ytzi/the-stack-dedup-python-scored · Datasets at Hugging Face

a12a09f22c4f5e88f0c6271dc4b2b3de7f615fa8

932

py

Python

server/migrations/versions/4a916694f1ba_add_initial_image_table.py

brodigan-e/capstone-POV

8ba8bf49e168a1c27a9a252d0f7af375a4e0bb5b

[ "MIT" ]

2

2020-10-02T20:49:48.000Z

2020-10-06T01:19:13.000Z

server/migrations/versions/4a916694f1ba_add_initial_image_table.py

brodigan-e/capstone-POV

8ba8bf49e168a1c27a9a252d0f7af375a4e0bb5b

[ "MIT" ]

15

2020-10-01T05:42:06.000Z

2020-12-07T22:48:22.000Z

server/migrations/versions/4a916694f1ba_add_initial_image_table.py

brodigan-e/capstone-POV

8ba8bf49e168a1c27a9a252d0f7af375a4e0bb5b

[ "MIT" ]

1

2020-11-12T20:47:57.000Z

"""Add Initial Image Table Revision ID: 4a916694f1ba Revises: Create Date: 2020-10-16 02:24:18.479608 """ import sqlalchemy as sa from alembic import op # revision identifiers, used by Alembic. revision = '4a916694f1ba' down_revision = None branch_labels = None depends_on = None def upgrade(): # ### commands auto generated by Alembic - please adjust! ### op.create_table('image_upload', sa.Column('id', sa.Integer(), nullable=False), sa.Column('title', sa.String(length=128), nullable=False), sa.Column('path_uuid', sa.String(length=32), nullable=False), sa.Column('uploadedAt', sa.DateTime(), nullable=False), sa.Column('isProcessed', sa.Boolean(), nullable=False), sa.PrimaryKeyConstraint('id') ) # ### end Alembic commands ### def downgrade(): # ### commands auto generated by Alembic - please adjust! ### op.drop_table('image_upload') # ### end Alembic commands ###

26.628571

65

0.683476

0

423

0.453863

a12aedcd932c89aac78464696ed1d71cb2034b31

9,969

py

Python

skyoffset/multisimplex.py

jonathansick/skyoffset

369f54d8a237f48cd56f550e80bf1d39b355bfcd

[ "BSD-3-Clause" ]

null

skyoffset/multisimplex.py

jonathansick/skyoffset

369f54d8a237f48cd56f550e80bf1d39b355bfcd

[ "BSD-3-Clause" ]

null

skyoffset/multisimplex.py

jonathansick/skyoffset

369f54d8a237f48cd56f550e80bf1d39b355bfcd

[ "BSD-3-Clause" ]

null

import os import logging import platform import time import multiprocessing import numpy import pymongo # Pure python/numpy import simplex from scalarobj import ScalarObjective # Cython/numpy import cyscalarobj import cysimplex class MultiStartSimplex(object): """Baseclass for multi-start recongerging simplex solvers.""" def __init__(self, dbname, cname, url, port): #super(MultiStartSimplex, self).__init__() self.dbname, cname, url, port = dbname, cname, url, port self.dbname = dbname self.cname = cname self.url = url self.port = port connection = pymongo.Connection(self.url, self.port) self.db = connection[self.dbname] self.collection = self.db[self.cname] def resetdb(self): """Delete existing entries in the mongodb collection for this multi simplex optimization.""" # Drop the collection, then recreate it self.db.drop_collection(self.cname) self.collection = self.db[self.cname] def _prep_log_file(self): self.startTime = time.clock() # for timing with close_log_file() logDir = os.path.dirname(self.logPath) if os.path.exists(logDir) is False: os.makedirs(logDir) logging.basicConfig(filename=self.logPath, level=logging.INFO) logging.info("STARTING NEW SIMPLEX OPTIMIZATION ====================") hostname = platform.node() now = time.localtime(time.time()) timeStamp = time.strftime("%y/%m/%d %H:%M:%S %Z", now) logging.info("MultiStartSimplex started on %s at %s" % (hostname, timeStamp)) def _close_log_file(self): endTime = time.clock() duration = (endTime - self.startTime) / 3600. logging.info("ENDING SIMPLEX OPTIMIZATION. Duration: %.2f hours" % duration) class SimplexScalarOffsetSolver(MultiStartSimplex): """Uses a Multi-Start and Reconverging algorithm for converging on the the set of scalar sky offsets that minimize coupled image differences. The optimization is persisted in real-time to MongoDB. This means that multiple computers could be running threads and adding results to the same pool. While optimization is running, it is possible to query for the best-to-date offset solution. """ def __init__(self, dbname="m31", cname="simplexscalar", url="localhost", port=27017): super(SimplexScalarOffsetSolver, self).__init__(dbname, cname, url, port) def multi_start(self, couplings, nTrials, logPath, initSigma=6e-10, restartSigma=1e-11, mp=True, cython=True, log_xtol=-6., log_ftol=-5.): """Start processing using the Multi-Start Reconverging algorithm. Parameters ---------- nTrials : int Number of times a simplex is started. initSigma : float Dispersion of offsets restartSigma : float Dispersion of offsets about a converged point when making a restart simplex. mp : bool If True, run simplexes in parallel with `multiprocessing`. cython : bool True to use the cython version of simplex. """ self.logPath = logPath self._prep_log_file() self.couplings = couplings if cython: self.objf = cyscalarobj.ScalarObjective(self.couplings) else: self.objf = ScalarObjective(self.couplings) ndim = self.objf.get_ndim() xtol = 10. ** log_xtol # frac error in offsets acceptable for conv ftol = 10. ** log_ftol # frac error in objective function acceptable maxiter = 100000 * ndim maxEvals = 100000 * ndim simplexArgs = {'xtol': xtol, 'ftol': ftol, 'maxiter': maxiter, 'maxfun': maxEvals, 'full_output': True, 'disp': True, 'retall': False, 'callback': None} dbArgs = {'dbname': self.dbname, 'cname': self.cname, 'url': self.url, 'port': self.port} # Create initial simplexes argsQueue = [] for n in xrange(nTrials): sim = numpy.zeros([ndim + 1, ndim], dtype=numpy.float64) for i in xrange(ndim + 1): sim[i, :] = initSigma * numpy.random.standard_normal(ndim) args = [sim, cython, self.couplings, simplexArgs, restartSigma, xtol, n, nTrials, self.logPath, dbArgs] argsQueue.append(args) # Run the queue pool = None if mp: pool = multiprocessing.Pool(processes=multiprocessing.cpu_count(), maxtasksperchild=None) pool.map(_simplexWorker, argsQueue) pool.close() pool.join() pool.terminate() else: map(_simplexWorker, argsQueue) self._close_log_file() def find_best_offsets(self): """Queries the mongodb collection of simplex runs to find the optimal result. Returns a dictionary of scalar offsets, keyed by the field name. """ bestEnergy = 1e99 # running tally of best optimization result bestOffsets = {} recs = self.collection.find({}, ['best_fopt', 'best_offsets']) for rec in recs: if rec['best_fopt'] < bestEnergy: bestEnergy = rec['best_fopt'] bestOffsets = rec['best_offsets'] # Normalize these offsets so that the net offset is zero netOffset = 0. fieldCount = 0 for field, offset in bestOffsets.iteritems(): netOffset += offset fieldCount += 1 print "Net offset %.2e" % netOffset netOffset = netOffset / fieldCount for field, offset in bestOffsets.iteritems(): bestOffsets[field] = offset - netOffset return bestOffsets def init_func(): print multiprocessing.current_process().name def _simplexWorker(argsList): """multiprocessing worker function for doing multi-trial simplex solving. This essentially replaces the multi_start_simplex function in simplex.py But this exists because it implicitly specifies the target function for the optimization; multiprocessing can't pickle a function object. This simplex worker has the ability to restart at the site of convergence by constructing a simplex that is randomly distributed about the best vertex. The simplex keeps reconverging from perturbed simplex until the reconverged minimum matches the previous minimum. That is, I believe I have a global minimum if the simplex returns to where it started. """ startTime = time.clock() sim, useCython, couplings, kwargs, restartSigma, xTol, n, nTrials, logFilePath, dbArgs = argsList if useCython: objf = cyscalarobj.ScalarObjective(couplings) else: objf = ScalarObjective(couplings) # Choose the simplex code if useCython: nm_simplex = cysimplex.nm_simplex else: nm_simplex = simplex.nm_simplex #print "Running simplex %i/%i"% (n,nTrials) Ndim = sim.shape[1] _evalObjFunc = lambda offsets, objF: objF.compute(offsets) # These variables keep track of how the code performs totalFCalls = 0 nRestarts = 0 # Initial simplex compute _xOpt, _fOpt, _nIters, _nFcalls, _warnflag = nm_simplex(objf, sim, **kwargs) bestFOpt = _fOpt bestXOpt = _xOpt.copy() totalFCalls += _nFcalls # These arrays list the running tally of restarts vs best fopt vs total f calls restartTally = [nRestarts] bestFOptTally = [bestFOpt] totalFCallTally = [totalFCalls] # initiate restarts while True: nRestarts += 1 sim = numpy.zeros([Ndim+1, Ndim], dtype=numpy.float64) sim[0,:] = bestXOpt.copy() # first vertex is the best point for i in xrange(1,Ndim+1): # rest are randomly distributed. sim[i,:] = restartSigma*numpy.random.standard_normal(Ndim) + bestXOpt _xOpt, _fOpt, _nIters, _nFcalls, _warnflag = nm_simplex(objf, sim, **kwargs) totalFCalls += _nFcalls # Ensure that the point has converged convergenceFrac = (_xOpt - bestXOpt) / bestXOpt if len(numpy.where(convergenceFrac > xTol)[0]) > 0: # do another restart of the simplex if _fOpt < bestFOpt: # but we did find a new minimum bestFOpt = _fOpt bestXOpt = _xOpt.copy() restartTally.append(nRestarts) bestFOptTally.append(bestFOpt) totalFCallTally.append(totalFCalls) else: # we're converged break # Report this in the log runtime = time.clock() - startTime if logFilePath is not None: logging.basicConfig(filename=logFilePath,level=logging.INFO) logging.info("%i/%i converged to %.4e in %.2f minutes, %i local restarts" % (n, nTrials, bestFOpt, runtime/60., nRestarts)) # Dictionary stores the history of restarts, as well as teh best solution # as a field offset dictionary (we're breaking reusability here... just # to make things faster.) convergenceHistory = {"total_calls": totalFCalls, "n_restarts": nRestarts, "runtime": runtime, "best_offsets": objf.get_best_offsets(), "best_fopt": bestFOpt, "restart_hist": restartTally, "fopt_hist": bestFOptTally, "fcall_hist": totalFCallTally} # Connect to MongoDB and add our convergence history! try: connection = pymongo.Connection(dbArgs['url'], dbArgs['port']) db = connection[dbArgs['dbname']] collection = db[dbArgs['cname']] collection.insert(convergenceHistory, safe=True) except pymongo.errors.AutoReconnect: logging.info("pymongo.errors.AutoReconnect on %i"%n) # collection.database.connection.disconnect()

39.403162

131

0.634467

5,667

0.568462

0

3,659

0.367038

a12b99b03f4c428fc4fbd3c7f3bfcb53005d0cea

695

py

Python

netsuitesdk/api/custom_records.py

cart-com/netsuite-sdk-py

9c759b631f7a194efb86c06e1935cdc2856200d3

[ "MIT" ]

null

netsuitesdk/api/custom_records.py

cart-com/netsuite-sdk-py

9c759b631f7a194efb86c06e1935cdc2856200d3

[ "MIT" ]

null

netsuitesdk/api/custom_records.py

cart-com/netsuite-sdk-py

9c759b631f7a194efb86c06e1935cdc2856200d3

[ "MIT" ]

null

from netsuitesdk.internal.utils import PaginatedSearch from .base import ApiBase import logging logger = logging.getLogger(__name__) class CustomRecords(ApiBase): def __init__(self, ns_client): ApiBase.__init__(self, ns_client=ns_client, type_name='CustomRecordType') def get_all_by_id(self, internalId): cr_type = self.ns_client.CustomRecordSearchBasic( recType=self.ns_client.CustomRecordType( internalId=internalId ) ) ps = PaginatedSearch(client=self.ns_client, type_name='CustomRecordType', search_record=cr_type, pageSize=20) return list(self._paginated_search_to_generator(paginated_search=ps))

33.095238

117

0.728058

557

0.801439

0

36

0.051799

a12be00ef3b06e0094c89aa20c5aafe79c822021

343

py

Python

Support/renameCNVNatorOutput.py

zhongmicai/SV_population

81987865c9b67be5e358cb1b966bb69cc303abee

[ "MIT" ]

18

2019-03-18T00:08:18.000Z

2021-10-19T06:21:56.000Z

Support/renameCNVNatorOutput.py

zhongmicai/SV_population

81987865c9b67be5e358cb1b966bb69cc303abee

[ "MIT" ]

5

2018-11-06T15:18:17.000Z

2020-07-24T09:31:08.000Z

Support/renameCNVNatorOutput.py

zhongmicai/SV_population

81987865c9b67be5e358cb1b966bb69cc303abee

[ "MIT" ]

2

2019-11-13T10:28:58.000Z

2021-09-07T08:25:12.000Z

#!/usr/bin/env python3 import os vcfdir='/home/matt/Plasmodium/Pf_SV/Data' for ID in os.listdir(vcfdir): nameID = '_'.join(ID.split('.')[0].split('_')[:-1]) coreID = nameID.split('_')[-1] if coreID[:3] == 'ERR': os.system('cp {0}.cnvs {1}_DEL.cnvs'.format(coreID, nameID)) os.system('cp {0}.cnvs {1}_DUP.cnvs'.format(coreID, nameID))

28.583333

62

0.641399

0

125

0.364431

a12cb244767dfa01e9b581f3a545006ea34d4ac7

1,568

py

Python

string_1/hello_name.py

nhutnamhcmus/coding-bat-solutions

5f780a4027a6c3523a72961db1bad547c997fdc6

[ "MIT" ]

1

2020-09-19T18:02:13.000Z

string_1/hello_name.py

nhutnamhcmus/coding-bat-solutions

5f780a4027a6c3523a72961db1bad547c997fdc6

[ "MIT" ]

null

string_1/hello_name.py

nhutnamhcmus/coding-bat-solutions

5f780a4027a6c3523a72961db1bad547c997fdc6

[ "MIT" ]

null

# ======================================================================================================================================= # VNU-HCM, University of Science # Department Computer Science, Faculty of Information Technology # Authors: Nhut-Nam Le (Tich Phan Suy Rong) # © 2020 """ Given a string name, e.g. "Bob", return a greeting of the form "Hello Bob!". For example test case: hello_name('Bob') → 'Hello Bob!' hello_name('Alice') → 'Hello Alice!' hello_name('X') → 'Hello X!' """ import unittest def hello_name(name): return "Hello " + name + "!" class TestHelloName(unittest.TestCase): def test_case_00(self): self.assertEqual(hello_name('Bob'), 'Hello Bob!') def test_case_01(self): self.assertEqual(hello_name('Alice'), 'Hello Alice!') def test_case_02(self): self.assertEqual(hello_name('X'), 'Hello X!') def test_case_03(self): self.assertEqual(hello_name('Dolly'), 'Hello Dolly!') def test_case_04(self): self.assertEqual(hello_name('Alpha'), 'Hello Alpha!') def test_case_05(self): self.assertEqual(hello_name('Omega'), 'Hello Omega!') def test_case_06(self): self.assertEqual(hello_name('Goodbye'), 'Hello Goodbye!') def test_case_07(self): self.assertEqual(hello_name('ho ho ho'), 'Hello ho ho ho!') def test_case_08(self): self.assertEqual(hello_name('xyz!'), 'Hello xyz!!') def test_case_09(self): self.assertEqual(hello_name('Hello'), 'Hello Hello!') if __name__ == "__main__": unittest.main()

27.508772

137

0.598852

944

0.599365

0

725

0.460317

a12f2dc13e43b20caf3450c97b9fa9395b547d8a

335

py

Python

materials/ch_04/escape_str.py

epsilonxe/RMUTT_09090016

863dd8a6471b560831b742da4aec27209c294df5

[ "MIT" ]

null

materials/ch_04/escape_str.py

epsilonxe/RMUTT_09090016

863dd8a6471b560831b742da4aec27209c294df5

[ "MIT" ]

null

materials/ch_04/escape_str.py

epsilonxe/RMUTT_09090016

863dd8a6471b560831b742da4aec27209c294df5

[ "MIT" ]

null

text1 = '''ABCDEF GHIJKL MNOPQRS TUVWXYZ ''' text2 = 'ABCDEF\ GHIJKL\ MNOPQRS\ TUVWXYZ' text3 = 'ABCD\'EF\'GHIJKL' text4 = 'ABCDEF\nGHIJKL\nMNOPQRS\nTUVWXYZ' text5 = 'ABCDEF\fGHIJKL\fMNOPQRS\fTUVWXYZ' print(text1) print('-' * 25) print(text2) print('-' * 25) print(text3) print('-' * 25) print(text4) print('-' * 25) print(text5)

12.884615

42

0.671642

0

168

0.501493

a1300bc0639e795122958402aa1f3b4e0ab96874

823

py

Python

pygears/cookbook/reduce2.py

Risto97/pygears

19393e85101a16762cb3bbbf3010946ef69217f2

[ "MIT" ]

null

pygears/cookbook/reduce2.py

Risto97/pygears

19393e85101a16762cb3bbbf3010946ef69217f2

[ "MIT" ]

null

pygears/cookbook/reduce2.py

Risto97/pygears

19393e85101a16762cb3bbbf3010946ef69217f2

[ "MIT" ]

null

from pygears import gear, Intf from pygears.common import czip from pygears.typing import Tuple, Uint, Union, Queue from pygears.common import fmap, demux, decoupler, fifo, union_collapse from pygears.cookbook import priority_mux, replicate TCfg = Tuple[{'reduce_size': Uint['w_reduce_size'], 'init': 't_acc'}] @gear def reduce2(din, cfg: TCfg, *, f, max_size): acctype = cfg.dtype['init'] qtype = Queue[acctype, din.dtype.lvl - 1] temp_res = Intf(dtype=qtype) cfg_rep = cfg | replicate sec_opnd = (cfg_rep, temp_res) \ | priority_mux \ | fmap(f=union_collapse, fcat=czip, lvl=1) result = czip(din, sec_opnd) | decoupler | fmap(f=f, fcat=czip, lvl=2) acc, fin_res = result | Union[qtype, qtype] | demux acc | fifo(intfs=[temp_res], depth=max_size) return fin_res

29.392857

74

0.684083

0

508

0.617254

0

47

0.057108

a1309a770978d986e457fb2177d6163ed7ae8ec0

313

py

Python

atcoder/abc166D_i_hate_factorization.py

da-edra/kyopro

ad531d15bcccf6aafdaaef3cc69db850b0f7c471

[ "BSD-3-Clause" ]

2

2020-08-31T17:19:07.000Z

2021-01-08T21:35:48.000Z

atcoder/abc166D_i_hate_factorization.py

edglaz/kyopro

b8ac4f6873418ad20ad417e46d731c35a8062c0d

[ "BSD-3-Clause" ]

null

atcoder/abc166D_i_hate_factorization.py

edglaz/kyopro

b8ac4f6873418ad20ad417e46d731c35a8062c0d

[ "BSD-3-Clause" ]

null

# unihernandez22 # https://atcoder.jp/contests/abc166/tasks/abc166_d # math, brute force n = int(input()) for a in range(n): breaked = True for b in range(-1000, 1000): if a**5 - b**5 == n: print(a, b) break; else: breaked = False if breaked: break

20.866667

51

0.539936

0

86

0.27476

a130aee35a17b1d7653613de1de880f9a3444608

305

py

Python

packages/grid/apps/worker/src/main/core/database/groups/groups.py

exityan/PySyft

35166c487a5be57f9ad28929ed88a8ba6bdd5aeb

[ "Apache-2.0" ]

425

2019-09-22T06:14:53.000Z

2022-03-30T02:17:34.000Z

packages/grid/apps/worker/src/main/core/database/groups/groups.py

Metrix1010/PySyft

6477f64b63dc285059c3766deab3993653cead2e

[ "Apache-2.0" ]

352

2019-09-17T15:32:51.000Z

2022-03-12T01:07:35.000Z

packages/grid/apps/worker/src/main/core/database/groups/groups.py

Metrix1010/PySyft

6477f64b63dc285059c3766deab3993653cead2e

[ "Apache-2.0" ]

208

2019-09-18T18:32:10.000Z

2022-03-24T01:10:11.000Z

# grid relative from .. import BaseModel from .. import db class Group(BaseModel): __tablename__ = "group" id = db.Column(db.Integer(), primary_key=True, autoincrement=True) name = db.Column(db.String(255)) def __str__(self): return f"<Group id: {self.id}, name: {self.name}>"

21.785714

70

0.655738

243

0.796721

0

65

0.213115

a130d81a095f620365d47a00f587d3671ea0c357

2,416

py

Python

libraries/urx_python/urx_scripts/demo_apple_tree.py

giacomotomasi/tennisball_demo

f71cd552e64fe21533abe47b986db6999947c3a9

[ "Apache-2.0" ]

null

libraries/urx_python/urx_scripts/demo_apple_tree.py

giacomotomasi/tennisball_demo

f71cd552e64fe21533abe47b986db6999947c3a9

[ "Apache-2.0" ]

null

libraries/urx_python/urx_scripts/demo_apple_tree.py

giacomotomasi/tennisball_demo

f71cd552e64fe21533abe47b986db6999947c3a9

[ "Apache-2.0" ]

null

import urx import logging import time if __name__ == "__main__": logging.basicConfig(level=logging.WARN) #gripper_remove_pos = [0.0755, -0.2824, 0.3477, -0.0387, -3.0754, 0.4400] # rest position (good to place/remove gripper) rob = urx.Robot("192.168.56.1") #rob.set_tcp((0,0,0,0,0,0)) #rob.set_payload(0.5, (0,0,0)) home_pos = [-0.0153, -0.4213, 0.3469, 1.2430, 2.6540, -0.9590] appro1 = [-0.0762, -0.5575, 0.3546, 0.6110, 2.7090, -1.7840] apple1 = [-0.1042, -0.6244, 0.3209, 1.4510, 1.9160, -1.4980] get_far1 = [-0.0510, -0.5086, 0.3215, 0.4900, 2.6510, -1.8690] appro2 = [-0.1767, -0.4281, 0.3204, 1.8210, 2.0030, -1.5280] apple2 = [-0.2129, -0.4926, 0.2951, 1.8210, 2.0030, -1.5280] get_far2 = [-0.1324, -0.3790, 0.3112, 1.8210, 2.0030, -1.5280] appro_place = [0.3571, -0.3540, 0.3563, 1.2360, 2.8850, -0.0780] place_pos = [0.3571, -0.3540, 0.2983, 1.2360, 2.8850, -0.0780] try: v = 0.2 a = 0.3 rob.set_digital_out(0,0) # initialize gripper # open gripper rob.set_digital_out(0, 1) time.sleep(0.5) rob.set_digital_out(0,0) pose = rob.getl() #gives a lists with 6 elements (x, y, z, rx, ry, rz) --> rotation vector #print("robot tcp is at: ", pose) # move to home position #rob.movej(joint_pose, acc=a, vel=v) # it takes as inputs the joints goal values! rob.movej_to_pose(home_pos, acc=a, vel=0.3) time.sleep(0.01) # move towards the first apple to pick (approach it, move to a suitable grabbing position, get away) rob.movej_to_pose(appro1, acc=a, vel=v) time.sleep(0.01) rob.movel(apple1, acc=a, vel=v) # close gripper rob.set_digital_out(0, 1) time.sleep(0.5) rob.set_digital_out(0,0) time.sleep(1) rob.movel(get_far1, a, v) #move towards the place position rob.movej_to_pose(appro_place, a, vel=0.3) time.sleep(0.01) rob.movel(place_pos, a, v) # open gripper rob.set_digital_out(0, 1) time.sleep(0.5) rob.set_digital_out(0,0) time.sleep(1) rob.movel(appro_place, a, v) # move to home position rob.movej_to_pose(home_pos, a, v) pose_final = rob.getl() print("robot tcp is at (final): ", pose_final) finally: rob.close()

32.213333

124

0.577815

0

655

0.271109

a1311c3c3114e32c6b986776dfaae1a0d9bb6825

403

py

Python

solution/data_structure2/1302/main.py

jungyoonoh/baekjoon-1

2b4437a4b5e06244fa47fae6c7b7be0157d0f94f

[ "MIT" ]

2,236

2019-08-05T00:36:59.000Z

2022-03-31T16:03:53.000Z

solution/data_structure2/1302/main.py

juy4556/baekjoon

bc0b0a0ebaa45a5bbd32751f84c458a9cfdd9f92

[ "MIT" ]

225

2020-12-17T10:20:45.000Z

2022-01-05T17:44:16.000Z

solution/data_structure2/1302/main.py

juy4556/baekjoon

bc0b0a0ebaa45a5bbd32751f84c458a9cfdd9f92

[ "MIT" ]

602

2019-08-05T00:46:25.000Z

2022-03-31T13:38:23.000Z

# Authored by : gusdn3477 # Co-authored by : - # Link : http://boj.kr/8adc986ae26b461eadd65abdff3cfba9 import sys def input(): return sys.stdin.readline().rstrip() N = int(input()) book = {} for i in range(N): name = input() if name not in book: book[name] = 1 else: book[name] += 1 book = list(book.items()) book.sort(key = lambda x : (-x[1],x[0])) print(book[0][0])

19.190476

55

0.600496

0

100

0.248139

a13162f4cb62e368c73037f36a88c321b285f2d8

1,152

py

Python

testflows/_core/utils/sort.py

testflows/TestFlows-Core

0aa17247dffd2f7199465031ab16cc4f12c9cfb0

[ "Apache-2.0" ]

3

2020-06-25T19:23:19.000Z

2021-10-20T19:29:56.000Z

testflows/_core/utils/sort.py

testflows/TestFlows-Core

0aa17247dffd2f7199465031ab16cc4f12c9cfb0

[ "Apache-2.0" ]

null

testflows/_core/utils/sort.py

testflows/TestFlows-Core

0aa17247dffd2f7199465031ab16cc4f12c9cfb0

[ "Apache-2.0" ]

1

2020-02-24T12:31:45.000Z

# Copyright 2020 Katteli Inc. # TestFlows.com Open-Source Software Testing Framework (http://testflows.com) # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import re def human(l, key=None): """Sort in human readable format. Credit: https://blog.codinghorror.com/sorting-for-humans-natural-sort-order/ :key: optional function to retrieve the key from the element """ get_key = key if get_key is None: get_key = lambda x: x convert = lambda text: int(text) if text.isdigit() else text alphanum_key = lambda key: [ convert(c) for c in re.split('([0-9]+)', get_key(key)) ] l.sort(key=alphanum_key) return l

38.4

89

0.717882

0

839

0.728299

a13291eccf29b835c30e820b06c59c45c1cf58bf

3,220

py

Python

tests/build/test_flash.py

cyliangtw/mbed-tools

69c600c0a5ac1eb0d52b481b5ba020da8bb73d33

[ "Apache-2.0" ]

39

2020-04-03T13:52:34.000Z

2022-03-23T13:08:22.000Z

tests/build/test_flash.py

cyliangtw/mbed-tools

69c600c0a5ac1eb0d52b481b5ba020da8bb73d33

[ "Apache-2.0" ]

306

2020-02-06T18:08:43.000Z

2022-03-25T14:50:18.000Z

tests/build/test_flash.py

cyliangtw/mbed-tools

69c600c0a5ac1eb0d52b481b5ba020da8bb73d33

[ "Apache-2.0" ]

23

2020-03-17T11:42:23.000Z

2022-01-30T02:56:18.000Z

# # Copyright (c) 2020-2021 Arm Limited and Contributors. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # import pathlib import tempfile from unittest import TestCase, mock from mbed_tools.build.flash import flash_binary, _build_binary_file_path, _flash_dev from mbed_tools.build.exceptions import BinaryFileNotFoundError from tests.build.factories import DeviceFactory @mock.patch("mbed_tools.build.flash._build_binary_file_path") @mock.patch("mbed_tools.build.flash._flash_dev") class TestFlashBinary(TestCase): def test_check_flashing(self, _flash_dev, _build_binary_file_path): test_device = DeviceFactory() _flash_dev.return_value = True with tempfile.TemporaryDirectory() as tmpDir: base_dir = pathlib.Path(tmpDir) build_dir = base_dir / "cmake_build" build_dir.mkdir() bin_file = base_dir.name + ".bin" bin_file = build_dir / bin_file bin_file.touch() _build_binary_file_path.return_value = bin_file flash_binary(test_device.mount_points[0].resolve(), base_dir, build_dir, "TEST", False) _build_binary_file_path.assert_called_once_with(base_dir, build_dir, False) _flash_dev.assert_called_once_with(test_device.mount_points[0].resolve(), bin_file) class TestBuildBinFilePath(TestCase): def test_build_bin_file_path(self): with tempfile.TemporaryDirectory() as tmpDir: base_dir = pathlib.Path(tmpDir) build_dir = base_dir / "cmake_build" build_dir.mkdir() bin_file = base_dir.name + ".bin" bin_file = build_dir / bin_file bin_file.touch() self.assertEqual(_build_binary_file_path(base_dir, build_dir, False), bin_file) def test_build_hex_file_path(self): with tempfile.TemporaryDirectory() as tmpDir: base_dir = pathlib.Path(tmpDir) build_dir = base_dir / "cmake_build" build_dir.mkdir() bin_file = base_dir.name + ".hex" bin_file = build_dir / bin_file bin_file.touch() self.assertEqual(_build_binary_file_path(base_dir, build_dir, True), bin_file) def test_missing_binary_file(self): with tempfile.TemporaryDirectory() as tmpDir: base_dir = pathlib.Path(tmpDir) build_dir = base_dir / "cmake_build" build_dir.mkdir() with self.assertRaises(BinaryFileNotFoundError): _build_binary_file_path(base_dir, build_dir, False) @mock.patch("mbed_tools.build.flash.shutil.copy") class TestCopyToDevice(TestCase): def test_copy_to_device(self, copy): test_device = DeviceFactory() with tempfile.TemporaryDirectory() as tmpDir: base_dir = pathlib.Path(tmpDir) build_dir = base_dir / "cmake_build" build_dir.mkdir() bin_file = base_dir.name + ".bin" bin_file = build_dir / bin_file bin_file.touch() _flash_dev(test_device.mount_points[0].resolve(), bin_file) copy.assert_called_once_with(bin_file, test_device.mount_points[0].resolve(), follow_symlinks=False)

36.590909

112

0.675776

2,663

0.827019

0

1,581

0.490994

0

329

0.102174

a133567cd81f4bb8edf05a69d95e9fb2d7bf451d

2,795

py

Python

packettotal_sdk/search_tools.py

RogerDeng/HoneyBot

3843ec6d684786091ced053857d1718ef1fa495c

[ "MIT" ]

67

2019-08-16T05:03:19.000Z

2021-11-25T01:48:23.000Z

packettotal_sdk/search_tools.py

RogerDeng/HoneyBot

3843ec6d684786091ced053857d1718ef1fa495c

[ "MIT" ]

1

2020-09-01T02:40:31.000Z

packettotal_sdk/search_tools.py

RogerDeng/HoneyBot

3843ec6d684786091ced053857d1718ef1fa495c

[ "MIT" ]

16

2020-02-20T12:38:40.000Z

2022-03-22T17:45:25.000Z

import time import typing import requests from sys import stderr from datetime import datetime from packettotal_sdk import packettotal_api class SearchTools(packettotal_api.PacketTotalApi): def __init__(self, api_key: str): """ :param api_key: An API authentication token """ super().__init__(api_key) def search_by_pcap(self, pcap_file_obj: typing.BinaryIO) -> requests.Response: """ Search by a pcap/pcapng file, get list list of similar packet captures :param pcap_file_obj: A file like object that provides a .read() interface (E.G open('path_to_pcap.pcap, 'rb') ) :return: A request.Response instance, containing a graph of similar pcaps with matched terms """ response = super().analyze(pcap_file_obj) if response.status_code == 200: sim_response = super().pcap_similar(response.json()['pcap_metadata']['md5']) elif response.status_code == 202: pcap_id = response.json()['id'] info_response = super().pcap_info(pcap_id) while info_response.status_code == 404: print('[{}] Waiting for {} to finish analyzing.'.format(datetime.utcnow(), pcap_id)) info_response = super().pcap_info(response.json()['id']) time.sleep(10) print('[{}] Fetching results for {}.'.format(datetime.utcnow(), pcap_id)) time.sleep(5) sim_response = super().pcap_similar(response.json()['id']) else: return response return sim_response def search_by_iocs(self, ioc_file: typing.TextIO) -> requests.Response: """ Search up to 100 IOC terms at once, and get matching packet captures :param ioc_file: A file like object that provides a .read() interface (E.G open('path_to_iocs.txt, 'r') contents are line delim :return: A request.Response instance containing the search results containing at least one matching IOC """ text = ioc_file.read() delim = '\n' if '\r\n' in text[0:2048]: delim = '\r\n' elif '\r' in text[0:2048]: delim = '\r' elif ',' in text[0:2048]: delim = ',' elif '\t' in text[0:2048]: delim = '\t' text_delimd = text.split(delim) search_str = '' for i, ioc in enumerate(text_delimd[0: -2]): search_str += '"{}" OR '.format(ioc.strip()) if i > 100: print('Warning searching only the first 100 IOC terms of {}.'.format(len(text_delimd)), file=stderr) break search_str += '"{}"'.format(text_delimd[-1].strip()) response = super().search(search_str) return response

39.366197

120

0.594633

2,651

0.948479

0

966

0.345617

a133fa0afcdcf42b74dd45b66f95e50ddbf7734f

41

py

Python

actfw_core/v4l2/__init__.py

Idein/actfw-core

44c979bbe5d32d068eed20b7d565a6de2fb9acd3

[ "MIT" ]

2

2021-03-15T11:44:37.000Z

2021-05-12T09:58:35.000Z

actfw_core/v4l2/__init__.py

Idein/actfw-core

44c979bbe5d32d068eed20b7d565a6de2fb9acd3

[ "MIT" ]

28

2020-12-24T02:53:37.000Z

2022-03-14T09:02:28.000Z

actfw_core/v4l2/__init__.py

Idein/actfw-core

44c979bbe5d32d068eed20b7d565a6de2fb9acd3

[ "MIT" ]

null

from . import types, video # noqa: F401

20.5

40

0.682927

0

12

0.292683

a13428de836fe2ca966877503cf126c867ad3cd6

531

py

Python

xos/synchronizers/openstack/model_policies/model_policy_Sliver.py

xmaruto/mcord

3678a3d10c3703c2b73f396c293faebf0c82a4f4

[ "Apache-2.0" ]

null

xos/synchronizers/openstack/model_policies/model_policy_Sliver.py

xmaruto/mcord

3678a3d10c3703c2b73f396c293faebf0c82a4f4

[ "Apache-2.0" ]

null

xos/synchronizers/openstack/model_policies/model_policy_Sliver.py

xmaruto/mcord

3678a3d10c3703c2b73f396c293faebf0c82a4f4

[ "Apache-2.0" ]

null

def handle(instance): from core.models import Controller, ControllerSlice, ControllerNetwork, NetworkSlice networks = [ns.network for ns in NetworkSlice.objects.filter(slice=instance.slice)] controller_networks = ControllerNetwork.objects.filter(network__in=networks, controller=instance.node.site_deployment.controller) for cn in controller_networks: if (cn.lazy_blocked): cn.lazy_blocked=False cn.backend_register = '{}' cn.save()

37.928571

116

0.6742

0

4

0.007533

a1357146c1bfe43fcbbabe34684a165daba3ef28

4,987

py

Python

tests/unit/test_s3.py

tejuafonja/SDGym

7c20c588a4c9f5940885467406e73274a5b01a8e

[ "MIT" ]

19

2019-05-23T14:27:02.000Z

2019-12-08T16:04:20.000Z

tests/unit/test_s3.py

tejuafonja/SDGym

7c20c588a4c9f5940885467406e73274a5b01a8e

[ "MIT" ]

11

2019-05-30T21:29:27.000Z

2019-12-10T16:49:28.000Z

tests/unit/test_s3.py

tejuafonja/SDGym

7c20c588a4c9f5940885467406e73274a5b01a8e

[ "MIT" ]

11

2019-05-23T14:27:06.000Z

2020-01-02T14:29:00.000Z

from unittest.mock import Mock, patch import pandas as pd from sdgym.s3 import is_s3_path, parse_s3_path, write_csv, write_file def test_is_s3_path_with_local_dir(): """Test the ``sdgym.s3.is_s3_path`` function with a local directory. If the path is not an s3 path, it should return ``False``. Input: - path to a local directory Output: - False """ # setup path = 'path/to/local/dir' # run result = is_s3_path(path) # asserts assert not result def test_is_s3_path_with_s3_bucket(): """Test the ``sdgym.s3.is_s3_path`` function with an s3 directory. If the path is an s3 path, it should return ``True``. Input: - path to an s3 directory Output: - True """ # setup path = 's3://my-bucket/my/path' # run result = is_s3_path(path) # asserts assert result def test_parse_s3_path_bucket_only(): """Test the ``sdgym.s3.parse_s3_path`` function with an s3 path. If the s3 path contains only the bucket name, the returned tuple should be ``(bucket_name, '')``. Input: - path to s3 bucket Output: - ('my-bucket', '') """ # setup expected_bucket_name = 'my-bucket' expected_key_prefix = '' path = f's3://{expected_bucket_name}/{expected_key_prefix}' # run bucket_name, key_prefix = parse_s3_path(path) # asserts assert bucket_name == expected_bucket_name assert key_prefix == expected_key_prefix def test_parse_s3_path_bucket_and_dir_path(): """Test the `sdgym.s3.parse_s3_path`` function with an s3 path. If the s3 path contains the bucket and a sub directory, the returned tuple should be ``(bucket_name, subdirectory)``. Input: - path to s3 directory Output: - ('my-bucket', 'path/to/dir') """ # setup expected_bucket_name = 'my-bucket' expected_key_prefix = 'path/to/dir' path = f's3://{expected_bucket_name}/{expected_key_prefix}' # run bucket_name, key_prefix = parse_s3_path(path) # asserts assert bucket_name == expected_bucket_name assert key_prefix == expected_key_prefix def test_write_file(tmpdir): """Test the `sdgym.s3.write_file`` function with a local path. If the path is a local path, a file with the correct contents should be created at the specified path. Input: - contents of the local file - path to the local file - aws_key is None - aws_secret is None Output: - None Side effects: - file creation at the specified path with the given contents """ # setup content_str = 'test_content' path = f'{tmpdir}/test.txt' # run write_file(content_str.encode('utf-8'), path, None, None) # asserts with open(path, 'r') as f: assert f.read() == content_str @patch('sdgym.s3.boto3') def test_write_file_s3(boto3_mock): """Test the `sdgym.s3.write_file`` function with an s3 path. If the path is an s3 path, a file with the given contents should be created at the specified s3 path. Input: - contents of the s3 file - path to the s3 file location - aws_key for aws authentication - aws_secret for aws authentication Output: - None Side effects: - s3 client creation with aws credentials (aws_key, aws_secret) - s3 method call to create a file in the given bucket with the given contents """ # setup content_str = 'test_content' bucket_name = 'my-bucket' key = 'test.txt' path = f's3://{bucket_name}/{key}' aws_key = 'my-key' aws_secret = 'my-secret' s3_mock = Mock() boto3_mock.client.return_value = s3_mock # run write_file(content_str.encode('utf-8'), path, aws_key, aws_secret) # asserts boto3_mock.client.assert_called_once_with( 's3', aws_access_key_id=aws_key, aws_secret_access_key=aws_secret ) s3_mock.put_object.assert_called_once_with( Bucket=bucket_name, Key=key, Body=content_str.encode('utf-8'), ContentEncoding='', ) @patch('sdgym.s3.write_file') def test_write_csv(write_file_mock): """Test the ``sdgym.s3.write_csv`` function. If ``write_csv`` is called with a DataFrame, ``write_file`` should be called with the expected DataFrame contents. Input: - data to be written to the csv file - path of the desired csv file - aws_key is None - aws_secret is None Output: - None Side effects: - call to write_file with the correct contents and path """ # setup data = pd.DataFrame({"col1": [1, 2], "col2": [3, 4]}) path = 'tmp/path' # run write_csv(data, path, None, None) # asserts input_data = pd.DataFrame({"col1": [1, 2], "col2": [3, 4]}) expected_content = input_data.to_csv(index=False).encode('utf-8') write_file_mock.assert_called_once_with( expected_content, path, None, None )

23.195349

72

0.645278

0

2,157

0.432525

0

2,861

0.573692

a1360dd0640d6fe332d03889c6a40e96f3ddedfb

3,227

py

Python

vet_care/scripts/generate_from_history.py

neerajvkn/vet_care

14914b22e7a83265d736f9f9dc5186271ae62d66

[ "MIT" ]

2

2020-11-23T11:14:32.000Z

2021-02-03T06:40:33.000Z

vet_care/scripts/generate_from_history.py

neerajvkn/vet_care

14914b22e7a83265d736f9f9dc5186271ae62d66

[ "MIT" ]

null

vet_care/scripts/generate_from_history.py

neerajvkn/vet_care

14914b22e7a83265d736f9f9dc5186271ae62d66

[ "MIT" ]

7

2019-11-16T14:36:33.000Z

2021-08-25T07:54:51.000Z

import csv import datetime import frappe # bench execute vet_care.scripts.generate_from_history.execute --args "['./data/important_data.csv']" def execute(filename): patient_activities = [] not_created = [] with open(filename, 'r') as csvfile: reader = csv.DictReader(csvfile) for row in reader: timestamp = int(row.get('Date')) cirrusvet_id = row.get('AnimalID') description = row.get('Notes') date = datetime.datetime.utcfromtimestamp(timestamp).strftime('%Y-%m-%d') patient = _get_patient_via_cirrusvet_id(cirrusvet_id) if patient: patient_activity = _pick_or_new_patient_activity(patient_activities, patient, date) patient_activity.append('items', {'description': description}) patient_activities.append(patient_activity) else: not_created.append(cirrusvet_id) created = 0 total = len(patient_activities) for patient_activity in patient_activities: patient_activity.save() created = created + 1 print(f'Created ${created}/${total} patient activities') print(not_created) # bench execute vet_care.scripts.generate_from_history.execute --args "['./data/important_data.csv', ['1010', '2920']]" def execute_with_filter(filename, missing_animals): patient_activities = [] not_created = [] with open(filename, 'r') as csvfile: reader = csv.DictReader(csvfile) for row in reader: timestamp = int(row.get('Date')) cirrusvet_id = row.get('AnimalID') description = row.get('Notes') if cirrusvet_id in missing_animals: date = datetime.datetime.utcfromtimestamp(timestamp).strftime('%Y-%m-%d') patient = _get_patient_via_cirrusvet_id(cirrusvet_id) if patient: patient_activity = _pick_or_new_patient_activity(patient_activities, patient, date) patient_activity.append('items', {'description': description}) patient_activities.append(patient_activity) else: not_created.append(cirrusvet_id) created = 0 total = len(patient_activities) for patient_activity in patient_activities: patient_activity.save() created = created + 1 print(f'Created ${created}/${total} patient activities') print(not_created) def _pick_or_new_patient_activity(patient_activities, patient, date): def filter_activity(activity): return activity.patient == patient and activity.posting_date == date existing = list(filter(filter_activity, patient_activities)) if existing: return existing[0] return frappe.get_doc({ 'doctype': 'Patient Activity', 'patient': patient, 'posting_date': date }) def _get_patient_via_cirrusvet_id(cirrusvet_id): patient_data = frappe.db.sql( """SELECT name FROM `tabPatient` WHERE vc_cirrusvet=%s""", cirrusvet_id, as_dict=True ) if patient_data: return patient_data[0].get('name') return None

37.964706

119

0.634645

0

543

0.168268

a1362909e583305f43ba83685760d08284ce8f25

594

py

Python

aws_interface/cloud/auth/delete_sessions.py

hubaimaster/aws-interface

162dd056546d58b6eb29afcae1c3c2d78e4309b2

[ "Apache-2.0" ]

53

2018-10-02T05:58:54.000Z

2020-09-15T08:58:26.000Z

aws_interface/cloud/auth/delete_sessions.py

hubaimaster/aws-interface

162dd056546d58b6eb29afcae1c3c2d78e4309b2

[ "Apache-2.0" ]

52

2018-09-26T05:16:09.000Z

2022-03-11T23:51:14.000Z

aws_interface/cloud/auth/delete_sessions.py

hubaimaster/aws-interface

162dd056546d58b6eb29afcae1c3c2d78e4309b2

[ "Apache-2.0" ]

10

2019-03-11T16:35:14.000Z

2019-10-23T08:03:54.000Z

from cloud.permission import Permission, NeedPermission # Define the input output format of the function. # This information is used when creating the *SDK*. info = { 'input_format': { 'session_ids': ['str'], }, 'output_format': { 'success': 'bool' }, 'description': 'Delete sessions' } @NeedPermission(Permission.Run.Auth.delete_sessions) def do(data, resource): body = {} params = data['params'] session_ids = params.get('session_ids') success = resource.db_delete_item_batch(session_ids) body['success'] = success return body

22.846154

56

0.6633

0

266

0.447811

0

222

0.373737

a137958aa6262c5d4af45fea5f852cfe4e0fb7c7

5,509

py

Python

plugin/autoWHUT.py

PPeanutButter/MediaServer

a6a0b3f424ca3fc4ea73d78db380ec3cc882bfd2

[ "MIT" ]

2

2021-09-23T15:09:25.000Z

2022-01-16T01:04:07.000Z

plugin/autoWHUT.py

PPeanutButter/MediaServer

a6a0b3f424ca3fc4ea73d78db380ec3cc882bfd2

[ "MIT" ]

1

2022-02-23T04:00:16.000Z

2022-02-23T04:10:06.000Z

plugin/autoWHUT.py

PPeanutButter/MediaServer

a6a0b3f424ca3fc4ea73d78db380ec3cc882bfd2

[ "MIT" ]

1

2021-09-23T15:09:26.000Z

# coding=<utf-8> import requests import re import socket import base64 import psutil import pywifi from pywifi import const import subprocess import os import time def get_host_ip(): try: s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) s.connect(('8.8.8.8', 80)) ip = s.getsockname()[0] finally: s.close() return ip def encrypt(password): password = base64.b64encode(password.encode('utf-8')) return password.decode('utf-8') def getNetIfAddr(): dic = psutil.net_if_addrs() mac = '' for adapter in dic: print(adapter) if adapter != 'wls1': continue snicList = dic[adapter] mac = '' ipv4 = '' ipv6 = '' for snic in snicList: if snic.family.name in {'AF_LINK', 'AF_PACKET'}: mac = snic.address elif snic.family.name == 'AF_INET': ipv4 = snic.address elif snic.family.name == 'AF_INET6': ipv6 = snic.address print('%s, %s, %s, %s' % (adapter, mac, ipv4, ipv6)) return mac def get_mac_address(): return getNetIfAddr().lower() class AutoWHUT: def get_param(self, username: str, password: str, cookies: str): header = { 'Origin': 'http://172.30.16.34', 'Referer': 'http://172.30.16.34/srun_portal_pc.php?ac_id=1&cmd=login&switchip=172.30.14.104&mac=84:ef:18' ':91:e5:5b&ip=' + get_host_ip() + '&essid=WHUT-WLAN6&apname=JB-JH-J4-0901-E&apgroup=WHUT-WLAN-Dual&url=http://www.gstatic.com' '/generate_204', 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) ' 'Chrome/70.0.3538.102 Safari/537.36 Edge/18.18362', 'Accept': '*/*', 'Accept-Language': 'zh-CN', 'Content-Type': 'application/x-www-form-urlencoded; charset=UTF-8', 'X-Requested-With': 'XMLHttpRequest', 'Accept-Encoding': 'gzip, deflate', 'Host': '172.30.16.34', 'Connection': 'Keep-Alive', 'Pragma': 'no-cache', 'Cookie': cookies } data = 'action=login&username=&password=&ac_id=64&user_ip=&nas_ip=&user_mac=&save_me=1&ajax=1' data = re.sub("username=.*?&", "username=" + username + '&', data) data = re.sub("password=.*?&", "password={B}" + encrypt(password) + '&', data) data = re.sub("user_ip=.*?&", "user_ip=" + get_host_ip() + '&', data) data = re.sub("user_mac=.*?&", "user_mac=" + get_mac_address() + '&', data) return header, data def sign_in(self): try: username = '' password = '' cookies = 'login=bQ0pOyR6IXU7PJaQQqRAcBPxGAvxAcrvEe0UJsVvdkTHxMBomR2HUS3oxriFtDiSt7XrDS' \ '%2BmurcIcGKHmgRZbb8fUGzw%2FUGvJFIjk0nAVIEwPGYVt7br7b5u1t4sMp' \ '%2BAfr4VZ5VcKPDr8eaBrOt2YRrH9Bdy6bogpY89dPj' \ '%2BzwrVuc4xmFUoWD8peECGHshewZRrIVvucbx652F2TRxF3VtHNL9H0fs5GjjmJjQMtecd; ' \ 'NSC_tsvo_4l_TH=ffffffffaf160e3a45525d5f4f58455e445a4a423660; ' \ 'login=bQ0pOyR6IXU7PJaQQqRAcBPxGAvxAcrvEe0UJsVvdkTHxMBomR2HUS3oxriFtDiSt7XrDS' \ '%2BmurcIcGKHmgRZbb8fUGzw%2FUGvJFIjk0nAVIEwPGYVt7br7b5u1t4sMp' \ '%2BAfr4VZ5VcKPDr8eaBrOt2YRrH9Bdy6bogpY89dPj' \ '%2BzwrVuc4xmFUoWD8peECGHshewZRrIVvucbx652F2TRxF3VtHNL9H0fs5GjjmJjQMtecd ' header, data = self.get_param(username, password, cookies) print(data) result = requests.post('http://172.30.16.34/include/auth_action.php', headers=header, data=data) print(result.text, '\n{}\n'.format('*' * 79), result.encoding) except BaseException as arg: print(arg) class WifiManager: def __init__(self): self.wifi = pywifi.PyWiFi() self.ifaces = self.wifi.interfaces()[1] self.autoWHUT = AutoWHUT() self.sleepTime = 1 def is_connected_wifi(self): return self.ifaces.status() == const.IFACE_CONNECTED def get_current_wifi(self): cmd = 'netsh wlan show interfaces' p = subprocess.Popen(cmd, stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True) ret = p.stdout.read() ret = ret.decode('gbk') index = ret.find("SSID") if index > 0: return ret[index:].split(':')[1].split('\r\n')[0].strip() else: return None def check_net(self): try: result = requests.post('http://www.baidu.com') return result.text.find("?cmd=redirect") == -1 except Exception: return False def auto_check(self): if self.is_connected_wifi(): if not self.check_net(): self.autoWHUT.sign_in() print("2s") self.sleepTime = 2 else: self.sleepTime = 60 print("60s") else: self.sleepTime = 4 print("no wifi") def start(self): while True: self.auto_check() time.sleep(self.sleepTime) if __name__ == '__main__': wifiManager = WifiManager() wifiManager.start()

34.43125

117

0.554547

4,252

0.771828

0

1,703

0.309131

a137f706cc16a7ddd946b13b277853a20e68de35

3,096

py

Python

active_subspaces/gradients.py

ftalbrecht/active_subspaces

64817a19db250e4b01bcd51055ad0f7d2a5665b8

[ "MIT" ]

51

2015-04-24T13:52:00.000Z

2022-02-16T13:30:39.000Z

active_subspaces/gradients.py

JIMMY-KSU/active_subspaces

64817a19db250e4b01bcd51055ad0f7d2a5665b8

[ "MIT" ]

10

2015-02-03T01:00:09.000Z

2022-03-06T07:48:46.000Z

active_subspaces/gradients.py

JIMMY-KSU/active_subspaces

64817a19db250e4b01bcd51055ad0f7d2a5665b8

[ "MIT" ]

44

2015-01-12T06:05:59.000Z

2022-02-02T18:53:34.000Z

"""Utilities for approximating gradients.""" import numpy as np from utils.misc import process_inputs from utils.simrunners import SimulationRunner def local_linear_gradients(X, f, p=None, weights=None): """Estimate a collection of gradients from input/output pairs. Given a set of input/output pairs, choose subsets of neighboring points and build a local linear model for each subset. The gradients of these local linear models comprise estimates of sampled gradients. Parameters ---------- X : ndarray M-by-m matrix that contains the m-dimensional inputs f : ndarray M-by-1 matrix that contains scalar outputs p : int, optional how many nearest neighbors to use when constructing the local linear model (default 1) weights : ndarray, optional M-by-1 matrix that contains the weights for each observation (default None) Returns ------- df : ndarray M-by-m matrix that contains estimated partial derivatives approximated by the local linear models Notes ----- If `p` is not specified, the default value is floor(1.7*m). """ X, M, m = process_inputs(X) if M<=m: raise Exception('Not enough samples for local linear models.') if p is None: p = int(np.minimum(np.floor(1.7*m), M)) elif not isinstance(p, int): raise TypeError('p must be an integer.') if p < m+1 or p > M: raise Exception('p must be between m+1 and M') if weights is None: weights = np.ones((M, 1)) / M MM = np.minimum(int(np.ceil(10*m*np.log(m))), M-1) df = np.zeros((MM, m)) for i in range(MM): ii = np.random.randint(M) x = X[ii,:] D2 = np.sum((X - x)**2, axis=1) ind = np.argsort(D2) ind = ind[D2 != 0] A = np.hstack((np.ones((p,1)), X[ind[:p],:])) * np.sqrt(weights[ii]) b = f[ind[:p]] * np.sqrt(weights[ii]) u = np.linalg.lstsq(A, b)[0] df[i,:] = u[1:].T return df def finite_difference_gradients(X, fun, h=1e-6): """Compute finite difference gradients with a given interface. Parameters ---------- X : ndarray M-by-m matrix that contains the points to estimate the gradients with finite differences fun : function function that returns the simulation's quantity of interest given inputs h : float, optional the finite difference step size (default 1e-6) Returns ------- df : ndarray M-by-m matrix that contains estimated partial derivatives approximated by finite differences """ X, M, m = process_inputs(X) # points to run simulations including the perturbed inputs XX = np.kron(np.ones((m+1, 1)),X) + \ h*np.kron(np.vstack((np.zeros((1, m)), np.eye(m))), np.ones((M, 1))) # run the simulation if isinstance(fun, SimulationRunner): F = fun.run(XX) else: F = SimulationRunner(fun).run(XX) df = (F[M:].reshape((m, M)).transpose() - F[:M]) / h return df.reshape((M,m))

31.591837

80

0.609173

0

1,737

0.561047

a1385e4aefd67a6e8363bc3fce53670aa1ea871f

6,861

py

Python

covidaid/tools/read_data.py

sabuj7177/CovidProject

b4b7bcfa5ace165520507f489dc74da7b695e2f0

[ "Apache-2.0" ]

null

covidaid/tools/read_data.py

sabuj7177/CovidProject

b4b7bcfa5ace165520507f489dc74da7b695e2f0

[ "Apache-2.0" ]

null

covidaid/tools/read_data.py

sabuj7177/CovidProject

b4b7bcfa5ace165520507f489dc74da7b695e2f0

[ "Apache-2.0" ]

null

# encoding: utf-8 """ Read images and corresponding labels. """ import torch from torch.utils.data import Dataset from PIL import Image import os import random class ChestXrayDataSetTest(Dataset): def __init__(self, image_list_file, transform=None, combine_pneumonia=False): """ Create the Data Loader. Since class 3 (Covid) has limited covidaid_data, dataset size will be accordingly at train time. Code is written in generic form to assume last class as the rare class Args: image_list_file: path to the file containing images with corresponding labels. transform: optional transform to be applied on a sample. combine_pneumonia: True for combining Baterial and Viral Pneumonias into one class """ self.NUM_CLASSES = 3 if combine_pneumonia else 4 # Set of images for each class image_names = [] with open(image_list_file, "r") as f: for line in f: items = line.split() image_name = items[0] label = int(items[1]) image_names.append((image_name, label)) self.image_names = image_names self.transform = transform def __getitem__(self, index): """ Args: index: the index of item Returns: image and its labels """ def __one_hot_encode(l): v = [0] * self.NUM_CLASSES v[l] = 1 return v image_name, label = self.image_names[index] label = __one_hot_encode(label) image = Image.open(image_name).convert('RGB') if self.transform is not None: image = self.transform(image) return image, torch.FloatTensor(label) def __len__(self): return len(self.image_names) class ChestXrayDataSet(Dataset): def __init__(self, image_list_file, transform=None, combine_pneumonia=False): """ Create the Data Loader. Since class 3 (Covid) has limited covidaid_data, dataset size will be accordingly at train time. Code is written in generic form to assume last class as the rare class Args: image_list_file: path to the file containing images with corresponding labels. transform: optional transform to be applied on a sample. combine_pneumonia: True for combining Baterial and Viral Pneumonias into one class """ self.NUM_CLASSES = 3 if combine_pneumonia else 4 # Set of images for each class image_names = [[] for _ in range(self.NUM_CLASSES)] with open(image_list_file, "r") as f: for line in f: items = line.split() image_name = items[0] label = int(items[1]) image_names[label].append(image_name) self.image_names = image_names self.transform = transform label_dist = [len(cnames) for cnames in image_names] # Number of images of each class desired self.num_covid = int(label_dist[-1]) if combine_pneumonia: covid_factor = 7.0 self.num_normal = int(self.num_covid * covid_factor) self.num_pneumonia = int(self.num_covid * covid_factor) self.total = self.num_covid + self.num_pneumonia + self.num_normal self.loss_weight_minus = torch.FloatTensor([self.num_normal, self.num_pneumonia, self.num_covid]).unsqueeze(0).cuda() / self.total self.loss_weight_plus = 1.0 - self.loss_weight_minus else: covid_factor = 5.0 self.num_normal = int(self.num_covid * covid_factor) self.num_viral = int(self.num_covid * covid_factor) self.num_bact = int(self.num_covid * covid_factor) self.total = self.num_covid + self.num_viral + self.num_bact + self.num_normal self.loss_weight_minus = torch.FloatTensor([self.num_normal, self.num_bact, self.num_viral, self.num_covid]).unsqueeze(0).cuda() / self.total self.loss_weight_plus = 1.0 - self.loss_weight_minus # print (self.loss_weight_plus, self.loss_weight_minus) if combine_pneumonia: self.partitions = [self.num_covid, self.num_covid + self.num_normal, self.num_covid + self.num_normal + self.num_pneumonia] else: self.partitions = [self.num_covid, self.num_covid + self.num_normal, self.num_covid + self.num_normal + self.num_bact, self.num_covid + self.num_normal + self.num_bact + self.num_viral] assert len(self.partitions) == self.NUM_CLASSES def __getitem__(self, index): """ Args: index: the index of item Returns: image and its labels """ def __one_hot_encode(l): v = [0] * self.NUM_CLASSES v[l] = 1 return v image_name = None # print (index, self.partitions, len(self), sum([len(cnames) for cnames in self.image_names])) if index < self.partitions[0]: # Return a covid image data_idx = index image_name = self.image_names[self.NUM_CLASSES - 1][data_idx] label = __one_hot_encode(self.NUM_CLASSES - 1) else: # Return non-covid image for l in range(1, self.NUM_CLASSES): if index < self.partitions[l]: class_idx = l - 1 label = __one_hot_encode(class_idx) # Return a random image image_name = random.choice(self.image_names[class_idx]) break assert image_name is not None image = Image.open(image_name).convert('RGB') if self.transform is not None: image = self.transform(image) return image, torch.FloatTensor(label) def __len__(self): return self.partitions[-1] def loss(self, output, target): """ Binary weighted cross-entropy loss for each class """ weight_plus = torch.autograd.Variable(self.loss_weight_plus.repeat(1, target.size(0)).view(-1, self.loss_weight_plus.size(1)).cuda()) weight_neg = torch.autograd.Variable(self.loss_weight_minus.repeat(1, target.size(0)).view(-1, self.loss_weight_minus.size(1)).cuda()) loss = output pmask = (target >= 0.5).data nmask = (target < 0.5).data epsilon = 1e-15 loss[pmask] = (loss[pmask] + epsilon).log() * weight_plus[pmask] loss[nmask] = (1-loss[nmask] + epsilon).log() * weight_plus[nmask] loss = -loss.sum() return loss

36.887097

153

0.594957

6,694

0.97566

0

1,737

0.25317

a13861d4cfee522305c9e242f88c3b1859a889ba

7,996

py

Python

helper/evaluator.py

manipopopo/TC-ResNet

7dff6f4f865f1e63ff705d8e0267cf3b9a0d70a3

[ "Apache-2.0" ]

185

2019-04-06T12:54:25.000Z

2022-03-24T12:06:59.000Z

helper/evaluator.py

manipopopo/TC-ResNet

7dff6f4f865f1e63ff705d8e0267cf3b9a0d70a3

[ "Apache-2.0" ]

23

2019-05-15T09:19:01.000Z

2022-02-10T00:07:03.000Z

helper/evaluator.py

manipopopo/TC-ResNet

7dff6f4f865f1e63ff705d8e0267cf3b9a0d70a3

[ "Apache-2.0" ]

61

2019-04-06T12:33:46.000Z

2022-03-01T06:41:53.000Z

import csv import sys from pathlib import Path from abc import abstractmethod import numpy as np import tensorflow as tf from tqdm import tqdm import common.tf_utils as tf_utils import metrics.manager as metric_manager from common.model_loader import Ckpt from common.utils import format_text from common.utils import get_logger from helper.base import AudioBase from metrics.summaries import BaseSummaries from metrics.summaries import Summaries class Evaluator(object): def __init__(self, model, session, args, dataset, dataset_name, name): self.log = get_logger(name) self.model = model self.session = session self.args = args self.dataset = dataset self.dataset_name = dataset_name if Path(self.args.checkpoint_path).is_dir(): latest_checkpoint = tf.train.latest_checkpoint(self.args.checkpoint_path) if latest_checkpoint is not None: self.args.checkpoint_path = latest_checkpoint self.log.info(f"Get latest checkpoint and update to it: {self.args.checkpoint_path}") self.watch_path = self._build_watch_path() self.session.run(tf.global_variables_initializer()) self.session.run(tf.local_variables_initializer()) self.ckpt_loader = Ckpt( session=session, include_scopes=args.checkpoint_include_scopes, exclude_scopes=args.checkpoint_exclude_scopes, ignore_missing_vars=args.ignore_missing_vars, use_ema=self.args.use_ema, ema_decay=self.args.ema_decay, ) @abstractmethod def setup_metric_manager(self): raise NotImplementedError @abstractmethod def setup_metric_ops(self): raise NotImplementedError @abstractmethod def build_non_tensor_data_from_eval_dict(self, eval_dict, **kwargs): raise NotImplementedError @abstractmethod def setup_dataset_iterator(self): raise NotImplementedError def _build_watch_path(self): if Path(self.args.checkpoint_path).is_dir(): return Path(self.args.checkpoint_path) else: return Path(self.args.checkpoint_path).parent def build_evaluation_step(self, checkpoint_path): if "-" in checkpoint_path and checkpoint_path.split("-")[-1].isdigit(): return int(checkpoint_path.split("-")[-1]) else: return 0 def build_checkpoint_paths(self, checkpoint_path): checkpoint_glob = Path(checkpoint_path + "*") checkpoint_path = Path(checkpoint_path) return checkpoint_glob, checkpoint_path def build_miscellaneous_path(self, name): target_dir = self.watch_path / "miscellaneous" / self.dataset_name / name if not target_dir.exists(): target_dir.mkdir(parents=True) return target_dir def setup_best_keeper(self): metric_with_modes = self.metric_manager.get_best_keep_metric_with_modes() self.log.debug(metric_with_modes) self.best_keeper = tf_utils.BestKeeper( metric_with_modes, self.dataset_name, self.watch_path, self.log, ) def evaluate_once(self, checkpoint_path): self.log.info("Evaluation started") self.setup_dataset_iterator() self.ckpt_loader.load(checkpoint_path) step = self.build_evaluation_step(checkpoint_path) checkpoint_glob, checkpoint_path = self.build_checkpoint_paths(checkpoint_path) self.session.run(tf.local_variables_initializer()) eval_metric_dict = self.run_evaluation(step, is_training=False) best_keep_metric_dict = self.metric_manager.filter_best_keep_metric(eval_metric_dict) is_keep, metrics_keep = self.best_keeper.monitor(self.dataset_name, best_keep_metric_dict) if self.args.save_best_keeper: meta_info = { "step": step, "model_size": self.model.total_params, } self.best_keeper.remove_old_best(self.dataset_name, metrics_keep) self.best_keeper.save_best(self.dataset_name, metrics_keep, checkpoint_glob) self.best_keeper.remove_temp_dir() self.best_keeper.save_scores(self.dataset_name, metrics_keep, best_keep_metric_dict, meta_info) self.metric_manager.write_evaluation_summaries(step=step, collection_keys=[BaseSummaries.KEY_TYPES.DEFAULT]) self.metric_manager.log_metrics(step=step) self.log.info("Evaluation finished") if step >= self.args.max_step_from_restore: self.log.info("Evaluation stopped") sys.exit() def build_train_directory(self): if Path(self.args.checkpoint_path).is_dir(): return str(self.args.checkpoint_path) else: return str(Path(self.args.checkpoint_path).parent) @staticmethod def add_arguments(parser): g = parser.add_argument_group("(Evaluator) arguments") g.add_argument("--valid_type", default="loop", type=str, choices=["loop", "once"]) g.add_argument("--max_outputs", default=5, type=int) g.add_argument("--maximum_num_labels_for_metric", default=10, type=int, help="Maximum number of labels for using class-specific metrics(e.g. precision/recall/f1score)") g.add_argument("--no-save_best_keeper", dest="save_best_keeper", action="store_false") g.add_argument("--save_best_keeper", dest="save_best_keeper", action="store_true") g.set_defaults(save_best_keeper=True) g.add_argument("--no-flatten_output", dest="flatten_output", action="store_false") g.add_argument("--flatten_output", dest="flatten_output", action="store_true") g.set_defaults(flatten_output=False) g.add_argument("--max_step_from_restore", default=1e20, type=int) class SingleLabelAudioEvaluator(Evaluator, AudioBase): def __init__(self, model, session, args, dataset, dataset_name): super().__init__(model, session, args, dataset, dataset_name, "SingleLabelAudioEvaluator") self.setup_dataset_related_attr() self.setup_metric_manager() self.setup_metric_ops() self.setup_best_keeper() def setup_dataset_related_attr(self): assert len(self.dataset.label_names) == self.args.num_classes self.use_class_metrics = len(self.dataset.label_names) < self.args.maximum_num_labels_for_metric def setup_metric_manager(self): self.metric_manager = metric_manager.AudioMetricManager( is_training=False, use_class_metrics=self.use_class_metrics, exclude_metric_names=self.args.exclude_metric_names, summary=Summaries( session=self.session, train_dir=self.build_train_directory(), is_training=False, base_name=self.dataset.dataset_split_name, max_summary_outputs=self.args.max_summary_outputs, ), ) def setup_metric_ops(self): losses = self.build_basic_loss_ops() self.metric_tf_op = self.metric_manager.build_metric_ops({ "dataset_split_name": self.dataset_name, "label_names": self.dataset.label_names, "losses": losses, "learning_rate": None, "wavs": self.model.audio_original, }) def build_non_tensor_data_from_eval_dict(self, eval_dict, **kwargs): return { "dataset_split_name": self.dataset.dataset_split_name, "label_names": self.dataset.label_names, "predictions_onehot": eval_dict["predictions_onehot"], "labels_onehot": eval_dict["labels_onehot"], } def setup_dataset_iterator(self): self.dataset.setup_iterator( self.session, self.dataset.placeholders, self.dataset.data, )

37.539906

119

0.674212

7,541

0.943097

0

1,368

0.171086

0

786

0.098299

a139c61e93bd3d976aaa5d706da3d269f7d52385

7,483

py

Python

src/sync.py

neybar/icloud-drive-docker

c7e59400c01b304c0f8ed7fd0b3ea2a623623b2e

[ "BSD-3-Clause" ]

null

src/sync.py

neybar/icloud-drive-docker

c7e59400c01b304c0f8ed7fd0b3ea2a623623b2e

[ "BSD-3-Clause" ]

null

src/sync.py

neybar/icloud-drive-docker

c7e59400c01b304c0f8ed7fd0b3ea2a623623b2e

[ "BSD-3-Clause" ]

null

__author__ = 'Mandar Patil (mandarons@pm.me)' import datetime import os import re import time from pathlib import Path from shutil import copyfileobj, rmtree from pyicloud import PyiCloudService, utils, exceptions from src import config_parser from src import notify def wanted_file(filters, file_path, verbose=False): if not file_path: return False if not filters or len(filters) == 0: return True for file_extension in filters: if re.search(f'{file_extension}$', file_path, re.IGNORECASE): return True if verbose: print(f'Skipping the unwanted file {file_path}') return False def wanted_folder(filters, root, folder_path, verbose=False): if not filters or not folder_path or not root or len(filters) == 0: # Nothing to filter, return True return True # Something to filter folder_path = Path(folder_path) for folder in filters: child_path = Path(os.path.join(os.path.abspath(root), folder.removeprefix('/').removesuffix('/'))) if folder_path in child_path.parents or child_path in folder_path.parents or folder_path == child_path: return True return False def wanted_parent_folder(filters, root, folder_path, verbose=False): if not filters or not folder_path or not root or len(filters) == 0: return True folder_path = Path(folder_path) for folder in filters: child_path = Path(os.path.join(os.path.abspath(root), folder.removeprefix('/').removesuffix('/'))) if child_path in folder_path.parents or folder_path == child_path: return True return False def process_folder(item, destination_path, filters, root, verbose=False): if not (item and destination_path and filters and root): return None new_directory = os.path.join(destination_path, item.name) if not wanted_folder(filters=filters, folder_path=new_directory, root=root, verbose=verbose): if verbose: print(f'Skipping the unwanted folder {new_directory}...') return None os.makedirs(new_directory, exist_ok=True) return new_directory def file_exists(item, local_file, verbose=False): if item and local_file and os.path.isfile(local_file): local_file_modified_time = int(os.path.getmtime(local_file)) remote_file_modified_time = int(item.date_modified.timestamp()) local_file_size = os.path.getsize(local_file) remote_file_size = item.size if local_file_modified_time == remote_file_modified_time and local_file_size == remote_file_size: if verbose: print(f'No changes detected. Skipping the file {local_file}') return True return False def download_file(item, local_file, verbose=False): if not (item and local_file): return False if verbose: print(f'Downloading {local_file} ...') try: with item.open(stream=True) as response: with open(local_file, 'wb') as file_out: copyfileobj(response.raw, file_out) item_modified_time = time.mktime(item.date_modified.timetuple()) os.utime(local_file, (item_modified_time, item_modified_time)) except (exceptions.PyiCloudAPIResponseException, FileNotFoundError, Exception) as e: print(f'Failed to download {local_file}: {str(e)}') return False return True def process_file(item, destination_path, filters, files, verbose=False): if not (item and destination_path and files is not None): return False local_file = os.path.join(destination_path, item.name) if not wanted_file(filters=filters, file_path=local_file, verbose=verbose): return False files.add(local_file) if file_exists(item=item, local_file=local_file, verbose=verbose): return False download_file(item=item, local_file=local_file, verbose=verbose) return True def remove_obsolete(destination_path, files, verbose=False): removed_paths = set() if not (destination_path and files is not None): return removed_paths for path in Path(destination_path).rglob('*'): local_file = str(path.absolute()) if local_file not in files: if verbose: print(f'Removing {local_file}') if path.is_file(): path.unlink(missing_ok=True) removed_paths.add(local_file) elif path.is_dir(): rmtree(local_file) removed_paths.add(local_file) return removed_paths def sync_directory(drive, destination_path, items, root, top=True, filters=None, remove=False, verbose=False): files = set() if drive and destination_path and items and root: for i in items: item = drive[i] if item.type == 'folder': new_folder = process_folder(item=item, destination_path=destination_path, filters=filters['folders'] if 'folders' in filters else None, root=root, verbose=verbose) if not new_folder: continue files.add(new_folder) files.update(sync_directory(drive=item, destination_path=new_folder, items=item.dir(), root=root, top=False, filters=filters, verbose=verbose)) elif item.type == 'file': if wanted_parent_folder(filters=filters['folders'], root=root, folder_path=destination_path, verbose=verbose): process_file(item=item, destination_path=destination_path, filters=filters['file_extensions'] if 'file_extensions' in filters else None, files=files, verbose=verbose) if top and remove: remove_obsolete(destination_path=destination_path, files=files, verbose=verbose) return files def sync_drive(): last_send = None while True: config = config_parser.read_config() verbose = config_parser.get_verbose(config=config) username = config_parser.get_username(config=config) destination_path = config_parser.prepare_destination(config=config) if username and destination_path: try: api = PyiCloudService(apple_id=username, password=utils.get_password_from_keyring(username=username)) if not api.requires_2sa: sync_directory(drive=api.drive, destination_path=destination_path, root=destination_path, items=api.drive.dir(), top=True, filters=config['filters'], remove=config_parser.get_remove_obsolete(config=config), verbose=verbose) else: print('Error: 2FA is required. Please log in.') last_send = notify.send(config, last_send) except exceptions.PyiCloudNoStoredPasswordAvailableException: print('password is not stored in keyring. Please save the password in keyring.') sleep_for = config_parser.get_sync_interval(config=config) next_sync = (datetime.datetime.now() + datetime.timedelta(minutes=sleep_for)).strftime('%l:%M%p %Z on %b %d, %Y') print(f'Resyncing at {next_sync} ...') if sleep_for < 0: break time.sleep(sleep_for)

42.276836

117

0.647735

0

621

0.082988

a13a98235b9b2f72025d1bf03dbd61547e3c8d9f

2,163

py

Python

sphinx-sources/Examples/Interference/MultiSlit.py

jccmak/lightpipes

1a296fe08bdd97fc9a0e11f92bab25c85f68e57d

[ "BSD-3-Clause" ]

null

sphinx-sources/Examples/Interference/MultiSlit.py

jccmak/lightpipes

1a296fe08bdd97fc9a0e11f92bab25c85f68e57d

[ "BSD-3-Clause" ]

null

sphinx-sources/Examples/Interference/MultiSlit.py

jccmak/lightpipes

1a296fe08bdd97fc9a0e11f92bab25c85f68e57d

[ "BSD-3-Clause" ]

null

#! python3 import numpy as np import matplotlib.pyplot as plt from LightPipes import * """ MultiSlit.py Demonstrates the RowOfFields command. Two wavelengths are used to show the principles of a grating. cc Fred van Goor, June 2020. """ wavelength=1000*nm Dlambda=150*nm size=11*mm N=2000 N2=int(N/2) SlitSeparation=0.5*mm f=30*cm Nslits=20 SlitHeight=5*mm SlitWidth=0.1*mm Nheight=int(SlitHeight/size*N) Nwidth=int(SlitWidth/size*N) Fslit=np.ones((Nheight,Nwidth)) F1=Begin(size,wavelength,N) F1=RowOfFields(F1,Fslit,Nslits,SlitSeparation) Islits=Intensity(F1) F1=Lens(F1,f) F1=Forvard(F1,f) F11=Interpol(F1,size,N,magnif=4) Iscreen1=Intensity(F11) F2=Begin(size,wavelength+Dlambda,N) F2=RowOfFields(F2,Fslit,Nslits,SlitSeparation) F2=Lens(F2,f) F2=Forvard(F2,f) F22=Interpol(F2,size,N,magnif=4) Iscreen2=Intensity(F22) X=np.arange(N) X=(X/N-1/2)*size/mm s= r'LightPipes for Python,' + '\n' +\ r'MultiSlit.py'+ '\n\n'\ r'size = {:4.2f} mm'.format(size/mm) + '\n' +\ r'$\lambda$ = {:4.2f} nm'.format(wavelength/nm) + '\n' +\ r'$\Delta\lambda$ = {:4.2f} nm'.format(Dlambda/nm) + '\n' +\ r'N = {:d}'.format(N) + '\n' +\ r'width of the slits: {:4.2f} mm'.format(SlitWidth/mm) + '\n' +\ r'height of the slits: {:4.2f} mm'.format(SlitHeight/mm) + '\n' +\ r'separation of the slits: {:4.2f} mm'.format(SlitSeparation/mm) + '\n' +\ r'number of slits: {:d}'.format(Nslits) + '\n' +\ r'focal length lens: {:4.2f} cm'.format(f/cm) + '\n\n' +\ r'${\copyright}$ Fred van Goor, May 2020' fig=plt.figure(figsize=(10,6)) ax1 = fig.add_subplot(221) ax2 = fig.add_subplot(222);#ax2.set_ylim(bottom=900,top=1100) ax3 = fig.add_subplot(223) ax4 = fig.add_subplot(224) ax1.imshow(Islits,cmap='gray',aspect='equal');ax1.axis('off'); ax1.set_title('Screen with slits') ax2.imshow(Iscreen1+Iscreen2,cmap='jet',aspect='equal');ax2.axis('off'); ax2.set_title('Intensity distribution at the focus of the lens') #ax2.margins(x=0, y=-0.45) ax3.plot(X,(Iscreen1+Iscreen2)[N2]); ax3.set_xlabel('x [mm]'); ax3.set_ylabel('Intensity [a.u.]'); ax3.set_title('Cross section of intensity at the focus') ax4.text(0,0,s); ax4.axis('off') plt.show()

31.808824

155

0.680536

0

793

0.36662

a13baec342fa639fe6142ecd977281a346771177

389

py

Python

genshimacro/__init__.py

trac-hacks/trac-GenshiMacro

d9da1a50f6d73904fdda2e9e7cbc4c056b929267

[ "BSD-3-Clause" ]

1

2015-02-19T21:08:53.000Z

genshimacro/__init__.py

ejucovy/trac-GenshiMacro

d9da1a50f6d73904fdda2e9e7cbc4c056b929267

[ "BSD-3-Clause" ]

null

genshimacro/__init__.py

ejucovy/trac-GenshiMacro

d9da1a50f6d73904fdda2e9e7cbc4c056b929267

[ "BSD-3-Clause" ]

null

from genshi.template import MarkupTemplate from trac.core import * from trac.web.chrome import Chrome from trac.wiki.macros import WikiMacroBase class GenshiMacro(WikiMacroBase): def expand_macro(self, formatter, name, text, args): template = MarkupTemplate(text) chrome = Chrome(self.env) return template.generate(**chrome.populate_data(formatter.req, {}))

29.923077

75

0.742931

241

0.619537

0

a13d6b6264ad2abf3168edf6c36418b077a9e067

2,110

py

Python

scripts/WIPS2015/WIPS_anydiag_time.py

eclee25/flu-SDI-exploratory-age

2f5a4d97b84d2116e179e85fe334edf4556aa946

[ "MIT" ]

3

2018-03-29T23:02:43.000Z

2020-08-10T12:01:50.000Z

scripts/WIPS2015/WIPS_anydiag_time.py

eclee25/flu-SDI-exploratory-age

2f5a4d97b84d2116e179e85fe334edf4556aa946

[ "MIT" ]

null

scripts/WIPS2015/WIPS_anydiag_time.py

eclee25/flu-SDI-exploratory-age

2f5a4d97b84d2116e179e85fe334edf4556aa946

[ "MIT" ]

null

#!/usr/bin/python ############################################## ###Python template ###Author: Elizabeth Lee ###Date: 10/11/14 ###Function: Any diagnosis per 100,000 population vs. week number for flu weeks (wks 40-20). Population size is from the calendar year of the week of calculation. ###Import data: SQL_export/anydiag_outpatient_allweeks.csv ### branch from v2/Supp_anydiag_time.py ###Command Line: python WIPS_anydiag_time.py ############################################## ### notes ### # Incidence per 100,000 is normalized by total population by second calendar year of the flu season ### packages/modules ### import csv import matplotlib.pyplot as plt ## local modules ## import functions_v5 as fxn ### data structures ### ### functions ### ### data files ### anydiagin = open('/home/elee/Dropbox/Elizabeth_Bansal_Lab/SDI_Data/explore/SQL_export/anydiag_allweeks_outpatient.csv','r') anydiagin.readline() # rm header anydiag = csv.reader(anydiagin, delimiter=',') ### called/local plotting parameters ### ps = fxn.pseasons fw = fxn.gp_fluweeks sl = fxn.gp_seasonlabels colvec = fxn.gp_colors wklab = fxn.gp_weeklabels fs = 24 fssml = 16 ### program ### # dict_wk[week] = seasonnum, dict_any[week] = visits per 100,000 in US population in calendar year of week,d_any53ls[seasonnum] = [anydiag wk 40 per 100000, anydiag wk 41 per 100000,...] d_wk, d_any, d_any53ls = fxn.week_anydiag_processing(anydiag) # plot values for s in ps: plt.plot(xrange(53), d_any53ls[s], marker = fxn.gp_marker, color = colvec[s-2], label = sl[s-2], linewidth = fxn.gp_linewidth) plt.fill([7, 8, 8, 7], [0, 0, 4000, 4000], facecolor='grey', alpha=0.4) plt.fill([12, 14, 14, 12], [0, 0, 4000, 4000], facecolor='grey', alpha=0.4) plt.xlim([0, fw-1]) plt.xticks(range(53)[::5], wklab[::5]) plt.ylim([0, 4000]) plt.xlabel('Week Number', fontsize=fs) plt.ylabel('Outpatient Visit per 100,000', fontsize=fs) plt.legend(loc='upper right') plt.savefig('/home/elee/Dropbox/Department/Presentations/2015_WIPS/Figures/anydiag_time.png', transparent=False, bbox_inches='tight', pad_inches=0) plt.close() # plt.show()

32.461538

186

0.691469

0

1,223

0.579621

a13d78de55aa35e5195b6d00dd9af4b319aa1688

5,290

py

Python

misc/src/scheduler_plugin.py

hivesolutions/colony_plugins

cfd8fb2ac58037e01002966704b8a642feb37895

[ "Apache-1.1" ]

1

2016-10-30T09:51:06.000Z

misc/src/scheduler_plugin.py

hivesolutions/colony_plugins

cfd8fb2ac58037e01002966704b8a642feb37895

[ "Apache-1.1" ]

1

2015-12-29T18:51:07.000Z

misc/src/scheduler_plugin.py

hivesolutions/colony_plugins

cfd8fb2ac58037e01002966704b8a642feb37895

[ "Apache-1.1" ]

1

2018-01-26T12:54:13.000Z

#!/usr/bin/python # -*- coding: utf-8 -*- # Hive Colony Framework # Copyright (c) 2008-2020 Hive Solutions Lda. # # This file is part of Hive Colony Framework. # # Hive Colony Framework is free software: you can redistribute it and/or modify # it under the terms of the Apache License as published by the Apache # Foundation, either version 2.0 of the License, or (at your option) any # later version. # # Hive Colony Framework 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 # Apache License for more details. # # You should have received a copy of the Apache License along with # Hive Colony Framework. If not, see <http://www.apache.org/licenses/>. __author__ = "João Magalhães <joamag@hive.pt>" """ The author(s) of the module """ __version__ = "1.0.0" """ The version of the module """ __revision__ = "$LastChangedRevision$" """ The revision number of the module """ __date__ = "$LastChangedDate$" """ The last change date of the module """ __copyright__ = "Copyright (c) 2008-2020 Hive Solutions Lda." """ The copyright for the module """ __license__ = "Apache License, Version 2.0" """ The license for the module """ import colony class SchedulerPlugin(colony.Plugin): """ The main class for the Scheduler plugin. """ id = "pt.hive.colony.plugins.misc.scheduler" name = "Scheduler" description = "A plugin to manage the scheduling of tasks" version = "1.0.0" author = "Hive Solutions Lda. <development@hive.pt>" platforms = [ colony.CPYTHON_ENVIRONMENT ] capabilities = [ "main", "scheduler", "console_command_extension" ] dependencies = [ colony.PluginDependency("pt.hive.colony.plugins.misc.guid"), colony.PluginDependency("pt.hive.colony.plugins.console") ] main_modules = [ "scheduler_c" ] def load_plugin(self): colony.Plugin.load_plugin(self) import scheduler_c self.system = scheduler_c.Scheduler(self) self.console = scheduler_c.ConsoleScheduler(self) self.release_ready_semaphore() def end_load_plugin(self): colony.Plugin.end_load_plugin(self) self.system.load_scheduler() def unload_plugin(self): colony.Plugin.unload_plugin(self) self.system.unload_scheduler() self.release_ready_semaphore() def end_unload_plugin(self): colony.Plugin.end_unload_plugin(self) self.release_ready_semaphore() @colony.set_configuration_property def set_configuration_property(self, property_name, property): colony.Plugin.set_configuration_property(self, property_name, property) @colony.unset_configuration_property def unset_configuration_property(self, property_name): colony.Plugin.unset_configuration_property(self, property_name) def get_console_extension_name(self): return self.console.get_console_extension_name() def get_commands_map(self): return self.console.get_commands_map() def register_task(self, task, time): return self.system.register_task(task, time) def register_task_absolute(self, task, absolute_time): return self.system.register_task_absolute(task, absolute_time) def register_task_date_time(self, task, date_time): return self.system.register_task_date_time(task, date_time) def register_task_date_time_absolute(self, task, absolute_date_time): return self.system.register_task_date_time_absolute(task, absolute_date_time) def register_task_recursive(self, task, time, recursion_list): return self.system.register_task_recursive(task, time, recursion_list) def register_task_absolute_recursive(self, task, absolute_time, recursion_list): return self.system.register_task_absolute_recursive(task, absolute_time, recursion_list) def register_task_date_time_recursive(self, task, date_time, recursion_list): return self.system.register_task_date_time_recursive(task, date_time, recursion_list) def register_task_date_time_absolute_recursive(self, task, absolute_date_time, recursion_list): return self.system.register_task_date_time_absolute_recursive(task, absolute_date_time, recursion_list) def unregister_task(self, task): return self.system.unregister_task(task) def get_task_class(self): """ Retrieves the class that represents a task in the current scope. :rtype: Class :return: The task class for the current scope. """ return self.system.get_task_class() @colony.set_configuration_property_method("startup_configuration") def startup_configuration_set_configuration_property(self, property_name, property): self.system.set_startup_configuration_property(property) @colony.unset_configuration_property_method("startup_configuration") def startup_configuration_unset_configuration_property(self, property_name): self.system.unset_startup_configuration_property()

35.986395

112

0.709074

3,969

0.75

0

784

0.148148

0

1,713

0.323696

a13e0be2220cebb57badaee86dd77ccad221768a

3,458

py

Python

source/vistas/ui/controls/gl_camera.py

VISTAS-IVES/pyvistas

2de1541c0fb40ccbac4014af758ff329ba0677b1

[ "BSD-3-Clause" ]

1

2017-08-26T20:18:38.000Z

source/vistas/ui/controls/gl_camera.py

VISTAS-IVES/pyvistas

2de1541c0fb40ccbac4014af758ff329ba0677b1

[ "BSD-3-Clause" ]

89

2017-06-10T21:03:16.000Z

2022-03-11T23:19:56.000Z

source/vistas/ui/controls/gl_camera.py

VISTAS-IVES/pyvistas

2de1541c0fb40ccbac4014af758ff329ba0677b1

[ "BSD-3-Clause" ]

1

2019-03-05T21:44:29.000Z

import os import wx from vistas.core.graphics.camera_interactor import * from vistas.core.graphics.overlay import BasicOverlayButton from vistas.core.paths import get_resources_directory from vistas.ui.events import CameraChangedEvent from vistas.ui.utils import get_main_window class GLCameraControls(wx.EvtHandler): """ Event handler for controlling the camera interaction for a GLCanvas. Allows a user to switch between different camera interaction modes. """ SPHERE = 0 FREELOOK = 1 PAN = 2 def __init__(self, gl_canvas, camera): super().__init__() self.camera = camera self.canvas = gl_canvas self.visible = False self.sphere_button = BasicOverlayButton( os.path.join(get_resources_directory(), 'images', 'glyphicons-372-global.png'), (0, 0) ) self.sphere_button.opaque = True self.freelook_button = BasicOverlayButton( os.path.join(get_resources_directory(), 'images', 'glyphicons-52-eye-open.png'), (0, 0) ) self.pan_button = BasicOverlayButton( os.path.join(get_resources_directory(), 'images', 'glyphicons-187-move.png'), (0, 0) ) self.camera_interactor = SphereInteractor(camera=self.camera) self.reposition() self.show() self.canvas.Bind(wx.EVT_SIZE, lambda event: self.reposition()) self.sphere_button.Bind(wx.EVT_BUTTON, lambda event: self.set_type(self.SPHERE)) self.freelook_button.Bind(wx.EVT_BUTTON, lambda event: self.set_type(self.FREELOOK)) self.pan_button.Bind(wx.EVT_BUTTON, lambda event: self.set_type(self.PAN)) def reset(self): self.set_type(self.camera_interactor.camera_type, False) def reposition(self): width = self.canvas.GetSize().width y_offset = 10 for button in (self.sphere_button, self.freelook_button, self.pan_button): button.position = (width - button.size[0], y_offset) y_offset += 5 + button.size[1] self.canvas.Refresh() def show(self): if not self.visible: self.canvas.overlay.add_button(self.sphere_button) self.canvas.overlay.add_button(self.freelook_button) self.canvas.overlay.add_button(self.pan_button) self.visible = True def hide(self): if self.visible: self.canvas.overlay.remove_button(self.sphere_button) self.canvas.overlay.remove_button(self.freelook_button) self.canvas.overlay.remove_button(self.pan_button) self.visible = False def set_type(self, interactor, send_event=True): self.sphere_button.opaque = False self.freelook_button.opaque = False self.pan_button.opaque = False if interactor in (self.SPHERE, CameraInteractor.SPHERE): self.sphere_button.opaque = True self.camera_interactor = SphereInteractor(self.camera, False) elif interactor in (self.FREELOOK, CameraInteractor.FREELOOK): self.freelook_button.opaque = True self.camera_interactor = FreelookInteractor(self.camera, False) elif interactor in (self.PAN, CameraInteractor.PAN): self.pan_button.opaque = True self.camera_interactor = PanInteractor(self.camera, False) self.canvas.Refresh() if send_event: wx.PostEvent(get_main_window(), CameraChangedEvent())

34.237624

114

0.669751

3,174

0.917872

0

260

0.075188

a13f0a11b4555fcfbf9c924b7e7de9f674331ec4

8,678

py

Python

src/_sever_qt4.py

Joy917/fast-transfer

dfbcf5c4239da3d550b721500dff05fb6d40b756

[ "MIT" ]

null

src/_sever_qt4.py

Joy917/fast-transfer

dfbcf5c4239da3d550b721500dff05fb6d40b756

[ "MIT" ]

null

src/_sever_qt4.py

Joy917/fast-transfer

dfbcf5c4239da3d550b721500dff05fb6d40b756

[ "MIT" ]

null

# -*- coding: utf-8 -*- # Form implementation generated from reading ui file 'D:\SVNzhangy\fast-transfer\src\_sever.ui' # # Created by: PyQt4 UI code generator 4.11.4 # # WARNING! All changes made in this file will be lost! from PySide import QtCore, QtGui try: _fromUtf8 = QtCore.QString.fromUtf8 except AttributeError: def _fromUtf8(s): return s try: _encoding = QtGui.QApplication.UnicodeUTF8 def _translate(context, text, disambig): return QtGui.QApplication.translate(context, text, disambig, _encoding) except AttributeError: def _translate(context, text, disambig): return QtGui.QApplication.translate(context, text, disambig) class Ui_Form(object): def setupUi(self, Form): Form.setObjectName(_fromUtf8("Form")) Form.resize(798, 732) self.gridLayout = QtGui.QGridLayout(Form) self.gridLayout.setObjectName(_fromUtf8("gridLayout")) self.groupBox_2 = QtGui.QGroupBox(Form) self.groupBox_2.setObjectName(_fromUtf8("groupBox_2")) self.verticalLayout_2 = QtGui.QVBoxLayout(self.groupBox_2) self.verticalLayout_2.setObjectName(_fromUtf8("verticalLayout_2")) self.horizontalLayout = QtGui.QHBoxLayout() self.horizontalLayout.setObjectName(_fromUtf8("horizontalLayout")) spacerItem = QtGui.QSpacerItem(20, 20, QtGui.QSizePolicy.Minimum, QtGui.QSizePolicy.Minimum) self.horizontalLayout.addItem(spacerItem) self.checkBox_time = QtGui.QCheckBox(self.groupBox_2) self.checkBox_time.setObjectName(_fromUtf8("checkBox_time")) self.horizontalLayout.addWidget(self.checkBox_time) self.dateTimeEdit_start = QtGui.QDateTimeEdit(self.groupBox_2) self.dateTimeEdit_start.setDateTime(QtCore.QDateTime(QtCore.QDate(2017, 1, 1), QtCore.QTime(0, 0, 0))) self.dateTimeEdit_start.setCalendarPopup(True) self.dateTimeEdit_start.setObjectName(_fromUtf8("dateTimeEdit_start")) self.horizontalLayout.addWidget(self.dateTimeEdit_start) self.label_2 = QtGui.QLabel(self.groupBox_2) self.label_2.setObjectName(_fromUtf8("label_2")) self.horizontalLayout.addWidget(self.label_2) self.dateTimeEdit_end = QtGui.QDateTimeEdit(self.groupBox_2) self.dateTimeEdit_end.setDateTime(QtCore.QDateTime(QtCore.QDate(2018, 1, 1), QtCore.QTime(0, 0, 0))) self.dateTimeEdit_end.setCalendarPopup(True) self.dateTimeEdit_end.setObjectName(_fromUtf8("dateTimeEdit_end")) self.horizontalLayout.addWidget(self.dateTimeEdit_end) spacerItem1 = QtGui.QSpacerItem(40, 20, QtGui.QSizePolicy.Expanding, QtGui.QSizePolicy.Minimum) self.horizontalLayout.addItem(spacerItem1) self.verticalLayout_2.addLayout(self.horizontalLayout) self.horizontalLayout_3 = QtGui.QHBoxLayout() self.horizontalLayout_3.setObjectName(_fromUtf8("horizontalLayout_3")) spacerItem2 = QtGui.QSpacerItem(20, 20, QtGui.QSizePolicy.Minimum, QtGui.QSizePolicy.Minimum) self.horizontalLayout_3.addItem(spacerItem2) self.checkBox_ip = QtGui.QCheckBox(self.groupBox_2) self.checkBox_ip.setObjectName(_fromUtf8("checkBox_ip")) self.horizontalLayout_3.addWidget(self.checkBox_ip) self.lineEdit_ip = QtGui.QLineEdit(self.groupBox_2) self.lineEdit_ip.setObjectName(_fromUtf8("lineEdit_ip")) self.horizontalLayout_3.addWidget(self.lineEdit_ip) spacerItem3 = QtGui.QSpacerItem(40, 20, QtGui.QSizePolicy.MinimumExpanding, QtGui.QSizePolicy.Minimum) self.horizontalLayout_3.addItem(spacerItem3) self.verticalLayout_2.addLayout(self.horizontalLayout_3) self.horizontalLayout_4 = QtGui.QHBoxLayout() self.horizontalLayout_4.setObjectName(_fromUtf8("horizontalLayout_4")) spacerItem4 = QtGui.QSpacerItem(20, 20, QtGui.QSizePolicy.Minimum, QtGui.QSizePolicy.Minimum) self.horizontalLayout_4.addItem(spacerItem4) self.checkBox_fuzzy = QtGui.QCheckBox(self.groupBox_2) self.checkBox_fuzzy.setObjectName(_fromUtf8("checkBox_fuzzy")) self.horizontalLayout_4.addWidget(self.checkBox_fuzzy) self.lineEdit_fuzzysearch = QtGui.QLineEdit(self.groupBox_2) self.lineEdit_fuzzysearch.setObjectName(_fromUtf8("lineEdit_fuzzysearch")) self.horizontalLayout_4.addWidget(self.lineEdit_fuzzysearch) spacerItem5 = QtGui.QSpacerItem(40, 20, QtGui.QSizePolicy.Expanding, QtGui.QSizePolicy.Minimum) self.horizontalLayout_4.addItem(spacerItem5) self.verticalLayout_2.addLayout(self.horizontalLayout_4) self.gridLayout.addWidget(self.groupBox_2, 1, 0, 1, 2) self.groupBox = QtGui.QGroupBox(Form) self.groupBox.setObjectName(_fromUtf8("groupBox")) self.verticalLayout = QtGui.QVBoxLayout(self.groupBox) self.verticalLayout.setObjectName(_fromUtf8("verticalLayout")) self.textBrowser_log = QtGui.QTextBrowser(self.groupBox) self.textBrowser_log.viewport().setProperty("cursor", QtGui.QCursor(QtCore.Qt.IBeamCursor)) self.textBrowser_log.setMouseTracking(True) self.textBrowser_log.setObjectName(_fromUtf8("textBrowser_log")) self.verticalLayout.addWidget(self.textBrowser_log) self.horizontalLayout_2 = QtGui.QHBoxLayout() self.horizontalLayout_2.setObjectName(_fromUtf8("horizontalLayout_2")) self.lineEdit_pagenumStart = QtGui.QLineEdit(self.groupBox) self.lineEdit_pagenumStart.setMaximumSize(QtCore.QSize(50, 16777215)) self.lineEdit_pagenumStart.setObjectName(_fromUtf8("lineEdit_pagenumStart")) self.horizontalLayout_2.addWidget(self.lineEdit_pagenumStart) self.label_3 = QtGui.QLabel(self.groupBox) self.label_3.setMaximumSize(QtCore.QSize(20, 16777215)) self.label_3.setObjectName(_fromUtf8("label_3")) self.horizontalLayout_2.addWidget(self.label_3) self.lineEdit_pagenumEnd = QtGui.QLineEdit(self.groupBox) self.lineEdit_pagenumEnd.setMaximumSize(QtCore.QSize(50, 16777215)) self.lineEdit_pagenumEnd.setObjectName(_fromUtf8("lineEdit_pagenumEnd")) self.horizontalLayout_2.addWidget(self.lineEdit_pagenumEnd) spacerItem6 = QtGui.QSpacerItem(40, 20, QtGui.QSizePolicy.Expanding, QtGui.QSizePolicy.Minimum) self.horizontalLayout_2.addItem(spacerItem6) self.pushButton_pageup = QtGui.QPushButton(self.groupBox) self.pushButton_pageup.setObjectName(_fromUtf8("pushButton_pageup")) self.horizontalLayout_2.addWidget(self.pushButton_pageup) self.pushButton_pagedown = QtGui.QPushButton(self.groupBox) self.pushButton_pagedown.setObjectName(_fromUtf8("pushButton_pagedown")) self.horizontalLayout_2.addWidget(self.pushButton_pagedown) self.verticalLayout.addLayout(self.horizontalLayout_2) self.gridLayout.addWidget(self.groupBox, 0, 0, 1, 2) self.horizontalLayout_5 = QtGui.QHBoxLayout() self.horizontalLayout_5.setObjectName(_fromUtf8("horizontalLayout_5")) self.label_notice = QtGui.QLabel(Form) self.label_notice.setMinimumSize(QtCore.QSize(600, 0)) self.label_notice.setObjectName(_fromUtf8("label_notice")) self.horizontalLayout_5.addWidget(self.label_notice) spacerItem7 = QtGui.QSpacerItem(40, 20, QtGui.QSizePolicy.Expanding, QtGui.QSizePolicy.Minimum) self.horizontalLayout_5.addItem(spacerItem7) self.pushButton_check = QtGui.QPushButton(Form) self.pushButton_check.setObjectName(_fromUtf8("pushButton_check")) self.horizontalLayout_5.addWidget(self.pushButton_check) self.gridLayout.addLayout(self.horizontalLayout_5, 2, 0, 1, 2) self.retranslateUi(Form) QtCore.QMetaObject.connectSlotsByName(Form) def retranslateUi(self, Form): Form.setWindowTitle(_translate("Form", "LogManager", None)) self.groupBox_2.setTitle(_translate("Form", "Search Setting", None)) self.checkBox_time.setText(_translate("Form", "time：", None)) self.label_2.setText(_translate("Form", "-----", None)) self.checkBox_ip.setText(_translate("Form", "IP： ", None)) self.checkBox_fuzzy.setText(_translate("Form", "fuzzy：", None)) self.groupBox.setTitle(_translate("Form", "Log Display", None)) self.label_3.setText(_translate("Form", "---", None)) self.pushButton_pageup.setText(_translate("Form", "page up ", None)) self.pushButton_pagedown.setText(_translate("Form", "page down", None)) self.label_notice.setText(_translate("Form", "Notice:", None)) self.pushButton_check.setText(_translate("Form", "Check", None))

58.635135

110

0.735999

7,999

0.921119

0

857

0.098687

a1423e6a2572b095e511d07a5f47171e04381471

4,579

py

Python

aleph/tests/test_documents_api.py

gazeti/aleph

f6714c4be038471cfdc6408bfe88dc9e2ed28452

[ "MIT" ]

1

2017-07-28T12:54:09.000Z

aleph/tests/test_documents_api.py

gazeti/aleph

f6714c4be038471cfdc6408bfe88dc9e2ed28452

[ "MIT" ]

7

2017-08-16T12:49:23.000Z

2018-02-16T10:22:11.000Z

aleph/tests/test_documents_api.py

gazeti/aleph

f6714c4be038471cfdc6408bfe88dc9e2ed28452

[ "MIT" ]

6

2017-07-26T12:29:53.000Z

2017-08-18T09:35:50.000Z

import json from aleph.tests.util import TestCase class DocumentsApiTestCase(TestCase): def setUp(self): super(DocumentsApiTestCase, self).setUp() self.load_fixtures('docs.yaml') def test_index(self): res = self.client.get('/api/1/documents') assert res.status_code == 200, res self.login(is_admin=True) res = self.client.get('/api/1/documents') assert res.status_code == 200, res assert res.json['total'] == 4, res.json fix = '720badc9cfa9a80fc455239f86c56273dc5c8291' res = self.client.get('/api/1/documents?content_hash=%s' % fix) assert res.status_code == 200, res assert res.json['total'] == 1, res.json assert res.json['results'][0]['content_hash'] == fix, res.json def test_view(self): doc_id = 1000 res = self.client.get('/api/1/documents/%s' % doc_id) assert res.status_code == 200, res assert res.json['foreign_id'] == 'test1', res res = self.client.get('/api/1/documents/328984') assert res.status_code == 404, res def test_view_tables(self): doc_id = 1003 res = self.client.get('/api/1/documents/%s/tables/0' % doc_id) assert res.status_code == 200, res assert 'sheet_name' in res.json, res.json res = self.client.get('/api/1/documents/%s/tables/444' % doc_id) assert res.status_code == 404, res def test_view_records(self): res = self.client.get('/api/1/documents/1003/records') assert res.status_code == 200, res assert 'results' in res.json, res.json assert len(res.json['results']) == 10, res.json def test_view_record_by_id(self): doc_id = 1000 res = self.client.get('/api/1/documents/%s/records/1' % doc_id) assert res.status_code == 200, res assert 'banana' in res.json['text'], res assert 'total' not in res.json['text'], res res = self.client.get('/api/1/documents/%s/records/2' % doc_id) assert 'total' in res.json['text'], res res = self.client.get('/api/1/documents/%s/records/2000' % doc_id) assert res.status_code == 404, res def test_records_search(self): res = self.client.get('/api/1/documents/1003/records?q=kwazulu') assert res.status_code == 200, res assert res.json['total'] == 1, res.json def test_view_pdf(self): res = self.client.get('/api/1/documents/1003/pdf') assert res.status_code == 400, res res = self.client.get('/api/1/documents/1000/pdf') assert res.status_code == 404, res def test_view_references(self): doc_id = 1001 res = self.client.get('/api/1/documents/%s/references' % doc_id) assert res.status_code == 403, res self.login(is_admin=True) res = self.client.get('/api/1/documents/%s/references' % doc_id) assert res.status_code == 200, res assert 'results' in res.json, res.json # assert len(res.json['results']) == 2, res.json def test_update_simple(self): url = '/api/1/documents/1000' res = self.client.get(url) assert res.status_code == 200, res data = res.json res = self.client.post(url, data=json.dumps(data), content_type='application/json') assert res.status_code == 403, res.json data['title'] = 'Eaten by a pumpkin' self.login(is_admin=True) res = self.client.post(url, data=json.dumps(data), content_type='application/json') assert res.status_code == 200, res.json assert res.json['title'] == data['title'], res.json def test_update_invalid(self): url = '/api/1/documents/1000' ores = self.client.get(url) self.login(is_admin=True) data = ores.json.copy() data['countries'] = ['xz'] res = self.client.post(url, data=json.dumps(data), content_type='application/json') assert res.status_code == 400, res.json data = ores.json.copy() data['urls'] = ['lalala'] res = self.client.post(url, data=json.dumps(data), content_type='application/json') assert res.status_code == 400, res.json data = ores.json.copy() data['dates'] = ['2011-XX-XX'] res = self.client.post(url, data=json.dumps(data), content_type='application/json') assert res.status_code == 400, res.json

37.227642

74

0.592487

4,525

0.988207

0

932

0.203538

a143abc8dbbd62332b147ee1258deecef9896d32

649

py

Python

acropolis.py

andreasa13/Flask_WebApp_TripAdvisor

ea77291280676128b224da02c4938a42bbbb5200

[ "MIT" ]

null

acropolis.py

andreasa13/Flask_WebApp_TripAdvisor

ea77291280676128b224da02c4938a42bbbb5200

[ "MIT" ]

1

2021-12-13T20:52:54.000Z

acropolis.py

andreasagap/Flask_WebApp_TripAdvisor

06fd682248ea12ee440834719c113ec974635dd0

[ "MIT" ]

1

2021-06-09T18:29:33.000Z

import json import pandas as pd from geopy.geocoders import Nominatim def getAcropolisStatistics(): data = pd.read_csv("Analytics/demographics_old.csv") ratings_acropolis = len(data) gender = data.gender.str.lower().value_counts() ages = data.age_group.value_counts() return ratings_acropolis, gender["man"], gender["woman"], ages def getAcropolisMap(): data = pd.read_csv("Analytics/demographics.csv") dictionary = {} for i, v in data.geocode.value_counts().items(): dictionary[i] = v #a = pd.read_json(data.geocode.value_counts(), typ='series', orient='records') return json.dumps(dictionary)

28.217391

82

0.70416

0

150

0.231125

a146f1a5836a0723e015b88316d930723a68dc51

1,464

py

Python

share/pegasus/init/split/daxgen.py

fengggli/pegasus

b68f588d90eb2b832086ed627d61414691f8ba95

[ "Apache-2.0" ]

null

share/pegasus/init/split/daxgen.py

fengggli/pegasus

b68f588d90eb2b832086ed627d61414691f8ba95

[ "Apache-2.0" ]

null

share/pegasus/init/split/daxgen.py

fengggli/pegasus

b68f588d90eb2b832086ed627d61414691f8ba95

[ "Apache-2.0" ]

null

#!/usr/bin/env python import os import pwd import sys import time from Pegasus.DAX3 import * # The name of the DAX file is the first argument if len(sys.argv) != 2: sys.stderr.write("Usage: %s DAXFILE\n" % (sys.argv[0])) sys.exit(1) daxfile = sys.argv[1] USER = pwd.getpwuid(os.getuid())[0] # Create a abstract dag dax = ADAG("split") # Add some workflow-level metadata dax.metadata("creator", "%s@%s" % (USER, os.uname()[1])) dax.metadata("created", time.ctime()) webpage = File("pegasus.html") # the split job that splits the webpage into smaller chunks split = Job("split") split.addArguments("-l","100","-a","1",webpage,"part.") split.uses(webpage, link=Link.INPUT) # associate the label with the job. all jobs with same label # are run with PMC when doing job clustering split.addProfile( Profile("pegasus","label","p1")) dax.addJob(split) # we do a parmeter sweep on the first 4 chunks created for c in "abcd": part = File("part.%s" % c) split.uses(part, link=Link.OUTPUT, transfer=False, register=False) count = File("count.txt.%s" % c) wc = Job("wc") wc.addProfile( Profile("pegasus","label","p1")) wc.addArguments("-l",part) wc.setStdout(count) wc.uses(part, link=Link.INPUT) wc.uses(count, link=Link.OUTPUT, transfer=True, register=True) dax.addJob(wc) #adding dependency dax.depends(wc, split) f = open(daxfile, "w") dax.writeXML(f) f.close() print "Generated dax %s" %daxfile

25.684211

70

0.672814

0

556

0.379781

a147e22d5aeaabe35ccc4c56ea5539f536e24407

3,685

py

Python

lbrynet/wallet/ledger.py

ttkopec/lbry

03415415ed397730e6f691f527f51b429a834ed5

[ "MIT" ]

null

lbrynet/wallet/ledger.py

ttkopec/lbry

03415415ed397730e6f691f527f51b429a834ed5

[ "MIT" ]

110

2018-11-26T05:41:35.000Z

2021-08-03T15:37:20.000Z

lbrynet/wallet/ledger.py

ttkopec/lbry

03415415ed397730e6f691f527f51b429a834ed5

[ "MIT" ]

1

2018-09-20T22:15:59.000Z

import logging from six import int2byte from binascii import unhexlify from twisted.internet import defer from .resolve import Resolver from lbryschema.error import URIParseError from lbryschema.uri import parse_lbry_uri from torba.baseledger import BaseLedger from .account import Account from .network import Network from .database import WalletDatabase from .transaction import Transaction from .header import Headers, UnvalidatedHeaders log = logging.getLogger(__name__) class MainNetLedger(BaseLedger): name = 'LBRY Credits' symbol = 'LBC' network_name = 'mainnet' account_class = Account database_class = WalletDatabase headers_class = Headers network_class = Network transaction_class = Transaction secret_prefix = int2byte(0x1c) pubkey_address_prefix = int2byte(0x55) script_address_prefix = int2byte(0x7a) extended_public_key_prefix = unhexlify('0488b21e') extended_private_key_prefix = unhexlify('0488ade4') max_target = 0x0000ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff genesis_hash = '9c89283ba0f3227f6c03b70216b9f665f0118d5e0fa729cedf4fb34d6a34f463' genesis_bits = 0x1f00ffff target_timespan = 150 default_fee_per_byte = 50 default_fee_per_name_char = 200000 def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.fee_per_name_char = self.config.get('fee_per_name_char', self.default_fee_per_name_char) @property def resolver(self): return Resolver(self.headers.claim_trie_root, self.headers.height, self.transaction_class, hash160_to_address=self.hash160_to_address, network=self.network) @defer.inlineCallbacks def resolve(self, page, page_size, *uris): for uri in uris: try: parse_lbry_uri(uri) except URIParseError as err: defer.returnValue({'error': err.message}) resolutions = yield self.network.get_values_for_uris(self.headers.hash().decode(), *uris) return (yield self.resolver._handle_resolutions(resolutions, uris, page, page_size)) @defer.inlineCallbacks def get_claim_by_claim_id(self, claim_id): result = (yield self.network.get_claims_by_ids(claim_id)).pop(claim_id, {}) return (yield self.resolver.get_certificate_and_validate_result(result)) @defer.inlineCallbacks def get_claim_by_outpoint(self, txid, nout): claims = (yield self.network.get_claims_in_tx(txid)) or [] for claim in claims: if claim['nout'] == nout: return (yield self.resolver.get_certificate_and_validate_result(claim)) return 'claim not found' @defer.inlineCallbacks def start(self): yield super().start() yield defer.DeferredList([ a.maybe_migrate_certificates() for a in self.accounts ]) class TestNetLedger(MainNetLedger): network_name = 'testnet' pubkey_address_prefix = int2byte(111) script_address_prefix = int2byte(196) extended_public_key_prefix = unhexlify('043587cf') extended_private_key_prefix = unhexlify('04358394') class RegTestLedger(MainNetLedger): network_name = 'regtest' headers_class = UnvalidatedHeaders pubkey_address_prefix = int2byte(111) script_address_prefix = int2byte(196) extended_public_key_prefix = unhexlify('043587cf') extended_private_key_prefix = unhexlify('04358394') max_target = 0x7fffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff genesis_hash = '6e3fcf1299d4ec5d79c3a4c91d624a4acf9e2e173d95a1a0504f677669687556' genesis_bits = 0x207fffff target_timespan = 1

34.12037

101

0.735414

3,195

0.867028

1,074

0.291452

1,404

0.381004

0

287

0.077883

a14898fc9eb718d11bd7d8fbc8f0101300add0a6

297

py

Python

MATA37-ILP 2021.2/JUDE/Lista 3 e Prova 3 - Loop/lista3_D.py

jeffersonraimon/Programming-UFBA

6a6803bfd0e6aa72f8c2b9ffa120792d73c727ca

[ "MIT" ]

1

2021-12-09T12:55:56.000Z

MATA37-ILP 2021.2/JUDE/Lista 3 e Prova 3 - Loop/lista3_D.py

jeffersonraimon/Programming-UFBA

6a6803bfd0e6aa72f8c2b9ffa120792d73c727ca

[ "MIT" ]

null

MATA37-ILP 2021.2/JUDE/Lista 3 e Prova 3 - Loop/lista3_D.py

jeffersonraimon/Programming-UFBA

6a6803bfd0e6aa72f8c2b9ffa120792d73c727ca

[ "MIT" ]

1

2022-02-21T12:01:53.000Z

T = int(input()) P = int(input()) controle = 0 #Uso para guardar o valor maior que o limite while P != 0: P = int(input()) if P >= T: controle = 1 #coloquei 1 so pra ser diferente de 0 if controle == 1: print("ALARME") else: print("O Havai pode dormir tranquilo")

14.142857

58

0.592593

0

120

0.40404

a1489d0338a6be1fe32c5e1421435901d7f812f7

1,387

py

Python

dopamine/fetch_cam_train/fetch_cam/test/fetch_dis_error.py

kbehouse/dopamine

1922481d9c23d6c3cf3ee3ec06e613c6eb87cbc1

[ "Apache-2.0" ]

null

dopamine/fetch_cam_train/fetch_cam/test/fetch_dis_error.py

kbehouse/dopamine

1922481d9c23d6c3cf3ee3ec06e613c6eb87cbc1

[ "Apache-2.0" ]

null

dopamine/fetch_cam_train/fetch_cam/test/fetch_dis_error.py

kbehouse/dopamine

1922481d9c23d6c3cf3ee3ec06e613c6eb87cbc1

[ "Apache-2.0" ]

null

import numpy as np import gym import time from matplotlib import pyplot as plt from fetch_cam import FetchCameraEnv from fsm import FSM dis_tolerance = 0.0001 # 1mm env = FetchCameraEnv() obs = env.reset() done = False want_pos = (obs['eeinfo'][0]).copy() ori_pos = (obs['eeinfo'][0]).copy() print('---ori_pos = ' , obs['eeinfo'][0],'----') step = 0 robot_step = 0 s_time = time.time() while True: # env.render() now_pos = obs['eeinfo'][0] dis = np.linalg.norm(now_pos - want_pos) print('dis = ',dis) if dis < dis_tolerance: x, y, z, g = 0.01, 0.01, 0.01, 0. want_pos = obs['eeinfo'][0] + np.array([x, y, z]) print('want_pos =' , want_pos) step +=1 if step>=11: break else: x, y, z, g = 0., 0.0, 0., 0. a = np.array([x, y, z, g]) obs, r, done, info = env.step(a) robot_step +=1 if abs(x) > 0 or abs(y) > 0 or abs(z) > 0 : diff_x = obs['eeinfo'][0] - want_pos # print("pre_obs['eeinfo'][0] = ", pre_x) print("obs['eeinfo'][0] = {}, diff_x={}".format( obs['eeinfo'][0], diff_x) ) # time.sleep(0.5) print('---final_pos = ' , obs['eeinfo'][0],'----') print('---pos_diff = ' , obs['eeinfo'][0] - ori_pos,'----') print('step = {}, robot_step={}'.format(step, robot_step)) print('use time = {:.2f}'.format(time.time()-s_time))

24.333333

85

0.539293

0

314

0.226388

a1490edf966fa802ac0a01963e5d3d0e3138778b

5,091

py

Python

pyHarvest_build_151223/pyHarvest_Analyse_Data_v1.py

bl305/pyHarvest

d4c62d443ca657f9d31245c3c3f24c741cf2ae0b

[ "CC0-1.0" ]

null

pyHarvest_build_151223/pyHarvest_Analyse_Data_v1.py

bl305/pyHarvest

d4c62d443ca657f9d31245c3c3f24c741cf2ae0b

[ "CC0-1.0" ]

null

pyHarvest_build_151223/pyHarvest_Analyse_Data_v1.py

bl305/pyHarvest

d4c62d443ca657f9d31245c3c3f24c741cf2ae0b

[ "CC0-1.0" ]

null

# coding=utf-8 from packages import * import os #SET PARAMETERS myverbosity=-1 mymaxencode=5 TXT_filetypes=( #simple text files 'txt','lst', #config files 'ini','cfg', #programming languages 'c','cpp', #scripts 'vbs','py','pl') XLS_filetypes=('xls','xlsx') DOC_filetypes=('doc',) DOCX_filetypes=('docx',) PDF_filetypes=('pdf',) #TEMPLATE FILES myXLSpath=r'c:\LENBAL\Trainings\Securitytube_Python_Expert_PRIVATE\My_Network_Discovery_Project\Main_Program\AllTestFiles\XLS\test.xlsx' myTXTpath=r'c:\LENBAL\Trainings\Securitytube_Python_Expert_PRIVATE\My_Network_Discovery_Project\Main_Program\AllTestFiles\TXT\normal.txt' #myTXTpath=r'c:\LENBAL\Trainings\Securitytube_Python_Expert_PRIVATE\My_Network_Discovery_Project\Main_Program\AllTestFiles\TXT\unicode.txt' #myTXTpath=r'c:\LENBAL\Trainings\Securitytube_Python_Expert_PRIVATE\My_Network_Discovery_Project\Main_Program\AllTestFiles\TXT\unicode_big.txt' #myTXTpath=r'c:\LENBAL\Trainings\Securitytube_Python_Expert_PRIVATE\My_Network_Discovery_Project\Main_Program\AllTestFiles\TXT\unicode_utf8.txt' #myTXTpath=r'c:\LENBAL\Trainings\Securitytube_Python_Expert_PRIVATE\My_Network_Discovery_Project\Main_Program\AllTestFiles\TXT\x.txt' #myPDFpath=r'c:\LENBAL\Trainings\Securitytube_Python_Expert_PRIVATE\My_Network_Discovery_Project\Main_Program\AllTestFiles\PDF\test.pdf' #myPDFpath=r'c:\LENBAL\Trainings\Securitytube_Python_Expert_PRIVATE\My_Network_Discovery_Project\Main_Program\AllTestFiles\PDF\xtest.pdf' myPDFpath=r'c:\LENBAL\Trainings\Securitytube_Python_Expert_PRIVATE\My_Network_Discovery_Project\Main_Program\AllTestFiles\PDF\ztest.pdf' myDOCpath=r'c:\LENBAL\Trainings\Securitytube_Python_Expert_PRIVATE\My_Network_Discovery_Project\Main_Program\AllTestFiles\DOC\xtest.doc' myDOCXpath=r'c:\LENBAL\Trainings\Securitytube_Python_Expert_PRIVATE\My_Network_Discovery_Project\Main_Program\AllTestFiles\DOC\xtest.docx' mydirpath=r'c:\LENBAL\Trainings\Securitytube_Python_Expert_PRIVATE\My_Network_Discovery_Project\Main_Program\AllTestFiles' #mydirpath=r'c:\LENBAL\Trainings\Securitytube_Python_Expert_PRIVATE\My_Network_Discovery_Project\Main_Program\DataGathered' #mypath=myTXTpath #mypath=myXLSpath #mypath=myPDFpath #mypath=myDOCpath #mypath=myDOCXpath #PROGRAM START def process_myfile(thepath,verbosity=0): #Select file type fileextension="" result=() if '.' in thepath: fileextension = thepath.rsplit('.', 1)[1] if fileextension in DOC_filetypes: doc_match=doc_full_search_tuple(thepath,myverbosity) if doc_match: result+=(doc_match,'doc') if verbosity>1: print doc_match elif fileextension in DOCX_filetypes: docx_match=docx_full_search_tuple(thepath,myverbosity) if docx_match: result+=(docx_match,'docx') if verbosity>1: print docx_match elif fileextension in XLS_filetypes: #PROCESS XLS #xls_match=xls_full_search_tuple(thepath,verbosity=myverbosity) xls_match=xls_full_search_tuple(thepath,myverbosity) if xls_match: result+=(xls_match,'xlsx') if verbosity>1: print xls_match #print xls_match[-1] elif fileextension in PDF_filetypes: pdf_match=pdf_full_search_tuple(thepath,myverbosity) if pdf_match: result+=(pdf_match,'pdf') if verbosity>1: print pdf_match #print pdf_match[-1] elif fileextension in TXT_filetypes: #PROCESS TXT #txt_match=txt_full_search_tuple(thepath,maxencode=mymaxencode,verbosity=myverbosity) txt_match=txt_full_search_tuple(thepath,mymaxencode,myverbosity) if txt_match: result+=(txt_match,'txt') if verbosity>1: print txt_match #print txt_match[-1] else: print "[-] UNKNOWN filetype",thepath return result def process_localdir(localdir,recursive=0): results=() if recursive==0: #files = [ f for f in os.listdir(localdir) if os.path.isfile(os.path.join(localdir,f)) ] for files in os.listdir(localdir): if os.path.isfile(os.path.join(localdir,files)): abspath=os.path.join(localdir,files) abspath = os.path.normpath(abspath).replace('//','/') #print abspath results+=(abspath,) else: for subdir, dirs, files in os.walk(localdir): for file in files: abspath=os.path.join(subdir,file) abspath = os.path.normpath(abspath).replace('//','/') #print abspath results+=(abspath,) return results #print "##########################Main Program Started##########################" #ANALYSE A SPECIFIC FILE #process_myfile(mypath) #ANALYSE ALL FILES IN A SPECIFIED DIRECTORY filesindir=process_localdir(mydirpath,1) Analysisconn, Analysisc = db_connect(Analysis_sqlite_file) create_host_db(Analysisconn, Analysis_create_script,print_out=False) filecount=len(filesindir) filecounter=1 if filecount==0: print "No files to analyse" for fn in range(len(filesindir)): mytext=process_myfile(filesindir[fn]) print "Analysing file %d/%d %s"%(filecounter,filecount,filesindir[fn]) filecounter+=1 if mytext: ftype=mytext[1] mytextdata=mytext[0] insert_analysis_data(Analysisc,Analysis_table_name,mytextdata,ftype,print_out=False) db_commit(Analysisconn) pass db_commit(Analysisconn) db_close(Analysisconn) print (raw_input('Press Enter to Exit!'))

36.891304

144

0.792772

0

2,651

0.520723

a1499e6c4207a38f095d2e507e2c6116418ae733

2,732

py

Python

functions/update_modeling_results.py

zheng-da/covid19-severity-prediction

205ab5aa13a5e91a4c23ccd73e65939e4003626b

[ "MIT" ]

2

2020-05-15T14:42:02.000Z

2020-05-22T08:51:47.000Z

functions/update_modeling_results.py

rahul263-stack/covid19-severity-prediction

f581adb2fccb12d5ab3f3c59ee120f484703edf5

[ "MIT" ]

null

functions/update_modeling_results.py

rahul263-stack/covid19-severity-prediction

f581adb2fccb12d5ab3f3c59ee120f484703edf5

[ "MIT" ]

null

import numpy as np import pandas as pd from os.path import join as oj import os import pygsheets import pandas as pd import sys import inspect from datetime import datetime, timedelta currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) parentdir = os.path.dirname(currentdir) sys.path.append(parentdir) sys.path.append(parentdir + '/modeling') import load_data from fit_and_predict import fit_and_predict_ensemble from functions import merge_data from viz import viz_interactive import matplotlib.pyplot as plt import plotly.express as px import plotly def predictions_plot(df_county, NUM_DAYS_LIST, num_days_in_past, output_key): today = datetime.today().strftime("%B %d") day_past = (datetime.now() - timedelta(days=num_days_in_past)).strftime("%B %d") pred_key = f'Predicted deaths by {today}\n(predicted on {day_past})' deaths_key = f'Actual deaths by {today}' d = df_county.rename(columns={ output_key: pred_key, 'tot_deaths': deaths_key, }) minn = min(min(d[pred_key]), min(d[deaths_key])) + 1 maxx = max(max(d[pred_key]), max(d[deaths_key])) px.colors.DEFAULT_PLOTLY_COLORS[:3] = ['rgb(239,138,98)','rgb(247,247,247)','rgb(103,169,207)'] fig = px.scatter(d, x=deaths_key, y=pred_key, size='PopulationEstimate2018', hover_name="CountyName", hover_data=["CountyName", 'StateName'], log_x=True, log_y=True) fig.update_layout(shapes=[ dict( type= 'line', yref= 'y', y0=minn, y1=maxx, xref= 'x', x0=minn, x1=maxx, opacity=0.2 ) ]) fig.update_layout( paper_bgcolor='rgba(0,0,0,255)', plot_bgcolor='rgba(0,0,0,255)', template='plotly_dark', title='County-level predictions' ) plotly.offline.plot(fig, filename=oj(parentdir, 'results', 'predictions.html'), auto_open=False) if __name__ == '__main__': print('loading data...') NUM_DAYS_LIST = [1, 2, 3, 4, 5, 6, 7] df_county = load_data.load_county_level(data_dir=oj(parentdir, 'data')) num_days_in_past = 3 output_key = f'Predicted Deaths {num_days_in_past}-day' df_county = fit_and_predict_ensemble(df_county, outcome='deaths', mode='eval_mode', target_day=np.array([num_days_in_past]), output_key=output_key) df_county[output_key] = [v[0] for v in df_county[output_key].values] predictions_plot(df_county, NUM_DAYS_LIST, num_days_in_past, output_key)

35.947368

100

0.625915

0

439

0.160688

a14c5c58cf2881b62cfe95e034f42cf5c934399c

4,582

py

Python

zun/tests/unit/common/test_rpc.py

wanghuiict/zun

2f4a3a2ba06d7ca83002418d4003ee5dece70952

[ "Apache-2.0" ]

83

2016-09-14T22:06:26.000Z

2022-01-27T03:49:52.000Z

zun/tests/unit/common/test_rpc.py

wanghuiict/zun

2f4a3a2ba06d7ca83002418d4003ee5dece70952

[ "Apache-2.0" ]

2

2017-06-22T21:58:47.000Z

2019-04-10T03:17:44.000Z

zun/tests/unit/common/test_rpc.py

wanghuiict/zun

2f4a3a2ba06d7ca83002418d4003ee5dece70952

[ "Apache-2.0" ]

54

2016-09-29T10:16:02.000Z

2022-01-28T19:12:49.000Z

# Copyright 2017 OpenStack Foundation # All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. from unittest import mock from oslo_serialization import jsonutils as json from zun.common import context from zun.common import rpc from zun.tests import base class TestRpc(base.TestCase): def test_add_extra_exmods(self): rpc.EXTRA_EXMODS = [] rpc.add_extra_exmods('foo', 'bar') self.assertEqual(['foo', 'bar'], rpc.EXTRA_EXMODS) def test_clear_extra_exmods(self): rpc.EXTRA_EXMODS = ['foo', 'bar'] rpc.clear_extra_exmods() self.assertEqual(0, len(rpc.EXTRA_EXMODS)) def test_serialize_entity(self): with mock.patch.object(json, 'to_primitive') as mock_prim: rpc.JsonPayloadSerializer.serialize_entity('context', 'entity') mock_prim.assert_called_once_with('entity', convert_instances=True) class TestRequestContextSerializer(base.TestCase): def setUp(self): super(TestRequestContextSerializer, self).setUp() self.mock_base = mock.Mock() self.ser = rpc.RequestContextSerializer(self.mock_base) self.ser_null = rpc.RequestContextSerializer(None) def test_serialize_entity(self): self.mock_base.serialize_entity.return_value = 'foo' ser_ent = self.ser.serialize_entity('context', 'entity') self.mock_base.serialize_entity.assert_called_once_with('context', 'entity') self.assertEqual('foo', ser_ent) def test_serialize_entity_null_base(self): ser_ent = self.ser_null.serialize_entity('context', 'entity') self.assertEqual('entity', ser_ent) def test_deserialize_entity(self): self.mock_base.deserialize_entity.return_value = 'foo' deser_ent = self.ser.deserialize_entity('context', 'entity') self.mock_base.deserialize_entity.assert_called_once_with('context', 'entity') self.assertEqual('foo', deser_ent) def test_deserialize_entity_null_base(self): deser_ent = self.ser_null.deserialize_entity('context', 'entity') self.assertEqual('entity', deser_ent) def test_serialize_context(self): context = mock.Mock() self.ser.serialize_context(context) context.to_dict.assert_called_once_with() @mock.patch.object(context, 'RequestContext') def test_deserialize_context(self, mock_req): self.ser.deserialize_context('context') mock_req.from_dict.assert_called_once_with('context') class TestProfilerRequestContextSerializer(base.TestCase): def setUp(self): super(TestProfilerRequestContextSerializer, self).setUp() self.ser = rpc.ProfilerRequestContextSerializer(mock.Mock()) @mock.patch('zun.common.rpc.profiler') def test_serialize_context(self, mock_profiler): prof = mock_profiler.get.return_value prof.hmac_key = 'swordfish' prof.get_base_id.return_value = 'baseid' prof.get_id.return_value = 'parentid' context = mock.Mock() context.to_dict.return_value = {'project_id': 'test'} self.assertEqual({ 'project_id': 'test', 'trace_info': { 'hmac_key': 'swordfish', 'base_id': 'baseid', 'parent_id': 'parentid' } }, self.ser.serialize_context(context)) @mock.patch('zun.common.rpc.profiler') def test_deserialize_context(self, mock_profiler): serialized = {'project_id': 'test', 'trace_info': { 'hmac_key': 'swordfish', 'base_id': 'baseid', 'parent_id': 'parentid'}} context = self.ser.deserialize_context(serialized) self.assertEqual('test', context.project_id) mock_profiler.init.assert_called_once_with( hmac_key='swordfish', base_id='baseid', parent_id='parentid')

34.19403

78

0.654518

3,775

0.823876

0

1,402

0.30598

0

1,172

0.255784

a14da1829b09a4bac353d3762281e3ef271e99d4

26,935

py

Python

skidl/Pin.py

arjenroodselaar/skidl

0bf801bd3b74e6ef94bd9aa1b68eef756b568276

[ "MIT" ]

null

skidl/Pin.py

arjenroodselaar/skidl

0bf801bd3b74e6ef94bd9aa1b68eef756b568276

[ "MIT" ]

null

skidl/Pin.py

arjenroodselaar/skidl

0bf801bd3b74e6ef94bd9aa1b68eef756b568276

[ "MIT" ]

1

2020-09-21T23:31:41.000Z

# -*- coding: utf-8 -*- # MIT license # # Copyright (C) 2018 by XESS Corp. # # 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. """ Handles part pins. """ from __future__ import absolute_import, division, print_function, unicode_literals from builtins import range, super from collections import defaultdict from copy import copy from enum import IntEnum from future import standard_library from .Alias import * from .baseobj import SkidlBaseObject from .defines import * from .logger import erc_logger, logger from .utilities import * standard_library.install_aliases() class Pin(SkidlBaseObject): """ A class for storing data about pins for a part. Args: attribs: Key/value pairs of attributes to add to the library. Attributes: nets: The electrical nets this pin is connected to (can be >1). part: Link to the Part object this pin belongs to. func: Pin function such as PinType.types.INPUT. do_erc: When false, the pin is not checked for ERC violations. """ # Various types of pins. types = IntEnum( "types", ( "INPUT", "OUTPUT", "BIDIR", "TRISTATE", "PASSIVE", "UNSPEC", "PWRIN", "PWROUT", "OPENCOLL", "OPENEMIT", "PULLUP", "PULLDN", "NOCONNECT", ), ) @classmethod def add_type(cls, *pin_types): """ Add new pin type identifiers to the list of pin types. Args: pin_types: Strings identifying zero or more pin types. """ cls.types = IntEnum("types", [m.name for m in cls.types] + list(pin_types)) # Also add the pin types as attributes of the Pin class so # existing SKiDL part libs will still work (e.g. Pin.INPUT # still works as well as the newer Pin.types.INPUT). for m in cls.types: setattr(cls, m.name, m) # Various drive levels a pin can output. # The order of these is important! The first entry has the weakest # drive and the drive increases for each successive entry. drives = IntEnum( "drives", ( "NOCONNECT", # NC pin drive. "NONE", # No drive capability (like an input pin). "PASSIVE", # Small drive capability, but less than a pull-up or pull-down. "PULLUPDN", # Pull-up or pull-down capability. "ONESIDE", # Can pull high (open-emitter) or low (open-collector). "TRISTATE", # Can pull high/low and be in high-impedance state. "PUSHPULL", # Can actively drive high or low. "POWER", # A power supply or ground line. ), ) # Information about the various types of pins: # function: A string describing the pin's function. # drive: The drive capability of the pin. # rcv_min: The minimum amount of drive the pin must receive to function. # rcv_max: The maximum amount of drive the pin can receive and still function. pin_info = { types.INPUT: { "function": "INPUT", "func_str": "INPUT", "drive": drives.NONE, "max_rcv": drives.POWER, "min_rcv": drives.PASSIVE, }, types.OUTPUT: { "function": "OUTPUT", "func_str": "OUTPUT", "drive": drives.PUSHPULL, "max_rcv": drives.PASSIVE, "min_rcv": drives.NONE, }, types.BIDIR: { "function": "BIDIRECTIONAL", "func_str": "BIDIR", "drive": drives.TRISTATE, "max_rcv": drives.POWER, "min_rcv": drives.NONE, }, types.TRISTATE: { "function": "TRISTATE", "func_str": "TRISTATE", "drive": drives.TRISTATE, "max_rcv": drives.TRISTATE, "min_rcv": drives.NONE, }, types.PASSIVE: { "function": "PASSIVE", "func_str": "PASSIVE", "drive": drives.PASSIVE, "max_rcv": drives.POWER, "min_rcv": drives.NONE, }, types.PULLUP: { "function": "PULLUP", "func_str": "PULLUP", "drive": drives.PULLUPDN, "max_rcv": drives.POWER, "min_rcv": drives.NONE, }, types.PULLDN: { "function": "PULLDN", "func_str": "PULLDN", "drive": drives.PULLUPDN, "max_rcv": drives.POWER, "min_rcv": drives.NONE, }, types.UNSPEC: { "function": "UNSPECIFIED", "func_str": "UNSPEC", "drive": drives.NONE, "max_rcv": drives.POWER, "min_rcv": drives.NONE, }, types.PWRIN: { "function": "POWER-IN", "func_str": "PWRIN", "drive": drives.NONE, "max_rcv": drives.POWER, "min_rcv": drives.POWER, }, types.PWROUT: { "function": "POWER-OUT", "func_str": "PWROUT", "drive": drives.POWER, "max_rcv": drives.PASSIVE, "min_rcv": drives.NONE, }, types.OPENCOLL: { "function": "OPEN-COLLECTOR", "func_str": "OPENCOLL", "drive": drives.ONESIDE, "max_rcv": drives.TRISTATE, "min_rcv": drives.NONE, }, types.OPENEMIT: { "function": "OPEN-EMITTER", "func_str": "OPENEMIT", "drive": drives.ONESIDE, "max_rcv": drives.TRISTATE, "min_rcv": drives.NONE, }, types.NOCONNECT: { "function": "NO-CONNECT", "func_str": "NOCONNECT", "drive": drives.NOCONNECT, "max_rcv": drives.NOCONNECT, "min_rcv": drives.NOCONNECT, }, } def __init__(self, **attribs): super().__init__() self.nets = [] self.part = None self.name = "" self.num = "" self.do_erc = True self.func = self.types.UNSPEC # Pin function defaults to unspecified. # Attach additional attributes to the pin. for k, v in list(attribs.items()): setattr(self, k, v) def copy(self, num_copies=None, **attribs): """ Return copy or list of copies of a pin including any net connection. Args: num_copies: Number of copies to make of pin. Keyword Args: attribs: Name/value pairs for setting attributes for the pin. Notes: An instance of a pin can be copied just by calling it like so:: p = Pin() # Create a pin. p_copy = p() # This is a copy of the pin. """ # If the number of copies is None, then a single copy will be made # and returned as a scalar (not a list). Otherwise, the number of # copies will be set by the num_copies parameter or the number of # values supplied for each part attribute. num_copies_attribs = find_num_copies(**attribs) return_list = (num_copies is not None) or (num_copies_attribs > 1) if num_copies is None: num_copies = max(1, num_copies_attribs) # Check that a valid number of copies is requested. if not isinstance(num_copies, int): log_and_raise( logger, ValueError, "Can't make a non-integer number ({}) of copies of a pin!".format( num_copies ), ) if num_copies < 0: log_and_raise( logger, ValueError, "Can't make a negative number ({}) of copies of a pin!".format( num_copies ), ) copies = [] for _ in range(num_copies): # Make a shallow copy of the pin. cpy = copy(self) # The copy is not on a net, yet. cpy.nets = [] # Connect the new pin to the same net as the original. if self.nets: self.nets[0] += cpy # Copy the aliases for the pin if it has them. cpy.aliases = self.aliases # Attach additional attributes to the pin. for k, v in list(attribs.items()): setattr(cpy, k, v) copies.append(cpy) # Return a list of the copies made or just a single copy. if return_list: return copies return copies[0] # Make copies with the multiplication operator or by calling the object. __call__ = copy def __mul__(self, num_copies): if num_copies is None: num_copies = 0 return self.copy(num_copies=num_copies) __rmul__ = __mul__ def __getitem__(self, *ids): """ Return the pin if the indices resolve to a single index of 0. Args: ids: A list of indices. These can be individual numbers, net names, nested lists, or slices. Returns: The pin, otherwise None or raises an Exception. """ # Resolve the indices. indices = list(set(expand_indices(0, self.width - 1, False, *ids))) if indices is None or len(indices) == 0: return None if len(indices) > 1: log_and_raise( logger, ValueError, "Can't index a pin with multiple indices." ) if indices[0] != 0: log_and_raise(logger, ValueError, "Can't use a non-zero index for a pin.") return self def __setitem__(self, ids, *pins_nets_buses): """ You can't assign to Pins. You must use the += operator. This method is a work-around that allows the use of the += for making connections to pins while prohibiting direct assignment. Python processes something like net[0] += Net() as follows:: 1. Pin.__getitem__ is called with '0' as the index. This returns a single Pin. 2. The Pin.__iadd__ method is passed the pin and the thing to connect to it (a Net in this case). This method makes the actual connection to the net. Then it creates an iadd_flag attribute in the object it returns. 3. Finally, Pin.__setitem__ is called. If the iadd_flag attribute is true in the passed argument, then __setitem__ was entered as part of processing the += operator. If there is no iadd_flag attribute, then __setitem__ was entered as a result of using a direct assignment, which is not allowed. """ # If the iadd_flag is set, then it's OK that we got # here and don't issue an error. Also, delete the flag. if getattr(pins_nets_buses[0], "iadd_flag", False): del pins_nets_buses[0].iadd_flag return # No iadd_flag or it wasn't set. This means a direct assignment # was made to the pin, which is not allowed. log_and_raise(logger, TypeError, "Can't assign to a Net! Use the += operator.") def __iter__(self): """ Return an iterator for stepping through the pin. """ # You can only iterate a Pin one time. return (self for i in [0]) # Return generator expr. def is_connected(self): """Return true if a pin is connected to a net (but not a no-connect net).""" from .Net import Net, NCNet if not self.nets: # This pin is not connected to any nets. return False # Get the types of things this pin is connected to. net_types = set([type(n) for n in self.nets]) if set([NCNet]) == net_types: # This pin is only connected to no-connect nets. return False if set([Net]) == net_types: # This pin is only connected to normal nets. return True if set([Net, NCNet]) == net_types: # Can't be connected to both normal and no-connect nets! log_and_raise( logger, ValueError, "{} is connected to both normal and no-connect nets!".format( self.erc_desc() ), ) # This is just strange... log_and_raise( logger, ValueError, "{} is connected to something strange: {}.".format( self.erc_desc(), self.nets ), ) def is_attached(self, pin_net_bus): """Return true if this pin is attached to the given pin, net or bus.""" from .Net import Net from .Pin import Pin if not self.is_connected(): return False if isinstance(pin_net_bus, Pin): if pin_net_bus.is_connected(): return pin_net_bus.net.is_attached(self.net) return False if isinstance(pin_net_bus, Net): return pin_net_bus.is_attached(self.net) if isinstance(pin_net_bus, Bus): for net in pin_net_bus[:]: if self.net.is_attached(net): return True return False log_and_raise( logger, ValueError, "Pins can't be attached to {}!".format(type(pin_net_bus)), ) def connect(self, *pins_nets_buses): """ Return the pin after connecting it to one or more nets or pins. Args: pins_nets_buses: One or more Pin, Net or Bus objects or lists/tuples of them. Returns: The updated pin with the new connections. Notes: You can connect nets or pins to a pin like so:: p = Pin() # Create a pin. n = Net() # Create a net. p += net # Connect the net to the pin. """ from .Net import Net from .ProtoNet import ProtoNet # Go through all the pins and/or nets and connect them to this pin. for pn in expand_buses(flatten(pins_nets_buses)): if isinstance(pn, ProtoNet): pn += self elif isinstance(pn, Pin): # Connecting pin-to-pin. if self.is_connected(): # If self is already connected to a net, then add the # other pin to the same net. self.nets[0] += pn elif pn.is_connected(): # If self is unconnected but the other pin is, then # connect self to the other pin's net. pn.nets[0] += self else: # Neither pin is connected to a net, so create a net # in the same circuit as the pin and attach both to it. Net(circuit=self.part.circuit).connect(self, pn) elif isinstance(pn, Net): # Connecting pin-to-net, so just connect the pin to the net. pn += self else: log_and_raise( logger, TypeError, "Cannot attach non-Pin/non-Net {} to {}.".format( type(pn), self.erc_desc() ), ) # Set the flag to indicate this result came from the += operator. self.iadd_flag = True # pylint: disable=attribute-defined-outside-init return self # Connect a net to a pin using the += operator. __iadd__ = connect def disconnect(self): """Disconnect this pin from all nets.""" if not self.net: return for n in self.nets: n.disconnect(self) self.nets = [] def get_nets(self): """Return a list containing the Net objects connected to this pin.""" return self.nets def get_pins(self): """Return a list containing this pin.""" return to_list(self) def create_network(self): """Create a network from a single pin.""" from .Network import Network ntwk = Network() ntwk.append(self) return ntwk def __and__(self, obj): """Attach a pin and another part/pin/net in serial.""" from .Network import Network return Network(self) & obj def __rand__(self, obj): """Attach a pin and another part/pin/net in serial.""" from .Network import Network return obj & Network(self) def __or__(self, obj): """Attach a pin and another part/pin/net in parallel.""" from .Network import Network return Network(self) | obj def __ror__(self, obj): """Attach a pin and another part/pin/net in parallel.""" from .Network import Network return obj | Network(self) def chk_conflict(self, other_pin): """Check for electrical rule conflicts between this pin and another.""" if not self.do_erc or not other_pin.do_erc: return [erc_result, erc_msg] = conflict_matrix[self.func][other_pin.func] # Return if the pins are compatible. if erc_result == OK: return # Otherwise, generate an error or warning message. if not erc_msg: erc_msg = " ".join( ( self.pin_info[self.func]["function"], "connected to", other_pin.pin_info[other_pin.func]["function"], ) ) n = self.net.name p1 = self.erc_desc() p2 = other_pin.erc_desc() msg = "Pin conflict on net {n}, {p1} <==> {p2} ({erc_msg})".format(**locals()) if erc_result == WARNING: erc_logger.warning(msg) else: erc_logger.error(msg) def erc_desc(self): """Return a string describing this pin for ERC.""" desc = "{func} pin {num}/{name} of {part}".format( part=self.part.erc_desc(), num=self.num, name=self.name, func=Pin.pin_info[self.func]["function"], ) return desc def get_pin_info(self): num = getattr(self, "num", "???") names = [getattr(self, "name", "???")] names.extend(self.aliases) names = ",".join(names) func = Pin.pin_info[self.func]["function"] return num, names, func def __str__(self): """Return a description of this pin as a string.""" ref = getattr(self.part, "ref", "???") num, names, func = self.get_pin_info() return "Pin {ref}/{num}/{names}/{func}".format(**locals()) __repr__ = __str__ def export(self): """Return a string to recreate a Pin object.""" attribs = [] for k in ["num", "name", "func", "do_erc"]: v = getattr(self, k, None) if v: if k == "func": # Assign the pin function using the actual name of the # function, not its numerical value (in case that changes # in the future if more pin functions are added). v = "Pin.types." + Pin.pin_info[v]["func_str"] else: v = repr(v) attribs.append("{}={}".format(k, v)) return "Pin({})".format(",".join(attribs)) @property def net(self): """Return one of the nets the pin is connected to.""" if self.nets: return self.nets[0] return None @property def width(self): """Return width of a Pin, which is always 1.""" return 1 @property def drive(self): """ Get, set and delete the drive strength of this pin. """ try: return self._drive except AttributeError: # Drive unspecified, so use default drive for this type of pin. return self.pin_info[self.func]["drive"] @drive.setter def drive(self, drive): self._drive = drive @drive.deleter def drive(self): try: del self._drive except AttributeError: pass def __bool__(self): """Any valid Pin is True.""" return True __nonzero__ = __bool__ # Python 2 compatibility. ############################################################################## class PhantomPin(Pin): """ A pin type that exists solely to tie two pinless nets together. It will not participate in generating any netlists. """ def __init__(self, **attribs): super().__init__(**attribs) self.nets = [] self.part = None self.do_erc = False ############################################################################## class PinList(list): """ A list of Pin objects that's meant to look something like a Pin to a Part. This is used for vector I/O of XSPICE parts. """ def __init__(self, num, name, part): super().__init__() # The list needs the following attributes to behave like a Pin. self.num = num self.name = name self.part = part def __getitem__(self, i): """ Get a Pin from the list. Add Pin objects to the list if they don't exist. """ if i >= len(self): self.extend([Pin(num=j, part=self.part) for j in range(len(self), i + 1)]) return super().__getitem__(i) def copy(self): """ Return a copy of a PinList for use when a Part is copied. """ cpy = PinList(self.num, self.name, self.part) for pin in self: cpy += pin.copy() return cpy def disconnect(self): """Disconnect all the pins in the list.""" for pin in self: pin.disconnect() ############################################################################## # This will make all the Pin.drive members into attributes of the Pin class # so things like Pin.INPUT will work as well as Pin.types.INPUT. Pin.add_type() # Create the pin conflict matrix as a defaultdict of defaultdicts which # returns OK if the given element is not in the matrix. This would indicate # the pin types used to index that element have no contention if connected. conflict_matrix = defaultdict(lambda: defaultdict(lambda: [OK, ""])) # Add the non-OK pin connections to the matrix. conflict_matrix[Pin.types.OUTPUT][Pin.types.OUTPUT] = [ERROR, ""] conflict_matrix[Pin.types.TRISTATE][Pin.types.OUTPUT] = [WARNING, ""] conflict_matrix[Pin.types.UNSPEC][Pin.types.INPUT] = [WARNING, ""] conflict_matrix[Pin.types.UNSPEC][Pin.types.OUTPUT] = [WARNING, ""] conflict_matrix[Pin.types.UNSPEC][Pin.types.BIDIR] = [WARNING, ""] conflict_matrix[Pin.types.UNSPEC][Pin.types.TRISTATE] = [WARNING, ""] conflict_matrix[Pin.types.UNSPEC][Pin.types.PASSIVE] = [WARNING, ""] conflict_matrix[Pin.types.UNSPEC][Pin.types.PULLUP] = [WARNING, ""] conflict_matrix[Pin.types.UNSPEC][Pin.types.PULLDN] = [WARNING, ""] conflict_matrix[Pin.types.UNSPEC][Pin.types.UNSPEC] = [WARNING, ""] conflict_matrix[Pin.types.PWRIN][Pin.types.TRISTATE] = [WARNING, ""] conflict_matrix[Pin.types.PWRIN][Pin.types.UNSPEC] = [WARNING, ""] conflict_matrix[Pin.types.PWROUT][Pin.types.OUTPUT] = [ERROR, ""] conflict_matrix[Pin.types.PWROUT][Pin.types.BIDIR] = [WARNING, ""] conflict_matrix[Pin.types.PWROUT][Pin.types.TRISTATE] = [ERROR, ""] conflict_matrix[Pin.types.PWROUT][Pin.types.UNSPEC] = [WARNING, ""] conflict_matrix[Pin.types.PWROUT][Pin.types.PWROUT] = [ERROR, ""] conflict_matrix[Pin.types.OPENCOLL][Pin.types.OUTPUT] = [ERROR, ""] conflict_matrix[Pin.types.OPENCOLL][Pin.types.TRISTATE] = [ERROR, ""] conflict_matrix[Pin.types.OPENCOLL][Pin.types.UNSPEC] = [WARNING, ""] conflict_matrix[Pin.types.OPENCOLL][Pin.types.PWROUT] = [ERROR, ""] conflict_matrix[Pin.types.OPENEMIT][Pin.types.OUTPUT] = [ERROR, ""] conflict_matrix[Pin.types.OPENEMIT][Pin.types.BIDIR] = [WARNING, ""] conflict_matrix[Pin.types.OPENEMIT][Pin.types.TRISTATE] = [WARNING, ""] conflict_matrix[Pin.types.OPENEMIT][Pin.types.UNSPEC] = [WARNING, ""] conflict_matrix[Pin.types.OPENEMIT][Pin.types.PWROUT] = [ERROR, ""] conflict_matrix[Pin.types.NOCONNECT][Pin.types.INPUT] = [ERROR, ""] conflict_matrix[Pin.types.NOCONNECT][Pin.types.OUTPUT] = [ERROR, ""] conflict_matrix[Pin.types.NOCONNECT][Pin.types.BIDIR] = [ERROR, ""] conflict_matrix[Pin.types.NOCONNECT][Pin.types.TRISTATE] = [ERROR, ""] conflict_matrix[Pin.types.NOCONNECT][Pin.types.PASSIVE] = [ERROR, ""] conflict_matrix[Pin.types.NOCONNECT][Pin.types.PULLUP] = [ERROR, ""] conflict_matrix[Pin.types.NOCONNECT][Pin.types.PULLDN] = [ERROR, ""] conflict_matrix[Pin.types.NOCONNECT][Pin.types.UNSPEC] = [ERROR, ""] conflict_matrix[Pin.types.NOCONNECT][Pin.types.PWRIN] = [ERROR, ""] conflict_matrix[Pin.types.NOCONNECT][Pin.types.PWROUT] = [ERROR, ""] conflict_matrix[Pin.types.NOCONNECT][Pin.types.OPENCOLL] = [ERROR, ""] conflict_matrix[Pin.types.NOCONNECT][Pin.types.OPENEMIT] = [ERROR, ""] conflict_matrix[Pin.types.NOCONNECT][Pin.types.NOCONNECT] = [ERROR, ""] conflict_matrix[Pin.types.PULLUP][Pin.types.PULLUP] = [ WARNING, "Multiple pull-ups connected.", ] conflict_matrix[Pin.types.PULLDN][Pin.types.PULLDN] = [ WARNING, "Multiple pull-downs connected.", ] conflict_matrix[Pin.types.PULLUP][Pin.types.PULLDN] = [ ERROR, "Pull-up connected to pull-down.", ] # Fill-in the other half of the symmetrical contention matrix by looking # for entries that != OK at position (r,c) and copying them to position # (c,r). cols = list(conflict_matrix.keys()) for c in cols: for r in list(conflict_matrix[c].keys()): conflict_matrix[r][c] = conflict_matrix[c][r]

34.57638

87

0.569742

21,292

0.790496

0

1,338

0.049675

0

11,516

0.427548

a14dc76d87023f8e5ab3f4a7babd9708c41bf004

34,030

py

Python

Project1/cl1_p1_wsd.py

Sanghyun-Hong/NLPProjects

9f81fa680946648f64ac25e5ca8197e9f3386deb

[ "MIT" ]

null

Project1/cl1_p1_wsd.py

Sanghyun-Hong/NLPProjects

9f81fa680946648f64ac25e5ca8197e9f3386deb

[ "MIT" ]

null

Project1/cl1_p1_wsd.py

Sanghyun-Hong/NLPProjects

9f81fa680946648f64ac25e5ca8197e9f3386deb

[ "MIT" ]

null

import numpy as np import operator # SHHONG: custom modules imported import json import random import itertools from math import pow, log from collections import Counter import os import sys sys.stdout = open(os.devnull, 'w') """ CMSC723 / INST725 / LING723 -- Fall 2016 Project 1: Implementing Word Sense Disambiguation Systems """ """ read one of train, dev, test subsets subset - one of train, dev, test output is a tuple of three lists labels: one of the 6 possible senses <cord, division, formation, phone, product, text > targets: the index within the text of the token to be disambiguated texts: a list of tokenized and normalized text input (note that there can be multiple sentences) """ import nltk #### added dev_manual to the subset of allowable files def read_dataset(subset): labels = [] texts = [] targets = [] if subset in ['train', 'dev', 'test', 'dev_manual']: with open('data/wsd_'+subset+'.txt') as inp_hndl: for example in inp_hndl: label, text = example.strip().split('\t') text = nltk.word_tokenize(text.lower().replace('" ','"')) if 'line' in text: ambig_ix = text.index('line') elif 'lines' in text: ambig_ix = text.index('lines') else: ldjal targets.append(ambig_ix) labels.append(label) texts.append(text) return (labels, targets, texts) else: print '>>>> invalid input !!! <<<<<' """ computes f1-score of the classification accuracy gold_labels - is a list of the gold labels predicted_labels - is a list of the predicted labels output is a tuple of the micro averaged score and the macro averaged score """ import sklearn.metrics #### changed method name from eval because of naming conflict with python keyword def eval_performance(gold_labels, predicted_labels): return ( sklearn.metrics.f1_score(gold_labels, predicted_labels, average='micro'), sklearn.metrics.f1_score(gold_labels, predicted_labels, average='macro') ) """ a helper method that takes a list of predictions and writes them to a file (1 prediction per line) predictions - list of predictions (strings) file_name - name of the output file """ def write_predictions(predictions, file_name): with open(file_name, 'w') as outh: for p in predictions: outh.write(p+'\n') """ Trains a naive bayes model with bag of words features and computes the accuracy on the test set train_texts, train_targets, train_labels are as described in read_dataset above The same thing applies to the reset of the parameters. """ def run_bow_naivebayes_classifier(train_texts, train_targets, train_labels, dev_texts, dev_targets, dev_labels, test_texts, test_targets, test_labels): # control variables improved = True alpha = 0.04 silent = True # Part 2.1 (c_s/c_sw) c_s = dict.fromkeys(set(train_labels), 0) multiples = list(itertools.product(c_s.keys(), ['time', 'loss', 'export'])) c_sw = dict.fromkeys(multiples, 0) t_w = [each_word for each_text in train_texts for each_word in each_text] multiples = list(itertools.product(c_s.keys(), t_w)) t_sw = dict.fromkeys(multiples, 0) for idx, label in enumerate(train_labels): cur_text = train_texts[idx] # compute c_s c_s[label] += len(cur_text) # compute c_sw time_cnt = cur_text.count('time') loss_cnt = cur_text.count('loss') export_cnt = cur_text.count('export') c_sw[(label, 'time')] += time_cnt c_sw[(label, 'loss')] += loss_cnt c_sw[(label, 'export')] += export_cnt # compute t_sw (total occurances): of (label, word): occurances for each_word in cur_text: t_sw[(label, each_word)] += 1 # total # of distinct words: will be used for smoothing t_dw = Counter(t_w) if not silent: print '{:<11} | {:<10} | {:<10} | {:<10} | {:<10} | {:<10} | {:<10} |'.\ format('s', 'cord', 'division', 'formation', 'phone', 'product', 'text') print '------------------------------------------------------------------------------------------' print '{:<11} | {:<10} | {:<10} | {:<10} | {:<10} | {:<10} | {:<10} |'.\ format('c(s)', c_s['cord'], c_s['division'], c_s['formation'], c_s['phone'], c_s['product'], c_s['text']) print '{:<11} | {:<10} | {:<10} | {:<10} | {:<10} | {:<10} | {:<10} |'.\ format('c(s,time)', c_sw[('cord', 'time')], c_sw[('division', 'time')], c_sw[('formation', 'time')], \ c_sw[('phone', 'time')], c_sw[('product', 'time')], c_sw[('text', 'time')]) print '{:<11} | {:<10} | {:<10} | {:<10} | {:<10} | {:<10} | {:<10} |'.\ format('c(s,loss)', c_sw[('cord', 'loss')], c_sw[('division', 'loss')], c_sw[('formation', 'loss')], \ c_sw[('phone', 'loss')], c_sw[('product', 'loss')], c_sw[('text', 'loss')]) print '{:<11} | {:<10} | {:<10} | {:<10} | {:<10} | {:<10} | {:<10} |'.\ format('c(s,export)', c_sw[('cord', 'export')], c_sw[('division', 'export')], c_sw[('formation', 'export')], \ c_sw[('phone', 'export')], c_sw[('product', 'export')], c_sw[('text', 'export')]) print '------------------------------------------------------------------------------------------' print ' total distinct words: %d ' % (len(t_dw.keys())) # Part 2.2 (p_s/p_ws) total_occurances = float(sum(c_s.values())) label_count = Counter(train_labels) p_s = {key: (value / float( sum( label_count.values() )) ) for key, value in label_count.iteritems()} if improved: p_ws = {key: ( (value + alpha) / \ (float(c_s[key[0]]) + alpha*len(t_dw.keys())) ) \ for key, value in c_sw.iteritems()} t_ws = {key: ( (value + alpha) / \ (float(c_s[key[0]]) + alpha*len(t_dw.keys())) ) \ for key, value in t_sw.iteritems()} else: p_ws = {key: (value / float(c_s[key[0]])) for key, value in c_sw.iteritems()} t_ws = {key: (value / float(c_s[key[0]])) for key, value in t_sw.iteritems()} # normalization steps norm_denominators = { 'time': 0.0, 'loss': 0.0, 'export': 0.0 } for key, value in p_ws.iteritems(): norm_denominators[key[1]] += value p_ws_norm = {key: (value / norm_denominators[key[1]]) for key, value in p_ws.iteritems()} p_ws = p_ws_norm if not silent: print '------------------------------------------------------------------------------------------' print '{:<11} | {:<10.8f} | {:<10.8f} | {:<10.8f} | {:<10.8f} | {:<10.8f} | {:<10.8f} |'.\ format('p(s)', p_s['cord'], p_s['division'], p_s['formation'], p_s['phone'], p_s['product'], p_s['text']) print '{:<11} | {:<10.8f} | {:<10.8f} | {:<10.8f} | {:<10.8f} | {:<10.8f} | {:<10.8f} |'.\ format('p(time|s)', p_ws[('cord', 'time')], p_ws[('division', 'time')], p_ws[('formation', 'time')], \ p_ws[('phone', 'time')], p_ws[('product', 'time')], p_ws[('text', 'time')]) print '{:<11} | {:<10.8f} | {:<10.8f} | {:<10.8f} | {:<10.8f} | {:<10.8f} | {:<10.8f} |'.\ format('p(loss|s)', p_ws[('cord', 'loss')], p_ws[('division', 'loss')], p_ws[('formation', 'loss')], \ p_ws[('phone', 'loss')], p_ws[('product', 'loss')], p_ws[('text', 'loss')]) print '{:<11} | {:<10.8f} | {:<10.8f} | {:<10.8f} | {:<10.8f} | {:<10.8f} | {:<10.8f} |'.\ format('p(export|s)', p_ws[('cord', 'export')], p_ws[('division', 'export')], p_ws[('formation', 'export')], \ p_ws[('phone', 'export')], p_ws[('product', 'export')], p_ws[('text', 'export')]) # Part 2.3 (p_sxd, on the 1st line on test set) p_sxd = dict.fromkeys(c_s.keys(), 0.0) lp_sxd = dict.fromkeys(c_s.keys(), 0.0) cur_text = dev_texts[0] for key in p_sxd.keys(): # compute p for each class if improved: tp_sxd = p_s[key] tlp_sxd = log(p_s[key]) else: tp_sxd = p_s[key] # compute for each word for each_word in cur_text: if t_ws.has_key((key, each_word)): if improved: tp_sxd *= t_ws[(key, each_word)] tlp_sxd += log(t_ws[(key, each_word)]) else: tp_sxd *= t_ws[(key, each_word)] # add to the dict if improved: p_sxd[key] = tp_sxd lp_sxd[key] = tlp_sxd else: p_sxd[key] = tp_sxd if not silent: print '------------------------------------------------------------------------------------------' print ' %s | %s | %s | %s | %s | %s | %s |' % \ ('p(s|X)', p_sxd['cord'], p_sxd['division'], p_sxd['formation'], \ p_sxd['phone'], p_sxd['product'], p_sxd['text']) print '------------------------------------------------------------------------------------------' print ' 1st label in dev : %s ' % (dev_labels[0]) print ' 1st text in dev[:5]: %s ' % (dev_texts[0][:5]) if improved: print '------------------------------------------------------------------------------------------' print '{:<11} | {:<10.8f} | {:<10.8f} | {:<10.8f} | {:<10.8f} | {:<10.8f} | {:<10.8f} |'.\ format('log(p(s|X))', lp_sxd['cord'], lp_sxd['division'], lp_sxd['formation'], \ lp_sxd['phone'], lp_sxd['product'], lp_sxd['text']) # Part 2.4: compute all the prob on the test dataset p_sx = list() for idx, text in enumerate(test_texts): t_prob = dict.fromkeys(c_s.keys(), 0.0) for key in t_prob.keys(): # compute p for each class if improved: tp_sxt = log(p_s[key]) else: tp_sxt = p_s[key] for each_word in text: if t_ws.has_key((key, each_word)): if improved: tp_sxt += log(t_ws[(key, each_word)]) else: tp_sxt *= t_ws[(key, each_word)] # add to the dict t_prob[key] = tp_sxt # add dict to the entire list p_sx.append(t_prob) # Part 2.4 (run the classifier for all) labels_predicted = list() for idx, label in enumerate(test_labels): maximum_probs = max(p_sx[idx].values()) label_prediction = [key for key, value in p_sx[idx].iteritems() if value == maximum_probs] label_prediction = random.choice(label_prediction) # based on the prob labels_predicted.append(label_prediction) naivebayes_performance = eval_performance(test_labels, labels_predicted) # save the implementation to the file with open('q4p2.txt', 'wb') as q4p2_output: for each_label in labels_predicted: q4p2_output.write(each_label+'\n') # Part 2.5 (do more tuning for the classifier) # - Laplace smoothing # - Log likelihoods if not silent: print '------------------------------------------------------------------------------------------' return 'Naive Bayes: micro/macro = [%.2f, %.2f] @ (alpha: %s)' % \ (naivebayes_performance[0]*100, naivebayes_performance[1]*100, alpha) ## extract all the distinct words from a set of texts ## return a dictionary {word:index} that maps each word to a unique index def extract_all_words(texts,prev_set=set()): all_words = prev_set for t in texts: for w in t: all_words.add(w) all_words_idx = {} for i,w in enumerate(all_words): all_words_idx[w] = i return all_words_idx ## extract all distinct labels from a dataset ## return a dictionary {label:index} that maps each label to a unique index def extract_all_labels(labels): distinct_labels = list(set(labels)) all_labels_idx = {} for i,l in enumerate(distinct_labels): all_labels_idx[l] = i return all_labels_idx ## construct a bow feature matrix for a set of instances ## the returned matrix has the size NUM_INSTANCES X NUM_FEATURES def extract_features(all_words_idx,all_labels_idx,texts): NUM_FEATURES = len(all_words_idx.keys()) NUM_INSTANCES = len(texts) features_matrix = np.zeros((NUM_INSTANCES,NUM_FEATURES)) for i,instance in enumerate(texts): for word in instance: if all_words_idx.get(word,None) is None: continue features_matrix[i][all_words_idx[word]] += 1 return features_matrix ## compute the feature vector for a set of words and a given label ## the features are computed as described in Slide #19 of: ## http://www.cs.umd.edu/class/fall2016/cmsc723/slides/slides_02.pdf def get_features_for_label(instance,label,class_labels): num_labels = len(class_labels) num_feats = len(instance) feats = np.zeros(len(instance)*num_labels+1) assert len(feats[num_feats*label:num_feats*label+num_feats]) == len(instance) feats[num_feats*label:num_feats*label+num_feats] = instance return feats ## get the predicted label for a given instance ## the predicted label is the one with the highest dot product of theta*feature_vector ## return the predicted label, the dot product scores for all labels and the features computed for all labels for that instance def get_predicted_label(inst,class_labels,theta): all_labels_scores = {} all_labels_features = {} for lbl in class_labels: feat_vec = get_features_for_label(inst,lbl,class_labels) assert len(feat_vec) == len(theta) all_labels_scores[lbl] = np.dot(feat_vec,theta) predicted_label = max(all_labels_scores.iteritems(), key=operator.itemgetter(1))[0] return predicted_label ## train the perceptron by iterating over the entire training dataset ## the algorithm is an implementation of the pseudocode from Slide #23 of: ## http://www.cs.umd.edu/class/fall2016/cmsc723/slides/slides_03.pdf def train_perceptron(train_features,train_labels,class_labels,num_features): NO_MAX_ITERATIONS = 20 np.random.seed(0) theta = np.zeros(num_features) print '# Training Instances:',len(train_features) num_iterations = 0 cnt_updates_total = 0 cnt_updates_prev = 0 m = np.zeros(num_features) print '# Total Updates / # Current Iteration Updates:' for piter in range(NO_MAX_ITERATIONS): shuffled_indices = np.arange(len(train_features)) np.random.shuffle(shuffled_indices) cnt_updates_crt = 0 for i in shuffled_indices: inst = train_features[i] actual_label = train_labels[i] predicted_label = get_predicted_label(inst,class_labels,theta) if predicted_label != actual_label: cnt_updates_total += 1 cnt_updates_crt += 1 theta = theta + get_features_for_label(inst,actual_label,class_labels) - get_features_for_label(inst,predicted_label,class_labels) m = m + theta num_iterations += 1 print cnt_updates_total,'/',cnt_updates_crt if cnt_updates_crt == 0: break theta = m/cnt_updates_total print '# Iterations:',piter print '# Iterations over instances:',num_iterations print '# Total Updates:',cnt_updates_total return theta ## return the predictions of the perceptron on a test set def test_perceptron(theta,test_features,test_labels,class_labels): predictions = [] for inst in test_features: predicted_label = get_predicted_label(inst,class_labels,theta) predictions.append(predicted_label) return predictions """ Trains a perceptron model with bag of words features and computes the accuracy on the test set train_texts, train_targets, train_labels are as described in read_dataset above The same thing applies to the reset of the parameters. """ def run_bow_perceptron_classifier(train_texts, train_targets,train_labels, dev_texts, dev_targets,dev_labels, test_texts, test_targets, test_labels): all_words_idx = extract_all_words(train_texts) all_labels_idx = extract_all_labels(train_labels) num_features = len(all_words_idx.keys())*len(all_labels_idx.keys())+1 class_labels = all_labels_idx.values() train_features = extract_features(all_words_idx,all_labels_idx,train_texts) train_labels = map(lambda e: all_labels_idx[e],train_labels) test_features = extract_features(all_words_idx,all_labels_idx,test_texts) test_labels = map(lambda e: all_labels_idx[e],test_labels) for l in class_labels: inst = train_features[0] ffl = get_features_for_label(inst,l,class_labels) assert False not in (inst == ffl[l*len(inst):(l+1)*len(inst)]) theta = train_perceptron(train_features,train_labels,class_labels,num_features) test_predictions = test_perceptron(theta,test_features,test_labels,class_labels) eval_test = eval_performance(test_labels,test_predictions) inverse_labels_index = {} for k in all_labels_idx.keys(): inverse_labels_index[all_labels_idx[k]] = k test_predictions_names = map(lambda e: inverse_labels_index[e],test_predictions) with open('q3p3.txt', 'wb') as file_output: for each_label in test_predictions_names: file_output.write(each_label+'\n') return ('test-micro=%d%%, test-macro=%d%%' % (int(eval_test[0]*100),int(eval_test[1]*100))) """ Trains a naive bayes model with bag of words features + two additional features and computes the accuracy on the test set train_texts, train_targets, train_labels are as described in read_dataset above The same thing applies to the reset of the parameters. """ def run_extended_bow_naivebayes_classifier(train_texts, train_targets,train_labels, dev_texts, dev_targets,dev_labels, test_texts, test_targets, test_labels): # control variables improved = True alpha = 0.04 silent = True RUN_EXP = 'Both' # set to 'B', None, or 'Both' # feature extensions (A) if 'A' in RUN_EXP: train_features, dev_features, test_features = get_feature_A(train_texts, train_targets, train_labels, dev_texts, dev_targets, dev_labels, test_texts, test_targets, test_labels) for idx, each_text in enumerate(train_texts): each_text.append(str(float(train_features[idx]))) for idx, each_text in enumerate(dev_texts): each_text.append(str(float(dev_features[idx]))) for idx, each_text in enumerate(test_texts): each_text.append(str(float(test_features[idx]))) # feature extensions (B) elif 'B' in RUN_EXP: train_features, dev_features, test_features = get_feature_B(train_texts, train_targets, train_labels, dev_texts, dev_targets, dev_labels, test_texts, test_targets, test_labels) for idx, each_text in enumerate(train_texts): each_text.append(str(int(train_features[idx]))) for idx, each_text in enumerate(dev_texts): each_text.append(str(int(dev_features[idx]))) for idx, each_text in enumerate(test_texts): each_text.append(str(int(test_features[idx]))) # feature extensions with both two A and B elif 'Both' in RUN_EXP: train_features_A, dev_features_A, test_features_A = get_feature_A(train_texts, train_targets, train_labels, dev_texts, dev_targets, dev_labels, test_texts, test_targets, test_labels) train_features_B, dev_features_B, test_features_B = get_feature_B(train_texts, train_targets, train_labels, dev_texts, dev_targets, dev_labels, test_texts, test_targets, test_labels) for idx, each_text in enumerate(train_texts): each_text.append(str(float(train_features_A[idx]))) each_text.append(str(int(train_features_B[idx]))) for idx, each_text in enumerate(dev_texts): each_text.append(str(float(dev_features_A[idx]))) each_text.append(str(intern(train_features_B[idx]))) for idx, each_text in enumerate(test_texts): each_text.append(str(float(test_features_A[idx]))) each_text.append(str(int(train_features_B[idx]))) else: train_features, dev_features, test_features = None, None, None if not silent: print ' extension of the Naive Bayes classifier w. feature set: [%s] ' % (RUN_EXP) print '------------------------------------------------------------------------------------------' # Part 2.1 (c_s/c_sw) c_s = dict.fromkeys(set(train_labels), 0) multiples = list(itertools.product(c_s.keys(), ['time', 'loss', 'export'])) c_sw = dict.fromkeys(multiples, 0) t_w = [each_word for each_text in train_texts for each_word in each_text] multiples = list(itertools.product(c_s.keys(), t_w)) t_sw = dict.fromkeys(multiples, 0) for idx, label in enumerate(train_labels): cur_text = train_texts[idx] # compute c_s c_s[label] += len(cur_text) # compute c_sw time_cnt = cur_text.count('time') loss_cnt = cur_text.count('loss') export_cnt = cur_text.count('export') c_sw[(label, 'time')] += time_cnt c_sw[(label, 'loss')] += loss_cnt c_sw[(label, 'export')] += export_cnt # compute t_sw (total occurances): of (label, word): occurances for each_word in cur_text: t_sw[(label, each_word)] += 1 # total # of distinct words: will be used for smoothing t_dw = Counter(t_w) if not silent: print '{:<11} | {:<10} | {:<10} | {:<10} | {:<10} | {:<10} | {:<10} |'.\ format('s', 'cord', 'division', 'formation', 'phone', 'product', 'text') print '------------------------------------------------------------------------------------------' print '{:<11} | {:<10} | {:<10} | {:<10} | {:<10} | {:<10} | {:<10} |'.\ format('c(s)', c_s['cord'], c_s['division'], c_s['formation'], c_s['phone'], c_s['product'], c_s['text']) print '{:<11} | {:<10} | {:<10} | {:<10} | {:<10} | {:<10} | {:<10} |'.\ format('c(s,time)', c_sw[('cord', 'time')], c_sw[('division', 'time')], c_sw[('formation', 'time')], \ c_sw[('phone', 'time')], c_sw[('product', 'time')], c_sw[('text', 'time')]) print '{:<11} | {:<10} | {:<10} | {:<10} | {:<10} | {:<10} | {:<10} |'.\ format('c(s,loss)', c_sw[('cord', 'loss')], c_sw[('division', 'loss')], c_sw[('formation', 'loss')], \ c_sw[('phone', 'loss')], c_sw[('product', 'loss')], c_sw[('text', 'loss')]) print '{:<11} | {:<10} | {:<10} | {:<10} | {:<10} | {:<10} | {:<10} |'.\ format('c(s,export)', c_sw[('cord', 'export')], c_sw[('division', 'export')], c_sw[('formation', 'export')], \ c_sw[('phone', 'export')], c_sw[('product', 'export')], c_sw[('text', 'export')]) print '------------------------------------------------------------------------------------------' print ' total distinct words: %d ' % (len(t_dw.keys())) # Part 2.2 (p_s/p_ws) total_occurances = float(sum(c_s.values())) label_count = Counter(train_labels) p_s = {key: (value / float( sum( label_count.values() )) ) for key, value in label_count.iteritems()} if improved: p_ws = {key: ( (value + alpha) / \ (float(c_s[key[0]]) + alpha*len(t_dw.keys())) ) \ for key, value in c_sw.iteritems()} t_ws = {key: ( (value + alpha) / \ (float(c_s[key[0]]) + alpha*len(t_dw.keys())) ) \ for key, value in t_sw.iteritems()} else: p_ws = {key: (value / float(c_s[key[0]])) for key, value in c_sw.iteritems()} t_ws = {key: (value / float(c_s[key[0]])) for key, value in t_sw.iteritems()} # normalization steps norm_denominators = { 'time': 0.0, 'loss': 0.0, 'export': 0.0 } for key, value in p_ws.iteritems(): norm_denominators[key[1]] += value p_ws_norm = {key: (value / norm_denominators[key[1]]) for key, value in p_ws.iteritems()} p_ws = p_ws_norm if not silent: print '------------------------------------------------------------------------------------------' print '{:<11} | {:<10.8f} | {:<10.8f} | {:<10.8f} | {:<10.8f} | {:<10.8f} | {:<10.8f} |'.\ format('p(s)', p_s['cord'], p_s['division'], p_s['formation'], p_s['phone'], p_s['product'], p_s['text']) print '{:<11} | {:<10.8f} | {:<10.8f} | {:<10.8f} | {:<10.8f} | {:<10.8f} | {:<10.8f} |'.\ format('p(time|s)', p_ws[('cord', 'time')], p_ws[('division', 'time')], p_ws[('formation', 'time')], \ p_ws[('phone', 'time')], p_ws[('product', 'time')], p_ws[('text', 'time')]) print '{:<11} | {:<10.8f} | {:<10.8f} | {:<10.8f} | {:<10.8f} | {:<10.8f} | {:<10.8f} |'.\ format('p(loss|s)', p_ws[('cord', 'loss')], p_ws[('division', 'loss')], p_ws[('formation', 'loss')], \ p_ws[('phone', 'loss')], p_ws[('product', 'loss')], p_ws[('text', 'loss')]) print '{:<11} | {:<10.8f} | {:<10.8f} | {:<10.8f} | {:<10.8f} | {:<10.8f} | {:<10.8f} |'.\ format('p(export|s)', p_ws[('cord', 'export')], p_ws[('division', 'export')], p_ws[('formation', 'export')], \ p_ws[('phone', 'export')], p_ws[('product', 'export')], p_ws[('text', 'export')]) # Part 2.3 (p_sxd, on the 1st line on test set) p_sxd = dict.fromkeys(c_s.keys(), 0.0) lp_sxd = dict.fromkeys(c_s.keys(), 0.0) cur_text = dev_texts[0] for key in p_sxd.keys(): # compute p for each class if improved: tp_sxd = p_s[key] tlp_sxd = log(p_s[key]) else: tp_sxd = p_s[key] # compute for each word for each_word in cur_text: if t_ws.has_key((key, each_word)): if improved: tp_sxd *= t_ws[(key, each_word)] tlp_sxd += log(t_ws[(key, each_word)]) else: tp_sxd *= t_ws[(key, each_word)] # add to the dict if improved: p_sxd[key] = tp_sxd lp_sxd[key] = tlp_sxd else: p_sxd[key] = tp_sxd if not silent: print '------------------------------------------------------------------------------------------' print ' %s | %s | %s | %s | %s | %s | %s |' % \ ('p(s|X)', p_sxd['cord'], p_sxd['division'], p_sxd['formation'], \ p_sxd['phone'], p_sxd['product'], p_sxd['text']) print '------------------------------------------------------------------------------------------' print ' 1st label in dev : %s ' % (dev_labels[0]) print ' 1st text in dev[:5]: %s ' % (dev_texts[0][:5]) if improved: print '------------------------------------------------------------------------------------------' print '{:<11} | {:<10.8f} | {:<10.8f} | {:<10.8f} | {:<10.8f} | {:<10.8f} | {:<10.8f} |'.\ format('log(p(s|X))', lp_sxd['cord'], lp_sxd['division'], lp_sxd['formation'], \ lp_sxd['phone'], lp_sxd['product'], lp_sxd['text']) # Part 2.4: compute all the prob on the test dataset p_sx = list() for idx, text in enumerate(test_texts): t_prob = dict.fromkeys(c_s.keys(), 0.0) for key in t_prob.keys(): # compute p for each class if improved: tp_sxt = log(p_s[key]) else: tp_sxt = p_s[key] for each_word in text: if t_ws.has_key((key, each_word)): if improved: tp_sxt += log(t_ws[(key, each_word)]) else: tp_sxt *= t_ws[(key, each_word)] # add to the dict t_prob[key] = tp_sxt # add dict to the entire list p_sx.append(t_prob) # Part 2.4 (run the classifier for all) labels_predicted = list() for idx, label in enumerate(test_labels): maximum_probs = max(p_sx[idx].values()) label_prediction = [key for key, value in p_sx[idx].iteritems() if value == maximum_probs] label_prediction = random.choice(label_prediction) # based on the prob labels_predicted.append(label_prediction) naivebayes_performance = eval_performance(test_labels, labels_predicted) # save the implementation to the file with open('q4p4_nb.txt', 'wb') as q4p4_nb_output: for each_label in labels_predicted: q4p4_nb_output.write(each_label+'\n') # Part 2.5 (do more tuning for the classifier) # - Laplace smoothing # - Log likelihoods if not silent: print '------------------------------------------------------------------------------------------' return 'Naive Bayes: micro/macro = [%.2f, %.2f] @ (alpha: %s)' % \ (naivebayes_performance[0]*100, naivebayes_performance[1]*100, alpha) ## this feature is just a random number generated for each instance def get_feature_A(train_texts, train_targets,train_labels, dev_texts, dev_targets,dev_labels, test_texts, test_targets, test_label): # call this everytime, makes the same random number np.random.seed(0) train_feature_vector = np.random.random_sample((len(train_texts),)) dev_feature_vector = np.random.random_sample((len(dev_texts),)) test_feature_vector = np.random.random_sample((len(test_texts),)) return train_feature_vector,dev_feature_vector,test_feature_vector ## this feature encodes the number of distinct words in each instance def get_feature_B(train_texts, train_targets,train_labels, dev_texts, dev_targets,dev_labels, test_texts, test_targets, test_label): train_feature_vector = np.zeros(len(train_texts)) dev_feature_vector = np.zeros(len(dev_texts)) test_feature_vector = np.zeros(len(test_texts)) for i,text in enumerate(train_texts): nw = len(set(text)) train_feature_vector[i] = nw for i,text in enumerate(dev_texts): nw = len(set(text)) dev_feature_vector[i] = nw for i,text in enumerate(test_texts): nw = len(set(text)) test_feature_vector[i] = nw return train_feature_vector,dev_feature_vector,test_feature_vector """ Trains a perceptron model with bag of words features + two additional features and computes the accuracy on the test set train_texts, train_targets, train_labels are as described in read_dataset above The same thing applies to the reset of the parameters. """ def run_extended_bow_perceptron_classifier(train_texts, train_targets,train_labels, dev_texts, dev_targets,dev_labels, test_texts, test_targets, test_labels): RUN_EXP_A = True # set to True for running on feature A RUN_EXP_B = True # set to True for running on feature B num_extra_features = 0 if RUN_EXP_A: train_new_feature_vectorA,dev_new_feature_vectorA,test_new_feature_vectorA = get_feature_A(train_texts, train_targets,train_labels, dev_texts, dev_targets,dev_labels, test_texts, test_targets, test_labels) num_extra_features += 1 if RUN_EXP_B: train_new_feature_vectorB,dev_new_feature_vectorB,test_new_feature_vectorB = get_feature_B(train_texts, train_targets,train_labels, dev_texts, dev_targets,dev_labels, test_texts, test_targets, test_labels) num_extra_features += 1 all_words_idx = extract_all_words(train_texts) all_labels_idx = extract_all_labels(train_labels) num_features = (len(all_words_idx.keys())+num_extra_features)*len(all_labels_idx.keys())+1 class_labels = all_labels_idx.values() train_features = extract_features(all_words_idx,all_labels_idx,train_texts) train_labels = map(lambda e: all_labels_idx[e],train_labels) test_features = extract_features(all_words_idx,all_labels_idx,test_texts) test_labels = map(lambda e: all_labels_idx[e],test_labels) if RUN_EXP_A: train_features = np.c_[train_features, train_new_feature_vectorA] test_features = np.c_[test_features, test_new_feature_vectorA] if RUN_EXP_B: train_features = np.c_[train_features, train_new_feature_vectorB] test_features = np.c_[test_features, test_new_feature_vectorB] for l in class_labels: inst = train_features[0] ffl = get_features_for_label(inst,l,class_labels) assert False not in (inst == ffl[l*len(inst):(l+1)*len(inst)]) theta = train_perceptron(train_features,train_labels,class_labels,num_features) test_predictions = test_perceptron(theta,test_features,test_labels,class_labels) eval_test = eval_performance(test_labels,test_predictions) inverse_labels_index = {} for k in all_labels_idx.keys(): inverse_labels_index[all_labels_idx[k]] = k test_predictions_names = map(lambda e: inverse_labels_index[e],test_predictions) with open('q4p4_pn.txt', 'wb') as file_output: for each_label in test_predictions_names: file_output.write(each_label+'\n') return ('test-micro=%d%%, test-macro=%d%%' % (int(eval_test[0]*100),int(eval_test[1]*100))) # Part 1.1 def run_most_frequent_class_classifier(train_texts, train_targets,train_labels, dev_texts, dev_targets,dev_labels, test_texts, test_targets, test_labels): labels_freq = {} for l in train_labels: if labels_freq.get(l,None) is None: labels_freq[l] = 0 labels_freq[l] += 1 most_frequent_label = max(labels_freq.iteritems(), key=operator.itemgetter(1))[0] train_pred = [most_frequent_label]*len(train_labels) dev_pred = [most_frequent_label]*len(dev_labels) assert train_pred[2] == train_labels[2] eval_train = eval_performance(train_labels,train_pred) eval_dev = eval_performance(dev_labels,dev_pred) return ('training-micro=%d%%, training-macro=%d%%, dev-micro=%d%%, dev-macro=%d%%' % (int(eval_train[0]*100),int(eval_train[1]*100),int(eval_dev[0]*100),int(eval_dev[1]*100))) # Part 1.2 def run_inner_annotator_agreement(train_texts, train_targets,train_labels, dev_texts, dev_targets,dev_labels, test_texts, test_targets, test_labels): dev_labels_manual, dev_targets_manual, dev_texts_manual = read_dataset('dev_manual') return '%.2f' % sklearn.metrics.cohen_kappa_score(dev_labels[:20],dev_labels_manual) """ Main (able to change the classifier to other ones) """ if __name__ == "__main__": # reading, tokenizing, and normalizing data train_labels, train_targets, train_texts = read_dataset('train') dev_labels, dev_targets, dev_texts = read_dataset('dev') test_labels, test_targets, test_texts = read_dataset('test') #running the classifier test_scores = run_bow_perceptron_classifier(train_texts, train_targets, train_labels, dev_texts, dev_targets, dev_labels, test_texts, test_targets, test_labels) print test_scores

43.075949

211

0.614634

0

10,731

0.315339

a14fb8c57a2911a94e991dd47b577ec949e53771

640

py

Python

Week 7 Web pages/Task05.py

retverd/python_hse

cb9bfb092c1cf68ae0c53b9919ca24a71a8cbf88

[ "MIT" ]

null

Week 7 Web pages/Task05.py

retverd/python_hse

cb9bfb092c1cf68ae0c53b9919ca24a71a8cbf88

[ "MIT" ]

null

Week 7 Web pages/Task05.py

retverd/python_hse

cb9bfb092c1cf68ae0c53b9919ca24a71a8cbf88

[ "MIT" ]

null

# Мы сохранили страницу с википедии про языки программирования и сохранили по адресу # https://stepik.org/media/attachments/lesson/209717/1.html # # Скачайте её с помощью скрипта на Питоне и посчитайте, какой язык упоминается чаще Python или C++ (ответ должен быть # одной из этих двух строк). Необходимо просто подсчитать количество вхождений слова Python или C++ как подстроки. from urllib.request import urlopen response = urlopen('https://stepik.org/media/attachments/lesson/209717/1.html') html = response.read().decode('utf-8') c = html.count('C++') p = html.count('Python') if c > p: print('C++') else: print('Python')

30.47619

117

0.739063

0

709

0.803855

a150c0cbc599ebc411b4f81c6fa3b0405cf1395b

31,794

py

Python

tests/test_bio/test_cell.py

jfaccioni/clovars

64e24286a2dc185490384aeb08027d88eb9462c4

[ "MIT" ]

null

tests/test_bio/test_cell.py

jfaccioni/clovars

64e24286a2dc185490384aeb08027d88eb9462c4

[ "MIT" ]

null

tests/test_bio/test_cell.py

jfaccioni/clovars

64e24286a2dc185490384aeb08027d88eb9462c4

[ "MIT" ]

null

import unittest from unittest import mock from unittest.mock import MagicMock from clovars.abstract import Circle from clovars.bio import Cell, Treatment from clovars.scientific import ConstantCellSignal, CellSignal, GaussianCellSignal, Gaussian from clovars.utils import SimulationError from tests import NotEmptyTestCase class TestCell(NotEmptyTestCase): """Class representing unit-tests for clovars.bio.cell.Cell class.""" default_delta = 100 control_treatment = Treatment( name="Control", division_curve=Gaussian(loc=24.0, scale=5), death_curve=Gaussian(loc=32, scale=5), ) @classmethod def setUpClass(cls) -> None: """Sets up the entire test suite by setting the default Treatment.""" pass def setUp(self) -> None: """Sets up the test case subject (a Cell instance).""" self.cell = Cell() # def test_cell_has_default_treatment_class_attribute(self) -> None: # """Tests whether a Cell has a "default_treatment" class attribute (a Treatment instance).""" # self.assertTrue(hasattr(self.cell, 'default_treatment')) # self.assertTrue(hasattr(Cell, 'default_treatment')) # self.assertIsInstance(self.cell.default_treatment, Treatment) def test_cell_has_name_attribute(self) -> None: """Tests whether a Cell has a "name" attribute (a string).""" self.assertTrue(hasattr(self.cell, 'name')) self.assertIsInstance(self.cell.name, str) def test_cell_has_max_speed_attribute(self) -> None: """Tests whether a Cell has a "max_speed" attribute (a float value).""" self.assertTrue(hasattr(self.cell, 'max_speed')) self.assertIsInstance(self.cell.max_speed, float) def test_cell_has_fate_attribute(self) -> None: """Tests whether a Cell has a "fate" attribute (a string).""" self.assertTrue(hasattr(self.cell, 'fate')) self.assertIsInstance(self.cell.fate, str) def test_fate_attribute_starts_as_migration(self) -> None: """Tests whether a Cell starts with its "fate" attribute set to "migration".""" self.assertEqual(Cell().fate, "migration") def test_cell_has_seconds_since_birth_attribute(self) -> None: """Tests whether a Cell has a "seconds_since_birth" attribute (an integer).""" self.assertTrue(hasattr(self.cell, 'seconds_since_birth')) self.assertIsInstance(self.cell.seconds_since_birth, int) def test_seconds_since_birth_attribute_starts_at_zero(self) -> None: """Tests whether a Cell starts with its "seconds_since_birth" attribute set to 0.""" self.assertEqual(Cell().seconds_since_birth, 0) def test_cell_has_alive_attribute(self) -> None: """Tests whether a Cell has an "alive" attribute (a boolean value).""" self.assertTrue(hasattr(self.cell, 'alive')) self.assertIsInstance(self.cell.alive, bool) def test_alive_attribute_starts_true(self) -> None: """Tests whether a Cell starts with its "alive" attribute set to True.""" self.assertEqual(Cell().alive, True) def test_cell_has_senescent_attribute(self) -> None: """Tests whether a Cell has a "senescent" attribute (a boolean value).""" self.assertTrue(hasattr(self.cell, 'senescent')) self.assertIsInstance(self.cell.senescent, bool) def test_senescent_attribute_starts_false(self) -> None: """Tests whether a Cell starts with its "senescent" attribute set to False.""" self.assertEqual(Cell().senescent, False) def test_cell_has_fitness_memory_attribute(self) -> None: """Tests whether a Cell has a "fitness_memory" attribute (a float).""" self.assertTrue(hasattr(self.cell, 'fitness_memory')) self.assertIsInstance(self.cell.fitness_memory, float) def test_fitness_memory_outside_zero_one_range_raises_error(self) -> None: """ Tests whether a Cell raises a SimulationError only when its "fitness_memory" attribute is initialized outside the [0, 1] interval. """ for fitness_memory in [0.0, 0.2, 0.4, 0.6, 0.8, 1.0]: try: Cell(fitness_memory=fitness_memory) except SimulationError: self.fail( "SimulationError was unexpectedly raised when initializing Cell" f" with fitness_memory = {fitness_memory}" ) for fitness_memory in [-0.1, 1.1]: with self.assertRaises(SimulationError): Cell(fitness_memory=fitness_memory) def test_cell_has_division_threshold_attribute(self) -> None: """Tests whether a Cell has a "division_threshold" attribute (a float).""" self.assertTrue(hasattr(self.cell, 'division_threshold')) self.assertIsInstance(self.cell.division_threshold, float) def test_division_threshold_outside_zero_one_range_raises_error(self) -> None: """ Tests whether a Cell raises a SimulationError only when its "division_threshold" attribute is initialized outside the [0, 1] interval. """ for division_threshold in [0.0, 0.2, 0.4, 0.6, 0.8, 1.0]: try: Cell(division_threshold=division_threshold) except SimulationError: self.fail( "SimulationError was unexpectedly raised when initializing Cell" f" with division_threshold = {division_threshold}" ) for division_threshold in [-0.1, 1.1]: with self.assertRaises(SimulationError): Cell(division_threshold=division_threshold) def test_cell_division_threshold_attribute_is_between_zero_and_one(self) -> None: """ Tests whether the "division_threshold" attribute (random float value) lies between 0 and 1 when it is initialized as a None value. """ for _ in range(10): cell = Cell(division_threshold=None) with self.subTest(cell=cell): self.assertGreaterEqual(cell.division_threshold, 0) self.assertLessEqual(cell.division_threshold, 1) def test_cell_has_death_threshold_attribute(self) -> None: """Tests whether a Cell has a "death_threshold" attribute (a float).""" self.assertTrue(hasattr(self.cell, 'death_threshold')) self.assertIsInstance(self.cell.death_threshold, float) def test_death_threshold_outside_zero_one_range_raises_error(self) -> None: """ Tests whether a Cell raises a SimulationError only when its "death_threshold" attribute is initialized outside the [0, 1] interval. """ for death_threshold in [0.0, 0.2, 0.4, 0.6, 0.8, 1.0]: try: Cell(death_threshold=death_threshold) except SimulationError: self.fail( "SimulationError was unexpectedly raised when initializing Cell" f" with death_threshold = {death_threshold}" ) for death_threshold in [-0.1, 1.1]: with self.assertRaises(SimulationError): Cell(death_threshold=death_threshold) def test_cell_death_threshold_attribute_is_between_zero_and_one(self) -> None: """ Tests whether the "death_threshold" attribute (random float value) lies between 0 and 1 when it is initialized as a None value. """ for _ in range(10): cell = Cell(death_threshold=None) with self.subTest(cell=cell): self.assertGreaterEqual(cell.death_threshold, 0) self.assertLessEqual(cell.death_threshold, 1) def test_cell_has_death_threshold_attribute_is_between_zero_and_one(self) -> None: """Tests whether the "death_threshold" attribute (random float value) lies between 0 and 1.""" for _ in range(10): cell = Cell() with self.subTest(cell=cell): self.assertGreaterEqual(cell.death_threshold, 0) self.assertLessEqual(cell.death_threshold, 1) def test_cell_has_circle_attribute(self) -> None: """Tests whether a Cell has a "circle" attribute (a Circle instance).""" self.assertTrue(hasattr(self.cell, 'circle')) self.assertIsInstance(self.cell.circle, Circle) def test_cell_has_signal_attribute(self) -> None: """Tests whether a Cell has a "signal" attribute (a CellSignal instance).""" self.assertTrue(hasattr(self.cell, 'signal')) self.assertIsInstance(self.cell.signal, CellSignal) def test_cell_uses_a_constant_signal_if_signal_argument_is_none(self) -> None: """Tests whether a Cell uses a ConstantCellSignal instance when initialized with signal=None.""" cell = Cell(signal=None) self.assertIsInstance(cell.signal, ConstantCellSignal) def test_cell_has_treatment_attribute(self) -> None: """Tests whether a Cell has a "treatment" attribute (a Treatment instance).""" self.assertTrue(hasattr(self.cell, 'treatment')) self.assertIsInstance(self.cell.treatment, Treatment) # def test_cell_uses_the_default_treatment_if_treatment_argument_is_none(self) -> None: # """Tests whether a Cell uses the "default_treatment" class attribute when initialized with treatment=None.""" # cell = Cell(signal=None) # self.assertIs(cell.treatment, self.cell.default_treatment) def test_calculate_division_chance_method_returns_chance_depending_on_the_cell_seconds_since_birth(self) -> None: """ Tests whether the "calculate_division_chance" method returns a chance between [0, 1] proportional to the Cell's age. """ self.cell.treatment = self.control_treatment # division stats: 24 (+-5) hours self.cell.seconds_since_birth = 0 # Very low chance of dividing right after birth self.assertLess(self.cell.calculate_division_chance(delta=self.default_delta), 0.1) self.cell.seconds_since_birth = 60 * 60 * 1000 # Very high chance of dividing after 1000 h self.assertGreater(self.cell.calculate_division_chance(delta=self.default_delta), 0.9) def test_calculate_death_chance_method_returns_chance_depending_on_the_cell_seconds_since_birth(self) -> None: """ Tests whether the "calculate_death_chance" method returns a chance between [0, 1] proportional to the Cell's age. """ self.cell.treatment = self.control_treatment # death stats: 24 (+-5) hours self.cell.seconds_since_birth = 0 # Very low chance of dying right after birth self.assertLess(self.cell.calculate_death_chance(delta=self.default_delta), 0.1) self.cell.seconds_since_birth = 60 * 60 * 1000 # Very high chance of dying after 1000 h self.assertGreater(self.cell.calculate_death_chance(delta=self.default_delta), 0.9) def test_cell_has_circle_attributes_as_properties(self) -> None: """Tests whether a Cell exposes relevant Circle attributes as properties.""" test_cell = Cell(x=10.0, y=20.0, radius=5.0) for attr_name in ['x', 'y', 'radius', 'center', 'area']: with self.subTest(attr_name=attr_name): try: value = getattr(test_cell, attr_name) self.assertEqual(value, getattr(test_cell.circle, attr_name)) except AttributeError: self.fail(f"Test failed: could not get attribute {attr_name} in Cell instance {test_cell}") def test_cell_is_able_to_set_circle_attributes(self) -> None: """Tests whether a Cell is able to directly set its "x", "y" and "radius" Circle attributes.""" test_cell = Cell(x=10.0, y=20.0, radius=5.0) for attr_name in ['x', 'y', 'radius']: with self.subTest(attr_name=attr_name): try: setattr(test_cell, attr_name, 1.0) except AttributeError: self.fail(f"Test failed: could not set attribute {attr_name} in Cell instance {test_cell}") def test_cell_distance_to_method_calculates_cell_distance_using_circles(self) -> None: """Tests whether the "distance_to" method uses Circles to calculate distance between Cells.""" other_cell = Cell() with mock.patch("clovars.abstract.Circle.distance_to") as mock_circle_distance_to: self.cell.distance_to(other_cell=other_cell) mock_circle_distance_to.assert_called_once_with(other_cell.circle) def test_cell_distance_to_method_raises_type_error_if_argument_is_not_a_cell(self) -> None: """ Tests whether the "distance_to" method raises a TypeError only when the other_cell argument is not an actual Cell instance. """ valid_argument = Cell() try: self.cell.distance_to(other_cell=valid_argument) except TypeError: self.fail("Cell raised TypeError unexpectedly!") invalid_argument = "WHATEVER ELSE" with self.assertRaises(TypeError): self.cell.distance_to(other_cell=invalid_argument) # noqa def test_cell_has_hours_since_birth_property(self) -> None: """Tests whether a Cell has an "hours_since_birth" property (a float).""" self.assertTrue(hasattr(self.cell, 'hours_since_birth')) self.assertIsInstance(self.cell.hours_since_birth, float) def test_hours_since_birth_calculations_are_correct(self) -> None: """Tests whether the "hours_since_birth" property correctly calculates the Cell's hours since birth.""" for seconds, hours in [(0, 0.0), (60, 1/60), (3600, 1.0), (7200, 2.0), (9000, 2.5)]: with self.subTest(seconds=seconds, hours=hours): self.cell.seconds_since_birth = seconds self.assertEqual(self.cell.hours_since_birth, hours) def test_cell_has_branch_name_property(self) -> None: """Tests whether a Cell has a "branch_name" property (a string).""" self.assertTrue(hasattr(self.cell, 'branch_name')) self.assertIsInstance(self.cell.branch_name, str) def test_branch_name_returns_root_name_up_to_first_division(self) -> None: """Tests whether the "branch_name" property returns the Cell's root name, including the branch number.""" for cell_name, branch_name in [('1', '1'), ('3b.1', '3b'), ('15e-5.1.2', '15e-5'), ('4d-3.2.2.1.2', '4d-3')]: with self.subTest(cell_name=cell_name, branch_name=branch_name): self.cell.name = cell_name self.assertEqual(self.cell.branch_name, branch_name) def test_cell_has_colony_name_property(self) -> None: """Tests whether a Cell has a "colony_name" property (a string).""" self.assertTrue(hasattr(self.cell, 'colony_name')) self.assertIsInstance(self.cell.colony_name, str) def test_colony_name_returns_root_name_up_to_branch_name(self) -> None: """Tests whether the "colony_name" property returns the Cell's root name, excluding the branch number.""" for cell_name, colony_name in [('1', '1'), ('3b.1', '3b'), ('15e-5.1.2', '15e'), ('4d-3.2.2.1.2', '4d')]: with self.subTest(cell_name=cell_name, colony_name=colony_name): self.cell.name = cell_name self.assertEqual(self.cell.colony_name, colony_name) def test_cell_has_generation_property(self) -> None: """Tests whether a Cell has a "generation" property (an integer).""" self.assertTrue(hasattr(self.cell, 'generation')) self.assertIsInstance(self.cell.generation, int) def test_generation_returns_cell_name_prefix(self) -> None: """ Tests whether the "generation" property returns the number of times that the Cell has divided based on its name. """ for cell_name, generation in [('1', 0), ('3b.1', 1), ('15e-5.1.2', 2), ('4d-3.2.2.1.2', 4)]: with self.subTest(cell_name=cell_name, generation=generation): self.cell.name = cell_name self.assertEqual(self.cell.generation, generation) def test_cell_has_signal_value_property(self) -> None: """Tests whether a Cell has a "signal_value" property (a float).""" self.assertTrue(hasattr(self.cell, 'signal_value')) self.assertIsInstance(self.cell.signal_value, float) def test_signal_value_returns_current_signal_value(self) -> None: """Tests whether the "signal_value" property returns the CellSignal's current value.""" signal = GaussianCellSignal() test_cell = Cell(signal=signal) for _ in range(10): signal.oscillate(current_seconds=0) current_signal_value = signal.value with self.subTest(current_signal_value=current_signal_value): self.assertEqual(test_cell.signal_value, current_signal_value) def test_set_cell_fate_method_sets_fate_to_death_if_cell_should_die(self) -> None: """ Tests whether the "set_cell_fate" method sets the Cell fate to "death" if the "should_die" method returns True. """ with mock.patch('clovars.bio.Cell.should_die', return_value=True): self.cell.set_cell_fate(delta=self.default_delta) self.assertEqual(self.cell.fate, "death") def test_should_die_returns_boolean_based_on_death_chance_and_threshold(self) -> None: """Tests whether the "should_die" method returns True/False depending on the Cell's death chance.""" self.cell.death_threshold = 1.1 # death chance is in [0, 1], cell never dies here self.assertFalse(self.cell.should_die(delta=self.default_delta)) self.cell.death_threshold = -0.1 # death chance is in [0, 1], cell always dies here self.assertTrue(self.cell.should_die(delta=self.default_delta)) def test_set_cell_fate_method_sets_fate_to_division_if_cell_should_divide(self) -> None: """ Tests whether the "set_cell_fate" method sets the Cell fate to "division" if the "should_die" method returns False and "should_divide" returns True. """ with ( mock.patch('clovars.bio.Cell.should_die', return_value=False), mock.patch('clovars.bio.Cell.should_divide', return_value=True), ): self.cell.set_cell_fate(delta=self.default_delta) self.assertEqual(self.cell.fate, "division") def test_should_divide_returns_boolean_based_on_division_chance_and_threshold(self) -> None: """Tests whether the "should_divide" method returns True/False depending on the Cell's division chance.""" self.cell.division_threshold = 1.1 # death chance is in [0, 1], cell never dies here self.assertFalse(self.cell.should_divide(delta=self.default_delta)) self.cell.division_threshold = -0.1 # death chance is in [0, 1], cell always dies here self.assertTrue(self.cell.should_divide(delta=self.default_delta)) def test_set_cell_fate_method_sets_fate_to_migration_if_cell_should_not_die_nor_divide(self) -> None: """ Tests whether the "set_cell_fate" method sets the Cell fate to "migration" if both "should_die" and "should_divide" methods returns False. """ with ( mock.patch('clovars.bio.Cell.should_die', return_value=False), mock.patch('clovars.bio.Cell.should_divide', return_value=False), ): self.cell.set_cell_fate(delta=self.default_delta) self.assertEqual(self.cell.fate, "migration") @mock.patch('clovars.bio.Cell.migrate') @mock.patch('clovars.bio.Cell.divide') @mock.patch('clovars.bio.Cell.die') def test_pass_time_method_calls_die_if_cell_fate_is_to_die( self, mock_die: MagicMock, mock_divide: MagicMock, mock_migrate: MagicMock, ) -> None: """Tests whether the "pass_time" method calls the "die" method if the Cell fate is set to "death".""" self.cell.fate = 'death' self.cell.pass_time(delta=self.default_delta, current_seconds=0) mock_die.assert_called_once() mock_divide.assert_not_called() mock_migrate.assert_not_called() def test_pass_time_method_returns_none_if_cell_fate_is_to_die(self) -> None: """Tests whether the "pass_time" method returns None if the Cell fate is set to "death".""" self.cell.fate = 'death' return_value = self.cell.pass_time(delta=self.default_delta, current_seconds=0) self.assertIsNone(return_value) @mock.patch('clovars.bio.Cell.migrate') @mock.patch('clovars.bio.Cell.divide') @mock.patch('clovars.bio.Cell.die') def test_pass_time_method_calls_divide_if_cell_fate_is_to_divide( self, mock_die: MagicMock, mock_divide: MagicMock, mock_migrate: MagicMock, ) -> None: """Tests whether the "pass_time" method calls the "divide" method if the Cell fate is set to "division".""" self.cell.fate = 'division' self.cell.pass_time(delta=self.default_delta, current_seconds=0) mock_die.assert_not_called() mock_divide.assert_called_once() mock_migrate.assert_not_called() def test_pass_time_method_returns_a_tuple_of_child_cells_if_cell_fate_is_to_divide(self) -> None: """Tests whether the "pass_time" method returns a tuple of child Cells if the Cell fate is set to "division".""" self.cell.fate = 'division' return_value = self.cell.pass_time(delta=self.default_delta, current_seconds=0) self.assertIsInstance(return_value, tuple) for thing in return_value: self.assertIsInstance(thing, Cell) self.assertIsNot(thing, self.cell) @mock.patch('clovars.bio.Cell.migrate') @mock.patch('clovars.bio.Cell.divide') @mock.patch('clovars.bio.Cell.die') def test_pass_time_method_calls_migrate_if_cell_fate_is_to_migrate( self, mock_die: MagicMock, mock_divide: MagicMock, mock_migrate: MagicMock, ) -> None: """Tests whether the "pass_time" method calls the "migrate" method if the Cell fate is set to "migration".""" self.cell.fate = 'migration' self.cell.pass_time(delta=self.default_delta, current_seconds=0) mock_die.assert_not_called() mock_divide.assert_not_called() mock_migrate.assert_called_once() def test_pass_time_method_returns_the_same_cell_if_cell_fate_is_to_migrate(self) -> None: """Tests whether the "pass_time" method returns the own Cell instance if the Cell fate is set to "migration".""" self.cell.fate = 'migration' return_value = self.cell.pass_time(delta=self.default_delta, current_seconds=0) self.assertIsInstance(return_value, Cell) self.assertIs(return_value, self.cell) def test_pass_time_method_raises_value_error_if_cell_fate_is_unexpected(self) -> None: """Tests whether the "pass_time" method raises a ValueError if the Cell fate value is unexpected.""" self.cell.fate = 'UNEXPECTED VALUE!' with self.assertRaises(ValueError): self.cell.pass_time(delta=self.default_delta, current_seconds=0) def test_die_method_sets_the_state_of_the_alive_flag_to_false(self) -> None: """Tests whether the "die" method sets the state of the "alive" flag to False.""" self.assertTrue(self.cell.alive) self.cell.die() self.assertFalse(self.cell.alive) def test_divide_method_returns_a_tuple_of_two_cells_with_matching_names(self) -> None: """Tests whether the "divide" returns a tuple of two child Cells with matching names (ending in .1 and .2).""" children = self.cell.divide(delta=self.default_delta) self.assertIsInstance(children[0], Cell) self.assertEqual(children[0].name, self.cell.name + '.1') self.assertIsInstance(children[1], Cell) self.assertEqual(children[1].name, self.cell.name + '.2') def test_get_child_cell_returns_a_new_cell_instance(self) -> None: """Tests whether the "get_child_cell" method returns a new Cell instance.""" child_cell = self.cell.get_child_cell(delta=self.default_delta, branch_name='') self.assertIsInstance(child_cell, Cell) self.assertIsNot(child_cell, self.cell) def test_get_child_cell_adds_the_branch_name_to_the_parent_cell_name(self) -> None: """Tests whether the Cell returned from "get_child_cell" has the same base name as its parent + branch name.""" for branch_name in ['1', '2', 'BRANCH_NAME', '...', '']: child_cell = self.cell.get_child_cell(delta=self.default_delta, branch_name=branch_name) with self.subTest(branch_name=branch_name): self.assertEqual(child_cell.name, f"{self.cell.name}.{branch_name}") def test_get_child_cell_method_moves_cell(self) -> None: """Tests whether the "migrate" method moves the Cell from its previous position.""" previous_cell_center = self.cell.center same_cell = self.cell.migrate(delta=self.default_delta) self.assertNotEqual(same_cell.center, previous_cell_center) # unlikely to be equal, but it may happen... def test_get_child_cell_copies_attributes_from_parent_cell(self) -> None: """Tests whether the Cell returned from "get_child_cell" has some identical attributes as its parent.""" child_cell = self.cell.get_child_cell(delta=self.default_delta, branch_name='') for attr_name in ['max_speed', 'radius', 'fitness_memory', 'treatment']: with self.subTest(attr_name=attr_name): self.assertEqual(getattr(child_cell, attr_name), getattr(self.cell, attr_name)) def test_get_child_cell_calls_get_child_fitness_to_assign_a_the_child_thresholds(self) -> None: """ Tests whether the Cell returned from "get_child_cell" has a division and death threshold values returned from the parent's "get_child_fitness" method. """ mock_fitness = (0.1, 0.2) with mock.patch.object(self.cell, 'get_child_fitness', return_value=mock_fitness) as mock_get_cell_fitness: child_cell = self.cell.get_child_cell(delta=self.default_delta, branch_name='') mock_get_cell_fitness.assert_called() self.assertIn(child_cell.division_threshold, mock_fitness) self.assertIn(child_cell.death_threshold, mock_fitness) def test_get_child_cell_uses_signal_split_to_assign_a_new_signal_to_child_cell(self) -> None: """ Tests whether the Cell returned from "get_child_cell" has a signal returned from the parent's signal's "split" method. """ with mock.patch('clovars.scientific.CellSignal.split') as mock_split: child_cell = self.cell.get_child_cell(delta=self.default_delta, branch_name='') mock_split.assert_called_once() self.assertIs(child_cell.signal, mock_split.return_value) def test_get_new_xy_coordinates_method_returns_a_tuple_of_floats(self) -> None: """Tests whether the "get_new_xy_coordinates" method returns a tuple of floats.""" xy = self.cell.get_new_xy_coordinates(delta=self.default_delta, event_name='migration') self.assertIsInstance(xy, tuple) for thing in xy: self.assertIsInstance(thing, float) def test_get_new_xy_coordinates_method_raises_value_error_if_event_name_is_not_migration_or_division(self) -> None: """ Tests whether the "get_new_xy_coordinates" raises a ValueError if the event name argument isn't "migration" or "division". """ for event_name in ['migration', 'division']: with self.subTest(event_name=event_name): try: self.cell.get_new_xy_coordinates(delta=self.default_delta, event_name='migration') except ValueError: self.fail(f'Call to "get_new_xy_coordinates" failed unexpectedly with event_name="{event_name}"') with self.assertRaises(ValueError): self.cell.get_new_xy_coordinates(delta=self.default_delta, event_name="INVALID EVENT NAME") def test_get_new_xy_coordinates_method_uses_smaller_search_radius_on_division(self) -> None: """Tests whether the "get_new_xy_coordinates" uses a smaller search radius when the event name is "division".""" with mock.patch('clovars.bio.cell.Circle') as mock_circle_init_migration: self.cell.get_new_xy_coordinates(delta=self.default_delta, event_name='migration') migration_radius = mock_circle_init_migration.call_args[1]['radius'] with mock.patch('clovars.bio.cell.Circle') as mock_circle_init_division: self.cell.get_new_xy_coordinates(delta=self.default_delta, event_name='division') division_radius = mock_circle_init_division.call_args[1]['radius'] self.assertGreater(migration_radius, division_radius) def test_get_child_fitness_method_returns_tuple_of_floats(self) -> None: """ Tests whether the "get_child_fitness" method returns a tuple of floats representing the child Cell's division and death thresholds. """ return_value = self.cell.get_child_fitness() self.assertIsInstance(return_value, tuple) with self.assertSequenceNotEmpty(return_value): for thing in return_value: self.assertIsInstance(thing, float) def test_get_child_fitness_method_returns_values_from_bounded_brownian_fluctuation_function(self) -> None: """ Tests whether the "get_child_fitness" method returns values from the "bounded_brownian_fluctuation_function" function using the appropriate parameters from the Cell. """ with mock.patch('clovars.bio.cell.bounded_brownian_motion') as mock_brownian_motion: self.cell.get_child_fitness() mock_brownian_motion.assert_any_call(current_value=self.cell.division_threshold, scale=self.cell.fitness_memory) mock_brownian_motion.assert_any_call(current_value=self.cell.death_threshold, scale=self.cell.fitness_memory) def test_migrate_method_returns_the_same_cell(self) -> None: """Tests whether the "migrate" method returns the same Cell.""" same_cell = self.cell.migrate(delta=self.default_delta) self.assertIs(same_cell, self.cell) def test_migrate_method_adds_delta_seconds_to_the_cell_seconds_since_birth(self) -> None: """Tests whether the "migrate" method adds delta seconds to the Cell's "seconds_since_birth" attribute.""" previous_seconds_since_birth = self.cell.seconds_since_birth same_cell = self.cell.migrate(delta=self.default_delta) self.assertEqual(same_cell.seconds_since_birth, previous_seconds_since_birth + self.default_delta) def test_migrate_method_moves_cell(self) -> None: """Tests whether the "migrate" method moves the Cell from its previous position.""" previous_cell_center = self.cell.center same_cell = self.cell.migrate(delta=self.default_delta) self.assertNotEqual(same_cell.center, previous_cell_center) # unlikely to be equal, but it may happen... def test_fluctuate_signal_method_calls_signal_oscillate_method(self) -> None: """Tests whether the "fluctuate_signal" method calls the signal's "oscillate" method.""" self.cell.signal = (signal_mock := MagicMock()) self.cell.fluctuate_signal(current_seconds=0) signal_mock.oscillate.assert_called_once_with(current_seconds=0) if __name__ == '__main__': unittest.main()

53.345638

120

0.686482

31,418

0.988174

0

2,151

0.067654

0

10,440

0.328364

a151ad0affbfcc7813c745ba76d87908fc3a227a

2,959

py

Python

nutsml/examples/pytorch_/mnist/mlp_train.py

maet3608/nuts-ml

2551612a47bc6e9efa534eda0db5d8c5def51887

[ "Apache-2.0" ]

39

2017-02-07T03:22:41.000Z

2021-11-24T20:27:57.000Z

nutsml/examples/pytorch_/mnist/mlp_train.py

maet3608/nuts-ml

2551612a47bc6e9efa534eda0db5d8c5def51887

[ "Apache-2.0" ]

19

2017-02-13T22:22:30.000Z

2019-01-31T04:13:39.000Z

nutsml/examples/pytorch_/mnist/mlp_train.py

maet3608/nuts-ml

2551612a47bc6e9efa534eda0db5d8c5def51887

[ "Apache-2.0" ]

13

2017-06-01T13:44:54.000Z

2020-09-08T04:51:36.000Z

""" .. module:: cnn_train :synopsis: Example nuts-ml pipeline for training a MLP on MNIST """ import torch import torch.nn.functional as F import torch.nn as nn import torch.optim as optim import nutsflow as nf import nutsml as nm import numpy as np from nutsml.network import PytorchNetwork from utils import download_mnist, load_mnist class Model(nn.Module): """Pytorch model""" def __init__(self, device): """Construct model on given device, e.g. 'cpu' or 'cuda'""" super(Model, self).__init__() self.fc1 = nn.Linear(28 * 28, 500) self.fc2 = nn.Linear(500, 256) self.fc3 = nn.Linear(256, 10) self.to(device) # set device before constructing optimizer # required properties of a model to be wrapped as PytorchNetwork! self.device = device # 'cuda', 'cuda:0' or 'gpu' self.losses = nn.CrossEntropyLoss() # can be list of loss functions self.optimizer = optim.Adam(self.parameters()) def forward(self, x): """Forward pass through network for input x""" x = x.view(-1, 28 * 28) x = F.relu(self.fc1(x)) x = F.relu(self.fc2(x)) x = self.fc3(x) return x def accuracy(y_true, y_pred): """Compute accuracy""" from sklearn.metrics import accuracy_score y_pred = [yp.argmax() for yp in y_pred] return 100 * accuracy_score(y_true, y_pred) def evaluate(network, x, y): """Evaluate network performance (here accuracy)""" metrics = [accuracy] build_batch = (nm.BuildBatch(64) .input(0, 'vector', 'float32') .output(1, 'number', 'int64')) acc = zip(x, y) >> build_batch >> network.evaluate(metrics) return acc def train(network, epochs=3): """Train network for given number of epochs""" print('loading data...') filepath = download_mnist() x_train, y_train, x_test, y_test = load_mnist(filepath) plot = nm.PlotLines(None, every_sec=0.2) build_batch = (nm.BuildBatch(128) .input(0, 'vector', 'float32') .output(1, 'number', 'int64')) for epoch in range(epochs): print('epoch', epoch + 1) losses = (zip(x_train, y_train) >> nf.PrintProgress(x_train) >> nf.Shuffle(1000) >> build_batch >> network.train() >> plot >> nf.Collect()) acc_test = evaluate(network, x_test, y_test) acc_train = evaluate(network, x_train, y_train) print('train loss : {:.6f}'.format(np.mean(losses))) print('train acc : {:.1f}'.format(acc_train)) print('test acc : {:.1f}'.format(acc_test)) if __name__ == '__main__': print('creating model...') device = 'cuda' if torch.cuda.is_available() else 'cpu' model = Model(device) network = PytorchNetwork(model) # network.load_weights() network.print_layers((28 * 28,)) print('training network...') train(network, epochs=3)

31.147368

76

0.613045

859

0.290301

0

739

0.249747

a152a29b6edc8d593cb4451e6903d733b234650c

2,317

py

Python

get_image.py

DanielJamesEvans/spectrophotometer_code

10957590a4b49fe91ec6a0111ef83da63cc4ee67

[ "MIT" ]

3

2019-08-31T16:43:10.000Z

2019-10-07T20:35:13.000Z

get_image.py

DanielJamesEvans/spectrophotometer_code

10957590a4b49fe91ec6a0111ef83da63cc4ee67

[ "MIT" ]

null

get_image.py

DanielJamesEvans/spectrophotometer_code

10957590a4b49fe91ec6a0111ef83da63cc4ee67

[ "MIT" ]

1

2019-08-31T19:10:40.000Z

"""This code contains functions called by gui.py. This software is licensed under the MIT license. """ import time from picamera.array import PiRGBArray from picamera import PiCamera from gpiozero import LED import numpy as np from PIL import Image __author__ = "Daniel James Evans" __copyright__ = "Copyright 2019, Daniel James Evans" __license__ = "MIT" camera = PiCamera() camera.resolution = (640, 480) camera.framerate = 24 time.sleep(0.5) def get_color_image(): """Take a color image using the camera. Return as a numpy array.""" led = LED(4) led.on() output = np.empty((480, 640, 3), dtype=np.uint8) camera.capture(output, "rgb") led.off() return output def get_bw_image(): """Return a numpy array of a grayscale image from the camera. I couldn't figure out the proper way to do this, so the function saves the image as bw.png. The function takes multiple pictures and averages the values from each picture. This is done to reduce noise.""" led = LED(4) led.on() # I couldn't find a way for the # camera to pass a grayscale # image directly to numpy. So # the code saves a grayscale # image file then reads it. camera.color_effects = (128, 128) camera.capture("bw.png") image_pil = Image.open("bw.png") image_arr_1 = np.array(image_pil) time.sleep(0.1) camera.capture("bw.png") image_pil = Image.open("bw.png") image_arr_2 = np.array(image_pil) time.sleep(0.1) camera.capture("bw.png") image_pil = Image.open("bw.png") image_arr_3 = np.array(image_pil) time.sleep(0.1) camera.capture("bw.png") image_pil = Image.open("bw.png") image_arr_4 = np.array(image_pil) time.sleep(0.1) camera.capture("bw.png") image_pil = Image.open("bw.png") image_arr_5 = np.array(image_pil) image_arr = (image_arr_1.astype(np.int16) + image_arr_2.astype(np.int16) + image_arr_3.astype(np.int16) + image_arr_4.astype(np.int16) + image_arr_5.astype(np.int16)) / 5 image_arr = image_arr.astype(np.uint8) camera.color_effects = None led.off() # Each pixel has 3 values (plus a 4th). # But the values are identical # (+/- 1) because of camera.color_effects. return image_arr[:, :, 1]

25.461538

79

0.662063

0

857

0.369875

a1536df44cebf44b8ca6b21340ed07ba5ea74a42

15,346

py

Python

rave_ec/Lib/ec_mcgill.py

DanielMichelson/drqc_article

cd7df2f7290adedb557bbc6ba484d30039a23ce2

[ "CC-BY-4.0" ]

null

rave_ec/Lib/ec_mcgill.py

DanielMichelson/drqc_article

cd7df2f7290adedb557bbc6ba484d30039a23ce2

[ "CC-BY-4.0" ]

null

rave_ec/Lib/ec_mcgill.py

DanielMichelson/drqc_article

cd7df2f7290adedb557bbc6ba484d30039a23ce2

[ "CC-BY-4.0" ]

null

''' Copyright (C) 2016 The Crown (i.e. Her Majesty the Queen in Right of Canada) This file is an add-on to RAVE. RAVE is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. RAVE 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 Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with RAVE. If not, see <http://www.gnu.org/licenses/>. ''' ## # McGill format reader # McGill indices are base 1, except the bin_number! ## # @file # @author Daniel Michelson, Environment and Climate Change Canada # @date 2016-01-22 import time import _rave, _raveio import _polarvolume, _polarscan, _polarscanparam from Proj import dr from numpy import * HEADER_LENGTH = 4096 RECORD_LENGTH = 2048 SEGMENT_LENGTH = 19 SEGMENTS = 107 NRAYS = 360 SCANT = 10 # Time in seconds to acquire a sweep. QUANTITIES = {1 : "DBZH", 4 : "VRADH", 16 : "ZDR", 17 : "PHIDP", 18 : "RHOHV", 19 : "KDP"} # Only 1 and 4 are available # esteps are the times in seconds between tilts in the ascending scan strategy # These are real times from an acquisition in April 2012. They are used to # adjust the timing metadata backwards, as McGill timestamps the end of data # acquisition. They are indicative only, but the best we can do. esteps = (0.921875, 0.914062, 0.914062, 1.04688, 0.976562, 1.00000, 0.984375, 1.02344, 1.47656, 1.33594, 1.17188, 1.71094, 2.17188, 2.82812, 3.12500, 3.32031, 3.71875, 3.92969, 4.44531, 4.83594, 5.13281, 5.22656, 5.29688, 0.0) # Last value is a dummy ## Empty generic container, to be populated # @param object class McGill(object): def __init__(self): pass ## Is this a McGill file? # @param string containing the input file name # @returns True if the file is a McGill file, otherwise False def isMcGill(filename): fd = open(filename) s = fd.read(6) fd.close() return s == "mcgill" ## Reads the contents of a McGill file, according to # http://deneb.tor.ec.gc.ca/urpdoc/reference/science/mcgill_volume_scan.html # Attribute naming follows this document. # The generic container is used to represent the contents of the file as: # mobj : top-level McGill() object # mobj.logical_records : a list of McGill objects containing one logical record each # mobj.logical_records[index].segments : a list of 107 McGill objects, each # representing a segment # @param string input file name # @returns McGill object representing the file contents def readMcGill(filename): mobj = McGill() fd = open(filename) # Start reading header fd.seek(46*2) #mobj.dum0 = fd.read(46*2) mobj.number_Logical_Records = int(fromstring(fd.read(2), int16)) fd.seek(3*2, 1) #mobj.dum1 = fd.read(3*2) mobj.Volume_Scan_Format = int(fromstring(fd.read(2), int16)) fd.seek(5*2, 1) #mobj.dum2 = fd.read(2*5) mobj.hours = int(fromstring(fd.read(4), int32)) mobj.minutes = int(fromstring(fd.read(4), int32)) mobj.seconds = int(fromstring(fd.read(4), int32)) mobj.day = int(fromstring(fd.read(4), int32)) mobj.month = int(fromstring(fd.read(4), int32)) mobj.year = int(fromstring(fd.read(4), int32)) mobj.radar_Id = int(fromstring(fd.read(4), int32)) mobj.radar_latitude = float(fromstring(fd.read(4), float32)) mobj.radar_longitude = float(fromstring(fd.read(4), float32)) mobj.number_elevations = int(fromstring(fd.read(4), int32)) mobj.elevation_angles = [] for i in range(mobj.number_elevations): mobj.elevation_angles.append(float(fromstring(fd.read(4), float32))) mobj.azimuth_offset = int(fromstring(fd.read(2), int16)) mobj.viraq_flag = fd.read(2) mobj.clutter_filter = fd.read(2) fd.seek(315*2, 1) #mobj.dum3 = fd.read(315*2) mobj.met_param = int(fromstring(fd.read(2), int16)) fd.seek(2 ,1) #mobj.dum4 = fd.read(2) mobj.value_offset = float(fromstring(fd.read(4), float32)) mobj.cal_slope = float(fromstring(fd.read(4), float32)) mobj.antenna_programme = int(fromstring(fd.read(2), int16)) fd.seek(4, 1) #mobj.dum5 = fd.read(2) #mobj.dum6 = fd.read(2) mobj.cscan_format = int(fromstring(fd.read(2), int16)) mobj.range_unfolded = int(fromstring(fd.read(2), int16)) mobj.vad_velocity_unfolded = int(fromstring(fd.read(2), int16)) mobj.numb_vad_unf_pts = [] for i in range(mobj.number_elevations): mobj.numb_vad_unf_pts.append(int(fromstring(fd.read(2), int16))) mobj.numb_range_unf_pts = [] for i in range(mobj.number_elevations): mobj.numb_range_unf_pts.append(int(fromstring(fd.read(2), int16))) mobj.range_bins_array_size = int(fromstring(fd.read(2), int16)) fd.seek(2, 1) #mobj.dum7 = fd.read(2) mobj.shift_cscan_flag = int(fromstring(fd.read(2), int16)) mobj.shift_speed = int(fromstring(fd.read(2), int16)) mobj.shift_dir = int(fromstring(fd.read(2), int16)) fd.seek(48*4, 1) #mobj.dum8 = fd.read(24*4) #mobj.dum9 = fd.read(24*4) mobj.vert_grad_unfolded = int(fromstring(fd.read(2), int16)) mobj.numb_vert_grad_unf_pts = [] for i in range(mobj.number_elevations): mobj.numb_vert_grad_unf_pts.append(int(fromstring(fd.read(2), int16))) fd.seek(12, 1) #mobj.dum10 = fd.read(4) # documentation says 2 bytes, but it's 4 #mobj.dum11 = fd.read(4) #mobj.dum12 = fd.read(4) mobj.radial_grad_unfolded = int(fromstring(fd.read(2), int16)) mobj.numb_radial_grad_unf_pts = [] for i in range(mobj.number_elevations): mobj.numb_radial_grad_unf_pts.append(int(fromstring(fd.read(2), int16))) mobj.prf1 = [] for i in range(mobj.number_elevations): mobj.prf1.append(int(fromstring(fd.read(2), int16))) mobj.prf2 = [] for i in range(mobj.number_elevations): mobj.prf2.append(int(fromstring(fd.read(2), int16))) mobj.nyq_range = [] for i in range(mobj.number_elevations): mobj.nyq_range.append(int(fromstring(fd.read(2), int16))) mobj.max_range = [] for i in range(mobj.number_elevations): mobj.max_range.append(int(fromstring(fd.read(2), int16))) mobj.nyq_vel = [] for i in range(mobj.number_elevations): mobj.nyq_vel.append(float(fromstring(fd.read(4), float32))) mobj.max_vel = [] for i in range(mobj.number_elevations): mobj.max_vel.append(float(fromstring(fd.read(4), float32))) mobj.usable_elv = [] for i in range(mobj.number_elevations): mobj.usable_elv.append(int(fromstring(fd.read(1), uint8))) mobj.prev_sub_area_speed, mobj.prev_sub_area_dir = [], [] for i in range(9): mobj.prev_sub_area_speed.append(int(fromstring(fd.read(2), int16))) for i in range(9): mobj.prev_sub_area_dir.append(int(fromstring(fd.read(2), int16))) #mobj.dum_pad = fd.read(1166*2) # Start reading data, by logical record mobj.logical_records = [] fd.seek(HEADER_LENGTH) last_record = 0 while last_record == 0: lr = McGill() record = fd.read(RECORD_LENGTH) lr.high = int(fromstring(record[0], uint8)) lr.low = int(fromstring(record[1], uint8)) lr.logical_record_number = 64 * lr.high + lr.low last_record = int(fromstring(record[2], uint8)) lr.beginning_elevation_number = int(fromstring(record[3], uint8)) lr.end_elevation_number = int(fromstring(record[4], uint8)) lr.segstr = record[14:2047] lr.segments = [] # Read SEGMENTS, each SEGMENT_LENGTH bytes long. segpos = 0 for i in range(SEGMENTS): seg = McGill() this_seg = lr.segstr[segpos:segpos+SEGMENT_LENGTH] seg.N = int(fromstring(this_seg[0], uint8)) # Data segment if 1 <= seg.N <= 30: seg.type = "data" seg.high = int(fromstring(this_seg[1], uint8)) seg.low = int(fromstring(this_seg[2], uint8)) seg.bin_number = 16 * (seg.N - 1)# + 1 seg.radial_number = 64 * seg.high + seg.low seg.data = fromstring(this_seg[3:], uint8) # Elevation segment elif 31 <= seg.N <= 55: seg.type = "elevation" seg.elevation_number = seg.N - 31 seg.elevation_angle = mobj.elevation_angles[seg.elevation_number-1] # End-of-data segment can be ignored elif seg.N == 63: seg.type = "eod" # For some reason, there are segments of type 0, which are # undocumented. Ignore these. if seg.N > 0: lr.segments.append(seg) segpos += SEGMENT_LENGTH mobj.logical_records.append(lr) fd.close() return mobj ## Takes the output of readMcGill and creates contiguous scans of data. # This is done by pasting the contents of each McGill segment into the # equivalent position in the corresponding contiguous scan. # @param McGill object representing file contents def makeScans(mobj): mobj.scans = [] # Create empty arrays for each scan for i in range(mobj.number_elevations): mobj.scans.append(zeros((NRAYS, 120+(60*2)+(60*4)), uint8)) # Populate them for lr in mobj.logical_records: for seg in lr.segments: # Elevation segment types always preceed data types if seg.type == "elevation": scan = seg.elevation_number -1 elif seg.type == "data": ray = seg.radial_number - 1 # Bins 112-119 are 1 km, 120-128 are 2 km, 112-135 km if seg.bin_number == 112: part1 = seg.data[:8] part2 = repeat(seg.data[8:], 2) data = concatenate([part1, part2]) frombin = 112 # All 2 km, 136-231 km elif 128 <= seg.bin_number < 176: data = repeat(seg.data, 2) diff = (seg.bin_number - 128) / 16.0 frombin = 136 + 32 * diff # 16 and 32 combo makes no sense? # Bins 176-179 are 2 km, 180-239 are 4 km, 232-287 km elif seg.bin_number == 176: part1 = repeat(seg.data[:4], 2) part2 = repeat(seg.data[4:], 4) data = concatenate([part1, part2]) frombin = 232 # All 4 km, 288- km elif 192 <= seg.bin_number: data = repeat(seg.data, 4) diff = (seg.bin_number - 192) / 32.0 frombin = 288 + 64 * diff # 32 and 64 combo makes no sense? # All 1 km, 0-111 km else: data = seg.data frombin = seg.bin_number tobin = int(frombin) + len(data) mobj.scans[scan][ray][frombin:tobin] = data ## McGill data times are the end of data acquisition. This function guestimates # the beginning dates and times of each scan in the volume. # @param McGill object representing file contents def adjustTimes(mobj): startdate, starttime, enddate, endtime = [], [], [], [] tt = (mobj.year, mobj.month, mobj.day, mobj.hours, mobj.minutes, mobj.seconds, 0, 0, 0) epochs = time.mktime(tt) - (sum(esteps) + SCANT*mobj.number_elevations) for i in range(mobj.number_elevations): start = time.gmtime(epochs) startdate.append(time.strftime("%Y%m%d", start)) starttime.append(time.strftime("%H%M%S", start)) epochs += SCANT end = time.gmtime(epochs) enddate.append(time.strftime("%Y%m%d", end)) endtime.append(time.strftime("%H%M%S", end)) epochs += esteps[i] mobj.startdate = startdate mobj.starttime = starttime mobj.enddate = enddate mobj.endtime = endtime ## Creates a PVOL from the McGill object # @param McGill object representing file contents # @returns BALTRAD/ODIM PVOL object def makePVOL(mobj): pvol = _polarvolume.new() pvol.source = "NOD:cawmn,PLC:McGill QC" pvol.longitude = mobj.radar_longitude * dr pvol.latitude = mobj.radar_latitude * dr pvol.height = 76.0 # From a URP Site.conf file pvol.beamwidth = 0.85 * dr # From a URP Site.conf file pvol.date = mobj.startdate[0] pvol.time = mobj.starttime[0] pvol.addAttribute("how/simulated", "False") pvol.addAttribute("how/system", "McGill") pvol.addAttribute("how/TXtype", "klystron") pvol.addAttribute("how/polmode", "simultaneous-dual") pvol.addAttribute("how/wavelength", 10.4) # According to the McGill spec pvol.addAttribute("how/rpm", 6.0) # According to the McGill spec for i in range(mobj.number_elevations): scan = _polarscan.new() scan.elangle = mobj.elevation_angles[i] * dr scan.rscale = 1000.0 scan.rstart = 0.25 # According to URP decoder scan.a1gate = 0 # Unknown scan.startdate = mobj.startdate[i] scan.starttime = mobj.starttime[i] scan.enddate = mobj.enddate[i] scan.endtime = mobj.endtime[i] scan.addAttribute("how/astart", 0.5) # According to the McGill spec scan.addAttribute("how/lowprf", mobj.prf1[i]) # PRFs are identical #scan.addAttribute("how/midprf", ) scan.addAttribute("how/highprf", mobj.prf2[i]) param = _polarscanparam.new() param.quantity = QUANTITIES[mobj.met_param] # Only DBZH and VRADH param.nodata = 255.0 # Unknown param.undetect = 0.0 # Implied param.gain = mobj.cal_slope param.offset = mobj.value_offset param.setData(mobj.scans[i]) scan.addParameter(param) pvol.addScan(scan) return pvol ## Each PVOL contains only one moment, so merge several of these into one. # Assume the first PVOL contains DBZH and the second VRADH. # @param list of (two) PVOLs # @returns PVOL object containing (both) moments per scan. def mergePVOLs(pvols): refl, wind = pvols for i in range(wind.getNumberOfScans()): zscan, vscan = refl.getScan(i), wind.getScan(i) vradh = vscan.getParameter("VRADH") zscan.addParameter(vradh) return refl ## Reads McGill data from file and returns a BALTRAD/ODIM PVOL object for a # single moment # @param string of McGill file # @returns PVOL object containing one moment for each scan. def file2pvol(filename): mobj = readMcGill(filename) makeScans(mobj) adjustTimes(mobj) return makePVOL(mobj) ## Reads McGill data from two files into a single BALTRAD/ODIM PVOL # @param string of the McGill file containing reflectivity (DBZH) # @param string of the McGill file containing radial wind velocity (VRADH) # @returns PVOL object containing both moments per scan def read(zfile, vfile): refl = file2pvol(zfile) wind = file2pvol(vfile) return mergePVOLs([refl, wind]) if __name__=="__main__": pass

37.891358

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58

0.003779

0

4,821

0.314154

a1537d70484481dc31d44d35ec4975bba8b264f5

1,038

py

Python

product/migrations/0001_initial.py

dnetochaves/e-commerce

97c2266934b6db883d520381520130b0472e9db4

[ "MIT" ]

null

product/migrations/0001_initial.py

dnetochaves/e-commerce

97c2266934b6db883d520381520130b0472e9db4

[ "MIT" ]

null

product/migrations/0001_initial.py

dnetochaves/e-commerce

97c2266934b6db883d520381520130b0472e9db4

[ "MIT" ]

null

# Generated by Django 3.1.4 on 2020-12-27 15:03 from django.db import migrations, models class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='Product', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=255)), ('short_description', models.TextField(max_length=255)), ('long_description', models.TextField()), ('image', models.ImageField(blank=True, null=True, upload_to='product_pictures/%Y/%m')), ('slug', models.SlugField(unique=True)), ('price_marketing', models.FloatField()), ('price_marketing_promotion', models.FloatField(default=0)), ('FIELDNAME', models.CharField(choices=[('V', 'Variação'), ('S', 'Simples')], default='V', max_length=1)), ], ), ]

35.793103

122

0.575145

947

0.910577

0

229

0.220192

a155e11f0e425a96e53ea2166d51415855a2b463

921

py

Python

src/python/setup.py

Basasuya/tsne-cuda

dc518acd9fdf9109952ffe57d6cf12363e3ffd2c

[ "BSD-3-Clause" ]

2

2021-04-30T16:48:47.000Z

2021-05-21T08:49:13.000Z

src/python/setup.py

Basasuya/tsne-cuda

dc518acd9fdf9109952ffe57d6cf12363e3ffd2c

[ "BSD-3-Clause" ]

null

src/python/setup.py

Basasuya/tsne-cuda

dc518acd9fdf9109952ffe57d6cf12363e3ffd2c

[ "BSD-3-Clause" ]

1

2021-04-25T23:11:05.000Z

from setuptools import setup setup( name='tsnecuda', version='2.1.0', author='Chan, David M., Huang, Forrest., Rao, Roshan.', author_email='davidchan@berkeley.edu', packages=['tsnecuda', 'tsnecuda.test'], package_data={'tsnecuda': ['libtsnecuda.so']}, scripts=[], url='https://github.com/CannyLab/tsne-cuda', license='LICENSE.txt', description='CUDA Implementation of T-SNE with Python bindings', long_description=open('README.txt').read(), install_requires=[ 'numpy >= 1.14.1', ], classifiers=[ 'Programming Language :: Python :: 3.6', 'Operating System :: POSIX :: Linux', 'Intended Audience :: Developers', 'Intended Audience :: Science/Research', 'Topic :: Scientific/Engineering :: Artificial Intelligence' ], keywords=[ 'TSNE', 'CUDA', 'Machine Learning', 'AI' ] )

27.909091

68

0.598263

0

512

0.555917

a15747184e94e78f55f7ab475ca0b1abe33741e3

107,889

py

Python

programs/parallels.py

ETCBC/parallells

f45f6cc3c4f933dba6e649f49cdb14a40dcf333f

[ "MIT" ]

4

2017-10-01T05:14:59.000Z

2020-09-09T09:41:26.000Z

programs/parallels.py

ETCBC/parallells

f45f6cc3c4f933dba6e649f49cdb14a40dcf333f

[ "MIT" ]

null

programs/parallels.py

ETCBC/parallells

f45f6cc3c4f933dba6e649f49cdb14a40dcf333f

[ "MIT" ]

1

2020-10-16T13:21:51.000Z

#!/usr/bin/env python # coding: utf-8 # <h1>Table of Contents</h1> # <div class="toc" style="margin-top: 1em;"><ul class="toc-item"><li><a href="#0.1-Motivation" data-toc-modified-id="0.1-Motivation-1">1  0.1 Motivation</a></li><li><a href="#0.3-Open-Source" data-toc-modified-id="0.3-Open-Source-2">2  0.3 Open Source</a></li><li><a href="#0.4-What-are-parallel-passages?" data-toc-modified-id="0.4-What-are-parallel-passages?-3">3  0.4 What are parallel passages?</a></li><li><a href="#0.5-Authors" data-toc-modified-id="0.5-Authors-4">4  0.5 Authors</a></li><li><a href="#0.6-Status" data-toc-modified-id="0.6-Status-5">5  0.6 Status</a></li><li><a href="#2.1-Assessing-the-outcomes" data-toc-modified-id="2.1-Assessing-the-outcomes-6">6  2.1 Assessing the outcomes</a><ul class="toc-item"><li><a href="#2.1.1-Assessment-criteria" data-toc-modified-id="2.1.1-Assessment-criteria-6.1">6.1  2.1.1 Assessment criteria</a></li></ul></li><li><a href="#3.1-Similarity" data-toc-modified-id="3.1-Similarity-7">7  3.1 Similarity</a><ul class="toc-item"><li><a href="#3.1.1-SET" data-toc-modified-id="3.1.1-SET-7.1">7.1  3.1.1 SET</a></li><li><a href="#3.1.2-LCS" data-toc-modified-id="3.1.2-LCS-7.2">7.2  3.1.2 LCS</a></li></ul></li><li><a href="#3.2-Performance" data-toc-modified-id="3.2-Performance-8">8  3.2 Performance</a></li><li><a href="#4.1-Chunking" data-toc-modified-id="4.1-Chunking-9">9  4.1 Chunking</a><ul class="toc-item"><li><a href="#4.1.1-Fixed-chunking" data-toc-modified-id="4.1.1-Fixed-chunking-9.1">9.1  4.1.1 Fixed chunking</a></li><li><a href="#4.1.2-Object-chunking" data-toc-modified-id="4.1.2-Object-chunking-9.2">9.2  4.1.2 Object chunking</a></li></ul></li><li><a href="#4.2-Preparing" data-toc-modified-id="4.2-Preparing-10">10  4.2 Preparing</a></li><li><a href="#4.3-Cliques" data-toc-modified-id="4.3-Cliques-11">11  4.3 Cliques</a><ul class="toc-item"><li><a href="#4.3.1-Organizing-the-cliques" data-toc-modified-id="4.3.1-Organizing-the-cliques-11.1">11.1  4.3.1 Organizing the cliques</a></li><li><a href="#4.3.2-Evaluating-clique-sets" data-toc-modified-id="4.3.2-Evaluating-clique-sets-11.2">11.2  4.3.2 Evaluating clique sets</a></li></ul></li><li><a href="#5.1-Loading-the-feature-data" data-toc-modified-id="5.1-Loading-the-feature-data-12">12  5.1 Loading the feature data</a></li><li><a href="#5.2-Configuration" data-toc-modified-id="5.2-Configuration-13">13  5.2 Configuration</a></li><li><a href="#5.3-Experiment-settings" data-toc-modified-id="5.3-Experiment-settings-14">14  5.3 Experiment settings</a></li><li><a href="#5.4-Chunking" data-toc-modified-id="5.4-Chunking-15">15  5.4 Chunking</a></li><li><a href="#5.5-Preparing" data-toc-modified-id="5.5-Preparing-16">16  5.5 Preparing</a><ul class="toc-item"><li><a href="#5.5.1-Preparing-for-SET-comparison" data-toc-modified-id="5.5.1-Preparing-for-SET-comparison-16.1">16.1  5.5.1 Preparing for SET comparison</a></li><li><a href="#5.5.2-Preparing-for-LCS-comparison" data-toc-modified-id="5.5.2-Preparing-for-LCS-comparison-16.2">16.2  5.5.2 Preparing for LCS comparison</a></li></ul></li><li><a href="#5.6-Similarity-computation" data-toc-modified-id="5.6-Similarity-computation-17">17  5.6 Similarity computation</a><ul class="toc-item"><li><a href="#5.6.1-SET-similarity" data-toc-modified-id="5.6.1-SET-similarity-17.1">17.1  5.6.1 SET similarity</a></li><li><a href="#5.6.2-LCS-similarity" data-toc-modified-id="5.6.2-LCS-similarity-17.2">17.2  5.6.2 LCS similarity</a></li></ul></li><li><a href="#5.7-Cliques" data-toc-modified-id="5.7-Cliques-18">18  5.7 Cliques</a></li><li><a href="#5.7.1-Selecting-passages" data-toc-modified-id="5.7.1-Selecting-passages-19">19  5.7.1 Selecting passages</a></li><li><a href="#5.7.2-Growing-cliques" data-toc-modified-id="5.7.2-Growing-cliques-20">20  5.7.2 Growing cliques</a></li><li><a href="#5.8-Output" data-toc-modified-id="5.8-Output-21">21  5.8 Output</a><ul class="toc-item"><li><a href="#5.8.1-Format-definitions" data-toc-modified-id="5.8.1-Format-definitions-21.1">21.1  5.8.1 Format definitions</a></li><li><a href="#5.8.2-Formatting-clique-lists" data-toc-modified-id="5.8.2-Formatting-clique-lists-21.2">21.2  5.8.2 Formatting clique lists</a></li><li><a href="#5.8.3-Compiling-the-table-of-experiments" data-toc-modified-id="5.8.3-Compiling-the-table-of-experiments-21.3">21.3  5.8.3 Compiling the table of experiments</a></li><li><a href="#5.8.4-High-level-formatting-functions" data-toc-modified-id="5.8.4-High-level-formatting-functions-21.4">21.4  5.8.4 High level formatting functions</a></li></ul></li><li><a href="#5.9-Running-experiments" data-toc-modified-id="5.9-Running-experiments-22">22  5.9 Running experiments</a></li><li><a href="#Discussion" data-toc-modified-id="Discussion-23">23  Discussion</a></li></ul></div> # <img align="right" src="images/dans-small.png"/> # <img align="right" src="images/tf-small.png"/> # <img align="right" src="images/etcbc.png"/> # # # # Parallel Passages in the MT # # # 0. Introduction # # ## 0.1 Motivation # We want to make a list of **all** parallel passages in the Masoretic Text (MT) of the Hebrew Bible. # # Here is a quote that triggered Dirk to write this notebook: # # > Finally, the Old Testament Parallels module in Accordance is a helpful resource that enables the researcher to examine 435 sets of parallel texts, or in some cases very similar wording in different texts, in both the MT and translation, but the large number of sets of texts in this database should not fool one to think it is complete or even nearly complete for all parallel writings in the Hebrew Bible. # # Robert Rezetko and Ian Young. # Historical linguistics & Biblical Hebrew. Steps Toward an Integrated Approach. # *Ancient Near East Monographs, Number9*. SBL Press Atlanta. 2014. # [PDF Open access available](https://www.google.nl/url?sa=t&rct=j&q=&esrc=s&source=web&cd=2&ved=0CCgQFjAB&url=http%3A%2F%2Fwww.sbl-site.org%2Fassets%2Fpdfs%2Fpubs%2F9781628370461_OA.pdf&ei=2QSdVf-vAYSGzAPArJeYCg&usg=AFQjCNFA3TymYlsebQ0MwXq2FmJCSHNUtg&sig2=LaXuAC5k3V7fSXC6ZVx05w&bvm=bv.96952980,d.bGQ) # <img align="right" width="50%" src="parallel.png"/> # # ## 0.3 Open Source # This is an IPython notebook. # It contains a working program to carry out the computations needed to obtain the results reported here. # # You can download this notebook and run it on your computer, provided you have # [Text-Fabric](https://github.com/Dans-labs/text-fabric) installed. # # It is a pity that we cannot compare our results with the Accordance resource mentioned above, # since that resource has not been published in an accessible manner. # We also do not have the information how this resource has been constructed on the basis of the raw data. # In contrast with that, we present our results in a completely reproducible manner. # This notebook itself can serve as the method of replication, # provided you have obtained the necessary resources. # See [sources](https://github.com/ETCBC/shebanq/wiki/Sources), which are all Open Access. # # ## 0.4 What are parallel passages? # The notion of *parallel passage* is not a simple, straightforward one. # There are parallels on the basis of lexical content in the passages on the one hand, # but on the other hand there are also correspondences in certain syntactical structures, # or even in similarities in text structure. # # In this notebook we do select a straightforward notion of parallel, based on lexical content only. # We investigate two measures of similarity, one that ignores word order completely, # and one that takes word order into account. # # Two kinds of short-comings of this approach must be mentioned: # # 1. We will not find parallels based on non-lexical criteria (unless they are also lexical parallels) # 1. We will find too many parallels: certain short sentences (and he said), or formula like passages (and the word of God came to Moses) occur so often that they have a more subtle bearing on whether there is a common text history. # # For a more full treatment of parallel passages, see # # **Wido Th. van Peursen and Eep Talstra**: # Computer-Assisted Analysis of Parallel Texts in the Bible - # The Case of 2 Kings xviii-xix and its Parallels in Isaiah and Chronicles. # *Vetus Testamentum* 57, pp. 45-72. # 2007, Brill, Leiden. # # Note that our method fails to identify any parallels with Chronica_II 32. # Van Peursen and Talstra state about this chapter and 2 Kings 18: # # > These chapters differ so much, that it is sometimes impossible to establish # which verses should be considered parallel. # # In this notebook we produce a set of *cliques*, # a clique being a set of passages that are *quite* similar, based on lexical information. # # # ## 0.5 Authors # This notebook is by Dirk Roorda and owes a lot to discussions with Martijn Naaijer. # # [Dirk Roorda](mailto:dirk.roorda@dans.knaw.nl) while discussing ideas with # [Martijn Naaijer](mailto:m.naaijer@vu.nl). # # # ## 0.6 Status # # * **modified: 2017-09-28** Is now part of a pipeline for transferring data from the ETCBC to Text-Fabric. # * **modified: 2016-03-03** Added experiments based on chapter chunks and lower similarities. # # 165 experiments have been carried out, of which 18 with promising results. # All results can be easily inspected, just by clicking in your browser. # One of the experiments has been chosen as the basis for # [crossref](https://shebanq.ancient-data.org/hebrew/note?version=4b&id=Mnxjcm9zc3JlZg__&tp=txt_tb1&nget=v) # annotations in SHEBANQ. # # # 1. Results # # Click in a green cell to see interesting results. The numbers in the cell indicate # # * the number of passages that have a variant elsewhere # * the number of *cliques* they form (cliques are sets of similar passages) # * the number of passages in the biggest clique # # Below the results is an account of the method that we used, followed by the actual code to produce these results. # # Pipeline # See [operation](https://github.com/ETCBC/pipeline/blob/master/README.md#operation) # for how to run this script in the pipeline. # # The pipeline comes in action in Section [6a](#6a) below: TF features. # # Caveat # # This notebook makes use of a new feature of text-fabric, first present in 2.3.15. # Make sure to upgrade first. # # ``` # sudo -H pip3 install --upgrade text-fabric # ``` # In[1]: import sys import os import re import collections import pickle import math import difflib import yaml from difflib import SequenceMatcher from IPython.display import HTML import matplotlib.pyplot as plt from tf.core.helpers import formatMeta # pip3 install python-Levenshtein # In[2]: from Levenshtein import ratio # In[3]: import utils from tf.fabric import Fabric # In[4]: get_ipython().run_line_magic("load_ext", "autoreload") # noqa F821 get_ipython().run_line_magic("autoreload", "2") # noqa F821 get_ipython().run_line_magic("matplotlib", "inline") # noqa F821 # In[2]: # In[5]: if "SCRIPT" not in locals(): # SCRIPT = False SCRIPT = False FORCE = True FORCE_MATRIX = False LANG_FEATURE = "languageISO" OCC_FEATURE = "g_cons" LEX_FEATURE = "lex" TEXT_FEATURE = "g_word_utf8" TRAILER_FEATURE = "trailer_utf8" CORE_NAME = "bhsa" NAME = "parallels" VERSION = "2021" # In[6]: def stop(good=False): if SCRIPT: sys.exit(0 if good else 1) # In[3]: # In[7]: # run this cell after all other cells if False and not SCRIPT: HTML(other_exps) # # 2. Experiments # # We have conducted 165 experiments, all corresponding to a specific choice of parameters. # Every experiment is an attempt to identify variants and collect them in *cliques*. # # The table gives an overview of the experiments conducted. # # Every *row* corresponds to a particular way of chunking and a method of measuring the similarity. # # There are *columns* for each similarity *threshold* that we have tried. # The idea is that chunks are similar if their similarity is above the threshold. # # The outcomes of one experiment have been added to SHEBANQ as the note set # [crossref](https://shebanq.ancient-data.org/hebrew/note?version=4b&id=Mnxjcm9zc3JlZg__&tp=txt_tb1&nget=v). # The experiment chosen for this is currently # # * *chunking*: **object verse** # * *similarity method*: **SET** # * *similarity threshold*: **65** # # # ## 2.1 Assessing the outcomes # # Not all experiments lead to useful results. # We have indicated the value of a result by a color coding, based on objective characteristics, # such as the number of parallel passages, the number of cliques, the size of the greatest clique, and the way of chunking. # These numbers are shown in the cells. # # ### 2.1.1 Assessment criteria # # If the method is based on *fixed* chunks, we deprecated the method and the results. # Because two perfectly similar verses could be missed if a 100-word wide window that shifts over the text aligns differently with both verses, which will usually be the case. # # Otherwise, we consider the *ll*, the length of the longest clique, and *nc*, the number of cliques. # We set three quality parameters: # * `REC_CLIQUE_RATIO` = 5 : recommended clique ratio # * `DUB_CLIQUE_RATIO` = 15 : dubious clique ratio # * `DEP_CLIQUE_RATIO` = 25 : deprecated clique ratio # # where the *clique ratio* is $100 (ll/nc)$, # i.e. the length of the longest clique divided by the number of cliques as percentage. # # An experiment is *recommended* if its clique ratio is between the recommended and dubious clique ratios. # # It is *dubious* if its clique ratio is between the dubious and deprecated clique ratios. # # It is *deprecated* if its clique ratio is above the deprecated clique ratio. # # # 2.2 Inspecting results # If you click on the hyperlink in the cell, you are taken to a page that gives you # all the details of the results: # # 1. A link to a file with all *cliques* (which are the sets of similar passages) # 1. A list of links to chapter-by-chapter diff files (for cliques with just two members), and only for # experiments with outcomes that are labeled as *promising* or *unassessed quality* or *mixed results*. # # To get into the variants quickly, inspect the list (2) and click through # to see the actual variant material in chapter context. # # Not all variants occur here, so continue with (1) to see the remaining cliques. # # Sometimes in (2) a chapter diff file does not indicate clearly the relevant common part of both chapters. # In that case you have to consult the big list (1) # # All these results can be downloaded from the # [SHEBANQ github repo](https://github.com/ETCBC/shebanq/tree/master/static/docs/tools/parallel/files) # After downloading the whole directory, open ``experiments.html`` in your browser. # # 3. Method # # Here we discuss the method we used to arrive at a list of parallel passages # in the Masoretic Text (MT) of the Hebrew Bible. # # ## 3.1 Similarity # # We have to find passages in the MT that are *similar*. # Therefore we *chunk* the text in some way, and then compute the similarities between pairs of chunks. # # There are many ways to define and compute similarity between texts. # Here, we have tried two methods ``SET`` and ``LCS``. # Both methods define similarity as the fraction of common material with respect to the total material. # # ### 3.1.1 SET # # The ``SET`` method reduces textual chunks to *sets* of *lexemes*. # This method abstracts from the order and number of occurrences of words in chunks. # # We use as measure for the similarity of chunks $C_1$ and $C_2$ (taken as sets): # # $$ s_{\rm set}(C_1, C_2) = {\vert C_1 \cap C_2\vert \over \vert C_1 \cup C_2 \vert} $$ # # where $\vert X \vert$ is the number of elements in set $X$. # # ### 3.1.2 LCS # # The ``LCS`` method is less reductive: chunks are *strings* of *lexemes*, # so the order and number of occurrences of words is retained. # # We use as measure for the similarity of chunks $C_1$ and $C_2$ (taken as strings): # # $$ s_{\rm lcs}(C_1, C_2) = {\vert {\rm LCS}(C_1,C_2)\vert \over \vert C_1\vert + \vert C_2 \vert - # \vert {\rm LCS}(C_1,C_2)\vert} $$ # # where ${\rm LCS}(C_1, C_2)$ is the # [longest common subsequence](https://en.wikipedia.org/wiki/Longest_common_subsequence_problem) # of $C_1$ and $C_2$ and # $\vert X\vert$ is the length of sequence $X$. # # It remains to be seen whether we need the extra sophistication of ``LCS``. # The risk is that ``LCS`` could fail to spot related passages when there is a large amount of transposition going on. # The results should have the last word. # # We need to compute the LCS efficiently, and for this we used the python ``Levenshtein`` module: # # ``pip install python-Levenshtein`` # # whose documentation is # [here](http://www.coli.uni-saarland.de/courses/LT1/2011/slides/Python-Levenshtein.html). # # ## 3.2 Performance # # Similarity computation is the part where the heavy lifting occurs. # It is basically quadratic in the number of chunks, so if you have verses as chunks (~ 23,000), # you need to do ~ 270,000,000 similarity computations, and if you use sentences (~ 64,000), # you need to do ~ 2,000,000,000 ones! # The computation of a single similarity should be *really* fast. # # Besides that, we use two ways to economize: # # * after having computed a matrix for a specific set of parameter values, we save the matrix to disk; # new runs can load the matrix from disk in a matter of seconds; # * we do not store low similarity values in the matrix, low being < ``MATRIX_THRESHOLD``. # # The ``LCS`` method is more complicated. # We have tried the ``ratio`` method from the ``difflib`` package that is present in the standard python distribution. # This is unbearably slow for our purposes. # The ``ratio`` method in the ``Levenshtein`` package is much quicker. # # See the table for an indication of the amount of work to create the similarity matrix # and the performance per similarity method. # # The *matrix threshold* is the lower bound of similarities that are stored in the matrix. # If a pair of chunks has a lower similarity, no entry will be made in the matrix. # # The computing has been done on a Macbook Air (11", mid 2012, 1.7 GHz Intel Core i5, 8GB RAM). # # |chunk type |chunk size|similarity method|matrix threshold|# of comparisons|size of matrix (KB)|computing time (min)| # |:----------|---------:|----------------:|---------------:|---------------:|------------------:|-------------------:| # |fixed |100 |LCS |60 | 9,003,646| 7| ? | # |fixed |100 |SET |50 | 9,003,646| 7| ? | # |fixed |50 |LCS |60 | 36,197,286| 37| ? | # |fixed |50 |SET |50 | 36,197,286| 18| ? | # |fixed |20 |LCS |60 | 227,068,705| 2,400| ? | # |fixed |20 |SET |50 | 227,068,705| 113| ? | # |fixed |10 |LCS |60 | 909,020,841| 59,000| ? | # |fixed |10 |SET |50 | 909,020,841| 1,800| ? | # |object |verse |LCS |60 | 269,410,078| 2,300| 31| # |object |verse |SET |50 | 269,410,078| 509| 14| # |object |half_verse|LCS |60 | 1,016,396,241| 40,000| 50| # |object |half_verse|SET |50 | 1,016,396,241| 3,600| 41| # |object |sentence |LCS |60 | 2,055,975,750| 212,000| 68| # |object |sentence |SET |50 | 2,055,975,750| 82,000| 63| # # 4. Workflow # # ## 4.1 Chunking # # There are several ways to chunk the text: # # * fixed chunks of approximately ``CHUNK_SIZE`` words # * by object, such as verse, sentence and even chapter # # After chunking, we prepare the chunks for similarity measuring. # # ### 4.1.1 Fixed chunking # Fixed chunking is unnatural, but if the chunk size is small, it can yield fair results. # The results are somewhat difficult to inspect, because they generally do not respect constituent boundaries. # It is to be expected that fixed chunks in variant passages will be mutually *out of phase*, # meaning that the chunks involved in these passages are not aligned with each other. # So they will have a lower similarity than they could have if they were aligned. # This is a source of artificial noise in the outcome and/or missed cases. # # If the chunking respects "natural" boundaries in the text, there is far less misalignment. # # ### 4.1.2 Object chunking # We can also chunk by object, such as verse, half_verse or sentence. # # Chunking by *verse* is very much like chunking in fixed chunks of size 20, performance-wise. # # Chunking by *half_verse* is comparable to fixed chunks of size 10. # # Chunking by *sentence* will generate an enormous amount of # false positives, because there are very many very short sentences (down to 1-word) in the text. # Besides that, the performance overhead is huge. # # The *half_verses* seem to be a very interesting candidate. # They are smaller than verses, but there are less *degenerate cases* compared to with sentences. # From the table above it can be read that half verses require only half as many similarity computations as sentences. # # # ## 4.2 Preparing # # We prepare the chunks for the application of the chosen method of similarity computation (``SET`` or ``LCS``). # # In both cases we reduce the text to a sequence of transliterated consonantal *lexemes* without disambiguation. # In fact, we go one step further: we remove the consonants (aleph, wav, yod) that are often silent. # # For ``SET``, we represent each chunk as the set of its reduced lexemes. # # For ``LCS``, we represent each chunk as the string obtained by joining its reduced lexemes separated by white spaces. # # ## 4.3 Cliques # # After having computed a sufficient part of the similarity matrix, we set a value for ``SIMILARITY_THRESHOLD``. # All pairs of chunks having at least that similarity are deemed *interesting*. # # We organize the members of such pairs in *cliques*, groups of chunks of which each member is # similar (*similarity* > ``SIMILARITY_THRESHOLD``) to at least one other member. # # We start with no cliques and walk through the pairs whose similarity is above ``SIMILARITY_THRESHOLD``, # and try to put each member into a clique. # # If there is not yet a clique, we make the member in question into a new singleton clique. # # If there are cliques, we find the cliques that have a member similar to the member in question. # If we find several, we merge them all into one clique. # # If there is no such clique, we put the member in a new singleton clique. # # NB: Cliques may *drift*, meaning that they contain members that are completely different from each other. # They are in the same clique, because there is a path of pairwise similar members leading from the one chunk to the other. # # ### 4.3.1 Organizing the cliques # In order to handle cases where there are many corresponding verses in corresponding chapters, we produce # chapter-by-chapter diffs in the following way. # # We make a list of all chapters that are involved in cliques. # This yields a list of chapter cliques. # For all *binary* chapters cliques, we generate a colorful diff rendering (as HTML) for the complete two chapters. # # We only do this for *promising* experiments. # # ### 4.3.2 Evaluating clique sets # # Not all clique sets are equally worth while. # For example, if we set the ``SIMILARITY_THRESHOLD`` too low, we might get one gigantic clique, especially # in combination with a fine-grained chunking. In other words: we suffer from *clique drifting*. # # We detect clique drifting by looking at the size of the largest clique. # If that is large compared to the total number of chunks, we deem the results unsatisfactory. # # On the other hand, when the ``SIMILARITY_THRESHOLD`` is too high, you might miss a lot of correspondences, # especially when chunks are large, or when we have fixed-size chunks that are out of phase. # # We deem the results of experiments based on a partitioning into fixed length chunks as unsatisfactory, although it # might be interesting to inspect what exactly the damage is. # # At the moment, we have not yet analyzed the relative merits of the similarity methods ``SET`` and ``LCS``. # # 5. Implementation # # # The rest is code. From here we fire up the engines and start computing. # In[8]: PICKLE_PROTOCOL = 3 # # Setting up the context: source file and target directories # # The conversion is executed in an environment of directories, so that sources, temp files and # results are in convenient places and do not have to be shifted around. # In[5]: # In[9]: repoBase = os.path.expanduser("~/github/etcbc") coreRepo = "{}/{}".format(repoBase, CORE_NAME) thisRepo = "{}/{}".format(repoBase, NAME) # In[10]: coreTf = "{}/tf/{}".format(coreRepo, VERSION) # In[11]: allTemp = "{}/_temp".format(thisRepo) thisTemp = "{}/_temp/{}".format(thisRepo, VERSION) thisTempTf = "{}/tf".format(thisTemp) # In[12]: thisTf = "{}/tf/{}".format(thisRepo, VERSION) thisNotes = "{}/shebanq/{}".format(thisRepo, VERSION) # In[6]: # In[13]: notesFile = "crossrefNotes.csv" if not os.path.exists(thisNotes): os.makedirs(thisNotes) # # Test # # Check whether this conversion is needed in the first place. # Only when run as a script. # In[7]: # In[14]: if SCRIPT: (good, work) = utils.mustRun( None, "{}/.tf/{}.tfx".format(thisTf, "crossref"), force=FORCE ) if not good: stop(good=False) if not work: stop(good=True) # ## 5.1 Loading the feature data # # We load the features we need from the BHSA core database. # In[8]: # In[15]: utils.caption(4, "Load the existing TF dataset") TF = Fabric(locations=coreTf, modules=[""]) # In[9]: # In[16]: api = TF.load( """ otype {} {} {} book chapter verse number """.format( LEX_FEATURE, TEXT_FEATURE, TRAILER_FEATURE, ) ) api.makeAvailableIn(globals()) # ## 5.2 Configuration # # Here are the parameters on which the results crucially depend. # # There are also parameters that control the reporting of the results, such as file locations. # In[10]: # In[17]: # chunking CHUNK_LABELS = {True: "fixed", False: "object"} CHUNK_LBS = {True: "F", False: "O"} CHUNK_SIZES = (100, 50, 20, 10) CHUNK_OBJECTS = ("chapter", "verse", "half_verse", "sentence") # In[18]: # preparing EXCLUDED_CONS = r"[>WJ=/\[]" # weed out weak consonants EXCLUDED_PAT = re.compile(EXCLUDED_CONS) # In[19]: # similarity MATRIX_THRESHOLD = 50 SIM_METHODS = ("SET", "LCS") SIMILARITIES = (100, 95, 90, 85, 80, 75, 70, 65, 60, 55, 50, 45, 40, 35, 30) # In[20]: # printing DEP_CLIQUE_RATIO = 25 DUB_CLIQUE_RATIO = 15 REC_CLIQUE_RATIO = 5 LARGE_CLIQUE_SIZE = 50 CLIQUES_PER_FILE = 50 # In[21]: # assessing results VALUE_LABELS = dict( mis="no results available", rec="promising results: recommended", dep="messy results: deprecated", dub="mixed quality: take care", out="method deprecated", nor="unassessed quality: inspection needed", lr="this experiment is the last one run", ) # note that the TF_TABLE and LOCAL_BASE_COMP are deliberately # located in the version independent # part of the tempdir. # Here the results of expensive calculations are stored, # to be used by all versions # In[22]: # crossrefs for TF TF_TABLE = "{}/parallelTable.tsv".format(allTemp) # In[23]: # crossrefs for SHEBANQ SHEBANQ_MATRIX = (False, "verse", "SET") SHEBANQ_SIMILARITY = 65 SHEBANQ_TOOL = "parallel" CROSSREF_STATUS = "!" CROSSREF_KEYWORD = "crossref" # In[24]: # progress indication VERBOSE = False MEGA = 1000000 KILO = 1000 SIMILARITY_PROGRESS = 5 * MEGA CLIQUES_PROGRESS = 1 * KILO # In[25]: # locations and hyperlinks LOCAL_BASE_COMP = "{}/calculus".format(allTemp) LOCAL_BASE_OUTP = "files" EXPERIMENT_DIR = "experiments" EXPERIMENT_FILE = "experiments" EXPERIMENT_PATH = "{}/{}.txt".format(LOCAL_BASE_OUTP, EXPERIMENT_FILE) EXPERIMENT_HTML = "{}/{}.html".format(LOCAL_BASE_OUTP, EXPERIMENT_FILE) NOTES_FILE = "crossref" NOTES_PATH = "{}/{}.csv".format(LOCAL_BASE_OUTP, NOTES_FILE) STORED_CLIQUE_DIR = "stored/cliques" STORED_MATRIX_DIR = "stored/matrices" STORED_CHUNK_DIR = "stored/chunks" CHAPTER_DIR = "chapters" CROSSREF_DB_FILE = "crossrefdb.csv" CROSSREF_DB_PATH = "{}/{}".format(LOCAL_BASE_OUTP, CROSSREF_DB_FILE) # ## 5.3 Experiment settings # # For each experiment we have to adapt the configuration settings to the parameters that define the experiment. # In[11]: # In[26]: def reset_params(): global CHUNK_FIXED, CHUNK_SIZE, CHUNK_OBJECT, CHUNK_LB, CHUNK_DESC global SIMILARITY_METHOD, SIMILARITY_THRESHOLD, MATRIX_THRESHOLD global meta meta = collections.OrderedDict() # chunking CHUNK_FIXED = None # kind of chunking: fixed size or by object CHUNK_SIZE = None # only relevant for CHUNK_FIXED = True CHUNK_OBJECT = ( None # only relevant for CHUNK_FIXED = False; see CHUNK_OBJECTS in next cell ) CHUNK_LB = None # computed from CHUNK_FIXED, CHUNK_SIZE, CHUNK_OBJ CHUNK_DESC = None # computed from CHUNK_FIXED, CHUNK_SIZE, CHUNK_OBJ # similarity MATRIX_THRESHOLD = ( None # minimal similarity used to fill the matrix of similarities ) SIMILARITY_METHOD = None # see SIM_METHODS in next cell SIMILARITY_THRESHOLD = ( None # minimal similarity used to put elements together in cliques ) meta = collections.OrderedDict() # In[27]: def set_matrix_threshold(sim_m=None, chunk_o=None): global MATRIX_THRESHOLD the_sim_m = SIMILARITY_METHOD if sim_m is None else sim_m the_chunk_o = CHUNK_OBJECT if chunk_o is None else chunk_o MATRIX_THRESHOLD = 50 if the_sim_m == "SET" else 60 if the_sim_m == "SET": if the_chunk_o == "chapter": MATRIX_THRESHOLD = 30 else: MATRIX_THRESHOLD = 50 else: if the_chunk_o == "chapter": MATRIX_THRESHOLD = 55 else: MATRIX_THRESHOLD = 60 # In[28]: def do_params_chunk(chunk_f, chunk_i): global CHUNK_FIXED, CHUNK_SIZE, CHUNK_OBJECT, CHUNK_LB, CHUNK_DESC do_chunk = False if ( chunk_f != CHUNK_FIXED or (chunk_f and chunk_i != CHUNK_SIZE) or (not chunk_f and chunk_i != CHUNK_OBJECT) ): do_chunk = True CHUNK_FIXED = chunk_f if chunk_f: CHUNK_SIZE = chunk_i else: CHUNK_OBJECT = chunk_i CHUNK_LB = CHUNK_LBS[CHUNK_FIXED] CHUNK_DESC = CHUNK_SIZE if CHUNK_FIXED else CHUNK_OBJECT for p in ( "{}/{}".format(LOCAL_BASE_OUTP, EXPERIMENT_DIR), "{}/{}".format(LOCAL_BASE_COMP, STORED_CHUNK_DIR), ): if not os.path.exists(p): os.makedirs(p) return do_chunk # In[29]: def do_params(chunk_f, chunk_i, sim_m, sim_thr): global CHUNK_FIXED, CHUNK_SIZE, CHUNK_OBJECT, CHUNK_LB, CHUNK_DESC global SIMILARITY_METHOD, SIMILARITY_THRESHOLD, MATRIX_THRESHOLD global meta do_chunk = False do_prep = False do_sim = False do_clique = False meta = collections.OrderedDict() if ( chunk_f != CHUNK_FIXED or (chunk_f and chunk_i != CHUNK_SIZE) or (not chunk_f and chunk_i != CHUNK_OBJECT) ): do_chunk = True do_prep = True do_sim = True do_clique = True CHUNK_FIXED = chunk_f if chunk_f: CHUNK_SIZE = chunk_i else: CHUNK_OBJECT = chunk_i if sim_m != SIMILARITY_METHOD: do_prep = True do_sim = True do_clique = True SIMILARITY_METHOD = sim_m if sim_thr != SIMILARITY_THRESHOLD: do_clique = True SIMILARITY_THRESHOLD = sim_thr set_matrix_threshold() if SIMILARITY_THRESHOLD < MATRIX_THRESHOLD: return (False, False, False, False, True) CHUNK_LB = CHUNK_LBS[CHUNK_FIXED] CHUNK_DESC = CHUNK_SIZE if CHUNK_FIXED else CHUNK_OBJECT meta["CHUNK TYPE"] = ( "FIXED {}".format(CHUNK_SIZE) if CHUNK_FIXED else "OBJECT {}".format(CHUNK_OBJECT) ) meta["MATRIX THRESHOLD"] = MATRIX_THRESHOLD meta["SIMILARITY METHOD"] = SIMILARITY_METHOD meta["SIMILARITY THRESHOLD"] = SIMILARITY_THRESHOLD for p in ( "{}/{}".format(LOCAL_BASE_OUTP, EXPERIMENT_DIR), "{}/{}".format(LOCAL_BASE_OUTP, CHAPTER_DIR), "{}/{}".format(LOCAL_BASE_COMP, STORED_CLIQUE_DIR), "{}/{}".format(LOCAL_BASE_COMP, STORED_MATRIX_DIR), "{}/{}".format(LOCAL_BASE_COMP, STORED_CHUNK_DIR), ): if not os.path.exists(p): os.makedirs(p) return (do_chunk, do_prep, do_sim, do_clique, False) # In[30]: reset_params() # ## 5.4 Chunking # # We divide the text into chunks to be compared. The result is ``chunks``, # which is a list of lists. # Every chunk is a list of word nodes. # In[12]: # In[31]: def chunking(do_chunk): global chunks, book_rank if not do_chunk: TF.info( "CHUNKING ({} {}): already chunked into {} chunks".format( CHUNK_LB, CHUNK_DESC, len(chunks) ) ) meta["# CHUNKS"] = len(chunks) return chunk_path = "{}/{}/chunk_{}_{}".format( LOCAL_BASE_COMP, STORED_CHUNK_DIR, CHUNK_LB, CHUNK_DESC, ) if os.path.exists(chunk_path): with open(chunk_path, "rb") as f: chunks = pickle.load(f) TF.info( "CHUNKING ({} {}): Loaded: {:>5} chunks".format( CHUNK_LB, CHUNK_DESC, len(chunks), ) ) else: TF.info("CHUNKING ({} {})".format(CHUNK_LB, CHUNK_DESC)) chunks = [] book_rank = {} for b in F.otype.s("book"): book_name = F.book.v(b) book_rank[book_name] = b words = L.d(b, otype="word") nwords = len(words) if CHUNK_FIXED: nchunks = nwords // CHUNK_SIZE if nchunks == 0: nchunks = 1 common_incr = nwords special_incr = 0 else: rem = nwords % CHUNK_SIZE common_incr = rem // nchunks special_incr = rem % nchunks word_in_chunk = -1 cur_chunk = -1 these_chunks = [] for w in words: word_in_chunk += 1 if word_in_chunk == 0 or ( word_in_chunk >= CHUNK_SIZE + common_incr + (1 if cur_chunk < special_incr else 0) ): word_in_chunk = 0 these_chunks.append([]) cur_chunk += 1 these_chunks[-1].append(w) else: these_chunks = [ L.d(c, otype="word") for c in L.d(b, otype=CHUNK_OBJECT) ] chunks.extend(these_chunks) chunkvolume = sum(len(c) for c in these_chunks) if VERBOSE: TF.info( "CHUNKING ({} {}): {:<20s} {:>5} words; {:>5} chunks; sizes {:>5} to {:>5}; {:>5}".format( CHUNK_LB, CHUNK_DESC, book_name, nwords, len(these_chunks), min(len(c) for c in these_chunks), max(len(c) for c in these_chunks), "OK" if chunkvolume == nwords else "ERROR", ) ) with open(chunk_path, "wb") as f: pickle.dump(chunks, f, protocol=PICKLE_PROTOCOL) TF.info("CHUNKING ({} {}): Made {} chunks".format(CHUNK_LB, CHUNK_DESC, len(chunks))) meta["# CHUNKS"] = len(chunks) # ## 5.5 Preparing # # In order to compute similarities between chunks, we have to compile each chunk into the information that really matters for the comparison. This is dependent on the chosen method of similarity computing. # # ### 5.5.1 Preparing for SET comparison # # We reduce words to their lexemes (dictionary entries) and from them we also remove the aleph, wav, and yods. # The lexeme feature also contains characters (`/ [ =`) to disambiguate homonyms. We also remove these. # If we end up with something empty, we skip it. # Eventually, we take the set of these reduced word lexemes, so that we effectively ignore order and multiplicity of words. In other words: the resulting similarity will be based on lexeme content. # # ### 5.5.2 Preparing for LCS comparison # # Again, we reduce words to their lexemes as for the SET preparation, and we do the same weeding of consonants and empty strings. But then we concatenate everything, separated by a space. So we preserve order and multiplicity. # In[13]: # In[32]: def preparing(do_prepare): global chunk_data if not do_prepare: TF.info( "PREPARING ({} {} {}): Already prepared".format( CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD ) ) return TF.info("PREPARING ({} {} {})".format(CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD)) chunk_data = [] if SIMILARITY_METHOD == "SET": for c in chunks: words = ( EXCLUDED_PAT.sub("", Fs(LEX_FEATURE).v(w).replace("<", "O")) for w in c ) clean_words = (w for w in words if w != "") this_data = frozenset(clean_words) chunk_data.append(this_data) else: for c in chunks: words = ( EXCLUDED_PAT.sub("", Fs(LEX_FEATURE).v(w).replace("<", "O")) for w in c ) clean_words = (w for w in words if w != "") this_data = " ".join(clean_words) chunk_data.append(this_data) TF.info( "PREPARING ({} {} {}): Done {} chunks.".format( CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, len(chunk_data) ) ) # ## 5.6 Similarity computation # # Here we implement our two ways of similarity computation. # Both need a massive amount of work, especially for experiments with many small chunks. # The similarities are stored in a ``matrix``, a data structure that stores a similarity number for each pair of chunk indexes. # Most pair of chunks will be dissimilar. In order to save space, we do not store similarities below a certain threshold. # We store matrices for re-use. # # ### 5.6.1 SET similarity # The core is an operation on the sets, associated with the chunks by the prepare step. We take the cardinality of the intersection divided by the cardinality of the union. # Intuitively, we compute the proportion of what two chunks have in common against their total material. # # In case the union is empty (both chunks have yielded an empty set), we deem the chunks not to be interesting as a parallel pair, and we set the similarity to 0. # # ### 5.6.2 LCS similarity # The core is the method `ratio()`, taken from the Levenshtein module. # Remember that the preparation step yielded a space separated string of lexemes, and these strings are compared on the basis of edit distance. # In[14]: # In[33]: def similarity_post(): nequals = len({x for x in chunk_dist if chunk_dist[x] >= 100}) cmin = min(chunk_dist.values()) if len(chunk_dist) else "!empty set!" cmax = max(chunk_dist.values()) if len(chunk_dist) else "!empty set!" meta["LOWEST AVAILABLE SIMILARITY"] = cmin meta["HIGHEST AVAILABLE SIMILARITY"] = cmax meta["# EQUAL COMPARISONS"] = nequals TF.info( "SIMILARITY ({} {} {} M>{}): similarities between {} and {}. {} are 100%".format( CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, cmin, cmax, nequals, ) ) # In[34]: def similarity(do_sim): global chunk_dist total_chunks = len(chunks) total_distances = total_chunks * (total_chunks - 1) // 2 meta["# SIMILARITY COMPARISONS"] = total_distances SIMILARITY_PROGRESS = total_distances // 100 if SIMILARITY_PROGRESS >= MEGA: sim_unit = MEGA sim_lb = "M" else: sim_unit = KILO sim_lb = "K" if not do_sim: TF.info( "SIMILARITY ({} {} {} M>{}): Using {:>5} {} ({}) comparisons with {} entries in matrix".format( CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, total_distances // sim_unit, sim_lb, total_distances, len(chunk_dist), ) ) meta["# STORED SIMILARITIES"] = len(chunk_dist) similarity_post() return matrix_path = "{}/{}/matrix_{}_{}_{}_{}".format( LOCAL_BASE_COMP, STORED_MATRIX_DIR, CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, ) if os.path.exists(matrix_path): with open(matrix_path, "rb") as f: chunk_dist = pickle.load(f) TF.info( "SIMILARITY ({} {} {} M>{}): Loaded: {:>5} {} ({}) comparisons with {} entries in matrix".format( CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, total_distances // sim_unit, sim_lb, total_distances, len(chunk_dist), ) ) meta["# STORED SIMILARITIES"] = len(chunk_dist) similarity_post() return TF.info( "SIMILARITY ({} {} {} M>{}): Computing {:>5} {} ({}) comparisons and saving entries in matrix".format( CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, total_distances // sim_unit, sim_lb, total_distances, ) ) chunk_dist = {} wc = 0 wt = 0 if SIMILARITY_METHOD == "SET": # method SET: all chunks have been reduced to sets, ratio between lengths of intersection and union for i in range(total_chunks): c_i = chunk_data[i] for j in range(i + 1, total_chunks): c_j = chunk_data[j] u = len(c_i | c_j) # HERE COMES THE SIMILARITY COMPUTATION d = 100 * len(c_i & c_j) / u if u != 0 else 0 # HERE WE STORE THE OUTCOME if d >= MATRIX_THRESHOLD: chunk_dist[(i, j)] = d wc += 1 wt += 1 if wc == SIMILARITY_PROGRESS: wc = 0 TF.info( "SIMILARITY ({} {} {} M>{}): Computed {:>5} {} comparisons and saved {} entries in matrix".format( CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, wt // sim_unit, sim_lb, len(chunk_dist), ) ) elif SIMILARITY_METHOD == "LCS": # method LCS: chunks are sequence aligned, ratio between length of all common parts and total length for i in range(total_chunks): c_i = chunk_data[i] for j in range(i + 1, total_chunks): c_j = chunk_data[j] # HERE COMES THE SIMILARITY COMPUTATION d = 100 * ratio(c_i, c_j) # HERE WE STORE THE OUTCOME if d >= MATRIX_THRESHOLD: chunk_dist[(i, j)] = d wc += 1 wt += 1 if wc == SIMILARITY_PROGRESS: wc = 0 TF.info( "SIMILARITY ({} {} {} M>{}): Computed {:>5} {} comparisons and saved {} entries in matrix".format( CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, wt // sim_unit, sim_lb, len(chunk_dist), ) ) with open(matrix_path, "wb") as f: pickle.dump(chunk_dist, f, protocol=PICKLE_PROTOCOL) TF.info( "SIMILARITY ({} {} {} M>{}): Computed {:>5} {} ({}) comparisons and saved {} entries in matrix".format( CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, wt // sim_unit, sim_lb, wt, len(chunk_dist), ) ) meta["# STORED SIMILARITIES"] = len(chunk_dist) similarity_post() # ## 5.7 Cliques # # Based on the value for the ``SIMILARITY_THRESHOLD`` we use the similarity matrix to pick the *interesting* # similar pairs out of it. From these pairs we lump together our cliques. # # Our list of experiments will select various values for ``SIMILARITY_THRESHOLD``, which will result # in various types of clique behavior. # # We store computed cliques for re-use. # # ## 5.7.1 Selecting passages # # We take all pairs from the similarity matrix which are above the threshold, and add both members to a list of passages. # # ## 5.7.2 Growing cliques # We inspect all passages in our set, and try to add them to the cliques we are growing. # We start with an empty set of cliques. # Each passage is added to a clique with which it has *enough familiarity*, otherwise it is added to a new clique. # *Enough familiarity means*: the passage is similar to at least one member of the clique, and the similarity is at least ``SIMILARITY_THRESHOLD``. # It is possible that a passage is thus added to more than one clique. In that case, those cliques are merged. # This may lead to growing very large cliques if ``SIMILARITY_THRESHOLD`` is too low. # In[15]: # In[35]: def key_chunk(i): c = chunks[i] w = c[0] return ( -len(c), L.u(w, otype="book")[0], L.u(w, otype="chapter")[0], L.u(w, otype="verse")[0], ) # In[36]: def meta_clique_pre(): global similars, passages TF.info( "CLIQUES ({} {} {} M>{} S>{}): inspecting the similarity matrix".format( CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD, ) ) similars = {x for x in chunk_dist if chunk_dist[x] >= SIMILARITY_THRESHOLD} passage_set = set() for (i, j) in similars: passage_set.add(i) passage_set.add(j) passages = sorted(passage_set, key=key_chunk) meta["# SIMILAR COMPARISONS"] = len(similars) meta["# SIMILAR PASSAGES"] = len(passages) # In[37]: def meta_clique_pre2(): TF.info( "CLIQUES ({} {} {} M>{} S>{}): {} relevant similarities between {} passages".format( CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD, len(similars), len(passages), ) ) # In[38]: def meta_clique_post(): global l_c_l meta["# CLIQUES"] = len(cliques) scliques = collections.Counter() for c in cliques: scliques[len(c)] += 1 l_c_l = max(scliques.keys()) if len(scliques) > 0 else 0 totmn = 0 totcn = 0 for (ln, n) in sorted(scliques.items(), key=lambda x: x[0]): totmn += ln * n totcn += n if VERBOSE: TF.info( "CLIQUES ({} {} {} M>{} S>{}): {:>4} cliques of length {:>4}".format( CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD, n, ln, ) ) meta["# CLIQUES of LENGTH {:>4}".format(ln)] = n TF.info( "CLIQUES ({} {} {} M>{} S>{}): {} members in {} cliques".format( CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD, totmn, totcn, ) ) # In[39]: def cliqueing(do_clique): global cliques if not do_clique: TF.info( "CLIQUES ({} {} {} M>{} S>{}): Already loaded {} cliques out of {} candidates from {} comparisons".format( CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD, len(cliques), len(passages), len(similars), ) ) meta_clique_pre2() meta_clique_post() return TF.info( "CLIQUES ({} {} {} M>{} S>{}): fetching similars and chunk candidates".format( CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD, ) ) meta_clique_pre() meta_clique_pre2() clique_path = "{}/{}/clique_{}_{}_{}_{}_{}".format( LOCAL_BASE_COMP, STORED_CLIQUE_DIR, CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD, ) if os.path.exists(clique_path): with open(clique_path, "rb") as f: cliques = pickle.load(f) TF.info( "CLIQUES ({} {} {} M>{} S>{}): Loaded: {:>5} cliques out of {:>6} chunks from {} comparisons".format( CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD, len(cliques), len(passages), len(similars), ) ) meta_clique_post() return TF.info( "CLIQUES ({} {} {} M>{} S>{}): Composing cliques out of {:>6} chunks from {} comparisons".format( CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD, len(passages), len(similars), ) ) cliques_unsorted = [] np = 0 npc = 0 for i in passages: added = None removable = set() for (k, c) in enumerate(cliques_unsorted): origc = tuple(c) for j in origc: d = ( chunk_dist.get((i, j), 0) if i < j else chunk_dist.get((j, i), 0) if j = SIMILARITY_THRESHOLD: if ( added is None ): # the passage has not been added to any clique yet c.add(i) added = k # remember that we added the passage to this clique else: # the passage has alreay been added to another clique: # we merge this clique with that one cliques_unsorted[added] |= c removable.add( k ) # we remember that we have merged this clicque into another one, # so we can throw away this clicque later break if added is None: cliques_unsorted.append({i}) else: if len(removable): cliques_unsorted = [ c for (k, c) in enumerate(cliques_unsorted) if k not in removable ] np += 1 npc += 1 if npc == CLIQUES_PROGRESS: npc = 0 TF.info( "CLIQUES ({} {} {} M>{} S>{}): Composed {:>5} cliques out of {:>6} chunks".format( CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD, len(cliques_unsorted), np, ) ) cliques = sorted([tuple(sorted(c, key=key_chunk)) for c in cliques_unsorted]) with open(clique_path, "wb") as f: pickle.dump(cliques, f, protocol=PICKLE_PROTOCOL) meta_clique_post() TF.info( "CLIQUES ({} {} {} M>{} S>{}): Composed and saved {:>5} cliques out of {:>6} chunks from {} comparisons".format( CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD, len(cliques), len(passages), len(similars), ) ) # ## 5.8 Output # # We deliver the output of our experiments in various ways, all in HTML. # # We generate chapter based diff outputs with color-highlighted differences between the chapters for every pair of chapters that merit it. # # For every (*good*) experiment, we produce a big list of its cliques, and for # every such clique, we produce a diff-view of its members. # # Big cliques will be split into several files. # # Clique listings will also contain metadata: the value of the experiment parameters. # # ### 5.8.1 Format definitions # Here are the definitions for formatting the (HTML) output. # In[16]: # In[40]: # clique lists css = """ td.vl { font-family: Verdana, Arial, sans-serif; font-size: small; text-align: right; color: #aaaaaa; width: 10%; direction: ltr; border-left: 2px solid #aaaaaa; border-right: 2px solid #aaaaaa; } td.ht { font-family: Ezra SIL, SBL Hebrew, Verdana, sans-serif; font-size: x-large; line-height: 1.7; text-align: right; direction: rtl; } table.ht { width: 100%; direction: rtl; border-collapse: collapse; } td.ht { border-left: 2px solid #aaaaaa; border-right: 2px solid #aaaaaa; } tr.ht.tb { border-top: 2px solid #aaaaaa; border-left: 2px solid #aaaaaa; border-right: 2px solid #aaaaaa; } tr.ht.bb { border-bottom: 2px solid #aaaaaa; border-left: 2px solid #aaaaaa; border-right: 2px solid #aaaaaa; } span.m { background-color: #aaaaff; } span.f { background-color: #ffaaaa; } span.x { background-color: #ffffaa; color: #bb0000; } span.delete { background-color: #ffaaaa; } span.insert { background-color: #aaffaa; } span.replace { background-color: #ffff00; } """ # In[41]: # chapter diffs diffhead = """ <head> <meta http-equiv="Content-Type" content="text/html; charset=UTF-8" /> <title></title> <style type="text/css"> table.diff { font-family: Ezra SIL, SBL Hebrew, Verdana, sans-serif; font-size: x-large; text-align: right; } .diff_header {background-color:#e0e0e0} td.diff_header {text-align:right} .diff_next {background-color:#c0c0c0} .diff_add {background-color:#aaffaa} .diff_chg {background-color:#ffff77} .diff_sub {background-color:#ffaaaa} </style> </head> """ # In[42]: # table of experiments ecss = """ <style type="text/css"> .mis {background-color: #cccccc;} .rec {background-color: #aaffaa;} .dep {background-color: #ffaaaa;} .dub {background-color: #ffddaa;} .out {background-color: #ffddff;} .nor {background-color: #fcfcff;} .ps {font-weight: normal;} .mx {font-style: italic;} .cl {font-weight: bold;} .lr {font-weight: bold; background-color: #ffffaa;} p,td {font-family: 'Helvetica Neue', Helvetica, Arial, sans-serif; font-size: small;} td {border: 1pt solid #000000; padding: 4pt;} table {border: 1pt solid #000000; border-collapse: collapse;} </style> """ # In[43]: legend = """ <table> <tr><td class="mis">{mis}</td></tr> <tr><td class="rec">{rec}</td></tr> <tr><td class="dep">{dep}</td></tr> <tr><td class="dub">{dub}</td></tr> <tr><td class="out">{out}</td></tr> <tr><td class="nor">{nor}</td></tr> </table> """.format( **VALUE_LABELS ) # ### 5.8.2 Formatting clique lists # In[17]: # In[44]: def xterse_chunk(i): chunk = chunks[i] fword = chunk[0] book = L.u(fword, otype="book")[0] chapter = L.u(fword, otype="chapter")[0] return (book, chapter) # In[45]: def xterse_clique(ii): return tuple(sorted({xterse_chunk(i) for i in ii})) # In[46]: def terse_chunk(i): chunk = chunks[i] fword = chunk[0] book = L.u(fword, otype="book")[0] chapter = L.u(fword, otype="chapter")[0] verse = L.u(fword, otype="verse")[0] return (book, chapter, verse) # In[47]: def terse_clique(ii): return tuple(sorted({terse_chunk(i) for i in ii})) # In[48]: def verse_chunk(i): (bk, ch, vs) = i book = F.book.v(bk) chapter = F.chapter.v(ch) verse = F.verse.v(vs) text = "".join( "{}{}".format(Fs(TEXT_FEATURE).v(w), Fs(TRAILER_FEATURE).v(w)) for w in L.d(vs, otype="word") ) verse_label = '<td class="vl">{} {}:{}</td>'.format(book, chapter, verse) htext = '{}<td class="ht">{}</td>'.format(verse_label, text) return '<tr class="ht">{}</tr>'.format(htext) # In[49]: def verse_clique(ii): return '<table class="ht">{}</table>\n'.format( "".join(verse_chunk(i) for i in sorted(ii)) ) # In[50]: def condense(vlabels): cnd = "" (cur_b, cur_c) = (None, None) for (b, c, v) in vlabels: c = str(c) v = str(v) sep = ( "" if cur_b is None else ". " if cur_b != b else "; " if cur_c != c else ", " ) show_b = b + " " if cur_b != b else "" show_c = c + ":" if cur_b != b or cur_c != c else "" (cur_b, cur_c) = (b, c) cnd += "{}{}{}{}".format(sep, show_b, show_c, v) return cnd # In[51]: def print_diff(a, b): arep = "" brep = "" for (lb, ai, aj, bi, bj) in SequenceMatcher( isjunk=None, a=a, b=b, autojunk=False ).get_opcodes(): if lb == "equal": arep += a[ai:aj] brep += b[bi:bj] elif lb == "delete": arep += '{}'.format(lb, a[ai:aj]) elif lb == "insert": brep += '{}'.format(lb, b[bi:bj]) else: arep += '{}'.format(lb, a[ai:aj]) brep += '{}'.format(lb, b[bi:bj]) return (arep, brep) # In[52]: def print_chunk_fine(prev, text, verse_labels, prevlabels): if prev is None: return """ <tr class="ht tb bb"><td class="vl">{}</td><td class="ht">{}</td></tr> """.format( condense(verse_labels), text, ) else: (prevline, textline) = print_diff(prev, text) return """ <tr class="ht tb"><td class="vl">{}</td><td class="ht">{}</td></tr> <tr class="ht bb"><td class="vl">{}</td><td class="ht">{}</td></tr> """.format( condense(prevlabels) if prevlabels is not None else "previous", prevline, condense(verse_labels), textline, ) # In[53]: def print_chunk_coarse(text, verse_labels): return """ <tr class="ht tb bb"><td class="vl">{}</td><td class="ht">{}</td></tr> """.format( condense(verse_labels), text, ) # In[54]: def print_clique(ii, ncliques): return ( print_clique_fine(ii) if len(ii) < ncliques * DEP_CLIQUE_RATIO / 100 else print_clique_coarse(ii) ) # In[55]: def print_clique_fine(ii): condensed = collections.OrderedDict() for i in sorted(ii, key=lambda c: (-len(chunks[c]), c)): chunk = chunks[i] fword = chunk[0] book = F.book.v(L.u(fword, otype="book")[0]) chapter = F.chapter.v(L.u(fword, otype="chapter")[0]) verse = F.verse.v(L.u(fword, otype="verse")[0]) text = "".join( "{}{}".format(Fs(TEXT_FEATURE).v(w), Fs(TRAILER_FEATURE).v(w)) for w in chunk ) condensed.setdefault(text, []).append((book, chapter, verse)) result = [] nv = len(condensed.items()) prev = None for (text, verse_labels) in condensed.items(): if prev is None: if nv == 1: result.append(print_chunk_fine(None, text, verse_labels, None)) else: prev = text prevlabels = verse_labels continue else: result.append(print_chunk_fine(prev, text, verse_labels, prevlabels)) prev = text prevlabels = None return '<table class="ht">{}</table>\n'.format("".join(result)) # In[56]: def print_clique_coarse(ii): condensed = collections.OrderedDict() for i in sorted(ii, key=lambda c: (-len(chunks[c]), c))[0:LARGE_CLIQUE_SIZE]: chunk = chunks[i] fword = chunk[0] book = F.book.v(L.u(fword, otype="book")[0]) chapter = F.chapter.v(L.u(fword, otype="chapter")[0]) verse = F.verse.v(L.u(fword, otype="verse")[0]) text = "".join( "{}{}".format(Fs(TEXT_FEATURE).v(w), Fs(TRAILER_FEATURE).v(w)) for w in chunk ) condensed.setdefault(text, []).append((book, chapter, verse)) result = [] for (text, verse_labels) in condensed.items(): result.append(print_chunk_coarse(text, verse_labels)) if len(ii) > LARGE_CLIQUE_SIZE: result.append( print_chunk_coarse("+ {} ...".format(len(ii) - LARGE_CLIQUE_SIZE), []) ) return '<table class="ht">{}</table>\n'.format("".join(result)) # In[57]: def index_clique(bnm, n, ii, ncliques): return ( index_clique_fine(bnm, n, ii) if len(ii) < ncliques * DEP_CLIQUE_RATIO / 100 else index_clique_coarse(bnm, n, ii) ) # In[58]: def index_clique_fine(bnm, n, ii): verse_labels = [] for i in sorted(ii, key=lambda c: (-len(chunks[c]), c)): chunk = chunks[i] fword = chunk[0] book = F.book.v(L.u(fword, otype="book")[0]) chapter = F.chapter.v(L.u(fword, otype="chapter")[0]) verse = F.verse.v(L.u(fword, otype="verse")[0]) verse_labels.append((book, chapter, verse)) reffl = "{}_{}".format(bnm, n // CLIQUES_PER_FILE) return '{} <a href="{}.html#c_{}">{}</a>'.format( n, reffl, n, condense(verse_labels), ) # In[59]: def index_clique_coarse(bnm, n, ii): verse_labels = [] for i in sorted(ii, key=lambda c: (-len(chunks[c]), c))[0:LARGE_CLIQUE_SIZE]: chunk = chunks[i] fword = chunk[0] book = F.book.v(L.u(fword, otype="book")[0]) chapter = F.chapter.v(L.u(fword, otype="chapter")[0]) verse = F.verse.v(L.u(fword, otype="verse")[0]) verse_labels.append((book, chapter, verse)) reffl = "{}_{}".format(bnm, n // CLIQUES_PER_FILE) extra = ( "+ {} ...".format(len(ii) - LARGE_CLIQUE_SIZE) if len(ii) > LARGE_CLIQUE_SIZE else "" ) return '{} <a href="{}.html#c_{}">{}{}</a>'.format( n, reffl, n, condense(verse_labels), extra, ) # In[60]: def lines_chapter(c): lines = [] for v in L.d(c, otype="verse"): vl = F.verse.v(v) text = "".join( "{}{}".format(Fs(TEXT_FEATURE).v(w), Fs(TRAILER_FEATURE).v(w)) for w in L.d(v, otype="word") ) lines.append("{} {}".format(vl, text.replace("\n", " "))) return lines # In[61]: def compare_chapters(c1, c2, lb1, lb2): dh = difflib.HtmlDiff(wrapcolumn=80) table_html = dh.make_table( lines_chapter(c1), lines_chapter(c2), fromdesc=lb1, todesc=lb2, context=False, numlines=5, ) htext = """<html>{}<body>{}</body></html>""".format(diffhead, table_html) return htext # ### 5.8.3 Compiling the table of experiments # # Here we generate the table of experiments, complete with the coloring according to their assessments. # In[18]: # In[62]: # generate the table of experiments def gen_html(standalone=False): global other_exps TF.info( "EXPERIMENT: Generating html report{}".format( "(standalone)" if standalone else "" ) ) stats = collections.Counter() pre = ( """ <html> <head> <meta http-equiv="Content-Type" content="text/html; charset=UTF-8" /> {} </head> <body> """.format( ecss ) if standalone else "" ) post = ( """ </body></html> """ if standalone else "" ) experiments = """ {} {} <table> <tr><th>chunk type</th><th>chunk size</th><th>similarity method</th>{}</tr> """.format( pre, legend, "".join("<th>{}</th>".format(sim_thr) for sim_thr in SIMILARITIES) ) for chunk_f in (True, False): if chunk_f: chunk_items = CHUNK_SIZES else: chunk_items = CHUNK_OBJECTS chunk_lb = CHUNK_LBS[chunk_f] for chunk_i in chunk_items: for sim_m in SIM_METHODS: set_matrix_threshold(sim_m=sim_m, chunk_o=chunk_i) these_outputs = outputs.get(MATRIX_THRESHOLD, {}) experiments += "<tr><td>{}</td><td>{}</td><td>{}</td>".format( CHUNK_LABELS[chunk_f], chunk_i, sim_m, ) for sim_thr in SIMILARITIES: okey = (chunk_lb, chunk_i, sim_m, sim_thr) values = these_outputs.get(okey) if values is None: result = '<td class="mis"> </td>' stats["mis"] += 1 else: (npassages, ncliques, longest_clique_len) = values cls = assess_exp( chunk_f, npassages, ncliques, longest_clique_len ) stats[cls] += 1 (lr_el, lr_lb) = ("", "") if ( CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, SIMILARITY_THRESHOLD, ) == ( chunk_lb, chunk_i, sim_m, sim_thr, ): lr_el = '*' lr_lb = VALUE_LABELS["lr"] result = """ <td class="{}" title="{}">{} {} <a target="_blank" href="{}{}/{}_{}_{}_M{}_S{}.html">{}</a> {} </td>""".format( cls, lr_lb, lr_el, npassages, "" if standalone else LOCAL_BASE_OUTP + "/", EXPERIMENT_DIR, chunk_lb, chunk_i, sim_m, MATRIX_THRESHOLD, sim_thr, ncliques, longest_clique_len, ) experiments += result experiments += "</tr>\n" experiments += "</table>\n{}".format(post) if standalone: with open(EXPERIMENT_HTML, "w") as f: f.write(experiments) else: other_exps = experiments for stat in sorted(stats): TF.info("EXPERIMENT: {:>3} {}".format(stats[stat], VALUE_LABELS[stat])) TF.info("EXPERIMENT: Generated html report") # ### 5.8.4 High level formatting functions # # Here everything concerning output is brought together. # In[19]: # In[63]: def assess_exp(cf, np, nc, ll): return ( "out" if cf else "rec" if ll > nc * REC_CLIQUE_RATIO / 100 and ll <= nc * DUB_CLIQUE_RATIO / 100 else "dep" if ll > nc * DEP_CLIQUE_RATIO / 100 else "dub" if ll > nc * DUB_CLIQUE_RATIO / 100 else "nor" ) # In[64]: def printing(): global outputs, bin_cliques, base_name TF.info( "PRINT ({} {} {} M>{} S>{}): sorting out cliques".format( CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD, ) ) xt_cliques = { xterse_clique(c) for c in cliques } # chapter cliques as tuples of (b, ch) tuples bin_cliques = { c for c in xt_cliques if len(c) == 2 } # chapter cliques with exactly two chapters # all chapters that occur in binary chapter cliques meta["# BINARY CHAPTER DIFFS"] = len(bin_cliques) # We generate one kind of info for binary chapter cliques (the majority of cases). # The remaining cases are verse cliques that do not occur in such chapters, e.g. because they # have member chunks in the same chapter, or in multiple (more than two) chapters. ncliques = len(cliques) chapters_ok = assess_exp(CHUNK_FIXED, len(passages), ncliques, l_c_l) in { "rec", "nor", "dub", } cdoing = "involving" if chapters_ok else "skipping" TF.info( "PRINT ({} {} {} M>{} S>{}): formatting {} cliques {} {} binary chapter diffs".format( CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD, ncliques, cdoing, len(bin_cliques), ) ) meta_html = "\n".join("{:<40} : {:>10}".format(k, str(meta[k])) for k in meta) base_name = "{}_{}_{}_M{}_S{}".format( CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD, ) param_spec = """ <table> <tr><th>chunking method</th><td>{}</td></tr> <tr><th>chunking description</th><td>{}</td></tr> <tr><th>similarity method</th><td>{}</td></tr> <tr><th>similarity threshold</th><td>{}</td></tr> </table> """.format( CHUNK_LABELS[CHUNK_FIXED], CHUNK_DESC, SIMILARITY_METHOD, SIMILARITY_THRESHOLD, ) param_lab = "chunk-{}-{}-sim-{}-m{}-s{}".format( CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD, ) index_name = base_name all_name = "{}_{}".format("all", base_name) cliques_name = "{}_{}".format("clique", base_name) clique_links = [] clique_links.append( ("{}/{}.html".format(base_name, all_name), "Big list of all cliques") ) nexist = 0 nnew = 0 if chapters_ok: chapter_diffs = [] TF.info( "PRINT ({} {} {} M>{} S>{}): Chapter diffs needed: {}".format( CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD, len(bin_cliques), ) ) bcc_text = "These results look good, so a binary chapter comparison has been generated" for cl in sorted(bin_cliques): lb1 = "{} {}".format(F.book.v(cl[0][0]), F.chapter.v(cl[0][1])) lb2 = "{} {}".format(F.book.v(cl[1][0]), F.chapter.v(cl[1][1])) hfilename = "{}_vs_{}.html".format(lb1, lb2).replace(" ", "_") hfilepath = "{}/{}/{}".format(LOCAL_BASE_OUTP, CHAPTER_DIR, hfilename) chapter_diffs.append( ( lb1, cl[0][1], lb2, cl[1][1], "{}/{}/{}/{}".format( SHEBANQ_TOOL, LOCAL_BASE_OUTP, CHAPTER_DIR, hfilename, ), ) ) if not os.path.exists(hfilepath): htext = compare_chapters(cl[0][1], cl[1][1], lb1, lb2) with open(hfilepath, "w") as f: f.write(htext) if VERBOSE: TF.info( "PRINT ({} {} {} M>{} S>{}): written {}".format( CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD, hfilename, ) ) nnew += 1 else: nexist += 1 clique_links.append( ( "../{}/{}".format(CHAPTER_DIR, hfilename), "{} versus {}".format(lb1, lb2), ) ) TF.info( "PRINT ({} {} {} M>{} S>{}): Chapter diffs: {} newly created and {} already existing".format( CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD, nnew, nexist, ) ) else: bcc_text = "These results look dubious at best, so no binary chapter comparison has been generated" allgeni_html = ( index_clique(cliques_name, i, c, ncliques) for (i, c) in enumerate(cliques) ) allgen_htmls = [] allgen_html = "" for (i, c) in enumerate(cliques): if i % CLIQUES_PER_FILE == 0: if i > 0: allgen_htmls.append(allgen_html) allgen_html = "" allgen_html += '<h3><a name="c_{}">Clique {}</a></h3>\n{}'.format( i, i, print_clique(c, ncliques) ) allgen_htmls.append(allgen_html) index_html_tpl = """ {} <h1>Binary chapter comparisons</h1> {} {} """ content_file_tpl = """<html> <head> <meta http-equiv="Content-Type" content="text/html; charset=UTF-8" /> <title>{}</title> <style type="text/css"> {} </style> </head> <body> <h1>{}</h1> {} <a href="#meta">more parameters and stats</a> {} <h1><a name="meta">Parameters and stats</a></h1> <pre>{}</pre> </body> </html>""" a_tpl_file = '<a target="_blank" href="{}">{}</a>' index_html_file = index_html_tpl.format( a_tpl_file.format(*clique_links[0]), bcc_text, "\n".join(a_tpl_file.format(*c) for c in clique_links[1:]), ) listing_html = "{}\n".format( "\n".join(allgeni_html), ) for (subdir, fname, content_html, tit) in ( (None, index_name, index_html_file, "Index " + param_lab), (base_name, all_name, listing_html, "Listing " + param_lab), (base_name, cliques_name, allgen_htmls, "Cliques " + param_lab), ): subdir = "" if subdir is None else (subdir + "/") subdirabs = "{}/{}/{}".format(LOCAL_BASE_OUTP, EXPERIMENT_DIR, subdir) if not os.path.exists(subdirabs): os.makedirs(subdirabs) if type(content_html) is list: for (i, c_h) in enumerate(content_html): fn = "{}_{}".format(fname, i) t = "{}_{}".format(tit, i) with open( "{}/{}/{}{}.html".format( LOCAL_BASE_OUTP, EXPERIMENT_DIR, subdir, fn ), "w", ) as f: f.write( content_file_tpl.format(t, css, t, param_spec, c_h, meta_html) ) else: with open( "{}/{}/{}{}.html".format( LOCAL_BASE_OUTP, EXPERIMENT_DIR, subdir, fname ), "w", ) as f: f.write( content_file_tpl.format( tit, css, tit, param_spec, content_html, meta_html ) ) destination = outputs.setdefault(MATRIX_THRESHOLD, {}) destination[(CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, SIMILARITY_THRESHOLD)] = ( len(passages), len(cliques), l_c_l, ) TF.info( "PRINT ({} {} {} M>{} S>{}): formatted {} cliques ({} files) {} {} binary chapter diffs".format( CHUNK_LB, CHUNK_DESC, SIMILARITY_METHOD, MATRIX_THRESHOLD, SIMILARITY_THRESHOLD, len(cliques), len(allgen_htmls), cdoing, len(bin_cliques), ) ) # ## 5.9 Running experiments # # The workflows of doing a single experiment, and then all experiments, are defined. # In[20]: # In[65]: outputs = {} # In[66]: def writeoutputs(): global outputs with open(EXPERIMENT_PATH, "wb") as f: pickle.dump(outputs, f, protocol=PICKLE_PROTOCOL) # In[67]: def readoutputs(): global outputs if not os.path.exists(EXPERIMENT_PATH): outputs = {} else: with open(EXPERIMENT_PATH, "rb") as f: outputs = pickle.load(f) # In[68]: def do_experiment(chunk_f, chunk_i, sim_m, sim_thr, do_index): if do_index: readoutputs() (do_chunk, do_prep, do_sim, do_clique, skip) = do_params( chunk_f, chunk_i, sim_m, sim_thr ) if skip: return chunking(do_chunk) preparing(do_prep) similarity(do_sim) cliqueing(do_clique) printing() if do_index: writeoutputs() gen_html() # In[69]: def do_only_chunk(chunk_f, chunk_i): do_chunk = do_params_chunk(chunk_f, chunk_i) chunking(do_chunk) # In[70]: def reset_experiments(): global outputs readoutputs() outputs = {} reset_params() writeoutputs() gen_html() # In[71]: def do_all_experiments(no_fixed=False, only_object=None): global outputs reset_experiments() for chunk_f in (False,) if no_fixed else (True, False): if chunk_f: chunk_items = CHUNK_SIZES else: chunk_items = CHUNK_OBJECTS if only_object is None else (only_object,) for chunk_i in chunk_items: for sim_m in SIM_METHODS: for sim_thr in SIMILARITIES: do_experiment(chunk_f, chunk_i, sim_m, sim_thr, False) writeoutputs() gen_html() gen_html(standalone=True) # In[72]: def do_all_chunks(no_fixed=False, only_object=None): global outputs reset_experiments() for chunk_f in (False,) if no_fixed else (True, False): if chunk_f: chunk_items = CHUNK_SIZES else: chunk_items = CHUNK_OBJECTS if only_object is None else (only_object,) for chunk_i in chunk_items: do_only_chunk(chunk_f, chunk_i) # In[73]: def show_all_experiments(): readoutputs() gen_html() gen_html(standalone=True) # # 6a # # TF features # # Based on selected similarity matrices, we produce an # edge features between verses, containing weighted links to parallel verses. # # The features to deliver are called `crossrefSET` and `crossrefLCS` and `crossref`. # # These are edge feature, both are symmetric, and hence redundant. # For every node, the *from* and *to* edges are identical. # # The `SET` variant consists of set based similarity, the `LCS` one on longest common subsequence # similarity. # # The `crossref` feature takes the union of both methods, with the average confidence. # # The weight is the similarity as percentage integer as it comes from the similarity matrix. # # ## Discussion # We only produce the results of the similarity computation (the matrix), we do not do the cliqueing. # There are many ways to make cliques, and that can easily be done by users of the data, once the # matrix results are in place. # We also do not produce pretty outputs, chapter diffs and other goodies. # Just the raw similarity data. # # The matrix computation is expensive. # We use fixed settings: # * verse chunks # * `SET` method / `LCS` method, # * matrix threshold 50 / 60 # * similarity threshold 75 # # That is, we compute a matrix that contains all pairs with similarity above 50 or 60 # depending on whether we do the `SET` method or the `LCS` method. # # From that matrix, we only use the similarities above 75. # This gives us room to play without recomputing the matrix. # # We do not want to redo this computation if it can be avoided. # # Verse similarity is not something that is very sensitive to change in the encoding. # It is very likely that similar verses in one version of the data agree with similar # verses in all other versions. # # However, the node numbers of verses may change from version to version, so that part # must be done again for each version. # # This is how we proceed: # * the matrix computation gives us triples (v1, v2, w), where v1, v2 are verse nodes and d is there similarity # * we store the result of the matrix computation in a csv file with the following fields: # * method, v1, v1Ref, v2, v2Ref, d, where v1Ref and v2Ref are verse references, # each containing exactly 3 fields: book, chapter, verse # * NB: the similarity table has only one entry for each pair of similar verses per method. # If (v1, v2) is in the table, (v2, v1) is not in the table, per method. # # When we run this notebook for the pipeline, we check for the presence of this file. # If it is present, we uses the vRefs in it to compute the verse nodes that are valid for the # version we are going to produce. # That gives us all the data we need, so we can skip the matrix computation. # # If the file is not present, we have to compute the matrix. # There will be a parameter, called FORCE_MATRIX, which can enforce a re-computation of the matrix. # We need some utility function geared to TF feature production. # The `get_verse()` function is simpler, and we do not have to run full experiments. # In[21]: # In[74]: def writeSimTable(similars): with open(TF_TABLE, "w") as h: for entry in similars: h.write("{}\n".format("\t".join(str(x) for x in entry))) # In[75]: def readSimTable(): similars = [] stats = set() with open(TF_TABLE) as h: for line in h: ( method, v1, v2, sim, book1, chapter1, verse1, book2, chapter2, verse2, ) = line.rstrip("\n").split("\t") verseNode1 = T.nodeFromSection((book1, int(chapter1), int(verse1))) verseNode2 = T.nodeFromSection((book2, int(chapter2), int(verse2))) if verseNode1 != int(v1): stats.add(verseNode1) if verseNode2 != int(v2): stats.add(verseNode2) similars.append( ( method, verseNode1, verseNode2, int(sim), book1, int(chapter1), int(verse1), book2, int(chapter2), int(verse2), ) ) nStats = len(stats) if nStats: utils.caption( 0, "\t\tINFO: {} verse nodes have been changed between versions".format( nStats ), ) utils.caption(0, "\t\tINFO: We will save and use the recomputed ones") writeSimTable(similars) else: utils.caption( 0, "\t\tINFO: All verse nodes are the same as in the previous version" ) return similars # In[76]: def makeSimTable(): similars = [] for (method, similarityCutoff) in ( ("SET", 75), ("LCS", 75), ): (do_chunk, do_prep, do_sim, do_clique, skip) = do_params( False, "verse", method, similarityCutoff ) chunking(do_chunk) preparing(do_prep) similarity(do_sim or FORCE_MATRIX) theseSimilars = [] for ((chunk1, chunk2), sim) in sorted( (x, d) for (x, d) in chunk_dist.items() if d >= similarityCutoff ): verseNode1 = L.u(chunks[chunk1][0], otype="verse")[0] verseNode2 = L.u(chunks[chunk2][0], otype="verse")[0] simInt = int(round(sim)) heading1 = T.sectionFromNode(verseNode1) heading2 = T.sectionFromNode(verseNode2) theseSimilars.append( (method, verseNode1, verseNode2, simInt, *heading1, *heading2) ) utils.caption( 0, "\tMethod {}: found {} similar pairs of verses".format( method, len(theseSimilars) ), ) similars.extend(theseSimilars) writeSimTable(similars) return similars # In[22]: # In[77]: utils.caption(4, "CROSSREFS: Fetching crossrefs") # In[78]: xTable = os.path.exists(TF_TABLE) if FORCE_MATRIX: utils.caption( 0, "\t{} requested of {}".format( "Recomputing" if xTable else "computing", TF_TABLE, ), ) else: if xTable: utils.caption(0, "\tReading existing {}".format(TF_TABLE)) else: utils.caption(0, "\tComputing missing {}".format(TF_TABLE)) # In[79]: if FORCE_MATRIX or not xTable: similars = makeSimTable() else: similars = readSimTable() # In[23]: # In[80]: if not SCRIPT: print("\n".join(sorted(repr(sim) for sim in similars if sim[0] == "LCS")[0:10])) print("\n".join(sorted(repr(sim) for sim in similars if sim[0] == "SET")[0:10])) # In[81]: crossrefData = {} otherMethod = dict(LCS="SET", SET="LCS") # In[82]: for (method, v1, v2, sim, *x) in similars: crossrefData.setdefault(method, {}).setdefault(v1, {})[v2] = sim crossrefData.setdefault(method, {}).setdefault(v2, {})[v1] = sim omethod = otherMethod[method] otherSim = crossrefData.get(omethod, {}).get(v1, {}).get(v2, None) thisSim = sim if otherSim is None else int(round((otherSim + sim) / 2)) crossrefData.setdefault("", {}).setdefault(v1, {})[v2] = thisSim crossrefData.setdefault("", {}).setdefault(v2, {})[v1] = thisSim # # Generating parallels module for Text-Fabric # # We generate the feature `crossref`. # It is an edge feature between verse nodes, with the similarity as weight. # In[89]: utils.caption(4, "Writing TF parallel features") # In[90]: newFeatureStr = "crossref crossrefSET crossrefLCS" newFeatures = newFeatureStr.strip().split() # In[91]: genericMetaPath = f"{thisRepo}/yaml/generic.yaml" parallelsMetaPath = f"{thisRepo}/yaml/parallels.yaml" with open(genericMetaPath) as fh: genericMeta = yaml.load(fh, Loader=yaml.FullLoader) genericMeta["version"] = VERSION with open(parallelsMetaPath) as fh: parallelsMeta = formatMeta(yaml.load(fh, Loader=yaml.FullLoader)) metaData = {"": genericMeta, **parallelsMeta} # In[92]: nodeFeatures = dict() edgeFeatures = dict() for method in [""] + list(otherMethod): edgeFeatures["crossref{}".format(method)] = crossrefData[method] # In[93]: for newFeature in newFeatures: metaData[newFeature]["valueType"] = "int" metaData[newFeature]["edgeValues"] = True # In[94]: TF = Fabric(locations=thisTempTf, silent=True) TF.save(nodeFeatures=nodeFeatures, edgeFeatures=edgeFeatures, metaData=metaData) # # Generating simple crossref notes for SHEBANQ # We base them on the average of both methods, we supply the confidence. # In[33]: # In[ ]: MAX_REFS = 10 # In[ ]: def condenseX(vlabels): cnd = [] (cur_b, cur_c) = (None, None) for (b, c, v, d) in vlabels: sep = ( "" if cur_b is None else ". " if cur_b != b else "; " if cur_c != c else ", " ) show_b = b + " " if cur_b != b else "" show_c = str(c) + ":" if cur_b != b or cur_c != c else "" (cur_b, cur_c) = (b, c) cnd.append("{}[{}{}{}{}]".format(sep, show_b, show_c, v, d)) return cnd # In[ ]: crossrefBase = crossrefData[""] # In[ ]: refsGrouped = [] nCrossrefs = 0 for (x, refs) in crossrefBase.items(): vys = sorted(refs.keys()) nCrossrefs += len(vys) currefs = [] for vy in vys: nr = len(currefs) if nr == MAX_REFS: refsGrouped.append((x, tuple(currefs))) currefs = [] currefs.append(vy) if len(currefs): refsGrouped.append((x, tuple(currefs))) # In[33]: refsCompiled = [] for (x, vys) in refsGrouped: vysd = [ (*T.sectionFromNode(vy, lang="la"), " ~{}%".format(crossrefBase[x][vy])) for vy in vys ] vysl = condenseX(vysd) these_refs = [] for (i, vy) in enumerate(vysd): link_text = vysl[i] link_target = "{} {}:{}".format(vy[0], vy[1], vy[2]) these_refs.append("{}({})".format(link_text, link_target)) refsCompiled.append((x, " ".join(these_refs))) utils.caption( 0, "Compiled {} cross references into {} notes".format(nCrossrefs, len(refsCompiled)), ) # In[34]: # In[ ]: sfields = """ version book chapter verse clause_atom is_shared is_published status keywords ntext """.strip().split() # In[ ]: sfields_fmt = ("{}\t" * (len(sfields) - 1)) + "{}\n" # In[ ]: ofs = open("{}/{}".format(thisNotes, notesFile), "w") ofs.write("{}\n".format("\t".join(sfields))) # In[ ]: for (v, refs) in refsCompiled: firstWord = L.d(v, otype="word")[0] ca = F.number.v(L.u(firstWord, otype="clause_atom")[0]) (bk, ch, vs) = T.sectionFromNode(v, lang="la") ofs.write( sfields_fmt.format( VERSION, bk, ch, vs, ca, "T", "", CROSSREF_STATUS, CROSSREF_KEYWORD, refs, ) ) # In[34]: utils.caption(0, "Generated {} notes".format(len(refsCompiled))) ofs.close() # # Diffs # # Check differences with previous versions. # In[35]: # In[35]: utils.checkDiffs(thisTempTf, thisTf, only=set(newFeatures)) # # Deliver # # Copy the new TF feature from the temporary location where it has been created to its final destination. # In[36]: # In[36]: utils.deliverDataset(thisTempTf, thisTf) # # Compile TF # In[38]: # In[ ]: utils.caption(4, "Load and compile the new TF features") # In[38]: TF = Fabric(locations=[coreTf, thisTf], modules=[""]) api = TF.load(newFeatureStr) api.makeAvailableIn(globals()) # # Examples # We list all the crossrefs that the verses of Genesis 10 are involved in. # In[39]: # In[ ]: utils.caption(4, "Test: crossrefs of Genesis 10") # In[ ]: chapter = ("Genesis", 10) chapterNode = T.nodeFromSection(chapter) startVerses = {} # In[39]: for method in ["", "SET", "LCS"]: utils.caption(0, "\tMethod {}".format(method)) for verseNode in L.d(chapterNode, otype="verse"): crossrefs = Es("crossref{}".format(method)).f(verseNode) if crossrefs: startVerses[T.sectionFromNode(verseNode)] = crossrefs utils.caption(0, "\t\t{} start verses".format(len(startVerses))) for (start, crossrefs) in sorted(startVerses.items()): utils.caption(0, "\t\t{} {}:{}".format(*start), continuation=True) for (target, confidence) in crossrefs: utils.caption( 0, "\t\t{:>20} {:<20} confidende {:>3}%".format( "-" * 10 + ">", "{} {}:{}".format(*T.sectionFromNode(target)), confidence, ), ) # In[29]: # In[29]: if SCRIPT: stop(good=True) # # 6b. SHEBANQ annotations # # The code below generates extensive crossref notes for `4b`, including clique overviews and chapter diffs. # But since the pipeline in October 2017, we generate much simpler notes. # That code is above. # # We retain this code here, in case we want to expand the crossref functionality in the future again. # # Based on selected similarity matrices, we produce a SHEBANQ note set of cross references for similar passages. # In[30]: # In[ ]: def get_verse(i, ca=False): return get_verse_w(chunks[i][0], ca=ca) # In[ ]: def get_verse_o(o, ca=False): return get_verse_w(L.d(o, otype="word")[0], ca=ca) # In[ ]: def get_verse_w(w, ca=False): book = F.book.v(L.u(w, otype="book")[0]) chapter = F.chapter.v(L.u(w, otype="chapter")[0]) verse = F.verse.v(L.u(w, otype="verse")[0]) if ca: ca = F.number.v(L.u(w, otype="clause_atom")[0]) return (book, chapter, verse, ca) if ca else (book, chapter, verse) # In[ ]: def key_verse(x): return (book_rank[x[0]], int(x[1]), int(x[2])) # In[ ]: MAX_REFS = 10 # In[ ]: def condensex(vlabels): cnd = [] (cur_b, cur_c) = (None, None) for (b, c, v, d) in vlabels: sep = ( "" if cur_b is None else ". " if cur_b != b else "; " if cur_c != c else ", " ) show_b = b + " " if cur_b != b else "" show_c = c + ":" if cur_b != b or cur_c != c else "" (cur_b, cur_c) = (b, c) cnd.append("{}{}{}{}{}".format(sep, show_b, show_c, v, d)) return cnd # In[ ]: dfields = """ book1 chapter1 verse1 book2 chapter2 verse2 similarity """.strip().split() # In[ ]: dfields_fmt = ("{}\t" * (len(dfields) - 1)) + "{}\n" # In[ ]: def get_crossrefs(): global crossrefs TF.info("CROSSREFS: Fetching crossrefs") crossrefs_proto = {} crossrefs = {} (chunk_f, chunk_i, sim_m) = SHEBANQ_MATRIX sim_thr = SHEBANQ_SIMILARITY (do_chunk, do_prep, do_sim, do_clique, skip) = do_params( chunk_f, chunk_i, sim_m, sim_thr ) if skip: return TF.info( "CROSSREFS ({} {} {} S>{})".format(CHUNK_LBS[chunk_f], chunk_i, sim_m, sim_thr) ) crossrefs_proto = {x for x in chunk_dist.items() if x[1] >= sim_thr} TF.info( "CROSSREFS ({} {} {} S>{}): found {} pairs".format( CHUNK_LBS[chunk_f], chunk_i, sim_m, sim_thr, len(crossrefs_proto), ) ) f = open(CROSSREF_DB_PATH, "w") f.write("{}\n".format("\t".join(dfields))) for ((x, y), d) in crossrefs_proto: vx = get_verse(x) vy = get_verse(y) rd = int(round(d)) crossrefs.setdefault(x, {})[vy] = rd crossrefs.setdefault(y, {})[vx] = rd f.write(dfields_fmt.format(*(vx + vy + (rd,)))) total = sum(len(x) for x in crossrefs.values()) f.close() TF.info( "CROSSREFS: Found {} crossreferences and wrote {} pairs".format( total, len(crossrefs_proto) ) ) # In[ ]: def get_specific_crossrefs(chunk_f, chunk_i, sim_m, sim_thr, write_to): (do_chunk, do_prep, do_sim, do_clique, skip) = do_params( chunk_f, chunk_i, sim_m, sim_thr ) if skip: return chunking(do_chunk) preparing(do_prep) similarity(do_sim) TF.info("CROSSREFS: Fetching crossrefs") crossrefs_proto = {} crossrefs = {} (do_chunk, do_prep, do_sim, do_clique, skip) = do_params( chunk_f, chunk_i, sim_m, sim_thr ) if skip: return TF.info( "CROSSREFS ({} {} {} S>{})".format(CHUNK_LBS[chunk_f], chunk_i, sim_m, sim_thr) ) crossrefs_proto = {x for x in chunk_dist.items() if x[1] >= sim_thr} TF.info( "CROSSREFS ({} {} {} S>{}): found {} pairs".format( CHUNK_LBS[chunk_f], chunk_i, sim_m, sim_thr, len(crossrefs_proto), ) ) f = open("files/{}".format(write_to), "w") f.write("{}\n".format("\t".join(dfields))) for ((x, y), d) in crossrefs_proto: vx = get_verse(x) vy = get_verse(y) rd = int(round(d)) crossrefs.setdefault(x, {})[vy] = rd crossrefs.setdefault(y, {})[vx] = rd f.write(dfields_fmt.format(*(vx + vy + (rd,)))) total = sum(len(x) for x in crossrefs.values()) f.close() TF.info( "CROSSREFS: Found {} crossreferences and wrote {} pairs".format( total, len(crossrefs_proto) ) ) # In[ ]: def compile_refs(): global refs_compiled refs_grouped = [] for x in sorted(crossrefs): refs = crossrefs[x] vys = sorted(refs.keys(), key=key_verse) currefs = [] for vy in vys: nr = len(currefs) if nr == MAX_REFS: refs_grouped.append((x, tuple(currefs))) currefs = [] currefs.append(vy) if len(currefs): refs_grouped.append((x, tuple(currefs))) refs_compiled = [] for (x, vys) in refs_grouped: vysd = [(vy[0], vy[1], vy[2], " ~{}%".format(crossrefs[x][vy])) for vy in vys] vysl = condensex(vysd) these_refs = [] for (i, vy) in enumerate(vysd): link_text = vysl[i] link_target = "{} {}:{}".format(vy[0], vy[1], vy[2]) these_refs.append("[{}]({})".format(link_text, link_target)) refs_compiled.append((x, " ".join(these_refs))) TF.info( "CROSSREFS: Compiled cross references into {} notes".format(len(refs_compiled)) ) # In[ ]: def get_chapter_diffs(): global chapter_diffs chapter_diffs = [] for cl in sorted(bin_cliques): lb1 = "{} {}".format(F.book.v(cl[0][0]), F.chapter.v(cl[0][1])) lb2 = "{} {}".format(F.book.v(cl[1][0]), F.chapter.v(cl[1][1])) hfilename = "{}_vs_{}.html".format(lb1, lb2).replace(" ", "_") chapter_diffs.append( ( lb1, cl[0][1], lb2, cl[1][1], "{}/{}/{}/{}".format( SHEBANQ_TOOL, LOCAL_BASE_OUTP, CHAPTER_DIR, hfilename, ), ) ) TF.info("CROSSREFS: Added {} chapter diffs".format(2 * len(chapter_diffs))) # In[ ]: def get_clique_refs(): global clique_refs clique_refs = [] for (i, c) in enumerate(cliques): for j in c: seq = i // CLIQUES_PER_FILE clique_refs.append( ( j, i, "{}/{}/{}/{}/clique_{}_{}.html#c_{}".format( SHEBANQ_TOOL, LOCAL_BASE_OUTP, EXPERIMENT_DIR, base_name, base_name, seq, i, ), ) ) TF.info("CROSSREFS: Added {} clique references".format(len(clique_refs))) # In[ ]: sfields = """ version book chapter verse clause_atom is_shared is_published status keywords ntext """.strip().split() # In[ ]: sfields_fmt = ("{}\t" * (len(sfields) - 1)) + "{}\n" # In[ ]: def generate_notes(): with open(NOTES_PATH, "w") as f: f.write("{}\n".format("\t".join(sfields))) x = next(F.otype.s("word")) (bk, ch, vs, ca) = get_verse(x, ca=True) f.write( sfields_fmt.format( VERSION, bk, ch, vs, ca, "T", "", CROSSREF_STATUS, CROSSREF_KEYWORD, """The crossref notes are the result of a computation without manual tweaks. Parameters: chunk by verse, similarity method SET with threshold 65. [Here](tool=parallel) is an account of the generation method.""".replace( "\n", " " ), ) ) for (lb1, ch1, lb2, ch2, fl) in chapter_diffs: (bk1, ch1, vs1, ca1) = get_verse_o(ch1, ca=True) (bk2, ch2, vs2, ca2) = get_verse_o(ch2, ca=True) f.write( sfields_fmt.format( VERSION, bk1, ch1, vs1, ca1, "T", "", CROSSREF_STATUS, CROSSREF_KEYWORD, "[chapter diff with {}](tool:{})".format(lb2, fl), ) ) f.write( sfields_fmt.format( VERSION, bk2, ch2, vs2, ca2, "T", "", CROSSREF_STATUS, CROSSREF_KEYWORD, "[chapter diff with {}](tool:{})".format(lb1, fl), ) ) for (x, refs) in refs_compiled: (bk, ch, vs, ca) = get_verse(x, ca=True) f.write( sfields_fmt.format( VERSION, bk, ch, vs, ca, "T", "", CROSSREF_STATUS, CROSSREF_KEYWORD, refs, ) ) for (chunk, clique, fl) in clique_refs: (bk, ch, vs, ca) = get_verse(chunk, ca=True) f.write( sfields_fmt.format( VERSION, bk, ch, vs, ca, "T", "", CROSSREF_STATUS, CROSSREF_KEYWORD, "[all variants (clique {})](tool:{})".format(clique, fl), ) ) TF.info( "CROSSREFS: Generated {} notes".format( 1 + len(refs_compiled) + 2 * len(chapter_diffs) + len(clique_refs) ) ) # In[30]: def crossrefs2shebanq(): expr = SHEBANQ_MATRIX + (SHEBANQ_SIMILARITY,) do_experiment(*(expr + (True,))) get_crossrefs() compile_refs() get_chapter_diffs() get_clique_refs() generate_notes() # # 7. Main # # In the cell below you can select the experiments you want to carry out. # # The previous cells contain just definitions and parameters. # The next cell will do work. # # If none of the matrices and cliques have been computed before on the system where this runs, doing all experiments might take multiple hours (4-8). # In[ ]: # In[ ]: reset_params() # do_experiment(False, 'sentence', 'LCS', 60, False) # In[ ]: do_all_experiments() # do_all_experiments(no_fixed=True, only_object='chapter') # crossrefs2shebanq() # show_all_experiments() # get_specific_crossrefs(False, 'verse', 'LCS', 60, 'crossrefs_lcs_db.txt') # do_all_chunks() # In[ ]: # In[ ]: HTML(ecss) # # 8. Overview of the similarities # # Here are the plots of two similarity matrices # * with verses as chunks and SET as similarity method # * with verses as chunks and LCS as similarity method # # Horizontally you see the degree of similarity from 0 to 100%, vertically the number of pairs that have that (rounded) similarity. This axis is logarithmic. # In[ ]: # In[ ]: do_experiment(False, "verse", "SET", 60, False) distances = collections.Counter() for (x, d) in chunk_dist.items(): distances[int(round(d))] += 1 # In[ ]: x = range(MATRIX_THRESHOLD, 101) fig = plt.figure(figsize=[15, 4]) plt.plot(x, [math.log(max((1, distances[y]))) for y in x], "b-") plt.axis([MATRIX_THRESHOLD, 101, 0, 15]) plt.xlabel("similarity as %") plt.ylabel("log # similarities") plt.xticks(x, x, rotation="vertical") plt.margins(0.2) plt.subplots_adjust(bottom=0.15) plt.title("distances") # In[ ]: # In[ ]: do_experiment(False, "verse", "LCS", 60, False) distances = collections.Counter() for (x, d) in chunk_dist.items(): distances[int(round(d))] += 1 # In[ ]: x = range(MATRIX_THRESHOLD, 101) fig = plt.figure(figsize=[15, 4]) plt.plot(x, [math.log(max((1, distances[y]))) for y in x], "b-") plt.axis([MATRIX_THRESHOLD, 101, 0, 15]) plt.xlabel("similarity as %") plt.ylabel("log # similarities") plt.xticks(x, x, rotation="vertical") plt.margins(0.2) plt.subplots_adjust(bottom=0.15) plt.title("distances") # In[ ]:

30.896048

6,888

0.579503

0

51,128

0.473894

a157d32f7b13b416fb6bf59f5d4cfdbbe25ce080

4,870

py

Python

src/python/pants/goal/initialize_reporting.py

WamBamBoozle/pants

98cadfa1a5d337146903eb66548cfe955f2627b3

[ "Apache-2.0" ]

null

src/python/pants/goal/initialize_reporting.py

WamBamBoozle/pants

98cadfa1a5d337146903eb66548cfe955f2627b3

[ "Apache-2.0" ]

null

src/python/pants/goal/initialize_reporting.py

WamBamBoozle/pants

98cadfa1a5d337146903eb66548cfe955f2627b3

[ "Apache-2.0" ]

null

# coding=utf-8 # Copyright 2014 Pants project contributors (see CONTRIBUTORS.md). # Licensed under the Apache License, Version 2.0 (see LICENSE). from __future__ import (absolute_import, division, generators, nested_scopes, print_function, unicode_literals, with_statement) import errno import os import sys from six import StringIO from pants.reporting.html_reporter import HtmlReporter from pants.reporting.plaintext_reporter import PlainTextReporter from pants.reporting.quiet_reporter import QuietReporter from pants.reporting.report import Report, ReportingError from pants.reporting.reporting_server import ReportingServerManager from pants.util.dirutil import safe_mkdir, safe_rmtree def initial_reporting(config, run_tracker): """Sets up the initial reporting configuration. Will be changed after we parse cmd-line flags. """ reports_dir = os.path.join(config.getdefault('pants_workdir'), 'reports') link_to_latest = os.path.join(reports_dir, 'latest') run_id = run_tracker.run_info.get_info('id') if run_id is None: raise ReportingError('No run_id set') run_dir = os.path.join(reports_dir, run_id) safe_rmtree(run_dir) html_dir = os.path.join(run_dir, 'html') safe_mkdir(html_dir) try: if os.path.lexists(link_to_latest): os.unlink(link_to_latest) os.symlink(run_dir, link_to_latest) except OSError as e: # Another run may beat us to deletion or creation. if not (e.errno == errno.EEXIST or e.errno == errno.ENOENT): raise report = Report() # Capture initial console reporting into a buffer. We'll do something with it once # we know what the cmd-line flag settings are. outfile = StringIO() capturing_reporter_settings = PlainTextReporter.Settings(outfile=outfile, log_level=Report.INFO, color=False, indent=True, timing=False, cache_stats=False) capturing_reporter = PlainTextReporter(run_tracker, capturing_reporter_settings) report.add_reporter('capturing', capturing_reporter) # Set up HTML reporting. We always want that. template_dir = config.get('reporting', 'reports_template_dir') html_reporter_settings = HtmlReporter.Settings(log_level=Report.INFO, html_dir=html_dir, template_dir=template_dir) html_reporter = HtmlReporter(run_tracker, html_reporter_settings) report.add_reporter('html', html_reporter) # Add some useful RunInfo. run_tracker.run_info.add_info('default_report', html_reporter.report_path()) (_, port) = ReportingServerManager.get_current_server_pid_and_port() if port: run_tracker.run_info.add_info('report_url', 'http://localhost:{}/run/{}'.format(port, run_id)) return report def update_reporting(options, is_quiet_task, run_tracker): """Updates reporting config once we've parsed cmd-line flags.""" # Get any output silently buffered in the old console reporter, and remove it. old_outfile = run_tracker.report.remove_reporter('capturing').settings.outfile old_outfile.flush() buffered_output = old_outfile.getvalue() old_outfile.close() log_level = Report.log_level_from_string(options.level or 'info') # Ideally, we'd use terminfo or somesuch to discover whether a # terminal truly supports color, but most that don't set TERM=dumb. color = (options.colors) and (os.getenv('TERM') != 'dumb') timing = options.time cache_stats = options.time # TODO: Separate flag for this? if options.quiet or is_quiet_task: console_reporter = QuietReporter(run_tracker, QuietReporter.Settings(log_level=log_level, color=color)) else: # Set up the new console reporter. settings = PlainTextReporter.Settings(log_level=log_level, outfile=sys.stdout, color=color, indent=True, timing=timing, cache_stats=cache_stats) console_reporter = PlainTextReporter(run_tracker, settings) console_reporter.emit(buffered_output) console_reporter.flush() run_tracker.report.add_reporter('console', console_reporter) if options.logdir: # Also write plaintext logs to a file. This is completely separate from the html reports. safe_mkdir(options.logdir) run_id = run_tracker.run_info.get_info('id') outfile = open(os.path.join(options.logdir, '{}.log'.format(run_id)), 'w') settings = PlainTextReporter.Settings(log_level=log_level, outfile=outfile, color=False, indent=True, timing=True, cache_stats=True) logfile_reporter = PlainTextReporter(run_tracker, settings) logfile_reporter.emit(buffered_output) logfile_reporter.flush() run_tracker.report.add_reporter('logfile', logfile_reporter)

42.719298

98

0.716222

0

1,145

0.235113

a1586b7c08a86b032589e3a797f710af94eef3ed

4,947

py

Python

ResolvePageSwitcher.py

IgorRidanovic/DaVinciResolve-PageSwitcher

5a771d8fa319454dbcf986b8921e5fa0c665baa9

[ "MIT" ]

17

2018-06-01T07:30:33.000Z

2021-12-22T21:05:29.000Z

ResolvePageSwitcher.py

IgorRidanovic/DaVinciResolve-PageSwitcher

5a771d8fa319454dbcf986b8921e5fa0c665baa9

[ "MIT" ]

2

2018-10-23T17:32:45.000Z

2020-12-09T07:48:06.000Z

ResolvePageSwitcher.py

IgorRidanovic/DaVinciResolve-PageSwitcher

5a771d8fa319454dbcf986b8921e5fa0c665baa9

[ "MIT" ]

5

2018-09-06T02:11:56.000Z

2020-10-25T11:25:22.000Z

#! /usr/bin/env python # -*- coding: utf-8 -*- # DaVinci Resolve scripting proof of concept. Resolve page external switcher. # Local or TCP/IP control mode. # Refer to Resolve V15 public beta 2 scripting API documentation for host setup. # Copyright 2018 Igor Riđanović, www.hdhead.com from PyQt4 import QtCore, QtGui import sys import socket # If API module not found assume we're working as a remote control try: import DaVinciResolveScript #Instantiate Resolve object resolve = DaVinciResolveScript.scriptapp('Resolve') checkboxState = False except ImportError: print 'Resolve API not found.' checkboxState = True try: _fromUtf8 = QtCore.QString.fromUtf8 except AttributeError: def _fromUtf8(s): return s try: _encoding = QtGui.QApplication.UnicodeUTF8 def _translate(context, text, disambig): return QtGui.QApplication.translate(context, text, disambig, _encoding) except AttributeError: def _translate(context, text, disambig): return QtGui.QApplication.translate(context, text, disambig) class Ui_Form(object): def setupUi(self, Form): Form.setObjectName(_fromUtf8('Resolve Page Switcher')) Form.resize(561, 88) Form.setStyleSheet(_fromUtf8('background-color: #282828;\ border-color: #555555;\ color: #929292;\ font-size: 13px;'\ )) self.horizontalLayout = QtGui.QHBoxLayout(Form) self.horizontalLayout.setObjectName(_fromUtf8('horizontalLayout')) self.mediaButton = QtGui.QPushButton(Form) self.mediaButton.setObjectName(_fromUtf8('mediaButton')) self.horizontalLayout.addWidget(self.mediaButton) self.editButton = QtGui.QPushButton(Form) self.editButton.setObjectName(_fromUtf8('editButton')) self.horizontalLayout.addWidget(self.editButton) self.fusionButton = QtGui.QPushButton(Form) self.fusionButton.setObjectName(_fromUtf8('fusionButton')) self.horizontalLayout.addWidget(self.fusionButton) self.colorButton = QtGui.QPushButton(Form) self.colorButton.setObjectName(_fromUtf8('colorButton')) self.horizontalLayout.addWidget(self.colorButton) self.fairlightButton = QtGui.QPushButton(Form) self.fairlightButton.setObjectName(_fromUtf8('fairlightButton')) self.horizontalLayout.addWidget(self.fairlightButton) self.deliverButton = QtGui.QPushButton(Form) self.deliverButton.setObjectName(_fromUtf8('deliverButton')) self.horizontalLayout.addWidget(self.deliverButton) self.tcpipcheckBox = QtGui.QCheckBox(Form) self.tcpipcheckBox.setObjectName(_fromUtf8('tcpipcheckBox')) self.tcpipcheckBox.setChecked(checkboxState) self.horizontalLayout.addWidget(self.tcpipcheckBox) self.mediaButton.clicked.connect(lambda: self.pageswitch('media')) self.editButton.clicked.connect(lambda: self.pageswitch('edit')) self.fusionButton.clicked.connect(lambda: self.pageswitch('fusion')) self.colorButton.clicked.connect(lambda: self.pageswitch('color')) self.fairlightButton.clicked.connect(lambda: self.pageswitch('fairlight')) self.deliverButton.clicked.connect(lambda: self.pageswitch('deliver')) self.mediaButton.setStyleSheet(_fromUtf8('background-color: #181818;')) self.editButton.setStyleSheet(_fromUtf8('background-color: #181818;')) self.fusionButton.setStyleSheet(_fromUtf8('background-color: #181818;')) self.colorButton.setStyleSheet(_fromUtf8('background-color: #181818;')) self.fairlightButton.setStyleSheet(_fromUtf8('background-color: #181818;')) self.deliverButton.setStyleSheet(_fromUtf8('background-color: #181818;')) self.retranslateUi(Form) QtCore.QMetaObject.connectSlotsByName(Form) def retranslateUi(self, Form): Form.setWindowTitle(_translate('Resolve Page Switcher',\ 'Resolve Page Switcher', None)) self.mediaButton.setText(_translate('Form', 'Media', None)) self.editButton.setText(_translate('Form', 'Edit', None)) self.fusionButton.setText(_translate('Form', 'Fusion', None)) self.colorButton.setText(_translate('Form', 'Color', None)) self.fairlightButton.setText(_translate('Form', 'Fairlight', None)) self.deliverButton.setText(_translate('Form', 'Deliver', None)) self.tcpipcheckBox.setText(_translate("Form", "TCP/IP remote", None)) def send(self, message): s = socket.socket() try: s.connect((server, port)) except socket.error: print 'Server unavailable. Exiting.' s.send(message) return s.recv(32) def pageswitch(self, page): # Send page name to server to switch remote Resolve's page if self.tcpipcheckBox.isChecked(): response = self.send(page) print 'Server echo:', response # Switch local Resolve's page if API is available else: try: resolve.OpenPage(page) print 'Switched to', page except NameError: print 'Resolve API not found. Run in remote mode instead?' if __name__ == '__main__': # Assign server parameters server = '192.168.1.1' port = 7779 app = QtGui.QApplication(sys.argv) Form = QtGui.QWidget() ui = Ui_Form() ui.setupUi(Form) Form.show() sys.exit(app.exec_())

36.91791

80

0.761472

3,703

0.748232

0

1,291

0.260861

a15a0aec2c8adfc46228db42100cded4658cf98f

14,022

py

Python

Make Data Files.py

micitz/Dune_Aspect_Ratio_XB_Paper

25395219886facb3a7e68835e8aae406dbff0b4d

[ "MIT" ]

null

Make Data Files.py

micitz/Dune_Aspect_Ratio_XB_Paper

25395219886facb3a7e68835e8aae406dbff0b4d

[ "MIT" ]

null

Make Data Files.py

micitz/Dune_Aspect_Ratio_XB_Paper

25395219886facb3a7e68835e8aae406dbff0b4d

[ "MIT" ]

null

""" All the data sources are scattered around the D drive, this script organizes it and consolidates it into the "Data" subfolder in the "Chapter 2 Dune Aspect Ratio" folder. Michael Itzkin, 5/6/2020 """ import shutil as sh import pandas as pd import numpy as np import os # Set the data directory to save files into DATA_DIR = os.path.join('..', 'Data') # Set the directory with most of the XBeach data XB_DIR = os.path.join('..', '..', 'XBeach Modelling', 'Dune Complexity Experiments') def bogue_lidar_data(): """ Load all Bogue Banks morphometrics from 1997-2016 and return a dataframe of aspect ratios and natural dune volumes """ # Set a list of years years = [1997, 1998, 1999, 2000, 2004, 2005, 2010, 2011, 2014, 2016] # Set an empty dataframe morpho = pd.DataFrame() # Loop through the years and load the data for year in years: # Set a path to the data and load path = os.path.join('..', '..', 'Chapter 1 Sand Fences', 'Data', f'Morphometrics for Bogue {year}.csv') temp = pd.read_csv(path, delimiter=',', header=0) # Add a column for the year temp['Year'] = year # Append the data to the main dataframe morpho = pd.concat([morpho, temp]) # Make a new dataframe with just aspect ratios and volumes data = pd.DataFrame() data['Year'] = morpho['Year'] data['Ratio'] = (morpho['y_crest'] - morpho['y_toe']) / (morpho['x_heel'] - morpho['x_toe']) data['Volume'] = morpho['Natural Dune Volume'] # Save the Dataframe to the data folder save_name = os.path.join(DATA_DIR, 'Bogue Banks Volumes and Aspect Ratios.csv') data.to_csv(save_name, index=False) print(f'File Saved: {save_name}') def initial_profiles(): """ Take all the initial profiles and place them into a Dataframe to save as a .csv Make a column for the experiment names, a column for the X-grids, and columns for the profiles """ # Set the experiment names. The initial profiles are the same regardless of # the surge level so just take from the half surge simulations experiments = ['Toes Joined', 'Crests Joined', 'Heels Joined', 'Fenced'] # Set an empty dataframe profiles = pd.DataFrame() # Loop through the experiments for experiment in experiments: # Set a path to the profiles PROFILE_DIR = os.path.join(XB_DIR, f'{experiment} Half Surge') # Load the x-grid x_grid_fname = os.path.join(PROFILE_DIR, 'Dune Complexity 1 1', 'x.grd') x_grid = np.loadtxt(x_grid_fname) # Load the dunes dune_1 = np.loadtxt(fname=os.path.join(PROFILE_DIR, 'Dune Complexity 1 1', 'bed.dep')) dune_2 = np.loadtxt(fname=os.path.join(PROFILE_DIR, 'Dune Complexity 20 1', 'bed.dep')) dune_3 = np.loadtxt(fname=os.path.join(PROFILE_DIR, 'Dune Complexity 40 1', 'bed.dep')) dune_4 = np.loadtxt(fname=os.path.join(PROFILE_DIR, 'Dune Complexity 60 1', 'bed.dep')) dune_5 = np.loadtxt(fname=os.path.join(PROFILE_DIR, 'Dune Complexity -20 1', 'bed.dep')) dune_6 = np.loadtxt(fname=os.path.join(PROFILE_DIR, 'Dune Complexity -40 1', 'bed.dep')) dune_7 = np.loadtxt(fname=os.path.join(PROFILE_DIR, 'Dune Complexity -60 1', 'bed.dep')) # Put all of the stretched dunes into a dataframe dune_dict = { 'Experiment': experiment.replace('Joined', 'Aligned'), 'X': x_grid, '1 pct': dune_1, '20 pct': dune_2, '40 pct': dune_3, '60 pct': dune_4, '-20 pct': dune_5, '-40 pct': dune_6, '-60 pct': dune_7, } dune_data = pd.DataFrame(data=dune_dict) # Concatenate the Dataframes profiles = pd.concat([profiles, dune_data]) # Save the Dataframe to the data folder save_name = os.path.join(DATA_DIR, 'Initial Profiles.csv') profiles.to_csv(save_name, index=False) print(f'File Saved: {save_name}') def initial_ratios(): """ Make a .csv file with the initial dune aspect ratios and dune volumes for the profiles used in the simulations """ # Set the experiment names. The initial profiles are the same regardless of # the surge level so just take from the half surge simulations experiments = ['Toes Joined', 'Crests Joined', 'Heels Joined', 'Fenced'] # Set an empty dataframe ratios = pd.DataFrame() # Loop through the experiments for experiment in experiments: # Load the initial dune ratios init_ratio_fname = os.path.join(XB_DIR, f'{experiment} Half Surge', 'Setup Data', 'Initial Dune Ratios.csv') init_ratios = pd.read_csv(init_ratio_fname, delimiter=',', header=None, names=['Stretch', 'Ratio', 'Volume']) # Add a column for the experiment name init_ratios['Experiment'] = experiment.replace('Joined', 'Aligned') # Concatenate the data ratios = pd.concat([ratios, init_ratios]) # Save the Dataframe to the data folder save_name = os.path.join(DATA_DIR, 'Initial Dune Ratios.csv') ratios.to_csv(save_name, index=False) print(f'File Saved: {save_name}') def joaquin_and_florence(): """ Load the storm surge time series' from Tropical Storm Joaquin and Hurricane Florence, put them in a .csv file """ # Loop through the storms for storm in ['Joaquin', 'Florence']: # Load the tide predictions and observations as a Pandas dataframe filename = os.path.join(XB_DIR, 'Setup Data', f'{storm}.csv') if storm == 'Joaquin': parse_dates_cols = ['Date', 'Time'] data_columns = ['Time', 'Predicted', 'Observed'] else: parse_dates_cols = ['Date', 'Time (GMT)'] data_columns = ['Time', 'Predicted', 'Preliminary', 'Observed'] data = pd.read_csv(filename, delimiter=',', parse_dates=[parse_dates_cols], header=0) data.columns = data_columns # Calculate the non-tidal residual data['NTR'] = data['Observed'] - data['Predicted'] # Load the time data times = data['Time'].tolist() data['String Times'] = [t.strftime('%Y-%m-%d %H') for t in times] # Save the DataFrame as a .csv save_name = os.path.join(DATA_DIR, f'{storm}.csv') data.to_csv(save_name, index=False) def move_csv_output(): """ Take the .csv files and move them into the "Data" folder, then rename them from "xboutput.nc" to the name of the simulation """ # Set lists with the dune configurations, storm surge # modifications, storm duration increases, and dune aspect # ratio stretches dunes = ['Toes Joined', 'Crests Joined', 'Heels Joined', 'Fenced'] surges = ['Half', 'Normal', 'One Half'] durations = [1, 12, 18, 24, 36, 48] stretches = [-60, -40, -20, 1, 20, 40, 60] # Loop through the dunes and surges for dune in dunes: for surge in surges: # Set the experiment folder name experiment_name = f'{dune} {surge} Surge' experiment_folder = os.path.join(XB_DIR, experiment_name) # Make a target folder to move the runs into save_folder = os.path.join(DATA_DIR, 'XBeach Morphometrics', experiment_name) if not os.path.exists(save_folder): os.mkdir(save_folder) # Loop through the dunes and durations within the experiment for stretch in stretches: for duration in durations: # Set the simulation folder run_name = f'Dune Complexity {stretch} {duration}' simulation_folder = os.path.join(experiment_folder, run_name) # Set the XBeach output file as the source. Set the destination # name. Then copy the file over source = os.path.join(simulation_folder, f'{run_name} Morphometrics.csv') if os.path.exists(source): destination = os.path.join(save_folder, f'{run_name} Morphometrics.csv') if not os.path.exists(destination): sh.copy(source, destination) print(f'File Successfully Copied: {destination}') else: print(f'File already exists: {destination}') else: print(f'FILE DOES NOT EXIST: {source}') def move_field_data(): """ Move the field data morphometrics from 2017 and 2018 into the data folder """ # Set the years years = [2017, 2018] # Set a path to the field data field_dir = os.path.join('..', '..', 'Bogue Banks Field Data') # Loop through the years for year in years: # Identify the source file source = os.path.join(field_dir, str(year), f'Morphometrics for Bogue Banks {year}.csv') # Set the target destination = os.path.join(DATA_DIR, f'Morphometrics for Bogue Banks {year}.csv') # Copy the file sh.copy(source, destination) def move_netcdf_output(): """ Take the netCDF files and move them into the "Data" folder, then rename them from "xboutput.nc" to the name of the simulation """ # Set lists with the dune configurations, storm surge # modifications, storm duration increases, and dune aspect # ratio stretches dunes = ['Toes Joined', 'Crests Joined', 'Heels Joined', 'Fenced'] surges = ['Half', 'Normal', 'One Half'] durations = [1, 12, 18, 24, 36, 48] stretches = [-60, -40, -20, 1, 20, 40, 60] # Loop through the dunes and surges for dune in dunes: for surge in surges: # Set the experiment folder name experiment_name = f'{dune} {surge} Surge' experiment_folder = os.path.join(XB_DIR, experiment_name) # Make a target folder to move the runs into save_folder = os.path.join(DATA_DIR, 'XBeach Output', experiment_name) if not os.path.exists(save_folder): os.mkdir(save_folder) # Loop through the dunes and durations within the experiment for stretch in stretches: for duration in durations: # Set the simulation folder run_name = f'Dune Complexity {stretch} {duration}' simulation_folder = os.path.join(experiment_folder, run_name) # Set the XBeach output file as the source. Set the destination # name. Then copy the file over source = os.path.join(simulation_folder, 'xboutput.nc') if os.path.exists(source): destination = os.path.join(save_folder, f'{run_name}.nc') if not os.path.exists(destination): sh.copy(source, destination) print(f'File Successfully Copied: {destination}') else: print(f'File already exists: {destination}') else: print(f'FILE DOES NOT EXIST: {source}') def surge_time_series(): """ Put all the storm time series' into a .csv file that can be loaded as a DataFrame """ # Set a list of storm surge modifiers # and storm duration increases surges, surge_labels = [0.5, 1.0, 1.5], ['Half', 'Normal', 'One Half'] durations = [1, 12, 18, 24, 36, 48] # Make an empty DataFrame to loop into surge_df = pd.DataFrame() # Loop through the surges for surge, label in zip(surges, surge_labels): # Loop through the durations for duration in durations: # The DataFrame won't work if the columns are different # lengths so place them all in a preset 125 "hour" long # array so that they'll fit in the DataFrame time_series = np.full((1, 125), fill_value=np.nan)[0] # Load the data and place it in the time series NaN array filename = os.path.join(XB_DIR, f'Toes Joined {label} Surge', f'Dune Complexity 1 {duration}', 'ntr.txt') ntr = np.genfromtxt(filename, dtype=np.float32) time_series[:len(ntr)] = ntr # Place the time series in the dict surge_df[f'{label} {duration}'] = time_series # Save the DataFrame as a .csv file save_name = os.path.join(DATA_DIR, 'Storm Surge Time Series.csv') surge_df.to_csv(save_name, index=False) def main(): """ Main program function to consolidate all the data sources """ # Make a .csv file with the initial profiles used # initial_profiles() # Make a .csv file with the initial dune ratios # initial_ratios() # Make a .csv file with all the natural dune volumes # and aspect ratios measured from Bogue Banks LiDAR # bogue_lidar_data() # Make a .csv file with the storm surge time # series' for all the model runs # surge_time_series() # Make a .csv file with storm surge data # for Tropical Storm Joaquin and Hurricane Florence # joaquin_and_florence() # Move the netCDF output files into the Data folder # and rename them for the run name. Move the .csv # files with the morphometrics from the runs too # move_csv_output() # move_netcdf_output() # Move the Bogue Banks field data morphometrics # from 2017 and 2018 into the data folder move_field_data() if __name__ == '__main__': main()

36.80315

118

0.597276

0

6,984

0.498074

a15ae079911483a5e3b82012f76254443eb7a059

339

py

Python

counter-test-applications/lr100000/linear-regr-100k.py

EsperLiu/vPython

f1005f011d6d9fd079cf72e8f78bab6d95a9f993

[ "0BSD" ]

1

2021-11-21T03:31:32.000Z

counter-test-applications/lr100000/linear-regr-100k.py

EsperLiu/vPython

f1005f011d6d9fd079cf72e8f78bab6d95a9f993

[ "0BSD" ]

null

counter-test-applications/lr100000/linear-regr-100k.py

EsperLiu/vPython

f1005f011d6d9fd079cf72e8f78bab6d95a9f993

[ "0BSD" ]

1

2021-11-28T05:57:55.000Z

def end_of_import(): return 0 def end_of_init(): return 0 def end_of_computing(): return 0 import numpy as np from sklearn.linear_model import LinearRegression end_of_import() X = np.array(range(0,100000)).reshape(-1, 1) # y = 2x + 3 y = np.dot(X, 2) + 3 end_of_init() reg = LinearRegression().fit(X, y) end_of_computing()

16.95

49

0.696165

0

12

0.035398

a15b3e54d6303597b66c9ac9aa7e5fefcc34013d

262

py

Python

python/bitcoin/ch04/04_08.py

gangserver/py_test

869bdfa5c94c3b6a15b87e0c3de6b2cdaca821f4

[ "Apache-2.0" ]

null

python/bitcoin/ch04/04_08.py

gangserver/py_test

869bdfa5c94c3b6a15b87e0c3de6b2cdaca821f4

[ "Apache-2.0" ]

null

python/bitcoin/ch04/04_08.py

gangserver/py_test

869bdfa5c94c3b6a15b87e0c3de6b2cdaca821f4

[ "Apache-2.0" ]

null

import requests url = "https://api.korbit.co.kr/v1/ticker/detailed?currency_pair=btc_krw" r = requests.get(url) bitcoin = r.json() print(bitcoin) print(type(bitcoin)) print(bitcoin['last']) print(bitcoin['bid']) print(bitcoin['ask']) print(bitcoin['volume'])

18.714286

73

0.725191

0

91

0.347328

a15b9e2b4f9954059a9f62e3b0c43fda6866814f

3,938

py

Python

jackselect/indicator.py

SpotlightKid/jack-select

acb6cfa5a48846fa7640373d4976d4df1ab0bbd7

[ "MIT" ]

12

2016-03-30T18:32:35.000Z

2022-01-18T21:12:51.000Z

jackselect/indicator.py

SpotlightKid/jack-select

acb6cfa5a48846fa7640373d4976d4df1ab0bbd7

[ "MIT" ]

8

2018-09-03T15:26:51.000Z

2020-04-20T14:44:00.000Z

jackselect/indicator.py

SpotlightKid/jack-select

acb6cfa5a48846fa7640373d4976d4df1ab0bbd7

[ "MIT" ]

null

"""A convenience class for a GTK 3 system tray indicator.""" from pkg_resources import resource_filename import gi gi.require_version('Gtk', '3.0') # noqa from gi.repository import Gtk from gi.repository.GdkPixbuf import Pixbuf class Indicator: """This class defines a standard GTK3 system tray indicator. Class Indicator can be easily reused in any other project. """ def __init__(self, icon, title=None): """Create indicator icon and add menu. Args: icon (str): path to initial icon that will be shown on system panel """ self._icon_cache = {} self.icon = Gtk.StatusIcon.new_from_pixbuf(self._get_icon(icon)) self.menu = Gtk.Menu() self.icon.connect('activate', self.on_popup_menu_open) self.icon.connect('popup-menu', self.on_popup_menu_open) if title: self.icon.set_title(title) def _get_icon(self, icon): """Return icon from package as GdkPixbuf.Pixbuf. Extracts the image from package to a file, stores it in the icon cache if it's not in there yet and returns it. Otherwise just returns the image stored in the cache. """ if icon not in self._icon_cache: filename = resource_filename(__name__, "images/%s" % icon) self._icon_cache[icon] = Pixbuf.new_from_file(filename) return self._icon_cache[icon] def set_icon(self, icon): """Set new icon in system tray. Args: icon (str): path to file with new icon """ self.icon.set_from_pixbuf(self._get_icon(icon)) def set_tooltip(self, callback): self.icon.set_has_tooltip(True) self.icon.connect("query-tooltip", callback) def clear_menu(self): """Clear all entries from the main menu.""" self.menu = Gtk.Menu() def add_menu_item(self, command=None, title=None, icon=None, enabled=True, is_check=False, active=False, menu=None, data=None): """Add mouse right click menu item. Args: command (callable): function that will be called after left mouse click on title title (str): label that will be shown in menu icon (str): name of icon stored in application package active (bool): whether the menu entry can be activated (default: True) data (obj): arbitrary data to associate with the menu entry """ if icon: m_item = Gtk.ImageMenuItem(title) image = Gtk.Image.new_from_pixbuf(self._get_icon(icon)) m_item.set_image(image) elif is_check: m_item = Gtk.CheckMenuItem(title) m_item.set_active(active) else: m_item = Gtk.MenuItem(title) if command: m_item.connect('toggled' if is_check else 'activate', command) m_item.set_sensitive(enabled) m_item.data = data if menu: menu.append(m_item) else: self.menu.append(m_item) return m_item def add_submenu(self, title): """Add a sub menu popup menu.""" submenu = Gtk.Menu() m_item = Gtk.MenuItem(title) m_item.set_submenu(submenu) self.menu.append(m_item) return submenu def add_separator(self): """Add separator between labels in the popup menu.""" m_item = Gtk.SeparatorMenuItem() self.menu.append(m_item) def on_popup_menu_open(self, widget=None, button=None, *args): """Some action requested opening the popup menu.""" self.menu.popup(None, None, Gtk.StatusIcon.position_menu, widget or self.icon, button or 1, Gtk.get_current_event_time()) def on_popup_menu_close(self, widget=None, button=None, *args): """Some action requested closing the popup menu.""" self.menu.popdown()

32.278689

94

0.61935

3,704

0.940579

0

1,446

0.367191

a15c583b91868493579d97f1c0cb3471ef7cba0e

442

py

Python

myaxf/migrations/0011_minebtns_is_used.py

Pyrans/test1806

1afc62e09bbebf74521b4b6fdafde8eeaa260ed9

[ "Apache-2.0" ]

null

myaxf/migrations/0011_minebtns_is_used.py

Pyrans/test1806

1afc62e09bbebf74521b4b6fdafde8eeaa260ed9

[ "Apache-2.0" ]

null

myaxf/migrations/0011_minebtns_is_used.py

Pyrans/test1806

1afc62e09bbebf74521b4b6fdafde8eeaa260ed9

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- # Generated by Django 1.11.7 on 2018-11-06 01:54 from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('myaxf', '0010_minebtns'), ] operations = [ migrations.AddField( model_name='minebtns', name='is_used', field=models.BooleanField(default=True), ), ]

21.047619

52

0.608597

284

0.642534

0

112

0.253394

a15d304cf1b066b2781b604c9736d8b3d3f4ed26

3,342

py

Python

components/PyTorch/pytorch-kfp-components/setup.py

nostro-im/pipelines

39f5b6b74040abbf4b764cbd5b422d7548723d9e

[ "Apache-2.0" ]

2,860

2018-05-24T04:55:01.000Z

2022-03-31T13:49:56.000Z

components/PyTorch/pytorch-kfp-components/setup.py

nostro-im/pipelines

39f5b6b74040abbf4b764cbd5b422d7548723d9e

[ "Apache-2.0" ]

7,331

2018-05-16T09:03:26.000Z

2022-03-31T23:22:04.000Z

components/PyTorch/pytorch-kfp-components/setup.py

nostro-im/pipelines

39f5b6b74040abbf4b764cbd5b422d7548723d9e

[ "Apache-2.0" ]

1,359

2018-05-15T11:05:41.000Z

2022-03-31T09:42:09.000Z

#!/usr/bin/env/python3 # # Copyright (c) Facebook, Inc. and its affiliates. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Setup script.""" import importlib import os import types from setuptools import setup, find_packages def make_required_install_packages(): return [ "pytorch-lightning>=1.4.0", "torch>=1.7.1", "torch-model-archiver", ] def make_required_test_packages(): return make_required_install_packages() + [ "mock>=4.0.0", "flake8>=3.0.0", "pylint", "pytest>=6.0.0", "wget", "pandas", "minio" ] def make_dependency_links(): return [] def detect_version(base_path): loader = importlib.machinery.SourceFileLoader( fullname="version", path=os.path.join(base_path, "pytorch_kfp_components/__init__.py"), ) version = types.ModuleType(loader.name) loader.exec_module(version) return version.__version__ if __name__ == "__main__": relative_directory = os.path.relpath( os.path.dirname(os.path.abspath(__file__))) version = detect_version(relative_directory) setup( name="pytorch-kfp-components", version=version, description="PyTorch Kubeflow Pipeline", url="https://github.com/kubeflow/pipelines/tree/master/components/PyTorch/pytorch-kfp-components/", author="The PyTorch Kubeflow Pipeline Components authors", author_email="pytorch-kfp-components@fb.com", license="Apache License 2.0", extra_requires={"tests": make_required_test_packages()}, include_package_data=True, python_requires=">=3.6", install_requires=make_required_install_packages(), dependency_links=make_dependency_links(), keywords=[ "Kubeflow Pipelines", "KFP", "ML workflow", "PyTorch", ], classifiers=[ "Development Status :: 4 - Beta", "Operating System :: Unix", "Operating System :: MacOS", "Intended Audience :: Developers", "Intended Audience :: Education", "Intended Audience :: Science/Research", "License :: OSI Approved :: Apache Software License", "Programming Language :: Python :: 3 :: Only", "Topic :: Scientific/Engineering", "Topic :: Scientific/Engineering :: Artificial Intelligence", "Topic :: Software Development", "Topic :: Software Development :: Libraries", "Topic :: Software Development :: Libraries :: Python Modules", ], package_dir={ "pytorch_kfp_components": os.path.join(relative_directory, "pytorch_kfp_components") }, packages=find_packages(where=relative_directory), )

31.528302

107

0.635548

0

1,684

0.50389

a15d6cd6a92c370d9583f2a5012f9737df67a02a

10,453

py

Python

generate_pipelines.py

phorne-uncharted/d3m-primitives

77d900b9dd6ab4b2b330f4e969dabcdc419c73e1

[ "MIT" ]

null

generate_pipelines.py

phorne-uncharted/d3m-primitives

77d900b9dd6ab4b2b330f4e969dabcdc419c73e1

[ "MIT" ]

null

generate_pipelines.py

phorne-uncharted/d3m-primitives

77d900b9dd6ab4b2b330f4e969dabcdc419c73e1

[ "MIT" ]

null

""" Utility to get generate all submission pipelines for all primitives. This script assumes that `generate_annotations.py` has already been run. """ import os import subprocess import shutil import fire from kf_d3m_primitives.data_preprocessing.data_cleaning.data_cleaning_pipeline import DataCleaningPipeline from kf_d3m_primitives.data_preprocessing.text_summarization.duke_pipeline import DukePipeline from kf_d3m_primitives.data_preprocessing.geocoding_forward.goat_forward_pipeline import GoatForwardPipeline from kf_d3m_primitives.data_preprocessing.geocoding_reverse.goat_reverse_pipeline import GoatReversePipeline from kf_d3m_primitives.data_preprocessing.data_typing.simon_pipeline import SimonPipeline from kf_d3m_primitives.clustering.spectral_clustering.spectral_clustering_pipeline import SpectralClusteringPipeline from kf_d3m_primitives.clustering.k_means.storc_pipeline import StorcPipeline from kf_d3m_primitives.clustering.hdbscan.hdbscan_pipeline import HdbscanPipeline from kf_d3m_primitives.dimensionality_reduction.tsne.tsne_pipeline import TsnePipeline from kf_d3m_primitives.feature_selection.pca_features.pca_features_pipeline import PcaFeaturesPipeline from kf_d3m_primitives.feature_selection.rf_features.rf_features_pipeline import RfFeaturesPipeline from kf_d3m_primitives.natural_language_processing.sent2vec.sent2vec_pipeline import Sent2VecPipeline from kf_d3m_primitives.object_detection.retinanet.object_detection_retinanet_pipeline import ObjectDetectionRNPipeline from kf_d3m_primitives.image_classification.imagenet_transfer_learning.gator_pipeline import GatorPipeline from kf_d3m_primitives.ts_classification.knn.kanine_pipeline import KaninePipeline from kf_d3m_primitives.ts_classification.lstm_fcn.lstm_fcn_pipeline import LstmFcnPipeline from kf_d3m_primitives.ts_forecasting.vector_autoregression.var_pipeline import VarPipeline from kf_d3m_primitives.ts_forecasting.deep_ar.deepar_pipeline import DeepARPipeline from kf_d3m_primitives.ts_forecasting.nbeats.nbeats_pipeline import NBEATSPipeline from kf_d3m_primitives.remote_sensing.classifier.mlp_classifier_pipeline import MlpClassifierPipeline def generate_pipelines(gpu = False): gpu_prims = [ "d3m.primitives.classification.inceptionV3_image_feature.Gator", "d3m.primitives.object_detection.retina_net.ObjectDetectionRN", "d3m.primitives.time_series_classification.convolutional_neural_net.LSTM_FCN", "d3m.primitives.feature_extraction.nk_sent2vec.Sent2Vec", "d3m.primitives.remote_sensing.mlp.MlpClassifier" ] prims_to_pipelines = { "d3m.primitives.data_cleaning.column_type_profiler.Simon": [ (SimonPipeline(), ('185_baseball_MIN_METADATA',)) ], "d3m.primitives.data_cleaning.geocoding.Goat_forward": [ (GoatForwardPipeline(), ('LL0_acled_reduced_MIN_METADATA',)) ], "d3m.primitives.data_cleaning.geocoding.Goat_reverse": [ (GoatReversePipeline(), ('LL0_acled_reduced_MIN_METADATA',)) ], "d3m.primitives.feature_extraction.nk_sent2vec.Sent2Vec": [ (Sent2VecPipeline(), ('LL1_TXT_CLS_apple_products_sentiment_MIN_METADATA',)) ], "d3m.primitives.clustering.k_means.Sloth": [ (StorcPipeline(), ('66_chlorineConcentration_MIN_METADATA',)) ], "d3m.primitives.clustering.hdbscan.Hdbscan": [ (HdbscanPipeline(), ('SEMI_1044_eye_movements_MIN_METADATA',)) ], "d3m.primitives.clustering.spectral_graph.SpectralClustering": [ (SpectralClusteringPipeline(), ('SEMI_1044_eye_movements_MIN_METADATA',)) ], "d3m.primitives.dimensionality_reduction.t_distributed_stochastic_neighbor_embedding.Tsne": [ (TsnePipeline(), ('SEMI_1044_eye_movements_MIN_METADATA',)) ], "d3m.primitives.time_series_classification.k_neighbors.Kanine": [ (KaninePipeline(), ('66_chlorineConcentration_MIN_METADATA',)) ], "d3m.primitives.time_series_classification.convolutional_neural_net.LSTM_FCN": [ (LstmFcnPipeline(), ( '66_chlorineConcentration_MIN_METADATA', "LL1_Adiac_MIN_METADATA", "LL1_ArrowHead_MIN_METADATA", "LL1_Cricket_Y_MIN_METADATA", "LL1_ECG200_MIN_METADATA", "LL1_ElectricDevices_MIN_METADATA", "LL1_FISH_MIN_METADATA", "LL1_FaceFour_MIN_METADATA", "LL1_HandOutlines_MIN_METADATA", "LL1_Haptics_MIN_METADATA", "LL1_ItalyPowerDemand_MIN_METADATA", "LL1_Meat_MIN_METADATA", "LL1_OSULeaf_MIN_METADATA", )), (LstmFcnPipeline(attention_lstm=True), ( '66_chlorineConcentration_MIN_METADATA', "LL1_Adiac_MIN_METADATA", "LL1_ArrowHead_MIN_METADATA", "LL1_Cricket_Y_MIN_METADATA", "LL1_ECG200_MIN_METADATA", "LL1_ElectricDevices_MIN_METADATA", "LL1_FISH_MIN_METADATA", "LL1_FaceFour_MIN_METADATA", "LL1_HandOutlines_MIN_METADATA", "LL1_Haptics_MIN_METADATA", "LL1_ItalyPowerDemand_MIN_METADATA", "LL1_Meat_MIN_METADATA", "LL1_OSULeaf_MIN_METADATA", )) ], "d3m.primitives.time_series_forecasting.vector_autoregression.VAR": [ (VarPipeline(), ( '56_sunspots_MIN_METADATA', '56_sunspots_monthly_MIN_METADATA', 'LL1_736_population_spawn_MIN_METADATA', 'LL1_736_stock_market_MIN_METADATA', 'LL1_terra_canopy_height_long_form_s4_100_MIN_METADATA', "LL1_terra_canopy_height_long_form_s4_90_MIN_METADATA", "LL1_terra_canopy_height_long_form_s4_80_MIN_METADATA", "LL1_terra_canopy_height_long_form_s4_70_MIN_METADATA", 'LL1_terra_leaf_angle_mean_long_form_s4_MIN_METADATA', 'LL1_PHEM_Monthly_Malnutrition_MIN_METADATA', 'LL1_PHEM_weeklyData_malnutrition_MIN_METADATA', )) ], "d3m.primitives.time_series_forecasting.lstm.DeepAR": [ (DeepARPipeline(prediction_length = 21, context_length = 21), ('56_sunspots_MIN_METADATA',)), (DeepARPipeline(prediction_length = 38, context_length = 38), ('56_sunspots_monthly_MIN_METADATA',)), (DeepARPipeline(prediction_length = 60, context_length = 30), ('LL1_736_population_spawn_MIN_METADATA',)), (DeepARPipeline(prediction_length = 34, context_length = 17), ('LL1_736_stock_market_MIN_METADATA',)), ], "d3m.primitives.time_series_forecasting.feed_forward_neural_net.NBEATS": [ (NBEATSPipeline(prediction_length = 21), ('56_sunspots_MIN_METADATA',)), (NBEATSPipeline(prediction_length = 38), ('56_sunspots_monthly_MIN_METADATA',)), (NBEATSPipeline(prediction_length = 60), ('LL1_736_population_spawn_MIN_METADATA',)), (NBEATSPipeline(prediction_length = 34), ('LL1_736_stock_market_MIN_METADATA',)), ], "d3m.primitives.object_detection.retina_net.ObjectDetectionRN": [ (ObjectDetectionRNPipeline(), ( 'LL1_tidy_terra_panicle_detection_MIN_METADATA', 'LL1_penn_fudan_pedestrian_MIN_METADATA' )) ], "d3m.primitives.data_cleaning.data_cleaning.Datacleaning": [ (DataCleaningPipeline(), ('185_baseball_MIN_METADATA',)) ], "d3m.primitives.data_cleaning.text_summarization.Duke": [ (DukePipeline(), ('185_baseball_MIN_METADATA',)) ], "d3m.primitives.feature_selection.pca_features.Pcafeatures": [ (PcaFeaturesPipeline(), ('185_baseball_MIN_METADATA',)) ], "d3m.primitives.feature_selection.rffeatures.Rffeatures": [ (RfFeaturesPipeline(), ('185_baseball_MIN_METADATA',)) ], "d3m.primitives.classification.inceptionV3_image_feature.Gator": [ (GatorPipeline(), ( "124_174_cifar10_MIN_METADATA", "124_188_usps_MIN_METADATA", "124_214_coil20_MIN_METADATA", "uu_101_object_categories_MIN_METADATA", )) ], "d3m.primitives.remote_sensing.mlp.MlpClassifier": [ (MlpClassifierPipeline(), ('LL1_bigearth_landuse_detection',)) ] } for primitive, pipelines in prims_to_pipelines.items(): if gpu: if primitive not in gpu_prims: continue else: if primitive in gpu_prims: continue os.chdir(f'/annotations/{primitive}') os.chdir(os.listdir('.')[0]) if not os.path.isdir('pipelines'): os.mkdir('pipelines') else: [os.remove(f'pipelines/{pipeline}') for pipeline in os.listdir('pipelines')] if not os.path.isdir('pipeline_runs'): os.mkdir('pipeline_runs') else: [os.remove(f'pipeline_runs/{pipeline_run}') for pipeline_run in os.listdir('pipeline_runs')] if not os.path.isdir(f'/pipeline_scores/{primitive.split(".")[-1]}'): os.mkdir(f'/pipeline_scores/{primitive.split(".")[-1]}') for pipeline, datasets in pipelines: pipeline.write_pipeline(output_dir = './pipelines') for dataset in datasets: print(f'Generating pipeline for {primitive.split(".")[-1]} on {dataset}') if primitive.split(".")[-1] in ['Duke', 'Sloth']: pipeline.fit_produce( dataset, output_yml_dir = './pipeline_runs', submission = True ) else: if primitive.split(".")[-1] == 'NBEATS': shutil.rmtree(f'/scratch_dir/nbeats') pipeline.fit_score( dataset, output_yml_dir = './pipeline_runs', output_score_dir = f'/pipeline_scores/{primitive.split(".")[-1]}', submission = True ) os.system('gzip -r pipeline_runs') if __name__ == '__main__': fire.Fire(generate_pipelines)

50.990244

118

0.672534

0

4,322

0.41347

a16015f7fdd109191a18e2ce3c5cc5cd31b338c6

210

py

Python

gorynych/ontologies/gch/edges/basic/__init__.py

vurmux/gorynych

d721e8cdb61f7c7ee6bc4bd31026605df15f2d9d

[ "Apache-2.0" ]

null

gorynych/ontologies/gch/edges/basic/__init__.py

vurmux/gorynych

d721e8cdb61f7c7ee6bc4bd31026605df15f2d9d

[ "Apache-2.0" ]

null

gorynych/ontologies/gch/edges/basic/__init__.py

vurmux/gorynych

d721e8cdb61f7c7ee6bc4bd31026605df15f2d9d

[ "Apache-2.0" ]

null

__all__ = [ "aggregation", "association", "composition", "connection", "containment", "dependency", "includes", "membership", "ownership", "responsibility", "usage" ]

16.153846

21

0.557143

0

132

0.628571

a162116929e58d2ceb5db3d4712dce3ef830f40a

3,851

py

Python

square.py

chriswilson1982/black-and-white

e275e6f534aa51f12f4545730b627ce280aae8c3

[ "MIT" ]

null

square.py

chriswilson1982/black-and-white

e275e6f534aa51f12f4545730b627ce280aae8c3

[ "MIT" ]

null

square.py

chriswilson1982/black-and-white

e275e6f534aa51f12f4545730b627ce280aae8c3

[ "MIT" ]

2

2020-06-05T04:37:08.000Z

2020-09-30T06:15:22.000Z

# coding: utf-8 """Square module. Represents the squares on the game grid. """ from scene import * from common import * import sound class Square (SpriteNode): """Represents the squares on the game grid. Main properties are their row and column (used for path calculation) and state (corresponds to color in the game). """ def __init__(self, row, col, position, size, state, color): self.row = row self.col = col self.position = position self.size = size self.color = color self.z_position = 0.2 self.state = state self.last_state = state self.press = False self.star = False if self.state == 1: self.color = color1 if self.state == 2: self.color = color2 def set_color(self): self.color = all_colors[self.state - 1] # Find neighbouring white squares def white_neighbours(self, square_list): white_neighbours = [] for s in square_list: if (((s.row == self.row - 1) and (s.col == self.col)) or ((s.row == self.row + 1) and (s.col == self.col)) or ((s.row == self.row) and (s.col == self.col - 1)) or ((s.row == self.row) and (s.col == self.col + 1))) and s.state == 2: white_neighbours.append(s) return white_neighbours # Find squares to toggle when square pressed def toggle_neighbours(self, squares): for square in squares: if square.row >= self.row - 1 and square.row <= self.row + 1 and square.col >= self.col - 1 and square.col <= self.col + 1 and not (square.row == self.row and square.col == self.col) and (square.state == 1 or square.state == 2): square.toggle() # Square pressed def pressed(self): # If power-up 3 active if self.parent.can_flip: self.toggle() sound.play_effect(reds_away) return # State saved so power-up 2 can unlock self.last_state = self.state self.press = True self.z_position = 0.3 self.run_action(pressed_action_1) self.state = 0 self.color = color3 # Bonus star destroyed if star square pressed if self.star: self.star = False self.parent.star_square = None self.star_icon.run_action( A.sequence(A.scale_to(0, 0.5), A.remove())) sound.play_effect(star_away_sound) self.parent.level_label.text = "Goodbye star!" else: sound.play_effect(tap_sound) # Square toggles between black and white def toggle(self): # Ignore if square already pressed if self.state == 0: return if self.rotation == 0: self.run_action(toggle_action_1) else: self.run_action(toggle_action_2) if self.state == 1: self.state = 2 self.color = color2 elif self.state == 2: self.state = 1 self.color = color1 if self.star: self.go_star() self.scene.sparkle(self.color, self.position, image='shp:RoundRect', spread=square_size, n=2) # Creates star icon if this square is the randomly selected star square def go_star(self): # Remove star icon first, if it exists try: self.star_icon.run_action(A.remove()) except: pass self.star = True # Star icon depends on square color if self.state == 1: tex = Texture('typw:Star') elif self.state == 2: tex = Texture('typb:Star') self.star_icon = SpriteNode( texture=tex, position=self.position, size=(square_size - 5, square_size - 5)) self.star_icon.z_position = 0.6 self.parent.add_child(self.star_icon)

33.780702

243

0.578811

3,714

0.964425

0

717

0.186185

a1635f7424a1cd00dce9eb1d4e2acface083e3bd

1,128

py

Python

coocurrence_loader.py

miselico/KGlove

2bcbce3d14ed5173a319d80bfff95be6486b41e2

[ "MIT" ]

2

2021-11-05T09:27:57.000Z

2022-02-25T12:33:14.000Z

coocurrence_loader.py

miselico/KGlove

2bcbce3d14ed5173a319d80bfff95be6486b41e2

[ "MIT" ]

null

coocurrence_loader.py

miselico/KGlove

2bcbce3d14ed5173a319d80bfff95be6486b41e2

[ "MIT" ]

1

2022-02-25T12:37:47.000Z

import pathlib from struct import unpack from typing import BinaryIO, List, Optional, Tuple, cast import numpy as np import scipy.sparse def _read_little_endian_crec(file: BinaryIO ) -> Optional[Tuple[int, int, float]]: le_int = file.read(16) # https://docs.python.org/3/library/struct.html#format-strings if len(le_int) == 0: return None crec = cast(Tuple[int, int, float], unpack('<iid', le_int)) return crec def load(cooccurrence_file_content: BinaryIO) -> scipy.sparse.coo_matrix: row: List[int] = [] column: List[int] = [] data: List[float] = [] while (cooccurrence_file_content.readable()): crec = _read_little_endian_crec(cooccurrence_file_content) if crec is None: break row.append(crec[0]) column.append(crec[1]) data.append(crec[2]) result = scipy.sparse.coo_matrix((data, (row, column)), dtype=np.float64) return result if __name__ == "__main__": p = pathlib.Path("output/cooccurrence_file.bin") with open(p, 'rb') as file: m = load(file) print(m.tocsc())

28.2

77

0.639184

0

112

0.099291

a163e601ea9b0587f0a7996da2ea54d7b047cc87

597

py

Python

api_app/migrations/0001_initial.py

DurkinDevelopment/coinbase_api

0cea72234d481d09ff906f7bc064cfe16111c785

[ "MIT" ]

null

api_app/migrations/0001_initial.py

DurkinDevelopment/coinbase_api

0cea72234d481d09ff906f7bc064cfe16111c785

[ "MIT" ]

null

api_app/migrations/0001_initial.py

DurkinDevelopment/coinbase_api

0cea72234d481d09ff906f7bc064cfe16111c785

[ "MIT" ]

null

# Generated by Django 3.2.12 on 2022-02-15 02:57 from django.db import migrations, models class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='SpotPrice', fields=[ ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('currency', models.CharField(max_length=200)), ('amount', models.FloatField()), ('timestamp', models.DateField()), ], ), ]

24.875

117

0.562814

503

0.842546

0

96

0.160804

a163f9dace925925161f417c4fc2f6f13d99f9d2

924

py

Python

Kalender/views.py

RamonvdW/nhb-apps

5a9f840bfe066cd964174515c06b806a7b170c69

[ "BSD-3-Clause-Clear" ]

1

2021-12-22T13:11:12.000Z

Kalender/views.py

RamonvdW/nhb-apps

5a9f840bfe066cd964174515c06b806a7b170c69

[ "BSD-3-Clause-Clear" ]

9

2020-10-28T07:07:05.000Z

2021-06-28T20:05:37.000Z

Kalender/views.py

RamonvdW/nhb-apps

5a9f840bfe066cd964174515c06b806a7b170c69

[ "BSD-3-Clause-Clear" ]

null

# -*- coding: utf-8 -*- # Copyright (c) 2021 Ramon van der Winkel. # All rights reserved. # Licensed under BSD-3-Clause-Clear. See LICENSE file for details. from django.views.generic import View from django.urls import reverse from django.http import HttpResponseRedirect from Functie.rol import Rollen, rol_get_huidige from .view_maand import get_url_huidige_maand class KalenderLandingPageView(View): """ Deze pagina is puur voor het doorsturen naar een van de andere pagina's afhankelijk van de gekozen rol. """ @staticmethod def get(request, *args, **kwargs): rol_nu = rol_get_huidige(request) if rol_nu == Rollen.ROL_BB: url = reverse('Kalender:manager') elif rol_nu == Rollen.ROL_HWL: url = reverse('Kalender:vereniging') else: url = get_url_huidige_maand() return HttpResponseRedirect(url) # end of file

26.4

79

0.683983

534

0.577922

0

365

0.395022

0

331

0.358225

a166142b9f7a87deb268c549d8183c79b3298038

9,511

py

Python

profile.py

giswqs/Depression-filling-1D

3c0ed86bbbe6f0b8573212a3efd59375dc7be45e

[ "MIT" ]

1

2022-02-27T14:40:00.000Z

profile.py

giswqs/Depression-filling-1D

3c0ed86bbbe6f0b8573212a3efd59375dc7be45e

[ "MIT" ]

null

profile.py

giswqs/Depression-filling-1D

3c0ed86bbbe6f0b8573212a3efd59375dc7be45e

[ "MIT" ]

null

import os import numpy as np # class for depression class Depression: def __init__(self, id, width, depth, area, pour_elev, min_elev, points, internal_pts): self.id = id self.width = width self.depth = depth self.area = area self.pour_elev = pour_elev self.min_elev = min_elev self.points = points self.internal_pts = internal_pts # read profile values from CSV def read_csv(in_csv, header = True, col_index = 1): with open(in_csv) as f: lines = f.readlines() if header: lines = lines[1:] values = [] for line in lines: line = line.strip() value = line.split(",")[col_index - 1] values.append(float(value)) return values def write_csv(in_csv, out_csv, col_name, in_values): with open(in_csv) as f: lines = f.readlines() header = lines[0].strip() + "," + col_name + '\n' lines.pop(0) out_lines = [] for index, line in enumerate(lines): line = line.strip() line = line + ',' + str(in_values[index]) +'\n' out_lines.append(line) with open(out_csv, 'w') as ff: ff.write(header) ff.writelines(out_lines) # check the depression type of a point based on its neighbors def check_dep_type(value_list, index): if index == 0: if value_list[0] <= value_list[1]: return 'ascending' else: return 'descending' elif index == (len(value_list) - 1): if value_list[len(value_list) - 2] >= value_list[len(value_list) - 1]: return 'descending' else: return 'ascending' else: if (value_list[index] == value_list[index - 1]) and (value_list[index]) == value_list[index + 1]: return 'flat' elif (value_list[index] <= value_list[index - 1]) and (value_list[index]) <= value_list[index + 1]: return 'depression' elif (value_list[index] > value_list[index - 1]) and (value_list[index] < value_list[index + 1]): return 'ascending' elif (value_list[index] > value_list[index + 1]) and (value_list[index] < value_list[index - 1]): return 'descending' else: return 'unknown' # find forward ascending neighbors def find_ascending(value_list, index): ascending_loc = [] cursor = index while (cursor < (len(value_list) - 1 )) and (value_list[cursor] < value_list[cursor + 1]): ascending_loc.append(cursor) cursor = cursor + 1 ascending_loc.append(cursor) # print(ascending_loc) return set(ascending_loc) # find forward descending neighbors def find_descending(value_list, index): descending_loc = [] cursor = index while (cursor < (len(value_list) - 1 )) and (value_list[cursor] > value_list[cursor + 1]): descending_loc.append(cursor) cursor = cursor + 1 descending_loc.append(cursor) return set(descending_loc) # find backward descending neighbors def find_descending_backward(value_list, index): descending_loc = [] cursor = index while (cursor > 0) and (value_list[cursor] < value_list[cursor - 1]): descending_loc.append(cursor) cursor = cursor - 1 descending_loc.append(cursor) return set(descending_loc[::-1]) # find all points associated with a depression based on one point def find_single_depression(value_list, index): dep_loc = [] ascending_loc = list(find_ascending(value_list, index)) # ascending_loc = ascending_loc.sort() # print(ascending_loc.sort()) descending_loc = list(find_descending_backward(value_list, index)) dep_loc = descending_loc + ascending_loc return set(dep_loc) # remove acending edge and descending edge def process_edges(value_list): size = len(value_list) pts_list = set(range(size)) left_edge = find_ascending(value_list, 0) if len(left_edge) > 0: for item in left_edge: pts_list.remove(item) right_edge = find_descending_backward(value_list, size - 1) if len(right_edge) > 0: for item in right_edge: pts_list.remove(item) return pts_list # get depression width, height, and area def get_width_depth_area(value_list, pts_set): min_index = min(pts_set) max_index = max(pts_set) left_elev = value_list[min_index] right_elev = value_list[max_index] pts_list = list(pts_set) pts_arr = np.array(value_list)[pts_list] min_value = np.min(pts_arr) pour_value = min([left_elev, right_elev]) depth = pour_value - min_value new_pts_arr = pts_arr[pts_arr <= pour_value] width = new_pts_arr.size area = pour_value * new_pts_arr.size - np.sum(new_pts_arr) new_pts_set = pts_set.copy() for item in pts_set: if value_list[item] > pour_value: new_pts_set.remove(item) return width, depth, area, pour_value, new_pts_set # find all depressions recursively def find_depressions(in_values, in_width = 0, in_depth = 0, in_area = 0, dep_list = []): size = len(in_values) global_set = process_edges(in_values) num_arr = np.array(in_values) # dep_list = [] while len(global_set) > 0: # print("Remaining: {}".format(len(global_set))) # if len(global_set) == 2: # print(global_set) tmp_arr = num_arr[list(global_set)] min_value = np.min(tmp_arr) min_candidates = list(np.where(num_arr == min_value)[0]) min_index = min_candidates[0] if len(min_candidates) > 1: for item in min_candidates: items = [item - 1, item, item + 1] con = all(elem in items for elem in global_set) if con : min_index = item break else: ascending_loc_tmp = find_ascending(in_values, item) descending_loc_tmp = find_descending_backward(in_values, item) dep_loc_tmp = list(ascending_loc_tmp) + list(descending_loc_tmp) max_value = np.max(num_arr[dep_loc_tmp]) num_arr[dep_loc_tmp] = max_value for item in dep_loc_tmp: if item in global_set: global_set.remove(item) min_index = -1 elif len(global_set) < 3: global_set_tmp = global_set.copy() for item in global_set_tmp: global_set.remove(item) min_index = -1 if min_index != -1: dep_index = find_single_depression(list(num_arr), min_index) # print(dep_index) width, depth, area, pour_elev, dep_tmp_set = get_width_depth_area(in_values, dep_index) # print(dep_tmp_set) # if len(dep_tmp_set) == 1: # print('stop') # print(pour_elev) if (width >= in_width) and (depth >= in_depth) and (area > in_area): print("************************************") print("depression loc: {}".format(dep_index)) print("min candidates: {}".format(min_candidates)) # print("Pour elevation: {}".format(pour_elev)) print("width = {}; depth = {}; area = {}; pour = {}".format(width, round(depth, 2), round(area, 2), pour_elev)) # dep_list.append(dep_index) id = len(dep_list) + 1 dep_list.append(Depression(id, width, round(depth, 4), round(area, 4), pour_elev, min_value, dep_index, dep_tmp_set)) for item in dep_index: if item in global_set: global_set.remove(item) # elif len(dep_tmp_set) == 1: # continue else: # print(dep_tmp_set) for item in dep_tmp_set: num_arr[item] = pour_elev global_set.add(item) # for dep in dep_list: # print(dep) print("Number of depressions: {}".format(len(dep_list))) return dep_list def fill_depressions(in_values, dep_list): for dep in dep_list: points = dep.points internal_pts = dep.internal_pts pour_elev = dep.pour_elev for point in internal_pts: in_values[point] = pour_elev return in_values def get_hierarchy(in_csv, out_dir, width = 0, height = 0, area = 0): pass if __name__ == '__main__': # ************************ change the following parameters if needed ******************************** # width = 0 height = 0 area = 0 work_dir = os.path.dirname(__file__) in_csv = os.path.join(work_dir, 'data/profile1.csv') out_csv = in_csv.replace('.csv', '_level1.csv') values = read_csv(in_csv, header=True, col_index=4) size = len(values) print("Total number of rows: {}".format(size)) dep_type = check_dep_type(values, 557) # print(dep_type) dep_pts = find_single_depression(values, index = 1087) # print(dep_pts) dep_list = find_depressions(values, in_width = 3, in_depth = 0) out_values = fill_depressions(values, dep_list) # print(out_values) write_csv(in_csv, out_csv, "LEVEL-1", out_values) # print(get_width_depth_area(values, dep_pts)) # ************************************************************************************************** #

33.255245

133

0.588161

346

0.036379

0

1,535

0.161392

a166258a27d4639c261790d1e5d9c74ab19c0e5f

4,544

py

Python

data/make_joint_comp_inc_data.py

gcunhase/StackedDeBERT

82777114fd99cafc6e2a3d760e774f007c563245

[ "MIT" ]

32

2020-01-03T09:53:03.000Z

2021-09-07T07:23:26.000Z

data/make_joint_comp_inc_data.py

gcunhase/StackedDeBERT

82777114fd99cafc6e2a3d760e774f007c563245

[ "MIT" ]

null

data/make_joint_comp_inc_data.py

gcunhase/StackedDeBERT

82777114fd99cafc6e2a3d760e774f007c563245

[ "MIT" ]

6

2020-01-21T06:50:21.000Z

2021-01-22T08:04:00.000Z

import argparse import os import csv import random from utils import ensure_dir, get_project_path from collections import defaultdict # POS-tag for irrelevant tag selection import nltk nltk.download('punkt') nltk.download('averaged_perceptron_tagger') __author__ = "Gwena Cunha" def write_tsv(intention_dir_path, filename, keys, dict): file_test = open(intention_dir_path + "/" + filename, 'wt') dict_writer = csv.writer(file_test, delimiter='\t') dict_writer.writerow(keys) r = zip(*dict.values()) for d in r: dict_writer.writerow(d) def make_dataset(root_data_dir, complete_data_dir, incomplete_data_dir, results_dir): """ :param root_data_dir: directory to save data :param complete_data_dir: subdirectory with complete data :param incomplete_data_dir: subdirectory with incomplete data :param results_dir: subdirectory with incomplete data :return: """ print("Making incomplete intention classification dataset...") complete_data_dir_path = root_data_dir + '/' + complete_data_dir incomplete_data_dir_path = root_data_dir + '/' + incomplete_data_dir results_dir_path = root_data_dir + '/' + results_dir ensure_dir(results_dir_path) # Traverse all sub-directories files_dictionary = defaultdict(lambda: []) for sub_dir in os.walk(complete_data_dir_path): if len(sub_dir[1]) == 0: data_name = sub_dir[0].split('/')[-1] files_dictionary[data_name] = sub_dir[2] # Open train and test tsv files for k, v in files_dictionary.items(): save_path = results_dir_path + '/' + k ensure_dir(save_path) for comp_v_i, inc_v_i in zip(['test.tsv', 'train.tsv'], ['test_withMissingWords.tsv', 'train_withMissingWords.tsv']): complete_tsv_file = open(complete_data_dir_path + '/' + k + '/' + comp_v_i, 'r') incomplete_tsv_file = open(incomplete_data_dir_path + '/' + k + '/' + inc_v_i, 'r') reader_complete = csv.reader(complete_tsv_file, delimiter='\t') reader_incomplete = csv.reader(incomplete_tsv_file, delimiter='\t') sentences, labels, missing_words_arr, targets = [], [], [], [] row_count = 0 for row_comp, row_inc in zip(reader_complete, reader_incomplete): if row_count != 0: # Incomplete sentences.append(row_inc[0]) labels.append(row_inc[1]) missing_words_arr.append(row_inc[2]) targets.append(row_comp[0]) if 'train' in comp_v_i: # Complete sentences.append(row_comp[0]) labels.append(row_comp[1]) missing_words_arr.append('') targets.append(row_comp[0]) row_count += 1 # Shuffle if 'train' in comp_v_i: c = list(zip(sentences, labels, missing_words_arr, targets)) random.shuffle(c) sentences, labels, missing_words_arr, targets = zip(*c) # Save train, test, val in files in the format (sentence, label) keys = ['sentence', 'label', 'missing', 'target'] data_dict = {'sentence': sentences, 'label': labels, 'missing': missing_words_arr, 'target': targets} write_tsv(save_path, comp_v_i, keys, data_dict) print("Complete + Incomplete intention classification dataset completed") def init_args(): parser = argparse.ArgumentParser(description="Script to make intention recognition dataset") parser.add_argument('--root_data_dir', type=str, default=get_project_path() + "/data", help='Directory to save subdirectories, needs to be an absolute path') parser.add_argument('--complete_data_dir', type=str, default="complete_data", help='Subdirectory with complete data') parser.add_argument('--incomplete_data_dir', type=str, default="incomplete_data_tfidf_lower_0.8_noMissingTag", help='Subdirectory with incomplete data') parser.add_argument('--results_dir', type=str, default="comp_with_incomplete_data_tfidf_lower_0.8_noMissingTag", help='Subdirectory to save Joint Complete and Incomplete data') return parser.parse_args() if __name__ == '__main__': args = init_args() make_dataset(args.root_data_dir, args.complete_data_dir, args.incomplete_data_dir, args.results_dir)

42.867925

125

0.645026

0

1,279

0.28147

a16669ec079300a0633ffd694b38772760885089

4,989

py

Python

recipes/models.py

JakubKoralewski/django-recipes

3794c6a96fb0765e2e3cebfc3968dae88e4f084c

[ "MIT" ]

null

recipes/models.py

JakubKoralewski/django-recipes

3794c6a96fb0765e2e3cebfc3968dae88e4f084c

[ "MIT" ]

5

2021-03-19T03:49:52.000Z

2021-06-10T19:16:05.000Z

recipes/models.py

JakubKoralewski/django-recipes

3794c6a96fb0765e2e3cebfc3968dae88e4f084c

[ "MIT" ]

null

from typing import List, Dict, Union from django.db import models # Create your models here. # https://en.wikipedia.org/wiki/Cooking_weights_and_measures class AmountType(models.TextChoices): GRAMS = ('g', 'grams') KILOGRAMS = ('kg', 'kilograms') MILLILITERS = ('ml', 'milliliters') TABLE_SPOONS = ('tbsp', 'tablespoons') TEA_SPOONS = ('tsp', 'teaspoons') COUNT = ('x', 'items') class Author(models.Model): name = models.CharField(max_length=100) def __str__(self): return f'Author: "{self.name}"' class IngredientType(models.IntegerChoices): DAIRY_PRODUCT = (0, 'Dairy product') VEGETABLE = (1, 'Vegetable') FRUIT = (2, 'Fruit') MEAT = (3, 'Meat') FLOUR_LIKE = (4, 'Flour-like') RICE_LIKE = (5, 'Rice-like') OTHER = (100, 'Unknown') class Ingredient(models.Model): name = models.CharField(max_length=200) photo = models.URLField(max_length=400) type = models.IntegerField(choices=IngredientType.choices, default=IngredientType.OTHER) def __str__(self): return f'Ingredient: "{self.name}"' class IngredientToBeAdded: name: str amount: Union[float, int] amount_type: str photo: str type: int # cuz choices are ints def __str__(self): return ','.join(self.__dict__.items()) def set_field(self, key: str, val): key = key.lower() if key == 'name': if not isinstance(val, str): raise Exception('name should be str') self.name = val elif key == 'photo': if not isinstance(val, str): raise Exception('photo should be str') self.photo = val elif key == 'amount': if not isinstance(val, int) and not isinstance(val, float): try: val = float(val) except: raise Exception('amount of ingredient should be number') self.amount = val elif key == 'amount_type': if not isinstance(val, str): raise Exception('amount type should be string') self.amount_type = val elif key == 'type': if not isinstance(val, int): try: val = int(val) except: raise Exception('type of ingredient should be int') self.type = val else: raise Exception(f'Unknown ingredient field tried to be saved: "{key}" of value: "{val}"') class StepToBeAdded: description: str photo: str ingredients: List[IngredientToBeAdded] def __init__(self): self.ingredients = [] def __str__(self): return ','.join(self.__dict__.items()) class Recipe(models.Model): name = models.CharField(max_length=200) photo = models.URLField(max_length=400, null=True, blank=True) author = models.ForeignKey(Author, on_delete=models.CASCADE, null=True) pub_date = models.DateTimeField(auto_now_add=True, editable=False, help_text='Published date') votes = models.PositiveIntegerField(default=0) steps = models.PositiveIntegerField(default=0) @classmethod def from_form(cls, name: str, photo: str, author: str): self = Recipe(name=name, photo=photo) try: maybe_existing_author = Author.objects.get(name__iexact=author) self.author = maybe_existing_author except Author.DoesNotExist: new_author = Author(name=author) new_author.save() self.author = new_author return self def add_steps(self, steps: Union[List[StepToBeAdded]]): if isinstance(steps, list): for step in steps: self.add_step(step) elif isinstance(steps, dict): for step in steps.values(): self.add_step(step) else: raise Exception("invalid type of steps added to recipes") def add_step(self, step: StepToBeAdded): if not self.id: raise Exception('Add the Recipe to the database before inserting steps!') new_step = StepsOfRecipe(recipe=self, step_amt=self.steps, description=step.description) new_step.save() self.steps += 1 self.save() for ing in step.ingredients: ingredient: Ingredient try: maybe_ingredient = Ingredient.objects.get(name__iexact=ing.name) ingredient = maybe_ingredient except Ingredient.DoesNotExist: new_ingredient = Ingredient(name=ing.name, photo=ing.photo, type=ing.type) new_ingredient.save() ingredient = new_ingredient step_ing = IngredientsOfStep( step=new_step, ingredient=ingredient, amount=ing.amount, amount_type=ing.amount_type ) step_ing.save() def __str__(self): return f'Recipe: name="{self.name}" author="{self.author.name if self.author else "no author"}"' class StepsOfRecipe(models.Model): recipe = models.ForeignKey(Recipe, on_delete=models.CASCADE) step_amt = models.PositiveIntegerField(default=0) description = models.TextField() def __str__(self): return f'{self.step_amt + 1}-th step of {self.recipe.name}' class IngredientsOfStep(models.Model): step = models.ForeignKey(StepsOfRecipe, on_delete=models.CASCADE) ingredient = models.ForeignKey(Ingredient, on_delete=models.CASCADE) amount = models.DecimalField(decimal_places=1, max_digits=100) amount_type = models.CharField(max_length=10, choices=AmountType.choices, default=AmountType.COUNT) def __str__(self): return f"Ingredient of {self.ingredient.name}'s {self.step.step_amt + 1}-th step"

28.83815

100

0.71798

4,806

0.963319

0

351

0.070355

0

907

0.1818

a166f12db4d713441e75c22cdaa77f074c8a2431

835

py

Python

zoneh/conf.py

RaminAT/zoneh

73c8e66d76cbd0aa51551e21740d88ff439158a9

[ "MIT" ]

8

2019-05-27T07:21:51.000Z

2021-09-14T21:26:53.000Z

zoneh/conf.py

RaminAT/zoneh

73c8e66d76cbd0aa51551e21740d88ff439158a9

[ "MIT" ]

5

2020-04-08T12:10:44.000Z

2021-02-11T01:51:41.000Z

zoneh/conf.py

RaminAT/zoneh

73c8e66d76cbd0aa51551e21740d88ff439158a9

[ "MIT" ]

5

2020-03-29T17:04:05.000Z

2021-09-14T21:26:58.000Z

"""Config module.""" import json import logging import os from zoneh.exceptions import ConfigError _log = logging.getLogger(__name__) _CONFIG_FILE = 'config.json' def _load_config(): """Load telegram and filters configuration from config file.""" if not os.path.isfile(_CONFIG_FILE): err_msg = f'Cannot find {_CONFIG_FILE} configuration file' _log.error(err_msg) raise ConfigError(err_msg) with open(_CONFIG_FILE, 'r') as fd: config = fd.read() try: config = json.loads(config) except json.decoder.JSONDecodeError: err_msg = f'Malformed JSON in {_CONFIG_FILE} configuration file' _log.error(err_msg) raise ConfigError(err_msg) return config _CONF = _load_config() def get_config(): """Return config as singleton.""" return _CONF

22.567568

72

0.68024

0

234

0.28024

a1676b1833d7b48b6064b056da63a6fba24af86a

3,629

py

Python

mlogger.py

morris178/mqtt-data-logger

75e0fbbe0311ecaba8c905df356d6f7d8a0e3615

[ "MIT" ]

null

mlogger.py

morris178/mqtt-data-logger

75e0fbbe0311ecaba8c905df356d6f7d8a0e3615

[ "MIT" ]

null

mlogger.py

morris178/mqtt-data-logger

75e0fbbe0311ecaba8c905df356d6f7d8a0e3615

[ "MIT" ]

null

###demo code provided by Steve Cope at www.steves-internet-guide.com ##email steve@steves-internet-guide.com ###Free to use for any purpose """ implements data logging class """ import time, os, json, logging ############### class m_logger(object): """Class for logging data to a file. You can set the maximim bunber of messages in a file the default is 1000. When the file is full a new file is created.Log files are store under a root directoy and a sub directory that uses the timestamp for the directory name Log file data is flushed immediately to disk so that data is not lost. Data can be stored as plain text or in JSON format """ def __init__(self, log_dir="mlogs", log_recs=1000, number_logs=0): self.log_dir = log_dir self.log_recs = log_recs self.number_logs = number_logs self.count = 0 self.log_dir = self.create_log_dir(self.log_dir) self.fo = self.get_log_name(self.log_dir, self.count) self.new_file_flag = 0 self.writecount = 0 self.timenow = time.time() self.flush_flag = True self.flush_time = 2 # flush logs to disk every 2 seconds def __flushlogs(self): # write to disk self.fo.flush() # logging.info("flushing logs") os.fsync(self.fo.fileno()) self.timenow = time.time() def __del__(self): if not self.fo.closed: print("closing log file") self.fo.close() def close_file(self): if not self.fo.closed: print("closing log file") self.fo.close() def create_log_dir(self, log_dir): """Function for creating new log directories using the timestamp for the name""" self.t = time.localtime(time.time()) self.time_stamp = (str(self.t[1]) + "-" + str(self.t[2]) + "-" + str(self.t[3]) + "-" + str(self.t[4])) logging.info("creating sub directory" + str(self.time_stamp)) try: os.stat(self.log_dir) except: os.mkdir(self.log_dir) self.log_sub_dir = self.log_dir + "/" + self.time_stamp try: os.stat(self.log_sub_dir) except: os.mkdir(self.log_sub_dir) return (self.log_sub_dir) def get_log_name(self, log_dir, count): """get log files and directories""" self.log_numbr = "{0:003d}".format(count) logging.info("s is" + str(self.log_numbr)) self.file_name = self.log_dir + "/" + "log" + self.log_numbr logging.info("creating log file " + self.file_name) f = open(self.file_name, 'w') # clears file if it exists f.close() f = open(self.file_name, 'a') return (f) def log_json(self, data): jdata = json.dumps(data) + "\n" self.log_data(jdata) def log_data(self, data): self.data = data try: self.fo.write(data) self.writecount += 1 self.__flushlogs() if self.writecount >= self.log_recs: self.count += 1 # counts number of logs if self.count > self.number_logs and self.number_logs != 0: logging.info("too many logs: starting from 0") self.count = 0 # reset self.fo = self.get_log_name(self.log_dir, self.count) self.writecount = 0 except BaseException as e: logging.error("Error on_data: %s" % str(e)) return False return True

36.656566

76

0.572058

3,390

0.934142

0

1,041

0.286856

a16793db9e30c478f5f315f915ced2b2053b7849

6,299

py

Python

ptools/lipytools/little_methods.py

piteren/ptools_module

5117d06d7dea4716b573b93d5feb10137966c373

[ "MIT" ]

null

ptools/lipytools/little_methods.py

piteren/ptools_module

5117d06d7dea4716b573b93d5feb10137966c373

[ "MIT" ]

null

ptools/lipytools/little_methods.py

piteren/ptools_module

5117d06d7dea4716b573b93d5feb10137966c373

[ "MIT" ]

null

""" 2018 (c) piteren some little methods (but frequently used) for Python """ from collections import OrderedDict import csv import inspect import json import os import pickle import random import shutil import string import time from typing import List, Callable, Any, Optional # prepares function parameters dictionary def get_params(function: Callable): params_dict = {'without_defaults':[], 'with_defaults':OrderedDict()} if function: specs = inspect.getfullargspec(function) params = specs.args if not params: params = [] vals = specs.defaults if not vals: vals = () while len(params) > len(vals): params_dict['without_defaults'].append(params.pop(0)) params_dict['with_defaults'] = {k: v for k,v in zip(params,vals)} return params_dict # short(compressed) scientific notation for floats def short_scin( fl: float, precision:int= 1): sh = f'{fl:.{precision}E}' sh = sh.replace('+0','') sh = sh.replace('+','') sh = sh.replace('-0','-') sh = sh.replace('E','e') return sh # returns sting from float, always of given width def float_to_str( num: float, width: int= 7): if width < 5: width = 5 scientific_decimals = width-6 if width>6 else 0 ff = f'{num:.{scientific_decimals}E}' if 1000 > num > 0.0001: ff = str(num)[:width] if len(ff)<width: ff += '0'*(width-len(ff)) return ff # *********************************************************************************************** file readers / writers # ********************************************* for raise_exception=False each reader will return None if file not found def r_pickle( # pickle reader file_path, obj_type= None, # if obj_type is given checks for compatibility with given type raise_exception= False): if not os.path.isfile(file_path): if raise_exception: raise FileNotFoundError(f'file {file_path} not exists!') return None # obj = pickle.load(open(file_path, 'rb')) << replaced by: with open(file_path, 'rb') as file: obj = pickle.load(file) if obj_type: assert type(obj) is obj_type, f'ERROR: obj from file is not {str(obj_type)} type !!!' return obj def w_pickle( # pickle writer obj, file_path): with open(file_path, 'wb') as file: pickle.dump(obj, file) def r_json( # json reader file_path, raise_exception= False): if not os.path.isfile(file_path): if raise_exception: raise FileNotFoundError(f'file {file_path} not exists!') return None with open(file_path, 'r', encoding='utf-8') as file: return json.load(file) def w_json( # json writer data: dict, file_path): with open(file_path, 'w', encoding='utf-8') as file: json.dump(data, file, indent=4, ensure_ascii=False) def r_jsonl( # jsonl reader file_path, raise_exception=False): if not os.path.isfile(file_path): if raise_exception: raise FileNotFoundError(f'file {file_path} not exists!') return None with open(file_path, 'r', encoding='utf-8') as file: return [json.loads(line) for line in file] def w_jsonl( # jsonl writer data: List[dict], file_path): with open(file_path, 'w', encoding='utf-8') as file: for d in data: json.dump(d, file, ensure_ascii=False) file.write('\n') def r_csv( # csv reader file_path, raise_exception= False): if not os.path.isfile(file_path): if raise_exception: raise FileNotFoundError(f'file {file_path} not exists!') return None with open(file_path, newline='') as f: reader = csv.reader(f) return [row for row in reader][1:] # returns timestamp string def stamp( year= False, date= True, letters: Optional[int]= 3): random.seed(time.time()) if date: if year: stp = time.strftime('%y%m%d_%H%M') else: stp = time.strftime('%m%d_%H%M') else: stp = '' if letters: if date: stp += '_' stp += ''.join([random.choice(string.ascii_letters) for _ in range(letters)]) return stp # returns nice string of given list def list_str(ls: List[Any], limit:Optional[int]=200): lstr = [str(e) for e in ls] lstr = '; '.join(lstr) if limit: lstr = lstr[:limit] return lstr # prints nested dict def print_nested_dict(dc: dict, ind_scale=2, line_limit=200): tpD = { dict: 'D', list: 'L', tuple: 'T', str: 'S'} def __prn_root(root: dict, ind, ind_scale=2, line_limit=line_limit): spacer = ' ' * ind * ind_scale for k in sorted(list(root.keys())): tp = tpD.get(type(root[k]),'O') ln = len(root[k]) if tp in tpD.values() else '' exmpl = '' if tp!='D': exmpl = str(root[k]) if line_limit: if len(exmpl)>line_limit: exmpl = f'{exmpl[:line_limit]}..' exmpl = f' : {exmpl}' print(f'{spacer}{k} [{tp}.{ln}]{exmpl}') if type(root[k]) is dict: __prn_root(root[k],ind+1,ind_scale) __prn_root(dc,ind=0,ind_scale=ind_scale) # prepares folder, creates or flushes def prep_folder( folder_path :str, # folder path flush_non_empty= False): if flush_non_empty and os.path.isdir(folder_path): shutil.rmtree(folder_path) os.makedirs(folder_path, exist_ok=True) # random <0;1> probability function def prob(p: float) -> bool: return random.random() < p # terminal progress bar def progress_ ( iteration: float or int, # current iteration total: float or int, # total iterations prefix: str= '', # prefix string suffix: str= '', # suffix string length: int= 20, fill: str= '█', print_end: str= ''): prog = iteration / total if prog > 1: prog = 1 filled_length = int(length * prog) bar = fill * filled_length + '-' * (length - filled_length) print(f'\r{prefix} |{bar}| {prog*100:.1f}% {suffix}', end = print_end) if prog == 1: print()

30.877451

120

0.582156

0

1,508

0.239327

a16884524638226d0ba06be614706d7a5f91b5dc

2,135

py

Python

tests/test.py

zephenryus/botw-grass

31adaebd69b56c4177bcdaf8e933fee5e8bc8433

[ "MIT" ]

1

2020-10-11T07:07:31.000Z

tests/test.py

zephenryus/botw-grass

31adaebd69b56c4177bcdaf8e933fee5e8bc8433

[ "MIT" ]

null

tests/test.py

zephenryus/botw-grass

31adaebd69b56c4177bcdaf8e933fee5e8bc8433

[ "MIT" ]

1

2020-10-11T07:07:33.000Z

import filecmp import hashlib import json import grass def grass_to_json(): """ Tests reading of grass file and exports data as a json file """ data = grass.read_grass("assets/5000000000.grass.extm") print("Saving file output/5000000000.grass.extm.json...") with open("output/5000000000.grass.extm.json", "w+") as outfile: out_obj = [] for entry in data: out_obj.append(entry.__dict__) outfile.write(json.dumps(out_obj, indent=4, separators=(',', ': '))) def grass_to_binary_string(): """ Tests that data is recompiled correctly and matches the original file """ data = grass.read_grass("assets/5000000000.grass.extm") binary_data = grass.compile_grass(data) hash_md5 = hashlib.md5() with open("assets/5000000000.grass.extm", "rb") as infile: for chunk in iter(lambda: infile.read(4096), b""): hash_md5.update(chunk) file_hash = hash_md5.hexdigest() hash_md5 = hashlib.md5() pos = 0 for chunk in iter(lambda: binary_data[pos:pos + 4096], b""): pos += 4096 hash_md5.update(chunk) string_hash = hash_md5.hexdigest() print("The file and binary string are the same: {0}".format(file_hash == string_hash)) def grass_to_binary_file(): """ Tests reading data from grass file then writes the same data back as a binary """ data = grass.read_grass("assets/5000000000.grass.extm") grass.write_grass(data, "output/5000000000.grass.extm") print("The files are the same: {0}".format( filecmp.cmp("assets/5000000000.grass.extm", "output/5000000000.grass.extm"))) def grass_to_image(): """ Tests reading data from grass file then generating height and color map images """ data = grass.read_grass("assets/5000000000.grass.extm") grass.generate_height_map(data, 'output/5000000000.grass.extm.height.tiff') grass.generate_color_map(data, 'output/5000000000.grass.extm.color.tiff') def main(): grass_to_json() grass_to_binary_string() grass_to_binary_file() grass_to_image() if __name__ == "__main__": main()

28.092105

90

0.6726

0

861

0.403279

a16900fa8a0412a37028d1da77ef8f912a14e56f

259

py

Python

Control/control_common.py

TomE8/drones

c92865556dd3df2d5f5b73589cd48e413bff3a3a

[ "MIT" ]

14

2018-10-29T00:52:18.000Z

2022-03-23T20:07:11.000Z

Control/control_common.py

TomE8/drones

c92865556dd3df2d5f5b73589cd48e413bff3a3a

[ "MIT" ]

4

2020-07-12T05:19:05.000Z

2020-09-20T12:40:47.000Z

Control/control_common.py

TomE8/drones

c92865556dd3df2d5f5b73589cd48e413bff3a3a

[ "MIT" ]

2

2019-03-08T01:36:47.000Z

2019-09-12T04:07:19.000Z

class AxisIndex(): #TODO: read this value from config file LEFT_RIGHT=0 FORWARD_BACKWARDS=1 ROTATE=2 UP_DOWN=3 class ButtonIndex(): TRIGGER = 0 SIDE_BUTTON = 1 HOVERING = 2 EXIT = 10 class ThresHold(): SENDING_TIME = 0.5

17.266667

58

0.648649

254

0.980695

0

39

0.150579

a16aa7de0e511402c80303f34d1d2b678e7b0256

446

py

Python

tests/LayoutTest.py

lakhman/restructuredWeb

a8aff9f96c63415fdefe6832f923a6d395d4ebdd

[ "MIT" ]

2

2021-05-19T15:43:26.000Z

2021-05-19T16:07:00.000Z

tests/LayoutTest.py

lakhman/restructuredWeb

a8aff9f96c63415fdefe6832f923a6d395d4ebdd

[ "MIT" ]

null

tests/LayoutTest.py

lakhman/restructuredWeb

a8aff9f96c63415fdefe6832f923a6d395d4ebdd

[ "MIT" ]

1

2021-05-19T15:43:44.000Z

# -*- coding: utf-8 -*- from .BaseTest import BaseTest class LayoutTest(BaseTest): def test_layout_switch(self): """ Test layout switch """ self.do_component_fixture_test_with_real_sphinx('layout', 'layout') def test_layout_multiple(self): """ Test multiple layout directives throw an error """ self.do_component_fixture_test_with_real_sphinx('layout', 'multiple-error')

26.235294

83

0.650224

388

0.869955

0

175

0.392377

a16aadbd9d67147c97cce0ae81ac212da4c01e1c

2,472

py

Python

.leetcode/16.3-sum-closest.2.py

KuiyuanFu/PythonLeetCode

8962df2fa838eb7ae48fa59de272ba55a89756d8

[ "MIT" ]

null

.leetcode/16.3-sum-closest.2.py

KuiyuanFu/PythonLeetCode

8962df2fa838eb7ae48fa59de272ba55a89756d8

[ "MIT" ]

null

.leetcode/16.3-sum-closest.2.py

KuiyuanFu/PythonLeetCode

8962df2fa838eb7ae48fa59de272ba55a89756d8

[ "MIT" ]

null

# @lc app=leetcode id=16 lang=python3 # # [16] 3Sum Closest # # https://leetcode.com/problems/3sum-closest/description/ # # algorithms # Medium (46.33%) # Likes: 3080 # Dislikes: 169 # Total Accepted: 570.4K # Total Submissions: 1.2M # Testcase Example: '[-1,2,1,-4]\n1' # # Given an array nums of n integers and an integer target, find three integers # in nums such that the sum is closest to target. Return the sum of the three # integers. You may assume that each input would have exactly one solution. # # # Example 1: # # # Input: nums = [-1,2,1,-4], target = 1 # Output: 2 # Explanation: The sum that is closest to the target is 2. (-1 + 2 + 1 = # 2). # # # # Constraints: # # # 3 <= nums.length <= 10^3 # -10^3 <= nums[i] <= 10^3 # -10^4 <= target <= 10^4 # # # # @lc tags=array;two-pointers # @lc imports=start from imports import * # @lc imports=end # @lc idea=start # # 给定一个数组，求数组中三个元素和最接近目标的和。 # 使用双指针法。首先对数组排序，确定第一个值，之后在剩下的数组中，使用双指针法找最小的差值。因为有序，所以可以通过左右移动指针，来修改剩余两个数的和的大小变化方向。之后判断是否重复来剪枝。 # # @lc idea=end # @lc group=two-pointers # @lc rank=10 # @lc code=start class Solution: def threeSumClosest(self, nums: List[int], target: int) -> int: # dic = {} # for n in nums: # if not dic.__contains__(n): # dic[n] = 1 # elif dic[n] < 3: # dic[n] += 1 # nums = [] # for i in list(dic.keys()): # nums += [i]*dic[i] nums.sort() s = nums[0] + nums[1] + nums[2] dif = abs(s - target) for i in range(len(nums) - 2): # 重复元素。 if i > 0 and nums[i] == nums[i - 1]: continue l = i + 1 r = len(nums) - 1 t = target - nums[i] while l < r: if abs(t - nums[l] - nums[r]) < dif: dif = abs(t - nums[l] - nums[r]) s = nums[i] + nums[l] + nums[r] # 确定方向 if t - nums[l] - nums[r] > 0: l = l + 1 else: r = r - 1 if dif == 0: break return s pass # @lc code=end # @lc main=start if __name__ == '__main__': print('Example 1:') print('Input : ') print('nums = [-1,2,1,-4], target = 1') print('Output :') print(str(Solution().threeSumClosest([-1, 2, 1, -4], 1))) print('Exception :') print('2') print() pass # @lc main=end

22.071429

95

0.506068

1,088

0.398535

0

1,599

0.585714

a16be12b3f57a68c02b41dfe786a31910f86a92e

2,142

py

Python

test/test_functions/test_michalewicz.py

carefree0910/botorch

c0b252baba8f16a4ea2eb3f99c266fba47418b1f

[ "MIT" ]

null

test/test_functions/test_michalewicz.py

carefree0910/botorch

c0b252baba8f16a4ea2eb3f99c266fba47418b1f

[ "MIT" ]

null

test/test_functions/test_michalewicz.py

carefree0910/botorch

c0b252baba8f16a4ea2eb3f99c266fba47418b1f

[ "MIT" ]

1

2019-05-07T23:53:08.000Z

#! /usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved import unittest import torch from botorch.test_functions.michalewicz import ( GLOBAL_MAXIMIZER, GLOBAL_MAXIMUM, neg_michalewicz, ) class TestNegMichalewicz(unittest.TestCase): def test_single_eval_neg_michalewicz(self, cuda=False): device = torch.device("cuda") if cuda else torch.device("cpu") for dtype in (torch.float, torch.double): X = torch.zeros(10, device=device, dtype=dtype) res = neg_michalewicz(X) self.assertEqual(res.dtype, dtype) self.assertEqual(res.device.type, device.type) self.assertEqual(res.shape, torch.Size()) def test_single_eval_neg_michalewicz_cuda(self): if torch.cuda.is_available(): self.test_single_eval_neg_michalewicz(cuda=True) def test_batch_eval_neg_michalewicz(self, cuda=False): device = torch.device("cuda") if cuda else torch.device("cpu") for dtype in (torch.float, torch.double): X = torch.zeros(2, 10, device=device, dtype=dtype) res = neg_michalewicz(X) self.assertEqual(res.dtype, dtype) self.assertEqual(res.device.type, device.type) self.assertEqual(res.shape, torch.Size([2])) def test_batch_eval_neg_michalewicz_cuda(self): if torch.cuda.is_available(): self.test_batch_eval_neg_michalewicz(cuda=True) def test_neg_michalewicz_global_maximum(self, cuda=False): device = torch.device("cuda") if cuda else torch.device("cpu") for dtype in (torch.float, torch.double): X = torch.tensor( GLOBAL_MAXIMIZER, device=device, dtype=dtype, requires_grad=True ) res = neg_michalewicz(X) res.backward() self.assertAlmostEqual(res.item(), GLOBAL_MAXIMUM, places=4) self.assertLess(X.grad.abs().max().item(), 1e-3) def test_neg_michalewicz_global_maximum_cuda(self): if torch.cuda.is_available(): self.test_neg_michalewicz_global_maximum(cuda=False)

38.25

80

0.661531

1,898

0.886088

0

126

0.058824

a16cdf1f2057c870dd857dd5ffd7ccfb349decff

1,122

py

Python

example_scripts/write_mztab_result.py

gavswe/pyqms

299cd4d96b78611ebbe43e0ac625909c6a8d8fd9

[ "MIT" ]

23

2017-06-28T07:53:42.000Z

2022-02-20T02:46:37.000Z

example_scripts/write_mztab_result.py

gavswe/pyqms

299cd4d96b78611ebbe43e0ac625909c6a8d8fd9

[ "MIT" ]

23

2019-05-15T18:05:18.000Z

2022-01-21T13:27:11.000Z

example_scripts/write_mztab_result.py

gavswe/pyqms

299cd4d96b78611ebbe43e0ac625909c6a8d8fd9

[ "MIT" ]

11

2017-06-26T13:22:57.000Z

2022-03-31T23:35:14.000Z

#!/usr/bin/env python3 # encoding: utf-8 """ pyQms ----- Python module for fast and accurate mass spectrometry data quantification :license: MIT, see LICENSE.txt for more details Authors: * Leufken, J. * Niehues, A. * Sarin, L.P. * Hippler, M. * Leidel, S.A. * Fufezan, C. """ import pickle import sys def main(result_pkl=None): """ usage: ./write_mztab_results.py <Path2ResultPkl> Will write all results of a result pkl into a .mztab file. Please refer to Documentation of :doc:`results` for further information. Note: Please note that the ouput in mzTab format is still in beta stage. Since pyQms is a raw quantification tool, some meta data has to be passed/set manually by the user. """ results_class = pickle.load(open(result_pkl, "rb")) results_class.write_result_mztab( output_file_name="{0}_results.mztab".format(result_pkl) ) if __name__ == "__main__": if len(sys.argv) < 2: print(main.__doc__) else: main(result_pkl=sys.argv[1])

21.169811

78

0.622995

0

798

0.71123

a16d009cfff8e6fc878e82ac94cf0ba2221a05c0

5,516

py

Python

dbservice/dbprovider/MessageDAO.py

artyomche9/digest_bot

480e9038ac1f42a10a9a333a72b9e38fa9fe8385

[ "MIT" ]

11

2019-10-25T12:42:03.000Z

2020-04-03T09:43:49.000Z

dbservice/dbprovider/MessageDAO.py

maybe-hello-world/digestbot

480e9038ac1f42a10a9a333a72b9e38fa9fe8385

[ "MIT" ]

13

2020-12-12T12:33:55.000Z

2021-09-09T15:00:57.000Z

dbservice/dbprovider/MessageDAO.py

artyomche9/digest_bot

480e9038ac1f42a10a9a333a72b9e38fa9fe8385

[ "MIT" ]

5

2019-10-06T09:55:24.000Z

2019-10-21T16:36:56.000Z

from decimal import Decimal from typing import List, Any from common.Enums import SortingType from models import Message from .engine import db_engine, DBEngine class MessageDAO: def __init__(self, engine: DBEngine): self.engine = engine @staticmethod def __make_insert_values_from_messages_array(messages: List[Message]) -> List[tuple]: return [ ( message.username, message.text, Decimal(message.timestamp), message.reply_count, message.reply_users_count, message.reactions_rate, message.thread_length, message.channel_id, ) for message in messages ] @staticmethod def __request_messages_to_message_class(request_messages: List[Any]) -> List[Message]: return [Message(**message) for message in request_messages] @staticmethod def __make_link_update_values_from_messages_array(messages: List[Message]) -> List[tuple]: return [(x.link, Decimal(x.timestamp), x.channel_id) for x in messages] async def create_messages(self, messages: List[Message]) -> None: request = f""" INSERT INTO message (username, text, timestamp, reply_count, reply_users_count, reactions_rate, thread_length, channel_id) VALUES ($1, $2, $3, $4, $5, $6, $7, $8); """ sequence = self.__make_insert_values_from_messages_array(messages) await self.engine.make_execute_many(request, sequence) async def upsert_messages(self, messages: List[Message]) -> None: request = f""" INSERT INTO message (username, text, timestamp, reply_count, reply_users_count, reactions_rate, thread_length, channel_id) VALUES ($1, $2, $3, $4, $5, $6, $7, $8) ON CONFLICT (timestamp, channel_id) DO UPDATE SET reply_count = EXCLUDED.reply_count, reply_users_count = EXCLUDED.reply_users_count, reactions_rate = EXCLUDED.reactions_rate, thread_length = EXCLUDED.thread_length; """ sequence = self.__make_insert_values_from_messages_array(messages) await self.engine.make_execute_many(request, sequence) async def get_messages_without_links(self) -> List[Message]: request = f"SELECT * FROM message WHERE link IS NULL;" messages = await self.engine.make_fetch_rows(request) return self.__request_messages_to_message_class(messages) async def update_message_links(self, messages: List[Message]) -> None: request = f" UPDATE message SET link=($1) WHERE timestamp=($2) AND channel_id=($3)" sequence = self.__make_link_update_values_from_messages_array(messages) await self.engine.make_execute_many(request, sequence) async def get_top_messages( self, after_ts: str, user_id: str, sorting_type: SortingType = SortingType.REPLIES, top_count: int = 10 ) -> List[Message]: request = f""" SELECT * FROM message WHERE timestamp >= $1 AND username NOT IN (SELECT ignore_username FROM IgnoreList WHERE author_username = $3) ORDER BY {sorting_type.value} DESC LIMIT $2; """ messages = await self.engine.make_fetch_rows(request, after_ts, top_count, user_id) return self.__request_messages_to_message_class(messages) async def get_top_messages_by_channel_id( self, channel_id: str, after_ts: str, user_id: str, sorting_type: SortingType = SortingType.REPLIES, top_count: int = 10, ) -> List[Message]: request = f""" SELECT * FROM message WHERE channel_id=$1 AND timestamp >= $2 AND username NOT IN (SELECT ignore_username FROM IgnoreList WHERE author_username = $4) ORDER BY {sorting_type.value} DESC LIMIT $3; """ messages = await self.engine.make_fetch_rows( request, channel_id, after_ts, top_count, user_id ) return self.__request_messages_to_message_class(messages) async def get_top_messages_by_preset_name( self, preset_name: str, after_ts: str, user_id: str, sorting_type: SortingType = SortingType.REPLIES, top_count: int = 10, ) -> List[Message]: request = f""" WITH presets AS ( SELECT * FROM preset WHERE name = $1 AND (username = $2 OR username IS NULL) ORDER BY username NULLS LAST LIMIT 1 ) SELECT message.* FROM message JOIN presets preset ON message.channel_id=ANY(preset.channel_ids) WHERE message.timestamp >= $3 AND message.username NOT IN (SELECT ignore_username FROM IgnoreList WHERE author_username = $2) ORDER BY {sorting_type.value} DESC LIMIT $4; """ messages = await self.engine.make_fetch_rows( request, preset_name, user_id, after_ts, top_count ) return self.__request_messages_to_message_class(messages) message_dao = MessageDAO(db_engine)

36.773333

99

0.603336

5,313

0.963198

0

857

0.155366

4,308

0.781001

2,084

0.37781

a16f85e6fac2fb3f5423a543ab4b85436a1f1301

196

py

Python

Chapter09/fuzzing.py

firebitsbr/Penetration-Testing-with-Shellcode

2d756bccace6b727e050b2010ebf23e08d221fdc

[ "MIT" ]

30

2018-05-15T21:45:09.000Z

2022-03-23T20:04:25.000Z

Chapter09/fuzzing.py

naveenselvan/Penetration-Testing-with-Shellcode

2d756bccace6b727e050b2010ebf23e08d221fdc

[ "MIT" ]

1

2020-10-19T13:03:32.000Z

2020-11-24T05:50:17.000Z

Chapter09/fuzzing.py

naveenselvan/Penetration-Testing-with-Shellcode

2d756bccace6b727e050b2010ebf23e08d221fdc

[ "MIT" ]

18

2018-02-20T21:21:23.000Z

2022-01-26T04:19:28.000Z

#!/usr/bin/python import socket import sys junk = 'A'*500 s=socket.socket(socket.AF_INET,socket.SOCK_STREAM) connect = s.connect(('192.168.129.128',21)) s.recv(1024) s.send('USER '+junk+'\r\n')

17.818182

50

0.69898

0

50

0.255102

a17081dce9dfbf674f07300258797fe7e68a0847

1,746

py

Python

017. Letter Combinations of a Phone Number.py

youhusky/Facebook_Prepare

4045bcb652537711b3680b2aa17204ae73c6bde8

[ "MIT" ]

6

2017-10-30T05:35:46.000Z

2020-12-15T06:51:52.000Z

017. Letter Combinations of a Phone Number.py

youhusky/Facebook_Prepare

4045bcb652537711b3680b2aa17204ae73c6bde8

[ "MIT" ]

1

2017-10-30T04:11:31.000Z

2017-10-30T05:46:24.000Z

017. Letter Combinations of a Phone Number.py

youhusky/Facebook_Prepare

4045bcb652537711b3680b2aa17204ae73c6bde8

[ "MIT" ]

2

2020-09-03T07:14:02.000Z

2021-05-21T19:19:57.000Z

# Given a digit string, return all possible letter combinations that the number could represent. # A mapping of digit to letters (just like on the telephone buttons) is given below. # Input:Digit string "23" # Output: ["ad", "ae", "af", "bd", "be", "bf", "cd", "ce", "cf"]. # DFS backtracking class Solution(object): def letterCombinations(self, digits): """ O(2^n) :type digits: str :rtype: List[str] """ if not digits: return [] res = [] dic = { '2': 'abc', '3': 'def', '4': 'ghi', '5': 'jkl', '6': 'mno', '7': 'pqrs', '8': 'tuv', '9': 'wxyz' } self.dfs(res, '',dic,digits,0) return res def dfs(self, res, temp, dic,digits,index): if len(temp) == len(digits): res.append(temp) return # focus on ! # digits[index] -> 2: generate a,b,c for letter in dic[digits[index]]: self.dfs(res, temp+letter, dic, digits, index+1) class Solution2(object): def letterCombinations(self, password): if not password: return [] res = [] dic = {'a':"12", 'c':"34"} for char in password: if char not in dic: dic[char] = char self.dfs(res, "", dic, password, 0) return res def dfs(self, res, temp, dic, password, index): if index == len(password): res.append(temp) return for letter in dic[password[index]]: self.dfs(res, temp+letter, dic, password, index+1) m = Solution2() print m.letterCombinations('abc')

27.28125

96

0.4874

1,374

0.786942

0

507

0.290378

a172ea5b14e8133a222d02986a593e89323cad7c

847

py

Python

FreeBSD/bsd_netstats_poller.py

failedrequest/telegraf-plugins

9cda0612a912f219fa84724f12af1f428483a37a

[ "BSD-2-Clause" ]

null

FreeBSD/bsd_netstats_poller.py

failedrequest/telegraf-plugins

9cda0612a912f219fa84724f12af1f428483a37a

[ "BSD-2-Clause" ]

null

FreeBSD/bsd_netstats_poller.py

failedrequest/telegraf-plugins

9cda0612a912f219fa84724f12af1f428483a37a

[ "BSD-2-Clause" ]

null

#!/usr/bin/env python3 # 3/21/2021 # Updated for python3 # A Simple sysctl to telegraf plugin for freebsd's netstats ip info from freebsd_sysctl import Sysctl as sysctl import subprocess as sp import re import json import sys import pprint as pp hostname = sysctl("kern.hostname").value netstat_data = {} points_netstat = {} netstat_output = sp.check_output(["netstat", "-s", "-p", "ip", "--libxo", "json", "/dev/null"],universal_newlines=True) netstat_data = json.loads(netstat_output) for x in netstat_data["statistics"]: for k,v in netstat_data["statistics"][x].items(): points_netstat[k] = v def points_to_influx(points): field_tags= ",".join(["{k}={v}".format(k=str(x[0]), v=x[1]) for x in list(points_netstat.items())]) print(("bsd_netstat,type=netstat {}").format(field_tags)) points_to_influx(points_netstat)

22.289474

119

0.709563

0

248

0.292798

a1730ed2d00a6babe52f239de2d480281d939967

13,395

py

Python

ixnetwork_restpy/testplatform/sessions/ixnetwork/topology/packetinlist_10d8adb40e4e05f4b37904f2c6428ca9.py

OpenIxia/ixnetwork_restpy

f628db450573a104f327cf3c737ca25586e067ae

[ "MIT" ]

20

2019-05-07T01:59:14.000Z

2022-02-11T05:24:47.000Z

ixnetwork_restpy/testplatform/sessions/ixnetwork/topology/packetinlist_10d8adb40e4e05f4b37904f2c6428ca9.py

OpenIxia/ixnetwork_restpy

f628db450573a104f327cf3c737ca25586e067ae

[ "MIT" ]

60

2019-04-03T18:59:35.000Z

2022-02-22T12:05:05.000Z

ixnetwork_restpy/testplatform/sessions/ixnetwork/topology/packetinlist_10d8adb40e4e05f4b37904f2c6428ca9.py

OpenIxia/ixnetwork_restpy

f628db450573a104f327cf3c737ca25586e067ae

[ "MIT" ]

13

2019-05-20T10:48:31.000Z

2021-10-06T07:45:44.000Z

# MIT LICENSE # # Copyright 1997 - 2020 by IXIA Keysight # # 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. from ixnetwork_restpy.base import Base from ixnetwork_restpy.files import Files from typing import List, Any, Union class PacketInList(Base): """Openflow Switch PacketIn Configuration The PacketInList class encapsulates a list of packetInList resources that are managed by the system. A list of resources can be retrieved from the server using the PacketInList.find() method. """ __slots__ = () _SDM_NAME = 'packetInList' _SDM_ATT_MAP = { 'AuxiliaryId': 'auxiliaryId', 'Count': 'count', 'DescriptiveName': 'descriptiveName', 'FlowTable': 'flowTable', 'InPort': 'inPort', 'Name': 'name', 'PacketInName': 'packetInName', 'PhysicalInPort': 'physicalInPort', 'SendPacketIn': 'sendPacketIn', 'SwitchName': 'switchName', } _SDM_ENUM_MAP = { } def __init__(self, parent, list_op=False): super(PacketInList, self).__init__(parent, list_op) @property def AuxiliaryId(self): # type: () -> 'Multivalue' """ Returns ------- - obj(ixnetwork_restpy.multivalue.Multivalue): The identifier for auxiliary connections. """ from ixnetwork_restpy.multivalue import Multivalue return Multivalue(self, self._get_attribute(self._SDM_ATT_MAP['AuxiliaryId'])) @property def Count(self): # type: () -> int """ Returns ------- - number: Number of elements inside associated multiplier-scaled container object, e.g. number of devices inside a Device Group. """ return self._get_attribute(self._SDM_ATT_MAP['Count']) @property def DescriptiveName(self): # type: () -> str """ Returns ------- - str: Longer, more descriptive name for element. It's not guaranteed to be unique like -name-, but may offer more context. """ return self._get_attribute(self._SDM_ATT_MAP['DescriptiveName']) @property def FlowTable(self): # type: () -> 'Multivalue' """ Returns ------- - obj(ixnetwork_restpy.multivalue.Multivalue): If selected, the Switch looks up for each PacketIn configured in the Flow Table. """ from ixnetwork_restpy.multivalue import Multivalue return Multivalue(self, self._get_attribute(self._SDM_ATT_MAP['FlowTable'])) @property def InPort(self): # type: () -> 'Multivalue' """ Returns ------- - obj(ixnetwork_restpy.multivalue.Multivalue): The Switch Port on which, this Packet has come. """ from ixnetwork_restpy.multivalue import Multivalue return Multivalue(self, self._get_attribute(self._SDM_ATT_MAP['InPort'])) @property def Name(self): # type: () -> str """ Returns ------- - str: Name of NGPF element, guaranteed to be unique in Scenario """ return self._get_attribute(self._SDM_ATT_MAP['Name']) @Name.setter def Name(self, value): # type: (str) -> None self._set_attribute(self._SDM_ATT_MAP['Name'], value) @property def PacketInName(self): # type: () -> 'Multivalue' """ Returns ------- - obj(ixnetwork_restpy.multivalue.Multivalue): The description of the packet-in. """ from ixnetwork_restpy.multivalue import Multivalue return Multivalue(self, self._get_attribute(self._SDM_ATT_MAP['PacketInName'])) @property def PhysicalInPort(self): # type: () -> 'Multivalue' """ Returns ------- - obj(ixnetwork_restpy.multivalue.Multivalue): The physical In port value for this PacketIn range. It is the underlying physical port when packet is received on a logical port. """ from ixnetwork_restpy.multivalue import Multivalue return Multivalue(self, self._get_attribute(self._SDM_ATT_MAP['PhysicalInPort'])) @property def SendPacketIn(self): # type: () -> 'Multivalue' """ Returns ------- - obj(ixnetwork_restpy.multivalue.Multivalue): If selected, the Switch starts sending PacketIn messages when the session comes up. """ from ixnetwork_restpy.multivalue import Multivalue return Multivalue(self, self._get_attribute(self._SDM_ATT_MAP['SendPacketIn'])) @property def SwitchName(self): # type: () -> str """ Returns ------- - str: Parent Switch Name """ return self._get_attribute(self._SDM_ATT_MAP['SwitchName']) def update(self, Name=None): # type: (str) -> PacketInList """Updates packetInList resource on the server. This method has some named parameters with a type: obj (Multivalue). The Multivalue class has documentation that details the possible values for those named parameters. Args ---- - Name (str): Name of NGPF element, guaranteed to be unique in Scenario Raises ------ - ServerError: The server has encountered an uncategorized error condition """ return self._update(self._map_locals(self._SDM_ATT_MAP, locals())) def add(self, Name=None): # type: (str) -> PacketInList """Adds a new packetInList resource on the json, only valid with config assistant Args ---- - Name (str): Name of NGPF element, guaranteed to be unique in Scenario Returns ------- - self: This instance with all currently retrieved packetInList resources using find and the newly added packetInList resources available through an iterator or index Raises ------ - Exception: if this function is not being used with config assistance """ return self._add_xpath(self._map_locals(self._SDM_ATT_MAP, locals())) def find(self, Count=None, DescriptiveName=None, Name=None, SwitchName=None): # type: (int, str, str, str) -> PacketInList """Finds and retrieves packetInList resources from the server. All named parameters are evaluated on the server using regex. The named parameters can be used to selectively retrieve packetInList resources from the server. To retrieve an exact match ensure the parameter value starts with ^ and ends with $ By default the find method takes no parameters and will retrieve all packetInList resources from the server. Args ---- - Count (number): Number of elements inside associated multiplier-scaled container object, e.g. number of devices inside a Device Group. - DescriptiveName (str): Longer, more descriptive name for element. It's not guaranteed to be unique like -name-, but may offer more context. - Name (str): Name of NGPF element, guaranteed to be unique in Scenario - SwitchName (str): Parent Switch Name Returns ------- - self: This instance with matching packetInList resources retrieved from the server available through an iterator or index Raises ------ - ServerError: The server has encountered an uncategorized error condition """ return self._select(self._map_locals(self._SDM_ATT_MAP, locals())) def read(self, href): """Retrieves a single instance of packetInList data from the server. Args ---- - href (str): An href to the instance to be retrieved Returns ------- - self: This instance with the packetInList resources from the server available through an iterator or index Raises ------ - NotFoundError: The requested resource does not exist on the server - ServerError: The server has encountered an uncategorized error condition """ return self._read(href) def SendPause(self, *args, **kwargs): # type: (*Any, **Any) -> Union[List[str], None] """Executes the sendPause operation on the server. Pause Sending PacketIn sendPause(Arg2=list, async_operation=bool)list ---------------------------------------------- - Arg2 (list(number)): List of PacketIn. - async_operation (bool=False): True to execute the operation asynchronously. Any subsequent rest api calls made through the Connection class will block until the operation is complete. - Returns list(str): ID to associate each async action invocation Raises ------ - NotFoundError: The requested resource does not exist on the server - ServerError: The server has encountered an uncategorized error condition """ payload = { "Arg1": self.href } for i in range(len(args)): payload['Arg%s' % (i + 2)] = args[i] for item in kwargs.items(): payload[item[0]] = item[1] return self._execute('sendPause', payload=payload, response_object=None) def SendStart(self, *args, **kwargs): # type: (*Any, **Any) -> Union[List[str], None] """Executes the sendStart operation on the server. Start Sending PacketIn sendStart(Arg2=list, async_operation=bool)list ---------------------------------------------- - Arg2 (list(number)): List of PacketIn. - async_operation (bool=False): True to execute the operation asynchronously. Any subsequent rest api calls made through the Connection class will block until the operation is complete. - Returns list(str): ID to associate each async action invocation Raises ------ - NotFoundError: The requested resource does not exist on the server - ServerError: The server has encountered an uncategorized error condition """ payload = { "Arg1": self.href } for i in range(len(args)): payload['Arg%s' % (i + 2)] = args[i] for item in kwargs.items(): payload[item[0]] = item[1] return self._execute('sendStart', payload=payload, response_object=None) def SendStop(self, *args, **kwargs): # type: (*Any, **Any) -> Union[List[str], None] """Executes the sendStop operation on the server. Stop Sending PacketIn sendStop(Arg2=list, async_operation=bool)list --------------------------------------------- - Arg2 (list(number)): List of PacketIn. - async_operation (bool=False): True to execute the operation asynchronously. Any subsequent rest api calls made through the Connection class will block until the operation is complete. - Returns list(str): ID to associate each async action invocation Raises ------ - NotFoundError: The requested resource does not exist on the server - ServerError: The server has encountered an uncategorized error condition """ payload = { "Arg1": self.href } for i in range(len(args)): payload['Arg%s' % (i + 2)] = args[i] for item in kwargs.items(): payload[item[0]] = item[1] return self._execute('sendStop', payload=payload, response_object=None) def get_device_ids(self, PortNames=None, AuxiliaryId=None, FlowTable=None, InPort=None, PacketInName=None, PhysicalInPort=None, SendPacketIn=None): """Base class infrastructure that gets a list of packetInList device ids encapsulated by this object. Use the optional regex parameters in the method to refine the list of device ids encapsulated by this object. Args ---- - PortNames (str): optional regex of port names - AuxiliaryId (str): optional regex of auxiliaryId - FlowTable (str): optional regex of flowTable - InPort (str): optional regex of inPort - PacketInName (str): optional regex of packetInName - PhysicalInPort (str): optional regex of physicalInPort - SendPacketIn (str): optional regex of sendPacketIn Returns ------- - list(int): A list of device ids that meets the regex criteria provided in the method parameters Raises ------ - ServerError: The server has encountered an uncategorized error condition """ return self._get_ngpf_device_ids(locals())

40.468278

193

0.642255

12,161

0.907876

0

3,618

0.270101

0

9,597

0.716461

a173546fb4be8c1b52e29b792d62de5b919bbc8f

97

py

Python

Python/Phani.py

baroood/Hacktoberfest-2k17

87383df4bf705358866a5a4120dd678a3f2acd3e

[ "MIT" ]

28

2017-10-04T19:42:26.000Z

2021-03-26T04:00:48.000Z

Python/Phani.py

baroood/Hacktoberfest-2k17

87383df4bf705358866a5a4120dd678a3f2acd3e

[ "MIT" ]

375

2017-09-28T02:58:37.000Z

2019-10-31T09:10:38.000Z

Python/Phani.py

baroood/Hacktoberfest-2k17

87383df4bf705358866a5a4120dd678a3f2acd3e

[ "MIT" ]

519

2017-09-28T02:40:29.000Z

2021-02-15T08:29:17.000Z

a = input("Enter the first number") b = input("Enter the second number") print('the sum is',a+b)

24.25

36

0.680412

0

61

0.628866

a1735e027f0563b68478c5ef69b57c79d02303e9

1,108

py

Python

servicecatalog_factory/constants_test.py

micwha/aws-service-catalog-factory

c50a922d64e3d47fd56dbe261d841d81f872f0fb

[ "Apache-2.0" ]

null

servicecatalog_factory/constants_test.py

micwha/aws-service-catalog-factory

c50a922d64e3d47fd56dbe261d841d81f872f0fb

[ "Apache-2.0" ]

null

servicecatalog_factory/constants_test.py

micwha/aws-service-catalog-factory

c50a922d64e3d47fd56dbe261d841d81f872f0fb

[ "Apache-2.0" ]

null

# Copyright 2019 Amazon.com, Inc. or its affiliates. All Rights Reserved. # SPDX-License-Identifier: Apache-2.0 from pytest import fixture @fixture def sut(): from servicecatalog_factory import constants return constants def test_bootstrap_stack_name(sut): # setup expected_result = "servicecatalog-factory" # execute # verify assert sut.BOOTSTRAP_STACK_NAME == expected_result def test_service_catalog_factory_repo_name(sut): # setup expected_result = "ServiceCatalogFactory" # execute # verify assert sut.SERVICE_CATALOG_FACTORY_REPO_NAME == expected_result def test_non_recoverable_states(sut): # setup expected_result = [ "ROLLBACK_COMPLETE", "CREATE_IN_PROGRESS", "ROLLBACK_IN_PROGRESS", "DELETE_IN_PROGRESS", "UPDATE_IN_PROGRESS", "UPDATE_COMPLETE_CLEANUP_IN_PROGRESS", "UPDATE_ROLLBACK_IN_PROGRESS", "UPDATE_ROLLBACK_COMPLETE_CLEANUP_IN_PROGRESS", "REVIEW_IN_PROGRESS", ] # execute # verify assert sut.NON_RECOVERABLE_STATES == expected_result

22.612245

73

0.712094

0

90

0.081227

0

462

0.416968

a173f091bd6a84a9640f8e5bfa3ab824665803fb

1,038

py

Python

django/contrib/contenttypes/tests/models.py

benjaoming/django

6dbe979b4d9396e1b307c7d27388c97c13beb21c

[ "BSD-3-Clause" ]

2

2015-01-21T15:45:07.000Z

2015-02-21T02:38:13.000Z

env/lib/python2.7/site-packages/django/contrib/contenttypes/tests/models.py

luiscarlosgph/nas

e5acee61e8bbf12c34785fe971ce7df8dee775d4

[ "MIT" ]

10

2019-12-26T17:31:31.000Z

2022-03-21T22:17:33.000Z

env/lib/python2.7/site-packages/django/contrib/contenttypes/tests/models.py

luiscarlosgph/nas

e5acee61e8bbf12c34785fe971ce7df8dee775d4

[ "MIT" ]

1

2020-05-25T08:55:19.000Z

from django.db import models from django.utils.encoding import python_2_unicode_compatible from django.utils.http import urlquote class ConcreteModel(models.Model): name = models.CharField(max_length=10) class ProxyModel(ConcreteModel): class Meta: proxy = True @python_2_unicode_compatible class FooWithoutUrl(models.Model): """ Fake model not defining ``get_absolute_url`` for ContentTypesTests.test_shortcut_view_without_get_absolute_url() """ name = models.CharField(max_length=30, unique=True) def __str__(self): return self.name class FooWithUrl(FooWithoutUrl): """ Fake model defining ``get_absolute_url`` for ContentTypesTests.test_shortcut_view(). """ def get_absolute_url(self): return "/users/%s/" % urlquote(self.name) class FooWithBrokenAbsoluteUrl(FooWithoutUrl): """ Fake model defining a ``get_absolute_url`` method containing an error """ def get_absolute_url(self): return "/users/%s/" % self.unknown_field

23.590909

73

0.716763

864

0.83237

0

305

0.293834

0

345

0.33237

a174909b1f9a6d386413fccc83ffd4e52629d864

75,049

py

Python

tests/unit/utils/test_docker.py

springborland/salt

bee85e477d57e9a171884e54fefb9a59d0835ed0

[ "Apache-2.0" ]

1

2020-04-09T03:25:10.000Z

tests/unit/utils/test_docker.py

springborland/salt

bee85e477d57e9a171884e54fefb9a59d0835ed0

[ "Apache-2.0" ]

null

tests/unit/utils/test_docker.py

springborland/salt

bee85e477d57e9a171884e54fefb9a59d0835ed0

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- """ tests.unit.utils.test_docker ============================ Test the funcs in salt.utils.docker and salt.utils.docker.translate """ # Import Python Libs from __future__ import absolute_import, print_function, unicode_literals import copy import functools import logging import os # Import salt libs import salt.config import salt.loader import salt.utils.docker.translate.container import salt.utils.docker.translate.network import salt.utils.platform from salt.exceptions import CommandExecutionError # Import 3rd-party libs from salt.ext import six from salt.utils.docker.translate import helpers as translate_helpers # Import Salt Testing Libs from tests.support.unit import TestCase log = logging.getLogger(__name__) class Assert(object): def __init__(self, translator): self.translator = translator def __call__(self, func): self.func = func return functools.wraps(func)( # pylint: disable=unnecessary-lambda lambda testcase, *args, **kwargs: self.wrap(testcase, *args, **kwargs) # pylint: enable=unnecessary-lambda ) def wrap(self, *args, **kwargs): raise NotImplementedError def test_stringlist(self, testcase, name): alias = self.translator.ALIASES_REVMAP.get(name) # Using file paths here because "volumes" must be passed through this # set of assertions and it requires absolute paths. if salt.utils.platform.is_windows(): data = [r"c:\foo", r"c:\bar", r"c:\baz"] else: data = ["/foo", "/bar", "/baz"] for item in (name, alias): if item is None: continue testcase.assertEqual( salt.utils.docker.translate_input( self.translator, **{item: ",".join(data)} ), testcase.apply_defaults({name: data}), ) testcase.assertEqual( salt.utils.docker.translate_input(self.translator, **{item: data}), testcase.apply_defaults({name: data}), ) if name != "volumes": # Test coercing to string testcase.assertEqual( salt.utils.docker.translate_input( self.translator, **{item: ["one", 2]} ), testcase.apply_defaults({name: ["one", "2"]}), ) if alias is not None: # Test collision # sorted() used here because we want to confirm that we discard the # alias' value and go with the unsorted version. test_kwargs = {name: data, alias: sorted(data)} testcase.assertEqual( salt.utils.docker.translate_input( self.translator, ignore_collisions=True, **test_kwargs ), testcase.apply_defaults({name: test_kwargs[name]}), ) with testcase.assertRaisesRegex( CommandExecutionError, "is an alias for.+cannot both be used" ): salt.utils.docker.translate_input( self.translator, ignore_collisions=False, **test_kwargs ) def test_key_value(self, testcase, name, delimiter): """ Common logic for key/value pair testing. IP address validation is turned off here, and must be done separately in the wrapped function. """ alias = self.translator.ALIASES_REVMAP.get(name) expected = {"foo": "bar", "baz": "qux"} vals = "foo{0}bar,baz{0}qux".format(delimiter) for item in (name, alias): if item is None: continue for val in (vals, vals.split(",")): testcase.assertEqual( salt.utils.docker.translate_input( self.translator, validate_ip_addrs=False, **{item: val} ), testcase.apply_defaults({name: expected}), ) # Dictionary input testcase.assertEqual( salt.utils.docker.translate_input( self.translator, validate_ip_addrs=False, **{item: expected} ), testcase.apply_defaults({name: expected}), ) # "Dictlist" input from states testcase.assertEqual( salt.utils.docker.translate_input( self.translator, validate_ip_addrs=False, **{item: [{"foo": "bar"}, {"baz": "qux"}]} ), testcase.apply_defaults({name: expected}), ) if alias is not None: # Test collision test_kwargs = {name: vals, alias: "hello{0}world".format(delimiter)} testcase.assertEqual( salt.utils.docker.translate_input( self.translator, validate_ip_addrs=False, ignore_collisions=True, **test_kwargs ), testcase.apply_defaults({name: expected}), ) with testcase.assertRaisesRegex( CommandExecutionError, "is an alias for.+cannot both be used" ): salt.utils.docker.translate_input( self.translator, validate_ip_addrs=False, ignore_collisions=False, **test_kwargs ) class assert_bool(Assert): """ Test a boolean value """ def wrap(self, testcase, *args, **kwargs): # pylint: disable=arguments-differ # Strip off the "test_" from the function name name = self.func.__name__[5:] alias = self.translator.ALIASES_REVMAP.get(name) for item in (name, alias): if item is None: continue testcase.assertEqual( salt.utils.docker.translate_input(self.translator, **{item: True}), testcase.apply_defaults({name: True}), ) # These two are contrived examples, but they will test bool-ifying # a non-bool value to ensure proper input format. testcase.assertEqual( salt.utils.docker.translate_input(self.translator, **{item: "foo"}), testcase.apply_defaults({name: True}), ) testcase.assertEqual( salt.utils.docker.translate_input(self.translator, **{item: 0}), testcase.apply_defaults({name: False}), ) if alias is not None: # Test collision test_kwargs = {name: True, alias: False} testcase.assertEqual( salt.utils.docker.translate_input( self.translator, ignore_collisions=True, **test_kwargs ), testcase.apply_defaults({name: test_kwargs[name]}), ) with testcase.assertRaisesRegex( CommandExecutionError, "is an alias for.+cannot both be used" ): salt.utils.docker.translate_input( self.translator, ignore_collisions=False, **test_kwargs ) return self.func(testcase, *args, **kwargs) class assert_int(Assert): """ Test an integer value """ def wrap(self, testcase, *args, **kwargs): # pylint: disable=arguments-differ # Strip off the "test_" from the function name name = self.func.__name__[5:] alias = self.translator.ALIASES_REVMAP.get(name) for item in (name, alias): if item is None: continue for val in (100, "100"): testcase.assertEqual( salt.utils.docker.translate_input(self.translator, **{item: val}), testcase.apply_defaults({name: 100}), ) # Error case: non-numeric value passed with testcase.assertRaisesRegex( CommandExecutionError, "'foo' is not an integer" ): salt.utils.docker.translate_input(self.translator, **{item: "foo"}) if alias is not None: # Test collision test_kwargs = {name: 100, alias: 200} testcase.assertEqual( salt.utils.docker.translate_input( self.translator, ignore_collisions=True, **test_kwargs ), testcase.apply_defaults({name: test_kwargs[name]}), ) with testcase.assertRaisesRegex( CommandExecutionError, "is an alias for.+cannot both be used" ): salt.utils.docker.translate_input( self.translator, ignore_collisions=False, **test_kwargs ) return self.func(testcase, *args, **kwargs) class assert_string(Assert): """ Test that item is a string or is converted to one """ def wrap(self, testcase, *args, **kwargs): # pylint: disable=arguments-differ # Strip off the "test_" from the function name name = self.func.__name__[5:] alias = self.translator.ALIASES_REVMAP.get(name) # Using file paths here because "working_dir" must be passed through # this set of assertions and it requires absolute paths. if salt.utils.platform.is_windows(): data = r"c:\foo" else: data = "/foo" for item in (name, alias): if item is None: continue testcase.assertEqual( salt.utils.docker.translate_input(self.translator, **{item: data}), testcase.apply_defaults({name: data}), ) if name != "working_dir": # Test coercing to string testcase.assertEqual( salt.utils.docker.translate_input(self.translator, **{item: 123}), testcase.apply_defaults({name: "123"}), ) if alias is not None: # Test collision test_kwargs = {name: data, alias: data} testcase.assertEqual( salt.utils.docker.translate_input( self.translator, ignore_collisions=True, **test_kwargs ), testcase.apply_defaults({name: test_kwargs[name]}), ) with testcase.assertRaisesRegex( CommandExecutionError, "is an alias for.+cannot both be used" ): salt.utils.docker.translate_input( self.translator, ignore_collisions=False, **test_kwargs ) return self.func(testcase, *args, **kwargs) class assert_int_or_string(Assert): """ Test an integer or string value """ def wrap(self, testcase, *args, **kwargs): # pylint: disable=arguments-differ # Strip off the "test_" from the function name name = self.func.__name__[5:] alias = self.translator.ALIASES_REVMAP.get(name) for item in (name, alias): if item is None: continue testcase.assertEqual( salt.utils.docker.translate_input(self.translator, **{item: 100}), testcase.apply_defaults({name: 100}), ) testcase.assertEqual( salt.utils.docker.translate_input(self.translator, **{item: "100M"}), testcase.apply_defaults({name: "100M"}), ) if alias is not None: # Test collision test_kwargs = {name: 100, alias: "100M"} testcase.assertEqual( salt.utils.docker.translate_input( self.translator, ignore_collisions=True, **test_kwargs ), testcase.apply_defaults({name: test_kwargs[name]}), ) with testcase.assertRaisesRegex( CommandExecutionError, "is an alias for.+cannot both be used" ): salt.utils.docker.translate_input( self.translator, ignore_collisions=False, **test_kwargs ) return self.func(testcase, *args, **kwargs) class assert_stringlist(Assert): """ Test a comma-separated or Python list of strings """ def wrap(self, testcase, *args, **kwargs): # pylint: disable=arguments-differ # Strip off the "test_" from the function name name = self.func.__name__[5:] self.test_stringlist(testcase, name) return self.func(testcase, *args, **kwargs) class assert_dict(Assert): """ Dictionaries should be untouched, dictlists should be repacked and end up as a single dictionary. """ def wrap(self, testcase, *args, **kwargs): # pylint: disable=arguments-differ # Strip off the "test_" from the function name name = self.func.__name__[5:] alias = self.translator.ALIASES_REVMAP.get(name) expected = {"foo": "bar", "baz": "qux"} for item in (name, alias): if item is None: continue testcase.assertEqual( salt.utils.docker.translate_input(self.translator, **{item: expected}), testcase.apply_defaults({name: expected}), ) # "Dictlist" input from states testcase.assertEqual( salt.utils.docker.translate_input( self.translator, **{item: [{x: y} for x, y in six.iteritems(expected)]} ), testcase.apply_defaults({name: expected}), ) # Error case: non-dictionary input with testcase.assertRaisesRegex( CommandExecutionError, "'foo' is not a dictionary" ): salt.utils.docker.translate_input(self.translator, **{item: "foo"}) if alias is not None: # Test collision test_kwargs = {name: "foo", alias: "bar"} testcase.assertEqual( salt.utils.docker.translate_input( self.translator, ignore_collisions=True, **test_kwargs ), testcase.apply_defaults({name: test_kwargs[name]}), ) with testcase.assertRaisesRegex( CommandExecutionError, "is an alias for.+cannot both be used" ): salt.utils.docker.translate_input( self.translator, ignore_collisions=False, **test_kwargs ) return self.func(testcase, *args, **kwargs) class assert_cmd(Assert): """ Test for a string, or a comma-separated or Python list of strings. This is different from a stringlist in that we do not do any splitting. This decorator is used both by the "command" and "entrypoint" arguments. """ def wrap(self, testcase, *args, **kwargs): # pylint: disable=arguments-differ # Strip off the "test_" from the function name name = self.func.__name__[5:] alias = self.translator.ALIASES_REVMAP.get(name) for item in (name, alias): if item is None: continue testcase.assertEqual( salt.utils.docker.translate_input(self.translator, **{item: "foo bar"}), testcase.apply_defaults({name: "foo bar"}), ) testcase.assertEqual( salt.utils.docker.translate_input( self.translator, **{item: ["foo", "bar"]} ), testcase.apply_defaults({name: ["foo", "bar"]}), ) # Test coercing to string testcase.assertEqual( salt.utils.docker.translate_input(self.translator, **{item: 123}), testcase.apply_defaults({name: "123"}), ) testcase.assertEqual( salt.utils.docker.translate_input( self.translator, **{item: ["one", 2]} ), testcase.apply_defaults({name: ["one", "2"]}), ) if alias is not None: # Test collision test_kwargs = {name: "foo", alias: "bar"} testcase.assertEqual( salt.utils.docker.translate_input( self.translator, ignore_collisions=True, **test_kwargs ), testcase.apply_defaults({name: test_kwargs[name]}), ) with testcase.assertRaisesRegex( CommandExecutionError, "is an alias for.+cannot both be used" ): salt.utils.docker.translate_input( self.translator, ignore_collisions=False, **test_kwargs ) return self.func(testcase, *args, **kwargs) class assert_key_colon_value(Assert): """ Test a key/value pair with parameters passed as key:value pairs """ def wrap(self, testcase, *args, **kwargs): # pylint: disable=arguments-differ # Strip off the "test_" from the function name name = self.func.__name__[5:] self.test_key_value(testcase, name, ":") return self.func(testcase, *args, **kwargs) class assert_key_equals_value(Assert): """ Test a key/value pair with parameters passed as key=value pairs """ def wrap(self, testcase, *args, **kwargs): # pylint: disable=arguments-differ # Strip off the "test_" from the function name name = self.func.__name__[5:] self.test_key_value(testcase, name, "=") if name == "labels": self.test_stringlist(testcase, name) return self.func(testcase, *args, **kwargs) class assert_labels(Assert): def wrap(self, testcase, *args, **kwargs): # pylint: disable=arguments-differ # Strip off the "test_" from the function name name = self.func.__name__[5:] alias = self.translator.ALIASES_REVMAP.get(name) labels = ["foo", "bar=baz", {"hello": "world"}] expected = {"foo": "", "bar": "baz", "hello": "world"} for item in (name, alias): if item is None: continue testcase.assertEqual( salt.utils.docker.translate_input(self.translator, **{item: labels}), testcase.apply_defaults({name: expected}), ) # Error case: Passed a mutli-element dict in dictlist bad_labels = copy.deepcopy(labels) bad_labels[-1]["bad"] = "input" with testcase.assertRaisesRegex( CommandExecutionError, r"Invalid label$s$" ): salt.utils.docker.translate_input(self.translator, **{item: bad_labels}) return self.func(testcase, *args, **kwargs) class assert_device_rates(Assert): """ Tests for device_{read,write}_{bps,iops}. The bps values have a "Rate" value expressed in bytes/kb/mb/gb, while the iops values have a "Rate" expressed as a simple integer. """ def wrap(self, testcase, *args, **kwargs): # pylint: disable=arguments-differ # Strip off the "test_" from the function name name = self.func.__name__[5:] alias = self.translator.ALIASES_REVMAP.get(name) for item in (name, alias): if item is None: continue # Error case: Not an absolute path path = os.path.join("foo", "bar", "baz") with testcase.assertRaisesRegex( CommandExecutionError, "Path '{0}' is not absolute".format(path.replace("\\", "\\\\")), ): salt.utils.docker.translate_input( self.translator, **{item: "{0}:1048576".format(path)} ) if name.endswith("_bps"): # Both integer bytes and a string providing a shorthand for kb, # mb, or gb can be used, so we need to test for both. expected = ({}, []) vals = "/dev/sda:1048576,/dev/sdb:1048576" for val in (vals, vals.split(",")): testcase.assertEqual( salt.utils.docker.translate_input( self.translator, **{item: val} ), testcase.apply_defaults( { name: [ {"Path": "/dev/sda", "Rate": 1048576}, {"Path": "/dev/sdb", "Rate": 1048576}, ] } ), ) vals = "/dev/sda:1mb,/dev/sdb:5mb" for val in (vals, vals.split(",")): testcase.assertEqual( salt.utils.docker.translate_input( self.translator, **{item: val} ), testcase.apply_defaults( { name: [ {"Path": "/dev/sda", "Rate": "1mb"}, {"Path": "/dev/sdb", "Rate": "5mb"}, ] } ), ) if alias is not None: # Test collision test_kwargs = { name: "/dev/sda:1048576,/dev/sdb:1048576", alias: "/dev/sda:1mb,/dev/sdb:5mb", } testcase.assertEqual( salt.utils.docker.translate_input( self.translator, ignore_collisions=True, **test_kwargs ), testcase.apply_defaults( { name: [ {"Path": "/dev/sda", "Rate": 1048576}, {"Path": "/dev/sdb", "Rate": 1048576}, ] } ), ) with testcase.assertRaisesRegex( CommandExecutionError, "is an alias for.+cannot both be used" ): salt.utils.docker.translate_input( self.translator, ignore_collisions=False, **test_kwargs ) else: # The "Rate" value must be an integer vals = "/dev/sda:1000,/dev/sdb:500" for val in (vals, vals.split(",")): testcase.assertEqual( salt.utils.docker.translate_input( self.translator, **{item: val} ), testcase.apply_defaults( { name: [ {"Path": "/dev/sda", "Rate": 1000}, {"Path": "/dev/sdb", "Rate": 500}, ] } ), ) # Test non-integer input expected = ( {}, {item: "Rate '5mb' for path '/dev/sdb' is non-numeric"}, [], ) vals = "/dev/sda:1000,/dev/sdb:5mb" for val in (vals, vals.split(",")): with testcase.assertRaisesRegex( CommandExecutionError, "Rate '5mb' for path '/dev/sdb' is non-numeric", ): salt.utils.docker.translate_input( self.translator, **{item: val} ) if alias is not None: # Test collision test_kwargs = { name: "/dev/sda:1000,/dev/sdb:500", alias: "/dev/sda:888,/dev/sdb:999", } testcase.assertEqual( salt.utils.docker.translate_input( self.translator, ignore_collisions=True, **test_kwargs ), testcase.apply_defaults( { name: [ {"Path": "/dev/sda", "Rate": 1000}, {"Path": "/dev/sdb", "Rate": 500}, ] } ), ) with testcase.assertRaisesRegex( CommandExecutionError, "is an alias for.+cannot both be used" ): salt.utils.docker.translate_input( self.translator, ignore_collisions=False, **test_kwargs ) return self.func(testcase, *args, **kwargs) class assert_subnet(Assert): """ Test an IPv4 or IPv6 subnet """ def wrap(self, testcase, *args, **kwargs): # pylint: disable=arguments-differ # Strip off the "test_" from the function name name = self.func.__name__[5:] alias = self.translator.ALIASES_REVMAP.get(name) for item in (name, alias): if item is None: continue for val in ("127.0.0.1/32", "::1/128"): log.debug("Verifying '%s' is a valid subnet", val) testcase.assertEqual( salt.utils.docker.translate_input( self.translator, validate_ip_addrs=True, **{item: val} ), testcase.apply_defaults({name: val}), ) # Error case: invalid subnet caught by validation for val in ( "127.0.0.1", "999.999.999.999/24", "10.0.0.0/33", "::1", "feaz::1/128", "::1/129", ): log.debug("Verifying '%s' is not a valid subnet", val) with testcase.assertRaisesRegex( CommandExecutionError, "'{0}' is not a valid subnet".format(val) ): salt.utils.docker.translate_input( self.translator, validate_ip_addrs=True, **{item: val} ) # This is not valid input but it will test whether or not subnet # validation happened val = "foo" testcase.assertEqual( salt.utils.docker.translate_input( self.translator, validate_ip_addrs=False, **{item: val} ), testcase.apply_defaults({name: val}), ) if alias is not None: # Test collision test_kwargs = {name: "10.0.0.0/24", alias: "192.168.50.128/25"} testcase.assertEqual( salt.utils.docker.translate_input( self.translator, ignore_collisions=True, **test_kwargs ), testcase.apply_defaults({name: test_kwargs[name]}), ) with testcase.assertRaisesRegex( CommandExecutionError, "is an alias for.+cannot both be used" ): salt.utils.docker.translate_input( self.translator, ignore_collisions=False, **test_kwargs ) return self.func(testcase, *args, **kwargs) class TranslateBase(TestCase): maxDiff = None translator = None # Must be overridden in the subclass def apply_defaults(self, ret, skip_translate=None): if skip_translate is not True: defaults = getattr(self.translator, "DEFAULTS", {}) for key, val in six.iteritems(defaults): if key not in ret: ret[key] = val return ret @staticmethod def normalize_ports(ret): """ When we translate exposed ports, we can end up with a mixture of ints (representing TCP ports) and tuples (representing UDP ports). Python 2 will sort an iterable containing these mixed types, but Python 3 will not. This helper is used to munge the ports in the return data so that the resulting list is sorted in a way that can reliably be compared to the expected results in the test. This helper should only be needed for port_bindings and ports. """ if "ports" in ret[0]: tcp_ports = [] udp_ports = [] for item in ret[0]["ports"]: if isinstance(item, six.integer_types): tcp_ports.append(item) else: udp_ports.append(item) ret[0]["ports"] = sorted(tcp_ports) + sorted(udp_ports) return ret def tearDown(self): """ Test skip_translate kwarg """ name = self.id().split(".")[-1][5:] # The below is not valid input for the Docker API, but these # assertions confirm that we successfully skipped translation. for val in (True, name, [name]): self.assertEqual( salt.utils.docker.translate_input( self.translator, skip_translate=val, **{name: "foo"} ), self.apply_defaults({name: "foo"}, skip_translate=val), ) class TranslateContainerInputTestCase(TranslateBase): """ Tests for salt.utils.docker.translate_input(), invoked using salt.utils.docker.translate.container as the translator module. """ translator = salt.utils.docker.translate.container @staticmethod def normalize_ports(ret): """ When we translate exposed ports, we can end up with a mixture of ints (representing TCP ports) and tuples (representing UDP ports). Python 2 will sort an iterable containing these mixed types, but Python 3 will not. This helper is used to munge the ports in the return data so that the resulting list is sorted in a way that can reliably be compared to the expected results in the test. This helper should only be needed for port_bindings and ports. """ if "ports" in ret: tcp_ports = [] udp_ports = [] for item in ret["ports"]: if isinstance(item, six.integer_types): tcp_ports.append(item) else: udp_ports.append(item) ret["ports"] = sorted(tcp_ports) + sorted(udp_ports) return ret @assert_bool(salt.utils.docker.translate.container) def test_auto_remove(self): """ Should be a bool or converted to one """ def test_binds(self): """ Test the "binds" kwarg. Any volumes not defined in the "volumes" kwarg should be added to the results. """ self.assertEqual( salt.utils.docker.translate_input( self.translator, binds="/srv/www:/var/www:ro", volumes="/testing" ), {"binds": ["/srv/www:/var/www:ro"], "volumes": ["/testing", "/var/www"]}, ) self.assertEqual( salt.utils.docker.translate_input( self.translator, binds=["/srv/www:/var/www:ro"], volumes="/testing" ), {"binds": ["/srv/www:/var/www:ro"], "volumes": ["/testing", "/var/www"]}, ) self.assertEqual( salt.utils.docker.translate_input( self.translator, binds={"/srv/www": {"bind": "/var/www", "mode": "ro"}}, volumes="/testing", ), { "binds": {"/srv/www": {"bind": "/var/www", "mode": "ro"}}, "volumes": ["/testing", "/var/www"], }, ) @assert_int(salt.utils.docker.translate.container) def test_blkio_weight(self): """ Should be an int or converted to one """ def test_blkio_weight_device(self): """ Should translate a list of PATH:WEIGHT pairs to a list of dictionaries with the following format: {'Path': PATH, 'Weight': WEIGHT} """ for val in ("/dev/sda:100,/dev/sdb:200", ["/dev/sda:100", "/dev/sdb:200"]): self.assertEqual( salt.utils.docker.translate_input( self.translator, blkio_weight_device="/dev/sda:100,/dev/sdb:200" ), { "blkio_weight_device": [ {"Path": "/dev/sda", "Weight": 100}, {"Path": "/dev/sdb", "Weight": 200}, ] }, ) # Error cases with self.assertRaisesRegex( CommandExecutionError, r"'foo' contains 1 value$s$ $expected 2$" ): salt.utils.docker.translate_input( self.translator, blkio_weight_device="foo" ) with self.assertRaisesRegex( CommandExecutionError, r"'foo:bar:baz' contains 3 value$s$ $expected 2$" ): salt.utils.docker.translate_input( self.translator, blkio_weight_device="foo:bar:baz" ) with self.assertRaisesRegex( CommandExecutionError, r"Weight 'foo' for path '/dev/sdb' is not an integer" ): salt.utils.docker.translate_input( self.translator, blkio_weight_device=["/dev/sda:100", "/dev/sdb:foo"] ) @assert_stringlist(salt.utils.docker.translate.container) def test_cap_add(self): """ Should be a list of strings or converted to one """ @assert_stringlist(salt.utils.docker.translate.container) def test_cap_drop(self): """ Should be a list of strings or converted to one """ @assert_cmd(salt.utils.docker.translate.container) def test_command(self): """ Can either be a string or a comma-separated or Python list of strings. """ @assert_string(salt.utils.docker.translate.container) def test_cpuset_cpus(self): """ Should be a string or converted to one """ @assert_string(salt.utils.docker.translate.container) def test_cpuset_mems(self): """ Should be a string or converted to one """ @assert_int(salt.utils.docker.translate.container) def test_cpu_group(self): """ Should be an int or converted to one """ @assert_int(salt.utils.docker.translate.container) def test_cpu_period(self): """ Should be an int or converted to one """ @assert_int(salt.utils.docker.translate.container) def test_cpu_shares(self): """ Should be an int or converted to one """ @assert_bool(salt.utils.docker.translate.container) def test_detach(self): """ Should be a bool or converted to one """ @assert_device_rates(salt.utils.docker.translate.container) def test_device_read_bps(self): """ CLI input is a list of PATH:RATE pairs, but the API expects a list of dictionaries in the format [{'Path': path, 'Rate': rate}] """ @assert_device_rates(salt.utils.docker.translate.container) def test_device_read_iops(self): """ CLI input is a list of PATH:RATE pairs, but the API expects a list of dictionaries in the format [{'Path': path, 'Rate': rate}] """ @assert_device_rates(salt.utils.docker.translate.container) def test_device_write_bps(self): """ CLI input is a list of PATH:RATE pairs, but the API expects a list of dictionaries in the format [{'Path': path, 'Rate': rate}] """ @assert_device_rates(salt.utils.docker.translate.container) def test_device_write_iops(self): """ CLI input is a list of PATH:RATE pairs, but the API expects a list of dictionaries in the format [{'Path': path, 'Rate': rate}] """ @assert_stringlist(salt.utils.docker.translate.container) def test_devices(self): """ Should be a list of strings or converted to one """ @assert_stringlist(salt.utils.docker.translate.container) def test_dns_opt(self): """ Should be a list of strings or converted to one """ @assert_stringlist(salt.utils.docker.translate.container) def test_dns_search(self): """ Should be a list of strings or converted to one """ def test_dns(self): """ While this is a stringlist, it also supports IP address validation, so it can't use the test_stringlist decorator because we need to test both with and without validation, and it isn't necessary to make all other stringlist tests also do that same kind of testing. """ for val in ("8.8.8.8,8.8.4.4", ["8.8.8.8", "8.8.4.4"]): self.assertEqual( salt.utils.docker.translate_input( self.translator, dns=val, validate_ip_addrs=True, ), {"dns": ["8.8.8.8", "8.8.4.4"]}, ) # Error case: invalid IP address caught by validation for val in ("8.8.8.888,8.8.4.4", ["8.8.8.888", "8.8.4.4"]): with self.assertRaisesRegex( CommandExecutionError, r"'8.8.8.888' is not a valid IP address" ): salt.utils.docker.translate_input( self.translator, dns=val, validate_ip_addrs=True, ) # This is not valid input but it will test whether or not IP address # validation happened. for val in ("foo,bar", ["foo", "bar"]): self.assertEqual( salt.utils.docker.translate_input( self.translator, dns=val, validate_ip_addrs=False, ), {"dns": ["foo", "bar"]}, ) @assert_string(salt.utils.docker.translate.container) def test_domainname(self): """ Should be a list of strings or converted to one """ @assert_cmd(salt.utils.docker.translate.container) def test_entrypoint(self): """ Can either be a string or a comma-separated or Python list of strings. """ @assert_key_equals_value(salt.utils.docker.translate.container) def test_environment(self): """ Can be passed in several formats but must end up as a dictionary mapping keys to values """ def test_extra_hosts(self): """ Can be passed as a list of key:value pairs but can't be simply tested using @assert_key_colon_value since we need to test both with and without IP address validation. """ for val in ("web1:10.9.8.7,web2:10.9.8.8", ["web1:10.9.8.7", "web2:10.9.8.8"]): self.assertEqual( salt.utils.docker.translate_input( self.translator, extra_hosts=val, validate_ip_addrs=True, ), {"extra_hosts": {"web1": "10.9.8.7", "web2": "10.9.8.8"}}, ) # Error case: invalid IP address caught by validation for val in ( "web1:10.9.8.299,web2:10.9.8.8", ["web1:10.9.8.299", "web2:10.9.8.8"], ): with self.assertRaisesRegex( CommandExecutionError, r"'10.9.8.299' is not a valid IP address" ): salt.utils.docker.translate_input( self.translator, extra_hosts=val, validate_ip_addrs=True, ) # This is not valid input but it will test whether or not IP address # validation happened. for val in ("foo:bar,baz:qux", ["foo:bar", "baz:qux"]): self.assertEqual( salt.utils.docker.translate_input( self.translator, extra_hosts=val, validate_ip_addrs=False, ), {"extra_hosts": {"foo": "bar", "baz": "qux"}}, ) @assert_stringlist(salt.utils.docker.translate.container) def test_group_add(self): """ Should be a list of strings or converted to one """ @assert_string(salt.utils.docker.translate.container) def test_hostname(self): """ Should be a string or converted to one """ @assert_string(salt.utils.docker.translate.container) def test_ipc_mode(self): """ Should be a string or converted to one """ @assert_string(salt.utils.docker.translate.container) def test_isolation(self): """ Should be a string or converted to one """ @assert_labels(salt.utils.docker.translate.container) def test_labels(self): """ Can be passed as a list of key=value pairs or a dictionary, and must ultimately end up as a dictionary. """ @assert_key_colon_value(salt.utils.docker.translate.container) def test_links(self): """ Can be passed as a list of key:value pairs or a dictionary, and must ultimately end up as a dictionary. """ def test_log_config(self): """ This is a mixture of log_driver and log_opt, which get combined into a dictionary. log_driver is a simple string, but log_opt can be passed in several ways, so we need to test them all. """ expected = ( {"log_config": {"Type": "foo", "Config": {"foo": "bar", "baz": "qux"}}}, {}, [], ) for val in ( "foo=bar,baz=qux", ["foo=bar", "baz=qux"], [{"foo": "bar"}, {"baz": "qux"}], {"foo": "bar", "baz": "qux"}, ): self.assertEqual( salt.utils.docker.translate_input( self.translator, log_driver="foo", log_opt="foo=bar,baz=qux" ), {"log_config": {"Type": "foo", "Config": {"foo": "bar", "baz": "qux"}}}, ) # Ensure passing either `log_driver` or `log_opt` alone works self.assertEqual( salt.utils.docker.translate_input(self.translator, log_driver="foo"), {"log_config": {"Type": "foo", "Config": {}}}, ) self.assertEqual( salt.utils.docker.translate_input( self.translator, log_opt={"foo": "bar", "baz": "qux"} ), {"log_config": {"Type": "none", "Config": {"foo": "bar", "baz": "qux"}}}, ) @assert_key_equals_value(salt.utils.docker.translate.container) def test_lxc_conf(self): """ Can be passed as a list of key=value pairs or a dictionary, and must ultimately end up as a dictionary. """ @assert_string(salt.utils.docker.translate.container) def test_mac_address(self): """ Should be a string or converted to one """ @assert_int_or_string(salt.utils.docker.translate.container) def test_mem_limit(self): """ Should be a string or converted to one """ @assert_int(salt.utils.docker.translate.container) def test_mem_swappiness(self): """ Should be an int or converted to one """ @assert_int_or_string(salt.utils.docker.translate.container) def test_memswap_limit(self): """ Should be a string or converted to one """ @assert_string(salt.utils.docker.translate.container) def test_name(self): """ Should be a string or converted to one """ @assert_bool(salt.utils.docker.translate.container) def test_network_disabled(self): """ Should be a bool or converted to one """ @assert_string(salt.utils.docker.translate.container) def test_network_mode(self): """ Should be a string or converted to one """ @assert_bool(salt.utils.docker.translate.container) def test_oom_kill_disable(self): """ Should be a bool or converted to one """ @assert_int(salt.utils.docker.translate.container) def test_oom_score_adj(self): """ Should be an int or converted to one """ @assert_string(salt.utils.docker.translate.container) def test_pid_mode(self): """ Should be a string or converted to one """ @assert_int(salt.utils.docker.translate.container) def test_pids_limit(self): """ Should be an int or converted to one """ def test_port_bindings(self): """ This has several potential formats and can include port ranges. It needs its own test. """ # ip:hostPort:containerPort - Bind a specific IP and port on the host # to a specific port within the container. bindings = ( "10.1.2.3:8080:80,10.1.2.3:8888:80,10.4.5.6:3333:3333," "10.7.8.9:14505-14506:4505-4506,10.1.2.3:8080:81/udp," "10.1.2.3:8888:81/udp,10.4.5.6:3334:3334/udp," "10.7.8.9:15505-15506:5505-5506/udp" ) for val in (bindings, bindings.split(",")): self.assertEqual( self.normalize_ports( salt.utils.docker.translate_input( self.translator, port_bindings=val, ) ), { "port_bindings": { 80: [("10.1.2.3", 8080), ("10.1.2.3", 8888)], 3333: ("10.4.5.6", 3333), 4505: ("10.7.8.9", 14505), 4506: ("10.7.8.9", 14506), "81/udp": [("10.1.2.3", 8080), ("10.1.2.3", 8888)], "3334/udp": ("10.4.5.6", 3334), "5505/udp": ("10.7.8.9", 15505), "5506/udp": ("10.7.8.9", 15506), }, "ports": [ 80, 3333, 4505, 4506, (81, "udp"), (3334, "udp"), (5505, "udp"), (5506, "udp"), ], }, ) # ip::containerPort - Bind a specific IP and an ephemeral port to a # specific port within the container. bindings = ( "10.1.2.3::80,10.1.2.3::80,10.4.5.6::3333,10.7.8.9::4505-4506," "10.1.2.3::81/udp,10.1.2.3::81/udp,10.4.5.6::3334/udp," "10.7.8.9::5505-5506/udp" ) for val in (bindings, bindings.split(",")): self.assertEqual( self.normalize_ports( salt.utils.docker.translate_input( self.translator, port_bindings=val, ) ), { "port_bindings": { 80: [("10.1.2.3",), ("10.1.2.3",)], 3333: ("10.4.5.6",), 4505: ("10.7.8.9",), 4506: ("10.7.8.9",), "81/udp": [("10.1.2.3",), ("10.1.2.3",)], "3334/udp": ("10.4.5.6",), "5505/udp": ("10.7.8.9",), "5506/udp": ("10.7.8.9",), }, "ports": [ 80, 3333, 4505, 4506, (81, "udp"), (3334, "udp"), (5505, "udp"), (5506, "udp"), ], }, ) # hostPort:containerPort - Bind a specific port on all of the host's # interfaces to a specific port within the container. bindings = ( "8080:80,8888:80,3333:3333,14505-14506:4505-4506,8080:81/udp," "8888:81/udp,3334:3334/udp,15505-15506:5505-5506/udp" ) for val in (bindings, bindings.split(",")): self.assertEqual( self.normalize_ports( salt.utils.docker.translate_input( self.translator, port_bindings=val, ) ), { "port_bindings": { 80: [8080, 8888], 3333: 3333, 4505: 14505, 4506: 14506, "81/udp": [8080, 8888], "3334/udp": 3334, "5505/udp": 15505, "5506/udp": 15506, }, "ports": [ 80, 3333, 4505, 4506, (81, "udp"), (3334, "udp"), (5505, "udp"), (5506, "udp"), ], }, ) # containerPort - Bind an ephemeral port on all of the host's # interfaces to a specific port within the container. bindings = "80,3333,4505-4506,81/udp,3334/udp,5505-5506/udp" for val in (bindings, bindings.split(",")): self.assertEqual( self.normalize_ports( salt.utils.docker.translate_input( self.translator, port_bindings=val, ) ), { "port_bindings": { 80: None, 3333: None, 4505: None, 4506: None, "81/udp": None, "3334/udp": None, "5505/udp": None, "5506/udp": None, }, "ports": [ 80, 3333, 4505, 4506, (81, "udp"), (3334, "udp"), (5505, "udp"), (5506, "udp"), ], }, ) # Test a mixture of different types of input bindings = ( "10.1.2.3:8080:80,10.4.5.6::3333,14505-14506:4505-4506," "9999-10001,10.1.2.3:8080:81/udp,10.4.5.6::3334/udp," "15505-15506:5505-5506/udp,19999-20001/udp" ) for val in (bindings, bindings.split(",")): self.assertEqual( self.normalize_ports( salt.utils.docker.translate_input( self.translator, port_bindings=val, ) ), { "port_bindings": { 80: ("10.1.2.3", 8080), 3333: ("10.4.5.6",), 4505: 14505, 4506: 14506, 9999: None, 10000: None, 10001: None, "81/udp": ("10.1.2.3", 8080), "3334/udp": ("10.4.5.6",), "5505/udp": 15505, "5506/udp": 15506, "19999/udp": None, "20000/udp": None, "20001/udp": None, }, "ports": [ 80, 3333, 4505, 4506, 9999, 10000, 10001, (81, "udp"), (3334, "udp"), (5505, "udp"), (5506, "udp"), (19999, "udp"), (20000, "udp"), (20001, "udp"), ], }, ) # Error case: too many items (max 3) with self.assertRaisesRegex( CommandExecutionError, r"'10.1.2.3:8080:80:123' is an invalid port binding " r"definition $at most 3 components are allowed, found 4$", ): salt.utils.docker.translate_input( self.translator, port_bindings="10.1.2.3:8080:80:123" ) # Error case: port range start is greater than end for val in ( "10.1.2.3:5555-5554:1111-1112", "10.1.2.3:1111-1112:5555-5554", "10.1.2.3::5555-5554", "5555-5554:1111-1112", "1111-1112:5555-5554", "5555-5554", ): with self.assertRaisesRegex( CommandExecutionError, r"Start of port range $5555$ cannot be greater than end " r"of port range $5554$", ): salt.utils.docker.translate_input( self.translator, port_bindings=val, ) # Error case: non-numeric port range for val in ( "10.1.2.3:foo:1111-1112", "10.1.2.3:1111-1112:foo", "10.1.2.3::foo", "foo:1111-1112", "1111-1112:foo", "foo", ): with self.assertRaisesRegex( CommandExecutionError, "'foo' is non-numeric or an invalid port range" ): salt.utils.docker.translate_input( self.translator, port_bindings=val, ) # Error case: misatched port range for val in ("10.1.2.3:1111-1113:1111-1112", "1111-1113:1111-1112"): with self.assertRaisesRegex( CommandExecutionError, r"Host port range $1111-1113$ does not have the same " r"number of ports as the container port range $1111-1112$", ): salt.utils.docker.translate_input(self.translator, port_bindings=val) for val in ("10.1.2.3:1111-1112:1111-1113", "1111-1112:1111-1113"): with self.assertRaisesRegex( CommandExecutionError, r"Host port range $1111-1112$ does not have the same " r"number of ports as the container port range $1111-1113$", ): salt.utils.docker.translate_input( self.translator, port_bindings=val, ) # Error case: empty host port or container port with self.assertRaisesRegex( CommandExecutionError, "Empty host port in port binding definition ':1111'" ): salt.utils.docker.translate_input(self.translator, port_bindings=":1111") with self.assertRaisesRegex( CommandExecutionError, "Empty container port in port binding definition '1111:'", ): salt.utils.docker.translate_input(self.translator, port_bindings="1111:") with self.assertRaisesRegex( CommandExecutionError, "Empty port binding definition found" ): salt.utils.docker.translate_input(self.translator, port_bindings="") def test_ports(self): """ Ports can be passed as a comma-separated or Python list of port numbers, with '/tcp' being optional for TCP ports. They must ultimately be a list of port definitions, in which an integer denotes a TCP port, and a tuple in the format (port_num, 'udp') denotes a UDP port. Also, the port numbers must end up as integers. None of the decorators will suffice so this one must be tested specially. """ for val in ( "1111,2222/tcp,3333/udp,4505-4506", [1111, "2222/tcp", "3333/udp", "4505-4506"], ["1111", "2222/tcp", "3333/udp", "4505-4506"], ): self.assertEqual( self.normalize_ports( salt.utils.docker.translate_input(self.translator, ports=val,) ), {"ports": [1111, 2222, 4505, 4506, (3333, "udp")]}, ) # Error case: non-integer and non/string value for val in (1.0, [1.0]): with self.assertRaisesRegex( CommandExecutionError, "'1.0' is not a valid port definition" ): salt.utils.docker.translate_input( self.translator, ports=val, ) # Error case: port range start is greater than end with self.assertRaisesRegex( CommandExecutionError, r"Start of port range $5555$ cannot be greater than end of " r"port range $5554$", ): salt.utils.docker.translate_input( self.translator, ports="5555-5554", ) @assert_bool(salt.utils.docker.translate.container) def test_privileged(self): """ Should be a bool or converted to one """ @assert_bool(salt.utils.docker.translate.container) def test_publish_all_ports(self): """ Should be a bool or converted to one """ @assert_bool(salt.utils.docker.translate.container) def test_read_only(self): """ Should be a bool or converted to one """ def test_restart_policy(self): """ Input is in the format "name[:retry_count]", but the API wants it in the format {'Name': name, 'MaximumRetryCount': retry_count} """ name = "restart_policy" alias = "restart" for item in (name, alias): # Test with retry count self.assertEqual( salt.utils.docker.translate_input( self.translator, **{item: "on-failure:5"} ), {name: {"Name": "on-failure", "MaximumRetryCount": 5}}, ) # Test without retry count self.assertEqual( salt.utils.docker.translate_input( self.translator, **{item: "on-failure"} ), {name: {"Name": "on-failure", "MaximumRetryCount": 0}}, ) # Error case: more than one policy passed with self.assertRaisesRegex( CommandExecutionError, "Only one policy is permitted" ): salt.utils.docker.translate_input( self.translator, **{item: "on-failure,always"} ) # Test collision test_kwargs = {name: "on-failure:5", alias: "always"} self.assertEqual( salt.utils.docker.translate_input( self.translator, ignore_collisions=True, **test_kwargs ), {name: {"Name": "on-failure", "MaximumRetryCount": 5}}, ) with self.assertRaisesRegex( CommandExecutionError, "'restart' is an alias for 'restart_policy'" ): salt.utils.docker.translate_input( self.translator, ignore_collisions=False, **test_kwargs ) @assert_stringlist(salt.utils.docker.translate.container) def test_security_opt(self): """ Should be a list of strings or converted to one """ @assert_int_or_string(salt.utils.docker.translate.container) def test_shm_size(self): """ Should be a string or converted to one """ @assert_bool(salt.utils.docker.translate.container) def test_stdin_open(self): """ Should be a bool or converted to one """ @assert_string(salt.utils.docker.translate.container) def test_stop_signal(self): """ Should be a string or converted to one """ @assert_int(salt.utils.docker.translate.container) def test_stop_timeout(self): """ Should be an int or converted to one """ @assert_key_equals_value(salt.utils.docker.translate.container) def test_storage_opt(self): """ Can be passed in several formats but must end up as a dictionary mapping keys to values """ @assert_key_equals_value(salt.utils.docker.translate.container) def test_sysctls(self): """ Can be passed in several formats but must end up as a dictionary mapping keys to values """ @assert_dict(salt.utils.docker.translate.container) def test_tmpfs(self): """ Can be passed in several formats but must end up as a dictionary mapping keys to values """ @assert_bool(salt.utils.docker.translate.container) def test_tty(self): """ Should be a bool or converted to one """ def test_ulimits(self): """ Input is in the format "name=soft_limit[:hard_limit]", but the API wants it in the format {'Name': name, 'Soft': soft_limit, 'Hard': hard_limit} """ # Test with and without hard limit ulimits = "nofile=1024:2048,nproc=50" for val in (ulimits, ulimits.split(",")): self.assertEqual( salt.utils.docker.translate_input(self.translator, ulimits=val,), { "ulimits": [ {"Name": "nofile", "Soft": 1024, "Hard": 2048}, {"Name": "nproc", "Soft": 50, "Hard": 50}, ] }, ) # Error case: Invalid format with self.assertRaisesRegex( CommandExecutionError, r"Ulimit definition 'nofile:1024:2048' is not in the format " r"type=soft_limit\[:hard_limit\]", ): salt.utils.docker.translate_input( self.translator, ulimits="nofile:1024:2048" ) # Error case: Invalid format with self.assertRaisesRegex( CommandExecutionError, r"Limit 'nofile=foo:2048' contains non-numeric value$s$", ): salt.utils.docker.translate_input( self.translator, ulimits="nofile=foo:2048" ) def test_user(self): """ Must be either username (string) or uid (int). An int passed as a string (e.g. '0') should be converted to an int. """ # Username passed as string self.assertEqual( salt.utils.docker.translate_input(self.translator, user="foo"), {"user": "foo"}, ) for val in (0, "0"): self.assertEqual( salt.utils.docker.translate_input(self.translator, user=val), {"user": 0}, ) # Error case: non string/int passed with self.assertRaisesRegex( CommandExecutionError, "Value must be a username or uid" ): salt.utils.docker.translate_input(self.translator, user=["foo"]) # Error case: negative int passed with self.assertRaisesRegex(CommandExecutionError, "'-1' is an invalid uid"): salt.utils.docker.translate_input(self.translator, user=-1) @assert_string(salt.utils.docker.translate.container) def test_userns_mode(self): """ Should be a bool or converted to one """ @assert_string(salt.utils.docker.translate.container) def test_volume_driver(self): """ Should be a bool or converted to one """ @assert_stringlist(salt.utils.docker.translate.container) def test_volumes(self): """ Should be a list of absolute paths """ # Error case: Not an absolute path path = os.path.join("foo", "bar", "baz") with self.assertRaisesRegex( CommandExecutionError, "'{0}' is not an absolute path".format(path.replace("\\", "\\\\")), ): salt.utils.docker.translate_input(self.translator, volumes=path) @assert_stringlist(salt.utils.docker.translate.container) def test_volumes_from(self): """ Should be a list of strings or converted to one """ @assert_string(salt.utils.docker.translate.container) def test_working_dir(self): """ Should be a single absolute path """ # Error case: Not an absolute path path = os.path.join("foo", "bar", "baz") with self.assertRaisesRegex( CommandExecutionError, "'{0}' is not an absolute path".format(path.replace("\\", "\\\\")), ): salt.utils.docker.translate_input(self.translator, working_dir=path) class TranslateNetworkInputTestCase(TranslateBase): """ Tests for salt.utils.docker.translate_input(), invoked using salt.utils.docker.translate.network as the translator module. """ translator = salt.utils.docker.translate.network ip_addrs = { True: ("10.1.2.3", "::1"), False: ("FOO", "0.9.800.1000", "feaz::1", "aj01::feac"), } @assert_string(salt.utils.docker.translate.network) def test_driver(self): """ Should be a string or converted to one """ @assert_key_equals_value(salt.utils.docker.translate.network) def test_options(self): """ Can be passed in several formats but must end up as a dictionary mapping keys to values """ @assert_dict(salt.utils.docker.translate.network) def test_ipam(self): """ Must be a dict """ @assert_bool(salt.utils.docker.translate.network) def test_check_duplicate(self): """ Should be a bool or converted to one """ @assert_bool(salt.utils.docker.translate.network) def test_internal(self): """ Should be a bool or converted to one """ @assert_labels(salt.utils.docker.translate.network) def test_labels(self): """ Can be passed as a list of key=value pairs or a dictionary, and must ultimately end up as a dictionary. """ @assert_bool(salt.utils.docker.translate.network) def test_enable_ipv6(self): """ Should be a bool or converted to one """ @assert_bool(salt.utils.docker.translate.network) def test_attachable(self): """ Should be a bool or converted to one """ @assert_bool(salt.utils.docker.translate.network) def test_ingress(self): """ Should be a bool or converted to one """ @assert_string(salt.utils.docker.translate.network) def test_ipam_driver(self): """ Should be a bool or converted to one """ @assert_key_equals_value(salt.utils.docker.translate.network) def test_ipam_opts(self): """ Can be passed in several formats but must end up as a dictionary mapping keys to values """ def ipam_pools(self): """ Must be a list of dictionaries (not a dictlist) """ good_pool = { "subnet": "10.0.0.0/24", "iprange": "10.0.0.128/25", "gateway": "10.0.0.254", "aux_addresses": { "foo.bar.tld": "10.0.0.20", "hello.world.tld": "10.0.0.21", }, } bad_pools = [ { "subnet": "10.0.0.0/33", "iprange": "10.0.0.128/25", "gateway": "10.0.0.254", "aux_addresses": { "foo.bar.tld": "10.0.0.20", "hello.world.tld": "10.0.0.21", }, }, { "subnet": "10.0.0.0/24", "iprange": "foo/25", "gateway": "10.0.0.254", "aux_addresses": { "foo.bar.tld": "10.0.0.20", "hello.world.tld": "10.0.0.21", }, }, { "subnet": "10.0.0.0/24", "iprange": "10.0.0.128/25", "gateway": "10.0.0.256", "aux_addresses": { "foo.bar.tld": "10.0.0.20", "hello.world.tld": "10.0.0.21", }, }, { "subnet": "10.0.0.0/24", "iprange": "10.0.0.128/25", "gateway": "10.0.0.254", "aux_addresses": { "foo.bar.tld": "10.0.0.20", "hello.world.tld": "999.0.0.21", }, }, ] self.assertEqual( salt.utils.docker.translate_input(self.translator, ipam_pools=[good_pool],), {"ipam_pools": [good_pool]}, ) for bad_pool in bad_pools: with self.assertRaisesRegex(CommandExecutionError, "not a valid"): salt.utils.docker.translate_input( self.translator, ipam_pools=[good_pool, bad_pool] ) @assert_subnet(salt.utils.docker.translate.network) def test_subnet(self): """ Must be an IPv4 or IPv6 subnet """ @assert_subnet(salt.utils.docker.translate.network) def test_iprange(self): """ Must be an IPv4 or IPv6 subnet """ def test_gateway(self): """ Must be an IPv4 or IPv6 address """ for val in self.ip_addrs[True]: self.assertEqual( salt.utils.docker.translate_input( self.translator, validate_ip_addrs=True, gateway=val, ), self.apply_defaults({"gateway": val}), ) for val in self.ip_addrs[False]: with self.assertRaisesRegex( CommandExecutionError, "'{0}' is not a valid IP address".format(val) ): salt.utils.docker.translate_input( self.translator, validate_ip_addrs=True, gateway=val, ) self.assertEqual( salt.utils.docker.translate_input( self.translator, validate_ip_addrs=False, gateway=val, ), self.apply_defaults( { "gateway": val if isinstance(val, six.string_types) else six.text_type(val) } ), ) @assert_key_equals_value(salt.utils.docker.translate.network) def test_aux_addresses(self): """ Must be a mapping of hostnames to IP addresses """ name = "aux_addresses" alias = "aux_address" for item in (name, alias): for val in self.ip_addrs[True]: addresses = {"foo.bar.tld": val} self.assertEqual( salt.utils.docker.translate_input( self.translator, validate_ip_addrs=True, **{item: addresses} ), self.apply_defaults({name: addresses}), ) for val in self.ip_addrs[False]: addresses = {"foo.bar.tld": val} with self.assertRaisesRegex( CommandExecutionError, "'{0}' is not a valid IP address".format(val) ): salt.utils.docker.translate_input( self.translator, validate_ip_addrs=True, **{item: addresses} ) self.assertEqual( salt.utils.docker.translate_input( self.translator, validate_ip_addrs=False, aux_addresses=addresses, ), self.apply_defaults({name: addresses}), ) class DockerTranslateHelperTestCase(TestCase): """ Tests for a couple helper functions in salt.utils.docker.translate """ def test_get_port_def(self): """ Test translation of port definition (1234, '1234/tcp', '1234/udp', etc.) into the format which docker-py uses (integer for TCP ports, 'port_num/udp' for UDP ports). """ # Test TCP port (passed as int, no protocol passed) self.assertEqual(translate_helpers.get_port_def(2222), 2222) # Test TCP port (passed as str, no protocol passed) self.assertEqual(translate_helpers.get_port_def("2222"), 2222) # Test TCP port (passed as str, with protocol passed) self.assertEqual(translate_helpers.get_port_def("2222", "tcp"), 2222) # Test TCP port (proto passed in port_num, with passed proto ignored). # This is a contrived example as we would never invoke the function in # this way, but it tests that we are taking the port number from the # port_num argument and ignoring the passed protocol. self.assertEqual(translate_helpers.get_port_def("2222/tcp", "udp"), 2222) # Test UDP port (passed as int) self.assertEqual(translate_helpers.get_port_def(2222, "udp"), (2222, "udp")) # Test UDP port (passed as string) self.assertEqual(translate_helpers.get_port_def("2222", "udp"), (2222, "udp")) # Test UDP port (proto passed in port_num self.assertEqual(translate_helpers.get_port_def("2222/udp"), (2222, "udp")) def test_get_port_range(self): """ Test extracting the start and end of a port range from a port range expression (e.g. 4505-4506) """ # Passing a single int should return the start and end as the same value self.assertEqual(translate_helpers.get_port_range(2222), (2222, 2222)) # Same as above but with port number passed as a string self.assertEqual(translate_helpers.get_port_range("2222"), (2222, 2222)) # Passing a port range self.assertEqual(translate_helpers.get_port_range("2222-2223"), (2222, 2223)) # Error case: port range start is greater than end with self.assertRaisesRegex( ValueError, r"Start of port range $2222$ cannot be greater than end of " r"port range $2221$", ): translate_helpers.get_port_range("2222-2221") # Error case: non-numeric input with self.assertRaisesRegex( ValueError, "'2222-bar' is non-numeric or an invalid port range" ): translate_helpers.get_port_range("2222-bar")

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0.20981

0

23,282

0.310224

a174c86a4c793d497c49fdd9127b5aea515b4346

400

py

Python

utils/middleware.py

DavidRoldan523/elenas_test

8b520fae68a275654a42ad761713c9c932d17a76

[ "MIT" ]

null

utils/middleware.py

DavidRoldan523/elenas_test

8b520fae68a275654a42ad761713c9c932d17a76

[ "MIT" ]

null

utils/middleware.py

DavidRoldan523/elenas_test

8b520fae68a275654a42ad761713c9c932d17a76

[ "MIT" ]

null

from django.http import HttpResponse from django.utils.deprecation import MiddlewareMixin class HealthCheckMiddleware: def __init__(self, get_response): self.get_response = get_response def __call__(self, request): if request.META["PATH_INFO"] == "/health-check/": return HttpResponse("ok") response = self.get_response(request) return response

28.571429

57

0.7

308

0.77

0

31

0.0775

a175602faa0357ee58584137efdc7c85d289bf89

3,317

py

Python

bot/exts/evergreen/catify.py

chincholikarsalil/sir-lancebot

05ba3de5c99b30a8eba393da1460fae255373457

[ "MIT" ]

null

bot/exts/evergreen/catify.py

chincholikarsalil/sir-lancebot

05ba3de5c99b30a8eba393da1460fae255373457

[ "MIT" ]

null

bot/exts/evergreen/catify.py

chincholikarsalil/sir-lancebot

05ba3de5c99b30a8eba393da1460fae255373457

[ "MIT" ]

null

import random from contextlib import suppress from typing import Optional from discord import AllowedMentions, Embed, Forbidden from discord.ext import commands from bot.constants import Cats, Colours, NEGATIVE_REPLIES from bot.utils import helpers class Catify(commands.Cog): """Cog for the catify command.""" def __init__(self, bot: commands.Bot): self.bot = bot @commands.command(aliases=["ᓚᘏᗢify", "ᓚᘏᗢ"]) @commands.cooldown(1, 5, commands.BucketType.user) async def catify(self, ctx: commands.Context, *, text: Optional[str]) -> None: """ Convert the provided text into a cat themed sentence by interspercing cats throughout text. If no text is given then the users nickname is edited. """ if not text: display_name = ctx.author.display_name if len(display_name) > 26: embed = Embed( title=random.choice(NEGATIVE_REPLIES), description=( "Your display name is too long to be catified! " "Please change it to be under 26 characters." ), color=Colours.soft_red ) await ctx.send(embed=embed) return else: display_name += f" | {random.choice(Cats.cats)}" await ctx.send(f"Your catified nickname is: `{display_name}`", allowed_mentions=AllowedMentions.none()) with suppress(Forbidden): await ctx.author.edit(nick=display_name) else: if len(text) >= 1500: embed = Embed( title=random.choice(NEGATIVE_REPLIES), description="Submitted text was too large! Please submit something under 1500 characters.", color=Colours.soft_red ) await ctx.send(embed=embed) return string_list = text.split() for index, name in enumerate(string_list): name = name.lower() if "cat" in name: if random.randint(0, 5) == 5: string_list[index] = name.replace("cat", f"**{random.choice(Cats.cats)}**") else: string_list[index] = name.replace("cat", random.choice(Cats.cats)) for element in Cats.cats: if element in name: string_list[index] = name.replace(element, "cat") string_len = len(string_list) // 3 or len(string_list) for _ in range(random.randint(1, string_len)): # insert cat at random index if random.randint(0, 5) == 5: string_list.insert(random.randint(0, len(string_list)), f"**{random.choice(Cats.cats)}**") else: string_list.insert(random.randint(0, len(string_list)), random.choice(Cats.cats)) text = helpers.suppress_links(" ".join(string_list)) await ctx.send( f">>> {text}", allowed_mentions=AllowedMentions.none() ) def setup(bot: commands.Bot) -> None: """Loads the catify cog.""" bot.add_cog(Catify(bot))

37.269663

119

0.545674

2,974

0.893361

0

2,836

0.851907

2,720

0.817062

643

0.193151

a1773cd4561ed64fe6472e04a837e283a5378aa9

1,763

py

Python

data/ebmnlp/stream.py

bepnye/tf_ner

c68b9f489e56e0ec8cfb02b7115d2b07d721ac6f

[ "Apache-2.0" ]

null

data/ebmnlp/stream.py

bepnye/tf_ner

c68b9f489e56e0ec8cfb02b7115d2b07d721ac6f

[ "Apache-2.0" ]

null

data/ebmnlp/stream.py

bepnye/tf_ner

c68b9f489e56e0ec8cfb02b7115d2b07d721ac6f

[ "Apache-2.0" ]

null

import os import data_utils from pathlib import Path top_path = Path(os.path.dirname(os.path.abspath(__file__))) EBM_NLP = Path('/Users/ben/Desktop/ebm_nlp/repo/ebm_nlp_2_00/') NO_LABEL = '0' def overwrite_tags(new_tags, tags): for i, t in enumerate(new_tags): if t != NO_LABEL: tags[i] = t def get_tags(d): pmid_tags = {} for e in ['participants', 'interventions', 'outcomes']: for a in (EBM_NLP / 'annotations' / 'aggregated' / 'starting_spans' / e / d).glob('*.ann'): pmid = a.stem.split('.')[0] tags = a.open().read().split() tags = [e[0] if t == '1' else NO_LABEL for t in tags] if pmid not in pmid_tags: pmid_tags[pmid] = tags else: overwrite_tags(tags, pmid_tags[pmid]) return pmid_tags def get_words(pmids): return { pmid: (EBM_NLP / 'documents' / '{}.tokens'.format(pmid)).open().read().split() for pmid in pmids } def get_seqs(tag_d, word_d, keys): tag_seqs = [] word_seqs = [] for k in keys: words, tags = data_utils.generate_seqs(word_d[k], tag_d[k]) tag_seqs += tags word_seqs += words return word_seqs, tag_seqs TRAIN_TAG_D = get_tags(Path('train/')) TRAIN_PMIDS = sorted(TRAIN_TAG_D.keys()) TRAIN_WORD_D = get_words(TRAIN_PMIDS) TRAIN_WORDS, TRAIN_TAGS = get_seqs(TRAIN_TAG_D, TRAIN_WORD_D, TRAIN_PMIDS) TEST_TAG_D = get_tags(Path('test/gold/')) TEST_PMIDS = sorted(TEST_TAG_D.keys()) TEST_WORD_D = get_words(TEST_PMIDS) TEST_WORDS, TEST_TAGS = get_seqs(TEST_TAG_D, TEST_WORD_D, TEST_PMIDS) def train_words(): return TRAIN_WORDS def train_tags(): return TRAIN_TAGS def test_words(): return TEST_WORDS def test_tags(): return TEST_TAGS def word_embeddings(): return ((top_path / '..' / 'embeddings' / 'glove.840B.300d.txt').open(), 300)

28.435484

109

0.683494

0

222

0.125922

a178917c391e8c7d6cc84a889a8b3efdf43b8cd9

16,753

py

Python

Kernels/Research/FFT/config/fft.py

WoodData/EndpointAI

8e4d145ff45cf5559ab009eba4f423e944dc6975

[ "Apache-2.0" ]

190

2020-09-22T02:14:29.000Z

2022-03-28T02:35:57.000Z

Kernels/Research/FFT/config/fft.py

chuancqc/EndpointAI

ab67cefeae3c06f1c93f66812bcf988c14e72ff1

[ "Apache-2.0" ]

2

2021-08-30T10:06:22.000Z

2021-11-05T20:37:58.000Z

Kernels/Research/FFT/config/fft.py

chuancqc/EndpointAI

ab67cefeae3c06f1c93f66812bcf988c14e72ff1

[ "Apache-2.0" ]

80

2020-09-13T17:48:56.000Z

2022-03-19T10:45:05.000Z

# # # Copyright (C) 2010-2021 ARM Limited or its affiliates. All rights reserved. # # SPDX-License-Identifier: Apache-2.0 # # Licensed under the Apache License, Version 2.0 (the License); you may # not use this file except in compliance with the License. # You may obtain a copy of the License at # # www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an AS IS BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # from sympy.ntheory import factorint import numpy as np from sympy.combinatorics import Permutation import io import math from config.strtools import * import itertools import struct import config.formats # Conversion of double to fixed point values # # - 8000 gives 8000 in C (int16) # So when it is multiplied it will give the wrong sign for the result # of the multiplication except if DSPE instructions with saturation are used # to compute the negate (and we should get 7FFF). # # So for cortex-m without DSP extension, we should try to use 8001 # It is done but not yet tested. def to_q63(v,dspe): r = int(round(v * 2**63)) if (r > 0x07FFFFFFFFFFFFFFF): r = 0x07FFFFFFFFFFFFFFF if (r < -0x08000000000000000): if dspe: r = -0x08000000000000000 else: r = -0x07FFFFFFFFFFFFFFF return ("0x%s" % format(struct.unpack('<Q', struct.pack('<q', r))[0],'016X')) def to_q31(v,dspe): r = int(round(v * 2**31)) if (r > 0x07FFFFFFF): r = 0x07FFFFFFF if (r < -0x080000000): if dspe: r = -0x080000000 else: r = -0x07FFFFFFF return ("0x%s" % format(struct.unpack('<I', struct.pack('<i', r))[0],'08X')) def to_q15(v,dspe): r = int(round(v * 2**15)) if (r > 0x07FFF): r = 0x07FFF if (r < -0x08000): if dspe: r = -0x08000 else: r = -0x07FFF return ("0x%s" % format(struct.unpack('<H', struct.pack('<h', r))[0],'04X')) def to_q7(v,dspe): r = int(round(v * 2**7)) if (r > 0x07F): r = 0x07F if (r < -0x080):# if dspe: r = -0x080 else: r = -0x07F return ("0x%s" % format(struct.unpack('<B', struct.pack('<b', r))[0],'02X')) Q7=1 Q15=2 Q31=3 F16=4 F32=5 F64=6 # In the final C++ code, we have a loop for a given radix. # The input list here has not grouped the factors. # The list need to be transformed into a list of pair. # The pair being (radix,exponent) def groupFactors(factors): n = 0 current=-1 result=[] for f in factors: if f != current: if current != -1: result = result + [current,n] current=f n=1 else: n=n+1 result = result + [current,n] return(result) # Compute the grouped factors for the the FFT length originaln # where the only possible radix are in primitiveFactors list. def getFactors(primitiveFactors,originaln): factors=[] length=[] primitiveFactors.sort(reverse=True) n = originaln while (n > 1) and primitiveFactors: if (n % primitiveFactors[0] == 0): factors.append(primitiveFactors[0]) n = n // primitiveFactors[0] else: primitiveFactors=primitiveFactors[1:] # When lowest factors are at the beginning (like 2) # we use a special implementation of the loopcore template # and it is removing some cycles. # So, we will get (for instance) 2x8x8x8 instead of 8x8x8x2 factors.reverse() for f in factors: originaln = originaln // f length.append(originaln) groupedfactors=groupFactors(factors) return(groupedfactors,factors,length) # Apply the radix decomposition to compute the input -> output permutation # computed by the FFT. def radixReverse(f,n): a=np.array(range(0,n)).reshape(f) r = list(range(0,len(f))) r.reverse() r = tuple(r) a = np.transpose(a,r) return(a.reshape(n)) def radixPermutation(factors,n): a = radixReverse(factors,n) tps = [] vectorizable=True for c in Permutation.from_sequence(a).cyclic_form: if (len(c)>2): vectorizable = False for i in range(len(c)-1,0,-1): # 2 because those are indexes in an array of complex numbers but # with a real type. tps.append([2*c[i], 2*c[i-1]]) return(np.array(tps,dtype=int).flatten(),vectorizable) # CFFT Twiddle table def cfft_twiddle(n): a=2.0*math.pi*np.linspace(0,n,num=n,endpoint=False)/n c=np.cos(-a) s=np.sin(-a) r = np.empty((c.size + s.size,), dtype=c.dtype) r[0::2] = c r[1::2] = s return(r) # RFFT twiddle for the merge and split steps. def rfft_twiddle(n): a=2.0j*math.pi*np.linspace(0,n//2,num=n // 2,endpoint=False)/n z=-1.0j * np.exp(-a) r = z.view(dtype=np.float64) return(r) # Compute the twiddle tables def twiddle(transform,n): if transform=="CFFT": return(cfft_twiddle(n)) if transform=="RFFT": return(rfft_twiddle(n)) return(None) NB_ELEMS_PER_LINE=3 # Generate C array content for a given datatype def printFloat64Array(f,n): nb=0 for s in n: print("%.20f, " % s,end="",file=f) nb = nb + 1 if nb == NB_ELEMS_PER_LINE: nb=0 print("",file=f) def printFloat32Array(f,n): nb=0 for s in n: print("%.20ff, " % s,end="",file=f) nb = nb + 1 if nb == NB_ELEMS_PER_LINE: nb=0 print("",file=f) def printFloat16Array(f,n): nb=0 for s in n: print("%.8ff16, " % s,end="",file=f) nb = nb + 1 if nb == NB_ELEMS_PER_LINE: nb=0 print("",file=f) def printQ31Array(f,mode,n): DSPE=False if mode == "DSP": DSPE=True nb=0 for s in n: print(to_q31(s,DSPE) + ", ",end="",file=f) nb = nb + 1 if nb == NB_ELEMS_PER_LINE: nb=0 print("",file=f) def printQ15Array(f,mode,n): DSPE=False if mode == "DSP": DSPE=True nb=0 for s in n: print(to_q15(s,DSPE) + ", ",end="",file=f) nb = nb + 1 if nb == NB_ELEMS_PER_LINE: nb=0 print("",file=f) def printQ7Array(f,mode,n): DSPE=False if mode == "DSP": DSPE=True nb=0 for s in n: print(to_q7(s,DSPE) + ", ",end="",file=f) nb = nb + 1 if nb == NB_ELEMS_PER_LINE: nb=0 print("",file=f) # Print a C array # Using the type, dpse mode, name # (dpse mode is for knowing if 0x8000 must be generated as 8000 or 8001 # to avoid sign issues when multiplying with the twiddles) def printArray(f,ctype,mode,name,a): nbSamples = len(a) define = "NB_" + name.upper() n = a.reshape(len(a)) print("__ALIGNED(8) const %s %s[%s]={" % (ctype,name,define),file=f) if ctype == "float64_t": printFloat64Array(f,n) if ctype == "float32_t": printFloat32Array(f,n) if ctype == "float16_t": printFloat16Array(f,n) if ctype == "Q31": printQ31Array(f,mode,n) if ctype == "Q15": printQ15Array(f,mode,n) if ctype == "Q7": printQ7Array(f,mode,n) print("};",file=f) # Convert a float value to a given datatype. def convertToDatatype(r,ctype,mode): DSPE=False if mode == "DSP": DSPE=True if ctype == "float64_t": result = "%.20f" % r if ctype == "float32_t": result = "%.20ff" % r if ctype == "float16_t": result = "%.20ff16" % r if ctype == "Q31": result = "Q31(%s)" % to_q31(r,DSPE) if ctype == "Q15": result = "Q15(%s)" % to_q15(r,DSPE) if ctype == "Q7": result = "Q7(%s)" % to_q7(r,DSPE) return(result) def printArrayHeader(f,ctype,name,nbSamples): define = "NB_" + name.upper() print("#define %s %d" % (define, nbSamples),file=f) print("extern __ALIGNED(8) const %s %s[%s];\n" % (ctype,name,define),file=f) # Print UINT arrays for permutations. def printUInt32Array(f,name,a): nbSamples = len(a) define = "NB_" + name.upper() n = a.reshape(len(a)) print("__ALIGNED(8) const uint32_t %s[%s]={" % (name,define),file=f) nb=0 for s in n: print("%d, " % s,end="",file=f) nb = nb + 1 if nb == NB_ELEMS_PER_LINE: nb=0 print("",file=f) print("};",file=f) def printUInt16Array(f,name,a): nbSamples = len(a) define = "NB_" + name.upper() n = a.reshape(len(a)) print("__ALIGNED(8) const uint16_t %s[%s]={" % (name,define),file=f) nb=0 for s in n: print("%d, " % s,end="",file=f) nb = nb + 1 if nb == NB_ELEMS_PER_LINE: nb=0 print("",file=f) print("};",file=f) def printUInt32ArrayHeader(f,name,a): nbSamples = len(a) define = "NB_" + name.upper() n = a.reshape(len(a)) print("#define %s %d" % (define, nbSamples),file=f) print("extern __ALIGNED(8) const uint32_t %s[%s];\n" % (name,define),file=f) def printUInt16ArrayHeader(f,name,a): nbSamples = len(a) define = "NB_" + name.upper() n = a.reshape(len(a)) print("#define %s %d" % (define, nbSamples),file=f) print("extern __ALIGNED(8) const uint16_t %s[%s];\n" % (name,define),file=f) def getCtype(t): if t == 'f64': return("float64_t") if t == 'f32': return("float32_t") if t == 'f16': return("float16_t") if t == 'q31': return("Q31") if t == 'q15': return("Q15") if t == 'q7': return("Q7") return("void") # Configuration structures for CFFT and RFFT cfftconfig = """cfftconfig<%s> config%d={ .normalization=%s, .nbPerms=%s, .perms=perm%d, .nbTwiddle=%s, .twiddle=twiddle%d, .nbGroupedFactors=%d, .nbFactors=%d, .factors=factors%d, .lengths=lengths%d, .format=%d, .reversalVectorizable=%d };""" rfftconfig = """rfftconfig<%s> config%d={ .nbTwiddle=%s, .twiddle=twiddle%d };""" fftconfigHeader = """extern %sconfig<%s> config%d;""" fftFactorArray = """const uint16_t factors%d[%d]=%s;\n""" fftLengthArray = """const uint16_t lengths%d[%d]=%s;\n""" # Descriptino of a permutation class Perm: PermID = 0 # Grouped factors and factors. def getFactors(core,nb,datatype): _groupedFactors,_factors,_lens=getFactors(core.radix(datatype,nb),nb) return(_factors) def __init__(self,core,nb,datatype): Perm.PermID = Perm.PermID + 1 self._nb=nb self._id = Perm.PermID self._radixUsed=set([]) self._groupedFactors,self._factors,self._lens=getFactors(core.radix(datatype,nb),nb) self._perms = None self._core=core self._isvectorizable=False def permutations(self): _permFactors=list(itertools.chain(*[self._core.getPermFactor(x) for x in self._factors])) #print(_permFactors) self._perms,self._isvectorizable = radixPermutation(_permFactors[::-1],self._nb) @property def isVectorizable(self): return(self._isvectorizable) @property def permID(self): return(self._id) @property def perms(self): if self._perms is not None: return(self._perms) else: self.permutations() return(self._perms) @property def factors(self): return(self._factors) @property def nbGroupedFactors(self): return(int(len(self._groupedFactors)/2)) @property def nbFactors(self): return(len(self._factors)) def writePermHeader(self,h): printUInt16ArrayHeader(h,"perm%d" % self.permID,self.perms) def writePermCode(self,c): printUInt16Array(c,"perm%d" % self.permID,self.perms) def writeFactorDesc(self,c): radixList="{%s}" % joinStr([str(x) for x in self._groupedFactors]) lengthList="{%s}" % joinStr([str(x) for x in self._lens]) print(fftFactorArray % (self.permID,2*self.nbGroupedFactors,radixList),file=c); print(fftLengthArray % (self.permID,len(self._lens),lengthList),file=c); class Twiddle: TwiddleId = 0 def __init__(self,transform,nb,datatype,mode): Twiddle.TwiddleId = Twiddle.TwiddleId + 1 self._id = Twiddle.TwiddleId self._datatype = datatype self._nb=nb self._twiddle = None self._transform=transform self._mode=mode @property def twiddleID(self): return(self._id) @property def datatype(self): return(self._datatype) @property def samples(self): if self._twiddle is None: self._twiddle=twiddle(self._transform,self._nb) return(self._twiddle) @property def nbSamples(self): return(self._nb) @property def nbTwiddles(self): if self._transform=="RFFT": return(self._nb // 2) else: return(self._nb) def writeTwidHeader(self,h): ctype=getCtype(self.datatype) # Twiddle is a complex array so 2*nbSamples must be used printArrayHeader(h,ctype,"twiddle%d" % self.twiddleID,2*self.nbTwiddles) def writeTwidCode(self,c): ctype=getCtype(self.datatype) printArray(c,ctype,self._mode,"twiddle%d" % self.twiddleID,self.samples) class Config: ConfigID = 0 def __init__(self,transform,twiddle,perms,coreMode): Config.ConfigID = Config.ConfigID + 1 self._id = Config.ConfigID self._twiddle=twiddle self._perms=perms self._transform=transform self._coreMode=coreMode @property def transform(self): return(self._transform) @property def configID(self): return(self._id) @property def perms(self): return(self._perms) @property def twiddle(self): return(self._twiddle) @property def nbSamples(self): return(self.twiddle.nbSamples) def writeConfigHeader(self,c): ctype=getCtype(self.twiddle.datatype) print(fftconfigHeader % (self.transform.lower(),ctype,self.configID),file=c) def writeConfigCode(self,c): ctype=getCtype(self.twiddle.datatype) twiddleLen = "NB_" + ("twiddle%d"% self.twiddle.twiddleID).upper() if self.transform == "RFFT": print(rfftconfig % (ctype,self.configID,twiddleLen,self.twiddle.twiddleID),file=c) else: normfactor = 1.0 / self.twiddle.nbSamples normFactorStr = convertToDatatype(normfactor,ctype,self._coreMode) permsLen = "NB_" + ("perm%d"% self.perms.permID).upper() outputFormat = 0 #print(self.twiddle.datatype) #print(self.twiddle.nbSamples) #print(self.perms.factors) # For fixed point, each stage will change the output format. # We need to cmpute the final format of the FFT # and record it in the initialization structure # so that the user can easily know how to recover the # input format (q31, q15). It is encoded as a shift value. # The shift to apply to recover the input format # But applying this shift will saturate the result in general. if self.twiddle.datatype == "q15" or self.twiddle.datatype == "q31": for f in self.perms.factors: #print(f,self.twiddle.datatype,self._coreMode) # The file "formats.py" is decribing the format of each radix # and is used to compute the format of the FFT based # on the decomposition of its length. # # Currently (since there is no vector version for fixed point) # this is not taking into account the format change that may # be implied by the vectorization in case it may be different # from the scalar version. formatForSize = config.formats.formats[f][self._coreMode] outputFormat += formatForSize[self.twiddle.datatype] vectorizable=0 if self.perms.isVectorizable: vectorizable = 1 print(cfftconfig % (ctype,self.configID,normFactorStr,permsLen,self.perms.permID, twiddleLen,self.twiddle.twiddleID,self.perms.nbGroupedFactors,self.perms.nbFactors, self.perms.permID,self.perms.permID,outputFormat,vectorizable ),file=c)

27.463934

98

0.595595

6,312

0.376768

0

1,333

0.079568

0

4,460

0.266221

a179d95ca52452ffb3320f8150fc8f1ca9d9de24

1,275

py

Python

classification/resnetOnnx_inference_dynamicInput.py

SahilChachra/Onnx-Deposit

6cdf03903639166a43e0c809b67621a1aa2449dd

[ "BSD-3-Clause" ]

null

classification/resnetOnnx_inference_dynamicInput.py

SahilChachra/Onnx-Deposit

6cdf03903639166a43e0c809b67621a1aa2449dd

[ "BSD-3-Clause" ]

null

classification/resnetOnnx_inference_dynamicInput.py

SahilChachra/Onnx-Deposit

6cdf03903639166a43e0c809b67621a1aa2449dd

[ "BSD-3-Clause" ]

null

''' This inference script takes in images of dynamic size Runs inference in batch ** In this images have been resized but not need for this script ''' import onnx import onnxruntime as ort import numpy as np import cv2 from imagenet_classlist import get_class import os model_path = 'resnet18.onnx' model = onnx.load(model_path) image_path = "../sample_images" try: print("Checking model...") onnx.checker.check_model(model) onnx.helper.printable_graph(model.graph) print("Model checked...") print("Running inference...") ort_session = ort.InferenceSession(model_path) img_list = [] for image in os.listdir(image_path): img = cv2.imread(os.path.join(image_path, image), cv2.IMREAD_COLOR) img = cv2.resize(img, ((224, 224))) img = np.moveaxis(img, -1, 0) # (Batch_size, channels, width, heigth) img_list.append(img/255.0) # Normalize the image outputs = ort_session.run(None, {"input":img_list}) out = np.array(outputs) for image_num, image_name in zip(range(out.shape[1]), os.listdir(image_path)): index = out[0][image_num] print("Image : {0}, Class : {1}".format(image_name, get_class(np.argmax(index)))) except Exception as e: print("Exception occured : ", e)

28.977273

89

0.680784

0

358

0.280784

a17a4e7f440bd9450eae4bfedcba472184cfe212

3,857

py

Python

demo/Master/TaskMaker.py

build2last/JOCC

8eedaa923c6444a32e53e03fdd2a85a8031c46f5

[ "MIT" ]

null

demo/Master/TaskMaker.py

build2last/JOCC

8eedaa923c6444a32e53e03fdd2a85a8031c46f5

[ "MIT" ]

null

demo/Master/TaskMaker.py

build2last/JOCC

8eedaa923c6444a32e53e03fdd2a85a8031c46f5

[ "MIT" ]

null

# coding:utf-8 import time import MySQLdb import conf import Server # Another way to load data to MySQL: # load data infile "C://ProgramData/MySQL/MySQL Server 5.7/Uploads/track_info_url_0_part0.txt" ignore into table develop.task(mid, url); # doing: load data infile "C://ProgramData/MySQL/MySQL Server 5.7/Uploads/track_info_url_1_part1.txt" ignore into table develop.task(mid, url); class Master: def __init__(self): CREATE_TABLE_SQL = ( """CREATE TABLE IF NOT EXISTS `task` ( `mid` varchar(50) NOT NULL, `status` tinyint(1) NOT NULL DEFAULT '0' COMMENT '0:未分配 1:已分配未反馈 2:已完成', `worker` varchar(45) DEFAULT NULL, `url` varchar(600) NOT NULL, PRIMARY KEY (`mid`) ) ENGINE=InnoDB DEFAULT CHARSET=utf8 COMMENT='Give out tasks for distributed crawler.';""") # Create table in MySQL conn = MySQLdb.connect(host=conf.HOST, user=conf.USER, passwd=conf.PASS, db=conf.DB_NAME, port=conf.DBPORT, charset='utf8') cursor = conn.cursor() cursor.execute(CREATE_TABLE_SQL) conn.commit() conn.close() self.mid = set() self.urls = set() def generate_task(self, task_file): """Promise to provide data with corrent format""" with open(task_file) as fr: while fr: task = fr.readline().strip().split("\t") if '' not in task: yield(task) else: continue def load_func(self, conn, items): #特点：内存占用小 cursor = conn.cursor() counter = 0 for item in items: try: insert_sql = """insert IGNORE into {table_name} ({column1}, {column2}) VALUES (%s, %s)""".format(table_name="task", column1="mid", column2="url") cursor.execute(insert_sql, (item[0], item[1])) counter += 1 conn.commit() except Exception as e: print(e) print("Load %d items success"%counter) def load_fast(self, conn, items): #Fail cursor = conn.cursor() insert_sql = """insert into {table_name} ({column1}, {column2}) VALUES (%s, %s)""".format(table_name="task", column1="mid", column2="url") paras = [] for i in items: if i[0] not in self.mid and i[1] not in self.urls: self.mid.add(i[0]) self.urls.add(i[1]) paras.append((i[0], i[1])) counter = len(paras) print(counter) try: print("inserting") for index in range(len(paras))[::10000]: para = paras[index:index+10000] cursor.executemany(insert_sql, para) print("Load items success") except Exception as e: print(e) conn.commit() def load_task(self, task_file): try: conn = MySQLdb.connect(host=conf.HOST, user=conf.USER, passwd=conf.PASS, db=conf.DB_NAME, port=conf.DBPORT, charset='utf8') tasks = self.generate_task(task_file) self.load_func(conn, tasks) #self.load_fast(conn, tasks) except Exception as e: print(e) finally: conn.close() def main(): master = Master() task_files = ["track_info_url_5_part0.txt","track_info_url_5_part1.txt","track_info_url_6_part1.txt","track_info_url_8_part0.txt","track_info_url_8_part1.txt","track_info_url_9_part0.txt","track_info_url_9_part1.txt"] for task_file in task_files: print("Processing %s"%task_file) path = r"C:\ProgramData\MySQL\MySQL Server 5.7\Uploads\\" + task_file master.load_task(path) if __name__ == '__main__': tick = time.time() main() tock = time.time() print("Cost %d s"%(tock - tick))

38.959596

221

0.577392

2,942

0.75494

326

0.083654

0

1,392

0.357198

a17ebf74350b134333915aa09bd51888d3742c03

770

py

Python

Inclass/python/sort_order_testing/sort_order.py

chenchuw/EC602-Design-by-Software

c233c9d08a67abc47235282fedd866d67ccaf4ce

[ "MIT" ]

null

Inclass/python/sort_order_testing/sort_order.py

chenchuw/EC602-Design-by-Software

c233c9d08a67abc47235282fedd866d67ccaf4ce

[ "MIT" ]

null

Inclass/python/sort_order_testing/sort_order.py

chenchuw/EC602-Design-by-Software

c233c9d08a67abc47235282fedd866d67ccaf4ce

[ "MIT" ]

1

2022-01-11T20:23:47.000Z

#!/Users/francischen/opt/anaconda3/bin/python #pythons sorts are STABLE: order is the same as original in tie. # sort: key, reverse q = ['two','twelve','One','3'] #sort q, result being a modified list. nothing is returned q.sort() print(q) q = ['two','twelve','One','3',"this has lots of t's"] q.sort(reverse=True) print(q) def f(x): return x.count('t') q.sort(key = f) print(q) q = ['twelve','two','One','3',"this has lots of t's"] q.sort(key=f) print(q) #Multiple sorts q = ['twelve','two','One','3',"this has lots of t's"] q.sort() q.sort(key=f) # sort based on 1,2,and then 3 # sort 3, then sort 2, then sort 1 print(q) def complicated(x): return(x.count('t'),len(x),x) q = ['two','otw','wot','Z','t','tt','longer t'] q.sort(key=complicated) print(q)

18.333333

64

0.62987

0

457

0.593506

a17ec4639df7fdbb530566bb66941b664210b137

96

py

Python

bhinneka/utils.py

kangfend/scrapy-bhinneka

a4a6e4ae5295e8bf83b213c1dace9c7de70f128c

[ "MIT" ]

1

2016-10-04T10:10:05.000Z

bhinneka/utils.py

kangfend/scrapy-bhinneka

a4a6e4ae5295e8bf83b213c1dace9c7de70f128c

[ "MIT" ]

null

bhinneka/utils.py

kangfend/scrapy-bhinneka

a4a6e4ae5295e8bf83b213c1dace9c7de70f128c

[ "MIT" ]

null

from bhinneka.settings import BASE_URL def get_absolute_url(path): return BASE_URL + path

16

38

0.78125

0

a17ef045f77adc98f9fc666a8b89d72884c7ebf6

287

py

Python

tests/test_vsan/vars.py

wardy3/mdssdk

393102fab146917a3893b6aa2bd6a0449ad491c5

[ "Apache-2.0" ]

4

2020-12-13T20:02:43.000Z

2022-02-27T23:36:58.000Z

tests/test_vsan/vars.py

wardy3/mdssdk

393102fab146917a3893b6aa2bd6a0449ad491c5

[ "Apache-2.0" ]

13

2020-09-23T07:30:15.000Z

2022-03-30T01:12:25.000Z

tests/test_vsan/vars.py

wardy3/mdssdk

393102fab146917a3893b6aa2bd6a0449ad491c5

[ "Apache-2.0" ]

12

2020-05-11T09:33:21.000Z

2022-03-18T11:11:28.000Z

import logging import random log = logging.getLogger(__name__) reserved_id = [4079, 4094] boundary_id = [0, 4095] # No need to have end=4094 as there are some inbetween vsans reserved for fport-channel-trunk def get_random_id(start=2, end=400): return random.randint(start, end)

22.076923

93

0.756098

0

93

0.324042

a17f452cabac62c273c6e040b99703605a01fbfa

1,403

py

Python

testing_ideas/try_pymed_package/try_pymed_and_ss_api.py

hschilling/data-collection-and-prep

b70ab54fd887592bad05d5748f492fc2f9ef0f6f

[ "Unlicense" ]

null

testing_ideas/try_pymed_package/try_pymed_and_ss_api.py

hschilling/data-collection-and-prep

b70ab54fd887592bad05d5748f492fc2f9ef0f6f

[ "Unlicense" ]

41

2021-01-01T14:01:30.000Z

2021-01-27T20:17:21.000Z

testing_ideas/try_pymed_package/try_pymed_and_ss_api.py

hschilling/data-collection-and-prep

b70ab54fd887592bad05d5748f492fc2f9ef0f6f

[ "Unlicense" ]

5

2021-02-08T14:19:35.000Z

2021-10-19T12:10:55.000Z

# Use the pymed package to call the PubMed API to get lots of papers from, in this case, JEB from pymed import PubMed import pandas as pd import requests _REQUESTS_TIMEOUT = 3.0 df_jeb = pd.DataFrame(columns=['title', 'abstract']) df_jeb = df_jeb.convert_dtypes() pubmed = PubMed(tool="MyTool", email="my@email.address") # query = '("The Journal of experimental biology"[Journal]) AND (("2002/01/01"[Date - Publication] : "3000"[Date - Publication]))' query = '("The Journal of experimental biology"[Journal]) AND (("2002/01/01"[Date - Publication] : "2018/10/10"[Date - Publication]))' # results = pubmed.query(query, max_results=10000) results = pubmed.query(query, max_results=100) for r in results: doi = "http://dx.doi.org/" + r.doi if r.doi else '' df_jeb = df_jeb.append( {'title': r.title, 'abstract': r.abstract, 'doi': doi, 'pmid': f"https://pubmed.ncbi.nlm.nih.gov/{r.pubmed_id}/", }, ignore_index=True) ss_api_url = f'https://api.semanticscholar.org/v1/paper/{r.doi}' response = requests.get(ss_api_url, timeout=_REQUESTS_TIMEOUT) ss_api_results = response.json() print('is open access', ss_api_results['is_open_access']) if r.title.startswith("Bumb"): print(response) print('is open access', ss_api_results['is_open_access']) df_jeb.to_csv("pubmed_titles_abstracts_doi_pmid_100_only.csv")

40.085714

134

0.68211

0

708

0.504633

a17f65f1db1e9d6fc0255b219c8e7f7acd085081

287

py

Python

simple_functions/__init__.py

JihaoXin/ci_acse1

6ba30368cc2000bb13aab0dc213837d530753612

[ "MIT" ]

null

simple_functions/__init__.py

JihaoXin/ci_acse1

6ba30368cc2000bb13aab0dc213837d530753612

[ "MIT" ]

null

simple_functions/__init__.py

JihaoXin/ci_acse1

6ba30368cc2000bb13aab0dc213837d530753612

[ "MIT" ]

null

from .functions1 import my_sum, factorial from .constants import pi from .print import myprint from pkg_resources import get_distribution, DistributionNotFound try: __version__ = get_distribution(__name__).version except DistributionNotFound: # package is not installed pass

28.7

64

0.811847

0

26

0.090592

a17f75ddc89a6583319e9dcd13c17dded131aa22

1,259

bzl

Python

tools/build_defs/native_tools/tool_access.bzl

slsyy/rules_foreign_cc

34ab7f86a3ab1b2381cb4820d08a1c892f55bf54

[ "Apache-2.0" ]

null

tools/build_defs/native_tools/tool_access.bzl

slsyy/rules_foreign_cc

34ab7f86a3ab1b2381cb4820d08a1c892f55bf54

[ "Apache-2.0" ]

null

tools/build_defs/native_tools/tool_access.bzl

slsyy/rules_foreign_cc

34ab7f86a3ab1b2381cb4820d08a1c892f55bf54

[ "Apache-2.0" ]

null

# buildifier: disable=module-docstring load(":native_tools_toolchain.bzl", "access_tool") def get_cmake_data(ctx): return _access_and_expect_label_copied("@rules_foreign_cc//tools/build_defs:cmake_toolchain", ctx, "cmake") def get_ninja_data(ctx): return _access_and_expect_label_copied("@rules_foreign_cc//tools/build_defs:ninja_toolchain", ctx, "ninja") def get_make_data(ctx): return _access_and_expect_label_copied("@rules_foreign_cc//tools/build_defs:make_toolchain", ctx, "make") def _access_and_expect_label_copied(toolchain_type_, ctx, tool_name): tool_data = access_tool(toolchain_type_, ctx, tool_name) if tool_data.target: # This could be made more efficient by changing the # toolchain to provide the executable as a target cmd_file = tool_data for f in tool_data.target.files.to_list(): if f.path.endswith("/" + tool_data.path): cmd_file = f break return struct( deps = [tool_data.target], # as the tool will be copied into tools directory path = "$EXT_BUILD_ROOT/{}".format(cmd_file.path), ) else: return struct( deps = [], path = tool_data.path, )

38.151515

111

0.669579

0

430

0.341541

a1813bf8f98dea1f19c9411401522d50224116bd

5,805

py

Python

tests/test_model.py

jakehadar/py-snake

3c19d572afb275768c504c66d331b5727515fd71

[ "MIT" ]

null

tests/test_model.py

jakehadar/py-snake

3c19d572afb275768c504c66d331b5727515fd71

[ "MIT" ]

null

tests/test_model.py

jakehadar/py-snake

3c19d572afb275768c504c66d331b5727515fd71

[ "MIT" ]

1

2021-11-30T10:14:32.000Z

# -*- coding: utf-8 -*- import sys import pytest from snake.common import Frame, Point, BoundaryCollision, SelfCollision from snake.config import GameConfig from snake.model import SnakeModel @pytest.fixture def config(): config = GameConfig() config.solid_walls = True config.initial_food_count = 0 config.food_increase_interval = 0 return config @pytest.fixture def model(config): """Initial state (T0).""" frame = Frame(10, 10) m = SnakeModel(frame, config) return m class TestSnakeModelInitialState: def test_length(self, model): assert len(model) == 1 def test_score(self, model): assert model.score == 0 def test_occupied_locations(self, model): assert {model.head_location} == set(model.occupied_locations) def test_empty_locations(self, model): assert model.head_location not in model.empty_locations def test_available_food_locations(self, model): assert model.available_food_locations == model.empty_locations @pytest.fixture def model2(model): """Initial state (T0) + 3 steps forward, where each spot had food.""" model.face_up() model.food_locations.append(model.head_location + Point(0, 1)) model.food_locations.append(model.head_location + Point(0, 2)) model.food_locations.append(model.head_location + Point(0, 3)) model.step() model.step() model.step() return model class TestSnakeEatsAndGrows: def test_length(self, model2): assert len(model2) == 4 def test_score(self, model2): assert model2.score == 3 class TestBoundaryCollision: def test_raises_scenario_1(self, config): model = SnakeModel(Frame(3, 3), config) model.face_up() with pytest.raises(BoundaryCollision): model.step() model.step() def test_raises_scenario_2(self, config): model = SnakeModel(Frame(3, 3), config) model.face_down() with pytest.raises(BoundaryCollision): model.step() model.step() def test_raises_scenario_3(self, config): model = SnakeModel(Frame(3, 3), config) model.face_left() with pytest.raises(BoundaryCollision): model.step() model.step() def test_raises_scenario_4(self, config): model = SnakeModel(Frame(3, 3), config) model.face_right() with pytest.raises(BoundaryCollision): model.step() model.step() class TestSelfCollision: def test_valid_scenario_raises(self, model): """Snake turns into itself.""" model.face_up() model.step(should_grow=True) model.step(should_grow=True) model.step(should_grow=True) model.face_right() model.step() model.face_down() model.step() model.face_left() with pytest.raises(SelfCollision): model.step() # The scenarios below should never raise def test_scenario_1a(self, model): model.face_up() model.step(should_grow=True) model.face_down() model.step() def test_scenario_1b(self, model): model.face_down() model.step(should_grow=True) model.face_up() model.step() def test_scenario_1c(self, model): model.face_left() model.step(should_grow=True) model.face_right() model.step() def test_scenario_1d(self, model): model.face_right() model.step(should_grow=True) model.face_left() model.step() @pytest.mark.skipif(sys.version_info.major == 3, reason='Non-critical test failure from Python2.') def test_scenario_2a(self, model): model.face_up() model.step(should_grow=True) model.face_left() model.face_down() model.step() @pytest.mark.skipif(sys.version_info.major == 3, reason='Non-critical test failure from Python2.') def test_scenario_2b(self, model): model.face_up() model.step(should_grow=True) model.face_right() model.face_down() model.step() @pytest.mark.skipif(sys.version_info.major == 3, reason='Non-critical test failure from Python2.') def test_scenario_3a(self, model): model.face_down() model.step(should_grow=True) model.face_left() model.face_up() model.step() @pytest.mark.skipif(sys.version_info.major == 3, reason='Non-critical test failure from Python2.') def test_scenario_3b(self, model): model.face_down() model.step(should_grow=True) model.face_right() model.face_up() model.step() @pytest.mark.skipif(sys.version_info.major == 3, reason='Non-critical test failure from Python2.') def test_scenario_4a(self, model): model.face_left() model.step(should_grow=True) model.face_down() model.face_right() model.step() @pytest.mark.skipif(sys.version_info.major == 3, reason='Non-critical test failure from Python2.') def test_scenario_4b(self, model): model.face_left() model.step(should_grow=True) model.face_up() model.face_right() model.step() @pytest.mark.skipif(sys.version_info.major == 3, reason='Non-critical test failure from Python2.') def test_scenario_5a(self, model): model.face_right() model.step(should_grow=True) model.face_down() model.face_left() model.step() @pytest.mark.skipif(sys.version_info.major == 3, reason='Non-critical test failure from Python2.') def test_scenario_5b(self, model): model.face_right() model.step(should_grow=True) model.face_up() model.face_left() model.step()

28.880597

102

0.64186

4,880

0.840655

0

2,887

0.49733

0

515

0.088717

a1823c37136cd59bed9a94266ef25fc93fb40d71

255

py

Python

gallery/photo/urls.py

andyjohn23/django-photo

e65ee3ab6fdad3a9d836d32b7f1026efcc728a41

[ "MIT" ]

null

gallery/photo/urls.py

andyjohn23/django-photo

e65ee3ab6fdad3a9d836d32b7f1026efcc728a41

[ "MIT" ]

null

gallery/photo/urls.py

andyjohn23/django-photo

e65ee3ab6fdad3a9d836d32b7f1026efcc728a41

[ "MIT" ]

null

from django.urls import path from . import views urlpatterns = [ path('', views.index, name="index"), path('category/<category>/', views.CategoryListView.as_view(), name="category"), path('search/', views.image_search, name='image-search'), ]

31.875

84

0.686275

0

64

0.25098

a182a47e0e9e4e6e3cf93dede6480b43b9da9679

381

py

Python

book2/s4_ex2.py

Felipe-Tommaselli/Python4everbody_Michigan

f4f940c15a4b165b144d14ead79d583bf31b805b

[ "MIT" ]

null

book2/s4_ex2.py

Felipe-Tommaselli/Python4everbody_Michigan

f4f940c15a4b165b144d14ead79d583bf31b805b

[ "MIT" ]

null

book2/s4_ex2.py

Felipe-Tommaselli/Python4everbody_Michigan

f4f940c15a4b165b144d14ead79d583bf31b805b

[ "MIT" ]

null

fname = input("Enter file name: ") if len(fname) < 1 : fname = "mbox-short.txt" list = list() f = open(fname) count = 0 for line in f: line = line.rstrip() list = line.split() if list == []: continue elif list[0].lower() == 'from': count += 1 print(list[1]) print("There were", count, "lines in the file with From as the first word")

25.4

75

0.564304

0

100

0.262467

a183121368090836638181c5ae887b713f923588

6,358

py

Python

fedsimul/models/mnist/mclr.py

cshjin/fedsimul

1e2b9a9d9034fbc679dfaff059c42dea5642971d

[ "MIT" ]

11

2021-05-07T01:28:26.000Z

2022-03-10T08:23:16.000Z

fedsimul/models/mnist/mclr.py

cshjin/fedsimul

1e2b9a9d9034fbc679dfaff059c42dea5642971d

[ "MIT" ]

2

2021-08-13T10:12:13.000Z

2021-08-31T02:03:20.000Z

fedsimul/models/mnist/mclr.py

cshjin/fedsimul

1e2b9a9d9034fbc679dfaff059c42dea5642971d

[ "MIT" ]

1

2021-06-08T07:23:22.000Z

import numpy as np import tensorflow as tf from tqdm import trange from fedsimul.utils.model_utils import batch_data from fedsimul.utils.tf_utils import graph_size from fedsimul.utils.tf_utils import process_grad class Model(object): ''' This is the tf model for the MNIST dataset with multiple class learner regression. Images are 28px by 28px. ''' def __init__(self, num_classes, optimizer, gpu_id=0, seed=1): """ Initialize the learner. Args: num_classes: int optimizer: tf.train.Optimizer gpu_id: int, default 0 seed: int, default 1 """ # params self.num_classes = num_classes # create computation graph self.graph = tf.Graph() with self.graph.as_default(): tf.set_random_seed(123 + seed) _created = self.create_model(optimizer) self.features = _created[0] self.labels = _created[1] self.train_op = _created[2] self.grads = _created[3] self.eval_metric_ops = _created[4] self.loss = _created[5] self.saver = tf.train.Saver() # set the gpu resources gpu_options = tf.compat.v1.GPUOptions(visible_device_list="{}".format(gpu_id), allow_growth=True) config = tf.compat.v1.ConfigProto(gpu_options=gpu_options) self.sess = tf.Session(graph=self.graph, config=config) # self.sess = tf.Session(graph=self.graph) # REVIEW: find memory footprint and compute cost of the model self.size = graph_size(self.graph) with self.graph.as_default(): self.sess.run(tf.global_variables_initializer()) metadata = tf.RunMetadata() opts = tf.profiler.ProfileOptionBuilder.float_operation() self.flops = tf.profiler.profile(self.graph, run_meta=metadata, cmd='scope', options=opts).total_float_ops def create_model(self, optimizer): """ Model function for Logistic Regression. Args: optimizer: tf.train.Optimizer Returns: tuple: (features, labels, train_op, grads, eval_metric_ops, loss) """ features = tf.placeholder(tf.float32, shape=[None, 784], name='features') labels = tf.placeholder(tf.int64, shape=[None, ], name='labels') logits = tf.layers.dense(inputs=features, units=self.num_classes, kernel_regularizer=tf.contrib.layers.l2_regularizer(0.001)) predictions = { "classes": tf.argmax(input=logits, axis=1), "probabilities": tf.nn.softmax(logits, name="softmax_tensor") } loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits) grads_and_vars = optimizer.compute_gradients(loss) grads, _ = zip(*grads_and_vars) train_op = optimizer.apply_gradients(grads_and_vars, global_step=tf.train.get_global_step()) eval_metric_ops = tf.count_nonzero(tf.equal(labels, predictions["classes"])) return features, labels, train_op, grads, eval_metric_ops, loss def set_params(self, latest_params=None, momentum=False, gamma=0.9): """ Set parameters from server Args: latest_params: list list of tf.Variables momentum: boolean gamma: float TODO: update variable with its local variable and the value from latest_params TODO: DO NOT set_params from the global, instead, use the global gradient to update """ if latest_params is not None: with self.graph.as_default(): # previous gradient all_vars = tf.trainable_variables() for variable, value in zip(all_vars, latest_params): if momentum: curr_val = self.sess.run(variable) new_val = gamma * curr_val + (1 - gamma) * value # TODO: use `assign` function instead of `load` variable.load(new_val, self.sess) else: variable.load(value, self.sess) def get_params(self): """ Get model parameters. Returns: model_params: list list of tf.Variables """ with self.graph.as_default(): model_params = self.sess.run(tf.trainable_variables()) return model_params def get_gradients(self, data, model_len): """ Access gradients of a given dataset. Args: data: dict model_len: int Returns: num_samples: int grads: tuple """ grads = np.zeros(model_len) num_samples = len(data['y']) with self.graph.as_default(): model_grads = self.sess.run(self.grads, feed_dict={self.features: data['x'], self.labels: data['y']}) grads = process_grad(model_grads) return num_samples, grads def solve_inner(self, data, num_epochs=1, batch_size=32): '''Solves local optimization problem. Args: data: dict with format {'x':[], 'y':[]} num_epochs: int batch_size: int Returns: soln: list comp: float ''' for _ in trange(num_epochs, desc='Epoch: ', leave=False, ncols=120): for X, y in batch_data(data, batch_size): with self.graph.as_default(): self.sess.run(self.train_op, feed_dict={self.features: X, self.labels: y}) soln = self.get_params() comp = num_epochs * (len(data['y']) // batch_size) * batch_size * self.flops return soln, comp def test(self, data): ''' Args: data: dict of the form {'x': [], 'y': []} Returns: tot_correct: int loss: float ''' with self.graph.as_default(): tot_correct, loss = self.sess.run([self.eval_metric_ops, self.loss], feed_dict={self.features: data['x'], self.labels: data['y']}) return tot_correct, loss def close(self): self.sess.close()

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