixed-size CLA # -1 for non-fixed-size CLA # # TODO: for Ron: once the appropriate value is placed in TM # constructor, see if we should eliminate this parameter from # description.py. 'maxSegmentsPerCell': 128, # Initial Permanence # TODO: need better explanation 'initialPerm': 0.21, # Permanence Increment 'permanenceInc': 0.1, # Permanence Decrement # If set to None, will automatically default to tpPermanenceInc # value. 'permanenceDec' : 0.1, 'globalDecay': 0.0, 'maxAge': 0, # Minimum number of active synapses for a segment to be considered # during search for the best-matching segments. # None=use default # Replaces: tpMinThreshold 'minThreshold': 12, # Segment activation threshold. # A segment is active if it has >= tpSegmentActivationThreshold # connected synapses that are active due to infActiveState # None=use default # Replaces: tpActivationThreshold 'activationThreshold': 16, 'outputType': 'normal', # "Pay Attention Mode" length. This tells the TM how many new # elements to append to the end of a learned sequence at a time. # Smaller values are better for datasets with short sequences, # higher values are better for datasets with long sequences. 'pamLength': 1, }, 'clParams': { 'regionName' : 'SDRClassifierRegion', # Classifier diagnostic output verbosity control; # 0: silent; [1..6]: increasing levels of verbosity 'verbosity' : 0, # This controls how fast the classifier learns/forgets. Higher values # make it adapt faster and forget older patterns faster. 'alpha': 0.001, # This is set after the call to updateConfigFromSubConfig and is # computed from the aggregationInfo and predictAheadTime. 'steps': '1', }, 'anomalyParams': { u'anomalyCacheRecords': None, u'autoDetectThreshold': None, u'autoDetectWaitRecords': None}, 'trainSPNetOnlyIfRequested': False, }, } # end of config dictionary # Adjust base config dictionary for any modifications if imported from a # sub-experiment updateConfigFromSubConfig(config) # Compute predictionSteps based on the predictAheadTime and the aggregation # period, which may be permuted over. if config['predictAheadTime'] is not None: predictionSteps = int(round(aggregationDivide( config['predictAheadTime'], config['aggregationInfo']))) assert (predictionSteps >= 1) config['modelParams']['clParams']['steps'] = str(predictionSteps) # Adjust config by applying ValueGetterBase-derived # futures. NOTE: this MUST be called after updateConfigFromSubConfig() in order # to support value-getter-based substitutions from the sub-experiment (if any) applyValueGettersToContainer(config) control = { # The environment that the current model is being run in "environment": 'nupic', # Input stream specification per py/nupic/frameworks/opf/jsonschema/stream_def.json. # 'dataset' : { u'info': u'test_hotgym', u'streams': [ { u'columns': [u'*'], u'info': u'test data', u'source': u'file://swarming/test_data.csv'}], u'version': 1}, # Iteration count: maximum number of iterations. Each iteration corresponds # to one record from the (possibly aggregated) dataset. The task is # terminated when either number of iterations reaches iterationCount or # all records in the (possibly aggregated) database have been processed, # whichever occurs first. # # iterationCount of -1 = iterate over the entire dataset 'iterationCount' : -1, # A dictionary containing all the supplementary parameters for inference "inferenceArgs":{u'predictedField': u'consumption', u'predictionSteps': [1]}, # Metrics: A list of MetricSpecs that instantiate the metrics that are # computed for this experiment 'metrics':[ MetricSpec(field=u'consumption', metric='multiStep', inferenceElement='multiStepBestPredictions', params={'window': 1000, 'steps': [1], 'errorMetric': 'altMAPE'}), ], # Logged Metrics: A sequence of regular expressions that specify which of # the metrics from the Inference Specifications section MUST be logged for # every prediction. The regex's correspond to the automatically generated # metric labels. This is similar to the way the optimization metric is # specified in permutations.py. 'loggedMetrics': ['.*'], } descriptionInterface = ExperimentDescriptionAPI(modelConfig=config, control=control)

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26,215

permutations.py

numenta_nupic-legacy/tests/swarming/nupic/swarming/experiments/simple_cla_multistep/permutations.py

# ---------------------------------------------------------------------- # Numenta Platform for Intelligent Computing (NuPIC) # Copyright (C) 2013, Numenta, Inc. Unless you have an agreement # with Numenta, Inc., for a separate license for this software code, the # following terms and conditions apply: # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU Affero Public License version 3 as # published by the Free Software Foundation. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. # See the GNU Affero Public License for more details. # # You should have received a copy of the GNU Affero Public License # along with this program. If not, see http://www.gnu.org/licenses. # # http://numenta.org/licenses/ # ---------------------------------------------------------------------- """ Template file used by ExpGenerator to generate the actual permutations.py file by replacing $XXXXXXXX tokens with desired values. This permutations.py file was generated by: '/Users/ronmarianetti/nupic/eng/lib/python2.6/site-packages/nupicengine/frameworks/opf/expGenerator/ExpGenerator.pyc' """ import os from nupic.swarming.permutation_helpers import * # The name of the field being predicted. Any allowed permutation MUST contain # the prediction field. # (generated from PREDICTION_FIELD) predictedField = 'consumption' permutations = { 'aggregationInfo': { 'days': 0, 'fields': [ (u'timestamp', 'first'), (u'gym', 'first'), (u'consumption', 'sum')], 'hours': 1, 'microseconds': 0, 'milliseconds': 0, 'minutes': 0, 'months': 0, 'seconds': 0, 'weeks': 0, 'years': 0}, 'modelParams': { 'inferenceType': PermuteChoices(['NontemporalMultiStep', 'TemporalMultiStep']), 'sensorParams': { 'encoders': { u'timestamp_timeOfDay': PermuteEncoder( fieldName='timestamp', encoderClass='DateEncoder.timeOfDay', w=21, radius=PermuteFloat(0.5, 12)), u'timestamp_dayOfWeek': PermuteEncoder( fieldName='timestamp', encoderClass='DateEncoder.dayOfWeek', w=21, radius=PermuteFloat(1, 6)), u'timestamp_weekend': PermuteEncoder( fieldName='timestamp', encoderClass='DateEncoder.weekend', w=21, radius=PermuteChoices([1])), u'consumption': PermuteEncoder( fieldName='consumption', encoderClass='AdaptiveScalarEncoder', w=21, n=PermuteInt(28, 521), clipInput=True), u'_classifierInput': dict( fieldname='consumption', classifierOnly=True, type='AdaptiveScalarEncoder', w=21, n=PermuteInt(28, 521), clipInput=True), }, }, 'spParams': { 'synPermInactiveDec': PermuteFloat(0.005, 0.1), }, 'tmParams': { 'activationThreshold': PermuteInt(12, 16), 'minThreshold': PermuteInt(9, 12), 'pamLength': PermuteInt(1, 5), }, 'clParams': { 'alpha': PermuteFloat(0.0001, 0.1), }, } } # Fields selected for final hypersearch report; # NOTE: These values are used as regular expressions by RunPermutations.py's # report generator # (fieldname values generated from PERM_PREDICTED_FIELD_NAME) report = [ '.*consumption.*', ] # Permutation optimization setting: either minimize or maximize metric # used by RunPermutations. # NOTE: The value is used as a regular expressions by RunPermutations.py's # report generator # (generated from minimize = "multiStepBestPredictions:multiStep:errorMetric='altMAPE':steps=\[1\]:window=1000:field=consumption") minimize = "multiStepBestPredictions:multiStep:errorMetric='altMAPE':steps=\[1\]:window=1000:field=consumption" minParticlesPerSwarm = None def permutationFilter(perm): """ This function can be used to selectively filter out specific permutation combinations. It is called by RunPermutations for every possible permutation of the variables in the permutations dict. It should return True for valid a combination of permutation values and False for an invalid one. Parameters: --------------------------------------------------------- perm: dict of one possible combination of name:value pairs chosen from permutations. """ # An example of how to use this #if perm['__consumption_encoder']['maxval'] > 300: # return False; # return True

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numenta/nupic-legacy

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26,216

description.py

numenta_nupic-legacy/tests/swarming/nupic/swarming/experiments/simpleV2/description.py

# ---------------------------------------------------------------------- # Numenta Platform for Intelligent Computing (NuPIC) # Copyright (C) 2013, Numenta, Inc. Unless you have an agreement # with Numenta, Inc., for a separate license for this software code, the # following terms and conditions apply: # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU Affero Public License version 3 as # published by the Free Software Foundation. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. # See the GNU Affero Public License for more details. # # You should have received a copy of the GNU Affero Public License # along with this program. If not, see http://www.gnu.org/licenses. # # http://numenta.org/licenses/ # ---------------------------------------------------------------------- """ Template file used by the OPF Experiment Generator to generate the actual description.py file by replacing $XXXXXXXX tokens with desired values. This description.py file was generated by: '/Users/ronmarianetti/nupic/eng/lib/python2.6/site-packages/nupic/frameworks/opf/expGenerator/experiment_generator.py' """ from nupic.frameworks.opf.exp_description_api import ExperimentDescriptionAPI from nupic.frameworks.opf.exp_description_helpers import ( updateConfigFromSubConfig, applyValueGettersToContainer, DeferredDictLookup) from nupic.frameworks.opf.htm_prediction_model_callbacks import * from nupic.frameworks.opf.metrics import MetricSpec from nupic.frameworks.opf.opf_utils import (InferenceType, InferenceElement) from nupic.support import aggregationDivide from nupic.frameworks.opf.opf_task_driver import ( IterationPhaseSpecLearnOnly, IterationPhaseSpecInferOnly, IterationPhaseSpecLearnAndInfer) # Model Configuration Dictionary: # # Define the model parameters and adjust for any modifications if imported # from a sub-experiment. # # These fields might be modified by a sub-experiment; this dict is passed # between the sub-experiment and base experiment # # # NOTE: Use of DEFERRED VALUE-GETTERs: dictionary fields and list elements # within the config dictionary may be assigned futures derived from the # ValueGetterBase class, such as DeferredDictLookup. # This facility is particularly handy for enabling substitution of values in # the config dictionary from other values in the config dictionary, which is # needed by permutation.py-based experiments. These values will be resolved # during the call to applyValueGettersToContainer(), # which we call after the base experiment's config dictionary is updated from # the sub-experiment. See ValueGetterBase and # DeferredDictLookup for more details about value-getters. # # For each custom encoder parameter to be exposed to the sub-experiment/ # permutation overrides, define a variable in this section, using key names # beginning with a single underscore character to avoid collisions with # pre-defined keys (e.g., _dsEncoderFieldName2_N). # # Example: # config = dict( # _dsEncoderFieldName2_N = 70, # _dsEncoderFieldName2_W = 5, # dsEncoderSchema = [ # base=dict( # fieldname='Name2', type='ScalarEncoder', # name='Name2', minval=0, maxval=270, clipInput=True, # n=DeferredDictLookup('_dsEncoderFieldName2_N'), # w=DeferredDictLookup('_dsEncoderFieldName2_W')), # ], # ) # updateConfigFromSubConfig(config) # applyValueGettersToContainer(config) config = { # Type of model that the rest of these parameters apply to. 'model': "HTMPrediction", # Version that specifies the format of the config. 'version': 1, # Intermediate variables used to compute fields in modelParams and also # referenced from the control section. 'aggregationInfo': { 'days': 0, 'fields': [ (u'timestamp', 'first'), (u'gym', 'first'), (u'consumption', 'mean'), (u'address', 'first')], 'hours': 0, 'microseconds': 0, 'milliseconds': 0, 'minutes': 0, 'months': 0, 'seconds': 0, 'weeks': 0, 'years': 0}, 'predictAheadTime': None, # Model parameter dictionary. 'modelParams': { # The type of inference that this model will perform 'inferenceType': 'TemporalNextStep', 'sensorParams': { # Sensor diagnostic output verbosity control; # if > 0: sensor region will print out on screen what it's sensing # at each step 0: silent; >=1: some info; >=2: more info; # >=3: even more info (see compute() in py/regions/RecordSensor.py) 'verbosity' : 0, # Example: # dsEncoderSchema = [ # DeferredDictLookup('__field_name_encoder'), # ], # # (value generated from DS_ENCODER_SCHEMA) 'encoders': { 'address': { 'fieldname': u'address', 'n': 300, 'name': u'address', 'type': 'SDRCategoryEncoder', 'w': 21}, 'consumption': { 'clipInput': True, 'fieldname': u'consumption', 'maxval': 200, 'minval': 0, 'n': 1500, 'name': u'consumption', 'type': 'ScalarEncoder', 'w': 21}, 'gym': { 'fieldname': u'gym', 'n': 300, 'name': u'gym', 'type': 'SDRCategoryEncoder', 'w': 21}, 'timestamp_dayOfWeek': { 'dayOfWeek': (7, 3), 'fieldname': u'timestamp', 'name': u'timestamp_dayOfWeek', 'type': 'DateEncoder'}, 'timestamp_timeOfDay': { 'fieldname': u'timestamp', 'name': u'timestamp_timeOfDay', 'timeOfDay': (7, 8), 'type': 'DateEncoder'}}, # A dictionary specifying the period for automatically-generated # resets from a RecordSensor; # # None = disable automatically-generated resets (also disabled if # all of the specified values evaluate to 0). # Valid keys is the desired combination of the following: # days, hours, minutes, seconds, milliseconds, microseconds, weeks # # Example for 1.5 days: sensorAutoReset = dict(days=1,hours=12), # # (value generated from SENSOR_AUTO_RESET) 'sensorAutoReset' : None, }, 'spEnable': True, 'spParams': { # SP diagnostic output verbosity control; # 0: silent; >=1: some info; >=2: more info; 'spVerbosity' : 0, 'globalInhibition': 1, # Number of cell columns in the cortical region (same number for # SP and TM) # (see also tpNCellsPerCol) 'columnCount': 2048, 'inputWidth': 0, # SP inhibition control (absolute value); # Maximum number of active columns in the SP region's output (when # there are more, the weaker ones are suppressed) 'numActiveColumnsPerInhArea': 40, 'seed': 1956, # potentialPct # What percent of the columns's receptive field is available # for potential synapses. At initialization time, we will # choose potentialPct * (2*potentialRadius+1)^2 'potentialPct': 0.5, # The default connected threshold. Any synapse whose # permanence value is above the connected threshold is # a "connected synapse", meaning it can contribute to the # cell's firing. Typical value is 0.10. Cells whose activity # level before inhibition falls below minDutyCycleBeforeInh # will have their own internal synPermConnectedCell # threshold set below this default value. # (This concept applies to both SP and TM and so 'cells' # is correct here as opposed to 'columns') 'synPermConnected': 0.1, 'synPermActiveInc': 0.1, 'synPermInactiveDec': 0.01, }, # Controls whether TM is enabled or disabled; # TM is necessary for making temporal predictions, such as predicting # the next inputs. Without TM, the model is only capable of # reconstructing missing sensor inputs (via SP). 'tmEnable' : True, 'tmParams': { # TM diagnostic output verbosity control; # 0: silent; [1..6]: increasing levels of verbosity # (see verbosity in nupic/trunk/py/nupic/research/backtracking_tm.py and backtracking_tm_cpp.py) 'verbosity': 0, # Number of cell columns in the cortical region (same number for # SP and TM) # (see also tpNCellsPerCol) 'columnCount': 2048, # The number of cells (i.e., states), allocated per column. 'cellsPerColumn': 32, 'inputWidth': 2048, 'seed': 1960, # Temporal Pooler implementation selector (see _getTPClass in # CLARegion.py). 'temporalImp': 'cpp', # New Synapse formation count # NOTE: If None, use spNumActivePerInhArea # # TODO: need better explanation 'newSynapseCount': 15, # Maximum number of synapses per segment # > 0 for fixed-size CLA # -1 for non-fixed-size CLA # # TODO: for Ron: once the appropriate value is placed in TM # constructor, see if we should eliminate this parameter from # description.py. 'maxSynapsesPerSegment': 32, # Maximum number of segments per cell # > 0 for fixed-size CLA # -1 for non-fixed-size CLA # # TODO: for Ron: once the appropriate value is placed in TM # constructor, see if we should eliminate this parameter from # description.py. 'maxSegmentsPerCell': 128, # Initial Permanence # TODO: need better explanation 'initialPerm': 0.21, # Permanence Increment 'permanenceInc': 0.1, # Permanence Decrement # If set to None, will automatically default to tpPermanenceInc # value. 'permanenceDec' : 0.1, 'globalDecay': 0.0, 'maxAge': 0, # Minimum number of active synapses for a segment to be considered # during search for the best-matching segments. # None=use default # Replaces: tpMinThreshold 'minThreshold': 12, # Segment activation threshold. # A segment is active if it has >= tpSegmentActivationThreshold # connected synapses that are active due to infActiveState # None=use default # Replaces: tpActivationThreshold 'activationThreshold': 16, 'outputType': 'normal', # "Pay Attention Mode" length. This tells the TM how many new # elements to append to the end of a learned sequence at a time. # Smaller values are better for datasets with short sequences, # higher values are better for datasets with long sequences. 'pamLength': 1, }, 'clParams': { 'regionName' : 'SDRClassifierRegion', # Classifier diagnostic output verbosity control; # 0: silent; [1..6]: increasing levels of verbosity 'verbosity' : 0, # This controls how fast the classifier learns/forgets. Higher values # make it adapt faster and forget older patterns faster. 'alpha': 0.001, # This is set after the call to updateConfigFromSubConfig and is # computed from the aggregationInfo and predictAheadTime. 'steps': '1', }, 'trainSPNetOnlyIfRequested': False, }, } # end of config dictionary # Adjust base config dictionary for any modifications if imported from a # sub-experiment updateConfigFromSubConfig(config) # Compute predictionSteps based on the predictAheadTime and the aggregation # period, which may be permuted over. if config['predictAheadTime'] is not None: predictionSteps = int(round(aggregationDivide( config['predictAheadTime'], config['aggregationInfo']))) assert (predictionSteps >= 1) config['modelParams']['clParams']['steps'] = str(predictionSteps) # Adjust config by applying ValueGetterBase-derived # futures. NOTE: this MUST be called after updateConfigFromSubConfig() in order # to support value-getter-based substitutions from the sub-experiment (if any) applyValueGettersToContainer(config) control = { # The environment that the current model is being run in "environment": 'nupic', # Input stream specification per py/nupicengine/cluster/database/StreamDef.json. # 'dataset' : { u'info': u'test_NoProviders', u'streams': [ { u'columns': [u'*'], u'info': u'test data', u'source': u'file://swarming/test_data.csv'}], u'version': 1}, # Iteration count: maximum number of iterations. Each iteration corresponds # to one record from the (possibly aggregated) dataset. The task is # terminated when either number of iterations reaches iterationCount or # all records in the (possibly aggregated) database have been processed, # whichever occurs first. # # iterationCount of -1 = iterate over the entire dataset #'iterationCount' : ITERATION_COUNT, # Metrics: A list of MetricSpecs that instantiate the metrics that are # computed for this experiment 'metrics':[ MetricSpec(field=u'consumption', inferenceElement=InferenceElement.prediction, metric='rmse'), ], # Logged Metrics: A sequence of regular expressions that specify which of # the metrics from the Inference Specifications section MUST be logged for # every prediction. The regex's correspond to the automatically generated # metric labels. This is similar to the way the optimization metric is # specified in permutations.py. 'loggedMetrics': ['.*nupicScore.*'], } descriptionInterface = ExperimentDescriptionAPI(modelConfig=config, control=control)

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26,217

permutations.py

numenta_nupic-legacy/tests/swarming/nupic/swarming/experiments/simpleV2/permutations.py

# ---------------------------------------------------------------------- # Numenta Platform for Intelligent Computing (NuPIC) # Copyright (C) 2013, Numenta, Inc. Unless you have an agreement # with Numenta, Inc., for a separate license for this software code, the # following terms and conditions apply: # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU Affero Public License version 3 as # published by the Free Software Foundation. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. # See the GNU Affero Public License for more details. # # You should have received a copy of the GNU Affero Public License # along with this program. If not, see http://www.gnu.org/licenses. # # http://numenta.org/licenses/ # ---------------------------------------------------------------------- """ Template file used by ExpGenerator to generate the actual permutations.py file by replacing $XXXXXXXX tokens with desired values. This permutations.py file was generated by: '/Users/ronmarianetti/nupic/eng/lib/python2.6/site-packages/nupic/frameworks/opf/expGenerator/experiment_generator.py' """ import os from nupic.swarming.permutation_helpers import * # The name of the field being predicted. Any allowed permutation MUST contain # the prediction field. # (generated from PREDICTION_FIELD) predictedField = 'consumption' permutations = { 'modelParams': { 'sensorParams': { 'encoders': { 'gym': PermuteEncoder(fieldName='gym', encoderClass='SDRCategoryEncoder', w=7, n=100), 'timestamp_dayOfWeek': PermuteEncoder(fieldName='timestamp', encoderClass='DateEncoder.dayOfWeek', radius=PermuteChoices([1, 3]), w=7), 'timestamp_timeOfDay': PermuteEncoder(fieldName='timestamp', encoderClass='DateEncoder.timeOfDay', radius=PermuteChoices([1, 8]), w=7), 'consumption': PermuteEncoder(fieldName='consumption', encoderClass='ScalarEncoder', maxval=PermuteInt(100, 300, 25), n=PermuteInt(13, 500, 20), w=7, minval=0), 'address': PermuteEncoder(fieldName='address', encoderClass='SDRCategoryEncoder', w=7, n=100), }, }, 'tmParams': { 'minThreshold': PermuteInt(9, 12), 'activationThreshold': PermuteInt(12, 16), }, } } # Fields selected for final hypersearch report; # NOTE: These values are used as regular expressions by RunPermutations.py's # report generator # (fieldname values generated from PERM_PREDICTED_FIELD_NAME) report = [ '.*consumption.*', ] # Permutation optimization setting: either minimize or maximize metric # used by RunPermutations. # NOTE: The value is used as a regular expressions by RunPermutations.py's # report generator # (generated from minimize = 'prediction:rmse:field=consumption') minimize = 'prediction:rmse:field=consumption' def dummyModelParams(perm): """ This function can be used for Hypersearch algorithm development. When present, Hypersearch doesn't actually run the CLA model in the OPF, but instead runs a dummy model. This function returns the dummy model params that will be used. See the OPFDummyModelRunner class source code (in nupic.swarming.ModelRunner) for a description of the schema for the dummy model params. """ errScore = 50 errScore += abs(perm['modelParams']['sensorParams']['encoders']\ ['consumption']['maxval'] - 250) errScore += abs(perm['modelParams']['sensorParams']['encoders']\ ['consumption']['n'] - 53) if perm['modelParams']['sensorParams']['encoders']['address'] is not None: errScore -= 20 if perm['modelParams']['sensorParams']['encoders']['gym'] is not None: errScore -= 10 # Make models that contain the __timestamp_timeOfDay encoder run a bit # slower so we can test that we successfully kill running models waitTime = None if eval(os.environ.get('NTA_TEST_variableWaits', 'False')): if perm['modelParams']['sensorParams']['encoders']\ ['timestamp_timeOfDay'] is not None: waitTime = 0.01 dummyModelParams = dict( metricValue = errScore, iterations = int(os.environ.get('NTA_TEST_numIterations', '1')), waitTime = waitTime, sysExitModelRange = os.environ.get('NTA_TEST_sysExitModelRange', None), errModelRange = os.environ.get('NTA_TEST_errModelRange', None), jobFailErr = bool(os.environ.get('NTA_TEST_jobFailErr', False)) ) return dummyModelParams def permutationFilter(perm): """ This function can be used to selectively filter out specific permutation combinations. It is called by RunPermutations for every possible permutation of the variables in the permutations dict. It should return True for valid a combination of permutation values and False for an invalid one. Parameters: --------------------------------------------------------- perm: dict of one possible combination of name:value pairs chosen from permutations. """ # An example of how to use this limit = int(os.environ.get('NTA_TEST_maxvalFilter', 300)) if perm['modelParams']['sensorParams']['encoders']['consumption']['maxval'] > limit: return False; return True

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26,218

description.py

numenta_nupic-legacy/tests/swarming/nupic/swarming/experiments/dummy_multi_v2/description.py

# ---------------------------------------------------------------------- # Numenta Platform for Intelligent Computing (NuPIC) # Copyright (C) 2013, Numenta, Inc. Unless you have an agreement # with Numenta, Inc., for a separate license for this software code, the # following terms and conditions apply: # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU Affero Public License version 3 as # published by the Free Software Foundation. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. # See the GNU Affero Public License for more details. # # You should have received a copy of the GNU Affero Public License # along with this program. If not, see http://www.gnu.org/licenses. # # http://numenta.org/licenses/ # ---------------------------------------------------------------------- """ Template file used by the OPF Experiment Generator to generate the actual description.py file by replacing $XXXXXXXX tokens with desired values. This description.py file was generated by: '/Users/ronmarianetti/nupic/eng/lib/python2.6/site-packages/nupic/frameworks/opf/expGenerator/experiment_generator.py' """ from nupic.frameworks.opf.exp_description_api import ExperimentDescriptionAPI from nupic.frameworks.opf.exp_description_helpers import ( updateConfigFromSubConfig, applyValueGettersToContainer, DeferredDictLookup) from nupic.frameworks.opf.htm_prediction_model_callbacks import * from nupic.frameworks.opf.metrics import MetricSpec from nupic.frameworks.opf.opf_utils import (InferenceType, InferenceElement) from nupic.support import aggregationDivide from nupic.frameworks.opf.opf_task_driver import ( IterationPhaseSpecLearnOnly, IterationPhaseSpecInferOnly, IterationPhaseSpecLearnAndInfer) # Model Configuration Dictionary: # # Define the model parameters and adjust for any modifications if imported # from a sub-experiment. # # These fields might be modified by a sub-experiment; this dict is passed # between the sub-experiment and base experiment # # # NOTE: Use of DEFERRED VALUE-GETTERs: dictionary fields and list elements # within the config dictionary may be assigned futures derived from the # ValueGetterBase class, such as DeferredDictLookup. # This facility is particularly handy for enabling substitution of values in # the config dictionary from other values in the config dictionary, which is # needed by permutation.py-based experiments. These values will be resolved # during the call to applyValueGettersToContainer(), # which we call after the base experiment's config dictionary is updated from # the sub-experiment. See ValueGetterBase and # DeferredDictLookup for more details about value-getters. # # For each custom encoder parameter to be exposed to the sub-experiment/ # permutation overrides, define a variable in this section, using key names # beginning with a single underscore character to avoid collisions with # pre-defined keys (e.g., _dsEncoderFieldName2_N). # # Example: # config = dict( # _dsEncoderFieldName2_N = 70, # _dsEncoderFieldName2_W = 5, # dsEncoderSchema = [ # base=dict( # fieldname='Name2', type='ScalarEncoder', # name='Name2', minval=0, maxval=270, clipInput=True, # n=DeferredDictLookup('_dsEncoderFieldName2_N'), # w=DeferredDictLookup('_dsEncoderFieldName2_W')), # ], # ) # updateConfigFromSubConfig(config) # applyValueGettersToContainer(config) config = { # Type of model that the rest of these parameters apply to. 'model': "HTMPrediction", # Version that specifies the format of the config. 'version': 1, # Intermediate variables used to compute fields in modelParams and also # referenced from the control section. 'aggregationInfo': { 'days': 0, 'fields': [ (u'timestamp', 'first'), (u'gym', 'first'), (u'consumption', 'mean'), (u'address', 'first')], 'hours': 0, 'microseconds': 0, 'milliseconds': 0, 'minutes': 0, 'months': 0, 'seconds': 0, 'weeks': 0, 'years': 0}, 'predictAheadTime': None, # Model parameter dictionary. 'modelParams': { # The type of inference that this model will perform 'inferenceType': 'TemporalNextStep', 'sensorParams': { # Sensor diagnostic output verbosity control; # if > 0: sensor region will print out on screen what it's sensing # at each step 0: silent; >=1: some info; >=2: more info; # >=3: even more info (see compute() in py/regions/RecordSensor.py) 'verbosity' : 0, # Example: # dsEncoderSchema = [ # DeferredDictLookup('__field_name_encoder'), # ], # # (value generated from DS_ENCODER_SCHEMA) 'encoders': { 'address': { 'fieldname': u'address', 'n': 300, 'name': u'address', 'type': 'SDRCategoryEncoder', 'w': 21}, 'consumption': { 'clipInput': True, 'fieldname': u'consumption', 'maxval': 200, 'minval': 0, 'n': 1500, 'name': u'consumption', 'type': 'ScalarEncoder', 'w': 21}, 'gym': { 'fieldname': u'gym', 'n': 300, 'name': u'gym', 'type': 'SDRCategoryEncoder', 'w': 21}, 'timestamp_dayOfWeek': { 'dayOfWeek': (7, 3), 'fieldname': u'timestamp', 'name': u'timestamp_dayOfWeek', 'type': 'DateEncoder'}, 'timestamp_timeOfDay': { 'fieldname': u'timestamp', 'name': u'timestamp_timeOfDay', 'timeOfDay': (7, 8), 'type': 'DateEncoder'}}, # A dictionary specifying the period for automatically-generated # resets from a RecordSensor; # # None = disable automatically-generated resets (also disabled if # all of the specified values evaluate to 0). # Valid keys is the desired combination of the following: # days, hours, minutes, seconds, milliseconds, microseconds, weeks # # Example for 1.5 days: sensorAutoReset = dict(days=1,hours=12), # # (value generated from SENSOR_AUTO_RESET) 'sensorAutoReset' : None, }, 'spEnable': True, 'spParams': { # SP diagnostic output verbosity control; # 0: silent; >=1: some info; >=2: more info; 'spVerbosity' : 0, 'globalInhibition': 1, # Number of cell columns in the cortical region (same number for # SP and TM) # (see also tpNCellsPerCol) 'columnCount': 2048, 'inputWidth': 0, # SP inhibition control (absolute value); # Maximum number of active columns in the SP region's output (when # there are more, the weaker ones are suppressed) 'numActiveColumnsPerInhArea': 40, 'seed': 1956, # potentialPct # What percent of the columns's receptive field is available # for potential synapses. At initialization time, we will # choose potentialPct * (2*potentialRadius+1)^2 'potentialPct': 0.5, # The default connected threshold. Any synapse whose # permanence value is above the connected threshold is # a "connected synapse", meaning it can contribute to the # cell's firing. Typical value is 0.10. Cells whose activity # level before inhibition falls below minDutyCycleBeforeInh # will have their own internal synPermConnectedCell # threshold set below this default value. # (This concept applies to both SP and TM and so 'cells' # is correct here as opposed to 'columns') 'synPermConnected': 0.1, 'synPermActiveInc': 0.1, 'synPermInactiveDec': 0.01, }, # Controls whether TM is enabled or disabled; # TM is necessary for making temporal predictions, such as predicting # the next inputs. Without TM, the model is only capable of # reconstructing missing sensor inputs (via SP). 'tmEnable' : True, 'tmParams': { # TM diagnostic output verbosity control; # 0: silent; [1..6]: increasing levels of verbosity # (see verbosity in nupic/trunk/py/nupic/research/backtracking_tm.py and backtracking_tm_cpp.py) 'verbosity': 0, # Number of cell columns in the cortical region (same number for # SP and TM) # (see also tpNCellsPerCol) 'columnCount': 2048, # The number of cells (i.e., states), allocated per column. 'cellsPerColumn': 32, 'inputWidth': 2048, 'seed': 1960, # Temporal Pooler implementation selector (see _getTPClass in # CLARegion.py). 'temporalImp': 'cpp', # New Synapse formation count # NOTE: If None, use spNumActivePerInhArea # # TODO: need better explanation 'newSynapseCount': 15, # Maximum number of synapses per segment # > 0 for fixed-size CLA # -1 for non-fixed-size CLA # # TODO: for Ron: once the appropriate value is placed in TM # constructor, see if we should eliminate this parameter from # description.py. 'maxSynapsesPerSegment': 32, # Maximum number of segments per cell # > 0 for fixed-size CLA # -1 for non-fixed-size CLA # # TODO: for Ron: once the appropriate value is placed in TM # constructor, see if we should eliminate this parameter from # description.py. 'maxSegmentsPerCell': 128, # Initial Permanence # TODO: need better explanation 'initialPerm': 0.21, # Permanence Increment 'permanenceInc': 0.1, # Permanence Decrement # If set to None, will automatically default to tpPermanenceInc # value. 'permanenceDec' : 0.1, 'globalDecay': 0.0, 'maxAge': 0, # Minimum number of active synapses for a segment to be considered # during search for the best-matching segments. # None=use default # Replaces: tpMinThreshold 'minThreshold': 12, # Segment activation threshold. # A segment is active if it has >= tpSegmentActivationThreshold # connected synapses that are active due to infActiveState # None=use default # Replaces: tpActivationThreshold 'activationThreshold': 16, 'outputType': 'normal', # "Pay Attention Mode" length. This tells the TM how many new # elements to append to the end of a learned sequence at a time. # Smaller values are better for datasets with short sequences, # higher values are better for datasets with long sequences. 'pamLength': 1, }, 'clParams': { 'regionName' : 'SDRClassifierRegion', # Classifier diagnostic output verbosity control; # 0: silent; [1..6]: increasing levels of verbosity 'verbosity' : 0, # This controls how fast the classifier learns/forgets. Higher values # make it adapt faster and forget older patterns faster. 'alpha': 0.001, # This is set after the call to updateConfigFromSubConfig and is # computed from the aggregationInfo and predictAheadTime. 'steps': '1', }, 'trainSPNetOnlyIfRequested': False, }, } # end of config dictionary # Adjust base config dictionary for any modifications if imported from a # sub-experiment updateConfigFromSubConfig(config) # Compute predictionSteps based on the predictAheadTime and the aggregation # period, which may be permuted over. if config['predictAheadTime'] is not None: predictionSteps = int(round(aggregationDivide( config['predictAheadTime'], config['aggregationInfo']))) assert (predictionSteps >= 1) config['modelParams']['clParams']['steps'] = str(predictionSteps) # Adjust config by applying ValueGetterBase-derived # futures. NOTE: this MUST be called after updateConfigFromSubConfig() in order # to support value-getter-based substitutions from the sub-experiment (if any) applyValueGettersToContainer(config) control = { # The environment that the current model is being run in "environment": 'nupic', # Input stream specification per py/nupicengine/cluster/database/StreamDef.json. # 'dataset' : {u'info': u'test_NoProviders', u'streams': [ { u'columns': [u'*'], u'info': u'test data', u'source': u'file://swarming/test_data.csv'}], u'version': 1}, # Iteration count: maximum number of iterations. Each iteration corresponds # to one record from the (possibly aggregated) dataset. The task is # terminated when either number of iterations reaches iterationCount or # all records in the (possibly aggregated) database have been processed, # whichever occurs first. # # iterationCount of -1 = iterate over the entire dataset #'iterationCount' : ITERATION_COUNT, # Metrics: A list of MetricSpecs that instantiate the metrics that are # computed for this experiment 'metrics':[ MetricSpec(field=u'consumption', inferenceElement=InferenceElement.prediction, metric='rmse'), ], # Logged Metrics: A sequence of regular expressions that specify which of # the metrics from the Inference Specifications section MUST be logged for # every prediction. The regex's correspond to the automatically generated # metric labels. This is similar to the way the optimization metric is # specified in permutations.py. 'loggedMetrics': [".*nupicScore.*"], } descriptionInterface = ExperimentDescriptionAPI(modelConfig=config, control=control)

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26,219

permutations.py

numenta_nupic-legacy/tests/swarming/nupic/swarming/experiments/dummy_multi_v2/permutations.py

# ---------------------------------------------------------------------- # Numenta Platform for Intelligent Computing (NuPIC) # Copyright (C) 2013, Numenta, Inc. Unless you have an agreement # with Numenta, Inc., for a separate license for this software code, the # following terms and conditions apply: # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU Affero Public License version 3 as # published by the Free Software Foundation. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. # See the GNU Affero Public License for more details. # # You should have received a copy of the GNU Affero Public License # along with this program. If not, see http://www.gnu.org/licenses. # # http://numenta.org/licenses/ # ---------------------------------------------------------------------- """ Template file used by ExpGenerator to generate the actual permutations.py file by replacing $XXXXXXXX tokens with desired values. This permutations.py file was generated by: '/Users/ronmarianetti/nupic/eng/lib/python2.6/site-packages/nupic/frameworks/opf/expGenerator/experiment_generator.py' """ import os from nupic.swarming.permutation_helpers import * # The name of the field being predicted. Any allowed permutation MUST contain # the prediction field. # (generated from PREDICTION_FIELD) predictedField = 'consumption' numModels = int(os.environ.get('NTA_TEST_max_num_models',10)) permutations = { '__model_num' : PermuteInt(0, numModels-1, 1), 'modelParams': { 'sensorParams': { 'encoders': { 'gym': PermuteEncoder(fieldName='gym', encoderClass='SDRCategoryEncoder', w=7, n=100), 'timestamp_dayOfWeek': PermuteEncoder(fieldName='timestamp', encoderClass='DateEncoder.dayOfWeek', radius=PermuteChoices([1, 3]), w=7), 'timestamp_timeOfDay': PermuteEncoder(fieldName='timestamp', encoderClass='DateEncoder.timeOfDay', radius=PermuteChoices([1, 8]), w=7), 'consumption': PermuteEncoder(fieldName='consumption', encoderClass='ScalarEncoder', maxval=PermuteInt(100, 300, 25), n=PermuteInt(13, 500, 20), w=7, minval=0), 'address': PermuteEncoder(fieldName='address', encoderClass='SDRCategoryEncoder', w=7, n=100), }, }, 'tmParams': { 'minThreshold': PermuteInt(9, 12), 'activationThreshold': PermuteInt(12, 16), }, } } # Fields selected for final hypersearch report; # NOTE: These values are used as regular expressions by RunPermutations.py's # report generator # (fieldname values generated from PERM_PREDICTED_FIELD_NAME) report = [ '.*consumption.*', ] # Permutation optimization setting: either minimize or maximize metric # used by RunPermutations. # NOTE: The value is used as a regular expressions by RunPermutations.py's # report generator # (generated from minimize = 'prediction:rmse:field=consumption') minimize = 'prediction:rmse:field=consumption' def permutationFilter(perm): """ This function can be used to selectively filter out specific permutation combinations. It is called by RunPermutations for every possible permutation of the variables in the permutations dict. It should return True for valid a combination of permutation values and False for an invalid one. Parameters: --------------------------------------------------------- perm: dict of one possible combination of name:value pairs chosen from permutations. """ # An example of how to use this limit = int(os.environ.get('NTA_TEST_maxvalFilter', 300)) if perm['modelParams']['sensorParams']['encoders']['consumption']['maxval'] > limit: return False; return True

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26,220

description.py

numenta_nupic-legacy/tests/swarming/nupic/swarming/experiments/field_contrib_temporal/description.py

# ---------------------------------------------------------------------- # Numenta Platform for Intelligent Computing (NuPIC) # Copyright (C) 2013, Numenta, Inc. Unless you have an agreement # with Numenta, Inc., for a separate license for this software code, the # following terms and conditions apply: # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU Affero Public License version 3 as # published by the Free Software Foundation. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. # See the GNU Affero Public License for more details. # # You should have received a copy of the GNU Affero Public License # along with this program. If not, see http://www.gnu.org/licenses. # # http://numenta.org/licenses/ # ---------------------------------------------------------------------- """ Template file used by the OPF Experiment Generator to generate the actual description.py file by replacing $XXXXXXXX tokens with desired values. This description.py file was generated by: '/Users/ronmarianetti/nupic/eng/lib/python2.6/site-packages/nupic/frameworks/opf/expGenerator/experiment_generator.py' """ from nupic.frameworks.opf.exp_description_api import ExperimentDescriptionAPI from nupic.frameworks.opf.exp_description_helpers import ( updateConfigFromSubConfig, applyValueGettersToContainer, DeferredDictLookup) from nupic.frameworks.opf.htm_prediction_model_callbacks import * from nupic.frameworks.opf.metrics import MetricSpec from nupic.frameworks.opf.opf_utils import (InferenceType, InferenceElement) from nupic.support import aggregationDivide from nupic.frameworks.opf.opf_task_driver import ( IterationPhaseSpecLearnOnly, IterationPhaseSpecInferOnly, IterationPhaseSpecLearnAndInfer) # Model Configuration Dictionary: # # Define the model parameters and adjust for any modifications if imported # from a sub-experiment. # # These fields might be modified by a sub-experiment; this dict is passed # between the sub-experiment and base experiment # # # NOTE: Use of DEFERRED VALUE-GETTERs: dictionary fields and list elements # within the config dictionary may be assigned futures derived from the # ValueGetterBase class, such as DeferredDictLookup. # This facility is particularly handy for enabling substitution of values in # the config dictionary from other values in the config dictionary, which is # needed by permutation.py-based experiments. These values will be resolved # during the call to applyValueGettersToContainer(), # which we call after the base experiment's config dictionary is updated from # the sub-experiment. See ValueGetterBase and # DeferredDictLookup for more details about value-getters. # # For each custom encoder parameter to be exposed to the sub-experiment/ # permutation overrides, define a variable in this section, using key names # beginning with a single underscore character to avoid collisions with # pre-defined keys (e.g., _dsEncoderFieldName2_N). # # Example: # config = dict( # _dsEncoderFieldName2_N = 70, # _dsEncoderFieldName2_W = 5, # dsEncoderSchema = [ # base=dict( # fieldname='Name2', type='ScalarEncoder', # name='Name2', minval=0, maxval=270, clipInput=True, # n=DeferredDictLookup('_dsEncoderFieldName2_N'), # w=DeferredDictLookup('_dsEncoderFieldName2_W')), # ], # ) # updateConfigFromSubConfig(config) # applyValueGettersToContainer(config) config = { # Type of model that the rest of these parameters apply to. 'model': "HTMPrediction", # Version that specifies the format of the config. 'version': 1, # Intermediate variables used to compute fields in modelParams and also # referenced from the control section. 'aggregationInfo': { 'days': 0, 'fields': [ (u'timestamp', 'first'), (u'gym', 'first'), (u'consumption', 'mean'), (u'address', 'first')], 'hours': 0, 'microseconds': 0, 'milliseconds': 0, 'minutes': 0, 'months': 0, 'seconds': 0, 'weeks': 0, 'years': 0}, 'predictAheadTime': None, # Model parameter dictionary. 'modelParams': { # The type of inference that this model will perform 'inferenceType': 'TemporalNextStep', 'sensorParams': { # Sensor diagnostic output verbosity control; # if > 0: sensor region will print out on screen what it's sensing # at each step 0: silent; >=1: some info; >=2: more info; # >=3: even more info (see compute() in py/regions/RecordSensor.py) 'verbosity' : 0, # Example: # dsEncoderSchema = [ # DeferredDictLookup('__field_name_encoder'), # ], # # (value generated from DS_ENCODER_SCHEMA) 'encoders': { 'address': { 'fieldname': u'address', 'n': 300, 'name': u'address', 'type': 'SDRCategoryEncoder', 'w': 21}, 'consumption': { 'clipInput': True, 'fieldname': u'consumption', 'maxval': 200, 'minval': 0, 'n': 1500, 'name': u'consumption', 'type': 'ScalarEncoder', 'w': 21}, 'gym': { 'fieldname': u'gym', 'n': 300, 'name': u'gym', 'type': 'SDRCategoryEncoder', 'w': 21}, 'timestamp_dayOfWeek': { 'dayOfWeek': (7, 3), 'fieldname': u'timestamp', 'name': u'timestamp_dayOfWeek', 'type': 'DateEncoder'}, 'timestamp_timeOfDay': { 'fieldname': u'timestamp', 'name': u'timestamp_timeOfDay', 'timeOfDay': (7, 8), 'type': 'DateEncoder'}}, # A dictionary specifying the period for automatically-generated # resets from a RecordSensor; # # None = disable automatically-generated resets (also disabled if # all of the specified values evaluate to 0). # Valid keys is the desired combination of the following: # days, hours, minutes, seconds, milliseconds, microseconds, weeks # # Example for 1.5 days: sensorAutoReset = dict(days=1,hours=12), # # (value generated from SENSOR_AUTO_RESET) 'sensorAutoReset' : None, }, 'spEnable': True, 'spParams': { # SP diagnostic output verbosity control; # 0: silent; >=1: some info; >=2: more info; 'spVerbosity' : 0, 'globalInhibition': 1, # Number of cell columns in the cortical region (same number for # SP and TM) # (see also tpNCellsPerCol) 'columnCount': 2048, 'inputWidth': 0, # SP inhibition control (absolute value); # Maximum number of active columns in the SP region's output (when # there are more, the weaker ones are suppressed) 'numActiveColumnsPerInhArea': 40, 'seed': 1956, # potentialPct # What percent of the columns's receptive field is available # for potential synapses. At initialization time, we will # choose potentialPct * (2*potentialRadius+1)^2 'potentialPct': 0.5, # The default connected threshold. Any synapse whose # permanence value is above the connected threshold is # a "connected synapse", meaning it can contribute to the # cell's firing. Typical value is 0.10. Cells whose activity # level before inhibition falls below minDutyCycleBeforeInh # will have their own internal synPermConnectedCell # threshold set below this default value. # (This concept applies to both SP and TM and so 'cells' # is correct here as opposed to 'columns') 'synPermConnected': 0.1, 'synPermActiveInc': 0.1, 'synPermInactiveDec': 0.01, }, # Controls whether TM is enabled or disabled; # TM is necessary for making temporal predictions, such as predicting # the next inputs. Without TM, the model is only capable of # reconstructing missing sensor inputs (via SP). 'tmEnable' : True, 'tmParams': { # TM diagnostic output verbosity control; # 0: silent; [1..6]: increasing levels of verbosity # (see verbosity in nupic/trunk/py/nupic/research/backtracking_tm.py and backtracking_tm_cpp.py) 'verbosity': 0, # Number of cell columns in the cortical region (same number for # SP and TM) # (see also tpNCellsPerCol) 'columnCount': 2048, # The number of cells (i.e., states), allocated per column. 'cellsPerColumn': 32, 'inputWidth': 2048, 'seed': 1960, # Temporal Pooler implementation selector (see _getTPClass in # CLARegion.py). 'temporalImp': 'cpp', # New Synapse formation count # NOTE: If None, use spNumActivePerInhArea # # TODO: need better explanation 'newSynapseCount': 15, # Maximum number of synapses per segment # > 0 for fixed-size CLA # -1 for non-fixed-size CLA # # TODO: for Ron: once the appropriate value is placed in TM # constructor, see if we should eliminate this parameter from # description.py. 'maxSynapsesPerSegment': 32, # Maximum number of segments per cell # > 0 for fixed-size CLA # -1 for non-fixed-size CLA # # TODO: for Ron: once the appropriate value is placed in TM # constructor, see if we should eliminate this parameter from # description.py. 'maxSegmentsPerCell': 128, # Initial Permanence # TODO: need better explanation 'initialPerm': 0.21, # Permanence Increment 'permanenceInc': 0.1, # Permanence Decrement # If set to None, will automatically default to tpPermanenceInc # value. 'permanenceDec' : 0.1, 'globalDecay': 0.0, 'maxAge': 0, # Minimum number of active synapses for a segment to be considered # during search for the best-matching segments. # None=use default # Replaces: tpMinThreshold 'minThreshold': 12, # Segment activation threshold. # A segment is active if it has >= tpSegmentActivationThreshold # connected synapses that are active due to infActiveState # None=use default # Replaces: tpActivationThreshold 'activationThreshold': 16, 'outputType': 'normal', # "Pay Attention Mode" length. This tells the TM how many new # elements to append to the end of a learned sequence at a time. # Smaller values are better for datasets with short sequences, # higher values are better for datasets with long sequences. 'pamLength': 1, }, 'clParams': { 'regionName' : 'SDRClassifierRegion', # Classifier diagnostic output verbosity control; # 0: silent; [1..6]: increasing levels of verbosity 'verbosity' : 0, # This controls how fast the classifier learns/forgets. Higher values # make it adapt faster and forget older patterns faster. 'alpha': 0.001, # This is set after the call to updateConfigFromSubConfig and is # computed from the aggregationInfo and predictAheadTime. 'steps': '1', }, 'trainSPNetOnlyIfRequested': False, }, } # end of config dictionary # Adjust base config dictionary for any modifications if imported from a # sub-experiment updateConfigFromSubConfig(config) # Compute predictionSteps based on the predictAheadTime and the aggregation # period, which may be permuted over. if config['predictAheadTime'] is not None: predictionSteps = int(round(aggregationDivide( config['predictAheadTime'], config['aggregationInfo']))) assert (predictionSteps >= 1) config['modelParams']['clParams']['steps'] = str(predictionSteps) # Adjust config by applying ValueGetterBase-derived # futures. NOTE: this MUST be called after updateConfigFromSubConfig() in order # to support value-getter-based substitutions from the sub-experiment (if any) applyValueGettersToContainer(config) control = { # The environment that the current model is being run in "environment": 'nupic', # Input stream specification per py/nupicengine/cluster/database/StreamDef.json. # 'dataset' : { u'info': u'test_NoProviders', u'streams': [ { u'columns': [u'*'], u'info': u'test data', u'source': u'file://swarming/test_data.csv'}], u'version': 1}, # Iteration count: maximum number of iterations. Each iteration corresponds # to one record from the (possibly aggregated) dataset. The task is # terminated when either number of iterations reaches iterationCount or # all records in the (possibly aggregated) database have been processed, # whichever occurs first. # # iterationCount of -1 = iterate over the entire dataset #'iterationCount' : ITERATION_COUNT, # Metrics: A list of MetricSpecs that instantiate the metrics that are # computed for this experiment 'metrics':[ MetricSpec(field=u'consumption', inferenceElement=InferenceElement.prediction, metric='rmse'), ], # Logged Metrics: A sequence of regular expressions that specify which of # the metrics from the Inference Specifications section MUST be logged for # every prediction. The regex's correspond to the automatically generated # metric labels. This is similar to the way the optimization metric is # specified in permutations.py. 'loggedMetrics': ['.*nupicScore.*'], } descriptionInterface = ExperimentDescriptionAPI(modelConfig=config, control=control)

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26,221

permutations.py

numenta_nupic-legacy/tests/swarming/nupic/swarming/experiments/field_contrib_temporal/permutations.py

# ---------------------------------------------------------------------- # Numenta Platform for Intelligent Computing (NuPIC) # Copyright (C) 2013, Numenta, Inc. Unless you have an agreement # with Numenta, Inc., for a separate license for this software code, the # following terms and conditions apply: # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU Affero Public License version 3 as # published by the Free Software Foundation. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. # See the GNU Affero Public License for more details. # # You should have received a copy of the GNU Affero Public License # along with this program. If not, see http://www.gnu.org/licenses. # # http://numenta.org/licenses/ # ---------------------------------------------------------------------- """ Template file used by ExpGenerator to generate the actual permutations.py file by replacing $XXXXXXXX tokens with desired values. This permutations.py file was generated by: '/Users/ronmarianetti/nupic/eng/lib/python2.6/site-packages/nupic/frameworks/opf/expGenerator/experiment_generator.py' """ import os from nupic.swarming.permutation_helpers import * # The name of the field being predicted. Any allowed permutation MUST contain # the prediction field. # (generated from PREDICTION_FIELD) predictedField = 'consumption' permutations = { 'modelParams': { 'sensorParams': { 'encoders': { 'gym': PermuteEncoder(fieldName='gym', encoderClass='SDRCategoryEncoder', w=7, n=100), 'timestamp_dayOfWeek': PermuteEncoder(fieldName='timestamp', encoderClass='DateEncoder.dayOfWeek', radius=PermuteChoices([1, 3]), w=7), 'timestamp_timeOfDay': PermuteEncoder(fieldName='timestamp', encoderClass='DateEncoder.timeOfDay', radius=PermuteChoices([1, 8]), w=7), 'consumption': PermuteEncoder(fieldName='consumption', encoderClass='ScalarEncoder', maxval=PermuteInt(100, 300, 25), n=PermuteInt(13, 500, 20), w=7, minval=0), 'address': PermuteEncoder(fieldName='address', encoderClass='SDRCategoryEncoder', w=7, n=100), }, }, 'tmParams': { 'minThreshold': PermuteInt(9, 12), 'activationThreshold': PermuteInt(12, 16), }, } } # Fields selected for final hypersearch report; # NOTE: These values are used as regular expressions by RunPermutations.py's # report generator # (fieldname values generated from PERM_PREDICTED_FIELD_NAME) report = [ '.*consumption.*', ] # Permutation optimization setting: either minimize or maximize metric # used by RunPermutations. # NOTE: The value is used as a regular expressions by RunPermutations.py's # report generator # (generated from minimize = 'prediction:rmse:field=consumption') minimize = 'prediction:rmse:field=consumption' def dummyModelParams(perm): """ This function can be used for Hypersearch algorithm development. When present, Hypersearch doesn't actually run the CLA model in the OPF, but instead run a dummy model. This function returns the dummy model params that will be used. See the OPFDummyModelRunner class source code (in nupic.swarming.ModelRunner) for a description of the schema for the dummy model params. """ errScore = 100 if perm['modelParams']['sensorParams']['encoders']['address'] is not None: errScore -= 0 if perm['modelParams']['sensorParams']['encoders']['gym'] is not None: errScore -= 10 if perm['modelParams']['sensorParams']['encoders']['timestamp_timeOfDay'] \ is not None: errScore -= 20 if perm['modelParams']['sensorParams']['encoders']['timestamp_dayOfWeek'] \ is not None: errScore -= 50 dummyModelParams = dict( metricValue = errScore, metricFunctions = None, ) return dummyModelParams def permutationFilter(perm): """ This function can be used to selectively filter out specific permutation combinations. It is called by RunPermutations for every possible permutation of the variables in the permutations dict. It should return True for valid a combination of permutation values and False for an invalid one. Parameters: --------------------------------------------------------- perm: dict of one possible combination of name:value pairs chosen from permutations. """ # An example of how to use this limit = int(os.environ.get('NTA_TEST_maxvalFilter', 300)) if perm['modelParams']['sensorParams']['encoders']['consumption']['maxval'] > limit: return False; return True

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9/5/2024, 5:13:42 PM (Europe/Amsterdam)

26,222

description.py

numenta_nupic-legacy/tests/swarming/nupic/swarming/experiments/smart_speculation_spatial_classification/description.py

# ---------------------------------------------------------------------- # Numenta Platform for Intelligent Computing (NuPIC) # Copyright (C) 2013, Numenta, Inc. Unless you have an agreement # with Numenta, Inc., for a separate license for this software code, the # following terms and conditions apply: # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU Affero Public License version 3 as # published by the Free Software Foundation. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. # See the GNU Affero Public License for more details. # # You should have received a copy of the GNU Affero Public License # along with this program. If not, see http://www.gnu.org/licenses. # # http://numenta.org/licenses/ # ---------------------------------------------------------------------- """ Template file used by the OPF Experiment Generator to generate the actual description.py file by replacing $XXXXXXXX tokens with desired values. This description.py file was generated by: '/Users/ronmarianetti/nupic/eng/lib/python2.6/site-packages/nupic/frameworks/opf/expGenerator/experiment_generator.py' """ from nupic.frameworks.opf.exp_description_api import ExperimentDescriptionAPI from nupic.frameworks.opf.exp_description_helpers import ( updateConfigFromSubConfig, applyValueGettersToContainer, DeferredDictLookup) from nupic.frameworks.opf.htm_prediction_model_callbacks import * from nupic.frameworks.opf.metrics import MetricSpec from nupic.frameworks.opf.opf_utils import (InferenceType, InferenceElement) from nupic.support import aggregationDivide from nupic.frameworks.opf.opf_task_driver import ( IterationPhaseSpecLearnOnly, IterationPhaseSpecInferOnly, IterationPhaseSpecLearnAndInfer) # Model Configuration Dictionary: # # Define the model parameters and adjust for any modifications if imported # from a sub-experiment. # # These fields might be modified by a sub-experiment; this dict is passed # between the sub-experiment and base experiment # # # NOTE: Use of DEFERRED VALUE-GETTERs: dictionary fields and list elements # within the config dictionary may be assigned futures derived from the # ValueGetterBase class, such as DeferredDictLookup. # This facility is particularly handy for enabling substitution of values in # the config dictionary from other values in the config dictionary, which is # needed by permutation.py-based experiments. These values will be resolved # during the call to applyValueGettersToContainer(), # which we call after the base experiment's config dictionary is updated from # the sub-experiment. See ValueGetterBase and # DeferredDictLookup for more details about value-getters. # # For each custom encoder parameter to be exposed to the sub-experiment/ # permutation overrides, define a variable in this section, using key names # beginning with a single underscore character to avoid collisions with # pre-defined keys (e.g., _dsEncoderFieldName2_N). # # Example: # config = dict( # _dsEncoderFieldName2_N = 70, # _dsEncoderFieldName2_W = 5, # dsEncoderSchema = [ # base=dict( # fieldname='Name2', type='ScalarEncoder', # name='Name2', minval=0, maxval=270, clipInput=True, # n=DeferredDictLookup('_dsEncoderFieldName2_N'), # w=DeferredDictLookup('_dsEncoderFieldName2_W')), # ], # ) # updateConfigFromSubConfig(config) # applyValueGettersToContainer(config) config = { # Type of model that the rest of these parameters apply to. 'model': "HTMPrediction", # Version that specifies the format of the config. 'version': 1, # Intermediate variables used to compute fields in modelParams and also # referenced from the control section. 'aggregationInfo': { 'days': 0, 'fields': [], 'hours': 0, 'microseconds': 0, 'milliseconds': 0, 'minutes': 0, 'months': 0, 'seconds': 0, 'weeks': 0, 'years': 0}, 'predictAheadTime': None, # Model parameter dictionary. 'modelParams': { # The type of inference that this model will perform 'inferenceType': 'NontemporalClassification', 'sensorParams': { # Sensor diagnostic output verbosity control; # if > 0: sensor region will print out on screen what it's sensing # at each step 0: silent; >=1: some info; >=2: more info; # >=3: even more info (see compute() in py/regions/RecordSensor.py) 'verbosity' : 0, # Example: # dsEncoderSchema = [ # DeferredDictLookup('__field_name_encoder'), # ], # # (value generated from DS_ENCODER_SCHEMA) 'encoders': { u'A': { 'fieldname': u'daynight', 'n': 300, 'name': u'daynight', 'type': 'SDRCategoryEncoder', 'w': 21}, u'B': { 'fieldname': u'daynight', 'n': 300, 'name': u'daynight', 'type': 'SDRCategoryEncoder', 'w': 21}, u'C': { 'fieldname': u'precip', 'n': 300, 'name': u'precip', 'type': 'SDRCategoryEncoder', 'w': 21}, u'D': { 'clipInput': True, 'fieldname': u'visitor_winloss', 'maxval': 0.78600000000000003, 'minval': 0.0, 'n': 150, 'name': u'visitor_winloss', 'type': 'AdaptiveScalarEncoder', 'w': 21}, u'E': { 'clipInput': True, 'fieldname': u'home_winloss', 'maxval': 0.69999999999999996, 'minval': 0.0, 'n': 150, 'name': u'home_winloss', 'type': 'AdaptiveScalarEncoder', 'w': 21}, u'F': { 'dayOfWeek': (7, 1), 'fieldname': u'timestamp', 'name': u'timestamp_dayOfWeek', 'type': 'DateEncoder'}, u'G': { 'fieldname': u'timestamp', 'name': u'timestamp_timeOfDay', 'timeOfDay': (7, 1), 'type': 'DateEncoder'}, u'_classifierInput': { 'clipInput': True, 'fieldname': u'attendance', 'classifierOnly': True, 'maxval': 36067, 'minval': 0, 'n': 150, 'name': u'attendance', 'type': 'AdaptiveScalarEncoder', 'w': 21}}, # A dictionary specifying the period for automatically-generated # resets from a RecordSensor; # # None = disable automatically-generated resets (also disabled if # all of the specified values evaluate to 0). # Valid keys is the desired combination of the following: # days, hours, minutes, seconds, milliseconds, microseconds, weeks # # Example for 1.5 days: sensorAutoReset = dict(days=1,hours=12), # # (value generated from SENSOR_AUTO_RESET) 'sensorAutoReset' : None, }, 'spEnable': True, 'spParams': { # SP diagnostic output verbosity control; # 0: silent; >=1: some info; >=2: more info; 'spVerbosity' : 0, 'globalInhibition': 1, # Number of cell columns in the cortical region (same number for # SP and TM) # (see also tpNCellsPerCol) 'columnCount': 2048, 'inputWidth': 0, # SP inhibition control (absolute value); # Maximum number of active columns in the SP region's output (when # there are more, the weaker ones are suppressed) 'numActiveColumnsPerInhArea': 40, 'seed': 1956, # potentialPct # What percent of the columns's receptive field is available # for potential synapses. At initialization time, we will # choose potentialPct * (2*potentialRadius+1)^2 'potentialPct': 1.0, # The default connected threshold. Any synapse whose # permanence value is above the connected threshold is # a "connected synapse", meaning it can contribute to the # cell's firing. Typical value is 0.10. Cells whose activity # level before inhibition falls below minDutyCycleBeforeInh # will have their own internal synPermConnectedCell # threshold set below this default value. # (This concept applies to both SP and TM and so 'cells' # is correct here as opposed to 'columns') 'synPermConnected': 0.1, 'synPermActiveInc': 0.1, 'synPermInactiveDec': 0.01, }, # Controls whether TM is enabled or disabled; # TM is necessary for making temporal predictions, such as predicting # the next inputs. Without TM, the model is only capable of # reconstructing missing sensor inputs (via SP). 'tmEnable' : True, 'tmParams': { # TM diagnostic output verbosity control; # 0: silent; [1..6]: increasing levels of verbosity # (see verbosity in nupic/trunk/py/nupic/research/backtracking_tm.py and backtracking_tm_cpp.py) 'verbosity': 0, # Number of cell columns in the cortical region (same number for # SP and TM) # (see also tpNCellsPerCol) 'columnCount': 2048, # The number of cells (i.e., states), allocated per column. 'cellsPerColumn': 32, 'inputWidth': 2048, 'seed': 1960, # Temporal Pooler implementation selector (see _getTPClass in # CLARegion.py). 'temporalImp': 'cpp', # New Synapse formation count # NOTE: If None, use spNumActivePerInhArea # # TODO: need better explanation 'newSynapseCount': 15, # Maximum number of synapses per segment # > 0 for fixed-size CLA # -1 for non-fixed-size CLA # # TODO: for Ron: once the appropriate value is placed in TM # constructor, see if we should eliminate this parameter from # description.py. 'maxSynapsesPerSegment': 32, # Maximum number of segments per cell # > 0 for fixed-size CLA # -1 for non-fixed-size CLA # # TODO: for Ron: once the appropriate value is placed in TM # constructor, see if we should eliminate this parameter from # description.py. 'maxSegmentsPerCell': 128, # Initial Permanence # TODO: need better explanation 'initialPerm': 0.21, # Permanence Increment 'permanenceInc': 0.1, # Permanence Decrement # If set to None, will automatically default to tpPermanenceInc # value. 'permanenceDec' : 0.1, 'globalDecay': 0.0, 'maxAge': 0, # Minimum number of active synapses for a segment to be considered # during search for the best-matching segments. # None=use default # Replaces: tpMinThreshold 'minThreshold': 12, # Segment activation threshold. # A segment is active if it has >= tpSegmentActivationThreshold # connected synapses that are active due to infActiveState # None=use default # Replaces: tpActivationThreshold 'activationThreshold': 16, 'outputType': 'normal', # "Pay Attention Mode" length. This tells the TM how many new # elements to append to the end of a learned sequence at a time. # Smaller values are better for datasets with short sequences, # higher values are better for datasets with long sequences. 'pamLength': 1, }, 'clParams': { 'regionName' : 'SDRClassifierRegion', # Classifier diagnostic output verbosity control; # 0: silent; [1..6]: increasing levels of verbosity 'verbosity' : 0, # This controls how fast the classifier learns/forgets. Higher values # make it adapt faster and forget older patterns faster. 'alpha': 0.001, # This is set after the call to updateConfigFromSubConfig and is # computed from the aggregationInfo and predictAheadTime. 'steps': '1', }, 'trainSPNetOnlyIfRequested': False, }, } # end of config dictionary # Adjust base config dictionary for any modifications if imported from a # sub-experiment updateConfigFromSubConfig(config) # Compute predictionSteps based on the predictAheadTime and the aggregation # period, which may be permuted over. if config['predictAheadTime'] is not None: predictionSteps = int(round(aggregationDivide( config['predictAheadTime'], config['aggregationInfo']))) assert (predictionSteps >= 1) config['modelParams']['clParams']['steps'] = str(predictionSteps) # Adjust config by applying ValueGetterBase-derived # futures. NOTE: this MUST be called after updateConfigFromSubConfig() in order # to support value-getter-based substitutions from the sub-experiment (if any) applyValueGettersToContainer(config) control = { # The environment that the current model is being run in "environment": 'nupic', # Input stream specification per py/nupicengine/cluster/database/StreamDef.json. # 'dataset' : { u'info': u'baseball benchmark test', u'streams': [ { u'columns': [ u'daynight', u'precip', u'home_winloss', u'visitor_winloss', u'attendance', u'timestamp'], u'info': u'OAK01.csv', u'source': u'file://extra/baseball_stadium/OAK01reformatted.csv'}], u'version': 1}, # Iteration count: maximum number of iterations. Each iteration corresponds # to one record from the (possibly aggregated) dataset. The task is # terminated when either number of iterations reaches iterationCount or # all records in the (possibly aggregated) database have been processed, # whichever occurs first. # # iterationCount of -1 = iterate over the entire dataset #'iterationCount' : ITERATION_COUNT, # Metrics: A list of MetricSpecs that instantiate the metrics that are # computed for this experiment 'metrics':[ MetricSpec(field=u'attendance', metric='multiStep', inferenceElement='multiStepBestPredictions', params={'window': 1000, 'steps': [0], 'errorMetric': 'aae'}), ], # Logged Metrics: A sequence of regular expressions that specify which of # the metrics from the Inference Specifications section MUST be logged for # every prediction. The regex's correspond to the automatically generated # metric labels. This is similar to the way the optimization metric is # specified in permutations.py. 'loggedMetrics': ['.*'], } descriptionInterface = ExperimentDescriptionAPI(modelConfig=config, control=control)

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26,223

permutations.py

numenta_nupic-legacy/tests/swarming/nupic/swarming/experiments/smart_speculation_spatial_classification/permutations.py

# ---------------------------------------------------------------------- # Numenta Platform for Intelligent Computing (NuPIC) # Copyright (C) 2013, Numenta, Inc. Unless you have an agreement # with Numenta, Inc., for a separate license for this software code, the # following terms and conditions apply: # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU Affero Public License version 3 as # published by the Free Software Foundation. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. # See the GNU Affero Public License for more details. # # You should have received a copy of the GNU Affero Public License # along with this program. If not, see http://www.gnu.org/licenses. # # http://numenta.org/licenses/ # ---------------------------------------------------------------------- """ Template file used by ExpGenerator to generate the actual permutations.py file by replacing $XXXXXXXX tokens with desired values. This permutations.py file was generated by: '/Users/ronmarianetti/nupic/eng/lib/python2.6/site-packages/nupic/frameworks/opf/expGenerator/experiment_generator.py' """ import os from nupic.swarming.permutation_helpers import * # The name of the field being predicted. Any allowed permutation MUST contain # the prediction field. # (generated from PREDICTION_FIELD) predictedField = 'attendance' permutations = { 'modelParams': { 'sensorParams': { 'encoders': { 'A': PermuteEncoder(fieldName='daynight', encoderClass='SDRCategoryEncoder', w=7, n=100), 'B': PermuteEncoder(fieldName='daynight', encoderClass='SDRCategoryEncoder', w=7, n=100), 'C': PermuteEncoder(fieldName='precip', encoderClass='SDRCategoryEncoder', w=7, n=100), '_classifierInput': dict(fieldNname='attendance', classifierOnly=True, type='AdaptiveScalarEncoder', maxval=36067, n=PermuteInt(13, 500, 25), clipInput=True, w=7, minval=0), }, }, } } # Fields selected for final hypersearch report; # NOTE: These values are used as regular expressions by RunPermutations.py's # report generator # (fieldname values generated from PERM_PREDICTED_FIELD_NAME) report = [ '.*attendance.*', ] # Permutation optimization setting: either minimize or maximize metric # used by RunPermutations. # NOTE: The value is used as a regular expressions by RunPermutations.py's # report generator # (generated from minimize = 'nonprediction:aae:window=1000:field=attendance') minimize = "multiStepBestPredictions:multiStep:errorMetric='aae':steps=\[0\]:window=1000:field=attendance" def dummyModelParams(perm): """ This function can be used for Hypersearch algorithm development. When present, we don't actually run the CLA model in the OPF, but instead run a dummy model. This function returns the dummy model params that will be used. See the OPFDummyModelRunner class source code (in nupic.swarming.ModelRunner) for a description of the schema for the dummy model params. """ errScore = 500 if not perm['modelParams']['sensorParams']['encoders']['A'] is None: errScore -= 40 if not perm['modelParams']['sensorParams']['encoders']['B'] is None: errScore -= 30 if not perm['modelParams']['sensorParams']['encoders']['C'] is None: errScore -= 20 delay = 0 # Make the best model in sprint 0 run slower so that we create more # speculative models encoderCount = 0 encoders = perm['modelParams']['sensorParams']['encoders'] for field,encoder in encoders.items(): if encoder is not None: encoderCount += 1 # NOTE: The _classifierInput is always present. It seems that speculation # favors fields that are not done yet, so make the worse fields take # longer so that the speculative particles choose them in the second # sprint. if encoderCount == 2: delay = 0.1 # Make speculative swarms that should be killed take longer... elif encoderCount == 3 \ and perm['modelParams']['sensorParams']['encoders']["A"] is None: delay = 0.2 # Make the best possible combination take the longest so that we have # an opportunity to kill other swarms before we finish elif encoderCount == 3: delay = 0.3 dummyModelParams = dict( metricValue = errScore, metricFunctions = None, delay=delay, ) return dummyModelParams def permutationFilter(perm): """ This function can be used to selectively filter out specific permutation combinations. It is called by RunPermutations for every possible permutation of the variables in the permutations dict. It should return True for valid a combination of permutation values and False for an invalid one. Parameters: --------------------------------------------------------- perm: dict of one possible combination of name:value pairs chosen from permutations. """ # An example of how to use this #if perm['__consumption_encoder']['maxval'] > 300: # return False; # return True

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26,224

description.py

numenta_nupic-legacy/tests/swarming/nupic/swarming/experiments/max_branching_temporal/description.py

# ---------------------------------------------------------------------- # Numenta Platform for Intelligent Computing (NuPIC) # Copyright (C) 2013, Numenta, Inc. Unless you have an agreement # with Numenta, Inc., for a separate license for this software code, the # following terms and conditions apply: # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU Affero Public License version 3 as # published by the Free Software Foundation. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. # See the GNU Affero Public License for more details. # # You should have received a copy of the GNU Affero Public License # along with this program. If not, see http://www.gnu.org/licenses. # # http://numenta.org/licenses/ # ---------------------------------------------------------------------- """ Template file used by the OPF Experiment Generator to generate the actual description.py file by replacing $XXXXXXXX tokens with desired values. This description.py file was generated by: '/Users/ronmarianetti/nupic/eng/lib/python2.6/site-packages/nupic/frameworks/opf/expGenerator/experiment_generator.py' """ from nupic.frameworks.opf.exp_description_api import ExperimentDescriptionAPI from nupic.frameworks.opf.exp_description_helpers import ( updateConfigFromSubConfig, applyValueGettersToContainer, DeferredDictLookup) from nupic.frameworks.opf.htm_prediction_model_callbacks import * from nupic.frameworks.opf.metrics import MetricSpec from nupic.frameworks.opf.opf_utils import (InferenceType, InferenceElement) from nupic.support import aggregationDivide from nupic.frameworks.opf.opf_task_driver import ( IterationPhaseSpecLearnOnly, IterationPhaseSpecInferOnly, IterationPhaseSpecLearnAndInfer) # Model Configuration Dictionary: # # Define the model parameters and adjust for any modifications if imported # from a sub-experiment. # # These fields might be modified by a sub-experiment; this dict is passed # between the sub-experiment and base experiment # # # NOTE: Use of DEFERRED VALUE-GETTERs: dictionary fields and list elements # within the config dictionary may be assigned futures derived from the # ValueGetterBase class, such as DeferredDictLookup. # This facility is particularly handy for enabling substitution of values in # the config dictionary from other values in the config dictionary, which is # needed by permutation.py-based experiments. These values will be resolved # during the call to applyValueGettersToContainer(), # which we call after the base experiment's config dictionary is updated from # the sub-experiment. See ValueGetterBase and # DeferredDictLookup for more details about value-getters. # # For each custom encoder parameter to be exposed to the sub-experiment/ # permutation overrides, define a variable in this section, using key names # beginning with a single underscore character to avoid collisions with # pre-defined keys (e.g., _dsEncoderFieldName2_N). # # Example: # config = dict( # _dsEncoderFieldName2_N = 70, # _dsEncoderFieldName2_W = 5, # dsEncoderSchema = [ # base=dict( # fieldname='Name2', type='ScalarEncoder', # name='Name2', minval=0, maxval=270, clipInput=True, # n=DeferredDictLookup('_dsEncoderFieldName2_N'), # w=DeferredDictLookup('_dsEncoderFieldName2_W')), # ], # ) # updateConfigFromSubConfig(config) # applyValueGettersToContainer(config) config = { # Type of model that the rest of these parameters apply to. 'model': "HTMPrediction", # Version that specifies the format of the config. 'version': 1, # Intermediate variables used to compute fields in modelParams and also # referenced from the control section. 'aggregationInfo': { 'days': 0, 'fields': [], 'hours': 0, 'microseconds': 0, 'milliseconds': 0, 'minutes': 0, 'months': 0, 'seconds': 0, 'weeks': 0, 'years': 0}, 'predictAheadTime': None, # Model parameter dictionary. 'modelParams': { # The type of inference that this model will perform 'inferenceType': 'TemporalNextStep', 'sensorParams': { # Sensor diagnostic output verbosity control; # if > 0: sensor region will print out on screen what it's sensing # at each step 0: silent; >=1: some info; >=2: more info; # >=3: even more info (see compute() in py/regions/RecordSensor.py) 'verbosity' : 0, # Example: # dsEncoderSchema = [ # DeferredDictLookup('__field_name_encoder'), # ], # # (value generated from DS_ENCODER_SCHEMA) 'encoders': { u'attendance': { 'clipInput': True, 'fieldname': u'attendance', 'maxval': 36067, 'minval': 0, 'n': 150, 'name': u'attendance', 'type': 'AdaptiveScalarEncoder', 'w': 21}, u'daynight': { 'fieldname': u'daynight', 'n': 300, 'name': u'daynight', 'type': 'SDRCategoryEncoder', 'w': 21}, u'home_winloss': { 'clipInput': True, 'fieldname': u'home_winloss', 'maxval': 0.69999999999999996, 'minval': 0.0, 'n': 150, 'name': u'home_winloss', 'type': 'AdaptiveScalarEncoder', 'w': 21}, u'precip': { 'fieldname': u'precip', 'n': 300, 'name': u'precip', 'type': 'SDRCategoryEncoder', 'w': 21}, u'timestamp_dayOfWeek': { 'dayOfWeek': (7, 1), 'fieldname': u'timestamp', 'name': u'timestamp_dayOfWeek', 'type': 'DateEncoder'}, u'timestamp_timeOfDay': { 'fieldname': u'timestamp', 'name': u'timestamp_timeOfDay', 'timeOfDay': (7, 1), 'type': 'DateEncoder'}, u'visitor_winloss': { 'clipInput': True, 'fieldname': u'visitor_winloss', 'maxval': 0.78600000000000003, 'minval': 0.0, 'n': 150, 'name': u'visitor_winloss', 'type': 'AdaptiveScalarEncoder', 'w': 21}}, # A dictionary specifying the period for automatically-generated # resets from a RecordSensor; # # None = disable automatically-generated resets (also disabled if # all of the specified values evaluate to 0). # Valid keys is the desired combination of the following: # days, hours, minutes, seconds, milliseconds, microseconds, weeks # # Example for 1.5 days: sensorAutoReset = dict(days=1,hours=12), # # (value generated from SENSOR_AUTO_RESET) 'sensorAutoReset' : None, }, 'spEnable': True, 'spParams': { # SP diagnostic output verbosity control; # 0: silent; >=1: some info; >=2: more info; 'spVerbosity' : 0, 'globalInhibition': 1, # Number of cell columns in the cortical region (same number for # SP and TM) # (see also tpNCellsPerCol) 'columnCount': 2048, 'inputWidth': 0, # SP inhibition control (absolute value); # Maximum number of active columns in the SP region's output (when # there are more, the weaker ones are suppressed) 'numActiveColumnsPerInhArea': 40, 'seed': 1956, # potentialPct # What percent of the columns's receptive field is available # for potential synapses. At initialization time, we will # choose potentialPct * (2*potentialRadius+1)^2 'potentialPct': 1.0, # The default connected threshold. Any synapse whose # permanence value is above the connected threshold is # a "connected synapse", meaning it can contribute to the # cell's firing. Typical value is 0.10. Cells whose activity # level before inhibition falls below minDutyCycleBeforeInh # will have their own internal synPermConnectedCell # threshold set below this default value. # (This concept applies to both SP and TM and so 'cells' # is correct here as opposed to 'columns') 'synPermConnected': 0.1, 'synPermActiveInc': 0.1, 'synPermInactiveDec': 0.01, }, # Controls whether TM is enabled or disabled; # TM is necessary for making temporal predictions, such as predicting # the next inputs. Without TM, the model is only capable of # reconstructing missing sensor inputs (via SP). 'tmEnable' : True, 'tmParams': { # TM diagnostic output verbosity control; # 0: silent; [1..6]: increasing levels of verbosity # (see verbosity in nupic/trunk/py/nupic/research/backtracking_tm.py and backtracking_tm_cpp.py) 'verbosity': 0, # Number of cell columns in the cortical region (same number for # SP and TM) # (see also tpNCellsPerCol) 'columnCount': 2048, # The number of cells (i.e., states), allocated per column. 'cellsPerColumn': 32, 'inputWidth': 2048, 'seed': 1960, # Temporal Pooler implementation selector (see _getTPClass in # CLARegion.py). 'temporalImp': 'cpp', # New Synapse formation count # NOTE: If None, use spNumActivePerInhArea # # TODO: need better explanation 'newSynapseCount': 15, # Maximum number of synapses per segment # > 0 for fixed-size CLA # -1 for non-fixed-size CLA # # TODO: for Ron: once the appropriate value is placed in TM # constructor, see if we should eliminate this parameter from # description.py. 'maxSynapsesPerSegment': 32, # Maximum number of segments per cell # > 0 for fixed-size CLA # -1 for non-fixed-size CLA # # TODO: for Ron: once the appropriate value is placed in TM # constructor, see if we should eliminate this parameter from # description.py. 'maxSegmentsPerCell': 128, # Initial Permanence # TODO: need better explanation 'initialPerm': 0.21, # Permanence Increment 'permanenceInc': 0.1, # Permanence Decrement # If set to None, will automatically default to tpPermanenceInc # value. 'permanenceDec' : 0.1, 'globalDecay': 0.0, 'maxAge': 0, # Minimum number of active synapses for a segment to be considered # during search for the best-matching segments. # None=use default # Replaces: tpMinThreshold 'minThreshold': 12, # Segment activation threshold. # A segment is active if it has >= tpSegmentActivationThreshold # connected synapses that are active due to infActiveState # None=use default # Replaces: tpActivationThreshold 'activationThreshold': 16, 'outputType': 'normal', # "Pay Attention Mode" length. This tells the TM how many new # elements to append to the end of a learned sequence at a time. # Smaller values are better for datasets with short sequences, # higher values are better for datasets with long sequences. 'pamLength': 1, }, 'clParams': { 'regionName' : 'SDRClassifierRegion', # Classifier diagnostic output verbosity control; # 0: silent; [1..6]: increasing levels of verbosity 'verbosity' : 0, # This controls how fast the classifier learns/forgets. Higher values # make it adapt faster and forget older patterns faster. 'alpha': 0.001, # This is set after the call to updateConfigFromSubConfig and is # computed from the aggregationInfo and predictAheadTime. 'steps': '1', }, 'trainSPNetOnlyIfRequested': False, }, } # end of config dictionary # Adjust base config dictionary for any modifications if imported from a # sub-experiment updateConfigFromSubConfig(config) # Compute predictionSteps based on the predictAheadTime and the aggregation # period, which may be permuted over. if config['predictAheadTime'] is not None: predictionSteps = int(round(aggregationDivide( config['predictAheadTime'], config['aggregationInfo']))) assert (predictionSteps >= 1) config['modelParams']['clParams']['steps'] = str(predictionSteps) # Adjust config by applying ValueGetterBase-derived # futures. NOTE: this MUST be called after updateConfigFromSubConfig() in order # to support value-getter-based substitutions from the sub-experiment (if any) applyValueGettersToContainer(config) control = { # The environment that the current model is being run in "environment": 'nupic', # Input stream specification per py/nupicengine/cluster/database/StreamDef.json. # 'dataset' : { u'info': u'baseball benchmark test', u'streams': [ { u'columns': [ u'daynight', u'precip', u'home_winloss', u'visitor_winloss', u'attendance', u'timestamp'], u'info': u'OAK01.csv', u'source': u'file://extra/baseball_stadium/OAK01reformatted.csv'}], u'version': 1}, # Iteration count: maximum number of iterations. Each iteration corresponds # to one record from the (possibly aggregated) dataset. The task is # terminated when either number of iterations reaches iterationCount or # all records in the (possibly aggregated) database have been processed, # whichever occurs first. # # iterationCount of -1 = iterate over the entire dataset #'iterationCount' : ITERATION_COUNT, # Metrics: A list of MetricSpecs that instantiate the metrics that are # computed for this experiment 'metrics':[ MetricSpec(field=u'attendance', inferenceElement=InferenceElement.prediction, metric='aae', params={'window': 1000}), MetricSpec(field=u'attendance', inferenceElement=InferenceElement.prediction, metric='trivial_aae', params={'window': 1000}), MetricSpec(field=u'attendance', inferenceElement=InferenceElement.encodings, metric='nupicScore_scalar', params={'frequencyWindow': 1000, 'movingAverageWindow': 1000}), MetricSpec(field=u'attendance', inferenceElement=InferenceElement.encodings, metric='nupicScore_scalar', params={'frequencyWindow': 1000}) ], 'inferenceArgs':dict(testFields=['attendance']), # Logged Metrics: A sequence of regular expressions that specify which of # the metrics from the Inference Specifications section MUST be logged for # every prediction. The regex's correspond to the automatically generated # metric labels. This is similar to the way the optimization metric is # specified in permutations.py. 'loggedMetrics': ['.*nupicScore.*'], } descriptionInterface = ExperimentDescriptionAPI(modelConfig=config, control=control)

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26,225

permutations.py

numenta_nupic-legacy/tests/swarming/nupic/swarming/experiments/max_branching_temporal/permutations.py

# ---------------------------------------------------------------------- # Numenta Platform for Intelligent Computing (NuPIC) # Copyright (C) 2013, Numenta, Inc. Unless you have an agreement # with Numenta, Inc., for a separate license for this software code, the # following terms and conditions apply: # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU Affero Public License version 3 as # published by the Free Software Foundation. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. # See the GNU Affero Public License for more details. # # You should have received a copy of the GNU Affero Public License # along with this program. If not, see http://www.gnu.org/licenses. # # http://numenta.org/licenses/ # ---------------------------------------------------------------------- """ Template file used by ExpGenerator to generate the actual permutations.py file by replacing $XXXXXXXX tokens with desired values. This permutations.py file was generated by: '/Users/ronmarianetti/nupic/eng/lib/python2.6/site-packages/nupic/frameworks/opf/expGenerator/experiment_generator.py' """ import os from nupic.swarming.permutation_helpers import * # The name of the field being predicted. Any allowed permutation MUST contain # the prediction field. # (generated from PREDICTION_FIELD) predictedField = 'attendance' permutations = { 'modelParams': { 'sensorParams': { 'encoders': { 'attendance': PermuteEncoder(fieldName='attendance', encoderClass='AdaptiveScalarEncoder', maxval=36067, n=PermuteInt(13, 500, 25), clipInput=True, w=7, minval=0), 'visitor_winloss': PermuteEncoder(fieldName='visitor_winloss', encoderClass='AdaptiveScalarEncoder', maxval=0.786, n=PermuteInt(13, 500, 25), clipInput=True, w=7, minval=0), 'timestamp_dayOfWeek': PermuteEncoder(fieldName='timestamp', encoderClass='DateEncoder.dayOfWeek', radius=PermuteChoices([1, 3]), w=7), 'precip': PermuteEncoder(fieldName='precip', encoderClass='SDRCategoryEncoder', w=7, n=100), 'timestamp_timeOfDay': PermuteEncoder(fieldName='timestamp', encoderClass='DateEncoder.timeOfDay', radius=PermuteChoices([1, 8]), w=7), 'daynight': PermuteEncoder(fieldName='daynight', encoderClass='SDRCategoryEncoder', w=7, n=100), 'home_winloss': PermuteEncoder(fieldName='home_winloss', encoderClass='AdaptiveScalarEncoder', maxval=0.7, n=PermuteInt(13, 500, 25), clipInput=True, w=7, minval=0), }, }, 'tmParams': { 'minThreshold': PermuteInt(9, 12), 'activationThreshold': PermuteInt(12, 16), }, } } # Fields selected for final hypersearch report; # NOTE: These values are used as regular expressions by RunPermutations.py's # report generator # (fieldname values generated from PERM_PREDICTED_FIELD_NAME) report = [ '.*attendance.*', ] # Permutation optimization setting: either minimize or maximize metric # used by RunPermutations. # NOTE: The value is used as a regular expressions by RunPermutations.py's # report generator # (generated from minimize = 'nonprediction:aae:window=1000:field=attendance') minimize = 'prediction:aae:window=1000:field=attendance' def dummyModelParams(perm): """ This function can be used for Hypersearch algorithm development. When present, we don't actually run the CLA model in the OPF, but instead run a dummy model. This function returns the dummy model params that will be used. See the OPFDummyModelRunner class source code (in nupic.swarming.ModelRunner) for a description of the schema for the dummy model params. """ #By contribution order from most to least A,B,C,D,E,F #Want A,E combo to give the most contribution followed by A,D then A,C errScore = 500 #A if not perm['modelParams']['sensorParams']['encoders']['visitor_winloss'] \ is None: errScore -= 6 if not perm['modelParams']['sensorParams']['encoders']['daynight'] \ is None: errScore -= 90 if not perm['modelParams']['sensorParams']['encoders']['precip'] \ is None: errScore -= 40 if not perm['modelParams']['sensorParams']['encoders']\ ['timestamp_dayOfWeek'] is None: errScore -= 30 if not perm['modelParams']['sensorParams']['encoders']\ ['timestamp_timeOfDay'] is None: errScore -= 20 #B if not perm['modelParams']['sensorParams']['encoders']['home_winloss'] \ is None: errScore -= 5 #C if not perm['modelParams']['sensorParams']['encoders']\ ['timestamp_timeOfDay'] is None: errScore -= 4 #D if not perm['modelParams']['sensorParams']['encoders']\ ['timestamp_dayOfWeek'] is None: errScore -= 3 #E if not perm['modelParams']['sensorParams']['encoders']['precip'] is None: errScore -= 2 #F if not perm['modelParams']['sensorParams']['encoders']['daynight'] is None: errScore += 2 dummyModelParams = dict( metricValue = errScore, metricFunctions = None, ) return dummyModelParams def permutationFilter(perm): """ This function can be used to selectively filter out specific permutation combinations. It is called by RunPermutations for every possible permutation of the variables in the permutations dict. It should return True for valid a combination of permutation values and False for an invalid one. Parameters: --------------------------------------------------------- perm: dict of one possible combination of name:value pairs chosen from permutations. """ # An example of how to use this #if perm['__consumption_encoder']['maxval'] > 300: # return False; # return True

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description.py

numenta_nupic-legacy/tests/swarming/nupic/swarming/experiments/field_threshold_temporal/description.py

# ---------------------------------------------------------------------- # Numenta Platform for Intelligent Computing (NuPIC) # Copyright (C) 2013, Numenta, Inc. Unless you have an agreement # with Numenta, Inc., for a separate license for this software code, the # following terms and conditions apply: # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU Affero Public License version 3 as # published by the Free Software Foundation. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. # See the GNU Affero Public License for more details. # # You should have received a copy of the GNU Affero Public License # along with this program. If not, see http://www.gnu.org/licenses. # # http://numenta.org/licenses/ # ---------------------------------------------------------------------- """ Template file used by the OPF Experiment Generator to generate the actual description.py file by replacing $XXXXXXXX tokens with desired values. This description.py file was generated by: '/Users/ronmarianetti/nupic/eng/lib/python2.6/site-packages/nupic/frameworks/opf/expGenerator/experiment_generator.py' """ from nupic.frameworks.opf.exp_description_api import ExperimentDescriptionAPI from nupic.frameworks.opf.exp_description_helpers import ( updateConfigFromSubConfig, applyValueGettersToContainer, DeferredDictLookup) from nupic.frameworks.opf.htm_prediction_model_callbacks import * from nupic.frameworks.opf.metrics import MetricSpec from nupic.frameworks.opf.opf_utils import (InferenceType, InferenceElement) from nupic.support import aggregationDivide from nupic.frameworks.opf.opf_task_driver import ( IterationPhaseSpecLearnOnly, IterationPhaseSpecInferOnly, IterationPhaseSpecLearnAndInfer) # Model Configuration Dictionary: # # Define the model parameters and adjust for any modifications if imported # from a sub-experiment. # # These fields might be modified by a sub-experiment; this dict is passed # between the sub-experiment and base experiment # # # NOTE: Use of DEFERRED VALUE-GETTERs: dictionary fields and list elements # within the config dictionary may be assigned futures derived from the # ValueGetterBase class, such as DeferredDictLookup. # This facility is particularly handy for enabling substitution of values in # the config dictionary from other values in the config dictionary, which is # needed by permutation.py-based experiments. These values will be resolved # during the call to applyValueGettersToContainer(), # which we call after the base experiment's config dictionary is updated from # the sub-experiment. See ValueGetterBase and # DeferredDictLookup for more details about value-getters. # # For each custom encoder parameter to be exposed to the sub-experiment/ # permutation overrides, define a variable in this section, using key names # beginning with a single underscore character to avoid collisions with # pre-defined keys (e.g., _dsEncoderFieldName2_N). # # Example: # config = dict( # _dsEncoderFieldName2_N = 70, # _dsEncoderFieldName2_W = 5, # dsEncoderSchema = [ # base=dict( # fieldname='Name2', type='ScalarEncoder', # name='Name2', minval=0, maxval=270, clipInput=True, # n=DeferredDictLookup('_dsEncoderFieldName2_N'), # w=DeferredDictLookup('_dsEncoderFieldName2_W')), # ], # ) # updateConfigFromSubConfig(config) # applyValueGettersToContainer(config) config = { # Type of model that the rest of these parameters apply to. 'model': "HTMPrediction", # Version that specifies the format of the config. 'version': 1, # Intermediate variables used to compute fields in modelParams and also # referenced from the control section. 'aggregationInfo': { 'days': 0, 'fields': [], 'hours': 0, 'microseconds': 0, 'milliseconds': 0, 'minutes': 0, 'months': 0, 'seconds': 0, 'weeks': 0, 'years': 0}, 'predictAheadTime': None, # Model parameter dictionary. 'modelParams': { # The type of inference that this model will perform 'inferenceType': 'TemporalNextStep', 'sensorParams': { # Sensor diagnostic output verbosity control; # if > 0: sensor region will print out on screen what it's sensing # at each step 0: silent; >=1: some info; >=2: more info; # >=3: even more info (see compute() in py/regions/RecordSensor.py) 'verbosity' : 0, # Example: # dsEncoderSchema = [ # DeferredDictLookup('__field_name_encoder'), # ], # # (value generated from DS_ENCODER_SCHEMA) 'encoders': { u'attendance': { 'clipInput': True, 'fieldname': u'attendance', 'maxval': 36067, 'minval': 0, 'n': 150, 'name': u'attendance', 'type': 'AdaptiveScalarEncoder', 'w': 21}, u'daynight': { 'fieldname': u'daynight', 'n': 300, 'name': u'daynight', 'type': 'SDRCategoryEncoder', 'w': 21}, u'home_winloss': { 'clipInput': True, 'fieldname': u'home_winloss', 'maxval': 0.69999999999999996, 'minval': 0.0, 'n': 150, 'name': u'home_winloss', 'type': 'AdaptiveScalarEncoder', 'w': 21}, u'precip': { 'fieldname': u'precip', 'n': 300, 'name': u'precip', 'type': 'SDRCategoryEncoder', 'w': 21}, u'timestamp_dayOfWeek': { 'dayOfWeek': (7, 1), 'fieldname': u'timestamp', 'name': u'timestamp_dayOfWeek', 'type': 'DateEncoder'}, u'timestamp_timeOfDay': { 'fieldname': u'timestamp', 'name': u'timestamp_timeOfDay', 'timeOfDay': (7, 1), 'type': 'DateEncoder'}, u'visitor_winloss': { 'clipInput': True, 'fieldname': u'visitor_winloss', 'maxval': 0.78600000000000003, 'minval': 0.0, 'n': 150, 'name': u'visitor_winloss', 'type': 'AdaptiveScalarEncoder', 'w': 21}}, # A dictionary specifying the period for automatically-generated # resets from a RecordSensor; # # None = disable automatically-generated resets (also disabled if # all of the specified values evaluate to 0). # Valid keys is the desired combination of the following: # days, hours, minutes, seconds, milliseconds, microseconds, weeks # # Example for 1.5 days: sensorAutoReset = dict(days=1,hours=12), # # (value generated from SENSOR_AUTO_RESET) 'sensorAutoReset' : None, }, 'spEnable': True, 'spParams': { # SP diagnostic output verbosity control; # 0: silent; >=1: some info; >=2: more info; 'spVerbosity' : 0, 'globalInhibition': 1, # Number of cell columns in the cortical region (same number for # SP and TM) # (see also tpNCellsPerCol) 'columnCount': 2048, 'inputWidth': 0, # SP inhibition control (absolute value); # Maximum number of active columns in the SP region's output (when # there are more, the weaker ones are suppressed) 'numActiveColumnsPerInhArea': 40, 'seed': 1956, # potentialPct # What percent of the columns's receptive field is available # for potential synapses. At initialization time, we will # choose potentialPct * (2*potentialRadius+1)^2 'potentialPct': 1.0, # The default connected threshold. Any synapse whose # permanence value is above the connected threshold is # a "connected synapse", meaning it can contribute to the # cell's firing. Typical value is 0.10. Cells whose activity # level before inhibition falls below minDutyCycleBeforeInh # will have their own internal synPermConnectedCell # threshold set below this default value. # (This concept applies to both SP and TM and so 'cells' # is correct here as opposed to 'columns') 'synPermConnected': 0.1, 'synPermActiveInc': 0.1, 'synPermInactiveDec': 0.01, }, # Controls whether TM is enabled or disabled; # TM is necessary for making temporal predictions, such as predicting # the next inputs. Without TM, the model is only capable of # reconstructing missing sensor inputs (via SP). 'tmEnable' : True, 'tmParams': { # TM diagnostic output verbosity control; # 0: silent; [1..6]: increasing levels of verbosity # (see verbosity in nupic/trunk/py/nupic/research/backtracking_tm.py and backtracking_tm_cpp.py) 'verbosity': 0, # Number of cell columns in the cortical region (same number for # SP and TM) # (see also tpNCellsPerCol) 'columnCount': 2048, # The number of cells (i.e., states), allocated per column. 'cellsPerColumn': 32, 'inputWidth': 2048, 'seed': 1960, # Temporal Pooler implementation selector (see _getTPClass in # CLARegion.py). 'temporalImp': 'cpp', # New Synapse formation count # NOTE: If None, use spNumActivePerInhArea # # TODO: need better explanation 'newSynapseCount': 15, # Maximum number of synapses per segment # > 0 for fixed-size CLA # -1 for non-fixed-size CLA # # TODO: for Ron: once the appropriate value is placed in TM # constructor, see if we should eliminate this parameter from # description.py. 'maxSynapsesPerSegment': 32, # Maximum number of segments per cell # > 0 for fixed-size CLA # -1 for non-fixed-size CLA # # TODO: for Ron: once the appropriate value is placed in TM # constructor, see if we should eliminate this parameter from # description.py. 'maxSegmentsPerCell': 128, # Initial Permanence # TODO: need better explanation 'initialPerm': 0.21, # Permanence Increment 'permanenceInc': 0.1, # Permanence Decrement # If set to None, will automatically default to tpPermanenceInc # value. 'permanenceDec' : 0.1, 'globalDecay': 0.0, 'maxAge': 0, # Minimum number of active synapses for a segment to be considered # during search for the best-matching segments. # None=use default # Replaces: tpMinThreshold 'minThreshold': 12, # Segment activation threshold. # A segment is active if it has >= tpSegmentActivationThreshold # connected synapses that are active due to infActiveState # None=use default # Replaces: tpActivationThreshold 'activationThreshold': 16, 'outputType': 'normal', # "Pay Attention Mode" length. This tells the TM how many new # elements to append to the end of a learned sequence at a time. # Smaller values are better for datasets with short sequences, # higher values are better for datasets with long sequences. 'pamLength': 1, }, 'clParams': { 'regionName' : 'SDRClassifierRegion', # Classifier diagnostic output verbosity control; # 0: silent; [1..6]: increasing levels of verbosity 'verbosity' : 0, # This controls how fast the classifier learns/forgets. Higher values # make it adapt faster and forget older patterns faster. 'alpha': 0.001, # This is set after the call to updateConfigFromSubConfig and is # computed from the aggregationInfo and predictAheadTime. 'steps': '1', }, 'trainSPNetOnlyIfRequested': False, }, } # end of config dictionary # Adjust base config dictionary for any modifications if imported from a # sub-experiment updateConfigFromSubConfig(config) # Compute predictionSteps based on the predictAheadTime and the aggregation # period, which may be permuted over. if config['predictAheadTime'] is not None: predictionSteps = int(round(aggregationDivide( config['predictAheadTime'], config['aggregationInfo']))) assert (predictionSteps >= 1) config['modelParams']['clParams']['steps'] = str(predictionSteps) # Adjust config by applying ValueGetterBase-derived # futures. NOTE: this MUST be called after updateConfigFromSubConfig() in order # to support value-getter-based substitutions from the sub-experiment (if any) applyValueGettersToContainer(config) control = { # The environment that the current model is being run in "environment": 'nupic', # Input stream specification per py/nupicengine/cluster/database/StreamDef.json. # 'dataset' : { u'info': u'baseball benchmark test', u'streams': [ { u'columns': [ u'daynight', u'precip', u'home_winloss', u'visitor_winloss', u'attendance', u'timestamp'], u'info': u'OAK01.csv', u'source': u'file://extra/baseball_stadium/OAK01reformatted.csv'}], u'version': 1}, # Iteration count: maximum number of iterations. Each iteration corresponds # to one record from the (possibly aggregated) dataset. The task is # terminated when either number of iterations reaches iterationCount or # all records in the (possibly aggregated) database have been processed, # whichever occurs first. # # iterationCount of -1 = iterate over the entire dataset #'iterationCount' : ITERATION_COUNT, # Metrics: A list of MetricSpecs that instantiate the metrics that are # computed for this experiment 'metrics':[ MetricSpec(field=u'attendance', inferenceElement=InferenceElement.prediction, metric='aae', params={'window': 1000}), MetricSpec(field=u'attendance', inferenceElement=InferenceElement.prediction, metric='trivial_aae', params={'window': 1000}), MetricSpec(field=u'attendance', inferenceElement=InferenceElement.encodings, metric='nupicScore_scalar', params={'frequencyWindow': 1000, 'movingAverageWindow': 1000}), MetricSpec(field=u'attendance', inferenceElement=InferenceElement.encodings, metric='nupicScore_scalar', params={'frequencyWindow': 1000}) ], # Logged Metrics: A sequence of regular expressions that specify which of # the metrics from the Inference Specifications section MUST be logged for # every prediction. The regex's correspond to the automatically generated # metric labels. This is similar to the way the optimization metric is # specified in permutations.py. 'loggedMetrics': ['.*nupicScore.*'], } descriptionInterface = ExperimentDescriptionAPI(modelConfig=config, control=control)

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permutations.py

numenta_nupic-legacy/tests/swarming/nupic/swarming/experiments/field_threshold_temporal/permutations.py

# ---------------------------------------------------------------------- # Numenta Platform for Intelligent Computing (NuPIC) # Copyright (C) 2013, Numenta, Inc. Unless you have an agreement # with Numenta, Inc., for a separate license for this software code, the # following terms and conditions apply: # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU Affero Public License version 3 as # published by the Free Software Foundation. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. # See the GNU Affero Public License for more details. # # You should have received a copy of the GNU Affero Public License # along with this program. If not, see http://www.gnu.org/licenses. # # http://numenta.org/licenses/ # ---------------------------------------------------------------------- """ Template file used by ExpGenerator to generate the actual permutations.py file by replacing $XXXXXXXX tokens with desired values. This permutations.py file was generated by: '/Users/ronmarianetti/nupic/eng/lib/python2.6/site-packages/nupic/frameworks/opf/expGenerator/experiment_generator.py' """ import os from nupic.swarming.permutation_helpers import * # The name of the field being predicted. Any allowed permutation MUST contain # the prediction field. # (generated from PREDICTION_FIELD) predictedField = 'attendance' permutations = { 'modelParams': { 'sensorParams': { 'encoders': { 'attendance': PermuteEncoder(fieldName='attendance', encoderClass='AdaptiveScalarEncoder', maxval=36067, n=PermuteInt(13, 500, 25), clipInput=True, w=7, minval=0), 'visitor_winloss': PermuteEncoder(fieldName='visitor_winloss', encoderClass='AdaptiveScalarEncoder', maxval=0.786, n=PermuteInt(13, 500, 25), clipInput=True, w=7, minval=0), 'timestamp_dayOfWeek': PermuteEncoder(fieldName='timestamp', encoderClass='DateEncoder.dayOfWeek', radius=PermuteChoices([1, 3]), w=7), 'precip': PermuteEncoder(fieldName='precip', encoderClass='SDRCategoryEncoder', w=7, n=100), 'timestamp_timeOfDay': PermuteEncoder(fieldName='timestamp', encoderClass='DateEncoder.timeOfDay', radius=PermuteChoices([1, 8]), w=7), 'daynight': PermuteEncoder(fieldName='daynight', encoderClass='SDRCategoryEncoder', w=7, n=100), 'home_winloss': PermuteEncoder(fieldName='home_winloss', encoderClass='AdaptiveScalarEncoder', maxval=0.7, n=PermuteInt(13, 500, 25), clipInput=True, w=7, minval=0), }, }, 'tmParams': { 'minThreshold': PermuteInt(9, 12), 'activationThreshold': PermuteInt(12, 16), }, } } # Fields selected for final hypersearch report; # NOTE: These values are used as regular expressions by RunPermutations.py's # report generator # (fieldname values generated from PERM_PREDICTED_FIELD_NAME) report = [ '.*attendance.*', ] # Permutation optimization setting: either minimize or maximize metric # used by RunPermutations. # NOTE: The value is used as a regular expressions by RunPermutations.py's # report generator # (generated from minimize = 'nonprediction:aae:window=1000:field=attendance') minimize = 'prediction:aae:window=1000:field=attendance' def dummyModelParams(perm): """ This function can be used for Hypersearch algorithm development. When present, we don't actually run the CLA model in the OPF, but instead run a dummy model. This function returns the dummy model params that will be used. See the OPFDummyModelRunner class source code (in nupic.swarming.ModelRunner) for a description of the schema for the dummy model params. """ #By contribution order from most to least A,B,C,D,E,F #Want A,E combo to give the most contribution followed by A,D then A,C errScore = 100 #A if not perm['modelParams']['sensorParams']['encoders']['visitor_winloss'] \ is None: errScore -= 25 #B if not perm['modelParams']['sensorParams']['encoders']['home_winloss'] \ is None: errScore -= 20 #C if not perm['modelParams']['sensorParams']['encoders']\ ['timestamp_timeOfDay'] is None: errScore -= 15 #D if not perm['modelParams']['sensorParams']['encoders']\ ['timestamp_dayOfWeek'] is None: errScore -= 10 #E if not perm['modelParams']['sensorParams']['encoders']['precip'] is None: errScore -= 5 #F if not perm['modelParams']['sensorParams']['encoders']['daynight'] is None: errScore += 10 dummyModelParams = dict( metricValue = errScore, metricFunctions = None, ) return dummyModelParams def permutationFilter(perm): """ This function can be used to selectively filter out specific permutation combinations. It is called by RunPermutations for every possible permutation of the variables in the permutations dict. It should return True for valid a combination of permutation values and False for an invalid one. Parameters: --------------------------------------------------------- perm: dict of one possible combination of name:value pairs chosen from permutations. """ # An example of how to use this #if perm['__consumption_encoder']['maxval'] > 300: # return False; # return True

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numenta/nupic-legacy

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26,228

description.py

numenta_nupic-legacy/tests/swarming/nupic/swarming/experiments/dummyV2/description.py

# ---------------------------------------------------------------------- # Numenta Platform for Intelligent Computing (NuPIC) # Copyright (C) 2013, Numenta, Inc. Unless you have an agreement # with Numenta, Inc., for a separate license for this software code, the # following terms and conditions apply: # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU Affero Public License version 3 as # published by the Free Software Foundation. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. # See the GNU Affero Public License for more details. # # You should have received a copy of the GNU Affero Public License # along with this program. If not, see http://www.gnu.org/licenses. # # http://numenta.org/licenses/ # ---------------------------------------------------------------------- """ Template file used by the OPF Experiment Generator to generate the actual description.py file by replacing $XXXXXXXX tokens with desired values. This description.py file was generated by: '/Users/ronmarianetti/nupic/eng/lib/python2.6/site-packages/nupic/frameworks/opf/expGenerator/experiment_generator.py' """ from nupic.frameworks.opf.exp_description_api import ExperimentDescriptionAPI from nupic.frameworks.opf.exp_description_helpers import ( updateConfigFromSubConfig, applyValueGettersToContainer, DeferredDictLookup) from nupic.frameworks.opf.htm_prediction_model_callbacks import * from nupic.frameworks.opf.metrics import MetricSpec from nupic.frameworks.opf.opf_utils import (InferenceType, InferenceElement) from nupic.support import aggregationDivide from nupic.frameworks.opf.opf_task_driver import ( IterationPhaseSpecLearnOnly, IterationPhaseSpecInferOnly, IterationPhaseSpecLearnAndInfer) # Model Configuration Dictionary: # # Define the model parameters and adjust for any modifications if imported # from a sub-experiment. # # These fields might be modified by a sub-experiment; this dict is passed # between the sub-experiment and base experiment # # # NOTE: Use of DEFERRED VALUE-GETTERs: dictionary fields and list elements # within the config dictionary may be assigned futures derived from the # ValueGetterBase class, such as DeferredDictLookup. # This facility is particularly handy for enabling substitution of values in # the config dictionary from other values in the config dictionary, which is # needed by permutation.py-based experiments. These values will be resolved # during the call to applyValueGettersToContainer(), # which we call after the base experiment's config dictionary is updated from # the sub-experiment. See ValueGetterBase and # DeferredDictLookup for more details about value-getters. # # For each custom encoder parameter to be exposed to the sub-experiment/ # permutation overrides, define a variable in this section, using key names # beginning with a single underscore character to avoid collisions with # pre-defined keys (e.g., _dsEncoderFieldName2_N). # # Example: # config = dict( # _dsEncoderFieldName2_N = 70, # _dsEncoderFieldName2_W = 5, # dsEncoderSchema = [ # base=dict( # fieldname='Name2', type='ScalarEncoder', # name='Name2', minval=0, maxval=270, clipInput=True, # n=DeferredDictLookup('_dsEncoderFieldName2_N'), # w=DeferredDictLookup('_dsEncoderFieldName2_W')), # ], # ) # updateConfigFromSubConfig(config) # applyValueGettersToContainer(config) config = { # Type of model that the rest of these parameters apply to. 'model': "HTMPrediction", # Version that specifies the format of the config. 'version': 1, # Intermediate variables used to compute fields in modelParams and also # referenced from the control section. 'aggregationInfo': { 'days': 0, 'fields': [ (u'timestamp', 'first'), (u'gym', 'first'), (u'consumption', 'mean'), (u'address', 'first')], 'hours': 0, 'microseconds': 0, 'milliseconds': 0, 'minutes': 0, 'months': 0, 'seconds': 0, 'weeks': 0, 'years': 0}, 'predictAheadTime': None, # Model parameter dictionary. 'modelParams': { # The type of inference that this model will perform 'inferenceType': 'TemporalNextStep', 'sensorParams': { # Sensor diagnostic output verbosity control; # if > 0: sensor region will print out on screen what it's sensing # at each step 0: silent; >=1: some info; >=2: more info; # >=3: even more info (see compute() in py/regions/RecordSensor.py) 'verbosity' : 0, # Example: # dsEncoderSchema = [ # DeferredDictLookup('__field_name_encoder'), # ], # # (value generated from DS_ENCODER_SCHEMA) 'encoders': { 'address': { 'fieldname': u'address', 'n': 300, 'name': u'address', 'type': 'SDRCategoryEncoder', 'w': 21}, 'consumption': { 'clipInput': True, 'fieldname': u'consumption', 'maxval': 200, 'minval': 0, 'n': 1500, 'name': u'consumption', 'type': 'ScalarEncoder', 'w': 21}, 'gym': { 'fieldname': u'gym', 'n': 600, 'name': u'gym', 'type': 'SDRCategoryEncoder', 'w': 21}, 'timestamp_dayOfWeek': { 'dayOfWeek': (7, 3), 'fieldname': u'timestamp', 'name': u'timestamp_dayOfWeek', 'type': 'DateEncoder'}, 'timestamp_timeOfDay': { 'fieldname': u'timestamp', 'name': u'timestamp_timeOfDay', 'timeOfDay': (7, 8), 'type': 'DateEncoder'}}, # A dictionary specifying the period for automatically-generated # resets from a RecordSensor; # # None = disable automatically-generated resets (also disabled if # all of the specified values evaluate to 0). # Valid keys is the desired combination of the following: # days, hours, minutes, seconds, milliseconds, microseconds, weeks # # Example for 1.5 days: sensorAutoReset = dict(days=1,hours=12), # # (value generated from SENSOR_AUTO_RESET) 'sensorAutoReset' : None, }, 'spEnable': True, 'spParams': { # SP diagnostic output verbosity control; # 0: silent; >=1: some info; >=2: more info; 'spVerbosity' : 0, 'globalInhibition': 1, # Number of cell columns in the cortical region (same number for # SP and TM) # (see also tpNCellsPerCol) 'columnCount': 2048, 'inputWidth': 0, # SP inhibition control (absolute value); # Maximum number of active columns in the SP region's output (when # there are more, the weaker ones are suppressed) 'numActiveColumnsPerInhArea': 40, 'seed': 1956, # potentialPct # What percent of the columns's receptive field is available # for potential synapses. At initialization time, we will # choose potentialPct * (2*potentialRadius+1)^2 'potentialPct': 0.5, # The default connected threshold. Any synapse whose # permanence value is above the connected threshold is # a "connected synapse", meaning it can contribute to the # cell's firing. Typical value is 0.10. Cells whose activity # level before inhibition falls below minDutyCycleBeforeInh # will have their own internal synPermConnectedCell # threshold set below this default value. # (This concept applies to both SP and TM and so 'cells' # is correct here as opposed to 'columns') 'synPermConnected': 0.1, 'synPermActiveInc': 0.1, 'synPermInactiveDec': 0.01, }, # Controls whether TM is enabled or disabled; # TM is necessary for making temporal predictions, such as predicting # the next inputs. Without TM, the model is only capable of # reconstructing missing sensor inputs (via SP). 'tmEnable' : True, 'tmParams': { # TM diagnostic output verbosity control; # 0: silent; [1..6]: increasing levels of verbosity # (see verbosity in nupic/trunk/py/nupic/research/backtracking_tm.py and backtracking_tm_cpp.py) 'verbosity': 0, # Number of cell columns in the cortical region (same number for # SP and TM) # (see also tpNCellsPerCol) 'columnCount': 2048, # The number of cells (i.e., states), allocated per column. 'cellsPerColumn': 32, 'inputWidth': 2048, 'seed': 1960, # Temporal Pooler implementation selector (see _getTPClass in # CLARegion.py). 'temporalImp': 'cpp', # New Synapse formation count # NOTE: If None, use spNumActivePerInhArea # # TODO: need better explanation 'newSynapseCount': 15, # Maximum number of synapses per segment # > 0 for fixed-size CLA # -1 for non-fixed-size CLA # # TODO: for Ron: once the appropriate value is placed in TM # constructor, see if we should eliminate this parameter from # description.py. 'maxSynapsesPerSegment': 32, # Maximum number of segments per cell # > 0 for fixed-size CLA # -1 for non-fixed-size CLA # # TODO: for Ron: once the appropriate value is placed in TM # constructor, see if we should eliminate this parameter from # description.py. 'maxSegmentsPerCell': 128, # Initial Permanence # TODO: need better explanation 'initialPerm': 0.21, # Permanence Increment 'permanenceInc': 0.1, # Permanence Decrement # If set to None, will automatically default to tpPermanenceInc # value. 'permanenceDec' : 0.1, 'globalDecay': 0.0, 'maxAge': 0, # Minimum number of active synapses for a segment to be considered # during search for the best-matching segments. # None=use default # Replaces: tpMinThreshold 'minThreshold': 12, # Segment activation threshold. # A segment is active if it has >= tpSegmentActivationThreshold # connected synapses that are active due to infActiveState # None=use default # Replaces: tpActivationThreshold 'activationThreshold': 16, 'outputType': 'normal', # "Pay Attention Mode" length. This tells the TM how many new # elements to append to the end of a learned sequence at a time. # Smaller values are better for datasets with short sequences, # higher values are better for datasets with long sequences. 'pamLength': 1, }, 'clParams': { 'regionName' : 'SDRClassifierRegion', # Classifier diagnostic output verbosity control; # 0: silent; [1..6]: increasing levels of verbosity 'verbosity' : 0, # This controls how fast the classifier learns/forgets. Higher values # make it adapt faster and forget older patterns faster. 'alpha': 0.001, # This is set after the call to updateConfigFromSubConfig and is # computed from the aggregationInfo and predictAheadTime. 'steps': '1', }, 'trainSPNetOnlyIfRequested': False, }, } # end of config dictionary # Adjust base config dictionary for any modifications if imported from a # sub-experiment updateConfigFromSubConfig(config) # Compute predictionSteps based on the predictAheadTime and the aggregation # period, which may be permuted over. if config['predictAheadTime'] is not None: predictionSteps = int(round(aggregationDivide( config['predictAheadTime'], config['aggregationInfo']))) assert (predictionSteps >= 1) config['modelParams']['clParams']['steps'] = str(predictionSteps) # Adjust config by applying ValueGetterBase-derived # futures. NOTE: this MUST be called after updateConfigFromSubConfig() in order # to support value-getter-based substitutions from the sub-experiment (if any) applyValueGettersToContainer(config) control = { # The environment that the current model is being run in "environment": 'nupic', # Input stream specification per py/nupicengine/cluster/database/StreamDef.json. # 'dataset' : {u'info': u'test_NoProviders', u'streams': [ { u'columns': [u'*'], u'info': "test data", u'source': "file://swarming/test_data.csv"}], u'version': 1}, # Iteration count: maximum number of iterations. Each iteration corresponds # to one record from the (possibly aggregated) dataset. The task is # terminated when either number of iterations reaches iterationCount or # all records in the (possibly aggregated) database have been processed, # whichever occurs first. # # iterationCount of -1 = iterate over the entire dataset #'iterationCount' : ITERATION_COUNT, # Metrics: A list of MetricSpecs that instantiate the metrics that are # computed for this experiment 'metrics':[ MetricSpec(field=u'consumption',inferenceElement=InferenceElement.prediction, metric='rmse'), ], # Logged Metrics: A sequence of regular expressions that specify which of # the metrics from the Inference Specifications section MUST be logged for # every prediction. The regex's correspond to the automatically generated # metric labels. This is similar to the way the optimization metric is # specified in permutations.py. } descriptionInterface = ExperimentDescriptionAPI(modelConfig=config, control=control)

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26,229

permutations.py

numenta_nupic-legacy/tests/swarming/nupic/swarming/experiments/dummyV2/permutations.py

# ---------------------------------------------------------------------- # Numenta Platform for Intelligent Computing (NuPIC) # Copyright (C) 2013, Numenta, Inc. Unless you have an agreement # with Numenta, Inc., for a separate license for this software code, the # following terms and conditions apply: # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU Affero Public License version 3 as # published by the Free Software Foundation. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. # See the GNU Affero Public License for more details. # # You should have received a copy of the GNU Affero Public License # along with this program. If not, see http://www.gnu.org/licenses. # # http://numenta.org/licenses/ # ---------------------------------------------------------------------- """ Template file used by ExpGenerator to generate the actual permutations.py file by replacing $XXXXXXXX tokens with desired values. This permutations.py file was generated by: '/Users/ronmarianetti/nupic/eng/lib/python2.6/site-packages/nupic/frameworks/opf/expGenerator/experiment_generator.py' """ import os from nupic.swarming.permutation_helpers import * # The name of the field being predicted. Any allowed permutation MUST contain # the prediction field. # (generated from PREDICTION_FIELD) predictedField = 'consumption' permutations = { 'modelParams': { 'sensorParams': { 'encoders': { 'gym': PermuteEncoder(fieldName='gym', encoderClass='SDRCategoryEncoder', w=21, n=300), 'timestamp_dayOfWeek': PermuteEncoder(fieldName='timestamp', encoderClass='DateEncoder.dayOfWeek', radius=PermuteChoices([1, 3]), w=7), 'timestamp_timeOfDay': PermuteEncoder(fieldName='timestamp', encoderClass='DateEncoder.timeOfDay', radius=PermuteChoices([1, 8]), w=7), 'consumption': PermuteEncoder(fieldName='consumption', encoderClass='ScalarEncoder', maxval=PermuteInt(100, 300, 25), n=PermuteInt(39, 1500, 60), w=21, minval=0), 'address': PermuteEncoder(fieldName='address', encoderClass='SDRCategoryEncoder', w=21, n=300), }, }, 'tmParams': { 'minThreshold': PermuteInt(9, 12), 'activationThreshold': PermuteInt(12, 16), }, } } # Fields selected for final hypersearch report; # NOTE: These values are used as regular expressions by RunPermutations.py's # report generator # (fieldname values generated from PERM_PREDICTED_FIELD_NAME) report = [ '.*consumption.*', ] # Permutation optimization setting: either minimize or maximize metric # used by RunPermutations. # NOTE: The value is used as a regular expressions by RunPermutations.py's # report generator # (generated from minimize = 'prediction:rmse:field=consumption') minimize = 'prediction:rmse:field=consumption' def permutationFilter(perm): """ This function can be used to selectively filter out specific permutation combinations. It is called by RunPermutations for every possible permutation of the variables in the permutations dict. It should return True for valid a combination of permutation values and False for an invalid one. Parameters: --------------------------------------------------------- perm: dict of one possible combination of name:value pairs chosen from permutations. """ # An example of how to use this if perm['modelParams']['sensorParams']['encoders']['consumption']['maxval'] > 250: return False; return True

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LICENSE.tweepy-2.1.txt

numenta_nupic-legacy/external/licenses/LICENSE.tweepy-2.1.txt

MIT License Copyright (c) 2009-2010 Joshua Roesslein Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

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numenta/nupic-legacy

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26,231

pkg_resources.py

numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/pkg_resources.py

"""Package resource API -------------------- A resource is a logical file contained within a package, or a logical subdirectory thereof. The package resource API expects resource names to have their path parts separated with ``/``, *not* whatever the local path separator is. Do not use os.path operations to manipulate resource names being passed into the API. The package resource API is designed to work with normal filesystem packages, .egg files, and unpacked .egg files. It can also work in a limited way with .zip files and with custom PEP 302 loaders that support the ``get_data()`` method. """ import sys, os, zipimport, time, re, imp, new try: frozenset except NameError: from sets import ImmutableSet as frozenset from os import utime, rename, unlink # capture these to bypass sandboxing from os import open as os_open def get_supported_platform(): """Return this platform's maximum compatible version. distutils.util.get_platform() normally reports the minimum version of Mac OS X that would be required to *use* extensions produced by distutils. But what we want when checking compatibility is to know the version of Mac OS X that we are *running*. To allow usage of packages that explicitly require a newer version of Mac OS X, we must also know the current version of the OS. If this condition occurs for any other platform with a version in its platform strings, this function should be extended accordingly. """ plat = get_build_platform(); m = macosVersionString.match(plat) if m is not None and sys.platform == "darwin": try: plat = 'macosx-%s-%s' % ('.'.join(_macosx_vers()[:2]), m.group(3)) except ValueError: pass # not Mac OS X return plat __all__ = [ # Basic resource access and distribution/entry point discovery 'require', 'run_script', 'get_provider', 'get_distribution', 'load_entry_point', 'get_entry_map', 'get_entry_info', 'iter_entry_points', 'resource_string', 'resource_stream', 'resource_filename', 'resource_listdir', 'resource_exists', 'resource_isdir', # Environmental control 'declare_namespace', 'working_set', 'add_activation_listener', 'find_distributions', 'set_extraction_path', 'cleanup_resources', 'get_default_cache', # Primary implementation classes 'Environment', 'WorkingSet', 'ResourceManager', 'Distribution', 'Requirement', 'EntryPoint', # Exceptions 'ResolutionError','VersionConflict','DistributionNotFound','UnknownExtra', 'ExtractionError', # Parsing functions and string utilities 'parse_requirements', 'parse_version', 'safe_name', 'safe_version', 'get_platform', 'compatible_platforms', 'yield_lines', 'split_sections', 'safe_extra', 'to_filename', # filesystem utilities 'ensure_directory', 'normalize_path', # Distribution "precedence" constants 'EGG_DIST', 'BINARY_DIST', 'SOURCE_DIST', 'CHECKOUT_DIST', 'DEVELOP_DIST', # "Provider" interfaces, implementations, and registration/lookup APIs 'IMetadataProvider', 'IResourceProvider', 'FileMetadata', 'PathMetadata', 'EggMetadata', 'EmptyProvider', 'empty_provider', 'NullProvider', 'EggProvider', 'DefaultProvider', 'ZipProvider', 'register_finder', 'register_namespace_handler', 'register_loader_type', 'fixup_namespace_packages', 'get_importer', # Deprecated/backward compatibility only 'run_main', 'AvailableDistributions', ] class ResolutionError(Exception): """Abstract base for dependency resolution errors""" def __repr__(self): return self.__class__.__name__+repr(self.args) class VersionConflict(ResolutionError): """An already-installed version conflicts with the requested version""" class DistributionNotFound(ResolutionError): """A requested distribution was not found""" class UnknownExtra(ResolutionError): """Distribution doesn't have an "extra feature" of the given name""" _provider_factories = {} PY_MAJOR = sys.version[:3] EGG_DIST = 3 BINARY_DIST = 2 SOURCE_DIST = 1 CHECKOUT_DIST = 0 DEVELOP_DIST = -1 def register_loader_type(loader_type, provider_factory): """Register `provider_factory` to make providers for `loader_type` `loader_type` is the type or class of a PEP 302 ``module.__loader__``, and `provider_factory` is a function that, passed a *module* object, returns an ``IResourceProvider`` for that module. """ _provider_factories[loader_type] = provider_factory def get_provider(moduleOrReq): """Return an IResourceProvider for the named module or requirement""" if isinstance(moduleOrReq,Requirement): return working_set.find(moduleOrReq) or require(str(moduleOrReq))[0] try: module = sys.modules[moduleOrReq] except KeyError: __import__(moduleOrReq) module = sys.modules[moduleOrReq] loader = getattr(module, '__loader__', None) return _find_adapter(_provider_factories, loader)(module) def _macosx_vers(_cache=[]): if not _cache: info = os.popen('/usr/bin/sw_vers').read().splitlines() for line in info: key, value = line.split(None, 1) if key == 'ProductVersion:': _cache.append(value.strip().split(".")) break else: raise ValueError, "What?!" return _cache[0] def _macosx_arch(machine): return {'PowerPC':'ppc', 'Power_Macintosh':'ppc'}.get(machine,machine) def get_build_platform(): """Return this platform's string for platform-specific distributions XXX Currently this is the same as ``distutils.util.get_platform()``, but it needs some hacks for Linux and Mac OS X. """ from distutils.util import get_platform plat = get_platform() if sys.platform == "darwin" and not plat.startswith('macosx-'): try: version = _macosx_vers() machine = os.uname()[4].replace(" ", "_") return "macosx-%d.%d-%s" % (int(version[0]), int(version[1]), _macosx_arch(machine)) except ValueError: # if someone is running a non-Mac darwin system, this will fall # through to the default implementation pass return plat macosVersionString = re.compile(r"macosx-(\d+)\.(\d+)-(.*)") darwinVersionString = re.compile(r"darwin-(\d+)\.(\d+)\.(\d+)-(.*)") get_platform = get_build_platform # XXX backward compat def compatible_platforms(provided,required): """Can code for the `provided` platform run on the `required` platform? Returns true if either platform is ``None``, or the platforms are equal. XXX Needs compatibility checks for Linux and other unixy OSes. """ if provided is None or required is None or provided==required: return True # easy case # Mac OS X special cases reqMac = macosVersionString.match(required) if reqMac: provMac = macosVersionString.match(provided) # is this a Mac package? if not provMac: # this is backwards compatibility for packages built before # setuptools 0.6. All packages built after this point will # use the new macosx designation. provDarwin = darwinVersionString.match(provided) if provDarwin: dversion = int(provDarwin.group(1)) macosversion = "%s.%s" % (reqMac.group(1), reqMac.group(2)) if dversion == 7 and macosversion >= "10.3" or \ dversion == 8 and macosversion >= "10.4": #import warnings #warnings.warn("Mac eggs should be rebuilt to " # "use the macosx designation instead of darwin.", # category=DeprecationWarning) return True return False # egg isn't macosx or legacy darwin # are they the same major version and machine type? if provMac.group(1) != reqMac.group(1) or \ provMac.group(3) != reqMac.group(3): return False # is the required OS major update >= the provided one? if int(provMac.group(2)) > int(reqMac.group(2)): return False return True # XXX Linux and other platforms' special cases should go here return False def run_script(dist_spec, script_name): """Locate distribution `dist_spec` and run its `script_name` script""" ns = sys._getframe(1).f_globals name = ns['__name__'] ns.clear() ns['__name__'] = name require(dist_spec)[0].run_script(script_name, ns) run_main = run_script # backward compatibility def get_distribution(dist): """Return a current distribution object for a Requirement or string""" if isinstance(dist,basestring): dist = Requirement.parse(dist) if isinstance(dist,Requirement): dist = get_provider(dist) if not isinstance(dist,Distribution): raise TypeError("Expected string, Requirement, or Distribution", dist) return dist def load_entry_point(dist, group, name): """Return `name` entry point of `group` for `dist` or raise ImportError""" return get_distribution(dist).load_entry_point(group, name) def get_entry_map(dist, group=None): """Return the entry point map for `group`, or the full entry map""" return get_distribution(dist).get_entry_map(group) def get_entry_info(dist, group, name): """Return the EntryPoint object for `group`+`name`, or ``None``""" return get_distribution(dist).get_entry_info(group, name) class IMetadataProvider: def has_metadata(name): """Does the package's distribution contain the named metadata?""" def get_metadata(name): """The named metadata resource as a string""" def get_metadata_lines(name): """Yield named metadata resource as list of non-blank non-comment lines Leading and trailing whitespace is stripped from each line, and lines with ``#`` as the first non-blank character are omitted.""" def metadata_isdir(name): """Is the named metadata a directory? (like ``os.path.isdir()``)""" def metadata_listdir(name): """List of metadata names in the directory (like ``os.listdir()``)""" def run_script(script_name, namespace): """Execute the named script in the supplied namespace dictionary""" class IResourceProvider(IMetadataProvider): """An object that provides access to package resources""" def get_resource_filename(manager, resource_name): """Return a true filesystem path for `resource_name` `manager` must be an ``IResourceManager``""" def get_resource_stream(manager, resource_name): """Return a readable file-like object for `resource_name` `manager` must be an ``IResourceManager``""" def get_resource_string(manager, resource_name): """Return a string containing the contents of `resource_name` `manager` must be an ``IResourceManager``""" def has_resource(resource_name): """Does the package contain the named resource?""" def resource_isdir(resource_name): """Is the named resource a directory? (like ``os.path.isdir()``)""" def resource_listdir(resource_name): """List of resource names in the directory (like ``os.listdir()``)""" class WorkingSet(object): """A collection of active distributions on sys.path (or a similar list)""" def __init__(self, entries=None): """Create working set from list of path entries (default=sys.path)""" self.entries = [] self.entry_keys = {} self.by_key = {} self.callbacks = [] if entries is None: entries = sys.path for entry in entries: self.add_entry(entry) def add_entry(self, entry): """Add a path item to ``.entries``, finding any distributions on it ``find_distributions(entry,False)`` is used to find distributions corresponding to the path entry, and they are added. `entry` is always appended to ``.entries``, even if it is already present. (This is because ``sys.path`` can contain the same value more than once, and the ``.entries`` of the ``sys.path`` WorkingSet should always equal ``sys.path``.) """ self.entry_keys.setdefault(entry, []) self.entries.append(entry) for dist in find_distributions(entry, True): self.add(dist, entry, False) def __contains__(self,dist): """True if `dist` is the active distribution for its project""" return self.by_key.get(dist.key) == dist def find(self, req): """Find a distribution matching requirement `req` If there is an active distribution for the requested project, this returns it as long as it meets the version requirement specified by `req`. But, if there is an active distribution for the project and it does *not* meet the `req` requirement, ``VersionConflict`` is raised. If there is no active distribution for the requested project, ``None`` is returned. """ dist = self.by_key.get(req.key) if dist is not None and dist not in req: raise VersionConflict(dist,req) # XXX add more info else: return dist def iter_entry_points(self, group, name=None): """Yield entry point objects from `group` matching `name` If `name` is None, yields all entry points in `group` from all distributions in the working set, otherwise only ones matching both `group` and `name` are yielded (in distribution order). """ for dist in self: entries = dist.get_entry_map(group) if name is None: for ep in entries.values(): yield ep elif name in entries: yield entries[name] def run_script(self, requires, script_name): """Locate distribution for `requires` and run `script_name` script""" ns = sys._getframe(1).f_globals name = ns['__name__'] ns.clear() ns['__name__'] = name self.require(requires)[0].run_script(script_name, ns) def __iter__(self): """Yield distributions for non-duplicate projects in the working set The yield order is the order in which the items' path entries were added to the working set. """ seen = {} for item in self.entries: for key in self.entry_keys[item]: if key not in seen: seen[key]=1 yield self.by_key[key] def add(self, dist, entry=None, insert=True): """Add `dist` to working set, associated with `entry` If `entry` is unspecified, it defaults to the ``.location`` of `dist`. On exit from this routine, `entry` is added to the end of the working set's ``.entries`` (if it wasn't already present). `dist` is only added to the working set if it's for a project that doesn't already have a distribution in the set. If it's added, any callbacks registered with the ``subscribe()`` method will be called. """ if insert: dist.insert_on(self.entries, entry) if entry is None: entry = dist.location keys = self.entry_keys.setdefault(entry,[]) keys2 = self.entry_keys.setdefault(dist.location,[]) if dist.key in self.by_key: return # ignore hidden distros self.by_key[dist.key] = dist if dist.key not in keys: keys.append(dist.key) if dist.key not in keys2: keys2.append(dist.key) self._added_new(dist) def resolve(self, requirements, env=None, installer=None): """List all distributions needed to (recursively) meet `requirements` `requirements` must be a sequence of ``Requirement`` objects. `env`, if supplied, should be an ``Environment`` instance. If not supplied, it defaults to all distributions available within any entry or distribution in the working set. `installer`, if supplied, will be invoked with each requirement that cannot be met by an already-installed distribution; it should return a ``Distribution`` or ``None``. """ requirements = list(requirements)[::-1] # set up the stack processed = {} # set of processed requirements best = {} # key -> dist to_activate = [] while requirements: req = requirements.pop(0) # process dependencies breadth-first if req in processed: # Ignore cyclic or redundant dependencies continue dist = best.get(req.key) if dist is None: # Find the best distribution and add it to the map dist = self.by_key.get(req.key) if dist is None: if env is None: env = Environment(self.entries) dist = best[req.key] = env.best_match(req, self, installer) if dist is None: raise DistributionNotFound(req) # XXX put more info here to_activate.append(dist) if dist not in req: # Oops, the "best" so far conflicts with a dependency raise VersionConflict(dist,req) # XXX put more info here requirements.extend(dist.requires(req.extras)[::-1]) processed[req] = True return to_activate # return list of distros to activate def find_plugins(self, plugin_env, full_env=None, installer=None, fallback=True ): """Find all activatable distributions in `plugin_env` Example usage:: distributions, errors = working_set.find_plugins( Environment(plugin_dirlist) ) map(working_set.add, distributions) # add plugins+libs to sys.path print "Couldn't load", errors # display errors The `plugin_env` should be an ``Environment`` instance that contains only distributions that are in the project's "plugin directory" or directories. The `full_env`, if supplied, should be an ``Environment`` contains all currently-available distributions. If `full_env` is not supplied, one is created automatically from the ``WorkingSet`` this method is called on, which will typically mean that every directory on ``sys.path`` will be scanned for distributions. `installer` is a standard installer callback as used by the ``resolve()`` method. The `fallback` flag indicates whether we should attempt to resolve older versions of a plugin if the newest version cannot be resolved. This method returns a 2-tuple: (`distributions`, `error_info`), where `distributions` is a list of the distributions found in `plugin_env` that were loadable, along with any other distributions that are needed to resolve their dependencies. `error_info` is a dictionary mapping unloadable plugin distributions to an exception instance describing the error that occurred. Usually this will be a ``DistributionNotFound`` or ``VersionConflict`` instance. """ plugin_projects = list(plugin_env) plugin_projects.sort() # scan project names in alphabetic order error_info = {} distributions = {} if full_env is None: env = Environment(self.entries) env += plugin_env else: env = full_env + plugin_env shadow_set = self.__class__([]) map(shadow_set.add, self) # put all our entries in shadow_set for project_name in plugin_projects: for dist in plugin_env[project_name]: req = [dist.as_requirement()] try: resolvees = shadow_set.resolve(req, env, installer) except ResolutionError,v: error_info[dist] = v # save error info if fallback: continue # try the next older version of project else: break # give up on this project, keep going else: map(shadow_set.add, resolvees) distributions.update(dict.fromkeys(resolvees)) # success, no need to try any more versions of this project break distributions = list(distributions) distributions.sort() return distributions, error_info def require(self, *requirements): """Ensure that distributions matching `requirements` are activated `requirements` must be a string or a (possibly-nested) sequence thereof, specifying the distributions and versions required. The return value is a sequence of the distributions that needed to be activated to fulfill the requirements; all relevant distributions are included, even if they were already activated in this working set. """ needed = self.resolve(parse_requirements(requirements)) for dist in needed: self.add(dist) return needed def subscribe(self, callback): """Invoke `callback` for all distributions (including existing ones)""" if callback in self.callbacks: return self.callbacks.append(callback) for dist in self: callback(dist) def _added_new(self, dist): for callback in self.callbacks: callback(dist) class Environment(object): """Searchable snapshot of distributions on a search path""" def __init__(self, search_path=None, platform=get_supported_platform(), python=PY_MAJOR): """Snapshot distributions available on a search path Any distributions found on `search_path` are added to the environment. `search_path` should be a sequence of ``sys.path`` items. If not supplied, ``sys.path`` is used. `platform` is an optional string specifying the name of the platform that platform-specific distributions must be compatible with. If unspecified, it defaults to the current platform. `python` is an optional string naming the desired version of Python (e.g. ``'2.4'``); it defaults to the current version. You may explicitly set `platform` (and/or `python`) to ``None`` if you wish to map *all* distributions, not just those compatible with the running platform or Python version. """ self._distmap = {} self._cache = {} self.platform = platform self.python = python self.scan(search_path) def can_add(self, dist): """Is distribution `dist` acceptable for this environment? The distribution must match the platform and python version requirements specified when this environment was created, or False is returned. """ return (self.python is None or dist.py_version is None or dist.py_version==self.python) \ and compatible_platforms(dist.platform,self.platform) def remove(self, dist): """Remove `dist` from the environment""" self._distmap[dist.key].remove(dist) def scan(self, search_path=None): """Scan `search_path` for distributions usable in this environment Any distributions found are added to the environment. `search_path` should be a sequence of ``sys.path`` items. If not supplied, ``sys.path`` is used. Only distributions conforming to the platform/python version defined at initialization are added. """ if search_path is None: search_path = sys.path for item in search_path: for dist in find_distributions(item): self.add(dist) def __getitem__(self,project_name): """Return a newest-to-oldest list of distributions for `project_name` """ try: return self._cache[project_name] except KeyError: project_name = project_name.lower() if project_name not in self._distmap: return [] if project_name not in self._cache: dists = self._cache[project_name] = self._distmap[project_name] _sort_dists(dists) return self._cache[project_name] def add(self,dist): """Add `dist` if we ``can_add()`` it and it isn't already added""" if self.can_add(dist) and dist.has_version(): dists = self._distmap.setdefault(dist.key,[]) if dist not in dists: dists.append(dist) if dist.key in self._cache: _sort_dists(self._cache[dist.key]) def best_match(self, req, working_set, installer=None): """Find distribution best matching `req` and usable on `working_set` This calls the ``find(req)`` method of the `working_set` to see if a suitable distribution is already active. (This may raise ``VersionConflict`` if an unsuitable version of the project is already active in the specified `working_set`.) If a suitable distribution isn't active, this method returns the newest distribution in the environment that meets the ``Requirement`` in `req`. If no suitable distribution is found, and `installer` is supplied, then the result of calling the environment's ``obtain(req, installer)`` method will be returned. """ dist = working_set.find(req) if dist is not None: return dist for dist in self[req.key]: if dist in req: return dist return self.obtain(req, installer) # try and download/install def obtain(self, requirement, installer=None): """Obtain a distribution matching `requirement` (e.g. via download) Obtain a distro that matches requirement (e.g. via download). In the base ``Environment`` class, this routine just returns ``installer(requirement)``, unless `installer` is None, in which case None is returned instead. This method is a hook that allows subclasses to attempt other ways of obtaining a distribution before falling back to the `installer` argument.""" if installer is not None: return installer(requirement) def __iter__(self): """Yield the unique project names of the available distributions""" for key in self._distmap.keys(): if self[key]: yield key def __iadd__(self, other): """In-place addition of a distribution or environment""" if isinstance(other,Distribution): self.add(other) elif isinstance(other,Environment): for project in other: for dist in other[project]: self.add(dist) else: raise TypeError("Can't add %r to environment" % (other,)) return self def __add__(self, other): """Add an environment or distribution to an environment""" new = self.__class__([], platform=None, python=None) for env in self, other: new += env return new AvailableDistributions = Environment # XXX backward compatibility class ExtractionError(RuntimeError): """An error occurred extracting a resource The following attributes are available from instances of this exception: manager The resource manager that raised this exception cache_path The base directory for resource extraction original_error The exception instance that caused extraction to fail """ class ResourceManager: """Manage resource extraction and packages""" extraction_path = None def __init__(self): self.cached_files = {} def resource_exists(self, package_or_requirement, resource_name): """Does the named resource exist?""" return get_provider(package_or_requirement).has_resource(resource_name) def resource_isdir(self, package_or_requirement, resource_name): """Is the named resource an existing directory?""" return get_provider(package_or_requirement).resource_isdir( resource_name ) def resource_filename(self, package_or_requirement, resource_name): """Return a true filesystem path for specified resource""" return get_provider(package_or_requirement).get_resource_filename( self, resource_name ) def resource_stream(self, package_or_requirement, resource_name): """Return a readable file-like object for specified resource""" return get_provider(package_or_requirement).get_resource_stream( self, resource_name ) def resource_string(self, package_or_requirement, resource_name): """Return specified resource as a string""" return get_provider(package_or_requirement).get_resource_string( self, resource_name ) def resource_listdir(self, package_or_requirement, resource_name): """List the contents of the named resource directory""" return get_provider(package_or_requirement).resource_listdir( resource_name ) def extraction_error(self): """Give an error message for problems extracting file(s)""" old_exc = sys.exc_info()[1] cache_path = self.extraction_path or get_default_cache() err = ExtractionError("""Can't extract file(s) to egg cache The following error occurred while trying to extract file(s) to the Python egg cache: %s The Python egg cache directory is currently set to: %s Perhaps your account does not have write access to this directory? You can change the cache directory by setting the PYTHON_EGG_CACHE environment variable to point to an accessible directory. """ % (old_exc, cache_path) ) err.manager = self err.cache_path = cache_path err.original_error = old_exc raise err def get_cache_path(self, archive_name, names=()): """Return absolute location in cache for `archive_name` and `names` The parent directory of the resulting path will be created if it does not already exist. `archive_name` should be the base filename of the enclosing egg (which may not be the name of the enclosing zipfile!), including its ".egg" extension. `names`, if provided, should be a sequence of path name parts "under" the egg's extraction location. This method should only be called by resource providers that need to obtain an extraction location, and only for names they intend to extract, as it tracks the generated names for possible cleanup later. """ extract_path = self.extraction_path or get_default_cache() target_path = os.path.join(extract_path, archive_name+'-tmp', *names) try: ensure_directory(target_path) except: self.extraction_error() self.cached_files[target_path] = 1 return target_path def postprocess(self, tempname, filename): """Perform any platform-specific postprocessing of `tempname` This is where Mac header rewrites should be done; other platforms don't have anything special they should do. Resource providers should call this method ONLY after successfully extracting a compressed resource. They must NOT call it on resources that are already in the filesystem. `tempname` is the current (temporary) name of the file, and `filename` is the name it will be renamed to by the caller after this routine returns. """ if os.name == 'posix': # Make the resource executable mode = ((os.stat(tempname).st_mode) | 0555) & 07777 os.chmod(tempname, mode) def set_extraction_path(self, path): """Set the base path where resources will be extracted to, if needed. If you do not call this routine before any extractions take place, the path defaults to the return value of ``get_default_cache()``. (Which is based on the ``PYTHON_EGG_CACHE`` environment variable, with various platform-specific fallbacks. See that routine's documentation for more details.) Resources are extracted to subdirectories of this path based upon information given by the ``IResourceProvider``. You may set this to a temporary directory, but then you must call ``cleanup_resources()`` to delete the extracted files when done. There is no guarantee that ``cleanup_resources()`` will be able to remove all extracted files. (Note: you may not change the extraction path for a given resource manager once resources have been extracted, unless you first call ``cleanup_resources()``.) """ if self.cached_files: raise ValueError( "Can't change extraction path, files already extracted" ) self.extraction_path = path def cleanup_resources(self, force=False): """ Delete all extracted resource files and directories, returning a list of the file and directory names that could not be successfully removed. This function does not have any concurrency protection, so it should generally only be called when the extraction path is a temporary directory exclusive to a single process. This method is not automatically called; you must call it explicitly or register it as an ``atexit`` function if you wish to ensure cleanup of a temporary directory used for extractions. """ # XXX def get_default_cache(): """Determine the default cache location This returns the ``PYTHON_EGG_CACHE`` environment variable, if set. Otherwise, on Windows, it returns a "Python-Eggs" subdirectory of the "Application Data" directory. On all other systems, it's "~/.python-eggs". """ try: return os.environ['PYTHON_EGG_CACHE'] except KeyError: pass if os.name!='nt': return os.path.expanduser('~/.python-eggs') app_data = 'Application Data' # XXX this may be locale-specific! app_homes = [ (('APPDATA',), None), # best option, should be locale-safe (('USERPROFILE',), app_data), (('HOMEDRIVE','HOMEPATH'), app_data), (('HOMEPATH',), app_data), (('HOME',), None), (('WINDIR',), app_data), # 95/98/ME ] for keys, subdir in app_homes: dirname = '' for key in keys: if key in os.environ: dirname = os.path.join(dirname, os.environ[key]) else: break else: if subdir: dirname = os.path.join(dirname,subdir) return os.path.join(dirname, 'Python-Eggs') else: raise RuntimeError( "Please set the PYTHON_EGG_CACHE enviroment variable" ) def safe_name(name): """Convert an arbitrary string to a standard distribution name Any runs of non-alphanumeric/. characters are replaced with a single '-'. """ return re.sub('[^A-Za-z0-9.]+', '-', name) def safe_version(version): """Convert an arbitrary string to a standard version string Spaces become dots, and all other non-alphanumeric characters become dashes, with runs of multiple dashes condensed to a single dash. """ version = version.replace(' ','.') return re.sub('[^A-Za-z0-9.]+', '-', version) def safe_extra(extra): """Convert an arbitrary string to a standard 'extra' name Any runs of non-alphanumeric characters are replaced with a single '_', and the result is always lowercased. """ return re.sub('[^A-Za-z0-9.]+', '_', extra).lower() def to_filename(name): """Convert a project or version name to its filename-escaped form Any '-' characters are currently replaced with '_'. """ return name.replace('-','_') class NullProvider: """Try to implement resources and metadata for arbitrary PEP 302 loaders""" egg_name = None egg_info = None loader = None def __init__(self, module): self.loader = getattr(module, '__loader__', None) self.module_path = os.path.dirname(getattr(module, '__file__', '')) def get_resource_filename(self, manager, resource_name): return self._fn(self.module_path, resource_name) def get_resource_stream(self, manager, resource_name): return StringIO(self.get_resource_string(manager, resource_name)) def get_resource_string(self, manager, resource_name): return self._get(self._fn(self.module_path, resource_name)) def has_resource(self, resource_name): return self._has(self._fn(self.module_path, resource_name)) def has_metadata(self, name): return self.egg_info and self._has(self._fn(self.egg_info,name)) def get_metadata(self, name): if not self.egg_info: return "" return self._get(self._fn(self.egg_info,name)) def get_metadata_lines(self, name): return yield_lines(self.get_metadata(name)) def resource_isdir(self,resource_name): return self._isdir(self._fn(self.module_path, resource_name)) def metadata_isdir(self,name): return self.egg_info and self._isdir(self._fn(self.egg_info,name)) def resource_listdir(self,resource_name): return self._listdir(self._fn(self.module_path,resource_name)) def metadata_listdir(self,name): if self.egg_info: return self._listdir(self._fn(self.egg_info,name)) return [] def run_script(self,script_name,namespace): script = 'scripts/'+script_name if not self.has_metadata(script): raise ResolutionError("No script named %r" % script_name) script_text = self.get_metadata(script).replace('\r\n','\n') script_text = script_text.replace('\r','\n') script_filename = self._fn(self.egg_info,script) namespace['__file__'] = script_filename if os.path.exists(script_filename): execfile(script_filename, namespace, namespace) else: from linecache import cache cache[script_filename] = ( len(script_text), 0, script_text.split('\n'), script_filename ) script_code = compile(script_text,script_filename,'exec') exec script_code in namespace, namespace def _has(self, path): raise NotImplementedError( "Can't perform this operation for unregistered loader type" ) def _isdir(self, path): raise NotImplementedError( "Can't perform this operation for unregistered loader type" ) def _listdir(self, path): raise NotImplementedError( "Can't perform this operation for unregistered loader type" ) def _fn(self, base, resource_name): return os.path.join(base, *resource_name.split('/')) def _get(self, path): if hasattr(self.loader, 'get_data'): return self.loader.get_data(path) raise NotImplementedError( "Can't perform this operation for loaders without 'get_data()'" ) register_loader_type(object, NullProvider) class EggProvider(NullProvider): """Provider based on a virtual filesystem""" def __init__(self,module): NullProvider.__init__(self,module) self._setup_prefix() def _setup_prefix(self): # we assume here that our metadata may be nested inside a "basket" # of multiple eggs; that's why we use module_path instead of .archive path = self.module_path old = None while path!=old: if path.lower().endswith('.egg'): self.egg_name = os.path.basename(path) self.egg_info = os.path.join(path, 'EGG-INFO') self.egg_root = path break old = path path, base = os.path.split(path) class DefaultProvider(EggProvider): """Provides access to package resources in the filesystem""" def _has(self, path): return os.path.exists(path) def _isdir(self,path): return os.path.isdir(path) def _listdir(self,path): return os.listdir(path) def get_resource_stream(self, manager, resource_name): return open(self._fn(self.module_path, resource_name), 'rb') def _get(self, path): stream = open(path, 'rb') try: return stream.read() finally: stream.close() register_loader_type(type(None), DefaultProvider) class EmptyProvider(NullProvider): """Provider that returns nothing for all requests""" _isdir = _has = lambda self,path: False _get = lambda self,path: '' _listdir = lambda self,path: [] module_path = None def __init__(self): pass empty_provider = EmptyProvider() class ZipProvider(EggProvider): """Resource support for zips and eggs""" eagers = None def __init__(self, module): EggProvider.__init__(self,module) self.zipinfo = zipimport._zip_directory_cache[self.loader.archive] self.zip_pre = self.loader.archive+os.sep def _zipinfo_name(self, fspath): # Convert a virtual filename (full path to file) into a zipfile subpath # usable with the zipimport directory cache for our target archive if fspath.startswith(self.zip_pre): return fspath[len(self.zip_pre):] raise AssertionError( "%s is not a subpath of %s" % (fspath,self.zip_pre) ) def _parts(self,zip_path): # Convert a zipfile subpath into an egg-relative path part list fspath = self.zip_pre+zip_path # pseudo-fs path if fspath.startswith(self.egg_root+os.sep): return fspath[len(self.egg_root)+1:].split(os.sep) raise AssertionError( "%s is not a subpath of %s" % (fspath,self.egg_root) ) def get_resource_filename(self, manager, resource_name): if not self.egg_name: raise NotImplementedError( "resource_filename() only supported for .egg, not .zip" ) # no need to lock for extraction, since we use temp names zip_path = self._resource_to_zip(resource_name) eagers = self._get_eager_resources() if '/'.join(self._parts(zip_path)) in eagers: for name in eagers: self._extract_resource(manager, self._eager_to_zip(name)) return self._extract_resource(manager, zip_path) def _extract_resource(self, manager, zip_path): if zip_path in self._index(): for name in self._index()[zip_path]: last = self._extract_resource( manager, os.path.join(zip_path, name) ) return os.path.dirname(last) # return the extracted directory name zip_stat = self.zipinfo[zip_path] t,d,size = zip_stat[5], zip_stat[6], zip_stat[3] date_time = ( (d>>9)+1980, (d>>5)&0xF, d&0x1F, # ymd (t&0xFFFF)>>11, (t>>5)&0x3F, (t&0x1F) * 2, 0, 0, -1 # hms, etc. ) timestamp = time.mktime(date_time) try: real_path = manager.get_cache_path( self.egg_name, self._parts(zip_path) ) if os.path.isfile(real_path): stat = os.stat(real_path) if stat.st_size==size and stat.st_mtime==timestamp: # size and stamp match, don't bother extracting return real_path outf, tmpnam = _mkstemp(".$extract", dir=os.path.dirname(real_path)) os.write(outf, self.loader.get_data(zip_path)) os.close(outf) utime(tmpnam, (timestamp,timestamp)) manager.postprocess(tmpnam, real_path) try: rename(tmpnam, real_path) except os.error: if os.path.isfile(real_path): stat = os.stat(real_path) if stat.st_size==size and stat.st_mtime==timestamp: # size and stamp match, somebody did it just ahead of # us, so we're done return real_path elif os.name=='nt': # Windows, del old file and retry unlink(real_path) rename(tmpnam, real_path) return real_path raise except os.error: manager.extraction_error() # report a user-friendly error return real_path def _get_eager_resources(self): if self.eagers is None: eagers = [] for name in ('native_libs.txt', 'eager_resources.txt'): if self.has_metadata(name): eagers.extend(self.get_metadata_lines(name)) self.eagers = eagers return self.eagers def _index(self): try: return self._dirindex except AttributeError: ind = {} for path in self.zipinfo: parts = path.split(os.sep) while parts: parent = os.sep.join(parts[:-1]) if parent in ind: ind[parent].append(parts[-1]) break else: ind[parent] = [parts.pop()] self._dirindex = ind return ind def _has(self, fspath): zip_path = self._zipinfo_name(fspath) return zip_path in self.zipinfo or zip_path in self._index() def _isdir(self,fspath): return self._zipinfo_name(fspath) in self._index() def _listdir(self,fspath): return list(self._index().get(self._zipinfo_name(fspath), ())) def _eager_to_zip(self,resource_name): return self._zipinfo_name(self._fn(self.egg_root,resource_name)) def _resource_to_zip(self,resource_name): return self._zipinfo_name(self._fn(self.module_path,resource_name)) register_loader_type(zipimport.zipimporter, ZipProvider) class FileMetadata(EmptyProvider): """Metadata handler for standalone PKG-INFO files Usage:: metadata = FileMetadata("/path/to/PKG-INFO") This provider rejects all data and metadata requests except for PKG-INFO, which is treated as existing, and will be the contents of the file at the provided location. """ def __init__(self,path): self.path = path def has_metadata(self,name): return name=='PKG-INFO' def get_metadata(self,name): if name=='PKG-INFO': return open(self.path,'rU').read() raise KeyError("No metadata except PKG-INFO is available") def get_metadata_lines(self,name): return yield_lines(self.get_metadata(name)) class PathMetadata(DefaultProvider): """Metadata provider for egg directories Usage:: # Development eggs: egg_info = "/path/to/PackageName.egg-info" base_dir = os.path.dirname(egg_info) metadata = PathMetadata(base_dir, egg_info) dist_name = os.path.splitext(os.path.basename(egg_info))[0] dist = Distribution(basedir,project_name=dist_name,metadata=metadata) # Unpacked egg directories: egg_path = "/path/to/PackageName-ver-pyver-etc.egg" metadata = PathMetadata(egg_path, os.path.join(egg_path,'EGG-INFO')) dist = Distribution.from_filename(egg_path, metadata=metadata) """ def __init__(self, path, egg_info): self.module_path = path self.egg_info = egg_info class EggMetadata(ZipProvider): """Metadata provider for .egg files""" def __init__(self, importer): """Create a metadata provider from a zipimporter""" self.zipinfo = zipimport._zip_directory_cache[importer.archive] self.zip_pre = importer.archive+os.sep self.loader = importer if importer.prefix: self.module_path = os.path.join(importer.archive, importer.prefix) else: self.module_path = importer.archive self._setup_prefix() class ImpWrapper: """PEP 302 Importer that wraps Python's "normal" import algorithm""" def __init__(self, path=None): self.path = path def find_module(self, fullname, path=None): subname = fullname.split(".")[-1] if subname != fullname and self.path is None: return None if self.path is None: path = None else: path = [self.path] try: file, filename, etc = imp.find_module(subname, path) except ImportError: return None return ImpLoader(file, filename, etc) class ImpLoader: """PEP 302 Loader that wraps Python's "normal" import algorithm""" def __init__(self, file, filename, etc): self.file = file self.filename = filename self.etc = etc def load_module(self, fullname): try: mod = imp.load_module(fullname, self.file, self.filename, self.etc) finally: if self.file: self.file.close() # Note: we don't set __loader__ because we want the module to look # normal; i.e. this is just a wrapper for standard import machinery return mod def get_importer(path_item): """Retrieve a PEP 302 "importer" for the given path item If there is no importer, this returns a wrapper around the builtin import machinery. The returned importer is only cached if it was created by a path hook. """ try: importer = sys.path_importer_cache[path_item] except KeyError: for hook in sys.path_hooks: try: importer = hook(path_item) except ImportError: pass else: break else: importer = None sys.path_importer_cache.setdefault(path_item,importer) if importer is None: try: importer = ImpWrapper(path_item) except ImportError: pass return importer try: from pkgutil import get_importer, ImpImporter except ImportError: pass # Python 2.3 or 2.4, use our own implementation else: ImpWrapper = ImpImporter # Python 2.5, use pkgutil's implementation del ImpLoader, ImpImporter _distribution_finders = {} def register_finder(importer_type, distribution_finder): """Register `distribution_finder` to find distributions in sys.path items `importer_type` is the type or class of a PEP 302 "Importer" (sys.path item handler), and `distribution_finder` is a callable that, passed a path item and the importer instance, yields ``Distribution`` instances found on that path item. See ``pkg_resources.find_on_path`` for an example.""" _distribution_finders[importer_type] = distribution_finder def find_distributions(path_item, only=False): """Yield distributions accessible via `path_item`""" importer = get_importer(path_item) finder = _find_adapter(_distribution_finders, importer) return finder(importer, path_item, only) def find_in_zip(importer, path_item, only=False): metadata = EggMetadata(importer) if metadata.has_metadata('PKG-INFO'): yield Distribution.from_filename(path_item, metadata=metadata) if only: return # don't yield nested distros for subitem in metadata.resource_listdir('/'): if subitem.endswith('.egg'): subpath = os.path.join(path_item, subitem) for dist in find_in_zip(zipimport.zipimporter(subpath), subpath): yield dist register_finder(zipimport.zipimporter, find_in_zip) def StringIO(*args, **kw): """Thunk to load the real StringIO on demand""" global StringIO try: from cStringIO import StringIO except ImportError: from StringIO import StringIO return StringIO(*args,**kw) def find_nothing(importer, path_item, only=False): return () register_finder(object,find_nothing) def find_on_path(importer, path_item, only=False): """Yield distributions accessible on a sys.path directory""" path_item = _normalize_cached(path_item) if os.path.isdir(path_item): if path_item.lower().endswith('.egg'): # unpacked egg yield Distribution.from_filename( path_item, metadata=PathMetadata( path_item, os.path.join(path_item,'EGG-INFO') ) ) else: # scan for .egg and .egg-info in directory for entry in os.listdir(path_item): lower = entry.lower() if lower.endswith('.egg-info'): fullpath = os.path.join(path_item, entry) if os.path.isdir(fullpath): # egg-info directory, allow getting metadata metadata = PathMetadata(path_item, fullpath) else: metadata = FileMetadata(fullpath) yield Distribution.from_location( path_item,entry,metadata,precedence=DEVELOP_DIST ) elif not only and lower.endswith('.egg'): for dist in find_distributions(os.path.join(path_item, entry)): yield dist elif not only and lower.endswith('.egg-link'): for line in file(os.path.join(path_item, entry)): if not line.strip(): continue for item in find_distributions(os.path.join(path_item,line.rstrip())): yield item break register_finder(ImpWrapper,find_on_path) _namespace_handlers = {} _namespace_packages = {} def register_namespace_handler(importer_type, namespace_handler): """Register `namespace_handler` to declare namespace packages `importer_type` is the type or class of a PEP 302 "Importer" (sys.path item handler), and `namespace_handler` is a callable like this:: def namespace_handler(importer,path_entry,moduleName,module): # return a path_entry to use for child packages Namespace handlers are only called if the importer object has already agreed that it can handle the relevant path item, and they should only return a subpath if the module __path__ does not already contain an equivalent subpath. For an example namespace handler, see ``pkg_resources.file_ns_handler``. """ _namespace_handlers[importer_type] = namespace_handler def _handle_ns(packageName, path_item): """Ensure that named package includes a subpath of path_item (if needed)""" importer = get_importer(path_item) if importer is None: return None loader = importer.find_module(packageName) if loader is None: return None module = sys.modules.get(packageName) if module is None: module = sys.modules[packageName] = new.module(packageName) module.__path__ = []; _set_parent_ns(packageName) elif not hasattr(module,'__path__'): raise TypeError("Not a package:", packageName) handler = _find_adapter(_namespace_handlers, importer) subpath = handler(importer,path_item,packageName,module) if subpath is not None: path = module.__path__; path.append(subpath) loader.load_module(packageName); module.__path__ = path return subpath def declare_namespace(packageName): """Declare that package 'packageName' is a namespace package""" imp.acquire_lock() try: if packageName in _namespace_packages: return path, parent = sys.path, None if '.' in packageName: parent = '.'.join(packageName.split('.')[:-1]) declare_namespace(parent) __import__(parent) try: path = sys.modules[parent].__path__ except AttributeError: raise TypeError("Not a package:", parent) # Track what packages are namespaces, so when new path items are added, # they can be updated _namespace_packages.setdefault(parent,[]).append(packageName) _namespace_packages.setdefault(packageName,[]) for path_item in path: # Ensure all the parent's path items are reflected in the child, # if they apply _handle_ns(packageName, path_item) finally: imp.release_lock() def fixup_namespace_packages(path_item, parent=None): """Ensure that previously-declared namespace packages include path_item""" imp.acquire_lock() try: for package in _namespace_packages.get(parent,()): subpath = _handle_ns(package, path_item) if subpath: fixup_namespace_packages(subpath,package) finally: imp.release_lock() def file_ns_handler(importer, path_item, packageName, module): """Compute an ns-package subpath for a filesystem or zipfile importer""" subpath = os.path.join(path_item, packageName.split('.')[-1]) normalized = _normalize_cached(subpath) for item in module.__path__: if _normalize_cached(item)==normalized: break else: # Only return the path if it's not already there return subpath register_namespace_handler(ImpWrapper,file_ns_handler) register_namespace_handler(zipimport.zipimporter,file_ns_handler) def null_ns_handler(importer, path_item, packageName, module): return None register_namespace_handler(object,null_ns_handler) def normalize_path(filename): """Normalize a file/dir name for comparison purposes""" return os.path.normcase(os.path.realpath(filename)) def _normalize_cached(filename,_cache={}): try: return _cache[filename] except KeyError: _cache[filename] = result = normalize_path(filename) return result def _set_parent_ns(packageName): parts = packageName.split('.') name = parts.pop() if parts: parent = '.'.join(parts) setattr(sys.modules[parent], name, sys.modules[packageName]) def yield_lines(strs): """Yield non-empty/non-comment lines of a ``basestring`` or sequence""" if isinstance(strs,basestring): for s in strs.splitlines(): s = s.strip() if s and not s.startswith('#'): # skip blank lines/comments yield s else: for ss in strs: for s in yield_lines(ss): yield s LINE_END = re.compile(r"\s*(#.*)?$").match # whitespace and comment CONTINUE = re.compile(r"\s*\\\s*(#.*)?$").match # line continuation DISTRO = re.compile(r"\s*((\w|[-.])+)").match # Distribution or extra VERSION = re.compile(r"\s*(<=?|>=?|==|!=)\s*((\w|[-.])+)").match # ver. info COMMA = re.compile(r"\s*,").match # comma between items OBRACKET = re.compile(r"\s*\[").match CBRACKET = re.compile(r"\s*\]").match MODULE = re.compile(r"\w+(\.\w+)*$").match EGG_NAME = re.compile( r"(?P<name>[^-]+)" r"( -(?P<ver>[^-]+) (-py(?P<pyver>[^-]+) (-(?P<plat>.+))? )? )?", re.VERBOSE | re.IGNORECASE ).match component_re = re.compile(r'(\d+ | [a-z]+ | \.| -)', re.VERBOSE) replace = {'pre':'c', 'preview':'c','-':'final-','rc':'c','dev':'@'}.get def _parse_version_parts(s): for part in component_re.split(s): part = replace(part,part) if not part or part=='.': continue if part[:1] in '0123456789': yield part.zfill(8) # pad for numeric comparison else: yield '*'+part yield '*final' # ensure that alpha/beta/candidate are before final def parse_version(s): """Convert a version string to a chronologically-sortable key This is a rough cross between distutils' StrictVersion and LooseVersion; if you give it versions that would work with StrictVersion, then it behaves the same; otherwise it acts like a slightly-smarter LooseVersion. It is *possible* to create pathological version coding schemes that will fool this parser, but they should be very rare in practice. The returned value will be a tuple of strings. Numeric portions of the version are padded to 8 digits so they will compare numerically, but without relying on how numbers compare relative to strings. Dots are dropped, but dashes are retained. Trailing zeros between alpha segments or dashes are suppressed, so that e.g. "2.4.0" is considered the same as "2.4". Alphanumeric parts are lower-cased. The algorithm assumes that strings like "-" and any alpha string that alphabetically follows "final" represents a "patch level". So, "2.4-1" is assumed to be a branch or patch of "2.4", and therefore "2.4.1" is considered newer than "2.4-1", whic in turn is newer than "2.4". Strings like "a", "b", "c", "alpha", "beta", "candidate" and so on (that come before "final" alphabetically) are assumed to be pre-release versions, so that the version "2.4" is considered newer than "2.4a1". Finally, to handle miscellaneous cases, the strings "pre", "preview", and "rc" are treated as if they were "c", i.e. as though they were release candidates, and therefore are not as new as a version string that does not contain them. """ parts = [] for part in _parse_version_parts(s.lower()): if part.startswith('*'): if part<'*final': # remove '-' before a prerelease tag while parts and parts[-1]=='*final-': parts.pop() # remove trailing zeros from each series of numeric parts while parts and parts[-1]=='00000000': parts.pop() parts.append(part) return tuple(parts) class EntryPoint(object): """Object representing an advertised importable object""" def __init__(self, name, module_name, attrs=(), extras=(), dist=None): if not MODULE(module_name): raise ValueError("Invalid module name", module_name) self.name = name self.module_name = module_name self.attrs = tuple(attrs) self.extras = Requirement.parse(("x[%s]" % ','.join(extras))).extras self.dist = dist def __str__(self): s = "%s = %s" % (self.name, self.module_name) if self.attrs: s += ':' + '.'.join(self.attrs) if self.extras: s += ' [%s]' % ','.join(self.extras) return s def __repr__(self): return "EntryPoint.parse(%r)" % str(self) def load(self, require=True, env=None, installer=None): if require: self.require(env, installer) entry = __import__(self.module_name, globals(),globals(), ['__name__']) for attr in self.attrs: try: entry = getattr(entry,attr) except AttributeError: raise ImportError("%r has no %r attribute" % (entry,attr)) return entry def require(self, env=None, installer=None): if self.extras and not self.dist: raise UnknownExtra("Can't require() without a distribution", self) map(working_set.add, working_set.resolve(self.dist.requires(self.extras),env,installer)) #@classmethod def parse(cls, src, dist=None): """Parse a single entry point from string `src` Entry point syntax follows the form:: name = some.module:some.attr [extra1,extra2] The entry name and module name are required, but the ``:attrs`` and ``[extras]`` parts are optional """ try: attrs = extras = () name,value = src.split('=',1) if '[' in value: value,extras = value.split('[',1) req = Requirement.parse("x["+extras) if req.specs: raise ValueError extras = req.extras if ':' in value: value,attrs = value.split(':',1) if not MODULE(attrs.rstrip()): raise ValueError attrs = attrs.rstrip().split('.') except ValueError: raise ValueError( "EntryPoint must be in 'name=module:attrs [extras]' format", src ) else: return cls(name.strip(), value.strip(), attrs, extras, dist) parse = classmethod(parse) #@classmethod def parse_group(cls, group, lines, dist=None): """Parse an entry point group""" if not MODULE(group): raise ValueError("Invalid group name", group) this = {} for line in yield_lines(lines): ep = cls.parse(line, dist) if ep.name in this: raise ValueError("Duplicate entry point", group, ep.name) this[ep.name]=ep return this parse_group = classmethod(parse_group) #@classmethod def parse_map(cls, data, dist=None): """Parse a map of entry point groups""" if isinstance(data,dict): data = data.items() else: data = split_sections(data) maps = {} for group, lines in data: if group is None: if not lines: continue raise ValueError("Entry points must be listed in groups") group = group.strip() if group in maps: raise ValueError("Duplicate group name", group) maps[group] = cls.parse_group(group, lines, dist) return maps parse_map = classmethod(parse_map) class Distribution(object): """Wrap an actual or potential sys.path entry w/metadata""" def __init__(self, location=None, metadata=None, project_name=None, version=None, py_version=PY_MAJOR, platform=None, precedence = EGG_DIST ): self.project_name = safe_name(project_name or 'Unknown') if version is not None: self._version = safe_version(version) self.py_version = py_version self.platform = platform self.location = location self.precedence = precedence self._provider = metadata or empty_provider #@classmethod def from_location(cls,location,basename,metadata=None,**kw): project_name, version, py_version, platform = [None]*4 basename, ext = os.path.splitext(basename) if ext.lower() in (".egg",".egg-info"): match = EGG_NAME(basename) if match: project_name, version, py_version, platform = match.group( 'name','ver','pyver','plat' ) return cls( location, metadata, project_name=project_name, version=version, py_version=py_version, platform=platform, **kw ) from_location = classmethod(from_location) hashcmp = property( lambda self: ( getattr(self,'parsed_version',()), self.precedence, self.key, -len(self.location or ''), self.location, self.py_version, self.platform ) ) def __cmp__(self, other): return cmp(self.hashcmp, other) def __hash__(self): return hash(self.hashcmp) # These properties have to be lazy so that we don't have to load any # metadata until/unless it's actually needed. (i.e., some distributions # may not know their name or version without loading PKG-INFO) #@property def key(self): try: return self._key except AttributeError: self._key = key = self.project_name.lower() return key key = property(key) #@property def parsed_version(self): try: return self._parsed_version except AttributeError: self._parsed_version = pv = parse_version(self.version) return pv parsed_version = property(parsed_version) #@property def version(self): try: return self._version except AttributeError: for line in self._get_metadata('PKG-INFO'): if line.lower().startswith('version:'): self._version = safe_version(line.split(':',1)[1].strip()) return self._version else: raise ValueError( "Missing 'Version:' header and/or PKG-INFO file", self ) version = property(version) #@property def _dep_map(self): try: return self.__dep_map except AttributeError: dm = self.__dep_map = {None: []} for name in 'requires.txt', 'depends.txt': for extra,reqs in split_sections(self._get_metadata(name)): if extra: extra = safe_extra(extra) dm.setdefault(extra,[]).extend(parse_requirements(reqs)) return dm _dep_map = property(_dep_map) def requires(self,extras=()): """List of Requirements needed for this distro if `extras` are used""" dm = self._dep_map deps = [] deps.extend(dm.get(None,())) for ext in extras: try: deps.extend(dm[safe_extra(ext)]) except KeyError: raise UnknownExtra( "%s has no such extra feature %r" % (self, ext) ) return deps def _get_metadata(self,name): if self.has_metadata(name): for line in self.get_metadata_lines(name): yield line def activate(self,path=None): """Ensure distribution is importable on `path` (default=sys.path)""" if path is None: path = sys.path self.insert_on(path) if path is sys.path: fixup_namespace_packages(self.location) map(declare_namespace, self._get_metadata('namespace_packages.txt')) def egg_name(self): """Return what this distribution's standard .egg filename should be""" filename = "%s-%s-py%s" % ( to_filename(self.project_name), to_filename(self.version), self.py_version or PY_MAJOR ) if self.platform: filename += '-'+self.platform return filename def __repr__(self): if self.location: return "%s (%s)" % (self,self.location) else: return str(self) def __str__(self): try: version = getattr(self,'version',None) except ValueError: version = None version = version or "[unknown version]" return "%s %s" % (self.project_name,version) def __getattr__(self,attr): """Delegate all unrecognized public attributes to .metadata provider""" if attr.startswith('_'): raise AttributeError,attr return getattr(self._provider, attr) #@classmethod def from_filename(cls,filename,metadata=None, **kw): return cls.from_location( _normalize_cached(filename), os.path.basename(filename), metadata, **kw ) from_filename = classmethod(from_filename) def as_requirement(self): """Return a ``Requirement`` that matches this distribution exactly""" return Requirement.parse('%s==%s' % (self.project_name, self.version)) def load_entry_point(self, group, name): """Return the `name` entry point of `group` or raise ImportError""" ep = self.get_entry_info(group,name) if ep is None: raise ImportError("Entry point %r not found" % ((group,name),)) return ep.load() def get_entry_map(self, group=None): """Return the entry point map for `group`, or the full entry map""" try: ep_map = self._ep_map except AttributeError: ep_map = self._ep_map = EntryPoint.parse_map( self._get_metadata('entry_points.txt'), self ) if group is not None: return ep_map.get(group,{}) return ep_map def get_entry_info(self, group, name): """Return the EntryPoint object for `group`+`name`, or ``None``""" return self.get_entry_map(group).get(name) def insert_on(self, path, loc = None): """Insert self.location in path before its nearest parent directory""" loc = loc or self.location if not loc: return if path is sys.path: self.check_version_conflict() nloc = _normalize_cached(loc) bdir = os.path.dirname(nloc) npath= map(_normalize_cached, path) bp = None for p, item in enumerate(npath): if item==nloc: break elif item==bdir and self.precedence==EGG_DIST: # if it's an .egg, give it precedence over its directory path.insert(p, loc) npath.insert(p, nloc) break else: path.append(loc) return # p is the spot where we found or inserted loc; now remove duplicates while 1: try: np = npath.index(nloc, p+1) except ValueError: break else: del npath[np], path[np] p = np # ha! return def check_version_conflict(self): if self.key=='setuptools': return # ignore the inevitable setuptools self-conflicts :( nsp = dict.fromkeys(self._get_metadata('namespace_packages.txt')) loc = normalize_path(self.location) for modname in self._get_metadata('top_level.txt'): if (modname not in sys.modules or modname in nsp or modname in _namespace_packages ): continue fn = getattr(sys.modules[modname], '__file__', None) if fn and normalize_path(fn).startswith(loc): continue issue_warning( "Module %s was already imported from %s, but %s is being added" " to sys.path" % (modname, fn, self.location), ) def has_version(self): try: self.version except ValueError: issue_warning("Unbuilt egg for "+repr(self)) return False return True def clone(self,**kw): """Copy this distribution, substituting in any changed keyword args""" for attr in ( 'project_name', 'version', 'py_version', 'platform', 'location', 'precedence' ): kw.setdefault(attr, getattr(self,attr,None)) kw.setdefault('metadata', self._provider) return self.__class__(**kw) #@property def extras(self): return [dep for dep in self._dep_map if dep] extras = property(extras) def issue_warning(*args,**kw): level = 1 g = globals() try: # find the first stack frame that is *not* code in # the pkg_resources module, to use for the warning while sys._getframe(level).f_globals is g: level += 1 except ValueError: pass from warnings import warn warn(stacklevel = level+1, *args, **kw) def parse_requirements(strs): """Yield ``Requirement`` objects for each specification in `strs` `strs` must be an instance of ``basestring``, or a (possibly-nested) iterable thereof. """ # create a steppable iterator, so we can handle \-continuations lines = iter(yield_lines(strs)) def scan_list(ITEM,TERMINATOR,line,p,groups,item_name): items = [] while not TERMINATOR(line,p): if CONTINUE(line,p): try: line = lines.next(); p = 0 except StopIteration: raise ValueError( "\\ must not appear on the last nonblank line" ) match = ITEM(line,p) if not match: raise ValueError("Expected "+item_name+" in",line,"at",line[p:]) items.append(match.group(*groups)) p = match.end() match = COMMA(line,p) if match: p = match.end() # skip the comma elif not TERMINATOR(line,p): raise ValueError( "Expected ',' or end-of-list in",line,"at",line[p:] ) match = TERMINATOR(line,p) if match: p = match.end() # skip the terminator, if any return line, p, items for line in lines: match = DISTRO(line) if not match: raise ValueError("Missing distribution spec", line) project_name = match.group(1) p = match.end() extras = [] match = OBRACKET(line,p) if match: p = match.end() line, p, extras = scan_list( DISTRO, CBRACKET, line, p, (1,), "'extra' name" ) line, p, specs = scan_list(VERSION,LINE_END,line,p,(1,2),"version spec") specs = [(op,safe_version(val)) for op,val in specs] yield Requirement(project_name, specs, extras) def _sort_dists(dists): tmp = [(dist.hashcmp,dist) for dist in dists] tmp.sort() dists[::-1] = [d for hc,d in tmp] class Requirement: def __init__(self, project_name, specs, extras): """DO NOT CALL THIS UNDOCUMENTED METHOD; use Requirement.parse()!""" self.unsafe_name, project_name = project_name, safe_name(project_name) self.project_name, self.key = project_name, project_name.lower() index = [(parse_version(v),state_machine[op],op,v) for op,v in specs] index.sort() self.specs = [(op,ver) for parsed,trans,op,ver in index] self.index, self.extras = index, tuple(map(safe_extra,extras)) self.hashCmp = ( self.key, tuple([(op,parsed) for parsed,trans,op,ver in index]), frozenset(self.extras) ) self.__hash = hash(self.hashCmp) def __str__(self): specs = ','.join([''.join(s) for s in self.specs]) extras = ','.join(self.extras) if extras: extras = '[%s]' % extras return '%s%s%s' % (self.project_name, extras, specs) def __eq__(self,other): return isinstance(other,Requirement) and self.hashCmp==other.hashCmp def __contains__(self,item): if isinstance(item,Distribution): if item.key <> self.key: return False if self.index: item = item.parsed_version # only get if we need it elif isinstance(item,basestring): item = parse_version(item) last = None for parsed,trans,op,ver in self.index: action = trans[cmp(item,parsed)] if action=='F': return False elif action=='T': return True elif action=='+': last = True elif action=='-' or last is None: last = False if last is None: last = True # no rules encountered return last def __hash__(self): return self.__hash def __repr__(self): return "Requirement.parse(%r)" % str(self) #@staticmethod def parse(s): reqs = list(parse_requirements(s)) if reqs: if len(reqs)==1: return reqs[0] raise ValueError("Expected only one requirement", s) raise ValueError("No requirements found", s) parse = staticmethod(parse) state_machine = { # =>< '<' : '--T', '<=': 'T-T', '>' : 'F+F', '>=': 'T+F', '==': 'T..', '!=': 'F++', } def _get_mro(cls): """Get an mro for a type or classic class""" if not isinstance(cls,type): class cls(cls,object): pass return cls.__mro__[1:] return cls.__mro__ def _find_adapter(registry, ob): """Return an adapter factory for `ob` from `registry`""" for t in _get_mro(getattr(ob, '__class__', type(ob))): if t in registry: return registry[t] def ensure_directory(path): """Ensure that the parent directory of `path` exists""" dirname = os.path.dirname(path) if not os.path.isdir(dirname): os.makedirs(dirname) def split_sections(s): """Split a string or iterable thereof into (section,content) pairs Each ``section`` is a stripped version of the section header ("[section]") and each ``content`` is a list of stripped lines excluding blank lines and comment-only lines. If there are any such lines before the first section header, they're returned in a first ``section`` of ``None``. """ section = None content = [] for line in yield_lines(s): if line.startswith("["): if line.endswith("]"): if section or content: yield section, content section = line[1:-1].strip() content = [] else: raise ValueError("Invalid section heading", line) else: content.append(line) # wrap up last segment yield section, content def _mkstemp(*args,**kw): from tempfile import mkstemp old_open = os.open try: os.open = os_open # temporarily bypass sandboxing return mkstemp(*args,**kw) finally: os.open = old_open # and then put it back # Set up global resource manager _manager = ResourceManager() def _initialize(g): for name in dir(_manager): if not name.startswith('_'): g[name] = getattr(_manager, name) _initialize(globals()) # Prepare the master working set and make the ``require()`` API available working_set = WorkingSet() try: # Does the main program list any requirements? from __main__ import __requires__ except ImportError: pass # No: just use the default working set based on sys.path else: # Yes: ensure the requirements are met, by prefixing sys.path if necessary try: working_set.require(__requires__) except VersionConflict: # try it without defaults already on sys.path working_set = WorkingSet([]) # by starting with an empty path for dist in working_set.resolve( parse_requirements(__requires__), Environment() ): working_set.add(dist) for entry in sys.path: # add any missing entries from sys.path if entry not in working_set.entries: working_set.add_entry(entry) sys.path[:] = working_set.entries # then copy back to sys.path require = working_set.require iter_entry_points = working_set.iter_entry_points add_activation_listener = working_set.subscribe run_script = working_set.run_script run_main = run_script # backward compatibility # Activate all distributions already on sys.path, and ensure that # all distributions added to the working set in the future (e.g. by # calling ``require()``) will get activated as well. add_activation_listener(lambda dist: dist.activate()) working_set.entries=[]; map(working_set.add_entry,sys.path) # match order

83,809

Python

.py

1,862

35.66971

94

0.622621

numenta/nupic-legacy

6,330

1,556

464

AGPL-3.0

9/5/2024, 5:13:42 PM (Europe/Amsterdam)

26,232

tzfile.py

numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/pytz/tzfile.py

''' $Id: tzfile.py,v 1.8 2004/06/03 00:15:24 zenzen Exp $ ''' from cStringIO import StringIO from datetime import datetime, timedelta from struct import unpack, calcsize from pytz.tzinfo import StaticTzInfo, DstTzInfo, memorized_ttinfo from pytz.tzinfo import memorized_datetime, memorized_timedelta def build_tzinfo(zone, fp): head_fmt = '>4s 16x 6l' head_size = calcsize(head_fmt) (magic,ttisgmtcnt,ttisstdcnt,leapcnt, timecnt,typecnt,charcnt) = unpack(head_fmt, fp.read(head_size)) # Make sure it is a tzinfo(5) file assert magic == 'TZif' # Read out the transition times, localtime indices and ttinfo structures. data_fmt = '>%(timecnt)dl %(timecnt)dB %(ttinfo)s %(charcnt)ds' % dict( timecnt=timecnt, ttinfo='lBB'*typecnt, charcnt=charcnt) data_size = calcsize(data_fmt) data = unpack(data_fmt, fp.read(data_size)) # make sure we unpacked the right number of values assert len(data) == 2 * timecnt + 3 * typecnt + 1 transitions = [memorized_datetime(trans) for trans in data[:timecnt]] lindexes = list(data[timecnt:2 * timecnt]) ttinfo_raw = data[2 * timecnt:-1] tznames_raw = data[-1] del data # Process ttinfo into separate structs ttinfo = [] tznames = {} i = 0 while i < len(ttinfo_raw): # have we looked up this timezone name yet? tzname_offset = ttinfo_raw[i+2] if tzname_offset not in tznames: nul = tznames_raw.find('\0', tzname_offset) if nul < 0: nul = len(tznames_raw) tznames[tzname_offset] = tznames_raw[tzname_offset:nul] ttinfo.append((ttinfo_raw[i], bool(ttinfo_raw[i+1]), tznames[tzname_offset])) i += 3 # Now build the timezone object if len(transitions) == 0: ttinfo[0][0], ttinfo[0][2] cls = type(zone, (StaticTzInfo,), dict( zone=zone, _utcoffset=memorized_timedelta(ttinfo[0][0]), _tzname=ttinfo[0][2])) else: # Early dates use the first standard time ttinfo i = 0 while ttinfo[i][1]: i += 1 if ttinfo[i] == ttinfo[lindexes[0]]: transitions[0] = datetime.min else: transitions.insert(0, datetime.min) lindexes.insert(0, i) # calculate transition info transition_info = [] for i in range(len(transitions)): inf = ttinfo[lindexes[i]] utcoffset = inf[0] if not inf[1]: dst = 0 else: for j in range(i-1, -1, -1): prev_inf = ttinfo[lindexes[j]] if not prev_inf[1]: break dst = inf[0] - prev_inf[0] # dst offset tzname = inf[2] # Round utcoffset and dst to the nearest minute or the # datetime library will complain. Conversions to these timezones # might be up to plus or minus 30 seconds out, but it is # the best we can do. utcoffset = int((utcoffset + 30) / 60) * 60 dst = int((dst + 30) / 60) * 60 transition_info.append(memorized_ttinfo(utcoffset, dst, tzname)) cls = type(zone, (DstTzInfo,), dict( zone=zone, _utc_transition_times=transitions, _transition_info=transition_info)) return cls() if __name__ == '__main__': import os.path from pprint import pprint base = os.path.join(os.path.dirname(__file__), 'zoneinfo') tz = build_tzinfo('Australia/Melbourne', open(os.path.join(base,'Australia','Melbourne'), 'rb')) tz = build_tzinfo('US/Eastern', open(os.path.join(base,'US','Eastern'), 'rb')) pprint(tz._utc_transition_times) #print tz.asPython(4) #print tz.transitions_mapping

3,937

Python

.py

98

30.72449

77

0.583617

numenta/nupic-legacy

6,330

1,556

464

AGPL-3.0

9/5/2024, 5:13:42 PM (Europe/Amsterdam)

26,233

__init__.py

numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/pytz/__init__.py

''' datetime.tzinfo timezone definitions generated from the Olson timezone database: ftp://elsie.nci.nih.gov/pub/tz*.tar.gz See the datetime section of the Python Library Reference for information on how to use these modules. ''' # The Olson database has historically been updated about 4 times a year OLSON_VERSION = '2008c' VERSION = OLSON_VERSION #VERSION = OLSON_VERSION + '.2' __version__ = OLSON_VERSION OLSEN_VERSION = OLSON_VERSION # Old releases had this misspelling __all__ = [ 'timezone', 'utc', 'country_timezones', 'AmbiguousTimeError', 'UnknownTimeZoneError', 'all_timezones', 'all_timezones_set', 'common_timezones', 'common_timezones_set', ] import sys, datetime, os.path, gettext try: from pkg_resources import resource_stream except ImportError: resource_stream = None from tzinfo import AmbiguousTimeError, unpickler from tzfile import build_tzinfo # Use 2.3 sets module implementation if set builtin is not available try: set except NameError: from sets import Set as set def open_resource(name): """Open a resource from the zoneinfo subdir for reading. Uses the pkg_resources module if available. """ if resource_stream is not None: return resource_stream(__name__, 'zoneinfo/' + name) else: name_parts = name.lstrip('/').split('/') for part in name_parts: if part == os.path.pardir or os.path.sep in part: raise ValueError('Bad path segment: %r' % part) filename = os.path.join(os.path.dirname(__file__), 'zoneinfo', *name_parts) return open(filename, 'rb') # Enable this when we get some translations? # We want an i18n API that is useful to programs using Python's gettext # module, as well as the Zope3 i18n package. Perhaps we should just provide # the POT file and translations, and leave it up to callers to make use # of them. # # t = gettext.translation( # 'pytz', os.path.join(os.path.dirname(__file__), 'locales'), # fallback=True # ) # def _(timezone_name): # """Translate a timezone name using the current locale, returning Unicode""" # return t.ugettext(timezone_name) class UnknownTimeZoneError(KeyError): '''Exception raised when pytz is passed an unknown timezone. >>> isinstance(UnknownTimeZoneError(), LookupError) True This class is actually a subclass of KeyError to provide backwards compatibility with code relying on the undocumented behavior of earlier pytz releases. >>> isinstance(UnknownTimeZoneError(), KeyError) True ''' pass _tzinfo_cache = {} def timezone(zone): r''' Return a datetime.tzinfo implementation for the given timezone >>> from datetime import datetime, timedelta >>> utc = timezone('UTC') >>> eastern = timezone('US/Eastern') >>> eastern.zone 'US/Eastern' >>> timezone(u'US/Eastern') is eastern True >>> utc_dt = datetime(2002, 10, 27, 6, 0, 0, tzinfo=utc) >>> loc_dt = utc_dt.astimezone(eastern) >>> fmt = '%Y-%m-%d %H:%M:%S %Z (%z)' >>> loc_dt.strftime(fmt) '2002-10-27 01:00:00 EST (-0500)' >>> (loc_dt - timedelta(minutes=10)).strftime(fmt) '2002-10-27 00:50:00 EST (-0500)' >>> eastern.normalize(loc_dt - timedelta(minutes=10)).strftime(fmt) '2002-10-27 01:50:00 EDT (-0400)' >>> (loc_dt + timedelta(minutes=10)).strftime(fmt) '2002-10-27 01:10:00 EST (-0500)' Raises UnknownTimeZoneError if passed an unknown zone. >>> timezone('Asia/Shangri-La') Traceback (most recent call last): ... UnknownTimeZoneError: 'Asia/Shangri-La' >>> timezone(u'\N{TRADE MARK SIGN}') Traceback (most recent call last): ... UnknownTimeZoneError: u'\u2122' ''' if zone.upper() == 'UTC': return utc try: zone = zone.encode('US-ASCII') except UnicodeEncodeError: # All valid timezones are ASCII raise UnknownTimeZoneError(zone) zone = _unmunge_zone(zone) if zone not in _tzinfo_cache: if zone in all_timezones_set: _tzinfo_cache[zone] = build_tzinfo(zone, open_resource(zone)) else: raise UnknownTimeZoneError(zone) return _tzinfo_cache[zone] def _unmunge_zone(zone): """Undo the time zone name munging done by older versions of pytz.""" return zone.replace('_plus_', '+').replace('_minus_', '-') ZERO = datetime.timedelta(0) HOUR = datetime.timedelta(hours=1) class UTC(datetime.tzinfo): """UTC Identical to the reference UTC implementation given in Python docs except that it unpickles using the single module global instance defined beneath this class declaration. Also contains extra attributes and methods to match other pytz tzinfo instances. """ zone = "UTC" def utcoffset(self, dt): return ZERO def tzname(self, dt): return "UTC" def dst(self, dt): return ZERO def __reduce__(self): return _UTC, () def localize(self, dt, is_dst=False): '''Convert naive time to local time''' if dt.tzinfo is not None: raise ValueError, 'Not naive datetime (tzinfo is already set)' return dt.replace(tzinfo=self) def normalize(self, dt, is_dst=False): '''Correct the timezone information on the given datetime''' if dt.tzinfo is None: raise ValueError, 'Naive time - no tzinfo set' return dt.replace(tzinfo=self) def __repr__(self): return "<UTC>" def __str__(self): return "UTC" UTC = utc = UTC() # UTC is a singleton def _UTC(): """Factory function for utc unpickling. Makes sure that unpickling a utc instance always returns the same module global. These examples belong in the UTC class above, but it is obscured; or in the README.txt, but we are not depending on Python 2.4 so integrating the README.txt examples with the unit tests is not trivial. >>> import datetime, pickle >>> dt = datetime.datetime(2005, 3, 1, 14, 13, 21, tzinfo=utc) >>> naive = dt.replace(tzinfo=None) >>> p = pickle.dumps(dt, 1) >>> naive_p = pickle.dumps(naive, 1) >>> len(p), len(naive_p), len(p) - len(naive_p) (60, 43, 17) >>> new = pickle.loads(p) >>> new == dt True >>> new is dt False >>> new.tzinfo is dt.tzinfo True >>> utc is UTC is timezone('UTC') True >>> utc is timezone('GMT') False """ return utc _UTC.__safe_for_unpickling__ = True def _p(*args): """Factory function for unpickling pytz tzinfo instances. Just a wrapper around tzinfo.unpickler to save a few bytes in each pickle by shortening the path. """ return unpickler(*args) _p.__safe_for_unpickling__ = True _country_timezones_cache = {} def country_timezones(iso3166_code): """Return a list of timezones used in a particular country. iso3166_code is the two letter code used to identify the country. >>> country_timezones('ch') ['Europe/Zurich'] >>> country_timezones('CH') ['Europe/Zurich'] >>> country_timezones(u'ch') ['Europe/Zurich'] >>> country_timezones('XXX') Traceback (most recent call last): ... KeyError: 'XXX' """ iso3166_code = iso3166_code.upper() if not _country_timezones_cache: zone_tab = open_resource('zone.tab') for line in zone_tab: if line.startswith('#'): continue code, coordinates, zone = line.split(None, 4)[:3] try: _country_timezones_cache[code].append(zone) except KeyError: _country_timezones_cache[code] = [zone] return _country_timezones_cache[iso3166_code] # Time-zone info based solely on fixed offsets class _FixedOffset(datetime.tzinfo): zone = None # to match the standard pytz API def __init__(self, minutes): if abs(minutes) >= 1440: raise ValueError("absolute offset is too large", minutes) self._minutes = minutes self._offset = datetime.timedelta(minutes=minutes) def utcoffset(self, dt): return self._offset def __reduce__(self): return FixedOffset, (self._minutes, ) def dst(self, dt): return None def tzname(self, dt): return None def __repr__(self): return 'pytz.FixedOffset(%d)' % self._minutes def localize(self, dt, is_dst=False): '''Convert naive time to local time''' if dt.tzinfo is not None: raise ValueError, 'Not naive datetime (tzinfo is already set)' return dt.replace(tzinfo=self) def normalize(self, dt, is_dst=False): '''Correct the timezone information on the given datetime''' if dt.tzinfo is None: raise ValueError, 'Naive time - no tzinfo set' return dt.replace(tzinfo=self) def FixedOffset(offset, _tzinfos = {}): """return a fixed-offset timezone based off a number of minutes. >>> one = FixedOffset(-330) >>> one pytz.FixedOffset(-330) >>> one.utcoffset(datetime.datetime.now()) datetime.timedelta(-1, 66600) >>> two = FixedOffset(1380) >>> two pytz.FixedOffset(1380) >>> two.utcoffset(datetime.datetime.now()) datetime.timedelta(0, 82800) The datetime.timedelta must be between the range of -1 and 1 day, non-inclusive. >>> FixedOffset(1440) Traceback (most recent call last): ... ValueError: ('absolute offset is too large', 1440) >>> FixedOffset(-1440) Traceback (most recent call last): ... ValueError: ('absolute offset is too large', -1440) An offset of 0 is special-cased to return UTC. >>> FixedOffset(0) is UTC True There should always be only one instance of a FixedOffset per timedelta. This should be true for multiple creation calls. >>> FixedOffset(-330) is one True >>> FixedOffset(1380) is two True It should also be true for pickling. >>> import pickle >>> pickle.loads(pickle.dumps(one)) is one True >>> pickle.loads(pickle.dumps(two)) is two True """ if offset == 0: return UTC info = _tzinfos.get(offset) if info is None: # We haven't seen this one before. we need to save it. # Use setdefault to avoid a race condition and make sure we have # only one info = _tzinfos.setdefault(offset, _FixedOffset(offset)) return info FixedOffset.__safe_for_unpickling__ = True def _test(): import doctest, os, sys sys.path.insert(0, os.pardir) import pytz return doctest.testmod(pytz) if __name__ == '__main__': _test() common_timezones = \ ['Africa/Abidjan', 'Africa/Accra', 'Africa/Addis_Ababa', 'Africa/Algiers', 'Africa/Asmara', 'Africa/Asmera', 'Africa/Bamako', 'Africa/Bangui', 'Africa/Banjul', 'Africa/Bissau', 'Africa/Blantyre', 'Africa/Brazzaville', 'Africa/Bujumbura', 'Africa/Cairo', 'Africa/Casablanca', 'Africa/Ceuta', 'Africa/Conakry', 'Africa/Dakar', 'Africa/Dar_es_Salaam', 'Africa/Djibouti', 'Africa/Douala', 'Africa/El_Aaiun', 'Africa/Freetown', 'Africa/Gaborone', 'Africa/Harare', 'Africa/Johannesburg', 'Africa/Kampala', 'Africa/Khartoum', 'Africa/Kigali', 'Africa/Kinshasa', 'Africa/Lagos', 'Africa/Libreville', 'Africa/Lome', 'Africa/Luanda', 'Africa/Lubumbashi', 'Africa/Lusaka', 'Africa/Malabo', 'Africa/Maputo', 'Africa/Maseru', 'Africa/Mbabane', 'Africa/Mogadishu', 'Africa/Monrovia', 'Africa/Nairobi', 'Africa/Ndjamena', 'Africa/Niamey', 'Africa/Nouakchott', 'Africa/Ouagadougou', 'Africa/Porto-Novo', 'Africa/Sao_Tome', 'Africa/Timbuktu', 'Africa/Tripoli', 'Africa/Tunis', 'Africa/Windhoek', 'America/Adak', 'America/Anchorage', 'America/Anguilla', 'America/Antigua', 'America/Araguaina', 'America/Aruba', 'America/Asuncion', 'America/Atikokan', 'America/Atka', 'America/Bahia', 'America/Barbados', 'America/Belem', 'America/Belize', 'America/Blanc-Sablon', 'America/Boa_Vista', 'America/Bogota', 'America/Boise', 'America/Buenos_Aires', 'America/Cambridge_Bay', 'America/Campo_Grande', 'America/Cancun', 'America/Caracas', 'America/Catamarca', 'America/Cayenne', 'America/Cayman', 'America/Chicago', 'America/Chihuahua', 'America/Coral_Harbour', 'America/Cordoba', 'America/Costa_Rica', 'America/Cuiaba', 'America/Curacao', 'America/Danmarkshavn', 'America/Dawson', 'America/Dawson_Creek', 'America/Denver', 'America/Detroit', 'America/Dominica', 'America/Edmonton', 'America/Eirunepe', 'America/El_Salvador', 'America/Ensenada', 'America/Fort_Wayne', 'America/Fortaleza', 'America/Glace_Bay', 'America/Godthab', 'America/Goose_Bay', 'America/Grand_Turk', 'America/Grenada', 'America/Guadeloupe', 'America/Guatemala', 'America/Guayaquil', 'America/Guyana', 'America/Halifax', 'America/Havana', 'America/Hermosillo', 'America/Indianapolis', 'America/Inuvik', 'America/Iqaluit', 'America/Jamaica', 'America/Jujuy', 'America/Juneau', 'America/Knox_IN', 'America/La_Paz', 'America/Lima', 'America/Los_Angeles', 'America/Louisville', 'America/Maceio', 'America/Managua', 'America/Manaus', 'America/Marigot', 'America/Martinique', 'America/Mazatlan', 'America/Mendoza', 'America/Menominee', 'America/Merida', 'America/Mexico_City', 'America/Miquelon', 'America/Moncton', 'America/Monterrey', 'America/Montevideo', 'America/Montreal', 'America/Montserrat', 'America/Nassau', 'America/New_York', 'America/Nipigon', 'America/Nome', 'America/Noronha', 'America/Panama', 'America/Pangnirtung', 'America/Paramaribo', 'America/Phoenix', 'America/Port-au-Prince', 'America/Port_of_Spain', 'America/Porto_Acre', 'America/Porto_Velho', 'America/Puerto_Rico', 'America/Rainy_River', 'America/Rankin_Inlet', 'America/Recife', 'America/Regina', 'America/Resolute', 'America/Rio_Branco', 'America/Rosario', 'America/Santiago', 'America/Santo_Domingo', 'America/Sao_Paulo', 'America/Scoresbysund', 'America/Shiprock', 'America/St_Barthelemy', 'America/St_Johns', 'America/St_Kitts', 'America/St_Lucia', 'America/St_Thomas', 'America/St_Vincent', 'America/Swift_Current', 'America/Tegucigalpa', 'America/Thule', 'America/Thunder_Bay', 'America/Tijuana', 'America/Toronto', 'America/Tortola', 'America/Vancouver', 'America/Virgin', 'America/Whitehorse', 'America/Winnipeg', 'America/Yakutat', 'America/Yellowknife', 'Antarctica/Casey', 'Antarctica/Davis', 'Antarctica/DumontDUrville', 'Antarctica/Mawson', 'Antarctica/McMurdo', 'Antarctica/Palmer', 'Antarctica/Rothera', 'Antarctica/South_Pole', 'Antarctica/Syowa', 'Antarctica/Vostok', 'Arctic/Longyearbyen', 'Asia/Aden', 'Asia/Almaty', 'Asia/Amman', 'Asia/Anadyr', 'Asia/Aqtau', 'Asia/Aqtobe', 'Asia/Ashgabat', 'Asia/Ashkhabad', 'Asia/Baghdad', 'Asia/Bahrain', 'Asia/Baku', 'Asia/Bangkok', 'Asia/Beirut', 'Asia/Bishkek', 'Asia/Brunei', 'Asia/Calcutta', 'Asia/Choibalsan', 'Asia/Chongqing', 'Asia/Chungking', 'Asia/Colombo', 'Asia/Dacca', 'Asia/Damascus', 'Asia/Dhaka', 'Asia/Dili', 'Asia/Dubai', 'Asia/Dushanbe', 'Asia/Gaza', 'Asia/Harbin', 'Asia/Ho_Chi_Minh', 'Asia/Hong_Kong', 'Asia/Hovd', 'Asia/Irkutsk', 'Asia/Istanbul', 'Asia/Jakarta', 'Asia/Jayapura', 'Asia/Jerusalem', 'Asia/Kabul', 'Asia/Kamchatka', 'Asia/Karachi', 'Asia/Kashgar', 'Asia/Katmandu', 'Asia/Kolkata', 'Asia/Krasnoyarsk', 'Asia/Kuala_Lumpur', 'Asia/Kuching', 'Asia/Kuwait', 'Asia/Macao', 'Asia/Macau', 'Asia/Magadan', 'Asia/Makassar', 'Asia/Manila', 'Asia/Muscat', 'Asia/Nicosia', 'Asia/Novosibirsk', 'Asia/Omsk', 'Asia/Oral', 'Asia/Phnom_Penh', 'Asia/Pontianak', 'Asia/Pyongyang', 'Asia/Qatar', 'Asia/Qyzylorda', 'Asia/Rangoon', 'Asia/Riyadh', 'Asia/Saigon', 'Asia/Sakhalin', 'Asia/Samarkand', 'Asia/Seoul', 'Asia/Shanghai', 'Asia/Singapore', 'Asia/Taipei', 'Asia/Tashkent', 'Asia/Tbilisi', 'Asia/Tehran', 'Asia/Tel_Aviv', 'Asia/Thimbu', 'Asia/Thimphu', 'Asia/Tokyo', 'Asia/Ujung_Pandang', 'Asia/Ulaanbaatar', 'Asia/Ulan_Bator', 'Asia/Urumqi', 'Asia/Vientiane', 'Asia/Vladivostok', 'Asia/Yakutsk', 'Asia/Yekaterinburg', 'Asia/Yerevan', 'Atlantic/Azores', 'Atlantic/Bermuda', 'Atlantic/Canary', 'Atlantic/Cape_Verde', 'Atlantic/Faeroe', 'Atlantic/Faroe', 'Atlantic/Jan_Mayen', 'Atlantic/Madeira', 'Atlantic/Reykjavik', 'Atlantic/South_Georgia', 'Atlantic/St_Helena', 'Atlantic/Stanley', 'Australia/ACT', 'Australia/Adelaide', 'Australia/Brisbane', 'Australia/Broken_Hill', 'Australia/Canberra', 'Australia/Currie', 'Australia/Darwin', 'Australia/Eucla', 'Australia/Hobart', 'Australia/LHI', 'Australia/Lindeman', 'Australia/Lord_Howe', 'Australia/Melbourne', 'Australia/NSW', 'Australia/North', 'Australia/Perth', 'Australia/Queensland', 'Australia/South', 'Australia/Sydney', 'Australia/Tasmania', 'Australia/Victoria', 'Australia/West', 'Australia/Yancowinna', 'Brazil/Acre', 'Brazil/DeNoronha', 'Brazil/East', 'Brazil/West', 'Canada/Atlantic', 'Canada/Central', 'Canada/East-Saskatchewan', 'Canada/Eastern', 'Canada/Mountain', 'Canada/Newfoundland', 'Canada/Pacific', 'Canada/Saskatchewan', 'Canada/Yukon', 'Chile/Continental', 'Chile/EasterIsland', 'Europe/Amsterdam', 'Europe/Andorra', 'Europe/Athens', 'Europe/Belfast', 'Europe/Belgrade', 'Europe/Berlin', 'Europe/Bratislava', 'Europe/Brussels', 'Europe/Bucharest', 'Europe/Budapest', 'Europe/Chisinau', 'Europe/Copenhagen', 'Europe/Dublin', 'Europe/Gibraltar', 'Europe/Guernsey', 'Europe/Helsinki', 'Europe/Isle_of_Man', 'Europe/Istanbul', 'Europe/Jersey', 'Europe/Kaliningrad', 'Europe/Kiev', 'Europe/Lisbon', 'Europe/Ljubljana', 'Europe/London', 'Europe/Luxembourg', 'Europe/Madrid', 'Europe/Malta', 'Europe/Mariehamn', 'Europe/Minsk', 'Europe/Monaco', 'Europe/Moscow', 'Europe/Nicosia', 'Europe/Oslo', 'Europe/Paris', 'Europe/Podgorica', 'Europe/Prague', 'Europe/Riga', 'Europe/Rome', 'Europe/Samara', 'Europe/San_Marino', 'Europe/Sarajevo', 'Europe/Simferopol', 'Europe/Skopje', 'Europe/Sofia', 'Europe/Stockholm', 'Europe/Tallinn', 'Europe/Tirane', 'Europe/Tiraspol', 'Europe/Uzhgorod', 'Europe/Vaduz', 'Europe/Vatican', 'Europe/Vienna', 'Europe/Vilnius', 'Europe/Volgograd', 'Europe/Warsaw', 'Europe/Zagreb', 'Europe/Zaporozhye', 'Europe/Zurich', 'GMT', 'Indian/Antananarivo', 'Indian/Chagos', 'Indian/Christmas', 'Indian/Cocos', 'Indian/Comoro', 'Indian/Kerguelen', 'Indian/Mahe', 'Indian/Maldives', 'Indian/Mauritius', 'Indian/Mayotte', 'Indian/Reunion', 'Mexico/BajaNorte', 'Mexico/BajaSur', 'Mexico/General', 'Pacific/Apia', 'Pacific/Auckland', 'Pacific/Chatham', 'Pacific/Easter', 'Pacific/Efate', 'Pacific/Enderbury', 'Pacific/Fakaofo', 'Pacific/Fiji', 'Pacific/Funafuti', 'Pacific/Galapagos', 'Pacific/Gambier', 'Pacific/Guadalcanal', 'Pacific/Guam', 'Pacific/Honolulu', 'Pacific/Johnston', 'Pacific/Kiritimati', 'Pacific/Kosrae', 'Pacific/Kwajalein', 'Pacific/Majuro', 'Pacific/Marquesas', 'Pacific/Midway', 'Pacific/Nauru', 'Pacific/Niue', 'Pacific/Norfolk', 'Pacific/Noumea', 'Pacific/Pago_Pago', 'Pacific/Palau', 'Pacific/Pitcairn', 'Pacific/Ponape', 'Pacific/Port_Moresby', 'Pacific/Rarotonga', 'Pacific/Saipan', 'Pacific/Samoa', 'Pacific/Tahiti', 'Pacific/Tarawa', 'Pacific/Tongatapu', 'Pacific/Truk', 'Pacific/Wake', 'Pacific/Wallis', 'Pacific/Yap', 'US/Alaska', 'US/Aleutian', 'US/Arizona', 'US/Central', 'US/East-Indiana', 'US/Eastern', 'US/Hawaii', 'US/Indiana-Starke', 'US/Michigan', 'US/Mountain', 'US/Pacific', 'US/Pacific-New', 'US/Samoa', 'UTC'] common_timezones_set = set(common_timezones) all_timezones = \ ['Africa/Abidjan', 'Africa/Accra', 'Africa/Addis_Ababa', 'Africa/Algiers', 'Africa/Asmara', 'Africa/Asmera', 'Africa/Bamako', 'Africa/Bangui', 'Africa/Banjul', 'Africa/Bissau', 'Africa/Blantyre', 'Africa/Brazzaville', 'Africa/Bujumbura', 'Africa/Cairo', 'Africa/Casablanca', 'Africa/Ceuta', 'Africa/Conakry', 'Africa/Dakar', 'Africa/Dar_es_Salaam', 'Africa/Djibouti', 'Africa/Douala', 'Africa/El_Aaiun', 'Africa/Freetown', 'Africa/Gaborone', 'Africa/Harare', 'Africa/Johannesburg', 'Africa/Kampala', 'Africa/Khartoum', 'Africa/Kigali', 'Africa/Kinshasa', 'Africa/Lagos', 'Africa/Libreville', 'Africa/Lome', 'Africa/Luanda', 'Africa/Lubumbashi', 'Africa/Lusaka', 'Africa/Malabo', 'Africa/Maputo', 'Africa/Maseru', 'Africa/Mbabane', 'Africa/Mogadishu', 'Africa/Monrovia', 'Africa/Nairobi', 'Africa/Ndjamena', 'Africa/Niamey', 'Africa/Nouakchott', 'Africa/Ouagadougou', 'Africa/Porto-Novo', 'Africa/Sao_Tome', 'Africa/Timbuktu', 'Africa/Tripoli', 'Africa/Tunis', 'Africa/Windhoek', 'America/Adak', 'America/Anchorage', 'America/Anguilla', 'America/Antigua', 'America/Araguaina', 'America/Argentina/Buenos_Aires', 'America/Argentina/Catamarca', 'America/Argentina/ComodRivadavia', 'America/Argentina/Cordoba', 'America/Argentina/Jujuy', 'America/Argentina/La_Rioja', 'America/Argentina/Mendoza', 'America/Argentina/Rio_Gallegos', 'America/Argentina/San_Juan', 'America/Argentina/San_Luis', 'America/Argentina/Tucuman', 'America/Argentina/Ushuaia', 'America/Aruba', 'America/Asuncion', 'America/Atikokan', 'America/Atka', 'America/Bahia', 'America/Barbados', 'America/Belem', 'America/Belize', 'America/Blanc-Sablon', 'America/Boa_Vista', 'America/Bogota', 'America/Boise', 'America/Buenos_Aires', 'America/Cambridge_Bay', 'America/Campo_Grande', 'America/Cancun', 'America/Caracas', 'America/Catamarca', 'America/Cayenne', 'America/Cayman', 'America/Chicago', 'America/Chihuahua', 'America/Coral_Harbour', 'America/Cordoba', 'America/Costa_Rica', 'America/Cuiaba', 'America/Curacao', 'America/Danmarkshavn', 'America/Dawson', 'America/Dawson_Creek', 'America/Denver', 'America/Detroit', 'America/Dominica', 'America/Edmonton', 'America/Eirunepe', 'America/El_Salvador', 'America/Ensenada', 'America/Fort_Wayne', 'America/Fortaleza', 'America/Glace_Bay', 'America/Godthab', 'America/Goose_Bay', 'America/Grand_Turk', 'America/Grenada', 'America/Guadeloupe', 'America/Guatemala', 'America/Guayaquil', 'America/Guyana', 'America/Halifax', 'America/Havana', 'America/Hermosillo', 'America/Indiana/Indianapolis', 'America/Indiana/Knox', 'America/Indiana/Marengo', 'America/Indiana/Petersburg', 'America/Indiana/Tell_City', 'America/Indiana/Vevay', 'America/Indiana/Vincennes', 'America/Indiana/Winamac', 'America/Indianapolis', 'America/Inuvik', 'America/Iqaluit', 'America/Jamaica', 'America/Jujuy', 'America/Juneau', 'America/Kentucky/Louisville', 'America/Kentucky/Monticello', 'America/Knox_IN', 'America/La_Paz', 'America/Lima', 'America/Los_Angeles', 'America/Louisville', 'America/Maceio', 'America/Managua', 'America/Manaus', 'America/Marigot', 'America/Martinique', 'America/Mazatlan', 'America/Mendoza', 'America/Menominee', 'America/Merida', 'America/Mexico_City', 'America/Miquelon', 'America/Moncton', 'America/Monterrey', 'America/Montevideo', 'America/Montreal', 'America/Montserrat', 'America/Nassau', 'America/New_York', 'America/Nipigon', 'America/Nome', 'America/Noronha', 'America/North_Dakota/Center', 'America/North_Dakota/New_Salem', 'America/Panama', 'America/Pangnirtung', 'America/Paramaribo', 'America/Phoenix', 'America/Port-au-Prince', 'America/Port_of_Spain', 'America/Porto_Acre', 'America/Porto_Velho', 'America/Puerto_Rico', 'America/Rainy_River', 'America/Rankin_Inlet', 'America/Recife', 'America/Regina', 'America/Resolute', 'America/Rio_Branco', 'America/Rosario', 'America/Santiago', 'America/Santo_Domingo', 'America/Sao_Paulo', 'America/Scoresbysund', 'America/Shiprock', 'America/St_Barthelemy', 'America/St_Johns', 'America/St_Kitts', 'America/St_Lucia', 'America/St_Thomas', 'America/St_Vincent', 'America/Swift_Current', 'America/Tegucigalpa', 'America/Thule', 'America/Thunder_Bay', 'America/Tijuana', 'America/Toronto', 'America/Tortola', 'America/Vancouver', 'America/Virgin', 'America/Whitehorse', 'America/Winnipeg', 'America/Yakutat', 'America/Yellowknife', 'Antarctica/Casey', 'Antarctica/Davis', 'Antarctica/DumontDUrville', 'Antarctica/Mawson', 'Antarctica/McMurdo', 'Antarctica/Palmer', 'Antarctica/Rothera', 'Antarctica/South_Pole', 'Antarctica/Syowa', 'Antarctica/Vostok', 'Arctic/Longyearbyen', 'Asia/Aden', 'Asia/Almaty', 'Asia/Amman', 'Asia/Anadyr', 'Asia/Aqtau', 'Asia/Aqtobe', 'Asia/Ashgabat', 'Asia/Ashkhabad', 'Asia/Baghdad', 'Asia/Bahrain', 'Asia/Baku', 'Asia/Bangkok', 'Asia/Beirut', 'Asia/Bishkek', 'Asia/Brunei', 'Asia/Calcutta', 'Asia/Choibalsan', 'Asia/Chongqing', 'Asia/Chungking', 'Asia/Colombo', 'Asia/Dacca', 'Asia/Damascus', 'Asia/Dhaka', 'Asia/Dili', 'Asia/Dubai', 'Asia/Dushanbe', 'Asia/Gaza', 'Asia/Harbin', 'Asia/Ho_Chi_Minh', 'Asia/Hong_Kong', 'Asia/Hovd', 'Asia/Irkutsk', 'Asia/Istanbul', 'Asia/Jakarta', 'Asia/Jayapura', 'Asia/Jerusalem', 'Asia/Kabul', 'Asia/Kamchatka', 'Asia/Karachi', 'Asia/Kashgar', 'Asia/Katmandu', 'Asia/Kolkata', 'Asia/Krasnoyarsk', 'Asia/Kuala_Lumpur', 'Asia/Kuching', 'Asia/Kuwait', 'Asia/Macao', 'Asia/Macau', 'Asia/Magadan', 'Asia/Makassar', 'Asia/Manila', 'Asia/Muscat', 'Asia/Nicosia', 'Asia/Novosibirsk', 'Asia/Omsk', 'Asia/Oral', 'Asia/Phnom_Penh', 'Asia/Pontianak', 'Asia/Pyongyang', 'Asia/Qatar', 'Asia/Qyzylorda', 'Asia/Rangoon', 'Asia/Riyadh', 'Asia/Saigon', 'Asia/Sakhalin', 'Asia/Samarkand', 'Asia/Seoul', 'Asia/Shanghai', 'Asia/Singapore', 'Asia/Taipei', 'Asia/Tashkent', 'Asia/Tbilisi', 'Asia/Tehran', 'Asia/Tel_Aviv', 'Asia/Thimbu', 'Asia/Thimphu', 'Asia/Tokyo', 'Asia/Ujung_Pandang', 'Asia/Ulaanbaatar', 'Asia/Ulan_Bator', 'Asia/Urumqi', 'Asia/Vientiane', 'Asia/Vladivostok', 'Asia/Yakutsk', 'Asia/Yekaterinburg', 'Asia/Yerevan', 'Atlantic/Azores', 'Atlantic/Bermuda', 'Atlantic/Canary', 'Atlantic/Cape_Verde', 'Atlantic/Faeroe', 'Atlantic/Faroe', 'Atlantic/Jan_Mayen', 'Atlantic/Madeira', 'Atlantic/Reykjavik', 'Atlantic/South_Georgia', 'Atlantic/St_Helena', 'Atlantic/Stanley', 'Australia/ACT', 'Australia/Adelaide', 'Australia/Brisbane', 'Australia/Broken_Hill', 'Australia/Canberra', 'Australia/Currie', 'Australia/Darwin', 'Australia/Eucla', 'Australia/Hobart', 'Australia/LHI', 'Australia/Lindeman', 'Australia/Lord_Howe', 'Australia/Melbourne', 'Australia/NSW', 'Australia/North', 'Australia/Perth', 'Australia/Queensland', 'Australia/South', 'Australia/Sydney', 'Australia/Tasmania', 'Australia/Victoria', 'Australia/West', 'Australia/Yancowinna', 'Brazil/Acre', 'Brazil/DeNoronha', 'Brazil/East', 'Brazil/West', 'CET', 'CST6CDT', 'Canada/Atlantic', 'Canada/Central', 'Canada/East-Saskatchewan', 'Canada/Eastern', 'Canada/Mountain', 'Canada/Newfoundland', 'Canada/Pacific', 'Canada/Saskatchewan', 'Canada/Yukon', 'Chile/Continental', 'Chile/EasterIsland', 'Cuba', 'EET', 'EST', 'EST5EDT', 'Egypt', 'Eire', 'Etc/GMT', 'Etc/GMT+0', 'Etc/GMT+1', 'Etc/GMT+10', 'Etc/GMT+11', 'Etc/GMT+12', 'Etc/GMT+2', 'Etc/GMT+3', 'Etc/GMT+4', 'Etc/GMT+5', 'Etc/GMT+6', 'Etc/GMT+7', 'Etc/GMT+8', 'Etc/GMT+9', 'Etc/GMT-0', 'Etc/GMT-1', 'Etc/GMT-10', 'Etc/GMT-11', 'Etc/GMT-12', 'Etc/GMT-13', 'Etc/GMT-14', 'Etc/GMT-2', 'Etc/GMT-3', 'Etc/GMT-4', 'Etc/GMT-5', 'Etc/GMT-6', 'Etc/GMT-7', 'Etc/GMT-8', 'Etc/GMT-9', 'Etc/GMT0', 'Etc/Greenwich', 'Etc/UCT', 'Etc/UTC', 'Etc/Universal', 'Etc/Zulu', 'Europe/Amsterdam', 'Europe/Andorra', 'Europe/Athens', 'Europe/Belfast', 'Europe/Belgrade', 'Europe/Berlin', 'Europe/Bratislava', 'Europe/Brussels', 'Europe/Bucharest', 'Europe/Budapest', 'Europe/Chisinau', 'Europe/Copenhagen', 'Europe/Dublin', 'Europe/Gibraltar', 'Europe/Guernsey', 'Europe/Helsinki', 'Europe/Isle_of_Man', 'Europe/Istanbul', 'Europe/Jersey', 'Europe/Kaliningrad', 'Europe/Kiev', 'Europe/Lisbon', 'Europe/Ljubljana', 'Europe/London', 'Europe/Luxembourg', 'Europe/Madrid', 'Europe/Malta', 'Europe/Mariehamn', 'Europe/Minsk', 'Europe/Monaco', 'Europe/Moscow', 'Europe/Nicosia', 'Europe/Oslo', 'Europe/Paris', 'Europe/Podgorica', 'Europe/Prague', 'Europe/Riga', 'Europe/Rome', 'Europe/Samara', 'Europe/San_Marino', 'Europe/Sarajevo', 'Europe/Simferopol', 'Europe/Skopje', 'Europe/Sofia', 'Europe/Stockholm', 'Europe/Tallinn', 'Europe/Tirane', 'Europe/Tiraspol', 'Europe/Uzhgorod', 'Europe/Vaduz', 'Europe/Vatican', 'Europe/Vienna', 'Europe/Vilnius', 'Europe/Volgograd', 'Europe/Warsaw', 'Europe/Zagreb', 'Europe/Zaporozhye', 'Europe/Zurich', 'GB', 'GB-Eire', 'GMT', 'GMT+0', 'GMT-0', 'GMT0', 'Greenwich', 'HST', 'Hongkong', 'Iceland', 'Indian/Antananarivo', 'Indian/Chagos', 'Indian/Christmas', 'Indian/Cocos', 'Indian/Comoro', 'Indian/Kerguelen', 'Indian/Mahe', 'Indian/Maldives', 'Indian/Mauritius', 'Indian/Mayotte', 'Indian/Reunion', 'Iran', 'Israel', 'Jamaica', 'Japan', 'Kwajalein', 'Libya', 'MET', 'MST', 'MST7MDT', 'Mexico/BajaNorte', 'Mexico/BajaSur', 'Mexico/General', 'NZ', 'NZ-CHAT', 'Navajo', 'PRC', 'PST8PDT', 'Pacific/Apia', 'Pacific/Auckland', 'Pacific/Chatham', 'Pacific/Easter', 'Pacific/Efate', 'Pacific/Enderbury', 'Pacific/Fakaofo', 'Pacific/Fiji', 'Pacific/Funafuti', 'Pacific/Galapagos', 'Pacific/Gambier', 'Pacific/Guadalcanal', 'Pacific/Guam', 'Pacific/Honolulu', 'Pacific/Johnston', 'Pacific/Kiritimati', 'Pacific/Kosrae', 'Pacific/Kwajalein', 'Pacific/Majuro', 'Pacific/Marquesas', 'Pacific/Midway', 'Pacific/Nauru', 'Pacific/Niue', 'Pacific/Norfolk', 'Pacific/Noumea', 'Pacific/Pago_Pago', 'Pacific/Palau', 'Pacific/Pitcairn', 'Pacific/Ponape', 'Pacific/Port_Moresby', 'Pacific/Rarotonga', 'Pacific/Saipan', 'Pacific/Samoa', 'Pacific/Tahiti', 'Pacific/Tarawa', 'Pacific/Tongatapu', 'Pacific/Truk', 'Pacific/Wake', 'Pacific/Wallis', 'Pacific/Yap', 'Poland', 'Portugal', 'ROC', 'ROK', 'Singapore', 'Turkey', 'UCT', 'US/Alaska', 'US/Aleutian', 'US/Arizona', 'US/Central', 'US/East-Indiana', 'US/Eastern', 'US/Hawaii', 'US/Indiana-Starke', 'US/Michigan', 'US/Mountain', 'US/Pacific', 'US/Pacific-New', 'US/Samoa', 'UTC', 'Universal', 'W-SU', 'WET', 'Zulu', 'posixrules'] all_timezones_set = set(all_timezones)

30,648

Python

.py

1,312

20.38872

81

0.694118

numenta/nupic-legacy

6,330

1,556

464

AGPL-3.0

9/5/2024, 5:13:42 PM (Europe/Amsterdam)

26,234

tzinfo.py

numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/pytz/tzinfo.py

'''Base classes and helpers for building zone specific tzinfo classes''' from datetime import datetime, timedelta, tzinfo from bisect import bisect_right try: set except NameError: from sets import Set as set import pytz __all__ = [] _timedelta_cache = {} def memorized_timedelta(seconds): '''Create only one instance of each distinct timedelta''' try: return _timedelta_cache[seconds] except KeyError: delta = timedelta(seconds=seconds) _timedelta_cache[seconds] = delta return delta _epoch = datetime.utcfromtimestamp(0) _datetime_cache = {0: _epoch} def memorized_datetime(seconds): '''Create only one instance of each distinct datetime''' try: return _datetime_cache[seconds] except KeyError: # NB. We can't just do datetime.utcfromtimestamp(seconds) as this # fails with negative values under Windows (Bug #90096) dt = _epoch + timedelta(seconds=seconds) _datetime_cache[seconds] = dt return dt _ttinfo_cache = {} def memorized_ttinfo(*args): '''Create only one instance of each distinct tuple''' try: return _ttinfo_cache[args] except KeyError: ttinfo = ( memorized_timedelta(args[0]), memorized_timedelta(args[1]), args[2] ) _ttinfo_cache[args] = ttinfo return ttinfo _notime = memorized_timedelta(0) def _to_seconds(td): '''Convert a timedelta to seconds''' return td.seconds + td.days * 24 * 60 * 60 class BaseTzInfo(tzinfo): # Overridden in subclass _utcoffset = None _tzname = None zone = None def __str__(self): return self.zone class StaticTzInfo(BaseTzInfo): '''A timezone that has a constant offset from UTC These timezones are rare, as most regions have changed their offset from UTC at some point in their history ''' def fromutc(self, dt): '''See datetime.tzinfo.fromutc''' return (dt + self._utcoffset).replace(tzinfo=self) def utcoffset(self,dt): '''See datetime.tzinfo.utcoffset''' return self._utcoffset def dst(self,dt): '''See datetime.tzinfo.dst''' return _notime def tzname(self,dt): '''See datetime.tzinfo.tzname''' return self._tzname def localize(self, dt, is_dst=False): '''Convert naive time to local time''' if dt.tzinfo is not None: raise ValueError, 'Not naive datetime (tzinfo is already set)' return dt.replace(tzinfo=self) def normalize(self, dt, is_dst=False): '''Correct the timezone information on the given datetime''' if dt.tzinfo is None: raise ValueError, 'Naive time - no tzinfo set' return dt.replace(tzinfo=self) def __repr__(self): return '<StaticTzInfo %r>' % (self.zone,) def __reduce__(self): # Special pickle to zone remains a singleton and to cope with # database changes. return pytz._p, (self.zone,) class DstTzInfo(BaseTzInfo): '''A timezone that has a variable offset from UTC The offset might change if daylight savings time comes into effect, or at a point in history when the region decides to change their timezone definition. ''' # Overridden in subclass _utc_transition_times = None # Sorted list of DST transition times in UTC _transition_info = None # [(utcoffset, dstoffset, tzname)] corresponding # to _utc_transition_times entries zone = None # Set in __init__ _tzinfos = None _dst = None # DST offset def __init__(self, _inf=None, _tzinfos=None): if _inf: self._tzinfos = _tzinfos self._utcoffset, self._dst, self._tzname = _inf else: _tzinfos = {} self._tzinfos = _tzinfos self._utcoffset, self._dst, self._tzname = self._transition_info[0] _tzinfos[self._transition_info[0]] = self for inf in self._transition_info[1:]: if inf not in _tzinfos: _tzinfos[inf] = self.__class__(inf, _tzinfos) def fromutc(self, dt): '''See datetime.tzinfo.fromutc''' dt = dt.replace(tzinfo=None) idx = max(0, bisect_right(self._utc_transition_times, dt) - 1) inf = self._transition_info[idx] return (dt + inf[0]).replace(tzinfo=self._tzinfos[inf]) def normalize(self, dt): '''Correct the timezone information on the given datetime If date arithmetic crosses DST boundaries, the tzinfo is not magically adjusted. This method normalizes the tzinfo to the correct one. To test, first we need to do some setup >>> from pytz import timezone >>> utc = timezone('UTC') >>> eastern = timezone('US/Eastern') >>> fmt = '%Y-%m-%d %H:%M:%S %Z (%z)' We next create a datetime right on an end-of-DST transition point, the instant when the wallclocks are wound back one hour. >>> utc_dt = datetime(2002, 10, 27, 6, 0, 0, tzinfo=utc) >>> loc_dt = utc_dt.astimezone(eastern) >>> loc_dt.strftime(fmt) '2002-10-27 01:00:00 EST (-0500)' Now, if we subtract a few minutes from it, note that the timezone information has not changed. >>> before = loc_dt - timedelta(minutes=10) >>> before.strftime(fmt) '2002-10-27 00:50:00 EST (-0500)' But we can fix that by calling the normalize method >>> before = eastern.normalize(before) >>> before.strftime(fmt) '2002-10-27 01:50:00 EDT (-0400)' ''' if dt.tzinfo is None: raise ValueError, 'Naive time - no tzinfo set' # Convert dt in localtime to UTC offset = dt.tzinfo._utcoffset dt = dt.replace(tzinfo=None) dt = dt - offset # convert it back, and return it return self.fromutc(dt) def localize(self, dt, is_dst=False): '''Convert naive time to local time. This method should be used to construct localtimes, rather than passing a tzinfo argument to a datetime constructor. is_dst is used to determine the correct timezone in the ambigous period at the end of daylight savings time. >>> from pytz import timezone >>> fmt = '%Y-%m-%d %H:%M:%S %Z (%z)' >>> amdam = timezone('Europe/Amsterdam') >>> dt = datetime(2004, 10, 31, 2, 0, 0) >>> loc_dt1 = amdam.localize(dt, is_dst=True) >>> loc_dt2 = amdam.localize(dt, is_dst=False) >>> loc_dt1.strftime(fmt) '2004-10-31 02:00:00 CEST (+0200)' >>> loc_dt2.strftime(fmt) '2004-10-31 02:00:00 CET (+0100)' >>> str(loc_dt2 - loc_dt1) '1:00:00' Use is_dst=None to raise an AmbiguousTimeError for ambiguous times at the end of daylight savings >>> try: ... loc_dt1 = amdam.localize(dt, is_dst=None) ... except AmbiguousTimeError: ... print 'Oops' Oops >>> loc_dt1 = amdam.localize(dt, is_dst=None) Traceback (most recent call last): [...] AmbiguousTimeError: 2004-10-31 02:00:00 is_dst defaults to False >>> amdam.localize(dt) == amdam.localize(dt, False) True ''' if dt.tzinfo is not None: raise ValueError, 'Not naive datetime (tzinfo is already set)' # Find the possibly correct timezones. We probably just have one, # but we might end up with two if we are in the end-of-DST # transition period. Or possibly more in some particularly confused # location... possible_loc_dt = set() for tzinfo in self._tzinfos.values(): loc_dt = tzinfo.normalize(dt.replace(tzinfo=tzinfo)) if loc_dt.replace(tzinfo=None) == dt: possible_loc_dt.add(loc_dt) if len(possible_loc_dt) == 1: return possible_loc_dt.pop() # If told to be strict, raise an exception since we have an # ambiguous case if is_dst is None: raise AmbiguousTimeError(dt) # Filter out the possiblilities that don't match the requested # is_dst filtered_possible_loc_dt = [ p for p in possible_loc_dt if bool(p.tzinfo._dst) == is_dst ] # Hopefully we only have one possibility left. Return it. if len(filtered_possible_loc_dt) == 1: return filtered_possible_loc_dt[0] if len(filtered_possible_loc_dt) == 0: filtered_possible_loc_dt = list(possible_loc_dt) # If we get this far, we have in a wierd timezone transition # where the clocks have been wound back but is_dst is the same # in both (eg. Europe/Warsaw 1915 when they switched to CET). # At this point, we just have to guess unless we allow more # hints to be passed in (such as the UTC offset or abbreviation), # but that is just getting silly. # # Choose the earliest (by UTC) applicable timezone. def mycmp(a,b): return cmp( a.replace(tzinfo=None) - a.tzinfo._utcoffset, b.replace(tzinfo=None) - b.tzinfo._utcoffset, ) filtered_possible_loc_dt.sort(mycmp) return filtered_possible_loc_dt[0] def utcoffset(self, dt): '''See datetime.tzinfo.utcoffset''' return self._utcoffset def dst(self, dt): '''See datetime.tzinfo.dst''' return self._dst def tzname(self, dt): '''See datetime.tzinfo.tzname''' return self._tzname def __repr__(self): if self._dst: dst = 'DST' else: dst = 'STD' if self._utcoffset > _notime: return '<DstTzInfo %r %s+%s %s>' % ( self.zone, self._tzname, self._utcoffset, dst ) else: return '<DstTzInfo %r %s%s %s>' % ( self.zone, self._tzname, self._utcoffset, dst ) def __reduce__(self): # Special pickle to zone remains a singleton and to cope with # database changes. return pytz._p, ( self.zone, _to_seconds(self._utcoffset), _to_seconds(self._dst), self._tzname ) class AmbiguousTimeError(Exception): '''Exception raised when attempting to create an ambiguous wallclock time. At the end of a DST transition period, a particular wallclock time will occur twice (once before the clocks are set back, once after). Both possibilities may be correct, unless further information is supplied. See DstTzInfo.normalize() for more info ''' def unpickler(zone, utcoffset=None, dstoffset=None, tzname=None): """Factory function for unpickling pytz tzinfo instances. This is shared for both StaticTzInfo and DstTzInfo instances, because database changes could cause a zones implementation to switch between these two base classes and we can't break pickles on a pytz version upgrade. """ # Raises a KeyError if zone no longer exists, which should never happen # and would be a bug. tz = pytz.timezone(zone) # A StaticTzInfo - just return it if utcoffset is None: return tz # This pickle was created from a DstTzInfo. We need to # determine which of the list of tzinfo instances for this zone # to use in order to restore the state of any datetime instances using # it correctly. utcoffset = memorized_timedelta(utcoffset) dstoffset = memorized_timedelta(dstoffset) try: return tz._tzinfos[(utcoffset, dstoffset, tzname)] except KeyError: # The particular state requested in this timezone no longer exists. # This indicates a corrupt pickle, or the timezone database has been # corrected violently enough to make this particular # (utcoffset,dstoffset) no longer exist in the zone, or the # abbreviation has been changed. pass # See if we can find an entry differing only by tzname. Abbreviations # get changed from the initial guess by the database maintainers to # match reality when this information is discovered. for localized_tz in tz._tzinfos.values(): if (localized_tz._utcoffset == utcoffset and localized_tz._dst == dstoffset): return localized_tz # This (utcoffset, dstoffset) information has been removed from the # zone. Add it back. This might occur when the database maintainers have # corrected incorrect information. datetime instances using this # incorrect information will continue to do so, exactly as they were # before being pickled. This is purely an overly paranoid safety net - I # doubt this will ever been needed in real life. inf = (utcoffset, dstoffset, tzname) tz._tzinfos[inf] = tz.__class__(inf, tz._tzinfos) return tz._tzinfos[inf]

13,161

Python

.py

311

33.749196

79

0.624139

numenta/nupic-legacy

6,330

1,556

464

AGPL-3.0

9/5/2024, 5:13:42 PM (Europe/Amsterdam)

26,235

reference.py

numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/pytz/reference.py

''' Reference tzinfo implementations from the Python docs. Used for testing against as they are only correct for the years 1987 to 2006. Do not use these for real code. ''' from datetime import tzinfo, timedelta, datetime from pytz import utc, UTC, HOUR, ZERO # A class building tzinfo objects for fixed-offset time zones. # Note that FixedOffset(0, "UTC") is a different way to build a # UTC tzinfo object. class FixedOffset(tzinfo): """Fixed offset in minutes east from UTC.""" def __init__(self, offset, name): self.__offset = timedelta(minutes = offset) self.__name = name def utcoffset(self, dt): return self.__offset def tzname(self, dt): return self.__name def dst(self, dt): return ZERO # A class capturing the platform's idea of local time. import time as _time STDOFFSET = timedelta(seconds = -_time.timezone) if _time.daylight: DSTOFFSET = timedelta(seconds = -_time.altzone) else: DSTOFFSET = STDOFFSET DSTDIFF = DSTOFFSET - STDOFFSET class LocalTimezone(tzinfo): def utcoffset(self, dt): if self._isdst(dt): return DSTOFFSET else: return STDOFFSET def dst(self, dt): if self._isdst(dt): return DSTDIFF else: return ZERO def tzname(self, dt): return _time.tzname[self._isdst(dt)] def _isdst(self, dt): tt = (dt.year, dt.month, dt.day, dt.hour, dt.minute, dt.second, dt.weekday(), 0, -1) stamp = _time.mktime(tt) tt = _time.localtime(stamp) return tt.tm_isdst > 0 Local = LocalTimezone() # A complete implementation of current DST rules for major US time zones. def first_sunday_on_or_after(dt): days_to_go = 6 - dt.weekday() if days_to_go: dt += timedelta(days_to_go) return dt # In the US, DST starts at 2am (standard time) on the first Sunday in April. DSTSTART = datetime(1, 4, 1, 2) # and ends at 2am (DST time; 1am standard time) on the last Sunday of Oct. # which is the first Sunday on or after Oct 25. DSTEND = datetime(1, 10, 25, 1) class USTimeZone(tzinfo): def __init__(self, hours, reprname, stdname, dstname): self.stdoffset = timedelta(hours=hours) self.reprname = reprname self.stdname = stdname self.dstname = dstname def __repr__(self): return self.reprname def tzname(self, dt): if self.dst(dt): return self.dstname else: return self.stdname def utcoffset(self, dt): return self.stdoffset + self.dst(dt) def dst(self, dt): if dt is None or dt.tzinfo is None: # An exception may be sensible here, in one or both cases. # It depends on how you want to treat them. The default # fromutc() implementation (called by the default astimezone() # implementation) passes a datetime with dt.tzinfo is self. return ZERO assert dt.tzinfo is self # Find first Sunday in April & the last in October. start = first_sunday_on_or_after(DSTSTART.replace(year=dt.year)) end = first_sunday_on_or_after(DSTEND.replace(year=dt.year)) # Can't compare naive to aware objects, so strip the timezone from # dt first. if start <= dt.replace(tzinfo=None) < end: return HOUR else: return ZERO Eastern = USTimeZone(-5, "Eastern", "EST", "EDT") Central = USTimeZone(-6, "Central", "CST", "CDT") Mountain = USTimeZone(-7, "Mountain", "MST", "MDT") Pacific = USTimeZone(-8, "Pacific", "PST", "PDT")

3,649

Python

.py

97

30.989691

76

0.641965

numenta/nupic-legacy

6,330

1,556

464

AGPL-3.0

9/5/2024, 5:13:42 PM (Europe/Amsterdam)

26,236

colors.py

numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/colors.py

""" A module for converting numbers or color arguments to *RGB* or *RGBA* *RGB* and *RGBA* are sequences of, respectively, 3 or 4 floats in the range 0-1. This module includes functions and classes for color specification conversions, and for mapping numbers to colors in a 1-D array of colors called a colormap. Colormapping typically involves two steps: a data array is first mapped onto the range 0-1 using an instance of :class:`Normalize` or of a subclass; then this number in the 0-1 range is mapped to a color using an instance of a subclass of :class:`Colormap`. Two are provided here: :class:`LinearSegmentedColormap`, which is used to generate all the built-in colormap instances, but is also useful for making custom colormaps, and :class:`ListedColormap`, which is used for generating a custom colormap from a list of color specifications. The module also provides a single instance, *colorConverter*, of the :class:`ColorConverter` class providing methods for converting single color specifications or sequences of them to *RGB* or *RGBA*. Commands which take color arguments can use several formats to specify the colors. For the basic builtin colors, you can use a single letter - b : blue - g : green - r : red - c : cyan - m : magenta - y : yellow - k : black - w : white Gray shades can be given as a string encoding a float in the 0-1 range, e.g.:: color = '0.75' For a greater range of colors, you have two options. You can specify the color using an html hex string, as in:: color = '#eeefff' or you can pass an *R* , *G* , *B* tuple, where each of *R* , *G* , *B* are in the range [0,1]. Finally, legal html names for colors, like 'red', 'burlywood' and 'chartreuse' are supported. """ import re import numpy as np from numpy import ma import matplotlib.cbook as cbook parts = np.__version__.split('.') NP_MAJOR, NP_MINOR = map(int, parts[:2]) # true if clip supports the out kwarg NP_CLIP_OUT = NP_MAJOR>=1 and NP_MINOR>=2 cnames = { 'aliceblue' : '#F0F8FF', 'antiquewhite' : '#FAEBD7', 'aqua' : '#00FFFF', 'aquamarine' : '#7FFFD4', 'azure' : '#F0FFFF', 'beige' : '#F5F5DC', 'bisque' : '#FFE4C4', 'black' : '#000000', 'blanchedalmond' : '#FFEBCD', 'blue' : '#0000FF', 'blueviolet' : '#8A2BE2', 'brown' : '#A52A2A', 'burlywood' : '#DEB887', 'cadetblue' : '#5F9EA0', 'chartreuse' : '#7FFF00', 'chocolate' : '#D2691E', 'coral' : '#FF7F50', 'cornflowerblue' : '#6495ED', 'cornsilk' : '#FFF8DC', 'crimson' : '#DC143C', 'cyan' : '#00FFFF', 'darkblue' : '#00008B', 'darkcyan' : '#008B8B', 'darkgoldenrod' : '#B8860B', 'darkgray' : '#A9A9A9', 'darkgreen' : '#006400', 'darkkhaki' : '#BDB76B', 'darkmagenta' : '#8B008B', 'darkolivegreen' : '#556B2F', 'darkorange' : '#FF8C00', 'darkorchid' : '#9932CC', 'darkred' : '#8B0000', 'darksalmon' : '#E9967A', 'darkseagreen' : '#8FBC8F', 'darkslateblue' : '#483D8B', 'darkslategray' : '#2F4F4F', 'darkturquoise' : '#00CED1', 'darkviolet' : '#9400D3', 'deeppink' : '#FF1493', 'deepskyblue' : '#00BFFF', 'dimgray' : '#696969', 'dodgerblue' : '#1E90FF', 'firebrick' : '#B22222', 'floralwhite' : '#FFFAF0', 'forestgreen' : '#228B22', 'fuchsia' : '#FF00FF', 'gainsboro' : '#DCDCDC', 'ghostwhite' : '#F8F8FF', 'gold' : '#FFD700', 'goldenrod' : '#DAA520', 'gray' : '#808080', 'green' : '#008000', 'greenyellow' : '#ADFF2F', 'honeydew' : '#F0FFF0', 'hotpink' : '#FF69B4', 'indianred' : '#CD5C5C', 'indigo' : '#4B0082', 'ivory' : '#FFFFF0', 'khaki' : '#F0E68C', 'lavender' : '#E6E6FA', 'lavenderblush' : '#FFF0F5', 'lawngreen' : '#7CFC00', 'lemonchiffon' : '#FFFACD', 'lightblue' : '#ADD8E6', 'lightcoral' : '#F08080', 'lightcyan' : '#E0FFFF', 'lightgoldenrodyellow' : '#FAFAD2', 'lightgreen' : '#90EE90', 'lightgrey' : '#D3D3D3', 'lightpink' : '#FFB6C1', 'lightsalmon' : '#FFA07A', 'lightseagreen' : '#20B2AA', 'lightskyblue' : '#87CEFA', 'lightslategray' : '#778899', 'lightsteelblue' : '#B0C4DE', 'lightyellow' : '#FFFFE0', 'lime' : '#00FF00', 'limegreen' : '#32CD32', 'linen' : '#FAF0E6', 'magenta' : '#FF00FF', 'maroon' : '#800000', 'mediumaquamarine' : '#66CDAA', 'mediumblue' : '#0000CD', 'mediumorchid' : '#BA55D3', 'mediumpurple' : '#9370DB', 'mediumseagreen' : '#3CB371', 'mediumslateblue' : '#7B68EE', 'mediumspringgreen' : '#00FA9A', 'mediumturquoise' : '#48D1CC', 'mediumvioletred' : '#C71585', 'midnightblue' : '#191970', 'mintcream' : '#F5FFFA', 'mistyrose' : '#FFE4E1', 'moccasin' : '#FFE4B5', 'navajowhite' : '#FFDEAD', 'navy' : '#000080', 'oldlace' : '#FDF5E6', 'olive' : '#808000', 'olivedrab' : '#6B8E23', 'orange' : '#FFA500', 'orangered' : '#FF4500', 'orchid' : '#DA70D6', 'palegoldenrod' : '#EEE8AA', 'palegreen' : '#98FB98', 'palevioletred' : '#AFEEEE', 'papayawhip' : '#FFEFD5', 'peachpuff' : '#FFDAB9', 'peru' : '#CD853F', 'pink' : '#FFC0CB', 'plum' : '#DDA0DD', 'powderblue' : '#B0E0E6', 'purple' : '#800080', 'red' : '#FF0000', 'rosybrown' : '#BC8F8F', 'royalblue' : '#4169E1', 'saddlebrown' : '#8B4513', 'salmon' : '#FA8072', 'sandybrown' : '#FAA460', 'seagreen' : '#2E8B57', 'seashell' : '#FFF5EE', 'sienna' : '#A0522D', 'silver' : '#C0C0C0', 'skyblue' : '#87CEEB', 'slateblue' : '#6A5ACD', 'slategray' : '#708090', 'snow' : '#FFFAFA', 'springgreen' : '#00FF7F', 'steelblue' : '#4682B4', 'tan' : '#D2B48C', 'teal' : '#008080', 'thistle' : '#D8BFD8', 'tomato' : '#FF6347', 'turquoise' : '#40E0D0', 'violet' : '#EE82EE', 'wheat' : '#F5DEB3', 'white' : '#FFFFFF', 'whitesmoke' : '#F5F5F5', 'yellow' : '#FFFF00', 'yellowgreen' : '#9ACD32', } # add british equivs for k, v in cnames.items(): if k.find('gray')>=0: k = k.replace('gray', 'grey') cnames[k] = v def is_color_like(c): 'Return *True* if *c* can be converted to *RGB*' try: colorConverter.to_rgb(c) return True except ValueError: return False def rgb2hex(rgb): 'Given a len 3 rgb tuple of 0-1 floats, return the hex string' return '#%02x%02x%02x' % tuple([round(val*255) for val in rgb]) hexColorPattern = re.compile("\A#[a-fA-F0-9]{6}\Z") def hex2color(s): """ Take a hex string *s* and return the corresponding rgb 3-tuple Example: #efefef -> (0.93725, 0.93725, 0.93725) """ if not isinstance(s, basestring): raise TypeError('hex2color requires a string argument') if hexColorPattern.match(s) is None: raise ValueError('invalid hex color string "%s"' % s) return tuple([int(n, 16)/255.0 for n in (s[1:3], s[3:5], s[5:7])]) class ColorConverter: """ Provides methods for converting color specifications to *RGB* or *RGBA* Caching is used for more efficient conversion upon repeated calls with the same argument. Ordinarily only the single instance instantiated in this module, *colorConverter*, is needed. """ colors = { 'b' : (0.0, 0.0, 1.0), 'g' : (0.0, 0.5, 0.0), 'r' : (1.0, 0.0, 0.0), 'c' : (0.0, 0.75, 0.75), 'm' : (0.75, 0, 0.75), 'y' : (0.75, 0.75, 0), 'k' : (0.0, 0.0, 0.0), 'w' : (1.0, 1.0, 1.0), } cache = {} def to_rgb(self, arg): """ Returns an *RGB* tuple of three floats from 0-1. *arg* can be an *RGB* or *RGBA* sequence or a string in any of several forms: 1) a letter from the set 'rgbcmykw' 2) a hex color string, like '#00FFFF' 3) a standard name, like 'aqua' 4) a float, like '0.4', indicating gray on a 0-1 scale if *arg* is *RGBA*, the *A* will simply be discarded. """ try: return self.cache[arg] except KeyError: pass except TypeError: # could be unhashable rgb seq arg = tuple(arg) try: return self.cache[arg] except KeyError: pass except TypeError: raise ValueError( 'to_rgb: arg "%s" is unhashable even inside a tuple' % (str(arg),)) try: if cbook.is_string_like(arg): color = self.colors.get(arg, None) if color is None: str1 = cnames.get(arg, arg) if str1.startswith('#'): color = hex2color(str1) else: fl = float(arg) if fl < 0 or fl > 1: raise ValueError( 'gray (string) must be in range 0-1') color = tuple([fl]*3) elif cbook.iterable(arg): if len(arg) > 4 or len(arg) < 3: raise ValueError( 'sequence length is %d; must be 3 or 4'%len(arg)) color = tuple(arg[:3]) if [x for x in color if (float(x) < 0) or (x > 1)]: # This will raise TypeError if x is not a number. raise ValueError('number in rbg sequence outside 0-1 range') else: raise ValueError('cannot convert argument to rgb sequence') self.cache[arg] = color except (KeyError, ValueError, TypeError), exc: raise ValueError('to_rgb: Invalid rgb arg "%s"\n%s' % (str(arg), exc)) # Error messages could be improved by handling TypeError # separately; but this should be rare and not too hard # for the user to figure out as-is. return color def to_rgba(self, arg, alpha=None): """ Returns an *RGBA* tuple of four floats from 0-1. For acceptable values of *arg*, see :meth:`to_rgb`. If *arg* is an *RGBA* sequence and *alpha* is not *None*, *alpha* will replace the original *A*. """ try: if not cbook.is_string_like(arg) and cbook.iterable(arg): if len(arg) == 4: if [x for x in arg if (float(x) < 0) or (x > 1)]: # This will raise TypeError if x is not a number. raise ValueError('number in rbga sequence outside 0-1 range') if alpha is None: return tuple(arg) if alpha < 0.0 or alpha > 1.0: raise ValueError("alpha must be in range 0-1") return arg[0], arg[1], arg[2], arg[3] * alpha r,g,b = arg[:3] if [x for x in (r,g,b) if (float(x) < 0) or (x > 1)]: raise ValueError('number in rbg sequence outside 0-1 range') else: r,g,b = self.to_rgb(arg) if alpha is None: alpha = 1.0 return r,g,b,alpha except (TypeError, ValueError), exc: raise ValueError('to_rgba: Invalid rgba arg "%s"\n%s' % (str(arg), exc)) def to_rgba_array(self, c, alpha=None): """ Returns a numpy array of *RGBA* tuples. Accepts a single mpl color spec or a sequence of specs. Special case to handle "no color": if *c* is "none" (case-insensitive), then an empty array will be returned. Same for an empty list. """ try: if c.lower() == 'none': return np.zeros((0,4), dtype=np.float_) except AttributeError: pass if len(c) == 0: return np.zeros((0,4), dtype=np.float_) try: result = np.array([self.to_rgba(c, alpha)], dtype=np.float_) except ValueError: if isinstance(c, np.ndarray): if c.ndim != 2 and c.dtype.kind not in 'SU': raise ValueError("Color array must be two-dimensional") result = np.zeros((len(c), 4)) for i, cc in enumerate(c): result[i] = self.to_rgba(cc, alpha) # change in place return np.asarray(result, np.float_) colorConverter = ColorConverter() def makeMappingArray(N, data): """Create an *N* -element 1-d lookup table *data* represented by a list of x,y0,y1 mapping correspondences. Each element in this list represents how a value between 0 and 1 (inclusive) represented by x is mapped to a corresponding value between 0 and 1 (inclusive). The two values of y are to allow for discontinuous mapping functions (say as might be found in a sawtooth) where y0 represents the value of y for values of x <= to that given, and y1 is the value to be used for x > than that given). The list must start with x=0, end with x=1, and all values of x must be in increasing order. Values between the given mapping points are determined by simple linear interpolation. The function returns an array "result" where ``result[x*(N-1)]`` gives the closest value for values of x between 0 and 1. """ try: adata = np.array(data) except: raise TypeError("data must be convertable to an array") shape = adata.shape if len(shape) != 2 and shape[1] != 3: raise ValueError("data must be nx3 format") x = adata[:,0] y0 = adata[:,1] y1 = adata[:,2] if x[0] != 0. or x[-1] != 1.0: raise ValueError( "data mapping points must start with x=0. and end with x=1") if np.sometrue(np.sort(x)-x): raise ValueError( "data mapping points must have x in increasing order") # begin generation of lookup table x = x * (N-1) lut = np.zeros((N,), np.float) xind = np.arange(float(N)) ind = np.searchsorted(x, xind)[1:-1] lut[1:-1] = ( ((xind[1:-1] - x[ind-1]) / (x[ind] - x[ind-1])) * (y0[ind] - y1[ind-1]) + y1[ind-1]) lut[0] = y1[0] lut[-1] = y0[-1] # ensure that the lut is confined to values between 0 and 1 by clipping it np.clip(lut, 0.0, 1.0) #lut = where(lut > 1., 1., lut) #lut = where(lut < 0., 0., lut) return lut class Colormap: """Base class for all scalar to rgb mappings Important methods: * :meth:`set_bad` * :meth:`set_under` * :meth:`set_over` """ def __init__(self, name, N=256): """ Public class attributes: :attr:`N` : number of rgb quantization levels :attr:`name` : name of colormap """ self.name = name self.N = N self._rgba_bad = (0.0, 0.0, 0.0, 0.0) # If bad, don't paint anything. self._rgba_under = None self._rgba_over = None self._i_under = N self._i_over = N+1 self._i_bad = N+2 self._isinit = False def __call__(self, X, alpha=1.0, bytes=False): """ *X* is either a scalar or an array (of any dimension). If scalar, a tuple of rgba values is returned, otherwise an array with the new shape = oldshape+(4,). If the X-values are integers, then they are used as indices into the array. If they are floating point, then they must be in the interval (0.0, 1.0). Alpha must be a scalar. If bytes is False, the rgba values will be floats on a 0-1 scale; if True, they will be uint8, 0-255. """ if not self._isinit: self._init() alpha = min(alpha, 1.0) # alpha must be between 0 and 1 alpha = max(alpha, 0.0) self._lut[:-3, -1] = alpha mask_bad = None if not cbook.iterable(X): vtype = 'scalar' xa = np.array([X]) else: vtype = 'array' xma = ma.asarray(X) xa = xma.filled(0) mask_bad = ma.getmask(xma) if xa.dtype.char in np.typecodes['Float']: np.putmask(xa, xa==1.0, 0.9999999) #Treat 1.0 as slightly less than 1. # The following clip is fast, and prevents possible # conversion of large positive values to negative integers. if NP_CLIP_OUT: np.clip(xa * self.N, -1, self.N, out=xa) else: xa = np.clip(xa * self.N, -1, self.N) xa = xa.astype(int) # Set the over-range indices before the under-range; # otherwise the under-range values get converted to over-range. np.putmask(xa, xa>self.N-1, self._i_over) np.putmask(xa, xa<0, self._i_under) if mask_bad is not None and mask_bad.shape == xa.shape: np.putmask(xa, mask_bad, self._i_bad) if bytes: lut = (self._lut * 255).astype(np.uint8) else: lut = self._lut rgba = np.empty(shape=xa.shape+(4,), dtype=lut.dtype) lut.take(xa, axis=0, mode='clip', out=rgba) # twice as fast as lut[xa]; # using the clip or wrap mode and providing an # output array speeds it up a little more. if vtype == 'scalar': rgba = tuple(rgba[0,:]) return rgba def set_bad(self, color = 'k', alpha = 1.0): '''Set color to be used for masked values. ''' self._rgba_bad = colorConverter.to_rgba(color, alpha) if self._isinit: self._set_extremes() def set_under(self, color = 'k', alpha = 1.0): '''Set color to be used for low out-of-range values. Requires norm.clip = False ''' self._rgba_under = colorConverter.to_rgba(color, alpha) if self._isinit: self._set_extremes() def set_over(self, color = 'k', alpha = 1.0): '''Set color to be used for high out-of-range values. Requires norm.clip = False ''' self._rgba_over = colorConverter.to_rgba(color, alpha) if self._isinit: self._set_extremes() def _set_extremes(self): if self._rgba_under: self._lut[self._i_under] = self._rgba_under else: self._lut[self._i_under] = self._lut[0] if self._rgba_over: self._lut[self._i_over] = self._rgba_over else: self._lut[self._i_over] = self._lut[self.N-1] self._lut[self._i_bad] = self._rgba_bad def _init(): '''Generate the lookup table, self._lut''' raise NotImplementedError("Abstract class only") def is_gray(self): if not self._isinit: self._init() return (np.alltrue(self._lut[:,0] == self._lut[:,1]) and np.alltrue(self._lut[:,0] == self._lut[:,2])) class LinearSegmentedColormap(Colormap): """Colormap objects based on lookup tables using linear segments. The lookup table is generated using linear interpolation for each primary color, with the 0-1 domain divided into any number of segments. """ def __init__(self, name, segmentdata, N=256): """Create color map from linear mapping segments segmentdata argument is a dictionary with a red, green and blue entries. Each entry should be a list of *x*, *y0*, *y1* tuples, forming rows in a table. Example: suppose you want red to increase from 0 to 1 over the bottom half, green to do the same over the middle half, and blue over the top half. Then you would use:: cdict = {'red': [(0.0, 0.0, 0.0), (0.5, 1.0, 1.0), (1.0, 1.0, 1.0)], 'green': [(0.0, 0.0, 0.0), (0.25, 0.0, 0.0), (0.75, 1.0, 1.0), (1.0, 1.0, 1.0)], 'blue': [(0.0, 0.0, 0.0), (0.5, 0.0, 0.0), (1.0, 1.0, 1.0)]} Each row in the table for a given color is a sequence of *x*, *y0*, *y1* tuples. In each sequence, *x* must increase monotonically from 0 to 1. For any input value *z* falling between *x[i]* and *x[i+1]*, the output value of a given color will be linearly interpolated between *y1[i]* and *y0[i+1]*:: row i: x y0 y1 / / row i+1: x y0 y1 Hence y0 in the first row and y1 in the last row are never used. .. seealso:: :func:`makeMappingArray` """ self.monochrome = False # True only if all colors in map are identical; # needed for contouring. Colormap.__init__(self, name, N) self._segmentdata = segmentdata def _init(self): self._lut = np.ones((self.N + 3, 4), np.float) self._lut[:-3, 0] = makeMappingArray(self.N, self._segmentdata['red']) self._lut[:-3, 1] = makeMappingArray(self.N, self._segmentdata['green']) self._lut[:-3, 2] = makeMappingArray(self.N, self._segmentdata['blue']) self._isinit = True self._set_extremes() class ListedColormap(Colormap): """Colormap object generated from a list of colors. This may be most useful when indexing directly into a colormap, but it can also be used to generate special colormaps for ordinary mapping. """ def __init__(self, colors, name = 'from_list', N = None): """ Make a colormap from a list of colors. *colors* a list of matplotlib color specifications, or an equivalent Nx3 floating point array (*N* rgb values) *name* a string to identify the colormap *N* the number of entries in the map. The default is *None*, in which case there is one colormap entry for each element in the list of colors. If:: N < len(colors) the list will be truncated at *N*. If:: N > len(colors) the list will be extended by repetition. """ self.colors = colors self.monochrome = False # True only if all colors in map are identical; # needed for contouring. if N is None: N = len(self.colors) else: if cbook.is_string_like(self.colors): self.colors = [self.colors] * N self.monochrome = True elif cbook.iterable(self.colors): self.colors = list(self.colors) # in case it was a tuple if len(self.colors) == 1: self.monochrome = True if len(self.colors) < N: self.colors = list(self.colors) * N del(self.colors[N:]) else: try: gray = float(self.colors) except TypeError: pass else: self.colors = [gray] * N self.monochrome = True Colormap.__init__(self, name, N) def _init(self): rgb = np.array([colorConverter.to_rgb(c) for c in self.colors], np.float) self._lut = np.zeros((self.N + 3, 4), np.float) self._lut[:-3, :-1] = rgb self._lut[:-3, -1] = 1 self._isinit = True self._set_extremes() class Normalize: """ Normalize a given value to the 0-1 range """ def __init__(self, vmin=None, vmax=None, clip=False): """ If *vmin* or *vmax* is not given, they are taken from the input's minimum and maximum value respectively. If *clip* is *True* and the given value falls outside the range, the returned value will be 0 or 1, whichever is closer. Returns 0 if:: vmin==vmax Works with scalars or arrays, including masked arrays. If *clip* is *True*, masked values are set to 1; otherwise they remain masked. Clipping silently defeats the purpose of setting the over, under, and masked colors in the colormap, so it is likely to lead to surprises; therefore the default is *clip* = *False*. """ self.vmin = vmin self.vmax = vmax self.clip = clip def __call__(self, value, clip=None): if clip is None: clip = self.clip if cbook.iterable(value): vtype = 'array' val = ma.asarray(value).astype(np.float) else: vtype = 'scalar' val = ma.array([value]).astype(np.float) self.autoscale_None(val) vmin, vmax = self.vmin, self.vmax if vmin > vmax: raise ValueError("minvalue must be less than or equal to maxvalue") elif vmin==vmax: return 0.0 * val else: if clip: mask = ma.getmask(val) val = ma.array(np.clip(val.filled(vmax), vmin, vmax), mask=mask) result = (val-vmin) * (1.0/(vmax-vmin)) if vtype == 'scalar': result = result[0] return result def inverse(self, value): if not self.scaled(): raise ValueError("Not invertible until scaled") vmin, vmax = self.vmin, self.vmax if cbook.iterable(value): val = ma.asarray(value) return vmin + val * (vmax - vmin) else: return vmin + value * (vmax - vmin) def autoscale(self, A): ''' Set *vmin*, *vmax* to min, max of *A*. ''' self.vmin = ma.minimum(A) self.vmax = ma.maximum(A) def autoscale_None(self, A): ' autoscale only None-valued vmin or vmax' if self.vmin is None: self.vmin = ma.minimum(A) if self.vmax is None: self.vmax = ma.maximum(A) def scaled(self): 'return true if vmin and vmax set' return (self.vmin is not None and self.vmax is not None) class LogNorm(Normalize): """ Normalize a given value to the 0-1 range on a log scale """ def __call__(self, value, clip=None): if clip is None: clip = self.clip if cbook.iterable(value): vtype = 'array' val = ma.asarray(value).astype(np.float) else: vtype = 'scalar' val = ma.array([value]).astype(np.float) self.autoscale_None(val) vmin, vmax = self.vmin, self.vmax if vmin > vmax: raise ValueError("minvalue must be less than or equal to maxvalue") elif vmin<=0: raise ValueError("values must all be positive") elif vmin==vmax: return 0.0 * val else: if clip: mask = ma.getmask(val) val = ma.array(np.clip(val.filled(vmax), vmin, vmax), mask=mask) result = (ma.log(val)-np.log(vmin))/(np.log(vmax)-np.log(vmin)) if vtype == 'scalar': result = result[0] return result def inverse(self, value): if not self.scaled(): raise ValueError("Not invertible until scaled") vmin, vmax = self.vmin, self.vmax if cbook.iterable(value): val = ma.asarray(value) return vmin * ma.power((vmax/vmin), val) else: return vmin * pow((vmax/vmin), value) class BoundaryNorm(Normalize): ''' Generate a colormap index based on discrete intervals. Unlike :class:`Normalize` or :class:`LogNorm`, :class:`BoundaryNorm` maps values to integers instead of to the interval 0-1. Mapping to the 0-1 interval could have been done via piece-wise linear interpolation, but using integers seems simpler, and reduces the number of conversions back and forth between integer and floating point. ''' def __init__(self, boundaries, ncolors, clip=False): ''' *boundaries* a monotonically increasing sequence *ncolors* number of colors in the colormap to be used If:: b[i] <= v < b[i+1] then v is mapped to color j; as i varies from 0 to len(boundaries)-2, j goes from 0 to ncolors-1. Out-of-range values are mapped to -1 if low and ncolors if high; these are converted to valid indices by :meth:`Colormap.__call__` . ''' self.clip = clip self.vmin = boundaries[0] self.vmax = boundaries[-1] self.boundaries = np.asarray(boundaries) self.N = len(self.boundaries) self.Ncmap = ncolors if self.N-1 == self.Ncmap: self._interp = False else: self._interp = True def __call__(self, x, clip=None): if clip is None: clip = self.clip x = ma.asarray(x) mask = ma.getmaskarray(x) xx = x.filled(self.vmax+1) if clip: np.clip(xx, self.vmin, self.vmax) iret = np.zeros(x.shape, dtype=np.int16) for i, b in enumerate(self.boundaries): iret[xx>=b] = i if self._interp: iret = (iret * (float(self.Ncmap-1)/(self.N-2))).astype(np.int16) iret[xx<self.vmin] = -1 iret[xx>=self.vmax] = self.Ncmap ret = ma.array(iret, mask=mask) if ret.shape == () and not mask: ret = int(ret) # assume python scalar return ret def inverse(self, value): return ValueError("BoundaryNorm is not invertible") class NoNorm(Normalize): ''' Dummy replacement for Normalize, for the case where we want to use indices directly in a :class:`~matplotlib.cm.ScalarMappable` . ''' def __call__(self, value, clip=None): return value def inverse(self, value): return value # compatibility with earlier class names that violated convention: normalize = Normalize no_norm = NoNorm

31,676

Python

.py

767

32.179922

85

0.531855

numenta/nupic-legacy

6,330

1,556

464

AGPL-3.0

9/5/2024, 5:13:42 PM (Europe/Amsterdam)

26,237

transforms.py

numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/transforms.py

""" matplotlib includes a framework for arbitrary geometric transformations that is used determine the final position of all elements drawn on the canvas. Transforms are composed into trees of :class:`TransformNode` objects whose actual value depends on their children. When the contents of children change, their parents are automatically invalidated. The next time an invalidated transform is accessed, it is recomputed to reflect those changes. This invalidation/caching approach prevents unnecessary recomputations of transforms, and contributes to better interactive performance. For example, here is a graph of the transform tree used to plot data to the graph: .. image:: ../_static/transforms.png The framework can be used for both affine and non-affine transformations. However, for speed, we want use the backend renderers to perform affine transformations whenever possible. Therefore, it is possible to perform just the affine or non-affine part of a transformation on a set of data. The affine is always assumed to occur after the non-affine. For any transform:: full transform == non-affine part + affine part The backends are not expected to handle non-affine transformations themselves. """ import numpy as np from numpy import ma from matplotlib._path import affine_transform from numpy.linalg import inv from weakref import WeakKeyDictionary import warnings try: set except NameError: from sets import Set as set import cbook from path import Path from _path import count_bboxes_overlapping_bbox, update_path_extents DEBUG = False if DEBUG: import warnings MaskedArray = ma.MaskedArray class TransformNode(object): """ :class:`TransformNode` is the base class for anything that participates in the transform tree and needs to invalidate its parents or be invalidated. This includes classes that are not really transforms, such as bounding boxes, since some transforms depend on bounding boxes to compute their values. """ _gid = 0 # Invalidation may affect only the affine part. If the # invalidation was "affine-only", the _invalid member is set to # INVALID_AFFINE_ONLY INVALID_NON_AFFINE = 1 INVALID_AFFINE = 2 INVALID = INVALID_NON_AFFINE | INVALID_AFFINE # Some metadata about the transform, used to determine whether an # invalidation is affine-only is_affine = False is_bbox = False # If pass_through is True, all ancestors will always be # invalidated, even if 'self' is already invalid. pass_through = False def __init__(self): """ Creates a new :class:`TransformNode`. """ # Parents are stored in a WeakKeyDictionary, so that if the # parents are deleted, references from the children won't keep # them alive. self._parents = WeakKeyDictionary() # TransformNodes start out as invalid until their values are # computed for the first time. self._invalid = 1 def __copy__(self, *args): raise NotImplementedError( "TransformNode instances can not be copied. " + "Consider using frozen() instead.") __deepcopy__ = __copy__ def invalidate(self): """ Invalidate this :class:`TransformNode` and all of its ancestors. Should be called any time the transform changes. """ # If we are an affine transform being changed, we can set the # flag to INVALID_AFFINE_ONLY value = (self.is_affine) and self.INVALID_AFFINE or self.INVALID # Shortcut: If self is already invalid, that means its parents # are as well, so we don't need to do anything. if self._invalid == value: return if not len(self._parents): self._invalid = value return # Invalidate all ancestors of self using pseudo-recursion. stack = [self] while len(stack): root = stack.pop() # Stop at subtrees that have already been invalidated if root._invalid != value or root.pass_through: root._invalid = self.INVALID stack.extend(root._parents.keys()) def set_children(self, *children): """ Set the children of the transform, to let the invalidation system know which transforms can invalidate this transform. Should be called from the constructor of any transforms that depend on other transforms. """ for child in children: child._parents[self] = None if DEBUG: _set_children = set_children def set_children(self, *children): self._set_children(*children) self._children = children set_children.__doc__ = _set_children.__doc__ def frozen(self): """ Returns a frozen copy of this transform node. The frozen copy will not update when its children change. Useful for storing a previously known state of a transform where ``copy.deepcopy()`` might normally be used. """ return self if DEBUG: def write_graphviz(self, fobj, highlight=[]): """ For debugging purposes. Writes the transform tree rooted at 'self' to a graphviz "dot" format file. This file can be run through the "dot" utility to produce a graph of the transform tree. Affine transforms are marked in blue. Bounding boxes are marked in yellow. *fobj*: A Python file-like object """ seen = set() def recurse(root): if root in seen: return seen.add(root) props = {} label = root.__class__.__name__ if root._invalid: label = '[%s]' % label if root in highlight: props['style'] = 'bold' props['shape'] = 'box' props['label'] = '"%s"' % label props = ' '.join(['%s=%s' % (key, val) for key, val in props.items()]) fobj.write('%s [%s];\n' % (hash(root), props)) if hasattr(root, '_children'): for child in root._children: name = '?' for key, val in root.__dict__.items(): if val is child: name = key break fobj.write('%s -> %s [label="%s", fontsize=10];\n' % ( hash(root), hash(child), name)) recurse(child) fobj.write("digraph G {\n") recurse(self) fobj.write("}\n") else: def write_graphviz(self, fobj, highlight=[]): return class BboxBase(TransformNode): """ This is the base class of all bounding boxes, and provides read-only access to its data. A mutable bounding box is provided by the :class:`Bbox` class. The canonical representation is as two points, with no restrictions on their ordering. Convenience properties are provided to get the left, bottom, right and top edges and width and height, but these are not stored explicity. """ is_bbox = True is_affine = True #* Redundant: Removed for performance # # def __init__(self): # TransformNode.__init__(self) if DEBUG: def _check(points): if ma.isMaskedArray(points): warnings.warn("Bbox bounds are a masked array.") points = np.asarray(points) if (points[1,0] - points[0,0] == 0 or points[1,1] - points[0,1] == 0): warnings.warn("Singular Bbox.") _check = staticmethod(_check) def frozen(self): return Bbox(self.get_points().copy()) frozen.__doc__ = TransformNode.__doc__ def __array__(self, *args, **kwargs): return self.get_points() def is_unit(self): """ Returns True if the :class:`Bbox` is the unit bounding box from (0, 0) to (1, 1). """ return list(self.get_points().flatten()) == [0., 0., 1., 1.] def _get_x0(self): return self.get_points()[0, 0] x0 = property(_get_x0, None, None, """ (property) :attr:`x0` is the first of the pair of *x* coordinates that define the bounding box. :attr:`x0` is not guaranteed to be less than :attr:`x1`. If you require that, use :attr:`xmin`.""") def _get_y0(self): return self.get_points()[0, 1] y0 = property(_get_y0, None, None, """ (property) :attr:`y0` is the first of the pair of *y* coordinates that define the bounding box. :attr:`y0` is not guaranteed to be less than :attr:`y1`. If you require that, use :attr:`ymin`.""") def _get_x1(self): return self.get_points()[1, 0] x1 = property(_get_x1, None, None, """ (property) :attr:`x1` is the second of the pair of *x* coordinates that define the bounding box. :attr:`x1` is not guaranteed to be greater than :attr:`x0`. If you require that, use :attr:`xmax`.""") def _get_y1(self): return self.get_points()[1, 1] y1 = property(_get_y1, None, None, """ (property) :attr:`y1` is the second of the pair of *y* coordinates that define the bounding box. :attr:`y1` is not guaranteed to be greater than :attr:`y0`. If you require that, use :attr:`ymax`.""") def _get_p0(self): return self.get_points()[0] p0 = property(_get_p0, None, None, """ (property) :attr:`p0` is the first pair of (*x*, *y*) coordinates that define the bounding box. It is not guaranteed to be the bottom-left corner. For that, use :attr:`min`.""") def _get_p1(self): return self.get_points()[1] p1 = property(_get_p1, None, None, """ (property) :attr:`p1` is the second pair of (*x*, *y*) coordinates that define the bounding box. It is not guaranteed to be the top-right corner. For that, use :attr:`max`.""") def _get_xmin(self): return min(self.get_points()[:, 0]) xmin = property(_get_xmin, None, None, """ (property) :attr:`xmin` is the left edge of the bounding box.""") def _get_ymin(self): return min(self.get_points()[:, 1]) ymin = property(_get_ymin, None, None, """ (property) :attr:`ymin` is the bottom edge of the bounding box.""") def _get_xmax(self): return max(self.get_points()[:, 0]) xmax = property(_get_xmax, None, None, """ (property) :attr:`xmax` is the right edge of the bounding box.""") def _get_ymax(self): return max(self.get_points()[:, 1]) ymax = property(_get_ymax, None, None, """ (property) :attr:`ymax` is the top edge of the bounding box.""") def _get_min(self): return [min(self.get_points()[:, 0]), min(self.get_points()[:, 1])] min = property(_get_min, None, None, """ (property) :attr:`min` is the bottom-left corner of the bounding box.""") def _get_max(self): return [max(self.get_points()[:, 0]), max(self.get_points()[:, 1])] max = property(_get_max, None, None, """ (property) :attr:`max` is the top-right corner of the bounding box.""") def _get_intervalx(self): return self.get_points()[:, 0] intervalx = property(_get_intervalx, None, None, """ (property) :attr:`intervalx` is the pair of *x* coordinates that define the bounding box. It is not guaranteed to be sorted from left to right.""") def _get_intervaly(self): return self.get_points()[:, 1] intervaly = property(_get_intervaly, None, None, """ (property) :attr:`intervaly` is the pair of *y* coordinates that define the bounding box. It is not guaranteed to be sorted from bottom to top.""") def _get_width(self): points = self.get_points() return points[1, 0] - points[0, 0] width = property(_get_width, None, None, """ (property) The width of the bounding box. It may be negative if :attr:`x1` < :attr:`x0`.""") def _get_height(self): points = self.get_points() return points[1, 1] - points[0, 1] height = property(_get_height, None, None, """ (property) The height of the bounding box. It may be negative if :attr:`y1` < :attr:`y0`.""") def _get_size(self): points = self.get_points() return points[1] - points[0] size = property(_get_size, None, None, """ (property) The width and height of the bounding box. May be negative, in the same way as :attr:`width` and :attr:`height`.""") def _get_bounds(self): x0, y0, x1, y1 = self.get_points().flatten() return (x0, y0, x1 - x0, y1 - y0) bounds = property(_get_bounds, None, None, """ (property) Returns (:attr:`x0`, :attr:`y0`, :attr:`width`, :attr:`height`).""") def _get_extents(self): return self.get_points().flatten().copy() extents = property(_get_extents, None, None, """ (property) Returns (:attr:`x0`, :attr:`y0`, :attr:`x1`, :attr:`y1`).""") def get_points(self): return NotImplementedError() def containsx(self, x): """ Returns True if *x* is between or equal to :attr:`x0` and :attr:`x1`. """ x0, x1 = self.intervalx return ((x0 < x1 and (x >= x0 and x <= x1)) or (x >= x1 and x <= x0)) def containsy(self, y): """ Returns True if *y* is between or equal to :attr:`y0` and :attr:`y1`. """ y0, y1 = self.intervaly return ((y0 < y1 and (y >= y0 and y <= y1)) or (y >= y1 and y <= y0)) def contains(self, x, y): """ Returns *True* if (*x*, *y*) is a coordinate inside the bounding box or on its edge. """ return self.containsx(x) and self.containsy(y) def overlaps(self, other): """ Returns True if this bounding box overlaps with the given bounding box *other*. """ ax1, ay1, ax2, ay2 = self._get_extents() bx1, by1, bx2, by2 = other._get_extents() if ax2 < ax1: ax2, ax1 = ax1, ax2 if ay2 < ay1: ay2, ay1 = ay1, ay2 if bx2 < bx1: bx2, bx1 = bx1, bx2 if by2 < by1: by2, by1 = by1, by2 return not ((bx2 < ax1) or (by2 < ay1) or (bx1 > ax2) or (by1 > ay2)) def fully_containsx(self, x): """ Returns True if *x* is between but not equal to :attr:`x0` and :attr:`x1`. """ x0, x1 = self.intervalx return ((x0 < x1 and (x > x0 and x < x1)) or (x > x1 and x < x0)) def fully_containsy(self, y): """ Returns True if *y* is between but not equal to :attr:`y0` and :attr:`y1`. """ y0, y1 = self.intervaly return ((y0 < y1 and (x > y0 and x < y1)) or (x > y1 and x < y0)) def fully_contains(self, x, y): """ Returns True if (*x*, *y*) is a coordinate inside the bounding box, but not on its edge. """ return self.fully_containsx(x) \ and self.fully_containsy(y) def fully_overlaps(self, other): """ Returns True if this bounding box overlaps with the given bounding box *other*, but not on its edge alone. """ ax1, ay1, ax2, ay2 = self._get_extents() bx1, by1, bx2, by2 = other._get_extents() if ax2 < ax1: ax2, ax1 = ax1, ax2 if ay2 < ay1: ay2, ay1 = ay1, ay2 if bx2 < bx1: bx2, bx1 = bx1, bx2 if by2 < by1: by2, by1 = by1, by2 return not ((bx2 <= ax1) or (by2 <= ay1) or (bx1 >= ax2) or (by1 >= ay2)) def transformed(self, transform): """ Return a new :class:`Bbox` object, statically transformed by the given transform. """ return Bbox(transform.transform(self.get_points())) def inverse_transformed(self, transform): """ Return a new :class:`Bbox` object, statically transformed by the inverse of the given transform. """ return Bbox(transform.inverted().transform(self.get_points())) coefs = {'C': (0.5, 0.5), 'SW': (0,0), 'S': (0.5, 0), 'SE': (1.0, 0), 'E': (1.0, 0.5), 'NE': (1.0, 1.0), 'N': (0.5, 1.0), 'NW': (0, 1.0), 'W': (0, 0.5)} def anchored(self, c, container = None): """ Return a copy of the :class:`Bbox`, shifted to position *c* within a container. *c*: may be either: * a sequence (*cx*, *cy*) where *cx* and *cy* range from 0 to 1, where 0 is left or bottom and 1 is right or top * a string: - 'C' for centered - 'S' for bottom-center - 'SE' for bottom-left - 'E' for left - etc. Optional argument *container* is the box within which the :class:`Bbox` is positioned; it defaults to the initial :class:`Bbox`. """ if container is None: container = self l, b, w, h = container.bounds if isinstance(c, str): cx, cy = self.coefs[c] else: cx, cy = c L, B, W, H = self.bounds return Bbox(self._points + [(l + cx * (w-W)) - L, (b + cy * (h-H)) - B]) def shrunk(self, mx, my): """ Return a copy of the :class:`Bbox`, shrunk by the factor *mx* in the *x* direction and the factor *my* in the *y* direction. The lower left corner of the box remains unchanged. Normally *mx* and *my* will be less than 1, but this is not enforced. """ w, h = self.size return Bbox([self._points[0], self._points[0] + [mx * w, my * h]]) def shrunk_to_aspect(self, box_aspect, container = None, fig_aspect = 1.0): """ Return a copy of the :class:`Bbox`, shrunk so that it is as large as it can be while having the desired aspect ratio, *box_aspect*. If the box coordinates are relative---that is, fractions of a larger box such as a figure---then the physical aspect ratio of that figure is specified with *fig_aspect*, so that *box_aspect* can also be given as a ratio of the absolute dimensions, not the relative dimensions. """ assert box_aspect > 0 and fig_aspect > 0 if container is None: container = self w, h = container.size H = w * box_aspect/fig_aspect if H <= h: W = w else: W = h * fig_aspect/box_aspect H = h return Bbox([self._points[0], self._points[0] + (W, H)]) def splitx(self, *args): """ e.g., ``bbox.splitx(f1, f2, ...)`` Returns a list of new :class:`Bbox` objects formed by splitting the original one with vertical lines at fractional positions *f1*, *f2*, ... """ boxes = [] xf = [0] + list(args) + [1] x0, y0, x1, y1 = self._get_extents() w = x1 - x0 for xf0, xf1 in zip(xf[:-1], xf[1:]): boxes.append(Bbox([[x0 + xf0 * w, y0], [x0 + xf1 * w, y1]])) return boxes def splity(self, *args): """ e.g., ``bbox.splitx(f1, f2, ...)`` Returns a list of new :class:`Bbox` objects formed by splitting the original one with horizontal lines at fractional positions *f1*, *f2*, ... """ boxes = [] yf = [0] + list(args) + [1] x0, y0, x1, y1 = self._get_extents() h = y1 - y0 for yf0, yf1 in zip(yf[:-1], yf[1:]): boxes.append(Bbox([[x0, y0 + yf0 * h], [x1, y0 + yf1 * h]])) return boxes def count_contains(self, vertices): """ Count the number of vertices contained in the :class:`Bbox`. *vertices* is a Nx2 Numpy array. """ if len(vertices) == 0: return 0 vertices = np.asarray(vertices) x0, y0, x1, y1 = self._get_extents() dx0 = np.sign(vertices[:, 0] - x0) dy0 = np.sign(vertices[:, 1] - y0) dx1 = np.sign(vertices[:, 0] - x1) dy1 = np.sign(vertices[:, 1] - y1) inside = (abs(dx0 + dx1) + abs(dy0 + dy1)) <= 2 return np.sum(inside) def count_overlaps(self, bboxes): """ Count the number of bounding boxes that overlap this one. bboxes is a sequence of :class:`BboxBase` objects """ return count_bboxes_overlapping_bbox(self, bboxes) def expanded(self, sw, sh): """ Return a new :class:`Bbox` which is this :class:`Bbox` expanded around its center by the given factors *sw* and *sh*. """ width = self.width height = self.height deltaw = (sw * width - width) / 2.0 deltah = (sh * height - height) / 2.0 a = np.array([[-deltaw, -deltah], [deltaw, deltah]]) return Bbox(self._points + a) def padded(self, p): """ Return a new :class:`Bbox` that is padded on all four sides by the given value. """ points = self._points return Bbox(points + [[-p, -p], [p, p]]) def translated(self, tx, ty): """ Return a copy of the :class:`Bbox`, statically translated by *tx* and *ty*. """ return Bbox(self._points + (tx, ty)) def corners(self): """ Return an array of points which are the four corners of this rectangle. For example, if this :class:`Bbox` is defined by the points (*a*, *b*) and (*c*, *d*), :meth:`corners` returns (*a*, *b*), (*a*, *d*), (*c*, *b*) and (*c*, *d*). """ l, b, r, t = self.get_points().flatten() return np.array([[l, b], [l, t], [r, b], [r, t]]) def rotated(self, radians): """ Return a new bounding box that bounds a rotated version of this bounding box by the given radians. The new bounding box is still aligned with the axes, of course. """ corners = self.corners() corners_rotated = Affine2D().rotate(radians).transform(corners) bbox = Bbox.unit() bbox.update_from_data_xy(corners_rotated, ignore=True) return bbox #@staticmethod def union(bboxes): """ Return a :class:`Bbox` that contains all of the given bboxes. """ assert(len(bboxes)) if len(bboxes) == 1: return bboxes[0] x0 = np.inf y0 = np.inf x1 = -np.inf y1 = -np.inf for bbox in bboxes: points = bbox.get_points() xs = points[:, 0] ys = points[:, 1] x0 = min(x0, np.min(xs)) y0 = min(y0, np.min(ys)) x1 = max(x1, np.max(xs)) y1 = max(y1, np.max(ys)) return Bbox.from_extents(x0, y0, x1, y1) union = staticmethod(union) class Bbox(BboxBase): """ A mutable bounding box. """ def __init__(self, points): """ *points*: a 2x2 numpy array of the form [[x0, y0], [x1, y1]] If you need to create a :class:`Bbox` object from another form of data, consider the static methods :meth:`unit`, :meth:`from_bounds` and :meth:`from_extents`. """ BboxBase.__init__(self) self._points = np.asarray(points, np.float_) self._minpos = np.array([0.0000001, 0.0000001]) self._ignore = True if DEBUG: ___init__ = __init__ def __init__(self, points): self._check(points) self.___init__(points) def invalidate(self): self._check(self._points) TransformNode.invalidate(self) _unit_values = np.array([[0.0, 0.0], [1.0, 1.0]], np.float_) #@staticmethod def unit(): """ (staticmethod) Create a new unit :class:`Bbox` from (0, 0) to (1, 1). """ return Bbox(Bbox._unit_values.copy()) unit = staticmethod(unit) #@staticmethod def from_bounds(x0, y0, width, height): """ (staticmethod) Create a new :class:`Bbox` from *x0*, *y0*, *width* and *height*. *width* and *height* may be negative. """ return Bbox.from_extents(x0, y0, x0 + width, y0 + height) from_bounds = staticmethod(from_bounds) #@staticmethod def from_extents(*args): """ (staticmethod) Create a new Bbox from *left*, *bottom*, *right* and *top*. The *y*-axis increases upwards. """ points = np.array(args, dtype=np.float_).reshape(2, 2) return Bbox(points) from_extents = staticmethod(from_extents) def __repr__(self): return 'Bbox(%s)' % repr(self._points) __str__ = __repr__ def ignore(self, value): """ Set whether the existing bounds of the box should be ignored by subsequent calls to :meth:`update_from_data` or :meth:`update_from_data_xy`. *value*: - When True, subsequent calls to :meth:`update_from_data` will ignore the existing bounds of the :class:`Bbox`. - When False, subsequent calls to :meth:`update_from_data` will include the existing bounds of the :class:`Bbox`. """ self._ignore = value def update_from_data(self, x, y, ignore=None): """ Update the bounds of the :class:`Bbox` based on the passed in data. After updating, the bounds will have positive *width* and *height*; *x0* and *y0* will be the minimal values. *x*: a numpy array of *x*-values *y*: a numpy array of *y*-values *ignore*: - when True, ignore the existing bounds of the :class:`Bbox`. - when False, include the existing bounds of the :class:`Bbox`. - when None, use the last value passed to :meth:`ignore`. """ warnings.warn( "update_from_data requires a memory copy -- please replace with update_from_data_xy") xy = np.hstack((x.reshape((len(x), 1)), y.reshape((len(y), 1)))) return self.update_from_data_xy(xy, ignore) def update_from_path(self, path, ignore=None, updatex=True, updatey=True): """ Update the bounds of the :class:`Bbox` based on the passed in data. After updating, the bounds will have positive *width* and *height*; *x0* and *y0* will be the minimal values. *path*: a :class:`~matplotlib.path.Path` instance *ignore*: - when True, ignore the existing bounds of the :class:`Bbox`. - when False, include the existing bounds of the :class:`Bbox`. - when None, use the last value passed to :meth:`ignore`. *updatex*: when True, update the x values *updatey*: when True, update the y values """ if ignore is None: ignore = self._ignore if path.vertices.size == 0: return points, minpos, changed = update_path_extents( path, None, self._points, self._minpos, ignore) if changed: self.invalidate() if updatex: self._points[:,0] = points[:,0] self._minpos[0] = minpos[0] if updatey: self._points[:,1] = points[:,1] self._minpos[1] = minpos[1] def update_from_data_xy(self, xy, ignore=None, updatex=True, updatey=True): """ Update the bounds of the :class:`Bbox` based on the passed in data. After updating, the bounds will have positive *width* and *height*; *x0* and *y0* will be the minimal values. *xy*: a numpy array of 2D points *ignore*: - when True, ignore the existing bounds of the :class:`Bbox`. - when False, include the existing bounds of the :class:`Bbox`. - when None, use the last value passed to :meth:`ignore`. *updatex*: when True, update the x values *updatey*: when True, update the y values """ if len(xy) == 0: return path = Path(xy) self.update_from_path(path, ignore=ignore, updatex=updatex, updatey=updatey) def _set_x0(self, val): self._points[0, 0] = val self.invalidate() x0 = property(BboxBase._get_x0, _set_x0) def _set_y0(self, val): self._points[0, 1] = val self.invalidate() y0 = property(BboxBase._get_y0, _set_y0) def _set_x1(self, val): self._points[1, 0] = val self.invalidate() x1 = property(BboxBase._get_x1, _set_x1) def _set_y1(self, val): self._points[1, 1] = val self.invalidate() y1 = property(BboxBase._get_y1, _set_y1) def _set_p0(self, val): self._points[0] = val self.invalidate() p0 = property(BboxBase._get_p0, _set_p0) def _set_p1(self, val): self._points[1] = val self.invalidate() p1 = property(BboxBase._get_p1, _set_p1) def _set_intervalx(self, interval): self._points[:, 0] = interval self.invalidate() intervalx = property(BboxBase._get_intervalx, _set_intervalx) def _set_intervaly(self, interval): self._points[:, 1] = interval self.invalidate() intervaly = property(BboxBase._get_intervaly, _set_intervaly) def _set_bounds(self, bounds): l, b, w, h = bounds points = np.array([[l, b], [l+w, b+h]], np.float_) if np.any(self._points != points): self._points = points self.invalidate() bounds = property(BboxBase._get_bounds, _set_bounds) def _get_minpos(self): return self._minpos minpos = property(_get_minpos) def _get_minposx(self): return self._minpos[0] minposx = property(_get_minposx) def _get_minposy(self): return self._minpos[1] minposy = property(_get_minposy) def get_points(self): """ Get the points of the bounding box directly as a numpy array of the form: [[x0, y0], [x1, y1]]. """ self._invalid = 0 return self._points def set_points(self, points): """ Set the points of the bounding box directly from a numpy array of the form: [[x0, y0], [x1, y1]]. No error checking is performed, as this method is mainly for internal use. """ if np.any(self._points != points): self._points = points self.invalidate() def set(self, other): """ Set this bounding box from the "frozen" bounds of another :class:`Bbox`. """ if np.any(self._points != other.get_points()): self._points = other.get_points() self.invalidate() class TransformedBbox(BboxBase): """ A :class:`Bbox` that is automatically transformed by a given transform. When either the child bounding box or transform changes, the bounds of this bbox will update accordingly. """ def __init__(self, bbox, transform): """ *bbox*: a child :class:`Bbox` *transform*: a 2D :class:`Transform` """ assert bbox.is_bbox assert isinstance(transform, Transform) assert transform.input_dims == 2 assert transform.output_dims == 2 BboxBase.__init__(self) self._bbox = bbox self._transform = transform self.set_children(bbox, transform) self._points = None def __repr__(self): return "TransformedBbox(%s, %s)" % (self._bbox, self._transform) __str__ = __repr__ def get_points(self): if self._invalid: points = self._transform.transform(self._bbox.get_points()) if ma.isMaskedArray(points): points.putmask(0.0) points = np.asarray(points) self._points = points self._invalid = 0 return self._points get_points.__doc__ = Bbox.get_points.__doc__ if DEBUG: _get_points = get_points def get_points(self): points = self._get_points() self._check(points) return points class Transform(TransformNode): """ The base class of all :class:`TransformNode` instances that actually perform a transformation. All non-affine transformations should be subclasses of this class. New affine transformations should be subclasses of :class:`Affine2D`. Subclasses of this class should override the following members (at minimum): - :attr:`input_dims` - :attr:`output_dims` - :meth:`transform` - :attr:`is_separable` - :attr:`has_inverse` - :meth:`inverted` (if :meth:`has_inverse` can return True) If the transform needs to do something non-standard with :class:`mathplotlib.path.Path` objects, such as adding curves where there were once line segments, it should override: - :meth:`transform_path` """ # The number of input and output dimensions for this transform. # These must be overridden (with integers) in the subclass. input_dims = None output_dims = None # True if this transform as a corresponding inverse transform. has_inverse = False # True if this transform is separable in the x- and y- dimensions. is_separable = False #* Redundant: Removed for performance # # def __init__(self): # TransformNode.__init__(self) def __add__(self, other): """ Composes two transforms together such that *self* is followed by *other*. """ if isinstance(other, Transform): return composite_transform_factory(self, other) raise TypeError( "Can not add Transform to object of type '%s'" % type(other)) def __radd__(self, other): """ Composes two transforms together such that *self* is followed by *other*. """ if isinstance(other, Transform): return composite_transform_factory(other, self) raise TypeError( "Can not add Transform to object of type '%s'" % type(other)) def __array__(self, *args, **kwargs): """ Used by C/C++ -based backends to get at the array matrix data. """ return self.frozen().__array__() def transform(self, values): """ Performs the transformation on the given array of values. Accepts a numpy array of shape (N x :attr:`input_dims`) and returns a numpy array of shape (N x :attr:`output_dims`). """ raise NotImplementedError() def transform_affine(self, values): """ Performs only the affine part of this transformation on the given array of values. ``transform(values)`` is always equivalent to ``transform_affine(transform_non_affine(values))``. In non-affine transformations, this is generally a no-op. In affine transformations, this is equivalent to ``transform(values)``. Accepts a numpy array of shape (N x :attr:`input_dims`) and returns a numpy array of shape (N x :attr:`output_dims`). """ return values def transform_non_affine(self, values): """ Performs only the non-affine part of the transformation. ``transform(values)`` is always equivalent to ``transform_affine(transform_non_affine(values))``. In non-affine transformations, this is generally equivalent to ``transform(values)``. In affine transformations, this is always a no-op. Accepts a numpy array of shape (N x :attr:`input_dims`) and returns a numpy array of shape (N x :attr:`output_dims`). """ return self.transform(values) def get_affine(self): """ Get the affine part of this transform. """ return IdentityTransform() def transform_point(self, point): """ A convenience function that returns the transformed copy of a single point. The point is given as a sequence of length :attr:`input_dims`. The transformed point is returned as a sequence of length :attr:`output_dims`. """ assert len(point) == self.input_dims return self.transform(np.asarray([point]))[0] def transform_path(self, path): """ Returns a transformed copy of path. *path*: a :class:`~matplotlib.path.Path` instance. In some cases, this transform may insert curves into the path that began as line segments. """ return Path(self.transform(path.vertices), path.codes) def transform_path_affine(self, path): """ Returns a copy of path, transformed only by the affine part of this transform. *path*: a :class:`~matplotlib.path.Path` instance. ``transform_path(path)`` is equivalent to ``transform_path_affine(transform_path_non_affine(values))``. """ return path def transform_path_non_affine(self, path): """ Returns a copy of path, transformed only by the non-affine part of this transform. *path*: a :class:`~matplotlib.path.Path` instance. ``transform_path(path)`` is equivalent to ``transform_path_affine(transform_path_non_affine(values))``. """ return Path(self.transform_non_affine(path.vertices), path.codes) def transform_angles(self, angles, pts, radians=False, pushoff=1e-5): """ Performs transformation on a set of angles anchored at specific locations. The *angles* must be a column vector (i.e., numpy array). The *pts* must be a two-column numpy array of x,y positions (angle transforms currently only work in 2D). This array must have the same number of rows as *angles*. *radians* indicates whether or not input angles are given in radians (True) or degrees (False; the default). *pushoff* is the distance to move away from *pts* for determining transformed angles (see discussion of method below). The transformed angles are returned in an array with the same size as *angles*. The generic version of this method uses a very generic algorithm that transforms *pts*, as well as locations very close to *pts*, to find the angle in the transformed system. """ # Must be 2D if self.input_dims <> 2 or self.output_dims <> 2: raise NotImplementedError('Only defined in 2D') # pts must be array with 2 columns for x,y assert pts.shape[1] == 2 # angles must be a column vector and have same number of # rows as pts assert np.prod(angles.shape) == angles.shape[0] == pts.shape[0] # Convert to radians if desired if not radians: angles = angles / 180.0 * np.pi # Move a short distance away pts2 = pts + pushoff * np.c_[ np.cos(angles), np.sin(angles) ] # Transform both sets of points tpts = self.transform( pts ) tpts2 = self.transform( pts2 ) # Calculate transformed angles d = tpts2 - tpts a = np.arctan2( d[:,1], d[:,0] ) # Convert back to degrees if desired if not radians: a = a * 180.0 / np.pi return a def inverted(self): """ Return the corresponding inverse transformation. The return value of this method should be treated as temporary. An update to *self* does not cause a corresponding update to its inverted copy. ``x === self.inverted().transform(self.transform(x))`` """ raise NotImplementedError() class TransformWrapper(Transform): """ A helper class that holds a single child transform and acts equivalently to it. This is useful if a node of the transform tree must be replaced at run time with a transform of a different type. This class allows that replacement to correctly trigger invalidation. Note that :class:`TransformWrapper` instances must have the same input and output dimensions during their entire lifetime, so the child transform may only be replaced with another child transform of the same dimensions. """ pass_through = True is_affine = False def __init__(self, child): """ *child*: A class:`Transform` instance. This child may later be replaced with :meth:`set`. """ assert isinstance(child, Transform) Transform.__init__(self) self.input_dims = child.input_dims self.output_dims = child.output_dims self._set(child) self._invalid = 0 def __repr__(self): return "TransformWrapper(%r)" % self._child __str__ = __repr__ def frozen(self): return self._child.frozen() frozen.__doc__ = Transform.frozen.__doc__ def _set(self, child): self._child = child self.set_children(child) self.transform = child.transform self.transform_affine = child.transform_affine self.transform_non_affine = child.transform_non_affine self.transform_path = child.transform_path self.transform_path_affine = child.transform_path_affine self.transform_path_non_affine = child.transform_path_non_affine self.get_affine = child.get_affine self.inverted = child.inverted def set(self, child): """ Replace the current child of this transform with another one. The new child must have the same number of input and output dimensions as the current child. """ assert child.input_dims == self.input_dims assert child.output_dims == self.output_dims self._set(child) self._invalid = 0 self.invalidate() self._invalid = 0 def _get_is_separable(self): return self._child.is_separable is_separable = property(_get_is_separable) def _get_has_inverse(self): return self._child.has_inverse has_inverse = property(_get_has_inverse) class AffineBase(Transform): """ The base class of all affine transformations of any number of dimensions. """ is_affine = True def __init__(self): Transform.__init__(self) self._inverted = None def __array__(self, *args, **kwargs): return self.get_matrix() #@staticmethod def _concat(a, b): """ Concatenates two transformation matrices (represented as numpy arrays) together. """ return np.dot(b, a) _concat = staticmethod(_concat) def get_matrix(self): """ Get the underlying transformation matrix as a numpy array. """ raise NotImplementedError() def transform_non_affine(self, points): return points transform_non_affine.__doc__ = Transform.transform_non_affine.__doc__ def transform_path_affine(self, path): return self.transform_path(path) transform_path_affine.__doc__ = Transform.transform_path_affine.__doc__ def transform_path_non_affine(self, path): return path transform_path_non_affine.__doc__ = Transform.transform_path_non_affine.__doc__ def get_affine(self): return self get_affine.__doc__ = Transform.get_affine.__doc__ class Affine2DBase(AffineBase): """ The base class of all 2D affine transformations. 2D affine transformations are performed using a 3x3 numpy array:: a c e b d f 0 0 1 This class provides the read-only interface. For a mutable 2D affine transformation, use :class:`Affine2D`. Subclasses of this class will generally only need to override a constructor and :meth:`get_matrix` that generates a custom 3x3 matrix. """ input_dims = 2 output_dims = 2 #* Redundant: Removed for performance # # def __init__(self): # Affine2DBase.__init__(self) def frozen(self): return Affine2D(self.get_matrix().copy()) frozen.__doc__ = AffineBase.frozen.__doc__ def _get_is_separable(self): mtx = self.get_matrix() return mtx[0, 1] == 0.0 and mtx[1, 0] == 0.0 is_separable = property(_get_is_separable) def __array__(self, *args, **kwargs): return self.get_matrix() def to_values(self): """ Return the values of the matrix as a sequence (a,b,c,d,e,f) """ mtx = self.get_matrix() return tuple(mtx[:2].swapaxes(0, 1).flatten()) #@staticmethod def matrix_from_values(a, b, c, d, e, f): """ (staticmethod) Create a new transformation matrix as a 3x3 numpy array of the form:: a c e b d f 0 0 1 """ return np.array([[a, c, e], [b, d, f], [0.0, 0.0, 1.0]], np.float_) matrix_from_values = staticmethod(matrix_from_values) def transform(self, points): mtx = self.get_matrix() if isinstance(points, MaskedArray): tpoints = affine_transform(points.data, mtx) return ma.MaskedArray(tpoints, mask=ma.getmask(points)) return affine_transform(points, mtx) def transform_point(self, point): mtx = self.get_matrix() return affine_transform(point, mtx) transform_point.__doc__ = AffineBase.transform_point.__doc__ if DEBUG: _transform = transform def transform(self, points): # The major speed trap here is just converting to the # points to an array in the first place. If we can use # more arrays upstream, that should help here. if (not ma.isMaskedArray(points) and not isinstance(points, np.ndarray)): warnings.warn( ('A non-numpy array of type %s was passed in for ' + 'transformation. Please correct this.') % type(values)) return self._transform(points) transform.__doc__ = AffineBase.transform.__doc__ transform_affine = transform transform_affine.__doc__ = AffineBase.transform_affine.__doc__ def inverted(self): if self._inverted is None or self._invalid: mtx = self.get_matrix() self._inverted = Affine2D(inv(mtx)) self._invalid = 0 return self._inverted inverted.__doc__ = AffineBase.inverted.__doc__ class Affine2D(Affine2DBase): """ A mutable 2D affine transformation. """ def __init__(self, matrix = None): """ Initialize an Affine transform from a 3x3 numpy float array:: a c e b d f 0 0 1 If *matrix* is None, initialize with the identity transform. """ Affine2DBase.__init__(self) if matrix is None: matrix = np.identity(3) elif DEBUG: matrix = np.asarray(matrix, np.float_) assert matrix.shape == (3, 3) self._mtx = matrix self._invalid = 0 def __repr__(self): return "Affine2D(%s)" % repr(self._mtx) __str__ = __repr__ def __cmp__(self, other): if (isinstance(other, Affine2D) and (self.get_matrix() == other.get_matrix()).all()): return 0 return -1 #@staticmethod def from_values(a, b, c, d, e, f): """ (staticmethod) Create a new Affine2D instance from the given values:: a c e b d f 0 0 1 """ return Affine2D( np.array([a, c, e, b, d, f, 0.0, 0.0, 1.0], np.float_) .reshape((3,3))) from_values = staticmethod(from_values) def get_matrix(self): """ Get the underlying transformation matrix as a 3x3 numpy array:: a c e b d f 0 0 1 """ self._invalid = 0 return self._mtx def set_matrix(self, mtx): """ Set the underlying transformation matrix from a 3x3 numpy array:: a c e b d f 0 0 1 """ self._mtx = mtx self.invalidate() def set(self, other): """ Set this transformation from the frozen copy of another :class:`Affine2DBase` object. """ assert isinstance(other, Affine2DBase) self._mtx = other.get_matrix() self.invalidate() #@staticmethod def identity(): """ (staticmethod) Return a new :class:`Affine2D` object that is the identity transform. Unless this transform will be mutated later on, consider using the faster :class:`IdentityTransform` class instead. """ return Affine2D(np.identity(3)) identity = staticmethod(identity) def clear(self): """ Reset the underlying matrix to the identity transform. """ self._mtx = np.identity(3) self.invalidate() return self def rotate(self, theta): """ Add a rotation (in radians) to this transform in place. Returns *self*, so this method can easily be chained with more calls to :meth:`rotate`, :meth:`rotate_deg`, :meth:`translate` and :meth:`scale`. """ a = np.cos(theta) b = np.sin(theta) rotate_mtx = np.array( [[a, -b, 0.0], [b, a, 0.0], [0.0, 0.0, 1.0]], np.float_) self._mtx = np.dot(rotate_mtx, self._mtx) self.invalidate() return self def rotate_deg(self, degrees): """ Add a rotation (in degrees) to this transform in place. Returns *self*, so this method can easily be chained with more calls to :meth:`rotate`, :meth:`rotate_deg`, :meth:`translate` and :meth:`scale`. """ return self.rotate(degrees*np.pi/180.) def rotate_around(self, x, y, theta): """ Add a rotation (in radians) around the point (x, y) in place. Returns *self*, so this method can easily be chained with more calls to :meth:`rotate`, :meth:`rotate_deg`, :meth:`translate` and :meth:`scale`. """ return self.translate(-x, -y).rotate(theta).translate(x, y) def rotate_deg_around(self, x, y, degrees): """ Add a rotation (in degrees) around the point (x, y) in place. Returns *self*, so this method can easily be chained with more calls to :meth:`rotate`, :meth:`rotate_deg`, :meth:`translate` and :meth:`scale`. """ return self.translate(-x, -y).rotate_deg(degrees).translate(x, y) def translate(self, tx, ty): """ Adds a translation in place. Returns *self*, so this method can easily be chained with more calls to :meth:`rotate`, :meth:`rotate_deg`, :meth:`translate` and :meth:`scale`. """ translate_mtx = np.array( [[1.0, 0.0, tx], [0.0, 1.0, ty], [0.0, 0.0, 1.0]], np.float_) self._mtx = np.dot(translate_mtx, self._mtx) self.invalidate() return self def scale(self, sx, sy=None): """ Adds a scale in place. If *sy* is None, the same scale is applied in both the *x*- and *y*-directions. Returns *self*, so this method can easily be chained with more calls to :meth:`rotate`, :meth:`rotate_deg`, :meth:`translate` and :meth:`scale`. """ if sy is None: sy = sx scale_mtx = np.array( [[sx, 0.0, 0.0], [0.0, sy, 0.0], [0.0, 0.0, 1.0]], np.float_) self._mtx = np.dot(scale_mtx, self._mtx) self.invalidate() return self def _get_is_separable(self): mtx = self.get_matrix() return mtx[0, 1] == 0.0 and mtx[1, 0] == 0.0 is_separable = property(_get_is_separable) class IdentityTransform(Affine2DBase): """ A special class that does on thing, the identity transform, in a fast way. """ _mtx = np.identity(3) def frozen(self): return self frozen.__doc__ = Affine2DBase.frozen.__doc__ def __repr__(self): return "IdentityTransform()" __str__ = __repr__ def get_matrix(self): return self._mtx get_matrix.__doc__ = Affine2DBase.get_matrix.__doc__ def transform(self, points): return points transform.__doc__ = Affine2DBase.transform.__doc__ transform_affine = transform transform_affine.__doc__ = Affine2DBase.transform_affine.__doc__ transform_non_affine = transform transform_non_affine.__doc__ = Affine2DBase.transform_non_affine.__doc__ def transform_path(self, path): return path transform_path.__doc__ = Affine2DBase.transform_path.__doc__ transform_path_affine = transform_path transform_path_affine.__doc__ = Affine2DBase.transform_path_affine.__doc__ transform_path_non_affine = transform_path transform_path_non_affine.__doc__ = Affine2DBase.transform_path_non_affine.__doc__ def get_affine(self): return self get_affine.__doc__ = Affine2DBase.get_affine.__doc__ inverted = get_affine inverted.__doc__ = Affine2DBase.inverted.__doc__ class BlendedGenericTransform(Transform): """ A "blended" transform uses one transform for the *x*-direction, and another transform for the *y*-direction. This "generic" version can handle any given child transform in the *x*- and *y*-directions. """ input_dims = 2 output_dims = 2 is_separable = True pass_through = True def __init__(self, x_transform, y_transform): """ Create a new "blended" transform using *x_transform* to transform the *x*-axis and *y_transform* to transform the *y*-axis. You will generally not call this constructor directly but use the :func:`blended_transform_factory` function instead, which can determine automatically which kind of blended transform to create. """ # Here we ask: "Does it blend?" Transform.__init__(self) self._x = x_transform self._y = y_transform self.set_children(x_transform, y_transform) self._affine = None def _get_is_affine(self): return self._x.is_affine and self._y.is_affine is_affine = property(_get_is_affine) def frozen(self): return blended_transform_factory(self._x.frozen(), self._y.frozen()) frozen.__doc__ = Transform.frozen.__doc__ def __repr__(self): return "BlendedGenericTransform(%s,%s)" % (self._x, self._y) __str__ = __repr__ def transform(self, points): x = self._x y = self._y if x is y and x.input_dims == 2: return x.transform(points) if x.input_dims == 2: x_points = x.transform(points)[:, 0:1] else: x_points = x.transform(points[:, 0]) x_points = x_points.reshape((len(x_points), 1)) if y.input_dims == 2: y_points = y.transform(points)[:, 1:] else: y_points = y.transform(points[:, 1]) y_points = y_points.reshape((len(y_points), 1)) if isinstance(x_points, MaskedArray) or isinstance(y_points, MaskedArray): return ma.concatenate((x_points, y_points), 1) else: return np.concatenate((x_points, y_points), 1) transform.__doc__ = Transform.transform.__doc__ def transform_affine(self, points): return self.get_affine().transform(points) transform_affine.__doc__ = Transform.transform_affine.__doc__ def transform_non_affine(self, points): if self._x.is_affine and self._y.is_affine: return points return self.transform(points) transform_non_affine.__doc__ = Transform.transform_non_affine.__doc__ def inverted(self): return BlendedGenericTransform(self._x.inverted(), self._y.inverted()) inverted.__doc__ = Transform.inverted.__doc__ def get_affine(self): if self._invalid or self._affine is None: if self._x.is_affine and self._y.is_affine: if self._x == self._y: self._affine = self._x.get_affine() else: x_mtx = self._x.get_affine().get_matrix() y_mtx = self._y.get_affine().get_matrix() # This works because we already know the transforms are # separable, though normally one would want to set b and # c to zero. mtx = np.vstack((x_mtx[0], y_mtx[1], [0.0, 0.0, 1.0])) self._affine = Affine2D(mtx) else: self._affine = IdentityTransform() self._invalid = 0 return self._affine get_affine.__doc__ = Transform.get_affine.__doc__ class BlendedAffine2D(Affine2DBase): """ A "blended" transform uses one transform for the *x*-direction, and another transform for the *y*-direction. This version is an optimization for the case where both child transforms are of type :class:`Affine2DBase`. """ is_separable = True def __init__(self, x_transform, y_transform): """ Create a new "blended" transform using *x_transform* to transform the *x*-axis and *y_transform* to transform the *y*-axis. Both *x_transform* and *y_transform* must be 2D affine transforms. You will generally not call this constructor directly but use the :func:`blended_transform_factory` function instead, which can determine automatically which kind of blended transform to create. """ assert x_transform.is_affine assert y_transform.is_affine assert x_transform.is_separable assert y_transform.is_separable Transform.__init__(self) self._x = x_transform self._y = y_transform self.set_children(x_transform, y_transform) Affine2DBase.__init__(self) self._mtx = None def __repr__(self): return "BlendedAffine2D(%s,%s)" % (self._x, self._y) __str__ = __repr__ def get_matrix(self): if self._invalid: if self._x == self._y: self._mtx = self._x.get_matrix() else: x_mtx = self._x.get_matrix() y_mtx = self._y.get_matrix() # This works because we already know the transforms are # separable, though normally one would want to set b and # c to zero. self._mtx = np.vstack((x_mtx[0], y_mtx[1], [0.0, 0.0, 1.0])) self._inverted = None self._invalid = 0 return self._mtx get_matrix.__doc__ = Affine2DBase.get_matrix.__doc__ def blended_transform_factory(x_transform, y_transform): """ Create a new "blended" transform using *x_transform* to transform the *x*-axis and *y_transform* to transform the *y*-axis. A faster version of the blended transform is returned for the case where both child transforms are affine. """ if (isinstance(x_transform, Affine2DBase) and isinstance(y_transform, Affine2DBase)): return BlendedAffine2D(x_transform, y_transform) return BlendedGenericTransform(x_transform, y_transform) class CompositeGenericTransform(Transform): """ A composite transform formed by applying transform *a* then transform *b*. This "generic" version can handle any two arbitrary transformations. """ pass_through = True def __init__(self, a, b): """ Create a new composite transform that is the result of applying transform *a* then transform *b*. You will generally not call this constructor directly but use the :func:`composite_transform_factory` function instead, which can automatically choose the best kind of composite transform instance to create. """ assert a.output_dims == b.input_dims self.input_dims = a.input_dims self.output_dims = b.output_dims Transform.__init__(self) self._a = a self._b = b self.set_children(a, b) def frozen(self): self._invalid = 0 frozen = composite_transform_factory(self._a.frozen(), self._b.frozen()) if not isinstance(frozen, CompositeGenericTransform): return frozen.frozen() return frozen frozen.__doc__ = Transform.frozen.__doc__ def _get_is_affine(self): return self._a.is_affine and self._b.is_affine is_affine = property(_get_is_affine) def _get_is_separable(self): return self._a.is_separable and self._b.is_separable is_separable = property(_get_is_separable) def __repr__(self): return "CompositeGenericTransform(%s, %s)" % (self._a, self._b) __str__ = __repr__ def transform(self, points): return self._b.transform( self._a.transform(points)) transform.__doc__ = Transform.transform.__doc__ def transform_affine(self, points): return self.get_affine().transform(points) transform_affine.__doc__ = Transform.transform_affine.__doc__ def transform_non_affine(self, points): if self._a.is_affine and self._b.is_affine: return points return self._b.transform_non_affine( self._a.transform(points)) transform_non_affine.__doc__ = Transform.transform_non_affine.__doc__ def transform_path(self, path): return self._b.transform_path( self._a.transform_path(path)) transform_path.__doc__ = Transform.transform_path.__doc__ def transform_path_affine(self, path): return self._b.transform_path_affine( self._a.transform_path(path)) transform_path_affine.__doc__ = Transform.transform_path_affine.__doc__ def transform_path_non_affine(self, path): if self._a.is_affine and self._b.is_affine: return path return self._b.transform_path_non_affine( self._a.transform_path(path)) transform_path_non_affine.__doc__ = Transform.transform_path_non_affine.__doc__ def get_affine(self): if self._a.is_affine and self._b.is_affine: return Affine2D(np.dot(self._b.get_affine().get_matrix(), self._a.get_affine().get_matrix())) else: return self._b.get_affine() get_affine.__doc__ = Transform.get_affine.__doc__ def inverted(self): return CompositeGenericTransform(self._b.inverted(), self._a.inverted()) inverted.__doc__ = Transform.inverted.__doc__ class CompositeAffine2D(Affine2DBase): """ A composite transform formed by applying transform *a* then transform *b*. This version is an optimization that handles the case where both *a* and *b* are 2D affines. """ def __init__(self, a, b): """ Create a new composite transform that is the result of applying transform *a* then transform *b*. Both *a* and *b* must be instances of :class:`Affine2DBase`. You will generally not call this constructor directly but use the :func:`composite_transform_factory` function instead, which can automatically choose the best kind of composite transform instance to create. """ assert a.output_dims == b.input_dims self.input_dims = a.input_dims self.output_dims = b.output_dims assert a.is_affine assert b.is_affine Affine2DBase.__init__(self) self._a = a self._b = b self.set_children(a, b) self._mtx = None def __repr__(self): return "CompositeAffine2D(%s, %s)" % (self._a, self._b) __str__ = __repr__ def get_matrix(self): if self._invalid: self._mtx = np.dot( self._b.get_matrix(), self._a.get_matrix()) self._inverted = None self._invalid = 0 return self._mtx get_matrix.__doc__ = Affine2DBase.get_matrix.__doc__ def composite_transform_factory(a, b): """ Create a new composite transform that is the result of applying transform a then transform b. Shortcut versions of the blended transform are provided for the case where both child transforms are affine, or one or the other is the identity transform. Composite transforms may also be created using the '+' operator, e.g.:: c = a + b """ if isinstance(a, IdentityTransform): return b elif isinstance(b, IdentityTransform): return a elif isinstance(a, AffineBase) and isinstance(b, AffineBase): return CompositeAffine2D(a, b) return CompositeGenericTransform(a, b) class BboxTransform(Affine2DBase): """ :class:`BboxTransform` linearly transforms points from one :class:`Bbox` to another :class:`Bbox`. """ is_separable = True def __init__(self, boxin, boxout): """ Create a new :class:`BboxTransform` that linearly transforms points from *boxin* to *boxout*. """ assert boxin.is_bbox assert boxout.is_bbox Affine2DBase.__init__(self) self._boxin = boxin self._boxout = boxout self.set_children(boxin, boxout) self._mtx = None self._inverted = None def __repr__(self): return "BboxTransform(%s, %s)" % (self._boxin, self._boxout) __str__ = __repr__ def get_matrix(self): if self._invalid: inl, inb, inw, inh = self._boxin.bounds outl, outb, outw, outh = self._boxout.bounds x_scale = outw / inw y_scale = outh / inh if DEBUG and (x_scale == 0 or y_scale == 0): raise ValueError("Transforming from or to a singular bounding box.") self._mtx = np.array([[x_scale, 0.0 , (-inl*x_scale+outl)], [0.0 , y_scale, (-inb*y_scale+outb)], [0.0 , 0.0 , 1.0 ]], np.float_) self._inverted = None self._invalid = 0 return self._mtx get_matrix.__doc__ = Affine2DBase.get_matrix.__doc__ class BboxTransformTo(Affine2DBase): """ :class:`BboxTransformTo` is a transformation that linearly transforms points from the unit bounding box to a given :class:`Bbox`. """ is_separable = True def __init__(self, boxout): """ Create a new :class:`BboxTransformTo` that linearly transforms points from the unit bounding box to *boxout*. """ assert boxout.is_bbox Affine2DBase.__init__(self) self._boxout = boxout self.set_children(boxout) self._mtx = None self._inverted = None def __repr__(self): return "BboxTransformTo(%s)" % (self._boxout) __str__ = __repr__ def get_matrix(self): if self._invalid: outl, outb, outw, outh = self._boxout.bounds if DEBUG and (outw == 0 or outh == 0): raise ValueError("Transforming to a singular bounding box.") self._mtx = np.array([[outw, 0.0, outl], [ 0.0, outh, outb], [ 0.0, 0.0, 1.0]], np.float_) self._inverted = None self._invalid = 0 return self._mtx get_matrix.__doc__ = Affine2DBase.get_matrix.__doc__ class BboxTransformFrom(Affine2DBase): """ :class:`BboxTransformFrom` linearly transforms points from a given :class:`Bbox` to the unit bounding box. """ is_separable = True def __init__(self, boxin): assert boxin.is_bbox Affine2DBase.__init__(self) self._boxin = boxin self.set_children(boxin) self._mtx = None self._inverted = None def __repr__(self): return "BboxTransformFrom(%s)" % (self._boxin) __str__ = __repr__ def get_matrix(self): if self._invalid: inl, inb, inw, inh = self._boxin.bounds if DEBUG and (inw == 0 or inh == 0): raise ValueError("Transforming from a singular bounding box.") x_scale = 1.0 / inw y_scale = 1.0 / inh self._mtx = np.array([[x_scale, 0.0 , (-inl*x_scale)], [0.0 , y_scale, (-inb*y_scale)], [0.0 , 0.0 , 1.0 ]], np.float_) self._inverted = None self._invalid = 0 return self._mtx get_matrix.__doc__ = Affine2DBase.get_matrix.__doc__ class ScaledTranslation(Affine2DBase): """ A transformation that translates by *xt* and *yt*, after *xt* and *yt* have been transformad by the given transform *scale_trans*. """ def __init__(self, xt, yt, scale_trans): Affine2DBase.__init__(self) self._t = (xt, yt) self._scale_trans = scale_trans self.set_children(scale_trans) self._mtx = None self._inverted = None def __repr__(self): return "ScaledTranslation(%s)" % (self._t,) __str__ = __repr__ def get_matrix(self): if self._invalid: xt, yt = self._scale_trans.transform_point(self._t) self._mtx = np.array([[1.0, 0.0, xt], [0.0, 1.0, yt], [0.0, 0.0, 1.0]], np.float_) self._invalid = 0 self._inverted = None return self._mtx get_matrix.__doc__ = Affine2DBase.get_matrix.__doc__ class TransformedPath(TransformNode): """ A :class:`TransformedPath` caches a non-affine transformed copy of the :class:`~matplotlib.path.Path`. This cached copy is automatically updated when the non-affine part of the transform changes. """ def __init__(self, path, transform): """ Create a new :class:`TransformedPath` from the given :class:`~matplotlib.path.Path` and :class:`Transform`. """ assert isinstance(transform, Transform) TransformNode.__init__(self) self._path = path self._transform = transform self.set_children(transform) self._transformed_path = None self._transformed_points = None def _revalidate(self): if ((self._invalid & self.INVALID_NON_AFFINE == self.INVALID_NON_AFFINE) or self._transformed_path is None): self._transformed_path = \ self._transform.transform_path_non_affine(self._path) self._transformed_points = \ Path(self._transform.transform_non_affine(self._path.vertices)) self._invalid = 0 def get_transformed_points_and_affine(self): """ Return a copy of the child path, with the non-affine part of the transform already applied, along with the affine part of the path necessary to complete the transformation. Unlike :meth:`get_transformed_path_and_affine`, no interpolation will be performed. """ self._revalidate() return self._transformed_points, self.get_affine() def get_transformed_path_and_affine(self): """ Return a copy of the child path, with the non-affine part of the transform already applied, along with the affine part of the path necessary to complete the transformation. """ self._revalidate() return self._transformed_path, self.get_affine() def get_fully_transformed_path(self): """ Return a fully-transformed copy of the child path. """ if ((self._invalid & self.INVALID_NON_AFFINE == self.INVALID_NON_AFFINE) or self._transformed_path is None): self._transformed_path = \ self._transform.transform_path_non_affine(self._path) self._invalid = 0 return self._transform.transform_path_affine(self._transformed_path) def get_affine(self): return self._transform.get_affine() def nonsingular(vmin, vmax, expander=0.001, tiny=1e-15, increasing=True): ''' Ensure the endpoints of a range are finite and not too close together. "too close" means the interval is smaller than 'tiny' times the maximum absolute value. If they are too close, each will be moved by the 'expander'. If 'increasing' is True and vmin > vmax, they will be swapped, regardless of whether they are too close. If either is inf or -inf or nan, return - expander, expander. ''' if (not np.isfinite(vmin)) or (not np.isfinite(vmax)): return -expander, expander swapped = False if vmax < vmin: vmin, vmax = vmax, vmin swapped = True if vmax - vmin <= max(abs(vmin), abs(vmax)) * tiny: if vmin == 0.0: vmin = -expander vmax = expander else: vmin -= expander*abs(vmin) vmax += expander*abs(vmax) if swapped and not increasing: vmin, vmax = vmax, vmin return vmin, vmax def interval_contains(interval, val): a, b = interval return ( ((a < b) and (a <= val and b >= val)) or (b <= val and a >= val)) def interval_contains_open(interval, val): a, b = interval return ( ((a < b) and (a < val and b > val)) or (b < val and a > val)) def offset_copy(trans, fig, x=0.0, y=0.0, units='inches'): ''' Return a new transform with an added offset. args: trans is any transform kwargs: fig is the current figure; it can be None if units are 'dots' x, y give the offset units is 'inches', 'points' or 'dots' ''' if units == 'dots': return trans + Affine2D().translate(x, y) if fig is None: raise ValueError('For units of inches or points a fig kwarg is needed') if units == 'points': x /= 72.0 y /= 72.0 elif not units == 'inches': raise ValueError('units must be dots, points, or inches') return trans + ScaledTranslation(x, y, fig.dpi_scale_trans)

75,638

Python

.py

1,895

30.928232

97

0.584582

numenta/nupic-legacy

6,330

1,556

464

AGPL-3.0

9/5/2024, 5:13:42 PM (Europe/Amsterdam)

26,238

legend.py

numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/legend.py

""" Place a legend on the axes at location loc. Labels are a sequence of strings and loc can be a string or an integer specifying the legend location The location codes are 'best' : 0, (only implemented for axis legends) 'upper right' : 1, 'upper left' : 2, 'lower left' : 3, 'lower right' : 4, 'right' : 5, 'center left' : 6, 'center right' : 7, 'lower center' : 8, 'upper center' : 9, 'center' : 10, Return value is a sequence of text, line instances that make up the legend """ from __future__ import division import warnings import numpy as np from matplotlib import rcParams from matplotlib.artist import Artist from matplotlib.cbook import is_string_like, iterable, silent_list, safezip from matplotlib.font_manager import FontProperties from matplotlib.lines import Line2D from matplotlib.patches import Patch, Rectangle, Shadow, FancyBboxPatch from matplotlib.collections import LineCollection, RegularPolyCollection from matplotlib.transforms import Bbox from matplotlib.offsetbox import HPacker, VPacker, PackerBase, TextArea, DrawingArea class Legend(Artist): """ Place a legend on the axes at location loc. Labels are a sequence of strings and loc can be a string or an integer specifying the legend location The location codes are:: 'best' : 0, (only implemented for axis legends) 'upper right' : 1, 'upper left' : 2, 'lower left' : 3, 'lower right' : 4, 'right' : 5, 'center left' : 6, 'center right' : 7, 'lower center' : 8, 'upper center' : 9, 'center' : 10, loc can be a tuple of the noramilzed coordinate values with respect its parent. Return value is a sequence of text, line instances that make up the legend """ codes = {'best' : 0, # only implemented for axis legends 'upper right' : 1, 'upper left' : 2, 'lower left' : 3, 'lower right' : 4, 'right' : 5, 'center left' : 6, 'center right' : 7, 'lower center' : 8, 'upper center' : 9, 'center' : 10, } zorder = 5 def __str__(self): return "Legend" def __init__(self, parent, handles, labels, loc = None, numpoints = None, # the number of points in the legend line markerscale = None, # the relative size of legend markers vs. original scatterpoints = 3, # TODO: may be an rcParam scatteryoffsets=None, prop = None, # properties for the legend texts # the following dimensions are in axes coords pad = None, # deprecated; use borderpad labelsep = None, # deprecated; use labelspacing handlelen = None, # deprecated; use handlelength handletextsep = None, # deprecated; use handletextpad axespad = None, # deprecated; use borderaxespad # spacing & pad defined as a fractionof the font-size borderpad = None, # the whitespace inside the legend border labelspacing=None, #the vertical space between the legend entries handlelength=None, # the length of the legend handles handletextpad=None, # the pad between the legend handle and text borderaxespad=None, # the pad between the axes and legend border columnspacing=None, # spacing between columns ncol=1, # number of columns mode=None, # mode for horizontal distribution of columns. None, "expand" fancybox=None, # True use a fancy box, false use a rounded box, none use rc shadow = None, ): """ - *parent* : the artist that contains the legend - *handles* : a list of artists (lines, patches) to add to the legend - *labels* : a list of strings to label the legend Optional keyword arguments: ================ ================================================================== Keyword Description ================ ================================================================== loc a location code or a tuple of coordinates numpoints the number of points in the legend line prop the font property markerscale the relative size of legend markers vs. original fancybox if True, draw a frame with a round fancybox. If None, use rc shadow if True, draw a shadow behind legend scatteryoffsets a list of yoffsets for scatter symbols in legend borderpad the fractional whitespace inside the legend border labelspacing the vertical space between the legend entries handlelength the length of the legend handles handletextpad the pad between the legend handle and text borderaxespad the pad between the axes and legend border columnspacing the spacing between columns ================ ================================================================== The dimensions of pad and spacing are given as a fraction of the fontsize. Values from rcParams will be used if None. """ from matplotlib.axes import Axes # local import only to avoid circularity from matplotlib.figure import Figure # local import only to avoid circularity Artist.__init__(self) if prop is None: self.prop=FontProperties(size=rcParams["legend.fontsize"]) else: self.prop=prop self.fontsize = self.prop.get_size_in_points() propnames=['numpoints', 'markerscale', 'shadow', "columnspacing", "scatterpoints"] localdict = locals() for name in propnames: if localdict[name] is None: value = rcParams["legend."+name] else: value = localdict[name] setattr(self, name, value) # Take care the deprecated keywords deprecated_kwds = {"pad":"borderpad", "labelsep":"labelspacing", "handlelen":"handlelength", "handletextsep":"handletextpad", "axespad":"borderaxespad"} # convert values of deprecated keywords (ginve in axes coords) # to new vaules in a fraction of the font size # conversion factor bbox = parent.bbox axessize_fontsize = min(bbox.width, bbox.height)/self.fontsize for k, v in deprecated_kwds.items(): # use deprecated value if not None and if their newer # counter part is None. if localdict[k] is not None and localdict[v] is None: warnings.warn("Use '%s' instead of '%s'." % (v, k), DeprecationWarning) setattr(self, v, localdict[k]*axessize_fontsize) continue # Otherwise, use new keywords if localdict[v] is None: setattr(self, v, rcParams["legend."+v]) else: setattr(self, v, localdict[v]) del localdict self._ncol = ncol if self.numpoints <= 0: raise ValueError("numpoints must be >= 0; it was %d"% numpoints) # introduce y-offset for handles of the scatter plot if scatteryoffsets is None: self._scatteryoffsets = np.array([3./8., 4./8., 2.5/8.]) else: self._scatteryoffsets = np.asarray(scatteryoffsets) reps = int(self.numpoints / len(self._scatteryoffsets)) + 1 self._scatteryoffsets = np.tile(self._scatteryoffsets, reps)[:self.scatterpoints] # _legend_box is an OffsetBox instance that contains all # legend items and will be initialized from _init_legend_box() # method. self._legend_box = None if isinstance(parent,Axes): self.isaxes = True self.set_figure(parent.figure) elif isinstance(parent,Figure): self.isaxes = False self.set_figure(parent) else: raise TypeError("Legend needs either Axes or Figure as parent") self.parent = parent if loc is None: loc = rcParams["legend.loc"] if not self.isaxes and loc in [0,'best']: loc = 'upper right' if is_string_like(loc): if loc not in self.codes: if self.isaxes: warnings.warn('Unrecognized location "%s". Falling back on "best"; ' 'valid locations are\n\t%s\n' % (loc, '\n\t'.join(self.codes.keys()))) loc = 0 else: warnings.warn('Unrecognized location "%s". Falling back on "upper right"; ' 'valid locations are\n\t%s\n' % (loc, '\n\t'.join(self.codes.keys()))) loc = 1 else: loc = self.codes[loc] if not self.isaxes and loc == 0: warnings.warn('Automatic legend placement (loc="best") not implemented for figure legend. ' 'Falling back on "upper right".') loc = 1 self._loc = loc self._mode = mode # We use FancyBboxPatch to draw a legend frame. The location # and size of the box will be updated during the drawing time. self.legendPatch = FancyBboxPatch( xy=(0.0, 0.0), width=1., height=1., facecolor='w', edgecolor='k', mutation_scale=self.fontsize, snap=True ) # The width and height of the legendPatch will be set (in the # draw()) to the length that includes the padding. Thus we set # pad=0 here. if fancybox is None: fancybox = rcParams["legend.fancybox"] if fancybox == True: self.legendPatch.set_boxstyle("round",pad=0, rounding_size=0.2) else: self.legendPatch.set_boxstyle("square",pad=0) self._set_artist_props(self.legendPatch) self._drawFrame = True # init with null renderer self._init_legend_box(handles, labels) self._last_fontsize_points = self.fontsize def _set_artist_props(self, a): """ set the boilerplate props for artists added to axes """ a.set_figure(self.figure) for c in self.get_children(): c.set_figure(self.figure) a.set_transform(self.get_transform()) def _findoffset_best(self, width, height, xdescent, ydescent, renderer): "Heper function to locate the legend at its best position" ox, oy = self._find_best_position(width, height, renderer) return ox+xdescent, oy+ydescent def _findoffset_loc(self, width, height, xdescent, ydescent, renderer): "Heper function to locate the legend using the location code" if iterable(self._loc) and len(self._loc)==2: # when loc is a tuple of axes(or figure) coordinates. fx, fy = self._loc bbox = self.parent.bbox x, y = bbox.x0 + bbox.width * fx, bbox.y0 + bbox.height * fy else: bbox = Bbox.from_bounds(0, 0, width, height) x, y = self._get_anchored_bbox(self._loc, bbox, self.parent.bbox, renderer) return x+xdescent, y+ydescent def draw(self, renderer): "Draw everything that belongs to the legend" if not self.get_visible(): return self._update_legend_box(renderer) renderer.open_group('legend') # find_offset function will be provided to _legend_box and # _legend_box will draw itself at the location of the return # value of the find_offset. if self._loc == 0: _findoffset = self._findoffset_best else: _findoffset = self._findoffset_loc def findoffset(width, height, xdescent, ydescent): return _findoffset(width, height, xdescent, ydescent, renderer) self._legend_box.set_offset(findoffset) fontsize = renderer.points_to_pixels(self.fontsize) # if mode == fill, set the width of the legend_box to the # width of the paret (minus pads) if self._mode in ["expand"]: pad = 2*(self.borderaxespad+self.borderpad)*fontsize self._legend_box.set_width(self.parent.bbox.width-pad) if self._drawFrame: # update the location and size of the legend bbox = self._legend_box.get_window_extent(renderer) self.legendPatch.set_bounds(bbox.x0, bbox.y0, bbox.width, bbox.height) self.legendPatch.set_mutation_scale(fontsize) if self.shadow: shadow = Shadow(self.legendPatch, 2, -2) shadow.draw(renderer) self.legendPatch.draw(renderer) self._legend_box.draw(renderer) renderer.close_group('legend') def _approx_text_height(self, renderer=None): """ Return the approximate height of the text. This is used to place the legend handle. """ if renderer is None: return self.fontsize else: return renderer.points_to_pixels(self.fontsize) def _init_legend_box(self, handles, labels): """ Initiallize the legend_box. The legend_box is an instance of the OffsetBox, which is packed with legend handles and texts. Once packed, their location is calculated during the drawing time. """ fontsize = self.fontsize # legend_box is a HPacker, horizontally packed with # columns. Each column is a VPacker, vertically packed with # legend items. Each legend item is HPacker packed with # legend handleBox and labelBox. handleBox is an instance of # offsetbox.DrawingArea which contains legend handle. labelBox # is an instance of offsetbox.TextArea which contains legend # text. text_list = [] # the list of text instances handle_list = [] # the list of text instances label_prop = dict(verticalalignment='baseline', horizontalalignment='left', fontproperties=self.prop, ) labelboxes = [] for l in labels: textbox = TextArea(l, textprops=label_prop, multilinebaseline=True, minimumdescent=True) text_list.append(textbox._text) labelboxes.append(textbox) handleboxes = [] # The approximate height and descent of text. These values are # only used for plotting the legend handle. height = self._approx_text_height() * 0.7 descent = 0. # each handle needs to be drawn inside a box of (x, y, w, h) = # (0, -descent, width, height). And their corrdinates should # be given in the display coordinates. # NOTE : the coordinates will be updated again in # _update_legend_box() method. # The transformation of each handle will be automatically set # to self.get_trasnform(). If the artist does not uses its # default trasnform (eg, Collections), you need to # manually set their transform to the self.get_transform(). for handle in handles: if isinstance(handle, RegularPolyCollection): npoints = self.scatterpoints else: npoints = self.numpoints if npoints > 1: # we put some pad here to compensate the size of the # marker xdata = np.linspace(0.3*fontsize, (self.handlelength-0.3)*fontsize, npoints) xdata_marker = xdata elif npoints == 1: xdata = np.linspace(0, self.handlelength*fontsize, 2) xdata_marker = [0.5*self.handlelength*fontsize] if isinstance(handle, Line2D): ydata = ((height-descent)/2.)*np.ones(xdata.shape, float) legline = Line2D(xdata, ydata) legline.update_from(handle) self._set_artist_props(legline) # after update legline.set_clip_box(None) legline.set_clip_path(None) legline.set_drawstyle('default') legline.set_marker('None') handle_list.append(legline) legline_marker = Line2D(xdata_marker, ydata[:len(xdata_marker)]) legline_marker.update_from(handle) self._set_artist_props(legline_marker) legline_marker.set_clip_box(None) legline_marker.set_clip_path(None) legline_marker.set_linestyle('None') # we don't want to add this to the return list because # the texts and handles are assumed to be in one-to-one # correpondence. legline._legmarker = legline_marker elif isinstance(handle, Patch): p = Rectangle(xy=(0., 0.), width = self.handlelength*fontsize, height=(height-descent), ) p.update_from(handle) self._set_artist_props(p) p.set_clip_box(None) p.set_clip_path(None) handle_list.append(p) elif isinstance(handle, LineCollection): ydata = ((height-descent)/2.)*np.ones(xdata.shape, float) legline = Line2D(xdata, ydata) self._set_artist_props(legline) legline.set_clip_box(None) legline.set_clip_path(None) lw = handle.get_linewidth()[0] dashes = handle.get_dashes()[0] color = handle.get_colors()[0] legline.set_color(color) legline.set_linewidth(lw) legline.set_dashes(dashes) handle_list.append(legline) elif isinstance(handle, RegularPolyCollection): #ydata = self._scatteryoffsets ydata = height*self._scatteryoffsets size_max, size_min = max(handle.get_sizes()),\ min(handle.get_sizes()) # we may need to scale these sizes by "markerscale" # attribute. But other handle types does not seem # to care about this attribute and it is currently ignored. if self.scatterpoints < 4: sizes = [.5*(size_max+size_min), size_max, size_min] else: sizes = (size_max-size_min)*np.linspace(0,1,self.scatterpoints)+size_min p = type(handle)(handle.get_numsides(), rotation=handle.get_rotation(), sizes=sizes, offsets=zip(xdata_marker,ydata), transOffset=self.get_transform(), ) p.update_from(handle) p.set_figure(self.figure) p.set_clip_box(None) p.set_clip_path(None) handle_list.append(p) else: handle_list.append(None) handlebox = DrawingArea(width=self.handlelength*fontsize, height=height, xdescent=0., ydescent=descent) handle = handle_list[-1] handlebox.add_artist(handle) if hasattr(handle, "_legmarker"): handlebox.add_artist(handle._legmarker) handleboxes.append(handlebox) # We calculate number of lows in each column. The first # (num_largecol) columns will have (nrows+1) rows, and remaing # (num_smallcol) columns will have (nrows) rows. nrows, num_largecol = divmod(len(handleboxes), self._ncol) num_smallcol = self._ncol-num_largecol # starting index of each column and number of rows in it. largecol = safezip(range(0, num_largecol*(nrows+1), (nrows+1)), [nrows+1] * num_largecol) smallcol = safezip(range(num_largecol*(nrows+1), len(handleboxes), nrows), [nrows] * num_smallcol) handle_label = safezip(handleboxes, labelboxes) columnbox = [] for i0, di in largecol+smallcol: # pack handleBox and labelBox into itemBox itemBoxes = [HPacker(pad=0, sep=self.handletextpad*fontsize, children=[h, t], align="baseline") for h, t in handle_label[i0:i0+di]] # minimumdescent=False for the text of the last row of the column itemBoxes[-1].get_children()[1].set_minimumdescent(False) # pack columnBox columnbox.append(VPacker(pad=0, sep=self.labelspacing*fontsize, align="baseline", children=itemBoxes)) if self._mode == "expand": mode = "expand" else: mode = "fixed" sep = self.columnspacing*fontsize self._legend_box = HPacker(pad=self.borderpad*fontsize, sep=sep, align="baseline", mode=mode, children=columnbox) self._legend_box.set_figure(self.figure) self.texts = text_list self.legendHandles = handle_list def _update_legend_box(self, renderer): """ Update the dimension of the legend_box. This is required becuase the paddings, the hadle size etc. depends on the dpi of the renderer. """ # fontsize in points. fontsize = renderer.points_to_pixels(self.fontsize) if self._last_fontsize_points == fontsize: # no update is needed return # each handle needs to be drawn inside a box of # (x, y, w, h) = (0, -descent, width, height). # And their corrdinates should be given in the display coordinates. # The approximate height and descent of text. These values are # only used for plotting the legend handle. height = self._approx_text_height(renderer) * 0.7 descent = 0. for handle in self.legendHandles: if isinstance(handle, RegularPolyCollection): npoints = self.scatterpoints else: npoints = self.numpoints if npoints > 1: # we put some pad here to compensate the size of the # marker xdata = np.linspace(0.3*fontsize, (self.handlelength-0.3)*fontsize, npoints) xdata_marker = xdata elif npoints == 1: xdata = np.linspace(0, self.handlelength*fontsize, 2) xdata_marker = [0.5*self.handlelength*fontsize] if isinstance(handle, Line2D): legline = handle ydata = ((height-descent)/2.)*np.ones(xdata.shape, float) legline.set_data(xdata, ydata) legline_marker = legline._legmarker legline_marker.set_data(xdata_marker, ydata[:len(xdata_marker)]) elif isinstance(handle, Patch): p = handle p.set_bounds(0., 0., self.handlelength*fontsize, (height-descent), ) elif isinstance(handle, RegularPolyCollection): p = handle ydata = height*self._scatteryoffsets p.set_offsets(zip(xdata_marker,ydata)) # correction factor cor = fontsize / self._last_fontsize_points # helper function to iterate over all children def all_children(parent): yield parent for c in parent.get_children(): for cc in all_children(c): yield cc #now update paddings for box in all_children(self._legend_box): if isinstance(box, PackerBase): box.pad = box.pad * cor box.sep = box.sep * cor elif isinstance(box, DrawingArea): box.width = self.handlelength*fontsize box.height = height box.xdescent = 0. box.ydescent=descent self._last_fontsize_points = fontsize def _auto_legend_data(self): """ Returns list of vertices and extents covered by the plot. Returns a two long list. First element is a list of (x, y) vertices (in display-coordinates) covered by all the lines and line collections, in the legend's handles. Second element is a list of bounding boxes for all the patches in the legend's handles. """ assert self.isaxes # should always hold because function is only called internally ax = self.parent vertices = [] bboxes = [] lines = [] for handle in ax.lines: assert isinstance(handle, Line2D) path = handle.get_path() trans = handle.get_transform() tpath = trans.transform_path(path) lines.append(tpath) for handle in ax.patches: assert isinstance(handle, Patch) if isinstance(handle, Rectangle): transform = handle.get_data_transform() bboxes.append(handle.get_bbox().transformed(transform)) else: transform = handle.get_transform() bboxes.append(handle.get_path().get_extents(transform)) return [vertices, bboxes, lines] def draw_frame(self, b): 'b is a boolean. Set draw frame to b' self._drawFrame = b def get_children(self): 'return a list of child artists' children = [] if self._legend_box: children.append(self._legend_box) return children def get_frame(self): 'return the Rectangle instance used to frame the legend' return self.legendPatch def get_lines(self): 'return a list of lines.Line2D instances in the legend' return [h for h in self.legendHandles if isinstance(h, Line2D)] def get_patches(self): 'return a list of patch instances in the legend' return silent_list('Patch', [h for h in self.legendHandles if isinstance(h, Patch)]) def get_texts(self): 'return a list of text.Text instance in the legend' return silent_list('Text', self.texts) def get_window_extent(self): 'return a extent of the the legend' return self.legendPatch.get_window_extent() def _get_anchored_bbox(self, loc, bbox, parentbbox, renderer): """ Place the *bbox* inside the *parentbbox* according to a given location code. Return the (x,y) coordinate of the bbox. - loc: a location code in range(1, 11). This corresponds to the possible values for self._loc, excluding "best". - bbox: bbox to be placed, display coodinate units. - parentbbox: a parent box which will contain the bbox. In display coordinates. """ assert loc in range(1,11) # called only internally BEST, UR, UL, LL, LR, R, CL, CR, LC, UC, C = range(11) anchor_coefs={UR:"NE", UL:"NW", LL:"SW", LR:"SE", R:"E", CL:"W", CR:"E", LC:"S", UC:"N", C:"C"} c = anchor_coefs[loc] fontsize = renderer.points_to_pixels(self.fontsize) container = parentbbox.padded(-(self.borderaxespad) * fontsize) anchored_box = bbox.anchored(c, container=container) return anchored_box.x0, anchored_box.y0 def _find_best_position(self, width, height, renderer, consider=None): """ Determine the best location to place the legend. `consider` is a list of (x, y) pairs to consider as a potential lower-left corner of the legend. All are display coords. """ assert self.isaxes # should always hold because function is only called internally verts, bboxes, lines = self._auto_legend_data() bbox = Bbox.from_bounds(0, 0, width, height) consider = [self._get_anchored_bbox(x, bbox, self.parent.bbox, renderer) for x in range(1, len(self.codes))] #tx, ty = self.legendPatch.get_x(), self.legendPatch.get_y() candidates = [] for l, b in consider: legendBox = Bbox.from_bounds(l, b, width, height) badness = 0 badness = legendBox.count_contains(verts) badness += legendBox.count_overlaps(bboxes) for line in lines: if line.intersects_bbox(legendBox): badness += 1 ox, oy = l, b if badness == 0: return ox, oy candidates.append((badness, (l, b))) # rather than use min() or list.sort(), do this so that we are assured # that in the case of two equal badnesses, the one first considered is # returned. # NOTE: list.sort() is stable.But leave as it is for now. -JJL minCandidate = candidates[0] for candidate in candidates: if candidate[0] < minCandidate[0]: minCandidate = candidate ox, oy = minCandidate[1] return ox, oy

30,705

Python

.py

647

34.256569

116

0.56115

numenta/nupic-legacy

6,330

1,556

464

AGPL-3.0

9/5/2024, 5:13:42 PM (Europe/Amsterdam)

26,239

mlab.py

numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/mlab.py

""" Numerical python functions written for compatability with matlab(TM) commands with the same names. Matlab(TM) compatible functions ------------------------------- :func:`cohere` Coherence (normalized cross spectral density) :func:`csd` Cross spectral density uing Welch's average periodogram :func:`detrend` Remove the mean or best fit line from an array :func:`find` Return the indices where some condition is true; numpy.nonzero is similar but more general. :func:`griddata` interpolate irregularly distributed data to a regular grid. :func:`prctile` find the percentiles of a sequence :func:`prepca` Principal Component Analysis :func:`psd` Power spectral density uing Welch's average periodogram :func:`rk4` A 4th order runge kutta integrator for 1D or ND systems :func:`specgram` Spectrogram (power spectral density over segments of time) Miscellaneous functions ------------------------- Functions that don't exist in matlab(TM), but are useful anyway: :meth:`cohere_pairs` Coherence over all pairs. This is not a matlab function, but we compute coherence a lot in my lab, and we compute it for a lot of pairs. This function is optimized to do this efficiently by caching the direct FFTs. :meth:`rk4` A 4th order Runge-Kutta ODE integrator in case you ever find yourself stranded without scipy (and the far superior scipy.integrate tools) record array helper functions ------------------------------- A collection of helper methods for numpyrecord arrays .. _htmlonly:: See :ref:`misc-examples-index` :meth:`rec2txt` pretty print a record array :meth:`rec2csv` store record array in CSV file :meth:`csv2rec` import record array from CSV file with type inspection :meth:`rec_append_fields` adds field(s)/array(s) to record array :meth:`rec_drop_fields` drop fields from record array :meth:`rec_join` join two record arrays on sequence of fields :meth:`rec_groupby` summarize data by groups (similar to SQL GROUP BY) :meth:`rec_summarize` helper code to filter rec array fields into new fields For the rec viewer functions(e rec2csv), there are a bunch of Format objects you can pass into the functions that will do things like color negative values red, set percent formatting and scaling, etc. Example usage:: r = csv2rec('somefile.csv', checkrows=0) formatd = dict( weight = FormatFloat(2), change = FormatPercent(2), cost = FormatThousands(2), ) rec2excel(r, 'test.xls', formatd=formatd) rec2csv(r, 'test.csv', formatd=formatd) scroll = rec2gtk(r, formatd=formatd) win = gtk.Window() win.set_size_request(600,800) win.add(scroll) win.show_all() gtk.main() Deprecated functions --------------------- The following are deprecated; please import directly from numpy (with care--function signatures may differ): :meth:`conv` convolution (numpy.convolve) :meth:`corrcoef` The matrix of correlation coefficients :meth:`hist` Histogram (numpy.histogram) :meth:`linspace` Linear spaced array from min to max :meth:`load` load ASCII file - use numpy.loadtxt :meth:`meshgrid` Make a 2D grid from 2 1 arrays (numpy.meshgrid) :meth:`polyfit` least squares best polynomial fit of x to y (numpy.polyfit) :meth:`polyval` evaluate a vector for a vector of polynomial coeffs (numpy.polyval) :meth:`save` save ASCII file - use numpy.savetxt :meth:`trapz` trapeziodal integration (trapz(x,y) -> numpy.trapz(y,x)) :meth:`vander` the Vandermonde matrix (numpy.vander) """ from __future__ import division import csv, warnings, copy, os import numpy as np ma = np.ma from matplotlib import verbose import matplotlib.nxutils as nxutils import matplotlib.cbook as cbook # set is a new builtin function in 2.4; delete the following when # support for 2.3 is dropped. try: set except NameError: from sets import Set as set def linspace(*args, **kw): warnings.warn("use numpy.linspace", DeprecationWarning) return np.linspace(*args, **kw) def meshgrid(x,y): warnings.warn("use numpy.meshgrid", DeprecationWarning) return np.meshgrid(x,y) def mean(x, dim=None): warnings.warn("Use numpy.mean(x) or x.mean()", DeprecationWarning) if len(x)==0: return None return np.mean(x, axis=dim) def logspace(xmin,xmax,N): return np.exp(np.linspace(np.log(xmin), np.log(xmax), N)) def _norm(x): "return sqrt(x dot x)" return np.sqrt(np.dot(x,x)) def window_hanning(x): "return x times the hanning window of len(x)" return np.hanning(len(x))*x def window_none(x): "No window function; simply return x" return x #from numpy import convolve as conv def conv(x, y, mode=2): 'convolve x with y' warnings.warn("Use numpy.convolve(x, y, mode='full')", DeprecationWarning) return np.convolve(x,y,mode) def detrend(x, key=None): if key is None or key=='constant': return detrend_mean(x) elif key=='linear': return detrend_linear(x) def demean(x, axis=0): "Return x minus its mean along the specified axis" x = np.asarray(x) if axis: ind = [slice(None)] * axis ind.append(np.newaxis) return x - x.mean(axis)[ind] return x - x.mean(axis) def detrend_mean(x): "Return x minus the mean(x)" return x - x.mean() def detrend_none(x): "Return x: no detrending" return x def detrend_linear(y): "Return y minus best fit line; 'linear' detrending " # This is faster than an algorithm based on linalg.lstsq. x = np.arange(len(y), dtype=np.float_) C = np.cov(x, y, bias=1) b = C[0,1]/C[0,0] a = y.mean() - b*x.mean() return y - (b*x + a) #This is a helper function that implements the commonality between the #psd, csd, and spectrogram. It is *NOT* meant to be used outside of mlab def _spectral_helper(x, y, NFFT=256, Fs=2, detrend=detrend_none, window=window_hanning, noverlap=0, pad_to=None, sides='default', scale_by_freq=None): #The checks for if y is x are so that we can use the same function to #implement the core of psd(), csd(), and spectrogram() without doing #extra calculations. We return the unaveraged Pxy, freqs, and t. same_data = y is x #Make sure we're dealing with a numpy array. If y and x were the same #object to start with, keep them that way x = np.asarray(x) if not same_data: y = np.asarray(y) # zero pad x and y up to NFFT if they are shorter than NFFT if len(x)<NFFT: n = len(x) x = np.resize(x, (NFFT,)) x[n:] = 0 if not same_data and len(y)<NFFT: n = len(y) y = np.resize(y, (NFFT,)) y[n:] = 0 if pad_to is None: pad_to = NFFT if scale_by_freq is None: warnings.warn("psd, csd, and specgram have changed to scale their " "densities by the sampling frequency for better MatLab " "compatibility. You can pass scale_by_freq=False to disable " "this behavior. Also, one-sided densities are scaled by a " "factor of 2.") scale_by_freq = True # For real x, ignore the negative frequencies unless told otherwise if (sides == 'default' and np.iscomplexobj(x)) or sides == 'twosided': numFreqs = pad_to scaling_factor = 1. elif sides in ('default', 'onesided'): numFreqs = pad_to//2 + 1 scaling_factor = 2. else: raise ValueError("sides must be one of: 'default', 'onesided', or " "'twosided'") # Matlab divides by the sampling frequency so that density function # has units of dB/Hz and can be integrated by the plotted frequency # values. Perform the same scaling here. if scale_by_freq: scaling_factor /= Fs if cbook.iterable(window): assert(len(window) == NFFT) windowVals = window else: windowVals = window(np.ones((NFFT,), x.dtype)) step = NFFT - noverlap ind = np.arange(0, len(x) - NFFT + 1, step) n = len(ind) Pxy = np.zeros((numFreqs,n), np.complex_) # do the ffts of the slices for i in range(n): thisX = x[ind[i]:ind[i]+NFFT] thisX = windowVals * detrend(thisX) fx = np.fft.fft(thisX, n=pad_to) if same_data: fy = fx else: thisY = y[ind[i]:ind[i]+NFFT] thisY = windowVals * detrend(thisY) fy = np.fft.fft(thisY, n=pad_to) Pxy[:,i] = np.conjugate(fx[:numFreqs]) * fy[:numFreqs] # Scale the spectrum by the norm of the window to compensate for # windowing loss; see Bendat & Piersol Sec 11.5.2. Also include # scaling factors for one-sided densities and dividing by the sampling # frequency, if desired. Pxy *= scaling_factor / (np.abs(windowVals)**2).sum() t = 1./Fs * (ind + NFFT / 2.) freqs = float(Fs) / pad_to * np.arange(numFreqs) return Pxy, freqs, t #Split out these keyword docs so that they can be used elsewhere kwdocd = dict() kwdocd['PSD'] =""" Keyword arguments: *NFFT*: integer The number of data points used in each block for the FFT. Must be even; a power 2 is most efficient. The default value is 256. *Fs*: scalar The sampling frequency (samples per time unit). It is used to calculate the Fourier frequencies, freqs, in cycles per time unit. The default value is 2. *detrend*: callable The function applied to each segment before fft-ing, designed to remove the mean or linear trend. Unlike in matlab, where the *detrend* parameter is a vector, in matplotlib is it a function. The :mod:`~matplotlib.pylab` module defines :func:`~matplotlib.pylab.detrend_none`, :func:`~matplotlib.pylab.detrend_mean`, and :func:`~matplotlib.pylab.detrend_linear`, but you can use a custom function as well. *window*: callable or ndarray A function or a vector of length *NFFT*. To create window vectors see :func:`window_hanning`, :func:`window_none`, :func:`numpy.blackman`, :func:`numpy.hamming`, :func:`numpy.bartlett`, :func:`scipy.signal`, :func:`scipy.signal.get_window`, etc. The default is :func:`window_hanning`. If a function is passed as the argument, it must take a data segment as an argument and return the windowed version of the segment. *noverlap*: integer The number of points of overlap between blocks. The default value is 0 (no overlap). *pad_to*: integer The number of points to which the data segment is padded when performing the FFT. This can be different from *NFFT*, which specifies the number of data points used. While not increasing the actual resolution of the psd (the minimum distance between resolvable peaks), this can give more points in the plot, allowing for more detail. This corresponds to the *n* parameter in the call to fft(). The default is None, which sets *pad_to* equal to *NFFT* *sides*: [ 'default' | 'onesided' | 'twosided' ] Specifies which sides of the PSD to return. Default gives the default behavior, which returns one-sided for real data and both for complex data. 'onesided' forces the return of a one-sided PSD, while 'twosided' forces two-sided. *scale_by_freq*: boolean Specifies whether the resulting density values should be scaled by the scaling frequency, which gives density in units of Hz^-1. This allows for integration over the returned frequency values. The default is True for MatLab compatibility. """ def psd(x, NFFT=256, Fs=2, detrend=detrend_none, window=window_hanning, noverlap=0, pad_to=None, sides='default', scale_by_freq=None): """ The power spectral density by Welch's average periodogram method. The vector *x* is divided into *NFFT* length blocks. Each block is detrended by the function *detrend* and windowed by the function *window*. *noverlap* gives the length of the overlap between blocks. The absolute(fft(block))**2 of each segment are averaged to compute *Pxx*, with a scaling to correct for power loss due to windowing. If len(*x*) < *NFFT*, it will be zero padded to *NFFT*. *x* Array or sequence containing the data %(PSD)s Returns the tuple (*Pxx*, *freqs*). Refs: Bendat & Piersol -- Random Data: Analysis and Measurement Procedures, John Wiley & Sons (1986) """ Pxx,freqs = csd(x, x, NFFT, Fs, detrend, window, noverlap, pad_to, sides, scale_by_freq) return Pxx.real,freqs psd.__doc__ = psd.__doc__ % kwdocd def csd(x, y, NFFT=256, Fs=2, detrend=detrend_none, window=window_hanning, noverlap=0, pad_to=None, sides='default', scale_by_freq=None): """ The cross power spectral density by Welch's average periodogram method. The vectors *x* and *y* are divided into *NFFT* length blocks. Each block is detrended by the function *detrend* and windowed by the function *window*. *noverlap* gives the length of the overlap between blocks. The product of the direct FFTs of *x* and *y* are averaged over each segment to compute *Pxy*, with a scaling to correct for power loss due to windowing. If len(*x*) < *NFFT* or len(*y*) < *NFFT*, they will be zero padded to *NFFT*. *x*, *y* Array or sequence containing the data %(PSD)s Returns the tuple (*Pxy*, *freqs*). Refs: Bendat & Piersol -- Random Data: Analysis and Measurement Procedures, John Wiley & Sons (1986) """ Pxy, freqs, t = _spectral_helper(x, y, NFFT, Fs, detrend, window, noverlap, pad_to, sides, scale_by_freq) if len(Pxy.shape) == 2 and Pxy.shape[1]>1: Pxy = Pxy.mean(axis=1) return Pxy, freqs csd.__doc__ = csd.__doc__ % kwdocd def specgram(x, NFFT=256, Fs=2, detrend=detrend_none, window=window_hanning, noverlap=128, pad_to=None, sides='default', scale_by_freq=None): """ Compute a spectrogram of data in *x*. Data are split into *NFFT* length segements and the PSD of each section is computed. The windowing function *window* is applied to each segment, and the amount of overlap of each segment is specified with *noverlap*. If *x* is real (i.e. non-complex) only the spectrum of the positive frequencie is returned. If *x* is complex then the complete spectrum is returned. %(PSD)s Returns a tuple (*Pxx*, *freqs*, *t*): - *Pxx*: 2-D array, columns are the periodograms of successive segments - *freqs*: 1-D array of frequencies corresponding to the rows in Pxx - *t*: 1-D array of times corresponding to midpoints of segments. .. seealso:: :func:`psd`: :func:`psd` differs in the default overlap; in returning the mean of the segment periodograms; and in not returning times. """ assert(NFFT > noverlap) Pxx, freqs, t = _spectral_helper(x, x, NFFT, Fs, detrend, window, noverlap, pad_to, sides, scale_by_freq) Pxx = Pxx.real #Needed since helper implements generically if (np.iscomplexobj(x) and sides == 'default') or sides == 'twosided': # center the frequency range at zero freqs = np.concatenate((freqs[NFFT/2:]-Fs,freqs[:NFFT/2])) Pxx = np.concatenate((Pxx[NFFT/2:,:],Pxx[:NFFT/2,:]),0) return Pxx, freqs, t specgram.__doc__ = specgram.__doc__ % kwdocd _coh_error = """Coherence is calculated by averaging over *NFFT* length segments. Your signal is too short for your choice of *NFFT*. """ def cohere(x, y, NFFT=256, Fs=2, detrend=detrend_none, window=window_hanning, noverlap=0, pad_to=None, sides='default', scale_by_freq=None): """ The coherence between *x* and *y*. Coherence is the normalized cross spectral density: .. math:: C_{xy} = \\frac{|P_{xy}|^2}{P_{xx}P_{yy}} *x*, *y* Array or sequence containing the data %(PSD)s The return value is the tuple (*Cxy*, *f*), where *f* are the frequencies of the coherence vector. For cohere, scaling the individual densities by the sampling frequency has no effect, since the factors cancel out. .. seealso:: :func:`psd` and :func:`csd`: For information about the methods used to compute :math:`P_{xy}`, :math:`P_{xx}` and :math:`P_{yy}`. """ if len(x)<2*NFFT: raise ValueError(_coh_error) Pxx, f = psd(x, NFFT, Fs, detrend, window, noverlap, pad_to, sides, scale_by_freq) Pyy, f = psd(y, NFFT, Fs, detrend, window, noverlap, pad_to, sides, scale_by_freq) Pxy, f = csd(x, y, NFFT, Fs, detrend, window, noverlap, pad_to, sides, scale_by_freq) Cxy = np.divide(np.absolute(Pxy)**2, Pxx*Pyy) Cxy.shape = (len(f),) return Cxy, f cohere.__doc__ = cohere.__doc__ % kwdocd def corrcoef(*args): """ corrcoef(*X*) where *X* is a matrix returns a matrix of correlation coefficients for the columns of *X* corrcoef(*x*, *y*) where *x* and *y* are vectors returns the matrix of correlation coefficients for *x* and *y*. Numpy arrays can be real or complex. The correlation matrix is defined from the covariance matrix *C* as .. math:: r_{ij} = \\frac{C_{ij}}{\\sqrt{C_{ii}C_{jj}}} """ warnings.warn("Use numpy.corrcoef", DeprecationWarning) kw = dict(rowvar=False) return np.corrcoef(*args, **kw) def polyfit(*args, **kwargs): u""" polyfit(*x*, *y*, *N*) Do a best fit polynomial of order *N* of *y* to *x*. Return value is a vector of polynomial coefficients [pk ... p1 p0]. Eg, for *N*=2:: p2*x0^2 + p1*x0 + p0 = y1 p2*x1^2 + p1*x1 + p0 = y1 p2*x2^2 + p1*x2 + p0 = y2 ..... p2*xk^2 + p1*xk + p0 = yk Method: if *X* is a the Vandermonde Matrix computed from *x* (see `vandermonds <http://mathworld.wolfram.com/VandermondeMatrix.html>`_), then the polynomial least squares solution is given by the '*p*' in X*p = y where *X* is a (len(*x*) \N{MULTIPLICATION SIGN} *N* + 1) matrix, *p* is a *N*+1 length vector, and *y* is a (len(*x*) \N{MULTIPLICATION SIGN} 1) vector. This equation can be solved as .. math:: p = (X_t X)^-1 X_t y where :math:`X_t` is the transpose of *X* and -1 denotes the inverse. Numerically, however, this is not a good method, so we use :func:`numpy.linalg.lstsq`. For more info, see `least squares fitting <http://mathworld.wolfram.com/LeastSquaresFittingPolynomial.html>`_, but note that the *k*'s and *n*'s in the superscripts and subscripts on that page. The linear algebra is correct, however. .. seealso:: :func:`polyval` """ warnings.warn("use numpy.poyfit", DeprecationWarning) return np.polyfit(*args, **kwargs) def polyval(*args, **kwargs): """ *y* = polyval(*p*, *x*) *p* is a vector of polynomial coeffients and *y* is the polynomial evaluated at *x*. Example code to remove a polynomial (quadratic) trend from y:: p = polyfit(x, y, 2) trend = polyval(p, x) resid = y - trend .. seealso:: :func:`polyfit` """ warnings.warn("use numpy.polyval", DeprecationWarning) return np.polyval(*args, **kwargs) def vander(*args, **kwargs): """ *X* = vander(*x*, *N* = *None*) The Vandermonde matrix of vector *x*. The *i*-th column of *X* is the the *i*-th power of *x*. *N* is the maximum power to compute; if *N* is *None* it defaults to len(*x*). """ warnings.warn("Use numpy.vander()", DeprecationWarning) return np.vander(*args, **kwargs) def donothing_callback(*args): pass def cohere_pairs( X, ij, NFFT=256, Fs=2, detrend=detrend_none, window=window_hanning, noverlap=0, preferSpeedOverMemory=True, progressCallback=donothing_callback, returnPxx=False): u""" Cxy, Phase, freqs = cohere_pairs(X, ij, ...) Compute the coherence for all pairs in *ij*. *X* is a (*numSamples*, *numCols*) numpy array. *ij* is a list of tuples (*i*, *j*). Each tuple is a pair of indexes into the columns of *X* for which you want to compute coherence. For example, if *X* has 64 columns, and you want to compute all nonredundant pairs, define *ij* as:: ij = [] for i in range(64): for j in range(i+1,64): ij.append( (i, j) ) The other function arguments, except for *preferSpeedOverMemory* (see below), are explained in the help string of :func:`psd`. Return value is a tuple (*Cxy*, *Phase*, *freqs*). - *Cxy*: a dictionary of (*i*, *j*) tuples -> coherence vector for that pair. I.e., ``Cxy[(i,j)] = cohere(X[:,i], X[:,j])``. Number of dictionary keys is ``len(ij)``. - *Phase*: a dictionary of phases of the cross spectral density at each frequency for each pair. The keys are ``(i,j)``. - *freqs*: a vector of frequencies, equal in length to either the coherence or phase vectors for any (*i*, *j*) key.. Eg, to make a coherence Bode plot:: subplot(211) plot( freqs, Cxy[(12,19)]) subplot(212) plot( freqs, Phase[(12,19)]) For a large number of pairs, :func:`cohere_pairs` can be much more efficient than just calling :func:`cohere` for each pair, because it caches most of the intensive computations. If *N* is the number of pairs, this function is O(N) for most of the heavy lifting, whereas calling cohere for each pair is O(N\N{SUPERSCRIPT TWO}). However, because of the caching, it is also more memory intensive, making 2 additional complex arrays with approximately the same number of elements as *X*. The parameter *preferSpeedOverMemory*, if *False*, limits the caching by only making one, rather than two, complex cache arrays. This is useful if memory becomes critical. Even when *preferSpeedOverMemory* is *False*, :func:`cohere_pairs` will still give significant performace gains over calling :func:`cohere` for each pair, and will use subtantially less memory than if *preferSpeedOverMemory* is *True*. In my tests with a (43000, 64) array over all non-redundant pairs, *preferSpeedOverMemory* = *True* delivered a 33% performace boost on a 1.7GHZ Athlon with 512MB RAM compared with *preferSpeedOverMemory* = *False*. But both solutions were more than 10x faster than naievly crunching all possible pairs through cohere. .. seealso:: :file:`test/cohere_pairs_test.py` in the src tree: For an example script that shows that this :func:`cohere_pairs` and :func:`cohere` give the same results for a given pair. """ numRows, numCols = X.shape # zero pad if X is too short if numRows < NFFT: tmp = X X = np.zeros( (NFFT, numCols), X.dtype) X[:numRows,:] = tmp del tmp numRows, numCols = X.shape # get all the columns of X that we are interested in by checking # the ij tuples seen = {} for i,j in ij: seen[i]=1; seen[j] = 1 allColumns = seen.keys() Ncols = len(allColumns) del seen # for real X, ignore the negative frequencies if np.iscomplexobj(X): numFreqs = NFFT else: numFreqs = NFFT//2+1 # cache the FFT of every windowed, detrended NFFT length segement # of every channel. If preferSpeedOverMemory, cache the conjugate # as well if cbook.iterable(window): assert(len(window) == NFFT) windowVals = window else: windowVals = window(np.ones((NFFT,), typecode(X))) ind = range(0, numRows-NFFT+1, NFFT-noverlap) numSlices = len(ind) FFTSlices = {} FFTConjSlices = {} Pxx = {} slices = range(numSlices) normVal = norm(windowVals)**2 for iCol in allColumns: progressCallback(i/Ncols, 'Cacheing FFTs') Slices = np.zeros( (numSlices,numFreqs), dtype=np.complex_) for iSlice in slices: thisSlice = X[ind[iSlice]:ind[iSlice]+NFFT, iCol] thisSlice = windowVals*detrend(thisSlice) Slices[iSlice,:] = fft(thisSlice)[:numFreqs] FFTSlices[iCol] = Slices if preferSpeedOverMemory: FFTConjSlices[iCol] = conjugate(Slices) Pxx[iCol] = np.divide(np.mean(absolute(Slices)**2), normVal) del Slices, ind, windowVals # compute the coherences and phases for all pairs using the # cached FFTs Cxy = {} Phase = {} count = 0 N = len(ij) for i,j in ij: count +=1 if count%10==0: progressCallback(count/N, 'Computing coherences') if preferSpeedOverMemory: Pxy = FFTSlices[i] * FFTConjSlices[j] else: Pxy = FFTSlices[i] * np.conjugate(FFTSlices[j]) if numSlices>1: Pxy = np.mean(Pxy) Pxy = np.divide(Pxy, normVal) Cxy[(i,j)] = np.divide(np.absolute(Pxy)**2, Pxx[i]*Pxx[j]) Phase[(i,j)] = np.arctan2(Pxy.imag, Pxy.real) freqs = Fs/NFFT*np.arange(numFreqs) if returnPxx: return Cxy, Phase, freqs, Pxx else: return Cxy, Phase, freqs def entropy(y, bins): r""" Return the entropy of the data in *y*. .. math:: \sum p_i \log_2(p_i) where :math:`p_i` is the probability of observing *y* in the :math:`i^{th}` bin of *bins*. *bins* can be a number of bins or a range of bins; see :func:`numpy.histogram`. Compare *S* with analytic calculation for a Gaussian:: x = mu + sigma * randn(200000) Sanalytic = 0.5 * ( 1.0 + log(2*pi*sigma**2.0) ) """ n,bins = np.histogram(y, bins) n = n.astype(np.float_) n = np.take(n, np.nonzero(n)[0]) # get the positive p = np.divide(n, len(y)) delta = bins[1]-bins[0] S = -1.0*np.sum(p*log(p)) + log(delta) #S = -1.0*np.sum(p*log(p)) return S def hist(y, bins=10, normed=0): """ Return the histogram of *y* with *bins* equally sized bins. If bins is an array, use those bins. Return value is (*n*, *x*) where *n* is the count for each bin in *x*. If *normed* is *False*, return the counts in the first element of the returned tuple. If *normed* is *True*, return the probability density :math:`\\frac{n}{(len(y)\mathrm{dbin}}`. If *y* has rank > 1, it will be raveled. If *y* is masked, only the unmasked values will be used. Credits: the Numeric 22 documentation """ warnings.warn("Use numpy.histogram()", DeprecationWarning) return np.histogram(y, bins=bins, range=None, normed=normed) def normpdf(x, *args): "Return the normal pdf evaluated at *x*; args provides *mu*, *sigma*" mu, sigma = args return 1./(np.sqrt(2*np.pi)*sigma)*np.exp(-0.5 * (1./sigma*(x - mu))**2) def levypdf(x, gamma, alpha): "Returm the levy pdf evaluated at *x* for params *gamma*, *alpha*" N = len(x) if N%2 != 0: raise ValueError, 'x must be an event length array; try\n' + \ 'x = np.linspace(minx, maxx, N), where N is even' dx = x[1]-x[0] f = 1/(N*dx)*np.arange(-N/2, N/2, np.float_) ind = np.concatenate([np.arange(N/2, N, int), np.arange(0, N/2, int)]) df = f[1]-f[0] cfl = exp(-gamma*np.absolute(2*pi*f)**alpha) px = np.fft.fft(np.take(cfl,ind)*df).astype(np.float_) return np.take(px, ind) def find(condition): "Return the indices where ravel(condition) is true" res, = np.nonzero(np.ravel(condition)) return res def trapz(x, y): """ Trapezoidal integral of *y*(*x*). """ warnings.warn("Use numpy.trapz(y,x) instead of trapz(x,y)", DeprecationWarning) return np.trapz(y, x) #if len(x)!=len(y): # raise ValueError, 'x and y must have the same length' #if len(x)<2: # raise ValueError, 'x and y must have > 1 element' #return np.sum(0.5*np.diff(x)*(y[1:]+y[:-1])) def longest_contiguous_ones(x): """ Return the indices of the longest stretch of contiguous ones in *x*, assuming *x* is a vector of zeros and ones. If there are two equally long stretches, pick the first. """ x = np.ravel(x) if len(x)==0: return np.array([]) ind = (x==0).nonzero()[0] if len(ind)==0: return np.arange(len(x)) if len(ind)==len(x): return np.array([]) y = np.zeros( (len(x)+2,), x.dtype) y[1:-1] = x dif = np.diff(y) up = (dif == 1).nonzero()[0]; dn = (dif == -1).nonzero()[0]; i = (dn-up == max(dn - up)).nonzero()[0][0] ind = np.arange(up[i], dn[i]) return ind def longest_ones(x): '''alias for longest_contiguous_ones''' return longest_contiguous_ones(x) def prepca(P, frac=0): """ Compute the principal components of *P*. *P* is a (*numVars*, *numObs*) array. *frac* is the minimum fraction of variance that a component must contain to be included. Return value is a tuple of the form (*Pcomponents*, *Trans*, *fracVar*) where: - *Pcomponents* : a (numVars, numObs) array - *Trans* : the weights matrix, ie, *Pcomponents* = *Trans* * *P* - *fracVar* : the fraction of the variance accounted for by each component returned A similar function of the same name was in the Matlab (TM) R13 Neural Network Toolbox but is not found in later versions; its successor seems to be called "processpcs". """ U,s,v = np.linalg.svd(P) varEach = s**2/P.shape[1] totVar = varEach.sum() fracVar = varEach/totVar ind = slice((fracVar>=frac).sum()) # select the components that are greater Trans = U[:,ind].transpose() # The transformed data Pcomponents = np.dot(Trans,P) return Pcomponents, Trans, fracVar[ind] def prctile(x, p = (0.0, 25.0, 50.0, 75.0, 100.0)): """ Return the percentiles of *x*. *p* can either be a sequence of percentile values or a scalar. If *p* is a sequence, the ith element of the return sequence is the *p*(i)-th percentile of *x*. If *p* is a scalar, the largest value of *x* less than or equal to the *p* percentage point in the sequence is returned. """ x = np.array(x).ravel() # we need a copy x.sort() Nx = len(x) if not cbook.iterable(p): return x[int(p*Nx/100.0)] p = np.asarray(p)* Nx/100.0 ind = p.astype(int) ind = np.where(ind>=Nx, Nx-1, ind) return x.take(ind) def prctile_rank(x, p): """ Return the rank for each element in *x*, return the rank 0..len(*p*). Eg if *p* = (25, 50, 75), the return value will be a len(*x*) array with values in [0,1,2,3] where 0 indicates the value is less than the 25th percentile, 1 indicates the value is >= the 25th and < 50th percentile, ... and 3 indicates the value is above the 75th percentile cutoff. *p* is either an array of percentiles in [0..100] or a scalar which indicates how many quantiles of data you want ranked. """ if not cbook.iterable(p): p = np.arange(100.0/p, 100.0, 100.0/p) else: p = np.asarray(p) if p.max()<=1 or p.min()<0 or p.max()>100: raise ValueError('percentiles should be in range 0..100, not 0..1') ptiles = prctile(x, p) return np.searchsorted(ptiles, x) def center_matrix(M, dim=0): """ Return the matrix *M* with each row having zero mean and unit std. If *dim* = 1 operate on columns instead of rows. (*dim* is opposite to the numpy axis kwarg.) """ M = np.asarray(M, np.float_) if dim: M = (M - M.mean(axis=0)) / M.std(axis=0) else: M = (M - M.mean(axis=1)[:,np.newaxis]) M = M / M.std(axis=1)[:,np.newaxis] return M def rk4(derivs, y0, t): """ Integrate 1D or ND system of ODEs using 4-th order Runge-Kutta. This is a toy implementation which may be useful if you find yourself stranded on a system w/o scipy. Otherwise use :func:`scipy.integrate`. *y0* initial state vector *t* sample times *derivs* returns the derivative of the system and has the signature ``dy = derivs(yi, ti)`` Example 1 :: ## 2D system def derivs6(x,t): d1 = x[0] + 2*x[1] d2 = -3*x[0] + 4*x[1] return (d1, d2) dt = 0.0005 t = arange(0.0, 2.0, dt) y0 = (1,2) yout = rk4(derivs6, y0, t) Example 2:: ## 1D system alpha = 2 def derivs(x,t): return -alpha*x + exp(-t) y0 = 1 yout = rk4(derivs, y0, t) If you have access to scipy, you should probably be using the scipy.integrate tools rather than this function. """ try: Ny = len(y0) except TypeError: yout = np.zeros( (len(t),), np.float_) else: yout = np.zeros( (len(t), Ny), np.float_) yout[0] = y0 i = 0 for i in np.arange(len(t)-1): thist = t[i] dt = t[i+1] - thist dt2 = dt/2.0 y0 = yout[i] k1 = np.asarray(derivs(y0, thist)) k2 = np.asarray(derivs(y0 + dt2*k1, thist+dt2)) k3 = np.asarray(derivs(y0 + dt2*k2, thist+dt2)) k4 = np.asarray(derivs(y0 + dt*k3, thist+dt)) yout[i+1] = y0 + dt/6.0*(k1 + 2*k2 + 2*k3 + k4) return yout def bivariate_normal(X, Y, sigmax=1.0, sigmay=1.0, mux=0.0, muy=0.0, sigmaxy=0.0): """ Bivariate Gaussian distribution for equal shape *X*, *Y*. See `bivariate normal <http://mathworld.wolfram.com/BivariateNormalDistribution.html>`_ at mathworld. """ Xmu = X-mux Ymu = Y-muy rho = sigmaxy/(sigmax*sigmay) z = Xmu**2/sigmax**2 + Ymu**2/sigmay**2 - 2*rho*Xmu*Ymu/(sigmax*sigmay) denom = 2*np.pi*sigmax*sigmay*np.sqrt(1-rho**2) return np.exp( -z/(2*(1-rho**2))) / denom def get_xyz_where(Z, Cond): """ *Z* and *Cond* are *M* x *N* matrices. *Z* are data and *Cond* is a boolean matrix where some condition is satisfied. Return value is (*x*, *y*, *z*) where *x* and *y* are the indices into *Z* and *z* are the values of *Z* at those indices. *x*, *y*, and *z* are 1D arrays. """ X,Y = np.indices(Z.shape) return X[Cond], Y[Cond], Z[Cond] def get_sparse_matrix(M,N,frac=0.1): """ Return a *M* x *N* sparse matrix with *frac* elements randomly filled. """ data = np.zeros((M,N))*0. for i in range(int(M*N*frac)): x = np.random.randint(0,M-1) y = np.random.randint(0,N-1) data[x,y] = np.random.rand() return data def dist(x,y): """ Return the distance between two points. """ d = x-y return np.sqrt(np.dot(d,d)) def dist_point_to_segment(p, s0, s1): """ Get the distance of a point to a segment. *p*, *s0*, *s1* are *xy* sequences This algorithm from http://softsurfer.com/Archive/algorithm_0102/algorithm_0102.htm#Distance%20to%20Ray%20or%20Segment """ p = np.asarray(p, np.float_) s0 = np.asarray(s0, np.float_) s1 = np.asarray(s1, np.float_) v = s1 - s0 w = p - s0 c1 = np.dot(w,v); if ( c1 <= 0 ): return dist(p, s0); c2 = np.dot(v,v) if ( c2 <= c1 ): return dist(p, s1); b = c1 / c2 pb = s0 + b * v; return dist(p, pb) def segments_intersect(s1, s2): """ Return *True* if *s1* and *s2* intersect. *s1* and *s2* are defined as:: s1: (x1, y1), (x2, y2) s2: (x3, y3), (x4, y4) """ (x1, y1), (x2, y2) = s1 (x3, y3), (x4, y4) = s2 den = ((y4-y3) * (x2-x1)) - ((x4-x3)*(y2-y1)) n1 = ((x4-x3) * (y1-y3)) - ((y4-y3)*(x1-x3)) n2 = ((x2-x1) * (y1-y3)) - ((y2-y1)*(x1-x3)) if den == 0: # lines parallel return False u1 = n1/den u2 = n2/den return 0.0 <= u1 <= 1.0 and 0.0 <= u2 <= 1.0 def fftsurr(x, detrend=detrend_none, window=window_none): """ Compute an FFT phase randomized surrogate of *x*. """ if cbook.iterable(window): x=window*detrend(x) else: x = window(detrend(x)) z = np.fft.fft(x) a = 2.*np.pi*1j phase = a * np.random.rand(len(x)) z = z*np.exp(phase) return np.fft.ifft(z).real def liaupunov(x, fprime): """ *x* is a very long trajectory from a map, and *fprime* returns the derivative of *x*. Returns : .. math:: \lambda = \\frac{1}{n}\\sum \\ln|f^'(x_i)| .. seealso:: Sec 10.5 Strogatz (1994) "Nonlinear Dynamics and Chaos". `Wikipedia article on Lyapunov Exponent <http://en.wikipedia.org/wiki/Lyapunov_exponent>`_. .. note:: What the function here calculates may not be what you really want; *caveat emptor*. It also seems that this function's name is badly misspelled. """ return np.mean(np.log(np.absolute(fprime(x)))) class FIFOBuffer: """ A FIFO queue to hold incoming *x*, *y* data in a rotating buffer using numpy arrays under the hood. It is assumed that you will call asarrays much less frequently than you add data to the queue -- otherwise another data structure will be faster. This can be used to support plots where data is added from a real time feed and the plot object wants to grab data from the buffer and plot it to screen less freqeuently than the incoming. If you set the *dataLim* attr to :class:`~matplotlib.transforms.BBox` (eg :attr:`matplotlib.Axes.dataLim`), the *dataLim* will be updated as new data come in. TODO: add a grow method that will extend nmax .. note:: mlab seems like the wrong place for this class. """ def __init__(self, nmax): """ Buffer up to *nmax* points. """ self._xa = np.zeros((nmax,), np.float_) self._ya = np.zeros((nmax,), np.float_) self._xs = np.zeros((nmax,), np.float_) self._ys = np.zeros((nmax,), np.float_) self._ind = 0 self._nmax = nmax self.dataLim = None self.callbackd = {} def register(self, func, N): """ Call *func* every time *N* events are passed; *func* signature is ``func(fifo)``. """ self.callbackd.setdefault(N, []).append(func) def add(self, x, y): """ Add scalar *x* and *y* to the queue. """ if self.dataLim is not None: xys = ((x,y),) self.dataLim.update(xys, -1) #-1 means use the default ignore setting ind = self._ind % self._nmax #print 'adding to fifo:', ind, x, y self._xs[ind] = x self._ys[ind] = y for N,funcs in self.callbackd.items(): if (self._ind%N)==0: for func in funcs: func(self) self._ind += 1 def last(self): """ Get the last *x*, *y* or *None*. *None* if no data set. """ if self._ind==0: return None, None ind = (self._ind-1) % self._nmax return self._xs[ind], self._ys[ind] def asarrays(self): """ Return *x* and *y* as arrays; their length will be the len of data added or *nmax*. """ if self._ind<self._nmax: return self._xs[:self._ind], self._ys[:self._ind] ind = self._ind % self._nmax self._xa[:self._nmax-ind] = self._xs[ind:] self._xa[self._nmax-ind:] = self._xs[:ind] self._ya[:self._nmax-ind] = self._ys[ind:] self._ya[self._nmax-ind:] = self._ys[:ind] return self._xa, self._ya def update_datalim_to_current(self): """ Update the *datalim* in the current data in the fifo. """ if self.dataLim is None: raise ValueError('You must first set the dataLim attr') x, y = self.asarrays() self.dataLim.update_numerix(x, y, True) def movavg(x,n): """ Compute the len(*n*) moving average of *x*. """ w = np.empty((n,), dtype=np.float_) w[:] = 1.0/n return np.convolve(x, w, mode='valid') def save(fname, X, fmt='%.18e',delimiter=' '): """ Save the data in *X* to file *fname* using *fmt* string to convert the data to strings. *fname* can be a filename or a file handle. If the filename ends in '.gz', the file is automatically saved in compressed gzip format. The :func:`load` function understands gzipped files transparently. Example usage:: save('test.out', X) # X is an array save('test1.out', (x,y,z)) # x,y,z equal sized 1D arrays save('test2.out', x) # x is 1D save('test3.out', x, fmt='%1.4e') # use exponential notation *delimiter* is used to separate the fields, eg. *delimiter* ',' for comma-separated values. """ if cbook.is_string_like(fname): if fname.endswith('.gz'): import gzip fh = gzip.open(fname,'wb') else: fh = file(fname,'w') elif hasattr(fname, 'seek'): fh = fname else: raise ValueError('fname must be a string or file handle') X = np.asarray(X) origShape = None if X.ndim == 1: origShape = X.shape X.shape = len(X), 1 for row in X: fh.write(delimiter.join([fmt%val for val in row]) + '\n') if origShape is not None: X.shape = origShape def load(fname,comments='#',delimiter=None, converters=None,skiprows=0, usecols=None, unpack=False, dtype=np.float_): """ Load ASCII data from *fname* into an array and return the array. The data must be regular, same number of values in every row *fname* can be a filename or a file handle. Support for gzipped files is automatic, if the filename ends in '.gz'. matfile data is not supported; for that, use :mod:`scipy.io.mio` module. Example usage:: X = load('test.dat') # data in two columns t = X[:,0] y = X[:,1] Alternatively, you can do the same with "unpack"; see below:: X = load('test.dat') # a matrix of data x = load('test.dat') # a single column of data - *comments*: the character used to indicate the start of a comment in the file - *delimiter* is a string-like character used to seperate values in the file. If *delimiter* is unspecified or *None*, any whitespace string is a separator. - *converters*, if not *None*, is a dictionary mapping column number to a function that will convert that column to a float (or the optional *dtype* if specified). Eg, if column 0 is a date string:: converters = {0:datestr2num} - *skiprows* is the number of rows from the top to skip. - *usecols*, if not *None*, is a sequence of integer column indexes to extract where 0 is the first column, eg ``usecols=[1,4,5]`` to extract just the 2nd, 5th and 6th columns - *unpack*, if *True*, will transpose the matrix allowing you to unpack into named arguments on the left hand side:: t,y = load('test.dat', unpack=True) # for two column data x,y,z = load('somefile.dat', usecols=[3,5,7], unpack=True) - *dtype*: the array will have this dtype. default: ``numpy.float_`` .. seealso:: See :file:`examples/pylab_examples/load_converter.py` in the source tree: Exercises many of these options. """ if converters is None: converters = {} fh = cbook.to_filehandle(fname) X = [] if delimiter==' ': # space splitting is a special case since x.split() is what # you want, not x.split(' ') def splitfunc(x): return x.split() else: def splitfunc(x): return x.split(delimiter) converterseq = None for i,line in enumerate(fh): if i<skiprows: continue line = line.split(comments, 1)[0].strip() if not len(line): continue if converterseq is None: converterseq = [converters.get(j,float) for j,val in enumerate(splitfunc(line))] if usecols is not None: vals = splitfunc(line) row = [converterseq[j](vals[j]) for j in usecols] else: row = [converterseq[j](val) for j,val in enumerate(splitfunc(line))] thisLen = len(row) X.append(row) X = np.array(X, dtype) r,c = X.shape if r==1 or c==1: X.shape = max(r,c), if unpack: return X.transpose() else: return X def slopes(x,y): """ SLOPES calculate the slope y'(x) Given data vectors X and Y SLOPES calculates Y'(X), i.e the slope of a curve Y(X). The slope is estimated using the slope obtained from that of a parabola through any three consecutive points. This method should be superior to that described in the appendix of A CONSISTENTLY WELL BEHAVED METHOD OF INTERPOLATION by Russel W. Stineman (Creative Computing July 1980) in at least one aspect: Circles for interpolation demand a known aspect ratio between x- and y-values. For many functions, however, the abscissa are given in different dimensions, so an aspect ratio is completely arbitrary. The parabola method gives very similar results to the circle method for most regular cases but behaves much better in special cases Norbert Nemec, Institute of Theoretical Physics, University or Regensburg, April 2006 Norbert.Nemec at physik.uni-regensburg.de (inspired by a original implementation by Halldor Bjornsson, Icelandic Meteorological Office, March 2006 halldor at vedur.is) """ # Cast key variables as float. x=np.asarray(x, np.float_) y=np.asarray(y, np.float_) yp=np.zeros(y.shape, np.float_) dx=x[1:] - x[:-1] dy=y[1:] - y[:-1] dydx = dy/dx yp[1:-1] = (dydx[:-1] * dx[1:] + dydx[1:] * dx[:-1])/(dx[1:] + dx[:-1]) yp[0] = 2.0 * dy[0]/dx[0] - yp[1] yp[-1] = 2.0 * dy[-1]/dx[-1] - yp[-2] return yp def stineman_interp(xi,x,y,yp=None): """ STINEMAN_INTERP Well behaved data interpolation. Given data vectors X and Y, the slope vector YP and a new abscissa vector XI the function stineman_interp(xi,x,y,yp) uses Stineman interpolation to calculate a vector YI corresponding to XI. Here's an example that generates a coarse sine curve, then interpolates over a finer abscissa: x = linspace(0,2*pi,20); y = sin(x); yp = cos(x) xi = linspace(0,2*pi,40); yi = stineman_interp(xi,x,y,yp); plot(x,y,'o',xi,yi) The interpolation method is described in the article A CONSISTENTLY WELL BEHAVED METHOD OF INTERPOLATION by Russell W. Stineman. The article appeared in the July 1980 issue of Creative Computing with a note from the editor stating that while they were not an academic journal but once in a while something serious and original comes in adding that this was "apparently a real solution" to a well known problem. For yp=None, the routine automatically determines the slopes using the "slopes" routine. X is assumed to be sorted in increasing order For values xi[j] < x[0] or xi[j] > x[-1], the routine tries a extrapolation. The relevance of the data obtained from this, of course, questionable... original implementation by Halldor Bjornsson, Icelandic Meteorolocial Office, March 2006 halldor at vedur.is completely reworked and optimized for Python by Norbert Nemec, Institute of Theoretical Physics, University or Regensburg, April 2006 Norbert.Nemec at physik.uni-regensburg.de """ # Cast key variables as float. x=np.asarray(x, np.float_) y=np.asarray(y, np.float_) assert x.shape == y.shape N=len(y) if yp is None: yp = slopes(x,y) else: yp=np.asarray(yp, np.float_) xi=np.asarray(xi, np.float_) yi=np.zeros(xi.shape, np.float_) # calculate linear slopes dx = x[1:] - x[:-1] dy = y[1:] - y[:-1] s = dy/dx #note length of s is N-1 so last element is #N-2 # find the segment each xi is in # this line actually is the key to the efficiency of this implementation idx = np.searchsorted(x[1:-1], xi) # now we have generally: x[idx[j]] <= xi[j] <= x[idx[j]+1] # except at the boundaries, where it may be that xi[j] < x[0] or xi[j] > x[-1] # the y-values that would come out from a linear interpolation: sidx = s.take(idx) xidx = x.take(idx) yidx = y.take(idx) xidxp1 = x.take(idx+1) yo = yidx + sidx * (xi - xidx) # the difference that comes when using the slopes given in yp dy1 = (yp.take(idx)- sidx) * (xi - xidx) # using the yp slope of the left point dy2 = (yp.take(idx+1)-sidx) * (xi - xidxp1) # using the yp slope of the right point dy1dy2 = dy1*dy2 # The following is optimized for Python. The solution actually # does more calculations than necessary but exploiting the power # of numpy, this is far more efficient than coding a loop by hand # in Python yi = yo + dy1dy2 * np.choose(np.array(np.sign(dy1dy2), np.int32)+1, ((2*xi-xidx-xidxp1)/((dy1-dy2)*(xidxp1-xidx)), 0.0, 1/(dy1+dy2),)) return yi def inside_poly(points, verts): """ points is a sequence of x,y points verts is a sequence of x,y vertices of a poygon return value is a sequence of indices into points for the points that are inside the polygon """ res, = np.nonzero(nxutils.points_inside_poly(points, verts)) return res def poly_below(ymin, xs, ys): """ given a arrays *xs* and *ys*, return the vertices of a polygon that has a scalar lower bound *ymin* and an upper bound at the *ys*. intended for use with Axes.fill, eg:: xv, yv = poly_below(0, x, y) ax.fill(xv, yv) """ return poly_between(xs, ys, xmin) def poly_between(x, ylower, yupper): """ given a sequence of x, ylower and yupper, return the polygon that fills the regions between them. ylower or yupper can be scalar or iterable. If they are iterable, they must be equal in length to x return value is x, y arrays for use with Axes.fill """ Nx = len(x) if not cbook.iterable(ylower): ylower = ylower*np.ones(Nx) if not cbook.iterable(yupper): yupper = yupper*np.ones(Nx) x = np.concatenate( (x, x[::-1]) ) y = np.concatenate( (yupper, ylower[::-1]) ) return x,y ### the following code was written and submitted by Fernando Perez ### from the ipython numutils package under a BSD license # begin fperez functions """ A set of convenient utilities for numerical work. Most of this module requires numpy or is meant to be used with it. Copyright (c) 2001-2004, Fernando Perez. <Fernando.Perez@colorado.edu> All rights reserved. This license was generated from the BSD license template as found in: http://www.opensource.org/licenses/bsd-license.php Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the IPython project nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. """ import operator import math #***************************************************************************** # Globals #**************************************************************************** # function definitions exp_safe_MIN = math.log(2.2250738585072014e-308) exp_safe_MAX = 1.7976931348623157e+308 def exp_safe(x): """ Compute exponentials which safely underflow to zero. Slow, but convenient to use. Note that numpy provides proper floating point exception handling with access to the underlying hardware. """ if type(x) is np.ndarray: return exp(np.clip(x,exp_safe_MIN,exp_safe_MAX)) else: return math.exp(x) def amap(fn,*args): """ amap(function, sequence[, sequence, ...]) -> array. Works like :func:`map`, but it returns an array. This is just a convenient shorthand for ``numpy.array(map(...))``. """ return np.array(map(fn,*args)) #from numpy import zeros_like def zeros_like(a): """ Return an array of zeros of the shape and typecode of *a*. """ warnings.warn("Use numpy.zeros_like(a)", DeprecationWarning) return np.zeros_like(a) #from numpy import sum as sum_flat def sum_flat(a): """ Return the sum of all the elements of *a*, flattened out. It uses ``a.flat``, and if *a* is not contiguous, a call to ``ravel(a)`` is made. """ warnings.warn("Use numpy.sum(a) or a.sum()", DeprecationWarning) return np.sum(a) #from numpy import mean as mean_flat def mean_flat(a): """ Return the mean of all the elements of *a*, flattened out. """ warnings.warn("Use numpy.mean(a) or a.mean()", DeprecationWarning) return np.mean(a) def rms_flat(a): """ Return the root mean square of all the elements of *a*, flattened out. """ return np.sqrt(np.mean(np.absolute(a)**2)) def l1norm(a): """ Return the *l1* norm of *a*, flattened out. Implemented as a separate function (not a call to :func:`norm` for speed). """ return np.sum(np.absolute(a)) def l2norm(a): """ Return the *l2* norm of *a*, flattened out. Implemented as a separate function (not a call to :func:`norm` for speed). """ return np.sqrt(np.sum(np.absolute(a)**2)) def norm_flat(a,p=2): """ norm(a,p=2) -> l-p norm of a.flat Return the l-p norm of *a*, considered as a flat array. This is NOT a true matrix norm, since arrays of arbitrary rank are always flattened. *p* can be a number or the string 'Infinity' to get the L-infinity norm. """ # This function was being masked by a more general norm later in # the file. We may want to simply delete it. if p=='Infinity': return np.amax(np.absolute(a)) else: return (np.sum(np.absolute(a)**p))**(1.0/p) def frange(xini,xfin=None,delta=None,**kw): """ frange([start,] stop[, step, keywords]) -> array of floats Return a numpy ndarray containing a progression of floats. Similar to :func:`numpy.arange`, but defaults to a closed interval. ``frange(x0, x1)`` returns ``[x0, x0+1, x0+2, ..., x1]``; *start* defaults to 0, and the endpoint *is included*. This behavior is different from that of :func:`range` and :func:`numpy.arange`. This is deliberate, since :func:`frange` will probably be more useful for generating lists of points for function evaluation, and endpoints are often desired in this use. The usual behavior of :func:`range` can be obtained by setting the keyword *closed* = 0, in this case, :func:`frange` basically becomes :func:numpy.arange`. When *step* is given, it specifies the increment (or decrement). All arguments can be floating point numbers. ``frange(x0,x1,d)`` returns ``[x0,x0+d,x0+2d,...,xfin]`` where *xfin* <= *x1*. :func:`frange` can also be called with the keyword *npts*. This sets the number of points the list should contain (and overrides the value *step* might have been given). :func:`numpy.arange` doesn't offer this option. Examples:: >>> frange(3) array([ 0., 1., 2., 3.]) >>> frange(3,closed=0) array([ 0., 1., 2.]) >>> frange(1,6,2) array([1, 3, 5]) or 1,3,5,7, depending on floating point vagueries >>> frange(1,6.5,npts=5) array([ 1. , 2.375, 3.75 , 5.125, 6.5 ]) """ #defaults kw.setdefault('closed',1) endpoint = kw['closed'] != 0 # funny logic to allow the *first* argument to be optional (like range()) # This was modified with a simpler version from a similar frange() found # at http://aspn.activestate.com/ASPN/Cookbook/Python/Recipe/66472 if xfin == None: xfin = xini + 0.0 xini = 0.0 if delta == None: delta = 1.0 # compute # of points, spacing and return final list try: npts=kw['npts'] delta=(xfin-xini)/float(npts-endpoint) except KeyError: npts = int(round((xfin-xini)/delta)) + endpoint #npts = int(floor((xfin-xini)/delta)*(1.0+1e-10)) + endpoint # round finds the nearest, so the endpoint can be up to # delta/2 larger than xfin. return np.arange(npts)*delta+xini # end frange() #import numpy.diag as diagonal_matrix def diagonal_matrix(diag): """ Return square diagonal matrix whose non-zero elements are given by the input array. """ warnings.warn("Use numpy.diag(d)", DeprecationWarning) return np.diag(diag) def identity(n, rank=2, dtype='l', typecode=None): """ Returns the identity matrix of shape (*n*, *n*, ..., *n*) (rank *r*). For ranks higher than 2, this object is simply a multi-index Kronecker delta:: / 1 if i0=i1=...=iR, id[i0,i1,...,iR] = -| \ 0 otherwise. Optionally a *dtype* (or typecode) may be given (it defaults to 'l'). Since rank defaults to 2, this function behaves in the default case (when only *n* is given) like ``numpy.identity(n)`` -- but surprisingly, it is much faster. """ if typecode is not None: warnings.warn("Use dtype kwarg instead of typecode", DeprecationWarning) dtype = typecode iden = np.zeros((n,)*rank, dtype) for i in range(n): idx = (i,)*rank iden[idx] = 1 return iden def base_repr (number, base = 2, padding = 0): """ Return the representation of a *number* in any given *base*. """ chars = '0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZ' if number < base: \ return (padding - 1) * chars [0] + chars [int (number)] max_exponent = int (math.log (number)/math.log (base)) max_power = long (base) ** max_exponent lead_digit = int (number/max_power) return chars [lead_digit] + \ base_repr (number - max_power * lead_digit, base, \ max (padding - 1, max_exponent)) def binary_repr(number, max_length = 1025): """ Return the binary representation of the input *number* as a string. This is more efficient than using :func:`base_repr` with base 2. Increase the value of max_length for very large numbers. Note that on 32-bit machines, 2**1023 is the largest integer power of 2 which can be converted to a Python float. """ #assert number < 2L << max_length shifts = map (operator.rshift, max_length * [number], \ range (max_length - 1, -1, -1)) digits = map (operator.mod, shifts, max_length * [2]) if not digits.count (1): return 0 digits = digits [digits.index (1):] return ''.join (map (repr, digits)).replace('L','') def log2(x,ln2 = math.log(2.0)): """ Return the log(*x*) in base 2. This is a _slow_ function but which is guaranteed to return the correct integer value if the input is an integer exact power of 2. """ try: bin_n = binary_repr(x)[1:] except (AssertionError,TypeError): return math.log(x)/ln2 else: if '1' in bin_n: return math.log(x)/ln2 else: return len(bin_n) def ispower2(n): """ Returns the log base 2 of *n* if *n* is a power of 2, zero otherwise. Note the potential ambiguity if *n* == 1: 2**0 == 1, interpret accordingly. """ bin_n = binary_repr(n)[1:] if '1' in bin_n: return 0 else: return len(bin_n) def isvector(X): """ Like the Matlab (TM) function with the same name, returns *True* if the supplied numpy array or matrix *X* looks like a vector, meaning it has a one non-singleton axis (i.e., it can have multiple axes, but all must have length 1, except for one of them). If you just want to see if the array has 1 axis, use X.ndim == 1. """ return np.prod(X.shape)==np.max(X.shape) #from numpy import fromfunction as fromfunction_kw def fromfunction_kw(function, dimensions, **kwargs): """ Drop-in replacement for :func:`numpy.fromfunction`. Allows passing keyword arguments to the desired function. Call it as (keywords are optional):: fromfunction_kw(MyFunction, dimensions, keywords) The function ``MyFunction`` is responsible for handling the dictionary of keywords it will receive. """ warnings.warn("Use numpy.fromfunction()", DeprecationWarning) return np.fromfunction(function, dimensions, **kwargs) ### end fperez numutils code def rem(x,y): """ Deprecated - see :func:`numpy.remainder` """ raise NotImplementedError('Deprecated - see numpy.remainder') def norm(x,y=2): """ Deprecated - see :func:`numpy.linalg.norm` """ raise NotImplementedError('Deprecated - see numpy.linalg.norm') def orth(A): """ Deprecated - needs clean room implementation """ raise NotImplementedError('Deprecated - needs clean room implementation') def rank(x): """ Deprecated - see :func:`numpy.rank` """ raise NotImplementedError('Deprecated - see numpy.rank') def sqrtm(x): """ Deprecated - needs clean room implementation """ raise NotImplementedError('Deprecated - see scipy.linalg.sqrtm') def mfuncC(f, x): """ Deprecated """ raise NotImplementedError('Deprecated - needs clean room implementation') def approx_real(x): """ Deprecated - needs clean room implementation """ raise NotImplementedError('Deprecated - needs clean room implementation') #helpers for loading, saving, manipulating and viewing numpy record arrays def safe_isnan(x): ':func:`numpy.isnan` for arbitrary types' if cbook.is_string_like(x): return False try: b = np.isnan(x) except NotImplementedError: return False except TypeError: return False else: return b def safe_isinf(x): ':func:`numpy.isinf` for arbitrary types' if cbook.is_string_like(x): return False try: b = np.isinf(x) except NotImplementedError: return False except TypeError: return False else: return b def rec_view(rec): """ Return a view of an ndarray as a recarray .. seealso:: http://projects.scipy.org/pipermail/numpy-discussion/2008-August/036429.html """ return rec.view(np.recarray) #return rec.view(dtype=(np.record, rec.dtype), type=np.recarray) def rec_append_field(rec, name, arr, dtype=None): """ Return a new record array with field name populated with data from array *arr*. This function is Deprecated. Please use :func:`rec_append_fields`. """ warnings.warn("use rec_append_fields", DeprecationWarning) return rec_append_fields(rec, name, arr, dtype) def rec_append_fields(rec, names, arrs, dtypes=None): """ Return a new record array with field names populated with data from arrays in *arrs*. If appending a single field, then *names*, *arrs* and *dtypes* do not have to be lists. They can just be the values themselves. """ if (not cbook.is_string_like(names) and cbook.iterable(names) \ and len(names) and cbook.is_string_like(names[0])): if len(names) != len(arrs): raise ValueError, "number of arrays do not match number of names" else: # we have only 1 name and 1 array names = [names] arrs = [arrs] arrs = map(np.asarray, arrs) if dtypes is None: dtypes = [a.dtype for a in arrs] elif not cbook.iterable(dtypes): dtypes = [dtypes] if len(arrs) != len(dtypes): if len(dtypes) == 1: dtypes = dtypes * len(arrs) else: raise ValueError, "dtypes must be None, a single dtype or a list" newdtype = np.dtype(rec.dtype.descr + zip(names, dtypes)) newrec = np.empty(rec.shape, dtype=newdtype) for field in rec.dtype.fields: newrec[field] = rec[field] for name, arr in zip(names, arrs): newrec[name] = arr return rec_view(newrec) def rec_drop_fields(rec, names): """ Return a new numpy record array with fields in *names* dropped. """ names = set(names) Nr = len(rec) newdtype = np.dtype([(name, rec.dtype[name]) for name in rec.dtype.names if name not in names]) newrec = np.empty(Nr, dtype=newdtype) for field in newdtype.names: newrec[field] = rec[field] return rec_view(newrec) def rec_groupby(r, groupby, stats): """ *r* is a numpy record array *groupby* is a sequence of record array attribute names that together form the grouping key. eg ('date', 'productcode') *stats* is a sequence of (*attr*, *func*, *outname*) tuples which will call ``x = func(attr)`` and assign *x* to the record array output with attribute *outname*. For example:: stats = ( ('sales', len, 'numsales'), ('sales', np.mean, 'avgsale') ) Return record array has *dtype* names for each attribute name in the the *groupby* argument, with the associated group values, and for each outname name in the *stats* argument, with the associated stat summary output. """ # build a dictionary from groupby keys-> list of indices into r with # those keys rowd = dict() for i, row in enumerate(r): key = tuple([row[attr] for attr in groupby]) rowd.setdefault(key, []).append(i) # sort the output by groupby keys keys = rowd.keys() keys.sort() rows = [] for key in keys: row = list(key) # get the indices for this groupby key ind = rowd[key] thisr = r[ind] # call each stat function for this groupby slice row.extend([func(thisr[attr]) for attr, func, outname in stats]) rows.append(row) # build the output record array with groupby and outname attributes attrs, funcs, outnames = zip(*stats) names = list(groupby) names.extend(outnames) return np.rec.fromrecords(rows, names=names) def rec_summarize(r, summaryfuncs): """ *r* is a numpy record array *summaryfuncs* is a list of (*attr*, *func*, *outname*) tuples which will apply *func* to the the array *r*[attr] and assign the output to a new attribute name *outname*. The returned record array is identical to *r*, with extra arrays for each element in *summaryfuncs*. """ names = list(r.dtype.names) arrays = [r[name] for name in names] for attr, func, outname in summaryfuncs: names.append(outname) arrays.append(np.asarray(func(r[attr]))) return np.rec.fromarrays(arrays, names=names) def rec_join(key, r1, r2, jointype='inner', defaults=None, r1postfix='1', r2postfix='2'): """ Join record arrays *r1* and *r2* on *key*; *key* is a tuple of field names -- if *key* is a string it is assumed to be a single attribute name. If *r1* and *r2* have equal values on all the keys in the *key* tuple, then their fields will be merged into a new record array containing the intersection of the fields of *r1* and *r2*. *r1* (also *r2*) must not have any duplicate keys. The *jointype* keyword can be 'inner', 'outer', 'leftouter'. To do a rightouter join just reverse *r1* and *r2*. The *defaults* keyword is a dictionary filled with ``{column_name:default_value}`` pairs. The keywords *r1postfix* and *r2postfix* are postfixed to column names (other than keys) that are both in *r1* and *r2*. """ if cbook.is_string_like(key): key = (key, ) for name in key: if name not in r1.dtype.names: raise ValueError('r1 does not have key field %s'%name) if name not in r2.dtype.names: raise ValueError('r2 does not have key field %s'%name) def makekey(row): return tuple([row[name] for name in key]) r1d = dict([(makekey(row),i) for i,row in enumerate(r1)]) r2d = dict([(makekey(row),i) for i,row in enumerate(r2)]) r1keys = set(r1d.keys()) r2keys = set(r2d.keys()) common_keys = r1keys & r2keys r1ind = np.array([r1d[k] for k in common_keys]) r2ind = np.array([r2d[k] for k in common_keys]) common_len = len(common_keys) left_len = right_len = 0 if jointype == "outer" or jointype == "leftouter": left_keys = r1keys.difference(r2keys) left_ind = np.array([r1d[k] for k in left_keys]) left_len = len(left_ind) if jointype == "outer": right_keys = r2keys.difference(r1keys) right_ind = np.array([r2d[k] for k in right_keys]) right_len = len(right_ind) def key_desc(name): 'if name is a string key, use the larger size of r1 or r2 before merging' dt1 = r1.dtype[name] if dt1.type != np.string_: return (name, dt1.descr[0][1]) dt2 = r1.dtype[name] assert dt2==dt1 if dt1.num>dt2.num: return (name, dt1.descr[0][1]) else: return (name, dt2.descr[0][1]) keydesc = [key_desc(name) for name in key] def mapped_r1field(name): """ The column name in *newrec* that corresponds to the column in *r1*. """ if name in key or name not in r2.dtype.names: return name else: return name + r1postfix def mapped_r2field(name): """ The column name in *newrec* that corresponds to the column in *r2*. """ if name in key or name not in r1.dtype.names: return name else: return name + r2postfix r1desc = [(mapped_r1field(desc[0]), desc[1]) for desc in r1.dtype.descr if desc[0] not in key] r2desc = [(mapped_r2field(desc[0]), desc[1]) for desc in r2.dtype.descr if desc[0] not in key] newdtype = np.dtype(keydesc + r1desc + r2desc) newrec = np.empty(common_len + left_len + right_len, dtype=newdtype) if jointype != 'inner' and defaults is not None: # fill in the defaults enmasse newrec_fields = newrec.dtype.fields.keys() for k, v in defaults.items(): if k in newrec_fields: newrec[k] = v for field in r1.dtype.names: newfield = mapped_r1field(field) if common_len: newrec[newfield][:common_len] = r1[field][r1ind] if (jointype == "outer" or jointype == "leftouter") and left_len: newrec[newfield][common_len:(common_len+left_len)] = r1[field][left_ind] for field in r2.dtype.names: newfield = mapped_r2field(field) if field not in key and common_len: newrec[newfield][:common_len] = r2[field][r2ind] if jointype == "outer" and right_len: newrec[newfield][-right_len:] = r2[field][right_ind] newrec.sort(order=key) return rec_view(newrec) def csv2rec(fname, comments='#', skiprows=0, checkrows=0, delimiter=',', converterd=None, names=None, missing='', missingd=None, use_mrecords=True): """ Load data from comma/space/tab delimited file in *fname* into a numpy record array and return the record array. If *names* is *None*, a header row is required to automatically assign the recarray names. The headers will be lower cased, spaces will be converted to underscores, and illegal attribute name characters removed. If *names* is not *None*, it is a sequence of names to use for the column names. In this case, it is assumed there is no header row. - *fname*: can be a filename or a file handle. Support for gzipped files is automatic, if the filename ends in '.gz' - *comments*: the character used to indicate the start of a comment in the file - *skiprows*: is the number of rows from the top to skip - *checkrows*: is the number of rows to check to validate the column data type. When set to zero all rows are validated. - *converted*: if not *None*, is a dictionary mapping column number or munged column name to a converter function. - *names*: if not None, is a list of header names. In this case, no header will be read from the file - *missingd* is a dictionary mapping munged column names to field values which signify that the field does not contain actual data and should be masked, e.g. '0000-00-00' or 'unused' - *missing*: a string whose value signals a missing field regardless of the column it appears in - *use_mrecords*: if True, return an mrecords.fromrecords record array if any of the data are missing If no rows are found, *None* is returned -- see :file:`examples/loadrec.py` """ if converterd is None: converterd = dict() if missingd is None: missingd = {} import dateutil.parser import datetime parsedate = dateutil.parser.parse fh = cbook.to_filehandle(fname) class FH: """ For space-delimited files, we want different behavior than comma or tab. Generally, we want multiple spaces to be treated as a single separator, whereas with comma and tab we want multiple commas to return multiple (empty) fields. The join/strip trick below effects this. """ def __init__(self, fh): self.fh = fh def close(self): self.fh.close() def seek(self, arg): self.fh.seek(arg) def fix(self, s): return ' '.join(s.split()) def next(self): return self.fix(self.fh.next()) def __iter__(self): for line in self.fh: yield self.fix(line) if delimiter==' ': fh = FH(fh) reader = csv.reader(fh, delimiter=delimiter) def process_skiprows(reader): if skiprows: for i, row in enumerate(reader): if i>=(skiprows-1): break return fh, reader process_skiprows(reader) def ismissing(name, val): "Should the value val in column name be masked?" if val == missing or val == missingd.get(name) or val == '': return True else: return False def with_default_value(func, default): def newfunc(name, val): if ismissing(name, val): return default else: return func(val) return newfunc def mybool(x): if x=='True': return True elif x=='False': return False else: raise ValueError('invalid bool') dateparser = dateutil.parser.parse mydateparser = with_default_value(dateparser, datetime.date(1,1,1)) myfloat = with_default_value(float, np.nan) myint = with_default_value(int, -1) mystr = with_default_value(str, '') mybool = with_default_value(mybool, None) def mydate(x): # try and return a date object d = dateparser(x) if d.hour>0 or d.minute>0 or d.second>0: raise ValueError('not a date') return d.date() mydate = with_default_value(mydate, datetime.date(1,1,1)) def get_func(name, item, func): # promote functions in this order funcmap = {mybool:myint,myint:myfloat, myfloat:mydate, mydate:mydateparser, mydateparser:mystr} try: func(name, item) except: if func==mystr: raise ValueError('Could not find a working conversion function') else: return get_func(name, item, funcmap[func]) # recurse else: return func # map column names that clash with builtins -- TODO - extend this list itemd = { 'return' : 'return_', 'file' : 'file_', 'print' : 'print_', } def get_converters(reader): converters = None for i, row in enumerate(reader): if i==0: converters = [mybool]*len(row) if checkrows and i>checkrows: break #print i, len(names), len(row) #print 'converters', zip(converters, row) for j, (name, item) in enumerate(zip(names, row)): func = converterd.get(j) if func is None: func = converterd.get(name) if func is None: #if not item.strip(): continue func = converters[j] if len(item.strip()): func = get_func(name, item, func) else: # how should we handle custom converters and defaults? func = with_default_value(func, None) converters[j] = func return converters # Get header and remove invalid characters needheader = names is None if needheader: for row in reader: #print 'csv2rec', row if len(row) and row[0].startswith(comments): continue headers = row break # remove these chars delete = set("""~!@#$%^&*()-=+~\|]}[{';: /?.>,<""") delete.add('"') names = [] seen = dict() for i, item in enumerate(headers): item = item.strip().lower().replace(' ', '_') item = ''.join([c for c in item if c not in delete]) if not len(item): item = 'column%d'%i item = itemd.get(item, item) cnt = seen.get(item, 0) if cnt>0: names.append(item + '_%d'%cnt) else: names.append(item) seen[item] = cnt+1 else: if cbook.is_string_like(names): names = [n.strip() for n in names.split(',')] # get the converter functions by inspecting checkrows converters = get_converters(reader) if converters is None: raise ValueError('Could not find any valid data in CSV file') # reset the reader and start over fh.seek(0) reader = csv.reader(fh, delimiter=delimiter) process_skiprows(reader) if needheader: skipheader = reader.next() # iterate over the remaining rows and convert the data to date # objects, ints, or floats as approriate rows = [] rowmasks = [] for i, row in enumerate(reader): if not len(row): continue if row[0].startswith(comments): continue rows.append([func(name, val) for func, name, val in zip(converters, names, row)]) rowmasks.append([ismissing(name, val) for name, val in zip(names, row)]) fh.close() if not len(rows): return None if use_mrecords and np.any(rowmasks): try: from numpy.ma import mrecords except ImportError: raise RuntimeError('numpy 1.05 or later is required for masked array support') else: r = mrecords.fromrecords(rows, names=names, mask=rowmasks) else: r = np.rec.fromrecords(rows, names=names) return r # a series of classes for describing the format intentions of various rec views class FormatObj: def tostr(self, x): return self.toval(x) def toval(self, x): return str(x) def fromstr(self, s): return s class FormatString(FormatObj): def tostr(self, x): val = repr(x) return val[1:-1] #class FormatString(FormatObj): # def tostr(self, x): # return '"%r"'%self.toval(x) class FormatFormatStr(FormatObj): def __init__(self, fmt): self.fmt = fmt def tostr(self, x): if x is None: return 'None' return self.fmt%self.toval(x) class FormatFloat(FormatFormatStr): def __init__(self, precision=4, scale=1.): FormatFormatStr.__init__(self, '%%1.%df'%precision) self.precision = precision self.scale = scale def toval(self, x): if x is not None: x = x * self.scale return x def fromstr(self, s): return float(s)/self.scale class FormatInt(FormatObj): def tostr(self, x): return '%d'%int(x) def toval(self, x): return int(x) def fromstr(self, s): return int(s) class FormatBool(FormatObj): def toval(self, x): return str(x) def fromstr(self, s): return bool(s) class FormatPercent(FormatFloat): def __init__(self, precision=4): FormatFloat.__init__(self, precision, scale=100.) class FormatThousands(FormatFloat): def __init__(self, precision=4): FormatFloat.__init__(self, precision, scale=1e-3) class FormatMillions(FormatFloat): def __init__(self, precision=4): FormatFloat.__init__(self, precision, scale=1e-6) class FormatDate(FormatObj): def __init__(self, fmt): self.fmt = fmt def toval(self, x): if x is None: return 'None' return x.strftime(self.fmt) def fromstr(self, x): import dateutil.parser return dateutil.parser.parse(x).date() class FormatDatetime(FormatDate): def __init__(self, fmt='%Y-%m-%d %H:%M:%S'): FormatDate.__init__(self, fmt) def fromstr(self, x): import dateutil.parser return dateutil.parser.parse(x) defaultformatd = { np.bool_ : FormatBool(), np.int16 : FormatInt(), np.int32 : FormatInt(), np.int64 : FormatInt(), np.float32 : FormatFloat(), np.float64 : FormatFloat(), np.object_ : FormatObj(), np.string_ : FormatString(), } def get_formatd(r, formatd=None): 'build a formatd guaranteed to have a key for every dtype name' if formatd is None: formatd = dict() for i, name in enumerate(r.dtype.names): dt = r.dtype[name] format = formatd.get(name) if format is None: format = defaultformatd.get(dt.type, FormatObj()) formatd[name] = format return formatd def csvformat_factory(format): format = copy.deepcopy(format) if isinstance(format, FormatFloat): format.scale = 1. # override scaling for storage format.fmt = '%r' return format def rec2txt(r, header=None, padding=3, precision=3): """ Returns a textual representation of a record array. *r*: numpy recarray *header*: list of column headers *padding*: space between each column *precision*: number of decimal places to use for floats. Set to an integer to apply to all floats. Set to a list of integers to apply precision individually. Precision for non-floats is simply ignored. Example:: precision=[0,2,3] Output:: ID Price Return ABC 12.54 0.234 XYZ 6.32 -0.076 """ if cbook.is_numlike(precision): precision = [precision]*len(r.dtype) def get_type(item,atype=int): tdict = {None:int, int:float, float:str} try: atype(str(item)) except: return get_type(item,tdict[atype]) return atype def get_justify(colname, column, precision): ntype = type(column[0]) if ntype==np.str or ntype==np.str_ or ntype==np.string0 or ntype==np.string_: length = max(len(colname),column.itemsize) return 0, length+padding, "%s" # left justify if ntype==np.int or ntype==np.int16 or ntype==np.int32 or ntype==np.int64 or ntype==np.int8 or ntype==np.int_: length = max(len(colname),np.max(map(len,map(str,column)))) return 1, length+padding, "%d" # right justify # JDH: my powerbook does not have np.float96 using np 1.3.0 """ In [2]: np.__version__ Out[2]: '1.3.0.dev5948' In [3]: !uname -a Darwin Macintosh-5.local 9.4.0 Darwin Kernel Version 9.4.0: Mon Jun 9 19:30:53 PDT 2008; root:xnu-1228.5.20~1/RELEASE_I386 i386 i386 In [4]: np.float96 --------------------------------------------------------------------------- AttributeError Traceback (most recent call la """ if ntype==np.float or ntype==np.float32 or ntype==np.float64 or (hasattr(np, 'float96') and (ntype==np.float96)) or ntype==np.float_: fmt = "%." + str(precision) + "f" length = max(len(colname),np.max(map(len,map(lambda x:fmt%x,column)))) return 1, length+padding, fmt # right justify return 0, max(len(colname),np.max(map(len,map(str,column))))+padding, "%s" if header is None: header = r.dtype.names justify_pad_prec = [get_justify(header[i],r.__getitem__(colname),precision[i]) for i, colname in enumerate(r.dtype.names)] justify_pad_prec_spacer = [] for i in range(len(justify_pad_prec)): just,pad,prec = justify_pad_prec[i] if i == 0: justify_pad_prec_spacer.append((just,pad,prec,0)) else: pjust,ppad,pprec = justify_pad_prec[i-1] if pjust == 0 and just == 1: justify_pad_prec_spacer.append((just,pad-padding,prec,0)) elif pjust == 1 and just == 0: justify_pad_prec_spacer.append((just,pad,prec,padding)) else: justify_pad_prec_spacer.append((just,pad,prec,0)) def format(item, just_pad_prec_spacer): just, pad, prec, spacer = just_pad_prec_spacer if just == 0: return spacer*' ' + str(item).ljust(pad) else: if get_type(item) == float: item = (prec%float(item)) elif get_type(item) == int: item = (prec%int(item)) return item.rjust(pad) textl = [] textl.append(''.join([format(colitem,justify_pad_prec_spacer[j]) for j, colitem in enumerate(header)])) for i, row in enumerate(r): textl.append(''.join([format(colitem,justify_pad_prec_spacer[j]) for j, colitem in enumerate(row)])) if i==0: textl[0] = textl[0].rstrip() text = os.linesep.join(textl) return text def rec2csv(r, fname, delimiter=',', formatd=None, missing='', missingd=None): """ Save the data from numpy recarray *r* into a comma-/space-/tab-delimited file. The record array dtype names will be used for column headers. *fname*: can be a filename or a file handle. Support for gzipped files is automatic, if the filename ends in '.gz' .. seealso:: :func:`csv2rec`: For information about *missing* and *missingd*, which can be used to fill in masked values into your CSV file. """ if missingd is None: missingd = dict() def with_mask(func): def newfunc(val, mask, mval): if mask: return mval else: return func(val) return newfunc formatd = get_formatd(r, formatd) funcs = [] for i, name in enumerate(r.dtype.names): funcs.append(with_mask(csvformat_factory(formatd[name]).tostr)) fh, opened = cbook.to_filehandle(fname, 'w', return_opened=True) writer = csv.writer(fh, delimiter=delimiter) header = r.dtype.names writer.writerow(header) # Our list of specials for missing values mvals = [] for name in header: mvals.append(missingd.get(name, missing)) ismasked = False if len(r): row = r[0] ismasked = hasattr(row, '_fieldmask') for row in r: if ismasked: row, rowmask = row.item(), row._fieldmask.item() else: rowmask = [False] * len(row) writer.writerow([func(val, mask, mval) for func, val, mask, mval in zip(funcs, row, rowmask, mvals)]) if opened: fh.close() def griddata(x,y,z,xi,yi): """ ``zi = griddata(x,y,z,xi,yi)`` fits a surface of the form *z* = *f*(*x*, *y*) to the data in the (usually) nonuniformly spaced vectors (*x*, *y*, *z*). :func:`griddata` interpolates this surface at the points specified by (*xi*, *yi*) to produce *zi*. *xi* and *yi* must describe a regular grid, can be either 1D or 2D, but must be monotonically increasing. A masked array is returned if any grid points are outside convex hull defined by input data (no extrapolation is done). Uses natural neighbor interpolation based on Delaunay triangulation. By default, this algorithm is provided by the :mod:`matplotlib.delaunay` package, written by Robert Kern. The triangulation algorithm in this package is known to fail on some nearly pathological cases. For this reason, a separate toolkit (:mod:`mpl_tookits.natgrid`) has been created that provides a more robust algorithm fof triangulation and interpolation. This toolkit is based on the NCAR natgrid library, which contains code that is not redistributable under a BSD-compatible license. When installed, this function will use the :mod:`mpl_toolkits.natgrid` algorithm, otherwise it will use the built-in :mod:`matplotlib.delaunay` package. The natgrid matplotlib toolkit can be downloaded from http://sourceforge.net/project/showfiles.php?group_id=80706&package_id=142792 """ try: from mpl_toolkits.natgrid import _natgrid, __version__ _use_natgrid = True except ImportError: import matplotlib.delaunay as delaunay from matplotlib.delaunay import __version__ _use_natgrid = False if not griddata._reported: if _use_natgrid: verbose.report('using natgrid version %s' % __version__) else: verbose.report('using delaunay version %s' % __version__) griddata._reported = True if xi.ndim != yi.ndim: raise TypeError("inputs xi and yi must have same number of dimensions (1 or 2)") if xi.ndim != 1 and xi.ndim != 2: raise TypeError("inputs xi and yi must be 1D or 2D.") if not len(x)==len(y)==len(z): raise TypeError("inputs x,y,z must all be 1D arrays of the same length") # remove masked points. if hasattr(z,'mask'): x = x.compress(z.mask == False) y = y.compress(z.mask == False) z = z.compressed() if _use_natgrid: # use natgrid toolkit if available. if xi.ndim == 2: xi = xi[0,:] yi = yi[:,0] # override default natgrid internal parameters. _natgrid.seti('ext',0) _natgrid.setr('nul',np.nan) # cast input arrays to doubles (this makes a copy) x = x.astype(np.float) y = y.astype(np.float) z = z.astype(np.float) xo = xi.astype(np.float) yo = yi.astype(np.float) if min(xo[1:]-xo[0:-1]) < 0 or min(yo[1:]-yo[0:-1]) < 0: raise ValueError, 'output grid defined by xi,yi must be monotone increasing' # allocate array for output (buffer will be overwritten by nagridd) zo = np.empty((yo.shape[0],xo.shape[0]), np.float) _natgrid.natgridd(x,y,z,xo,yo,zo) else: # use Robert Kern's delaunay package from scikits (default) if xi.ndim != yi.ndim: raise TypeError("inputs xi and yi must have same number of dimensions (1 or 2)") if xi.ndim != 1 and xi.ndim != 2: raise TypeError("inputs xi and yi must be 1D or 2D.") if xi.ndim == 1: xi,yi = np.meshgrid(xi,yi) # triangulate data tri = delaunay.Triangulation(x,y) # interpolate data interp = tri.nn_interpolator(z) zo = interp(xi,yi) # mask points on grid outside convex hull of input data. if np.any(np.isnan(zo)): zo = np.ma.masked_where(np.isnan(zo),zo) return zo griddata._reported = False ################################################## # Linear interpolation algorithms ################################################## def less_simple_linear_interpolation( x, y, xi, extrap=False ): """ This function provides simple (but somewhat less so than :func:`cbook.simple_linear_interpolation`) linear interpolation. :func:`simple_linear_interpolation` will give a list of point between a start and an end, while this does true linear interpolation at an arbitrary set of points. This is very inefficient linear interpolation meant to be used only for a small number of points in relatively non-intensive use cases. For real linear interpolation, use scipy. """ if cbook.is_scalar(xi): xi = [xi] x = np.asarray(x) y = np.asarray(y) xi = np.asarray(xi) s = list(y.shape) s[0] = len(xi) yi = np.tile( np.nan, s ) for ii,xx in enumerate(xi): bb = x == xx if np.any(bb): jj, = np.nonzero(bb) yi[ii] = y[jj[0]] elif xx<x[0]: if extrap: yi[ii] = y[0] elif xx>x[-1]: if extrap: yi[ii] = y[-1] else: jj, = np.nonzero(x<xx) jj = max(jj) yi[ii] = y[jj] + (xx-x[jj])/(x[jj+1]-x[jj]) * (y[jj+1]-y[jj]) return yi def slopes(x,y): """ :func:`slopes` calculates the slope *y*'(*x*) The slope is estimated using the slope obtained from that of a parabola through any three consecutive points. This method should be superior to that described in the appendix of A CONSISTENTLY WELL BEHAVED METHOD OF INTERPOLATION by Russel W. Stineman (Creative Computing July 1980) in at least one aspect: Circles for interpolation demand a known aspect ratio between *x*- and *y*-values. For many functions, however, the abscissa are given in different dimensions, so an aspect ratio is completely arbitrary. The parabola method gives very similar results to the circle method for most regular cases but behaves much better in special cases. Norbert Nemec, Institute of Theoretical Physics, University or Regensburg, April 2006 Norbert.Nemec at physik.uni-regensburg.de (inspired by a original implementation by Halldor Bjornsson, Icelandic Meteorological Office, March 2006 halldor at vedur.is) """ # Cast key variables as float. x=np.asarray(x, np.float_) y=np.asarray(y, np.float_) yp=np.zeros(y.shape, np.float_) dx=x[1:] - x[:-1] dy=y[1:] - y[:-1] dydx = dy/dx yp[1:-1] = (dydx[:-1] * dx[1:] + dydx[1:] * dx[:-1])/(dx[1:] + dx[:-1]) yp[0] = 2.0 * dy[0]/dx[0] - yp[1] yp[-1] = 2.0 * dy[-1]/dx[-1] - yp[-2] return yp def stineman_interp(xi,x,y,yp=None): """ Given data vectors *x* and *y*, the slope vector *yp* and a new abscissa vector *xi*, the function :func:`stineman_interp` uses Stineman interpolation to calculate a vector *yi* corresponding to *xi*. Here's an example that generates a coarse sine curve, then interpolates over a finer abscissa:: x = linspace(0,2*pi,20); y = sin(x); yp = cos(x) xi = linspace(0,2*pi,40); yi = stineman_interp(xi,x,y,yp); plot(x,y,'o',xi,yi) The interpolation method is described in the article A CONSISTENTLY WELL BEHAVED METHOD OF INTERPOLATION by Russell W. Stineman. The article appeared in the July 1980 issue of Creative Computing with a note from the editor stating that while they were: not an academic journal but once in a while something serious and original comes in adding that this was "apparently a real solution" to a well known problem. For *yp* = *None*, the routine automatically determines the slopes using the :func:`slopes` routine. *x* is assumed to be sorted in increasing order. For values ``xi[j] < x[0]`` or ``xi[j] > x[-1]``, the routine tries an extrapolation. The relevance of the data obtained from this, of course, is questionable... Original implementation by Halldor Bjornsson, Icelandic Meteorolocial Office, March 2006 halldor at vedur.is Completely reworked and optimized for Python by Norbert Nemec, Institute of Theoretical Physics, University or Regensburg, April 2006 Norbert.Nemec at physik.uni-regensburg.de """ # Cast key variables as float. x=np.asarray(x, np.float_) y=np.asarray(y, np.float_) assert x.shape == y.shape N=len(y) if yp is None: yp = slopes(x,y) else: yp=np.asarray(yp, np.float_) xi=np.asarray(xi, np.float_) yi=np.zeros(xi.shape, np.float_) # calculate linear slopes dx = x[1:] - x[:-1] dy = y[1:] - y[:-1] s = dy/dx #note length of s is N-1 so last element is #N-2 # find the segment each xi is in # this line actually is the key to the efficiency of this implementation idx = np.searchsorted(x[1:-1], xi) # now we have generally: x[idx[j]] <= xi[j] <= x[idx[j]+1] # except at the boundaries, where it may be that xi[j] < x[0] or xi[j] > x[-1] # the y-values that would come out from a linear interpolation: sidx = s.take(idx) xidx = x.take(idx) yidx = y.take(idx) xidxp1 = x.take(idx+1) yo = yidx + sidx * (xi - xidx) # the difference that comes when using the slopes given in yp dy1 = (yp.take(idx)- sidx) * (xi - xidx) # using the yp slope of the left point dy2 = (yp.take(idx+1)-sidx) * (xi - xidxp1) # using the yp slope of the right point dy1dy2 = dy1*dy2 # The following is optimized for Python. The solution actually # does more calculations than necessary but exploiting the power # of numpy, this is far more efficient than coding a loop by hand # in Python yi = yo + dy1dy2 * np.choose(np.array(np.sign(dy1dy2), np.int32)+1, ((2*xi-xidx-xidxp1)/((dy1-dy2)*(xidxp1-xidx)), 0.0, 1/(dy1+dy2),)) return yi ################################################## # Code related to things in and around polygons ################################################## def inside_poly(points, verts): """ *points* is a sequence of *x*, *y* points. *verts* is a sequence of *x*, *y* vertices of a polygon. Return value is a sequence of indices into points for the points that are inside the polygon. """ res, = np.nonzero(nxutils.points_inside_poly(points, verts)) return res def poly_below(xmin, xs, ys): """ Given a sequence of *xs* and *ys*, return the vertices of a polygon that has a horizontal base at *xmin* and an upper bound at the *ys*. *xmin* is a scalar. Intended for use with :meth:`matplotlib.axes.Axes.fill`, eg:: xv, yv = poly_below(0, x, y) ax.fill(xv, yv) """ if ma.isMaskedArray(xs) or ma.isMaskedArray(ys): nx = ma else: nx = np xs = nx.asarray(xs) ys = nx.asarray(ys) Nx = len(xs) Ny = len(ys) assert(Nx==Ny) x = xmin*nx.ones(2*Nx) y = nx.ones(2*Nx) x[:Nx] = xs y[:Nx] = ys y[Nx:] = ys[::-1] return x, y def poly_between(x, ylower, yupper): """ Given a sequence of *x*, *ylower* and *yupper*, return the polygon that fills the regions between them. *ylower* or *yupper* can be scalar or iterable. If they are iterable, they must be equal in length to *x*. Return value is *x*, *y* arrays for use with :meth:`matplotlib.axes.Axes.fill`. """ if ma.isMaskedArray(ylower) or ma.isMaskedArray(yupper) or ma.isMaskedArray(x): nx = ma else: nx = np Nx = len(x) if not cbook.iterable(ylower): ylower = ylower*nx.ones(Nx) if not cbook.iterable(yupper): yupper = yupper*nx.ones(Nx) x = nx.concatenate( (x, x[::-1]) ) y = nx.concatenate( (yupper, ylower[::-1]) ) return x,y def is_closed_polygon(X): """ Tests whether first and last object in a sequence are the same. These are presumably coordinates on a polygonal curve, in which case this function tests if that curve is closed. """ return np.all(X[0] == X[-1]) def contiguous_regions(mask): """ return a list of (ind0, ind1) such that mask[ind0:ind1].all() is True and we cover all such regions TODO: this is a pure python implementation which probably has a much faster numpy impl """ in_region = None boundaries = [] for i, val in enumerate(mask): if in_region is None and val: in_region = i elif in_region is not None and not val: boundaries.append((in_region, i)) in_region = None if in_region is not None: boundaries.append((in_region, i+1)) return boundaries ################################################## # Vector and path length geometry calculations ################################################## def vector_lengths( X, P=2., axis=None ): """ Finds the length of a set of vectors in *n* dimensions. This is like the :func:`numpy.norm` function for vectors, but has the ability to work over a particular axis of the supplied array or matrix. Computes ``(sum((x_i)^P))^(1/P)`` for each ``{x_i}`` being the elements of *X* along the given axis. If *axis* is *None*, compute over all elements of *X*. """ X = np.asarray(X) return (np.sum(X**(P),axis=axis))**(1./P) def distances_along_curve( X ): """ Computes the distance between a set of successive points in *N* dimensions. Where *X* is an *M* x *N* array or matrix. The distances between successive rows is computed. Distance is the standard Euclidean distance. """ X = np.diff( X, axis=0 ) return vector_lengths(X,axis=1) def path_length(X): """ Computes the distance travelled along a polygonal curve in *N* dimensions. Where *X* is an *M* x *N* array or matrix. Returns an array of length *M* consisting of the distance along the curve at each point (i.e., the rows of *X*). """ X = distances_along_curve(X) return np.concatenate( (np.zeros(1), np.cumsum(X)) ) def quad2cubic(q0x, q0y, q1x, q1y, q2x, q2y): """ Converts a quadratic Bezier curve to a cubic approximation. The inputs are the *x* and *y* coordinates of the three control points of a quadratic curve, and the output is a tuple of *x* and *y* coordinates of the four control points of the cubic curve. """ # c0x, c0y = q0x, q0y c1x, c1y = q0x + 2./3. * (q1x - q0x), q0y + 2./3. * (q1y - q0y) c2x, c2y = c1x + 1./3. * (q2x - q0x), c1y + 1./3. * (q2y - q0y) # c3x, c3y = q2x, q2y return q0x, q0y, c1x, c1y, c2x, c2y, q2x, q2y

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Python

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numenta/nupic-legacy

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9/5/2024, 5:13:42 PM (Europe/Amsterdam)

26,240

fontconfig_pattern.py

numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/fontconfig_pattern.py

""" A module for parsing and generating fontconfig patterns. See the `fontconfig pattern specification <http://www.fontconfig.org/fontconfig-user.html>`_ for more information. """ # Author : Michael Droettboom <mdroe@stsci.edu> # License : matplotlib license (PSF compatible) # This class is defined here because it must be available in: # - The old-style config framework (:file:`rcsetup.py`) # - The traits-based config framework (:file:`mpltraits.py`) # - The font manager (:file:`font_manager.py`) # It probably logically belongs in :file:`font_manager.py`, but # placing it in any of these places would have created cyclical # dependency problems, or an undesired dependency on traits even # when the traits-based config framework is not used. import re from matplotlib.pyparsing import Literal, ZeroOrMore, \ Optional, Regex, StringEnd, ParseException, Suppress family_punc = r'\\\-:,' family_unescape = re.compile(r'\\([%s])' % family_punc).sub family_escape = re.compile(r'([%s])' % family_punc).sub value_punc = r'\\=_:,' value_unescape = re.compile(r'\\([%s])' % value_punc).sub value_escape = re.compile(r'([%s])' % value_punc).sub class FontconfigPatternParser: """A simple pyparsing-based parser for fontconfig-style patterns. See the `fontconfig pattern specification <http://www.fontconfig.org/fontconfig-user.html>`_ for more information. """ _constants = { 'thin' : ('weight', 'light'), 'extralight' : ('weight', 'light'), 'ultralight' : ('weight', 'light'), 'light' : ('weight', 'light'), 'book' : ('weight', 'book'), 'regular' : ('weight', 'regular'), 'normal' : ('weight', 'normal'), 'medium' : ('weight', 'medium'), 'demibold' : ('weight', 'demibold'), 'semibold' : ('weight', 'semibold'), 'bold' : ('weight', 'bold'), 'extrabold' : ('weight', 'extra bold'), 'black' : ('weight', 'black'), 'heavy' : ('weight', 'heavy'), 'roman' : ('slant', 'normal'), 'italic' : ('slant', 'italic'), 'oblique' : ('slant', 'oblique'), 'ultracondensed' : ('width', 'ultra-condensed'), 'extracondensed' : ('width', 'extra-condensed'), 'condensed' : ('width', 'condensed'), 'semicondensed' : ('width', 'semi-condensed'), 'expanded' : ('width', 'expanded'), 'extraexpanded' : ('width', 'extra-expanded'), 'ultraexpanded' : ('width', 'ultra-expanded') } def __init__(self): family = Regex(r'([^%s]|(\\[%s]))*' % (family_punc, family_punc)) \ .setParseAction(self._family) size = Regex(r"([0-9]+\.?[0-9]*|\.[0-9]+)") \ .setParseAction(self._size) name = Regex(r'[a-z]+') \ .setParseAction(self._name) value = Regex(r'([^%s]|(\\[%s]))*' % (value_punc, value_punc)) \ .setParseAction(self._value) families =(family + ZeroOrMore( Literal(',') + family) ).setParseAction(self._families) point_sizes =(size + ZeroOrMore( Literal(',') + size) ).setParseAction(self._point_sizes) property =( (name + Suppress(Literal('=')) + value + ZeroOrMore( Suppress(Literal(',')) + value) ) | name ).setParseAction(self._property) pattern =(Optional( families) + Optional( Literal('-') + point_sizes) + ZeroOrMore( Literal(':') + property) + StringEnd() ) self._parser = pattern self.ParseException = ParseException def parse(self, pattern): """ Parse the given fontconfig *pattern* and return a dictionary of key/value pairs useful for initializing a :class:`font_manager.FontProperties` object. """ props = self._properties = {} try: self._parser.parseString(pattern) except self.ParseException, e: raise ValueError("Could not parse font string: '%s'\n%s" % (pattern, e)) self._properties = None return props def _family(self, s, loc, tokens): return [family_unescape(r'\1', str(tokens[0]))] def _size(self, s, loc, tokens): return [float(tokens[0])] def _name(self, s, loc, tokens): return [str(tokens[0])] def _value(self, s, loc, tokens): return [value_unescape(r'\1', str(tokens[0]))] def _families(self, s, loc, tokens): self._properties['family'] = [str(x) for x in tokens] return [] def _point_sizes(self, s, loc, tokens): self._properties['size'] = [str(x) for x in tokens] return [] def _property(self, s, loc, tokens): if len(tokens) == 1: if tokens[0] in self._constants: key, val = self._constants[tokens[0]] self._properties.setdefault(key, []).append(val) else: key = tokens[0] val = tokens[1:] self._properties.setdefault(key, []).extend(val) return [] parse_fontconfig_pattern = FontconfigPatternParser().parse def generate_fontconfig_pattern(d): """ Given a dictionary of key/value pairs, generates a fontconfig pattern string. """ props = [] families = '' size = '' for key in 'family style variant weight stretch file size'.split(): val = getattr(d, 'get_' + key)() if val is not None and val != []: if type(val) == list: val = [value_escape(r'\\\1', str(x)) for x in val if x is not None] if val != []: val = ','.join(val) props.append(":%s=%s" % (key, val)) return ''.join(props)

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Python

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154

31.253247

84

0.516005

numenta/nupic-legacy

6,330

1,556

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AGPL-3.0

9/5/2024, 5:13:42 PM (Europe/Amsterdam)

26,241

mpl.py

numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/mpl.py

from matplotlib import artist from matplotlib import axis from matplotlib import axes from matplotlib import cbook from matplotlib import collections from matplotlib import colors from matplotlib import colorbar from matplotlib import contour from matplotlib import dates from matplotlib import figure from matplotlib import finance from matplotlib import font_manager from matplotlib import image from matplotlib import legend from matplotlib import lines from matplotlib import mlab from matplotlib import cm from matplotlib import patches from matplotlib import quiver from matplotlib import rcParams from matplotlib import table from matplotlib import text from matplotlib import ticker from matplotlib import transforms from matplotlib import units from matplotlib import widgets

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numenta/nupic-legacy

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9/5/2024, 5:13:42 PM (Europe/Amsterdam)

26,242

pyplot.py

numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/pyplot.py

import sys import matplotlib from matplotlib import _pylab_helpers, interactive from matplotlib.cbook import dedent, silent_list, is_string_like, is_numlike from matplotlib.figure import Figure, figaspect from matplotlib.backend_bases import FigureCanvasBase from matplotlib.image import imread as _imread from matplotlib import rcParams, rcParamsDefault, get_backend from matplotlib.rcsetup import interactive_bk as _interactive_bk from matplotlib.artist import getp, get, Artist from matplotlib.artist import setp as _setp from matplotlib.axes import Axes from matplotlib.projections import PolarAxes from matplotlib import mlab # for csv2rec in plotfile from matplotlib.scale import get_scale_docs, get_scale_names from matplotlib import cm from matplotlib.cm import get_cmap # We may not need the following imports here: from matplotlib.colors import Normalize, normalize # latter for backwards compat. from matplotlib.lines import Line2D from matplotlib.text import Text, Annotation from matplotlib.patches import Polygon, Rectangle, Circle, Arrow from matplotlib.widgets import SubplotTool, Button, Slider, Widget from ticker import TickHelper, Formatter, FixedFormatter, NullFormatter,\ FuncFormatter, FormatStrFormatter, ScalarFormatter,\ LogFormatter, LogFormatterExponent, LogFormatterMathtext,\ Locator, IndexLocator, FixedLocator, NullLocator,\ LinearLocator, LogLocator, AutoLocator, MultipleLocator,\ MaxNLocator ## Backend detection ## def _backend_selection(): """ If rcParams['backend_fallback'] is true, check to see if the current backend is compatible with the current running event loop, and if not switches to a compatible one. """ backend = rcParams['backend'] if not rcParams['backend_fallback'] or \ backend not in _interactive_bk: return is_agg_backend = rcParams['backend'].endswith('Agg') if 'wx' in sys.modules and not backend in ('WX', 'WXAgg'): import wx if wx.App.IsMainLoopRunning(): rcParams['backend'] = 'wx' + 'Agg' * is_agg_backend elif 'qt' in sys.modules and not backend == 'QtAgg': import qt if not qt.qApp.startingUp(): # The mainloop is running. rcParams['backend'] = 'qtAgg' elif 'PyQt4.QtCore' in sys.modules and not backend == 'Qt4Agg': import PyQt4.QtGui if not PyQt4.QtGui.qApp.startingUp(): # The mainloop is running. rcParams['backend'] = 'qt4Agg' elif 'gtk' in sys.modules and not backend in ('GTK', 'GTKAgg', 'GTKCairo'): import gobject if gobject.MainLoop().is_running(): rcParams['backend'] = 'gtk' + 'Agg' * is_agg_backend elif 'Tkinter' in sys.modules and not backend == 'TkAgg': #import Tkinter pass #what if anything do we need to do for tkinter? _backend_selection() ## Global ## from matplotlib.backends import pylab_setup new_figure_manager, draw_if_interactive, show = pylab_setup() def findobj(o=None, match=None): if o is None: o = gcf() return o.findobj(match) findobj.__doc__ = Artist.findobj.__doc__ def switch_backend(newbackend): """ Switch the default backend to newbackend. This feature is **experimental**, and is only expected to work switching to an image backend. Eg, if you have a bunch of PostScript scripts that you want to run from an interactive ipython session, you may want to switch to the PS backend before running them to avoid having a bunch of GUI windows popup. If you try to interactively switch from one GUI backend to another, you will explode. Calling this command will close all open windows. """ close('all') global new_figure_manager, draw_if_interactive, show matplotlib.use(newbackend, warn=False) reload(matplotlib.backends) from matplotlib.backends import pylab_setup new_figure_manager, draw_if_interactive, show = pylab_setup() def isinteractive(): """ Return the interactive status """ return matplotlib.is_interactive() def ioff(): 'Turn interactive mode off.' matplotlib.interactive(False) def ion(): 'Turn interactive mode on.' matplotlib.interactive(True) def rc(*args, **kwargs): matplotlib.rc(*args, **kwargs) if matplotlib.rc.__doc__ is not None: rc.__doc__ = dedent(matplotlib.rc.__doc__) def rcdefaults(): matplotlib.rcdefaults() draw_if_interactive() if matplotlib.rcdefaults.__doc__ is not None: rcdefaults.__doc__ = dedent(matplotlib.rcdefaults.__doc__) # The current "image" (ScalarMappable) is tracked here on a # per-pylab-session basis: def gci(): """ Get the current :class:`~matplotlib.cm.ScalarMappable` instance (image or patch collection), or *None* if no images or patch collections have been defined. The commands :func:`~matplotlib.pyplot.imshow` and :func:`~matplotlib.pyplot.figimage` create :class:`~matplotlib.image.Image` instances, and the commands :func:`~matplotlib.pyplot.pcolor` and :func:`~matplotlib.pyplot.scatter` create :class:`~matplotlib.collections.Collection` instances. """ return gci._current gci._current = None def sci(im): """ Set the current image (target of colormap commands like :func:`~matplotlib.pyplot.jet`, :func:`~matplotlib.pyplot.hot` or :func:`~matplotlib.pyplot.clim`). """ gci._current = im ## Any Artist ## # (getp is simply imported) def setp(*args, **kwargs): ret = _setp(*args, **kwargs) draw_if_interactive() return ret if _setp.__doc__ is not None: setp.__doc__ = _setp.__doc__ ## Figures ## def figure(num=None, # autoincrement if None, else integer from 1-N figsize = None, # defaults to rc figure.figsize dpi = None, # defaults to rc figure.dpi facecolor = None, # defaults to rc figure.facecolor edgecolor = None, # defaults to rc figure.edgecolor frameon = True, FigureClass = Figure, **kwargs ): """ call signature:: figure(num=None, figsize=(8, 6), dpi=80, facecolor='w', edgecolor='k') Create a new figure and return a :class:`matplotlib.figure.Figure` instance. If *num* = *None*, the figure number will be incremented and a new figure will be created. The returned figure objects have a *number* attribute holding this number. If *num* is an integer, and ``figure(num)`` already exists, make it active and return the handle to it. If ``figure(num)`` does not exist it will be created. Numbering starts at 1, matlab style:: figure(1) If you are creating many figures, make sure you explicitly call "close" on the figures you are not using, because this will enable pylab to properly clean up the memory. Optional keyword arguments: ========= ======================================================= Keyword Description ========= ======================================================= figsize width x height in inches; defaults to rc figure.figsize dpi resolution; defaults to rc figure.dpi facecolor the background color; defaults to rc figure.facecolor edgecolor the border color; defaults to rc figure.edgecolor ========= ======================================================= rcParams defines the default values, which can be modified in the matplotlibrc file *FigureClass* is a :class:`~matplotlib.figure.Figure` or derived class that will be passed on to :meth:`new_figure_manager` in the backends which allows you to hook custom Figure classes into the pylab interface. Additional kwargs will be passed on to your figure init function. """ if figsize is None : figsize = rcParams['figure.figsize'] if dpi is None : dpi = rcParams['figure.dpi'] if facecolor is None : facecolor = rcParams['figure.facecolor'] if edgecolor is None : edgecolor = rcParams['figure.edgecolor'] if num is None: allnums = [f.num for f in _pylab_helpers.Gcf.get_all_fig_managers()] if allnums: num = max(allnums) + 1 else: num = 1 else: num = int(num) # crude validation of num argument figManager = _pylab_helpers.Gcf.get_fig_manager(num) if figManager is None: if get_backend().lower() == 'ps': dpi = 72 figManager = new_figure_manager(num, figsize=figsize, dpi=dpi, facecolor=facecolor, edgecolor=edgecolor, frameon=frameon, FigureClass=FigureClass, **kwargs) # make this figure current on button press event def make_active(event): _pylab_helpers.Gcf.set_active(figManager) cid = figManager.canvas.mpl_connect('button_press_event', make_active) figManager._cidgcf = cid _pylab_helpers.Gcf.set_active(figManager) figManager.canvas.figure.number = num draw_if_interactive() return figManager.canvas.figure def gcf(): "Return a handle to the current figure." figManager = _pylab_helpers.Gcf.get_active() if figManager is not None: return figManager.canvas.figure else: return figure() def get_current_fig_manager(): figManager = _pylab_helpers.Gcf.get_active() if figManager is None: gcf() # creates an active figure as a side effect figManager = _pylab_helpers.Gcf.get_active() return figManager # note we check for __doc__ is not None since py2exe optimize removes # the docstrings def connect(s, func): return get_current_fig_manager().canvas.mpl_connect(s, func) if FigureCanvasBase.mpl_connect.__doc__ is not None: connect.__doc__ = dedent(FigureCanvasBase.mpl_connect.__doc__) def disconnect(cid): return get_current_fig_manager().canvas.mpl_disconnect(cid) if FigureCanvasBase.mpl_disconnect.__doc__ is not None: disconnect.__doc__ = dedent(FigureCanvasBase.mpl_disconnect.__doc__) def close(*args): """ Close a figure window ``close()`` by itself closes the current figure ``close(num)`` closes figure number *num* ``close(h)`` where *h* is a :class:`Figure` instance, closes that figure ``close('all')`` closes all the figure windows """ if len(args)==0: figManager = _pylab_helpers.Gcf.get_active() if figManager is None: return else: figManager.canvas.mpl_disconnect(figManager._cidgcf) _pylab_helpers.Gcf.destroy(figManager.num) elif len(args)==1: arg = args[0] if arg=='all': for manager in _pylab_helpers.Gcf.get_all_fig_managers(): manager.canvas.mpl_disconnect(manager._cidgcf) _pylab_helpers.Gcf.destroy(manager.num) elif isinstance(arg, int): _pylab_helpers.Gcf.destroy(arg) elif isinstance(arg, Figure): for manager in _pylab_helpers.Gcf.get_all_fig_managers(): if manager.canvas.figure==arg: manager.canvas.mpl_disconnect(manager._cidgcf) _pylab_helpers.Gcf.destroy(manager.num) else: raise TypeError('Unrecognized argument type %s to close'%type(arg)) else: raise TypeError('close takes 0 or 1 arguments') def clf(): """ Clear the current figure """ gcf().clf() draw_if_interactive() def draw(): 'redraw the current figure' get_current_fig_manager().canvas.draw() def savefig(*args, **kwargs): fig = gcf() return fig.savefig(*args, **kwargs) if Figure.savefig.__doc__ is not None: savefig.__doc__ = dedent(Figure.savefig.__doc__) def ginput(*args, **kwargs): """ Blocking call to interact with the figure. This will wait for *n* clicks from the user and return a list of the coordinates of each click. If *timeout* is negative, does not timeout. """ return gcf().ginput(*args, **kwargs) if Figure.ginput.__doc__ is not None: ginput.__doc__ = dedent(Figure.ginput.__doc__) def waitforbuttonpress(*args, **kwargs): """ Blocking call to interact with the figure. This will wait for *n* key or mouse clicks from the user and return a list containing True's for keyboard clicks and False's for mouse clicks. If *timeout* is negative, does not timeout. """ return gcf().waitforbuttonpress(*args, **kwargs) if Figure.waitforbuttonpress.__doc__ is not None: waitforbuttonpress.__doc__ = dedent(Figure.waitforbuttonpress.__doc__) # Putting things in figures def figtext(*args, **kwargs): ret = gcf().text(*args, **kwargs) draw_if_interactive() return ret if Figure.text.__doc__ is not None: figtext.__doc__ = dedent(Figure.text.__doc__) def suptitle(*args, **kwargs): ret = gcf().suptitle(*args, **kwargs) draw_if_interactive() return ret if Figure.suptitle.__doc__ is not None: suptitle.__doc__ = dedent(Figure.suptitle.__doc__) def figimage(*args, **kwargs): # allow callers to override the hold state by passing hold=True|False ret = gcf().figimage(*args, **kwargs) draw_if_interactive() gci._current = ret return ret if Figure.figimage.__doc__ is not None: figimage.__doc__ = dedent(Figure.figimage.__doc__) + """ Addition kwargs: hold = [True|False] overrides default hold state""" def figlegend(handles, labels, loc, **kwargs): """ Place a legend in the figure. *labels* a sequence of strings *handles* a sequence of :class:`~matplotlib.lines.Line2D` or :class:`~matplotlib.patches.Patch` instances *loc* can be a string or an integer specifying the legend location A :class:`matplotlib.legend.Legend` instance is returned. Example:: figlegend( (line1, line2, line3), ('label1', 'label2', 'label3'), 'upper right' ) .. seealso:: :func:`~matplotlib.pyplot.legend`: For information about the location codes """ l = gcf().legend(handles, labels, loc, **kwargs) draw_if_interactive() return l ## Figure and Axes hybrid ## def hold(b=None): """ Set the hold state. If *b* is None (default), toggle the hold state, else set the hold state to boolean value *b*:: hold() # toggle hold hold(True) # hold is on hold(False) # hold is off When *hold* is *True*, subsequent plot commands will be added to the current axes. When *hold* is *False*, the current axes and figure will be cleared on the next plot command. """ fig = gcf() ax = fig.gca() fig.hold(b) ax.hold(b) # b=None toggles the hold state, so let's get get the current hold # state; but should pyplot hold toggle the rc setting - me thinks # not b = ax.ishold() rc('axes', hold=b) def ishold(): """ Return the hold status of the current axes """ return gca().ishold() def over(func, *args, **kwargs): """ over calls:: func(*args, **kwargs) with ``hold(True)`` and then restores the hold state. """ h = ishold() hold(True) func(*args, **kwargs) hold(h) ## Axes ## def axes(*args, **kwargs): """ Add an axes at position rect specified by: - ``axes()`` by itself creates a default full ``subplot(111)`` window axis. - ``axes(rect, axisbg='w')`` where *rect* = [left, bottom, width, height] in normalized (0, 1) units. *axisbg* is the background color for the axis, default white. - ``axes(h)`` where *h* is an axes instance makes *h* the current axis. An :class:`~matplotlib.axes.Axes` instance is returned. ======= ============ ================================================ kwarg Accepts Desctiption ======= ============ ================================================ axisbg color the axes background color frameon [True|False] display the frame? sharex otherax current axes shares xaxis attribute with otherax sharey otherax current axes shares yaxis attribute with otherax polar [True|False] use a polar axes? ======= ============ ================================================ Examples: * :file:`examples/pylab_examples/axes_demo.py` places custom axes. * :file:`examples/pylab_examples/shared_axis_demo.py` uses *sharex* and *sharey*. """ nargs = len(args) if len(args)==0: return subplot(111, **kwargs) if nargs>1: raise TypeError('Only one non keyword arg to axes allowed') arg = args[0] if isinstance(arg, Axes): a = gcf().sca(arg) else: rect = arg a = gcf().add_axes(rect, **kwargs) draw_if_interactive() return a def delaxes(*args): """ ``delaxes(ax)``: remove *ax* from the current figure. If *ax* doesn't exist, an error will be raised. ``delaxes()``: delete the current axes """ if not len(args): ax = gca() else: ax = args[0] ret = gcf().delaxes(ax) draw_if_interactive() return ret def gca(**kwargs): """ Return the current axis instance. This can be used to control axis properties either using set or the :class:`~matplotlib.axes.Axes` methods, for example, setting the xaxis range:: plot(t,s) set(gca(), 'xlim', [0,10]) or:: plot(t,s) a = gca() a.set_xlim([0,10]) """ ax = gcf().gca(**kwargs) return ax # More ways of creating axes: def subplot(*args, **kwargs): """ Create a subplot command, creating axes with:: subplot(numRows, numCols, plotNum) where *plotNum* = 1 is the first plot number and increasing *plotNums* fill rows first. max(*plotNum*) == *numRows* * *numCols* You can leave out the commas if *numRows* <= *numCols* <= *plotNum* < 10, as in:: subplot(211) # 2 rows, 1 column, first (upper) plot ``subplot(111)`` is the default axis. New subplots that overlap old will delete the old axes. If you do not want this behavior, use :meth:`matplotlib.figure.Figure.add_subplot` or the :func:`~matplotlib.pyplot.axes` command. Eg.:: from pylab import * plot([1,2,3]) # implicitly creates subplot(111) subplot(211) # overlaps, subplot(111) is killed plot(rand(12), rand(12)) subplot(212, axisbg='y') # creates 2nd subplot with yellow background Keyword arguments: *axisbg*: The background color of the subplot, which can be any valid color specifier. See :mod:`matplotlib.colors` for more information. *polar*: A boolean flag indicating whether the subplot plot should be a polar projection. Defaults to False. *projection*: A string giving the name of a custom projection to be used for the subplot. This projection must have been previously registered. See :func:`matplotlib.projections.register_projection` .. seealso:: :func:`~matplotlib.pyplot.axes`: For additional information on :func:`axes` and :func:`subplot` keyword arguments. :file:`examples/pylab_examples/polar_scatter.py` **Example:** .. plot:: mpl_examples/pylab_examples/subplot_demo.py """ fig = gcf() a = fig.add_subplot(*args, **kwargs) bbox = a.bbox byebye = [] for other in fig.axes: if other==a: continue if bbox.fully_overlaps(other.bbox): byebye.append(other) for ax in byebye: delaxes(ax) draw_if_interactive() return a def twinx(ax=None): """ Make a second axes overlay *ax* (or the current axes if *ax* is *None*) sharing the xaxis. The ticks for *ax2* will be placed on the right, and the *ax2* instance is returned. .. seealso:: :file:`examples/api_examples/two_scales.py` """ if ax is None: ax=gca() ax1 = ax.twinx() draw_if_interactive() return ax1 def twiny(ax=None): """ Make a second axes overlay *ax* (or the current axes if *ax* is *None*) sharing the yaxis. The ticks for *ax2* will be placed on the top, and the *ax2* instance is returned. """ if ax is None: ax=gca() ax1 = ax.twiny() draw_if_interactive() return ax1 def subplots_adjust(*args, **kwargs): """ call signature:: subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=None, hspace=None) Tune the subplot layout via the :class:`matplotlib.figure.SubplotParams` mechanism. The parameter meanings (and suggested defaults) are:: left = 0.125 # the left side of the subplots of the figure right = 0.9 # the right side of the subplots of the figure bottom = 0.1 # the bottom of the subplots of the figure top = 0.9 # the top of the subplots of the figure wspace = 0.2 # the amount of width reserved for blank space between subplots hspace = 0.2 # the amount of height reserved for white space between subplots The actual defaults are controlled by the rc file """ fig = gcf() fig.subplots_adjust(*args, **kwargs) draw_if_interactive() def subplot_tool(targetfig=None): """ Launch a subplot tool window for *targetfig* (default gcf). A :class:`matplotlib.widgets.SubplotTool` instance is returned. """ tbar = rcParams['toolbar'] # turn off the navigation toolbar for the toolfig rcParams['toolbar'] = 'None' if targetfig is None: manager = get_current_fig_manager() targetfig = manager.canvas.figure else: # find the manager for this figure for manager in _pylab_helpers.Gcf._activeQue: if manager.canvas.figure==targetfig: break else: raise RuntimeError('Could not find manager for targetfig') toolfig = figure(figsize=(6,3)) toolfig.subplots_adjust(top=0.9) ret = SubplotTool(targetfig, toolfig) rcParams['toolbar'] = tbar _pylab_helpers.Gcf.set_active(manager) # restore the current figure return ret def box(on=None): """ Turn the axes box on or off according to *on*. If *on* is *None*, toggle state. """ ax = gca() if on is None: on = not ax.get_frame_on() ax.set_frame_on(on) draw_if_interactive() def title(s, *args, **kwargs): """ Set the title of the current axis to *s*. Default font override is:: override = {'fontsize': 'medium', 'verticalalignment': 'bottom', 'horizontalalignment': 'center'} .. seealso:: :func:`~matplotlib.pyplot.text`: for information on how override and the optional args work. """ l = gca().set_title(s, *args, **kwargs) draw_if_interactive() return l ## Axis ## def axis(*v, **kwargs): """ Set/Get the axis properties: >>> axis() returns the current axes limits ``[xmin, xmax, ymin, ymax]``. >>> axis(v) sets the min and max of the x and y axes, with ``v = [xmin, xmax, ymin, ymax]``. >>> axis('off') turns off the axis lines and labels. >>> axis('equal') changes limits of *x* or *y* axis so that equal increments of *x* and *y* have the same length; a circle is circular. >>> axis('scaled') achieves the same result by changing the dimensions of the plot box instead of the axis data limits. >>> axis('tight') changes *x* and *y* axis limits such that all data is shown. If all data is already shown, it will move it to the center of the figure without modifying (*xmax* - *xmin*) or (*ymax* - *ymin*). Note this is slightly different than in matlab. >>> axis('image') is 'scaled' with the axis limits equal to the data limits. >>> axis('auto') and >>> axis('normal') are deprecated. They restore default behavior; axis limits are automatically scaled to make the data fit comfortably within the plot box. if ``len(*v)==0``, you can pass in *xmin*, *xmax*, *ymin*, *ymax* as kwargs selectively to alter just those limits without changing the others. The xmin, xmax, ymin, ymax tuple is returned .. seealso:: :func:`xlim`, :func:`ylim` """ ax = gca() v = ax.axis(*v, **kwargs) draw_if_interactive() return v def xlabel(s, *args, **kwargs): """ Set the *x* axis label of the current axis to *s* Default override is:: override = { 'fontsize' : 'small', 'verticalalignment' : 'top', 'horizontalalignment' : 'center' } .. seealso:: :func:`~matplotlib.pyplot.text`: For information on how override and the optional args work """ l = gca().set_xlabel(s, *args, **kwargs) draw_if_interactive() return l def ylabel(s, *args, **kwargs): """ Set the *y* axis label of the current axis to *s*. Defaults override is:: override = { 'fontsize' : 'small', 'verticalalignment' : 'center', 'horizontalalignment' : 'right', 'rotation'='vertical' : } .. seealso:: :func:`~matplotlib.pyplot.text`: For information on how override and the optional args work. """ l = gca().set_ylabel(s, *args, **kwargs) draw_if_interactive() return l def xlim(*args, **kwargs): """ Set/Get the xlimits of the current axes:: xmin, xmax = xlim() # return the current xlim xlim( (xmin, xmax) ) # set the xlim to xmin, xmax xlim( xmin, xmax ) # set the xlim to xmin, xmax If you do not specify args, you can pass the xmin and xmax as kwargs, eg.:: xlim(xmax=3) # adjust the max leaving min unchanged xlim(xmin=1) # adjust the min leaving max unchanged The new axis limits are returned as a length 2 tuple. """ ax = gca() ret = ax.set_xlim(*args, **kwargs) draw_if_interactive() return ret def ylim(*args, **kwargs): """ Set/Get the ylimits of the current axes:: ymin, ymax = ylim() # return the current ylim ylim( (ymin, ymax) ) # set the ylim to ymin, ymax ylim( ymin, ymax ) # set the ylim to ymin, ymax If you do not specify args, you can pass the *ymin* and *ymax* as kwargs, eg.:: ylim(ymax=3) # adjust the max leaving min unchanged ylim(ymin=1) # adjust the min leaving max unchanged The new axis limits are returned as a length 2 tuple. """ ax = gca() ret = ax.set_ylim(*args, **kwargs) draw_if_interactive() return ret def xscale(*args, **kwargs): """ call signature:: xscale(scale, **kwargs) Set the scaling for the x-axis: %(scale)s Different keywords may be accepted, depending on the scale: %(scale_docs)s """ ax = gca() ret = ax.set_xscale(*args, **kwargs) draw_if_interactive() return ret xscale.__doc__ = dedent(xscale.__doc__) % { 'scale': ' | '.join([repr(_x) for _x in get_scale_names()]), 'scale_docs': get_scale_docs()} def yscale(*args, **kwargs): """ call signature:: xscale(scale, **kwargs) Set the scaling for the y-axis: %(scale)s Different keywords may be accepted, depending on the scale: %(scale_docs)s """ ax = gca() ret = ax.set_yscale(*args, **kwargs) draw_if_interactive() return ret yscale.__doc__ = dedent(yscale.__doc__) % { 'scale': ' | '.join([repr(_x) for _x in get_scale_names()]), 'scale_docs': get_scale_docs()} def xticks(*args, **kwargs): """ Set/Get the xlimits of the current ticklocs and labels:: # return locs, labels where locs is an array of tick locations and # labels is an array of tick labels. locs, labels = xticks() # set the locations of the xticks xticks( arange(6) ) # set the locations and labels of the xticks xticks( arange(5), ('Tom', 'Dick', 'Harry', 'Sally', 'Sue') ) The keyword args, if any, are :class:`~matplotlib.text.Text` properties. """ ax = gca() if len(args)==0: locs = ax.get_xticks() labels = ax.get_xticklabels() elif len(args)==1: locs = ax.set_xticks(args[0]) labels = ax.get_xticklabels() elif len(args)==2: locs = ax.set_xticks(args[0]) labels = ax.set_xticklabels(args[1], **kwargs) else: raise TypeError('Illegal number of arguments to xticks') if len(kwargs): for l in labels: l.update(kwargs) draw_if_interactive() return locs, silent_list('Text xticklabel', labels) def yticks(*args, **kwargs): """ Set/Get the ylimits of the current ticklocs and labels:: # return locs, labels where locs is an array of tick locations and # labels is an array of tick labels. locs, labels = yticks() # set the locations of the yticks yticks( arange(6) ) # set the locations and labels of the yticks yticks( arange(5), ('Tom', 'Dick', 'Harry', 'Sally', 'Sue') ) The keyword args, if any, are :class:`~matplotlib.text.Text` properties. """ ax = gca() if len(args)==0: locs = ax.get_yticks() labels = ax.get_yticklabels() elif len(args)==1: locs = ax.set_yticks(args[0]) labels = ax.get_yticklabels() elif len(args)==2: locs = ax.set_yticks(args[0]) labels = ax.set_yticklabels(args[1], **kwargs) else: raise TypeError('Illegal number of arguments to yticks') if len(kwargs): for l in labels: l.update(kwargs) draw_if_interactive() return ( locs, silent_list('Text yticklabel', labels) ) def rgrids(*args, **kwargs): """ Set/Get the radial locations of the gridlines and ticklabels on a polar plot. call signatures:: lines, labels = rgrids() lines, labels = rgrids(radii, labels=None, angle=22.5, **kwargs) When called with no arguments, :func:`rgrid` simply returns the tuple (*lines*, *labels*), where *lines* is an array of radial gridlines (:class:`~matplotlib.lines.Line2D` instances) and *labels* is an array of tick labels (:class:`~matplotlib.text.Text` instances). When called with arguments, the labels will appear at the specified radial distances and angles. *labels*, if not *None*, is a len(*radii*) list of strings of the labels to use at each angle. If *labels* is None, the rformatter will be used Examples:: # set the locations of the radial gridlines and labels lines, labels = rgrids( (0.25, 0.5, 1.0) ) # set the locations and labels of the radial gridlines and labels lines, labels = rgrids( (0.25, 0.5, 1.0), ('Tom', 'Dick', 'Harry' ) """ ax = gca() if not isinstance(ax, PolarAxes): raise RuntimeError('rgrids only defined for polar axes') if len(args)==0: lines = ax.yaxis.get_ticklines() labels = ax.yaxis.get_ticklabels() else: lines, labels = ax.set_rgrids(*args, **kwargs) draw_if_interactive() return ( silent_list('Line2D rgridline', lines), silent_list('Text rgridlabel', labels) ) def thetagrids(*args, **kwargs): """ Set/Get the theta locations of the gridlines and ticklabels. If no arguments are passed, return a tuple (*lines*, *labels*) where *lines* is an array of radial gridlines (:class:`~matplotlib.lines.Line2D` instances) and *labels* is an array of tick labels (:class:`~matplotlib.text.Text` instances):: lines, labels = thetagrids() Otherwise the syntax is:: lines, labels = thetagrids(angles, labels=None, fmt='%d', frac = 1.1) set the angles at which to place the theta grids (these gridlines are equal along the theta dimension). *angles* is in degrees. *labels*, if not *None*, is a len(angles) list of strings of the labels to use at each angle. If *labels* is *None*, the labels will be ``fmt%angle``. *frac* is the fraction of the polar axes radius at which to place the label (1 is the edge). Eg. 1.05 is outside the axes and 0.95 is inside the axes. Return value is a list of tuples (*lines*, *labels*): - *lines* are :class:`~matplotlib.lines.Line2D` instances - *labels* are :class:`~matplotlib.text.Text` instances. Note that on input, the *labels* argument is a list of strings, and on output it is a list of :class:`~matplotlib.text.Text` instances. Examples:: # set the locations of the radial gridlines and labels lines, labels = thetagrids( range(45,360,90) ) # set the locations and labels of the radial gridlines and labels lines, labels = thetagrids( range(45,360,90), ('NE', 'NW', 'SW','SE') ) """ ax = gca() if not isinstance(ax, PolarAxes): raise RuntimeError('rgrids only defined for polar axes') if len(args)==0: lines = ax.xaxis.get_ticklines() labels = ax.xaxis.get_ticklabels() else: lines, labels = ax.set_thetagrids(*args, **kwargs) draw_if_interactive() return (silent_list('Line2D thetagridline', lines), silent_list('Text thetagridlabel', labels) ) ## Plotting Info ## def plotting(): """ Plotting commands =============== ========================================================= Command Description =============== ========================================================= axes Create a new axes axis Set or return the current axis limits bar make a bar chart boxplot make a box and whiskers chart cla clear current axes clabel label a contour plot clf clear a figure window close close a figure window colorbar add a colorbar to the current figure cohere make a plot of coherence contour make a contour plot contourf make a filled contour plot csd make a plot of cross spectral density draw force a redraw of the current figure errorbar make an errorbar graph figlegend add a legend to the figure figimage add an image to the figure, w/o resampling figtext add text in figure coords figure create or change active figure fill make filled polygons fill_between make filled polygons gca return the current axes gcf return the current figure gci get the current image, or None getp get a handle graphics property hist make a histogram hold set the hold state on current axes legend add a legend to the axes loglog a log log plot imread load image file into array imshow plot image data matshow display a matrix in a new figure preserving aspect pcolor make a pseudocolor plot plot make a line plot plotfile plot data from a flat file psd make a plot of power spectral density quiver make a direction field (arrows) plot rc control the default params savefig save the current figure scatter make a scatter plot setp set a handle graphics property semilogx log x axis semilogy log y axis show show the figures specgram a spectrogram plot stem make a stem plot subplot make a subplot (numrows, numcols, axesnum) table add a table to the axes text add some text at location x,y to the current axes title add a title to the current axes xlabel add an xlabel to the current axes ylabel add a ylabel to the current axes =============== ========================================================= The following commands will set the default colormap accordingly: * autumn * bone * cool * copper * flag * gray * hot * hsv * jet * pink * prism * spring * summer * winter * spectral """ pass def get_plot_commands(): return ( 'axes', 'axis', 'bar', 'boxplot', 'cla', 'clf', 'close', 'colorbar', 'cohere', 'csd', 'draw', 'errorbar', 'figlegend', 'figtext', 'figimage', 'figure', 'fill', 'gca', 'gcf', 'gci', 'get', 'gray', 'barh', 'jet', 'hist', 'hold', 'imread', 'imshow', 'legend', 'loglog', 'quiver', 'rc', 'pcolor', 'pcolormesh', 'plot', 'psd', 'savefig', 'scatter', 'set', 'semilogx', 'semilogy', 'show', 'specgram', 'stem', 'subplot', 'table', 'text', 'title', 'xlabel', 'ylabel', 'pie', 'polar') def colors(): """ This is a do nothing function to provide you with help on how matplotlib handles colors. Commands which take color arguments can use several formats to specify the colors. For the basic builtin colors, you can use a single letter ===== ======= Alias Color ===== ======= 'b' blue 'g' green 'r' red 'c' cyan 'm' magenta 'y' yellow 'k' black 'w' white ===== ======= For a greater range of colors, you have two options. You can specify the color using an html hex string, as in:: color = '#eeefff' or you can pass an R,G,B tuple, where each of R,G,B are in the range [0,1]. You can also use any legal html name for a color, for example:: color = 'red', color = 'burlywood' color = 'chartreuse' The example below creates a subplot with a dark slate gray background subplot(111, axisbg=(0.1843, 0.3098, 0.3098)) Here is an example that creates a pale turqoise title:: title('Is this the best color?', color='#afeeee') """ pass def colormaps(): """ matplotlib provides the following colormaps. * autumn * bone * cool * copper * flag * gray * hot * hsv * jet * pink * prism * spring * summer * winter * spectral You can set the colormap for an image, pcolor, scatter, etc, either as a keyword argument:: imshow(X, cmap=cm.hot) or post-hoc using the corresponding pylab interface function:: imshow(X) hot() jet() In interactive mode, this will update the colormap allowing you to see which one works best for your data. """ pass ## Plotting part 1: manually generated functions and wrappers ## from matplotlib.colorbar import colorbar_doc def colorbar(mappable=None, cax=None, ax=None, **kw): if mappable is None: mappable = gci() if ax is None: ax = gca() ret = gcf().colorbar(mappable, cax = cax, ax=ax, **kw) draw_if_interactive() return ret colorbar.__doc__ = colorbar_doc def clim(vmin=None, vmax=None): """ Set the color limits of the current image To apply clim to all axes images do:: clim(0, 0.5) If either *vmin* or *vmax* is None, the image min/max respectively will be used for color scaling. If you want to set the clim of multiple images, use, for example:: for im in gca().get_images(): im.set_clim(0, 0.05) """ im = gci() if im is None: raise RuntimeError('You must first define an image, eg with imshow') im.set_clim(vmin, vmax) draw_if_interactive() def imread(*args, **kwargs): return _imread(*args, **kwargs) if _imread.__doc__ is not None: imread.__doc__ = dedent(_imread.__doc__) def matshow(A, fignum=None, **kw): """ Display an array as a matrix in a new figure window. The origin is set at the upper left hand corner and rows (first dimension of the array) are displayed horizontally. The aspect ratio of the figure window is that of the array, unless this would make an excessively short or narrow figure. Tick labels for the xaxis are placed on top. With the exception of fignum, keyword arguments are passed to :func:`~matplotlib.pyplot.imshow`. *fignum*: [ None | integer | False ] By default, :func:`matshow` creates a new figure window with automatic numbering. If *fignum* is given as an integer, the created figure will use this figure number. Because of how :func:`matshow` tries to set the figure aspect ratio to be the one of the array, if you provide the number of an already existing figure, strange things may happen. If *fignum* is *False* or 0, a new figure window will **NOT** be created. """ if fignum is False or fignum is 0: ax = gca() else: # Extract actual aspect ratio of array and make appropriately sized figure fig = figure(fignum, figsize=figaspect(A)) ax = fig.add_axes([0.15, 0.09, 0.775, 0.775]) im = ax.matshow(A, **kw) gci._current = im draw_if_interactive() return im def polar(*args, **kwargs): """ call signature:: polar(theta, r, **kwargs) Make a polar plot. Multiple *theta*, *r* arguments are supported, with format strings, as in :func:`~matplotlib.pyplot.plot`. """ ax = gca(polar=True) ret = ax.plot(*args, **kwargs) draw_if_interactive() return ret def plotfile(fname, cols=(0,), plotfuncs=None, comments='#', skiprows=0, checkrows=5, delimiter=',', **kwargs): """ Plot the data in *fname* *cols* is a sequence of column identifiers to plot. An identifier is either an int or a string. If it is an int, it indicates the column number. If it is a string, it indicates the column header. matplotlib will make column headers lower case, replace spaces with underscores, and remove all illegal characters; so ``'Adj Close*'`` will have name ``'adj_close'``. - If len(*cols*) == 1, only that column will be plotted on the *y* axis. - If len(*cols*) > 1, the first element will be an identifier for data for the *x* axis and the remaining elements will be the column indexes for multiple subplots *plotfuncs*, if not *None*, is a dictionary mapping identifier to an :class:`~matplotlib.axes.Axes` plotting function as a string. Default is 'plot', other choices are 'semilogy', 'fill', 'bar', etc. You must use the same type of identifier in the *cols* vector as you use in the *plotfuncs* dictionary, eg., integer column numbers in both or column names in both. *comments*, *skiprows*, *checkrows*, and *delimiter* are all passed on to :func:`matplotlib.pylab.csv2rec` to load the data into a record array. kwargs are passed on to plotting functions. Example usage:: # plot the 2nd and 4th column against the 1st in two subplots plotfile(fname, (0,1,3)) # plot using column names; specify an alternate plot type for volume plotfile(fname, ('date', 'volume', 'adj_close'), plotfuncs={'volume': 'semilogy'}) """ fig = figure() if len(cols)<1: raise ValueError('must have at least one column of data') if plotfuncs is None: plotfuncs = dict() r = mlab.csv2rec(fname, comments=comments, skiprows=skiprows, checkrows=checkrows, delimiter=delimiter) def getname_val(identifier): 'return the name and column data for identifier' if is_string_like(identifier): return identifier, r[identifier] elif is_numlike(identifier): name = r.dtype.names[int(identifier)] return name, r[name] else: raise TypeError('identifier must be a string or integer') xname, x = getname_val(cols[0]) if len(cols)==1: ax1 = fig.add_subplot(1,1,1) funcname = plotfuncs.get(cols[0], 'plot') func = getattr(ax1, funcname) func(x, **kwargs) ax1.set_xlabel(xname) else: N = len(cols) for i in range(1,N): if i==1: ax = ax1 = fig.add_subplot(N-1,1,i) ax.grid(True) else: ax = fig.add_subplot(N-1,1,i, sharex=ax1) ax.grid(True) yname, y = getname_val(cols[i]) funcname = plotfuncs.get(cols[i], 'plot') func = getattr(ax, funcname) func(x, y, **kwargs) ax.set_ylabel(yname) if ax.is_last_row(): ax.set_xlabel(xname) else: ax.set_xlabel('') if xname=='date': fig.autofmt_xdate() draw_if_interactive() ## Plotting part 2: autogenerated wrappers for axes methods ## # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def acorr(*args, **kwargs): # allow callers to override the hold state by passing hold=True|False b = ishold() h = kwargs.pop('hold', None) if h is not None: hold(h) try: ret = gca().acorr(*args, **kwargs) draw_if_interactive() except: hold(b) raise hold(b) return ret if Axes.acorr.__doc__ is not None: acorr.__doc__ = dedent(Axes.acorr.__doc__) + """ Additional kwargs: hold = [True|False] overrides default hold state""" # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def arrow(*args, **kwargs): # allow callers to override the hold state by passing hold=True|False b = ishold() h = kwargs.pop('hold', None) if h is not None: hold(h) try: ret = gca().arrow(*args, **kwargs) draw_if_interactive() except: hold(b) raise hold(b) return ret if Axes.arrow.__doc__ is not None: arrow.__doc__ = dedent(Axes.arrow.__doc__) + """ Additional kwargs: hold = [True|False] overrides default hold state""" # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def axhline(*args, **kwargs): # allow callers to override the hold state by passing hold=True|False b = ishold() h = kwargs.pop('hold', None) if h is not None: hold(h) try: ret = gca().axhline(*args, **kwargs) draw_if_interactive() except: hold(b) raise hold(b) return ret if Axes.axhline.__doc__ is not None: axhline.__doc__ = dedent(Axes.axhline.__doc__) + """ Additional kwargs: hold = [True|False] overrides default hold state""" # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def axhspan(*args, **kwargs): # allow callers to override the hold state by passing hold=True|False b = ishold() h = kwargs.pop('hold', None) if h is not None: hold(h) try: ret = gca().axhspan(*args, **kwargs) draw_if_interactive() except: hold(b) raise hold(b) return ret if Axes.axhspan.__doc__ is not None: axhspan.__doc__ = dedent(Axes.axhspan.__doc__) + """ Additional kwargs: hold = [True|False] overrides default hold state""" # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def axvline(*args, **kwargs): # allow callers to override the hold state by passing hold=True|False b = ishold() h = kwargs.pop('hold', None) if h is not None: hold(h) try: ret = gca().axvline(*args, **kwargs) draw_if_interactive() except: hold(b) raise hold(b) return ret if Axes.axvline.__doc__ is not None: axvline.__doc__ = dedent(Axes.axvline.__doc__) + """ Additional kwargs: hold = [True|False] overrides default hold state""" # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def axvspan(*args, **kwargs): # allow callers to override the hold state by passing hold=True|False b = ishold() h = kwargs.pop('hold', None) if h is not None: hold(h) try: ret = gca().axvspan(*args, **kwargs) draw_if_interactive() except: hold(b) raise hold(b) return ret if Axes.axvspan.__doc__ is not None: axvspan.__doc__ = dedent(Axes.axvspan.__doc__) + """ Additional kwargs: hold = [True|False] overrides default hold state""" # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def bar(*args, **kwargs): # allow callers to override the hold state by passing hold=True|False b = ishold() h = kwargs.pop('hold', None) if h is not None: hold(h) try: ret = gca().bar(*args, **kwargs) draw_if_interactive() except: hold(b) raise hold(b) return ret if Axes.bar.__doc__ is not None: bar.__doc__ = dedent(Axes.bar.__doc__) + """ Additional kwargs: hold = [True|False] overrides default hold state""" # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def barh(*args, **kwargs): # allow callers to override the hold state by passing hold=True|False b = ishold() h = kwargs.pop('hold', None) if h is not None: hold(h) try: ret = gca().barh(*args, **kwargs) draw_if_interactive() except: hold(b) raise hold(b) return ret if Axes.barh.__doc__ is not None: barh.__doc__ = dedent(Axes.barh.__doc__) + """ Additional kwargs: hold = [True|False] overrides default hold state""" # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def broken_barh(*args, **kwargs): # allow callers to override the hold state by passing hold=True|False b = ishold() h = kwargs.pop('hold', None) if h is not None: hold(h) try: ret = gca().broken_barh(*args, **kwargs) draw_if_interactive() except: hold(b) raise hold(b) return ret if Axes.broken_barh.__doc__ is not None: broken_barh.__doc__ = dedent(Axes.broken_barh.__doc__) + """ Additional kwargs: hold = [True|False] overrides default hold state""" # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def boxplot(*args, **kwargs): # allow callers to override the hold state by passing hold=True|False b = ishold() h = kwargs.pop('hold', None) if h is not None: hold(h) try: ret = gca().boxplot(*args, **kwargs) draw_if_interactive() except: hold(b) raise hold(b) return ret if Axes.boxplot.__doc__ is not None: boxplot.__doc__ = dedent(Axes.boxplot.__doc__) + """ Additional kwargs: hold = [True|False] overrides default hold state""" # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def cohere(*args, **kwargs): # allow callers to override the hold state by passing hold=True|False b = ishold() h = kwargs.pop('hold', None) if h is not None: hold(h) try: ret = gca().cohere(*args, **kwargs) draw_if_interactive() except: hold(b) raise hold(b) return ret if Axes.cohere.__doc__ is not None: cohere.__doc__ = dedent(Axes.cohere.__doc__) + """ Additional kwargs: hold = [True|False] overrides default hold state""" # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def clabel(*args, **kwargs): # allow callers to override the hold state by passing hold=True|False b = ishold() h = kwargs.pop('hold', None) if h is not None: hold(h) try: ret = gca().clabel(*args, **kwargs) draw_if_interactive() except: hold(b) raise hold(b) return ret if Axes.clabel.__doc__ is not None: clabel.__doc__ = dedent(Axes.clabel.__doc__) + """ Additional kwargs: hold = [True|False] overrides default hold state""" # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def contour(*args, **kwargs): # allow callers to override the hold state by passing hold=True|False b = ishold() h = kwargs.pop('hold', None) if h is not None: hold(h) try: ret = gca().contour(*args, **kwargs) draw_if_interactive() except: hold(b) raise if ret._A is not None: gci._current = ret hold(b) return ret if Axes.contour.__doc__ is not None: contour.__doc__ = dedent(Axes.contour.__doc__) + """ Additional kwargs: hold = [True|False] overrides default hold state""" # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def contourf(*args, **kwargs): # allow callers to override the hold state by passing hold=True|False b = ishold() h = kwargs.pop('hold', None) if h is not None: hold(h) try: ret = gca().contourf(*args, **kwargs) draw_if_interactive() except: hold(b) raise if ret._A is not None: gci._current = ret hold(b) return ret if Axes.contourf.__doc__ is not None: contourf.__doc__ = dedent(Axes.contourf.__doc__) + """ Additional kwargs: hold = [True|False] overrides default hold state""" # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def csd(*args, **kwargs): # allow callers to override the hold state by passing hold=True|False b = ishold() h = kwargs.pop('hold', None) if h is not None: hold(h) try: ret = gca().csd(*args, **kwargs) draw_if_interactive() except: hold(b) raise hold(b) return ret if Axes.csd.__doc__ is not None: csd.__doc__ = dedent(Axes.csd.__doc__) + """ Additional kwargs: hold = [True|False] overrides default hold state""" # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def errorbar(*args, **kwargs): # allow callers to override the hold state by passing hold=True|False b = ishold() h = kwargs.pop('hold', None) if h is not None: hold(h) try: ret = gca().errorbar(*args, **kwargs) draw_if_interactive() except: hold(b) raise hold(b) return ret if Axes.errorbar.__doc__ is not None: errorbar.__doc__ = dedent(Axes.errorbar.__doc__) + """ Additional kwargs: hold = [True|False] overrides default hold state""" # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def fill(*args, **kwargs): # allow callers to override the hold state by passing hold=True|False b = ishold() h = kwargs.pop('hold', None) if h is not None: hold(h) try: ret = gca().fill(*args, **kwargs) draw_if_interactive() except: hold(b) raise hold(b) return ret if Axes.fill.__doc__ is not None: fill.__doc__ = dedent(Axes.fill.__doc__) + """ Additional kwargs: hold = [True|False] overrides default hold state""" # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def fill_between(*args, **kwargs): # allow callers to override the hold state by passing hold=True|False b = ishold() h = kwargs.pop('hold', None) if h is not None: hold(h) try: ret = gca().fill_between(*args, **kwargs) draw_if_interactive() except: hold(b) raise hold(b) return ret if Axes.fill_between.__doc__ is not None: fill_between.__doc__ = dedent(Axes.fill_between.__doc__) + """ Additional kwargs: hold = [True|False] overrides default hold state""" # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def hexbin(*args, **kwargs): # allow callers to override the hold state by passing hold=True|False b = ishold() h = kwargs.pop('hold', None) if h is not None: hold(h) try: ret = gca().hexbin(*args, **kwargs) draw_if_interactive() except: hold(b) raise gci._current = ret hold(b) return ret if Axes.hexbin.__doc__ is not None: hexbin.__doc__ = dedent(Axes.hexbin.__doc__) + """ Additional kwargs: hold = [True|False] overrides default hold state""" # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def hist(*args, **kwargs): # allow callers to override the hold state by passing hold=True|False b = ishold() h = kwargs.pop('hold', None) if h is not None: hold(h) try: ret = gca().hist(*args, **kwargs) draw_if_interactive() except: hold(b) raise hold(b) return ret if Axes.hist.__doc__ is not None: hist.__doc__ = dedent(Axes.hist.__doc__) + """ Additional kwargs: hold = [True|False] overrides default hold state""" # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def hlines(*args, **kwargs): # allow callers to override the hold state by passing hold=True|False b = ishold() h = kwargs.pop('hold', None) if h is not None: hold(h) try: ret = gca().hlines(*args, **kwargs) draw_if_interactive() except: hold(b) raise hold(b) return ret if Axes.hlines.__doc__ is not None: hlines.__doc__ = dedent(Axes.hlines.__doc__) + """ Additional kwargs: hold = [True|False] overrides default hold state""" # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def imshow(*args, **kwargs): # allow callers to override the hold state by passing hold=True|False b = ishold() h = kwargs.pop('hold', None) if h is not None: hold(h) try: ret = gca().imshow(*args, **kwargs) draw_if_interactive() except: hold(b) raise gci._current = ret hold(b) return ret if Axes.imshow.__doc__ is not None: imshow.__doc__ = dedent(Axes.imshow.__doc__) + """ Additional kwargs: hold = [True|False] overrides default hold state""" # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def loglog(*args, **kwargs): # allow callers to override the hold state by passing hold=True|False b = ishold() h = kwargs.pop('hold', None) if h is not None: hold(h) try: ret = gca().loglog(*args, **kwargs) draw_if_interactive() except: hold(b) raise hold(b) return ret if Axes.loglog.__doc__ is not None: loglog.__doc__ = dedent(Axes.loglog.__doc__) + """ Additional kwargs: hold = [True|False] overrides default hold state""" # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def pcolor(*args, **kwargs): # allow callers to override the hold state by passing hold=True|False b = ishold() h = kwargs.pop('hold', None) if h is not None: hold(h) try: ret = gca().pcolor(*args, **kwargs) draw_if_interactive() except: hold(b) raise gci._current = ret hold(b) return ret if Axes.pcolor.__doc__ is not None: pcolor.__doc__ = dedent(Axes.pcolor.__doc__) + """ Additional kwargs: hold = [True|False] overrides default hold state""" # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def pcolormesh(*args, **kwargs): # allow callers to override the hold state by passing hold=True|False b = ishold() h = kwargs.pop('hold', None) if h is not None: hold(h) try: ret = gca().pcolormesh(*args, **kwargs) draw_if_interactive() except: hold(b) raise gci._current = ret hold(b) return ret if Axes.pcolormesh.__doc__ is not None: pcolormesh.__doc__ = dedent(Axes.pcolormesh.__doc__) + """ Additional kwargs: hold = [True|False] overrides default hold state""" # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def pie(*args, **kwargs): # allow callers to override the hold state by passing hold=True|False b = ishold() h = kwargs.pop('hold', None) if h is not None: hold(h) try: ret = gca().pie(*args, **kwargs) draw_if_interactive() except: hold(b) raise hold(b) return ret if Axes.pie.__doc__ is not None: pie.__doc__ = dedent(Axes.pie.__doc__) + """ Additional kwargs: hold = [True|False] overrides default hold state""" # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def plot(*args, **kwargs): # allow callers to override the hold state by passing hold=True|False b = ishold() h = kwargs.pop('hold', None) if h is not None: hold(h) try: ret = gca().plot(*args, **kwargs) draw_if_interactive() except: hold(b) raise hold(b) return ret if Axes.plot.__doc__ is not None: plot.__doc__ = dedent(Axes.plot.__doc__) + """ Additional kwargs: hold = [True|False] overrides default hold state""" # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def plot_date(*args, **kwargs): # allow callers to override the hold state by passing hold=True|False b = ishold() h = kwargs.pop('hold', None) if h is not None: hold(h) try: ret = gca().plot_date(*args, **kwargs) draw_if_interactive() except: hold(b) raise hold(b) return ret if Axes.plot_date.__doc__ is not None: plot_date.__doc__ = dedent(Axes.plot_date.__doc__) + """ Additional kwargs: hold = [True|False] overrides default hold state""" # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def psd(*args, **kwargs): # allow callers to override the hold state by passing hold=True|False b = ishold() h = kwargs.pop('hold', None) if h is not None: hold(h) try: ret = gca().psd(*args, **kwargs) draw_if_interactive() except: hold(b) raise hold(b) return ret if Axes.psd.__doc__ is not None: psd.__doc__ = dedent(Axes.psd.__doc__) + """ Additional kwargs: hold = [True|False] overrides default hold state""" # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def quiver(*args, **kwargs): # allow callers to override the hold state by passing hold=True|False b = ishold() h = kwargs.pop('hold', None) if h is not None: hold(h) try: ret = gca().quiver(*args, **kwargs) draw_if_interactive() except: hold(b) raise gci._current = ret hold(b) return ret if Axes.quiver.__doc__ is not None: quiver.__doc__ = dedent(Axes.quiver.__doc__) + """ Additional kwargs: hold = [True|False] overrides default hold state""" # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def quiverkey(*args, **kwargs): # allow callers to override the hold state by passing hold=True|False b = ishold() h = kwargs.pop('hold', None) if h is not None: hold(h) try: ret = gca().quiverkey(*args, **kwargs) draw_if_interactive() except: hold(b) raise hold(b) return ret if Axes.quiverkey.__doc__ is not None: quiverkey.__doc__ = dedent(Axes.quiverkey.__doc__) + """ Additional kwargs: hold = [True|False] overrides default hold state""" # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def scatter(*args, **kwargs): # allow callers to override the hold state by passing hold=True|False b = ishold() h = kwargs.pop('hold', None) if h is not None: hold(h) try: ret = gca().scatter(*args, **kwargs) draw_if_interactive() except: hold(b) raise gci._current = ret hold(b) return ret if Axes.scatter.__doc__ is not None: scatter.__doc__ = dedent(Axes.scatter.__doc__) + """ Additional kwargs: hold = [True|False] overrides default hold state""" # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def semilogx(*args, **kwargs): # allow callers to override the hold state by passing hold=True|False b = ishold() h = kwargs.pop('hold', None) if h is not None: hold(h) try: ret = gca().semilogx(*args, **kwargs) draw_if_interactive() except: hold(b) raise hold(b) return ret if Axes.semilogx.__doc__ is not None: semilogx.__doc__ = dedent(Axes.semilogx.__doc__) + """ Additional kwargs: hold = [True|False] overrides default hold state""" # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def semilogy(*args, **kwargs): # allow callers to override the hold state by passing hold=True|False b = ishold() h = kwargs.pop('hold', None) if h is not None: hold(h) try: ret = gca().semilogy(*args, **kwargs) draw_if_interactive() except: hold(b) raise hold(b) return ret if Axes.semilogy.__doc__ is not None: semilogy.__doc__ = dedent(Axes.semilogy.__doc__) + """ Additional kwargs: hold = [True|False] overrides default hold state""" # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def specgram(*args, **kwargs): # allow callers to override the hold state by passing hold=True|False b = ishold() h = kwargs.pop('hold', None) if h is not None: hold(h) try: ret = gca().specgram(*args, **kwargs) draw_if_interactive() except: hold(b) raise gci._current = ret[-1] hold(b) return ret if Axes.specgram.__doc__ is not None: specgram.__doc__ = dedent(Axes.specgram.__doc__) + """ Additional kwargs: hold = [True|False] overrides default hold state""" # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def spy(*args, **kwargs): # allow callers to override the hold state by passing hold=True|False b = ishold() h = kwargs.pop('hold', None) if h is not None: hold(h) try: ret = gca().spy(*args, **kwargs) draw_if_interactive() except: hold(b) raise gci._current = ret hold(b) return ret if Axes.spy.__doc__ is not None: spy.__doc__ = dedent(Axes.spy.__doc__) + """ Additional kwargs: hold = [True|False] overrides default hold state""" # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def stem(*args, **kwargs): # allow callers to override the hold state by passing hold=True|False b = ishold() h = kwargs.pop('hold', None) if h is not None: hold(h) try: ret = gca().stem(*args, **kwargs) draw_if_interactive() except: hold(b) raise hold(b) return ret if Axes.stem.__doc__ is not None: stem.__doc__ = dedent(Axes.stem.__doc__) + """ Additional kwargs: hold = [True|False] overrides default hold state""" # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def step(*args, **kwargs): # allow callers to override the hold state by passing hold=True|False b = ishold() h = kwargs.pop('hold', None) if h is not None: hold(h) try: ret = gca().step(*args, **kwargs) draw_if_interactive() except: hold(b) raise hold(b) return ret if Axes.step.__doc__ is not None: step.__doc__ = dedent(Axes.step.__doc__) + """ Additional kwargs: hold = [True|False] overrides default hold state""" # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def vlines(*args, **kwargs): # allow callers to override the hold state by passing hold=True|False b = ishold() h = kwargs.pop('hold', None) if h is not None: hold(h) try: ret = gca().vlines(*args, **kwargs) draw_if_interactive() except: hold(b) raise hold(b) return ret if Axes.vlines.__doc__ is not None: vlines.__doc__ = dedent(Axes.vlines.__doc__) + """ Additional kwargs: hold = [True|False] overrides default hold state""" # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def xcorr(*args, **kwargs): # allow callers to override the hold state by passing hold=True|False b = ishold() h = kwargs.pop('hold', None) if h is not None: hold(h) try: ret = gca().xcorr(*args, **kwargs) draw_if_interactive() except: hold(b) raise hold(b) return ret if Axes.xcorr.__doc__ is not None: xcorr.__doc__ = dedent(Axes.xcorr.__doc__) + """ Additional kwargs: hold = [True|False] overrides default hold state""" # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def barbs(*args, **kwargs): # allow callers to override the hold state by passing hold=True|False b = ishold() h = kwargs.pop('hold', None) if h is not None: hold(h) try: ret = gca().barbs(*args, **kwargs) draw_if_interactive() except: hold(b) raise hold(b) return ret if Axes.barbs.__doc__ is not None: barbs.__doc__ = dedent(Axes.barbs.__doc__) + """ Additional kwargs: hold = [True|False] overrides default hold state""" # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def cla(*args, **kwargs): ret = gca().cla(*args, **kwargs) draw_if_interactive() return ret if Axes.cla.__doc__ is not None: cla.__doc__ = dedent(Axes.cla.__doc__) # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def grid(*args, **kwargs): ret = gca().grid(*args, **kwargs) draw_if_interactive() return ret if Axes.grid.__doc__ is not None: grid.__doc__ = dedent(Axes.grid.__doc__) # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def legend(*args, **kwargs): ret = gca().legend(*args, **kwargs) draw_if_interactive() return ret if Axes.legend.__doc__ is not None: legend.__doc__ = dedent(Axes.legend.__doc__) # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def table(*args, **kwargs): ret = gca().table(*args, **kwargs) draw_if_interactive() return ret if Axes.table.__doc__ is not None: table.__doc__ = dedent(Axes.table.__doc__) # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def text(*args, **kwargs): ret = gca().text(*args, **kwargs) draw_if_interactive() return ret if Axes.text.__doc__ is not None: text.__doc__ = dedent(Axes.text.__doc__) # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def annotate(*args, **kwargs): ret = gca().annotate(*args, **kwargs) draw_if_interactive() return ret if Axes.annotate.__doc__ is not None: annotate.__doc__ = dedent(Axes.annotate.__doc__) # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def autumn(): ''' set the default colormap to autumn and apply to current image if any. See help(colormaps) for more information ''' rc('image', cmap='autumn') im = gci() if im is not None: im.set_cmap(cm.autumn) draw_if_interactive() # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def bone(): ''' set the default colormap to bone and apply to current image if any. See help(colormaps) for more information ''' rc('image', cmap='bone') im = gci() if im is not None: im.set_cmap(cm.bone) draw_if_interactive() # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def cool(): ''' set the default colormap to cool and apply to current image if any. See help(colormaps) for more information ''' rc('image', cmap='cool') im = gci() if im is not None: im.set_cmap(cm.cool) draw_if_interactive() # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def copper(): ''' set the default colormap to copper and apply to current image if any. See help(colormaps) for more information ''' rc('image', cmap='copper') im = gci() if im is not None: im.set_cmap(cm.copper) draw_if_interactive() # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def flag(): ''' set the default colormap to flag and apply to current image if any. See help(colormaps) for more information ''' rc('image', cmap='flag') im = gci() if im is not None: im.set_cmap(cm.flag) draw_if_interactive() # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def gray(): ''' set the default colormap to gray and apply to current image if any. See help(colormaps) for more information ''' rc('image', cmap='gray') im = gci() if im is not None: im.set_cmap(cm.gray) draw_if_interactive() # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def hot(): ''' set the default colormap to hot and apply to current image if any. See help(colormaps) for more information ''' rc('image', cmap='hot') im = gci() if im is not None: im.set_cmap(cm.hot) draw_if_interactive() # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def hsv(): ''' set the default colormap to hsv and apply to current image if any. See help(colormaps) for more information ''' rc('image', cmap='hsv') im = gci() if im is not None: im.set_cmap(cm.hsv) draw_if_interactive() # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def jet(): ''' set the default colormap to jet and apply to current image if any. See help(colormaps) for more information ''' rc('image', cmap='jet') im = gci() if im is not None: im.set_cmap(cm.jet) draw_if_interactive() # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def pink(): ''' set the default colormap to pink and apply to current image if any. See help(colormaps) for more information ''' rc('image', cmap='pink') im = gci() if im is not None: im.set_cmap(cm.pink) draw_if_interactive() # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def prism(): ''' set the default colormap to prism and apply to current image if any. See help(colormaps) for more information ''' rc('image', cmap='prism') im = gci() if im is not None: im.set_cmap(cm.prism) draw_if_interactive() # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def spring(): ''' set the default colormap to spring and apply to current image if any. See help(colormaps) for more information ''' rc('image', cmap='spring') im = gci() if im is not None: im.set_cmap(cm.spring) draw_if_interactive() # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def summer(): ''' set the default colormap to summer and apply to current image if any. See help(colormaps) for more information ''' rc('image', cmap='summer') im = gci() if im is not None: im.set_cmap(cm.summer) draw_if_interactive() # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def winter(): ''' set the default colormap to winter and apply to current image if any. See help(colormaps) for more information ''' rc('image', cmap='winter') im = gci() if im is not None: im.set_cmap(cm.winter) draw_if_interactive() # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def spectral(): ''' set the default colormap to spectral and apply to current image if any. See help(colormaps) for more information ''' rc('image', cmap='spectral') im = gci() if im is not None: im.set_cmap(cm.spectral) draw_if_interactive()

77,521

Python

.py

2,170

29.981106

88

0.632719

numenta/nupic-legacy

6,330

1,556

464

AGPL-3.0

9/5/2024, 5:13:42 PM (Europe/Amsterdam)

26,243

quiver.py

numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/quiver.py

""" Support for plotting vector fields. Presently this contains Quiver and Barb. Quiver plots an arrow in the direction of the vector, with the size of the arrow related to the magnitude of the vector. Barbs are like quiver in that they point along a vector, but the magnitude of the vector is given schematically by the presence of barbs or flags on the barb. This will also become a home for things such as standard deviation ellipses, which can and will be derived very easily from the Quiver code. """ import numpy as np from numpy import ma import matplotlib.collections as collections import matplotlib.transforms as transforms import matplotlib.text as mtext import matplotlib.artist as martist import matplotlib.font_manager as font_manager from matplotlib.cbook import delete_masked_points from matplotlib.patches import CirclePolygon import math _quiver_doc = """ Plot a 2-D field of arrows. call signatures:: quiver(U, V, **kw) quiver(U, V, C, **kw) quiver(X, Y, U, V, **kw) quiver(X, Y, U, V, C, **kw) Arguments: *X*, *Y*: The x and y coordinates of the arrow locations (default is tail of arrow; see *pivot* kwarg) *U*, *V*: give the *x* and *y* components of the arrow vectors *C*: an optional array used to map colors to the arrows All arguments may be 1-D or 2-D arrays or sequences. If *X* and *Y* are absent, they will be generated as a uniform grid. If *U* and *V* are 2-D arrays but *X* and *Y* are 1-D, and if len(*X*) and len(*Y*) match the column and row dimensions of *U*, then *X* and *Y* will be expanded with :func:`numpy.meshgrid`. *U*, *V*, *C* may be masked arrays, but masked *X*, *Y* are not supported at present. Keyword arguments: *units*: ['width' | 'height' | 'dots' | 'inches' | 'x' | 'y' ] arrow units; the arrow dimensions *except for length* are in multiples of this unit. * 'width' or 'height': the width or height of the axes * 'dots' or 'inches': pixels or inches, based on the figure dpi * 'x' or 'y': *X* or *Y* data units The arrows scale differently depending on the units. For 'x' or 'y', the arrows get larger as one zooms in; for other units, the arrow size is independent of the zoom state. For 'width or 'height', the arrow size increases with the width and height of the axes, respectively, when the the window is resized; for 'dots' or 'inches', resizing does not change the arrows. *angles*: ['uv' | 'xy' | array] With the default 'uv', the arrow aspect ratio is 1, so that if *U*==*V* the angle of the arrow on the plot is 45 degrees CCW from the *x*-axis. With 'xy', the arrow points from (x,y) to (x+u, y+v). Alternatively, arbitrary angles may be specified as an array of values in degrees, CCW from the *x*-axis. *scale*: [ None | float ] data units per arrow unit, e.g. m/s per plot width; a smaller scale parameter makes the arrow longer. If *None*, a simple autoscaling algorithm is used, based on the average vector length and the number of vectors. *width*: shaft width in arrow units; default depends on choice of units, above, and number of vectors; a typical starting value is about 0.005 times the width of the plot. *headwidth*: scalar head width as multiple of shaft width, default is 3 *headlength*: scalar head length as multiple of shaft width, default is 5 *headaxislength*: scalar head length at shaft intersection, default is 4.5 *minshaft*: scalar length below which arrow scales, in units of head length. Do not set this to less than 1, or small arrows will look terrible! Default is 1 *minlength*: scalar minimum length as a multiple of shaft width; if an arrow length is less than this, plot a dot (hexagon) of this diameter instead. Default is 1. *pivot*: [ 'tail' | 'middle' | 'tip' ] The part of the arrow that is at the grid point; the arrow rotates about this point, hence the name *pivot*. *color*: [ color | color sequence ] This is a synonym for the :class:`~matplotlib.collections.PolyCollection` facecolor kwarg. If *C* has been set, *color* has no effect. The defaults give a slightly swept-back arrow; to make the head a triangle, make *headaxislength* the same as *headlength*. To make the arrow more pointed, reduce *headwidth* or increase *headlength* and *headaxislength*. To make the head smaller relative to the shaft, scale down all the head parameters. You will probably do best to leave minshaft alone. linewidths and edgecolors can be used to customize the arrow outlines. Additional :class:`~matplotlib.collections.PolyCollection` keyword arguments: %(PolyCollection)s """ % martist.kwdocd _quiverkey_doc = """ Add a key to a quiver plot. call signature:: quiverkey(Q, X, Y, U, label, **kw) Arguments: *Q*: The Quiver instance returned by a call to quiver. *X*, *Y*: The location of the key; additional explanation follows. *U*: The length of the key *label*: a string with the length and units of the key Keyword arguments: *coordinates* = [ 'axes' | 'figure' | 'data' | 'inches' ] Coordinate system and units for *X*, *Y*: 'axes' and 'figure' are normalized coordinate systems with 0,0 in the lower left and 1,1 in the upper right; 'data' are the axes data coordinates (used for the locations of the vectors in the quiver plot itself); 'inches' is position in the figure in inches, with 0,0 at the lower left corner. *color*: overrides face and edge colors from *Q*. *labelpos* = [ 'N' | 'S' | 'E' | 'W' ] Position the label above, below, to the right, to the left of the arrow, respectively. *labelsep*: Distance in inches between the arrow and the label. Default is 0.1 *labelcolor*: defaults to default :class:`~matplotlib.text.Text` color. *fontproperties*: A dictionary with keyword arguments accepted by the :class:`~matplotlib.font_manager.FontProperties` initializer: *family*, *style*, *variant*, *size*, *weight* Any additional keyword arguments are used to override vector properties taken from *Q*. The positioning of the key depends on *X*, *Y*, *coordinates*, and *labelpos*. If *labelpos* is 'N' or 'S', *X*, *Y* give the position of the middle of the key arrow. If *labelpos* is 'E', *X*, *Y* positions the head, and if *labelpos* is 'W', *X*, *Y* positions the tail; in either of these two cases, *X*, *Y* is somewhere in the middle of the arrow+label key object. """ class QuiverKey(martist.Artist): """ Labelled arrow for use as a quiver plot scale key. """ halign = {'N': 'center', 'S': 'center', 'E': 'left', 'W': 'right'} valign = {'N': 'bottom', 'S': 'top', 'E': 'center', 'W': 'center'} pivot = {'N': 'mid', 'S': 'mid', 'E': 'tip', 'W': 'tail'} def __init__(self, Q, X, Y, U, label, **kw): martist.Artist.__init__(self) self.Q = Q self.X = X self.Y = Y self.U = U self.coord = kw.pop('coordinates', 'axes') self.color = kw.pop('color', None) self.label = label self._labelsep_inches = kw.pop('labelsep', 0.1) self.labelsep = (self._labelsep_inches * Q.ax.figure.dpi) def on_dpi_change(fig): self.labelsep = (self._labelsep_inches * fig.dpi) self._initialized = False # simple brute force update # works because _init is called # at the start of draw. Q.ax.figure.callbacks.connect('dpi_changed', on_dpi_change) self.labelpos = kw.pop('labelpos', 'N') self.labelcolor = kw.pop('labelcolor', None) self.fontproperties = kw.pop('fontproperties', dict()) self.kw = kw _fp = self.fontproperties #boxprops = dict(facecolor='red') self.text = mtext.Text(text=label, # bbox=boxprops, horizontalalignment=self.halign[self.labelpos], verticalalignment=self.valign[self.labelpos], fontproperties=font_manager.FontProperties(**_fp)) if self.labelcolor is not None: self.text.set_color(self.labelcolor) self._initialized = False self.zorder = Q.zorder + 0.1 __init__.__doc__ = _quiverkey_doc def _init(self): if True: ##not self._initialized: self._set_transform() _pivot = self.Q.pivot self.Q.pivot = self.pivot[self.labelpos] self.verts = self.Q._make_verts(np.array([self.U]), np.zeros((1,))) self.Q.pivot = _pivot kw = self.Q.polykw kw.update(self.kw) self.vector = collections.PolyCollection(self.verts, offsets=[(self.X,self.Y)], transOffset=self.get_transform(), **kw) if self.color is not None: self.vector.set_color(self.color) self.vector.set_transform(self.Q.get_transform()) self._initialized = True def _text_x(self, x): if self.labelpos == 'E': return x + self.labelsep elif self.labelpos == 'W': return x - self.labelsep else: return x def _text_y(self, y): if self.labelpos == 'N': return y + self.labelsep elif self.labelpos == 'S': return y - self.labelsep else: return y def draw(self, renderer): self._init() self.vector.draw(renderer) x, y = self.get_transform().transform_point((self.X, self.Y)) self.text.set_x(self._text_x(x)) self.text.set_y(self._text_y(y)) self.text.draw(renderer) def _set_transform(self): if self.coord == 'data': self.set_transform(self.Q.ax.transData) elif self.coord == 'axes': self.set_transform(self.Q.ax.transAxes) elif self.coord == 'figure': self.set_transform(self.Q.ax.figure.transFigure) elif self.coord == 'inches': self.set_transform(self.Q.ax.figure.dpi_scale_trans) else: raise ValueError('unrecognized coordinates') def set_figure(self, fig): martist.Artist.set_figure(self, fig) self.text.set_figure(fig) def contains(self, mouseevent): # Maybe the dictionary should allow one to # distinguish between a text hit and a vector hit. if (self.text.contains(mouseevent)[0] or self.vector.contains(mouseevent)[0]): return True, {} return False, {} quiverkey_doc = _quiverkey_doc class Quiver(collections.PolyCollection): """ Specialized PolyCollection for arrows. The only API method is set_UVC(), which can be used to change the size, orientation, and color of the arrows; their locations are fixed when the class is instantiated. Possibly this method will be useful in animations. Much of the work in this class is done in the draw() method so that as much information as possible is available about the plot. In subsequent draw() calls, recalculation is limited to things that might have changed, so there should be no performance penalty from putting the calculations in the draw() method. """ def __init__(self, ax, *args, **kw): self.ax = ax X, Y, U, V, C = self._parse_args(*args) self.X = X self.Y = Y self.XY = np.hstack((X[:,np.newaxis], Y[:,np.newaxis])) self.N = len(X) self.scale = kw.pop('scale', None) self.headwidth = kw.pop('headwidth', 3) self.headlength = float(kw.pop('headlength', 5)) self.headaxislength = kw.pop('headaxislength', 4.5) self.minshaft = kw.pop('minshaft', 1) self.minlength = kw.pop('minlength', 1) self.units = kw.pop('units', 'width') self.angles = kw.pop('angles', 'uv') self.width = kw.pop('width', None) self.color = kw.pop('color', 'k') self.pivot = kw.pop('pivot', 'tail') kw.setdefault('facecolors', self.color) kw.setdefault('linewidths', (0,)) collections.PolyCollection.__init__(self, [], offsets=self.XY, transOffset=ax.transData, closed=False, **kw) self.polykw = kw self.set_UVC(U, V, C) self._initialized = False self.keyvec = None self.keytext = None def on_dpi_change(fig): self._new_UV = True # vertices depend on width, span # which in turn depend on dpi self._initialized = False # simple brute force update # works because _init is called # at the start of draw. self.ax.figure.callbacks.connect('dpi_changed', on_dpi_change) __init__.__doc__ = """ The constructor takes one required argument, an Axes instance, followed by the args and kwargs described by the following pylab interface documentation: %s""" % _quiver_doc def _parse_args(self, *args): X, Y, U, V, C = [None]*5 args = list(args) if len(args) == 3 or len(args) == 5: C = ma.asarray(args.pop(-1)).ravel() V = ma.asarray(args.pop(-1)) U = ma.asarray(args.pop(-1)) nn = np.shape(U) nc = nn[0] nr = 1 if len(nn) > 1: nr = nn[1] if len(args) == 2: # remaining after removing U,V,C X, Y = [np.array(a).ravel() for a in args] if len(X) == nc and len(Y) == nr: X, Y = [a.ravel() for a in np.meshgrid(X, Y)] else: indexgrid = np.meshgrid(np.arange(nc), np.arange(nr)) X, Y = [np.ravel(a) for a in indexgrid] return X, Y, U, V, C def _init(self): """initialization delayed until first draw; allow time for axes setup. """ # It seems that there are not enough event notifications # available to have this work on an as-needed basis at present. if True: ##not self._initialized: trans = self._set_transform() ax = self.ax sx, sy = trans.inverted().transform_point( (ax.bbox.width, ax.bbox.height)) self.span = sx sn = max(8, min(25, math.sqrt(self.N))) if self.width is None: self.width = 0.06 * self.span / sn def draw(self, renderer): self._init() if self._new_UV or self.angles == 'xy': verts = self._make_verts(self.U, self.V) self.set_verts(verts, closed=False) self._new_UV = False collections.PolyCollection.draw(self, renderer) def set_UVC(self, U, V, C=None): self.U = U.ravel() self.V = V.ravel() if C is not None: self.set_array(C.ravel()) self._new_UV = True def _set_transform(self): ax = self.ax if self.units in ('x', 'y'): if self.units == 'x': dx0 = ax.viewLim.width dx1 = ax.bbox.width else: dx0 = ax.viewLim.height dx1 = ax.bbox.height dx = dx1/dx0 else: if self.units == 'width': dx = ax.bbox.width elif self.units == 'height': dx = ax.bbox.height elif self.units == 'dots': dx = 1.0 elif self.units == 'inches': dx = ax.figure.dpi else: raise ValueError('unrecognized units') trans = transforms.Affine2D().scale(dx) self.set_transform(trans) return trans def _angles(self, U, V, eps=0.001): xy = self.ax.transData.transform(self.XY) uv = ma.hstack((U[:,np.newaxis], V[:,np.newaxis])).filled(0) xyp = self.ax.transData.transform(self.XY + eps * uv) dxy = xyp - xy ang = ma.arctan2(dxy[:,1], dxy[:,0]) return ang def _make_verts(self, U, V): uv = ma.asarray(U+V*1j) a = ma.absolute(uv) if self.scale is None: sn = max(10, math.sqrt(self.N)) scale = 1.8 * a.mean() * sn / self.span # crude auto-scaling self.scale = scale length = a/(self.scale*self.width) X, Y = self._h_arrows(length) if self.angles == 'xy': theta = self._angles(U, V).filled(0)[:,np.newaxis] elif self.angles == 'uv': theta = np.angle(ma.asarray(uv[..., np.newaxis]).filled(0)) else: theta = ma.asarray(self.angles*np.pi/180.0).filled(0) xy = (X+Y*1j) * np.exp(1j*theta)*self.width xy = xy[:,:,np.newaxis] XY = ma.concatenate((xy.real, xy.imag), axis=2) return XY def _h_arrows(self, length): """ length is in arrow width units """ # It might be possible to streamline the code # and speed it up a bit by using complex (x,y) # instead of separate arrays; but any gain would be slight. minsh = self.minshaft * self.headlength N = len(length) length = length.reshape(N, 1) # x, y: normal horizontal arrow x = np.array([0, -self.headaxislength, -self.headlength, 0], np.float64) x = x + np.array([0,1,1,1]) * length y = 0.5 * np.array([1, 1, self.headwidth, 0], np.float64) y = np.repeat(y[np.newaxis,:], N, axis=0) # x0, y0: arrow without shaft, for short vectors x0 = np.array([0, minsh-self.headaxislength, minsh-self.headlength, minsh], np.float64) y0 = 0.5 * np.array([1, 1, self.headwidth, 0], np.float64) ii = [0,1,2,3,2,1,0] X = x.take(ii, 1) Y = y.take(ii, 1) Y[:, 3:] *= -1 X0 = x0.take(ii) Y0 = y0.take(ii) Y0[3:] *= -1 shrink = length/minsh X0 = shrink * X0[np.newaxis,:] Y0 = shrink * Y0[np.newaxis,:] short = np.repeat(length < minsh, 7, axis=1) #print 'short', length < minsh # Now select X0, Y0 if short, otherwise X, Y X = ma.where(short, X0, X) Y = ma.where(short, Y0, Y) if self.pivot[:3] == 'mid': X -= 0.5 * X[:,3, np.newaxis] elif self.pivot[:3] == 'tip': X = X - X[:,3, np.newaxis] #numpy bug? using -= does not # work here unless we multiply # by a float first, as with 'mid'. tooshort = length < self.minlength if tooshort.any(): # Use a heptagonal dot: th = np.arange(0,7,1, np.float64) * (np.pi/3.0) x1 = np.cos(th) * self.minlength * 0.5 y1 = np.sin(th) * self.minlength * 0.5 X1 = np.repeat(x1[np.newaxis, :], N, axis=0) Y1 = np.repeat(y1[np.newaxis, :], N, axis=0) tooshort = ma.repeat(tooshort, 7, 1) X = ma.where(tooshort, X1, X) Y = ma.where(tooshort, Y1, Y) return X, Y quiver_doc = _quiver_doc _barbs_doc = """ Plot a 2-D field of barbs. call signatures:: barb(U, V, **kw) barb(U, V, C, **kw) barb(X, Y, U, V, **kw) barb(X, Y, U, V, C, **kw) Arguments: *X*, *Y*: The x and y coordinates of the barb locations (default is head of barb; see *pivot* kwarg) *U*, *V*: give the *x* and *y* components of the barb shaft *C*: an optional array used to map colors to the barbs All arguments may be 1-D or 2-D arrays or sequences. If *X* and *Y* are absent, they will be generated as a uniform grid. If *U* and *V* are 2-D arrays but *X* and *Y* are 1-D, and if len(*X*) and len(*Y*) match the column and row dimensions of *U*, then *X* and *Y* will be expanded with :func:`numpy.meshgrid`. *U*, *V*, *C* may be masked arrays, but masked *X*, *Y* are not supported at present. Keyword arguments: *length*: Length of the barb in points; the other parts of the barb are scaled against this. Default is 9 *pivot*: [ 'tip' | 'middle' ] The part of the arrow that is at the grid point; the arrow rotates about this point, hence the name *pivot*. Default is 'tip' *barbcolor*: [ color | color sequence ] Specifies the color all parts of the barb except any flags. This parameter is analagous to the *edgecolor* parameter for polygons, which can be used instead. However this parameter will override facecolor. *flagcolor*: [ color | color sequence ] Specifies the color of any flags on the barb. This parameter is analagous to the *facecolor* parameter for polygons, which can be used instead. However this parameter will override facecolor. If this is not set (and *C* has not either) then *flagcolor* will be set to match *barbcolor* so that the barb has a uniform color. If *C* has been set, *flagcolor* has no effect. *sizes*: A dictionary of coefficients specifying the ratio of a given feature to the length of the barb. Only those values one wishes to override need to be included. These features include: - 'spacing' - space between features (flags, full/half barbs) - 'height' - height (distance from shaft to top) of a flag or full barb - 'width' - width of a flag, twice the width of a full barb - 'emptybarb' - radius of the circle used for low magnitudes *fill_empty*: A flag on whether the empty barbs (circles) that are drawn should be filled with the flag color. If they are not filled, they will be drawn such that no color is applied to the center. Default is False *rounding*: A flag to indicate whether the vector magnitude should be rounded when allocating barb components. If True, the magnitude is rounded to the nearest multiple of the half-barb increment. If False, the magnitude is simply truncated to the next lowest multiple. Default is True *barb_increments*: A dictionary of increments specifying values to associate with different parts of the barb. Only those values one wishes to override need to be included. - 'half' - half barbs (Default is 5) - 'full' - full barbs (Default is 10) - 'flag' - flags (default is 50) *flip_barb*: Either a single boolean flag or an array of booleans. Single boolean indicates whether the lines and flags should point opposite to normal for all barbs. An array (which should be the same size as the other data arrays) indicates whether to flip for each individual barb. Normal behavior is for the barbs and lines to point right (comes from wind barbs having these features point towards low pressure in the Northern Hemisphere.) Default is False Barbs are traditionally used in meteorology as a way to plot the speed and direction of wind observations, but can technically be used to plot any two dimensional vector quantity. As opposed to arrows, which give vector magnitude by the length of the arrow, the barbs give more quantitative information about the vector magnitude by putting slanted lines or a triangle for various increments in magnitude, as show schematically below:: : /\ \\ : / \ \\ : / \ \ \\ : / \ \ \\ : ------------------------------ .. note the double \\ at the end of each line to make the figure .. render correctly The largest increment is given by a triangle (or "flag"). After those come full lines (barbs). The smallest increment is a half line. There is only, of course, ever at most 1 half line. If the magnitude is small and only needs a single half-line and no full lines or triangles, the half-line is offset from the end of the barb so that it can be easily distinguished from barbs with a single full line. The magnitude for the barb shown above would nominally be 65, using the standard increments of 50, 10, and 5. linewidths and edgecolors can be used to customize the barb. Additional :class:`~matplotlib.collections.PolyCollection` keyword arguments: %(PolyCollection)s """ % martist.kwdocd class Barbs(collections.PolyCollection): ''' Specialized PolyCollection for barbs. The only API method is :meth:`set_UVC`, which can be used to change the size, orientation, and color of the arrows. Locations are changed using the :meth:`set_offsets` collection method. Possibly this method will be useful in animations. There is one internal function :meth:`_find_tails` which finds exactly what should be put on the barb given the vector magnitude. From there :meth:`_make_barbs` is used to find the vertices of the polygon to represent the barb based on this information. ''' #This may be an abuse of polygons here to render what is essentially maybe #1 triangle and a series of lines. It works fine as far as I can tell #however. def __init__(self, ax, *args, **kw): self._pivot = kw.pop('pivot', 'tip') self._length = kw.pop('length', 7) barbcolor = kw.pop('barbcolor', None) flagcolor = kw.pop('flagcolor', None) self.sizes = kw.pop('sizes', dict()) self.fill_empty = kw.pop('fill_empty', False) self.barb_increments = kw.pop('barb_increments', dict()) self.rounding = kw.pop('rounding', True) self.flip = kw.pop('flip_barb', False) #Flagcolor and and barbcolor provide convenience parameters for setting #the facecolor and edgecolor, respectively, of the barb polygon. We #also work here to make the flag the same color as the rest of the barb #by default if None in (barbcolor, flagcolor): kw['edgecolors'] = 'face' if flagcolor: kw['facecolors'] = flagcolor elif barbcolor: kw['facecolors'] = barbcolor else: #Set to facecolor passed in or default to black kw.setdefault('facecolors', 'k') else: kw['edgecolors'] = barbcolor kw['facecolors'] = flagcolor #Parse out the data arrays from the various configurations supported x, y, u, v, c = self._parse_args(*args) self.x = x self.y = y xy = np.hstack((x[:,np.newaxis], y[:,np.newaxis])) #Make a collection barb_size = self._length**2 / 4 #Empirically determined collections.PolyCollection.__init__(self, [], (barb_size,), offsets=xy, transOffset=ax.transData, **kw) self.set_transform(transforms.IdentityTransform()) self.set_UVC(u, v, c) __init__.__doc__ = """ The constructor takes one required argument, an Axes instance, followed by the args and kwargs described by the following pylab interface documentation: %s""" % _barbs_doc def _find_tails(self, mag, rounding=True, half=5, full=10, flag=50): ''' Find how many of each of the tail pieces is necessary. Flag specifies the increment for a flag, barb for a full barb, and half for half a barb. Mag should be the magnitude of a vector (ie. >= 0). This returns a tuple of: (*number of flags*, *number of barbs*, *half_flag*, *empty_flag*) *half_flag* is a boolean whether half of a barb is needed, since there should only ever be one half on a given barb. *empty_flag* flag is an array of flags to easily tell if a barb is empty (too low to plot any barbs/flags. ''' #If rounding, round to the nearest multiple of half, the smallest #increment if rounding: mag = half * (mag / half + 0.5).astype(np.int) num_flags = np.floor(mag / flag).astype(np.int) mag = np.mod(mag, flag) num_barb = np.floor(mag / full).astype(np.int) mag = np.mod(mag, full) half_flag = mag >= half empty_flag = ~(half_flag | (num_flags > 0) | (num_barb > 0)) return num_flags, num_barb, half_flag, empty_flag def _make_barbs(self, u, v, nflags, nbarbs, half_barb, empty_flag, length, pivot, sizes, fill_empty, flip): ''' This function actually creates the wind barbs. *u* and *v* are components of the vector in the *x* and *y* directions, respectively. *nflags*, *nbarbs*, and *half_barb*, empty_flag* are, *respectively, the number of flags, number of barbs, flag for *half a barb, and flag for empty barb, ostensibly obtained *from :meth:`_find_tails`. *length* is the length of the barb staff in points. *pivot* specifies the point on the barb around which the entire barb should be rotated. Right now, valid options are 'head' and 'middle'. *sizes* is a dictionary of coefficients specifying the ratio of a given feature to the length of the barb. These features include: - *spacing*: space between features (flags, full/half barbs) - *height*: distance from shaft of top of a flag or full barb - *width* - width of a flag, twice the width of a full barb - *emptybarb* - radius of the circle used for low magnitudes *fill_empty* specifies whether the circle representing an empty barb should be filled or not (this changes the drawing of the polygon). *flip* is a flag indicating whether the features should be flipped to the other side of the barb (useful for winds in the southern hemisphere. This function returns list of arrays of vertices, defining a polygon for each of the wind barbs. These polygons have been rotated to properly align with the vector direction. ''' #These control the spacing and size of barb elements relative to the #length of the shaft spacing = length * sizes.get('spacing', 0.125) full_height = length * sizes.get('height', 0.4) full_width = length * sizes.get('width', 0.25) empty_rad = length * sizes.get('emptybarb', 0.15) #Controls y point where to pivot the barb. pivot_points = dict(tip=0.0, middle=-length/2.) #Check for flip if flip: full_height = -full_height endx = 0.0 endy = pivot_points[pivot.lower()] #Get the appropriate angle for the vector components. The offset is due #to the way the barb is initially drawn, going down the y-axis. This #makes sense in a meteorological mode of thinking since there 0 degrees #corresponds to north (the y-axis traditionally) angles = -(ma.arctan2(v, u) + np.pi/2) #Used for low magnitude. We just get the vertices, so if we make it #out here, it can be reused. The center set here should put the #center of the circle at the location(offset), rather than at the #same point as the barb pivot; this seems more sensible. circ = CirclePolygon((0,0), radius=empty_rad).get_verts() if fill_empty: empty_barb = circ else: #If we don't want the empty one filled, we make a degenerate polygon #that wraps back over itself empty_barb = np.concatenate((circ, circ[::-1])) barb_list = [] for index, angle in np.ndenumerate(angles): #If the vector magnitude is too weak to draw anything, plot an #empty circle instead if empty_flag[index]: #We can skip the transform since the circle has no preferred #orientation barb_list.append(empty_barb) continue poly_verts = [(endx, endy)] offset = length #Add vertices for each flag for i in range(nflags[index]): #The spacing that works for the barbs is a little to much for #the flags, but this only occurs when we have more than 1 flag. if offset != length: offset += spacing / 2. poly_verts.extend([[endx, endy + offset], [endx + full_height, endy - full_width/2 + offset], [endx, endy - full_width + offset]]) offset -= full_width + spacing #Add vertices for each barb. These really are lines, but works #great adding 3 vertices that basically pull the polygon out and #back down the line for i in range(nbarbs[index]): poly_verts.extend([(endx, endy + offset), (endx + full_height, endy + offset + full_width/2), (endx, endy + offset)]) offset -= spacing #Add the vertices for half a barb, if needed if half_barb[index]: #If the half barb is the first on the staff, traditionally it is #offset from the end to make it easy to distinguish from a barb #with a full one if offset == length: poly_verts.append((endx, endy + offset)) offset -= 1.5 * spacing poly_verts.extend([(endx, endy + offset), (endx + full_height/2, endy + offset + full_width/4), (endx, endy + offset)]) #Rotate the barb according the angle. Making the barb first and then #rotating it made the math for drawing the barb really easy. Also, #the transform framework makes doing the rotation simple. poly_verts = transforms.Affine2D().rotate(-angle).transform( poly_verts) barb_list.append(poly_verts) return barb_list #Taken shamelessly from Quiver def _parse_args(self, *args): X, Y, U, V, C = [None]*5 args = list(args) if len(args) == 3 or len(args) == 5: C = ma.asarray(args.pop(-1)).ravel() V = ma.asarray(args.pop(-1)) U = ma.asarray(args.pop(-1)) nn = np.shape(U) nc = nn[0] nr = 1 if len(nn) > 1: nr = nn[1] if len(args) == 2: # remaining after removing U,V,C X, Y = [np.array(a).ravel() for a in args] if len(X) == nc and len(Y) == nr: X, Y = [a.ravel() for a in np.meshgrid(X, Y)] else: indexgrid = np.meshgrid(np.arange(nc), np.arange(nr)) X, Y = [np.ravel(a) for a in indexgrid] return X, Y, U, V, C def set_UVC(self, U, V, C=None): self.u = ma.asarray(U).ravel() self.v = ma.asarray(V).ravel() if C is not None: c = ma.asarray(C).ravel() x,y,u,v,c = delete_masked_points(self.x.ravel(), self.y.ravel(), self.u, self.v, c) else: x,y,u,v = delete_masked_points(self.x.ravel(), self.y.ravel(), self.u, self.v) magnitude = np.sqrt(u*u + v*v) flags, barbs, halves, empty = self._find_tails(magnitude, self.rounding, **self.barb_increments) #Get the vertices for each of the barbs plot_barbs = self._make_barbs(u, v, flags, barbs, halves, empty, self._length, self._pivot, self.sizes, self.fill_empty, self.flip) self.set_verts(plot_barbs) #Set the color array if C is not None: self.set_array(c) #Update the offsets in case the masked data changed xy = np.hstack((x[:,np.newaxis], y[:,np.newaxis])) self._offsets = xy def set_offsets(self, xy): ''' Set the offsets for the barb polygons. This saves the offets passed in and actually sets version masked as appropriate for the existing U/V data. *offsets* should be a sequence. ACCEPTS: sequence of pairs of floats ''' self.x = xy[:,0] self.y = xy[:,1] x,y,u,v = delete_masked_points(self.x.ravel(), self.y.ravel(), self.u, self.v) xy = np.hstack((x[:,np.newaxis], y[:,np.newaxis])) collections.PolyCollection.set_offsets(self, xy) set_offsets.__doc__ = collections.PolyCollection.set_offsets.__doc__ barbs_doc = _barbs_doc

36,790

Python

.py

806

36.774194

80

0.608087

numenta/nupic-legacy

6,330

1,556

464

AGPL-3.0

9/5/2024, 5:13:42 PM (Europe/Amsterdam)

26,244

path.py

numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/path.py

""" Contains a class for managing paths (polylines). """ import math from weakref import WeakValueDictionary import numpy as np from numpy import ma from matplotlib._path import point_in_path, get_path_extents, \ point_in_path_collection, get_path_collection_extents, \ path_in_path, path_intersects_path, convert_path_to_polygons from matplotlib.cbook import simple_linear_interpolation class Path(object): """ :class:`Path` represents a series of possibly disconnected, possibly closed, line and curve segments. The underlying storage is made up of two parallel numpy arrays: - *vertices*: an Nx2 float array of vertices - *codes*: an N-length uint8 array of vertex types These two arrays always have the same length in the first dimension. For example, to represent a cubic curve, you must provide three vertices as well as three codes ``CURVE3``. The code types are: - ``STOP`` : 1 vertex (ignored) A marker for the end of the entire path (currently not required and ignored) - ``MOVETO`` : 1 vertex Pick up the pen and move to the given vertex. - ``LINETO`` : 1 vertex Draw a line from the current position to the given vertex. - ``CURVE3`` : 1 control point, 1 endpoint Draw a quadratic Bezier curve from the current position, with the given control point, to the given end point. - ``CURVE4`` : 2 control points, 1 endpoint Draw a cubic Bezier curve from the current position, with the given control points, to the given end point. - ``CLOSEPOLY`` : 1 vertex (ignored) Draw a line segment to the start point of the current polyline. Users of Path objects should not access the vertices and codes arrays directly. Instead, they should use :meth:`iter_segments` to get the vertex/code pairs. This is important, since many :class:`Path` objects, as an optimization, do not store a *codes* at all, but have a default one provided for them by :meth:`iter_segments`. Note also that the vertices and codes arrays should be treated as immutable -- there are a number of optimizations and assumptions made up front in the constructor that will not change when the data changes. """ # Path codes STOP = 0 # 1 vertex MOVETO = 1 # 1 vertex LINETO = 2 # 1 vertex CURVE3 = 3 # 2 vertices CURVE4 = 4 # 3 vertices CLOSEPOLY = 5 # 1 vertex NUM_VERTICES = [1, 1, 1, 2, 3, 1] code_type = np.uint8 def __init__(self, vertices, codes=None): """ Create a new path with the given vertices and codes. *vertices* is an Nx2 numpy float array, masked array or Python sequence. *codes* is an N-length numpy array or Python sequence of type :attr:`matplotlib.path.Path.code_type`. These two arrays must have the same length in the first dimension. If *codes* is None, *vertices* will be treated as a series of line segments. If *vertices* contains masked values, they will be converted to NaNs which are then handled correctly by the Agg PathIterator and other consumers of path data, such as :meth:`iter_segments`. """ if ma.isMaskedArray(vertices): vertices = vertices.astype(np.float_).filled(np.nan) else: vertices = np.asarray(vertices, np.float_) if codes is not None: codes = np.asarray(codes, self.code_type) assert codes.ndim == 1 assert len(codes) == len(vertices) assert vertices.ndim == 2 assert vertices.shape[1] == 2 self.should_simplify = (len(vertices) >= 128 and (codes is None or np.all(codes <= Path.LINETO))) self.has_nonfinite = not np.isfinite(vertices).all() self.codes = codes self.vertices = vertices #@staticmethod def make_compound_path(*args): """ (staticmethod) Make a compound path from a list of Path objects. Only polygons (not curves) are supported. """ for p in args: assert p.codes is None lengths = [len(x) for x in args] total_length = sum(lengths) vertices = np.vstack([x.vertices for x in args]) vertices.reshape((total_length, 2)) codes = Path.LINETO * np.ones(total_length) i = 0 for length in lengths: codes[i] = Path.MOVETO i += length return Path(vertices, codes) make_compound_path = staticmethod(make_compound_path) def __repr__(self): return "Path(%s, %s)" % (self.vertices, self.codes) def __len__(self): return len(self.vertices) def iter_segments(self, simplify=None): """ Iterates over all of the curve segments in the path. Each iteration returns a 2-tuple (*vertices*, *code*), where *vertices* is a sequence of 1 - 3 coordinate pairs, and *code* is one of the :class:`Path` codes. If *simplify* is provided, it must be a tuple (*width*, *height*) defining the size of the figure, in native units (e.g. pixels or points). Simplification implies both removing adjacent line segments that are very close to parallel, and removing line segments outside of the figure. The path will be simplified *only* if :attr:`should_simplify` is True, which is determined in the constructor by this criteria: - No curves - More than 128 vertices """ vertices = self.vertices if not len(vertices): return codes = self.codes len_vertices = len(vertices) isfinite = np.isfinite NUM_VERTICES = self.NUM_VERTICES MOVETO = self.MOVETO LINETO = self.LINETO CLOSEPOLY = self.CLOSEPOLY STOP = self.STOP if simplify is not None and self.should_simplify: polygons = self.to_polygons(None, *simplify) for vertices in polygons: yield vertices[0], MOVETO for v in vertices[1:]: yield v, LINETO elif codes is None: if self.has_nonfinite: next_code = MOVETO for v in vertices: if np.isfinite(v).all(): yield v, next_code next_code = LINETO else: next_code = MOVETO else: yield vertices[0], MOVETO for v in vertices[1:]: yield v, LINETO else: i = 0 was_nan = False while i < len_vertices: code = codes[i] if code == CLOSEPOLY: yield [], code i += 1 elif code == STOP: return else: num_vertices = NUM_VERTICES[int(code)] curr_vertices = vertices[i:i+num_vertices].flatten() if not isfinite(curr_vertices).all(): was_nan = True elif was_nan: yield curr_vertices[-2:], MOVETO was_nan = False else: yield curr_vertices, code i += num_vertices def transformed(self, transform): """ Return a transformed copy of the path. .. seealso:: :class:`matplotlib.transforms.TransformedPath`: A specialized path class that will cache the transformed result and automatically update when the transform changes. """ return Path(transform.transform(self.vertices), self.codes) def contains_point(self, point, transform=None): """ Returns *True* if the path contains the given point. If *transform* is not *None*, the path will be transformed before performing the test. """ if transform is not None: transform = transform.frozen() return point_in_path(point[0], point[1], self, transform) def contains_path(self, path, transform=None): """ Returns *True* if this path completely contains the given path. If *transform* is not *None*, the path will be transformed before performing the test. """ if transform is not None: transform = transform.frozen() return path_in_path(self, None, path, transform) def get_extents(self, transform=None): """ Returns the extents (*xmin*, *ymin*, *xmax*, *ymax*) of the path. Unlike computing the extents on the *vertices* alone, this algorithm will take into account the curves and deal with control points appropriately. """ from transforms import Bbox if transform is not None: transform = transform.frozen() return Bbox(get_path_extents(self, transform)) def intersects_path(self, other, filled=True): """ Returns *True* if this path intersects another given path. *filled*, when True, treats the paths as if they were filled. That is, if one path completely encloses the other, :meth:`intersects_path` will return True. """ return path_intersects_path(self, other, filled) def intersects_bbox(self, bbox, filled=True): """ Returns *True* if this path intersects a given :class:`~matplotlib.transforms.Bbox`. *filled*, when True, treats the path as if it was filled. That is, if one path completely encloses the other, :meth:`intersects_path` will return True. """ from transforms import BboxTransformTo rectangle = self.unit_rectangle().transformed( BboxTransformTo(bbox)) result = self.intersects_path(rectangle, filled) return result def interpolated(self, steps): """ Returns a new path resampled to length N x steps. Does not currently handle interpolating curves. """ vertices = simple_linear_interpolation(self.vertices, steps) codes = self.codes if codes is not None: new_codes = Path.LINETO * np.ones(((len(codes) - 1) * steps + 1, )) new_codes[0::steps] = codes else: new_codes = None return Path(vertices, new_codes) def to_polygons(self, transform=None, width=0, height=0): """ Convert this path to a list of polygons. Each polygon is an Nx2 array of vertices. In other words, each polygon has no ``MOVETO`` instructions or curves. This is useful for displaying in backends that do not support compound paths or Bezier curves, such as GDK. If *width* and *height* are both non-zero then the lines will be simplified so that vertices outside of (0, 0), (width, height) will be clipped. """ if len(self.vertices) == 0: return [] if transform is not None: transform = transform.frozen() if self.codes is None and (width == 0 or height == 0): if transform is None: return [self.vertices] else: return [transform.transform(self.vertices)] # Deal with the case where there are curves and/or multiple # subpaths (using extension code) return convert_path_to_polygons(self, transform, width, height) _unit_rectangle = None #@classmethod def unit_rectangle(cls): """ (staticmethod) Returns a :class:`Path` of the unit rectangle from (0, 0) to (1, 1). """ if cls._unit_rectangle is None: cls._unit_rectangle = \ Path([[0.0, 0.0], [1.0, 0.0], [1.0, 1.0], [0.0, 1.0], [0.0, 0.0]]) return cls._unit_rectangle unit_rectangle = classmethod(unit_rectangle) _unit_regular_polygons = WeakValueDictionary() #@classmethod def unit_regular_polygon(cls, numVertices): """ (staticmethod) Returns a :class:`Path` for a unit regular polygon with the given *numVertices* and radius of 1.0, centered at (0, 0). """ if numVertices <= 16: path = cls._unit_regular_polygons.get(numVertices) else: path = None if path is None: theta = (2*np.pi/numVertices * np.arange(numVertices + 1).reshape((numVertices + 1, 1))) # This initial rotation is to make sure the polygon always # "points-up" theta += np.pi / 2.0 verts = np.concatenate((np.cos(theta), np.sin(theta)), 1) path = Path(verts) cls._unit_regular_polygons[numVertices] = path return path unit_regular_polygon = classmethod(unit_regular_polygon) _unit_regular_stars = WeakValueDictionary() #@classmethod def unit_regular_star(cls, numVertices, innerCircle=0.5): """ (staticmethod) Returns a :class:`Path` for a unit regular star with the given numVertices and radius of 1.0, centered at (0, 0). """ if numVertices <= 16: path = cls._unit_regular_stars.get((numVertices, innerCircle)) else: path = None if path is None: ns2 = numVertices * 2 theta = (2*np.pi/ns2 * np.arange(ns2 + 1)) # This initial rotation is to make sure the polygon always # "points-up" theta += np.pi / 2.0 r = np.ones(ns2 + 1) r[1::2] = innerCircle verts = np.vstack((r*np.cos(theta), r*np.sin(theta))).transpose() path = Path(verts) cls._unit_regular_polygons[(numVertices, innerCircle)] = path return path unit_regular_star = classmethod(unit_regular_star) #@classmethod def unit_regular_asterisk(cls, numVertices): """ (staticmethod) Returns a :class:`Path` for a unit regular asterisk with the given numVertices and radius of 1.0, centered at (0, 0). """ return cls.unit_regular_star(numVertices, 0.0) unit_regular_asterisk = classmethod(unit_regular_asterisk) _unit_circle = None #@classmethod def unit_circle(cls): """ (staticmethod) Returns a :class:`Path` of the unit circle. The circle is approximated using cubic Bezier curves. This uses 8 splines around the circle using the approach presented here: Lancaster, Don. `Approximating a Circle or an Ellipse Using Four Bezier Cubic Splines <http://www.tinaja.com/glib/ellipse4.pdf>`_. """ if cls._unit_circle is None: MAGIC = 0.2652031 SQRTHALF = np.sqrt(0.5) MAGIC45 = np.sqrt((MAGIC*MAGIC) / 2.0) vertices = np.array( [[0.0, -1.0], [MAGIC, -1.0], [SQRTHALF-MAGIC45, -SQRTHALF-MAGIC45], [SQRTHALF, -SQRTHALF], [SQRTHALF+MAGIC45, -SQRTHALF+MAGIC45], [1.0, -MAGIC], [1.0, 0.0], [1.0, MAGIC], [SQRTHALF+MAGIC45, SQRTHALF-MAGIC45], [SQRTHALF, SQRTHALF], [SQRTHALF-MAGIC45, SQRTHALF+MAGIC45], [MAGIC, 1.0], [0.0, 1.0], [-MAGIC, 1.0], [-SQRTHALF+MAGIC45, SQRTHALF+MAGIC45], [-SQRTHALF, SQRTHALF], [-SQRTHALF-MAGIC45, SQRTHALF-MAGIC45], [-1.0, MAGIC], [-1.0, 0.0], [-1.0, -MAGIC], [-SQRTHALF-MAGIC45, -SQRTHALF+MAGIC45], [-SQRTHALF, -SQRTHALF], [-SQRTHALF+MAGIC45, -SQRTHALF-MAGIC45], [-MAGIC, -1.0], [0.0, -1.0], [0.0, -1.0]], np.float_) codes = cls.CURVE4 * np.ones(26) codes[0] = cls.MOVETO codes[-1] = cls.CLOSEPOLY cls._unit_circle = Path(vertices, codes) return cls._unit_circle unit_circle = classmethod(unit_circle) #@classmethod def arc(cls, theta1, theta2, n=None, is_wedge=False): """ (staticmethod) Returns an arc on the unit circle from angle *theta1* to angle *theta2* (in degrees). If *n* is provided, it is the number of spline segments to make. If *n* is not provided, the number of spline segments is determined based on the delta between *theta1* and *theta2*. Masionobe, L. 2003. `Drawing an elliptical arc using polylines, quadratic or cubic Bezier curves <http://www.spaceroots.org/documents/ellipse/index.html>`_. """ # degrees to radians theta1 *= np.pi / 180.0 theta2 *= np.pi / 180.0 twopi = np.pi * 2.0 halfpi = np.pi * 0.5 eta1 = np.arctan2(np.sin(theta1), np.cos(theta1)) eta2 = np.arctan2(np.sin(theta2), np.cos(theta2)) eta2 -= twopi * np.floor((eta2 - eta1) / twopi) if (theta2 - theta1 > np.pi) and (eta2 - eta1 < np.pi): eta2 += twopi # number of curve segments to make if n is None: n = int(2 ** np.ceil((eta2 - eta1) / halfpi)) if n < 1: raise ValueError("n must be >= 1 or None") deta = (eta2 - eta1) / n t = np.tan(0.5 * deta) alpha = np.sin(deta) * (np.sqrt(4.0 + 3.0 * t * t) - 1) / 3.0 steps = np.linspace(eta1, eta2, n + 1, True) cos_eta = np.cos(steps) sin_eta = np.sin(steps) xA = cos_eta[:-1] yA = sin_eta[:-1] xA_dot = -yA yA_dot = xA xB = cos_eta[1:] yB = sin_eta[1:] xB_dot = -yB yB_dot = xB if is_wedge: length = n * 3 + 4 vertices = np.zeros((length, 2), np.float_) codes = Path.CURVE4 * np.ones((length, ), Path.code_type) vertices[1] = [xA[0], yA[0]] codes[0:2] = [Path.MOVETO, Path.LINETO] codes[-2:] = [Path.LINETO, Path.CLOSEPOLY] vertex_offset = 2 end = length - 2 else: length = n * 3 + 1 vertices = np.zeros((length, 2), np.float_) codes = Path.CURVE4 * np.ones((length, ), Path.code_type) vertices[0] = [xA[0], yA[0]] codes[0] = Path.MOVETO vertex_offset = 1 end = length vertices[vertex_offset :end:3, 0] = xA + alpha * xA_dot vertices[vertex_offset :end:3, 1] = yA + alpha * yA_dot vertices[vertex_offset+1:end:3, 0] = xB - alpha * xB_dot vertices[vertex_offset+1:end:3, 1] = yB - alpha * yB_dot vertices[vertex_offset+2:end:3, 0] = xB vertices[vertex_offset+2:end:3, 1] = yB return Path(vertices, codes) arc = classmethod(arc) #@classmethod def wedge(cls, theta1, theta2, n=None): """ (staticmethod) Returns a wedge of the unit circle from angle *theta1* to angle *theta2* (in degrees). If *n* is provided, it is the number of spline segments to make. If *n* is not provided, the number of spline segments is determined based on the delta between *theta1* and *theta2*. """ return cls.arc(theta1, theta2, n, True) wedge = classmethod(wedge) _get_path_collection_extents = get_path_collection_extents def get_path_collection_extents(*args): """ Given a sequence of :class:`Path` objects, returns the bounding box that encapsulates all of them. """ from transforms import Bbox if len(args[1]) == 0: raise ValueError("No paths provided") return Bbox.from_extents(*_get_path_collection_extents(*args))

20,263

Python

.py

481

31.669439

82

0.577806

numenta/nupic-legacy

6,330

1,556

464

AGPL-3.0

9/5/2024, 5:13:42 PM (Europe/Amsterdam)

26,245

patches.py

numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/patches.py

# -*- coding: utf-8 -*- from __future__ import division import math import matplotlib as mpl import numpy as np import matplotlib.cbook as cbook import matplotlib.artist as artist import matplotlib.colors as colors import matplotlib.transforms as transforms from matplotlib.path import Path # these are not available for the object inspector until after the # class is built so we define an initial set here for the init # function and they will be overridden after object definition artist.kwdocd['Patch'] = """ ================= ============================================== Property Description ================= ============================================== alpha float animated [True | False] antialiased or aa [True | False] clip_box a matplotlib.transform.Bbox instance clip_on [True | False] edgecolor or ec any matplotlib color facecolor or fc any matplotlib color figure a matplotlib.figure.Figure instance fill [True | False] hatch unknown label any string linewidth or lw float lod [True | False] transform a matplotlib.transform transformation instance visible [True | False] zorder any number ================= ============================================== """ class Patch(artist.Artist): """ A patch is a 2D thingy with a face color and an edge color. If any of *edgecolor*, *facecolor*, *linewidth*, or *antialiased* are *None*, they default to their rc params setting. """ zorder = 1 def __str__(self): return str(self.__class__).split('.')[-1] def get_verts(self): """ Return a copy of the vertices used in this patch If the patch contains Bézier curves, the curves will be interpolated by line segments. To access the curves as curves, use :meth:`get_path`. """ trans = self.get_transform() path = self.get_path() polygons = path.to_polygons(trans) if len(polygons): return polygons[0] return [] def contains(self, mouseevent): """Test whether the mouse event occurred in the patch. Returns T/F, {} """ # This is a general version of contains that should work on any # patch with a path. However, patches that have a faster # algebraic solution to hit-testing should override this # method. if callable(self._contains): return self._contains(self,mouseevent) inside = self.get_path().contains_point( (mouseevent.x, mouseevent.y), self.get_transform()) return inside, {} def update_from(self, other): """ Updates this :class:`Patch` from the properties of *other*. """ artist.Artist.update_from(self, other) self.set_edgecolor(other.get_edgecolor()) self.set_facecolor(other.get_facecolor()) self.set_fill(other.get_fill()) self.set_hatch(other.get_hatch()) self.set_linewidth(other.get_linewidth()) self.set_linestyle(other.get_linestyle()) self.set_transform(other.get_data_transform()) self.set_figure(other.get_figure()) self.set_alpha(other.get_alpha()) def get_extents(self): """ Return a :class:`~matplotlib.transforms.Bbox` object defining the axis-aligned extents of the :class:`Patch`. """ return self.get_path().get_extents(self.get_transform()) def get_transform(self): """ Return the :class:`~matplotlib.transforms.Transform` applied to the :class:`Patch`. """ return self.get_patch_transform() + artist.Artist.get_transform(self) def get_data_transform(self): return artist.Artist.get_transform(self) def get_patch_transform(self): return transforms.IdentityTransform() def get_antialiased(self): """ Returns True if the :class:`Patch` is to be drawn with antialiasing. """ return self._antialiased get_aa = get_antialiased def get_edgecolor(self): """ Return the edge color of the :class:`Patch`. """ return self._edgecolor get_ec = get_edgecolor def get_facecolor(self): """ Return the face color of the :class:`Patch`. """ return self._facecolor get_fc = get_facecolor def get_linewidth(self): """ Return the line width in points. """ return self._linewidth get_lw = get_linewidth def get_linestyle(self): """ Return the linestyle. Will be one of ['solid' | 'dashed' | 'dashdot' | 'dotted'] """ return self._linestyle get_ls = get_linestyle def set_antialiased(self, aa): """ Set whether to use antialiased rendering ACCEPTS: [True | False] or None for default """ if aa is None: aa = mpl.rcParams['patch.antialiased'] self._antialiased = aa def set_aa(self, aa): """alias for set_antialiased""" return self.set_antialiased(aa) def set_edgecolor(self, color): """ Set the patch edge color ACCEPTS: mpl color spec, or None for default, or 'none' for no color """ if color is None: color = mpl.rcParams['patch.edgecolor'] self._edgecolor = color def set_ec(self, color): """alias for set_edgecolor""" return self.set_edgecolor(color) def set_facecolor(self, color): """ Set the patch face color ACCEPTS: mpl color spec, or None for default, or 'none' for no color """ if color is None: color = mpl.rcParams['patch.facecolor'] self._facecolor = color def set_fc(self, color): """alias for set_facecolor""" return self.set_facecolor(color) def set_linewidth(self, w): """ Set the patch linewidth in points ACCEPTS: float or None for default """ if w is None: w = mpl.rcParams['patch.linewidth'] self._linewidth = w def set_lw(self, lw): """alias for set_linewidth""" return self.set_linewidth(lw) def set_linestyle(self, ls): """ Set the patch linestyle ACCEPTS: ['solid' | 'dashed' | 'dashdot' | 'dotted'] """ if ls is None: ls = "solid" self._linestyle = ls def set_ls(self, ls): """alias for set_linestyle""" return self.set_linestyle(ls) def set_fill(self, b): """ Set whether to fill the patch ACCEPTS: [True | False] """ self.fill = b def get_fill(self): 'return whether fill is set' return self.fill def set_hatch(self, h): """ Set the hatching pattern hatch can be one of:: / - diagonal hatching \ - back diagonal | - vertical - - horizontal # - crossed x - crossed diagonal Letters can be combined, in which case all the specified hatchings are done. If same letter repeats, it increases the density of hatching in that direction. CURRENT LIMITATIONS: 1. Hatching is supported in the PostScript backend only. 2. Hatching is done with solid black lines of width 0. ACCEPTS: [ '/' | '\\' | '|' | '-' | '#' | 'x' ] """ self._hatch = h def get_hatch(self): 'Return the current hatching pattern' return self._hatch def draw(self, renderer): 'Draw the :class:`Patch` to the given *renderer*.' if not self.get_visible(): return #renderer.open_group('patch') gc = renderer.new_gc() if cbook.is_string_like(self._edgecolor) and self._edgecolor.lower()=='none': gc.set_linewidth(0) else: gc.set_foreground(self._edgecolor) gc.set_linewidth(self._linewidth) gc.set_linestyle(self._linestyle) gc.set_antialiased(self._antialiased) self._set_gc_clip(gc) gc.set_capstyle('projecting') gc.set_url(self._url) gc.set_snap(self._snap) if (not self.fill or self._facecolor is None or (cbook.is_string_like(self._facecolor) and self._facecolor.lower()=='none')): rgbFace = None gc.set_alpha(1.0) else: r, g, b, a = colors.colorConverter.to_rgba(self._facecolor, self._alpha) rgbFace = (r, g, b) gc.set_alpha(a) if self._hatch: gc.set_hatch(self._hatch ) path = self.get_path() transform = self.get_transform() tpath = transform.transform_path_non_affine(path) affine = transform.get_affine() renderer.draw_path(gc, tpath, affine, rgbFace) #renderer.close_group('patch') def get_path(self): """ Return the path of this patch """ raise NotImplementedError('Derived must override') def get_window_extent(self, renderer=None): return self.get_path().get_extents(self.get_transform()) artist.kwdocd['Patch'] = patchdoc = artist.kwdoc(Patch) for k in ('Rectangle', 'Circle', 'RegularPolygon', 'Polygon', 'Wedge', 'Arrow', 'FancyArrow', 'YAArrow', 'CirclePolygon', 'Ellipse', 'Arc', 'FancyBboxPatch'): artist.kwdocd[k] = patchdoc # define Patch.__init__ after the class so that the docstring can be # auto-generated. def __patch__init__(self, edgecolor=None, facecolor=None, linewidth=None, linestyle=None, antialiased = None, hatch = None, fill=True, **kwargs ): """ The following kwarg properties are supported %(Patch)s """ artist.Artist.__init__(self) if linewidth is None: linewidth = mpl.rcParams['patch.linewidth'] if linestyle is None: linestyle = "solid" if antialiased is None: antialiased = mpl.rcParams['patch.antialiased'] self.set_edgecolor(edgecolor) self.set_facecolor(facecolor) self.set_linewidth(linewidth) self.set_linestyle(linestyle) self.set_antialiased(antialiased) self.set_hatch(hatch) self.fill = fill self._combined_transform = transforms.IdentityTransform() if len(kwargs): artist.setp(self, **kwargs) __patch__init__.__doc__ = cbook.dedent(__patch__init__.__doc__) % artist.kwdocd Patch.__init__ = __patch__init__ class Shadow(Patch): def __str__(self): return "Shadow(%s)"%(str(self.patch)) def __init__(self, patch, ox, oy, props=None, **kwargs): """ Create a shadow of the given *patch* offset by *ox*, *oy*. *props*, if not *None*, is a patch property update dictionary. If *None*, the shadow will have have the same color as the face, but darkened. kwargs are %(Patch)s """ Patch.__init__(self) self.patch = patch self.props = props self._ox, self._oy = ox, oy self._update_transform() self._update() __init__.__doc__ = cbook.dedent(__init__.__doc__) % artist.kwdocd def _update(self): self.update_from(self.patch) if self.props is not None: self.update(self.props) else: r,g,b,a = colors.colorConverter.to_rgba(self.patch.get_facecolor()) rho = 0.3 r = rho*r g = rho*g b = rho*b self.set_facecolor((r,g,b,0.5)) self.set_edgecolor((r,g,b,0.5)) def _update_transform(self): self._shadow_transform = transforms.Affine2D().translate(self._ox, self._oy) def _get_ox(self): return self._ox def _set_ox(self, ox): self._ox = ox self._update_transform() def _get_oy(self): return self._oy def _set_oy(self, oy): self._oy = oy self._update_transform() def get_path(self): return self.patch.get_path() def get_patch_transform(self): return self.patch.get_patch_transform() + self._shadow_transform class Rectangle(Patch): """ Draw a rectangle with lower left at *xy* = (*x*, *y*) with specified *width* and *height*. """ def __str__(self): return self.__class__.__name__ \ + "(%g,%g;%gx%g)" % (self._x, self._y, self._width, self._height) def __init__(self, xy, width, height, **kwargs): """ *fill* is a boolean indicating whether to fill the rectangle Valid kwargs are: %(Patch)s """ Patch.__init__(self, **kwargs) self._x = xy[0] self._y = xy[1] self._width = width self._height = height # Note: This cannot be calculated until this is added to an Axes self._rect_transform = transforms.IdentityTransform() __init__.__doc__ = cbook.dedent(__init__.__doc__) % artist.kwdocd def get_path(self): """ Return the vertices of the rectangle """ return Path.unit_rectangle() def _update_patch_transform(self): """NOTE: This cannot be called until after this has been added to an Axes, otherwise unit conversion will fail. This maxes it very important to call the accessor method and not directly access the transformation member variable. """ x = self.convert_xunits(self._x) y = self.convert_yunits(self._y) width = self.convert_xunits(self._width) height = self.convert_yunits(self._height) bbox = transforms.Bbox.from_bounds(x, y, width, height) self._rect_transform = transforms.BboxTransformTo(bbox) def get_patch_transform(self): self._update_patch_transform() return self._rect_transform def contains(self, mouseevent): # special case the degenerate rectangle if self._width==0 or self._height==0: return False, {} x, y = self.get_transform().inverted().transform_point( (mouseevent.x, mouseevent.y)) return (x >= 0.0 and x <= 1.0 and y >= 0.0 and y <= 1.0), {} def get_x(self): "Return the left coord of the rectangle" return self._x def get_y(self): "Return the bottom coord of the rectangle" return self._y def get_xy(self): "Return the left and bottom coords of the rectangle" return self._x, self._y def get_width(self): "Return the width of the rectangle" return self._width def get_height(self): "Return the height of the rectangle" return self._height def set_x(self, x): """ Set the left coord of the rectangle ACCEPTS: float """ self._x = x def set_y(self, y): """ Set the bottom coord of the rectangle ACCEPTS: float """ self._y = y def set_xy(self, xy): """ Set the left and bottom coords of the rectangle ACCEPTS: 2-item sequence """ self._x, self._y = xy def set_width(self, w): """ Set the width rectangle ACCEPTS: float """ self._width = w def set_height(self, h): """ Set the width rectangle ACCEPTS: float """ self._height = h def set_bounds(self, *args): """ Set the bounds of the rectangle: l,b,w,h ACCEPTS: (left, bottom, width, height) """ if len(args)==0: l,b,w,h = args[0] else: l,b,w,h = args self._x = l self._y = b self._width = w self._height = h def get_bbox(self): return transforms.Bbox.from_bounds(self._x, self._y, self._width, self._height) xy = property(get_xy, set_xy) class RegularPolygon(Patch): """ A regular polygon patch. """ def __str__(self): return "Poly%d(%g,%g)"%(self._numVertices,self._xy[0],self._xy[1]) def __init__(self, xy, numVertices, radius=5, orientation=0, **kwargs): """ Constructor arguments: *xy* A length 2 tuple (*x*, *y*) of the center. *numVertices* the number of vertices. *radius* The distance from the center to each of the vertices. *orientation* rotates the polygon (in radians). Valid kwargs are: %(Patch)s """ self._xy = xy self._numVertices = numVertices self._orientation = orientation self._radius = radius self._path = Path.unit_regular_polygon(numVertices) self._poly_transform = transforms.Affine2D() self._update_transform() Patch.__init__(self, **kwargs) __init__.__doc__ = cbook.dedent(__init__.__doc__) % artist.kwdocd def _update_transform(self): self._poly_transform.clear() \ .scale(self.radius) \ .rotate(self.orientation) \ .translate(*self.xy) def _get_xy(self): return self._xy def _set_xy(self, xy): self._update_transform() xy = property(_get_xy, _set_xy) def _get_orientation(self): return self._orientation def _set_orientation(self, xy): self._orientation = xy orientation = property(_get_orientation, _set_orientation) def _get_radius(self): return self._radius def _set_radius(self, xy): self._radius = xy radius = property(_get_radius, _set_radius) def _get_numvertices(self): return self._numVertices def _set_numvertices(self, numVertices): self._numVertices = numVertices numvertices = property(_get_numvertices, _set_numvertices) def get_path(self): return self._path def get_patch_transform(self): self._update_transform() return self._poly_transform class PathPatch(Patch): """ A general polycurve path patch. """ def __str__(self): return "Poly((%g, %g) ...)" % tuple(self._path.vertices[0]) def __init__(self, path, **kwargs): """ *path* is a :class:`matplotlib.path.Path` object. Valid kwargs are: %(Patch)s .. seealso:: :class:`Patch`: For additional kwargs """ Patch.__init__(self, **kwargs) self._path = path __init__.__doc__ = cbook.dedent(__init__.__doc__) % artist.kwdocd def get_path(self): return self._path class Polygon(Patch): """ A general polygon patch. """ def __str__(self): return "Poly((%g, %g) ...)" % tuple(self._path.vertices[0]) def __init__(self, xy, closed=True, **kwargs): """ *xy* is a numpy array with shape Nx2. If *closed* is *True*, the polygon will be closed so the starting and ending points are the same. Valid kwargs are: %(Patch)s .. seealso:: :class:`Patch`: For additional kwargs """ Patch.__init__(self, **kwargs) xy = np.asarray(xy, np.float_) self._path = Path(xy) self.set_closed(closed) __init__.__doc__ = cbook.dedent(__init__.__doc__) % artist.kwdocd def get_path(self): return self._path def get_closed(self): return self._closed def set_closed(self, closed): self._closed = closed xy = self._get_xy() if closed: if len(xy) and (xy[0] != xy[-1]).any(): xy = np.concatenate([xy, [xy[0]]]) else: if len(xy)>2 and (xy[0]==xy[-1]).all(): xy = xy[0:-1] self._set_xy(xy) def get_xy(self): return self._path.vertices def set_xy(self, vertices): self._path = Path(vertices) _get_xy = get_xy _set_xy = set_xy xy = property( get_xy, set_xy, None, """Set/get the vertices of the polygon. This property is provided for backward compatibility with matplotlib 0.91.x only. New code should use :meth:`~matplotlib.patches.Polygon.get_xy` and :meth:`~matplotlib.patches.Polygon.set_xy` instead.""") class Wedge(Patch): """ Wedge shaped patch. """ def __str__(self): return "Wedge(%g,%g)"%(self.theta1,self.theta2) def __init__(self, center, r, theta1, theta2, width=None, **kwargs): """ Draw a wedge centered at *x*, *y* center with radius *r* that sweeps *theta1* to *theta2* (in degrees). If *width* is given, then a partial wedge is drawn from inner radius *r* - *width* to outer radius *r*. Valid kwargs are: %(Patch)s """ Patch.__init__(self, **kwargs) self.center = center self.r,self.width = r,width self.theta1,self.theta2 = theta1,theta2 # Inner and outer rings are connected unless the annulus is complete delta=theta2-theta1 if abs((theta2-theta1) - 360) <= 1e-12: theta1,theta2 = 0,360 connector = Path.MOVETO else: connector = Path.LINETO # Form the outer ring arc = Path.arc(theta1,theta2) if width is not None: # Partial annulus needs to draw the outter ring # followed by a reversed and scaled inner ring v1 = arc.vertices v2 = arc.vertices[::-1]*float(r-width)/r v = np.vstack([v1,v2,v1[0,:],(0,0)]) c = np.hstack([arc.codes,arc.codes,connector,Path.CLOSEPOLY]) c[len(arc.codes)]=connector else: # Wedge doesn't need an inner ring v = np.vstack([arc.vertices,[(0,0),arc.vertices[0,:],(0,0)]]) c = np.hstack([arc.codes,[connector,connector,Path.CLOSEPOLY]]) # Shift and scale the wedge to the final location. v *= r v += np.asarray(center) self._path = Path(v,c) self._patch_transform = transforms.IdentityTransform() __init__.__doc__ = cbook.dedent(__init__.__doc__) % artist.kwdocd def get_path(self): return self._path # COVERAGE NOTE: Not used internally or from examples class Arrow(Patch): """ An arrow patch. """ def __str__(self): return "Arrow()" _path = Path( [ [ 0.0, 0.1 ], [ 0.0, -0.1], [ 0.8, -0.1 ], [ 0.8, -0.3], [ 1.0, 0.0 ], [ 0.8, 0.3], [ 0.8, 0.1 ], [ 0.0, 0.1] ] ) def __init__( self, x, y, dx, dy, width=1.0, **kwargs ): """ Draws an arrow, starting at (*x*, *y*), direction and length given by (*dx*, *dy*) the width of the arrow is scaled by *width*. Valid kwargs are: %(Patch)s """ Patch.__init__(self, **kwargs) L = np.sqrt(dx**2+dy**2) or 1 # account for div by zero cx = float(dx)/L sx = float(dy)/L trans1 = transforms.Affine2D().scale(L, width) trans2 = transforms.Affine2D.from_values(cx, sx, -sx, cx, 0.0, 0.0) trans3 = transforms.Affine2D().translate(x, y) trans = trans1 + trans2 + trans3 self._patch_transform = trans.frozen() __init__.__doc__ = cbook.dedent(__init__.__doc__) % artist.kwdocd def get_path(self): return self._path def get_patch_transform(self): return self._patch_transform class FancyArrow(Polygon): """ Like Arrow, but lets you set head width and head height independently. """ def __str__(self): return "FancyArrow()" def __init__(self, x, y, dx, dy, width=0.001, length_includes_head=False, \ head_width=None, head_length=None, shape='full', overhang=0, \ head_starts_at_zero=False,**kwargs): """ Constructor arguments *length_includes_head*: *True* if head is counted in calculating the length. *shape*: ['full', 'left', 'right'] *overhang*: distance that the arrow is swept back (0 overhang means triangular shape). *head_starts_at_zero*: If *True*, the head starts being drawn at coordinate 0 instead of ending at coordinate 0. Valid kwargs are: %(Patch)s """ if head_width is None: head_width = 3 * width if head_length is None: head_length = 1.5 * head_width distance = np.sqrt(dx**2 + dy**2) if length_includes_head: length=distance else: length=distance+head_length if not length: verts = [] #display nothing if empty else: #start by drawing horizontal arrow, point at (0,0) hw, hl, hs, lw = head_width, head_length, overhang, width left_half_arrow = np.array([ [0.0,0.0], #tip [-hl, -hw/2.0], #leftmost [-hl*(1-hs), -lw/2.0], #meets stem [-length, -lw/2.0], #bottom left [-length, 0], ]) #if we're not including the head, shift up by head length if not length_includes_head: left_half_arrow += [head_length, 0] #if the head starts at 0, shift up by another head length if head_starts_at_zero: left_half_arrow += [head_length/2.0, 0] #figure out the shape, and complete accordingly if shape == 'left': coords = left_half_arrow else: right_half_arrow = left_half_arrow*[1,-1] if shape == 'right': coords = right_half_arrow elif shape == 'full': # The half-arrows contain the midpoint of the stem, # which we can omit from the full arrow. Including it # twice caused a problem with xpdf. coords=np.concatenate([left_half_arrow[:-1], right_half_arrow[-2::-1]]) else: raise ValueError, "Got unknown shape: %s" % shape cx = float(dx)/distance sx = float(dy)/distance M = np.array([[cx, sx],[-sx,cx]]) verts = np.dot(coords, M) + (x+dx, y+dy) Polygon.__init__(self, map(tuple, verts), **kwargs) __init__.__doc__ = cbook.dedent(__init__.__doc__) % artist.kwdocd class YAArrow(Patch): """ Yet another arrow class. This is an arrow that is defined in display space and has a tip at *x1*, *y1* and a base at *x2*, *y2*. """ def __str__(self): return "YAArrow()" def __init__(self, figure, xytip, xybase, width=4, frac=0.1, headwidth=12, **kwargs): """ Constructor arguments: *xytip* (*x*, *y*) location of arrow tip *xybase* (*x*, *y*) location the arrow base mid point *figure* The :class:`~matplotlib.figure.Figure` instance (fig.dpi) *width* The width of the arrow in points *frac* The fraction of the arrow length occupied by the head *headwidth* The width of the base of the arrow head in points Valid kwargs are: %(Patch)s """ self.figure = figure self.xytip = xytip self.xybase = xybase self.width = width self.frac = frac self.headwidth = headwidth Patch.__init__(self, **kwargs) __init__.__doc__ = cbook.dedent(__init__.__doc__) % artist.kwdocd def get_path(self): # Since this is dpi dependent, we need to recompute the path # every time. # the base vertices x1, y1 = self.xytip x2, y2 = self.xybase k1 = self.width*self.figure.dpi/72./2. k2 = self.headwidth*self.figure.dpi/72./2. xb1, yb1, xb2, yb2 = self.getpoints(x1, y1, x2, y2, k1) # a point on the segment 20% of the distance from the tip to the base theta = math.atan2(y2-y1, x2-x1) r = math.sqrt((y2-y1)**2. + (x2-x1)**2.) xm = x1 + self.frac * r * math.cos(theta) ym = y1 + self.frac * r * math.sin(theta) xc1, yc1, xc2, yc2 = self.getpoints(x1, y1, xm, ym, k1) xd1, yd1, xd2, yd2 = self.getpoints(x1, y1, xm, ym, k2) xs = self.convert_xunits([xb1, xb2, xc2, xd2, x1, xd1, xc1, xb1]) ys = self.convert_yunits([yb1, yb2, yc2, yd2, y1, yd1, yc1, yb1]) return Path(zip(xs, ys)) def get_patch_transform(self): return transforms.IdentityTransform() def getpoints(self, x1,y1,x2,y2, k): """ For line segment defined by (*x1*, *y1*) and (*x2*, *y2*) return the points on the line that is perpendicular to the line and intersects (*x2*, *y2*) and the distance from (*x2*, *y2*) of the returned points is *k*. """ x1,y1,x2,y2,k = map(float, (x1,y1,x2,y2,k)) if y2-y1 == 0: return x2, y2+k, x2, y2-k elif x2-x1 == 0: return x2+k, y2, x2-k, y2 m = (y2-y1)/(x2-x1) pm = -1./m a = 1 b = -2*y2 c = y2**2. - k**2.*pm**2./(1. + pm**2.) y3a = (-b + math.sqrt(b**2.-4*a*c))/(2.*a) x3a = (y3a - y2)/pm + x2 y3b = (-b - math.sqrt(b**2.-4*a*c))/(2.*a) x3b = (y3b - y2)/pm + x2 return x3a, y3a, x3b, y3b class CirclePolygon(RegularPolygon): """ A polygon-approximation of a circle patch. """ def __str__(self): return "CirclePolygon(%d,%d)"%self.center def __init__(self, xy, radius=5, resolution=20, # the number of vertices **kwargs): """ Create a circle at *xy* = (*x*, *y*) with given *radius*. This circle is approximated by a regular polygon with *resolution* sides. For a smoother circle drawn with splines, see :class:`~matplotlib.patches.Circle`. Valid kwargs are: %(Patch)s """ RegularPolygon.__init__(self, xy, resolution, radius, orientation=0, **kwargs) __init__.__doc__ = cbook.dedent(__init__.__doc__) % artist.kwdocd class Ellipse(Patch): """ A scale-free ellipse. """ def __str__(self): return "Ellipse(%s,%s;%sx%s)"%(self.center[0],self.center[1],self.width,self.height) def __init__(self, xy, width, height, angle=0.0, **kwargs): """ *xy* center of ellipse *width* length of horizontal axis *height* length of vertical axis *angle* rotation in degrees (anti-clockwise) Valid kwargs are: %(Patch)s """ Patch.__init__(self, **kwargs) self.center = xy self.width, self.height = width, height self.angle = angle self._path = Path.unit_circle() # Note: This cannot be calculated until this is added to an Axes self._patch_transform = transforms.IdentityTransform() __init__.__doc__ = cbook.dedent(__init__.__doc__) % artist.kwdocd def _recompute_transform(self): """NOTE: This cannot be called until after this has been added to an Axes, otherwise unit conversion will fail. This maxes it very important to call the accessor method and not directly access the transformation member variable. """ center = (self.convert_xunits(self.center[0]), self.convert_yunits(self.center[1])) width = self.convert_xunits(self.width) height = self.convert_yunits(self.height) self._patch_transform = transforms.Affine2D() \ .scale(width * 0.5, height * 0.5) \ .rotate_deg(self.angle) \ .translate(*center) def get_path(self): """ Return the vertices of the rectangle """ return self._path def get_patch_transform(self): self._recompute_transform() return self._patch_transform def contains(self,ev): if ev.x is None or ev.y is None: return False,{} x, y = self.get_transform().inverted().transform_point((ev.x, ev.y)) return (x*x + y*y) <= 1.0, {} class Circle(Ellipse): """ A circle patch. """ def __str__(self): return "Circle((%g,%g),r=%g)"%(self.center[0],self.center[1],self.radius) def __init__(self, xy, radius=5, **kwargs): """ Create true circle at center *xy* = (*x*, *y*) with given *radius*. Unlike :class:`~matplotlib.patches.CirclePolygon` which is a polygonal approximation, this uses Bézier splines and is much closer to a scale-free circle. Valid kwargs are: %(Patch)s """ if 'resolution' in kwargs: import warnings warnings.warn('Circle is now scale free. Use CirclePolygon instead!', DeprecationWarning) kwargs.pop('resolution') self.radius = radius Ellipse.__init__(self, xy, radius*2, radius*2, **kwargs) __init__.__doc__ = cbook.dedent(__init__.__doc__) % artist.kwdocd class Arc(Ellipse): """ An elliptical arc. Because it performs various optimizations, it can not be filled. The arc must be used in an :class:`~matplotlib.axes.Axes` instance---it can not be added directly to a :class:`~matplotlib.figure.Figure`---because it is optimized to only render the segments that are inside the axes bounding box with high resolution. """ def __str__(self): return "Arc(%s,%s;%sx%s)"%(self.center[0],self.center[1],self.width,self.height) def __init__(self, xy, width, height, angle=0.0, theta1=0.0, theta2=360.0, **kwargs): """ The following args are supported: *xy* center of ellipse *width* length of horizontal axis *height* length of vertical axis *angle* rotation in degrees (anti-clockwise) *theta1* starting angle of the arc in degrees *theta2* ending angle of the arc in degrees If *theta1* and *theta2* are not provided, the arc will form a complete ellipse. Valid kwargs are: %(Patch)s """ fill = kwargs.pop('fill') if fill: raise ValueError("Arc objects can not be filled") kwargs['fill'] = False Ellipse.__init__(self, xy, width, height, angle, **kwargs) self.theta1 = theta1 self.theta2 = theta2 __init__.__doc__ = cbook.dedent(__init__.__doc__) % artist.kwdocd def draw(self, renderer): """ Ellipses are normally drawn using an approximation that uses eight cubic bezier splines. The error of this approximation is 1.89818e-6, according to this unverified source: Lancaster, Don. Approximating a Circle or an Ellipse Using Four Bezier Cubic Splines. http://www.tinaja.com/glib/ellipse4.pdf There is a use case where very large ellipses must be drawn with very high accuracy, and it is too expensive to render the entire ellipse with enough segments (either splines or line segments). Therefore, in the case where either radius of the ellipse is large enough that the error of the spline approximation will be visible (greater than one pixel offset from the ideal), a different technique is used. In that case, only the visible parts of the ellipse are drawn, with each visible arc using a fixed number of spline segments (8). The algorithm proceeds as follows: 1. The points where the ellipse intersects the axes bounding box are located. (This is done be performing an inverse transformation on the axes bbox such that it is relative to the unit circle -- this makes the intersection calculation much easier than doing rotated ellipse intersection directly). This uses the "line intersecting a circle" algorithm from: Vince, John. Geometry for Computer Graphics: Formulae, Examples & Proofs. London: Springer-Verlag, 2005. 2. The angles of each of the intersection points are calculated. 3. Proceeding counterclockwise starting in the positive x-direction, each of the visible arc-segments between the pairs of vertices are drawn using the bezier arc approximation technique implemented in :meth:`matplotlib.path.Path.arc`. """ if not hasattr(self, 'axes'): raise RuntimeError('Arcs can only be used in Axes instances') self._recompute_transform() # Get the width and height in pixels width = self.convert_xunits(self.width) height = self.convert_yunits(self.height) width, height = self.get_transform().transform_point( (width, height)) inv_error = (1.0 / 1.89818e-6) * 0.5 if width < inv_error and height < inv_error: self._path = Path.arc(self.theta1, self.theta2) return Patch.draw(self, renderer) def iter_circle_intersect_on_line(x0, y0, x1, y1): dx = x1 - x0 dy = y1 - y0 dr2 = dx*dx + dy*dy D = x0*y1 - x1*y0 D2 = D*D discrim = dr2 - D2 # Single (tangential) intersection if discrim == 0.0: x = (D*dy) / dr2 y = (-D*dx) / dr2 yield x, y elif discrim > 0.0: # The definition of "sign" here is different from # np.sign: we never want to get 0.0 if dy < 0.0: sign_dy = -1.0 else: sign_dy = 1.0 sqrt_discrim = np.sqrt(discrim) for sign in (1., -1.): x = (D*dy + sign * sign_dy * dx * sqrt_discrim) / dr2 y = (-D*dx + sign * np.abs(dy) * sqrt_discrim) / dr2 yield x, y def iter_circle_intersect_on_line_seg(x0, y0, x1, y1): epsilon = 1e-9 if x1 < x0: x0e, x1e = x1, x0 else: x0e, x1e = x0, x1 if y1 < y0: y0e, y1e = y1, y0 else: y0e, y1e = y0, y1 x0e -= epsilon y0e -= epsilon x1e += epsilon y1e += epsilon for x, y in iter_circle_intersect_on_line(x0, y0, x1, y1): if x >= x0e and x <= x1e and y >= y0e and y <= y1e: yield x, y # Transforms the axes box_path so that it is relative to the unit # circle in the same way that it is relative to the desired # ellipse. box_path = Path.unit_rectangle() box_path_transform = transforms.BboxTransformTo(self.axes.bbox) + \ self.get_transform().inverted() box_path = box_path.transformed(box_path_transform) PI = np.pi TWOPI = PI * 2.0 RAD2DEG = 180.0 / PI DEG2RAD = PI / 180.0 theta1 = self.theta1 theta2 = self.theta2 thetas = {} # For each of the point pairs, there is a line segment for p0, p1 in zip(box_path.vertices[:-1], box_path.vertices[1:]): x0, y0 = p0 x1, y1 = p1 for x, y in iter_circle_intersect_on_line_seg(x0, y0, x1, y1): theta = np.arccos(x) if y < 0: theta = TWOPI - theta # Convert radians to angles theta *= RAD2DEG if theta > theta1 and theta < theta2: thetas[theta] = None thetas = thetas.keys() thetas.sort() thetas.append(theta2) last_theta = theta1 theta1_rad = theta1 * DEG2RAD inside = box_path.contains_point((np.cos(theta1_rad), np.sin(theta1_rad))) for theta in thetas: if inside: self._path = Path.arc(last_theta, theta, 8) Patch.draw(self, renderer) inside = False else: inside = True last_theta = theta def bbox_artist(artist, renderer, props=None, fill=True): """ This is a debug function to draw a rectangle around the bounding box returned by :meth:`~matplotlib.artist.Artist.get_window_extent` of an artist, to test whether the artist is returning the correct bbox. *props* is a dict of rectangle props with the additional property 'pad' that sets the padding around the bbox in points. """ if props is None: props = {} props = props.copy() # don't want to alter the pad externally pad = props.pop('pad', 4) pad = renderer.points_to_pixels(pad) bbox = artist.get_window_extent(renderer) l,b,w,h = bbox.bounds l-=pad/2. b-=pad/2. w+=pad h+=pad r = Rectangle(xy=(l,b), width=w, height=h, fill=fill, ) r.set_transform(transforms.IdentityTransform()) r.set_clip_on( False ) r.update(props) r.draw(renderer) def draw_bbox(bbox, renderer, color='k', trans=None): """ This is a debug function to draw a rectangle around the bounding box returned by :meth:`~matplotlib.artist.Artist.get_window_extent` of an artist, to test whether the artist is returning the correct bbox. """ l,b,w,h = bbox.get_bounds() r = Rectangle(xy=(l,b), width=w, height=h, edgecolor=color, fill=False, ) if trans is not None: r.set_transform(trans) r.set_clip_on( False ) r.draw(renderer) def _pprint_table(_table, leadingspace=2): """ Given the list of list of strings, return a string of REST table format. """ if leadingspace: pad = ' '*leadingspace else: pad = '' columns = [[] for cell in _table[0]] for row in _table: for column, cell in zip(columns, row): column.append(cell) col_len = [max([len(cell) for cell in column]) for column in columns] lines = [] table_formatstr = pad + ' '.join([('=' * cl) for cl in col_len]) lines.append('') lines.append(table_formatstr) lines.append(pad + ' '.join([cell.ljust(cl) for cell, cl in zip(_table[0], col_len)])) lines.append(table_formatstr) lines.extend([(pad + ' '.join([cell.ljust(cl) for cell, cl in zip(row, col_len)])) for row in _table[1:]]) lines.append(table_formatstr) lines.append('') return "\n".join(lines) def _pprint_styles(_styles, leadingspace=2): """ A helper function for the _Style class. Given the dictionary of (stylename : styleclass), return a formatted string listing all the styles. Used to update the documentation. """ if leadingspace: pad = ' '*leadingspace else: pad = '' names, attrss, clss = [], [], [] import inspect _table = [["Class", "Name", "Attrs"]] for name, cls in sorted(_styles.items()): args, varargs, varkw, defaults = inspect.getargspec(cls.__init__) if defaults: args = [(argname, argdefault) \ for argname, argdefault in zip(args[1:], defaults)] else: args = None if args is None: argstr = 'None' else: argstr = ",".join([("%s=%s" % (an, av)) for an, av in args]) #adding quotes for now to work around tex bug treating '-' as itemize _table.append([cls.__name__, "'%s'"%name, argstr]) return _pprint_table(_table) class _Style(object): """ A base class for the Styles. It is meant to be a container class, where actual styles are declared as subclass of it, and it provides some helper functions. """ def __new__(self, stylename, **kw): """ return the instance of the subclass with the given style name. """ # the "class" should have the _style_list attribute, which is # a dictionary of stylname, style class paie. _list = stylename.replace(" ","").split(",") _name = _list[0].lower() try: _cls = self._style_list[_name] except KeyError: raise ValueError("Unknown style : %s" % stylename) try: _args_pair = [cs.split("=") for cs in _list[1:]] _args = dict([(k, float(v)) for k, v in _args_pair]) except ValueError: raise ValueError("Incorrect style argument : %s" % stylename) _args.update(kw) return _cls(**_args) @classmethod def get_styles(klass): """ A class method which returns a dictionary of available styles. """ return klass._style_list @classmethod def pprint_styles(klass): """ A class method which returns a string of the available styles. """ return _pprint_styles(klass._style_list) class BoxStyle(_Style): """ :class:`BoxStyle` is a container class which defines several boxstyle classes, which are used for :class:`FancyBoxPatch`. A style object can be created as:: BoxStyle.Round(pad=0.2) or:: BoxStyle("Round", pad=0.2) or:: BoxStyle("Round, pad=0.2") Following boxstyle classes are defined. %(AvailableBoxstyles)s An instance of any boxstyle class is an callable object, whose call signature is:: __call__(self, x0, y0, width, height, mutation_size, aspect_ratio=1.) and returns a :class:`Path` instance. *x0*, *y0*, *width* and *height* specify the location and size of the box to be drawn. *mutation_scale* determines the overall size of the mutation (by which I mean the transformation of the rectangle to the fancy box). *mutation_aspect* determines the aspect-ratio of the mutation. .. plot:: mpl_examples/pylab_examples/fancybox_demo2.py """ _style_list = {} class _Base(object): """ :class:`BBoxTransmuterBase` and its derivatives are used to make a fancy box around a given rectangle. The :meth:`__call__` method returns the :class:`~matplotlib.path.Path` of the fancy box. This class is not an artist and actual drawing of the fancy box is done by the :class:`FancyBboxPatch` class. """ # The derived classes are required to be able to be initialized # w/o arguments, i.e., all its argument (except self) must have # the default values. def __init__(self): """ initializtion. """ super(BoxStyle._Base, self).__init__() def transmute(self, x0, y0, width, height, mutation_size): """ The transmute method is a very core of the :class:`BboxTransmuter` class and must be overriden in the subclasses. It receives the location and size of the rectangle, and the mutation_size, with which the amount of padding and etc. will be scaled. It returns a :class:`~matplotlib.path.Path` instance. """ raise NotImplementedError('Derived must override') def __call__(self, x0, y0, width, height, mutation_size, aspect_ratio=1.): """ Given the location and size of the box, return the path of the box around it. - *x0*, *y0*, *width*, *height* : location and size of the box - *mutation_size* : a reference scale for the mutation. - *aspect_ratio* : aspect-ration for the mutation. """ # The __call__ method is a thin wrapper around the transmute method # and take care of the aspect. if aspect_ratio is not None: # Squeeze the given height by the aspect_ratio y0, height = y0/aspect_ratio, height/aspect_ratio # call transmute method with squeezed height. path = self.transmute(x0, y0, width, height, mutation_size) vertices, codes = path.vertices, path.codes # Restore the height vertices[:,1] = vertices[:,1] * aspect_ratio return Path(vertices, codes) else: return self.transmute(x0, y0, width, height, mutation_size) class Square(_Base): """ A simple square box. """ def __init__(self, pad=0.3): """ *pad* amount of padding """ self.pad = pad super(BoxStyle.Square, self).__init__() def transmute(self, x0, y0, width, height, mutation_size): # padding pad = mutation_size * self.pad # width and height with padding added. width, height = width + 2.*pad, \ height + 2.*pad, # boundary of the padded box x0, y0 = x0-pad, y0-pad, x1, y1 = x0+width, y0 + height cp = [(x0, y0), (x1, y0), (x1, y1), (x0, y1), (x0, y0), (x0, y0)] com = [Path.MOVETO, Path.LINETO, Path.LINETO, Path.LINETO, Path.LINETO, Path.CLOSEPOLY] path = Path(cp, com) return path _style_list["square"] = Square class LArrow(_Base): """ (left) Arrow Box """ def __init__(self, pad=0.3): self.pad = pad super(BoxStyle.LArrow, self).__init__() def transmute(self, x0, y0, width, height, mutation_size): # padding pad = mutation_size * self.pad # width and height with padding added. width, height = width + 2.*pad, \ height + 2.*pad, # boundary of the padded box x0, y0 = x0-pad, y0-pad, x1, y1 = x0+width, y0 + height dx = (y1-y0)/2. dxx = dx*.5 # adjust x0. 1.4 <- sqrt(2) x0 = x0 + pad / 1.4 cp = [(x0+dxx, y0), (x1, y0), (x1, y1), (x0+dxx, y1), (x0+dxx, y1+dxx), (x0-dx, y0+dx), (x0+dxx, y0-dxx), # arrow (x0+dxx, y0), (x0+dxx, y0)] com = [Path.MOVETO, Path.LINETO, Path.LINETO, Path.LINETO, Path.LINETO, Path.LINETO, Path.LINETO, Path.LINETO, Path.CLOSEPOLY] path = Path(cp, com) return path _style_list["larrow"] = LArrow class RArrow(LArrow): """ (right) Arrow Box """ def __init__(self, pad=0.3): self.pad = pad super(BoxStyle.RArrow, self).__init__() def transmute(self, x0, y0, width, height, mutation_size): p = BoxStyle.LArrow.transmute(self, x0, y0, width, height, mutation_size) p.vertices[:,0] = 2*x0 + width - p.vertices[:,0] return p _style_list["rarrow"] = RArrow class Round(_Base): """ A box with round corners. """ def __init__(self, pad=0.3, rounding_size=None): """ *pad* amount of padding *rounding_size* rounding radius of corners. *pad* if None """ self.pad = pad self.rounding_size = rounding_size super(BoxStyle.Round, self).__init__() def transmute(self, x0, y0, width, height, mutation_size): # padding pad = mutation_size * self.pad # size of the roudning corner if self.rounding_size: dr = mutation_size * self.rounding_size else: dr = pad width, height = width + 2.*pad, \ height + 2.*pad, x0, y0 = x0-pad, y0-pad, x1, y1 = x0+width, y0 + height # Round corners are implemented as quadratic bezier. eg. # [(x0, y0-dr), (x0, y0), (x0+dr, y0)] for lower left corner. cp = [(x0+dr, y0), (x1-dr, y0), (x1, y0), (x1, y0+dr), (x1, y1-dr), (x1, y1), (x1-dr, y1), (x0+dr, y1), (x0, y1), (x0, y1-dr), (x0, y0+dr), (x0, y0), (x0+dr, y0), (x0+dr, y0)] com = [Path.MOVETO, Path.LINETO, Path.CURVE3, Path.CURVE3, Path.LINETO, Path.CURVE3, Path.CURVE3, Path.LINETO, Path.CURVE3, Path.CURVE3, Path.LINETO, Path.CURVE3, Path.CURVE3, Path.CLOSEPOLY] path = Path(cp, com) return path _style_list["round"] = Round class Round4(_Base): """ Another box with round edges. """ def __init__(self, pad=0.3, rounding_size=None): """ *pad* amount of padding *rounding_size* rounding size of edges. *pad* if None """ self.pad = pad self.rounding_size = rounding_size super(BoxStyle.Round4, self).__init__() def transmute(self, x0, y0, width, height, mutation_size): # padding pad = mutation_size * self.pad # roudning size. Use a half of the pad if not set. if self.rounding_size: dr = mutation_size * self.rounding_size else: dr = pad / 2. width, height = width + 2.*pad - 2*dr, \ height + 2.*pad - 2*dr, x0, y0 = x0-pad+dr, y0-pad+dr, x1, y1 = x0+width, y0 + height cp = [(x0, y0), (x0+dr, y0-dr), (x1-dr, y0-dr), (x1, y0), (x1+dr, y0+dr), (x1+dr, y1-dr), (x1, y1), (x1-dr, y1+dr), (x0+dr, y1+dr), (x0, y1), (x0-dr, y1-dr), (x0-dr, y0+dr), (x0, y0), (x0, y0)] com = [Path.MOVETO, Path.CURVE4, Path.CURVE4, Path.CURVE4, Path.CURVE4, Path.CURVE4, Path.CURVE4, Path.CURVE4, Path.CURVE4, Path.CURVE4, Path.CURVE4, Path.CURVE4, Path.CURVE4, Path.CLOSEPOLY] path = Path(cp, com) return path _style_list["round4"] = Round4 class Sawtooth(_Base): """ A sawtooth box. """ def __init__(self, pad=0.3, tooth_size=None): """ *pad* amount of padding *tooth_size* size of the sawtooth. pad* if None """ self.pad = pad self.tooth_size = tooth_size super(BoxStyle.Sawtooth, self).__init__() def _get_sawtooth_vertices(self, x0, y0, width, height, mutation_size): # padding pad = mutation_size * self.pad # size of sawtooth if self.tooth_size is None: tooth_size = self.pad * .5 * mutation_size else: tooth_size = self.tooth_size * mutation_size tooth_size2 = tooth_size / 2. width, height = width + 2.*pad - tooth_size, \ height + 2.*pad - tooth_size, # the sizes of the vertical and horizontal sawtooth are # separately adjusted to fit the given box size. dsx_n = int(round((width - tooth_size) / (tooth_size * 2))) * 2 dsx = (width - tooth_size) / dsx_n dsy_n = int(round((height - tooth_size) / (tooth_size * 2))) * 2 dsy = (height - tooth_size) / dsy_n x0, y0 = x0-pad+tooth_size2, y0-pad+tooth_size2 x1, y1 = x0+width, y0 + height bottom_saw_x = [x0] + \ [x0 + tooth_size2 + dsx*.5* i for i in range(dsx_n*2)] + \ [x1 - tooth_size2] bottom_saw_y = [y0] + \ [y0 - tooth_size2, y0, y0 + tooth_size2, y0] * dsx_n + \ [y0 - tooth_size2] right_saw_x = [x1] + \ [x1 + tooth_size2, x1, x1 - tooth_size2, x1] * dsx_n + \ [x1 + tooth_size2] right_saw_y = [y0] + \ [y0 + tooth_size2 + dsy*.5* i for i in range(dsy_n*2)] + \ [y1 - tooth_size2] top_saw_x = [x1] + \ [x1 - tooth_size2 - dsx*.5* i for i in range(dsx_n*2)] + \ [x0 + tooth_size2] top_saw_y = [y1] + \ [y1 + tooth_size2, y1, y1 - tooth_size2, y1] * dsx_n + \ [y1 + tooth_size2] left_saw_x = [x0] + \ [x0 - tooth_size2, x0, x0 + tooth_size2, x0] * dsy_n + \ [x0 - tooth_size2] left_saw_y = [y1] + \ [y1 - tooth_size2 - dsy*.5* i for i in range(dsy_n*2)] + \ [y0 + tooth_size2] saw_vertices = zip(bottom_saw_x, bottom_saw_y) + \ zip(right_saw_x, right_saw_y) + \ zip(top_saw_x, top_saw_y) + \ zip(left_saw_x, left_saw_y) + \ [(bottom_saw_x[0], bottom_saw_y[0])] return saw_vertices def transmute(self, x0, y0, width, height, mutation_size): saw_vertices = self._get_sawtooth_vertices(x0, y0, width, height, mutation_size) path = Path(saw_vertices) return path _style_list["sawtooth"] = Sawtooth class Roundtooth(Sawtooth): """ A roundtooth(?) box. """ def __init__(self, pad=0.3, tooth_size=None): """ *pad* amount of padding *tooth_size* size of the sawtooth. pad* if None """ super(BoxStyle.Roundtooth, self).__init__(pad, tooth_size) def transmute(self, x0, y0, width, height, mutation_size): saw_vertices = self._get_sawtooth_vertices(x0, y0, width, height, mutation_size) cp = [Path.MOVETO] + ([Path.CURVE3, Path.CURVE3] * ((len(saw_vertices)-1)//2)) path = Path(saw_vertices, cp) return path _style_list["roundtooth"] = Roundtooth __doc__ = cbook.dedent(__doc__) % \ {"AvailableBoxstyles": _pprint_styles(_style_list)} class FancyBboxPatch(Patch): """ Draw a fancy box around a rectangle with lower left at *xy*=(*x*, *y*) with specified width and height. :class:`FancyBboxPatch` class is similar to :class:`Rectangle` class, but it draws a fancy box around the rectangle. The transformation of the rectangle box to the fancy box is delegated to the :class:`BoxTransmuterBase` and its derived classes. """ def __str__(self): return self.__class__.__name__ \ + "FancyBboxPatch(%g,%g;%gx%g)" % (self._x, self._y, self._width, self._height) def __init__(self, xy, width, height, boxstyle="round", bbox_transmuter=None, mutation_scale=1., mutation_aspect=None, **kwargs): """ *xy* = lower left corner *width*, *height* *boxstyle* determines what kind of fancy box will be drawn. It can be a string of the style name with a comma separated attribute, or an instance of :class:`BoxStyle`. Following box styles are available. %(AvailableBoxstyles)s *mutation_scale* : a value with which attributes of boxstyle (e.g., pad) will be scaled. default=1. *mutation_aspect* : The height of the rectangle will be squeezed by this value before the mutation and the mutated box will be stretched by the inverse of it. default=None. Valid kwargs are: %(Patch)s """ Patch.__init__(self, **kwargs) self._x = xy[0] self._y = xy[1] self._width = width self._height = height if boxstyle == "custom": if bbox_transmuter is None: raise ValueError("bbox_transmuter argument is needed with custom boxstyle") self._bbox_transmuter = bbox_transmuter else: self.set_boxstyle(boxstyle) self._mutation_scale=mutation_scale self._mutation_aspect=mutation_aspect kwdoc = dict() kwdoc["AvailableBoxstyles"]=_pprint_styles(BoxStyle._style_list) kwdoc.update(artist.kwdocd) __init__.__doc__ = cbook.dedent(__init__.__doc__) % kwdoc del kwdoc def set_boxstyle(self, boxstyle=None, **kw): """ Set the box style. *boxstyle* can be a string with boxstyle name with optional comma-separated attributes. Alternatively, the attrs can be provided as keywords:: set_boxstyle("round,pad=0.2") set_boxstyle("round", pad=0.2) Old attrs simply are forgotten. Without argument (or with *boxstyle* = None), it returns available box styles. ACCEPTS: [ %(AvailableBoxstyles)s ] """ if boxstyle==None: return BoxStyle.pprint_styles() if isinstance(boxstyle, BoxStyle._Base): self._bbox_transmuter = boxstyle elif callable(boxstyle): self._bbox_transmuter = boxstyle else: self._bbox_transmuter = BoxStyle(boxstyle, **kw) kwdoc = dict() kwdoc["AvailableBoxstyles"]=_pprint_styles(BoxStyle._style_list) kwdoc.update(artist.kwdocd) set_boxstyle.__doc__ = cbook.dedent(set_boxstyle.__doc__) % kwdoc del kwdoc def set_mutation_scale(self, scale): """ Set the mutation scale. ACCEPTS: float """ self._mutation_scale=scale def get_mutation_scale(self): """ Return the mutation scale. """ return self._mutation_scale def set_mutation_aspect(self, aspect): """ Set the aspect ratio of the bbox mutation. ACCEPTS: float """ self._mutation_aspect=aspect def get_mutation_aspect(self): """ Return the aspect ratio of the bbox mutation. """ return self._mutation_aspect def get_boxstyle(self): "Return the boxstyle object" return self._bbox_transmuter def get_path(self): """ Return the mutated path of the rectangle """ _path = self.get_boxstyle()(self._x, self._y, self._width, self._height, self.get_mutation_scale(), self.get_mutation_aspect()) return _path # Following methods are borrowed from the Rectangle class. def get_x(self): "Return the left coord of the rectangle" return self._x def get_y(self): "Return the bottom coord of the rectangle" return self._y def get_width(self): "Return the width of the rectangle" return self._width def get_height(self): "Return the height of the rectangle" return self._height def set_x(self, x): """ Set the left coord of the rectangle ACCEPTS: float """ self._x = x def set_y(self, y): """ Set the bottom coord of the rectangle ACCEPTS: float """ self._y = y def set_width(self, w): """ Set the width rectangle ACCEPTS: float """ self._width = w def set_height(self, h): """ Set the width rectangle ACCEPTS: float """ self._height = h def set_bounds(self, *args): """ Set the bounds of the rectangle: l,b,w,h ACCEPTS: (left, bottom, width, height) """ if len(args)==0: l,b,w,h = args[0] else: l,b,w,h = args self._x = l self._y = b self._width = w self._height = h def get_bbox(self): return transforms.Bbox.from_bounds(self._x, self._y, self._width, self._height) from matplotlib.bezier import split_bezier_intersecting_with_closedpath from matplotlib.bezier import get_intersection, inside_circle, get_parallels from matplotlib.bezier import make_wedged_bezier2 from matplotlib.bezier import split_path_inout, get_cos_sin class ConnectionStyle(_Style): """ :class:`ConnectionStyle` is a container class which defines several connectionstyle classes, which is used to create a path between two points. These are mainly used with :class:`FancyArrowPatch`. A connectionstyle object can be either created as:: ConnectionStyle.Arc3(rad=0.2) or:: ConnectionStyle("Arc3", rad=0.2) or:: ConnectionStyle("Arc3, rad=0.2") The following classes are defined %(AvailableConnectorstyles)s An instance of any connection style class is an callable object, whose call signature is:: __call__(self, posA, posB, patchA=None, patchB=None, shrinkA=2., shrinkB=2.) and it returns a :class:`Path` instance. *posA* and *posB* are tuples of x,y coordinates of the two points to be connected. *patchA* (or *patchB*) is given, the returned path is clipped so that it start (or end) from the boundary of the patch. The path is further shrunk by *shrinkA* (or *shrinkB*) which is given in points. """ _style_list = {} class _Base(object): """ A base class for connectionstyle classes. The dervided needs to implement a *connect* methods whose call signature is:: connect(posA, posB) where posA and posB are tuples of x, y coordinates to be connected. The methods needs to return a path connecting two points. This base class defines a __call__ method, and few helper methods. """ class SimpleEvent: def __init__(self, xy): self.x, self.y = xy def _clip(self, path, patchA, patchB): """ Clip the path to the boundary of the patchA and patchB. The starting point of the path needed to be inside of the patchA and the end point inside the patch B. The *contains* methods of each patch object is utilized to test if the point is inside the path. """ if patchA: def insideA(xy_display): #xy_display = patchA.get_data_transform().transform_point(xy_data) xy_event = ConnectionStyle._Base.SimpleEvent(xy_display) return patchA.contains(xy_event)[0] try: left, right = split_path_inout(path, insideA) except ValueError: right = path path = right if patchB: def insideB(xy_display): #xy_display = patchB.get_data_transform().transform_point(xy_data) xy_event = ConnectionStyle._Base.SimpleEvent(xy_display) return patchB.contains(xy_event)[0] try: left, right = split_path_inout(path, insideB) except ValueError: left = path path = left return path def _shrink(self, path, shrinkA, shrinkB): """ Shrink the path by fixed size (in points) with shrinkA and shrinkB """ if shrinkA: x, y = path.vertices[0] insideA = inside_circle(x, y, shrinkA) left, right = split_path_inout(path, insideA) path = right if shrinkB: x, y = path.vertices[-1] insideB = inside_circle(x, y, shrinkB) left, right = split_path_inout(path, insideB) path = left return path def __call__(self, posA, posB, shrinkA=2., shrinkB=2., patchA=None, patchB=None): """ Calls the *connect* method to create a path between *posA* and *posB*. The path is clipped and shrinked. """ path = self.connect(posA, posB) clipped_path = self._clip(path, patchA, patchB) shrinked_path = self._shrink(clipped_path, shrinkA, shrinkB) return shrinked_path class Arc3(_Base): """ Creates a simple quadratic bezier curve between two points. The curve is created so that the middle contol points (C1) is located at the same distance from the start (C0) and end points(C2) and the distance of the C1 to the line connecting C0-C2 is *rad* times the distance of C0-C2. """ def __init__(self, rad=0.): """ *rad* curvature of the curve. """ self.rad = rad def connect(self, posA, posB): x1, y1 = posA x2, y2 = posB x12, y12 = (x1 + x2)/2., (y1 + y2)/2. dx, dy = x2 - x1, y2 - y1 f = self.rad cx, cy = x12 + f*dy, y12 - f*dx vertices = [(x1, y1), (cx, cy), (x2, y2)] codes = [Path.MOVETO, Path.CURVE3, Path.CURVE3] return Path(vertices, codes) _style_list["arc3"] = Arc3 class Angle3(_Base): """ Creates a simple quadratic bezier curve between two points. The middle control points is placed at the intersecting point of two lines which crosses the start (or end) point and has a angle of angleA (or angleB). """ def __init__(self, angleA=90, angleB=0): """ *angleA* starting angle of the path *angleB* ending angle of the path """ self.angleA = angleA self.angleB = angleB def connect(self, posA, posB): x1, y1 = posA x2, y2 = posB cosA, sinA = math.cos(self.angleA/180.*math.pi),\ math.sin(self.angleA/180.*math.pi), cosB, sinB = math.cos(self.angleB/180.*math.pi),\ math.sin(self.angleB/180.*math.pi), cx, cy = get_intersection(x1, y1, cosA, sinA, x2, y2, cosB, sinB) vertices = [(x1, y1), (cx, cy), (x2, y2)] codes = [Path.MOVETO, Path.CURVE3, Path.CURVE3] return Path(vertices, codes) _style_list["angle3"] = Angle3 class Angle(_Base): """ Creates a picewise continuous quadratic bezier path between two points. The path has a one passing-through point placed at the intersecting point of two lines which crosses the start (or end) point and has a angle of angleA (or angleB). The connecting edges are rounded with *rad*. """ def __init__(self, angleA=90, angleB=0, rad=0.): """ *angleA* starting angle of the path *angleB* ending angle of the path *rad* rounding radius of the edge """ self.angleA = angleA self.angleB = angleB self.rad = rad def connect(self, posA, posB): x1, y1 = posA x2, y2 = posB cosA, sinA = math.cos(self.angleA/180.*math.pi),\ math.sin(self.angleA/180.*math.pi), cosB, sinB = math.cos(self.angleB/180.*math.pi),\ -math.sin(self.angleB/180.*math.pi), cx, cy = get_intersection(x1, y1, cosA, sinA, x2, y2, cosB, sinB) vertices = [(x1, y1)] codes = [Path.MOVETO] if self.rad == 0.: vertices.append((cx, cy)) codes.append(Path.LINETO) else: vertices.extend([(cx - self.rad * cosA, cy - self.rad * sinA), (cx, cy), (cx + self.rad * cosB, cy + self.rad * sinB)]) codes.extend([Path.LINETO, Path.CURVE3, Path.CURVE3]) vertices.append((x2, y2)) codes.append(Path.LINETO) return Path(vertices, codes) _style_list["angle"] = Angle class Arc(_Base): """ Creates a picewise continuous quadratic bezier path between two points. The path can have two passing-through points, a point placed at the distance of armA and angle of angleA from point A, another point with respect to point B. The edges are rounded with *rad*. """ def __init__(self, angleA=0, angleB=0, armA=None, armB=None, rad=0.): """ *angleA* : starting angle of the path *angleB* : ending angle of the path *armA* : length of the starting arm *armB* : length of the ending arm *rad* : rounding radius of the edges """ self.angleA = angleA self.angleB = angleB self.armA = armA self.armB = armB self.rad = rad def connect(self, posA, posB): x1, y1 = posA x2, y2 = posB vertices = [(x1, y1)] rounded = [] codes = [Path.MOVETO] if self.armA: cosA = math.cos(self.angleA/180.*math.pi) sinA = math.sin(self.angleA/180.*math.pi) #x_armA, y_armB d = self.armA - self.rad rounded.append((x1 + d*cosA, y1 + d*sinA)) d = self.armA rounded.append((x1 + d*cosA, y1 + d*sinA)) if self.armB: cosB = math.cos(self.angleB/180.*math.pi) sinB = math.sin(self.angleB/180.*math.pi) x_armB, y_armB = x2 + self.armB*cosB, y2 + self.armB*sinB if rounded: xp, yp = rounded[-1] dx, dy = x_armB - xp, y_armB - yp dd = (dx*dx + dy*dy)**.5 rounded.append((xp + self.rad*dx/dd, yp + self.rad*dy/dd)) vertices.extend(rounded) codes.extend([Path.LINETO, Path.CURVE3, Path.CURVE3]) else: xp, yp = vertices[-1] dx, dy = x_armB - xp, y_armB - yp dd = (dx*dx + dy*dy)**.5 d = dd - self.rad rounded = [(xp + d*dx/dd, yp + d*dy/dd), (x_armB, y_armB)] if rounded: xp, yp = rounded[-1] dx, dy = x2 - xp, y2 - yp dd = (dx*dx + dy*dy)**.5 rounded.append((xp + self.rad*dx/dd, yp + self.rad*dy/dd)) vertices.extend(rounded) codes.extend([Path.LINETO, Path.CURVE3, Path.CURVE3]) vertices.append((x2, y2)) codes.append(Path.LINETO) return Path(vertices, codes) _style_list["arc"] = Arc __doc__ = cbook.dedent(__doc__) % \ {"AvailableConnectorstyles": _pprint_styles(_style_list)} class ArrowStyle(_Style): """ :class:`ArrowStyle` is a container class which defines several arrowstyle classes, which is used to create an arrow path along a given path. These are mainly used with :class:`FancyArrowPatch`. A arrowstyle object can be either created as:: ArrowStyle.Fancy(head_length=.4, head_width=.4, tail_width=.4) or:: ArrowStyle("Fancy", head_length=.4, head_width=.4, tail_width=.4) or:: ArrowStyle("Fancy, head_length=.4, head_width=.4, tail_width=.4") The following classes are defined %(AvailableArrowstyles)s An instance of any arrow style class is an callable object, whose call signature is:: __call__(self, path, mutation_size, linewidth, aspect_ratio=1.) and it returns a tuple of a :class:`Path` instance and a boolean value. *path* is a :class:`Path` instance along witch the arrow will be drawn. *mutation_size* and *aspect_ratio* has a same meaning as in :class:`BoxStyle`. *linewidth* is a line width to be stroked. This is meant to be used to correct the location of the head so that it does not overshoot the destination point, but not all classes support it. .. plot:: mpl_examples/pylab_examples/fancyarrow_demo.py """ _style_list = {} class _Base(object): """ Arrow Transmuter Base class ArrowTransmuterBase and its derivatives are used to make a fancy arrow around a given path. The __call__ method returns a path (which will be used to create a PathPatch instance) and a boolean value indicating the path is open therefore is not fillable. This class is not an artist and actual drawing of the fancy arrow is done by the FancyArrowPatch class. """ # The derived classes are required to be able to be initialized # w/o arguments, i.e., all its argument (except self) must have # the default values. def __init__(self): super(ArrowStyle._Base, self).__init__() @staticmethod def ensure_quadratic_bezier(path): """ Some ArrowStyle class only wokrs with a simple quaratic bezier curve (created with Arc3Connetion or Angle3Connector). This static method is to check if the provided path is a simple quadratic bezier curve and returns its control points if true. """ segments = list(path.iter_segments()) assert len(segments) == 2 assert segments[0][1] == Path.MOVETO assert segments[1][1] == Path.CURVE3 return list(segments[0][0]) + list(segments[1][0]) def transmute(self, path, mutation_size, linewidth): """ The transmute method is a very core of the ArrowStyle class and must be overriden in the subclasses. It receives the path object along which the arrow will be drawn, and the mutation_size, with which the amount arrow head and etc. will be scaled. It returns a Path instance. The linewidth may be used to adjust the the path so that it does not pass beyond the given points. """ raise NotImplementedError('Derived must override') def __call__(self, path, mutation_size, linewidth, aspect_ratio=1.): """ The __call__ method is a thin wrapper around the transmute method and take care of the aspect ratio. """ if aspect_ratio is not None: # Squeeze the given height by the aspect_ratio vertices, codes = path.vertices[:], path.codes[:] # Squeeze the height vertices[:,1] = vertices[:,1] / aspect_ratio path_shrinked = Path(vertices, codes) # call transmute method with squeezed height. path_mutated, closed = self.transmute(path_shrinked, linewidth, mutation_size) vertices, codes = path_mutated.vertices, path_mutated.codes # Restore the height vertices[:,1] = vertices[:,1] * aspect_ratio return Path(vertices, codes), closed else: return self.transmute(path, mutation_size, linewidth) class _Curve(_Base): """ A simple arrow which will work with any path instance. The returned path is simply concatenation of the original path + at most two paths representing the arrow at the begin point and the at the end point. The returned path is not closed and only meant to be stroked. """ def __init__(self, beginarrow=None, endarrow=None, head_length=.2, head_width=.1): """ The arrows are drawn if *beginarrow* and/or *endarrow* are true. *head_length* and *head_width* determines the size of the arrow relative to the *mutation scale*. """ self.beginarrow, self.endarrow = beginarrow, endarrow self.head_length, self.head_width = \ head_length, head_width super(ArrowStyle._Curve, self).__init__() def _get_pad_projected(self, x0, y0, x1, y1, linewidth): # when no arrow head is drawn dx, dy = x0 - x1, y0 - y1 cp_distance = math.sqrt(dx**2 + dy**2) # padx_projected, pady_projected : amount of pad to account # projection of the wedge padx_projected = (.5*linewidth) pady_projected = (.5*linewidth) # apply pad for projected edge ddx = padx_projected * dx / cp_distance ddy = pady_projected * dy / cp_distance return ddx, ddy def _get_arrow_wedge(self, x0, y0, x1, y1, head_dist, cos_t, sin_t, linewidth ): """ Return the paths for arrow heads. Since arrow lines are drawn with capstyle=projected, The arrow is goes beyond the desired point. This method also returns the amount of the path to be shrinked so that it does not overshoot. """ # arrow from x0, y0 to x1, y1 dx, dy = x0 - x1, y0 - y1 cp_distance = math.sqrt(dx**2 + dy**2) # padx_projected, pady_projected : amount of pad for account # the overshooting of the projection of the wedge padx_projected = (.5*linewidth / cos_t) pady_projected = (.5*linewidth / sin_t) # apply pad for projected edge ddx = padx_projected * dx / cp_distance ddy = pady_projected * dy / cp_distance # offset for arrow wedge dx, dy = dx / cp_distance * head_dist, dy / cp_distance * head_dist dx1, dy1 = cos_t * dx + sin_t * dy, -sin_t * dx + cos_t * dy dx2, dy2 = cos_t * dx - sin_t * dy, sin_t * dx + cos_t * dy vertices_arrow = [(x1+ddx+dx1, y1+ddy+dy1), (x1+ddx, y1++ddy), (x1+ddx+dx2, y1+ddy+dy2)] codes_arrow = [Path.MOVETO, Path.LINETO, Path.LINETO] return vertices_arrow, codes_arrow, ddx, ddy def transmute(self, path, mutation_size, linewidth): head_length, head_width = self.head_length * mutation_size, \ self.head_width * mutation_size head_dist = math.sqrt(head_length**2 + head_width**2) cos_t, sin_t = head_length / head_dist, head_width / head_dist # begin arrow x0, y0 = path.vertices[0] x1, y1 = path.vertices[1] if self.beginarrow: verticesA, codesA, ddxA, ddyA = \ self._get_arrow_wedge(x1, y1, x0, y0, head_dist, cos_t, sin_t, linewidth) else: verticesA, codesA = [], [] #ddxA, ddyA = self._get_pad_projected(x1, y1, x0, y0, linewidth) ddxA, ddyA = 0., 0., #self._get_pad_projected(x1, y1, x0, y0, linewidth) # end arrow x2, y2 = path.vertices[-2] x3, y3 = path.vertices[-1] if self.endarrow: verticesB, codesB, ddxB, ddyB = \ self._get_arrow_wedge(x2, y2, x3, y3, head_dist, cos_t, sin_t, linewidth) else: verticesB, codesB = [], [] ddxB, ddyB = 0., 0. #self._get_pad_projected(x2, y2, x3, y3, linewidth) # this simple code will not work if ddx, ddy is greater than # separation bettern vertices. vertices = np.concatenate([verticesA + [(x0+ddxA, y0+ddyA)], path.vertices[1:-1], [(x3+ddxB, y3+ddyB)] + verticesB]) codes = np.concatenate([codesA, path.codes, codesB]) p = Path(vertices, codes) return p, False class Curve(_Curve): """ A simple curve without any arrow head. """ def __init__(self): super(ArrowStyle.Curve, self).__init__( \ beginarrow=False, endarrow=False) _style_list["-"] = Curve class CurveA(_Curve): """ An arrow with a head at its begin point. """ def __init__(self, head_length=.4, head_width=.2): """ *head_length* length of the arrow head *head_width* width of the arrow head """ super(ArrowStyle.CurveA, self).__init__( \ beginarrow=True, endarrow=False, head_length=head_length, head_width=head_width ) _style_list["<-"] = CurveA class CurveB(_Curve): """ An arrow with a head at its end point. """ def __init__(self, head_length=.4, head_width=.2): """ *head_length* length of the arrow head *head_width* width of the arrow head """ super(ArrowStyle.CurveB, self).__init__( \ beginarrow=False, endarrow=True, head_length=head_length, head_width=head_width ) #_style_list["->"] = CurveB _style_list["->"] = CurveB class CurveAB(_Curve): """ An arrow with heads both at the begin and the end point. """ def __init__(self, head_length=.4, head_width=.2): """ *head_length* length of the arrow head *head_width* width of the arrow head """ super(ArrowStyle.CurveAB, self).__init__( \ beginarrow=True, endarrow=True, head_length=head_length, head_width=head_width ) #_style_list["<->"] = CurveAB _style_list["<->"] = CurveAB class _Bracket(_Base): def __init__(self, bracketA=None, bracketB=None, widthA=1., widthB=1., lengthA=0.2, lengthB=0.2, angleA=None, angleB=None, scaleA=None, scaleB=None ): self.bracketA, self.bracketB = bracketA, bracketB self.widthA, self.widthB = widthA, widthB self.lengthA, self.lengthB = lengthA, lengthB self.angleA, self.angleB = angleA, angleB self.scaleA, self.scaleB= scaleA, scaleB def _get_bracket(self, x0, y0, cos_t, sin_t, width, length, ): # arrow from x0, y0 to x1, y1 from matplotlib.bezier import get_normal_points x1, y1, x2, y2 = get_normal_points(x0, y0, cos_t, sin_t, width) dx, dy = length * cos_t, length * sin_t vertices_arrow = [(x1+dx, y1+dy), (x1, y1), (x2, y2), (x2+dx, y2+dy)] codes_arrow = [Path.MOVETO, Path.LINETO, Path.LINETO, Path.LINETO] return vertices_arrow, codes_arrow def transmute(self, path, mutation_size, linewidth): if self.scaleA is None: scaleA = mutation_size else: scaleA = self.scaleA if self.scaleB is None: scaleB = mutation_size else: scaleB = self.scaleB vertices_list, codes_list = [], [] if self.bracketA: x0, y0 = path.vertices[0] x1, y1 = path.vertices[1] cos_t, sin_t = get_cos_sin(x1, y1, x0, y0) verticesA, codesA = self._get_bracket(x0, y0, cos_t, sin_t, self.widthA*scaleA, self.legnthA*scaleA) vertices_list.append(verticesA) codes_list.append(codesA) vertices_list.append(path.vertices) codes_list.append(path.codes) if self.bracketB: x0, y0 = path.vertices[-1] x1, y1 = path.vertices[-2] cos_t, sin_t = get_cos_sin(x1, y1, x0, y0) verticesB, codesB = self._get_bracket(x0, y0, cos_t, sin_t, self.widthB*scaleB, self.lengthB*scaleB) vertices_list.append(verticesB) codes_list.append(codesB) vertices = np.concatenate(vertices_list) codes = np.concatenate(codes_list) p = Path(vertices, codes) return p, False class BracketB(_Bracket): """ An arrow with a bracket([) at its end. """ def __init__(self, widthB=1., lengthB=0.2, angleB=None): """ *widthB* width of the bracket *lengthB* length of the bracket *angleB* angle between the bracket and the line """ super(ArrowStyle.BracketB, self).__init__(None, True, widthB=widthB, lengthB=lengthB, angleB=None ) #_style_list["-["] = BracketB _style_list["-["] = BracketB class Simple(_Base): """ A simple arrow. Only works with a quadratic bezier curve. """ def __init__(self, head_length=.5, head_width=.5, tail_width=.2): """ *head_length* length of the arrow head *head_with* width of the arrow head *tail_width* width of the arrow tail """ self.head_length, self.head_width, self.tail_width = \ head_length, head_width, tail_width super(ArrowStyle.Simple, self).__init__() def transmute(self, path, mutation_size, linewidth): x0, y0, x1, y1, x2, y2 = self.ensure_quadratic_bezier(path) # divide the path into a head and a tail head_length = self.head_length * mutation_size in_f = inside_circle(x2, y2, head_length) arrow_path = [(x0, y0), (x1, y1), (x2, y2)] arrow_out, arrow_in = \ split_bezier_intersecting_with_closedpath(arrow_path, in_f, tolerence=0.01) # head head_width = self.head_width * mutation_size head_l, head_r = make_wedged_bezier2(arrow_in, head_width/2., wm=.5) # tail tail_width = self.tail_width * mutation_size tail_left, tail_right = get_parallels(arrow_out, tail_width/2.) head_right, head_left = head_r, head_l patch_path = [(Path.MOVETO, tail_right[0]), (Path.CURVE3, tail_right[1]), (Path.CURVE3, tail_right[2]), (Path.LINETO, head_right[0]), (Path.CURVE3, head_right[1]), (Path.CURVE3, head_right[2]), (Path.CURVE3, head_left[1]), (Path.CURVE3, head_left[0]), (Path.LINETO, tail_left[2]), (Path.CURVE3, tail_left[1]), (Path.CURVE3, tail_left[0]), (Path.LINETO, tail_right[0]), (Path.CLOSEPOLY, tail_right[0]), ] path = Path([p for c, p in patch_path], [c for c, p in patch_path]) return path, True _style_list["simple"] = Simple class Fancy(_Base): """ A fancy arrow. Only works with a quadratic bezier curve. """ def __init__(self, head_length=.4, head_width=.4, tail_width=.4): """ *head_length* length of the arrow head *head_with* width of the arrow head *tail_width* width of the arrow tail """ self.head_length, self.head_width, self.tail_width = \ head_length, head_width, tail_width super(ArrowStyle.Fancy, self).__init__() def transmute(self, path, mutation_size, linewidth): x0, y0, x1, y1, x2, y2 = self.ensure_quadratic_bezier(path) # divide the path into a head and a tail head_length = self.head_length * mutation_size arrow_path = [(x0, y0), (x1, y1), (x2, y2)] # path for head in_f = inside_circle(x2, y2, head_length) path_out, path_in = \ split_bezier_intersecting_with_closedpath(arrow_path, in_f, tolerence=0.01) path_head = path_in # path for head in_f = inside_circle(x2, y2, head_length*.8) path_out, path_in = \ split_bezier_intersecting_with_closedpath(arrow_path, in_f, tolerence=0.01) path_tail = path_out # head head_width = self.head_width * mutation_size head_l, head_r = make_wedged_bezier2(path_head, head_width/2., wm=.6) # tail tail_width = self.tail_width * mutation_size tail_left, tail_right = make_wedged_bezier2(path_tail, tail_width*.5, w1=1., wm=0.6, w2=0.3) # path for head in_f = inside_circle(x0, y0, tail_width*.3) path_in, path_out = \ split_bezier_intersecting_with_closedpath(arrow_path, in_f, tolerence=0.01) tail_start = path_in[-1] head_right, head_left = head_r, head_l patch_path = [(Path.MOVETO, tail_start), (Path.LINETO, tail_right[0]), (Path.CURVE3, tail_right[1]), (Path.CURVE3, tail_right[2]), (Path.LINETO, head_right[0]), (Path.CURVE3, head_right[1]), (Path.CURVE3, head_right[2]), (Path.CURVE3, head_left[1]), (Path.CURVE3, head_left[0]), (Path.LINETO, tail_left[2]), (Path.CURVE3, tail_left[1]), (Path.CURVE3, tail_left[0]), (Path.LINETO, tail_start), (Path.CLOSEPOLY, tail_start), ] patch_path2 = [(Path.MOVETO, tail_right[0]), (Path.CURVE3, tail_right[1]), (Path.CURVE3, tail_right[2]), (Path.LINETO, head_right[0]), (Path.CURVE3, head_right[1]), (Path.CURVE3, head_right[2]), (Path.CURVE3, head_left[1]), (Path.CURVE3, head_left[0]), (Path.LINETO, tail_left[2]), (Path.CURVE3, tail_left[1]), (Path.CURVE3, tail_left[0]), (Path.CURVE3, tail_start), (Path.CURVE3, tail_right[0]), (Path.CLOSEPOLY, tail_right[0]), ] path = Path([p for c, p in patch_path], [c for c, p in patch_path]) return path, True _style_list["fancy"] = Fancy class Wedge(_Base): """ Wedge(?) shape. Only wokrs with a quadratic bezier curve. The begin point has a width of the tail_width and the end point has a width of 0. At the middle, the width is shrink_factor*tail_width. """ def __init__(self, tail_width=.3, shrink_factor=0.5): """ *tail_width* width of the tail *shrink_factor* fraction of the arrow width at the middle point """ self.tail_width = tail_width self.shrink_factor = shrink_factor super(ArrowStyle.Wedge, self).__init__() def transmute(self, path, mutation_size, linewidth): x0, y0, x1, y1, x2, y2 = self.ensure_quadratic_bezier(path) arrow_path = [(x0, y0), (x1, y1), (x2, y2)] b_plus, b_minus = make_wedged_bezier2(arrow_path, self.tail_width * mutation_size / 2., wm=self.shrink_factor) patch_path = [(Path.MOVETO, b_plus[0]), (Path.CURVE3, b_plus[1]), (Path.CURVE3, b_plus[2]), (Path.LINETO, b_minus[2]), (Path.CURVE3, b_minus[1]), (Path.CURVE3, b_minus[0]), (Path.CLOSEPOLY, b_minus[0]), ] path = Path([p for c, p in patch_path], [c for c, p in patch_path]) return path, True _style_list["wedge"] = Wedge __doc__ = cbook.dedent(__doc__) % \ {"AvailableArrowstyles": _pprint_styles(_style_list)} class FancyArrowPatch(Patch): """ A fancy arrow patch. It draws an arrow using the :class:ArrowStyle. """ def __str__(self): return self.__class__.__name__ \ + "FancyArrowPatch(%g,%g,%g,%g,%g,%g)" % tuple(self._q_bezier) def __init__(self, posA=None, posB=None, path=None, arrowstyle="simple", arrow_transmuter=None, connectionstyle="arc3", connector=None, patchA=None, patchB=None, shrinkA=2., shrinkB=2., mutation_scale=1., mutation_aspect=None, **kwargs): """ If *posA* and *posB* is given, a path connecting two point are created according to the connectionstyle. The path will be clipped with *patchA* and *patchB* and further shirnked by *shrinkA* and *shrinkB*. An arrow is drawn along this resulting path using the *arrowstyle* parameter. If *path* provided, an arrow is drawn along this path and *patchA*, *patchB*, *shrinkA*, and *shrinkB* are ignored. The *connectionstyle* describes how *posA* and *posB* are connected. It can be an instance of the ConnectionStyle class (matplotlib.patches.ConnectionStlye) or a string of the connectionstyle name, with optional comma-separated attributes. The following connection styles are available. %(AvailableConnectorstyles)s The *arrowstyle* describes how the fancy arrow will be drawn. It can be string of the available arrowstyle names, with optional comma-separated attributes, or one of the ArrowStyle instance. The optional attributes are meant to be scaled with the *mutation_scale*. The following arrow styles are available. %(AvailableArrowstyles)s *mutation_scale* : a value with which attributes of arrowstyle (e.g., head_length) will be scaled. default=1. *mutation_aspect* : The height of the rectangle will be squeezed by this value before the mutation and the mutated box will be stretched by the inverse of it. default=None. Valid kwargs are: %(Patch)s """ if posA is not None and posB is not None and path is None: self._posA_posB = [posA, posB] if connectionstyle is None: connectionstyle = "arc3" self.set_connectionstyle(connectionstyle) elif posA is None and posB is None and path is not None: self._posA_posB = None self._connetors = None else: raise ValueError("either posA and posB, or path need to provided") self.patchA = patchA self.patchB = patchB self.shrinkA = shrinkA self.shrinkB = shrinkB Patch.__init__(self, **kwargs) self._path_original = path self.set_arrowstyle(arrowstyle) self._mutation_scale=mutation_scale self._mutation_aspect=mutation_aspect #self._draw_in_display_coordinate = True kwdoc = dict() kwdoc["AvailableArrowstyles"]=_pprint_styles(ArrowStyle._style_list) kwdoc["AvailableConnectorstyles"]=_pprint_styles(ConnectionStyle._style_list) kwdoc.update(artist.kwdocd) __init__.__doc__ = cbook.dedent(__init__.__doc__) % kwdoc del kwdoc def set_positions(self, posA, posB): """ set the begin end end positions of the connecting path. Use current vlaue if None. """ if posA is not None: self._posA_posB[0] = posA if posB is not None: self._posA_posB[1] = posB def set_patchA(self, patchA): """ set the begin patch. """ self.patchA = patchA def set_patchB(self, patchB): """ set the begin patch """ self.patchB = patchB def set_connectionstyle(self, connectionstyle, **kw): """ Set the connection style. *connectionstyle* can be a string with connectionstyle name with optional comma-separated attributes. Alternatively, the attrs can be probided as keywords. set_connectionstyle("arc,angleA=0,armA=30,rad=10") set_connectionstyle("arc", angleA=0,armA=30,rad=10) Old attrs simply are forgotten. Without argument (or with connectionstyle=None), return available styles as a list of strings. """ if connectionstyle==None: return ConnectionStyle.pprint_styles() if isinstance(connectionstyle, ConnectionStyle._Base): self._connector = connectionstyle elif callable(connectionstyle): # we may need check the calling convention of the given function self._connector = connectionstyle else: self._connector = ConnectionStyle(connectionstyle, **kw) def get_connectionstyle(self): """ Return the ConnectionStyle instance """ return self._connector def set_arrowstyle(self, arrowstyle=None, **kw): """ Set the arrow style. *arrowstyle* can be a string with arrowstyle name with optional comma-separated attributes. Alternatively, the attrs can be provided as keywords. set_arrowstyle("Fancy,head_length=0.2") set_arrowstyle("fancy", head_length=0.2) Old attrs simply are forgotten. Without argument (or with arrowstyle=None), return available box styles as a list of strings. """ if arrowstyle==None: return ArrowStyle.pprint_styles() if isinstance(arrowstyle, ConnectionStyle._Base): self._arrow_transmuter = arrowstyle else: self._arrow_transmuter = ArrowStyle(arrowstyle, **kw) def get_arrowstyle(self): """ Return the arrowstyle object """ return self._arrow_transmuter def set_mutation_scale(self, scale): """ Set the mutation scale. ACCEPTS: float """ self._mutation_scale=scale def get_mutation_scale(self): """ Return the mutation scale. """ return self._mutation_scale def set_mutation_aspect(self, aspect): """ Set the aspect ratio of the bbox mutation. ACCEPTS: float """ self._mutation_aspect=aspect def get_mutation_aspect(self): """ Return the aspect ratio of the bbox mutation. """ return self._mutation_aspect def get_path(self): """ return the path of the arrow in the data coordinate. Use get_path_in_displaycoord() medthod to retrieve the arrow path in the disaply coord. """ _path = self.get_path_in_displaycoord() return self.get_transform().inverted().transform_path(_path) def get_path_in_displaycoord(self): """ Return the mutated path of the arrow in the display coord """ if self._posA_posB is not None: posA = self.get_transform().transform_point(self._posA_posB[0]) posB = self.get_transform().transform_point(self._posA_posB[1]) _path = self.get_connectionstyle()(posA, posB, patchA=self.patchA, patchB=self.patchB, shrinkA=self.shrinkA, shrinkB=self.shrinkB ) else: _path = self.get_transform().transform_path(self._path_original) _path, closed = self.get_arrowstyle()(_path, self.get_mutation_scale(), self.get_linewidth(), self.get_mutation_aspect() ) if not closed: self.fill = False return _path def draw(self, renderer): if not self.get_visible(): return #renderer.open_group('patch') gc = renderer.new_gc() fill_orig = self.fill path = self.get_path_in_displaycoord() affine = transforms.IdentityTransform() if cbook.is_string_like(self._edgecolor) and self._edgecolor.lower()=='none': gc.set_linewidth(0) else: gc.set_foreground(self._edgecolor) gc.set_linewidth(self._linewidth) gc.set_linestyle(self._linestyle) gc.set_antialiased(self._antialiased) self._set_gc_clip(gc) gc.set_capstyle('round') if (not self.fill or self._facecolor is None or (cbook.is_string_like(self._facecolor) and self._facecolor.lower()=='none')): rgbFace = None gc.set_alpha(1.0) else: r, g, b, a = colors.colorConverter.to_rgba(self._facecolor, self._alpha) rgbFace = (r, g, b) gc.set_alpha(a) if self._hatch: gc.set_hatch(self._hatch ) renderer.draw_path(gc, path, affine, rgbFace) self.fill = fill_orig #renderer.close_group('patch')

110,325

Python

.py

2,673

29.265619

102

0.533629

numenta/nupic-legacy

6,330

1,556

464

AGPL-3.0

9/5/2024, 5:13:42 PM (Europe/Amsterdam)

26,246

cbook.py

numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/cbook.py

""" A collection of utility functions and classes. Many (but not all) from the Python Cookbook -- hence the name cbook """ from __future__ import generators import re, os, errno, sys, StringIO, traceback, locale, threading, types import time, datetime import warnings import numpy as np import numpy.ma as ma from weakref import ref major, minor1, minor2, s, tmp = sys.version_info # on some systems, locale.getpreferredencoding returns None, which can break unicode preferredencoding = locale.getpreferredencoding() def unicode_safe(s): if preferredencoding is None: return unicode(s) else: return unicode(s, preferredencoding) class converter: """ Base class for handling string -> python type with support for missing values """ def __init__(self, missing='Null', missingval=None): self.missing = missing self.missingval = missingval def __call__(self, s): if s==self.missing: return self.missingval return s def is_missing(self, s): return not s.strip() or s==self.missing class tostr(converter): 'convert to string or None' def __init__(self, missing='Null', missingval=''): converter.__init__(self, missing=missing, missingval=missingval) class todatetime(converter): 'convert to a datetime or None' def __init__(self, fmt='%Y-%m-%d', missing='Null', missingval=None): 'use a :func:`time.strptime` format string for conversion' converter.__init__(self, missing, missingval) self.fmt = fmt def __call__(self, s): if self.is_missing(s): return self.missingval tup = time.strptime(s, self.fmt) return datetime.datetime(*tup[:6]) class todate(converter): 'convert to a date or None' def __init__(self, fmt='%Y-%m-%d', missing='Null', missingval=None): 'use a :func:`time.strptime` format string for conversion' converter.__init__(self, missing, missingval) self.fmt = fmt def __call__(self, s): if self.is_missing(s): return self.missingval tup = time.strptime(s, self.fmt) return datetime.date(*tup[:3]) class tofloat(converter): 'convert to a float or None' def __init__(self, missing='Null', missingval=None): converter.__init__(self, missing) self.missingval = missingval def __call__(self, s): if self.is_missing(s): return self.missingval return float(s) class toint(converter): 'convert to an int or None' def __init__(self, missing='Null', missingval=None): converter.__init__(self, missing) def __call__(self, s): if self.is_missing(s): return self.missingval return int(s) class CallbackRegistry: """ Handle registering and disconnecting for a set of signals and callbacks:: signals = 'eat', 'drink', 'be merry' def oneat(x): print 'eat', x def ondrink(x): print 'drink', x callbacks = CallbackRegistry(signals) ideat = callbacks.connect('eat', oneat) iddrink = callbacks.connect('drink', ondrink) #tmp = callbacks.connect('drunk', ondrink) # this will raise a ValueError callbacks.process('drink', 123) # will call oneat callbacks.process('eat', 456) # will call ondrink callbacks.process('be merry', 456) # nothing will be called callbacks.disconnect(ideat) # disconnect oneat callbacks.process('eat', 456) # nothing will be called """ def __init__(self, signals): '*signals* is a sequence of valid signals' self.signals = set(signals) # callbacks is a dict mapping the signal to a dictionary # mapping callback id to the callback function self.callbacks = dict([(s, dict()) for s in signals]) self._cid = 0 def _check_signal(self, s): 'make sure *s* is a valid signal or raise a ValueError' if s not in self.signals: signals = list(self.signals) signals.sort() raise ValueError('Unknown signal "%s"; valid signals are %s'%(s, signals)) def connect(self, s, func): """ register *func* to be called when a signal *s* is generated func will be called """ self._check_signal(s) self._cid +=1 self.callbacks[s][self._cid] = func return self._cid def disconnect(self, cid): """ disconnect the callback registered with callback id *cid* """ for eventname, callbackd in self.callbacks.items(): try: del callbackd[cid] except KeyError: continue else: return def process(self, s, *args, **kwargs): """ process signal *s*. All of the functions registered to receive callbacks on *s* will be called with *\*args* and *\*\*kwargs* """ self._check_signal(s) for func in self.callbacks[s].values(): func(*args, **kwargs) class Scheduler(threading.Thread): """ Base class for timeout and idle scheduling """ idlelock = threading.Lock() id = 0 def __init__(self): threading.Thread.__init__(self) self.id = Scheduler.id self._stopped = False Scheduler.id += 1 self._stopevent = threading.Event() def stop(self): if self._stopped: return self._stopevent.set() self.join() self._stopped = True class Timeout(Scheduler): """ Schedule recurring events with a wait time in seconds """ def __init__(self, wait, func): Scheduler.__init__(self) self.wait = wait self.func = func def run(self): while not self._stopevent.isSet(): self._stopevent.wait(self.wait) Scheduler.idlelock.acquire() b = self.func(self) Scheduler.idlelock.release() if not b: break class Idle(Scheduler): """ Schedule callbacks when scheduler is idle """ # the prototype impl is a bit of a poor man's idle handler. It # just implements a short wait time. But it will provide a # placeholder for a proper impl ater waittime = 0.05 def __init__(self, func): Scheduler.__init__(self) self.func = func def run(self): while not self._stopevent.isSet(): self._stopevent.wait(Idle.waittime) Scheduler.idlelock.acquire() b = self.func(self) Scheduler.idlelock.release() if not b: break class silent_list(list): """ override repr when returning a list of matplotlib artists to prevent long, meaningless output. This is meant to be used for a homogeneous list of a give type """ def __init__(self, type, seq=None): self.type = type if seq is not None: self.extend(seq) def __repr__(self): return '<a list of %d %s objects>' % (len(self), self.type) def __str__(self): return '<a list of %d %s objects>' % (len(self), self.type) def strip_math(s): 'remove latex formatting from mathtext' remove = (r'\mathdefault', r'\rm', r'\cal', r'\tt', r'\it', '\\', '{', '}') s = s[1:-1] for r in remove: s = s.replace(r,'') return s class Bunch: """ Often we want to just collect a bunch of stuff together, naming each item of the bunch; a dictionary's OK for that, but a small do- nothing class is even handier, and prettier to use. Whenever you want to group a few variables: >>> point = Bunch(datum=2, squared=4, coord=12) >>> point.datum By: Alex Martelli From: http://aspn.activestate.com/ASPN/Cookbook/Python/Recipe/52308 """ def __init__(self, **kwds): self.__dict__.update(kwds) def unique(x): 'Return a list of unique elements of *x*' return dict([ (val, 1) for val in x]).keys() def iterable(obj): 'return true if *obj* is iterable' try: len(obj) except: return False return True def is_string_like(obj): 'Return True if *obj* looks like a string' if isinstance(obj, (str, unicode)): return True # numpy strings are subclass of str, ma strings are not if ma.isMaskedArray(obj): if obj.ndim == 0 and obj.dtype.kind in 'SU': return True else: return False try: obj + '' except (TypeError, ValueError): return False return True def is_sequence_of_strings(obj): """ Returns true if *obj* is iterable and contains strings """ if not iterable(obj): return False if is_string_like(obj): return False for o in obj: if not is_string_like(o): return False return True def is_writable_file_like(obj): 'return true if *obj* looks like a file object with a *write* method' return hasattr(obj, 'write') and callable(obj.write) def is_scalar(obj): 'return true if *obj* is not string like and is not iterable' return not is_string_like(obj) and not iterable(obj) def is_numlike(obj): 'return true if *obj* looks like a number' try: obj+1 except TypeError: return False else: return True def to_filehandle(fname, flag='r', return_opened=False): """ *fname* can be a filename or a file handle. Support for gzipped files is automatic, if the filename ends in .gz. *flag* is a read/write flag for :func:`file` """ if is_string_like(fname): if fname.endswith('.gz'): import gzip fh = gzip.open(fname, flag) else: fh = file(fname, flag) opened = True elif hasattr(fname, 'seek'): fh = fname opened = False else: raise ValueError('fname must be a string or file handle') if return_opened: return fh, opened return fh def is_scalar_or_string(val): return is_string_like(val) or not iterable(val) def flatten(seq, scalarp=is_scalar_or_string): """ this generator flattens nested containers such as >>> l=( ('John', 'Hunter'), (1,23), [[[[42,(5,23)]]]]) so that >>> for i in flatten(l): print i, John Hunter 1 23 42 5 23 By: Composite of Holger Krekel and Luther Blissett From: http://aspn.activestate.com/ASPN/Cookbook/Python/Recipe/121294 and Recipe 1.12 in cookbook """ for item in seq: if scalarp(item): yield item else: for subitem in flatten(item, scalarp): yield subitem class Sorter: """ Sort by attribute or item Example usage:: sort = Sorter() list = [(1, 2), (4, 8), (0, 3)] dict = [{'a': 3, 'b': 4}, {'a': 5, 'b': 2}, {'a': 0, 'b': 0}, {'a': 9, 'b': 9}] sort(list) # default sort sort(list, 1) # sort by index 1 sort(dict, 'a') # sort a list of dicts by key 'a' """ def _helper(self, data, aux, inplace): aux.sort() result = [data[i] for junk, i in aux] if inplace: data[:] = result return result def byItem(self, data, itemindex=None, inplace=1): if itemindex is None: if inplace: data.sort() result = data else: result = data[:] result.sort() return result else: aux = [(data[i][itemindex], i) for i in range(len(data))] return self._helper(data, aux, inplace) def byAttribute(self, data, attributename, inplace=1): aux = [(getattr(data[i],attributename),i) for i in range(len(data))] return self._helper(data, aux, inplace) # a couple of handy synonyms sort = byItem __call__ = byItem class Xlator(dict): """ All-in-one multiple-string-substitution class Example usage:: text = "Larry Wall is the creator of Perl" adict = { "Larry Wall" : "Guido van Rossum", "creator" : "Benevolent Dictator for Life", "Perl" : "Python", } print multiple_replace(adict, text) xlat = Xlator(adict) print xlat.xlat(text) """ def _make_regex(self): """ Build re object based on the keys of the current dictionary """ return re.compile("|".join(map(re.escape, self.keys()))) def __call__(self, match): """ Handler invoked for each regex *match* """ return self[match.group(0)] def xlat(self, text): """ Translate *text*, returns the modified text. """ return self._make_regex().sub(self, text) def soundex(name, len=4): """ soundex module conforming to Odell-Russell algorithm """ # digits holds the soundex values for the alphabet soundex_digits = '01230120022455012623010202' sndx = '' fc = '' # Translate letters in name to soundex digits for c in name.upper(): if c.isalpha(): if not fc: fc = c # Remember first letter d = soundex_digits[ord(c)-ord('A')] # Duplicate consecutive soundex digits are skipped if not sndx or (d != sndx[-1]): sndx += d # Replace first digit with first letter sndx = fc + sndx[1:] # Remove all 0s from the soundex code sndx = sndx.replace('0', '') # Return soundex code truncated or 0-padded to len characters return (sndx + (len * '0'))[:len] class Null: """ Null objects always and reliably "do nothing." """ def __init__(self, *args, **kwargs): pass def __call__(self, *args, **kwargs): return self def __str__(self): return "Null()" def __repr__(self): return "Null()" def __nonzero__(self): return 0 def __getattr__(self, name): return self def __setattr__(self, name, value): return self def __delattr__(self, name): return self def mkdirs(newdir, mode=0777): """ make directory *newdir* recursively, and set *mode*. Equivalent to :: > mkdir -p NEWDIR > chmod MODE NEWDIR """ try: if not os.path.exists(newdir): parts = os.path.split(newdir) for i in range(1, len(parts)+1): thispart = os.path.join(*parts[:i]) if not os.path.exists(thispart): os.makedirs(thispart, mode) except OSError, err: # Reraise the error unless it's about an already existing directory if err.errno != errno.EEXIST or not os.path.isdir(newdir): raise class GetRealpathAndStat: def __init__(self): self._cache = {} def __call__(self, path): result = self._cache.get(path) if result is None: realpath = os.path.realpath(path) if sys.platform == 'win32': stat_key = realpath else: stat = os.stat(realpath) stat_key = (stat.st_ino, stat.st_dev) result = realpath, stat_key self._cache[path] = result return result get_realpath_and_stat = GetRealpathAndStat() def dict_delall(d, keys): 'delete all of the *keys* from the :class:`dict` *d*' for key in keys: try: del d[key] except KeyError: pass class RingBuffer: """ class that implements a not-yet-full buffer """ def __init__(self,size_max): self.max = size_max self.data = [] class __Full: """ class that implements a full buffer """ def append(self, x): """ Append an element overwriting the oldest one. """ self.data[self.cur] = x self.cur = (self.cur+1) % self.max def get(self): """ return list of elements in correct order """ return self.data[self.cur:]+self.data[:self.cur] def append(self,x): """append an element at the end of the buffer""" self.data.append(x) if len(self.data) == self.max: self.cur = 0 # Permanently change self's class from non-full to full self.__class__ = __Full def get(self): """ Return a list of elements from the oldest to the newest. """ return self.data def __get_item__(self, i): return self.data[i % len(self.data)] def get_split_ind(seq, N): """ *seq* is a list of words. Return the index into seq such that:: len(' '.join(seq[:ind])<=N """ sLen = 0 # todo: use Alex's xrange pattern from the cbook for efficiency for (word, ind) in zip(seq, range(len(seq))): sLen += len(word) + 1 # +1 to account for the len(' ') if sLen>=N: return ind return len(seq) def wrap(prefix, text, cols): 'wrap *text* with *prefix* at length *cols*' pad = ' '*len(prefix.expandtabs()) available = cols - len(pad) seq = text.split(' ') Nseq = len(seq) ind = 0 lines = [] while ind<Nseq: lastInd = ind ind += get_split_ind(seq[ind:], available) lines.append(seq[lastInd:ind]) # add the prefix to the first line, pad with spaces otherwise ret = prefix + ' '.join(lines[0]) + '\n' for line in lines[1:]: ret += pad + ' '.join(line) + '\n' return ret # A regular expression used to determine the amount of space to # remove. It looks for the first sequence of spaces immediately # following the first newline, or at the beginning of the string. _find_dedent_regex = re.compile("(?:(?:\n\r?)|^)( *)\S") # A cache to hold the regexs that actually remove the indent. _dedent_regex = {} def dedent(s): """ Remove excess indentation from docstring *s*. Discards any leading blank lines, then removes up to n whitespace characters from each line, where n is the number of leading whitespace characters in the first line. It differs from textwrap.dedent in its deletion of leading blank lines and its use of the first non-blank line to determine the indentation. It is also faster in most cases. """ # This implementation has a somewhat obtuse use of regular # expressions. However, this function accounted for almost 30% of # matplotlib startup time, so it is worthy of optimization at all # costs. if not s: # includes case of s is None return '' match = _find_dedent_regex.match(s) if match is None: return s # This is the number of spaces to remove from the left-hand side. nshift = match.end(1) - match.start(1) if nshift == 0: return s # Get a regex that will remove *up to* nshift spaces from the # beginning of each line. If it isn't in the cache, generate it. unindent = _dedent_regex.get(nshift, None) if unindent is None: unindent = re.compile("\n\r? {0,%d}" % nshift) _dedent_regex[nshift] = unindent result = unindent.sub("\n", s).strip() return result def listFiles(root, patterns='*', recurse=1, return_folders=0): """ Recursively list files from Parmar and Martelli in the Python Cookbook """ import os.path, fnmatch # Expand patterns from semicolon-separated string to list pattern_list = patterns.split(';') # Collect input and output arguments into one bunch class Bunch: def __init__(self, **kwds): self.__dict__.update(kwds) arg = Bunch(recurse=recurse, pattern_list=pattern_list, return_folders=return_folders, results=[]) def visit(arg, dirname, files): # Append to arg.results all relevant files (and perhaps folders) for name in files: fullname = os.path.normpath(os.path.join(dirname, name)) if arg.return_folders or os.path.isfile(fullname): for pattern in arg.pattern_list: if fnmatch.fnmatch(name, pattern): arg.results.append(fullname) break # Block recursion if recursion was disallowed if not arg.recurse: files[:]=[] os.path.walk(root, visit, arg) return arg.results def get_recursive_filelist(args): """ Recurs all the files and dirs in *args* ignoring symbolic links and return the files as a list of strings """ files = [] for arg in args: if os.path.isfile(arg): files.append(arg) continue if os.path.isdir(arg): newfiles = listFiles(arg, recurse=1, return_folders=1) files.extend(newfiles) return [f for f in files if not os.path.islink(f)] def pieces(seq, num=2): "Break up the *seq* into *num* tuples" start = 0 while 1: item = seq[start:start+num] if not len(item): break yield item start += num def exception_to_str(s = None): sh = StringIO.StringIO() if s is not None: print >>sh, s traceback.print_exc(file=sh) return sh.getvalue() def allequal(seq): """ Return *True* if all elements of *seq* compare equal. If *seq* is 0 or 1 length, return *True* """ if len(seq)<2: return True val = seq[0] for i in xrange(1, len(seq)): thisval = seq[i] if thisval != val: return False return True def alltrue(seq): """ Return *True* if all elements of *seq* evaluate to *True*. If *seq* is empty, return *False*. """ if not len(seq): return False for val in seq: if not val: return False return True def onetrue(seq): """ Return *True* if one element of *seq* is *True*. It *seq* is empty, return *False*. """ if not len(seq): return False for val in seq: if val: return True return False def allpairs(x): """ return all possible pairs in sequence *x* Condensed by Alex Martelli from this thread_ on c.l.python .. _thread: http://groups.google.com/groups?q=all+pairs+group:*python*&hl=en&lr=&ie=UTF-8&selm=mailman.4028.1096403649.5135.python-list%40python.org&rnum=1 """ return [ (s, f) for i, f in enumerate(x) for s in x[i+1:] ] # python 2.2 dicts don't have pop--but we don't support 2.2 any more def popd(d, *args): """ Should behave like python2.3 :meth:`dict.pop` method; *d* is a :class:`dict`:: # returns value for key and deletes item; raises a KeyError if key # is not in dict val = popd(d, key) # returns value for key if key exists, else default. Delete key, # val item if it exists. Will not raise a KeyError val = popd(d, key, default) """ warnings.warn("Use native python dict.pop method", DeprecationWarning) # warning added 2008/07/22 if len(args)==1: key = args[0] val = d[key] del d[key] elif len(args)==2: key, default = args val = d.get(key, default) try: del d[key] except KeyError: pass return val class maxdict(dict): """ A dictionary with a maximum size; this doesn't override all the relevant methods to contrain size, just setitem, so use with caution """ def __init__(self, maxsize): dict.__init__(self) self.maxsize = maxsize self._killkeys = [] def __setitem__(self, k, v): if len(self)>=self.maxsize: del self[self._killkeys[0]] del self._killkeys[0] dict.__setitem__(self, k, v) self._killkeys.append(k) class Stack: """ Implement a stack where elements can be pushed on and you can move back and forth. But no pop. Should mimic home / back / forward in a browser """ def __init__(self, default=None): self.clear() self._default = default def __call__(self): 'return the current element, or None' if not len(self._elements): return self._default else: return self._elements[self._pos] def forward(self): 'move the position forward and return the current element' N = len(self._elements) if self._pos<N-1: self._pos += 1 return self() def back(self): 'move the position back and return the current element' if self._pos>0: self._pos -= 1 return self() def push(self, o): """ push object onto stack at current position - all elements occurring later than the current position are discarded """ self._elements = self._elements[:self._pos+1] self._elements.append(o) self._pos = len(self._elements)-1 return self() def home(self): 'push the first element onto the top of the stack' if not len(self._elements): return self.push(self._elements[0]) return self() def empty(self): return len(self._elements)==0 def clear(self): 'empty the stack' self._pos = -1 self._elements = [] def bubble(self, o): """ raise *o* to the top of the stack and return *o*. *o* must be in the stack """ if o not in self._elements: raise ValueError('Unknown element o') old = self._elements[:] self.clear() bubbles = [] for thiso in old: if thiso==o: bubbles.append(thiso) else: self.push(thiso) for thiso in bubbles: self.push(o) return o def remove(self, o): 'remove element *o* from the stack' if o not in self._elements: raise ValueError('Unknown element o') old = self._elements[:] self.clear() for thiso in old: if thiso==o: continue else: self.push(thiso) def popall(seq): 'empty a list' for i in xrange(len(seq)): seq.pop() def finddir(o, match, case=False): """ return all attributes of *o* which match string in match. if case is True require an exact case match. """ if case: names = [(name,name) for name in dir(o) if is_string_like(name)] else: names = [(name.lower(), name) for name in dir(o) if is_string_like(name)] match = match.lower() return [orig for name, orig in names if name.find(match)>=0] def reverse_dict(d): 'reverse the dictionary -- may lose data if values are not unique!' return dict([(v,k) for k,v in d.items()]) def report_memory(i=0): # argument may go away 'return the memory consumed by process' pid = os.getpid() if sys.platform=='sunos5': a2 = os.popen('ps -p %d -o osz' % pid).readlines() mem = int(a2[-1].strip()) elif sys.platform.startswith('linux'): a2 = os.popen('ps -p %d -o rss,sz' % pid).readlines() mem = int(a2[1].split()[1]) elif sys.platform.startswith('darwin'): a2 = os.popen('ps -p %d -o rss,vsz' % pid).readlines() mem = int(a2[1].split()[0]) return mem _safezip_msg = 'In safezip, len(args[0])=%d but len(args[%d])=%d' def safezip(*args): 'make sure *args* are equal len before zipping' Nx = len(args[0]) for i, arg in enumerate(args[1:]): if len(arg) != Nx: raise ValueError(_safezip_msg % (Nx, i+1, len(arg))) return zip(*args) def issubclass_safe(x, klass): 'return issubclass(x, klass) and return False on a TypeError' try: return issubclass(x, klass) except TypeError: return False class MemoryMonitor: def __init__(self, nmax=20000): self._nmax = nmax self._mem = np.zeros((self._nmax,), np.int32) self.clear() def clear(self): self._n = 0 self._overflow = False def __call__(self): mem = report_memory() if self._n < self._nmax: self._mem[self._n] = mem self._n += 1 else: self._overflow = True return mem def report(self, segments=4): n = self._n segments = min(n, segments) dn = int(n/segments) ii = range(0, n, dn) ii[-1] = n-1 print print 'memory report: i, mem, dmem, dmem/nloops' print 0, self._mem[0] for i in range(1, len(ii)): di = ii[i] - ii[i-1] if di == 0: continue dm = self._mem[ii[i]] - self._mem[ii[i-1]] print '%5d %5d %3d %8.3f' % (ii[i], self._mem[ii[i]], dm, dm / float(di)) if self._overflow: print "Warning: array size was too small for the number of calls." def xy(self, i0=0, isub=1): x = np.arange(i0, self._n, isub) return x, self._mem[i0:self._n:isub] def plot(self, i0=0, isub=1, fig=None): if fig is None: from pylab import figure, show fig = figure() ax = fig.add_subplot(111) ax.plot(*self.xy(i0, isub)) fig.canvas.draw() def print_cycles(objects, outstream=sys.stdout, show_progress=False): """ *objects* A list of objects to find cycles in. It is often useful to pass in gc.garbage to find the cycles that are preventing some objects from being garbage collected. *outstream* The stream for output. *show_progress* If True, print the number of objects reached as they are found. """ import gc from types import FrameType def print_path(path): for i, step in enumerate(path): # next "wraps around" next = path[(i + 1) % len(path)] outstream.write(" %s -- " % str(type(step))) if isinstance(step, dict): for key, val in step.items(): if val is next: outstream.write("[%s]" % repr(key)) break if key is next: outstream.write("[key] = %s" % repr(val)) break elif isinstance(step, list): outstream.write("[%d]" % step.index(next)) elif isinstance(step, tuple): outstream.write("( tuple )") else: outstream.write(repr(step)) outstream.write(" ->\n") outstream.write("\n") def recurse(obj, start, all, current_path): if show_progress: outstream.write("%d\r" % len(all)) all[id(obj)] = None referents = gc.get_referents(obj) for referent in referents: # If we've found our way back to the start, this is # a cycle, so print it out if referent is start: print_path(current_path) # Don't go back through the original list of objects, or # through temporary references to the object, since those # are just an artifact of the cycle detector itself. elif referent is objects or isinstance(referent, FrameType): continue # We haven't seen this object before, so recurse elif id(referent) not in all: recurse(referent, start, all, current_path + [obj]) for obj in objects: outstream.write("Examining: %r\n" % (obj,)) recurse(obj, obj, { }, []) class Grouper(object): """ This class provides a lightweight way to group arbitrary objects together into disjoint sets when a full-blown graph data structure would be overkill. Objects can be joined using :meth:`join`, tested for connectedness using :meth:`joined`, and all disjoint sets can be retreived by using the object as an iterator. The objects being joined must be hashable. For example: >>> g = grouper.Grouper() >>> g.join('a', 'b') >>> g.join('b', 'c') >>> g.join('d', 'e') >>> list(g) [['a', 'b', 'c'], ['d', 'e']] >>> g.joined('a', 'b') True >>> g.joined('a', 'c') True >>> g.joined('a', 'd') False """ def __init__(self, init=[]): mapping = self._mapping = {} for x in init: mapping[ref(x)] = [ref(x)] def __contains__(self, item): return ref(item) in self._mapping def clean(self): """ Clean dead weak references from the dictionary """ mapping = self._mapping for key, val in mapping.items(): if key() is None: del mapping[key] val.remove(key) def join(self, a, *args): """ Join given arguments into the same set. Accepts one or more arguments. """ mapping = self._mapping set_a = mapping.setdefault(ref(a), [ref(a)]) for arg in args: set_b = mapping.get(ref(arg)) if set_b is None: set_a.append(ref(arg)) mapping[ref(arg)] = set_a elif set_b is not set_a: if len(set_b) > len(set_a): set_a, set_b = set_b, set_a set_a.extend(set_b) for elem in set_b: mapping[elem] = set_a self.clean() def joined(self, a, b): """ Returns True if *a* and *b* are members of the same set. """ self.clean() mapping = self._mapping try: return mapping[ref(a)] is mapping[ref(b)] except KeyError: return False def __iter__(self): """ Iterate over each of the disjoint sets as a list. The iterator is invalid if interleaved with calls to join(). """ self.clean() class Token: pass token = Token() # Mark each group as we come across if by appending a token, # and don't yield it twice for group in self._mapping.itervalues(): if not group[-1] is token: yield [x() for x in group] group.append(token) # Cleanup the tokens for group in self._mapping.itervalues(): if group[-1] is token: del group[-1] def get_siblings(self, a): """ Returns all of the items joined with *a*, including itself. """ self.clean() siblings = self._mapping.get(ref(a), [ref(a)]) return [x() for x in siblings] def simple_linear_interpolation(a, steps): steps = np.floor(steps) new_length = ((len(a) - 1) * steps) + 1 new_shape = list(a.shape) new_shape[0] = new_length result = np.zeros(new_shape, a.dtype) result[0] = a[0] a0 = a[0:-1] a1 = a[1: ] delta = ((a1 - a0) / steps) for i in range(1, int(steps)): result[i::steps] = delta * i + a0 result[steps::steps] = a1 return result def recursive_remove(path): if os.path.isdir(path): for fname in glob.glob(os.path.join(path, '*')) + glob.glob(os.path.join(path, '.*')): if os.path.isdir(fname): recursive_remove(fname) os.removedirs(fname) else: os.remove(fname) #os.removedirs(path) else: os.remove(path) def delete_masked_points(*args): """ Find all masked and/or non-finite points in a set of arguments, and return the arguments with only the unmasked points remaining. Arguments can be in any of 5 categories: 1) 1-D masked arrays 2) 1-D ndarrays 3) ndarrays with more than one dimension 4) other non-string iterables 5) anything else The first argument must be in one of the first four categories; any argument with a length differing from that of the first argument (and hence anything in category 5) then will be passed through unchanged. Masks are obtained from all arguments of the correct length in categories 1, 2, and 4; a point is bad if masked in a masked array or if it is a nan or inf. No attempt is made to extract a mask from categories 2, 3, and 4 if :meth:`np.isfinite` does not yield a Boolean array. All input arguments that are not passed unchanged are returned as ndarrays after removing the points or rows corresponding to masks in any of the arguments. A vastly simpler version of this function was originally written as a helper for Axes.scatter(). """ if not len(args): return () if (is_string_like(args[0]) or not iterable(args[0])): raise ValueError("First argument must be a sequence") nrecs = len(args[0]) margs = [] seqlist = [False] * len(args) for i, x in enumerate(args): if (not is_string_like(x)) and iterable(x) and len(x) == nrecs: seqlist[i] = True if ma.isMA(x): if x.ndim > 1: raise ValueError("Masked arrays must be 1-D") else: x = np.asarray(x) margs.append(x) masks = [] # list of masks that are True where good for i, x in enumerate(margs): if seqlist[i]: if x.ndim > 1: continue # Don't try to get nan locations unless 1-D. if ma.isMA(x): masks.append(~ma.getmaskarray(x)) # invert the mask xd = x.data else: xd = x try: mask = np.isfinite(xd) if isinstance(mask, np.ndarray): masks.append(mask) except: #Fixme: put in tuple of possible exceptions? pass if len(masks): mask = reduce(np.logical_and, masks) igood = mask.nonzero()[0] if len(igood) < nrecs: for i, x in enumerate(margs): if seqlist[i]: margs[i] = x.take(igood, axis=0) for i, x in enumerate(margs): if seqlist[i] and ma.isMA(x): margs[i] = x.filled() return margs def unmasked_index_ranges(mask, compressed = True): ''' Find index ranges where *mask* is *False*. *mask* will be flattened if it is not already 1-D. Returns Nx2 :class:`numpy.ndarray` with each row the start and stop indices for slices of the compressed :class:`numpy.ndarray` corresponding to each of *N* uninterrupted runs of unmasked values. If optional argument *compressed* is *False*, it returns the start and stop indices into the original :class:`numpy.ndarray`, not the compressed :class:`numpy.ndarray`. Returns *None* if there are no unmasked values. Example:: y = ma.array(np.arange(5), mask = [0,0,1,0,0]) ii = unmasked_index_ranges(ma.getmaskarray(y)) # returns array [[0,2,] [2,4,]] y.compressed()[ii[1,0]:ii[1,1]] # returns array [3,4,] ii = unmasked_index_ranges(ma.getmaskarray(y), compressed=False) # returns array [[0, 2], [3, 5]] y.filled()[ii[1,0]:ii[1,1]] # returns array [3,4,] Prior to the transforms refactoring, this was used to support masked arrays in Line2D. ''' mask = mask.reshape(mask.size) m = np.concatenate(((1,), mask, (1,))) indices = np.arange(len(mask) + 1) mdif = m[1:] - m[:-1] i0 = np.compress(mdif == -1, indices) i1 = np.compress(mdif == 1, indices) assert len(i0) == len(i1) if len(i1) == 0: return None # Maybe this should be np.zeros((0,2), dtype=int) if not compressed: return np.concatenate((i0[:, np.newaxis], i1[:, np.newaxis]), axis=1) seglengths = i1 - i0 breakpoints = np.cumsum(seglengths) ic0 = np.concatenate(((0,), breakpoints[:-1])) ic1 = breakpoints return np.concatenate((ic0[:, np.newaxis], ic1[:, np.newaxis]), axis=1) # a dict to cross-map linestyle arguments _linestyles = [('-', 'solid'), ('--', 'dashed'), ('-.', 'dashdot'), (':', 'dotted')] ls_mapper = dict(_linestyles) ls_mapper.update([(ls[1], ls[0]) for ls in _linestyles]) def less_simple_linear_interpolation( x, y, xi, extrap=False ): """ This function has been moved to matplotlib.mlab -- please import it from there """ # deprecated from cbook in 0.98.4 warnings.warn('less_simple_linear_interpolation has been moved to matplotlib.mlab -- please import it from there', DeprecationWarning) import matplotlib.mlab as mlab return mlab.less_simple_linear_interpolation( x, y, xi, extrap=extrap ) def isvector(X): """ This function has been moved to matplotlib.mlab -- please import it from there """ # deprecated from cbook in 0.98.4 warnings.warn('isvector has been moved to matplotlib.mlab -- please import it from there', DeprecationWarning) import matplotlib.mlab as mlab return mlab.isvector( x, y, xi, extrap=extrap ) def vector_lengths( X, P=2., axis=None ): """ This function has been moved to matplotlib.mlab -- please import it from there """ # deprecated from cbook in 0.98.4 warnings.warn('vector_lengths has been moved to matplotlib.mlab -- please import it from there', DeprecationWarning) import matplotlib.mlab as mlab return mlab.vector_lengths( X, P=2., axis=axis ) def distances_along_curve( X ): """ This function has been moved to matplotlib.mlab -- please import it from there """ # deprecated from cbook in 0.98.4 warnings.warn('distances_along_curve has been moved to matplotlib.mlab -- please import it from there', DeprecationWarning) import matplotlib.mlab as mlab return mlab.distances_along_curve( X ) def path_length(X): """ This function has been moved to matplotlib.mlab -- please import it from there """ # deprecated from cbook in 0.98.4 warnings.warn('path_length has been moved to matplotlib.mlab -- please import it from there', DeprecationWarning) import matplotlib.mlab as mlab return mlab.path_length(X) def is_closed_polygon(X): """ This function has been moved to matplotlib.mlab -- please import it from there """ # deprecated from cbook in 0.98.4 warnings.warn('is_closed_polygon has been moved to matplotlib.mlab -- please import it from there', DeprecationWarning) import matplotlib.mlab as mlab return mlab.is_closed_polygon(X) def quad2cubic(q0x, q0y, q1x, q1y, q2x, q2y): """ This function has been moved to matplotlib.mlab -- please import it from there """ # deprecated from cbook in 0.98.4 warnings.warn('quad2cubic has been moved to matplotlib.mlab -- please import it from there', DeprecationWarning) import matplotlib.mlab as mlab return mlab.quad2cubic(q0x, q0y, q1x, q1y, q2x, q2y) if __name__=='__main__': assert( allequal([1,1,1]) ) assert(not allequal([1,1,0]) ) assert( allequal([]) ) assert( allequal(('a', 'a'))) assert( not allequal(('a', 'b')))

42,525

Python

.py

1,144

29.562937

159

0.601435

numenta/nupic-legacy

6,330

1,556

464

AGPL-3.0

9/5/2024, 5:13:42 PM (Europe/Amsterdam)

26,247

axes.py

numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/axes.py

from __future__ import division, generators import math, sys, warnings, datetime, new import numpy as np from numpy import ma import matplotlib rcParams = matplotlib.rcParams import matplotlib.artist as martist import matplotlib.axis as maxis import matplotlib.cbook as cbook import matplotlib.collections as mcoll import matplotlib.colors as mcolors import matplotlib.contour as mcontour import matplotlib.dates as mdates import matplotlib.font_manager as font_manager import matplotlib.image as mimage import matplotlib.legend as mlegend import matplotlib.lines as mlines import matplotlib.mlab as mlab import matplotlib.patches as mpatches import matplotlib.quiver as mquiver import matplotlib.scale as mscale import matplotlib.table as mtable import matplotlib.text as mtext import matplotlib.ticker as mticker import matplotlib.transforms as mtransforms iterable = cbook.iterable is_string_like = cbook.is_string_like def _process_plot_format(fmt): """ Process a matlab(TM) style color/line style format string. Return a (*linestyle*, *color*) tuple as a result of the processing. Default values are ('-', 'b'). Example format strings include: * 'ko': black circles * '.b': blue dots * 'r--': red dashed lines .. seealso:: :func:`~matplotlib.Line2D.lineStyles` and :func:`~matplotlib.pyplot.colors`: for all possible styles and color format string. """ linestyle = None marker = None color = None # Is fmt just a colorspec? try: color = mcolors.colorConverter.to_rgb(fmt) return linestyle, marker, color # Yes. except ValueError: pass # No, not just a color. # handle the multi char special cases and strip them from the # string if fmt.find('--')>=0: linestyle = '--' fmt = fmt.replace('--', '') if fmt.find('-.')>=0: linestyle = '-.' fmt = fmt.replace('-.', '') if fmt.find(' ')>=0: linestyle = 'None' fmt = fmt.replace(' ', '') chars = [c for c in fmt] for c in chars: if c in mlines.lineStyles: if linestyle is not None: raise ValueError( 'Illegal format string "%s"; two linestyle symbols' % fmt) linestyle = c elif c in mlines.lineMarkers: if marker is not None: raise ValueError( 'Illegal format string "%s"; two marker symbols' % fmt) marker = c elif c in mcolors.colorConverter.colors: if color is not None: raise ValueError( 'Illegal format string "%s"; two color symbols' % fmt) color = c else: raise ValueError( 'Unrecognized character %c in format string' % c) if linestyle is None and marker is None: linestyle = rcParams['lines.linestyle'] if linestyle is None: linestyle = 'None' if marker is None: marker = 'None' return linestyle, marker, color def set_default_color_cycle(clist): """ Change the default cycle of colors that will be used by the plot command. This must be called before creating the :class:`Axes` to which it will apply; it will apply to all future axes. *clist* is a sequence of mpl color specifiers """ _process_plot_var_args.defaultColors = clist[:] rcParams['lines.color'] = clist[0] class _process_plot_var_args: """ Process variable length arguments to the plot command, so that plot commands like the following are supported:: plot(t, s) plot(t1, s1, t2, s2) plot(t1, s1, 'ko', t2, s2) plot(t1, s1, 'ko', t2, s2, 'r--', t3, e3) an arbitrary number of *x*, *y*, *fmt* are allowed """ defaultColors = ['b','g','r','c','m','y','k'] def __init__(self, axes, command='plot'): self.axes = axes self.command = command self._clear_color_cycle() def _clear_color_cycle(self): self.colors = _process_plot_var_args.defaultColors[:] # if the default line color is a color format string, move it up # in the que try: ind = self.colors.index(rcParams['lines.color']) except ValueError: self.firstColor = rcParams['lines.color'] else: self.colors[0], self.colors[ind] = self.colors[ind], self.colors[0] self.firstColor = self.colors[0] self.Ncolors = len(self.colors) self.count = 0 def set_color_cycle(self, clist): self.colors = clist[:] self.firstColor = self.colors[0] self.Ncolors = len(self.colors) self.count = 0 def _get_next_cycle_color(self): if self.count==0: color = self.firstColor else: color = self.colors[int(self.count % self.Ncolors)] self.count += 1 return color def __call__(self, *args, **kwargs): if self.axes.xaxis is not None and self.axes.yaxis is not None: xunits = kwargs.pop( 'xunits', self.axes.xaxis.units) yunits = kwargs.pop( 'yunits', self.axes.yaxis.units) if xunits!=self.axes.xaxis.units: self.axes.xaxis.set_units(xunits) if yunits!=self.axes.yaxis.units: self.axes.yaxis.set_units(yunits) ret = self._grab_next_args(*args, **kwargs) return ret def set_lineprops(self, line, **kwargs): assert self.command == 'plot', 'set_lineprops only works with "plot"' for key, val in kwargs.items(): funcName = "set_%s"%key if not hasattr(line,funcName): raise TypeError, 'There is no line property "%s"'%key func = getattr(line,funcName) func(val) def set_patchprops(self, fill_poly, **kwargs): assert self.command == 'fill', 'set_patchprops only works with "fill"' for key, val in kwargs.items(): funcName = "set_%s"%key if not hasattr(fill_poly,funcName): raise TypeError, 'There is no patch property "%s"'%key func = getattr(fill_poly,funcName) func(val) def _xy_from_y(self, y): if self.axes.yaxis is not None: b = self.axes.yaxis.update_units(y) if b: return np.arange(len(y)), y, False if not ma.isMaskedArray(y): y = np.asarray(y) if len(y.shape) == 1: y = y[:,np.newaxis] nr, nc = y.shape x = np.arange(nr) if len(x.shape) == 1: x = x[:,np.newaxis] return x,y, True def _xy_from_xy(self, x, y): if self.axes.xaxis is not None and self.axes.yaxis is not None: bx = self.axes.xaxis.update_units(x) by = self.axes.yaxis.update_units(y) # right now multicol is not supported if either x or y are # unit enabled but this can be fixed.. if bx or by: return x, y, False x = ma.asarray(x) y = ma.asarray(y) if len(x.shape) == 1: x = x[:,np.newaxis] if len(y.shape) == 1: y = y[:,np.newaxis] nrx, ncx = x.shape nry, ncy = y.shape assert nrx == nry, 'Dimensions of x and y are incompatible' if ncx == ncy: return x, y, True if ncx == 1: x = np.repeat(x, ncy, axis=1) if ncy == 1: y = np.repeat(y, ncx, axis=1) assert x.shape == y.shape, 'Dimensions of x and y are incompatible' return x, y, True def _plot_1_arg(self, y, **kwargs): assert self.command == 'plot', 'fill needs at least 2 arguments' ret = [] x, y, multicol = self._xy_from_y(y) if multicol: for j in xrange(y.shape[1]): color = self._get_next_cycle_color() seg = mlines.Line2D(x, y[:,j], color = color, axes=self.axes, ) self.set_lineprops(seg, **kwargs) ret.append(seg) else: color = self._get_next_cycle_color() seg = mlines.Line2D(x, y, color = color, axes=self.axes, ) self.set_lineprops(seg, **kwargs) ret.append(seg) return ret def _plot_2_args(self, tup2, **kwargs): ret = [] if is_string_like(tup2[1]): assert self.command == 'plot', ('fill needs at least 2 non-string ' 'arguments') y, fmt = tup2 x, y, multicol = self._xy_from_y(y) linestyle, marker, color = _process_plot_format(fmt) def makeline(x, y): _color = color if _color is None: _color = self._get_next_cycle_color() seg = mlines.Line2D(x, y, color=_color, linestyle=linestyle, marker=marker, axes=self.axes, ) self.set_lineprops(seg, **kwargs) ret.append(seg) if multicol: for j in xrange(y.shape[1]): makeline(x[:,j], y[:,j]) else: makeline(x, y) return ret else: x, y = tup2 x, y, multicol = self._xy_from_xy(x, y) def makeline(x, y): color = self._get_next_cycle_color() seg = mlines.Line2D(x, y, color=color, axes=self.axes, ) self.set_lineprops(seg, **kwargs) ret.append(seg) def makefill(x, y): x = self.axes.convert_xunits(x) y = self.axes.convert_yunits(y) facecolor = self._get_next_cycle_color() seg = mpatches.Polygon(np.hstack( (x[:,np.newaxis],y[:,np.newaxis])), facecolor = facecolor, fill=True, closed=closed ) self.set_patchprops(seg, **kwargs) ret.append(seg) if self.command == 'plot': func = makeline else: closed = kwargs.get('closed', True) func = makefill if multicol: for j in xrange(y.shape[1]): func(x[:,j], y[:,j]) else: func(x, y) return ret def _plot_3_args(self, tup3, **kwargs): ret = [] x, y, fmt = tup3 x, y, multicol = self._xy_from_xy(x, y) linestyle, marker, color = _process_plot_format(fmt) def makeline(x, y): _color = color if _color is None: _color = self._get_next_cycle_color() seg = mlines.Line2D(x, y, color=_color, linestyle=linestyle, marker=marker, axes=self.axes, ) self.set_lineprops(seg, **kwargs) ret.append(seg) def makefill(x, y): facecolor = color x = self.axes.convert_xunits(x) y = self.axes.convert_yunits(y) seg = mpatches.Polygon(np.hstack( (x[:,np.newaxis],y[:,np.newaxis])), facecolor = facecolor, fill=True, closed=closed ) self.set_patchprops(seg, **kwargs) ret.append(seg) if self.command == 'plot': func = makeline else: closed = kwargs.get('closed', True) func = makefill if multicol: for j in xrange(y.shape[1]): func(x[:,j], y[:,j]) else: func(x, y) return ret def _grab_next_args(self, *args, **kwargs): remaining = args while 1: if len(remaining)==0: return if len(remaining)==1: for seg in self._plot_1_arg(remaining[0], **kwargs): yield seg remaining = [] continue if len(remaining)==2: for seg in self._plot_2_args(remaining, **kwargs): yield seg remaining = [] continue if len(remaining)==3: if not is_string_like(remaining[2]): raise ValueError, 'third arg must be a format string' for seg in self._plot_3_args(remaining, **kwargs): yield seg remaining=[] continue if is_string_like(remaining[2]): for seg in self._plot_3_args(remaining[:3], **kwargs): yield seg remaining=remaining[3:] else: for seg in self._plot_2_args(remaining[:2], **kwargs): yield seg remaining=remaining[2:] class Axes(martist.Artist): """ The :class:`Axes` contains most of the figure elements: :class:`~matplotlib.axis.Axis`, :class:`~matplotlib.axis.Tick`, :class:`~matplotlib.lines.Line2D`, :class:`~matplotlib.text.Text`, :class:`~matplotlib.patches.Polygon`, etc., and sets the coordinate system. The :class:`Axes` instance supports callbacks through a callbacks attribute which is a :class:`~matplotlib.cbook.CallbackRegistry` instance. The events you can connect to are 'xlim_changed' and 'ylim_changed' and the callback will be called with func(*ax*) where *ax* is the :class:`Axes` instance. """ name = "rectilinear" _shared_x_axes = cbook.Grouper() _shared_y_axes = cbook.Grouper() def __str__(self): return "Axes(%g,%g;%gx%g)" % tuple(self._position.bounds) def __init__(self, fig, rect, axisbg = None, # defaults to rc axes.facecolor frameon = True, sharex=None, # use Axes instance's xaxis info sharey=None, # use Axes instance's yaxis info label='', **kwargs ): """ Build an :class:`Axes` instance in :class:`~matplotlib.figure.Figure` *fig* with *rect=[left, bottom, width, height]* in :class:`~matplotlib.figure.Figure` coordinates Optional keyword arguments: ================ ========================================= Keyword Description ================ ========================================= *adjustable* [ 'box' | 'datalim' ] *alpha* float: the alpha transparency *anchor* [ 'C', 'SW', 'S', 'SE', 'E', 'NE', 'N', 'NW', 'W' ] *aspect* [ 'auto' | 'equal' | aspect_ratio ] *autoscale_on* [ *True* | *False* ] whether or not to autoscale the *viewlim* *axis_bgcolor* any matplotlib color, see :func:`~matplotlib.pyplot.colors` *axisbelow* draw the grids and ticks below the other artists *cursor_props* a (*float*, *color*) tuple *figure* a :class:`~matplotlib.figure.Figure` instance *frame_on* a boolean - draw the axes frame *label* the axes label *navigate* [ *True* | *False* ] *navigate_mode* [ 'PAN' | 'ZOOM' | None ] the navigation toolbar button status *position* [left, bottom, width, height] in class:`~matplotlib.figure.Figure` coords *sharex* an class:`~matplotlib.axes.Axes` instance to share the x-axis with *sharey* an class:`~matplotlib.axes.Axes` instance to share the y-axis with *title* the title string *visible* [ *True* | *False* ] whether the axes is visible *xlabel* the xlabel *xlim* (*xmin*, *xmax*) view limits *xscale* [%(scale)s] *xticklabels* sequence of strings *xticks* sequence of floats *ylabel* the ylabel strings *ylim* (*ymin*, *ymax*) view limits *yscale* [%(scale)s] *yticklabels* sequence of strings *yticks* sequence of floats ================ ========================================= """ % {'scale': ' | '.join([repr(x) for x in mscale.get_scale_names()])} martist.Artist.__init__(self) if isinstance(rect, mtransforms.Bbox): self._position = rect else: self._position = mtransforms.Bbox.from_bounds(*rect) self._originalPosition = self._position.frozen() self.set_axes(self) self.set_aspect('auto') self._adjustable = 'box' self.set_anchor('C') self._sharex = sharex self._sharey = sharey if sharex is not None: self._shared_x_axes.join(self, sharex) if sharex._adjustable == 'box': sharex._adjustable = 'datalim' #warnings.warn( # 'shared axes: "adjustable" is being changed to "datalim"') self._adjustable = 'datalim' if sharey is not None: self._shared_y_axes.join(self, sharey) if sharey._adjustable == 'box': sharey._adjustable = 'datalim' #warnings.warn( # 'shared axes: "adjustable" is being changed to "datalim"') self._adjustable = 'datalim' self.set_label(label) self.set_figure(fig) # this call may differ for non-sep axes, eg polar self._init_axis() if axisbg is None: axisbg = rcParams['axes.facecolor'] self._axisbg = axisbg self._frameon = frameon self._axisbelow = rcParams['axes.axisbelow'] self._hold = rcParams['axes.hold'] self._connected = {} # a dict from events to (id, func) self.cla() # funcs used to format x and y - fall back on major formatters self.fmt_xdata = None self.fmt_ydata = None self.set_cursor_props((1,'k')) # set the cursor properties for axes self._cachedRenderer = None self.set_navigate(True) self.set_navigate_mode(None) if len(kwargs): martist.setp(self, **kwargs) if self.xaxis is not None: self._xcid = self.xaxis.callbacks.connect('units finalize', self.relim) if self.yaxis is not None: self._ycid = self.yaxis.callbacks.connect('units finalize', self.relim) def get_window_extent(self, *args, **kwargs): ''' get the axes bounding box in display space; *args* and *kwargs* are empty ''' return self.bbox def _init_axis(self): "move this out of __init__ because non-separable axes don't use it" self.xaxis = maxis.XAxis(self) self.yaxis = maxis.YAxis(self) self._update_transScale() def set_figure(self, fig): """ Set the class:`~matplotlib.axes.Axes` figure accepts a class:`~matplotlib.figure.Figure` instance """ martist.Artist.set_figure(self, fig) self.bbox = mtransforms.TransformedBbox(self._position, fig.transFigure) #these will be updated later as data is added self.dataLim = mtransforms.Bbox.unit() self.viewLim = mtransforms.Bbox.unit() self.transScale = mtransforms.TransformWrapper( mtransforms.IdentityTransform()) self._set_lim_and_transforms() def _set_lim_and_transforms(self): """ set the *dataLim* and *viewLim* :class:`~matplotlib.transforms.Bbox` attributes and the *transScale*, *transData*, *transLimits* and *transAxes* transformations. """ self.transAxes = mtransforms.BboxTransformTo(self.bbox) # Transforms the x and y axis separately by a scale factor # It is assumed that this part will have non-linear components self.transScale = mtransforms.TransformWrapper( mtransforms.IdentityTransform()) # An affine transformation on the data, generally to limit the # range of the axes self.transLimits = mtransforms.BboxTransformFrom( mtransforms.TransformedBbox(self.viewLim, self.transScale)) # The parentheses are important for efficiency here -- they # group the last two (which are usually affines) separately # from the first (which, with log-scaling can be non-affine). self.transData = self.transScale + (self.transLimits + self.transAxes) self._xaxis_transform = mtransforms.blended_transform_factory( self.axes.transData, self.axes.transAxes) self._yaxis_transform = mtransforms.blended_transform_factory( self.axes.transAxes, self.axes.transData) def get_xaxis_transform(self): """ Get the transformation used for drawing x-axis labels, ticks and gridlines. The x-direction is in data coordinates and the y-direction is in axis coordinates. .. note:: This transformation is primarily used by the :class:`~matplotlib.axis.Axis` class, and is meant to be overridden by new kinds of projections that may need to place axis elements in different locations. """ return self._xaxis_transform def get_xaxis_text1_transform(self, pad_points): """ Get the transformation used for drawing x-axis labels, which will add the given amount of padding (in points) between the axes and the label. The x-direction is in data coordinates and the y-direction is in axis coordinates. Returns a 3-tuple of the form:: (transform, valign, halign) where *valign* and *halign* are requested alignments for the text. .. note:: This transformation is primarily used by the :class:`~matplotlib.axis.Axis` class, and is meant to be overridden by new kinds of projections that may need to place axis elements in different locations. """ return (self._xaxis_transform + mtransforms.ScaledTranslation(0, -1 * pad_points / 72.0, self.figure.dpi_scale_trans), "top", "center") def get_xaxis_text2_transform(self, pad_points): """ Get the transformation used for drawing the secondary x-axis labels, which will add the given amount of padding (in points) between the axes and the label. The x-direction is in data coordinates and the y-direction is in axis coordinates. Returns a 3-tuple of the form:: (transform, valign, halign) where *valign* and *halign* are requested alignments for the text. .. note:: This transformation is primarily used by the :class:`~matplotlib.axis.Axis` class, and is meant to be overridden by new kinds of projections that may need to place axis elements in different locations. """ return (self._xaxis_transform + mtransforms.ScaledTranslation(0, pad_points / 72.0, self.figure.dpi_scale_trans), "bottom", "center") def get_yaxis_transform(self): """ Get the transformation used for drawing y-axis labels, ticks and gridlines. The x-direction is in axis coordinates and the y-direction is in data coordinates. .. note:: This transformation is primarily used by the :class:`~matplotlib.axis.Axis` class, and is meant to be overridden by new kinds of projections that may need to place axis elements in different locations. """ return self._yaxis_transform def get_yaxis_text1_transform(self, pad_points): """ Get the transformation used for drawing y-axis labels, which will add the given amount of padding (in points) between the axes and the label. The x-direction is in axis coordinates and the y-direction is in data coordinates. Returns a 3-tuple of the form:: (transform, valign, halign) where *valign* and *halign* are requested alignments for the text. .. note:: This transformation is primarily used by the :class:`~matplotlib.axis.Axis` class, and is meant to be overridden by new kinds of projections that may need to place axis elements in different locations. """ return (self._yaxis_transform + mtransforms.ScaledTranslation(-1 * pad_points / 72.0, 0, self.figure.dpi_scale_trans), "center", "right") def get_yaxis_text2_transform(self, pad_points): """ Get the transformation used for drawing the secondary y-axis labels, which will add the given amount of padding (in points) between the axes and the label. The x-direction is in axis coordinates and the y-direction is in data coordinates. Returns a 3-tuple of the form:: (transform, valign, halign) where *valign* and *halign* are requested alignments for the text. .. note:: This transformation is primarily used by the :class:`~matplotlib.axis.Axis` class, and is meant to be overridden by new kinds of projections that may need to place axis elements in different locations. """ return (self._yaxis_transform + mtransforms.ScaledTranslation(pad_points / 72.0, 0, self.figure.dpi_scale_trans), "center", "left") def _update_transScale(self): self.transScale.set( mtransforms.blended_transform_factory( self.xaxis.get_transform(), self.yaxis.get_transform())) if hasattr(self, "lines"): for line in self.lines: line._transformed_path.invalidate() def get_position(self, original=False): 'Return the a copy of the axes rectangle as a Bbox' if original: return self._originalPosition.frozen() else: return self._position.frozen() def set_position(self, pos, which='both'): """ Set the axes position with:: pos = [left, bottom, width, height] in relative 0,1 coords, or *pos* can be a :class:`~matplotlib.transforms.Bbox` There are two position variables: one which is ultimately used, but which may be modified by :meth:`apply_aspect`, and a second which is the starting point for :meth:`apply_aspect`. Optional keyword arguments: *which* ========== ==================== value description ========== ==================== 'active' to change the first 'original' to change the second 'both' to change both ========== ==================== """ if not isinstance(pos, mtransforms.BboxBase): pos = mtransforms.Bbox.from_bounds(*pos) if which in ('both', 'active'): self._position.set(pos) if which in ('both', 'original'): self._originalPosition.set(pos) def reset_position(self): 'Make the original position the active position' pos = self.get_position(original=True) self.set_position(pos, which='active') def _set_artist_props(self, a): 'set the boilerplate props for artists added to axes' a.set_figure(self.figure) if not a.is_transform_set(): a.set_transform(self.transData) a.set_axes(self) def _gen_axes_patch(self): """ Returns the patch used to draw the background of the axes. It is also used as the clipping path for any data elements on the axes. In the standard axes, this is a rectangle, but in other projections it may not be. .. note:: Intended to be overridden by new projection types. """ return mpatches.Rectangle((0.0, 0.0), 1.0, 1.0) def cla(self): 'Clear the current axes' # Note: this is called by Axes.__init__() self.xaxis.cla() self.yaxis.cla() self.ignore_existing_data_limits = True self.callbacks = cbook.CallbackRegistry(('xlim_changed', 'ylim_changed')) if self._sharex is not None: # major and minor are class instances with # locator and formatter attributes self.xaxis.major = self._sharex.xaxis.major self.xaxis.minor = self._sharex.xaxis.minor x0, x1 = self._sharex.get_xlim() self.set_xlim(x0, x1, emit=False) self.xaxis.set_scale(self._sharex.xaxis.get_scale()) else: self.xaxis.set_scale('linear') if self._sharey is not None: self.yaxis.major = self._sharey.yaxis.major self.yaxis.minor = self._sharey.yaxis.minor y0, y1 = self._sharey.get_ylim() self.set_ylim(y0, y1, emit=False) self.yaxis.set_scale(self._sharey.yaxis.get_scale()) else: self.yaxis.set_scale('linear') self._autoscaleon = True self._update_transScale() # needed? self._get_lines = _process_plot_var_args(self) self._get_patches_for_fill = _process_plot_var_args(self, 'fill') self._gridOn = rcParams['axes.grid'] self.lines = [] self.patches = [] self.texts = [] self.tables = [] self.artists = [] self.images = [] self.legend_ = None self.collections = [] # collection.Collection instances self.grid(self._gridOn) props = font_manager.FontProperties(size=rcParams['axes.titlesize']) self.titleOffsetTrans = mtransforms.ScaledTranslation( 0.0, 5.0 / 72.0, self.figure.dpi_scale_trans) self.title = mtext.Text( x=0.5, y=1.0, text='', fontproperties=props, verticalalignment='bottom', horizontalalignment='center', ) self.title.set_transform(self.transAxes + self.titleOffsetTrans) self.title.set_clip_box(None) self._set_artist_props(self.title) # the patch draws the background of the axes. we want this to # be below the other artists; the axesPatch name is # deprecated. We use the frame to draw the edges so we are # setting the edgecolor to None self.patch = self.axesPatch = self._gen_axes_patch() self.patch.set_figure(self.figure) self.patch.set_facecolor(self._axisbg) self.patch.set_edgecolor('None') self.patch.set_linewidth(0) self.patch.set_transform(self.transAxes) # the frame draws the border around the axes and we want this # above. this is a place holder for a more sophisticated # artist that might just draw a left, bottom frame, or a # centered frame, etc the axesFrame name is deprecated self.frame = self.axesFrame = self._gen_axes_patch() self.frame.set_figure(self.figure) self.frame.set_facecolor('none') self.frame.set_edgecolor(rcParams['axes.edgecolor']) self.frame.set_linewidth(rcParams['axes.linewidth']) self.frame.set_transform(self.transAxes) self.frame.set_zorder(2.5) self.axison = True self.xaxis.set_clip_path(self.patch) self.yaxis.set_clip_path(self.patch) self._shared_x_axes.clean() self._shared_y_axes.clean() def clear(self): 'clear the axes' self.cla() def set_color_cycle(self, clist): """ Set the color cycle for any future plot commands on this Axes. clist is a list of mpl color specifiers. """ self._get_lines.set_color_cycle(clist) def ishold(self): 'return the HOLD status of the axes' return self._hold def hold(self, b=None): """ call signature:: hold(b=None) Set the hold state. If *hold* is *None* (default), toggle the *hold* state. Else set the *hold* state to boolean value *b*. Examples: * toggle hold: >>> hold() * turn hold on: >>> hold(True) * turn hold off >>> hold(False) When hold is True, subsequent plot commands will be added to the current axes. When hold is False, the current axes and figure will be cleared on the next plot command """ if b is None: self._hold = not self._hold else: self._hold = b def get_aspect(self): return self._aspect def set_aspect(self, aspect, adjustable=None, anchor=None): """ *aspect* ======== ================================================ value description ======== ================================================ 'auto' automatic; fill position rectangle with data 'normal' same as 'auto'; deprecated 'equal' same scaling from data to plot units for x and y num a circle will be stretched such that the height is num times the width. aspect=1 is the same as aspect='equal'. ======== ================================================ *adjustable* ========= ============================ value description ========= ============================ 'box' change physical size of axes 'datalim' change xlim or ylim ========= ============================ *anchor* ===== ===================== value description ===== ===================== 'C' centered 'SW' lower left corner 'S' middle of bottom edge 'SE' lower right corner etc. ===== ===================== """ if aspect in ('normal', 'auto'): self._aspect = 'auto' elif aspect == 'equal': self._aspect = 'equal' else: self._aspect = float(aspect) # raise ValueError if necessary if adjustable is not None: self.set_adjustable(adjustable) if anchor is not None: self.set_anchor(anchor) def get_adjustable(self): return self._adjustable def set_adjustable(self, adjustable): """ ACCEPTS: [ 'box' | 'datalim' ] """ if adjustable in ('box', 'datalim'): if self in self._shared_x_axes or self in self._shared_y_axes: if adjustable == 'box': raise ValueError( 'adjustable must be "datalim" for shared axes') self._adjustable = adjustable else: raise ValueError('argument must be "box", or "datalim"') def get_anchor(self): return self._anchor def set_anchor(self, anchor): """ *anchor* ===== ============ value description ===== ============ 'C' Center 'SW' bottom left 'S' bottom 'SE' bottom right 'E' right 'NE' top right 'N' top 'NW' top left 'W' left ===== ============ """ if anchor in mtransforms.Bbox.coefs.keys() or len(anchor) == 2: self._anchor = anchor else: raise ValueError('argument must be among %s' % ', '.join(mtransforms.BBox.coefs.keys())) def get_data_ratio(self): """ Returns the aspect ratio of the raw data. This method is intended to be overridden by new projection types. """ xmin,xmax = self.get_xbound() xsize = max(math.fabs(xmax-xmin), 1e-30) ymin,ymax = self.get_ybound() ysize = max(math.fabs(ymax-ymin), 1e-30) return ysize/xsize def apply_aspect(self, position=None): ''' Use :meth:`_aspect` and :meth:`_adjustable` to modify the axes box or the view limits. ''' if position is None: position = self.get_position(original=True) aspect = self.get_aspect() if aspect == 'auto': self.set_position( position , which='active') return if aspect == 'equal': A = 1 else: A = aspect #Ensure at drawing time that any Axes involved in axis-sharing # does not have its position changed. if self in self._shared_x_axes or self in self._shared_y_axes: if self._adjustable == 'box': self._adjustable = 'datalim' warnings.warn( 'shared axes: "adjustable" is being changed to "datalim"') figW,figH = self.get_figure().get_size_inches() fig_aspect = figH/figW if self._adjustable == 'box': box_aspect = A * self.get_data_ratio() pb = position.frozen() pb1 = pb.shrunk_to_aspect(box_aspect, pb, fig_aspect) self.set_position(pb1.anchored(self.get_anchor(), pb), 'active') return # reset active to original in case it had been changed # by prior use of 'box' self.set_position(position, which='active') xmin,xmax = self.get_xbound() xsize = max(math.fabs(xmax-xmin), 1e-30) ymin,ymax = self.get_ybound() ysize = max(math.fabs(ymax-ymin), 1e-30) l,b,w,h = position.bounds box_aspect = fig_aspect * (h/w) data_ratio = box_aspect / A y_expander = (data_ratio*xsize/ysize - 1.0) #print 'y_expander', y_expander # If y_expander > 0, the dy/dx viewLim ratio needs to increase if abs(y_expander) < 0.005: #print 'good enough already' return dL = self.dataLim xr = 1.05 * dL.width yr = 1.05 * dL.height xmarg = xsize - xr ymarg = ysize - yr Ysize = data_ratio * xsize Xsize = ysize / data_ratio Xmarg = Xsize - xr Ymarg = Ysize - yr xm = 0 # Setting these targets to, e.g., 0.05*xr does not seem to help. ym = 0 #print 'xmin, xmax, ymin, ymax', xmin, xmax, ymin, ymax #print 'xsize, Xsize, ysize, Ysize', xsize, Xsize, ysize, Ysize changex = (self in self._shared_y_axes and self not in self._shared_x_axes) changey = (self in self._shared_x_axes and self not in self._shared_y_axes) if changex and changey: warnings.warn("adjustable='datalim' cannot work with shared " "x and y axes") return if changex: adjust_y = False else: #print 'xmarg, ymarg, Xmarg, Ymarg', xmarg, ymarg, Xmarg, Ymarg if xmarg > xm and ymarg > ym: adjy = ((Ymarg > 0 and y_expander < 0) or (Xmarg < 0 and y_expander > 0)) else: adjy = y_expander > 0 #print 'y_expander, adjy', y_expander, adjy adjust_y = changey or adjy #(Ymarg > xmarg) if adjust_y: yc = 0.5*(ymin+ymax) y0 = yc - Ysize/2.0 y1 = yc + Ysize/2.0 self.set_ybound((y0, y1)) #print 'New y0, y1:', y0, y1 #print 'New ysize, ysize/xsize', y1-y0, (y1-y0)/xsize else: xc = 0.5*(xmin+xmax) x0 = xc - Xsize/2.0 x1 = xc + Xsize/2.0 self.set_xbound((x0, x1)) #print 'New x0, x1:', x0, x1 #print 'New xsize, ysize/xsize', x1-x0, ysize/(x1-x0) def axis(self, *v, **kwargs): ''' Convenience method for manipulating the x and y view limits and the aspect ratio of the plot. *kwargs* are passed on to :meth:`set_xlim` and :meth:`set_ylim` ''' if len(v)==1 and is_string_like(v[0]): s = v[0].lower() if s=='on': self.set_axis_on() elif s=='off': self.set_axis_off() elif s in ('equal', 'tight', 'scaled', 'normal', 'auto', 'image'): self.set_autoscale_on(True) self.set_aspect('auto') self.autoscale_view() # self.apply_aspect() if s=='equal': self.set_aspect('equal', adjustable='datalim') elif s == 'scaled': self.set_aspect('equal', adjustable='box', anchor='C') self.set_autoscale_on(False) # Req. by Mark Bakker elif s=='tight': self.autoscale_view(tight=True) self.set_autoscale_on(False) elif s == 'image': self.autoscale_view(tight=True) self.set_autoscale_on(False) self.set_aspect('equal', adjustable='box', anchor='C') else: raise ValueError('Unrecognized string %s to axis; ' 'try on or off' % s) xmin, xmax = self.get_xlim() ymin, ymax = self.get_ylim() return xmin, xmax, ymin, ymax try: v[0] except IndexError: emit = kwargs.get('emit', True) xmin = kwargs.get('xmin', None) xmax = kwargs.get('xmax', None) xmin, xmax = self.set_xlim(xmin, xmax, emit) ymin = kwargs.get('ymin', None) ymax = kwargs.get('ymax', None) ymin, ymax = self.set_ylim(ymin, ymax, emit) return xmin, xmax, ymin, ymax v = v[0] if len(v) != 4: raise ValueError('v must contain [xmin xmax ymin ymax]') self.set_xlim([v[0], v[1]]) self.set_ylim([v[2], v[3]]) return v def get_child_artists(self): """ Return a list of artists the axes contains. .. deprecated:: 0.98 """ raise DeprecationWarning('Use get_children instead') def get_frame(self): 'Return the axes Rectangle frame' warnings.warn('use ax.patch instead', DeprecationWarning) return self.patch def get_legend(self): 'Return the legend.Legend instance, or None if no legend is defined' return self.legend_ def get_images(self): 'return a list of Axes images contained by the Axes' return cbook.silent_list('AxesImage', self.images) def get_lines(self): 'Return a list of lines contained by the Axes' return cbook.silent_list('Line2D', self.lines) def get_xaxis(self): 'Return the XAxis instance' return self.xaxis def get_xgridlines(self): 'Get the x grid lines as a list of Line2D instances' return cbook.silent_list('Line2D xgridline', self.xaxis.get_gridlines()) def get_xticklines(self): 'Get the xtick lines as a list of Line2D instances' return cbook.silent_list('Text xtickline', self.xaxis.get_ticklines()) def get_yaxis(self): 'Return the YAxis instance' return self.yaxis def get_ygridlines(self): 'Get the y grid lines as a list of Line2D instances' return cbook.silent_list('Line2D ygridline', self.yaxis.get_gridlines()) def get_yticklines(self): 'Get the ytick lines as a list of Line2D instances' return cbook.silent_list('Line2D ytickline', self.yaxis.get_ticklines()) #### Adding and tracking artists def has_data(self): '''Return *True* if any artists have been added to axes. This should not be used to determine whether the *dataLim* need to be updated, and may not actually be useful for anything. ''' return ( len(self.collections) + len(self.images) + len(self.lines) + len(self.patches))>0 def add_artist(self, a): 'Add any :class:`~matplotlib.artist.Artist` to the axes' a.set_axes(self) self.artists.append(a) self._set_artist_props(a) a.set_clip_path(self.patch) a._remove_method = lambda h: self.artists.remove(h) def add_collection(self, collection, autolim=True): ''' add a :class:`~matplotlib.collections.Collection` instance to the axes ''' label = collection.get_label() if not label: collection.set_label('collection%d'%len(self.collections)) self.collections.append(collection) self._set_artist_props(collection) collection.set_clip_path(self.patch) if autolim: if collection._paths and len(collection._paths): self.update_datalim(collection.get_datalim(self.transData)) collection._remove_method = lambda h: self.collections.remove(h) def add_line(self, line): ''' Add a :class:`~matplotlib.lines.Line2D` to the list of plot lines ''' self._set_artist_props(line) line.set_clip_path(self.patch) self._update_line_limits(line) if not line.get_label(): line.set_label('_line%d'%len(self.lines)) self.lines.append(line) line._remove_method = lambda h: self.lines.remove(h) def _update_line_limits(self, line): p = line.get_path() if p.vertices.size > 0: self.dataLim.update_from_path(p, self.ignore_existing_data_limits, updatex=line.x_isdata, updatey=line.y_isdata) self.ignore_existing_data_limits = False def add_patch(self, p): """ Add a :class:`~matplotlib.patches.Patch` *p* to the list of axes patches; the clipbox will be set to the Axes clipping box. If the transform is not set, it will be set to :attr:`transData`. """ self._set_artist_props(p) p.set_clip_path(self.patch) self._update_patch_limits(p) self.patches.append(p) p._remove_method = lambda h: self.patches.remove(h) def _update_patch_limits(self, patch): 'update the data limits for patch *p*' # hist can add zero height Rectangles, which is useful to keep # the bins, counts and patches lined up, but it throws off log # scaling. We'll ignore rects with zero height or width in # the auto-scaling if (isinstance(patch, mpatches.Rectangle) and (patch.get_width()==0 or patch.get_height()==0)): return vertices = patch.get_path().vertices if vertices.size > 0: xys = patch.get_patch_transform().transform(vertices) if patch.get_data_transform() != self.transData: transform = (patch.get_data_transform() + self.transData.inverted()) xys = transform.transform(xys) self.update_datalim(xys, updatex=patch.x_isdata, updatey=patch.y_isdata) def add_table(self, tab): ''' Add a :class:`~matplotlib.tables.Table` instance to the list of axes tables ''' self._set_artist_props(tab) self.tables.append(tab) tab.set_clip_path(self.patch) tab._remove_method = lambda h: self.tables.remove(h) def relim(self): 'recompute the data limits based on current artists' # Collections are deliberately not supported (yet); see # the TODO note in artists.py. self.dataLim.ignore(True) self.ignore_existing_data_limits = True for line in self.lines: self._update_line_limits(line) for p in self.patches: self._update_patch_limits(p) def update_datalim(self, xys, updatex=True, updatey=True): 'Update the data lim bbox with seq of xy tups or equiv. 2-D array' # if no data is set currently, the bbox will ignore its # limits and set the bound to be the bounds of the xydata. # Otherwise, it will compute the bounds of it's current data # and the data in xydata if iterable(xys) and not len(xys): return if not ma.isMaskedArray(xys): xys = np.asarray(xys) self.dataLim.update_from_data_xy(xys, self.ignore_existing_data_limits, updatex=updatex, updatey=updatey) self.ignore_existing_data_limits = False def update_datalim_numerix(self, x, y): 'Update the data lim bbox with seq of xy tups' # if no data is set currently, the bbox will ignore it's # limits and set the bound to be the bounds of the xydata. # Otherwise, it will compute the bounds of it's current data # and the data in xydata if iterable(x) and not len(x): return self.dataLim.update_from_data(x, y, self.ignore_existing_data_limits) self.ignore_existing_data_limits = False def update_datalim_bounds(self, bounds): ''' Update the datalim to include the given :class:`~matplotlib.transforms.Bbox` *bounds* ''' self.dataLim.set(mtransforms.Bbox.union([self.dataLim, bounds])) def _process_unit_info(self, xdata=None, ydata=None, kwargs=None): 'look for unit *kwargs* and update the axis instances as necessary' if self.xaxis is None or self.yaxis is None: return #print 'processing', self.get_geometry() if xdata is not None: # we only need to update if there is nothing set yet. if not self.xaxis.have_units(): self.xaxis.update_units(xdata) #print '\tset from xdata', self.xaxis.units if ydata is not None: # we only need to update if there is nothing set yet. if not self.yaxis.have_units(): self.yaxis.update_units(ydata) #print '\tset from ydata', self.yaxis.units # process kwargs 2nd since these will override default units if kwargs is not None: xunits = kwargs.pop( 'xunits', self.xaxis.units) if xunits!=self.xaxis.units: #print '\tkw setting xunits', xunits self.xaxis.set_units(xunits) # If the units being set imply a different converter, # we need to update. if xdata is not None: self.xaxis.update_units(xdata) yunits = kwargs.pop('yunits', self.yaxis.units) if yunits!=self.yaxis.units: #print '\tkw setting yunits', yunits self.yaxis.set_units(yunits) # If the units being set imply a different converter, # we need to update. if ydata is not None: self.yaxis.update_units(ydata) def in_axes(self, mouseevent): ''' return *True* if the given *mouseevent* (in display coords) is in the Axes ''' return self.patch.contains(mouseevent)[0] def get_autoscale_on(self): """ Get whether autoscaling is applied on plot commands """ return self._autoscaleon def set_autoscale_on(self, b): """ Set whether autoscaling is applied on plot commands accepts: [ *True* | *False* ] """ self._autoscaleon = b def autoscale_view(self, tight=False, scalex=True, scaley=True): """ autoscale the view limits using the data limits. You can selectively autoscale only a single axis, eg, the xaxis by setting *scaley* to *False*. The autoscaling preserves any axis direction reversal that has already been done. """ # if image data only just use the datalim if not self._autoscaleon: return if scalex: xshared = self._shared_x_axes.get_siblings(self) dl = [ax.dataLim for ax in xshared] bb = mtransforms.BboxBase.union(dl) x0, x1 = bb.intervalx if scaley: yshared = self._shared_y_axes.get_siblings(self) dl = [ax.dataLim for ax in yshared] bb = mtransforms.BboxBase.union(dl) y0, y1 = bb.intervaly if (tight or (len(self.images)>0 and len(self.lines)==0 and len(self.patches)==0)): if scalex: self.set_xbound(x0, x1) if scaley: self.set_ybound(y0, y1) return if scalex: XL = self.xaxis.get_major_locator().view_limits(x0, x1) self.set_xbound(XL) if scaley: YL = self.yaxis.get_major_locator().view_limits(y0, y1) self.set_ybound(YL) #### Drawing def draw(self, renderer=None, inframe=False): "Draw everything (plot lines, axes, labels)" if renderer is None: renderer = self._cachedRenderer if renderer is None: raise RuntimeError('No renderer defined') if not self.get_visible(): return renderer.open_group('axes') self.apply_aspect() # the patch draws the background rectangle -- the frame below # will draw the edges if self.axison and self._frameon: self.patch.draw(renderer) artists = [] if len(self.images)<=1 or renderer.option_image_nocomposite(): for im in self.images: im.draw(renderer) else: # make a composite image blending alpha # list of (mimage.Image, ox, oy) mag = renderer.get_image_magnification() ims = [(im.make_image(mag),0,0) for im in self.images if im.get_visible()] l, b, r, t = self.bbox.extents width = mag*((round(r) + 0.5) - (round(l) - 0.5)) height = mag*((round(t) + 0.5) - (round(b) - 0.5)) im = mimage.from_images(height, width, ims) im.is_grayscale = False l, b, w, h = self.bbox.bounds # composite images need special args so they will not # respect z-order for now renderer.draw_image( round(l), round(b), im, self.bbox, self.patch.get_path(), self.patch.get_transform()) artists.extend(self.collections) artists.extend(self.patches) artists.extend(self.lines) artists.extend(self.texts) artists.extend(self.artists) if self.axison and not inframe: if self._axisbelow: self.xaxis.set_zorder(0.5) self.yaxis.set_zorder(0.5) else: self.xaxis.set_zorder(2.5) self.yaxis.set_zorder(2.5) artists.extend([self.xaxis, self.yaxis]) if not inframe: artists.append(self.title) artists.extend(self.tables) if self.legend_ is not None: artists.append(self.legend_) # the frame draws the edges around the axes patch -- we # decouple these so the patch can be in the background and the # frame in the foreground. if self.axison and self._frameon: artists.append(self.frame) dsu = [ (a.zorder, i, a) for i, a in enumerate(artists) if not a.get_animated() ] dsu.sort() for zorder, i, a in dsu: a.draw(renderer) renderer.close_group('axes') self._cachedRenderer = renderer def draw_artist(self, a): """ This method can only be used after an initial draw which caches the renderer. It is used to efficiently update Axes data (axis ticks, labels, etc are not updated) """ assert self._cachedRenderer is not None a.draw(self._cachedRenderer) def redraw_in_frame(self): """ This method can only be used after an initial draw which caches the renderer. It is used to efficiently update Axes data (axis ticks, labels, etc are not updated) """ assert self._cachedRenderer is not None self.draw(self._cachedRenderer, inframe=True) def get_renderer_cache(self): return self._cachedRenderer def __draw_animate(self): # ignore for now; broken if self._lastRenderer is None: raise RuntimeError('You must first call ax.draw()') dsu = [(a.zorder, a) for a in self.animated.keys()] dsu.sort() renderer = self._lastRenderer renderer.blit() for tmp, a in dsu: a.draw(renderer) #### Axes rectangle characteristics def get_frame_on(self): """ Get whether the axes rectangle patch is drawn """ return self._frameon def set_frame_on(self, b): """ Set whether the axes rectangle patch is drawn ACCEPTS: [ *True* | *False* ] """ self._frameon = b def get_axisbelow(self): """ Get whether axis below is true or not """ return self._axisbelow def set_axisbelow(self, b): """ Set whether the axis ticks and gridlines are above or below most artists ACCEPTS: [ *True* | *False* ] """ self._axisbelow = b def grid(self, b=None, **kwargs): """ call signature:: grid(self, b=None, **kwargs) Set the axes grids on or off; *b* is a boolean If *b* is *None* and ``len(kwargs)==0``, toggle the grid state. If *kwargs* are supplied, it is assumed that you want a grid and *b* is thus set to *True* *kawrgs* are used to set the grid line properties, eg:: ax.grid(color='r', linestyle='-', linewidth=2) Valid :class:`~matplotlib.lines.Line2D` kwargs are %(Line2D)s """ if len(kwargs): b = True self.xaxis.grid(b, **kwargs) self.yaxis.grid(b, **kwargs) grid.__doc__ = cbook.dedent(grid.__doc__) % martist.kwdocd def ticklabel_format(self, **kwargs): """ Convenience method for manipulating the ScalarFormatter used by default for linear axes. Optional keyword arguments: ============ ===================================== Keyword Description ============ ===================================== *style* [ 'sci' (or 'scientific') | 'plain' ] plain turns off scientific notation *scilimits* (m, n), pair of integers; if *style* is 'sci', scientific notation will be used for numbers outside the range 10`-m`:sup: to 10`n`:sup:. Use (0,0) to include all numbers. *axis* [ 'x' | 'y' | 'both' ] ============ ===================================== Only the major ticks are affected. If the method is called when the :class:`~matplotlib.ticker.ScalarFormatter` is not the :class:`~matplotlib.ticker.Formatter` being used, an :exc:`AttributeError` will be raised. """ style = kwargs.pop('style', '').lower() scilimits = kwargs.pop('scilimits', None) if scilimits is not None: try: m, n = scilimits m+n+1 # check that both are numbers except (ValueError, TypeError): raise ValueError("scilimits must be a sequence of 2 integers") axis = kwargs.pop('axis', 'both').lower() if style[:3] == 'sci': sb = True elif style in ['plain', 'comma']: sb = False if style == 'plain': cb = False else: cb = True raise NotImplementedError, "comma style remains to be added" elif style == '': sb = None else: raise ValueError, "%s is not a valid style value" try: if sb is not None: if axis == 'both' or axis == 'x': self.xaxis.major.formatter.set_scientific(sb) if axis == 'both' or axis == 'y': self.yaxis.major.formatter.set_scientific(sb) if scilimits is not None: if axis == 'both' or axis == 'x': self.xaxis.major.formatter.set_powerlimits(scilimits) if axis == 'both' or axis == 'y': self.yaxis.major.formatter.set_powerlimits(scilimits) except AttributeError: raise AttributeError( "This method only works with the ScalarFormatter.") def set_axis_off(self): """turn off the axis""" self.axison = False def set_axis_on(self): """turn on the axis""" self.axison = True def get_axis_bgcolor(self): 'Return the axis background color' return self._axisbg def set_axis_bgcolor(self, color): """ set the axes background color ACCEPTS: any matplotlib color - see :func:`~matplotlib.pyplot.colors` """ self._axisbg = color self.patch.set_facecolor(color) ### data limits, ticks, tick labels, and formatting def invert_xaxis(self): "Invert the x-axis." left, right = self.get_xlim() self.set_xlim(right, left) def xaxis_inverted(self): 'Returns True if the x-axis is inverted.' left, right = self.get_xlim() return right < left def get_xbound(self): """ Returns the x-axis numerical bounds where:: lowerBound < upperBound """ left, right = self.get_xlim() if left < right: return left, right else: return right, left def set_xbound(self, lower=None, upper=None): """ Set the lower and upper numerical bounds of the x-axis. This method will honor axes inversion regardless of parameter order. """ if upper is None and iterable(lower): lower,upper = lower old_lower,old_upper = self.get_xbound() if lower is None: lower = old_lower if upper is None: upper = old_upper if self.xaxis_inverted(): if lower < upper: self.set_xlim(upper, lower) else: self.set_xlim(lower, upper) else: if lower < upper: self.set_xlim(lower, upper) else: self.set_xlim(upper, lower) def get_xlim(self): """ Get the x-axis range [*xmin*, *xmax*] """ return tuple(self.viewLim.intervalx) def set_xlim(self, xmin=None, xmax=None, emit=True, **kwargs): """ call signature:: set_xlim(self, *args, **kwargs) Set the limits for the xaxis Returns the current xlimits as a length 2 tuple: [*xmin*, *xmax*] Examples:: set_xlim((valmin, valmax)) set_xlim(valmin, valmax) set_xlim(xmin=1) # xmax unchanged set_xlim(xmax=1) # xmin unchanged Keyword arguments: *ymin*: scalar the min of the ylim *ymax*: scalar the max of the ylim *emit*: [ True | False ] notify observers of lim change ACCEPTS: len(2) sequence of floats """ if xmax is None and iterable(xmin): xmin,xmax = xmin self._process_unit_info(xdata=(xmin, xmax)) if xmin is not None: xmin = self.convert_xunits(xmin) if xmax is not None: xmax = self.convert_xunits(xmax) old_xmin,old_xmax = self.get_xlim() if xmin is None: xmin = old_xmin if xmax is None: xmax = old_xmax xmin, xmax = mtransforms.nonsingular(xmin, xmax, increasing=False) xmin, xmax = self.xaxis.limit_range_for_scale(xmin, xmax) self.viewLim.intervalx = (xmin, xmax) if emit: self.callbacks.process('xlim_changed', self) # Call all of the other x-axes that are shared with this one for other in self._shared_x_axes.get_siblings(self): if other is not self: other.set_xlim(self.viewLim.intervalx, emit=False) if (other.figure != self.figure and other.figure.canvas is not None): other.figure.canvas.draw_idle() return xmin, xmax def get_xscale(self): 'return the xaxis scale string: %s' % ( ", ".join(mscale.get_scale_names())) return self.xaxis.get_scale() def set_xscale(self, value, **kwargs): """ call signature:: set_xscale(value) Set the scaling of the x-axis: %(scale)s ACCEPTS: [%(scale)s] Different kwargs are accepted, depending on the scale: %(scale_docs)s """ self.xaxis.set_scale(value, **kwargs) self.autoscale_view() self._update_transScale() set_xscale.__doc__ = cbook.dedent(set_xscale.__doc__) % { 'scale': ' | '.join([repr(x) for x in mscale.get_scale_names()]), 'scale_docs': mscale.get_scale_docs().strip()} def get_xticks(self, minor=False): 'Return the x ticks as a list of locations' return self.xaxis.get_ticklocs(minor=minor) def set_xticks(self, ticks, minor=False): """ Set the x ticks with list of *ticks* ACCEPTS: sequence of floats """ return self.xaxis.set_ticks(ticks, minor=minor) def get_xmajorticklabels(self): 'Get the xtick labels as a list of Text instances' return cbook.silent_list('Text xticklabel', self.xaxis.get_majorticklabels()) def get_xminorticklabels(self): 'Get the xtick labels as a list of Text instances' return cbook.silent_list('Text xticklabel', self.xaxis.get_minorticklabels()) def get_xticklabels(self, minor=False): 'Get the xtick labels as a list of Text instances' return cbook.silent_list('Text xticklabel', self.xaxis.get_ticklabels(minor=minor)) def set_xticklabels(self, labels, fontdict=None, minor=False, **kwargs): """ call signature:: set_xticklabels(labels, fontdict=None, minor=False, **kwargs) Set the xtick labels with list of strings *labels*. Return a list of axis text instances. *kwargs* set the :class:`~matplotlib.text.Text` properties. Valid properties are %(Text)s ACCEPTS: sequence of strings """ return self.xaxis.set_ticklabels(labels, fontdict, minor=minor, **kwargs) set_xticklabels.__doc__ = cbook.dedent( set_xticklabels.__doc__) % martist.kwdocd def invert_yaxis(self): "Invert the y-axis." left, right = self.get_ylim() self.set_ylim(right, left) def yaxis_inverted(self): 'Returns True if the y-axis is inverted.' left, right = self.get_ylim() return right < left def get_ybound(self): "Return y-axis numerical bounds in the form of lowerBound < upperBound" left, right = self.get_ylim() if left < right: return left, right else: return right, left def set_ybound(self, lower=None, upper=None): """Set the lower and upper numerical bounds of the y-axis. This method will honor axes inversion regardless of parameter order. """ if upper is None and iterable(lower): lower,upper = lower old_lower,old_upper = self.get_ybound() if lower is None: lower = old_lower if upper is None: upper = old_upper if self.yaxis_inverted(): if lower < upper: self.set_ylim(upper, lower) else: self.set_ylim(lower, upper) else: if lower < upper: self.set_ylim(lower, upper) else: self.set_ylim(upper, lower) def get_ylim(self): """ Get the y-axis range [*ymin*, *ymax*] """ return tuple(self.viewLim.intervaly) def set_ylim(self, ymin=None, ymax=None, emit=True, **kwargs): """ call signature:: set_ylim(self, *args, **kwargs): Set the limits for the yaxis; v = [ymin, ymax]:: set_ylim((valmin, valmax)) set_ylim(valmin, valmax) set_ylim(ymin=1) # ymax unchanged set_ylim(ymax=1) # ymin unchanged Keyword arguments: *ymin*: scalar the min of the ylim *ymax*: scalar the max of the ylim *emit*: [ True | False ] notify observers of lim change Returns the current ylimits as a length 2 tuple ACCEPTS: len(2) sequence of floats """ if ymax is None and iterable(ymin): ymin,ymax = ymin if ymin is not None: ymin = self.convert_yunits(ymin) if ymax is not None: ymax = self.convert_yunits(ymax) old_ymin,old_ymax = self.get_ylim() if ymin is None: ymin = old_ymin if ymax is None: ymax = old_ymax ymin, ymax = mtransforms.nonsingular(ymin, ymax, increasing=False) ymin, ymax = self.yaxis.limit_range_for_scale(ymin, ymax) self.viewLim.intervaly = (ymin, ymax) if emit: self.callbacks.process('ylim_changed', self) # Call all of the other y-axes that are shared with this one for other in self._shared_y_axes.get_siblings(self): if other is not self: other.set_ylim(self.viewLim.intervaly, emit=False) if (other.figure != self.figure and other.figure.canvas is not None): other.figure.canvas.draw_idle() return ymin, ymax def get_yscale(self): 'return the xaxis scale string: %s' % ( ", ".join(mscale.get_scale_names())) return self.yaxis.get_scale() def set_yscale(self, value, **kwargs): """ call signature:: set_yscale(value) Set the scaling of the y-axis: %(scale)s ACCEPTS: [%(scale)s] Different kwargs are accepted, depending on the scale: %(scale_docs)s """ self.yaxis.set_scale(value, **kwargs) self.autoscale_view() self._update_transScale() set_yscale.__doc__ = cbook.dedent(set_yscale.__doc__) % { 'scale': ' | '.join([repr(x) for x in mscale.get_scale_names()]), 'scale_docs': mscale.get_scale_docs().strip()} def get_yticks(self, minor=False): 'Return the y ticks as a list of locations' return self.yaxis.get_ticklocs(minor=minor) def set_yticks(self, ticks, minor=False): """ Set the y ticks with list of *ticks* ACCEPTS: sequence of floats Keyword arguments: *minor*: [ False | True ] Sets the minor ticks if True """ return self.yaxis.set_ticks(ticks, minor=minor) def get_ymajorticklabels(self): 'Get the xtick labels as a list of Text instances' return cbook.silent_list('Text yticklabel', self.yaxis.get_majorticklabels()) def get_yminorticklabels(self): 'Get the xtick labels as a list of Text instances' return cbook.silent_list('Text yticklabel', self.yaxis.get_minorticklabels()) def get_yticklabels(self, minor=False): 'Get the xtick labels as a list of Text instances' return cbook.silent_list('Text yticklabel', self.yaxis.get_ticklabels(minor=minor)) def set_yticklabels(self, labels, fontdict=None, minor=False, **kwargs): """ call signature:: set_yticklabels(labels, fontdict=None, minor=False, **kwargs) Set the ytick labels with list of strings *labels*. Return a list of :class:`~matplotlib.text.Text` instances. *kwargs* set :class:`~matplotlib.text.Text` properties for the labels. Valid properties are %(Text)s ACCEPTS: sequence of strings """ return self.yaxis.set_ticklabels(labels, fontdict, minor=minor, **kwargs) set_yticklabels.__doc__ = cbook.dedent( set_yticklabels.__doc__) % martist.kwdocd def xaxis_date(self, tz=None): """Sets up x-axis ticks and labels that treat the x data as dates. *tz* is the time zone to use in labeling dates. Defaults to rc value. """ xmin, xmax = self.dataLim.intervalx if xmin==0.: # no data has been added - let's set the default datalim. # We should probably use a better proxy for the datalim # have been updated than the ignore setting dmax = today = datetime.date.today() dmin = today-datetime.timedelta(days=10) self._process_unit_info(xdata=(dmin, dmax)) dmin, dmax = self.convert_xunits([dmin, dmax]) self.viewLim.intervalx = dmin, dmax self.dataLim.intervalx = dmin, dmax locator = self.xaxis.get_major_locator() if not isinstance(locator, mdates.DateLocator): locator = mdates.AutoDateLocator(tz) self.xaxis.set_major_locator(locator) # the autolocator uses the viewlim to pick the right date # locator, but it may not have correct viewlim before an # autoscale. If the viewlim is still zero..1, set it to the # datalim and the autoscaler will update it on request if self.viewLim.intervalx[0]==0.: self.viewLim.intervalx = tuple(self.dataLim.intervalx) locator.refresh() formatter = self.xaxis.get_major_formatter() if not isinstance(formatter, mdates.DateFormatter): formatter = mdates.AutoDateFormatter(locator, tz) self.xaxis.set_major_formatter(formatter) def yaxis_date(self, tz=None): """Sets up y-axis ticks and labels that treat the y data as dates. *tz* is the time zone to use in labeling dates. Defaults to rc value. """ ymin, ymax = self.dataLim.intervaly if ymin==0.: # no data has been added - let's set the default datalim. # We should probably use a better proxy for the datalim # have been updated than the ignore setting dmax = today = datetime.date.today() dmin = today-datetime.timedelta(days=10) self._process_unit_info(ydata=(dmin, dmax)) dmin, dmax = self.convert_yunits([dmin, dmax]) self.viewLim.intervaly = dmin, dmax self.dataLim.intervaly = dmin, dmax locator = self.yaxis.get_major_locator() if not isinstance(locator, mdates.DateLocator): locator = mdates.AutoDateLocator(tz) self.yaxis.set_major_locator(locator) # the autolocator uses the viewlim to pick the right date # locator, but it may not have correct viewlim before an # autoscale. If the viewlim is still zero..1, set it to the # datalim and the autoscaler will update it on request if self.viewLim.intervaly[0]==0.: self.viewLim.intervaly = tuple(self.dataLim.intervaly) locator.refresh() formatter = self.xaxis.get_major_formatter() if not isinstance(formatter, mdates.DateFormatter): formatter = mdates.AutoDateFormatter(locator, tz) self.yaxis.set_major_formatter(formatter) def format_xdata(self, x): """ Return *x* string formatted. This function will use the attribute self.fmt_xdata if it is callable, else will fall back on the xaxis major formatter """ try: return self.fmt_xdata(x) except TypeError: func = self.xaxis.get_major_formatter().format_data_short val = func(x) return val def format_ydata(self, y): """ Return y string formatted. This function will use the :attr:`fmt_ydata` attribute if it is callable, else will fall back on the yaxis major formatter """ try: return self.fmt_ydata(y) except TypeError: func = self.yaxis.get_major_formatter().format_data_short val = func(y) return val def format_coord(self, x, y): 'return a format string formatting the *x*, *y* coord' if x is None: x = '???' if y is None: y = '???' xs = self.format_xdata(x) ys = self.format_ydata(y) return 'x=%s, y=%s'%(xs,ys) #### Interactive manipulation def can_zoom(self): """ Return *True* if this axes support the zoom box """ return True def get_navigate(self): """ Get whether the axes responds to navigation commands """ return self._navigate def set_navigate(self, b): """ Set whether the axes responds to navigation toolbar commands ACCEPTS: [ True | False ] """ self._navigate = b def get_navigate_mode(self): """ Get the navigation toolbar button status: 'PAN', 'ZOOM', or None """ return self._navigate_mode def set_navigate_mode(self, b): """ Set the navigation toolbar button status; .. warning:: this is not a user-API function. """ self._navigate_mode = b def start_pan(self, x, y, button): """ Called when a pan operation has started. *x*, *y* are the mouse coordinates in display coords. button is the mouse button number: * 1: LEFT * 2: MIDDLE * 3: RIGHT .. note:: Intended to be overridden by new projection types. """ self._pan_start = cbook.Bunch( lim = self.viewLim.frozen(), trans = self.transData.frozen(), trans_inverse = self.transData.inverted().frozen(), bbox = self.bbox.frozen(), x = x, y = y ) def end_pan(self): """ Called when a pan operation completes (when the mouse button is up.) .. note:: Intended to be overridden by new projection types. """ del self._pan_start def drag_pan(self, button, key, x, y): """ Called when the mouse moves during a pan operation. *button* is the mouse button number: * 1: LEFT * 2: MIDDLE * 3: RIGHT *key* is a "shift" key *x*, *y* are the mouse coordinates in display coords. .. note:: Intended to be overridden by new projection types. """ def format_deltas(key, dx, dy): if key=='control': if(abs(dx)>abs(dy)): dy = dx else: dx = dy elif key=='x': dy = 0 elif key=='y': dx = 0 elif key=='shift': if 2*abs(dx) < abs(dy): dx=0 elif 2*abs(dy) < abs(dx): dy=0 elif(abs(dx)>abs(dy)): dy=dy/abs(dy)*abs(dx) else: dx=dx/abs(dx)*abs(dy) return (dx,dy) p = self._pan_start dx = x - p.x dy = y - p.y if dx == 0 and dy == 0: return if button == 1: dx, dy = format_deltas(key, dx, dy) result = p.bbox.translated(-dx, -dy) \ .transformed(p.trans_inverse) elif button == 3: try: dx = -dx / float(self.bbox.width) dy = -dy / float(self.bbox.height) dx, dy = format_deltas(key, dx, dy) if self.get_aspect() != 'auto': dx = 0.5 * (dx + dy) dy = dx alpha = np.power(10.0, (dx, dy)) start = p.trans_inverse.transform_point((p.x, p.y)) lim_points = p.lim.get_points() result = start + alpha * (lim_points - start) result = mtransforms.Bbox(result) except OverflowError: warnings.warn('Overflow while panning') return self.set_xlim(*result.intervalx) self.set_ylim(*result.intervaly) def get_cursor_props(self): """ return the cursor propertiess as a (*linewidth*, *color*) tuple, where *linewidth* is a float and *color* is an RGBA tuple """ return self._cursorProps def set_cursor_props(self, *args): """ Set the cursor property as:: ax.set_cursor_props(linewidth, color) or:: ax.set_cursor_props((linewidth, color)) ACCEPTS: a (*float*, *color*) tuple """ if len(args)==1: lw, c = args[0] elif len(args)==2: lw, c = args else: raise ValueError('args must be a (linewidth, color) tuple') c =mcolors.colorConverter.to_rgba(c) self._cursorProps = lw, c def connect(self, s, func): """ Register observers to be notified when certain events occur. Register with callback functions with the following signatures. The function has the following signature:: func(ax) # where ax is the instance making the callback. The following events can be connected to: 'xlim_changed','ylim_changed' The connection id is is returned - you can use this with disconnect to disconnect from the axes event """ raise DeprecationWarning('use the callbacks CallbackRegistry instance ' 'instead') def disconnect(self, cid): 'disconnect from the Axes event.' raise DeprecationWarning('use the callbacks CallbackRegistry instance ' 'instead') def get_children(self): 'return a list of child artists' children = [] children.append(self.xaxis) children.append(self.yaxis) children.extend(self.lines) children.extend(self.patches) children.extend(self.texts) children.extend(self.tables) children.extend(self.artists) children.extend(self.images) if self.legend_ is not None: children.append(self.legend_) children.extend(self.collections) children.append(self.title) children.append(self.patch) children.append(self.frame) return children def contains(self,mouseevent): """Test whether the mouse event occured in the axes. Returns T/F, {} """ if callable(self._contains): return self._contains(self,mouseevent) return self.patch.contains(mouseevent) def pick(self, *args): """ call signature:: pick(mouseevent) each child artist will fire a pick event if mouseevent is over the artist and the artist has picker set """ if len(args)>1: raise DeprecationWarning('New pick API implemented -- ' 'see API_CHANGES in the src distribution') martist.Artist.pick(self,args[0]) def __pick(self, x, y, trans=None, among=None): """ Return the artist under point that is closest to the *x*, *y*. If *trans* is *None*, *x*, and *y* are in window coords, (0,0 = lower left). Otherwise, *trans* is a :class:`~matplotlib.transforms.Transform` that specifies the coordinate system of *x*, *y*. The selection of artists from amongst which the pick function finds an artist can be narrowed using the optional keyword argument *among*. If provided, this should be either a sequence of permitted artists or a function taking an artist as its argument and returning a true value if and only if that artist can be selected. Note this algorithm calculates distance to the vertices of the polygon, so if you want to pick a patch, click on the edge! """ # MGDTODO: Needs updating if trans is not None: xywin = trans.transform_point((x,y)) else: xywin = x,y def dist_points(p1, p2): 'return the distance between two points' x1, y1 = p1 x2, y2 = p2 return math.sqrt((x1-x2)**2+(y1-y2)**2) def dist_x_y(p1, x, y): '*x* and *y* are arrays; return the distance to the closest point' x1, y1 = p1 return min(np.sqrt((x-x1)**2+(y-y1)**2)) def dist(a): if isinstance(a, Text): bbox = a.get_window_extent() l,b,w,h = bbox.bounds verts = (l,b), (l,b+h), (l+w,b+h), (l+w, b) xt, yt = zip(*verts) elif isinstance(a, Patch): path = a.get_path() tverts = a.get_transform().transform_path(path) xt, yt = zip(*tverts) elif isinstance(a, mlines.Line2D): xdata = a.get_xdata(orig=False) ydata = a.get_ydata(orig=False) xt, yt = a.get_transform().numerix_x_y(xdata, ydata) return dist_x_y(xywin, np.asarray(xt), np.asarray(yt)) artists = self.lines + self.patches + self.texts if callable(among): artists = filter(test, artists) elif iterable(among): amongd = dict([(k,1) for k in among]) artists = [a for a in artists if a in amongd] elif among is None: pass else: raise ValueError('among must be callable or iterable') if not len(artists): return None ds = [ (dist(a),a) for a in artists] ds.sort() return ds[0][1] #### Labelling def get_title(self): """ Get the title text string. """ return self.title.get_text() def set_title(self, label, fontdict=None, **kwargs): """ call signature:: set_title(label, fontdict=None, **kwargs): Set the title for the axes. kwargs are Text properties: %(Text)s ACCEPTS: str .. seealso:: :meth:`text`: for information on how override and the optional args work """ default = { 'fontsize':rcParams['axes.titlesize'], 'verticalalignment' : 'bottom', 'horizontalalignment' : 'center' } self.title.set_text(label) self.title.update(default) if fontdict is not None: self.title.update(fontdict) self.title.update(kwargs) return self.title set_title.__doc__ = cbook.dedent(set_title.__doc__) % martist.kwdocd def get_xlabel(self): """ Get the xlabel text string. """ label = self.xaxis.get_label() return label.get_text() def set_xlabel(self, xlabel, fontdict=None, **kwargs): """ call signature:: set_xlabel(xlabel, fontdict=None, **kwargs) Set the label for the xaxis. Valid kwargs are Text properties: %(Text)s ACCEPTS: str .. seealso:: :meth:`text`: for information on how override and the optional args work """ label = self.xaxis.get_label() label.set_text(xlabel) if fontdict is not None: label.update(fontdict) label.update(kwargs) return label set_xlabel.__doc__ = cbook.dedent(set_xlabel.__doc__) % martist.kwdocd def get_ylabel(self): """ Get the ylabel text string. """ label = self.yaxis.get_label() return label.get_text() def set_ylabel(self, ylabel, fontdict=None, **kwargs): """ call signature:: set_ylabel(ylabel, fontdict=None, **kwargs) Set the label for the yaxis Valid kwargs are Text properties: %(Text)s ACCEPTS: str .. seealso:: :meth:`text`: for information on how override and the optional args work """ label = self.yaxis.get_label() label.set_text(ylabel) if fontdict is not None: label.update(fontdict) label.update(kwargs) return label set_ylabel.__doc__ = cbook.dedent(set_ylabel.__doc__) % martist.kwdocd def text(self, x, y, s, fontdict=None, withdash=False, **kwargs): """ call signature:: text(x, y, s, fontdict=None, **kwargs) Add text in string *s* to axis at location *x*, *y*, data coordinates. Keyword arguments: *fontdict*: A dictionary to override the default text properties. If *fontdict* is *None*, the defaults are determined by your rc parameters. *withdash*: [ False | True ] Creates a :class:`~matplotlib.text.TextWithDash` instance instead of a :class:`~matplotlib.text.Text` instance. Individual keyword arguments can be used to override any given parameter:: text(x, y, s, fontsize=12) The default transform specifies that text is in data coords, alternatively, you can specify text in axis coords (0,0 is lower-left and 1,1 is upper-right). The example below places text in the center of the axes:: text(0.5, 0.5,'matplotlib', horizontalalignment='center', verticalalignment='center', transform = ax.transAxes) You can put a rectangular box around the text instance (eg. to set a background color) by using the keyword *bbox*. *bbox* is a dictionary of :class:`matplotlib.patches.Rectangle` properties. For example:: text(x, y, s, bbox=dict(facecolor='red', alpha=0.5)) Valid kwargs are :class:`matplotlib.text.Text` properties: %(Text)s """ default = { 'verticalalignment' : 'bottom', 'horizontalalignment' : 'left', #'verticalalignment' : 'top', 'transform' : self.transData, } # At some point if we feel confident that TextWithDash # is robust as a drop-in replacement for Text and that # the performance impact of the heavier-weight class # isn't too significant, it may make sense to eliminate # the withdash kwarg and simply delegate whether there's # a dash to TextWithDash and dashlength. if withdash: t = mtext.TextWithDash( x=x, y=y, text=s, ) else: t = mtext.Text( x=x, y=y, text=s, ) self._set_artist_props(t) t.update(default) if fontdict is not None: t.update(fontdict) t.update(kwargs) self.texts.append(t) t._remove_method = lambda h: self.texts.remove(h) #if t.get_clip_on(): t.set_clip_box(self.bbox) if 'clip_on' in kwargs: t.set_clip_box(self.bbox) return t text.__doc__ = cbook.dedent(text.__doc__) % martist.kwdocd def annotate(self, *args, **kwargs): """ call signature:: annotate(s, xy, xytext=None, xycoords='data', textcoords='data', arrowprops=None, **kwargs) Keyword arguments: %(Annotation)s .. plot:: mpl_examples/pylab_examples/annotation_demo2.py """ a = mtext.Annotation(*args, **kwargs) a.set_transform(mtransforms.IdentityTransform()) self._set_artist_props(a) if kwargs.has_key('clip_on'): a.set_clip_path(self.patch) self.texts.append(a) return a annotate.__doc__ = cbook.dedent(annotate.__doc__) % martist.kwdocd #### Lines and spans def axhline(self, y=0, xmin=0, xmax=1, **kwargs): """ call signature:: axhline(y=0, xmin=0, xmax=1, **kwargs) Axis Horizontal Line Draw a horizontal line at *y* from *xmin* to *xmax*. With the default values of *xmin* = 0 and *xmax* = 1, this line will always span the horizontal extent of the axes, regardless of the xlim settings, even if you change them, eg. with the :meth:`set_xlim` command. That is, the horizontal extent is in axes coords: 0=left, 0.5=middle, 1.0=right but the *y* location is in data coordinates. Return value is the :class:`~matplotlib.lines.Line2D` instance. kwargs are the same as kwargs to plot, and can be used to control the line properties. Eg., * draw a thick red hline at *y* = 0 that spans the xrange >>> axhline(linewidth=4, color='r') * draw a default hline at *y* = 1 that spans the xrange >>> axhline(y=1) * draw a default hline at *y* = .5 that spans the the middle half of the xrange >>> axhline(y=.5, xmin=0.25, xmax=0.75) Valid kwargs are :class:`~matplotlib.lines.Line2D` properties: %(Line2D)s .. seealso:: :meth:`axhspan`: for example plot and source code """ ymin, ymax = self.get_ybound() # We need to strip away the units for comparison with # non-unitized bounds yy = self.convert_yunits( y ) scaley = (yy<ymin) or (yy>ymax) trans = mtransforms.blended_transform_factory( self.transAxes, self.transData) l = mlines.Line2D([xmin,xmax], [y,y], transform=trans, **kwargs) l.x_isdata = False self.add_line(l) self.autoscale_view(scalex=False, scaley=scaley) return l axhline.__doc__ = cbook.dedent(axhline.__doc__) % martist.kwdocd def axvline(self, x=0, ymin=0, ymax=1, **kwargs): """ call signature:: axvline(x=0, ymin=0, ymax=1, **kwargs) Axis Vertical Line Draw a vertical line at *x* from *ymin* to *ymax*. With the default values of *ymin* = 0 and *ymax* = 1, this line will always span the vertical extent of the axes, regardless of the xlim settings, even if you change them, eg. with the :meth:`set_xlim` command. That is, the vertical extent is in axes coords: 0=bottom, 0.5=middle, 1.0=top but the *x* location is in data coordinates. Return value is the :class:`~matplotlib.lines.Line2D` instance. kwargs are the same as kwargs to plot, and can be used to control the line properties. Eg., * draw a thick red vline at *x* = 0 that spans the yrange >>> axvline(linewidth=4, color='r') * draw a default vline at *x* = 1 that spans the yrange >>> axvline(x=1) * draw a default vline at *x* = .5 that spans the the middle half of the yrange >>> axvline(x=.5, ymin=0.25, ymax=0.75) Valid kwargs are :class:`~matplotlib.lines.Line2D` properties: %(Line2D)s .. seealso:: :meth:`axhspan`: for example plot and source code """ xmin, xmax = self.get_xbound() # We need to strip away the units for comparison with # non-unitized bounds xx = self.convert_xunits( x ) scalex = (xx<xmin) or (xx>xmax) trans = mtransforms.blended_transform_factory( self.transData, self.transAxes) l = mlines.Line2D([x,x], [ymin,ymax] , transform=trans, **kwargs) l.y_isdata = False self.add_line(l) self.autoscale_view(scalex=scalex, scaley=False) return l axvline.__doc__ = cbook.dedent(axvline.__doc__) % martist.kwdocd def axhspan(self, ymin, ymax, xmin=0, xmax=1, **kwargs): """ call signature:: axhspan(ymin, ymax, xmin=0, xmax=1, **kwargs) Axis Horizontal Span. *y* coords are in data units and *x* coords are in axes (relative 0-1) units. Draw a horizontal span (rectangle) from *ymin* to *ymax*. With the default values of *xmin* = 0 and *xmax* = 1, this always spans the xrange, regardless of the xlim settings, even if you change them, eg. with the :meth:`set_xlim` command. That is, the horizontal extent is in axes coords: 0=left, 0.5=middle, 1.0=right but the *y* location is in data coordinates. Return value is a :class:`matplotlib.patches.Polygon` instance. Examples: * draw a gray rectangle from *y* = 0.25-0.75 that spans the horizontal extent of the axes >>> axhspan(0.25, 0.75, facecolor='0.5', alpha=0.5) Valid kwargs are :class:`~matplotlib.patches.Polygon` properties: %(Polygon)s **Example:** .. plot:: mpl_examples/pylab_examples/axhspan_demo.py """ trans = mtransforms.blended_transform_factory( self.transAxes, self.transData) # process the unit information self._process_unit_info( [xmin, xmax], [ymin, ymax], kwargs=kwargs ) # first we need to strip away the units xmin, xmax = self.convert_xunits( [xmin, xmax] ) ymin, ymax = self.convert_yunits( [ymin, ymax] ) verts = (xmin, ymin), (xmin, ymax), (xmax, ymax), (xmax, ymin) p = mpatches.Polygon(verts, **kwargs) p.set_transform(trans) p.x_isdata = False self.add_patch(p) return p axhspan.__doc__ = cbook.dedent(axhspan.__doc__) % martist.kwdocd def axvspan(self, xmin, xmax, ymin=0, ymax=1, **kwargs): """ call signature:: axvspan(xmin, xmax, ymin=0, ymax=1, **kwargs) Axis Vertical Span. *x* coords are in data units and *y* coords are in axes (relative 0-1) units. Draw a vertical span (rectangle) from *xmin* to *xmax*. With the default values of *ymin* = 0 and *ymax* = 1, this always spans the yrange, regardless of the ylim settings, even if you change them, eg. with the :meth:`set_ylim` command. That is, the vertical extent is in axes coords: 0=bottom, 0.5=middle, 1.0=top but the *y* location is in data coordinates. Return value is the :class:`matplotlib.patches.Polygon` instance. Examples: * draw a vertical green translucent rectangle from x=1.25 to 1.55 that spans the yrange of the axes >>> axvspan(1.25, 1.55, facecolor='g', alpha=0.5) Valid kwargs are :class:`~matplotlib.patches.Polygon` properties: %(Polygon)s .. seealso:: :meth:`axhspan`: for example plot and source code """ trans = mtransforms.blended_transform_factory( self.transData, self.transAxes) # process the unit information self._process_unit_info( [xmin, xmax], [ymin, ymax], kwargs=kwargs ) # first we need to strip away the units xmin, xmax = self.convert_xunits( [xmin, xmax] ) ymin, ymax = self.convert_yunits( [ymin, ymax] ) verts = [(xmin, ymin), (xmin, ymax), (xmax, ymax), (xmax, ymin)] p = mpatches.Polygon(verts, **kwargs) p.set_transform(trans) p.y_isdata = False self.add_patch(p) return p axvspan.__doc__ = cbook.dedent(axvspan.__doc__) % martist.kwdocd def hlines(self, y, xmin, xmax, colors='k', linestyles='solid', label='', **kwargs): """ call signature:: hlines(y, xmin, xmax, colors='k', linestyles='solid', **kwargs) Plot horizontal lines at each *y* from *xmin* to *xmax*. Returns the :class:`~matplotlib.collections.LineCollection` that was added. Required arguments: *y*: a 1-D numpy array or iterable. *xmin* and *xmax*: can be scalars or ``len(x)`` numpy arrays. If they are scalars, then the respective values are constant, else the widths of the lines are determined by *xmin* and *xmax*. Optional keyword arguments: *colors*: a line collections color argument, either a single color or a ``len(y)`` list of colors *linestyles*: [ 'solid' | 'dashed' | 'dashdot' | 'dotted' ] **Example:** .. plot:: mpl_examples/pylab_examples/hline_demo.py """ if kwargs.get('fmt') is not None: raise DeprecationWarning('hlines now uses a ' 'collections.LineCollection and not a ' 'list of Line2D to draw; see API_CHANGES') # We do the conversion first since not all unitized data is uniform y = self.convert_yunits( y ) xmin = self.convert_xunits( xmin ) xmax = self.convert_xunits( xmax ) if not iterable(y): y = [y] if not iterable(xmin): xmin = [xmin] if not iterable(xmax): xmax = [xmax] y = np.asarray(y) xmin = np.asarray(xmin) xmax = np.asarray(xmax) if len(xmin)==1: xmin = np.resize( xmin, y.shape ) if len(xmax)==1: xmax = np.resize( xmax, y.shape ) if len(xmin)!=len(y): raise ValueError, 'xmin and y are unequal sized sequences' if len(xmax)!=len(y): raise ValueError, 'xmax and y are unequal sized sequences' verts = [ ((thisxmin, thisy), (thisxmax, thisy)) for thisxmin, thisxmax, thisy in zip(xmin, xmax, y)] coll = mcoll.LineCollection(verts, colors=colors, linestyles=linestyles, label=label) self.add_collection(coll) coll.update(kwargs) minx = min(xmin.min(), xmax.min()) maxx = max(xmin.max(), xmax.max()) miny = y.min() maxy = y.max() corners = (minx, miny), (maxx, maxy) self.update_datalim(corners) self.autoscale_view() return coll hlines.__doc__ = cbook.dedent(hlines.__doc__) def vlines(self, x, ymin, ymax, colors='k', linestyles='solid', label='', **kwargs): """ call signature:: vlines(x, ymin, ymax, color='k', linestyles='solid') Plot vertical lines at each *x* from *ymin* to *ymax*. *ymin* or *ymax* can be scalars or len(*x*) numpy arrays. If they are scalars, then the respective values are constant, else the heights of the lines are determined by *ymin* and *ymax*. *colors* a line collections color args, either a single color or a len(*x*) list of colors *linestyles* one of [ 'solid' | 'dashed' | 'dashdot' | 'dotted' ] Returns the :class:`matplotlib.collections.LineCollection` that was added. kwargs are :class:`~matplotlib.collections.LineCollection` properties: %(LineCollection)s """ if kwargs.get('fmt') is not None: raise DeprecationWarning('vlines now uses a ' 'collections.LineCollection and not a ' 'list of Line2D to draw; see API_CHANGES') self._process_unit_info(xdata=x, ydata=ymin, kwargs=kwargs) # We do the conversion first since not all unitized data is uniform x = self.convert_xunits( x ) ymin = self.convert_yunits( ymin ) ymax = self.convert_yunits( ymax ) if not iterable(x): x = [x] if not iterable(ymin): ymin = [ymin] if not iterable(ymax): ymax = [ymax] x = np.asarray(x) ymin = np.asarray(ymin) ymax = np.asarray(ymax) if len(ymin)==1: ymin = np.resize( ymin, x.shape ) if len(ymax)==1: ymax = np.resize( ymax, x.shape ) if len(ymin)!=len(x): raise ValueError, 'ymin and x are unequal sized sequences' if len(ymax)!=len(x): raise ValueError, 'ymax and x are unequal sized sequences' Y = np.array([ymin, ymax]).T verts = [ ((thisx, thisymin), (thisx, thisymax)) for thisx, (thisymin, thisymax) in zip(x,Y)] #print 'creating line collection' coll = mcoll.LineCollection(verts, colors=colors, linestyles=linestyles, label=label) self.add_collection(coll) coll.update(kwargs) minx = min( x ) maxx = max( x ) miny = min( min(ymin), min(ymax) ) maxy = max( max(ymin), max(ymax) ) corners = (minx, miny), (maxx, maxy) self.update_datalim(corners) self.autoscale_view() return coll vlines.__doc__ = cbook.dedent(vlines.__doc__) % martist.kwdocd #### Basic plotting def plot(self, *args, **kwargs): """ Plot lines and/or markers to the :class:`~matplotlib.axes.Axes`. *args* is a variable length argument, allowing for multiple *x*, *y* pairs with an optional format string. For example, each of the following is legal:: plot(x, y) # plot x and y using default line style and color plot(x, y, 'bo') # plot x and y using blue circle markers plot(y) # plot y using x as index array 0..N-1 plot(y, 'r+') # ditto, but with red plusses If *x* and/or *y* is 2-dimensional, then the corresponding columns will be plotted. An arbitrary number of *x*, *y*, *fmt* groups can be specified, as in:: a.plot(x1, y1, 'g^', x2, y2, 'g-') Return value is a list of lines that were added. The following format string characters are accepted to control the line style or marker: ================ =============================== character description ================ =============================== '-' solid line style '--' dashed line style '-.' dash-dot line style ':' dotted line style '.' point marker ',' pixel marker 'o' circle marker 'v' triangle_down marker '^' triangle_up marker '<' triangle_left marker '>' triangle_right marker '1' tri_down marker '2' tri_up marker '3' tri_left marker '4' tri_right marker 's' square marker 'p' pentagon marker '*' star marker 'h' hexagon1 marker 'H' hexagon2 marker '+' plus marker 'x' x marker 'D' diamond marker 'd' thin_diamond marker '|' vline marker '_' hline marker ================ =============================== The following color abbreviations are supported: ========== ======== character color ========== ======== 'b' blue 'g' green 'r' red 'c' cyan 'm' magenta 'y' yellow 'k' black 'w' white ========== ======== In addition, you can specify colors in many weird and wonderful ways, including full names (``'green'``), hex strings (``'#008000'``), RGB or RGBA tuples (``(0,1,0,1)``) or grayscale intensities as a string (``'0.8'``). Of these, the string specifications can be used in place of a ``fmt`` group, but the tuple forms can be used only as ``kwargs``. Line styles and colors are combined in a single format string, as in ``'bo'`` for blue circles. The *kwargs* can be used to set line properties (any property that has a ``set_*`` method). You can use this to set a line label (for auto legends), linewidth, anitialising, marker face color, etc. Here is an example:: plot([1,2,3], [1,2,3], 'go-', label='line 1', linewidth=2) plot([1,2,3], [1,4,9], 'rs', label='line 2') axis([0, 4, 0, 10]) legend() If you make multiple lines with one plot command, the kwargs apply to all those lines, e.g.:: plot(x1, y1, x2, y2, antialised=False) Neither line will be antialiased. You do not need to use format strings, which are just abbreviations. All of the line properties can be controlled by keyword arguments. For example, you can set the color, marker, linestyle, and markercolor with:: plot(x, y, color='green', linestyle='dashed', marker='o', markerfacecolor='blue', markersize=12). See :class:`~matplotlib.lines.Line2D` for details. The kwargs are :class:`~matplotlib.lines.Line2D` properties: %(Line2D)s kwargs *scalex* and *scaley*, if defined, are passed on to :meth:`~matplotlib.axes.Axes.autoscale_view` to determine whether the *x* and *y* axes are autoscaled; the default is *True*. """ scalex = kwargs.pop( 'scalex', True) scaley = kwargs.pop( 'scaley', True) if not self._hold: self.cla() lines = [] for line in self._get_lines(*args, **kwargs): self.add_line(line) lines.append(line) self.autoscale_view(scalex=scalex, scaley=scaley) return lines plot.__doc__ = cbook.dedent(plot.__doc__) % martist.kwdocd def plot_date(self, x, y, fmt='bo', tz=None, xdate=True, ydate=False, **kwargs): """ call signature:: plot_date(x, y, fmt='bo', tz=None, xdate=True, ydate=False, **kwargs) Similar to the :func:`~matplotlib.pyplot.plot` command, except the *x* or *y* (or both) data is considered to be dates, and the axis is labeled accordingly. *x* and/or *y* can be a sequence of dates represented as float days since 0001-01-01 UTC. Keyword arguments: *fmt*: string The plot format string. *tz*: [ None | timezone string ] The time zone to use in labeling dates. If *None*, defaults to rc value. *xdate*: [ True | False ] If *True*, the *x*-axis will be labeled with dates. *ydate*: [ False | True ] If *True*, the *y*-axis will be labeled with dates. Note if you are using custom date tickers and formatters, it may be necessary to set the formatters/locators after the call to :meth:`plot_date` since :meth:`plot_date` will set the default tick locator to :class:`matplotlib.ticker.AutoDateLocator` (if the tick locator is not already set to a :class:`matplotlib.ticker.DateLocator` instance) and the default tick formatter to :class:`matplotlib.ticker.AutoDateFormatter` (if the tick formatter is not already set to a :class:`matplotlib.ticker.DateFormatter` instance). Valid kwargs are :class:`~matplotlib.lines.Line2D` properties: %(Line2D)s .. seealso:: :mod:`~matplotlib.dates`: for helper functions :func:`~matplotlib.dates.date2num`, :func:`~matplotlib.dates.num2date` and :func:`~matplotlib.dates.drange`: for help on creating the required floating point dates. """ if not self._hold: self.cla() ret = self.plot(x, y, fmt, **kwargs) if xdate: self.xaxis_date(tz) if ydate: self.yaxis_date(tz) self.autoscale_view() return ret plot_date.__doc__ = cbook.dedent(plot_date.__doc__) % martist.kwdocd def loglog(self, *args, **kwargs): """ call signature:: loglog(*args, **kwargs) Make a plot with log scaling on the *x* and *y* axis. :func:`~matplotlib.pyplot.loglog` supports all the keyword arguments of :func:`~matplotlib.pyplot.plot` and :meth:`matplotlib.axes.Axes.set_xscale` / :meth:`matplotlib.axes.Axes.set_yscale`. Notable keyword arguments: *basex*/*basey*: scalar > 1 base of the *x*/*y* logarithm *subsx*/*subsy*: [ None | sequence ] the location of the minor *x*/*y* ticks; *None* defaults to autosubs, which depend on the number of decades in the plot; see :meth:`matplotlib.axes.Axes.set_xscale` / :meth:`matplotlib.axes.Axes.set_yscale` for details The remaining valid kwargs are :class:`~matplotlib.lines.Line2D` properties: %(Line2D)s **Example:** .. plot:: mpl_examples/pylab_examples/log_demo.py """ if not self._hold: self.cla() dx = {'basex': kwargs.pop('basex', 10), 'subsx': kwargs.pop('subsx', None), } dy = {'basey': kwargs.pop('basey', 10), 'subsy': kwargs.pop('subsy', None), } self.set_xscale('log', **dx) self.set_yscale('log', **dy) b = self._hold self._hold = True # we've already processed the hold l = self.plot(*args, **kwargs) self._hold = b # restore the hold return l loglog.__doc__ = cbook.dedent(loglog.__doc__) % martist.kwdocd def semilogx(self, *args, **kwargs): """ call signature:: semilogx(*args, **kwargs) Make a plot with log scaling on the *x* axis. :func:`semilogx` supports all the keyword arguments of :func:`~matplotlib.pyplot.plot` and :meth:`matplotlib.axes.Axes.set_xscale`. Notable keyword arguments: *basex*: scalar > 1 base of the *x* logarithm *subsx*: [ None | sequence ] The location of the minor xticks; *None* defaults to autosubs, which depend on the number of decades in the plot; see :meth:`~matplotlib.axes.Axes.set_xscale` for details. The remaining valid kwargs are :class:`~matplotlib.lines.Line2D` properties: %(Line2D)s .. seealso:: :meth:`loglog`: For example code and figure """ if not self._hold: self.cla() d = {'basex': kwargs.pop( 'basex', 10), 'subsx': kwargs.pop( 'subsx', None), } self.set_xscale('log', **d) b = self._hold self._hold = True # we've already processed the hold l = self.plot(*args, **kwargs) self._hold = b # restore the hold return l semilogx.__doc__ = cbook.dedent(semilogx.__doc__) % martist.kwdocd def semilogy(self, *args, **kwargs): """ call signature:: semilogy(*args, **kwargs) Make a plot with log scaling on the *y* axis. :func:`semilogy` supports all the keyword arguments of :func:`~matplotlib.pylab.plot` and :meth:`matplotlib.axes.Axes.set_yscale`. Notable keyword arguments: *basey*: scalar > 1 Base of the *y* logarithm *subsy*: [ None | sequence ] The location of the minor yticks; *None* defaults to autosubs, which depend on the number of decades in the plot; see :meth:`~matplotlib.axes.Axes.set_yscale` for details. The remaining valid kwargs are :class:`~matplotlib.lines.Line2D` properties: %(Line2D)s .. seealso:: :meth:`loglog`: For example code and figure """ if not self._hold: self.cla() d = {'basey': kwargs.pop('basey', 10), 'subsy': kwargs.pop('subsy', None), } self.set_yscale('log', **d) b = self._hold self._hold = True # we've already processed the hold l = self.plot(*args, **kwargs) self._hold = b # restore the hold return l semilogy.__doc__ = cbook.dedent(semilogy.__doc__) % martist.kwdocd def acorr(self, x, **kwargs): """ call signature:: acorr(x, normed=False, detrend=mlab.detrend_none, usevlines=False, maxlags=None, **kwargs) Plot the autocorrelation of *x*. If *normed* = *True*, normalize the data by the autocorrelation at 0-th lag. *x* is detrended by the *detrend* callable (default no normalization). Data are plotted as ``plot(lags, c, **kwargs)`` Return value is a tuple (*lags*, *c*, *line*) where: - *lags* are a length 2*maxlags+1 lag vector - *c* is the 2*maxlags+1 auto correlation vector - *line* is a :class:`~matplotlib.lines.Line2D` instance returned by :meth:`plot` The default *linestyle* is None and the default *marker* is ``'o'``, though these can be overridden with keyword args. The cross correlation is performed with :func:`numpy.correlate` with *mode* = 2. If *usevlines* is *True*, :meth:`~matplotlib.axes.Axes.vlines` rather than :meth:`~matplotlib.axes.Axes.plot` is used to draw vertical lines from the origin to the acorr. Otherwise, the plot style is determined by the kwargs, which are :class:`~matplotlib.lines.Line2D` properties. *maxlags* is a positive integer detailing the number of lags to show. The default value of *None* will return all :math:`2 \mathrm{len}(x) - 1` lags. The return value is a tuple (*lags*, *c*, *linecol*, *b*) where - *linecol* is the :class:`~matplotlib.collections.LineCollection` - *b* is the *x*-axis. .. seealso:: :meth:`~matplotlib.axes.Axes.plot` or :meth:`~matplotlib.axes.Axes.vlines`: For documentation on valid kwargs. **Example:** :func:`~matplotlib.pyplot.xcorr` above, and :func:`~matplotlib.pyplot.acorr` below. **Example:** .. plot:: mpl_examples/pylab_examples/xcorr_demo.py """ return self.xcorr(x, x, **kwargs) acorr.__doc__ = cbook.dedent(acorr.__doc__) % martist.kwdocd def xcorr(self, x, y, normed=False, detrend=mlab.detrend_none, usevlines=False, maxlags=None, **kwargs): """ call signature:: xcorr(x, y, normed=False, detrend=mlab.detrend_none, usevlines=False, **kwargs): Plot the cross correlation between *x* and *y*. If *normed* = *True*, normalize the data by the cross correlation at 0-th lag. *x* and y are detrended by the *detrend* callable (default no normalization). *x* and *y* must be equal length. Data are plotted as ``plot(lags, c, **kwargs)`` Return value is a tuple (*lags*, *c*, *line*) where: - *lags* are a length ``2*maxlags+1`` lag vector - *c* is the ``2*maxlags+1`` auto correlation vector - *line* is a :class:`~matplotlib.lines.Line2D` instance returned by :func:`~matplotlib.pyplot.plot`. The default *linestyle* is *None* and the default *marker* is 'o', though these can be overridden with keyword args. The cross correlation is performed with :func:`numpy.correlate` with *mode* = 2. If *usevlines* is *True*: :func:`~matplotlib.pyplot.vlines` rather than :func:`~matplotlib.pyplot.plot` is used to draw vertical lines from the origin to the xcorr. Otherwise the plotstyle is determined by the kwargs, which are :class:`~matplotlib.lines.Line2D` properties. The return value is a tuple (*lags*, *c*, *linecol*, *b*) where *linecol* is the :class:`matplotlib.collections.LineCollection` instance and *b* is the *x*-axis. *maxlags* is a positive integer detailing the number of lags to show. The default value of *None* will return all ``(2*len(x)-1)`` lags. **Example:** :func:`~matplotlib.pyplot.xcorr` above, and :func:`~matplotlib.pyplot.acorr` below. **Example:** .. plot:: mpl_examples/pylab_examples/xcorr_demo.py """ Nx = len(x) if Nx!=len(y): raise ValueError('x and y must be equal length') x = detrend(np.asarray(x)) y = detrend(np.asarray(y)) c = np.correlate(x, y, mode=2) if normed: c/= np.sqrt(np.dot(x,x) * np.dot(y,y)) if maxlags is None: maxlags = Nx - 1 if maxlags >= Nx or maxlags < 1: raise ValueError('maglags must be None or strictly ' 'positive < %d'%Nx) lags = np.arange(-maxlags,maxlags+1) c = c[Nx-1-maxlags:Nx+maxlags] if usevlines: a = self.vlines(lags, [0], c, **kwargs) b = self.axhline(**kwargs) else: kwargs.setdefault('marker', 'o') kwargs.setdefault('linestyle', 'None') a, = self.plot(lags, c, **kwargs) b = None return lags, c, a, b xcorr.__doc__ = cbook.dedent(xcorr.__doc__) % martist.kwdocd def legend(self, *args, **kwargs): """ call signature:: legend(*args, **kwargs) Place a legend on the current axes at location *loc*. Labels are a sequence of strings and *loc* can be a string or an integer specifying the legend location. To make a legend with existing lines:: legend() :meth:`legend` by itself will try and build a legend using the label property of the lines/patches/collections. You can set the label of a line by doing:: plot(x, y, label='my data') or:: line.set_label('my data'). If label is set to '_nolegend_', the item will not be shown in legend. To automatically generate the legend from labels:: legend( ('label1', 'label2', 'label3') ) To make a legend for a list of lines and labels:: legend( (line1, line2, line3), ('label1', 'label2', 'label3') ) To make a legend at a given location, using a location argument:: legend( ('label1', 'label2', 'label3'), loc='upper left') or:: legend( (line1, line2, line3), ('label1', 'label2', 'label3'), loc=2) The location codes are =============== ============= Location String Location Code =============== ============= 'best' 0 'upper right' 1 'upper left' 2 'lower left' 3 'lower right' 4 'right' 5 'center left' 6 'center right' 7 'lower center' 8 'upper center' 9 'center' 10 =============== ============= If none of these are locations are suitable, loc can be a 2-tuple giving x,y in axes coords, ie:: loc = 0, 1 # left top loc = 0.5, 0.5 # center Keyword arguments: *isaxes*: [ True | False ] Indicates that this is an axes legend *numpoints*: integer The number of points in the legend line, default is 4 *prop*: [ None | FontProperties ] A :class:`matplotlib.font_manager.FontProperties` instance, or *None* to use rc settings. *pad*: [ None | scalar ] The fractional whitespace inside the legend border, between 0 and 1. If *None*, use rc settings. *markerscale*: [ None | scalar ] The relative size of legend markers vs. original. If *None*, use rc settings. *shadow*: [ None | False | True ] If *True*, draw a shadow behind legend. If *None*, use rc settings. *labelsep*: [ None | scalar ] The vertical space between the legend entries. If *None*, use rc settings. *handlelen*: [ None | scalar ] The length of the legend lines. If *None*, use rc settings. *handletextsep*: [ None | scalar ] The space between the legend line and legend text. If *None*, use rc settings. *axespad*: [ None | scalar ] The border between the axes and legend edge. If *None*, use rc settings. **Example:** .. plot:: mpl_examples/api/legend_demo.py """ def get_handles(): handles = self.lines[:] handles.extend(self.patches) handles.extend([c for c in self.collections if isinstance(c, mcoll.LineCollection)]) handles.extend([c for c in self.collections if isinstance(c, mcoll.RegularPolyCollection)]) return handles if len(args)==0: handles = [] labels = [] for handle in get_handles(): label = handle.get_label() if (label is not None and label != '' and not label.startswith('_')): handles.append(handle) labels.append(label) if len(handles) == 0: warnings.warn("No labeled objects found. " "Use label='...' kwarg on individual plots.") return None elif len(args)==1: # LABELS labels = args[0] handles = [h for h, label in zip(get_handles(), labels)] elif len(args)==2: if is_string_like(args[1]) or isinstance(args[1], int): # LABELS, LOC labels, loc = args handles = [h for h, label in zip(get_handles(), labels)] kwargs['loc'] = loc else: # LINES, LABELS handles, labels = args elif len(args)==3: # LINES, LABELS, LOC handles, labels, loc = args kwargs['loc'] = loc else: raise TypeError('Invalid arguments to legend') handles = cbook.flatten(handles) self.legend_ = mlegend.Legend(self, handles, labels, **kwargs) return self.legend_ #### Specialized plotting def step(self, x, y, *args, **kwargs): ''' call signature:: step(x, y, *args, **kwargs) Make a step plot. Additional keyword args to :func:`step` are the same as those for :func:`~matplotlib.pyplot.plot`. *x* and *y* must be 1-D sequences, and it is assumed, but not checked, that *x* is uniformly increasing. Keyword arguments: *where*: [ 'pre' | 'post' | 'mid' ] If 'pre', the interval from x[i] to x[i+1] has level y[i] If 'post', that interval has level y[i+1] If 'mid', the jumps in *y* occur half-way between the *x*-values. ''' where = kwargs.pop('where', 'pre') if where not in ('pre', 'post', 'mid'): raise ValueError("'where' argument to step must be " "'pre', 'post' or 'mid'") kwargs['linestyle'] = 'steps-' + where return self.plot(x, y, *args, **kwargs) def bar(self, left, height, width=0.8, bottom=None, color=None, edgecolor=None, linewidth=None, yerr=None, xerr=None, ecolor=None, capsize=3, align='edge', orientation='vertical', log=False, **kwargs ): """ call signature:: bar(left, height, width=0.8, bottom=0, color=None, edgecolor=None, linewidth=None, yerr=None, xerr=None, ecolor=None, capsize=3, align='edge', orientation='vertical', log=False) Make a bar plot with rectangles bounded by: *left*, *left* + *width*, *bottom*, *bottom* + *height* (left, right, bottom and top edges) *left*, *height*, *width*, and *bottom* can be either scalars or sequences Return value is a list of :class:`matplotlib.patches.Rectangle` instances. Required arguments: ======== =============================================== Argument Description ======== =============================================== *left* the x coordinates of the left sides of the bars *height* the heights of the bars ======== =============================================== Optional keyword arguments: =============== ========================================== Keyword Description =============== ========================================== *width* the widths of the bars *bottom* the y coordinates of the bottom edges of the bars *color* the colors of the bars *edgecolor* the colors of the bar edges *linewidth* width of bar edges; None means use default linewidth; 0 means don't draw edges. *xerr* if not None, will be used to generate errorbars on the bar chart *yerr* if not None, will be used to generate errorbars on the bar chart *ecolor* specifies the color of any errorbar *capsize* (default 3) determines the length in points of the error bar caps *align* 'edge' (default) | 'center' *orientation* 'vertical' | 'horizontal' *log* [False|True] False (default) leaves the orientation axis as-is; True sets it to log scale =============== ========================================== For vertical bars, *align* = 'edge' aligns bars by their left edges in left, while *align* = 'center' interprets these values as the *x* coordinates of the bar centers. For horizontal bars, *align* = 'edge' aligns bars by their bottom edges in bottom, while *align* = 'center' interprets these values as the *y* coordinates of the bar centers. The optional arguments *color*, *edgecolor*, *linewidth*, *xerr*, and *yerr* can be either scalars or sequences of length equal to the number of bars. This enables you to use bar as the basis for stacked bar charts, or candlestick plots. Other optional kwargs: %(Rectangle)s **Example:** A stacked bar chart. .. plot:: mpl_examples/pylab_examples/bar_stacked.py """ if not self._hold: self.cla() label = kwargs.pop('label', '') def make_iterable(x): if not iterable(x): return [x] else: return x # make them safe to take len() of _left = left left = make_iterable(left) height = make_iterable(height) width = make_iterable(width) _bottom = bottom bottom = make_iterable(bottom) linewidth = make_iterable(linewidth) adjust_ylim = False adjust_xlim = False if orientation == 'vertical': self._process_unit_info(xdata=left, ydata=height, kwargs=kwargs) if log: self.set_yscale('log') # size width and bottom according to length of left if _bottom is None: if self.get_yscale() == 'log': bottom = [1e-100] adjust_ylim = True else: bottom = [0] nbars = len(left) if len(width) == 1: width *= nbars if len(bottom) == 1: bottom *= nbars elif orientation == 'horizontal': self._process_unit_info(xdata=width, ydata=bottom, kwargs=kwargs) if log: self.set_xscale('log') # size left and height according to length of bottom if _left is None: if self.get_xscale() == 'log': left = [1e-100] adjust_xlim = True else: left = [0] nbars = len(bottom) if len(left) == 1: left *= nbars if len(height) == 1: height *= nbars else: raise ValueError, 'invalid orientation: %s' % orientation # do not convert to array here as unit info is lost #left = np.asarray(left) #height = np.asarray(height) #width = np.asarray(width) #bottom = np.asarray(bottom) if len(linewidth) < nbars: linewidth *= nbars if color is None: color = [None] * nbars else: color = list(mcolors.colorConverter.to_rgba_array(color)) if len(color) < nbars: color *= nbars if edgecolor is None: edgecolor = [None] * nbars else: edgecolor = list(mcolors.colorConverter.to_rgba_array(edgecolor)) if len(edgecolor) < nbars: edgecolor *= nbars if yerr is not None: if not iterable(yerr): yerr = [yerr]*nbars if xerr is not None: if not iterable(xerr): xerr = [xerr]*nbars # FIXME: convert the following to proper input validation # raising ValueError; don't use assert for this. assert len(left)==nbars, "argument 'left' must be %d or scalar" % nbars assert len(height)==nbars, ("argument 'height' must be %d or scalar" % nbars) assert len(width)==nbars, ("argument 'width' must be %d or scalar" % nbars) assert len(bottom)==nbars, ("argument 'bottom' must be %d or scalar" % nbars) if yerr is not None and len(yerr)!=nbars: raise ValueError( "bar() argument 'yerr' must be len(%s) or scalar" % nbars) if xerr is not None and len(xerr)!=nbars: raise ValueError( "bar() argument 'xerr' must be len(%s) or scalar" % nbars) patches = [] # lets do some conversions now since some types cannot be # subtracted uniformly if self.xaxis is not None: xconv = self.xaxis.converter if xconv is not None: units = self.xaxis.get_units() left = xconv.convert( left, units ) width = xconv.convert( width, units ) if self.yaxis is not None: yconv = self.yaxis.converter if yconv is not None : units = self.yaxis.get_units() bottom = yconv.convert( bottom, units ) height = yconv.convert( height, units ) if align == 'edge': pass elif align == 'center': if orientation == 'vertical': left = [left[i] - width[i]/2. for i in xrange(len(left))] elif orientation == 'horizontal': bottom = [bottom[i] - height[i]/2. for i in xrange(len(bottom))] else: raise ValueError, 'invalid alignment: %s' % align args = zip(left, bottom, width, height, color, edgecolor, linewidth) for l, b, w, h, c, e, lw in args: if h<0: b += h h = abs(h) if w<0: l += w w = abs(w) r = mpatches.Rectangle( xy=(l, b), width=w, height=h, facecolor=c, edgecolor=e, linewidth=lw, label=label ) label = '_nolegend_' r.update(kwargs) #print r.get_label(), label, 'label' in kwargs self.add_patch(r) patches.append(r) holdstate = self._hold self.hold(True) # ensure hold is on before plotting errorbars if xerr is not None or yerr is not None: if orientation == 'vertical': # using list comps rather than arrays to preserve unit info x = [l+0.5*w for l, w in zip(left, width)] y = [b+h for b,h in zip(bottom, height)] elif orientation == 'horizontal': # using list comps rather than arrays to preserve unit info x = [l+w for l,w in zip(left, width)] y = [b+0.5*h for b,h in zip(bottom, height)] self.errorbar( x, y, yerr=yerr, xerr=xerr, fmt=None, ecolor=ecolor, capsize=capsize) self.hold(holdstate) # restore previous hold state if adjust_xlim: xmin, xmax = self.dataLim.intervalx xmin = np.amin(width[width!=0]) # filter out the 0 width rects if xerr is not None: xmin = xmin - np.amax(xerr) xmin = max(xmin*0.9, 1e-100) self.dataLim.intervalx = (xmin, xmax) if adjust_ylim: ymin, ymax = self.dataLim.intervaly ymin = np.amin(height[height!=0]) # filter out the 0 height rects if yerr is not None: ymin = ymin - np.amax(yerr) ymin = max(ymin*0.9, 1e-100) self.dataLim.intervaly = (ymin, ymax) self.autoscale_view() return patches bar.__doc__ = cbook.dedent(bar.__doc__) % martist.kwdocd def barh(self, bottom, width, height=0.8, left=None, **kwargs): """ call signature:: barh(bottom, width, height=0.8, left=0, **kwargs) Make a horizontal bar plot with rectangles bounded by: *left*, *left* + *width*, *bottom*, *bottom* + *height* (left, right, bottom and top edges) *bottom*, *width*, *height*, and *left* can be either scalars or sequences Return value is a list of :class:`matplotlib.patches.Rectangle` instances. Required arguments: ======== ====================================================== Argument Description ======== ====================================================== *bottom* the vertical positions of the bottom edges of the bars *width* the lengths of the bars ======== ====================================================== Optional keyword arguments: =============== ========================================== Keyword Description =============== ========================================== *height* the heights (thicknesses) of the bars *left* the x coordinates of the left edges of the bars *color* the colors of the bars *edgecolor* the colors of the bar edges *linewidth* width of bar edges; None means use default linewidth; 0 means don't draw edges. *xerr* if not None, will be used to generate errorbars on the bar chart *yerr* if not None, will be used to generate errorbars on the bar chart *ecolor* specifies the color of any errorbar *capsize* (default 3) determines the length in points of the error bar caps *align* 'edge' (default) | 'center' *log* [False|True] False (default) leaves the horizontal axis as-is; True sets it to log scale =============== ========================================== Setting *align* = 'edge' aligns bars by their bottom edges in bottom, while *align* = 'center' interprets these values as the *y* coordinates of the bar centers. The optional arguments *color*, *edgecolor*, *linewidth*, *xerr*, and *yerr* can be either scalars or sequences of length equal to the number of bars. This enables you to use barh as the basis for stacked bar charts, or candlestick plots. other optional kwargs: %(Rectangle)s """ patches = self.bar(left=left, height=height, width=width, bottom=bottom, orientation='horizontal', **kwargs) return patches barh.__doc__ = cbook.dedent(barh.__doc__) % martist.kwdocd def broken_barh(self, xranges, yrange, **kwargs): """ call signature:: broken_barh(self, xranges, yrange, **kwargs) A collection of horizontal bars spanning *yrange* with a sequence of *xranges*. Required arguments: ========= ============================== Argument Description ========= ============================== *xranges* sequence of (*xmin*, *xwidth*) *yrange* sequence of (*ymin*, *ywidth*) ========= ============================== kwargs are :class:`matplotlib.collections.BrokenBarHCollection` properties: %(BrokenBarHCollection)s these can either be a single argument, ie:: facecolors = 'black' or a sequence of arguments for the various bars, ie:: facecolors = ('black', 'red', 'green') **Example:** .. plot:: mpl_examples/pylab_examples/broken_barh.py """ col = mcoll.BrokenBarHCollection(xranges, yrange, **kwargs) self.add_collection(col, autolim=True) self.autoscale_view() return col broken_barh.__doc__ = cbook.dedent(broken_barh.__doc__) % martist.kwdocd def stem(self, x, y, linefmt='b-', markerfmt='bo', basefmt='r-'): """ call signature:: stem(x, y, linefmt='b-', markerfmt='bo', basefmt='r-') A stem plot plots vertical lines (using *linefmt*) at each *x* location from the baseline to *y*, and places a marker there using *markerfmt*. A horizontal line at 0 is is plotted using *basefmt*. Return value is a tuple (*markerline*, *stemlines*, *baseline*). .. seealso:: `this document`__ for details :file:`examples/pylab_examples/stem_plot.py`: for a demo __ http://www.mathworks.com/access/helpdesk/help/techdoc/ref/stem.html """ remember_hold=self._hold if not self._hold: self.cla() self.hold(True) markerline, = self.plot(x, y, markerfmt) stemlines = [] for thisx, thisy in zip(x, y): l, = self.plot([thisx,thisx], [0, thisy], linefmt) stemlines.append(l) baseline, = self.plot([np.amin(x), np.amax(x)], [0,0], basefmt) self.hold(remember_hold) return markerline, stemlines, baseline def pie(self, x, explode=None, labels=None, colors=None, autopct=None, pctdistance=0.6, shadow=False, labeldistance=1.1): r""" call signature:: pie(x, explode=None, labels=None, colors=('b', 'g', 'r', 'c', 'm', 'y', 'k', 'w'), autopct=None, pctdistance=0.6, labeldistance=1.1, shadow=False) Make a pie chart of array *x*. The fractional area of each wedge is given by x/sum(x). If sum(x) <= 1, then the values of x give the fractional area directly and the array will not be normalized. Keyword arguments: *explode*: [ None | len(x) sequence ] If not *None*, is a len(*x*) array which specifies the fraction of the radius with which to offset each wedge. *colors*: [ None | color sequence ] A sequence of matplotlib color args through which the pie chart will cycle. *labels*: [ None | len(x) sequence of strings ] A sequence of strings providing the labels for each wedge *autopct*: [ None | format string | format function ] If not *None*, is a string or function used to label the wedges with their numeric value. The label will be placed inside the wedge. If it is a format string, the label will be ``fmt%pct``. If it is a function, it will be called. *pctdistance*: scalar The ratio between the center of each pie slice and the start of the text generated by *autopct*. Ignored if *autopct* is *None*; default is 0.6. *labeldistance*: scalar The radial distance at which the pie labels are drawn *shadow*: [ False | True ] Draw a shadow beneath the pie. The pie chart will probably look best if the figure and axes are square. Eg.:: figure(figsize=(8,8)) ax = axes([0.1, 0.1, 0.8, 0.8]) Return value: If *autopct* is None, return the tuple (*patches*, *texts*): - *patches* is a sequence of :class:`matplotlib.patches.Wedge` instances - *texts* is a list of the label :class:`matplotlib.text.Text` instances. If *autopct* is not *None*, return the tuple (*patches*, *texts*, *autotexts*), where *patches* and *texts* are as above, and *autotexts* is a list of :class:`~matplotlib.text.Text` instances for the numeric labels. """ self.set_frame_on(False) x = np.asarray(x).astype(np.float32) sx = float(x.sum()) if sx>1: x = np.divide(x,sx) if labels is None: labels = ['']*len(x) if explode is None: explode = [0]*len(x) assert(len(x)==len(labels)) assert(len(x)==len(explode)) if colors is None: colors = ('b', 'g', 'r', 'c', 'm', 'y', 'k', 'w') center = 0,0 radius = 1 theta1 = 0 i = 0 texts = [] slices = [] autotexts = [] for frac, label, expl in cbook.safezip(x,labels, explode): x, y = center theta2 = theta1 + frac thetam = 2*math.pi*0.5*(theta1+theta2) x += expl*math.cos(thetam) y += expl*math.sin(thetam) w = mpatches.Wedge((x,y), radius, 360.*theta1, 360.*theta2, facecolor=colors[i%len(colors)]) slices.append(w) self.add_patch(w) w.set_label(label) if shadow: # make sure to add a shadow after the call to # add_patch so the figure and transform props will be # set shad = mpatches.Shadow(w, -0.02, -0.02, #props={'facecolor':w.get_facecolor()} ) shad.set_zorder(0.9*w.get_zorder()) self.add_patch(shad) xt = x + labeldistance*radius*math.cos(thetam) yt = y + labeldistance*radius*math.sin(thetam) label_alignment = xt > 0 and 'left' or 'right' t = self.text(xt, yt, label, size=rcParams['xtick.labelsize'], horizontalalignment=label_alignment, verticalalignment='center') texts.append(t) if autopct is not None: xt = x + pctdistance*radius*math.cos(thetam) yt = y + pctdistance*radius*math.sin(thetam) if is_string_like(autopct): s = autopct%(100.*frac) elif callable(autopct): s = autopct(100.*frac) else: raise TypeError( 'autopct must be callable or a format string') t = self.text(xt, yt, s, horizontalalignment='center', verticalalignment='center') autotexts.append(t) theta1 = theta2 i += 1 self.set_xlim((-1.25, 1.25)) self.set_ylim((-1.25, 1.25)) self.set_xticks([]) self.set_yticks([]) if autopct is None: return slices, texts else: return slices, texts, autotexts def errorbar(self, x, y, yerr=None, xerr=None, fmt='-', ecolor=None, elinewidth=None, capsize=3, barsabove=False, lolims=False, uplims=False, xlolims=False, xuplims=False, **kwargs): """ call signature:: errorbar(x, y, yerr=None, xerr=None, fmt='-', ecolor=None, elinewidth=None, capsize=3, barsabove=False, lolims=False, uplims=False, xlolims=False, xuplims=False) Plot *x* versus *y* with error deltas in *yerr* and *xerr*. Vertical errorbars are plotted if *yerr* is not *None*. Horizontal errorbars are plotted if *xerr* is not *None*. *x*, *y*, *xerr*, and *yerr* can all be scalars, which plots a single error bar at *x*, *y*. Optional keyword arguments: *xerr*/*yerr*: [ scalar | N, Nx1, Nx2 array-like ] If a scalar number, len(N) array-like object, or an Nx1 array-like object, errorbars are drawn +/- value. If a rank-1, Nx2 Numpy array, errorbars are drawn at -column1 and +column2 *fmt*: '-' The plot format symbol for *y*. If *fmt* is *None*, just plot the errorbars with no line symbols. This can be useful for creating a bar plot with errorbars. *ecolor*: [ None | mpl color ] a matplotlib color arg which gives the color the errorbar lines; if *None*, use the marker color. *elinewidth*: scalar the linewidth of the errorbar lines. If *None*, use the linewidth. *capsize*: scalar the size of the error bar caps in points *barsabove*: [ True | False ] if *True*, will plot the errorbars above the plot symbols. Default is below. *lolims*/*uplims*/*xlolims*/*xuplims*: [ False | True ] These arguments can be used to indicate that a value gives only upper/lower limits. In that case a caret symbol is used to indicate this. lims-arguments may be of the same type as *xerr* and *yerr*. All other keyword arguments are passed on to the plot command for the markers, so you can add additional key=value pairs to control the errorbar markers. For example, this code makes big red squares with thick green edges:: x,y,yerr = rand(3,10) errorbar(x, y, yerr, marker='s', mfc='red', mec='green', ms=20, mew=4) where *mfc*, *mec*, *ms* and *mew* are aliases for the longer property names, *markerfacecolor*, *markeredgecolor*, *markersize* and *markeredgewith*. valid kwargs for the marker properties are %(Line2D)s Return value is a length 3 tuple. The first element is the :class:`~matplotlib.lines.Line2D` instance for the *y* symbol lines. The second element is a list of error bar cap lines, the third element is a list of :class:`~matplotlib.collections.LineCollection` instances for the horizontal and vertical error ranges. **Example:** .. plot:: mpl_examples/pylab_examples/errorbar_demo.py """ self._process_unit_info(xdata=x, ydata=y, kwargs=kwargs) if not self._hold: self.cla() # make sure all the args are iterable; use lists not arrays to # preserve units if not iterable(x): x = [x] if not iterable(y): y = [y] if xerr is not None: if not iterable(xerr): xerr = [xerr]*len(x) if yerr is not None: if not iterable(yerr): yerr = [yerr]*len(y) l0 = None if barsabove and fmt is not None: l0, = self.plot(x,y,fmt,**kwargs) barcols = [] caplines = [] lines_kw = {'label':'_nolegend_'} if elinewidth: lines_kw['linewidth'] = elinewidth else: if 'linewidth' in kwargs: lines_kw['linewidth']=kwargs['linewidth'] if 'lw' in kwargs: lines_kw['lw']=kwargs['lw'] if 'transform' in kwargs: lines_kw['transform'] = kwargs['transform'] # arrays fine here, they are booleans and hence not units if not iterable(lolims): lolims = np.asarray([lolims]*len(x), bool) else: lolims = np.asarray(lolims, bool) if not iterable(uplims): uplims = np.array([uplims]*len(x), bool) else: uplims = np.asarray(uplims, bool) if not iterable(xlolims): xlolims = np.array([xlolims]*len(x), bool) else: xlolims = np.asarray(xlolims, bool) if not iterable(xuplims): xuplims = np.array([xuplims]*len(x), bool) else: xuplims = np.asarray(xuplims, bool) def xywhere(xs, ys, mask): """ return xs[mask], ys[mask] where mask is True but xs and ys are not arrays """ assert len(xs)==len(ys) assert len(xs)==len(mask) xs = [thisx for thisx, b in zip(xs, mask) if b] ys = [thisy for thisy, b in zip(ys, mask) if b] return xs, ys if capsize > 0: plot_kw = { 'ms':2*capsize, 'label':'_nolegend_'} if 'markeredgewidth' in kwargs: plot_kw['markeredgewidth']=kwargs['markeredgewidth'] if 'mew' in kwargs: plot_kw['mew']=kwargs['mew'] if 'transform' in kwargs: plot_kw['transform'] = kwargs['transform'] if xerr is not None: if (iterable(xerr) and len(xerr)==2 and iterable(xerr[0]) and iterable(xerr[1])): # using list comps rather than arrays to preserve units left = [thisx-thiserr for (thisx, thiserr) in cbook.safezip(x,xerr[0])] right = [thisx+thiserr for (thisx, thiserr) in cbook.safezip(x,xerr[1])] else: # using list comps rather than arrays to preserve units left = [thisx-thiserr for (thisx, thiserr) in cbook.safezip(x,xerr)] right = [thisx+thiserr for (thisx, thiserr) in cbook.safezip(x,xerr)] barcols.append( self.hlines(y, left, right, **lines_kw ) ) if capsize > 0: if xlolims.any(): # can't use numpy logical indexing since left and # y are lists leftlo, ylo = xywhere(left, y, xlolims) caplines.extend( self.plot(leftlo, ylo, ls='None', marker=mlines.CARETLEFT, **plot_kw) ) xlolims = ~xlolims leftlo, ylo = xywhere(left, y, xlolims) caplines.extend( self.plot(leftlo, ylo, 'k|', **plot_kw) ) else: caplines.extend( self.plot(left, y, 'k|', **plot_kw) ) if xuplims.any(): rightup, yup = xywhere(right, y, xuplims) caplines.extend( self.plot(rightup, yup, ls='None', marker=mlines.CARETRIGHT, **plot_kw) ) xuplims = ~xuplims rightup, yup = xywhere(right, y, xuplims) caplines.extend( self.plot(rightup, yup, 'k|', **plot_kw) ) else: caplines.extend( self.plot(right, y, 'k|', **plot_kw) ) if yerr is not None: if (iterable(yerr) and len(yerr)==2 and iterable(yerr[0]) and iterable(yerr[1])): # using list comps rather than arrays to preserve units lower = [thisy-thiserr for (thisy, thiserr) in cbook.safezip(y,yerr[0])] upper = [thisy+thiserr for (thisy, thiserr) in cbook.safezip(y,yerr[1])] else: # using list comps rather than arrays to preserve units lower = [thisy-thiserr for (thisy, thiserr) in cbook.safezip(y,yerr)] upper = [thisy+thiserr for (thisy, thiserr) in cbook.safezip(y,yerr)] barcols.append( self.vlines(x, lower, upper, **lines_kw) ) if capsize > 0: if lolims.any(): xlo, lowerlo = xywhere(x, lower, lolims) caplines.extend( self.plot(xlo, lowerlo, ls='None', marker=mlines.CARETDOWN, **plot_kw) ) lolims = ~lolims xlo, lowerlo = xywhere(x, lower, lolims) caplines.extend( self.plot(xlo, lowerlo, 'k_', **plot_kw) ) else: caplines.extend( self.plot(x, lower, 'k_', **plot_kw) ) if uplims.any(): xup, upperup = xywhere(x, upper, uplims) caplines.extend( self.plot(xup, upperup, ls='None', marker=mlines.CARETUP, **plot_kw) ) uplims = ~uplims xup, upperup = xywhere(x, upper, uplims) caplines.extend( self.plot(xup, upperup, 'k_', **plot_kw) ) else: caplines.extend( self.plot(x, upper, 'k_', **plot_kw) ) if not barsabove and fmt is not None: l0, = self.plot(x,y,fmt,**kwargs) if ecolor is None: if l0 is None: ecolor = self._get_lines._get_next_cycle_color() else: ecolor = l0.get_color() for l in barcols: l.set_color(ecolor) for l in caplines: l.set_color(ecolor) self.autoscale_view() return (l0, caplines, barcols) errorbar.__doc__ = cbook.dedent(errorbar.__doc__) % martist.kwdocd def boxplot(self, x, notch=0, sym='b+', vert=1, whis=1.5, positions=None, widths=None): """ call signature:: boxplot(x, notch=0, sym='+', vert=1, whis=1.5, positions=None, widths=None) Make a box and whisker plot for each column of *x* or each vector in sequence *x*. The box extends from the lower to upper quartile values of the data, with a line at the median. The whiskers extend from the box to show the range of the data. Flier points are those past the end of the whiskers. - *notch* = 0 (default) produces a rectangular box plot. - *notch* = 1 will produce a notched box plot *sym* (default 'b+') is the default symbol for flier points. Enter an empty string ('') if you don't want to show fliers. - *vert* = 1 (default) makes the boxes vertical. - *vert* = 0 makes horizontal boxes. This seems goofy, but that's how Matlab did it. *whis* (default 1.5) defines the length of the whiskers as a function of the inner quartile range. They extend to the most extreme data point within ( ``whis*(75%-25%)`` ) data range. *positions* (default 1,2,...,n) sets the horizontal positions of the boxes. The ticks and limits are automatically set to match the positions. *widths* is either a scalar or a vector and sets the width of each box. The default is 0.5, or ``0.15*(distance between extreme positions)`` if that is smaller. *x* is an array or a sequence of vectors. Returns a dictionary mapping each component of the boxplot to a list of the :class:`matplotlib.lines.Line2D` instances created. **Example:** .. plot:: pyplots/boxplot_demo.py """ if not self._hold: self.cla() holdStatus = self._hold whiskers, caps, boxes, medians, fliers = [], [], [], [], [] # convert x to a list of vectors if hasattr(x, 'shape'): if len(x.shape) == 1: if hasattr(x[0], 'shape'): x = list(x) else: x = [x,] elif len(x.shape) == 2: nr, nc = x.shape if nr == 1: x = [x] elif nc == 1: x = [x.ravel()] else: x = [x[:,i] for i in xrange(nc)] else: raise ValueError, "input x can have no more than 2 dimensions" if not hasattr(x[0], '__len__'): x = [x] col = len(x) # get some plot info if positions is None: positions = range(1, col + 1) if widths is None: distance = max(positions) - min(positions) widths = min(0.15*max(distance,1.0), 0.5) if isinstance(widths, float) or isinstance(widths, int): widths = np.ones((col,), float) * widths # loop through columns, adding each to plot self.hold(True) for i,pos in enumerate(positions): d = np.ravel(x[i]) row = len(d) # get median and quartiles q1, med, q3 = mlab.prctile(d,[25,50,75]) # get high extreme iq = q3 - q1 hi_val = q3 + whis*iq wisk_hi = np.compress( d <= hi_val , d ) if len(wisk_hi) == 0: wisk_hi = q3 else: wisk_hi = max(wisk_hi) # get low extreme lo_val = q1 - whis*iq wisk_lo = np.compress( d >= lo_val, d ) if len(wisk_lo) == 0: wisk_lo = q1 else: wisk_lo = min(wisk_lo) # get fliers - if we are showing them flier_hi = [] flier_lo = [] flier_hi_x = [] flier_lo_x = [] if len(sym) != 0: flier_hi = np.compress( d > wisk_hi, d ) flier_lo = np.compress( d < wisk_lo, d ) flier_hi_x = np.ones(flier_hi.shape[0]) * pos flier_lo_x = np.ones(flier_lo.shape[0]) * pos # get x locations for fliers, whisker, whisker cap and box sides box_x_min = pos - widths[i] * 0.5 box_x_max = pos + widths[i] * 0.5 wisk_x = np.ones(2) * pos cap_x_min = pos - widths[i] * 0.25 cap_x_max = pos + widths[i] * 0.25 cap_x = [cap_x_min, cap_x_max] # get y location for median med_y = [med, med] # calculate 'regular' plot if notch == 0: # make our box vectors box_x = [box_x_min, box_x_max, box_x_max, box_x_min, box_x_min ] box_y = [q1, q1, q3, q3, q1 ] # make our median line vectors med_x = [box_x_min, box_x_max] # calculate 'notch' plot else: notch_max = med + 1.57*iq/np.sqrt(row) notch_min = med - 1.57*iq/np.sqrt(row) if notch_max > q3: notch_max = q3 if notch_min < q1: notch_min = q1 # make our notched box vectors box_x = [box_x_min, box_x_max, box_x_max, cap_x_max, box_x_max, box_x_max, box_x_min, box_x_min, cap_x_min, box_x_min, box_x_min ] box_y = [q1, q1, notch_min, med, notch_max, q3, q3, notch_max, med, notch_min, q1] # make our median line vectors med_x = [cap_x_min, cap_x_max] med_y = [med, med] # vertical or horizontal plot? if vert: def doplot(*args): return self.plot(*args) else: def doplot(*args): shuffled = [] for i in xrange(0, len(args), 3): shuffled.extend([args[i+1], args[i], args[i+2]]) return self.plot(*shuffled) whiskers.extend(doplot(wisk_x, [q1, wisk_lo], 'b--', wisk_x, [q3, wisk_hi], 'b--')) caps.extend(doplot(cap_x, [wisk_hi, wisk_hi], 'k-', cap_x, [wisk_lo, wisk_lo], 'k-')) boxes.extend(doplot(box_x, box_y, 'b-')) medians.extend(doplot(med_x, med_y, 'r-')) fliers.extend(doplot(flier_hi_x, flier_hi, sym, flier_lo_x, flier_lo, sym)) # fix our axes/ticks up a little if 1 == vert: setticks, setlim = self.set_xticks, self.set_xlim else: setticks, setlim = self.set_yticks, self.set_ylim newlimits = min(positions)-0.5, max(positions)+0.5 setlim(newlimits) setticks(positions) # reset hold status self.hold(holdStatus) return dict(whiskers=whiskers, caps=caps, boxes=boxes, medians=medians, fliers=fliers) def scatter(self, x, y, s=20, c='b', marker='o', cmap=None, norm=None, vmin=None, vmax=None, alpha=1.0, linewidths=None, faceted=True, verts=None, **kwargs): """ call signatures:: scatter(x, y, s=20, c='b', marker='o', cmap=None, norm=None, vmin=None, vmax=None, alpha=1.0, linewidths=None, verts=None, **kwargs) Make a scatter plot of *x* versus *y*, where *x*, *y* are 1-D sequences of the same length, *N*. Keyword arguments: *s*: size in points^2. It is a scalar or an array of the same length as *x* and *y*. *c*: a color. *c* can be a single color format string, or a sequence of color specifications of length *N*, or a sequence of *N* numbers to be mapped to colors using the *cmap* and *norm* specified via kwargs (see below). Note that *c* should not be a single numeric RGB or RGBA sequence because that is indistinguishable from an array of values to be colormapped. *c* can be a 2-D array in which the rows are RGB or RGBA, however. *marker*: can be one of: ===== ============== Value Description ===== ============== 's' square 'o' circle '^' triangle up '>' triangle right 'v' triangle down '<' triangle left 'd' diamond 'p' pentagram 'h' hexagon '8' octagon '+' plus 'x' cross ===== ============== The marker can also be a tuple (*numsides*, *style*, *angle*), which will create a custom, regular symbol. *numsides*: the number of sides *style*: the style of the regular symbol: ===== ============================================= Value Description ===== ============================================= 0 a regular polygon 1 a star-like symbol 2 an asterisk 3 a circle (*numsides* and *angle* is ignored) ===== ============================================= *angle*: the angle of rotation of the symbol Finally, *marker* can be (*verts*, 0): *verts* is a sequence of (*x*, *y*) vertices for a custom scatter symbol. Alternatively, use the kwarg combination *marker* = *None*, *verts* = *verts*. Any or all of *x*, *y*, *s*, and *c* may be masked arrays, in which case all masks will be combined and only unmasked points will be plotted. Other keyword arguments: the color mapping and normalization arguments will be used only if *c* is an array of floats. *cmap*: [ None | Colormap ] A :class:`matplotlib.colors.Colormap` instance. If *None*, defaults to rc ``image.cmap``. *cmap* is only used if *c* is an array of floats. *norm*: [ None | Normalize ] A :class:`matplotlib.colors.Normalize` instance is used to scale luminance data to 0, 1. If *None*, use the default :func:`normalize`. *norm* is only used if *c* is an array of floats. *vmin*/*vmax*: *vmin* and *vmax* are used in conjunction with norm to normalize luminance data. If either are None, the min and max of the color array *C* is used. Note if you pass a *norm* instance, your settings for *vmin* and *vmax* will be ignored. *alpha*: 0 <= scalar <= 1 The alpha value for the patches *linewidths*: [ None | scalar | sequence ] If *None*, defaults to (lines.linewidth,). Note that this is a tuple, and if you set the linewidths argument you must set it as a sequence of floats, as required by :class:`~matplotlib.collections.RegularPolyCollection`. Optional kwargs control the :class:`~matplotlib.collections.Collection` properties; in particular: *edgecolors*: 'none' to plot faces with no outlines *facecolors*: 'none' to plot unfilled outlines Here are the standard descriptions of all the :class:`~matplotlib.collections.Collection` kwargs: %(Collection)s A :class:`~matplotlib.collections.Collection` instance is returned. """ if not self._hold: self.cla() syms = { # a dict from symbol to (numsides, angle) 's' : (4,math.pi/4.0,0), # square 'o' : (20,3,0), # circle '^' : (3,0,0), # triangle up '>' : (3,math.pi/2.0,0), # triangle right 'v' : (3,math.pi,0), # triangle down '<' : (3,3*math.pi/2.0,0), # triangle left 'd' : (4,0,0), # diamond 'p' : (5,0,0), # pentagram 'h' : (6,0,0), # hexagon '8' : (8,0,0), # octagon '+' : (4,0,2), # plus 'x' : (4,math.pi/4.0,2) # cross } self._process_unit_info(xdata=x, ydata=y, kwargs=kwargs) x, y, s, c = cbook.delete_masked_points(x, y, s, c) if is_string_like(c) or cbook.is_sequence_of_strings(c): colors = mcolors.colorConverter.to_rgba_array(c, alpha) else: sh = np.shape(c) # The inherent ambiguity is resolved in favor of color # mapping, not interpretation as rgb or rgba: if len(sh) == 1 and sh[0] == len(x): colors = None # use cmap, norm after collection is created else: colors = mcolors.colorConverter.to_rgba_array(c, alpha) if not iterable(s): scales = (s,) else: scales = s if faceted: edgecolors = None else: edgecolors = 'none' warnings.warn( '''replace "faceted=False" with "edgecolors='none'"''', DeprecationWarning) #2008/04/18 sym = None symstyle = 0 # to be API compatible if marker is None and not (verts is None): marker = (verts, 0) verts = None if is_string_like(marker): # the standard way to define symbols using a string character sym = syms.get(marker) if sym is None and verts is None: raise ValueError('Unknown marker symbol to scatter') numsides, rotation, symstyle = syms[marker] elif iterable(marker): # accept marker to be: # (numsides, style, [angle]) # or # (verts[], style, [angle]) if len(marker)<2 or len(marker)>3: raise ValueError('Cannot create markersymbol from marker') if cbook.is_numlike(marker[0]): # (numsides, style, [angle]) if len(marker)==2: numsides, rotation = marker[0], 0. elif len(marker)==3: numsides, rotation = marker[0], marker[2] sym = True if marker[1] in (1,2): symstyle = marker[1] else: verts = np.asarray(marker[0]) if sym is not None: if symstyle==0: collection = mcoll.RegularPolyCollection( numsides, rotation, scales, facecolors = colors, edgecolors = edgecolors, linewidths = linewidths, offsets = zip(x,y), transOffset = self.transData, ) elif symstyle==1: collection = mcoll.StarPolygonCollection( numsides, rotation, scales, facecolors = colors, edgecolors = edgecolors, linewidths = linewidths, offsets = zip(x,y), transOffset = self.transData, ) elif symstyle==2: collection = mcoll.AsteriskPolygonCollection( numsides, rotation, scales, facecolors = colors, edgecolors = edgecolors, linewidths = linewidths, offsets = zip(x,y), transOffset = self.transData, ) elif symstyle==3: collection = mcoll.CircleCollection( scales, facecolors = colors, edgecolors = edgecolors, linewidths = linewidths, offsets = zip(x,y), transOffset = self.transData, ) else: rescale = np.sqrt(max(verts[:,0]**2+verts[:,1]**2)) verts /= rescale collection = mcoll.PolyCollection( (verts,), scales, facecolors = colors, edgecolors = edgecolors, linewidths = linewidths, offsets = zip(x,y), transOffset = self.transData, ) collection.set_transform(mtransforms.IdentityTransform()) collection.set_alpha(alpha) collection.update(kwargs) if colors is None: if norm is not None: assert(isinstance(norm, mcolors.Normalize)) if cmap is not None: assert(isinstance(cmap, mcolors.Colormap)) collection.set_array(np.asarray(c)) collection.set_cmap(cmap) collection.set_norm(norm) if vmin is not None or vmax is not None: collection.set_clim(vmin, vmax) else: collection.autoscale_None() temp_x = x temp_y = y minx = np.amin(temp_x) maxx = np.amax(temp_x) miny = np.amin(temp_y) maxy = np.amax(temp_y) w = maxx-minx h = maxy-miny # the pad is a little hack to deal with the fact that we don't # want to transform all the symbols whose scales are in points # to data coords to get the exact bounding box for efficiency # reasons. It can be done right if this is deemed important padx, pady = 0.05*w, 0.05*h corners = (minx-padx, miny-pady), (maxx+padx, maxy+pady) self.update_datalim( corners) self.autoscale_view() # add the collection last self.add_collection(collection) return collection scatter.__doc__ = cbook.dedent(scatter.__doc__) % martist.kwdocd def hexbin(self, x, y, C = None, gridsize = 100, bins = None, xscale = 'linear', yscale = 'linear', cmap=None, norm=None, vmin=None, vmax=None, alpha=1.0, linewidths=None, edgecolors='none', reduce_C_function = np.mean, **kwargs): """ call signature:: hexbin(x, y, C = None, gridsize = 100, bins = None, xscale = 'linear', yscale = 'linear', cmap=None, norm=None, vmin=None, vmax=None, alpha=1.0, linewidths=None, edgecolors='none' reduce_C_function = np.mean, **kwargs) Make a hexagonal binning plot of *x* versus *y*, where *x*, *y* are 1-D sequences of the same length, *N*. If *C* is None (the default), this is a histogram of the number of occurences of the observations at (x[i],y[i]). If *C* is specified, it specifies values at the coordinate (x[i],y[i]). These values are accumulated for each hexagonal bin and then reduced according to *reduce_C_function*, which defaults to numpy's mean function (np.mean). (If *C* is specified, it must also be a 1-D sequence of the same length as *x* and *y*.) *x*, *y* and/or *C* may be masked arrays, in which case only unmasked points will be plotted. Optional keyword arguments: *gridsize*: [ 100 | integer ] The number of hexagons in the *x*-direction, default is 100. The corresponding number of hexagons in the *y*-direction is chosen such that the hexagons are approximately regular. Alternatively, gridsize can be a tuple with two elements specifying the number of hexagons in the *x*-direction and the *y*-direction. *bins*: [ None | 'log' | integer | sequence ] If *None*, no binning is applied; the color of each hexagon directly corresponds to its count value. If 'log', use a logarithmic scale for the color map. Internally, :math:`log_{10}(i+1)` is used to determine the hexagon color. If an integer, divide the counts in the specified number of bins, and color the hexagons accordingly. If a sequence of values, the values of the lower bound of the bins to be used. *xscale*: [ 'linear' | 'log' ] Use a linear or log10 scale on the horizontal axis. *scale*: [ 'linear' | 'log' ] Use a linear or log10 scale on the vertical axis. Other keyword arguments controlling color mapping and normalization arguments: *cmap*: [ None | Colormap ] a :class:`matplotlib.cm.Colormap` instance. If *None*, defaults to rc ``image.cmap``. *norm*: [ None | Normalize ] :class:`matplotlib.colors.Normalize` instance is used to scale luminance data to 0,1. *vmin*/*vmax*: scalar *vmin* and *vmax* are used in conjunction with *norm* to normalize luminance data. If either are *None*, the min and max of the color array *C* is used. Note if you pass a norm instance, your settings for *vmin* and *vmax* will be ignored. *alpha*: scalar the alpha value for the patches *linewidths*: [ None | scalar ] If *None*, defaults to rc lines.linewidth. Note that this is a tuple, and if you set the linewidths argument you must set it as a sequence of floats, as required by :class:`~matplotlib.collections.RegularPolyCollection`. Other keyword arguments controlling the Collection properties: *edgecolors*: [ None | mpl color | color sequence ] If 'none', draws the edges in the same color as the fill color. This is the default, as it avoids unsightly unpainted pixels between the hexagons. If *None*, draws the outlines in the default color. If a matplotlib color arg or sequence of rgba tuples, draws the outlines in the specified color. Here are the standard descriptions of all the :class:`~matplotlib.collections.Collection` kwargs: %(Collection)s The return value is a :class:`~matplotlib.collections.PolyCollection` instance; use :meth:`~matplotlib.collection.PolyCollection.get_array` on this :class:`~matplotlib.collections.PolyCollection` to get the counts in each hexagon. **Example:** .. plot:: mpl_examples/pylab_examples/hexbin_demo.py """ if not self._hold: self.cla() self._process_unit_info(xdata=x, ydata=y, kwargs=kwargs) x, y, C = cbook.delete_masked_points(x, y, C) # Set the size of the hexagon grid if iterable(gridsize): nx, ny = gridsize else: nx = gridsize ny = int(nx/math.sqrt(3)) # Count the number of data in each hexagon x = np.array(x, float) y = np.array(y, float) if xscale=='log': x = np.log10(x) if yscale=='log': y = np.log10(y) xmin = np.amin(x) xmax = np.amax(x) ymin = np.amin(y) ymax = np.amax(y) # In the x-direction, the hexagons exactly cover the region from # xmin to xmax. Need some padding to avoid roundoff errors. padding = 1.e-9 * (xmax - xmin) xmin -= padding xmax += padding sx = (xmax-xmin) / nx sy = (ymax-ymin) / ny x = (x-xmin)/sx y = (y-ymin)/sy ix1 = np.round(x).astype(int) iy1 = np.round(y).astype(int) ix2 = np.floor(x).astype(int) iy2 = np.floor(y).astype(int) nx1 = nx + 1 ny1 = ny + 1 nx2 = nx ny2 = ny n = nx1*ny1+nx2*ny2 d1 = (x-ix1)**2 + 3.0 * (y-iy1)**2 d2 = (x-ix2-0.5)**2 + 3.0 * (y-iy2-0.5)**2 bdist = (d1<d2) if C is None: accum = np.zeros(n) # Create appropriate views into "accum" array. lattice1 = accum[:nx1*ny1] lattice2 = accum[nx1*ny1:] lattice1.shape = (nx1,ny1) lattice2.shape = (nx2,ny2) for i in xrange(len(x)): if bdist[i]: lattice1[ix1[i], iy1[i]]+=1 else: lattice2[ix2[i], iy2[i]]+=1 else: # create accumulation arrays lattice1 = np.empty((nx1,ny1),dtype=object) for i in xrange(nx1): for j in xrange(ny1): lattice1[i,j] = [] lattice2 = np.empty((nx2,ny2),dtype=object) for i in xrange(nx2): for j in xrange(ny2): lattice2[i,j] = [] for i in xrange(len(x)): if bdist[i]: lattice1[ix1[i], iy1[i]].append( C[i] ) else: lattice2[ix2[i], iy2[i]].append( C[i] ) for i in xrange(nx1): for j in xrange(ny1): vals = lattice1[i,j] if len(vals): lattice1[i,j] = reduce_C_function( vals ) else: lattice1[i,j] = np.nan for i in xrange(nx2): for j in xrange(ny2): vals = lattice2[i,j] if len(vals): lattice2[i,j] = reduce_C_function( vals ) else: lattice2[i,j] = np.nan accum = np.hstack(( lattice1.astype(float).ravel(), lattice2.astype(float).ravel())) good_idxs = ~np.isnan(accum) px = xmin + sx * np.array([ 0.5, 0.5, 0.0, -0.5, -0.5, 0.0]) py = ymin + sy * np.array([-0.5, 0.5, 1.0, 0.5, -0.5, -1.0]) / 3.0 polygons = np.zeros((6, n, 2), float) polygons[:,:nx1*ny1,0] = np.repeat(np.arange(nx1), ny1) polygons[:,:nx1*ny1,1] = np.tile(np.arange(ny1), nx1) polygons[:,nx1*ny1:,0] = np.repeat(np.arange(nx2) + 0.5, ny2) polygons[:,nx1*ny1:,1] = np.tile(np.arange(ny2), nx2) + 0.5 if C is not None: # remove accumulation bins with no data polygons = polygons[:,good_idxs,:] accum = accum[good_idxs] polygons = np.transpose(polygons, axes=[1,0,2]) polygons[:,:,0] *= sx polygons[:,:,1] *= sy polygons[:,:,0] += px polygons[:,:,1] += py if xscale=='log': polygons[:,:,0] = 10**(polygons[:,:,0]) xmin = 10**xmin xmax = 10**xmax self.set_xscale('log') if yscale=='log': polygons[:,:,1] = 10**(polygons[:,:,1]) ymin = 10**ymin ymax = 10**ymax self.set_yscale('log') if edgecolors=='none': edgecolors = 'face' collection = mcoll.PolyCollection( polygons, edgecolors = edgecolors, linewidths = linewidths, transOffset = self.transData, ) # Transform accum if needed if bins=='log': accum = np.log10(accum+1) elif bins!=None: if not iterable(bins): minimum, maximum = min(accum), max(accum) bins-=1 # one less edge than bins bins = minimum + (maximum-minimum)*np.arange(bins)/bins bins = np.sort(bins) accum = bins.searchsorted(accum) if norm is not None: assert(isinstance(norm, mcolors.Normalize)) if cmap is not None: assert(isinstance(cmap, mcolors.Colormap)) collection.set_array(accum) collection.set_cmap(cmap) collection.set_norm(norm) collection.set_alpha(alpha) collection.update(kwargs) if vmin is not None or vmax is not None: collection.set_clim(vmin, vmax) else: collection.autoscale_None() corners = ((xmin, ymin), (xmax, ymax)) self.update_datalim( corners) self.autoscale_view() # add the collection last self.add_collection(collection) return collection hexbin.__doc__ = cbook.dedent(hexbin.__doc__) % martist.kwdocd def arrow(self, x, y, dx, dy, **kwargs): """ call signature:: arrow(x, y, dx, dy, **kwargs) Draws arrow on specified axis from (*x*, *y*) to (*x* + *dx*, *y* + *dy*). Optional kwargs control the arrow properties: %(FancyArrow)s **Example:** .. plot:: mpl_examples/pylab_examples/arrow_demo.py """ a = mpatches.FancyArrow(x, y, dx, dy, **kwargs) self.add_artist(a) return a arrow.__doc__ = cbook.dedent(arrow.__doc__) % martist.kwdocd def quiverkey(self, *args, **kw): qk = mquiver.QuiverKey(*args, **kw) self.add_artist(qk) return qk quiverkey.__doc__ = mquiver.QuiverKey.quiverkey_doc def quiver(self, *args, **kw): if not self._hold: self.cla() q = mquiver.Quiver(self, *args, **kw) self.add_collection(q, False) self.update_datalim(q.XY) self.autoscale_view() return q quiver.__doc__ = mquiver.Quiver.quiver_doc def barbs(self, *args, **kw): """ %(barbs_doc)s **Example:** .. plot:: mpl_examples/pylab_examples/barb_demo.py """ if not self._hold: self.cla() b = mquiver.Barbs(self, *args, **kw) self.add_collection(b) self.update_datalim(b.get_offsets()) self.autoscale_view() return b barbs.__doc__ = cbook.dedent(barbs.__doc__) % { 'barbs_doc': mquiver.Barbs.barbs_doc} def fill(self, *args, **kwargs): """ call signature:: fill(*args, **kwargs) Plot filled polygons. *args* is a variable length argument, allowing for multiple *x*, *y* pairs with an optional color format string; see :func:`~matplotlib.pyplot.plot` for details on the argument parsing. For example, to plot a polygon with vertices at *x*, *y* in blue.:: ax.fill(x,y, 'b' ) An arbitrary number of *x*, *y*, *color* groups can be specified:: ax.fill(x1, y1, 'g', x2, y2, 'r') Return value is a list of :class:`~matplotlib.patches.Patch` instances that were added. The same color strings that :func:`~matplotlib.pyplot.plot` supports are supported by the fill format string. If you would like to fill below a curve, eg. shade a region between 0 and *y* along *x*, use :meth:`fill_between` The *closed* kwarg will close the polygon when *True* (default). kwargs control the Polygon properties: %(Polygon)s **Example:** .. plot:: mpl_examples/pylab_examples/fill_demo.py """ if not self._hold: self.cla() patches = [] for poly in self._get_patches_for_fill(*args, **kwargs): self.add_patch( poly ) patches.append( poly ) self.autoscale_view() return patches fill.__doc__ = cbook.dedent(fill.__doc__) % martist.kwdocd def fill_between(self, x, y1, y2=0, where=None, **kwargs): """ call signature:: fill_between(x, y1, y2=0, where=None, **kwargs) Create a :class:`~matplotlib.collections.PolyCollection` filling the regions between *y1* and *y2* where ``where==True`` *x* an N length np array of the x data *y1* an N length scalar or np array of the x data *y2* an N length scalar or np array of the x data *where* if None, default to fill between everywhere. If not None, it is a a N length numpy boolean array and the fill will only happen over the regions where ``where==True`` *kwargs* keyword args passed on to the :class:`PolyCollection` kwargs control the Polygon properties: %(PolyCollection)s .. plot:: mpl_examples/pylab_examples/fill_between.py """ # Handle united data, such as dates self._process_unit_info(xdata=x, ydata=y1, kwargs=kwargs) self._process_unit_info(ydata=y2) # Convert the arrays so we can work with them x = np.asarray(self.convert_xunits(x)) y1 = np.asarray(self.convert_yunits(y1)) y2 = np.asarray(self.convert_yunits(y2)) if not cbook.iterable(y1): y1 = np.ones_like(x)*y1 if not cbook.iterable(y2): y2 = np.ones_like(x)*y2 if where is None: where = np.ones(len(x), np.bool) where = np.asarray(where) assert( (len(x)==len(y1)) and (len(x)==len(y2)) and len(x)==len(where)) polys = [] for ind0, ind1 in mlab.contiguous_regions(where): theseverts = [] xslice = x[ind0:ind1] y1slice = y1[ind0:ind1] y2slice = y2[ind0:ind1] if not len(xslice): continue N = len(xslice) X = np.zeros((2*N+2, 2), np.float) # the purpose of the next two lines is for when y2 is a # scalar like 0 and we want the fill to go all the way # down to 0 even if none of the y1 sample points do X[0] = xslice[0], y2slice[0] X[N+1] = xslice[-1], y2slice[-1] X[1:N+1,0] = xslice X[1:N+1,1] = y1slice X[N+2:,0] = xslice[::-1] X[N+2:,1] = y2slice[::-1] polys.append(X) collection = mcoll.PolyCollection(polys, **kwargs) # now update the datalim and autoscale XY1 = np.array([x[where], y1[where]]).T XY2 = np.array([x[where], y2[where]]).T self.dataLim.update_from_data_xy(XY1, self.ignore_existing_data_limits, updatex=True, updatey=True) self.dataLim.update_from_data_xy(XY2, self.ignore_existing_data_limits, updatex=False, updatey=True) self.add_collection(collection) self.autoscale_view() return collection fill_between.__doc__ = cbook.dedent(fill_between.__doc__) % martist.kwdocd #### plotting z(x,y): imshow, pcolor and relatives, contour def imshow(self, X, cmap=None, norm=None, aspect=None, interpolation=None, alpha=1.0, vmin=None, vmax=None, origin=None, extent=None, shape=None, filternorm=1, filterrad=4.0, imlim=None, resample=None, url=None, **kwargs): """ call signature:: imshow(X, cmap=None, norm=None, aspect=None, interpolation=None, alpha=1.0, vmin=None, vmax=None, origin=None, extent=None, **kwargs) Display the image in *X* to current axes. *X* may be a float array, a uint8 array or a PIL image. If *X* is an array, *X* can have the following shapes: * MxN -- luminance (grayscale, float array only) * MxNx3 -- RGB (float or uint8 array) * MxNx4 -- RGBA (float or uint8 array) The value for each component of MxNx3 and MxNx4 float arrays should be in the range 0.0 to 1.0; MxN float arrays may be normalised. An :class:`matplotlib.image.AxesImage` instance is returned. Keyword arguments: *cmap*: [ None | Colormap ] A :class:`matplotlib.cm.Colormap` instance, eg. cm.jet. If *None*, default to rc ``image.cmap`` value. *cmap* is ignored when *X* has RGB(A) information *aspect*: [ None | 'auto' | 'equal' | scalar ] If 'auto', changes the image aspect ratio to match that of the axes If 'equal', and *extent* is *None*, changes the axes aspect ratio to match that of the image. If *extent* is not *None*, the axes aspect ratio is changed to match that of the extent. If *None*, default to rc ``image.aspect`` value. *interpolation*: Acceptable values are *None*, 'nearest', 'bilinear', 'bicubic', 'spline16', 'spline36', 'hanning', 'hamming', 'hermite', 'kaiser', 'quadric', 'catrom', 'gaussian', 'bessel', 'mitchell', 'sinc', 'lanczos', If *interpolation* is *None*, default to rc ``image.interpolation``. See also the *filternorm* and *filterrad* parameters *norm*: [ None | Normalize ] An :class:`matplotlib.colors.Normalize` instance; if *None*, default is ``normalization()``. This scales luminance -> 0-1 *norm* is only used for an MxN float array. *vmin*/*vmax*: [ None | scalar ] Used to scale a luminance image to 0-1. If either is *None*, the min and max of the luminance values will be used. Note if *norm* is not *None*, the settings for *vmin* and *vmax* will be ignored. *alpha*: scalar The alpha blending value, between 0 (transparent) and 1 (opaque) *origin*: [ None | 'upper' | 'lower' ] Place the [0,0] index of the array in the upper left or lower left corner of the axes. If *None*, default to rc ``image.origin``. *extent*: [ None | scalars (left, right, bottom, top) ] Eata values of the axes. The default assigns zero-based row, column indices to the *x*, *y* centers of the pixels. *shape*: [ None | scalars (columns, rows) ] For raw buffer images *filternorm*: A parameter for the antigrain image resize filter. From the antigrain documentation, if *filternorm* = 1, the filter normalizes integer values and corrects the rounding errors. It doesn't do anything with the source floating point values, it corrects only integers according to the rule of 1.0 which means that any sum of pixel weights must be equal to 1.0. So, the filter function must produce a graph of the proper shape. *filterrad*: The filter radius for filters that have a radius parameter, i.e. when interpolation is one of: 'sinc', 'lanczos' or 'blackman' Additional kwargs are :class:`~matplotlib.artist.Artist` properties: %(Artist)s **Example:** .. plot:: mpl_examples/pylab_examples/image_demo.py """ if not self._hold: self.cla() if norm is not None: assert(isinstance(norm, mcolors.Normalize)) if cmap is not None: assert(isinstance(cmap, mcolors.Colormap)) if aspect is None: aspect = rcParams['image.aspect'] self.set_aspect(aspect) im = mimage.AxesImage(self, cmap, norm, interpolation, origin, extent, filternorm=filternorm, filterrad=filterrad, resample=resample, **kwargs) im.set_data(X) im.set_alpha(alpha) self._set_artist_props(im) im.set_clip_path(self.patch) #if norm is None and shape is None: # im.set_clim(vmin, vmax) if vmin is not None or vmax is not None: im.set_clim(vmin, vmax) else: im.autoscale_None() im.set_url(url) xmin, xmax, ymin, ymax = im.get_extent() corners = (xmin, ymin), (xmax, ymax) self.update_datalim(corners) if self._autoscaleon: self.set_xlim((xmin, xmax)) self.set_ylim((ymin, ymax)) self.images.append(im) return im imshow.__doc__ = cbook.dedent(imshow.__doc__) % martist.kwdocd def _pcolorargs(self, funcname, *args): if len(args)==1: C = args[0] numRows, numCols = C.shape X, Y = np.meshgrid(np.arange(numCols+1), np.arange(numRows+1) ) elif len(args)==3: X, Y, C = args else: raise TypeError( 'Illegal arguments to %s; see help(%s)' % (funcname, funcname)) Nx = X.shape[-1] Ny = Y.shape[0] if len(X.shape) <> 2 or X.shape[0] == 1: x = X.reshape(1,Nx) X = x.repeat(Ny, axis=0) if len(Y.shape) <> 2 or Y.shape[1] == 1: y = Y.reshape(Ny, 1) Y = y.repeat(Nx, axis=1) if X.shape != Y.shape: raise TypeError( 'Incompatible X, Y inputs to %s; see help(%s)' % ( funcname, funcname)) return X, Y, C def pcolor(self, *args, **kwargs): """ call signatures:: pcolor(C, **kwargs) pcolor(X, Y, C, **kwargs) Create a pseudocolor plot of a 2-D array. *C* is the array of color values. *X* and *Y*, if given, specify the (*x*, *y*) coordinates of the colored quadrilaterals; the quadrilateral for C[i,j] has corners at:: (X[i, j], Y[i, j]), (X[i, j+1], Y[i, j+1]), (X[i+1, j], Y[i+1, j]), (X[i+1, j+1], Y[i+1, j+1]). Ideally the dimensions of *X* and *Y* should be one greater than those of *C*; if the dimensions are the same, then the last row and column of *C* will be ignored. Note that the the column index corresponds to the *x*-coordinate, and the row index corresponds to *y*; for details, see the :ref:`Grid Orientation <axes-pcolor-grid-orientation>` section below. If either or both of *X* and *Y* are 1-D arrays or column vectors, they will be expanded as needed into the appropriate 2-D arrays, making a rectangular grid. *X*, *Y* and *C* may be masked arrays. If either C[i, j], or one of the vertices surrounding C[i,j] (*X* or *Y* at [i, j], [i+1, j], [i, j+1],[i+1, j+1]) is masked, nothing is plotted. Keyword arguments: *cmap*: [ None | Colormap ] A :class:`matplotlib.cm.Colormap` instance. If *None*, use rc settings. norm: [ None | Normalize ] An :class:`matplotlib.colors.Normalize` instance is used to scale luminance data to 0,1. If *None*, defaults to :func:`normalize`. *vmin*/*vmax*: [ None | scalar ] *vmin* and *vmax* are used in conjunction with *norm* to normalize luminance data. If either are *None*, the min and max of the color array *C* is used. If you pass a *norm* instance, *vmin* and *vmax* will be ignored. *shading*: [ 'flat' | 'faceted' ] If 'faceted', a black grid is drawn around each rectangle; if 'flat', edges are not drawn. Default is 'flat', contrary to Matlab(TM). This kwarg is deprecated; please use 'edgecolors' instead: * shading='flat' -- edgecolors='None' * shading='faceted -- edgecolors='k' *edgecolors*: [ None | 'None' | color | color sequence] If *None*, the rc setting is used by default. If 'None', edges will not be visible. An mpl color or sequence of colors will set the edge color *alpha*: 0 <= scalar <= 1 the alpha blending value Return value is a :class:`matplotlib.collection.Collection` instance. .. _axes-pcolor-grid-orientation: The grid orientation follows the Matlab(TM) convention: an array *C* with shape (*nrows*, *ncolumns*) is plotted with the column number as *X* and the row number as *Y*, increasing up; hence it is plotted the way the array would be printed, except that the *Y* axis is reversed. That is, *C* is taken as *C*(*y*, *x*). Similarly for :func:`~matplotlib.pyplot.meshgrid`:: x = np.arange(5) y = np.arange(3) X, Y = meshgrid(x,y) is equivalent to: X = array([[0, 1, 2, 3, 4], [0, 1, 2, 3, 4], [0, 1, 2, 3, 4]]) Y = array([[0, 0, 0, 0, 0], [1, 1, 1, 1, 1], [2, 2, 2, 2, 2]]) so if you have:: C = rand( len(x), len(y)) then you need:: pcolor(X, Y, C.T) or:: pcolor(C.T) Matlab :func:`pcolor` always discards the last row and column of *C*, but matplotlib displays the last row and column if *X* and *Y* are not specified, or if *X* and *Y* have one more row and column than *C*. kwargs can be used to control the :class:`~matplotlib.collection.PolyCollection` properties: %(PolyCollection)s """ if not self._hold: self.cla() alpha = kwargs.pop('alpha', 1.0) norm = kwargs.pop('norm', None) cmap = kwargs.pop('cmap', None) vmin = kwargs.pop('vmin', None) vmax = kwargs.pop('vmax', None) shading = kwargs.pop('shading', 'flat') X, Y, C = self._pcolorargs('pcolor', *args) Ny, Nx = X.shape # convert to MA, if necessary. C = ma.asarray(C) X = ma.asarray(X) Y = ma.asarray(Y) mask = ma.getmaskarray(X)+ma.getmaskarray(Y) xymask = mask[0:-1,0:-1]+mask[1:,1:]+mask[0:-1,1:]+mask[1:,0:-1] # don't plot if C or any of the surrounding vertices are masked. mask = ma.getmaskarray(C)[0:Ny-1,0:Nx-1]+xymask newaxis = np.newaxis compress = np.compress ravelmask = (mask==0).ravel() X1 = compress(ravelmask, ma.filled(X[0:-1,0:-1]).ravel()) Y1 = compress(ravelmask, ma.filled(Y[0:-1,0:-1]).ravel()) X2 = compress(ravelmask, ma.filled(X[1:,0:-1]).ravel()) Y2 = compress(ravelmask, ma.filled(Y[1:,0:-1]).ravel()) X3 = compress(ravelmask, ma.filled(X[1:,1:]).ravel()) Y3 = compress(ravelmask, ma.filled(Y[1:,1:]).ravel()) X4 = compress(ravelmask, ma.filled(X[0:-1,1:]).ravel()) Y4 = compress(ravelmask, ma.filled(Y[0:-1,1:]).ravel()) npoly = len(X1) xy = np.concatenate((X1[:,newaxis], Y1[:,newaxis], X2[:,newaxis], Y2[:,newaxis], X3[:,newaxis], Y3[:,newaxis], X4[:,newaxis], Y4[:,newaxis], X1[:,newaxis], Y1[:,newaxis]), axis=1) verts = xy.reshape((npoly, 5, 2)) #verts = zip(zip(X1,Y1),zip(X2,Y2),zip(X3,Y3),zip(X4,Y4)) C = compress(ravelmask, ma.filled(C[0:Ny-1,0:Nx-1]).ravel()) if shading == 'faceted': edgecolors = (0,0,0,1), linewidths = (0.25,) else: edgecolors = 'face' linewidths = (1.0,) kwargs.setdefault('edgecolors', edgecolors) kwargs.setdefault('antialiaseds', (0,)) kwargs.setdefault('linewidths', linewidths) collection = mcoll.PolyCollection(verts, **kwargs) collection.set_alpha(alpha) collection.set_array(C) if norm is not None: assert(isinstance(norm, mcolors.Normalize)) if cmap is not None: assert(isinstance(cmap, mcolors.Colormap)) collection.set_cmap(cmap) collection.set_norm(norm) if vmin is not None or vmax is not None: collection.set_clim(vmin, vmax) else: collection.autoscale_None() self.grid(False) x = X.compressed() y = Y.compressed() minx = np.amin(x) maxx = np.amax(x) miny = np.amin(y) maxy = np.amax(y) corners = (minx, miny), (maxx, maxy) self.update_datalim( corners) self.autoscale_view() self.add_collection(collection) return collection pcolor.__doc__ = cbook.dedent(pcolor.__doc__) % martist.kwdocd def pcolormesh(self, *args, **kwargs): """ call signatures:: pcolormesh(C) pcolormesh(X, Y, C) pcolormesh(C, **kwargs) *C* may be a masked array, but *X* and *Y* may not. Masked array support is implemented via *cmap* and *norm*; in contrast, :func:`~matplotlib.pyplot.pcolor` simply does not draw quadrilaterals with masked colors or vertices. Keyword arguments: *cmap*: [ None | Colormap ] A :class:`matplotlib.cm.Colormap` instance. If None, use rc settings. *norm*: [ None | Normalize ] A :class:`matplotlib.colors.Normalize` instance is used to scale luminance data to 0,1. If None, defaults to :func:`normalize`. *vmin*/*vmax*: [ None | scalar ] *vmin* and *vmax* are used in conjunction with *norm* to normalize luminance data. If either are *None*, the min and max of the color array *C* is used. If you pass a *norm* instance, *vmin* and *vmax* will be ignored. *shading*: [ 'flat' | 'faceted' ] If 'faceted', a black grid is drawn around each rectangle; if 'flat', edges are not drawn. Default is 'flat', contrary to Matlab(TM). This kwarg is deprecated; please use 'edgecolors' instead: * shading='flat' -- edgecolors='None' * shading='faceted -- edgecolors='k' *edgecolors*: [ None | 'None' | color | color sequence] If None, the rc setting is used by default. If 'None', edges will not be visible. An mpl color or sequence of colors will set the edge color *alpha*: 0 <= scalar <= 1 the alpha blending value Return value is a :class:`matplotlib.collection.QuadMesh` object. kwargs can be used to control the :class:`matplotlib.collections.QuadMesh` properties: %(QuadMesh)s .. seealso:: :func:`~matplotlib.pyplot.pcolor`: For an explanation of the grid orientation and the expansion of 1-D *X* and/or *Y* to 2-D arrays. """ if not self._hold: self.cla() alpha = kwargs.pop('alpha', 1.0) norm = kwargs.pop('norm', None) cmap = kwargs.pop('cmap', None) vmin = kwargs.pop('vmin', None) vmax = kwargs.pop('vmax', None) shading = kwargs.pop('shading', 'flat') edgecolors = kwargs.pop('edgecolors', 'None') antialiased = kwargs.pop('antialiased', False) X, Y, C = self._pcolorargs('pcolormesh', *args) Ny, Nx = X.shape # convert to one dimensional arrays C = ma.ravel(C[0:Ny-1, 0:Nx-1]) # data point in each cell is value at # lower left corner X = X.ravel() Y = Y.ravel() coords = np.zeros(((Nx * Ny), 2), dtype=float) coords[:, 0] = X coords[:, 1] = Y if shading == 'faceted' or edgecolors != 'None': showedges = 1 else: showedges = 0 collection = mcoll.QuadMesh( Nx - 1, Ny - 1, coords, showedges, antialiased=antialiased) # kwargs are not used collection.set_alpha(alpha) collection.set_array(C) if norm is not None: assert(isinstance(norm, mcolors.Normalize)) if cmap is not None: assert(isinstance(cmap, mcolors.Colormap)) collection.set_cmap(cmap) collection.set_norm(norm) if vmin is not None or vmax is not None: collection.set_clim(vmin, vmax) else: collection.autoscale_None() self.grid(False) minx = np.amin(X) maxx = np.amax(X) miny = np.amin(Y) maxy = np.amax(Y) corners = (minx, miny), (maxx, maxy) self.update_datalim( corners) self.autoscale_view() self.add_collection(collection) return collection pcolormesh.__doc__ = cbook.dedent(pcolormesh.__doc__) % martist.kwdocd def pcolorfast(self, *args, **kwargs): """ pseudocolor plot of a 2-D array Experimental; this is a version of pcolor that does not draw lines, that provides the fastest possible rendering with the Agg backend, and that can handle any quadrilateral grid. Call signatures:: pcolor(C, **kwargs) pcolor(xr, yr, C, **kwargs) pcolor(x, y, C, **kwargs) pcolor(X, Y, C, **kwargs) C is the 2D array of color values corresponding to quadrilateral cells. Let (nr, nc) be its shape. C may be a masked array. ``pcolor(C, **kwargs)`` is equivalent to ``pcolor([0,nc], [0,nr], C, **kwargs)`` *xr*, *yr* specify the ranges of *x* and *y* corresponding to the rectangular region bounding *C*. If:: xr = [x0, x1] and:: yr = [y0,y1] then *x* goes from *x0* to *x1* as the second index of *C* goes from 0 to *nc*, etc. (*x0*, *y0*) is the outermost corner of cell (0,0), and (*x1*, *y1*) is the outermost corner of cell (*nr*-1, *nc*-1). All cells are rectangles of the same size. This is the fastest version. *x*, *y* are 1D arrays of length *nc* +1 and *nr* +1, respectively, giving the x and y boundaries of the cells. Hence the cells are rectangular but the grid may be nonuniform. The speed is intermediate. (The grid is checked, and if found to be uniform the fast version is used.) *X* and *Y* are 2D arrays with shape (*nr* +1, *nc* +1) that specify the (x,y) coordinates of the corners of the colored quadrilaterals; the quadrilateral for C[i,j] has corners at (X[i,j],Y[i,j]), (X[i,j+1],Y[i,j+1]), (X[i+1,j],Y[i+1,j]), (X[i+1,j+1],Y[i+1,j+1]). The cells need not be rectangular. This is the most general, but the slowest to render. It may produce faster and more compact output using ps, pdf, and svg backends, however. Note that the the column index corresponds to the x-coordinate, and the row index corresponds to y; for details, see the "Grid Orientation" section below. Optional keyword arguments: *cmap*: [ None | Colormap ] A cm Colormap instance from cm. If None, use rc settings. *norm*: [ None | Normalize ] An mcolors.Normalize instance is used to scale luminance data to 0,1. If None, defaults to normalize() *vmin*/*vmax*: [ None | scalar ] *vmin* and *vmax* are used in conjunction with norm to normalize luminance data. If either are *None*, the min and max of the color array *C* is used. If you pass a norm instance, *vmin* and *vmax* will be *None*. *alpha*: 0 <= scalar <= 1 the alpha blending value Return value is an image if a regular or rectangular grid is specified, and a QuadMesh collection in the general quadrilateral case. """ if not self._hold: self.cla() alpha = kwargs.pop('alpha', 1.0) norm = kwargs.pop('norm', None) cmap = kwargs.pop('cmap', None) vmin = kwargs.pop('vmin', None) vmax = kwargs.pop('vmax', None) if norm is not None: assert(isinstance(norm, mcolors.Normalize)) if cmap is not None: assert(isinstance(cmap, mcolors.Colormap)) C = args[-1] nr, nc = C.shape if len(args) == 1: style = "image" x = [0, nc] y = [0, nr] elif len(args) == 3: x, y = args[:2] x = np.asarray(x) y = np.asarray(y) if x.ndim == 1 and y.ndim == 1: if x.size == 2 and y.size == 2: style = "image" else: dx = np.diff(x) dy = np.diff(y) if (np.ptp(dx) < 0.01*np.abs(dx.mean()) and np.ptp(dy) < 0.01*np.abs(dy.mean())): style = "image" else: style = "pcolorimage" elif x.ndim == 2 and y.ndim == 2: style = "quadmesh" else: raise TypeError("arguments do not match valid signatures") else: raise TypeError("need 1 argument or 3 arguments") if style == "quadmesh": # convert to one dimensional arrays # This should also be moved to the QuadMesh class C = ma.ravel(C) # data point in each cell is value # at lower left corner X = x.ravel() Y = y.ravel() Nx = nc+1 Ny = nr+1 # The following needs to be cleaned up; the renderer # requires separate contiguous arrays for X and Y, # but the QuadMesh class requires the 2D array. coords = np.empty(((Nx * Ny), 2), np.float64) coords[:, 0] = X coords[:, 1] = Y # The QuadMesh class can also be changed to # handle relevant superclass kwargs; the initializer # should do much more than it does now. collection = mcoll.QuadMesh(nc, nr, coords, 0) collection.set_alpha(alpha) collection.set_array(C) collection.set_cmap(cmap) collection.set_norm(norm) self.add_collection(collection) xl, xr, yb, yt = X.min(), X.max(), Y.min(), Y.max() ret = collection else: # One of the image styles: xl, xr, yb, yt = x[0], x[-1], y[0], y[-1] if style == "image": im = mimage.AxesImage(self, cmap, norm, interpolation='nearest', origin='lower', extent=(xl, xr, yb, yt), **kwargs) im.set_data(C) im.set_alpha(alpha) self.images.append(im) ret = im if style == "pcolorimage": im = mimage.PcolorImage(self, x, y, C, cmap=cmap, norm=norm, alpha=alpha, **kwargs) self.images.append(im) ret = im self._set_artist_props(ret) if vmin is not None or vmax is not None: ret.set_clim(vmin, vmax) else: ret.autoscale_None() self.update_datalim(np.array([[xl, yb], [xr, yt]])) self.autoscale_view(tight=True) return ret def contour(self, *args, **kwargs): if not self._hold: self.cla() kwargs['filled'] = False return mcontour.ContourSet(self, *args, **kwargs) contour.__doc__ = mcontour.ContourSet.contour_doc def contourf(self, *args, **kwargs): if not self._hold: self.cla() kwargs['filled'] = True return mcontour.ContourSet(self, *args, **kwargs) contourf.__doc__ = mcontour.ContourSet.contour_doc def clabel(self, CS, *args, **kwargs): return CS.clabel(*args, **kwargs) clabel.__doc__ = mcontour.ContourSet.clabel.__doc__ def table(self, **kwargs): """ call signature:: table(cellText=None, cellColours=None, cellLoc='right', colWidths=None, rowLabels=None, rowColours=None, rowLoc='left', colLabels=None, colColours=None, colLoc='center', loc='bottom', bbox=None): Add a table to the current axes. Returns a :class:`matplotlib.table.Table` instance. For finer grained control over tables, use the :class:`~matplotlib.table.Table` class and add it to the axes with :meth:`~matplotlib.axes.Axes.add_table`. Thanks to John Gill for providing the class and table. kwargs control the :class:`~matplotlib.table.Table` properties: %(Table)s """ return mtable.table(self, **kwargs) table.__doc__ = cbook.dedent(table.__doc__) % martist.kwdocd def twinx(self): """ call signature:: ax = twinx() create a twin of Axes for generating a plot with a sharex x-axis but independent y axis. The y-axis of self will have ticks on left and the returned axes will have ticks on the right """ ax2 = self.figure.add_axes(self.get_position(True), sharex=self, frameon=False) ax2.yaxis.tick_right() ax2.yaxis.set_label_position('right') self.yaxis.tick_left() return ax2 def twiny(self): """ call signature:: ax = twiny() create a twin of Axes for generating a plot with a shared y-axis but independent x axis. The x-axis of self will have ticks on bottom and the returned axes will have ticks on the top """ ax2 = self.figure.add_axes(self.get_position(True), sharey=self, frameon=False) ax2.xaxis.tick_top() ax2.xaxis.set_label_position('top') self.xaxis.tick_bottom() return ax2 def get_shared_x_axes(self): 'Return a copy of the shared axes Grouper object for x axes' return self._shared_x_axes def get_shared_y_axes(self): 'Return a copy of the shared axes Grouper object for y axes' return self._shared_y_axes #### Data analysis def hist(self, x, bins=10, range=None, normed=False, cumulative=False, bottom=None, histtype='bar', align='mid', orientation='vertical', rwidth=None, log=False, **kwargs): """ call signature:: hist(x, bins=10, range=None, normed=False, cumulative=False, bottom=None, histtype='bar', align='mid', orientation='vertical', rwidth=None, log=False, **kwargs) Compute and draw the histogram of *x*. The return value is a tuple (*n*, *bins*, *patches*) or ([*n0*, *n1*, ...], *bins*, [*patches0*, *patches1*,...]) if the input contains multiple data. Keyword arguments: *bins*: Either an integer number of bins or a sequence giving the bins. *x* are the data to be binned. *x* can be an array, a 2D array with multiple data in its columns, or a list of arrays with data of different length. Note, if *bins* is an integer input argument=numbins, *bins* + 1 bin edges will be returned, compatible with the semantics of :func:`numpy.histogram` with the *new* = True argument. Unequally spaced bins are supported if *bins* is a sequence. *range*: The lower and upper range of the bins. Lower and upper outliers are ignored. If not provided, *range* is (x.min(), x.max()). Range has no effect if *bins* is a sequence. If *bins* is a sequence or *range* is specified, autoscaling is set off (*autoscale_on* is set to *False*) and the xaxis limits are set to encompass the full specified bin range. *normed*: If *True*, the first element of the return tuple will be the counts normalized to form a probability density, i.e., ``n/(len(x)*dbin)``. In a probability density, the integral of the histogram should be 1; you can verify that with a trapezoidal integration of the probability density function:: pdf, bins, patches = ax.hist(...) print np.sum(pdf * np.diff(bins)) *cumulative*: If *True*, then a histogram is computed where each bin gives the counts in that bin plus all bins for smaller values. The last bin gives the total number of datapoints. If *normed* is also *True* then the histogram is normalized such that the last bin equals 1. If *cumulative* evaluates to less than 0 (e.g. -1), the direction of accumulation is reversed. In this case, if *normed* is also *True*, then the histogram is normalized such that the first bin equals 1. *histtype*: [ 'bar' | 'barstacked' | 'step' | 'stepfilled' ] The type of histogram to draw. - 'bar' is a traditional bar-type histogram. If multiple data are given the bars are aranged side by side. - 'barstacked' is a bar-type histogram where multiple data are stacked on top of each other. - 'step' generates a lineplot that is by default unfilled. - 'stepfilled' generates a lineplot that is by default filled. *align*: ['left' | 'mid' | 'right' ] Controls how the histogram is plotted. - 'left': bars are centered on the left bin edges. - 'mid': bars are centered between the bin edges. - 'right': bars are centered on the right bin edges. *orientation*: [ 'horizontal' | 'vertical' ] If 'horizontal', :func:`~matplotlib.pyplot.barh` will be used for bar-type histograms and the *bottom* kwarg will be the left edges. *rwidth*: The relative width of the bars as a fraction of the bin width. If *None*, automatically compute the width. Ignored if *histtype* = 'step' or 'stepfilled'. *log*: If *True*, the histogram axis will be set to a log scale. If *log* is *True* and *x* is a 1D array, empty bins will be filtered out and only the non-empty (*n*, *bins*, *patches*) will be returned. kwargs are used to update the properties of the hist :class:`~matplotlib.patches.Rectangle` instances: %(Rectangle)s You can use labels for your histogram, and only the first :class:`~matplotlib.patches.Rectangle` gets the label (the others get the magic string '_nolegend_'. This will make the histograms work in the intuitive way for bar charts:: ax.hist(10+2*np.random.randn(1000), label='men') ax.hist(12+3*np.random.randn(1000), label='women', alpha=0.5) ax.legend() **Example:** .. plot:: mpl_examples/pylab_examples/histogram_demo.py """ if not self._hold: self.cla() # NOTE: the range keyword overwrites the built-in func range !!! # needs to be fixed in with numpy !!! if kwargs.get('width') is not None: raise DeprecationWarning( 'hist now uses the rwidth to give relative width ' 'and not absolute width') try: # make sure a copy is created: don't use asarray x = np.transpose(np.array(x)) if len(x.shape)==1: x.shape = (1,x.shape[0]) elif len(x.shape)==2 and x.shape[1]<x.shape[0]: warnings.warn('2D hist should be nsamples x nvariables; ' 'this looks transposed') except ValueError: # multiple hist with data of different length if iterable(x[0]) and not is_string_like(x[0]): tx = [] for i in xrange(len(x)): tx.append( np.array(x[i]) ) x = tx else: raise ValueError, 'Can not use providet data to create a histogram' # Check whether bins or range are given explicitly. In that # case do not autoscale axes. binsgiven = (cbook.iterable(bins) or range != None) # check the version of the numpy if np.__version__ < "1.3": # version 1.1 and 1.2 hist_kwargs = dict(range=range, normed=bool(normed), new=True) else: # version 1.3 and later, drop new=True hist_kwargs = dict(range=range, normed=bool(normed)) n = [] for i in xrange(len(x)): # this will automatically overwrite bins, # so that each histogram uses the same bins m, bins = np.histogram(x[i], bins, **hist_kwargs) n.append(m) if cumulative: slc = slice(None) if cbook.is_numlike(cumulative) and cumulative < 0: slc = slice(None,None,-1) if normed: n = [(m * np.diff(bins))[slc].cumsum()[slc] for m in n] else: n = [m[slc].cumsum()[slc] for m in n] patches = [] if histtype.startswith('bar'): totwidth = np.diff(bins) stacked = False if rwidth is not None: dr = min(1., max(0., rwidth)) elif len(n)>1: dr = 0.8 else: dr = 1.0 if histtype=='bar': width = dr*totwidth/len(n) dw = width if len(n)>1: boffset = -0.5*dr*totwidth*(1.-1./len(n)) else: boffset = 0.0 elif histtype=='barstacked': width = dr*totwidth boffset, dw = 0.0, 0.0 stacked = True else: raise ValueError, 'invalid histtype: %s' % histtype if align == 'mid' or align == 'edge': boffset += 0.5*totwidth elif align == 'right': boffset += totwidth elif align != 'left' and align != 'center': raise ValueError, 'invalid align: %s' % align if orientation == 'horizontal': for m in n: color = self._get_lines._get_next_cycle_color() patch = self.barh(bins[:-1]+boffset, m, height=width, left=bottom, align='center', log=log, color=color) patches.append(patch) if stacked: if bottom is None: bottom = 0.0 bottom += m boffset += dw elif orientation == 'vertical': for m in n: color = self._get_lines._get_next_cycle_color() patch = self.bar(bins[:-1]+boffset, m, width=width, bottom=bottom, align='center', log=log, color=color) patches.append(patch) if stacked: if bottom is None: bottom = 0.0 bottom += m boffset += dw else: raise ValueError, 'invalid orientation: %s' % orientation elif histtype.startswith('step'): x = np.zeros( 2*len(bins), np.float ) y = np.zeros( 2*len(bins), np.float ) x[0::2], x[1::2] = bins, bins if align == 'left' or align == 'center': x -= 0.5*(bins[1]-bins[0]) elif align == 'right': x += 0.5*(bins[1]-bins[0]) elif align != 'mid' and align != 'edge': raise ValueError, 'invalid align: %s' % align if log: y[0],y[-1] = 1e-100, 1e-100 if orientation == 'horizontal': self.set_xscale('log') elif orientation == 'vertical': self.set_yscale('log') fill = False if histtype == 'stepfilled': fill = True elif histtype != 'step': raise ValueError, 'invalid histtype: %s' % histtype for m in n: y[1:-1:2], y[2::2] = m, m if orientation == 'horizontal': x,y = y,x elif orientation != 'vertical': raise ValueError, 'invalid orientation: %s' % orientation color = self._get_lines._get_next_cycle_color() if fill: patches.append( self.fill(x, y, closed=False, facecolor=color) ) else: patches.append( self.fill(x, y, closed=False, edgecolor=color, fill=False) ) # adopted from adjust_x/ylim part of the bar method if orientation == 'horizontal': xmin, xmax = 0, self.dataLim.intervalx[1] for m in n: xmin = np.amin(m[m!=0]) # filter out the 0 height bins xmin = max(xmin*0.9, 1e-100) self.dataLim.intervalx = (xmin, xmax) elif orientation == 'vertical': ymin, ymax = 0, self.dataLim.intervaly[1] for m in n: ymin = np.amin(m[m!=0]) # filter out the 0 height bins ymin = max(ymin*0.9, 1e-100) self.dataLim.intervaly = (ymin, ymax) self.autoscale_view() else: raise ValueError, 'invalid histtype: %s' % histtype label = kwargs.pop('label', '') for patch in patches: for p in patch: p.update(kwargs) p.set_label(label) label = '_nolegend_' if binsgiven: self.set_autoscale_on(False) if orientation == 'vertical': self.autoscale_view(scalex=False, scaley=True) XL = self.xaxis.get_major_locator().view_limits(bins[0], bins[-1]) self.set_xbound(XL) else: self.autoscale_view(scalex=True, scaley=False) YL = self.yaxis.get_major_locator().view_limits(bins[0], bins[-1]) self.set_ybound(YL) if len(n)==1: return n[0], bins, cbook.silent_list('Patch', patches[0]) else: return n, bins, cbook.silent_list('Lists of Patches', patches) hist.__doc__ = cbook.dedent(hist.__doc__) % martist.kwdocd def psd(self, x, NFFT=256, Fs=2, Fc=0, detrend=mlab.detrend_none, window=mlab.window_hanning, noverlap=0, pad_to=None, sides='default', scale_by_freq=None, **kwargs): """ call signature:: psd(x, NFFT=256, Fs=2, Fc=0, detrend=mlab.detrend_none, window=mlab.window_hanning, noverlap=0, pad_to=None, sides='default', scale_by_freq=None, **kwargs) The power spectral density by Welch's average periodogram method. The vector *x* is divided into *NFFT* length segments. Each segment is detrended by function *detrend* and windowed by function *window*. *noverlap* gives the length of the overlap between segments. The :math:`|\mathrm{fft}(i)|^2` of each segment :math:`i` are averaged to compute *Pxx*, with a scaling to correct for power loss due to windowing. *Fs* is the sampling frequency. %(PSD)s *Fc*: integer The center frequency of *x* (defaults to 0), which offsets the x extents of the plot to reflect the frequency range used when a signal is acquired and then filtered and downsampled to baseband. Returns the tuple (*Pxx*, *freqs*). For plotting, the power is plotted as :math:`10\log_{10}(P_{xx})` for decibels, though *Pxx* itself is returned. References: Bendat & Piersol -- Random Data: Analysis and Measurement Procedures, John Wiley & Sons (1986) kwargs control the :class:`~matplotlib.lines.Line2D` properties: %(Line2D)s **Example:** .. plot:: mpl_examples/pylab_examples/psd_demo.py """ if not self._hold: self.cla() pxx, freqs = mlab.psd(x, NFFT, Fs, detrend, window, noverlap, pad_to, sides, scale_by_freq) pxx.shape = len(freqs), freqs += Fc if scale_by_freq in (None, True): psd_units = 'dB/Hz' else: psd_units = 'dB' self.plot(freqs, 10*np.log10(pxx), **kwargs) self.set_xlabel('Frequency') self.set_ylabel('Power Spectral Density (%s)' % psd_units) self.grid(True) vmin, vmax = self.viewLim.intervaly intv = vmax-vmin logi = int(np.log10(intv)) if logi==0: logi=.1 step = 10*logi #print vmin, vmax, step, intv, math.floor(vmin), math.ceil(vmax)+1 ticks = np.arange(math.floor(vmin), math.ceil(vmax)+1, step) self.set_yticks(ticks) return pxx, freqs psd_doc_dict = dict() psd_doc_dict.update(martist.kwdocd) psd_doc_dict.update(mlab.kwdocd) psd_doc_dict['PSD'] = cbook.dedent(psd_doc_dict['PSD']) psd.__doc__ = cbook.dedent(psd.__doc__) % psd_doc_dict def csd(self, x, y, NFFT=256, Fs=2, Fc=0, detrend=mlab.detrend_none, window=mlab.window_hanning, noverlap=0, pad_to=None, sides='default', scale_by_freq=None, **kwargs): """ call signature:: csd(x, y, NFFT=256, Fs=2, Fc=0, detrend=mlab.detrend_none, window=mlab.window_hanning, noverlap=0, pad_to=None, sides='default', scale_by_freq=None, **kwargs) The cross spectral density :math:`P_{xy}` by Welch's average periodogram method. The vectors *x* and *y* are divided into *NFFT* length segments. Each segment is detrended by function *detrend* and windowed by function *window*. The product of the direct FFTs of *x* and *y* are averaged over each segment to compute :math:`P_{xy}`, with a scaling to correct for power loss due to windowing. Returns the tuple (*Pxy*, *freqs*). *P* is the cross spectrum (complex valued), and :math:`10\log_{10}|P_{xy}|` is plotted. %(PSD)s *Fc*: integer The center frequency of *x* (defaults to 0), which offsets the x extents of the plot to reflect the frequency range used when a signal is acquired and then filtered and downsampled to baseband. References: Bendat & Piersol -- Random Data: Analysis and Measurement Procedures, John Wiley & Sons (1986) kwargs control the Line2D properties: %(Line2D)s **Example:** .. plot:: mpl_examples/pylab_examples/csd_demo.py .. seealso: :meth:`psd` For a description of the optional parameters. """ if not self._hold: self.cla() pxy, freqs = mlab.csd(x, y, NFFT, Fs, detrend, window, noverlap, pad_to, sides, scale_by_freq) pxy.shape = len(freqs), # pxy is complex freqs += Fc self.plot(freqs, 10*np.log10(np.absolute(pxy)), **kwargs) self.set_xlabel('Frequency') self.set_ylabel('Cross Spectrum Magnitude (dB)') self.grid(True) vmin, vmax = self.viewLim.intervaly intv = vmax-vmin step = 10*int(np.log10(intv)) ticks = np.arange(math.floor(vmin), math.ceil(vmax)+1, step) self.set_yticks(ticks) return pxy, freqs csd.__doc__ = cbook.dedent(csd.__doc__) % psd_doc_dict def cohere(self, x, y, NFFT=256, Fs=2, Fc=0, detrend=mlab.detrend_none, window=mlab.window_hanning, noverlap=0, pad_to=None, sides='default', scale_by_freq=None, **kwargs): """ call signature:: cohere(x, y, NFFT=256, Fs=2, Fc=0, detrend = mlab.detrend_none, window = mlab.window_hanning, noverlap=0, pad_to=None, sides='default', scale_by_freq=None, **kwargs) cohere the coherence between *x* and *y*. Coherence is the normalized cross spectral density: .. math:: C_{xy} = \\frac{|P_{xy}|^2}{P_{xx}P_{yy}} %(PSD)s *Fc*: integer The center frequency of *x* (defaults to 0), which offsets the x extents of the plot to reflect the frequency range used when a signal is acquired and then filtered and downsampled to baseband. The return value is a tuple (*Cxy*, *f*), where *f* are the frequencies of the coherence vector. kwargs are applied to the lines. References: * Bendat & Piersol -- Random Data: Analysis and Measurement Procedures, John Wiley & Sons (1986) kwargs control the :class:`~matplotlib.lines.Line2D` properties of the coherence plot: %(Line2D)s **Example:** .. plot:: mpl_examples/pylab_examples/cohere_demo.py """ if not self._hold: self.cla() cxy, freqs = mlab.cohere(x, y, NFFT, Fs, detrend, window, noverlap, scale_by_freq) freqs += Fc self.plot(freqs, cxy, **kwargs) self.set_xlabel('Frequency') self.set_ylabel('Coherence') self.grid(True) return cxy, freqs cohere.__doc__ = cbook.dedent(cohere.__doc__) % psd_doc_dict def specgram(self, x, NFFT=256, Fs=2, Fc=0, detrend=mlab.detrend_none, window=mlab.window_hanning, noverlap=128, cmap=None, xextent=None, pad_to=None, sides='default', scale_by_freq=None): """ call signature:: specgram(x, NFFT=256, Fs=2, Fc=0, detrend=mlab.detrend_none, window=mlab.window_hanning, noverlap=128, cmap=None, xextent=None, pad_to=None, sides='default', scale_by_freq=None) Compute a spectrogram of data in *x*. Data are split into *NFFT* length segments and the PSD of each section is computed. The windowing function *window* is applied to each segment, and the amount of overlap of each segment is specified with *noverlap*. %(PSD)s *Fc*: integer The center frequency of *x* (defaults to 0), which offsets the y extents of the plot to reflect the frequency range used when a signal is acquired and then filtered and downsampled to baseband. *cmap*: A :class:`matplotlib.cm.Colormap` instance; if *None* use default determined by rc *xextent*: The image extent along the x-axis. xextent = (xmin,xmax) The default is (0,max(bins)), where bins is the return value from :func:`mlab.specgram` Return value is (*Pxx*, *freqs*, *bins*, *im*): - *bins* are the time points the spectrogram is calculated over - *freqs* is an array of frequencies - *Pxx* is a len(times) x len(freqs) array of power - *im* is a :class:`matplotlib.image.AxesImage` instance Note: If *x* is real (i.e. non-complex), only the positive spectrum is shown. If *x* is complex, both positive and negative parts of the spectrum are shown. This can be overridden using the *sides* keyword argument. **Example:** .. plot:: mpl_examples/pylab_examples/specgram_demo.py """ if not self._hold: self.cla() Pxx, freqs, bins = mlab.specgram(x, NFFT, Fs, detrend, window, noverlap, pad_to, sides, scale_by_freq) Z = 10. * np.log10(Pxx) Z = np.flipud(Z) if xextent is None: xextent = 0, np.amax(bins) xmin, xmax = xextent freqs += Fc extent = xmin, xmax, freqs[0], freqs[-1] im = self.imshow(Z, cmap, extent=extent) self.axis('auto') return Pxx, freqs, bins, im specgram.__doc__ = cbook.dedent(specgram.__doc__) % psd_doc_dict del psd_doc_dict #So that this does not become an Axes attribute def spy(self, Z, precision=0, marker=None, markersize=None, aspect='equal', **kwargs): """ call signature:: spy(Z, precision=0, marker=None, markersize=None, aspect='equal', **kwargs) ``spy(Z)`` plots the sparsity pattern of the 2-D array *Z*. If *precision* is 0, any non-zero value will be plotted; else, values of :math:`|Z| > precision` will be plotted. For :class:`scipy.sparse.spmatrix` instances, there is a special case: if *precision* is 'present', any value present in the array will be plotted, even if it is identically zero. The array will be plotted as it would be printed, with the first index (row) increasing down and the second index (column) increasing to the right. By default aspect is 'equal', so that each array element occupies a square space; set the aspect kwarg to 'auto' to allow the plot to fill the plot box, or to any scalar number to specify the aspect ratio of an array element directly. Two plotting styles are available: image or marker. Both are available for full arrays, but only the marker style works for :class:`scipy.sparse.spmatrix` instances. If *marker* and *markersize* are *None*, an image will be returned and any remaining kwargs are passed to :func:`~matplotlib.pyplot.imshow`; else, a :class:`~matplotlib.lines.Line2D` object will be returned with the value of marker determining the marker type, and any remaining kwargs passed to the :meth:`~matplotlib.axes.Axes.plot` method. If *marker* and *markersize* are *None*, useful kwargs include: * *cmap* * *alpha* .. seealso:: :func:`~matplotlib.pyplot.imshow` For controlling colors, e.g. cyan background and red marks, use:: cmap = mcolors.ListedColormap(['c','r']) If *marker* or *markersize* is not *None*, useful kwargs include: * *marker* * *markersize* * *color* Useful values for *marker* include: * 's' square (default) * 'o' circle * '.' point * ',' pixel .. seealso:: :func:`~matplotlib.pyplot.plot` """ if precision is None: precision = 0 warnings.DeprecationWarning("Use precision=0 instead of None") # 2008/10/03 if marker is None and markersize is None and hasattr(Z, 'tocoo'): marker = 's' if marker is None and markersize is None: Z = np.asarray(Z) mask = np.absolute(Z)>precision if 'cmap' not in kwargs: kwargs['cmap'] = mcolors.ListedColormap(['w', 'k'], name='binary') nr, nc = Z.shape extent = [-0.5, nc-0.5, nr-0.5, -0.5] ret = self.imshow(mask, interpolation='nearest', aspect=aspect, extent=extent, origin='upper', **kwargs) else: if hasattr(Z, 'tocoo'): c = Z.tocoo() if precision == 'present': y = c.row x = c.col else: nonzero = np.absolute(c.data) > precision y = c.row[nonzero] x = c.col[nonzero] else: Z = np.asarray(Z) nonzero = np.absolute(Z)>precision y, x = np.nonzero(nonzero) if marker is None: marker = 's' if markersize is None: markersize = 10 marks = mlines.Line2D(x, y, linestyle='None', marker=marker, markersize=markersize, **kwargs) self.add_line(marks) nr, nc = Z.shape self.set_xlim(xmin=-0.5, xmax=nc-0.5) self.set_ylim(ymin=nr-0.5, ymax=-0.5) self.set_aspect(aspect) ret = marks self.title.set_y(1.05) self.xaxis.tick_top() self.xaxis.set_ticks_position('both') self.xaxis.set_major_locator(mticker.MaxNLocator(nbins=9, steps=[1, 2, 5, 10], integer=True)) self.yaxis.set_major_locator(mticker.MaxNLocator(nbins=9, steps=[1, 2, 5, 10], integer=True)) return ret def matshow(self, Z, **kwargs): ''' Plot a matrix or array as an image. The matrix will be shown the way it would be printed, with the first row at the top. Row and column numbering is zero-based. Argument: *Z* anything that can be interpreted as a 2-D array kwargs all are passed to :meth:`~matplotlib.axes.Axes.imshow`. :meth:`matshow` sets defaults for *extent*, *origin*, *interpolation*, and *aspect*; use care in overriding the *extent* and *origin* kwargs, because they interact. (Also, if you want to change them, you probably should be using imshow directly in your own version of matshow.) Returns: an :class:`matplotlib.image.AxesImage` instance. ''' Z = np.asarray(Z) nr, nc = Z.shape extent = [-0.5, nc-0.5, nr-0.5, -0.5] kw = {'extent': extent, 'origin': 'upper', 'interpolation': 'nearest', 'aspect': 'equal'} # (already the imshow default) kw.update(kwargs) im = self.imshow(Z, **kw) self.title.set_y(1.05) self.xaxis.tick_top() self.xaxis.set_ticks_position('both') self.xaxis.set_major_locator(mticker.MaxNLocator(nbins=9, steps=[1, 2, 5, 10], integer=True)) self.yaxis.set_major_locator(mticker.MaxNLocator(nbins=9, steps=[1, 2, 5, 10], integer=True)) return im class SubplotBase: """ Base class for subplots, which are :class:`Axes` instances with additional methods to facilitate generating and manipulating a set of :class:`Axes` within a figure. """ def __init__(self, fig, *args, **kwargs): """ *fig* is a :class:`matplotlib.figure.Figure` instance. *args* is the tuple (*numRows*, *numCols*, *plotNum*), where the array of subplots in the figure has dimensions *numRows*, *numCols*, and where *plotNum* is the number of the subplot being created. *plotNum* starts at 1 in the upper left corner and increases to the right. If *numRows* <= *numCols* <= *plotNum* < 10, *args* can be the decimal integer *numRows* * 100 + *numCols* * 10 + *plotNum*. """ self.figure = fig if len(args)==1: s = str(args[0]) if len(s) != 3: raise ValueError('Argument to subplot must be a 3 digits long') rows, cols, num = map(int, s) elif len(args)==3: rows, cols, num = args else: raise ValueError( 'Illegal argument to subplot') total = rows*cols num -= 1 # convert from matlab to python indexing # ie num in range(0,total) if num >= total: raise ValueError( 'Subplot number exceeds total subplots') self._rows = rows self._cols = cols self._num = num self.update_params() # _axes_class is set in the subplot_class_factory self._axes_class.__init__(self, fig, self.figbox, **kwargs) def get_geometry(self): 'get the subplot geometry, eg 2,2,3' return self._rows, self._cols, self._num+1 # COVERAGE NOTE: Never used internally or from examples def change_geometry(self, numrows, numcols, num): 'change subplot geometry, eg. from 1,1,1 to 2,2,3' self._rows = numrows self._cols = numcols self._num = num-1 self.update_params() self.set_position(self.figbox) def update_params(self): 'update the subplot position from fig.subplotpars' rows = self._rows cols = self._cols num = self._num pars = self.figure.subplotpars left = pars.left right = pars.right bottom = pars.bottom top = pars.top wspace = pars.wspace hspace = pars.hspace totWidth = right-left totHeight = top-bottom figH = totHeight/(rows + hspace*(rows-1)) sepH = hspace*figH figW = totWidth/(cols + wspace*(cols-1)) sepW = wspace*figW rowNum, colNum = divmod(num, cols) figBottom = top - (rowNum+1)*figH - rowNum*sepH figLeft = left + colNum*(figW + sepW) self.figbox = mtransforms.Bbox.from_bounds(figLeft, figBottom, figW, figH) self.rowNum = rowNum self.colNum = colNum self.numRows = rows self.numCols = cols if 0: print 'rcn', rows, cols, num print 'lbrt', left, bottom, right, top print 'self.figBottom', self.figBottom print 'self.figLeft', self.figLeft print 'self.figW', self.figW print 'self.figH', self.figH print 'self.rowNum', self.rowNum print 'self.colNum', self.colNum print 'self.numRows', self.numRows print 'self.numCols', self.numCols def is_first_col(self): return self.colNum==0 def is_first_row(self): return self.rowNum==0 def is_last_row(self): return self.rowNum==self.numRows-1 def is_last_col(self): return self.colNum==self.numCols-1 # COVERAGE NOTE: Never used internally or from examples def label_outer(self): """ set the visible property on ticklabels so xticklabels are visible only if the subplot is in the last row and yticklabels are visible only if the subplot is in the first column """ lastrow = self.is_last_row() firstcol = self.is_first_col() for label in self.get_xticklabels(): label.set_visible(lastrow) for label in self.get_yticklabels(): label.set_visible(firstcol) _subplot_classes = {} def subplot_class_factory(axes_class=None): # This makes a new class that inherits from SubclassBase and the # given axes_class (which is assumed to be a subclass of Axes). # This is perhaps a little bit roundabout to make a new class on # the fly like this, but it means that a new Subplot class does # not have to be created for every type of Axes. if axes_class is None: axes_class = Axes new_class = _subplot_classes.get(axes_class) if new_class is None: new_class = new.classobj("%sSubplot" % (axes_class.__name__), (SubplotBase, axes_class), {'_axes_class': axes_class}) _subplot_classes[axes_class] = new_class return new_class # This is provided for backward compatibility Subplot = subplot_class_factory() martist.kwdocd['Axes'] = martist.kwdocd['Subplot'] = martist.kwdoc(Axes) """ # this is some discarded code I was using to find the minimum positive # data point for some log scaling fixes. I realized there was a # cleaner way to do it, but am keeping this around as an example for # how to get the data out of the axes. Might want to make something # like this a method one day, or better yet make get_verts an Artist # method minx, maxx = self.get_xlim() if minx<=0 or maxx<=0: # find the min pos value in the data xs = [] for line in self.lines: xs.extend(line.get_xdata(orig=False)) for patch in self.patches: xs.extend([x for x,y in patch.get_verts()]) for collection in self.collections: xs.extend([x for x,y in collection.get_verts()]) posx = [x for x in xs if x>0] if len(posx): minx = min(posx) maxx = max(posx) # warning, probably breaks inverted axis self.set_xlim((0.1*minx, maxx)) """

259,904

Python

.py

5,861

32.439515

83

0.546114

numenta/nupic-legacy

6,330

1,556

464

AGPL-3.0

9/5/2024, 5:13:42 PM (Europe/Amsterdam)

26,248

dviread.py

numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/dviread.py

""" An experimental module for reading dvi files output by TeX. Several limitations make this not (currently) useful as a general-purpose dvi preprocessor. Interface:: dvi = Dvi(filename, 72) for page in dvi: # iterate over pages w, h, d = page.width, page.height, page.descent for x,y,font,glyph,width in page.text: fontname = font.texname pointsize = font.size ... for x,y,height,width in page.boxes: ... """ import errno import matplotlib import matplotlib.cbook as mpl_cbook import numpy as np import struct import subprocess _dvistate = mpl_cbook.Bunch(pre=0, outer=1, inpage=2, post_post=3, finale=4) class Dvi(object): """ A dvi ("device-independent") file, as produced by TeX. The current implementation only reads the first page and does not even attempt to verify the postamble. """ def __init__(self, filename, dpi): """ Initialize the object. This takes the filename as input and opens the file; actually reading the file happens when iterating through the pages of the file. """ matplotlib.verbose.report('Dvi: ' + filename, 'debug') self.file = open(filename, 'rb') self.dpi = dpi self.fonts = {} self.state = _dvistate.pre def __iter__(self): """ Iterate through the pages of the file. Returns (text, pages) pairs, where: text is a list of (x, y, fontnum, glyphnum, width) tuples boxes is a list of (x, y, height, width) tuples The coordinates are transformed into a standard Cartesian coordinate system at the dpi value given when initializing. The coordinates are floating point numbers, but otherwise precision is not lost and coordinate values are not clipped to integers. """ while True: have_page = self._read() if have_page: yield self._output() else: break def close(self): """ Close the underlying file if it is open. """ if not self.file.closed: self.file.close() def _output(self): """ Output the text and boxes belonging to the most recent page. page = dvi._output() """ minx, miny, maxx, maxy = np.inf, np.inf, -np.inf, -np.inf maxy_pure = -np.inf for elt in self.text + self.boxes: if len(elt) == 4: # box x,y,h,w = elt e = 0 # zero depth else: # glyph x,y,font,g,w = elt h = _mul2012(font._scale, font._tfm.height[g]) e = _mul2012(font._scale, font._tfm.depth[g]) minx = min(minx, x) miny = min(miny, y - h) maxx = max(maxx, x + w) maxy = max(maxy, y + e) maxy_pure = max(maxy_pure, y) if self.dpi is None: # special case for ease of debugging: output raw dvi coordinates return mpl_cbook.Bunch(text=self.text, boxes=self.boxes, width=maxx-minx, height=maxy_pure-miny, descent=maxy-maxy_pure) d = self.dpi / (72.27 * 2**16) # from TeX's "scaled points" to dpi units text = [ ((x-minx)*d, (maxy-y)*d, f, g, w*d) for (x,y,f,g,w) in self.text ] boxes = [ ((x-minx)*d, (maxy-y)*d, h*d, w*d) for (x,y,h,w) in self.boxes ] return mpl_cbook.Bunch(text=text, boxes=boxes, width=(maxx-minx)*d, height=(maxy_pure-miny)*d, descent=(maxy-maxy_pure)*d) def _read(self): """ Read one page from the file. Return True if successful, False if there were no more pages. """ while True: byte = ord(self.file.read(1)) self._dispatch(byte) # if self.state == _dvistate.inpage: # matplotlib.verbose.report( # 'Dvi._read: after %d at %f,%f' % # (byte, self.h, self.v), # 'debug-annoying') if byte == 140: # end of page return True if self.state == _dvistate.post_post: # end of file self.close() return False def _arg(self, nbytes, signed=False): """ Read and return an integer argument "nbytes" long. Signedness is determined by the "signed" keyword. """ str = self.file.read(nbytes) value = ord(str[0]) if signed and value >= 0x80: value = value - 0x100 for i in range(1, nbytes): value = 0x100*value + ord(str[i]) return value def _dispatch(self, byte): """ Based on the opcode "byte", read the correct kinds of arguments from the dvi file and call the method implementing that opcode with those arguments. """ if 0 <= byte <= 127: self._set_char(byte) elif byte == 128: self._set_char(self._arg(1)) elif byte == 129: self._set_char(self._arg(2)) elif byte == 130: self._set_char(self._arg(3)) elif byte == 131: self._set_char(self._arg(4, True)) elif byte == 132: self._set_rule(self._arg(4, True), self._arg(4, True)) elif byte == 133: self._put_char(self._arg(1)) elif byte == 134: self._put_char(self._arg(2)) elif byte == 135: self._put_char(self._arg(3)) elif byte == 136: self._put_char(self._arg(4, True)) elif byte == 137: self._put_rule(self._arg(4, True), self._arg(4, True)) elif byte == 138: self._nop() elif byte == 139: self._bop(*[self._arg(4, True) for i in range(11)]) elif byte == 140: self._eop() elif byte == 141: self._push() elif byte == 142: self._pop() elif byte == 143: self._right(self._arg(1, True)) elif byte == 144: self._right(self._arg(2, True)) elif byte == 145: self._right(self._arg(3, True)) elif byte == 146: self._right(self._arg(4, True)) elif byte == 147: self._right_w(None) elif byte == 148: self._right_w(self._arg(1, True)) elif byte == 149: self._right_w(self._arg(2, True)) elif byte == 150: self._right_w(self._arg(3, True)) elif byte == 151: self._right_w(self._arg(4, True)) elif byte == 152: self._right_x(None) elif byte == 153: self._right_x(self._arg(1, True)) elif byte == 154: self._right_x(self._arg(2, True)) elif byte == 155: self._right_x(self._arg(3, True)) elif byte == 156: self._right_x(self._arg(4, True)) elif byte == 157: self._down(self._arg(1, True)) elif byte == 158: self._down(self._arg(2, True)) elif byte == 159: self._down(self._arg(3, True)) elif byte == 160: self._down(self._arg(4, True)) elif byte == 161: self._down_y(None) elif byte == 162: self._down_y(self._arg(1, True)) elif byte == 163: self._down_y(self._arg(2, True)) elif byte == 164: self._down_y(self._arg(3, True)) elif byte == 165: self._down_y(self._arg(4, True)) elif byte == 166: self._down_z(None) elif byte == 167: self._down_z(self._arg(1, True)) elif byte == 168: self._down_z(self._arg(2, True)) elif byte == 169: self._down_z(self._arg(3, True)) elif byte == 170: self._down_z(self._arg(4, True)) elif 171 <= byte <= 234: self._fnt_num(byte-171) elif byte == 235: self._fnt_num(self._arg(1)) elif byte == 236: self._fnt_num(self._arg(2)) elif byte == 237: self._fnt_num(self._arg(3)) elif byte == 238: self._fnt_num(self._arg(4, True)) elif 239 <= byte <= 242: len = self._arg(byte-238) special = self.file.read(len) self._xxx(special) elif 243 <= byte <= 246: k = self._arg(byte-242, byte==246) c, s, d, a, l = [ self._arg(x) for x in (4, 4, 4, 1, 1) ] n = self.file.read(a+l) self._fnt_def(k, c, s, d, a, l, n) elif byte == 247: i, num, den, mag, k = [ self._arg(x) for x in (1, 4, 4, 4, 1) ] x = self.file.read(k) self._pre(i, num, den, mag, x) elif byte == 248: self._post() elif byte == 249: self._post_post() else: raise ValueError, "unknown command: byte %d"%byte def _pre(self, i, num, den, mag, comment): if self.state != _dvistate.pre: raise ValueError, "pre command in middle of dvi file" if i != 2: raise ValueError, "Unknown dvi format %d"%i if num != 25400000 or den != 7227 * 2**16: raise ValueError, "nonstandard units in dvi file" # meaning: TeX always uses those exact values, so it # should be enough for us to support those # (There are 72.27 pt to an inch so 7227 pt = # 7227 * 2**16 sp to 100 in. The numerator is multiplied # by 10^5 to get units of 10**-7 meters.) if mag != 1000: raise ValueError, "nonstandard magnification in dvi file" # meaning: LaTeX seems to frown on setting \mag, so # I think we can assume this is constant self.state = _dvistate.outer def _set_char(self, char): if self.state != _dvistate.inpage: raise ValueError, "misplaced set_char in dvi file" self._put_char(char) self.h += self.fonts[self.f]._width_of(char) def _set_rule(self, a, b): if self.state != _dvistate.inpage: raise ValueError, "misplaced set_rule in dvi file" self._put_rule(a, b) self.h += b def _put_char(self, char): if self.state != _dvistate.inpage: raise ValueError, "misplaced put_char in dvi file" font = self.fonts[self.f] if font._vf is None: self.text.append((self.h, self.v, font, char, font._width_of(char))) # matplotlib.verbose.report( # 'Dvi._put_char: %d,%d %d' %(self.h, self.v, char), # 'debug-annoying') else: scale = font._scale for x, y, f, g, w in font._vf[char].text: newf = DviFont(scale=_mul2012(scale, f._scale), tfm=f._tfm, texname=f.texname, vf=f._vf) self.text.append((self.h + _mul2012(x, scale), self.v + _mul2012(y, scale), newf, g, newf._width_of(g))) self.boxes.extend([(self.h + _mul2012(x, scale), self.v + _mul2012(y, scale), _mul2012(a, scale), _mul2012(b, scale)) for x, y, a, b in font._vf[char].boxes]) def _put_rule(self, a, b): if self.state != _dvistate.inpage: raise ValueError, "misplaced put_rule in dvi file" if a > 0 and b > 0: self.boxes.append((self.h, self.v, a, b)) # matplotlib.verbose.report( # 'Dvi._put_rule: %d,%d %d,%d' % (self.h, self.v, a, b), # 'debug-annoying') def _nop(self): pass def _bop(self, c0, c1, c2, c3, c4, c5, c6, c7, c8, c9, p): if self.state != _dvistate.outer: raise ValueError, \ "misplaced bop in dvi file (state %d)" % self.state self.state = _dvistate.inpage self.h, self.v, self.w, self.x, self.y, self.z = 0, 0, 0, 0, 0, 0 self.stack = [] self.text = [] # list of (x,y,fontnum,glyphnum) self.boxes = [] # list of (x,y,width,height) def _eop(self): if self.state != _dvistate.inpage: raise ValueError, "misplaced eop in dvi file" self.state = _dvistate.outer del self.h, self.v, self.w, self.x, self.y, self.z, self.stack def _push(self): if self.state != _dvistate.inpage: raise ValueError, "misplaced push in dvi file" self.stack.append((self.h, self.v, self.w, self.x, self.y, self.z)) def _pop(self): if self.state != _dvistate.inpage: raise ValueError, "misplaced pop in dvi file" self.h, self.v, self.w, self.x, self.y, self.z = self.stack.pop() def _right(self, b): if self.state != _dvistate.inpage: raise ValueError, "misplaced right in dvi file" self.h += b def _right_w(self, new_w): if self.state != _dvistate.inpage: raise ValueError, "misplaced w in dvi file" if new_w is not None: self.w = new_w self.h += self.w def _right_x(self, new_x): if self.state != _dvistate.inpage: raise ValueError, "misplaced x in dvi file" if new_x is not None: self.x = new_x self.h += self.x def _down(self, a): if self.state != _dvistate.inpage: raise ValueError, "misplaced down in dvi file" self.v += a def _down_y(self, new_y): if self.state != _dvistate.inpage: raise ValueError, "misplaced y in dvi file" if new_y is not None: self.y = new_y self.v += self.y def _down_z(self, new_z): if self.state != _dvistate.inpage: raise ValueError, "misplaced z in dvi file" if new_z is not None: self.z = new_z self.v += self.z def _fnt_num(self, k): if self.state != _dvistate.inpage: raise ValueError, "misplaced fnt_num in dvi file" self.f = k def _xxx(self, special): matplotlib.verbose.report( 'Dvi._xxx: encountered special: %s' % ''.join([(32 <= ord(ch) < 127) and ch or '<%02x>' % ord(ch) for ch in special]), 'debug') def _fnt_def(self, k, c, s, d, a, l, n): tfm = _tfmfile(n[-l:]) if c != 0 and tfm.checksum != 0 and c != tfm.checksum: raise ValueError, 'tfm checksum mismatch: %s'%n # It seems that the assumption behind the following check is incorrect: #if d != tfm.design_size: # raise ValueError, 'tfm design size mismatch: %d in dvi, %d in %s'%\ # (d, tfm.design_size, n) vf = _vffile(n[-l:]) self.fonts[k] = DviFont(scale=s, tfm=tfm, texname=n, vf=vf) def _post(self): if self.state != _dvistate.outer: raise ValueError, "misplaced post in dvi file" self.state = _dvistate.post_post # TODO: actually read the postamble and finale? # currently post_post just triggers closing the file def _post_post(self): raise NotImplementedError class DviFont(object): """ Object that holds a font's texname and size, supports comparison, and knows the widths of glyphs in the same units as the AFM file. There are also internal attributes (for use by dviread.py) that are _not_ used for comparison. The size is in Adobe points (converted from TeX points). """ __slots__ = ('texname', 'size', 'widths', '_scale', '_vf', '_tfm') def __init__(self, scale, tfm, texname, vf): self._scale, self._tfm, self.texname, self._vf = \ scale, tfm, texname, vf self.size = scale * (72.0 / (72.27 * 2**16)) try: nchars = max(tfm.width.iterkeys()) except ValueError: nchars = 0 self.widths = [ (1000*tfm.width.get(char, 0)) >> 20 for char in range(nchars) ] def __eq__(self, other): return self.__class__ == other.__class__ and \ self.texname == other.texname and self.size == other.size def __ne__(self, other): return not self.__eq__(other) def _width_of(self, char): """ Width of char in dvi units. For internal use by dviread.py. """ width = self._tfm.width.get(char, None) if width is not None: return _mul2012(width, self._scale) matplotlib.verbose.report( 'No width for char %d in font %s' % (char, self.texname), 'debug') return 0 class Vf(Dvi): """ A virtual font (\*.vf file) containing subroutines for dvi files. Usage:: vf = Vf(filename) glyph = vf[code] glyph.text, glyph.boxes, glyph.width """ def __init__(self, filename): Dvi.__init__(self, filename, 0) self._first_font = None self._chars = {} self._packet_ends = None self._read() self.close() def __getitem__(self, code): return self._chars[code] def _dispatch(self, byte): # If we are in a packet, execute the dvi instructions if self.state == _dvistate.inpage: byte_at = self.file.tell()-1 if byte_at == self._packet_ends: self._finalize_packet() # fall through elif byte_at > self._packet_ends: raise ValueError, "Packet length mismatch in vf file" else: if byte in (139, 140) or byte >= 243: raise ValueError, "Inappropriate opcode %d in vf file" % byte Dvi._dispatch(self, byte) return # We are outside a packet if byte < 242: # a short packet (length given by byte) cc, tfm = self._arg(1), self._arg(3) self._init_packet(byte, cc, tfm) elif byte == 242: # a long packet pl, cc, tfm = [ self._arg(x) for x in (4, 4, 4) ] self._init_packet(pl, cc, tfm) elif 243 <= byte <= 246: Dvi._dispatch(self, byte) elif byte == 247: # preamble i, k = self._arg(1), self._arg(1) x = self.file.read(k) cs, ds = self._arg(4), self._arg(4) self._pre(i, x, cs, ds) elif byte == 248: # postamble (just some number of 248s) self.state = _dvistate.post_post else: raise ValueError, "unknown vf opcode %d" % byte def _init_packet(self, pl, cc, tfm): if self.state != _dvistate.outer: raise ValueError, "Misplaced packet in vf file" self.state = _dvistate.inpage self._packet_ends = self.file.tell() + pl self._packet_char = cc self._packet_width = tfm self.h, self.v, self.w, self.x, self.y, self.z = 0, 0, 0, 0, 0, 0 self.stack, self.text, self.boxes = [], [], [] self.f = self._first_font def _finalize_packet(self): self._chars[self._packet_char] = mpl_cbook.Bunch( text=self.text, boxes=self.boxes, width = self._packet_width) self.state = _dvistate.outer def _pre(self, i, x, cs, ds): if self.state != _dvistate.pre: raise ValueError, "pre command in middle of vf file" if i != 202: raise ValueError, "Unknown vf format %d" % i if len(x): matplotlib.verbose.report('vf file comment: ' + x, 'debug') self.state = _dvistate.outer # cs = checksum, ds = design size def _fnt_def(self, k, *args): Dvi._fnt_def(self, k, *args) if self._first_font is None: self._first_font = k def _fix2comp(num): """ Convert from two's complement to negative. """ assert 0 <= num < 2**32 if num & 2**31: return num - 2**32 else: return num def _mul2012(num1, num2): """ Multiply two numbers in 20.12 fixed point format. """ # Separated into a function because >> has surprising precedence return (num1*num2) >> 20 class Tfm(object): """ A TeX Font Metric file. This implementation covers only the bare minimum needed by the Dvi class. Attributes: checksum: for verifying against dvi file design_size: design size of the font (in what units?) width[i]: width of character \#i, needs to be scaled by the factor specified in the dvi file (this is a dict because indexing may not start from 0) height[i], depth[i]: height and depth of character \#i """ __slots__ = ('checksum', 'design_size', 'width', 'height', 'depth') def __init__(self, filename): matplotlib.verbose.report('opening tfm file ' + filename, 'debug') file = open(filename, 'rb') try: header1 = file.read(24) lh, bc, ec, nw, nh, nd = \ struct.unpack('!6H', header1[2:14]) matplotlib.verbose.report( 'lh=%d, bc=%d, ec=%d, nw=%d, nh=%d, nd=%d' % ( lh, bc, ec, nw, nh, nd), 'debug') header2 = file.read(4*lh) self.checksum, self.design_size = \ struct.unpack('!2I', header2[:8]) # there is also encoding information etc. char_info = file.read(4*(ec-bc+1)) widths = file.read(4*nw) heights = file.read(4*nh) depths = file.read(4*nd) finally: file.close() self.width, self.height, self.depth = {}, {}, {} widths, heights, depths = \ [ struct.unpack('!%dI' % (len(x)/4), x) for x in (widths, heights, depths) ] for i in range(ec-bc): self.width[bc+i] = _fix2comp(widths[ord(char_info[4*i])]) self.height[bc+i] = _fix2comp(heights[ord(char_info[4*i+1]) >> 4]) self.depth[bc+i] = _fix2comp(depths[ord(char_info[4*i+1]) & 0xf]) class PsfontsMap(object): """ A psfonts.map formatted file, mapping TeX fonts to PS fonts. Usage: map = PsfontsMap('.../psfonts.map'); map['cmr10'] For historical reasons, TeX knows many Type-1 fonts by different names than the outside world. (For one thing, the names have to fit in eight characters.) Also, TeX's native fonts are not Type-1 but Metafont, which is nontrivial to convert to PostScript except as a bitmap. While high-quality conversions to Type-1 format exist and are shipped with modern TeX distributions, we need to know which Type-1 fonts are the counterparts of which native fonts. For these reasons a mapping is needed from internal font names to font file names. A texmf tree typically includes mapping files called e.g. psfonts.map, pdftex.map, dvipdfm.map. psfonts.map is used by dvips, pdftex.map by pdfTeX, and dvipdfm.map by dvipdfm. psfonts.map might avoid embedding the 35 PostScript fonts, while the pdf-related files perhaps only avoid the "Base 14" pdf fonts. But the user may have configured these files differently. """ __slots__ = ('_font',) def __init__(self, filename): self._font = {} file = open(filename, 'rt') try: self._parse(file) finally: file.close() def __getitem__(self, texname): result = self._font[texname] fn, enc = result.filename, result.encoding if fn is not None and not fn.startswith('/'): result.filename = find_tex_file(fn) if enc is not None and not enc.startswith('/'): result.encoding = find_tex_file(result.encoding) return result def _parse(self, file): """Parse each line into words.""" for line in file: line = line.strip() if line == '' or line.startswith('%'): continue words, pos = [], 0 while pos < len(line): if line[pos] == '"': # double quoted word pos += 1 end = line.index('"', pos) words.append(line[pos:end]) pos = end + 1 else: # ordinary word end = line.find(' ', pos+1) if end == -1: end = len(line) words.append(line[pos:end]) pos = end while pos < len(line) and line[pos] == ' ': pos += 1 self._register(words) def _register(self, words): """Register a font described by "words". The format is, AFAIK: texname fontname [effects and filenames] Effects are PostScript snippets like ".177 SlantFont", filenames begin with one or two less-than signs. A filename ending in enc is an encoding file, other filenames are font files. This can be overridden with a left bracket: <[foobar indicates an encoding file named foobar. There is some difference between <foo.pfb and <<bar.pfb in subsetting, but I have no example of << in my TeX installation. """ texname, psname = words[:2] effects, encoding, filename = [], None, None for word in words[2:]: if not word.startswith('<'): effects.append(word) else: word = word.lstrip('<') if word.startswith('['): assert encoding is None encoding = word[1:] elif word.endswith('.enc'): assert encoding is None encoding = word else: assert filename is None filename = word self._font[texname] = mpl_cbook.Bunch( texname=texname, psname=psname, effects=effects, encoding=encoding, filename=filename) class Encoding(object): """ Parses a \*.enc file referenced from a psfonts.map style file. The format this class understands is a very limited subset of PostScript. Usage (subject to change):: for name in Encoding(filename): whatever(name) """ __slots__ = ('encoding',) def __init__(self, filename): file = open(filename, 'rt') try: matplotlib.verbose.report('Parsing TeX encoding ' + filename, 'debug-annoying') self.encoding = self._parse(file) matplotlib.verbose.report('Result: ' + `self.encoding`, 'debug-annoying') finally: file.close() def __iter__(self): for name in self.encoding: yield name def _parse(self, file): result = [] state = 0 for line in file: comment_start = line.find('%') if comment_start > -1: line = line[:comment_start] line = line.strip() if state == 0: # Expecting something like /FooEncoding [ if '[' in line: state = 1 line = line[line.index('[')+1:].strip() if state == 1: if ']' in line: # ] def line = line[:line.index(']')] state = 2 words = line.split() for w in words: if w.startswith('/'): # Allow for /abc/def/ghi subwords = w.split('/') result.extend(subwords[1:]) else: raise ValueError, "Broken name in encoding file: " + w return result def find_tex_file(filename, format=None): """ Call kpsewhich to find a file in the texmf tree. If format is not None, it is used as the value for the --format option. See the kpathsea documentation for more information. Apparently most existing TeX distributions on Unix-like systems use kpathsea. I hear MikTeX (a popular distribution on Windows) doesn't use kpathsea, so what do we do? (TODO) """ cmd = ['kpsewhich'] if format is not None: cmd += ['--format=' + format] cmd += [filename] matplotlib.verbose.report('find_tex_file(%s): %s' \ % (filename,cmd), 'debug') pipe = subprocess.Popen(cmd, stdout=subprocess.PIPE) result = pipe.communicate()[0].rstrip() matplotlib.verbose.report('find_tex_file result: %s' % result, 'debug') return result def _read_nointr(pipe, bufsize=-1): while True: try: return pipe.read(bufsize) except OSError, e: if e.errno == errno.EINTR: continue else: raise # With multiple text objects per figure (e.g. tick labels) we may end # up reading the same tfm and vf files many times, so we implement a # simple cache. TODO: is this worth making persistent? _tfmcache = {} _vfcache = {} def _fontfile(texname, class_, suffix, cache): try: return cache[texname] except KeyError: pass filename = find_tex_file(texname + suffix) if filename: result = class_(filename) else: result = None cache[texname] = result return result def _tfmfile(texname): return _fontfile(texname, Tfm, '.tfm', _tfmcache) def _vffile(texname): return _fontfile(texname, Vf, '.vf', _vfcache) if __name__ == '__main__': import sys matplotlib.verbose.set_level('debug-annoying') fname = sys.argv[1] try: dpi = float(sys.argv[2]) except IndexError: dpi = None dvi = Dvi(fname, dpi) fontmap = PsfontsMap(find_tex_file('pdftex.map')) for page in dvi: print '=== new page ===' fPrev = None for x,y,f,c,w in page.text: if f != fPrev: print 'font', f.texname, 'scaled', f._scale/pow(2.0,20) fPrev = f print x,y,c, 32 <= c < 128 and chr(c) or '.', w for x,y,w,h in page.boxes: print x,y,'BOX',w,h

29,920

Python

.py

709

32.038082

91

0.546954

numenta/nupic-legacy

6,330

1,556

464

AGPL-3.0

9/5/2024, 5:13:42 PM (Europe/Amsterdam)

26,249

afm.py

numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/afm.py

""" This is a python interface to Adobe Font Metrics Files. Although a number of other python implementations exist (and may be more complete than mine) I decided not to go with them because either they were either 1) copyrighted or used a non-BSD compatible license 2) had too many dependencies and I wanted a free standing lib 3) Did more than I needed and it was easier to write my own than figure out how to just get what I needed from theirs It is pretty easy to use, and requires only built-in python libs:: >>> from afm import AFM >>> fh = file('ptmr8a.afm') >>> afm = AFM(fh) >>> afm.string_width_height('What the heck?') (6220.0, 683) >>> afm.get_fontname() 'Times-Roman' >>> afm.get_kern_dist('A', 'f') 0 >>> afm.get_kern_dist('A', 'y') -92.0 >>> afm.get_bbox_char('!') [130, -9, 238, 676] >>> afm.get_bbox_font() [-168, -218, 1000, 898] AUTHOR: John D. Hunter <jdh2358@gmail.com> """ import sys, os, re from _mathtext_data import uni2type1 #Convert string the a python type _to_int = int _to_float = float _to_str = str def _to_list_of_ints(s): s = s.replace(',', ' ') return [_to_int(val) for val in s.split()] def _to_list_of_floats(s): return [_to_float(val) for val in s.split()] def _to_bool(s): if s.lower().strip() in ('false', '0', 'no'): return False else: return True def _sanity_check(fh): """ Check if the file at least looks like AFM. If not, raise :exc:`RuntimeError`. """ # Remember the file position in case the caller wants to # do something else with the file. pos = fh.tell() try: line = fh.readline() finally: fh.seek(pos, 0) # AFM spec, Section 4: The StartFontMetrics keyword [followed by a # version number] must be the first line in the file, and the # EndFontMetrics keyword must be the last non-empty line in the # file. We just check the first line. if not line.startswith('StartFontMetrics'): raise RuntimeError('Not an AFM file') def _parse_header(fh): """ Reads the font metrics header (up to the char metrics) and returns a dictionary mapping *key* to *val*. *val* will be converted to the appropriate python type as necessary; eg: * 'False'->False * '0'->0 * '-168 -218 1000 898'-> [-168, -218, 1000, 898] Dictionary keys are StartFontMetrics, FontName, FullName, FamilyName, Weight, ItalicAngle, IsFixedPitch, FontBBox, UnderlinePosition, UnderlineThickness, Version, Notice, EncodingScheme, CapHeight, XHeight, Ascender, Descender, StartCharMetrics """ headerConverters = { 'StartFontMetrics': _to_float, 'FontName': _to_str, 'FullName': _to_str, 'FamilyName': _to_str, 'Weight': _to_str, 'ItalicAngle': _to_float, 'IsFixedPitch': _to_bool, 'FontBBox': _to_list_of_ints, 'UnderlinePosition': _to_int, 'UnderlineThickness': _to_int, 'Version': _to_str, 'Notice': _to_str, 'EncodingScheme': _to_str, 'CapHeight': _to_float, # Is the second version a mistake, or 'Capheight': _to_float, # do some AFM files contain 'Capheight'? -JKS 'XHeight': _to_float, 'Ascender': _to_float, 'Descender': _to_float, 'StdHW': _to_float, 'StdVW': _to_float, 'StartCharMetrics': _to_int, 'CharacterSet': _to_str, 'Characters': _to_int, } d = {} while 1: line = fh.readline() if not line: break line = line.rstrip() if line.startswith('Comment'): continue lst = line.split( ' ', 1 ) #print '%-s\t%-d line :: %-s' % ( fh.name, len(lst), line ) key = lst[0] if len( lst ) == 2: val = lst[1] else: val = '' #key, val = line.split(' ', 1) try: d[key] = headerConverters[key](val) except ValueError: print >>sys.stderr, 'Value error parsing header in AFM:', key, val continue except KeyError: print >>sys.stderr, 'Found an unknown keyword in AFM header (was %s)' % key continue if key=='StartCharMetrics': return d raise RuntimeError('Bad parse') def _parse_char_metrics(fh): """ Return a character metric dictionary. Keys are the ASCII num of the character, values are a (*wx*, *name*, *bbox*) tuple, where *wx* is the character width, *name* is the postscript language name, and *bbox* is a (*llx*, *lly*, *urx*, *ury*) tuple. This function is incomplete per the standard, but thus far parses all the sample afm files tried. """ ascii_d = {} name_d = {} while 1: line = fh.readline() if not line: break line = line.rstrip() if line.startswith('EndCharMetrics'): return ascii_d, name_d vals = line.split(';')[:4] if len(vals) !=4 : raise RuntimeError('Bad char metrics line: %s' % line) num = _to_int(vals[0].split()[1]) wx = _to_float(vals[1].split()[1]) name = vals[2].split()[1] bbox = _to_list_of_ints(vals[3][2:]) # Workaround: If the character name is 'Euro', give it the corresponding # character code, according to WinAnsiEncoding (see PDF Reference). if name == 'Euro': num = 128 if num != -1: ascii_d[num] = (wx, name, bbox) name_d[name] = (wx, bbox) raise RuntimeError('Bad parse') def _parse_kern_pairs(fh): """ Return a kern pairs dictionary; keys are (*char1*, *char2*) tuples and values are the kern pair value. For example, a kern pairs line like ``KPX A y -50`` will be represented as:: d[ ('A', 'y') ] = -50 """ line = fh.readline() if not line.startswith('StartKernPairs'): raise RuntimeError('Bad start of kern pairs data: %s'%line) d = {} while 1: line = fh.readline() if not line: break line = line.rstrip() if len(line)==0: continue if line.startswith('EndKernPairs'): fh.readline() # EndKernData return d vals = line.split() if len(vals)!=4 or vals[0]!='KPX': raise RuntimeError('Bad kern pairs line: %s'%line) c1, c2, val = vals[1], vals[2], _to_float(vals[3]) d[(c1,c2)] = val raise RuntimeError('Bad kern pairs parse') def _parse_composites(fh): """ Return a composites dictionary. Keys are the names of the composites. Values are a num parts list of composite information, with each element being a (*name*, *dx*, *dy*) tuple. Thus a composites line reading: CC Aacute 2 ; PCC A 0 0 ; PCC acute 160 170 ; will be represented as:: d['Aacute'] = [ ('A', 0, 0), ('acute', 160, 170) ] """ d = {} while 1: line = fh.readline() if not line: break line = line.rstrip() if len(line)==0: continue if line.startswith('EndComposites'): return d vals = line.split(';') cc = vals[0].split() name, numParts = cc[1], _to_int(cc[2]) pccParts = [] for s in vals[1:-1]: pcc = s.split() name, dx, dy = pcc[1], _to_float(pcc[2]), _to_float(pcc[3]) pccParts.append( (name, dx, dy) ) d[name] = pccParts raise RuntimeError('Bad composites parse') def _parse_optional(fh): """ Parse the optional fields for kern pair data and composites return value is a (*kernDict*, *compositeDict*) which are the return values from :func:`_parse_kern_pairs`, and :func:`_parse_composites` if the data exists, or empty dicts otherwise """ optional = { 'StartKernData' : _parse_kern_pairs, 'StartComposites' : _parse_composites, } d = {'StartKernData':{}, 'StartComposites':{}} while 1: line = fh.readline() if not line: break line = line.rstrip() if len(line)==0: continue key = line.split()[0] if key in optional: d[key] = optional[key](fh) l = ( d['StartKernData'], d['StartComposites'] ) return l def parse_afm(fh): """ Parse the Adobe Font Metics file in file handle *fh*. Return value is a (*dhead*, *dcmetrics*, *dkernpairs*, *dcomposite*) tuple where *dhead* is a :func:`_parse_header` dict, *dcmetrics* is a :func:`_parse_composites` dict, *dkernpairs* is a :func:`_parse_kern_pairs` dict (possibly {}), and *dcomposite* is a :func:`_parse_composites` dict (possibly {}) """ _sanity_check(fh) dhead = _parse_header(fh) dcmetrics_ascii, dcmetrics_name = _parse_char_metrics(fh) doptional = _parse_optional(fh) return dhead, dcmetrics_ascii, dcmetrics_name, doptional[0], doptional[1] class AFM: def __init__(self, fh): """ Parse the AFM file in file object *fh* """ (dhead, dcmetrics_ascii, dcmetrics_name, dkernpairs, dcomposite) = \ parse_afm(fh) self._header = dhead self._kern = dkernpairs self._metrics = dcmetrics_ascii self._metrics_by_name = dcmetrics_name self._composite = dcomposite def get_bbox_char(self, c, isord=False): if not isord: c=ord(c) wx, name, bbox = self._metrics[c] return bbox def string_width_height(self, s): """ Return the string width (including kerning) and string height as a (*w*, *h*) tuple. """ if not len(s): return 0,0 totalw = 0 namelast = None miny = 1e9 maxy = 0 for c in s: if c == '\n': continue wx, name, bbox = self._metrics[ord(c)] l,b,w,h = bbox # find the width with kerning try: kp = self._kern[ (namelast, name) ] except KeyError: kp = 0 totalw += wx + kp # find the max y thismax = b+h if thismax>maxy: maxy = thismax # find the min y thismin = b if thismin<miny: miny = thismin return totalw, maxy-miny def get_str_bbox_and_descent(self, s): """ Return the string bounding box """ if not len(s): return 0,0,0,0 totalw = 0 namelast = None miny = 1e9 maxy = 0 left = 0 if not isinstance(s, unicode): s = s.decode() for c in s: if c == '\n': continue name = uni2type1.get(ord(c), 'question') try: wx, bbox = self._metrics_by_name[name] except KeyError: name = 'question' wx, bbox = self._metrics_by_name[name] l,b,w,h = bbox if l<left: left = l # find the width with kerning try: kp = self._kern[ (namelast, name) ] except KeyError: kp = 0 totalw += wx + kp # find the max y thismax = b+h if thismax>maxy: maxy = thismax # find the min y thismin = b if thismin<miny: miny = thismin return left, miny, totalw, maxy-miny, -miny def get_str_bbox(self, s): """ Return the string bounding box """ return self.get_str_bbox_and_descent(s)[:4] def get_name_char(self, c, isord=False): """ Get the name of the character, ie, ';' is 'semicolon' """ if not isord: c=ord(c) wx, name, bbox = self._metrics[c] return name def get_width_char(self, c, isord=False): """ Get the width of the character from the character metric WX field """ if not isord: c=ord(c) wx, name, bbox = self._metrics[c] return wx def get_width_from_char_name(self, name): """ Get the width of the character from a type1 character name """ wx, bbox = self._metrics_by_name[name] return wx def get_height_char(self, c, isord=False): """ Get the height of character *c* from the bounding box. This is the ink height (space is 0) """ if not isord: c=ord(c) wx, name, bbox = self._metrics[c] return bbox[-1] def get_kern_dist(self, c1, c2): """ Return the kerning pair distance (possibly 0) for chars *c1* and *c2* """ name1, name2 = self.get_name_char(c1), self.get_name_char(c2) return self.get_kern_dist_from_name(name1, name2) def get_kern_dist_from_name(self, name1, name2): """ Return the kerning pair distance (possibly 0) for chars *name1* and *name2* """ try: return self._kern[ (name1, name2) ] except: return 0 def get_fontname(self): "Return the font name, eg, 'Times-Roman'" return self._header['FontName'] def get_fullname(self): "Return the font full name, eg, 'Times-Roman'" name = self._header.get('FullName') if name is None: # use FontName as a substitute name = self._header['FontName'] return name def get_familyname(self): "Return the font family name, eg, 'Times'" name = self._header.get('FamilyName') if name is not None: return name # FamilyName not specified so we'll make a guess name = self.get_fullname() extras = r'(?i)([ -](regular|plain|italic|oblique|bold|semibold|light|ultralight|extra|condensed))+$' return re.sub(extras, '', name) def get_weight(self): "Return the font weight, eg, 'Bold' or 'Roman'" return self._header['Weight'] def get_angle(self): "Return the fontangle as float" return self._header['ItalicAngle'] def get_capheight(self): "Return the cap height as float" return self._header['CapHeight'] def get_xheight(self): "Return the xheight as float" return self._header['XHeight'] def get_underline_thickness(self): "Return the underline thickness as float" return self._header['UnderlineThickness'] def get_horizontal_stem_width(self): """ Return the standard horizontal stem width as float, or *None* if not specified in AFM file. """ return self._header.get('StdHW', None) def get_vertical_stem_width(self): """ Return the standard vertical stem width as float, or *None* if not specified in AFM file. """ return self._header.get('StdVW', None) if __name__=='__main__': #pathname = '/usr/local/lib/R/afm/' pathname = '/usr/local/share/fonts/afms/adobe' for fname in os.listdir(pathname): fh = file(os.path.join(pathname,fname)) afm = AFM(fh) w,h = afm.string_width_height('John Hunter is the Man!')

15,057

Python

.py

416

28.298077

109

0.581062

numenta/nupic-legacy

6,330

1,556

464

AGPL-3.0

9/5/2024, 5:13:42 PM (Europe/Amsterdam)

26,250

figure.py

numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/figure.py

""" The figure module provides the top-level :class:`~matplotlib.artist.Artist`, the :class:`Figure`, which contains all the plot elements. The following classes are defined :class:`SubplotParams` control the default spacing of the subplots :class:`Figure` top level container for all plot elements """ import numpy as np import time import artist from artist import Artist from axes import Axes, SubplotBase, subplot_class_factory from cbook import flatten, allequal, Stack, iterable, dedent import _image import colorbar as cbar from image import FigureImage from matplotlib import rcParams from patches import Rectangle from text import Text, _process_text_args from legend import Legend from transforms import Affine2D, Bbox, BboxTransformTo, TransformedBbox from projections import projection_factory, get_projection_names, \ get_projection_class from matplotlib.blocking_input import BlockingMouseInput, BlockingKeyMouseInput import matplotlib.cbook as cbook class SubplotParams: """ A class to hold the parameters for a subplot """ def __init__(self, left=None, bottom=None, right=None, top=None, wspace=None, hspace=None): """ All dimensions are fraction of the figure width or height. All values default to their rc params The following attributes are available *left* = 0.125 the left side of the subplots of the figure *right* = 0.9 the right side of the subplots of the figure *bottom* = 0.1 the bottom of the subplots of the figure *top* = 0.9 the top of the subplots of the figure *wspace* = 0.2 the amount of width reserved for blank space between subplots *hspace* = 0.2 the amount of height reserved for white space between subplots *validate* make sure the params are in a legal state (*left*<*right*, etc) """ self.validate = True self.update(left, bottom, right, top, wspace, hspace) def update(self,left=None, bottom=None, right=None, top=None, wspace=None, hspace=None): """ Update the current values. If any kwarg is None, default to the current value, if set, otherwise to rc """ thisleft = getattr(self, 'left', None) thisright = getattr(self, 'right', None) thistop = getattr(self, 'top', None) thisbottom = getattr(self, 'bottom', None) thiswspace = getattr(self, 'wspace', None) thishspace = getattr(self, 'hspace', None) self._update_this('left', left) self._update_this('right', right) self._update_this('bottom', bottom) self._update_this('top', top) self._update_this('wspace', wspace) self._update_this('hspace', hspace) def reset(): self.left = thisleft self.right = thisright self.top = thistop self.bottom = thisbottom self.wspace = thiswspace self.hspace = thishspace if self.validate: if self.left>=self.right: reset() raise ValueError('left cannot be >= right') if self.bottom>=self.top: reset() raise ValueError('bottom cannot be >= top') def _update_this(self, s, val): if val is None: val = getattr(self, s, None) if val is None: key = 'figure.subplot.' + s val = rcParams[key] setattr(self, s, val) class Figure(Artist): """ The Figure instance supports callbacks through a *callbacks* attribute which is a :class:`matplotlib.cbook.CallbackRegistry` instance. The events you can connect to are 'dpi_changed', and the callback will be called with ``func(fig)`` where fig is the :class:`Figure` instance. The figure patch is drawn by a the attribute *patch* a :class:`matplotlib.patches.Rectangle` instance *suppressComposite* for multiple figure images, the figure will make composite images depending on the renderer option_image_nocomposite function. If suppressComposite is True|False, this will override the renderer """ def __str__(self): return "Figure(%gx%g)" % tuple(self.bbox.size) def __init__(self, figsize = None, # defaults to rc figure.figsize dpi = None, # defaults to rc figure.dpi facecolor = None, # defaults to rc figure.facecolor edgecolor = None, # defaults to rc figure.edgecolor linewidth = 1.0, # the default linewidth of the frame frameon = True, # whether or not to draw the figure frame subplotpars = None, # default to rc ): """ *figsize* w,h tuple in inches *dpi* dots per inch *facecolor* the figure patch facecolor; defaults to rc ``figure.facecolor`` *edgecolor* the figure patch edge color; defaults to rc ``figure.edgecolor`` *linewidth* the figure patch edge linewidth; the default linewidth of the frame *frameon* if False, suppress drawing the figure frame *subplotpars* a :class:`SubplotParams` instance, defaults to rc """ Artist.__init__(self) self.callbacks = cbook.CallbackRegistry(('dpi_changed', )) if figsize is None : figsize = rcParams['figure.figsize'] if dpi is None : dpi = rcParams['figure.dpi'] if facecolor is None: facecolor = rcParams['figure.facecolor'] if edgecolor is None: edgecolor = rcParams['figure.edgecolor'] self.dpi_scale_trans = Affine2D() self.dpi = dpi self.bbox_inches = Bbox.from_bounds(0, 0, *figsize) self.bbox = TransformedBbox(self.bbox_inches, self.dpi_scale_trans) self.frameon = frameon self.transFigure = BboxTransformTo(self.bbox) # the figurePatch name is deprecated self.patch = self.figurePatch = Rectangle( xy=(0,0), width=1, height=1, facecolor=facecolor, edgecolor=edgecolor, linewidth=linewidth, ) self._set_artist_props(self.patch) self._hold = rcParams['axes.hold'] self.canvas = None if subplotpars is None: subplotpars = SubplotParams() self.subplotpars = subplotpars self._axstack = Stack() # maintain the current axes self.axes = [] self.clf() self._cachedRenderer = None def _get_dpi(self): return self._dpi def _set_dpi(self, dpi): self._dpi = dpi self.dpi_scale_trans.clear().scale(dpi, dpi) self.callbacks.process('dpi_changed', self) dpi = property(_get_dpi, _set_dpi) def autofmt_xdate(self, bottom=0.2, rotation=30, ha='right'): """ Date ticklabels often overlap, so it is useful to rotate them and right align them. Also, a common use case is a number of subplots with shared xaxes where the x-axis is date data. The ticklabels are often long, and it helps to rotate them on the bottom subplot and turn them off on other subplots, as well as turn off xlabels. *bottom* the bottom of the subplots for :meth:`subplots_adjust` *rotation* the rotation of the xtick labels *ha* the horizontal alignment of the xticklabels """ allsubplots = np.alltrue([hasattr(ax, 'is_last_row') for ax in self.axes]) if len(self.axes)==1: for label in ax.get_xticklabels(): label.set_ha(ha) label.set_rotation(rotation) else: if allsubplots: for ax in self.get_axes(): if ax.is_last_row(): for label in ax.get_xticklabels(): label.set_ha(ha) label.set_rotation(rotation) else: for label in ax.get_xticklabels(): label.set_visible(False) ax.set_xlabel('') if allsubplots: self.subplots_adjust(bottom=bottom) def get_children(self): 'get a list of artists contained in the figure' children = [self.patch] children.extend(self.artists) children.extend(self.axes) children.extend(self.lines) children.extend(self.patches) children.extend(self.texts) children.extend(self.images) children.extend(self.legends) return children def contains(self, mouseevent): """ Test whether the mouse event occurred on the figure. Returns True,{} """ if callable(self._contains): return self._contains(self,mouseevent) #inside = mouseevent.x >= 0 and mouseevent.y >= 0 inside = self.bbox.contains(mouseevent.x,mouseevent.y) return inside,{} def get_window_extent(self, *args, **kwargs): 'get the figure bounding box in display space; kwargs are void' return self.bbox def suptitle(self, t, **kwargs): """ Add a centered title to the figure. kwargs are :class:`matplotlib.text.Text` properties. Using figure coordinates, the defaults are: - *x* = 0.5 the x location of text in figure coords - *y* = 0.98 the y location of the text in figure coords - *horizontalalignment* = 'center' the horizontal alignment of the text - *verticalalignment* = 'top' the vertical alignment of the text A :class:`matplotlib.text.Text` instance is returned. Example:: fig.subtitle('this is the figure title', fontsize=12) """ x = kwargs.pop('x', 0.5) y = kwargs.pop('y', 0.98) if ('horizontalalignment' not in kwargs) and ('ha' not in kwargs): kwargs['horizontalalignment'] = 'center' if ('verticalalignment' not in kwargs) and ('va' not in kwargs): kwargs['verticalalignment'] = 'top' t = self.text(x, y, t, **kwargs) return t def set_canvas(self, canvas): """ Set the canvas the contains the figure ACCEPTS: a FigureCanvas instance """ self.canvas = canvas def hold(self, b=None): """ Set the hold state. If hold is None (default), toggle the hold state. Else set the hold state to boolean value b. Eg:: hold() # toggle hold hold(True) # hold is on hold(False) # hold is off """ if b is None: self._hold = not self._hold else: self._hold = b def figimage(self, X, xo=0, yo=0, alpha=1.0, norm=None, cmap=None, vmin=None, vmax=None, origin=None): """ call signatures:: figimage(X, **kwargs) adds a non-resampled array *X* to the figure. :: figimage(X, xo, yo) with pixel offsets *xo*, *yo*, *X* must be a float array: * If *X* is MxN, assume luminance (grayscale) * If *X* is MxNx3, assume RGB * If *X* is MxNx4, assume RGBA Optional keyword arguments: ========= ========================================================== Keyword Description ========= ========================================================== xo or yo An integer, the *x* and *y* image offset in pixels cmap a :class:`matplotlib.cm.ColorMap` instance, eg cm.jet. If None, default to the rc ``image.cmap`` value norm a :class:`matplotlib.colors.Normalize` instance. The default is normalization(). This scales luminance -> 0-1 vmin|vmax are used to scale a luminance image to 0-1. If either is None, the min and max of the luminance values will be used. Note if you pass a norm instance, the settings for *vmin* and *vmax* will be ignored. alpha the alpha blending value, default is 1.0 origin [ 'upper' | 'lower' ] Indicates where the [0,0] index of the array is in the upper left or lower left corner of the axes. Defaults to the rc image.origin value ========= ========================================================== figimage complements the axes image (:meth:`~matplotlib.axes.Axes.imshow`) which will be resampled to fit the current axes. If you want a resampled image to fill the entire figure, you can define an :class:`~matplotlib.axes.Axes` with size [0,1,0,1]. An :class:`matplotlib.image.FigureImage` instance is returned. .. plot:: mpl_examples/pylab_examples/figimage_demo.py """ if not self._hold: self.clf() im = FigureImage(self, cmap, norm, xo, yo, origin) im.set_array(X) im.set_alpha(alpha) if norm is None: im.set_clim(vmin, vmax) self.images.append(im) return im def set_figsize_inches(self, *args, **kwargs): import warnings warnings.warn('Use set_size_inches instead!', DeprecationWarning) self.set_size_inches(*args, **kwargs) def set_size_inches(self, *args, **kwargs): """ set_size_inches(w,h, forward=False) Set the figure size in inches Usage:: fig.set_size_inches(w,h) # OR fig.set_size_inches((w,h) ) optional kwarg *forward=True* will cause the canvas size to be automatically updated; eg you can resize the figure window from the shell WARNING: forward=True is broken on all backends except GTK* and WX* ACCEPTS: a w,h tuple with w,h in inches """ forward = kwargs.get('forward', False) if len(args)==1: w,h = args[0] else: w,h = args dpival = self.dpi self.bbox_inches.p1 = w, h if forward: dpival = self.dpi canvasw = w*dpival canvash = h*dpival manager = getattr(self.canvas, 'manager', None) if manager is not None: manager.resize(int(canvasw), int(canvash)) def get_size_inches(self): return self.bbox_inches.p1 def get_edgecolor(self): 'Get the edge color of the Figure rectangle' return self.patch.get_edgecolor() def get_facecolor(self): 'Get the face color of the Figure rectangle' return self.patch.get_facecolor() def get_figwidth(self): 'Return the figwidth as a float' return self.bbox_inches.width def get_figheight(self): 'Return the figheight as a float' return self.bbox_inches.height def get_dpi(self): 'Return the dpi as a float' return self.dpi def get_frameon(self): 'get the boolean indicating frameon' return self.frameon def set_edgecolor(self, color): """ Set the edge color of the Figure rectangle ACCEPTS: any matplotlib color - see help(colors) """ self.patch.set_edgecolor(color) def set_facecolor(self, color): """ Set the face color of the Figure rectangle ACCEPTS: any matplotlib color - see help(colors) """ self.patch.set_facecolor(color) def set_dpi(self, val): """ Set the dots-per-inch of the figure ACCEPTS: float """ self.dpi = val def set_figwidth(self, val): """ Set the width of the figure in inches ACCEPTS: float """ self.bbox_inches.x1 = val def set_figheight(self, val): """ Set the height of the figure in inches ACCEPTS: float """ self.bbox_inches.y1 = val def set_frameon(self, b): """ Set whether the figure frame (background) is displayed or invisible ACCEPTS: boolean """ self.frameon = b def delaxes(self, a): 'remove a from the figure and update the current axes' self.axes.remove(a) self._axstack.remove(a) keys = [] for key, thisax in self._seen.items(): if a==thisax: del self._seen[key] for func in self._axobservers: func(self) def _make_key(self, *args, **kwargs): 'make a hashable key out of args and kwargs' def fixitems(items): #items may have arrays and lists in them, so convert them # to tuples for the key ret = [] for k, v in items: if iterable(v): v = tuple(v) ret.append((k,v)) return tuple(ret) def fixlist(args): ret = [] for a in args: if iterable(a): a = tuple(a) ret.append(a) return tuple(ret) key = fixlist(args), fixitems(kwargs.items()) return key def add_axes(self, *args, **kwargs): """ Add an a axes with axes rect [*left*, *bottom*, *width*, *height*] where all quantities are in fractions of figure width and height. kwargs are legal :class:`~matplotlib.axes.Axes` kwargs plus *projection* which sets the projection type of the axes. (For backward compatibility, ``polar=True`` may also be provided, which is equivalent to ``projection='polar'``). Valid values for *projection* are: %(list)s. Some of these projections support additional kwargs, which may be provided to :meth:`add_axes`:: rect = l,b,w,h fig.add_axes(rect) fig.add_axes(rect, frameon=False, axisbg='g') fig.add_axes(rect, polar=True) fig.add_axes(rect, projection='polar') fig.add_axes(ax) # add an Axes instance If the figure already has an axes with the same parameters, then it will simply make that axes current and return it. If you do not want this behavior, eg. you want to force the creation of a new axes, you must use a unique set of args and kwargs. The axes :attr:`~matplotlib.axes.Axes.label` attribute has been exposed for this purpose. Eg., if you want two axes that are otherwise identical to be added to the figure, make sure you give them unique labels:: fig.add_axes(rect, label='axes1') fig.add_axes(rect, label='axes2') The :class:`~matplotlib.axes.Axes` instance will be returned. The following kwargs are supported: %(Axes)s """ key = self._make_key(*args, **kwargs) if key in self._seen: ax = self._seen[key] self.sca(ax) return ax if not len(args): return if isinstance(args[0], Axes): a = args[0] assert(a.get_figure() is self) else: rect = args[0] ispolar = kwargs.pop('polar', False) projection = kwargs.pop('projection', None) if ispolar: if projection is not None and projection != 'polar': raise ValueError( "polar=True, yet projection='%s'. " + "Only one of these arguments should be supplied." % projection) projection = 'polar' a = projection_factory(projection, self, rect, **kwargs) self.axes.append(a) self._axstack.push(a) self.sca(a) self._seen[key] = a return a add_axes.__doc__ = dedent(add_axes.__doc__) % \ {'list': (", ".join(get_projection_names())), 'Axes': artist.kwdocd['Axes']} def add_subplot(self, *args, **kwargs): """ Add a subplot. Examples: fig.add_subplot(111) fig.add_subplot(1,1,1) # equivalent but more general fig.add_subplot(212, axisbg='r') # add subplot with red background fig.add_subplot(111, polar=True) # add a polar subplot fig.add_subplot(sub) # add Subplot instance sub *kwargs* are legal :class:`!matplotlib.axes.Axes` kwargs plus *projection*, which chooses a projection type for the axes. (For backward compatibility, *polar=True* may also be provided, which is equivalent to *projection='polar'*). Valid values for *projection* are: %(list)s. Some of these projections support additional *kwargs*, which may be provided to :meth:`add_axes`. The :class:`~matplotlib.axes.Axes` instance will be returned. If the figure already has a subplot with key (*args*, *kwargs*) then it will simply make that subplot current and return it. The following kwargs are supported: %(Axes)s """ kwargs = kwargs.copy() if not len(args): return if isinstance(args[0], SubplotBase): a = args[0] assert(a.get_figure() is self) else: ispolar = kwargs.pop('polar', False) projection = kwargs.pop('projection', None) if ispolar: if projection is not None and projection != 'polar': raise ValueError( "polar=True, yet projection='%s'. " + "Only one of these arguments should be supplied." % projection) projection = 'polar' projection_class = get_projection_class(projection) key = self._make_key(*args, **kwargs) if key in self._seen: ax = self._seen[key] if isinstance(ax, projection_class): self.sca(ax) return ax else: self.axes.remove(ax) self._axstack.remove(ax) a = subplot_class_factory(projection_class)(self, *args, **kwargs) self._seen[key] = a self.axes.append(a) self._axstack.push(a) self.sca(a) return a add_subplot.__doc__ = dedent(add_subplot.__doc__) % { 'list': ", ".join(get_projection_names()), 'Axes': artist.kwdocd['Axes']} def clf(self): """ Clear the figure """ self.suppressComposite = None self.callbacks = cbook.CallbackRegistry(('dpi_changed', )) for ax in tuple(self.axes): # Iterate over the copy. ax.cla() self.delaxes(ax) # removes ax from self.axes toolbar = getattr(self.canvas, 'toolbar', None) if toolbar is not None: toolbar.update() self._axstack.clear() self._seen = {} self.artists = [] self.lines = [] self.patches = [] self.texts=[] self.images = [] self.legends = [] self._axobservers = [] def clear(self): """ Clear the figure -- synonym for fig.clf """ self.clf() def draw(self, renderer): """ Render the figure using :class:`matplotlib.backend_bases.RendererBase` instance renderer """ # draw the figure bounding box, perhaps none for white figure #print 'figure draw' if not self.get_visible(): return renderer.open_group('figure') if self.frameon: self.patch.draw(renderer) # todo: respect zorder for p in self.patches: p.draw(renderer) for l in self.lines: l.draw(renderer) for a in self.artists: a.draw(renderer) # override the renderer default if self.suppressComposite # is not None composite = renderer.option_image_nocomposite() if self.suppressComposite is not None: composite = self.suppressComposite if len(self.images)<=1 or composite or not allequal([im.origin for im in self.images]): for im in self.images: im.draw(renderer) else: # make a composite image blending alpha # list of (_image.Image, ox, oy) mag = renderer.get_image_magnification() ims = [(im.make_image(mag), im.ox, im.oy) for im in self.images] im = _image.from_images(self.bbox.height * mag, self.bbox.width * mag, ims) im.is_grayscale = False l, b, w, h = self.bbox.bounds clippath, affine = self.get_transformed_clip_path_and_affine() renderer.draw_image(l, b, im, self.bbox, clippath, affine) # render the axes for a in self.axes: a.draw(renderer) # render the figure text for t in self.texts: t.draw(renderer) for legend in self.legends: legend.draw(renderer) renderer.close_group('figure') self._cachedRenderer = renderer self.canvas.draw_event(renderer) def draw_artist(self, a): """ draw :class:`matplotlib.artist.Artist` instance *a* only -- this is available only after the figure is drawn """ assert self._cachedRenderer is not None a.draw(self._cachedRenderer) def get_axes(self): return self.axes def legend(self, handles, labels, *args, **kwargs): """ Place a legend in the figure. Labels are a sequence of strings, handles is a sequence of :class:`~matplotlib.lines.Line2D` or :class:`~matplotlib.patches.Patch` instances, and loc can be a string or an integer specifying the legend location USAGE:: legend( (line1, line2, line3), ('label1', 'label2', 'label3'), 'upper right') The *loc* location codes are:: 'best' : 0, (currently not supported for figure legends) 'upper right' : 1, 'upper left' : 2, 'lower left' : 3, 'lower right' : 4, 'right' : 5, 'center left' : 6, 'center right' : 7, 'lower center' : 8, 'upper center' : 9, 'center' : 10, *loc* can also be an (x,y) tuple in figure coords, which specifies the lower left of the legend box. figure coords are (0,0) is the left, bottom of the figure and 1,1 is the right, top. The legend instance is returned. The following kwargs are supported *loc* the location of the legend *numpoints* the number of points in the legend line *prop* a :class:`matplotlib.font_manager.FontProperties` instance *pad* the fractional whitespace inside the legend border *markerscale* the relative size of legend markers vs. original *shadow* if True, draw a shadow behind legend *labelsep* the vertical space between the legend entries *handlelen* the length of the legend lines *handletextsep* the space between the legend line and legend text *axespad* the border between the axes and legend edge .. plot:: mpl_examples/pylab_examples/figlegend_demo.py """ handles = flatten(handles) l = Legend(self, handles, labels, *args, **kwargs) self.legends.append(l) return l def text(self, x, y, s, *args, **kwargs): """ Call signature:: figtext(x, y, s, fontdict=None, **kwargs) Add text to figure at location *x*, *y* (relative 0-1 coords). See :func:`~matplotlib.pyplot.text` for the meaning of the other arguments. kwargs control the :class:`~matplotlib.text.Text` properties: %(Text)s """ override = _process_text_args({}, *args, **kwargs) t = Text( x=x, y=y, text=s, ) t.update(override) self._set_artist_props(t) self.texts.append(t) return t text.__doc__ = dedent(text.__doc__) % artist.kwdocd def _set_artist_props(self, a): if a!= self: a.set_figure(self) a.set_transform(self.transFigure) def gca(self, **kwargs): """ Return the current axes, creating one if necessary The following kwargs are supported %(Axes)s """ ax = self._axstack() if ax is not None: ispolar = kwargs.get('polar', False) projection = kwargs.get('projection', None) if ispolar: if projection is not None and projection != 'polar': raise ValueError( "polar=True, yet projection='%s'. " + "Only one of these arguments should be supplied." % projection) projection = 'polar' projection_class = get_projection_class(projection) if isinstance(ax, projection_class): return ax return self.add_subplot(111, **kwargs) gca.__doc__ = dedent(gca.__doc__) % artist.kwdocd def sca(self, a): 'Set the current axes to be a and return a' self._axstack.bubble(a) for func in self._axobservers: func(self) return a def add_axobserver(self, func): 'whenever the axes state change, func(self) will be called' self._axobservers.append(func) def savefig(self, *args, **kwargs): """ call signature:: savefig(fname, dpi=None, facecolor='w', edgecolor='w', orientation='portrait', papertype=None, format=None, transparent=False): Save the current figure. The output formats available depend on the backend being used. Arguments: *fname*: A string containing a path to a filename, or a Python file-like object. If *format* is *None* and *fname* is a string, the output format is deduced from the extension of the filename. Keyword arguments: *dpi*: [ None | scalar > 0 ] The resolution in dots per inch. If *None* it will default to the value ``savefig.dpi`` in the matplotlibrc file. *facecolor*, *edgecolor*: the colors of the figure rectangle *orientation*: [ 'landscape' | 'portrait' ] not supported on all backends; currently only on postscript output *papertype*: One of 'letter', 'legal', 'executive', 'ledger', 'a0' through 'a10', 'b0' through 'b10'. Only supported for postscript output. *format*: One of the file extensions supported by the active backend. Most backends support png, pdf, ps, eps and svg. *transparent*: If *True*, the figure patch and axes patches will all be transparent. This is useful, for example, for displaying a plot on top of a colored background on a web page. The transparency of these patches will be restored to their original values upon exit of this function. """ for key in ('dpi', 'facecolor', 'edgecolor'): if key not in kwargs: kwargs[key] = rcParams['savefig.%s'%key] transparent = kwargs.pop('transparent', False) if transparent: original_figure_alpha = self.patch.get_alpha() self.patch.set_alpha(0.0) original_axes_alpha = [] for ax in self.axes: patch = ax.patch original_axes_alpha.append(patch.get_alpha()) patch.set_alpha(0.0) self.canvas.print_figure(*args, **kwargs) if transparent: self.patch.set_alpha(original_figure_alpha) for ax, alpha in zip(self.axes, original_axes_alpha): ax.patch.set_alpha(alpha) def colorbar(self, mappable, cax=None, ax=None, **kw): if ax is None: ax = self.gca() if cax is None: cax, kw = cbar.make_axes(ax, **kw) cax.hold(True) cb = cbar.Colorbar(cax, mappable, **kw) def on_changed(m): #print 'calling on changed', m.get_cmap().name cb.set_cmap(m.get_cmap()) cb.set_clim(m.get_clim()) cb.update_bruteforce(m) self.cbid = mappable.callbacksSM.connect('changed', on_changed) mappable.set_colorbar(cb, cax) self.sca(ax) return cb colorbar.__doc__ = ''' Create a colorbar for a ScalarMappable instance. Documentation for the pylab thin wrapper: %s '''% cbar.colorbar_doc def subplots_adjust(self, *args, **kwargs): """ fig.subplots_adjust(left=None, bottom=None, right=None, wspace=None, hspace=None) Update the :class:`SubplotParams` with *kwargs* (defaulting to rc where None) and update the subplot locations """ self.subplotpars.update(*args, **kwargs) import matplotlib.axes for ax in self.axes: if not isinstance(ax, matplotlib.axes.SubplotBase): # Check if sharing a subplots axis if ax._sharex is not None and isinstance(ax._sharex, matplotlib.axes.SubplotBase): ax._sharex.update_params() ax.set_position(ax._sharex.figbox) elif ax._sharey is not None and isinstance(ax._sharey, matplotlib.axes.SubplotBase): ax._sharey.update_params() ax.set_position(ax._sharey.figbox) else: ax.update_params() ax.set_position(ax.figbox) def ginput(self, n=1, timeout=30, show_clicks=True): """ call signature:: ginput(self, n=1, timeout=30, show_clicks=True) Blocking call to interact with the figure. This will wait for *n* clicks from the user and return a list of the coordinates of each click. If *timeout* is zero or negative, does not timeout. If *n* is zero or negative, accumulate clicks until a middle click (or potentially both mouse buttons at once) terminates the input. Right clicking cancels last input. The keyboard can also be used to select points in case your mouse does not have one or more of the buttons. The delete and backspace keys act like right clicking (i.e., remove last point), the enter key terminates input and any other key (not already used by the window manager) selects a point. """ blocking_mouse_input = BlockingMouseInput(self) return blocking_mouse_input(n=n, timeout=timeout, show_clicks=show_clicks) def waitforbuttonpress(self, timeout=-1): """ call signature:: waitforbuttonpress(self, timeout=-1) Blocking call to interact with the figure. This will return True is a key was pressed, False if a mouse button was pressed and None if *timeout* was reached without either being pressed. If *timeout* is negative, does not timeout. """ blocking_input = BlockingKeyMouseInput(self) return blocking_input(timeout=timeout) def figaspect(arg): """ Create a figure with specified aspect ratio. If *arg* is a number, use that aspect ratio. If *arg* is an array, figaspect will determine the width and height for a figure that would fit array preserving aspect ratio. The figure width, height in inches are returned. Be sure to create an axes with equal with and height, eg Example usage:: # make a figure twice as tall as it is wide w, h = figaspect(2.) fig = Figure(figsize=(w,h)) ax = fig.add_axes([0.1, 0.1, 0.8, 0.8]) ax.imshow(A, **kwargs) # make a figure with the proper aspect for an array A = rand(5,3) w, h = figaspect(A) fig = Figure(figsize=(w,h)) ax = fig.add_axes([0.1, 0.1, 0.8, 0.8]) ax.imshow(A, **kwargs) Thanks to Fernando Perez for this function """ isarray = hasattr(arg, 'shape') # min/max sizes to respect when autoscaling. If John likes the idea, they # could become rc parameters, for now they're hardwired. figsize_min = np.array((4.0,2.0)) # min length for width/height figsize_max = np.array((16.0,16.0)) # max length for width/height #figsize_min = rcParams['figure.figsize_min'] #figsize_max = rcParams['figure.figsize_max'] # Extract the aspect ratio of the array if isarray: nr,nc = arg.shape[:2] arr_ratio = float(nr)/nc else: arr_ratio = float(arg) # Height of user figure defaults fig_height = rcParams['figure.figsize'][1] # New size for the figure, keeping the aspect ratio of the caller newsize = np.array((fig_height/arr_ratio,fig_height)) # Sanity checks, don't drop either dimension below figsize_min newsize /= min(1.0,*(newsize/figsize_min)) # Avoid humongous windows as well newsize /= max(1.0,*(newsize/figsize_max)) # Finally, if we have a really funky aspect ratio, break it but respect # the min/max dimensions (we don't want figures 10 feet tall!) newsize = np.clip(newsize,figsize_min,figsize_max) return newsize artist.kwdocd['Figure'] = artist.kwdoc(Figure)

38,331

Python

.py

912

31.475877

100

0.581762

numenta/nupic-legacy

6,330

1,556

464

AGPL-3.0

9/5/2024, 5:13:42 PM (Europe/Amsterdam)

26,251

rcsetup.py

numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/rcsetup.py

""" The rcsetup module contains the default values and the validation code for customization using matplotlib's rc settings. Each rc setting is assigned a default value and a function used to validate any attempted changes to that setting. The default values and validation functions are defined in the rcsetup module, and are used to construct the rcParams global object which stores the settings and is referenced throughout matplotlib. These default values should be consistent with the default matplotlibrc file that actually reflects the values given here. Any additions or deletions to the parameter set listed here should also be visited to the :file:`matplotlibrc.template` in matplotlib's root source directory. """ import os import warnings from matplotlib.fontconfig_pattern import parse_fontconfig_pattern from matplotlib.colors import is_color_like #interactive_bk = ['gtk', 'gtkagg', 'gtkcairo', 'fltkagg', 'qtagg', 'qt4agg', # 'tkagg', 'wx', 'wxagg', 'cocoaagg'] # The capitalized forms are needed for ipython at present; this may # change for later versions. interactive_bk = ['GTK', 'GTKAgg', 'GTKCairo', 'FltkAgg', 'MacOSX', 'QtAgg', 'Qt4Agg', 'TkAgg', 'WX', 'WXAgg', 'CocoaAgg'] non_interactive_bk = ['agg', 'cairo', 'emf', 'gdk', 'pdf', 'ps', 'svg', 'template'] all_backends = interactive_bk + non_interactive_bk class ValidateInStrings: def __init__(self, key, valid, ignorecase=False): 'valid is a list of legal strings' self.key = key self.ignorecase = ignorecase def func(s): if ignorecase: return s.lower() else: return s self.valid = dict([(func(k),k) for k in valid]) def __call__(self, s): if self.ignorecase: s = s.lower() if s in self.valid: return self.valid[s] raise ValueError('Unrecognized %s string "%s": valid strings are %s' % (self.key, s, self.valid.values())) def validate_path_exists(s): 'If s is a path, return s, else False' if os.path.exists(s): return s else: raise RuntimeError('"%s" should be a path but it does not exist'%s) def validate_bool(b): 'Convert b to a boolean or raise' if type(b) is str: b = b.lower() if b in ('t', 'y', 'yes', 'on', 'true', '1', 1, True): return True elif b in ('f', 'n', 'no', 'off', 'false', '0', 0, False): return False else: raise ValueError('Could not convert "%s" to boolean' % b) def validate_bool_maybe_none(b): 'Convert b to a boolean or raise' if type(b) is str: b = b.lower() if b=='none': return None if b in ('t', 'y', 'yes', 'on', 'true', '1', 1, True): return True elif b in ('f', 'n', 'no', 'off', 'false', '0', 0, False): return False else: raise ValueError('Could not convert "%s" to boolean' % b) def validate_float(s): 'convert s to float or raise' try: return float(s) except ValueError: raise ValueError('Could not convert "%s" to float' % s) def validate_int(s): 'convert s to int or raise' try: return int(s) except ValueError: raise ValueError('Could not convert "%s" to int' % s) def validate_fonttype(s): 'confirm that this is a Postscript of PDF font type that we know how to convert to' fonttypes = { 'type3': 3, 'truetype': 42 } try: fonttype = validate_int(s) except ValueError: if s.lower() in fonttypes.keys(): return fonttypes[s.lower()] raise ValueError('Supported Postscript/PDF font types are %s' % fonttypes.keys()) else: if fonttype not in fonttypes.values(): raise ValueError('Supported Postscript/PDF font types are %s' % fonttypes.values()) return fonttype #validate_backend = ValidateInStrings('backend', all_backends, ignorecase=True) _validate_standard_backends = ValidateInStrings('backend', all_backends, ignorecase=True) def validate_backend(s): if s.startswith('module://'): return s else: return _validate_standard_backends(s) validate_numerix = ValidateInStrings('numerix',[ 'Numeric','numarray','numpy', ], ignorecase=True) validate_toolbar = ValidateInStrings('toolbar',[ 'None','classic','toolbar2', ], ignorecase=True) def validate_autolayout(v): if v: warnings.warn("figure.autolayout is not currently supported") class validate_nseq_float: def __init__(self, n): self.n = n def __call__(self, s): 'return a seq of n floats or raise' if type(s) is str: ss = s.split(',') if len(ss) != self.n: raise ValueError('You must supply exactly %d comma separated values'%self.n) try: return [float(val) for val in ss] except ValueError: raise ValueError('Could not convert all entries to floats') else: assert type(s) in (list,tuple) if len(s) != self.n: raise ValueError('You must supply exactly %d values'%self.n) return [float(val) for val in s] class validate_nseq_int: def __init__(self, n): self.n = n def __call__(self, s): 'return a seq of n ints or raise' if type(s) is str: ss = s.split(',') if len(ss) != self.n: raise ValueError('You must supply exactly %d comma separated values'%self.n) try: return [int(val) for val in ss] except ValueError: raise ValueError('Could not convert all entries to ints') else: assert type(s) in (list,tuple) if len(s) != self.n: raise ValueError('You must supply exactly %d values'%self.n) return [int(val) for val in s] def validate_color(s): 'return a valid color arg' if s.lower() == 'none': return 'None' if is_color_like(s): return s stmp = '#' + s if is_color_like(stmp): return stmp # If it is still valid, it must be a tuple. colorarg = s msg = '' if s.find(',')>=0: # get rid of grouping symbols stmp = ''.join([ c for c in s if c.isdigit() or c=='.' or c==',']) vals = stmp.split(',') if len(vals)!=3: msg = '\nColor tuples must be length 3' else: try: colorarg = [float(val) for val in vals] except ValueError: msg = '\nCould not convert all entries to floats' if not msg and is_color_like(colorarg): return colorarg raise ValueError('%s does not look like a color arg%s'%(s, msg)) def validate_stringlist(s): 'return a list' if type(s) is str: return [ v.strip() for v in s.split(',') ] else: assert type(s) in [list,tuple] return [ str(v) for v in s ] validate_orientation = ValidateInStrings('orientation',[ 'landscape', 'portrait', ]) def validate_aspect(s): if s in ('auto', 'equal'): return s try: return float(s) except ValueError: raise ValueError('not a valid aspect specification') def validate_fontsize(s): if type(s) is str: s = s.lower() if s in ['xx-small', 'x-small', 'small', 'medium', 'large', 'x-large', 'xx-large', 'smaller', 'larger']: return s try: return float(s) except ValueError: raise ValueError('not a valid font size') def validate_font_properties(s): parse_fontconfig_pattern(s) return s validate_fontset = ValidateInStrings('fontset', ['cm', 'stix', 'stixsans', 'custom']) validate_verbose = ValidateInStrings('verbose',[ 'silent', 'helpful', 'debug', 'debug-annoying', ]) validate_cairo_format = ValidateInStrings('cairo_format', ['png', 'ps', 'pdf', 'svg'], ignorecase=True) validate_ps_papersize = ValidateInStrings('ps_papersize',[ 'auto', 'letter', 'legal', 'ledger', 'a0', 'a1', 'a2','a3', 'a4', 'a5', 'a6', 'a7', 'a8', 'a9', 'a10', 'b0', 'b1', 'b2', 'b3', 'b4', 'b5', 'b6', 'b7', 'b8', 'b9', 'b10', ], ignorecase=True) def validate_ps_distiller(s): if type(s) is str: s = s.lower() if s in ('none',None): return None elif s in ('false', False): return False elif s in ('ghostscript', 'xpdf'): return s else: raise ValueError('matplotlibrc ps.usedistiller must either be none, ghostscript or xpdf') validate_joinstyle = ValidateInStrings('joinstyle',['miter', 'round', 'bevel'], ignorecase=True) validate_capstyle = ValidateInStrings('capstyle',['butt', 'round', 'projecting'], ignorecase=True) validate_negative_linestyle = ValidateInStrings('negative_linestyle',['solid', 'dashed'], ignorecase=True) def validate_negative_linestyle_legacy(s): try: res = validate_negative_linestyle(s) return res except ValueError: dashes = validate_nseq_float(2)(s) warnings.warn("Deprecated negative_linestyle specification; use 'solid' or 'dashed'") return (0, dashes) # (offset, (solid, blank)) validate_legend_loc = ValidateInStrings('legend_loc',[ 'best', 'upper right', 'upper left', 'lower left', 'lower right', 'right', 'center left', 'center right', 'lower center', 'upper center', 'center', ], ignorecase=True) class ValidateInterval: """ Value must be in interval """ def __init__(self, vmin, vmax, closedmin=True, closedmax=True): self.vmin = vmin self.vmax = vmax self.cmin = closedmin self.cmax = closedmax def __call__(self, s): try: s = float(s) except: raise RuntimeError('Value must be a float; found "%s"'%s) if self.cmin and s<self.vmin: raise RuntimeError('Value must be >= %f; found "%f"'%(self.vmin, s)) elif not self.cmin and s<=self.vmin: raise RuntimeError('Value must be > %f; found "%f"'%(self.vmin, s)) if self.cmax and s>self.vmax: raise RuntimeError('Value must be <= %f; found "%f"'%(self.vmax, s)) elif not self.cmax and s>=self.vmax: raise RuntimeError('Value must be < %f; found "%f"'%(self.vmax, s)) return s # a map from key -> value, converter defaultParams = { 'backend' : ['Agg', validate_backend], # agg is certainly present 'backend_fallback' : [True, validate_bool], # agg is certainly present 'numerix' : ['numpy', validate_numerix], 'maskedarray' : [False, validate_bool], 'toolbar' : ['toolbar2', validate_toolbar], 'datapath' : [None, validate_path_exists], # handled by _get_data_path_cached 'units' : [False, validate_bool], 'interactive' : [False, validate_bool], 'timezone' : ['UTC', str], # the verbosity setting 'verbose.level' : ['silent', validate_verbose], 'verbose.fileo' : ['sys.stdout', str], # line props 'lines.linewidth' : [1.0, validate_float], # line width in points 'lines.linestyle' : ['-', str], # solid line 'lines.color' : ['b', validate_color], # blue 'lines.marker' : ['None', str], # black 'lines.markeredgewidth' : [0.5, validate_float], 'lines.markersize' : [6, validate_float], # markersize, in points 'lines.antialiased' : [True, validate_bool], # antialised (no jaggies) 'lines.dash_joinstyle' : ['miter', validate_joinstyle], 'lines.solid_joinstyle' : ['miter', validate_joinstyle], 'lines.dash_capstyle' : ['butt', validate_capstyle], 'lines.solid_capstyle' : ['projecting', validate_capstyle], # patch props 'patch.linewidth' : [1.0, validate_float], # line width in points 'patch.edgecolor' : ['k', validate_color], # black 'patch.facecolor' : ['b', validate_color], # blue 'patch.antialiased' : [True, validate_bool], # antialised (no jaggies) # font props 'font.family' : ['sans-serif', str], # used by text object 'font.style' : ['normal', str], # 'font.variant' : ['normal', str], # 'font.stretch' : ['normal', str], # 'font.weight' : ['normal', str], # 'font.size' : [12.0, validate_float], # 'font.serif' : [['Bitstream Vera Serif', 'DejaVu Serif', 'New Century Schoolbook', 'Century Schoolbook L', 'Utopia', 'ITC Bookman', 'Bookman', 'Nimbus Roman No9 L','Times New Roman', 'Times','Palatino','Charter','serif'], validate_stringlist], 'font.sans-serif' : [['Bitstream Vera Sans', 'DejaVu Sans', 'Lucida Grande', 'Verdana', 'Geneva', 'Lucid', 'Arial', 'Helvetica', 'Avant Garde', 'sans-serif'], validate_stringlist], 'font.cursive' : [['Apple Chancery','Textile','Zapf Chancery', 'Sand','cursive'], validate_stringlist], 'font.fantasy' : [['Comic Sans MS','Chicago','Charcoal','Impact' 'Western','fantasy'], validate_stringlist], 'font.monospace' : [['Bitstream Vera Sans Mono', 'DejaVu Sans Mono', 'Andale Mono', 'Nimbus Mono L', 'Courier New', 'Courier','Fixed', 'Terminal','monospace'], validate_stringlist], # text props 'text.color' : ['k', validate_color], # black 'text.usetex' : [False, validate_bool], 'text.latex.unicode' : [False, validate_bool], 'text.latex.preamble' : [[''], validate_stringlist], 'text.dvipnghack' : [None, validate_bool_maybe_none], 'text.fontstyle' : ['normal', str], 'text.fontangle' : ['normal', str], 'text.fontvariant' : ['normal', str], 'text.fontweight' : ['normal', str], 'text.fontsize' : ['medium', validate_fontsize], 'mathtext.cal' : ['cursive', validate_font_properties], 'mathtext.rm' : ['serif', validate_font_properties], 'mathtext.tt' : ['monospace', validate_font_properties], 'mathtext.it' : ['serif:italic', validate_font_properties], 'mathtext.bf' : ['serif:bold', validate_font_properties], 'mathtext.sf' : ['sans\-serif', validate_font_properties], 'mathtext.fontset' : ['cm', validate_fontset], 'mathtext.fallback_to_cm' : [True, validate_bool], 'image.aspect' : ['equal', validate_aspect], # equal, auto, a number 'image.interpolation' : ['bilinear', str], 'image.cmap' : ['jet', str], # one of gray, jet, etc 'image.lut' : [256, validate_int], # lookup table 'image.origin' : ['upper', str], # lookup table 'image.resample' : [False, validate_bool], 'contour.negative_linestyle' : ['dashed', validate_negative_linestyle_legacy], # axes props 'axes.axisbelow' : [False, validate_bool], 'axes.hold' : [True, validate_bool], 'axes.facecolor' : ['w', validate_color], # background color; white 'axes.edgecolor' : ['k', validate_color], # edge color; black 'axes.linewidth' : [1.0, validate_float], # edge linewidth 'axes.titlesize' : ['large', validate_fontsize], # fontsize of the axes title 'axes.grid' : [False, validate_bool], # display grid or not 'axes.labelsize' : ['medium', validate_fontsize], # fontsize of the x any y labels 'axes.labelcolor' : ['k', validate_color], # color of axis label 'axes.formatter.limits' : [[-7, 7], validate_nseq_int(2)], # use scientific notation if log10 # of the axis range is smaller than the # first or larger than the second 'axes.unicode_minus' : [True, validate_bool], 'polaraxes.grid' : [True, validate_bool], # display polar grid or not #legend properties 'legend.fancybox' : [False,validate_bool], 'legend.loc' : ['upper right',validate_legend_loc], # at some point, this should be changed to 'best' 'legend.isaxes' : [True,validate_bool], # this option is internally ignored - it never served any useful purpose 'legend.numpoints' : [2, validate_int], # the number of points in the legend line 'legend.fontsize' : ['large', validate_fontsize], 'legend.pad' : [0, validate_float], # was 0.2, deprecated; the fractional whitespace inside the legend border 'legend.borderpad' : [0.4, validate_float], # units are fontsize 'legend.markerscale' : [1.0, validate_float], # the relative size of legend markers vs. original # the following dimensions are in axes coords 'legend.labelsep' : [0.010, validate_float], # the vertical space between the legend entries 'legend.handlelen' : [0.05, validate_float], # the length of the legend lines 'legend.handletextsep' : [0.02, validate_float], # the space between the legend line and legend text 'legend.axespad' : [0.02, validate_float], # the border between the axes and legend edge 'legend.shadow' : [False, validate_bool], 'legend.labelspacing' : [0.5, validate_float], # the vertical space between the legend entries 'legend.handlelength' : [2., validate_float], # the length of the legend lines 'legend.handletextpad' : [.8, validate_float], # the space between the legend line and legend text 'legend.borderaxespad' : [0.5, validate_float], # the border between the axes and legend edge 'legend.columnspacing' : [2., validate_float], # the border between the axes and legend edge 'legend.markerscale' : [1.0, validate_float], # the relative size of legend markers vs. original # the following dimensions are in axes coords 'legend.labelsep' : [0.010, validate_float], # the vertical space between the legend entries 'legend.handlelen' : [0.05, validate_float], # the length of the legend lines 'legend.handletextsep' : [0.02, validate_float], # the space between the legend line and legend text 'legend.axespad' : [0.5, validate_float], # the border between the axes and legend edge 'legend.shadow' : [False, validate_bool], # tick properties 'xtick.major.size' : [4, validate_float], # major xtick size in points 'xtick.minor.size' : [2, validate_float], # minor xtick size in points 'xtick.major.pad' : [4, validate_float], # distance to label in points 'xtick.minor.pad' : [4, validate_float], # distance to label in points 'xtick.color' : ['k', validate_color], # color of the xtick labels 'xtick.labelsize' : ['medium', validate_fontsize], # fontsize of the xtick labels 'xtick.direction' : ['in', str], # direction of xticks 'ytick.major.size' : [4, validate_float], # major ytick size in points 'ytick.minor.size' : [2, validate_float], # minor ytick size in points 'ytick.major.pad' : [4, validate_float], # distance to label in points 'ytick.minor.pad' : [4, validate_float], # distance to label in points 'ytick.color' : ['k', validate_color], # color of the ytick labels 'ytick.labelsize' : ['medium', validate_fontsize], # fontsize of the ytick labels 'ytick.direction' : ['in', str], # direction of yticks 'grid.color' : ['k', validate_color], # grid color 'grid.linestyle' : [':', str], # dotted 'grid.linewidth' : [0.5, validate_float], # in points # figure props # figure size in inches: width by height 'figure.figsize' : [ [8.0,6.0], validate_nseq_float(2)], 'figure.dpi' : [ 80, validate_float], # DPI 'figure.facecolor' : [ '0.75', validate_color], # facecolor; scalar gray 'figure.edgecolor' : [ 'w', validate_color], # edgecolor; white 'figure.autolayout' : [ False, validate_autolayout], 'figure.subplot.left' : [0.125, ValidateInterval(0, 1, closedmin=True, closedmax=True)], 'figure.subplot.right' : [0.9, ValidateInterval(0, 1, closedmin=True, closedmax=True)], 'figure.subplot.bottom' : [0.1, ValidateInterval(0, 1, closedmin=True, closedmax=True)], 'figure.subplot.top' : [0.9, ValidateInterval(0, 1, closedmin=True, closedmax=True)], 'figure.subplot.wspace' : [0.2, ValidateInterval(0, 1, closedmin=True, closedmax=False)], 'figure.subplot.hspace' : [0.2, ValidateInterval(0, 1, closedmin=True, closedmax=False)], 'savefig.dpi' : [100, validate_float], # DPI 'savefig.facecolor' : ['w', validate_color], # facecolor; white 'savefig.edgecolor' : ['w', validate_color], # edgecolor; white 'savefig.orientation' : ['portrait', validate_orientation], # edgecolor; white 'cairo.format' : ['png', validate_cairo_format], 'tk.window_focus' : [False, validate_bool], # Maintain shell focus for TkAgg 'tk.pythoninspect' : [False, validate_bool], # Set PYTHONINSPECT 'ps.papersize' : ['letter', validate_ps_papersize], # Set the papersize/type 'ps.useafm' : [False, validate_bool], # Set PYTHONINSPECT 'ps.usedistiller' : [False, validate_ps_distiller], # use ghostscript or xpdf to distill ps output 'ps.distiller.res' : [6000, validate_int], # dpi 'ps.fonttype' : [3, validate_fonttype], # 3 (Type3) or 42 (Truetype) 'pdf.compression' : [6, validate_int], # compression level from 0 to 9; 0 to disable 'pdf.inheritcolor' : [False, validate_bool], # ignore any color-setting commands from the frontend 'pdf.use14corefonts' : [False, validate_bool], # use only the 14 PDF core fonts # embedded in every PDF viewing application 'pdf.fonttype' : [3, validate_fonttype], # 3 (Type3) or 42 (Truetype) 'svg.image_inline' : [True, validate_bool], # write raster image data directly into the svg file 'svg.image_noscale' : [False, validate_bool], # suppress scaling of raster data embedded in SVG 'svg.embed_char_paths' : [True, validate_bool], # True to save all characters as paths in the SVG 'docstring.hardcopy' : [False, validate_bool], # set this when you want to generate hardcopy docstring 'plugins.directory' : ['.matplotlib_plugins', str], # where plugin directory is locate 'path.simplify' : [False, validate_bool], 'agg.path.chunksize' : [0, validate_int] # 0 to disable chunking; # recommend about 20000 to # enable. Experimental. } if __name__ == '__main__': rc = defaultParams rc['datapath'][0] = '/' for key in rc: if not rc[key][1](rc[key][0]) == rc[key][0]: print "%s: %s != %s"%(key, rc[key][1](rc[key][0]), rc[key][0])

23,344

Python

.py

448

44.694196

123

0.602805

numenta/nupic-legacy

6,330

1,556

464

AGPL-3.0

9/5/2024, 5:13:42 PM (Europe/Amsterdam)

26,252

dates.py

numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/dates.py

""" Matplotlib provides sophisticated date plotting capabilities, standing on the shoulders of python :mod:`datetime`, the add-on modules :mod:`pytz` and :mod:`dateutils`. :class:`datetime` objects are converted to floating point numbers which represent the number of days since 0001-01-01 UTC. The helper functions :func:`date2num`, :func:`num2date` and :func:`drange` are used to facilitate easy conversion to and from :mod:`datetime` and numeric ranges. A wide range of specific and general purpose date tick locators and formatters are provided in this module. See :mod:`matplotlib.ticker` for general information on tick locators and formatters. These are described below. All the matplotlib date converters, tickers and formatters are timezone aware, and the default timezone is given by the timezone parameter in your :file:`matplotlibrc` file. If you leave out a :class:`tz` timezone instance, the default from your rc file will be assumed. If you want to use a custom time zone, pass a :class:`pytz.timezone` instance with the tz keyword argument to :func:`num2date`, :func:`plot_date`, and any custom date tickers or locators you create. See `pytz <http://pytz.sourceforge.net>`_ for information on :mod:`pytz` and timezone handling. The `dateutil module <http://labix.org/python-dateutil>`_ provides additional code to handle date ticking, making it easy to place ticks on any kinds of dates. See examples below. Date tickers ------------ Most of the date tickers can locate single or multiple values. For example:: # tick on mondays every week loc = WeekdayLocator(byweekday=MO, tz=tz) # tick on mondays and saturdays loc = WeekdayLocator(byweekday=(MO, SA)) In addition, most of the constructors take an interval argument:: # tick on mondays every second week loc = WeekdayLocator(byweekday=MO, interval=2) The rrule locator allows completely general date ticking:: # tick every 5th easter rule = rrulewrapper(YEARLY, byeaster=1, interval=5) loc = RRuleLocator(rule) Here are all the date tickers: * :class:`MinuteLocator`: locate minutes * :class:`HourLocator`: locate hours * :class:`DayLocator`: locate specifed days of the month * :class:`WeekdayLocator`: Locate days of the week, eg MO, TU * :class:`MonthLocator`: locate months, eg 7 for july * :class:`YearLocator`: locate years that are multiples of base * :class:`RRuleLocator`: locate using a :class:`matplotlib.dates.rrulewrapper`. The :class:`rrulewrapper` is a simple wrapper around a :class:`dateutils.rrule` (`dateutil <https://moin.conectiva.com.br/DateUtil>`_) which allow almost arbitrary date tick specifications. See `rrule example <../examples/pylab_examples/date_demo_rrule.html>`_. Date formatters --------------- Here all all the date formatters: * :class:`DateFormatter`: use :func:`strftime` format strings * :class:`IndexDateFormatter`: date plots with implicit *x* indexing. """ import re, time, math, datetime import pytz # compatability for 2008c and older versions try: import pytz.zoneinfo except ImportError: pytz.zoneinfo = pytz.tzinfo pytz.zoneinfo.UTC = pytz.UTC import matplotlib import numpy as np import matplotlib.units as units import matplotlib.cbook as cbook import matplotlib.ticker as ticker from pytz import timezone from dateutil.rrule import rrule, MO, TU, WE, TH, FR, SA, SU, YEARLY, \ MONTHLY, WEEKLY, DAILY, HOURLY, MINUTELY, SECONDLY from dateutil.relativedelta import relativedelta import dateutil.parser __all__ = ( 'date2num', 'num2date', 'drange', 'epoch2num', 'num2epoch', 'mx2num', 'DateFormatter', 'IndexDateFormatter', 'DateLocator', 'RRuleLocator', 'YearLocator', 'MonthLocator', 'WeekdayLocator', 'DayLocator', 'HourLocator', 'MinuteLocator', 'SecondLocator', 'rrule', 'MO', 'TU', 'WE', 'TH', 'FR', 'SA', 'SU', 'YEARLY', 'MONTHLY', 'WEEKLY', 'DAILY', 'HOURLY', 'MINUTELY', 'SECONDLY', 'relativedelta', 'seconds', 'minutes', 'hours', 'weeks') UTC = pytz.timezone('UTC') def _get_rc_timezone(): s = matplotlib.rcParams['timezone'] return pytz.timezone(s) HOURS_PER_DAY = 24. MINUTES_PER_DAY = 60.*HOURS_PER_DAY SECONDS_PER_DAY = 60.*MINUTES_PER_DAY MUSECONDS_PER_DAY = 1e6*SECONDS_PER_DAY SEC_PER_MIN = 60 SEC_PER_HOUR = 3600 SEC_PER_DAY = SEC_PER_HOUR * 24 SEC_PER_WEEK = SEC_PER_DAY * 7 MONDAY, TUESDAY, WEDNESDAY, THURSDAY, FRIDAY, SATURDAY, SUNDAY = ( MO, TU, WE, TH, FR, SA, SU) WEEKDAYS = (MONDAY, TUESDAY, WEDNESDAY, THURSDAY, FRIDAY, SATURDAY, SUNDAY) def _to_ordinalf(dt): """ Convert :mod:`datetime` to the Gregorian date as UTC float days, preserving hours, minutes, seconds and microseconds. Return value is a :func:`float`. """ if hasattr(dt, 'tzinfo') and dt.tzinfo is not None: delta = dt.tzinfo.utcoffset(dt) if delta is not None: dt -= delta base = float(dt.toordinal()) if hasattr(dt, 'hour'): base += (dt.hour/HOURS_PER_DAY + dt.minute/MINUTES_PER_DAY + dt.second/SECONDS_PER_DAY + dt.microsecond/MUSECONDS_PER_DAY ) return base def _from_ordinalf(x, tz=None): """ Convert Gregorian float of the date, preserving hours, minutes, seconds and microseconds. Return value is a :class:`datetime`. """ if tz is None: tz = _get_rc_timezone() ix = int(x) dt = datetime.datetime.fromordinal(ix) remainder = float(x) - ix hour, remainder = divmod(24*remainder, 1) minute, remainder = divmod(60*remainder, 1) second, remainder = divmod(60*remainder, 1) microsecond = int(1e6*remainder) if microsecond<10: microsecond=0 # compensate for rounding errors dt = datetime.datetime( dt.year, dt.month, dt.day, int(hour), int(minute), int(second), microsecond, tzinfo=UTC).astimezone(tz) if microsecond>999990: # compensate for rounding errors dt += datetime.timedelta(microseconds=1e6-microsecond) return dt class strpdate2num: """ Use this class to parse date strings to matplotlib datenums when you know the date format string of the date you are parsing. See :file:`examples/load_demo.py`. """ def __init__(self, fmt): """ fmt: any valid strptime format is supported """ self.fmt = fmt def __call__(self, s): """s : string to be converted return value: a date2num float """ return date2num(datetime.datetime(*time.strptime(s, self.fmt)[:6])) def datestr2num(d): """ Convert a date string to a datenum using :func:`dateutil.parser.parse`. *d* can be a single string or a sequence of strings. """ if cbook.is_string_like(d): dt = dateutil.parser.parse(d) return date2num(dt) else: return date2num([dateutil.parser.parse(s) for s in d]) def date2num(d): """ *d* is either a :class:`datetime` instance or a sequence of datetimes. Return value is a floating point number (or sequence of floats) which gives number of days (fraction part represents hours, minutes, seconds) since 0001-01-01 00:00:00 UTC. """ if not cbook.iterable(d): return _to_ordinalf(d) else: return np.asarray([_to_ordinalf(val) for val in d]) def julian2num(j): 'Convert a Julian date (or sequence) to a matplotlib date (or sequence).' if cbook.iterable(j): j = np.asarray(j) return j + 1721425.5 def num2julian(n): 'Convert a matplotlib date (or sequence) to a Julian date (or sequence).' if cbook.iterable(n): n = np.asarray(n) return n - 1721425.5 def num2date(x, tz=None): """ *x* is a float value which gives number of days (fraction part represents hours, minutes, seconds) since 0001-01-01 00:00:00 UTC. Return value is a :class:`datetime` instance in timezone *tz* (default to rcparams TZ value). If *x* is a sequence, a sequence of :class:`datetime` objects will be returned. """ if tz is None: tz = _get_rc_timezone() if not cbook.iterable(x): return _from_ordinalf(x, tz) else: return [_from_ordinalf(val, tz) for val in x] def drange(dstart, dend, delta): """ Return a date range as float Gregorian ordinals. *dstart* and *dend* are :class:`datetime` instances. *delta* is a :class:`datetime.timedelta` instance. """ step = (delta.days + delta.seconds/SECONDS_PER_DAY + delta.microseconds/MUSECONDS_PER_DAY) f1 = _to_ordinalf(dstart) f2 = _to_ordinalf(dend) return np.arange(f1, f2, step) ### date tickers and formatters ### class DateFormatter(ticker.Formatter): """ Tick location is seconds since the epoch. Use a :func:`strftime` format string. Python only supports :mod:`datetime` :func:`strftime` formatting for years greater than 1900. Thanks to Andrew Dalke, Dalke Scientific Software who contributed the :func:`strftime` code below to include dates earlier than this year. """ illegal_s = re.compile(r"((^|[^%])(%%)*%s)") def __init__(self, fmt, tz=None): """ *fmt* is an :func:`strftime` format string; *tz* is the :class:`tzinfo` instance. """ if tz is None: tz = _get_rc_timezone() self.fmt = fmt self.tz = tz def __call__(self, x, pos=0): dt = num2date(x, self.tz) return self.strftime(dt, self.fmt) def set_tzinfo(self, tz): self.tz = tz def _findall(self, text, substr): # Also finds overlaps sites = [] i = 0 while 1: j = text.find(substr, i) if j == -1: break sites.append(j) i=j+1 return sites # Dalke: I hope I did this math right. Every 28 years the # calendar repeats, except through century leap years excepting # the 400 year leap years. But only if you're using the Gregorian # calendar. def strftime(self, dt, fmt): fmt = self.illegal_s.sub(r"\1", fmt) fmt = fmt.replace("%s", "s") if dt.year > 1900: return cbook.unicode_safe(dt.strftime(fmt)) year = dt.year # For every non-leap year century, advance by # 6 years to get into the 28-year repeat cycle delta = 2000 - year off = 6*(delta // 100 + delta // 400) year = year + off # Move to around the year 2000 year = year + ((2000 - year)//28)*28 timetuple = dt.timetuple() s1 = time.strftime(fmt, (year,) + timetuple[1:]) sites1 = self._findall(s1, str(year)) s2 = time.strftime(fmt, (year+28,) + timetuple[1:]) sites2 = self._findall(s2, str(year+28)) sites = [] for site in sites1: if site in sites2: sites.append(site) s = s1 syear = "%4d" % (dt.year,) for site in sites: s = s[:site] + syear + s[site+4:] return cbook.unicode_safe(s) class IndexDateFormatter(ticker.Formatter): """ Use with :class:`~matplotlib.ticker.IndexLocator` to cycle format strings by index. """ def __init__(self, t, fmt, tz=None): """ *t* is a sequence of dates (floating point days). *fmt* is a :func:`strftime` format string. """ if tz is None: tz = _get_rc_timezone() self.t = t self.fmt = fmt self.tz = tz def __call__(self, x, pos=0): 'Return the label for time *x* at position *pos*' ind = int(round(x)) if ind>=len(self.t) or ind<=0: return '' dt = num2date(self.t[ind], self.tz) return cbook.unicode_safe(dt.strftime(self.fmt)) class AutoDateFormatter(ticker.Formatter): """ This class attempts to figure out the best format to use. This is most useful when used with the :class:`AutoDateLocator`. """ # This can be improved by providing some user-level direction on # how to choose the best format (precedence, etc...) # Perhaps a 'struct' that has a field for each time-type where a # zero would indicate "don't show" and a number would indicate # "show" with some sort of priority. Same priorities could mean # show all with the same priority. # Or more simply, perhaps just a format string for each # possibility... def __init__(self, locator, tz=None): self._locator = locator self._formatter = DateFormatter("%b %d %Y %H:%M:%S %Z", tz) self._tz = tz def __call__(self, x, pos=0): scale = float( self._locator._get_unit() ) if ( scale == 365.0 ): self._formatter = DateFormatter("%Y", self._tz) elif ( scale == 30.0 ): self._formatter = DateFormatter("%b %Y", self._tz) elif ( (scale == 1.0) or (scale == 7.0) ): self._formatter = DateFormatter("%b %d %Y", self._tz) elif ( scale == (1.0/24.0) ): self._formatter = DateFormatter("%H:%M:%S %Z", self._tz) elif ( scale == (1.0/(24*60)) ): self._formatter = DateFormatter("%H:%M:%S %Z", self._tz) elif ( scale == (1.0/(24*3600)) ): self._formatter = DateFormatter("%H:%M:%S %Z", self._tz) else: self._formatter = DateFormatter("%b %d %Y %H:%M:%S %Z", self._tz) return self._formatter(x, pos) class rrulewrapper: def __init__(self, freq, **kwargs): self._construct = kwargs.copy() self._construct["freq"] = freq self._rrule = rrule(**self._construct) def set(self, **kwargs): self._construct.update(kwargs) self._rrule = rrule(**self._construct) def __getattr__(self, name): if name in self.__dict__: return self.__dict__[name] return getattr(self._rrule, name) class DateLocator(ticker.Locator): hms0d = {'byhour':0, 'byminute':0,'bysecond':0} def __init__(self, tz=None): """ *tz* is a :class:`tzinfo` instance. """ if tz is None: tz = _get_rc_timezone() self.tz = tz def set_tzinfo(self, tz): self.tz = tz def datalim_to_dt(self): dmin, dmax = self.axis.get_data_interval() return num2date(dmin, self.tz), num2date(dmax, self.tz) def viewlim_to_dt(self): vmin, vmax = self.axis.get_view_interval() return num2date(vmin, self.tz), num2date(vmax, self.tz) def _get_unit(self): """ Return how many days a unit of the locator is; used for intelligent autoscaling. """ return 1 def nonsingular(self, vmin, vmax): unit = self._get_unit() vmin -= 2*unit vmax += 2*unit return vmin, vmax class RRuleLocator(DateLocator): # use the dateutil rrule instance def __init__(self, o, tz=None): DateLocator.__init__(self, tz) self.rule = o def __call__(self): # if no data have been set, this will tank with a ValueError try: dmin, dmax = self.viewlim_to_dt() except ValueError: return [] if dmin>dmax: dmax, dmin = dmin, dmax delta = relativedelta(dmax, dmin) self.rule.set(dtstart=dmin-delta, until=dmax+delta) dates = self.rule.between(dmin, dmax, True) return date2num(dates) def _get_unit(self): """ Return how many days a unit of the locator is; used for intelligent autoscaling. """ freq = self.rule._rrule._freq if ( freq == YEARLY ): return 365 elif ( freq == MONTHLY ): return 30 elif ( freq == WEEKLY ): return 7 elif ( freq == DAILY ): return 1 elif ( freq == HOURLY ): return (1.0/24.0) elif ( freq == MINUTELY ): return (1.0/(24*60)) elif ( freq == SECONDLY ): return (1.0/(24*3600)) else: # error return -1 #or should this just return '1'? def autoscale(self): """ Set the view limits to include the data range. """ dmin, dmax = self.datalim_to_dt() if dmin>dmax: dmax, dmin = dmin, dmax delta = relativedelta(dmax, dmin) self.rule.set(dtstart=dmin-delta, until=dmax+delta) dmin, dmax = self.datalim_to_dt() vmin = self.rule.before(dmin, True) if not vmin: vmin=dmin vmax = self.rule.after(dmax, True) if not vmax: vmax=dmax vmin = date2num(vmin) vmax = date2num(vmax) return self.nonsingular(vmin, vmax) class AutoDateLocator(DateLocator): """ On autoscale, this class picks the best :class:`MultipleDateLocator` to set the view limits and the tick locations. """ def __init__(self, tz=None): DateLocator.__init__(self, tz) self._locator = YearLocator() self._freq = YEARLY def __call__(self): 'Return the locations of the ticks' self.refresh() return self._locator() def set_axis(self, axis): DateLocator.set_axis(self, axis) self._locator.set_axis(axis) def refresh(self): 'Refresh internal information based on current limits.' dmin, dmax = self.viewlim_to_dt() self._locator = self.get_locator(dmin, dmax) def _get_unit(self): if ( self._freq == YEARLY ): return 365.0 elif ( self._freq == MONTHLY ): return 30.0 elif ( self._freq == WEEKLY ): return 7.0 elif ( self._freq == DAILY ): return 1.0 elif ( self._freq == HOURLY ): return 1.0/24 elif ( self._freq == MINUTELY ): return 1.0/(24*60) elif ( self._freq == SECONDLY ): return 1.0/(24*3600) else: # error return -1 def autoscale(self): 'Try to choose the view limits intelligently.' dmin, dmax = self.datalim_to_dt() self._locator = self.get_locator(dmin, dmax) return self._locator.autoscale() def get_locator(self, dmin, dmax): 'Pick the best locator based on a distance.' delta = relativedelta(dmax, dmin) numYears = (delta.years * 1.0) numMonths = (numYears * 12.0) + delta.months numDays = (numMonths * 31.0) + delta.days numHours = (numDays * 24.0) + delta.hours numMinutes = (numHours * 60.0) + delta.minutes numSeconds = (numMinutes * 60.0) + delta.seconds numticks = 5 # self._freq = YEARLY interval = 1 bymonth = 1 bymonthday = 1 byhour = 0 byminute = 0 bysecond = 0 if ( numYears >= numticks ): self._freq = YEARLY elif ( numMonths >= numticks ): self._freq = MONTHLY bymonth = range(1, 13) if ( (0 <= numMonths) and (numMonths <= 14) ): interval = 1 # show every month elif ( (15 <= numMonths) and (numMonths <= 29) ): interval = 3 # show every 3 months elif ( (30 <= numMonths) and (numMonths <= 44) ): interval = 4 # show every 4 months else: # 45 <= numMonths <= 59 interval = 6 # show every 6 months elif ( numDays >= numticks ): self._freq = DAILY bymonth = None bymonthday = range(1, 32) if ( (0 <= numDays) and (numDays <= 9) ): interval = 1 # show every day elif ( (10 <= numDays) and (numDays <= 19) ): interval = 2 # show every 2 days elif ( (20 <= numDays) and (numDays <= 49) ): interval = 3 # show every 3 days elif ( (50 <= numDays) and (numDays <= 99) ): interval = 7 # show every 1 week else: # 100 <= numDays <= ~150 interval = 14 # show every 2 weeks elif ( numHours >= numticks ): self._freq = HOURLY bymonth = None bymonthday = None byhour = range(0, 24) # show every hour if ( (0 <= numHours) and (numHours <= 14) ): interval = 1 # show every hour elif ( (15 <= numHours) and (numHours <= 30) ): interval = 2 # show every 2 hours elif ( (30 <= numHours) and (numHours <= 45) ): interval = 3 # show every 3 hours elif ( (45 <= numHours) and (numHours <= 68) ): interval = 4 # show every 4 hours elif ( (68 <= numHours) and (numHours <= 90) ): interval = 6 # show every 6 hours else: # 90 <= numHours <= 120 interval = 12 # show every 12 hours elif ( numMinutes >= numticks ): self._freq = MINUTELY bymonth = None bymonthday = None byhour = None byminute = range(0, 60) if ( numMinutes > (10.0 * numticks) ): interval = 10 # end if elif ( numSeconds >= numticks ): self._freq = SECONDLY bymonth = None bymonthday = None byhour = None byminute = None bysecond = range(0, 60) if ( numSeconds > (10.0 * numticks) ): interval = 10 # end if else: # do what? # microseconds as floats, but floats from what reference point? pass rrule = rrulewrapper( self._freq, interval=interval, \ dtstart=dmin, until=dmax, \ bymonth=bymonth, bymonthday=bymonthday, \ byhour=byhour, byminute = byminute, \ bysecond=bysecond ) locator = RRuleLocator(rrule, self.tz) locator.set_axis(self.axis) locator.set_view_interval(*self.axis.get_view_interval()) locator.set_data_interval(*self.axis.get_data_interval()) return locator class YearLocator(DateLocator): """ Make ticks on a given day of each year that is a multiple of base. Examples:: # Tick every year on Jan 1st locator = YearLocator() # Tick every 5 years on July 4th locator = YearLocator(5, month=7, day=4) """ def __init__(self, base=1, month=1, day=1, tz=None): """ Mark years that are multiple of base on a given month and day (default jan 1). """ DateLocator.__init__(self, tz) self.base = ticker.Base(base) self.replaced = { 'month' : month, 'day' : day, 'hour' : 0, 'minute' : 0, 'second' : 0, 'tzinfo' : tz } def _get_unit(self): """ Return how many days a unit of the locator is; used for intelligent autoscaling. """ return 365 def __call__(self): dmin, dmax = self.viewlim_to_dt() ymin = self.base.le(dmin.year) ymax = self.base.ge(dmax.year) ticks = [dmin.replace(year=ymin, **self.replaced)] while 1: dt = ticks[-1] if dt.year>=ymax: return date2num(ticks) year = dt.year + self.base.get_base() ticks.append(dt.replace(year=year, **self.replaced)) def autoscale(self): """ Set the view limits to include the data range. """ dmin, dmax = self.datalim_to_dt() ymin = self.base.le(dmin.year) ymax = self.base.ge(dmax.year) vmin = dmin.replace(year=ymin, **self.replaced) vmax = dmax.replace(year=ymax, **self.replaced) vmin = date2num(vmin) vmax = date2num(vmax) return self.nonsingular(vmin, vmax) class MonthLocator(RRuleLocator): """ Make ticks on occurances of each month month, eg 1, 3, 12. """ def __init__(self, bymonth=None, bymonthday=1, interval=1, tz=None): """ Mark every month in *bymonth*; *bymonth* can be an int or sequence. Default is ``range(1,13)``, i.e. every month. *interval* is the interval between each iteration. For example, if ``interval=2``, mark every second occurance. """ if bymonth is None: bymonth=range(1,13) o = rrulewrapper(MONTHLY, bymonth=bymonth, bymonthday=bymonthday, interval=interval, **self.hms0d) RRuleLocator.__init__(self, o, tz) def _get_unit(self): """ Return how many days a unit of the locator is; used for intelligent autoscaling. """ return 30 class WeekdayLocator(RRuleLocator): """ Make ticks on occurances of each weekday. """ def __init__(self, byweekday=1, interval=1, tz=None): """ Mark every weekday in *byweekday*; *byweekday* can be a number or sequence. Elements of *byweekday* must be one of MO, TU, WE, TH, FR, SA, SU, the constants from :mod:`dateutils.rrule`. *interval* specifies the number of weeks to skip. For example, ``interval=2`` plots every second week. """ o = rrulewrapper(DAILY, byweekday=byweekday, interval=interval, **self.hms0d) RRuleLocator.__init__(self, o, tz) def _get_unit(self): """ return how many days a unit of the locator is; used for intelligent autoscaling. """ return 7 class DayLocator(RRuleLocator): """ Make ticks on occurances of each day of the month. For example, 1, 15, 30. """ def __init__(self, bymonthday=None, interval=1, tz=None): """ Mark every day in *bymonthday*; *bymonthday* can be an int or sequence. Default is to tick every day of the month: ``bymonthday=range(1,32)`` """ if bymonthday is None: bymonthday=range(1,32) o = rrulewrapper(DAILY, bymonthday=bymonthday, interval=interval, **self.hms0d) RRuleLocator.__init__(self, o, tz) def _get_unit(self): """ Return how many days a unit of the locator is; used for intelligent autoscaling. """ return 1 class HourLocator(RRuleLocator): """ Make ticks on occurances of each hour. """ def __init__(self, byhour=None, interval=1, tz=None): """ Mark every hour in *byhour*; *byhour* can be an int or sequence. Default is to tick every hour: ``byhour=range(24)`` *interval* is the interval between each iteration. For example, if ``interval=2``, mark every second occurrence. """ if byhour is None: byhour=range(24) rule = rrulewrapper(HOURLY, byhour=byhour, interval=interval, byminute=0, bysecond=0) RRuleLocator.__init__(self, rule, tz) def _get_unit(self): """ return how many days a unit of the locator is; use for intelligent autoscaling """ return 1/24. class MinuteLocator(RRuleLocator): """ Make ticks on occurances of each minute. """ def __init__(self, byminute=None, interval=1, tz=None): """ Mark every minute in *byminute*; *byminute* can be an int or sequence. Default is to tick every minute: ``byminute=range(60)`` *interval* is the interval between each iteration. For example, if ``interval=2``, mark every second occurrence. """ if byminute is None: byminute=range(60) rule = rrulewrapper(MINUTELY, byminute=byminute, interval=interval, bysecond=0) RRuleLocator.__init__(self, rule, tz) def _get_unit(self): """ Return how many days a unit of the locator is; used for intelligent autoscaling. """ return 1./(24*60) class SecondLocator(RRuleLocator): """ Make ticks on occurances of each second. """ def __init__(self, bysecond=None, interval=1, tz=None): """ Mark every second in *bysecond*; *bysecond* can be an int or sequence. Default is to tick every second: ``bysecond = range(60)`` *interval* is the interval between each iteration. For example, if ``interval=2``, mark every second occurrence. """ if bysecond is None: bysecond=range(60) rule = rrulewrapper(SECONDLY, bysecond=bysecond, interval=interval) RRuleLocator.__init__(self, rule, tz) def _get_unit(self): """ Return how many days a unit of the locator is; used for intelligent autoscaling. """ return 1./(24*60*60) def _close_to_dt(d1, d2, epsilon=5): 'Assert that datetimes *d1* and *d2* are within *epsilon* microseconds.' delta = d2-d1 mus = abs(delta.days*MUSECONDS_PER_DAY + delta.seconds*1e6 + delta.microseconds) assert(mus<epsilon) def _close_to_num(o1, o2, epsilon=5): 'Assert that float ordinals *o1* and *o2* are within *epsilon* microseconds.' delta = abs((o2-o1)*MUSECONDS_PER_DAY) assert(delta<epsilon) def epoch2num(e): """ Convert an epoch or sequence of epochs to the new date format, that is days since 0001. """ spd = 24.*3600. return 719163 + np.asarray(e)/spd def num2epoch(d): """ Convert days since 0001 to epoch. *d* can be a number or sequence. """ spd = 24.*3600. return (np.asarray(d)-719163)*spd def mx2num(mxdates): """ Convert mx :class:`datetime` instance (or sequence of mx instances) to the new date format. """ scalar = False if not cbook.iterable(mxdates): scalar = True mxdates = [mxdates] ret = epoch2num([m.ticks() for m in mxdates]) if scalar: return ret[0] else: return ret def date_ticker_factory(span, tz=None, numticks=5): """ Create a date locator with *numticks* (approx) and a date formatter for *span* in days. Return value is (locator, formatter). """ if span==0: span = 1/24. minutes = span*24*60 hours = span*24 days = span weeks = span/7. months = span/31. # approx years = span/365. if years>numticks: locator = YearLocator(int(years/numticks), tz=tz) # define fmt = '%Y' elif months>numticks: locator = MonthLocator(tz=tz) fmt = '%b %Y' elif weeks>numticks: locator = WeekdayLocator(tz=tz) fmt = '%a, %b %d' elif days>numticks: locator = DayLocator(interval=int(math.ceil(days/numticks)), tz=tz) fmt = '%b %d' elif hours>numticks: locator = HourLocator(interval=int(math.ceil(hours/numticks)), tz=tz) fmt = '%H:%M\n%b %d' elif minutes>numticks: locator = MinuteLocator(interval=int(math.ceil(minutes/numticks)), tz=tz) fmt = '%H:%M:%S' else: locator = MinuteLocator(tz=tz) fmt = '%H:%M:%S' formatter = DateFormatter(fmt, tz=tz) return locator, formatter def seconds(s): 'Return seconds as days.' return float(s)/SEC_PER_DAY def minutes(m): 'Return minutes as days.' return float(m)/MINUTES_PER_DAY def hours(h): 'Return hours as days.' return h/24. def weeks(w): 'Return weeks as days.' return w*7. class DateConverter(units.ConversionInterface): def axisinfo(unit): 'return the unit AxisInfo' if unit=='date': majloc = AutoDateLocator() majfmt = AutoDateFormatter(majloc) return units.AxisInfo( majloc = majloc, majfmt = majfmt, label='', ) else: return None axisinfo = staticmethod(axisinfo) def convert(value, unit): if units.ConversionInterface.is_numlike(value): return value return date2num(value) convert = staticmethod(convert) def default_units(x): 'Return the default unit for *x* or None' return 'date' default_units = staticmethod(default_units) units.registry[datetime.date] = DateConverter() units.registry[datetime.datetime] = DateConverter() if __name__=='__main__': #tz = None tz = pytz.timezone('US/Pacific') #tz = UTC dt = datetime.datetime(1011, 10, 9, 13, 44, 22, 101010, tzinfo=tz) x = date2num(dt) _close_to_dt(dt, num2date(x, tz)) #tz = _get_rc_timezone() d1 = datetime.datetime( 2000, 3, 1, tzinfo=tz) d2 = datetime.datetime( 2000, 3, 5, tzinfo=tz) #d1 = datetime.datetime( 2002, 1, 5, tzinfo=tz) #d2 = datetime.datetime( 2003, 12, 1, tzinfo=tz) delta = datetime.timedelta(hours=6) dates = drange(d1, d2, delta) # MGDTODO: Broken on transforms branch #print 'orig', d1 #print 'd2n and back', num2date(date2num(d1), tz) from _transforms import Value, Interval v1 = Value(date2num(d1)) v2 = Value(date2num(d2)) dlim = Interval(v1,v2) vlim = Interval(v1,v2) #locator = HourLocator(byhour=(3,15), tz=tz) #locator = MinuteLocator(byminute=(15,30,45), tz=tz) #locator = YearLocator(base=5, month=7, day=4, tz=tz) #locator = MonthLocator(bymonthday=15) locator = DayLocator(tz=tz) locator.set_data_interval(dlim) locator.set_view_interval(vlim) dmin, dmax = locator.autoscale() vlim.set_bounds(dmin, dmax) ticks = locator() fmt = '%Y-%m-%d %H:%M:%S %Z' formatter = DateFormatter(fmt, tz) #for t in ticks: print formatter(t) for t in dates: print formatter(t)

33,969

Python

.py

873

30.972509

81

0.600438

numenta/nupic-legacy

6,330

1,556

464

AGPL-3.0

9/5/2024, 5:13:42 PM (Europe/Amsterdam)

26,253

offsetbox.py

numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/offsetbox.py

""" The OffsetBox is a simple container artist. The child artist are meant to be drawn at a relative position to its parent. The [VH]Packer, DrawingArea and TextArea are derived from the OffsetBox. The [VH]Packer automatically adjust the relative postisions of their children, which should be instances of the OffsetBox. This is used to align similar artists together, e.g., in legend. The DrawingArea can contain any Artist as a child. The DrawingArea has a fixed width and height. The position of children relative to the parent is fixed. The TextArea is contains a single Text instance. The width and height of the TextArea instance is the width and height of the its child text. """ import matplotlib.transforms as mtransforms import matplotlib.artist as martist import matplotlib.text as mtext import numpy as np from matplotlib.patches import bbox_artist as mbbox_artist DEBUG=False # for debuging use def bbox_artist(*args, **kwargs): if DEBUG: mbbox_artist(*args, **kwargs) # _get_packed_offsets() and _get_aligned_offsets() are coded assuming # that we are packing boxes horizontally. But same function will be # used with vertical packing. def _get_packed_offsets(wd_list, total, sep, mode="fixed"): """ Geiven a list of (width, xdescent) of each boxes, calculate the total width and the x-offset positions of each items according to *mode*. xdescent is analagous to the usual descent, but along the x-direction. xdescent values are currently ignored. *wd_list* : list of (width, xdescent) of boxes to be packed. *sep* : spacing between boxes *total* : Intended total length. None if not used. *mode* : packing mode. 'fixed', 'expand', or 'equal'. """ w_list, d_list = zip(*wd_list) # d_list is currently not used. if mode == "fixed": offsets_ = np.add.accumulate([0]+[w + sep for w in w_list]) offsets = offsets_[:-1] if total is None: total = offsets_[-1] - sep return total, offsets elif mode == "expand": sep = (total - sum(w_list))/(len(w_list)-1.) offsets_ = np.add.accumulate([0]+[w + sep for w in w_list]) offsets = offsets_[:-1] return total, offsets elif mode == "equal": maxh = max(w_list) if total is None: total = (maxh+sep)*len(w_list) else: sep = float(total)/(len(w_list)) - maxh offsets = np.array([(maxh+sep)*i for i in range(len(w_list))]) return total, offsets else: raise ValueError("Unknown mode : %s" % (mode,)) def _get_aligned_offsets(hd_list, height, align="baseline"): """ Geiven a list of (height, descent) of each boxes, align the boxes with *align* and calculate the y-offsets of each boxes. total width and the offset positions of each items according to *mode*. xdescent is analagous to the usual descent, but along the x-direction. xdescent values are currently ignored. *hd_list* : list of (width, xdescent) of boxes to be aligned. *sep* : spacing between boxes *height* : Intended total length. None if not used. *align* : align mode. 'baseline', 'top', 'bottom', or 'center'. """ if height is None: height = max([h for h, d in hd_list]) if align == "baseline": height_descent = max([h-d for h, d in hd_list]) descent = max([d for h, d in hd_list]) height = height_descent + descent offsets = [0. for h, d in hd_list] elif align in ["left","top"]: descent=0. offsets = [d for h, d in hd_list] elif align in ["right","bottom"]: descent=0. offsets = [height-h+d for h, d in hd_list] elif align == "center": descent=0. offsets = [(height-h)*.5+d for h, d in hd_list] else: raise ValueError("Unknown Align mode : %s" % (align,)) return height, descent, offsets class OffsetBox(martist.Artist): """ The OffsetBox is a simple container artist. The child artist are meant to be drawn at a relative position to its parent. """ def __init__(self, *args, **kwargs): super(OffsetBox, self).__init__(*args, **kwargs) self._children = [] self._offset = (0, 0) def set_figure(self, fig): """ Set the figure accepts a class:`~matplotlib.figure.Figure` instance """ martist.Artist.set_figure(self, fig) for c in self.get_children(): c.set_figure(fig) def set_offset(self, xy): """ Set the offset accepts x, y, tuple, or a callable object. """ self._offset = xy def get_offset(self, width, height, xdescent, ydescent): """ Get the offset accepts extent of the box """ if callable(self._offset): return self._offset(width, height, xdescent, ydescent) else: return self._offset def set_width(self, width): """ Set the width accepts float """ self.width = width def set_height(self, height): """ Set the height accepts float """ self.height = height def get_children(self): """ Return a list of artists it contains. """ return self._children def get_extent_offsets(self, renderer): raise Exception("") def get_extent(self, renderer): """ Return with, height, xdescent, ydescent of box """ w, h, xd, yd, offsets = self.get_extent_offsets(renderer) return w, h, xd, yd def get_window_extent(self, renderer): ''' get the bounding box in display space. ''' w, h, xd, yd, offsets = self.get_extent_offsets(renderer) px, py = self.get_offset(w, h, xd, yd) return mtransforms.Bbox.from_bounds(px-xd, py-yd, w, h) def draw(self, renderer): """ Update the location of children if necessary and draw them to the given *renderer*. """ width, height, xdescent, ydescent, offsets = self.get_extent_offsets(renderer) px, py = self.get_offset(width, height, xdescent, ydescent) for c, (ox, oy) in zip(self.get_children(), offsets): c.set_offset((px+ox, py+oy)) c.draw(renderer) bbox_artist(self, renderer, fill=False, props=dict(pad=0.)) class PackerBase(OffsetBox): def __init__(self, pad=None, sep=None, width=None, height=None, align=None, mode=None, children=None): """ *pad* : boundary pad *sep* : spacing between items *width*, *height* : width and height of the container box. calculated if None. *align* : alignment of boxes *mode* : packing mode """ super(PackerBase, self).__init__() self.height = height self.width = width self.sep = sep self.pad = pad self.mode = mode self.align = align self._children = children class VPacker(PackerBase): """ The VPacker has its children packed vertically. It automatically adjust the relative postisions of children in the drawing time. """ def __init__(self, pad=None, sep=None, width=None, height=None, align="baseline", mode="fixed", children=None): """ *pad* : boundary pad *sep* : spacing between items *width*, *height* : width and height of the container box. calculated if None. *align* : alignment of boxes *mode* : packing mode """ super(VPacker, self).__init__(pad, sep, width, height, align, mode, children) def get_extent_offsets(self, renderer): """ update offset of childrens and return the extents of the box """ whd_list = [c.get_extent(renderer) for c in self.get_children()] whd_list = [(w, h, xd, (h-yd)) for w, h, xd, yd in whd_list] wd_list = [(w, xd) for w, h, xd, yd in whd_list] width, xdescent, xoffsets = _get_aligned_offsets(wd_list, self.width, self.align) pack_list = [(h, yd) for w,h,xd,yd in whd_list] height, yoffsets_ = _get_packed_offsets(pack_list, self.height, self.sep, self.mode) yoffsets = yoffsets_ + [yd for w,h,xd,yd in whd_list] ydescent = height - yoffsets[0] yoffsets = height - yoffsets #w, h, xd, h_yd = whd_list[-1] yoffsets = yoffsets - ydescent return width + 2*self.pad, height + 2*self.pad, \ xdescent+self.pad, ydescent+self.pad, \ zip(xoffsets, yoffsets) class HPacker(PackerBase): """ The HPacker has its children packed horizontally. It automatically adjust the relative postisions of children in the drawing time. """ def __init__(self, pad=None, sep=None, width=None, height=None, align="baseline", mode="fixed", children=None): """ *pad* : boundary pad *sep* : spacing between items *width*, *height* : width and height of the container box. calculated if None. *align* : alignment of boxes *mode* : packing mode """ super(HPacker, self).__init__(pad, sep, width, height, align, mode, children) def get_extent_offsets(self, renderer): """ update offset of childrens and return the extents of the box """ whd_list = [c.get_extent(renderer) for c in self.get_children()] if self.height is None: height_descent = max([h-yd for w,h,xd,yd in whd_list]) ydescent = max([yd for w,h,xd,yd in whd_list]) height = height_descent + ydescent else: height = self.height - 2*self._pad # width w/o pad hd_list = [(h, yd) for w, h, xd, yd in whd_list] height, ydescent, yoffsets = _get_aligned_offsets(hd_list, self.height, self.align) pack_list = [(w, xd) for w,h,xd,yd in whd_list] width, xoffsets_ = _get_packed_offsets(pack_list, self.width, self.sep, self.mode) xoffsets = xoffsets_ + [xd for w,h,xd,yd in whd_list] xdescent=whd_list[0][2] xoffsets = xoffsets - xdescent return width + 2*self.pad, height + 2*self.pad, \ xdescent + self.pad, ydescent + self.pad, \ zip(xoffsets, yoffsets) class DrawingArea(OffsetBox): """ The DrawingArea can contain any Artist as a child. The DrawingArea has a fixed width and height. The position of children relative to the parent is fixed. """ def __init__(self, width, height, xdescent=0., ydescent=0., clip=True): """ *width*, *height* : width and height of the container box. *xdescent*, *ydescent* : descent of the box in x- and y-direction. """ super(DrawingArea, self).__init__() self.width = width self.height = height self.xdescent = xdescent self.ydescent = ydescent self.offset_transform = mtransforms.Affine2D() self.offset_transform.clear() self.offset_transform.translate(0, 0) def get_transform(self): """ Return the :class:`~matplotlib.transforms.Transform` applied to the children """ return self.offset_transform def set_transform(self, t): """ set_transform is ignored. """ pass def set_offset(self, xy): """ set offset of the container. Accept : tuple of x,y cooridnate in disokay units. """ self._offset = xy self.offset_transform.clear() self.offset_transform.translate(xy[0], xy[1]) def get_offset(self): """ return offset of the container. """ return self._offset def get_window_extent(self, renderer): ''' get the bounding box in display space. ''' w, h, xd, yd = self.get_extent(renderer) ox, oy = self.get_offset() #w, h, xd, yd) return mtransforms.Bbox.from_bounds(ox-xd, oy-yd, w, h) def get_extent(self, renderer): """ Return with, height, xdescent, ydescent of box """ return self.width, self.height, self.xdescent, self.ydescent def add_artist(self, a): 'Add any :class:`~matplotlib.artist.Artist` to the container box' self._children.append(a) a.set_transform(self.get_transform()) def draw(self, renderer): """ Draw the children """ for c in self._children: c.draw(renderer) bbox_artist(self, renderer, fill=False, props=dict(pad=0.)) class TextArea(OffsetBox): """ The TextArea is contains a single Text instance. The text is placed at (0,0) with baseline+left alignment. The width and height of the TextArea instance is the width and height of the its child text. """ def __init__(self, s, textprops=None, multilinebaseline=None, minimumdescent=True, ): """ *s* : a string to be displayed. *textprops* : property dictionary for the text *multilinebaseline* : If True, baseline for multiline text is adjusted so that it is (approximatedly) center-aligned with singleline text. *minimumdescent* : If True, the box has a minimum descent of "p". """ if textprops is None: textprops = {} if not textprops.has_key("va"): textprops["va"]="baseline" self._text = mtext.Text(0, 0, s, **textprops) OffsetBox.__init__(self) self._children = [self._text] self.offset_transform = mtransforms.Affine2D() self.offset_transform.clear() self.offset_transform.translate(0, 0) self._baseline_transform = mtransforms.Affine2D() self._text.set_transform(self.offset_transform+self._baseline_transform) self._multilinebaseline = multilinebaseline self._minimumdescent = minimumdescent def set_multilinebaseline(self, t): """ Set multilinebaseline . If True, baseline for multiline text is adjusted so that it is (approximatedly) center-aligned with singleline text. """ self._multilinebaseline = t def get_multilinebaseline(self): """ get multilinebaseline . """ return self._multilinebaseline def set_minimumdescent(self, t): """ Set minimumdescent . If True, extent of the single line text is adjusted so that it has minimum descent of "p" """ self._minimumdescent = t def get_minimumdescent(self): """ get minimumdescent. """ return self._minimumdescent def set_transform(self, t): """ set_transform is ignored. """ pass def set_offset(self, xy): """ set offset of the container. Accept : tuple of x,y cooridnate in disokay units. """ self._offset = xy self.offset_transform.clear() self.offset_transform.translate(xy[0], xy[1]) def get_offset(self): """ return offset of the container. """ return self._offset def get_window_extent(self, renderer): ''' get the bounding box in display space. ''' w, h, xd, yd = self.get_extent(renderer) ox, oy = self.get_offset() #w, h, xd, yd) return mtransforms.Bbox.from_bounds(ox-xd, oy-yd, w, h) def get_extent(self, renderer): clean_line, ismath = self._text.is_math_text(self._text._text) _, h_, d_ = renderer.get_text_width_height_descent( "lp", self._text._fontproperties, ismath=False) bbox, info = self._text._get_layout(renderer) w, h = bbox.width, bbox.height line = info[0][0] # first line _, hh, dd = renderer.get_text_width_height_descent( clean_line, self._text._fontproperties, ismath=ismath) self._baseline_transform.clear() if len(info) > 1 and self._multilinebaseline: # multi line d = h-(hh-dd) # the baseline of the first line d_new = 0.5 * h - 0.5 * (h_ - d_) self._baseline_transform.translate(0, d - d_new) d = d_new else: # single line h_d = max(h_ - d_, h-dd) if self.get_minimumdescent(): ## to have a minimum descent, #i.e., "l" and "p" have same ## descents. d = max(dd, d_) else: d = dd h = h_d + d return w, h, 0., d def draw(self, renderer): """ Draw the children """ self._text.draw(renderer) bbox_artist(self, renderer, fill=False, props=dict(pad=0.))

17,728

Python

.py

450

29.648889

86

0.581216

numenta/nupic-legacy

6,330

1,556

464

AGPL-3.0

9/5/2024, 5:13:42 PM (Europe/Amsterdam)

26,254

_pylab_helpers.py

numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/_pylab_helpers.py

import sys, gc def error_msg(msg): print >>sys.stderr, msgs class Gcf(object): _activeQue = [] figs = {} def get_fig_manager(num): figManager = Gcf.figs.get(num, None) if figManager is not None: Gcf.set_active(figManager) return figManager get_fig_manager = staticmethod(get_fig_manager) def destroy(num): if not Gcf.has_fignum(num): return figManager = Gcf.figs[num] oldQue = Gcf._activeQue[:] Gcf._activeQue = [] for f in oldQue: if f != figManager: Gcf._activeQue.append(f) del Gcf.figs[num] #print len(Gcf.figs.keys()), len(Gcf._activeQue) figManager.destroy() gc.collect() destroy = staticmethod(destroy) def has_fignum(num): return num in Gcf.figs has_fignum = staticmethod(has_fignum) def get_all_fig_managers(): return Gcf.figs.values() get_all_fig_managers = staticmethod(get_all_fig_managers) def get_num_fig_managers(): return len(Gcf.figs.values()) get_num_fig_managers = staticmethod(get_num_fig_managers) def get_active(): if len(Gcf._activeQue)==0: return None else: return Gcf._activeQue[-1] get_active = staticmethod(get_active) def set_active(manager): oldQue = Gcf._activeQue[:] Gcf._activeQue = [] for m in oldQue: if m != manager: Gcf._activeQue.append(m) Gcf._activeQue.append(manager) Gcf.figs[manager.num] = manager set_active = staticmethod(set_active)

1,572

Python

.py

45

27.4

61

0.623265

numenta/nupic-legacy

6,330

1,556

464

AGPL-3.0

9/5/2024, 5:13:42 PM (Europe/Amsterdam)

26,255

collections.py

numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/collections.py

""" Classes for the efficient drawing of large collections of objects that share most properties, e.g. a large number of line segments or polygons. The classes are not meant to be as flexible as their single element counterparts (e.g. you may not be able to select all line styles) but they are meant to be fast for common use cases (e.g. a bunch of solid line segemnts) """ import copy, math, warnings import numpy as np from numpy import ma import matplotlib as mpl import matplotlib.cbook as cbook import matplotlib.colors as _colors # avoid conflict with kwarg import matplotlib.cm as cm import matplotlib.transforms as transforms import matplotlib.artist as artist import matplotlib.backend_bases as backend_bases import matplotlib.path as mpath import matplotlib.mlab as mlab class Collection(artist.Artist, cm.ScalarMappable): """ Base class for Collections. Must be subclassed to be usable. All properties in a collection must be sequences or scalars; if scalars, they will be converted to sequences. The property of the ith element of the collection is:: prop[i % len(props)] Keyword arguments and default values: * *edgecolors*: None * *facecolors*: None * *linewidths*: None * *antialiaseds*: None * *offsets*: None * *transOffset*: transforms.IdentityTransform() * *norm*: None (optional for :class:`matplotlib.cm.ScalarMappable`) * *cmap*: None (optional for :class:`matplotlib.cm.ScalarMappable`) *offsets* and *transOffset* are used to translate the patch after rendering (default no offsets). If any of *edgecolors*, *facecolors*, *linewidths*, *antialiaseds* are None, they default to their :data:`matplotlib.rcParams` patch setting, in sequence form. The use of :class:`~matplotlib.cm.ScalarMappable` is optional. If the :class:`~matplotlib.cm.ScalarMappable` matrix _A is not None (ie a call to set_array has been made), at draw time a call to scalar mappable will be made to set the face colors. """ _offsets = np.array([], np.float_) _transOffset = transforms.IdentityTransform() _transforms = [] zorder = 1 def __init__(self, edgecolors=None, facecolors=None, linewidths=None, linestyles='solid', antialiaseds = None, offsets = None, transOffset = None, norm = None, # optional for ScalarMappable cmap = None, # ditto pickradius = 5.0, urls = None, **kwargs ): """ Create a Collection %(Collection)s """ artist.Artist.__init__(self) cm.ScalarMappable.__init__(self, norm, cmap) self.set_edgecolor(edgecolors) self.set_facecolor(facecolors) self.set_linewidth(linewidths) self.set_linestyle(linestyles) self.set_antialiased(antialiaseds) self.set_urls(urls) self._uniform_offsets = None self._offsets = np.array([], np.float_) if offsets is not None: offsets = np.asarray(offsets) if len(offsets.shape) == 1: offsets = offsets[np.newaxis,:] # Make it Nx2. if transOffset is not None: self._offsets = offsets self._transOffset = transOffset else: self._uniform_offsets = offsets self._pickradius = pickradius self.update(kwargs) def _get_value(self, val): try: return (float(val), ) except TypeError: if cbook.iterable(val) and len(val): try: float(val[0]) except TypeError: pass # raise below else: return val raise TypeError('val must be a float or nonzero sequence of floats') def _get_bool(self, val): try: return (bool(val), ) except TypeError: if cbook.iterable(val) and len(val): try: bool(val[0]) except TypeError: pass # raise below else: return val raise TypeError('val must be a bool or nonzero sequence of them') def get_paths(self): raise NotImplementedError def get_transforms(self): return self._transforms def get_datalim(self, transData): transform = self.get_transform() transOffset = self._transOffset offsets = self._offsets paths = self.get_paths() if not transform.is_affine: paths = [transform.transform_path_non_affine(p) for p in paths] transform = transform.get_affine() if not transOffset.is_affine: offsets = transOffset.transform_non_affine(offsets) transOffset = transOffset.get_affine() offsets = np.asarray(offsets, np.float_) result = mpath.get_path_collection_extents( transform.frozen(), paths, self.get_transforms(), offsets, transOffset.frozen()) result = result.inverse_transformed(transData) return result def get_window_extent(self, renderer): bbox = self.get_datalim(transforms.IdentityTransform()) #TODO:check to ensure that this does not fail for #cases other than scatter plot legend return bbox def _prepare_points(self): """Point prep for drawing and hit testing""" transform = self.get_transform() transOffset = self._transOffset offsets = self._offsets paths = self.get_paths() if self.have_units(): paths = [] for path in self.get_paths(): vertices = path.vertices xs, ys = vertices[:, 0], vertices[:, 1] xs = self.convert_xunits(xs) ys = self.convert_yunits(ys) paths.append(mpath.Path(zip(xs, ys), path.codes)) if len(self._offsets): xs = self.convert_xunits(self._offsets[:0]) ys = self.convert_yunits(self._offsets[:1]) offsets = zip(xs, ys) offsets = np.asarray(offsets, np.float_) if not transform.is_affine: paths = [transform.transform_path_non_affine(path) for path in paths] transform = transform.get_affine() if not transOffset.is_affine: offsets = transOffset.transform_non_affine(offsets) transOffset = transOffset.get_affine() return transform, transOffset, offsets, paths def draw(self, renderer): if not self.get_visible(): return renderer.open_group(self.__class__.__name__) self.update_scalarmappable() clippath, clippath_trans = self.get_transformed_clip_path_and_affine() if clippath_trans is not None: clippath_trans = clippath_trans.frozen() transform, transOffset, offsets, paths = self._prepare_points() renderer.draw_path_collection( transform.frozen(), self.clipbox, clippath, clippath_trans, paths, self.get_transforms(), offsets, transOffset, self.get_facecolor(), self.get_edgecolor(), self._linewidths, self._linestyles, self._antialiaseds, self._urls) renderer.close_group(self.__class__.__name__) def contains(self, mouseevent): """ Test whether the mouse event occurred in the collection. Returns True | False, ``dict(ind=itemlist)``, where every item in itemlist contains the event. """ if callable(self._contains): return self._contains(self,mouseevent) if not self.get_visible(): return False,{} transform, transOffset, offsets, paths = self._prepare_points() ind = mpath.point_in_path_collection( mouseevent.x, mouseevent.y, self._pickradius, transform.frozen(), paths, self.get_transforms(), offsets, transOffset, len(self._facecolors)>0) return len(ind)>0,dict(ind=ind) def set_pickradius(self,pickradius): self.pickradius = 5 def get_pickradius(self): return self.pickradius def set_urls(self, urls): if urls is None: self._urls = [None,] else: self._urls = urls def get_urls(self): return self._urls def set_offsets(self, offsets): """ Set the offsets for the collection. *offsets* can be a scalar or a sequence. ACCEPTS: float or sequence of floats """ offsets = np.asarray(offsets, np.float_) if len(offsets.shape) == 1: offsets = offsets[np.newaxis,:] # Make it Nx2. #This decision is based on how they are initialized above if self._uniform_offsets is None: self._offsets = offsets else: self._uniform_offsets = offsets def get_offsets(self): """ Return the offsets for the collection. """ #This decision is based on how they are initialized above in __init__() if self._uniform_offsets is None: return self._offsets else: return self._uniform_offsets def set_linewidth(self, lw): """ Set the linewidth(s) for the collection. *lw* can be a scalar or a sequence; if it is a sequence the patches will cycle through the sequence ACCEPTS: float or sequence of floats """ if lw is None: lw = mpl.rcParams['patch.linewidth'] self._linewidths = self._get_value(lw) def set_linewidths(self, lw): """alias for set_linewidth""" return self.set_linewidth(lw) def set_lw(self, lw): """alias for set_linewidth""" return self.set_linewidth(lw) def set_linestyle(self, ls): """ Set the linestyle(s) for the collection. ACCEPTS: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) ] """ try: dashd = backend_bases.GraphicsContextBase.dashd if cbook.is_string_like(ls): if ls in dashd: dashes = [dashd[ls]] elif ls in cbook.ls_mapper: dashes = [dashd[cbook.ls_mapper[ls]]] else: raise ValueError() elif cbook.iterable(ls): try: dashes = [] for x in ls: if cbook.is_string_like(x): if x in dashd: dashes.append(dashd[x]) elif x in cbook.ls_mapper: dashes.append(dashd[cbook.ls_mapper[x]]) else: raise ValueError() elif cbook.iterable(x) and len(x) == 2: dashes.append(x) else: raise ValueError() except ValueError: if len(ls)==2: dashes = ls else: raise ValueError() else: raise ValueError() except ValueError: raise ValueError('Do not know how to convert %s to dashes'%ls) self._linestyles = dashes def set_linestyles(self, ls): """alias for set_linestyle""" return self.set_linestyle(ls) def set_dashes(self, ls): """alias for set_linestyle""" return self.set_linestyle(ls) def set_antialiased(self, aa): """ Set the antialiasing state for rendering. ACCEPTS: Boolean or sequence of booleans """ if aa is None: aa = mpl.rcParams['patch.antialiased'] self._antialiaseds = self._get_bool(aa) def set_antialiaseds(self, aa): """alias for set_antialiased""" return self.set_antialiased(aa) def set_color(self, c): """ Set both the edgecolor and the facecolor. ACCEPTS: matplotlib color arg or sequence of rgba tuples .. seealso:: :meth:`set_facecolor`, :meth:`set_edgecolor` """ self.set_facecolor(c) self.set_edgecolor(c) def set_facecolor(self, c): """ Set the facecolor(s) of the collection. *c* can be a matplotlib color arg (all patches have same color), or a sequence or rgba tuples; if it is a sequence the patches will cycle through the sequence ACCEPTS: matplotlib color arg or sequence of rgba tuples """ if c is None: c = mpl.rcParams['patch.facecolor'] self._facecolors_original = c self._facecolors = _colors.colorConverter.to_rgba_array(c, self._alpha) def set_facecolors(self, c): """alias for set_facecolor""" return self.set_facecolor(c) def get_facecolor(self): return self._facecolors get_facecolors = get_facecolor def get_edgecolor(self): if self._edgecolors == 'face': return self.get_facecolors() else: return self._edgecolors get_edgecolors = get_edgecolor def set_edgecolor(self, c): """ Set the edgecolor(s) of the collection. *c* can be a matplotlib color arg (all patches have same color), or a sequence or rgba tuples; if it is a sequence the patches will cycle through the sequence. If *c* is 'face', the edge color will always be the same as the face color. ACCEPTS: matplotlib color arg or sequence of rgba tuples """ if c == 'face': self._edgecolors = 'face' self._edgecolors_original = 'face' else: if c is None: c = mpl.rcParams['patch.edgecolor'] self._edgecolors_original = c self._edgecolors = _colors.colorConverter.to_rgba_array(c, self._alpha) def set_edgecolors(self, c): """alias for set_edgecolor""" return self.set_edgecolor(c) def set_alpha(self, alpha): """ Set the alpha tranparencies of the collection. *alpha* must be a float. ACCEPTS: float """ try: float(alpha) except TypeError: raise TypeError('alpha must be a float') else: artist.Artist.set_alpha(self, alpha) try: self._facecolors = _colors.colorConverter.to_rgba_array( self._facecolors_original, self._alpha) except (AttributeError, TypeError, IndexError): pass try: if self._edgecolors_original != 'face': self._edgecolors = _colors.colorConverter.to_rgba_array( self._edgecolors_original, self._alpha) except (AttributeError, TypeError, IndexError): pass def get_linewidths(self): return self._linewidths get_linewidth = get_linewidths def get_linestyles(self): return self._linestyles get_dashes = get_linestyle = get_linestyles def update_scalarmappable(self): """ If the scalar mappable array is not none, update colors from scalar data """ if self._A is None: return if self._A.ndim > 1: raise ValueError('Collections can only map rank 1 arrays') if len(self._facecolors): self._facecolors = self.to_rgba(self._A, self._alpha) else: self._edgecolors = self.to_rgba(self._A, self._alpha) def update_from(self, other): 'copy properties from other to self' artist.Artist.update_from(self, other) self._antialiaseds = other._antialiaseds self._edgecolors_original = other._edgecolors_original self._edgecolors = other._edgecolors self._facecolors_original = other._facecolors_original self._facecolors = other._facecolors self._linewidths = other._linewidths self._linestyles = other._linestyles self._pickradius = other._pickradius # these are not available for the object inspector until after the # class is built so we define an initial set here for the init # function and they will be overridden after object defn artist.kwdocd['Collection'] = """\ Valid Collection keyword arguments: * *edgecolors*: None * *facecolors*: None * *linewidths*: None * *antialiaseds*: None * *offsets*: None * *transOffset*: transforms.IdentityTransform() * *norm*: None (optional for :class:`matplotlib.cm.ScalarMappable`) * *cmap*: None (optional for :class:`matplotlib.cm.ScalarMappable`) *offsets* and *transOffset* are used to translate the patch after rendering (default no offsets) If any of *edgecolors*, *facecolors*, *linewidths*, *antialiaseds* are None, they default to their :data:`matplotlib.rcParams` patch setting, in sequence form. """ class QuadMesh(Collection): """ Class for the efficient drawing of a quadrilateral mesh. A quadrilateral mesh consists of a grid of vertices. The dimensions of this array are (*meshWidth* + 1, *meshHeight* + 1). Each vertex in the mesh has a different set of "mesh coordinates" representing its position in the topology of the mesh. For any values (*m*, *n*) such that 0 <= *m* <= *meshWidth* and 0 <= *n* <= *meshHeight*, the vertices at mesh coordinates (*m*, *n*), (*m*, *n* + 1), (*m* + 1, *n* + 1), and (*m* + 1, *n*) form one of the quadrilaterals in the mesh. There are thus (*meshWidth* * *meshHeight*) quadrilaterals in the mesh. The mesh need not be regular and the polygons need not be convex. A quadrilateral mesh is represented by a (2 x ((*meshWidth* + 1) * (*meshHeight* + 1))) numpy array *coordinates*, where each row is the *x* and *y* coordinates of one of the vertices. To define the function that maps from a data point to its corresponding color, use the :meth:`set_cmap` method. Each of these arrays is indexed in row-major order by the mesh coordinates of the vertex (or the mesh coordinates of the lower left vertex, in the case of the colors). For example, the first entry in *coordinates* is the coordinates of the vertex at mesh coordinates (0, 0), then the one at (0, 1), then at (0, 2) .. (0, meshWidth), (1, 0), (1, 1), and so on. """ def __init__(self, meshWidth, meshHeight, coordinates, showedges, antialiased=True): Collection.__init__(self) self._meshWidth = meshWidth self._meshHeight = meshHeight self._coordinates = coordinates self._showedges = showedges self._antialiased = antialiased self._paths = None self._bbox = transforms.Bbox.unit() self._bbox.update_from_data_xy(coordinates.reshape( ((meshWidth + 1) * (meshHeight + 1), 2))) # By converting to floats now, we can avoid that on every draw. self._coordinates = self._coordinates.reshape((meshHeight + 1, meshWidth + 1, 2)) self._coordinates = np.array(self._coordinates, np.float_) def get_paths(self, dataTrans=None): if self._paths is None: self._paths = self.convert_mesh_to_paths( self._meshWidth, self._meshHeight, self._coordinates) return self._paths #@staticmethod def convert_mesh_to_paths(meshWidth, meshHeight, coordinates): """ Converts a given mesh into a sequence of :class:`matplotlib.path.Path` objects for easier rendering by backends that do not directly support quadmeshes. This function is primarily of use to backend implementers. """ Path = mpath.Path if ma.isMaskedArray(coordinates): c = coordinates.data else: c = coordinates points = np.concatenate(( c[0:-1, 0:-1], c[0:-1, 1: ], c[1: , 1: ], c[1: , 0:-1], c[0:-1, 0:-1] ), axis=2) points = points.reshape((meshWidth * meshHeight, 5, 2)) return [Path(x) for x in points] convert_mesh_to_paths = staticmethod(convert_mesh_to_paths) def get_datalim(self, transData): return self._bbox def draw(self, renderer): if not self.get_visible(): return renderer.open_group(self.__class__.__name__) transform = self.get_transform() transOffset = self._transOffset offsets = self._offsets if self.have_units(): if len(self._offsets): xs = self.convert_xunits(self._offsets[:0]) ys = self.convert_yunits(self._offsets[:1]) offsets = zip(xs, ys) offsets = np.asarray(offsets, np.float_) if self.check_update('array'): self.update_scalarmappable() clippath, clippath_trans = self.get_transformed_clip_path_and_affine() if clippath_trans is not None: clippath_trans = clippath_trans.frozen() if not transform.is_affine: coordinates = self._coordinates.reshape( (self._coordinates.shape[0] * self._coordinates.shape[1], 2)) coordinates = transform.transform(coordinates) coordinates = coordinates.reshape(self._coordinates.shape) transform = transforms.IdentityTransform() else: coordinates = self._coordinates if not transOffset.is_affine: offsets = transOffset.transform_non_affine(offsets) transOffset = transOffset.get_affine() renderer.draw_quad_mesh( transform.frozen(), self.clipbox, clippath, clippath_trans, self._meshWidth, self._meshHeight, coordinates, offsets, transOffset, self.get_facecolor(), self._antialiased, self._showedges) renderer.close_group(self.__class__.__name__) class PolyCollection(Collection): def __init__(self, verts, sizes = None, closed = True, **kwargs): """ *verts* is a sequence of ( *verts0*, *verts1*, ...) where *verts_i* is a sequence of *xy* tuples of vertices, or an equivalent :mod:`numpy` array of shape (*nv*, 2). *sizes* is *None* (default) or a sequence of floats that scale the corresponding *verts_i*. The scaling is applied before the Artist master transform; if the latter is an identity transform, then the overall scaling is such that if *verts_i* specify a unit square, then *sizes_i* is the area of that square in points^2. If len(*sizes*) < *nv*, the additional values will be taken cyclically from the array. *closed*, when *True*, will explicitly close the polygon. %(Collection)s """ Collection.__init__(self,**kwargs) self._sizes = sizes self.set_verts(verts, closed) __init__.__doc__ = cbook.dedent(__init__.__doc__) % artist.kwdocd def set_verts(self, verts, closed=True): '''This allows one to delay initialization of the vertices.''' if closed: self._paths = [] for xy in verts: if np.ma.isMaskedArray(xy): if len(xy) and (xy[0] != xy[-1]).any(): xy = np.ma.concatenate([xy, [xy[0]]]) else: xy = np.asarray(xy) if len(xy) and (xy[0] != xy[-1]).any(): xy = np.concatenate([xy, [xy[0]]]) self._paths.append(mpath.Path(xy)) else: self._paths = [mpath.Path(xy) for xy in verts] def get_paths(self): return self._paths def draw(self, renderer): if self._sizes is not None: self._transforms = [ transforms.Affine2D().scale( (np.sqrt(x) * self.figure.dpi / 72.0)) for x in self._sizes] return Collection.draw(self, renderer) class BrokenBarHCollection(PolyCollection): """ A collection of horizontal bars spanning *yrange* with a sequence of *xranges*. """ def __init__(self, xranges, yrange, **kwargs): """ *xranges* sequence of (*xmin*, *xwidth*) *yrange* *ymin*, *ywidth* %(Collection)s """ ymin, ywidth = yrange ymax = ymin + ywidth verts = [ [(xmin, ymin), (xmin, ymax), (xmin+xwidth, ymax), (xmin+xwidth, ymin), (xmin, ymin)] for xmin, xwidth in xranges] PolyCollection.__init__(self, verts, **kwargs) __init__.__doc__ = cbook.dedent(__init__.__doc__) % artist.kwdocd @staticmethod def span_where(x, ymin, ymax, where, **kwargs): """ Create a BrokenBarHCollection to plot horizontal bars from over the regions in *x* where *where* is True. The bars range on the y-axis from *ymin* to *ymax* A :class:`BrokenBarHCollection` is returned. *kwargs* are passed on to the collection """ xranges = [] for ind0, ind1 in mlab.contiguous_regions(where): xslice = x[ind0:ind1] if not len(xslice): continue xranges.append((xslice[0], xslice[-1]-xslice[0])) collection = BrokenBarHCollection(xranges, [ymin, ymax-ymin], **kwargs) return collection class RegularPolyCollection(Collection): """Draw a collection of regular polygons with *numsides*.""" _path_generator = mpath.Path.unit_regular_polygon def __init__(self, numsides, rotation = 0 , sizes = (1,), **kwargs): """ *numsides* the number of sides of the polygon *rotation* the rotation of the polygon in radians *sizes* gives the area of the circle circumscribing the regular polygon in points^2 %(Collection)s Example: see :file:`examples/dynamic_collection.py` for complete example:: offsets = np.random.rand(20,2) facecolors = [cm.jet(x) for x in np.random.rand(20)] black = (0,0,0,1) collection = RegularPolyCollection( numsides=5, # a pentagon rotation=0, sizes=(50,), facecolors = facecolors, edgecolors = (black,), linewidths = (1,), offsets = offsets, transOffset = ax.transData, ) """ Collection.__init__(self,**kwargs) self._sizes = sizes self._numsides = numsides self._paths = [self._path_generator(numsides)] self._rotation = rotation self.set_transform(transforms.IdentityTransform()) __init__.__doc__ = cbook.dedent(__init__.__doc__) % artist.kwdocd def draw(self, renderer): self._transforms = [ transforms.Affine2D().rotate(-self._rotation).scale( (np.sqrt(x) * self.figure.dpi / 72.0) / np.sqrt(np.pi)) for x in self._sizes] return Collection.draw(self, renderer) def get_paths(self): return self._paths def get_numsides(self): return self._numsides def get_rotation(self): return self._rotation def get_sizes(self): return self._sizes class StarPolygonCollection(RegularPolyCollection): """ Draw a collection of regular stars with *numsides* points.""" _path_generator = mpath.Path.unit_regular_star class AsteriskPolygonCollection(RegularPolyCollection): """ Draw a collection of regular asterisks with *numsides* points.""" _path_generator = mpath.Path.unit_regular_asterisk class LineCollection(Collection): """ All parameters must be sequences or scalars; if scalars, they will be converted to sequences. The property of the ith line segment is:: prop[i % len(props)] i.e., the properties cycle if the ``len`` of props is less than the number of segments. """ zorder = 2 def __init__(self, segments, # Can be None. linewidths = None, colors = None, antialiaseds = None, linestyles = 'solid', offsets = None, transOffset = None, norm = None, cmap = None, pickradius = 5, **kwargs ): """ *segments* a sequence of (*line0*, *line1*, *line2*), where:: linen = (x0, y0), (x1, y1), ... (xm, ym) or the equivalent numpy array with two columns. Each line can be a different length. *colors* must be a sequence of RGBA tuples (eg arbitrary color strings, etc, not allowed). *antialiaseds* must be a sequence of ones or zeros *linestyles* [ 'solid' | 'dashed' | 'dashdot' | 'dotted' ] a string or dash tuple. The dash tuple is:: (offset, onoffseq), where *onoffseq* is an even length tuple of on and off ink in points. If *linewidths*, *colors*, or *antialiaseds* is None, they default to their rcParams setting, in sequence form. If *offsets* and *transOffset* are not None, then *offsets* are transformed by *transOffset* and applied after the segments have been transformed to display coordinates. If *offsets* is not None but *transOffset* is None, then the *offsets* are added to the segments before any transformation. In this case, a single offset can be specified as:: offsets=(xo,yo) and this value will be added cumulatively to each successive segment, so as to produce a set of successively offset curves. *norm* None (optional for :class:`matplotlib.cm.ScalarMappable`) *cmap* None (optional for :class:`matplotlib.cm.ScalarMappable`) *pickradius* is the tolerance for mouse clicks picking a line. The default is 5 pt. The use of :class:`~matplotlib.cm.ScalarMappable` is optional. If the :class:`~matplotlib.cm.ScalarMappable` matrix :attr:`~matplotlib.cm.ScalarMappable._A` is not None (ie a call to :meth:`~matplotlib.cm.ScalarMappable.set_array` has been made), at draw time a call to scalar mappable will be made to set the colors. """ if colors is None: colors = mpl.rcParams['lines.color'] if linewidths is None: linewidths = (mpl.rcParams['lines.linewidth'],) if antialiaseds is None: antialiaseds = (mpl.rcParams['lines.antialiased'],) self.set_linestyles(linestyles) colors = _colors.colorConverter.to_rgba_array(colors) Collection.__init__( self, edgecolors=colors, linewidths=linewidths, linestyles=linestyles, antialiaseds=antialiaseds, offsets=offsets, transOffset=transOffset, norm=norm, cmap=cmap, pickradius=pickradius, **kwargs) self.set_facecolors([]) self.set_segments(segments) def get_paths(self): return self._paths def set_segments(self, segments): if segments is None: return _segments = [] for seg in segments: if not np.ma.isMaskedArray(seg): seg = np.asarray(seg, np.float_) _segments.append(seg) if self._uniform_offsets is not None: _segments = self._add_offsets(_segments) self._paths = [mpath.Path(seg) for seg in _segments] set_verts = set_segments # for compatibility with PolyCollection def _add_offsets(self, segs): offsets = self._uniform_offsets Nsegs = len(segs) Noffs = offsets.shape[0] if Noffs == 1: for i in range(Nsegs): segs[i] = segs[i] + i * offsets else: for i in range(Nsegs): io = i%Noffs segs[i] = segs[i] + offsets[io:io+1] return segs def set_color(self, c): """ Set the color(s) of the line collection. *c* can be a matplotlib color arg (all patches have same color), or a sequence or rgba tuples; if it is a sequence the patches will cycle through the sequence ACCEPTS: matplotlib color arg or sequence of rgba tuples """ self._edgecolors = _colors.colorConverter.to_rgba_array(c) def color(self, c): """ Set the color(s) of the line collection. *c* can be a matplotlib color arg (all patches have same color), or a sequence or rgba tuples; if it is a sequence the patches will cycle through the sequence ACCEPTS: matplotlib color arg or sequence of rgba tuples """ warnings.warn('LineCollection.color deprecated; use set_color instead') return self.set_color(c) def get_color(self): return self._edgecolors get_colors = get_color # for compatibility with old versions class CircleCollection(Collection): """ A collection of circles, drawn using splines. """ def __init__(self, sizes, **kwargs): """ *sizes* Gives the area of the circle in points^2 %(Collection)s """ Collection.__init__(self,**kwargs) self._sizes = sizes self.set_transform(transforms.IdentityTransform()) self._paths = [mpath.Path.unit_circle()] __init__.__doc__ = cbook.dedent(__init__.__doc__) % artist.kwdocd def draw(self, renderer): # sizes is the area of the circle circumscribing the polygon # in points^2 self._transforms = [ transforms.Affine2D().scale( (np.sqrt(x) * self.figure.dpi / 72.0) / np.sqrt(np.pi)) for x in self._sizes] return Collection.draw(self, renderer) def get_paths(self): return self._paths class EllipseCollection(Collection): """ A collection of ellipses, drawn using splines. """ def __init__(self, widths, heights, angles, units='points', **kwargs): """ *widths*: sequence half-lengths of first axes (e.g., semi-major axis lengths) *heights*: sequence half-lengths of second axes *angles*: sequence angles of first axes, degrees CCW from the X-axis *units*: ['points' | 'inches' | 'dots' | 'width' | 'height' | 'x' | 'y'] units in which majors and minors are given; 'width' and 'height' refer to the dimensions of the axes, while 'x' and 'y' refer to the *offsets* data units. Additional kwargs inherited from the base :class:`Collection`: %(Collection)s """ Collection.__init__(self,**kwargs) self._widths = np.asarray(widths).ravel() self._heights = np.asarray(heights).ravel() self._angles = np.asarray(angles).ravel() *(np.pi/180.0) self._units = units self.set_transform(transforms.IdentityTransform()) self._transforms = [] self._paths = [mpath.Path.unit_circle()] self._initialized = False __init__.__doc__ = cbook.dedent(__init__.__doc__) % artist.kwdocd def _init(self): def on_dpi_change(fig): self._transforms = [] self.figure.callbacks.connect('dpi_changed', on_dpi_change) self._initialized = True def set_transforms(self): if not self._initialized: self._init() self._transforms = [] ax = self.axes fig = self.figure if self._units in ('x', 'y'): if self._units == 'x': dx0 = ax.viewLim.width dx1 = ax.bbox.width else: dx0 = ax.viewLim.height dx1 = ax.bbox.height sc = dx1/dx0 else: if self._units == 'inches': sc = fig.dpi elif self._units == 'points': sc = fig.dpi / 72.0 elif self._units == 'width': sc = ax.bbox.width elif self._units == 'height': sc = ax.bbox.height elif self._units == 'dots': sc = 1.0 else: raise ValueError('unrecognized units: %s' % self._units) _affine = transforms.Affine2D for x, y, a in zip(self._widths, self._heights, self._angles): trans = _affine().scale(x * sc, y * sc).rotate(a) self._transforms.append(trans) def draw(self, renderer): if True: ###not self._transforms: self.set_transforms() return Collection.draw(self, renderer) def get_paths(self): return self._paths class PatchCollection(Collection): """ A generic collection of patches. This makes it easier to assign a color map to a heterogeneous collection of patches. This also may improve plotting speed, since PatchCollection will draw faster than a large number of patches. """ def __init__(self, patches, match_original=False, **kwargs): """ *patches* a sequence of Patch objects. This list may include a heterogeneous assortment of different patch types. *match_original* If True, use the colors and linewidths of the original patches. If False, new colors may be assigned by providing the standard collection arguments, facecolor, edgecolor, linewidths, norm or cmap. If any of *edgecolors*, *facecolors*, *linewidths*, *antialiaseds* are None, they default to their :data:`matplotlib.rcParams` patch setting, in sequence form. The use of :class:`~matplotlib.cm.ScalarMappable` is optional. If the :class:`~matplotlib.cm.ScalarMappable` matrix _A is not None (ie a call to set_array has been made), at draw time a call to scalar mappable will be made to set the face colors. """ if match_original: def determine_facecolor(patch): if patch.fill: return patch.get_facecolor() return [0, 0, 0, 0] facecolors = [determine_facecolor(p) for p in patches] edgecolors = [p.get_edgecolor() for p in patches] linewidths = [p.get_linewidths() for p in patches] antialiaseds = [p.get_antialiased() for p in patches] Collection.__init__( self, edgecolors=edgecolors, facecolors=facecolors, linewidths=linewidths, linestyles='solid', antialiaseds = antialiaseds) else: Collection.__init__(self, **kwargs) paths = [p.get_transform().transform_path(p.get_path()) for p in patches] self._paths = paths def get_paths(self): return self._paths artist.kwdocd['Collection'] = patchstr = artist.kwdoc(Collection) for k in ('QuadMesh', 'PolyCollection', 'BrokenBarHCollection', 'RegularPolyCollection', 'StarPolygonCollection', 'PatchCollection', 'CircleCollection'): artist.kwdocd[k] = patchstr artist.kwdocd['LineCollection'] = artist.kwdoc(LineCollection)

39,876

Python

.py

939

32.13738

131

0.592839

numenta/nupic-legacy

6,330

1,556

464

AGPL-3.0

9/5/2024, 5:13:42 PM (Europe/Amsterdam)

26,256

finance.py

numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/finance.py

""" A collection of modules for collecting, analyzing and plotting financial data. User contributions welcome! """ #from __future__ import division import os, time, warnings from urllib import urlopen try: from hashlib import md5 except ImportError: from md5 import md5 #Deprecated in 2.5 try: import datetime except ImportError: raise ImportError('The finance module requires datetime support (python2.3)') import numpy as np from matplotlib import verbose, get_configdir from dates import date2num from matplotlib.cbook import Bunch from matplotlib.collections import LineCollection, PolyCollection from matplotlib.colors import colorConverter from lines import Line2D, TICKLEFT, TICKRIGHT from patches import Rectangle from matplotlib.transforms import Affine2D configdir = get_configdir() cachedir = os.path.join(configdir, 'finance.cache') def parse_yahoo_historical(fh, asobject=False, adjusted=True): """ Parse the historical data in file handle fh from yahoo finance and return results as a list of d, open, close, high, low, volume where d is a floating poing representation of date, as returned by date2num if adjust=True, use adjusted prices """ results = [] lines = fh.readlines() datefmt = None for line in lines[1:]: vals = line.split(',') if len(vals)!=7: continue datestr = vals[0] if datefmt is None: try: datefmt = '%Y-%m-%d' dt = datetime.date(*time.strptime(datestr, datefmt)[:3]) except ValueError: datefmt = '%d-%b-%y' # Old Yahoo--cached file? dt = datetime.date(*time.strptime(datestr, datefmt)[:3]) d = date2num(dt) open, high, low, close = [float(val) for val in vals[1:5]] volume = int(vals[5]) if adjusted: aclose = float(vals[6]) m = aclose/close open *= m high *= m low *= m close = aclose results.append((d, open, close, high, low, volume)) results.reverse() if asobject: if len(results)==0: return None else: date, open, close, high, low, volume = map(np.asarray, zip(*results)) return Bunch(date=date, open=open, close=close, high=high, low=low, volume=volume) else: return results def fetch_historical_yahoo(ticker, date1, date2, cachename=None): """ Fetch historical data for ticker between date1 and date2. date1 and date2 are datetime instances Ex: fh = fetch_historical_yahoo('^GSPC', d1, d2) cachename is the name of the local file cache. If None, will default to the md5 hash or the url (which incorporates the ticker and date range) a file handle is returned """ ticker = ticker.upper() d1 = (date1.month-1, date1.day, date1.year) d2 = (date2.month-1, date2.day, date2.year) urlFmt = 'http://table.finance.yahoo.com/table.csv?a=%d&b=%d&c=%d&d=%d&e=%d&f=%d&s=%s&y=0&g=d&ignore=.csv' url = urlFmt % (d1[0], d1[1], d1[2], d2[0], d2[1], d2[2], ticker) if cachename is None: cachename = os.path.join(cachedir, md5(url).hexdigest()) if os.path.exists(cachename): fh = file(cachename) verbose.report('Using cachefile %s for %s'%(cachename, ticker)) else: if not os.path.isdir(cachedir): os.mkdir(cachedir) fh = file(cachename, 'w') fh.write(urlopen(url).read()) fh.close() verbose.report('Saved %s data to cache file %s'%(ticker, cachename)) fh = file(cachename, 'r') return fh def quotes_historical_yahoo(ticker, date1, date2, asobject=False, adjusted=True, cachename=None): """ Get historical data for ticker between date1 and date2. date1 and date2 are datetime instances results are a list of tuples (d, open, close, high, low, volume) where d is a floating poing representation of date, as returned by date2num if asobject is True, the return val is an object with attrs date, open, close, high, low, volume, which are equal length arrays if adjust=True, use adjusted prices Ex: sp = f.quotes_historical_yahoo('^GSPC', d1, d2, asobject=True, adjusted=True) returns = (sp.open[1:] - sp.open[:-1])/sp.open[1:] [n,bins,patches] = hist(returns, 100) mu = mean(returns) sigma = std(returns) x = normpdf(bins, mu, sigma) plot(bins, x, color='red', lw=2) cachename is the name of the local file cache. If None, will default to the md5 hash or the url (which incorporates the ticker and date range) """ fh = fetch_historical_yahoo(ticker, date1, date2, cachename) try: ret = parse_yahoo_historical(fh, asobject, adjusted) except IOError, exc: warnings.warn('urlopen() failure\n' + url + '\n' + exc.strerror[1]) return None return ret def plot_day_summary(ax, quotes, ticksize=3, colorup='k', colordown='r', ): """ quotes is a list of (time, open, close, high, low, ...) tuples Represent the time, open, close, high, low as a vertical line ranging from low to high. The left tick is the open and the right tick is the close. time must be in float date format - see date2num ax : an Axes instance to plot to ticksize : open/close tick marker in points colorup : the color of the lines where close >= open colordown : the color of the lines where close < open return value is a list of lines added """ lines = [] for q in quotes: t, open, close, high, low = q[:5] if close>=open : color = colorup else : color = colordown vline = Line2D( xdata=(t, t), ydata=(low, high), color=color, antialiased=False, # no need to antialias vert lines ) oline = Line2D( xdata=(t, t), ydata=(open, open), color=color, antialiased=False, marker=TICKLEFT, markersize=ticksize, ) cline = Line2D( xdata=(t, t), ydata=(close, close), color=color, antialiased=False, markersize=ticksize, marker=TICKRIGHT) lines.extend((vline, oline, cline)) ax.add_line(vline) ax.add_line(oline) ax.add_line(cline) ax.autoscale_view() return lines def candlestick(ax, quotes, width=0.2, colorup='k', colordown='r', alpha=1.0): """ quotes is a list of (time, open, close, high, low, ...) tuples. As long as the first 5 elements of the tuples are these values, the tuple can be as long as you want (eg it may store volume). time must be in float days format - see date2num Plot the time, open, close, high, low as a vertical line ranging from low to high. Use a rectangular bar to represent the open-close span. If close >= open, use colorup to color the bar, otherwise use colordown ax : an Axes instance to plot to width : fraction of a day for the rectangle width colorup : the color of the rectangle where close >= open colordown : the color of the rectangle where close < open alpha : the rectangle alpha level return value is lines, patches where lines is a list of lines added and patches is a list of the rectangle patches added """ OFFSET = width/2.0 lines = [] patches = [] for q in quotes: t, open, close, high, low = q[:5] if close>=open : color = colorup lower = open height = close-open else : color = colordown lower = close height = open-close vline = Line2D( xdata=(t, t), ydata=(low, high), color='k', linewidth=0.5, antialiased=True, ) rect = Rectangle( xy = (t-OFFSET, lower), width = width, height = height, facecolor = color, edgecolor = color, ) rect.set_alpha(alpha) lines.append(vline) patches.append(rect) ax.add_line(vline) ax.add_patch(rect) ax.autoscale_view() return lines, patches def plot_day_summary2(ax, opens, closes, highs, lows, ticksize=4, colorup='k', colordown='r', ): """ Represent the time, open, close, high, low as a vertical line ranging from low to high. The left tick is the open and the right tick is the close. ax : an Axes instance to plot to ticksize : size of open and close ticks in points colorup : the color of the lines where close >= open colordown : the color of the lines where close < open return value is a list of lines added """ # note this code assumes if any value open, close, low, high is # missing they all are missing rangeSegments = [ ((i, low), (i, high)) for i, low, high in zip(xrange(len(lows)), lows, highs) if low != -1 ] # the ticks will be from ticksize to 0 in points at the origin and # we'll translate these to the i, close location openSegments = [ ((-ticksize, 0), (0, 0)) ] # the ticks will be from 0 to ticksize in points at the origin and # we'll translate these to the i, close location closeSegments = [ ((0, 0), (ticksize, 0)) ] offsetsOpen = [ (i, open) for i, open in zip(xrange(len(opens)), opens) if open != -1 ] offsetsClose = [ (i, close) for i, close in zip(xrange(len(closes)), closes) if close != -1 ] scale = ax.figure.dpi * (1.0/72.0) tickTransform = Affine2D().scale(scale, 0.0) r,g,b = colorConverter.to_rgb(colorup) colorup = r,g,b,1 r,g,b = colorConverter.to_rgb(colordown) colordown = r,g,b,1 colord = { True : colorup, False : colordown, } colors = [colord[open<close] for open, close in zip(opens, closes) if open!=-1 and close !=-1] assert(len(rangeSegments)==len(offsetsOpen)) assert(len(offsetsOpen)==len(offsetsClose)) assert(len(offsetsClose)==len(colors)) useAA = 0, # use tuple here lw = 1, # and here rangeCollection = LineCollection(rangeSegments, colors = colors, linewidths = lw, antialiaseds = useAA, ) openCollection = LineCollection(openSegments, colors = colors, antialiaseds = useAA, linewidths = lw, offsets = offsetsOpen, transOffset = ax.transData, ) openCollection.set_transform(tickTransform) closeCollection = LineCollection(closeSegments, colors = colors, antialiaseds = useAA, linewidths = lw, offsets = offsetsClose, transOffset = ax.transData, ) closeCollection.set_transform(tickTransform) minpy, maxx = (0, len(rangeSegments)) miny = min([low for low in lows if low !=-1]) maxy = max([high for high in highs if high != -1]) corners = (minpy, miny), (maxx, maxy) ax.update_datalim(corners) ax.autoscale_view() # add these last ax.add_collection(rangeCollection) ax.add_collection(openCollection) ax.add_collection(closeCollection) return rangeCollection, openCollection, closeCollection def candlestick2(ax, opens, closes, highs, lows, width=4, colorup='k', colordown='r', alpha=0.75, ): """ Represent the open, close as a bar line and high low range as a vertical line. ax : an Axes instance to plot to width : the bar width in points colorup : the color of the lines where close >= open colordown : the color of the lines where close < open alpha : bar transparency return value is lineCollection, barCollection """ # note this code assumes if any value open, close, low, high is # missing they all are missing delta = width/2. barVerts = [ ( (i-delta, open), (i-delta, close), (i+delta, close), (i+delta, open) ) for i, open, close in zip(xrange(len(opens)), opens, closes) if open != -1 and close!=-1 ] rangeSegments = [ ((i, low), (i, high)) for i, low, high in zip(xrange(len(lows)), lows, highs) if low != -1 ] r,g,b = colorConverter.to_rgb(colorup) colorup = r,g,b,alpha r,g,b = colorConverter.to_rgb(colordown) colordown = r,g,b,alpha colord = { True : colorup, False : colordown, } colors = [colord[open<close] for open, close in zip(opens, closes) if open!=-1 and close !=-1] assert(len(barVerts)==len(rangeSegments)) useAA = 0, # use tuple here lw = 0.5, # and here rangeCollection = LineCollection(rangeSegments, colors = ( (0,0,0,1), ), linewidths = lw, antialiaseds = useAA, ) barCollection = PolyCollection(barVerts, facecolors = colors, edgecolors = ( (0,0,0,1), ), antialiaseds = useAA, linewidths = lw, ) minx, maxx = 0, len(rangeSegments) miny = min([low for low in lows if low !=-1]) maxy = max([high for high in highs if high != -1]) corners = (minx, miny), (maxx, maxy) ax.update_datalim(corners) ax.autoscale_view() # add these last ax.add_collection(barCollection) ax.add_collection(rangeCollection) return rangeCollection, barCollection def volume_overlay(ax, opens, closes, volumes, colorup='k', colordown='r', width=4, alpha=1.0): """ Add a volume overlay to the current axes. The opens and closes are used to determine the color of the bar. -1 is missing. If a value is missing on one it must be missing on all ax : an Axes instance to plot to width : the bar width in points colorup : the color of the lines where close >= open colordown : the color of the lines where close < open alpha : bar transparency """ r,g,b = colorConverter.to_rgb(colorup) colorup = r,g,b,alpha r,g,b = colorConverter.to_rgb(colordown) colordown = r,g,b,alpha colord = { True : colorup, False : colordown, } colors = [colord[open<close] for open, close in zip(opens, closes) if open!=-1 and close !=-1] delta = width/2. bars = [ ( (i-delta, 0), (i-delta, v), (i+delta, v), (i+delta, 0)) for i, v in enumerate(volumes) if v != -1 ] barCollection = PolyCollection(bars, facecolors = colors, edgecolors = ( (0,0,0,1), ), antialiaseds = (0,), linewidths = (0.5,), ) corners = (0, 0), (len(bars), max(volumes)) ax.update_datalim(corners) ax.autoscale_view() # add these last return barCollection def volume_overlay2(ax, closes, volumes, colorup='k', colordown='r', width=4, alpha=1.0): """ Add a volume overlay to the current axes. The closes are used to determine the color of the bar. -1 is missing. If a value is missing on one it must be missing on all ax : an Axes instance to plot to width : the bar width in points colorup : the color of the lines where close >= open colordown : the color of the lines where close < open alpha : bar transparency nb: first point is not displayed - it is used only for choosing the right color """ return volume_overlay(ax,closes[:-1],closes[1:],volumes[1:],colorup,colordown,width,alpha) def volume_overlay3(ax, quotes, colorup='k', colordown='r', width=4, alpha=1.0): """ Add a volume overlay to the current axes. quotes is a list of (d, open, close, high, low, volume) and close-open is used to determine the color of the bar kwarg width : the bar width in points colorup : the color of the lines where close1 >= close0 colordown : the color of the lines where close1 < close0 alpha : bar transparency """ r,g,b = colorConverter.to_rgb(colorup) colorup = r,g,b,alpha r,g,b = colorConverter.to_rgb(colordown) colordown = r,g,b,alpha colord = { True : colorup, False : colordown, } dates, opens, closes, highs, lows, volumes = zip(*quotes) colors = [colord[close1>=close0] for close0, close1 in zip(closes[:-1], closes[1:]) if close0!=-1 and close1 !=-1] colors.insert(0,colord[closes[0]>=opens[0]]) right = width/2.0 left = -width/2.0 bars = [ ( (left, 0), (left, volume), (right, volume), (right, 0)) for d, open, close, high, low, volume in quotes] sx = ax.figure.dpi * (1.0/72.0) # scale for points sy = ax.bbox.height / ax.viewLim.height barTransform = Affine2D().scale(sx,sy) dates = [d for d, open, close, high, low, volume in quotes] offsetsBars = [(d, 0) for d in dates] useAA = 0, # use tuple here lw = 0.5, # and here barCollection = PolyCollection(bars, facecolors = colors, edgecolors = ( (0,0,0,1), ), antialiaseds = useAA, linewidths = lw, offsets = offsetsBars, transOffset = ax.transData, ) barCollection.set_transform(barTransform) minpy, maxx = (min(dates), max(dates)) miny = 0 maxy = max([volume for d, open, close, high, low, volume in quotes]) corners = (minpy, miny), (maxx, maxy) ax.update_datalim(corners) #print 'datalim', ax.dataLim.get_bounds() #print 'viewlim', ax.viewLim.get_bounds() ax.add_collection(barCollection) ax.autoscale_view() return barCollection def index_bar(ax, vals, facecolor='b', edgecolor='l', width=4, alpha=1.0, ): """ Add a bar collection graph with height vals (-1 is missing). ax : an Axes instance to plot to width : the bar width in points alpha : bar transparency """ facecolors = (colorConverter.to_rgba(facecolor, alpha),) edgecolors = (colorConverter.to_rgba(edgecolor, alpha),) right = width/2.0 left = -width/2.0 bars = [ ( (left, 0), (left, v), (right, v), (right, 0)) for v in vals if v != -1 ] sx = ax.figure.dpi * (1.0/72.0) # scale for points sy = ax.bbox.height / ax.viewLim.height barTransform = Affine2D().scale(sx,sy) offsetsBars = [ (i, 0) for i,v in enumerate(vals) if v != -1 ] barCollection = PolyCollection(bars, facecolors = facecolors, edgecolors = edgecolors, antialiaseds = (0,), linewidths = (0.5,), offsets = offsetsBars, transOffset = ax.transData, ) barCollection.set_transform(barTransform) minpy, maxx = (0, len(offsetsBars)) miny = 0 maxy = max([v for v in vals if v!=-1]) corners = (minpy, miny), (maxx, maxy) ax.update_datalim(corners) ax.autoscale_view() # add these last ax.add_collection(barCollection) return barCollection

20,558

Python

.py

480

32.9875

180

0.578723

numenta/nupic-legacy

6,330

1,556

464

AGPL-3.0

9/5/2024, 5:13:42 PM (Europe/Amsterdam)

26,257

scale.py

numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/scale.py

import textwrap import numpy as np from numpy import ma MaskedArray = ma.MaskedArray from cbook import dedent from ticker import NullFormatter, ScalarFormatter, LogFormatterMathtext, Formatter from ticker import NullLocator, LogLocator, AutoLocator, SymmetricalLogLocator, FixedLocator from transforms import Transform, IdentityTransform class ScaleBase(object): """ The base class for all scales. Scales are separable transformations, working on a single dimension. Any subclasses will want to override: - :attr:`name` - :meth:`get_transform` And optionally: - :meth:`set_default_locators_and_formatters` - :meth:`limit_range_for_scale` """ def get_transform(self): """ Return the :class:`~matplotlib.transforms.Transform` object associated with this scale. """ raise NotImplementedError def set_default_locators_and_formatters(self, axis): """ Set the :class:`~matplotlib.ticker.Locator` and :class:`~matplotlib.ticker.Formatter` objects on the given axis to match this scale. """ raise NotImplementedError def limit_range_for_scale(self, vmin, vmax, minpos): """ Returns the range *vmin*, *vmax*, possibly limited to the domain supported by this scale. *minpos* should be the minimum positive value in the data. This is used by log scales to determine a minimum value. """ return vmin, vmax class LinearScale(ScaleBase): """ The default linear scale. """ name = 'linear' def __init__(self, axis, **kwargs): pass def set_default_locators_and_formatters(self, axis): """ Set the locators and formatters to reasonable defaults for linear scaling. """ axis.set_major_locator(AutoLocator()) axis.set_major_formatter(ScalarFormatter()) axis.set_minor_locator(NullLocator()) axis.set_minor_formatter(NullFormatter()) def get_transform(self): """ The transform for linear scaling is just the :class:`~matplotlib.transforms.IdentityTransform`. """ return IdentityTransform() def _mask_non_positives(a): """ Return a Numpy masked array where all non-positive values are masked. If there are no non-positive values, the original array is returned. """ mask = a <= 0.0 if mask.any(): return ma.MaskedArray(a, mask=mask) return a class LogScale(ScaleBase): """ A standard logarithmic scale. Care is taken so non-positive values are not plotted. For computational efficiency (to push as much as possible to Numpy C code in the common cases), this scale provides different transforms depending on the base of the logarithm: - base 10 (:class:`Log10Transform`) - base 2 (:class:`Log2Transform`) - base e (:class:`NaturalLogTransform`) - arbitrary base (:class:`LogTransform`) """ name = 'log' class Log10Transform(Transform): input_dims = 1 output_dims = 1 is_separable = True base = 10.0 def transform(self, a): a = _mask_non_positives(a * 10.0) if isinstance(a, MaskedArray): return ma.log10(a) return np.log10(a) def inverted(self): return LogScale.InvertedLog10Transform() class InvertedLog10Transform(Transform): input_dims = 1 output_dims = 1 is_separable = True base = 10.0 def transform(self, a): return ma.power(10.0, a) / 10.0 def inverted(self): return LogScale.Log10Transform() class Log2Transform(Transform): input_dims = 1 output_dims = 1 is_separable = True base = 2.0 def transform(self, a): a = _mask_non_positives(a * 2.0) if isinstance(a, MaskedArray): return ma.log(a) / np.log(2) return np.log2(a) def inverted(self): return LogScale.InvertedLog2Transform() class InvertedLog2Transform(Transform): input_dims = 1 output_dims = 1 is_separable = True base = 2.0 def transform(self, a): return ma.power(2.0, a) / 2.0 def inverted(self): return LogScale.Log2Transform() class NaturalLogTransform(Transform): input_dims = 1 output_dims = 1 is_separable = True base = np.e def transform(self, a): a = _mask_non_positives(a * np.e) if isinstance(a, MaskedArray): return ma.log(a) return np.log(a) def inverted(self): return LogScale.InvertedNaturalLogTransform() class InvertedNaturalLogTransform(Transform): input_dims = 1 output_dims = 1 is_separable = True base = np.e def transform(self, a): return ma.power(np.e, a) / np.e def inverted(self): return LogScale.NaturalLogTransform() class LogTransform(Transform): input_dims = 1 output_dims = 1 is_separable = True def __init__(self, base): Transform.__init__(self) self.base = base def transform(self, a): a = _mask_non_positives(a * self.base) if isinstance(a, MaskedArray): return ma.log(a) / np.log(self.base) return np.log(a) / np.log(self.base) def inverted(self): return LogScale.InvertedLogTransform(self.base) class InvertedLogTransform(Transform): input_dims = 1 output_dims = 1 is_separable = True def __init__(self, base): Transform.__init__(self) self.base = base def transform(self, a): return ma.power(self.base, a) / self.base def inverted(self): return LogScale.LogTransform(self.base) def __init__(self, axis, **kwargs): """ *basex*/*basey*: The base of the logarithm *subsx*/*subsy*: Where to place the subticks between each major tick. Should be a sequence of integers. For example, in a log10 scale: ``[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]`` will place 10 logarithmically spaced minor ticks between each major tick. """ if axis.axis_name == 'x': base = kwargs.pop('basex', 10.0) subs = kwargs.pop('subsx', None) else: base = kwargs.pop('basey', 10.0) subs = kwargs.pop('subsy', None) if base == 10.0: self._transform = self.Log10Transform() elif base == 2.0: self._transform = self.Log2Transform() elif base == np.e: self._transform = self.NaturalLogTransform() else: self._transform = self.LogTransform(base) self.base = base self.subs = subs def set_default_locators_and_formatters(self, axis): """ Set the locators and formatters to specialized versions for log scaling. """ axis.set_major_locator(LogLocator(self.base)) axis.set_major_formatter(LogFormatterMathtext(self.base)) axis.set_minor_locator(LogLocator(self.base, self.subs)) axis.set_minor_formatter(NullFormatter()) def get_transform(self): """ Return a :class:`~matplotlib.transforms.Transform` instance appropriate for the given logarithm base. """ return self._transform def limit_range_for_scale(self, vmin, vmax, minpos): """ Limit the domain to positive values. """ return (vmin <= 0.0 and minpos or vmin, vmax <= 0.0 and minpos or vmax) class SymmetricalLogScale(ScaleBase): """ The symmetrical logarithmic scale is logarithmic in both the positive and negative directions from the origin. Since the values close to zero tend toward infinity, there is a need to have a range around zero that is linear. The parameter *linthresh* allows the user to specify the size of this range (-*linthresh*, *linthresh*). """ name = 'symlog' class SymmetricalLogTransform(Transform): input_dims = 1 output_dims = 1 is_separable = True def __init__(self, base, linthresh): Transform.__init__(self) self.base = base self.linthresh = linthresh self._log_base = np.log(base) self._linadjust = (np.log(linthresh) / self._log_base) / linthresh def transform(self, a): a = np.asarray(a) sign = np.sign(a) masked = ma.masked_inside(a, -self.linthresh, self.linthresh, copy=False) log = sign * ma.log(np.abs(masked)) / self._log_base if masked.mask.any(): return np.asarray(ma.where(masked.mask, a * self._linadjust, log)) else: return np.asarray(log) def inverted(self): return SymmetricalLogScale.InvertedSymmetricalLogTransform(self.base, self.linthresh) class InvertedSymmetricalLogTransform(Transform): input_dims = 1 output_dims = 1 is_separable = True def __init__(self, base, linthresh): Transform.__init__(self) self.base = base self.linthresh = linthresh self._log_base = np.log(base) self._log_linthresh = np.log(linthresh) / self._log_base self._linadjust = linthresh / (np.log(linthresh) / self._log_base) def transform(self, a): a = np.asarray(a) return np.where(a <= self._log_linthresh, np.where(a >= -self._log_linthresh, a * self._linadjust, -(np.power(self.base, -a))), np.power(self.base, a)) def inverted(self): return SymmetricalLogScale.SymmetricalLogTransform(self.base) def __init__(self, axis, **kwargs): """ *basex*/*basey*: The base of the logarithm *linthreshx*/*linthreshy*: The range (-*x*, *x*) within which the plot is linear (to avoid having the plot go to infinity around zero). *subsx*/*subsy*: Where to place the subticks between each major tick. Should be a sequence of integers. For example, in a log10 scale: ``[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]`` will place 10 logarithmically spaced minor ticks between each major tick. """ if axis.axis_name == 'x': base = kwargs.pop('basex', 10.0) linthresh = kwargs.pop('linthreshx', 2.0) subs = kwargs.pop('subsx', None) else: base = kwargs.pop('basey', 10.0) linthresh = kwargs.pop('linthreshy', 2.0) subs = kwargs.pop('subsy', None) self._transform = self.SymmetricalLogTransform(base, linthresh) self.base = base self.linthresh = linthresh self.subs = subs def set_default_locators_and_formatters(self, axis): """ Set the locators and formatters to specialized versions for symmetrical log scaling. """ axis.set_major_locator(SymmetricalLogLocator(self.get_transform())) axis.set_major_formatter(LogFormatterMathtext(self.base)) axis.set_minor_locator(SymmetricalLogLocator(self.get_transform(), self.subs)) axis.set_minor_formatter(NullFormatter()) def get_transform(self): """ Return a :class:`SymmetricalLogTransform` instance. """ return self._transform _scale_mapping = { 'linear' : LinearScale, 'log' : LogScale, 'symlog' : SymmetricalLogScale } def get_scale_names(): names = _scale_mapping.keys() names.sort() return names def scale_factory(scale, axis, **kwargs): """ Return a scale class by name. ACCEPTS: [ %(names)s ] """ scale = scale.lower() if scale is None: scale = 'linear' if scale not in _scale_mapping: raise ValueError("Unknown scale type '%s'" % scale) return _scale_mapping[scale](axis, **kwargs) scale_factory.__doc__ = dedent(scale_factory.__doc__) % \ {'names': " | ".join(get_scale_names())} def register_scale(scale_class): """ Register a new kind of scale. *scale_class* must be a subclass of :class:`ScaleBase`. """ _scale_mapping[scale_class.name] = scale_class def get_scale_docs(): """ Helper function for generating docstrings related to scales. """ docs = [] for name in get_scale_names(): scale_class = _scale_mapping[name] docs.append(" '%s'" % name) docs.append("") class_docs = dedent(scale_class.__init__.__doc__) class_docs = "".join([" %s\n" % x for x in class_docs.split("\n")]) docs.append(class_docs) docs.append("") return "\n".join(docs)

13,414

Python

.py

357

28.218487

97

0.590688

numenta/nupic-legacy

6,330

1,556

464

AGPL-3.0

9/5/2024, 5:13:42 PM (Europe/Amsterdam)

26,258

ticker.py

numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/ticker.py

""" Tick locating and formatting ============================ This module contains classes to support completely configurable tick locating and formatting. Although the locators know nothing about major or minor ticks, they are used by the Axis class to support major and minor tick locating and formatting. Generic tick locators and formatters are provided, as well as domain specific custom ones.. Tick locating ------------- The Locator class is the base class for all tick locators. The locators handle autoscaling of the view limits based on the data limits, and the choosing of tick locations. A useful semi-automatic tick locator is MultipleLocator. You initialize this with a base, eg 10, and it picks axis limits and ticks that are multiples of your base. The Locator subclasses defined here are :class:`NullLocator` No ticks :class:`FixedLocator` Tick locations are fixed :class:`IndexLocator` locator for index plots (eg. where x = range(len(y))) :class:`LinearLocator` evenly spaced ticks from min to max :class:`LogLocator` logarithmically ticks from min to max :class:`MultipleLocator` ticks and range are a multiple of base; either integer or float :class:`OldAutoLocator` choose a MultipleLocator and dyamically reassign it for intelligent ticking during navigation :class:`MaxNLocator` finds up to a max number of ticks at nice locations :class:`AutoLocator` :class:`MaxNLocator` with simple defaults. This is the default tick locator for most plotting. There are a number of locators specialized for date locations - see the dates module You can define your own locator by deriving from Locator. You must override the __call__ method, which returns a sequence of locations, and you will probably want to override the autoscale method to set the view limits from the data limits. If you want to override the default locator, use one of the above or a custom locator and pass it to the x or y axis instance. The relevant methods are:: ax.xaxis.set_major_locator( xmajorLocator ) ax.xaxis.set_minor_locator( xminorLocator ) ax.yaxis.set_major_locator( ymajorLocator ) ax.yaxis.set_minor_locator( yminorLocator ) The default minor locator is the NullLocator, eg no minor ticks on by default. Tick formatting --------------- Tick formatting is controlled by classes derived from Formatter. The formatter operates on a single tick value and returns a string to the axis. :class:`NullFormatter` no labels on the ticks :class:`FixedFormatter` set the strings manually for the labels :class:`FuncFormatter` user defined function sets the labels :class:`FormatStrFormatter` use a sprintf format string :class:`ScalarFormatter` default formatter for scalars; autopick the fmt string :class:`LogFormatter` formatter for log axes You can derive your own formatter from the Formatter base class by simply overriding the ``__call__`` method. The formatter class has access to the axis view and data limits. To control the major and minor tick label formats, use one of the following methods:: ax.xaxis.set_major_formatter( xmajorFormatter ) ax.xaxis.set_minor_formatter( xminorFormatter ) ax.yaxis.set_major_formatter( ymajorFormatter ) ax.yaxis.set_minor_formatter( yminorFormatter ) See :ref:`pylab_examples-major_minor_demo1` for an example of setting major an minor ticks. See the :mod:`matplotlib.dates` module for more information and examples of using date locators and formatters. """ from __future__ import division import math import numpy as np from matplotlib import rcParams from matplotlib import cbook from matplotlib import transforms as mtransforms class TickHelper: axis = None class DummyAxis: def __init__(self): self.dataLim = mtransforms.Bbox.unit() self.viewLim = mtransforms.Bbox.unit() def get_view_interval(self): return self.viewLim.intervalx def set_view_interval(self, vmin, vmax): self.viewLim.intervalx = vmin, vmax def get_data_interval(self): return self.dataLim.intervalx def set_data_interval(self, vmin, vmax): self.dataLim.intervalx = vmin, vmax def set_axis(self, axis): self.axis = axis def create_dummy_axis(self): if self.axis is None: self.axis = self.DummyAxis() def set_view_interval(self, vmin, vmax): self.axis.set_view_interval(vmin, vmax) def set_data_interval(self, vmin, vmax): self.axis.set_data_interval(vmin, vmax) def set_bounds(self, vmin, vmax): self.set_view_interval(vmin, vmax) self.set_data_interval(vmin, vmax) class Formatter(TickHelper): """ Convert the tick location to a string """ # some classes want to see all the locs to help format # individual ones locs = [] def __call__(self, x, pos=None): 'Return the format for tick val x at position pos; pos=None indicated unspecified' raise NotImplementedError('Derived must overide') def format_data(self,value): return self.__call__(value) def format_data_short(self,value): 'return a short string version' return self.format_data(value) def get_offset(self): return '' def set_locs(self, locs): self.locs = locs def fix_minus(self, s): """ some classes may want to replace a hyphen for minus with the proper unicode symbol as described `here <http://sourceforge.net/tracker/index.php?func=detail&aid=1962574&group_id=80706&atid=560720>`_. The default is to do nothing Note, if you use this method, eg in :meth`format_data` or call, you probably don't want to use it for :meth:`format_data_short` since the toolbar uses this for interative coord reporting and I doubt we can expect GUIs across platforms will handle the unicode correctly. So for now the classes that override :meth:`fix_minus` should have an explicit :meth:`format_data_short` method """ return s class NullFormatter(Formatter): 'Always return the empty string' def __call__(self, x, pos=None): 'Return the format for tick val *x* at position *pos*' return '' class FixedFormatter(Formatter): 'Return fixed strings for tick labels' def __init__(self, seq): """ seq is a sequence of strings. For positions `i<len(seq)` return *seq[i]* regardless of *x*. Otherwise return '' """ self.seq = seq self.offset_string = '' def __call__(self, x, pos=None): 'Return the format for tick val *x* at position *pos*' if pos is None or pos>=len(self.seq): return '' else: return self.seq[pos] def get_offset(self): return self.offset_string def set_offset_string(self, ofs): self.offset_string = ofs class FuncFormatter(Formatter): """ User defined function for formatting """ def __init__(self, func): self.func = func def __call__(self, x, pos=None): 'Return the format for tick val *x* at position *pos*' return self.func(x, pos) class FormatStrFormatter(Formatter): """ Use a format string to format the tick """ def __init__(self, fmt): self.fmt = fmt def __call__(self, x, pos=None): 'Return the format for tick val *x* at position *pos*' return self.fmt % x class OldScalarFormatter(Formatter): """ Tick location is a plain old number. """ def __call__(self, x, pos=None): 'Return the format for tick val *x* at position *pos*' xmin, xmax = self.axis.get_view_interval() d = abs(xmax - xmin) return self.pprint_val(x,d) def pprint_val(self, x, d): #if the number is not too big and it's an int, format it as an #int if abs(x)<1e4 and x==int(x): return '%d' % x if d < 1e-2: fmt = '%1.3e' elif d < 1e-1: fmt = '%1.3f' elif d > 1e5: fmt = '%1.1e' elif d > 10 : fmt = '%1.1f' elif d > 1 : fmt = '%1.2f' else: fmt = '%1.3f' s = fmt % x #print d, x, fmt, s tup = s.split('e') if len(tup)==2: mantissa = tup[0].rstrip('0').rstrip('.') sign = tup[1][0].replace('+', '') exponent = tup[1][1:].lstrip('0') s = '%se%s%s' %(mantissa, sign, exponent) else: s = s.rstrip('0').rstrip('.') return s class ScalarFormatter(Formatter): """ Tick location is a plain old number. If useOffset==True and the data range is much smaller than the data average, then an offset will be determined such that the tick labels are meaningful. Scientific notation is used for data < 1e-3 or data >= 1e4. """ def __init__(self, useOffset=True, useMathText=False): # useOffset allows plotting small data ranges with large offsets: # for example: [1+1e-9,1+2e-9,1+3e-9] # useMathText will render the offset and scientific notation in mathtext self._useOffset = useOffset self._usetex = rcParams['text.usetex'] self._useMathText = useMathText self.offset = 0 self.orderOfMagnitude = 0 self.format = '' self._scientific = True self._powerlimits = rcParams['axes.formatter.limits'] def fix_minus(self, s): 'use a unicode minus rather than hyphen' if rcParams['text.usetex'] or not rcParams['axes.unicode_minus']: return s else: return s.replace('-', u'\u2212') def __call__(self, x, pos=None): 'Return the format for tick val *x* at position *pos*' if len(self.locs)==0: return '' else: s = self.pprint_val(x) return self.fix_minus(s) def set_scientific(self, b): '''True or False to turn scientific notation on or off see also :meth:`set_powerlimits` ''' self._scientific = bool(b) def set_powerlimits(self, lims): ''' Sets size thresholds for scientific notation. e.g. ``xaxis.set_powerlimits((-3, 4))`` sets the pre-2007 default in which scientific notation is used for numbers less than 1e-3 or greater than 1e4. See also :meth:`set_scientific`. ''' assert len(lims) == 2, "argument must be a sequence of length 2" self._powerlimits = lims def format_data_short(self,value): 'return a short formatted string representation of a number' return '%1.3g'%value def format_data(self,value): 'return a formatted string representation of a number' s = self._formatSciNotation('%1.10e'% value) return self.fix_minus(s) def get_offset(self): """Return scientific notation, plus offset""" if len(self.locs)==0: return '' s = '' if self.orderOfMagnitude or self.offset: offsetStr = '' sciNotStr = '' if self.offset: offsetStr = self.format_data(self.offset) if self.offset > 0: offsetStr = '+' + offsetStr if self.orderOfMagnitude: if self._usetex or self._useMathText: sciNotStr = self.format_data(10**self.orderOfMagnitude) else: sciNotStr = '1e%d'% self.orderOfMagnitude if self._useMathText: if sciNotStr != '': sciNotStr = r'\times\mathdefault{%s}' % sciNotStr s = ''.join(('$',sciNotStr,r'\mathdefault{',offsetStr,'}$')) elif self._usetex: if sciNotStr != '': sciNotStr = r'\times%s' % sciNotStr s = ''.join(('$',sciNotStr,offsetStr,'$')) else: s = ''.join((sciNotStr,offsetStr)) return self.fix_minus(s) def set_locs(self, locs): 'set the locations of the ticks' self.locs = locs if len(self.locs) > 0: vmin, vmax = self.axis.get_view_interval() d = abs(vmax-vmin) if self._useOffset: self._set_offset(d) self._set_orderOfMagnitude(d) self._set_format() def _set_offset(self, range): # offset of 20,001 is 20,000, for example locs = self.locs if locs is None or not len(locs) or range == 0: self.offset = 0 return ave_loc = np.mean(locs) if ave_loc: # dont want to take log10(0) ave_oom = math.floor(math.log10(np.mean(np.absolute(locs)))) range_oom = math.floor(math.log10(range)) if np.absolute(ave_oom-range_oom) >= 3: # four sig-figs if ave_loc < 0: self.offset = math.ceil(np.max(locs)/10**range_oom)*10**range_oom else: self.offset = math.floor(np.min(locs)/10**(range_oom))*10**(range_oom) else: self.offset = 0 def _set_orderOfMagnitude(self,range): # if scientific notation is to be used, find the appropriate exponent # if using an numerical offset, find the exponent after applying the offset if not self._scientific: self.orderOfMagnitude = 0 return locs = np.absolute(self.locs) if self.offset: oom = math.floor(math.log10(range)) else: if locs[0] > locs[-1]: val = locs[0] else: val = locs[-1] if val == 0: oom = 0 else: oom = math.floor(math.log10(val)) if oom <= self._powerlimits[0]: self.orderOfMagnitude = oom elif oom >= self._powerlimits[1]: self.orderOfMagnitude = oom else: self.orderOfMagnitude = 0 def _set_format(self): # set the format string to format all the ticklabels # The floating point black magic (adding 1e-15 and formatting # to 8 digits) may warrant review and cleanup. locs = (np.asarray(self.locs)-self.offset) / 10**self.orderOfMagnitude+1e-15 sigfigs = [len(str('%1.8f'% loc).split('.')[1].rstrip('0')) \ for loc in locs] sigfigs.sort() self.format = '%1.' + str(sigfigs[-1]) + 'f' if self._usetex: self.format = '$%s$' % self.format elif self._useMathText: self.format = '$\mathdefault{%s}$' % self.format def pprint_val(self, x): xp = (x-self.offset)/10**self.orderOfMagnitude if np.absolute(xp) < 1e-8: xp = 0 return self.format % xp def _formatSciNotation(self, s): # transform 1e+004 into 1e4, for example tup = s.split('e') try: significand = tup[0].rstrip('0').rstrip('.') sign = tup[1][0].replace('+', '') exponent = tup[1][1:].lstrip('0') if self._useMathText or self._usetex: if significand == '1': # reformat 1x10^y as 10^y significand = '' if exponent: exponent = '10^{%s%s}'%(sign, exponent) if significand and exponent: return r'%s{\times}%s'%(significand, exponent) else: return r'%s%s'%(significand, exponent) else: s = ('%se%s%s' %(significand, sign, exponent)).rstrip('e') return s except IndexError, msg: return s class LogFormatter(Formatter): """ Format values for log axis; if attribute *decadeOnly* is True, only the decades will be labelled. """ def __init__(self, base=10.0, labelOnlyBase = True): """ *base* is used to locate the decade tick, which will be the only one to be labeled if *labelOnlyBase* is ``False`` """ self._base = base+0.0 self.labelOnlyBase=labelOnlyBase self.decadeOnly = True def base(self,base): 'change the *base* for labeling - warning: should always match the base used for :class:`LogLocator`' self._base=base def label_minor(self,labelOnlyBase): 'switch on/off minor ticks labeling' self.labelOnlyBase=labelOnlyBase def __call__(self, x, pos=None): 'Return the format for tick val *x* at position *pos*' vmin, vmax = self.axis.get_view_interval() d = abs(vmax - vmin) b=self._base if x == 0.0: return '0' sign = np.sign(x) # only label the decades fx = math.log(abs(x))/math.log(b) isDecade = self.is_decade(fx) if not isDecade and self.labelOnlyBase: s = '' elif x>10000: s= '%1.0e'%x elif x<1: s = '%1.0e'%x else : s = self.pprint_val(x,d) if sign == -1: s = '-%s' % s return self.fix_minus(s) def format_data(self,value): self.labelOnlyBase = False value = cbook.strip_math(self.__call__(value)) self.labelOnlyBase = True return value def format_data_short(self,value): 'return a short formatted string representation of a number' return '%1.3g'%value def is_decade(self, x): n = self.nearest_long(x) return abs(x-n)<1e-10 def nearest_long(self, x): if x==0: return 0L elif x>0: return long(x+0.5) else: return long(x-0.5) def pprint_val(self, x, d): #if the number is not too big and it's an int, format it as an #int if abs(x)<1e4 and x==int(x): return '%d' % x if d < 1e-2: fmt = '%1.3e' elif d < 1e-1: fmt = '%1.3f' elif d > 1e5: fmt = '%1.1e' elif d > 10 : fmt = '%1.1f' elif d > 1 : fmt = '%1.2f' else: fmt = '%1.3f' s = fmt % x #print d, x, fmt, s tup = s.split('e') if len(tup)==2: mantissa = tup[0].rstrip('0').rstrip('.') sign = tup[1][0].replace('+', '') exponent = tup[1][1:].lstrip('0') s = '%se%s%s' %(mantissa, sign, exponent) else: s = s.rstrip('0').rstrip('.') return s class LogFormatterExponent(LogFormatter): """ Format values for log axis; using ``exponent = log_base(value)`` """ def __call__(self, x, pos=None): 'Return the format for tick val *x* at position *pos*' vmin, vmax = self.axis.get_view_interval() vmin, vmax = mtransforms.nonsingular(vmin, vmax, expander = 0.05) d = abs(vmax-vmin) b=self._base if x == 0: return '0' sign = np.sign(x) # only label the decades fx = math.log(abs(x))/math.log(b) isDecade = self.is_decade(fx) if not isDecade and self.labelOnlyBase: s = '' #if 0: pass elif fx>10000: s= '%1.0e'%fx #elif x<1: s = '$10^{%d}$'%fx #elif x<1: s = '10^%d'%fx elif fx<1: s = '%1.0e'%fx else : s = self.pprint_val(fx,d) if sign == -1: s = '-%s' % s return self.fix_minus(s) class LogFormatterMathtext(LogFormatter): """ Format values for log axis; using ``exponent = log_base(value)`` """ def __call__(self, x, pos=None): 'Return the format for tick val *x* at position *pos*' b = self._base # only label the decades if x == 0: return '$0$' sign = np.sign(x) fx = math.log(abs(x))/math.log(b) isDecade = self.is_decade(fx) usetex = rcParams['text.usetex'] if sign == -1: sign_string = '-' else: sign_string = '' if not isDecade and self.labelOnlyBase: s = '' elif not isDecade: if usetex: s = r'$%s%d^{%.2f}$'% (sign_string, b, fx) else: s = '$\mathdefault{%s%d^{%.2f}}$'% (sign_string, b, fx) else: if usetex: s = r'$%s%d^{%d}$'% (sign_string, b, self.nearest_long(fx)) else: s = r'$\mathdefault{%s%d^{%d}}$'% (sign_string, b, self.nearest_long(fx)) return s class Locator(TickHelper): """ Determine the tick locations; Note, you should not use the same locator between different :class:`~matplotlib.axis.Axis` because the locator stores references to the Axis data and view limits """ def __call__(self): 'Return the locations of the ticks' raise NotImplementedError('Derived must override') def view_limits(self, vmin, vmax): """ select a scale for the range from vmin to vmax Normally This will be overridden. """ return mtransforms.nonsingular(vmin, vmax) def autoscale(self): 'autoscale the view limits' return self.view_limits(*self.axis.get_view_interval()) def pan(self, numsteps): 'Pan numticks (can be positive or negative)' ticks = self() numticks = len(ticks) vmin, vmax = self.axis.get_view_interval() vmin, vmax = mtransforms.nonsingular(vmin, vmax, expander = 0.05) if numticks>2: step = numsteps*abs(ticks[0]-ticks[1]) else: d = abs(vmax-vmin) step = numsteps*d/6. vmin += step vmax += step self.axis.set_view_interval(vmin, vmax, ignore=True) def zoom(self, direction): "Zoom in/out on axis; if direction is >0 zoom in, else zoom out" vmin, vmax = self.axis.get_view_interval() vmin, vmax = mtransforms.nonsingular(vmin, vmax, expander = 0.05) interval = abs(vmax-vmin) step = 0.1*interval*direction self.axis.set_view_interval(vmin + step, vmax - step, ignore=True) def refresh(self): 'refresh internal information based on current lim' pass class IndexLocator(Locator): """ Place a tick on every multiple of some base number of points plotted, eg on every 5th point. It is assumed that you are doing index plotting; ie the axis is 0, len(data). This is mainly useful for x ticks. """ def __init__(self, base, offset): 'place ticks on the i-th data points where (i-offset)%base==0' self._base = base self.offset = offset def __call__(self): 'Return the locations of the ticks' dmin, dmax = self.axis.get_data_interval() return np.arange(dmin + self.offset, dmax+1, self._base) class FixedLocator(Locator): """ Tick locations are fixed. If nbins is not None, the array of possible positions will be subsampled to keep the number of ticks <= nbins +1. """ def __init__(self, locs, nbins=None): self.locs = locs self.nbins = nbins if self.nbins is not None: self.nbins = max(self.nbins, 2) def __call__(self): 'Return the locations of the ticks' if self.nbins is None: return self.locs step = max(int(0.99 + len(self.locs) / float(self.nbins)), 1) return self.locs[::step] class NullLocator(Locator): """ No ticks """ def __call__(self): 'Return the locations of the ticks' return [] class LinearLocator(Locator): """ Determine the tick locations The first time this function is called it will try to set the number of ticks to make a nice tick partitioning. Thereafter the number of ticks will be fixed so that interactive navigation will be nice """ def __init__(self, numticks = None, presets=None): """ Use presets to set locs based on lom. A dict mapping vmin, vmax->locs """ self.numticks = numticks if presets is None: self.presets = {} else: self.presets = presets def __call__(self): 'Return the locations of the ticks' vmin, vmax = self.axis.get_view_interval() vmin, vmax = mtransforms.nonsingular(vmin, vmax, expander = 0.05) if vmax<vmin: vmin, vmax = vmax, vmin if (vmin, vmax) in self.presets: return self.presets[(vmin, vmax)] if self.numticks is None: self._set_numticks() if self.numticks==0: return [] ticklocs = np.linspace(vmin, vmax, self.numticks) return ticklocs def _set_numticks(self): self.numticks = 11 # todo; be smart here; this is just for dev def view_limits(self, vmin, vmax): 'Try to choose the view limits intelligently' if vmax<vmin: vmin, vmax = vmax, vmin if vmin==vmax: vmin-=1 vmax+=1 exponent, remainder = divmod(math.log10(vmax - vmin), 1) if remainder < 0.5: exponent -= 1 scale = 10**(-exponent) vmin = math.floor(scale*vmin)/scale vmax = math.ceil(scale*vmax)/scale return mtransforms.nonsingular(vmin, vmax) def closeto(x,y): if abs(x-y)<1e-10: return True else: return False class Base: 'this solution has some hacks to deal with floating point inaccuracies' def __init__(self, base): assert(base>0) self._base = base def lt(self, x): 'return the largest multiple of base < x' d,m = divmod(x, self._base) if closeto(m,0) and not closeto(m/self._base,1): return (d-1)*self._base return d*self._base def le(self, x): 'return the largest multiple of base <= x' d,m = divmod(x, self._base) if closeto(m/self._base,1): # was closeto(m, self._base) #looks like floating point error return (d+1)*self._base return d*self._base def gt(self, x): 'return the smallest multiple of base > x' d,m = divmod(x, self._base) if closeto(m/self._base,1): #looks like floating point error return (d+2)*self._base return (d+1)*self._base def ge(self, x): 'return the smallest multiple of base >= x' d,m = divmod(x, self._base) if closeto(m,0) and not closeto(m/self._base,1): return d*self._base return (d+1)*self._base def get_base(self): return self._base class MultipleLocator(Locator): """ Set a tick on every integer that is multiple of base in the view interval """ def __init__(self, base=1.0): self._base = Base(base) def __call__(self): 'Return the locations of the ticks' vmin, vmax = self.axis.get_view_interval() if vmax<vmin: vmin, vmax = vmax, vmin vmin = self._base.ge(vmin) base = self._base.get_base() n = (vmax - vmin + 0.001*base)//base locs = vmin + np.arange(n+1) * base return locs def view_limits(self, dmin, dmax): """ Set the view limits to the nearest multiples of base that contain the data """ vmin = self._base.le(dmin) vmax = self._base.ge(dmax) if vmin==vmax: vmin -=1 vmax +=1 return mtransforms.nonsingular(vmin, vmax) def scale_range(vmin, vmax, n = 1, threshold=100): dv = abs(vmax - vmin) maxabsv = max(abs(vmin), abs(vmax)) if maxabsv == 0 or dv/maxabsv < 1e-12: return 1.0, 0.0 meanv = 0.5*(vmax+vmin) if abs(meanv)/dv < threshold: offset = 0 elif meanv > 0: ex = divmod(math.log10(meanv), 1)[0] offset = 10**ex else: ex = divmod(math.log10(-meanv), 1)[0] offset = -10**ex ex = divmod(math.log10(dv/n), 1)[0] scale = 10**ex return scale, offset class MaxNLocator(Locator): """ Select no more than N intervals at nice locations. """ def __init__(self, nbins = 10, steps = None, trim = True, integer=False, symmetric=False): self._nbins = int(nbins) self._trim = trim self._integer = integer self._symmetric = symmetric if steps is None: self._steps = [1, 1.5, 2, 2.5, 3, 4, 5, 6, 8, 10] else: if int(steps[-1]) != 10: steps = list(steps) steps.append(10) self._steps = steps if integer: self._steps = [n for n in self._steps if divmod(n,1)[1] < 0.001] def bin_boundaries(self, vmin, vmax): nbins = self._nbins scale, offset = scale_range(vmin, vmax, nbins) if self._integer: scale = max(1, scale) vmin -= offset vmax -= offset raw_step = (vmax-vmin)/nbins scaled_raw_step = raw_step/scale for step in self._steps: if step < scaled_raw_step: continue step *= scale best_vmin = step*divmod(vmin, step)[0] best_vmax = best_vmin + step*nbins if (best_vmax >= vmax): break if self._trim: extra_bins = int(divmod((best_vmax - vmax), step)[0]) nbins -= extra_bins return (np.arange(nbins+1) * step + best_vmin + offset) def __call__(self): vmin, vmax = self.axis.get_view_interval() vmin, vmax = mtransforms.nonsingular(vmin, vmax, expander = 0.05) return self.bin_boundaries(vmin, vmax) def view_limits(self, dmin, dmax): if self._symmetric: maxabs = max(abs(dmin), abs(dmax)) dmin = -maxabs dmax = maxabs dmin, dmax = mtransforms.nonsingular(dmin, dmax, expander = 0.05) return np.take(self.bin_boundaries(dmin, dmax), [0,-1]) def decade_down(x, base=10): 'floor x to the nearest lower decade' lx = math.floor(math.log(x)/math.log(base)) return base**lx def decade_up(x, base=10): 'ceil x to the nearest higher decade' lx = math.ceil(math.log(x)/math.log(base)) return base**lx def is_decade(x,base=10): lx = math.log(x)/math.log(base) return lx==int(lx) class LogLocator(Locator): """ Determine the tick locations for log axes """ def __init__(self, base=10.0, subs=[1.0]): """ place ticks on the location= base**i*subs[j] """ self.base(base) self.subs(subs) self.numticks = 15 def base(self,base): """ set the base of the log scaling (major tick every base**i, i interger) """ self._base=base+0.0 def subs(self,subs): """ set the minor ticks the log scaling every base**i*subs[j] """ if subs is None: self._subs = None # autosub else: self._subs = np.asarray(subs)+0.0 def _set_numticks(self): self.numticks = 15 # todo; be smart here; this is just for dev def __call__(self): 'Return the locations of the ticks' b=self._base vmin, vmax = self.axis.get_view_interval() if vmin <= 0.0: vmin = self.axis.get_minpos() if vmin <= 0.0: raise ValueError( "Data has no positive values, and therefore can not be log-scaled.") vmin = math.log(vmin)/math.log(b) vmax = math.log(vmax)/math.log(b) if vmax<vmin: vmin, vmax = vmax, vmin numdec = math.floor(vmax)-math.ceil(vmin) if self._subs is None: # autosub if numdec>10: subs = np.array([1.0]) elif numdec>6: subs = np.arange(2.0, b, 2.0) else: subs = np.arange(2.0, b) else: subs = self._subs stride = 1 while numdec/stride+1 > self.numticks: stride += 1 decades = np.arange(math.floor(vmin), math.ceil(vmax)+stride, stride) if len(subs) > 1 or (len(subs == 1) and subs[0] != 1.0): ticklocs = [] for decadeStart in b**decades: ticklocs.extend( subs*decadeStart ) else: ticklocs = b**decades return np.array(ticklocs) def view_limits(self, vmin, vmax): 'Try to choose the view limits intelligently' if vmax<vmin: vmin, vmax = vmax, vmin minpos = self.axis.get_minpos() if minpos<=0: raise ValueError( "Data has no positive values, and therefore can not be log-scaled.") if vmin <= minpos: vmin = minpos if not is_decade(vmin,self._base): vmin = decade_down(vmin,self._base) if not is_decade(vmax,self._base): vmax = decade_up(vmax,self._base) if vmin==vmax: vmin = decade_down(vmin,self._base) vmax = decade_up(vmax,self._base) result = mtransforms.nonsingular(vmin, vmax) return result class SymmetricalLogLocator(Locator): """ Determine the tick locations for log axes """ def __init__(self, transform, subs=[1.0]): """ place ticks on the location= base**i*subs[j] """ self._transform = transform self._subs = subs self.numticks = 15 def _set_numticks(self): self.numticks = 15 # todo; be smart here; this is just for dev def __call__(self): 'Return the locations of the ticks' b = self._transform.base vmin, vmax = self.axis.get_view_interval() vmin, vmax = self._transform.transform((vmin, vmax)) if vmax<vmin: vmin, vmax = vmax, vmin numdec = math.floor(vmax)-math.ceil(vmin) if self._subs is None: if numdec>10: subs = np.array([1.0]) elif numdec>6: subs = np.arange(2.0, b, 2.0) else: subs = np.arange(2.0, b) else: subs = np.asarray(self._subs) stride = 1 while numdec/stride+1 > self.numticks: stride += 1 decades = np.arange(math.floor(vmin), math.ceil(vmax)+stride, stride) if len(subs) > 1 or subs[0] != 1.0: ticklocs = [] for decade in decades: ticklocs.extend(subs * (np.sign(decade) * b ** np.abs(decade))) else: ticklocs = np.sign(decades) * b ** np.abs(decades) return np.array(ticklocs) def view_limits(self, vmin, vmax): 'Try to choose the view limits intelligently' b = self._transform.base if vmax<vmin: vmin, vmax = vmax, vmin if not is_decade(abs(vmin), b): if vmin < 0: vmin = -decade_up(-vmin, b) else: vmin = decade_down(vmin, b) if not is_decade(abs(vmax), b): if vmax < 0: vmax = -decade_down(-vmax, b) else: vmax = decade_up(vmax, b) if vmin == vmax: if vmin < 0: vmin = -decade_up(-vmin, b) vmax = -decade_down(-vmax, b) else: vmin = decade_down(vmin, b) vmax = decade_up(vmax, b) result = mtransforms.nonsingular(vmin, vmax) return result class AutoLocator(MaxNLocator): def __init__(self): MaxNLocator.__init__(self, nbins=9, steps=[1, 2, 5, 10]) class OldAutoLocator(Locator): """ On autoscale this class picks the best MultipleLocator to set the view limits and the tick locs. """ def __init__(self): self._locator = LinearLocator() def __call__(self): 'Return the locations of the ticks' self.refresh() return self._locator() def refresh(self): 'refresh internal information based on current lim' vmin, vmax = self.axis.get_view_interval() vmin, vmax = mtransforms.nonsingular(vmin, vmax, expander = 0.05) d = abs(vmax-vmin) self._locator = self.get_locator(d) def view_limits(self, vmin, vmax): 'Try to choose the view limits intelligently' d = abs(vmax-vmin) self._locator = self.get_locator(d) return self._locator.view_limits(vmin, vmax) def get_locator(self, d): 'pick the best locator based on a distance' d = abs(d) if d<=0: locator = MultipleLocator(0.2) else: try: ld = math.log10(d) except OverflowError: raise RuntimeError('AutoLocator illegal data interval range') fld = math.floor(ld) base = 10**fld #if ld==fld: base = 10**(fld-1) #else: base = 10**fld if d >= 5*base : ticksize = base elif d >= 2*base : ticksize = base/2.0 else : ticksize = base/5.0 locator = MultipleLocator(ticksize) return locator __all__ = ('TickHelper', 'Formatter', 'FixedFormatter', 'NullFormatter', 'FuncFormatter', 'FormatStrFormatter', 'ScalarFormatter', 'LogFormatter', 'LogFormatterExponent', 'LogFormatterMathtext', 'Locator', 'IndexLocator', 'FixedLocator', 'NullLocator', 'LinearLocator', 'LogLocator', 'AutoLocator', 'MultipleLocator', 'MaxNLocator', )

37,420

Python

.py

970

29.780412

109

0.586351

numenta/nupic-legacy

6,330

1,556

464

AGPL-3.0

9/5/2024, 5:13:42 PM (Europe/Amsterdam)

26,259

artist.py

numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/artist.py

from __future__ import division import re, warnings import matplotlib import matplotlib.cbook as cbook from transforms import Bbox, IdentityTransform, TransformedBbox, TransformedPath from path import Path ## Note, matplotlib artists use the doc strings for set and get # methods to enable the introspection methods of setp and getp. Every # set_* method should have a docstring containing the line # # ACCEPTS: [ legal | values ] # # and aliases for setters and getters should have a docstring that # starts with 'alias for ', as in 'alias for set_somemethod' # # You may wonder why we use so much boiler-plate manually defining the # set_alias and get_alias functions, rather than using some clever # python trick. The answer is that I need to be able to manipulate # the docstring, and there is no clever way to do that in python 2.2, # as far as I can see - see # http://groups.google.com/groups?hl=en&lr=&threadm=mailman.5090.1098044946.5135.python-list%40python.org&rnum=1&prev=/groups%3Fq%3D__doc__%2Bauthor%253Ajdhunter%2540ace.bsd.uchicago.edu%26hl%3Den%26btnG%3DGoogle%2BSearch class Artist(object): """ Abstract base class for someone who renders into a :class:`FigureCanvas`. """ aname = 'Artist' zorder = 0 def __init__(self): self.figure = None self._transform = None self._transformSet = False self._visible = True self._animated = False self._alpha = 1.0 self.clipbox = None self._clippath = None self._clipon = True self._lod = False self._label = '' self._picker = None self._contains = None self.eventson = False # fire events only if eventson self._oid = 0 # an observer id self._propobservers = {} # a dict from oids to funcs self.axes = None self._remove_method = None self._url = None self.x_isdata = True # False to avoid updating Axes.dataLim with x self.y_isdata = True # with y self._snap = None def remove(self): """ Remove the artist from the figure if possible. The effect will not be visible until the figure is redrawn, e.g., with :meth:`matplotlib.axes.Axes.draw_idle`. Call :meth:`matplotlib.axes.Axes.relim` to update the axes limits if desired. Note: :meth:`~matplotlib.axes.Axes.relim` will not see collections even if the collection was added to axes with *autolim* = True. Note: there is no support for removing the artist's legend entry. """ # There is no method to set the callback. Instead the parent should set # the _remove_method attribute directly. This would be a protected # attribute if Python supported that sort of thing. The callback # has one parameter, which is the child to be removed. if self._remove_method != None: self._remove_method(self) else: raise NotImplementedError('cannot remove artist') # TODO: the fix for the collections relim problem is to move the # limits calculation into the artist itself, including the property # of whether or not the artist should affect the limits. Then there # will be no distinction between axes.add_line, axes.add_patch, etc. # TODO: add legend support def have_units(self): 'Return *True* if units are set on the *x* or *y* axes' ax = self.axes if ax is None or ax.xaxis is None: return False return ax.xaxis.have_units() or ax.yaxis.have_units() def convert_xunits(self, x): """For artists in an axes, if the xaxis has units support, convert *x* using xaxis unit type """ ax = getattr(self, 'axes', None) if ax is None or ax.xaxis is None: #print 'artist.convert_xunits no conversion: ax=%s'%ax return x return ax.xaxis.convert_units(x) def convert_yunits(self, y): """For artists in an axes, if the yaxis has units support, convert *y* using yaxis unit type """ ax = getattr(self, 'axes', None) if ax is None or ax.yaxis is None: return y return ax.yaxis.convert_units(y) def set_axes(self, axes): """ Set the :class:`~matplotlib.axes.Axes` instance in which the artist resides, if any. ACCEPTS: an :class:`~matplotlib.axes.Axes` instance """ self.axes = axes def get_axes(self): """ Return the :class:`~matplotlib.axes.Axes` instance the artist resides in, or *None* """ return self.axes def add_callback(self, func): """ Adds a callback function that will be called whenever one of the :class:`Artist`'s properties changes. Returns an *id* that is useful for removing the callback with :meth:`remove_callback` later. """ oid = self._oid self._propobservers[oid] = func self._oid += 1 return oid def remove_callback(self, oid): """ Remove a callback based on its *id*. .. seealso:: :meth:`add_callback` """ try: del self._propobservers[oid] except KeyError: pass def pchanged(self): """ Fire an event when property changed, calling all of the registered callbacks. """ for oid, func in self._propobservers.items(): func(self) def is_transform_set(self): """ Returns *True* if :class:`Artist` has a transform explicitly set. """ return self._transformSet def set_transform(self, t): """ Set the :class:`~matplotlib.transforms.Transform` instance used by this artist. ACCEPTS: :class:`~matplotlib.transforms.Transform` instance """ self._transform = t self._transformSet = True self.pchanged() def get_transform(self): """ Return the :class:`~matplotlib.transforms.Transform` instance used by this artist. """ if self._transform is None: self._transform = IdentityTransform() return self._transform def hitlist(self, event): """ List the children of the artist which contain the mouse event *event*. """ import traceback L = [] try: hascursor,info = self.contains(event) if hascursor: L.append(self) except: traceback.print_exc() print "while checking",self.__class__ for a in self.get_children(): L.extend(a.hitlist(event)) return L def get_children(self): """ Return a list of the child :class:`Artist`s this :class:`Artist` contains. """ return [] def contains(self, mouseevent): """Test whether the artist contains the mouse event. Returns the truth value and a dictionary of artist specific details of selection, such as which points are contained in the pick radius. See individual artists for details. """ if callable(self._contains): return self._contains(self,mouseevent) #raise NotImplementedError,str(self.__class__)+" needs 'contains' method" warnings.warn("'%s' needs 'contains' method" % self.__class__.__name__) return False,{} def set_contains(self,picker): """ Replace the contains test used by this artist. The new picker should be a callable function which determines whether the artist is hit by the mouse event:: hit, props = picker(artist, mouseevent) If the mouse event is over the artist, return *hit* = *True* and *props* is a dictionary of properties you want returned with the contains test. ACCEPTS: a callable function """ self._contains = picker def get_contains(self): """ Return the _contains test used by the artist, or *None* for default. """ return self._contains def pickable(self): 'Return *True* if :class:`Artist` is pickable.' return (self.figure is not None and self.figure.canvas is not None and self._picker is not None) def pick(self, mouseevent): """ call signature:: pick(mouseevent) each child artist will fire a pick event if *mouseevent* is over the artist and the artist has picker set """ # Pick self if self.pickable(): picker = self.get_picker() if callable(picker): inside,prop = picker(self,mouseevent) else: inside,prop = self.contains(mouseevent) if inside: self.figure.canvas.pick_event(mouseevent, self, **prop) # Pick children for a in self.get_children(): a.pick(mouseevent) def set_picker(self, picker): """ Set the epsilon for picking used by this artist *picker* can be one of the following: * *None*: picking is disabled for this artist (default) * A boolean: if *True* then picking will be enabled and the artist will fire a pick event if the mouse event is over the artist * A float: if picker is a number it is interpreted as an epsilon tolerance in points and the artist will fire off an event if it's data is within epsilon of the mouse event. For some artists like lines and patch collections, the artist may provide additional data to the pick event that is generated, e.g. the indices of the data within epsilon of the pick event * A function: if picker is callable, it is a user supplied function which determines whether the artist is hit by the mouse event:: hit, props = picker(artist, mouseevent) to determine the hit test. if the mouse event is over the artist, return *hit=True* and props is a dictionary of properties you want added to the PickEvent attributes. ACCEPTS: [None|float|boolean|callable] """ self._picker = picker def get_picker(self): 'Return the picker object used by this artist' return self._picker def is_figure_set(self): """ Returns True if the artist is assigned to a :class:`~matplotlib.figure.Figure`. """ return self.figure is not None def get_url(self): """ Returns the url """ return self._url def set_url(self, url): """ Sets the url for the artist """ self._url = url def get_snap(self): """ Returns the snap setting which may be: * True: snap vertices to the nearest pixel center * False: leave vertices as-is * None: (auto) If the path contains only rectilinear line segments, round to the nearest pixel center Only supported by the Agg backends. """ return self._snap def set_snap(self, snap): """ Sets the snap setting which may be: * True: snap vertices to the nearest pixel center * False: leave vertices as-is * None: (auto) If the path contains only rectilinear line segments, round to the nearest pixel center Only supported by the Agg backends. """ self._snap = snap def get_figure(self): """ Return the :class:`~matplotlib.figure.Figure` instance the artist belongs to. """ return self.figure def set_figure(self, fig): """ Set the :class:`~matplotlib.figure.Figure` instance the artist belongs to. ACCEPTS: a :class:`matplotlib.figure.Figure` instance """ self.figure = fig self.pchanged() def set_clip_box(self, clipbox): """ Set the artist's clip :class:`~matplotlib.transforms.Bbox`. ACCEPTS: a :class:`matplotlib.transforms.Bbox` instance """ self.clipbox = clipbox self.pchanged() def set_clip_path(self, path, transform=None): """ Set the artist's clip path, which may be: * a :class:`~matplotlib.patches.Patch` (or subclass) instance * a :class:`~matplotlib.path.Path` instance, in which case an optional :class:`~matplotlib.transforms.Transform` instance may be provided, which will be applied to the path before using it for clipping. * *None*, to remove the clipping path For efficiency, if the path happens to be an axis-aligned rectangle, this method will set the clipping box to the corresponding rectangle and set the clipping path to *None*. ACCEPTS: [ (:class:`~matplotlib.path.Path`, :class:`~matplotlib.transforms.Transform`) | :class:`~matplotlib.patches.Patch` | None ] """ from patches import Patch, Rectangle success = False if transform is None: if isinstance(path, Rectangle): self.clipbox = TransformedBbox(Bbox.unit(), path.get_transform()) self._clippath = None success = True elif isinstance(path, Patch): self._clippath = TransformedPath( path.get_path(), path.get_transform()) success = True if path is None: self._clippath = None success = True elif isinstance(path, Path): self._clippath = TransformedPath(path, transform) success = True if not success: print type(path), type(transform) raise TypeError("Invalid arguments to set_clip_path") self.pchanged() def get_alpha(self): """ Return the alpha value used for blending - not supported on all backends """ return self._alpha def get_visible(self): "Return the artist's visiblity" return self._visible def get_animated(self): "Return the artist's animated state" return self._animated def get_clip_on(self): 'Return whether artist uses clipping' return self._clipon def get_clip_box(self): 'Return artist clipbox' return self.clipbox def get_clip_path(self): 'Return artist clip path' return self._clippath def get_transformed_clip_path_and_affine(self): ''' Return the clip path with the non-affine part of its transformation applied, and the remaining affine part of its transformation. ''' if self._clippath is not None: return self._clippath.get_transformed_path_and_affine() return None, None def set_clip_on(self, b): """ Set whether artist uses clipping. ACCEPTS: [True | False] """ self._clipon = b self.pchanged() def _set_gc_clip(self, gc): 'Set the clip properly for the gc' if self._clipon: if self.clipbox is not None: gc.set_clip_rectangle(self.clipbox) gc.set_clip_path(self._clippath) else: gc.set_clip_rectangle(None) gc.set_clip_path(None) def draw(self, renderer, *args, **kwargs): 'Derived classes drawing method' if not self.get_visible(): return def set_alpha(self, alpha): """ Set the alpha value used for blending - not supported on all backends ACCEPTS: float (0.0 transparent through 1.0 opaque) """ self._alpha = alpha self.pchanged() def set_lod(self, on): """ Set Level of Detail on or off. If on, the artists may examine things like the pixel width of the axes and draw a subset of their contents accordingly ACCEPTS: [True | False] """ self._lod = on self.pchanged() def set_visible(self, b): """ Set the artist's visiblity. ACCEPTS: [True | False] """ self._visible = b self.pchanged() def set_animated(self, b): """ Set the artist's animation state. ACCEPTS: [True | False] """ self._animated = b self.pchanged() def update(self, props): """ Update the properties of this :class:`Artist` from the dictionary *prop*. """ store = self.eventson self.eventson = False changed = False for k,v in props.items(): func = getattr(self, 'set_'+k, None) if func is None or not callable(func): raise AttributeError('Unknown property %s'%k) func(v) changed = True self.eventson = store if changed: self.pchanged() def get_label(self): """ Get the label used for this artist in the legend. """ return self._label def set_label(self, s): """ Set the label to *s* for auto legend. ACCEPTS: any string """ self._label = s self.pchanged() def get_zorder(self): """ Return the :class:`Artist`'s zorder. """ return self.zorder def set_zorder(self, level): """ Set the zorder for the artist. Artists with lower zorder values are drawn first. ACCEPTS: any number """ self.zorder = level self.pchanged() def update_from(self, other): 'Copy properties from *other* to *self*.' self._transform = other._transform self._transformSet = other._transformSet self._visible = other._visible self._alpha = other._alpha self.clipbox = other.clipbox self._clipon = other._clipon self._clippath = other._clippath self._lod = other._lod self._label = other._label self.pchanged() def set(self, **kwargs): """ A tkstyle set command, pass *kwargs* to set properties """ ret = [] for k,v in kwargs.items(): k = k.lower() funcName = "set_%s"%k func = getattr(self,funcName) ret.extend( [func(v)] ) return ret def findobj(self, match=None): """ pyplot signature: findobj(o=gcf(), match=None) Recursively find all :class:matplotlib.artist.Artist instances contained in self. *match* can be - None: return all objects contained in artist (including artist) - function with signature ``boolean = match(artist)`` used to filter matches - class instance: eg Line2D. Only return artists of class type .. plot:: mpl_examples/pylab_examples/findobj_demo.py """ if match is None: # always return True def matchfunc(x): return True elif cbook.issubclass_safe(match, Artist): def matchfunc(x): return isinstance(x, match) elif callable(match): matchfunc = match else: raise ValueError('match must be None, an matplotlib.artist.Artist subclass, or a callable') artists = [] for c in self.get_children(): if matchfunc(c): artists.append(c) artists.extend([thisc for thisc in c.findobj(matchfunc) if matchfunc(thisc)]) if matchfunc(self): artists.append(self) return artists class ArtistInspector: """ A helper class to inspect an :class:`~matplotlib.artist.Artist` and return information about it's settable properties and their current values. """ def __init__(self, o): """ Initialize the artist inspector with an :class:`~matplotlib.artist.Artist` or sequence of :class:`Artists`. If a sequence is used, we assume it is a homogeneous sequence (all :class:`Artists` are of the same type) and it is your responsibility to make sure this is so. """ if cbook.iterable(o) and len(o): o = o[0] self.oorig = o if not isinstance(o, type): o = type(o) self.o = o self.aliasd = self.get_aliases() def get_aliases(self): """ Get a dict mapping *fullname* -> *alias* for each *alias* in the :class:`~matplotlib.artist.ArtistInspector`. Eg., for lines:: {'markerfacecolor': 'mfc', 'linewidth' : 'lw', } """ names = [name for name in dir(self.o) if (name.startswith('set_') or name.startswith('get_')) and callable(getattr(self.o,name))] aliases = {} for name in names: func = getattr(self.o, name) if not self.is_alias(func): continue docstring = func.__doc__ fullname = docstring[10:] aliases.setdefault(fullname[4:], {})[name[4:]] = None return aliases _get_valid_values_regex = re.compile(r"\n\s*ACCEPTS:\s*((?:.|\n)*?)(?:$|(?:\n\n))") def get_valid_values(self, attr): """ Get the legal arguments for the setter associated with *attr*. This is done by querying the docstring of the function *set_attr* for a line that begins with ACCEPTS: Eg., for a line linestyle, return [ '-' | '--' | '-.' | ':' | 'steps' | 'None' ] """ name = 'set_%s'%attr if not hasattr(self.o, name): raise AttributeError('%s has no function %s'%(self.o,name)) func = getattr(self.o, name) docstring = func.__doc__ if docstring is None: return 'unknown' if docstring.startswith('alias for '): return None match = self._get_valid_values_regex.search(docstring) if match is not None: return match.group(1).replace('\n', ' ') return 'unknown' def _get_setters_and_targets(self): """ Get the attribute strings and a full path to where the setter is defined for all setters in an object. """ setters = [] for name in dir(self.o): if not name.startswith('set_'): continue o = getattr(self.o, name) if not callable(o): continue func = o if self.is_alias(func): continue source_class = self.o.__module__ + "." + self.o.__name__ for cls in self.o.mro(): if name in cls.__dict__: source_class = cls.__module__ + "." + cls.__name__ break setters.append((name[4:], source_class + "." + name)) return setters def get_setters(self): """ Get the attribute strings with setters for object. Eg., for a line, return ``['markerfacecolor', 'linewidth', ....]``. """ return [prop for prop, target in self._get_setters_and_targets()] def is_alias(self, o): """ Return *True* if method object *o* is an alias for another function. """ ds = o.__doc__ if ds is None: return False return ds.startswith('alias for ') def aliased_name(self, s): """ return 'PROPNAME or alias' if *s* has an alias, else return PROPNAME. E.g. for the line markerfacecolor property, which has an alias, return 'markerfacecolor or mfc' and for the transform property, which does not, return 'transform' """ if s in self.aliasd: return s + ''.join([' or %s' % x for x in self.aliasd[s].keys()]) else: return s def aliased_name_rest(self, s, target): """ return 'PROPNAME or alias' if *s* has an alias, else return PROPNAME formatted for ReST E.g. for the line markerfacecolor property, which has an alias, return 'markerfacecolor or mfc' and for the transform property, which does not, return 'transform' """ if s in self.aliasd: aliases = ''.join([' or %s' % x for x in self.aliasd[s].keys()]) else: aliases = '' return ':meth:`%s <%s>`%s' % (s, target, aliases) def pprint_setters(self, prop=None, leadingspace=2): """ If *prop* is *None*, return a list of strings of all settable properies and their valid values. If *prop* is not *None*, it is a valid property name and that property will be returned as a string of property : valid values. """ if leadingspace: pad = ' '*leadingspace else: pad = '' if prop is not None: accepts = self.get_valid_values(prop) return '%s%s: %s' %(pad, prop, accepts) attrs = self._get_setters_and_targets() attrs.sort() lines = [] for prop, path in attrs: accepts = self.get_valid_values(prop) name = self.aliased_name(prop) lines.append('%s%s: %s' %(pad, name, accepts)) return lines def pprint_setters_rest(self, prop=None, leadingspace=2): """ If *prop* is *None*, return a list of strings of all settable properies and their valid values. Format the output for ReST If *prop* is not *None*, it is a valid property name and that property will be returned as a string of property : valid values. """ if leadingspace: pad = ' '*leadingspace else: pad = '' if prop is not None: accepts = self.get_valid_values(prop) return '%s%s: %s' %(pad, prop, accepts) attrs = self._get_setters_and_targets() attrs.sort() lines = [] ######## names = [self.aliased_name_rest(prop, target) for prop, target in attrs] accepts = [self.get_valid_values(prop) for prop, target in attrs] col0_len = max([len(n) for n in names]) col1_len = max([len(a) for a in accepts]) table_formatstr = pad + '='*col0_len + ' ' + '='*col1_len lines.append('') lines.append(table_formatstr) lines.append(pad + 'Property'.ljust(col0_len+3) + \ 'Description'.ljust(col1_len)) lines.append(table_formatstr) lines.extend([pad + n.ljust(col0_len+3) + a.ljust(col1_len) for n, a in zip(names, accepts)]) lines.append(table_formatstr) lines.append('') return lines ######## for prop, path in attrs: accepts = self.get_valid_values(prop) name = self.aliased_name_rest(prop, path) lines.append('%s%s: %s' %(pad, name, accepts)) return lines def pprint_getters(self): """ Return the getters and actual values as list of strings. """ o = self.oorig getters = [name for name in dir(o) if name.startswith('get_') and callable(getattr(o, name))] #print getters getters.sort() lines = [] for name in getters: func = getattr(o, name) if self.is_alias(func): continue try: val = func() except: continue if getattr(val, 'shape', ()) != () and len(val)>6: s = str(val[:6]) + '...' else: s = str(val) s = s.replace('\n', ' ') if len(s)>50: s = s[:50] + '...' name = self.aliased_name(name[4:]) lines.append(' %s = %s' %(name, s)) return lines def findobj(self, match=None): """ Recursively find all :class:`matplotlib.artist.Artist` instances contained in *self*. If *match* is not None, it can be - function with signature ``boolean = match(artist)`` - class instance: eg :class:`~matplotlib.lines.Line2D` used to filter matches. """ if match is None: # always return True def matchfunc(x): return True elif issubclass(match, Artist): def matchfunc(x): return isinstance(x, match) elif callable(match): matchfunc = func else: raise ValueError('match must be None, an matplotlib.artist.Artist subclass, or a callable') artists = [] for c in self.get_children(): if matchfunc(c): artists.append(c) artists.extend([thisc for thisc in c.findobj(matchfunc) if matchfunc(thisc)]) if matchfunc(self): artists.append(self) return artists def getp(o, property=None): """ Return the value of handle property. property is an optional string for the property you want to return Example usage:: getp(o) # get all the object properties getp(o, 'linestyle') # get the linestyle property *o* is a :class:`Artist` instance, eg :class:`~matplotllib.lines.Line2D` or an instance of a :class:`~matplotlib.axes.Axes` or :class:`matplotlib.text.Text`. If the *property* is 'somename', this function returns o.get_somename() :func:`getp` can be used to query all the gettable properties with ``getp(o)``. Many properties have aliases for shorter typing, e.g. 'lw' is an alias for 'linewidth'. In the output, aliases and full property names will be listed as: property or alias = value e.g.: linewidth or lw = 2 """ insp = ArtistInspector(o) if property is None: ret = insp.pprint_getters() print '\n'.join(ret) return func = getattr(o, 'get_' + property) return func() # alias get = getp def setp(h, *args, **kwargs): """ matplotlib supports the use of :func:`setp` ("set property") and :func:`getp` to set and get object properties, as well as to do introspection on the object. For example, to set the linestyle of a line to be dashed, you can do:: >>> line, = plot([1,2,3]) >>> setp(line, linestyle='--') If you want to know the valid types of arguments, you can provide the name of the property you want to set without a value:: >>> setp(line, 'linestyle') linestyle: [ '-' | '--' | '-.' | ':' | 'steps' | 'None' ] If you want to see all the properties that can be set, and their possible values, you can do:: >>> setp(line) ... long output listing omitted :func:`setp` operates on a single instance or a list of instances. If you are in query mode introspecting the possible values, only the first instance in the sequence is used. When actually setting values, all the instances will be set. E.g., suppose you have a list of two lines, the following will make both lines thicker and red:: >>> x = arange(0,1.0,0.01) >>> y1 = sin(2*pi*x) >>> y2 = sin(4*pi*x) >>> lines = plot(x, y1, x, y2) >>> setp(lines, linewidth=2, color='r') :func:`setp` works with the matlab(TM) style string/value pairs or with python kwargs. For example, the following are equivalent:: >>> setp(lines, 'linewidth', 2, 'color', r') # matlab style >>> setp(lines, linewidth=2, color='r') # python style """ insp = ArtistInspector(h) if len(kwargs)==0 and len(args)==0: print '\n'.join(insp.pprint_setters()) return if len(kwargs)==0 and len(args)==1: print insp.pprint_setters(prop=args[0]) return if not cbook.iterable(h): h = [h] else: h = cbook.flatten(h) if len(args)%2: raise ValueError('The set args must be string, value pairs') funcvals = [] for i in range(0, len(args)-1, 2): funcvals.append((args[i], args[i+1])) funcvals.extend(kwargs.items()) ret = [] for o in h: for s, val in funcvals: s = s.lower() funcName = "set_%s"%s func = getattr(o,funcName) ret.extend( [func(val)] ) return [x for x in cbook.flatten(ret)] def kwdoc(a): hardcopy = matplotlib.rcParams['docstring.hardcopy'] if hardcopy: return '\n'.join(ArtistInspector(a).pprint_setters_rest(leadingspace=2)) else: return '\n'.join(ArtistInspector(a).pprint_setters(leadingspace=2)) kwdocd = dict() kwdocd['Artist'] = kwdoc(Artist)

33,042

Python

.py

855

29.326316

221

0.58659

numenta/nupic-legacy

6,330

1,556

464

AGPL-3.0

9/5/2024, 5:13:42 PM (Europe/Amsterdam)

26,260

text.py

numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/text.py

""" Classes for including text in a figure. """ from __future__ import division import math import numpy as np from matplotlib import cbook from matplotlib import rcParams import artist from artist import Artist from cbook import is_string_like, maxdict from font_manager import FontProperties from patches import bbox_artist, YAArrow, FancyBboxPatch, \ FancyArrowPatch, Rectangle import transforms as mtransforms from transforms import Affine2D, Bbox from lines import Line2D import matplotlib.nxutils as nxutils def _process_text_args(override, fontdict=None, **kwargs): "Return an override dict. See :func:`~pyplot.text' docstring for info" if fontdict is not None: override.update(fontdict) override.update(kwargs) return override # Extracted from Text's method to serve as a function def get_rotation(rotation): """ Return the text angle as float. *rotation* may be 'horizontal', 'vertical', or a numeric value in degrees. """ if rotation in ('horizontal', None): angle = 0. elif rotation == 'vertical': angle = 90. else: angle = float(rotation) return angle%360 # these are not available for the object inspector until after the # class is build so we define an initial set here for the init # function and they will be overridden after object defn artist.kwdocd['Text'] = """ ========================== ========================================================================= Property Value ========================== ========================================================================= alpha float animated [True | False] backgroundcolor any matplotlib color bbox rectangle prop dict plus key 'pad' which is a pad in points clip_box a matplotlib.transform.Bbox instance clip_on [True | False] color any matplotlib color family [ 'serif' | 'sans-serif' | 'cursive' | 'fantasy' | 'monospace' ] figure a matplotlib.figure.Figure instance fontproperties a matplotlib.font_manager.FontProperties instance horizontalalignment or ha [ 'center' | 'right' | 'left' ] label any string linespacing float lod [True | False] multialignment ['left' | 'right' | 'center' ] name or fontname string eg, ['Sans' | 'Courier' | 'Helvetica' ...] position (x,y) rotation [ angle in degrees 'vertical' | 'horizontal' size or fontsize [ size in points | relative size eg 'smaller', 'x-large' ] style or fontstyle [ 'normal' | 'italic' | 'oblique'] text string transform a matplotlib.transform transformation instance variant [ 'normal' | 'small-caps' ] verticalalignment or va [ 'center' | 'top' | 'bottom' | 'baseline' ] visible [True | False] weight or fontweight [ 'normal' | 'bold' | 'heavy' | 'light' | 'ultrabold' | 'ultralight'] x float y float zorder any number ========================== ========================================================================= """ # TODO : This function may move into the Text class as a method. As a # matter of fact, The information from the _get_textbox function # should be available during the Text._get_layout() call, which is # called within the _get_textbox. So, it would better to move this # function as a method with some refactoring of _get_layout method. def _get_textbox(text, renderer): """ Calculate the bounding box of the text. Unlike :meth:`matplotlib.text.Text.get_extents` method, The bbox size of the text before the rotation is calculated. """ projected_xs = [] projected_ys = [] theta = text.get_rotation()/180.*math.pi tr = mtransforms.Affine2D().rotate(-theta) for t, wh, x, y in text._get_layout(renderer)[1]: w, h = wh xt1, yt1 = tr.transform_point((x, y)) xt2, yt2 = xt1+w, yt1+h projected_xs.extend([xt1, xt2]) projected_ys.extend([yt1, yt2]) xt_box, yt_box = min(projected_xs), min(projected_ys) w_box, h_box = max(projected_xs) - xt_box, max(projected_ys) - yt_box tr = mtransforms.Affine2D().rotate(theta) x_box, y_box = tr.transform_point((xt_box, yt_box)) return x_box, y_box, w_box, h_box class Text(Artist): """ Handle storing and drawing of text in window or data coordinates. """ zorder = 3 def __str__(self): return "Text(%g,%g,%s)"%(self._y,self._y,repr(self._text)) def __init__(self, x=0, y=0, text='', color=None, # defaults to rc params verticalalignment='bottom', horizontalalignment='left', multialignment=None, fontproperties=None, # defaults to FontProperties() rotation=None, linespacing=None, **kwargs ): """ Create a :class:`~matplotlib.text.Text` instance at *x*, *y* with string *text*. Valid kwargs are %(Text)s """ Artist.__init__(self) self.cached = maxdict(5) self._x, self._y = x, y if color is None: color = rcParams['text.color'] if fontproperties is None: fontproperties=FontProperties() elif is_string_like(fontproperties): fontproperties=FontProperties(fontproperties) self.set_text(text) self.set_color(color) self._verticalalignment = verticalalignment self._horizontalalignment = horizontalalignment self._multialignment = multialignment self._rotation = rotation self._fontproperties = fontproperties self._bbox = None self._bbox_patch = None # a FancyBboxPatch instance self._renderer = None if linespacing is None: linespacing = 1.2 # Maybe use rcParam later. self._linespacing = linespacing self.update(kwargs) #self.set_bbox(dict(pad=0)) def contains(self,mouseevent): """Test whether the mouse event occurred in the patch. In the case of text, a hit is true anywhere in the axis-aligned bounding-box containing the text. Returns True or False. """ if callable(self._contains): return self._contains(self,mouseevent) if not self.get_visible() or self._renderer is None: return False,{} l,b,w,h = self.get_window_extent().bounds r = l+w t = b+h xyverts = (l,b), (l, t), (r, t), (r, b) x, y = mouseevent.x, mouseevent.y inside = nxutils.pnpoly(x, y, xyverts) return inside,{} def _get_xy_display(self): 'get the (possibly unit converted) transformed x, y in display coords' x, y = self.get_position() return self.get_transform().transform_point((x,y)) def _get_multialignment(self): if self._multialignment is not None: return self._multialignment else: return self._horizontalalignment def get_rotation(self): 'return the text angle as float in degrees' return get_rotation(self._rotation) # string_or_number -> number def update_from(self, other): 'Copy properties from other to self' Artist.update_from(self, other) self._color = other._color self._multialignment = other._multialignment self._verticalalignment = other._verticalalignment self._horizontalalignment = other._horizontalalignment self._fontproperties = other._fontproperties.copy() self._rotation = other._rotation self._picker = other._picker self._linespacing = other._linespacing def _get_layout(self, renderer): key = self.get_prop_tup() if key in self.cached: return self.cached[key] horizLayout = [] thisx, thisy = 0.0, 0.0 xmin, ymin = 0.0, 0.0 width, height = 0.0, 0.0 lines = self._text.split('\n') whs = np.zeros((len(lines), 2)) horizLayout = np.zeros((len(lines), 4)) # Find full vertical extent of font, # including ascenders and descenders: tmp, heightt, bl = renderer.get_text_width_height_descent( 'lp', self._fontproperties, ismath=False) offsety = heightt * self._linespacing baseline = None for i, line in enumerate(lines): clean_line, ismath = self.is_math_text(line) w, h, d = renderer.get_text_width_height_descent( clean_line, self._fontproperties, ismath=ismath) if baseline is None: baseline = h - d whs[i] = w, h horizLayout[i] = thisx, thisy, w, h thisy -= offsety width = max(width, w) ymin = horizLayout[-1][1] ymax = horizLayout[0][1] + horizLayout[0][3] height = ymax-ymin xmax = xmin + width # get the rotation matrix M = Affine2D().rotate_deg(self.get_rotation()) offsetLayout = np.zeros((len(lines), 2)) offsetLayout[:] = horizLayout[:, 0:2] # now offset the individual text lines within the box if len(lines)>1: # do the multiline aligment malign = self._get_multialignment() if malign == 'center': offsetLayout[:, 0] += width/2.0 - horizLayout[:, 2] / 2.0 elif malign == 'right': offsetLayout[:, 0] += width - horizLayout[:, 2] # the corners of the unrotated bounding box cornersHoriz = np.array( [(xmin, ymin), (xmin, ymax), (xmax, ymax), (xmax, ymin)], np.float_) # now rotate the bbox cornersRotated = M.transform(cornersHoriz) txs = cornersRotated[:, 0] tys = cornersRotated[:, 1] # compute the bounds of the rotated box xmin, xmax = txs.min(), txs.max() ymin, ymax = tys.min(), tys.max() width = xmax - xmin height = ymax - ymin # Now move the box to the targe position offset the display bbox by alignment halign = self._horizontalalignment valign = self._verticalalignment # compute the text location in display coords and the offsets # necessary to align the bbox with that location if halign=='center': offsetx = (xmin + width/2.0) elif halign=='right': offsetx = (xmin + width) else: offsetx = xmin if valign=='center': offsety = (ymin + height/2.0) elif valign=='top': offsety = (ymin + height) elif valign=='baseline': offsety = (ymin + height) - baseline else: offsety = ymin xmin -= offsetx ymin -= offsety bbox = Bbox.from_bounds(xmin, ymin, width, height) # now rotate the positions around the first x,y position xys = M.transform(offsetLayout) xys -= (offsetx, offsety) xs, ys = xys[:, 0], xys[:, 1] ret = bbox, zip(lines, whs, xs, ys) self.cached[key] = ret return ret def set_bbox(self, rectprops): """ Draw a bounding box around self. rectprops are any settable properties for a rectangle, eg facecolor='red', alpha=0.5. t.set_bbox(dict(facecolor='red', alpha=0.5)) If rectprops has "boxstyle" key. A FancyBboxPatch is initialized with rectprops and will be drawn. The mutation scale of the FancyBboxPath is set to the fontsize. ACCEPTS: rectangle prop dict """ # The self._bbox_patch object is created only if rectprops has # boxstyle key. Otherwise, self._bbox will be set to the # rectprops and the bbox will be drawn using bbox_artist # function. This is to keep the backward compatibility. if rectprops is not None and "boxstyle" in rectprops: props = rectprops.copy() boxstyle = props.pop("boxstyle") bbox_transmuter = props.pop("bbox_transmuter", None) self._bbox_patch = FancyBboxPatch((0., 0.), 1., 1., boxstyle=boxstyle, bbox_transmuter=bbox_transmuter, transform=mtransforms.IdentityTransform(), **props) self._bbox = None else: self._bbox_patch = None self._bbox = rectprops def get_bbox_patch(self): """ Return the bbox Patch object. Returns None if the the FancyBboxPatch is not made. """ return self._bbox_patch def update_bbox_position_size(self, renderer): """ Update the location and the size of the bbox. This method should be used when the position and size of the bbox needs to be updated before actually drawing the bbox. """ # For arrow_patch, use textbox as patchA by default. if not isinstance(self.arrow_patch, FancyArrowPatch): return if self._bbox_patch: trans = self.get_transform() # don't use self.get_position here, which refers to text position # in Text, and dash position in TextWithDash: posx = float(self.convert_xunits(self._x)) posy = float(self.convert_yunits(self._y)) posx, posy = trans.transform_point((posx, posy)) x_box, y_box, w_box, h_box = _get_textbox(self, renderer) self._bbox_patch.set_bounds(0., 0., w_box, h_box) theta = self.get_rotation()/180.*math.pi tr = mtransforms.Affine2D().rotate(theta) tr = tr.translate(posx+x_box, posy+y_box) self._bbox_patch.set_transform(tr) fontsize_in_pixel = renderer.points_to_pixels(self.get_size()) self._bbox_patch.set_mutation_scale(fontsize_in_pixel) #self._bbox_patch.draw(renderer) else: props = self._bbox if props is None: props = {} props = props.copy() # don't want to alter the pad externally pad = props.pop('pad', 4) pad = renderer.points_to_pixels(pad) bbox = self.get_window_extent(renderer) l,b,w,h = bbox.bounds l-=pad/2. b-=pad/2. w+=pad h+=pad r = Rectangle(xy=(l,b), width=w, height=h, ) r.set_transform(mtransforms.IdentityTransform()) r.set_clip_on( False ) r.update(props) self.arrow_patch.set_patchA(r) def _draw_bbox(self, renderer, posx, posy): """ Update the location and the size of the bbox (FancyBoxPatch), and draw """ x_box, y_box, w_box, h_box = _get_textbox(self, renderer) self._bbox_patch.set_bounds(0., 0., w_box, h_box) theta = self.get_rotation()/180.*math.pi tr = mtransforms.Affine2D().rotate(theta) tr = tr.translate(posx+x_box, posy+y_box) self._bbox_patch.set_transform(tr) fontsize_in_pixel = renderer.points_to_pixels(self.get_size()) self._bbox_patch.set_mutation_scale(fontsize_in_pixel) self._bbox_patch.draw(renderer) def draw(self, renderer): """ Draws the :class:`Text` object to the given *renderer*. """ if renderer is not None: self._renderer = renderer if not self.get_visible(): return if self._text=='': return bbox, info = self._get_layout(renderer) trans = self.get_transform() # don't use self.get_position here, which refers to text position # in Text, and dash position in TextWithDash: posx = float(self.convert_xunits(self._x)) posy = float(self.convert_yunits(self._y)) posx, posy = trans.transform_point((posx, posy)) canvasw, canvash = renderer.get_canvas_width_height() # draw the FancyBboxPatch if self._bbox_patch: self._draw_bbox(renderer, posx, posy) gc = renderer.new_gc() gc.set_foreground(self._color) gc.set_alpha(self._alpha) gc.set_url(self._url) if self.get_clip_on(): gc.set_clip_rectangle(self.clipbox) if self._bbox: bbox_artist(self, renderer, self._bbox) angle = self.get_rotation() if rcParams['text.usetex']: for line, wh, x, y in info: x = x + posx y = y + posy if renderer.flipy(): y = canvash-y clean_line, ismath = self.is_math_text(line) renderer.draw_tex(gc, x, y, clean_line, self._fontproperties, angle) return for line, wh, x, y in info: x = x + posx y = y + posy if renderer.flipy(): y = canvash-y clean_line, ismath = self.is_math_text(line) renderer.draw_text(gc, x, y, clean_line, self._fontproperties, angle, ismath=ismath) def get_color(self): "Return the color of the text" return self._color def get_fontproperties(self): "Return the :class:`~font_manager.FontProperties` object" return self._fontproperties def get_font_properties(self): 'alias for get_fontproperties' return self.get_fontproperties def get_family(self): "Return the list of font families used for font lookup" return self._fontproperties.get_family() def get_fontfamily(self): 'alias for get_family' return self.get_family() def get_name(self): "Return the font name as string" return self._fontproperties.get_name() def get_style(self): "Return the font style as string" return self._fontproperties.get_style() def get_size(self): "Return the font size as integer" return self._fontproperties.get_size_in_points() def get_variant(self): "Return the font variant as a string" return self._fontproperties.get_variant() def get_fontvariant(self): 'alias for get_variant' return self.get_variant() def get_weight(self): "Get the font weight as string or number" return self._fontproperties.get_weight() def get_fontname(self): 'alias for get_name' return self.get_name() def get_fontstyle(self): 'alias for get_style' return self.get_style() def get_fontsize(self): 'alias for get_size' return self.get_size() def get_fontweight(self): 'alias for get_weight' return self.get_weight() def get_stretch(self): 'Get the font stretch as a string or number' return self._fontproperties.get_stretch() def get_fontstretch(self): 'alias for get_stretch' return self.get_stretch() def get_ha(self): 'alias for get_horizontalalignment' return self.get_horizontalalignment() def get_horizontalalignment(self): """ Return the horizontal alignment as string. Will be one of 'left', 'center' or 'right'. """ return self._horizontalalignment def get_position(self): "Return the position of the text as a tuple (*x*, *y*)" x = float(self.convert_xunits(self._x)) y = float(self.convert_yunits(self._y)) return x, y def get_prop_tup(self): """ Return a hashable tuple of properties. Not intended to be human readable, but useful for backends who want to cache derived information about text (eg layouts) and need to know if the text has changed. """ x, y = self.get_position() return (x, y, self._text, self._color, self._verticalalignment, self._horizontalalignment, hash(self._fontproperties), self._rotation, self.figure.dpi, id(self._renderer), ) def get_text(self): "Get the text as string" return self._text def get_va(self): 'alias for :meth:`getverticalalignment`' return self.get_verticalalignment() def get_verticalalignment(self): """ Return the vertical alignment as string. Will be one of 'top', 'center', 'bottom' or 'baseline'. """ return self._verticalalignment def get_window_extent(self, renderer=None, dpi=None): ''' Return a :class:`~matplotlib.transforms.Bbox` object bounding the text, in display units. In addition to being used internally, this is useful for specifying clickable regions in a png file on a web page. *renderer* defaults to the _renderer attribute of the text object. This is not assigned until the first execution of :meth:`draw`, so you must use this kwarg if you want to call :meth:`get_window_extent` prior to the first :meth:`draw`. For getting web page regions, it is simpler to call the method after saving the figure. *dpi* defaults to self.figure.dpi; the renderer dpi is irrelevant. For the web application, if figure.dpi is not the value used when saving the figure, then the value that was used must be specified as the *dpi* argument. ''' #return _unit_box if not self.get_visible(): return Bbox.unit() if dpi is not None: dpi_orig = self.figure.dpi self.figure.dpi = dpi if self._text == '': tx, ty = self._get_xy_display() return Bbox.from_bounds(tx,ty,0,0) if renderer is not None: self._renderer = renderer if self._renderer is None: raise RuntimeError('Cannot get window extent w/o renderer') bbox, info = self._get_layout(self._renderer) x, y = self.get_position() x, y = self.get_transform().transform_point((x, y)) bbox = bbox.translated(x, y) if dpi is not None: self.figure.dpi = dpi_orig return bbox def set_backgroundcolor(self, color): """ Set the background color of the text by updating the bbox. .. seealso:: :meth:`set_bbox` ACCEPTS: any matplotlib color """ if self._bbox is None: self._bbox = dict(facecolor=color, edgecolor=color) else: self._bbox.update(dict(facecolor=color)) def set_color(self, color): """ Set the foreground color of the text ACCEPTS: any matplotlib color """ # Make sure it is hashable, or get_prop_tup will fail. try: hash(color) except TypeError: color = tuple(color) self._color = color def set_ha(self, align): 'alias for set_horizontalalignment' self.set_horizontalalignment(align) def set_horizontalalignment(self, align): """ Set the horizontal alignment to one of ACCEPTS: [ 'center' | 'right' | 'left' ] """ legal = ('center', 'right', 'left') if align not in legal: raise ValueError('Horizontal alignment must be one of %s' % str(legal)) self._horizontalalignment = align def set_ma(self, align): 'alias for set_verticalalignment' self.set_multialignment(align) def set_multialignment(self, align): """ Set the alignment for multiple lines layout. The layout of the bounding box of all the lines is determined bu the horizontalalignment and verticalalignment properties, but the multiline text within that box can be ACCEPTS: ['left' | 'right' | 'center' ] """ legal = ('center', 'right', 'left') if align not in legal: raise ValueError('Horizontal alignment must be one of %s' % str(legal)) self._multialignment = align def set_linespacing(self, spacing): """ Set the line spacing as a multiple of the font size. Default is 1.2. ACCEPTS: float (multiple of font size) """ self._linespacing = spacing def set_family(self, fontname): """ Set the font family. May be either a single string, or a list of strings in decreasing priority. Each string may be either a real font name or a generic font class name. If the latter, the specific font names will be looked up in the :file:`matplotlibrc` file. ACCEPTS: [ FONTNAME | 'serif' | 'sans-serif' | 'cursive' | 'fantasy' | 'monospace' ] """ self._fontproperties.set_family(fontname) def set_variant(self, variant): """ Set the font variant, either 'normal' or 'small-caps'. ACCEPTS: [ 'normal' | 'small-caps' ] """ self._fontproperties.set_variant(variant) def set_fontvariant(self, variant): 'alias for set_variant' return self.set_variant(variant) def set_name(self, fontname): """alias for set_family""" return self.set_family(fontname) def set_fontname(self, fontname): """alias for set_family""" self.set_family(fontname) def set_style(self, fontstyle): """ Set the font style. ACCEPTS: [ 'normal' | 'italic' | 'oblique'] """ self._fontproperties.set_style(fontstyle) def set_fontstyle(self, fontstyle): 'alias for set_style' return self.set_style(fontstyle) def set_size(self, fontsize): """ Set the font size. May be either a size string, relative to the default font size, or an absolute font size in points. ACCEPTS: [ size in points | 'xx-small' | 'x-small' | 'small' | 'medium' | 'large' | 'x-large' | 'xx-large' ] """ self._fontproperties.set_size(fontsize) def set_fontsize(self, fontsize): 'alias for set_size' return self.set_size(fontsize) def set_weight(self, weight): """ Set the font weight. ACCEPTS: [ a numeric value in range 0-1000 | 'ultralight' | 'light' | 'normal' | 'regular' | 'book' | 'medium' | 'roman' | 'semibold' | 'demibold' | 'demi' | 'bold' | 'heavy' | 'extra bold' | 'black' ] """ self._fontproperties.set_weight(weight) def set_fontweight(self, weight): 'alias for set_weight' return self.set_weight(weight) def set_stretch(self, stretch): """ Set the font stretch (horizontal condensation or expansion). ACCEPTS: [ a numeric value in range 0-1000 | 'ultra-condensed' | 'extra-condensed' | 'condensed' | 'semi-condensed' | 'normal' | 'semi-expanded' | 'expanded' | 'extra-expanded' | 'ultra-expanded' ] """ self._fontproperties.set_stretch(stretch) def set_fontstretch(self, stretch): 'alias for set_stretch' return self.set_stretch(stretch) def set_position(self, xy): """ Set the (*x*, *y*) position of the text ACCEPTS: (x,y) """ self.set_x(xy[0]) self.set_y(xy[1]) def set_x(self, x): """ Set the *x* position of the text ACCEPTS: float """ self._x = x def set_y(self, y): """ Set the *y* position of the text ACCEPTS: float """ self._y = y def set_rotation(self, s): """ Set the rotation of the text ACCEPTS: [ angle in degrees | 'vertical' | 'horizontal' ] """ self._rotation = s def set_va(self, align): 'alias for set_verticalalignment' self.set_verticalalignment(align) def set_verticalalignment(self, align): """ Set the vertical alignment ACCEPTS: [ 'center' | 'top' | 'bottom' | 'baseline' ] """ legal = ('top', 'bottom', 'center', 'baseline') if align not in legal: raise ValueError('Vertical alignment must be one of %s' % str(legal)) self._verticalalignment = align def set_text(self, s): """ Set the text string *s* It may contain newlines (``\\n``) or math in LaTeX syntax. ACCEPTS: string or anything printable with '%s' conversion. """ self._text = '%s' % (s,) def is_math_text(self, s): """ Returns True if the given string *s* contains any mathtext. """ # Did we find an even number of non-escaped dollar signs? # If so, treat is as math text. dollar_count = s.count(r'$') - s.count(r'\$') even_dollars = (dollar_count > 0 and dollar_count % 2 == 0) if rcParams['text.usetex']: return s, 'TeX' if even_dollars: return s, True else: return s.replace(r'\$', '$'), False def set_fontproperties(self, fp): """ Set the font properties that control the text. *fp* must be a :class:`matplotlib.font_manager.FontProperties` object. ACCEPTS: a :class:`matplotlib.font_manager.FontProperties` instance """ if is_string_like(fp): fp = FontProperties(fp) self._fontproperties = fp.copy() def set_font_properties(self, fp): 'alias for set_fontproperties' self.set_fontproperties(fp) artist.kwdocd['Text'] = artist.kwdoc(Text) Text.__init__.im_func.__doc__ = cbook.dedent(Text.__init__.__doc__) % artist.kwdocd class TextWithDash(Text): """ This is basically a :class:`~matplotlib.text.Text` with a dash (drawn with a :class:`~matplotlib.lines.Line2D`) before/after it. It is intended to be a drop-in replacement for :class:`~matplotlib.text.Text`, and should behave identically to it when *dashlength* = 0.0. The dash always comes between the point specified by :meth:`~matplotlib.text.Text.set_position` and the text. When a dash exists, the text alignment arguments (*horizontalalignment*, *verticalalignment*) are ignored. *dashlength* is the length of the dash in canvas units. (default = 0.0). *dashdirection* is one of 0 or 1, where 0 draws the dash after the text and 1 before. (default = 0). *dashrotation* specifies the rotation of the dash, and should generally stay *None*. In this case :meth:`~matplotlib.text.TextWithDash.get_dashrotation` returns :meth:`~matplotlib.text.Text.get_rotation`. (I.e., the dash takes its rotation from the text's rotation). Because the text center is projected onto the dash, major deviations in the rotation cause what may be considered visually unappealing results. (default = *None*) *dashpad* is a padding length to add (or subtract) space between the text and the dash, in canvas units. (default = 3) *dashpush* "pushes" the dash and text away from the point specified by :meth:`~matplotlib.text.Text.set_position` by the amount in canvas units. (default = 0) .. note:: The alignment of the two objects is based on the bounding box of the :class:`~matplotlib.text.Text`, as obtained by :meth:`~matplotlib.artist.Artist.get_window_extent`. This, in turn, appears to depend on the font metrics as given by the rendering backend. Hence the quality of the "centering" of the label text with respect to the dash varies depending on the backend used. .. note:: I'm not sure that I got the :meth:`~matplotlib.text.TextWithDash.get_window_extent` right, or whether that's sufficient for providing the object bounding box. """ __name__ = 'textwithdash' def __str__(self): return "TextWithDash(%g,%g,%s)"%(self._x,self._y,repr(self._text)) def __init__(self, x=0, y=0, text='', color=None, # defaults to rc params verticalalignment='center', horizontalalignment='center', multialignment=None, fontproperties=None, # defaults to FontProperties() rotation=None, linespacing=None, dashlength=0.0, dashdirection=0, dashrotation=None, dashpad=3, dashpush=0, ): Text.__init__(self, x=x, y=y, text=text, color=color, verticalalignment=verticalalignment, horizontalalignment=horizontalalignment, multialignment=multialignment, fontproperties=fontproperties, rotation=rotation, linespacing=linespacing) # The position (x,y) values for text and dashline # are bogus as given in the instantiation; they will # be set correctly by update_coords() in draw() self.dashline = Line2D(xdata=(x, x), ydata=(y, y), color='k', linestyle='-') self._dashx = float(x) self._dashy = float(y) self._dashlength = dashlength self._dashdirection = dashdirection self._dashrotation = dashrotation self._dashpad = dashpad self._dashpush = dashpush #self.set_bbox(dict(pad=0)) def get_position(self): "Return the position of the text as a tuple (*x*, *y*)" x = float(self.convert_xunits(self._dashx)) y = float(self.convert_yunits(self._dashy)) return x, y def get_prop_tup(self): """ Return a hashable tuple of properties. Not intended to be human readable, but useful for backends who want to cache derived information about text (eg layouts) and need to know if the text has changed. """ props = [p for p in Text.get_prop_tup(self)] props.extend([self._x, self._y, self._dashlength, self._dashdirection, self._dashrotation, self._dashpad, self._dashpush]) return tuple(props) def draw(self, renderer): """ Draw the :class:`TextWithDash` object to the given *renderer*. """ self.update_coords(renderer) Text.draw(self, renderer) if self.get_dashlength() > 0.0: self.dashline.draw(renderer) def update_coords(self, renderer): """ Computes the actual *x*, *y* coordinates for text based on the input *x*, *y* and the *dashlength*. Since the rotation is with respect to the actual canvas's coordinates we need to map back and forth. """ dashx, dashy = self.get_position() dashlength = self.get_dashlength() # Shortcircuit this process if we don't have a dash if dashlength == 0.0: self._x, self._y = dashx, dashy return dashrotation = self.get_dashrotation() dashdirection = self.get_dashdirection() dashpad = self.get_dashpad() dashpush = self.get_dashpush() angle = get_rotation(dashrotation) theta = np.pi*(angle/180.0+dashdirection-1) cos_theta, sin_theta = np.cos(theta), np.sin(theta) transform = self.get_transform() # Compute the dash end points # The 'c' prefix is for canvas coordinates cxy = transform.transform_point((dashx, dashy)) cd = np.array([cos_theta, sin_theta]) c1 = cxy+dashpush*cd c2 = cxy+(dashpush+dashlength)*cd inverse = transform.inverted() (x1, y1) = inverse.transform_point(tuple(c1)) (x2, y2) = inverse.transform_point(tuple(c2)) self.dashline.set_data((x1, x2), (y1, y2)) # We now need to extend this vector out to # the center of the text area. # The basic problem here is that we're "rotating" # two separate objects but want it to appear as # if they're rotated together. # This is made non-trivial because of the # interaction between text rotation and alignment - # text alignment is based on the bbox after rotation. # We reset/force both alignments to 'center' # so we can do something relatively reasonable. # There's probably a better way to do this by # embedding all this in the object's transformations, # but I don't grok the transformation stuff # well enough yet. we = Text.get_window_extent(self, renderer=renderer) w, h = we.width, we.height # Watch for zeros if sin_theta == 0.0: dx = w dy = 0.0 elif cos_theta == 0.0: dx = 0.0 dy = h else: tan_theta = sin_theta/cos_theta dx = w dy = w*tan_theta if dy > h or dy < -h: dy = h dx = h/tan_theta cwd = np.array([dx, dy])/2 cwd *= 1+dashpad/np.sqrt(np.dot(cwd,cwd)) cw = c2+(dashdirection*2-1)*cwd newx, newy = inverse.transform_point(tuple(cw)) self._x, self._y = newx, newy # Now set the window extent # I'm not at all sure this is the right way to do this. we = Text.get_window_extent(self, renderer=renderer) self._twd_window_extent = we.frozen() self._twd_window_extent.update_from_data_xy(np.array([c1]), False) # Finally, make text align center Text.set_horizontalalignment(self, 'center') Text.set_verticalalignment(self, 'center') def get_window_extent(self, renderer=None): ''' Return a :class:`~matplotlib.transforms.Bbox` object bounding the text, in display units. In addition to being used internally, this is useful for specifying clickable regions in a png file on a web page. *renderer* defaults to the _renderer attribute of the text object. This is not assigned until the first execution of :meth:`draw`, so you must use this kwarg if you want to call :meth:`get_window_extent` prior to the first :meth:`draw`. For getting web page regions, it is simpler to call the method after saving the figure. ''' self.update_coords(renderer) if self.get_dashlength() == 0.0: return Text.get_window_extent(self, renderer=renderer) else: return self._twd_window_extent def get_dashlength(self): """ Get the length of the dash. """ return self._dashlength def set_dashlength(self, dl): """ Set the length of the dash. ACCEPTS: float (canvas units) """ self._dashlength = dl def get_dashdirection(self): """ Get the direction dash. 1 is before the text and 0 is after. """ return self._dashdirection def set_dashdirection(self, dd): """ Set the direction of the dash following the text. 1 is before the text and 0 is after. The default is 0, which is what you'd want for the typical case of ticks below and on the left of the figure. ACCEPTS: int (1 is before, 0 is after) """ self._dashdirection = dd def get_dashrotation(self): """ Get the rotation of the dash in degrees. """ if self._dashrotation == None: return self.get_rotation() else: return self._dashrotation def set_dashrotation(self, dr): """ Set the rotation of the dash, in degrees ACCEPTS: float (degrees) """ self._dashrotation = dr def get_dashpad(self): """ Get the extra spacing between the dash and the text, in canvas units. """ return self._dashpad def set_dashpad(self, dp): """ Set the "pad" of the TextWithDash, which is the extra spacing between the dash and the text, in canvas units. ACCEPTS: float (canvas units) """ self._dashpad = dp def get_dashpush(self): """ Get the extra spacing between the dash and the specified text position, in canvas units. """ return self._dashpush def set_dashpush(self, dp): """ Set the "push" of the TextWithDash, which is the extra spacing between the beginning of the dash and the specified position. ACCEPTS: float (canvas units) """ self._dashpush = dp def set_position(self, xy): """ Set the (*x*, *y*) position of the :class:`TextWithDash`. ACCEPTS: (x, y) """ self.set_x(xy[0]) self.set_y(xy[1]) def set_x(self, x): """ Set the *x* position of the :class:`TextWithDash`. ACCEPTS: float """ self._dashx = float(x) def set_y(self, y): """ Set the *y* position of the :class:`TextWithDash`. ACCEPTS: float """ self._dashy = float(y) def set_transform(self, t): """ Set the :class:`matplotlib.transforms.Transform` instance used by this artist. ACCEPTS: a :class:`matplotlib.transforms.Transform` instance """ Text.set_transform(self, t) self.dashline.set_transform(t) def get_figure(self): 'return the figure instance the artist belongs to' return self.figure def set_figure(self, fig): """ Set the figure instance the artist belong to. ACCEPTS: a :class:`matplotlib.figure.Figure` instance """ Text.set_figure(self, fig) self.dashline.set_figure(fig) artist.kwdocd['TextWithDash'] = artist.kwdoc(TextWithDash) class Annotation(Text): """ A :class:`~matplotlib.text.Text` class to make annotating things in the figure, such as :class:`~matplotlib.figure.Figure`, :class:`~matplotlib.axes.Axes`, :class:`~matplotlib.patches.Rectangle`, etc., easier. """ def __str__(self): return "Annotation(%g,%g,%s)"%(self.xy[0],self.xy[1],repr(self._text)) def __init__(self, s, xy, xytext=None, xycoords='data', textcoords=None, arrowprops=None, **kwargs): """ Annotate the *x*, *y* point *xy* with text *s* at *x*, *y* location *xytext*. (If *xytext* = *None*, defaults to *xy*, and if *textcoords* = *None*, defaults to *xycoords*). *arrowprops*, if not *None*, is a dictionary of line properties (see :class:`matplotlib.lines.Line2D`) for the arrow that connects annotation to the point. If the dictionary has a key *arrowstyle*, a FancyArrowPatch instance is created with the given dictionary and is drawn. Otherwise, a YAArow patch instance is created and drawn. Valid keys for YAArow are ========= ============================================================= Key Description ========= ============================================================= width the width of the arrow in points frac the fraction of the arrow length occupied by the head headwidth the width of the base of the arrow head in points shrink oftentimes it is convenient to have the arrowtip and base a bit away from the text and point being annotated. If *d* is the distance between the text and annotated point, shrink will shorten the arrow so the tip and base are shink percent of the distance *d* away from the endpoints. ie, ``shrink=0.05 is 5%%`` ? any key for :class:`matplotlib.patches.polygon` ========= ============================================================= Valid keys for FancyArrowPatch are =============== ====================================================== Key Description =============== ====================================================== arrowstyle the arrow style connectionstyle the connection style relpos default is (0.5, 0.5) patchA default is bounding box of the text patchB default is None shrinkA default is 2 points shrinkB default is 2 points mutation_scale default is text size (in points) mutation_aspect default is 1. ? any key for :class:`matplotlib.patches.PathPatch` =============== ====================================================== *xycoords* and *textcoords* are strings that indicate the coordinates of *xy* and *xytext*. ================= =================================================== Property Description ================= =================================================== 'figure points' points from the lower left corner of the figure 'figure pixels' pixels from the lower left corner of the figure 'figure fraction' 0,0 is lower left of figure and 1,1 is upper, right 'axes points' points from lower left corner of axes 'axes pixels' pixels from lower left corner of axes 'axes fraction' 0,1 is lower left of axes and 1,1 is upper right 'data' use the coordinate system of the object being annotated (default) 'offset points' Specify an offset (in points) from the *xy* value 'polar' you can specify *theta*, *r* for the annotation, even in cartesian plots. Note that if you are using a polar axes, you do not need to specify polar for the coordinate system since that is the native "data" coordinate system. ================= =================================================== If a 'points' or 'pixels' option is specified, values will be added to the bottom-left and if negative, values will be subtracted from the top-right. Eg:: # 10 points to the right of the left border of the axes and # 5 points below the top border xy=(10,-5), xycoords='axes points' Additional kwargs are Text properties: %(Text)s """ if xytext is None: xytext = xy if textcoords is None: textcoords = xycoords # we'll draw ourself after the artist we annotate by default x,y = self.xytext = xytext Text.__init__(self, x, y, s, **kwargs) self.xy = xy self.xycoords = xycoords self.textcoords = textcoords self.arrowprops = arrowprops self.arrow = None if arrowprops and arrowprops.has_key("arrowstyle"): self._arrow_relpos = arrowprops.pop("relpos", (0.5, 0.5)) self.arrow_patch = FancyArrowPatch((0, 0), (1,1), **arrowprops) else: self.arrow_patch = None __init__.__doc__ = cbook.dedent(__init__.__doc__) % artist.kwdocd def contains(self,event): t,tinfo = Text.contains(self,event) if self.arrow is not None: a,ainfo=self.arrow.contains(event) t = t or a # self.arrow_patch is currently not checked as this can be a line - JJ return t,tinfo def set_figure(self, fig): if self.arrow is not None: self.arrow.set_figure(fig) if self.arrow_patch is not None: self.arrow_patch.set_figure(fig) Artist.set_figure(self, fig) def _get_xy(self, x, y, s): if s=='data': trans = self.axes.transData x = float(self.convert_xunits(x)) y = float(self.convert_yunits(y)) return trans.transform_point((x, y)) elif s=='offset points': # convert the data point dx, dy = self.xy # prevent recursion if self.xycoords == 'offset points': return self._get_xy(dx, dy, 'data') dx, dy = self._get_xy(dx, dy, self.xycoords) # convert the offset dpi = self.figure.get_dpi() x *= dpi/72. y *= dpi/72. # add the offset to the data point x += dx y += dy return x, y elif s=='polar': theta, r = x, y x = r*np.cos(theta) y = r*np.sin(theta) trans = self.axes.transData return trans.transform_point((x,y)) elif s=='figure points': #points from the lower left corner of the figure dpi = self.figure.dpi l,b,w,h = self.figure.bbox.bounds r = l+w t = b+h x *= dpi/72. y *= dpi/72. if x<0: x = r + x if y<0: y = t + y return x,y elif s=='figure pixels': #pixels from the lower left corner of the figure l,b,w,h = self.figure.bbox.bounds r = l+w t = b+h if x<0: x = r + x if y<0: y = t + y return x, y elif s=='figure fraction': #(0,0) is lower left, (1,1) is upper right of figure trans = self.figure.transFigure return trans.transform_point((x,y)) elif s=='axes points': #points from the lower left corner of the axes dpi = self.figure.dpi l,b,w,h = self.axes.bbox.bounds r = l+w t = b+h if x<0: x = r + x*dpi/72. else: x = l + x*dpi/72. if y<0: y = t + y*dpi/72. else: y = b + y*dpi/72. return x, y elif s=='axes pixels': #pixels from the lower left corner of the axes l,b,w,h = self.axes.bbox.bounds r = l+w t = b+h if x<0: x = r + x else: x = l + x if y<0: y = t + y else: y = b + y return x, y elif s=='axes fraction': #(0,0) is lower left, (1,1) is upper right of axes trans = self.axes.transAxes return trans.transform_point((x, y)) def update_positions(self, renderer): x, y = self.xytext self._x, self._y = self._get_xy(x, y, self.textcoords) x, y = self.xy x, y = self._get_xy(x, y, self.xycoords) ox0, oy0 = self._x, self._y ox1, oy1 = x, y if self.arrowprops: x0, y0 = x, y l,b,w,h = self.get_window_extent(renderer).bounds r = l+w t = b+h xc = 0.5*(l+r) yc = 0.5*(b+t) d = self.arrowprops.copy() # Use FancyArrowPatch if self.arrowprops has "arrowstyle" key. # Otherwise, fallback to YAArrow. #if d.has_key("arrowstyle"): if self.arrow_patch: # adjust the starting point of the arrow relative to # the textbox. # TODO : Rotation needs to be accounted. relpos = self._arrow_relpos bbox = self.get_window_extent(renderer) ox0 = bbox.x0 + bbox.width * relpos[0] oy0 = bbox.y0 + bbox.height * relpos[1] # The arrow will be drawn from (ox0, oy0) to (ox1, # oy1). It will be first clipped by patchA and patchB. # Then it will be shrinked by shirnkA and shrinkB # (in points). If patch A is not set, self.bbox_patch # is used. self.arrow_patch.set_positions((ox0, oy0), (ox1,oy1)) mutation_scale = d.pop("mutation_scale", self.get_size()) mutation_scale = renderer.points_to_pixels(mutation_scale) self.arrow_patch.set_mutation_scale(mutation_scale) if self._bbox_patch: patchA = d.pop("patchA", self._bbox_patch) self.arrow_patch.set_patchA(patchA) else: patchA = d.pop("patchA", self._bbox) self.arrow_patch.set_patchA(patchA) else: # pick the x,y corner of the text bbox closest to point # annotated dsu = [(abs(val-x0), val) for val in l, r, xc] dsu.sort() _, x = dsu[0] dsu = [(abs(val-y0), val) for val in b, t, yc] dsu.sort() _, y = dsu[0] shrink = d.pop('shrink', 0.0) theta = math.atan2(y-y0, x-x0) r = math.sqrt((y-y0)**2. + (x-x0)**2.) dx = shrink*r*math.cos(theta) dy = shrink*r*math.sin(theta) width = d.pop('width', 4) headwidth = d.pop('headwidth', 12) frac = d.pop('frac', 0.1) self.arrow = YAArrow(self.figure, (x0+dx,y0+dy), (x-dx, y-dy), width=width, headwidth=headwidth, frac=frac, **d) self.arrow.set_clip_box(self.get_clip_box()) def draw(self, renderer): """ Draw the :class:`Annotation` object to the given *renderer*. """ self.update_positions(renderer) self.update_bbox_position_size(renderer) if self.arrow is not None: if self.arrow.figure is None and self.figure is not None: self.arrow.figure = self.figure self.arrow.draw(renderer) if self.arrow_patch is not None: if self.arrow_patch.figure is None and self.figure is not None: self.arrow_patch.figure = self.figure self.arrow_patch.draw(renderer) Text.draw(self, renderer) artist.kwdocd['Annotation'] = Annotation.__init__.__doc__

55,366

Python

.py

1,299

32.357968

209

0.564406

numenta/nupic-legacy

6,330

1,556

464

AGPL-3.0

9/5/2024, 5:13:42 PM (Europe/Amsterdam)

26,261

__init__.py

numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/__init__.py

""" This is an object-orient plotting library. A procedural interface is provided by the companion pylab module, which may be imported directly, e.g:: from pylab import * or using ipython:: ipython -pylab For the most part, direct use of the object-oriented library is encouraged when programming rather than working interactively. The exceptions are the pylab commands :func:`~matplotlib.pyplot.figure`, :func:`~matplotlib.pyplot.subplot`, :func:`~matplotlib.backends.backend_qt4agg.show`, and :func:`~pyplot.savefig`, which can greatly simplify scripting. Modules include: :mod:`matplotlib.axes` defines the :class:`~matplotlib.axes.Axes` class. Most pylab commands are wrappers for :class:`~matplotlib.axes.Axes` methods. The axes module is the highest level of OO access to the library. :mod:`matplotlib.figure` defines the :class:`~matplotlib.figure.Figure` class. :mod:`matplotlib.artist` defines the :class:`~matplotlib.artist.Artist` base class for all classes that draw things. :mod:`matplotlib.lines` defines the :class:`~matplotlib.lines.Line2D` class for drawing lines and markers :mod`matplotlib.patches` defines classes for drawing polygons :mod:`matplotlib.text` defines the :class:`~matplotlib.text.Text`, :class:`~matplotlib.text.TextWithDash`, and :class:`~matplotlib.text.Annotate` classes :mod:`matplotlib.image` defines the :class:`~matplotlib.image.AxesImage` and :class:`~matplotlib.image.FigureImage` classes :mod:`matplotlib.collections` classes for efficient drawing of groups of lines or polygons :mod:`matplotlib.colors` classes for interpreting color specifications and for making colormaps :mod:`matplotlib.cm` colormaps and the :class:`~matplotlib.image.ScalarMappable` mixin class for providing color mapping functionality to other classes :mod:`matplotlib.ticker` classes for calculating tick mark locations and for formatting tick labels :mod:`matplotlib.backends` a subpackage with modules for various gui libraries and output formats The base matplotlib namespace includes: :data:`~matplotlib.rcParams` a global dictionary of default configuration settings. It is initialized by code which may be overridded by a matplotlibrc file. :func:`~matplotlib.rc` a function for setting groups of rcParams values :func:`~matplotlib.use` a function for setting the matplotlib backend. If used, this function must be called immediately after importing matplotlib for the first time. In particular, it must be called **before** importing pylab (if pylab is imported). matplotlib is written by John D. Hunter (jdh2358 at gmail.com) and a host of others. """ from __future__ import generators __version__ = '0.98.5.2' __revision__ = '$Revision: 6660 $' __date__ = '$Date: 2008-12-18 06:10:51 -0600 (Thu, 18 Dec 2008) $' import os, re, shutil, subprocess, sys, warnings import distutils.sysconfig import distutils.version NEWCONFIG = False # Needed for toolkit setuptools support if 0: try: __import__('pkg_resources').declare_namespace(__name__) except ImportError: pass # must not have setuptools if not hasattr(sys, 'argv'): # for modpython sys.argv = ['modpython'] """ Manage user customizations through a rc file. The default file location is given in the following order - environment variable MATPLOTLIBRC - HOME/.matplotlib/matplotlibrc if HOME is defined - PATH/matplotlibrc where PATH is the return value of get_data_path() """ import sys, os, tempfile from rcsetup import defaultParams, validate_backend, validate_toolbar from rcsetup import validate_cairo_format major, minor1, minor2, s, tmp = sys.version_info _python24 = major>=2 and minor1>=4 # the havedate check was a legacy from old matplotlib which preceeded # datetime support _havedate = True #try: # import pkg_resources # pkg_resources is part of setuptools #except ImportError: _have_pkg_resources = False #else: _have_pkg_resources = True if not _python24: raise ImportError('matplotlib requires Python 2.4 or later') import numpy nn = numpy.__version__.split('.') if not (int(nn[0]) >= 1 and int(nn[1]) >= 1): raise ImportError( 'numpy 1.1 or later is required; you have %s' % numpy.__version__) def is_string_like(obj): if hasattr(obj, 'shape'): return 0 try: obj + '' except (TypeError, ValueError): return 0 return 1 def _is_writable_dir(p): """ p is a string pointing to a putative writable dir -- return True p is such a string, else False """ try: p + '' # test is string like except TypeError: return False try: t = tempfile.TemporaryFile(dir=p) t.write('1') t.close() except OSError: return False else: return True class Verbose: """ A class to handle reporting. Set the fileo attribute to any file instance to handle the output. Default is sys.stdout """ levels = ('silent', 'helpful', 'debug', 'debug-annoying') vald = dict( [(level, i) for i,level in enumerate(levels)]) # parse the verbosity from the command line; flags look like # --verbose-silent or --verbose-helpful _commandLineVerbose = None for arg in sys.argv[1:]: if not arg.startswith('--verbose-'): continue _commandLineVerbose = arg[10:] def __init__(self): self.set_level('silent') self.fileo = sys.stdout def set_level(self, level): 'set the verbosity to one of the Verbose.levels strings' if self._commandLineVerbose is not None: level = self._commandLineVerbose if level not in self.levels: raise ValueError('Illegal verbose string "%s". Legal values are %s'%(level, self.levels)) self.level = level def set_fileo(self, fname): std = { 'sys.stdout': sys.stdout, 'sys.stderr': sys.stderr, } if fname in std: self.fileo = std[fname] else: try: fileo = file(fname, 'w') except IOError: raise ValueError('Verbose object could not open log file "%s" for writing.\nCheck your matplotlibrc verbose.fileo setting'%fname) else: self.fileo = fileo def report(self, s, level='helpful'): """ print message s to self.fileo if self.level>=level. Return value indicates whether a message was issued """ if self.ge(level): print >>self.fileo, s return True return False def wrap(self, fmt, func, level='helpful', always=True): """ return a callable function that wraps func and reports it output through the verbose handler if current verbosity level is higher than level if always is True, the report will occur on every function call; otherwise only on the first time the function is called """ assert callable(func) def wrapper(*args, **kwargs): ret = func(*args, **kwargs) if (always or not wrapper._spoke): spoke = self.report(fmt%ret, level) if not wrapper._spoke: wrapper._spoke = spoke return ret wrapper._spoke = False wrapper.__doc__ = func.__doc__ return wrapper def ge(self, level): 'return true if self.level is >= level' return self.vald[self.level]>=self.vald[level] verbose=Verbose() def checkdep_dvipng(): try: s = subprocess.Popen(['dvipng','-version'], stdout=subprocess.PIPE, stderr=subprocess.PIPE) line = s.stdout.readlines()[1] v = line.split()[-1] return v except (IndexError, ValueError, OSError): return None def checkdep_ghostscript(): try: if sys.platform == 'win32': command_args = ['gswin32c', '--version'] else: command_args = ['gs', '--version'] s = subprocess.Popen(command_args, stdout=subprocess.PIPE, stderr=subprocess.PIPE) v = s.stdout.read()[:-1] return v except (IndexError, ValueError, OSError): return None def checkdep_tex(): try: s = subprocess.Popen(['tex','-version'], stdout=subprocess.PIPE, stderr=subprocess.PIPE) line = s.stdout.readlines()[0] pattern = '3\.1\d+' match = re.search(pattern, line) v = match.group(0) return v except (IndexError, ValueError, AttributeError, OSError): return None def checkdep_pdftops(): try: s = subprocess.Popen(['pdftops','-v'], stdout=subprocess.PIPE, stderr=subprocess.PIPE) for line in s.stderr: if 'version' in line: v = line.split()[-1] return v except (IndexError, ValueError, UnboundLocalError, OSError): return None def compare_versions(a, b): "return True if a is greater than or equal to b" if a: a = distutils.version.LooseVersion(a) b = distutils.version.LooseVersion(b) if a>=b: return True else: return False else: return False def checkdep_ps_distiller(s): if not s: return False flag = True gs_req = '7.07' gs_sugg = '7.07' gs_v = checkdep_ghostscript() if compare_versions(gs_v, gs_sugg): pass elif compare_versions(gs_v, gs_req): verbose.report(('ghostscript-%s found. ghostscript-%s or later ' 'is recommended to use the ps.usedistiller option.') % (gs_v, gs_sugg)) else: flag = False warnings.warn(('matplotlibrc ps.usedistiller option can not be used ' 'unless ghostscript-%s or later is installed on your system') % gs_req) if s == 'xpdf': pdftops_req = '3.0' pdftops_req_alt = '0.9' # poppler version numbers, ugh pdftops_v = checkdep_pdftops() if compare_versions(pdftops_v, pdftops_req): pass elif compare_versions(pdftops_v, pdftops_req_alt) and not \ compare_versions(pdftops_v, '1.0'): pass else: flag = False warnings.warn(('matplotlibrc ps.usedistiller can not be set to ' 'xpdf unless xpdf-%s or later is installed on your system') % pdftops_req) if flag: return s else: return False def checkdep_usetex(s): if not s: return False tex_req = '3.1415' gs_req = '7.07' gs_sugg = '7.07' dvipng_req = '1.5' flag = True tex_v = checkdep_tex() if compare_versions(tex_v, tex_req): pass else: flag = False warnings.warn(('matplotlibrc text.usetex option can not be used ' 'unless TeX-%s or later is ' 'installed on your system') % tex_req) dvipng_v = checkdep_dvipng() if compare_versions(dvipng_v, dvipng_req): pass else: flag = False warnings.warn( 'matplotlibrc text.usetex can not be used with *Agg ' 'backend unless dvipng-1.5 or later is ' 'installed on your system') gs_v = checkdep_ghostscript() if compare_versions(gs_v, gs_sugg): pass elif compare_versions(gs_v, gs_req): verbose.report(('ghostscript-%s found. ghostscript-%s or later is ' 'recommended for use with the text.usetex ' 'option.') % (gs_v, gs_sugg)) else: flag = False warnings.warn(('matplotlibrc text.usetex can not be used ' 'unless ghostscript-%s or later is ' 'installed on your system') % gs_req) return flag def _get_home(): """Find user's home directory if possible. Otherwise raise error. :see: http://mail.python.org/pipermail/python-list/2005-February/263921.html """ path='' try: path=os.path.expanduser("~") except: pass if not os.path.isdir(path): for evar in ('HOME', 'USERPROFILE', 'TMP'): try: path = os.environ[evar] if os.path.isdir(path): break except: pass if path: return path else: raise RuntimeError('please define environment variable $HOME') get_home = verbose.wrap('$HOME=%s', _get_home, always=False) def _get_configdir(): """ Return the string representing the configuration dir. default is HOME/.matplotlib. you can override this with the MPLCONFIGDIR environment variable """ configdir = os.environ.get('MPLCONFIGDIR') if configdir is not None: if not _is_writable_dir(configdir): raise RuntimeError('Could not write to MPLCONFIGDIR="%s"'%configdir) return configdir h = get_home() p = os.path.join(get_home(), '.matplotlib') if os.path.exists(p): if not _is_writable_dir(p): raise RuntimeError("'%s' is not a writable dir; you must set %s/.matplotlib to be a writable dir. You can also set environment variable MPLCONFIGDIR to any writable directory where you want matplotlib data stored "% (h, h)) else: if not _is_writable_dir(h): raise RuntimeError("Failed to create %s/.matplotlib; consider setting MPLCONFIGDIR to a writable directory for matplotlib configuration data"%h) os.mkdir(p) return p get_configdir = verbose.wrap('CONFIGDIR=%s', _get_configdir, always=False) def _get_data_path(): 'get the path to matplotlib data' if 'MATPLOTLIBDATA' in os.environ: path = os.environ['MATPLOTLIBDATA'] if not os.path.isdir(path): raise RuntimeError('Path in environment MATPLOTLIBDATA not a directory') return path path = os.sep.join([os.path.dirname(__file__), 'mpl-data']) if os.path.isdir(path): return path # setuptools' namespace_packages may highjack this init file # so need to try something known to be in matplotlib, not basemap import matplotlib.afm path = os.sep.join([os.path.dirname(matplotlib.afm.__file__), 'mpl-data']) if os.path.isdir(path): return path # py2exe zips pure python, so still need special check if getattr(sys,'frozen',None): path = os.path.join(os.path.split(sys.path[0])[0], 'mpl-data') if os.path.isdir(path): return path else: # Try again assuming we need to step up one more directory path = os.path.join(os.path.split(os.path.split(sys.path[0])[0])[0], 'mpl-data') if os.path.isdir(path): return path else: # Try again assuming sys.path[0] is a dir not a exe path = os.path.join(sys.path[0], 'mpl-data') if os.path.isdir(path): return path raise RuntimeError('Could not find the matplotlib data files') def _get_data_path_cached(): if defaultParams['datapath'][0] is None: defaultParams['datapath'][0] = _get_data_path() return defaultParams['datapath'][0] get_data_path = verbose.wrap('matplotlib data path %s', _get_data_path_cached, always=False) def get_example_data(fname): """ return a filehandle to one of the example files in mpl-data/example *fname* the name of one of the files in mpl-data/example """ datadir = os.path.join(get_data_path(), 'example') fullpath = os.path.join(datadir, fname) if not os.path.exists(fullpath): raise IOError('could not find matplotlib example file "%s" in data directory "%s"'%( fname, datadir)) return file(fullpath, 'rb') def get_py2exe_datafiles(): datapath = get_data_path() head, tail = os.path.split(datapath) d = {} for root, dirs, files in os.walk(datapath): # Need to explicitly remove cocoa_agg files or py2exe complains # NOTE I dont know why, but do as previous version if 'Matplotlib.nib' in files: files.remove('Matplotlib.nib') files = [os.path.join(root, filename) for filename in files] root = root.replace(tail, 'mpl-data') root = root[root.index('mpl-data'):] d[root] = files return d.items() def matplotlib_fname(): """ Return the path to the rc file Search order: * current working dir * environ var MATPLOTLIBRC * HOME/.matplotlib/matplotlibrc * MATPLOTLIBDATA/matplotlibrc """ oldname = os.path.join( os.getcwd(), '.matplotlibrc') if os.path.exists(oldname): print >> sys.stderr, """\ WARNING: Old rc filename ".matplotlibrc" found in working dir and and renamed to new default rc file name "matplotlibrc" (no leading"dot"). """ shutil.move('.matplotlibrc', 'matplotlibrc') home = get_home() oldname = os.path.join( home, '.matplotlibrc') if os.path.exists(oldname): configdir = get_configdir() newname = os.path.join(configdir, 'matplotlibrc') print >> sys.stderr, """\ WARNING: Old rc filename "%s" found and renamed to new default rc file name "%s"."""%(oldname, newname) shutil.move(oldname, newname) fname = os.path.join( os.getcwd(), 'matplotlibrc') if os.path.exists(fname): return fname if 'MATPLOTLIBRC' in os.environ: path = os.environ['MATPLOTLIBRC'] if os.path.exists(path): fname = os.path.join(path, 'matplotlibrc') if os.path.exists(fname): return fname fname = os.path.join(get_configdir(), 'matplotlibrc') if os.path.exists(fname): return fname path = get_data_path() # guaranteed to exist or raise fname = os.path.join(path, 'matplotlibrc') if not os.path.exists(fname): warnings.warn('Could not find matplotlibrc; using defaults') return fname _deprecated_map = { 'text.fontstyle': 'font.style', 'text.fontangle': 'font.style', 'text.fontvariant': 'font.variant', 'text.fontweight': 'font.weight', 'text.fontsize': 'font.size', 'tick.size' : 'tick.major.size', } class RcParams(dict): """ A dictionary object including validation validating functions are defined and associated with rc parameters in :mod:`matplotlib.rcsetup` """ validate = dict([ (key, converter) for key, (default, converter) in \ defaultParams.iteritems() ]) def __setitem__(self, key, val): try: if key in _deprecated_map.keys(): alt = _deprecated_map[key] warnings.warn('%s is deprecated in matplotlibrc. Use %s \ instead.'% (key, alt)) key = alt cval = self.validate[key](val) dict.__setitem__(self, key, cval) except KeyError: raise KeyError('%s is not a valid rc parameter.\ See rcParams.keys() for a list of valid parameters.'%key) def rc_params(fail_on_error=False): 'Return the default params updated from the values in the rc file' fname = matplotlib_fname() if not os.path.exists(fname): # this should never happen, default in mpl-data should always be found message = 'could not find rc file; returning defaults' ret = RcParams([ (key, default) for key, (default, converter) in \ defaultParams.iteritems() ]) warnings.warn(message) return ret cnt = 0 rc_temp = {} for line in file(fname): cnt += 1 strippedline = line.split('#',1)[0].strip() if not strippedline: continue tup = strippedline.split(':',1) if len(tup) !=2: warnings.warn('Illegal line #%d\n\t%s\n\tin file "%s"'%\ (cnt, line, fname)) continue key, val = tup key = key.strip() val = val.strip() if key in rc_temp: warnings.warn('Duplicate key in file "%s", line #%d'%(fname,cnt)) rc_temp[key] = (val, line, cnt) ret = RcParams([ (key, default) for key, (default, converter) in \ defaultParams.iteritems() ]) for key in ('verbose.level', 'verbose.fileo'): if key in rc_temp: val, line, cnt = rc_temp.pop(key) if fail_on_error: ret[key] = val # try to convert to proper type or raise else: try: ret[key] = val # try to convert to proper type or skip except Exception, msg: warnings.warn('Bad val "%s" on line #%d\n\t"%s"\n\tin file \ "%s"\n\t%s' % (val, cnt, line, fname, msg)) verbose.set_level(ret['verbose.level']) verbose.set_fileo(ret['verbose.fileo']) for key, (val, line, cnt) in rc_temp.iteritems(): if key in defaultParams: if fail_on_error: ret[key] = val # try to convert to proper type or raise else: try: ret[key] = val # try to convert to proper type or skip except Exception, msg: warnings.warn('Bad val "%s" on line #%d\n\t"%s"\n\tin file \ "%s"\n\t%s' % (val, cnt, line, fname, msg)) else: print >> sys.stderr, """ Bad key "%s" on line %d in %s. You probably need to get an updated matplotlibrc file from http://matplotlib.sf.net/_static/matplotlibrc or from the matplotlib source distribution""" % (key, cnt, fname) if ret['datapath'] is None: ret['datapath'] = get_data_path() if not ret['text.latex.preamble'] == ['']: verbose.report(""" ***************************************************************** You have the following UNSUPPORTED LaTeX preamble customizations: %s Please do not ask for support with these customizations active. ***************************************************************** """% '\n'.join(ret['text.latex.preamble']), 'helpful') verbose.report('loaded rc file %s'%fname) return ret # this is the instance used by the matplotlib classes rcParams = rc_params() rcParamsDefault = RcParams([ (key, default) for key, (default, converter) in \ defaultParams.iteritems() ]) rcParams['ps.usedistiller'] = checkdep_ps_distiller(rcParams['ps.usedistiller']) rcParams['text.usetex'] = checkdep_usetex(rcParams['text.usetex']) def rc(group, **kwargs): """ Set the current rc params. Group is the grouping for the rc, eg. for ``lines.linewidth`` the group is ``lines``, for ``axes.facecolor``, the group is ``axes``, and so on. Group may also be a list or tuple of group names, eg. (*xtick*, *ytick*). *kwargs* is a dictionary attribute name/value pairs, eg:: rc('lines', linewidth=2, color='r') sets the current rc params and is equivalent to:: rcParams['lines.linewidth'] = 2 rcParams['lines.color'] = 'r' The following aliases are available to save typing for interactive users: ===== ================= Alias Property ===== ================= 'lw' 'linewidth' 'ls' 'linestyle' 'c' 'color' 'fc' 'facecolor' 'ec' 'edgecolor' 'mew' 'markeredgewidth' 'aa' 'antialiased' ===== ================= Thus you could abbreviate the above rc command as:: rc('lines', lw=2, c='r') Note you can use python's kwargs dictionary facility to store dictionaries of default parameters. Eg, you can customize the font rc as follows:: font = {'family' : 'monospace', 'weight' : 'bold', 'size' : 'larger'} rc('font', **font) # pass in the font dict as kwargs This enables you to easily switch between several configurations. Use :func:`~matplotlib.pyplot.rcdefaults` to restore the default rc params after changes. """ aliases = { 'lw' : 'linewidth', 'ls' : 'linestyle', 'c' : 'color', 'fc' : 'facecolor', 'ec' : 'edgecolor', 'mew' : 'markeredgewidth', 'aa' : 'antialiased', } if is_string_like(group): group = (group,) for g in group: for k,v in kwargs.items(): name = aliases.get(k) or k key = '%s.%s' % (g, name) if key not in rcParams: raise KeyError('Unrecognized key "%s" for group "%s" and name "%s"' % (key, g, name)) rcParams[key] = v def rcdefaults(): """ Restore the default rc params - the ones that were created at matplotlib load time. """ rcParams.update(rcParamsDefault) if NEWCONFIG: #print "importing from reorganized config system!" try: from config import rcParams, rcdefaults, mplConfig, save_config verbose.set_level(rcParams['verbose.level']) verbose.set_fileo(rcParams['verbose.fileo']) except: from config import rcParams, rcdefaults _use_error_msg = """ This call to matplotlib.use() has no effect because the the backend has already been chosen; matplotlib.use() must be called *before* pylab, matplotlib.pyplot, or matplotlib.backends is imported for the first time. """ def use(arg, warn=True): """ Set the matplotlib backend to one of the known backends. The argument is case-insensitive. For the Cairo backend, the argument can have an extension to indicate the type of output. Example: use('cairo.pdf') will specify a default of pdf output generated by Cairo. Note: this function must be called *before* importing pylab for the first time; or, if you are not using pylab, it must be called before importing matplotlib.backends. If warn is True, a warning is issued if you try and callthis after pylab or pyplot have been loaded. In certain black magic use cases, eg pyplot.switch_backends, we are doing the reloading necessary to make the backend switch work (in some cases, eg pure image backends) so one can set warn=False to supporess the warnings """ if 'matplotlib.backends' in sys.modules: if warn: warnings.warn(_use_error_msg) return arg = arg.lower() if arg.startswith('module://'): name = arg else: be_parts = arg.split('.') name = validate_backend(be_parts[0]) rcParams['backend'] = name if name == 'cairo' and len(be_parts) > 1: rcParams['cairo.format'] = validate_cairo_format(be_parts[1]) def get_backend(): "Returns the current backend" return rcParams['backend'] def interactive(b): """ Set interactive mode to boolean b. If b is True, then draw after every plotting command, eg, after xlabel """ rcParams['interactive'] = b def is_interactive(): 'Return true if plot mode is interactive' b = rcParams['interactive'] return b def tk_window_focus(): """Return true if focus maintenance under TkAgg on win32 is on. This currently works only for python.exe and IPython.exe. Both IDLE and Pythonwin.exe fail badly when tk_window_focus is on.""" if rcParams['backend'] != 'TkAgg': return False return rcParams['tk.window_focus'] # Now allow command line to override # Allow command line access to the backend with -d (matlab compatible # flag) for s in sys.argv[1:]: if s.startswith('-d') and len(s) > 2: # look for a -d flag try: use(s[2:]) except (KeyError, ValueError): pass # we don't want to assume all -d flags are backends, eg -debug verbose.report('matplotlib version %s'%__version__) verbose.report('verbose.level %s'%verbose.level) verbose.report('interactive is %s'%rcParams['interactive']) verbose.report('units is %s'%rcParams['units']) verbose.report('platform is %s'%sys.platform) verbose.report('loaded modules: %s'%sys.modules.keys(), 'debug')

28,184

Python

.py

698

32.866762

236

0.628524

numenta/nupic-legacy

6,330

1,556

464

AGPL-3.0

9/5/2024, 5:13:42 PM (Europe/Amsterdam)

26,262

axes3d.py

numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/axes3d.py

raise NotImplementedError('axes3d is not supported in matplotlib-0.98. You may want to try the 0.91.x maintenance branch')

124

Python

.py

1

123

0.796748

numenta/nupic-legacy

6,330

1,556

464

AGPL-3.0

9/5/2024, 5:13:42 PM (Europe/Amsterdam)

26,263

type1font.py

numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/type1font.py

""" A class representing a Type 1 font. This version merely reads pfa and pfb files and splits them for embedding in pdf files. There is no support yet for subsetting or anything like that. Usage (subject to change): font = Type1Font(filename) clear_part, encrypted_part, finale = font.parts Source: Adobe Technical Note #5040, Supporting Downloadable PostScript Language Fonts. If extending this class, see also: Adobe Type 1 Font Format, Adobe Systems Incorporated, third printing, v1.1, 1993. ISBN 0-201-57044-0. """ import re import struct class Type1Font(object): def __init__(self, filename): file = open(filename, 'rb') try: data = self._read(file) finally: file.close() self.parts = self._split(data) #self._parse() def _read(self, file): rawdata = file.read() if not rawdata.startswith(chr(128)): return rawdata data = '' while len(rawdata) > 0: if not rawdata.startswith(chr(128)): raise RuntimeError, \ 'Broken pfb file (expected byte 128, got %d)' % \ ord(rawdata[0]) type = ord(rawdata[1]) if type in (1,2): length, = struct.unpack('<i', rawdata[2:6]) segment = rawdata[6:6+length] rawdata = rawdata[6+length:] if type == 1: # ASCII text: include verbatim data += segment elif type == 2: # binary data: encode in hexadecimal data += ''.join(['%02x' % ord(char) for char in segment]) elif type == 3: # end of file break else: raise RuntimeError, \ 'Unknown segment type %d in pfb file' % type return data def _split(self, data): """ Split the Type 1 font into its three main parts. The three parts are: (1) the cleartext part, which ends in a eexec operator; (2) the encrypted part; (3) the fixed part, which contains 512 ASCII zeros possibly divided on various lines, a cleartomark operator, and possibly something else. """ # Cleartext part: just find the eexec and skip whitespace idx = data.index('eexec') idx += len('eexec') while data[idx] in ' \t\r\n': idx += 1 len1 = idx # Encrypted part: find the cleartomark operator and count # zeros backward idx = data.rindex('cleartomark') - 1 zeros = 512 while zeros and data[idx] in ('0', '\n', '\r'): if data[idx] == '0': zeros -= 1 idx -= 1 if zeros: raise RuntimeError, 'Insufficiently many zeros in Type 1 font' # Convert encrypted part to binary (if we read a pfb file, we # may end up converting binary to hexadecimal to binary again; # but if we read a pfa file, this part is already in hex, and # I am not quite sure if even the pfb format guarantees that # it will be in binary). binary = ''.join([chr(int(data[i:i+2], 16)) for i in range(len1, idx, 2)]) return data[:len1], binary, data[idx:] _whitespace = re.compile(r'[\0\t\r\014\n ]+') _delim = re.compile(r'[()<>[]{}/%]') _token = re.compile(r'/{0,2}[^]\0\t\r\v\n ()<>{}/%[]+') _comment = re.compile(r'%[^\r\n\v]*') _instring = re.compile(r'[()\\]') def _parse(self): """ A very limited kind of parsing to find the Encoding of the font. """ def tokens(text): """ Yield pairs (position, token), ignoring comments and whitespace. Numbers count as tokens. """ pos = 0 while pos < len(text): match = self._comment.match(text[pos:]) or self._whitespace.match(text[pos:]) if match: pos += match.end() elif text[pos] == '(': start = pos pos += 1 depth = 1 while depth: match = self._instring.search(text[pos:]) if match is None: return if match.group() == '(': depth += 1 pos += 1 elif match.group() == ')': depth -= 1 pos += 1 else: pos += 2 yield (start, text[start:pos]) elif text[pos:pos+2] in ('<<', '>>'): yield (pos, text[pos:pos+2]) pos += 2 elif text[pos] == '<': start = pos pos += text[pos:].index('>') yield (start, text[start:pos]) else: match = self._token.match(text[pos:]) if match: yield (pos, match.group()) pos += match.end() else: yield (pos, text[pos]) pos += 1 enc_starts, enc_ends = None, None state = 0 # State transitions: # 0 -> /Encoding -> 1 # 1 -> StandardEncoding -> 2 -> def -> (ends) # 1 -> dup -> 4 -> put -> 5 # 5 -> dup -> 4 -> put -> 5 # 5 -> def -> (ends) for pos,token in tokens(self.parts[0]): if state == 0 and token == '/Encoding': enc_starts = pos state = 1 elif state == 1 and token == 'StandardEncoding': state = 2 elif state in (2,5) and token == 'def': enc_ends = pos+3 break elif state in (1,5) and token == 'dup': state = 4 elif state == 4 and token == 'put': state = 5 self.enc_starts, self.enc_ends = enc_starts, enc_ends if __name__ == '__main__': import sys font = Type1Font(sys.argv[1]) parts = font.parts print len(parts[0]), len(parts[1]), len(parts[2]) #print parts[0][font.enc_starts:font.enc_ends]

6,406

Python

.py

161

26.881988

93

0.48436

numenta/nupic-legacy

6,330

1,556

464

AGPL-3.0

9/5/2024, 5:13:42 PM (Europe/Amsterdam)

26,264

_cm.py

numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/_cm.py

""" Color data and pre-defined cmap objects. This is a helper for cm.py, originally part of that file. Separating the data (this file) from cm.py makes both easier to deal with. Objects visible in cm.py are the individual cmap objects ('autumn', etc.) and a dictionary, 'datad', including all of these objects. """ import matplotlib as mpl import matplotlib.colors as colors LUTSIZE = mpl.rcParams['image.lut'] _binary_data = { 'red' : ((0., 1., 1.), (1., 0., 0.)), 'green': ((0., 1., 1.), (1., 0., 0.)), 'blue' : ((0., 1., 1.), (1., 0., 0.)) } _bone_data = {'red': ((0., 0., 0.),(1.0, 1.0, 1.0)), 'green': ((0., 0., 0.),(1.0, 1.0, 1.0)), 'blue': ((0., 0., 0.),(1.0, 1.0, 1.0))} _autumn_data = {'red': ((0., 1.0, 1.0),(1.0, 1.0, 1.0)), 'green': ((0., 0., 0.),(1.0, 1.0, 1.0)), 'blue': ((0., 0., 0.),(1.0, 0., 0.))} _bone_data = {'red': ((0., 0., 0.),(0.746032, 0.652778, 0.652778),(1.0, 1.0, 1.0)), 'green': ((0., 0., 0.),(0.365079, 0.319444, 0.319444), (0.746032, 0.777778, 0.777778),(1.0, 1.0, 1.0)), 'blue': ((0., 0., 0.),(0.365079, 0.444444, 0.444444),(1.0, 1.0, 1.0))} _cool_data = {'red': ((0., 0., 0.), (1.0, 1.0, 1.0)), 'green': ((0., 1., 1.), (1.0, 0., 0.)), 'blue': ((0., 1., 1.), (1.0, 1., 1.))} _copper_data = {'red': ((0., 0., 0.),(0.809524, 1.000000, 1.000000),(1.0, 1.0, 1.0)), 'green': ((0., 0., 0.),(1.0, 0.7812, 0.7812)), 'blue': ((0., 0., 0.),(1.0, 0.4975, 0.4975))} _flag_data = {'red': ((0., 1., 1.),(0.015873, 1.000000, 1.000000), (0.031746, 0.000000, 0.000000),(0.047619, 0.000000, 0.000000), (0.063492, 1.000000, 1.000000),(0.079365, 1.000000, 1.000000), (0.095238, 0.000000, 0.000000),(0.111111, 0.000000, 0.000000), (0.126984, 1.000000, 1.000000),(0.142857, 1.000000, 1.000000), (0.158730, 0.000000, 0.000000),(0.174603, 0.000000, 0.000000), (0.190476, 1.000000, 1.000000),(0.206349, 1.000000, 1.000000), (0.222222, 0.000000, 0.000000),(0.238095, 0.000000, 0.000000), (0.253968, 1.000000, 1.000000),(0.269841, 1.000000, 1.000000), (0.285714, 0.000000, 0.000000),(0.301587, 0.000000, 0.000000), (0.317460, 1.000000, 1.000000),(0.333333, 1.000000, 1.000000), (0.349206, 0.000000, 0.000000),(0.365079, 0.000000, 0.000000), (0.380952, 1.000000, 1.000000),(0.396825, 1.000000, 1.000000), (0.412698, 0.000000, 0.000000),(0.428571, 0.000000, 0.000000), (0.444444, 1.000000, 1.000000),(0.460317, 1.000000, 1.000000), (0.476190, 0.000000, 0.000000),(0.492063, 0.000000, 0.000000), (0.507937, 1.000000, 1.000000),(0.523810, 1.000000, 1.000000), (0.539683, 0.000000, 0.000000),(0.555556, 0.000000, 0.000000), (0.571429, 1.000000, 1.000000),(0.587302, 1.000000, 1.000000), (0.603175, 0.000000, 0.000000),(0.619048, 0.000000, 0.000000), (0.634921, 1.000000, 1.000000),(0.650794, 1.000000, 1.000000), (0.666667, 0.000000, 0.000000),(0.682540, 0.000000, 0.000000), (0.698413, 1.000000, 1.000000),(0.714286, 1.000000, 1.000000), (0.730159, 0.000000, 0.000000),(0.746032, 0.000000, 0.000000), (0.761905, 1.000000, 1.000000),(0.777778, 1.000000, 1.000000), (0.793651, 0.000000, 0.000000),(0.809524, 0.000000, 0.000000), (0.825397, 1.000000, 1.000000),(0.841270, 1.000000, 1.000000), (0.857143, 0.000000, 0.000000),(0.873016, 0.000000, 0.000000), (0.888889, 1.000000, 1.000000),(0.904762, 1.000000, 1.000000), (0.920635, 0.000000, 0.000000),(0.936508, 0.000000, 0.000000), (0.952381, 1.000000, 1.000000),(0.968254, 1.000000, 1.000000), (0.984127, 0.000000, 0.000000),(1.0, 0., 0.)), 'green': ((0., 0., 0.),(0.015873, 1.000000, 1.000000), (0.031746, 0.000000, 0.000000),(0.063492, 0.000000, 0.000000), (0.079365, 1.000000, 1.000000),(0.095238, 0.000000, 0.000000), (0.126984, 0.000000, 0.000000),(0.142857, 1.000000, 1.000000), (0.158730, 0.000000, 0.000000),(0.190476, 0.000000, 0.000000), (0.206349, 1.000000, 1.000000),(0.222222, 0.000000, 0.000000), (0.253968, 0.000000, 0.000000),(0.269841, 1.000000, 1.000000), (0.285714, 0.000000, 0.000000),(0.317460, 0.000000, 0.000000), (0.333333, 1.000000, 1.000000),(0.349206, 0.000000, 0.000000), (0.380952, 0.000000, 0.000000),(0.396825, 1.000000, 1.000000), (0.412698, 0.000000, 0.000000),(0.444444, 0.000000, 0.000000), (0.460317, 1.000000, 1.000000),(0.476190, 0.000000, 0.000000), (0.507937, 0.000000, 0.000000),(0.523810, 1.000000, 1.000000), (0.539683, 0.000000, 0.000000),(0.571429, 0.000000, 0.000000), (0.587302, 1.000000, 1.000000),(0.603175, 0.000000, 0.000000), (0.634921, 0.000000, 0.000000),(0.650794, 1.000000, 1.000000), (0.666667, 0.000000, 0.000000),(0.698413, 0.000000, 0.000000), (0.714286, 1.000000, 1.000000),(0.730159, 0.000000, 0.000000), (0.761905, 0.000000, 0.000000),(0.777778, 1.000000, 1.000000), (0.793651, 0.000000, 0.000000),(0.825397, 0.000000, 0.000000), (0.841270, 1.000000, 1.000000),(0.857143, 0.000000, 0.000000), (0.888889, 0.000000, 0.000000),(0.904762, 1.000000, 1.000000), (0.920635, 0.000000, 0.000000),(0.952381, 0.000000, 0.000000), (0.968254, 1.000000, 1.000000),(0.984127, 0.000000, 0.000000), (1.0, 0., 0.)), 'blue': ((0., 0., 0.),(0.015873, 1.000000, 1.000000), (0.031746, 1.000000, 1.000000),(0.047619, 0.000000, 0.000000), (0.063492, 0.000000, 0.000000),(0.079365, 1.000000, 1.000000), (0.095238, 1.000000, 1.000000),(0.111111, 0.000000, 0.000000), (0.126984, 0.000000, 0.000000),(0.142857, 1.000000, 1.000000), (0.158730, 1.000000, 1.000000),(0.174603, 0.000000, 0.000000), (0.190476, 0.000000, 0.000000),(0.206349, 1.000000, 1.000000), (0.222222, 1.000000, 1.000000),(0.238095, 0.000000, 0.000000), (0.253968, 0.000000, 0.000000),(0.269841, 1.000000, 1.000000), (0.285714, 1.000000, 1.000000),(0.301587, 0.000000, 0.000000), (0.317460, 0.000000, 0.000000),(0.333333, 1.000000, 1.000000), (0.349206, 1.000000, 1.000000),(0.365079, 0.000000, 0.000000), (0.380952, 0.000000, 0.000000),(0.396825, 1.000000, 1.000000), (0.412698, 1.000000, 1.000000),(0.428571, 0.000000, 0.000000), (0.444444, 0.000000, 0.000000),(0.460317, 1.000000, 1.000000), (0.476190, 1.000000, 1.000000),(0.492063, 0.000000, 0.000000), (0.507937, 0.000000, 0.000000),(0.523810, 1.000000, 1.000000), (0.539683, 1.000000, 1.000000),(0.555556, 0.000000, 0.000000), (0.571429, 0.000000, 0.000000),(0.587302, 1.000000, 1.000000), (0.603175, 1.000000, 1.000000),(0.619048, 0.000000, 0.000000), (0.634921, 0.000000, 0.000000),(0.650794, 1.000000, 1.000000), (0.666667, 1.000000, 1.000000),(0.682540, 0.000000, 0.000000), (0.698413, 0.000000, 0.000000),(0.714286, 1.000000, 1.000000), (0.730159, 1.000000, 1.000000),(0.746032, 0.000000, 0.000000), (0.761905, 0.000000, 0.000000),(0.777778, 1.000000, 1.000000), (0.793651, 1.000000, 1.000000),(0.809524, 0.000000, 0.000000), (0.825397, 0.000000, 0.000000),(0.841270, 1.000000, 1.000000), (0.857143, 1.000000, 1.000000),(0.873016, 0.000000, 0.000000), (0.888889, 0.000000, 0.000000),(0.904762, 1.000000, 1.000000), (0.920635, 1.000000, 1.000000),(0.936508, 0.000000, 0.000000), (0.952381, 0.000000, 0.000000),(0.968254, 1.000000, 1.000000), (0.984127, 1.000000, 1.000000),(1.0, 0., 0.))} _gray_data = {'red': ((0., 0, 0), (1., 1, 1)), 'green': ((0., 0, 0), (1., 1, 1)), 'blue': ((0., 0, 0), (1., 1, 1))} _hot_data = {'red': ((0., 0.0416, 0.0416),(0.365079, 1.000000, 1.000000),(1.0, 1.0, 1.0)), 'green': ((0., 0., 0.),(0.365079, 0.000000, 0.000000), (0.746032, 1.000000, 1.000000),(1.0, 1.0, 1.0)), 'blue': ((0., 0., 0.),(0.746032, 0.000000, 0.000000),(1.0, 1.0, 1.0))} _hsv_data = {'red': ((0., 1., 1.),(0.158730, 1.000000, 1.000000), (0.174603, 0.968750, 0.968750),(0.333333, 0.031250, 0.031250), (0.349206, 0.000000, 0.000000),(0.666667, 0.000000, 0.000000), (0.682540, 0.031250, 0.031250),(0.841270, 0.968750, 0.968750), (0.857143, 1.000000, 1.000000),(1.0, 1.0, 1.0)), 'green': ((0., 0., 0.),(0.158730, 0.937500, 0.937500), (0.174603, 1.000000, 1.000000),(0.507937, 1.000000, 1.000000), (0.666667, 0.062500, 0.062500),(0.682540, 0.000000, 0.000000), (1.0, 0., 0.)), 'blue': ((0., 0., 0.),(0.333333, 0.000000, 0.000000), (0.349206, 0.062500, 0.062500),(0.507937, 1.000000, 1.000000), (0.841270, 1.000000, 1.000000),(0.857143, 0.937500, 0.937500), (1.0, 0.09375, 0.09375))} _jet_data = {'red': ((0., 0, 0), (0.35, 0, 0), (0.66, 1, 1), (0.89,1, 1), (1, 0.5, 0.5)), 'green': ((0., 0, 0), (0.125,0, 0), (0.375,1, 1), (0.64,1, 1), (0.91,0,0), (1, 0, 0)), 'blue': ((0., 0.5, 0.5), (0.11, 1, 1), (0.34, 1, 1), (0.65,0, 0), (1, 0, 0))} _pink_data = {'red': ((0., 0.1178, 0.1178),(0.015873, 0.195857, 0.195857), (0.031746, 0.250661, 0.250661),(0.047619, 0.295468, 0.295468), (0.063492, 0.334324, 0.334324),(0.079365, 0.369112, 0.369112), (0.095238, 0.400892, 0.400892),(0.111111, 0.430331, 0.430331), (0.126984, 0.457882, 0.457882),(0.142857, 0.483867, 0.483867), (0.158730, 0.508525, 0.508525),(0.174603, 0.532042, 0.532042), (0.190476, 0.554563, 0.554563),(0.206349, 0.576204, 0.576204), (0.222222, 0.597061, 0.597061),(0.238095, 0.617213, 0.617213), (0.253968, 0.636729, 0.636729),(0.269841, 0.655663, 0.655663), (0.285714, 0.674066, 0.674066),(0.301587, 0.691980, 0.691980), (0.317460, 0.709441, 0.709441),(0.333333, 0.726483, 0.726483), (0.349206, 0.743134, 0.743134),(0.365079, 0.759421, 0.759421), (0.380952, 0.766356, 0.766356),(0.396825, 0.773229, 0.773229), (0.412698, 0.780042, 0.780042),(0.428571, 0.786796, 0.786796), (0.444444, 0.793492, 0.793492),(0.460317, 0.800132, 0.800132), (0.476190, 0.806718, 0.806718),(0.492063, 0.813250, 0.813250), (0.507937, 0.819730, 0.819730),(0.523810, 0.826160, 0.826160), (0.539683, 0.832539, 0.832539),(0.555556, 0.838870, 0.838870), (0.571429, 0.845154, 0.845154),(0.587302, 0.851392, 0.851392), (0.603175, 0.857584, 0.857584),(0.619048, 0.863731, 0.863731), (0.634921, 0.869835, 0.869835),(0.650794, 0.875897, 0.875897), (0.666667, 0.881917, 0.881917),(0.682540, 0.887896, 0.887896), (0.698413, 0.893835, 0.893835),(0.714286, 0.899735, 0.899735), (0.730159, 0.905597, 0.905597),(0.746032, 0.911421, 0.911421), (0.761905, 0.917208, 0.917208),(0.777778, 0.922958, 0.922958), (0.793651, 0.928673, 0.928673),(0.809524, 0.934353, 0.934353), (0.825397, 0.939999, 0.939999),(0.841270, 0.945611, 0.945611), (0.857143, 0.951190, 0.951190),(0.873016, 0.956736, 0.956736), (0.888889, 0.962250, 0.962250),(0.904762, 0.967733, 0.967733), (0.920635, 0.973185, 0.973185),(0.936508, 0.978607, 0.978607), (0.952381, 0.983999, 0.983999),(0.968254, 0.989361, 0.989361), (0.984127, 0.994695, 0.994695),(1.0, 1.0, 1.0)), 'green': ((0., 0., 0.),(0.015873, 0.102869, 0.102869), (0.031746, 0.145479, 0.145479),(0.047619, 0.178174, 0.178174), (0.063492, 0.205738, 0.205738),(0.079365, 0.230022, 0.230022), (0.095238, 0.251976, 0.251976),(0.111111, 0.272166, 0.272166), (0.126984, 0.290957, 0.290957),(0.142857, 0.308607, 0.308607), (0.158730, 0.325300, 0.325300),(0.174603, 0.341178, 0.341178), (0.190476, 0.356348, 0.356348),(0.206349, 0.370899, 0.370899), (0.222222, 0.384900, 0.384900),(0.238095, 0.398410, 0.398410), (0.253968, 0.411476, 0.411476),(0.269841, 0.424139, 0.424139), (0.285714, 0.436436, 0.436436),(0.301587, 0.448395, 0.448395), (0.317460, 0.460044, 0.460044),(0.333333, 0.471405, 0.471405), (0.349206, 0.482498, 0.482498),(0.365079, 0.493342, 0.493342), (0.380952, 0.517549, 0.517549),(0.396825, 0.540674, 0.540674), (0.412698, 0.562849, 0.562849),(0.428571, 0.584183, 0.584183), (0.444444, 0.604765, 0.604765),(0.460317, 0.624669, 0.624669), (0.476190, 0.643958, 0.643958),(0.492063, 0.662687, 0.662687), (0.507937, 0.680900, 0.680900),(0.523810, 0.698638, 0.698638), (0.539683, 0.715937, 0.715937),(0.555556, 0.732828, 0.732828), (0.571429, 0.749338, 0.749338),(0.587302, 0.765493, 0.765493), (0.603175, 0.781313, 0.781313),(0.619048, 0.796819, 0.796819), (0.634921, 0.812029, 0.812029),(0.650794, 0.826960, 0.826960), (0.666667, 0.841625, 0.841625),(0.682540, 0.856040, 0.856040), (0.698413, 0.870216, 0.870216),(0.714286, 0.884164, 0.884164), (0.730159, 0.897896, 0.897896),(0.746032, 0.911421, 0.911421), (0.761905, 0.917208, 0.917208),(0.777778, 0.922958, 0.922958), (0.793651, 0.928673, 0.928673),(0.809524, 0.934353, 0.934353), (0.825397, 0.939999, 0.939999),(0.841270, 0.945611, 0.945611), (0.857143, 0.951190, 0.951190),(0.873016, 0.956736, 0.956736), (0.888889, 0.962250, 0.962250),(0.904762, 0.967733, 0.967733), (0.920635, 0.973185, 0.973185),(0.936508, 0.978607, 0.978607), (0.952381, 0.983999, 0.983999),(0.968254, 0.989361, 0.989361), (0.984127, 0.994695, 0.994695),(1.0, 1.0, 1.0)), 'blue': ((0., 0., 0.),(0.015873, 0.102869, 0.102869), (0.031746, 0.145479, 0.145479),(0.047619, 0.178174, 0.178174), (0.063492, 0.205738, 0.205738),(0.079365, 0.230022, 0.230022), (0.095238, 0.251976, 0.251976),(0.111111, 0.272166, 0.272166), (0.126984, 0.290957, 0.290957),(0.142857, 0.308607, 0.308607), (0.158730, 0.325300, 0.325300),(0.174603, 0.341178, 0.341178), (0.190476, 0.356348, 0.356348),(0.206349, 0.370899, 0.370899), (0.222222, 0.384900, 0.384900),(0.238095, 0.398410, 0.398410), (0.253968, 0.411476, 0.411476),(0.269841, 0.424139, 0.424139), (0.285714, 0.436436, 0.436436),(0.301587, 0.448395, 0.448395), (0.317460, 0.460044, 0.460044),(0.333333, 0.471405, 0.471405), (0.349206, 0.482498, 0.482498),(0.365079, 0.493342, 0.493342), (0.380952, 0.503953, 0.503953),(0.396825, 0.514344, 0.514344), (0.412698, 0.524531, 0.524531),(0.428571, 0.534522, 0.534522), (0.444444, 0.544331, 0.544331),(0.460317, 0.553966, 0.553966), (0.476190, 0.563436, 0.563436),(0.492063, 0.572750, 0.572750), (0.507937, 0.581914, 0.581914),(0.523810, 0.590937, 0.590937), (0.539683, 0.599824, 0.599824),(0.555556, 0.608581, 0.608581), (0.571429, 0.617213, 0.617213),(0.587302, 0.625727, 0.625727), (0.603175, 0.634126, 0.634126),(0.619048, 0.642416, 0.642416), (0.634921, 0.650600, 0.650600),(0.650794, 0.658682, 0.658682), (0.666667, 0.666667, 0.666667),(0.682540, 0.674556, 0.674556), (0.698413, 0.682355, 0.682355),(0.714286, 0.690066, 0.690066), (0.730159, 0.697691, 0.697691),(0.746032, 0.705234, 0.705234), (0.761905, 0.727166, 0.727166),(0.777778, 0.748455, 0.748455), (0.793651, 0.769156, 0.769156),(0.809524, 0.789314, 0.789314), (0.825397, 0.808969, 0.808969),(0.841270, 0.828159, 0.828159), (0.857143, 0.846913, 0.846913),(0.873016, 0.865261, 0.865261), (0.888889, 0.883229, 0.883229),(0.904762, 0.900837, 0.900837), (0.920635, 0.918109, 0.918109),(0.936508, 0.935061, 0.935061), (0.952381, 0.951711, 0.951711),(0.968254, 0.968075, 0.968075), (0.984127, 0.984167, 0.984167),(1.0, 1.0, 1.0))} _prism_data = {'red': ((0., 1., 1.),(0.031746, 1.000000, 1.000000), (0.047619, 0.000000, 0.000000),(0.063492, 0.000000, 0.000000), (0.079365, 0.666667, 0.666667),(0.095238, 1.000000, 1.000000), (0.126984, 1.000000, 1.000000),(0.142857, 0.000000, 0.000000), (0.158730, 0.000000, 0.000000),(0.174603, 0.666667, 0.666667), (0.190476, 1.000000, 1.000000),(0.222222, 1.000000, 1.000000), (0.238095, 0.000000, 0.000000),(0.253968, 0.000000, 0.000000), (0.269841, 0.666667, 0.666667),(0.285714, 1.000000, 1.000000), (0.317460, 1.000000, 1.000000),(0.333333, 0.000000, 0.000000), (0.349206, 0.000000, 0.000000),(0.365079, 0.666667, 0.666667), (0.380952, 1.000000, 1.000000),(0.412698, 1.000000, 1.000000), (0.428571, 0.000000, 0.000000),(0.444444, 0.000000, 0.000000), (0.460317, 0.666667, 0.666667),(0.476190, 1.000000, 1.000000), (0.507937, 1.000000, 1.000000),(0.523810, 0.000000, 0.000000), (0.539683, 0.000000, 0.000000),(0.555556, 0.666667, 0.666667), (0.571429, 1.000000, 1.000000),(0.603175, 1.000000, 1.000000), (0.619048, 0.000000, 0.000000),(0.634921, 0.000000, 0.000000), (0.650794, 0.666667, 0.666667),(0.666667, 1.000000, 1.000000), (0.698413, 1.000000, 1.000000),(0.714286, 0.000000, 0.000000), (0.730159, 0.000000, 0.000000),(0.746032, 0.666667, 0.666667), (0.761905, 1.000000, 1.000000),(0.793651, 1.000000, 1.000000), (0.809524, 0.000000, 0.000000),(0.825397, 0.000000, 0.000000), (0.841270, 0.666667, 0.666667),(0.857143, 1.000000, 1.000000), (0.888889, 1.000000, 1.000000),(0.904762, 0.000000, 0.000000), (0.920635, 0.000000, 0.000000),(0.936508, 0.666667, 0.666667), (0.952381, 1.000000, 1.000000),(0.984127, 1.000000, 1.000000), (1.0, 0.0, 0.0)), 'green': ((0., 0., 0.),(0.031746, 1.000000, 1.000000), (0.047619, 1.000000, 1.000000),(0.063492, 0.000000, 0.000000), (0.095238, 0.000000, 0.000000),(0.126984, 1.000000, 1.000000), (0.142857, 1.000000, 1.000000),(0.158730, 0.000000, 0.000000), (0.190476, 0.000000, 0.000000),(0.222222, 1.000000, 1.000000), (0.238095, 1.000000, 1.000000),(0.253968, 0.000000, 0.000000), (0.285714, 0.000000, 0.000000),(0.317460, 1.000000, 1.000000), (0.333333, 1.000000, 1.000000),(0.349206, 0.000000, 0.000000), (0.380952, 0.000000, 0.000000),(0.412698, 1.000000, 1.000000), (0.428571, 1.000000, 1.000000),(0.444444, 0.000000, 0.000000), (0.476190, 0.000000, 0.000000),(0.507937, 1.000000, 1.000000), (0.523810, 1.000000, 1.000000),(0.539683, 0.000000, 0.000000), (0.571429, 0.000000, 0.000000),(0.603175, 1.000000, 1.000000), (0.619048, 1.000000, 1.000000),(0.634921, 0.000000, 0.000000), (0.666667, 0.000000, 0.000000),(0.698413, 1.000000, 1.000000), (0.714286, 1.000000, 1.000000),(0.730159, 0.000000, 0.000000), (0.761905, 0.000000, 0.000000),(0.793651, 1.000000, 1.000000), (0.809524, 1.000000, 1.000000),(0.825397, 0.000000, 0.000000), (0.857143, 0.000000, 0.000000),(0.888889, 1.000000, 1.000000), (0.904762, 1.000000, 1.000000),(0.920635, 0.000000, 0.000000), (0.952381, 0.000000, 0.000000),(0.984127, 1.000000, 1.000000), (1.0, 1.0, 1.0)), 'blue': ((0., 0., 0.),(0.047619, 0.000000, 0.000000), (0.063492, 1.000000, 1.000000),(0.079365, 1.000000, 1.000000), (0.095238, 0.000000, 0.000000),(0.142857, 0.000000, 0.000000), (0.158730, 1.000000, 1.000000),(0.174603, 1.000000, 1.000000), (0.190476, 0.000000, 0.000000),(0.238095, 0.000000, 0.000000), (0.253968, 1.000000, 1.000000),(0.269841, 1.000000, 1.000000), (0.285714, 0.000000, 0.000000),(0.333333, 0.000000, 0.000000), (0.349206, 1.000000, 1.000000),(0.365079, 1.000000, 1.000000), (0.380952, 0.000000, 0.000000),(0.428571, 0.000000, 0.000000), (0.444444, 1.000000, 1.000000),(0.460317, 1.000000, 1.000000), (0.476190, 0.000000, 0.000000),(0.523810, 0.000000, 0.000000), (0.539683, 1.000000, 1.000000),(0.555556, 1.000000, 1.000000), (0.571429, 0.000000, 0.000000),(0.619048, 0.000000, 0.000000), (0.634921, 1.000000, 1.000000),(0.650794, 1.000000, 1.000000), (0.666667, 0.000000, 0.000000),(0.714286, 0.000000, 0.000000), (0.730159, 1.000000, 1.000000),(0.746032, 1.000000, 1.000000), (0.761905, 0.000000, 0.000000),(0.809524, 0.000000, 0.000000), (0.825397, 1.000000, 1.000000),(0.841270, 1.000000, 1.000000), (0.857143, 0.000000, 0.000000),(0.904762, 0.000000, 0.000000), (0.920635, 1.000000, 1.000000),(0.936508, 1.000000, 1.000000), (0.952381, 0.000000, 0.000000),(1.0, 0.0, 0.0))} _spring_data = {'red': ((0., 1., 1.),(1.0, 1.0, 1.0)), 'green': ((0., 0., 0.),(1.0, 1.0, 1.0)), 'blue': ((0., 1., 1.),(1.0, 0.0, 0.0))} _summer_data = {'red': ((0., 0., 0.),(1.0, 1.0, 1.0)), 'green': ((0., 0.5, 0.5),(1.0, 1.0, 1.0)), 'blue': ((0., 0.4, 0.4),(1.0, 0.4, 0.4))} _winter_data = {'red': ((0., 0., 0.),(1.0, 0.0, 0.0)), 'green': ((0., 0., 0.),(1.0, 1.0, 1.0)), 'blue': ((0., 1., 1.),(1.0, 0.5, 0.5))} _spectral_data = {'red': [(0.0, 0.0, 0.0), (0.05, 0.4667, 0.4667), (0.10, 0.5333, 0.5333), (0.15, 0.0, 0.0), (0.20, 0.0, 0.0), (0.25, 0.0, 0.0), (0.30, 0.0, 0.0), (0.35, 0.0, 0.0), (0.40, 0.0, 0.0), (0.45, 0.0, 0.0), (0.50, 0.0, 0.0), (0.55, 0.0, 0.0), (0.60, 0.0, 0.0), (0.65, 0.7333, 0.7333), (0.70, 0.9333, 0.9333), (0.75, 1.0, 1.0), (0.80, 1.0, 1.0), (0.85, 1.0, 1.0), (0.90, 0.8667, 0.8667), (0.95, 0.80, 0.80), (1.0, 0.80, 0.80)], 'green': [(0.0, 0.0, 0.0), (0.05, 0.0, 0.0), (0.10, 0.0, 0.0), (0.15, 0.0, 0.0), (0.20, 0.0, 0.0), (0.25, 0.4667, 0.4667), (0.30, 0.6000, 0.6000), (0.35, 0.6667, 0.6667), (0.40, 0.6667, 0.6667), (0.45, 0.6000, 0.6000), (0.50, 0.7333, 0.7333), (0.55, 0.8667, 0.8667), (0.60, 1.0, 1.0), (0.65, 1.0, 1.0), (0.70, 0.9333, 0.9333), (0.75, 0.8000, 0.8000), (0.80, 0.6000, 0.6000), (0.85, 0.0, 0.0), (0.90, 0.0, 0.0), (0.95, 0.0, 0.0), (1.0, 0.80, 0.80)], 'blue': [(0.0, 0.0, 0.0), (0.05, 0.5333, 0.5333), (0.10, 0.6000, 0.6000), (0.15, 0.6667, 0.6667), (0.20, 0.8667, 0.8667), (0.25, 0.8667, 0.8667), (0.30, 0.8667, 0.8667), (0.35, 0.6667, 0.6667), (0.40, 0.5333, 0.5333), (0.45, 0.0, 0.0), (0.5, 0.0, 0.0), (0.55, 0.0, 0.0), (0.60, 0.0, 0.0), (0.65, 0.0, 0.0), (0.70, 0.0, 0.0), (0.75, 0.0, 0.0), (0.80, 0.0, 0.0), (0.85, 0.0, 0.0), (0.90, 0.0, 0.0), (0.95, 0.0, 0.0), (1.0, 0.80, 0.80)]} autumn = colors.LinearSegmentedColormap('autumn', _autumn_data, LUTSIZE) bone = colors.LinearSegmentedColormap('bone ', _bone_data, LUTSIZE) binary = colors.LinearSegmentedColormap('binary ', _binary_data, LUTSIZE) cool = colors.LinearSegmentedColormap('cool', _cool_data, LUTSIZE) copper = colors.LinearSegmentedColormap('copper', _copper_data, LUTSIZE) flag = colors.LinearSegmentedColormap('flag', _flag_data, LUTSIZE) gray = colors.LinearSegmentedColormap('gray', _gray_data, LUTSIZE) hot = colors.LinearSegmentedColormap('hot', _hot_data, LUTSIZE) hsv = colors.LinearSegmentedColormap('hsv', _hsv_data, LUTSIZE) jet = colors.LinearSegmentedColormap('jet', _jet_data, LUTSIZE) pink = colors.LinearSegmentedColormap('pink', _pink_data, LUTSIZE) prism = colors.LinearSegmentedColormap('prism', _prism_data, LUTSIZE) spring = colors.LinearSegmentedColormap('spring', _spring_data, LUTSIZE) summer = colors.LinearSegmentedColormap('summer', _summer_data, LUTSIZE) winter = colors.LinearSegmentedColormap('winter', _winter_data, LUTSIZE) spectral = colors.LinearSegmentedColormap('spectral', _spectral_data, LUTSIZE) datad = { 'autumn': _autumn_data, 'bone': _bone_data, 'binary': _binary_data, 'cool': _cool_data, 'copper': _copper_data, 'flag': _flag_data, 'gray' : _gray_data, 'hot': _hot_data, 'hsv': _hsv_data, 'jet' : _jet_data, 'pink': _pink_data, 'prism': _prism_data, 'spring': _spring_data, 'summer': _summer_data, 'winter': _winter_data, 'spectral': _spectral_data } # 34 colormaps based on color specifications and designs # developed by Cynthia Brewer (http://colorbrewer.org). # The ColorBrewer palettes have been included under the terms # of an Apache-stype license (for details, see the file # LICENSE_COLORBREWER in the license directory of the matplotlib # source distribution). _Accent_data = {'blue': [(0.0, 0.49803921580314636, 0.49803921580314636), (0.14285714285714285, 0.83137255907058716, 0.83137255907058716), (0.2857142857142857, 0.52549022436141968, 0.52549022436141968), (0.42857142857142855, 0.60000002384185791, 0.60000002384185791), (0.5714285714285714, 0.69019609689712524, 0.69019609689712524), (0.7142857142857143, 0.49803921580314636, 0.49803921580314636), (0.8571428571428571, 0.090196080505847931, 0.090196080505847931), (1.0, 0.40000000596046448, 0.40000000596046448)], 'green': [(0.0, 0.78823530673980713, 0.78823530673980713), (0.14285714285714285, 0.68235296010971069, 0.68235296010971069), (0.2857142857142857, 0.75294119119644165, 0.75294119119644165), (0.42857142857142855, 1.0, 1.0), (0.5714285714285714, 0.42352941632270813, 0.42352941632270813), (0.7142857142857143, 0.0078431377187371254, 0.0078431377187371254), (0.8571428571428571, 0.35686275362968445, 0.35686275362968445), (1.0, 0.40000000596046448, 0.40000000596046448)], 'red': [(0.0, 0.49803921580314636, 0.49803921580314636), (0.14285714285714285, 0.7450980544090271, 0.7450980544090271), (0.2857142857142857, 0.99215686321258545, 0.99215686321258545), (0.42857142857142855, 1.0, 1.0), (0.5714285714285714, 0.21960784494876862, 0.21960784494876862), (0.7142857142857143, 0.94117647409439087, 0.94117647409439087), (0.8571428571428571, 0.74901962280273438, 0.74901962280273438), (1.0, 0.40000000596046448, 0.40000000596046448)]} _Blues_data = {'blue': [(0.0, 1.0, 1.0), (0.125, 0.9686274528503418, 0.9686274528503418), (0.25, 0.93725490570068359, 0.93725490570068359), (0.375, 0.88235294818878174, 0.88235294818878174), (0.5, 0.83921569585800171, 0.83921569585800171), (0.625, 0.7764706015586853, 0.7764706015586853), (0.75, 0.70980393886566162, 0.70980393886566162), (0.875, 0.61176472902297974, 0.61176472902297974), (1.0, 0.41960784792900085, 0.41960784792900085)], 'green': [(0.0, 0.9843137264251709, 0.9843137264251709), (0.125, 0.92156863212585449, 0.92156863212585449), (0.25, 0.85882353782653809, 0.85882353782653809), (0.375, 0.7921568751335144, 0.7921568751335144), (0.5, 0.68235296010971069, 0.68235296010971069), (0.625, 0.57254904508590698, 0.57254904508590698), (0.75, 0.44313725829124451, 0.44313725829124451), (0.875, 0.31764706969261169, 0.31764706969261169), (1.0, 0.18823529779911041, 0.18823529779911041)], 'red': [(0.0, 0.9686274528503418, 0.9686274528503418), (0.125, 0.87058824300765991, 0.87058824300765991), (0.25, 0.7764706015586853, 0.7764706015586853), (0.375, 0.61960786581039429, 0.61960786581039429), (0.5, 0.41960784792900085, 0.41960784792900085), (0.625, 0.25882354378700256, 0.25882354378700256), (0.75, 0.12941177189350128, 0.12941177189350128), (0.875, 0.031372550874948502, 0.031372550874948502), (1.0, 0.031372550874948502, 0.031372550874948502)]} _BrBG_data = {'blue': [(0.0, 0.019607843831181526, 0.019607843831181526), (0.10000000000000001, 0.039215687662363052, 0.039215687662363052), (0.20000000000000001, 0.17647059261798859, 0.17647059261798859), (0.29999999999999999, 0.49019607901573181, 0.49019607901573181), (0.40000000000000002, 0.76470589637756348, 0.76470589637756348), (0.5, 0.96078431606292725, 0.96078431606292725), (0.59999999999999998, 0.89803922176361084, 0.89803922176361084), (0.69999999999999996, 0.75686275959014893, 0.75686275959014893), (0.80000000000000004, 0.56078433990478516, 0.56078433990478516), (0.90000000000000002, 0.36862745881080627, 0.36862745881080627), (1.0, 0.18823529779911041, 0.18823529779911041)], 'green': [(0.0, 0.18823529779911041, 0.18823529779911041), (0.10000000000000001, 0.31764706969261169, 0.31764706969261169), (0.20000000000000001, 0.5058823823928833, 0.5058823823928833), (0.29999999999999999, 0.7607843279838562, 0.7607843279838562), (0.40000000000000002, 0.90980392694473267, 0.90980392694473267), (0.5, 0.96078431606292725, 0.96078431606292725), (0.59999999999999998, 0.91764706373214722, 0.91764706373214722), (0.69999999999999996, 0.80392158031463623, 0.80392158031463623), (0.80000000000000004, 0.59215688705444336, 0.59215688705444336), (0.90000000000000002, 0.40000000596046448, 0.40000000596046448), (1.0, 0.23529411852359772, 0.23529411852359772)], 'red': [(0.0, 0.32941177487373352, 0.32941177487373352), (0.10000000000000001, 0.54901963472366333, 0.54901963472366333), (0.20000000000000001, 0.74901962280273438, 0.74901962280273438), (0.29999999999999999, 0.87450981140136719, 0.87450981140136719), (0.40000000000000002, 0.96470588445663452, 0.96470588445663452), (0.5, 0.96078431606292725, 0.96078431606292725), (0.59999999999999998, 0.78039216995239258, 0.78039216995239258), (0.69999999999999996, 0.50196081399917603, 0.50196081399917603), (0.80000000000000004, 0.20784313976764679, 0.20784313976764679), (0.90000000000000002, 0.0039215688593685627, 0.0039215688593685627), (1.0, 0.0, 0.0)]} _BuGn_data = {'blue': [(0.0, 0.99215686321258545, 0.99215686321258545), (0.125, 0.97647058963775635, 0.97647058963775635), (0.25, 0.90196079015731812, 0.90196079015731812), (0.375, 0.78823530673980713, 0.78823530673980713), (0.5, 0.64313727617263794, 0.64313727617263794), (0.625, 0.46274510025978088, 0.46274510025978088), (0.75, 0.27058824896812439, 0.27058824896812439), (0.875, 0.17254902422428131, 0.17254902422428131), (1.0, 0.10588235408067703, 0.10588235408067703)], 'green': [(0.0, 0.98823529481887817, 0.98823529481887817), (0.125, 0.96078431606292725, 0.96078431606292725), (0.25, 0.92549020051956177, 0.92549020051956177), (0.375, 0.84705883264541626, 0.84705883264541626), (0.5, 0.7607843279838562, 0.7607843279838562), (0.625, 0.68235296010971069, 0.68235296010971069), (0.75, 0.54509806632995605, 0.54509806632995605), (0.875, 0.42745098471641541, 0.42745098471641541), (1.0, 0.26666668057441711, 0.26666668057441711)], 'red': [(0.0, 0.9686274528503418, 0.9686274528503418), (0.125, 0.89803922176361084, 0.89803922176361084), (0.25, 0.80000001192092896, 0.80000001192092896), (0.375, 0.60000002384185791, 0.60000002384185791), (0.5, 0.40000000596046448, 0.40000000596046448), (0.625, 0.25490197539329529, 0.25490197539329529), (0.75, 0.13725490868091583, 0.13725490868091583), (0.875, 0.0, 0.0), (1.0, 0.0, 0.0)]} _BuPu_data = {'blue': [(0.0, 0.99215686321258545, 0.99215686321258545), (0.125, 0.95686274766921997, 0.95686274766921997), (0.25, 0.90196079015731812, 0.90196079015731812), (0.375, 0.85490196943283081, 0.85490196943283081), (0.5, 0.7764706015586853, 0.7764706015586853), (0.625, 0.69411766529083252, 0.69411766529083252), (0.75, 0.61568629741668701, 0.61568629741668701), (0.875, 0.48627451062202454, 0.48627451062202454), (1.0, 0.29411765933036804, 0.29411765933036804)], 'green': [(0.0, 0.98823529481887817, 0.98823529481887817), (0.125, 0.92549020051956177, 0.92549020051956177), (0.25, 0.82745099067687988, 0.82745099067687988), (0.375, 0.73725491762161255, 0.73725491762161255), (0.5, 0.58823531866073608, 0.58823531866073608), (0.625, 0.41960784792900085, 0.41960784792900085), (0.75, 0.25490197539329529, 0.25490197539329529), (0.875, 0.058823529630899429, 0.058823529630899429), (1.0, 0.0, 0.0)], 'red': [(0.0, 0.9686274528503418, 0.9686274528503418), (0.125, 0.87843137979507446, 0.87843137979507446), (0.25, 0.74901962280273438, 0.74901962280273438), (0.375, 0.61960786581039429, 0.61960786581039429), (0.5, 0.54901963472366333, 0.54901963472366333), (0.625, 0.54901963472366333, 0.54901963472366333), (0.75, 0.53333336114883423, 0.53333336114883423), (0.875, 0.5058823823928833, 0.5058823823928833), (1.0, 0.30196079611778259, 0.30196079611778259)]} _Dark2_data = {'blue': [(0.0, 0.46666666865348816, 0.46666666865348816), (0.14285714285714285, 0.0078431377187371254, 0.0078431377187371254), (0.2857142857142857, 0.70196080207824707, 0.70196080207824707), (0.42857142857142855, 0.54117649793624878, 0.54117649793624878), (0.5714285714285714, 0.11764705926179886, 0.11764705926179886), (0.7142857142857143, 0.0078431377187371254, 0.0078431377187371254), (0.8571428571428571, 0.11372549086809158, 0.11372549086809158), (1.0, 0.40000000596046448, 0.40000000596046448)], 'green': [(0.0, 0.61960786581039429, 0.61960786581039429), (0.14285714285714285, 0.37254902720451355, 0.37254902720451355), (0.2857142857142857, 0.43921568989753723, 0.43921568989753723), (0.42857142857142855, 0.16078431904315948, 0.16078431904315948), (0.5714285714285714, 0.65098041296005249, 0.65098041296005249), (0.7142857142857143, 0.67058825492858887, 0.67058825492858887), (0.8571428571428571, 0.46274510025978088, 0.46274510025978088), (1.0, 0.40000000596046448, 0.40000000596046448)], 'red': [(0.0, 0.10588235408067703, 0.10588235408067703), (0.14285714285714285, 0.85098040103912354, 0.85098040103912354), (0.2857142857142857, 0.45882353186607361, 0.45882353186607361), (0.42857142857142855, 0.90588235855102539, 0.90588235855102539), (0.5714285714285714, 0.40000000596046448, 0.40000000596046448), (0.7142857142857143, 0.90196079015731812, 0.90196079015731812), (0.8571428571428571, 0.65098041296005249, 0.65098041296005249), (1.0, 0.40000000596046448, 0.40000000596046448)]} _GnBu_data = {'blue': [(0.0, 0.94117647409439087, 0.94117647409439087), (0.125, 0.85882353782653809, 0.85882353782653809), (0.25, 0.77254903316497803, 0.77254903316497803), (0.375, 0.70980393886566162, 0.70980393886566162), (0.5, 0.76862746477127075, 0.76862746477127075), (0.625, 0.82745099067687988, 0.82745099067687988), (0.75, 0.7450980544090271, 0.7450980544090271), (0.875, 0.67450982332229614, 0.67450982332229614), (1.0, 0.5058823823928833, 0.5058823823928833)], 'green': [(0.0, 0.98823529481887817, 0.98823529481887817), (0.125, 0.9529411792755127, 0.9529411792755127), (0.25, 0.92156863212585449, 0.92156863212585449), (0.375, 0.86666667461395264, 0.86666667461395264), (0.5, 0.80000001192092896, 0.80000001192092896), (0.625, 0.70196080207824707, 0.70196080207824707), (0.75, 0.54901963472366333, 0.54901963472366333), (0.875, 0.40784314274787903, 0.40784314274787903), (1.0, 0.25098040699958801, 0.25098040699958801)], 'red': [(0.0, 0.9686274528503418, 0.9686274528503418), (0.125, 0.87843137979507446, 0.87843137979507446), (0.25, 0.80000001192092896, 0.80000001192092896), (0.375, 0.65882354974746704, 0.65882354974746704), (0.5, 0.48235294222831726, 0.48235294222831726), (0.625, 0.30588236451148987, 0.30588236451148987), (0.75, 0.16862745583057404, 0.16862745583057404), (0.875, 0.031372550874948502, 0.031372550874948502), (1.0, 0.031372550874948502, 0.031372550874948502)]} _Greens_data = {'blue': [(0.0, 0.96078431606292725, 0.96078431606292725), (0.125, 0.87843137979507446, 0.87843137979507446), (0.25, 0.75294119119644165, 0.75294119119644165), (0.375, 0.60784316062927246, 0.60784316062927246), (0.5, 0.46274510025978088, 0.46274510025978088), (0.625, 0.364705890417099, 0.364705890417099), (0.75, 0.27058824896812439, 0.27058824896812439), (0.875, 0.17254902422428131, 0.17254902422428131), (1.0, 0.10588235408067703, 0.10588235408067703)], 'green': [(0.0, 0.98823529481887817, 0.98823529481887817), (0.125, 0.96078431606292725, 0.96078431606292725), (0.25, 0.91372549533843994, 0.91372549533843994), (0.375, 0.85098040103912354, 0.85098040103912354), (0.5, 0.76862746477127075, 0.76862746477127075), (0.625, 0.67058825492858887, 0.67058825492858887), (0.75, 0.54509806632995605, 0.54509806632995605), (0.875, 0.42745098471641541, 0.42745098471641541), (1.0, 0.26666668057441711, 0.26666668057441711)], 'red': [(0.0, 0.9686274528503418, 0.9686274528503418), (0.125, 0.89803922176361084, 0.89803922176361084), (0.25, 0.78039216995239258, 0.78039216995239258), (0.375, 0.63137257099151611, 0.63137257099151611), (0.5, 0.45490196347236633, 0.45490196347236633), (0.625, 0.25490197539329529, 0.25490197539329529), (0.75, 0.13725490868091583, 0.13725490868091583), (0.875, 0.0, 0.0), (1.0, 0.0, 0.0)]} _Greys_data = {'blue': [(0.0, 1.0, 1.0), (0.125, 0.94117647409439087, 0.94117647409439087), (0.25, 0.85098040103912354, 0.85098040103912354), (0.375, 0.74117648601531982, 0.74117648601531982), (0.5, 0.58823531866073608, 0.58823531866073608), (0.625, 0.45098039507865906, 0.45098039507865906), (0.75, 0.32156863808631897, 0.32156863808631897), (0.875, 0.14509804546833038, 0.14509804546833038), (1.0, 0.0, 0.0)], 'green': [(0.0, 1.0, 1.0), (0.125, 0.94117647409439087, 0.94117647409439087), (0.25, 0.85098040103912354, 0.85098040103912354), (0.375, 0.74117648601531982, 0.74117648601531982), (0.5, 0.58823531866073608, 0.58823531866073608), (0.625, 0.45098039507865906, 0.45098039507865906), (0.75, 0.32156863808631897, 0.32156863808631897), (0.875, 0.14509804546833038, 0.14509804546833038), (1.0, 0.0, 0.0)], 'red': [(0.0, 1.0, 1.0), (0.125, 0.94117647409439087, 0.94117647409439087), (0.25, 0.85098040103912354, 0.85098040103912354), (0.375, 0.74117648601531982, 0.74117648601531982), (0.5, 0.58823531866073608, 0.58823531866073608), (0.625, 0.45098039507865906, 0.45098039507865906), (0.75, 0.32156863808631897, 0.32156863808631897), (0.875, 0.14509804546833038, 0.14509804546833038), (1.0, 0.0, 0.0)]} _Oranges_data = {'blue': [(0.0, 0.92156863212585449, 0.92156863212585449), (0.125, 0.80784314870834351, 0.80784314870834351), (0.25, 0.63529413938522339, 0.63529413938522339), (0.375, 0.41960784792900085, 0.41960784792900085), (0.5, 0.23529411852359772, 0.23529411852359772), (0.625, 0.074509806931018829, 0.074509806931018829), (0.75, 0.0039215688593685627, 0.0039215688593685627), (0.875, 0.011764706112444401, 0.011764706112444401), (1.0, 0.015686275437474251, 0.015686275437474251)], 'green': [(0.0, 0.96078431606292725, 0.96078431606292725), (0.125, 0.90196079015731812, 0.90196079015731812), (0.25, 0.81568628549575806, 0.81568628549575806), (0.375, 0.68235296010971069, 0.68235296010971069), (0.5, 0.55294120311737061, 0.55294120311737061), (0.625, 0.4117647111415863, 0.4117647111415863), (0.75, 0.28235295414924622, 0.28235295414924622), (0.875, 0.21176470816135406, 0.21176470816135406), (1.0, 0.15294118225574493, 0.15294118225574493)], 'red': [(0.0, 1.0, 1.0), (0.125, 0.99607843160629272, 0.99607843160629272), (0.25, 0.99215686321258545, 0.99215686321258545), (0.375, 0.99215686321258545, 0.99215686321258545), (0.5, 0.99215686321258545, 0.99215686321258545), (0.625, 0.94509804248809814, 0.94509804248809814), (0.75, 0.85098040103912354, 0.85098040103912354), (0.875, 0.65098041296005249, 0.65098041296005249), (1.0, 0.49803921580314636, 0.49803921580314636)]} _OrRd_data = {'blue': [(0.0, 0.92549020051956177, 0.92549020051956177), (0.125, 0.78431373834609985, 0.78431373834609985), (0.25, 0.61960786581039429, 0.61960786581039429), (0.375, 0.51764708757400513, 0.51764708757400513), (0.5, 0.3490196168422699, 0.3490196168422699), (0.625, 0.28235295414924622, 0.28235295414924622), (0.75, 0.12156862765550613, 0.12156862765550613), (0.875, 0.0, 0.0), (1.0, 0.0, 0.0)], 'green': [(0.0, 0.9686274528503418, 0.9686274528503418), (0.125, 0.90980392694473267, 0.90980392694473267), (0.25, 0.83137255907058716, 0.83137255907058716), (0.375, 0.73333334922790527, 0.73333334922790527), (0.5, 0.55294120311737061, 0.55294120311737061), (0.625, 0.3960784375667572, 0.3960784375667572), (0.75, 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0.0), (0.72727272727272729, 0.83921569585800171, 0.83921569585800171), (0.81818181818181823, 0.60392159223556519, 0.60392159223556519), (0.90909090909090906, 0.60000002384185791, 0.60000002384185791), (1.0, 0.15686275064945221, 0.15686275064945221)], 'green': [(0.0, 0.80784314870834351, 0.80784314870834351), (0.090909090909090912, 0.47058823704719543, 0.47058823704719543), (0.18181818181818182, 0.87450981140136719, 0.87450981140136719), (0.27272727272727271, 0.62745100259780884, 0.62745100259780884), (0.36363636363636365, 0.60392159223556519, 0.60392159223556519), (0.45454545454545453, 0.10196078568696976, 0.10196078568696976), (0.54545454545454541, 0.74901962280273438, 0.74901962280273438), (0.63636363636363635, 0.49803921580314636, 0.49803921580314636), (0.72727272727272729, 0.69803923368453979, 0.69803923368453979), (0.81818181818181823, 0.23921568691730499, 0.23921568691730499), (0.90909090909090906, 1.0, 1.0), (1.0, 0.3490196168422699, 0.3490196168422699)], 'red': [(0.0, 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0.94901961088180542, 0.94901961088180542)]} _Pastel2_data = {'blue': [(0.0, 0.80392158031463623, 0.80392158031463623), (0.14285714285714285, 0.67450982332229614, 0.67450982332229614), (0.2857142857142857, 0.90980392694473267, 0.90980392694473267), (0.42857142857142855, 0.89411765336990356, 0.89411765336990356), (0.5714285714285714, 0.78823530673980713, 0.78823530673980713), (0.7142857142857143, 0.68235296010971069, 0.68235296010971069), (0.8571428571428571, 0.80000001192092896, 0.80000001192092896), (1.0, 0.80000001192092896, 0.80000001192092896)], 'green': [(0.0, 0.88627451658248901, 0.88627451658248901), (0.14285714285714285, 0.80392158031463623, 0.80392158031463623), (0.2857142857142857, 0.83529412746429443, 0.83529412746429443), (0.42857142857142855, 0.7921568751335144, 0.7921568751335144), (0.5714285714285714, 0.96078431606292725, 0.96078431606292725), (0.7142857142857143, 0.94901961088180542, 0.94901961088180542), (0.8571428571428571, 0.88627451658248901, 0.88627451658248901), (1.0, 0.80000001192092896, 0.80000001192092896)], 'red': [(0.0, 0.70196080207824707, 0.70196080207824707), (0.14285714285714285, 0.99215686321258545, 0.99215686321258545), (0.2857142857142857, 0.79607844352722168, 0.79607844352722168), (0.42857142857142855, 0.95686274766921997, 0.95686274766921997), (0.5714285714285714, 0.90196079015731812, 0.90196079015731812), (0.7142857142857143, 1.0, 1.0), (0.8571428571428571, 0.94509804248809814, 0.94509804248809814), (1.0, 0.80000001192092896, 0.80000001192092896)]} _PiYG_data = {'blue': [(0.0, 0.32156863808631897, 0.32156863808631897), (0.10000000000000001, 0.49019607901573181, 0.49019607901573181), (0.20000000000000001, 0.68235296010971069, 0.68235296010971069), (0.29999999999999999, 0.85490196943283081, 0.85490196943283081), (0.40000000000000002, 0.93725490570068359, 0.93725490570068359), (0.5, 0.9686274528503418, 0.9686274528503418), (0.59999999999999998, 0.81568628549575806, 0.81568628549575806), (0.69999999999999996, 0.52549022436141968, 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0.77254903316497803), (0.20000000000000001, 0.87058824300765991, 0.87058824300765991), (0.29999999999999999, 0.94509804248809814, 0.94509804248809814), (0.40000000000000002, 0.99215686321258545, 0.99215686321258545), (0.5, 0.9686274528503418, 0.9686274528503418), (0.59999999999999998, 0.90196079015731812, 0.90196079015731812), (0.69999999999999996, 0.72156864404678345, 0.72156864404678345), (0.80000000000000004, 0.49803921580314636, 0.49803921580314636), (0.90000000000000002, 0.30196079611778259, 0.30196079611778259), (1.0, 0.15294118225574493, 0.15294118225574493)]} _PRGn_data = {'blue': [(0.0, 0.29411765933036804, 0.29411765933036804), (0.10000000000000001, 0.51372551918029785, 0.51372551918029785), (0.20000000000000001, 0.67058825492858887, 0.67058825492858887), (0.29999999999999999, 0.81176471710205078, 0.81176471710205078), (0.40000000000000002, 0.90980392694473267, 0.90980392694473267), (0.5, 0.9686274528503418, 0.9686274528503418), (0.59999999999999998, 0.82745099067687988, 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0.34509804844856262, 0.34509804844856262)], 'green': [(0.0, 0.9686274528503418, 0.9686274528503418), (0.125, 0.90588235855102539, 0.90588235855102539), (0.25, 0.81960785388946533, 0.81960785388946533), (0.375, 0.74117648601531982, 0.74117648601531982), (0.5, 0.66274511814117432, 0.66274511814117432), (0.625, 0.56470590829849243, 0.56470590829849243), (0.75, 0.43921568989753723, 0.43921568989753723), (0.875, 0.35294118523597717, 0.35294118523597717), (1.0, 0.21960784494876862, 0.21960784494876862)], 'red': [(0.0, 1.0, 1.0), (0.125, 0.92549020051956177, 0.92549020051956177), (0.25, 0.81568628549575806, 0.81568628549575806), (0.375, 0.65098041296005249, 0.65098041296005249), (0.5, 0.45490196347236633, 0.45490196347236633), (0.625, 0.21176470816135406, 0.21176470816135406), (0.75, 0.019607843831181526, 0.019607843831181526), (0.875, 0.015686275437474251, 0.015686275437474251), (1.0, 0.0078431377187371254, 0.0078431377187371254)]} _PuBuGn_data = {'blue': [(0.0, 0.9843137264251709, 0.9843137264251709), (0.125, 0.94117647409439087, 0.94117647409439087), (0.25, 0.90196079015731812, 0.90196079015731812), (0.375, 0.85882353782653809, 0.85882353782653809), (0.5, 0.81176471710205078, 0.81176471710205078), (0.625, 0.75294119119644165, 0.75294119119644165), (0.75, 0.54117649793624878, 0.54117649793624878), (0.875, 0.3490196168422699, 0.3490196168422699), (1.0, 0.21176470816135406, 0.21176470816135406)], 'green': [(0.0, 0.9686274528503418, 0.9686274528503418), (0.125, 0.88627451658248901, 0.88627451658248901), (0.25, 0.81960785388946533, 0.81960785388946533), (0.375, 0.74117648601531982, 0.74117648601531982), (0.5, 0.66274511814117432, 0.66274511814117432), (0.625, 0.56470590829849243, 0.56470590829849243), (0.75, 0.5058823823928833, 0.5058823823928833), (0.875, 0.42352941632270813, 0.42352941632270813), (1.0, 0.27450981736183167, 0.27450981736183167)], 'red': [(0.0, 1.0, 1.0), (0.125, 0.92549020051956177, 0.92549020051956177), (0.25, 0.81568628549575806, 0.81568628549575806), (0.375, 0.65098041296005249, 0.65098041296005249), (0.5, 0.40392157435417175, 0.40392157435417175), (0.625, 0.21176470816135406, 0.21176470816135406), (0.75, 0.0078431377187371254, 0.0078431377187371254), (0.875, 0.0039215688593685627, 0.0039215688593685627), (1.0, 0.0039215688593685627, 0.0039215688593685627)]} _PuOr_data = {'blue': [(0.0, 0.031372550874948502, 0.031372550874948502), (0.10000000000000001, 0.023529412224888802, 0.023529412224888802), (0.20000000000000001, 0.078431375324726105, 0.078431375324726105), (0.29999999999999999, 0.38823530077934265, 0.38823530077934265), (0.40000000000000002, 0.7137255072593689, 0.7137255072593689), (0.5, 0.9686274528503418, 0.9686274528503418), (0.59999999999999998, 0.92156863212585449, 0.92156863212585449), (0.69999999999999996, 0.82352942228317261, 0.82352942228317261), (0.80000000000000004, 0.67450982332229614, 0.67450982332229614), (0.90000000000000002, 0.53333336114883423, 0.53333336114883423), (1.0, 0.29411765933036804, 0.29411765933036804)], 'green': [(0.0, 0.23137255012989044, 0.23137255012989044), (0.10000000000000001, 0.34509804844856262, 0.34509804844856262), (0.20000000000000001, 0.50980395078659058, 0.50980395078659058), (0.29999999999999999, 0.72156864404678345, 0.72156864404678345), (0.40000000000000002, 0.87843137979507446, 0.87843137979507446), (0.5, 0.9686274528503418, 0.9686274528503418), (0.59999999999999998, 0.85490196943283081, 0.85490196943283081), (0.69999999999999996, 0.67058825492858887, 0.67058825492858887), (0.80000000000000004, 0.45098039507865906, 0.45098039507865906), (0.90000000000000002, 0.15294118225574493, 0.15294118225574493), (1.0, 0.0, 0.0)], 'red': [(0.0, 0.49803921580314636, 0.49803921580314636), (0.10000000000000001, 0.70196080207824707, 0.70196080207824707), (0.20000000000000001, 0.87843137979507446, 0.87843137979507446), (0.29999999999999999, 0.99215686321258545, 0.99215686321258545), (0.40000000000000002, 0.99607843160629272, 0.99607843160629272), (0.5, 0.9686274528503418, 0.9686274528503418), (0.59999999999999998, 0.84705883264541626, 0.84705883264541626), (0.69999999999999996, 0.69803923368453979, 0.69803923368453979), (0.80000000000000004, 0.50196081399917603, 0.50196081399917603), (0.90000000000000002, 0.32941177487373352, 0.32941177487373352), (1.0, 0.17647059261798859, 0.17647059261798859)]} _PuRd_data = {'blue': [(0.0, 0.97647058963775635, 0.97647058963775635), (0.125, 0.93725490570068359, 0.93725490570068359), (0.25, 0.85490196943283081, 0.85490196943283081), (0.375, 0.78039216995239258, 0.78039216995239258), (0.5, 0.69019609689712524, 0.69019609689712524), (0.625, 0.54117649793624878, 0.54117649793624878), (0.75, 0.33725491166114807, 0.33725491166114807), (0.875, 0.26274511218070984, 0.26274511218070984), (1.0, 0.12156862765550613, 0.12156862765550613)], 'green': [(0.0, 0.95686274766921997, 0.95686274766921997), (0.125, 0.88235294818878174, 0.88235294818878174), (0.25, 0.72549021244049072, 0.72549021244049072), (0.375, 0.58039218187332153, 0.58039218187332153), (0.5, 0.3960784375667572, 0.3960784375667572), (0.625, 0.16078431904315948, 0.16078431904315948), (0.75, 0.070588238537311554, 0.070588238537311554), (0.875, 0.0, 0.0), (1.0, 0.0, 0.0)], 'red': [(0.0, 0.9686274528503418, 0.9686274528503418), (0.125, 0.90588235855102539, 0.90588235855102539), (0.25, 0.83137255907058716, 0.83137255907058716), (0.375, 0.78823530673980713, 0.78823530673980713), (0.5, 0.87450981140136719, 0.87450981140136719), (0.625, 0.90588235855102539, 0.90588235855102539), (0.75, 0.80784314870834351, 0.80784314870834351), (0.875, 0.59607845544815063, 0.59607845544815063), (1.0, 0.40392157435417175, 0.40392157435417175)]} _Purples_data = {'blue': [(0.0, 0.99215686321258545, 0.99215686321258545), (0.125, 0.96078431606292725, 0.96078431606292725), (0.25, 0.92156863212585449, 0.92156863212585449), (0.375, 0.86274510622024536, 0.86274510622024536), (0.5, 0.78431373834609985, 0.78431373834609985), (0.625, 0.729411780834198, 0.729411780834198), (0.75, 0.63921570777893066, 0.63921570777893066), (0.875, 0.56078433990478516, 0.56078433990478516), (1.0, 0.49019607901573181, 0.49019607901573181)], 'green': [(0.0, 0.9843137264251709, 0.9843137264251709), (0.125, 0.92941176891326904, 0.92941176891326904), (0.25, 0.85490196943283081, 0.85490196943283081), (0.375, 0.74117648601531982, 0.74117648601531982), (0.5, 0.60392159223556519, 0.60392159223556519), (0.625, 0.49019607901573181, 0.49019607901573181), (0.75, 0.31764706969261169, 0.31764706969261169), (0.875, 0.15294118225574493, 0.15294118225574493), (1.0, 0.0, 0.0)], 'red': [(0.0, 0.98823529481887817, 0.98823529481887817), (0.125, 0.93725490570068359, 0.93725490570068359), (0.25, 0.85490196943283081, 0.85490196943283081), (0.375, 0.73725491762161255, 0.73725491762161255), (0.5, 0.61960786581039429, 0.61960786581039429), (0.625, 0.50196081399917603, 0.50196081399917603), (0.75, 0.41568627953529358, 0.41568627953529358), (0.875, 0.32941177487373352, 0.32941177487373352), (1.0, 0.24705882370471954, 0.24705882370471954)]} _RdBu_data = {'blue': [(0.0, 0.12156862765550613, 0.12156862765550613), (0.10000000000000001, 0.16862745583057404, 0.16862745583057404), (0.20000000000000001, 0.30196079611778259, 0.30196079611778259), (0.29999999999999999, 0.50980395078659058, 0.50980395078659058), (0.40000000000000002, 0.78039216995239258, 0.78039216995239258), (0.5, 0.9686274528503418, 0.9686274528503418), (0.59999999999999998, 0.94117647409439087, 0.94117647409439087), (0.69999999999999996, 0.87058824300765991, 0.87058824300765991), (0.80000000000000004, 0.76470589637756348, 0.76470589637756348), (0.90000000000000002, 0.67450982332229614, 0.67450982332229614), (1.0, 0.3803921639919281, 0.3803921639919281)], 'green': [(0.0, 0.0, 0.0), (0.10000000000000001, 0.094117648899555206, 0.094117648899555206), (0.20000000000000001, 0.37647059559822083, 0.37647059559822083), (0.29999999999999999, 0.64705884456634521, 0.64705884456634521), (0.40000000000000002, 0.85882353782653809, 0.85882353782653809), (0.5, 0.9686274528503418, 0.9686274528503418), (0.59999999999999998, 0.89803922176361084, 0.89803922176361084), (0.69999999999999996, 0.77254903316497803, 0.77254903316497803), (0.80000000000000004, 0.57647061347961426, 0.57647061347961426), (0.90000000000000002, 0.40000000596046448, 0.40000000596046448), (1.0, 0.18823529779911041, 0.18823529779911041)], 'red': [(0.0, 0.40392157435417175, 0.40392157435417175), (0.10000000000000001, 0.69803923368453979, 0.69803923368453979), (0.20000000000000001, 0.83921569585800171, 0.83921569585800171), (0.29999999999999999, 0.95686274766921997, 0.95686274766921997), (0.40000000000000002, 0.99215686321258545, 0.99215686321258545), (0.5, 0.9686274528503418, 0.9686274528503418), (0.59999999999999998, 0.81960785388946533, 0.81960785388946533), (0.69999999999999996, 0.57254904508590698, 0.57254904508590698), (0.80000000000000004, 0.26274511218070984, 0.26274511218070984), (0.90000000000000002, 0.12941177189350128, 0.12941177189350128), (1.0, 0.019607843831181526, 0.019607843831181526)]} _RdGy_data = {'blue': [(0.0, 0.12156862765550613, 0.12156862765550613), (0.10000000000000001, 0.16862745583057404, 0.16862745583057404), (0.20000000000000001, 0.30196079611778259, 0.30196079611778259), (0.29999999999999999, 0.50980395078659058, 0.50980395078659058), (0.40000000000000002, 0.78039216995239258, 0.78039216995239258), (0.5, 1.0, 1.0), (0.59999999999999998, 0.87843137979507446, 0.87843137979507446), (0.69999999999999996, 0.729411780834198, 0.729411780834198), (0.80000000000000004, 0.52941179275512695, 0.52941179275512695), (0.90000000000000002, 0.30196079611778259, 0.30196079611778259), (1.0, 0.10196078568696976, 0.10196078568696976)], 'green': [(0.0, 0.0, 0.0), (0.10000000000000001, 0.094117648899555206, 0.094117648899555206), (0.20000000000000001, 0.37647059559822083, 0.37647059559822083), (0.29999999999999999, 0.64705884456634521, 0.64705884456634521), (0.40000000000000002, 0.85882353782653809, 0.85882353782653809), (0.5, 1.0, 1.0), (0.59999999999999998, 0.87843137979507446, 0.87843137979507446), (0.69999999999999996, 0.729411780834198, 0.729411780834198), (0.80000000000000004, 0.52941179275512695, 0.52941179275512695), (0.90000000000000002, 0.30196079611778259, 0.30196079611778259), (1.0, 0.10196078568696976, 0.10196078568696976)], 'red': [(0.0, 0.40392157435417175, 0.40392157435417175), (0.10000000000000001, 0.69803923368453979, 0.69803923368453979), (0.20000000000000001, 0.83921569585800171, 0.83921569585800171), (0.29999999999999999, 0.95686274766921997, 0.95686274766921997), (0.40000000000000002, 0.99215686321258545, 0.99215686321258545), (0.5, 1.0, 1.0), (0.59999999999999998, 0.87843137979507446, 0.87843137979507446), (0.69999999999999996, 0.729411780834198, 0.729411780834198), (0.80000000000000004, 0.52941179275512695, 0.52941179275512695), (0.90000000000000002, 0.30196079611778259, 0.30196079611778259), (1.0, 0.10196078568696976, 0.10196078568696976)]} _RdPu_data = {'blue': [(0.0, 0.9529411792755127, 0.9529411792755127), (0.125, 0.86666667461395264, 0.86666667461395264), (0.25, 0.75294119119644165, 0.75294119119644165), (0.375, 0.70980393886566162, 0.70980393886566162), (0.5, 0.63137257099151611, 0.63137257099151611), (0.625, 0.59215688705444336, 0.59215688705444336), (0.75, 0.49411764740943909, 0.49411764740943909), (0.875, 0.46666666865348816, 0.46666666865348816), (1.0, 0.41568627953529358, 0.41568627953529358)], 'green': [(0.0, 0.9686274528503418, 0.9686274528503418), (0.125, 0.87843137979507446, 0.87843137979507446), (0.25, 0.77254903316497803, 0.77254903316497803), (0.375, 0.62352943420410156, 0.62352943420410156), (0.5, 0.40784314274787903, 0.40784314274787903), (0.625, 0.20392157137393951, 0.20392157137393951), (0.75, 0.0039215688593685627, 0.0039215688593685627), (0.875, 0.0039215688593685627, 0.0039215688593685627), (1.0, 0.0, 0.0)], 'red': [(0.0, 1.0, 1.0), (0.125, 0.99215686321258545, 0.99215686321258545), (0.25, 0.98823529481887817, 0.98823529481887817), (0.375, 0.98039215803146362, 0.98039215803146362), (0.5, 0.9686274528503418, 0.9686274528503418), (0.625, 0.86666667461395264, 0.86666667461395264), (0.75, 0.68235296010971069, 0.68235296010971069), (0.875, 0.47843137383460999, 0.47843137383460999), (1.0, 0.28627452254295349, 0.28627452254295349)]} _RdYlBu_data = {'blue': [(0.0, 0.14901961386203766, 0.14901961386203766), (0.10000000149011612, 0.15294118225574493, 0.15294118225574493), (0.20000000298023224, 0.26274511218070984, 0.26274511218070984), (0.30000001192092896, 0.3803921639919281, 0.3803921639919281), (0.40000000596046448, 0.56470590829849243, 0.56470590829849243), (0.5, 0.74901962280273438, 0.74901962280273438), (0.60000002384185791, 0.97254902124404907, 0.97254902124404907), (0.69999998807907104, 0.91372549533843994, 0.91372549533843994), (0.80000001192092896, 0.81960785388946533, 0.81960785388946533), (0.89999997615814209, 0.70588237047195435, 0.70588237047195435), (1.0, 0.58431375026702881, 0.58431375026702881)], 'green': [(0.0, 0.0, 0.0), (0.10000000149011612, 0.18823529779911041, 0.18823529779911041), (0.20000000298023224, 0.42745098471641541, 0.42745098471641541), (0.30000001192092896, 0.68235296010971069, 0.68235296010971069), (0.40000000596046448, 0.87843137979507446, 0.87843137979507446), (0.5, 1.0, 1.0), (0.60000002384185791, 0.9529411792755127, 0.9529411792755127), (0.69999998807907104, 0.85098040103912354, 0.85098040103912354), (0.80000001192092896, 0.67843139171600342, 0.67843139171600342), (0.89999997615814209, 0.45882353186607361, 0.45882353186607361), (1.0, 0.21176470816135406, 0.21176470816135406)], 'red': [(0.0, 0.64705884456634521, 0.64705884456634521), (0.10000000149011612, 0.84313726425170898, 0.84313726425170898), (0.20000000298023224, 0.95686274766921997, 0.95686274766921997), (0.30000001192092896, 0.99215686321258545, 0.99215686321258545), (0.40000000596046448, 0.99607843160629272, 0.99607843160629272), (0.5, 1.0, 1.0), (0.60000002384185791, 0.87843137979507446, 0.87843137979507446), (0.69999998807907104, 0.67058825492858887, 0.67058825492858887), (0.80000001192092896, 0.45490196347236633, 0.45490196347236633), (0.89999997615814209, 0.27058824896812439, 0.27058824896812439), (1.0, 0.19215686619281769, 0.19215686619281769)]} _RdYlGn_data = {'blue': [(0.0, 0.14901961386203766, 0.14901961386203766), (0.10000000000000001, 0.15294118225574493, 0.15294118225574493), (0.20000000000000001, 0.26274511218070984, 0.26274511218070984), (0.29999999999999999, 0.3803921639919281, 0.3803921639919281), (0.40000000000000002, 0.54509806632995605, 0.54509806632995605), (0.5, 0.74901962280273438, 0.74901962280273438), (0.59999999999999998, 0.54509806632995605, 0.54509806632995605), (0.69999999999999996, 0.41568627953529358, 0.41568627953529358), (0.80000000000000004, 0.38823530077934265, 0.38823530077934265), (0.90000000000000002, 0.31372550129890442, 0.31372550129890442), (1.0, 0.21568627655506134, 0.21568627655506134)], 'green': [(0.0, 0.0, 0.0), (0.10000000000000001, 0.18823529779911041, 0.18823529779911041), (0.20000000000000001, 0.42745098471641541, 0.42745098471641541), (0.29999999999999999, 0.68235296010971069, 0.68235296010971069), (0.40000000000000002, 0.87843137979507446, 0.87843137979507446), (0.5, 1.0, 1.0), (0.59999999999999998, 0.93725490570068359, 0.93725490570068359), (0.69999999999999996, 0.85098040103912354, 0.85098040103912354), (0.80000000000000004, 0.74117648601531982, 0.74117648601531982), (0.90000000000000002, 0.59607845544815063, 0.59607845544815063), (1.0, 0.40784314274787903, 0.40784314274787903)], 'red': [(0.0, 0.64705884456634521, 0.64705884456634521), (0.10000000000000001, 0.84313726425170898, 0.84313726425170898), (0.20000000000000001, 0.95686274766921997, 0.95686274766921997), (0.29999999999999999, 0.99215686321258545, 0.99215686321258545), (0.40000000000000002, 0.99607843160629272, 0.99607843160629272), (0.5, 1.0, 1.0), (0.59999999999999998, 0.85098040103912354, 0.85098040103912354), (0.69999999999999996, 0.65098041296005249, 0.65098041296005249), (0.80000000000000004, 0.40000000596046448, 0.40000000596046448), (0.90000000000000002, 0.10196078568696976, 0.10196078568696976), (1.0, 0.0, 0.0)]} _Reds_data = {'blue': [(0.0, 0.94117647409439087, 0.94117647409439087), (0.125, 0.82352942228317261, 0.82352942228317261), (0.25, 0.63137257099151611, 0.63137257099151611), (0.375, 0.44705882668495178, 0.44705882668495178), (0.5, 0.29019609093666077, 0.29019609093666077), (0.625, 0.17254902422428131, 0.17254902422428131), (0.75, 0.11372549086809158, 0.11372549086809158), (0.875, 0.08235294371843338, 0.08235294371843338), (1.0, 0.050980392843484879, 0.050980392843484879)], 'green': [(0.0, 0.96078431606292725, 0.96078431606292725), (0.125, 0.87843137979507446, 0.87843137979507446), (0.25, 0.73333334922790527, 0.73333334922790527), (0.375, 0.57254904508590698, 0.57254904508590698), (0.5, 0.41568627953529358, 0.41568627953529358), (0.625, 0.23137255012989044, 0.23137255012989044), (0.75, 0.094117648899555206, 0.094117648899555206), (0.875, 0.058823529630899429, 0.058823529630899429), (1.0, 0.0, 0.0)], 'red': [(0.0, 1.0, 1.0), (0.125, 0.99607843160629272, 0.99607843160629272), (0.25, 0.98823529481887817, 0.98823529481887817), (0.375, 0.98823529481887817, 0.98823529481887817), (0.5, 0.9843137264251709, 0.9843137264251709), (0.625, 0.93725490570068359, 0.93725490570068359), (0.75, 0.79607844352722168, 0.79607844352722168), (0.875, 0.64705884456634521, 0.64705884456634521), (1.0, 0.40392157435417175, 0.40392157435417175)]} _Set1_data = {'blue': [(0.0, 0.10980392247438431, 0.10980392247438431), (0.125, 0.72156864404678345, 0.72156864404678345), (0.25, 0.29019609093666077, 0.29019609093666077), (0.375, 0.63921570777893066, 0.63921570777893066), (0.5, 0.0, 0.0), (0.625, 0.20000000298023224, 0.20000000298023224), (0.75, 0.15686275064945221, 0.15686275064945221), (0.875, 0.74901962280273438, 0.74901962280273438), (1.0, 0.60000002384185791, 0.60000002384185791)], 'green': [(0.0, 0.10196078568696976, 0.10196078568696976), (0.125, 0.49411764740943909, 0.49411764740943909), (0.25, 0.68627452850341797, 0.68627452850341797), (0.375, 0.30588236451148987, 0.30588236451148987), (0.5, 0.49803921580314636, 0.49803921580314636), (0.625, 1.0, 1.0), (0.75, 0.33725491166114807, 0.33725491166114807), (0.875, 0.5058823823928833, 0.5058823823928833), (1.0, 0.60000002384185791, 0.60000002384185791)], 'red': [(0.0, 0.89411765336990356, 0.89411765336990356), (0.125, 0.21568627655506134, 0.21568627655506134), (0.25, 0.30196079611778259, 0.30196079611778259), (0.375, 0.59607845544815063, 0.59607845544815063), (0.5, 1.0, 1.0), (0.625, 1.0, 1.0), (0.75, 0.65098041296005249, 0.65098041296005249), (0.875, 0.9686274528503418, 0.9686274528503418), (1.0, 0.60000002384185791, 0.60000002384185791)]} _Set2_data = {'blue': [(0.0, 0.64705884456634521, 0.64705884456634521), (0.14285714285714285, 0.38431373238563538, 0.38431373238563538), (0.2857142857142857, 0.79607844352722168, 0.79607844352722168), (0.42857142857142855, 0.76470589637756348, 0.76470589637756348), (0.5714285714285714, 0.32941177487373352, 0.32941177487373352), (0.7142857142857143, 0.18431372940540314, 0.18431372940540314), (0.8571428571428571, 0.58039218187332153, 0.58039218187332153), (1.0, 0.70196080207824707, 0.70196080207824707)], 'green': [(0.0, 0.7607843279838562, 0.7607843279838562), (0.14285714285714285, 0.55294120311737061, 0.55294120311737061), (0.2857142857142857, 0.62745100259780884, 0.62745100259780884), (0.42857142857142855, 0.54117649793624878, 0.54117649793624878), (0.5714285714285714, 0.84705883264541626, 0.84705883264541626), (0.7142857142857143, 0.85098040103912354, 0.85098040103912354), (0.8571428571428571, 0.76862746477127075, 0.76862746477127075), (1.0, 0.70196080207824707, 0.70196080207824707)], 'red': [(0.0, 0.40000000596046448, 0.40000000596046448), (0.14285714285714285, 0.98823529481887817, 0.98823529481887817), (0.2857142857142857, 0.55294120311737061, 0.55294120311737061), (0.42857142857142855, 0.90588235855102539, 0.90588235855102539), (0.5714285714285714, 0.65098041296005249, 0.65098041296005249), (0.7142857142857143, 1.0, 1.0), (0.8571428571428571, 0.89803922176361084, 0.89803922176361084), (1.0, 0.70196080207824707, 0.70196080207824707)]} _Set3_data = {'blue': [(0.0, 0.78039216995239258, 0.78039216995239258), (0.090909090909090912, 0.70196080207824707, 0.70196080207824707), (0.18181818181818182, 0.85490196943283081, 0.85490196943283081), (0.27272727272727271, 0.44705882668495178, 0.44705882668495178), (0.36363636363636365, 0.82745099067687988, 0.82745099067687988), (0.45454545454545453, 0.38431373238563538, 0.38431373238563538), (0.54545454545454541, 0.4117647111415863, 0.4117647111415863), (0.63636363636363635, 0.89803922176361084, 0.89803922176361084), (0.72727272727272729, 0.85098040103912354, 0.85098040103912354), (0.81818181818181823, 0.74117648601531982, 0.74117648601531982), (0.90909090909090906, 0.77254903316497803, 0.77254903316497803), (1.0, 0.43529412150382996, 0.43529412150382996)], 'green': [(0.0, 0.82745099067687988, 0.82745099067687988), (0.090909090909090912, 1.0, 1.0), (0.18181818181818182, 0.729411780834198, 0.729411780834198), (0.27272727272727271, 0.50196081399917603, 0.50196081399917603), (0.36363636363636365, 0.69411766529083252, 0.69411766529083252), (0.45454545454545453, 0.70588237047195435, 0.70588237047195435), (0.54545454545454541, 0.87058824300765991, 0.87058824300765991), (0.63636363636363635, 0.80392158031463623, 0.80392158031463623), (0.72727272727272729, 0.85098040103912354, 0.85098040103912354), (0.81818181818181823, 0.50196081399917603, 0.50196081399917603), (0.90909090909090906, 0.92156863212585449, 0.92156863212585449), (1.0, 0.92941176891326904, 0.92941176891326904)], 'red': [(0.0, 0.55294120311737061, 0.55294120311737061), (0.090909090909090912, 1.0, 1.0), (0.18181818181818182, 0.7450980544090271, 0.7450980544090271), (0.27272727272727271, 0.9843137264251709, 0.9843137264251709), (0.36363636363636365, 0.50196081399917603, 0.50196081399917603), (0.45454545454545453, 0.99215686321258545, 0.99215686321258545), (0.54545454545454541, 0.70196080207824707, 0.70196080207824707), (0.63636363636363635, 0.98823529481887817, 0.98823529481887817), (0.72727272727272729, 0.85098040103912354, 0.85098040103912354), (0.81818181818181823, 0.73725491762161255, 0.73725491762161255), (0.90909090909090906, 0.80000001192092896, 0.80000001192092896), (1.0, 1.0, 1.0)]} _Spectral_data = {'blue': [(0.0, 0.25882354378700256, 0.25882354378700256), (0.10000000000000001, 0.30980393290519714, 0.30980393290519714), (0.20000000000000001, 0.26274511218070984, 0.26274511218070984), (0.29999999999999999, 0.3803921639919281, 0.3803921639919281), (0.40000000000000002, 0.54509806632995605, 0.54509806632995605), (0.5, 0.74901962280273438, 0.74901962280273438), (0.59999999999999998, 0.59607845544815063, 0.59607845544815063), (0.69999999999999996, 0.64313727617263794, 0.64313727617263794), (0.80000000000000004, 0.64705884456634521, 0.64705884456634521), (0.90000000000000002, 0.74117648601531982, 0.74117648601531982), (1.0, 0.63529413938522339, 0.63529413938522339)], 'green': [(0.0, 0.0039215688593685627, 0.0039215688593685627), (0.10000000000000001, 0.24313725531101227, 0.24313725531101227), (0.20000000000000001, 0.42745098471641541, 0.42745098471641541), (0.29999999999999999, 0.68235296010971069, 0.68235296010971069), (0.40000000000000002, 0.87843137979507446, 0.87843137979507446), (0.5, 1.0, 1.0), (0.59999999999999998, 0.96078431606292725, 0.96078431606292725), (0.69999999999999996, 0.86666667461395264, 0.86666667461395264), (0.80000000000000004, 0.7607843279838562, 0.7607843279838562), (0.90000000000000002, 0.53333336114883423, 0.53333336114883423), (1.0, 0.30980393290519714, 0.30980393290519714)], 'red': [(0.0, 0.61960786581039429, 0.61960786581039429), (0.10000000000000001, 0.83529412746429443, 0.83529412746429443), (0.20000000000000001, 0.95686274766921997, 0.95686274766921997), (0.29999999999999999, 0.99215686321258545, 0.99215686321258545), (0.40000000000000002, 0.99607843160629272, 0.99607843160629272), (0.5, 1.0, 1.0), (0.59999999999999998, 0.90196079015731812, 0.90196079015731812), (0.69999999999999996, 0.67058825492858887, 0.67058825492858887), (0.80000000000000004, 0.40000000596046448, 0.40000000596046448), (0.90000000000000002, 0.19607843458652496, 0.19607843458652496), (1.0, 0.36862745881080627, 0.36862745881080627)]} _YlGn_data = {'blue': [(0.0, 0.89803922176361084, 0.89803922176361084), (0.125, 0.72549021244049072, 0.72549021244049072), (0.25, 0.63921570777893066, 0.63921570777893066), (0.375, 0.55686277151107788, 0.55686277151107788), (0.5, 0.47450980544090271, 0.47450980544090271), (0.625, 0.364705890417099, 0.364705890417099), (0.75, 0.26274511218070984, 0.26274511218070984), (0.875, 0.21568627655506134, 0.21568627655506134), (1.0, 0.16078431904315948, 0.16078431904315948)], 'green': [(0.0, 1.0, 1.0), (0.125, 0.98823529481887817, 0.98823529481887817), (0.25, 0.94117647409439087, 0.94117647409439087), (0.375, 0.86666667461395264, 0.86666667461395264), (0.5, 0.7764706015586853, 0.7764706015586853), (0.625, 0.67058825492858887, 0.67058825492858887), (0.75, 0.51764708757400513, 0.51764708757400513), (0.875, 0.40784314274787903, 0.40784314274787903), (1.0, 0.27058824896812439, 0.27058824896812439)], 'red': [(0.0, 1.0, 1.0), (0.125, 0.9686274528503418, 0.9686274528503418), (0.25, 0.85098040103912354, 0.85098040103912354), (0.375, 0.67843139171600342, 0.67843139171600342), (0.5, 0.47058823704719543, 0.47058823704719543), (0.625, 0.25490197539329529, 0.25490197539329529), (0.75, 0.13725490868091583, 0.13725490868091583), (0.875, 0.0, 0.0), (1.0, 0.0, 0.0)]} _YlGnBu_data = {'blue': [(0.0, 0.85098040103912354, 0.85098040103912354), (0.125, 0.69411766529083252, 0.69411766529083252), (0.25, 0.70588237047195435, 0.70588237047195435), (0.375, 0.73333334922790527, 0.73333334922790527), (0.5, 0.76862746477127075, 0.76862746477127075), (0.625, 0.75294119119644165, 0.75294119119644165), (0.75, 0.65882354974746704, 0.65882354974746704), (0.875, 0.58039218187332153, 0.58039218187332153), (1.0, 0.34509804844856262, 0.34509804844856262)], 'green': [(0.0, 1.0, 1.0), (0.125, 0.97254902124404907, 0.97254902124404907), (0.25, 0.91372549533843994, 0.91372549533843994), (0.375, 0.80392158031463623, 0.80392158031463623), (0.5, 0.7137255072593689, 0.7137255072593689), (0.625, 0.56862747669219971, 0.56862747669219971), (0.75, 0.36862745881080627, 0.36862745881080627), (0.875, 0.20392157137393951, 0.20392157137393951), (1.0, 0.11372549086809158, 0.11372549086809158)], 'red': [(0.0, 1.0, 1.0), (0.125, 0.92941176891326904, 0.92941176891326904), (0.25, 0.78039216995239258, 0.78039216995239258), (0.375, 0.49803921580314636, 0.49803921580314636), (0.5, 0.25490197539329529, 0.25490197539329529), (0.625, 0.11372549086809158, 0.11372549086809158), (0.75, 0.13333334028720856, 0.13333334028720856), (0.875, 0.14509804546833038, 0.14509804546833038), (1.0, 0.031372550874948502, 0.031372550874948502)]} _YlOrBr_data = {'blue': [(0.0, 0.89803922176361084, 0.89803922176361084), (0.125, 0.73725491762161255, 0.73725491762161255), (0.25, 0.56862747669219971, 0.56862747669219971), (0.375, 0.30980393290519714, 0.30980393290519714), (0.5, 0.16078431904315948, 0.16078431904315948), (0.625, 0.078431375324726105, 0.078431375324726105), (0.75, 0.0078431377187371254, 0.0078431377187371254), (0.875, 0.015686275437474251, 0.015686275437474251), (1.0, 0.023529412224888802, 0.023529412224888802)], 'green': [(0.0, 1.0, 1.0), (0.125, 0.9686274528503418, 0.9686274528503418), (0.25, 0.89019608497619629, 0.89019608497619629), (0.375, 0.76862746477127075, 0.76862746477127075), (0.5, 0.60000002384185791, 0.60000002384185791), (0.625, 0.43921568989753723, 0.43921568989753723), (0.75, 0.29803922772407532, 0.29803922772407532), (0.875, 0.20392157137393951, 0.20392157137393951), (1.0, 0.14509804546833038, 0.14509804546833038)], 'red': [(0.0, 1.0, 1.0), (0.125, 1.0, 1.0), (0.25, 0.99607843160629272, 0.99607843160629272), (0.375, 0.99607843160629272, 0.99607843160629272), (0.5, 0.99607843160629272, 0.99607843160629272), (0.625, 0.92549020051956177, 0.92549020051956177), (0.75, 0.80000001192092896, 0.80000001192092896), (0.875, 0.60000002384185791, 0.60000002384185791), (1.0, 0.40000000596046448, 0.40000000596046448)]} _YlOrRd_data = {'blue': [(0.0, 0.80000001192092896, 0.80000001192092896), (0.125, 0.62745100259780884, 0.62745100259780884), (0.25, 0.46274510025978088, 0.46274510025978088), (0.375, 0.29803922772407532, 0.29803922772407532), (0.5, 0.23529411852359772, 0.23529411852359772), (0.625, 0.16470588743686676, 0.16470588743686676), (0.75, 0.10980392247438431, 0.10980392247438431), (0.875, 0.14901961386203766, 0.14901961386203766), (1.0, 0.14901961386203766, 0.14901961386203766)], 'green': [(0.0, 1.0, 1.0), (0.125, 0.92941176891326904, 0.92941176891326904), (0.25, 0.85098040103912354, 0.85098040103912354), (0.375, 0.69803923368453979, 0.69803923368453979), (0.5, 0.55294120311737061, 0.55294120311737061), (0.625, 0.30588236451148987, 0.30588236451148987), (0.75, 0.10196078568696976, 0.10196078568696976), (0.875, 0.0, 0.0), (1.0, 0.0, 0.0)], 'red': [(0.0, 1.0, 1.0), (0.125, 1.0, 1.0), (0.25, 0.99607843160629272, 0.99607843160629272), (0.375, 0.99607843160629272, 0.99607843160629272), (0.5, 0.99215686321258545, 0.99215686321258545), (0.625, 0.98823529481887817, 0.98823529481887817), (0.75, 0.89019608497619629, 0.89019608497619629), (0.875, 0.74117648601531982, 0.74117648601531982), (1.0, 0.50196081399917603, 0.50196081399917603)]} # The next 7 palettes are from the Yorick scientific visalisation package, # an evolution of the GIST package, both by David H. Munro. # They are released under a BSD-like license (see LICENSE_YORICK in # the license directory of the matplotlib source distribution). _gist_earth_data = {'blue': [(0.0, 0.0, 0.0), (0.0042016808874905109, 0.18039216101169586, 0.18039216101169586), (0.0084033617749810219, 0.22745098173618317, 0.22745098173618317), (0.012605042196810246, 0.27058824896812439, 0.27058824896812439), (0.016806723549962044, 0.31764706969261169, 0.31764706969261169), (0.021008403971791267, 0.36078432202339172, 0.36078432202339172), (0.025210084393620491, 0.40784314274787903, 0.40784314274787903), (0.029411764815449715, 0.45490196347236633, 0.45490196347236633), (0.033613447099924088, 0.45490196347236633, 0.45490196347236633), (0.037815127521753311, 0.45490196347236633, 0.45490196347236633), (0.042016807943582535, 0.45490196347236633, 0.45490196347236633), (0.046218488365411758, 0.45490196347236633, 0.45490196347236633), (0.050420168787240982, 0.45882353186607361, 0.45882353186607361), 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(0.67226892709732056, 0.32941177487373352, 0.32941177487373352), (0.67647057771682739, 0.32549020648002625, 0.32549020648002625), (0.680672287940979, 0.32156863808631897, 0.32156863808631897), (0.68487393856048584, 0.31764706969261169, 0.31764706969261169), (0.68907564878463745, 0.31372550129890442, 0.31372550129890442), (0.69327729940414429, 0.30980393290519714, 0.30980393290519714), (0.6974790096282959, 0.30588236451148987, 0.30588236451148987), (0.70168066024780273, 0.30196079611778259, 0.30196079611778259), (0.70588237047195435, 0.29803922772407532, 0.29803922772407532), (0.71008402109146118, 0.29411765933036804, 0.29411765933036804), (0.71428573131561279, 0.29019609093666077, 0.29019609093666077), (0.71848738193511963, 0.28627452254295349, 0.28627452254295349), (0.72268909215927124, 0.28235295414924622, 0.28235295414924622), (0.72689074277877808, 0.27450981736183167, 0.27450981736183167), (0.73109245300292969, 0.27058824896812439, 0.27058824896812439), (0.73529410362243652, 0.26666668057441711, 0.26666668057441711), (0.73949581384658813, 0.26274511218070984, 0.26274511218070984), (0.74369746446609497, 0.25882354378700256, 0.25882354378700256), (0.74789917469024658, 0.25490197539329529, 0.25490197539329529), (0.75210082530975342, 0.25098040699958801, 0.25098040699958801), (0.75630253553390503, 0.24705882370471954, 0.24705882370471954), (0.76050418615341187, 0.24313725531101227, 0.24313725531101227), (0.76470589637756348, 0.23921568691730499, 0.23921568691730499), (0.76890754699707031, 0.23529411852359772, 0.23529411852359772), (0.77310925722122192, 0.23137255012989044, 0.23137255012989044), (0.77731090784072876, 0.22745098173618317, 0.22745098173618317), (0.78151261806488037, 0.22352941334247589, 0.22352941334247589), (0.78571426868438721, 0.21960784494876862, 0.21960784494876862), (0.78991597890853882, 0.21176470816135406, 0.21176470816135406), (0.79411762952804565, 0.20784313976764679, 0.20784313976764679), (0.79831933975219727, 0.20392157137393951, 0.20392157137393951), (0.8025209903717041, 0.20000000298023224, 0.20000000298023224), (0.80672270059585571, 0.19607843458652496, 0.19607843458652496), (0.81092435121536255, 0.19215686619281769, 0.19215686619281769), (0.81512606143951416, 0.18823529779911041, 0.18823529779911041), (0.819327712059021, 0.18431372940540314, 0.18431372940540314), (0.82352942228317261, 0.18039216101169586, 0.18039216101169586), (0.82773107290267944, 0.17647059261798859, 0.17647059261798859), (0.83193278312683105, 0.17254902422428131, 0.17254902422428131), (0.83613443374633789, 0.16862745583057404, 0.16862745583057404), (0.8403361439704895, 0.16470588743686676, 0.16470588743686676), (0.84453779458999634, 0.16078431904315948, 0.16078431904315948), (0.84873950481414795, 0.15686275064945221, 0.15686275064945221), (0.85294115543365479, 0.14901961386203766, 0.14901961386203766), (0.8571428656578064, 0.14509804546833038, 0.14509804546833038), (0.86134451627731323, 0.14117647707462311, 0.14117647707462311), (0.86554622650146484, 0.13725490868091583, 0.13725490868091583), (0.86974787712097168, 0.13333334028720856, 0.13333334028720856), (0.87394958734512329, 0.12941177189350128, 0.12941177189350128), (0.87815123796463013, 0.12549020349979401, 0.12549020349979401), (0.88235294818878174, 0.12156862765550613, 0.12156862765550613), (0.88655459880828857, 0.11764705926179886, 0.11764705926179886), (0.89075630903244019, 0.11372549086809158, 0.11372549086809158), (0.89495795965194702, 0.10980392247438431, 0.10980392247438431), (0.89915966987609863, 0.10588235408067703, 0.10588235408067703), (0.90336132049560547, 0.10196078568696976, 0.10196078568696976), (0.90756303071975708, 0.098039217293262482, 0.098039217293262482), (0.91176468133926392, 0.094117648899555206, 0.094117648899555206), (0.91596639156341553, 0.086274512112140656, 0.086274512112140656), (0.92016804218292236, 0.08235294371843338, 0.08235294371843338), (0.92436975240707397, 0.078431375324726105, 0.078431375324726105), (0.92857140302658081, 0.074509806931018829, 0.074509806931018829), (0.93277311325073242, 0.070588238537311554, 0.070588238537311554), (0.93697476387023926, 0.066666670143604279, 0.066666670143604279), (0.94117647409439087, 0.062745101749897003, 0.062745101749897003), (0.94537812471389771, 0.058823529630899429, 0.058823529630899429), (0.94957983493804932, 0.054901961237192154, 0.054901961237192154), (0.95378148555755615, 0.050980392843484879, 0.050980392843484879), (0.95798319578170776, 0.047058824449777603, 0.047058824449777603), (0.9621848464012146, 0.043137256056070328, 0.043137256056070328), (0.96638655662536621, 0.039215687662363052, 0.039215687662363052), (0.97058820724487305, 0.035294119268655777, 0.035294119268655777), (0.97478991746902466, 0.031372550874948502, 0.031372550874948502), (0.97899156808853149, 0.023529412224888802, 0.023529412224888802), (0.98319327831268311, 0.019607843831181526, 0.019607843831181526), (0.98739492893218994, 0.015686275437474251, 0.015686275437474251), (0.99159663915634155, 0.011764706112444401, 0.011764706112444401), (0.99579828977584839, 0.0078431377187371254, 0.0078431377187371254), (1.0, 0.0039215688593685627, 0.0039215688593685627)]} Accent = colors.LinearSegmentedColormap('Accent', _Accent_data, LUTSIZE) Blues = colors.LinearSegmentedColormap('Blues', _Blues_data, LUTSIZE) BrBG = colors.LinearSegmentedColormap('BrBG', _BrBG_data, LUTSIZE) BuGn = colors.LinearSegmentedColormap('BuGn', _BuGn_data, LUTSIZE) BuPu = colors.LinearSegmentedColormap('BuPu', _BuPu_data, LUTSIZE) Dark2 = colors.LinearSegmentedColormap('Dark2', _Dark2_data, LUTSIZE) GnBu = colors.LinearSegmentedColormap('GnBu', _GnBu_data, LUTSIZE) Greens = colors.LinearSegmentedColormap('Greens', _Greens_data, LUTSIZE) Greys = colors.LinearSegmentedColormap('Greys', _Greys_data, LUTSIZE) Oranges = colors.LinearSegmentedColormap('Oranges', _Oranges_data, LUTSIZE) OrRd = colors.LinearSegmentedColormap('OrRd', _OrRd_data, LUTSIZE) Paired = colors.LinearSegmentedColormap('Paired', _Paired_data, LUTSIZE) Pastel1 = colors.LinearSegmentedColormap('Pastel1', _Pastel1_data, LUTSIZE) Pastel2 = colors.LinearSegmentedColormap('Pastel2', _Pastel2_data, LUTSIZE) PiYG = colors.LinearSegmentedColormap('PiYG', _PiYG_data, LUTSIZE) PRGn = colors.LinearSegmentedColormap('PRGn', _PRGn_data, LUTSIZE) PuBu = colors.LinearSegmentedColormap('PuBu', _PuBu_data, LUTSIZE) PuBuGn = colors.LinearSegmentedColormap('PuBuGn', _PuBuGn_data, LUTSIZE) PuOr = colors.LinearSegmentedColormap('PuOr', _PuOr_data, LUTSIZE) PuRd = colors.LinearSegmentedColormap('PuRd', _PuRd_data, LUTSIZE) Purples = colors.LinearSegmentedColormap('Purples', _Purples_data, LUTSIZE) RdBu = colors.LinearSegmentedColormap('RdBu', _RdBu_data, LUTSIZE) RdGy = colors.LinearSegmentedColormap('RdGy', _RdGy_data, LUTSIZE) RdPu = colors.LinearSegmentedColormap('RdPu', _RdPu_data, LUTSIZE) RdYlBu = colors.LinearSegmentedColormap('RdYlBu', _RdYlBu_data, LUTSIZE) RdYlGn = colors.LinearSegmentedColormap('RdYlGn', _RdYlGn_data, LUTSIZE) Reds = colors.LinearSegmentedColormap('Reds', _Reds_data, LUTSIZE) Set1 = colors.LinearSegmentedColormap('Set1', _Set1_data, LUTSIZE) Set2 = colors.LinearSegmentedColormap('Set2', _Set2_data, LUTSIZE) Set3 = colors.LinearSegmentedColormap('Set3', _Set3_data, LUTSIZE) Spectral = colors.LinearSegmentedColormap('Spectral', _Spectral_data, LUTSIZE) YlGn = colors.LinearSegmentedColormap('YlGn', _YlGn_data, LUTSIZE) YlGnBu = colors.LinearSegmentedColormap('YlGnBu', _YlGnBu_data, LUTSIZE) YlOrBr = colors.LinearSegmentedColormap('YlOrBr', _YlOrBr_data, LUTSIZE) YlOrRd = colors.LinearSegmentedColormap('YlOrRd', _YlOrRd_data, LUTSIZE) gist_earth = colors.LinearSegmentedColormap('gist_earth', _gist_earth_data, LUTSIZE) gist_gray = colors.LinearSegmentedColormap('gist_gray', _gist_gray_data, LUTSIZE) gist_heat = colors.LinearSegmentedColormap('gist_heat', _gist_heat_data, LUTSIZE) gist_ncar = colors.LinearSegmentedColormap('gist_ncar', _gist_ncar_data, LUTSIZE) gist_rainbow = colors.LinearSegmentedColormap('gist_rainbow', _gist_rainbow_data, LUTSIZE) gist_stern = colors.LinearSegmentedColormap('gist_stern', _gist_stern_data, LUTSIZE) gist_yarg = colors.LinearSegmentedColormap('gist_yarg', _gist_yarg_data, LUTSIZE) datad['Accent']=_Accent_data datad['Blues']=_Blues_data datad['BrBG']=_BrBG_data datad['BuGn']=_BuGn_data datad['BuPu']=_BuPu_data datad['Dark2']=_Dark2_data datad['GnBu']=_GnBu_data datad['Greens']=_Greens_data datad['Greys']=_Greys_data datad['Oranges']=_Oranges_data datad['OrRd']=_OrRd_data datad['Paired']=_Paired_data datad['Pastel1']=_Pastel1_data datad['Pastel2']=_Pastel2_data datad['PiYG']=_PiYG_data datad['PRGn']=_PRGn_data datad['PuBu']=_PuBu_data datad['PuBuGn']=_PuBuGn_data datad['PuOr']=_PuOr_data datad['PuRd']=_PuRd_data datad['Purples']=_Purples_data datad['RdBu']=_RdBu_data datad['RdGy']=_RdGy_data datad['RdPu']=_RdPu_data datad['RdYlBu']=_RdYlBu_data datad['RdYlGn']=_RdYlGn_data datad['Reds']=_Reds_data datad['Set1']=_Set1_data datad['Set2']=_Set2_data datad['Set3']=_Set3_data datad['Spectral']=_Spectral_data datad['YlGn']=_YlGn_data datad['YlGnBu']=_YlGnBu_data datad['YlOrBr']=_YlOrBr_data datad['YlOrRd']=_YlOrRd_data datad['gist_earth']=_gist_earth_data datad['gist_gray']=_gist_gray_data datad['gist_heat']=_gist_heat_data datad['gist_ncar']=_gist_ncar_data datad['gist_rainbow']=_gist_rainbow_data datad['gist_stern']=_gist_stern_data datad['gist_yarg']=_gist_yarg_data # reverse all the colormaps. # reversed colormaps have '_r' appended to the name. def revcmap(data): data_r = {} for key, val in data.iteritems(): valnew = [(1.-a, b, c) for a, b, c in reversed(val)] data_r[key] = valnew return data_r cmapnames = datad.keys() for cmapname in cmapnames: cmapname_r = cmapname+'_r' cmapdat_r = revcmap(datad[cmapname]) datad[cmapname_r] = cmapdat_r locals()[cmapname_r] = colors.LinearSegmentedColormap(cmapname_r, cmapdat_r, LUTSIZE)

375,423

Python

.py

5,825

61.546609

92

0.782012

numenta/nupic-legacy

6,330

1,556

464

AGPL-3.0

9/5/2024, 5:13:42 PM (Europe/Amsterdam)

26,265

table.py

numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/table.py

""" Place a table below the x-axis at location loc. The table consists of a grid of cells. The grid need not be rectangular and can have holes. Cells are added by specifying their row and column. For the purposes of positioning the cell at (0, 0) is assumed to be at the top left and the cell at (max_row, max_col) is assumed to be at bottom right. You can add additional cells outside this range to have convenient ways of positioning more interesting grids. Author : John Gill <jng@europe.renre.com> Copyright : 2004 John Gill and John Hunter License : matplotlib license """ from __future__ import division import warnings import artist from artist import Artist from patches import Rectangle from cbook import is_string_like from text import Text from transforms import Bbox class Cell(Rectangle): """ A cell is a Rectangle with some associated text. """ PAD = 0.1 # padding between text and rectangle def __init__(self, xy, width, height, edgecolor='k', facecolor='w', fill=True, text='', loc=None, fontproperties=None ): # Call base Rectangle.__init__(self, xy, width=width, height=height, edgecolor=edgecolor, facecolor=facecolor, ) self.set_clip_on(False) # Create text object if loc is None: loc = 'right' self._loc = loc self._text = Text(x=xy[0], y=xy[1], text=text, fontproperties=fontproperties) self._text.set_clip_on(False) def set_transform(self, trans): Rectangle.set_transform(self, trans) # the text does not get the transform! def set_figure(self, fig): Rectangle.set_figure(self, fig) self._text.set_figure(fig) def get_text(self): 'Return the cell Text intance' return self._text def set_fontsize(self, size): self._text.set_fontsize(size) def get_fontsize(self): 'Return the cell fontsize' return self._text.get_fontsize() def auto_set_font_size(self, renderer): """ Shrink font size until text fits. """ fontsize = self.get_fontsize() required = self.get_required_width(renderer) while fontsize > 1 and required > self.get_width(): fontsize -= 1 self.set_fontsize(fontsize) required = self.get_required_width(renderer) return fontsize def draw(self, renderer): if not self.get_visible(): return # draw the rectangle Rectangle.draw(self, renderer) # position the text self._set_text_position(renderer) self._text.draw(renderer) def _set_text_position(self, renderer): """ Set text up so it draws in the right place. Currently support 'left', 'center' and 'right' """ bbox = self.get_window_extent(renderer) l, b, w, h = bbox.bounds # draw in center vertically self._text.set_verticalalignment('center') y = b + (h / 2.0) # now position horizontally if self._loc == 'center': self._text.set_horizontalalignment('center') x = l + (w / 2.0) elif self._loc == 'left': self._text.set_horizontalalignment('left') x = l + (w * self.PAD) else: self._text.set_horizontalalignment('right') x = l + (w * (1.0 - self.PAD)) self._text.set_position((x, y)) def get_text_bounds(self, renderer): """ Get text bounds in axes co-ordinates. """ bbox = self._text.get_window_extent(renderer) bboxa = bbox.inverse_transformed(self.get_data_transform()) return bboxa.bounds def get_required_width(self, renderer): """ Get width required for this cell. """ l,b,w,h = self.get_text_bounds(renderer) return w * (1.0 + (2.0 * self.PAD)) def set_text_props(self, **kwargs): 'update the text properties with kwargs' self._text.update(kwargs) class Table(Artist): """ Create a table of cells. Table can have (optional) row and column headers. Each entry in the table can be either text or patches. Column widths and row heights for the table can be specifified. Return value is a sequence of text, line and patch instances that make up the table """ codes = {'best' : 0, 'upper right' : 1, # default 'upper left' : 2, 'lower left' : 3, 'lower right' : 4, 'center left' : 5, 'center right' : 6, 'lower center' : 7, 'upper center' : 8, 'center' : 9, 'top right' : 10, 'top left' : 11, 'bottom left' : 12, 'bottom right' : 13, 'right' : 14, 'left' : 15, 'top' : 16, 'bottom' : 17, } FONTSIZE = 10 AXESPAD = 0.02 # the border between the axes and table edge def __init__(self, ax, loc=None, bbox=None): Artist.__init__(self) if is_string_like(loc) and loc not in self.codes: warnings.warn('Unrecognized location %s. Falling back on bottom; valid locations are\n%s\t' %(loc, '\n\t'.join(self.codes.keys()))) loc = 'bottom' if is_string_like(loc): loc = self.codes.get(loc, 1) self.set_figure(ax.figure) self._axes = ax self._loc = loc self._bbox = bbox # use axes coords self.set_transform(ax.transAxes) self._texts = [] self._cells = {} self._autoRows = [] self._autoColumns = [] self._autoFontsize = True self._cachedRenderer = None def add_cell(self, row, col, *args, **kwargs): """ Add a cell to the table. """ xy = (0,0) cell = Cell(xy, *args, **kwargs) cell.set_figure(self.figure) cell.set_transform(self.get_transform()) cell.set_clip_on(False) self._cells[(row, col)] = cell def _approx_text_height(self): return self.FONTSIZE/72.0*self.figure.dpi/self._axes.bbox.height * 1.2 def draw(self, renderer): # Need a renderer to do hit tests on mouseevent; assume the last one will do if renderer is None: renderer = self._cachedRenderer if renderer is None: raise RuntimeError('No renderer defined') self._cachedRenderer = renderer if not self.get_visible(): return renderer.open_group('table') self._update_positions(renderer) keys = self._cells.keys() keys.sort() for key in keys: self._cells[key].draw(renderer) #for c in self._cells.itervalues(): # c.draw(renderer) renderer.close_group('table') def _get_grid_bbox(self, renderer): """Get a bbox, in axes co-ordinates for the cells. Only include those in the range (0,0) to (maxRow, maxCol)""" boxes = [self._cells[pos].get_window_extent(renderer) for pos in self._cells.keys() if pos[0] >= 0 and pos[1] >= 0] bbox = Bbox.union(boxes) return bbox.inverse_transformed(self.get_transform()) def contains(self,mouseevent): """Test whether the mouse event occurred in the table. Returns T/F, {} """ if callable(self._contains): return self._contains(self,mouseevent) # TODO: Return index of the cell containing the cursor so that the user # doesn't have to bind to each one individually. if self._cachedRenderer is not None: boxes = [self._cells[pos].get_window_extent(self._cachedRenderer) for pos in self._cells.keys() if pos[0] >= 0 and pos[1] >= 0] bbox = bbox_all(boxes) return bbox.contains(mouseevent.x,mouseevent.y),{} else: return False,{} def get_children(self): 'Return the Artists contained by the table' return self._cells.values() get_child_artists = get_children # backward compatibility def get_window_extent(self, renderer): 'Return the bounding box of the table in window coords' boxes = [c.get_window_extent(renderer) for c in self._cells] return bbox_all(boxes) def _do_cell_alignment(self): """ Calculate row heights and column widths. Position cells accordingly. """ # Calculate row/column widths widths = {} heights = {} for (row, col), cell in self._cells.iteritems(): height = heights.setdefault(row, 0.0) heights[row] = max(height, cell.get_height()) width = widths.setdefault(col, 0.0) widths[col] = max(width, cell.get_width()) # work out left position for each column xpos = 0 lefts = {} cols = widths.keys() cols.sort() for col in cols: lefts[col] = xpos xpos += widths[col] ypos = 0 bottoms = {} rows = heights.keys() rows.sort() rows.reverse() for row in rows: bottoms[row] = ypos ypos += heights[row] # set cell positions for (row, col), cell in self._cells.iteritems(): cell.set_x(lefts[col]) cell.set_y(bottoms[row]) def auto_set_column_width(self, col): self._autoColumns.append(col) def _auto_set_column_width(self, col, renderer): """ Automagically set width for column. """ cells = [key for key in self._cells if key[1] == col] # find max width width = 0 for cell in cells: c = self._cells[cell] width = max(c.get_required_width(renderer), width) # Now set the widths for cell in cells: self._cells[cell].set_width(width) def auto_set_font_size(self, value=True): """ Automatically set font size. """ self._autoFontsize = value def _auto_set_font_size(self, renderer): if len(self._cells) == 0: return fontsize = self._cells.values()[0].get_fontsize() cells = [] for key, cell in self._cells.iteritems(): # ignore auto-sized columns if key[1] in self._autoColumns: continue size = cell.auto_set_font_size(renderer) fontsize = min(fontsize, size) cells.append(cell) # now set all fontsizes equal for cell in self._cells.itervalues(): cell.set_fontsize(fontsize) def scale(self, xscale, yscale): """ Scale column widths by xscale and row heights by yscale. """ for c in self._cells.itervalues(): c.set_width(c.get_width() * xscale) c.set_height(c.get_height() * yscale) def set_fontsize(self, size): """ Set the fontsize of the cell text ACCEPTS: a float in points """ for cell in self._cells.itervalues(): cell.set_fontsize(size) def _offset(self, ox, oy): 'Move all the artists by ox,oy (axes coords)' for c in self._cells.itervalues(): x, y = c.get_x(), c.get_y() c.set_x(x+ox) c.set_y(y+oy) def _update_positions(self, renderer): # called from renderer to allow more precise estimates of # widths and heights with get_window_extent # Do any auto width setting for col in self._autoColumns: self._auto_set_column_width(col, renderer) if self._autoFontsize: self._auto_set_font_size(renderer) # Align all the cells self._do_cell_alignment() bbox = self._get_grid_bbox(renderer) l,b,w,h = bbox.bounds if self._bbox is not None: # Position according to bbox rl, rb, rw, rh = self._bbox self.scale(rw/w, rh/h) ox = rl - l oy = rb - b self._do_cell_alignment() else: # Position using loc (BEST, UR, UL, LL, LR, CL, CR, LC, UC, C, TR, TL, BL, BR, R, L, T, B) = range(len(self.codes)) # defaults for center ox = (0.5-w/2)-l oy = (0.5-h/2)-b if self._loc in (UL, LL, CL): # left ox = self.AXESPAD - l if self._loc in (BEST, UR, LR, R, CR): # right ox = 1 - (l + w + self.AXESPAD) if self._loc in (BEST, UR, UL, UC): # upper oy = 1 - (b + h + self.AXESPAD) if self._loc in (LL, LR, LC): # lower oy = self.AXESPAD - b if self._loc in (LC, UC, C): # center x ox = (0.5-w/2)-l if self._loc in (CL, CR, C): # center y oy = (0.5-h/2)-b if self._loc in (TL, BL, L): # out left ox = - (l + w) if self._loc in (TR, BR, R): # out right ox = 1.0 - l if self._loc in (TR, TL, T): # out top oy = 1.0 - b if self._loc in (BL, BR, B): # out bottom oy = - (b + h) self._offset(ox, oy) def get_celld(self): 'return a dict of cells in the table' return self._cells def table(ax, cellText=None, cellColours=None, cellLoc='right', colWidths=None, rowLabels=None, rowColours=None, rowLoc='left', colLabels=None, colColours=None, colLoc='center', loc='bottom', bbox=None): """ TABLE(cellText=None, cellColours=None, cellLoc='right', colWidths=None, rowLabels=None, rowColours=None, rowLoc='left', colLabels=None, colColours=None, colLoc='center', loc='bottom', bbox=None) Factory function to generate a Table instance. Thanks to John Gill for providing the class and table. """ # Check we have some cellText if cellText is None: # assume just colours are needed rows = len(cellColours) cols = len(cellColours[0]) cellText = [[''] * rows] * cols rows = len(cellText) cols = len(cellText[0]) for row in cellText: assert len(row) == cols if cellColours is not None: assert len(cellColours) == rows for row in cellColours: assert len(row) == cols else: cellColours = ['w' * cols] * rows # Set colwidths if not given if colWidths is None: colWidths = [1.0/cols] * cols # Check row and column labels rowLabelWidth = 0 if rowLabels is None: if rowColours is not None: rowLabels = [''] * cols rowLabelWidth = colWidths[0] elif rowColours is None: rowColours = 'w' * rows if rowLabels is not None: assert len(rowLabels) == rows offset = 0 if colLabels is None: if colColours is not None: colLabels = [''] * rows offset = 1 elif colColours is None: colColours = 'w' * cols offset = 1 if rowLabels is not None: assert len(rowLabels) == rows # Set up cell colours if not given if cellColours is None: cellColours = ['w' * cols] * rows # Now create the table table = Table(ax, loc, bbox) height = table._approx_text_height() # Add the cells for row in xrange(rows): for col in xrange(cols): table.add_cell(row+offset, col, width=colWidths[col], height=height, text=cellText[row][col], facecolor=cellColours[row][col], loc=cellLoc) # Do column labels if colLabels is not None: for col in xrange(cols): table.add_cell(0, col, width=colWidths[col], height=height, text=colLabels[col], facecolor=colColours[col], loc=colLoc) # Do row labels if rowLabels is not None: for row in xrange(rows): table.add_cell(row+offset, -1, width=rowLabelWidth or 1e-15, height=height, text=rowLabels[row], facecolor=rowColours[row], loc=rowLoc) if rowLabelWidth == 0: table.auto_set_column_width(-1) ax.add_table(table) return table artist.kwdocd['Table'] = artist.kwdoc(Table)

16,757

Python

.py

431

29.053364

143

0.561941

numenta/nupic-legacy

6,330

1,556

464

AGPL-3.0

9/5/2024, 5:13:42 PM (Europe/Amsterdam)

26,266

lines.py

numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/lines.py

""" This module contains all the 2D line class which can draw with a variety of line styles, markers and colors. """ # TODO: expose cap and join style attrs from __future__ import division import numpy as np from numpy import ma from matplotlib import verbose import artist from artist import Artist from cbook import iterable, is_string_like, is_numlike, ls_mapper, dedent,\ flatten from colors import colorConverter from path import Path from transforms import Affine2D, Bbox, TransformedPath, IdentityTransform from matplotlib import rcParams # special-purpose marker identifiers: (TICKLEFT, TICKRIGHT, TICKUP, TICKDOWN, CARETLEFT, CARETRIGHT, CARETUP, CARETDOWN) = range(8) # COVERAGE NOTE: Never called internally or from examples def unmasked_index_ranges(mask, compressed = True): warnings.warn("Import this directly from matplotlib.cbook", DeprecationWarning) # Warning added 2008/07/22 from matplotlib.cbook import unmasked_index_ranges as _unmasked_index_ranges return _unmasked_index_ranges(mask, compressed=compressed) def segment_hits(cx, cy, x, y, radius): """ Determine if any line segments are within radius of a point. Returns the list of line segments that are within that radius. """ # Process single points specially if len(x) < 2: res, = np.nonzero( (cx - x)**2 + (cy - y)**2 <= radius**2 ) return res # We need to lop the last element off a lot. xr,yr = x[:-1],y[:-1] # Only look at line segments whose nearest point to C on the line # lies within the segment. dx,dy = x[1:]-xr, y[1:]-yr Lnorm_sq = dx**2+dy**2 # Possibly want to eliminate Lnorm==0 u = ( (cx-xr)*dx + (cy-yr)*dy )/Lnorm_sq candidates = (u>=0) & (u<=1) #if any(candidates): print "candidates",xr[candidates] # Note that there is a little area near one side of each point # which will be near neither segment, and another which will # be near both, depending on the angle of the lines. The # following radius test eliminates these ambiguities. point_hits = (cx - x)**2 + (cy - y)**2 <= radius**2 #if any(point_hits): print "points",xr[candidates] candidates = candidates & ~(point_hits[:-1] | point_hits[1:]) # For those candidates which remain, determine how far they lie away # from the line. px,py = xr+u*dx,yr+u*dy line_hits = (cx-px)**2 + (cy-py)**2 <= radius**2 #if any(line_hits): print "lines",xr[candidates] line_hits = line_hits & candidates points, = point_hits.ravel().nonzero() lines, = line_hits.ravel().nonzero() #print points,lines return np.concatenate((points,lines)) class Line2D(Artist): """ A line - the line can have both a solid linestyle connecting all the vertices, and a marker at each vertex. Additionally, the drawing of the solid line is influenced by the drawstyle, eg one can create "stepped" lines in various styles. """ lineStyles = _lineStyles = { # hidden names deprecated '-' : '_draw_solid', '--' : '_draw_dashed', '-.' : '_draw_dash_dot', ':' : '_draw_dotted', 'None' : '_draw_nothing', ' ' : '_draw_nothing', '' : '_draw_nothing', } _drawStyles_l = { 'default' : '_draw_lines', 'steps-mid' : '_draw_steps_mid', 'steps-pre' : '_draw_steps_pre', 'steps-post' : '_draw_steps_post', } _drawStyles_s = { 'steps' : '_draw_steps_pre', } drawStyles = {} drawStyles.update(_drawStyles_l) drawStyles.update(_drawStyles_s) markers = _markers = { # hidden names deprecated '.' : '_draw_point', ',' : '_draw_pixel', 'o' : '_draw_circle', 'v' : '_draw_triangle_down', '^' : '_draw_triangle_up', '<' : '_draw_triangle_left', '>' : '_draw_triangle_right', '1' : '_draw_tri_down', '2' : '_draw_tri_up', '3' : '_draw_tri_left', '4' : '_draw_tri_right', 's' : '_draw_square', 'p' : '_draw_pentagon', '*' : '_draw_star', 'h' : '_draw_hexagon1', 'H' : '_draw_hexagon2', '+' : '_draw_plus', 'x' : '_draw_x', 'D' : '_draw_diamond', 'd' : '_draw_thin_diamond', '|' : '_draw_vline', '_' : '_draw_hline', TICKLEFT : '_draw_tickleft', TICKRIGHT : '_draw_tickright', TICKUP : '_draw_tickup', TICKDOWN : '_draw_tickdown', CARETLEFT : '_draw_caretleft', CARETRIGHT : '_draw_caretright', CARETUP : '_draw_caretup', CARETDOWN : '_draw_caretdown', 'None' : '_draw_nothing', ' ' : '_draw_nothing', '' : '_draw_nothing', } filled_markers = ('o', '^', 'v', '<', '>', 's', 'd', 'D', 'h', 'H', 'p', '*') zorder = 2 validCap = ('butt', 'round', 'projecting') validJoin = ('miter', 'round', 'bevel') def __str__(self): if self._label != "": return "Line2D(%s)"%(self._label) elif hasattr(self, '_x') and len(self._x) > 3: return "Line2D((%g,%g),(%g,%g),...,(%g,%g))"\ %(self._x[0],self._y[0],self._x[0],self._y[0],self._x[-1],self._y[-1]) elif hasattr(self, '_x'): return "Line2D(%s)"\ %(",".join(["(%g,%g)"%(x,y) for x,y in zip(self._x,self._y)])) else: return "Line2D()" def __init__(self, xdata, ydata, linewidth = None, # all Nones default to rc linestyle = None, color = None, marker = None, markersize = None, markeredgewidth = None, markeredgecolor = None, markerfacecolor = None, antialiased = None, dash_capstyle = None, solid_capstyle = None, dash_joinstyle = None, solid_joinstyle = None, pickradius = 5, drawstyle = None, **kwargs ): """ Create a :class:`~matplotlib.lines.Line2D` instance with *x* and *y* data in sequences *xdata*, *ydata*. The kwargs are :class:`~matplotlib.lines.Line2D` properties: %(Line2D)s See :meth:`set_linestyle` for a decription of the line styles, :meth:`set_marker` for a description of the markers, and :meth:`set_drawstyle` for a description of the draw styles. """ Artist.__init__(self) #convert sequences to numpy arrays if not iterable(xdata): raise RuntimeError('xdata must be a sequence') if not iterable(ydata): raise RuntimeError('ydata must be a sequence') if linewidth is None : linewidth=rcParams['lines.linewidth'] if linestyle is None : linestyle=rcParams['lines.linestyle'] if marker is None : marker=rcParams['lines.marker'] if color is None : color=rcParams['lines.color'] if markersize is None : markersize=rcParams['lines.markersize'] if antialiased is None : antialiased=rcParams['lines.antialiased'] if dash_capstyle is None : dash_capstyle=rcParams['lines.dash_capstyle'] if dash_joinstyle is None : dash_joinstyle=rcParams['lines.dash_joinstyle'] if solid_capstyle is None : solid_capstyle=rcParams['lines.solid_capstyle'] if solid_joinstyle is None : solid_joinstyle=rcParams['lines.solid_joinstyle'] if drawstyle is None : drawstyle='default' self.set_dash_capstyle(dash_capstyle) self.set_dash_joinstyle(dash_joinstyle) self.set_solid_capstyle(solid_capstyle) self.set_solid_joinstyle(solid_joinstyle) self.set_linestyle(linestyle) self.set_drawstyle(drawstyle) self.set_linewidth(linewidth) self.set_color(color) self.set_marker(marker) self.set_antialiased(antialiased) self.set_markersize(markersize) self._dashSeq = None self.set_markerfacecolor(markerfacecolor) self.set_markeredgecolor(markeredgecolor) self.set_markeredgewidth(markeredgewidth) self._point_size_reduction = 0.5 self.verticalOffset = None # update kwargs before updating data to give the caller a # chance to init axes (and hence unit support) self.update(kwargs) self.pickradius = pickradius if is_numlike(self._picker): self.pickradius = self._picker self._xorig = np.asarray([]) self._yorig = np.asarray([]) self._invalid = True self.set_data(xdata, ydata) def contains(self, mouseevent): """ Test whether the mouse event occurred on the line. The pick radius determines the precision of the location test (usually within five points of the value). Use :meth:`~matplotlib.lines.Line2D.get_pickradius` or :meth:`~matplotlib.lines.Line2D.set_pickradius` to view or modify it. Returns *True* if any values are within the radius along with ``{'ind': pointlist}``, where *pointlist* is the set of points within the radius. TODO: sort returned indices by distance """ if callable(self._contains): return self._contains(self,mouseevent) if not is_numlike(self.pickradius): raise ValueError,"pick radius should be a distance" # Make sure we have data to plot if self._invalid: self.recache() if len(self._xy)==0: return False,{} # Convert points to pixels path, affine = self._transformed_path.get_transformed_path_and_affine() path = affine.transform_path(path) xy = path.vertices xt = xy[:, 0] yt = xy[:, 1] # Convert pick radius from points to pixels if self.figure == None: warning.warn('no figure set when check if mouse is on line') pixels = self.pickradius else: pixels = self.figure.dpi/72. * self.pickradius # Check for collision if self._linestyle in ['None',None]: # If no line, return the nearby point(s) d = (xt-mouseevent.x)**2 + (yt-mouseevent.y)**2 ind, = np.nonzero(np.less_equal(d, pixels**2)) else: # If line, return the nearby segment(s) ind = segment_hits(mouseevent.x,mouseevent.y,xt,yt,pixels) # Debugging message if False and self._label != u'': print "Checking line",self._label,"at",mouseevent.x,mouseevent.y print 'xt', xt print 'yt', yt #print 'dx,dy', (xt-mouseevent.x)**2., (yt-mouseevent.y)**2. print 'ind',ind # Return the point(s) within radius return len(ind)>0,dict(ind=ind) def get_pickradius(self): 'return the pick radius used for containment tests' return self.pickradius def setpickradius(self,d): """Sets the pick radius used for containment tests ACCEPTS: float distance in points """ self.pickradius = d def set_picker(self,p): """Sets the event picker details for the line. ACCEPTS: float distance in points or callable pick function ``fn(artist, event)`` """ if callable(p): self._contains = p else: self.pickradius = p self._picker = p def get_window_extent(self, renderer): bbox = Bbox.unit() bbox.update_from_data_xy(self.get_transform().transform(self.get_xydata()), ignore=True) # correct for marker size, if any if self._marker is not None: ms = (self._markersize / 72.0 * self.figure.dpi) * 0.5 bbox = bbox.padded(ms) return bbox def set_axes(self, ax): Artist.set_axes(self, ax) if ax.xaxis is not None: self._xcid = ax.xaxis.callbacks.connect('units', self.recache) if ax.yaxis is not None: self._ycid = ax.yaxis.callbacks.connect('units', self.recache) set_axes.__doc__ = Artist.set_axes.__doc__ def set_data(self, *args): """ Set the x and y data ACCEPTS: 2D array """ if len(args)==1: x, y = args[0] else: x, y = args not_masked = 0 if not ma.isMaskedArray(x): x = np.asarray(x) not_masked += 1 if not ma.isMaskedArray(y): y = np.asarray(y) not_masked += 1 if (not_masked < 2 or (x is not self._xorig and (x.shape != self._xorig.shape or np.any(x != self._xorig))) or (y is not self._yorig and (y.shape != self._yorig.shape or np.any(y != self._yorig)))): self._xorig = x self._yorig = y self._invalid = True def recache(self): #if self.axes is None: print 'recache no axes' #else: print 'recache units', self.axes.xaxis.units, self.axes.yaxis.units if ma.isMaskedArray(self._xorig) or ma.isMaskedArray(self._yorig): x = ma.asarray(self.convert_xunits(self._xorig), float) y = ma.asarray(self.convert_yunits(self._yorig), float) x = ma.ravel(x) y = ma.ravel(y) else: x = np.asarray(self.convert_xunits(self._xorig), float) y = np.asarray(self.convert_yunits(self._yorig), float) x = np.ravel(x) y = np.ravel(y) if len(x)==1 and len(y)>1: x = x * np.ones(y.shape, float) if len(y)==1 and len(x)>1: y = y * np.ones(x.shape, float) if len(x) != len(y): raise RuntimeError('xdata and ydata must be the same length') x = x.reshape((len(x), 1)) y = y.reshape((len(y), 1)) if ma.isMaskedArray(x) or ma.isMaskedArray(y): self._xy = ma.concatenate((x, y), 1) else: self._xy = np.concatenate((x, y), 1) self._x = self._xy[:, 0] # just a view self._y = self._xy[:, 1] # just a view # Masked arrays are now handled by the Path class itself self._path = Path(self._xy) self._transformed_path = TransformedPath(self._path, self.get_transform()) self._invalid = False def set_transform(self, t): """ set the Transformation instance used by this artist ACCEPTS: a :class:`matplotlib.transforms.Transform` instance """ Artist.set_transform(self, t) self._invalid = True # self._transformed_path = TransformedPath(self._path, self.get_transform()) def _is_sorted(self, x): "return true if x is sorted" if len(x)<2: return 1 return np.alltrue(x[1:]-x[0:-1]>=0) def draw(self, renderer): if self._invalid: self.recache() renderer.open_group('line2d') if not self._visible: return gc = renderer.new_gc() self._set_gc_clip(gc) gc.set_foreground(self._color) gc.set_antialiased(self._antialiased) gc.set_linewidth(self._linewidth) gc.set_alpha(self._alpha) if self.is_dashed(): cap = self._dashcapstyle join = self._dashjoinstyle else: cap = self._solidcapstyle join = self._solidjoinstyle gc.set_joinstyle(join) gc.set_capstyle(cap) gc.set_snap(self.get_snap()) funcname = self._lineStyles.get(self._linestyle, '_draw_nothing') if funcname != '_draw_nothing': tpath, affine = self._transformed_path.get_transformed_path_and_affine() self._lineFunc = getattr(self, funcname) funcname = self.drawStyles.get(self._drawstyle, '_draw_lines') drawFunc = getattr(self, funcname) drawFunc(renderer, gc, tpath, affine.frozen()) if self._marker is not None: gc = renderer.new_gc() self._set_gc_clip(gc) gc.set_foreground(self.get_markeredgecolor()) gc.set_linewidth(self._markeredgewidth) gc.set_alpha(self._alpha) funcname = self._markers.get(self._marker, '_draw_nothing') if funcname != '_draw_nothing': tpath, affine = self._transformed_path.get_transformed_points_and_affine() markerFunc = getattr(self, funcname) markerFunc(renderer, gc, tpath, affine.frozen()) renderer.close_group('line2d') def get_antialiased(self): return self._antialiased def get_color(self): return self._color def get_drawstyle(self): return self._drawstyle def get_linestyle(self): return self._linestyle def get_linewidth(self): return self._linewidth def get_marker(self): return self._marker def get_markeredgecolor(self): if (is_string_like(self._markeredgecolor) and self._markeredgecolor == 'auto'): if self._marker in self.filled_markers: return 'k' else: return self._color else: return self._markeredgecolor return self._markeredgecolor def get_markeredgewidth(self): return self._markeredgewidth def get_markerfacecolor(self): if (self._markerfacecolor is None or (is_string_like(self._markerfacecolor) and self._markerfacecolor.lower()=='none') ): return self._markerfacecolor elif (is_string_like(self._markerfacecolor) and self._markerfacecolor.lower() == 'auto'): return self._color else: return self._markerfacecolor def get_markersize(self): return self._markersize def get_data(self, orig=True): """ Return the xdata, ydata. If *orig* is *True*, return the original data """ return self.get_xdata(orig=orig), self.get_ydata(orig=orig) def get_xdata(self, orig=True): """ Return the xdata. If *orig* is *True*, return the original data, else the processed data. """ if orig: return self._xorig if self._invalid: self.recache() return self._x def get_ydata(self, orig=True): """ Return the ydata. If *orig* is *True*, return the original data, else the processed data. """ if orig: return self._yorig if self._invalid: self.recache() return self._y def get_path(self): """ Return the :class:`~matplotlib.path.Path` object associated with this line. """ if self._invalid: self.recache() return self._path def get_xydata(self): """ Return the *xy* data as a Nx2 numpy array. """ if self._invalid: self.recache() return self._xy def set_antialiased(self, b): """ True if line should be drawin with antialiased rendering ACCEPTS: [True | False] """ self._antialiased = b def set_color(self, color): """ Set the color of the line ACCEPTS: any matplotlib color """ self._color = color def set_drawstyle(self, drawstyle): """ Set the drawstyle of the plot 'default' connects the points with lines. The steps variants produce step-plots. 'steps' is equivalent to 'steps-pre' and is maintained for backward-compatibility. ACCEPTS: [ 'default' | 'steps' | 'steps-pre' | 'steps-mid' | 'steps-post' ] """ self._drawstyle = drawstyle def set_linewidth(self, w): """ Set the line width in points ACCEPTS: float value in points """ self._linewidth = w def set_linestyle(self, linestyle): """ Set the linestyle of the line (also accepts drawstyles) ================ ================= linestyle description ================ ================= '-' solid '--' dashed '-.' dash_dot ':' dotted 'None' draw nothing ' ' draw nothing '' draw nothing ================ ================= 'steps' is equivalent to 'steps-pre' and is maintained for backward-compatibility. .. seealso:: :meth:`set_drawstyle` ACCEPTS: [ '-' | '--' | '-.' | ':' | 'None' | ' ' | '' ] and any drawstyle in combination with a linestyle, e.g. 'steps--'. """ # handle long drawstyle names before short ones ! for ds in flatten([k.keys() for k in (self._drawStyles_l, self._drawStyles_s)], is_string_like): if linestyle.startswith(ds): self.set_drawstyle(ds) if len(linestyle) > len(ds): linestyle = linestyle[len(ds):] else: linestyle = '-' if linestyle not in self._lineStyles: if linestyle in ls_mapper: linestyle = ls_mapper[linestyle] else: verbose.report('Unrecognized line style %s, %s' % (linestyle, type(linestyle))) if linestyle in [' ','']: linestyle = 'None' self._linestyle = linestyle def set_marker(self, marker): """ Set the line marker ========== ========================== marker description ========== ========================== '.' point ',' pixel 'o' circle 'v' triangle_down '^' triangle_up '<' triangle_left '>' triangle_right '1' tri_down '2' tri_up '3' tri_left '4' tri_right 's' square 'p' pentagon '*' star 'h' hexagon1 'H' hexagon2 '+' plus 'x' x 'D' diamond 'd' thin_diamond '|' vline '_' hline TICKLEFT tickleft TICKRIGHT tickright TICKUP tickup TICKDOWN tickdown CARETLEFT caretleft CARETRIGHT caretright CARETUP caretup CARETDOWN caretdown 'None' nothing ' ' nothing '' nothing ========== ========================== ACCEPTS: [ '+' | '*' | ',' | '.' | '1' | '2' | '3' | '4' | '<' | '>' | 'D' | 'H' | '^' | '_' | 'd' | 'h' | 'o' | 'p' | 's' | 'v' | 'x' | '|' | TICKUP | TICKDOWN | TICKLEFT | TICKRIGHT | 'None' | ' ' | '' ] """ if marker not in self._markers: verbose.report('Unrecognized marker style %s, %s' % (marker, type(marker))) if marker in [' ','']: marker = 'None' self._marker = marker self._markerFunc = self._markers[marker] def set_markeredgecolor(self, ec): """ Set the marker edge color ACCEPTS: any matplotlib color """ if ec is None : ec = 'auto' self._markeredgecolor = ec def set_markeredgewidth(self, ew): """ Set the marker edge width in points ACCEPTS: float value in points """ if ew is None : ew = rcParams['lines.markeredgewidth'] self._markeredgewidth = ew def set_markerfacecolor(self, fc): """ Set the marker face color ACCEPTS: any matplotlib color """ if fc is None : fc = 'auto' self._markerfacecolor = fc def set_markersize(self, sz): """ Set the marker size in points ACCEPTS: float """ self._markersize = sz def set_xdata(self, x): """ Set the data np.array for x ACCEPTS: 1D array """ x = np.asarray(x) self.set_data(x, self._yorig) def set_ydata(self, y): """ Set the data np.array for y ACCEPTS: 1D array """ y = np.asarray(y) self.set_data(self._xorig, y) def set_dashes(self, seq): """ Set the dash sequence, sequence of dashes with on off ink in points. If seq is empty or if seq = (None, None), the linestyle will be set to solid. ACCEPTS: sequence of on/off ink in points """ if seq == (None, None) or len(seq)==0: self.set_linestyle('-') else: self.set_linestyle('--') self._dashSeq = seq # TODO: offset ignored for now def _draw_lines(self, renderer, gc, path, trans): self._lineFunc(renderer, gc, path, trans) def _draw_steps_pre(self, renderer, gc, path, trans): vertices = self._xy steps = ma.zeros((2*len(vertices)-1, 2), np.float_) steps[0::2, 0], steps[1::2, 0] = vertices[:, 0], vertices[:-1, 0] steps[0::2, 1], steps[1:-1:2, 1] = vertices[:, 1], vertices[1:, 1] path = Path(steps) path = path.transformed(self.get_transform()) self._lineFunc(renderer, gc, path, IdentityTransform()) def _draw_steps_post(self, renderer, gc, path, trans): vertices = self._xy steps = ma.zeros((2*len(vertices)-1, 2), np.float_) steps[::2, 0], steps[1:-1:2, 0] = vertices[:, 0], vertices[1:, 0] steps[0::2, 1], steps[1::2, 1] = vertices[:, 1], vertices[:-1, 1] path = Path(steps) path = path.transformed(self.get_transform()) self._lineFunc(renderer, gc, path, IdentityTransform()) def _draw_steps_mid(self, renderer, gc, path, trans): vertices = self._xy steps = ma.zeros((2*len(vertices), 2), np.float_) steps[1:-1:2, 0] = 0.5 * (vertices[:-1, 0] + vertices[1:, 0]) steps[2::2, 0] = 0.5 * (vertices[:-1, 0] + vertices[1:, 0]) steps[0, 0] = vertices[0, 0] steps[-1, 0] = vertices[-1, 0] steps[0::2, 1], steps[1::2, 1] = vertices[:, 1], vertices[:, 1] path = Path(steps) path = path.transformed(self.get_transform()) self._lineFunc(renderer, gc, path, IdentityTransform()) def _draw_nothing(self, *args, **kwargs): pass def _draw_solid(self, renderer, gc, path, trans): gc.set_linestyle('solid') renderer.draw_path(gc, path, trans) def _draw_dashed(self, renderer, gc, path, trans): gc.set_linestyle('dashed') if self._dashSeq is not None: gc.set_dashes(0, self._dashSeq) renderer.draw_path(gc, path, trans) def _draw_dash_dot(self, renderer, gc, path, trans): gc.set_linestyle('dashdot') renderer.draw_path(gc, path, trans) def _draw_dotted(self, renderer, gc, path, trans): gc.set_linestyle('dotted') renderer.draw_path(gc, path, trans) def _draw_point(self, renderer, gc, path, path_trans): w = renderer.points_to_pixels(self._markersize) * \ self._point_size_reduction * 0.5 gc.set_snap(renderer.points_to_pixels(self._markersize) > 3.0) rgbFace = self._get_rgb_face() transform = Affine2D().scale(w) renderer.draw_markers( gc, Path.unit_circle(), transform, path, path_trans, rgbFace) _draw_pixel_transform = Affine2D().translate(-0.5, -0.5) def _draw_pixel(self, renderer, gc, path, path_trans): rgbFace = self._get_rgb_face() gc.set_snap(False) renderer.draw_markers(gc, Path.unit_rectangle(), self._draw_pixel_transform, path, path_trans, rgbFace) def _draw_circle(self, renderer, gc, path, path_trans): w = renderer.points_to_pixels(self._markersize) * 0.5 gc.set_snap(renderer.points_to_pixels(self._markersize) > 3.0) rgbFace = self._get_rgb_face() transform = Affine2D().scale(w, w) renderer.draw_markers( gc, Path.unit_circle(), transform, path, path_trans, rgbFace) _triangle_path = Path([[0.0, 1.0], [-1.0, -1.0], [1.0, -1.0], [0.0, 1.0]]) def _draw_triangle_up(self, renderer, gc, path, path_trans): gc.set_snap(renderer.points_to_pixels(self._markersize) >= 5.0) offset = 0.5*renderer.points_to_pixels(self._markersize) transform = Affine2D().scale(offset, offset) rgbFace = self._get_rgb_face() renderer.draw_markers(gc, self._triangle_path, transform, path, path_trans, rgbFace) def _draw_triangle_down(self, renderer, gc, path, path_trans): gc.set_snap(renderer.points_to_pixels(self._markersize) >= 5.0) offset = 0.5*renderer.points_to_pixels(self._markersize) transform = Affine2D().scale(offset, -offset) rgbFace = self._get_rgb_face() renderer.draw_markers(gc, self._triangle_path, transform, path, path_trans, rgbFace) def _draw_triangle_left(self, renderer, gc, path, path_trans): gc.set_snap(renderer.points_to_pixels(self._markersize) >= 5.0) offset = 0.5*renderer.points_to_pixels(self._markersize) transform = Affine2D().scale(offset, offset).rotate_deg(90) rgbFace = self._get_rgb_face() renderer.draw_markers(gc, self._triangle_path, transform, path, path_trans, rgbFace) def _draw_triangle_right(self, renderer, gc, path, path_trans): gc.set_snap(renderer.points_to_pixels(self._markersize) >= 5.0) offset = 0.5*renderer.points_to_pixels(self._markersize) transform = Affine2D().scale(offset, offset).rotate_deg(-90) rgbFace = self._get_rgb_face() renderer.draw_markers(gc, self._triangle_path, transform, path, path_trans, rgbFace) def _draw_square(self, renderer, gc, path, path_trans): gc.set_snap(renderer.points_to_pixels(self._markersize) >= 2.0) side = renderer.points_to_pixels(self._markersize) transform = Affine2D().translate(-0.5, -0.5).scale(side) rgbFace = self._get_rgb_face() renderer.draw_markers(gc, Path.unit_rectangle(), transform, path, path_trans, rgbFace) def _draw_diamond(self, renderer, gc, path, path_trans): gc.set_snap(renderer.points_to_pixels(self._markersize) >= 5.0) side = renderer.points_to_pixels(self._markersize) transform = Affine2D().translate(-0.5, -0.5).rotate_deg(45).scale(side) rgbFace = self._get_rgb_face() renderer.draw_markers(gc, Path.unit_rectangle(), transform, path, path_trans, rgbFace) def _draw_thin_diamond(self, renderer, gc, path, path_trans): gc.set_snap(renderer.points_to_pixels(self._markersize) >= 3.0) offset = renderer.points_to_pixels(self._markersize) transform = Affine2D().translate(-0.5, -0.5) \ .rotate_deg(45).scale(offset * 0.6, offset) rgbFace = self._get_rgb_face() renderer.draw_markers(gc, Path.unit_rectangle(), transform, path, path_trans, rgbFace) def _draw_pentagon(self, renderer, gc, path, path_trans): gc.set_snap(renderer.points_to_pixels(self._markersize) >= 5.0) offset = 0.5 * renderer.points_to_pixels(self._markersize) transform = Affine2D().scale(offset) rgbFace = self._get_rgb_face() renderer.draw_markers(gc, Path.unit_regular_polygon(5), transform, path, path_trans, rgbFace) def _draw_star(self, renderer, gc, path, path_trans): gc.set_snap(renderer.points_to_pixels(self._markersize) >= 5.0) offset = 0.5 * renderer.points_to_pixels(self._markersize) transform = Affine2D().scale(offset) rgbFace = self._get_rgb_face() _starpath = Path.unit_regular_star(5, innerCircle=0.381966) renderer.draw_markers(gc, _starpath, transform, path, path_trans, rgbFace) def _draw_hexagon1(self, renderer, gc, path, path_trans): gc.set_snap(renderer.points_to_pixels(self._markersize) >= 5.0) offset = 0.5 * renderer.points_to_pixels(self._markersize) transform = Affine2D().scale(offset) rgbFace = self._get_rgb_face() renderer.draw_markers(gc, Path.unit_regular_polygon(6), transform, path, path_trans, rgbFace) def _draw_hexagon2(self, renderer, gc, path, path_trans): gc.set_snap(renderer.points_to_pixels(self._markersize) >= 5.0) offset = 0.5 * renderer.points_to_pixels(self._markersize) transform = Affine2D().scale(offset).rotate_deg(30) rgbFace = self._get_rgb_face() renderer.draw_markers(gc, Path.unit_regular_polygon(6), transform, path, path_trans, rgbFace) _line_marker_path = Path([[0.0, -1.0], [0.0, 1.0]]) def _draw_vline(self, renderer, gc, path, path_trans): gc.set_snap(renderer.points_to_pixels(self._markersize) >= 1.0) offset = 0.5*renderer.points_to_pixels(self._markersize) transform = Affine2D().scale(offset) renderer.draw_markers(gc, self._line_marker_path, transform, path, path_trans) def _draw_hline(self, renderer, gc, path, path_trans): gc.set_snap(renderer.points_to_pixels(self._markersize) >= 1.0) offset = 0.5*renderer.points_to_pixels(self._markersize) transform = Affine2D().scale(offset).rotate_deg(90) renderer.draw_markers(gc, self._line_marker_path, transform, path, path_trans) _tickhoriz_path = Path([[0.0, 0.0], [1.0, 0.0]]) def _draw_tickleft(self, renderer, gc, path, path_trans): gc.set_snap(renderer.points_to_pixels(self._markersize) >= 1.0) offset = renderer.points_to_pixels(self._markersize) marker_transform = Affine2D().scale(-offset, 1.0) renderer.draw_markers(gc, self._tickhoriz_path, marker_transform, path, path_trans) def _draw_tickright(self, renderer, gc, path, path_trans): gc.set_snap(renderer.points_to_pixels(self._markersize) >= 1.0) offset = renderer.points_to_pixels(self._markersize) marker_transform = Affine2D().scale(offset, 1.0) renderer.draw_markers(gc, self._tickhoriz_path, marker_transform, path, path_trans) _tickvert_path = Path([[-0.0, 0.0], [-0.0, 1.0]]) def _draw_tickup(self, renderer, gc, path, path_trans): gc.set_snap(renderer.points_to_pixels(self._markersize) >= 1.0) offset = renderer.points_to_pixels(self._markersize) marker_transform = Affine2D().scale(1.0, offset) renderer.draw_markers(gc, self._tickvert_path, marker_transform, path, path_trans) def _draw_tickdown(self, renderer, gc, path, path_trans): gc.set_snap(renderer.points_to_pixels(self._markersize) >= 1.0) offset = renderer.points_to_pixels(self._markersize) marker_transform = Affine2D().scale(1.0, -offset) renderer.draw_markers(gc, self._tickvert_path, marker_transform, path, path_trans) _plus_path = Path([[-1.0, 0.0], [1.0, 0.0], [0.0, -1.0], [0.0, 1.0]], [Path.MOVETO, Path.LINETO, Path.MOVETO, Path.LINETO]) def _draw_plus(self, renderer, gc, path, path_trans): gc.set_snap(renderer.points_to_pixels(self._markersize) >= 3.0) offset = 0.5*renderer.points_to_pixels(self._markersize) transform = Affine2D().scale(offset) renderer.draw_markers(gc, self._plus_path, transform, path, path_trans) _tri_path = Path([[0.0, 0.0], [0.0, -1.0], [0.0, 0.0], [0.8, 0.5], [0.0, 0.0], [-0.8, 0.5]], [Path.MOVETO, Path.LINETO, Path.MOVETO, Path.LINETO, Path.MOVETO, Path.LINETO]) def _draw_tri_down(self, renderer, gc, path, path_trans): gc.set_snap(renderer.points_to_pixels(self._markersize) >= 5.0) offset = 0.5*renderer.points_to_pixels(self._markersize) transform = Affine2D().scale(offset) renderer.draw_markers(gc, self._tri_path, transform, path, path_trans) def _draw_tri_up(self, renderer, gc, path, path_trans): gc.set_snap(renderer.points_to_pixels(self._markersize) >= 5.0) offset = 0.5*renderer.points_to_pixels(self._markersize) transform = Affine2D().scale(offset).rotate_deg(180) renderer.draw_markers(gc, self._tri_path, transform, path, path_trans) def _draw_tri_left(self, renderer, gc, path, path_trans): gc.set_snap(renderer.points_to_pixels(self._markersize) >= 5.0) offset = 0.5*renderer.points_to_pixels(self._markersize) transform = Affine2D().scale(offset).rotate_deg(90) renderer.draw_markers(gc, self._tri_path, transform, path, path_trans) def _draw_tri_right(self, renderer, gc, path, path_trans): gc.set_snap(renderer.points_to_pixels(self._markersize) >= 5.0) offset = 0.5*renderer.points_to_pixels(self._markersize) transform = Affine2D().scale(offset).rotate_deg(270) renderer.draw_markers(gc, self._tri_path, transform, path, path_trans) _caret_path = Path([[-1.0, 1.5], [0.0, 0.0], [1.0, 1.5]]) def _draw_caretdown(self, renderer, gc, path, path_trans): gc.set_snap(renderer.points_to_pixels(self._markersize) >= 3.0) offset = 0.5*renderer.points_to_pixels(self._markersize) transform = Affine2D().scale(offset) renderer.draw_markers(gc, self._caret_path, transform, path, path_trans) def _draw_caretup(self, renderer, gc, path, path_trans): gc.set_snap(renderer.points_to_pixels(self._markersize) >= 3.0) offset = 0.5*renderer.points_to_pixels(self._markersize) transform = Affine2D().scale(offset).rotate_deg(180) renderer.draw_markers(gc, self._caret_path, transform, path, path_trans) def _draw_caretleft(self, renderer, gc, path, path_trans): gc.set_snap(renderer.points_to_pixels(self._markersize) >= 3.0) offset = 0.5*renderer.points_to_pixels(self._markersize) transform = Affine2D().scale(offset).rotate_deg(270) renderer.draw_markers(gc, self._caret_path, transform, path, path_trans) def _draw_caretright(self, renderer, gc, path, path_trans): gc.set_snap(renderer.points_to_pixels(self._markersize) >= 3.0) offset = 0.5*renderer.points_to_pixels(self._markersize) transform = Affine2D().scale(offset).rotate_deg(90) renderer.draw_markers(gc, self._caret_path, transform, path, path_trans) _x_path = Path([[-1.0, -1.0], [1.0, 1.0], [-1.0, 1.0], [1.0, -1.0]], [Path.MOVETO, Path.LINETO, Path.MOVETO, Path.LINETO]) def _draw_x(self, renderer, gc, path, path_trans): gc.set_snap(renderer.points_to_pixels(self._markersize) >= 3.0) offset = 0.5*renderer.points_to_pixels(self._markersize) transform = Affine2D().scale(offset) renderer.draw_markers(gc, self._x_path, transform, path, path_trans) def update_from(self, other): 'copy properties from other to self' Artist.update_from(self, other) self._linestyle = other._linestyle self._linewidth = other._linewidth self._color = other._color self._markersize = other._markersize self._markerfacecolor = other._markerfacecolor self._markeredgecolor = other._markeredgecolor self._markeredgewidth = other._markeredgewidth self._dashSeq = other._dashSeq self._dashcapstyle = other._dashcapstyle self._dashjoinstyle = other._dashjoinstyle self._solidcapstyle = other._solidcapstyle self._solidjoinstyle = other._solidjoinstyle self._linestyle = other._linestyle self._marker = other._marker self._drawstyle = other._drawstyle def _get_rgb_face(self): facecolor = self.get_markerfacecolor() if is_string_like(facecolor) and facecolor.lower()=='none': rgbFace = None else: rgbFace = colorConverter.to_rgb(facecolor) return rgbFace # some aliases.... def set_aa(self, val): 'alias for set_antialiased' self.set_antialiased(val) def set_c(self, val): 'alias for set_color' self.set_color(val) def set_ls(self, val): 'alias for set_linestyle' self.set_linestyle(val) def set_lw(self, val): 'alias for set_linewidth' self.set_linewidth(val) def set_mec(self, val): 'alias for set_markeredgecolor' self.set_markeredgecolor(val) def set_mew(self, val): 'alias for set_markeredgewidth' self.set_markeredgewidth(val) def set_mfc(self, val): 'alias for set_markerfacecolor' self.set_markerfacecolor(val) def set_ms(self, val): 'alias for set_markersize' self.set_markersize(val) def get_aa(self): 'alias for get_antialiased' return self.get_antialiased() def get_c(self): 'alias for get_color' return self.get_color() def get_ls(self): 'alias for get_linestyle' return self.get_linestyle() def get_lw(self): 'alias for get_linewidth' return self.get_linewidth() def get_mec(self): 'alias for get_markeredgecolor' return self.get_markeredgecolor() def get_mew(self): 'alias for get_markeredgewidth' return self.get_markeredgewidth() def get_mfc(self): 'alias for get_markerfacecolor' return self.get_markerfacecolor() def get_ms(self): 'alias for get_markersize' return self.get_markersize() def set_dash_joinstyle(self, s): """ Set the join style for dashed linestyles ACCEPTS: ['miter' | 'round' | 'bevel'] """ s = s.lower() if s not in self.validJoin: raise ValueError('set_dash_joinstyle passed "%s";\n' % (s,) + 'valid joinstyles are %s' % (self.validJoin,)) self._dashjoinstyle = s def set_solid_joinstyle(self, s): """ Set the join style for solid linestyles ACCEPTS: ['miter' | 'round' | 'bevel'] """ s = s.lower() if s not in self.validJoin: raise ValueError('set_solid_joinstyle passed "%s";\n' % (s,) + 'valid joinstyles are %s' % (self.validJoin,)) self._solidjoinstyle = s def get_dash_joinstyle(self): """ Get the join style for dashed linestyles """ return self._dashjoinstyle def get_solid_joinstyle(self): """ Get the join style for solid linestyles """ return self._solidjoinstyle def set_dash_capstyle(self, s): """ Set the cap style for dashed linestyles ACCEPTS: ['butt' | 'round' | 'projecting'] """ s = s.lower() if s not in self.validCap: raise ValueError('set_dash_capstyle passed "%s";\n' % (s,) + 'valid capstyles are %s' % (self.validCap,)) self._dashcapstyle = s def set_solid_capstyle(self, s): """ Set the cap style for solid linestyles ACCEPTS: ['butt' | 'round' | 'projecting'] """ s = s.lower() if s not in self.validCap: raise ValueError('set_solid_capstyle passed "%s";\n' % (s,) + 'valid capstyles are %s' % (self.validCap,)) self._solidcapstyle = s def get_dash_capstyle(self): """ Get the cap style for dashed linestyles """ return self._dashcapstyle def get_solid_capstyle(self): """ Get the cap style for solid linestyles """ return self._solidcapstyle def is_dashed(self): 'return True if line is dashstyle' return self._linestyle in ('--', '-.', ':') class VertexSelector: """ Manage the callbacks to maintain a list of selected vertices for :class:`matplotlib.lines.Line2D`. Derived classes should override :meth:`~matplotlib.lines.VertexSelector.process_selected` to do something with the picks. Here is an example which highlights the selected verts with red circles:: import numpy as np import matplotlib.pyplot as plt import matplotlib.lines as lines class HighlightSelected(lines.VertexSelector): def __init__(self, line, fmt='ro', **kwargs): lines.VertexSelector.__init__(self, line) self.markers, = self.axes.plot([], [], fmt, **kwargs) def process_selected(self, ind, xs, ys): self.markers.set_data(xs, ys) self.canvas.draw() fig = plt.figure() ax = fig.add_subplot(111) x, y = np.random.rand(2, 30) line, = ax.plot(x, y, 'bs-', picker=5) selector = HighlightSelected(line) plt.show() """ def __init__(self, line): """ Initialize the class with a :class:`matplotlib.lines.Line2D` instance. The line should already be added to some :class:`matplotlib.axes.Axes` instance and should have the picker property set. """ if not hasattr(line, 'axes'): raise RuntimeError('You must first add the line to the Axes') if line.get_picker() is None: raise RuntimeError('You must first set the picker property of the line') self.axes = line.axes self.line = line self.canvas = self.axes.figure.canvas self.cid = self.canvas.mpl_connect('pick_event', self.onpick) self.ind = set() def process_selected(self, ind, xs, ys): """ Default "do nothing" implementation of the :meth:`process_selected` method. *ind* are the indices of the selected vertices. *xs* and *ys* are the coordinates of the selected vertices. """ pass def onpick(self, event): 'When the line is picked, update the set of selected indicies.' if event.artist is not self.line: return for i in event.ind: if i in self.ind: self.ind.remove(i) else: self.ind.add(i) ind = list(self.ind) ind.sort() xdata, ydata = self.line.get_data() self.process_selected(ind, xdata[ind], ydata[ind]) lineStyles = Line2D._lineStyles lineMarkers = Line2D._markers drawStyles = Line2D.drawStyles artist.kwdocd['Line2D'] = artist.kwdoc(Line2D) # You can not set the docstring of an instancemethod, # but you can on the underlying function. Go figure. Line2D.__init__.im_func.__doc__ = dedent(Line2D.__init__.__doc__) % artist.kwdocd

48,233

Python

.py

1,116

33.258065

90

0.572304

numenta/nupic-legacy

6,330

1,556

464

AGPL-3.0

9/5/2024, 5:13:42 PM (Europe/Amsterdam)

26,267

backend_bases.py

numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/backend_bases.py

""" Abstract base classes define the primitives that renderers and graphics contexts must implement to serve as a matplotlib backend :class:`RendererBase` An abstract base class to handle drawing/rendering operations. :class:`FigureCanvasBase` The abstraction layer that separates the :class:`matplotlib.figure.Figure` from the backend specific details like a user interface drawing area :class:`GraphicsContextBase` An abstract base class that provides color, line styles, etc... :class:`Event` The base class for all of the matplotlib event handling. Derived classes suh as :class:`KeyEvent` and :class:`MouseEvent` store the meta data like keys and buttons pressed, x and y locations in pixel and :class:`~matplotlib.axes.Axes` coordinates. """ from __future__ import division import os, warnings, time import numpy as np import matplotlib.cbook as cbook import matplotlib.colors as colors import matplotlib.transforms as transforms import matplotlib.widgets as widgets from matplotlib import rcParams class RendererBase: """An abstract base class to handle drawing/rendering operations. The following methods *must* be implemented in the backend: * :meth:`draw_path` * :meth:`draw_image` * :meth:`draw_text` * :meth:`get_text_width_height_descent` The following methods *should* be implemented in the backend for optimization reasons: * :meth:`draw_markers` * :meth:`draw_path_collection` * :meth:`draw_quad_mesh` """ def __init__(self): self._texmanager = None def open_group(self, s): """ Open a grouping element with label *s*. Is only currently used by :mod:`~matplotlib.backends.backend_svg` """ pass def close_group(self, s): """ Close a grouping element with label *s* Is only currently used by :mod:`~matplotlib.backends.backend_svg` """ pass def draw_path(self, gc, path, transform, rgbFace=None): """ Draws a :class:`~matplotlib.path.Path` instance using the given affine transform. """ raise NotImplementedError def draw_markers(self, gc, marker_path, marker_trans, path, trans, rgbFace=None): """ Draws a marker at each of the vertices in path. This includes all vertices, including control points on curves. To avoid that behavior, those vertices should be removed before calling this function. *gc* the :class:`GraphicsContextBase` instance *marker_trans* is an affine transform applied to the marker. *trans* is an affine transform applied to the path. This provides a fallback implementation of draw_markers that makes multiple calls to :meth:`draw_path`. Some backends may want to override this method in order to draw the marker only once and reuse it multiple times. """ tpath = trans.transform_path(path) for vertices, codes in tpath.iter_segments(): if len(vertices): x,y = vertices[-2:] self.draw_path(gc, marker_path, marker_trans + transforms.Affine2D().translate(x, y), rgbFace) def draw_path_collection(self, master_transform, cliprect, clippath, clippath_trans, paths, all_transforms, offsets, offsetTrans, facecolors, edgecolors, linewidths, linestyles, antialiaseds, urls): """ Draws a collection of paths, selecting drawing properties from the lists *facecolors*, *edgecolors*, *linewidths*, *linestyles* and *antialiaseds*. *offsets* is a list of offsets to apply to each of the paths. The offsets in *offsets* are first transformed by *offsetTrans* before being applied. This provides a fallback implementation of :meth:`draw_path_collection` that makes multiple calls to draw_path. Some backends may want to override this in order to render each set of path data only once, and then reference that path multiple times with the different offsets, colors, styles etc. The generator methods :meth:`_iter_collection_raw_paths` and :meth:`_iter_collection` are provided to help with (and standardize) the implementation across backends. It is highly recommended to use those generators, so that changes to the behavior of :meth:`draw_path_collection` can be made globally. """ path_ids = [] for path, transform in self._iter_collection_raw_paths( master_transform, paths, all_transforms): path_ids.append((path, transform)) for xo, yo, path_id, gc, rgbFace in self._iter_collection( path_ids, cliprect, clippath, clippath_trans, offsets, offsetTrans, facecolors, edgecolors, linewidths, linestyles, antialiaseds, urls): path, transform = path_id transform = transforms.Affine2D(transform.get_matrix()).translate(xo, yo) self.draw_path(gc, path, transform, rgbFace) def draw_quad_mesh(self, master_transform, cliprect, clippath, clippath_trans, meshWidth, meshHeight, coordinates, offsets, offsetTrans, facecolors, antialiased, showedges): """ This provides a fallback implementation of :meth:`draw_quad_mesh` that generates paths and then calls :meth:`draw_path_collection`. """ from matplotlib.collections import QuadMesh paths = QuadMesh.convert_mesh_to_paths( meshWidth, meshHeight, coordinates) if showedges: edgecolors = np.array([[0.0, 0.0, 0.0, 1.0]], np.float_) linewidths = np.array([1.0], np.float_) else: edgecolors = facecolors linewidths = np.array([0.0], np.float_) return self.draw_path_collection( master_transform, cliprect, clippath, clippath_trans, paths, [], offsets, offsetTrans, facecolors, edgecolors, linewidths, [], [antialiased], [None]) def _iter_collection_raw_paths(self, master_transform, paths, all_transforms): """ This is a helper method (along with :meth:`_iter_collection`) to make it easier to write a space-efficent :meth:`draw_path_collection` implementation in a backend. This method yields all of the base path/transform combinations, given a master transform, a list of paths and list of transforms. The arguments should be exactly what is passed in to :meth:`draw_path_collection`. The backend should take each yielded path and transform and create an object that can be referenced (reused) later. """ Npaths = len(paths) Ntransforms = len(all_transforms) N = max(Npaths, Ntransforms) if Npaths == 0: return transform = transforms.IdentityTransform() for i in xrange(N): path = paths[i % Npaths] if Ntransforms: transform = all_transforms[i % Ntransforms] yield path, transform + master_transform def _iter_collection(self, path_ids, cliprect, clippath, clippath_trans, offsets, offsetTrans, facecolors, edgecolors, linewidths, linestyles, antialiaseds, urls): """ This is a helper method (along with :meth:`_iter_collection_raw_paths`) to make it easier to write a space-efficent :meth:`draw_path_collection` implementation in a backend. This method yields all of the path, offset and graphics context combinations to draw the path collection. The caller should already have looped over the results of :meth:`_iter_collection_raw_paths` to draw this collection. The arguments should be the same as that passed into :meth:`draw_path_collection`, with the exception of *path_ids*, which is a list of arbitrary objects that the backend will use to reference one of the paths created in the :meth:`_iter_collection_raw_paths` stage. Each yielded result is of the form:: xo, yo, path_id, gc, rgbFace where *xo*, *yo* is an offset; *path_id* is one of the elements of *path_ids*; *gc* is a graphics context and *rgbFace* is a color to use for filling the path. """ Npaths = len(path_ids) Noffsets = len(offsets) N = max(Npaths, Noffsets) Nfacecolors = len(facecolors) Nedgecolors = len(edgecolors) Nlinewidths = len(linewidths) Nlinestyles = len(linestyles) Naa = len(antialiaseds) Nurls = len(urls) if (Nfacecolors == 0 and Nedgecolors == 0) or Npaths == 0: return if Noffsets: toffsets = offsetTrans.transform(offsets) gc = self.new_gc() gc.set_clip_rectangle(cliprect) if clippath is not None: clippath = transforms.TransformedPath(clippath, clippath_trans) gc.set_clip_path(clippath) if Nfacecolors == 0: rgbFace = None if Nedgecolors == 0: gc.set_linewidth(0.0) xo, yo = 0, 0 for i in xrange(N): path_id = path_ids[i % Npaths] if Noffsets: xo, yo = toffsets[i % Noffsets] if Nfacecolors: rgbFace = facecolors[i % Nfacecolors] if Nedgecolors: gc.set_foreground(edgecolors[i % Nedgecolors]) if Nlinewidths: gc.set_linewidth(linewidths[i % Nlinewidths]) if Nlinestyles: gc.set_dashes(*linestyles[i % Nlinestyles]) if rgbFace is not None and len(rgbFace)==4: gc.set_alpha(rgbFace[-1]) rgbFace = rgbFace[:3] gc.set_antialiased(antialiaseds[i % Naa]) if Nurls: gc.set_url(urls[i % Nurls]) yield xo, yo, path_id, gc, rgbFace def get_image_magnification(self): """ Get the factor by which to magnify images passed to :meth:`draw_image`. Allows a backend to have images at a different resolution to other artists. """ return 1.0 def draw_image(self, x, y, im, bbox, clippath=None, clippath_trans=None): """ Draw the image instance into the current axes; *x* is the distance in pixels from the left hand side of the canvas. *y* the distance from the origin. That is, if origin is upper, y is the distance from top. If origin is lower, y is the distance from bottom *im* the :class:`matplotlib._image.Image` instance *bbox* a :class:`matplotlib.transforms.Bbox` instance for clipping, or None """ raise NotImplementedError def option_image_nocomposite(self): """ overwrite this method for renderers that do not necessarily want to rescale and composite raster images. (like SVG) """ return False def draw_tex(self, gc, x, y, s, prop, angle, ismath='TeX!'): raise NotImplementedError def draw_text(self, gc, x, y, s, prop, angle, ismath=False): """ Draw the text instance *gc* the :class:`GraphicsContextBase` instance *x* the x location of the text in display coords *y* the y location of the text in display coords *s* a :class:`matplotlib.text.Text` instance *prop* a :class:`matplotlib.font_manager.FontProperties` instance *angle* the rotation angle in degrees **backend implementers note** When you are trying to determine if you have gotten your bounding box right (which is what enables the text layout/alignment to work properly), it helps to change the line in text.py:: if 0: bbox_artist(self, renderer) to if 1, and then the actual bounding box will be blotted along with your text. """ raise NotImplementedError def flipy(self): """ Return true if y small numbers are top for renderer Is used for drawing text (:mod:`matplotlib.text`) and images (:mod:`matplotlib.image`) only """ return True def get_canvas_width_height(self): 'return the canvas width and height in display coords' return 1, 1 def get_texmanager(self): """ return the :class:`matplotlib.texmanager.TexManager` instance """ if self._texmanager is None: from matplotlib.texmanager import TexManager self._texmanager = TexManager() return self._texmanager def get_text_width_height_descent(self, s, prop, ismath): """ get the width and height, and the offset from the bottom to the baseline (descent), in display coords of the string s with :class:`~matplotlib.font_manager.FontProperties` prop """ raise NotImplementedError def new_gc(self): """ Return an instance of a :class:`GraphicsContextBase` """ return GraphicsContextBase() def points_to_pixels(self, points): """ Convert points to display units *points* a float or a numpy array of float return points converted to pixels You need to override this function (unless your backend doesn't have a dpi, eg, postscript or svg). Some imaging systems assume some value for pixels per inch:: points to pixels = points * pixels_per_inch/72.0 * dpi/72.0 """ return points def strip_math(self, s): return cbook.strip_math(s) def start_rasterizing(self): pass def stop_rasterizing(self): pass class GraphicsContextBase: """ An abstract base class that provides color, line styles, etc... """ # a mapping from dash styles to suggested offset, dash pairs dashd = { 'solid' : (None, None), 'dashed' : (0, (6.0, 6.0)), 'dashdot' : (0, (3.0, 5.0, 1.0, 5.0)), 'dotted' : (0, (1.0, 3.0)), } def __init__(self): self._alpha = 1.0 self._antialiased = 1 # use 0,1 not True, False for extension code self._capstyle = 'butt' self._cliprect = None self._clippath = None self._dashes = None, None self._joinstyle = 'miter' self._linestyle = 'solid' self._linewidth = 1 self._rgb = (0.0, 0.0, 0.0) self._hatch = None self._url = None self._snap = None def copy_properties(self, gc): 'Copy properties from gc to self' self._alpha = gc._alpha self._antialiased = gc._antialiased self._capstyle = gc._capstyle self._cliprect = gc._cliprect self._clippath = gc._clippath self._dashes = gc._dashes self._joinstyle = gc._joinstyle self._linestyle = gc._linestyle self._linewidth = gc._linewidth self._rgb = gc._rgb self._hatch = gc._hatch self._url = gc._url self._snap = gc._snap def get_alpha(self): """ Return the alpha value used for blending - not supported on all backends """ return self._alpha def get_antialiased(self): "Return true if the object should try to do antialiased rendering" return self._antialiased def get_capstyle(self): """ Return the capstyle as a string in ('butt', 'round', 'projecting') """ return self._capstyle def get_clip_rectangle(self): """ Return the clip rectangle as a :class:`~matplotlib.transforms.Bbox` instance """ return self._cliprect def get_clip_path(self): """ Return the clip path in the form (path, transform), where path is a :class:`~matplotlib.path.Path` instance, and transform is an affine transform to apply to the path before clipping. """ if self._clippath is not None: return self._clippath.get_transformed_path_and_affine() return None, None def get_dashes(self): """ Return the dash information as an offset dashlist tuple. The dash list is a even size list that gives the ink on, ink off in pixels. See p107 of to PostScript `BLUEBOOK <http://www-cdf.fnal.gov/offline/PostScript/BLUEBOOK.PDF>`_ for more info. Default value is None """ return self._dashes def get_joinstyle(self): """ Return the line join style as one of ('miter', 'round', 'bevel') """ return self._joinstyle def get_linestyle(self, style): """ Return the linestyle: one of ('solid', 'dashed', 'dashdot', 'dotted'). """ return self._linestyle def get_linewidth(self): """ Return the line width in points as a scalar """ return self._linewidth def get_rgb(self): """ returns a tuple of three floats from 0-1. color can be a matlab format string, a html hex color string, or a rgb tuple """ return self._rgb def get_url(self): """ returns a url if one is set, None otherwise """ return self._url def get_snap(self): """ returns the snap setting which may be: * True: snap vertices to the nearest pixel center * False: leave vertices as-is * None: (auto) If the path contains only rectilinear line segments, round to the nearest pixel center """ return self._snap def set_alpha(self, alpha): """ Set the alpha value used for blending - not supported on all backends """ self._alpha = alpha def set_antialiased(self, b): """ True if object should be drawn with antialiased rendering """ # use 0, 1 to make life easier on extension code trying to read the gc if b: self._antialiased = 1 else: self._antialiased = 0 def set_capstyle(self, cs): """ Set the capstyle as a string in ('butt', 'round', 'projecting') """ if cs in ('butt', 'round', 'projecting'): self._capstyle = cs else: raise ValueError('Unrecognized cap style. Found %s' % cs) def set_clip_rectangle(self, rectangle): """ Set the clip rectangle with sequence (left, bottom, width, height) """ self._cliprect = rectangle def set_clip_path(self, path): """ Set the clip path and transformation. Path should be a :class:`~matplotlib.transforms.TransformedPath` instance. """ assert path is None or isinstance(path, transforms.TransformedPath) self._clippath = path def set_dashes(self, dash_offset, dash_list): """ Set the dash style for the gc. *dash_offset* is the offset (usually 0). *dash_list* specifies the on-off sequence as points. ``(None, None)`` specifies a solid line """ self._dashes = dash_offset, dash_list def set_foreground(self, fg, isRGB=False): """ Set the foreground color. fg can be a matlab format string, a html hex color string, an rgb unit tuple, or a float between 0 and 1. In the latter case, grayscale is used. The :class:`GraphicsContextBase` converts colors to rgb internally. If you know the color is rgb already, you can set ``isRGB=True`` to avoid the performace hit of the conversion """ if isRGB: self._rgb = fg else: self._rgb = colors.colorConverter.to_rgba(fg) def set_graylevel(self, frac): """ Set the foreground color to be a gray level with *frac* """ self._rgb = (frac, frac, frac) def set_joinstyle(self, js): """ Set the join style to be one of ('miter', 'round', 'bevel') """ if js in ('miter', 'round', 'bevel'): self._joinstyle = js else: raise ValueError('Unrecognized join style. Found %s' % js) def set_linewidth(self, w): """ Set the linewidth in points """ self._linewidth = w def set_linestyle(self, style): """ Set the linestyle to be one of ('solid', 'dashed', 'dashdot', 'dotted'). """ try: offset, dashes = self.dashd[style] except: raise ValueError('Unrecognized linestyle: %s' % style) self._linestyle = style self.set_dashes(offset, dashes) def set_url(self, url): """ Sets the url for links in compatible backends """ self._url = url def set_snap(self, snap): """ Sets the snap setting which may be: * True: snap vertices to the nearest pixel center * False: leave vertices as-is * None: (auto) If the path contains only rectilinear line segments, round to the nearest pixel center """ self._snap = snap def set_hatch(self, hatch): """ Sets the hatch style for filling """ self._hatch = hatch def get_hatch(self): """ Gets the current hatch style """ return self._hatch class Event: """ A matplotlib event. Attach additional attributes as defined in :meth:`FigureCanvasBase.mpl_connect`. The following attributes are defined and shown with their default values *name* the event name *canvas* the FigureCanvas instance generating the event *guiEvent* the GUI event that triggered the matplotlib event """ def __init__(self, name, canvas,guiEvent=None): self.name = name self.canvas = canvas self.guiEvent = guiEvent class IdleEvent(Event): """ An event triggered by the GUI backend when it is idle -- useful for passive animation """ pass class DrawEvent(Event): """ An event triggered by a draw operation on the canvas In addition to the :class:`Event` attributes, the following event attributes are defined: *renderer* the :class:`RendererBase` instance for the draw event """ def __init__(self, name, canvas, renderer): Event.__init__(self, name, canvas) self.renderer = renderer class ResizeEvent(Event): """ An event triggered by a canvas resize In addition to the :class:`Event` attributes, the following event attributes are defined: *width* width of the canvas in pixels *height* height of the canvas in pixels """ def __init__(self, name, canvas): Event.__init__(self, name, canvas) self.width, self.height = canvas.get_width_height() class LocationEvent(Event): """ A event that has a screen location The following additional attributes are defined and shown with their default values In addition to the :class:`Event` attributes, the following event attributes are defined: *x* x position - pixels from left of canvas *y* y position - pixels from bottom of canvas *inaxes* the :class:`~matplotlib.axes.Axes` instance if mouse is over axes *xdata* x coord of mouse in data coords *ydata* y coord of mouse in data coords """ x = None # x position - pixels from left of canvas y = None # y position - pixels from right of canvas inaxes = None # the Axes instance if mouse us over axes xdata = None # x coord of mouse in data coords ydata = None # y coord of mouse in data coords # the last event that was triggered before this one lastevent = None def __init__(self, name, canvas, x, y,guiEvent=None): """ *x*, *y* in figure coords, 0,0 = bottom, left """ Event.__init__(self, name, canvas,guiEvent=guiEvent) self.x = x self.y = y if x is None or y is None: # cannot check if event was in axes if no x,y info self.inaxes = None self._update_enter_leave() return # Find all axes containing the mouse axes_list = [a for a in self.canvas.figure.get_axes() if a.in_axes(self)] if len(axes_list) == 0: # None found self.inaxes = None self._update_enter_leave() return elif (len(axes_list) > 1): # Overlap, get the highest zorder axCmp = lambda _x,_y: cmp(_x.zorder, _y.zorder) axes_list.sort(axCmp) self.inaxes = axes_list[-1] # Use the highest zorder else: # Just found one hit self.inaxes = axes_list[0] try: xdata, ydata = self.inaxes.transData.inverted().transform_point((x, y)) except ValueError: self.xdata = None self.ydata = None else: self.xdata = xdata self.ydata = ydata self._update_enter_leave() def _update_enter_leave(self): 'process the figure/axes enter leave events' if LocationEvent.lastevent is not None: last = LocationEvent.lastevent if last.inaxes!=self.inaxes: # process axes enter/leave events if last.inaxes is not None: last.canvas.callbacks.process('axes_leave_event', last) if self.inaxes is not None: self.canvas.callbacks.process('axes_enter_event', self) else: # process a figure enter event if self.inaxes is not None: self.canvas.callbacks.process('axes_enter_event', self) LocationEvent.lastevent = self class MouseEvent(LocationEvent): """ A mouse event ('button_press_event', 'button_release_event', 'scroll_event', 'motion_notify_event'). In addition to the :class:`Event` and :class:`LocationEvent` attributes, the following attributes are defined: *button* button pressed None, 1, 2, 3, 'up', 'down' (up and down are used for scroll events) *key* the key pressed: None, chr(range(255), 'shift', 'win', or 'control' *step* number of scroll steps (positive for 'up', negative for 'down') Example usage:: def on_press(event): print 'you pressed', event.button, event.xdata, event.ydata cid = fig.canvas.mpl_connect('button_press_event', on_press) """ x = None # x position - pixels from left of canvas y = None # y position - pixels from right of canvas button = None # button pressed None, 1, 2, 3 inaxes = None # the Axes instance if mouse us over axes xdata = None # x coord of mouse in data coords ydata = None # y coord of mouse in data coords step = None # scroll steps for scroll events def __init__(self, name, canvas, x, y, button=None, key=None, step=0, guiEvent=None): """ x, y in figure coords, 0,0 = bottom, left button pressed None, 1, 2, 3, 'up', 'down' """ LocationEvent.__init__(self, name, canvas, x, y, guiEvent=guiEvent) self.button = button self.key = key self.step = step class PickEvent(Event): """ a pick event, fired when the user picks a location on the canvas sufficiently close to an artist. Attrs: all the :class:`Event` attributes plus *mouseevent* the :class:`MouseEvent` that generated the pick *artist* the :class:`~matplotlib.artist.Artist` picked other extra class dependent attrs -- eg a :class:`~matplotlib.lines.Line2D` pick may define different extra attributes than a :class:`~matplotlib.collections.PatchCollection` pick event Example usage:: line, = ax.plot(rand(100), 'o', picker=5) # 5 points tolerance def on_pick(event): thisline = event.artist xdata, ydata = thisline.get_data() ind = event.ind print 'on pick line:', zip(xdata[ind], ydata[ind]) cid = fig.canvas.mpl_connect('pick_event', on_pick) """ def __init__(self, name, canvas, mouseevent, artist, guiEvent=None, **kwargs): Event.__init__(self, name, canvas, guiEvent) self.mouseevent = mouseevent self.artist = artist self.__dict__.update(kwargs) class KeyEvent(LocationEvent): """ A key event (key press, key release). Attach additional attributes as defined in :meth:`FigureCanvasBase.mpl_connect`. In addition to the :class:`Event` and :class:`LocationEvent` attributes, the following attributes are defined: *key* the key pressed: None, chr(range(255), shift, win, or control This interface may change slightly when better support for modifier keys is included. Example usage:: def on_key(event): print 'you pressed', event.key, event.xdata, event.ydata cid = fig.canvas.mpl_connect('key_press_event', on_key) """ def __init__(self, name, canvas, key, x=0, y=0, guiEvent=None): LocationEvent.__init__(self, name, canvas, x, y, guiEvent=guiEvent) self.key = key class FigureCanvasBase: """ The canvas the figure renders into. Public attributes *figure* A :class:`matplotlib.figure.Figure` instance """ events = [ 'resize_event', 'draw_event', 'key_press_event', 'key_release_event', 'button_press_event', 'button_release_event', 'scroll_event', 'motion_notify_event', 'pick_event', 'idle_event', 'figure_enter_event', 'figure_leave_event', 'axes_enter_event', 'axes_leave_event' ] def __init__(self, figure): figure.set_canvas(self) self.figure = figure # a dictionary from event name to a dictionary that maps cid->func self.callbacks = cbook.CallbackRegistry(self.events) self.widgetlock = widgets.LockDraw() self._button = None # the button pressed self._key = None # the key pressed self._lastx, self._lasty = None, None self.button_pick_id = self.mpl_connect('button_press_event',self.pick) self.scroll_pick_id = self.mpl_connect('scroll_event',self.pick) if False: ## highlight the artists that are hit self.mpl_connect('motion_notify_event',self.onHilite) ## delete the artists that are clicked on #self.mpl_disconnect(self.button_pick_id) #self.mpl_connect('button_press_event',self.onRemove) def onRemove(self, ev): """ Mouse event processor which removes the top artist under the cursor. Connect this to the 'mouse_press_event' using:: canvas.mpl_connect('mouse_press_event',canvas.onRemove) """ def sort_artists(artists): # This depends on stable sort and artists returned # from get_children in z order. L = [ (h.zorder, h) for h in artists ] L.sort() return [ h for zorder, h in L ] # Find the top artist under the cursor under = sort_artists(self.figure.hitlist(ev)) h = None if under: h = under[-1] # Try deleting that artist, or its parent if you # can't delete the artist while h: print "Removing",h if h.remove(): self.draw_idle() break parent = None for p in under: if h in p.get_children(): parent = p break h = parent def onHilite(self, ev): """ Mouse event processor which highlights the artists under the cursor. Connect this to the 'motion_notify_event' using:: canvas.mpl_connect('motion_notify_event',canvas.onHilite) """ if not hasattr(self,'_active'): self._active = dict() under = self.figure.hitlist(ev) enter = [a for a in under if a not in self._active] leave = [a for a in self._active if a not in under] print "within:"," ".join([str(x) for x in under]) #print "entering:",[str(a) for a in enter] #print "leaving:",[str(a) for a in leave] # On leave restore the captured colour for a in leave: if hasattr(a,'get_color'): a.set_color(self._active[a]) elif hasattr(a,'get_edgecolor'): a.set_edgecolor(self._active[a][0]) a.set_facecolor(self._active[a][1]) del self._active[a] # On enter, capture the color and repaint the artist # with the highlight colour. Capturing colour has to # be done first in case the parent recolouring affects # the child. for a in enter: if hasattr(a,'get_color'): self._active[a] = a.get_color() elif hasattr(a,'get_edgecolor'): self._active[a] = (a.get_edgecolor(),a.get_facecolor()) else: self._active[a] = None for a in enter: if hasattr(a,'get_color'): a.set_color('red') elif hasattr(a,'get_edgecolor'): a.set_edgecolor('red') a.set_facecolor('lightblue') else: self._active[a] = None self.draw_idle() def pick(self, mouseevent): if not self.widgetlock.locked(): self.figure.pick(mouseevent) def blit(self, bbox=None): """ blit the canvas in bbox (default entire canvas) """ pass def resize(self, w, h): """ set the canvas size in pixels """ pass def draw_event(self, renderer): """ This method will be call all functions connected to the 'draw_event' with a :class:`DrawEvent` """ s = 'draw_event' event = DrawEvent(s, self, renderer) self.callbacks.process(s, event) def resize_event(self): """ This method will be call all functions connected to the 'resize_event' with a :class:`ResizeEvent` """ s = 'resize_event' event = ResizeEvent(s, self) self.callbacks.process(s, event) def key_press_event(self, key, guiEvent=None): """ This method will be call all functions connected to the 'key_press_event' with a :class:`KeyEvent` """ self._key = key s = 'key_press_event' event = KeyEvent(s, self, key, self._lastx, self._lasty, guiEvent=guiEvent) self.callbacks.process(s, event) def key_release_event(self, key, guiEvent=None): """ This method will be call all functions connected to the 'key_release_event' with a :class:`KeyEvent` """ s = 'key_release_event' event = KeyEvent(s, self, key, self._lastx, self._lasty, guiEvent=guiEvent) self.callbacks.process(s, event) self._key = None def pick_event(self, mouseevent, artist, **kwargs): """ This method will be called by artists who are picked and will fire off :class:`PickEvent` callbacks registered listeners """ s = 'pick_event' event = PickEvent(s, self, mouseevent, artist, **kwargs) self.callbacks.process(s, event) def scroll_event(self, x, y, step, guiEvent=None): """ Backend derived classes should call this function on any scroll wheel event. x,y are the canvas coords: 0,0 is lower, left. button and key are as defined in MouseEvent. This method will be call all functions connected to the 'scroll_event' with a :class:`MouseEvent` instance. """ if step >= 0: self._button = 'up' else: self._button = 'down' s = 'scroll_event' mouseevent = MouseEvent(s, self, x, y, self._button, self._key, step=step, guiEvent=guiEvent) self.callbacks.process(s, mouseevent) def button_press_event(self, x, y, button, guiEvent=None): """ Backend derived classes should call this function on any mouse button press. x,y are the canvas coords: 0,0 is lower, left. button and key are as defined in :class:`MouseEvent`. This method will be call all functions connected to the 'button_press_event' with a :class:`MouseEvent` instance. """ self._button = button s = 'button_press_event' mouseevent = MouseEvent(s, self, x, y, button, self._key, guiEvent=guiEvent) self.callbacks.process(s, mouseevent) def button_release_event(self, x, y, button, guiEvent=None): """ Backend derived classes should call this function on any mouse button release. *x* the canvas coordinates where 0=left *y* the canvas coordinates where 0=bottom *guiEvent* the native UI event that generated the mpl event This method will be call all functions connected to the 'button_release_event' with a :class:`MouseEvent` instance. """ s = 'button_release_event' event = MouseEvent(s, self, x, y, button, self._key, guiEvent=guiEvent) self.callbacks.process(s, event) self._button = None def motion_notify_event(self, x, y, guiEvent=None): """ Backend derived classes should call this function on any motion-notify-event. *x* the canvas coordinates where 0=left *y* the canvas coordinates where 0=bottom *guiEvent* the native UI event that generated the mpl event This method will be call all functions connected to the 'motion_notify_event' with a :class:`MouseEvent` instance. """ self._lastx, self._lasty = x, y s = 'motion_notify_event' event = MouseEvent(s, self, x, y, self._button, self._key, guiEvent=guiEvent) self.callbacks.process(s, event) def leave_notify_event(self, guiEvent=None): """ Backend derived classes should call this function when leaving canvas *guiEvent* the native UI event that generated the mpl event """ self.callbacks.process('figure_leave_event', LocationEvent.lastevent) LocationEvent.lastevent = None def enter_notify_event(self, guiEvent=None): """ Backend derived classes should call this function when entering canvas *guiEvent* the native UI event that generated the mpl event """ event = Event('figure_enter_event', self, guiEvent) self.callbacks.process('figure_enter_event', event) def idle_event(self, guiEvent=None): 'call when GUI is idle' s = 'idle_event' event = IdleEvent(s, self, guiEvent=guiEvent) self.callbacks.process(s, event) def draw(self, *args, **kwargs): """ Render the :class:`~matplotlib.figure.Figure` """ pass def draw_idle(self, *args, **kwargs): """ :meth:`draw` only if idle; defaults to draw but backends can overrride """ self.draw(*args, **kwargs) def draw_cursor(self, event): """ Draw a cursor in the event.axes if inaxes is not None. Use native GUI drawing for efficiency if possible """ pass def get_width_height(self): """ return the figure width and height in points or pixels (depending on the backend), truncated to integers """ return int(self.figure.bbox.width), int(self.figure.bbox.height) filetypes = { 'emf': 'Enhanced Metafile', 'eps': 'Encapsulated Postscript', 'pdf': 'Portable Document Format', 'png': 'Portable Network Graphics', 'ps' : 'Postscript', 'raw': 'Raw RGBA bitmap', 'rgba': 'Raw RGBA bitmap', 'svg': 'Scalable Vector Graphics', 'svgz': 'Scalable Vector Graphics' } # All of these print_* functions do a lazy import because # a) otherwise we'd have cyclical imports, since all of these # classes inherit from FigureCanvasBase # b) so we don't import a bunch of stuff the user may never use def print_emf(self, *args, **kwargs): from backends.backend_emf import FigureCanvasEMF # lazy import emf = self.switch_backends(FigureCanvasEMF) return emf.print_emf(*args, **kwargs) def print_eps(self, *args, **kwargs): from backends.backend_ps import FigureCanvasPS # lazy import ps = self.switch_backends(FigureCanvasPS) return ps.print_eps(*args, **kwargs) def print_pdf(self, *args, **kwargs): from backends.backend_pdf import FigureCanvasPdf # lazy import pdf = self.switch_backends(FigureCanvasPdf) return pdf.print_pdf(*args, **kwargs) def print_png(self, *args, **kwargs): from backends.backend_agg import FigureCanvasAgg # lazy import agg = self.switch_backends(FigureCanvasAgg) return agg.print_png(*args, **kwargs) def print_ps(self, *args, **kwargs): from backends.backend_ps import FigureCanvasPS # lazy import ps = self.switch_backends(FigureCanvasPS) return ps.print_ps(*args, **kwargs) def print_raw(self, *args, **kwargs): from backends.backend_agg import FigureCanvasAgg # lazy import agg = self.switch_backends(FigureCanvasAgg) return agg.print_raw(*args, **kwargs) print_bmp = print_rgb = print_raw def print_svg(self, *args, **kwargs): from backends.backend_svg import FigureCanvasSVG # lazy import svg = self.switch_backends(FigureCanvasSVG) return svg.print_svg(*args, **kwargs) def print_svgz(self, *args, **kwargs): from backends.backend_svg import FigureCanvasSVG # lazy import svg = self.switch_backends(FigureCanvasSVG) return svg.print_svgz(*args, **kwargs) def get_supported_filetypes(self): return self.filetypes def get_supported_filetypes_grouped(self): groupings = {} for ext, name in self.filetypes.items(): groupings.setdefault(name, []).append(ext) groupings[name].sort() return groupings def print_figure(self, filename, dpi=None, facecolor='w', edgecolor='w', orientation='portrait', format=None, **kwargs): """ Render the figure to hardcopy. Set the figure patch face and edge colors. This is useful because some of the GUIs have a gray figure face color background and you'll probably want to override this on hardcopy. Arguments are: *filename* can also be a file object on image backends *orientation* only currently applies to PostScript printing. *dpi* the dots per inch to save the figure in; if None, use savefig.dpi *facecolor* the facecolor of the figure *edgecolor* the edgecolor of the figure *orientation* ' landscape' | 'portrait' (not supported on all backends) *format* when set, forcibly set the file format to save to """ if format is None: if cbook.is_string_like(filename): format = os.path.splitext(filename)[1][1:] if format is None or format == '': format = self.get_default_filetype() if cbook.is_string_like(filename): filename = filename.rstrip('.') + '.' + format format = format.lower() method_name = 'print_%s' % format if (format not in self.filetypes or not hasattr(self, method_name)): formats = self.filetypes.keys() formats.sort() raise ValueError( 'Format "%s" is not supported.\n' 'Supported formats: ' '%s.' % (format, ', '.join(formats))) if dpi is None: dpi = rcParams['savefig.dpi'] origDPI = self.figure.dpi origfacecolor = self.figure.get_facecolor() origedgecolor = self.figure.get_edgecolor() self.figure.dpi = dpi self.figure.set_facecolor(facecolor) self.figure.set_edgecolor(edgecolor) try: result = getattr(self, method_name)( filename, dpi=dpi, facecolor=facecolor, edgecolor=edgecolor, orientation=orientation, **kwargs) finally: self.figure.dpi = origDPI self.figure.set_facecolor(origfacecolor) self.figure.set_edgecolor(origedgecolor) self.figure.set_canvas(self) #self.figure.canvas.draw() ## seems superfluous return result def get_default_filetype(self): raise NotImplementedError def set_window_title(self, title): """ Set the title text of the window containing the figure. Note that this has no effect if there is no window (eg, a PS backend). """ if hasattr(self, "manager"): self.manager.set_window_title(title) def switch_backends(self, FigureCanvasClass): """ instantiate an instance of FigureCanvasClass This is used for backend switching, eg, to instantiate a FigureCanvasPS from a FigureCanvasGTK. Note, deep copying is not done, so any changes to one of the instances (eg, setting figure size or line props), will be reflected in the other """ newCanvas = FigureCanvasClass(self.figure) return newCanvas def mpl_connect(self, s, func): """ Connect event with string *s* to *func*. The signature of *func* is:: def func(event) where event is a :class:`matplotlib.backend_bases.Event`. The following events are recognized - 'button_press_event' - 'button_release_event' - 'draw_event' - 'key_press_event' - 'key_release_event' - 'motion_notify_event' - 'pick_event' - 'resize_event' - 'scroll_event' For the location events (button and key press/release), if the mouse is over the axes, the variable ``event.inaxes`` will be set to the :class:`~matplotlib.axes.Axes` the event occurs is over, and additionally, the variables ``event.xdata`` and ``event.ydata`` will be defined. This is the mouse location in data coords. See :class:`~matplotlib.backend_bases.KeyEvent` and :class:`~matplotlib.backend_bases.MouseEvent` for more info. Return value is a connection id that can be used with :meth:`~matplotlib.backend_bases.Event.mpl_disconnect`. Example usage:: def on_press(event): print 'you pressed', event.button, event.xdata, event.ydata cid = canvas.mpl_connect('button_press_event', on_press) """ return self.callbacks.connect(s, func) def mpl_disconnect(self, cid): """ disconnect callback id cid Example usage:: cid = canvas.mpl_connect('button_press_event', on_press) #...later canvas.mpl_disconnect(cid) """ return self.callbacks.disconnect(cid) def flush_events(self): """ Flush the GUI events for the figure. Implemented only for backends with GUIs. """ raise NotImplementedError def start_event_loop(self,timeout): """ Start an event loop. This is used to start a blocking event loop so that interactive functions, such as ginput and waitforbuttonpress, can wait for events. This should not be confused with the main GUI event loop, which is always running and has nothing to do with this. This is implemented only for backends with GUIs. """ raise NotImplementedError def stop_event_loop(self): """ Stop an event loop. This is used to stop a blocking event loop so that interactive functions, such as ginput and waitforbuttonpress, can wait for events. This is implemented only for backends with GUIs. """ raise NotImplementedError def start_event_loop_default(self,timeout=0): """ Start an event loop. This is used to start a blocking event loop so that interactive functions, such as ginput and waitforbuttonpress, can wait for events. This should not be confused with the main GUI event loop, which is always running and has nothing to do with this. This function provides default event loop functionality based on time.sleep that is meant to be used until event loop functions for each of the GUI backends can be written. As such, it throws a deprecated warning. Call signature:: start_event_loop_default(self,timeout=0) This call blocks until a callback function triggers stop_event_loop() or *timeout* is reached. If *timeout* is <=0, never timeout. """ str = "Using default event loop until function specific" str += " to this GUI is implemented" warnings.warn(str,DeprecationWarning) if timeout <= 0: timeout = np.inf timestep = 0.01 counter = 0 self._looping = True while self._looping and counter*timestep < timeout: self.flush_events() time.sleep(timestep) counter += 1 def stop_event_loop_default(self): """ Stop an event loop. This is used to stop a blocking event loop so that interactive functions, such as ginput and waitforbuttonpress, can wait for events. Call signature:: stop_event_loop_default(self) """ self._looping = False class FigureManagerBase: """ Helper class for matlab mode, wraps everything up into a neat bundle Public attibutes: *canvas* A :class:`FigureCanvasBase` instance *num* The figure nuamber """ def __init__(self, canvas, num): self.canvas = canvas canvas.manager = self # store a pointer to parent self.num = num self.canvas.mpl_connect('key_press_event', self.key_press) def destroy(self): pass def full_screen_toggle (self): pass def resize(self, w, h): 'For gui backends: resize window in pixels' pass def key_press(self, event): # these bindings happen whether you are over an axes or not #if event.key == 'q': # self.destroy() # how cruel to have to destroy oneself! # return if event.key == 'f': self.full_screen_toggle() # *h*ome or *r*eset mnemonic elif event.key == 'h' or event.key == 'r' or event.key == "home": self.canvas.toolbar.home() # c and v to enable left handed quick navigation elif event.key == 'left' or event.key == 'c' or event.key == 'backspace': self.canvas.toolbar.back() elif event.key == 'right' or event.key == 'v': self.canvas.toolbar.forward() # *p*an mnemonic elif event.key == 'p': self.canvas.toolbar.pan() # z*o*om mnemonic elif event.key == 'o': self.canvas.toolbar.zoom() elif event.key == 's': self.canvas.toolbar.save_figure(self.canvas.toolbar) if event.inaxes is None: return # the mouse has to be over an axes to trigger these if event.key == 'g': event.inaxes.grid() self.canvas.draw() elif event.key == 'l': ax = event.inaxes scale = ax.get_yscale() if scale=='log': ax.set_yscale('linear') ax.figure.canvas.draw() elif scale=='linear': ax.set_yscale('log') ax.figure.canvas.draw() elif event.key is not None and (event.key.isdigit() and event.key!='0') or event.key=='a': # 'a' enables all axes if event.key!='a': n=int(event.key)-1 for i, a in enumerate(self.canvas.figure.get_axes()): if event.x is not None and event.y is not None and a.in_axes(event): if event.key=='a': a.set_navigate(True) else: a.set_navigate(i==n) def show_popup(self, msg): """ Display message in a popup -- GUI only """ pass def set_window_title(self, title): """ Set the title text of the window containing the figure. Note that this has no effect if there is no window (eg, a PS backend). """ pass # cursors class Cursors: #namespace HAND, POINTER, SELECT_REGION, MOVE = range(4) cursors = Cursors() class NavigationToolbar2: """ Base class for the navigation cursor, version 2 backends must implement a canvas that handles connections for 'button_press_event' and 'button_release_event'. See :meth:`FigureCanvasBase.mpl_connect` for more information They must also define :meth:`save_figure` save the current figure :meth:`set_cursor` if you want the pointer icon to change :meth:`_init_toolbar` create your toolbar widget :meth:`draw_rubberband` (optional) draw the zoom to rect "rubberband" rectangle :meth:`press` (optional) whenever a mouse button is pressed, you'll be notified with the event :meth:`release` (optional) whenever a mouse button is released, you'll be notified with the event :meth:`dynamic_update` (optional) dynamically update the window while navigating :meth:`set_message` (optional) display message :meth:`set_history_buttons` (optional) you can change the history back / forward buttons to indicate disabled / enabled state. That's it, we'll do the rest! """ def __init__(self, canvas): self.canvas = canvas canvas.toolbar = self # a dict from axes index to a list of view limits self._views = cbook.Stack() self._positions = cbook.Stack() # stack of subplot positions self._xypress = None # the location and axis info at the time of the press self._idPress = None self._idRelease = None self._active = None self._lastCursor = None self._init_toolbar() self._idDrag=self.canvas.mpl_connect('motion_notify_event', self.mouse_move) self._button_pressed = None # determined by the button pressed at start self.mode = '' # a mode string for the status bar self.set_history_buttons() def set_message(self, s): 'display a message on toolbar or in status bar' pass def back(self, *args): 'move back up the view lim stack' self._views.back() self._positions.back() self.set_history_buttons() self._update_view() def dynamic_update(self): pass def draw_rubberband(self, event, x0, y0, x1, y1): 'draw a rectangle rubberband to indicate zoom limits' pass def forward(self, *args): 'move forward in the view lim stack' self._views.forward() self._positions.forward() self.set_history_buttons() self._update_view() def home(self, *args): 'restore the original view' self._views.home() self._positions.home() self.set_history_buttons() self._update_view() def _init_toolbar(self): """ This is where you actually build the GUI widgets (called by __init__). The icons ``home.xpm``, ``back.xpm``, ``forward.xpm``, ``hand.xpm``, ``zoom_to_rect.xpm`` and ``filesave.xpm`` are standard across backends (there are ppm versions in CVS also). You just need to set the callbacks home : self.home back : self.back forward : self.forward hand : self.pan zoom_to_rect : self.zoom filesave : self.save_figure You only need to define the last one - the others are in the base class implementation. """ raise NotImplementedError def mouse_move(self, event): #print 'mouse_move', event.button if not event.inaxes or not self._active: if self._lastCursor != cursors.POINTER: self.set_cursor(cursors.POINTER) self._lastCursor = cursors.POINTER else: if self._active=='ZOOM': if self._lastCursor != cursors.SELECT_REGION: self.set_cursor(cursors.SELECT_REGION) self._lastCursor = cursors.SELECT_REGION if self._xypress: x, y = event.x, event.y lastx, lasty, a, ind, lim, trans = self._xypress[0] self.draw_rubberband(event, x, y, lastx, lasty) elif (self._active=='PAN' and self._lastCursor != cursors.MOVE): self.set_cursor(cursors.MOVE) self._lastCursor = cursors.MOVE if event.inaxes and event.inaxes.get_navigate(): try: s = event.inaxes.format_coord(event.xdata, event.ydata) except ValueError: pass except OverflowError: pass else: if len(self.mode): self.set_message('%s : %s' % (self.mode, s)) else: self.set_message(s) else: self.set_message(self.mode) def pan(self,*args): 'Activate the pan/zoom tool. pan with left button, zoom with right' # set the pointer icon and button press funcs to the # appropriate callbacks if self._active == 'PAN': self._active = None else: self._active = 'PAN' if self._idPress is not None: self._idPress = self.canvas.mpl_disconnect(self._idPress) self.mode = '' if self._idRelease is not None: self._idRelease = self.canvas.mpl_disconnect(self._idRelease) self.mode = '' if self._active: self._idPress = self.canvas.mpl_connect( 'button_press_event', self.press_pan) self._idRelease = self.canvas.mpl_connect( 'button_release_event', self.release_pan) self.mode = 'pan/zoom mode' self.canvas.widgetlock(self) else: self.canvas.widgetlock.release(self) for a in self.canvas.figure.get_axes(): a.set_navigate_mode(self._active) self.set_message(self.mode) def press(self, event): 'this will be called whenver a mouse button is pressed' pass def press_pan(self, event): 'the press mouse button in pan/zoom mode callback' if event.button == 1: self._button_pressed=1 elif event.button == 3: self._button_pressed=3 else: self._button_pressed=None return x, y = event.x, event.y # push the current view to define home if stack is empty if self._views.empty(): self.push_current() self._xypress=[] for i, a in enumerate(self.canvas.figure.get_axes()): if x is not None and y is not None and a.in_axes(event) and a.get_navigate(): a.start_pan(x, y, event.button) self._xypress.append((a, i)) self.canvas.mpl_disconnect(self._idDrag) self._idDrag=self.canvas.mpl_connect('motion_notify_event', self.drag_pan) self.press(event) def press_zoom(self, event): 'the press mouse button in zoom to rect mode callback' if event.button == 1: self._button_pressed=1 elif event.button == 3: self._button_pressed=3 else: self._button_pressed=None return x, y = event.x, event.y # push the current view to define home if stack is empty if self._views.empty(): self.push_current() self._xypress=[] for i, a in enumerate(self.canvas.figure.get_axes()): if x is not None and y is not None and a.in_axes(event) \ and a.get_navigate() and a.can_zoom(): self._xypress.append(( x, y, a, i, a.viewLim.frozen(), a.transData.frozen())) self.press(event) def push_current(self): 'push the current view limits and position onto the stack' lims = []; pos = [] for a in self.canvas.figure.get_axes(): xmin, xmax = a.get_xlim() ymin, ymax = a.get_ylim() lims.append( (xmin, xmax, ymin, ymax) ) # Store both the original and modified positions pos.append( ( a.get_position(True).frozen(), a.get_position().frozen() ) ) self._views.push(lims) self._positions.push(pos) self.set_history_buttons() def release(self, event): 'this will be called whenever mouse button is released' pass def release_pan(self, event): 'the release mouse button callback in pan/zoom mode' self.canvas.mpl_disconnect(self._idDrag) self._idDrag=self.canvas.mpl_connect('motion_notify_event', self.mouse_move) for a, ind in self._xypress: a.end_pan() if not self._xypress: return self._xypress = [] self._button_pressed=None self.push_current() self.release(event) self.draw() def drag_pan(self, event): 'the drag callback in pan/zoom mode' for a, ind in self._xypress: #safer to use the recorded button at the press than current button: #multiple button can get pressed during motion... a.drag_pan(self._button_pressed, event.key, event.x, event.y) self.dynamic_update() def release_zoom(self, event): 'the release mouse button callback in zoom to rect mode' if not self._xypress: return last_a = [] for cur_xypress in self._xypress: x, y = event.x, event.y lastx, lasty, a, ind, lim, trans = cur_xypress # ignore singular clicks - 5 pixels is a threshold if abs(x-lastx)<5 or abs(y-lasty)<5: self._xypress = None self.release(event) self.draw() return x0, y0, x1, y1 = lim.extents # zoom to rect inverse = a.transData.inverted() lastx, lasty = inverse.transform_point( (lastx, lasty) ) x, y = inverse.transform_point( (x, y) ) Xmin,Xmax=a.get_xlim() Ymin,Ymax=a.get_ylim() # detect twinx,y axes and avoid double zooming twinx, twiny = False, False if last_a: for la in last_a: if a.get_shared_x_axes().joined(a,la): twinx=True if a.get_shared_y_axes().joined(a,la): twiny=True last_a.append(a) if twinx: x0, x1 = Xmin, Xmax else: if Xmin < Xmax: if x<lastx: x0, x1 = x, lastx else: x0, x1 = lastx, x if x0 < Xmin: x0=Xmin if x1 > Xmax: x1=Xmax else: if x>lastx: x0, x1 = x, lastx else: x0, x1 = lastx, x if x0 > Xmin: x0=Xmin if x1 < Xmax: x1=Xmax if twiny: y0, y1 = Ymin, Ymax else: if Ymin < Ymax: if y<lasty: y0, y1 = y, lasty else: y0, y1 = lasty, y if y0 < Ymin: y0=Ymin if y1 > Ymax: y1=Ymax else: if y>lasty: y0, y1 = y, lasty else: y0, y1 = lasty, y if y0 > Ymin: y0=Ymin if y1 < Ymax: y1=Ymax if self._button_pressed == 1: a.set_xlim((x0, x1)) a.set_ylim((y0, y1)) elif self._button_pressed == 3: if a.get_xscale()=='log': alpha=np.log(Xmax/Xmin)/np.log(x1/x0) rx1=pow(Xmin/x0,alpha)*Xmin rx2=pow(Xmax/x0,alpha)*Xmin else: alpha=(Xmax-Xmin)/(x1-x0) rx1=alpha*(Xmin-x0)+Xmin rx2=alpha*(Xmax-x0)+Xmin if a.get_yscale()=='log': alpha=np.log(Ymax/Ymin)/np.log(y1/y0) ry1=pow(Ymin/y0,alpha)*Ymin ry2=pow(Ymax/y0,alpha)*Ymin else: alpha=(Ymax-Ymin)/(y1-y0) ry1=alpha*(Ymin-y0)+Ymin ry2=alpha*(Ymax-y0)+Ymin a.set_xlim((rx1, rx2)) a.set_ylim((ry1, ry2)) self.draw() self._xypress = None self._button_pressed = None self.push_current() self.release(event) def draw(self): 'redraw the canvases, update the locators' for a in self.canvas.figure.get_axes(): xaxis = getattr(a, 'xaxis', None) yaxis = getattr(a, 'yaxis', None) locators = [] if xaxis is not None: locators.append(xaxis.get_major_locator()) locators.append(xaxis.get_minor_locator()) if yaxis is not None: locators.append(yaxis.get_major_locator()) locators.append(yaxis.get_minor_locator()) for loc in locators: loc.refresh() self.canvas.draw() def _update_view(self): '''update the viewlim and position from the view and position stack for each axes ''' lims = self._views() if lims is None: return pos = self._positions() if pos is None: return for i, a in enumerate(self.canvas.figure.get_axes()): xmin, xmax, ymin, ymax = lims[i] a.set_xlim((xmin, xmax)) a.set_ylim((ymin, ymax)) # Restore both the original and modified positions a.set_position( pos[i][0], 'original' ) a.set_position( pos[i][1], 'active' ) self.draw() def save_figure(self, *args): 'save the current figure' raise NotImplementedError def set_cursor(self, cursor): """ Set the current cursor to one of the :class:`Cursors` enums values """ pass def update(self): 'reset the axes stack' self._views.clear() self._positions.clear() self.set_history_buttons() def zoom(self, *args): 'activate zoom to rect mode' if self._active == 'ZOOM': self._active = None else: self._active = 'ZOOM' if self._idPress is not None: self._idPress=self.canvas.mpl_disconnect(self._idPress) self.mode = '' if self._idRelease is not None: self._idRelease=self.canvas.mpl_disconnect(self._idRelease) self.mode = '' if self._active: self._idPress = self.canvas.mpl_connect('button_press_event', self.press_zoom) self._idRelease = self.canvas.mpl_connect('button_release_event', self.release_zoom) self.mode = 'Zoom to rect mode' self.canvas.widgetlock(self) else: self.canvas.widgetlock.release(self) for a in self.canvas.figure.get_axes(): a.set_navigate_mode(self._active) self.set_message(self.mode) def set_history_buttons(self): 'enable or disable back/forward button' pass

69,740

Python

.py

1,718

30.583236

98

0.59163

numenta/nupic-legacy

6,330

1,556

464

AGPL-3.0

9/5/2024, 5:13:42 PM (Europe/Amsterdam)

26,268

_mathtext_data.py

numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/_mathtext_data.py

""" font data tables for truetype and afm computer modern fonts """ # this dict maps symbol names to fontnames, glyphindex. To get the # glyph index from the character code, you have to use get_charmap """ from matplotlib.ft2font import FT2Font font = FT2Font('/usr/local/share/matplotlib/cmr10.ttf') items = font.get_charmap().items() items.sort() for charcode, glyphind in items: print charcode, glyphind """ latex_to_bakoma = { r'\oint' : ('cmex10', 45), r'\bigodot' : ('cmex10', 50), r'\bigoplus' : ('cmex10', 55), r'\bigotimes' : ('cmex10', 59), r'\sum' : ('cmex10', 51), r'\prod' : ('cmex10', 24), r'\int' : ('cmex10', 56), r'\bigcup' : ('cmex10', 28), r'\bigcap' : ('cmex10', 60), r'\biguplus' : ('cmex10', 32), r'\bigwedge' : ('cmex10', 4), r'\bigvee' : ('cmex10', 37), r'\coprod' : ('cmex10', 42), r'\__sqrt__' : ('cmex10', 48), r'\leftbrace' : ('cmex10', 92), r'{' : ('cmex10', 92), r'\{' : ('cmex10', 92), r'\rightbrace' : ('cmex10', 130), r'}' : ('cmex10', 130), r'\}' : ('cmex10', 130), r'\leftangle' : ('cmex10', 97), r'\rightangle' : ('cmex10', 64), r'\langle' : ('cmex10', 97), r'\rangle' : ('cmex10', 64), r'\widehat' : ('cmex10', 15), r'\widetilde' : ('cmex10', 52), r'\omega' : ('cmmi10', 29), r'\varepsilon' : ('cmmi10', 20), r'\vartheta' : ('cmmi10', 22), r'\varrho' : ('cmmi10', 61), r'\varsigma' : ('cmmi10', 41), r'\varphi' : ('cmmi10', 6), r'\leftharpoonup' : ('cmmi10', 108), r'\leftharpoondown' : ('cmmi10', 68), r'\rightharpoonup' : ('cmmi10', 117), r'\rightharpoondown' : ('cmmi10', 77), r'\triangleright' : ('cmmi10', 130), r'\triangleleft' : ('cmmi10', 89), r'.' : ('cmmi10', 51), r',' : ('cmmi10', 44), r'<' : ('cmmi10', 99), r'/' : ('cmmi10', 98), r'>' : ('cmmi10', 107), r'\flat' : ('cmmi10', 131), r'\natural' : ('cmmi10', 90), r'\sharp' : ('cmmi10', 50), r'\smile' : ('cmmi10', 97), r'\frown' : ('cmmi10', 58), r'\ell' : ('cmmi10', 102), r'\imath' : ('cmmi10', 8), r'\jmath' : ('cmmi10', 65), r'\wp' : ('cmmi10', 14), r'\alpha' : ('cmmi10', 13), r'\beta' : ('cmmi10', 35), r'\gamma' : ('cmmi10', 24), r'\delta' : ('cmmi10', 38), r'\epsilon' : ('cmmi10', 54), r'\zeta' : ('cmmi10', 10), r'\eta' : ('cmmi10', 5), r'\theta' : ('cmmi10', 18), r'\iota' : ('cmmi10', 28), r'\lambda' : ('cmmi10', 9), r'\mu' : ('cmmi10', 32), r'\nu' : ('cmmi10', 34), r'\xi' : ('cmmi10', 7), r'\pi' : ('cmmi10', 36), r'\kappa' : ('cmmi10', 30), r'\rho' : ('cmmi10', 39), r'\sigma' : ('cmmi10', 21), r'\tau' : ('cmmi10', 43), r'\upsilon' : ('cmmi10', 25), r'\phi' : ('cmmi10', 42), r'\chi' : ('cmmi10', 17), r'\psi' : ('cmmi10', 31), r'|' : ('cmsy10', 47), r'\|' : ('cmsy10', 47), r'(' : ('cmr10', 119), r'\leftparen' : ('cmr10', 119), r'\rightparen' : ('cmr10', 68), r')' : ('cmr10', 68), r'+' : ('cmr10', 76), r'0' : ('cmr10', 40), r'1' : ('cmr10', 100), r'2' : ('cmr10', 49), r'3' : ('cmr10', 110), r'4' : ('cmr10', 59), r'5' : ('cmr10', 120), r'6' : ('cmr10', 69), r'7' : ('cmr10', 127), r'8' : ('cmr10', 77), r'9' : ('cmr10', 22), r':' : ('cmr10', 85), r';' : ('cmr10', 31), r'=' : ('cmr10', 41), r'\leftbracket' : ('cmr10', 62), r'[' : ('cmr10', 62), r'\rightbracket' : ('cmr10', 72), r']' : ('cmr10', 72), r'\%' : ('cmr10', 48), r'%' : ('cmr10', 48), r'\$' : ('cmr10', 99), r'@' : ('cmr10', 111), r'\_' : ('cmtt10', 79), r'\Gamma' : ('cmr10', 19), r'\Delta' : ('cmr10', 6), r'\Theta' : ('cmr10', 7), r'\Lambda' : ('cmr10', 14), r'\Xi' : ('cmr10', 3), r'\Pi' : ('cmr10', 17), r'\Sigma' : ('cmr10', 10), r'\Upsilon' : ('cmr10', 11), r'\Phi' : ('cmr10', 9), r'\Psi' : ('cmr10', 15), r'\Omega' : ('cmr10', 12), # these are mathml names, I think. I'm just using them for the # tex methods noted r'\circumflexaccent' : ('cmr10', 124), # for \hat r'\combiningbreve' : ('cmr10', 81), # for \breve r'\combiningoverline' : ('cmr10', 131), # for \bar r'\combininggraveaccent' : ('cmr10', 114), # for \grave r'\combiningacuteaccent' : ('cmr10', 63), # for \accute r'\combiningdiaeresis' : ('cmr10', 91), # for \ddot r'\combiningtilde' : ('cmr10', 75), # for \tilde r'\combiningrightarrowabove' : ('cmmi10', 110), # for \vec r'\combiningdotabove' : ('cmr10', 26), # for \dot r'\leftarrow' : ('cmsy10', 10), r'\uparrow' : ('cmsy10', 25), r'\downarrow' : ('cmsy10', 28), r'\leftrightarrow' : ('cmsy10', 24), r'\nearrow' : ('cmsy10', 99), r'\searrow' : ('cmsy10', 57), r'\simeq' : ('cmsy10', 108), r'\Leftarrow' : ('cmsy10', 104), r'\Rightarrow' : ('cmsy10', 112), r'\Uparrow' : ('cmsy10', 60), r'\Downarrow' : ('cmsy10', 68), r'\Leftrightarrow' : ('cmsy10', 51), r'\nwarrow' : ('cmsy10', 65), r'\swarrow' : ('cmsy10', 116), r'\propto' : ('cmsy10', 15), r'\prime' : ('cmsy10', 73), r"'" : ('cmsy10', 73), r'\infty' : ('cmsy10', 32), r'\in' : ('cmsy10', 59), r'\ni' : ('cmsy10', 122), r'\bigtriangleup' : ('cmsy10', 80), r'\bigtriangledown' : ('cmsy10', 132), r'\slash' : ('cmsy10', 87), r'\forall' : ('cmsy10', 21), r'\exists' : ('cmsy10', 5), r'\neg' : ('cmsy10', 20), r'\emptyset' : ('cmsy10', 33), r'\Re' : ('cmsy10', 95), r'\Im' : ('cmsy10', 52), r'\top' : ('cmsy10', 100), r'\bot' : ('cmsy10', 11), r'\aleph' : ('cmsy10', 26), r'\cup' : ('cmsy10', 6), r'\cap' : ('cmsy10', 19), r'\uplus' : ('cmsy10', 58), r'\wedge' : ('cmsy10', 43), r'\vee' : ('cmsy10', 96), r'\vdash' : ('cmsy10', 109), r'\dashv' : ('cmsy10', 66), r'\lfloor' : ('cmsy10', 117), r'\rfloor' : ('cmsy10', 74), r'\lceil' : ('cmsy10', 123), r'\rceil' : ('cmsy10', 81), r'\lbrace' : ('cmsy10', 92), r'\rbrace' : ('cmsy10', 105), r'\mid' : ('cmsy10', 47), r'\vert' : ('cmsy10', 47), r'\Vert' : ('cmsy10', 44), r'\updownarrow' : ('cmsy10', 94), r'\Updownarrow' : ('cmsy10', 53), r'\backslash' : ('cmsy10', 126), r'\wr' : ('cmsy10', 101), r'\nabla' : ('cmsy10', 110), r'\sqcup' : ('cmsy10', 67), r'\sqcap' : ('cmsy10', 118), r'\sqsubseteq' : ('cmsy10', 75), r'\sqsupseteq' : ('cmsy10', 124), r'\S' : ('cmsy10', 129), r'\dag' : ('cmsy10', 71), r'\ddag' : ('cmsy10', 127), r'\P' : ('cmsy10', 130), r'\clubsuit' : ('cmsy10', 18), r'\diamondsuit' : ('cmsy10', 34), r'\heartsuit' : ('cmsy10', 22), r'-' : ('cmsy10', 17), r'\cdot' : ('cmsy10', 78), r'\times' : ('cmsy10', 13), r'*' : ('cmsy10', 9), r'\ast' : ('cmsy10', 9), r'\div' : ('cmsy10', 31), r'\diamond' : ('cmsy10', 48), r'\pm' : ('cmsy10', 8), r'\mp' : ('cmsy10', 98), r'\oplus' : ('cmsy10', 16), r'\ominus' : ('cmsy10', 56), r'\otimes' : ('cmsy10', 30), r'\oslash' : ('cmsy10', 107), r'\odot' : ('cmsy10', 64), r'\bigcirc' : ('cmsy10', 115), r'\circ' : ('cmsy10', 72), r'\bullet' : ('cmsy10', 84), r'\asymp' : ('cmsy10', 121), r'\equiv' : ('cmsy10', 35), r'\subseteq' : ('cmsy10', 103), r'\supseteq' : ('cmsy10', 42), r'\leq' : ('cmsy10', 14), r'\geq' : ('cmsy10', 29), r'\preceq' : ('cmsy10', 79), r'\succeq' : ('cmsy10', 131), r'\sim' : ('cmsy10', 27), r'\approx' : ('cmsy10', 23), r'\subset' : ('cmsy10', 50), r'\supset' : ('cmsy10', 86), r'\ll' : ('cmsy10', 85), r'\gg' : ('cmsy10', 40), r'\prec' : ('cmsy10', 93), r'\succ' : ('cmsy10', 49), r'\rightarrow' : ('cmsy10', 12), r'\to' : ('cmsy10', 12), r'\spadesuit' : ('cmsy10', 7), } latex_to_cmex = { r'\__sqrt__' : 112, r'\bigcap' : 92, r'\bigcup' : 91, r'\bigodot' : 75, r'\bigoplus' : 77, r'\bigotimes' : 79, r'\biguplus' : 93, r'\bigvee' : 95, r'\bigwedge' : 94, r'\coprod' : 97, r'\int' : 90, r'\leftangle' : 173, r'\leftbrace' : 169, r'\oint' : 73, r'\prod' : 89, r'\rightangle' : 174, r'\rightbrace' : 170, r'\sum' : 88, r'\widehat' : 98, r'\widetilde' : 101, } latex_to_standard = { r'\cong' : ('psyr', 64), r'\Delta' : ('psyr', 68), r'\Phi' : ('psyr', 70), r'\Gamma' : ('psyr', 89), r'\alpha' : ('psyr', 97), r'\beta' : ('psyr', 98), r'\chi' : ('psyr', 99), r'\delta' : ('psyr', 100), r'\varepsilon' : ('psyr', 101), r'\phi' : ('psyr', 102), r'\gamma' : ('psyr', 103), r'\eta' : ('psyr', 104), r'\iota' : ('psyr', 105), r'\varpsi' : ('psyr', 106), r'\kappa' : ('psyr', 108), r'\nu' : ('psyr', 110), r'\pi' : ('psyr', 112), r'\theta' : ('psyr', 113), r'\rho' : ('psyr', 114), r'\sigma' : ('psyr', 115), r'\tau' : ('psyr', 116), r'\upsilon' : ('psyr', 117), r'\varpi' : ('psyr', 118), r'\omega' : ('psyr', 119), r'\xi' : ('psyr', 120), r'\psi' : ('psyr', 121), r'\zeta' : ('psyr', 122), r'\sim' : ('psyr', 126), r'\leq' : ('psyr', 163), r'\infty' : ('psyr', 165), r'\clubsuit' : ('psyr', 167), r'\diamondsuit' : ('psyr', 168), r'\heartsuit' : ('psyr', 169), r'\spadesuit' : ('psyr', 170), r'\leftrightarrow' : ('psyr', 171), r'\leftarrow' : ('psyr', 172), r'\uparrow' : ('psyr', 173), r'\rightarrow' : ('psyr', 174), r'\downarrow' : ('psyr', 175), r'\pm' : ('psyr', 176), r'\geq' : ('psyr', 179), r'\times' : ('psyr', 180), r'\propto' : ('psyr', 181), r'\partial' : ('psyr', 182), r'\bullet' : ('psyr', 183), r'\div' : ('psyr', 184), r'\neq' : ('psyr', 185), r'\equiv' : ('psyr', 186), r'\approx' : ('psyr', 187), r'\ldots' : ('psyr', 188), r'\aleph' : ('psyr', 192), r'\Im' : ('psyr', 193), r'\Re' : ('psyr', 194), r'\wp' : ('psyr', 195), r'\otimes' : ('psyr', 196), r'\oplus' : ('psyr', 197), r'\oslash' : ('psyr', 198), r'\cap' : ('psyr', 199), r'\cup' : ('psyr', 200), r'\supset' : ('psyr', 201), r'\supseteq' : ('psyr', 202), r'\subset' : ('psyr', 204), r'\subseteq' : ('psyr', 205), r'\in' : ('psyr', 206), r'\notin' : ('psyr', 207), r'\angle' : ('psyr', 208), r'\nabla' : ('psyr', 209), r'\textregistered' : ('psyr', 210), r'\copyright' : ('psyr', 211), r'\texttrademark' : ('psyr', 212), r'\Pi' : ('psyr', 213), r'\prod' : ('psyr', 213), r'\surd' : ('psyr', 214), r'\__sqrt__' : ('psyr', 214), r'\cdot' : ('psyr', 215), r'\urcorner' : ('psyr', 216), r'\vee' : ('psyr', 217), r'\wedge' : ('psyr', 218), r'\Leftrightarrow' : ('psyr', 219), r'\Leftarrow' : ('psyr', 220), r'\Uparrow' : ('psyr', 221), r'\Rightarrow' : ('psyr', 222), r'\Downarrow' : ('psyr', 223), r'\Diamond' : ('psyr', 224), r'\langle' : ('psyr', 225), r'\Sigma' : ('psyr', 229), r'\sum' : ('psyr', 229), r'\forall' : ('psyr', 34), r'\exists' : ('psyr', 36), r'\lceil' : ('psyr', 233), r'\lbrace' : ('psyr', 123), r'\Psi' : ('psyr', 89), r'\bot' : ('psyr', 0136), r'\Omega' : ('psyr', 0127), r'\leftbracket' : ('psyr', 0133), r'\rightbracket' : ('psyr', 0135), r'\leftbrace' : ('psyr', 123), r'\leftparen' : ('psyr', 050), r'\prime' : ('psyr', 0242), r'\sharp' : ('psyr', 043), r'\slash' : ('psyr', 057), r'\Lamda' : ('psyr', 0114), r'\neg' : ('psyr', 0330), r'\Upsilon' : ('psyr', 0241), r'\rightbrace' : ('psyr', 0175), r'\rfloor' : ('psyr', 0373), r'\lambda' : ('psyr', 0154), r'\to' : ('psyr', 0256), r'\Xi' : ('psyr', 0130), r'\emptyset' : ('psyr', 0306), r'\lfloor' : ('psyr', 0353), r'\rightparen' : ('psyr', 051), r'\rceil' : ('psyr', 0371), r'\ni' : ('psyr', 047), r'\epsilon' : ('psyr', 0145), r'\Theta' : ('psyr', 0121), r'\langle' : ('psyr', 0341), r'\leftangle' : ('psyr', 0341), r'\rangle' : ('psyr', 0361), r'\rightangle' : ('psyr', 0361), r'\rbrace' : ('psyr', 0175), r'\circ' : ('psyr', 0260), r'\diamond' : ('psyr', 0340), r'\mu' : ('psyr', 0155), r'\mid' : ('psyr', 0352), r'\imath' : ('pncri8a', 105), r'\%' : ('pncr8a', 37), r'\$' : ('pncr8a', 36), r'\{' : ('pncr8a', 123), r'\}' : ('pncr8a', 125), r'\backslash' : ('pncr8a', 92), r'\ast' : ('pncr8a', 42), r'\circumflexaccent' : ('pncri8a', 124), # for \hat r'\combiningbreve' : ('pncri8a', 81), # for \breve r'\combininggraveaccent' : ('pncri8a', 114), # for \grave r'\combiningacuteaccent' : ('pncri8a', 63), # for \accute r'\combiningdiaeresis' : ('pncri8a', 91), # for \ddot r'\combiningtilde' : ('pncri8a', 75), # for \tilde r'\combiningrightarrowabove' : ('pncri8a', 110), # for \vec r'\combiningdotabove' : ('pncri8a', 26), # for \dot } # Automatically generated. type12uni = {'uni24C8': 9416, 'aring': 229, 'uni22A0': 8864, 'uni2292': 8850, 'quotedblright': 8221, 'uni03D2': 978, 'uni2215': 8725, 'uni03D0': 976, 'V': 86, 'dollar': 36, 'uni301E': 12318, 'uni03D5': 981, 'four': 52, 'uni25A0': 9632, 'uni013C': 316, 'uni013B': 315, 'uni013E': 318, 'Yacute': 221, 'uni25DE': 9694, 'uni013F': 319, 'uni255A': 9562, 'uni2606': 9734, 'uni0180': 384, 'uni22B7': 8887, 'uni044F': 1103, 'uni22B5': 8885, 'uni22B4': 8884, 'uni22AE': 8878, 'uni22B2': 8882, 'uni22B1': 8881, 'uni22B0': 8880, 'uni25CD': 9677, 'uni03CE': 974, 'uni03CD': 973, 'uni03CC': 972, 'uni03CB': 971, 'uni03CA': 970, 'uni22B8': 8888, 'uni22C9': 8905, 'uni0449': 1097, 'uni20DD': 8413, 'uni20DC': 8412, 'uni20DB': 8411, 'uni2231': 8753, 'uni25CF': 9679, 'uni306E': 12398, 'uni03D1': 977, 'uni01A1': 417, 'uni20D7': 8407, 'uni03D6': 982, 'uni2233': 8755, 'uni20D2': 8402, 'uni20D1': 8401, 'uni20D0': 8400, 'P': 80, 'uni22BE': 8894, 'uni22BD': 8893, 'uni22BC': 8892, 'uni22BB': 8891, 'underscore': 95, 'uni03C8': 968, 'uni03C7': 967, 'uni0328': 808, 'uni03C5': 965, 'uni03C4': 964, 'uni03C3': 963, 'uni03C2': 962, 'uni03C1': 961, 'uni03C0': 960, 'uni2010': 8208, 'uni0130': 304, 'uni0133': 307, 'uni0132': 306, 'uni0135': 309, 'uni0134': 308, 'uni0137': 311, 'uni0136': 310, 'uni0139': 313, 'uni0138': 312, 'uni2244': 8772, 'uni229A': 8858, 'uni2571': 9585, 'uni0278': 632, 'uni2239': 8761, 'p': 112, 'uni3019': 12313, 'uni25CB': 9675, 'uni03DB': 987, 'uni03DC': 988, 'uni03DA': 986, 'uni03DF': 991, 'uni03DD': 989, 'uni013D': 317, 'uni220A': 8714, 'uni220C': 8716, 'uni220B': 8715, 'uni220E': 8718, 'uni220D': 8717, 'uni220F': 8719, 'uni22CC': 8908, 'Otilde': 213, 'uni25E5': 9701, 'uni2736': 10038, 'perthousand': 8240, 'zero': 48, 'uni279B': 10139, 'dotlessi': 305, 'uni2279': 8825, 'Scaron': 352, 'zcaron': 382, 'uni21D8': 8664, 'egrave': 232, 'uni0271': 625, 'uni01AA': 426, 'uni2332': 9010, 'section': 167, 'uni25E4': 9700, 'Icircumflex': 206, 'ntilde': 241, 'uni041E': 1054, 'ampersand': 38, 'uni041C': 1052, 'uni041A': 1050, 'uni22AB': 8875, 'uni21DB': 8667, 'dotaccent': 729, 'uni0416': 1046, 'uni0417': 1047, 'uni0414': 1044, 'uni0415': 1045, 'uni0412': 1042, 'uni0413': 1043, 'degree': 176, 'uni0411': 1041, 'K': 75, 'uni25EB': 9707, 'uni25EF': 9711, 'uni0418': 1048, 'uni0419': 1049, 'uni2263': 8803, 'uni226E': 8814, 'uni2251': 8785, 'uni02C8': 712, 'uni2262': 8802, 'acircumflex': 226, 'uni22B3': 8883, 'uni2261': 8801, 'uni2394': 9108, 'Aring': 197, 'uni2260': 8800, 'uni2254': 8788, 'uni0436': 1078, 'uni2267': 8807, 'k': 107, 'uni22C8': 8904, 'uni226A': 8810, 'uni231F': 8991, 'smalltilde': 732, 'uni2201': 8705, 'uni2200': 8704, 'uni2203': 8707, 'uni02BD': 701, 'uni2205': 8709, 'uni2204': 8708, 'Agrave': 192, 'uni2206': 8710, 'uni2209': 8713, 'uni2208': 8712, 'uni226D': 8813, 'uni2264': 8804, 'uni263D': 9789, 'uni2258': 8792, 'uni02D3': 723, 'uni02D2': 722, 'uni02D1': 721, 'uni02D0': 720, 'uni25E1': 9697, 'divide': 247, 'uni02D5': 725, 'uni02D4': 724, 'ocircumflex': 244, 'uni2524': 9508, 'uni043A': 1082, 'uni24CC': 9420, 'asciitilde': 126, 'uni22B9': 8889, 'uni24D2': 9426, 'uni211E': 8478, 'uni211D': 8477, 'uni24DD': 9437, 'uni211A': 8474, 'uni211C': 8476, 'uni211B': 8475, 'uni25C6': 9670, 'uni017F': 383, 'uni017A': 378, 'uni017C': 380, 'uni017B': 379, 'uni0346': 838, 'uni22F1': 8945, 'uni22F0': 8944, 'two': 50, 'uni2298': 8856, 'uni24D1': 9425, 'E': 69, 'uni025D': 605, 'scaron': 353, 'uni2322': 8994, 'uni25E3': 9699, 'uni22BF': 8895, 'F': 70, 'uni0440': 1088, 'uni255E': 9566, 'uni22BA': 8890, 'uni0175': 373, 'uni0174': 372, 'uni0177': 375, 'uni0176': 374, 'bracketleft': 91, 'uni0170': 368, 'uni0173': 371, 'uni0172': 370, 'asciicircum': 94, 'uni0179': 377, 'uni2590': 9616, 'uni25E2': 9698, 'uni2119': 8473, 'uni2118': 8472, 'uni25CC': 9676, 'f': 102, 'ordmasculine': 186, 'uni229B': 8859, 'uni22A1': 8865, 'uni2111': 8465, 'uni2110': 8464, 'uni2113': 8467, 'uni2112': 8466, 'mu': 181, 'uni2281': 8833, 'paragraph': 182, 'nine': 57, 'uni25EC': 9708, 'v': 118, 'uni040C': 1036, 'uni0113': 275, 'uni22D0': 8912, 'uni21CC': 8652, 'uni21CB': 8651, 'uni21CA': 8650, 'uni22A5': 8869, 'uni21CF': 8655, 'uni21CE': 8654, 'uni21CD': 8653, 'guilsinglleft': 8249, 'backslash': 92, 'uni2284': 8836, 'uni224E': 8782, 'uni224D': 8781, 'uni224F': 8783, 'uni224A': 8778, 'uni2287': 8839, 'uni224C': 8780, 'uni224B': 8779, 'uni21BD': 8637, 'uni2286': 8838, 'uni030F': 783, 'uni030D': 781, 'uni030E': 782, 'uni030B': 779, 'uni030C': 780, 'uni030A': 778, 'uni026E': 622, 'uni026D': 621, 'six': 54, 'uni026A': 618, 'uni026C': 620, 'uni25C1': 9665, 'uni20D6': 8406, 'uni045B': 1115, 'uni045C': 1116, 'uni256B': 9579, 'uni045A': 1114, 'uni045F': 1119, 'uni045E': 1118, 'A': 65, 'uni2569': 9577, 'uni0458': 1112, 'uni0459': 1113, 'uni0452': 1106, 'uni0453': 1107, 'uni2562': 9570, 'uni0451': 1105, 'uni0456': 1110, 'uni0457': 1111, 'uni0454': 1108, 'uni0455': 1109, 'icircumflex': 238, 'uni0307': 775, 'uni0304': 772, 'uni0305': 773, 'uni0269': 617, 'uni0268': 616, 'uni0300': 768, 'uni0301': 769, 'uni0265': 613, 'uni0264': 612, 'uni0267': 615, 'uni0266': 614, 'uni0261': 609, 'uni0260': 608, 'uni0263': 611, 'uni0262': 610, 'a': 97, 'uni2207': 8711, 'uni2247': 8775, 'uni2246': 8774, 'uni2241': 8769, 'uni2240': 8768, 'uni2243': 8771, 'uni2242': 8770, 'uni2312': 8978, 'ogonek': 731, 'uni2249': 8777, 'uni2248': 8776, 'uni3030': 12336, 'q': 113, 'uni21C2': 8642, 'uni21C1': 8641, 'uni21C0': 8640, 'uni21C7': 8647, 'uni21C6': 8646, 'uni21C5': 8645, 'uni21C4': 8644, 'uni225F': 8799, 'uni212C': 8492, 'uni21C8': 8648, 'uni2467': 9319, 'oacute': 243, 'uni028F': 655, 'uni028E': 654, 'uni026F': 623, 'uni028C': 652, 'uni028B': 651, 'uni028A': 650, 'uni2510': 9488, 'ograve': 242, 'edieresis': 235, 'uni22CE': 8910, 'uni22CF': 8911, 'uni219F': 8607, 'comma': 44, 'uni22CA': 8906, 'uni0429': 1065, 'uni03C6': 966, 'uni0427': 1063, 'uni0426': 1062, 'uni0425': 1061, 'uni0424': 1060, 'uni0423': 1059, 'uni0422': 1058, 'uni0421': 1057, 'uni0420': 1056, 'uni2465': 9317, 'uni24D0': 9424, 'uni2464': 9316, 'uni0430': 1072, 'otilde': 245, 'uni2661': 9825, 'uni24D6': 9430, 'uni2466': 9318, 'uni24D5': 9429, 'uni219A': 8602, 'uni2518': 9496, 'uni22B6': 8886, 'uni2461': 9313, 'uni24D4': 9428, 'uni2460': 9312, 'uni24EA': 9450, 'guillemotright': 187, 'ecircumflex': 234, 'greater': 62, 'uni2011': 8209, 'uacute': 250, 'uni2462': 9314, 'L': 76, 'bullet': 8226, 'uni02A4': 676, 'uni02A7': 679, 'cedilla': 184, 'uni02A2': 674, 'uni2015': 8213, 'uni22C4': 8900, 'uni22C5': 8901, 'uni22AD': 8877, 'uni22C7': 8903, 'uni22C0': 8896, 'uni2016': 8214, 'uni22C2': 8898, 'uni22C3': 8899, 'uni24CF': 9423, 'uni042F': 1071, 'uni042E': 1070, 'uni042D': 1069, 'ydieresis': 255, 'l': 108, 'logicalnot': 172, 'uni24CA': 9418, 'uni0287': 647, 'uni0286': 646, 'uni0285': 645, 'uni0284': 644, 'uni0283': 643, 'uni0282': 642, 'uni0281': 641, 'uni027C': 636, 'uni2664': 9828, 'exclamdown': 161, 'uni25C4': 9668, 'uni0289': 649, 'uni0288': 648, 'uni039A': 922, 'endash': 8211, 'uni2640': 9792, 'uni20E4': 8420, 'uni0473': 1139, 'uni20E1': 8417, 'uni2642': 9794, 'uni03B8': 952, 'uni03B9': 953, 'agrave': 224, 'uni03B4': 948, 'uni03B5': 949, 'uni03B6': 950, 'uni03B7': 951, 'uni03B0': 944, 'uni03B1': 945, 'uni03B2': 946, 'uni03B3': 947, 'uni2555': 9557, 'Adieresis': 196, 'germandbls': 223, 'Odieresis': 214, 'space': 32, 'uni0126': 294, 'uni0127': 295, 'uni0124': 292, 'uni0125': 293, 'uni0122': 290, 'uni0123': 291, 'uni0120': 288, 'uni0121': 289, 'quoteright': 8217, 'uni2560': 9568, 'uni2556': 9558, 'ucircumflex': 251, 'uni2561': 9569, 'uni2551': 9553, 'uni25B2': 9650, 'uni2550': 9552, 'uni2563': 9571, 'uni2553': 9555, 'G': 71, 'uni2564': 9572, 'uni2552': 9554, 'quoteleft': 8216, 'uni2565': 9573, 'uni2572': 9586, 'uni2568': 9576, 'uni2566': 9574, 'W': 87, 'uni214A': 8522, 'uni012F': 303, 'uni012D': 301, 'uni012E': 302, 'uni012B': 299, 'uni012C': 300, 'uni255C': 9564, 'uni012A': 298, 'uni2289': 8841, 'Q': 81, 'uni2320': 8992, 'uni2321': 8993, 'g': 103, 'uni03BD': 957, 'uni03BE': 958, 'uni03BF': 959, 'uni2282': 8834, 'uni2285': 8837, 'uni03BA': 954, 'uni03BB': 955, 'uni03BC': 956, 'uni2128': 8488, 'uni25B7': 9655, 'w': 119, 'uni0302': 770, 'uni03DE': 990, 'uni25DA': 9690, 'uni0303': 771, 'uni0463': 1123, 'uni0462': 1122, 'uni3018': 12312, 'uni2514': 9492, 'question': 63, 'uni25B3': 9651, 'uni24E1': 9441, 'one': 49, 'uni200A': 8202, 'uni2278': 8824, 'ring': 730, 'uni0195': 405, 'figuredash': 8210, 'uni22EC': 8940, 'uni0339': 825, 'uni0338': 824, 'uni0337': 823, 'uni0336': 822, 'uni0335': 821, 'uni0333': 819, 'uni0332': 818, 'uni0331': 817, 'uni0330': 816, 'uni01C1': 449, 'uni01C0': 448, 'uni01C3': 451, 'uni01C2': 450, 'uni2353': 9043, 'uni0308': 776, 'uni2218': 8728, 'uni2219': 8729, 'uni2216': 8726, 'uni2217': 8727, 'uni2214': 8724, 'uni0309': 777, 'uni2609': 9737, 'uni2213': 8723, 'uni2210': 8720, 'uni2211': 8721, 'uni2245': 8773, 'B': 66, 'uni25D6': 9686, 'iacute': 237, 'uni02E6': 742, 'uni02E7': 743, 'uni02E8': 744, 'uni02E9': 745, 'uni221D': 8733, 'uni221E': 8734, 'Ydieresis': 376, 'uni221C': 8732, 'uni22D7': 8919, 'uni221A': 8730, 'R': 82, 'uni24DC': 9436, 'uni033F': 831, 'uni033E': 830, 'uni033C': 828, 'uni033B': 827, 'uni033A': 826, 'b': 98, 'uni228A': 8842, 'uni22DB': 8923, 'uni2554': 9556, 'uni046B': 1131, 'uni046A': 1130, 'r': 114, 'uni24DB': 9435, 'Ccedilla': 199, 'minus': 8722, 'uni24DA': 9434, 'uni03F0': 1008, 'uni03F1': 1009, 'uni20AC': 8364, 'uni2276': 8822, 'uni24C0': 9408, 'uni0162': 354, 'uni0163': 355, 'uni011E': 286, 'uni011D': 285, 'uni011C': 284, 'uni011B': 283, 'uni0164': 356, 'uni0165': 357, 'Lslash': 321, 'uni0168': 360, 'uni0169': 361, 'uni25C9': 9673, 'uni02E5': 741, 'uni21C3': 8643, 'uni24C4': 9412, 'uni24E2': 9442, 'uni2277': 8823, 'uni013A': 314, 'uni2102': 8450, 'Uacute': 218, 'uni2317': 8983, 'uni2107': 8455, 'uni221F': 8735, 'yacute': 253, 'uni3012': 12306, 'Ucircumflex': 219, 'uni015D': 349, 'quotedbl': 34, 'uni25D9': 9689, 'uni2280': 8832, 'uni22AF': 8879, 'onehalf': 189, 'uni221B': 8731, 'Thorn': 222, 'uni2226': 8742, 'M': 77, 'uni25BA': 9658, 'uni2463': 9315, 'uni2336': 9014, 'eight': 56, 'uni2236': 8758, 'multiply': 215, 'uni210C': 8460, 'uni210A': 8458, 'uni21C9': 8649, 'grave': 96, 'uni210E': 8462, 'uni0117': 279, 'uni016C': 364, 'uni0115': 277, 'uni016A': 362, 'uni016F': 367, 'uni0112': 274, 'uni016D': 365, 'uni016E': 366, 'Ocircumflex': 212, 'uni2305': 8965, 'm': 109, 'uni24DF': 9439, 'uni0119': 281, 'uni0118': 280, 'uni20A3': 8355, 'uni20A4': 8356, 'uni20A7': 8359, 'uni2288': 8840, 'uni24C3': 9411, 'uni251C': 9500, 'uni228D': 8845, 'uni222F': 8751, 'uni222E': 8750, 'uni222D': 8749, 'uni222C': 8748, 'uni222B': 8747, 'uni222A': 8746, 'uni255B': 9563, 'Ugrave': 217, 'uni24DE': 9438, 'guilsinglright': 8250, 'uni250A': 9482, 'Ntilde': 209, 'uni0279': 633, 'questiondown': 191, 'uni256C': 9580, 'Atilde': 195, 'uni0272': 626, 'uni0273': 627, 'uni0270': 624, 'ccedilla': 231, 'uni0276': 630, 'uni0277': 631, 'uni0274': 628, 'uni0275': 629, 'uni2252': 8786, 'uni041F': 1055, 'uni2250': 8784, 'Z': 90, 'uni2256': 8790, 'uni2257': 8791, 'copyright': 169, 'uni2255': 8789, 'uni043D': 1085, 'uni043E': 1086, 'uni043F': 1087, 'yen': 165, 'uni041D': 1053, 'uni043B': 1083, 'uni043C': 1084, 'uni21B0': 8624, 'uni21B1': 8625, 'uni21B2': 8626, 'uni21B3': 8627, 'uni21B4': 8628, 'uni21B5': 8629, 'uni21B6': 8630, 'uni21B7': 8631, 'uni21B8': 8632, 'Eacute': 201, 'uni2311': 8977, 'uni2310': 8976, 'uni228F': 8847, 'uni25DB': 9691, 'uni21BA': 8634, 'uni21BB': 8635, 'uni21BC': 8636, 'uni2017': 8215, 'uni21BE': 8638, 'uni21BF': 8639, 'uni231C': 8988, 'H': 72, 'uni0293': 659, 'uni2202': 8706, 'uni22A4': 8868, 'uni231E': 8990, 'uni2232': 8754, 'uni225B': 8795, 'uni225C': 8796, 'uni24D9': 9433, 'uni225A': 8794, 'uni0438': 1080, 'uni0439': 1081, 'uni225D': 8797, 'uni225E': 8798, 'uni0434': 1076, 'X': 88, 'uni007F': 127, 'uni0437': 1079, 'Idieresis': 207, 'uni0431': 1073, 'uni0432': 1074, 'uni0433': 1075, 'uni22AC': 8876, 'uni22CD': 8909, 'uni25A3': 9635, 'bar': 124, 'uni24BB': 9403, 'uni037E': 894, 'uni027B': 635, 'h': 104, 'uni027A': 634, 'uni027F': 639, 'uni027D': 637, 'uni027E': 638, 'uni2227': 8743, 'uni2004': 8196, 'uni2225': 8741, 'uni2224': 8740, 'uni2223': 8739, 'uni2222': 8738, 'uni2221': 8737, 'uni2220': 8736, 'x': 120, 'uni2323': 8995, 'uni2559': 9561, 'uni2558': 9560, 'uni2229': 8745, 'uni2228': 8744, 'udieresis': 252, 'uni029D': 669, 'ordfeminine': 170, 'uni22CB': 8907, 'uni233D': 9021, 'uni0428': 1064, 'uni24C6': 9414, 'uni22DD': 8925, 'uni24C7': 9415, 'uni015C': 348, 'uni015B': 347, 'uni015A': 346, 'uni22AA': 8874, 'uni015F': 351, 'uni015E': 350, 'braceleft': 123, 'uni24C5': 9413, 'uni0410': 1040, 'uni03AA': 938, 'uni24C2': 9410, 'uni03AC': 940, 'uni03AB': 939, 'macron': 175, 'uni03AD': 941, 'uni03AF': 943, 'uni0294': 660, 'uni0295': 661, 'uni0296': 662, 'uni0297': 663, 'uni0290': 656, 'uni0291': 657, 'uni0292': 658, 'atilde': 227, 'Acircumflex': 194, 'uni2370': 9072, 'uni24C1': 9409, 'uni0298': 664, 'uni0299': 665, 'Oslash': 216, 'uni029E': 670, 'C': 67, 'quotedblleft': 8220, 'uni029B': 667, 'uni029C': 668, 'uni03A9': 937, 'uni03A8': 936, 'S': 83, 'uni24C9': 9417, 'uni03A1': 929, 'uni03A0': 928, 'exclam': 33, 'uni03A5': 933, 'uni03A4': 932, 'uni03A7': 935, 'Zcaron': 381, 'uni2133': 8499, 'uni2132': 8498, 'uni0159': 345, 'uni0158': 344, 'uni2137': 8503, 'uni2005': 8197, 'uni2135': 8501, 'uni2134': 8500, 'uni02BA': 698, 'uni2033': 8243, 'uni0151': 337, 'uni0150': 336, 'uni0157': 343, 'equal': 61, 'uni0155': 341, 'uni0154': 340, 's': 115, 'uni233F': 9023, 'eth': 240, 'uni24BE': 9406, 'uni21E9': 8681, 'uni2060': 8288, 'Egrave': 200, 'uni255D': 9565, 'uni24CD': 9421, 'uni21E1': 8673, 'uni21B9': 8633, 'hyphen': 45, 'uni01BE': 446, 'uni01BB': 443, 'period': 46, 'igrave': 236, 'uni01BA': 442, 'uni2296': 8854, 'uni2297': 8855, 'uni2294': 8852, 'uni2295': 8853, 'colon': 58, 'uni2293': 8851, 'uni2290': 8848, 'uni2291': 8849, 'uni032D': 813, 'uni032E': 814, 'uni032F': 815, 'uni032A': 810, 'uni032B': 811, 'uni032C': 812, 'uni231D': 8989, 'Ecircumflex': 202, 'uni24D7': 9431, 'uni25DD': 9693, 'trademark': 8482, 'Aacute': 193, 'cent': 162, 'uni0445': 1093, 'uni266E': 9838, 'uni266D': 9837, 'uni266B': 9835, 'uni03C9': 969, 'uni2003': 8195, 'uni2047': 8263, 'lslash': 322, 'uni03A6': 934, 'uni2043': 8259, 'uni250C': 9484, 'uni2040': 8256, 'uni255F': 9567, 'uni24CB': 9419, 'uni0472': 1138, 'uni0446': 1094, 'uni0474': 1140, 'uni0475': 1141, 'uni2508': 9480, 'uni2660': 9824, 'uni2506': 9478, 'uni2502': 9474, 'c': 99, 'uni2500': 9472, 'N': 78, 'uni22A6': 8870, 'uni21E7': 8679, 'uni2130': 8496, 'uni2002': 8194, 'breve': 728, 'uni0442': 1090, 'Oacute': 211, 'uni229F': 8863, 'uni25C7': 9671, 'uni229D': 8861, 'uni229E': 8862, 'guillemotleft': 171, 'uni0329': 809, 'uni24E5': 9445, 'uni011F': 287, 'uni0324': 804, 'uni0325': 805, 'uni0326': 806, 'uni0327': 807, 'uni0321': 801, 'uni0322': 802, 'n': 110, 'uni2032': 8242, 'uni2269': 8809, 'uni2268': 8808, 'uni0306': 774, 'uni226B': 8811, 'uni21EA': 8682, 'uni0166': 358, 'uni203B': 8251, 'uni01B5': 437, 'idieresis': 239, 'uni02BC': 700, 'uni01B0': 432, 'braceright': 125, 'seven': 55, 'uni02BB': 699, 'uni011A': 282, 'uni29FB': 10747, 'brokenbar': 166, 'uni2036': 8246, 'uni25C0': 9664, 'uni0156': 342, 'uni22D5': 8917, 'uni0258': 600, 'ugrave': 249, 'uni22D6': 8918, 'uni22D1': 8913, 'uni2034': 8244, 'uni22D3': 8915, 'uni22D2': 8914, 'uni203C': 8252, 'uni223E': 8766, 'uni02BF': 703, 'uni22D9': 8921, 'uni22D8': 8920, 'uni25BD': 9661, 'uni25BE': 9662, 'uni25BF': 9663, 'uni041B': 1051, 'periodcentered': 183, 'uni25BC': 9660, 'uni019E': 414, 'uni019B': 411, 'uni019A': 410, 'uni2007': 8199, 'uni0391': 913, 'uni0390': 912, 'uni0393': 915, 'uni0392': 914, 'uni0395': 917, 'uni0394': 916, 'uni0397': 919, 'uni0396': 918, 'uni0399': 921, 'uni0398': 920, 'uni25C8': 9672, 'uni2468': 9320, 'sterling': 163, 'uni22EB': 8939, 'uni039C': 924, 'uni039B': 923, 'uni039E': 926, 'uni039D': 925, 'uni039F': 927, 'I': 73, 'uni03E1': 993, 'uni03E0': 992, 'uni2319': 8985, 'uni228B': 8843, 'uni25B5': 9653, 'uni25B6': 9654, 'uni22EA': 8938, 'uni24B9': 9401, 'uni044E': 1102, 'uni0199': 409, 'uni2266': 8806, 'Y': 89, 'uni22A2': 8866, 'Eth': 208, 'uni266F': 9839, 'emdash': 8212, 'uni263B': 9787, 'uni24BD': 9405, 'uni22DE': 8926, 'uni0360': 864, 'uni2557': 9559, 'uni22DF': 8927, 'uni22DA': 8922, 'uni22DC': 8924, 'uni0361': 865, 'i': 105, 'uni24BF': 9407, 'uni0362': 866, 'uni263E': 9790, 'uni028D': 653, 'uni2259': 8793, 'uni0323': 803, 'uni2265': 8805, 'daggerdbl': 8225, 'y': 121, 'uni010A': 266, 'plusminus': 177, 'less': 60, 'uni21AE': 8622, 'uni0315': 789, 'uni230B': 8971, 'uni21AF': 8623, 'uni21AA': 8618, 'uni21AC': 8620, 'uni21AB': 8619, 'uni01FB': 507, 'uni01FC': 508, 'uni223A': 8762, 'uni01FA': 506, 'uni01FF': 511, 'uni01FD': 509, 'uni01FE': 510, 'uni2567': 9575, 'uni25E0': 9696, 'uni0104': 260, 'uni0105': 261, 'uni0106': 262, 'uni0107': 263, 'uni0100': 256, 'uni0101': 257, 'uni0102': 258, 'uni0103': 259, 'uni2038': 8248, 'uni2009': 8201, 'uni2008': 8200, 'uni0108': 264, 'uni0109': 265, 'uni02A1': 673, 'uni223B': 8763, 'uni226C': 8812, 'uni25AC': 9644, 'uni24D3': 9427, 'uni21E0': 8672, 'uni21E3': 8675, 'Udieresis': 220, 'uni21E2': 8674, 'D': 68, 'uni21E5': 8677, 'uni2621': 9761, 'uni21D1': 8657, 'uni203E': 8254, 'uni22C6': 8902, 'uni21E4': 8676, 'uni010D': 269, 'uni010E': 270, 'uni010F': 271, 'five': 53, 'T': 84, 'uni010B': 267, 'uni010C': 268, 'uni2605': 9733, 'uni2663': 9827, 'uni21E6': 8678, 'uni24B6': 9398, 'uni22C1': 8897, 'oslash': 248, 'acute': 180, 'uni01F0': 496, 'd': 100, 'OE': 338, 'uni22E3': 8931, 'Igrave': 204, 'uni2308': 8968, 'uni2309': 8969, 'uni21A9': 8617, 't': 116, 'uni2313': 8979, 'uni03A3': 931, 'uni21A4': 8612, 'uni21A7': 8615, 'uni21A6': 8614, 'uni21A1': 8609, 'uni21A0': 8608, 'uni21A3': 8611, 'uni21A2': 8610, 'parenright': 41, 'uni256A': 9578, 'uni25DC': 9692, 'uni24CE': 9422, 'uni042C': 1068, 'uni24E0': 9440, 'uni042B': 1067, 'uni0409': 1033, 'uni0408': 1032, 'uni24E7': 9447, 'uni25B4': 9652, 'uni042A': 1066, 'uni228E': 8846, 'uni0401': 1025, 'adieresis': 228, 'uni0403': 1027, 'quotesingle': 39, 'uni0405': 1029, 'uni0404': 1028, 'uni0407': 1031, 'uni0406': 1030, 'uni229C': 8860, 'uni2306': 8966, 'uni2253': 8787, 'twodotenleader': 8229, 'uni2131': 8497, 'uni21DA': 8666, 'uni2234': 8756, 'uni2235': 8757, 'uni01A5': 421, 'uni2237': 8759, 'uni2230': 8752, 'uni02CC': 716, 'slash': 47, 'uni01A0': 416, 'ellipsis': 8230, 'uni2299': 8857, 'uni2238': 8760, 'numbersign': 35, 'uni21A8': 8616, 'uni223D': 8765, 'uni01AF': 431, 'uni223F': 8767, 'uni01AD': 429, 'uni01AB': 427, 'odieresis': 246, 'uni223C': 8764, 'uni227D': 8829, 'uni0280': 640, 'O': 79, 'uni227E': 8830, 'uni21A5': 8613, 'uni22D4': 8916, 'uni25D4': 9684, 'uni227F': 8831, 'uni0435': 1077, 'uni2302': 8962, 'uni2669': 9833, 'uni24E3': 9443, 'uni2720': 10016, 'uni22A8': 8872, 'uni22A9': 8873, 'uni040A': 1034, 'uni22A7': 8871, 'oe': 339, 'uni040B': 1035, 'uni040E': 1038, 'uni22A3': 8867, 'o': 111, 'uni040F': 1039, 'Edieresis': 203, 'uni25D5': 9685, 'plus': 43, 'uni044D': 1101, 'uni263C': 9788, 'uni22E6': 8934, 'uni2283': 8835, 'uni258C': 9612, 'uni219E': 8606, 'uni24E4': 9444, 'uni2136': 8502, 'dagger': 8224, 'uni24B7': 9399, 'uni219B': 8603, 'uni22E5': 8933, 'three': 51, 'uni210B': 8459, 'uni2534': 9524, 'uni24B8': 9400, 'uni230A': 8970, 'hungarumlaut': 733, 'parenleft': 40, 'uni0148': 328, 'uni0149': 329, 'uni2124': 8484, 'uni2125': 8485, 'uni2126': 8486, 'uni2127': 8487, 'uni0140': 320, 'uni2129': 8489, 'uni25C5': 9669, 'uni0143': 323, 'uni0144': 324, 'uni0145': 325, 'uni0146': 326, 'uni0147': 327, 'uni210D': 8461, 'fraction': 8260, 'uni2031': 8241, 'uni2196': 8598, 'uni2035': 8245, 'uni24E6': 9446, 'uni016B': 363, 'uni24BA': 9402, 'uni266A': 9834, 'uni0116': 278, 'uni2115': 8469, 'registered': 174, 'J': 74, 'uni25DF': 9695, 'uni25CE': 9678, 'uni273D': 10045, 'dieresis': 168, 'uni212B': 8491, 'uni0114': 276, 'uni212D': 8493, 'uni212E': 8494, 'uni212F': 8495, 'uni014A': 330, 'uni014B': 331, 'uni014C': 332, 'uni014D': 333, 'uni014E': 334, 'uni014F': 335, 'uni025E': 606, 'uni24E8': 9448, 'uni0111': 273, 'uni24E9': 9449, 'Ograve': 210, 'j': 106, 'uni2195': 8597, 'uni2194': 8596, 'uni2197': 8599, 'uni2037': 8247, 'uni2191': 8593, 'uni2190': 8592, 'uni2193': 8595, 'uni2192': 8594, 'uni29FA': 10746, 'uni2713': 10003, 'z': 122, 'uni2199': 8601, 'uni2198': 8600, 'uni2667': 9831, 'ae': 230, 'uni0448': 1096, 'semicolon': 59, 'uni2666': 9830, 'uni038F': 911, 'uni0444': 1092, 'uni0447': 1095, 'uni038E': 910, 'uni0441': 1089, 'uni038C': 908, 'uni0443': 1091, 'uni038A': 906, 'uni0250': 592, 'uni0251': 593, 'uni0252': 594, 'uni0253': 595, 'uni0254': 596, 'at': 64, 'uni0256': 598, 'uni0257': 599, 'uni0167': 359, 'uni0259': 601, 'uni228C': 8844, 'uni2662': 9826, 'uni0319': 793, 'uni0318': 792, 'uni24BC': 9404, 'uni0402': 1026, 'uni22EF': 8943, 'Iacute': 205, 'uni22ED': 8941, 'uni22EE': 8942, 'uni0311': 785, 'uni0310': 784, 'uni21E8': 8680, 'uni0312': 786, 'percent': 37, 'uni0317': 791, 'uni0316': 790, 'uni21D6': 8662, 'uni21D7': 8663, 'uni21D4': 8660, 'uni21D5': 8661, 'uni21D2': 8658, 'uni21D3': 8659, 'uni21D0': 8656, 'uni2138': 8504, 'uni2270': 8816, 'uni2271': 8817, 'uni2272': 8818, 'uni2273': 8819, 'uni2274': 8820, 'uni2275': 8821, 'bracketright': 93, 'uni21D9': 8665, 'uni21DF': 8671, 'uni21DD': 8669, 'uni21DE': 8670, 'AE': 198, 'uni03AE': 942, 'uni227A': 8826, 'uni227B': 8827, 'uni227C': 8828, 'asterisk': 42, 'aacute': 225, 'uni226F': 8815, 'uni22E2': 8930, 'uni0386': 902, 'uni22E0': 8928, 'uni22E1': 8929, 'U': 85, 'uni22E7': 8935, 'uni22E4': 8932, 'uni0387': 903, 'uni031A': 794, 'eacute': 233, 'uni22E8': 8936, 'uni22E9': 8937, 'uni24D8': 9432, 'uni025A': 602, 'uni025B': 603, 'uni025C': 604, 'e': 101, 'uni0128': 296, 'uni025F': 607, 'uni2665': 9829, 'thorn': 254, 'uni0129': 297, 'uni253C': 9532, 'uni25D7': 9687, 'u': 117, 'uni0388': 904, 'uni0389': 905, 'uni0255': 597, 'uni0171': 369, 'uni0384': 900, 'uni0385': 901, 'uni044A': 1098, 'uni252C': 9516, 'uni044C': 1100, 'uni044B': 1099} uni2type1 = dict([(v,k) for k,v in type12uni.items()]) tex2uni = { 'widehat': 0x0302, 'widetilde': 0x0303, 'langle': 0x27e8, 'rangle': 0x27e9, 'perp': 0x27c2, 'neq': 0x2260, 'Join': 0x2a1d, 'leqslant': 0x2a7d, 'geqslant': 0x2a7e, 'lessapprox': 0x2a85, 'gtrapprox': 0x2a86, 'lesseqqgtr': 0x2a8b, 'gtreqqless': 0x2a8c, 'triangleeq': 0x225c, 'eqslantless': 0x2a95, 'eqslantgtr': 0x2a96, 'backepsilon': 0x03f6, 'precapprox': 0x2ab7, 'succapprox': 0x2ab8, 'fallingdotseq': 0x2252, 'subseteqq': 0x2ac5, 'supseteqq': 0x2ac6, 'varpropto': 0x221d, 'precnapprox': 0x2ab9, 'succnapprox': 0x2aba, 'subsetneqq': 0x2acb, 'supsetneqq': 0x2acc, 'lnapprox': 0x2ab9, 'gnapprox': 0x2aba, 'longleftarrow': 0x27f5, 'longrightarrow': 0x27f6, 'longleftrightarrow': 0x27f7, 'Longleftarrow': 0x27f8, 'Longrightarrow': 0x27f9, 'Longleftrightarrow': 0x27fa, 'longmapsto': 0x27fc, 'leadsto': 0x21dd, 'dashleftarrow': 0x290e, 'dashrightarrow': 0x290f, 'circlearrowleft': 0x21ba, 'circlearrowright': 0x21bb, 'leftrightsquigarrow': 0x21ad, 'leftsquigarrow': 0x219c, 'rightsquigarrow': 0x219d, 'Game': 0x2141, 'hbar': 0x0127, 'hslash': 0x210f, 'ldots': 0x22ef, 'vdots': 0x22ee, 'doteqdot': 0x2251, 'doteq': 8784, 'partial': 8706, 'gg': 8811, 'asymp': 8781, 'blacktriangledown': 9662, 'otimes': 8855, 'nearrow': 8599, 'varpi': 982, 'vee': 8744, 'vec': 8407, 'smile': 8995, 'succnsim': 8937, 'gimel': 8503, 'vert': 124, '|': 124, 'varrho': 1009, 'P': 182, 'approxident': 8779, 'Swarrow': 8665, 'textasciicircum': 94, 'imageof': 8887, 'ntriangleleft': 8938, 'nleq': 8816, 'div': 247, 'nparallel': 8742, 'Leftarrow': 8656, 'lll': 8920, 'oiint': 8751, 'ngeq': 8817, 'Theta': 920, 'origof': 8886, 'blacksquare': 9632, 'solbar': 9023, 'neg': 172, 'sum': 8721, 'Vdash': 8873, 'coloneq': 8788, 'degree': 176, 'bowtie': 8904, 'blacktriangleright': 9654, 'varsigma': 962, 'leq': 8804, 'ggg': 8921, 'lneqq': 8808, 'scurel': 8881, 'stareq': 8795, 'BbbN': 8469, 'nLeftarrow': 8653, 'nLeftrightarrow': 8654, 'k': 808, 'bot': 8869, 'BbbC': 8450, 'Lsh': 8624, 'leftleftarrows': 8647, 'BbbZ': 8484, 'digamma': 989, 'BbbR': 8477, 'BbbP': 8473, 'BbbQ': 8474, 'vartriangleright': 8883, 'succsim': 8831, 'wedge': 8743, 'lessgtr': 8822, 'veebar': 8891, 'mapsdown': 8615, 'Rsh': 8625, 'chi': 967, 'prec': 8826, 'nsubseteq': 8840, 'therefore': 8756, 'eqcirc': 8790, 'textexclamdown': 161, 'nRightarrow': 8655, 'flat': 9837, 'notin': 8713, 'llcorner': 8990, 'varepsilon': 949, 'bigtriangleup': 9651, 'aleph': 8501, 'dotminus': 8760, 'upsilon': 965, 'Lambda': 923, 'cap': 8745, 'barleftarrow': 8676, 'mu': 956, 'boxplus': 8862, 'mp': 8723, 'circledast': 8859, 'tau': 964, 'in': 8712, 'backslash': 92, 'varnothing': 8709, 'sharp': 9839, 'eqsim': 8770, 'gnsim': 8935, 'Searrow': 8664, 'updownarrows': 8645, 'heartsuit': 9825, 'trianglelefteq': 8884, 'ddag': 8225, 'sqsubseteq': 8849, 'mapsfrom': 8612, 'boxbar': 9707, 'sim': 8764, 'Nwarrow': 8662, 'nequiv': 8802, 'succ': 8827, 'vdash': 8866, 'Leftrightarrow': 8660, 'parallel': 8741, 'invnot': 8976, 'natural': 9838, 'ss': 223, 'uparrow': 8593, 'nsim': 8769, 'hookrightarrow': 8618, 'Equiv': 8803, 'approx': 8776, 'Vvdash': 8874, 'nsucc': 8833, 'leftrightharpoons': 8651, 'Re': 8476, 'boxminus': 8863, 'equiv': 8801, 'Lleftarrow': 8666, 'thinspace': 8201, 'll': 8810, 'Cup': 8915, 'measeq': 8798, 'upharpoonleft': 8639, 'lq': 8216, 'Upsilon': 933, 'subsetneq': 8842, 'greater': 62, 'supsetneq': 8843, 'Cap': 8914, 'L': 321, 'spadesuit': 9824, 'lrcorner': 8991, 'not': 824, 'bar': 772, 'rightharpoonaccent': 8401, 'boxdot': 8865, 'l': 322, 'leftharpoondown': 8637, 'bigcup': 8899, 'iint': 8748, 'bigwedge': 8896, 'downharpoonleft': 8643, 'textasciitilde': 126, 'subset': 8834, 'leqq': 8806, 'mapsup': 8613, 'nvDash': 8877, 'looparrowleft': 8619, 'nless': 8814, 'rightarrowbar': 8677, 'Vert': 8214, 'downdownarrows': 8650, 'uplus': 8846, 'simeq': 8771, 'napprox': 8777, 'ast': 8727, 'twoheaduparrow': 8607, 'doublebarwedge': 8966, 'Sigma': 931, 'leftharpoonaccent': 8400, 'ntrianglelefteq': 8940, 'nexists': 8708, 'times': 215, 'measuredangle': 8737, 'bumpeq': 8783, 'carriagereturn': 8629, 'adots': 8944, 'checkmark': 10003, 'lambda': 955, 'xi': 958, 'rbrace': 125, 'rbrack': 93, 'Nearrow': 8663, 'maltese': 10016, 'clubsuit': 9827, 'top': 8868, 'overarc': 785, 'varphi': 966, 'Delta': 916, 'iota': 953, 'nleftarrow': 8602, 'candra': 784, 'supset': 8835, 'triangleleft': 9665, 'gtreqless': 8923, 'ntrianglerighteq': 8941, 'quad': 8195, 'Xi': 926, 'gtrdot': 8919, 'leftthreetimes': 8907, 'minus': 8722, 'preccurlyeq': 8828, 'nleftrightarrow': 8622, 'lambdabar': 411, 'blacktriangle': 9652, 'kernelcontraction': 8763, 'Phi': 934, 'angle': 8736, 'spadesuitopen': 9828, 'eqless': 8924, 'mid': 8739, 'varkappa': 1008, 'Ldsh': 8626, 'updownarrow': 8597, 'beta': 946, 'textquotedblleft': 8220, 'rho': 961, 'alpha': 945, 'intercal': 8890, 'beth': 8502, 'grave': 768, 'acwopencirclearrow': 8634, 'nmid': 8740, 'nsupset': 8837, 'sigma': 963, 'dot': 775, 'Rightarrow': 8658, 'turnednot': 8985, 'backsimeq': 8909, 'leftarrowtail': 8610, 'approxeq': 8778, 'curlyeqsucc': 8927, 'rightarrowtail': 8611, 'Psi': 936, 'copyright': 169, 'yen': 165, 'vartriangleleft': 8882, 'rasp': 700, 'triangleright': 9655, 'precsim': 8830, 'infty': 8734, 'geq': 8805, 'updownarrowbar': 8616, 'precnsim': 8936, 'H': 779, 'ulcorner': 8988, 'looparrowright': 8620, 'ncong': 8775, 'downarrow': 8595, 'circeq': 8791, 'subseteq': 8838, 'bigstar': 9733, 'prime': 8242, 'lceil': 8968, 'Rrightarrow': 8667, 'oiiint': 8752, 'curlywedge': 8911, 'vDash': 8872, 'lfloor': 8970, 'ddots': 8945, 'exists': 8707, 'underbar': 817, 'Pi': 928, 'leftrightarrows': 8646, 'sphericalangle': 8738, 'coprod': 8720, 'circledcirc': 8858, 'gtrsim': 8819, 'gneqq': 8809, 'between': 8812, 'theta': 952, 'complement': 8705, 'arceq': 8792, 'nVdash': 8878, 'S': 167, 'wr': 8768, 'wp': 8472, 'backcong': 8780, 'lasp': 701, 'c': 807, 'nabla': 8711, 'dotplus': 8724, 'eta': 951, 'forall': 8704, 'eth': 240, 'colon': 58, 'sqcup': 8852, 'rightrightarrows': 8649, 'sqsupset': 8848, 'mapsto': 8614, 'bigtriangledown': 9661, 'sqsupseteq': 8850, 'propto': 8733, 'pi': 960, 'pm': 177, 'dots': 8230, 'nrightarrow': 8603, 'textasciiacute': 180, 'Doteq': 8785, 'breve': 774, 'sqcap': 8851, 'twoheadrightarrow': 8608, 'kappa': 954, 'vartriangle': 9653, 'diamondsuit': 9826, 'pitchfork': 8916, 'blacktriangleleft': 9664, 'nprec': 8832, 'vdots': 8942, 'curvearrowright': 8631, 'barwedge': 8892, 'multimap': 8888, 'textquestiondown': 191, 'cong': 8773, 'rtimes': 8906, 'rightzigzagarrow': 8669, 'rightarrow': 8594, 'leftarrow': 8592, '__sqrt__': 8730, 'twoheaddownarrow': 8609, 'oint': 8750, 'bigvee': 8897, 'eqdef': 8797, 'sterling': 163, 'phi': 981, 'Updownarrow': 8661, 'backprime': 8245, 'emdash': 8212, 'Gamma': 915, 'i': 305, 'rceil': 8969, 'leftharpoonup': 8636, 'Im': 8465, 'curvearrowleft': 8630, 'wedgeq': 8793, 'fallingdotseq': 8786, 'curlyeqprec': 8926, 'questeq': 8799, 'less': 60, 'upuparrows': 8648, 'tilde': 771, 'textasciigrave': 96, 'smallsetminus': 8726, 'ell': 8467, 'cup': 8746, 'danger': 9761, 'nVDash': 8879, 'cdotp': 183, 'cdots': 8943, 'hat': 770, 'eqgtr': 8925, 'enspace': 8194, 'psi': 968, 'frown': 8994, 'acute': 769, 'downzigzagarrow': 8623, 'ntriangleright': 8939, 'cupdot': 8845, 'circleddash': 8861, 'oslash': 8856, 'mho': 8487, 'd': 803, 'sqsubset': 8847, 'cdot': 8901, 'Omega': 937, 'OE': 338, 'veeeq': 8794, 'Finv': 8498, 't': 865, 'leftrightarrow': 8596, 'swarrow': 8601, 'rightthreetimes': 8908, 'rightleftharpoons': 8652, 'lesssim': 8818, 'searrow': 8600, 'because': 8757, 'gtrless': 8823, 'star': 8902, 'nsubset': 8836, 'zeta': 950, 'dddot': 8411, 'bigcirc': 9675, 'Supset': 8913, 'circ': 8728, 'slash': 8725, 'ocirc': 778, 'prod': 8719, 'twoheadleftarrow': 8606, 'daleth': 8504, 'upharpoonright': 8638, 'odot': 8857, 'Uparrow': 8657, 'O': 216, 'hookleftarrow': 8617, 'trianglerighteq': 8885, 'nsime': 8772, 'oe': 339, 'nwarrow': 8598, 'o': 248, 'ddddot': 8412, 'downharpoonright': 8642, 'succcurlyeq': 8829, 'gamma': 947, 'scrR': 8475, 'dag': 8224, 'thickspace': 8197, 'frakZ': 8488, 'lessdot': 8918, 'triangledown': 9663, 'ltimes': 8905, 'scrB': 8492, 'endash': 8211, 'scrE': 8496, 'scrF': 8497, 'scrH': 8459, 'scrI': 8464, 'rightharpoondown': 8641, 'scrL': 8466, 'scrM': 8499, 'frakC': 8493, 'nsupseteq': 8841, 'circledR': 174, 'circledS': 9416, 'ngtr': 8815, 'bigcap': 8898, 'scre': 8495, 'Downarrow': 8659, 'scrg': 8458, 'overleftrightarrow': 8417, 'scro': 8500, 'lnsim': 8934, 'eqcolon': 8789, 'curlyvee': 8910, 'urcorner': 8989, 'lbrace': 123, 'Bumpeq': 8782, 'delta': 948, 'boxtimes': 8864, 'overleftarrow': 8406, 'prurel': 8880, 'clubsuitopen': 9831, 'cwopencirclearrow': 8635, 'geqq': 8807, 'rightleftarrows': 8644, 'ac': 8766, 'ae': 230, 'int': 8747, 'rfloor': 8971, 'risingdotseq': 8787, 'nvdash': 8876, 'diamond': 8900, 'ddot': 776, 'backsim': 8765, 'oplus': 8853, 'triangleq': 8796, 'check': 780, 'ni': 8715, 'iiint': 8749, 'ne': 8800, 'lesseqgtr': 8922, 'obar': 9021, 'supseteq': 8839, 'nu': 957, 'AA': 8491, 'AE': 198, 'models': 8871, 'ominus': 8854, 'dashv': 8867, 'omega': 969, 'rq': 8217, 'Subset': 8912, 'rightharpoonup': 8640, 'Rdsh': 8627, 'bullet': 8729, 'divideontimes': 8903, 'lbrack': 91, 'textquotedblright': 8221, 'Colon': 8759, '%': 37, '$': 36, '{': 123, '}': 125, '_': 95, 'imath': 0x131, 'circumflexaccent' : 770, 'combiningbreve' : 774, 'combiningoverline' : 772, 'combininggraveaccent' : 768, 'combiningacuteaccent' : 769, 'combiningdiaeresis' : 776, 'combiningtilde' : 771, 'combiningrightarrowabove' : 8407, 'combiningdotabove' : 775, 'to': 8594, 'succeq': 8829, 'emptyset': 8709, 'leftparen': 40, 'rightparen': 41, 'bigoplus': 10753, 'leftangle': 10216, 'rightangle': 10217, 'leftbrace': 124, 'rightbrace': 125, 'jmath': 567, 'bigodot': 10752, 'preceq': 8828, 'biguplus': 10756, 'epsilon': 949, 'vartheta': 977, 'bigotimes': 10754 } # Each element is a 4-tuple of the form: # src_start, src_end, dst_font, dst_start # stix_virtual_fonts = { 'bb': { 'rm': [ (0x0030, 0x0039, 'rm', 0x1d7d8), # 0-9 (0x0041, 0x0042, 'rm', 0x1d538), # A-B (0x0043, 0x0043, 'rm', 0x2102), # C (0x0044, 0x0047, 'rm', 0x1d53b), # D-G (0x0048, 0x0048, 'rm', 0x210d), # H (0x0049, 0x004d, 'rm', 0x1d540), # I-M (0x004e, 0x004e, 'rm', 0x2115), # N (0x004f, 0x004f, 'rm', 0x1d546), # O (0x0050, 0x0051, 'rm', 0x2119), # P-Q (0x0052, 0x0052, 'rm', 0x211d), # R (0x0053, 0x0059, 'rm', 0x1d54a), # S-Y (0x005a, 0x005a, 'rm', 0x2124), # Z (0x0061, 0x007a, 'rm', 0x1d552), # a-z (0x0393, 0x0393, 'rm', 0x213e), # \Gamma (0x03a0, 0x03a0, 'rm', 0x213f), # \Pi (0x03a3, 0x03a3, 'rm', 0x2140), # \Sigma (0x03b3, 0x03b3, 'rm', 0x213d), # \gamma (0x03c0, 0x03c0, 'rm', 0x213c), # \pi ], 'it': [ (0x0030, 0x0039, 'rm', 0x1d7d8), # 0-9 (0x0041, 0x0042, 'it', 0xe154), # A-B (0x0043, 0x0043, 'it', 0x2102), # C (missing in beta STIX fonts) (0x0044, 0x0044, 'it', 0x2145), # D (0x0045, 0x0047, 'it', 0xe156), # E-G (0x0048, 0x0048, 'it', 0x210d), # H (missing in beta STIX fonts) (0x0049, 0x004d, 'it', 0xe159), # I-M (0x004e, 0x004e, 'it', 0x2115), # N (missing in beta STIX fonts) (0x004f, 0x004f, 'it', 0xe15e), # O (0x0050, 0x0051, 'it', 0x2119), # P-Q (missing in beta STIX fonts) (0x0052, 0x0052, 'it', 0x211d), # R (missing in beta STIX fonts) (0x0053, 0x0059, 'it', 0xe15f), # S-Y (0x005a, 0x005a, 'it', 0x2124), # Z (missing in beta STIX fonts) (0x0061, 0x0063, 'it', 0xe166), # a-c (0x0064, 0x0065, 'it', 0x2146), # d-e (0x0066, 0x0068, 'it', 0xe169), # f-h (0x0069, 0x006a, 'it', 0x2148), # i-j (0x006b, 0x007a, 'it', 0xe16c), # k-z (0x0393, 0x0393, 'it', 0x213e), # \Gamma (missing in beta STIX fonts) (0x03a0, 0x03a0, 'it', 0x213f), # \Pi (0x03a3, 0x03a3, 'it', 0x2140), # \Sigma (missing in beta STIX fonts) (0x03b3, 0x03b3, 'it', 0x213d), # \gamma (missing in beta STIX fonts) (0x03c0, 0x03c0, 'it', 0x213c), # \pi ], 'bf': [ (0x0030, 0x0039, 'rm', 0x1d7d8), # 0-9 (0x0041, 0x005a, 'bf', 0xe38a), # A-Z (0x0061, 0x007a, 'bf', 0xe39d), # a-z (0x0393, 0x0393, 'bf', 0x213e), # \Gamma (0x03a0, 0x03a0, 'bf', 0x213f), # \Pi (0x03a3, 0x03a3, 'bf', 0x2140), # \Sigma (0x03b3, 0x03b3, 'bf', 0x213d), # \gamma (0x03c0, 0x03c0, 'bf', 0x213c), # \pi ], }, 'cal': [ (0x0041, 0x005a, 'it', 0xe22d), # A-Z ], 'circled': { 'rm': [ (0x0030, 0x0030, 'rm', 0x24ea), # 0 (0x0031, 0x0039, 'rm', 0x2460), # 1-9 (0x0041, 0x005a, 'rm', 0x24b6), # A-Z (0x0061, 0x007a, 'rm', 0x24d0) # a-z ], 'it': [ (0x0030, 0x0030, 'rm', 0x24ea), # 0 (0x0031, 0x0039, 'rm', 0x2460), # 1-9 (0x0041, 0x005a, 'it', 0x24b6), # A-Z (0x0061, 0x007a, 'it', 0x24d0) # a-z ], 'bf': [ (0x0030, 0x0030, 'bf', 0x24ea), # 0 (0x0031, 0x0039, 'bf', 0x2460), # 1-9 (0x0041, 0x005a, 'bf', 0x24b6), # A-Z (0x0061, 0x007a, 'bf', 0x24d0) # a-z ], }, 'frak': { 'rm': [ (0x0041, 0x0042, 'rm', 0x1d504), # A-B (0x0043, 0x0043, 'rm', 0x212d), # C (0x0044, 0x0047, 'rm', 0x1d507), # D-G (0x0048, 0x0048, 'rm', 0x210c), # H (0x0049, 0x0049, 'rm', 0x2111), # I (0x004a, 0x0051, 'rm', 0x1d50d), # J-Q (0x0052, 0x0052, 'rm', 0x211c), # R (0x0053, 0x0059, 'rm', 0x1d516), # S-Y (0x005a, 0x005a, 'rm', 0x2128), # Z (0x0061, 0x007a, 'rm', 0x1d51e), # a-z ], 'it': [ (0x0041, 0x0042, 'rm', 0x1d504), # A-B (0x0043, 0x0043, 'rm', 0x212d), # C (0x0044, 0x0047, 'rm', 0x1d507), # D-G (0x0048, 0x0048, 'rm', 0x210c), # H (0x0049, 0x0049, 'rm', 0x2111), # I (0x004a, 0x0051, 'rm', 0x1d50d), # J-Q (0x0052, 0x0052, 'rm', 0x211c), # R (0x0053, 0x0059, 'rm', 0x1d516), # S-Y (0x005a, 0x005a, 'rm', 0x2128), # Z (0x0061, 0x007a, 'rm', 0x1d51e), # a-z ], 'bf': [ (0x0041, 0x005a, 'bf', 0x1d56c), # A-Z (0x0061, 0x007a, 'bf', 0x1d586), # a-z ], }, 'scr': [ (0x0041, 0x0041, 'it', 0x1d49c), # A (0x0042, 0x0042, 'it', 0x212c), # B (0x0043, 0x0044, 'it', 0x1d49e), # C-D (0x0045, 0x0046, 'it', 0x2130), # E-F (0x0047, 0x0047, 'it', 0x1d4a2), # G (0x0048, 0x0048, 'it', 0x210b), # H (0x0049, 0x0049, 'it', 0x2110), # I (0x004a, 0x004b, 'it', 0x1d4a5), # J-K (0x004c, 0x004c, 'it', 0x2112), # L (0x004d, 0x003d, 'it', 0x2113), # M (0x004e, 0x0051, 'it', 0x1d4a9), # N-Q (0x0052, 0x0052, 'it', 0x211b), # R (0x0053, 0x005a, 'it', 0x1d4ae), # S-Z (0x0061, 0x0064, 'it', 0x1d4b6), # a-d (0x0065, 0x0065, 'it', 0x212f), # e (0x0066, 0x0066, 'it', 0x1d4bb), # f (0x0067, 0x0067, 'it', 0x210a), # g (0x0068, 0x006e, 'it', 0x1d4bd), # h-n (0x006f, 0x006f, 'it', 0x2134), # o (0x0070, 0x007a, 'it', 0x1d4c5), # p-z ], 'sf': { 'rm': [ (0x0030, 0x0039, 'rm', 0x1d7e2), # 0-9 (0x0041, 0x005a, 'rm', 0x1d5a0), # A-Z (0x0061, 0x007a, 'rm', 0x1d5ba), # a-z (0x0391, 0x03a9, 'rm', 0xe17d), # \Alpha-\Omega (0x03b1, 0x03c9, 'rm', 0xe196), # \alpha-\omega (0x03d1, 0x03d1, 'rm', 0xe1b0), # theta variant (0x03d5, 0x03d5, 'rm', 0xe1b1), # phi variant (0x03d6, 0x03d6, 'rm', 0xe1b3), # pi variant (0x03f1, 0x03f1, 'rm', 0xe1b2), # rho variant (0x03f5, 0x03f5, 'rm', 0xe1af), # lunate epsilon (0x2202, 0x2202, 'rm', 0xe17c), # partial differential ], 'it': [ # These numerals are actually upright. We don't actually # want italic numerals ever. (0x0030, 0x0039, 'rm', 0x1d7e2), # 0-9 (0x0041, 0x005a, 'it', 0x1d608), # A-Z (0x0061, 0x007a, 'it', 0x1d622), # a-z (0x0391, 0x03a9, 'rm', 0xe17d), # \Alpha-\Omega (0x03b1, 0x03c9, 'it', 0xe1d8), # \alpha-\omega (0x03d1, 0x03d1, 'it', 0xe1f2), # theta variant (0x03d5, 0x03d5, 'it', 0xe1f3), # phi variant (0x03d6, 0x03d6, 'it', 0xe1f5), # pi variant (0x03f1, 0x03f1, 'it', 0xe1f4), # rho variant (0x03f5, 0x03f5, 'it', 0xe1f1), # lunate epsilon ], 'bf': [ (0x0030, 0x0039, 'bf', 0x1d7ec), # 0-9 (0x0041, 0x005a, 'bf', 0x1d5d4), # A-Z (0x0061, 0x007a, 'bf', 0x1d5ee), # a-z (0x0391, 0x03a9, 'bf', 0x1d756), # \Alpha-\Omega (0x03b1, 0x03c9, 'bf', 0x1d770), # \alpha-\omega (0x03d1, 0x03d1, 'bf', 0x1d78b), # theta variant (0x03d5, 0x03d5, 'bf', 0x1d78d), # phi variant (0x03d6, 0x03d6, 'bf', 0x1d78f), # pi variant (0x03f0, 0x03f0, 'bf', 0x1d78c), # kappa variant (0x03f1, 0x03f1, 'bf', 0x1d78e), # rho variant (0x03f5, 0x03f5, 'bf', 0x1d78a), # lunate epsilon (0x2202, 0x2202, 'bf', 0x1d789), # partial differential (0x2207, 0x2207, 'bf', 0x1d76f), # \Nabla ], }, 'tt': [ (0x0030, 0x0039, 'rm', 0x1d7f6), # 0-9 (0x0041, 0x005a, 'rm', 0x1d670), # A-Z (0x0061, 0x007a, 'rm', 0x1d68a) # a-z ], }

57,988

Python

.py

2,510

20.624701

82

0.541793

numenta/nupic-legacy

6,330

1,556

464

AGPL-3.0

9/5/2024, 5:13:42 PM (Europe/Amsterdam)

26,269

units.py

numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/units.py

""" The classes here provide support for using custom classes with matplotlib, eg those that do not expose the array interface but know how to converter themselves to arrays. It also supoprts classes with units and units conversion. Use cases include converters for custom objects, eg a list of datetime objects, as well as for objects that are unit aware. We don't assume any particular units implementation, rather a units implementation must provide a ConversionInterface, and the register with the Registry converter dictionary. For example, here is a complete implementation which support plotting with native datetime objects import matplotlib.units as units import matplotlib.dates as dates import matplotlib.ticker as ticker import datetime class DateConverter(units.ConversionInterface): def convert(value, unit): 'convert value to a scalar or array' return dates.date2num(value) convert = staticmethod(convert) def axisinfo(unit): 'return major and minor tick locators and formatters' if unit!='date': return None majloc = dates.AutoDateLocator() majfmt = dates.AutoDateFormatter(majloc) return AxisInfo(majloc=majloc, majfmt=majfmt, label='date') axisinfo = staticmethod(axisinfo) def default_units(x): 'return the default unit for x or None' return 'date' default_units = staticmethod(default_units) # finally we register our object type with a converter units.registry[datetime.date] = DateConverter() """ import numpy as np from matplotlib.cbook import iterable, is_numlike class AxisInfo: 'information to support default axis labeling and tick labeling' def __init__(self, majloc=None, minloc=None, majfmt=None, minfmt=None, label=None): """ majloc and minloc: TickLocators for the major and minor ticks majfmt and minfmt: TickFormatters for the major and minor ticks label: the default axis label If any of the above are None, the axis will simply use the default """ self.majloc = majloc self.minloc = minloc self.majfmt = majfmt self.minfmt = minfmt self.label = label class ConversionInterface: """ The minimal interface for a converter to take custom instances (or sequences) and convert them to values mpl can use """ def axisinfo(unit): 'return an units.AxisInfo instance for unit' return None axisinfo = staticmethod(axisinfo) def default_units(x): 'return the default unit for x or None' return None default_units = staticmethod(default_units) def convert(obj, unit): """ convert obj using unit. If obj is a sequence, return the converted sequence. The ouput must be a sequence of scalars that can be used by the numpy array layer """ return obj convert = staticmethod(convert) def is_numlike(x): """ The matplotlib datalim, autoscaling, locators etc work with scalars which are the units converted to floats given the current unit. The converter may be passed these floats, or arrays of them, even when units are set. Derived conversion interfaces may opt to pass plain-ol unitless numbers through the conversion interface and this is a helper function for them. """ if iterable(x): for thisx in x: return is_numlike(thisx) else: return is_numlike(x) is_numlike = staticmethod(is_numlike) class Registry(dict): """ register types with conversion interface """ def __init__(self): dict.__init__(self) self._cached = {} def get_converter(self, x): 'get the converter interface instance for x, or None' if not len(self): return None # nothing registered #DISABLED idx = id(x) #DISABLED cached = self._cached.get(idx) #DISABLED if cached is not None: return cached converter = None classx = getattr(x, '__class__', None) if classx is not None: converter = self.get(classx) if converter is None and iterable(x): # if this is anything but an object array, we'll assume # there are no custom units if isinstance(x, np.ndarray) and x.dtype != np.object: return None for thisx in x: converter = self.get_converter( thisx ) return converter #DISABLED self._cached[idx] = converter return converter registry = Registry()

4,810

Python

.py

118

32.415254

74

0.658165

numenta/nupic-legacy

6,330

1,556

464

AGPL-3.0

9/5/2024, 5:13:42 PM (Europe/Amsterdam)

26,270

contour.py

numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/contour.py

""" These are classes to support contour plotting and labelling for the axes class """ from __future__ import division import warnings import matplotlib as mpl import numpy as np from numpy import ma import matplotlib._cntr as _cntr import matplotlib.path as path import matplotlib.ticker as ticker import matplotlib.cm as cm import matplotlib.colors as colors import matplotlib.collections as collections import matplotlib.font_manager as font_manager import matplotlib.text as text import matplotlib.cbook as cbook import matplotlib.mlab as mlab # Import needed for adding manual selection capability to clabel from matplotlib.blocking_input import BlockingContourLabeler # We can't use a single line collection for contour because a line # collection can have only a single line style, and we want to be able to have # dashed negative contours, for example, and solid positive contours. # We could use a single polygon collection for filled contours, but it # seems better to keep line and filled contours similar, with one collection # per level. class ContourLabeler: '''Mixin to provide labelling capability to ContourSet''' def clabel(self, *args, **kwargs): """ call signature:: clabel(cs, **kwargs) adds labels to line contours in *cs*, where *cs* is a :class:`~matplotlib.contour.ContourSet` object returned by contour. :: clabel(cs, v, **kwargs) only labels contours listed in *v*. Optional keyword arguments: *fontsize*: See http://matplotlib.sf.net/fonts.html *colors*: - if *None*, the color of each label matches the color of the corresponding contour - if one string color, e.g. *colors* = 'r' or *colors* = 'red', all labels will be plotted in this color - if a tuple of matplotlib color args (string, float, rgb, etc), different labels will be plotted in different colors in the order specified *inline*: controls whether the underlying contour is removed or not. Default is *True*. *inline_spacing*: space in pixels to leave on each side of label when placing inline. Defaults to 5. This spacing will be exact for labels at locations where the contour is straight, less so for labels on curved contours. *fmt*: a format string for the label. Default is '%1.3f' Alternatively, this can be a dictionary matching contour levels with arbitrary strings to use for each contour level (i.e., fmt[level]=string) *manual*: if *True*, contour labels will be placed manually using mouse clicks. Click the first button near a contour to add a label, click the second button (or potentially both mouse buttons at once) to finish adding labels. The third button can be used to remove the last label added, but only if labels are not inline. Alternatively, the keyboard can be used to select label locations (enter to end label placement, delete or backspace act like the third mouse button, and any other key will select a label location). .. plot:: mpl_examples/pylab_examples/contour_demo.py """ """ NOTES on how this all works: clabel basically takes the input arguments and uses them to add a list of "label specific" attributes to the ContourSet object. These attributes are all of the form label* and names should be fairly self explanatory. Once these attributes are set, clabel passes control to the labels method (case of automatic label placement) or BlockingContourLabeler (case of manual label placement). """ fontsize = kwargs.get('fontsize', None) inline = kwargs.get('inline', 1) inline_spacing = kwargs.get('inline_spacing', 5) self.labelFmt = kwargs.get('fmt', '%1.3f') _colors = kwargs.get('colors', None) # Detect if manual selection is desired and remove from argument list self.labelManual=kwargs.get('manual',False) if len(args) == 0: levels = self.levels indices = range(len(self.levels)) elif len(args) == 1: levlabs = list(args[0]) indices, levels = [], [] for i, lev in enumerate(self.levels): if lev in levlabs: indices.append(i) levels.append(lev) if len(levels) < len(levlabs): msg = "Specified levels " + str(levlabs) msg += "\n don't match available levels " msg += str(self.levels) raise ValueError(msg) else: raise TypeError("Illegal arguments to clabel, see help(clabel)") self.labelLevelList = levels self.labelIndiceList = indices self.labelFontProps = font_manager.FontProperties() if fontsize == None: font_size = int(self.labelFontProps.get_size_in_points()) else: if type(fontsize) not in [int, float, str]: raise TypeError("Font size must be an integer number.") # Can't it be floating point, as indicated in line above? else: if type(fontsize) == str: font_size = int(self.labelFontProps.get_size_in_points()) else: self.labelFontProps.set_size(fontsize) font_size = fontsize self.labelFontSizeList = [font_size] * len(levels) if _colors == None: self.labelMappable = self self.labelCValueList = np.take(self.cvalues, self.labelIndiceList) else: cmap = colors.ListedColormap(_colors, N=len(self.labelLevelList)) self.labelCValueList = range(len(self.labelLevelList)) self.labelMappable = cm.ScalarMappable(cmap = cmap, norm = colors.NoNorm()) #self.labelTexts = [] # Initialized in ContourSet.__init__ #self.labelCValues = [] # same self.labelXYs = [] if self.labelManual: print 'Select label locations manually using first mouse button.' print 'End manual selection with second mouse button.' if not inline: print 'Remove last label by clicking third mouse button.' blocking_contour_labeler = BlockingContourLabeler(self) blocking_contour_labeler(inline,inline_spacing) else: self.labels(inline,inline_spacing) # Hold on to some old attribute names. These are depricated and will # be removed in the near future (sometime after 2008-08-01), but keeping # for now for backwards compatibility self.cl = self.labelTexts self.cl_xy = self.labelXYs self.cl_cvalues = self.labelCValues self.labelTextsList = cbook.silent_list('text.Text', self.labelTexts) return self.labelTextsList def print_label(self, linecontour,labelwidth): "if contours are too short, don't plot a label" lcsize = len(linecontour) if lcsize > 10 * labelwidth: return 1 xmax = np.amax(linecontour[:,0]) xmin = np.amin(linecontour[:,0]) ymax = np.amax(linecontour[:,1]) ymin = np.amin(linecontour[:,1]) lw = labelwidth if (xmax - xmin) > 1.2* lw or (ymax - ymin) > 1.2 * lw: return 1 else: return 0 def too_close(self, x,y, lw): "if there's a label already nearby, find a better place" if self.labelXYs != []: dist = [np.sqrt((x-loc[0]) ** 2 + (y-loc[1]) ** 2) for loc in self.labelXYs] for d in dist: if d < 1.2*lw: return 1 else: return 0 else: return 0 def get_label_coords(self, distances, XX, YY, ysize, lw): """ labels are ploted at a location with the smallest dispersion of the contour from a straight line unless there's another label nearby, in which case the second best place on the contour is picked up if there's no good place a label isplotted at the beginning of the contour """ hysize = int(ysize/2) adist = np.argsort(distances) for ind in adist: x, y = XX[ind][hysize], YY[ind][hysize] if self.too_close(x,y, lw): continue else: return x,y, ind ind = adist[0] x, y = XX[ind][hysize], YY[ind][hysize] return x,y, ind def get_label_width(self, lev, fmt, fsize): "get the width of the label in points" if cbook.is_string_like(lev): lw = (len(lev)) * fsize else: lw = (len(self.get_text(lev,fmt))) * fsize return lw def get_real_label_width( self, lev, fmt, fsize ): """ This computes actual onscreen label width. This uses some black magic to determine onscreen extent of non-drawn label. This magic may not be very robust. """ # Find middle of axes xx = np.mean( np.asarray(self.ax.axis()).reshape(2,2), axis=1 ) # Temporarily create text object t = text.Text( xx[0], xx[1] ) self.set_label_props( t, self.get_text(lev,fmt), 'k' ) # Some black magic to get onscreen extent # NOTE: This will only work for already drawn figures, as the canvas # does not have a renderer otherwise. This is the reason this function # can't be integrated into the rest of the code. bbox = t.get_window_extent(renderer=self.ax.figure.canvas.renderer) # difference in pixel extent of image lw = np.diff(bbox.corners()[0::2,0])[0] return lw def set_label_props(self, label, text, color): "set the label properties - color, fontsize, text" label.set_text(text) label.set_color(color) label.set_fontproperties(self.labelFontProps) label.set_clip_box(self.ax.bbox) def get_text(self, lev, fmt): "get the text of the label" if cbook.is_string_like(lev): return lev else: if isinstance(fmt,dict): return fmt[lev] else: return fmt%lev def locate_label(self, linecontour, labelwidth): """find a good place to plot a label (relatively flat part of the contour) and the angle of rotation for the text object """ nsize= len(linecontour) if labelwidth > 1: xsize = int(np.ceil(nsize/labelwidth)) else: xsize = 1 if xsize == 1: ysize = nsize else: ysize = labelwidth XX = np.resize(linecontour[:,0],(xsize, ysize)) YY = np.resize(linecontour[:,1],(xsize, ysize)) #I might have fouled up the following: yfirst = YY[:,0].reshape(xsize, 1) ylast = YY[:,-1].reshape(xsize, 1) xfirst = XX[:,0].reshape(xsize, 1) xlast = XX[:,-1].reshape(xsize, 1) s = (yfirst-YY) * (xlast-xfirst) - (xfirst-XX) * (ylast-yfirst) L = np.sqrt((xlast-xfirst)**2+(ylast-yfirst)**2).ravel() dist = np.add.reduce(([(abs(s)[i]/L[i]) for i in range(xsize)]),-1) x,y,ind = self.get_label_coords(dist, XX, YY, ysize, labelwidth) #print 'ind, x, y', ind, x, y # There must be a more efficient way... lc = [tuple(l) for l in linecontour] dind = lc.index((x,y)) #print 'dind', dind #dind = list(linecontour).index((x,y)) return x, y, dind def calc_label_rot_and_inline( self, slc, ind, lw, lc=None, spacing=5 ): """ This function calculates the appropriate label rotation given the linecontour coordinates in screen units, the index of the label location and the label width. It will also break contour and calculate inlining if *lc* is not empty (lc defaults to the empty list if None). *spacing* is the space around the label in pixels to leave empty. Do both of these tasks at once to avoid calling mlab.path_length multiple times, which is relatively costly. The method used here involves calculating the path length along the contour in pixel coordinates and then looking approximately label width / 2 away from central point to determine rotation and then to break contour if desired. """ if lc is None: lc = [] # Half the label width hlw = lw/2.0 # Check if closed and, if so, rotate contour so label is at edge closed = mlab.is_closed_polygon(slc) if closed: slc = np.r_[ slc[ind:-1], slc[:ind+1] ] if len(lc): # Rotate lc also if not empty lc = np.r_[ lc[ind:-1], lc[:ind+1] ] ind = 0 # Path length in pixel space pl = mlab.path_length(slc) pl = pl-pl[ind] # Use linear interpolation to get points around label xi = np.array( [ -hlw, hlw ] ) if closed: # Look at end also for closed contours dp = np.array([pl[-1],0]) else: dp = np.zeros_like(xi) ll = mlab.less_simple_linear_interpolation( pl, slc, dp+xi, extrap=True ) # get vector in pixel space coordinates from one point to other dd = np.diff( ll, axis=0 ).ravel() # Get angle of vector - must be calculated in pixel space for # text rotation to work correctly if np.all(dd==0): # Must deal with case of zero length label rotation = 0.0 else: rotation = np.arctan2(dd[1], dd[0]) * 180.0 / np.pi # Fix angle so text is never upside-down if rotation > 90: rotation = rotation - 180.0 if rotation < -90: rotation = 180.0 + rotation # Break contour if desired nlc = [] if len(lc): # Expand range by spacing xi = dp + xi + np.array([-spacing,spacing]) # Get indices near points of interest I = mlab.less_simple_linear_interpolation( pl, np.arange(len(pl)), xi, extrap=False ) # If those indices aren't beyond contour edge, find x,y if (not np.isnan(I[0])) and int(I[0])<>I[0]: xy1 = mlab.less_simple_linear_interpolation( pl, lc, [ xi[0] ] ) if (not np.isnan(I[1])) and int(I[1])<>I[1]: xy2 = mlab.less_simple_linear_interpolation( pl, lc, [ xi[1] ] ) # Make integer I = [ np.floor(I[0]), np.ceil(I[1]) ] # Actually break contours if closed: # This will remove contour if shorter than label if np.all(~np.isnan(I)): nlc.append( np.r_[ xy2, lc[I[1]:I[0]+1], xy1 ] ) else: # These will remove pieces of contour if they have length zero if not np.isnan(I[0]): nlc.append( np.r_[ lc[:I[0]+1], xy1 ] ) if not np.isnan(I[1]): nlc.append( np.r_[ xy2, lc[I[1]:] ] ) # The current implementation removes contours completely # covered by labels. Uncomment line below to keep # original contour if this is the preferred behavoir. #if not len(nlc): nlc = [ lc ] return (rotation,nlc) def add_label(self,x,y,rotation,lev,cvalue): dx,dy = self.ax.transData.inverted().transform_point((x,y)) t = text.Text(dx, dy, rotation = rotation, horizontalalignment='center', verticalalignment='center') color = self.labelMappable.to_rgba(cvalue,alpha=self.alpha) _text = self.get_text(lev,self.labelFmt) self.set_label_props(t, _text, color) self.labelTexts.append(t) self.labelCValues.append(cvalue) self.labelXYs.append((x,y)) # Add label to plot here - useful for manual mode label selection self.ax.add_artist(t) def pop_label(self,index=-1): '''Defaults to removing last label, but any index can be supplied''' self.labelCValues.pop(index) t = self.labelTexts.pop(index) t.remove() def labels(self, inline, inline_spacing): trans = self.ax.transData # A bit of shorthand for icon, lev, fsize, cvalue in zip( self.labelIndiceList, self.labelLevelList, self.labelFontSizeList, self.labelCValueList ): con = self.collections[icon] lw = self.get_label_width(lev, self.labelFmt, fsize) additions = [] paths = con.get_paths() for segNum, linepath in enumerate(paths): lc = linepath.vertices # Line contour slc0 = trans.transform(lc) # Line contour in screen coords # For closed polygons, add extra point to avoid division by # zero in print_label and locate_label. Other than these # functions, this is not necessary and should probably be # eventually removed. if mlab.is_closed_polygon( lc ): slc = np.r_[ slc0, slc0[1:2,:] ] else: slc = slc0 if self.print_label(slc,lw): # Check if long enough for a label x,y,ind = self.locate_label(slc, lw) if inline: lcarg = lc else: lcarg = None rotation,new=self.calc_label_rot_and_inline( slc0, ind, lw, lcarg, inline_spacing ) # Actually add the label self.add_label(x,y,rotation,lev,cvalue) # If inline, add new contours if inline: for n in new: # Add path if not empty or single point if len(n)>1: additions.append( path.Path(n) ) else: # If not adding label, keep old path additions.append(linepath) # After looping over all segments on a contour, remove old # paths and add new ones if inlining if inline: del paths[:] paths.extend(additions) class ContourSet(cm.ScalarMappable, ContourLabeler): """ Create and store a set of contour lines or filled regions. User-callable method: clabel Useful attributes: ax: the axes object in which the contours are drawn collections: a silent_list of LineCollections or PolyCollections levels: contour levels layers: same as levels for line contours; half-way between levels for filled contours. See _process_colors method. """ def __init__(self, ax, *args, **kwargs): """ Draw contour lines or filled regions, depending on whether keyword arg 'filled' is False (default) or True. The first argument of the initializer must be an axes object. The remaining arguments and keyword arguments are described in ContourSet.contour_doc. """ self.ax = ax self.levels = kwargs.get('levels', None) self.filled = kwargs.get('filled', False) self.linewidths = kwargs.get('linewidths', None) self.linestyles = kwargs.get('linestyles', 'solid') self.alpha = kwargs.get('alpha', 1.0) self.origin = kwargs.get('origin', None) self.extent = kwargs.get('extent', None) cmap = kwargs.get('cmap', None) self.colors = kwargs.get('colors', None) norm = kwargs.get('norm', None) self.extend = kwargs.get('extend', 'neither') self.antialiased = kwargs.get('antialiased', True) self.nchunk = kwargs.get('nchunk', 0) self.locator = kwargs.get('locator', None) if (isinstance(norm, colors.LogNorm) or isinstance(self.locator, ticker.LogLocator)): self.logscale = True if norm is None: norm = colors.LogNorm() if self.extend is not 'neither': raise ValueError('extend kwarg does not work yet with log scale') else: self.logscale = False if self.origin is not None: assert(self.origin in ['lower', 'upper', 'image']) if self.extent is not None: assert(len(self.extent) == 4) if cmap is not None: assert(isinstance(cmap, colors.Colormap)) if self.colors is not None and cmap is not None: raise ValueError('Either colors or cmap must be None') if self.origin == 'image': self.origin = mpl.rcParams['image.origin'] x, y, z = self._contour_args(*args) # also sets self.levels, # self.layers if self.colors is not None: cmap = colors.ListedColormap(self.colors, N=len(self.layers)) if self.filled: self.collections = cbook.silent_list('collections.PolyCollection') else: self.collections = cbook.silent_list('collections.LineCollection') # label lists must be initialized here self.labelTexts = [] self.labelCValues = [] kw = {'cmap': cmap} if norm is not None: kw['norm'] = norm cm.ScalarMappable.__init__(self, **kw) # sets self.cmap; self._process_colors() _mask = ma.getmask(z) if _mask is ma.nomask: _mask = None if self.filled: if self.linewidths is not None: warnings.warn('linewidths is ignored by contourf') C = _cntr.Cntr(x, y, z.filled(), _mask) lowers = self._levels[:-1] uppers = self._levels[1:] for level, level_upper in zip(lowers, uppers): nlist = C.trace(level, level_upper, points = 0, nchunk = self.nchunk) col = collections.PolyCollection(nlist, antialiaseds = (self.antialiased,), edgecolors= 'none', alpha=self.alpha) self.ax.add_collection(col) self.collections.append(col) else: tlinewidths = self._process_linewidths() self.tlinewidths = tlinewidths tlinestyles = self._process_linestyles() C = _cntr.Cntr(x, y, z.filled(), _mask) for level, width, lstyle in zip(self.levels, tlinewidths, tlinestyles): nlist = C.trace(level, points = 0) col = collections.LineCollection(nlist, linewidths = width, linestyle = lstyle, alpha=self.alpha) if level < 0.0 and self.monochrome: ls = mpl.rcParams['contour.negative_linestyle'] col.set_linestyle(ls) col.set_label('_nolegend_') self.ax.add_collection(col, False) self.collections.append(col) self.changed() # set the colors x0 = ma.minimum(x) x1 = ma.maximum(x) y0 = ma.minimum(y) y1 = ma.maximum(y) self.ax.update_datalim([(x0,y0), (x1,y1)]) self.ax.autoscale_view() def changed(self): tcolors = [ (tuple(rgba),) for rgba in self.to_rgba(self.cvalues, alpha=self.alpha)] self.tcolors = tcolors for color, collection in zip(tcolors, self.collections): collection.set_alpha(self.alpha) collection.set_color(color) for label, cv in zip(self.labelTexts, self.labelCValues): label.set_alpha(self.alpha) label.set_color(self.labelMappable.to_rgba(cv)) # add label colors cm.ScalarMappable.changed(self) def _autolev(self, z, N): ''' Select contour levels to span the data. We need two more levels for filled contours than for line contours, because for the latter we need to specify the lower and upper boundary of each range. For example, a single contour boundary, say at z = 0, requires only one contour line, but two filled regions, and therefore three levels to provide boundaries for both regions. ''' if self.locator is None: if self.logscale: self.locator = ticker.LogLocator() else: self.locator = ticker.MaxNLocator(N+1) self.locator.create_dummy_axis() zmax = self.zmax zmin = self.zmin self.locator.set_bounds(zmin, zmax) lev = self.locator() zmargin = (zmax - zmin) * 0.000001 # so z < (zmax + zmargin) if zmax >= lev[-1]: lev[-1] += zmargin if zmin <= lev[0]: if self.logscale: lev[0] = 0.99 * zmin else: lev[0] -= zmargin self._auto = True if self.filled: return lev return lev[1:-1] def _initialize_x_y(self, z): ''' Return X, Y arrays such that contour(Z) will match imshow(Z) if origin is not None. The center of pixel Z[i,j] depends on origin: if origin is None, x = j, y = i; if origin is 'lower', x = j + 0.5, y = i + 0.5; if origin is 'upper', x = j + 0.5, y = Nrows - i - 0.5 If extent is not None, x and y will be scaled to match, as in imshow. If origin is None and extent is not None, then extent will give the minimum and maximum values of x and y. ''' if z.ndim != 2: raise TypeError("Input must be a 2D array.") else: Ny, Nx = z.shape if self.origin is None: # Not for image-matching. if self.extent is None: return np.meshgrid(np.arange(Nx), np.arange(Ny)) else: x0,x1,y0,y1 = self.extent x = np.linspace(x0, x1, Nx) y = np.linspace(y0, y1, Ny) return np.meshgrid(x, y) # Match image behavior: if self.extent is None: x0,x1,y0,y1 = (0, Nx, 0, Ny) else: x0,x1,y0,y1 = self.extent dx = float(x1 - x0)/Nx dy = float(y1 - y0)/Ny x = x0 + (np.arange(Nx) + 0.5) * dx y = y0 + (np.arange(Ny) + 0.5) * dy if self.origin == 'upper': y = y[::-1] return np.meshgrid(x,y) def _check_xyz(self, args): ''' For functions like contour, check that the dimensions of the input arrays match; if x and y are 1D, convert them to 2D using meshgrid. Possible change: I think we should make and use an ArgumentError Exception class (here and elsewhere). ''' # We can strip away the x and y units x = self.ax.convert_xunits( args[0] ) y = self.ax.convert_yunits( args[1] ) x = np.asarray(x, dtype=np.float64) y = np.asarray(y, dtype=np.float64) z = ma.asarray(args[2], dtype=np.float64) if z.ndim != 2: raise TypeError("Input z must be a 2D array.") else: Ny, Nx = z.shape if x.shape == z.shape and y.shape == z.shape: return x,y,z if x.ndim != 1 or y.ndim != 1: raise TypeError("Inputs x and y must be 1D or 2D.") nx, = x.shape ny, = y.shape if nx != Nx or ny != Ny: raise TypeError("Length of x must be number of columns in z,\n" + "and length of y must be number of rows.") x,y = np.meshgrid(x,y) return x,y,z def _contour_args(self, *args): if self.filled: fn = 'contourf' else: fn = 'contour' Nargs = len(args) if Nargs <= 2: z = ma.asarray(args[0], dtype=np.float64) x, y = self._initialize_x_y(z) elif Nargs <=4: x,y,z = self._check_xyz(args[:3]) else: raise TypeError("Too many arguments to %s; see help(%s)" % (fn,fn)) self.zmax = ma.maximum(z) self.zmin = ma.minimum(z) if self.logscale and self.zmin <= 0: z = ma.masked_where(z <= 0, z) warnings.warn('Log scale: values of z <=0 have been masked') self.zmin = z.min() self._auto = False if self.levels is None: if Nargs == 1 or Nargs == 3: lev = self._autolev(z, 7) else: # 2 or 4 args level_arg = args[-1] try: if type(level_arg) == int: lev = self._autolev(z, level_arg) else: lev = np.asarray(level_arg).astype(np.float64) except: raise TypeError( "Last %s arg must give levels; see help(%s)" % (fn,fn)) if self.filled and len(lev) < 2: raise ValueError("Filled contours require at least 2 levels.") # Workaround for cntr.c bug wrt masked interior regions: #if filled: # z = ma.masked_array(z.filled(-1e38)) # It's not clear this is any better than the original bug. self.levels = lev #if self._auto and self.extend in ('both', 'min', 'max'): # raise TypeError("Auto level selection is inconsistent " # + "with use of 'extend' kwarg") self._levels = list(self.levels) if self.extend in ('both', 'min'): self._levels.insert(0, min(self.levels[0],self.zmin) - 1) if self.extend in ('both', 'max'): self._levels.append(max(self.levels[-1],self.zmax) + 1) self._levels = np.asarray(self._levels) self.vmin = np.amin(self.levels) # alternative would be self.layers self.vmax = np.amax(self.levels) if self.extend in ('both', 'min'): self.vmin = 2 * self.levels[0] - self.levels[1] if self.extend in ('both', 'max'): self.vmax = 2 * self.levels[-1] - self.levels[-2] self.layers = self._levels # contour: a line is a thin layer if self.filled: self.layers = 0.5 * (self._levels[:-1] + self._levels[1:]) if self.extend in ('both', 'min'): self.layers[0] = 0.5 * (self.vmin + self._levels[1]) if self.extend in ('both', 'max'): self.layers[-1] = 0.5 * (self.vmax + self._levels[-2]) return (x, y, z) def _process_colors(self): """ Color argument processing for contouring. Note that we base the color mapping on the contour levels, not on the actual range of the Z values. This means we don't have to worry about bad values in Z, and we always have the full dynamic range available for the selected levels. The color is based on the midpoint of the layer, except for an extended end layers. """ self.monochrome = self.cmap.monochrome if self.colors is not None: i0, i1 = 0, len(self.layers) if self.extend in ('both', 'min'): i0 = -1 if self.extend in ('both', 'max'): i1 = i1 + 1 self.cvalues = range(i0, i1) self.set_norm(colors.NoNorm()) else: self.cvalues = self.layers if not self.norm.scaled(): self.set_clim(self.vmin, self.vmax) if self.extend in ('both', 'max', 'min'): self.norm.clip = False self.set_array(self.layers) # self.tcolors are set by the "changed" method def _process_linewidths(self): linewidths = self.linewidths Nlev = len(self.levels) if linewidths is None: tlinewidths = [(mpl.rcParams['lines.linewidth'],)] *Nlev else: if cbook.iterable(linewidths) and len(linewidths) < Nlev: linewidths = list(linewidths) * int(np.ceil(Nlev/len(linewidths))) elif not cbook.iterable(linewidths) and type(linewidths) in [int, float]: linewidths = [linewidths] * Nlev tlinewidths = [(w,) for w in linewidths] return tlinewidths def _process_linestyles(self): linestyles = self.linestyles Nlev = len(self.levels) if linestyles is None: tlinestyles = ['solid'] * Nlev else: if cbook.is_string_like(linestyles): tlinestyles = [linestyles] * Nlev elif cbook.iterable(linestyles) and len(linestyles) <= Nlev: tlinestyles = list(linestyles) * int(np.ceil(Nlev/len(linestyles))) return tlinestyles def get_alpha(self): '''returns alpha to be applied to all ContourSet artists''' return self.alpha def set_alpha(self, alpha): '''sets alpha for all ContourSet artists''' self.alpha = alpha self.changed() contour_doc = """ :func:`~matplotlib.pyplot.contour` and :func:`~matplotlib.pyplot.contourf` draw contour lines and filled contours, respectively. Except as noted, function signatures and return values are the same for both versions. :func:`~matplotlib.pyplot.contourf` differs from the Matlab (TM) version in that it does not draw the polygon edges, because the contouring engine yields simply connected regions with branch cuts. To draw the edges, add line contours with calls to :func:`~matplotlib.pyplot.contour`. call signatures:: contour(Z) make a contour plot of an array *Z*. The level values are chosen automatically. :: contour(X,Y,Z) *X*, *Y* specify the (*x*, *y*) coordinates of the surface :: contour(Z,N) contour(X,Y,Z,N) contour *N* automatically-chosen levels. :: contour(Z,V) contour(X,Y,Z,V) draw contour lines at the values specified in sequence *V* :: contourf(..., V) fill the (len(*V*)-1) regions between the values in *V* :: contour(Z, **kwargs) Use keyword args to control colors, linewidth, origin, cmap ... see below for more details. *X*, *Y*, and *Z* must be arrays with the same dimensions. *Z* may be a masked array, but filled contouring may not handle internal masked regions correctly. ``C = contour(...)`` returns a :class:`~matplotlib.contour.ContourSet` object. Optional keyword arguments: *colors*: [ None | string | (mpl_colors) ] If *None*, the colormap specified by cmap will be used. If a string, like 'r' or 'red', all levels will be plotted in this color. If a tuple of matplotlib color args (string, float, rgb, etc), different levels will be plotted in different colors in the order specified. *alpha*: float The alpha blending value *cmap*: [ None | Colormap ] A cm :class:`~matplotlib.cm.Colormap` instance or *None*. If *cmap* is *None* and *colors* is *None*, a default Colormap is used. *norm*: [ None | Normalize ] A :class:`matplotlib.colors.Normalize` instance for scaling data values to colors. If *norm* is *None* and *colors* is *None*, the default linear scaling is used. *origin*: [ None | 'upper' | 'lower' | 'image' ] If *None*, the first value of *Z* will correspond to the lower left corner, location (0,0). If 'image', the rc value for ``image.origin`` will be used. This keyword is not active if *X* and *Y* are specified in the call to contour. *extent*: [ None | (x0,x1,y0,y1) ] If *origin* is not *None*, then *extent* is interpreted as in :func:`matplotlib.pyplot.imshow`: it gives the outer pixel boundaries. In this case, the position of Z[0,0] is the center of the pixel, not a corner. If *origin* is *None*, then (*x0*, *y0*) is the position of Z[0,0], and (*x1*, *y1*) is the position of Z[-1,-1]. This keyword is not active if *X* and *Y* are specified in the call to contour. *locator*: [ None | ticker.Locator subclass ] If *locator* is None, the default :class:`~matplotlib.ticker.MaxNLocator` is used. The locator is used to determine the contour levels if they are not given explicitly via the *V* argument. *extend*: [ 'neither' | 'both' | 'min' | 'max' ] Unless this is 'neither', contour levels are automatically added to one or both ends of the range so that all data are included. These added ranges are then mapped to the special colormap values which default to the ends of the colormap range, but can be set via :meth:`matplotlib.cm.Colormap.set_under` and :meth:`matplotlib.cm.Colormap.set_over` methods. contour-only keyword arguments: *linewidths*: [ None | number | tuple of numbers ] If *linewidths* is *None*, the default width in ``lines.linewidth`` in ``matplotlibrc`` is used. If a number, all levels will be plotted with this linewidth. If a tuple, different levels will be plotted with different linewidths in the order specified *linestyles*: [None | 'solid' | 'dashed' | 'dashdot' | 'dotted' ] If *linestyles* is *None*, the 'solid' is used. *linestyles* can also be an iterable of the above strings specifying a set of linestyles to be used. If this iterable is shorter than the number of contour levels it will be repeated as necessary. If contour is using a monochrome colormap and the contour level is less than 0, then the linestyle specified in ``contour.negative_linestyle`` in ``matplotlibrc`` will be used. contourf-only keyword arguments: *antialiased*: [ True | False ] enable antialiasing *nchunk*: [ 0 | integer ] If 0, no subdivision of the domain. Specify a positive integer to divide the domain into subdomains of roughly *nchunk* by *nchunk* points. This may never actually be advantageous, so this option may be removed. Chunking introduces artifacts at the chunk boundaries unless *antialiased* is *False*. **Example:** .. plot:: mpl_examples/pylab_examples/contour_demo.py """ def find_nearest_contour( self, x, y, indices=None, pixel=True ): """ Finds contour that is closest to a point. Defaults to measuring distance in pixels (screen space - useful for manual contour labeling), but this can be controlled via a keyword argument. Returns a tuple containing the contour, segment, index of segment, x & y of segment point and distance to minimum point. Call signature:: conmin,segmin,imin,xmin,ymin,dmin = find_nearest_contour( self, x, y, indices=None, pixel=True ) Optional keyword arguments:: *indices*: Indexes of contour levels to consider when looking for nearest point. Defaults to using all levels. *pixel*: If *True*, measure distance in pixel space, if not, measure distance in axes space. Defaults to *True*. """ # This function uses a method that is probably quite # inefficient based on converting each contour segment to # pixel coordinates and then comparing the given point to # those coordinates for each contour. This will probably be # quite slow for complex contours, but for normal use it works # sufficiently well that the time is not noticeable. # Nonetheless, improvements could probably be made. if indices==None: indices = range(len(self.levels)) dmin = 1e10 conmin = None segmin = None xmin = None ymin = None for icon in indices: con = self.collections[icon] paths = con.get_paths() for segNum, linepath in enumerate(paths): lc = linepath.vertices # transfer all data points to screen coordinates if desired if pixel: lc = self.ax.transData.transform(lc) ds = (lc[:,0]-x)**2 + (lc[:,1]-y)**2 d = min( ds ) if d < dmin: dmin = d conmin = icon segmin = segNum imin = mpl.mlab.find( ds == d )[0] xmin = lc[imin,0] ymin = lc[imin,1] return (conmin,segmin,imin,xmin,ymin,dmin)

42,063

Python

.py

912

34.394737

85

0.573364

numenta/nupic-legacy

6,330

1,556

464

AGPL-3.0

9/5/2024, 5:13:42 PM (Europe/Amsterdam)

26,271

widgets.py

numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/widgets.py

""" GUI Neutral widgets All of these widgets require you to predefine an Axes instance and pass that as the first arg. matplotlib doesn't try to be too smart in layout -- you have to figure out how wide and tall you want your Axes to be to accommodate your widget. """ import numpy as np from mlab import dist from patches import Circle, Rectangle from lines import Line2D from transforms import blended_transform_factory class LockDraw: """ some widgets, like the cursor, draw onto the canvas, and this is not desirable under all circumstaces, like when the toolbar is in zoom-to-rect mode and drawing a rectangle. The module level "lock" allows someone to grab the lock and prevent other widgets from drawing. Use matplotlib.widgets.lock(someobj) to pr """ def __init__(self): self._owner = None def __call__(self, o): 'reserve the lock for o' if not self.available(o): raise ValueError('already locked') self._owner = o def release(self, o): 'release the lock' if not self.available(o): raise ValueError('you do not own this lock') self._owner = None def available(self, o): 'drawing is available to o' return not self.locked() or self.isowner(o) def isowner(self, o): 'o owns the lock' return self._owner is o def locked(self): 'the lock is held' return self._owner is not None class Widget: """ OK, I couldn't resist; abstract base class for mpl GUI neutral widgets """ drawon = True eventson = True class Button(Widget): """ A GUI neutral button The following attributes are accesible ax - the Axes the button renders into label - a text.Text instance color - the color of the button when not hovering hovercolor - the color of the button when hovering Call "on_clicked" to connect to the button """ def __init__(self, ax, label, image=None, color='0.85', hovercolor='0.95'): """ ax is the Axes instance the button will be placed into label is a string which is the button text image if not None, is an image to place in the button -- can be any legal arg to imshow (numpy array, matplotlib Image instance, or PIL image) color is the color of the button when not activated hovercolor is the color of the button when the mouse is over it """ if image is not None: ax.imshow(image) self.label = ax.text(0.5, 0.5, label, verticalalignment='center', horizontalalignment='center', transform=ax.transAxes) self.cnt = 0 self.observers = {} self.ax = ax ax.figure.canvas.mpl_connect('button_press_event', self._click) ax.figure.canvas.mpl_connect('motion_notify_event', self._motion) ax.set_navigate(False) ax.set_axis_bgcolor(color) ax.set_xticks([]) ax.set_yticks([]) self.color = color self.hovercolor = hovercolor self._lastcolor = color def _click(self, event): if event.inaxes != self.ax: return if not self.eventson: return for cid, func in self.observers.items(): func(event) def _motion(self, event): if event.inaxes==self.ax: c = self.hovercolor else: c = self.color if c != self._lastcolor: self.ax.set_axis_bgcolor(c) self._lastcolor = c if self.drawon: self.ax.figure.canvas.draw() def on_clicked(self, func): """ When the button is clicked, call this func with event A connection id is returned which can be used to disconnect """ cid = self.cnt self.observers[cid] = func self.cnt += 1 return cid def disconnect(self, cid): 'remove the observer with connection id cid' try: del self.observers[cid] except KeyError: pass class Slider(Widget): """ A slider representing a floating point range The following attributes are defined ax : the slider axes.Axes instance val : the current slider value vline : a Line2D instance representing the initial value poly : A patch.Polygon instance which is the slider valfmt : the format string for formatting the slider text label : a text.Text instance, the slider label closedmin : whether the slider is closed on the minimum closedmax : whether the slider is closed on the maximum slidermin : another slider - if not None, this slider must be > slidermin slidermax : another slider - if not None, this slider must be < slidermax dragging : allow for mouse dragging on slider Call on_changed to connect to the slider event """ def __init__(self, ax, label, valmin, valmax, valinit=0.5, valfmt='%1.2f', closedmin=True, closedmax=True, slidermin=None, slidermax=None, dragging=True, **kwargs): """ Create a slider from valmin to valmax in axes ax; valinit - the slider initial position label - the slider label valfmt - used to format the slider value closedmin and closedmax - indicate whether the slider interval is closed slidermin and slidermax - be used to contrain the value of this slider to the values of other sliders. additional kwargs are passed on to self.poly which is the matplotlib.patches.Rectangle which draws the slider. See the matplotlib.patches.Rectangle documentation for legal property names (eg facecolor, edgecolor, alpha, ...) """ self.ax = ax self.valmin = valmin self.valmax = valmax self.val = valinit self.valinit = valinit self.poly = ax.axvspan(valmin,valinit,0,1, **kwargs) self.vline = ax.axvline(valinit,0,1, color='r', lw=1) self.valfmt=valfmt ax.set_yticks([]) ax.set_xlim((valmin, valmax)) ax.set_xticks([]) ax.set_navigate(False) ax.figure.canvas.mpl_connect('button_press_event', self._update) if dragging: ax.figure.canvas.mpl_connect('motion_notify_event', self._update) self.label = ax.text(-0.02, 0.5, label, transform=ax.transAxes, verticalalignment='center', horizontalalignment='right') self.valtext = ax.text(1.02, 0.5, valfmt%valinit, transform=ax.transAxes, verticalalignment='center', horizontalalignment='left') self.cnt = 0 self.observers = {} self.closedmin = closedmin self.closedmax = closedmax self.slidermin = slidermin self.slidermax = slidermax def _update(self, event): 'update the slider position' if event.button !=1: return if event.inaxes != self.ax: return val = event.xdata if not self.closedmin and val<=self.valmin: return if not self.closedmax and val>=self.valmax: return if self.slidermin is not None: if val<=self.slidermin.val: return if self.slidermax is not None: if val>=self.slidermax.val: return self.set_val(val) def set_val(self, val): xy = self.poly.xy xy[-1] = val, 0 xy[-2] = val, 1 self.poly.xy = xy self.valtext.set_text(self.valfmt%val) if self.drawon: self.ax.figure.canvas.draw() self.val = val if not self.eventson: return for cid, func in self.observers.items(): func(val) def on_changed(self, func): """ When the slider valud is changed, call this func with the new slider position A connection id is returned which can be used to disconnect """ cid = self.cnt self.observers[cid] = func self.cnt += 1 return cid def disconnect(self, cid): 'remove the observer with connection id cid' try: del self.observers[cid] except KeyError: pass def reset(self): "reset the slider to the initial value if needed" if (self.val != self.valinit): self.set_val(self.valinit) class CheckButtons(Widget): """ A GUI neutral radio button The following attributes are exposed ax - the Axes instance the buttons are in labels - a list of text.Text instances lines - a list of (line1, line2) tuples for the x's in the check boxes. These lines exist for each box, but have set_visible(False) when box is not checked rectangles - a list of patch.Rectangle instances Connect to the CheckButtons with the on_clicked method """ def __init__(self, ax, labels, actives): """ Add check buttons to axes.Axes instance ax labels is a len(buttons) list of labels as strings actives is a len(buttons) list of booleans indicating whether the button is active """ ax.set_xticks([]) ax.set_yticks([]) ax.set_navigate(False) if len(labels)>1: dy = 1./(len(labels)+1) ys = np.linspace(1-dy, dy, len(labels)) else: dy = 0.25 ys = [0.5] cnt = 0 axcolor = ax.get_axis_bgcolor() self.labels = [] self.lines = [] self.rectangles = [] lineparams = {'color':'k', 'linewidth':1.25, 'transform':ax.transAxes, 'solid_capstyle':'butt'} for y, label in zip(ys, labels): t = ax.text(0.25, y, label, transform=ax.transAxes, horizontalalignment='left', verticalalignment='center') w, h = dy/2., dy/2. x, y = 0.05, y-h/2. p = Rectangle(xy=(x,y), width=w, height=h, facecolor=axcolor, transform=ax.transAxes) l1 = Line2D([x, x+w], [y+h, y], **lineparams) l2 = Line2D([x, x+w], [y, y+h], **lineparams) l1.set_visible(actives[cnt]) l2.set_visible(actives[cnt]) self.labels.append(t) self.rectangles.append(p) self.lines.append((l1,l2)) ax.add_patch(p) ax.add_line(l1) ax.add_line(l2) cnt += 1 ax.figure.canvas.mpl_connect('button_press_event', self._clicked) self.ax = ax self.cnt = 0 self.observers = {} def _clicked(self, event): if event.button !=1 : return if event.inaxes != self.ax: return for p,t,lines in zip(self.rectangles, self.labels, self.lines): if (t.get_window_extent().contains(event.x, event.y) or p.get_window_extent().contains(event.x, event.y) ): l1, l2 = lines l1.set_visible(not l1.get_visible()) l2.set_visible(not l2.get_visible()) thist = t break else: return if self.drawon: self.ax.figure.canvas.draw() if not self.eventson: return for cid, func in self.observers.items(): func(thist.get_text()) def on_clicked(self, func): """ When the button is clicked, call this func with button label A connection id is returned which can be used to disconnect """ cid = self.cnt self.observers[cid] = func self.cnt += 1 return cid def disconnect(self, cid): 'remove the observer with connection id cid' try: del self.observers[cid] except KeyError: pass class RadioButtons(Widget): """ A GUI neutral radio button The following attributes are exposed ax - the Axes instance the buttons are in activecolor - the color of the button when clicked labels - a list of text.Text instances circles - a list of patch.Circle instances Connect to the RadioButtons with the on_clicked method """ def __init__(self, ax, labels, active=0, activecolor='blue'): """ Add radio buttons to axes.Axes instance ax labels is a len(buttons) list of labels as strings active is the index into labels for the button that is active activecolor is the color of the button when clicked """ self.activecolor = activecolor ax.set_xticks([]) ax.set_yticks([]) ax.set_navigate(False) dy = 1./(len(labels)+1) ys = np.linspace(1-dy, dy, len(labels)) cnt = 0 axcolor = ax.get_axis_bgcolor() self.labels = [] self.circles = [] for y, label in zip(ys, labels): t = ax.text(0.25, y, label, transform=ax.transAxes, horizontalalignment='left', verticalalignment='center') if cnt==active: facecolor = activecolor else: facecolor = axcolor p = Circle(xy=(0.15, y), radius=0.05, facecolor=facecolor, transform=ax.transAxes) self.labels.append(t) self.circles.append(p) ax.add_patch(p) cnt += 1 ax.figure.canvas.mpl_connect('button_press_event', self._clicked) self.ax = ax self.cnt = 0 self.observers = {} def _clicked(self, event): if event.button !=1 : return if event.inaxes != self.ax: return xy = self.ax.transAxes.inverted().transform_point((event.x, event.y)) pclicked = np.array([xy[0], xy[1]]) def inside(p): pcirc = np.array([p.center[0], p.center[1]]) return dist(pclicked, pcirc) < p.radius for p,t in zip(self.circles, self.labels): if t.get_window_extent().contains(event.x, event.y) or inside(p): inp = p thist = t break else: return for p in self.circles: if p==inp: color = self.activecolor else: color = self.ax.get_axis_bgcolor() p.set_facecolor(color) if self.drawon: self.ax.figure.canvas.draw() if not self.eventson: return for cid, func in self.observers.items(): func(thist.get_text()) def on_clicked(self, func): """ When the button is clicked, call this func with button label A connection id is returned which can be used to disconnect """ cid = self.cnt self.observers[cid] = func self.cnt += 1 return cid def disconnect(self, cid): 'remove the observer with connection id cid' try: del self.observers[cid] except KeyError: pass class SubplotTool(Widget): """ A tool to adjust to subplot params of fig """ def __init__(self, targetfig, toolfig): """ targetfig is the figure to adjust toolfig is the figure to embed the the subplot tool into. If None, a default pylab figure will be created. If you are using this from the GUI """ self.targetfig = targetfig toolfig.subplots_adjust(left=0.2, right=0.9) class toolbarfmt: def __init__(self, slider): self.slider = slider def __call__(self, x, y): fmt = '%s=%s'%(self.slider.label.get_text(), self.slider.valfmt) return fmt%x self.axleft = toolfig.add_subplot(711) self.axleft.set_title('Click on slider to adjust subplot param') self.axleft.set_navigate(False) self.sliderleft = Slider(self.axleft, 'left', 0, 1, targetfig.subplotpars.left, closedmax=False) self.sliderleft.on_changed(self.funcleft) self.axbottom = toolfig.add_subplot(712) self.axbottom.set_navigate(False) self.sliderbottom = Slider(self.axbottom, 'bottom', 0, 1, targetfig.subplotpars.bottom, closedmax=False) self.sliderbottom.on_changed(self.funcbottom) self.axright = toolfig.add_subplot(713) self.axright.set_navigate(False) self.sliderright = Slider(self.axright, 'right', 0, 1, targetfig.subplotpars.right, closedmin=False) self.sliderright.on_changed(self.funcright) self.axtop = toolfig.add_subplot(714) self.axtop.set_navigate(False) self.slidertop = Slider(self.axtop, 'top', 0, 1, targetfig.subplotpars.top, closedmin=False) self.slidertop.on_changed(self.functop) self.axwspace = toolfig.add_subplot(715) self.axwspace.set_navigate(False) self.sliderwspace = Slider(self.axwspace, 'wspace', 0, 1, targetfig.subplotpars.wspace, closedmax=False) self.sliderwspace.on_changed(self.funcwspace) self.axhspace = toolfig.add_subplot(716) self.axhspace.set_navigate(False) self.sliderhspace = Slider(self.axhspace, 'hspace', 0, 1, targetfig.subplotpars.hspace, closedmax=False) self.sliderhspace.on_changed(self.funchspace) # constraints self.sliderleft.slidermax = self.sliderright self.sliderright.slidermin = self.sliderleft self.sliderbottom.slidermax = self.slidertop self.slidertop.slidermin = self.sliderbottom bax = toolfig.add_axes([0.8, 0.05, 0.15, 0.075]) self.buttonreset = Button(bax, 'Reset') sliders = (self.sliderleft, self.sliderbottom, self.sliderright, self.slidertop, self.sliderwspace, self.sliderhspace, ) def func(event): thisdrawon = self.drawon self.drawon = False # store the drawon state of each slider bs = [] for slider in sliders: bs.append(slider.drawon) slider.drawon = False # reset the slider to the initial position for slider in sliders: slider.reset() # reset drawon for slider, b in zip(sliders, bs): slider.drawon = b # draw the canvas self.drawon = thisdrawon if self.drawon: toolfig.canvas.draw() self.targetfig.canvas.draw() # during reset there can be a temporary invalid state # depending on the order of the reset so we turn off # validation for the resetting validate = toolfig.subplotpars.validate toolfig.subplotpars.validate = False self.buttonreset.on_clicked(func) toolfig.subplotpars.validate = validate def funcleft(self, val): self.targetfig.subplots_adjust(left=val) if self.drawon: self.targetfig.canvas.draw() def funcright(self, val): self.targetfig.subplots_adjust(right=val) if self.drawon: self.targetfig.canvas.draw() def funcbottom(self, val): self.targetfig.subplots_adjust(bottom=val) if self.drawon: self.targetfig.canvas.draw() def functop(self, val): self.targetfig.subplots_adjust(top=val) if self.drawon: self.targetfig.canvas.draw() def funcwspace(self, val): self.targetfig.subplots_adjust(wspace=val) if self.drawon: self.targetfig.canvas.draw() def funchspace(self, val): self.targetfig.subplots_adjust(hspace=val) if self.drawon: self.targetfig.canvas.draw() class Cursor: """ A horizontal and vertical line span the axes that and move with the pointer. You can turn off the hline or vline spectively with the attributes horizOn =True|False: controls visibility of the horizontal line vertOn =True|False: controls visibility of the horizontal line And the visibility of the cursor itself with visible attribute """ def __init__(self, ax, useblit=False, **lineprops): """ Add a cursor to ax. If useblit=True, use the backend dependent blitting features for faster updates (GTKAgg only now). lineprops is a dictionary of line properties. See examples/widgets/cursor.py. """ self.ax = ax self.canvas = ax.figure.canvas self.canvas.mpl_connect('motion_notify_event', self.onmove) self.canvas.mpl_connect('draw_event', self.clear) self.visible = True self.horizOn = True self.vertOn = True self.useblit = useblit self.lineh = ax.axhline(ax.get_ybound()[0], visible=False, **lineprops) self.linev = ax.axvline(ax.get_xbound()[0], visible=False, **lineprops) self.background = None self.needclear = False def clear(self, event): 'clear the cursor' if self.useblit: self.background = self.canvas.copy_from_bbox(self.ax.bbox) self.linev.set_visible(False) self.lineh.set_visible(False) def onmove(self, event): 'on mouse motion draw the cursor if visible' if event.inaxes != self.ax: self.linev.set_visible(False) self.lineh.set_visible(False) if self.needclear: self.canvas.draw() self.needclear = False return self.needclear = True if not self.visible: return self.linev.set_xdata((event.xdata, event.xdata)) self.lineh.set_ydata((event.ydata, event.ydata)) self.linev.set_visible(self.visible and self.vertOn) self.lineh.set_visible(self.visible and self.horizOn) self._update() def _update(self): if self.useblit: if self.background is not None: self.canvas.restore_region(self.background) self.ax.draw_artist(self.linev) self.ax.draw_artist(self.lineh) self.canvas.blit(self.ax.bbox) else: self.canvas.draw_idle() return False class MultiCursor: """ Provide a vertical line cursor shared between multiple axes from matplotlib.widgets import MultiCursor from pylab import figure, show, nx t = nx.arange(0.0, 2.0, 0.01) s1 = nx.sin(2*nx.pi*t) s2 = nx.sin(4*nx.pi*t) fig = figure() ax1 = fig.add_subplot(211) ax1.plot(t, s1) ax2 = fig.add_subplot(212, sharex=ax1) ax2.plot(t, s2) multi = MultiCursor(fig.canvas, (ax1, ax2), color='r', lw=1) show() """ def __init__(self, canvas, axes, useblit=True, **lineprops): self.canvas = canvas self.axes = axes xmin, xmax = axes[-1].get_xlim() xmid = 0.5*(xmin+xmax) self.lines = [ax.axvline(xmid, visible=False, **lineprops) for ax in axes] self.visible = True self.useblit = useblit self.background = None self.needclear = False self.canvas.mpl_connect('motion_notify_event', self.onmove) self.canvas.mpl_connect('draw_event', self.clear) def clear(self, event): 'clear the cursor' if self.useblit: self.background = self.canvas.copy_from_bbox(self.canvas.figure.bbox) for line in self.lines: line.set_visible(False) def onmove(self, event): if event.inaxes is None: return if not self.canvas.widgetlock.available(self): return self.needclear = True if not self.visible: return for line in self.lines: line.set_xdata((event.xdata, event.xdata)) line.set_visible(self.visible) self._update() def _update(self): if self.useblit: if self.background is not None: self.canvas.restore_region(self.background) for ax, line in zip(self.axes, self.lines): ax.draw_artist(line) self.canvas.blit(self.canvas.figure.bbox) else: self.canvas.draw_idle() class SpanSelector: """ Select a min/max range of the x or y axes for a matplotlib Axes Example usage: ax = subplot(111) ax.plot(x,y) def onselect(vmin, vmax): print vmin, vmax span = SpanSelector(ax, onselect, 'horizontal') onmove_callback is an optional callback that will be called on mouse move with the span range """ def __init__(self, ax, onselect, direction, minspan=None, useblit=False, rectprops=None, onmove_callback=None): """ Create a span selector in ax. When a selection is made, clear the span and call onselect with onselect(vmin, vmax) and clear the span. direction must be 'horizontal' or 'vertical' If minspan is not None, ignore events smaller than minspan The span rect is drawn with rectprops; default rectprops = dict(facecolor='red', alpha=0.5) set the visible attribute to False if you want to turn off the functionality of the span selector """ if rectprops is None: rectprops = dict(facecolor='red', alpha=0.5) assert direction in ['horizontal', 'vertical'], 'Must choose horizontal or vertical for direction' self.direction = direction self.ax = None self.canvas = None self.visible = True self.cids=[] self.rect = None self.background = None self.pressv = None self.rectprops = rectprops self.onselect = onselect self.onmove_callback = onmove_callback self.useblit = useblit self.minspan = minspan # Needed when dragging out of axes self.buttonDown = False self.prev = (0, 0) self.new_axes(ax) def new_axes(self,ax): self.ax = ax if self.canvas is not ax.figure.canvas: for cid in self.cids: self.canvas.mpl_disconnect(cid) self.canvas = ax.figure.canvas self.cids.append(self.canvas.mpl_connect('motion_notify_event', self.onmove)) self.cids.append(self.canvas.mpl_connect('button_press_event', self.press)) self.cids.append(self.canvas.mpl_connect('button_release_event', self.release)) self.cids.append(self.canvas.mpl_connect('draw_event', self.update_background)) if self.direction == 'horizontal': trans = blended_transform_factory(self.ax.transData, self.ax.transAxes) w,h = 0,1 else: trans = blended_transform_factory(self.ax.transAxes, self.ax.transData) w,h = 1,0 self.rect = Rectangle( (0,0), w, h, transform=trans, visible=False, **self.rectprops ) if not self.useblit: self.ax.add_patch(self.rect) def update_background(self, event): 'force an update of the background' if self.useblit: self.background = self.canvas.copy_from_bbox(self.ax.bbox) def ignore(self, event): 'return True if event should be ignored' return event.inaxes!=self.ax or not self.visible or event.button !=1 def press(self, event): 'on button press event' if self.ignore(event): return self.buttonDown = True self.rect.set_visible(self.visible) if self.direction == 'horizontal': self.pressv = event.xdata else: self.pressv = event.ydata return False def release(self, event): 'on button release event' if self.pressv is None or (self.ignore(event) and not self.buttonDown): return self.buttonDown = False self.rect.set_visible(False) self.canvas.draw() vmin = self.pressv if self.direction == 'horizontal': vmax = event.xdata or self.prev[0] else: vmax = event.ydata or self.prev[1] if vmin>vmax: vmin, vmax = vmax, vmin span = vmax - vmin if self.minspan is not None and span<self.minspan: return self.onselect(vmin, vmax) self.pressv = None return False def update(self): 'draw using newfangled blit or oldfangled draw depending on useblit' if self.useblit: if self.background is not None: self.canvas.restore_region(self.background) self.ax.draw_artist(self.rect) self.canvas.blit(self.ax.bbox) else: self.canvas.draw_idle() return False def onmove(self, event): 'on motion notify event' if self.pressv is None or self.ignore(event): return x, y = event.xdata, event.ydata self.prev = x, y if self.direction == 'horizontal': v = x else: v = y minv, maxv = v, self.pressv if minv>maxv: minv, maxv = maxv, minv if self.direction == 'horizontal': self.rect.set_x(minv) self.rect.set_width(maxv-minv) else: self.rect.set_y(minv) self.rect.set_height(maxv-minv) if self.onmove_callback is not None: vmin = self.pressv if self.direction == 'horizontal': vmax = event.xdata or self.prev[0] else: vmax = event.ydata or self.prev[1] if vmin>vmax: vmin, vmax = vmax, vmin self.onmove_callback(vmin, vmax) self.update() return False # For backwards compatibility only! class HorizontalSpanSelector(SpanSelector): def __init__(self, ax, onselect, **kwargs): import warnings warnings.warn('Use SpanSelector instead!', DeprecationWarning) SpanSelector.__init__(self, ax, onselect, 'horizontal', **kwargs) class RectangleSelector: """ Select a min/max range of the x axes for a matplotlib Axes Example usage:: from matplotlib.widgets import RectangleSelector from pylab import * def onselect(eclick, erelease): 'eclick and erelease are matplotlib events at press and release' print ' startposition : (%f, %f)' % (eclick.xdata, eclick.ydata) print ' endposition : (%f, %f)' % (erelease.xdata, erelease.ydata) print ' used button : ', eclick.button def toggle_selector(event): print ' Key pressed.' if event.key in ['Q', 'q'] and toggle_selector.RS.active: print ' RectangleSelector deactivated.' toggle_selector.RS.set_active(False) if event.key in ['A', 'a'] and not toggle_selector.RS.active: print ' RectangleSelector activated.' toggle_selector.RS.set_active(True) x = arange(100)/(99.0) y = sin(x) fig = figure ax = subplot(111) ax.plot(x,y) toggle_selector.RS = RectangleSelector(ax, onselect, drawtype='line') connect('key_press_event', toggle_selector) show() """ def __init__(self, ax, onselect, drawtype='box', minspanx=None, minspany=None, useblit=False, lineprops=None, rectprops=None, spancoords='data'): """ Create a selector in ax. When a selection is made, clear the span and call onselect with onselect(pos_1, pos_2) and clear the drawn box/line. There pos_i are arrays of length 2 containing the x- and y-coordinate. If minspanx is not None then events smaller than minspanx in x direction are ignored(it's the same for y). The rect is drawn with rectprops; default rectprops = dict(facecolor='red', edgecolor = 'black', alpha=0.5, fill=False) The line is drawn with lineprops; default lineprops = dict(color='black', linestyle='-', linewidth = 2, alpha=0.5) Use type if you want the mouse to draw a line, a box or nothing between click and actual position ny setting drawtype = 'line', drawtype='box' or drawtype = 'none'. spancoords is one of 'data' or 'pixels'. If 'data', minspanx and minspanx will be interpreted in the same coordinates as the x and ya axis, if 'pixels', they are in pixels """ self.ax = ax self.visible = True self.canvas = ax.figure.canvas self.canvas.mpl_connect('motion_notify_event', self.onmove) self.canvas.mpl_connect('button_press_event', self.press) self.canvas.mpl_connect('button_release_event', self.release) self.canvas.mpl_connect('draw_event', self.update_background) self.active = True # for activation / deactivation self.to_draw = None self.background = None if drawtype == 'none': drawtype = 'line' # draw a line but make it self.visible = False # invisible if drawtype == 'box': if rectprops is None: rectprops = dict(facecolor='white', edgecolor = 'black', alpha=0.5, fill=False) self.rectprops = rectprops self.to_draw = Rectangle((0,0), 0, 1,visible=False,**self.rectprops) self.ax.add_patch(self.to_draw) if drawtype == 'line': if lineprops is None: lineprops = dict(color='black', linestyle='-', linewidth = 2, alpha=0.5) self.lineprops = lineprops self.to_draw = Line2D([0,0],[0,0],visible=False,**self.lineprops) self.ax.add_line(self.to_draw) self.onselect = onselect self.useblit = useblit self.minspanx = minspanx self.minspany = minspany assert(spancoords in ('data', 'pixels')) self.spancoords = spancoords self.drawtype = drawtype # will save the data (position at mouseclick) self.eventpress = None # will save the data (pos. at mouserelease) self.eventrelease = None def update_background(self, event): 'force an update of the background' if self.useblit: self.background = self.canvas.copy_from_bbox(self.ax.bbox) def ignore(self, event): 'return True if event should be ignored' # If RectangleSelector is not active : if not self.active: return True # If canvas was locked if not self.canvas.widgetlock.available(self): return True # If no button was pressed yet ignore the event if it was out # of the axes if self.eventpress == None: return event.inaxes!= self.ax # If a button was pressed, check if the release-button is the # same. return (event.inaxes!=self.ax or event.button != self.eventpress.button) def press(self, event): 'on button press event' # Is the correct button pressed within the correct axes? if self.ignore(event): return # make the drawed box/line visible get the click-coordinates, # button, ... self.to_draw.set_visible(self.visible) self.eventpress = event return False def release(self, event): 'on button release event' if self.eventpress is None or self.ignore(event): return # make the box/line invisible again self.to_draw.set_visible(False) self.canvas.draw() # release coordinates, button, ... self.eventrelease = event if self.spancoords=='data': xmin, ymin = self.eventpress.xdata, self.eventpress.ydata xmax, ymax = self.eventrelease.xdata, self.eventrelease.ydata # calculate dimensions of box or line get values in the right # order elif self.spancoords=='pixels': xmin, ymin = self.eventpress.x, self.eventpress.y xmax, ymax = self.eventrelease.x, self.eventrelease.y else: raise ValueError('spancoords must be "data" or "pixels"') if xmin>xmax: xmin, xmax = xmax, xmin if ymin>ymax: ymin, ymax = ymax, ymin spanx = xmax - xmin spany = ymax - ymin xproblems = self.minspanx is not None and spanx<self.minspanx yproblems = self.minspany is not None and spany<self.minspany if (self.drawtype=='box') and (xproblems or yproblems): """Box to small""" # check if drawed distance (if it exists) is return # not to small in neither x nor y-direction if (self.drawtype=='line') and (xproblems and yproblems): """Line to small""" # check if drawed distance (if it exists) is return # not to small in neither x nor y-direction self.onselect(self.eventpress, self.eventrelease) # call desired function self.eventpress = None # reset the variables to their self.eventrelease = None # inital values return False def update(self): 'draw using newfangled blit or oldfangled draw depending on useblit' if self.useblit: if self.background is not None: self.canvas.restore_region(self.background) self.ax.draw_artist(self.to_draw) self.canvas.blit(self.ax.bbox) else: self.canvas.draw_idle() return False def onmove(self, event): 'on motion notify event if box/line is wanted' if self.eventpress is None or self.ignore(event): return x,y = event.xdata, event.ydata # actual position (with # (button still pressed) if self.drawtype == 'box': minx, maxx = self.eventpress.xdata, x # click-x and actual mouse-x miny, maxy = self.eventpress.ydata, y # click-y and actual mouse-y if minx>maxx: minx, maxx = maxx, minx # get them in the right order if miny>maxy: miny, maxy = maxy, miny self.to_draw.set_x(minx) # set lower left of box self.to_draw.set_y(miny) self.to_draw.set_width(maxx-minx) # set width and height of box self.to_draw.set_height(maxy-miny) self.update() return False if self.drawtype == 'line': self.to_draw.set_data([self.eventpress.xdata, x], [self.eventpress.ydata, y]) self.update() return False def set_active(self, active): """ Use this to activate / deactivate the RectangleSelector from your program with an boolean variable 'active'. """ self.active = active def get_active(self): """ to get status of active mode (boolean variable)""" return self.active class Lasso(Widget): def __init__(self, ax, xy, callback=None, useblit=True): self.axes = ax self.figure = ax.figure self.canvas = self.figure.canvas self.useblit = useblit if useblit: self.background = self.canvas.copy_from_bbox(self.axes.bbox) x, y = xy self.verts = [(x,y)] self.line = Line2D([x], [y], linestyle='-', color='black', lw=2) self.axes.add_line(self.line) self.callback = callback self.cids = [] self.cids.append(self.canvas.mpl_connect('button_release_event', self.onrelease)) self.cids.append(self.canvas.mpl_connect('motion_notify_event', self.onmove)) def onrelease(self, event): if self.verts is not None: self.verts.append((event.xdata, event.ydata)) if len(self.verts)>2: self.callback(self.verts) self.axes.lines.remove(self.line) self.verts = None for cid in self.cids: self.canvas.mpl_disconnect(cid) def onmove(self, event): if self.verts is None: return if event.inaxes != self.axes: return if event.button!=1: return self.verts.append((event.xdata, event.ydata)) self.line.set_data(zip(*self.verts)) if self.useblit: self.canvas.restore_region(self.background) self.axes.draw_artist(self.line) self.canvas.blit(self.axes.bbox) else: self.canvas.draw_idle()

40,833

Python

.py

968

31.97624

115

0.601723

numenta/nupic-legacy

6,330

1,556

464

AGPL-3.0

9/5/2024, 5:13:42 PM (Europe/Amsterdam)

26,272

cm.py

numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/cm.py

""" This module contains the instantiations of color mapping classes """ import numpy as np from numpy import ma import matplotlib as mpl import matplotlib.colors as colors import matplotlib.cbook as cbook from matplotlib._cm import * def get_cmap(name=None, lut=None): """ Get a colormap instance, defaulting to rc values if *name* is None """ if name is None: name = mpl.rcParams['image.cmap'] if lut is None: lut = mpl.rcParams['image.lut'] assert(name in datad.keys()) return colors.LinearSegmentedColormap(name, datad[name], lut) class ScalarMappable: """ This is a mixin class to support scalar -> RGBA mapping. Handles normalization and colormapping """ def __init__(self, norm=None, cmap=None): """ *norm* is an instance of :class:`colors.Normalize` or one of its subclasses, used to map luminance to 0-1. *cmap* is a :mod:`cm` colormap instance, for example :data:`cm.jet` """ self.callbacksSM = cbook.CallbackRegistry(( 'changed',)) if cmap is None: cmap = get_cmap() if norm is None: norm = colors.Normalize() self._A = None self.norm = norm self.cmap = cmap self.colorbar = None self.update_dict = {'array':False} def set_colorbar(self, im, ax): 'set the colorbar image and axes associated with mappable' self.colorbar = im, ax def to_rgba(self, x, alpha=1.0, bytes=False): '''Return a normalized rgba array corresponding to *x*. If *x* is already an rgb array, insert *alpha*; if it is already rgba, return it unchanged. If *bytes* is True, return rgba as 4 uint8s instead of 4 floats. ''' try: if x.ndim == 3: if x.shape[2] == 3: if x.dtype == np.uint8: alpha = np.array(alpha*255, np.uint8) m, n = x.shape[:2] xx = np.empty(shape=(m,n,4), dtype = x.dtype) xx[:,:,:3] = x xx[:,:,3] = alpha elif x.shape[2] == 4: xx = x else: raise ValueError("third dimension must be 3 or 4") if bytes and xx.dtype != np.uint8: xx = (xx * 255).astype(np.uint8) return xx except AttributeError: pass x = ma.asarray(x) x = self.norm(x) x = self.cmap(x, alpha=alpha, bytes=bytes) return x def set_array(self, A): 'Set the image array from numpy array *A*' self._A = A self.update_dict['array'] = True def get_array(self): 'Return the array' return self._A def get_cmap(self): 'return the colormap' return self.cmap def get_clim(self): 'return the min, max of the color limits for image scaling' return self.norm.vmin, self.norm.vmax def set_clim(self, vmin=None, vmax=None): """ set the norm limits for image scaling; if *vmin* is a length2 sequence, interpret it as ``(vmin, vmax)`` which is used to support setp ACCEPTS: a length 2 sequence of floats """ if (vmin is not None and vmax is None and cbook.iterable(vmin) and len(vmin)==2): vmin, vmax = vmin if vmin is not None: self.norm.vmin = vmin if vmax is not None: self.norm.vmax = vmax self.changed() def set_cmap(self, cmap): """ set the colormap for luminance data ACCEPTS: a colormap """ if cmap is None: cmap = get_cmap() self.cmap = cmap self.changed() def set_norm(self, norm): 'set the normalization instance' if norm is None: norm = colors.Normalize() self.norm = norm self.changed() def autoscale(self): """ Autoscale the scalar limits on the norm instance using the current array """ if self._A is None: raise TypeError('You must first set_array for mappable') self.norm.autoscale(self._A) self.changed() def autoscale_None(self): """ Autoscale the scalar limits on the norm instance using the current array, changing only limits that are None """ if self._A is None: raise TypeError('You must first set_array for mappable') self.norm.autoscale_None(self._A) self.changed() def add_checker(self, checker): """ Add an entry to a dictionary of boolean flags that are set to True when the mappable is changed. """ self.update_dict[checker] = False def check_update(self, checker): """ If mappable has changed since the last check, return True; else return False """ if self.update_dict[checker]: self.update_dict[checker] = False return True return False def changed(self): """ Call this whenever the mappable is changed to notify all the callbackSM listeners to the 'changed' signal """ self.callbacksSM.process('changed', self) for key in self.update_dict: self.update_dict[key] = True

5,385

Python

.py

148

27.027027

71

0.576997

numenta/nupic-legacy

6,330

1,556

464

AGPL-3.0

9/5/2024, 5:13:42 PM (Europe/Amsterdam)

26,273

pylab.py

numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/pylab.py

""" This is a procedural interface to the matplotlib object-oriented plotting library. The following plotting commands are provided; the majority have Matlab(TM) analogs and similar argument. _Plotting commands acorr - plot the autocorrelation function annotate - annotate something in the figure arrow - add an arrow to the axes axes - Create a new axes axhline - draw a horizontal line across axes axvline - draw a vertical line across axes axhspan - draw a horizontal bar across axes axvspan - draw a vertical bar across axes axis - Set or return the current axis limits bar - make a bar chart barh - a horizontal bar chart broken_barh - a set of horizontal bars with gaps box - set the axes frame on/off state boxplot - make a box and whisker plot cla - clear current axes clabel - label a contour plot clf - clear a figure window clim - adjust the color limits of the current image close - close a figure window colorbar - add a colorbar to the current figure cohere - make a plot of coherence contour - make a contour plot contourf - make a filled contour plot csd - make a plot of cross spectral density delaxes - delete an axes from the current figure draw - Force a redraw of the current figure errorbar - make an errorbar graph figlegend - make legend on the figure rather than the axes figimage - make a figure image figtext - add text in figure coords figure - create or change active figure fill - make filled polygons findobj - recursively find all objects matching some criteria gca - return the current axes gcf - return the current figure gci - get the current image, or None getp - get a handle graphics property grid - set whether gridding is on hist - make a histogram hold - set the axes hold state ioff - turn interaction mode off ion - turn interaction mode on isinteractive - return True if interaction mode is on imread - load image file into array imshow - plot image data ishold - return the hold state of the current axes legend - make an axes legend loglog - a log log plot matshow - display a matrix in a new figure preserving aspect pcolor - make a pseudocolor plot pcolormesh - make a pseudocolor plot using a quadrilateral mesh pie - make a pie chart plot - make a line plot plot_date - plot dates plotfile - plot column data from an ASCII tab/space/comma delimited file pie - pie charts polar - make a polar plot on a PolarAxes psd - make a plot of power spectral density quiver - make a direction field (arrows) plot rc - control the default params rgrids - customize the radial grids and labels for polar savefig - save the current figure scatter - make a scatter plot setp - set a handle graphics property semilogx - log x axis semilogy - log y axis show - show the figures specgram - a spectrogram plot spy - plot sparsity pattern using markers or image stem - make a stem plot subplot - make a subplot (numrows, numcols, axesnum) subplots_adjust - change the params controlling the subplot positions of current figure subplot_tool - launch the subplot configuration tool suptitle - add a figure title table - add a table to the plot text - add some text at location x,y to the current axes thetagrids - customize the radial theta grids and labels for polar title - add a title to the current axes xcorr - plot the autocorrelation function of x and y xlim - set/get the xlimits ylim - set/get the ylimits xticks - set/get the xticks yticks - set/get the yticks xlabel - add an xlabel to the current axes ylabel - add a ylabel to the current axes autumn - set the default colormap to autumn bone - set the default colormap to bone cool - set the default colormap to cool copper - set the default colormap to copper flag - set the default colormap to flag gray - set the default colormap to gray hot - set the default colormap to hot hsv - set the default colormap to hsv jet - set the default colormap to jet pink - set the default colormap to pink prism - set the default colormap to prism spring - set the default colormap to spring summer - set the default colormap to summer winter - set the default colormap to winter spectral - set the default colormap to spectral _Event handling connect - register an event handler disconnect - remove a connected event handler _Matrix commands cumprod - the cumulative product along a dimension cumsum - the cumulative sum along a dimension detrend - remove the mean or besdt fit line from an array diag - the k-th diagonal of matrix diff - the n-th differnce of an array eig - the eigenvalues and eigen vectors of v eye - a matrix where the k-th diagonal is ones, else zero find - return the indices where a condition is nonzero fliplr - flip the rows of a matrix up/down flipud - flip the columns of a matrix left/right linspace - a linear spaced vector of N values from min to max inclusive logspace - a log spaced vector of N values from min to max inclusive meshgrid - repeat x and y to make regular matrices ones - an array of ones rand - an array from the uniform distribution [0,1] randn - an array from the normal distribution rot90 - rotate matrix k*90 degress counterclockwise squeeze - squeeze an array removing any dimensions of length 1 tri - a triangular matrix tril - a lower triangular matrix triu - an upper triangular matrix vander - the Vandermonde matrix of vector x svd - singular value decomposition zeros - a matrix of zeros _Probability levypdf - The levy probability density function from the char. func. normpdf - The Gaussian probability density function rand - random numbers from the uniform distribution randn - random numbers from the normal distribution _Statistics corrcoef - correlation coefficient cov - covariance matrix amax - the maximum along dimension m mean - the mean along dimension m median - the median along dimension m amin - the minimum along dimension m norm - the norm of vector x prod - the product along dimension m ptp - the max-min along dimension m std - the standard deviation along dimension m asum - the sum along dimension m _Time series analysis bartlett - M-point Bartlett window blackman - M-point Blackman window cohere - the coherence using average periodiogram csd - the cross spectral density using average periodiogram fft - the fast Fourier transform of vector x hamming - M-point Hamming window hanning - M-point Hanning window hist - compute the histogram of x kaiser - M length Kaiser window psd - the power spectral density using average periodiogram sinc - the sinc function of array x _Dates date2num - convert python datetimes to numeric representation drange - create an array of numbers for date plots num2date - convert numeric type (float days since 0001) to datetime _Other angle - the angle of a complex array griddata - interpolate irregularly distributed data to a regular grid load - load ASCII data into array polyfit - fit x, y to an n-th order polynomial polyval - evaluate an n-th order polynomial roots - the roots of the polynomial coefficients in p save - save an array to an ASCII file trapz - trapezoidal integration __end """ import sys, warnings from cbook import flatten, is_string_like, exception_to_str, popd, \ silent_list, iterable, dedent import numpy as np from numpy import ma from matplotlib import mpl # pulls in most modules from matplotlib.dates import date2num, num2date,\ datestr2num, strpdate2num, drange,\ epoch2num, num2epoch, mx2num,\ DateFormatter, IndexDateFormatter, DateLocator,\ RRuleLocator, YearLocator, MonthLocator, WeekdayLocator,\ DayLocator, HourLocator, MinuteLocator, SecondLocator,\ rrule, MO, TU, WE, TH, FR, SA, SU, YEARLY, MONTHLY,\ WEEKLY, DAILY, HOURLY, MINUTELY, SECONDLY, relativedelta import matplotlib.dates # bring all the symbols in so folks can import them from # pylab in one fell swoop from matplotlib.mlab import window_hanning, window_none,\ conv, detrend, detrend_mean, detrend_none, detrend_linear,\ polyfit, polyval, entropy, normpdf, griddata,\ levypdf, find, trapz, prepca, rem, norm, orth, rank,\ sqrtm, prctile, center_matrix, rk4, exp_safe, amap,\ sum_flat, mean_flat, rms_flat, l1norm, l2norm, norm, frange,\ diagonal_matrix, base_repr, binary_repr, log2, ispower2,\ bivariate_normal, load, save from matplotlib.mlab import stineman_interp, slopes, \ stineman_interp, inside_poly, poly_below, poly_between, \ is_closed_polygon, path_length, distances_along_curve, vector_lengths from numpy import * from numpy.fft import * from numpy.random import * from numpy.linalg import * from matplotlib.mlab import window_hanning, window_none, conv, detrend, demean, \ detrend_mean, detrend_none, detrend_linear, entropy, normpdf, levypdf, \ find, longest_contiguous_ones, longest_ones, prepca, prctile, prctile_rank, \ center_matrix, rk4, bivariate_normal, get_xyz_where, get_sparse_matrix, dist, \ dist_point_to_segment, segments_intersect, fftsurr, liaupunov, movavg, \ save, load, exp_safe, \ amap, rms_flat, l1norm, l2norm, norm_flat, frange, diagonal_matrix, identity, \ base_repr, binary_repr, log2, ispower2, fromfunction_kw, rem, norm, orth, rank, sqrtm,\ mfuncC, approx_real, rec_append_field, rec_drop_fields, rec_join, csv2rec, rec2csv, isvector from matplotlib.pyplot import * # provide the recommended module abbrevs in the pylab namespace import matplotlib.pyplot as plt import numpy as np

10,245

Python

.py

223

42.587444

97

0.722867

numenta/nupic-legacy

6,330

1,556

464

AGPL-3.0

9/5/2024, 5:13:42 PM (Europe/Amsterdam)

26,274

bezier.py

numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/bezier.py

""" A module providing some utility functions regarding bezier path manipulation. """ import numpy as np from math import sqrt from matplotlib.path import Path from operator import xor # some functions def get_intersection(cx1, cy1, cos_t1, sin_t1, cx2, cy2, cos_t2, sin_t2): """ return a intersecting point between a line through (cx1, cy1) and having angle t1 and a line through (cx2, cy2) and angle t2. """ # line1 => sin_t1 * (x - cx1) - cos_t1 * (y - cy1) = 0. # line1 => sin_t1 * x + cos_t1 * y = sin_t1*cx1 - cos_t1*cy1 line1_rhs = sin_t1 * cx1 - cos_t1 * cy1 line2_rhs = sin_t2 * cx2 - cos_t2 * cy2 # rhs matrix a, b = sin_t1, -cos_t1 c, d = sin_t2, -cos_t2 ad_bc = a*d-b*c if ad_bc == 0.: raise ValueError("Given lines do not intersect") #rhs_inverse a_, b_ = d, -b c_, d_ = -c, a a_, b_, c_, d_ = [k / ad_bc for k in [a_, b_, c_, d_]] x = a_* line1_rhs + b_ * line2_rhs y = c_* line1_rhs + d_ * line2_rhs return x, y def get_normal_points(cx, cy, cos_t, sin_t, length): """ For a line passing through (*cx*, *cy*) and having a angle *t*, return locations of the two points located along its perpendicular line at the distance of *length*. """ if length == 0.: return cx, cy, cx, cy cos_t1, sin_t1 = sin_t, -cos_t cos_t2, sin_t2 = -sin_t, cos_t x1, y1 = length*cos_t1 + cx, length*sin_t1 + cy x2, y2 = length*cos_t2 + cx, length*sin_t2 + cy return x1, y1, x2, y2 ## BEZIER routines # subdividing bezier curve # http://www.cs.mtu.edu/~shene/COURSES/cs3621/NOTES/spline/Bezier/bezier-sub.html def _de_casteljau1(beta, t): next_beta = beta[:-1] * (1-t) + beta[1:] * t return next_beta def split_de_casteljau(beta, t): """split a bezier segment defined by its controlpoints *beta* into two separate segment divided at *t* and return their control points. """ beta = np.asarray(beta) beta_list = [beta] while True: beta = _de_casteljau1(beta, t) beta_list.append(beta) if len(beta) == 1: break left_beta = [beta[0] for beta in beta_list] right_beta = [beta[-1] for beta in reversed(beta_list)] return left_beta, right_beta def find_bezier_t_intersecting_with_closedpath(bezier_point_at_t, inside_closedpath, t0=0., t1=1., tolerence=0.01): """ Find a parameter t0 and t1 of the given bezier path which bounds the intersecting points with a provided closed path(*inside_closedpath*). Search starts from *t0* and *t1* and it uses a simple bisecting algorithm therefore one of the end point must be inside the path while the orther doesn't. The search stop when |t0-t1| gets smaller than the given tolerence. value for - bezier_point_at_t : a function which returns x, y coordinates at *t* - inside_closedpath : return True if the point is insed the path """ # inside_closedpath : function start = bezier_point_at_t(t0) end = bezier_point_at_t(t1) start_inside = inside_closedpath(start) end_inside = inside_closedpath(end) if not xor(start_inside, end_inside): raise ValueError("the segment does not seemed to intersect with the path") while 1: # return if the distance is smaller than the tolerence if (start[0]-end[0])**2 + (start[1]-end[1])**2 < tolerence**2: return t0, t1 # calculate the middle point middle_t = 0.5*(t0+t1) middle = bezier_point_at_t(middle_t) middle_inside = inside_closedpath(middle) if xor(start_inside, middle_inside): t1 = middle_t end = middle end_inside = middle_inside else: t0 = middle_t start = middle start_inside = middle_inside class BezierSegment: """ A simple class of a 2-dimensional bezier segment """ # Highrt order bezier lines can be supported by simplying adding # correcponding values. _binom_coeff = {1:np.array([1., 1.]), 2:np.array([1., 2., 1.]), 3:np.array([1., 3., 3., 1.])} def __init__(self, control_points): """ *control_points* : location of contol points. It needs have a shpae of n * 2, where n is the order of the bezier line. 1<= n <= 3 is supported. """ _o = len(control_points) self._orders = np.arange(_o) _coeff = BezierSegment._binom_coeff[_o - 1] _control_points = np.asarray(control_points) xx = _control_points[:,0] yy = _control_points[:,1] self._px = xx * _coeff self._py = yy * _coeff def point_at_t(self, t): "evaluate a point at t" one_minus_t_powers = np.power(1.-t, self._orders)[::-1] t_powers = np.power(t, self._orders) tt = one_minus_t_powers * t_powers _x = sum(tt * self._px) _y = sum(tt * self._py) return _x, _y def split_bezier_intersecting_with_closedpath(bezier, inside_closedpath, tolerence=0.01): """ bezier : control points of the bezier segment inside_closedpath : a function which returns true if the point is inside the path """ bz = BezierSegment(bezier) bezier_point_at_t = bz.point_at_t t0, t1 = find_bezier_t_intersecting_with_closedpath(bezier_point_at_t, inside_closedpath, tolerence=tolerence) _left, _right = split_de_casteljau(bezier, (t0+t1)/2.) return _left, _right def find_r_to_boundary_of_closedpath(inside_closedpath, xy, cos_t, sin_t, rmin=0., rmax=1., tolerence=0.01): """ Find a radius r (centered at *xy*) between *rmin* and *rmax* at which it intersect with the path. inside_closedpath : function cx, cy : center cos_t, sin_t : cosine and sine for the angle rmin, rmax : """ cx, cy = xy def _f(r): return cos_t*r + cx, sin_t*r + cy find_bezier_t_intersecting_with_closedpath(_f, inside_closedpath, t0=rmin, t1=rmax, tolerence=tolerence) ## matplotlib specific def split_path_inout(path, inside, tolerence=0.01, reorder_inout=False): """ divide a path into two segment at the point where inside(x, y) becomes False. """ path_iter = path.iter_segments() ctl_points, command = path_iter.next() begin_inside = inside(ctl_points[-2:]) # true if begin point is inside bezier_path = None ctl_points_old = ctl_points concat = np.concatenate iold=0 i = 1 for ctl_points, command in path_iter: iold=i i += len(ctl_points)/2 if inside(ctl_points[-2:]) != begin_inside: bezier_path = concat([ctl_points_old[-2:], ctl_points]) break ctl_points_old = ctl_points if bezier_path is None: raise ValueError("The path does not seem to intersect with the patch") bp = zip(bezier_path[::2], bezier_path[1::2]) left, right = split_bezier_intersecting_with_closedpath(bp, inside, tolerence) if len(left) == 2: codes_left = [Path.LINETO] codes_right = [Path.MOVETO, Path.LINETO] elif len(left) == 3: codes_left = [Path.CURVE3, Path.CURVE3] codes_right = [Path.MOVETO, Path.CURVE3, Path.CURVE3] elif len(left) == 4: codes_left = [Path.CURVE4, Path.CURVE4, Path.CURVE4] codes_right = [Path.MOVETO, Path.CURVE4, Path.CURVE4, Path.CURVE4] else: raise ValueError() verts_left = left[1:] verts_right = right[:] #i += 1 if path.codes is None: path_in = Path(concat([path.vertices[:i], verts_left])) path_out = Path(concat([verts_right, path.vertices[i:]])) else: path_in = Path(concat([path.vertices[:iold], verts_left]), concat([path.codes[:iold], codes_left])) path_out = Path(concat([verts_right, path.vertices[i:]]), concat([codes_right, path.codes[i:]])) if reorder_inout and begin_inside == False: path_in, path_out = path_out, path_in return path_in, path_out def inside_circle(cx, cy, r): r2 = r**2 def _f(xy): x, y = xy return (x-cx)**2 + (y-cy)**2 < r2 return _f # quadratic bezier lines def get_cos_sin(x0, y0, x1, y1): dx, dy = x1-x0, y1-y0 d = (dx*dx + dy*dy)**.5 return dx/d, dy/d def get_parallels(bezier2, width): """ Given the quadraitc bezier control points *bezier2*, returns control points of quadrativ bezier lines roughly parralel to given one separated by *width*. """ # The parallel bezier lines constructed by following ways. # c1 and c2 are contol points representing the begin and end of the bezier line. # cm is the middle point c1x, c1y = bezier2[0] cmx, cmy = bezier2[1] c2x, c2y = bezier2[2] # t1 and t2 is the anlge between c1 and cm, cm, c2. # They are also a angle of the tangential line of the path at c1 and c2 cos_t1, sin_t1 = get_cos_sin(c1x, c1y, cmx, cmy) cos_t2, sin_t2 = get_cos_sin(cmx, cmy, c2x, c2y) # find c1_left, c1_right which are located along the lines # throught c1 and perpendicular to the tangential lines of the # bezier path at a distance of width. Same thing for c2_left and # c2_right with respect to c2. c1x_left, c1y_left, c1x_right, c1y_right = \ get_normal_points(c1x, c1y, cos_t1, sin_t1, width) c2x_left, c2y_left, c2x_right, c2y_right = \ get_normal_points(c2x, c2y, cos_t2, sin_t2, width) # find cm_left which is the intersectng point of a line through # c1_left with angle t1 and a line throught c2_left with angle # t2. Same with cm_right. cmx_left, cmy_left = get_intersection(c1x_left, c1y_left, cos_t1, sin_t1, c2x_left, c2y_left, cos_t2, sin_t2) cmx_right, cmy_right = get_intersection(c1x_right, c1y_right, cos_t1, sin_t1, c2x_right, c2y_right, cos_t2, sin_t2) # the parralel bezier lines are created with control points of # [c1_left, cm_left, c2_left] and [c1_right, cm_right, c2_right] path_left = [(c1x_left, c1y_left), (cmx_left, cmy_left), (c2x_left, c2y_left)] path_right = [(c1x_right, c1y_right), (cmx_right, cmy_right), (c2x_right, c2y_right)] return path_left, path_right def make_wedged_bezier2(bezier2, length, shrink_factor=0.5): """ Being similar to get_parallels, returns control points of two quadrativ bezier lines having a width roughly parralel to given one separated by *width*. """ xx1, yy1 = bezier2[2] xx2, yy2 = bezier2[1] xx3, yy3 = bezier2[0] cx, cy = xx3, yy3 x0, y0 = xx2, yy2 dist = sqrt((x0-cx)**2 + (y0-cy)**2) cos_t, sin_t = (x0-cx)/dist, (y0-cy)/dist, x1, y1, x2, y2 = get_normal_points(cx, cy, cos_t, sin_t, length) xx12, yy12 = (xx1+xx2)/2., (yy1+yy2)/2., xx23, yy23 = (xx2+xx3)/2., (yy2+yy3)/2., dist = sqrt((xx12-xx23)**2 + (yy12-yy23)**2) cos_t, sin_t = (xx12-xx23)/dist, (yy12-yy23)/dist, xm1, ym1, xm2, ym2 = get_normal_points(xx2, yy2, cos_t, sin_t, length*shrink_factor) l_plus = [(x1, y1), (xm1, ym1), (xx1, yy1)] l_minus = [(x2, y2), (xm2, ym2), (xx1, yy1)] return l_plus, l_minus def find_control_points(c1x, c1y, mmx, mmy, c2x, c2y): """ Find control points of the bezier line throught c1, mm, c2. We simply assume that c1, mm, c2 which have parameteric value 0, 0.5, and 1. """ cmx = .5 * (4*mmx - (c1x + c2x)) cmy = .5 * (4*mmy - (c1y + c2y)) return [(c1x, c1y), (cmx, cmy), (c2x, c2y)] def make_wedged_bezier2(bezier2, width, w1=1., wm=0.5, w2=0.): """ Being similar to get_parallels, returns control points of two quadrativ bezier lines having a width roughly parralel to given one separated by *width*. """ # c1, cm, c2 c1x, c1y = bezier2[0] cmx, cmy = bezier2[1] c3x, c3y = bezier2[2] # t1 and t2 is the anlge between c1 and cm, cm, c3. # They are also a angle of the tangential line of the path at c1 and c3 cos_t1, sin_t1 = get_cos_sin(c1x, c1y, cmx, cmy) cos_t2, sin_t2 = get_cos_sin(cmx, cmy, c3x, c3y) # find c1_left, c1_right which are located along the lines # throught c1 and perpendicular to the tangential lines of the # bezier path at a distance of width. Same thing for c3_left and # c3_right with respect to c3. c1x_left, c1y_left, c1x_right, c1y_right = \ get_normal_points(c1x, c1y, cos_t1, sin_t1, width*w1) c3x_left, c3y_left, c3x_right, c3y_right = \ get_normal_points(c3x, c3y, cos_t2, sin_t2, width*w2) # find c12, c23 and c123 which are middle points of c1-cm, cm-c3 and c12-c23 c12x, c12y = (c1x+cmx)*.5, (c1y+cmy)*.5 c23x, c23y = (cmx+c3x)*.5, (cmy+c3y)*.5 c123x, c123y = (c12x+c23x)*.5, (c12y+c23y)*.5 # tangential angle of c123 (angle between c12 and c23) cos_t123, sin_t123 = get_cos_sin(c12x, c12y, c23x, c23y) c123x_left, c123y_left, c123x_right, c123y_right = \ get_normal_points(c123x, c123y, cos_t123, sin_t123, width*wm) path_left = find_control_points(c1x_left, c1y_left, c123x_left, c123y_left, c3x_left, c3y_left) path_right = find_control_points(c1x_right, c1y_right, c123x_right, c123y_right, c3x_right, c3y_right) return path_left, path_right if 0: path = Path([(0, 0), (1, 0), (2, 2)], [Path.MOVETO, Path.CURVE3, Path.CURVE3]) left, right = divide_path_inout(path, inside) clf() ax = gca()

14,387

Python

.py

328

35.088415

104

0.593996

numenta/nupic-legacy

6,330

1,556

464

AGPL-3.0

9/5/2024, 5:13:42 PM (Europe/Amsterdam)

26,275

colorbar.py

numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/colorbar.py

''' Colorbar toolkit with two classes and a function: :class:`ColorbarBase` the base class with full colorbar drawing functionality. It can be used as-is to make a colorbar for a given colormap; a mappable object (e.g., image) is not needed. :class:`Colorbar` the derived class for use with images or contour plots. :func:`make_axes` a function for resizing an axes and adding a second axes suitable for a colorbar The :meth:`~matplotlib.figure.Figure.colorbar` method uses :func:`make_axes` and :class:`Colorbar`; the :func:`~matplotlib.pyplot.colorbar` function is a thin wrapper over :meth:`~matplotlib.figure.Figure.colorbar`. ''' import numpy as np import matplotlib as mpl import matplotlib.colors as colors import matplotlib.cm as cm import matplotlib.ticker as ticker import matplotlib.cbook as cbook import matplotlib.lines as lines import matplotlib.patches as patches import matplotlib.collections as collections import matplotlib.contour as contour make_axes_kw_doc = ''' ========== ==================================================== Property Description ========== ==================================================== *fraction* 0.15; fraction of original axes to use for colorbar *pad* 0.05 if vertical, 0.15 if horizontal; fraction of original axes between colorbar and new image axes *shrink* 1.0; fraction by which to shrink the colorbar *aspect* 20; ratio of long to short dimensions ========== ==================================================== ''' colormap_kw_doc = ''' =========== ==================================================== Property Description =========== ==================================================== *extend* [ 'neither' | 'both' | 'min' | 'max' ] If not 'neither', make pointed end(s) for out-of- range values. These are set for a given colormap using the colormap set_under and set_over methods. *spacing* [ 'uniform' | 'proportional' ] Uniform spacing gives each discrete color the same space; proportional makes the space proportional to the data interval. *ticks* [ None | list of ticks | Locator object ] If None, ticks are determined automatically from the input. *format* [ None | format string | Formatter object ] If None, the :class:`~matplotlib.ticker.ScalarFormatter` is used. If a format string is given, e.g. '%.3f', that is used. An alternative :class:`~matplotlib.ticker.Formatter` object may be given instead. *drawedges* [ False | True ] If true, draw lines at color boundaries. =========== ==================================================== The following will probably be useful only in the context of indexed colors (that is, when the mappable has norm=NoNorm()), or other unusual circumstances. ============ =================================================== Property Description ============ =================================================== *boundaries* None or a sequence *values* None or a sequence which must be of length 1 less than the sequence of *boundaries*. For each region delimited by adjacent entries in *boundaries*, the color mapped to the corresponding value in values will be used. ============ =================================================== ''' colorbar_doc = ''' Add a colorbar to a plot. Function signatures for the :mod:`~matplotlib.pyplot` interface; all but the first are also method signatures for the :meth:`~matplotlib.figure.Figure.colorbar` method:: colorbar(**kwargs) colorbar(mappable, **kwargs) colorbar(mappable, cax=cax, **kwargs) colorbar(mappable, ax=ax, **kwargs) arguments: *mappable* the :class:`~matplotlib.image.Image`, :class:`~matplotlib.contour.ContourSet`, etc. to which the colorbar applies; this argument is mandatory for the :meth:`~matplotlib.figure.Figure.colorbar` method but optional for the :func:`~matplotlib.pyplot.colorbar` function, which sets the default to the current image. keyword arguments: *cax* None | axes object into which the colorbar will be drawn *ax* None | parent axes object from which space for a new colorbar axes will be stolen Additional keyword arguments are of two kinds: axes properties: %s colorbar properties: %s If *mappable* is a :class:`~matplotlib.contours.ContourSet`, its *extend* kwarg is included automatically. Note that the *shrink* kwarg provides a simple way to keep a vertical colorbar, for example, from being taller than the axes of the mappable to which the colorbar is attached; but it is a manual method requiring some trial and error. If the colorbar is too tall (or a horizontal colorbar is too wide) use a smaller value of *shrink*. For more precise control, you can manually specify the positions of the axes objects in which the mappable and the colorbar are drawn. In this case, do not use any of the axes properties kwargs. returns: :class:`~matplotlib.colorbar.Colorbar` instance; see also its base class, :class:`~matplotlib.colorbar.ColorbarBase`. Call the :meth:`~matplotlib.colorbar.ColorbarBase.set_label` method to label the colorbar. ''' % (make_axes_kw_doc, colormap_kw_doc) class ColorbarBase(cm.ScalarMappable): ''' Draw a colorbar in an existing axes. This is a base class for the :class:`Colorbar` class, which is the basis for the :func:`~matplotlib.pyplot.colorbar` method and pylab function. It is also useful by itself for showing a colormap. If the *cmap* kwarg is given but *boundaries* and *values* are left as None, then the colormap will be displayed on a 0-1 scale. To show the under- and over-value colors, specify the *norm* as:: colors.Normalize(clip=False) To show the colors versus index instead of on the 0-1 scale, use:: norm=colors.NoNorm. Useful attributes: :attr:`ax` the Axes instance in which the colorbar is drawn :attr:`lines` a LineCollection if lines were drawn, otherwise None :attr:`dividers` a LineCollection if *drawedges* is True, otherwise None Useful public methods are :meth:`set_label` and :meth:`add_lines`. ''' _slice_dict = {'neither': slice(0,1000000), 'both': slice(1,-1), 'min': slice(1,1000000), 'max': slice(0,-1)} def __init__(self, ax, cmap=None, norm=None, alpha=1.0, values=None, boundaries=None, orientation='vertical', extend='neither', spacing='uniform', # uniform or proportional ticks=None, format=None, drawedges=False, filled=True, ): self.ax = ax if cmap is None: cmap = cm.get_cmap() if norm is None: norm = colors.Normalize() self.alpha = alpha cm.ScalarMappable.__init__(self, cmap=cmap, norm=norm) self.values = values self.boundaries = boundaries self.extend = extend self._inside = self._slice_dict[extend] self.spacing = spacing self.orientation = orientation self.drawedges = drawedges self.filled = filled self.solids = None self.lines = None self.dividers = None self.set_label('') if cbook.iterable(ticks): self.locator = ticker.FixedLocator(ticks, nbins=len(ticks)) else: self.locator = ticks # Handle default in _ticker() if format is None: if isinstance(self.norm, colors.LogNorm): self.formatter = ticker.LogFormatter() else: self.formatter = ticker.ScalarFormatter() elif cbook.is_string_like(format): self.formatter = ticker.FormatStrFormatter(format) else: self.formatter = format # Assume it is a Formatter # The rest is in a method so we can recalculate when clim changes. self.draw_all() def draw_all(self): ''' Calculate any free parameters based on the current cmap and norm, and do all the drawing. ''' self._process_values() self._find_range() X, Y = self._mesh() C = self._values[:,np.newaxis] self._config_axes(X, Y) if self.filled: self._add_solids(X, Y, C) self._set_label() def _config_axes(self, X, Y): ''' Make an axes patch and outline. ''' ax = self.ax ax.set_frame_on(False) ax.set_navigate(False) xy = self._outline(X, Y) ax.update_datalim(xy) ax.set_xlim(*ax.dataLim.intervalx) ax.set_ylim(*ax.dataLim.intervaly) self.outline = lines.Line2D(xy[:, 0], xy[:, 1], color=mpl.rcParams['axes.edgecolor'], linewidth=mpl.rcParams['axes.linewidth']) ax.add_artist(self.outline) self.outline.set_clip_box(None) self.outline.set_clip_path(None) c = mpl.rcParams['axes.facecolor'] self.patch = patches.Polygon(xy, edgecolor=c, facecolor=c, linewidth=0.01, zorder=-1) ax.add_artist(self.patch) ticks, ticklabels, offset_string = self._ticker() if self.orientation == 'vertical': ax.set_xticks([]) ax.yaxis.set_label_position('right') ax.yaxis.set_ticks_position('right') ax.set_yticks(ticks) ax.set_yticklabels(ticklabels) ax.yaxis.get_major_formatter().set_offset_string(offset_string) else: ax.set_yticks([]) ax.xaxis.set_label_position('bottom') ax.set_xticks(ticks) ax.set_xticklabels(ticklabels) ax.xaxis.get_major_formatter().set_offset_string(offset_string) def _set_label(self): if self.orientation == 'vertical': self.ax.set_ylabel(self._label, **self._labelkw) else: self.ax.set_xlabel(self._label, **self._labelkw) def set_label(self, label, **kw): ''' Label the long axis of the colorbar ''' self._label = label self._labelkw = kw self._set_label() def _outline(self, X, Y): ''' Return *x*, *y* arrays of colorbar bounding polygon, taking orientation into account. ''' N = X.shape[0] ii = [0, 1, N-2, N-1, 2*N-1, 2*N-2, N+1, N, 0] x = np.take(np.ravel(np.transpose(X)), ii) y = np.take(np.ravel(np.transpose(Y)), ii) x = x.reshape((len(x), 1)) y = y.reshape((len(y), 1)) if self.orientation == 'horizontal': return np.hstack((y, x)) return np.hstack((x, y)) def _edges(self, X, Y): ''' Return the separator line segments; helper for _add_solids. ''' N = X.shape[0] # Using the non-array form of these line segments is much # simpler than making them into arrays. if self.orientation == 'vertical': return [zip(X[i], Y[i]) for i in range(1, N-1)] else: return [zip(Y[i], X[i]) for i in range(1, N-1)] def _add_solids(self, X, Y, C): ''' Draw the colors using :meth:`~matplotlib.axes.Axes.pcolor`; optionally add separators. ''' ## Change to pcolorfast after fixing bugs in some backends... if self.orientation == 'vertical': args = (X, Y, C) else: args = (np.transpose(Y), np.transpose(X), np.transpose(C)) kw = {'cmap':self.cmap, 'norm':self.norm, 'shading':'flat', 'alpha':self.alpha} # Save, set, and restore hold state to keep pcolor from # clearing the axes. Ordinarily this will not be needed, # since the axes object should already have hold set. _hold = self.ax.ishold() self.ax.hold(True) col = self.ax.pcolor(*args, **kw) self.ax.hold(_hold) #self.add_observer(col) # We should observe, not be observed... self.solids = col if self.drawedges: self.dividers = collections.LineCollection(self._edges(X,Y), colors=(mpl.rcParams['axes.edgecolor'],), linewidths=(0.5*mpl.rcParams['axes.linewidth'],) ) self.ax.add_collection(self.dividers) def add_lines(self, levels, colors, linewidths): ''' Draw lines on the colorbar. ''' N = len(levels) dummy, y = self._locate(levels) if len(y) <> N: raise ValueError("levels are outside colorbar range") x = np.array([0.0, 1.0]) X, Y = np.meshgrid(x,y) if self.orientation == 'vertical': xy = [zip(X[i], Y[i]) for i in range(N)] else: xy = [zip(Y[i], X[i]) for i in range(N)] col = collections.LineCollection(xy, linewidths=linewidths) self.lines = col col.set_color(colors) self.ax.add_collection(col) def _ticker(self): ''' Return two sequences: ticks (colorbar data locations) and ticklabels (strings). ''' locator = self.locator formatter = self.formatter if locator is None: if self.boundaries is None: if isinstance(self.norm, colors.NoNorm): nv = len(self._values) base = 1 + int(nv/10) locator = ticker.IndexLocator(base=base, offset=0) elif isinstance(self.norm, colors.BoundaryNorm): b = self.norm.boundaries locator = ticker.FixedLocator(b, nbins=10) elif isinstance(self.norm, colors.LogNorm): locator = ticker.LogLocator() else: locator = ticker.MaxNLocator() else: b = self._boundaries[self._inside] locator = ticker.FixedLocator(b, nbins=10) if isinstance(self.norm, colors.NoNorm): intv = self._values[0], self._values[-1] else: intv = self.vmin, self.vmax locator.create_dummy_axis() formatter.create_dummy_axis() locator.set_view_interval(*intv) locator.set_data_interval(*intv) formatter.set_view_interval(*intv) formatter.set_data_interval(*intv) b = np.array(locator()) b, ticks = self._locate(b) formatter.set_locs(b) ticklabels = [formatter(t, i) for i, t in enumerate(b)] offset_string = formatter.get_offset() return ticks, ticklabels, offset_string def _process_values(self, b=None): ''' Set the :attr:`_boundaries` and :attr:`_values` attributes based on the input boundaries and values. Input boundaries can be *self.boundaries* or the argument *b*. ''' if b is None: b = self.boundaries if b is not None: self._boundaries = np.asarray(b, dtype=float) if self.values is None: self._values = 0.5*(self._boundaries[:-1] + self._boundaries[1:]) if isinstance(self.norm, colors.NoNorm): self._values = (self._values + 0.00001).astype(np.int16) return self._values = np.array(self.values) return if self.values is not None: self._values = np.array(self.values) if self.boundaries is None: b = np.zeros(len(self.values)+1, 'd') b[1:-1] = 0.5*(self._values[:-1] - self._values[1:]) b[0] = 2.0*b[1] - b[2] b[-1] = 2.0*b[-2] - b[-3] self._boundaries = b return self._boundaries = np.array(self.boundaries) return # Neither boundaries nor values are specified; # make reasonable ones based on cmap and norm. if isinstance(self.norm, colors.NoNorm): b = self._uniform_y(self.cmap.N+1) * self.cmap.N - 0.5 v = np.zeros((len(b)-1,), dtype=np.int16) v[self._inside] = np.arange(self.cmap.N, dtype=np.int16) if self.extend in ('both', 'min'): v[0] = -1 if self.extend in ('both', 'max'): v[-1] = self.cmap.N self._boundaries = b self._values = v return elif isinstance(self.norm, colors.BoundaryNorm): b = list(self.norm.boundaries) if self.extend in ('both', 'min'): b = [b[0]-1] + b if self.extend in ('both', 'max'): b = b + [b[-1] + 1] b = np.array(b) v = np.zeros((len(b)-1,), dtype=float) bi = self.norm.boundaries v[self._inside] = 0.5*(bi[:-1] + bi[1:]) if self.extend in ('both', 'min'): v[0] = b[0] - 1 if self.extend in ('both', 'max'): v[-1] = b[-1] + 1 self._boundaries = b self._values = v return else: if not self.norm.scaled(): self.norm.vmin = 0 self.norm.vmax = 1 b = self.norm.inverse(self._uniform_y(self.cmap.N+1)) if self.extend in ('both', 'min'): b[0] = b[0] - 1 if self.extend in ('both', 'max'): b[-1] = b[-1] + 1 self._process_values(b) def _find_range(self): ''' Set :attr:`vmin` and :attr:`vmax` attributes to the first and last boundary excluding extended end boundaries. ''' b = self._boundaries[self._inside] self.vmin = b[0] self.vmax = b[-1] def _central_N(self): '''number of boundaries **before** extension of ends''' nb = len(self._boundaries) if self.extend == 'both': nb -= 2 elif self.extend in ('min', 'max'): nb -= 1 return nb def _extended_N(self): ''' Based on the colormap and extend variable, return the number of boundaries. ''' N = self.cmap.N + 1 if self.extend == 'both': N += 2 elif self.extend in ('min', 'max'): N += 1 return N def _uniform_y(self, N): ''' Return colorbar data coordinates for *N* uniformly spaced boundaries, plus ends if required. ''' if self.extend == 'neither': y = np.linspace(0, 1, N) else: if self.extend == 'both': y = np.zeros(N + 2, 'd') y[0] = -0.05 y[-1] = 1.05 elif self.extend == 'min': y = np.zeros(N + 1, 'd') y[0] = -0.05 else: y = np.zeros(N + 1, 'd') y[-1] = 1.05 y[self._inside] = np.linspace(0, 1, N) return y def _proportional_y(self): ''' Return colorbar data coordinates for the boundaries of a proportional colorbar. ''' if isinstance(self.norm, colors.BoundaryNorm): b = self._boundaries[self._inside] y = (self._boundaries - self._boundaries[0]) y = y / (self._boundaries[-1] - self._boundaries[0]) else: y = self.norm(self._boundaries.copy()) if self.extend in ('both', 'min'): y[0] = -0.05 if self.extend in ('both', 'max'): y[-1] = 1.05 yi = y[self._inside] norm = colors.Normalize(yi[0], yi[-1]) y[self._inside] = norm(yi) return y def _mesh(self): ''' Return X,Y, the coordinate arrays for the colorbar pcolormesh. These are suitable for a vertical colorbar; swapping and transposition for a horizontal colorbar are done outside this function. ''' x = np.array([0.0, 1.0]) if self.spacing == 'uniform': y = self._uniform_y(self._central_N()) else: y = self._proportional_y() self._y = y X, Y = np.meshgrid(x,y) if self.extend in ('min', 'both'): X[0,:] = 0.5 if self.extend in ('max', 'both'): X[-1,:] = 0.5 return X, Y def _locate(self, x): ''' Given a possible set of color data values, return the ones within range, together with their corresponding colorbar data coordinates. ''' if isinstance(self.norm, (colors.NoNorm, colors.BoundaryNorm)): b = self._boundaries xn = x xout = x else: # Do calculations using normalized coordinates so # as to make the interpolation more accurate. b = self.norm(self._boundaries, clip=False).filled() # We do our own clipping so that we can allow a tiny # bit of slop in the end point ticks to allow for # floating point errors. xn = self.norm(x, clip=False).filled() in_cond = (xn > -0.001) & (xn < 1.001) xn = np.compress(in_cond, xn) xout = np.compress(in_cond, x) # The rest is linear interpolation with clipping. y = self._y N = len(b) ii = np.minimum(np.searchsorted(b, xn), N-1) i0 = np.maximum(ii - 1, 0) #db = b[ii] - b[i0] db = np.take(b, ii) - np.take(b, i0) db = np.where(i0==ii, 1.0, db) #dy = y[ii] - y[i0] dy = np.take(y, ii) - np.take(y, i0) z = np.take(y, i0) + (xn-np.take(b,i0))*dy/db return xout, z def set_alpha(self, alpha): self.alpha = alpha class Colorbar(ColorbarBase): def __init__(self, ax, mappable, **kw): mappable.autoscale_None() # Ensure mappable.norm.vmin, vmax # are set when colorbar is called, # even if mappable.draw has not yet # been called. This will not change # vmin, vmax if they are already set. self.mappable = mappable kw['cmap'] = mappable.cmap kw['norm'] = mappable.norm kw['alpha'] = mappable.get_alpha() if isinstance(mappable, contour.ContourSet): CS = mappable kw['boundaries'] = CS._levels kw['values'] = CS.cvalues kw['extend'] = CS.extend #kw['ticks'] = CS._levels kw.setdefault('ticks', ticker.FixedLocator(CS.levels, nbins=10)) kw['filled'] = CS.filled ColorbarBase.__init__(self, ax, **kw) if not CS.filled: self.add_lines(CS) else: ColorbarBase.__init__(self, ax, **kw) def add_lines(self, CS): ''' Add the lines from a non-filled :class:`~matplotlib.contour.ContourSet` to the colorbar. ''' if not isinstance(CS, contour.ContourSet) or CS.filled: raise ValueError('add_lines is only for a ContourSet of lines') tcolors = [c[0] for c in CS.tcolors] tlinewidths = [t[0] for t in CS.tlinewidths] # The following was an attempt to get the colorbar lines # to follow subsequent changes in the contour lines, # but more work is needed: specifically, a careful # look at event sequences, and at how # to make one object track another automatically. #tcolors = [col.get_colors()[0] for col in CS.collections] #tlinewidths = [col.get_linewidth()[0] for lw in CS.collections] #print 'tlinewidths:', tlinewidths ColorbarBase.add_lines(self, CS.levels, tcolors, tlinewidths) def update_bruteforce(self, mappable): ''' Manually change any contour line colors. This is called when the image or contour plot to which this colorbar belongs is changed. ''' # We are using an ugly brute-force method: clearing and # redrawing the whole thing. The problem is that if any # properties have been changed by methods other than the # colorbar methods, those changes will be lost. self.ax.cla() self.draw_all() #if self.vmin != self.norm.vmin or self.vmax != self.norm.vmax: # self.ax.cla() # self.draw_all() if isinstance(self.mappable, contour.ContourSet): CS = self.mappable if not CS.filled: self.add_lines(CS) #if self.lines is not None: # tcolors = [c[0] for c in CS.tcolors] # self.lines.set_color(tcolors) #Fixme? Recalculate boundaries, ticks if vmin, vmax have changed. #Fixme: Some refactoring may be needed; we should not # be recalculating everything if there was a simple alpha # change. def make_axes(parent, **kw): orientation = kw.setdefault('orientation', 'vertical') fraction = kw.pop('fraction', 0.15) shrink = kw.pop('shrink', 1.0) aspect = kw.pop('aspect', 20) #pb = transforms.PBox(parent.get_position()) pb = parent.get_position(original=True).frozen() if orientation == 'vertical': pad = kw.pop('pad', 0.05) x1 = 1.0-fraction pb1, pbx, pbcb = pb.splitx(x1-pad, x1) pbcb = pbcb.shrunk(1.0, shrink).anchored('C', pbcb) anchor = (0.0, 0.5) panchor = (1.0, 0.5) else: pad = kw.pop('pad', 0.15) pbcb, pbx, pb1 = pb.splity(fraction, fraction+pad) pbcb = pbcb.shrunk(shrink, 1.0).anchored('C', pbcb) aspect = 1.0/aspect anchor = (0.5, 1.0) panchor = (0.5, 0.0) parent.set_position(pb1) parent.set_anchor(panchor) fig = parent.get_figure() cax = fig.add_axes(pbcb) cax.set_aspect(aspect, anchor=anchor, adjustable='box') return cax, kw make_axes.__doc__ =''' Resize and reposition a parent axes, and return a child axes suitable for a colorbar:: cax, kw = make_axes(parent, **kw) Keyword arguments may include the following (with defaults): *orientation* 'vertical' or 'horizontal' %s All but the first of these are stripped from the input kw set. Returns (cax, kw), the child axes and the reduced kw dictionary. ''' % make_axes_kw_doc

27,260

Python

.py

656

31.289634

93

0.554571

numenta/nupic-legacy

6,330

1,556

464

AGPL-3.0

9/5/2024, 5:13:42 PM (Europe/Amsterdam)

26,276

texmanager.py

numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/texmanager.py

""" This module supports embedded TeX expressions in matplotlib via dvipng and dvips for the raster and postscript backends. The tex and dvipng/dvips information is cached in ~/.matplotlib/tex.cache for reuse between sessions Requirements: * latex * \*Agg backends: dvipng * PS backend: latex w/ psfrag, dvips, and Ghostscript 8.51 (older versions do not work properly) Backends: * \*Agg * PS * PDF For raster output, you can get RGBA numpy arrays from TeX expressions as follows:: texmanager = TexManager() s = '\\TeX\\ is Number $\\displaystyle\\sum_{n=1}^\\infty\\frac{-e^{i\pi}}{2^n}$!' Z = self.texmanager.get_rgba(s, size=12, dpi=80, rgb=(1,0,0)) To enable tex rendering of all text in your matplotlib figure, set text.usetex in your matplotlibrc file (http://matplotlib.sf.net/matplotlibrc) or include these two lines in your script:: from matplotlib import rc rc('text', usetex=True) """ import copy, glob, os, shutil, sys, warnings try: from hashlib import md5 except ImportError: from md5 import md5 #Deprecated in 2.5 import distutils.version import numpy as np import matplotlib as mpl from matplotlib import rcParams from matplotlib._png import read_png DEBUG = False if sys.platform.startswith('win'): cmd_split = '&' else: cmd_split = ';' def dvipng_hack_alpha(): stdin, stdout = os.popen4('dvipng -version') for line in stdout: if line.startswith('dvipng '): version = line.split()[-1] mpl.verbose.report('Found dvipng version %s'% version, 'helpful') version = distutils.version.LooseVersion(version) return version < distutils.version.LooseVersion('1.6') raise RuntimeError('Could not obtain dvipng version') class TexManager: """ Convert strings to dvi files using TeX, caching the results to a working dir """ oldpath = mpl.get_home() if oldpath is None: oldpath = mpl.get_data_path() oldcache = os.path.join(oldpath, '.tex.cache') configdir = mpl.get_configdir() texcache = os.path.join(configdir, 'tex.cache') if os.path.exists(oldcache): print >> sys.stderr, """\ WARNING: found a TeX cache dir in the deprecated location "%s". Moving it to the new default location "%s"."""%(oldcache, texcache) shutil.move(oldcache, texcache) if not os.path.exists(texcache): os.mkdir(texcache) _dvipng_hack_alpha = dvipng_hack_alpha() # mappable cache of rgba_arrayd = {} grey_arrayd = {} postscriptd = {} pscnt = 0 serif = ('cmr', '') sans_serif = ('cmss', '') monospace = ('cmtt', '') cursive = ('pzc', r'\usepackage{chancery}') font_family = 'serif' font_families = ('serif', 'sans-serif', 'cursive', 'monospace') font_info = {'new century schoolbook': ('pnc', r'\renewcommand{\rmdefault}{pnc}'), 'bookman': ('pbk', r'\renewcommand{\rmdefault}{pbk}'), 'times': ('ptm', r'\usepackage{mathptmx}'), 'palatino': ('ppl', r'\usepackage{mathpazo}'), 'zapf chancery': ('pzc', r'\usepackage{chancery}'), 'cursive': ('pzc', r'\usepackage{chancery}'), 'charter': ('pch', r'\usepackage{charter}'), 'serif': ('cmr', ''), 'sans-serif': ('cmss', ''), 'helvetica': ('phv', r'\usepackage{helvet}'), 'avant garde': ('pag', r'\usepackage{avant}'), 'courier': ('pcr', r'\usepackage{courier}'), 'monospace': ('cmtt', ''), 'computer modern roman': ('cmr', ''), 'computer modern sans serif': ('cmss', ''), 'computer modern typewriter': ('cmtt', '')} _rc_cache = None _rc_cache_keys = ('text.latex.preamble', )\ + tuple(['font.'+n for n in ('family', ) + font_families]) def __init__(self): if not os.path.isdir(self.texcache): os.mkdir(self.texcache) ff = rcParams['font.family'].lower() if ff in self.font_families: self.font_family = ff else: mpl.verbose.report('The %s font family is not compatible with LaTeX. serif will be used by default.' % ff, 'helpful') self.font_family = 'serif' fontconfig = [self.font_family] for font_family, font_family_attr in \ [(ff, ff.replace('-', '_')) for ff in self.font_families]: for font in rcParams['font.'+font_family]: if font.lower() in self.font_info: found_font = self.font_info[font.lower()] setattr(self, font_family_attr, self.font_info[font.lower()]) if DEBUG: print 'family: %s, font: %s, info: %s'%(font_family, font, self.font_info[font.lower()]) break else: if DEBUG: print '$s font is not compatible with usetex' else: mpl.verbose.report('No LaTeX-compatible font found for the %s font family in rcParams. Using default.' % ff, 'helpful') setattr(self, font_family_attr, self.font_info[font_family]) fontconfig.append(getattr(self, font_family_attr)[0]) self._fontconfig = ''.join(fontconfig) # The following packages and commands need to be included in the latex # file's preamble: cmd = [self.serif[1], self.sans_serif[1], self.monospace[1]] if self.font_family == 'cursive': cmd.append(self.cursive[1]) while r'\usepackage{type1cm}' in cmd: cmd.remove(r'\usepackage{type1cm}') cmd = '\n'.join(cmd) self._font_preamble = '\n'.join([r'\usepackage{type1cm}', cmd, r'\usepackage{textcomp}']) def get_basefile(self, tex, fontsize, dpi=None): """ returns a filename based on a hash of the string, fontsize, and dpi """ s = ''.join([tex, self.get_font_config(), '%f'%fontsize, self.get_custom_preamble(), str(dpi or '')]) # make sure hash is consistent for all strings, regardless of encoding: bytes = unicode(s).encode('utf-8') return os.path.join(self.texcache, md5(bytes).hexdigest()) def get_font_config(self): """Reinitializes self if relevant rcParams on have changed.""" if self._rc_cache is None: self._rc_cache = dict([(k,None) for k in self._rc_cache_keys]) changed = [par for par in self._rc_cache_keys if rcParams[par] != \ self._rc_cache[par]] if changed: if DEBUG: print 'DEBUG following keys changed:', changed for k in changed: if DEBUG: print 'DEBUG %-20s: %-10s -> %-10s' % \ (k, self._rc_cache[k], rcParams[k]) # deepcopy may not be necessary, but feels more future-proof self._rc_cache[k] = copy.deepcopy(rcParams[k]) if DEBUG: print 'DEBUG RE-INIT\nold fontconfig:', self._fontconfig self.__init__() if DEBUG: print 'DEBUG fontconfig:', self._fontconfig return self._fontconfig def get_font_preamble(self): """ returns a string containing font configuration for the tex preamble """ return self._font_preamble def get_custom_preamble(self): """returns a string containing user additions to the tex preamble""" return '\n'.join(rcParams['text.latex.preamble']) def _get_shell_cmd(self, *args): """ On windows, changing directories can be complicated by the presence of multiple drives. get_shell_cmd deals with this issue. """ if sys.platform == 'win32': command = ['%s'% os.path.splitdrive(self.texcache)[0]] else: command = [] command.extend(args) return ' && '.join(command) def make_tex(self, tex, fontsize): """ Generate a tex file to render the tex string at a specific font size returns the file name """ basefile = self.get_basefile(tex, fontsize) texfile = '%s.tex'%basefile fh = file(texfile, 'w') custom_preamble = self.get_custom_preamble() fontcmd = {'sans-serif' : r'{\sffamily %s}', 'monospace' : r'{\ttfamily %s}'}.get(self.font_family, r'{\rmfamily %s}') tex = fontcmd % tex if rcParams['text.latex.unicode']: unicode_preamble = """\usepackage{ucs} \usepackage[utf8x]{inputenc}""" else: unicode_preamble = '' s = r"""\documentclass{article} %s %s %s \usepackage[papersize={72in,72in}, body={70in,70in}, margin={1in,1in}]{geometry} \pagestyle{empty} \begin{document} \fontsize{%f}{%f}%s \end{document} """ % (self._font_preamble, unicode_preamble, custom_preamble, fontsize, fontsize*1.25, tex) if rcParams['text.latex.unicode']: fh.write(s.encode('utf8')) else: try: fh.write(s) except UnicodeEncodeError, err: mpl.verbose.report("You are using unicode and latex, but have " "not enabled the matplotlib 'text.latex.unicode' " "rcParam.", 'helpful') raise fh.close() return texfile def make_dvi(self, tex, fontsize): """ generates a dvi file containing latex's layout of tex string returns the file name """ basefile = self.get_basefile(tex, fontsize) dvifile = '%s.dvi'% basefile if DEBUG or not os.path.exists(dvifile): texfile = self.make_tex(tex, fontsize) outfile = basefile+'.output' command = self._get_shell_cmd('cd "%s"'% self.texcache, 'latex -interaction=nonstopmode %s > "%s"'\ %(os.path.split(texfile)[-1], outfile)) mpl.verbose.report(command, 'debug') exit_status = os.system(command) try: fh = file(outfile) report = fh.read() fh.close() except IOError: report = 'No latex error report available.' if exit_status: raise RuntimeError(('LaTeX was not able to process the following \ string:\n%s\nHere is the full report generated by LaTeX: \n\n'% repr(tex)) + report) else: mpl.verbose.report(report, 'debug') for fname in glob.glob(basefile+'*'): if fname.endswith('dvi'): pass elif fname.endswith('tex'): pass else: try: os.remove(fname) except OSError: pass return dvifile def make_png(self, tex, fontsize, dpi): """ generates a png file containing latex's rendering of tex string returns the filename """ basefile = self.get_basefile(tex, fontsize, dpi) pngfile = '%s.png'% basefile # see get_rgba for a discussion of the background if DEBUG or not os.path.exists(pngfile): dvifile = self.make_dvi(tex, fontsize) outfile = basefile+'.output' command = self._get_shell_cmd('cd "%s"' % self.texcache, 'dvipng -bg Transparent -D %s -T tight -o \ "%s" "%s" > "%s"'%(dpi, os.path.split(pngfile)[-1], os.path.split(dvifile)[-1], outfile)) mpl.verbose.report(command, 'debug') exit_status = os.system(command) try: fh = file(outfile) report = fh.read() fh.close() except IOError: report = 'No dvipng error report available.' if exit_status: raise RuntimeError('dvipng was not able to \ process the flowing file:\n%s\nHere is the full report generated by dvipng: \ \n\n'% dvifile + report) else: mpl.verbose.report(report, 'debug') try: os.remove(outfile) except OSError: pass return pngfile def make_ps(self, tex, fontsize): """ generates a postscript file containing latex's rendering of tex string returns the file name """ basefile = self.get_basefile(tex, fontsize) psfile = '%s.epsf'% basefile if DEBUG or not os.path.exists(psfile): dvifile = self.make_dvi(tex, fontsize) outfile = basefile+'.output' command = self._get_shell_cmd('cd "%s"'% self.texcache, 'dvips -q -E -o "%s" "%s" > "%s"'\ %(os.path.split(psfile)[-1], os.path.split(dvifile)[-1], outfile)) mpl.verbose.report(command, 'debug') exit_status = os.system(command) fh = file(outfile) if exit_status: raise RuntimeError('dvipng was not able to \ process the flowing file:\n%s\nHere is the full report generated by dvipng: \ \n\n'% dvifile + fh.read()) else: mpl.verbose.report(fh.read(), 'debug') fh.close() os.remove(outfile) return psfile def get_ps_bbox(self, tex, fontsize): """ returns a list containing the postscript bounding box for latex's rendering of the tex string """ psfile = self.make_ps(tex, fontsize) ps = file(psfile) for line in ps: if line.startswith('%%BoundingBox:'): return [int(val) for val in line.split()[1:]] raise RuntimeError('Could not parse %s'%psfile) def get_grey(self, tex, fontsize=None, dpi=None): """returns the alpha channel""" key = tex, self.get_font_config(), fontsize, dpi alpha = self.grey_arrayd.get(key) if alpha is None: pngfile = self.make_png(tex, fontsize, dpi) X = read_png(os.path.join(self.texcache, pngfile)) if rcParams['text.dvipnghack'] is not None: hack = rcParams['text.dvipnghack'] else: hack = self._dvipng_hack_alpha if hack: # hack the alpha channel # dvipng assumed a constant background, whereas we want to # overlay these rasters with antialiasing over arbitrary # backgrounds that may have other figure elements under them. # When you set dvipng -bg Transparent, it actually makes the # alpha channel 1 and does the background compositing and # antialiasing itself and puts the blended data in the rgb # channels. So what we do is extract the alpha information # from the red channel, which is a blend of the default dvipng # background (white) and foreground (black). So the amount of # red (or green or blue for that matter since white and black # blend to a grayscale) is the alpha intensity. Once we # extract the correct alpha information, we assign it to the # alpha channel properly and let the users pick their rgb. In # this way, we can overlay tex strings on arbitrary # backgrounds with antialiasing # # red = alpha*red_foreground + (1-alpha)*red_background # # Since the foreground is black (0) and the background is # white (1) this reduces to red = 1-alpha or alpha = 1-red #alpha = npy.sqrt(1-X[:,:,0]) # should this be sqrt here? alpha = 1-X[:,:,0] else: alpha = X[:,:,-1] self.grey_arrayd[key] = alpha return alpha def get_rgba(self, tex, fontsize=None, dpi=None, rgb=(0,0,0)): """ Returns latex's rendering of the tex string as an rgba array """ if not fontsize: fontsize = rcParams['font.size'] if not dpi: dpi = rcParams['savefig.dpi'] r,g,b = rgb key = tex, self.get_font_config(), fontsize, dpi, tuple(rgb) Z = self.rgba_arrayd.get(key) if Z is None: alpha = self.get_grey(tex, fontsize, dpi) Z = np.zeros((alpha.shape[0], alpha.shape[1], 4), np.float) Z[:,:,0] = r Z[:,:,1] = g Z[:,:,2] = b Z[:,:,3] = alpha self.rgba_arrayd[key] = Z return Z

16,818

Python

.py

377

33.469496

135

0.565069

numenta/nupic-legacy

6,330

1,556

464

AGPL-3.0

9/5/2024, 5:13:42 PM (Europe/Amsterdam)

26,277

axis.py

numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/axis.py

""" Classes for the ticks and x and y axis """ from __future__ import division from matplotlib import rcParams import matplotlib.artist as artist import matplotlib.cbook as cbook import matplotlib.font_manager as font_manager import matplotlib.lines as mlines import matplotlib.patches as mpatches import matplotlib.scale as mscale import matplotlib.text as mtext import matplotlib.ticker as mticker import matplotlib.transforms as mtransforms import matplotlib.units as munits class Tick(artist.Artist): """ Abstract base class for the axis ticks, grid lines and labels 1 refers to the bottom of the plot for xticks and the left for yticks 2 refers to the top of the plot for xticks and the right for yticks Publicly accessible attributes: :attr:`tick1line` a Line2D instance :attr:`tick2line` a Line2D instance :attr:`gridline` a Line2D instance :attr:`label1` a Text instance :attr:`label2` a Text instance :attr:`gridOn` a boolean which determines whether to draw the tickline :attr:`tick1On` a boolean which determines whether to draw the 1st tickline :attr:`tick2On` a boolean which determines whether to draw the 2nd tickline :attr:`label1On` a boolean which determines whether to draw tick label :attr:`label2On` a boolean which determines whether to draw tick label """ def __init__(self, axes, loc, label, size = None, # points gridOn = None, # defaults to axes.grid tick1On = True, tick2On = True, label1On = True, label2On = False, major = True, ): """ bbox is the Bound2D bounding box in display coords of the Axes loc is the tick location in data coords size is the tick size in relative, axes coords """ artist.Artist.__init__(self) if gridOn is None: gridOn = rcParams['axes.grid'] self.set_figure(axes.figure) self.axes = axes name = self.__name__.lower() if size is None: if major: size = rcParams['%s.major.size'%name] pad = rcParams['%s.major.pad'%name] else: size = rcParams['%s.minor.size'%name] pad = rcParams['%s.minor.pad'%name] self._tickdir = rcParams['%s.direction'%name] if self._tickdir == 'in': self._xtickmarkers = (mlines.TICKUP, mlines.TICKDOWN) self._ytickmarkers = (mlines.TICKRIGHT, mlines.TICKLEFT) self._pad = pad else: self._xtickmarkers = (mlines.TICKDOWN, mlines.TICKUP) self._ytickmarkers = (mlines.TICKLEFT, mlines.TICKRIGHT) self._pad = pad + size self._loc = loc self._size = size self.tick1line = self._get_tick1line() self.tick2line = self._get_tick2line() self.gridline = self._get_gridline() self.label1 = self._get_text1() self.label = self.label1 # legacy name self.label2 = self._get_text2() self.gridOn = gridOn self.tick1On = tick1On self.tick2On = tick2On self.label1On = label1On self.label2On = label2On self.update_position(loc) def get_children(self): children = [self.tick1line, self.tick2line, self.gridline, self.label1, self.label2] return children def set_clip_path(self, clippath, transform=None): artist.Artist.set_clip_path(self, clippath, transform) #self.tick1line.set_clip_path(clippath, transform) #self.tick2line.set_clip_path(clippath, transform) self.gridline.set_clip_path(clippath, transform) set_clip_path.__doc__ = artist.Artist.set_clip_path.__doc__ def get_pad_pixels(self): return self.figure.dpi * self._pad / 72.0 def contains(self, mouseevent): """ Test whether the mouse event occured in the Tick marks. This function always returns false. It is more useful to test if the axis as a whole contains the mouse rather than the set of tick marks. """ if callable(self._contains): return self._contains(self,mouseevent) return False,{} def set_pad(self, val): """ Set the tick label pad in points ACCEPTS: float """ self._pad = val def get_pad(self): 'Get the value of the tick label pad in points' return self._pad def _get_text1(self): 'Get the default Text 1 instance' pass def _get_text2(self): 'Get the default Text 2 instance' pass def _get_tick1line(self): 'Get the default line2D instance for tick1' pass def _get_tick2line(self): 'Get the default line2D instance for tick2' pass def _get_gridline(self): 'Get the default grid Line2d instance for this tick' pass def get_loc(self): 'Return the tick location (data coords) as a scalar' return self._loc def draw(self, renderer): if not self.get_visible(): return renderer.open_group(self.__name__) midPoint = mtransforms.interval_contains(self.get_view_interval(), self.get_loc()) if midPoint: if self.gridOn: self.gridline.draw(renderer) if self.tick1On: self.tick1line.draw(renderer) if self.tick2On: self.tick2line.draw(renderer) if self.label1On: self.label1.draw(renderer) if self.label2On: self.label2.draw(renderer) renderer.close_group(self.__name__) def set_label1(self, s): """ Set the text of ticklabel ACCEPTS: str """ self.label1.set_text(s) set_label = set_label1 def set_label2(self, s): """ Set the text of ticklabel2 ACCEPTS: str """ self.label2.set_text(s) def _set_artist_props(self, a): a.set_figure(self.figure) #if isinstance(a, mlines.Line2D): a.set_clip_box(self.axes.bbox) def get_view_interval(self): 'return the view Interval instance for the axis this tick is ticking' raise NotImplementedError('Derived must override') def set_view_interval(self, vmin, vmax, ignore=False): raise NotImplementedError('Derived must override') class XTick(Tick): """ Contains all the Artists needed to make an x tick - the tick line, the label text and the grid line """ __name__ = 'xtick' def _get_text1(self): 'Get the default Text instance' # the y loc is 3 points below the min of y axis # get the affine as an a,b,c,d,tx,ty list # x in data coords, y in axes coords #t = mtext.Text( trans, vert, horiz = self.axes.get_xaxis_text1_transform(self._pad) size = rcParams['xtick.labelsize'] t = mtext.Text( x=0, y=0, fontproperties=font_manager.FontProperties(size=size), color=rcParams['xtick.color'], verticalalignment=vert, horizontalalignment=horiz, ) t.set_transform(trans) self._set_artist_props(t) return t def _get_text2(self): 'Get the default Text 2 instance' # x in data coords, y in axes coords #t = mtext.Text( trans, vert, horiz = self.axes.get_xaxis_text2_transform(self._pad) t = mtext.Text( x=0, y=1, fontproperties=font_manager.FontProperties(size=rcParams['xtick.labelsize']), color=rcParams['xtick.color'], verticalalignment=vert, horizontalalignment=horiz, ) t.set_transform(trans) self._set_artist_props(t) return t def _get_tick1line(self): 'Get the default line2D instance' # x in data coords, y in axes coords l = mlines.Line2D(xdata=(0,), ydata=(0,), color='k', linestyle = 'None', marker = self._xtickmarkers[0], markersize=self._size, ) l.set_transform(self.axes.get_xaxis_transform()) self._set_artist_props(l) return l def _get_tick2line(self): 'Get the default line2D instance' # x in data coords, y in axes coords l = mlines.Line2D( xdata=(0,), ydata=(1,), color='k', linestyle = 'None', marker = self._xtickmarkers[1], markersize=self._size, ) l.set_transform(self.axes.get_xaxis_transform()) self._set_artist_props(l) return l def _get_gridline(self): 'Get the default line2D instance' # x in data coords, y in axes coords l = mlines.Line2D(xdata=(0.0, 0.0), ydata=(0, 1.0), color=rcParams['grid.color'], linestyle=rcParams['grid.linestyle'], linewidth=rcParams['grid.linewidth'], ) l.set_transform(self.axes.get_xaxis_transform()) self._set_artist_props(l) return l def update_position(self, loc): 'Set the location of tick in data coords with scalar *loc*' x = loc nonlinear = (hasattr(self.axes, 'yaxis') and self.axes.yaxis.get_scale() != 'linear' or hasattr(self.axes, 'xaxis') and self.axes.xaxis.get_scale() != 'linear') if self.tick1On: self.tick1line.set_xdata((x,)) if self.tick2On: self.tick2line.set_xdata((x,)) if self.gridOn: self.gridline.set_xdata((x,)) if self.label1On: self.label1.set_x(x) if self.label2On: self.label2.set_x(x) if nonlinear: self.tick1line._invalid = True self.tick2line._invalid = True self.gridline._invalid = True self._loc = loc def get_view_interval(self): 'return the Interval instance for this axis view limits' return self.axes.viewLim.intervalx def set_view_interval(self, vmin, vmax, ignore = False): if ignore: self.axes.viewLim.intervalx = vmin, vmax else: Vmin, Vmax = self.get_view_interval() self.axes.viewLim.intervalx = min(vmin, Vmin), max(vmax, Vmax) def get_minpos(self): return self.axes.dataLim.minposx def get_data_interval(self): 'return the Interval instance for this axis data limits' return self.axes.dataLim.intervalx class YTick(Tick): """ Contains all the Artists needed to make a Y tick - the tick line, the label text and the grid line """ __name__ = 'ytick' # how far from the y axis line the right of the ticklabel are def _get_text1(self): 'Get the default Text instance' # x in axes coords, y in data coords #t = mtext.Text( trans, vert, horiz = self.axes.get_yaxis_text1_transform(self._pad) t = mtext.Text( x=0, y=0, fontproperties=font_manager.FontProperties(size=rcParams['ytick.labelsize']), color=rcParams['ytick.color'], verticalalignment=vert, horizontalalignment=horiz, ) t.set_transform(trans) #t.set_transform( self.axes.transData ) self._set_artist_props(t) return t def _get_text2(self): 'Get the default Text instance' # x in axes coords, y in data coords #t = mtext.Text( trans, vert, horiz = self.axes.get_yaxis_text2_transform(self._pad) t = mtext.Text( x=1, y=0, fontproperties=font_manager.FontProperties(size=rcParams['ytick.labelsize']), color=rcParams['ytick.color'], verticalalignment=vert, horizontalalignment=horiz, ) t.set_transform(trans) self._set_artist_props(t) return t def _get_tick1line(self): 'Get the default line2D instance' # x in axes coords, y in data coords l = mlines.Line2D( (0,), (0,), color='k', marker = self._ytickmarkers[0], linestyle = 'None', markersize=self._size, ) l.set_transform(self.axes.get_yaxis_transform()) self._set_artist_props(l) return l def _get_tick2line(self): 'Get the default line2D instance' # x in axes coords, y in data coords l = mlines.Line2D( (1,), (0,), color='k', marker = self._ytickmarkers[1], linestyle = 'None', markersize=self._size, ) l.set_transform(self.axes.get_yaxis_transform()) self._set_artist_props(l) return l def _get_gridline(self): 'Get the default line2D instance' # x in axes coords, y in data coords l = mlines.Line2D( xdata=(0,1), ydata=(0, 0), color=rcParams['grid.color'], linestyle=rcParams['grid.linestyle'], linewidth=rcParams['grid.linewidth'], ) l.set_transform(self.axes.get_yaxis_transform()) self._set_artist_props(l) return l def update_position(self, loc): 'Set the location of tick in data coords with scalar loc' y = loc nonlinear = (hasattr(self.axes, 'yaxis') and self.axes.yaxis.get_scale() != 'linear' or hasattr(self.axes, 'xaxis') and self.axes.xaxis.get_scale() != 'linear') if self.tick1On: self.tick1line.set_ydata((y,)) if self.tick2On: self.tick2line.set_ydata((y,)) if self.gridOn: self.gridline.set_ydata((y, )) if self.label1On: self.label1.set_y( y ) if self.label2On: self.label2.set_y( y ) if nonlinear: self.tick1line._invalid = True self.tick2line._invalid = True self.gridline._invalid = True self._loc = loc def get_view_interval(self): 'return the Interval instance for this axis view limits' return self.axes.viewLim.intervaly def set_view_interval(self, vmin, vmax, ignore = False): if ignore: self.axes.viewLim.intervaly = vmin, vmax else: Vmin, Vmax = self.get_view_interval() self.axes.viewLim.intervaly = min(vmin, Vmin), max(vmax, Vmax) def get_minpos(self): return self.axes.dataLim.minposy def get_data_interval(self): 'return the Interval instance for this axis data limits' return self.axes.dataLim.intervaly class Ticker: locator = None formatter = None class Axis(artist.Artist): """ Public attributes * :attr:`transData` - transform data coords to display coords * :attr:`transAxis` - transform axis coords to display coords """ LABELPAD = 5 OFFSETTEXTPAD = 3 def __str__(self): return self.__class__.__name__ \ + "(%f,%f)"%tuple(self.axes.transAxes.transform_point((0,0))) def __init__(self, axes, pickradius=15): """ Init the axis with the parent Axes instance """ artist.Artist.__init__(self) self.set_figure(axes.figure) self.axes = axes self.major = Ticker() self.minor = Ticker() self.callbacks = cbook.CallbackRegistry(('units', 'units finalize')) #class dummy: # locator = None # formatter = None #self.major = dummy() #self.minor = dummy() self._autolabelpos = True self.label = self._get_label() self.offsetText = self._get_offset_text() self.majorTicks = [] self.minorTicks = [] self.pickradius = pickradius self.cla() self.set_scale('linear') def set_label_coords(self, x, y, transform=None): """ Set the coordinates of the label. By default, the x coordinate of the y label is determined by the tick label bounding boxes, but this can lead to poor alignment of multiple ylabels if there are multiple axes. Ditto for the y coodinate of the x label. You can also specify the coordinate system of the label with the transform. If None, the default coordinate system will be the axes coordinate system (0,0) is (left,bottom), (0.5, 0.5) is middle, etc """ self._autolabelpos = False if transform is None: transform = self.axes.transAxes self.label.set_transform(transform) self.label.set_position((x, y)) def get_transform(self): return self._scale.get_transform() def get_scale(self): return self._scale.name def set_scale(self, value, **kwargs): self._scale = mscale.scale_factory(value, self, **kwargs) self._scale.set_default_locators_and_formatters(self) def limit_range_for_scale(self, vmin, vmax): return self._scale.limit_range_for_scale(vmin, vmax, self.get_minpos()) def get_children(self): children = [self.label] majorticks = self.get_major_ticks() minorticks = self.get_minor_ticks() children.extend(majorticks) children.extend(minorticks) return children def cla(self): 'clear the current axis' self.set_major_locator(mticker.AutoLocator()) self.set_major_formatter(mticker.ScalarFormatter()) self.set_minor_locator(mticker.NullLocator()) self.set_minor_formatter(mticker.NullFormatter()) # Clear the callback registry for this axis, or it may "leak" self.callbacks = cbook.CallbackRegistry(('units', 'units finalize')) # whether the grids are on self._gridOnMajor = rcParams['axes.grid'] self._gridOnMinor = False self.label.set_text('') self._set_artist_props(self.label) # build a few default ticks; grow as necessary later; only # define 1 so properties set on ticks will be copied as they # grow cbook.popall(self.majorTicks) cbook.popall(self.minorTicks) self.majorTicks.extend([self._get_tick(major=True)]) self.minorTicks.extend([self._get_tick(major=False)]) self._lastNumMajorTicks = 1 self._lastNumMinorTicks = 1 self.converter = None self.units = None self.set_units(None) def set_clip_path(self, clippath, transform=None): artist.Artist.set_clip_path(self, clippath, transform) majorticks = self.get_major_ticks() minorticks = self.get_minor_ticks() for child in self.majorTicks + self.minorTicks: child.set_clip_path(clippath, transform) def get_view_interval(self): 'return the Interval instance for this axis view limits' raise NotImplementedError('Derived must override') def set_view_interval(self, vmin, vmax, ignore=False): raise NotImplementedError('Derived must override') def get_data_interval(self): 'return the Interval instance for this axis data limits' raise NotImplementedError('Derived must override') def set_data_interval(self): 'Set the axis data limits' raise NotImplementedError('Derived must override') def _set_artist_props(self, a): if a is None: return a.set_figure(self.figure) def iter_ticks(self): """ Iterate through all of the major and minor ticks. """ majorLocs = self.major.locator() majorTicks = self.get_major_ticks(len(majorLocs)) self.major.formatter.set_locs(majorLocs) majorLabels = [self.major.formatter(val, i) for i, val in enumerate(majorLocs)] minorLocs = self.minor.locator() minorTicks = self.get_minor_ticks(len(minorLocs)) self.minor.formatter.set_locs(minorLocs) minorLabels = [self.minor.formatter(val, i) for i, val in enumerate(minorLocs)] major_minor = [ (majorTicks, majorLocs, majorLabels), (minorTicks, minorLocs, minorLabels)] for group in major_minor: for tick in zip(*group): yield tick def get_ticklabel_extents(self, renderer): """ Get the extents of the tick labels on either side of the axes. """ ticklabelBoxes = [] ticklabelBoxes2 = [] interval = self.get_view_interval() for tick, loc, label in self.iter_ticks(): if tick is None: continue if not mtransforms.interval_contains(interval, loc): continue tick.update_position(loc) tick.set_label1(label) tick.set_label2(label) if tick.label1On and tick.label1.get_visible(): extent = tick.label1.get_window_extent(renderer) ticklabelBoxes.append(extent) if tick.label2On and tick.label2.get_visible(): extent = tick.label2.get_window_extent(renderer) ticklabelBoxes2.append(extent) if len(ticklabelBoxes): bbox = mtransforms.Bbox.union(ticklabelBoxes) else: bbox = mtransforms.Bbox.from_extents(0, 0, 0, 0) if len(ticklabelBoxes2): bbox2 = mtransforms.Bbox.union(ticklabelBoxes2) else: bbox2 = mtransforms.Bbox.from_extents(0, 0, 0, 0) return bbox, bbox2 def draw(self, renderer, *args, **kwargs): 'Draw the axis lines, grid lines, tick lines and labels' ticklabelBoxes = [] ticklabelBoxes2 = [] if not self.get_visible(): return renderer.open_group(__name__) interval = self.get_view_interval() for tick, loc, label in self.iter_ticks(): if tick is None: continue if not mtransforms.interval_contains(interval, loc): continue tick.update_position(loc) tick.set_label1(label) tick.set_label2(label) tick.draw(renderer) if tick.label1On and tick.label1.get_visible(): extent = tick.label1.get_window_extent(renderer) ticklabelBoxes.append(extent) if tick.label2On and tick.label2.get_visible(): extent = tick.label2.get_window_extent(renderer) ticklabelBoxes2.append(extent) # scale up the axis label box to also find the neighbors, not # just the tick labels that actually overlap note we need a # *copy* of the axis label box because we don't wan't to scale # the actual bbox self._update_label_position(ticklabelBoxes, ticklabelBoxes2) self.label.draw(renderer) self._update_offset_text_position(ticklabelBoxes, ticklabelBoxes2) self.offsetText.set_text( self.major.formatter.get_offset() ) self.offsetText.draw(renderer) if 0: # draw the bounding boxes around the text for debug for tick in majorTicks: label = tick.label1 mpatches.bbox_artist(label, renderer) mpatches.bbox_artist(self.label, renderer) renderer.close_group(__name__) def _get_label(self): raise NotImplementedError('Derived must override') def _get_offset_text(self): raise NotImplementedError('Derived must override') def get_gridlines(self): 'Return the grid lines as a list of Line2D instance' ticks = self.get_major_ticks() return cbook.silent_list('Line2D gridline', [tick.gridline for tick in ticks]) def get_label(self): 'Return the axis label as a Text instance' return self.label def get_offset_text(self): 'Return the axis offsetText as a Text instance' return self.offsetText def get_pickradius(self): 'Return the depth of the axis used by the picker' return self.pickradius def get_majorticklabels(self): 'Return a list of Text instances for the major ticklabels' ticks = self.get_major_ticks() labels1 = [tick.label1 for tick in ticks if tick.label1On] labels2 = [tick.label2 for tick in ticks if tick.label2On] return cbook.silent_list('Text major ticklabel', labels1+labels2) def get_minorticklabels(self): 'Return a list of Text instances for the minor ticklabels' ticks = self.get_minor_ticks() labels1 = [tick.label1 for tick in ticks if tick.label1On] labels2 = [tick.label2 for tick in ticks if tick.label2On] return cbook.silent_list('Text minor ticklabel', labels1+labels2) def get_ticklabels(self, minor=False): 'Return a list of Text instances for ticklabels' if minor: return self.get_minorticklabels() return self.get_majorticklabels() def get_majorticklines(self): 'Return the major tick lines as a list of Line2D instances' lines = [] ticks = self.get_major_ticks() for tick in ticks: lines.append(tick.tick1line) lines.append(tick.tick2line) return cbook.silent_list('Line2D ticklines', lines) def get_minorticklines(self): 'Return the minor tick lines as a list of Line2D instances' lines = [] ticks = self.get_minor_ticks() for tick in ticks: lines.append(tick.tick1line) lines.append(tick.tick2line) return cbook.silent_list('Line2D ticklines', lines) def get_ticklines(self, minor=False): 'Return the tick lines as a list of Line2D instances' if minor: return self.get_minorticklines() return self.get_majorticklines() def get_majorticklocs(self): "Get the major tick locations in data coordinates as a numpy array" return self.major.locator() def get_minorticklocs(self): "Get the minor tick locations in data coordinates as a numpy array" return self.minor.locator() def get_ticklocs(self, minor=False): "Get the tick locations in data coordinates as a numpy array" if minor: return self.minor.locator() return self.major.locator() def _get_tick(self, major): 'return the default tick intsance' raise NotImplementedError('derived must override') def _copy_tick_props(self, src, dest): 'Copy the props from src tick to dest tick' if src is None or dest is None: return dest.label1.update_from(src.label1) dest.label2.update_from(src.label2) dest.tick1line.update_from(src.tick1line) dest.tick2line.update_from(src.tick2line) dest.gridline.update_from(src.gridline) dest.tick1On = src.tick1On dest.tick2On = src.tick2On dest.label1On = src.label1On dest.label2On = src.label2On def get_major_locator(self): 'Get the locator of the major ticker' return self.major.locator def get_minor_locator(self): 'Get the locator of the minor ticker' return self.minor.locator def get_major_formatter(self): 'Get the formatter of the major ticker' return self.major.formatter def get_minor_formatter(self): 'Get the formatter of the minor ticker' return self.minor.formatter def get_major_ticks(self, numticks=None): 'get the tick instances; grow as necessary' if numticks is None: numticks = len(self.get_major_locator()()) if len(self.majorTicks) < numticks: # update the new tick label properties from the old for i in range(numticks - len(self.majorTicks)): tick = self._get_tick(major=True) self.majorTicks.append(tick) if self._lastNumMajorTicks < numticks: protoTick = self.majorTicks[0] for i in range(self._lastNumMajorTicks, len(self.majorTicks)): tick = self.majorTicks[i] if self._gridOnMajor: tick.gridOn = True self._copy_tick_props(protoTick, tick) self._lastNumMajorTicks = numticks ticks = self.majorTicks[:numticks] return ticks def get_minor_ticks(self, numticks=None): 'get the minor tick instances; grow as necessary' if numticks is None: numticks = len(self.get_minor_locator()()) if len(self.minorTicks) < numticks: # update the new tick label properties from the old for i in range(numticks - len(self.minorTicks)): tick = self._get_tick(major=False) self.minorTicks.append(tick) if self._lastNumMinorTicks < numticks: protoTick = self.minorTicks[0] for i in range(self._lastNumMinorTicks, len(self.minorTicks)): tick = self.minorTicks[i] if self._gridOnMinor: tick.gridOn = True self._copy_tick_props(protoTick, tick) self._lastNumMinorTicks = numticks ticks = self.minorTicks[:numticks] return ticks def grid(self, b=None, which='major', **kwargs): """ Set the axis grid on or off; b is a boolean use *which* = 'major' | 'minor' to set the grid for major or minor ticks if *b* is *None* and len(kwargs)==0, toggle the grid state. If *kwargs* are supplied, it is assumed you want the grid on and *b* will be set to True *kwargs* are used to set the line properties of the grids, eg, xax.grid(color='r', linestyle='-', linewidth=2) """ if len(kwargs): b = True if which.lower().find('minor')>=0: if b is None: self._gridOnMinor = not self._gridOnMinor else: self._gridOnMinor = b for tick in self.minorTicks: # don't use get_ticks here! if tick is None: continue tick.gridOn = self._gridOnMinor if len(kwargs): artist.setp(tick.gridline,**kwargs) else: if b is None: self._gridOnMajor = not self._gridOnMajor else: self._gridOnMajor = b for tick in self.majorTicks: # don't use get_ticks here! if tick is None: continue tick.gridOn = self._gridOnMajor if len(kwargs): artist.setp(tick.gridline,**kwargs) def update_units(self, data): """ introspect *data* for units converter and update the axis.converter instance if necessary. Return *True* is *data* is registered for unit conversion """ converter = munits.registry.get_converter(data) if converter is None: return False self.converter = converter default = self.converter.default_units(data) #print 'update units: default="%s", units=%s"'%(default, self.units) if default is not None and self.units is None: self.set_units(default) self._update_axisinfo() return True def _update_axisinfo(self): """ check the axis converter for the stored units to see if the axis info needs to be updated """ if self.converter is None: return info = self.converter.axisinfo(self.units) if info is None: return if info.majloc is not None and self.major.locator!=info.majloc: self.set_major_locator(info.majloc) if info.minloc is not None and self.minor.locator!=info.minloc: self.set_minor_locator(info.minloc) if info.majfmt is not None and self.major.formatter!=info.majfmt: self.set_major_formatter(info.majfmt) if info.minfmt is not None and self.minor.formatter!=info.minfmt: self.set_minor_formatter(info.minfmt) if info.label is not None: label = self.get_label() label.set_text(info.label) def have_units(self): return self.converter is not None or self.units is not None def convert_units(self, x): if self.converter is None: self.converter = munits.registry.get_converter(x) if self.converter is None: #print 'convert_units returning identity: units=%s, converter=%s'%(self.units, self.converter) return x ret = self.converter.convert(x, self.units) #print 'convert_units converting: axis=%s, units=%s, converter=%s, in=%s, out=%s'%(self, self.units, self.converter, x, ret) return ret def set_units(self, u): """ set the units for axis ACCEPTS: a units tag """ pchanged = False if u is None: self.units = None pchanged = True else: if u!=self.units: self.units = u #print 'setting units', self.converter, u, munits.registry.get_converter(u) pchanged = True if pchanged: self._update_axisinfo() self.callbacks.process('units') self.callbacks.process('units finalize') def get_units(self): 'return the units for axis' return self.units def set_major_formatter(self, formatter): """ Set the formatter of the major ticker ACCEPTS: A :class:`~matplotlib.ticker.Formatter` instance """ self.major.formatter = formatter formatter.set_axis(self) def set_minor_formatter(self, formatter): """ Set the formatter of the minor ticker ACCEPTS: A :class:`~matplotlib.ticker.Formatter` instance """ self.minor.formatter = formatter formatter.set_axis(self) def set_major_locator(self, locator): """ Set the locator of the major ticker ACCEPTS: a :class:`~matplotlib.ticker.Locator` instance """ self.major.locator = locator locator.set_axis(self) def set_minor_locator(self, locator): """ Set the locator of the minor ticker ACCEPTS: a :class:`~matplotlib.ticker.Locator` instance """ self.minor.locator = locator locator.set_axis(self) def set_pickradius(self, pickradius): """ Set the depth of the axis used by the picker ACCEPTS: a distance in points """ self.pickradius = pickradius def set_ticklabels(self, ticklabels, *args, **kwargs): """ Set the text values of the tick labels. Return a list of Text instances. Use *kwarg* *minor=True* to select minor ticks. ACCEPTS: sequence of strings """ #ticklabels = [str(l) for l in ticklabels] minor = kwargs.pop('minor', False) if minor: self.set_minor_formatter(mticker.FixedFormatter(ticklabels)) ticks = self.get_minor_ticks() else: self.set_major_formatter( mticker.FixedFormatter(ticklabels) ) ticks = self.get_major_ticks() self.set_major_formatter( mticker.FixedFormatter(ticklabels) ) ret = [] for i, tick in enumerate(ticks): if i<len(ticklabels): tick.label1.set_text(ticklabels[i]) ret.append(tick.label1) tick.label1.update(kwargs) return ret def set_ticks(self, ticks, minor=False): """ Set the locations of the tick marks from sequence ticks ACCEPTS: sequence of floats """ ### XXX if the user changes units, the information will be lost here ticks = self.convert_units(ticks) if len(ticks) > 1: xleft, xright = self.get_view_interval() if xright > xleft: self.set_view_interval(min(ticks), max(ticks)) else: self.set_view_interval(max(ticks), min(ticks)) if minor: self.set_minor_locator(mticker.FixedLocator(ticks)) return self.get_minor_ticks(len(ticks)) else: self.set_major_locator( mticker.FixedLocator(ticks) ) return self.get_major_ticks(len(ticks)) def _update_label_position(self, bboxes, bboxes2): """ Update the label position based on the sequence of bounding boxes of all the ticklabels """ raise NotImplementedError('Derived must override') def _update_offset_text_postion(self, bboxes, bboxes2): """ Update the label position based on the sequence of bounding boxes of all the ticklabels """ raise NotImplementedError('Derived must override') def pan(self, numsteps): 'Pan *numsteps* (can be positive or negative)' self.major.locator.pan(numsteps) def zoom(self, direction): "Zoom in/out on axis; if *direction* is >0 zoom in, else zoom out" self.major.locator.zoom(direction) class XAxis(Axis): __name__ = 'xaxis' axis_name = 'x' def contains(self,mouseevent): """Test whether the mouse event occured in the x axis. """ if callable(self._contains): return self._contains(self,mouseevent) x,y = mouseevent.x,mouseevent.y try: trans = self.axes.transAxes.inverted() xaxes,yaxes = trans.transform_point((x,y)) except ValueError: return False, {} l,b = self.axes.transAxes.transform_point((0,0)) r,t = self.axes.transAxes.transform_point((1,1)) inaxis = xaxes>=0 and xaxes<=1 and ( (y<b and y>b-self.pickradius) or (y>t and y<t+self.pickradius)) return inaxis, {} def _get_tick(self, major): return XTick(self.axes, 0, '', major=major) def _get_label(self): # x in axes coords, y in display coords (to be updated at draw # time by _update_label_positions) label = mtext.Text(x=0.5, y=0, fontproperties = font_manager.FontProperties(size=rcParams['axes.labelsize']), color = rcParams['axes.labelcolor'], verticalalignment='top', horizontalalignment='center', ) label.set_transform( mtransforms.blended_transform_factory( self.axes.transAxes, mtransforms.IdentityTransform() )) self._set_artist_props(label) self.label_position='bottom' return label def _get_offset_text(self): # x in axes coords, y in display coords (to be updated at draw time) offsetText = mtext.Text(x=1, y=0, fontproperties = font_manager.FontProperties(size=rcParams['xtick.labelsize']), color = rcParams['xtick.color'], verticalalignment='top', horizontalalignment='right', ) offsetText.set_transform( mtransforms.blended_transform_factory( self.axes.transAxes, mtransforms.IdentityTransform() )) self._set_artist_props(offsetText) self.offset_text_position='bottom' return offsetText def get_label_position(self): """ Return the label position (top or bottom) """ return self.label_position def set_label_position(self, position): """ Set the label position (top or bottom) ACCEPTS: [ 'top' | 'bottom' ] """ assert position == 'top' or position == 'bottom' if position == 'top': self.label.set_verticalalignment('bottom') else: self.label.set_verticalalignment('top') self.label_position=position def _update_label_position(self, bboxes, bboxes2): """ Update the label position based on the sequence of bounding boxes of all the ticklabels """ if not self._autolabelpos: return x,y = self.label.get_position() if self.label_position == 'bottom': if not len(bboxes): bottom = self.axes.bbox.ymin else: bbox = mtransforms.Bbox.union(bboxes) bottom = bbox.y0 self.label.set_position( (x, bottom - self.LABELPAD*self.figure.dpi / 72.0)) else: if not len(bboxes2): top = self.axes.bbox.ymax else: bbox = mtransforms.Bbox.union(bboxes2) top = bbox.y1 self.label.set_position( (x, top+self.LABELPAD*self.figure.dpi / 72.0)) def _update_offset_text_position(self, bboxes, bboxes2): """ Update the offset_text position based on the sequence of bounding boxes of all the ticklabels """ x,y = self.offsetText.get_position() if not len(bboxes): bottom = self.axes.bbox.ymin else: bbox = mtransforms.Bbox.union(bboxes) bottom = bbox.y0 self.offsetText.set_position((x, bottom-self.OFFSETTEXTPAD*self.figure.dpi/72.0)) def get_text_heights(self, renderer): """ Returns the amount of space one should reserve for text above and below the axes. Returns a tuple (above, below) """ bbox, bbox2 = self.get_ticklabel_extents(renderer) # MGDTODO: Need a better way to get the pad padPixels = self.majorTicks[0].get_pad_pixels() above = 0.0 if bbox2.height: above += bbox2.height + padPixels below = 0.0 if bbox.height: below += bbox.height + padPixels if self.get_label_position() == 'top': above += self.label.get_window_extent(renderer).height + padPixels else: below += self.label.get_window_extent(renderer).height + padPixels return above, below def set_ticks_position(self, position): """ Set the ticks position (top, bottom, both, default or none) both sets the ticks to appear on both positions, but does not change the tick labels. default resets the tick positions to the default: ticks on both positions, labels at bottom. none can be used if you don't want any ticks. ACCEPTS: [ 'top' | 'bottom' | 'both' | 'default' | 'none' ] """ assert position in ('top', 'bottom', 'both', 'default', 'none') ticks = list( self.get_major_ticks() ) # a copy ticks.extend( self.get_minor_ticks() ) if position == 'top': for t in ticks: t.tick1On = False t.tick2On = True t.label1On = False t.label2On = True elif position == 'bottom': for t in ticks: t.tick1On = True t.tick2On = False t.label1On = True t.label2On = False elif position == 'default': for t in ticks: t.tick1On = True t.tick2On = True t.label1On = True t.label2On = False elif position == 'none': for t in ticks: t.tick1On = False t.tick2On = False else: for t in ticks: t.tick1On = True t.tick2On = True for t in ticks: t.update_position(t._loc) def tick_top(self): 'use ticks only on top' self.set_ticks_position('top') def tick_bottom(self): 'use ticks only on bottom' self.set_ticks_position('bottom') def get_ticks_position(self): """ Return the ticks position (top, bottom, default or unknown) """ majt=self.majorTicks[0] mT=self.minorTicks[0] majorTop=(not majt.tick1On) and majt.tick2On and (not majt.label1On) and majt.label2On minorTop=(not mT.tick1On) and mT.tick2On and (not mT.label1On) and mT.label2On if majorTop and minorTop: return 'top' MajorBottom=majt.tick1On and (not majt.tick2On) and majt.label1On and (not majt.label2On) MinorBottom=mT.tick1On and (not mT.tick2On) and mT.label1On and (not mT.label2On) if MajorBottom and MinorBottom: return 'bottom' majorDefault=majt.tick1On and majt.tick2On and majt.label1On and (not majt.label2On) minorDefault=mT.tick1On and mT.tick2On and mT.label1On and (not mT.label2On) if majorDefault and minorDefault: return 'default' return 'unknown' def get_view_interval(self): 'return the Interval instance for this axis view limits' return self.axes.viewLim.intervalx def set_view_interval(self, vmin, vmax, ignore=False): if ignore: self.axes.viewLim.intervalx = vmin, vmax else: Vmin, Vmax = self.get_view_interval() self.axes.viewLim.intervalx = min(vmin, Vmin), max(vmax, Vmax) def get_minpos(self): return self.axes.dataLim.minposx def get_data_interval(self): 'return the Interval instance for this axis data limits' return self.axes.dataLim.intervalx def set_data_interval(self, vmin, vmax, ignore=False): 'return the Interval instance for this axis data limits' if ignore: self.axes.dataLim.intervalx = vmin, vmax else: Vmin, Vmax = self.get_data_interval() self.axes.dataLim.intervalx = min(vmin, Vmin), max(vmax, Vmax) class YAxis(Axis): __name__ = 'yaxis' axis_name = 'y' def contains(self,mouseevent): """Test whether the mouse event occurred in the y axis. Returns *True* | *False* """ if callable(self._contains): return self._contains(self,mouseevent) x,y = mouseevent.x,mouseevent.y try: trans = self.axes.transAxes.inverted() xaxes,yaxes = trans.transform_point((x,y)) except ValueError: return False, {} l,b = self.axes.transAxes.transform_point((0,0)) r,t = self.axes.transAxes.transform_point((1,1)) inaxis = yaxes>=0 and yaxes<=1 and ( (x<l and x>l-self.pickradius) or (x>r and x<r+self.pickradius)) return inaxis, {} def _get_tick(self, major): return YTick(self.axes, 0, '', major=major) def _get_label(self): # x in display coords (updated by _update_label_position) # y in axes coords label = mtext.Text(x=0, y=0.5, # todo: get the label position fontproperties=font_manager.FontProperties(size=rcParams['axes.labelsize']), color = rcParams['axes.labelcolor'], verticalalignment='center', horizontalalignment='right', rotation='vertical', ) label.set_transform( mtransforms.blended_transform_factory( mtransforms.IdentityTransform(), self.axes.transAxes) ) self._set_artist_props(label) self.label_position='left' return label def _get_offset_text(self): # x in display coords, y in axes coords (to be updated at draw time) offsetText = mtext.Text(x=0, y=0.5, fontproperties = font_manager.FontProperties(size=rcParams['ytick.labelsize']), color = rcParams['ytick.color'], verticalalignment = 'bottom', horizontalalignment = 'left', ) offsetText.set_transform(mtransforms.blended_transform_factory( self.axes.transAxes, mtransforms.IdentityTransform()) ) self._set_artist_props(offsetText) self.offset_text_position='left' return offsetText def get_label_position(self): """ Return the label position (left or right) """ return self.label_position def set_label_position(self, position): """ Set the label position (left or right) ACCEPTS: [ 'left' | 'right' ] """ assert position == 'left' or position == 'right' if position == 'right': self.label.set_horizontalalignment('left') else: self.label.set_horizontalalignment('right') self.label_position=position def _update_label_position(self, bboxes, bboxes2): """ Update the label position based on the sequence of bounding boxes of all the ticklabels """ if not self._autolabelpos: return x,y = self.label.get_position() if self.label_position == 'left': if not len(bboxes): left = self.axes.bbox.xmin else: bbox = mtransforms.Bbox.union(bboxes) left = bbox.x0 self.label.set_position( (left-self.LABELPAD*self.figure.dpi/72.0, y)) else: if not len(bboxes2): right = self.axes.bbox.xmax else: bbox = mtransforms.Bbox.union(bboxes2) right = bbox.x1 self.label.set_position( (right+self.LABELPAD*self.figure.dpi/72.0, y)) def _update_offset_text_position(self, bboxes, bboxes2): """ Update the offset_text position based on the sequence of bounding boxes of all the ticklabels """ x,y = self.offsetText.get_position() top = self.axes.bbox.ymax self.offsetText.set_position((x, top+self.OFFSETTEXTPAD*self.figure.dpi/72.0)) def set_offset_position(self, position): assert position == 'left' or position == 'right' x,y = self.offsetText.get_position() if position == 'left': x = 0 else: x = 1 self.offsetText.set_ha(position) self.offsetText.set_position((x,y)) def get_text_widths(self, renderer): bbox, bbox2 = self.get_ticklabel_extents(renderer) # MGDTODO: Need a better way to get the pad padPixels = self.majorTicks[0].get_pad_pixels() left = 0.0 if bbox.width: left += bbox.width + padPixels right = 0.0 if bbox2.width: right += bbox2.width + padPixels if self.get_label_position() == 'left': left += self.label.get_window_extent(renderer).width + padPixels else: right += self.label.get_window_extent(renderer).width + padPixels return left, right def set_ticks_position(self, position): """ Set the ticks position (left, right, both or default) both sets the ticks to appear on both positions, but does not change the tick labels. default resets the tick positions to the default: ticks on both positions, labels on the left. ACCEPTS: [ 'left' | 'right' | 'both' | 'default' | 'none' ] """ assert position in ('left', 'right', 'both', 'default', 'none') ticks = list( self.get_major_ticks() ) # a copy ticks.extend( self.get_minor_ticks() ) if position == 'right': self.set_offset_position('right') for t in ticks: t.tick1On = False t.tick2On = True t.label1On = False t.label2On = True elif position == 'left': self.set_offset_position('left') for t in ticks: t.tick1On = True t.tick2On = False t.label1On = True t.label2On = False elif position == 'default': self.set_offset_position('left') for t in ticks: t.tick1On = True t.tick2On = True t.label1On = True t.label2On = False elif position == 'none': for t in ticks: t.tick1On = False t.tick2On = False else: self.set_offset_position('left') for t in ticks: t.tick1On = True t.tick2On = True def tick_right(self): 'use ticks only on right' self.set_ticks_position('right') def tick_left(self): 'use ticks only on left' self.set_ticks_position('left') def get_ticks_position(self): """ Return the ticks position (left, right, both or unknown) """ majt=self.majorTicks[0] mT=self.minorTicks[0] majorRight=(not majt.tick1On) and majt.tick2On and (not majt.label1On) and majt.label2On minorRight=(not mT.tick1On) and mT.tick2On and (not mT.label1On) and mT.label2On if majorRight and minorRight: return 'right' majorLeft=majt.tick1On and (not majt.tick2On) and majt.label1On and (not majt.label2On) minorLeft=mT.tick1On and (not mT.tick2On) and mT.label1On and (not mT.label2On) if majorLeft and minorLeft: return 'left' majorDefault=majt.tick1On and majt.tick2On and majt.label1On and (not majt.label2On) minorDefault=mT.tick1On and mT.tick2On and mT.label1On and (not mT.label2On) if majorDefault and minorDefault: return 'default' return 'unknown' def get_view_interval(self): 'return the Interval instance for this axis view limits' return self.axes.viewLim.intervaly def set_view_interval(self, vmin, vmax, ignore=False): if ignore: self.axes.viewLim.intervaly = vmin, vmax else: Vmin, Vmax = self.get_view_interval() self.axes.viewLim.intervaly = min(vmin, Vmin), max(vmax, Vmax) def get_minpos(self): return self.axes.dataLim.minposy def get_data_interval(self): 'return the Interval instance for this axis data limits' return self.axes.dataLim.intervaly def set_data_interval(self, vmin, vmax, ignore=False): 'return the Interval instance for this axis data limits' if ignore: self.axes.dataLim.intervaly = vmin, vmax else: Vmin, Vmax = self.get_data_interval() self.axes.dataLim.intervaly = min(vmin, Vmin), max(vmax, Vmax)

54,453

Python

.py

1,305

31.527969

132

0.604136

numenta/nupic-legacy

6,330

1,556

464

AGPL-3.0

9/5/2024, 5:13:42 PM (Europe/Amsterdam)

26,278

windowing.py

numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/windowing.py

from matplotlib import rcParams try: if not rcParams['tk.window_focus']: raise ImportError from _windowing import GetForegroundWindow, SetForegroundWindow except ImportError: def GetForegroundWindow(): return 0 def SetForegroundWindow(hwnd): pass class FocusManager: def __init__(self): self._shellWindow = GetForegroundWindow() def __del__(self): SetForegroundWindow(self._shellWindow)

454

Python

.py

15

24.8

67

0.715596

numenta/nupic-legacy

6,330

1,556

464

AGPL-3.0

9/5/2024, 5:13:42 PM (Europe/Amsterdam)

26,279

font_manager.py

numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/font_manager.py

""" A module for finding, managing, and using fonts across platforms. This module provides a single :class:`FontManager` instance that can be shared across backends and platforms. The :func:`findfont` function returns the best TrueType (TTF) font file in the local or system font path that matches the specified :class:`FontProperties` instance. The :class:`FontManager` also handles Adobe Font Metrics (AFM) font files for use by the PostScript backend. The design is based on the `W3C Cascading Style Sheet, Level 1 (CSS1) font specification <http://www.w3.org/TR/1998/REC-CSS2-19980512/>`_. Future versions may implement the Level 2 or 2.1 specifications. Experimental support is included for using `fontconfig <http://www.fontconfig.org>`_ on Unix variant plaforms (Linux, OS X, Solaris). To enable it, set the constant ``USE_FONTCONFIG`` in this file to ``True``. Fontconfig has the advantage that it is the standard way to look up fonts on X11 platforms, so if a font is installed, it is much more likely to be found. """ """ KNOWN ISSUES - documentation - font variant is untested - font stretch is incomplete - font size is incomplete - font size_adjust is incomplete - default font algorithm needs improvement and testing - setWeights function needs improvement - 'light' is an invalid weight value, remove it. - update_fonts not implemented Authors : John Hunter <jdhunter@ace.bsd.uchicago.edu> Paul Barrett <Barrett@STScI.Edu> Michael Droettboom <mdroe@STScI.edu> Copyright : John Hunter (2004,2005), Paul Barrett (2004,2005) License : matplotlib license (PSF compatible) The font directory code is from ttfquery, see license/LICENSE_TTFQUERY. """ import os, sys, glob try: set except NameError: from sets import Set as set import matplotlib from matplotlib import afm from matplotlib import ft2font from matplotlib import rcParams, get_configdir from matplotlib.cbook import is_string_like from matplotlib.fontconfig_pattern import \ parse_fontconfig_pattern, generate_fontconfig_pattern try: import cPickle as pickle except ImportError: import pickle USE_FONTCONFIG = False verbose = matplotlib.verbose font_scalings = { 'xx-small' : 0.579, 'x-small' : 0.694, 'small' : 0.833, 'medium' : 1.0, 'large' : 1.200, 'x-large' : 1.440, 'xx-large' : 1.728, 'larger' : 1.2, 'smaller' : 0.833, None : 1.0} stretch_dict = { 'ultra-condensed' : 100, 'extra-condensed' : 200, 'condensed' : 300, 'semi-condensed' : 400, 'normal' : 500, 'semi-expanded' : 600, 'expanded' : 700, 'extra-expanded' : 800, 'ultra-expanded' : 900} weight_dict = { 'ultralight' : 100, 'light' : 200, 'normal' : 400, 'regular' : 400, 'book' : 400, 'medium' : 500, 'roman' : 500, 'semibold' : 600, 'demibold' : 600, 'demi' : 600, 'bold' : 700, 'heavy' : 800, 'extra bold' : 800, 'black' : 900} font_family_aliases = set([ 'serif', 'sans-serif', 'cursive', 'fantasy', 'monospace', 'sans']) # OS Font paths MSFolders = \ r'Software\Microsoft\Windows\CurrentVersion\Explorer\Shell Folders' MSFontDirectories = [ r'SOFTWARE\Microsoft\Windows NT\CurrentVersion\Fonts', r'SOFTWARE\Microsoft\Windows\CurrentVersion\Fonts'] X11FontDirectories = [ # an old standard installation point "/usr/X11R6/lib/X11/fonts/TTF/", # here is the new standard location for fonts "/usr/share/fonts/", # documented as a good place to install new fonts "/usr/local/share/fonts/", # common application, not really useful "/usr/lib/openoffice/share/fonts/truetype/", ] OSXFontDirectories = [ "/Library/Fonts/", "/Network/Library/Fonts/", "/System/Library/Fonts/" ] if not USE_FONTCONFIG: home = os.environ.get('HOME') if home is not None: # user fonts on OSX path = os.path.join(home, 'Library', 'Fonts') OSXFontDirectories.append(path) path = os.path.join(home, '.fonts') X11FontDirectories.append(path) def get_fontext_synonyms(fontext): """ Return a list of file extensions extensions that are synonyms for the given file extension *fileext*. """ return {'ttf': ('ttf', 'otf'), 'otf': ('ttf', 'otf'), 'afm': ('afm',)}[fontext] def win32FontDirectory(): """ Return the user-specified font directory for Win32. This is looked up from the registry key:: \\HKEY_CURRENT_USER\Software\Microsoft\Windows\CurrentVersion\Explorer\Shell Folders\Fonts If the key is not found, $WINDIR/Fonts will be returned. """ try: import _winreg except ImportError: pass # Fall through to default else: try: user = _winreg.OpenKey(_winreg.HKEY_CURRENT_USER, MSFolders) try: try: return _winreg.QueryValueEx(user, 'Fonts')[0] except OSError: pass # Fall through to default finally: _winreg.CloseKey(user) except OSError: pass # Fall through to default return os.path.join(os.environ['WINDIR'], 'Fonts') def win32InstalledFonts(directory=None, fontext='ttf'): """ Search for fonts in the specified font directory, or use the system directories if none given. A list of TrueType font filenames are returned by default, or AFM fonts if *fontext* == 'afm'. """ import _winreg if directory is None: directory = win32FontDirectory() fontext = get_fontext_synonyms(fontext) key, items = None, {} for fontdir in MSFontDirectories: try: local = _winreg.OpenKey(_winreg.HKEY_LOCAL_MACHINE, fontdir) except OSError: continue if not local: files = [] for ext in fontext: files.extend(glob.glob(os.path.join(directory, '*.'+ext))) return files try: for j in range(_winreg.QueryInfoKey(local)[1]): try: key, direc, any = _winreg.EnumValue( local, j) if not os.path.dirname(direc): direc = os.path.join(directory, direc) direc = os.path.abspath(direc).lower() if os.path.splitext(direc)[1][1:] in fontext: items[direc] = 1 except EnvironmentError: continue except WindowsError: continue return items.keys() finally: _winreg.CloseKey(local) return None def OSXFontDirectory(): """ Return the system font directories for OS X. This is done by starting at the list of hardcoded paths in :attr:`OSXFontDirectories` and returning all nested directories within them. """ fontpaths = [] def add(arg,directory,files): fontpaths.append(directory) for fontdir in OSXFontDirectories: try: if os.path.isdir(fontdir): os.path.walk(fontdir, add, None) except (IOError, OSError, TypeError, ValueError): pass return fontpaths def OSXInstalledFonts(directory=None, fontext='ttf'): """ Get list of font files on OS X - ignores font suffix by default. """ if directory is None: directory = OSXFontDirectory() fontext = get_fontext_synonyms(fontext) files = [] for path in directory: if fontext is None: files.extend(glob.glob(os.path.join(path,'*'))) else: for ext in fontext: files.extend(glob.glob(os.path.join(path, '*.'+ext))) files.extend(glob.glob(os.path.join(path, '*.'+ext.upper()))) return files def x11FontDirectory(): """ Return the system font directories for X11. This is done by starting at the list of hardcoded paths in :attr:`X11FontDirectories` and returning all nested directories within them. """ fontpaths = [] def add(arg,directory,files): fontpaths.append(directory) for fontdir in X11FontDirectories: try: if os.path.isdir(fontdir): os.path.walk(fontdir, add, None) except (IOError, OSError, TypeError, ValueError): pass return fontpaths def get_fontconfig_fonts(fontext='ttf'): """ Grab a list of all the fonts that are being tracked by fontconfig by making a system call to ``fc-list``. This is an easy way to grab all of the fonts the user wants to be made available to applications, without needing knowing where all of them reside. """ try: import commands except ImportError: return {} fontext = get_fontext_synonyms(fontext) fontfiles = {} status, output = commands.getstatusoutput("fc-list file") if status == 0: for line in output.split('\n'): fname = line.split(':')[0] if (os.path.splitext(fname)[1][1:] in fontext and os.path.exists(fname)): fontfiles[fname] = 1 return fontfiles def findSystemFonts(fontpaths=None, fontext='ttf'): """ Search for fonts in the specified font paths. If no paths are given, will use a standard set of system paths, as well as the list of fonts tracked by fontconfig if fontconfig is installed and available. A list of TrueType fonts are returned by default with AFM fonts as an option. """ fontfiles = {} fontexts = get_fontext_synonyms(fontext) if fontpaths is None: if sys.platform == 'win32': fontdir = win32FontDirectory() fontpaths = [fontdir] # now get all installed fonts directly... for f in win32InstalledFonts(fontdir): base, ext = os.path.splitext(f) if len(ext)>1 and ext[1:].lower() in fontexts: fontfiles[f] = 1 else: fontpaths = x11FontDirectory() # check for OS X & load its fonts if present if sys.platform == 'darwin': for f in OSXInstalledFonts(fontext=fontext): fontfiles[f] = 1 for f in get_fontconfig_fonts(fontext): fontfiles[f] = 1 elif isinstance(fontpaths, (str, unicode)): fontpaths = [fontpaths] for path in fontpaths: files = [] for ext in fontexts: files.extend(glob.glob(os.path.join(path, '*.'+ext))) files.extend(glob.glob(os.path.join(path, '*.'+ext.upper()))) for fname in files: fontfiles[os.path.abspath(fname)] = 1 return [fname for fname in fontfiles.keys() if os.path.exists(fname)] def weight_as_number(weight): """ Return the weight property as a numeric value. String values are converted to their corresponding numeric value. """ if isinstance(weight, str): try: weight = weight_dict[weight.lower()] except KeyError: weight = 400 elif weight in range(100, 1000, 100): pass else: raise ValueError, 'weight not a valid integer' return weight class FontEntry(object): """ A class for storing Font properties. It is used when populating the font lookup dictionary. """ def __init__(self, fname ='', name ='', style ='normal', variant='normal', weight ='normal', stretch='normal', size ='medium', ): self.fname = fname self.name = name self.style = style self.variant = variant self.weight = weight self.stretch = stretch try: self.size = str(float(size)) except ValueError: self.size = size def ttfFontProperty(font): """ A function for populating the :class:`FontKey` by extracting information from the TrueType font file. *font* is a :class:`FT2Font` instance. """ name = font.family_name # Styles are: italic, oblique, and normal (default) sfnt = font.get_sfnt() sfnt2 = sfnt.get((1,0,0,2)) sfnt4 = sfnt.get((1,0,0,4)) if sfnt2: sfnt2 = sfnt2.lower() else: sfnt2 = '' if sfnt4: sfnt4 = sfnt4.lower() else: sfnt4 = '' if sfnt4.find('oblique') >= 0: style = 'oblique' elif sfnt4.find('italic') >= 0: style = 'italic' elif sfnt2.find('regular') >= 0: style = 'normal' elif font.style_flags & ft2font.ITALIC: style = 'italic' else: style = 'normal' # Variants are: small-caps and normal (default) # !!!! Untested if name.lower() in ['capitals', 'small-caps']: variant = 'small-caps' else: variant = 'normal' # Weights are: 100, 200, 300, 400 (normal: default), 500 (medium), # 600 (semibold, demibold), 700 (bold), 800 (heavy), 900 (black) # lighter and bolder are also allowed. weight = None for w in weight_dict.keys(): if sfnt4.find(w) >= 0: weight = w break if not weight: if font.style_flags & ft2font.BOLD: weight = 700 else: weight = 400 weight = weight_as_number(weight) # Stretch can be absolute and relative # Absolute stretches are: ultra-condensed, extra-condensed, condensed, # semi-condensed, normal, semi-expanded, expanded, extra-expanded, # and ultra-expanded. # Relative stretches are: wider, narrower # Child value is: inherit # !!!! Incomplete if sfnt4.find('narrow') >= 0 or sfnt4.find('condensed') >= 0 or \ sfnt4.find('cond') >= 0: stretch = 'condensed' elif sfnt4.find('demi cond') >= 0: stretch = 'semi-condensed' elif sfnt4.find('wide') >= 0 or sfnt4.find('expanded') >= 0: stretch = 'expanded' else: stretch = 'normal' # Sizes can be absolute and relative. # Absolute sizes are: xx-small, x-small, small, medium, large, x-large, # and xx-large. # Relative sizes are: larger, smaller # Length value is an absolute font size, e.g. 12pt # Percentage values are in 'em's. Most robust specification. # !!!! Incomplete if font.scalable: size = 'scalable' else: size = str(float(font.get_fontsize())) # !!!! Incomplete size_adjust = None return FontEntry(font.fname, name, style, variant, weight, stretch, size) def afmFontProperty(fontpath, font): """ A function for populating a :class:`FontKey` instance by extracting information from the AFM font file. *font* is a class:`AFM` instance. """ name = font.get_familyname() # Styles are: italic, oblique, and normal (default) if font.get_angle() != 0 or name.lower().find('italic') >= 0: style = 'italic' elif name.lower().find('oblique') >= 0: style = 'oblique' else: style = 'normal' # Variants are: small-caps and normal (default) # !!!! Untested if name.lower() in ['capitals', 'small-caps']: variant = 'small-caps' else: variant = 'normal' # Weights are: 100, 200, 300, 400 (normal: default), 500 (medium), # 600 (semibold, demibold), 700 (bold), 800 (heavy), 900 (black) # lighter and bolder are also allowed. weight = weight_as_number(font.get_weight().lower()) # Stretch can be absolute and relative # Absolute stretches are: ultra-condensed, extra-condensed, condensed, # semi-condensed, normal, semi-expanded, expanded, extra-expanded, # and ultra-expanded. # Relative stretches are: wider, narrower # Child value is: inherit # !!!! Incomplete stretch = 'normal' # Sizes can be absolute and relative. # Absolute sizes are: xx-small, x-small, small, medium, large, x-large, # and xx-large. # Relative sizes are: larger, smaller # Length value is an absolute font size, e.g. 12pt # Percentage values are in 'em's. Most robust specification. # All AFM fonts are apparently scalable. size = 'scalable' # !!!! Incomplete size_adjust = None return FontEntry(fontpath, name, style, variant, weight, stretch, size) def createFontList(fontfiles, fontext='ttf'): """ A function to create a font lookup list. The default is to create a list of TrueType fonts. An AFM font list can optionally be created. """ fontlist = [] # Add fonts from list of known font files. seen = {} for fpath in fontfiles: verbose.report('createFontDict: %s' % (fpath), 'debug') fname = os.path.split(fpath)[1] if fname in seen: continue else: seen[fname] = 1 if fontext == 'afm': try: fh = open(fpath, 'r') except: verbose.report("Could not open font file %s" % fpath) continue try: try: font = afm.AFM(fh) finally: fh.close() except RuntimeError: verbose.report("Could not parse font file %s"%fpath) continue prop = afmFontProperty(fpath, font) else: try: font = ft2font.FT2Font(str(fpath)) except RuntimeError: verbose.report("Could not open font file %s"%fpath) continue except UnicodeError: verbose.report("Cannot handle unicode filenames") #print >> sys.stderr, 'Bad file is', fpath continue try: prop = ttfFontProperty(font) except: continue fontlist.append(prop) return fontlist class FontProperties(object): """ A class for storing and manipulating font properties. The font properties are those described in the `W3C Cascading Style Sheet, Level 1 <http://www.w3.org/TR/1998/REC-CSS2-19980512/>`_ font specification. The six properties are: - family: A list of font names in decreasing order of priority. The items may include a generic font family name, either 'serif', 'sans-serif', 'cursive', 'fantasy', or 'monospace'. In that case, the actual font to be used will be looked up from the associated rcParam in :file:`matplotlibrc`. - style: Either 'normal', 'italic' or 'oblique'. - variant: Either 'normal' or 'small-caps'. - stretch: A numeric value in the range 0-1000 or one of 'ultra-condensed', 'extra-condensed', 'condensed', 'semi-condensed', 'normal', 'semi-expanded', 'expanded', 'extra-expanded' or 'ultra-expanded' - weight: A numeric value in the range 0-1000 or one of 'ultralight', 'light', 'normal', 'regular', 'book', 'medium', 'roman', 'semibold', 'demibold', 'demi', 'bold', 'heavy', 'extra bold', 'black' - size: Either an relative value of 'xx-small', 'x-small', 'small', 'medium', 'large', 'x-large', 'xx-large' or an absolute font size, e.g. 12 The default font property for TrueType fonts (as specified in the default :file:`matplotlibrc` file) is:: sans-serif, normal, normal, normal, normal, scalable. Alternatively, a font may be specified using an absolute path to a .ttf file, by using the *fname* kwarg. The preferred usage of font sizes is to use the relative values, e.g. 'large', instead of absolute font sizes, e.g. 12. This approach allows all text sizes to be made larger or smaller based on the font manager's default font size, i.e. by using the :meth:`FontManager.set_default_size` method. This class will also accept a `fontconfig <http://www.fontconfig.org/>`_ pattern, if it is the only argument provided. See the documentation on `fontconfig patterns <http://www.fontconfig.org/fontconfig-user.html>`_. This support does not require fontconfig to be installed. We are merely borrowing its pattern syntax for use here. Note that matplotlib's internal font manager and fontconfig use a different algorithm to lookup fonts, so the results of the same pattern may be different in matplotlib than in other applications that use fontconfig. """ def __init__(self, family = None, style = None, variant= None, weight = None, stretch= None, size = None, fname = None, # if this is set, it's a hardcoded filename to use _init = None # used only by copy() ): self._family = None self._slant = None self._variant = None self._weight = None self._stretch = None self._size = None self._file = None # This is used only by copy() if _init is not None: self.__dict__.update(_init.__dict__) return if is_string_like(family): # Treat family as a fontconfig pattern if it is the only # parameter provided. if (style is None and variant is None and weight is None and stretch is None and size is None and fname is None): self.set_fontconfig_pattern(family) return self.set_family(family) self.set_style(style) self.set_variant(variant) self.set_weight(weight) self.set_stretch(stretch) self.set_file(fname) self.set_size(size) def _parse_fontconfig_pattern(self, pattern): return parse_fontconfig_pattern(pattern) def __hash__(self): l = self.__dict__.items() l.sort() return hash(repr(l)) def __str__(self): return self.get_fontconfig_pattern() def get_family(self): """ Return a list of font names that comprise the font family. """ if self._family is None: family = rcParams['font.family'] if is_string_like(family): return [family] return family return self._family def get_name(self): """ Return the name of the font that best matches the font properties. """ return ft2font.FT2Font(str(findfont(self))).family_name def get_style(self): """ Return the font style. Values are: 'normal', 'italic' or 'oblique'. """ if self._slant is None: return rcParams['font.style'] return self._slant get_slant = get_style def get_variant(self): """ Return the font variant. Values are: 'normal' or 'small-caps'. """ if self._variant is None: return rcParams['font.variant'] return self._variant def get_weight(self): """ Set the font weight. Options are: A numeric value in the range 0-1000 or one of 'light', 'normal', 'regular', 'book', 'medium', 'roman', 'semibold', 'demibold', 'demi', 'bold', 'heavy', 'extra bold', 'black' """ if self._weight is None: return rcParams['font.weight'] return self._weight def get_stretch(self): """ Return the font stretch or width. Options are: 'ultra-condensed', 'extra-condensed', 'condensed', 'semi-condensed', 'normal', 'semi-expanded', 'expanded', 'extra-expanded', 'ultra-expanded'. """ if self._stretch is None: return rcParams['font.stretch'] return self._stretch def get_size(self): """ Return the font size. """ if self._size is None: return rcParams['font.size'] return self._size def get_size_in_points(self): if self._size is not None: try: return float(self._size) except ValueError: pass default_size = fontManager.get_default_size() return default_size * font_scalings.get(self._size) def get_file(self): """ Return the filename of the associated font. """ return self._file def get_fontconfig_pattern(self): """ Get a fontconfig pattern suitable for looking up the font as specified with fontconfig's ``fc-match`` utility. See the documentation on `fontconfig patterns <http://www.fontconfig.org/fontconfig-user.html>`_. This support does not require fontconfig to be installed or support for it to be enabled. We are merely borrowing its pattern syntax for use here. """ return generate_fontconfig_pattern(self) def set_family(self, family): """ Change the font family. May be either an alias (generic name is CSS parlance), such as: 'serif', 'sans-serif', 'cursive', 'fantasy', or 'monospace', or a real font name. """ if family is None: self._family = None else: if is_string_like(family): family = [family] self._family = family set_name = set_family def set_style(self, style): """ Set the font style. Values are: 'normal', 'italic' or 'oblique'. """ if style not in ('normal', 'italic', 'oblique', None): raise ValueError("style must be normal, italic or oblique") self._slant = style set_slant = set_style def set_variant(self, variant): """ Set the font variant. Values are: 'normal' or 'small-caps'. """ if variant not in ('normal', 'small-caps', None): raise ValueError("variant must be normal or small-caps") self._variant = variant def set_weight(self, weight): """ Set the font weight. May be either a numeric value in the range 0-1000 or one of 'ultralight', 'light', 'normal', 'regular', 'book', 'medium', 'roman', 'semibold', 'demibold', 'demi', 'bold', 'heavy', 'extra bold', 'black' """ if weight is not None: try: weight = int(weight) if weight < 0 or weight > 1000: raise ValueError() except ValueError: if weight not in weight_dict: raise ValueError("weight is invalid") self._weight = weight def set_stretch(self, stretch): """ Set the font stretch or width. Options are: 'ultra-condensed', 'extra-condensed', 'condensed', 'semi-condensed', 'normal', 'semi-expanded', 'expanded', 'extra-expanded' or 'ultra-expanded', or a numeric value in the range 0-1000. """ if stretch is not None: try: stretch = int(stretch) if stretch < 0 or stretch > 1000: raise ValueError() except ValueError: if stretch not in stretch_dict: raise ValueError("stretch is invalid") self._stretch = stretch def set_size(self, size): """ Set the font size. Either an relative value of 'xx-small', 'x-small', 'small', 'medium', 'large', 'x-large', 'xx-large' or an absolute font size, e.g. 12. """ if size is not None: try: size = float(size) except ValueError: if size is not None and size not in font_scalings: raise ValueError("size is invalid") self._size = size def set_file(self, file): """ Set the filename of the fontfile to use. In this case, all other properties will be ignored. """ self._file = file def set_fontconfig_pattern(self, pattern): """ Set the properties by parsing a fontconfig *pattern*. See the documentation on `fontconfig patterns <http://www.fontconfig.org/fontconfig-user.html>`_. This support does not require fontconfig to be installed or support for it to be enabled. We are merely borrowing its pattern syntax for use here. """ for key, val in self._parse_fontconfig_pattern(pattern).items(): if type(val) == list: getattr(self, "set_" + key)(val[0]) else: getattr(self, "set_" + key)(val) def copy(self): """Return a deep copy of self""" return FontProperties(_init = self) def ttfdict_to_fnames(d): """ flatten a ttfdict to all the filenames it contains """ fnames = [] for named in d.values(): for styled in named.values(): for variantd in styled.values(): for weightd in variantd.values(): for stretchd in weightd.values(): for fname in stretchd.values(): fnames.append(fname) return fnames def pickle_dump(data, filename): """ Equivalent to pickle.dump(data, open(filename, 'w')) but closes the file to prevent filehandle leakage. """ fh = open(filename, 'w') try: pickle.dump(data, fh) finally: fh.close() def pickle_load(filename): """ Equivalent to pickle.load(open(filename, 'r')) but closes the file to prevent filehandle leakage. """ fh = open(filename, 'r') try: data = pickle.load(fh) finally: fh.close() return data class FontManager: """ On import, the :class:`FontManager` singleton instance creates a list of TrueType fonts based on the font properties: name, style, variant, weight, stretch, and size. The :meth:`findfont` method does a nearest neighbor search to find the font that most closely matches the specification. If no good enough match is found, a default font is returned. """ def __init__(self, size=None, weight='normal'): self.__default_weight = weight self.default_size = size paths = [os.path.join(rcParams['datapath'], 'fonts', 'ttf'), os.path.join(rcParams['datapath'], 'fonts', 'afm')] # Create list of font paths for pathname in ['TTFPATH', 'AFMPATH']: if pathname in os.environ: ttfpath = os.environ[pathname] if ttfpath.find(';') >= 0: #win32 style paths.extend(ttfpath.split(';')) elif ttfpath.find(':') >= 0: # unix style paths.extend(ttfpath.split(':')) else: paths.append(ttfpath) verbose.report('font search path %s'%(str(paths))) # Load TrueType fonts and create font dictionary. self.ttffiles = findSystemFonts(paths) + findSystemFonts() for fname in self.ttffiles: verbose.report('trying fontname %s' % fname, 'debug') if fname.lower().find('vera.ttf')>=0: self.defaultFont = fname break else: # use anything self.defaultFont = self.ttffiles[0] self.ttflist = createFontList(self.ttffiles) if rcParams['pdf.use14corefonts']: # Load only the 14 PDF core fonts. These fonts do not need to be # embedded; every PDF viewing application is required to have them: # Helvetica, Helvetica-Bold, Helvetica-Oblique, Helvetica-BoldOblique, # Courier, Courier-Bold, Courier-Oblique, Courier-BoldOblique, # Times-Roman, Times-Bold, Times-Italic, Times-BoldItalic, Symbol, # ZapfDingbats. afmpath = os.path.join(rcParams['datapath'],'fonts','pdfcorefonts') afmfiles = findSystemFonts(afmpath, fontext='afm') self.afmlist = createFontList(afmfiles, fontext='afm') else: self.afmfiles = findSystemFonts(paths, fontext='afm') + \ findSystemFonts(fontext='afm') self.afmlist = createFontList(self.afmfiles, fontext='afm') self.ttf_lookup_cache = {} self.afm_lookup_cache = {} def get_default_weight(self): """ Return the default font weight. """ return self.__default_weight def get_default_size(self): """ Return the default font size. """ if self.default_size is None: return rcParams['font.size'] return self.default_size def set_default_weight(self, weight): """ Set the default font weight. The initial value is 'normal'. """ self.__default_weight = weight def set_default_size(self, size): """ Set the default font size in points. The initial value is set by ``font.size`` in rc. """ self.default_size = size def update_fonts(self, filenames): """ Update the font dictionary with new font files. Currently not implemented. """ # !!!! Needs implementing raise NotImplementedError # Each of the scoring functions below should return a value between # 0.0 (perfect match) and 1.0 (terrible match) def score_family(self, families, family2): """ Returns a match score between the list of font families in *families* and the font family name *family2*. An exact match anywhere in the list returns 0.0. A match by generic font name will return 0.1. No match will return 1.0. """ for i, family1 in enumerate(families): if family1.lower() in font_family_aliases: if family1 == 'sans': family1 == 'sans-serif' options = rcParams['font.' + family1] if family2 in options: idx = options.index(family2) return 0.1 * (float(idx) / len(options)) elif family1.lower() == family2.lower(): return 0.0 return 1.0 def score_style(self, style1, style2): """ Returns a match score between *style1* and *style2*. An exact match returns 0.0. A match between 'italic' and 'oblique' returns 0.1. No match returns 1.0. """ if style1 == style2: return 0.0 elif style1 in ('italic', 'oblique') and \ style2 in ('italic', 'oblique'): return 0.1 return 1.0 def score_variant(self, variant1, variant2): """ Returns a match score between *variant1* and *variant2*. An exact match returns 0.0, otherwise 1.0. """ if variant1 == variant2: return 0.0 else: return 1.0 def score_stretch(self, stretch1, stretch2): """ Returns a match score between *stretch1* and *stretch2*. The result is the absolute value of the difference between the CSS numeric values of *stretch1* and *stretch2*, normalized between 0.0 and 1.0. """ try: stretchval1 = int(stretch1) except ValueError: stretchval1 = stretch_dict.get(stretch1, 500) try: stretchval2 = int(stretch2) except ValueError: stretchval2 = stretch_dict.get(stretch2, 500) return abs(stretchval1 - stretchval2) / 1000.0 def score_weight(self, weight1, weight2): """ Returns a match score between *weight1* and *weight2*. The result is the absolute value of the difference between the CSS numeric values of *weight1* and *weight2*, normalized between 0.0 and 1.0. """ try: weightval1 = int(weight1) except ValueError: weightval1 = weight_dict.get(weight1, 500) try: weightval2 = int(weight2) except ValueError: weightval2 = weight_dict.get(weight2, 500) return abs(weightval1 - weightval2) / 1000.0 def score_size(self, size1, size2): """ Returns a match score between *size1* and *size2*. If *size2* (the size specified in the font file) is 'scalable', this function always returns 0.0, since any font size can be generated. Otherwise, the result is the absolute distance between *size1* and *size2*, normalized so that the usual range of font sizes (6pt - 72pt) will lie between 0.0 and 1.0. """ if size2 == 'scalable': return 0.0 # Size value should have already been try: sizeval1 = float(size1) except ValueError: sizeval1 = self.default_size * font_scalings(size1) try: sizeval2 = float(size2) except ValueError: return 1.0 return abs(sizeval1 - sizeval2) / 72.0 def findfont(self, prop, fontext='ttf'): """ Search the font list for the font that most closely matches the :class:`FontProperties` *prop*. :meth:`findfont` performs a nearest neighbor search. Each font is given a similarity score to the target font properties. The first font with the highest score is returned. If no matches below a certain threshold are found, the default font (usually Vera Sans) is returned. The result is cached, so subsequent lookups don't have to perform the O(n) nearest neighbor search. See the `W3C Cascading Style Sheet, Level 1 <http://www.w3.org/TR/1998/REC-CSS2-19980512/>`_ documentation for a description of the font finding algorithm. """ debug = False if prop is None: return self.defaultFont if is_string_like(prop): prop = FontProperties(prop) fname = prop.get_file() if fname is not None: verbose.report('findfont returning %s'%fname, 'debug') return fname if fontext == 'afm': font_cache = self.afm_lookup_cache fontlist = self.afmlist else: font_cache = self.ttf_lookup_cache fontlist = self.ttflist cached = font_cache.get(hash(prop)) if cached: return cached best_score = 1e64 best_font = None for font in fontlist: # Matching family should have highest priority, so it is multiplied # by 10.0 score = \ self.score_family(prop.get_family(), font.name) * 10.0 + \ self.score_style(prop.get_style(), font.style) + \ self.score_variant(prop.get_variant(), font.variant) + \ self.score_weight(prop.get_weight(), font.weight) + \ self.score_stretch(prop.get_stretch(), font.stretch) + \ self.score_size(prop.get_size(), font.size) if score < best_score: best_score = score best_font = font if score == 0: break if best_font is None or best_score >= 10.0: verbose.report('findfont: Could not match %s. Returning %s' % (prop, self.defaultFont)) result = self.defaultFont else: verbose.report('findfont: Matching %s to %s (%s) with score of %f' % (prop, best_font.name, best_font.fname, best_score)) result = best_font.fname font_cache[hash(prop)] = result return result _is_opentype_cff_font_cache = {} def is_opentype_cff_font(filename): """ Returns True if the given font is a Postscript Compact Font Format Font embedded in an OpenType wrapper. Used by the PostScript and PDF backends that can not subset these fonts. """ if os.path.splitext(filename)[1].lower() == '.otf': result = _is_opentype_cff_font_cache.get(filename) if result is None: fd = open(filename, 'rb') tag = fd.read(4) fd.close() result = (tag == 'OTTO') _is_opentype_cff_font_cache[filename] = result return result return False # The experimental fontconfig-based backend. if USE_FONTCONFIG and sys.platform != 'win32': import re def fc_match(pattern, fontext): import commands fontexts = get_fontext_synonyms(fontext) ext = "." + fontext status, output = commands.getstatusoutput('fc-match -sv "%s"' % pattern) if status == 0: for match in _fc_match_regex.finditer(output): file = match.group(1) if os.path.splitext(file)[1][1:] in fontexts: return file return None _fc_match_regex = re.compile(r'\sfile:\s+"([^"]*)"') _fc_match_cache = {} def findfont(prop, fontext='ttf'): if not is_string_like(prop): prop = prop.get_fontconfig_pattern() cached = _fc_match_cache.get(prop) if cached is not None: return cached result = fc_match(prop, fontext) if result is None: result = fc_match(':', fontext) _fc_match_cache[prop] = result return result else: _fmcache = os.path.join(get_configdir(), 'fontList.cache') fontManager = None def _rebuild(): global fontManager fontManager = FontManager() pickle_dump(fontManager, _fmcache) verbose.report("generated new fontManager") try: fontManager = pickle_load(_fmcache) fontManager.default_size = None verbose.report("Using fontManager instance from %s" % _fmcache) except: _rebuild() def findfont(prop, **kw): global fontManager font = fontManager.findfont(prop, **kw) if not os.path.exists(font): verbose.report("%s returned by pickled fontManager does not exist" % font) _rebuild() font = fontManager.findfont(prop, **kw) return font

42,655

Python

.py

1,121

29.15165

96

0.595298

numenta/nupic-legacy

6,330

1,556

464

AGPL-3.0

9/5/2024, 5:13:42 PM (Europe/Amsterdam)

26,280

image.py

numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/image.py

""" The image module supports basic image loading, rescaling and display operations. """ from __future__ import division import os, warnings import numpy as np from numpy import ma from matplotlib import rcParams from matplotlib import artist as martist from matplotlib import colors as mcolors from matplotlib import cm # For clarity, names from _image are given explicitly in this module: from matplotlib import _image from matplotlib import _png # For user convenience, the names from _image are also imported into # the image namespace: from matplotlib._image import * class AxesImage(martist.Artist, cm.ScalarMappable): zorder = 1 # map interpolation strings to module constants _interpd = { 'nearest' : _image.NEAREST, 'bilinear' : _image.BILINEAR, 'bicubic' : _image.BICUBIC, 'spline16' : _image.SPLINE16, 'spline36' : _image.SPLINE36, 'hanning' : _image.HANNING, 'hamming' : _image.HAMMING, 'hermite' : _image.HERMITE, 'kaiser' : _image.KAISER, 'quadric' : _image.QUADRIC, 'catrom' : _image.CATROM, 'gaussian' : _image.GAUSSIAN, 'bessel' : _image.BESSEL, 'mitchell' : _image.MITCHELL, 'sinc' : _image.SINC, 'lanczos' : _image.LANCZOS, 'blackman' : _image.BLACKMAN, } # reverse interp dict _interpdr = dict([ (v,k) for k,v in _interpd.items()]) interpnames = _interpd.keys() def __str__(self): return "AxesImage(%g,%g;%gx%g)" % tuple(self.axes.bbox.bounds) def __init__(self, ax, cmap = None, norm = None, interpolation=None, origin=None, extent=None, filternorm=1, filterrad=4.0, resample = False, **kwargs ): """ interpolation and cmap default to their rc settings cmap is a colors.Colormap instance norm is a colors.Normalize instance to map luminance to 0-1 extent is data axes (left, right, bottom, top) for making image plots registered with data plots. Default is to label the pixel centers with the zero-based row and column indices. Additional kwargs are matplotlib.artist properties """ martist.Artist.__init__(self) cm.ScalarMappable.__init__(self, norm, cmap) if origin is None: origin = rcParams['image.origin'] self.origin = origin self._extent = extent self.set_filternorm(filternorm) self.set_filterrad(filterrad) self._filterrad = filterrad self.set_interpolation(interpolation) self.set_resample(resample) self.axes = ax self._imcache = None self.update(kwargs) def get_size(self): 'Get the numrows, numcols of the input image' if self._A is None: raise RuntimeError('You must first set the image array') return self._A.shape[:2] def set_alpha(self, alpha): """ Set the alpha value used for blending - not supported on all backends ACCEPTS: float """ martist.Artist.set_alpha(self, alpha) self._imcache = None def changed(self): """ Call this whenever the mappable is changed so observers can update state """ self._imcache = None self._rgbacache = None cm.ScalarMappable.changed(self) def make_image(self, magnification=1.0): if self._A is None: raise RuntimeError('You must first set the image array or the image attribute') xmin, xmax, ymin, ymax = self.get_extent() dxintv = xmax-xmin dyintv = ymax-ymin # the viewport scale factor sx = dxintv/self.axes.viewLim.width sy = dyintv/self.axes.viewLim.height numrows, numcols = self._A.shape[:2] if sx > 2: x0 = (self.axes.viewLim.x0-xmin)/dxintv * numcols ix0 = max(0, int(x0 - self._filterrad)) x1 = (self.axes.viewLim.x1-xmin)/dxintv * numcols ix1 = min(numcols, int(x1 + self._filterrad)) xslice = slice(ix0, ix1) xmin_old = xmin xmin = xmin_old + ix0*dxintv/numcols xmax = xmin_old + ix1*dxintv/numcols dxintv = xmax - xmin sx = dxintv/self.axes.viewLim.width else: xslice = slice(0, numcols) if sy > 2: y0 = (self.axes.viewLim.y0-ymin)/dyintv * numrows iy0 = max(0, int(y0 - self._filterrad)) y1 = (self.axes.viewLim.y1-ymin)/dyintv * numrows iy1 = min(numrows, int(y1 + self._filterrad)) if self.origin == 'upper': yslice = slice(numrows-iy1, numrows-iy0) else: yslice = slice(iy0, iy1) ymin_old = ymin ymin = ymin_old + iy0*dyintv/numrows ymax = ymin_old + iy1*dyintv/numrows dyintv = ymax - ymin sy = dyintv/self.axes.viewLim.height else: yslice = slice(0, numrows) if xslice != self._oldxslice or yslice != self._oldyslice: self._imcache = None self._oldxslice = xslice self._oldyslice = yslice if self._imcache is None: if self._A.dtype == np.uint8 and len(self._A.shape) == 3: im = _image.frombyte(self._A[yslice,xslice,:], 0) im.is_grayscale = False else: if self._rgbacache is None: x = self.to_rgba(self._A, self._alpha) self._rgbacache = x else: x = self._rgbacache im = _image.fromarray(x[yslice,xslice], 0) if len(self._A.shape) == 2: im.is_grayscale = self.cmap.is_gray() else: im.is_grayscale = False self._imcache = im if self.origin=='upper': im.flipud_in() else: im = self._imcache fc = self.axes.patch.get_facecolor() bg = mcolors.colorConverter.to_rgba(fc, 0) im.set_bg( *bg) # image input dimensions im.reset_matrix() numrows, numcols = im.get_size() im.set_interpolation(self._interpd[self._interpolation]) im.set_resample(self._resample) # the viewport translation tx = (xmin-self.axes.viewLim.x0)/dxintv * numcols ty = (ymin-self.axes.viewLim.y0)/dyintv * numrows l, b, r, t = self.axes.bbox.extents widthDisplay = (round(r) + 0.5) - (round(l) - 0.5) heightDisplay = (round(t) + 0.5) - (round(b) - 0.5) widthDisplay *= magnification heightDisplay *= magnification im.apply_translation(tx, ty) # resize viewport to display rx = widthDisplay / numcols ry = heightDisplay / numrows im.apply_scaling(rx*sx, ry*sy) im.resize(int(widthDisplay+0.5), int(heightDisplay+0.5), norm=self._filternorm, radius=self._filterrad) return im def draw(self, renderer, *args, **kwargs): if not self.get_visible(): return if (self.axes.get_xscale() != 'linear' or self.axes.get_yscale() != 'linear'): warnings.warn("Images are not supported on non-linear axes.") im = self.make_image(renderer.get_image_magnification()) im._url = self.get_url() l, b, widthDisplay, heightDisplay = self.axes.bbox.bounds clippath, affine = self.get_transformed_clip_path_and_affine() renderer.draw_image(round(l), round(b), im, self.axes.bbox.frozen(), clippath, affine) def contains(self, mouseevent): """Test whether the mouse event occured within the image. """ if callable(self._contains): return self._contains(self,mouseevent) # TODO: make sure this is consistent with patch and patch # collection on nonlinear transformed coordinates. # TODO: consider returning image coordinates (shouldn't # be too difficult given that the image is rectilinear x, y = mouseevent.xdata, mouseevent.ydata xmin, xmax, ymin, ymax = self.get_extent() if xmin > xmax: xmin,xmax = xmax,xmin if ymin > ymax: ymin,ymax = ymax,ymin #print x, y, xmin, xmax, ymin, ymax if x is not None and y is not None: inside = x>=xmin and x<=xmax and y>=ymin and y<=ymax else: inside = False return inside,{} def write_png(self, fname, noscale=False): """Write the image to png file with fname""" im = self.make_image() if noscale: numrows, numcols = im.get_size() im.reset_matrix() im.set_interpolation(0) im.resize(numcols, numrows) im.flipud_out() rows, cols, buffer = im.as_rgba_str() _png.write_png(buffer, cols, rows, fname) def set_data(self, A, shape=None): """ Set the image array ACCEPTS: numpy/PIL Image A""" # check if data is PIL Image without importing Image if hasattr(A,'getpixel'): self._A = pil_to_array(A) elif ma.isMA(A): self._A = A else: self._A = np.asarray(A) # assume array if self._A.dtype != np.uint8 and not np.can_cast(self._A.dtype, np.float): raise TypeError("Image data can not convert to float") if (self._A.ndim not in (2, 3) or (self._A.ndim == 3 and self._A.shape[-1] not in (3, 4))): raise TypeError("Invalid dimensions for image data") self._imcache =None self._rgbacache = None self._oldxslice = None self._oldyslice = None def set_array(self, A): """ retained for backwards compatibility - use set_data instead ACCEPTS: numpy array A or PIL Image""" # This also needs to be here to override the inherited # cm.ScalarMappable.set_array method so it is not invoked # by mistake. self.set_data(A) def set_extent(self, extent): """extent is data axes (left, right, bottom, top) for making image plots """ self._extent = extent xmin, xmax, ymin, ymax = extent corners = (xmin, ymin), (xmax, ymax) self.axes.update_datalim(corners) if self.axes._autoscaleon: self.axes.set_xlim((xmin, xmax)) self.axes.set_ylim((ymin, ymax)) def get_interpolation(self): """ Return the interpolation method the image uses when resizing. One of 'nearest', 'bilinear', 'bicubic', 'spline16', 'spline36', 'hanning', 'hamming', 'hermite', 'kaiser', 'quadric', 'catrom', 'gaussian', 'bessel', 'mitchell', 'sinc', 'lanczos', """ return self._interpolation def set_interpolation(self, s): """ Set the interpolation method the image uses when resizing. ACCEPTS: ['nearest' | 'bilinear' | 'bicubic' | 'spline16' | 'spline36' | 'hanning' | 'hamming' | 'hermite' | 'kaiser' | 'quadric' | 'catrom' | 'gaussian' | 'bessel' | 'mitchell' | 'sinc' | 'lanczos' | ] """ if s is None: s = rcParams['image.interpolation'] s = s.lower() if s not in self._interpd: raise ValueError('Illegal interpolation string') self._interpolation = s def set_resample(self, v): if v is None: v = rcParams['image.resample'] self._resample = v def get_interpolation(self): return self._resample def get_extent(self): 'get the image extent: left, right, bottom, top' if self._extent is not None: return self._extent else: sz = self.get_size() #print 'sz', sz numrows, numcols = sz if self.origin == 'upper': return (-0.5, numcols-0.5, numrows-0.5, -0.5) else: return (-0.5, numcols-0.5, -0.5, numrows-0.5) def set_filternorm(self, filternorm): """Set whether the resize filter norms the weights -- see help for imshow ACCEPTS: 0 or 1 """ if filternorm: self._filternorm = 1 else: self._filternorm = 0 def get_filternorm(self): 'return the filternorm setting' return self._filternorm def set_filterrad(self, filterrad): """Set the resize filter radius only applicable to some interpolation schemes -- see help for imshow ACCEPTS: positive float """ r = float(filterrad) assert(r>0) self._filterrad = r def get_filterrad(self): 'return the filterrad setting' return self._filterrad class NonUniformImage(AxesImage): def __init__(self, ax, **kwargs ): interp = kwargs.pop('interpolation', 'nearest') AxesImage.__init__(self, ax, **kwargs) AxesImage.set_interpolation(self, interp) def make_image(self, magnification=1.0): if self._A is None: raise RuntimeError('You must first set the image array') x0, y0, v_width, v_height = self.axes.viewLim.bounds l, b, r, t = self.axes.bbox.extents width = (round(r) + 0.5) - (round(l) - 0.5) height = (round(t) + 0.5) - (round(b) - 0.5) width *= magnification height *= magnification im = _image.pcolor(self._Ax, self._Ay, self._A, height, width, (x0, x0+v_width, y0, y0+v_height), self._interpd[self._interpolation]) fc = self.axes.patch.get_facecolor() bg = mcolors.colorConverter.to_rgba(fc, 0) im.set_bg(*bg) im.is_grayscale = self.is_grayscale return im def set_data(self, x, y, A): x = np.asarray(x,np.float32) y = np.asarray(y,np.float32) if not ma.isMA(A): A = np.asarray(A) if len(x.shape) != 1 or len(y.shape) != 1\ or A.shape[0:2] != (y.shape[0], x.shape[0]): raise TypeError("Axes don't match array shape") if len(A.shape) not in [2, 3]: raise TypeError("Can only plot 2D or 3D data") if len(A.shape) == 3 and A.shape[2] not in [1, 3, 4]: raise TypeError("3D arrays must have three (RGB) or four (RGBA) color components") if len(A.shape) == 3 and A.shape[2] == 1: A.shape = A.shape[0:2] if len(A.shape) == 2: if A.dtype != np.uint8: A = (self.cmap(self.norm(A))*255).astype(np.uint8) self.is_grayscale = self.cmap.is_gray() else: A = np.repeat(A[:,:,np.newaxis], 4, 2) A[:,:,3] = 255 self.is_grayscale = True else: if A.dtype != np.uint8: A = (255*A).astype(np.uint8) if A.shape[2] == 3: B = zeros(tuple(list(A.shape[0:2]) + [4]), np.uint8) B[:,:,0:3] = A B[:,:,3] = 255 A = B self.is_grayscale = False self._A = A self._Ax = x self._Ay = y self._imcache = None def set_array(self, *args): raise NotImplementedError('Method not supported') def set_interpolation(self, s): if s != None and not s in ('nearest','bilinear'): raise NotImplementedError('Only nearest neighbor and bilinear interpolations are supported') AxesImage.set_interpolation(self, s) def get_extent(self): if self._A is None: raise RuntimeError('Must set data first') return self._Ax[0], self._Ax[-1], self._Ay[0], self._Ay[-1] def set_filternorm(self, s): pass def set_filterrad(self, s): pass def set_norm(self, norm): if self._A is not None: raise RuntimeError('Cannot change colors after loading data') cm.ScalarMappable.set_norm(self, norm) def set_cmap(self, cmap): if self._A is not None: raise RuntimeError('Cannot change colors after loading data') cm.ScalarMappable.set_cmap(self, norm) class PcolorImage(martist.Artist, cm.ScalarMappable): ''' Make a pcolor-style plot with an irregular rectangular grid. This uses a variation of the original irregular image code, and it is used by pcolorfast for the corresponding grid type. ''' def __init__(self, ax, x=None, y=None, A=None, cmap = None, norm = None, **kwargs ): """ cmap defaults to its rc setting cmap is a colors.Colormap instance norm is a colors.Normalize instance to map luminance to 0-1 Additional kwargs are matplotlib.artist properties """ martist.Artist.__init__(self) cm.ScalarMappable.__init__(self, norm, cmap) self.axes = ax self._rgbacache = None self.update(kwargs) self.set_data(x, y, A) def make_image(self, magnification=1.0): if self._A is None: raise RuntimeError('You must first set the image array') fc = self.axes.patch.get_facecolor() bg = mcolors.colorConverter.to_rgba(fc, 0) bg = (np.array(bg)*255).astype(np.uint8) l, b, r, t = self.axes.bbox.extents width = (round(r) + 0.5) - (round(l) - 0.5) height = (round(t) + 0.5) - (round(b) - 0.5) width = width * magnification height = height * magnification if self.check_update('array'): A = self.to_rgba(self._A, alpha=self._alpha, bytes=True) self._rgbacache = A if self._A.ndim == 2: self.is_grayscale = self.cmap.is_gray() else: A = self._rgbacache vl = self.axes.viewLim im = _image.pcolor2(self._Ax, self._Ay, A, height, width, (vl.x0, vl.x1, vl.y0, vl.y1), bg) im.is_grayscale = self.is_grayscale return im def draw(self, renderer, *args, **kwargs): if not self.get_visible(): return im = self.make_image(renderer.get_image_magnification()) renderer.draw_image(round(self.axes.bbox.xmin), round(self.axes.bbox.ymin), im, self.axes.bbox.frozen(), *self.get_transformed_clip_path_and_affine()) def set_data(self, x, y, A): if not ma.isMA(A): A = np.asarray(A) if x is None: x = np.arange(0, A.shape[1]+1, dtype=np.float64) else: x = np.asarray(x, np.float64).ravel() if y is None: y = np.arange(0, A.shape[0]+1, dtype=np.float64) else: y = np.asarray(y, np.float64).ravel() if A.shape[:2] != (y.size-1, x.size-1): print A.shape print y.size print x.size raise ValueError("Axes don't match array shape") if A.ndim not in [2, 3]: raise ValueError("A must be 2D or 3D") if A.ndim == 3 and A.shape[2] == 1: A.shape = A.shape[:2] self.is_grayscale = False if A.ndim == 3: if A.shape[2] in [3, 4]: if (A[:,:,0] == A[:,:,1]).all() and (A[:,:,0] == A[:,:,2]).all(): self.is_grayscale = True else: raise ValueError("3D arrays must have RGB or RGBA as last dim") self._A = A self._Ax = x self._Ay = y self.update_dict['array'] = True def set_array(self, *args): raise NotImplementedError('Method not supported') def set_alpha(self, alpha): """ Set the alpha value used for blending - not supported on all backends ACCEPTS: float """ martist.Artist.set_alpha(self, alpha) self.update_dict['array'] = True class FigureImage(martist.Artist, cm.ScalarMappable): zorder = 1 def __init__(self, fig, cmap = None, norm = None, offsetx = 0, offsety = 0, origin=None, **kwargs ): """ cmap is a colors.Colormap instance norm is a colors.Normalize instance to map luminance to 0-1 kwargs are an optional list of Artist keyword args """ martist.Artist.__init__(self) cm.ScalarMappable.__init__(self, norm, cmap) if origin is None: origin = rcParams['image.origin'] self.origin = origin self.figure = fig self.ox = offsetx self.oy = offsety self.update(kwargs) self.magnification = 1.0 def contains(self, mouseevent): """Test whether the mouse event occured within the image. """ if callable(self._contains): return self._contains(self,mouseevent) xmin, xmax, ymin, ymax = self.get_extent() xdata, ydata = mouseevent.x, mouseevent.y #print xdata, ydata, xmin, xmax, ymin, ymax if xdata is not None and ydata is not None: inside = xdata>=xmin and xdata<=xmax and ydata>=ymin and ydata<=ymax else: inside = False return inside,{} def get_size(self): 'Get the numrows, numcols of the input image' if self._A is None: raise RuntimeError('You must first set the image array') return self._A.shape[:2] def get_extent(self): 'get the image extent: left, right, bottom, top' numrows, numcols = self.get_size() return (-0.5+self.ox, numcols-0.5+self.ox, -0.5+self.oy, numrows-0.5+self.oy) def make_image(self, magnification=1.0): if self._A is None: raise RuntimeError('You must first set the image array') x = self.to_rgba(self._A, self._alpha) self.magnification = magnification # if magnification is not one, we need to resize ismag = magnification!=1 #if ismag: raise RuntimeError if ismag: isoutput = 0 else: isoutput = 1 im = _image.fromarray(x, isoutput) fc = self.figure.get_facecolor() im.set_bg( *mcolors.colorConverter.to_rgba(fc, 0) ) im.is_grayscale = (self.cmap.name == "gray" and len(self._A.shape) == 2) if ismag: numrows, numcols = self.get_size() numrows *= magnification numcols *= magnification im.set_interpolation(_image.NEAREST) im.resize(numcols, numrows) if self.origin=='upper': im.flipud_out() return im def draw(self, renderer, *args, **kwargs): if not self.get_visible(): return # todo: we should be able to do some cacheing here im = self.make_image(renderer.get_image_magnification()) renderer.draw_image(round(self.ox), round(self.oy), im, self.figure.bbox, *self.get_transformed_clip_path_and_affine()) def write_png(self, fname): """Write the image to png file with fname""" im = self.make_image() rows, cols, buffer = im.as_rgba_str() _png.write_png(buffer, cols, rows, fname) def imread(fname): """ Return image file in *fname* as :class:`numpy.array`. Return value is a :class:`numpy.array`. For grayscale images, the return array is MxN. For RGB images, the return value is MxNx3. For RGBA images the return value is MxNx4. matplotlib can only read PNGs natively, but if `PIL <http://www.pythonware.com/products/pil/>`_ is installed, it will use it to load the image and return an array (if possible) which can be used with :func:`~matplotlib.pyplot.imshow`. TODO: support RGB and grayscale return values in _image.readpng """ def pilread(): 'try to load the image with PIL or return None' try: import Image except ImportError: return None image = Image.open( fname ) return pil_to_array(image) handlers = {'png' :_png.read_png, } basename, ext = os.path.splitext(fname) ext = ext.lower()[1:] if ext not in handlers.keys(): im = pilread() if im is None: raise ValueError('Only know how to handle extensions: %s; with PIL installed matplotlib can handle more images' % handlers.keys()) return im handler = handlers[ext] return handler(fname) def pil_to_array( pilImage ): """ load a PIL image and return it as a numpy array of uint8. For grayscale images, the return array is MxN. For RGB images, the return value is MxNx3. For RGBA images the return value is MxNx4 """ def toarray(im): 'return a 1D array of floats' x_str = im.tostring('raw',im.mode,0,-1) x = np.fromstring(x_str,np.uint8) return x if pilImage.mode in ('RGBA', 'RGBX'): im = pilImage # no need to convert images elif pilImage.mode=='L': im = pilImage # no need to luminance images # return MxN luminance array x = toarray(im) x.shape = im.size[1], im.size[0] return x elif pilImage.mode=='RGB': #return MxNx3 RGB array im = pilImage # no need to RGB images x = toarray(im) x.shape = im.size[1], im.size[0], 3 return x else: # try to convert to an rgba image try: im = pilImage.convert('RGBA') except ValueError: raise RuntimeError('Unknown image mode') # return MxNx4 RGBA array x = toarray(im) x.shape = im.size[1], im.size[0], 4 return x def thumbnail(infile, thumbfile, scale=0.1, interpolation='bilinear', preview=False): """ make a thumbnail of image in *infile* with output filename *thumbfile*. *infile* the image file -- must be PNG or PIL readable if you have `PIL <http://www.pythonware.com/products/pil/>`_ installed *thumbfile* the thumbnail filename *scale* the scale factor for the thumbnail *interpolation* the interpolation scheme used in the resampling *preview* if True, the default backend (presumably a user interface backend) will be used which will cause a figure to be raised if :func:`~matplotlib.pyplot.show` is called. If it is False, a pure image backend will be used depending on the extension, 'png'->FigureCanvasAgg, 'pdf'->FigureCanvasPDF, 'svg'->FigureCanvasSVG See examples/misc/image_thumbnail.py. .. htmlonly:: :ref:`misc-image_thumbnail` Return value is the figure instance containing the thumbnail """ basedir, basename = os.path.split(infile) baseout, extout = os.path.splitext(thumbfile) im = imread(infile) rows, cols, depth = im.shape # this doesn't really matter, it will cancel in the end, but we # need it for the mpl API dpi = 100 height = float(rows)/dpi*scale width = float(cols)/dpi*scale extension = extout.lower() if preview: # let the UI backend do everything import matplotlib.pyplot as plt fig = plt.figure(figsize=(width, height), dpi=dpi) else: if extension=='.png': from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas elif extension=='.pdf': from matplotlib.backends.backend_pdf import FigureCanvasPDF as FigureCanvas elif extension=='.svg': from matplotlib.backends.backend_svg import FigureCanvasSVG as FigureCanvas else: raise ValueError("Can only handle extensions 'png', 'svg' or 'pdf'") from matplotlib.figure import Figure fig = Figure(figsize=(width, height), dpi=dpi) canvas = FigureCanvas(fig) ax = fig.add_axes([0,0,1,1], aspect='auto', frameon=False, xticks=[], yticks=[]) basename, ext = os.path.splitext(basename) ax.imshow(im, aspect='auto', resample=True, interpolation='bilinear') fig.savefig(thumbfile, dpi=dpi) return fig

28,764

Python

.py

713

30.262272

142

0.574613

numenta/nupic-legacy

6,330

1,556

464

AGPL-3.0

9/5/2024, 5:13:42 PM (Europe/Amsterdam)

26,281

mathtext.py

numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/mathtext.py

r""" :mod:`~matplotlib.mathtext` is a module for parsing a subset of the TeX math syntax and drawing them to a matplotlib backend. For a tutorial of its usage see :ref:`mathtext-tutorial`. This document is primarily concerned with implementation details. The module uses pyparsing_ to parse the TeX expression. .. _pyparsing: http://pyparsing.wikispaces.com/ The Bakoma distribution of the TeX Computer Modern fonts, and STIX fonts are supported. There is experimental support for using arbitrary fonts, but results may vary without proper tweaking and metrics for those fonts. If you find TeX expressions that don't parse or render properly, please email mdroe@stsci.edu, but please check KNOWN ISSUES below first. """ from __future__ import division import os from cStringIO import StringIO from math import ceil try: set except NameError: from sets import Set as set import unicodedata from warnings import warn from numpy import inf, isinf import numpy as np from matplotlib.pyparsing import Combine, Group, Optional, Forward, \ Literal, OneOrMore, ZeroOrMore, ParseException, Empty, \ ParseResults, Suppress, oneOf, StringEnd, ParseFatalException, \ FollowedBy, Regex, ParserElement # Enable packrat parsing ParserElement.enablePackrat() from matplotlib.afm import AFM from matplotlib.cbook import Bunch, get_realpath_and_stat, \ is_string_like, maxdict from matplotlib.ft2font import FT2Font, FT2Image, KERNING_DEFAULT, LOAD_FORCE_AUTOHINT, LOAD_NO_HINTING from matplotlib.font_manager import findfont, FontProperties from matplotlib._mathtext_data import latex_to_bakoma, \ latex_to_standard, tex2uni, latex_to_cmex, stix_virtual_fonts from matplotlib import get_data_path, rcParams import matplotlib.colors as mcolors import matplotlib._png as _png #################### ############################################################################## # FONTS def get_unicode_index(symbol): """get_unicode_index(symbol) -> integer Return the integer index (from the Unicode table) of symbol. *symbol* can be a single unicode character, a TeX command (i.e. r'\pi'), or a Type1 symbol name (i.e. 'phi'). """ # From UTF #25: U+2212 minus sign is the preferred # representation of the unary and binary minus sign rather than # the ASCII-derived U+002D hyphen-minus, because minus sign is # unambiguous and because it is rendered with a more desirable # length, usually longer than a hyphen. if symbol == '-': return 0x2212 try:# This will succeed if symbol is a single unicode char return ord(symbol) except TypeError: pass try:# Is symbol a TeX symbol (i.e. \alpha) return tex2uni[symbol.strip("\\")] except KeyError: message = """'%(symbol)s' is not a valid Unicode character or TeX/Type1 symbol"""%locals() raise ValueError, message class MathtextBackend(object): """ The base class for the mathtext backend-specific code. The purpose of :class:`MathtextBackend` subclasses is to interface between mathtext and a specific matplotlib graphics backend. Subclasses need to override the following: - :meth:`render_glyph` - :meth:`render_filled_rect` - :meth:`get_results` And optionally, if you need to use a Freetype hinting style: - :meth:`get_hinting_type` """ def __init__(self): self.fonts_object = None def set_canvas_size(self, w, h, d): 'Dimension the drawing canvas' self.width = w self.height = h self.depth = d def render_glyph(self, ox, oy, info): """ Draw a glyph described by *info* to the reference point (*ox*, *oy*). """ raise NotImplementedError() def render_filled_rect(self, x1, y1, x2, y2): """ Draw a filled black rectangle from (*x1*, *y1*) to (*x2*, *y2*). """ raise NotImplementedError() def get_results(self, box): """ Return a backend-specific tuple to return to the backend after all processing is done. """ raise NotImplementedError() def get_hinting_type(self): """ Get the Freetype hinting type to use with this particular backend. """ return LOAD_NO_HINTING class MathtextBackendBbox(MathtextBackend): """ A backend whose only purpose is to get a precise bounding box. Only required for the Agg backend. """ def __init__(self, real_backend): MathtextBackend.__init__(self) self.bbox = [0, 0, 0, 0] self.real_backend = real_backend def _update_bbox(self, x1, y1, x2, y2): self.bbox = [min(self.bbox[0], x1), min(self.bbox[1], y1), max(self.bbox[2], x2), max(self.bbox[3], y2)] def render_glyph(self, ox, oy, info): self._update_bbox(ox + info.metrics.xmin, oy - info.metrics.ymax, ox + info.metrics.xmax, oy - info.metrics.ymin) def render_rect_filled(self, x1, y1, x2, y2): self._update_bbox(x1, y1, x2, y2) def get_results(self, box): orig_height = box.height orig_depth = box.depth ship(0, 0, box) bbox = self.bbox bbox = [bbox[0] - 1, bbox[1] - 1, bbox[2] + 1, bbox[3] + 1] self._switch_to_real_backend() self.fonts_object.set_canvas_size( bbox[2] - bbox[0], (bbox[3] - bbox[1]) - orig_depth, (bbox[3] - bbox[1]) - orig_height) ship(-bbox[0], -bbox[1], box) return self.fonts_object.get_results(box) def get_hinting_type(self): return self.real_backend.get_hinting_type() def _switch_to_real_backend(self): self.fonts_object.mathtext_backend = self.real_backend self.real_backend.fonts_object = self.fonts_object self.real_backend.ox = self.bbox[0] self.real_backend.oy = self.bbox[1] class MathtextBackendAggRender(MathtextBackend): """ Render glyphs and rectangles to an FTImage buffer, which is later transferred to the Agg image by the Agg backend. """ def __init__(self): self.ox = 0 self.oy = 0 self.image = None MathtextBackend.__init__(self) def set_canvas_size(self, w, h, d): MathtextBackend.set_canvas_size(self, w, h, d) self.image = FT2Image(ceil(w), ceil(h + d)) def render_glyph(self, ox, oy, info): info.font.draw_glyph_to_bitmap( self.image, ox, oy - info.metrics.ymax, info.glyph) def render_rect_filled(self, x1, y1, x2, y2): height = max(int(y2 - y1) - 1, 0) if height == 0: center = (y2 + y1) / 2.0 y = int(center - (height + 1) / 2.0) else: y = int(y1) self.image.draw_rect_filled(int(x1), y, ceil(x2), y + height) def get_results(self, box): return (self.ox, self.oy, self.width, self.height + self.depth, self.depth, self.image, self.fonts_object.get_used_characters()) def get_hinting_type(self): return LOAD_FORCE_AUTOHINT def MathtextBackendAgg(): return MathtextBackendBbox(MathtextBackendAggRender()) class MathtextBackendBitmapRender(MathtextBackendAggRender): def get_results(self, box): return self.image, self.depth def MathtextBackendBitmap(): """ A backend to generate standalone mathtext images. No additional matplotlib backend is required. """ return MathtextBackendBbox(MathtextBackendBitmapRender()) class MathtextBackendPs(MathtextBackend): """ Store information to write a mathtext rendering to the PostScript backend. """ def __init__(self): self.pswriter = StringIO() self.lastfont = None def render_glyph(self, ox, oy, info): oy = self.height - oy + info.offset postscript_name = info.postscript_name fontsize = info.fontsize symbol_name = info.symbol_name if (postscript_name, fontsize) != self.lastfont: ps = """/%(postscript_name)s findfont %(fontsize)s scalefont setfont """ % locals() self.lastfont = postscript_name, fontsize self.pswriter.write(ps) ps = """%(ox)f %(oy)f moveto /%(symbol_name)s glyphshow\n """ % locals() self.pswriter.write(ps) def render_rect_filled(self, x1, y1, x2, y2): ps = "%f %f %f %f rectfill\n" % (x1, self.height - y2, x2 - x1, y2 - y1) self.pswriter.write(ps) def get_results(self, box): ship(0, -self.depth, box) #print self.depth return (self.width, self.height + self.depth, self.depth, self.pswriter, self.fonts_object.get_used_characters()) class MathtextBackendPdf(MathtextBackend): """ Store information to write a mathtext rendering to the PDF backend. """ def __init__(self): self.glyphs = [] self.rects = [] def render_glyph(self, ox, oy, info): filename = info.font.fname oy = self.height - oy + info.offset self.glyphs.append( (ox, oy, filename, info.fontsize, info.num, info.symbol_name)) def render_rect_filled(self, x1, y1, x2, y2): self.rects.append((x1, self.height - y2, x2 - x1, y2 - y1)) def get_results(self, box): ship(0, -self.depth, box) return (self.width, self.height + self.depth, self.depth, self.glyphs, self.rects, self.fonts_object.get_used_characters()) class MathtextBackendSvg(MathtextBackend): """ Store information to write a mathtext rendering to the SVG backend. """ def __init__(self): self.svg_glyphs = [] self.svg_rects = [] def render_glyph(self, ox, oy, info): oy = self.height - oy + info.offset thetext = unichr(info.num) self.svg_glyphs.append( (info.font, info.fontsize, thetext, ox, oy, info.metrics)) def render_rect_filled(self, x1, y1, x2, y2): self.svg_rects.append( (x1, self.height - y1 + 1, x2 - x1, y2 - y1)) def get_results(self, box): ship(0, -self.depth, box) svg_elements = Bunch(svg_glyphs = self.svg_glyphs, svg_rects = self.svg_rects) return (self.width, self.height + self.depth, self.depth, svg_elements, self.fonts_object.get_used_characters()) class MathtextBackendCairo(MathtextBackend): """ Store information to write a mathtext rendering to the Cairo backend. """ def __init__(self): self.glyphs = [] self.rects = [] def render_glyph(self, ox, oy, info): oy = oy - info.offset - self.height thetext = unichr(info.num) self.glyphs.append( (info.font, info.fontsize, thetext, ox, oy)) def render_rect_filled(self, x1, y1, x2, y2): self.rects.append( (x1, y1 - self.height, x2 - x1, y2 - y1)) def get_results(self, box): ship(0, -self.depth, box) return (self.width, self.height + self.depth, self.depth, self.glyphs, self.rects) class Fonts(object): """ An abstract base class for a system of fonts to use for mathtext. The class must be able to take symbol keys and font file names and return the character metrics. It also delegates to a backend class to do the actual drawing. """ def __init__(self, default_font_prop, mathtext_backend): """ *default_font_prop*: A :class:`~matplotlib.font_manager.FontProperties` object to use for the default non-math font, or the base font for Unicode (generic) font rendering. *mathtext_backend*: A subclass of :class:`MathTextBackend` used to delegate the actual rendering. """ self.default_font_prop = default_font_prop self.mathtext_backend = mathtext_backend # Make these classes doubly-linked self.mathtext_backend.fonts_object = self self.used_characters = {} def destroy(self): """ Fix any cyclical references before the object is about to be destroyed. """ self.used_characters = None def get_kern(self, font1, fontclass1, sym1, fontsize1, font2, fontclass2, sym2, fontsize2, dpi): """ Get the kerning distance for font between *sym1* and *sym2*. *fontX*: one of the TeX font names:: tt, it, rm, cal, sf, bf or default (non-math) *fontclassX*: TODO *symX*: a symbol in raw TeX form. e.g. '1', 'x' or '\sigma' *fontsizeX*: the fontsize in points *dpi*: the current dots-per-inch """ return 0. def get_metrics(self, font, font_class, sym, fontsize, dpi): """ *font*: one of the TeX font names:: tt, it, rm, cal, sf, bf or default (non-math) *font_class*: TODO *sym*: a symbol in raw TeX form. e.g. '1', 'x' or '\sigma' *fontsize*: font size in points *dpi*: current dots-per-inch Returns an object with the following attributes: - *advance*: The advance distance (in points) of the glyph. - *height*: The height of the glyph in points. - *width*: The width of the glyph in points. - *xmin*, *xmax*, *ymin*, *ymax* - the ink rectangle of the glyph - *iceberg* - the distance from the baseline to the top of the glyph. This corresponds to TeX's definition of "height". """ info = self._get_info(font, font_class, sym, fontsize, dpi) return info.metrics def set_canvas_size(self, w, h, d): """ Set the size of the buffer used to render the math expression. Only really necessary for the bitmap backends. """ self.width, self.height, self.depth = ceil(w), ceil(h), ceil(d) self.mathtext_backend.set_canvas_size(self.width, self.height, self.depth) def render_glyph(self, ox, oy, facename, font_class, sym, fontsize, dpi): """ Draw a glyph at - *ox*, *oy*: position - *facename*: One of the TeX face names - *font_class*: - *sym*: TeX symbol name or single character - *fontsize*: fontsize in points - *dpi*: The dpi to draw at. """ info = self._get_info(facename, font_class, sym, fontsize, dpi) realpath, stat_key = get_realpath_and_stat(info.font.fname) used_characters = self.used_characters.setdefault( stat_key, (realpath, set())) used_characters[1].add(info.num) self.mathtext_backend.render_glyph(ox, oy, info) def render_rect_filled(self, x1, y1, x2, y2): """ Draw a filled rectangle from (*x1*, *y1*) to (*x2*, *y2*). """ self.mathtext_backend.render_rect_filled(x1, y1, x2, y2) def get_xheight(self, font, fontsize, dpi): """ Get the xheight for the given *font* and *fontsize*. """ raise NotImplementedError() def get_underline_thickness(self, font, fontsize, dpi): """ Get the line thickness that matches the given font. Used as a base unit for drawing lines such as in a fraction or radical. """ raise NotImplementedError() def get_used_characters(self): """ Get the set of characters that were used in the math expression. Used by backends that need to subset fonts so they know which glyphs to include. """ return self.used_characters def get_results(self, box): """ Get the data needed by the backend to render the math expression. The return value is backend-specific. """ return self.mathtext_backend.get_results(box) def get_sized_alternatives_for_symbol(self, fontname, sym): """ Override if your font provides multiple sizes of the same symbol. Should return a list of symbols matching *sym* in various sizes. The expression renderer will select the most appropriate size for a given situation from this list. """ return [(fontname, sym)] class TruetypeFonts(Fonts): """ A generic base class for all font setups that use Truetype fonts (through FT2Font). """ class CachedFont: def __init__(self, font): self.font = font self.charmap = font.get_charmap() self.glyphmap = dict( [(glyphind, ccode) for ccode, glyphind in self.charmap.iteritems()]) def __repr__(self): return repr(self.font) def __init__(self, default_font_prop, mathtext_backend): Fonts.__init__(self, default_font_prop, mathtext_backend) self.glyphd = {} self._fonts = {} filename = findfont(default_font_prop) default_font = self.CachedFont(FT2Font(str(filename))) self._fonts['default'] = default_font def destroy(self): self.glyphd = None Fonts.destroy(self) def _get_font(self, font): if font in self.fontmap: basename = self.fontmap[font] else: basename = font cached_font = self._fonts.get(basename) if cached_font is None: font = FT2Font(basename) cached_font = self.CachedFont(font) self._fonts[basename] = cached_font self._fonts[font.postscript_name] = cached_font self._fonts[font.postscript_name.lower()] = cached_font return cached_font def _get_offset(self, cached_font, glyph, fontsize, dpi): if cached_font.font.postscript_name == 'Cmex10': return glyph.height/64.0/2.0 + 256.0/64.0 * dpi/72.0 return 0. def _get_info(self, fontname, font_class, sym, fontsize, dpi): key = fontname, font_class, sym, fontsize, dpi bunch = self.glyphd.get(key) if bunch is not None: return bunch cached_font, num, symbol_name, fontsize, slanted = \ self._get_glyph(fontname, font_class, sym, fontsize) font = cached_font.font font.set_size(fontsize, dpi) glyph = font.load_char( num, flags=self.mathtext_backend.get_hinting_type()) xmin, ymin, xmax, ymax = [val/64.0 for val in glyph.bbox] offset = self._get_offset(cached_font, glyph, fontsize, dpi) metrics = Bunch( advance = glyph.linearHoriAdvance/65536.0, height = glyph.height/64.0, width = glyph.width/64.0, xmin = xmin, xmax = xmax, ymin = ymin+offset, ymax = ymax+offset, # iceberg is the equivalent of TeX's "height" iceberg = glyph.horiBearingY/64.0 + offset, slanted = slanted ) result = self.glyphd[key] = Bunch( font = font, fontsize = fontsize, postscript_name = font.postscript_name, metrics = metrics, symbol_name = symbol_name, num = num, glyph = glyph, offset = offset ) return result def get_xheight(self, font, fontsize, dpi): cached_font = self._get_font(font) cached_font.font.set_size(fontsize, dpi) pclt = cached_font.font.get_sfnt_table('pclt') if pclt is None: # Some fonts don't store the xHeight, so we do a poor man's xHeight metrics = self.get_metrics(font, 'it', 'x', fontsize, dpi) return metrics.iceberg xHeight = (pclt['xHeight'] / 64.0) * (fontsize / 12.0) * (dpi / 100.0) return xHeight def get_underline_thickness(self, font, fontsize, dpi): # This function used to grab underline thickness from the font # metrics, but that information is just too un-reliable, so it # is now hardcoded. return ((0.75 / 12.0) * fontsize * dpi) / 72.0 def get_kern(self, font1, fontclass1, sym1, fontsize1, font2, fontclass2, sym2, fontsize2, dpi): if font1 == font2 and fontsize1 == fontsize2: info1 = self._get_info(font1, fontclass1, sym1, fontsize1, dpi) info2 = self._get_info(font2, fontclass2, sym2, fontsize2, dpi) font = info1.font return font.get_kerning(info1.num, info2.num, KERNING_DEFAULT) / 64.0 return Fonts.get_kern(self, font1, fontclass1, sym1, fontsize1, font2, fontclass2, sym2, fontsize2, dpi) class BakomaFonts(TruetypeFonts): """ Use the Bakoma TrueType fonts for rendering. Symbols are strewn about a number of font files, each of which has its own proprietary 8-bit encoding. """ _fontmap = { 'cal' : 'cmsy10', 'rm' : 'cmr10', 'tt' : 'cmtt10', 'it' : 'cmmi10', 'bf' : 'cmb10', 'sf' : 'cmss10', 'ex' : 'cmex10' } fontmap = {} def __init__(self, *args, **kwargs): self._stix_fallback = StixFonts(*args, **kwargs) TruetypeFonts.__init__(self, *args, **kwargs) if not len(self.fontmap): for key, val in self._fontmap.iteritems(): fullpath = findfont(val) self.fontmap[key] = fullpath self.fontmap[val] = fullpath _slanted_symbols = set(r"\int \oint".split()) def _get_glyph(self, fontname, font_class, sym, fontsize): symbol_name = None if fontname in self.fontmap and sym in latex_to_bakoma: basename, num = latex_to_bakoma[sym] slanted = (basename == "cmmi10") or sym in self._slanted_symbols try: cached_font = self._get_font(basename) except RuntimeError: pass else: symbol_name = cached_font.font.get_glyph_name(num) num = cached_font.glyphmap[num] elif len(sym) == 1: slanted = (fontname == "it") try: cached_font = self._get_font(fontname) except RuntimeError: pass else: num = ord(sym) gid = cached_font.charmap.get(num) if gid is not None: symbol_name = cached_font.font.get_glyph_name( cached_font.charmap[num]) if symbol_name is None: return self._stix_fallback._get_glyph( fontname, font_class, sym, fontsize) return cached_font, num, symbol_name, fontsize, slanted # The Bakoma fonts contain many pre-sized alternatives for the # delimiters. The AutoSizedChar class will use these alternatives # and select the best (closest sized) glyph. _size_alternatives = { '(' : [('rm', '('), ('ex', '\xa1'), ('ex', '\xb3'), ('ex', '\xb5'), ('ex', '\xc3')], ')' : [('rm', ')'), ('ex', '\xa2'), ('ex', '\xb4'), ('ex', '\xb6'), ('ex', '\x21')], '{' : [('cal', '{'), ('ex', '\xa9'), ('ex', '\x6e'), ('ex', '\xbd'), ('ex', '\x28')], '}' : [('cal', '}'), ('ex', '\xaa'), ('ex', '\x6f'), ('ex', '\xbe'), ('ex', '\x29')], # The fourth size of '[' is mysteriously missing from the BaKoMa # font, so I've ommitted it for both '[' and ']' '[' : [('rm', '['), ('ex', '\xa3'), ('ex', '\x68'), ('ex', '\x22')], ']' : [('rm', ']'), ('ex', '\xa4'), ('ex', '\x69'), ('ex', '\x23')], r'\lfloor' : [('ex', '\xa5'), ('ex', '\x6a'), ('ex', '\xb9'), ('ex', '\x24')], r'\rfloor' : [('ex', '\xa6'), ('ex', '\x6b'), ('ex', '\xba'), ('ex', '\x25')], r'\lceil' : [('ex', '\xa7'), ('ex', '\x6c'), ('ex', '\xbb'), ('ex', '\x26')], r'\rceil' : [('ex', '\xa8'), ('ex', '\x6d'), ('ex', '\xbc'), ('ex', '\x27')], r'\langle' : [('ex', '\xad'), ('ex', '\x44'), ('ex', '\xbf'), ('ex', '\x2a')], r'\rangle' : [('ex', '\xae'), ('ex', '\x45'), ('ex', '\xc0'), ('ex', '\x2b')], r'\__sqrt__' : [('ex', '\x70'), ('ex', '\x71'), ('ex', '\x72'), ('ex', '\x73')], r'\backslash': [('ex', '\xb2'), ('ex', '\x2f'), ('ex', '\xc2'), ('ex', '\x2d')], r'/' : [('rm', '/'), ('ex', '\xb1'), ('ex', '\x2e'), ('ex', '\xcb'), ('ex', '\x2c')], r'\widehat' : [('rm', '\x5e'), ('ex', '\x62'), ('ex', '\x63'), ('ex', '\x64')], r'\widetilde': [('rm', '\x7e'), ('ex', '\x65'), ('ex', '\x66'), ('ex', '\x67')], r'<' : [('cal', 'h'), ('ex', 'D')], r'>' : [('cal', 'i'), ('ex', 'E')] } for alias, target in [('\leftparen', '('), ('\rightparent', ')'), ('\leftbrace', '{'), ('\rightbrace', '}'), ('\leftbracket', '['), ('\rightbracket', ']')]: _size_alternatives[alias] = _size_alternatives[target] def get_sized_alternatives_for_symbol(self, fontname, sym): return self._size_alternatives.get(sym, [(fontname, sym)]) class UnicodeFonts(TruetypeFonts): """ An abstract base class for handling Unicode fonts. While some reasonably complete Unicode fonts (such as DejaVu) may work in some situations, the only Unicode font I'm aware of with a complete set of math symbols is STIX. This class will "fallback" on the Bakoma fonts when a required symbol can not be found in the font. """ fontmap = {} use_cmex = True def __init__(self, *args, **kwargs): # This must come first so the backend's owner is set correctly if rcParams['mathtext.fallback_to_cm']: self.cm_fallback = BakomaFonts(*args, **kwargs) else: self.cm_fallback = None TruetypeFonts.__init__(self, *args, **kwargs) if not len(self.fontmap): for texfont in "cal rm tt it bf sf".split(): prop = rcParams['mathtext.' + texfont] font = findfont(prop) self.fontmap[texfont] = font prop = FontProperties('cmex10') font = findfont(prop) self.fontmap['ex'] = font _slanted_symbols = set(r"\int \oint".split()) def _map_virtual_font(self, fontname, font_class, uniindex): return fontname, uniindex def _get_glyph(self, fontname, font_class, sym, fontsize): found_symbol = False if self.use_cmex: uniindex = latex_to_cmex.get(sym) if uniindex is not None: fontname = 'ex' found_symbol = True if not found_symbol: try: uniindex = get_unicode_index(sym) found_symbol = True except ValueError: uniindex = ord('?') warn("No TeX to unicode mapping for '%s'" % sym.encode('ascii', 'backslashreplace'), MathTextWarning) fontname, uniindex = self._map_virtual_font( fontname, font_class, uniindex) # Only characters in the "Letter" class should be italicized in 'it' # mode. Greek capital letters should be Roman. if found_symbol: new_fontname = fontname if fontname == 'it': if uniindex < 0x10000: unistring = unichr(uniindex) if (not unicodedata.category(unistring)[0] == "L" or unicodedata.name(unistring).startswith("GREEK CAPITAL")): new_fontname = 'rm' slanted = (new_fontname == 'it') or sym in self._slanted_symbols found_symbol = False try: cached_font = self._get_font(new_fontname) except RuntimeError: pass else: try: glyphindex = cached_font.charmap[uniindex] found_symbol = True except KeyError: pass if not found_symbol: if self.cm_fallback: warn("Substituting with a symbol from Computer Modern.", MathTextWarning) return self.cm_fallback._get_glyph( fontname, 'it', sym, fontsize) else: if fontname == 'it' and isinstance(self, StixFonts): return self._get_glyph('rm', font_class, sym, fontsize) warn("Font '%s' does not have a glyph for '%s'" % (fontname, sym.encode('ascii', 'backslashreplace')), MathTextWarning) warn("Substituting with a dummy symbol.", MathTextWarning) fontname = 'rm' new_fontname = fontname cached_font = self._get_font(fontname) uniindex = 0xA4 # currency character, for lack of anything better glyphindex = cached_font.charmap[uniindex] slanted = False symbol_name = cached_font.font.get_glyph_name(glyphindex) return cached_font, uniindex, symbol_name, fontsize, slanted def get_sized_alternatives_for_symbol(self, fontname, sym): if self.cm_fallback: return self.cm_fallback.get_sized_alternatives_for_symbol( fontname, sym) return [(fontname, sym)] class StixFonts(UnicodeFonts): """ A font handling class for the STIX fonts. In addition to what UnicodeFonts provides, this class: - supports "virtual fonts" which are complete alpha numeric character sets with different font styles at special Unicode code points, such as "Blackboard". - handles sized alternative characters for the STIXSizeX fonts. """ _fontmap = { 'rm' : 'STIXGeneral', 'it' : 'STIXGeneral:italic', 'bf' : 'STIXGeneral:weight=bold', 'nonunirm' : 'STIXNonUnicode', 'nonuniit' : 'STIXNonUnicode:italic', 'nonunibf' : 'STIXNonUnicode:weight=bold', 0 : 'STIXGeneral', 1 : 'STIXSize1', 2 : 'STIXSize2', 3 : 'STIXSize3', 4 : 'STIXSize4', 5 : 'STIXSize5' } fontmap = {} use_cmex = False cm_fallback = False _sans = False def __init__(self, *args, **kwargs): TruetypeFonts.__init__(self, *args, **kwargs) if not len(self.fontmap): for key, name in self._fontmap.iteritems(): fullpath = findfont(name) self.fontmap[key] = fullpath self.fontmap[name] = fullpath def _map_virtual_font(self, fontname, font_class, uniindex): # Handle these "fonts" that are actually embedded in # other fonts. mapping = stix_virtual_fonts.get(fontname) if self._sans and mapping is None: mapping = stix_virtual_fonts['sf'] doing_sans_conversion = True else: doing_sans_conversion = False if mapping is not None: if isinstance(mapping, dict): mapping = mapping[font_class] # Binary search for the source glyph lo = 0 hi = len(mapping) while lo < hi: mid = (lo+hi)//2 range = mapping[mid] if uniindex < range[0]: hi = mid elif uniindex <= range[1]: break else: lo = mid + 1 if uniindex >= range[0] and uniindex <= range[1]: uniindex = uniindex - range[0] + range[3] fontname = range[2] elif not doing_sans_conversion: # This will generate a dummy character uniindex = 0x1 fontname = 'it' # Handle private use area glyphs if (fontname in ('it', 'rm', 'bf') and uniindex >= 0xe000 and uniindex <= 0xf8ff): fontname = 'nonuni' + fontname return fontname, uniindex _size_alternatives = {} def get_sized_alternatives_for_symbol(self, fontname, sym): alternatives = self._size_alternatives.get(sym) if alternatives: return alternatives alternatives = [] try: uniindex = get_unicode_index(sym) except ValueError: return [(fontname, sym)] fix_ups = { ord('<'): 0x27e8, ord('>'): 0x27e9 } uniindex = fix_ups.get(uniindex, uniindex) for i in range(6): cached_font = self._get_font(i) glyphindex = cached_font.charmap.get(uniindex) if glyphindex is not None: alternatives.append((i, unichr(uniindex))) self._size_alternatives[sym] = alternatives return alternatives class StixSansFonts(StixFonts): """ A font handling class for the STIX fonts (that uses sans-serif characters by default). """ _sans = True class StandardPsFonts(Fonts): """ Use the standard postscript fonts for rendering to backend_ps Unlike the other font classes, BakomaFont and UnicodeFont, this one requires the Ps backend. """ basepath = os.path.join( get_data_path(), 'fonts', 'afm' ) fontmap = { 'cal' : 'pzcmi8a', # Zapf Chancery 'rm' : 'pncr8a', # New Century Schoolbook 'tt' : 'pcrr8a', # Courier 'it' : 'pncri8a', # New Century Schoolbook Italic 'sf' : 'phvr8a', # Helvetica 'bf' : 'pncb8a', # New Century Schoolbook Bold None : 'psyr' # Symbol } def __init__(self, default_font_prop): Fonts.__init__(self, default_font_prop, MathtextBackendPs()) self.glyphd = {} self.fonts = {} filename = findfont(default_font_prop, fontext='afm') default_font = AFM(file(filename, 'r')) default_font.fname = filename self.fonts['default'] = default_font self.pswriter = StringIO() def _get_font(self, font): if font in self.fontmap: basename = self.fontmap[font] else: basename = font cached_font = self.fonts.get(basename) if cached_font is None: fname = os.path.join(self.basepath, basename + ".afm") cached_font = AFM(file(fname, 'r')) cached_font.fname = fname self.fonts[basename] = cached_font self.fonts[cached_font.get_fontname()] = cached_font return cached_font def _get_info (self, fontname, font_class, sym, fontsize, dpi): 'load the cmfont, metrics and glyph with caching' key = fontname, sym, fontsize, dpi tup = self.glyphd.get(key) if tup is not None: return tup # Only characters in the "Letter" class should really be italicized. # This class includes greek letters, so we're ok if (fontname == 'it' and (len(sym) > 1 or not unicodedata.category(unicode(sym)).startswith("L"))): fontname = 'rm' found_symbol = False if sym in latex_to_standard: fontname, num = latex_to_standard[sym] glyph = chr(num) found_symbol = True elif len(sym) == 1: glyph = sym num = ord(glyph) found_symbol = True else: warn("No TeX to built-in Postscript mapping for '%s'" % sym, MathTextWarning) slanted = (fontname == 'it') font = self._get_font(fontname) if found_symbol: try: symbol_name = font.get_name_char(glyph) except KeyError: warn("No glyph in standard Postscript font '%s' for '%s'" % (font.postscript_name, sym), MathTextWarning) found_symbol = False if not found_symbol: glyph = sym = '?' num = ord(glyph) symbol_name = font.get_name_char(glyph) offset = 0 scale = 0.001 * fontsize xmin, ymin, xmax, ymax = [val * scale for val in font.get_bbox_char(glyph)] metrics = Bunch( advance = font.get_width_char(glyph) * scale, width = font.get_width_char(glyph) * scale, height = font.get_height_char(glyph) * scale, xmin = xmin, xmax = xmax, ymin = ymin+offset, ymax = ymax+offset, # iceberg is the equivalent of TeX's "height" iceberg = ymax + offset, slanted = slanted ) self.glyphd[key] = Bunch( font = font, fontsize = fontsize, postscript_name = font.get_fontname(), metrics = metrics, symbol_name = symbol_name, num = num, glyph = glyph, offset = offset ) return self.glyphd[key] def get_kern(self, font1, fontclass1, sym1, fontsize1, font2, fontclass2, sym2, fontsize2, dpi): if font1 == font2 and fontsize1 == fontsize2: info1 = self._get_info(font1, fontclass1, sym1, fontsize1, dpi) info2 = self._get_info(font2, fontclass2, sym2, fontsize2, dpi) font = info1.font return (font.get_kern_dist(info1.glyph, info2.glyph) * 0.001 * fontsize1) return Fonts.get_kern(self, font1, fontclass1, sym1, fontsize1, font2, fontclass2, sym2, fontsize2, dpi) def get_xheight(self, font, fontsize, dpi): cached_font = self._get_font(font) return cached_font.get_xheight() * 0.001 * fontsize def get_underline_thickness(self, font, fontsize, dpi): cached_font = self._get_font(font) return cached_font.get_underline_thickness() * 0.001 * fontsize ############################################################################## # TeX-LIKE BOX MODEL # The following is based directly on the document 'woven' from the # TeX82 source code. This information is also available in printed # form: # # Knuth, Donald E.. 1986. Computers and Typesetting, Volume B: # TeX: The Program. Addison-Wesley Professional. # # The most relevant "chapters" are: # Data structures for boxes and their friends # Shipping pages out (Ship class) # Packaging (hpack and vpack) # Data structures for math mode # Subroutines for math mode # Typesetting math formulas # # Many of the docstrings below refer to a numbered "node" in that # book, e.g. node123 # # Note that (as TeX) y increases downward, unlike many other parts of # matplotlib. # How much text shrinks when going to the next-smallest level. GROW_FACTOR # must be the inverse of SHRINK_FACTOR. SHRINK_FACTOR = 0.7 GROW_FACTOR = 1.0 / SHRINK_FACTOR # The number of different sizes of chars to use, beyond which they will not # get any smaller NUM_SIZE_LEVELS = 4 # Percentage of x-height of additional horiz. space after sub/superscripts SCRIPT_SPACE = 0.2 # Percentage of x-height that sub/superscripts drop below the baseline SUBDROP = 0.3 # Percentage of x-height that superscripts drop below the baseline SUP1 = 0.5 # Percentage of x-height that subscripts drop below the baseline SUB1 = 0.0 # Percentage of x-height that superscripts are offset relative to the subscript DELTA = 0.18 class MathTextWarning(Warning): pass class Node(object): """ A node in the TeX box model """ def __init__(self): self.size = 0 def __repr__(self): return self.__internal_repr__() def __internal_repr__(self): return self.__class__.__name__ def get_kerning(self, next): return 0.0 def shrink(self): """ Shrinks one level smaller. There are only three levels of sizes, after which things will no longer get smaller. """ self.size += 1 def grow(self): """ Grows one level larger. There is no limit to how big something can get. """ self.size -= 1 def render(self, x, y): pass class Box(Node): """ Represents any node with a physical location. """ def __init__(self, width, height, depth): Node.__init__(self) self.width = width self.height = height self.depth = depth def shrink(self): Node.shrink(self) if self.size < NUM_SIZE_LEVELS: self.width *= SHRINK_FACTOR self.height *= SHRINK_FACTOR self.depth *= SHRINK_FACTOR def grow(self): Node.grow(self) self.width *= GROW_FACTOR self.height *= GROW_FACTOR self.depth *= GROW_FACTOR def render(self, x1, y1, x2, y2): pass class Vbox(Box): """ A box with only height (zero width). """ def __init__(self, height, depth): Box.__init__(self, 0., height, depth) class Hbox(Box): """ A box with only width (zero height and depth). """ def __init__(self, width): Box.__init__(self, width, 0., 0.) class Char(Node): """ Represents a single character. Unlike TeX, the font information and metrics are stored with each :class:`Char` to make it easier to lookup the font metrics when needed. Note that TeX boxes have a width, height, and depth, unlike Type1 and Truetype which use a full bounding box and an advance in the x-direction. The metrics must be converted to the TeX way, and the advance (if different from width) must be converted into a :class:`Kern` node when the :class:`Char` is added to its parent :class:`Hlist`. """ def __init__(self, c, state): Node.__init__(self) self.c = c self.font_output = state.font_output assert isinstance(state.font, (str, unicode, int)) self.font = state.font self.font_class = state.font_class self.fontsize = state.fontsize self.dpi = state.dpi # The real width, height and depth will be set during the # pack phase, after we know the real fontsize self._update_metrics() def __internal_repr__(self): return '`%s`' % self.c def _update_metrics(self): metrics = self._metrics = self.font_output.get_metrics( self.font, self.font_class, self.c, self.fontsize, self.dpi) if self.c == ' ': self.width = metrics.advance else: self.width = metrics.width self.height = metrics.iceberg self.depth = -(metrics.iceberg - metrics.height) def is_slanted(self): return self._metrics.slanted def get_kerning(self, next): """ Return the amount of kerning between this and the given character. Called when characters are strung together into :class:`Hlist` to create :class:`Kern` nodes. """ advance = self._metrics.advance - self.width kern = 0. if isinstance(next, Char): kern = self.font_output.get_kern( self.font, self.font_class, self.c, self.fontsize, next.font, next.font_class, next.c, next.fontsize, self.dpi) return advance + kern def render(self, x, y): """ Render the character to the canvas """ self.font_output.render_glyph( x, y, self.font, self.font_class, self.c, self.fontsize, self.dpi) def shrink(self): Node.shrink(self) if self.size < NUM_SIZE_LEVELS: self.fontsize *= SHRINK_FACTOR self.width *= SHRINK_FACTOR self.height *= SHRINK_FACTOR self.depth *= SHRINK_FACTOR def grow(self): Node.grow(self) self.fontsize *= GROW_FACTOR self.width *= GROW_FACTOR self.height *= GROW_FACTOR self.depth *= GROW_FACTOR class Accent(Char): """ The font metrics need to be dealt with differently for accents, since they are already offset correctly from the baseline in TrueType fonts. """ def _update_metrics(self): metrics = self._metrics = self.font_output.get_metrics( self.font, self.font_class, self.c, self.fontsize, self.dpi) self.width = metrics.xmax - metrics.xmin self.height = metrics.ymax - metrics.ymin self.depth = 0 def shrink(self): Char.shrink(self) self._update_metrics() def grow(self): Char.grow(self) self._update_metrics() def render(self, x, y): """ Render the character to the canvas. """ self.font_output.render_glyph( x - self._metrics.xmin, y + self._metrics.ymin, self.font, self.font_class, self.c, self.fontsize, self.dpi) class List(Box): """ A list of nodes (either horizontal or vertical). """ def __init__(self, elements): Box.__init__(self, 0., 0., 0.) self.shift_amount = 0. # An arbitrary offset self.children = elements # The child nodes of this list # The following parameters are set in the vpack and hpack functions self.glue_set = 0. # The glue setting of this list self.glue_sign = 0 # 0: normal, -1: shrinking, 1: stretching self.glue_order = 0 # The order of infinity (0 - 3) for the glue def __repr__(self): return '[%s <%.02f %.02f %.02f %.02f> %s]' % ( self.__internal_repr__(), self.width, self.height, self.depth, self.shift_amount, ' '.join([repr(x) for x in self.children])) def _determine_order(self, totals): """ A helper function to determine the highest order of glue used by the members of this list. Used by vpack and hpack. """ o = 0 for i in range(len(totals) - 1, 0, -1): if totals[i] != 0.0: o = i break return o def _set_glue(self, x, sign, totals, error_type): o = self._determine_order(totals) self.glue_order = o self.glue_sign = sign if totals[o] != 0.: self.glue_set = x / totals[o] else: self.glue_sign = 0 self.glue_ratio = 0. if o == 0: if len(self.children): warn("%s %s: %r" % (error_type, self.__class__.__name__, self), MathTextWarning) def shrink(self): for child in self.children: child.shrink() Box.shrink(self) if self.size < NUM_SIZE_LEVELS: self.shift_amount *= SHRINK_FACTOR self.glue_set *= SHRINK_FACTOR def grow(self): for child in self.children: child.grow() Box.grow(self) self.shift_amount *= GROW_FACTOR self.glue_set *= GROW_FACTOR class Hlist(List): """ A horizontal list of boxes. """ def __init__(self, elements, w=0., m='additional', do_kern=True): List.__init__(self, elements) if do_kern: self.kern() self.hpack() def kern(self): """ Insert :class:`Kern` nodes between :class:`Char` nodes to set kerning. The :class:`Char` nodes themselves determine the amount of kerning they need (in :meth:`~Char.get_kerning`), and this function just creates the linked list in the correct way. """ new_children = [] num_children = len(self.children) if num_children: for i in range(num_children): elem = self.children[i] if i < num_children - 1: next = self.children[i + 1] else: next = None new_children.append(elem) kerning_distance = elem.get_kerning(next) if kerning_distance != 0.: kern = Kern(kerning_distance) new_children.append(kern) self.children = new_children # This is a failed experiment to fake cross-font kerning. # def get_kerning(self, next): # if len(self.children) >= 2 and isinstance(self.children[-2], Char): # if isinstance(next, Char): # print "CASE A" # return self.children[-2].get_kerning(next) # elif isinstance(next, Hlist) and len(next.children) and isinstance(next.children[0], Char): # print "CASE B" # result = self.children[-2].get_kerning(next.children[0]) # print result # return result # return 0.0 def hpack(self, w=0., m='additional'): """ The main duty of :meth:`hpack` is to compute the dimensions of the resulting boxes, and to adjust the glue if one of those dimensions is pre-specified. The computed sizes normally enclose all of the material inside the new box; but some items may stick out if negative glue is used, if the box is overfull, or if a ``\\vbox`` includes other boxes that have been shifted left. - *w*: specifies a width - *m*: is either 'exactly' or 'additional'. Thus, ``hpack(w, 'exactly')`` produces a box whose width is exactly *w*, while ``hpack(w, 'additional')`` yields a box whose width is the natural width plus *w*. The default values produce a box with the natural width. """ # I don't know why these get reset in TeX. Shift_amount is pretty # much useless if we do. #self.shift_amount = 0. h = 0. d = 0. x = 0. total_stretch = [0.] * 4 total_shrink = [0.] * 4 for p in self.children: if isinstance(p, Char): x += p.width h = max(h, p.height) d = max(d, p.depth) elif isinstance(p, Box): x += p.width if not isinf(p.height) and not isinf(p.depth): s = getattr(p, 'shift_amount', 0.) h = max(h, p.height - s) d = max(d, p.depth + s) elif isinstance(p, Glue): glue_spec = p.glue_spec x += glue_spec.width total_stretch[glue_spec.stretch_order] += glue_spec.stretch total_shrink[glue_spec.shrink_order] += glue_spec.shrink elif isinstance(p, Kern): x += p.width self.height = h self.depth = d if m == 'additional': w += x self.width = w x = w - x if x == 0.: self.glue_sign = 0 self.glue_order = 0 self.glue_ratio = 0. return if x > 0.: self._set_glue(x, 1, total_stretch, "Overfull") else: self._set_glue(x, -1, total_shrink, "Underfull") class Vlist(List): """ A vertical list of boxes. """ def __init__(self, elements, h=0., m='additional'): List.__init__(self, elements) self.vpack() def vpack(self, h=0., m='additional', l=float(inf)): """ The main duty of :meth:`vpack` is to compute the dimensions of the resulting boxes, and to adjust the glue if one of those dimensions is pre-specified. - *h*: specifies a height - *m*: is either 'exactly' or 'additional'. - *l*: a maximum height Thus, ``vpack(h, 'exactly')`` produces a box whose height is exactly *h*, while ``vpack(h, 'additional')`` yields a box whose height is the natural height plus *h*. The default values produce a box with the natural width. """ # I don't know why these get reset in TeX. Shift_amount is pretty # much useless if we do. # self.shift_amount = 0. w = 0. d = 0. x = 0. total_stretch = [0.] * 4 total_shrink = [0.] * 4 for p in self.children: if isinstance(p, Box): x += d + p.height d = p.depth if not isinf(p.width): s = getattr(p, 'shift_amount', 0.) w = max(w, p.width + s) elif isinstance(p, Glue): x += d d = 0. glue_spec = p.glue_spec x += glue_spec.width total_stretch[glue_spec.stretch_order] += glue_spec.stretch total_shrink[glue_spec.shrink_order] += glue_spec.shrink elif isinstance(p, Kern): x += d + p.width d = 0. elif isinstance(p, Char): raise RuntimeError("Internal mathtext error: Char node found in Vlist.") self.width = w if d > l: x += d - l self.depth = l else: self.depth = d if m == 'additional': h += x self.height = h x = h - x if x == 0: self.glue_sign = 0 self.glue_order = 0 self.glue_ratio = 0. return if x > 0.: self._set_glue(x, 1, total_stretch, "Overfull") else: self._set_glue(x, -1, total_shrink, "Underfull") class Rule(Box): """ A :class:`Rule` node stands for a solid black rectangle; it has *width*, *depth*, and *height* fields just as in an :class:`Hlist`. However, if any of these dimensions is inf, the actual value will be determined by running the rule up to the boundary of the innermost enclosing box. This is called a "running dimension." The width is never running in an :class:`Hlist`; the height and depth are never running in a :class:`Vlist`. """ def __init__(self, width, height, depth, state): Box.__init__(self, width, height, depth) self.font_output = state.font_output def render(self, x, y, w, h): self.font_output.render_rect_filled(x, y, x + w, y + h) class Hrule(Rule): """ Convenience class to create a horizontal rule. """ def __init__(self, state): thickness = state.font_output.get_underline_thickness( state.font, state.fontsize, state.dpi) height = depth = thickness * 0.5 Rule.__init__(self, inf, height, depth, state) class Vrule(Rule): """ Convenience class to create a vertical rule. """ def __init__(self, state): thickness = state.font_output.get_underline_thickness( state.font, state.fontsize, state.dpi) Rule.__init__(self, thickness, inf, inf, state) class Glue(Node): """ Most of the information in this object is stored in the underlying :class:`GlueSpec` class, which is shared between multiple glue objects. (This is a memory optimization which probably doesn't matter anymore, but it's easier to stick to what TeX does.) """ def __init__(self, glue_type, copy=False): Node.__init__(self) self.glue_subtype = 'normal' if is_string_like(glue_type): glue_spec = GlueSpec.factory(glue_type) elif isinstance(glue_type, GlueSpec): glue_spec = glue_type else: raise ArgumentError("glue_type must be a glue spec name or instance.") if copy: glue_spec = glue_spec.copy() self.glue_spec = glue_spec def shrink(self): Node.shrink(self) if self.size < NUM_SIZE_LEVELS: if self.glue_spec.width != 0.: self.glue_spec = self.glue_spec.copy() self.glue_spec.width *= SHRINK_FACTOR def grow(self): Node.grow(self) if self.glue_spec.width != 0.: self.glue_spec = self.glue_spec.copy() self.glue_spec.width *= GROW_FACTOR class GlueSpec(object): """ See :class:`Glue`. """ def __init__(self, width=0., stretch=0., stretch_order=0, shrink=0., shrink_order=0): self.width = width self.stretch = stretch self.stretch_order = stretch_order self.shrink = shrink self.shrink_order = shrink_order def copy(self): return GlueSpec( self.width, self.stretch, self.stretch_order, self.shrink, self.shrink_order) def factory(cls, glue_type): return cls._types[glue_type] factory = classmethod(factory) GlueSpec._types = { 'fil': GlueSpec(0., 1., 1, 0., 0), 'fill': GlueSpec(0., 1., 2, 0., 0), 'filll': GlueSpec(0., 1., 3, 0., 0), 'neg_fil': GlueSpec(0., 0., 0, 1., 1), 'neg_fill': GlueSpec(0., 0., 0, 1., 2), 'neg_filll': GlueSpec(0., 0., 0, 1., 3), 'empty': GlueSpec(0., 0., 0, 0., 0), 'ss': GlueSpec(0., 1., 1, -1., 1) } # Some convenient ways to get common kinds of glue class Fil(Glue): def __init__(self): Glue.__init__(self, 'fil') class Fill(Glue): def __init__(self): Glue.__init__(self, 'fill') class Filll(Glue): def __init__(self): Glue.__init__(self, 'filll') class NegFil(Glue): def __init__(self): Glue.__init__(self, 'neg_fil') class NegFill(Glue): def __init__(self): Glue.__init__(self, 'neg_fill') class NegFilll(Glue): def __init__(self): Glue.__init__(self, 'neg_filll') class SsGlue(Glue): def __init__(self): Glue.__init__(self, 'ss') class HCentered(Hlist): """ A convenience class to create an :class:`Hlist` whose contents are centered within its enclosing box. """ def __init__(self, elements): Hlist.__init__(self, [SsGlue()] + elements + [SsGlue()], do_kern=False) class VCentered(Hlist): """ A convenience class to create a :class:`Vlist` whose contents are centered within its enclosing box. """ def __init__(self, elements): Vlist.__init__(self, [SsGlue()] + elements + [SsGlue()]) class Kern(Node): """ A :class:`Kern` node has a width field to specify a (normally negative) amount of spacing. This spacing correction appears in horizontal lists between letters like A and V when the font designer said that it looks better to move them closer together or further apart. A kern node can also appear in a vertical list, when its *width* denotes additional spacing in the vertical direction. """ def __init__(self, width): Node.__init__(self) self.width = width def __repr__(self): return "k%.02f" % self.width def shrink(self): Node.shrink(self) if self.size < NUM_SIZE_LEVELS: self.width *= SHRINK_FACTOR def grow(self): Node.grow(self) self.width *= GROW_FACTOR class SubSuperCluster(Hlist): """ :class:`SubSuperCluster` is a sort of hack to get around that fact that this code do a two-pass parse like TeX. This lets us store enough information in the hlist itself, namely the nucleus, sub- and super-script, such that if another script follows that needs to be attached, it can be reconfigured on the fly. """ def __init__(self): self.nucleus = None self.sub = None self.super = None Hlist.__init__(self, []) class AutoHeightChar(Hlist): """ :class:`AutoHeightChar` will create a character as close to the given height and depth as possible. When using a font with multiple height versions of some characters (such as the BaKoMa fonts), the correct glyph will be selected, otherwise this will always just return a scaled version of the glyph. """ def __init__(self, c, height, depth, state, always=False): alternatives = state.font_output.get_sized_alternatives_for_symbol( state.font, c) state = state.copy() target_total = height + depth for fontname, sym in alternatives: state.font = fontname char = Char(sym, state) if char.height + char.depth >= target_total: break factor = target_total / (char.height + char.depth) state.fontsize *= factor char = Char(sym, state) shift = (depth - char.depth) Hlist.__init__(self, [char]) self.shift_amount = shift class AutoWidthChar(Hlist): """ :class:`AutoWidthChar` will create a character as close to the given width as possible. When using a font with multiple width versions of some characters (such as the BaKoMa fonts), the correct glyph will be selected, otherwise this will always just return a scaled version of the glyph. """ def __init__(self, c, width, state, always=False, char_class=Char): alternatives = state.font_output.get_sized_alternatives_for_symbol( state.font, c) state = state.copy() for fontname, sym in alternatives: state.font = fontname char = char_class(sym, state) if char.width >= width: break factor = width / char.width state.fontsize *= factor char = char_class(sym, state) Hlist.__init__(self, [char]) self.width = char.width class Ship(object): """ Once the boxes have been set up, this sends them to output. Since boxes can be inside of boxes inside of boxes, the main work of :class:`Ship` is done by two mutually recursive routines, :meth:`hlist_out` and :meth:`vlist_out`, which traverse the :class:`Hlist` nodes and :class:`Vlist` nodes inside of horizontal and vertical boxes. The global variables used in TeX to store state as it processes have become member variables here. """ def __call__(self, ox, oy, box): self.max_push = 0 # Deepest nesting of push commands so far self.cur_s = 0 self.cur_v = 0. self.cur_h = 0. self.off_h = ox self.off_v = oy + box.height self.hlist_out(box) def clamp(value): if value < -1000000000.: return -1000000000. if value > 1000000000.: return 1000000000. return value clamp = staticmethod(clamp) def hlist_out(self, box): cur_g = 0 cur_glue = 0. glue_order = box.glue_order glue_sign = box.glue_sign base_line = self.cur_v left_edge = self.cur_h self.cur_s += 1 self.max_push = max(self.cur_s, self.max_push) clamp = self.clamp for p in box.children: if isinstance(p, Char): p.render(self.cur_h + self.off_h, self.cur_v + self.off_v) self.cur_h += p.width elif isinstance(p, Kern): self.cur_h += p.width elif isinstance(p, List): # node623 if len(p.children) == 0: self.cur_h += p.width else: edge = self.cur_h self.cur_v = base_line + p.shift_amount if isinstance(p, Hlist): self.hlist_out(p) else: # p.vpack(box.height + box.depth, 'exactly') self.vlist_out(p) self.cur_h = edge + p.width self.cur_v = base_line elif isinstance(p, Box): # node624 rule_height = p.height rule_depth = p.depth rule_width = p.width if isinf(rule_height): rule_height = box.height if isinf(rule_depth): rule_depth = box.depth if rule_height > 0 and rule_width > 0: self.cur_v = baseline + rule_depth p.render(self.cur_h + self.off_h, self.cur_v + self.off_v, rule_width, rule_height) self.cur_v = baseline self.cur_h += rule_width elif isinstance(p, Glue): # node625 glue_spec = p.glue_spec rule_width = glue_spec.width - cur_g if glue_sign != 0: # normal if glue_sign == 1: # stretching if glue_spec.stretch_order == glue_order: cur_glue += glue_spec.stretch cur_g = round(clamp(float(box.glue_set) * cur_glue)) elif glue_spec.shrink_order == glue_order: cur_glue += glue_spec.shrink cur_g = round(clamp(float(box.glue_set) * cur_glue)) rule_width += cur_g self.cur_h += rule_width self.cur_s -= 1 def vlist_out(self, box): cur_g = 0 cur_glue = 0. glue_order = box.glue_order glue_sign = box.glue_sign self.cur_s += 1 self.max_push = max(self.max_push, self.cur_s) left_edge = self.cur_h self.cur_v -= box.height top_edge = self.cur_v clamp = self.clamp for p in box.children: if isinstance(p, Kern): self.cur_v += p.width elif isinstance(p, List): if len(p.children) == 0: self.cur_v += p.height + p.depth else: self.cur_v += p.height self.cur_h = left_edge + p.shift_amount save_v = self.cur_v p.width = box.width if isinstance(p, Hlist): self.hlist_out(p) else: self.vlist_out(p) self.cur_v = save_v + p.depth self.cur_h = left_edge elif isinstance(p, Box): rule_height = p.height rule_depth = p.depth rule_width = p.width if isinf(rule_width): rule_width = box.width rule_height += rule_depth if rule_height > 0 and rule_depth > 0: self.cur_v += rule_height p.render(self.cur_h + self.off_h, self.cur_v + self.off_v, rule_width, rule_height) elif isinstance(p, Glue): glue_spec = p.glue_spec rule_height = glue_spec.width - cur_g if glue_sign != 0: # normal if glue_sign == 1: # stretching if glue_spec.stretch_order == glue_order: cur_glue += glue_spec.stretch cur_g = round(clamp(float(box.glue_set) * cur_glue)) elif glue_spec.shrink_order == glue_order: # shrinking cur_glue += glue_spec.shrink cur_g = round(clamp(float(box.glue_set) * cur_glue)) rule_height += cur_g self.cur_v += rule_height elif isinstance(p, Char): raise RuntimeError("Internal mathtext error: Char node found in vlist") self.cur_s -= 1 ship = Ship() ############################################################################## # PARSER def Error(msg): """ Helper class to raise parser errors. """ def raise_error(s, loc, toks): raise ParseFatalException(msg + "\n" + s) empty = Empty() empty.setParseAction(raise_error) return empty class Parser(object): """ This is the pyparsing-based parser for math expressions. It actually parses full strings *containing* math expressions, in that raw text may also appear outside of pairs of ``$``. The grammar is based directly on that in TeX, though it cuts a few corners. """ _binary_operators = set(r''' + * \pm \sqcap \rhd \mp \sqcup \unlhd \times \vee \unrhd \div \wedge \oplus \ast \setminus \ominus \star \wr \otimes \circ \diamond \oslash \bullet \bigtriangleup \odot \cdot \bigtriangledown \bigcirc \cap \triangleleft \dagger \cup \triangleright \ddagger \uplus \lhd \amalg'''.split()) _relation_symbols = set(r''' = < > : \leq \geq \equiv \models \prec \succ \sim \perp \preceq \succeq \simeq \mid \ll \gg \asymp \parallel \subset \supset \approx \bowtie \subseteq \supseteq \cong \Join \sqsubset \sqsupset \neq \smile \sqsubseteq \sqsupseteq \doteq \frown \in \ni \propto \vdash \dashv'''.split()) _arrow_symbols = set(r''' \leftarrow \longleftarrow \uparrow \Leftarrow \Longleftarrow \Uparrow \rightarrow \longrightarrow \downarrow \Rightarrow \Longrightarrow \Downarrow \leftrightarrow \longleftrightarrow \updownarrow \Leftrightarrow \Longleftrightarrow \Updownarrow \mapsto \longmapsto \nearrow \hookleftarrow \hookrightarrow \searrow \leftharpoonup \rightharpoonup \swarrow \leftharpoondown \rightharpoondown \nwarrow \rightleftharpoons \leadsto'''.split()) _spaced_symbols = _binary_operators | _relation_symbols | _arrow_symbols _punctuation_symbols = set(r', ; . ! \ldotp \cdotp'.split()) _overunder_symbols = set(r''' \sum \prod \coprod \bigcap \bigcup \bigsqcup \bigvee \bigwedge \bigodot \bigotimes \bigoplus \biguplus '''.split()) _overunder_functions = set( r"lim liminf limsup sup max min".split()) _dropsub_symbols = set(r'''\int \oint'''.split()) _fontnames = set("rm cal it tt sf bf default bb frak circled scr".split()) _function_names = set(""" arccos csc ker min arcsin deg lg Pr arctan det lim sec arg dim liminf sin cos exp limsup sinh cosh gcd ln sup cot hom log tan coth inf max tanh""".split()) _ambiDelim = set(r""" | \| / \backslash \uparrow \downarrow \updownarrow \Uparrow \Downarrow \Updownarrow .""".split()) _leftDelim = set(r"( [ { < \lfloor \langle \lceil".split()) _rightDelim = set(r") ] } > \rfloor \rangle \rceil".split()) def __init__(self): # All forward declarations are here font = Forward().setParseAction(self.font).setName("font") latexfont = Forward() subsuper = Forward().setParseAction(self.subsuperscript).setName("subsuper") placeable = Forward().setName("placeable") simple = Forward().setName("simple") autoDelim = Forward().setParseAction(self.auto_sized_delimiter) self._expression = Forward().setParseAction(self.finish).setName("finish") float = Regex(r"[-+]?([0-9]+\.?[0-9]*|\.[0-9]+)") lbrace = Literal('{').suppress() rbrace = Literal('}').suppress() start_group = (Optional(latexfont) - lbrace) start_group.setParseAction(self.start_group) end_group = rbrace.copy() end_group.setParseAction(self.end_group) bslash = Literal('\\') accent = oneOf(self._accent_map.keys() + list(self._wide_accents)) function = oneOf(list(self._function_names)) fontname = oneOf(list(self._fontnames)) latex2efont = oneOf(['math' + x for x in self._fontnames]) space =(FollowedBy(bslash) + oneOf([r'\ ', r'\/', r'\,', r'\;', r'\quad', r'\qquad', r'\!']) ).setParseAction(self.space).setName('space') customspace =(Literal(r'\hspace') - (( lbrace - float - rbrace ) | Error(r"Expected \hspace{n}")) ).setParseAction(self.customspace).setName('customspace') unicode_range = u"\U00000080-\U0001ffff" symbol =(Regex(UR"([a-zA-Z0-9 +\-*/<>=:,.;!'@()\[\]|%s])|(\\[%%${}\[\]_|])" % unicode_range) | (Combine( bslash + oneOf(tex2uni.keys()) ) + FollowedBy(Regex("[^a-zA-Z]"))) ).setParseAction(self.symbol).leaveWhitespace() c_over_c =(Suppress(bslash) + oneOf(self._char_over_chars.keys()) ).setParseAction(self.char_over_chars) accent = Group( Suppress(bslash) + accent - placeable ).setParseAction(self.accent).setName("accent") function =(Suppress(bslash) + function ).setParseAction(self.function).setName("function") group = Group( start_group + ZeroOrMore( autoDelim ^ simple) - end_group ).setParseAction(self.group).setName("group") font <<(Suppress(bslash) + fontname) latexfont <<(Suppress(bslash) + latex2efont) frac = Group( Suppress(Literal(r"\frac")) + ((group + group) | Error(r"Expected \frac{num}{den}")) ).setParseAction(self.frac).setName("frac") sqrt = Group( Suppress(Literal(r"\sqrt")) + Optional( Suppress(Literal("[")) - Regex("[0-9]+") - Suppress(Literal("]")), default = None ) + (group | Error("Expected \sqrt{value}")) ).setParseAction(self.sqrt).setName("sqrt") placeable <<(accent ^ function ^ (c_over_c | symbol) ^ group ^ frac ^ sqrt ) simple <<(space | customspace | font | subsuper ) subsuperop = oneOf(["_", "^"]) subsuper << Group( ( Optional(placeable) + OneOrMore( subsuperop - placeable ) ) | placeable ) ambiDelim = oneOf(list(self._ambiDelim)) leftDelim = oneOf(list(self._leftDelim)) rightDelim = oneOf(list(self._rightDelim)) autoDelim <<(Suppress(Literal(r"\left")) + ((leftDelim | ambiDelim) | Error("Expected a delimiter")) + Group( autoDelim ^ OneOrMore(simple)) + Suppress(Literal(r"\right")) + ((rightDelim | ambiDelim) | Error("Expected a delimiter")) ) math = OneOrMore( autoDelim ^ simple ).setParseAction(self.math).setName("math") math_delim = ~bslash + Literal('$') non_math = Regex(r"(?:(?:\\[$])|[^$])*" ).setParseAction(self.non_math).setName("non_math").leaveWhitespace() self._expression << ( non_math + ZeroOrMore( Suppress(math_delim) + Optional(math) + (Suppress(math_delim) | Error("Expected end of math '$'")) + non_math ) ) + StringEnd() self.clear() def clear(self): """ Clear any state before parsing. """ self._expr = None self._state_stack = None self._em_width_cache = {} def parse(self, s, fonts_object, fontsize, dpi): """ Parse expression *s* using the given *fonts_object* for output, at the given *fontsize* and *dpi*. Returns the parse tree of :class:`Node` instances. """ self._state_stack = [self.State(fonts_object, 'default', 'rm', fontsize, dpi)] try: self._expression.parseString(s) except ParseException, err: raise ValueError("\n".join([ "", err.line, " " * (err.column - 1) + "^", str(err)])) return self._expr # The state of the parser is maintained in a stack. Upon # entering and leaving a group { } or math/non-math, the stack # is pushed and popped accordingly. The current state always # exists in the top element of the stack. class State(object): """ Stores the state of the parser. States are pushed and popped from a stack as necessary, and the "current" state is always at the top of the stack. """ def __init__(self, font_output, font, font_class, fontsize, dpi): self.font_output = font_output self._font = font self.font_class = font_class self.fontsize = fontsize self.dpi = dpi def copy(self): return Parser.State( self.font_output, self.font, self.font_class, self.fontsize, self.dpi) def _get_font(self): return self._font def _set_font(self, name): if name in ('it', 'rm', 'bf'): self.font_class = name self._font = name font = property(_get_font, _set_font) def get_state(self): """ Get the current :class:`State` of the parser. """ return self._state_stack[-1] def pop_state(self): """ Pop a :class:`State` off of the stack. """ self._state_stack.pop() def push_state(self): """ Push a new :class:`State` onto the stack which is just a copy of the current state. """ self._state_stack.append(self.get_state().copy()) def finish(self, s, loc, toks): #~ print "finish", toks self._expr = Hlist(toks) return [self._expr] def math(self, s, loc, toks): #~ print "math", toks hlist = Hlist(toks) self.pop_state() return [hlist] def non_math(self, s, loc, toks): #~ print "non_math", toks s = toks[0].replace(r'\$', '$') symbols = [Char(c, self.get_state()) for c in s] hlist = Hlist(symbols) # We're going into math now, so set font to 'it' self.push_state() self.get_state().font = 'it' return [hlist] def _make_space(self, percentage): # All spaces are relative to em width state = self.get_state() key = (state.font, state.fontsize, state.dpi) width = self._em_width_cache.get(key) if width is None: metrics = state.font_output.get_metrics( state.font, 'it', 'm', state.fontsize, state.dpi) width = metrics.advance self._em_width_cache[key] = width return Kern(width * percentage) _space_widths = { r'\ ' : 0.3, r'\,' : 0.4, r'\;' : 0.8, r'\quad' : 1.6, r'\qquad' : 3.2, r'\!' : -0.4, r'\/' : 0.4 } def space(self, s, loc, toks): assert(len(toks)==1) num = self._space_widths[toks[0]] box = self._make_space(num) return [box] def customspace(self, s, loc, toks): return [self._make_space(float(toks[1]))] def symbol(self, s, loc, toks): # print "symbol", toks c = toks[0] try: char = Char(c, self.get_state()) except ValueError: raise ParseFatalException("Unknown symbol: %s" % c) if c in self._spaced_symbols: return [Hlist( [self._make_space(0.2), char, self._make_space(0.2)] , do_kern = False)] elif c in self._punctuation_symbols: return [Hlist( [char, self._make_space(0.2)] , do_kern = False)] return [char] _char_over_chars = { # The first 2 entires in the tuple are (font, char, sizescale) for # the two symbols under and over. The third element is the space # (in multiples of underline height) r'AA' : ( ('rm', 'A', 1.0), (None, '\circ', 0.5), 0.0), } def char_over_chars(self, s, loc, toks): sym = toks[0] state = self.get_state() thickness = state.font_output.get_underline_thickness( state.font, state.fontsize, state.dpi) under_desc, over_desc, space = \ self._char_over_chars.get(sym, (None, None, 0.0)) if under_desc is None: raise ParseFatalException("Error parsing symbol") over_state = state.copy() if over_desc[0] is not None: over_state.font = over_desc[0] over_state.fontsize *= over_desc[2] over = Accent(over_desc[1], over_state) under_state = state.copy() if under_desc[0] is not None: under_state.font = under_desc[0] under_state.fontsize *= under_desc[2] under = Char(under_desc[1], under_state) width = max(over.width, under.width) over_centered = HCentered([over]) over_centered.hpack(width, 'exactly') under_centered = HCentered([under]) under_centered.hpack(width, 'exactly') return Vlist([ over_centered, Vbox(0., thickness * space), under_centered ]) _accent_map = { r'hat' : r'\circumflexaccent', r'breve' : r'\combiningbreve', r'bar' : r'\combiningoverline', r'grave' : r'\combininggraveaccent', r'acute' : r'\combiningacuteaccent', r'ddot' : r'\combiningdiaeresis', r'tilde' : r'\combiningtilde', r'dot' : r'\combiningdotabove', r'vec' : r'\combiningrightarrowabove', r'"' : r'\combiningdiaeresis', r"`" : r'\combininggraveaccent', r"'" : r'\combiningacuteaccent', r'~' : r'\combiningtilde', r'.' : r'\combiningdotabove', r'^' : r'\circumflexaccent' } _wide_accents = set(r"widehat widetilde".split()) def accent(self, s, loc, toks): assert(len(toks)==1) state = self.get_state() thickness = state.font_output.get_underline_thickness( state.font, state.fontsize, state.dpi) if len(toks[0]) != 2: raise ParseFatalException("Error parsing accent") accent, sym = toks[0] if accent in self._wide_accents: accent = AutoWidthChar( '\\' + accent, sym.width, state, char_class=Accent) else: accent = Accent(self._accent_map[accent], state) centered = HCentered([accent]) centered.hpack(sym.width, 'exactly') return Vlist([ centered, Vbox(0., thickness * 2.0), Hlist([sym]) ]) def function(self, s, loc, toks): #~ print "function", toks self.push_state() state = self.get_state() state.font = 'rm' hlist = Hlist([Char(c, state) for c in toks[0]]) self.pop_state() hlist.function_name = toks[0] return hlist def start_group(self, s, loc, toks): self.push_state() # Deal with LaTeX-style font tokens if len(toks): self.get_state().font = toks[0][4:] return [] def group(self, s, loc, toks): grp = Hlist(toks[0]) return [grp] def end_group(self, s, loc, toks): self.pop_state() return [] def font(self, s, loc, toks): assert(len(toks)==1) name = toks[0] self.get_state().font = name return [] def is_overunder(self, nucleus): if isinstance(nucleus, Char): return nucleus.c in self._overunder_symbols elif isinstance(nucleus, Hlist) and hasattr(nucleus, 'function_name'): return nucleus.function_name in self._overunder_functions return False def is_dropsub(self, nucleus): if isinstance(nucleus, Char): return nucleus.c in self._dropsub_symbols return False def is_slanted(self, nucleus): if isinstance(nucleus, Char): return nucleus.is_slanted() return False def subsuperscript(self, s, loc, toks): assert(len(toks)==1) # print 'subsuperscript', toks nucleus = None sub = None super = None if len(toks[0]) == 1: return toks[0].asList() elif len(toks[0]) == 2: op, next = toks[0] nucleus = Hbox(0.0) if op == '_': sub = next else: super = next elif len(toks[0]) == 3: nucleus, op, next = toks[0] if op == '_': sub = next else: super = next elif len(toks[0]) == 5: nucleus, op1, next1, op2, next2 = toks[0] if op1 == op2: if op1 == '_': raise ParseFatalException("Double subscript") else: raise ParseFatalException("Double superscript") if op1 == '_': sub = next1 super = next2 else: super = next1 sub = next2 else: raise ParseFatalException( "Subscript/superscript sequence is too long. " "Use braces { } to remove ambiguity.") state = self.get_state() rule_thickness = state.font_output.get_underline_thickness( state.font, state.fontsize, state.dpi) xHeight = state.font_output.get_xheight( state.font, state.fontsize, state.dpi) # Handle over/under symbols, such as sum or integral if self.is_overunder(nucleus): vlist = [] shift = 0. width = nucleus.width if super is not None: super.shrink() width = max(width, super.width) if sub is not None: sub.shrink() width = max(width, sub.width) if super is not None: hlist = HCentered([super]) hlist.hpack(width, 'exactly') vlist.extend([hlist, Kern(rule_thickness * 3.0)]) hlist = HCentered([nucleus]) hlist.hpack(width, 'exactly') vlist.append(hlist) if sub is not None: hlist = HCentered([sub]) hlist.hpack(width, 'exactly') vlist.extend([Kern(rule_thickness * 3.0), hlist]) shift = hlist.height + hlist.depth + rule_thickness * 2.0 vlist = Vlist(vlist) vlist.shift_amount = shift + nucleus.depth * 0.5 result = Hlist([vlist]) return [result] # Handle regular sub/superscripts shift_up = nucleus.height - SUBDROP * xHeight if self.is_dropsub(nucleus): shift_down = nucleus.depth + SUBDROP * xHeight else: shift_down = SUBDROP * xHeight if super is None: # node757 sub.shrink() x = Hlist([sub]) # x.width += SCRIPT_SPACE * xHeight shift_down = max(shift_down, SUB1) clr = x.height - (abs(xHeight * 4.0) / 5.0) shift_down = max(shift_down, clr) x.shift_amount = shift_down else: super.shrink() x = Hlist([super, Kern(SCRIPT_SPACE * xHeight)]) # x.width += SCRIPT_SPACE * xHeight clr = SUP1 * xHeight shift_up = max(shift_up, clr) clr = x.depth + (abs(xHeight) / 4.0) shift_up = max(shift_up, clr) if sub is None: x.shift_amount = -shift_up else: # Both sub and superscript sub.shrink() y = Hlist([sub]) # y.width += SCRIPT_SPACE * xHeight shift_down = max(shift_down, SUB1 * xHeight) clr = (2.0 * rule_thickness - ((shift_up - x.depth) - (y.height - shift_down))) if clr > 0.: shift_up += clr shift_down += clr if self.is_slanted(nucleus): x.shift_amount = DELTA * (shift_up + shift_down) x = Vlist([x, Kern((shift_up - x.depth) - (y.height - shift_down)), y]) x.shift_amount = shift_down result = Hlist([nucleus, x]) return [result] def frac(self, s, loc, toks): assert(len(toks)==1) assert(len(toks[0])==2) state = self.get_state() thickness = state.font_output.get_underline_thickness( state.font, state.fontsize, state.dpi) num, den = toks[0] num.shrink() den.shrink() cnum = HCentered([num]) cden = HCentered([den]) width = max(num.width, den.width) + thickness * 10. cnum.hpack(width, 'exactly') cden.hpack(width, 'exactly') vlist = Vlist([cnum, # numerator Vbox(0, thickness * 2.0), # space Hrule(state), # rule Vbox(0, thickness * 4.0), # space cden # denominator ]) # Shift so the fraction line sits in the middle of the # equals sign metrics = state.font_output.get_metrics( state.font, 'it', '=', state.fontsize, state.dpi) shift = (cden.height - ((metrics.ymax + metrics.ymin) / 2 - thickness * 3.0)) vlist.shift_amount = shift hlist = Hlist([vlist, Hbox(thickness * 2.)]) return [hlist] def sqrt(self, s, loc, toks): #~ print "sqrt", toks root, body = toks[0] state = self.get_state() thickness = state.font_output.get_underline_thickness( state.font, state.fontsize, state.dpi) # Determine the height of the body, and add a little extra to # the height so it doesn't seem cramped height = body.height - body.shift_amount + thickness * 5.0 depth = body.depth + body.shift_amount check = AutoHeightChar(r'\__sqrt__', height, depth, state, always=True) height = check.height - check.shift_amount depth = check.depth + check.shift_amount # Put a little extra space to the left and right of the body padded_body = Hlist([Hbox(thickness * 2.0), body, Hbox(thickness * 2.0)]) rightside = Vlist([Hrule(state), Fill(), padded_body]) # Stretch the glue between the hrule and the body rightside.vpack(height + (state.fontsize * state.dpi) / (100.0 * 12.0), depth, 'exactly') # Add the root and shift it upward so it is above the tick. # The value of 0.6 is a hard-coded hack ;) if root is None: root = Box(check.width * 0.5, 0., 0.) else: root = Hlist([Char(x, state) for x in root]) root.shrink() root.shrink() root_vlist = Vlist([Hlist([root])]) root_vlist.shift_amount = -height * 0.6 hlist = Hlist([root_vlist, # Root # Negative kerning to put root over tick Kern(-check.width * 0.5), check, # Check rightside]) # Body return [hlist] def auto_sized_delimiter(self, s, loc, toks): #~ print "auto_sized_delimiter", toks front, middle, back = toks state = self.get_state() height = max([x.height for x in middle]) depth = max([x.depth for x in middle]) parts = [] # \left. and \right. aren't supposed to produce any symbols if front != '.': parts.append(AutoHeightChar(front, height, depth, state)) parts.extend(middle.asList()) if back != '.': parts.append(AutoHeightChar(back, height, depth, state)) hlist = Hlist(parts) return hlist ### ############################################################################## # MAIN class MathTextParser(object): _parser = None _backend_mapping = { 'bitmap': MathtextBackendBitmap, 'agg' : MathtextBackendAgg, 'ps' : MathtextBackendPs, 'pdf' : MathtextBackendPdf, 'svg' : MathtextBackendSvg, 'cairo' : MathtextBackendCairo, 'macosx': MathtextBackendAgg, } _font_type_mapping = { 'cm' : BakomaFonts, 'stix' : StixFonts, 'stixsans' : StixSansFonts, 'custom' : UnicodeFonts } def __init__(self, output): """ Create a MathTextParser for the given backend *output*. """ self._output = output.lower() self._cache = maxdict(50) def parse(self, s, dpi = 72, prop = None): """ Parse the given math expression *s* at the given *dpi*. If *prop* is provided, it is a :class:`~matplotlib.font_manager.FontProperties` object specifying the "default" font to use in the math expression, used for all non-math text. The results are cached, so multiple calls to :meth:`parse` with the same expression should be fast. """ if prop is None: prop = FontProperties() cacheKey = (s, dpi, hash(prop)) result = self._cache.get(cacheKey) if result is not None: return result if self._output == 'ps' and rcParams['ps.useafm']: font_output = StandardPsFonts(prop) else: backend = self._backend_mapping[self._output]() fontset = rcParams['mathtext.fontset'] fontset_class = self._font_type_mapping.get(fontset.lower()) if fontset_class is not None: font_output = fontset_class(prop, backend) else: raise ValueError( "mathtext.fontset must be either 'cm', 'stix', " "'stixsans', or 'custom'") fontsize = prop.get_size_in_points() # This is a class variable so we don't rebuild the parser # with each request. if self._parser is None: self.__class__._parser = Parser() box = self._parser.parse(s, font_output, fontsize, dpi) font_output.set_canvas_size(box.width, box.height, box.depth) result = font_output.get_results(box) self._cache[cacheKey] = result # Free up the transient data structures self._parser.clear() # Fix cyclical references font_output.destroy() font_output.mathtext_backend.fonts_object = None font_output.mathtext_backend = None return result def to_mask(self, texstr, dpi=120, fontsize=14): """ *texstr* A valid mathtext string, eg r'IQ: $\sigma_i=15$' *dpi* The dots-per-inch to render the text *fontsize* The font size in points Returns a tuple (*array*, *depth*) - *array* is an NxM uint8 alpha ubyte mask array of rasterized tex. - depth is the offset of the baseline from the bottom of the image in pixels. """ assert(self._output=="bitmap") prop = FontProperties(size=fontsize) ftimage, depth = self.parse(texstr, dpi=dpi, prop=prop) x = ftimage.as_array() return x, depth def to_rgba(self, texstr, color='black', dpi=120, fontsize=14): """ *texstr* A valid mathtext string, eg r'IQ: $\sigma_i=15$' *color* Any matplotlib color argument *dpi* The dots-per-inch to render the text *fontsize* The font size in points Returns a tuple (*array*, *depth*) - *array* is an NxM uint8 alpha ubyte mask array of rasterized tex. - depth is the offset of the baseline from the bottom of the image in pixels. """ x, depth = self.to_mask(texstr, dpi=dpi, fontsize=fontsize) r, g, b = mcolors.colorConverter.to_rgb(color) RGBA = np.zeros((x.shape[0], x.shape[1], 4), dtype=np.uint8) RGBA[:,:,0] = int(255*r) RGBA[:,:,1] = int(255*g) RGBA[:,:,2] = int(255*b) RGBA[:,:,3] = x return RGBA, depth def to_png(self, filename, texstr, color='black', dpi=120, fontsize=14): """ Writes a tex expression to a PNG file. Returns the offset of the baseline from the bottom of the image in pixels. *filename* A writable filename or fileobject *texstr* A valid mathtext string, eg r'IQ: $\sigma_i=15$' *color* A valid matplotlib color argument *dpi* The dots-per-inch to render the text *fontsize* The font size in points Returns the offset of the baseline from the bottom of the image in pixels. """ rgba, depth = self.to_rgba(texstr, color=color, dpi=dpi, fontsize=fontsize) numrows, numcols, tmp = rgba.shape _png.write_png(rgba.tostring(), numcols, numrows, filename) return depth def get_depth(self, texstr, dpi=120, fontsize=14): """ Returns the offset of the baseline from the bottom of the image in pixels. *texstr* A valid mathtext string, eg r'IQ: $\sigma_i=15$' *dpi* The dots-per-inch to render the text *fontsize* The font size in points """ assert(self._output=="bitmap") prop = FontProperties(size=fontsize) ftimage, depth = self.parse(texstr, dpi=dpi, prop=prop) return depth

101,723

Python

.py

2,503

29.952457

106

0.53857

numenta/nupic-legacy

6,330

1,556

464

AGPL-3.0

9/5/2024, 5:13:42 PM (Europe/Amsterdam)

26,282

blocking_input.py

numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/blocking_input.py

""" This provides several classes used for blocking interaction with figure windows: :class:`BlockingInput` creates a callable object to retrieve events in a blocking way for interactive sessions :class:`BlockingKeyMouseInput` creates a callable object to retrieve key or mouse clicks in a blocking way for interactive sessions. Note: Subclass of BlockingInput. Used by waitforbuttonpress :class:`BlockingMouseInput` creates a callable object to retrieve mouse clicks in a blocking way for interactive sessions. Note: Subclass of BlockingInput. Used by ginput :class:`BlockingContourLabeler` creates a callable object to retrieve mouse clicks in a blocking way that will then be used to place labels on a ContourSet Note: Subclass of BlockingMouseInput. Used by clabel """ import time import numpy as np from matplotlib import path, verbose from matplotlib.cbook import is_sequence_of_strings class BlockingInput(object): """ Class that creates a callable object to retrieve events in a blocking way. """ def __init__(self, fig, eventslist=()): self.fig = fig assert is_sequence_of_strings(eventslist), "Requires a sequence of event name strings" self.eventslist = eventslist def on_event(self, event): """ Event handler that will be passed to the current figure to retrieve events. """ # Add a new event to list - using a separate function is # overkill for the base class, but this is consistent with # subclasses self.add_event(event) verbose.report("Event %i" % len(self.events)) # This will extract info from events self.post_event() # Check if we have enough events already if len(self.events) >= self.n and self.n > 0: self.fig.canvas.stop_event_loop() def post_event(self): """For baseclass, do nothing but collect events""" pass def cleanup(self): """Disconnect all callbacks""" for cb in self.callbacks: self.fig.canvas.mpl_disconnect(cb) self.callbacks=[] def add_event(self,event): """For base class, this just appends an event to events.""" self.events.append(event) def pop_event(self,index=-1): """ This removes an event from the event list. Defaults to removing last event, but an index can be supplied. Note that this does not check that there are events, much like the normal pop method. If not events exist, this will throw an exception. """ self.events.pop(index) def pop(self,index=-1): self.pop_event(index) pop.__doc__=pop_event.__doc__ def __call__(self, n=1, timeout=30 ): """ Blocking call to retrieve n events """ assert isinstance(n, int), "Requires an integer argument" self.n = n self.events = [] self.callbacks = [] # Ensure that the figure is shown self.fig.show() # connect the events to the on_event function call for n in self.eventslist: self.callbacks.append( self.fig.canvas.mpl_connect(n, self.on_event) ) try: # Start event loop self.fig.canvas.start_event_loop(timeout=timeout) finally: # Run even on exception like ctrl-c # Disconnect the callbacks self.cleanup() # Return the events in this case return self.events class BlockingMouseInput(BlockingInput): """ Class that creates a callable object to retrieve mouse clicks in a blocking way. This class will also retrieve keyboard clicks and treat them like appropriate mouse clicks (delete and backspace are like mouse button 3, enter is like mouse button 2 and all others are like mouse button 1). """ def __init__(self, fig): BlockingInput.__init__(self, fig=fig, eventslist=('button_press_event', 'key_press_event') ) def post_event(self): """ This will be called to process events """ assert len(self.events)>0, "No events yet" if self.events[-1].name == 'key_press_event': self.key_event() else: self.mouse_event() def mouse_event(self): '''Process a mouse click event''' event = self.events[-1] button = event.button if button == 3: self.button3(event) elif button == 2: self.button2(event) else: self.button1(event) def key_event(self): ''' Process a key click event. This maps certain keys to appropriate mouse click events. ''' event = self.events[-1] key = event.key if key == 'backspace' or key == 'delete': self.button3(event) elif key == 'enter': self.button2(event) else: self.button1(event) def button1( self, event ): """ Will be called for any event involving a button other than button 2 or 3. This will add a click if it is inside axes. """ if event.inaxes: self.add_click(event) else: # If not a valid click, remove from event list BlockingInput.pop(self) def button2( self, event ): """ Will be called for any event involving button 2. Button 2 ends blocking input. """ # Remove last event just for cleanliness BlockingInput.pop(self) # This will exit even if not in infinite mode. This is # consistent with matlab and sometimes quite useful, but will # require the user to test how many points were actually # returned before using data. self.fig.canvas.stop_event_loop() def button3( self, event ): """ Will be called for any event involving button 3. Button 3 removes the last click. """ # Remove this last event BlockingInput.pop(self) # Now remove any existing clicks if possible if len(self.events)>0: self.pop() def add_click(self,event): """ This add the coordinates of an event to the list of clicks """ self.clicks.append((event.xdata,event.ydata)) verbose.report("input %i: %f,%f" % (len(self.clicks),event.xdata, event.ydata)) # If desired plot up click if self.show_clicks: self.marks.extend( event.inaxes.plot([event.xdata,], [event.ydata,], 'r+') ) self.fig.canvas.draw() def pop_click(self,index=-1): """ This removes a click from the list of clicks. Defaults to removing the last click. """ self.clicks.pop(index) if self.show_clicks: mark = self.marks.pop(index) mark.remove() self.fig.canvas.draw() def pop(self,index=-1): """ This removes a click and the associated event from the object. Defaults to removing the last click, but any index can be supplied. """ self.pop_click(index) BlockingInput.pop(self,index) def cleanup(self): # clean the figure if self.show_clicks: for mark in self.marks: mark.remove() self.marks = [] self.fig.canvas.draw() # Call base class to remove callbacks BlockingInput.cleanup(self) def __call__(self, n=1, timeout=30, show_clicks=True): """ Blocking call to retrieve n coordinate pairs through mouse clicks. """ self.show_clicks = show_clicks self.clicks = [] self.marks = [] BlockingInput.__call__(self,n=n,timeout=timeout) return self.clicks class BlockingContourLabeler( BlockingMouseInput ): """ Class that creates a callable object that uses mouse clicks or key clicks on a figure window to place contour labels. """ def __init__(self,cs): self.cs = cs BlockingMouseInput.__init__(self, fig=cs.ax.figure ) def button1(self,event): """ This will be called if an event involving a button other than 2 or 3 occcurs. This will add a label to a contour. """ # Shorthand cs = self.cs if event.inaxes == cs.ax: conmin,segmin,imin,xmin,ymin = cs.find_nearest_contour( event.x, event.y, cs.labelIndiceList)[:5] # Get index of nearest level in subset of levels used for labeling lmin = cs.labelIndiceList.index(conmin) # Coordinates of contour paths = cs.collections[conmin].get_paths() lc = paths[segmin].vertices # In pixel/screen space slc = cs.ax.transData.transform(lc) # Get label width for rotating labels and breaking contours lw = cs.get_label_width(cs.labelLevelList[lmin], cs.labelFmt, cs.labelFontSizeList[lmin]) """ # requires python 2.5 # Figure out label rotation. rotation,nlc = cs.calc_label_rot_and_inline( slc, imin, lw, lc if self.inline else [], self.inline_spacing ) """ # Figure out label rotation. if self.inline: lcarg = lc else: lcarg = None rotation,nlc = cs.calc_label_rot_and_inline( slc, imin, lw, lcarg, self.inline_spacing ) cs.add_label(xmin,ymin,rotation,cs.labelLevelList[lmin], cs.labelCValueList[lmin]) if self.inline: # Remove old, not looping over paths so we can do this up front paths.pop(segmin) # Add paths if not empty or single point for n in nlc: if len(n)>1: paths.append( path.Path(n) ) self.fig.canvas.draw() else: # Remove event if not valid BlockingInput.pop(self) def button3(self,event): """ This will be called if button 3 is clicked. This will remove a label if not in inline mode. Unfortunately, if one is doing inline labels, then there is currently no way to fix the broken contour - once humpty-dumpty is broken, he can't be put back together. In inline mode, this does nothing. """ # Remove this last event - not too important for clabel use # since clabel normally doesn't have a maximum number of # events, but best for cleanliness sake. BlockingInput.pop(self) if self.inline: pass else: self.cs.pop_label() self.cs.ax.figure.canvas.draw() def __call__(self,inline,inline_spacing=5,n=-1,timeout=-1): self.inline=inline self.inline_spacing=inline_spacing BlockingMouseInput.__call__(self,n=n,timeout=timeout, show_clicks=False) class BlockingKeyMouseInput(BlockingInput): """ Class that creates a callable object to retrieve a single mouse or keyboard click """ def __init__(self, fig): BlockingInput.__init__(self, fig=fig, eventslist=('button_press_event','key_press_event') ) def post_event(self): """ Determines if it is a key event """ assert len(self.events)>0, "No events yet" self.keyormouse = self.events[-1].name == 'key_press_event' def __call__(self, timeout=30): """ Blocking call to retrieve a single mouse or key click Returns True if key click, False if mouse, or None if timeout """ self.keyormouse = None BlockingInput.__call__(self,n=1,timeout=timeout) return self.keyormouse

12,119

Python

.py

306

29.859477

127

0.601877

numenta/nupic-legacy

6,330

1,556

464

AGPL-3.0

9/5/2024, 5:13:42 PM (Europe/Amsterdam)

26,283

interpolate.py

numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/delaunay/interpolate.py

import numpy as np from matplotlib._delaunay import compute_planes, linear_interpolate_grid, nn_interpolate_grid from matplotlib._delaunay import nn_interpolate_unstructured __all__ = ['LinearInterpolator', 'NNInterpolator'] def slice2gridspec(key): """Convert a 2-tuple of slices to start,stop,steps for x and y. key -- (slice(ystart,ystop,ystep), slice(xtart, xstop, xstep)) For now, the only accepted step values are imaginary integers (interpreted in the same way numpy.mgrid, etc. do). """ if ((len(key) != 2) or (not isinstance(key[0], slice)) or (not isinstance(key[1], slice))): raise ValueError("only 2-D slices, please") x0 = key[1].start x1 = key[1].stop xstep = key[1].step if not isinstance(xstep, complex) or int(xstep.real) != xstep.real: raise ValueError("only the [start:stop:numsteps*1j] form supported") xstep = int(xstep.imag) y0 = key[0].start y1 = key[0].stop ystep = key[0].step if not isinstance(ystep, complex) or int(ystep.real) != ystep.real: raise ValueError("only the [start:stop:numsteps*1j] form supported") ystep = int(ystep.imag) return x0, x1, xstep, y0, y1, ystep class LinearInterpolator(object): """Interpolate a function defined on the nodes of a triangulation by using the planes defined by the three function values at each corner of the triangles. LinearInterpolator(triangulation, z, default_value=numpy.nan) triangulation -- Triangulation instance z -- the function values at each node of the triangulation default_value -- a float giving the default value should the interpolating point happen to fall outside of the convex hull of the triangulation At the moment, the only regular rectangular grids are supported for interpolation. vals = interp[ystart:ystop:ysteps*1j, xstart:xstop:xsteps*1j] vals would then be a (ysteps, xsteps) array containing the interpolated values. These arguments are interpreted the same way as numpy.mgrid. Attributes: planes -- (ntriangles, 3) array of floats specifying the plane for each triangle. Linear Interpolation -------------------- Given the Delauany triangulation (or indeed *any* complete triangulation) we can interpolate values inside the convex hull by locating the enclosing triangle of the interpolation point and returning the value at that point of the plane defined by the three node values. f = planes[tri,0]*x + planes[tri,1]*y + planes[tri,2] The interpolated function is C0 continuous across the convex hull of the input points. It is C1 continuous across the convex hull except for the nodes and the edges of the triangulation. """ def __init__(self, triangulation, z, default_value=np.nan): self.triangulation = triangulation self.z = np.asarray(z, dtype=np.float64) self.default_value = default_value self.planes = compute_planes(triangulation.x, triangulation.y, self.z, triangulation.triangle_nodes) def __getitem__(self, key): x0, x1, xstep, y0, y1, ystep = slice2gridspec(key) grid = linear_interpolate_grid(x0, x1, xstep, y0, y1, ystep, self.default_value, self.planes, self.triangulation.x, self.triangulation.y, self.triangulation.triangle_nodes, self.triangulation.triangle_neighbors) return grid class NNInterpolator(object): """Interpolate a function defined on the nodes of a triangulation by the natural neighbors method. NNInterpolator(triangulation, z, default_value=numpy.nan) triangulation -- Triangulation instance z -- the function values at each node of the triangulation default_value -- a float giving the default value should the interpolating point happen to fall outside of the convex hull of the triangulation At the moment, the only regular rectangular grids are supported for interpolation. vals = interp[ystart:ystop:ysteps*1j, xstart:xstop:xsteps*1j] vals would then be a (ysteps, xsteps) array containing the interpolated values. These arguments are interpreted the same way as numpy.mgrid. Natural Neighbors Interpolation ------------------------------- One feature of the Delaunay triangulation is that for each triangle, its circumcircle contains no other point (although in degenerate cases, like squares, other points may be *on* the circumcircle). One can also construct what is called the Voronoi diagram from a Delaunay triangulation by connecting the circumcenters of the triangles to those of their neighbors to form a tesselation of irregular polygons covering the plane and containing only one node from the triangulation. Each point in one node's Voronoi polygon is closer to that node than any other node. To compute the Natural Neighbors interpolant, we consider adding the interpolation point to the triangulation. We define the natural neighbors of this point as the set of nodes participating in Delaunay triangles whose circumcircles contain the point. To restore the Delaunay-ness of the triangulation, one would only have to alter those triangles and Voronoi polygons. The new Voronooi diagram would have a polygon around the inserted point. This polygon would "steal" area from the original Voronoi polygons. For each node i in the natural neighbors set, we compute the area stolen from its original Voronoi polygon, stolen[i]. We define the natural neighbors coordinates phi[i] = stolen[i] / sum(stolen,axis=0) We then use these phi[i] to weight the corresponding function values from the input data z to compute the interpolated value. The interpolated surface is C1-continuous except at the nodes themselves across the convex hull of the input points. One can find the set of points that a given node will affect by computing the union of the areas covered by the circumcircles of each Delaunay triangle that node participates in. """ def __init__(self, triangulation, z, default_value=np.nan): self.triangulation = triangulation self.z = np.asarray(z, dtype=np.float64) self.default_value = default_value def __getitem__(self, key): x0, x1, xstep, y0, y1, ystep = slice2gridspec(key) grid = nn_interpolate_grid(x0, x1, xstep, y0, y1, ystep, self.default_value, self.triangulation.x, self.triangulation.y, self.z, self.triangulation.circumcenters, self.triangulation.triangle_nodes, self.triangulation.triangle_neighbors) return grid def __call__(self, intx, inty): intz = nn_interpolate_unstructured(intx, inty, self.default_value, self.triangulation.x, self.triangulation.y, self.z, self.triangulation.circumcenters, self.triangulation.triangle_nodes, self.triangulation.triangle_neighbors) return intz

7,068

Python

.py

127

49.086614

93

0.721338

numenta/nupic-legacy

6,330

1,556

464

AGPL-3.0

9/5/2024, 5:13:42 PM (Europe/Amsterdam)

26,284

testfuncs.py

numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/delaunay/testfuncs.py

"""Some test functions for bivariate interpolation. Most of these have been yoinked from ACM TOMS 792. http://netlib.org/toms/792 """ import numpy as np from triangulate import Triangulation class TestData(dict): def __init__(self, *args, **kwds): dict.__init__(self, *args, **kwds) self.__dict__ = self class TestDataSet(object): def __init__(self, **kwds): self.__dict__.update(kwds) data = TestData( franke100=TestDataSet( x=np.array([ 0.0227035, 0.0539888, 0.0217008, 0.0175129, 0.0019029, -0.0509685, 0.0395408, -0.0487061, 0.0315828, -0.0418785, 0.1324189, 0.1090271, 0.1254439, 0.093454 , 0.0767578, 0.1451874, 0.0626494, 0.1452734, 0.0958668, 0.0695559, 0.2645602, 0.2391645, 0.208899 , 0.2767329, 0.1714726, 0.2266781, 0.1909212, 0.1867647, 0.2304634, 0.2426219, 0.3663168, 0.3857662, 0.3832392, 0.3179087, 0.3466321, 0.3776591, 0.3873159, 0.3812917, 0.3795364, 0.2803515, 0.4149771, 0.4277679, 0.420001 , 0.4663631, 0.4855658, 0.4092026, 0.4792578, 0.4812279, 0.3977761, 0.4027321, 0.5848691, 0.5730076, 0.6063893, 0.5013894, 0.5741311, 0.6106955, 0.5990105, 0.5380621, 0.6096967, 0.5026188, 0.6616928, 0.6427836, 0.6396475, 0.6703963, 0.7001181, 0.633359 , 0.6908947, 0.6895638, 0.6718889, 0.6837675, 0.7736939, 0.7635332, 0.7410424, 0.8258981, 0.7306034, 0.8086609, 0.8214531, 0.729064 , 0.8076643, 0.8170951, 0.8424572, 0.8684053, 0.8366923, 0.9418461, 0.8478122, 0.8599583, 0.91757 , 0.8596328, 0.9279871, 0.8512805, 1.044982 , 0.9670631, 0.9857884, 0.9676313, 1.0129299, 0.965704 , 1.0019855, 1.0359297, 1.0414677, 0.9471506]), y=np.array([-0.0310206, 0.1586742, 0.2576924, 0.3414014, 0.4943596, 0.5782854, 0.6993418, 0.7470194, 0.9107649, 0.996289 , 0.050133 , 0.0918555, 0.2592973, 0.3381592, 0.4171125, 0.5615563, 0.6552235, 0.7524066, 0.9146523, 0.9632421, 0.0292939, 0.0602303, 0.2668783, 0.3696044, 0.4801738, 0.5940595, 0.6878797, 0.8185576, 0.9046507, 0.9805412, 0.0396955, 0.0684484, 0.2389548, 0.3124129, 0.4902989, 0.5199303, 0.6445227, 0.8203789, 0.8938079, 0.9711719, -0.0284618, 0.1560965, 0.2262471, 0.3175094, 0.3891417, 0.5084949, 0.6324247, 0.7511007, 0.8489712, 0.9978728, -0.0271948, 0.127243 , 0.2709269, 0.3477728, 0.4259422, 0.6084711, 0.6733781, 0.7235242, 0.9242411, 1.0308762, 0.0255959, 0.0707835, 0.2008336, 0.3259843, 0.4890704, 0.5096324, 0.669788 , 0.7759569, 0.9366096, 1.0064516, 0.0285374, 0.1021403, 0.1936581, 0.3235775, 0.4714228, 0.6091595, 0.6685053, 0.8022808, 0.847679 , 1.0512371, 0.0380499, 0.0902048, 0.2083092, 0.3318491, 0.4335632, 0.5910139, 0.6307383, 0.8144841, 0.904231 , 0.969603 , -0.01209 , 0.1334114, 0.2695844, 0.3795281, 0.4396054, 0.5044425, 0.6941519, 0.7459923, 0.8682081, 0.9801409])), franke33=TestDataSet( x=np.array([ 5.00000000e-02, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.00000000e-01, 1.00000000e-01, 1.50000000e-01, 2.00000000e-01, 2.50000000e-01, 3.00000000e-01, 3.50000000e-01, 5.00000000e-01, 5.00000000e-01, 5.50000000e-01, 6.00000000e-01, 6.00000000e-01, 6.00000000e-01, 6.50000000e-01, 7.00000000e-01, 7.00000000e-01, 7.00000000e-01, 7.50000000e-01, 7.50000000e-01, 7.50000000e-01, 8.00000000e-01, 8.00000000e-01, 8.50000000e-01, 9.00000000e-01, 9.00000000e-01, 9.50000000e-01, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00]), y=np.array([ 4.50000000e-01, 5.00000000e-01, 1.00000000e+00, 0.00000000e+00, 1.50000000e-01, 7.50000000e-01, 3.00000000e-01, 1.00000000e-01, 2.00000000e-01, 3.50000000e-01, 8.50000000e-01, 0.00000000e+00, 1.00000000e+00, 9.50000000e-01, 2.50000000e-01, 6.50000000e-01, 8.50000000e-01, 7.00000000e-01, 2.00000000e-01, 6.50000000e-01, 9.00000000e-01, 1.00000000e-01, 3.50000000e-01, 8.50000000e-01, 4.00000000e-01, 6.50000000e-01, 2.50000000e-01, 3.50000000e-01, 8.00000000e-01, 9.00000000e-01, 0.00000000e+00, 5.00000000e-01, 1.00000000e+00])), lawson25=TestDataSet( x=np.array([ 0.1375, 0.9125, 0.7125, 0.225 , -0.05 , 0.475 , 0.05 , 0.45 , 1.0875, 0.5375, -0.0375, 0.1875, 0.7125, 0.85 , 0.7 , 0.275 , 0.45 , 0.8125, 0.45 , 1. , 0.5 , 0.1875, 0.5875, 1.05 , 0.1 ]), y=np.array([ 0.975 , 0.9875 , 0.7625 , 0.8375 , 0.4125 , 0.6375 , -0.05 , 1.0375 , 0.55 , 0.8 , 0.75 , 0.575 , 0.55 , 0.4375 , 0.3125 , 0.425 , 0.2875 , 0.1875 , -0.0375 , 0.2625 , 0.4625 , 0.2625 , 0.125 , -0.06125, 0.1125 ])), random100=TestDataSet( x=np.array([ 0.0096326, 0.0216348, 0.029836 , 0.0417447, 0.0470462, 0.0562965, 0.0646857, 0.0740377, 0.0873907, 0.0934832, 0.1032216, 0.1110176, 0.1181193, 0.1251704, 0.132733 , 0.1439536, 0.1564861, 0.1651043, 0.1786039, 0.1886405, 0.2016706, 0.2099886, 0.2147003, 0.2204141, 0.2343715, 0.240966 , 0.252774 , 0.2570839, 0.2733365, 0.2853833, 0.2901755, 0.2964854, 0.3019725, 0.3125695, 0.3307163, 0.3378504, 0.3439061, 0.3529922, 0.3635507, 0.3766172, 0.3822429, 0.3869838, 0.3973137, 0.4170708, 0.4255588, 0.4299218, 0.4372839, 0.4705033, 0.4736655, 0.4879299, 0.494026 , 0.5055324, 0.5162593, 0.5219219, 0.5348529, 0.5483213, 0.5569571, 0.5638611, 0.5784908, 0.586395 , 0.5929148, 0.5987839, 0.6117561, 0.6252296, 0.6331381, 0.6399048, 0.6488972, 0.6558537, 0.6677405, 0.6814074, 0.6887812, 0.6940896, 0.7061687, 0.7160957, 0.7317445, 0.7370798, 0.746203 , 0.7566957, 0.7699998, 0.7879347, 0.7944014, 0.8164468, 0.8192794, 0.8368405, 0.8500993, 0.8588255, 0.8646496, 0.8792329, 0.8837536, 0.8900077, 0.8969894, 0.9044917, 0.9083947, 0.9203972, 0.9347906, 0.9434519, 0.9490328, 0.9569571, 0.9772067, 0.9983493]), y=np.array([ 0.3083158, 0.2450434, 0.8613847, 0.0977864, 0.3648355, 0.7156339, 0.5311312, 0.9755672, 0.1781117, 0.5452797, 0.1603881, 0.7837139, 0.9982015, 0.6910589, 0.104958 , 0.8184662, 0.7086405, 0.4456593, 0.1178342, 0.3189021, 0.9668446, 0.7571834, 0.2016598, 0.3232444, 0.4368583, 0.8907869, 0.064726 , 0.5692618, 0.2947027, 0.4332426, 0.3347464, 0.7436284, 0.1066265, 0.8845357, 0.515873 , 0.9425637, 0.4799701, 0.1783069, 0.114676 , 0.8225797, 0.2270688, 0.4073598, 0.887508 , 0.7631616, 0.9972804, 0.4959884, 0.3410421, 0.249812 , 0.6409007, 0.105869 , 0.5411969, 0.0089792, 0.8784268, 0.5515874, 0.4038952, 0.1654023, 0.2965158, 0.3660356, 0.0366554, 0.950242 , 0.2638101, 0.9277386, 0.5377694, 0.7374676, 0.4674627, 0.9186109, 0.0416884, 0.1291029, 0.6763676, 0.8444238, 0.3273328, 0.1893879, 0.0645923, 0.0180147, 0.8904992, 0.4160648, 0.4688995, 0.2174508, 0.5734231, 0.8853319, 0.8018436, 0.6388941, 0.8931002, 0.1000558, 0.2789506, 0.9082948, 0.3259159, 0.8318747, 0.0508513, 0.970845 , 0.5120548, 0.2859716, 0.9581641, 0.6183429, 0.3779934, 0.4010423, 0.9478657, 0.7425486, 0.8883287, 0.549675 ])), uniform9=TestDataSet( x=np.array([ 1.25000000e-01, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.25000000e-01, 1.25000000e-01, 1.25000000e-01, 1.25000000e-01, 1.25000000e-01, 1.25000000e-01, 1.25000000e-01, 1.25000000e-01, 2.50000000e-01, 2.50000000e-01, 2.50000000e-01, 2.50000000e-01, 2.50000000e-01, 2.50000000e-01, 2.50000000e-01, 2.50000000e-01, 2.50000000e-01, 3.75000000e-01, 3.75000000e-01, 3.75000000e-01, 3.75000000e-01, 3.75000000e-01, 3.75000000e-01, 3.75000000e-01, 3.75000000e-01, 3.75000000e-01, 5.00000000e-01, 5.00000000e-01, 5.00000000e-01, 5.00000000e-01, 5.00000000e-01, 5.00000000e-01, 5.00000000e-01, 5.00000000e-01, 5.00000000e-01, 6.25000000e-01, 6.25000000e-01, 6.25000000e-01, 6.25000000e-01, 6.25000000e-01, 6.25000000e-01, 6.25000000e-01, 6.25000000e-01, 6.25000000e-01, 7.50000000e-01, 7.50000000e-01, 7.50000000e-01, 7.50000000e-01, 7.50000000e-01, 7.50000000e-01, 7.50000000e-01, 7.50000000e-01, 7.50000000e-01, 8.75000000e-01, 8.75000000e-01, 8.75000000e-01, 8.75000000e-01, 8.75000000e-01, 8.75000000e-01, 8.75000000e-01, 8.75000000e-01, 8.75000000e-01, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00]), y=np.array([ 0.00000000e+00, 1.25000000e-01, 2.50000000e-01, 3.75000000e-01, 5.00000000e-01, 6.25000000e-01, 7.50000000e-01, 8.75000000e-01, 1.00000000e+00, 0.00000000e+00, 1.25000000e-01, 2.50000000e-01, 3.75000000e-01, 5.00000000e-01, 6.25000000e-01, 7.50000000e-01, 8.75000000e-01, 1.00000000e+00, 0.00000000e+00, 1.25000000e-01, 2.50000000e-01, 3.75000000e-01, 5.00000000e-01, 6.25000000e-01, 7.50000000e-01, 8.75000000e-01, 1.00000000e+00, 0.00000000e+00, 1.25000000e-01, 2.50000000e-01, 3.75000000e-01, 5.00000000e-01, 6.25000000e-01, 7.50000000e-01, 8.75000000e-01, 1.00000000e+00, 0.00000000e+00, 1.25000000e-01, 2.50000000e-01, 3.75000000e-01, 5.00000000e-01, 6.25000000e-01, 7.50000000e-01, 8.75000000e-01, 1.00000000e+00, 0.00000000e+00, 1.25000000e-01, 2.50000000e-01, 3.75000000e-01, 5.00000000e-01, 6.25000000e-01, 7.50000000e-01, 8.75000000e-01, 1.00000000e+00, 0.00000000e+00, 1.25000000e-01, 2.50000000e-01, 3.75000000e-01, 5.00000000e-01, 6.25000000e-01, 7.50000000e-01, 8.75000000e-01, 1.00000000e+00, 0.00000000e+00, 1.25000000e-01, 2.50000000e-01, 3.75000000e-01, 5.00000000e-01, 6.25000000e-01, 7.50000000e-01, 8.75000000e-01, 1.00000000e+00, 0.00000000e+00, 1.25000000e-01, 2.50000000e-01, 3.75000000e-01, 5.00000000e-01, 6.25000000e-01, 7.50000000e-01, 8.75000000e-01, 1.00000000e+00])), ) def constant(x, y): return np.ones(x.shape, x.dtype) constant.title = 'Constant' def xramp(x, y): return x xramp.title = 'X Ramp' def yramp(x, y): return y yramp.title = 'Y Ramp' def exponential(x, y): x = x*9 y = y*9 x1 = x+1.0 x2 = x-2.0 x4 = x-4.0 x7 = x-7.0 y1 = x+1.0 y2 = y-2.0 y3 = y-3.0 y7 = y-7.0 f = (0.75 * np.exp(-(x2*x2+y2*y2)/4.0) + 0.75 * np.exp(-x1*x1/49.0 - y1/10.0) + 0.5 * np.exp(-(x7*x7 + y3*y3)/4.0) - 0.2 * np.exp(-x4*x4 -y7*y7)) return f exponential.title = 'Exponential and Some Gaussians' def cliff(x, y): f = np.tanh(9.0*(y-x) + 1.0)/9.0 return f cliff.title = 'Cliff' def saddle(x, y): f = (1.25 + np.cos(5.4*y))/(6.0 + 6.0*(3*x-1.0)**2) return f saddle.title = 'Saddle' def gentle(x, y): f = np.exp(-5.0625*((x-0.5)**2+(y-0.5)**2))/3.0 return f gentle.title = 'Gentle Peak' def steep(x, y): f = np.exp(-20.25*((x-0.5)**2+(y-0.5)**2))/3.0 return f steep.title = 'Steep Peak' def sphere(x, y): circle = 64-81*((x-0.5)**2 + (y-0.5)**2) f = np.where(circle >= 0, np.sqrt(np.clip(circle,0,100)) - 0.5, 0.0) return f sphere.title = 'Sphere' def trig(x, y): f = 2.0*np.cos(10.0*x)*np.sin(10.0*y) + np.sin(10.0*x*y) return f trig.title = 'Cosines and Sines' def gauss(x, y): x = 5.0-10.0*x y = 5.0-10.0*y g1 = np.exp(-x*x/2) g2 = np.exp(-y*y/2) f = g1 + 0.75*g2*(1 + g1) return f gauss.title = 'Gaussian Peak and Gaussian Ridges' def cloverleaf(x, y): ex = np.exp((10.0-20.0*x)/3.0) ey = np.exp((10.0-20.0*y)/3.0) logitx = 1.0/(1.0+ex) logity = 1.0/(1.0+ey) f = (((20.0/3.0)**3 * ex*ey)**2 * (logitx*logity)**5 * (ex-2.0*logitx)*(ey-2.0*logity)) return f cloverleaf.title = 'Cloverleaf' def cosine_peak(x, y): circle = np.hypot(80*x-40.0, 90*y-45.) f = np.exp(-0.04*circle) * np.cos(0.15*circle) return f cosine_peak.title = 'Cosine Peak' allfuncs = [exponential, cliff, saddle, gentle, steep, sphere, trig, gauss, cloverleaf, cosine_peak] class LinearTester(object): name = 'Linear' def __init__(self, xrange=(0.0, 1.0), yrange=(0.0, 1.0), nrange=101, npoints=250): self.xrange = xrange self.yrange = yrange self.nrange = nrange self.npoints = npoints rng = np.random.RandomState(1234567890) self.x = rng.uniform(xrange[0], xrange[1], size=npoints) self.y = rng.uniform(yrange[0], yrange[1], size=npoints) self.tri = Triangulation(self.x, self.y) def replace_data(self, dataset): self.x = dataset.x self.y = dataset.y self.tri = Triangulation(self.x, self.y) def interpolator(self, func): z = func(self.x, self.y) return self.tri.linear_extrapolator(z, bbox=self.xrange+self.yrange) def plot(self, func, interp=True, plotter='imshow'): import matplotlib as mpl from matplotlib import pylab as pl if interp: lpi = self.interpolator(func) z = lpi[self.yrange[0]:self.yrange[1]:complex(0,self.nrange), self.xrange[0]:self.xrange[1]:complex(0,self.nrange)] else: y, x = np.mgrid[self.yrange[0]:self.yrange[1]:complex(0,self.nrange), self.xrange[0]:self.xrange[1]:complex(0,self.nrange)] z = func(x, y) z = np.where(np.isinf(z), 0.0, z) extent = (self.xrange[0], self.xrange[1], self.yrange[0], self.yrange[1]) pl.ioff() pl.clf() pl.hot() # Some like it hot if plotter == 'imshow': pl.imshow(np.nan_to_num(z), interpolation='nearest', extent=extent, origin='lower') elif plotter == 'contour': Y, X = np.ogrid[self.yrange[0]:self.yrange[1]:complex(0,self.nrange), self.xrange[0]:self.xrange[1]:complex(0,self.nrange)] pl.contour(np.ravel(X), np.ravel(Y), z, 20) x = self.x y = self.y lc = mpl.collections.LineCollection(np.array([((x[i], y[i]), (x[j], y[j])) for i, j in self.tri.edge_db]), colors=[(0,0,0,0.2)]) ax = pl.gca() ax.add_collection(lc) if interp: title = '%s Interpolant' % self.name else: title = 'Reference' if hasattr(func, 'title'): pl.title('%s: %s' % (func.title, title)) else: pl.title(title) pl.show() pl.ion() class NNTester(LinearTester): name = 'Natural Neighbors' def interpolator(self, func): z = func(self.x, self.y) return self.tri.nn_extrapolator(z, bbox=self.xrange+self.yrange) def plotallfuncs(allfuncs=allfuncs): from matplotlib import pylab as pl pl.ioff() nnt = NNTester(npoints=1000) lpt = LinearTester(npoints=1000) for func in allfuncs: print func.title nnt.plot(func, interp=False, plotter='imshow') pl.savefig('%s-ref-img.png' % func.func_name) nnt.plot(func, interp=True, plotter='imshow') pl.savefig('%s-nn-img.png' % func.func_name) lpt.plot(func, interp=True, plotter='imshow') pl.savefig('%s-lin-img.png' % func.func_name) nnt.plot(func, interp=False, plotter='contour') pl.savefig('%s-ref-con.png' % func.func_name) nnt.plot(func, interp=True, plotter='contour') pl.savefig('%s-nn-con.png' % func.func_name) lpt.plot(func, interp=True, plotter='contour') pl.savefig('%s-lin-con.png' % func.func_name) pl.ion() def plot_dt(tri, colors=None): import matplotlib as mpl from matplotlib import pylab as pl if colors is None: colors = [(0,0,0,0.2)] lc = mpl.collections.LineCollection(np.array([((tri.x[i], tri.y[i]), (tri.x[j], tri.y[j])) for i, j in tri.edge_db]), colors=colors) ax = pl.gca() ax.add_collection(lc) pl.draw_if_interactive() def plot_vo(tri, colors=None): import matplotlib as mpl from matplotlib import pylab as pl if colors is None: colors = [(0,1,0,0.2)] lc = mpl.collections.LineCollection(np.array( [(tri.circumcenters[i], tri.circumcenters[j]) for i in xrange(len(tri.circumcenters)) for j in tri.triangle_neighbors[i] if j != -1]), colors=colors) ax = pl.gca() ax.add_collection(lc) pl.draw_if_interactive() def plot_cc(tri, edgecolor=None): import matplotlib as mpl from matplotlib import pylab as pl if edgecolor is None: edgecolor = (0,0,1,0.2) dxy = (np.array([(tri.x[i], tri.y[i]) for i,j,k in tri.triangle_nodes]) - tri.circumcenters) r = np.hypot(dxy[:,0], dxy[:,1]) ax = pl.gca() for i in xrange(len(r)): p = mpl.patches.Circle(tri.circumcenters[i], r[i], resolution=100, edgecolor=edgecolor, facecolor=(1,1,1,0), linewidth=0.2) ax.add_patch(p) pl.draw_if_interactive() def quality(func, mesh, interpolator='nn', n=33): """Compute a quality factor (the quantity r**2 from TOMS792). interpolator must be in ('linear', 'nn'). """ fz = func(mesh.x, mesh.y) tri = Triangulation(mesh.x, mesh.y) intp = getattr(tri, interpolator+'_extrapolator')(fz, bbox=(0.,1.,0.,1.)) Y, X = np.mgrid[0:1:complex(0,n),0:1:complex(0,n)] Z = func(X, Y) iz = intp[0:1:complex(0,n),0:1:complex(0,n)] #nans = np.isnan(iz) #numgood = n*n - np.sum(np.array(nans.flat, np.int32)) numgood = n*n SE = (Z - iz)**2 SSE = np.sum(SE.flat) meanZ = np.sum(Z.flat) / numgood SM = (Z - meanZ)**2 SSM = np.sum(SM.flat) r2 = 1.0 - SSE/SSM print func.func_name, r2, SSE, SSM, numgood return r2 def allquality(interpolator='nn', allfuncs=allfuncs, data=data, n=33): results = {} kv = data.items() kv.sort() for name, mesh in kv: reslist = results.setdefault(name, []) for func in allfuncs: reslist.append(quality(func, mesh, interpolator, n)) return results def funky(): x0 = np.array([0.25, 0.3, 0.5, 0.6, 0.6]) y0 = np.array([0.2, 0.35, 0.0, 0.25, 0.65]) tx = 0.46 ty = 0.23 t0 = Triangulation(x0, y0) t1 = Triangulation(np.hstack((x0, [tx])), np.hstack((y0, [ty]))) return t0, t1

20,890

Python

.py

415

39.6

100

0.551422

numenta/nupic-legacy

6,330

1,556

464

AGPL-3.0

9/5/2024, 5:13:42 PM (Europe/Amsterdam)

26,285

__init__.py

numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/delaunay/__init__.py

"""Delaunay triangulation and interpolation tools. :Author: Robert Kern <robert.kern@gmail.com> :Copyright: Copyright 2005 Robert Kern. :License: BSD-style license. See LICENSE.txt in the scipy source directory. """ from matplotlib._delaunay import delaunay from triangulate import * from interpolate import * __version__ = "0.1"

332

Python

.py

9

35.666667

75

0.794393

numenta/nupic-legacy

6,330

1,556

464

AGPL-3.0

9/5/2024, 5:13:42 PM (Europe/Amsterdam)

26,286

triangulate.py

numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/delaunay/triangulate.py

import warnings try: set except NameError: from sets import Set as set import numpy as np from matplotlib._delaunay import delaunay from interpolate import LinearInterpolator, NNInterpolator __all__ = ['Triangulation', 'DuplicatePointWarning'] class DuplicatePointWarning(RuntimeWarning): """Duplicate points were passed in to the triangulation routine. """ class Triangulation(object): """A Delaunay triangulation of points in a plane. Triangulation(x, y) x, y -- the coordinates of the points as 1-D arrays of floats Let us make the following definitions: npoints = number of points input nedges = number of edges in the triangulation ntriangles = number of triangles in the triangulation point_id = an integer identifying a particular point (specifically, an index into x and y), range(0, npoints) edge_id = an integer identifying a particular edge, range(0, nedges) triangle_id = an integer identifying a particular triangle range(0, ntriangles) Attributes: (all should be treated as read-only to maintain consistency) x, y -- the coordinates of the points as 1-D arrays of floats. circumcenters -- (ntriangles, 2) array of floats giving the (x,y) coordinates of the circumcenters of each triangle (indexed by a triangle_id). edge_db -- (nedges, 2) array of point_id's giving the points forming each edge in no particular order; indexed by an edge_id. triangle_nodes -- (ntriangles, 3) array of point_id's giving the points forming each triangle in counter-clockwise order; indexed by a triangle_id. triangle_neighbors -- (ntriangles, 3) array of triangle_id's giving the neighboring triangle; indexed by a triangle_id. The value can also be -1 meaning that that edge is on the convex hull of the points and there is no neighbor on that edge. The values are ordered such that triangle_neighbors[tri, i] corresponds with the edge *opposite* triangle_nodes[tri, i]. As such, these neighbors are also in counter-clockwise order. hull -- list of point_id's giving the nodes which form the convex hull of the point set. This list is sorted in counter-clockwise order. """ def __init__(self, x, y): self.x = np.asarray(x, dtype=np.float64) self.y = np.asarray(y, dtype=np.float64) if self.x.shape != self.y.shape or len(self.x.shape) != 1: raise ValueError("x,y must be equal-length 1-D arrays") self.old_shape = self.x.shape j_unique = self._collapse_duplicate_points() if j_unique.shape != self.x.shape: warnings.warn( "Input data contains duplicate x,y points; some values are ignored.", DuplicatePointWarning, ) self.j_unique = j_unique self.x = self.x[self.j_unique] self.y = self.y[self.j_unique] else: self.j_unique = None self.circumcenters, self.edge_db, self.triangle_nodes, \ self.triangle_neighbors = delaunay(self.x, self.y) self.hull = self._compute_convex_hull() def _collapse_duplicate_points(self): """Generate index array that picks out unique x,y points. This appears to be required by the underlying delaunay triangulation code. """ # Find the indices of the unique entries j_sorted = np.lexsort(keys=(self.x, self.y)) mask_unique = np.hstack([ True, (np.diff(self.x[j_sorted]) != 0) | (np.diff(self.y[j_sorted]) != 0), ]) return j_sorted[mask_unique] def _compute_convex_hull(self): """Extract the convex hull from the triangulation information. The output will be a list of point_id's in counter-clockwise order forming the convex hull of the data set. """ border = (self.triangle_neighbors == -1) edges = {} edges.update(dict(zip(self.triangle_nodes[border[:,0]][:,1], self.triangle_nodes[border[:,0]][:,2]))) edges.update(dict(zip(self.triangle_nodes[border[:,1]][:,2], self.triangle_nodes[border[:,1]][:,0]))) edges.update(dict(zip(self.triangle_nodes[border[:,2]][:,0], self.triangle_nodes[border[:,2]][:,1]))) # Take an arbitrary starting point and its subsequent node hull = list(edges.popitem()) while edges: hull.append(edges.pop(hull[-1])) # hull[-1] == hull[0], so remove hull[-1] hull.pop() return hull def linear_interpolator(self, z, default_value=np.nan): """Get an object which can interpolate within the convex hull by assigning a plane to each triangle. z -- an array of floats giving the known function values at each point in the triangulation. """ z = np.asarray(z, dtype=np.float64) if z.shape != self.old_shape: raise ValueError("z must be the same shape as x and y") if self.j_unique is not None: z = z[self.j_unique] return LinearInterpolator(self, z, default_value) def nn_interpolator(self, z, default_value=np.nan): """Get an object which can interpolate within the convex hull by the natural neighbors method. z -- an array of floats giving the known function values at each point in the triangulation. """ z = np.asarray(z, dtype=np.float64) if z.shape != self.old_shape: raise ValueError("z must be the same shape as x and y") if self.j_unique is not None: z = z[self.j_unique] return NNInterpolator(self, z, default_value) def prep_extrapolator(self, z, bbox=None): if bbox is None: bbox = (self.x[0], self.x[0], self.y[0], self.y[0]) minx, maxx, miny, maxy = np.asarray(bbox, np.float64) minx = min(minx, np.minimum.reduce(self.x)) miny = min(miny, np.minimum.reduce(self.y)) maxx = max(maxx, np.maximum.reduce(self.x)) maxy = max(maxy, np.maximum.reduce(self.y)) M = max((maxx-minx)/2, (maxy-miny)/2) midx = (minx + maxx)/2.0 midy = (miny + maxy)/2.0 xp, yp= np.array([[midx+3*M, midx, midx-3*M], [midy, midy+3*M, midy-3*M]]) x1 = np.hstack((self.x, xp)) y1 = np.hstack((self.y, yp)) newtri = self.__class__(x1, y1) # do a least-squares fit to a plane to make pseudo-data xy1 = np.ones((len(self.x), 3), np.float64) xy1[:,0] = self.x xy1[:,1] = self.y from numpy.dual import lstsq c, res, rank, s = lstsq(xy1, z) zp = np.hstack((z, xp*c[0] + yp*c[1] + c[2])) return newtri, zp def nn_extrapolator(self, z, bbox=None, default_value=np.nan): newtri, zp = self.prep_extrapolator(z, bbox) return newtri.nn_interpolator(zp, default_value) def linear_extrapolator(self, z, bbox=None, default_value=np.nan): newtri, zp = self.prep_extrapolator(z, bbox) return newtri.linear_interpolator(zp, default_value) def node_graph(self): """Return a graph of node_id's pointing to node_id's. The arcs of the graph correspond to the edges in the triangulation. {node_id: set([node_id, ...]), ...} """ g = {} for i, j in self.edge_db: s = g.setdefault(i, set()) s.add(j) s = g.setdefault(j, set()) s.add(i) return g

7,732

Python

.py

163

37.957055

85

0.616111

numenta/nupic-legacy

6,330

1,556

464

AGPL-3.0

9/5/2024, 5:13:42 PM (Europe/Amsterdam)

26,287

polar.py

numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/projections/polar.py

import math import numpy as npy import matplotlib rcParams = matplotlib.rcParams from matplotlib.artist import kwdocd from matplotlib.axes import Axes from matplotlib import cbook from matplotlib.patches import Circle from matplotlib.path import Path from matplotlib.ticker import Formatter, Locator from matplotlib.transforms import Affine2D, Affine2DBase, Bbox, \ BboxTransformTo, IdentityTransform, Transform, TransformWrapper class PolarAxes(Axes): """ A polar graph projection, where the input dimensions are *theta*, *r*. Theta starts pointing east and goes anti-clockwise. """ name = 'polar' class PolarTransform(Transform): """ The base polar transform. This handles projection *theta* and *r* into Cartesian coordinate space *x* and *y*, but does not perform the ultimate affine transformation into the correct position. """ input_dims = 2 output_dims = 2 is_separable = False def __init__(self, resolution): """ Create a new polar transform. Resolution is the number of steps to interpolate between each input line segment to approximate its path in curved polar space. """ Transform.__init__(self) self._resolution = resolution def transform(self, tr): xy = npy.zeros(tr.shape, npy.float_) t = tr[:, 0:1] r = tr[:, 1:2] x = xy[:, 0:1] y = xy[:, 1:2] x[:] = r * npy.cos(t) y[:] = r * npy.sin(t) return xy transform.__doc__ = Transform.transform.__doc__ transform_non_affine = transform transform_non_affine.__doc__ = Transform.transform_non_affine.__doc__ def transform_path(self, path): vertices = path.vertices t = vertices[:, 0:1] t[t != (npy.pi * 2.0)] %= (npy.pi * 2.0) if len(vertices) == 2 and vertices[0, 0] == vertices[1, 0]: return Path(self.transform(vertices), path.codes) ipath = path.interpolated(self._resolution) return Path(self.transform(ipath.vertices), ipath.codes) transform_path.__doc__ = Transform.transform_path.__doc__ transform_path_non_affine = transform_path transform_path_non_affine.__doc__ = Transform.transform_path_non_affine.__doc__ def inverted(self): return PolarAxes.InvertedPolarTransform(self._resolution) inverted.__doc__ = Transform.inverted.__doc__ class PolarAffine(Affine2DBase): """ The affine part of the polar projection. Scales the output so that maximum radius rests on the edge of the axes circle. """ def __init__(self, scale_transform, limits): u""" *limits* is the view limit of the data. The only part of its bounds that is used is ymax (for the radius maximum). The theta range is always fixed to (0, 2\u03c0). """ Affine2DBase.__init__(self) self._scale_transform = scale_transform self._limits = limits self.set_children(scale_transform, limits) self._mtx = None def get_matrix(self): if self._invalid: limits_scaled = self._limits.transformed(self._scale_transform) ymax = limits_scaled.ymax affine = Affine2D() \ .scale(0.5 / ymax) \ .translate(0.5, 0.5) self._mtx = affine.get_matrix() self._inverted = None self._invalid = 0 return self._mtx get_matrix.__doc__ = Affine2DBase.get_matrix.__doc__ class InvertedPolarTransform(Transform): """ The inverse of the polar transform, mapping Cartesian coordinate space *x* and *y* back to *theta* and *r*. """ input_dims = 2 output_dims = 2 is_separable = False def __init__(self, resolution): Transform.__init__(self) self._resolution = resolution def transform(self, xy): x = xy[:, 0:1] y = xy[:, 1:] r = npy.sqrt(x*x + y*y) theta = npy.arccos(x / r) theta = npy.where(y < 0, 2 * npy.pi - theta, theta) return npy.concatenate((theta, r), 1) transform.__doc__ = Transform.transform.__doc__ def inverted(self): return PolarAxes.PolarTransform(self._resolution) inverted.__doc__ = Transform.inverted.__doc__ class ThetaFormatter(Formatter): u""" Used to format the *theta* tick labels. Converts the native unit of radians into degrees and adds a degree symbol (\u00b0). """ def __call__(self, x, pos=None): # \u00b0 : degree symbol if rcParams['text.usetex'] and not rcParams['text.latex.unicode']: return r"$%0.0f^\circ$" % ((x / npy.pi) * 180.0) else: # we use unicode, rather than mathtext with \circ, so # that it will work correctly with any arbitrary font # (assuming it has a degree sign), whereas $5\circ$ # will only work correctly with one of the supported # math fonts (Computer Modern and STIX) return u"%0.0f\u00b0" % ((x / npy.pi) * 180.0) class RadialLocator(Locator): """ Used to locate radius ticks. Ensures that all ticks are strictly positive. For all other tasks, it delegates to the base :class:`~matplotlib.ticker.Locator` (which may be different depending on the scale of the *r*-axis. """ def __init__(self, base): self.base = base def __call__(self): ticks = self.base() return [x for x in ticks if x > 0] def autoscale(self): return self.base.autoscale() def pan(self, numsteps): return self.base.pan(numsteps) def zoom(self, direction): return self.base.zoom(direction) def refresh(self): return self.base.refresh() RESOLUTION = 75 def __init__(self, *args, **kwargs): """ Create a new Polar Axes for a polar plot. """ self._rpad = 0.05 self.resolution = kwargs.pop('resolution', self.RESOLUTION) Axes.__init__(self, *args, **kwargs) self.set_aspect('equal', adjustable='box', anchor='C') self.cla() __init__.__doc__ = Axes.__init__.__doc__ def cla(self): Axes.cla(self) self.title.set_y(1.05) self.xaxis.set_major_formatter(self.ThetaFormatter()) angles = npy.arange(0.0, 360.0, 45.0) self.set_thetagrids(angles) self.yaxis.set_major_locator(self.RadialLocator(self.yaxis.get_major_locator())) self.grid(rcParams['polaraxes.grid']) self.xaxis.set_ticks_position('none') self.yaxis.set_ticks_position('none') def _set_lim_and_transforms(self): self.transAxes = BboxTransformTo(self.bbox) # Transforms the x and y axis separately by a scale factor # It is assumed that this part will have non-linear components self.transScale = TransformWrapper(IdentityTransform()) # A (possibly non-linear) projection on the (already scaled) data self.transProjection = self.PolarTransform(self.resolution) # An affine transformation on the data, generally to limit the # range of the axes self.transProjectionAffine = self.PolarAffine(self.transScale, self.viewLim) # The complete data transformation stack -- from data all the # way to display coordinates self.transData = self.transScale + self.transProjection + \ (self.transProjectionAffine + self.transAxes) # This is the transform for theta-axis ticks. It is # equivalent to transData, except it always puts r == 1.0 at # the edge of the axis circle. self._xaxis_transform = ( self.transProjection + self.PolarAffine(IdentityTransform(), Bbox.unit()) + self.transAxes) # The theta labels are moved from radius == 0.0 to radius == 1.1 self._theta_label1_position = Affine2D().translate(0.0, 1.1) self._xaxis_text1_transform = ( self._theta_label1_position + self._xaxis_transform) self._theta_label2_position = Affine2D().translate(0.0, 1.0 / 1.1) self._xaxis_text2_transform = ( self._theta_label2_position + self._xaxis_transform) # This is the transform for r-axis ticks. It scales the theta # axis so the gridlines from 0.0 to 1.0, now go from 0.0 to # 2pi. self._yaxis_transform = ( Affine2D().scale(npy.pi * 2.0, 1.0) + self.transData) # The r-axis labels are put at an angle and padded in the r-direction self._r_label1_position = Affine2D().translate(22.5, self._rpad) self._yaxis_text1_transform = ( self._r_label1_position + Affine2D().scale(1.0 / 360.0, 1.0) + self._yaxis_transform ) self._r_label2_position = Affine2D().translate(22.5, self._rpad) self._yaxis_text2_transform = ( self._r_label2_position + Affine2D().scale(1.0 / 360.0, 1.0) + self._yaxis_transform ) def get_xaxis_transform(self): return self._xaxis_transform def get_xaxis_text1_transform(self, pad): return self._xaxis_text1_transform, 'center', 'center' def get_xaxis_text2_transform(self, pad): return self._xaxis_text2_transform, 'center', 'center' def get_yaxis_transform(self): return self._yaxis_transform def get_yaxis_text1_transform(self, pad): return self._yaxis_text1_transform, 'center', 'center' def get_yaxis_text2_transform(self, pad): return self._yaxis_text2_transform, 'center', 'center' def _gen_axes_patch(self): return Circle((0.5, 0.5), 0.5) def set_rmax(self, rmax): self.viewLim.y1 = rmax angle = self._r_label1_position.to_values()[4] self._r_label1_position.clear().translate( angle, rmax * self._rpad) self._r_label2_position.clear().translate( angle, -rmax * self._rpad) def get_rmax(self): return self.viewLim.ymax def set_yscale(self, *args, **kwargs): Axes.set_yscale(self, *args, **kwargs) self.yaxis.set_major_locator( self.RadialLocator(self.yaxis.get_major_locator())) set_rscale = Axes.set_yscale set_rticks = Axes.set_yticks def set_thetagrids(self, angles, labels=None, frac=None, **kwargs): """ Set the angles at which to place the theta grids (these gridlines are equal along the theta dimension). *angles* is in degrees. *labels*, if not None, is a ``len(angles)`` list of strings of the labels to use at each angle. If *labels* is None, the labels will be ``fmt %% angle`` *frac* is the fraction of the polar axes radius at which to place the label (1 is the edge). Eg. 1.05 is outside the axes and 0.95 is inside the axes. Return value is a list of tuples (*line*, *label*), where *line* is :class:`~matplotlib.lines.Line2D` instances and the *label* is :class:`~matplotlib.text.Text` instances. kwargs are optional text properties for the labels: %(Text)s ACCEPTS: sequence of floats """ angles = npy.asarray(angles, npy.float_) self.set_xticks(angles * (npy.pi / 180.0)) if labels is not None: self.set_xticklabels(labels) if frac is not None: self._theta_label1_position.clear().translate(0.0, frac) self._theta_label2_position.clear().translate(0.0, 1.0 / frac) for t in self.xaxis.get_ticklabels(): t.update(kwargs) return self.xaxis.get_ticklines(), self.xaxis.get_ticklabels() set_thetagrids.__doc__ = cbook.dedent(set_thetagrids.__doc__) % kwdocd def set_rgrids(self, radii, labels=None, angle=None, rpad=None, **kwargs): """ Set the radial locations and labels of the *r* grids. The labels will appear at radial distances *radii* at the given *angle* in degrees. *labels*, if not None, is a ``len(radii)`` list of strings of the labels to use at each radius. If *labels* is None, the built-in formatter will be used. *rpad* is a fraction of the max of *radii* which will pad each of the radial labels in the radial direction. Return value is a list of tuples (*line*, *label*), where *line* is :class:`~matplotlib.lines.Line2D` instances and the *label* is :class:`~matplotlib.text.Text` instances. kwargs are optional text properties for the labels: %(Text)s ACCEPTS: sequence of floats """ radii = npy.asarray(radii) rmin = radii.min() if rmin <= 0: raise ValueError('radial grids must be strictly positive') self.set_yticks(radii) if labels is not None: self.set_yticklabels(labels) if angle is None: angle = self._r_label1_position.to_values()[4] if rpad is not None: self._rpad = rpad rmax = self.get_rmax() self._r_label1_position.clear().translate(angle, self._rpad * rmax) self._r_label2_position.clear().translate(angle, -self._rpad * rmax) for t in self.yaxis.get_ticklabels(): t.update(kwargs) return self.yaxis.get_ticklines(), self.yaxis.get_ticklabels() set_rgrids.__doc__ = cbook.dedent(set_rgrids.__doc__) % kwdocd def set_xscale(self, scale, *args, **kwargs): if scale != 'linear': raise NotImplementedError("You can not set the xscale on a polar plot.") def set_xlim(self, *args, **kargs): # The xlim is fixed, no matter what you do self.viewLim.intervalx = (0.0, npy.pi * 2.0) def format_coord(self, theta, r): """ Return a format string formatting the coordinate using Unicode characters. """ theta /= math.pi # \u03b8: lower-case theta # \u03c0: lower-case pi # \u00b0: degree symbol return u'\u03b8=%0.3f\u03c0 (%0.3f\u00b0), r=%0.3f' % (theta, theta * 180.0, r) def get_data_ratio(self): ''' Return the aspect ratio of the data itself. For a polar plot, this should always be 1.0 ''' return 1.0 ### Interactive panning def can_zoom(self): """ Return True if this axes support the zoom box """ return False def start_pan(self, x, y, button): angle = self._r_label1_position.to_values()[4] / 180.0 * npy.pi mode = '' if button == 1: epsilon = npy.pi / 45.0 t, r = self.transData.inverted().transform_point((x, y)) if t >= angle - epsilon and t <= angle + epsilon: mode = 'drag_r_labels' elif button == 3: mode = 'zoom' self._pan_start = cbook.Bunch( rmax = self.get_rmax(), trans = self.transData.frozen(), trans_inverse = self.transData.inverted().frozen(), r_label_angle = self._r_label1_position.to_values()[4], x = x, y = y, mode = mode ) def end_pan(self): del self._pan_start def drag_pan(self, button, key, x, y): p = self._pan_start if p.mode == 'drag_r_labels': startt, startr = p.trans_inverse.transform_point((p.x, p.y)) t, r = p.trans_inverse.transform_point((x, y)) # Deal with theta dt0 = t - startt dt1 = startt - t if abs(dt1) < abs(dt0): dt = abs(dt1) * sign(dt0) * -1.0 else: dt = dt0 * -1.0 dt = (dt / npy.pi) * 180.0 rpad = self._r_label1_position.to_values()[5] self._r_label1_position.clear().translate( p.r_label_angle - dt, rpad) self._r_label2_position.clear().translate( p.r_label_angle - dt, -rpad) elif p.mode == 'zoom': startt, startr = p.trans_inverse.transform_point((p.x, p.y)) t, r = p.trans_inverse.transform_point((x, y)) dr = r - startr # Deal with r scale = r / startr self.set_rmax(p.rmax / scale) # These are a couple of aborted attempts to project a polar plot using # cubic bezier curves. # def transform_path(self, path): # twopi = 2.0 * npy.pi # halfpi = 0.5 * npy.pi # vertices = path.vertices # t0 = vertices[0:-1, 0] # t1 = vertices[1: , 0] # td = npy.where(t1 > t0, t1 - t0, twopi - (t0 - t1)) # maxtd = td.max() # interpolate = npy.ceil(maxtd / halfpi) # if interpolate > 1.0: # vertices = self.interpolate(vertices, interpolate) # vertices = self.transform(vertices) # result = npy.zeros((len(vertices) * 3 - 2, 2), npy.float_) # codes = mpath.Path.CURVE4 * npy.ones((len(vertices) * 3 - 2, ), mpath.Path.code_type) # result[0] = vertices[0] # codes[0] = mpath.Path.MOVETO # kappa = 4.0 * ((npy.sqrt(2.0) - 1.0) / 3.0) # kappa = 0.5 # p0 = vertices[0:-1] # p1 = vertices[1: ] # x0 = p0[:, 0:1] # y0 = p0[:, 1: ] # b0 = ((y0 - x0) - y0) / ((x0 + y0) - x0) # a0 = y0 - b0*x0 # x1 = p1[:, 0:1] # y1 = p1[:, 1: ] # b1 = ((y1 - x1) - y1) / ((x1 + y1) - x1) # a1 = y1 - b1*x1 # x = -(a0-a1) / (b0-b1) # y = a0 + b0*x # xk = (x - x0) * kappa + x0 # yk = (y - y0) * kappa + y0 # result[1::3, 0:1] = xk # result[1::3, 1: ] = yk # xk = (x - x1) * kappa + x1 # yk = (y - y1) * kappa + y1 # result[2::3, 0:1] = xk # result[2::3, 1: ] = yk # result[3::3] = p1 # print vertices[-2:] # print result[-2:] # return mpath.Path(result, codes) # twopi = 2.0 * npy.pi # halfpi = 0.5 * npy.pi # vertices = path.vertices # t0 = vertices[0:-1, 0] # t1 = vertices[1: , 0] # td = npy.where(t1 > t0, t1 - t0, twopi - (t0 - t1)) # maxtd = td.max() # interpolate = npy.ceil(maxtd / halfpi) # print "interpolate", interpolate # if interpolate > 1.0: # vertices = self.interpolate(vertices, interpolate) # result = npy.zeros((len(vertices) * 3 - 2, 2), npy.float_) # codes = mpath.Path.CURVE4 * npy.ones((len(vertices) * 3 - 2, ), mpath.Path.code_type) # result[0] = vertices[0] # codes[0] = mpath.Path.MOVETO # kappa = 4.0 * ((npy.sqrt(2.0) - 1.0) / 3.0) # tkappa = npy.arctan(kappa) # hyp_kappa = npy.sqrt(kappa*kappa + 1.0) # t0 = vertices[0:-1, 0] # t1 = vertices[1: , 0] # r0 = vertices[0:-1, 1] # r1 = vertices[1: , 1] # td = npy.where(t1 > t0, t1 - t0, twopi - (t0 - t1)) # td_scaled = td / (npy.pi * 0.5) # rd = r1 - r0 # r0kappa = r0 * kappa * td_scaled # r1kappa = r1 * kappa * td_scaled # ravg_kappa = ((r1 + r0) / 2.0) * kappa * td_scaled # result[1::3, 0] = t0 + (tkappa * td_scaled) # result[1::3, 1] = r0*hyp_kappa # # result[1::3, 1] = r0 / npy.cos(tkappa * td_scaled) # npy.sqrt(r0*r0 + ravg_kappa*ravg_kappa) # result[2::3, 0] = t1 - (tkappa * td_scaled) # result[2::3, 1] = r1*hyp_kappa # # result[2::3, 1] = r1 / npy.cos(tkappa * td_scaled) # npy.sqrt(r1*r1 + ravg_kappa*ravg_kappa) # result[3::3, 0] = t1 # result[3::3, 1] = r1 # print vertices[:6], result[:6], t0[:6], t1[:6], td[:6], td_scaled[:6], tkappa # result = self.transform(result) # return mpath.Path(result, codes) # transform_path_non_affine = transform_path

20,981

Python

.py

472

35.819915

108

0.555801

numenta/nupic-legacy

6,330

1,556

464

AGPL-3.0

9/5/2024, 5:13:42 PM (Europe/Amsterdam)

26,288

__init__.py

numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/projections/__init__.py

from geo import AitoffAxes, HammerAxes, LambertAxes from polar import PolarAxes from matplotlib import axes class ProjectionRegistry(object): """ Manages the set of projections available to the system. """ def __init__(self): self._all_projection_types = {} def register(self, *projections): """ Register a new set of projection(s). """ for projection in projections: name = projection.name self._all_projection_types[name] = projection def get_projection_class(self, name): """ Get a projection class from its *name*. """ return self._all_projection_types[name] def get_projection_names(self): """ Get a list of the names of all projections currently registered. """ names = self._all_projection_types.keys() names.sort() return names projection_registry = ProjectionRegistry() projection_registry.register( axes.Axes, PolarAxes, AitoffAxes, HammerAxes, LambertAxes) def register_projection(cls): projection_registry.register(cls) def get_projection_class(projection=None): """ Get a projection class from its name. If *projection* is None, a standard rectilinear projection is returned. """ if projection is None: projection = 'rectilinear' try: return projection_registry.get_projection_class(projection) except KeyError: raise ValueError("Unknown projection '%s'" % projection) def projection_factory(projection, figure, rect, **kwargs): """ Get a new projection instance. *projection* is a projection name. *figure* is a figure to add the axes to. *rect* is a :class:`~matplotlib.transforms.Bbox` object specifying the location of the axes within the figure. Any other kwargs are passed along to the specific projection constructor being used. """ return get_projection_class(projection)(figure, rect, **kwargs) def get_projection_names(): """ Get a list of acceptable projection names. """ return projection_registry.get_projection_names()

2,179

Python

.py

66

26.969697

70

0.679389

numenta/nupic-legacy

6,330

1,556

464

AGPL-3.0

9/5/2024, 5:13:42 PM (Europe/Amsterdam)

26,289

geo.py

numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/projections/geo.py

import math import numpy as np import numpy.ma as ma import matplotlib rcParams = matplotlib.rcParams from matplotlib.artist import kwdocd from matplotlib.axes import Axes from matplotlib import cbook from matplotlib.patches import Circle from matplotlib.path import Path from matplotlib.ticker import Formatter, Locator, NullLocator, FixedLocator, NullFormatter from matplotlib.transforms import Affine2D, Affine2DBase, Bbox, \ BboxTransformTo, IdentityTransform, Transform, TransformWrapper class GeoAxes(Axes): """ An abstract base class for geographic projections """ class ThetaFormatter(Formatter): """ Used to format the theta tick labels. Converts the native unit of radians into degrees and adds a degree symbol. """ def __init__(self, round_to=1.0): self._round_to = round_to def __call__(self, x, pos=None): degrees = (x / np.pi) * 180.0 degrees = round(degrees / self._round_to) * self._round_to if rcParams['text.usetex'] and not rcParams['text.latex.unicode']: return r"$%0.0f^\circ$" % degrees else: return u"%0.0f\u00b0" % degrees RESOLUTION = 75 def cla(self): Axes.cla(self) self.set_longitude_grid(30) self.set_latitude_grid(15) self.set_longitude_grid_ends(75) self.xaxis.set_minor_locator(NullLocator()) self.yaxis.set_minor_locator(NullLocator()) self.xaxis.set_ticks_position('none') self.yaxis.set_ticks_position('none') self.grid(rcParams['axes.grid']) Axes.set_xlim(self, -np.pi, np.pi) Axes.set_ylim(self, -np.pi / 2.0, np.pi / 2.0) def _set_lim_and_transforms(self): # A (possibly non-linear) projection on the (already scaled) data self.transProjection = self._get_core_transform(self.RESOLUTION) self.transAffine = self._get_affine_transform() self.transAxes = BboxTransformTo(self.bbox) # The complete data transformation stack -- from data all the # way to display coordinates self.transData = \ self.transProjection + \ self.transAffine + \ self.transAxes # This is the transform for longitude ticks. self._xaxis_pretransform = \ Affine2D() \ .scale(1.0, self._longitude_cap * 2.0) \ .translate(0.0, -self._longitude_cap) self._xaxis_transform = \ self._xaxis_pretransform + \ self.transData self._xaxis_text1_transform = \ Affine2D().scale(1.0, 0.0) + \ self.transData + \ Affine2D().translate(0.0, 4.0) self._xaxis_text2_transform = \ Affine2D().scale(1.0, 0.0) + \ self.transData + \ Affine2D().translate(0.0, -4.0) # This is the transform for latitude ticks. yaxis_stretch = Affine2D().scale(np.pi * 2.0, 1.0).translate(-np.pi, 0.0) yaxis_space = Affine2D().scale(1.0, 1.1) self._yaxis_transform = \ yaxis_stretch + \ self.transData yaxis_text_base = \ yaxis_stretch + \ self.transProjection + \ (yaxis_space + \ self.transAffine + \ self.transAxes) self._yaxis_text1_transform = \ yaxis_text_base + \ Affine2D().translate(-8.0, 0.0) self._yaxis_text2_transform = \ yaxis_text_base + \ Affine2D().translate(8.0, 0.0) def _get_affine_transform(self): transform = self._get_core_transform(1) xscale, _ = transform.transform_point((np.pi, 0)) _, yscale = transform.transform_point((0, np.pi / 2.0)) return Affine2D() \ .scale(0.5 / xscale, 0.5 / yscale) \ .translate(0.5, 0.5) def get_xaxis_transform(self): return self._xaxis_transform def get_xaxis_text1_transform(self, pad): return self._xaxis_text1_transform, 'bottom', 'center' def get_xaxis_text2_transform(self, pad): return self._xaxis_text2_transform, 'top', 'center' def get_yaxis_transform(self): return self._yaxis_transform def get_yaxis_text1_transform(self, pad): return self._yaxis_text1_transform, 'center', 'right' def get_yaxis_text2_transform(self, pad): return self._yaxis_text2_transform, 'center', 'left' def _gen_axes_patch(self): return Circle((0.5, 0.5), 0.5) def set_yscale(self, *args, **kwargs): if args[0] != 'linear': raise NotImplementedError set_xscale = set_yscale def set_xlim(self, *args, **kwargs): Axes.set_xlim(self, -np.pi, np.pi) Axes.set_ylim(self, -np.pi / 2.0, np.pi / 2.0) set_ylim = set_xlim def format_coord(self, long, lat): 'return a format string formatting the coordinate' long = long * (180.0 / np.pi) lat = lat * (180.0 / np.pi) if lat >= 0.0: ns = 'N' else: ns = 'S' if long >= 0.0: ew = 'E' else: ew = 'W' return u'%f\u00b0%s, %f\u00b0%s' % (abs(lat), ns, abs(long), ew) def set_longitude_grid(self, degrees): """ Set the number of degrees between each longitude grid. """ number = (360.0 / degrees) + 1 self.xaxis.set_major_locator( FixedLocator( np.linspace(-np.pi, np.pi, number, True)[1:-1])) self._logitude_degrees = degrees self.xaxis.set_major_formatter(self.ThetaFormatter(degrees)) def set_latitude_grid(self, degrees): """ Set the number of degrees between each longitude grid. """ number = (180.0 / degrees) + 1 self.yaxis.set_major_locator( FixedLocator( np.linspace(-np.pi / 2.0, np.pi / 2.0, number, True)[1:-1])) self._latitude_degrees = degrees self.yaxis.set_major_formatter(self.ThetaFormatter(degrees)) def set_longitude_grid_ends(self, degrees): """ Set the latitude(s) at which to stop drawing the longitude grids. """ self._longitude_cap = degrees * (np.pi / 180.0) self._xaxis_pretransform \ .clear() \ .scale(1.0, self._longitude_cap * 2.0) \ .translate(0.0, -self._longitude_cap) def get_data_ratio(self): ''' Return the aspect ratio of the data itself. ''' return 1.0 ### Interactive panning def can_zoom(self): """ Return True if this axes support the zoom box """ return False def start_pan(self, x, y, button): pass def end_pan(self): pass def drag_pan(self, button, key, x, y): pass class AitoffAxes(GeoAxes): name = 'aitoff' class AitoffTransform(Transform): """ The base Aitoff transform. """ input_dims = 2 output_dims = 2 is_separable = False def __init__(self, resolution): """ Create a new Aitoff transform. Resolution is the number of steps to interpolate between each input line segment to approximate its path in curved Aitoff space. """ Transform.__init__(self) self._resolution = resolution def transform(self, ll): longitude = ll[:, 0:1] latitude = ll[:, 1:2] # Pre-compute some values half_long = longitude / 2.0 cos_latitude = np.cos(latitude) alpha = np.arccos(cos_latitude * np.cos(half_long)) # Mask this array, or we'll get divide-by-zero errors alpha = ma.masked_where(alpha == 0.0, alpha) # We want unnormalized sinc. numpy.sinc gives us normalized sinc_alpha = ma.sin(alpha) / alpha x = (cos_latitude * np.sin(half_long)) / sinc_alpha y = (np.sin(latitude) / sinc_alpha) x.set_fill_value(0.0) y.set_fill_value(0.0) return np.concatenate((x.filled(), y.filled()), 1) transform.__doc__ = Transform.transform.__doc__ transform_non_affine = transform transform_non_affine.__doc__ = Transform.transform_non_affine.__doc__ def transform_path(self, path): vertices = path.vertices ipath = path.interpolated(self._resolution) return Path(self.transform(ipath.vertices), ipath.codes) transform_path.__doc__ = Transform.transform_path.__doc__ transform_path_non_affine = transform_path transform_path_non_affine.__doc__ = Transform.transform_path_non_affine.__doc__ def inverted(self): return AitoffAxes.InvertedAitoffTransform(self._resolution) inverted.__doc__ = Transform.inverted.__doc__ class InvertedAitoffTransform(Transform): input_dims = 2 output_dims = 2 is_separable = False def __init__(self, resolution): Transform.__init__(self) self._resolution = resolution def transform(self, xy): # MGDTODO: Math is hard ;( return xy transform.__doc__ = Transform.transform.__doc__ def inverted(self): return AitoffAxes.AitoffTransform(self._resolution) inverted.__doc__ = Transform.inverted.__doc__ def __init__(self, *args, **kwargs): self._longitude_cap = np.pi / 2.0 GeoAxes.__init__(self, *args, **kwargs) self.set_aspect(0.5, adjustable='box', anchor='C') self.cla() def _get_core_transform(self, resolution): return self.AitoffTransform(resolution) class HammerAxes(GeoAxes): name = 'hammer' class HammerTransform(Transform): """ The base Hammer transform. """ input_dims = 2 output_dims = 2 is_separable = False def __init__(self, resolution): """ Create a new Hammer transform. Resolution is the number of steps to interpolate between each input line segment to approximate its path in curved Hammer space. """ Transform.__init__(self) self._resolution = resolution def transform(self, ll): longitude = ll[:, 0:1] latitude = ll[:, 1:2] # Pre-compute some values half_long = longitude / 2.0 cos_latitude = np.cos(latitude) sqrt2 = np.sqrt(2.0) alpha = 1.0 + cos_latitude * np.cos(half_long) x = (2.0 * sqrt2) * (cos_latitude * np.sin(half_long)) / alpha y = (sqrt2 * np.sin(latitude)) / alpha return np.concatenate((x, y), 1) transform.__doc__ = Transform.transform.__doc__ transform_non_affine = transform transform_non_affine.__doc__ = Transform.transform_non_affine.__doc__ def transform_path(self, path): vertices = path.vertices ipath = path.interpolated(self._resolution) return Path(self.transform(ipath.vertices), ipath.codes) transform_path.__doc__ = Transform.transform_path.__doc__ transform_path_non_affine = transform_path transform_path_non_affine.__doc__ = Transform.transform_path_non_affine.__doc__ def inverted(self): return HammerAxes.InvertedHammerTransform(self._resolution) inverted.__doc__ = Transform.inverted.__doc__ class InvertedHammerTransform(Transform): input_dims = 2 output_dims = 2 is_separable = False def __init__(self, resolution): Transform.__init__(self) self._resolution = resolution def transform(self, xy): x = xy[:, 0:1] y = xy[:, 1:2] quarter_x = 0.25 * x half_y = 0.5 * y z = np.sqrt(1.0 - quarter_x*quarter_x - half_y*half_y) longitude = 2 * np.arctan((z*x) / (2.0 * (2.0*z*z - 1.0))) latitude = np.arcsin(y*z) return np.concatenate((longitude, latitude), 1) transform.__doc__ = Transform.transform.__doc__ def inverted(self): return HammerAxes.HammerTransform(self._resolution) inverted.__doc__ = Transform.inverted.__doc__ def __init__(self, *args, **kwargs): self._longitude_cap = np.pi / 2.0 GeoAxes.__init__(self, *args, **kwargs) self.set_aspect(0.5, adjustable='box', anchor='C') self.cla() def _get_core_transform(self, resolution): return self.HammerTransform(resolution) class MollweideAxes(GeoAxes): name = 'mollweide' class MollweideTransform(Transform): """ The base Mollweide transform. """ input_dims = 2 output_dims = 2 is_separable = False def __init__(self, resolution): """ Create a new Mollweide transform. Resolution is the number of steps to interpolate between each input line segment to approximate its path in curved Mollweide space. """ Transform.__init__(self) self._resolution = resolution def transform(self, ll): longitude = ll[:, 0:1] latitude = ll[:, 1:2] aux = 2.0 * np.arcsin((2.0 * latitude) / np.pi) x = (2.0 * np.sqrt(2.0) * longitude * np.cos(aux)) / np.pi y = (np.sqrt(2.0) * np.sin(aux)) return np.concatenate((x, y), 1) transform.__doc__ = Transform.transform.__doc__ transform_non_affine = transform transform_non_affine.__doc__ = Transform.transform_non_affine.__doc__ def transform_path(self, path): vertices = path.vertices ipath = path.interpolated(self._resolution) return Path(self.transform(ipath.vertices), ipath.codes) transform_path.__doc__ = Transform.transform_path.__doc__ transform_path_non_affine = transform_path transform_path_non_affine.__doc__ = Transform.transform_path_non_affine.__doc__ def inverted(self): return MollweideAxes.InvertedMollweideTransform(self._resolution) inverted.__doc__ = Transform.inverted.__doc__ class InvertedMollweideTransform(Transform): input_dims = 2 output_dims = 2 is_separable = False def __init__(self, resolution): Transform.__init__(self) self._resolution = resolution def transform(self, xy): # MGDTODO: Math is hard ;( return xy transform.__doc__ = Transform.transform.__doc__ def inverted(self): return MollweideAxes.MollweideTransform(self._resolution) inverted.__doc__ = Transform.inverted.__doc__ def __init__(self, *args, **kwargs): self._longitude_cap = np.pi / 2.0 GeoAxes.__init__(self, *args, **kwargs) self.set_aspect(0.5, adjustable='box', anchor='C') self.cla() def _get_core_transform(self, resolution): return self.MollweideTransform(resolution) class LambertAxes(GeoAxes): name = 'lambert' class LambertTransform(Transform): """ The base Lambert transform. """ input_dims = 2 output_dims = 2 is_separable = False def __init__(self, center_longitude, center_latitude, resolution): """ Create a new Lambert transform. Resolution is the number of steps to interpolate between each input line segment to approximate its path in curved Lambert space. """ Transform.__init__(self) self._resolution = resolution self._center_longitude = center_longitude self._center_latitude = center_latitude def transform(self, ll): longitude = ll[:, 0:1] latitude = ll[:, 1:2] clong = self._center_longitude clat = self._center_latitude cos_lat = np.cos(latitude) sin_lat = np.sin(latitude) diff_long = longitude - clong cos_diff_long = np.cos(diff_long) inner_k = (1.0 + np.sin(clat)*sin_lat + np.cos(clat)*cos_lat*cos_diff_long) # Prevent divide-by-zero problems inner_k = np.where(inner_k == 0.0, 1e-15, inner_k) k = np.sqrt(2.0 / inner_k) x = k*cos_lat*np.sin(diff_long) y = k*(np.cos(clat)*sin_lat - np.sin(clat)*cos_lat*cos_diff_long) return np.concatenate((x, y), 1) transform.__doc__ = Transform.transform.__doc__ transform_non_affine = transform transform_non_affine.__doc__ = Transform.transform_non_affine.__doc__ def transform_path(self, path): vertices = path.vertices ipath = path.interpolated(self._resolution) return Path(self.transform(ipath.vertices), ipath.codes) transform_path.__doc__ = Transform.transform_path.__doc__ transform_path_non_affine = transform_path transform_path_non_affine.__doc__ = Transform.transform_path_non_affine.__doc__ def inverted(self): return LambertAxes.InvertedLambertTransform( self._center_longitude, self._center_latitude, self._resolution) inverted.__doc__ = Transform.inverted.__doc__ class InvertedLambertTransform(Transform): input_dims = 2 output_dims = 2 is_separable = False def __init__(self, center_longitude, center_latitude, resolution): Transform.__init__(self) self._resolution = resolution self._center_longitude = center_longitude self._center_latitude = center_latitude def transform(self, xy): x = xy[:, 0:1] y = xy[:, 1:2] clong = self._center_longitude clat = self._center_latitude p = np.sqrt(x*x + y*y) p = np.where(p == 0.0, 1e-9, p) c = 2.0 * np.arcsin(0.5 * p) sin_c = np.sin(c) cos_c = np.cos(c) lat = np.arcsin(cos_c*np.sin(clat) + ((y*sin_c*np.cos(clat)) / p)) long = clong + np.arctan( (x*sin_c) / (p*np.cos(clat)*cos_c - y*np.sin(clat)*sin_c)) return np.concatenate((long, lat), 1) transform.__doc__ = Transform.transform.__doc__ def inverted(self): return LambertAxes.LambertTransform( self._center_longitude, self._center_latitude, self._resolution) inverted.__doc__ = Transform.inverted.__doc__ def __init__(self, *args, **kwargs): self._longitude_cap = np.pi / 2.0 self._center_longitude = kwargs.pop("center_longitude", 0.0) self._center_latitude = kwargs.pop("center_latitude", 0.0) GeoAxes.__init__(self, *args, **kwargs) self.set_aspect('equal', adjustable='box', anchor='C') self.cla() def cla(self): GeoAxes.cla(self) self.yaxis.set_major_formatter(NullFormatter()) def _get_core_transform(self, resolution): return self.LambertTransform( self._center_longitude, self._center_latitude, resolution) def _get_affine_transform(self): return Affine2D() \ .scale(0.25) \ .translate(0.5, 0.5)

19,738

Python

.py

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90

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numenta/nupic-legacy

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1,556

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AGPL-3.0

9/5/2024, 5:13:42 PM (Europe/Amsterdam)

26,290

_nc_imports.py

numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/numerix/_nc_imports.py

from Numeric import array, ravel, reshape, shape, alltrue, sometrue from Numeric import Int8, UInt8, Int16, UInt16, Int32, UInt32, \ Float32, Float64, Complex32, Complex64, Float, Int, Complex from numpy import isnan as _isnan class _TypeNamespace: """Numeric compatible type aliases for use with extension functions.""" Int8 = Int8 UInt8 = UInt8 Int16 = Int16 UInt16 = UInt16 Int32 = Int32 UInt32 = UInt32 Float32 = Float32 Float64 = Float64 Complex32 = Complex32 Complex64 = Complex64 nx = _TypeNamespace() def isnan(a): """y = isnan(x) returns True where x is Not-A-Number""" return reshape(array([_isnan(i) for i in ravel(a)],'b'), shape(a)) def all(a, axis=None): '''Numpy-compatible version of all()''' if axis is None: return alltrue(ravel(a)) else: return alltrue(a, axis) def any(a, axis=None): if axis is None: return sometrue(ravel(a)) else: return sometrue(a, axis) # inf is useful for testing infinities in results of array divisions # (which don't raise exceptions) inf = infty = infinity = Infinity = (array([1])/0.0)[0]

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numenta/nupic-legacy

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AGPL-3.0

9/5/2024, 5:13:42 PM (Europe/Amsterdam)

26,291

_na_imports.py

numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/numerix/_na_imports.py

"""Imports from numarray for numerix, the numarray/Numeric interchangeability module. These array functions are used when numarray is chosen. """ from numarray import Int8, UInt8, Int16, UInt16, Int32, UInt32, \ Float32, Float64, Complex32, Complex64, Float, Int, Complex,\ typecode import numarray.ieeespecial as _ieee inf = infinity = infty = Infinity = _ieee.inf isnan = _ieee.isnan class _TypeNamespace: """Numeric compatible type aliases for use with extension functions.""" Int8 = typecode[Int8] UInt8 = typecode[UInt8] Int16 = typecode[Int16] UInt16 = typecode[UInt16] Int32 = typecode[Int32] #UInt32 = typecode[UInt32] # Todd: this appears broken Float32 = typecode[Float32] Float64 = typecode[Float64] Complex32 = typecode[Complex32] Complex64 = typecode[Complex64] nx = _TypeNamespace() from numarray import asarray, dot, fromlist, NumArray, shape, alltrue from numarray import all as _all def all(a, axis=None): '''Numpy-compatible version of all()''' if axis is None: return _all(a) return alltrue(a, axis) class _Matrix(NumArray): """_Matrix is a ported, stripped down version of the Numeric Matrix class which supplies only matrix multiplication. """ def _rc(self, a): if len(shape(a)) == 0: return a else: return Matrix(a) def __mul__(self, other): aother = asarray(other) #if len(aother.shape) == 0: # return self._rc(self*aother) #else: # return self._rc(dot(self, aother)) #return self._rc(dot(self, aother)) return dot(self, aother) def __rmul__(self, other): aother = asarray(other) if len(aother.shape) == 0: return self._rc(aother*self) else: return self._rc(dot(aother, self)) def __imul__(self,other): aother = asarray(other) self[:] = dot(self, aother) return self def Matrix(data, typecode=None, copy=1, savespace=0): """Matrix constructs new matrices from 2D nested lists of numbers""" if isinstance(data, type("")): raise TypeError("numerix Matrix does not support Numeric matrix string notation. Use nested lists.") a = fromlist(data, type=typecode) if a.rank == 0: a.shape = (1,1) elif a.rank == 1: a.shape = (1,) + a.shape a.__class__ = _Matrix return a

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numenta/nupic-legacy

6,330

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9/5/2024, 5:13:42 PM (Europe/Amsterdam)

26,292

__init__.py

numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/numerix/__init__.py

""" numerix imports either Numeric or numarray based on various selectors. 0. If the value "--numpy","--numarray" or "--Numeric" is specified on the command line, then numerix imports the specified array package. 1. The value of numerix in matplotlibrc: either Numeric or numarray 2. If none of the above is done, the default array package is Numeric. Because the matplotlibrc always provides *some* value for numerix (it has it's own system of default values), this default is most likely never used. To summarize: the commandline is examined first, the rc file second, and the default array package is Numeric. """ import sys, os, struct from matplotlib import rcParams, verbose which = None, None use_maskedarray = None # First, see if --numarray or --Numeric was specified on the command # line: for a in sys.argv: if a in ["--Numeric", "--numeric", "--NUMERIC", "--Numarray", "--numarray", "--NUMARRAY", "--NumPy", "--numpy", "--NUMPY", "--Numpy", ]: which = a[2:], "command line" if a == "--maskedarray": use_maskedarray = True if a == "--ma": use_maskedarray = False try: del a except NameError: pass if which[0] is None: try: # In theory, rcParams always has *some* value for numerix. which = rcParams['numerix'], "rc" except KeyError: pass if use_maskedarray is None: try: use_maskedarray = rcParams['maskedarray'] except KeyError: use_maskedarray = False # If all the above fail, default to Numeric. Most likely not used. if which[0] is None: which = "numeric", "defaulted" which = which[0].strip().lower(), which[1] if which[0] not in ["numeric", "numarray", "numpy"]: raise ValueError("numerix selector must be either 'Numeric', 'numarray', or 'numpy' but the value obtained from the %s was '%s'." % (which[1], which[0])) if which[0] == "numarray": import warnings warnings.warn("numarray use as a numerix backed for matplotlib is deprecated", DeprecationWarning, stacklevel=1) #from na_imports import * from numarray import * from _na_imports import nx, inf, infinity, Infinity, Matrix, isnan, all from numarray.numeric import nonzero from numarray.convolve import cross_correlate, convolve import numarray version = 'numarray %s'%numarray.__version__ nan = struct.unpack('d', struct.pack('Q', 0x7ff8000000000000))[0] elif which[0] == "numeric": import warnings warnings.warn("Numeric use as a numerix backed for matplotlib is deprecated", DeprecationWarning, stacklevel=1) #from nc_imports import * from Numeric import * from _nc_imports import nx, inf, infinity, Infinity, isnan, all, any from Matrix import Matrix import Numeric version = 'Numeric %s'%Numeric.__version__ nan = struct.unpack('d', struct.pack('Q', 0x7ff8000000000000))[0] elif which[0] == "numpy": try: import numpy.oldnumeric as numpy from numpy.oldnumeric import * except ImportError: import numpy from numpy import * print 'except asarray', asarray from _sp_imports import nx, infinity, rand, randn, isnan, all, any from _sp_imports import UInt8, UInt16, UInt32, Infinity try: from numpy.oldnumeric.matrix import Matrix except ImportError: Matrix = matrix version = 'numpy %s' % numpy.__version__ from numpy import nan else: raise RuntimeError("invalid numerix selector") # Some changes are only applicable to the new numpy: if (which[0] == 'numarray' or which[0] == 'numeric'): from mlab import amin, amax newaxis = NewAxis def typecode(a): return a.typecode() def iscontiguous(a): return a.iscontiguous() def byteswapped(a): return a.byteswapped() def itemsize(a): return a.itemsize() def angle(a): return arctan2(a.imag, a.real) else: # We've already checked for a valid numerix selector, # so assume numpy. from mlab import amin, amax newaxis = NewAxis from numpy import angle def typecode(a): return a.dtype.char def iscontiguous(a): return a.flags.contiguous def byteswapped(a): return a.byteswap() def itemsize(a): return a.itemsize verbose.report('numerix %s'%version) # a bug fix for blas numeric suggested by Fernando Perez matrixmultiply=dot asum = sum def _import_fail_message(module, version): """Prints a message when the array package specific version of an extension fails to import correctly. """ _dict = { "which" : which[0], "module" : module, "specific" : version + module } print """ The import of the %(which)s version of the %(module)s module, %(specific)s, failed. This is is either because %(which)s was unavailable when matplotlib was compiled, because a dependency of %(specific)s could not be satisfied, or because the build flag for this module was turned off in setup.py. If it appears that %(specific)s was not built, make sure you have a working copy of %(which)s and then re-install matplotlib. Otherwise, the following traceback gives more details:\n""" % _dict g = globals() l = locals() __import__('ma', g, l) __import__('fft', g, l) __import__('linear_algebra', g, l) __import__('random_array', g, l) __import__('mlab', g, l) la = linear_algebra ra = random_array

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26,293

_sp_imports.py

numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/numerix/_sp_imports.py

try: from numpy.oldnumeric import Int8, UInt8, \ Int16, UInt16, \ Int32, UInt32, \ Float32, Float64, \ Complex32, Complex64, \ Float, Int, Complex except ImportError: from numpy import Int8, UInt8, \ Int16, UInt16, \ Int32, UInt32, \ Float32, Float64, \ Complex32, Complex64, \ Float, Int, Complex class _TypeNamespace: """Numeric compatible type aliases for use with extension functions.""" Int8 = Int8 UInt8 = UInt8 Int16 = Int16 UInt16 = UInt16 Int32 = Int32 UInt32 = UInt32 Float32 = Float32 Float64 = Float64 Complex32 = Complex32 Complex64 = Complex64 nx = _TypeNamespace() from numpy import inf, infty, Infinity from numpy.random import rand, randn infinity = Infinity from numpy import all, isnan, any

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26,294

__init__.py

numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/numerix/linear_algebra/__init__.py

from matplotlib.numerix import which if which[0] == "numarray": from numarray.linear_algebra import * elif which[0] == "numeric": from LinearAlgebra import * elif which[0] == "numpy": try: from numpy.oldnumeric.linear_algebra import * except ImportError: from numpy.linalg.old import * else: raise RuntimeError("invalid numerix selector")

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26,295

__init__.py

numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/numerix/random_array/__init__.py

from matplotlib.numerix import which if which[0] == "numarray": from numarray.random_array import * elif which[0] == "numeric": from RandomArray import * elif which[0] == "numpy": try: from numpy.oldnumeric.random_array import * except ImportError: from numpy.random import * else: raise RuntimeError("invalid numerix selector")

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26,296

__init__.py

numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/numerix/npyma/__init__.py

from matplotlib.numerix import use_maskedarray if use_maskedarray: from maskedarray import * print "using maskedarray" else: try: from numpy.ma import * # numpy 1.05 and later except ImportError: from numpy.core.ma import * # earlier #print "using ma"

298

Python

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numenta/nupic-legacy

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26,297

__init__.py

numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/numerix/mlab/__init__.py

from matplotlib.numerix import which if which[0] == "numarray": from numarray.linear_algebra.mlab import * elif which[0] == "numeric": from MLab import * elif which[0] == "numpy": try: from numpy.oldnumeric.mlab import * except ImportError: from numpy.lib.mlab import * else: raise RuntimeError("invalid numerix selector") amin = min amax = max

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numenta/nupic-legacy

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26,298

__init__.py

numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/numerix/ma/__init__.py

from matplotlib.numerix import which, use_maskedarray if which[0] == "numarray": from numarray.ma import * nomask = None getmaskorNone = getmask elif which[0] == "numeric": from MA import * nomask = None getmaskorNone = getmask elif which[0] == "numpy": if use_maskedarray: from maskedarray import * print "using maskedarray" else: try: from numpy.ma import * # numpy 1.05 and later except ImportError: from numpy.core.ma import * # earlier #print "using ma" def getmaskorNone(obj): _msk = getmask(obj) if _msk is nomask: return None return _msk else: raise RuntimeError("invalid numerix selector")

749

Python

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numenta/nupic-legacy

6,330

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26,299

__init__.py

numenta_nupic-legacy/external/linux32/lib/python2.6/site-packages/matplotlib/numerix/fft/__init__.py

from matplotlib.numerix import which if which[0] == "numarray": from numarray.fft import * elif which[0] == "numeric": from FFT import * elif which[0] == "numpy": try: from numpy.oldnumeric.fft import * except ImportError: from numpy.dft.old import * else: raise RuntimeError("invalid numerix selector")

341

Python

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numenta/nupic-legacy

6,330

1,556

464

AGPL-3.0

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