ytzi/the-stack-dedup-python-filtered-docstrings

6848d05c72fa374d993685eb4210477d11796461

1,006

py

Python

profiler/undecorate_for_profiling.py

co2meal/-bnpy-dev

74f69afde6c9dac8de4c074842df53ae87a15ac1

[ "BSD-3-Clause" ]

null

profiler/undecorate_for_profiling.py

co2meal/-bnpy-dev

74f69afde6c9dac8de4c074842df53ae87a15ac1

[ "BSD-3-Clause" ]

null

profiler/undecorate_for_profiling.py

co2meal/-bnpy-dev

74f69afde6c9dac8de4c074842df53ae87a15ac1

[ "BSD-3-Clause" ]

null

''' undecorate_for_profiling.py Explore all the python functions in the user-specified directory, and remove decoration @profile from appropriate functions ''' import os if __name__ == '__main__': import argparse parser = argparse.ArgumentParser() parser.add_argument('rootdir') args = parser.parse_args() main(args.rootdir)

29.588235

69

0.624254

''' undecorate_for_profiling.py Explore all the python functions in the user-specified directory, and remove decoration @profile from appropriate functions ''' import os def main(bnpyrootdir): list_of_files = {} for (dirpath, contentdirs, contentfiles) in os.walk(bnpyrootdir): for fname in contentfiles: if fname[-3:] == '.py': fullpathkey = os.sep.join([dirpath, fname]) list_of_files[fullpathkey] = fname for origPath in list_of_files.keys(): profPath = origPath + 'CLEAN' profFileObj = open(profPath, 'w') with open(origPath, 'r') as f: for line in f.readlines(): if line.count('@profile') == 0: profFileObj.write(line) profFileObj.close() os.rename(profPath, origPath) if __name__ == '__main__': import argparse parser = argparse.ArgumentParser() parser.add_argument('rootdir') args = parser.parse_args() main(args.rootdir)

635

0

23

e0006a8f256b6783c06d6c65cdca6a156de2c8d9

1,734

py

Python

Proxy/MachineLearn/classification.py

Crispae/BBPRED

226c6347d986da4b0f573f1b7a978b9418d0eeb4

[ "MIT" ]

null

Proxy/MachineLearn/classification.py

Crispae/BBPRED

226c6347d986da4b0f573f1b7a978b9418d0eeb4

[ "MIT" ]

null

Proxy/MachineLearn/classification.py

Crispae/BBPRED

226c6347d986da4b0f573f1b7a978b9418d0eeb4

[ "MIT" ]

null

## classification.py __all__ = ["Lazy",] from .lazy import LazyClassifier from .utils import * from sklearn.model_selection import train_test_split import pandas

23.12

140

0.672434

## classification.py __all__ = ["Lazy",] from .lazy import LazyClassifier from .utils import * from sklearn.model_selection import train_test_split import pandas class Lazy: def _lazy_split(self,descriptors_data,test_size,random_state): if Data_frame_validator(descriptors_data): data = descriptors_data.drop("Target",axis=1) Target = descriptors_data["Target"] X_train, X_test, y_train, y_test = train_test_split(data, Target,test_size=test_size,random_state =random_state,stratify=Target) return [X_train,X_test,y_train,y_test] def _lazy_classifier(self,tests,verbose,ignore_warnings): clf = LazyClassifier(verbose=verbose,ignore_warnings=ignore_warnings, custom_metric=None) models,predictions = clf.fit(*tests) return (models,predictions) def lazy_classify(self,descriptors,test_size=0.3,random_state=42,verbose=False,ignore_warnings=True): return self._lazy_classifier(self._lazy_split(descriptors,test_size,random_state),verbose=verbose,ignore_warnings=ignore_warnings) class custom: pass class NaiveByes(custom): # def Naive_fit(self): # def pick_best(X_train, X_test, y_train, y_test,): # best = (None, 0) # for var_smoothing in range(-7, 1): # clf = GaussianNB(var_smoothing=pow(10, var_smoothing)) # clf.fit(X_train, y_train) # y_pred = clf.predict(X_test) # accuracy = (y_pred == y_test).sum() # if accuracy > best[1]: # best = (clf, accuracy) # print('best accuracy', best[1] / len(y_test)) # return best[0] # model = pick_best(*cl_data1,) pass class Svm(custom): pass

824

523

181

9d438aadf58244488ff98e5078d8104573590578

3,099

py

Python

pkgs/sdk-pkg/src/genie/libs/sdk/libs/abstracted_libs/iosxr/subsection.py

jbronikowski/genielibs

200a34e5fe4838a27b5a80d5973651b2e34ccafb

[ "Apache-2.0" ]

94

2018-04-30T20:29:15.000Z

2022-03-29T13:40:31.000Z

pkgs/sdk-pkg/src/genie/libs/sdk/libs/abstracted_libs/iosxr/subsection.py

jbronikowski/genielibs

200a34e5fe4838a27b5a80d5973651b2e34ccafb

[ "Apache-2.0" ]

67

2018-12-06T21:08:09.000Z

2022-03-29T18:00:46.000Z

pkgs/sdk-pkg/src/genie/libs/sdk/libs/abstracted_libs/iosxr/subsection.py

jbronikowski/genielibs

200a34e5fe4838a27b5a80d5973651b2e34ccafb

[ "Apache-2.0" ]

49

2018-06-29T18:59:03.000Z

2022-03-10T02:07:59.000Z

# Python import logging from os import path # Abstract from genie.abstract import Lookup # Parser from genie.libs import parser from genie.metaparser.util.exceptions import SchemaEmptyParserError # unicon from unicon.eal.dialogs import Statement, Dialog log = logging.getLogger(__name__) def save_device_information(device, **kwargs): """Install the commit packages. This is for IOSXR devices. Args: Mandatory: device (`obj`) : Device object. Returns: True: Result is PASSED False: Result is PASSX Raises: None Example: >>> save_device_information(device=Device()) """ # Checking the config-register has 0x2 # if not configure 0x2 # RP/0/RSP1/CPU0:PE1#admin config-register 0x2 if device.is_ha: conn = device.active else: conn = device # Install commit ( when thre are package to bring up features) # from admin prompt conn.admin_execute('install commit') def get_default_dir(device): """ Get the default directory of this device Args: Mandatory: device (`obj`) : Device object. Returns: default_dir (`str`): Default directory of the system Raises: Exception Example: >>> get_default_dir(device=device) """ try: lookup = Lookup.from_device(device) parsed_dict = lookup.parser.show_platform.Dir(device=device).parse() if ":" in parsed_dict['dir']['dir_name']: default_dir = parsed_dict['dir']['dir_name'] else: default_dir = '' except SchemaEmptyParserError as e: raise Exception("No output when executing 'dir' command") from e except Exception as e: raise Exception("Unable to execute 'dir' command") from e # Return default_dir to caller log.info("Default directory on '{d}' is '{dir}'".format(d=device.name, dir=default_dir)) return default_dir def configure_replace(device, file_location, timeout=60, file_name=None): """Configure replace on device Args: device (`obj`): Device object file_location (`str`): File location timeout (`int`): Timeout value in seconds file_name (`str`): File name Returns: None Raises: pyATS Results """ if file_name: file_location = '{}{}'.format( file_location, file_name) try: # check if file exist device.execute.error_pattern.append('.*Path does not exist.*') device.execute("dir {}".format(file_location)) except Exception: raise Exception("File {} does not exist".format(file_location)) dialog = Dialog([ Statement(pattern=r'\[no\]', action='sendline(y)', loop_continue=True, continue_timer=False)]) device.configure("load {}\ncommit replace".format(file_location), timeout=timeout, reply=dialog)

26.042017

77

0.601162

# Python import logging from os import path # Abstract from genie.abstract import Lookup # Parser from genie.libs import parser from genie.metaparser.util.exceptions import SchemaEmptyParserError # unicon from unicon.eal.dialogs import Statement, Dialog log = logging.getLogger(__name__) def save_device_information(device, **kwargs): """Install the commit packages. This is for IOSXR devices. Args: Mandatory: device (`obj`) : Device object. Returns: True: Result is PASSED False: Result is PASSX Raises: None Example: >>> save_device_information(device=Device()) """ # Checking the config-register has 0x2 # if not configure 0x2 # RP/0/RSP1/CPU0:PE1#admin config-register 0x2 if device.is_ha: conn = device.active else: conn = device # Install commit ( when thre are package to bring up features) # from admin prompt conn.admin_execute('install commit') def get_default_dir(device): """ Get the default directory of this device Args: Mandatory: device (`obj`) : Device object. Returns: default_dir (`str`): Default directory of the system Raises: Exception Example: >>> get_default_dir(device=device) """ try: lookup = Lookup.from_device(device) parsed_dict = lookup.parser.show_platform.Dir(device=device).parse() if ":" in parsed_dict['dir']['dir_name']: default_dir = parsed_dict['dir']['dir_name'] else: default_dir = '' except SchemaEmptyParserError as e: raise Exception("No output when executing 'dir' command") from e except Exception as e: raise Exception("Unable to execute 'dir' command") from e # Return default_dir to caller log.info("Default directory on '{d}' is '{dir}'".format(d=device.name, dir=default_dir)) return default_dir def configure_replace(device, file_location, timeout=60, file_name=None): """Configure replace on device Args: device (`obj`): Device object file_location (`str`): File location timeout (`int`): Timeout value in seconds file_name (`str`): File name Returns: None Raises: pyATS Results """ if file_name: file_location = '{}{}'.format( file_location, file_name) try: # check if file exist device.execute.error_pattern.append('.*Path does not exist.*') device.execute("dir {}".format(file_location)) except Exception: raise Exception("File {} does not exist".format(file_location)) dialog = Dialog([ Statement(pattern=r'\[no\]', action='sendline(y)', loop_continue=True, continue_timer=False)]) device.configure("load {}\ncommit replace".format(file_location), timeout=timeout, reply=dialog)

0

fb43dcb45f5d7511c1b7ad5465521087e7f16242

3,879

py

Python

open_fmri/apps/dataset/migrations/0001_initial.py

rwblair/open_fmri

5e3052878b6d514553a074a6d9d44fe740daa034

[ "BSD-3-Clause" ]

5

2016-01-18T20:54:18.000Z

2021-02-10T10:43:59.000Z

open_fmri/apps/dataset/migrations/0001_initial.py

rwblair/open_fmri

5e3052878b6d514553a074a6d9d44fe740daa034

[ "BSD-3-Clause" ]

25

2015-12-02T17:37:45.000Z

2018-02-05T22:07:51.000Z

open_fmri/apps/dataset/migrations/0001_initial.py

rwblair/open_fmri

5e3052878b6d514553a074a6d9d44fe740daa034

[ "BSD-3-Clause" ]

6

2015-11-19T23:26:47.000Z

2021-02-10T10:44:01.000Z

# -*- coding: utf-8 -*- from __future__ import unicode_literals from django.db import migrations, models import django.core.validators

41.265957

224

0.565094

# -*- coding: utf-8 -*- from __future__ import unicode_literals from django.db import migrations, models import django.core.validators class Migration(migrations.Migration): dependencies = [ ] operations = [ migrations.CreateModel( name='Dataset', fields=[ ('id', models.AutoField(primary_key=True, verbose_name='ID', serialize=False, auto_created=True)), ('workflow_stage', models.CharField(default='SUBMITTED', choices=[('SUBMITTED', 'Submitted'), ('IN_PROCESS', 'In Process'), ('STAGED', 'Staged'), ('SHARED', 'Shared'), ('REVIEW', 'Review')], max_length=200)), ('project_name', models.CharField(max_length=255)), ('summary', models.TextField()), ('sample_size', models.IntegerField()), ('scanner_type', models.TextField()), ('accession_number', models.CharField(max_length=200)), ('acknowledgements', models.TextField()), ('license_title', models.CharField(max_length=255)), ('license_url', models.TextField(validators=[django.core.validators.URLValidator()])), ('aws_link_title', models.CharField(max_length=255)), ('aws_link_url', models.TextField(validators=[django.core.validators.URLValidator()])), ], ), migrations.CreateModel( name='Investigator', fields=[ ('id', models.AutoField(primary_key=True, verbose_name='ID', serialize=False, auto_created=True)), ('investigator', models.CharField(max_length=200)), ], ), migrations.CreateModel( name='PublicationDocument', fields=[ ('id', models.AutoField(primary_key=True, verbose_name='ID', serialize=False, auto_created=True)), ('document', models.FileField(upload_to='')), ], ), migrations.CreateModel( name='PublicationFullText', fields=[ ('id', models.AutoField(primary_key=True, verbose_name='ID', serialize=False, auto_created=True)), ('full_text', models.TextField()), ], ), migrations.CreateModel( name='PublicationPubMedLink', fields=[ ('id', models.AutoField(primary_key=True, verbose_name='ID', serialize=False, auto_created=True)), ('title', models.CharField(max_length=255)), ('url', models.TextField(validators=[django.core.validators.URLValidator()])), ], ), migrations.CreateModel( name='Task', fields=[ ('id', models.AutoField(primary_key=True, verbose_name='ID', serialize=False, auto_created=True)), ('cogat_id', models.TextField()), ('name', models.TextField()), ], ), migrations.AddField( model_name='dataset', name='investigator', field=models.ManyToManyField(to='dataset.Investigator'), ), migrations.AddField( model_name='dataset', name='publication_document', field=models.ManyToManyField(to='dataset.PublicationDocument'), ), migrations.AddField( model_name='dataset', name='publication_full_text', field=models.ManyToManyField(to='dataset.PublicationFullText'), ), migrations.AddField( model_name='dataset', name='publication_pubmed_link', field=models.ManyToManyField(to='dataset.PublicationPubMedLink'), ), migrations.AddField( model_name='dataset', name='task', field=models.ManyToManyField(to='dataset.Task'), ), ]

0

3,719

23

9fc19f32a9cf06da8897f17f045d1bb765cb8c81

2,849

py

Python

packages/core/minos-microservice-saga/tests/test_saga/test_executions/test_repositories/test_abc.py

minos-framework/minos-python

9a6ad6783361f3d8a497a088808b55ea7a938c6c

[ "MIT" ]

247

2022-01-24T14:55:30.000Z

2022-03-25T12:06:17.000Z

packages/core/minos-microservice-saga/tests/test_saga/test_executions/test_repositories/test_abc.py

minos-framework/minos-python

9a6ad6783361f3d8a497a088808b55ea7a938c6c

[ "MIT" ]

168

2022-01-24T14:54:31.000Z

2022-03-31T09:31:09.000Z

packages/core/minos-microservice-saga/tests/test_saga/test_executions/test_repositories/test_abc.py

minos-framework/minos-python

9a6ad6783361f3d8a497a088808b55ea7a938c6c

[ "MIT" ]

21

2022-02-06T17:25:58.000Z

2022-03-27T04:50:29.000Z

import unittest from unittest.mock import ( AsyncMock, call, ) from uuid import ( UUID, ) from minos.saga import ( SagaExecution, SagaExecutionRepository, ) from tests.utils import ( ADD_ORDER, SagaTestCase, ) if __name__ == "__main__": unittest.main()

29.371134

69

0.692524

import unittest from unittest.mock import ( AsyncMock, call, ) from uuid import ( UUID, ) from minos.saga import ( SagaExecution, SagaExecutionRepository, ) from tests.utils import ( ADD_ORDER, SagaTestCase, ) class _SagaExecutionRepository(SagaExecutionRepository): async def _store(self, execution: SagaExecution) -> None: """For testing purposes.""" async def _load(self, uuid: UUID) -> SagaExecution: """For testing purposes.""" async def _delete(self, key: UUID) -> None: """For testing purposes.""" class TestSagaExecutionRepository(SagaTestCase): async def test_store(self): mock = AsyncMock() repository = _SagaExecutionRepository() repository._store = mock execution = SagaExecution.from_definition(ADD_ORDER) await repository.store(execution) self.assertEqual([call(execution)], mock.call_args_list) async def test_load(self): execution = SagaExecution.from_definition(ADD_ORDER) repository = _SagaExecutionRepository() mock = AsyncMock(return_value=execution) repository._load = mock observed = await repository.load(execution.uuid) self.assertEqual(execution, observed) self.assertEqual([call(execution.uuid)], mock.call_args_list) async def test_load_from_str(self): execution = SagaExecution.from_definition(ADD_ORDER) repository = _SagaExecutionRepository() mock = AsyncMock(return_value=execution) repository._load = mock observed = await repository.load(str(execution.uuid)) self.assertEqual(execution, observed) self.assertEqual([call(execution.uuid)], mock.call_args_list) async def test_delete(self): execution = SagaExecution.from_definition(ADD_ORDER) repository = _SagaExecutionRepository() mock = AsyncMock(return_value=execution) repository._delete = mock await repository.delete(execution) self.assertEqual([call(execution.uuid)], mock.call_args_list) async def test_delete_from_uuid(self): execution = SagaExecution.from_definition(ADD_ORDER) repository = _SagaExecutionRepository() mock = AsyncMock(return_value=execution) repository._delete = mock await repository.delete(execution.uuid) self.assertEqual([call(execution.uuid)], mock.call_args_list) async def test_delete_from_str(self): execution = SagaExecution.from_definition(ADD_ORDER) repository = _SagaExecutionRepository() mock = AsyncMock(return_value=execution) repository._delete = mock await repository.delete(str(execution.uuid)) self.assertEqual([call(execution.uuid)], mock.call_args_list) if __name__ == "__main__": unittest.main()

2,014

338

207

fc3a1fcfc078a5900b3d3012da99b9ea49b5adb1

768

py

Python

data_assimilation/forwardModel.py

MagneticEarth/book.magneticearth.org

c8c1e3403b682a508a61053ce330b0e891992ef3

[ "CC-BY-4.0" ]

null

data_assimilation/forwardModel.py

MagneticEarth/book.magneticearth.org

c8c1e3403b682a508a61053ce330b0e891992ef3

[ "CC-BY-4.0" ]

null

data_assimilation/forwardModel.py

MagneticEarth/book.magneticearth.org

c8c1e3403b682a508a61053ce330b0e891992ef3

[ "CC-BY-4.0" ]

null

import numpy as np from scipy.integrate import solve_ivp import matplotlib.pyplot as plt

26.482759

83

0.605469

import numpy as np from scipy.integrate import solve_ivp import matplotlib.pyplot as plt def Lorenz63(t, y, sig, rho, beta): # Lorenz '63 model out = np.zeros_like(y) out[0] = sig * ( y[1] - y[0] ) out[1] = y[0] * ( rho - y[2] ) - y[1] out[2] = y[0] * y[1] - beta * y[2] return out def forwardModel_r( xt0, time, rayleigh, prandtl, b): # perform integration of Lorentz63 model # default integrator for solve_ivp is RK4 rho = rayleigh beta = b sig = prandtl myParams = np.array( [sig, rho, beta], dtype=float ) tstart = time[0] tend = time[-1] y0 = np.array( xt0, dtype=float ) sol = solve_ivp( Lorenz63, [tstart,tend], y0, args=myParams, dense_output=True) xt = sol.sol(time) return xt

632

0

47

25fcb95f8a4a06a86c76be9be03f3139ee47ed77

8,133

py

Python

samples/example_by.py

jokva/windrose

99a2f636a6558a29e7ded63d0d233f25dc7986b6

[ "CECILL-B", "BSD-3-Clause" ]

null

samples/example_by.py

jokva/windrose

99a2f636a6558a29e7ded63d0d233f25dc7986b6

[ "CECILL-B", "BSD-3-Clause" ]

null

samples/example_by.py

jokva/windrose

99a2f636a6558a29e7ded63d0d233f25dc7986b6

[ "CECILL-B", "BSD-3-Clause" ]

1

2020-10-04T18:48:35.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- from __future__ import print_function """ sample using "by" keyword """ import click # import matplotlib # matplotlib.use("Agg") # import matplotlib as mpl import matplotlib.pyplot as plt import matplotlib.cm as cm import numpy as np import pandas as pd from windrose import (WindroseAxes, FIGSIZE_DEFAULT, DPI_DEFAULT) class Layout(object): """ Inspired from PdfPages https://github.com/matplotlib/matplotlib/blob/master/lib/matplotlib/backends/backend_pdf.py - PdfPages http://matplotlib.org/api/backend_pdf_api.html http://matplotlib.org/examples/pylab_examples/multipage_pdf.html Inspired also from FFMpegWriter http://matplotlib.org/examples/animation/moviewriter.html https://github.com/matplotlib/matplotlib/blob/master/lib/matplotlib/animation.py MovieWriter """ @property S_FIGSIZE_DEFAULT = ",".join(map(str, FIGSIZE_DEFAULT)) @click.command() @click.option("--filename", default="samples/sample_wind_poitiers.csv", help="Input filename") @click.option("--filename_out", default="windrose_animation.mp4", help="Output filename") @click.option("--dpi", default=DPI_DEFAULT, help="Dot per inch for plot generation") @click.option("--figsize", default=S_FIGSIZE_DEFAULT, help="Figure size x,y - default=%s" % S_FIGSIZE_DEFAULT) @click.option("--fps", default=7, help="Number of frame per seconds for video generation") @click.option("--bins_min", default=0.01, help="Bins minimum value") @click.option("--bins_max", default=20, help="Bins maximum value") @click.option("--bins_step", default=2, help="Bins step value") if __name__ == "__main__": main()

31.523256

110

0.624985

#!/usr/bin/env python # -*- coding: utf-8 -*- from __future__ import print_function """ sample using "by" keyword """ import click # import matplotlib # matplotlib.use("Agg") # import matplotlib as mpl import matplotlib.pyplot as plt import matplotlib.cm as cm import numpy as np import pandas as pd from windrose import (WindroseAxes, FIGSIZE_DEFAULT, DPI_DEFAULT) class AxCollection(object): def __init__(self, fig=None, *args, **kwargs): if fig is None: self.fig = plt.figure(figsize=FIGSIZE_DEFAULT, dpi=DPI_DEFAULT, facecolor='w', edgecolor='w') else: self.fig = fig def animate(self): pass def show(self): pass class Layout(object): """ Inspired from PdfPages https://github.com/matplotlib/matplotlib/blob/master/lib/matplotlib/backends/backend_pdf.py - PdfPages http://matplotlib.org/api/backend_pdf_api.html http://matplotlib.org/examples/pylab_examples/multipage_pdf.html Inspired also from FFMpegWriter http://matplotlib.org/examples/animation/moviewriter.html https://github.com/matplotlib/matplotlib/blob/master/lib/matplotlib/animation.py MovieWriter """ def __init__(self, ncols=4, nrows=6, nsheets=1): self.ncols = ncols self.nrows = nrows self.nsheets = nsheets self._resize() self._i = 0 @property def fig(self): return self._array_fig def _resize(self): # self._array_ax = np.empty((self.nsheets, self.nrows, self.ncols), dtype=object) self._array_ax = np.empty(self.nsheets, dtype=object) # self._array_ax.fill(None) self._array_fig = np.empty(self.nsheets, dtype=object) # self._array_fig.fill(None) for i in range(self.nsheets): fig, axs = plt.subplots(nrows=self.nrows, ncols=self.ncols) # print(fig, axs) self._array_fig[i] = fig self._array_ax[i] = axs def __repr__(self): s = """<Layout cols: %s rows: %s sheets: %s >""" % (self.ncols, self.nrows, self.nsheets) return s def __enter__(self, *args, **kwargs): print("enter %s %s" % (args, kwargs)) return self def __exit__(self, type, value, traceback): # print("exit %s %s" % (args, kwargs)) print("exit %s %s %s" % (type, value, traceback)) # print("exit") self.close() def close(self): print("close") def saveax(self): print("saveax") self._i += 1 class NormalLayout(Layout): def __init__(self): super(NormalLayout, self).__init__() S_FIGSIZE_DEFAULT = ",".join(map(str, FIGSIZE_DEFAULT)) def by_func_yearly(dt): return dt.year def by_func_monthly(dt): return dt.year, dt.month def by_func_daily(dt): return dt.year, dt.month, dt.day @click.command() @click.option("--filename", default="samples/sample_wind_poitiers.csv", help="Input filename") @click.option("--filename_out", default="windrose_animation.mp4", help="Output filename") @click.option("--dpi", default=DPI_DEFAULT, help="Dot per inch for plot generation") @click.option("--figsize", default=S_FIGSIZE_DEFAULT, help="Figure size x,y - default=%s" % S_FIGSIZE_DEFAULT) @click.option("--fps", default=7, help="Number of frame per seconds for video generation") @click.option("--bins_min", default=0.01, help="Bins minimum value") @click.option("--bins_max", default=20, help="Bins maximum value") @click.option("--bins_step", default=2, help="Bins step value") def main(filename, dpi, figsize, fps, bins_min, bins_max, bins_step, filename_out): # convert figsize (string like "8,9" to a list of float [8.0, 9.0] figsize = figsize.split(",") figsize = map(float, figsize) # Read CSV file to a Pandas DataFrame df_all = pd.read_csv(filename) df_all['Timestamp'] = pd.to_datetime(df_all['Timestamp']) df_all = df_all.set_index('Timestamp') df_all.index = df_all.index.tz_localize('UTC').tz_convert('UTC') # df_all = df_all.iloc[-10000:,:] df_all = df_all.ix['2011-07-01':'2011-12-31'] # Get Numpy arrays from DataFrame direction_all = df_all['direction'].values var_all = df_all['speed'].values index_all = df_all.index.to_datetime() # Fixed: .values -> to_datetime() by_all = df_all.index.map(by_func_monthly) by_unique = np.unique(by_all) print(by_unique) (ncols, nrows, nsheets) = (4, 3, 2) # noqa # layout = Layout(4, 3, 2) # ncols, nrows, nsheets # layout = Layout(ncols, nrows, nsheets) # layout = Layout(4, 6, 1) # layout.save(ax) # layout.to_pdf("filename.pdf") # layout.to_video("filename.mp4") # fig, ax = plt.subplots(nrows=2, ncols=3) # with Layout(4, 6, 1) as layout: # print(layout) # #layout.save(ax) def tuple_position(i, ncols, nrows): i_sheet, sheet_pos = divmod(i, ncols * nrows) i_row, i_col = divmod(sheet_pos, ncols) return i_sheet, i_row, i_col def position_from_tuple(t, ncols, nrows): i_sheet, i_row, i_col = t return i_sheet * ncols * nrows + i_row * ncols + i_col assert tuple_position(0, ncols, nrows) == (0, 0, 0) assert tuple_position(1, ncols, nrows) == (0, 0, 1) assert tuple_position(2, ncols, nrows) == (0, 0, 2) assert tuple_position(3, ncols, nrows) == (0, 0, 3) assert tuple_position(4, ncols, nrows) == (0, 1, 0) assert tuple_position(5, ncols, nrows) == (0, 1, 1) assert tuple_position(6, ncols, nrows) == (0, 1, 2) assert tuple_position(7, ncols, nrows) == (0, 1, 3) assert tuple_position(8, ncols, nrows) == (0, 2, 0) assert tuple_position(9, ncols, nrows) == (0, 2, 1) assert tuple_position(10, ncols, nrows) == (0, 2, 2) assert tuple_position(11, ncols, nrows) == (0, 2, 3) assert tuple_position(12, ncols, nrows) == (1, 0, 0) assert tuple_position(13, ncols, nrows) == (1, 0, 1) assert tuple_position(14, ncols, nrows) == (1, 0, 2) assert tuple_position(15, ncols, nrows) == (1, 0, 3) assert tuple_position(16, ncols, nrows) == (1, 1, 0) assert tuple_position(17, ncols, nrows) == (1, 1, 1) assert position_from_tuple((0, 0, 0), ncols, nrows) == 0 assert position_from_tuple((1, 0, 0), ncols, nrows) == ncols * nrows assert position_from_tuple((2, 0, 0), ncols, nrows) == 2 * ncols * nrows assert position_from_tuple((1, 0, 1), ncols, nrows) == ncols * nrows + 1 assert position_from_tuple((1, 1, 1), ncols, nrows) == ncols * nrows + ncols + 1 assert position_from_tuple((1, 2, 3), ncols, nrows) == ncols * nrows + 2 * ncols + 3 for i in range(20): t = tuple_position(i, ncols, nrows) assert position_from_tuple(t, ncols, nrows) == i # layout = NormalLayout() # with layout.append() as ax: # pass # layout.show() # Define bins bins = np.arange(bins_min, bins_max, bins_step) for by_value in by_unique: # by_value = (2011, 5) # mask = (by == by_value).all(axis=1) # ToFix: see http://stackoverflow.com/questions/32005403/boolean-indexing-with-numpy-array-and-tuples mask = (pd.Series(by_all) == by_value).values # print(mask) index = index_all[mask] var = var_all[mask] direction = direction_all[mask] # Create figure # fig = plt.figure(figsize=figsize, dpi=dpi, facecolor='w', edgecolor='w') # Same as above, but with contours over each filled region... ax = WindroseAxes.from_ax() ax.contourf(direction, var, bins=bins, cmap=cm.hot) ax.contour(direction, var, bins=bins, colors='black') fontname = "Courier" # title = by_value dt1 = index[0] dt2 = index[-1] # dt1 = df.index[mask][0] # dt2 = df.index[mask][-1] # td = dt2 - dt1 title = "From %s\n to %s" % (dt1, dt2) ax.set_title(title, fontname=fontname) ax.set_legend() plt.show() # time.sleep(10) # print("Save file to '%s'" % filename_out) if __name__ == "__main__": main()

5,964

12

458

8aa086dfd06626e4b4e36485c4d38dd75160c536

8,316

py

Python

gt/reid.py

solapark/frcnn_keras_original

3561d1de18f41868efc9cec927761613d75a5dc3

[ "Apache-2.0" ]

null

gt/reid.py

solapark/frcnn_keras_original

3561d1de18f41868efc9cec927761613d75a5dc3

[ "Apache-2.0" ]

null

gt/reid.py

solapark/frcnn_keras_original

3561d1de18f41868efc9cec927761613d75a5dc3

[ "Apache-2.0" ]

null

import numpy as np import os, sys sys.path.append(os.path.dirname(os.path.abspath(os.path.dirname(__file__)))) import utility from epipolar import EPIPOLAR import cv2 if __name__ == '__main__': from option import args import pickle with open('/home/sap/frcnn_keras/mv_train_two_reid.pickle', 'rb') as f: reid_pickle = pickle.load(f) pred_box, pred_box_emb, pred_box_prob, reid_box_gt = reid_pickle reid = REID(args) reid_box_pred, is_valid = reid.get_reid_box(pred_box, pred_box_emb, pred_box_prob) print('reid_box_pred.shape', reid_box_pred.shape, 'is_valid', is_valid.shape) pred_box_batch, pred_box_emb_batch, pred_box_prob_batch = list(map(lambda a : np.expand_dims(a, 0), [pred_box, pred_box_emb, pred_box_prob])) reid_box_pred_batch, is_valid_batch = reid.get_batch(pred_box_batch, pred_box_emb_batch, pred_box_prob_batch) print('reid_box_pred_batch.shape', reid_box_pred_batch.shape, 'is_valid_batch', is_valid_batch.shape) print(np.array_equal(reid_box_pred_batch[0], reid_box_pred), np.array_equal(is_valid_batch[0], is_valid)) ''' is_valid = np.ones((self.num_nms, self.num_valid_cam)) with open('/home/sap/frcnn_keras/pred_box_is_valid.pickle', 'wb') as f: pickle.dump(is_valid, f) for i in range(10) : print('gt', reid_box_gt[i]) print('pred', reid_box_pred[i]) print('valid', is_valid[i]) if(np.array_equal(reid_box_gt, reid_box_pred)) : print('good') else : print('bad') '''

46.983051

162

0.641775

import numpy as np import os, sys sys.path.append(os.path.dirname(os.path.abspath(os.path.dirname(__file__)))) import utility from epipolar import EPIPOLAR import cv2 class REID: def __init__(self, args): self.num_valid_cam = args.num_valid_cam self.num_nms = args.num_nms self.batch_size = args.batch_size self.rpn_stride = args.rpn_stride self.reid_min_emb_dist = args.reid_min_emb_dist self.cam_idx = np.repeat(np.arange(self.num_valid_cam), self.num_nms).reshape(self.num_valid_cam, self.num_nms, 1) self.num_nms_arange = np.arange(self.num_nms) self.num_nms_arange_repeat = np.repeat(self.num_nms_arange, self.num_valid_cam).reshape(self.num_nms, self.num_valid_cam, 1).transpose(1, 0, 2) self.box_idx_stack = np.concatenate([self.cam_idx, self.num_nms_arange_repeat], 2).reshape(self.num_valid_cam*self.num_nms, 2) #(num_valid_cam*num_nms, 2) self.epipolar = EPIPOLAR(args) self.args = args def get_min_emb_dist_idx(self, emb, embs, thresh = np.zeros(0), is_want_dist = 0, epi_dist = np.zeros(0)): ''' Args : emb (shape : m, n) embs (shape : m, k, n) thresh_dist : lower thersh. throw away too small dist (shape : m, ) Return : min_dist_idx (shape : m, 1) ''' emb_ref = emb[:, np.newaxis, :] dist = utility.calc_emb_dist(emb_ref, embs) #(m, k) if epi_dist.any() : dist[np.where(epi_dist > self.args.epi_dist_thresh)] = np.inf if(thresh.size) : thresh = thresh[:, np.newaxis] #(m, 1) dist[dist<=thresh] = np.inf min_dist_idx = np.argmin(dist, 1) #(m, ) if(is_want_dist): min_dist = dist[np.arange(len(dist)), min_dist_idx] return min_dist_idx, min_dist return min_dist_idx def get_ref(self, pred_box_prob, pred_box, pred_box_emb) : pred_box_prob_stack = np.reshape(pred_box_prob, (self.num_valid_cam*self.num_nms, )) top_N_box_idx = self.box_idx_stack[pred_box_prob_stack.argsort()[-self.num_nms:]] top_N_box_idx = tuple(top_N_box_idx.T) ref_cam_idx = top_N_box_idx[0] ref_box = pred_box[top_N_box_idx] ref_emb = pred_box_emb[top_N_box_idx] return ref_cam_idx, ref_box, ref_emb def get_ref_cam_idx_batch(self, pred_box_prob_batch, pred_box_batch, pred_box_emb_batch) : ref_cam_idx_batch = [] for pred_box_prob, pred_box, pred_box_emb in zip(pred_box_prob_batch, pred_box_batch, pred_box_emb_batch) : ref_cam_idx, _, _ = self.get_ref(pred_box_prob, pred_box, pred_box_emb) ref_cam_idx_batch.append(ref_cam_idx) return np.array(ref_cam_idx_batch) def get_batch(self, *args): reid_box_pred_batch, is_valid_batch, dist_batch = [], [], [] for args_one_batch in zip(*args) : reid_box_pred, is_valid, dist = self.get_reid_box(*args_one_batch) reid_box_pred_batch.append(reid_box_pred) is_valid_batch.append(is_valid) dist_batch.append(dist) return np.array(reid_box_pred_batch), np.array(is_valid_batch), np.array(dist_batch) def get_reid_box(self, pred_box, pred_box_emb, pred_box_prob, extrins, debug_img_np): """get ref idx, postive idx, negative idx for reid training Args : pred_box : x1, y1, x2, y2 #(num_valid_cam, 300, 4) pred_box_emb #(num_valid_cam, 300, featsize) pred_box_prob #(num_valid_cam, 300) extrins #(num_cam, 3, 3) Return : reid_box #(300, num_valid_cam, 4) is_valid #(300, num_valid_cam) dist #(300, num_valid_cam) distance from ref box """ reid_box = np.zeros((self.num_nms, self.num_valid_cam, 4)) is_valid = np.ones((self.num_nms, self.num_valid_cam)) dist = np.zeros((self.num_nms, self.num_valid_cam)) ref_cam_idx, ref_box, ref_emb = self.get_ref(pred_box_prob, pred_box, pred_box_emb) reid_box[self.num_nms_arange, ref_cam_idx] = ref_box self.epipolar.reset(extrins, debug_img_np) for offset in range(1, self.num_valid_cam): target_cam_idx = (ref_cam_idx + offset) % self.num_valid_cam cand_emb = pred_box_emb[target_cam_idx] cand_box = pred_box[target_cam_idx] epi_dist = np.ones((self.num_nms, self.num_nms)) for i in range(self.num_nms): epi_dist[i] = self.epipolar.get_epipolar_dist(ref_cam_idx[i], target_cam_idx[i], ref_box[i].reshape(-1, 4), cand_box[i]) min_dist_idx, min_dist = self.get_min_emb_dist_idx(ref_emb, cand_emb, is_want_dist=True, epi_dist = epi_dist) reid_box[self.num_nms_arange, target_cam_idx] = pred_box[target_cam_idx, min_dist_idx] dist[self.num_nms_arange, target_cam_idx] = min_dist invalid_idx = np.where(min_dist > self.reid_min_emb_dist) invalid_nms_idx = self.num_nms_arange[invalid_idx] invalid_target_cam_idx = target_cam_idx[invalid_idx] is_valid[invalid_nms_idx, invalid_target_cam_idx] = 0 return reid_box, is_valid, dist def draw_reid_batch(self, box_batch, is_valid_batch, ref_cam_idx_batch, imgs_batch, dist_batch, waitKey=0): box_batch = box_batch.astype(int)*self.rpn_stride for batch_idx in range(self.batch_size): imgs_in_one_batch = imgs_batch[batch_idx] boxes_in_one_batch = box_batch[batch_idx] is_valids_in_one_batch = is_valid_batch[batch_idx] ref_cam_idx_in_one_batch = ref_cam_idx_batch[batch_idx] dist_in_one_batch = dist_batch[batch_idx] img_list = list(imgs_in_one_batch) for box_idx in range(self.num_nms) : boxes = boxes_in_one_batch[box_idx] is_valids = is_valids_in_one_batch[box_idx] ref_cam_idx = ref_cam_idx_in_one_batch[box_idx] dists = dist_in_one_batch[box_idx] result_img_list = [] for cam_idx in range(self.num_valid_cam): box = boxes[cam_idx] is_valid = is_valids[cam_idx] dist = dists[cam_idx] if cam_idx == ref_cam_idx : color = (0, 0, 255) elif is_valid : color = (0, 0, 100) else : color = (0, 0, 0) reuslt_img = utility.draw_box(img_list[cam_idx], box, name = None, color = color, is_show = False, text = 'dist : %.2f'%dist) result_img_list.append(reuslt_img) concat_img = utility.get_concat_img(result_img_list) cv2.imshow('reid', concat_img) cv2.waitKey(waitKey) if __name__ == '__main__': from option import args import pickle with open('/home/sap/frcnn_keras/mv_train_two_reid.pickle', 'rb') as f: reid_pickle = pickle.load(f) pred_box, pred_box_emb, pred_box_prob, reid_box_gt = reid_pickle reid = REID(args) reid_box_pred, is_valid = reid.get_reid_box(pred_box, pred_box_emb, pred_box_prob) print('reid_box_pred.shape', reid_box_pred.shape, 'is_valid', is_valid.shape) pred_box_batch, pred_box_emb_batch, pred_box_prob_batch = list(map(lambda a : np.expand_dims(a, 0), [pred_box, pred_box_emb, pred_box_prob])) reid_box_pred_batch, is_valid_batch = reid.get_batch(pred_box_batch, pred_box_emb_batch, pred_box_prob_batch) print('reid_box_pred_batch.shape', reid_box_pred_batch.shape, 'is_valid_batch', is_valid_batch.shape) print(np.array_equal(reid_box_pred_batch[0], reid_box_pred), np.array_equal(is_valid_batch[0], is_valid)) ''' is_valid = np.ones((self.num_nms, self.num_valid_cam)) with open('/home/sap/frcnn_keras/pred_box_is_valid.pickle', 'wb') as f: pickle.dump(is_valid, f) for i in range(10) : print('gt', reid_box_gt[i]) print('pred', reid_box_pred[i]) print('valid', is_valid[i]) if(np.array_equal(reid_box_gt, reid_box_pred)) : print('good') else : print('bad') '''

3,683

3,084

23

869daa2374c26f25d0517f0a880b5bc63581fcf1

3,413

py

Python

dagflow/sdk.py

GodQ/autoflow

74954dafb9cdb16c29b9f3a7d081a3f3a12e808a

[ "Apache-2.0" ]

1

2019-06-20T15:31:13.000Z

dagflow/sdk.py

GodQ/dagflow

74954dafb9cdb16c29b9f3a7d081a3f3a12e808a

[ "Apache-2.0" ]

null

dagflow/sdk.py

GodQ/dagflow

74954dafb9cdb16c29b9f3a7d081a3f3a12e808a

[ "Apache-2.0" ]

null

__author__ = 'godq' import os import sys from dagflow.flow_operation import send_start_flow_msg as sdk_send_start_flow_msg, \ send_finish_step_msg as sdk_send_finish_step_msg from dagflow.loader import get_DagRepo_Object dag_repo = get_DagRepo_Object()

31.601852

98

0.745971

__author__ = 'godq' import os import sys from dagflow.flow_operation import send_start_flow_msg as sdk_send_start_flow_msg, \ send_finish_step_msg as sdk_send_finish_step_msg from dagflow.loader import get_DagRepo_Object dag_repo = get_DagRepo_Object() def send_finish_step_msg(dag_name, dag_run_id, step_name, status=None, message=None, result=None): return sdk_send_finish_step_msg(dag_name, dag_run_id, step_name, status, message, result) def send_start_flow_msg(dag_name, dag_run_id): return sdk_send_start_flow_msg(dag_name, dag_run_id) def register_dag(dag_def): assert isinstance(dag_def, dict) dag_name = dag_def.get("name") dag_repo.add_dag(dag_name=dag_name, content=dag_def) # print("Dag {} created successfully".format(dag_name)) def update_dag(dag_def): assert isinstance(dag_def, dict) dag_name = dag_def.get("name") dag_repo.update_dag(dag_name=dag_name, content=dag_def) # print("Dag {} updated successfully".format(dag_name)) def start_event_center(): # if in user folder, load user's plugins cwd = os.path.abspath(os.getcwd()) if os.path.isdir("plugins"): os.environ["USER_PLUGINS_PATH"] = os.path.join(cwd, "plugins") sys.path.append(cwd) from dagflow.event_center.event_center import start_event_center start_event_center() def start_worker(worker_count=None): from dagflow.utils.command import run_cmd from dagflow.config import Config # if in user folder, load user's plugins cwd = os.path.abspath(os.getcwd()) if os.path.isdir("plugins"): os.environ["USER_PLUGINS_PATH"] = os.path.join(cwd, "plugins") if not worker_count: worker_count = Config.celery_configs.get("worker_count", 1) cmd = "celery worker -A dagflow.executors.celery_executor -c {}".format(worker_count) print(cmd) run_cmd(cmd, daemon=True) # print("Dagflow worker has started successfully") def get_dag(dag_name): assert dag_name return dag_repo.find_dag(dag_name) def run_dag(dag_name, dag_run_id=None): import time from dagflow.flow_operation import send_start_flow_msg if not dag_run_id: dag_run_id = str(time.time()) send_start_flow_msg(dag_name, dag_run_id) # print("Dag {} started successfully with dag_run_id {}".format(dag_name, dag_run_id)) return dag_run_id def stop_dag_run(dag_name, dag_run_id): assert dag_name and dag_run_id dag_repo.stop_dag_run(dag_name, dag_run_id) # print("Dag {} stopped successfully with dag_run_id {}".format(dag_name, dag_run_id)) def get_dag_run(dag_name, dag_run_id): assert dag_name and dag_run_id return dag_repo.find_dag_run(dag_name, dag_run_id) def list_dags(detail=False): from dagflow.loader import get_DagRepo_Object repo = get_DagRepo_Object() detail = str(detail).strip().lower() detail = True if detail == "true" else False dag_list = repo.list_dags(detail=detail) return dag_list def list_dag_runs(dag_name): from dagflow.loader import get_DagRepo_Object repo = get_DagRepo_Object() dag_run_list = repo.list_dag_runs(dag_name=dag_name) return dag_run_list def list_dag_run_events(dag_name, dag_run_id): from dagflow.loader import get_DagRepo_Object repo = get_DagRepo_Object() dag_run_events_list = repo.find_dag_run_events(dag_name=dag_name, dag_run_id=dag_run_id) return dag_run_events_list

2,844

0

299

39e69d64294449e607d3625ba49e9a9d1fcc26c9

3,965

py

Python

writer.py

MrBoogie27/PlagiarismPrograms

05564131c6849747a9a7b8c56961d488cb5a2755

[ "MIT" ]

null

writer.py

MrBoogie27/PlagiarismPrograms

05564131c6849747a9a7b8c56961d488cb5a2755

[ "MIT" ]

null

writer.py

MrBoogie27/PlagiarismPrograms

05564131c6849747a9a7b8c56961d488cb5a2755

[ "MIT" ]

null

from common import compare_array from prepare_hash import run_binary_hasher import psycopg2 import tempfile from psycopg2 import sql TABLE_NAME='runs' TABLE_TEXT_MATCHES= 'text_matches' COLUMNS=('id', 'content', 'ASTHash') COLUMNS_MATCH=('first_runs_id', 'second_runs_id', 'match_AST_v1') PROBLEM_ID=3 COUNT_LIMIT = 100

38.495146

87

0.538462

from common import compare_array from prepare_hash import run_binary_hasher import psycopg2 import tempfile from psycopg2 import sql TABLE_NAME='runs' TABLE_TEXT_MATCHES= 'text_matches' COLUMNS=('id', 'content', 'ASTHash') COLUMNS_MATCH=('first_runs_id', 'second_runs_id', 'match_AST_v1') PROBLEM_ID=3 COUNT_LIMIT = 100 def get_connect(args): return psycopg2.connect(dbname=args.database, user=args.db_user, password=args.password, host=args.host) def insert_hash(row, update_data): with tempfile.NamedTemporaryFile(mode="w+t", suffix='.cpp') as fp: fp.writelines(row[1]) fp.seek(0) try: hashes = run_binary_hasher("./hasher_AST_tool", fp.name) update_data[row[0]] = hashes except Exception as e: print('error for {}'.format(row[0])) def all_update(cursor, update_data): sql_command = """UPDATE {} SET {} = %s WHERE {} = %s""" stmt = sql.SQL(sql_command).format( sql.Identifier(TABLE_NAME), sql.Identifier(COLUMNS[2]), sql.Identifier(COLUMNS[0]) ) # print(update_data) for key, hashes in update_data.items(): cursor.execute(stmt, (hashes, key)) print("updated {}".format(key)) def writer_hasher(args): update_data = {} with get_connect(args) as conn: with conn.cursor() as cursor: stmt = sql.SQL('SELECT {} FROM {} where problems_id = %s LIMIT %s').format( sql.SQL(',').join(map(sql.Identifier, COLUMNS)), sql.Identifier(TABLE_NAME) ) cursor.execute(stmt, (PROBLEM_ID, COUNT_LIMIT)) for row in cursor: if row[2] is None: insert_hash(row, update_data) all_update(cursor, update_data) def update_compared(cursor, all_compares): sql_command = """UPDATE {} SET {} = %s WHERE {} = %s and {} = %s""" stmt = sql.SQL(sql_command).format( sql.Identifier(TABLE_TEXT_MATCHES), sql.Identifier(COLUMNS_MATCH[2]), sql.Identifier(COLUMNS_MATCH[0]), sql.Identifier(COLUMNS_MATCH[1]) ) for fst_key, snd_key, comparison in all_compares: cursor.execute(stmt, (comparison, fst_key, snd_key)) print("updated comparison for {} and {}".format(fst_key, snd_key)) def writer_similarity(args): RUN_TABLE = 'runs' with get_connect(args) as conn: with conn.cursor() as cursor: stmt = sql.SQL("""SELECT first_runs_id, second_runs_id, fst_run."ASTHash" as fst_hash, snd_run."ASTHash" as snd_hash FROM {} JOIN {} as fst_run ON {}.first_runs_id = fst_run.id JOIN {} as snd_run ON {}.second_runs_id = snd_run.id WHERE {}.problems_id = %s and "match_AST_v1" IS NULL and fst_run."ASTHash" IS NOT NULL and snd_run."ASTHash" IS NOT NULL LIMIT %s""").format( sql.Identifier(TABLE_TEXT_MATCHES), sql.Identifier(RUN_TABLE), sql.Identifier(TABLE_TEXT_MATCHES), sql.Identifier(RUN_TABLE), sql.Identifier(TABLE_TEXT_MATCHES), sql.Identifier(TABLE_TEXT_MATCHES) ) cursor.execute(stmt, (PROBLEM_ID, COUNT_LIMIT)) all_compares = [] for row in cursor: compared = compare_array(row[2], row[3]) all_compares.append((row[0], row[1], compared)) update_compared(cursor, all_compares)

3,505

0

138

5c92b9919c5b5d586563d3d52b13ac345250227f

262

py

Python

api/static.py

hartliddell/api

73d44d2271c01fe7540fedeee9174c4032cbbbc0

[ "MIT" ]

null

api/static.py

hartliddell/api

73d44d2271c01fe7540fedeee9174c4032cbbbc0

[ "MIT" ]

null

api/static.py

hartliddell/api

73d44d2271c01fe7540fedeee9174c4032cbbbc0

[ "MIT" ]

null

"""Define a custom static storage class.""" from django.contrib.staticfiles.storage import ManifestStaticFilesStorage class RyrManifestStaticFilesStorage(ManifestStaticFilesStorage): """Define a custom static storage class.""" manifest_strict = False

29.111111

73

0.79771

"""Define a custom static storage class.""" from django.contrib.staticfiles.storage import ManifestStaticFilesStorage class RyrManifestStaticFilesStorage(ManifestStaticFilesStorage): """Define a custom static storage class.""" manifest_strict = False

0

f3e92c79550d52d83e93cc3237d16de166bd98cb

4,394

py

Python

todos/tests.py

shiniao/todoz

4cb2cf492f6cfac5e037da6e7b3b674ef548e62a

[ "MIT" ]

1

2020-01-13T03:32:11.000Z

todos/tests.py

shiniao/todoz

4cb2cf492f6cfac5e037da6e7b3b674ef548e62a

[ "MIT" ]

6

2021-05-10T19:58:23.000Z

2022-02-26T20:29:39.000Z

todos/tests.py

shiniao/todoz

4cb2cf492f6cfac5e037da6e7b3b674ef548e62a

[ "MIT" ]

null

# Create your tests here. import json from django.contrib.auth.models import User from django.test import TestCase, Client from .models import Todo from django.utils import timezone import datetime class TestTodosModel(TestCase): """测试数据库model""" class TestTodosViews(TestCase): """测试视图函数""" # TODO test def test_todo_put(self): """测试更新土豆""" data = { 'title': '抽烟', } uuid = self.user1.todos.all()[0].uuid rsp = self.client.put('/api/v1/todos/{}/'.format(uuid), data=data, content_type='application/json') self.assertEqual(rsp.status_code, 200) todo = self.user1.todos.all()[0] self.assertEqual(todo.title, data['title']) def test_todo_delete(self): """测试删除土豆""" uuid = self.user1.todos.all()[0].uuid rsp = self.client.delete('/api/v1/todos/{}/'.format(uuid)) print(rsp.content) self.assertEqual(rsp.status_code, 200) with self.assertRaises(Todo.DoesNotExist) as e: self.user1.todos.get(uuid=uuid) def test_todo_field_error(self): """测试字段不正确情况下报错""" data = { 'titl': '抽烟', } uuid = self.user1.todos.all()[0].uuid rsp = self.client.put('/api/v1/todos/{}/'.format(uuid), data=data, content_type='application/json') self.assertEqual(rsp.status_code, 400) class TestAuth(TestCase): """测试jwt认证"""

32.072993

85

0.574192

# Create your tests here. import json from django.contrib.auth.models import User from django.test import TestCase, Client from .models import Todo from django.utils import timezone import datetime class TestTodosModel(TestCase): """测试数据库model""" def test_is_past(self): # 未来土豆 future_date = timezone.now() + datetime.timedelta(days=30) future_todo = Todo(created=future_date) self.assertIs(future_todo.is_past(), False) # 过去土豆 past_date = timezone.now() + datetime.timedelta(days=-1) past_todo = Todo(expired=past_date) self.assertIs(past_todo.is_past(), True) # 现在土豆 now_todo = Todo(created=timezone.now()) self.assertIs(now_todo.is_past(), False) class TestTodosViews(TestCase): """测试视图函数""" def setUp(self) -> None: self.user1 = User.objects.create_user(username='user1', password='123123') self.user2 = User.objects.create_user(username='user2', password='123123') for i in range(5): Todo.objects.create( title='test{}'.format(i), owner=self.user1) data = { "username": "user1", "password": "123123" } # 获取token rsp = self.client.post('/auth/login/', data, content_type='application/json') token = rsp.json()['message'] self.client = Client( HTTP_AUTHORIZATION='Bearer {}'.format(token), HTTP_CONTENT_TYPE='application/json') # TODO test def test_todo_remove_past(self): pass def test_todos_get(self): rsp = self.client.get('/api/v1/todos/', {'per_page': 3, 'page': 1}) self.assertEqual(rsp.status_code, 200, rsp.json()) self.assertEqual(rsp.json()['message']['count'], 3) self.assertEqual(len(rsp.json()['message']['todos']), 3) def test_todos_post(self): todo = { 'title': '烫头' } rsp = self.client.post('/api/v1/todos/', data=json.dumps(todo), content_type='application/json') print(rsp.json()) self.assertEqual(rsp.status_code, 200) dtodo = self.user1.todos.get(title=todo['title']) self.assertEqual(dtodo.title, todo['title']) def test_todo_get(self): uuid = self.user1.todos.all()[0].uuid rsp = self.client.get('/api/v1/todos/{}/'.format(uuid)) self.assertEqual(rsp.status_code, 200, rsp.json()) self.assertEqual(rsp.json()['message']['title'], 'test0') def test_todo_put(self): """测试更新土豆""" data = { 'title': '抽烟', } uuid = self.user1.todos.all()[0].uuid rsp = self.client.put('/api/v1/todos/{}/'.format(uuid), data=data, content_type='application/json') self.assertEqual(rsp.status_code, 200) todo = self.user1.todos.all()[0] self.assertEqual(todo.title, data['title']) def test_todo_delete(self): """测试删除土豆""" uuid = self.user1.todos.all()[0].uuid rsp = self.client.delete('/api/v1/todos/{}/'.format(uuid)) print(rsp.content) self.assertEqual(rsp.status_code, 200) with self.assertRaises(Todo.DoesNotExist) as e: self.user1.todos.get(uuid=uuid) def test_todo_field_error(self): """测试字段不正确情况下报错""" data = { 'titl': '抽烟', } uuid = self.user1.todos.all()[0].uuid rsp = self.client.put('/api/v1/todos/{}/'.format(uuid), data=data, content_type='application/json') self.assertEqual(rsp.status_code, 400) class TestAuth(TestCase): """测试jwt认证""" def setUp(self) -> None: self.user = User.objects.create_user(username='auth_user', password='123456') def test_token(self): data = { "username": "auth_user", "password": "123456" } rsp = self.client.post('/auth/login/', data, content_type='application/json') self.assertEqual(rsp.status_code, 200, rsp.json()) def test_auth_requirement(self): pass class TestUtils(TestCase): def test_http_methods_required(self): rsp = self.client.get('/auth/login/') self.assertEqual(rsp.status_code, 405)

2,581

5

292

955eed560456dca1edea91950d78789cf913e924

4,962

py

Python

quadtree.py

eug/quadtree

7a3154f06b8f52fd8338a7b73b5a8329c399281f

[ "MIT" ]

1

2021-09-02T07:57:29.000Z

quadtree.py

eug/quadtree

7a3154f06b8f52fd8338a7b73b5a8329c399281f

[ "MIT" ]

null

quadtree.py

eug/quadtree

7a3154f06b8f52fd8338a7b73b5a8329c399281f

[ "MIT" ]

1

2020-11-05T05:57:37.000Z

import bisect from scipy.spatial.distance import euclidean from common import (NO_QUADRANT, NORTH_EAST, NORTH_WEST, SOUTH_EAST, SOUTH_WEST, Boundary, Point, belongs, compute_knn, intersects, quadrants) from node import TreeNode # Constants for tuple access optimization BOUNDARY = 0 POINTS = 1

32.012903

78

0.601169

import bisect from scipy.spatial.distance import euclidean from common import (NO_QUADRANT, NORTH_EAST, NORTH_WEST, SOUTH_EAST, SOUTH_WEST, Boundary, Point, belongs, compute_knn, intersects, quadrants) from node import TreeNode # Constants for tuple access optimization BOUNDARY = 0 POINTS = 1 class StaticQuadTree: def __init__(self, dimension=1, max_depth=4): self.max_depth = max_depth self._quadrants = [0] * int(((4 ** (max_depth + 1))-1)/3) self._quadrants[0] = (Boundary(Point(0, 0), dimension), set()) self._decompose(self._quadrants[0][BOUNDARY], 0, 0) def _decompose(self, boundary, depth, parent): if depth == self.max_depth: return x, y = boundary.center dm = boundary.dimension / 2 index0 = 4 * parent + NORTH_WEST index1 = 4 * parent + NORTH_EAST index2 = 4 * parent + SOUTH_EAST index3 = 4 * parent + SOUTH_WEST self._quadrants[index0] = (Boundary(Point(x - dm, y + dm), dm), set()) self._quadrants[index1] = (Boundary(Point(x + dm, y + dm), dm), set()) self._quadrants[index2] = (Boundary(Point(x + dm, y - dm), dm), set()) self._quadrants[index3] = (Boundary(Point(x - dm, y - dm), dm), set()) self._decompose(self._quadrants[index0][BOUNDARY], depth + 1, index0) self._decompose(self._quadrants[index1][BOUNDARY], depth + 1, index1) self._decompose(self._quadrants[index2][BOUNDARY], depth + 1, index2) self._decompose(self._quadrants[index3][BOUNDARY], depth + 1, index3) def index(self, point): idx = 0 q = quadrants(self._quadrants[idx][BOUNDARY], point) if q == NO_QUADRANT: return for _ in range(0, self.max_depth): idx = 4 * idx + q q = quadrants(self._quadrants[idx][BOUNDARY], point) return idx def __len__(self): return sum(len(q[1]) for q in self._quadrants) def __iter__(self): return (point for quad in self._quadrants for point in quad[POINTS]) def __contains__(self, point): return point in self._quadrants[self.index(point)][POINTS] def insert(self, point): self._quadrants[self.index(point)][POINTS].add(point) def remove(self, point): if not isinstance(point): return False try: self._quadrants[self.index(point)][POINTS].remove(point) return True except: return False def update(self, new_point, old_point): if not isinstance(new_point, Point) or \ not isinstance(old_point, Point): return False try: self._quadrants[self.index(old_point)][POINTS].remove(old_point) self._quadrants[self.index(new_point)][POINTS].add(new_point) return True except: return False def query_range(self, boundary): if not isinstance(boundary, Boundary): return ([]) for quadrant in self._quadrants: if intersects(quadrant[BOUNDARY], boundary): for point in quadrant[POINTS]: if belongs(boundary, point): yield point def knn(self, point, k, factor=.1): if not isinstance(point, Point) or k <= 0 or factor <= 0: return [] if len(self) < k: points = self.query_range(self._quadrants[BOUNDARY]) return compute_knn(points, point, k) points_count = 0 dimension = factor while points_count <= k: dimension += factor points_count = self._count_points(Boundary(point, dimension)) points = self.query_range(Boundary(point, dimension)) return compute_knn(points, point, k) def _count_points(self, boundary): count = 0 for quadrant in self._quadrants: if intersects(quadrant[BOUNDARY], boundary): for point in quadrant[POINTS]: if belongs(boundary, point): count += 1 return count class DynamicQuadTree: def __init__(self, dimension=1, max_points=1, max_depth=4): self.max_points = max_points self.max_depth = max_depth self.root = TreeNode(Point(0, 0), dimension, max_points, max_depth, 0) def __len__(self): return len(self.root) def __iter__(self): return iter(self.root) def __contains__(self, point): return self.root.exist(point) def insert(self, point): return self.root.insert(point) def remove(self, point): return self.root.remove(point) def update(self, new_point, old_point): return self.root.update(new_point, old_point) def query_range(self, boundary): return self.root.query_range(boundary) def knn(self, point, k): return self.root.knn(point, k)

4,010

1

613

7289ee6470e2a8f5fb3a4ac360a43fd613597c4a

523

py

Python

competition/alibaba.py

Max-PJB/python-learning2

e8b05bef1574ee9abf8c90497e94ef20a7f4e3bd

[ "MIT" ]

null

competition/alibaba.py

Max-PJB/python-learning2

e8b05bef1574ee9abf8c90497e94ef20a7f4e3bd

[ "MIT" ]

null

competition/alibaba.py

Max-PJB/python-learning2

e8b05bef1574ee9abf8c90497e94ef20a7f4e3bd

[ "MIT" ]

null

# N, a, b, c, d = list(map(int, input().split())) # # # def jc(x): # r = 1 # for i in range(1, x + 1): # r *= i # return r # # res = int(jc(N * N) / (jc(a) * jc(b) * jc(c) * jc(d))) % # print(res) from collections import defaultdict n = int(input()) edges = defaultdict(list) for _ in range(n - 1): u, v = list(map(int, input().split())) edges[u].append(v) print(subtree(1))

17.433333

68

0.529637

# N, a, b, c, d = list(map(int, input().split())) # # # def jc(x): # r = 1 # for i in range(1, x + 1): # r *= i # return r # # res = int(jc(N * N) / (jc(a) * jc(b) * jc(c) * jc(d))) % # print(res) from collections import defaultdict n = int(input()) edges = defaultdict(list) for _ in range(n - 1): u, v = list(map(int, input().split())) edges[u].append(v) def subtree(k): if edges[k]: return max(list(map(subtree, edges[k]))) + len(edges[k]) - 1 else: return 0 print(subtree(1))

107

0

23

5a8a1562596c85fca9c274efc7e80bd787910272

4,948

py

Python

neo4j/__main__.py

ank-forked/neo4j-python-driver

f7857791051e0a7499aea9da5f92256cef7eb014

[ "Apache-2.0" ]

null

neo4j/__main__.py

ank-forked/neo4j-python-driver

f7857791051e0a7499aea9da5f92256cef7eb014

[ "Apache-2.0" ]

null

neo4j/__main__.py

ank-forked/neo4j-python-driver

f7857791051e0a7499aea9da5f92256cef7eb014

[ "Apache-2.0" ]

null

#!/usr/bin/env python # -*- encoding: utf-8 -*- # Copyright (c) 2002-2015 "Neo Technology," # Network Engine for Objects in Lund AB [http://neotechnology.com] # # This file is part of Neo4j. # # 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 __future__ import unicode_literals from argparse import ArgumentParser from json import loads as json_loads import logging from sys import stdout, stderr from neo4j.session import GraphDatabase, CypherError class ColourFormatter(logging.Formatter): """ Colour formatter for pretty log output. """ class Watcher(object): """ Log watcher for debug output. """ handlers = {} if __name__ == "__main__": main()

37.203008

92

0.599232

#!/usr/bin/env python # -*- encoding: utf-8 -*- # Copyright (c) 2002-2015 "Neo Technology," # Network Engine for Objects in Lund AB [http://neotechnology.com] # # This file is part of Neo4j. # # 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 __future__ import unicode_literals from argparse import ArgumentParser from json import loads as json_loads import logging from sys import stdout, stderr from neo4j.session import GraphDatabase, CypherError class ColourFormatter(logging.Formatter): """ Colour formatter for pretty log output. """ def format(self, record): s = super(ColourFormatter, self).format(record) if record.levelno == logging.CRITICAL: return "\x1b[31;1m%s\x1b[0m" % s # bright red elif record.levelno == logging.ERROR: return "\x1b[33;1m%s\x1b[0m" % s # bright yellow elif record.levelno == logging.WARNING: return "\x1b[33m%s\x1b[0m" % s # yellow elif record.levelno == logging.INFO: return "\x1b[36m%s\x1b[0m" % s # cyan elif record.levelno == logging.DEBUG: return "\x1b[34m%s\x1b[0m" % s # blue else: return s class Watcher(object): """ Log watcher for debug output. """ handlers = {} def __init__(self, logger_name): super(Watcher, self).__init__() self.logger_name = logger_name self.logger = logging.getLogger(self.logger_name) self.formatter = ColourFormatter("%(asctime)s %(message)s") def watch(self, level=logging.INFO, out=stdout): try: self.logger.removeHandler(self.handlers[self.logger_name]) except KeyError: pass handler = logging.StreamHandler(out) handler.setFormatter(self.formatter) self.handlers[self.logger_name] = handler self.logger.addHandler(handler) self.logger.setLevel(level) def main(): parser = ArgumentParser(description="Execute one or more Cypher statements using Bolt.") parser.add_argument("statement", nargs="+") parser.add_argument("-u", "--url", default="bolt://localhost", metavar="CONNECTION_URL") parser.add_argument("-p", "--parameter", action="append", metavar="NAME=VALUE") parser.add_argument("-q", "--quiet", action="store_true") parser.add_argument("-s", "--secure", action="store_true") parser.add_argument("-v", "--verbose", action="count") parser.add_argument("-x", "--times", type=int, default=1) parser.add_argument("-z", "--summarize", action="store_true") args = parser.parse_args() if args.verbose: level = logging.INFO if args.verbose == 1 else logging.DEBUG Watcher("neo4j").watch(level, stderr) parameters = {} for parameter in args.parameter or []: name, _, value = parameter.partition("=") if value == "" and name in parameters: del parameters[name] else: try: parameters[name] = json_loads(value) except ValueError: parameters[name] = value driver = GraphDatabase.driver(args.url, secure=args.secure) session = driver.session() for _ in range(args.times): for statement in args.statement: try: result = session.run(statement, parameters) except CypherError as error: stderr.write("%s: %s\r\n" % (error.code, error.message)) else: if not args.quiet: has_results = False for i, record in enumerate(result): has_results = True if i == 0: stdout.write("%s\r\n" % "\t".join(record.__keys__)) stdout.write("%s\r\n" % "\t".join(map(repr, record))) if has_results: stdout.write("\r\n") if args.summarize: summary = result.summarize() stdout.write("Statement : %r\r\n" % summary.statement) stdout.write("Parameters : %r\r\n" % summary.parameters) stdout.write("Statement Type : %r\r\n" % summary.statement_type) stdout.write("Statistics : %r\r\n" % summary.statistics) stdout.write("\r\n") session.close() if __name__ == "__main__": main()

3,659

0

104

b1351f952c91be994aa5c5780c6c740a6d7dd951

1,506

py

Python

naga/shared/trainer.py

bright1993ff66/emoji2vec

08281486c3b3c6da9c5ecc13e140baefc6e48326

[ "MIT" ]

173

2016-10-03T18:28:13.000Z

2019-09-26T10:36:54.000Z

naga/shared/trainer.py

bright1993ff66/emoji2vec

08281486c3b3c6da9c5ecc13e140baefc6e48326

[ "MIT" ]

2

2016-10-25T18:28:48.000Z

2019-08-04T21:50:12.000Z

naga/shared/trainer.py

bright1993ff66/emoji2vec

08281486c3b3c6da9c5ecc13e140baefc6e48326

[ "MIT" ]

39

2016-10-04T13:35:29.000Z

2019-09-11T18:06:54.000Z

import tensorflow as tf class Trainer(object): """ Object representing a TensorFlow trainer. """

32.73913

108

0.579681

import tensorflow as tf class Trainer(object): """ Object representing a TensorFlow trainer. """ def __init__(self, optimizer, max_epochs, hooks=[]): self.loss = None self.optimizer = optimizer self.max_epochs = max_epochs self.hooks = hooks def __call__(self, batcher, placeholders, loss, model=None, session=None): self.loss = loss minimization_op = self.optimizer.minimize(loss) close_session_after_training = False if session is None: session = tf.Session() close_session_after_training = True # no session existed before, we provide a temporary session init = tf.initialize_all_variables() session.run(init) epoch = 1 iteration = 1 while epoch < self.max_epochs: for values in batcher: feed_dict = {} for i in range(0, len(placeholders)): feed_dict[placeholders[i]] = values[i] _, current_loss = session.run([minimization_op, loss], feed_dict=feed_dict) current_loss = sum(current_loss) for hook in self.hooks: hook(session, epoch, iteration, model, current_loss) iteration += 1 # calling post-epoch hooks for hook in self.hooks: hook(session, epoch, 0, model, 0) epoch += 1 if close_session_after_training: session.close()

1,341

0

54

916dd0a0fef78307533ccaae6243fb5d10a059e7

4,107

py

Python

test_dp_setting_initial_conditions_bem.py

whitews/dpconverge

0e00a7f1c0be8bd291cb68598ca2b2eaa1a47448

[ "BSD-3-Clause" ]

null

test_dp_setting_initial_conditions_bem.py

whitews/dpconverge

0e00a7f1c0be8bd291cb68598ca2b2eaa1a47448

[ "BSD-3-Clause" ]

null

test_dp_setting_initial_conditions_bem.py

whitews/dpconverge

0e00a7f1c0be8bd291cb68598ca2b2eaa1a47448

[ "BSD-3-Clause" ]

null

from dpconverge.data_set import DataSet import numpy as np import pandas as pd from matplotlib import pyplot from sklearn.datasets.samples_generator import make_blobs n_features = 2 points_per_feature = 100 centers = [[2, 1.35], [2, 2], [2, 3], [2.5, 1.5], [2.5, 2], [2.5, 2.5]] blob1, y1 = make_blobs( n_samples=1000, n_features=1, centers=centers[0], cluster_std=[0.1, 0.15], random_state=1 ) blob2, y2 = make_blobs( n_samples=6000, n_features=1, centers=centers[1], cluster_std=[0.2, 0.3], random_state=2 ) blob3, y3 = make_blobs( n_samples=3000, n_features=1, centers=centers[2], cluster_std=[0.2, 0.1], random_state=2 ) blob4, y4 = make_blobs( n_samples=250, n_features=1, centers=centers[3], cluster_std=[0.1, 0.1], random_state=2 ) blob5, y5 = make_blobs( n_samples=250, n_features=1, centers=centers[4], cluster_std=[0.1, 0.1], random_state=3 ) ds = DataSet(parameter_count=2) ds.add_blob(1, blob1) ds.add_blob(2, blob2) ds.add_blob(3, blob3) ds.add_blob(4, blob4) ds.add_blob(5, blob5) # ds.plot_blobs(ds.classifications, x_lim=[0, 4], y_lim=[0, 4]) component_count = 128 iteration_count = 5000 # use multiple runs of BEM to estimate the number of components # and get initial conditions max_log_like = None # the highest value for all runs converged = False results = [] # will be a list of dicts to convert to a DataFrame while not converged: print component_count new_comp_counts = [] for seed in range(1, 17): ds.results = None # reset results ds.cluster( component_count=component_count, burn_in=0, iteration_count=iteration_count, random_seed=seed, model='bem' ) log_like = ds.get_log_likelihood_trace()[0] print log_like if log_like > max_log_like: max_log_like = log_like # if the new log_like is close to the max (within 1%), # see if there are any empty components (pi < 0.0001) if abs(max_log_like - log_like) < abs(max_log_like * 0.01): tmp_comp_count = np.sum(ds.raw_results.pis > 0.0001) new_comp_counts.append(tmp_comp_count) # save good run to our results results.append( { 'comp': component_count, 'true_comp': tmp_comp_count, 'seed': seed, 'log_like': log_like, 'pis': ds.raw_results.pis, 'mus': ds.raw_results.mus, 'sigmas': ds.raw_results.sigmas } ) # ds.plot_classifications(0) if len(new_comp_counts) > 0: if int(np.mean(new_comp_counts)) < component_count: component_count = int(np.mean(new_comp_counts)) else: converged = True else: converged = True results_df = pd.DataFrame( results, columns=['comp', 'true_comp', 'seed', 'log_like'] ) min_comp_count = results_df.comp.min() best_index = results_df[results_df.comp == min_comp_count].log_like.argmax() best_run = results[best_index] ds.results = None ds.cluster( component_count=best_run['comp'], burn_in=0, iteration_count=iteration_count, random_seed=best_run['seed'], model='bem' ) log_like = ds.get_log_likelihood_trace()[0] print log_like ds.plot_classifications(0) # Re-run a chain using the initial conditions from the last iteration last_iter = ds.raw_results.get_iteration(0) initial_conditions = { 'pis': last_iter.pis.flatten(), 'mus': last_iter.mus, 'sigmas': last_iter.sigmas } # reset DataSet results ds.results = None ds.cluster( component_count=best_run['comp'], burn_in=0, iteration_count=iteration_count, random_seed=1, initial_conditions=initial_conditions ) ds.plot_log_likelihood_trace() pyplot.show() valid_components = ds.get_valid_components() for i in range(best_run['comp']): ds.plot_iteration_traces(i) ds.plot_animated_trace() pass

22.944134

76

0.637448

from dpconverge.data_set import DataSet import numpy as np import pandas as pd from matplotlib import pyplot from sklearn.datasets.samples_generator import make_blobs n_features = 2 points_per_feature = 100 centers = [[2, 1.35], [2, 2], [2, 3], [2.5, 1.5], [2.5, 2], [2.5, 2.5]] blob1, y1 = make_blobs( n_samples=1000, n_features=1, centers=centers[0], cluster_std=[0.1, 0.15], random_state=1 ) blob2, y2 = make_blobs( n_samples=6000, n_features=1, centers=centers[1], cluster_std=[0.2, 0.3], random_state=2 ) blob3, y3 = make_blobs( n_samples=3000, n_features=1, centers=centers[2], cluster_std=[0.2, 0.1], random_state=2 ) blob4, y4 = make_blobs( n_samples=250, n_features=1, centers=centers[3], cluster_std=[0.1, 0.1], random_state=2 ) blob5, y5 = make_blobs( n_samples=250, n_features=1, centers=centers[4], cluster_std=[0.1, 0.1], random_state=3 ) ds = DataSet(parameter_count=2) ds.add_blob(1, blob1) ds.add_blob(2, blob2) ds.add_blob(3, blob3) ds.add_blob(4, blob4) ds.add_blob(5, blob5) # ds.plot_blobs(ds.classifications, x_lim=[0, 4], y_lim=[0, 4]) component_count = 128 iteration_count = 5000 # use multiple runs of BEM to estimate the number of components # and get initial conditions max_log_like = None # the highest value for all runs converged = False results = [] # will be a list of dicts to convert to a DataFrame while not converged: print component_count new_comp_counts = [] for seed in range(1, 17): ds.results = None # reset results ds.cluster( component_count=component_count, burn_in=0, iteration_count=iteration_count, random_seed=seed, model='bem' ) log_like = ds.get_log_likelihood_trace()[0] print log_like if log_like > max_log_like: max_log_like = log_like # if the new log_like is close to the max (within 1%), # see if there are any empty components (pi < 0.0001) if abs(max_log_like - log_like) < abs(max_log_like * 0.01): tmp_comp_count = np.sum(ds.raw_results.pis > 0.0001) new_comp_counts.append(tmp_comp_count) # save good run to our results results.append( { 'comp': component_count, 'true_comp': tmp_comp_count, 'seed': seed, 'log_like': log_like, 'pis': ds.raw_results.pis, 'mus': ds.raw_results.mus, 'sigmas': ds.raw_results.sigmas } ) # ds.plot_classifications(0) if len(new_comp_counts) > 0: if int(np.mean(new_comp_counts)) < component_count: component_count = int(np.mean(new_comp_counts)) else: converged = True else: converged = True results_df = pd.DataFrame( results, columns=['comp', 'true_comp', 'seed', 'log_like'] ) min_comp_count = results_df.comp.min() best_index = results_df[results_df.comp == min_comp_count].log_like.argmax() best_run = results[best_index] ds.results = None ds.cluster( component_count=best_run['comp'], burn_in=0, iteration_count=iteration_count, random_seed=best_run['seed'], model='bem' ) log_like = ds.get_log_likelihood_trace()[0] print log_like ds.plot_classifications(0) # Re-run a chain using the initial conditions from the last iteration last_iter = ds.raw_results.get_iteration(0) initial_conditions = { 'pis': last_iter.pis.flatten(), 'mus': last_iter.mus, 'sigmas': last_iter.sigmas } # reset DataSet results ds.results = None ds.cluster( component_count=best_run['comp'], burn_in=0, iteration_count=iteration_count, random_seed=1, initial_conditions=initial_conditions ) ds.plot_log_likelihood_trace() pyplot.show() valid_components = ds.get_valid_components() for i in range(best_run['comp']): ds.plot_iteration_traces(i) ds.plot_animated_trace() pass

0

495def4124e4516ef147f20dbb2acf3dd3301455

102

py

Python

vit_pytorch/__init__.py

WilliamAshbee/vit-pytorch

a033eae5f6ad1c609c06b762371cc43ca3930662

[ "MIT" ]

null

vit_pytorch/__init__.py

WilliamAshbee/vit-pytorch

a033eae5f6ad1c609c06b762371cc43ca3930662

[ "MIT" ]

null

vit_pytorch/__init__.py

WilliamAshbee/vit-pytorch

a033eae5f6ad1c609c06b762371cc43ca3930662

[ "MIT" ]

null

from vit_pytorch.vit import ViT from vit_pytorch.vit3d import ViT3d from vit_pytorch.dino import Dino

25.5

35

0.852941

from vit_pytorch.vit import ViT from vit_pytorch.vit3d import ViT3d from vit_pytorch.dino import Dino

0

754d3a88e63c470befd4e37201725706f9d4214f

2,469

py

Python

pincer/objects/message/component.py

Kylianalex/Pincer

7ca530798a696c70e7d5c939902653575e3d8054

[ "MIT" ]

1

2021-11-04T13:20:23.000Z

pincer/objects/message/component.py

Kylianalex/Pincer

7ca530798a696c70e7d5c939902653575e3d8054

[ "MIT" ]

1

2021-10-31T11:41:42.000Z

pincer/objects/message/component.py

Kylianalex/Pincer

7ca530798a696c70e7d5c939902653575e3d8054

[ "MIT" ]

1

2021-11-17T13:55:07.000Z

# Copyright Pincer 2021-Present # Full MIT License can be found in `LICENSE` at the project root. from __future__ import annotations from dataclasses import dataclass from typing import TYPE_CHECKING from ...utils.api_object import APIObject from ...utils.types import MISSING if TYPE_CHECKING: from typing import List from ..app.select_menu import SelectOption from ..message.button import ButtonStyle from ..message.emoji import Emoji from ...utils.types import APINullable @dataclass(repr=False) class MessageComponent(APIObject): """Represents a Discord Message Component object Attributes ---------- type: :class:`int` Component type options: List[:class:`~pincer.objects.app.select_menu.SelectOption`] The choices in the select, max 25 custom_id: APINullable[:class:`str`] A developer-defined identifier for the component, max 100 characters disabled: APINullable[:class:`bool`] Whether the component is disabled, defaults to `False` style: APINullable[:class:`~pincer.objects.message.button.ButtonStyle`] One of button styles label: APINullable[:class:`str`] Text that appears on the button, max 80 characters emoji: APINullable[:class:`~pincer.objects.message.emoji.Emoji`] ``name``, ``id``, and ``animated`` url: APINullable[:class:`str`] A url for link-style buttons placeholder: APINullable[:class:`str`] Custom placeholder text if nothing is selected, max 100 characters min_values: APINullable[:class:`int`] The minimum number of items that must be chosen; |default| ``1``, min ``0``, max ``25`` max_values: APINullable[:class:`int`] The maximum number of items that can be chosen; |default| ``1``, max ``25`` components: APINullable[List[:class:`~pincer.objects.message.component.MessageComponent`]] A list of child components """ # noqa: E501 type: int options: List[SelectOption] = MISSING custom_id: APINullable[str] = MISSING disabled: APINullable[bool] = False style: APINullable[ButtonStyle] = MISSING label: APINullable[str] = MISSING emoji: APINullable[Emoji] = MISSING url: APINullable[str] = MISSING placeholder: APINullable[str] = MISSING min_values: APINullable[int] = 1 max_values: APINullable[int] = 1 components: APINullable[List[MessageComponent]] = MISSING

34.774648

94

0.687323

# Copyright Pincer 2021-Present # Full MIT License can be found in `LICENSE` at the project root. from __future__ import annotations from dataclasses import dataclass from typing import TYPE_CHECKING from ...utils.api_object import APIObject from ...utils.types import MISSING if TYPE_CHECKING: from typing import List from ..app.select_menu import SelectOption from ..message.button import ButtonStyle from ..message.emoji import Emoji from ...utils.types import APINullable @dataclass(repr=False) class MessageComponent(APIObject): """Represents a Discord Message Component object Attributes ---------- type: :class:`int` Component type options: List[:class:`~pincer.objects.app.select_menu.SelectOption`] The choices in the select, max 25 custom_id: APINullable[:class:`str`] A developer-defined identifier for the component, max 100 characters disabled: APINullable[:class:`bool`] Whether the component is disabled, defaults to `False` style: APINullable[:class:`~pincer.objects.message.button.ButtonStyle`] One of button styles label: APINullable[:class:`str`] Text that appears on the button, max 80 characters emoji: APINullable[:class:`~pincer.objects.message.emoji.Emoji`] ``name``, ``id``, and ``animated`` url: APINullable[:class:`str`] A url for link-style buttons placeholder: APINullable[:class:`str`] Custom placeholder text if nothing is selected, max 100 characters min_values: APINullable[:class:`int`] The minimum number of items that must be chosen; |default| ``1``, min ``0``, max ``25`` max_values: APINullable[:class:`int`] The maximum number of items that can be chosen; |default| ``1``, max ``25`` components: APINullable[List[:class:`~pincer.objects.message.component.MessageComponent`]] A list of child components """ # noqa: E501 type: int options: List[SelectOption] = MISSING custom_id: APINullable[str] = MISSING disabled: APINullable[bool] = False style: APINullable[ButtonStyle] = MISSING label: APINullable[str] = MISSING emoji: APINullable[Emoji] = MISSING url: APINullable[str] = MISSING placeholder: APINullable[str] = MISSING min_values: APINullable[int] = 1 max_values: APINullable[int] = 1 components: APINullable[List[MessageComponent]] = MISSING

0

d1746864f4611777bffb76d6b2649465292c511c

1,771

py

Python

resources/unite_tax_to_newick.py

colinbrislawn/hundo

38f5da8e63fdbd314e99f4eff3668c8adb5a0a5f

[ "MIT" ]

null

resources/unite_tax_to_newick.py

colinbrislawn/hundo

38f5da8e63fdbd314e99f4eff3668c8adb5a0a5f

[ "MIT" ]

null

resources/unite_tax_to_newick.py

colinbrislawn/hundo

38f5da8e63fdbd314e99f4eff3668c8adb5a0a5f

[ "MIT" ]

null

#!/usr/bin/env python # coding=utf-8 """ """ import click import os from collections import defaultdict from hundo.fasta import read_fasta, format_fasta_record @click.group() @click.pass_context def cli(obj): """ """ @cli.command("tax-to-newick") @click.argument("tax", type=click.File("r")) @click.argument("fasta", type=click.File("r")) @click.argument("outfasta", type=click.File("w")) @click.argument("outmap", type=click.File("w")) @click.argument("outtre", type=click.File("w")) def tax_to_newick(tax, fasta, outfasta, outmap, outtre): """ Tax and FASTA input files represent clusters at 99%% identity via: https://unite.ut.ee/sh_files/sh_mothur_release_10.10.2017.zip """ t = tree() saved = set() for line in tax: toks = line.strip().split("\t") taxonomies = toks[1].strip(";").split(";") if not taxonomies[0] == "k__Fungi": continue assert(len(taxonomies) == 7) tree_add(t, taxonomies) print(toks[0], taxonomies[6], sep="\t", file=outmap) saved.add(toks[0]) tree_str = tree_to_newick(t) print(tree_str, file=outtre) for name, seq in read_fasta(fasta): if name in saved: print(format_fasta_record(name, seq), file=outfasta) if __name__ == '__main__': cli()

25.666667

70

0.578769

#!/usr/bin/env python # coding=utf-8 """ """ import click import os from collections import defaultdict from hundo.fasta import read_fasta, format_fasta_record @click.group() @click.pass_context def cli(obj): """ """ @cli.command("tax-to-newick") @click.argument("tax", type=click.File("r")) @click.argument("fasta", type=click.File("r")) @click.argument("outfasta", type=click.File("w")) @click.argument("outmap", type=click.File("w")) @click.argument("outtre", type=click.File("w")) def tax_to_newick(tax, fasta, outfasta, outmap, outtre): """ Tax and FASTA input files represent clusters at 99%% identity via: https://unite.ut.ee/sh_files/sh_mothur_release_10.10.2017.zip """ def tree(): return defaultdict(tree) def tree_add(t, path): for node in path: t = t[node] def tree_to_newick(root): items = [] for k in root.keys(): s = '' if len(root[k].keys()) > 0: sub_tree = tree_to_newick(root[k]) if sub_tree != '': s += '(' + sub_tree + ')' s += k items.append(s) return ','.join(items) t = tree() saved = set() for line in tax: toks = line.strip().split("\t") taxonomies = toks[1].strip(";").split(";") if not taxonomies[0] == "k__Fungi": continue assert(len(taxonomies) == 7) tree_add(t, taxonomies) print(toks[0], taxonomies[6], sep="\t", file=outmap) saved.add(toks[0]) tree_str = tree_to_newick(t) print(tree_str, file=outtre) for name, seq in read_fasta(fasta): if name in saved: print(format_fasta_record(name, seq), file=outfasta) if __name__ == '__main__': cli()

390

0

80

40c987a08a1ef8707bf650d224f8475fede83800

1,026

py

Python

venv/lib/python3.6/site-packages/marshmallow/base.py

aitoehigie/britecore_flask

eef1873dbe6b2cc21f770bc6dec783007ae4493b

[ "MIT" ]

null

venv/lib/python3.6/site-packages/marshmallow/base.py

aitoehigie/britecore_flask

eef1873dbe6b2cc21f770bc6dec783007ae4493b

[ "MIT" ]

1

2021-06-01T23:32:38.000Z

venv/lib/python3.6/site-packages/marshmallow/base.py

aitoehigie/britecore_flask

eef1873dbe6b2cc21f770bc6dec783007ae4493b

[ "MIT" ]

null

# -*- coding: utf-8 -*- """Abstract base classes. These are necessary to avoid circular imports between core.py and fields.py. """ import copy class FieldABC(object): """Abstract base class from which all Field classes inherit. """ parent = None name = None class SchemaABC(object): """Abstract base class from which all Schemas inherit."""

21.829787

76

0.658869

# -*- coding: utf-8 -*- """Abstract base classes. These are necessary to avoid circular imports between core.py and fields.py. """ import copy class FieldABC(object): """Abstract base class from which all Field classes inherit. """ parent = None name = None def serialize(self, attr, obj, accessor=None): raise NotImplementedError def deserialize(self, value): raise NotImplementedError def _serialize(self, value, attr, obj): raise NotImplementedError def _deserialize(self, value, attr, ob): raise NotImplementedError def __deepcopy__(self, memo): ret = copy.copy(self) return ret class SchemaABC(object): """Abstract base class from which all Schemas inherit.""" def dump(self, obj): raise NotImplementedError def dumps(self, obj, *args, **kwargs): raise NotImplementedError def load(self, data): raise NotImplementedError def loads(self, data): raise NotImplementedError

416

0

243

e8d6ac802de2774ffc5d0f5f58cb2e9373e7792f

2,501

py

Python

CRNNHandle.py

zuoyuwei/crnn_torch_trt

ba1f7e8d113a25325389ba2435cef9a548e55210

[ "MIT" ]

null

CRNNHandle.py

zuoyuwei/crnn_torch_trt

ba1f7e8d113a25325389ba2435cef9a548e55210

[ "MIT" ]

null

CRNNHandle.py

zuoyuwei/crnn_torch_trt

ba1f7e8d113a25325389ba2435cef9a548e55210

[ "MIT" ]

1

2020-07-29T05:20:04.000Z

import torch from torchvision import transforms import os import cv2 import time import numpy as np # alphabetfrom .keys import alphabet import params from torch.autograd import Variable from PIL import Image from utils import strLabelConverter,resizeNormalize converter = strLabelConverter(params.alphabet) # converter = strLabelConverter(''.join(alphabet))

27.788889

88

0.572171

import torch from torchvision import transforms import os import cv2 import time import numpy as np # alphabetfrom .keys import alphabet import params from torch.autograd import Variable from PIL import Image from utils import strLabelConverter,resizeNormalize converter = strLabelConverter(params.alphabet) # converter = strLabelConverter(''.join(alphabet)) class CRNNHandle(): def __init__(self,model_path , net , gpu_id=None ): ''' 初始化pytorch模型 :param model_path: 模型地址(可以是模型的参数或者参数和计算图一起保存的文件) :param net: 网络计算图，如果在model_path中指定的是参数的保存路径，则需要给出网络的计算图 :param gpu_id: 在哪一块gpu上运行 ''' if gpu_id is not None and isinstance(gpu_id, int) and torch.cuda.is_available(): self.device = torch.device("cuda:{}".format(gpu_id)) else: self.device = torch.device("cpu") self.net = torch.load(model_path, map_location=self.device) print('device:', self.device) if net is not None: # 如果网络计算图和参数是分开保存的，就执行参数加载 net = net.to(self.device) try: sk = {} for k in self.net: sk[k.replace("module.","")] = self.net[k] # sk[k[7:]] = self.net[k] net.load_state_dict(sk) except Exception as e: print(e) net.load_state_dict(self.net) self.net = net print('load model') self.net.eval() def predict(self, im): """ 预测 """ image = im.convert('L') scale = image.size[1] * 1.0 / 32 w = image.size[0] / scale w = int(w) img = image.resize([1000, 32], Image.BILINEAR) tft = transforms.ToTensor() image = tft(img) image.sub_(0.5).div_(0.5) # transformer = resizeNormalize((1024, 32)) # image = transformer(image) # image = image.to(self.device) image = image.view(1, *image.size()) image = Variable(image) preds = self.net(image) rr = preds.cpu().detach().numpy() _, preds = preds.max(2) preds = preds.transpose(1, 0).contiguous().view(-1) preds_size = Variable(torch.LongTensor([preds.size(0)])) # preds_size = Variable(torch.IntTensor([preds.size(0)])) sim_pred = converter.decode(preds.data, preds_size.data, raw=False) return sim_pred def train(self, images, labels): return

27

2,284

23

077dc1cf93a34eabf2a6423dfc342a97bc442c61

6,331

py

Python

tests/integration/worker/fixture.py

geometry-labs/icon-governance

2f084de358525808c6f05ab99a686463d2273c8b

[ "Apache-2.0" ]

null

tests/integration/worker/fixture.py

geometry-labs/icon-governance

2f084de358525808c6f05ab99a686463d2273c8b

[ "Apache-2.0" ]

23

2021-10-04T19:13:57.000Z

2022-02-16T23:50:15.000Z

tests/integration/worker/fixture.py

geometry-labs/icon-governance

2f084de358525808c6f05ab99a686463d2273c8b

[ "Apache-2.0" ]

null

peers = { ("54.95.16.98", "hxdabfd26d6c01038acae8081580fcce86c802fd01"), ("54.184.241.211", "hx016401bba6a5474e08c925b6390e1ef1d8e0adc9"), ("35.170.9.187", "hx9fa9d224306b0722099d30471b3c2306421aead7"), ("65.108.47.101", "hx54d6f19c3d16b2ef23c09c885ca1ba776aaa80e2"), ("13.91.36.115", "hxc5e0b88cb9092bbd8b004a517996139334752f62"), ("3.139.211.90", "hx9780bfcd8d33c50f56e37f5b27313433c28eb8d8"), ("18.142.22.234", "hx4e39d214d1e682f2acba7a08c4f7591fb5baaade"), ("44.231.211.22", "hx5f5c750fef3fb5cbce15951bf2266f1e412a7797"), ("174.138.3.225", "hx6f89b2c25c15f6294c79810221753131067ed3f8"), ("3.37.81.62", "hxaf33a0c15dbf52b76590422cbb7e2d835034cdf6"), ("44.198.151.153", "hxc72bb8230e602072dc8e1b3abf9a08fd1db7464b"), ("44.235.153.121", "hxfdcd12d6cab5d684859076ded73c726f29b1ea7b"), ("144.91.102.161", "hxd2de5d155251ff62bf2f6c2aa616da6472886165"), ("35.76.191.119", "hxd0f86e4f465dadbb722b8946fcc3ce192f9298d2"), ("35.226.20.97", "hxf08bd5835fdb53dc7c764a5f4dd4e2e6359324e8"), ("3.224.23.76", 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66.642105

70

0.751856

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0

d4db0e97f8dd644aa8041e83eae4beaa62181324

1,952

py

Python

Scripts_Python/Neuronal_20200417/CGAN/Discriminator.py

IshanBaliyan/DEEP-TFM_with_cGAN

8d711c025367031197e5b8c7c768fc9fbea406ce

[ "MIT" ]

1

2021-08-17T14:47:37.000Z

Scripts_Python/Neuronal_20200417/CGAN/Discriminator.py

IshanBaliyan/DEEP-TFM_with_cGAN

8d711c025367031197e5b8c7c768fc9fbea406ce

[ "MIT" ]

null

Scripts_Python/Neuronal_20200417/CGAN/Discriminator.py

IshanBaliyan/DEEP-TFM_with_cGAN

8d711c025367031197e5b8c7c768fc9fbea406ce

[ "MIT" ]

1

2020-12-11T23:53:43.000Z

import numpy as np import torch import torch.nn.functional as F import torch.nn as nn

33.655172

59

0.552254

import numpy as np import torch import torch.nn.functional as F import torch.nn as nn class Discriminator(nn.Module): def __init__(self, hidden_dim=1024): super(Discriminator, self).__init__() self.hidden_dim = hidden_dim self.conv1 = nn.Conv2d(33, 16, 4, 2, 1) self.conv2 = nn.Conv2d(16, 32, 4, 2, 1) self.conv3 = nn.Conv2d(32, 64, 4, 2, 1) self.conv4 = nn.Conv2d(64, 128, 4, 2, 1) self.conv5 = nn.Conv2d(128, 256, 4, 2, 1) self.conv6 = nn.Conv2d(256, 512, 4, 2, 1) self.re = nn.LeakyReLU(0.2, True) self.bn1 = nn.BatchNorm2d(16) self.bn2 = nn.BatchNorm2d(32) self.bn3 = nn.BatchNorm2d(64) self.bn4 = nn.BatchNorm2d(128) self.bn5 = nn.BatchNorm2d(256) self.bn7 = nn.BatchNorm2d(512) self.bn6 = nn.BatchNorm2d(1) self.dconv = nn.ConvTranspose2d(512, 256, 4, 2, 1) self.dconv1 = nn.ConvTranspose2d(256, 128, 4, 2, 1) self.dconv2 = nn.ConvTranspose2d(128, 64, 4, 2, 1) self.dconv3 = nn.ConvTranspose2d(64, 32, 4, 2, 1) self.dconv4 = nn.ConvTranspose2d(32, 16, 4, 2, 1) self.dconv5 = nn.ConvTranspose2d(16, 1, 4, 2, 1) def forward(self, x): e1 = self.re(self.bn1(self.conv1(x))) e2 = self.re(self.bn2(self.conv2(e1))) e3 = self.re(self.bn3(self.conv3(e2))) e4 = self.re(self.bn4(self.conv4(e3))) e5 = self.re(self.bn5(self.conv5(e4))) e6 = self.re(self.bn7(self.conv6(e5))) d6 = self.dconv(e6) d6 = self.re(self.bn5(d6)) d5 = self.dconv1(d6) d5 = self.re(self.bn4(d5)) d4 = self.dconv2(d5) d4 = self.re(self.bn3(d4)) d3 = self.dconv3(d4) d3 = self.re(self.bn2(d3)) d2 = self.dconv4(d3) d2 = self.re(self.bn1(d2)) d1 = self.dconv5(d2) out = self.re(self.bn6(d1)) return out

1,769

10

84

68ae7d329b80fdf5298ee6cf2403df2d82abe871

12,483

py

Python

maci/distributions/real_nvp_bijector.py

bbrito/mapr2

5aa1a4c85c28918d9f16e5544793bf5574d7c49e

[ "Apache-2.0" ]

35

2019-01-13T17:55:03.000Z

2022-02-23T17:06:53.000Z

maci/distributions/real_nvp_bijector.py

arita37/mapr2

57f76875a4a6aed1850d3fb8604683bfe8a0e09b

[ "Apache-2.0" ]

18

2019-03-10T23:12:00.000Z

2022-03-21T22:17:09.000Z

maci/distributions/real_nvp_bijector.py

arita37/mapr2

57f76875a4a6aed1850d3fb8604683bfe8a0e09b

[ "Apache-2.0" ]

19

2019-01-13T20:47:00.000Z

2021-11-09T05:59:13.000Z

"""RealNVP bijector.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import tensorflow as tf import numpy as np ConditionalBijector = tf.contrib.distributions.bijectors.ConditionalBijector __all__ = [ "RealNVPBijector", ] def checkerboard(shape, parity='even', dtype=tf.bool): """TODO: Implement for dimensions >1""" if len(shape) > 1: raise NotImplementedError( "checkerboard not yet implemented for dimensions >1") unit = (tf.constant((True, False)) if parity == 'even' else tf.constant((False, True))) num_elements = np.prod(shape) tiled = tf.tile(unit, ((num_elements // 2) + 1, ))[:num_elements] return tf.cast(tf.reshape(tiled, shape), dtype) class CouplingBijector(ConditionalBijector): """TODO""" def __init__(self, parity, translation_fn, scale_fn, event_ndims=0, validate_args=False, name="coupling_bijector"): """Instantiates the `CouplingBijector` bijector. Args: TODO event_ndims: Python scalar indicating the number of dimensions associated with a particular draw from the distribution. validate_args: Python `bool` indicating whether arguments should be checked for correctness. name: Python `str` name given to ops managed by this object. Raises: ValueError: if TODO happens """ self._graph_parents = [] self._name = name self._validate_args = validate_args self.parity = parity self.translation_fn = translation_fn self.scale_fn = scale_fn super().__init__(event_ndims=event_ndims, validate_args=validate_args, name=name) # TODO: Properties def _maybe_assert_valid_x(self, x): """TODO""" if not self.validate_args: return x raise NotImplementedError("_maybe_assert_valid_x") def _maybe_assert_valid_y(self, y): """TODO""" if not self.validate_args: return y raise NotImplementedError("_maybe_assert_valid_y") class RealNVPBijector(ConditionalBijector): """TODO""" def __init__(self, num_coupling_layers=2, translation_hidden_sizes=(25,), scale_hidden_sizes=(25,), event_ndims=0, validate_args=False, name="real_nvp"): """Instantiates the `RealNVPBijector` bijector. Args: TODO event_ndims: Python scalar indicating the number of dimensions associated with a particular draw from the distribution. validate_args: Python `bool` indicating whether arguments should be checked for correctness. name: Python `str` name given to ops managed by this object. Raises: ValueError: if TODO happens """ self._graph_parents = [] self._name = name self._validate_args = validate_args self._num_coupling_layers = num_coupling_layers self._translation_hidden_sizes = tuple(translation_hidden_sizes) self._scale_hidden_sizes = tuple(scale_hidden_sizes) self.build() super().__init__(event_ndims=event_ndims, validate_args=validate_args, name=name) # TODO: Properties def _maybe_assert_valid_x(self, x): """TODO""" if not self.validate_args: return x raise NotImplementedError("_maybe_assert_valid_x") def _maybe_assert_valid_y(self, y): """TODO""" if not self.validate_args: return y raise NotImplementedError("_maybe_assert_valid_y")

32.936675

79

0.564448

"""RealNVP bijector.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import tensorflow as tf import numpy as np ConditionalBijector = tf.contrib.distributions.bijectors.ConditionalBijector __all__ = [ "RealNVPBijector", ] def checkerboard(shape, parity='even', dtype=tf.bool): """TODO: Implement for dimensions >1""" if len(shape) > 1: raise NotImplementedError( "checkerboard not yet implemented for dimensions >1") unit = (tf.constant((True, False)) if parity == 'even' else tf.constant((False, True))) num_elements = np.prod(shape) tiled = tf.tile(unit, ((num_elements // 2) + 1, ))[:num_elements] return tf.cast(tf.reshape(tiled, shape), dtype) def feedforward_net(inputs, layer_sizes, activation_fn=tf.nn.tanh, output_nonlinearity=None, regularizer=None): prev_size = inputs.get_shape().as_list()[-1] out = inputs for i, layer_size in enumerate(layer_sizes): weight_initializer = tf.contrib.layers.xavier_initializer() weight = tf.get_variable( name="weight_{i}".format(i=i), shape=(prev_size, layer_size), initializer=weight_initializer, regularizer=regularizer) bias_initializer = tf.initializers.random_normal() bias = tf.get_variable( name="bias_{i}".format(i=i), shape=(layer_size, ), initializer=bias_initializer) prev_size = layer_size z = tf.matmul(out, weight) + bias if i < len(layer_sizes) - 1 and activation_fn is not None: out = activation_fn(z) elif i == len(layer_sizes) - 1 and output_nonlinearity is not None: out = output_nonlinearity(z) else: out = z return out class CouplingBijector(ConditionalBijector): """TODO""" def __init__(self, parity, translation_fn, scale_fn, event_ndims=0, validate_args=False, name="coupling_bijector"): """Instantiates the `CouplingBijector` bijector. Args: TODO event_ndims: Python scalar indicating the number of dimensions associated with a particular draw from the distribution. validate_args: Python `bool` indicating whether arguments should be checked for correctness. name: Python `str` name given to ops managed by this object. Raises: ValueError: if TODO happens """ self._graph_parents = [] self._name = name self._validate_args = validate_args self.parity = parity self.translation_fn = translation_fn self.scale_fn = scale_fn super().__init__(event_ndims=event_ndims, validate_args=validate_args, name=name) # TODO: Properties def _forward(self, x, **condition_kwargs): self._maybe_assert_valid_x(x) D = x.shape[1] if self.parity == 'even': masked_x = x[:, :D//2] non_masked_x = x[:, D//2:] else: non_masked_x = x[:, :D//2] masked_x = x[:, D//2:] with tf.variable_scope("{name}/scale".format(name=self.name), reuse=tf.AUTO_REUSE): # s(x_{1:d}) in paper scale = self.scale_fn(masked_x, condition_kwargs['condition'], non_masked_x.shape[-1]) with tf.variable_scope("{name}/translation".format(name=self.name), reuse=tf.AUTO_REUSE): # t(x_{1:d}) in paper translation = self.translation_fn(masked_x, condition_kwargs['condition'], non_masked_x.shape[-1]) # exp(s(b*x)) in paper exp_scale = tf.check_numerics( tf.exp(scale), "tf.exp(scale) contains NaNs or infs") # y_{d+1:D} = x_{d+1:D} * exp(s(x_{1:d})) + t(x_{1:d}) part_1 = masked_x part_2 = non_masked_x * exp_scale + translation to_concat = ( (part_1, part_2) if self.parity == 'even' else (part_2, part_1) ) outputs = tf.concat(to_concat, axis=1) return outputs def _forward_log_det_jacobian(self, x, **condition_kwargs): self._maybe_assert_valid_x(x) D = x.shape[1] masked_slice = ( slice(None, D//2) if self.parity == 'even' else slice(D//2, None)) masked_x = x[:, masked_slice] nonlinearity_output_size = D - masked_x.shape[1] # TODO: scale and translation could be merged into a single network with tf.variable_scope("{name}/scale".format(name=self.name), reuse=tf.AUTO_REUSE): scale = self.scale_fn( masked_x, **condition_kwargs, output_size=nonlinearity_output_size) log_det_jacobian = tf.reduce_sum( scale, axis=tuple(range(1, len(x.shape)))) return log_det_jacobian def _inverse(self, y, **condition_kwargs): self._maybe_assert_valid_y(y) condition = condition_kwargs["condition"] D = y.shape[1] if self.parity == 'even': masked_y = y[:, :D//2] non_masked_y = y[:, D//2:] else: non_masked_y = y[:, :D//2] masked_y = y[:, D//2:] with tf.variable_scope("{name}/scale".format(name=self.name), reuse=tf.AUTO_REUSE): # s(y_{1:d}) in paper scale = self.scale_fn(masked_y, condition, non_masked_y.shape[-1]) with tf.variable_scope("{name}/translation".format(name=self.name), reuse=tf.AUTO_REUSE): # t(y_{1:d}) in paper translation = self.translation_fn(masked_y, condition, non_masked_y.shape[-1]) exp_scale = tf.exp(-scale) # y_{d+1:D} = (y_{d+1:D} - t(y_{1:d})) * exp(-s(y_{1:d})) part_1 = masked_y part_2 = (non_masked_y - translation) * exp_scale to_concat = ( (part_1, part_2) if self.parity == 'even' else (part_2, part_1) ) outputs = tf.concat(to_concat, axis=1) return outputs def _inverse_log_det_jacobian(self, y, **condition_kwargs): self._maybe_assert_valid_y(y) condition = condition_kwargs["condition"] D = y.shape[1] masked_slice = ( slice(None, D//2) if self.parity == 'even' else slice(D//2, None)) masked_y = y[:, masked_slice] nonlinearity_output_size = D - masked_y.shape[1] # TODO: scale and translation could be merged into a single network with tf.variable_scope("{name}/scale".format(name=self.name), reuse=tf.AUTO_REUSE): scale = self.scale_fn(masked_y, condition, nonlinearity_output_size) log_det_jacobian = -tf.reduce_sum( scale, axis=tuple(range(1, len(y.shape)))) return log_det_jacobian def _maybe_assert_valid_x(self, x): """TODO""" if not self.validate_args: return x raise NotImplementedError("_maybe_assert_valid_x") def _maybe_assert_valid_y(self, y): """TODO""" if not self.validate_args: return y raise NotImplementedError("_maybe_assert_valid_y") class RealNVPBijector(ConditionalBijector): """TODO""" def __init__(self, num_coupling_layers=2, translation_hidden_sizes=(25,), scale_hidden_sizes=(25,), event_ndims=0, validate_args=False, name="real_nvp"): """Instantiates the `RealNVPBijector` bijector. Args: TODO event_ndims: Python scalar indicating the number of dimensions associated with a particular draw from the distribution. validate_args: Python `bool` indicating whether arguments should be checked for correctness. name: Python `str` name given to ops managed by this object. Raises: ValueError: if TODO happens """ self._graph_parents = [] self._name = name self._validate_args = validate_args self._num_coupling_layers = num_coupling_layers self._translation_hidden_sizes = tuple(translation_hidden_sizes) self._scale_hidden_sizes = tuple(scale_hidden_sizes) self.build() super().__init__(event_ndims=event_ndims, validate_args=validate_args, name=name) # TODO: Properties def build(self): num_coupling_layers = self._num_coupling_layers translation_hidden_sizes = self._translation_hidden_sizes scale_hidden_sizes = self._scale_hidden_sizes def translation_wrapper(inputs, condition, output_size): return feedforward_net( tf.concat((inputs, condition), axis=1), # TODO: should allow multi_dimensional inputs/outputs layer_sizes=(*translation_hidden_sizes, output_size)) def scale_wrapper(inputs, condition, output_size): return feedforward_net( tf.concat((inputs, condition), axis=1), # TODO: should allow multi_dimensional inputs/outputs layer_sizes=(*scale_hidden_sizes, output_size)) self.layers = [ CouplingBijector( parity=('even', 'odd')[i % 2], name="coupling_{i}".format(i=i), translation_fn=translation_wrapper, scale_fn=scale_wrapper) for i in range(1, num_coupling_layers + 1) ] def _forward(self, x, **condition_kwargs): self._maybe_assert_valid_x(x) out = x for layer in self.layers: out = layer.forward(out, **condition_kwargs) return out def _forward_log_det_jacobian(self, x, **condition_kwargs): self._maybe_assert_valid_x(x) sum_log_det_jacobians = tf.reduce_sum( tf.zeros_like(x), axis=tuple(range(1, len(x.shape)))) out = x for layer in self.layers: log_det_jacobian = layer.forward_log_det_jacobian( out, **condition_kwargs) out = layer.forward(out, **condition_kwargs) assert (sum_log_det_jacobians.shape.as_list() == log_det_jacobian.shape.as_list()) sum_log_det_jacobians += log_det_jacobian return sum_log_det_jacobians def _inverse(self, y, **condition_kwargs): self._maybe_assert_valid_y(y) out = y for layer in reversed(self.layers): out = layer.inverse(out, **condition_kwargs) return out def _inverse_log_det_jacobian(self, y, **condition_kwargs): self._maybe_assert_valid_y(y) sum_log_det_jacobians = tf.reduce_sum( tf.zeros_like(y), axis=tuple(range(1, len(y.shape)))) out = y for layer in reversed(self.layers): log_det_jacobian = layer.inverse_log_det_jacobian( out, **condition_kwargs) out = layer.inverse(out, **condition_kwargs) assert (sum_log_det_jacobians.shape.as_list() == log_det_jacobian.shape.as_list()) sum_log_det_jacobians += log_det_jacobian return sum_log_det_jacobians def _maybe_assert_valid_x(self, x): """TODO""" if not self.validate_args: return x raise NotImplementedError("_maybe_assert_valid_x") def _maybe_assert_valid_y(self, y): """TODO""" if not self.validate_args: return y raise NotImplementedError("_maybe_assert_valid_y")

8,279

0

266

e42334d7ea3bc94404b733cfe0daa07bc8199160

3,904

py

Python

politician/views.py

adborden/WeVoteBase

7fd612aee1d3638c8a74cc81873ce0687f62cf33

[ "MIT" ]

null

politician/views.py

adborden/WeVoteBase

7fd612aee1d3638c8a74cc81873ce0687f62cf33

[ "MIT" ]

null

politician/views.py

adborden/WeVoteBase

7fd612aee1d3638c8a74cc81873ce0687f62cf33

[ "MIT" ]

1

2020-03-04T00:22:39.000Z

# politician/views.py # Brought to you by We Vote. Be good. # -*- coding: UTF-8 -*- from django.http import HttpResponseRedirect from django.shortcuts import get_object_or_404, render from django.contrib import messages from django.contrib.messages import get_messages from django.core.urlresolvers import reverse from django.views import generic from django.views.generic import TemplateView from django.utils import timezone from politician.forms import TagNewForm from politician.models import Politician, PoliticianTagLink from tag.models import Tag # TODO Next step is to get Twitter vacuum working so we can pull in Tweets automatically based on tags/handles def politician_tag_new_view(request, politician_id): """ Form to add a new link tying a politician to twitter tags :param request: :return: """ messages_on_stage = get_messages(request) # for message in messages_on_stage: # if message.level is ERROR: politician_on_stage = get_object_or_404(Politician, id=politician_id) try: tag_link_list = politician_on_stage.tag_link.all() except PoliticianTagLink.DoesNotExist: tag_link_list = None template_values = { 'politician_on_stage': politician_on_stage, 'tag_link_list': tag_link_list, 'messages_on_stage': messages_on_stage, } return render(request, 'politician/politician_tag_new.html', template_values) def politician_tag_new_test_view(request, politician_id): """ Form to add a new link tying a politician to twitter tags :param request: :return: """ tag_new_form = TagNewForm() politician_on_stage = get_object_or_404(Politician, id=politician_id) # TODO Find the tags attached to this politician try: tag_list = PoliticianTagLink.objects.get(politician=politician_on_stage) except PoliticianTagLink.DoesNotExist: tag_list = None template_values = { 'tag_new_form': tag_new_form, 'politician_on_stage': politician_on_stage, 'tag_list': tag_list, } return render(request, 'politician/politician_tag_new_test.html', template_values) def politician_tag_new_process_view(request, politician_id): """ Process the form to add a new link tying a politician to twitter tags """ politician_on_stage = get_object_or_404(Politician, id=politician_id) new_tag = request.POST['new_tag'] # If an invalid tag didn't come in, redirect back to tag_new if not is_tag_valid(new_tag): messages.add_message(request, messages.INFO, 'That is not a valid tag. Please enter a different tag.') return HttpResponseRedirect(reverse('politician:politician_tag_new', args=(politician_id,))) new_tag_temp, created = Tag.objects.get_or_create(hashtag_text=new_tag) new_tag_link = PoliticianTagLink(tag=new_tag_temp, politician=politician_on_stage) new_tag_link.save() return HttpResponseRedirect(reverse('politician:politician_detail', args=(politician_id,)))

36.485981

147

0.742059

# politician/views.py # Brought to you by We Vote. Be good. # -*- coding: UTF-8 -*- from django.http import HttpResponseRedirect from django.shortcuts import get_object_or_404, render from django.contrib import messages from django.contrib.messages import get_messages from django.core.urlresolvers import reverse from django.views import generic from django.views.generic import TemplateView from django.utils import timezone from politician.forms import TagNewForm from politician.models import Politician, PoliticianTagLink from tag.models import Tag class PoliticianIndexView(generic.ListView): template_name = 'politician/politician_list.html' context_object_name = 'politician_list' def get_queryset(self): """""" return Politician.objects.order_by('last_name') # TODO Next step is to get Twitter vacuum working so we can pull in Tweets automatically based on tags/handles def politician_detail_view(request, politician_id): politician_on_stage = get_object_or_404(Politician, id=politician_id) # post_list = Post.objects.filter template_values = { 'politician_on_stage': politician_on_stage, # 'post_list': tag_list, # This is for prototyping only -- we want to move very quickly to posts being pulled onto the page via javascript } return render(request, 'politician/politician_detail.html', template_values) def politician_tag_new_view(request, politician_id): """ Form to add a new link tying a politician to twitter tags :param request: :return: """ messages_on_stage = get_messages(request) # for message in messages_on_stage: # if message.level is ERROR: politician_on_stage = get_object_or_404(Politician, id=politician_id) try: tag_link_list = politician_on_stage.tag_link.all() except PoliticianTagLink.DoesNotExist: tag_link_list = None template_values = { 'politician_on_stage': politician_on_stage, 'tag_link_list': tag_link_list, 'messages_on_stage': messages_on_stage, } return render(request, 'politician/politician_tag_new.html', template_values) def politician_tag_new_test_view(request, politician_id): """ Form to add a new link tying a politician to twitter tags :param request: :return: """ tag_new_form = TagNewForm() politician_on_stage = get_object_or_404(Politician, id=politician_id) # TODO Find the tags attached to this politician try: tag_list = PoliticianTagLink.objects.get(politician=politician_on_stage) except PoliticianTagLink.DoesNotExist: tag_list = None template_values = { 'tag_new_form': tag_new_form, 'politician_on_stage': politician_on_stage, 'tag_list': tag_list, } return render(request, 'politician/politician_tag_new_test.html', template_values) def politician_tag_new_process_view(request, politician_id): """ Process the form to add a new link tying a politician to twitter tags """ politician_on_stage = get_object_or_404(Politician, id=politician_id) new_tag = request.POST['new_tag'] # If an invalid tag didn't come in, redirect back to tag_new if not is_tag_valid(new_tag): messages.add_message(request, messages.INFO, 'That is not a valid tag. Please enter a different tag.') return HttpResponseRedirect(reverse('politician:politician_tag_new', args=(politician_id,))) new_tag_temp, created = Tag.objects.get_or_create(hashtag_text=new_tag) new_tag_link = PoliticianTagLink(tag=new_tag_temp, politician=politician_on_stage) new_tag_link.save() return HttpResponseRedirect(reverse('politician:politician_detail', args=(politician_id,))) def is_tag_valid(new_tag): if not bool(new_tag.strip()): # If this doesn't evaluate true here, then it is empty and isn't valid return False return True

601

221

68

4ed568d66cbb02189b36d1ec6924ebb87e827d1e

6,209

py

Python

tests/compare_updates.py

cassianobecker/msbm

6ce22f93f63071dc3ca722d499db376ea678eb23

[ "MIT" ]

null

tests/compare_updates.py

cassianobecker/msbm

6ce22f93f63071dc3ca722d499db376ea678eb23

[ "MIT" ]

null

tests/compare_updates.py

cassianobecker/msbm

6ce22f93f63071dc3ca722d499db376ea678eb23

[ "MIT" ]

null

import sys sys.path.insert(0, '..') import updates_msbm_vi_iter import updates_msbm_vi import updates_msbm2_vi_iter import updates_msbm2_vi import os import util import init_msbm_vi as im import numpy as np import numpy.random as npr import pdb # ########################################################### # ########################################################### # ########################################################### if __name__ == '__main__': file_url = os.path.join('..', 'experiments', 'two_prototype', 'data', 'twoprototype_105_250.pickle') remove_self_loops = False updater_einsum = updates_msbm_vi updater_iter = updates_msbm_vi_iter runner = TestUpdates(updater_einsum, updater_iter, file_url, remove_self_loops) runner.test_all() updater_einsum = updates_msbm2_vi updater_iter = updates_msbm2_vi_iter runner = TestUpdates(updater_einsum, updater_iter, file_url, remove_self_loops) runner.test_all() remove_self_loops = True updater_einsum = updates_msbm_vi updater_iter = updates_msbm_vi_iter runner = TestUpdates(updater_einsum, updater_iter, file_url, remove_self_loops) runner.test_all() updater_einsum = updates_msbm2_vi updater_iter = updates_msbm2_vi_iter runner = TestUpdates(updater_einsum, updater_iter, file_url, remove_self_loops) runner.test_all()

35.079096

113

0.565792

import sys sys.path.insert(0, '..') import updates_msbm_vi_iter import updates_msbm_vi import updates_msbm2_vi_iter import updates_msbm2_vi import os import util import init_msbm_vi as im import numpy as np import numpy.random as npr import pdb class TestUpdates: def __init__(self, updater_einsum, updater_iter, file_url, remove_self_loops): print("\n\n=====================================================================================") print("Comparing '{:}' with '{:}' with remove_self_loops={:}" .format(updater_einsum.__name__, updater_iter.__name__, remove_self_loops)) print("=====================================================================================") data = util.load_data(file_url) self.data = data print('') K = 8 data['X'] = data['X'][:K, :, :] data['NON_X'] = data['NON_X'][:K, :, :] data['K'] = K data['Y'] = data['Y'][:K, :] prior = dict() prior['ALPHA_0'] = 0.5 prior['BETA_0'] = 0.5 prior['NU_0'] = 0.5 prior['ZETA_0'] = 0.5 self.prior = prior # assigning hyper-parameters from ground truth (cheating) hyper = dict() hyper['M'] = data['M'] hyper['Q'] = data['Q'] self.hyper = hyper # initialize moments mom = dict() npr.seed(1) mode = 'random' mom['ALPHA'] = im.init_ALPHA(data, hyper, mode) mom['BETA'] = im.init_BETA(data, hyper, mode) mom['NU'] = im.init_NU(data, hyper, mode) mom['ZETA'] = im.init_ZETA(data, hyper, mode) mom['MU'] = im.init_MU(data, hyper, mode) mom['LOG_MU'] = np.log(mom['MU']) mom['TAU'] = im.init_TAU(data, hyper, mode) mom['LOG_TAU'] = np.log(mom['TAU']) self.mom = mom par = dict() par['MAX_ITER'] = 1000 par['TOL_ELBO'] = 1.e-16 par['ALG'] = 'cavi' par['kappa'] = 1.0 self.par = par self.remove_self_loops = remove_self_loops self.msbm_einsum = updater_einsum self.msbm_iter = updater_iter def test_update_Pi(self): print('--- Pi ---') NEW_ALPHA1, NEW_BETA1 = self.msbm_einsum.update_Pi(self.data, self.prior, self.hyper, self.mom, self.par, remove_self_loops=self.remove_self_loops) NEW_ALPHA2, NEW_BETA2 = self.msbm_iter.update_Pi(self.data, self.prior, self.hyper, self.mom, self.par, remove_self_loops=self.remove_self_loops) print("--ALPHA:") self.eval_diff(NEW_ALPHA1, NEW_ALPHA2) print("--BETA:") self.eval_diff(NEW_BETA1, NEW_BETA2) def test_update_Z(self): print('--- Z ---') NEW_LOG_TAU1 = self.msbm_einsum.update_Z(self.data, self.prior, self.hyper, self.mom, self.par, remove_self_loops=self.remove_self_loops) NEW_LOG_TAU2 = self.msbm_iter.update_Z(self.data, self.prior, self.hyper, self.mom, self.par, remove_self_loops=self.remove_self_loops) self.eval_diff(NEW_LOG_TAU1, NEW_LOG_TAU2) def test_update_Y(self): print('--- Y ---') NEW_LOG_MU1 = self.msbm_einsum.update_Y(self.data, self.prior, self.hyper, self.mom, self.par, remove_self_loops=self.remove_self_loops) NEW_LOG_MU2 = self.msbm_iter.update_Y(self.data, self.prior, self.hyper, self.mom, self.par, remove_self_loops=self.remove_self_loops) self.eval_diff(NEW_LOG_MU1, NEW_LOG_MU2) def test_update_gamma(self): print('--- Gamma ---') NEW_NU1 = self.msbm_einsum.update_gamma(self.data, self.prior, self.hyper, self.mom, self.par, remove_self_loops=self.remove_self_loops) NEW_NU2 = self.msbm_iter.update_gamma(self.data, self.prior, self.hyper, self.mom, self.par) self.eval_diff(NEW_NU1, NEW_NU2) def test_update_rho(self): print('--- Rho ---') NEW_ZETA1 = self.msbm_einsum.update_rho(self.data, self.prior, self.hyper, self.mom, self.par, remove_self_loops=self.remove_self_loops) NEW_ZETA2 = self.msbm_iter.update_rho(self.data, self.prior, self.hyper, self.mom, self.par) self.eval_diff(NEW_ZETA1, NEW_ZETA2) def test_all(self): self.test_update_Pi() self.test_update_Z() self.test_update_Y() self.test_update_gamma() self.test_update_rho() def eval_diff(self, X1, X2): ind = np.unravel_index(np.argmax(X1 - X2), X1.shape) diff = (X1 - X2).ravel() print('Mean abs entry-wise error: {:1.3e}'.format(np.mean(np.abs(diff)))) print('Max abs entry-wise error: {:1.3e}'.format(np.max(np.abs(diff)))) print('On entry with index:') print(ind) # ########################################################### # ########################################################### # ########################################################### if __name__ == '__main__': file_url = os.path.join('..', 'experiments', 'two_prototype', 'data', 'twoprototype_105_250.pickle') remove_self_loops = False updater_einsum = updates_msbm_vi updater_iter = updates_msbm_vi_iter runner = TestUpdates(updater_einsum, updater_iter, file_url, remove_self_loops) runner.test_all() updater_einsum = updates_msbm2_vi updater_iter = updates_msbm2_vi_iter runner = TestUpdates(updater_einsum, updater_iter, file_url, remove_self_loops) runner.test_all() remove_self_loops = True updater_einsum = updates_msbm_vi updater_iter = updates_msbm_vi_iter runner = TestUpdates(updater_einsum, updater_iter, file_url, remove_self_loops) runner.test_all() updater_einsum = updates_msbm2_vi updater_iter = updates_msbm2_vi_iter runner = TestUpdates(updater_einsum, updater_iter, file_url, remove_self_loops) runner.test_all()

4,600

-3

239

90aec8fbaf7418768c7f684cf8a72125b93b8c1d

595

py

Python

module/tools/extra/graph_clean.py

ObliviousJamie/opic-prototype

a925ce9faa38b9a6c8976d4c63b47349a53fd07e

[ "BSD-3-Clause" ]

null

module/tools/extra/graph_clean.py

ObliviousJamie/opic-prototype

a925ce9faa38b9a6c8976d4c63b47349a53fd07e

[ "BSD-3-Clause" ]

null

module/tools/extra/graph_clean.py

ObliviousJamie/opic-prototype

a925ce9faa38b9a6c8976d4c63b47349a53fd07e

[ "BSD-3-Clause" ]

null

import networkx as nx

27.045455

73

0.633613

import networkx as nx class GraphClean: @staticmethod def prune_unconnected_components(graph): current = graph # Remove self loops for vertex in graph.nodes_with_selfloops(): graph.remove_edge(vertex, vertex) if not nx.is_connected(graph): connected_subgraphs = nx.connected_component_subgraphs(graph) current = next(connected_subgraphs) for sub_graph in connected_subgraphs: if len(sub_graph.nodes) > len(current.nodes): current = sub_graph return current

508

41

23

4f7f939a65964f9c1af988a3620892d451aa3135

46,951

py

Python

utensor_cgen/backend/operators.py

dboyliao/utensor_cgen

aacd3adf4ee2a521a8eb2e75807fe3c1c0d1e1e5

[ "Apache-2.0" ]

1

2017-12-29T17:40:49.000Z

utensor_cgen/backend/operators.py

dboyliao/utensor_cgen

aacd3adf4ee2a521a8eb2e75807fe3c1c0d1e1e5

[ "Apache-2.0" ]

1

2017-12-28T02:25:45.000Z

utensor_cgen/backend/operators.py

dboyliao/utensor_cgen

aacd3adf4ee2a521a8eb2e75807fe3c1c0d1e1e5

[ "Apache-2.0" ]

3

2017-12-27T17:15:38.000Z

2017-12-29T06:43:00.000Z

# -*- coding:utf8 -*- r''' TODO: remove all tensorflow graph construction in `build_op_info` ''' import os import numpy as np import idx2numpy as idx2np import tensorflow as tf from utensor_cgen.ir import OperationInfo, TensorInfo from utensor_cgen.ir.converter import (AttrValueConverter, DataTypeConverter, GenericTensorConverterMixin) from utensor_cgen.logger import logger from utensor_cgen.matcher import OpEqualityDelegate, _morphism from utensor_cgen.transformer.optimizer import RefCntOptimizer from utensor_cgen.utils import NamescopedKWArgsParser from .snippets import * # pylint: disable=W0401,W0614 __all__ = ['OperatorFactory', 'OpNotSupportedError'] @OperatorFactory.register @OpEqualityDelegate.is_compatible_with("Inline", _morphism.Const2InlineMorphism) @OperatorFactory.register @OpEqualityDelegate.is_associative( permutations=((0, 1), (1, 0)) ) @OperatorFactory.register @OperatorFactory.register @OperatorFactory.register @OperatorFactory.register @OperatorFactory.register @OperatorFactory.register @OperatorFactory.register @OperatorFactory.register @OperatorFactory.register @OperatorFactory.register @OperatorFactory.register @OperatorFactory.register @OperatorFactory.register @OperatorFactory.register @OperatorFactory.register @OperatorFactory.register @OperatorFactory.register @OperatorFactory.register @OperatorFactory.register @OperatorFactory.register @OperatorFactory.register @OperatorFactory.register @OperatorFactory.register @OperatorFactory.register #hard coding to uint8_t uint8_t int32_t for now @OperatorFactory.register @OperatorFactory.register @OpEqualityDelegate.is_compatible_with("Const", _morphism.Inline2ConstMorphism) @OperatorFactory.register @OperatorFactory.register @OperatorFactory.register @OperatorFactory.register @OperatorFactory.register @OperatorFactory.register

37.32194

114

0.629188

# -*- coding:utf8 -*- r''' TODO: remove all tensorflow graph construction in `build_op_info` ''' import os import numpy as np import idx2numpy as idx2np import tensorflow as tf from utensor_cgen.ir import OperationInfo, TensorInfo from utensor_cgen.ir.converter import (AttrValueConverter, DataTypeConverter, GenericTensorConverterMixin) from utensor_cgen.logger import logger from utensor_cgen.matcher import OpEqualityDelegate, _morphism from utensor_cgen.transformer.optimizer import RefCntOptimizer from utensor_cgen.utils import NamescopedKWArgsParser from .snippets import * # pylint: disable=W0401,W0614 __all__ = ['OperatorFactory', 'OpNotSupportedError'] class OpNotSupportedError(Exception): pass class OperatorFactory(): # Can easily do something smarter _operators = {} def createOperatorSnippet(self, op_info, **kwargs): op_type = op_info.op_type if op_type not in self._operators: err_msg = "unsupported op type in uTensor: {op.name}, {op.op_type}".format(op=op_info) raise ValueError(err_msg) op = self._operators[op_type](op_info, **kwargs) # Create desired object return op.snippet # Ops know how to create their snippets @classmethod def get_opertor(cls, op_type): op_cls = cls._operators.get(op_type) if op_cls is None: raise OpNotSupportedError( '{} not supported in utensor_cgen'.format(op_type) ) return op_cls @classmethod def build_op_info(cls, *args, ugraph, op_type, name, **kwargs): op_cls = cls._operators.get(op_type, None) if op_cls is None: err_msg = "unsupported op type in uTensor: {}".format(op_type) raise OpNotSupportedError(err_msg) return op_cls.build_op_info(ugraph, name, *args, **kwargs) @classmethod def register(cls, op_cls): cls._operators[op_cls.op_type] = op_cls return op_cls @classmethod def support_op_types(cls): """Return the set of all supported ops """ return set(cls._operators.keys()) @classmethod def is_supported(cls, op_type): if op_type != 'Placeholder' and op_type not in cls._operators: return False return True class _Operator(object): def __init__(self): self.name = "" self._snippet = None @property def snippet(self): return self._snippet @classmethod def build_op_info(cls, ugraph, name, *args, **kwargs): raise NotImplementedError('%s does not have build_op_info method' % cls) @OperatorFactory.register @OpEqualityDelegate.is_compatible_with("Inline", _morphism.Const2InlineMorphism) class _ConstOperator(_Operator): op_type = "Const" def __init__(self, op_info, **kwargs): out_tensor_info = op_info.output_tensors[0] out_tname, out_dtype = (out_tensor_info.name, out_tensor_info.dtype) parser = NamescopedKWArgsParser(RefCntOptimizer.KWARGS_NAMESCOPE, op_info.op_attr) ref_count = parser.get('ref_counts', [0])[0] pre_tname = self._tf_prepare_tensor_name(out_tname) idx_fname = "{}.idx".format(pre_tname) idx_dir = kwargs['idx_dir'] embed_data_dir = kwargs.get('embed_data_dir', os.path.join("/fs", idx_dir)) self._snippet = CreateTensorIdxSnippet(embed_data_dir, out_tname, idx_fname=idx_fname, np_dtype=out_dtype, ref_count=ref_count) idx_path = os.path.join(idx_dir, idx_fname) value = op_info.op_attr['value'].value self._tf_save_data(idx_path, value) @classmethod def build_op_info(cls, ugraph, name, value, **kwargs): generic_value = GenericTensorConverterMixin.__utensor_generic_type__( np_array=value ) return OperationInfo( name=name, input_tensors=[], output_tensors=[ TensorInfo( name='{}:0'.format(name), op_name=name, dtype=value.dtype, shape=list(value.shape), ugraph=ugraph ) ], op_type=cls.op_type, op_attr={ 'value': AttrValueConverter.__utensor_generic_type__( value_name='tensor', value=generic_value ), 'dtype': AttrValueConverter.__utensor_generic_type__( value_name='type', value=DataTypeConverter.get_tf_value(value.dtype) ) }, ugraph=ugraph, backend=kwargs.get('backend', 'tensorflow') ) def _tf_prepare_tensor_name(self, tensor_name): """Replace all ':' and '/' with '_' in a given tensor name """ prepared = tensor_name.replace(":", "_").replace("/", "_") return prepared def _tf_save_data(self, path, value): np_array = value.np_array if np_array.shape == (): np_array = np.array([np_array]) with open(path, "wb") as fid: idx2np.convert_to_file(fid, np_array) logger.info("saving %s", path) @OperatorFactory.register @OpEqualityDelegate.is_associative( permutations=((0, 1), (1, 0)) ) class _AddOperator(_Operator): op_type = "Add" # tf op type def __init__(self, op_info, **kwargs): _Operator.__init__(self) inputs = [tensor_info.name for tensor_info in op_info.input_tensors] output = op_info.output_tensors[0].name tf_dtype = op_info.input_tensors[0].dtype parser = NamescopedKWArgsParser(RefCntOptimizer.KWARGS_NAMESCOPE, op_info.op_attr) ref_count = parser.get('ref_counts', [0])[0] to_eval = parser.get('to_eval', False) self._snippet = AddOpSnippet(inputs, output, tf_dtype, ref_count, to_eval) @classmethod def build_op_info(cls, ugraph, name, tensor_x, tensor_y, **kwargs): # broadcast the shape and promote types dummy_x = np.empty(tensor_x.shape) dummy_y = np.empty(tensor_y.shape) output_shape = np.broadcast(dummy_x, dummy_y).shape output_dtype = np.promote_types(tensor_x.dtype, tensor_y.dtype) return OperationInfo( name=name, input_tensors=[tensor_x, tensor_y], output_tensors=[ TensorInfo( name='{}:0'.format(name), op_name=name, dtype=output_dtype, shape=list(output_shape), ugraph=ugraph ) ], op_type=cls.op_type, op_attr={ 'T': AttrValueConverter.__utensor_generic_type__( value_name='type', value=DataTypeConverter.get_tf_value(output_dtype) ) }, ugraph=ugraph, backend=kwargs.get('backend', 'tensorflow') ) @OperatorFactory.register class _ArgMaxOperator(_Operator): op_type = "ArgMax" def __init__(self, op_info, **kwargs): _Operator.__init__(self) inputs = [tensor_info.name for tensor_info in op_info.input_tensors] out_tensor_info = op_info.output_tensors[0] output, out_dtype = out_tensor_info.name, out_tensor_info.dtype in_dtype = op_info.input_tensors[0].dtype data_manager = kwargs['data_manager'] parser = NamescopedKWArgsParser(RefCntOptimizer.KWARGS_NAMESCOPE, op_info.op_attr, data_manager, op_info) ref_count = parser.get('ref_counts', [0])[0] to_eval = parser.get('to_eval', False) address = parser.get('address', []) self._snippet = ArgMaxOpSnippet(inputs, output, in_dtype, out_dtype, ref_count, to_eval, address) @classmethod def build_op_info(cls, ugraph, name, input_tensor, dtype=np.dtype('int64'), axis=0, **kwargs): if isinstance(axis, int): axis, = ugraph.add_op( np.array(axis, dtype=np.dtype('int32')), op_type='Const', name='{}/axis'.format(name) ) dummy_in = np.empty(input_tensor.shape, dtype=input_tensor.dtype) graph = tf.Graph() with graph.as_default(): dummy_out = tf.math.argmax( dummy_in, axis=axis.op.op_attr['value'].value.np_array, name='dummy', output_type=tf.as_dtype(dtype) ) node_def = [node for node in graph.as_graph_def().node if node.name=='dummy'][0] output_shape = dummy_out.shape.as_list() op_attr = { k: AttrValueConverter.get_generic_value(v) for k, v in node_def.attr.items() } return OperationInfo( name=name, op_type=cls.op_type, input_tensors=[input_tensor, axis], output_tensors=[ TensorInfo( name='{}:0'.format(name), op_name=name, dtype=dtype, shape=output_shape, ugraph=ugraph ) ], op_attr=op_attr, ugraph=ugraph, backend=kwargs.get('backend', 'tensorflow') ) @OperatorFactory.register class _DequantizeOperator(_Operator): op_type = "Dequantize" def __init__(self, op_info, **kwargs): _Operator.__init__(self) inputs = [tensor_info.name for tensor_info in op_info.input_tensors] out_tensor_info = op_info.output_tensors[0] data_manager = kwargs['data_manager'] output, out_dtype = out_tensor_info.name, out_tensor_info.dtype parser = NamescopedKWArgsParser(RefCntOptimizer.KWARGS_NAMESCOPE, op_info.op_attr, data_manager, op_info) ref_count = parser.get('ref_counts', [0])[0] to_eval = parser.get('to_eval', False) address = parser.get('address', []) self._snippet = DequantizeOpSnippet(inputs, output, out_dtype, ref_count, to_eval, address) @OperatorFactory.register class _MaxOperator(_Operator): op_type = "Max" def __init__(self, op_info, **kwargs): _Operator.__init__(self) inputs = [tensor_info.name for tensor_info in op_info.input_tensors] out_tensor_info = op_info.output_tensors[0] data_manager = kwargs['data_manager'] output, out_dtype, out_shape = (out_tensor_info.name, out_tensor_info.dtype, out_tensor_info.shape) # FIXME: automatic alloc for uTensor fail if not out_shape: out_shape = [1] parser = NamescopedKWArgsParser(RefCntOptimizer.KWARGS_NAMESCOPE, op_info.op_attr, data_manager, op_info) ref_count = parser.get('ref_counts', [0])[0] to_eval = parser.get('to_eval', False) address = parser.get('address', []) self._snippet = MaxOpSnippet(inputs, output, out_dtype, out_shape, ref_count, to_eval, address) @classmethod def build_op_info(cls, ugraph, name, tensor, axis=-1, keepdims=False, **kwargs): if isinstance(axis, int): axis, = ugraph.add_op( np.array(axis, dtype=np.dtype('int32')), op_type='Const', name='{}/axis'.format(name) ) dummy_in = np.empty(tensor.shape, dtype=tensor.dtype) graph = tf.Graph() with graph.as_default(): dummy_out = tf.reduce_max( dummy_in, axis=axis.op.op_attr['value'].value.np_array, keepdims=keepdims, name='dummy' ) node_def = [node for node in graph.as_graph_def().node if node.name == 'dummy'][0] return OperationInfo( name=name, input_tensors=[tensor, axis], output_tensors=[ TensorInfo( name='{}:0'.format(name), op_name=name, dtype=tensor.dtype, shape=dummy_out.shape.as_list(), ugraph=ugraph ) ], op_type=cls.op_type, op_attr={ k: AttrValueConverter.get_generic_value(v) for k, v in node_def.attr.items() }, backend=kwargs.get('backend', 'tensorflow'), ugraph=ugraph ) @OperatorFactory.register class _MinOperator(_Operator): op_type = "Min" def __init__(self, op_info, **kwargs): _Operator.__init__(self) inputs = [tensor_info.name for tensor_info in op_info.input_tensors] out_info = op_info.output_tensors[0] output, out_dtype, out_shape = (out_info.name, out_info.dtype, out_info.shape) # FIXME: automatic alloc for uTensor fail if not out_shape: out_shape = [1] parser = NamescopedKWArgsParser(RefCntOptimizer.KWARGS_NAMESCOPE, op_info.op_attr) ref_count = parser.get('ref_counts', [0])[0] to_eval = parser.get('to_eval', False) self._snippet = MinOpSnippet(inputs, output, out_dtype, out_shape, ref_count, to_eval) @classmethod def build_op_info(cls, ugraph, name, tensor, axis=-1, keepdims=False, **kwargs): if isinstance(axis, int): axis, = ugraph.add_op( np.array(axis, dtype=np.dtype('int32')), op_type='Const', name='{}/axis'.format(name) ) dummy_in = np.empty(tensor.shape, dtype=tensor.dtype) graph = tf.Graph() with graph.as_default(): dummy_out = tf.reduce_min( dummy_in, axis=axis.op.op_attr['value'].value.np_array, keepdims=keepdims, name='dummy' ) node_def = [node for node in graph.as_graph_def().node if node.name == 'dummy'][0] output_shape = dummy_out.shape.as_list() return OperationInfo( name=name, input_tensors=[tensor, axis], output_tensors=[ TensorInfo( name='{}:0'.format(name), op_name=name, dtype=tensor.dtype, shape=output_shape, ugraph=ugraph, ) ], op_type=cls.op_type, backend=kwargs.get('backend', 'tensorflow'), ugraph=ugraph, op_attr={ k: AttrValueConverter.get_generic_value(v) for k, v in node_def.attr.items() } ) @OperatorFactory.register class _MaxPool(_Operator): op_type = "MaxPool" def __init__(self, op_info, **kwargs): _Operator.__init__(self) inputs = [tensor_info.name for tensor_info in op_info.input_tensors] output = op_info.output_tensors[0].name dtype = op_info.output_tensors[0].dtype ksize = op_info.op_attr['ksize'].value.ints_value strides = op_info.op_attr['strides'].value.ints_value padding = op_info.op_attr['padding'].value.decode('utf8') parser = NamescopedKWArgsParser(RefCntOptimizer.KWARGS_NAMESCOPE, op_info.op_attr) ref_count = parser.get('ref_counts', [0])[0] to_eval = parser.get('to_eval', False) self._snippet = MaxPoolSnippet(inputs, output, dtype, ksize, strides, padding, ref_count, to_eval) @classmethod def build_op_info( cls, ugraph, name, tensor, ksize_height, ksize_width, stride_height, stride_width, padding='SAME', **kwargs ): dummy_arr = np.empty(tensor.shape, dtype=tensor.dtype) graph = tf.Graph() with graph.as_default(): tf_tensor = tf.nn.max_pool( dummy_arr, ksize=[1, ksize_height, ksize_width, 1], strides=[1, stride_height, stride_width, 1], padding=padding, name='dummy' ) output_shape = tf_tensor.shape.as_list() graph_def = graph.as_graph_def() node_def = [node for node in graph_def.node if node.name == 'dummy'][0] return OperationInfo( name=name, input_tensors=[tensor], output_tensors=[ TensorInfo( name='{}:0'.format(name), op_name=name, dtype=tensor.dtype, shape=output_shape, ugraph=ugraph ) ], op_type=cls.op_type, backend=kwargs.get('backend', 'tensorflow'), ugraph=ugraph, op_attr={ k: AttrValueConverter.get_generic_value(v) for k, v in node_def.attr.items() } ) @OperatorFactory.register class _QuantizedMaxPool(_Operator): op_type = "QuantizedMaxPool" def __init__(self, op_info, **kwargs): _Operator.__init__(self) inputs = [tensor_info.name for tensor_info in op_info.input_tensors] outputs = [tensor_info.name for tensor_info in op_info.output_tensors] dtype = op_info.output_tensors[0].dtype ksize = op_info.op_attr['ksize'].value.ints_value strides = op_info.op_attr['strides'].value.ints_value padding = op_info.op_attr['padding'].value.decode('utf8') parser = NamescopedKWArgsParser(RefCntOptimizer.KWARGS_NAMESCOPE, op_info.op_attr) ref_counts = parser.get('ref_counts', []) to_eval = parser.get('to_eval', False) self._snippet = QuantizedMaxPoolSnippet(inputs, outputs, dtype, ksize, strides, padding, ref_counts, to_eval) @OperatorFactory.register class _MinOperator(_Operator): op_type = "Min" def __init__(self, op_info, **kwargs): _Operator.__init__(self) inputs = [tensor_info.name for tensor_info in op_info.input_tensors] out_info = op_info.output_tensors[0] data_manager = kwargs['data_manager'] output, out_dtype, out_shape = (out_info.name, out_info.dtype, out_info.shape) # FIXME: automatic alloc for uTensor fail if not out_shape: out_shape = [1] parser = NamescopedKWArgsParser(RefCntOptimizer.KWARGS_NAMESCOPE, op_info.op_attr, data_manager, op_info) ref_count = parser.get('ref_counts', [0])[0] to_eval = parser.get('to_eval', False) address = parser.get('address', []) self._snippet = MinOpSnippet(inputs, output, out_dtype, out_shape, ref_count, to_eval, address) @classmethod def build_op_info(cls, ugraph, name, tensor, axis=-1, keepdims=False, **kwargs): if isinstance(axis, int): axis, = ugraph.add_op( np.array(axis, dtype=np.dtype('int32')), op_type='Const', name='{}/axis'.format(name) ) dummy_in = np.empty(tensor.shape, dtype=tensor.dtype) graph = tf.Graph() with graph.as_default(): dummy_out = tf.reduce_min( dummy_in, axis=axis.op.op_attr['value'].value.np_array, keepdims=keepdims, name='dummy' ) node_def = [node for node in graph.as_graph_def().node if node.name == 'dummy'][0] output_shape = dummy_out.shape.as_list() return OperationInfo( name=name, input_tensors=[tensor, axis], output_tensors=[ TensorInfo( name='{}:0'.format(name), op_name=name, dtype=tensor.dtype, shape=output_shape, ugraph=ugraph, ) ], op_type=cls.op_type, backend=kwargs.get('backend', 'tensorflow'), ugraph=ugraph, op_attr={ k: AttrValueConverter.get_generic_value(v) for k, v in node_def.attr.items() } ) @OperatorFactory.register class _QuantizeV2Operator(_Operator): op_type = "QuantizeV2" def __init__(self, op_info, **kwargs): _Operator.__init__(self) inputs = [tensor_info.name for tensor_info in op_info.input_tensors] outputs = [tensor_info.name for tensor_info in op_info.output_tensors] out_dtype = op_info.output_tensors[0].dtype data_manager = kwargs['data_manager'] parser = NamescopedKWArgsParser(RefCntOptimizer.KWARGS_NAMESCOPE, op_info.op_attr, data_manager, op_info) ref_counts = parser.get('ref_counts', []) to_eval = parser.get('to_eval', False) address = parser.get('address', []) self._snippet = QuantizeV2OpSnippet(inputs, outputs, out_dtype, ref_counts, to_eval, address) @OperatorFactory.register class _MatMulOperator(_Operator): op_type = "MatMul" def __init__(self, op_info, **kwargs): _Operator.__init__(self) inputs = [tensor_info.name for tensor_info in op_info.input_tensors] output = op_info.output_tensors[0].name in_tensor_info = op_info.input_tensors[0] x_dtype, w_dtype, out_dtype = (op_info.input_tensors[0].dtype, op_info.input_tensors[1].dtype, op_info.output_tensors[0].dtype) parser = NamescopedKWArgsParser(RefCntOptimizer.KWARGS_NAMESCOPE, op_info.op_attr) ref_count = parser.get('ref_counts', [0])[0] to_eval = parser.get('to_eval', False) self._snippet = MatMulOpSnippet(inputs, output, x_dtype, w_dtype, out_dtype, ref_count, to_eval) @classmethod def build_op_info(cls, ugraph, name, tensor_x, tensor_w, **kwargs): dtype_x = tensor_x.dtype dtype_w = tensor_w.dtype out_dtype = np.promote_types(dtype_x, dtype_w) if tensor_x.shape[-1] != tensor_w.shape[0]: raise ValueError( 'dimension mismatch: {},{}'.format(tensor_x.shape, tensor_w.shape) ) return OperationInfo( name=name, input_tensors=[ tensor_x, tensor_w ], output_tensors=[ TensorInfo( name='{}:0'.format(name), op_name=name, dtype=out_dtype, shape=tensor_x.shape[:-1]+tensor_w.shape[1:], ugraph=ugraph ) ], op_type=cls.op_type, op_attr={ 'T': AttrValueConverter.__utensor_generic_type__( value_name='type', value=DataTypeConverter.get_tf_value(out_dtype) ), 'transpose_a': AttrValueConverter.__utensor_generic_type__( value_name='b', value=kwargs.get('transpose_x', False) ), 'transpose_b': AttrValueConverter.__utensor_generic_type__( value_name='b', value=kwargs.get('tranpose_w', False) ) }, ugraph=ugraph, backend=kwargs.get('backend', 'tensorflow') ) @OperatorFactory.register class _QuantizedMatMulOperator(_Operator): op_type = "QuantizedMatMul" def __init__(self, op_info, **kwargs): _Operator.__init__(self) inputs = [tensor_info.name for tensor_info in op_info.input_tensors] outputs = [tensor_info.name for tensor_info in op_info.output_tensors] in_tensor_info = op_info.input_tensors[0] x_dtype, w_dtype, out_dtype = (op_info.input_tensors[0].dtype, op_info.input_tensors[1].dtype, op_info.output_tensors[0].dtype) data_manager = kwargs['data_manager'] parser = NamescopedKWArgsParser(RefCntOptimizer.KWARGS_NAMESCOPE, op_info.op_attr, data_manager, op_info) ref_counts = parser.get('ref_counts', []) to_eval = parser.get('to_eval', False) address = parser.get('address', []) self._snippet = QuantizedMatMulOpSnippet(inputs, outputs, x_dtype, w_dtype, out_dtype, ref_counts, to_eval, address) @OperatorFactory.register class _ReluOperator(_Operator): op_type = "Relu" def __init__(self, op_info, **kwargs): _Operator.__init__(self) inputs = [tensor_info.name for tensor_info in op_info.input_tensors] output = op_info.output_tensors[0].name in_dtype, out_dtype = (op_info.input_tensors[0].dtype, op_info.output_tensors[0].dtype) #NT: why separate this out? #DB: I don't know, it's in the uTensor C code parser = NamescopedKWArgsParser(RefCntOptimizer.KWARGS_NAMESCOPE, op_info.op_attr) ref_count = parser.get('ref_counts', [0])[0] to_eval = parser.get('to_eval', False) self._snippet = ReluOpSnippet(inputs, output, in_dtype, out_dtype, ref_count, to_eval) @classmethod def build_op_info(cls, ugraph, name, tensor, **kwargs): return OperationInfo( name=name, input_tensors=[tensor], output_tensors=[ TensorInfo( name='{}:0'.format(name), op_name=name, dtype=tensor.dtype, shape=tensor.shape[:], ugraph=ugraph ) ], op_type=cls.op_type, op_attr={ 'T': AttrValueConverter.__utensor_generic_type__( value_name='type', value=DataTypeConverter.get_tf_value(tensor.dtype) ) }, ugraph=ugraph, backend=kwargs.get('backend', 'tensorflow') ) @OperatorFactory.register class _QuantizedReluOperator(_Operator): op_type = "QuantizedRelu" def __init__(self, op_info, **kwargs): _Operator.__init__(self) inputs = [tensor_info.name for tensor_info in op_info.input_tensors] outputs = [tensor_info.name for tensor_info in op_info.output_tensors] in_dtype, qout_dtype = (op_info.input_tensors[0].dtype, op_info.output_tensors[0].dtype) #NT: why separate this out? #DB: I don't know, it's in the uTensor C code data_manager = kwargs['data_manager'] out_dtypes = [tensor_info.dtype for tensor_info in op_info.output_tensors[1:]] parser = NamescopedKWArgsParser(RefCntOptimizer.KWARGS_NAMESCOPE, op_info.op_attr, data_manager, op_info) ref_counts = parser.get('ref_counts', []) to_eval = parser.get('to_eval', False) address = parser.get('address', []) self._snippet = QuantizedReluOpSnippet(inputs, outputs, in_dtype, out_dtypes, qout_dtype, ref_counts, to_eval, address) @OperatorFactory.register class _QuantizedAddOperator(_Operator): op_type = "QuantizedAdd" def __init__(self, op_info, **kwargs): _Operator.__init__(self) inputs = [tensor_info.name for tensor_info in op_info.input_tensors] outputs = [tensor_info.name for tensor_info in op_info.output_tensors] x_dtype, w_dtype, out_dtype = (op_info.input_tensors[0].dtype, op_info.input_tensors[1].dtype, op_info.output_tensors[0].dtype) data_manager = kwargs['data_manager'] parser = NamescopedKWArgsParser(RefCntOptimizer.KWARGS_NAMESCOPE, op_info.op_attr, data_manager, op_info) ref_counts = parser.get('ref_counts', []) to_eval = parser.get('to_eval', False) address = parser.get('address', []) self._snippet = QuantizedAddOpSnippet(inputs, outputs, x_dtype, w_dtype, out_dtype, ref_counts, to_eval, address) @OperatorFactory.register class _QuantizedMulOperator(_Operator): op_type = "QuantizedMul" def __init__(self, op_info, **kwargs): _Operator.__init__(self) inputs = [tensor_info.name for tensor_info in op_info.input_tensors] outputs = [tensor_info.name for tensor_info in op_info.output_tensors] x_dtype, w_dtype, out_dtype = (op_info.input_tensors[0].dtype, op_info.input_tensors[1].dtype, op_info.output_tensors[0].dtype) parser = NamescopedKWArgsParser(RefCntOptimizer.KWARGS_NAMESCOPE, op_info.op_attr) ref_counts = parser.get('ref_counts', []) to_eval = parser.get('to_eval', False) self._snippet = QuantizedMulOpSnippet(inputs, outputs, x_dtype, w_dtype, out_dtype, ref_counts, to_eval) @OperatorFactory.register class _RequantizationRangeOperator(_Operator): op_type = "RequantizationRange" def __init__(self, op_info, **kwargs): _Operator.__init__(self) inputs = [tensor_info.name for tensor_info in op_info.input_tensors] outputs = [tensor_info.name for tensor_info in op_info.output_tensors] out_dtype = op_info.output_tensors[0].dtype data_manager = kwargs['data_manager'] parser = NamescopedKWArgsParser(RefCntOptimizer.KWARGS_NAMESCOPE, op_info.op_attr, data_manager, op_info) ref_counts = parser.get('ref_counts', []) to_eval = parser.get('to_eval', False) address = parser.get('address', []) self._snippet = RequantizationRangeOpSnippet(inputs, outputs, out_dtype, ref_counts, to_eval, address) @OperatorFactory.register class _RequantizeOperator(_Operator): op_type = "Requantize" def __init__(self, op_info, **kwargs): _Operator.__init__(self) inputs = [tensor_info.name for tensor_info in op_info.input_tensors] outputs = [tensor_info.name for tensor_info in op_info.output_tensors] qout_dtype = op_info.output_tensors[0].dtype range_dtype = op_info.output_tensors[1].dtype data_manager = kwargs['data_manager'] parser = NamescopedKWArgsParser(RefCntOptimizer.KWARGS_NAMESCOPE, op_info.op_attr, data_manager, op_info) ref_counts = parser.get('ref_counts', []) to_eval = parser.get('to_eval', False) address = parser.get('address', []) self._snippet = RequantizeOpSnippet(inputs, outputs, qout_dtype, range_dtype, ref_counts, to_eval, address) @OperatorFactory.register class _ReshapeOperator(_Operator): op_type = "Reshape" def __init__(self, op_info, **kwargs): _Operator.__init__(self) inputs = [tensor_info.name for tensor_info in op_info.input_tensors] output = op_info.output_tensors[0].name data_manager = kwargs['data_manager'] parser = NamescopedKWArgsParser(RefCntOptimizer.KWARGS_NAMESCOPE, op_info.op_attr, data_manager, op_info) ref_count = parser.get('ref_counts', [0])[0] to_eval = parser.get('to_eval', False) address = parser.get('address', []) dtype = op_info.input_tensors[0].dtype self._snippet = ReshapeOpSnippet(inputs, output, dtype, ref_count, to_eval, address) @OperatorFactory.register class _QuantizedReshapeOperator(_Operator): op_type = "QuantizedReshape" def __init__(self, op_info, **kwargs): _Operator.__init__(self) inputs = [tensor_info.name for tensor_info in op_info.input_tensors] outputs = [tensor_info.name for tensor_info in op_info.output_tensors] parser = NamescopedKWArgsParser(RefCntOptimizer.KWARGS_NAMESCOPE, op_info.op_attr) ref_counts = parser.get('ref_counts', []) to_eval = parser.get('to_eval', False) self._snippet = QuantizedReshapeOpSnippet(inputs=inputs, outputs=outputs, ref_counts=ref_counts, to_eval=to_eval) @OperatorFactory.register class _CMSIS_NN_FCOperator(_Operator): op_type="CMSIS_NN_FC" def __init__(self, op_info, **kwargs): _Operator.__init__(self) #import pdb; pdb.set_trace() # Note order of inputs/outputs is preserved inputs = [tensor_info.name for tensor_info in op_info.input_tensors] output = op_info.output_tensors[0].name out_dtype = op_info.output_tensors[0].dtype in_dtypes = [tensor_info.dtype for tensor_info in op_info.input_tensors] assert (op_info.input_tensors[0].shape[1] == None or op_info.input_tensors[0].shape[1] == 1) parser = NamescopedKWArgsParser(RefCntOptimizer.KWARGS_NAMESCOPE, op_info.op_attr) ref_counts = parser.get('ref_counts', []) to_eval = parser.get('to_eval', False) self._snippet = CMSISNNFCOpSnippet(inputs=inputs, output=output, ref_counts=ref_counts, in_dtypes=in_dtypes, out_dtype=out_dtype, to_eval=to_eval) @OperatorFactory.register class _Conv2DOperator(_Operator): op_type = "Conv2D" def __init__(self, op_info, **kwargs): _Operator.__init__(self) inputs = [tensor_info.name for tensor_info in op_info.input_tensors] output = op_info.output_tensors[0].name in_dtype, filter_dtype = (op_info.input_tensors[0].dtype, op_info.input_tensors[1].dtype) out_dtype = op_info.output_tensors[0].dtype strides = op_info.op_attr["strides"].value.ints_value padding = op_info.op_attr["padding"].value.decode('utf8') parser = NamescopedKWArgsParser(RefCntOptimizer.KWARGS_NAMESCOPE, op_info.op_attr) ref_count = parser.get('ref_counts', [0])[0] to_eval = parser.get('to_eval', False) self._snippet = Conv2DOpSnippet(inputs, output, strides, padding, in_dtype=in_dtype, filter_dtype=filter_dtype, out_dtype=out_dtype, ref_count=ref_count, to_eval=to_eval) @classmethod def build_op_info(cls, ugraph, name, tensor_x, tensor_w, stride_height, stride_width, padding='SAME', **kwargs): # dboy: I'm too lazy to implement the padding algorithm again # simply call tf to find out the output shape dummy_x = np.empty(tensor_x.shape, dtype=tensor_x.dtype) dummy_w = np.empty(tensor_w.shape, dtype=tensor_w.dtype) graph = tf.Graph() with graph.as_default(): dummy_out = tf.nn.conv2d( dummy_x, dummy_w, strides=[1, stride_height, stride_width, 1], padding=padding, name='dummy' ) node_def = [node for node in graph.as_graph_def().node if node.name == 'dummy'][0] output_shape = dummy_out.shape.as_list() output_dtype = np.promote_types(tensor_x.dtype, tensor_w.dtype) op_attr = { k: AttrValueConverter.get_generic_value(v) for k, v in node_def.attr.items() } return OperationInfo( name=name, input_tensors=[tensor_x, tensor_w], output_tensors=[ TensorInfo( name='{}:0'.format(name), op_name=name, dtype=output_dtype, shape=output_shape, ugraph=ugraph, ) ], op_type=cls.op_type, op_attr=op_attr, ugraph=ugraph, backend=kwargs.get('backend', 'tensorflow'), ) @OperatorFactory.register class _FusedConv2DMaxpoolOperator(_Operator): op_type = "FusedConv2DMaxpool" def __init__(self, op_info, **kwargs): _Operator.__init__(self) inputs = [tensor_info.name for tensor_info in op_info.input_tensors] output = op_info.output_tensors[0].name in_dtype, filter_dtype = (op_info.input_tensors[0].dtype, op_info.input_tensors[1].dtype) out_dtype = op_info.output_tensors[0].dtype strides = op_info.op_attr["strides"].value.ints_value ksize = op_info.op_attr["ksize"].value.ints_value padding = op_info.op_attr["padding"].value.decode('utf8') parser = NamescopedKWArgsParser(RefCntOptimizer.KWARGS_NAMESCOPE, op_info.op_attr) ref_count = parser.get('ref_counts', [0])[0] to_eval = parser.get('to_eval', False) self._snippet = FusedConv2DMaxpoolOpSnippet(inputs, output, strides, ksize, padding, in_dtype=in_dtype, filter_dtype=filter_dtype, out_dtype=out_dtype, ref_count=ref_count, to_eval=to_eval) @OperatorFactory.register class _QuantizedFusedConv2DMaxpoolOperator(_Operator): op_type = "QuantizedFusedConv2DMaxpool" def __init__(self, op_info, **kwargs): _Operator.__init__(self) inputs = [tensor_info.name for tensor_info in op_info.input_tensors] outputs = [tensor_info.name for tensor_info in op_info.output_tensors] in_dtype, filter_dtype = (op_info.input_tensors[0].dtype, op_info.input_tensors[1].dtype) out_dtypes = [tensor_info.dtype for tensor_info in op_info.output_tensors] strides = op_info.op_attr['_utensor_conv']["strides"].value.ints_value ksize = op_info.op_attr['_utensor_pool']["ksize"].value.ints_value padding = op_info.op_attr['_utensor_conv']["padding"].value.decode('utf8') parser = NamescopedKWArgsParser(RefCntOptimizer.KWARGS_NAMESCOPE, op_info.op_attr) ref_counts = parser.get('ref_counts', None) to_eval = parser.get('to_eval', False) self._snippet = QuantizedFusedConv2DMaxpoolOpSnippet( inputs, outputs, strides, ksize, padding, in_dtype=in_dtype, filter_dtype=filter_dtype, out_dtypes=out_dtypes, ref_counts=ref_counts, to_eval=to_eval ) @OperatorFactory.register class _Conv2DQuantOperator(_Operator): op_type = "QuantizedConv2D" def __init__(self, op_info, **kwargs): _Operator.__init__(self) inputs = [tensor_info.name for tensor_info in op_info.input_tensors] outputs = [tensor_info.name for tensor_info in op_info.output_tensors] in_dtype, filter_dtype = (op_info.input_tensors[0].dtype, op_info.input_tensors[1].dtype) out_dtypes = [tensor_info.dtype for tensor_info in op_info.output_tensors] strides = op_info.op_attr["strides"].value.ints_value padding = op_info.op_attr["padding"].value.decode('utf8') parser = NamescopedKWArgsParser(RefCntOptimizer.KWARGS_NAMESCOPE, op_info.op_attr) ref_counts = parser.get('ref_counts', []) to_eval = parser.get('to_eval', False) self._snippet = Conv2DQuantOpSnippet(inputs, outputs, strides, padding, in_dtype=in_dtype, filter_dtype=filter_dtype, out_dtypes=out_dtypes, ref_counts=ref_counts, to_eval=to_eval) @OperatorFactory.register class _Uint8Q7OriginOperator(_Operator): op_type = "Uint8Q7OriginOp" def __init__(self, op_info, **kwargs): _Operator.__init__(self) inputs = [tensor_info.name for tensor_info in op_info.input_tensors] output = op_info.output_tensors[0].name parser = NamescopedKWArgsParser(RefCntOptimizer.KWARGS_NAMESCOPE, op_info.op_attr) ref_count = parser.get('ref_counts', [0])[0] to_eval = parser.get('to_eval', False) self._snippet = Uint8Q7OriginSnippet(inputs, output, ref_count, to_eval) #hard coding to uint8_t uint8_t int32_t for now @OperatorFactory.register class _QuantRangeForMultiplication_u8_u8_int32_Operator(_Operator): op_type = "QuantRangeForMultiplicationu8u8int32Op" def __init__(self, op_info, **kwargs): _Operator.__init__(self) inputs = [tensor_info.name for tensor_info in op_info.input_tensors] outputs = [tensor_info.name for tensor_info in op_info.output_tensors] if op_info.output_tensors[0].dtype != op_info.output_tensors[1].dtype: assert "output tensors must have the same data type" #output_type = op_info.output_tensors[0].dtype #FIXME: hard coding the output to int32 for now output_type = np.dtype([('qint32', '<i4')]) parser = NamescopedKWArgsParser(RefCntOptimizer.KWARGS_NAMESCOPE, op_info.op_attr) ref_counts = parser.get('ref_counts', []) to_eval = parser.get('to_eval', False) self._snippet = QuantRangeForMultiplicationSnippet(inputs, outputs, output_type, ref_counts, to_eval) @OperatorFactory.register @OpEqualityDelegate.is_compatible_with("Const", _morphism.Inline2ConstMorphism) class _InlineOperator(_Operator): op_type = "Inline" def __init__(self, op_info, **kwargs): out_tensor_info = op_info.output_tensors[0] out_tname, out_dtype, tensor_shape = (out_tensor_info.name, out_tensor_info.dtype, out_tensor_info.shape) parser = NamescopedKWArgsParser(RefCntOptimizer.KWARGS_NAMESCOPE, op_info.op_attr) ref_count = parser.get('ref_counts', [0])[0] pre_tname = self._prepare_tensor_name(out_tname) inline_tname = self._prepare_inline_array_name(out_tname) value = op_info.op_attr['value'].value.np_array.flatten() self._snippet = CreateTensorBinarySnippet(out_tname, tensor_shape=tensor_shape, tf_dtype=out_dtype, sptr_name=pre_tname, inline_name=inline_tname, ref_count=ref_count) weight_snippet = WeightSnippet(inline_tname, out_dtype, tensor_shape, value) weight_container = kwargs['weight_container'] weight_container.add_snippet(weight_snippet) def _prepare_tensor_name(self, tensor_name): prepared = tensor_name.replace(":", "_").replace("/", "_") return prepared def _prepare_inline_array_name(self, tensor_name): inline = tensor_name.replace(":", "_").replace("/", "_") preapred = "inline_{}".format(inline) return preapred @OperatorFactory.register class _RamOperator(_Operator): op_type = "Ram" def __init__(self, op_info, **kwargs): out_tensor_info = op_info.output_tensors[0] out_tname, out_dtype, tensor_shape = (out_tensor_info.name, out_tensor_info.dtype, out_tensor_info.shape) parser = NamescopedKWArgsParser(RefCntOptimizer.KWARGS_NAMESCOPE, op_info.op_attr) ref_count = parser.get('ref_counts', [0])[0] pre_tname = self._prepare_tensor_name(out_tname) #inline_tname = self._prepare_inline_array_name(out_tname) #value = op_info.op_attr['value'].value.np_array.flatten() self._snippet = CreateTensorRamSnippet(out_tname, tensor_shape=tensor_shape, tf_dtype=out_dtype, sptr_name=pre_tname, ref_count=ref_count) def _prepare_tensor_name(self, tensor_name): prepared = tensor_name.replace(":", "_").replace("/", "_") return prepared @OperatorFactory.register class _ShapeOperator(_Operator): op_type = "Shape" def __init__(self, op_info, **kwargs): _Operator.__init__(self) inputs = [tensor_info.name for tensor_info in op_info.input_tensors] output = op_info.output_tensors[0].name parser = NamescopedKWArgsParser(RefCntOptimizer.KWARGS_NAMESCOPE, op_info.op_attr) ref_count = parser.get('ref_counts', [0])[0] to_eval = parser.get('to_eval', True) out_dtype = op_info.output_tensors[0].dtype self._snippet = ShapeOpSnippet(inputs, output, out_dtype, ref_count, to_eval) @OperatorFactory.register class _StridedSliceOperator(_Operator): op_type = "StridedSlice" def __init__(self, op_info, **kwargs): _Operator.__init__(self) inputs = [tensor_info.name for tensor_info in op_info.input_tensors] output = op_info.output_tensors[0].name parser = NamescopedKWArgsParser(RefCntOptimizer.KWARGS_NAMESCOPE, op_info.op_attr) ref_count = parser.get('ref_counts', [0])[0] to_eval = parser.get('to_eval', True) dtype = op_info.input_tensors[0].dtype out_dtype = op_info.output_tensors[0].dtype begin_mask = op_info.op_attr['begin_mask'].value ellipsis_mask = op_info.op_attr['ellipsis_mask'].value end_mask = op_info.op_attr['end_mask'].value new_axis_mask = op_info.op_attr['begin_mask'].value shrink_axis_mask = op_info.op_attr['shrink_axis_mask'].value self._snippet = StridedSliceOpSnippet(inputs, output, dtype, out_dtype, begin_mask, ellipsis_mask, end_mask, new_axis_mask, shrink_axis_mask, ref_count, to_eval) @OperatorFactory.register class _PackOperator(_Operator): op_type = "Pack" def __init__(self, op_info, **kwargs): _Operator.__init__(self) inputs = [tensor_info.name for tensor_info in op_info.input_tensors] output = op_info.output_tensors[0].name parser = NamescopedKWArgsParser(RefCntOptimizer.KWARGS_NAMESCOPE, op_info.op_attr) ref_count = parser.get('ref_counts', [0])[0] to_eval = parser.get('to_eval', True) dtype = op_info.input_tensors[0].dtype out_dtype = op_info.output_tensors[0].dtype N = op_info.op_attr['N'].value axis = op_info.op_attr['axis'].value self._snippet = PackOpSnippet(inputs, output, dtype, out_dtype, N, axis, ref_count, to_eval) @OperatorFactory.register class _SoftmaxOperator(_Operator): # NOTE: softmax in tf is a composite op, no trivial way # to construct the op_info if we want to support # tf quantization for softmax op. We simply just # support uTensor softmax only. op_type = "Softmax" def __init__(self, op_info, **kwargs): _Operator.__init__(self) input_tname = op_info.input_tensors[0].name output_tname = op_info.output_tensors[0].name parser = NamescopedKWArgsParser(RefCntOptimizer.KWARGS_NAMESCOPE, op_info.op_attr) ref_count = parser.get('ref_counts', [0])[0] to_eval = parser.get('to_eval', True) out_dtype = op_info.output_tensors[0].dtype in_dtype = op_info.input_tensors[0].dtype self._snippet = SoftmaxOpSnippet( input_tname, output_tname, in_dtype, out_dtype, ref_count, to_eval ) @OperatorFactory.register class _GatherOperator(_Operator): op_type = "Gather" # tf op type def __init__(self, op_info, **kwargs): _Operator.__init__(self) inputs = [tensor_info.name for tensor_info in op_info.input_tensors] output = op_info.output_tensors[0].name tf_dtype = op_info.input_tensors[0].dtype parser = NamescopedKWArgsParser(RefCntOptimizer.KWARGS_NAMESCOPE, op_info.op_attr) ref_count = parser.get('ref_counts', [0])[0] to_eval = parser.get('to_eval', False) self._snippet = GatherOpSnippet(inputs, output, tf_dtype, ref_count, to_eval)

40,455

3,736

817

f1f76e0897cbb9b84dcbbc0fc3e50e81b0c75e9b

2,993

py

Python

ndg/security/server/test/unit/wsgi/authn/test_httpbasicauth.py

cedadev/ndg_security_server

6873cc0de1a01ad05ddcbeb3f074a33923dc1ca1

[ "BSD-3-Clause" ]

null

ndg/security/server/test/unit/wsgi/authn/test_httpbasicauth.py

cedadev/ndg_security_server

6873cc0de1a01ad05ddcbeb3f074a33923dc1ca1

[ "BSD-3-Clause" ]

null

ndg/security/server/test/unit/wsgi/authn/test_httpbasicauth.py

cedadev/ndg_security_server

6873cc0de1a01ad05ddcbeb3f074a33923dc1ca1

[ "BSD-3-Clause" ]

1

2017-12-05T17:31:08.000Z

#!/usr/bin/env python """Unit tests for WSGI HTTP Basic Auth handler NERC DataGrid Project """ __author__ = "P J Kershaw" __date__ = "13/10/09" __copyright__ = "(C) 2009 Science and Technology Facilities Council" __license__ = "BSD - see LICENSE file in top-level directory" __contact__ = "Philip.Kershaw@stfc.ac.uk" __revision__ = '$Id$' import logging logging.basicConfig(level=logging.DEBUG) import unittest import urllib.request, urllib.error, urllib.parse import base64 import paste.fixture from paste.httpexceptions import HTTPUnauthorized from ndg.security.server.test.base import BaseTestCase from ndg.security.server.wsgi.httpbasicauth import HttpBasicAuthMiddleware class TestAuthnApp(object): '''Test Application for the Authentication handler to protect''' response = b"Test HTTP Basic Authentication application" if __name__ == "__main__": unittest.main()

32.182796

80

0.636151

#!/usr/bin/env python """Unit tests for WSGI HTTP Basic Auth handler NERC DataGrid Project """ __author__ = "P J Kershaw" __date__ = "13/10/09" __copyright__ = "(C) 2009 Science and Technology Facilities Council" __license__ = "BSD - see LICENSE file in top-level directory" __contact__ = "Philip.Kershaw@stfc.ac.uk" __revision__ = '$Id$' import logging logging.basicConfig(level=logging.DEBUG) import unittest import urllib.request, urllib.error, urllib.parse import base64 import paste.fixture from paste.httpexceptions import HTTPUnauthorized from ndg.security.server.test.base import BaseTestCase from ndg.security.server.wsgi.httpbasicauth import HttpBasicAuthMiddleware class TestAuthnApp(object): '''Test Application for the Authentication handler to protect''' response = b"Test HTTP Basic Authentication application" def __init__(self, app_conf, **local_conf): pass def __call__(self, environ, start_response): if environ['PATH_INFO'] == '/test_200': status = "200 OK" else: status = "404 Not found" start_response(status, [('Content-length', str(len(TestAuthnApp.response))), ('Content-type', 'text/plain')]) return [TestAuthnApp.response] class HttpBasicAuthPluginMiddleware(object): USERNAME = b'testuser' PASSWORD = b'password' def __init__(self, app): self._app = app def __call__(self, environ, start_response): def authenticate(environ, username, password): if username == HttpBasicAuthPluginMiddleware.USERNAME and \ password == HttpBasicAuthPluginMiddleware.PASSWORD: return else: raise HTTPUnauthorized("Invalid credentials") environ['authenticate'] = authenticate return self._app(environ, start_response) class HttpBasicAuthMiddlewareTestCase(BaseTestCase): SERVICE_PORTNUM = 10443 def __init__(self, *args, **kwargs): app = TestAuthnApp({}) app = HttpBasicAuthMiddleware.filter_app_factory(app, {}, prefix='', authnFunc='authenticate') self.wsgiapp = HttpBasicAuthPluginMiddleware(app) self.app = paste.fixture.TestApp(self.wsgiapp) BaseTestCase.__init__(self, *args, **kwargs) def test01PasteFixture(self): username = HttpBasicAuthPluginMiddleware.USERNAME password = HttpBasicAuthPluginMiddleware.PASSWORD base64String = base64.encodestring(b'%s:%s' % (username, password))[:-1] authHeader = "Basic %s" % base64String headers = {'Authorization': authHeader} url = '/test_200' response = self.app.get(url, headers=headers, status=200) print(response) if __name__ == "__main__": unittest.main()

1,708

260

116

0c2450da8b64ba940ddbb47766c3145d609c2f49

4,725

py

Python

src/tankoh2/contour.py

sfreund-DLR/tankoh2

92ff080f7034a7eb1cdabed5089c79fd01af4d11

[ "MIT", "BSD-3-Clause" ]

null

src/tankoh2/contour.py

sfreund-DLR/tankoh2

92ff080f7034a7eb1cdabed5089c79fd01af4d11

[ "MIT", "BSD-3-Clause" ]

27

2021-11-03T19:53:00.000Z

2022-03-28T12:43:30.000Z

src/tankoh2/contour.py

sfreund-DLR/tankoh2

92ff080f7034a7eb1cdabed5089c79fd01af4d11

[ "MIT", "BSD-3-Clause" ]

null

"""methods for liners and domes""" import numpy as np from tankoh2 import pychain from tankoh2.service import log from tankoh2.exception import Tankoh2Error from tankoh2.utilities import updateName, copyAsJson # ######################################################################################### # Create Liner # ######################################################################################### def domeContourLength(dome): """Returns the contour length of a dome""" contourCoords = np.array([dome.getXCoords(), dome.getRCoords()]).T contourDiffs = contourCoords[1:,:] - contourCoords[:-1] contourLength = np.sum(np.linalg.norm(contourDiffs, axis=1)) return contourLength def getDome(cylinderRadius, polarOpening, domeType=None, x=None, r=None): """ :param cylinderRadius: radius of the cylinder :param polarOpening: polar opening radius :param domeType: pychain.winding.DOME_TYPES.ISOTENSOID or pychain.winding.DOME_TYPES.CIRCLE :param x: x-coordinates of a custom dome contour :param r: radius-coordinates of a custom dome contour. r[0] starts at cylinderRadius """ if domeType is None: domeType = pychain.winding.DOME_TYPES.ISOTENSOID elif isinstance(domeType, str): domeType = domeType.lower() if domeType == 'isotensoid': domeType = pychain.winding.DOME_TYPES.ISOTENSOID elif domeType == 'circle': domeType = pychain.winding.DOME_TYPES.CIRCLE else: raise Tankoh2Error(f'wrong dome type "{domeType}". Valid dome types: [isotensoid, circle]') # build dome dome = pychain.winding.Dome() dome.buildDome(cylinderRadius, polarOpening, domeType) if x is not None and r is not None: if not np.allclose(r[0], cylinderRadius): raise Tankoh2Error('cylinderRadius and r-vector do not fit') if not np.allclose(r[-1], polarOpening): raise Tankoh2Error('polarOpening and r-vector do not fit') dome.setPoints(x, r) return dome def getLiner(dome, length, linerFilename=None, linerName=None, dome2 = None, nodeNumber = 500): """Creates a liner :param dome: dome instance :param length: zylindrical length of liner :param linerFilename: if given, the liner is saved to this file for visualization in µChainWind :param linerName: name of the liner written to the file :return: """ # create a symmetric liner with dome information and cylinder length liner = pychain.winding.Liner() # spline for winding calculation is left on default of 1.0 if dome2: contourLength = length + domeContourLength(dome) + domeContourLength(dome2) else: contourLength = length / 2 + domeContourLength(dome) # use half model (one dome, half cylinder) deltaLengthSpline = contourLength / nodeNumber # just use half side if dome2 is not None: log.info("Creat unsymmetric vessel") liner.buildFromDomes(dome, dome2, length, deltaLengthSpline) else: log.info("Create symmetric vessel") liner.buildFromDome(dome, length, deltaLengthSpline) if linerFilename: liner.saveToFile(linerFilename) updateName(linerFilename, linerName, ['liner']) copyAsJson(linerFilename, 'liner') liner.loadFromFile(linerFilename) return liner

37.8

116

0.631534

"""methods for liners and domes""" import numpy as np from tankoh2 import pychain from tankoh2.service import log from tankoh2.exception import Tankoh2Error from tankoh2.utilities import updateName, copyAsJson # ######################################################################################### # Create Liner # ######################################################################################### def getReducedDomePoints(contourFilename, spacing, contourOutFilename=None): # load contour from file Data = np.loadtxt(contourFilename) if 1: contourPoints = np.abs(Data) contourPoints[:, 0] -= contourPoints[0, 0] # reduce points redContourPoints = contourPoints[::spacing, :] if not np.allclose(redContourPoints[-1, :], contourPoints[-1, :]): redContourPoints = np.append(redContourPoints, [contourPoints[-1, :]], axis=0) if contourOutFilename: np.savetxt(contourOutFilename, redContourPoints, delimiter=',') Xvec, rVec = redContourPoints[:, 0], redContourPoints[:, 1] else: Xvec = abs(Data[:, 0]) Xvec = Xvec - Xvec[0] rVec = abs(Data[:, 1]) # reduce data points log.info(len(Xvec) - 1) index = np.linspace(0, dpoints * int((len(Xvec) / dpoints)), int((len(Xvec) / dpoints)) + 1, dtype=np.int16) arr = [len(Xvec) - 1] index = np.append(index, arr) Xvec = Xvec[index] rVec = rVec[index] # save liner contour for loading in mikroWind with open(fileNameReducedDomeContour, "w") as contour: for i in range(len(Xvec)): contour.write(str(Xvec[i]) + ',' + str(rVec[i]) + '\n') return Xvec, rVec def domeContourLength(dome): """Returns the contour length of a dome""" contourCoords = np.array([dome.getXCoords(), dome.getRCoords()]).T contourDiffs = contourCoords[1:,:] - contourCoords[:-1] contourLength = np.sum(np.linalg.norm(contourDiffs, axis=1)) return contourLength def getDome(cylinderRadius, polarOpening, domeType=None, x=None, r=None): """ :param cylinderRadius: radius of the cylinder :param polarOpening: polar opening radius :param domeType: pychain.winding.DOME_TYPES.ISOTENSOID or pychain.winding.DOME_TYPES.CIRCLE :param x: x-coordinates of a custom dome contour :param r: radius-coordinates of a custom dome contour. r[0] starts at cylinderRadius """ if domeType is None: domeType = pychain.winding.DOME_TYPES.ISOTENSOID elif isinstance(domeType, str): domeType = domeType.lower() if domeType == 'isotensoid': domeType = pychain.winding.DOME_TYPES.ISOTENSOID elif domeType == 'circle': domeType = pychain.winding.DOME_TYPES.CIRCLE else: raise Tankoh2Error(f'wrong dome type "{domeType}". Valid dome types: [isotensoid, circle]') # build dome dome = pychain.winding.Dome() dome.buildDome(cylinderRadius, polarOpening, domeType) if x is not None and r is not None: if not np.allclose(r[0], cylinderRadius): raise Tankoh2Error('cylinderRadius and r-vector do not fit') if not np.allclose(r[-1], polarOpening): raise Tankoh2Error('polarOpening and r-vector do not fit') dome.setPoints(x, r) return dome def getLiner(dome, length, linerFilename=None, linerName=None, dome2 = None, nodeNumber = 500): """Creates a liner :param dome: dome instance :param length: zylindrical length of liner :param linerFilename: if given, the liner is saved to this file for visualization in µChainWind :param linerName: name of the liner written to the file :return: """ # create a symmetric liner with dome information and cylinder length liner = pychain.winding.Liner() # spline for winding calculation is left on default of 1.0 if dome2: contourLength = length + domeContourLength(dome) + domeContourLength(dome2) else: contourLength = length / 2 + domeContourLength(dome) # use half model (one dome, half cylinder) deltaLengthSpline = contourLength / nodeNumber # just use half side if dome2 is not None: log.info("Creat unsymmetric vessel") liner.buildFromDomes(dome, dome2, length, deltaLengthSpline) else: log.info("Create symmetric vessel") liner.buildFromDome(dome, length, deltaLengthSpline) if linerFilename: liner.saveToFile(linerFilename) updateName(linerFilename, linerName, ['liner']) copyAsJson(linerFilename, 'liner') liner.loadFromFile(linerFilename) return liner

1,298

0

23

d9f39d299ff10da061d80b7dd42549838fdc0966

206

py

Python

test/test_local_grid_client.py

mari-linhares/Grid

e06a13f24667160b91cd5f682983453072877f30

[ "Apache-2.0" ]

null

test/test_local_grid_client.py

mari-linhares/Grid

e06a13f24667160b91cd5f682983453072877f30

[ "Apache-2.0" ]

null

test/test_local_grid_client.py

mari-linhares/Grid

e06a13f24667160b91cd5f682983453072877f30

[ "Apache-2.0" ]

null

import syft as sy import torch as th from grid.client import GridClient

18.727273

56

0.718447

import syft as sy import torch as th from grid.client import GridClient def test_local_grid_client(): hook = sy.TorchHook(th) gr_client = GridClient(addr="http://127.0.0.1:5000") assert True

109

0

23

f11ef309eb4e3feab167a5e7a7494a790d48b331

7,450

py

Python

tests/test_arg_check.py

lizeyan/tensorkit

2997a5914ec3c3ec72f91eb5906b5ee878fdc020

[ "MIT" ]

null

tests/test_arg_check.py

lizeyan/tensorkit

2997a5914ec3c3ec72f91eb5906b5ee878fdc020

[ "MIT" ]

null

tests/test_arg_check.py

lizeyan/tensorkit

2997a5914ec3c3ec72f91eb5906b5ee878fdc020

[ "MIT" ]

2

2020-10-15T06:41:32.000Z

2021-01-27T12:55:11.000Z

import unittest import pytest import tensorkit as tk from tensorkit import tensor as T from tensorkit.arg_check import * from tests.helper import *

38.402062

82

0.475168

import unittest import pytest import tensorkit as tk from tensorkit import tensor as T from tensorkit.arg_check import * from tests.helper import * class ArgCheckTestCase(TestCase): def test_validate_positive_int(self): for v in [1, 2, 3]: self.assertEqual(validate_positive_int('v', v), v) with pytest.raises(ValueError, match='`v` must be a positive int: ' 'got -1'): _ = validate_positive_int('v', -1) def test_validate_layer(self): layer = tk.layers.Linear(5, 3) for v in [layer, tk.layers.jit_compile(layer)]: self.assertIs(validate_layer('v', v), v) with pytest.raises(TypeError, match='`v` is required to be a layer: got 123'): _ = validate_layer('v', 123) def test_validate_layer_factory(self): for v in [tk.layers.Linear, (lambda: tk.layers.Linear(5, 3))]: self.assertIs(validate_layer_factory('v', v), v) with pytest.raises(TypeError, match='`v` is required to be a layer factory: ' 'got 123'): _ = validate_layer_factory('v', 123) def test_get_layer_from_layer_or_factory(self): factory = lambda in_features, out_features: \ tk.layers.Linear(in_features, out_features) layer = factory(5, 3) for v in [layer, tk.layers.jit_compile(layer), tk.layers.Linear, factory]: out = get_layer_from_layer_or_factory( 'v', v, args=(5,), kwargs=dict(out_features=3)) if isinstance(v, T.Module): self.assertIs(out, v) else: self.assertIsInstance(out, tk.layers.Linear) self.assertEqual(out.in_features, 5) self.assertEqual(out.out_features, 3) with pytest.raises(TypeError, match='`v` is required to be a layer or a layer ' 'factory: got 123'): _ = get_layer_from_layer_or_factory('v', 123) def test_validate_conv_size(self): for spatial_ndims in (1, 2, 3): self.assertEqual( validate_conv_size('v', 2, spatial_ndims), [2] * spatial_ndims ) self.assertEqual( validate_conv_size('v', [1, 2, 3][:spatial_ndims], spatial_ndims), [1, 2, 3][:spatial_ndims] ) self.assertEqual( validate_conv_size('v', (1, 2, 3)[:spatial_ndims], spatial_ndims), [1, 2, 3][:spatial_ndims] ) with pytest.raises(ValueError, match=r'`v` must be either a positive integer, or ' r'a sequence of positive integers of length ' r'`3`: got \[1, 2\]'): _ = validate_conv_size('v', [1, 2], 3), with pytest.raises(ValueError, match=r'`v` must be either a positive integer, or ' r'a sequence of positive integers of length ' r'`3`: got \[1, 2, 0\]'): _ = validate_conv_size('v', [1, 2, 0], 3) def test_validate_padding(self): for spatial_ndims in (1, 2, 3): self.assertEqual( validate_padding( 'none', kernel_size=[5, 6, 7][:spatial_ndims], dilation=[1, 2, 3][:spatial_ndims], spatial_ndims=spatial_ndims, ), [(0, 0)] * spatial_ndims ) self.assertEqual( validate_padding( 'full', kernel_size=[5, 6, 7][:spatial_ndims], dilation=[1, 2, 3][:spatial_ndims], spatial_ndims=spatial_ndims, ), [(4, 4), (10, 10), (18, 18)][:spatial_ndims] ) self.assertEqual( validate_padding( 'half', kernel_size=[4, 5, 6][:spatial_ndims], dilation=[1, 2, 3][:spatial_ndims], spatial_ndims=spatial_ndims, ), [(1, 2), (4, 4), (7, 8)][:spatial_ndims] ) self.assertEqual( validate_padding( 0, kernel_size=[5, 6, 7][:spatial_ndims], dilation=[1, 2, 3][:spatial_ndims], spatial_ndims=spatial_ndims, ), [(0, 0)] * spatial_ndims ) self.assertEqual( validate_padding( [(3, 4), 4, (4, 5)][:spatial_ndims], kernel_size=[5, 6, 7][:spatial_ndims], dilation=[1, 2, 3][:spatial_ndims], spatial_ndims=spatial_ndims, ), [(3, 4), (4, 4), (4, 5)][:spatial_ndims] ) self.assertEqual( validate_padding( (3, 4, 5)[:spatial_ndims], kernel_size=[5, 6, 7][:spatial_ndims], dilation=[1, 2, 3][:spatial_ndims], spatial_ndims=spatial_ndims, ), [(3, 3), (4, 4), (5, 5)][:spatial_ndims] ) msg_prefix = ( r'`padding` must be a non-negative integer, a ' r'sequence of non-negative integers of length ' r'`3`, "none", "half" or "full": got ' ) with pytest.raises(ValueError, match=msg_prefix + r'-1'): _ = validate_padding(-1, [1] * 3, [1] * 3, 3) with pytest.raises(ValueError, match=msg_prefix + r'\[1, 2\]'): _ = validate_padding([1, 2], [1] * 3, [1] * 3, 3) with pytest.raises(ValueError, match=msg_prefix + r'\[1, 2, -1\]'): _ = validate_padding([1, 2, -1], [1] * 3, [1] * 3, 3) def test_validate_output_padding(self): for spatial_ndims in (1, 2, 3): self.assertEqual( validate_output_padding( 0, stride=[1, 2, 3][: spatial_ndims], dilation=[1, 2, 3][:spatial_ndims], spatial_ndims=spatial_ndims, ), [0] * spatial_ndims ) self.assertEqual( validate_output_padding( [1, 2, 3][:spatial_ndims], stride=[4, 5, 6][: spatial_ndims], dilation=[3, 4, 5][:spatial_ndims], spatial_ndims=spatial_ndims, ), [1, 2, 3][:spatial_ndims], ) err_msg = ( r'`output_padding` must be a non-negative integer, or a sequence ' r'of non-negative integers, and must be smaller than either ' r'`stride` or `dilation`' ) with pytest.raises(ValueError, match=err_msg): _ = validate_output_padding(-1, [4] * 3, [4] * 3, 3) with pytest.raises(ValueError, match=err_msg): _ = validate_output_padding([1, 2], [4] * 3, [4] * 3, 3) with pytest.raises(ValueError, match=err_msg): _ = validate_output_padding([1, 2, -1], [4] * 3, [4] * 3, 3)

7,075

12

212

9928c1c459906e3027e54332db9b7b9f35517297

930

py

Python

levels/chock_a_block.py

ungood/stockfighter-py

8fd6cc70177227164f3984670d352a89d9f0b0d5

[ "MIT" ]

null

levels/chock_a_block.py

ungood/stockfighter-py

8fd6cc70177227164f3984670d352a89d9f0b0d5

[ "MIT" ]

null

levels/chock_a_block.py

ungood/stockfighter-py

8fd6cc70177227164f3984670d352a89d9f0b0d5

[ "MIT" ]

null

#from stockfighter import Stockfighter import os, time # sf = Stockfighter() # # level = sf.levels['chock_a_block'] # info = level.start() # print(info) # # sf = Stockfighter() # print(sf.heartbeat()) # # venue = sf.venues['PVIEX'] # # stock = venue.stocks['SOF'] # for stock in venue.stocks: # print(stock) # # ORDER_SIZE = 50 # remaining = 100000 - 42823 # goal = 9103 # # def run(): # while(remaining > 0): # quote = stock.quote() # size = quote['askSize'] # if(size < 1): # continue # time.sleep(1) # ask = quote['ask'] # if(ask > goal): # continue # time.sleep(1) # order = min(remaining, size, ORDER_SIZE) # if order > 0: # print('Placing order for {} at {}. Remaining: {}'.format(order, ask, remaining)) # stock.buy(ACCOUNT, ask, order) # remaining -= order #(venue='CENOEX', account='SAS22786391')

22.142857

90

0.588172

#from stockfighter import Stockfighter import os, time def create_parser(parent): pass # sf = Stockfighter() # # level = sf.levels['chock_a_block'] # info = level.start() # print(info) # # sf = Stockfighter() # print(sf.heartbeat()) # # venue = sf.venues['PVIEX'] # # stock = venue.stocks['SOF'] # for stock in venue.stocks: # print(stock) # # ORDER_SIZE = 50 # remaining = 100000 - 42823 # goal = 9103 # # def run(): # while(remaining > 0): # quote = stock.quote() # size = quote['askSize'] # if(size < 1): # continue # time.sleep(1) # ask = quote['ask'] # if(ask > goal): # continue # time.sleep(1) # order = min(remaining, size, ORDER_SIZE) # if order > 0: # print('Placing order for {} at {}. Remaining: {}'.format(order, ask, remaining)) # stock.buy(ACCOUNT, ask, order) # remaining -= order #(venue='CENOEX', account='SAS22786391')

14

0

23

f7e3107009c02c524f668486aee1490c29db37a3

3,471

py

Python

matmul.py

zangkaiqiang/pyaam

3c59026df17fb0b4588797026d5a2fe64d05fca9

[ "MIT" ]

2

2020-07-06T18:18:25.000Z

2021-01-20T08:05:21.000Z

matmul.py

zangkaiqiang/pyaam

3c59026df17fb0b4588797026d5a2fe64d05fca9

[ "MIT" ]

null

matmul.py

zangkaiqiang/pyaam

3c59026df17fb0b4588797026d5a2fe64d05fca9

[ "MIT" ]

3

2021-01-11T07:16:42.000Z

2021-07-28T11:37:01.000Z

######################################################################## # # License: BSD # Created: October 11, 2013 # Author: Francesc Alted # ######################################################################## """ Implementation of an out of core matrix-matrix multiplication for PyTables. """ import sys, math import numpy as np import tables as tb _MB = 2**20 OOC_BUFFER_SIZE = 32*_MB """The buffer size for out-of-core operations. """ def dot(a, b, out=None): """ Matrix multiplication of two 2-D arrays. Parameters ---------- a : array_like First argument. b : array_like Second argument. out : array_like, optional Output argument. This must have the exact kind that would be returned if it was not used. Returns ------- output : CArray or scalar Returns the dot product of `a` and `b`. If `a` and `b` are both scalars or both 1-D arrays then a scalar is returned; otherwise a new CArray (in file dot.h5:/out) is returned. If `out` parameter is provided, then it is returned instead. Raises ------ ValueError If the last dimension of `a` is not the same size as the second-to-last dimension of `b`. """ if len(a.shape) != 2 or len(b.shape) != 2: raise (ValueError, "only 2-D matrices supported") if a.shape[1] != b.shape[0]: raise (ValueError, "last dimension of `a` does not match first dimension of `b`") l, m, n = a.shape[0], a.shape[1], b.shape[1] if out is not None: if out.shape != (l, n): raise (ValueError, "`out` array does not have the correct shape") else: f = tb.openFile('dot.h5', 'w') filters = tb.Filters(complevel=5, complib='blosc') out = f.createCArray(f.root, 'out', tb.Atom.from_dtype(a.dtype), shape=(l, n), filters=filters) # Compute a good block size buffersize = OOC_BUFFER_SIZE bl = math.sqrt(buffersize / out.dtype.itemsize) bl = 2**int(math.log(bl, 2)) for i in range(0, l, bl): for j in range(0, n, bl): for k in range(0, m, bl): a0 = a[i:min(i+bl, l), k:min(k+bl, m)] b0 = b[k:min(k+bl, m), j:min(j+bl, n)] out[i:i+bl, j:j+bl] += np.dot(a0, b0) return out if __name__ == "__main__": """Small benchmark for comparison against numpy.dot() speed""" from time import time # Matrix dimensions L, M, N = 1000, 100, 2000 print "Multiplying (%d, %d) x (%d, %d) matrices" % (L, M, M, N) a = np.linspace(0, 1, L*M).reshape(L, M) b = np.linspace(0, 1, M*N).reshape(M, N) t0 = time() cdot = np.dot(a,b) print "Time for np.dot->", round(time()-t0, 3), cdot.shape f = tb.openFile('matrix-pt.h5', 'w') l, m, n = a.shape[0], a.shape[1], b.shape[1] filters = tb.Filters(complevel=5, complib='blosc') ad = f.createCArray(f.root, 'a', tb.Float64Atom(), (l,m), filters=filters) ad[:] = a bd = f.createCArray(f.root, 'b', tb.Float64Atom(), (m,n), filters=filters) bd[:] = b cd = f.createCArray(f.root, 'c', tb.Float64Atom(), (l,n), filters=filters) t0 = time() dot(a, b, out=cd) print "Time for ooc dot->", round(time()-t0, 3), cd.shape np.testing.assert_almost_equal(cd, cdot) f.close()

28.925

77

0.53414

######################################################################## # # License: BSD # Created: October 11, 2013 # Author: Francesc Alted # ######################################################################## """ Implementation of an out of core matrix-matrix multiplication for PyTables. """ import sys, math import numpy as np import tables as tb _MB = 2**20 OOC_BUFFER_SIZE = 32*_MB """The buffer size for out-of-core operations. """ def dot(a, b, out=None): """ Matrix multiplication of two 2-D arrays. Parameters ---------- a : array_like First argument. b : array_like Second argument. out : array_like, optional Output argument. This must have the exact kind that would be returned if it was not used. Returns ------- output : CArray or scalar Returns the dot product of `a` and `b`. If `a` and `b` are both scalars or both 1-D arrays then a scalar is returned; otherwise a new CArray (in file dot.h5:/out) is returned. If `out` parameter is provided, then it is returned instead. Raises ------ ValueError If the last dimension of `a` is not the same size as the second-to-last dimension of `b`. """ if len(a.shape) != 2 or len(b.shape) != 2: raise (ValueError, "only 2-D matrices supported") if a.shape[1] != b.shape[0]: raise (ValueError, "last dimension of `a` does not match first dimension of `b`") l, m, n = a.shape[0], a.shape[1], b.shape[1] if out is not None: if out.shape != (l, n): raise (ValueError, "`out` array does not have the correct shape") else: f = tb.openFile('dot.h5', 'w') filters = tb.Filters(complevel=5, complib='blosc') out = f.createCArray(f.root, 'out', tb.Atom.from_dtype(a.dtype), shape=(l, n), filters=filters) # Compute a good block size buffersize = OOC_BUFFER_SIZE bl = math.sqrt(buffersize / out.dtype.itemsize) bl = 2**int(math.log(bl, 2)) for i in range(0, l, bl): for j in range(0, n, bl): for k in range(0, m, bl): a0 = a[i:min(i+bl, l), k:min(k+bl, m)] b0 = b[k:min(k+bl, m), j:min(j+bl, n)] out[i:i+bl, j:j+bl] += np.dot(a0, b0) return out if __name__ == "__main__": """Small benchmark for comparison against numpy.dot() speed""" from time import time # Matrix dimensions L, M, N = 1000, 100, 2000 print "Multiplying (%d, %d) x (%d, %d) matrices" % (L, M, M, N) a = np.linspace(0, 1, L*M).reshape(L, M) b = np.linspace(0, 1, M*N).reshape(M, N) t0 = time() cdot = np.dot(a,b) print "Time for np.dot->", round(time()-t0, 3), cdot.shape f = tb.openFile('matrix-pt.h5', 'w') l, m, n = a.shape[0], a.shape[1], b.shape[1] filters = tb.Filters(complevel=5, complib='blosc') ad = f.createCArray(f.root, 'a', tb.Float64Atom(), (l,m), filters=filters) ad[:] = a bd = f.createCArray(f.root, 'b', tb.Float64Atom(), (m,n), filters=filters) bd[:] = b cd = f.createCArray(f.root, 'c', tb.Float64Atom(), (l,n), filters=filters) t0 = time() dot(a, b, out=cd) print "Time for ooc dot->", round(time()-t0, 3), cd.shape np.testing.assert_almost_equal(cd, cdot) f.close()

0

4bfa331a60755af393427cbeb23700e010675ae3

3,487

py

Python

English/CdeC.py

ehultee/glacio-CdeC

2cab0a2593abe9e23ced8704be9c794fbbce0576

[ "MIT" ]

2

2019-07-01T16:42:35.000Z

2021-11-07T20:03:30.000Z

English/CdeC.py

ehultee/glacio-CdeC

2cab0a2593abe9e23ced8704be9c794fbbce0576

[ "MIT" ]

18

2019-07-02T16:46:54.000Z

2019-08-02T18:19:15.000Z

English/CdeC.py

ehultee/glacio-CdeC

2cab0a2593abe9e23ced8704be9c794fbbce0576

[ "MIT" ]

2

2019-07-02T16:20:33.000Z

2021-01-27T11:47:14.000Z

## Helper functions to clean up Clubes de Ciencia notebooks ## 5 July 2019 EHU import xarray as xr import pandas as pd import numpy as np from oggm import utils def ice_to_freshwater(icevol, rho_ice=900, rho_water=1000): """Cleanly convert volume of glacial ice (km3) to equivalent volume fresh water (liter). Arguments: icevol = volume of ice to convert, in km3 rho_ice = density of glacial ice (default 900 kg/m3) rho_water = density of freshwater (default 1000 kg/m3) """ km3_to_ltr = 1E12 water_vol_km3 = icevol * rho_ice / rho_water return water_vol_km3 * km3_to_ltr def read_run_results(gdir, filesuffix=None): """Reads the output diagnostics of a simulation and puts the data in a pandas dataframe. Parameters ---------- gdir : the glacier directory filesuffix : the file identifier Returns ------- a pandas Dataframe with monthly temp and precip """ with xr.open_dataset(gdir.get_filepath('model_diagnostics', filesuffix=filesuffix)) as ds: ds = ds.load() # Lemgth needs filtering ts = ds.length_m.to_series() ts = ts.rolling(12*3).min() ts.iloc[0:12*3] = ts.iloc[12*3] # Volume change delta_vol = np.append(ds.volume_m3.data[1:] - ds.volume_m3.data[0:-1], [0]) if ds.calendar_month[0] == 10 and gdir.cenlat < 0: # this is to cover up a bug in OGGM _, m = utils.hydrodate_to_calendardate(ds.hydro_year.data, ds.hydro_month.data, start_month=4) ds.calendar_month[:] = m odf = pd.DataFrame() odf['length_m'] = ts odf['volume_m3'] = ds.volume_m3 odf['delta_water_m3'] = delta_vol * 0.9 odf['month'] = ds.calendar_month return odf def read_climate_statistics(gdir): """Reads the annual cycle of climate for [1985-2015] at the glacier terminus elevation. Parameters ---------- gdir : the glacier directory Returns ------- a pandas Dataframe with monthly average temp and precip """ with xr.open_dataset(gdir.get_filepath('climate_monthly')) as ds: ds = ds.load() ds = ds.sel(time=slice('1985', '2015')) dsm = ds.groupby('time.month').mean(dim='time') odf = pd.DataFrame() odf['temp_celcius'] = dsm.temp.to_series() odf['prcp_mm_mth'] = dsm.prcp.to_series() # We correct for altitude difference d = utils.glacier_statistics(gdir) odf['temp_celcius'] += (ds.ref_hgt - d['flowline_min_elev']) * 0.0065 return odf def plot_xz_bed(x, bed, ax=None, ylim=None): """This function implements a glacier bed, prepared axes and a legend in altitude vs. distance along a glacier plot. Based on function of the same name in OGGM-Edu, but adds explicit axes argument. Parameters ---------- x : ndarray distance along glacier (all steps in km) bed : ndarray bed rock Parameters (Optional) ---------- ax : matplotlib axes instance on which to plot If None, calls plt.gca() ylim : tuple, y-limits of plot If None, calls ax.get_ylim() """ if ax is None: ax = plt.gca() if ylim is None: ylim = ax.get_ylim() ax.plot(x, bed, color='k', label='Bedrock', linestyle=':', linewidth=1.5) ax.set_xlabel('Distance along glacier [km]') ax.set_ylabel('Altitude [m]') ax.set_ylim(ylim) ax.legend(loc='best', frameon=False)

30.587719

102

0.629481

## Helper functions to clean up Clubes de Ciencia notebooks ## 5 July 2019 EHU import xarray as xr import pandas as pd import numpy as np from oggm import utils def ice_to_freshwater(icevol, rho_ice=900, rho_water=1000): """Cleanly convert volume of glacial ice (km3) to equivalent volume fresh water (liter). Arguments: icevol = volume of ice to convert, in km3 rho_ice = density of glacial ice (default 900 kg/m3) rho_water = density of freshwater (default 1000 kg/m3) """ km3_to_ltr = 1E12 water_vol_km3 = icevol * rho_ice / rho_water return water_vol_km3 * km3_to_ltr def read_run_results(gdir, filesuffix=None): """Reads the output diagnostics of a simulation and puts the data in a pandas dataframe. Parameters ---------- gdir : the glacier directory filesuffix : the file identifier Returns ------- a pandas Dataframe with monthly temp and precip """ with xr.open_dataset(gdir.get_filepath('model_diagnostics', filesuffix=filesuffix)) as ds: ds = ds.load() # Lemgth needs filtering ts = ds.length_m.to_series() ts = ts.rolling(12*3).min() ts.iloc[0:12*3] = ts.iloc[12*3] # Volume change delta_vol = np.append(ds.volume_m3.data[1:] - ds.volume_m3.data[0:-1], [0]) if ds.calendar_month[0] == 10 and gdir.cenlat < 0: # this is to cover up a bug in OGGM _, m = utils.hydrodate_to_calendardate(ds.hydro_year.data, ds.hydro_month.data, start_month=4) ds.calendar_month[:] = m odf = pd.DataFrame() odf['length_m'] = ts odf['volume_m3'] = ds.volume_m3 odf['delta_water_m3'] = delta_vol * 0.9 odf['month'] = ds.calendar_month return odf def read_climate_statistics(gdir): """Reads the annual cycle of climate for [1985-2015] at the glacier terminus elevation. Parameters ---------- gdir : the glacier directory Returns ------- a pandas Dataframe with monthly average temp and precip """ with xr.open_dataset(gdir.get_filepath('climate_monthly')) as ds: ds = ds.load() ds = ds.sel(time=slice('1985', '2015')) dsm = ds.groupby('time.month').mean(dim='time') odf = pd.DataFrame() odf['temp_celcius'] = dsm.temp.to_series() odf['prcp_mm_mth'] = dsm.prcp.to_series() # We correct for altitude difference d = utils.glacier_statistics(gdir) odf['temp_celcius'] += (ds.ref_hgt - d['flowline_min_elev']) * 0.0065 return odf def plot_xz_bed(x, bed, ax=None, ylim=None): """This function implements a glacier bed, prepared axes and a legend in altitude vs. distance along a glacier plot. Based on function of the same name in OGGM-Edu, but adds explicit axes argument. Parameters ---------- x : ndarray distance along glacier (all steps in km) bed : ndarray bed rock Parameters (Optional) ---------- ax : matplotlib axes instance on which to plot If None, calls plt.gca() ylim : tuple, y-limits of plot If None, calls ax.get_ylim() """ if ax is None: ax = plt.gca() if ylim is None: ylim = ax.get_ylim() ax.plot(x, bed, color='k', label='Bedrock', linestyle=':', linewidth=1.5) ax.set_xlabel('Distance along glacier [km]') ax.set_ylabel('Altitude [m]') ax.set_ylim(ylim) ax.legend(loc='best', frameon=False)

0

891a6a2c0dd55610ffc22400d6c2676a3191cb6f

1,543

py

Python

skoleintra/schildren.py

svalgaard/fskintra

3ccf656ef1450e541c902d4c00ea1dadcf82085c

[ "BSD-2-Clause-FreeBSD" ]

9

2015-08-12T09:54:04.000Z

2021-06-21T08:35:39.000Z

skoleintra/schildren.py

svalgaard/fskintra

3ccf656ef1450e541c902d4c00ea1dadcf82085c

[ "BSD-2-Clause-FreeBSD" ]

29

2015-01-03T21:13:20.000Z

2020-11-12T08:23:56.000Z

skoleintra/schildren.py

svalgaard/fskintra

3ccf656ef1450e541c902d4c00ea1dadcf82085c

[ "BSD-2-Clause-FreeBSD" ]

11

2015-02-25T20:24:56.000Z

2018-11-16T07:37:37.000Z

# -*- coding: utf-8 -*- import re import config import surllib # Map of children => urlPrefix # 'Andrea 0A' => '/parent/1234/Andrea/' _children = None def getChildren(): '''Returns of list of "available" children in the system''' global _children if not _children: _children = dict() seen = set() config.log(u'Henter liste af børn') data = surllib.skoleLogin() # Name of "First child" fst = data.find(id="sk-personal-menu-button").text.strip() for a in data.findAll('a', href=re.compile('^(/[^/]*){3}/Index$')): url = a['href'].rsplit('/', 1)[0].rstrip('/') if url in seen: continue seen.add(url) name = a.text.strip() or fst if name not in _children: config.log(u'Barn %s => %s' % (name, url), 2) _children[name] = url cns = sorted(_children.keys(), key=ckey) config.log(u'Følgende børn blev fundet: ' + u', '.join(cns)) return sorted(_children.keys(), key=ckey)

27.553571

75

0.596889

# -*- coding: utf-8 -*- import re import config import surllib # Map of children => urlPrefix # 'Andrea 0A' => '/parent/1234/Andrea/' _children = None def getChildren(): '''Returns of list of "available" children in the system''' global _children def ckey(n): return tuple(n.rsplit(' ', 1)[::-1]) if not _children: _children = dict() seen = set() config.log(u'Henter liste af børn') data = surllib.skoleLogin() # Name of "First child" fst = data.find(id="sk-personal-menu-button").text.strip() for a in data.findAll('a', href=re.compile('^(/[^/]*){3}/Index$')): url = a['href'].rsplit('/', 1)[0].rstrip('/') if url in seen: continue seen.add(url) name = a.text.strip() or fst if name not in _children: config.log(u'Barn %s => %s' % (name, url), 2) _children[name] = url cns = sorted(_children.keys(), key=ckey) config.log(u'Følgende børn blev fundet: ' + u', '.join(cns)) return sorted(_children.keys(), key=ckey) def getChildURLPrefix(cname): getChildren() assert(cname in _children) return surllib.absurl(_children[cname]) def getChildURL(cname, suffix): # Guessing a bug in forældre intra as weekplan urls have the following # format: parent/CHILD_ID/CHILD_NAMEitem/weeklyplansandhomework/list assert(suffix.startswith('/') or suffix.startswith('item/')) return getChildURLPrefix(cname) + suffix

399

0

73

b6fea9e2c246a5af265492ae8abcfc853cc92e50

297

py

Python

aula01/par_impar.py

Doni-zete/Praticas-Python

36a877a9f22f9992550fb6e3bdb89c751d6299ef

[ "MIT" ]

null

aula01/par_impar.py

Doni-zete/Praticas-Python

36a877a9f22f9992550fb6e3bdb89c751d6299ef

[ "MIT" ]

null

aula01/par_impar.py

Doni-zete/Praticas-Python

36a877a9f22f9992550fb6e3bdb89c751d6299ef

[ "MIT" ]

null

""" Decobrir se um numero é impar oou par """ print(25*"-") while True: numero = int(input("Digite um numero: ")) if (numero % 2) == 0: print(f"Numero digitado, {numero} é PAR: ") elif(numero % 2) != 0: print(f"Numero digitado, {numero} é IMPAR: ") print(25*"-")

21.214286

53

0.555556

""" Decobrir se um numero é impar oou par """ print(25*"-") while True: numero = int(input("Digite um numero: ")) if (numero % 2) == 0: print(f"Numero digitado, {numero} é PAR: ") elif(numero % 2) != 0: print(f"Numero digitado, {numero} é IMPAR: ") print(25*"-")

0

0167fb62df138c757952327fadf399fc12f32100

3,435

py

Python

tests/python/test_plotting.py

MichaelChirico/xgboost

028bdc174086d22dcda4130ca5955efca9a0eed7

[ "Apache-2.0" ]

1

2022-01-04T23:38:14.000Z

tests/python/test_plotting.py

Nihilitior/xgboost

7366d3b20cad8e28ecef67d5130c71e81bb0b088

[ "Apache-2.0" ]

40

2021-09-10T06:17:11.000Z

2022-03-19T19:30:56.000Z

tests/python/test_plotting.py

Nihilitior/xgboost

7366d3b20cad8e28ecef67d5130c71e81bb0b088

[ "Apache-2.0" ]

1

2018-12-09T14:30:38.000Z

import json import numpy as np import xgboost as xgb import testing as tm import pytest try: import matplotlib matplotlib.use('Agg') from matplotlib.axes import Axes from graphviz import Source except ImportError: pass pytestmark = pytest.mark.skipif(**tm.no_multiple(tm.no_matplotlib(), tm.no_graphviz())) dpath = 'demo/data/agaricus.txt.train'

35.05102

77

0.560116

import json import numpy as np import xgboost as xgb import testing as tm import pytest try: import matplotlib matplotlib.use('Agg') from matplotlib.axes import Axes from graphviz import Source except ImportError: pass pytestmark = pytest.mark.skipif(**tm.no_multiple(tm.no_matplotlib(), tm.no_graphviz())) dpath = 'demo/data/agaricus.txt.train' class TestPlotting: def test_plotting(self): m = xgb.DMatrix(dpath) booster = xgb.train({'max_depth': 2, 'eta': 1, 'objective': 'binary:logistic'}, m, num_boost_round=2) ax = xgb.plot_importance(booster) assert isinstance(ax, Axes) assert ax.get_title() == 'Feature importance' assert ax.get_xlabel() == 'F score' assert ax.get_ylabel() == 'Features' assert len(ax.patches) == 4 ax = xgb.plot_importance(booster, color='r', title='t', xlabel='x', ylabel='y') assert isinstance(ax, Axes) assert ax.get_title() == 't' assert ax.get_xlabel() == 'x' assert ax.get_ylabel() == 'y' assert len(ax.patches) == 4 for p in ax.patches: assert p.get_facecolor() == (1.0, 0, 0, 1.0) # red ax = xgb.plot_importance(booster, color=['r', 'r', 'b', 'b'], title=None, xlabel=None, ylabel=None) assert isinstance(ax, Axes) assert ax.get_title() == '' assert ax.get_xlabel() == '' assert ax.get_ylabel() == '' assert len(ax.patches) == 4 assert ax.patches[0].get_facecolor() == (1.0, 0, 0, 1.0) # red assert ax.patches[1].get_facecolor() == (1.0, 0, 0, 1.0) # red assert ax.patches[2].get_facecolor() == (0, 0, 1.0, 1.0) # blue assert ax.patches[3].get_facecolor() == (0, 0, 1.0, 1.0) # blue g = xgb.to_graphviz(booster, num_trees=0) assert isinstance(g, Source) ax = xgb.plot_tree(booster, num_trees=0) assert isinstance(ax, Axes) def test_importance_plot_lim(self): np.random.seed(1) dm = xgb.DMatrix(np.random.randn(100, 100), label=[0, 1] * 50) bst = xgb.train({}, dm) assert len(bst.get_fscore()) == 71 ax = xgb.plot_importance(bst) assert ax.get_xlim() == (0., 11.) assert ax.get_ylim() == (-1., 71.) ax = xgb.plot_importance(bst, xlim=(0, 5), ylim=(10, 71)) assert ax.get_xlim() == (0., 5.) assert ax.get_ylim() == (10., 71.) def run_categorical(self, tree_method: str) -> None: X, y = tm.make_categorical(1000, 31, 19, onehot=False) reg = xgb.XGBRegressor( enable_categorical=True, n_estimators=10, tree_method=tree_method ) reg.fit(X, y) trees = reg.get_booster().get_dump(dump_format="json") for tree in trees: j_tree = json.loads(tree) assert "leaf" in j_tree.keys() or isinstance( j_tree["split_condition"], list ) graph = xgb.to_graphviz(reg, num_trees=len(j_tree) - 1) assert isinstance(graph, Source) ax = xgb.plot_tree(reg, num_trees=len(j_tree) - 1) assert isinstance(ax, Axes) @pytest.mark.skipif(**tm.no_pandas()) def test_categorical(self) -> None: self.run_categorical("approx")

2,845

147

23

982d1343458738175e9ac9c51f09078a5d70f3fc

1,386

py

Python

tests/components/test_branch.py

haowen-xu/tfsnippet-pre-alpha

31eb2cf692ac25b95cc815aaca53754d6db42d9f

[ "MIT" ]

null

tests/components/test_branch.py

haowen-xu/tfsnippet-pre-alpha

31eb2cf692ac25b95cc815aaca53754d6db42d9f

[ "MIT" ]

null

tests/components/test_branch.py

haowen-xu/tfsnippet-pre-alpha

31eb2cf692ac25b95cc815aaca53754d6db42d9f

[ "MIT" ]

null

# -*- coding: utf-8 -*- import unittest import six import tensorflow as tf from tfsnippet.components import DictMapper, Linear, Dense from tests.helper import TestCase if __name__ == '__main__': unittest.main()

30.130435

71

0.565657

# -*- coding: utf-8 -*- import unittest import six import tensorflow as tf from tfsnippet.components import DictMapper, Linear, Dense from tests.helper import TestCase class DictMapperTestCase(TestCase): def test_construction(self): net = DictMapper({ 'a': Linear(2), 'b': Dense(2), 'c': lambda x: x * tf.get_variable('y', initializer=0.) }) inputs = tf.placeholder(dtype=tf.float32, shape=[None, 2]) output = net(inputs) self.assertIsInstance(output, dict) self.assertEqual(sorted(output.keys()), ['a', 'b', 'c']) for v in six.itervalues(output): self.assertIsInstance(v, tf.Tensor) _ = net(inputs) self.assertEqual( sorted(v.name for v in tf.global_variables()), ['dense/fully_connected/biases:0', 'dense/fully_connected/weights:0', 'dict_mapper/c/y:0', 'linear/fully_connected/biases:0', 'linear/fully_connected/weights:0'] ) def test_invalid_key(self): for k in ['.', '', '90ab', 'abc.def']: with self.assertRaisesRegex( ValueError, 'The key for `DictMapper` must be a valid ' 'Python identifier.*'): _ = DictMapper({k: lambda x: x}) if __name__ == '__main__': unittest.main()

1,075

14

77

dc034e6d00493ce71c20bb1a4fa5cb0e5ade07f9

1,076

py

Python

server.py

MusaTamzid05/simple_image_api

ea7a2b255c47732d7f79f7f9c59576eced23f3c6

[ "MIT" ]

null

server.py

MusaTamzid05/simple_image_api

ea7a2b255c47732d7f79f7f9c59576eced23f3c6

[ "MIT" ]

null

server.py

MusaTamzid05/simple_image_api

ea7a2b255c47732d7f79f7f9c59576eced23f3c6

[ "MIT" ]

null

from flask import Flask from flask_restful import Resource from flask_restful import Api import numpy as np import cv2 import werkzeug from flask_restful import reqparse parser = reqparse.RequestParser() parser.add_argument("file", type = werkzeug.datastructures.FileStorage, location = "files") app = Flask(__name__) api = Api(app) import base64 api.add_resource(ImageServer, "/") if __name__ == "__main__": app.run(debug = True, port = 5000)

19.925926

91

0.672862

from flask import Flask from flask_restful import Resource from flask_restful import Api import numpy as np import cv2 import werkzeug from flask_restful import reqparse parser = reqparse.RequestParser() parser.add_argument("file", type = werkzeug.datastructures.FileStorage, location = "files") app = Flask(__name__) api = Api(app) import base64 class ImageServer(Resource): def _decode(self, image): image = np.fromstring(image, np.uint8) image = cv2.imdecode(image, cv2.IMREAD_UNCHANGED) return image def _encode(self, image): _, encoded_image = cv2.imencode(".jpg", image) image_data = base64.b64encode(encoded_image).decode("utf-8") return f"data:image/jpeg;base64,{image_data}" def post(self): data = parser.parse_args() image = data["file"].read() image = self._decode(image = image) image = self._encode(image = image) return {"image" : image} api.add_resource(ImageServer, "/") if __name__ == "__main__": app.run(debug = True, port = 5000)

502

7

104

762c4a4873f93d04b2ca0adffa5772acd9975495

1,428

py

Python

access-analyzer/step-functions-archive-findings/functions/evaluate-access-analyzer-finding/app.py

lulukelu/aws-iam-permissions-guardrails

cae485e3d8589c85f55c50c442ce47916345e00d

[ "Apache-2.0" ]

88

2020-04-02T02:56:27.000Z

2022-03-18T13:22:02.000Z

access-analyzer/step-functions-archive-findings/functions/evaluate-access-analyzer-finding/app.py

lulukelu/aws-iam-permissions-guardrails

cae485e3d8589c85f55c50c442ce47916345e00d

[ "Apache-2.0" ]

45

2020-06-26T11:11:28.000Z

2021-08-17T15:31:47.000Z

access-analyzer/step-functions-archive-findings/functions/evaluate-access-analyzer-finding/app.py

lulukelu/aws-iam-permissions-guardrails

cae485e3d8589c85f55c50c442ce47916345e00d

[ "Apache-2.0" ]

32

2020-04-02T02:56:28.000Z

2021-12-20T18:53:04.000Z

import logging logger = logging.getLogger() logger.setLevel(logging.INFO) import boto3 #Evaluate Risk Level #Return True to raise alert if risk level exceeds threshold #Return False to Archive finding

31.733333

111

0.740196

import logging logger = logging.getLogger() logger.setLevel(logging.INFO) import boto3 #Evaluate Risk Level #Return True to raise alert if risk level exceeds threshold #Return False to Archive finding def should_raise_alert(finding_details, tags, additional_context): if "error" in finding_details: logger.error(f"Error in finding {finding_details['error']} for resource {finding_details['resource']}") return True if ( finding_details["isPublic"] and not is_allowed_public(finding_details, tags, additional_context) ): return True elif ( "IsAllowedToShare" in tags and tags["IsAllowedToShare"]=="true" and "Environment" in tags and tags["Environment"]=="development" and "key_aliases" in additional_context and "alias/DevelopmentKey" in additional_context["key_aliases"] ): return False return True def is_allowed_public(finding_details, tags, additional_context): #customize logic #for example, Data Classification is Confidential, return False if "Data Classification" in tags and tags["Data Classification"]=="Confidential": return False return True def handler(event,context): finding_details=event["detail"] tags=event["guid"]["tags"] additional_context=event["guid"]["additional_context"] if should_raise_alert(finding_details,tags,additional_context): return {"status":"NOTIFY"} else: return {"status":"ARCHIVE"}

1,157

0

68

2e3fe88d47f02aa45547236534d02086aa6a58e7

3,372

py

Python

MobileRevelator/python/android_gls.py

ohunecker/MR

b0c93436c7964d87a0b8154f8b7662b1731124b9

[ "MIT" ]

98

2019-02-03T22:50:24.000Z

2022-03-17T12:50:56.000Z

MobileRevelator/python/android_gls.py

cewatkins/MR

5ba553fd0eb4c1d80842074a553119486f005822

[ "MIT" ]

10

2019-03-14T20:12:10.000Z

2020-05-23T10:37:54.000Z

MobileRevelator/python/android_gls.py

cewatkins/MR

5ba553fd0eb4c1d80842074a553119486f005822

[ "MIT" ]

30

2019-02-03T22:50:27.000Z

2022-03-30T12:37:30.000Z

#Pluginname="GLS Tracking (Android)" #Type=App import os import json import tempfile

42.15

148

0.472123

#Pluginname="GLS Tracking (Android)" #Type=App import os import json import tempfile def convertdata(filenames): zfields=[] row=0 for fsname in filenames: filename=tempfile.gettempdir()+"/"+fsname[fsname.rfind("/")+1:] if ctx.fs_file_extract(fsname,filename): print("Running GLS conversion: "+filename[filename.rfind("/")+1:]) with open(filename,'rb') as rt: dat=json.loads(rt.read().decode()) desc="" if ("innerResult") in dat: zfield={} root=dat["innerResult"] if "expeditionDate" in root: desc+="ExpeditionDate:"+root["expeditionDate"]+";" if "recipient" in root: desc+="Recipient:"+root["recipient"]+";" if "recipient" in root: desc+="Sender:"+root["sender"]+";" if "parcelNumber" in root: desc+="ParcelNumber:"+root["parcelNumber"]+";" if "stepList" in root: st=root["stepList"] for child in st: if "dateStep" in child: desc+="[DateStep:"+child["dateStep"]+";" if "note" in child: desc+="Note:"+child["note"]+";" if "place" in child: desc+="Place:"+child["place"]+";" if "timeStep" in child: desc+="TimeStep:"+child["timeStep"]+";" if "statusTitle" in child: desc+="Status:"+child["statusTitle"]+"]" zfield["ID"]=str(row) zfield["Type"]="" zfield["Package"]="" zfield["Duration"]="" zfield["Filename"]=fsname zfield["Timestamp"]="" zfield["Other content"]=desc row+=1 zfields.append(zfield) os.remove(filename) rows=len(zfields) #print(zfields) for i in range(0,rows): zfield=zfields[i] oldpos=0 newpos=int(i/rows*100) if (oldpos<newpos): oldpos=newpos ctx.gui_setMainProgressBar(oldpos) ctx.gui_set_data(i,0,zfield["ID"]) ctx.gui_set_data(i,1,zfield["Type"]) ctx.gui_set_data(i,2,zfield["Package"]) ctx.gui_set_data(i,3,zfield["Timestamp"]) ctx.gui_set_data(i,4,zfield["Duration"]) ctx.gui_set_data(i,5,zfield["Other content"]) ctx.gui_set_data(i,6,zfield["Filename"]) def main(): ctx.gui_setMainLabel("GLS: Parsing Parcels"); ctx.gui_setMainProgressBar(0) headers=["rowid (int)","Type (QString)", "Package (QString)","Timestamp (int)","Duration (int)","Other_Content (QString)","Filename (QString)"] ctx.gui_set_headers(headers) filenames=ctx.pluginfilenames() convertdata(filenames) ctx.gui_update() ctx.gui_setMainLabel("Status: Idle.") ctx.gui_setMainProgressBar(0) return "Finished running plugin."

3,230

0

49

1129bbca49af3c3ae848c54738eec81269b88739

983

py

Python

utf7.py

CthUlhUzzz/unicode_crafter

b1b6b54e13d9afdf20c58abffe7a4986e35628d0

[ "WTFPL" ]

null

utf7.py

CthUlhUzzz/unicode_crafter

b1b6b54e13d9afdf20c58abffe7a4986e35628d0

[ "WTFPL" ]

null

utf7.py

CthUlhUzzz/unicode_crafter

b1b6b54e13d9afdf20c58abffe7a4986e35628d0

[ "WTFPL" ]

null

# Module for UTF-7 encoding from base64 import b64encode from utf16 import utf16_encode, UTF16_MAXIMUM_CODEPOINT DIRECT_CHARACTERS = '\'(),-./:?' UTF7_MAXIMUM_CODEPOINT = UTF16_MAXIMUM_CODEPOINT

31.709677

95

0.603255

# Module for UTF-7 encoding from base64 import b64encode from utf16 import utf16_encode, UTF16_MAXIMUM_CODEPOINT DIRECT_CHARACTERS = '\'(),-./:?' UTF7_MAXIMUM_CODEPOINT = UTF16_MAXIMUM_CODEPOINT def utf7_encode(codepoints, direct_characters=DIRECT_CHARACTERS): assert '+' not in direct_characters result_str = b'' for i in codepoints: to_encode_str = [] # if codepoint <= 127 if i <= 0x7f: # if char not '+' and not in direct_characters if i != 0x2b and chr(i) not in direct_characters: to_encode_str.append(i) else: result_str += chr(i).encode('ascii') else: if to_encode_str: result_str += b'+' + b64encode(utf16_encode(to_encode_str)).rstrip(b'=') + b'-' to_encode_str = [] if to_encode_str: result_str += b'+' + b64encode(utf16_encode(to_encode_str)).rstrip(b'=') + b'-' return result_str

761

0

23

48337410cf4a7f2042310786ee08e0b9f74679d0

6,939

py

Python

ping_me/ping.py

harshcrop/ping-me

274a07fd07356255763f516a47b37d9472041dcf

[ "Apache-2.0" ]

null

ping_me/ping.py

harshcrop/ping-me

274a07fd07356255763f516a47b37d9472041dcf

[ "Apache-2.0" ]

null

ping_me/ping.py

harshcrop/ping-me

274a07fd07356255763f516a47b37d9472041dcf

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- """Command line execution listener module of ping-me""" from __future__ import print_function from dateutil import parser import argparse import datetime import getpass import hashlib import os import parsedatetime import sys import time import ping_me.authenticate import ping_me.engine home = os.path.expanduser("~") cal = parsedatetime.Calendar() def main(): """Parse the arguments using argparse package""" argparser = argparse.ArgumentParser(description='ping-me') argparser.add_argument("-e", action="store_true", default=False) argparser.add_argument("-V", "--version", action="store_true", default=False) argparser.add_argument("-d", "--date", action="store", dest="DATE", default=None, nargs="+") argparser.add_argument("-t", "--time", action="store", dest="TIME", default=None, nargs="+") argparser.add_argument("message", action="store", help="Message", default=None, nargs="*") argparser.add_argument("-v", action="store_true", default=False) args = argparser.parse_args() process(args) def process(args): """Process the arguments. Call engine if flags are used.""" if args.e: detailed_usage() sys.exit(2) if args.version: import release print(release.__version__) sys.exit(2) if args.DATE is not None and args.TIME is not None: message = ' '.join(args.message).lstrip('to ') date_time = parser.parse(' '.join(args.DATE) + ' ' + ' '.join(args.TIME)) if len(message) == 0: print("What is the message of your reminder?\n") print("Use ping-me -h for help\n") sys.exit(2) ping_me.engine.engine(message, date_time.year, date_time.month, date_time.day, date_time.hour, date_time.minute, args.v) elif args.TIME is not None: m_time = parser.parse(' '.join(args.TIME)) c_time = datetime.datetime.now() if (m_time - c_time).days == -1: m_time += datetime.timedelta(1) message = ' '.join(args.message).lstrip('to ') if len(message) == 0: print("What is the message of your reminder?\n") print("Use ping-me -h for help\n") sys.exit(2) ping_me.engine.engine(message, m_time.year, m_time.month, m_time.day, m_time.hour, m_time.minute, args.v) elif args.DATE is not None: c_time = repr(time.localtime().tm_hour) + ":" + \ repr(time.localtime().tm_min) m_date = parser.parse(' '.join(args.DATE) + ' ' + c_time) message = ' '.join(args.message).lstrip('to ') if len(message) == 0: print("What is the message of your reminder?\n") print("Use ping-me -h for help\n") sys.exit(2) ping_me.engine.engine(message, m_date.year, m_date.month, m_date.day, m_date.hour, m_date.minute, args.v) else: if len(args.message) == 0: sys.stderr.write("Use ping-me -h for help\n") sys.exit(2) elif len(args.message) == 1 and args.message == ['config']: ping_me.authenticate.newuser() elif len(args.message) == 1 and args.message == ['reconfig']: reconfig() else: nlp_process(args) def nlp_process(args): """Process arguments using Natural Language Processing.""" # If there is something like "to do something in 2 mins" try: mins_index = args.message.index('mins') args.message[mins_index] = 'minutes' except ValueError: pass to_parse = ' '.join(args.message) try: m_date = cal.nlp(to_parse)[0][0] except TypeError: print("Sorry, couldn't understand your message. Try again.") sys.exit(2) # Remove the keywords keywords = cal.nlp(to_parse)[0][-1].split() for word in keywords: args.message.remove(word) # Remove redundant word 'this' try: args.message.remove('this') except ValueError: pass if 'to' in args.message: args.message.remove('to') message = ' '.join(args.message) ping_me.engine.engine(message, m_date.year, m_date.month, m_date.day, m_date.hour, m_date.minute, args.v) def detailed_usage(): """Detailed documentation of ping-me.""" print("Welcome to the detailed documentation of ping-me !") # Inspired from 'import this' s = " "; l = "_ "; r = " _"; f = "/"; b = "\\"; p = "|"; d = "— " print(s*6 + l*5 + s + l*4 + r + s*12 + l + r*5 + s*2 + r + s*8 + l + s*7 + l*4) print(s*5 + f + s*8 + f + s*5 + f + s*4 + f + s + b + s*10 + f + s + f + s*12 + f + s + b + s*6 + f + s + p + s*6 + f + s*7) print(s*4 + f + s*8 + f + s*5 + f + s*4 + f + s*3 + b + s*8 + f + s + f + s*12 + f + s*3 + b + s*4 + f + s*2 + p + s*5 + f + s*7) print(s*3 + f + r*4 + f + s*5 + f + s*4 + f + s*5 + b + s*6 + f + s + f + s*2 + r*4 + s*2 + f + 5*s + b + s*2 + f + s*3 + p + s*4 + f + l*4) print(s*2 + f + s*14 + f + s*4 + f + s*7 + b + s*4 + f + s + f + s*9 + f + s*2 + f + s*7 + b + f + s*4 + p + s*3 + f + s*7) print(s + f + s*14 + f + s*4 + f + s*9 + b + s*2 + f + s + f + s*9 + f + s*2 + f + s*14 + p + s*2 + f + s*7) print(f + s*11 + d*4 + f + s*11 + b + f + s + f + (r*5)[1:] + f + s*2 + f + s*15 + p + s + f + (r*4)[1:]) print("") print("ping-me works well with time and date flags already. " + "Use 'ping-me -h' for that option. " + "However, ping-me is smart enough to work without flags.\n") print("Examples : ") print("\t\t1. ping-me to call mom tonight") print("\t\t2. ping-me to buy milk early today") print("\t\t3. ping-me to go home seven days from now") print("\t\t4. ping-me to take a nap this afternoon") print("\t\t5. ping-me to go workout next month") print("") print("Report (and track process on fixing) bugs on " + "https://github.com/OrkoHunter/ping-me. Or simply write a mail " + "to Himanshu Mishra at himanshumishra[at]iitkgp[dot]ac[dot]in") def reconfig(): """Reconfigure the user. Removes all the information of existing one.""" if not os.path.exists(home + "/.pingmeconfig"): ping_me.authenticate.newuser() else: old_pass = hashlib.md5(getpass.getpass("Old Password : " + "").rstrip()).hexdigest() if old_pass == ping_me.authenticate.extract_password(): ping_me.authenticate.newuser() else: print("Authentication failed.") sys.exit(2) if __name__ == "__main__": main()

39.651429

78

0.542729

# -*- coding: utf-8 -*- """Command line execution listener module of ping-me""" from __future__ import print_function from dateutil import parser import argparse import datetime import getpass import hashlib import os import parsedatetime import sys import time import ping_me.authenticate import ping_me.engine home = os.path.expanduser("~") cal = parsedatetime.Calendar() def main(): """Parse the arguments using argparse package""" argparser = argparse.ArgumentParser(description='ping-me') argparser.add_argument("-e", action="store_true", default=False) argparser.add_argument("-V", "--version", action="store_true", default=False) argparser.add_argument("-d", "--date", action="store", dest="DATE", default=None, nargs="+") argparser.add_argument("-t", "--time", action="store", dest="TIME", default=None, nargs="+") argparser.add_argument("message", action="store", help="Message", default=None, nargs="*") argparser.add_argument("-v", action="store_true", default=False) args = argparser.parse_args() process(args) def process(args): """Process the arguments. Call engine if flags are used.""" if args.e: detailed_usage() sys.exit(2) if args.version: import release print(release.__version__) sys.exit(2) if args.DATE is not None and args.TIME is not None: message = ' '.join(args.message).lstrip('to ') date_time = parser.parse(' '.join(args.DATE) + ' ' + ' '.join(args.TIME)) if len(message) == 0: print("What is the message of your reminder?\n") print("Use ping-me -h for help\n") sys.exit(2) ping_me.engine.engine(message, date_time.year, date_time.month, date_time.day, date_time.hour, date_time.minute, args.v) elif args.TIME is not None: m_time = parser.parse(' '.join(args.TIME)) c_time = datetime.datetime.now() if (m_time - c_time).days == -1: m_time += datetime.timedelta(1) message = ' '.join(args.message).lstrip('to ') if len(message) == 0: print("What is the message of your reminder?\n") print("Use ping-me -h for help\n") sys.exit(2) ping_me.engine.engine(message, m_time.year, m_time.month, m_time.day, m_time.hour, m_time.minute, args.v) elif args.DATE is not None: c_time = repr(time.localtime().tm_hour) + ":" + \ repr(time.localtime().tm_min) m_date = parser.parse(' '.join(args.DATE) + ' ' + c_time) message = ' '.join(args.message).lstrip('to ') if len(message) == 0: print("What is the message of your reminder?\n") print("Use ping-me -h for help\n") sys.exit(2) ping_me.engine.engine(message, m_date.year, m_date.month, m_date.day, m_date.hour, m_date.minute, args.v) else: if len(args.message) == 0: sys.stderr.write("Use ping-me -h for help\n") sys.exit(2) elif len(args.message) == 1 and args.message == ['config']: ping_me.authenticate.newuser() elif len(args.message) == 1 and args.message == ['reconfig']: reconfig() else: nlp_process(args) def nlp_process(args): """Process arguments using Natural Language Processing.""" # If there is something like "to do something in 2 mins" try: mins_index = args.message.index('mins') args.message[mins_index] = 'minutes' except ValueError: pass to_parse = ' '.join(args.message) try: m_date = cal.nlp(to_parse)[0][0] except TypeError: print("Sorry, couldn't understand your message. Try again.") sys.exit(2) # Remove the keywords keywords = cal.nlp(to_parse)[0][-1].split() for word in keywords: args.message.remove(word) # Remove redundant word 'this' try: args.message.remove('this') except ValueError: pass if 'to' in args.message: args.message.remove('to') message = ' '.join(args.message) ping_me.engine.engine(message, m_date.year, m_date.month, m_date.day, m_date.hour, m_date.minute, args.v) def detailed_usage(): """Detailed documentation of ping-me.""" print("Welcome to the detailed documentation of ping-me !") # Inspired from 'import this' s = " "; l = "_ "; r = " _"; f = "/"; b = "\\"; p = "|"; d = "— " print(s*6 + l*5 + s + l*4 + r + s*12 + l + r*5 + s*2 + r + s*8 + l + s*7 + l*4) print(s*5 + f + s*8 + f + s*5 + f + s*4 + f + s + b + s*10 + f + s + f + s*12 + f + s + b + s*6 + f + s + p + s*6 + f + s*7) print(s*4 + f + s*8 + f + s*5 + f + s*4 + f + s*3 + b + s*8 + f + s + f + s*12 + f + s*3 + b + s*4 + f + s*2 + p + s*5 + f + s*7) print(s*3 + f + r*4 + f + s*5 + f + s*4 + f + s*5 + b + s*6 + f + s + f + s*2 + r*4 + s*2 + f + 5*s + b + s*2 + f + s*3 + p + s*4 + f + l*4) print(s*2 + f + s*14 + f + s*4 + f + s*7 + b + s*4 + f + s + f + s*9 + f + s*2 + f + s*7 + b + f + s*4 + p + s*3 + f + s*7) print(s + f + s*14 + f + s*4 + f + s*9 + b + s*2 + f + s + f + s*9 + f + s*2 + f + s*14 + p + s*2 + f + s*7) print(f + s*11 + d*4 + f + s*11 + b + f + s + f + (r*5)[1:] + f + s*2 + f + s*15 + p + s + f + (r*4)[1:]) print("") print("ping-me works well with time and date flags already. " + "Use 'ping-me -h' for that option. " + "However, ping-me is smart enough to work without flags.\n") print("Examples : ") print("\t\t1. ping-me to call mom tonight") print("\t\t2. ping-me to buy milk early today") print("\t\t3. ping-me to go home seven days from now") print("\t\t4. ping-me to take a nap this afternoon") print("\t\t5. ping-me to go workout next month") print("") print("Report (and track process on fixing) bugs on " + "https://github.com/OrkoHunter/ping-me. Or simply write a mail " + "to Himanshu Mishra at himanshumishra[at]iitkgp[dot]ac[dot]in") def reconfig(): """Reconfigure the user. Removes all the information of existing one.""" if not os.path.exists(home + "/.pingmeconfig"): ping_me.authenticate.newuser() else: old_pass = hashlib.md5(getpass.getpass("Old Password : " + "").rstrip()).hexdigest() if old_pass == ping_me.authenticate.extract_password(): ping_me.authenticate.newuser() else: print("Authentication failed.") sys.exit(2) if __name__ == "__main__": main()

0

5cb7d5f6b4498ffbcd46a0a7bcd0f240d3cdfa19

443

py

Python

test/forecast.py

sambacha/pyblock

f8f207de36a2f91dfe5f61681eba0e371cb0c552

[ "MIT" ]

null

test/forecast.py

sambacha/pyblock

f8f207de36a2f91dfe5f61681eba0e371cb0c552

[ "MIT" ]

null

test/forecast.py

sambacha/pyblock

f8f207de36a2f91dfe5f61681eba0e371cb0c552

[ "MIT" ]

null

"""Test workflow of forecasting model.""" import sys sys.path.append("../Forecasting") import model def test_forecast(): """Optimize an ARIMA model and predict a few data points.""" START = 5 END = 10 print("Forecasting...") f = model.Forecast("../Forecasting/blockchain.csv") f.optimizeARIMA(range(5), range(5), range(5), f.endog, f.exog) pred = f.predictARIMA(START, END) assert len(pred) == (END - START)

26.058824

66

0.650113

"""Test workflow of forecasting model.""" import sys sys.path.append("../Forecasting") import model def test_forecast(): """Optimize an ARIMA model and predict a few data points.""" START = 5 END = 10 print("Forecasting...") f = model.Forecast("../Forecasting/blockchain.csv") f.optimizeARIMA(range(5), range(5), range(5), f.endog, f.exog) pred = f.predictARIMA(START, END) assert len(pred) == (END - START)

0

6286afe34c783abf3fab1f6006078941470e0023

6,221

py

Python

prototxt_basic.py

niekai1982/MobileNet-SSD

0fb307ff049865338017627d4f082a8fd8b12016

[ "MIT" ]

null

prototxt_basic.py

niekai1982/MobileNet-SSD

0fb307ff049865338017627d4f082a8fd8b12016

[ "MIT" ]

null

prototxt_basic.py

niekai1982/MobileNet-SSD

0fb307ff049865338017627d4f082a8fd8b12016

[ "MIT" ]

null

# prototxt_basic # ----------------------------------------------------------------

36.380117

100

0.561968

# prototxt_basic def data(txt_file, info): txt_file.write('name: "mxnet-mdoel"\n') txt_file.write('layer {\n') txt_file.write(' name: "data"\n') txt_file.write(' type: "Input"\n') txt_file.write(' top: "data"\n') txt_file.write(' input_param {\n') txt_file.write(' shape: { dim: 10 dim: 3 dim: 224 dim: 224 }\n') # TODO txt_file.write(' }\n') txt_file.write('}\n') txt_file.write('\n') def Convolution(txt_file, info): print('test') if info['attr']['no_bias'] == 'True': bias_term = 'false' else: bias_term = 'true' txt_file.write('layer {\n') txt_file.write(' bottom: "%s"\n' % info['bottom'][0]) txt_file.write(' top: "%s"\n' % info['top']) txt_file.write(' name: "%s"\n' % info['top']) txt_file.write(' type: "Convolution"\n') txt_file.write(' convolution_param {\n') txt_file.write(' num_output: %s\n' % info['attr']['num_filter']) txt_file.write(' kernel_size: %s\n' % info['attr']['kernel'].split('(')[1].split(',')[0]) # TODO txt_file.write(' pad: %s\n' % info['attr']['pad'].split('(')[1].split(',')[0]) # TODO txt_file.write(' group: %s\n' % info['attr']['num_group']) txt_file.write(' stride: %s\n' % info['attr']['stride'].split('(')[1].split(',')[0]) txt_file.write(' bias_term: %s\n' % bias_term) txt_file.write(' }\n') if 'share' in info.keys() and info['share']: txt_file.write(' param {\n') txt_file.write(' name: "%s"\n' % info['params'][0]) txt_file.write(' }\n') txt_file.write('}\n') txt_file.write('\n') def ChannelwiseConvolution(txt_file, info): Convolution(txt_file, info) def BatchNorm(txt_file, info): txt_file.write('layer {\n') txt_file.write(' bottom: "%s"\n' % info['bottom'][0]) txt_file.write(' top: "%s"\n' % info['top']) txt_file.write(' name: "%s"\n' % info['top']) txt_file.write(' type: "BatchNorm"\n') txt_file.write(' batch_norm_param {\n') txt_file.write(' use_global_stats: true\n') # TODO txt_file.write(' moving_average_fraction: 0.9\n') # TODO txt_file.write(' eps: 0.001\n') # TODO txt_file.write(' }\n') txt_file.write('}\n') # if info['fix_gamma'] is "False": # TODO txt_file.write('layer {\n') txt_file.write(' bottom: "%s"\n' % info['top']) txt_file.write(' top: "%s"\n' % info['top']) txt_file.write(' name: "%s_scale"\n' % info['top']) txt_file.write(' type: "Scale"\n') txt_file.write(' scale_param { bias_term: true }\n') txt_file.write('}\n') txt_file.write('\n') pass def Activation(txt_file, info): txt_file.write('layer {\n') txt_file.write(' bottom: "%s"\n' % info['bottom'][0]) txt_file.write(' top: "%s"\n' % info['top']) txt_file.write(' name: "%s"\n' % info['top']) txt_file.write(' type: "ReLU"\n') # TODO txt_file.write('}\n') txt_file.write('\n') pass def Concat(txt_file, info): txt_file.write('layer {\n') txt_file.write(' name: "%s"\n' % info['top']) txt_file.write(' type: "Concat"\n') for bottom_i in info['bottom']: txt_file.write(' bottom: "%s"\n' % bottom_i) txt_file.write(' top: "%s"\n' % info['top']) txt_file.write('}\n') txt_file.write('\n') pass def ElementWiseSum(txt_file, info): txt_file.write('layer {\n') txt_file.write(' name: "%s"\n' % info['top']) txt_file.write(' type: "Eltwise"\n') for bottom_i in info['bottom']: txt_file.write(' bottom: "%s"\n' % bottom_i) txt_file.write(' top: "%s"\n' % info['top']) txt_file.write('}\n') txt_file.write('\n') pass def Pooling(txt_file, info): pool_type = 'AVE' if info['param']['pool_type'] == 'avg' else 'MAX' txt_file.write('layer {\n') txt_file.write(' bottom: "%s"\n' % info['bottom'][0]) txt_file.write(' top: "%s"\n' % info['top']) txt_file.write(' name: "%s"\n' % info['top']) txt_file.write(' type: "Pooling"\n') txt_file.write(' pooling_param {\n') txt_file.write(' pool: %s\n' % pool_type) # TODO txt_file.write(' kernel_size: %s\n' % info['param']['kernel'].split('(')[1].split(',')[0]) txt_file.write(' stride: %s\n' % info['param']['stride'].split('(')[1].split(',')[0]) txt_file.write(' pad: %s\n' % info['param']['pad'].split('(')[1].split(',')[0]) txt_file.write(' }\n') txt_file.write('}\n') txt_file.write('\n') pass def FullyConnected(txt_file, info): txt_file.write('layer {\n') txt_file.write(' bottom: "%s"\n' % info['bottom'][0]) txt_file.write(' top: "%s"\n' % info['top']) txt_file.write(' name: "%s"\n' % info['top']) txt_file.write(' type: "InnerProduct"\n') txt_file.write(' inner_product_param {\n') txt_file.write(' num_output: %s\n' % info['param']['num_hidden']) txt_file.write(' }\n') txt_file.write('}\n') txt_file.write('\n') pass def Flatten(txt_file, info): pass def SoftmaxOutput(txt_file, info): pass # ---------------------------------------------------------------- def write_node(txt_file, info): if 'label' in info['name']: return if info['op'] == 'null' and info['name'] == 'data': data(txt_file, info) elif info['op'] == 'Convolution': Convolution(txt_file, info) elif info['op'] == 'ChannelwiseConvolution': ChannelwiseConvolution(txt_file, info) elif info['op'] == 'BatchNorm': BatchNorm(txt_file, info) elif info['op'] == 'Activation': Activation(txt_file, info) elif info['op'] == 'ElementWiseSum': ElementWiseSum(txt_file, info) elif info['op'] == '_Plus': ElementWiseSum(txt_file, info) elif info['op'] == 'Concat': Concat(txt_file, info) elif info['op'] == 'Pooling': Pooling(txt_file, info) elif info['op'] == 'Flatten': Flatten(txt_file, info) elif info['op'] == 'FullyConnected': FullyConnected(txt_file, info) elif info['op'] == 'SoftmaxOutput': SoftmaxOutput(txt_file, info) else: sys.exit("Warning! Unknown mxnet op:{}".format(info['op']))

5,849

0

281

0255284ab4263a817a1bbf24a0fc9d68856eaa0f

3,769

py

Python

tests/test_gregorian_calendar.py

ibalagurov/laughing-train

24c345b22230e695ddd461368f118d1f2bb8d379

[ "MIT" ]

null

tests/test_gregorian_calendar.py

ibalagurov/laughing-train

24c345b22230e695ddd461368f118d1f2bb8d379

[ "MIT" ]

null

tests/test_gregorian_calendar.py

ibalagurov/laughing-train

24c345b22230e695ddd461368f118d1f2bb8d379

[ "MIT" ]

null

""" Tests for checking gregorian calendar date. Astronomical year contains 365,2425 days: 365 for usual year and 366 for leap Leap years: 0.2425 is 97 / 400 or 1/4 - 1/100 + 1/400 It means: - each 4th year is leap, except 3 of 4 round dates - 2004, 2008, 2012 and etc are leap - 2000, 2400, 2800 and etc. is leap - 2100, 2200, 2300, 2500, 2600, 2700, 2900 and etc. are NOT leap - for 400 years 97 are leap """ import pytest from src.calendar import date_to_gregorian, GregorianDate @pytest.mark.parametrize( "day, month, year", [ pytest.param(1, 1, 1, id="Minimum supported date"), pytest.param(31, 12, 9999, id="Maximum supported date"), pytest.param(29, 2, 4, id="First supported usual 4th leap year"), pytest.param(29, 2, 2020, id="Usual 4th leap year"), pytest.param(29, 2, 9996, id="Last supported usual 4th leap year"), pytest.param(29, 2, 400, id="First supported round leap year"), pytest.param(29, 2, 2000, id="Usual round leap year"), pytest.param(29, 2, 9600, id="Last supported round leap year"), ], ) def test_correct_date_format(day, month, year): """Check correct date""" result: GregorianDate = date_to_gregorian(day=day, month=month, year=year) assert ( result.correct ), f"Correct date '{day}-{month}-{year}'(day-month-year) is recognized as incorrect" def test_400_years_contain_97_leap_years(): """Check property of count leap years for 400 consecutive years""" start_year = 2000 leap_years = [ year for year in range(start_year, start_year + 400) if date_to_gregorian(year=year, month=2, day=29).correct ] actual_count = len(leap_years) expected_count = 97 assert actual_count == expected_count, ( f"For 400 consecutive years '{expected_count}' should be leap. " f"But actual count: '{actual_count}'. " f"Years, recognized as leap:\n{leap_years}" ) @pytest.mark.parametrize( "day, month, year", [ # 29th february for not leap years pytest.param(29, 2, 1, id="First supported usual year"), pytest.param(29, 2, 2021, id="Usual year"), pytest.param(29, 2, 9999, id="Last supported usual year"), pytest.param(29, 2, 100, id="First supported round usual year"), pytest.param(29, 2, 2100, id="Usual round year"), pytest.param(29, 2, 9900, id="Last supported round usual year"), # day format pytest.param(32, 1, 1900, id="Nonexistent 32th day"), pytest.param(31, 4, 1900, id="Nonexistent 31th day"), pytest.param(0, 1, 1900, id="Nonexistent 0th day"), pytest.param(-1, 1, 1900, id="Negative day"), # month format pytest.param(1, 0, 1900, id="Nonexistent 0th day"), pytest.param(1, 13, 1900, id="Nonexistent 13th month"), pytest.param(1, -1, 1900, id="Negative month"), ], ) def test_incorrect_date_format(day, month, year): """Check incorrect date""" result: GregorianDate = date_to_gregorian(day=day, month=month, year=year) assert ( not result.correct ), f"Incorrect date '{day}-{month}-{year}'(day-month-year) is recognized as correct" @pytest.mark.parametrize( "day, month, year", [ pytest.param(31, 1, 0, id="Unsupported bottom boundary year"), pytest.param(1, 1, 10_000, id="Unsupported top boundary year"), pytest.param(31, 1, -1, id="Negative year"), ], ) def test_unsupported_date_format(day, month, year): """Check unsupported date""" result: GregorianDate = date_to_gregorian(day=day, month=month, year=year) assert ( not result.supported ), f"Unsupported date '{day}-{month}-{year}'(day-month-year) is recognized as supported"

36.592233

92

0.645795

""" Tests for checking gregorian calendar date. Astronomical year contains 365,2425 days: 365 for usual year and 366 for leap Leap years: 0.2425 is 97 / 400 or 1/4 - 1/100 + 1/400 It means: - each 4th year is leap, except 3 of 4 round dates - 2004, 2008, 2012 and etc are leap - 2000, 2400, 2800 and etc. is leap - 2100, 2200, 2300, 2500, 2600, 2700, 2900 and etc. are NOT leap - for 400 years 97 are leap """ import pytest from src.calendar import date_to_gregorian, GregorianDate @pytest.mark.parametrize( "day, month, year", [ pytest.param(1, 1, 1, id="Minimum supported date"), pytest.param(31, 12, 9999, id="Maximum supported date"), pytest.param(29, 2, 4, id="First supported usual 4th leap year"), pytest.param(29, 2, 2020, id="Usual 4th leap year"), pytest.param(29, 2, 9996, id="Last supported usual 4th leap year"), pytest.param(29, 2, 400, id="First supported round leap year"), pytest.param(29, 2, 2000, id="Usual round leap year"), pytest.param(29, 2, 9600, id="Last supported round leap year"), ], ) def test_correct_date_format(day, month, year): """Check correct date""" result: GregorianDate = date_to_gregorian(day=day, month=month, year=year) assert ( result.correct ), f"Correct date '{day}-{month}-{year}'(day-month-year) is recognized as incorrect" def test_400_years_contain_97_leap_years(): """Check property of count leap years for 400 consecutive years""" start_year = 2000 leap_years = [ year for year in range(start_year, start_year + 400) if date_to_gregorian(year=year, month=2, day=29).correct ] actual_count = len(leap_years) expected_count = 97 assert actual_count == expected_count, ( f"For 400 consecutive years '{expected_count}' should be leap. " f"But actual count: '{actual_count}'. " f"Years, recognized as leap:\n{leap_years}" ) @pytest.mark.parametrize( "day, month, year", [ # 29th february for not leap years pytest.param(29, 2, 1, id="First supported usual year"), pytest.param(29, 2, 2021, id="Usual year"), pytest.param(29, 2, 9999, id="Last supported usual year"), pytest.param(29, 2, 100, id="First supported round usual year"), pytest.param(29, 2, 2100, id="Usual round year"), pytest.param(29, 2, 9900, id="Last supported round usual year"), # day format pytest.param(32, 1, 1900, id="Nonexistent 32th day"), pytest.param(31, 4, 1900, id="Nonexistent 31th day"), pytest.param(0, 1, 1900, id="Nonexistent 0th day"), pytest.param(-1, 1, 1900, id="Negative day"), # month format pytest.param(1, 0, 1900, id="Nonexistent 0th day"), pytest.param(1, 13, 1900, id="Nonexistent 13th month"), pytest.param(1, -1, 1900, id="Negative month"), ], ) def test_incorrect_date_format(day, month, year): """Check incorrect date""" result: GregorianDate = date_to_gregorian(day=day, month=month, year=year) assert ( not result.correct ), f"Incorrect date '{day}-{month}-{year}'(day-month-year) is recognized as correct" @pytest.mark.parametrize( "day, month, year", [ pytest.param(31, 1, 0, id="Unsupported bottom boundary year"), pytest.param(1, 1, 10_000, id="Unsupported top boundary year"), pytest.param(31, 1, -1, id="Negative year"), ], ) def test_unsupported_date_format(day, month, year): """Check unsupported date""" result: GregorianDate = date_to_gregorian(day=day, month=month, year=year) assert ( not result.supported ), f"Unsupported date '{day}-{month}-{year}'(day-month-year) is recognized as supported"

0

f317684016dd3310bb8a93c672100e2f58bb2b7f

159

py

Python

arithmetic10.py

indraputra147/belajarpython

13ed3e73a75f25cc6c2c0e1fc7af17ffa53e5760

[ "MIT" ]

null

arithmetic10.py

indraputra147/belajarpython

13ed3e73a75f25cc6c2c0e1fc7af17ffa53e5760

[ "MIT" ]

null

arithmetic10.py

indraputra147/belajarpython

13ed3e73a75f25cc6c2c0e1fc7af17ffa53e5760

[ "MIT" ]

null

import math a = int(input("a = ")) b = int(input("b = ")) c = a + b d = a - b e = a * b f = a / b g = a % b h = math.log10(a) i = a**b print(c,d,e,f,g,h,i)

10.6

22

0.446541

import math a = int(input("a = ")) b = int(input("b = ")) c = a + b d = a - b e = a * b f = a / b g = a % b h = math.log10(a) i = a**b print(c,d,e,f,g,h,i)

0

feda14de5a8d251a97859d636e823d8fc7099816

193

py

Python

section_3/3_decorators_and_context_mgrs/decos.py

hgohel/Python-for-Everyday-Life

957963e67dca8c2d20a86fc7e66e818c80d013aa

[ "MIT" ]

43

2018-04-09T11:59:11.000Z

2022-01-29T14:27:37.000Z

section_3/3_decorators_and_context_mgrs/decos.py

hgohel/Python-for-Everyday-Life

957963e67dca8c2d20a86fc7e66e818c80d013aa

[ "MIT" ]

12

2019-11-03T16:50:39.000Z

2021-09-07T23:52:37.000Z

section_3/3_decorators_and_context_mgrs/decos.py

hgohel/Python-for-Everyday-Life

957963e67dca8c2d20a86fc7e66e818c80d013aa

[ "MIT" ]

45

2018-05-10T21:40:46.000Z

2022-03-01T05:50:07.000Z

# -*- coding: utf-8 -*- # !/usr/bin/env python3

24.125

40

0.595855

# -*- coding: utf-8 -*- # !/usr/bin/env python3 def greet(func): def decorated_func(*args, **kwargs): print('Hello!') return func(*args, **kwargs) return decorated_func

123

0

23

38a8181ed5c0474062640cb8571e1aa5db1c0d30

7,383

py

Python

server/generateconfig.py

ehackify/hnp

ba0e10e9ca390616dfa3888ceafc94672f41d26d

[ "MIT" ]

2

2020-04-29T09:58:21.000Z

2020-05-08T20:23:33.000Z

server/generateconfig.py

ehackify/hnp

ba0e10e9ca390616dfa3888ceafc94672f41d26d

[ "MIT" ]

1

2020-05-01T11:00:58.000Z

server/generateconfig.py

ehackify/hnp

ba0e10e9ca390616dfa3888ceafc94672f41d26d

[ "MIT" ]

null

""" This is a helper script meant to generate a working config.py file from the config template. """ from getpass import getpass import json import os.path from random import choice import string import sys from urllib2 import urlopen import argparse el = string.ascii_letters + string.digits rand_str = lambda n: ''.join(choice(el) for _ in range(n)) if __name__ == '__main__': generate_config()

39.908108

111

0.629554

""" This is a helper script meant to generate a working config.py file from the config template. """ from getpass import getpass import json import os.path from random import choice import string import sys from urllib2 import urlopen import argparse el = string.ascii_letters + string.digits rand_str = lambda n: ''.join(choice(el) for _ in range(n)) def get_pub_ip(): sock = urlopen('http://icanhazip.com/') ip = sock.read().rstrip() sock.close() return ip def generate_config(): # Check if config file already exists if os.path.isfile('config.py'): print('config.py already exists') sys.exit() pub_ip = get_pub_ip() default_base_url = 'http://{}'.format(pub_ip) default_honeymap_url = '{}:3000'.format(default_base_url) default_log_path = '/var/log/hnp/hnp.log' localconfig = {} localconfig['SECRET_KEY'] = rand_str(32) localconfig['DEPLOY_KEY'] = rand_str(8) is_unattended = False # Get and parse args for command unattended install parser_description = 'This is a help script to generate a working config.py file from the config template.' parser = argparse.ArgumentParser(description=parser_description) subparsers = parser.add_subparsers(help='commands') parser_generate = subparsers.add_parser('generate', help='Generate a config.py and prompt for options') parser_generate.set_defaults(which='generate') parser_unatt = subparsers.add_parser('unattended', help='Unattended install') parser_unatt.set_defaults(which='unattended') parser_unatt.add_argument('-e', '--email', type=str, required=True, help='Superuser email address') parser_unatt.add_argument('-p', '--password', type=str, required=True, help='Superuser password') parser_unatt.add_argument('-b', '--base_url', type=str, default=default_base_url, help='Server base url') parser_unatt.add_argument('-y', '--honeymap_url', type=str, default=default_honeymap_url, help='Honeymap url') parser_unatt.add_argument('-m', '--mail_server', type=str, default='localhost', help='Mail server address') parser_unatt.add_argument('-s', '--mail_port', type=int, default=25, help='Mail server port') parser_unatt.add_argument('--mail_tls', action='store_true', help='Use TLS for mail') parser_unatt.add_argument('--mail_ssl', action='store_true', help='Use SSL for mail') parser_unatt.add_argument('--mail_user', type=str, default='', help='Mail username') parser_unatt.add_argument('--mail_pass', type=str, default='', help='Mail password') parser_unatt.add_argument('--mail_sender', type=str, default='', help='Mail sender') parser_unatt.add_argument('-l', '--log_file_path', type=str, default=default_log_path, help='Log file path') parser_unatt.add_argument('-d', '--debug', action='store_true', help='Run in debug mode') if (len(sys.argv) < 2): args = parser.parse_args(['generate']) else: args = parser.parse_args(sys.argv[1:]) # check for unattended install if args.which is 'unattended': is_unattended = True if is_unattended: # Collect values from arguments debug = args.debug email = args.email password = args.password server_base_url= args.base_url honeymap_url = args.honeymap_url mail_server = args.mail_server mail_port = args.mail_port mail_tls = args.mail_tls mail_ssl = args.mail_ssl mail_username = args.mail_user mail_password = args.mail_pass default_mail_sender = args.mail_sender log_file_path = args.log_file_path else: # Collect values from user debug = raw_input('Do you wish to run in Debug mode?: y/n ') while debug not in ['y', 'n']: debug = raw_input('Please y or n ') debug = True if debug == 'y' else False email = raw_input('Superuser email: ') while '@' not in email: email = raw_input('Superuser email (must be valid): ') while True: password = getpass('Superuser password: ') while not password: password = getpass('Superuser password (cannot be blank): ') password2 = getpass('Superuser password: (again): ') while not password2: password2 = getpass('Superuser password (again; cannot be blank): ') if password == password2: break else: print "Passwords did not match. Try again" server_base_url = raw_input('Server base url ["{}"]: '.format(default_base_url)) if server_base_url.endswith('/'): server_base_url = server_base_url[:-1] server_base_url = server_base_url if server_base_url.strip() else default_base_url default_honeymap_url = '{}:3000'.format(server_base_url) honeymap_url = raw_input('Honeymap url ["{}"]: '.format(default_honeymap_url)) if honeymap_url.endswith('/'): honeymap_url = honeymap_url[:-1] mail_server = raw_input('Mail server address ["localhost"]: ') mail_port = raw_input('Mail server port [25]: ') mail_tls = raw_input('Use TLS for email?: y/n ') while mail_tls not in ['y', 'n']: mail_tls = raw_input('Please y or n ') mail_ssl = raw_input('Use SSL for email?: y/n ') while mail_ssl not in ['y', 'n']: mail_ssl = raw_input('Please y or n ') mail_username = raw_input('Mail server username [""]: ') mail_password = getpass('Mail server password [""]: ') default_mail_sender = raw_input('Mail default sender [""]: ') log_file_path = raw_input('Path for log file ["{}"]: '.format(default_log_path)) honeymap_url = honeymap_url if honeymap_url.strip() else default_honeymap_url log_file_path = log_file_path if log_file_path else default_log_path localconfig['DEBUG'] = debug localconfig['SUPERUSER_EMAIL'] = email localconfig['SUPERUSER_PASSWORD'] = password localconfig['SERVER_BASE_URL'] = server_base_url localconfig['HONEYMAP_URL'] = honeymap_url localconfig['MAIL_SERVER'] = mail_server if mail_server else "localhost" localconfig['MAIL_PORT'] = mail_port if mail_port else 25 localconfig['MAIL_USE_TLS'] = 'y' == mail_tls localconfig['MAIL_USE_SSL'] = 'y' == mail_ssl localconfig['MAIL_USERNAME'] = mail_username if mail_username else '' localconfig['MAIL_PASSWORD'] = mail_password if mail_password else '' localconfig['DEFAULT_MAIL_SENDER'] = default_mail_sender if default_mail_sender else "" localconfig['LOG_FILE_PATH'] = log_file_path with open('config.py.template', 'r') as templfile,\ open('config.py', 'w') as confile: templ = templfile.read() for key, setting in localconfig.iteritems(): templ = templ.replace('{{' + key + '}}', str(setting)) confile.write(templ) if __name__ == '__main__': generate_config()

6,930

0

46

080aa373a3a2c4c389161bee26616e1cb5da3628

12,129

py

Python

cogs/admin.py

fossabot/Pawbot

6fb5d6c16adc02b155a70df91a44c930eddb493f

[ "MIT" ]

null

cogs/admin.py

fossabot/Pawbot

6fb5d6c16adc02b155a70df91a44c930eddb493f

[ "MIT" ]

null

cogs/admin.py

fossabot/Pawbot

6fb5d6c16adc02b155a70df91a44c930eddb493f

[ "MIT" ]

null

import time import aiohttp import traceback import discord import textwrap import io import json from dhooks import Webhook from utils.chat_formatting import pagify from contextlib import redirect_stdout from copy import copy from typing import Union from utils import repo, default, http, dataIO from discord.ext import commands

36.423423

236

0.574986

import time import aiohttp import traceback import discord import textwrap import io import json from dhooks import Webhook from utils.chat_formatting import pagify from contextlib import redirect_stdout from copy import copy from typing import Union from utils import repo, default, http, dataIO from discord.ext import commands class Admin: def __init__(self, bot): self.bot = bot self.config = default.get("config.json") self._last_result = None self.sessions = set() @staticmethod def cleanup_code(content): """Automatically removes code blocks from the code.""" # remove ```py\n``` if content.startswith('```') and content.endswith('```'): return '\n'.join(content.split('\n')[1:-1]) # remove `foo` return content.strip('` \n') @staticmethod def get_syntax_error(e): if e.text is None: return f'```py\n{e.__class__.__name__}: {e}\n```' return f'```py\n{e.text}{"^":>{e.offset}}\n{e.__class__.__name__}: {e}```' @commands.command() async def amiadmin(self, ctx): """ Are you admin? """ if ctx.author.id in self.config.owners: await ctx.send(f"Yes **{ctx.author.name}** you are admin! ✅") elif ctx.author.id in self.config.contributors: await ctx.send(f"No, but you're a contributor **{ctx.author.name}** 💙") elif ctx.author.id in self.config.friends: await ctx.send(f"No, but you're a friend of Paws **{ctx.author.name}** 💜") else: await ctx.send(f"No, heck off **{ctx.author.name}**.") @commands.command() @commands.check(repo.is_owner) async def reload(self, ctx, name: str): """ Reloads an extension. """ try: self.bot.unload_extension(f"cogs.{name}") self.bot.load_extension(f"cogs.{name}") except FileNotFoundError as e: return await ctx.send(f"```\n{e}```") await ctx.send(f"Reloaded extension **{name}.py**") @commands.command() @commands.check(repo.is_owner) async def reboot(self, ctx): """ Reboot the bot """ await ctx.send('Rebooting now...') time.sleep(1) await self.bot.logout() @commands.command() @commands.check(repo.is_owner) async def load(self, ctx, name: str): """ Reloads an extension. """ try: self.bot.load_extension(f"cogs.{name}") except FileNotFoundError as e: await ctx.send(f"```diff\n- {e}```") return await ctx.send(f"Loaded extension **{name}.py**") @commands.command() @commands.check(repo.is_owner) async def unload(self, ctx, name: str): """ Reloads an extension. """ try: self.bot.unload_extension(f"cogs.{name}") except FileNotFoundError as e: await ctx.send(f"```diff\n- {e}```") return await ctx.send(f"Unloaded extension **{name}.py**") @commands.group() @commands.check(repo.is_owner) async def change(self, ctx): if ctx.invoked_subcommand is None: _help = await ctx.bot.formatter.format_help_for(ctx, ctx.command) for page in _help: await ctx.send(page) @change.command(name="playing") @commands.check(repo.is_owner) async def change_playing(self, ctx, *, playing: str): """ Change playing status. """ try: await self.bot.change_presence( activity=discord.Game(type=0, name=playing), status=discord.Status.online ) dataIO.change_value("config.json", "playing", playing) await ctx.send(f"Successfully changed playing status to **{playing}**") except discord.InvalidArgument as err: await ctx.send(err) except Exception as e: await ctx.send(e) @change.command(name="username") @commands.check(repo.is_owner) async def change_username(self, ctx, *, name: str): """ Change username. """ try: await self.bot.user.edit(username=name) await ctx.send(f"Successfully changed username to **{name}**") except discord.HTTPException as err: await ctx.send(err) @change.command(name="nickname") @commands.check(repo.is_owner) async def change_nickname(self, ctx, *, name: str = None): """ Change nickname. """ try: await ctx.guild.me.edit(nick=name) if name: await ctx.send(f"Successfully changed nickname to **{name}**") else: await ctx.send("Successfully removed nickname") except Exception as err: await ctx.send(err) @change.command(name="avatar") @commands.check(repo.is_owner) async def change_avatar(self, ctx, url: str = None): """ Change avatar. """ if url is None and len(ctx.message.attachments) == 1: url = ctx.message.attachments[0].url else: url = url.strip('<>') try: bio = await http.get(url, res_method="read") await self.bot.user.edit(avatar=bio) await ctx.send(f"Successfully changed the avatar. Currently using:\n{url}") except aiohttp.InvalidURL: await ctx.send("The URL is invalid...") except discord.InvalidArgument: await ctx.send("This URL does not contain a usable image") except discord.HTTPException as err: await ctx.send(err) @commands.command() @commands.check(repo.is_owner) async def steal(self, ctx, emojiname, url: str = None): """Steals emojis""" if emojiname is None or "http" in emojiname: return await ctx.send("No emoji name provided") if url is None and len(ctx.message.attachments) == 1: url = ctx.message.attachments[0].url else: url = url.strip('<>') try: botguild = self.bot.get_guild(423879867457863680) bio = await http.get(url, res_method="read") await botguild.create_custom_emoji(name=emojiname, image=bio) await ctx.message.delete() await ctx.send(f"Successfully stolen emoji.") except aiohttp.InvalidURL: await ctx.send("The URL is invalid...") except discord.InvalidArgument: await ctx.send("This URL does not contain a usable image") except discord.HTTPException as err: await ctx.send(err) @commands.command(pass_context=True, name='eval') @commands.check(repo.is_owner) async def _eval(self, ctx, *, body: str): """Evaluates a code""" env = { 'bot': self.bot, 'ctx': ctx, 'channel': ctx.channel, 'author': ctx.author, 'guild': ctx.guild, 'message': ctx.message, '_': self._last_result } if "bot.http.token" in body: return await ctx.send(f"You can't take my token {ctx.author.name}") env.update(globals()) body = self.cleanup_code(body) stdout = io.StringIO() to_compile = f'async def func():\n{textwrap.indent(body, " ")}' try: exec(to_compile, env) except Exception as e: return await ctx.send(f'```py\n{e.__class__.__name__}: {e}\n```') func = env['func'] try: with redirect_stdout(stdout): ret = await func() except Exception as e: value = stdout.getvalue() await ctx.send(f'```py\n{value}{traceback.format_exc()}\n```') else: value = stdout.getvalue() reactiontosend = self.bot.get_emoji(508388437661843483) await ctx.message.add_reaction(reactiontosend) if ret is None: if value: await ctx.send(f'```py\n{value}\n```') else: if self.config.token in ret: ret = self.config.realtoken self._last_result = ret await ctx.send(f'Inputted code:\n```py\n{body}\n```\n\nOutputted Code:\n```py\n{value}{ret}\n```') @commands.group(aliases=["as"]) @commands.check(repo.is_owner) async def sudo(self, ctx): """Run a cmd under an altered context """ if ctx.invoked_subcommand is None: await ctx.send("...") @sudo.command(aliases=["u", "--u", "--user", "user"]) @commands.check(repo.is_owner) async def sudo_user(self, ctx, who: Union[discord.Member, discord.User], *, command: str): """Run a cmd under someone else's name """ msg = copy(ctx.message) msg.author = who msg.content = ctx.prefix + command new_ctx = await self.bot.get_context(msg) await self.bot.invoke(new_ctx) @sudo.command(aliases=["c", "--c", "--channel", "channel"]) @commands.check(repo.is_owner) async def sudo_channel(self, ctx, chid: int, *, command: str): """Run a command as another user.""" cmd = copy(ctx.message) cmd.channel = self.bot.get_channel(chid) cmd.content = ctx.prefix + command new_ctx = await self.bot.get_context(cmd) await self.bot.invoke(new_ctx) @commands.command() @commands.check(repo.is_owner) async def cogs(self, ctx): mod = ", ".join(list(self.bot.cogs)) await ctx.send(f"The current modules are:\n```\n{mod}\n```") @commands.command(aliases=['gsi']) @commands.check(repo.is_owner) async def getserverinfo(self, ctx, *, guild_id: int): """ Makes me get the information from a guild id""" guild = self.bot.get_guild(guild_id) if guild is None: return await ctx.send("Hmph.. I got nothing..") members = set(guild.members) bots = filter(lambda m: m.bot, members) bots = set(bots) members = len(members) - len(bots) if guild == ctx.guild: roles = " ".join([x.mention for x in guild.roles != "@everyone"]) else: roles = ", ".join([x.name for x in guild.roles if x.name != "@everyone"]) info = discord.Embed(title="Guild info", description=f"» Name: {guild.name}\n» Members/Bots: `{members}:{len(bots)}`"f"\n» Owner: {guild.owner}\n» Created at: {guild.created_at}"f"\n» Roles: {roles}", color=discord.Color.blue()) info.set_thumbnail(url=guild.icon_url) await ctx.send(embed=info) @commands.command(alisases=['bsl']) @commands.check(repo.is_owner) async def botservers(self, ctx): """Lists servers""" owner = ctx.author guilds = sorted(list(self.bot.guilds), key=lambda s: s.name.lower()) msg = "" for i, guild in enumerate(guilds, 1): members = set(guild.members) bots = filter(lambda m: m.bot, members) bots = set(bots) members = len(members) - len(bots) msg += "`{}:` {}, `{}` `{} members, {} bots` \n".format(i, guild.name, guild.id, members, len(bots)) for page in pagify(msg, ['\n']): await ctx.send(page) @commands.command(aliases=["webhooktest"]) @commands.check(repo.is_owner) async def whtest(self, ctx, whlink: str, *, texttosend): try: await ctx.message.delete() hook = Webhook(whlink, is_async=True) await hook.send(texttosend) await hook.close() except ValueError: return await ctx.send("I couldn't send the message..") @commands.command() @commands.check(repo.is_owner) async def blacklist(self, ctx, uid: int): with open("blacklist.json", "r+") as file: content = json.load(file) content["blacklist"].append(uid) file.seek(0) json.dump(content, file) file.truncate() await ctx.send(f"I have successfully blacklisted the id **{uid}**") def setup(bot): bot.add_cog(Admin(bot))

1,273

10,490

46

ff3989ef53fcbd5d6a133b5ce43e595a8a5131de

9,683

py

Python

SIX_DOF.py

HarrisonLeece/Circinus

d5934f9d59f6b63635d5d053e48339292394c106

[ "MIT" ]

null

SIX_DOF.py

HarrisonLeece/Circinus

d5934f9d59f6b63635d5d053e48339292394c106

[ "MIT" ]

null

SIX_DOF.py

HarrisonLeece/Circinus

d5934f9d59f6b63635d5d053e48339292394c106

[ "MIT" ]

null

''' @Authors: Harrison Leece, James Hribal, Max Fung, Nils Heidenreich @Purpose: Explore 6DOF rocket trajectory, esspecially quaternion rotation Learning resources: https://eater.net/quaternions ''' import numpy as np import oyaml as yaml import math class Rotator: ''' https://en.wikipedia.org/wiki/Quaternions_and_spatial_rotation#Using_quaternions_as_rotations This function should take inputs: 'Cartesian, unit rotation-axis (Vector), Rotation Angle in radians (Theta) and form a quaternion vector ''' ''' https://math.stackexchange.com/questions/40164/how-do-you-rotate-a-vector-by-a-unit-quaternion ''' ''' https://en.wikipedia.org/wiki/Quaternion#Hamilton_product https://math.stackexchange.com/questions/40164/how-do-you-rotate-a-vector-by-a-unit-quaternion ''' ''' Convert some arbitrary vector to a unit vector (divide components by the magnitude) ''' ''' Checker function to verify a vector of arbitrary length is a unit vector Tolerance variable to allow 'close enough' cases to succeed ''' ''' https://en.wikipedia.org/wiki/Quaternions_and_spatial_rotation#Using_quaternions_as_rotations q' = q2q1 q1 first, then q2 USE QUATERNION MULTIPLICATION RULE: v*w where v and w are both quaternions with no real part v*w = v x w - v * w v*w where v and w are both quaternions with real part s and t (see wikipedia) v*w = (s + v)*(t + w) = (st - v * w)+(sw + tv + v x w) ''' class Rocket(Rotator): ''' Calculate the angle of attack (alpha) in radians using the rocket's velocity direction and rotation state. Return alpha ''' ''' Use environmental data regarding gust velocity and rocket geometry to estimate the rotation axis and rotation magnitude (radians) of a rocket Return a rotation quaternion for this axis and magnitude ''' ''' Use the angle of attack, drag+lift coefficients and rocket geometry to estimate the rotation axis and rotation magnitude (radians) of a rocket Return a rotation quaternion for this axis and magnitude ''' ''' Place holder - calcultes tvc rotation Not needed for final version Return a rotation quaternion for this axis and magnitude ''' ''' lock rotation of the craft despite forces acting on the body useful for constraining rocket to a rail at launch for example (Prevents integration of accelerations to velocities) ''' ''' Unlocks rotation ''' class Environment(): ''' The environment object helps compartmentalize environmental data (atmospheric temperature, pressure, gusts etc...). The object can then be accessed to fetch atmospheric or environmental data for the rotator object desired ''' ''' For these be sure to check which altitude you are working with. For now I have it as altitude relative to center of the earth ''' ''' The Reference object is a Fixed Earth, centered at 0,0,0 with no rotation ''' ''' Inherits the Reference (Non-rotating earth) and creates a rotating earth ''' class Launchpad(RotatingEarth): ''' Turn the coordinates of the launch site into spherical coordinates and set as the position of the object RRS coordinates: fmt=dms 35 degrees, 21 minutes, 2 seconds North 117 degrees, 48 minutes, 30 seconds West fmts:>> 'dd' << decimal degree, >> 'dmm' << degree + decimal minutes >> dms << degrees, minutes, and seconds Format input as nested lists, North first, then west list = [[35,21,2],[117,48,39]] ''' if __name__ == '__main__': with open('rocket_info.yaml') as rocket_info: rocket_data = yaml.load(rocket_info, Loader=yaml.FullLoader) rot_tester = Rotator() rot_tester.report_body_vector() rot_quaternion = np.array([[-.707],[0], [.707],[0]]) rot_tester.rotate_body(rot_quaternion) rot_tester.report_body_vector() rocenv = Environment(None, None) rocket = Rocket(rocket_data, rocenv)

35.996283

115

0.627388

''' @Authors: Harrison Leece, James Hribal, Max Fung, Nils Heidenreich @Purpose: Explore 6DOF rocket trajectory, esspecially quaternion rotation Learning resources: https://eater.net/quaternions ''' import numpy as np import oyaml as yaml import math class Rotator: def __init__(self): self.re = 0; self.i = 0; self.j = 0; self.k = 1 self.body_vector = np.array([[0],[1],[0],[0]]) ''' https://en.wikipedia.org/wiki/Quaternions_and_spatial_rotation#Using_quaternions_as_rotations This function should take inputs: 'Cartesian, unit rotation-axis (Vector), Rotation Angle in radians (Theta) and form a quaternion vector ''' def form_quaternion(self, vector, theta): assert self.vector_is_unit(vector), 'Class: Rotator, Fxn: form_quaternion, vector is not a unit quaternion' r = np.cos(theta/2) i = -1*np.sin(theta/2)*vector[0] j = -1*np.sin(theta/2)*vector[1] k = -1*np.sin(theta/2)*vector[2] quaternion = np.array([[r],[i],[j],[k]]) return quaternion ''' https://math.stackexchange.com/questions/40164/how-do-you-rotate-a-vector-by-a-unit-quaternion ''' def rotate_body(self, quaternion): left = quaternion right = np.array([[quaternion[0]], -quaternion[1], -quaternion[2], -quaternion[3]]) h1 = self.hamilton_product(left, self.body_vector) print('H1: {}'.format(h1)) self.body_vector = self.hamilton_product(h1,right) ''' https://en.wikipedia.org/wiki/Quaternion#Hamilton_product https://math.stackexchange.com/questions/40164/how-do-you-rotate-a-vector-by-a-unit-quaternion ''' def hamilton_product(self, vec1, vec2): a1 = vec1[0]; a2 = vec2[0] b1 = vec1[1]; b2 = vec2[1] c1 = vec1[2]; c2 = vec2[2] d1 = vec1[3]; d2 = vec2[3] r = float(a1*a2 - b1*b2 - c1*c2 - d1*d2) x = float(a1*b2 + b1*a2 + c1*d2 - d1*c2) y = float(a1*c2 - b1*d2 + c1*a2 + d1*b2) z = float(d1*d2 + b1*c2 - c1*b2 + d1*a2) return np.array([[r],[x],[y],[z]]) def report_body_vector(self): print(self.body_vector) ''' Convert some arbitrary vector to a unit vector (divide components by the magnitude) ''' def unitify_vector(self, vector): mag = np.linalg.norm(vector) return vector/mag ''' Checker function to verify a vector of arbitrary length is a unit vector Tolerance variable to allow 'close enough' cases to succeed ''' def vector_is_unit(vec): squares = [x*x for x in vec] vec_sum = np.sum(squares) norm = np.sqrt(vec_sum) tolerance = .01 if abs(norm-1) < tolerance: return true return false ''' https://en.wikipedia.org/wiki/Quaternions_and_spatial_rotation#Using_quaternions_as_rotations q' = q2q1 q1 first, then q2 USE QUATERNION MULTIPLICATION RULE: v*w where v and w are both quaternions with no real part v*w = v x w - v * w v*w where v and w are both quaternions with real part s and t (see wikipedia) v*w = (s + v)*(t + w) = (st - v * w)+(sw + tv + v x w) ''' def combine_quaternions(self, q1, q2): re1 = q1[0]; re2 = q2[0] q1 = q1[1:3]; q2 = q2[1:3] cross = np.cross(q2, q1); dot = np.dot(q2, q1) re_prime = (re1*re2 - dot) temp = re1*q2 + re2*q1 + cross q_prime = np.array([[re_prime],[temp[0]],[temp[1]],[temp[2]]]) return q_prime class Rocket(Rotator): def __init__(self, rocket_data, environment_obj, reference=None, units='english'): super().__init__() ''' abs_orientation is a (unit) vector representing the orientation of the rocket relative to the launch location axis or global axis ''' self.abs_orientation = np.array([[0],[0],[1]]) ''' rel_orientation is a (unit) vector representing the orientation of the rocket relative to the velocity vector (defined as [0,0,1] if velocity is zero) ''' self.rel_orientation = np.array([[0],[0],[1]]) self.velocity = np.array([[0],[0],[0]]) self.abs_position = np.array([[0],[0],[0]]) ''' TODO: Get reltaive position and velocities, this object compared to reference object ''' self.relative_position = np.array([[None],[None],[None]]) self.relative_velocity = np.array([[None],[None],[None]]) ''' Calculate the angle of attack (alpha) in radians using the rocket's velocity direction and rotation state. Return alpha ''' def calculate_alpha(self): pass ''' Use environmental data regarding gust velocity and rocket geometry to estimate the rotation axis and rotation magnitude (radians) of a rocket Return a rotation quaternion for this axis and magnitude ''' def calculate_gust_rotation(self): pass ''' Use the angle of attack, drag+lift coefficients and rocket geometry to estimate the rotation axis and rotation magnitude (radians) of a rocket Return a rotation quaternion for this axis and magnitude ''' def calculate_drag_rotation(self): pass ''' Place holder - calcultes tvc rotation Not needed for final version Return a rotation quaternion for this axis and magnitude ''' def calculate_tvc_rotation(self): pass ''' lock rotation of the craft despite forces acting on the body useful for constraining rocket to a rail at launch for example (Prevents integration of accelerations to velocities) ''' def lock_rotation(self): pass ''' Unlocks rotation ''' def unlock_rotation(self): pass class Environment(): ''' The environment object helps compartmentalize environmental data (atmospheric temperature, pressure, gusts etc...). The object can then be accessed to fetch atmospheric or environmental data for the rotator object desired ''' def __init__(self, rocket_position, seed, units='english'): self.EARTH_MASS_SLUG = 4.0948607276025*10**23 self.EARTH_MASS_KG = 5.972 * 10**24 self.EARTH_RADIUS_MI = 3958.8 self.EARTH_RADIUS_FT = 20902000 self.EARTH_RADIUS_M = 6371000 self.units = units ''' Use the arguments to fetch data from functions ''' pass ''' For these be sure to check which altitude you are working with. For now I have it as altitude relative to center of the earth ''' def calc_gravity(self, altitude): if self.units == 'english': altitude = altitude * 0.3048 g = (self.EARTH_MASS_KG*6.67408*10**(-11))/(altitude**2) return g def fetch_atm_pressure(self, altitude): pass def fetch_atm_temperature(self, altitude): pass def fetch_atm_density(self, altitude): pass ''' The Reference object is a Fixed Earth, centered at 0,0,0 with no rotation ''' class Reference(): def __init__(self): self.position = np.array([[0],[0],[0]]) ''' Inherits the Reference (Non-rotating earth) and creates a rotating earth ''' class RotatingEarth(Reference): def __init__(self): self.position= np.array([[0],[0],[0]]) #Angular velocity in radians/s self.angular_velocity = 7.2921159 * 10**-5 #Angular displacement around the z axis self.angular_dispalcement = 0 def rotate_step(self, step): self.angular_dispalcement = self.angular_dispalcement + self.angular_velocity*step class Launchpad(RotatingEarth): def __init__(self, units='english'): self.EARTH_RADIUS_FT = 20902000 self.EARTH_RADIUS_M = 6371000 #set units for this object self.units = units #Using spherical coordinates for this (r,theta,phi) self.position= np.array([[self.get_radius],[0],[0]]) #Angular velocity in radians/s self.angular_velocity = 7.2921159 * 10**-5 #Angular displacement around the z axis self.angular_dispalcement = 0 def get_radius(self): if self.units == 'english': return self.EARTH_RADIUS_FT return self.EARTH_RADIUS_M ''' Turn the coordinates of the launch site into spherical coordinates and set as the position of the object RRS coordinates: fmt=dms 35 degrees, 21 minutes, 2 seconds North 117 degrees, 48 minutes, 30 seconds West fmts:>> 'dd' << decimal degree, >> 'dmm' << degree + decimal minutes >> dms << degrees, minutes, and seconds Format input as nested lists, North first, then west list = [[35,21,2],[117,48,39]] ''' def resolve_coordinates(self, input, fmt='dms'): #parse the input list into radians in format: [North Radians], [West Radians] radians_list = [] for list in input: degrees = list[0] if (fmt == 'dmm' or fmt == 'dms'): decimals = list[1]*1/60 if (fmt == 'dms'): decimals = decimals + list[2]*1/3600 degrees = degrees + decimals #convert degrees to radian radians_list.append(degrees*np.pi/180) # if __name__ == '__main__': with open('rocket_info.yaml') as rocket_info: rocket_data = yaml.load(rocket_info, Loader=yaml.FullLoader) rot_tester = Rotator() rot_tester.report_body_vector() rot_quaternion = np.array([[-.707],[0], [.707],[0]]) rot_tester.rotate_body(rot_quaternion) rot_tester.report_body_vector() rocenv = Environment(None, None) rocket = Rocket(rocket_data, rocenv)

4,917

7

725

480b454c4f3f99e6503621c21cc4b0ccfce7fa43

1,683

py

Python

warrior/Actions/DevActions/dev_actions.py

pavithra-gowda/warrior

19b153310552b986b86b5470fcfea9547a74c3a9

[ "Apache-2.0" ]

null

warrior/Actions/DevActions/dev_actions.py

pavithra-gowda/warrior

19b153310552b986b86b5470fcfea9547a74c3a9

[ "Apache-2.0" ]

1

2021-12-13T20:04:13.000Z

warrior/Actions/DevActions/dev_actions.py

pavithra-gowda/warrior

19b153310552b986b86b5470fcfea9547a74c3a9

[ "Apache-2.0" ]

null

import Framework.Utils as Utils from Framework.Utils import data_utils from Fremework.Utils.testcase_Utils import pNote class MyActions(object): """" Default __init__ field must be used when using classes for keywords """ def full_name(self, student, first_name= 'first', last_name= 'last', full_name= 'first last'): """ combine first and last name """ # status will be used to save the status of the test that wheather it is failed or pass status = True # we will return the dictionary of keys and value to maintain the logs log_dic={} wdesc= 'combine first and last name' full_name = None data = data_Utils.get_credentials(self.datafile, student, [first_name, last_name, full_name]) if first_name and last_name: pNote("first name is {0}".format(first_name)) pNote("last name is {0}".format(last_name)) temp_full_name = first_name + ' ' + last_name if temp_full_name != full_name: status= False pNote('full name is {0}'.format(full_name)) else: pNote("names are not provided") status = False log_dic["student"]= student log_dic["first_names"]= first_name log_dic["second_name"]= second_name log_dic["full_name"]= full_name return status, log_dic

36.586957

101

0.641711

import Framework.Utils as Utils from Framework.Utils import data_utils from Fremework.Utils.testcase_Utils import pNote class MyActions(object): """" Default __init__ field must be used when using classes for keywords """ def __init__(self): self.resultfile = Utils.config_Utils.resultfile self.datafile = Utils.config_Utils.datafile self.logsdir = Utils.config_Utils.logsdir self.filename = Utils.config_Utils.filename self.logfile = Utils.config_Utils.logfile def full_name(self, student, first_name= 'first', last_name= 'last', full_name= 'first last'): """ combine first and last name """ # status will be used to save the status of the test that wheather it is failed or pass status = True # we will return the dictionary of keys and value to maintain the logs log_dic={} wdesc= 'combine first and last name' full_name = None data = data_Utils.get_credentials(self.datafile, student, [first_name, last_name, full_name]) if first_name and last_name: pNote("first name is {0}".format(first_name)) pNote("last name is {0}".format(last_name)) temp_full_name = first_name + ' ' + last_name if temp_full_name != full_name: status= False pNote('full name is {0}'.format(full_name)) else: pNote("names are not provided") status = False log_dic["student"]= student log_dic["first_names"]= first_name log_dic["second_name"]= second_name log_dic["full_name"]= full_name return status, log_dic

258

0

26

51104acb8b2c3ea9fc29d8399745835746c384e2

413

py

Python

scripts/avsb/visualize.py

maryprimary/frg

e789439f599eb884a6220ae5b471cf610b0c2b2a

[ "MIT" ]

null

scripts/avsb/visualize.py

maryprimary/frg

e789439f599eb884a6220ae5b471cf610b0c2b2a

[ "MIT" ]

12

2021-02-04T06:46:36.000Z

2021-07-01T00:43:38.000Z

scripts/avsb/visualize.py

maryprimary/frg

e789439f599eb884a6220ae5b471cf610b0c2b2a

[ "MIT" ]

null

'''显示结果''' import numpy from helpers.drawer import draw_heatmap def main(): '''入口''' lval = 5.20 rpath = 'heatmap8/avsb' uval = numpy.load('{0}/{1:.2f}U.npy'.format(rpath, lval)) draw_heatmap(uval[0, 0, 0, 0, :, :, 0]) draw_heatmap(uval[1, 1, 1, 1, :, :, 0]) draw_heatmap(uval[1, 0, 0, 1, :, :, 0]) draw_heatmap(uval[0, 1, 1, 0, :, :, 0]) if __name__ == '__main__': main()

21.736842

61

0.544794

'''显示结果''' import numpy from helpers.drawer import draw_heatmap def main(): '''入口''' lval = 5.20 rpath = 'heatmap8/avsb' uval = numpy.load('{0}/{1:.2f}U.npy'.format(rpath, lval)) draw_heatmap(uval[0, 0, 0, 0, :, :, 0]) draw_heatmap(uval[1, 1, 1, 1, :, :, 0]) draw_heatmap(uval[1, 0, 0, 1, :, :, 0]) draw_heatmap(uval[0, 1, 1, 0, :, :, 0]) if __name__ == '__main__': main()

0

d2c57943c6d4a8aba77f42d9392defc82e3aa234

1,509

py

Python

tests/test_j2sparql.py

vliz-be-opsci/pykg2tbl

0455c5b58a0bde5e3453cd2242e89f7870d49d68

[ "MIT" ]

null

tests/test_j2sparql.py

vliz-be-opsci/pykg2tbl

0455c5b58a0bde5e3453cd2242e89f7870d49d68

[ "MIT" ]

null

tests/test_j2sparql.py

vliz-be-opsci/pykg2tbl

0455c5b58a0bde5e3453cd2242e89f7870d49d68

[ "MIT" ]

null

import unittest import pytest import sys import os from util4tests import enable_test_logging, run_single_test, log from pykg2tbl import KG2TblService, KGFileSource, KG2EndpointSource, J2SparqlBuilder ALL_TRIPLES_SPARQL = "SELECT * WHERE { ?s ?p ?o. } LIMIT 10" BODC_ENDPOINT = "http://vocab.nerc.ac.uk/sparql/sparql" if __name__ == "__main__": run_single_test(__file__)

36.804878

102

0.71173

import unittest import pytest import sys import os from util4tests import enable_test_logging, run_single_test, log from pykg2tbl import KG2TblService, KGFileSource, KG2EndpointSource, J2SparqlBuilder ALL_TRIPLES_SPARQL = "SELECT * WHERE { ?s ?p ?o. } LIMIT 10" BODC_ENDPOINT = "http://vocab.nerc.ac.uk/sparql/sparql" class TestBuilder(unittest.TestCase): def test_basic_query_sparql(self): template_folder = os.path.join(os.path.abspath(os.path.dirname(__file__)), 'sparql_templates') j2sqb = J2SparqlBuilder(template_folder) qry = j2sqb.build_sparql_query("all.sparql") self.assertIsNotNone(qry, "result qry should exist") self.assertEqual('''SELECT * WHERE { ?s ?p ?o. } LIMIT 10''',qry,'unexpected qry result') def test_get_variables_sparql_query(self): #TODO write test to get all the variables from a sparql template template_folder = os.path.join(os.path.abspath(os.path.dirname(__file__)), 'sparql_templates') log.debug(f"template folder = {template_folder}") j2sqb = J2SparqlBuilder(template_folder) variables = j2sqb.variables_in_query(name="bodc_find.sparql") log.info(f"all variables {variables}") self.assertIsNotNone(variables,'variables should exist') def test_injested_query_sparql(self): #test a sparql template who uses variables to make a sparql query to see if it works pass if __name__ == "__main__": run_single_test(__file__)

1,003

16

111

acc786eefab7f0810d55f87c6a7ce8c231f23c01

18,029

py

Python

tutorials/evoked/30_eeg_erp.py

ts2-lescot/mne-python

e4b16dc57a6a188aa06332b73d911e8131972522

[ "BSD-3-Clause" ]

null

tutorials/evoked/30_eeg_erp.py

ts2-lescot/mne-python

e4b16dc57a6a188aa06332b73d911e8131972522

[ "BSD-3-Clause" ]

1

2021-04-24T05:21:19.000Z

2021-04-27T07:47:52.000Z

tutorials/evoked/30_eeg_erp.py

ts2-lescot/mne-python

e4b16dc57a6a188aa06332b73d911e8131972522

[ "BSD-3-Clause" ]

1

2021-01-07T23:08:52.000Z

""" .. _tut-erp: EEG processing and Event Related Potentials (ERPs) ================================================== This tutorial shows how to perform standard ERP analyses in MNE-Python. Most of the material here is covered in other tutorials too, but for convenience the functions and methods most useful for ERP analyses are collected here, with links to other tutorials where more detailed information is given. As usual we'll start by importing the modules we need and loading some example data. Instead of parsing the events from the raw data's :term:`stim channel` (like we do in :ref:`this tutorial <tut-events-vs-annotations>`), we'll load the events from an external events file. Finally, to speed up computations so our documentation server can handle them, we'll crop the raw data from ~4.5 minutes down to 90 seconds. """ import os import numpy as np import matplotlib.pyplot as plt import mne sample_data_folder = mne.datasets.sample.data_path() sample_data_raw_file = os.path.join(sample_data_folder, 'MEG', 'sample', 'sample_audvis_filt-0-40_raw.fif') raw = mne.io.read_raw_fif(sample_data_raw_file, preload=False) sample_data_events_file = os.path.join(sample_data_folder, 'MEG', 'sample', 'sample_audvis_filt-0-40_raw-eve.fif') events = mne.read_events(sample_data_events_file) raw.crop(tmax=90) # in seconds; happens in-place # discard events >90 seconds (not strictly necessary: avoids some warnings) events = events[events[:, 0] <= raw.last_samp] ############################################################################### # The file that we loaded has already been partially processed: 3D sensor # locations have been saved as part of the ``.fif`` file, the data have been # low-pass filtered at 40 Hz, and a common average reference is set for the # EEG channels, stored as a projector (see :ref:`section-avg-ref-proj` in the # :ref:`tut-set-eeg-ref` tutorial for more info about when you may want to do # this). We'll discuss how to do each of these below. # # Since this is a combined EEG+MEG dataset, let's start by restricting the data # to just the EEG and EOG channels. This will cause the other projectors saved # in the file (which apply only to magnetometer channels) to be removed. By # looking at the measurement info we can see that we now have 59 EEG channels # and 1 EOG channel. raw.pick(['eeg', 'eog']).load_data() raw.info ############################################################################### # Channel names and types # ^^^^^^^^^^^^^^^^^^^^^^^ # # In practice it's quite common to have some channels labelled as EEG that are # actually EOG channels. `~mne.io.Raw` objects have a # `~mne.io.Raw.set_channel_types` method that you can use to change a channel # that is labeled as ``eeg`` into an ``eog`` type. You can also rename channels # using the `~mne.io.Raw.rename_channels` method. Detailed examples of both of # these methods can be found in the tutorial :ref:`tut-raw-class`. In this data # the channel types are all correct already, so for now we'll just rename the # channels to remove a space and a leading zero in the channel names, and # convert to lowercase: channel_renaming_dict = {name: name.replace(' 0', '').lower() for name in raw.ch_names} _ = raw.rename_channels(channel_renaming_dict) # happens in-place ############################################################################### # Channel locations # ^^^^^^^^^^^^^^^^^ # # The tutorial :ref:`tut-sensor-locations` describes MNE-Python's handling of # sensor positions in great detail. To briefly summarize: MNE-Python # distinguishes :term:`montages <montage>` (which contain sensor positions in # 3D: ``x``, ``y``, ``z``, in meters) from :term:`layouts <layout>` (which # define 2D arrangements of sensors for plotting approximate overhead diagrams # of sensor positions). Additionally, montages may specify *idealized* sensor # positions (based on, e.g., an idealized spherical headshape model) or they # may contain *realistic* sensor positions obtained by digitizing the 3D # locations of the sensors when placed on the actual subject's head. # # This dataset has realistic digitized 3D sensor locations saved as part of the # ``.fif`` file, so we can view the sensor locations in 2D or 3D using the # `~mne.io.Raw.plot_sensors` method: raw.plot_sensors(show_names=True) fig = raw.plot_sensors('3d') ############################################################################### # If you're working with a standard montage like the `10-20 <ten_twenty_>`_ # system, you can add sensor locations to the data like this: # ``raw.set_montage('standard_1020')``. See :ref:`tut-sensor-locations` for # info on what other standard montages are built-in to MNE-Python. # # If you have digitized realistic sensor locations, there are dedicated # functions for loading those digitization files into MNE-Python; see # :ref:`reading-dig-montages` for discussion and :ref:`dig-formats` for a list # of supported formats. Once loaded, the digitized sensor locations can be # added to the data by passing the loaded montage object to # ``raw.set_montage()``. # # # Setting the EEG reference # ^^^^^^^^^^^^^^^^^^^^^^^^^ # # As mentioned above, this data already has an EEG common average reference # added as a :term:`projector`. We can view the effect of this on the raw data # by plotting with and without the projector applied: for proj in (False, True): fig = raw.plot(n_channels=5, proj=proj, scalings=dict(eeg=50e-6)) fig.subplots_adjust(top=0.9) # make room for title ref = 'Average' if proj else 'No' fig.suptitle(f'{ref} reference', size='xx-large', weight='bold') ############################################################################### # The referencing scheme can be changed with the function # `mne.set_eeg_reference` (which by default operates on a *copy* of the data) # or the `raw.set_eeg_reference() <mne.io.Raw.set_eeg_reference>` method (which # always modifies the data in-place). The tutorial :ref:`tut-set-eeg-ref` shows # several examples of this. # # # Filtering # ^^^^^^^^^ # # MNE-Python has extensive support for different ways of filtering data. For a # general discussion of filter characteristics and MNE-Python defaults, see # :ref:`disc-filtering`. For practical examples of how to apply filters to your # data, see :ref:`tut-filter-resample`. Here, we'll apply a simple high-pass # filter for illustration: raw.filter(l_freq=0.1, h_freq=None) ############################################################################### # Evoked responses: epoching and averaging # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ # # The general process for extracting evoked responses from continuous data is # to use the `~mne.Epochs` constructor, and then average the resulting epochs # to create an `~mne.Evoked` object. In MNE-Python, events are represented as # a :class:`NumPy array <numpy.ndarray>` of sample numbers and integer event # codes. The event codes are stored in the last column of the events array: np.unique(events[:, -1]) ############################################################################### # The :ref:`tut-event-arrays` tutorial discusses event arrays in more detail. # Integer event codes are mapped to more descriptive text using a Python # :class:`dictionary <dict>` usually called ``event_id``. This mapping is # determined by your experiment code (i.e., it reflects which event codes you # chose to use to represent different experimental events or conditions). For # the :ref:`sample-dataset` data has the following mapping: event_dict = {'auditory/left': 1, 'auditory/right': 2, 'visual/left': 3, 'visual/right': 4, 'face': 5, 'buttonpress': 32} ############################################################################### # Now we can extract epochs from the continuous data. An interactive plot # allows you to click on epochs to mark them as "bad" and drop them from the # analysis (it is not interactive on the documentation website, but will be # when you run `epochs.plot() <mne.Epochs.plot>` in a Python console). epochs = mne.Epochs(raw, events, event_id=event_dict, tmin=-0.3, tmax=0.7, preload=True) fig = epochs.plot() ############################################################################### # It is also possible to automatically drop epochs, when first creating them or # later on, by providing maximum peak-to-peak signal value thresholds (pass to # the `~mne.Epochs` constructor as the ``reject`` parameter; see # :ref:`tut-reject-epochs-section` for details). You can also do this after # the epochs are already created, using the `~mne.Epochs.drop_bad` method: reject_criteria = dict(eeg=100e-6, # 100 µV eog=200e-6) # 200 µV _ = epochs.drop_bad(reject=reject_criteria) ############################################################################### # Next we generate a barplot of which channels contributed most to epochs # getting rejected. If one channel is responsible for lots of epoch rejections, # it may be worthwhile to mark that channel as "bad" in the `~mne.io.Raw` # object and then re-run epoching (fewer channels w/ more good epochs may be # preferable to keeping all channels but losing many epochs). See # :ref:`tut-bad-channels` for more info. epochs.plot_drop_log() ############################################################################### # Another way in which epochs can be automatically dropped is if the # `~mne.io.Raw` object they're extracted from contains :term:`annotations` that # begin with either ``bad`` or ``edge`` ("edge" annotations are automatically # inserted when concatenating two separate `~mne.io.Raw` objects together). See # :ref:`tut-reject-data-spans` for more information about annotation-based # epoch rejection. # # Now that we've dropped the bad epochs, let's look at our evoked responses for # some conditions we care about. Here the `~mne.Epochs.average` method will # create and `~mne.Evoked` object, which we can then plot. Notice that we\ # select which condition we want to average using the square-bracket indexing # (like a :class:`dictionary <dict>`); that returns a smaller epochs object # containing just the epochs from that condition, to which we then apply the # `~mne.Epochs.average` method: l_aud = epochs['auditory/left'].average() l_vis = epochs['visual/left'].average() ############################################################################### # These `~mne.Evoked` objects have their own interactive plotting method # (though again, it won't be interactive on the documentation website): # click-dragging a span of time will generate a scalp field topography for that # time span. Here we also demonstrate built-in color-coding the channel traces # by location: fig1 = l_aud.plot() fig2 = l_vis.plot(spatial_colors=True) ############################################################################### # Scalp topographies can also be obtained non-interactively with the # `~mne.Evoked.plot_topomap` method. Here we display topomaps of the average # field in 50 ms time windows centered at -200 ms, 100 ms, and 400 ms. l_aud.plot_topomap(times=[-0.2, 0.1, 0.4], average=0.05) ############################################################################### # Considerable customization of these plots is possible, see the docstring of # `~mne.Evoked.plot_topomap` for details. # # There is also a built-in method for combining "butterfly" plots of the # signals with scalp topographies, called `~mne.Evoked.plot_joint`. Like # `~mne.Evoked.plot_topomap` you can specify times for the scalp topographies # or you can let the method choose times automatically, as is done here: l_aud.plot_joint() ############################################################################### # Global field power (GFP) # ^^^^^^^^^^^^^^^^^^^^^^^^ # # Global field power :footcite:`Lehmann1980,Lehmann1984,Murray2008` is, # generally speaking, a measure of agreement of the signals picked up by all # sensors across the entire scalp: if all sensors have the same value at a # given time point, the GFP will be zero at that time point; if the signals # differ, the GFP will be non-zero at that time point. GFP # peaks may reflect "interesting" brain activity, warranting further # investigation. Mathematically, the GFP is the population standard # deviation across all sensors, calculated separately for every time point. # # You can plot the GFP using `evoked.plot(gfp=True) <mne.Evoked.plot>`. The GFP # trace will be black if ``spatial_colors=True`` and green otherwise. The EEG # reference does not affect the GFP: # sphinx_gallery_thumbnail_number=11 for evk in (l_aud, l_vis): evk.plot(gfp=True, spatial_colors=True, ylim=dict(eeg=[-12, 12])) ############################################################################### # To plot the GFP by itself you can pass ``gfp='only'`` (this makes it easier # to read off the GFP data values, because the scale is aligned): l_aud.plot(gfp='only') ############################################################################### # As stated above, the GFP is the population standard deviation of the signal # across channels. To compute it manually, we can leverage the fact that # `evoked.data <mne.Evoked.data>` is a :class:`NumPy array <numpy.ndarray>`, # and verify by plotting it using matplotlib commands: gfp = l_aud.data.std(axis=0, ddof=0) # Reproducing the MNE-Python plot style seen above fig, ax = plt.subplots() ax.plot(l_aud.times, gfp * 1e6, color='lime') ax.fill_between(l_aud.times, gfp * 1e6, color='lime', alpha=0.2) ax.set(xlabel='Time (s)', ylabel='GFP (µV)', title='EEG') ############################################################################### # Analyzing regions of interest (ROIs): averaging across channels # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ # # Since our sample data is responses to left and right auditory and visual # stimuli, we may want to compare left versus right ROIs. To average across # channels in a region of interest, we first find the channel indices we want. # Looking back at the 2D sensor plot above, we might choose the following for # left and right ROIs: left = ['eeg17', 'eeg18', 'eeg25', 'eeg26'] right = ['eeg23', 'eeg24', 'eeg34', 'eeg35'] left_ix = mne.pick_channels(l_aud.info['ch_names'], include=left) right_ix = mne.pick_channels(l_aud.info['ch_names'], include=right) ############################################################################### # Now we can create a new Evoked with 2 virtual channels (one for each ROI): roi_dict = dict(left_ROI=left_ix, right_ROI=right_ix) roi_evoked = mne.channels.combine_channels(l_aud, roi_dict, method='mean') print(roi_evoked.info['ch_names']) roi_evoked.plot() ############################################################################### # Comparing conditions # ^^^^^^^^^^^^^^^^^^^^ # # If we wanted to compare our auditory and visual stimuli, a useful function is # `mne.viz.plot_compare_evokeds`. By default this will combine all channels in # each evoked object using global field power (or RMS for MEG channels); here # instead we specify to combine by averaging, and restrict it to a subset of # channels by passing ``picks``: evokeds = dict(auditory=l_aud, visual=l_vis) picks = [f'eeg{n}' for n in range(10, 15)] mne.viz.plot_compare_evokeds(evokeds, picks=picks, combine='mean') ############################################################################### # We can also easily get confidence intervals by treating each epoch as a # separate observation using the `~mne.Epochs.iter_evoked` method. A confidence # interval across subjects could also be obtained, by passing a list of # `~mne.Evoked` objects (one per subject) to the # `~mne.viz.plot_compare_evokeds` function. evokeds = dict(auditory=list(epochs['auditory/left'].iter_evoked()), visual=list(epochs['visual/left'].iter_evoked())) mne.viz.plot_compare_evokeds(evokeds, combine='mean', picks=picks) ############################################################################### # We can also compare conditions by subtracting one `~mne.Evoked` object from # another using the `mne.combine_evoked` function (this function also allows # pooling of epochs without subtraction). aud_minus_vis = mne.combine_evoked([l_aud, l_vis], weights=[1, -1]) aud_minus_vis.plot_joint() ############################################################################### # .. warning:: # # The code above yields an **equal-weighted difference**. If you have # imbalanced trial numbers, you might want to equalize the number of events # per condition first by using `epochs.equalize_event_counts() # <mne.Epochs.equalize_event_counts>` before averaging. # # # Grand averages # ^^^^^^^^^^^^^^ # # To compute grand averages across conditions (or subjects), you can pass a # list of `~mne.Evoked` objects to `mne.grand_average`. The result is another # `~mne.Evoked` object. grand_average = mne.grand_average([l_aud, l_vis]) print(grand_average) ############################################################################### # For combining *conditions* it is also possible to make use of :term:`HED` # tags in the condition names when selecting which epochs to average. For # example, we have the condition names: list(event_dict) ############################################################################### # We can select the auditory conditions (left and right together) by passing: epochs['auditory'].average() ############################################################################### # see :ref:`tut-section-subselect-epochs` for details. # # The tutorials :ref:`tut-epochs-class` and :ref:`tut-evoked-class` have many # more details about working with the `~mne.Epochs` and `~mne.Evoked` classes. # # .. _ten_twenty: https://en.wikipedia.org/wiki/10%E2%80%9320_system_(EEG) # # # References # ---------- # .. footbibliography::

47.32021

79

0.645349

""" .. _tut-erp: EEG processing and Event Related Potentials (ERPs) ================================================== This tutorial shows how to perform standard ERP analyses in MNE-Python. Most of the material here is covered in other tutorials too, but for convenience the functions and methods most useful for ERP analyses are collected here, with links to other tutorials where more detailed information is given. As usual we'll start by importing the modules we need and loading some example data. Instead of parsing the events from the raw data's :term:`stim channel` (like we do in :ref:`this tutorial <tut-events-vs-annotations>`), we'll load the events from an external events file. Finally, to speed up computations so our documentation server can handle them, we'll crop the raw data from ~4.5 minutes down to 90 seconds. """ import os import numpy as np import matplotlib.pyplot as plt import mne sample_data_folder = mne.datasets.sample.data_path() sample_data_raw_file = os.path.join(sample_data_folder, 'MEG', 'sample', 'sample_audvis_filt-0-40_raw.fif') raw = mne.io.read_raw_fif(sample_data_raw_file, preload=False) sample_data_events_file = os.path.join(sample_data_folder, 'MEG', 'sample', 'sample_audvis_filt-0-40_raw-eve.fif') events = mne.read_events(sample_data_events_file) raw.crop(tmax=90) # in seconds; happens in-place # discard events >90 seconds (not strictly necessary: avoids some warnings) events = events[events[:, 0] <= raw.last_samp] ############################################################################### # The file that we loaded has already been partially processed: 3D sensor # locations have been saved as part of the ``.fif`` file, the data have been # low-pass filtered at 40 Hz, and a common average reference is set for the # EEG channels, stored as a projector (see :ref:`section-avg-ref-proj` in the # :ref:`tut-set-eeg-ref` tutorial for more info about when you may want to do # this). We'll discuss how to do each of these below. # # Since this is a combined EEG+MEG dataset, let's start by restricting the data # to just the EEG and EOG channels. This will cause the other projectors saved # in the file (which apply only to magnetometer channels) to be removed. By # looking at the measurement info we can see that we now have 59 EEG channels # and 1 EOG channel. raw.pick(['eeg', 'eog']).load_data() raw.info ############################################################################### # Channel names and types # ^^^^^^^^^^^^^^^^^^^^^^^ # # In practice it's quite common to have some channels labelled as EEG that are # actually EOG channels. `~mne.io.Raw` objects have a # `~mne.io.Raw.set_channel_types` method that you can use to change a channel # that is labeled as ``eeg`` into an ``eog`` type. You can also rename channels # using the `~mne.io.Raw.rename_channels` method. Detailed examples of both of # these methods can be found in the tutorial :ref:`tut-raw-class`. In this data # the channel types are all correct already, so for now we'll just rename the # channels to remove a space and a leading zero in the channel names, and # convert to lowercase: channel_renaming_dict = {name: name.replace(' 0', '').lower() for name in raw.ch_names} _ = raw.rename_channels(channel_renaming_dict) # happens in-place ############################################################################### # Channel locations # ^^^^^^^^^^^^^^^^^ # # The tutorial :ref:`tut-sensor-locations` describes MNE-Python's handling of # sensor positions in great detail. To briefly summarize: MNE-Python # distinguishes :term:`montages <montage>` (which contain sensor positions in # 3D: ``x``, ``y``, ``z``, in meters) from :term:`layouts <layout>` (which # define 2D arrangements of sensors for plotting approximate overhead diagrams # of sensor positions). Additionally, montages may specify *idealized* sensor # positions (based on, e.g., an idealized spherical headshape model) or they # may contain *realistic* sensor positions obtained by digitizing the 3D # locations of the sensors when placed on the actual subject's head. # # This dataset has realistic digitized 3D sensor locations saved as part of the # ``.fif`` file, so we can view the sensor locations in 2D or 3D using the # `~mne.io.Raw.plot_sensors` method: raw.plot_sensors(show_names=True) fig = raw.plot_sensors('3d') ############################################################################### # If you're working with a standard montage like the `10-20 <ten_twenty_>`_ # system, you can add sensor locations to the data like this: # ``raw.set_montage('standard_1020')``. See :ref:`tut-sensor-locations` for # info on what other standard montages are built-in to MNE-Python. # # If you have digitized realistic sensor locations, there are dedicated # functions for loading those digitization files into MNE-Python; see # :ref:`reading-dig-montages` for discussion and :ref:`dig-formats` for a list # of supported formats. Once loaded, the digitized sensor locations can be # added to the data by passing the loaded montage object to # ``raw.set_montage()``. # # # Setting the EEG reference # ^^^^^^^^^^^^^^^^^^^^^^^^^ # # As mentioned above, this data already has an EEG common average reference # added as a :term:`projector`. We can view the effect of this on the raw data # by plotting with and without the projector applied: for proj in (False, True): fig = raw.plot(n_channels=5, proj=proj, scalings=dict(eeg=50e-6)) fig.subplots_adjust(top=0.9) # make room for title ref = 'Average' if proj else 'No' fig.suptitle(f'{ref} reference', size='xx-large', weight='bold') ############################################################################### # The referencing scheme can be changed with the function # `mne.set_eeg_reference` (which by default operates on a *copy* of the data) # or the `raw.set_eeg_reference() <mne.io.Raw.set_eeg_reference>` method (which # always modifies the data in-place). The tutorial :ref:`tut-set-eeg-ref` shows # several examples of this. # # # Filtering # ^^^^^^^^^ # # MNE-Python has extensive support for different ways of filtering data. For a # general discussion of filter characteristics and MNE-Python defaults, see # :ref:`disc-filtering`. For practical examples of how to apply filters to your # data, see :ref:`tut-filter-resample`. Here, we'll apply a simple high-pass # filter for illustration: raw.filter(l_freq=0.1, h_freq=None) ############################################################################### # Evoked responses: epoching and averaging # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ # # The general process for extracting evoked responses from continuous data is # to use the `~mne.Epochs` constructor, and then average the resulting epochs # to create an `~mne.Evoked` object. In MNE-Python, events are represented as # a :class:`NumPy array <numpy.ndarray>` of sample numbers and integer event # codes. The event codes are stored in the last column of the events array: np.unique(events[:, -1]) ############################################################################### # The :ref:`tut-event-arrays` tutorial discusses event arrays in more detail. # Integer event codes are mapped to more descriptive text using a Python # :class:`dictionary <dict>` usually called ``event_id``. This mapping is # determined by your experiment code (i.e., it reflects which event codes you # chose to use to represent different experimental events or conditions). For # the :ref:`sample-dataset` data has the following mapping: event_dict = {'auditory/left': 1, 'auditory/right': 2, 'visual/left': 3, 'visual/right': 4, 'face': 5, 'buttonpress': 32} ############################################################################### # Now we can extract epochs from the continuous data. An interactive plot # allows you to click on epochs to mark them as "bad" and drop them from the # analysis (it is not interactive on the documentation website, but will be # when you run `epochs.plot() <mne.Epochs.plot>` in a Python console). epochs = mne.Epochs(raw, events, event_id=event_dict, tmin=-0.3, tmax=0.7, preload=True) fig = epochs.plot() ############################################################################### # It is also possible to automatically drop epochs, when first creating them or # later on, by providing maximum peak-to-peak signal value thresholds (pass to # the `~mne.Epochs` constructor as the ``reject`` parameter; see # :ref:`tut-reject-epochs-section` for details). You can also do this after # the epochs are already created, using the `~mne.Epochs.drop_bad` method: reject_criteria = dict(eeg=100e-6, # 100 µV eog=200e-6) # 200 µV _ = epochs.drop_bad(reject=reject_criteria) ############################################################################### # Next we generate a barplot of which channels contributed most to epochs # getting rejected. If one channel is responsible for lots of epoch rejections, # it may be worthwhile to mark that channel as "bad" in the `~mne.io.Raw` # object and then re-run epoching (fewer channels w/ more good epochs may be # preferable to keeping all channels but losing many epochs). See # :ref:`tut-bad-channels` for more info. epochs.plot_drop_log() ############################################################################### # Another way in which epochs can be automatically dropped is if the # `~mne.io.Raw` object they're extracted from contains :term:`annotations` that # begin with either ``bad`` or ``edge`` ("edge" annotations are automatically # inserted when concatenating two separate `~mne.io.Raw` objects together). See # :ref:`tut-reject-data-spans` for more information about annotation-based # epoch rejection. # # Now that we've dropped the bad epochs, let's look at our evoked responses for # some conditions we care about. Here the `~mne.Epochs.average` method will # create and `~mne.Evoked` object, which we can then plot. Notice that we\ # select which condition we want to average using the square-bracket indexing # (like a :class:`dictionary <dict>`); that returns a smaller epochs object # containing just the epochs from that condition, to which we then apply the # `~mne.Epochs.average` method: l_aud = epochs['auditory/left'].average() l_vis = epochs['visual/left'].average() ############################################################################### # These `~mne.Evoked` objects have their own interactive plotting method # (though again, it won't be interactive on the documentation website): # click-dragging a span of time will generate a scalp field topography for that # time span. Here we also demonstrate built-in color-coding the channel traces # by location: fig1 = l_aud.plot() fig2 = l_vis.plot(spatial_colors=True) ############################################################################### # Scalp topographies can also be obtained non-interactively with the # `~mne.Evoked.plot_topomap` method. Here we display topomaps of the average # field in 50 ms time windows centered at -200 ms, 100 ms, and 400 ms. l_aud.plot_topomap(times=[-0.2, 0.1, 0.4], average=0.05) ############################################################################### # Considerable customization of these plots is possible, see the docstring of # `~mne.Evoked.plot_topomap` for details. # # There is also a built-in method for combining "butterfly" plots of the # signals with scalp topographies, called `~mne.Evoked.plot_joint`. Like # `~mne.Evoked.plot_topomap` you can specify times for the scalp topographies # or you can let the method choose times automatically, as is done here: l_aud.plot_joint() ############################################################################### # Global field power (GFP) # ^^^^^^^^^^^^^^^^^^^^^^^^ # # Global field power :footcite:`Lehmann1980,Lehmann1984,Murray2008` is, # generally speaking, a measure of agreement of the signals picked up by all # sensors across the entire scalp: if all sensors have the same value at a # given time point, the GFP will be zero at that time point; if the signals # differ, the GFP will be non-zero at that time point. GFP # peaks may reflect "interesting" brain activity, warranting further # investigation. Mathematically, the GFP is the population standard # deviation across all sensors, calculated separately for every time point. # # You can plot the GFP using `evoked.plot(gfp=True) <mne.Evoked.plot>`. The GFP # trace will be black if ``spatial_colors=True`` and green otherwise. The EEG # reference does not affect the GFP: # sphinx_gallery_thumbnail_number=11 for evk in (l_aud, l_vis): evk.plot(gfp=True, spatial_colors=True, ylim=dict(eeg=[-12, 12])) ############################################################################### # To plot the GFP by itself you can pass ``gfp='only'`` (this makes it easier # to read off the GFP data values, because the scale is aligned): l_aud.plot(gfp='only') ############################################################################### # As stated above, the GFP is the population standard deviation of the signal # across channels. To compute it manually, we can leverage the fact that # `evoked.data <mne.Evoked.data>` is a :class:`NumPy array <numpy.ndarray>`, # and verify by plotting it using matplotlib commands: gfp = l_aud.data.std(axis=0, ddof=0) # Reproducing the MNE-Python plot style seen above fig, ax = plt.subplots() ax.plot(l_aud.times, gfp * 1e6, color='lime') ax.fill_between(l_aud.times, gfp * 1e6, color='lime', alpha=0.2) ax.set(xlabel='Time (s)', ylabel='GFP (µV)', title='EEG') ############################################################################### # Analyzing regions of interest (ROIs): averaging across channels # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ # # Since our sample data is responses to left and right auditory and visual # stimuli, we may want to compare left versus right ROIs. To average across # channels in a region of interest, we first find the channel indices we want. # Looking back at the 2D sensor plot above, we might choose the following for # left and right ROIs: left = ['eeg17', 'eeg18', 'eeg25', 'eeg26'] right = ['eeg23', 'eeg24', 'eeg34', 'eeg35'] left_ix = mne.pick_channels(l_aud.info['ch_names'], include=left) right_ix = mne.pick_channels(l_aud.info['ch_names'], include=right) ############################################################################### # Now we can create a new Evoked with 2 virtual channels (one for each ROI): roi_dict = dict(left_ROI=left_ix, right_ROI=right_ix) roi_evoked = mne.channels.combine_channels(l_aud, roi_dict, method='mean') print(roi_evoked.info['ch_names']) roi_evoked.plot() ############################################################################### # Comparing conditions # ^^^^^^^^^^^^^^^^^^^^ # # If we wanted to compare our auditory and visual stimuli, a useful function is # `mne.viz.plot_compare_evokeds`. By default this will combine all channels in # each evoked object using global field power (or RMS for MEG channels); here # instead we specify to combine by averaging, and restrict it to a subset of # channels by passing ``picks``: evokeds = dict(auditory=l_aud, visual=l_vis) picks = [f'eeg{n}' for n in range(10, 15)] mne.viz.plot_compare_evokeds(evokeds, picks=picks, combine='mean') ############################################################################### # We can also easily get confidence intervals by treating each epoch as a # separate observation using the `~mne.Epochs.iter_evoked` method. A confidence # interval across subjects could also be obtained, by passing a list of # `~mne.Evoked` objects (one per subject) to the # `~mne.viz.plot_compare_evokeds` function. evokeds = dict(auditory=list(epochs['auditory/left'].iter_evoked()), visual=list(epochs['visual/left'].iter_evoked())) mne.viz.plot_compare_evokeds(evokeds, combine='mean', picks=picks) ############################################################################### # We can also compare conditions by subtracting one `~mne.Evoked` object from # another using the `mne.combine_evoked` function (this function also allows # pooling of epochs without subtraction). aud_minus_vis = mne.combine_evoked([l_aud, l_vis], weights=[1, -1]) aud_minus_vis.plot_joint() ############################################################################### # .. warning:: # # The code above yields an **equal-weighted difference**. If you have # imbalanced trial numbers, you might want to equalize the number of events # per condition first by using `epochs.equalize_event_counts() # <mne.Epochs.equalize_event_counts>` before averaging. # # # Grand averages # ^^^^^^^^^^^^^^ # # To compute grand averages across conditions (or subjects), you can pass a # list of `~mne.Evoked` objects to `mne.grand_average`. The result is another # `~mne.Evoked` object. grand_average = mne.grand_average([l_aud, l_vis]) print(grand_average) ############################################################################### # For combining *conditions* it is also possible to make use of :term:`HED` # tags in the condition names when selecting which epochs to average. For # example, we have the condition names: list(event_dict) ############################################################################### # We can select the auditory conditions (left and right together) by passing: epochs['auditory'].average() ############################################################################### # see :ref:`tut-section-subselect-epochs` for details. # # The tutorials :ref:`tut-epochs-class` and :ref:`tut-evoked-class` have many # more details about working with the `~mne.Epochs` and `~mne.Evoked` classes. # # .. _ten_twenty: https://en.wikipedia.org/wiki/10%E2%80%9320_system_(EEG) # # # References # ---------- # .. footbibliography::

0

e3bdde5ca15cdab92e7ceb01b1e2eb574d075a1d

164

py

Python

problemsets/Codeforces/Python/A1095.py

juarezpaulino/coderemite

a4649d3f3a89d234457032d14a6646b3af339ac1

[ "Apache-2.0" ]

null

problemsets/Codeforces/Python/A1095.py

juarezpaulino/coderemite

a4649d3f3a89d234457032d14a6646b3af339ac1

[ "Apache-2.0" ]

null

problemsets/Codeforces/Python/A1095.py

juarezpaulino/coderemite

a4649d3f3a89d234457032d14a6646b3af339ac1

[ "Apache-2.0" ]

null

""" * * Author: Juarez Paulino(coderemite) * Email: juarez.paulino@gmail.com * """ n,k=int(input()),0 s,x,t='',1,input() while k<n: s+=t[k];k+=x;x+=1 print(s)

16.4

38

0.579268

""" * * Author: Juarez Paulino(coderemite) * Email: juarez.paulino@gmail.com * """ n,k=int(input()),0 s,x,t='',1,input() while k<n: s+=t[k];k+=x;x+=1 print(s)

0

13523c859982e146b74d5680ca538f2bf774a03e

2,746

py

Python

axonius_api_client/cli/grp_system/grp_central_core/cmd_restore_from_aws_s3.py

rwils83/axonius_api_client

1990ed4d1287482a4648dc51edcaa5eb08255f5b

[ "MIT" ]

null

axonius_api_client/cli/grp_system/grp_central_core/cmd_restore_from_aws_s3.py

rwils83/axonius_api_client

1990ed4d1287482a4648dc51edcaa5eb08255f5b

[ "MIT" ]

3

2021-05-18T14:28:30.000Z

2021-09-06T20:01:56.000Z

axonius_api_client/cli/grp_system/grp_central_core/cmd_restore_from_aws_s3.py

rwils83/axonius_api_client

1990ed4d1287482a4648dc51edcaa5eb08255f5b

[ "MIT" ]

null

# -*- coding: utf-8 -*- """Command line interface for Axonius API Client.""" from ....tools import json_dump from ...context import CONTEXT_SETTINGS, click from ...options import AUTH, add_options KEY_NAME = click.option( "--key-name", "-kn", "key_name", help="Key name of file object in [bucket_name] to restore", required=True, show_envvar=True, show_default=True, ) BUCKET_NAME = click.option( "--bucket-name", "-bn", "bucket_name", default=None, help="Name of bucket in S3 to get [key_name] from", show_envvar=True, show_default=True, ) ACCESS_KEY_ID = click.option( "--access-key-id", "-aki", "access_key_id", default=None, help="AWS Access Key Id to use to access [bucket_name]", show_envvar=True, show_default=True, ) SECRET_ACCESS_KEY = click.option( "--secret-access-key", "-sak", "secret_access_key", default=None, help="AWS Secret Access Key to use to access [bucket_name]", show_envvar=True, show_default=True, ) PRESHARED_KEY = click.option( "--preshared-key", "-pk", "preshared_key", default=None, help="Password to use to decrypt [key_name]", show_envvar=True, show_default=True, ) ALLOW_RE_RESTORE = click.option( "--allow-re-restore/--no-allow-re-restore", "-arr/-narr", "allow_re_restore", help="Restore [key_name] even if it has already been restored", is_flag=True, default=False, show_envvar=True, show_default=True, ) DELETE_BACKUPS = click.option( "--delete-backups/--no-delete-backups", "-db/-ndb", "delete_backups", help="Delete [key_name] from [bucket_name] after restore has finished", is_flag=True, default=None, show_envvar=True, show_default=True, ) OPTIONS = [ *AUTH, ACCESS_KEY_ID, SECRET_ACCESS_KEY, PRESHARED_KEY, ALLOW_RE_RESTORE, DELETE_BACKUPS, BUCKET_NAME, KEY_NAME, ] EPILOG = """ If values for these options are not provided, they will default to the settings under Global Settings > Amazon S3 Settings: \b * bucket-name: Amazon S3 bucket name * access-key-id: AWS Access Key Id * secret-access-key: AWS Secret Access Key * preshared-key: Backup encryption passphrase """ @click.command( name="restore-from-aws-s3", context_settings=CONTEXT_SETTINGS, epilog=EPILOG, ) @add_options(OPTIONS) @click.pass_context def cmd(ctx, url, key, secret, **kwargs): """Perform a manual restore of a backup in AWS S3.""" client = ctx.obj.start_client(url=url, key=key, secret=secret) with ctx.obj.exc_wrap(wraperror=ctx.obj.wraperror): data = client.system.central_core.restore_from_aws_s3(**kwargs) click.secho(json_dump(data))

23.878261

75

0.671158

# -*- coding: utf-8 -*- """Command line interface for Axonius API Client.""" from ....tools import json_dump from ...context import CONTEXT_SETTINGS, click from ...options import AUTH, add_options KEY_NAME = click.option( "--key-name", "-kn", "key_name", help="Key name of file object in [bucket_name] to restore", required=True, show_envvar=True, show_default=True, ) BUCKET_NAME = click.option( "--bucket-name", "-bn", "bucket_name", default=None, help="Name of bucket in S3 to get [key_name] from", show_envvar=True, show_default=True, ) ACCESS_KEY_ID = click.option( "--access-key-id", "-aki", "access_key_id", default=None, help="AWS Access Key Id to use to access [bucket_name]", show_envvar=True, show_default=True, ) SECRET_ACCESS_KEY = click.option( "--secret-access-key", "-sak", "secret_access_key", default=None, help="AWS Secret Access Key to use to access [bucket_name]", show_envvar=True, show_default=True, ) PRESHARED_KEY = click.option( "--preshared-key", "-pk", "preshared_key", default=None, help="Password to use to decrypt [key_name]", show_envvar=True, show_default=True, ) ALLOW_RE_RESTORE = click.option( "--allow-re-restore/--no-allow-re-restore", "-arr/-narr", "allow_re_restore", help="Restore [key_name] even if it has already been restored", is_flag=True, default=False, show_envvar=True, show_default=True, ) DELETE_BACKUPS = click.option( "--delete-backups/--no-delete-backups", "-db/-ndb", "delete_backups", help="Delete [key_name] from [bucket_name] after restore has finished", is_flag=True, default=None, show_envvar=True, show_default=True, ) OPTIONS = [ *AUTH, ACCESS_KEY_ID, SECRET_ACCESS_KEY, PRESHARED_KEY, ALLOW_RE_RESTORE, DELETE_BACKUPS, BUCKET_NAME, KEY_NAME, ] EPILOG = """ If values for these options are not provided, they will default to the settings under Global Settings > Amazon S3 Settings: \b * bucket-name: Amazon S3 bucket name * access-key-id: AWS Access Key Id * secret-access-key: AWS Secret Access Key * preshared-key: Backup encryption passphrase """ @click.command( name="restore-from-aws-s3", context_settings=CONTEXT_SETTINGS, epilog=EPILOG, ) @add_options(OPTIONS) @click.pass_context def cmd(ctx, url, key, secret, **kwargs): """Perform a manual restore of a backup in AWS S3.""" client = ctx.obj.start_client(url=url, key=key, secret=secret) with ctx.obj.exc_wrap(wraperror=ctx.obj.wraperror): data = client.system.central_core.restore_from_aws_s3(**kwargs) click.secho(json_dump(data))

0

f20965bf4a26da4bfb598e4cc77e1c9347070578

496

py

Python

algoanim/sorts/yslow.py

Gaming32/Python-AlgoAnim

c6df06e263f52d57ca91471830ff8fa14f1d85db

[ "MIT" ]

null

algoanim/sorts/yslow.py

Gaming32/Python-AlgoAnim

c6df06e263f52d57ca91471830ff8fa14f1d85db

[ "MIT" ]

null

algoanim/sorts/yslow.py

Gaming32/Python-AlgoAnim

c6df06e263f52d57ca91471830ff8fa14f1d85db

[ "MIT" ]

null

from algoanim.array import Array from algoanim.sort import Sort SORT_CLASS = YSlowSort

18.37037

40

0.461694

from algoanim.array import Array from algoanim.sort import Sort def yslow(A, l, r): if r - l > 0: if A[r] < A[l]: A[l], A[r] = A[r], A[l] m = (r - l + 1) // 2 for _ in [0, 1]: A = yslow(A, l, r-m) A = yslow(A, l + m, r) A = yslow(A, l + 1, r - 1) return A class YSlowSort(Sort): name = 'YSlow Sort' def run(self, array: Array) -> None: yslow(array, 0, len(array) - 1) SORT_CLASS = YSlowSort

307

52

46

5e317ecf395a2349de6f2351a075fe488ace4261

22,311

py

Python

tests/test_parser.py

shaljam/streaming-form-data

65f764fe521c38db681c3ef384d6b998496df79b

[ "MIT" ]

null

tests/test_parser.py

shaljam/streaming-form-data

65f764fe521c38db681c3ef384d6b998496df79b

[ "MIT" ]

null

tests/test_parser.py

shaljam/streaming-form-data

65f764fe521c38db681c3ef384d6b998496df79b

[ "MIT" ]

1

2020-10-13T03:21:46.000Z

from contextlib import contextmanager from io import BytesIO import hashlib from numpy import random import pytest from requests_toolbelt import MultipartEncoder from streaming_form_data import ParseFailedException, StreamingFormDataParser from streaming_form_data.targets import ( BaseTarget, FileTarget, SHA256Target, ValueTarget, ) from streaming_form_data.validators import MaxSizeValidator, ValidationError @contextmanager # The following tests have been added from tornado's # MultipartFormDataTestCase # https://github.com/tornadoweb/tornado/blob/master/tornado/test/httputil_test.py

23.836538

81

0.640626

from contextlib import contextmanager from io import BytesIO import hashlib from numpy import random import pytest from requests_toolbelt import MultipartEncoder from streaming_form_data import ParseFailedException, StreamingFormDataParser from streaming_form_data.targets import ( BaseTarget, FileTarget, SHA256Target, ValueTarget, ) from streaming_form_data.validators import MaxSizeValidator, ValidationError @contextmanager def local_seed(seed): state = random.get_state() try: random.seed(seed) yield finally: random.set_state(state) def get_random_bytes(size, seed): with local_seed(seed): return random.bytes(size) def open_dataset(filename): if filename == 'file.txt': filedata = b'this is a txt file\r\n' * 10 elif filename == 'image-600x400.png': filedata = get_random_bytes(1780, 600) elif filename == 'image-2560x1600.png': filedata = get_random_bytes(11742, 2560) elif filename == 'image-500k.png': filedata = get_random_bytes(437814, 500) elif filename == 'image-high-res.jpg': filedata = get_random_bytes(9450866, 945) elif filename == 'empty.html': filedata = b'' elif filename == 'hyphen-hyphen.txt': filedata = b'--' elif filename == 'LF.txt': filedata = b'\n' elif filename == 'CRLF.txt': filedata = b'\r\n' elif filename == '1M.dat': filedata = get_random_bytes(1024 * 1024, 1024) elif filename == '1M-1.dat': filedata = get_random_bytes(1024 * 1024 - 1, 1024 - 1) elif filename == '1M+1.dat': filedata = get_random_bytes(1024 * 1024 + 1, 1024 + 1) else: raise Exception('Unknown file name: ' + filename) return BytesIO(filedata) def encoded_dataset(filename): with open_dataset(filename) as dataset_: fields = {filename: (filename, dataset_, 'text/plain')} encoder = MultipartEncoder(fields=fields) return (encoder.content_type, encoder.to_string()) def test_smoke(): encoder = MultipartEncoder(fields={'name': 'hello'}) parser = StreamingFormDataParser( headers={'Content-Type': encoder.content_type} ) parser.data_received(encoder.to_string()) def test_basic_single(): target = ValueTarget() encoder = MultipartEncoder(fields={'value': 'hello world'}) parser = StreamingFormDataParser( headers={'Content-Type': encoder.content_type} ) parser.register('value', target) parser.data_received(encoder.to_string()) assert target.value == b'hello world' assert target._started assert target._finished def test_case_insensitive_content_type(): content_type_header = 'Content-Type' for header_key in ( content_type_header, content_type_header.lower(), content_type_header.upper(), 'cOnTeNt-tYPe', ): target = ValueTarget() encoder = MultipartEncoder(fields={'value': 'hello world'}) parser = StreamingFormDataParser( headers={header_key: encoder.content_type} ) parser.register('value', target) parser.data_received(encoder.to_string()) assert target.value == b'hello world' def test_missing_content_type(): with pytest.raises(ParseFailedException): StreamingFormDataParser({}) with pytest.raises(ParseFailedException): StreamingFormDataParser({'key': 'value'}) def test_incorrect_content_type(): for value in ( 'multipart/mixed; boundary=1234', 'multipart/form-data', 'multipart/form-data; delimiter=1234', ): with pytest.raises(ParseFailedException): StreamingFormDataParser({'Content-Type': value}) def test_basic_multiple(): first = ValueTarget() second = ValueTarget() third = ValueTarget() encoder = MultipartEncoder( fields={'first': 'foo', 'second': 'bar', 'third': 'baz'} ) parser = StreamingFormDataParser( headers={'Content-Type': encoder.content_type} ) parser.register('first', first) parser.register('second', second) parser.register('third', third) parser.data_received(encoder.to_string()) assert first.value == b'foo' assert second.value == b'bar' assert third.value == b'baz' def test_chunked_single(): expected_value = 'hello world' target = ValueTarget() encoder = MultipartEncoder(fields={'value': expected_value}) body = encoder.to_string() parser = StreamingFormDataParser( headers={'Content-Type': encoder.content_type} ) parser.register('value', target) index = body.index(b'world') parser.data_received(body[:index]) parser.data_received(body[index:]) assert target.value == expected_value.encode('utf-8') def test_chunked_multiple(): expected_first_value = 'foo' * 1000 expected_second_value = 'bar' * 1000 expected_third_value = 'baz' * 1000 first = ValueTarget() second = ValueTarget() third = ValueTarget() encoder = MultipartEncoder( fields={ 'first': expected_first_value, 'second': expected_second_value, 'third': expected_third_value, } ) body = encoder.to_string() parser = StreamingFormDataParser( headers={'Content-Type': encoder.content_type} ) parser.register('first', first) parser.register('second', second) parser.register('third', third) chunks = [] size = 100 while len(body): chunks.append(body[:size]) body = body[size:] for chunk in chunks: parser.data_received(chunk) assert first.value == expected_first_value.encode('utf-8') assert second.value == expected_second_value.encode('utf-8') assert third.value == expected_third_value.encode('utf-8') def test_break_chunk_at_boundary(): expected_first_value = 'hello' * 500 expected_second_value = 'hello' * 500 first = ValueTarget() second = ValueTarget() encoder = MultipartEncoder( fields={'first': 'hello' * 500, 'second': 'hello' * 500} ) body = encoder.to_string() boundary = encoder.boundary.encode('utf-8') parser = StreamingFormDataParser( headers={'Content-Type': encoder.content_type} ) parser.register('first', first) parser.register('second', second) index = body[50:].index(boundary) + 5 parser.data_received(body[:index]) parser.data_received(body[index:]) assert first.value == expected_first_value.encode('utf-8') assert second.value == expected_second_value.encode('utf-8') def test_file_content_single(): filenames = ( 'file.txt', 'image-600x400.png', 'image-2560x1600.png', 'empty.html', 'hyphen-hyphen.txt', 'LF.txt', 'CRLF.txt', '1M.dat', '1M-1.dat', '1M+1.dat', ) for filename in filenames: with open_dataset(filename) as dataset_: expected_value = dataset_.read() content_type, body = encoded_dataset(filename) target = ValueTarget() parser = StreamingFormDataParser( headers={'Content-Type': content_type} ) parser.register(filename, target) parser.data_received(body) assert target.value == expected_value def test_file_content_multiple(): with open_dataset('file.txt') as dataset_: expected_value = dataset_.read() content_type, body = encoded_dataset('file.txt') txt = ValueTarget() parser = StreamingFormDataParser(headers={'Content-Type': content_type}) parser.register('file.txt', txt) size = 50 chunks = [] while body: chunks.append(body[:size]) body = body[size:] for chunk in chunks: parser.data_received(chunk) assert txt.value == expected_value def test_file_content_varying_chunk_size(): with open_dataset('file.txt') as dataset_: expected_value = dataset_.read() content_type, body = encoded_dataset('file.txt') for index in range(len(body)): txt = ValueTarget() parser = StreamingFormDataParser( headers={'Content-Type': content_type} ) parser.register('file.txt', txt) parser.data_received(body[:index]) parser.data_received(body[index:]) assert txt.value == expected_value def test_mixed_content_varying_chunk_size(): with open_dataset('file.txt') as dataset_: expected_value = dataset_.read() with open_dataset('file.txt') as dataset_: fields = { 'name': 'hello world', 'age': '10', 'cv.txt': ('file.txt', dataset_, 'text/plain'), } encoder = MultipartEncoder(fields=fields) body = encoder.to_string() content_type = encoder.content_type for index in range(len(body)): name = ValueTarget() age = ValueTarget() cv = ValueTarget() parser = StreamingFormDataParser( headers={'Content-Type': content_type} ) parser.register('name', name) parser.register('age', age) parser.register('cv.txt', cv) parser.data_received(body[:index]) parser.data_received(body[index:]) assert name.value == b'hello world' assert age.value == b'10' assert cv.value == expected_value def test_parameter_contains_crlf(): target = ValueTarget() encoder = MultipartEncoder(fields={'value': 'hello\r\nworld'}) parser = StreamingFormDataParser( headers={'Content-Type': encoder.content_type} ) parser.register('value', target) parser.data_received(encoder.to_string()) assert target.value == b'hello\r\nworld' def test_parameter_ends_with_crlf(): target = ValueTarget() encoder = MultipartEncoder(fields={'value': 'hello\r\n'}) parser = StreamingFormDataParser( headers={'Content-Type': encoder.content_type} ) parser.register('value', target) parser.data_received(encoder.to_string()) assert target.value == b'hello\r\n' def test_parameter_starts_with_crlf(): target = ValueTarget() encoder = MultipartEncoder(fields={'value': '\r\nworld'}) parser = StreamingFormDataParser( headers={'Content-Type': encoder.content_type} ) parser.register('value', target) parser.data_received(encoder.to_string()) assert target.value == b'\r\nworld' def test_parameter_contains_part_of_delimiter(): data = b'''\ --1234 Content-Disposition: form-data; name="files"; filename="ab.txt" Foo --123 --1234--'''.replace( b'\n', b'\r\n' ) target = ValueTarget() parser = StreamingFormDataParser( headers={'Content-Type': 'multipart/form-data; boundary=1234'} ) parser.register('files', target) parser.data_received(data) assert target.multipart_filename == 'ab.txt' assert target.value == b'Foo\r\n--123' assert target._started assert target._finished def test_multiple_files(): txt_filename = 'file.txt' png_filename = 'image-600x400.png' with open_dataset(txt_filename) as dataset_: expected_txt = dataset_.read() with open_dataset(png_filename) as dataset_: expected_png = dataset_.read() txt_target = ValueTarget() png_target = ValueTarget() with open_dataset(txt_filename) as txt_file, open_dataset( png_filename ) as png_file: encoder = MultipartEncoder( fields={ txt_filename: (txt_filename, txt_file, 'application/plain'), png_filename: (png_filename, png_file, 'image/png'), } ) parser = StreamingFormDataParser( headers={'Content-Type': encoder.content_type} ) parser.register(txt_filename, txt_target) parser.register(png_filename, png_target) parser.data_received(encoder.to_string()) assert txt_target.value == expected_txt assert png_target.value == expected_png def test_large_file(): for filename in [ 'image-500k.png', 'image-2560x1600.png', 'image-600x400.png', 'image-high-res.jpg', ]: with open_dataset(filename) as dataset_: expected_value = dataset_.read() content_type, body = encoded_dataset(filename) target = ValueTarget() parser = StreamingFormDataParser( headers={'Content-Type': content_type} ) parser.register(filename, target) parser.data_received(body) assert target.value == expected_value # The following tests have been added from tornado's # MultipartFormDataTestCase # https://github.com/tornadoweb/tornado/blob/master/tornado/test/httputil_test.py def test_file_upload(): data = b'''\ --1234 Content-Disposition: form-data; name="files"; filename="ab.txt" Foo --1234--'''.replace( b'\n', b'\r\n' ) target = ValueTarget() parser = StreamingFormDataParser( headers={'Content-Type': 'multipart/form-data; boundary=1234'} ) parser.register('files', target) parser.data_received(data) assert target.multipart_filename == 'ab.txt' assert target.value == b'Foo' assert target._started assert target._finished def test_unquoted_names(): data = b'''\ --1234 Content-Disposition: form-data; name=files; filename=ab.txt Foo --1234--'''.replace( b'\n', b'\r\n' ) target = ValueTarget() parser = StreamingFormDataParser( headers={'Content-Type': 'multipart/form-data; boundary=1234'} ) parser.register('files', target) parser.data_received(data) assert target.value == b'Foo' def test_special_filenames(): filenames = [ 'a;b.txt', 'a"b.txt', 'a";b.txt', 'a;"b.txt', 'a";";.txt', 'a\\"b.txt', 'a\\b.txt', ] for filename in filenames: data = ( '''\ --1234 Content-Disposition: form-data; name=files; filename={} Foo --1234--'''.format( filename ) .replace('\n', '\r\n') .encode('utf-8') ) target = ValueTarget() parser = StreamingFormDataParser( headers={'Content-Type': 'multipart/form-data; boundary=1234'} ) parser.register('files', target) parser.data_received(data) assert target.value == b'Foo' def test_boundary_starts_and_ends_with_quotes(): data = b'''\ --1234 Content-Disposition: form-data; name="files"; filename="ab.txt" Foo --1234--'''.replace( b'\n', b'\r\n' ) target = ValueTarget() parser = StreamingFormDataParser( headers={'Content-Type': 'multipart/form-data; boundary="1234"'} ) parser.register('files', target) parser.data_received(data) assert target.multipart_filename == 'ab.txt' assert target.value == b'Foo' def test_missing_headers(): data = '''\ --1234 Foo --1234--'''.replace( '\n', '\r\n' ).encode( 'utf-8' ) target = ValueTarget() parser = StreamingFormDataParser( headers={'Content-Type': 'multipart/form-data; boundary=1234'} ) parser.register('files', target) parser.data_received(data) assert target.value == b'' def test_invalid_content_disposition(): data = b'''\ --1234 Content-Disposition: invalid; name="files"; filename="ab.txt" Foo --1234--'''.replace( b'\n', b'\r\n' ) target = ValueTarget() parser = StreamingFormDataParser( headers={'Content-Type': 'multipart/form-data; boundary=1234'} ) parser.register('files', target) with pytest.raises(ParseFailedException): parser.data_received(data) assert target.value == b'' def test_without_name_parameter(): data = b'''\ --1234 Content-Disposition: form-data; filename="ab.txt" Foo --1234--'''.replace( b'\n', b'\r\n' ) target = ValueTarget() parser = StreamingFormDataParser( headers={'Content-Type': 'multipart/form-data; boundary=1234'} ) parser.register('files', target) parser.data_received(data) assert target.value == b'' def test_data_after_final_boundary(): data = b'''\ --1234 Content-Disposition: form-data; name="files"; filename="ab.txt" Foo --1234-- '''.replace( b'\n', b'\r\n' ) target = ValueTarget() parser = StreamingFormDataParser( headers={'Content-Type': 'multipart/form-data; boundary=1234'} ) parser.register('files', target) parser.data_received(data) assert target.value == b'Foo' def test_register_after_data_received(): encoder = MultipartEncoder(fields={'name': 'hello'}) parser = StreamingFormDataParser( headers={'Content-Type': encoder.content_type} ) parser.data_received(encoder.to_string()) with pytest.raises(ParseFailedException): parser.register('name', ValueTarget()) def test_missing_filename_directive(): data = b'''\ --1234 Content-Disposition: form-data; name="files" Foo --1234-- '''.replace( b'\n', b'\r\n' ) target = ValueTarget() assert not target.multipart_filename parser = StreamingFormDataParser( headers={'Content-Type': 'multipart/form-data; boundary=1234'} ) parser.register('files', target) parser.data_received(data) assert target.value == b'Foo' assert not target.multipart_filename def test_filename_passed_to_target(): filename = 'file.txt' content_type, body = encoded_dataset(filename) target = ValueTarget() assert not target.multipart_filename parser = StreamingFormDataParser(headers={'Content-Type': content_type}) parser.register(filename, target) parser.data_received(body) assert target.multipart_filename == filename def test_target_raises_exception(): filename = 'file.txt' content_type, body = encoded_dataset(filename) class BadTarget(BaseTarget): def data_received(self, data): raise ValueError() target = BadTarget() parser = StreamingFormDataParser(headers={'Content-Type': content_type}) parser.register(filename, target) with pytest.raises(ValueError): parser.data_received(body) def test_target_exceeds_max_size(): data = b'''\ --1234 Content-Disposition: form-data; name="files"; filename="ab.txt" Foo --1234--'''.replace( b'\n', b'\r\n' ) target = ValueTarget(validator=MaxSizeValidator(1)) parser = StreamingFormDataParser( headers={'Content-Type': 'multipart/form-data; boundary=1234'} ) parser.register('files', target) with pytest.raises(ValidationError): parser.data_received(data) assert target._started assert target._finished def test_file_target_exceeds_max_size(): data = b'''\ --1234 Content-Disposition: form-data; name="files"; filename="ab.txt" Foo --1234--'''.replace( b'\n', b'\r\n' ) target = FileTarget('/tmp/file.txt', validator=MaxSizeValidator(1)) parser = StreamingFormDataParser( headers={'Content-Type': 'multipart/form-data; boundary=1234'} ) parser.register('files', target) with pytest.raises(ValidationError): parser.data_received(data) assert target._started assert target._finished def test_content_type_passed_to_target(): filename = 'image-600x400.png' with open_dataset(filename) as dataset_: expected_data = dataset_.read() target = ValueTarget() with open_dataset(filename) as file_: encoder = MultipartEncoder( fields={filename: (filename, file_, 'image/png')} ) parser = StreamingFormDataParser( headers={'Content-Type': encoder.content_type} ) parser.register(filename, target) parser.data_received(encoder.to_string()) assert target.value == expected_data assert target.multipart_content_type == 'image/png' def test_multiple_targets(): filename = 'image-600x400.png' with open_dataset(filename) as dataset_: expected_data = dataset_.read() value_target = ValueTarget() sha256_target = SHA256Target() with open_dataset(filename) as file_: encoder = MultipartEncoder( fields={filename: (filename, file_, 'image/png')} ) parser = StreamingFormDataParser( headers={'Content-Type': encoder.content_type} ) parser.register(filename, value_target) parser.register(filename, sha256_target) assert not value_target.value assert sha256_target.value == hashlib.sha256(b'').hexdigest() parser.data_received(encoder.to_string()) assert value_target.value == expected_data assert sha256_target.value == hashlib.sha256(expected_data).hexdigest() def test_extra_headers(): # example from https://tools.ietf.org/html/rfc2388 data = b'''\ --1234 Content-Disposition: form-data; name="files" Content-Type: text/plain;charset=windows-1250 Content-Transfer-Encoding: quoted-printable Joe owes =80100. --1234--'''.replace( b'\n', b'\r\n' ) target = ValueTarget() parser = StreamingFormDataParser( headers={'Content-Type': 'multipart/form-data; boundary=1234'} ) parser.register('files', target) parser.data_received(data) assert target.value == b'Joe owes =80100.' def test_case_insensitive_content_disposition_header(): content_disposition_header = 'Content-Disposition' for header in ( content_disposition_header, content_disposition_header.lower(), content_disposition_header.upper(), ): data = b'''\ --1234 {header}: form-data; name="files"; filename="ab.txt" Foo --1234--'''.replace( b'\n', b'\r\n' ).replace( b'{header}', header.encode('utf-8') ) target = ValueTarget() parser = StreamingFormDataParser( headers={'Content-Type': 'multipart/form-data; boundary=1234'} ) parser.register('files', target) parser.data_received(data) assert target.value == b'Foo'

20,716

0

942

6a4411330754e041753c15c87e2436746005b50d

1,998

py

Python

oshi/lme.py

netgroup/Dreamer-Management-Scripts

11fe627ff2fb601f0b1c41c42ae4a6f8a9f5cb21

[ "Apache-2.0" ]

null

oshi/lme.py

netgroup/Dreamer-Management-Scripts

11fe627ff2fb601f0b1c41c42ae4a6f8a9f5cb21

[ "Apache-2.0" ]

null

oshi/lme.py

netgroup/Dreamer-Management-Scripts

11fe627ff2fb601f0b1c41c42ae4a6f8a9f5cb21

[ "Apache-2.0" ]

null

#!/usr/bin/python import re import sys import os from subprocess import Popen,PIPE if __name__ == '__main__': push_rules()

25.615385

108

0.683183

#!/usr/bin/python import re import sys import os from subprocess import Popen,PIPE def get_if_index(in_if_name): output = Popen(['ovs-vsctl find Interface name=%s' % in_if_name], shell=True, stdout=PIPE).communicate()[0] if output != None and output != "" : return re.search( r'ofport(.*): (\d*)', output).group(2) else: print "Error Port Not Available" sys.exit(-2) def add_flow(rule): output = Popen([rule], shell=True, stdout=PIPE).communicate()[0] def translate_rule(rule): # ports reg exp out_port = re.compile('output:(.*?),') in_port = re.compile('in_port=(.*?),') out_port_end = "," #test if rule has in_port if 'in_port' in rule and not re.search(in_port, rule): print "Error Wrong In Port" sys.exit(-2) elif 'in_port' in rule and re.search(in_port, rule): in_if_name = in_port.search(rule).group(1) in_if_index = get_if_index(in_if_name) rule = re.sub(in_port, "in_port="+in_if_index+",", rule) #test if rule has output_port if 'output' in rule and not re.search(out_port, rule): #print "output: not followed by comma, retry.." out_port = re.compile('output:(.*?)\"(\Z)') out_port_end = "\"" if not re.search(out_port, rule): print "Error Wrong Output Port" sys.exit(-2) out_if_name = out_port.search(rule).group(1) out_if_index = get_if_index(out_if_name) rule = re.sub(out_port, "output:"+out_if_index+out_port_end, rule) elif 'output' in rule and re.search(out_port, rule): out_if_name = out_port.search(rule).group(1) out_if_index = get_if_index(out_if_name) rule = re.sub(out_port, "output:"+out_if_index+out_port_end, rule) return rule def push_rules(): path = "lmerules.sh" if os.path.exists(path) == False: print "Error Rules File Not Exists" sys.exit(-2) filesh = open(path, 'r') lines = filesh.readlines() for line in lines: if "start" not in line and "end" not in line: rule = line[:-1] rule = translate_rule(rule) add_flow(rule) if __name__ == '__main__': push_rules()

1,764

0

92

f2d19c9aaacf2f6a53e0a55c017ae4dcf96f3238

3,034

py

Python

taxon/backends/memory.py

jdp/taxon

822ac9c92d3aa57484e328c99a5af8a8002991d6

[ "MIT" ]

7

2015-01-10T07:25:24.000Z

2018-05-04T17:47:42.000Z

taxon/backends/memory.py

jdp/taxon

822ac9c92d3aa57484e328c99a5af8a8002991d6

[ "MIT" ]

null

taxon/backends/memory.py

jdp/taxon

822ac9c92d3aa57484e328c99a5af8a8002991d6

[ "MIT" ]

null

import operator try: from collections import Counter except ImportError: from ._counter import Counter from .backend import Backend from ..query import Query

31.604167

86

0.542518

import operator try: from collections import Counter except ImportError: from ._counter import Counter from .backend import Backend from ..query import Query class MemoryBackend(Backend): def __init__(self): self.empty() def tag_items(self, tag, *items): if tag not in self.tags: self.tags[tag] = 0 self.tagged[tag] = set() new_items = set(items) - self.tagged[tag] if len(new_items) == 0: return [] self.tags[tag] += len(new_items) self.tagged[tag].update(set(new_items)) self.items += Counter(new_items) return list(new_items) def untag_items(self, tag, *items): old_items = set(items) & self.tagged[tag] if len(old_items) == 0: return [] self.tags[tag] -= len(old_items) self.tagged[tag] -= set(old_items) self.items -= Counter(old_items) return list(old_items) def remove_items(self, *items): removed = [] for item in set(items): if item not in self.items: continue for tag in self.all_tags(): if item not in self.tagged[tag]: continue self.tagged[tag] -= set([item]) self.tags[tag] -= 1 self.items[item] -= 1 removed.append(item) return removed def all_tags(self): return [tag[0] for tag in self.tags.items() if tag[1] > 0] def all_items(self): return [item[0] for item in self.items.items() if item[1] > 0] def query(self, q): if isinstance(q, Query): fn, args = q.freeze() return self._raw_query(fn, args) elif isinstance(q, tuple): fn, args = q return self._raw_query(fn, args) else: raise ValueError def _raw_query(self, fn, args): if fn == 'tag': if len(args) == 1: return None, self.tagged.get(args[0], []) else: groups = [self.tagged.get(tag, []) for tag in args] return None, reduce(operator.add, groups) elif fn == 'and': results = [set(items) for _, items in [self._raw_query(*a) for a in args]] return None, reduce(operator.__and__, results) elif fn == 'or': results = [set(items) for _, items in [self._raw_query(*a) for a in args]] return None, reduce(operator.__or__, results) elif fn == 'not': results = [set(items) for _, items in [self._raw_query(*a) for a in args]] results.insert(0, set(self.all_items())) return None, reduce(operator.sub, results) else: raise ValueError def empty(self): self.tagged = dict() self.items = Counter() self.tags = Counter() def __str__(self): return unicode(self).encode('utf-8') def __unicode__(self): return u"%s()" % (self.__class__.__name__)

2,539

8

319

0ac474e1e7533b08c8837044f8fe017e777d82e6

364

py

Python

experiment_data_and_analysis/AccuracyAnalysis/mayavi_example.py

JakeFountain/Spooky

e0a2d4ea878467d6bc7f385220f29c85fd65a190

[ "Apache-2.0" ]

null

experiment_data_and_analysis/AccuracyAnalysis/mayavi_example.py

JakeFountain/Spooky

e0a2d4ea878467d6bc7f385220f29c85fd65a190

[ "Apache-2.0" ]

null

experiment_data_and_analysis/AccuracyAnalysis/mayavi_example.py

JakeFountain/Spooky

e0a2d4ea878467d6bc7f385220f29c85fd65a190

[ "Apache-2.0" ]

2

2019-03-12T02:06:32.000Z

2019-05-12T15:29:41.000Z

from mayavi import mlab n_mer, n_long = 6, 11 dphi = np.pi / 1000.0 phi = np.arange(0.0, 2 * pi + 0.5 * dphi, dphi) mu = phi * n_mer x = np.cos(mu) * (1 + np.cos(n_long * mu / n_mer) * 0.5) y = np.sin(mu) * (1 + np.cos(n_long * mu / n_mer) * 0.5) z = np.sin(n_long * mu / n_mer) * 0.5 t = np.sin(mu) mlab.plot3d(x, y, z, t, tube_radius=0.025, colormap='Spectral')

33.090909

63

0.590659

from mayavi import mlab n_mer, n_long = 6, 11 dphi = np.pi / 1000.0 phi = np.arange(0.0, 2 * pi + 0.5 * dphi, dphi) mu = phi * n_mer x = np.cos(mu) * (1 + np.cos(n_long * mu / n_mer) * 0.5) y = np.sin(mu) * (1 + np.cos(n_long * mu / n_mer) * 0.5) z = np.sin(n_long * mu / n_mer) * 0.5 t = np.sin(mu) mlab.plot3d(x, y, z, t, tube_radius=0.025, colormap='Spectral')

0

1274f1dac29488da85dd79cccab14802e253602b

689

py

Python

src/absolute_uri.py

nigma/djutil

85b7c21acbcd4d3e8cef4246cdb5049cbede8748

[ "MIT" ]

1

2015-04-16T14:43:25.000Z

src/absolute_uri.py

nigma/djutil

85b7c21acbcd4d3e8cef4246cdb5049cbede8748

[ "MIT" ]

null

src/absolute_uri.py

nigma/djutil

85b7c21acbcd4d3e8cef4246cdb5049cbede8748

[ "MIT" ]

null

#-*- coding: utf-8 -*- from __future__ import unicode_literals try: from urlparse import urljoin except ImportError: from urllib.parse import urljoin from django.contrib.sites.models import get_current_site

27.56

66

0.718433

#-*- coding: utf-8 -*- from __future__ import unicode_literals try: from urlparse import urljoin except ImportError: from urllib.parse import urljoin from django.contrib.sites.models import get_current_site def add_domain(path, domain, secure=False): if path.startswith("http://") or path.startswith("https://"): return path domain = ("https://" if secure else "http://") + domain return urljoin(domain, path) def build_site_url(path, request=None): current_site = get_current_site(request=request) domain = current_site.domain secure = request.is_secure() if request is not None else False return add_domain(path, domain, secure=secure)

423

0

46

f8369411bcf3950b58aef28e018e3ef6c0a4a4f1

367

py

Python

revolver/group.py

michaelcontento/revolver

bbae82df0804ff2708a82fd0016b776664ee2deb

[ "Apache-2.0" ]

1

2015-05-16T17:55:26.000Z

revolver/group.py

michaelcontento/revolver

bbae82df0804ff2708a82fd0016b776664ee2deb

[ "Apache-2.0" ]

null

revolver/group.py

michaelcontento/revolver

bbae82df0804ff2708a82fd0016b776664ee2deb

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- from __future__ import absolute_import, division, with_statement from cuisine import group_check as get from cuisine import group_create as create from cuisine import group_ensure as ensure from cuisine import group_user_add as user_add from cuisine import group_user_check as user_check from cuisine import group_user_ensure as user_ensure

33.363636

64

0.836512

# -*- coding: utf-8 -*- from __future__ import absolute_import, division, with_statement from cuisine import group_check as get from cuisine import group_create as create from cuisine import group_ensure as ensure from cuisine import group_user_add as user_add from cuisine import group_user_check as user_check from cuisine import group_user_ensure as user_ensure

0

29951beb8b1b68a35ede1131be3ff6ad070a45f5

3,777

py

Python

evaluation/consuming_service_generator.py

Peniac/NSE-CSC

7bb313b59f3b92c1349b03bc11e8fb17512e1518

[ "Apache-2.0" ]

null

evaluation/consuming_service_generator.py

Peniac/NSE-CSC

7bb313b59f3b92c1349b03bc11e8fb17512e1518

[ "Apache-2.0" ]

null

evaluation/consuming_service_generator.py

Peniac/NSE-CSC

7bb313b59f3b92c1349b03bc11e8fb17512e1518

[ "Apache-2.0" ]

null

''' Definitions 1. consuming service: is a network service that requires the consumption of additional VNF(s) that pertain to a different service, i.e., providing services. 2. CSC engaged VNFs (w.r.t. the consuming service): are exactly two distinct VNFs. One VNF forwards the traffic to the receiver CSC engaged VNF of the providing service, whereas the other VNF receives the traffic, which is now processed by (a subset of) the providing service. General assumptions 1. Every network service is modelled as a directed graph. Nodes represent VNFs, and edges represent virtual links. VNFs require CPU, and edges require bandwidth. 2. We consider sequential services, e.g., 'A->B->C' is OK, whereas 'A->B,A->C,B->C' is NOT OK Environment 1. CPU in [0.72, 1.44, 2.16, 2.88, 3.6GHz] (randomly) 2. bandwidth in [10Mbps, 200Mbps] (randomly) 3. length (i.e., number of VNFs per service) in [3,8] (randomly) 4. The two CSC engaged VNFs are randomly sampled (without replacement) 5. Duration in [3,10] time intervals ''' # dependencies import networkx as nx import random def initializeConsumingService(providing_services, service_index, time): ''' Function that generates a consuming service, in the form of a graph. The consuming service pairs with a providing service, and the corresponding consuming service graph adds VNFs and edges for the CSC, also. ''' # the providing service that the consuming service will pair with providing_service = random.choice(providing_services) # list of possible VNF CPU requirements CPUs = [0.72, 1.44, 2.16, 2.88, 3.6] CPUs = [round(cpu/7.2,2) for cpu in CPUs] # the length of the network service service_length = random.randint(3,8) # list of CSC VNF indices first_CSC_engaged_VNF = random.choice(range(service_length-1)) CSC_engaged_VNFs = [first_CSC_engaged_VNF, first_CSC_engaged_VNF+1] # create empty directional graph G = nx.DiGraph(id = service_index, type = 'consuming', provider_pair = providing_service.graph['id'], expires_in = time + random.randint(3,10)) # populate the consuming service graph with VNF nodes for j in range(service_length): if j not in CSC_engaged_VNFs: VNF_type = 'VNF' else: VNF_type = 'C_CSC_VNF' G.add_node("C{0}VNF{1}".format(service_index,j), type = VNF_type, cpu = random.choice(CPUs), serid = service_index, sertype = 'consumer') nodes = list(G.nodes()) # add edges between VNFs sequentially for j in range(service_length-1): G.add_edge(nodes[j],nodes[j+1], source = nodes[j], dest= nodes[j+1], bandwidth = random.randrange(10,100), sertype = 'consuming') # the corresponding CSC VNF indices of the providing service CSC_engaged_VNFs_provider = [n for n in providing_service.nodes if providing_service.nodes[n]['type'] == 'CSC_VNF'] # add the CSC nodes of the providing service to the consuming service for j in CSC_engaged_VNFs_provider: G.add_node(j, type = 'P_CSC_VNF', sertype = 'provider') # add the 2 CSC-engaged edges # from consuming to providing G.add_edge(nodes[CSC_engaged_VNFs[0]], CSC_engaged_VNFs_provider[0], source = nodes[CSC_engaged_VNFs[0]], dest = CSC_engaged_VNFs_provider[0], bandwidth = random.randrange(10,100), sertype = 'providing') # from providing to consuming if len(CSC_engaged_VNFs_provider) == 2: G.add_edge(CSC_engaged_VNFs_provider[1], nodes[CSC_engaged_VNFs[1]], source = CSC_engaged_VNFs_provider[1], dest = nodes[CSC_engaged_VNFs[1]], bandwidth = random.randrange(10,100), sertype = 'providing') else: G.add_edge(CSC_engaged_VNFs_provider[0], nodes[CSC_engaged_VNFs[1]], source = CSC_engaged_VNFs_provider[0], dest = nodes[CSC_engaged_VNFs[1]], bandwidth = random.randrange(10,100), sertype = 'providing') return G

53.197183

278

0.735769

''' Definitions 1. consuming service: is a network service that requires the consumption of additional VNF(s) that pertain to a different service, i.e., providing services. 2. CSC engaged VNFs (w.r.t. the consuming service): are exactly two distinct VNFs. One VNF forwards the traffic to the receiver CSC engaged VNF of the providing service, whereas the other VNF receives the traffic, which is now processed by (a subset of) the providing service. General assumptions 1. Every network service is modelled as a directed graph. Nodes represent VNFs, and edges represent virtual links. VNFs require CPU, and edges require bandwidth. 2. We consider sequential services, e.g., 'A->B->C' is OK, whereas 'A->B,A->C,B->C' is NOT OK Environment 1. CPU in [0.72, 1.44, 2.16, 2.88, 3.6GHz] (randomly) 2. bandwidth in [10Mbps, 200Mbps] (randomly) 3. length (i.e., number of VNFs per service) in [3,8] (randomly) 4. The two CSC engaged VNFs are randomly sampled (without replacement) 5. Duration in [3,10] time intervals ''' # dependencies import networkx as nx import random def initializeConsumingService(providing_services, service_index, time): ''' Function that generates a consuming service, in the form of a graph. The consuming service pairs with a providing service, and the corresponding consuming service graph adds VNFs and edges for the CSC, also. ''' # the providing service that the consuming service will pair with providing_service = random.choice(providing_services) # list of possible VNF CPU requirements CPUs = [0.72, 1.44, 2.16, 2.88, 3.6] CPUs = [round(cpu/7.2,2) for cpu in CPUs] # the length of the network service service_length = random.randint(3,8) # list of CSC VNF indices first_CSC_engaged_VNF = random.choice(range(service_length-1)) CSC_engaged_VNFs = [first_CSC_engaged_VNF, first_CSC_engaged_VNF+1] # create empty directional graph G = nx.DiGraph(id = service_index, type = 'consuming', provider_pair = providing_service.graph['id'], expires_in = time + random.randint(3,10)) # populate the consuming service graph with VNF nodes for j in range(service_length): if j not in CSC_engaged_VNFs: VNF_type = 'VNF' else: VNF_type = 'C_CSC_VNF' G.add_node("C{0}VNF{1}".format(service_index,j), type = VNF_type, cpu = random.choice(CPUs), serid = service_index, sertype = 'consumer') nodes = list(G.nodes()) # add edges between VNFs sequentially for j in range(service_length-1): G.add_edge(nodes[j],nodes[j+1], source = nodes[j], dest= nodes[j+1], bandwidth = random.randrange(10,100), sertype = 'consuming') # the corresponding CSC VNF indices of the providing service CSC_engaged_VNFs_provider = [n for n in providing_service.nodes if providing_service.nodes[n]['type'] == 'CSC_VNF'] # add the CSC nodes of the providing service to the consuming service for j in CSC_engaged_VNFs_provider: G.add_node(j, type = 'P_CSC_VNF', sertype = 'provider') # add the 2 CSC-engaged edges # from consuming to providing G.add_edge(nodes[CSC_engaged_VNFs[0]], CSC_engaged_VNFs_provider[0], source = nodes[CSC_engaged_VNFs[0]], dest = CSC_engaged_VNFs_provider[0], bandwidth = random.randrange(10,100), sertype = 'providing') # from providing to consuming if len(CSC_engaged_VNFs_provider) == 2: G.add_edge(CSC_engaged_VNFs_provider[1], nodes[CSC_engaged_VNFs[1]], source = CSC_engaged_VNFs_provider[1], dest = nodes[CSC_engaged_VNFs[1]], bandwidth = random.randrange(10,100), sertype = 'providing') else: G.add_edge(CSC_engaged_VNFs_provider[0], nodes[CSC_engaged_VNFs[1]], source = CSC_engaged_VNFs_provider[0], dest = nodes[CSC_engaged_VNFs[1]], bandwidth = random.randrange(10,100), sertype = 'providing') return G

0

553ba126daefa4225f49cd1ed880ebdf7092f4f9

428

py

Python

quickstart/models.py

gladsonvm/drf-nested

e53cfa116c76ce573207401035edfac6a46cd1be

[ "MIT" ]

null

quickstart/models.py

gladsonvm/drf-nested

e53cfa116c76ce573207401035edfac6a46cd1be

[ "MIT" ]

null

quickstart/models.py

gladsonvm/drf-nested

e53cfa116c76ce573207401035edfac6a46cd1be

[ "MIT" ]

null

from django.db import models from django.contrib.auth.models import User # Create your models here.

25.176471

70

0.752336

from django.db import models from django.contrib.auth.models import User # Create your models here. class UserProfile(models.Model): add_info = models.CharField(null=True, blank=True, max_length=100) user = models.OneToOneField(User) class nestedmodel(models.Model): info = models.CharField(null=True, blank=True, max_length=100) user = models.ForeignKey(User) profile = models.ForeignKey(UserProfile)

0

278

46

f2c87a50078598a9e64acc7a01d1bb775a171f62

3,915

py

Python

check_backup_age.py

martialblog/check_backup_age

1203a458be6f27ed30fcdb49b1f8c8a80a695782

[ "MIT" ]

null

check_backup_age.py

martialblog/check_backup_age

1203a458be6f27ed30fcdb49b1f8c8a80a695782

[ "MIT" ]

null

check_backup_age.py

martialblog/check_backup_age

1203a458be6f27ed30fcdb49b1f8c8a80a695782

[ "MIT" ]

null

#!/usr/bin/env python3 import sys import os import argparse import re import datetime import subprocess class EXIT(): """ Exit codes from: https://docs.icinga.com/latest/en/pluginapi.html """ OK = 0 WARN = 1 CRIT = 2 UNKOWN = 3 def commandline(args): """ Settings for the commandline arguments. Returns the parsed arguments. """ parser = argparse.ArgumentParser(description='Checks the timestamps for files in a directory.') parser.add_argument("-p", "--path", required=True, help="Path to offline backup list file or directory") parser.add_argument("-w", "--warning", help="Threshold for warnings in days. Default: 2 Days") parser.add_argument("-c", "--critical", help="Threshold for criticals in days. Default: 5 Days") parser.add_argument("-f", "--format", help="Format of the date in the file. Default: Y-m-d") parser.add_argument("-r", "--regex", help="Regular Expression to extract date from file. Default: [0-9]{4}-[0-9]{2}-[0-9]{2}") parser.add_argument("-v", "--verbose", help="Increase output verbosity", action="store_true") parser.set_defaults(verbose=False, critical=5, warning=2) return parser.parse_args(args) def readdata(path): """ Checks if the path exists, then reads the file or directory and returns the data. """ if not os.path.exists(path): print('No such path {0}'.format(path)) sys.exit(EXIT.WARN) if os.path.isfile(path): with open(path) as f: data = f.read() elif os.path.isdir(path): data = subprocess.check_output(['ls', '--full-time', path]) data = data.decode("utf-8").rstrip('\n') return data def extract_dates(data, date_format='%Y-%m-%d', date_regex='[0-9]{4}-[0-9]{2}-[0-9]{2}'): """ Extracts dates from a string using regular expressions, then converts the dates to datetime objects and returns a list. """ dates = [] regex = re.compile(date_regex) date_strings = regex.findall(data) for date_string in date_strings: dates.append(datetime.datetime.strptime(date_string, date_format).date()) return sorted(dates) def check_delta(delta, warn, crit): """ Checks the category of the calculated delta (OK, WARN, FAIL) and exits the program accordingly. """ last_backup = 'Last backup was {0} days ago'.format(delta.days) isokay = delta.days < warn iswarn = delta.days >= warn and delta.days < crit iscrit = delta.days >= crit if isokay: print('OK - ' + last_backup) sys.exit(EXIT.OK) elif iswarn: print('WARN - ' + last_backup) sys.exit(EXIT.WARN) elif iscrit: print('CRIT - ' + last_backup) sys.exit(EXIT.CRIT) else: print('UNKNOWN - Not really sure what is happening') sys.exit(EXIT.UNKOWN) def calculate_delta(dates): """ Calculates how far the gives dates deviate from today's date. Returns a datetime.timedelta object. """ today = datetime.datetime.today().date() delta = 0 for i in range(0, len(dates)): delta = -(dates[i] - today) # If there are to dates in the file for example if not isinstance(delta, datetime.timedelta): print('UNKNOWN - Probably error while reading the file') sys.exit(EXIT.UNKOWN) return delta if __name__ == "__main__": args = commandline(sys.argv[1:]) main(args)

25.422078

123

0.603831

#!/usr/bin/env python3 import sys import os import argparse import re import datetime import subprocess class EXIT(): """ Exit codes from: https://docs.icinga.com/latest/en/pluginapi.html """ OK = 0 WARN = 1 CRIT = 2 UNKOWN = 3 def commandline(args): """ Settings for the commandline arguments. Returns the parsed arguments. """ parser = argparse.ArgumentParser(description='Checks the timestamps for files in a directory.') parser.add_argument("-p", "--path", required=True, help="Path to offline backup list file or directory") parser.add_argument("-w", "--warning", help="Threshold for warnings in days. Default: 2 Days") parser.add_argument("-c", "--critical", help="Threshold for criticals in days. Default: 5 Days") parser.add_argument("-f", "--format", help="Format of the date in the file. Default: Y-m-d") parser.add_argument("-r", "--regex", help="Regular Expression to extract date from file. Default: [0-9]{4}-[0-9]{2}-[0-9]{2}") parser.add_argument("-v", "--verbose", help="Increase output verbosity", action="store_true") parser.set_defaults(verbose=False, critical=5, warning=2) return parser.parse_args(args) def readdata(path): """ Checks if the path exists, then reads the file or directory and returns the data. """ if not os.path.exists(path): print('No such path {0}'.format(path)) sys.exit(EXIT.WARN) if os.path.isfile(path): with open(path) as f: data = f.read() elif os.path.isdir(path): data = subprocess.check_output(['ls', '--full-time', path]) data = data.decode("utf-8").rstrip('\n') return data def extract_dates(data, date_format='%Y-%m-%d', date_regex='[0-9]{4}-[0-9]{2}-[0-9]{2}'): """ Extracts dates from a string using regular expressions, then converts the dates to datetime objects and returns a list. """ dates = [] regex = re.compile(date_regex) date_strings = regex.findall(data) for date_string in date_strings: dates.append(datetime.datetime.strptime(date_string, date_format).date()) return sorted(dates) def check_delta(delta, warn, crit): """ Checks the category of the calculated delta (OK, WARN, FAIL) and exits the program accordingly. """ last_backup = 'Last backup was {0} days ago'.format(delta.days) isokay = delta.days < warn iswarn = delta.days >= warn and delta.days < crit iscrit = delta.days >= crit if isokay: print('OK - ' + last_backup) sys.exit(EXIT.OK) elif iswarn: print('WARN - ' + last_backup) sys.exit(EXIT.WARN) elif iscrit: print('CRIT - ' + last_backup) sys.exit(EXIT.CRIT) else: print('UNKNOWN - Not really sure what is happening') sys.exit(EXIT.UNKOWN) def calculate_delta(dates): """ Calculates how far the gives dates deviate from today's date. Returns a datetime.timedelta object. """ today = datetime.datetime.today().date() delta = 0 for i in range(0, len(dates)): delta = -(dates[i] - today) # If there are to dates in the file for example if not isinstance(delta, datetime.timedelta): print('UNKNOWN - Probably error while reading the file') sys.exit(EXIT.UNKOWN) return delta def main(args): path = str(args.path) crit = int(args.critical) warn = int(args.warning) rdata = readdata(path) dates = extract_dates(rdata) delta = calculate_delta(dates) check_delta(delta=delta, warn=warn, crit=crit) if __name__ == "__main__": args = commandline(sys.argv[1:]) main(args)

228

0

23

5e8f943f19843c86705229924cd80deefbcb73fd

2,170

py

Python

ooobuild/lo/xml/sax/x_fast_token_handler.py

Amourspirit/ooo_uno_tmpl

64e0c86fd68f24794acc22d63d8d32ae05dd12b8

[ "Apache-2.0" ]

null

ooobuild/lo/xml/sax/x_fast_token_handler.py

Amourspirit/ooo_uno_tmpl

64e0c86fd68f24794acc22d63d8d32ae05dd12b8

[ "Apache-2.0" ]

null

ooobuild/lo/xml/sax/x_fast_token_handler.py

Amourspirit/ooo_uno_tmpl

64e0c86fd68f24794acc22d63d8d32ae05dd12b8

[ "Apache-2.0" ]

null

# coding: utf-8 # # Copyright 2022 :Barry-Thomas-Paul: Moss # # 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. # # Interface Class # this is a auto generated file generated by Cheetah # Libre Office Version: 7.3 # Namespace: com.sun.star.xml.sax import typing from abc import abstractmethod from ...uno.x_interface import XInterface as XInterface_8f010a43 class XFastTokenHandler(XInterface_8f010a43): """ interface to translate XML strings to integer tokens. An instance of this interface can be registered at a XFastParser. It should be able to translate all XML names (element local names, attribute local names and constant attribute values) to integer tokens. A token value must be greater or equal to zero and less than FastToken.NAMESPACE. If a string identifier is not known to this instance, FastToken.DONTKNOW is returned. See Also: `API XFastTokenHandler <https://api.libreoffice.org/docs/idl/ref/interfacecom_1_1sun_1_1star_1_1xml_1_1sax_1_1XFastTokenHandler.html>`_ """ __ooo_ns__: str = 'com.sun.star.xml.sax' __ooo_full_ns__: str = 'com.sun.star.xml.sax.XFastTokenHandler' __ooo_type_name__: str = 'interface' __pyunointerface__: str = 'com.sun.star.xml.sax.XFastTokenHandler' @abstractmethod def getTokenFromUTF8(self, Identifier: 'typing.Tuple[int, ...]') -> int: """ returns an integer token for the given string """ @abstractmethod def getUTF8Identifier(self, Token: int) -> 'typing.Tuple[int, ...]': """ returns an identifier for the given integer token as a byte sequence encoded in UTF-8. """ __all__ = ['XFastTokenHandler']

40.185185

208

0.731336

# coding: utf-8 # # Copyright 2022 :Barry-Thomas-Paul: Moss # # 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. # # Interface Class # this is a auto generated file generated by Cheetah # Libre Office Version: 7.3 # Namespace: com.sun.star.xml.sax import typing from abc import abstractmethod from ...uno.x_interface import XInterface as XInterface_8f010a43 class XFastTokenHandler(XInterface_8f010a43): """ interface to translate XML strings to integer tokens. An instance of this interface can be registered at a XFastParser. It should be able to translate all XML names (element local names, attribute local names and constant attribute values) to integer tokens. A token value must be greater or equal to zero and less than FastToken.NAMESPACE. If a string identifier is not known to this instance, FastToken.DONTKNOW is returned. See Also: `API XFastTokenHandler <https://api.libreoffice.org/docs/idl/ref/interfacecom_1_1sun_1_1star_1_1xml_1_1sax_1_1XFastTokenHandler.html>`_ """ __ooo_ns__: str = 'com.sun.star.xml.sax' __ooo_full_ns__: str = 'com.sun.star.xml.sax.XFastTokenHandler' __ooo_type_name__: str = 'interface' __pyunointerface__: str = 'com.sun.star.xml.sax.XFastTokenHandler' @abstractmethod def getTokenFromUTF8(self, Identifier: 'typing.Tuple[int, ...]') -> int: """ returns an integer token for the given string """ @abstractmethod def getUTF8Identifier(self, Token: int) -> 'typing.Tuple[int, ...]': """ returns an identifier for the given integer token as a byte sequence encoded in UTF-8. """ __all__ = ['XFastTokenHandler']

0

220fea1a916c09c767cc2366cd7504e5daae93e3

5,216

py

Python

cap2/pipeline/short_read/amrs.py

nanusefue/CAP2-1

670b343ac7629fe0e64e86263ae420b01952f427

[ "MIT" ]

9

2020-07-10T15:45:12.000Z

2022-01-19T10:44:13.000Z

cap2/pipeline/short_read/amrs.py

nanusefue/CAP2-1

670b343ac7629fe0e64e86263ae420b01952f427

[ "MIT" ]

14

2020-06-15T16:04:54.000Z

2022-03-12T01:05:47.000Z

cap2/pipeline/short_read/amrs.py

nanusefue/CAP2-1

670b343ac7629fe0e64e86263ae420b01952f427

[ "MIT" ]

5

2021-01-05T01:26:48.000Z

2022-01-23T11:20:49.000Z

import luigi import subprocess from os.path import join, dirname, basename from ..utils.cap_task import CapTask from ..config import PipelineConfig from ..utils.conda import CondaPackage from ..preprocessing.clean_reads import CleanReads from ..databases.amr_db import GrootDB, MegaResDB, CardDB

31.047619

96

0.568252

import luigi import subprocess from os.path import join, dirname, basename from ..utils.cap_task import CapTask from ..config import PipelineConfig from ..utils.conda import CondaPackage from ..preprocessing.clean_reads import CleanReads from ..databases.amr_db import GrootDB, MegaResDB, CardDB class GrootAMR(CapTask): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.pkg = CondaPackage( package="groot==1.1.2", executable="groot", channel="bioconda", config_filename=self.config_filename, ) self.config = PipelineConfig(self.config_filename) self.out_dir = self.config.out_dir self.db = GrootDB(config_filename=self.config_filename) self.reads = CleanReads( sample_name=self.sample_name, pe1=self.pe1, pe2=self.pe2, config_filename=self.config_filename ) @classmethod def _module_name(cls): return 'groot' def requires(self): return self.pkg, self.db, self.reads @classmethod def version(cls): return 'v1.0.0' @classmethod def dependencies(cls): return ['groot==1.1.2', GrootDB, CleanReads] def output(self): return { 'alignment': self.get_target('alignment', 'bam'), } def _run(self): align_cmd = f'{self.pkg.bin} align ' align_cmd += f'-i {self.db.groot_index} -f {self.reads.reads[0]},{self.reads.reads[1]} ' align_cmd += f'-p {self.cores} > {self.output()["alignment"].path}' report_cmd = f'{self.pkg.bin} report -i {self.output()["alignment"].path} ' report_cmd += '--lowCov --plotCov' rm_cmd = f'rm {self.output()["alignment"].path}' subprocess.check_call(align_cmd + ' && ' + report_cmd + ' | ' + rm_cmd, shell=True) class MegaRes(CapTask): thresh = luigi.FloatParameter(default=80.0) def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.pkg = CondaPackage( package="resistome_analyzer", executable="resistome_analyzer", channel="bioconda", config_filename=self.config_filename, ) self.aligner = CondaPackage( package="bowtie2", executable="bowtie2", channel="bioconda", config_filename=self.config_filename, ) self.config = PipelineConfig(self.config_filename) self.out_dir = self.config.out_dir self.db = MegaResDB(config_filename=self.config_filename) self.reads = CleanReads( sample_name=self.sample_name, pe1=self.pe1, pe2=self.pe2, config_filename=self.config_filename ) def module_name(self): return 'megares' def requires(self): return self.pkg, self.aligner, self.db, self.reads def output(self): mytarget = lambda el: luigi.LocalTarget(join(self.out_dir, el)) return { 'sam': self.get_target('sam', 'sam'), 'gene': self.get_target('gene', 'tsv'), 'group': self.get_target('group', 'tsv'), 'classus': self.get_target('classus', 'tsv'), 'mech': self.get_target('mech', 'tsv'), } def _run(self): sam_file = self.output()["sam"].path cmd1 = ( f'{self.aligner.bin} ' f'-p {threads} ' '--very-sensitive ' f' -x {self.db.bowtie2_index} ' f' -1 {self.reads.reads[0]} ' f' -2 {self.reads.reads[1]} ' '| samtools view -F 4 ' f'> {sam_file} ' ) cmd2 = ( f'{self.pkg.bin} ' f'-ref_fp {self.db.fasta} ' f'-sam_fp {sam_file} ' f'-annot_fp {self.db.annotations} ' f'-gene_fp {self.output()["gene"].path} ' f'-group_fp {self.output()["group"].path} ' f'-class_fp {self.output()["classus"].path} ' f'-mech_fp {self.output()["mech"].path} ' f'-t {self.thresh}' ) subprocess.call(cmd1 + ' && ' + cmd2, shell=True) class CARD(CapTask): sample_name = luigi.Parameter() pe1 = luigi.Parameter() pe2 = luigi.Parameter() config_filename = luigi.Parameter() cores = luigi.IntParameter(default=1) def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.pkg = CondaPackage( package="megares", executable="megares", channel="bioconda", config_filename=self.config_filename, ) self.config = PipelineConfig(self.config_filename) self.out_dir = self.config.out_dir self.db = CardDB(config_filename=self.config_filename) self.reads = CleanReads( sample_name=self.sample_name, pe1=self.pe1, pe2=self.pe2, config_filename=self.config_filename ) def requires(self): return self.pkg, self.db, self.reads def output(self): pass def run(self): cmd = ( ) subprocess.call(cmd, shell=True)

4,137

709

69

a94350db177c24901064b6f7d2929cd25fa5ae9d

3,981

py

Python

apps/users/managers.py

dlooto/driver-vision

676256891971df1d5eee990be54fb31f485d0ae3

[ "MIT" ]

2

2020-06-16T01:52:47.000Z

2021-03-24T08:34:23.000Z

apps/users/managers.py

dlooto/driver-vision

676256891971df1d5eee990be54fb31f485d0ae3

[ "MIT" ]

2

2020-02-12T03:09:54.000Z

2020-06-05T22:47:17.000Z

apps/users/managers.py

dlooto/driver-vision

676256891971df1d5eee990be54fb31f485d0ae3

[ "MIT" ]

null

#coding=utf-8 # # Created on Mar 21, 2014, by Junn # # from django.contrib.auth.models import BaseUserManager from django.utils import timezone from utils import eggs, logs, http from django.core.cache import cache VALID_ATTRS = ('nickname', 'email', 'phone', 'gender', 'avatar')

32.900826

107

0.542828

#coding=utf-8 # # Created on Mar 21, 2014, by Junn # # from django.contrib.auth.models import BaseUserManager from django.utils import timezone from utils import eggs, logs, http from django.core.cache import cache VALID_ATTRS = ('nickname', 'email', 'phone', 'gender', 'avatar') def mk_key(id): return 'u%s' % id class CustomUserManager(BaseUserManager): def _create_user(self, username, password=None, is_active=True, **extra_fields): """ Creates and saves a User with the given username, email and password. """ now = timezone.now() if not username: raise ValueError('The given username must be set') user = self.model(username=username, is_staff=False, is_active=is_active, is_superuser=False, last_login=now, date_joined=now, **extra_fields) user.set_password(password) user.save(using=self._db) return user def get_by_phone(self, phone): try: return self.get(username=phone) except self.model.DoesNotExist: return None def update_user(self, user, req): data = req.DATA for attr in VALID_ATTRS: #双重循环, 以后需要改进算法 if attr in data: setattr(user, attr, data.get(attr)) user.save(using=self._db) return user def create_superuser(self, username, password, **extra_fields): u = self._create_user(username, password, **extra_fields) u.is_staff = True u.is_superuser = True u.save(using=self._db) return u def create_open_user(self, source_site, openid, access_token, expires_in, open_name='', avatar_url=''): '''创建第3方登录账号 @param source_site: 第3方平台名称 @param openid: 用户在第3方平台的账号id @param access_token: 第3方平台的访问token @param expires_in: access_token的超时时间 @param open_name: 用户在第3方平台的昵称 @param avatar_url: 用户在第3方平台的头像url ''' from auth.models import OpenAccount try: ## 第3方平台注册用户不允许直接登录, 除非重置了密码(重置密码需要先绑定手机号) user = self._create_user( username=eggs.gen_uuid1(), nickname=open_name, acct_type='O' ) try: user.save_avatar(http.request_file(avatar_url)) #请求远端获取图片并保存 except Exception, e: logs.err(__name__, eggs.lineno(), e) pass user.update_pdu(1) #设置头像标识位 user.update_pdu(2) #设置昵称标识位 user.save() user.cache() open_acct = OpenAccount(user=user, source_site=source_site, openid=openid, access_token=access_token, expires_in=int(expires_in) ) open_acct.save() return True, user except Exception, e: logs.err(__name__, eggs.lineno(), e) return False, u'账号绑定异常' ############################################################ cache methods def cache_all(self): #TODO: abstract these cache_xxx method into base class ... users = self.all() for u in users: u.cache() logs.info('====================================> All user entities cached.') def get_cached(self, uid): #TODO: using cache later... '''return cached user object''' user = cache.get(mk_key(uid)) if not user: try: user = self.get(id=int(uid)) user.cache() except self.model.DoesNotExist: logs.err(__name__, eggs.lineno(), 'User not found: %s' % uid) return None except Exception, e: logs.err(__name__, eggs.lineno(), 'get_cached user error: %s' % e) return None return user

836

3,055

50

9c6838ed116deb3a2770d9b6ad5a6c062dbeb8a7

430

py

Python

Python/WebCam GUI Filters/Gray.py

abhijeet007rocks8/Useful-Scripts

2c8bd8c1cca4960c2333806194af7341497269e1

[ "MIT" ]

32

2021-10-02T07:30:48.000Z

2022-03-20T13:43:32.000Z

Python/WebCam GUI Filters/Gray.py

abhijeet007rocks8/Useful-Scripts

2c8bd8c1cca4960c2333806194af7341497269e1

[ "MIT" ]

170

2021-10-02T07:13:00.000Z

2022-03-31T20:40:51.000Z

Python/WebCam GUI Filters/Gray.py

abhijeet007rocks8/Useful-Scripts

2c8bd8c1cca4960c2333806194af7341497269e1

[ "MIT" ]

69

2021-10-02T07:30:53.000Z

2022-03-30T08:25:54.000Z

import cv2 import numpy as np import mediapipe as mp cap = cv2.VideoCapture(0) ret, frame = cap. read () while (True): ret, frame = cap. read () frame = cv2.flip(frame,1) gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) cv2.imshow('Gray Filter', gray) if cv2.waitKey(10) & 0xFF==ord('q'): break if cv2.waitKey(10) & 0xFF==ord('s'): import email_sender cap. release () cv2.destroyAllWindows()

25.294118

50

0.64186

import cv2 import numpy as np import mediapipe as mp cap = cv2.VideoCapture(0) ret, frame = cap. read () while (True): ret, frame = cap. read () frame = cv2.flip(frame,1) gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) cv2.imshow('Gray Filter', gray) if cv2.waitKey(10) & 0xFF==ord('q'): break if cv2.waitKey(10) & 0xFF==ord('s'): import email_sender cap. release () cv2.destroyAllWindows()

0

8bb0a1ae516f4e3eaeb925931a16ef163067e5bd

1,537

py

Python

tests/test_models.py

alexwlchan/docstore

dcffa76cb74e685e5ac027be6536d9662cad460f

[ "MIT" ]

40

2019-01-13T18:46:18.000Z

2022-03-26T00:41:53.000Z

tests/test_models.py

alexwlchan/docstore

dcffa76cb74e685e5ac027be6536d9662cad460f

[ "MIT" ]

48

2019-03-02T10:42:42.000Z

2022-03-21T08:26:11.000Z

tests/test_models.py

alexwlchan/docstore

dcffa76cb74e685e5ac027be6536d9662cad460f

[ "MIT" ]

1

2021-07-13T22:46:36.000Z

import datetime import uuid import pytest from docstore.models import Dimensions, Document, File, Thumbnail, from_json, to_json @pytest.mark.parametrize("documents", [[1, 2, 3], {"a", "b", "c"}])

25.196721

85

0.62069

import datetime import uuid import pytest from docstore.models import Dimensions, Document, File, Thumbnail, from_json, to_json def is_recent(ds): return (datetime.datetime.now() - ds).seconds < 2 def test_document_defaults(): d1 = Document(title="My test document") assert uuid.UUID(d1.id) assert is_recent(d1.date_saved) assert d1.tags == [] assert d1.files == [] d2 = Document(title="A different document") assert d1.id != d2.id def test_file_defaults(): f = File( filename="cats.jpg", path="files/c/cats.jpg", size=100, checksum="sha256:123", thumbnail=Thumbnail( path="thumbnails/c/cats.jpg", dimensions=Dimensions(400, 300), tint_color="#ffffff", ), ) uuid.UUID(f.id) assert is_recent(f.date_saved) def test_can_serialise_document_to_json(): f = File( filename="cats.jpg", path="files/c/cats.jpg", size=100, checksum="sha256:123", thumbnail=Thumbnail( path="thumbnails/c/cats.jpg", dimensions=Dimensions(400, 300), tint_color="#ffffff", ), ) documents = [Document(title="Another test document", files=[f])] assert from_json(to_json(documents)) == documents @pytest.mark.parametrize("documents", [[1, 2, 3], {"a", "b", "c"}]) def test_to_json_with_bad_list_is_typeerror(documents): with pytest.raises(TypeError, match=r"Expected type List\[Document\]!"): to_json(documents)

1,219

0

114

97bbb23607a0663fc7bb7eb63651800524c61af0

778

py

Python

setup.py

JanStgmnn/meta-labs-python

a05caba7e5eb0630b304e2aedf5d4a4aa3036f44

[ "MIT" ]

1

2021-05-24T19:02:08.000Z

setup.py

JanStgmnn/meta-labs-python

a05caba7e5eb0630b304e2aedf5d4a4aa3036f44

[ "MIT" ]

null

setup.py

JanStgmnn/meta-labs-python

a05caba7e5eb0630b304e2aedf5d4a4aa3036f44

[ "MIT" ]

null

import setuptools with open("README.md", "r", encoding="utf-8") as fh: long_description = fh.read() setuptools.setup( name="metalabs_sdk", # Replace with your own username version="0.1.1", author="Jeffrey Annaraj", author_email="jannaraj@baffled.dev", description="SDK for MetaLabs API ", long_description=long_description, long_description_content_type="text/markdown", url="https://github.com/JAnnaraj/meta-labs_sdk", packages=setuptools.find_packages(), classifiers=[ "Programming Language :: Python :: 3", "License :: OSI Approved :: MIT License", "Operating System :: OS Independent", ], python_requires='>=3.6', keywords='metalabs', install_requires=['urllib3'] )

32.416667

58

0.650386

import setuptools with open("README.md", "r", encoding="utf-8") as fh: long_description = fh.read() setuptools.setup( name="metalabs_sdk", # Replace with your own username version="0.1.1", author="Jeffrey Annaraj", author_email="jannaraj@baffled.dev", description="SDK for MetaLabs API ", long_description=long_description, long_description_content_type="text/markdown", url="https://github.com/JAnnaraj/meta-labs_sdk", packages=setuptools.find_packages(), classifiers=[ "Programming Language :: Python :: 3", "License :: OSI Approved :: MIT License", "Operating System :: OS Independent", ], python_requires='>=3.6', keywords='metalabs', install_requires=['urllib3'] )

0

ed85d29525ec40eeaef723354135ec53e29dbcf5

1,415

py

Python

azure-mgmt-web/azure/mgmt/web/models/file_system_application_logs_config.py

HydAu/AzureSDKForPython

5cbe34e9e0b8ea1faacc9f205633ccc0b885c0f3

[ "Apache-2.0" ]

null

azure-mgmt-web/azure/mgmt/web/models/file_system_application_logs_config.py

HydAu/AzureSDKForPython

5cbe34e9e0b8ea1faacc9f205633ccc0b885c0f3

[ "Apache-2.0" ]

null

azure-mgmt-web/azure/mgmt/web/models/file_system_application_logs_config.py

HydAu/AzureSDKForPython

5cbe34e9e0b8ea1faacc9f205633ccc0b885c0f3

[ "Apache-2.0" ]

null

# coding=utf-8 # -------------------------------------------------------------------------- # Copyright (c) Microsoft and contributors. 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. # # Code generated by Microsoft (R) AutoRest Code Generator. # Changes may cause incorrect behavior and will be lost if the code is # regenerated. # -------------------------------------------------------------------------- from msrest.serialization import Model class FileSystemApplicationLogsConfig(Model): """ Application logs to file system configuration :param level: Log level. Possible values include: 'Off', 'Verbose', 'Information', 'Warning', 'Error' :type level: str or :class:`LogLevel <azure.mgmt.web.models.LogLevel>` """ _attribute_map = { 'level': {'key': 'level', 'type': 'LogLevel'}, }

35.375

76

0.640989

# coding=utf-8 # -------------------------------------------------------------------------- # Copyright (c) Microsoft and contributors. 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. # # Code generated by Microsoft (R) AutoRest Code Generator. # Changes may cause incorrect behavior and will be lost if the code is # regenerated. # -------------------------------------------------------------------------- from msrest.serialization import Model class FileSystemApplicationLogsConfig(Model): """ Application logs to file system configuration :param level: Log level. Possible values include: 'Off', 'Verbose', 'Information', 'Warning', 'Error' :type level: str or :class:`LogLevel <azure.mgmt.web.models.LogLevel>` """ _attribute_map = { 'level': {'key': 'level', 'type': 'LogLevel'}, } def __init__(self, level=None): self.level = level

37

0

27

b82ef50205094d9997859ea3617022b4bad7350f

63

py

Python

__init__.py

DavidJRobertson/kicad_scripts

4fbc687033260ea6ec919717e0d37ca0d7a9cf37

[ "BSD-3-Clause" ]

368

2016-07-27T06:42:36.000Z

2022-03-31T23:11:25.000Z

__init__.py

mehmetcanbudak/kicad_scripts

7a874fd59d6162636062032e4d2c66b205b52fbe

[ "BSD-3-Clause" ]

37

2017-06-01T08:15:20.000Z

2022-02-19T17:51:34.000Z

__init__.py

mehmetcanbudak/kicad_scripts

7a874fd59d6162636062032e4d2c66b205b52fbe

[ "BSD-3-Clause" ]

114

2017-01-05T05:08:16.000Z

2022-03-28T06:03:15.000Z

from __future__ import absolute_import from . import teardrops

21

38

0.857143

from __future__ import absolute_import from . import teardrops

0

3e19b4d28b24361d206d389e5a2aa96dbbe59313

1,252

py

Python

ndk/definitions/contact.py

VunkLai/ndk

76894d2b81297ed0b7b48a35227d919d50e8fb64

[ "MIT" ]

1

2020-10-23T07:02:52.000Z

ndk/definitions/contact.py

VunkLai/ndk

76894d2b81297ed0b7b48a35227d919d50e8fb64

[ "MIT" ]

null

ndk/definitions/contact.py

VunkLai/ndk

76894d2b81297ed0b7b48a35227d919d50e8fb64

[ "MIT" ]

null

import attr from ndk.construct import Construct from ndk.directives import * from ndk.options import contact as options @attr.s

32.102564

70

0.746006

import attr from ndk.construct import Construct from ndk.directives import * from ndk.options import contact as options @attr.s class ContactDirective(Construct): __object_type__ = 'contact' contact_name = PrimaryKey() alias = StringField() contactgroups = OneToMany('ContactGroup') minimum_importance = IntegerField() host_notifications_enabled = BooleanField(required=True) service_notifications_enabled = BooleanField(required=True) host_notifications_period = OneToOne( 'TimePeriod', required=True) service_notifications_period = OneToOne( 'TimePeriod', required=True) host_notifications_options = ChoiceField( options.HostNotifications, required=True) service_notifications_options = ChoiceField( options.ServiceNotifications, required=True) host_notification_commands = OneToOne('Command', required=True) service_notification_commands = OneToOne('Command', required=True) email = StringField() pager = StringField() addressx = StringField() can_submit_commands = BooleanField() retain_status_information = BooleanField() retain_nonstatus_information = BooleanField() @property def pk(self): return self.contact_name

25

1,073

22

45e2397e2dec44f82a2719f8b237ca83b5d8a294

17,416

py

Python

examples/task_seq2seq_autotitle.py

sijunx/bert4keras

eb2d7a5ccdf89d724a0e62d55a5292faaf01f395

[ "Apache-2.0" ]

null

examples/task_seq2seq_autotitle.py

sijunx/bert4keras

eb2d7a5ccdf89d724a0e62d55a5292faaf01f395

[ "Apache-2.0" ]

null

examples/task_seq2seq_autotitle.py

sijunx/bert4keras

eb2d7a5ccdf89d724a0e62d55a5292faaf01f395

[ "Apache-2.0" ]

null

#! -*- coding: utf-8 -*- # bert做Seq2Seq任务，采用UNILM方案 # 介绍链接：https://kexue.fm/archives/6933 from __future__ import print_function import glob import os import numpy as np import sys from bert4keras.backend import keras, K from bert4keras.layers import Loss from bert4keras.models import build_transformer_model, tf from bert4keras.tokenizers import Tokenizer, load_vocab from bert4keras.optimizers import Adam from bert4keras.snippets import sequence_padding, open from bert4keras.snippets import DataGenerator, AutoRegressiveDecoder from keras.models import Model from examples import modeling from examples.my_args import arg_dic from tensorflow.python.framework.graph_util import convert_variables_to_constants from keras import backend as K from tensorflow.python.platform import gfile # parameter ========================== wkdir = '/Users/xusijun/Documents/NLP009/bert4keras-master001/keras_to_tensorflow-master' pb_filename = 'model070.pb' # 基本参数 maxlen = 256 batch_size = 16 # steps_per_epoch = 1000 steps_per_epoch = 1000 # epochs = 10000 epochs = 10 # bert配置 # config_path = '/root/kg/bert/chinese_wwm_L-12_H-768_A-12/bert_config.json' # checkpoint_path = '/root/kg/bert/chinese_wwm_L-12_H-768_A-12/bert_model.ckpt' # dict_path = '/root/kg/bert/chinese_wwm_L-12_H-768_A-12/vocab.txt' # config_path = '/Users/xusijun/Documents/NLP009/bert4keras-master001/chinese_wwm_L-12_H-768_A-12/bert_config.json' # checkpoint_path = '/Users/xusijun/Documents/NLP009/bert4keras-master001/chinese_wwm_L-12_H-768_A-12/bert_model.ckpt' # dict_path = '/Users/xusijun/Documents/NLP009/bert4keras-master001/chinese_wwm_L-12_H-768_A-12/vocab.txt' config_path = '/Users/xusijun/Documents/NLP009/bert4keras-master001/albert_tiny_google_zh_489k/albert_config.json' checkpoint_path = '/Users/xusijun/Documents/NLP009/bert4keras-master001/albert_tiny_google_zh_489k/albert_model.ckpt' dict_path = '/Users/xusijun/Documents/NLP009/bert4keras-master001/albert_tiny_google_zh_489k/vocab.txt' # 训练样本。THUCNews数据集，每个样本保存为一个txt。 # txts = glob.glob('/root/thuctc/THUCNews/*/*.txt') # txts = glob.glob('/Users/xusijun/Documents/NLP009/bert4keras-master001/MyNews/*/*.txt') txts = glob.glob('/Users/xusijun/Documents/NLP009/bert4keras-master001/THUCNews/*/*.txt') # 加载并精简词表，建立分词器 # token_dict, keep_tokens = load_vocab( # dict_path=dict_path, # simplified=True, # startswith=['[PAD]', '[UNK]', '[CLS]', '[SEP]'], # ) token_dict = load_vocab( dict_path=dict_path, # startswith=['[PAD]', '[UNK]', '[CLS]', '[SEP]'], ) tokenizer = Tokenizer(token_dict, do_lower_case=True) class data_generator(DataGenerator): """数据生成器 """ class CrossEntropy(Loss): """交叉熵作为loss，并mask掉输入部分 """ model = build_transformer_model( config_path, checkpoint_path, application='unilm', # keep_tokens=keep_tokens, # 只保留keep_tokens中的字，精简原字表 keep_tokens=None, # 只保留keep_tokens中的字，精简原字表 ) output = CrossEntropy(2)(model.inputs + model.outputs) model = Model(model.inputs, output) model.compile(optimizer=Adam(1e-5)) model.summary() class AutoTitle(AutoRegressiveDecoder): """seq2seq解码器 """ @AutoRegressiveDecoder.wraps(default_rtype='probas') autotitle = AutoTitle(start_id=None, end_id=tokenizer._token_end_id, maxlen=32) # save model to pb ==================== def freeze_session(session, keep_var_names=None, output_names=None, clear_devices=True): """ Freezes the state of a session into a pruned computation graph. Creates a new computation graph where variable nodes are replaced by constants taking their current value in the session. The new graph will be pruned so subgraphs that are not necessary to compute the requested outputs are removed. @param session The TensorFlow session to be frozen. @param keep_var_names A list of variable names that should not be frozen, or None to freeze all the variables in the graph. @param output_names Names of the relevant graph outputs. @param clear_devices Remove the device directives from the graph for better portability. @return The frozen graph definition. """ graph = session.graph with graph.as_default(): freeze_var_names = list(set(v.op.name for v in tf.global_variables()).difference(keep_var_names or [])) output_names = output_names or [] output_names += [v.op.name for v in tf.global_variables()] input_graph_def = graph.as_graph_def() if clear_devices: for node in input_graph_def.node: node.device = "" frozen_graph = convert_variables_to_constants(session, input_graph_def, output_names, freeze_var_names) return frozen_graph class Evaluator(keras.callbacks.Callback): """评估与保存 """ if __name__ == '__main__': model.load_weights('./myFile70.h5') just_show() evaluator = Evaluator() train_generator = data_generator(txts, batch_size) model.fit( train_generator.forfit(), steps_per_epoch=steps_per_epoch, epochs=epochs, callbacks=[evaluator] ) else: model.load_weights('./best_model003.weights')

44.65641

1,517

0.676906

#! -*- coding: utf-8 -*- # bert做Seq2Seq任务，采用UNILM方案 # 介绍链接：https://kexue.fm/archives/6933 from __future__ import print_function import glob import os import numpy as np import sys from bert4keras.backend import keras, K from bert4keras.layers import Loss from bert4keras.models import build_transformer_model, tf from bert4keras.tokenizers import Tokenizer, load_vocab from bert4keras.optimizers import Adam from bert4keras.snippets import sequence_padding, open from bert4keras.snippets import DataGenerator, AutoRegressiveDecoder from keras.models import Model from examples import modeling from examples.my_args import arg_dic from tensorflow.python.framework.graph_util import convert_variables_to_constants from keras import backend as K from tensorflow.python.platform import gfile # parameter ========================== wkdir = '/Users/xusijun/Documents/NLP009/bert4keras-master001/keras_to_tensorflow-master' pb_filename = 'model070.pb' # 基本参数 maxlen = 256 batch_size = 16 # steps_per_epoch = 1000 steps_per_epoch = 1000 # epochs = 10000 epochs = 10 # bert配置 # config_path = '/root/kg/bert/chinese_wwm_L-12_H-768_A-12/bert_config.json' # checkpoint_path = '/root/kg/bert/chinese_wwm_L-12_H-768_A-12/bert_model.ckpt' # dict_path = '/root/kg/bert/chinese_wwm_L-12_H-768_A-12/vocab.txt' # config_path = '/Users/xusijun/Documents/NLP009/bert4keras-master001/chinese_wwm_L-12_H-768_A-12/bert_config.json' # checkpoint_path = '/Users/xusijun/Documents/NLP009/bert4keras-master001/chinese_wwm_L-12_H-768_A-12/bert_model.ckpt' # dict_path = '/Users/xusijun/Documents/NLP009/bert4keras-master001/chinese_wwm_L-12_H-768_A-12/vocab.txt' config_path = '/Users/xusijun/Documents/NLP009/bert4keras-master001/albert_tiny_google_zh_489k/albert_config.json' checkpoint_path = '/Users/xusijun/Documents/NLP009/bert4keras-master001/albert_tiny_google_zh_489k/albert_model.ckpt' dict_path = '/Users/xusijun/Documents/NLP009/bert4keras-master001/albert_tiny_google_zh_489k/vocab.txt' # 训练样本。THUCNews数据集，每个样本保存为一个txt。 # txts = glob.glob('/root/thuctc/THUCNews/*/*.txt') # txts = glob.glob('/Users/xusijun/Documents/NLP009/bert4keras-master001/MyNews/*/*.txt') txts = glob.glob('/Users/xusijun/Documents/NLP009/bert4keras-master001/THUCNews/*/*.txt') # 加载并精简词表，建立分词器 # token_dict, keep_tokens = load_vocab( # dict_path=dict_path, # simplified=True, # startswith=['[PAD]', '[UNK]', '[CLS]', '[SEP]'], # ) token_dict = load_vocab( dict_path=dict_path, # startswith=['[PAD]', '[UNK]', '[CLS]', '[SEP]'], ) tokenizer = Tokenizer(token_dict, do_lower_case=True) class data_generator(DataGenerator): """数据生成器 """ def __iter__(self, random=False): batch_token_ids, batch_segment_ids = [], [] for is_end, txt in self.sample(random): text = open(txt, encoding='utf-8').read() text = text.split('\n') if len(text) > 1: title = text[0] content = '\n'.join(text[1:]) token_ids, segment_ids = tokenizer.encode( content, title, maxlen=maxlen ) batch_token_ids.append(token_ids) batch_segment_ids.append(segment_ids) if len(batch_token_ids) == self.batch_size or is_end: batch_token_ids = sequence_padding(batch_token_ids) batch_segment_ids = sequence_padding(batch_segment_ids) yield [batch_token_ids, batch_segment_ids], None batch_token_ids, batch_segment_ids = [], [] class CrossEntropy(Loss): """交叉熵作为loss，并mask掉输入部分 """ def compute_loss(self, inputs, mask=None): y_true, y_mask, y_pred = inputs y_true = y_true[:, 1:] # 目标token_ids y_mask = y_mask[:, 1:] # segment_ids，刚好指示了要预测的部分 y_pred = y_pred[:, :-1] # 预测序列，错开一位 loss = K.sparse_categorical_crossentropy(y_true, y_pred) loss = K.sum(loss * y_mask) / K.sum(y_mask) return loss model = build_transformer_model( config_path, checkpoint_path, application='unilm', # keep_tokens=keep_tokens, # 只保留keep_tokens中的字，精简原字表 keep_tokens=None, # 只保留keep_tokens中的字，精简原字表 ) output = CrossEntropy(2)(model.inputs + model.outputs) model = Model(model.inputs, output) model.compile(optimizer=Adam(1e-5)) model.summary() class AutoTitle(AutoRegressiveDecoder): """seq2seq解码器 """ @AutoRegressiveDecoder.wraps(default_rtype='probas') def predict(self, inputs, output_ids, states): print("--------------------- 开始 ---------------------") print("prdict inputs:", inputs) print("prdict output_ids:", output_ids) print("prdict states:", states) token_ids, segment_ids = inputs token_ids = np.concatenate([token_ids, output_ids], 1) segment_ids = np.concatenate([segment_ids, np.ones_like(output_ids)], 1) print("predict token_ids:", token_ids) print("predict segment_ids:", segment_ids) topk = 1 proba = model.predict([token_ids, segment_ids]) print("proba:", proba) log_proba = np.log(proba + 1e-6) # 取对数，方便计算 print("log_proba:", log_proba) icount =0 maxIndex = 0 maxValue = -9999.0 temp = 78 while(icount<len(proba[0][temp])): if(proba[0][temp][icount] > maxValue): maxValue = proba[0][temp][icount] maxIndex = icount icount = icount+1 print("maxIndex:", maxIndex, " maxValue:", maxValue) # maxIndex: 8125 maxValue: 0.27502504 return self.last_token(model).predict([token_ids, segment_ids]) # print("result", scores) # print("states", states) # icount =0 # maxIndex = 0 # maxValue = -9999.0 # while(icount<len(scores[0])): # if(scores[0][icount] > maxValue): # maxValue = scores[0][icount] # maxIndex = icount # icount = icount+1 # print("maxIndex:", maxIndex, " maxValue:", maxValue) # print("--------------------- 结束 ---------------------") # return scores, states def generate(self, text, topk=1): max_c_len = maxlen - self.maxlen token_ids, segment_ids = tokenizer.encode(text, maxlen=max_c_len) # print('token_ids: ', len(token_ids), token_ids) # print('segment_ids: ', len(segment_ids), segment_ids) output_ids = self.beam_search([token_ids, segment_ids], topk=topk) # 基于beam search x01 = output_ids[0] # x02 = output_ids[1] # x03 = output_ids[2] # x04 = output_ids[3] # x05 = output_ids[4] y01 = tokenizer.decode(x01) print("y01:", y01) # y02 = tokenizer.decode(x02) # print("y02:", y02) # y03 = tokenizer.decode(x03) # print("y03:", y03) # y04 = tokenizer.decode(x04) # print("y04:", y04) # y05 = tokenizer.decode(x05) # print("y05:", y05) return tokenizer.decode(output_ids[0]) autotitle = AutoTitle(start_id=None, end_id=tokenizer._token_end_id, maxlen=32) # save model to pb ==================== def freeze_session(session, keep_var_names=None, output_names=None, clear_devices=True): """ Freezes the state of a session into a pruned computation graph. Creates a new computation graph where variable nodes are replaced by constants taking their current value in the session. The new graph will be pruned so subgraphs that are not necessary to compute the requested outputs are removed. @param session The TensorFlow session to be frozen. @param keep_var_names A list of variable names that should not be frozen, or None to freeze all the variables in the graph. @param output_names Names of the relevant graph outputs. @param clear_devices Remove the device directives from the graph for better portability. @return The frozen graph definition. """ graph = session.graph with graph.as_default(): freeze_var_names = list(set(v.op.name for v in tf.global_variables()).difference(keep_var_names or [])) output_names = output_names or [] output_names += [v.op.name for v in tf.global_variables()] input_graph_def = graph.as_graph_def() if clear_devices: for node in input_graph_def.node: node.device = "" frozen_graph = convert_variables_to_constants(session, input_graph_def, output_names, freeze_var_names) return frozen_graph def my_keras_to_pb(): # save keras model as tf pb files =============== frozen_graph = freeze_session(K.get_session(), output_names=[out.op.name for out in model.outputs]) tf.train.write_graph(frozen_graph, wkdir, pb_filename, as_text=False) # # load & inference the model ================== with tf.Session() as sess: # load model from pb file with gfile.FastGFile(wkdir+'/'+pb_filename,'rb') as f: graph_def = tf.GraphDef() graph_def.ParseFromString(f.read()) sess.graph.as_default() g_in = tf.import_graph_def(graph_def) # write to tensorboard (check tensorboard for each op names) writer = tf.summary.FileWriter(wkdir+'/log/') writer.add_graph(sess.graph) writer.flush() writer.close() # print all operation names print('\n===== ouptut operation names =====\n') for op in sess.graph.get_operations(): print(op) # inference by the model (op name must comes with :0 to specify the index of its output) tensor_output = sess.graph.get_tensor_by_name('cross_entropy_1/Identity:0') # Input-Token:0 tensor_input = sess.graph.get_tensor_by_name('Input-Token:0') # Input-Segment:0 seg_input = sess.graph.get_tensor_by_name('Input-Segment:0') text = '夏天来临，皮肤在强烈紫外线的照射下，晒伤不可避免，因此，晒后及时修复显得尤为重要，否则可能会造成长期伤害。专家表示，选择晒后护肤品要慎重，芦荟凝胶是最安全，有效的一种选择，晒伤严重者，还请及时就医。' # max_c_len = maxlen - self.maxlen # max_c_len = maxlen - 56 + 3 # token_ids, segment_ids = tokenizer.encode(text, maxlen=max_c_len) # # x = np.vstack((np.random.rand(1000,10),-np.random.rand(1000,10))) # y = np.vstack((np.ones((1000,1)),np.zeros((1000,1)))) # x = [[2, 2352, 6702, 2234, 758, 5407, 2127, 578, 7404, 1642, 6269, 6293, 991, 670, 1399, 4393, 670, 5340, 1189, 731, 6707, 2666, 6512, 1119, 2590, 1301, 3]] # y = [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]] # token_ids, segment_ids = inputs x = np.array([[2, 2352, 6702, 2234, 758, 5407, 2127, 578, 7404, 1642, 6269, 6293, 991, 670, 1399, 4393, 670, 5340, 1189, 731, 6707, 2666, 6512, 1119, 2590, 1301, 3]]) y = np.array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]) # token_ids = np.concatenate([token_ids, output_ids], 1) # segment_ids = np.concatenate([segment_ids, np.ones_like(output_ids)], 1) # print("predict token_ids:", token_ids) # print("predict segment_ids:", segment_ids) # print(x.shape) # print(y.shape) # predictions = sess.run(tensor_output, {tensor_input: x, seg_input: y}) # # print('\n===== output predicted results =====\n') # print(predictions) print('xxxxxxxxxx') def my_test001(): text1 = '语言模型' # text2 = "你好" tokens1 = tokenizer.tokenize(text1) print(tokens1) # tokens2 = tokenizer.tokenize(text2) # print(tokens2) # indices_new, segments_new = tokenizer.encode(text1, text2, max_length=512) indices_new, segments_new = tokenizer.encode(text1) print(indices_new[:10]) # [101, 6427, 6241, 3563, 1798, 102, 0, 0, 0, 0] print(segments_new[:10]) # [0, 0, 0, 0, 0, 0, 0, 0, 0, 0] # 提取特征 predicts_new = model.predict([np.array([indices_new]), np.array([segments_new])])[0] for i, token in enumerate(tokens1): print(token, predicts_new[i].tolist()[:5]) # for i, token in enumerate(tokens2): # print(token, predicts_new[i].tolist()[:5]) print("xxxxx") def my_test002(): #加载语言模型 #model = build_bert_model(config_path=config_path, checkpoint_path=checkpoint_path, with_mlm=True) token_ids, segment_ids = tokenizer.encode(u'科学技术是第一生产力') # mask掉“技术” # token_ids[3] = token_ids[4] = token_dict['[MASK]'] token_ids[3] = token_ids[4] = token_dict['[UNK]'] # 用mlm模型预测被mask掉的部分 probas = model.predict([np.array([token_ids]), np.array([segment_ids])])[0] mask01 = tokenizer.decode(probas[3:5].argmax(axis=1)) print(mask01) # 结果正是“技术” token_ids, segment_ids = tokenizer.encode(u'数学是利用符号语言研究数量、结构、变化以及空间等概念的一门学科') # mask掉“技术” #token_ids[1] = token_ids[2] = tokenizer._token_dict['[MASK]'] # 用mlm模型预测被mask掉的部分 probas = model.predict([np.array([token_ids]), np.array([segment_ids])])[0] print(tokenizer.decode(probas[1:3].argmax(axis=1))) # 结果正是“数学” print("xxx") def just_show(): # s1 = u'记者傅亚雨沈阳报道来到沈阳，国奥队依然没有摆脱雨水的困扰。7月31日下午6点，国奥队的日常训练再度受到大雨的干扰，无奈之下队员们只慢跑了25分钟就草草收场。31日上午10点，国奥队在奥体中心外场训练的时候，天就是阴沉沉的，气象预报显示当天下午沈阳就有大雨，但幸好队伍上午的训练并没有受到任何干扰。　　下午6点，当球队抵达训练场时，大雨已经下了几个小时，而且丝毫没有停下来的意思。抱着试一试的态度，球队开始了当天下午的例行训练，25分钟过去了，天气没有任何转好的迹象，为了保护球员们，国奥队决定中止当天的训练，全队立即返回酒店。　　在雨中训练对足球队来说并不是什么稀罕事，但在奥运会即将开始之前，全队变得“娇贵”了。在沈阳最后一周的训练，国奥队首先要保证现有的球员不再出现意外的伤病情况以免影响正式比赛，因此这一阶段控制训练受伤、控制感冒等疾病的出现被队伍放在了相当重要的位置。而抵达沈阳之后，中后卫冯萧霆就一直没有训练，冯萧霆是7月27日在长春患上了感冒，因此也没有参加29日跟塞尔维亚的热身赛。队伍介绍说，冯萧霆并没有出现发烧症状，但为了安全起见，这两天还是让他静养休息，等感冒彻底好了之后再恢复训练。由于有了冯萧霆这个例子，因此国奥队对雨中训练就显得特别谨慎，主要是担心球员们受凉而引发感冒，造成非战斗减员。而女足队员马晓旭在热身赛中受伤导致无缘奥运的前科，也让在沈阳的国奥队现在格外警惕，“训练中不断嘱咐队员们要注意动作，我们可不能再出这样的事情了。”一位工作人员表示。　　从长春到沈阳，雨水一路伴随着国奥队，“也邪了，我们走到哪儿雨就下到哪儿，在长春几次训练都被大雨给搅和了，没想到来沈阳又碰到这种事情。”一位国奥球员也对雨水的“青睐”有些不解。' # s2 = u'新浪体育讯　主场战胜沈阳东进豪取主场六连胜的中甲劲旅延边队再传好消息。今日上午，延边州体育局与韩国认证农产品生产者协会达成赞助意向，该协会将赞助延边足球俱乐部10亿韩币(约合560万人民币)，力助延足2011赛季实现冲超。　　无偿赞助只因为延足感动此番，韩国认证农产品生产者协会为延足提供的10亿韩币赞助大单基本上都是无偿的，唯一的回报就是希望延边州体育局能够帮助该协会的产品打入延边市场做一些协调工作。说起无偿赞助延足，韩国认证农产品生产者协会中央会会长吴亨根(O Hyung-Kun)先生表示，只因延边足球让他感动。　　据吴亨根介绍，在收到延边足球俱乐部赞助提议后，他很快就做出了赞助决定。“延边足球运动员很有天赋，只要在资金上能提供有力的支持，一定会成为一流球队。”在了解了延足球员目前的训练比赛状况后，今日吴亨根还以个人名义为延边队捐了三台全自动洗衣机。　　其实，吴亨根也曾经是个足球人，他就是韩国全北现代俱乐部的创始人。1993年他创立了全北队，1994年韩国的汽车巨擘现代汽车正式入主全北队，而球队也更名成今日所用的全北现代。2006年全北现代战胜叙利亚卡马拉队夺得亚冠联赛冠军，中国球员冯潇霆目前就在这支球队效力。　　除了这10亿韩币赞助，吴亨根还表示，中甲联赛结束后，他将把延边队带到韩国进行冬训，与全北的大学生球队进行训练比赛，通过以赛代练让延足充分为下赛季实现冲超夯实基础。　　冲超早动手经营更规范　　联赛还剩三轮，目前延边队排名第三，极有望取得征战中甲六年来的最佳战绩(此前最好排名第六)。冲超是延边队一直的梦想，延边州体育局与俱乐部方面都希望在2011赛季完成冲超大业，让延边足球重新回归国内顶级行列。要想冲超就要未雨绸缪，本赛季尚未结束，延足冲超的各项准备工作便已展开。　　本赛季延边队依然为资金所困，俱乐部经理李虎恩难辞其咎。今年7月，延边州体育局委托媒体人出身的郑宪哲先生先期运作经营延边足球俱乐部，为下赛季早作准备。年轻的郑宪哲接手后也为俱乐部经营带来了新思路，短短的两个月间，就为延足搞定了如此大单的韩国赞助意向。另外，下赛季延边队的比赛装备目前也已落实，韩国世达(STAR)体育用品公司将成为新的装备赞助商，为延足提供全套比赛训练装备，预计金额达100万人民币。　　在未来延边足球俱乐部经营思路上，延边州体育局副局长于长龙表示，要对目前俱乐部的经营进行彻底改造，以求更加适应现代足球的发展理念，在政府支持的基础上，大胆尝试市场化运作，引进韩国足球俱乐部经营运作理念，在经营、服务、宣传等方面全方位提升俱乐部的整体水平。于长龙还透露，本赛季最后一轮客场同上海东亚比赛结束后，延边足球俱乐部将在上海举行招商会，向更多企业宣传推介延边足球，实现走出去招商。而接下来，延足还将陆续前往青岛、深圳、大连等地展开招商工作。　　酝酿十年规划打造血性之师　　据悉，延边州体育局与延边足球俱乐部近期正在酝酿推出延足未来十年的一个中长期规划，其中最首要的任务就是要在未来三年在中超站稳脚跟。如果按照这一规划的设想，至少下赛季延足要完成冲超，此后再图站稳中超。　　于长龙希望，能够在未来把延边队打造成一支文明之师、忠诚之师、血性之师、战斗之师，在继承朝鲜族球员勇猛顽强的优良传统基础上，更加彰显朝鲜族民族文化的底蕴和内涵，让延边队成为一支忠诚家乡，充满血性，真正为足球而战的足坛劲旅。　　据悉，此番敲定赞助意向只是延足为冲超迈出的第一步，如何有效转变俱乐部经营理念、如何规范运作将是摆在延边州体育局面前的一个新课题。接下来，体育局与俱乐部还将推出一系列新动作，为冲超增添筹码。　　(木木)' # s3 = u'日前，江苏丹阳市延陵镇的一些西瓜种植户，因为滥用膨大剂又加上暴雨，导致不少西瓜纷纷“爆炸”，并因此影响了今年的西瓜总体销量。尽管专家一再强调膨大剂本身并无危害，但是滥用的话却易引发一连串问题。花果山CEO提问：辟谣的目的是什么？消除大众对膨化剂的恐惧？继续吃膨大剂西瓜？瓜农无疑是可悲的。根本不可能自己种西瓜的消费者呢？只能吃膨大剂西瓜么？谣言粉碎机：果壳网谣言粉碎机关心的不只是给大家一个“简单的答案”。我们通过对问题的梳理，给大家提供更全面的信息，让大家能更好的做出“自己的选择”。同时，果壳网谣言粉碎机也希望向大家提供一些理性看待问题的思路。这样的思路不仅是针对一事一人的，它关涉到的是我们的生活态度与看待世界的方法。CAPA-Real-柏志提问：氯吡脲使用后，会在水果中残留吗？人体食用后对人体有些什么影响？谣言粉碎机：会有一定的残留，一般在生长初期使用，时间越长残留越少。少量的接触，对人的影响很小。具体的毒理学实验数据，果壳的文章里有详细的说明。' s1 = '针对最近我国赴比利时留学人员接连发生入境时被比海关拒绝或者办理身份证明时被比警方要求限期出境的事件，教育部提醒赴比利时留学人员应注意严格遵守比方相关规定。' # s2 = u'程序员最爱' # s3 = u'身体素质' for s in [s1]: print(u'生成标题:', autotitle.generate(s)) print() class Evaluator(keras.callbacks.Callback): """评估与保存 """ def __init__(self): self.lowest = 1e10 def on_epoch_end(self, epoch, logs=None): # 保存最优 if logs['loss'] <= self.lowest: self.lowest = logs['loss'] # model.save_weights('./best_model.weights') # model.save_weights('/Users/xusijun/Documents/NLP009/bert4keras-master001/tensorflow-for-java-master/best_model03.weights') model.save('./myFile70.h5') # model.save('/Users/xusijun/Documents/NLP009/bert4keras-master001/tensorflow-for-java-master/myFile04') # tf.saved_model.save(model, '/Users/xusijun/Documents/NLP009/bert4keras-master001/examples/') # tf.keras.models.save_model(model, '/Users/xusijun/Documents/NLP009/bert4keras-master001/examples/') # 演示效果 just_show() # 我的保存 my_keras_to_pb() # pb模型 # save_PBmodel() if __name__ == '__main__': model.load_weights('./myFile70.h5') just_show() evaluator = Evaluator() train_generator = data_generator(txts, batch_size) model.fit( train_generator.forfit(), steps_per_epoch=steps_per_epoch, epochs=epochs, callbacks=[evaluator] ) else: model.load_weights('./best_model003.weights')

17,796

0

250

792aa9f0d16f371e267c4f2fcb7bdd16c329c1d1

706

py

Python

solutions/791/791-yongjoonseo.py

iknoom/LeetCode-Solutions

85c034dfaf1455bcd69c19a2009197934d83f08e

[ "MIT" ]

4

2021-01-13T11:37:57.000Z

2021-01-17T04:56:46.000Z

solutions/791/791-yongjoonseo.py

iknoom/LeetCode-Solutions

85c034dfaf1455bcd69c19a2009197934d83f08e

[ "MIT" ]

9

2021-01-21T11:16:29.000Z

2021-02-23T14:27:00.000Z

solutions/791/791-yongjoonseo.py

iknoom/LeetCode-Solutions

85c034dfaf1455bcd69c19a2009197934d83f08e

[ "MIT" ]

14

2021-01-14T14:36:07.000Z

2021-02-05T09:17:10.000Z

# check # lowercase letters # count all the letters of T which S contains # save indices of letters in T

27.153846

54

0.447592

# check # lowercase letters # count all the letters of T which S contains # save indices of letters in T class Solution: def customSortString(self, S: str, T: str) -> str: result = [0] * len(T) indices = [] counts = dict() for i in range(len(T)): if T[i] in S: indices.append(i) if T[i] in counts: counts[T[i]] += 1 else: counts[T[i]] = 1 else: result[i] = T[i] i = 0 for char in S: left = counts.get(char) while left: result[indices[i]] = char i += 1 left -= 1 return ''.join(result)

558

-6

49

0773b38a64aebeeea57ebbff37f79409955ec330

2,699

py

Python

hata/ext/rpc/utils.py

albertopoljak/hata

96d0b3182eb4f5291eaf36bd23d521787c6b01f1

[ "0BSD" ]

null

hata/ext/rpc/utils.py

albertopoljak/hata

96d0b3182eb4f5291eaf36bd23d521787c6b01f1

[ "0BSD" ]

null

hata/ext/rpc/utils.py

albertopoljak/hata

96d0b3182eb4f5291eaf36bd23d521787c6b01f1

[ "0BSD" ]

1

2020-09-17T20:10:15.000Z

__all__ = () from sys import platform as PLATFORM from os.path import join as join_paths from os import listdir as list_directory, environ as ENVIRONMENTAL_VARIABLES from tempfile import gettempdir as get_temporary_directory from scarletio import set_docs from .constants import PAYLOAD_KEY_EVENT, EVENT_ERROR, PAYLOAD_KEY_DATA from. exceptions import DiscordRPCError if PLATFORM in ('linux', 'darwin'): TEMPORARY_DIRECTORY = ENVIRONMENTAL_VARIABLES.get('XDG_RUNTIME_DIR', None) if (TEMPORARY_DIRECTORY is None): TEMPORARY_DIRECTORY = ENVIRONMENTAL_VARIABLES.get('TMPDIR', None) if (TEMPORARY_DIRECTORY is None): TEMPORARY_DIRECTORY = ENVIRONMENTAL_VARIABLES.get('TMP', None) if (TEMPORARY_DIRECTORY is None): TEMPORARY_DIRECTORY = ENVIRONMENTAL_VARIABLES.get('TEMP', None) if (TEMPORARY_DIRECTORY is None): TEMPORARY_DIRECTORY = get_temporary_directory() elif PLATFORM == 'win32': TEMPORARY_DIRECTORY = '\\\\?\\pipe' else: set_docs(get_ipc_path, """ Gets Discord inter process communication path. Parameters ---------- pipe : `None` or `str` # TODO Returns ------- path : `None` or `str` """) def check_for_error(data): """ Checks whether the given data contains an errors. Parameters ---------- data : `dict` of (`str`, `Any`) items Data received from Discord. Raises ------ DiscordRPCError """ try: event = data[PAYLOAD_KEY_EVENT] except KeyError: pass else: if event == EVENT_ERROR: error_data = data[PAYLOAD_KEY_DATA] error_code = error_data['code'] error_message = error_data['message'] raise DiscordRPCError(error_code, error_message)

27.824742

79

0.614672

__all__ = () from sys import platform as PLATFORM from os.path import join as join_paths from os import listdir as list_directory, environ as ENVIRONMENTAL_VARIABLES from tempfile import gettempdir as get_temporary_directory from scarletio import set_docs from .constants import PAYLOAD_KEY_EVENT, EVENT_ERROR, PAYLOAD_KEY_DATA from. exceptions import DiscordRPCError if PLATFORM in ('linux', 'darwin'): TEMPORARY_DIRECTORY = ENVIRONMENTAL_VARIABLES.get('XDG_RUNTIME_DIR', None) if (TEMPORARY_DIRECTORY is None): TEMPORARY_DIRECTORY = ENVIRONMENTAL_VARIABLES.get('TMPDIR', None) if (TEMPORARY_DIRECTORY is None): TEMPORARY_DIRECTORY = ENVIRONMENTAL_VARIABLES.get('TMP', None) if (TEMPORARY_DIRECTORY is None): TEMPORARY_DIRECTORY = ENVIRONMENTAL_VARIABLES.get('TEMP', None) if (TEMPORARY_DIRECTORY is None): TEMPORARY_DIRECTORY = get_temporary_directory() def get_ipc_path(pipe): ipc = f'discord-ipc-{pipe}' for path in (None, 'snap.discord', 'app/com.discordapp.Discord'): if path is None: full_path = TEMPORARY_DIRECTORY else: full_path = join_paths(TEMPORARY_DIRECTORY) for node_name in list_directory(full_path): if node_name.startswith(ipc): return join_paths(full_path, node_name) return None elif PLATFORM == 'win32': TEMPORARY_DIRECTORY = '\\\\?\\pipe' def get_ipc_path(pipe): ipc = f'discord-ipc-{pipe}' for node_name in list_directory(TEMPORARY_DIRECTORY): if node_name.startswith(ipc): return join_paths(TEMPORARY_DIRECTORY, node_name) return None else: def get_ipc_path(pipe): return None set_docs(get_ipc_path, """ Gets Discord inter process communication path. Parameters ---------- pipe : `None` or `str` # TODO Returns ------- path : `None` or `str` """) def check_for_error(data): """ Checks whether the given data contains an errors. Parameters ---------- data : `dict` of (`str`, `Any`) items Data received from Discord. Raises ------ DiscordRPCError """ try: event = data[PAYLOAD_KEY_EVENT] except KeyError: pass else: if event == EVENT_ERROR: error_data = data[PAYLOAD_KEY_DATA] error_code = error_data['code'] error_message = error_data['message'] raise DiscordRPCError(error_code, error_message)

748

0

88

ce80efef261b5805f2ccac2cfca1a8c03c9fd576

31,202

py

Python

cdam_convert_twine.py

jerrytron/twine-story-export

72574627969e8fd79ad246b7532874f223e2c88f

[ "MIT" ]

2

2017-08-26T12:25:31.000Z

2021-06-01T19:35:25.000Z

cdam_convert_twine.py

jerrytron/twine-story-export

72574627969e8fd79ad246b7532874f223e2c88f

[ "MIT" ]

null

cdam_convert_twine.py

jerrytron/twine-story-export

72574627969e8fd79ad246b7532874f223e2c88f

[ "MIT" ]

null

#!/usr/bin/env python # encoding=utf8 import os import re import sys import struct import pprint import random import argparse import datetime import tiddlywiki as tiddly import cdam_gen_files as gen import importlib import bitarray importlib.reload(sys) # sys.setdefaultencoding('utf8') VERSION = "1.0" BINARY_VER = "1.0.5" # For holding binary variable keys and values. VARIABLES = {} FLAGS = {} TITLE_MAP = {} STORY_MAP = {} PASSAGES = {} STORY_TITLE = "" STORY_AUTHOR = "" STORY_SUBTITLE = "" STORY_CREDITS = "" STORY_VERSION = "" STORY_CONTACT = "" STORY_LANGUAGE = "" REPORT = "" OPERATION_TEST = bytearray() TOTAL_OPS = 0 VERBOSE = False LINEAR = False HTML = False SEED = None PP = pprint.PrettyPrinter(indent = 4) kAppend = "<append>" kContinue = "<continue>" kContinueCopy = '<continue>' kGotoTempTag = "-GOTO-" if __name__ == '__main__': #global _UPDATE #global _FORCE main()

34.325633

196

0.56538

#!/usr/bin/env python # encoding=utf8 import os import re import sys import struct import pprint import random import argparse import datetime import tiddlywiki as tiddly import cdam_gen_files as gen import importlib import bitarray importlib.reload(sys) # sys.setdefaultencoding('utf8') VERSION = "1.0" BINARY_VER = "1.0.5" # For holding binary variable keys and values. VARIABLES = {} FLAGS = {} TITLE_MAP = {} STORY_MAP = {} PASSAGES = {} STORY_TITLE = "" STORY_AUTHOR = "" STORY_SUBTITLE = "" STORY_CREDITS = "" STORY_VERSION = "" STORY_CONTACT = "" STORY_LANGUAGE = "" REPORT = "" OPERATION_TEST = bytearray() TOTAL_OPS = 0 VERBOSE = False LINEAR = False HTML = False SEED = None PP = pprint.PrettyPrinter(indent = 4) kAppend = "<append>" kContinue = "<continue>" kContinueCopy = '<continue>' kGotoTempTag = "-GOTO-" class CDAMParser(argparse.ArgumentParser): def error(self, message): sys.stderr.write('error: %s\n' % message) self.print_help() sys.exit(2) class CDAMTwine(tiddly.Tiddler): def GetPassages(self): return self.tiddlers def main(): global STORY_TITLE global STORY_AUTHOR global STORY_SUBTITLE global STORY_CREDITS global STORY_CONTACT global STORY_LANGUAGE global STORY_VERSION global LINEAR global HTML # To Make a Linear Story: # python ./cdam_convert_twine.py --title parser = CDAMParser(description='CDAM Twine Source Code Converter') parser.add_argument('--dirname', default='NONE', action='store', help='Directory name for story on the file system.') parser.add_argument('--title', default='Untitled', action='store', help='The story title.') parser.add_argument('--subtitle', default='NONE', action='store', help='The story subtitle.') parser.add_argument('--author', default='Anonymous', action='store', help='The author of the story.') parser.add_argument('--pubdate', default='', action='store', help='The date this story was published') parser.add_argument('--credits', default='', action='store', help='Additional story credits.') parser.add_argument('--contact', default='Follow creator @j3rrytron online!', action='store', help='Misc contact info.') parser.add_argument('--lang', default='eng', action='store', help='Up to four character language code.') parser.add_argument('--ver', default='0.0.0', action='store', help='Story version in three parts, x.x.x') parser.add_argument('--source', default='', action='store', help='The Twine source code file.') parser.add_argument('--output', default='./', action='store', help='The location to create the output files.') parser.add_argument('--filename', default='', action='store', help='The output filename.') parser.add_argument('--json', action='store_true', help='Output as a JSON text file.') parser.add_argument('--linear', action='store_true', help='Output as a linear text file for humans.') parser.add_argument('--html', action='store_true', help='Output as html document.') parser.add_argument('--randseed', default='', action='store', help='Optional seed to control random output.') parser.add_argument('--binary', action='store_true', help='Output as a CDAM binary for the Choosatron v2.') parser.add_argument('--verbose', action='store_true', help='Print additional info, including warnings.') parser.add_argument('--operation', action='store_true', help='Output operations file too for debugging.') parser.add_argument('--family', action='store_true', help='Mark this story as family friendly.') parser.add_argument('--vartext', action='store_true', help='This story uses variable text logic.') parser.add_argument('--mono', action='store_true', help='This story requires a monospaced font.') #parser.add_argument('--update', action='store_true', help='Attempt to safely add to/update existing files without damaging existing data.') #parser.add_argument('--force', action='store_true', help='Overwrite files that already exist.') args = parser.parse_args() STORY_SUBTITLE = args.subtitle STORY_CREDITS = args.credits STORY_CONTACT = args.contact STORY_LANGUAGE = args.lang STORY_VERSION = args.ver print("--- " + args.title + " ---") if args.randseed: SEED = int(args.randseed) random.seed(SEED) else: SEED = datetime.datetime.now().microsecond #print "Random Seed for " + args.title + ": " + str(SEED) random.seed(SEED) LINEAR = args.linear HTML = args.html if HTML: LINEAR = True # Uncomment to override output and place wherever source was. #args.output = os.path.dirname(args.source) VERBOSE = args.verbose if VERBOSE: print(args.title) FLAGS['family_friendly'] = args.family FLAGS['variable_text'] = args.vartext FLAGS['retry'] = True # Just default true for now. FLAGS['monospace'] = args.mono storyWiki = LoadSource(args.source) if storyWiki == False: return result = BuildCDAMStory(storyWiki) if result == False: return if args.dirname.upper() in PASSAGES: print("[ERROR] Value of --dirname can't be the same as a passage title. Passage already exists named: " + args.dirname.upper()) return SimplifyNaming() genFile = gen.CDAMGenFiles() if args.binary == True: if args.title != "Untitled": STORY_TITLE = args.title if args.author != "Anonymous": STORY_AUTHOR = args.author # Generate Story Body storyBody = genFile.GenerateBody(STORY_MAP, PASSAGES, VARIABLES) if storyBody == False: return if len(VARIABLES) == 0: FLAGS['logic'] = False else: FLAGS['logic'] = True if 'ending_quality' not in FLAGS: FLAGS['ending_quality'] = False if 'points' not in FLAGS: FLAGS['points'] = False # Currently no images are supported. FLAGS['images'] = False # Generate Story Header storyHeader = genFile.GenerateHeader(args.lang, args.title, args.subtitle, args.author, args.pubdate, args.credits, args.contact, BINARY_VER, args.ver, FLAGS, len(storyBody), len(VARIABLES)) if storyHeader == False: return bookPath = STORY_TITLE.lower().replace(" ", "_") + "_BIN.dam" if args.filename != "": bookPath = args.filename + "_BIN.dam" bookPath = os.path.join(args.output, bookPath) if os.path.exists(bookPath): os.remove(bookPath) genFile.WriteToFile(bookPath, storyHeader + storyBody) if args.operation: opPath = STORY_TITLE.lower().replace(" ", "_") + "_OPS.dam" if args.filename != "": opPath = args.filename + "_OPS.dam" opPath = os.path.join(args.output, opPath) opData = bytearray() opData += bytearray(struct.pack('<H', TOTAL_OPS)) opData += OPERATION_TEST genFile.WriteToFile(opPath, opData) elif args.linear == True: if args.title != "Untitled": STORY_TITLE = args.title if args.author != "Anonymous": STORY_AUTHOR = args.author if HTML: bookPath = STORY_TITLE.lower().replace(" ", "_") + "_LINEAR.html" if args.filename != "": bookPath = args.filename + "_LINEAR.html" else: bookPath = STORY_TITLE.lower().replace(" ", "_") + "_LINEAR.txt" if args.filename != "": bookPath = args.filename + "_LINEAR.txt" bookPath = os.path.join(args.output, bookPath) book = "" if HTML: # Look for an HTML header to insert. sourcePath = os.path.dirname(args.source) headerPath = os.path.join(sourcePath, "header.txt") try: file = open(headerPath, 'r') book += file.read() except IOError: print("[WARNING] No HTML header found at: " + headerPath) book += "Title: " + STORY_TITLE + "\nSubtitle: " + STORY_SUBTITLE + "\nAuthor: " + STORY_AUTHOR book += "\nCredits: " + STORY_CREDITS + "\nContact: " + STORY_CONTACT + "\nLanguage: " + STORY_LANGUAGE + "\nVersion: " + STORY_VERSION + "\nSeed: " + str(SEED) + "\n\n\n" psgList = [] newMap = {} allKeys = list(PASSAGES.keys()) key = "0" p = PASSAGES[key] psgList.append(p) allKeys.remove(key) newMap[key] = key index = 0 while len(allKeys) > 0: index += 1 if "cs" in p and len(p["cs"]) == 1 and p["cs"][0]["link"] in allKeys: p = PASSAGES[p["cs"][0]["link"]] key = p["key"] # Map from old to new index. newMap[key] = str(index) if key in allKeys: allKeys.remove(key) psgList.append(p) else: key = random.choice(allKeys) # If this passage has a single entrance, that passage should be # put in first. if "ik" in PASSAGES[key]: while len(PASSAGES[key]["ik"]) == 1: # Keep tracing back until we find the first passage in a series # of single paths, or until we hit a passage already used. if PASSAGES[key]["ik"][0] in allKeys: key = PASSAGES[key]["ik"][0] else: break if key in allKeys: allKeys.remove(key) p = PASSAGES[key] newMap[key] = str(index) psgList.append(p) index = 0 for psg in psgList: book += linearPassageText(psg, newMap) index += 1 if index < len(psgList): book += "\n\n\n" # Look for an HTML footer to insert. if HTML: sourcePath = os.path.dirname(args.source) footerPath = os.path.join(sourcePath, "footer.txt") try: file = open(footerPath, 'r') book += file.read() #print book except IOError: print("[WARNING] No HTML footer found at: " + footerPath) if os.path.exists(bookPath): os.remove(bookPath) genFile.WriteToFile(bookPath, book) else: result = False; if args.json == False: result = genFile.UpdateManifest(args.output, args.title, args.dirname, args.author, args.json) if result == False: print("[ERROR] Failed to update manifest.") else: result = args.dirname result = genFile.BuildCDAMStory(result, STORY_MAP, PASSAGES, args.output, args.title, args.author, args.json) if result == False: print("[ERROR] Failed to build story.") print("--- Complete! ---\n") #print STORY_MAP #print PASSAGES def linearPassageText(aPassage, aMap): global HTML psgText = "" goto = " (go to " key = aMap[aPassage["key"]] if HTML: psgText += "" + "[" + key + "] " + aPassage['pt'] + "" psgText += "\n" else: psgText += "[" + key + "] " + aPassage['pt'] if aPassage['en'] == True: psgText += "\n--- THE END ---" #if aPassage['eq'] == 1: # psgText += "\n* - THE END"#* Oh no! Better luck next adventure. * - THE END" #elif aPassage['eq'] == 2: # psgText += "\n** - THE END"#** I'm sure you can do better. ** - THE END" #elif aPassage['eq'] == 3: # psgText += "\n*** - THE END"#*** You win some, you lose some. *** - THE END" #elif aPassage['eq'] == 4: # psgText += "\n**** - THE END"#**** Not too bad! **** - THE END" #elif aPassage['eq'] == 5: # psgText += "\n***** - THE END"#***** Congratulations! You sure know your stuff. ***** - THE END" else: choiceText = "" if HTML == False: # Add a delimeter so we know it is done choiceText += "\n---" for choice in aPassage['choices']: m = re.search(kGotoTempTag, psgText) if HTML: if psgText[m.start() - 1] == '\n': choiceText += ("" + choice['text'] + "" + "" + goto + aMap[choice['link']] + ")") else: choiceText += ("" + choice['text'] + "" + "" + goto + aMap[choice['link']] + ")") else: choiceText += ("\n- " + choice['text'] + goto + aMap[choice['link']] + ")") psgText = re.sub(kGotoTempTag, choiceText, psgText, 1); choiceText = "" return psgText def linearPassageTextFull(aPassages, aStoryMap, aKey): psgText = "" goto = " (go to " p = aPassages[aKey] m = aStoryMap[aKey] psgText += "[" + aKey + "] " + p['pt'] # Add a delimeter so we know it is done psgText += "\n---" if p['en'] == True: if p['eq'] == 1: psgText += "\n* - THE END"#* Oh no! Better luck next adventure. * - THE END" elif p['eq'] == 2: psgText += "\n** - THE END"#** I'm sure you can do better. ** - THE END" elif p['eq'] == 3: psgText += "\n*** - THE END"#*** You win some, you lose some. *** - THE END" elif p['eq'] == 4: psgText += "\n**** - THE END"#**** Not too bad! **** - THE END" elif p['eq'] == 5: psgText += "\n***** - THE END"#***** Congratulations! You sure know your stuff. ***** - THE END" else: if len(p['cs']) == 1: psgText += ("\n- " + p['cs'][0] + goto + m[0] + ")") else: for index in range(0, len(p['cs'])): psgText += ("\n- " + p['cs'][index] + goto + m[index] + ")") return psgText def twineBuild(storySource, path, storyDir, title, author): STORY_MAP.clear() PASSAGES.clear() result = BuildCDAMStory(storySource) if result == False: return SimplifyNaming() genFile = gen.CDAMGenFiles() result = genFile.UpdateManifest(path, title, storyDir, author) if result == False: print("[ERROR] Failed to update manifest.") result = genFile.BuildCDAMStory(storyDir, STORY_MAP, PASSAGES, path, title, author) if result == False: print("[ERROR] Failed to build story.") def LoadSource(path): try: file = open(path, 'r') except IOError: print("[ERROR] File not found: " + path) return False sourceStr = file.read() file.close() # Start reading from the first ':' character index = 0 for char in sourceStr: if char == ':': break index += 1 sourceStr = sourceStr[index:] wiki = tiddly.TiddlyWiki() wiki.addTwee(sourceStr) return wiki def BuildCDAMStory(wiki): global STORY_TITLE global STORY_AUTHOR global LINEAR for key in list(wiki.tiddlers.keys()): upKey = key.strip().upper() if upKey not in list(wiki.tiddlers.keys()): wiki.tiddlers[upKey] = wiki.tiddlers[key] del wiki.tiddlers[key] for key in wiki.tiddlers: if wiki.tiddlers[key].title == "StoryTitle": if STORY_TITLE == "": STORY_TITLE = wiki.tiddlers[key].text continue if wiki.tiddlers[key].title == "StorySubtitle": continue if wiki.tiddlers[key].title == "StoryAuthor": if STORY_AUTHOR == "": STORY_AUTHOR = wiki.tiddlers[key].text continue #print "Passage: " + key passage = ParseForAttributes(wiki.tiddlers[key].tags) if passage == False: continue # Is this the starting passage? if key == "START": if "ps" not in passage: passage["ps"] = 0 if "cp" not in passage: passage["cp"] = 0 if "sv" not in passage: passage["sv"] = "1.0" else: if "ps" in passage: if VERBOSE: print("[WARNING] Only set perfect score ('ps' or 'perfect') in the story passage titled 'Start'.") del passage["ps"] if "cp" in passage: if VERBOSE: print("[WARNING] Only set continue penalty ('cp' or 'penalty') in the story passage titled 'Start'.") del passage["cp"] if "sv" in passage: if VERBOSE: print("[WARNING] Only set story version ('sv' or 'version') in the story passage titled 'Start'.") del passage["sv"] passage["pv"] = VERSION if "pp" not in passage: passage["pp"] = 0 else: # Set the 'points' flag. FLAGS['points'] = True rss = wiki.tiddlers[key].toRss() choicePairs = ParseForChoices(rss.description) #PP.pprint(choicePairs) # Print pretty! passage["pt"] = ParseForBody(rss.description) if type(choicePairs) is bool: # No choices in this passage. if choicePairs == True: if "eq" not in passage: if VERBOSE: print("[WARNING] Ending quality 'eq' not set for " + key) # Default to average. passage["eq"] = 3 else: # Set the 'ending quality' flag. FLAGS['ending_quality'] = True STORY_MAP[key] = passage["eq"] passage["en"] = True if "cc" not in passage: passage["cc"] = True else: print("[ERROR] Failed to parse for choices.") return False if type(choicePairs) is list: nodes = [] choices = [] for item in choicePairs: nodes.append(item['link'].strip().upper()) choices.append(item['text']) if ValidateChoices(wiki.tiddlers, nodes) == False: print("[ERROR] Failed to validate choices for node.") return False else: STORY_MAP[key] = nodes passage["en"] = False #passage["cs"] = choices #passage["ck"] = nodes passage["cs"] = choicePairs #print "Validating passage for node " + key if ValidatePassage(passage) == False: print("[ERROR] Failed to validate passage.") return False else: PASSAGES[key] = passage #print PASSAGES def ParseOperation(opParts, iteration): global REPORT data = bytearray() REPORT += "( " types = "" leftName = "" rightName = "" command = opParts.pop(0) leftType = opParts.pop(0) leftValue = bytearray() rightValue = bytearray() #print "Command: " + command #print "LeftType: " + leftType if leftType == "cmd": types += "0011" leftValue = ParseOperation(opParts, iteration + 1) REPORT += " " + command + " " else: tempValue = opParts.pop(0) if leftType == "var": #print tempValue leftName = tempValue types += "0010" if leftName not in VARIABLES: VARIABLES[leftName] = len(VARIABLES) REPORT += leftName + "[" + str(VARIABLES[leftName]) + "] " + command + " " #print "Var #: " + str(VARIABLES[leftName]) leftValue = bytearray(struct.pack('<H', VARIABLES[leftName])) else: types += "0001" leftValue = bytearray(struct.pack('<H', int(tempValue))) REPORT += str(tempValue) + " " + command + " " #print str(leftValue) rightType = opParts.pop(0) #print "RightType: " + rightType rightPrintVal = 0 if rightType == "cmd": types += "0011" rightValue = ParseOperation(opParts, iteration + 1) else: tempValue = opParts.pop(0) if rightType == "var": #print tempValue rightName = tempValue types += "0010" if rightName not in VARIABLES: VARIABLES[rightName] = len(VARIABLES) #print "Index: " + str(VARIABLES[rightName]) rightValue = bytearray(struct.pack('<H', VARIABLES[rightName])) else: types += "0001" rightValue = bytearray(struct.pack('<H', int(tempValue))) rightPrintVal = tempValue #print str(rightValue) data += bitarray.bitarray(types) if command == "equal" or command == "==": data += bytes.fromhex('01') elif command == "not_equal" or command == "!=": data += bytes.fromhex('02') elif command == "greater" or command == ">": data += bytes.fromhex('03') elif command == "less" or command == "<": data += bytes.fromhex('04') elif command == "greater_equal" or command == ">=": data += bytes.fromhex('05') elif command == "less_equal" or command == "<=": data += bytes.fromhex('06') elif command == "and": data += bytes.fromhex('07') elif command == "or": data += bytes.fromhex('08') elif command == "xor": data += bytes.fromhex('09') elif command == "nand": data += bytes.fromhex('0A') elif command == "nor": data += bytes.fromhex('0B') elif command == "xnor": data += bytes.fromhex('0C') elif command == "visible": data += bytes.fromhex('0D') elif command == "mod" or command == "%": data += bytes.fromhex('0E') elif command == "set" or command == "=": data += bytes.fromhex('0F') elif command == "plus" or command == "add" or command == "+": data += bytes.fromhex('10') elif command == "minus" or command == "-": data += bytes.fromhex('11') elif command == "multiply" or command == "mult" or command == "*": data += bytes.fromhex('12') elif command == "divide" or command == "/": data += bytes.fromhex('13') elif command == "rand" or command == "random": data += bytes.fromhex('14') elif command == "dice" or command == "roll": data += bytes.fromhex('15') elif command == "if": data += bytes.fromhex('16') if rightType == "var": REPORT += rightName + "[" + str(VARIABLES[rightName]) + "]" elif rightType == "raw": REPORT += str(rightPrintVal) REPORT += " )" data += leftValue data += rightValue return data def ParseForAttributes(tags): global REPORT global OPERATION_TEST global TOTAL_OPS attributes = {} attributes["vu"] = [] attributes["cvu"] = {} #attributes["cvu"]["totalBytes"] = 0 attributes["cdc"] = {} for attr in tags: attr = attr.lower() if attr == "ignore": return False #print attr pair = attr.split(':') #if pair[0] == "vars": # pair.pop(0) # for var in pair: # varSet = var.split('|') # VARIABLES[varSet[0]] = { "default" : varSet[1], "index" : len(VARIABLES) } if pair[0] == "vu": print(pair) pair.pop(0) REPORT = "" data = bytearray() data = ParseOperation(pair, 0) print(":".join("{:02x}".format(ord(chr(c))) for c in data)) OPERATION_TEST += data TOTAL_OPS += 1 print(REPORT) #updates = { "operation" : pair[1], "leftType" : pair[2], "leftValue" : pair[3], "rightType" : pair[4], "rightValue" : pair[5] } #if updates["leftType"] == "var": # if updates["leftValue"] not in VARIABLES: # print "New var added: " + updates["leftValue"] # VARIABLES[updates["leftValue"]] = { "default" : 0, "index" : len(VARIABLES) } #if updates["rightType"] == "var": # if updates["rightValue"] not in VARIABLES: # print "New var added: " + updates["rightValue"] # VARIABLES[updates["rightValue"]] = { "default" : 0, "index" : len(VARIABLES) } attributes["vu"].append(data) elif pair[0] == "choice": pair.pop(0) index = int(pair.pop(0)) - 1 if attributes["cvu"].setdefault(index, None) == None: attributes["cvu"][index] = { "data" : [], "totalBytes" : 0} opType = pair.pop(0) REPORT = "" data = bytearray() data = ParseOperation(pair, 0) OPERATION_TEST += data TOTAL_OPS += 1 #attributes["cvu"]["totalBytes"] = 0 #components = { "valueOne" : pair[3], "operation" : pair[4], "valueTwoType" : pair[5], "valueTwo" : pair[6] } if opType == "vu": # Value updates print("[VU] " + str(index) + " : " + REPORT) #if attributes["cvu"].setdefault(index, None) == None: #print "Fresh Choice: " + str(index) #attributes["cvu"][index] = { "data" : [], "totalBytes" : 0} #attributes["cvu"][index]["data"] = bytearray() #attributes["cvu"][index]["totalBytes"] = 0 attributes["cvu"][index]["data"].append(data) attributes["cvu"][index]["totalBytes"] += len(data) elif opType == "dc": # Display conditionals print("[DC] " + str(index) + " : " + REPORT) attributes["cdc"].setdefault(index, []).append(data) elif len(pair) == 2: # Set Default Values if pair[0] == "pp" or pair[0] == "points": attributes["pp"] = int(pair[1]) elif pair[0] == "eq" or pair[0] == "quality": attributes["eq"] = int(pair[1]) elif pair[0] == "cc" or pair[0] == "continue": if pair[1] in ['true', '1', 't']: attributes["cc"] = True elif pair[1] in ['false', '0', 'f']: attributes["cc"] = False else: if VERBOSE: print("[WARNING] Invalid boolean value provided for tag: " + pair[0]) elif pair[0] == "ps" or pair[0] == "perfect": attributes["ps"] = int(pair[1]) elif pair[0] == "cp" or pair[0] == "penalty": attributes["cp"] = int(pair[1]) elif pair[0] == "lc" or pair[0] == "color": if VERBOSE: print("[WARNING] Color not currently supported.") #attributes["lc"] = int(pair[1]) elif pair[0] == "sn" or pair[0] == "sound": if VERBOSE: print("[WARNING] Sound not currently supported.") #attributes["sn"] = int(pair[1]) elif pair[0] == "sv" or pair[0] == "version": attributes["sv"] = pair[1] return attributes def ParseForChoices(bodyText): global LINEAR global HTML # Cleanse choices of carriage returns. bodyText = bodyText.replace('\r', '\n') if HTML: bodyText = bodyText.replace('\n\n', ' \n') #else: #bodyText = bodyText.replace('\n\n', '\n') choices = [] # Search for either [[Choice Text|Choice Key]] or [[Choice Key]] and warn about missing text. matchCount = len(re.findall(r"\n*\[\[([^\[\]|]+)(?:\|([\w\d\s]+))?\]\]", bodyText)) for index in range(0, matchCount): m = re.search(r"\n*\[\[([^\[\]|]+)(?:\|([\w\d\s]+))?\]\]", bodyText) #for m in re.finditer(r"\[\[([^\[\]|]+)(?:\|([\w\d\s]+))?\]\]", text): # For [[Run away.|1B]], m.group(0) is whole match, m.group(1) = 'Run away.', and m.group(2) = '1B' # For [[Run away.]], same but there is no m.group(2) choice = {} choice['index'] = m.start() choice['length'] = m.end() - m.start() text = m.group(1) link = m.group(2) # No link means copy text & link text are the same. if not link: link = text # Link is meant for auto-jumping. if text.lower() == kAppend: if len(choices) == 0: # If only a choice key, label it for an auto jump to the passage. if LINEAR: text = "Continue..." else: text = "*" else: print("[ERROR] Can only have a single auto-jump choice per passage.") return False elif text.lower() == kContinue: text = kContinueCopy # Set to <continue> elif text.lower() == 'continue': text = kContinueCopy # Set to <continue> elif text.lower() == 'continue...': text = kContinueCopy # Set to <continue> choice['link'] = link.strip().upper() choice['text'] = text.strip() choices.append(choice) replaceChoices = "" if LINEAR: replaceChoices = kGotoTempTag bodyText = re.sub(r"\n*\s*\[\[([^\[\]|]+)(?:\|([\w\d\s]+))?\]\]\s*", replaceChoices, bodyText, 1) if len(choices) == 0: return True return choices def ParseForBody(text): global LINEAR global HTML # Cleanse of carriage returns (but leave newlines!). # body = text body = body.replace('\r', '\n') if HTML: body = body.replace('\n\n', ' \n') #else: #body = body.replace('\n\n', '\n') replaceChoices = "" if LINEAR: replaceChoices = kGotoTempTag body = re.sub(r"\n*\s*\[\[([^\[\]|]+)(?:\|([\w\d\s]+))?\]\]\s*", replaceChoices, text) return body def ValidateChoices(tiddlers, nodes): #print tiddlers for node in nodes: if node not in tiddlers: #print tiddlers print("[ERROR] Choice key found without matching passage: " + node) return False return True def ValidatePassage(passage): if "cc" in passage: if passage["cc"] == True and passage["en"] == False: if VERBOSE: print("[WARNING] Continue flag useless if a passage isn't an ending. Setting False.") passage["cc"] = False elif passage["cc"] == True and passage["eq"] == 5: #print "[WARNING] Continue flag should be false if ending quality is 5." passage["cc"] = False if passage["en"] == True and "eq" not in passage: print("[ERROR] Ending Quality (eq|quality) missing from ending passage.") return False if "eq" in passage: if passage["eq"] > 5 or passage["eq"] < 1: print("[ERROR] Ending Quality (eq|quality) value outside range of 1-5.") return False if passage["pp"] > 255 or passage["pp"] < 0: print("[ERROR] Points (pp|points) value outside range of 0-255.") return False def SimplifyNaming(): i = 1 newMap = STORY_MAP.copy() STORY_MAP.clear() newPassages = PASSAGES.copy() PASSAGES.clear() for titleKey in newMap: upTitleKey = titleKey.strip().upper() if upTitleKey != "START": # Create a map from all passage titles to its new numbered title. TITLE_MAP[upTitleKey] = str(i) i += 1 else: TITLE_MAP["START"] = "0" for titleKey in newMap: upTitleKey = titleKey.strip().upper() if type(newMap[upTitleKey]) is list: i = 0 for val in newMap[upTitleKey]: # Links always referenced in uppercase. #print "HERE: " + titlekey + " : " + i newMap[upTitleKey][i] = TITLE_MAP[val.strip().upper()] i += 1 STORY_MAP[TITLE_MAP[upTitleKey]] = newMap[upTitleKey] PASSAGES[TITLE_MAP[upTitleKey]] = newPassages[upTitleKey] PASSAGES[TITLE_MAP[upTitleKey]]['key'] = TITLE_MAP[upTitleKey] # Create array for all incoming links on a passage. for key in PASSAGES: psg = PASSAGES[key] if "cs" in psg and len(psg["cs"]) > 0: for choice in psg["cs"]: choice["link"] = TITLE_MAP[choice["link"].strip().upper()] psgKey = choice["link"].strip().upper() if "ik" not in PASSAGES[psgKey]: PASSAGES[psgKey]["ik"] = [""] PASSAGES[psgKey]["ik"].append(psg["key"]) if __name__ == '__main__': #global _UPDATE #global _FORCE main()

29,872

32

395

757f80125b8c8f5468871f3caa0abaecb1d48b89

3,847

py

Python

autolab_core/primitives.py

SnehalD14/autolab_core

c271f1f84283ab5d368618eb85754a549aeae4a3

[ "Apache-2.0" ]

23

2021-04-02T09:02:04.000Z

2022-03-22T05:31:03.000Z

autolab_core/primitives.py

SnehalD14/autolab_core

c271f1f84283ab5d368618eb85754a549aeae4a3

[ "Apache-2.0" ]

35

2021-04-12T09:41:05.000Z

2022-03-26T13:32:46.000Z

autolab_core/primitives.py

SnehalD14/autolab_core

c271f1f84283ab5d368618eb85754a549aeae4a3

[ "Apache-2.0" ]

16

2021-03-30T11:55:45.000Z

2022-03-30T07:10:59.000Z

""" Common geometric primitives. Author: Jeff Mahler """ import numpy as np class Box(object): """A 2D box or 3D rectangular prism. Attributes ---------- dims : :obj:`numpy.ndarray` of float Maximal extent in x, y, and (optionally) z. width : float Maximal extent in x. height : float Maximal extent in y. area : float Area of projection onto xy plane. min_pt : :obj:`numpy.ndarray` of float The minimum x, y, and (optionally) z points. max_pt : :obj:`numpy.ndarray` of float The maximum x, y, and (optionally) z points. center : :obj:`numpy.ndarray` of float The center of the box in 2 or 3D coords. frame : :obj:`str` The frame in which this box is placed. """ def __init__(self, min_pt, max_pt, frame='unspecified'): """Initialize a box. Parameters ---------- min_pt : :obj:`numpy.ndarray` of float The minimum x, y, and (optionally) z points. max_pt : :obj:`numpy.ndarray` of float The maximum x, y, and (optionally) z points. frame : :obj:`str` The frame in which this box is placed. Raises ------ ValueError If max_pt is not strictly larger than min_pt in all dims. """ if np.any((max_pt - min_pt) < 0): raise ValueError('Min point must be smaller than max point') self._min_pt = min_pt self._max_pt = max_pt self._frame = frame @property def dims(self): """:obj:`numpy.ndarray` of float: Maximal extent in x, y, and (optionally) z """ return self._max_pt - self._min_pt @property def width(self): """float: Maximal extent in x. """ return int(np.round(self.dims[1])) @property def height(self): """float: Maximal extent in y. """ return int(np.round(self.dims[0])) @property def area(self): """float: Area of projection onto xy plane. """ return self.width * self.height @property def min_pt(self): """:obj:`numpy.ndarray` of float: The minimum x, y, and (optionally) z points. """ return self._min_pt @property def max_pt(self): """:obj:`numpy.ndarray` of float: The maximum x, y, and (optionally) z points. """ return self._max_pt @property def center(self): """:obj:`numpy.ndarray` of float: The center of the box in 2 or 3D coords. """ return self.min_pt + self.dims / 2.0 @property def ci(self): """float value of center i coordinate""" return self.center[0] @property def cj(self): """float value of center j coordinate""" return self.center[1] @property def frame(self): """:obj:`str`: The frame in which this box is placed. """ return self._frame class Contour(object): """ A set of pixels forming the boundary of an object of interest in an image. Attributes ---------- boundary_pixels : :obj:`numpy.ndarray` Nx2 array of pixel coordinates on the boundary of a contour bounding_box : :obj:`Box` smallest box containing the contour area : float area of the contour num_pixels : int number of pixels along the boundary """ @property

26.531034

86

0.570315

""" Common geometric primitives. Author: Jeff Mahler """ import numpy as np class Box(object): """A 2D box or 3D rectangular prism. Attributes ---------- dims : :obj:`numpy.ndarray` of float Maximal extent in x, y, and (optionally) z. width : float Maximal extent in x. height : float Maximal extent in y. area : float Area of projection onto xy plane. min_pt : :obj:`numpy.ndarray` of float The minimum x, y, and (optionally) z points. max_pt : :obj:`numpy.ndarray` of float The maximum x, y, and (optionally) z points. center : :obj:`numpy.ndarray` of float The center of the box in 2 or 3D coords. frame : :obj:`str` The frame in which this box is placed. """ def __init__(self, min_pt, max_pt, frame='unspecified'): """Initialize a box. Parameters ---------- min_pt : :obj:`numpy.ndarray` of float The minimum x, y, and (optionally) z points. max_pt : :obj:`numpy.ndarray` of float The maximum x, y, and (optionally) z points. frame : :obj:`str` The frame in which this box is placed. Raises ------ ValueError If max_pt is not strictly larger than min_pt in all dims. """ if np.any((max_pt - min_pt) < 0): raise ValueError('Min point must be smaller than max point') self._min_pt = min_pt self._max_pt = max_pt self._frame = frame @property def dims(self): """:obj:`numpy.ndarray` of float: Maximal extent in x, y, and (optionally) z """ return self._max_pt - self._min_pt @property def width(self): """float: Maximal extent in x. """ return int(np.round(self.dims[1])) @property def height(self): """float: Maximal extent in y. """ return int(np.round(self.dims[0])) @property def area(self): """float: Area of projection onto xy plane. """ return self.width * self.height @property def min_pt(self): """:obj:`numpy.ndarray` of float: The minimum x, y, and (optionally) z points. """ return self._min_pt @property def max_pt(self): """:obj:`numpy.ndarray` of float: The maximum x, y, and (optionally) z points. """ return self._max_pt @property def center(self): """:obj:`numpy.ndarray` of float: The center of the box in 2 or 3D coords. """ return self.min_pt + self.dims / 2.0 @property def ci(self): """float value of center i coordinate""" return self.center[0] @property def cj(self): """float value of center j coordinate""" return self.center[1] @property def frame(self): """:obj:`str`: The frame in which this box is placed. """ return self._frame class Contour(object): """ A set of pixels forming the boundary of an object of interest in an image. Attributes ---------- boundary_pixels : :obj:`numpy.ndarray` Nx2 array of pixel coordinates on the boundary of a contour bounding_box : :obj:`Box` smallest box containing the contour area : float area of the contour num_pixels : int number of pixels along the boundary """ def __init__(self, boundary_pixels, area=0.0, frame='unspecified'): self.boundary_pixels = boundary_pixels.squeeze() self.bounding_box = Box(np.min(self.boundary_pixels, axis=0), np.max(self.boundary_pixels, axis=0), frame) self.area = area @property def num_pixels(self): return self.boundary_pixels.shape[0]

352

0

52

f174753cba9198ba54b664cbc54c27b45c67aedf

1,699

py

Python

test/test_events.py

klevio/python-sparkpost

007fb26ff5d046a639a88273265fd0775573a8e2

[ "Apache-2.0" ]

null

test/test_events.py

klevio/python-sparkpost

007fb26ff5d046a639a88273265fd0775573a8e2

[ "Apache-2.0" ]

null

test/test_events.py

klevio/python-sparkpost

007fb26ff5d046a639a88273265fd0775573a8e2

[ "Apache-2.0" ]

null

import pytest import responses from sparkpost import SparkPost from sparkpost.exceptions import SparkPostAPIException @responses.activate @responses.activate @responses.activate @responses.activate

25.742424

75

0.616245

import pytest import responses from sparkpost import SparkPost from sparkpost.exceptions import SparkPostAPIException @responses.activate def test_success_events_message(): responses.add( responses.GET, 'https://api.sparkpost.com/api/v1/events/message', status=200, content_type='application/json', body='{"results": []}' ) sp = SparkPost('fake-key') results = sp.events.message.list() assert results == [] @responses.activate def test_fail_events_message(): responses.add( responses.GET, 'https://api.sparkpost.com/api/v1/events/message', status=500, content_type='application/json', body=""" {"errors": [{"message": "You failed", "description": "More Info"}]} """ ) with pytest.raises(SparkPostAPIException): sp = SparkPost('fake-key') sp.events.message.list() @responses.activate def test_success_events_ingest(): responses.add( responses.GET, 'https://api.sparkpost.com/api/v1/events/ingest', status=200, content_type='application/json', body='{"results": []}' ) sp = SparkPost('fake-key') results = sp.events.ingest.list() assert results == [] @responses.activate def test_fail_events_ingest(): responses.add( responses.GET, 'https://api.sparkpost.com/api/v1/events/ingest', status=500, content_type='application/json', body=""" {"errors": [{"message": "You failed", "description": "More Info"}]} """ ) with pytest.raises(SparkPostAPIException): sp = SparkPost('fake-key') sp.events.ingest.list()

1,404

0

88

76276d63683f2ee0d6c3b6270c2b69939a2ed7ab

195

py

Python

src/util.py

sajtizsolt/dumas

4b7e307535bcc93a75784449bc44055d6dd0730b

[ "MIT" ]

3

2021-08-17T08:14:40.000Z

2021-09-05T10:21:11.000Z

src/util.py

sajtizsolt/dumas

4b7e307535bcc93a75784449bc44055d6dd0730b

[ "MIT" ]

null

src/util.py

sajtizsolt/dumas

4b7e307535bcc93a75784449bc44055d6dd0730b

[ "MIT" ]

null

import sys

19.5

43

0.74359

import sys def print_exception_and_exit(): print_message_and_exit(sys.exc_info()[1]) def print_message_and_exit(message): print('\n Error:') print(message, file=sys.stderr) sys.exit()

138

0

46

7f796b84e36ede142a0744e292e9d72736a1a043

1,984

py

Python

3.7.0/lldb-3.7.0.src/test/functionalities/plugins/commands/TestPluginCommands.py

androm3da/clang_sles

2ba6d0711546ad681883c42dfb8661b842806695

[ "MIT" ]

3

2016-02-10T14:18:40.000Z

2018-02-05T03:15:56.000Z

3.7.0/lldb-3.7.0.src/test/functionalities/plugins/commands/TestPluginCommands.py

androm3da/clang_sles

2ba6d0711546ad681883c42dfb8661b842806695

[ "MIT" ]

1

2016-02-10T15:40:03.000Z

3.7.0/lldb-3.7.0.src/test/functionalities/plugins/commands/TestPluginCommands.py

androm3da/clang_sles

2ba6d0711546ad681883c42dfb8661b842806695

[ "MIT" ]

null

""" Test that plugins that load commands work correctly. """ import os, time import re import unittest2 import lldb from lldbtest import * import lldbutil if __name__ == '__main__': import atexit lldb.SBDebugger.Initialize() atexit.register(lambda: lldb.SBDebugger.Terminate()) unittest2.main()

29.176471

122

0.673387

""" Test that plugins that load commands work correctly. """ import os, time import re import unittest2 import lldb from lldbtest import * import lldbutil class PluginCommandTestCase(TestBase): mydir = TestBase.compute_mydir(__file__) def setUp(self): # Call super's setUp(). TestBase.setUp(self) self.lib_dir = os.environ["LLDB_LIB_DIR"] self.implib_dir = os.environ["LLDB_IMPLIB_DIR"] @expectedFailureFreeBSD('llvm.org/pr17430') @skipIfNoSBHeaders @skipIfHostIncompatibleWithRemote # Requires a compatible arch and platform to link against the host's built lldb lib. def test_load_plugin(self): """Test that plugins that load commands work correctly.""" plugin_name = "plugin" if sys.platform.startswith("darwin"): plugin_lib_name = "lib%s.dylib" % plugin_name else: plugin_lib_name = "lib%s.so" % plugin_name # Invoke the library build rule. self.buildLibrary("plugin.cpp", plugin_name) debugger = lldb.SBDebugger.Create() retobj = lldb.SBCommandReturnObject() retval = debugger.GetCommandInterpreter().HandleCommand("plugin load %s" % plugin_lib_name, retobj) retobj.Clear() retval = debugger.GetCommandInterpreter().HandleCommand("plugin_loaded_command child abc def ghi",retobj) if self.TraceOn(): print retobj.GetOutput() self.expect(retobj,substrs = ['abc def ghi'], exe=False) retobj.Clear() # check that abbreviations work correctly in plugin commands. retval = debugger.GetCommandInterpreter().HandleCommand("plugin_loaded_ ch abc def ghi",retobj) if self.TraceOn(): print retobj.GetOutput() self.expect(retobj,substrs = ['abc def ghi'], exe=False) if __name__ == '__main__': import atexit lldb.SBDebugger.Initialize() atexit.register(lambda: lldb.SBDebugger.Terminate()) unittest2.main()

162

1,485

23

d32e2039cde94b64ddb6c5940d4f74d989163e2b

1,923

py

Python

ros2/code_map_localization/code_map_localization/webcam.py

wzli/CodeMapLocalization

613c021cccbcb4c0f1d42252a9bcb6396b230bea

[ "MIT" ]

2

2020-07-12T16:02:20.000Z

2020-09-06T14:08:43.000Z

ros2/code_map_localization/code_map_localization/webcam.py

wzli/CodeMapLocalization

613c021cccbcb4c0f1d42252a9bcb6396b230bea

[ "MIT" ]

null

ros2/code_map_localization/code_map_localization/webcam.py

wzli/CodeMapLocalization

613c021cccbcb4c0f1d42252a9bcb6396b230bea

[ "MIT" ]

null

import rclpy from rclpy.node import Node from geometry_msgs.msg import PoseStamped from code_map_localization_msgs.msg import Localization from .convert_message import convert_to_ros_msgs from codemap.webcam import WebCamLocalization import ctypes import time libcodemap = ctypes.cdll.LoadLibrary('libcodemap.so') if __name__ == '__main__': main()

36.283019

78

0.702028

import rclpy from rclpy.node import Node from geometry_msgs.msg import PoseStamped from code_map_localization_msgs.msg import Localization from .convert_message import convert_to_ros_msgs from codemap.webcam import WebCamLocalization import ctypes import time libcodemap = ctypes.cdll.LoadLibrary('libcodemap.so') class CodeMapLocalizationWebcam(Node): def __init__(self): super().__init__('code_map_localization') # create publisher self.pose_publisher = self.create_publisher(PoseStamped, 'pose', 1) self.localization_publisher = self.create_publisher( Localization, 'localization', 1) # parse serial device capture_device = self.declare_parameter("capture_device") capture_device = capture_device._value if capture_device else 0 # parse frame_id self.frame_id = self.declare_parameter("frame_id") self.frame_id = self.frame_id._value if self.frame_id else 'map' # ceate webcam stream self.webcam_stream = WebCamLocalization(libcodemap, capture_device) # print start message self.get_logger().info( f'Webcam Localization started on capture device {capture_device}') # poll every 10ms self.timer = self.create_timer(0.03, self.rx_timer_callback) self.start_time = time.time() def rx_timer_callback(self): loc_msg = self.webcam_stream.update() if loc_msg is not None: loc_msg['timestamp'] = int((time.time() - self.start_time) * 1000) pose_msg, localization_msg = convert_to_ros_msgs(loc_msg) pose_msg.header.frame_id = self.frame_id self.pose_publisher.publish(pose_msg) self.localization_publisher.publish(localization_msg) def main(args=None): rclpy.init(args=args) node = CodeMapLocalizationWebcam() rclpy.spin(node) if __name__ == '__main__': main()

1,450

17

99

598d337979ee892594fed1712e2db68a0df4498d

2,017

py

Python

snakypy/dotctrl/actions/unlink.py

williamcanin/dotctrl

c3d8f07efce777cf67c478e96a03afbe37c0107e

[ "MIT" ]

6

2021-04-20T23:17:28.000Z

2022-01-29T21:17:00.000Z

snakypy/dotctrl/actions/unlink.py

williamcanin/dotctrl

c3d8f07efce777cf67c478e96a03afbe37c0107e

[ "MIT" ]

5

2021-05-27T11:33:45.000Z

2021-06-28T08:03:00.000Z

snakypy/dotctrl/actions/unlink.py

williamcanin/dotctrl

c3d8f07efce777cf67c478e96a03afbe37c0107e

[ "MIT" ]

1

2021-06-23T05:03:33.000Z

from contextlib import suppress from os import remove from os.path import islink, join from sys import exit from snakypy.helpers import FG, printer from snakypy.dotctrl.config.base import Base from snakypy.dotctrl.utils import check_init, join_two, listing_files, rm_garbage_config

34.775862

88

0.530987

from contextlib import suppress from os import remove from os.path import islink, join from sys import exit from snakypy.helpers import FG, printer from snakypy.dotctrl.config.base import Base from snakypy.dotctrl.utils import check_init, join_two, listing_files, rm_garbage_config class UnlinkCommand(Base): def __init__(self, root, home): Base.__init__(self, root, home) def main(self, arguments: dict) -> bool: """Method to unlink point files from the repository with their place of origin.""" check_init(self.ROOT) rm_garbage_config(self.HOME, self.repo_path, self.config_path) if arguments["--element"]: file_home = join_two(self.HOME, arguments["--element"]) if islink(file_home): with suppress(Exception): remove(file_home) return True printer( f'Element "{file_home}" not unlinked. Element not found.', foreground=FG().ERROR, ) return False else: objects = [ *listing_files(self.repo_path, only_rc_files=True), *self.data, ] for item in objects: file_home = join(self.HOME, item) if not islink(file_home) and not arguments["--force"]: printer( "Unlinked elements were found. Use the --element option " "to unlink unique links or use --force.", foreground=FG().WARNING, ) exit(0) if islink(file_home): with suppress(Exception): remove(file_home) if len(objects) == 0: printer( "Nothing to unlinked, en masse. Empty list of elements.", foreground=FG().WARNING, ) return False return True

50

1,659

23

437e22c8435e0564c4897390e72feb8a3af89a11

1,191

py

Python

arhuaco/sensors/source/log_metrics.py

kuronosec/arhuaco

6eec1691dd03b2e3726ae8c2101588b45d58b6d7

[ "Apache-2.0" ]

1

2020-08-08T02:17:34.000Z

arhuaco/sensors/source/log_metrics.py

kuronosec/arhuaco

6eec1691dd03b2e3726ae8c2101588b45d58b6d7

[ "Apache-2.0" ]

null

arhuaco/sensors/source/log_metrics.py

kuronosec/arhuaco

6eec1691dd03b2e3726ae8c2101588b45d58b6d7

[ "Apache-2.0" ]

null

29.775

68

0.598657

# Copyright (c) 2019 Andres Gomez Ramirez. # All Rights Reserved. import sys import time import subprocess import logging import os.path import time from arhuaco.sensors.source.source import Source class LogMetrics(Source): def __init__(self, dataPath): # Initialize entities super(LogMetrics, self).__init__() self.dataPath = dataPath def get_data_iterator(self): # Collect data from log file command_log = ("tail -f %s" % self.dataPath) while not os.path.exists(self.dataPath): time.sleep(1) logging.info("Starting the log collection %s" % command_log) proc_log = subprocess.Popen(command_log, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True) # Extract data from the logs while proc_log.poll() is None: line = proc_log.stdout.readline() yield line.decode('utf-8') logging.info(proc_log.poll()) logging.info('Finalyzing log collection.') proc_log.terminate() def get_data_source(self): return None

883

4

104

c5ccd7c06343a21e1742dea0fdd5652c02cb7257

4,685

py

Python

__init__.py

in4lio/yupp

38d4002d2f07c31940b2be572a1c205d6bf63546

[ "MIT" ]

44

2015-09-15T17:14:05.000Z

2021-08-22T10:35:05.000Z

__init__.py

in4lio/yupp

38d4002d2f07c31940b2be572a1c205d6bf63546

[ "MIT" ]

null

__init__.py

in4lio/yupp

38d4002d2f07c31940b2be572a1c205d6bf63546

[ "MIT" ]

1

2015-09-22T22:27:28.000Z

r""" http://github.com/in4lio/yupp/ __ __ _____ _____ /\ \ /\ \ /\ _ \ _ \ \ \ \_\/ \_\/ \_\ \ \_\ \ \ \__ /\____/\ __/\ __/ \/_/\_\/___/\ \_\/\ \_\/ \/_/ \/_/ \/_/ Python 'yupp' Codec Support """ from __future__ import absolute_import from __future__ import division from __future__ import print_function from __future__ import unicode_literals from builtins import str from future import standard_library standard_library.install_aliases() import codecs from encodings import utf_8, search_function from .pp.yulic import VERSION, DESCRIPTION, HOLDER, EMAIL from .pp.yup import cli from .pp.yup import proc_file as translate # --------------------------------------------------------------------------- __pp_name__ = 'yupp' __version__ = VERSION __description__ = DESCRIPTION __author__ = HOLDER __author_email__ = EMAIL __url__ = 'http://github.com/in4lio/yupp/' # --------------------------------------------------------------------------- def read_header( fn ): ''' Read shebang and magic comment from the source file. ''' header = '' try: with open( fn, 'r' ) as f: header = f.readline() if 'coding:' not in header: header += f.readline() except: pass return header # --------------------------------------------------------------------------- # --------------------------------------------------------------------------- # --------------------------------------------------------------------------- # --------------------------------------------------------------------------- # --------------------------------------------------------------------------- # --------------------------------------------------------------------------- codecs.register( yupp_search_function )

29.099379

85

0.522519

r""" http://github.com/in4lio/yupp/ __ __ _____ _____ /\ \ /\ \ /\ _ \ _ \ \ \ \_\/ \_\/ \_\ \ \_\ \ \ \__ /\____/\ __/\ __/ \/_/\_\/___/\ \_\/\ \_\/ \/_/ \/_/ \/_/ Python 'yupp' Codec Support """ from __future__ import absolute_import from __future__ import division from __future__ import print_function from __future__ import unicode_literals from builtins import str from future import standard_library standard_library.install_aliases() import codecs from encodings import utf_8, search_function from .pp.yulic import VERSION, DESCRIPTION, HOLDER, EMAIL from .pp.yup import cli from .pp.yup import proc_file as translate # --------------------------------------------------------------------------- __pp_name__ = 'yupp' __version__ = VERSION __description__ = DESCRIPTION __author__ = HOLDER __author_email__ = EMAIL __url__ = 'http://github.com/in4lio/yupp/' # --------------------------------------------------------------------------- def read_header( fn ): ''' Read shebang and magic comment from the source file. ''' header = '' try: with open( fn, 'r' ) as f: header = f.readline() if 'coding:' not in header: header += f.readline() except: pass return header # --------------------------------------------------------------------------- def decode_stream( fn, _stream ): from ast import parse from .pp.yup import proc_stream from .pylib import yutraceback try: ok, code, fn_o, shrink = proc_stream( _stream, fn ) except Exception: yutraceback.print_exc( None ) ok = False if not ok: return '' # -- replace the filename of source file in traceback yutraceback.substitution( fn, fn_o, shrink ) # -- check syntax of the preprocessed code try: parse( code, fn_o ) except SyntaxError: yutraceback.print_exc( 0 ) code = '' return code # -- or using a dirty hack execfile( fn_o ) return '' # --------------------------------------------------------------------------- def decoder_factory( basecodec ): # ----------------------------------- def decode( input, errors='strict' ): from io import StringIO from sys import argv data, bytesencoded = basecodec.decode( input, errors ) fn = argv[ 0 ] return decode_stream( fn, StringIO( read_header( fn ) + data )), bytesencoded return decode # --------------------------------------------------------------------------- def incremental_decoder_factory( basecodec ): # ----------------------------------- class IncrementalDecoder( codecs.BufferedIncrementalDecoder ): def _buffer_decode( self, input, errors, final ): if input and final: return decoder_factory( basecodec )( input, errors ) # -- we don't support incremental decoding return '', 0 return IncrementalDecoder # --------------------------------------------------------------------------- def stream_decoder_factory( basecodec ): # ----------------------------------- class StreamReader( basecodec.StreamReader ): def __init__( self, *args, **kwargs ): from io import StringIO basecodec.StreamReader.__init__( self, *args, **kwargs ) self.stream = StringIO( decode_stream( self.stream.name, self.stream )) return StreamReader # --------------------------------------------------------------------------- def yupp_search_function( coding ): if not coding.lower().startswith( __pp_name__ ): return None dot = coding.find( '.' ) if dot != -1: # -- coding: yupp.<encoding> if dot != len( __pp_name__ ): # -- wrong coding format return None basecodec = search_function( coding[( dot + 1 ): ]) if basecodec is None: # -- unknown <encoding> return None else: if len( coding ) != len( __pp_name__ ): # -- wrong coding format return None # -- default encoding: UTF-8 basecodec = utf_8 return codecs.CodecInfo( name=__pp_name__, encode=basecodec.encode, decode=decoder_factory( basecodec ), incrementalencoder=basecodec.IncrementalEncoder, incrementaldecoder=incremental_decoder_factory( basecodec ), streamwriter=basecodec.StreamWriter, streamreader=stream_decoder_factory( basecodec ) ) # --------------------------------------------------------------------------- codecs.register( yupp_search_function )

2,724

0

110

6f4fefcc76d7ffae881a56693f6ae63af3836838

1,430

py

Python

rtmplib/packet.py

genba2/pinybotbeta-enhanced

564ae7c363ee00ad2ae0e05d74e08e58de3d1d2f

[ "MIT" ]

null

rtmplib/packet.py

genba2/pinybotbeta-enhanced

564ae7c363ee00ad2ae0e05d74e08e58de3d1d2f

[ "MIT" ]

null

rtmplib/packet.py

genba2/pinybotbeta-enhanced

564ae7c363ee00ad2ae0e05d74e08e58de3d1d2f

[ "MIT" ]

null

import time HANDSHAKE_LENGTH = 1536 class Handshake(object): """ A handshake packet. @ivar first: The first 4 bytes of the packet, represented as an unsigned long. @type first: 32bit unsigned int. @ivar second: The second 4 bytes of the packet, represented as an unsigned long. @type second: 32bit unsigned int. @ivar payload: A blob of data which makes up the rest of the packet. This must be C{HANDSHAKE_LENGTH} - 8 bytes in length. @type payload: C{str} @ivar timestamp: Timestamp that this packet was created (in milliseconds). @type timestamp: C{int} """ first = None second = None payload = None timestamp = None def encode(self, stream_buffer): """ Encodes this packet to a stream. """ stream_buffer.write_ulong(self.first or 0) stream_buffer.write_ulong(self.second or 0) stream_buffer.write(self.payload) def decode(self, stream_buffer): """ Decodes this packet from a stream. """ self.first = stream_buffer.read_ulong() self.second = stream_buffer.read_ulong() self.payload = stream_buffer.read(HANDSHAKE_LENGTH - 8)

26.481481

78

0.633566

import time HANDSHAKE_LENGTH = 1536 class Handshake(object): """ A handshake packet. @ivar first: The first 4 bytes of the packet, represented as an unsigned long. @type first: 32bit unsigned int. @ivar second: The second 4 bytes of the packet, represented as an unsigned long. @type second: 32bit unsigned int. @ivar payload: A blob of data which makes up the rest of the packet. This must be C{HANDSHAKE_LENGTH} - 8 bytes in length. @type payload: C{str} @ivar timestamp: Timestamp that this packet was created (in milliseconds). @type timestamp: C{int} """ first = None second = None payload = None timestamp = None def __init__(self, **kwargs): timestamp = kwargs.get('timestamp', None) if timestamp is None: kwargs['timestamp'] = int(time.time()) self.__dict__.update(kwargs) def encode(self, stream_buffer): """ Encodes this packet to a stream. """ stream_buffer.write_ulong(self.first or 0) stream_buffer.write_ulong(self.second or 0) stream_buffer.write(self.payload) def decode(self, stream_buffer): """ Decodes this packet from a stream. """ self.first = stream_buffer.read_ulong() self.second = stream_buffer.read_ulong() self.payload = stream_buffer.read(HANDSHAKE_LENGTH - 8)

178

0

27

433a9cc460319ac1dc362de667e3e4fbb75f3448

1,052

py

Python

kits/python/mediocre/main.py

ppinchuk/Lux-Design-2021

8a04ad48c6749cafc9aca986f14e75daaa31c789

[ "Apache-2.0" ]

null

kits/python/mediocre/main.py

ppinchuk/Lux-Design-2021

8a04ad48c6749cafc9aca986f14e75daaa31c789

[ "Apache-2.0" ]

null

kits/python/mediocre/main.py

ppinchuk/Lux-Design-2021

8a04ad48c6749cafc9aca986f14e75daaa31c789

[ "Apache-2.0" ]

null

from typing import Dict import sys from mediocre_agent import agent if __name__ == "__main__": def read_input(): """ Reads input from stdin """ try: return input() except EOFError as eof: raise SystemExit(eof) step = 0 observation = Observation() observation["updates"] = [] observation["step"] = 0 player_id = 0 while True: inputs = read_input() observation["updates"].append(inputs) if step == 0: player_id = int(observation["updates"][0]) observation.player = player_id if inputs == "D_DONE": actions = agent(observation, None) observation["updates"] = [] step += 1 observation["step"] = step print(",".join(actions)) print("D_FINISH")

26.974359

54

0.521863

from typing import Dict import sys from mediocre_agent import agent if __name__ == "__main__": def read_input(): """ Reads input from stdin """ try: return input() except EOFError as eof: raise SystemExit(eof) step = 0 class Observation(Dict[str, any]): def __init__(self, player=0) -> None: self.player = player # self.updates = [] # self.step = 0 observation = Observation() observation["updates"] = [] observation["step"] = 0 player_id = 0 while True: inputs = read_input() observation["updates"].append(inputs) if step == 0: player_id = int(observation["updates"][0]) observation.player = player_id if inputs == "D_DONE": actions = agent(observation, None) observation["updates"] = [] step += 1 observation["step"] = step print(",".join(actions)) print("D_FINISH")

109

13

57

c0f7fbc32344fbe01dd0f5e9a00e97f8421dc665

650

py

Python

core/agent.py

ihgalis/queue_simulation

a49412417cedbdb1fe7943390a6f805489c33aaa

[ "MIT" ]

null

core/agent.py

ihgalis/queue_simulation

a49412417cedbdb1fe7943390a6f805489c33aaa

[ "MIT" ]

null

core/agent.py

ihgalis/queue_simulation

a49412417cedbdb1fe7943390a6f805489c33aaa

[ "MIT" ]

null

class Agent(object): """ represents the agent who takes the calls from the queue """ def __init__(self, id, free, minutes_till_ready=0): """ constructor just sets the id :param name: string """ self.id = id self.free = free self.minutes_till_ready = minutes_till_ready @staticmethod def consume(caller_list): """ consumes callers from the queue and chats with the caller. :param caller_list: :return: """ temp_caller = caller_list.consume_caller() print("agent consumes - " + str(temp_caller.chat()))

23.214286

60

0.578462

class Agent(object): """ represents the agent who takes the calls from the queue """ def __init__(self, id, free, minutes_till_ready=0): """ constructor just sets the id :param name: string """ self.id = id self.free = free self.minutes_till_ready = minutes_till_ready @staticmethod def consume(caller_list): """ consumes callers from the queue and chats with the caller. :param caller_list: :return: """ temp_caller = caller_list.consume_caller() print("agent consumes - " + str(temp_caller.chat()))

0

c262beba650fdb2c95f431d3157ae61c710ef51a

324

py

Python

config/wsgi.py

drixselecta/homebytwo

29d26ce9f5586943e3b64c95aa4ce9ea7263bd10

[ "MIT" ]

7

2018-03-10T20:58:59.000Z

2021-08-22T17:18:09.000Z

config/wsgi.py

HomebyTwo/homebytwo

29d26ce9f5586943e3b64c95aa4ce9ea7263bd10

[ "MIT" ]

69

2017-02-01T21:15:43.000Z

2022-02-26T09:33:27.000Z

config/wsgi.py

drixselecta/homebytwo

29d26ce9f5586943e3b64c95aa4ce9ea7263bd10

[ "MIT" ]

null

from os import environ from pathlib import Path from django.core.wsgi import get_wsgi_application from config import get_project_root_path, import_env_vars import_env_vars(Path(get_project_root_path(), "envdir")) environ.setdefault("DJANGO_SETTINGS_MODULE", "config.settings.base") application = get_wsgi_application()

24.923077

68

0.833333

from os import environ from pathlib import Path from django.core.wsgi import get_wsgi_application from config import get_project_root_path, import_env_vars import_env_vars(Path(get_project_root_path(), "envdir")) environ.setdefault("DJANGO_SETTINGS_MODULE", "config.settings.base") application = get_wsgi_application()

0

592da9c69b2699031221663c32e6918ce48f5588

18

py

Python

acmicpc/15733/15733.py

love-adela/algorithm

4ccd02173c96f8369962f1fd4e5166a221690fa2

[ "MIT" ]

3

2019-03-09T05:19:23.000Z

2019-04-06T09:26:36.000Z

acmicpc/15733/15733.py

love-adela/algorithm

4ccd02173c96f8369962f1fd4e5166a221690fa2

[ "MIT" ]

1

2020-02-23T10:38:04.000Z

acmicpc/15733/15733.py

love-adela/algorithm

4ccd02173c96f8369962f1fd4e5166a221690fa2

[ "MIT" ]

2

2017-11-20T14:06:06.000Z

2020-07-20T00:17:47.000Z

print("I'm Sexy")

9

17

0.611111

print("I'm Sexy")

0

1f5d038875e16e0fe3a4bf4b7a051aa57494670c

1,343

py

Python

run_init_images.py

alchem0x2A/paper.ZnVO

b36839ee52867c6892177b6152daa7a5b4fd4109

[ "MIT" ]

null

run_init_images.py

alchem0x2A/paper.ZnVO

b36839ee52867c6892177b6152daa7a5b4fd4109

[ "MIT" ]

null

run_init_images.py

alchem0x2A/paper.ZnVO

b36839ee52867c6892177b6152daa7a5b4fd4109

[ "MIT" ]

null

import sys import os, os.path # May need this for the path issue for gpaw-python sys.path.append(os.path.dirname(os.path.abspath(__file__))) from src.structure import get_structure from src.supercell import make_super, add_adatom from src.neb import neb, calc_img import shutil from ase.parallel import paropen, parprint, world, rank, broadcast from ase.visualize import view # Name=Zn, Co if __name__ == "__main__": assert len(sys.argv) == 3 mater = sys.argv[1] imag = sys.argv[2] main(name=mater, imag=imag)

27.979167

81

0.613552

import sys import os, os.path # May need this for the path issue for gpaw-python sys.path.append(os.path.dirname(os.path.abspath(__file__))) from src.structure import get_structure from src.supercell import make_super, add_adatom from src.neb import neb, calc_img import shutil from ase.parallel import paropen, parprint, world, rank, broadcast from ase.visualize import view # Name=Zn, Co def main(name, imag="init", root="/cluster/scratch/ttian/ZnVO", clean=False): assert imag in ("init", "final") if name not in ("Zn", "Co"): return False # Directory if rank == 0: base_dir = os.path.join(root, "{}V2O5".format(name)) if clean: shutil.rmtree(base_dir, ignore_errors=True) if not os.path.exists(base_dir): os.makedirs(base_dir) world.barrier() if clean: return # on all ranks base_dir = os.path.join(root, "{}V2O5".format(name)) if imag == "init": calc_img(base_dir=base_dir, scaled_pos=(0, 0, 1 / 2), index=imag) else: calc_img(base_dir=base_dir, scaled_pos=(1 / 2, 1 / 2, 1 / 2), index=imag) return 0 if __name__ == "__main__": assert len(sys.argv) == 3 mater = sys.argv[1] imag = sys.argv[2] main(name=mater, imag=imag)

791

0

22

9d8dfad20f0cd219f29ef974ce7e0abe3aeec538

959

py

Python

360agent/plugins/gpu.py

vfuse/360agent

947e5ffe6a9e2ef22665f4b2b98c882e698fb201

[ "BSD-3-Clause" ]

88

2017-01-26T14:26:37.000Z

2021-12-31T17:07:03.000Z

360agent/plugins/gpu.py

vfuse/360agent

947e5ffe6a9e2ef22665f4b2b98c882e698fb201

[ "BSD-3-Clause" ]

26

2016-12-27T12:28:16.000Z

2022-02-24T08:11:45.000Z

360agent/plugins/gpu.py

vfuse/360agent

947e5ffe6a9e2ef22665f4b2b98c882e698fb201

[ "BSD-3-Clause" ]

28

2017-04-11T08:40:00.000Z

2021-10-05T06:43:04.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- import plugins import sys if __name__ == '__main__': Plugin().execute()

28.205882

87

0.535975

#!/usr/bin/env python # -*- coding: utf-8 -*- import plugins import sys class Plugin(plugins.BasePlugin): __name__ = 'gpu' def run(self, *unused): ''' expirimental plugin used to collect GPU load from OpenHardWareMonitor (Windows) ''' data = {} if sys.platform == "win32": try: import wmi except: return 'wmi module not installed.' try: w = wmi.WMI(namespace="root\OpenHardwareMonitor") temperature_infos = w.Sensor() for sensor in temperature_infos: if sensor.SensorType==u'Load' and sensor.Name==u'GPU Core': data[sensor.Parent.replace('/','-').strip('-')] = sensor.Value except: return 'Could not fetch GPU Load data from OpenHardwareMonitor.' return data if __name__ == '__main__': Plugin().execute()

0

812

23

c08b1e1fb17569d5d4677af2fce155f334018648

3,794

py

Python

core/argo/core/network/ResEnc.py

szokejokepu/natural-rws

bb1ad4ca3ec714e6bf071d2136593dc853492b68

[ "MIT" ]

null

core/argo/core/network/ResEnc.py

szokejokepu/natural-rws

bb1ad4ca3ec714e6bf071d2136593dc853492b68

[ "MIT" ]

null

core/argo/core/network/ResEnc.py

szokejokepu/natural-rws

bb1ad4ca3ec714e6bf071d2136593dc853492b68

[ "MIT" ]

null

import tensorflow as tf import sonnet as snt from .build_utils import residual_stack, maybe_set_l2_conv_contractive_regularizer from .AbstractResNetLayer import AbstractResNetLayer class ResEnc(AbstractResNetLayer): """ res enc used in VQ """ #TODO remove biases before batch norm, see if it makes any difference. Remove dropouts?

39.113402

114

0.495519

import tensorflow as tf import sonnet as snt from .build_utils import residual_stack, maybe_set_l2_conv_contractive_regularizer from .AbstractResNetLayer import AbstractResNetLayer class ResEnc(AbstractResNetLayer): """ res enc used in VQ """ #TODO remove biases before batch norm, see if it makes any difference. Remove dropouts? def __init__(self, num_hiddens, num_residual_layers, num_residual_hiddens, activation, is_training, name='ResEnc', prob_drop=0.1, bn_momentum=0.99, bn_renormalization=True, creg_scale=None, **extra_params): super().__init__(num_hiddens, num_residual_layers, num_residual_hiddens, activation, is_training, name=name, prob_drop=prob_drop, bn_momentum=bn_momentum, bn_renormalization=bn_renormalization, creg_scale=creg_scale, **extra_params) def _build(self, x): # h_pre = x conv1 = snt.Conv2D( output_channels=self._num_hiddens / 2, kernel_shape=(4, 4), stride=(2, 2), # use_bias=False, **self._extra_params, name="enc_1") h = conv1(x) maybe_set_l2_conv_contractive_regularizer(conv1, h, self._activation, self._creg_scale, name="enc_1_creg") h = self._dropout(h, training=self._is_training) h = tf.layers.batch_normalization(h, training=self._is_training, momentum=self._bn_momentum, renorm=self._bn_renormalization, renorm_momentum=self._bn_momentum, renorm_clipping=self._renorm_clipping, name="batch_norm_1") h = self._activation(h) conv2 = snt.Conv2D( output_channels=self._num_hiddens, kernel_shape=(4, 4), stride=(2, 2), # use_bias=False, **self._extra_params, name="enc_2") h = conv2(h) maybe_set_l2_conv_contractive_regularizer(conv2, h, self._activation, self._creg_scale, name="enc_2_creg") h = self._dropout(h, training=self._is_training) h = tf.layers.batch_normalization(h, training=self._is_training, momentum=self._bn_momentum, renorm=self._bn_renormalization, renorm_momentum=self._bn_momentum, renorm_clipping=self._renorm_clipping, name="batch_norm_2") h = self._activation(h) h = residual_stack( h, self._num_hiddens, self._num_residual_layers, self._num_residual_hiddens, activation=self._activation, training=self._is_training, prob_drop=self._prob_drop, momentum=self._bn_momentum, renorm=self._bn_renormalization, renorm_momentum=self._bn_momentum, renorm_clipping=self._renorm_clipping, creg_scale = self._creg_scale, **self._extra_params ) return h

3,395

0

53

904a75d44462d0b84d10192273337b4d499672b8

1,366

py

Python

xrpl/models/transactions/payment_channel_fund.py

mDuo13/xrpl-py

70f927dcd2dbb8644b3e210b0a8de2a214e71e3d

[ "0BSD" ]

null

xrpl/models/transactions/payment_channel_fund.py

mDuo13/xrpl-py

70f927dcd2dbb8644b3e210b0a8de2a214e71e3d

[ "0BSD" ]

null

xrpl/models/transactions/payment_channel_fund.py

mDuo13/xrpl-py

70f927dcd2dbb8644b3e210b0a8de2a214e71e3d

[ "0BSD" ]

null

""" Represents a PaymentChannelFund transaction on the XRP Ledger. A PaymentChannelFund transaction adds additional XRP to an open payment channel, and optionally updates the expiration time of the channel. Only the source address of the channel can use this transaction. `See PaymentChannelFund <https://xrpl.org/paymentchannelfund.html>`_ """ from dataclasses import dataclass, field from typing import Optional from xrpl.models.required import REQUIRED from xrpl.models.transactions.transaction import Transaction, TransactionType from xrpl.models.utils import require_kwargs_on_init @require_kwargs_on_init @dataclass(frozen=True) class PaymentChannelFund(Transaction): """ Represents a PaymentChannelFund transaction on the XRP Ledger. A PaymentChannelFund transaction adds additional XRP to an open payment channel, and optionally updates the expiration time of the channel. Only the source address of the channel can use this transaction. `See PaymentChannelFund <https://xrpl.org/paymentchannelfund.html>`_ """ #: This field is required. channel: str = REQUIRED # type: ignore #: This field is required. amount: str = REQUIRED # type: ignore expiration: Optional[int] = None transaction_type: TransactionType = field( default=TransactionType.PAYMENT_CHANNEL_FUND, init=False, )

35.947368

86

0.770864

""" Represents a PaymentChannelFund transaction on the XRP Ledger. A PaymentChannelFund transaction adds additional XRP to an open payment channel, and optionally updates the expiration time of the channel. Only the source address of the channel can use this transaction. `See PaymentChannelFund <https://xrpl.org/paymentchannelfund.html>`_ """ from dataclasses import dataclass, field from typing import Optional from xrpl.models.required import REQUIRED from xrpl.models.transactions.transaction import Transaction, TransactionType from xrpl.models.utils import require_kwargs_on_init @require_kwargs_on_init @dataclass(frozen=True) class PaymentChannelFund(Transaction): """ Represents a PaymentChannelFund transaction on the XRP Ledger. A PaymentChannelFund transaction adds additional XRP to an open payment channel, and optionally updates the expiration time of the channel. Only the source address of the channel can use this transaction. `See PaymentChannelFund <https://xrpl.org/paymentchannelfund.html>`_ """ #: This field is required. channel: str = REQUIRED # type: ignore #: This field is required. amount: str = REQUIRED # type: ignore expiration: Optional[int] = None transaction_type: TransactionType = field( default=TransactionType.PAYMENT_CHANNEL_FUND, init=False, )

0

65b2babb163c94ccd29863798b7089a565f8bf1e

13,980

py

Python

nlcpy/ufuncs/operations.py

SX-Aurora/nlcpy

0a53eec8778073bc48b12687b7ce37ab2bf2b7e0

[ "BSD-3-Clause" ]

11

2020-07-31T02:21:55.000Z

2022-03-10T03:12:11.000Z

nlcpy/ufuncs/operations.py

SX-Aurora/nlcpy

0a53eec8778073bc48b12687b7ce37ab2bf2b7e0

[ "BSD-3-Clause" ]

null

nlcpy/ufuncs/operations.py

SX-Aurora/nlcpy

0a53eec8778073bc48b12687b7ce37ab2bf2b7e0

[ "BSD-3-Clause" ]

null

# # * The source code in this file is developed independently by NEC Corporation. # # # NLCPy License # # # Copyright (c) 2020-2021 NEC Corporation # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # * Redistributions of source code must retain the above copyright notice, # this list of conditions and the following disclaimer. # * Redistributions in binary form must reproduce the above copyright notice, # this list of conditions and the following disclaimer in the documentation # and/or other materials provided with the distribution. # * Neither NEC Corporation nor the names of its contributors may be # used to endorse or promote products derived from this software # without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND # ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED # WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE # DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE # FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES # (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; # LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND # ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT # (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS # SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. # import numpy from nlcpy.ufuncs import ufuncs from nlcpy.ufuncs import casting from nlcpy.ufuncs import err from nlcpy.ufuncs import ufunc_docs # ---------------------------------------------------------------------------- # ufunc operations # see: https://docs.scipy.org/doc/numpy/reference/ufuncs.html # ---------------------------------------------------------------------------- # math_operations add = ufuncs.create_ufunc( 'nlcpy_add', numpy.add.types, err._add_error_check, doc=ufunc_docs._add_doc ) subtract = ufuncs.create_ufunc( 'nlcpy_subtract', casting._subtract_types, err._subtract_error_check, doc=ufunc_docs._subtract_doc ) multiply = ufuncs.create_ufunc( 'nlcpy_multiply', numpy.multiply.types, err._multiply_error_check, doc=ufunc_docs._multiply_doc ) true_divide = ufuncs.create_ufunc( 'nlcpy_true_divide', casting._true_divide_types, err._true_divide_error_check, doc=ufunc_docs._true_divide_doc ) # ufunc_operation(divide,orig_types,valid_error_check)dnl divide = true_divide logaddexp = ufuncs.create_ufunc( 'nlcpy_logaddexp', numpy.logaddexp.types, err._logaddexp_error_check, doc=ufunc_docs._logaddexp_doc ) logaddexp2 = ufuncs.create_ufunc( 'nlcpy_logaddexp2', numpy.logaddexp2.types, err._logaddexp2_error_check, doc=ufunc_docs._logaddexp2_doc ) floor_divide = ufuncs.create_ufunc( 'nlcpy_floor_divide', numpy.floor_divide.types, err._floor_divide_error_check, doc=ufunc_docs._floor_divide_doc ) negative = ufuncs.create_ufunc( 'nlcpy_negative', casting._negative_types, err._negative_error_check, doc=ufunc_docs._negative_doc ) positive = ufuncs.create_ufunc( 'nlcpy_positive', casting._positive_types, err._positive_error_check, doc=ufunc_docs._positive_doc ) power = ufuncs.create_ufunc( 'nlcpy_power', numpy.power.types, err._power_error_check, doc=ufunc_docs._power_doc ) remainder = ufuncs.create_ufunc( 'nlcpy_remainder', casting._remainder_types, err._remainder_error_check, doc=ufunc_docs._remainder_doc ) # ufunc_operation(mod,orig_types,valid_error_check)dnl mod = remainder fmod = ufuncs.create_ufunc( 'nlcpy_fmod', casting._fmod_types, err._fmod_error_check, doc=ufunc_docs._fmod_doc ) # ufunc_operation(divmod,numpy_types,valid_error_check)dnl absolute = ufuncs.create_ufunc( 'nlcpy_absolute', numpy.absolute.types, err._absolute_error_check, doc=ufunc_docs._absolute_doc ) fabs = ufuncs.create_ufunc( 'nlcpy_fabs', casting._fabs_types, err._fabs_error_check, doc=ufunc_docs._fabs_doc ) rint = ufuncs.create_ufunc( 'nlcpy_rint', numpy.rint.types, err._rint_error_check, doc=ufunc_docs._rint_doc ) sign = ufuncs.create_ufunc( 'nlcpy_sign', casting._sign_types, err._sign_error_check, doc=ufunc_docs._sign_doc ) heaviside = ufuncs.create_ufunc( 'nlcpy_heaviside', numpy.heaviside.types, err._heaviside_error_check, doc=ufunc_docs._heaviside_doc ) conjugate = ufuncs.create_ufunc( 'nlcpy_conjugate', numpy.conjugate.types, err._conjugate_error_check, doc=ufunc_docs._conjugate_doc ) # ufunc_operation(conj,numpy_types,valid_error_check)dnl conj = conjugate exp = ufuncs.create_ufunc( 'nlcpy_exp', numpy.exp.types, err._exp_error_check, doc=ufunc_docs._exp_doc ) exp2 = ufuncs.create_ufunc( 'nlcpy_exp2', numpy.exp2.types, err._exp2_error_check, doc=ufunc_docs._exp2_doc ) log = ufuncs.create_ufunc( 'nlcpy_log', numpy.log.types, err._log_error_check, doc=ufunc_docs._log_doc ) log2 = ufuncs.create_ufunc( 'nlcpy_log2', numpy.log2.types, err._log2_error_check, doc=ufunc_docs._log2_doc ) log10 = ufuncs.create_ufunc( 'nlcpy_log10', numpy.log10.types, err._log10_error_check, doc=ufunc_docs._log10_doc ) expm1 = ufuncs.create_ufunc( 'nlcpy_expm1', numpy.expm1.types, err._expm1_error_check, doc=ufunc_docs._expm1_doc ) log1p = ufuncs.create_ufunc( 'nlcpy_log1p', numpy.log1p.types, err._log1p_error_check, doc=ufunc_docs._log1p_doc ) sqrt = ufuncs.create_ufunc( 'nlcpy_sqrt', numpy.sqrt.types, err._sqrt_error_check, doc=ufunc_docs._sqrt_doc ) square = ufuncs.create_ufunc( 'nlcpy_square', numpy.square.types, err._square_error_check, doc=ufunc_docs._square_doc ) cbrt = ufuncs.create_ufunc( 'nlcpy_cbrt', casting._cbrt_types, err._cbrt_error_check, doc=ufunc_docs._cbrt_doc ) reciprocal = ufuncs.create_ufunc( 'nlcpy_reciprocal', numpy.reciprocal.types, err._reciprocal_error_check, doc=ufunc_docs._reciprocal_doc ) # ufunc_operation(gcd)dnl # ufunc_operation(lcm)dnl # bit-twiddling functions bitwise_and = ufuncs.create_ufunc( 'nlcpy_bitwise_and', casting._bitwise_and_types, err._bitwise_and_error_check, doc=ufunc_docs._bitwise_and_doc ) bitwise_or = ufuncs.create_ufunc( 'nlcpy_bitwise_or', casting._bitwise_or_types, err._bitwise_or_error_check, doc=ufunc_docs._bitwise_or_doc ) bitwise_xor = ufuncs.create_ufunc( 'nlcpy_bitwise_xor', casting._bitwise_xor_types, err._bitwise_xor_error_check, doc=ufunc_docs._bitwise_xor_doc ) invert = ufuncs.create_ufunc( 'nlcpy_invert', casting._invert_types, err._invert_error_check, doc=ufunc_docs._invert_doc ) left_shift = ufuncs.create_ufunc( 'nlcpy_left_shift', casting._left_shift_types, err._left_shift_error_check, doc=ufunc_docs._left_shift_doc ) right_shift = ufuncs.create_ufunc( 'nlcpy_right_shift', casting._right_shift_types, err._right_shift_error_check, doc=ufunc_docs._right_shift_doc ) # comparison functions greater = ufuncs.create_ufunc( 'nlcpy_greater', numpy.greater.types, err._greater_error_check, doc=ufunc_docs._greater_doc ) greater_equal = ufuncs.create_ufunc( 'nlcpy_greater_equal', numpy.greater_equal.types, err._greater_equal_error_check, doc=ufunc_docs._greater_equal_doc ) less = ufuncs.create_ufunc( 'nlcpy_less', numpy.less.types, err._less_error_check, doc=ufunc_docs._less_doc ) less_equal = ufuncs.create_ufunc( 'nlcpy_less_equal', numpy.less_equal.types, err._less_equal_error_check, doc=ufunc_docs._less_equal_doc ) not_equal = ufuncs.create_ufunc( 'nlcpy_not_equal', numpy.not_equal.types, err._not_equal_error_check, doc=ufunc_docs._not_equal_doc ) equal = ufuncs.create_ufunc( 'nlcpy_equal', numpy.equal.types, err._equal_error_check, doc=ufunc_docs._equal_doc ) logical_and = ufuncs.create_ufunc( 'nlcpy_logical_and', numpy.logical_and.types, err._logical_and_error_check, doc=ufunc_docs._logical_and_doc ) logical_or = ufuncs.create_ufunc( 'nlcpy_logical_or', numpy.logical_or.types, err._logical_or_error_check, doc=ufunc_docs._logical_or_doc ) logical_xor = ufuncs.create_ufunc( 'nlcpy_logical_xor', numpy.logical_xor.types, err._logical_xor_error_check, doc=ufunc_docs._logical_xor_doc ) logical_not = ufuncs.create_ufunc( 'nlcpy_logical_not', numpy.logical_not.types, err._logical_not_error_check, doc=ufunc_docs._logical_not_doc ) minimum = ufuncs.create_ufunc( 'nlcpy_minimum', numpy.minimum.types, err._minimum_error_check, doc=ufunc_docs._minimum_doc ) maximum = ufuncs.create_ufunc( 'nlcpy_maximum', numpy.maximum.types, err._maximum_error_check, doc=ufunc_docs._maximum_doc ) fmax = ufuncs.create_ufunc( 'nlcpy_fmax', numpy.fmax.types, err._fmax_error_check, doc=ufunc_docs._fmax_doc ) fmin = ufuncs.create_ufunc( 'nlcpy_fmin', numpy.fmin.types, err._fmin_error_check, doc=ufunc_docs._fmin_doc ) # trigonometric functions sin = ufuncs.create_ufunc( 'nlcpy_sin', numpy.sin.types, err._sin_error_check, doc=ufunc_docs._sin_doc ) cos = ufuncs.create_ufunc( 'nlcpy_cos', numpy.cos.types, err._cos_error_check, doc=ufunc_docs._cos_doc ) tan = ufuncs.create_ufunc( 'nlcpy_tan', numpy.tan.types, err._tan_error_check, doc=ufunc_docs._tan_doc ) arcsin = ufuncs.create_ufunc( 'nlcpy_arcsin', numpy.arcsin.types, err._arcsin_error_check, doc=ufunc_docs._arcsin_doc ) arccos = ufuncs.create_ufunc( 'nlcpy_arccos', numpy.arccos.types, err._arccos_error_check, doc=ufunc_docs._arccos_doc ) arctan = ufuncs.create_ufunc( 'nlcpy_arctan', numpy.arctan.types, err._arctan_error_check, doc=ufunc_docs._arctan_doc ) arctan2 = ufuncs.create_ufunc( 'nlcpy_arctan2', casting._arctan2_types, err._arctan2_error_check, doc=ufunc_docs._arctan2_doc ) hypot = ufuncs.create_ufunc( 'nlcpy_hypot', casting._hypot_types, err._hypot_error_check, doc=ufunc_docs._hypot_doc ) sinh = ufuncs.create_ufunc( 'nlcpy_sinh', numpy.sinh.types, err._sinh_error_check, doc=ufunc_docs._sinh_doc ) cosh = ufuncs.create_ufunc( 'nlcpy_cosh', numpy.cosh.types, err._cosh_error_check, doc=ufunc_docs._cosh_doc ) tanh = ufuncs.create_ufunc( 'nlcpy_tanh', numpy.tanh.types, err._tanh_error_check, doc=ufunc_docs._tanh_doc ) arcsinh = ufuncs.create_ufunc( 'nlcpy_arcsinh', numpy.arcsinh.types, err._arcsinh_error_check, doc=ufunc_docs._arcsinh_doc ) arccosh = ufuncs.create_ufunc( 'nlcpy_arccosh', numpy.arccosh.types, err._arccosh_error_check, doc=ufunc_docs._arccosh_doc ) arctanh = ufuncs.create_ufunc( 'nlcpy_arctanh', numpy.arctanh.types, err._arctanh_error_check, doc=ufunc_docs._arctanh_doc ) deg2rad = ufuncs.create_ufunc( 'nlcpy_deg2rad', casting._deg2rad_types, err._deg2rad_error_check, doc=ufunc_docs._deg2rad_doc ) rad2deg = ufuncs.create_ufunc( 'nlcpy_rad2deg', casting._rad2deg_types, err._rad2deg_error_check, doc=ufunc_docs._rad2deg_doc ) degrees = ufuncs.create_ufunc( 'nlcpy_degrees', casting._degrees_types, err._degrees_error_check, doc=ufunc_docs._degrees_doc ) radians = ufuncs.create_ufunc( 'nlcpy_radians', casting._radians_types, err._radians_error_check, doc=ufunc_docs._radians_doc ) # floating functions isfinite = ufuncs.create_ufunc( 'nlcpy_isfinite', numpy.isfinite.types, err._isfinite_error_check, doc=ufunc_docs._isfinite_doc ) isinf = ufuncs.create_ufunc( 'nlcpy_isinf', numpy.isinf.types, err._isinf_error_check, doc=ufunc_docs._isinf_doc ) isnan = ufuncs.create_ufunc( 'nlcpy_isnan', numpy.isnan.types, err._isnan_error_check, doc=ufunc_docs._isnan_doc ) # ufunc_operation(isnat,numpy_types,valid_error_check)dnl signbit = ufuncs.create_ufunc( 'nlcpy_signbit', numpy.signbit.types, err._signbit_error_check, doc=ufunc_docs._signbit_doc ) copysign = ufuncs.create_ufunc( 'nlcpy_copysign', numpy.copysign.types, err._copysign_error_check, doc=ufunc_docs._copysign_doc ) nextafter = ufuncs.create_ufunc( 'nlcpy_nextafter', numpy.nextafter.types, err._nextafter_error_check, doc=ufunc_docs._nextafter_doc ) spacing = ufuncs.create_ufunc( 'nlcpy_spacing', numpy.spacing.types, err._spacing_error_check, doc=ufunc_docs._spacing_doc ) # ufunc_operation(modf,numpy_types,valid_error_check)dnl ldexp = ufuncs.create_ufunc( 'nlcpy_ldexp', numpy.ldexp.types, err._ldexp_error_check, doc=ufunc_docs._ldexp_doc ) # ufunc_operation(frexp)dnl floor = ufuncs.create_ufunc( 'nlcpy_floor', casting._floor_types, err._floor_error_check, doc=ufunc_docs._floor_doc ) ceil = ufuncs.create_ufunc( 'nlcpy_ceil', casting._ceil_types, err._ceil_error_check, doc=ufunc_docs._ceil_doc ) trunc = ufuncs.create_ufunc( 'nlcpy_trunc', numpy.trunc.types, err._trunc_error_check, doc=ufunc_docs._trunc_doc ) # matmul matmul = ufuncs.create_ufunc( 'nlcpy_matmul', numpy.matmul.types, None, doc=ufunc_docs._matmul_doc ) # end of operator functions

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0.722318

# # * The source code in this file is developed independently by NEC Corporation. # # # NLCPy License # # # Copyright (c) 2020-2021 NEC Corporation # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # * Redistributions of source code must retain the above copyright notice, # this list of conditions and the following disclaimer. # * Redistributions in binary form must reproduce the above copyright notice, # this list of conditions and the following disclaimer in the documentation # and/or other materials provided with the distribution. # * Neither NEC Corporation nor the names of its contributors may be # used to endorse or promote products derived from this software # without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND # ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED # WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE # DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE # FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES # (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; # LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND # ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT # (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS # SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. # import numpy from nlcpy.ufuncs import ufuncs from nlcpy.ufuncs import casting from nlcpy.ufuncs import err from nlcpy.ufuncs import ufunc_docs # ---------------------------------------------------------------------------- # ufunc operations # see: https://docs.scipy.org/doc/numpy/reference/ufuncs.html # ---------------------------------------------------------------------------- # math_operations add = ufuncs.create_ufunc( 'nlcpy_add', numpy.add.types, err._add_error_check, doc=ufunc_docs._add_doc ) subtract = ufuncs.create_ufunc( 'nlcpy_subtract', casting._subtract_types, err._subtract_error_check, doc=ufunc_docs._subtract_doc ) multiply = ufuncs.create_ufunc( 'nlcpy_multiply', numpy.multiply.types, err._multiply_error_check, doc=ufunc_docs._multiply_doc ) true_divide = ufuncs.create_ufunc( 'nlcpy_true_divide', casting._true_divide_types, err._true_divide_error_check, doc=ufunc_docs._true_divide_doc ) # ufunc_operation(divide,orig_types,valid_error_check)dnl divide = true_divide logaddexp = ufuncs.create_ufunc( 'nlcpy_logaddexp', numpy.logaddexp.types, err._logaddexp_error_check, doc=ufunc_docs._logaddexp_doc ) logaddexp2 = ufuncs.create_ufunc( 'nlcpy_logaddexp2', numpy.logaddexp2.types, err._logaddexp2_error_check, doc=ufunc_docs._logaddexp2_doc ) floor_divide = ufuncs.create_ufunc( 'nlcpy_floor_divide', numpy.floor_divide.types, err._floor_divide_error_check, doc=ufunc_docs._floor_divide_doc ) negative = ufuncs.create_ufunc( 'nlcpy_negative', casting._negative_types, err._negative_error_check, doc=ufunc_docs._negative_doc ) positive = ufuncs.create_ufunc( 'nlcpy_positive', casting._positive_types, err._positive_error_check, doc=ufunc_docs._positive_doc ) power = ufuncs.create_ufunc( 'nlcpy_power', numpy.power.types, err._power_error_check, doc=ufunc_docs._power_doc ) remainder = ufuncs.create_ufunc( 'nlcpy_remainder', casting._remainder_types, err._remainder_error_check, doc=ufunc_docs._remainder_doc ) # ufunc_operation(mod,orig_types,valid_error_check)dnl mod = remainder fmod = ufuncs.create_ufunc( 'nlcpy_fmod', casting._fmod_types, err._fmod_error_check, doc=ufunc_docs._fmod_doc ) # ufunc_operation(divmod,numpy_types,valid_error_check)dnl absolute = ufuncs.create_ufunc( 'nlcpy_absolute', numpy.absolute.types, err._absolute_error_check, doc=ufunc_docs._absolute_doc ) fabs = ufuncs.create_ufunc( 'nlcpy_fabs', casting._fabs_types, err._fabs_error_check, doc=ufunc_docs._fabs_doc ) rint = ufuncs.create_ufunc( 'nlcpy_rint', numpy.rint.types, err._rint_error_check, doc=ufunc_docs._rint_doc ) sign = ufuncs.create_ufunc( 'nlcpy_sign', casting._sign_types, err._sign_error_check, doc=ufunc_docs._sign_doc ) heaviside = ufuncs.create_ufunc( 'nlcpy_heaviside', numpy.heaviside.types, err._heaviside_error_check, doc=ufunc_docs._heaviside_doc ) conjugate = ufuncs.create_ufunc( 'nlcpy_conjugate', numpy.conjugate.types, err._conjugate_error_check, doc=ufunc_docs._conjugate_doc ) # ufunc_operation(conj,numpy_types,valid_error_check)dnl conj = conjugate exp = ufuncs.create_ufunc( 'nlcpy_exp', numpy.exp.types, err._exp_error_check, doc=ufunc_docs._exp_doc ) exp2 = ufuncs.create_ufunc( 'nlcpy_exp2', numpy.exp2.types, err._exp2_error_check, doc=ufunc_docs._exp2_doc ) log = ufuncs.create_ufunc( 'nlcpy_log', numpy.log.types, err._log_error_check, doc=ufunc_docs._log_doc ) log2 = ufuncs.create_ufunc( 'nlcpy_log2', numpy.log2.types, err._log2_error_check, doc=ufunc_docs._log2_doc ) log10 = ufuncs.create_ufunc( 'nlcpy_log10', numpy.log10.types, err._log10_error_check, doc=ufunc_docs._log10_doc ) expm1 = ufuncs.create_ufunc( 'nlcpy_expm1', numpy.expm1.types, err._expm1_error_check, doc=ufunc_docs._expm1_doc ) log1p = ufuncs.create_ufunc( 'nlcpy_log1p', numpy.log1p.types, err._log1p_error_check, doc=ufunc_docs._log1p_doc ) sqrt = ufuncs.create_ufunc( 'nlcpy_sqrt', numpy.sqrt.types, err._sqrt_error_check, doc=ufunc_docs._sqrt_doc ) square = ufuncs.create_ufunc( 'nlcpy_square', numpy.square.types, err._square_error_check, doc=ufunc_docs._square_doc ) cbrt = ufuncs.create_ufunc( 'nlcpy_cbrt', casting._cbrt_types, err._cbrt_error_check, doc=ufunc_docs._cbrt_doc ) reciprocal = ufuncs.create_ufunc( 'nlcpy_reciprocal', numpy.reciprocal.types, err._reciprocal_error_check, doc=ufunc_docs._reciprocal_doc ) # ufunc_operation(gcd)dnl # ufunc_operation(lcm)dnl # bit-twiddling functions bitwise_and = ufuncs.create_ufunc( 'nlcpy_bitwise_and', casting._bitwise_and_types, err._bitwise_and_error_check, doc=ufunc_docs._bitwise_and_doc ) bitwise_or = ufuncs.create_ufunc( 'nlcpy_bitwise_or', casting._bitwise_or_types, err._bitwise_or_error_check, doc=ufunc_docs._bitwise_or_doc ) bitwise_xor = ufuncs.create_ufunc( 'nlcpy_bitwise_xor', casting._bitwise_xor_types, err._bitwise_xor_error_check, doc=ufunc_docs._bitwise_xor_doc ) invert = ufuncs.create_ufunc( 'nlcpy_invert', casting._invert_types, err._invert_error_check, doc=ufunc_docs._invert_doc ) left_shift = ufuncs.create_ufunc( 'nlcpy_left_shift', casting._left_shift_types, err._left_shift_error_check, doc=ufunc_docs._left_shift_doc ) right_shift = ufuncs.create_ufunc( 'nlcpy_right_shift', casting._right_shift_types, err._right_shift_error_check, doc=ufunc_docs._right_shift_doc ) # comparison functions greater = ufuncs.create_ufunc( 'nlcpy_greater', numpy.greater.types, err._greater_error_check, doc=ufunc_docs._greater_doc ) greater_equal = ufuncs.create_ufunc( 'nlcpy_greater_equal', numpy.greater_equal.types, err._greater_equal_error_check, doc=ufunc_docs._greater_equal_doc ) less = ufuncs.create_ufunc( 'nlcpy_less', numpy.less.types, err._less_error_check, doc=ufunc_docs._less_doc ) less_equal = ufuncs.create_ufunc( 'nlcpy_less_equal', numpy.less_equal.types, err._less_equal_error_check, doc=ufunc_docs._less_equal_doc ) not_equal = ufuncs.create_ufunc( 'nlcpy_not_equal', numpy.not_equal.types, err._not_equal_error_check, doc=ufunc_docs._not_equal_doc ) equal = ufuncs.create_ufunc( 'nlcpy_equal', numpy.equal.types, err._equal_error_check, doc=ufunc_docs._equal_doc ) logical_and = ufuncs.create_ufunc( 'nlcpy_logical_and', numpy.logical_and.types, err._logical_and_error_check, doc=ufunc_docs._logical_and_doc ) logical_or = ufuncs.create_ufunc( 'nlcpy_logical_or', numpy.logical_or.types, err._logical_or_error_check, doc=ufunc_docs._logical_or_doc ) logical_xor = ufuncs.create_ufunc( 'nlcpy_logical_xor', numpy.logical_xor.types, err._logical_xor_error_check, doc=ufunc_docs._logical_xor_doc ) logical_not = ufuncs.create_ufunc( 'nlcpy_logical_not', numpy.logical_not.types, err._logical_not_error_check, doc=ufunc_docs._logical_not_doc ) minimum = ufuncs.create_ufunc( 'nlcpy_minimum', numpy.minimum.types, err._minimum_error_check, doc=ufunc_docs._minimum_doc ) maximum = ufuncs.create_ufunc( 'nlcpy_maximum', numpy.maximum.types, err._maximum_error_check, doc=ufunc_docs._maximum_doc ) fmax = ufuncs.create_ufunc( 'nlcpy_fmax', numpy.fmax.types, err._fmax_error_check, doc=ufunc_docs._fmax_doc ) fmin = ufuncs.create_ufunc( 'nlcpy_fmin', numpy.fmin.types, err._fmin_error_check, doc=ufunc_docs._fmin_doc ) # trigonometric functions sin = ufuncs.create_ufunc( 'nlcpy_sin', numpy.sin.types, err._sin_error_check, doc=ufunc_docs._sin_doc ) cos = ufuncs.create_ufunc( 'nlcpy_cos', numpy.cos.types, err._cos_error_check, doc=ufunc_docs._cos_doc ) tan = ufuncs.create_ufunc( 'nlcpy_tan', numpy.tan.types, err._tan_error_check, doc=ufunc_docs._tan_doc ) arcsin = ufuncs.create_ufunc( 'nlcpy_arcsin', numpy.arcsin.types, err._arcsin_error_check, doc=ufunc_docs._arcsin_doc ) arccos = ufuncs.create_ufunc( 'nlcpy_arccos', numpy.arccos.types, err._arccos_error_check, doc=ufunc_docs._arccos_doc ) arctan = ufuncs.create_ufunc( 'nlcpy_arctan', numpy.arctan.types, err._arctan_error_check, doc=ufunc_docs._arctan_doc ) arctan2 = ufuncs.create_ufunc( 'nlcpy_arctan2', casting._arctan2_types, err._arctan2_error_check, doc=ufunc_docs._arctan2_doc ) hypot = ufuncs.create_ufunc( 'nlcpy_hypot', casting._hypot_types, err._hypot_error_check, doc=ufunc_docs._hypot_doc ) sinh = ufuncs.create_ufunc( 'nlcpy_sinh', numpy.sinh.types, err._sinh_error_check, doc=ufunc_docs._sinh_doc ) cosh = ufuncs.create_ufunc( 'nlcpy_cosh', numpy.cosh.types, err._cosh_error_check, doc=ufunc_docs._cosh_doc ) tanh = ufuncs.create_ufunc( 'nlcpy_tanh', numpy.tanh.types, err._tanh_error_check, doc=ufunc_docs._tanh_doc ) arcsinh = ufuncs.create_ufunc( 'nlcpy_arcsinh', numpy.arcsinh.types, err._arcsinh_error_check, doc=ufunc_docs._arcsinh_doc ) arccosh = ufuncs.create_ufunc( 'nlcpy_arccosh', numpy.arccosh.types, err._arccosh_error_check, doc=ufunc_docs._arccosh_doc ) arctanh = ufuncs.create_ufunc( 'nlcpy_arctanh', numpy.arctanh.types, err._arctanh_error_check, doc=ufunc_docs._arctanh_doc ) deg2rad = ufuncs.create_ufunc( 'nlcpy_deg2rad', casting._deg2rad_types, err._deg2rad_error_check, doc=ufunc_docs._deg2rad_doc ) rad2deg = ufuncs.create_ufunc( 'nlcpy_rad2deg', casting._rad2deg_types, err._rad2deg_error_check, doc=ufunc_docs._rad2deg_doc ) degrees = ufuncs.create_ufunc( 'nlcpy_degrees', casting._degrees_types, err._degrees_error_check, doc=ufunc_docs._degrees_doc ) radians = ufuncs.create_ufunc( 'nlcpy_radians', casting._radians_types, err._radians_error_check, doc=ufunc_docs._radians_doc ) # floating functions isfinite = ufuncs.create_ufunc( 'nlcpy_isfinite', numpy.isfinite.types, err._isfinite_error_check, doc=ufunc_docs._isfinite_doc ) isinf = ufuncs.create_ufunc( 'nlcpy_isinf', numpy.isinf.types, err._isinf_error_check, doc=ufunc_docs._isinf_doc ) isnan = ufuncs.create_ufunc( 'nlcpy_isnan', numpy.isnan.types, err._isnan_error_check, doc=ufunc_docs._isnan_doc ) # ufunc_operation(isnat,numpy_types,valid_error_check)dnl signbit = ufuncs.create_ufunc( 'nlcpy_signbit', numpy.signbit.types, err._signbit_error_check, doc=ufunc_docs._signbit_doc ) copysign = ufuncs.create_ufunc( 'nlcpy_copysign', numpy.copysign.types, err._copysign_error_check, doc=ufunc_docs._copysign_doc ) nextafter = ufuncs.create_ufunc( 'nlcpy_nextafter', numpy.nextafter.types, err._nextafter_error_check, doc=ufunc_docs._nextafter_doc ) spacing = ufuncs.create_ufunc( 'nlcpy_spacing', numpy.spacing.types, err._spacing_error_check, doc=ufunc_docs._spacing_doc ) # ufunc_operation(modf,numpy_types,valid_error_check)dnl ldexp = ufuncs.create_ufunc( 'nlcpy_ldexp', numpy.ldexp.types, err._ldexp_error_check, doc=ufunc_docs._ldexp_doc ) # ufunc_operation(frexp)dnl floor = ufuncs.create_ufunc( 'nlcpy_floor', casting._floor_types, err._floor_error_check, doc=ufunc_docs._floor_doc ) ceil = ufuncs.create_ufunc( 'nlcpy_ceil', casting._ceil_types, err._ceil_error_check, doc=ufunc_docs._ceil_doc ) trunc = ufuncs.create_ufunc( 'nlcpy_trunc', numpy.trunc.types, err._trunc_error_check, doc=ufunc_docs._trunc_doc ) # matmul matmul = ufuncs.create_ufunc( 'nlcpy_matmul', numpy.matmul.types, None, doc=ufunc_docs._matmul_doc ) # end of operator functions

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