xszheng2020/the_stack_dedup_python_hits_6 · Datasets at Hugging Face

e2f550e1be14fc4de3d3f2c4274e4cb59e59e02d

3,550

py

Python

lib/click-6.6/tests/test_formatting.py

brianrodri/google_appengine

ec4e7cdfa1afd99de23b0a32eb94563fe5e6ef43

[ "Apache-2.0" ]

26

2015-01-20T08:02:38.000Z

2020-06-10T04:57:41.000Z

lib/click-6.6/tests/test_formatting.py

brianrodri/google_appengine

ec4e7cdfa1afd99de23b0a32eb94563fe5e6ef43

[ "Apache-2.0" ]

4

2016-02-28T05:53:54.000Z

2017-01-03T07:39:50.000Z

lib/click-6.6/tests/test_formatting.py

brianrodri/google_appengine

ec4e7cdfa1afd99de23b0a32eb94563fe5e6ef43

[ "Apache-2.0" ]

13

2016-02-28T00:14:23.000Z

2021-05-03T15:47:36.000Z

# -*- coding: utf-8 -*- import click def test_basic_functionality(runner): @click.command() def cli(): """First paragraph. This is a very long second paragraph and not correctly wrapped but it will be rewrapped. \b This is a paragraph without rewrapping. \b 1 2 3 And this is a paragraph that will be rewrapped again. """ result = runner.invoke(cli, ['--help'], terminal_width=60) assert not result.exception assert result.output.splitlines() == [ 'Usage: cli [OPTIONS]', '', ' First paragraph.', '', ' This is a very long second paragraph and not correctly', ' wrapped but it will be rewrapped.', '', ' This is', ' a paragraph', ' without rewrapping.', '', ' 1', ' 2', ' 3', '', ' And this is a paragraph that will be rewrapped again.', '', 'Options:', ' --help Show this message and exit.', ] def test_wrapping_long_options_strings(runner): @click.group() def cli(): """Top level command """ @cli.group() def a_very_long(): """Second level """ @a_very_long.command() @click.argument('first') @click.argument('second') @click.argument('third') @click.argument('fourth') @click.argument('fifth') @click.argument('sixth') def command(): """A command. """ # 54 is chosen as a length where the second line is one character # longer than the maximum length. result = runner.invoke(cli, ['a_very_long', 'command', '--help'], terminal_width=54) assert not result.exception assert result.output.splitlines() == [ 'Usage: cli a_very_long command [OPTIONS] FIRST SECOND', ' THIRD FOURTH FIFTH', ' SIXTH', '', ' A command.', '', 'Options:', ' --help Show this message and exit.', ] def test_wrapping_long_command_name(runner): @click.group() def cli(): """Top level command """ @cli.group() def a_very_very_very_long(): """Second level """ @a_very_very_very_long.command() @click.argument('first') @click.argument('second') @click.argument('third') @click.argument('fourth') @click.argument('fifth') @click.argument('sixth') def command(): """A command. """ result = runner.invoke(cli, ['a_very_very_very_long', 'command', '--help'], terminal_width=54) assert not result.exception assert result.output.splitlines() == [ 'Usage: cli a_very_very_very_long command ', ' [OPTIONS] FIRST SECOND THIRD FOURTH FIFTH', ' SIXTH', '', ' A command.', '', 'Options:', ' --help Show this message and exit.', ] def test_formatting_empty_help_lines(runner): @click.command() def cli(): """Top level command """ result = runner.invoke(cli, ['--help']) assert not result.exception assert result.output.splitlines() == [ 'Usage: cli [OPTIONS]', '', ' Top level command', '', '', '', 'Options:', ' --help Show this message and exit.', ]

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py

Python

test/example_algorithm.py

fluxtransport/hazel2

4121df2fa6bf96bf8f193f287bbf11c70c5a519e

[ "MIT" ]

17

2018-08-31T11:13:59.000Z

2022-01-12T02:30:56.000Z

test/example_algorithm.py

fluxtransport/hazel2

4121df2fa6bf96bf8f193f287bbf11c70c5a519e

[ "MIT" ]

26

2018-04-03T15:09:21.000Z

2021-05-27T10:10:45.000Z

test/example_algorithm.py

fluxtransport/hazel2

4121df2fa6bf96bf8f193f287bbf11c70c5a519e

[ "MIT" ]

3

2018-05-01T13:47:21.000Z

2019-09-23T20:49:08.000Z

import numpy as np import matplotlib.pyplot as pl import hazel import h5py from scipy.optimize import minimize import gc # Test a single inversion in non-iterator mode mod = hazel.Model('conf_single.ini', working_mode='inversion', verbose=2) mod.read_observation() mod.open_output() mod.invert() mod.write_output() mod.close_output() final = np.loadtxt('photospheres/model_photosphere.1d', skiprows=4) start = np.loadtxt('photospheres/model_photosphere_200.1d', skiprows=4) f = h5py.File('output.h5') pl.plot(f['ph1']['T'][0,0,:], label='inverted') pl.plot(final[:,1], label='target') pl.plot(start[:,1], 'x', label='initial') f.close() pl.legend() mod = hazel.Model('conf_single.ini', working_mode='inversion', verbose=2) mod.read_observation() mod.open_output() mod.invert_external(minimize, method='Nelder-Mead') mod.write_output() mod.close_output() final = np.loadtxt('photospheres/model_photosphere.1d', skiprows=4) start = np.loadtxt('photospheres/model_photosphere_200.1d', skiprows=4) f = h5py.File('output.h5') pl.figure() pl.plot(f['ph1']['T'][0,0,:], label='inverted') pl.plot(final[:,1], label='target') pl.plot(start[:,1], 'x', label='initial') f.close() pl.legend() pl.show()

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py

Python

lxdspawner/__init__.py

KeioAIConsortium/jupyterhub-lxd-spawner

7c5123990cfa51fb1214b7d5c7eb882dda6a50c6

[ "MIT" ]

1

2021-11-25T01:17:51.000Z

lxdspawner/__init__.py

KeioAIConsortium/jupyterhub-lxd-spawner

7c5123990cfa51fb1214b7d5c7eb882dda6a50c6

[ "MIT" ]

9

2020-05-29T05:36:28.000Z

2021-03-13T09:21:26.000Z

lxdspawner/__init__.py

KeioAIConsortium/jupyterhub-lxd-spawner

7c5123990cfa51fb1214b7d5c7eb882dda6a50c6

[ "MIT" ]

null

from .spawner import LXDSpawner

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py

Python

oscars_test.py

srio/shadow3-scripts

10712641333c29ca9854e9cc60d86cb321f3762b

[ "MIT" ]

1

2019-10-30T10:06:15.000Z

oscars_test.py

srio/shadow3-scripts

10712641333c29ca9854e9cc60d86cb321f3762b

[ "MIT" ]

null

oscars_test.py

srio/shadow3-scripts

10712641333c29ca9854e9cc60d86cb321f3762b

[ "MIT" ]

null

# coding: utf-8 # Plots inline for notebook #get_ipython().run_line_magic('matplotlib', 'inline') # Import the OSCARS SR module from srxraylib.plot.gol import set_qt import oscars.sr from oscars.plots_mpl import * from oscars.parametric_surfaces import PSCylinder import numpy def undulator_spectrum(): # Create a new OSCARS object. Default to 8 threads and always use the GPU if available osr = oscars.sr.sr(nthreads=8, gpu=1) # Clear all existing fields and create an undulator field osr.clear_bfields() osr.add_bfield_undulator(bfield=[0, 1, 0], period=[0, 0, 0.050], nperiods=31) # Define simple electron beam osr.set_particle_beam(energy_GeV=3, x0=[0, 0, -1], current=0.5) # Define the start and stop times for the calculation osr.set_ctstartstop(0, 2) # Calculate spectrum at 30 [m] spectrum = osr.calculate_spectrum(obs=[0, 0, 30], energy_range_eV=[100, 2000]) # Optionally import the plotting tools (matplotlib) # Plot spectrum plot_spectrum(spectrum) def undulator_flux(): # # coding: utf-8 # # # Plots inline for notebook # # get_ipython().run_line_magic('matplotlib', 'inline') # # # Import the OSCARS SR module # import oscars.sr # # # Optionally import the plotting tools (matplotlib) # from oscars.plots_mpl import * # Create a new OSCARS object. Default to 8 threads and always use the GPU if available osr = oscars.sr.sr(nthreads=8, gpu=1) # Clear all existing fields and create an undulator field osr.clear_bfields() osr.add_bfield_undulator(bfield=[0, 1, 0], period=[0, 0, 0.050], nperiods=31) # Define simple electron beam osr.set_particle_beam(energy_GeV=3, x0=[0, 0, -1], current=0.5) # Define the start and stop times for the calculation osr.set_ctstartstop(0, 2) # Calculate spectrum at 30 [m]. Note use of the nthreads argument. flux = osr.calculate_flux_rectangle( plane='XY', energy_eV=143.8, width=[0.01, 0.01], npoints=[101, 101], translation=[0, 0, 30] ) # Plot flux plot_flux(flux) def undulator_power_density(): # # coding: utf-8 # # # Plots inline for notebook # get_ipython().run_line_magic('matplotlib', 'inline') # # # Import the OSCARS SR module # import oscars.sr # # # Optionally import the plotting tools (matplotlib) # from oscars.plots_mpl import * # Create a new OSCARS object. Default to 8 threads and always use the GPU if available osr = oscars.sr.sr(nthreads=8, gpu=1) # Clear all existing fields and create an undulator field osr.clear_bfields() osr.add_bfield_undulator(bfield=[0, 1, 0], period=[0, 0, 0.050], nperiods=31) # Define simple electron beam osr.set_particle_beam(energy_GeV=3, x0=[0, 0, -1], current=0.5) # Define the start and stop times for the calculation osr.set_ctstartstop(0, 2) # Calculate spectrum at 30 [m]. Note use of the nthreads argument. power_density = osr.calculate_power_density_rectangle( plane='XY', width=[0.05, 0.05], npoints=[101, 101], translation=[0, 0, 30] ) # Plot power density plot_power_density(power_density) def undulator_3d_power_density(): # # coding: utf-8 # # # Plots inline for notebook # get_ipython().run_line_magic('matplotlib', 'inline') # # # Import the OSCARS SR module # import oscars.sr # # # Import the 3D and parametric surfaces utilities # from oscars.plots3d_mpl import * # from oscars.parametric_surfaces import * # Create a new OSCARS object. Default to 8 threads and always use the GPU if available osr = oscars.sr.sr(nthreads=8, gpu=1) # Clear all existing fields and create an undulator field osr.clear_bfields() osr.add_bfield_undulator(bfield=[0, 1, 0], period=[0, 0, 0.050], nperiods=31) # Define simple electron beam osr.set_particle_beam(energy_GeV=3, x0=[0, 0, -1], current=0.5) # Define the start and stop times for the calculation osr.set_ctstartstop(0, 2) # First create the surface of interest cylinder = PSCylinder(R=0.020, L=0.010, nu=101, nv=101) # Run calculation and plotting pd = power_density_3d(osr, cylinder, rotations=[osr.pi() / 2, 0, 0], translation=[0, 0, 30]) def example_032_undulator_flux(): # # Import the OSCARS SR module # import oscars.sr # # # Import basic plot utilities (matplotlib). You don't need these to run OSCARS, but it's used here for basic plots # from oscars.plots_mpl import * # Create a new OSCARS object. Default to 8 threads and always use the GPU if available osr = oscars.sr.sr(nthreads=8, gpu=1) # Clear any existing fields (just good habit in notebook style) and add an undulator field osr.clear_bfields() osr.add_bfield_undulator(bfield=[0, 1, 0], period=[0, 0, 0.049], nperiods=21) # Just to check the field that we added seems visually correct plot_bfield(osr) # Setup beam similar to NSLSII osr.clear_particle_beams() osr.set_particle_beam(x0=[0, 0, -1], energy_GeV=3, current=0.500) # Set the start and stop times for the calculation osr.set_ctstartstop(0, 2) # Run the particle trajectory calculation trajectory = osr.calculate_trajectory() # Plot the trajectory position and velocity plot_trajectory_position(trajectory) plot_trajectory_velocity(trajectory) # Calculate spectrum zoom spectrum = osr.calculate_spectrum(obs=[0, 0, 30], energy_range_eV=[145, 160], npoints=200) plot_spectrum(spectrum) flux = osr.calculate_flux_rectangle( plane='XY', energy_eV=153, width=[0.01, 0.01], npoints=[101, 101], translation=[0, 0, 30] ) plot_flux(flux) def example_042_undulator_power_density(): # # Import the OSCARS SR module # import oscars.sr # # # Import basic plot utilities (matplotlib). You don't need these to run OSCARS, but it's used here for basic plots # from oscars.plots_mpl import * # Create a new OSCARS object. Default to 8 threads and always use the GPU if available osr = oscars.sr.sr(nthreads=8, gpu=1) # Clear any existing fields (just good habit in notebook style) and add an undulator field osr.clear_bfields() osr.add_bfield_undulator(bfield=[0, 1, 0], period=[0, 0, 0.049], nperiods=21) # Just to check the field that we added seems visually correct plot_bfield(osr) # Setup beam similar to NSLSII osr.clear_particle_beams() osr.set_particle_beam(x0=[0, 0, -1], energy_GeV=3, current=0.500) # Set the start and stop times for the calculation osr.set_ctstartstop(0, 2) # Run the particle trajectory calculation trajectory = osr.calculate_trajectory() # Plot the trajectory position and velocity plot_trajectory_position(trajectory) plot_trajectory_velocity(trajectory) power_density = osr.calculate_power_density_rectangle( plane='XY', width=[0.05, 0.05], npoints=[101, 101], translation=[0, 0, 30] ) plot_power_density(power_density) def example_001_dipole_trajectory(): # Import the OSCARS SR module # import oscars.sr # # # Import basic plot utilities. You don't need these to run OSCARS, but it's used here for basic plots # from oscars.plots_mpl import * # Create a new OSCARS object. Default to 8 threads and always use the GPU if available osr = oscars.sr.sr(nthreads=8, gpu=1) # Clear any existing fields (just good habit in notebook style) and add an undulator field osr.clear_bfields() osr.add_bfield_uniform(bfield=[0, -0.4, 0], width=[0, 0, 1]) # Just to check the field that we added seems visually correct plot_bfield(osr) # Setup beam similar to NSLSII osr.clear_particle_beams() osr.set_particle_beam(x0=[0, 0, -1], energy_GeV=3, current=0.500) # Set the start and stop times for the calculation osr.set_ctstartstop(0, 2) # Verify input information - print all to screen osr.print_all() # Run the particle trajectory calculation trajectory = osr.calculate_trajectory() # Plot the trajectory position and velocity plot_trajectory_position(trajectory) plot_trajectory_velocity(trajectory) # Setup beam similar to NSLSII osr.clear_particle_beams() osr.set_particle_beam(energy_GeV=3, current=0.500) # Set the start and stop times for the calculation osr.set_ctstartstop(-1, 1) # Run the particle trajectory calculation trajectory = osr.calculate_trajectory() # Plot the trajectory position and velocity plot_trajectory_position(trajectory) plot_trajectory_velocity(trajectory) def undulator_radiation_srio(): # # Import the OSCARS SR module # import oscars.sr # # # Import basic plot utilities (matplotlib). You don't need these to run OSCARS, but it's used here for basic plots # from oscars.plots_mpl import * # Create a new OSCARS object. Default to 8 threads and always use the GPU if available osr = oscars.sr.sr(nthreads=8, gpu=1) # Clear any existing fields (just good habit in notebook style) and add an undulator field osr.clear_bfields() osr.add_bfield_undulator(bfield=[0, 0.69, 0], period=[0, 0, 0.038], nperiods=55) # # Just to check the field that we added seems visually correct # plot_bfield(osr) # Setup beam similar to NSLSII osr.clear_particle_beams() osr.set_particle_beam(x0=[0, 0, -2], energy_GeV=2.0, current=0.500) # Set the start and stop times for the calculation osr.set_ctstartstop(0, 4) # # Run the particle trajectory calculation # trajectory = osr.calculate_trajectory() # # Plot the trajectory position and velocity # plot_trajectory_position(trajectory) # plot_trajectory_velocity(trajectory) # # Calculate spectrum zoom # spectrum = osr.calculate_spectrum(obs=[0, 0, 10], energy_range_eV=[100, 400], npoints=2000) # # print(">>>",spectrum) # plot_spectrum(spectrum) import time t0 = time.time() flux = osr.calculate_flux_rectangle( plane='XY', energy_eV=249+6, width=[0.0025, 0.0025], npoints=[101, 101], translation=[0, 0, 10] ) print(">>>>",time.time()-t0) plot_flux(flux) print(">>>", flux,type(flux)) print(numpy.array(flux).shape) def undulator_radiation_howard(): osr = oscars.sr.sr(nthreads=8, gpu=1) osr.clear_bfields() K = 0.6 import scipy.constants as codata B = K * 2 * numpy.pi * codata.m_e * codata.c / (codata.e * 0.0288 ) osr.add_bfield_undulator(bfield=[0, B, 0], period=[0, 0, 0.0288], nperiods=138) osr.clear_particle_beams() osr.set_particle_beam(x0=[0, 0, -138*0.0288], energy_GeV=2.0, current=0.500) # Set the start and stop times for the calculation osr.set_ctstartstop(0, 4) # # Run the particle trajectory calculation # trajectory = osr.calculate_trajectory() # # Plot the trajectory position and velocity # plot_trajectory_position(trajectory) # plot_trajectory_velocity(trajectory) # # Calculate spectrum zoom # spectrum = osr.calculate_spectrum(obs=[0, 0, 10], energy_range_eV=[100, 400], npoints=2000) # # print(">>>",spectrum) # plot_spectrum(spectrum) import time t0 = time.time() flux = osr.calculate_flux_rectangle( plane='XY', energy_eV=830, #1117.74, width=[0.006, 0.006], npoints=[251, 251], translation=[0, 0, 13] ) print(">>>>", time.time() - t0) plot_flux(flux) print(">>>", flux, type(flux)) print(numpy.array(flux).shape) if __name__ == "__main__": set_qt() # undulator_spectrum() # undulator_flux() # undulator_power_density() # undulator_3d_power_density() #?????????? # example_032_undulator_flux() # example_042_undulator_power_density() # example_001_dipole_trajectory() undulator_radiation_howard()

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46b7d92ab60cce8857403f110c10a7f7acc9e8c1

137

py

Python

pyrimidine/local_search/__init__.py

Freakwill/pyrimidine

ff05998f110a69a002180d0dae2ae514a5807cfb

[ "MIT" ]

1

2021-03-04T17:03:14.000Z

pyrimidine/local_search/__init__.py

Freakwill/pyrimidine

ff05998f110a69a002180d0dae2ae514a5807cfb

[ "MIT" ]

null

pyrimidine/local_search/__init__.py

Freakwill/pyrimidine

ff05998f110a69a002180d0dae2ae514a5807cfb

[ "MIT" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- from .simulated_annealing import * from .random_walk import * from .tabu_search import *

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py

Python

dvc/utils/collections.py

vyloy/dvc

60c89adeb5dcc293d8661d6aabeb1da6d05466f5

[ "Apache-2.0" ]

1

2019-04-16T19:51:03.000Z

dvc/utils/collections.py

vyloy/dvc

60c89adeb5dcc293d8661d6aabeb1da6d05466f5

[ "Apache-2.0" ]

null

dvc/utils/collections.py

vyloy/dvc

60c89adeb5dcc293d8661d6aabeb1da6d05466f5

[ "Apache-2.0" ]

null

from __future__ import unicode_literals # just simple check for Nones and emtpy strings def compact(args): return list(filter(bool, args))

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6

46ddd2452e2167c4d040cbcb09e2c5f4dfefaa71

1,111

py

Python

operation.py

lucasma8795/chess

2d8a1f6472dc12e83bace2eb7e8329358edb6b4c

[ "Unlicense" ]

null

operation.py

lucasma8795/chess

2d8a1f6472dc12e83bace2eb7e8329358edb6b4c

[ "Unlicense" ]

null

operation.py

lucasma8795/chess

2d8a1f6472dc12e83bace2eb7e8329358edb6b4c

[ "Unlicense" ]

null

class Operation(): def __init__(self): pass class op_pawn_move(Operation): def __init__(self, old_pos, new_pos, two_squares=False): super().__init__() self.old_pos = old_pos self.new_pos = new_pos self.two_squares = two_squares class op_en_passant(Operation): def __init__(self, old_pos, new_pos, ep_pos): super().__init__() self.old_pos = old_pos self.new_pos = new_pos self.ep_pos = ep_pos class op_move(Operation): def __init__(self, old_pos, new_pos): super().__init__() self.old_pos = old_pos self.new_pos = new_pos class op_capture(Operation): def __init__(self, old_pos, new_pos): super().__init__() self.old_pos = old_pos self.new_pos = new_pos class op_castle(Operation): def __init__(self, old_pos, new_pos, color, side): super().__init__() self.old_pos = old_pos self.new_pos = new_pos self.color = color # white, black self.side = side # 0: queenside(left), 1: kingside(right) class op_promotion(Operation): def __init__(self, old_pos, new_pos, color): super().__init__() self.old_pos = old_pos self.new_pos = new_pos self.color = color

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0

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0

null

0

1

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6

645c421a7c0a65c7f4ed2f6036aaba467e9a0a8a

7,307

py

Python

tests/unit/test_install.py

markharley/pip-install-privates

a55b82020db0813ed8bac95e175edf5e8363bf32

[ "MIT" ]

null

tests/unit/test_install.py

markharley/pip-install-privates

a55b82020db0813ed8bac95e175edf5e8363bf32

[ "MIT" ]

null

tests/unit/test_install.py

markharley/pip-install-privates

a55b82020db0813ed8bac95e175edf5e8363bf32

[ "MIT" ]

null

import os import tempfile from unittest import TestCase from pip_install_privates.install import collect_requirements class TestInstall(TestCase): def _create_reqs_file(self, reqs): with tempfile.NamedTemporaryFile(delete=False) as f: f.write('\n'.join(reqs).encode('utf-8')) self.addCleanup(os.unlink, f.name) return f.name def test_considers_all_requirements_in_file(self): fname = self._create_reqs_file(['mock==2.0.0', 'nose==1.3.7', 'fso==0.3.1']) ret = collect_requirements(fname) self.assertEqual(ret, ['mock==2.0.0', 'nose==1.3.7', 'fso==0.3.1']) def test_removes_comments(self): fname = self._create_reqs_file(['mock==2.0.0', '# for testing', 'nose==1.3.7', 'fso==0.3.1']) ret = collect_requirements(fname) self.assertEqual(ret, ['mock==2.0.0', 'nose==1.3.7', 'fso==0.3.1']) def test_removes_trailing_comments(self): fname = self._create_reqs_file(['mock==2.0.0', 'nose==1.3.7 # for testing', 'fso==0.3.1']) ret = collect_requirements(fname) self.assertEqual(ret, ['mock==2.0.0', 'nose==1.3.7', 'fso==0.3.1']) def test_skips_empty_lines(self): fname = self._create_reqs_file(['mock==2.0.0', '', 'nose==1.3.7', '', 'fso==0.3.1']) ret = collect_requirements(fname) self.assertEqual(ret, ['mock==2.0.0', 'nose==1.3.7', 'fso==0.3.1']) def test_strips_whitespaces(self): fname = self._create_reqs_file([' mock==2.0.0 ', ' ', 'nose==1.3.7 ']) ret = collect_requirements(fname) self.assertEqual(ret, ['mock==2.0.0', 'nose==1.3.7']) def test_reads_included_files(self): basename = self._create_reqs_file(['mock==2.0.0', 'nose==1.3.7']) fname = self._create_reqs_file(['-r {}'.format(basename), 'fso==0.3.1']) ret = collect_requirements(fname) self.assertEqual(ret, ['mock==2.0.0', 'nose==1.3.7', 'fso==0.3.1']) def test_reads_chain_of_included_files(self): file1 = self._create_reqs_file(['mock==2.0.0', 'nose==1.3.7']) file2 = self._create_reqs_file(['-r {}'.format(file1), 'Django==1.10']) file3 = self._create_reqs_file(['amqp==1.4.7', '-r {}'.format(file2), 'six==1.10.0']) file4 = self._create_reqs_file(['-r {}'.format(file3), 'fso==0.3.1']) ret = collect_requirements(file4) self.assertEqual(ret, ['amqp==1.4.7', 'mock==2.0.0', 'nose==1.3.7', 'Django==1.10', 'six==1.10.0', 'fso==0.3.1']) def test_honors_vcs_urls(self): fname = self._create_reqs_file(['git+https://github.com/ByteInternet/...']) ret = collect_requirements(fname) self.assertEqual(ret, ['git+https://github.com/ByteInternet/...']) def test_transforms_vcs_git_url_to_oauth(self): fname = self._create_reqs_file(['git+git@github.com:ByteInternet/...']) ret = collect_requirements(fname, transform_with_token='my-token') self.assertEqual(ret, ['git+https://my-token:x-oauth-basic@github.com/ByteInternet/...']) def test_transforms_vcs_git_url_to_oauth_dashe_option(self): fname = self._create_reqs_file(['-e git+git@github.com:ByteInternet/...']) ret = collect_requirements(fname, transform_with_token='my-token') self.assertEqual(ret, ['-e', 'git+https://my-token:x-oauth-basic@github.com/ByteInternet/...']) def test_transforms_vcs_ssh_url_to_oauth(self): fname = self._create_reqs_file(['git+ssh://git@github.com/ByteInternet/...']) ret = collect_requirements(fname, transform_with_token='my-token') self.assertEqual(ret, ['git+https://my-token:x-oauth-basic@github.com/ByteInternet/...']) def test_transforms_vcs_ssh_url_to_oauth_dashe_option(self): fname = self._create_reqs_file(['-e git+ssh://git@github.com/ByteInternet/...']) ret = collect_requirements(fname, transform_with_token='my-token') self.assertEqual(ret, ['-e', 'git+https://my-token:x-oauth-basic@github.com/ByteInternet/...']) def test_transforms_urls_in_included_files(self): file1 = self._create_reqs_file(['mock==2.0.0', '-e git+git@github.com:ByteInternet/...', 'nose==1.3.7']) fname = self._create_reqs_file(['-r {}'.format(file1), 'fso==0.3.1']) ret = collect_requirements(fname, transform_with_token='my-token') self.assertEqual(ret, ['mock==2.0.0', '-e', 'git+https://my-token:x-oauth-basic@github.com/ByteInternet/...', 'nose==1.3.7', 'fso==0.3.1']) def test_transforms_git_plus_git_urls_to_regular_url_if_no_token_provided(self): file1 = self._create_reqs_file(['mock==2.0.0', '-e git+git@github.com:ByteInternet/...', 'nose==1.3.7']) fname = self._create_reqs_file(['-r {}'.format(file1), 'fso==0.3.1']) ret = collect_requirements(fname) self.assertEqual(ret, ['mock==2.0.0', '-e', 'git+https://github.com/ByteInternet/...', 'nose==1.3.7', 'fso==0.3.1']) def test_transforms_git_plus_ssh_urls_to_regular_url_if_no_token_provided(self): file1 = self._create_reqs_file(['mock==2.0.0', '-e git+ssh://git@github.com/ByteInternet/...', 'nose==1.3.7']) fname = self._create_reqs_file(['-r {}'.format(file1), 'fso==0.3.1']) ret = collect_requirements(fname) self.assertEqual(ret, ['mock==2.0.0', '-e', 'git+https://github.com/ByteInternet/...', 'nose==1.3.7', 'fso==0.3.1']) def test_transforms_git_plus_https_urls_to_https_url_with_oauth_token_if_token_provided(self): file1 = self._create_reqs_file(['mock==2.0.0', 'git+https://github.com/ByteInternet/...', 'nose==1.3.7']) fname = self._create_reqs_file(['-r {}'.format(file1), 'fso==0.3.1']) ret = collect_requirements(fname, transform_with_token='my-token') self.assertEqual(ret, ['mock==2.0.0', 'git+https://my-token:x-oauth-basic@github.com/ByteInternet/...', 'nose==1.3.7', 'fso==0.3.1']) def test_transforms_editable_git_plus_https_urls_to_editable_https_url_with_oauth_token_if_token_provided(self): file1 = self._create_reqs_file(['mock==2.0.0', '-e git+https://github.com/ByteInternet/...', 'nose==1.3.7']) fname = self._create_reqs_file(['-r {}'.format(file1), 'fso==0.3.1']) ret = collect_requirements(fname, transform_with_token='my-token') self.assertEqual(ret, ['mock==2.0.0', '-e', 'git+https://my-token:x-oauth-basic@github.com/ByteInternet/...', 'nose==1.3.7', 'fso==0.3.1']) def test_does_not_transform_git_plus_https_urls_to_https_url_with_oauth_token_if_no_token_provided(self): file1 = self._create_reqs_file(['mock==2.0.0', '-e git+https://github.com/ByteInternet/...', 'nose==1.3.7']) fname = self._create_reqs_file(['-r {}'.format(file1), 'fso==0.3.1']) ret = collect_requirements(fname) self.assertEqual(ret, ['mock==2.0.0', '-e', 'git+https://github.com/ByteInternet/...', 'nose==1.3.7', 'fso==0.3.1'])

47.75817

118

0.608458

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7,307

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0.096805

0.120172

0.8598

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0.805913

0.802575

0

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7,307

152

119

48.072368

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0.504762

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false

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0.238095

0

null

0

1

0

null

0

6

646d6e4235411c4ee4a21ed373eb71a093cd02cb

179

py

Python

matid/__init__.py

markus1978/matid

dad7a79db727015a3ad0a50962e351f6bf724e4d

[ "Apache-2.0" ]

20

2018-06-25T10:04:58.000Z

2021-07-09T06:15:06.000Z

matid/__init__.py

markus1978/matid

dad7a79db727015a3ad0a50962e351f6bf724e4d

[ "Apache-2.0" ]

7

2019-02-28T11:19:14.000Z

2020-11-27T10:16:09.000Z

matid/__init__.py

markus1978/matid

dad7a79db727015a3ad0a50962e351f6bf724e4d

[ "Apache-2.0" ]

5

2018-11-23T10:02:12.000Z

2021-06-30T12:41:45.000Z

from matid.classification.classifier import Classifier from matid.classification.periodicfinder import PeriodicFinder from matid.symmetry.symmetryanalyzer import SymmetryAnalyzer

44.75

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0.899441

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179

8.944444

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0.285714

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0.067039

179

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0.964072

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0

null

0

1

0

1

0

1

0

6

6473ac17ff682dd87cd41748c7ec4698347a9b13

196

py

Python

modules/dials/algorithms/background/median/__init__.py

jorgediazjr/dials-dev20191018

77d66c719b5746f37af51ad593e2941ed6fbba17

[ "BSD-3-Clause" ]

null

modules/dials/algorithms/background/median/__init__.py

jorgediazjr/dials-dev20191018

77d66c719b5746f37af51ad593e2941ed6fbba17

[ "BSD-3-Clause" ]

null

modules/dials/algorithms/background/median/__init__.py

jorgediazjr/dials-dev20191018

77d66c719b5746f37af51ad593e2941ed6fbba17

[ "BSD-3-Clause" ]

1

2020-02-04T15:39:06.000Z

from __future__ import absolute_import, division, print_function from dials.algorithms.background.median.algorithm import BackgroundAlgorithm from dials_algorithms_background_median_ext import *

39.2

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0.887755

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196

7.130435

0.608696

0.109756

0.231707

0.353659

0.426829

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0.076531

196

4

77

49

0.906077

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true

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0.333333

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1

0

1

0

null

0

1

0

1

0

6

648b275bc7cd5a4ba6b055f739ed44b8ed35745d

41

py

Python

core/exception/__init__.py

ryanolee/pager-duty-sync

1fd88634e461b5db647d856bc6b59f990944685e

[ "MIT" ]

null

core/exception/__init__.py

ryanolee/pager-duty-sync

1fd88634e461b5db647d856bc6b59f990944685e

[ "MIT" ]

2

2020-09-27T18:19:17.000Z

2021-06-29T09:21:04.000Z

core/exception/__init__.py

ryanolee/pager-duty-sync

1fd88634e461b5db647d856bc6b59f990944685e

[ "MIT" ]

null

from .httpExceptions import HTTPException

41

0.902439

4

41

9.25

1

0

0.073171

41

1

41

0.973684

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

6492c020f5fcfef7237266ba7de4fa58ee7a6c87

102

py

Python

aws_mock/requests/modify_subnet_attribute.py

enaydanov/aws_mock

4ad3dca270ad164693e85741d5e92f845c34aa01

[ "Apache-2.0" ]

null

aws_mock/requests/modify_subnet_attribute.py

enaydanov/aws_mock

4ad3dca270ad164693e85741d5e92f845c34aa01

[ "Apache-2.0" ]

1

2021-10-21T21:06:29.000Z

aws_mock/requests/modify_subnet_attribute.py

bentsi/aws_mock

d6c1b963e02b4cd3602722e7135f4d65f6a71d3e

[ "Apache-2.0" ]

1

2021-11-08T14:20:36.000Z

from aws_mock.lib import aws_response @aws_response def modify_subnet_attribute() -> None: pass

14.571429

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102

4.933333

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0.297297

0

0.156863

102

6

39

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0

1

0.25

true

0.25

0

0.5

0

1

0

null

1

0

1

0

null

0

1

0

6

649a7cdf7291e8bb3799fdb69b8b932c130f83e7

7,097

py

Python

loldib/getratings/models/NA/na_cassiopeia/na_cassiopeia_sup.py

koliupy/loldib

c9ab94deb07213cdc42b5a7c26467cdafaf81b7f

[ "Apache-2.0" ]

null

loldib/getratings/models/NA/na_cassiopeia/na_cassiopeia_sup.py

koliupy/loldib

c9ab94deb07213cdc42b5a7c26467cdafaf81b7f

[ "Apache-2.0" ]

null

loldib/getratings/models/NA/na_cassiopeia/na_cassiopeia_sup.py

koliupy/loldib

c9ab94deb07213cdc42b5a7c26467cdafaf81b7f

[ "Apache-2.0" ]

null

from getratings.models.ratings import Ratings class NA_Cassiopeia_Sup_Aatrox(Ratings): pass class NA_Cassiopeia_Sup_Ahri(Ratings): pass class NA_Cassiopeia_Sup_Akali(Ratings): pass class NA_Cassiopeia_Sup_Alistar(Ratings): pass class NA_Cassiopeia_Sup_Amumu(Ratings): pass class NA_Cassiopeia_Sup_Anivia(Ratings): pass class NA_Cassiopeia_Sup_Annie(Ratings): pass class NA_Cassiopeia_Sup_Ashe(Ratings): pass class NA_Cassiopeia_Sup_AurelionSol(Ratings): pass class NA_Cassiopeia_Sup_Azir(Ratings): pass class NA_Cassiopeia_Sup_Bard(Ratings): pass class NA_Cassiopeia_Sup_Blitzcrank(Ratings): pass class NA_Cassiopeia_Sup_Brand(Ratings): pass class NA_Cassiopeia_Sup_Braum(Ratings): pass class NA_Cassiopeia_Sup_Caitlyn(Ratings): pass class NA_Cassiopeia_Sup_Camille(Ratings): pass class NA_Cassiopeia_Sup_Cassiopeia(Ratings): pass class NA_Cassiopeia_Sup_Chogath(Ratings): pass class NA_Cassiopeia_Sup_Corki(Ratings): pass class NA_Cassiopeia_Sup_Darius(Ratings): pass class NA_Cassiopeia_Sup_Diana(Ratings): pass class NA_Cassiopeia_Sup_Draven(Ratings): pass class NA_Cassiopeia_Sup_DrMundo(Ratings): pass class NA_Cassiopeia_Sup_Ekko(Ratings): pass class NA_Cassiopeia_Sup_Elise(Ratings): pass class NA_Cassiopeia_Sup_Evelynn(Ratings): pass class NA_Cassiopeia_Sup_Ezreal(Ratings): pass class NA_Cassiopeia_Sup_Fiddlesticks(Ratings): pass class NA_Cassiopeia_Sup_Fiora(Ratings): pass class NA_Cassiopeia_Sup_Fizz(Ratings): pass class NA_Cassiopeia_Sup_Galio(Ratings): pass class NA_Cassiopeia_Sup_Gangplank(Ratings): pass class NA_Cassiopeia_Sup_Garen(Ratings): pass class NA_Cassiopeia_Sup_Gnar(Ratings): pass class NA_Cassiopeia_Sup_Gragas(Ratings): pass class NA_Cassiopeia_Sup_Graves(Ratings): pass class NA_Cassiopeia_Sup_Hecarim(Ratings): pass class NA_Cassiopeia_Sup_Heimerdinger(Ratings): pass class NA_Cassiopeia_Sup_Illaoi(Ratings): pass class NA_Cassiopeia_Sup_Irelia(Ratings): pass class NA_Cassiopeia_Sup_Ivern(Ratings): pass class NA_Cassiopeia_Sup_Janna(Ratings): pass class NA_Cassiopeia_Sup_JarvanIV(Ratings): pass class NA_Cassiopeia_Sup_Jax(Ratings): pass class NA_Cassiopeia_Sup_Jayce(Ratings): pass class NA_Cassiopeia_Sup_Jhin(Ratings): pass class NA_Cassiopeia_Sup_Jinx(Ratings): pass class NA_Cassiopeia_Sup_Kalista(Ratings): pass class NA_Cassiopeia_Sup_Karma(Ratings): pass class NA_Cassiopeia_Sup_Karthus(Ratings): pass class NA_Cassiopeia_Sup_Kassadin(Ratings): pass class NA_Cassiopeia_Sup_Katarina(Ratings): pass class NA_Cassiopeia_Sup_Kayle(Ratings): pass class NA_Cassiopeia_Sup_Kayn(Ratings): pass class NA_Cassiopeia_Sup_Kennen(Ratings): pass class NA_Cassiopeia_Sup_Khazix(Ratings): pass class NA_Cassiopeia_Sup_Kindred(Ratings): pass class NA_Cassiopeia_Sup_Kled(Ratings): pass class NA_Cassiopeia_Sup_KogMaw(Ratings): pass class NA_Cassiopeia_Sup_Leblanc(Ratings): pass class NA_Cassiopeia_Sup_LeeSin(Ratings): pass class NA_Cassiopeia_Sup_Leona(Ratings): pass class NA_Cassiopeia_Sup_Lissandra(Ratings): pass class NA_Cassiopeia_Sup_Lucian(Ratings): pass class NA_Cassiopeia_Sup_Lulu(Ratings): pass class NA_Cassiopeia_Sup_Lux(Ratings): pass class NA_Cassiopeia_Sup_Malphite(Ratings): pass class NA_Cassiopeia_Sup_Malzahar(Ratings): pass class NA_Cassiopeia_Sup_Maokai(Ratings): pass class NA_Cassiopeia_Sup_MasterYi(Ratings): pass class NA_Cassiopeia_Sup_MissFortune(Ratings): pass class NA_Cassiopeia_Sup_MonkeyKing(Ratings): pass class NA_Cassiopeia_Sup_Mordekaiser(Ratings): pass class NA_Cassiopeia_Sup_Morgana(Ratings): pass class NA_Cassiopeia_Sup_Nami(Ratings): pass class NA_Cassiopeia_Sup_Nasus(Ratings): pass class NA_Cassiopeia_Sup_Nautilus(Ratings): pass class NA_Cassiopeia_Sup_Nidalee(Ratings): pass class NA_Cassiopeia_Sup_Nocturne(Ratings): pass class NA_Cassiopeia_Sup_Nunu(Ratings): pass class NA_Cassiopeia_Sup_Olaf(Ratings): pass class NA_Cassiopeia_Sup_Orianna(Ratings): pass class NA_Cassiopeia_Sup_Ornn(Ratings): pass class NA_Cassiopeia_Sup_Pantheon(Ratings): pass class NA_Cassiopeia_Sup_Poppy(Ratings): pass class NA_Cassiopeia_Sup_Quinn(Ratings): pass class NA_Cassiopeia_Sup_Rakan(Ratings): pass class NA_Cassiopeia_Sup_Rammus(Ratings): pass class NA_Cassiopeia_Sup_RekSai(Ratings): pass class NA_Cassiopeia_Sup_Renekton(Ratings): pass class NA_Cassiopeia_Sup_Rengar(Ratings): pass class NA_Cassiopeia_Sup_Riven(Ratings): pass class NA_Cassiopeia_Sup_Rumble(Ratings): pass class NA_Cassiopeia_Sup_Ryze(Ratings): pass class NA_Cassiopeia_Sup_Sejuani(Ratings): pass class NA_Cassiopeia_Sup_Shaco(Ratings): pass class NA_Cassiopeia_Sup_Shen(Ratings): pass class NA_Cassiopeia_Sup_Shyvana(Ratings): pass class NA_Cassiopeia_Sup_Singed(Ratings): pass class NA_Cassiopeia_Sup_Sion(Ratings): pass class NA_Cassiopeia_Sup_Sivir(Ratings): pass class NA_Cassiopeia_Sup_Skarner(Ratings): pass class NA_Cassiopeia_Sup_Sona(Ratings): pass class NA_Cassiopeia_Sup_Soraka(Ratings): pass class NA_Cassiopeia_Sup_Swain(Ratings): pass class NA_Cassiopeia_Sup_Syndra(Ratings): pass class NA_Cassiopeia_Sup_TahmKench(Ratings): pass class NA_Cassiopeia_Sup_Taliyah(Ratings): pass class NA_Cassiopeia_Sup_Talon(Ratings): pass class NA_Cassiopeia_Sup_Taric(Ratings): pass class NA_Cassiopeia_Sup_Teemo(Ratings): pass class NA_Cassiopeia_Sup_Thresh(Ratings): pass class NA_Cassiopeia_Sup_Tristana(Ratings): pass class NA_Cassiopeia_Sup_Trundle(Ratings): pass class NA_Cassiopeia_Sup_Tryndamere(Ratings): pass class NA_Cassiopeia_Sup_TwistedFate(Ratings): pass class NA_Cassiopeia_Sup_Twitch(Ratings): pass class NA_Cassiopeia_Sup_Udyr(Ratings): pass class NA_Cassiopeia_Sup_Urgot(Ratings): pass class NA_Cassiopeia_Sup_Varus(Ratings): pass class NA_Cassiopeia_Sup_Vayne(Ratings): pass class NA_Cassiopeia_Sup_Veigar(Ratings): pass class NA_Cassiopeia_Sup_Velkoz(Ratings): pass class NA_Cassiopeia_Sup_Vi(Ratings): pass class NA_Cassiopeia_Sup_Viktor(Ratings): pass class NA_Cassiopeia_Sup_Vladimir(Ratings): pass class NA_Cassiopeia_Sup_Volibear(Ratings): pass class NA_Cassiopeia_Sup_Warwick(Ratings): pass class NA_Cassiopeia_Sup_Xayah(Ratings): pass class NA_Cassiopeia_Sup_Xerath(Ratings): pass class NA_Cassiopeia_Sup_XinZhao(Ratings): pass class NA_Cassiopeia_Sup_Yasuo(Ratings): pass class NA_Cassiopeia_Sup_Yorick(Ratings): pass class NA_Cassiopeia_Sup_Zac(Ratings): pass class NA_Cassiopeia_Sup_Zed(Ratings): pass class NA_Cassiopeia_Sup_Ziggs(Ratings): pass class NA_Cassiopeia_Sup_Zilean(Ratings): pass class NA_Cassiopeia_Sup_Zyra(Ratings): pass

17.019185

47

0.784839

972

7,097

5.304527

0.151235

0.187355

0.455004

0.535299

0.823701

0

0.156545

7,097

416

48

17.060096

0.861343

0

0.498195

0

1

0

true

0.498195

0.00361

0

0.501805

0

null

0

1

0

null

0

1

0

6

64b725ad878c5678871563ccb1019a7f54db1797

22,388

py

Python

tests/test_smartCompare.py

salesforce/smartACL

b8ccf5003689e32e9cf10512df84117f95ed86c2

[ "BSD-3-Clause" ]

3

2019-10-10T16:53:06.000Z

2020-12-29T22:48:29.000Z

tests/test_smartCompare.py

salesforce/smartACL

b8ccf5003689e32e9cf10512df84117f95ed86c2

[ "BSD-3-Clause" ]

5

2018-03-02T08:30:52.000Z

2021-07-26T10:53:18.000Z

tests/test_smartCompare.py

salesforce/smartACL

b8ccf5003689e32e9cf10512df84117f95ed86c2

[ "BSD-3-Clause" ]

3

2018-02-27T14:41:31.000Z

2019-08-06T04:41:21.000Z

# Copyright (c) 2018, salesforce.com, inc. # All rights reserved. # Licensed under the BSD 3-Clause license. # For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause import unittest import sys import os from smartACL import linkdef from smartACL import link_cisco from smartACL import link_juniper from smartACL import smartACL class smartTest(unittest.TestCase): def setUp(self): self.filet1 = 'tests/test_data/test_acl_smartCompare1' self.filet2 = 'tests/test_data/test_acl_smartCompare2' self.filet2a = 'tests/test_data/test_acl_smartCompare2a' self.filet3 = 'tests/test_data/test_acl_smartCompare3' self.filet4 = 'tests/test_data/test_acl_smartCompare4' self.filet5 = 'tests/test_data/test_acl_smartCompare5' self.filet6 = 'tests/test_data/test_acl_smartCompare6' self.filet7 = 'tests/test_data/test_acl_smartCompare7' self.filet8 = 'tests/test_data/test_acl_smartCompare8' self.filet9 = 'tests/test_data/test_acl_smartCompare9' self.filet10 = 'tests/test_data/test_acl_smartCompare10' self.filet11 = 'tests/test_data/test_acl_smartCompare11' self.filet12 = 'tests/test_data/test_acl_smartCompare12' self.filet13 = 'tests/test_data/test_acl_smartCompare13' self.filet14 = 'tests/test_data/test_acl_smartCompare14' self.filet15 = 'tests/test_data/test_acl_smartCompare15' self.filet16 = 'tests/test_data/test_acl_smartCompare16' self.filet17 = 'tests/test_data/test_acl_smartCompare17' self.filet18 = 'tests/test_data/test_acl_smartCompare18' self.filet19 = 'tests/test_data/test_acl_smartCompare19' self.results_t1_t2 = ['permit tcp 10.230.0.0 0.0.0.127 10.240.0.0 0.0.0.127', 'permit tcp 10.231.69.128 0.0.0.127 10.0.0.0 0.0.0.63 eq 7080', 'permit tcp 10.231.69.128 0.0.0.127 10.0.0.128 0.0.0.127 eq 7080', 'permit tcp 10.231.69.128 0.0.0.127 10.0.0.64 0.0.0.63 eq 7080'] self.results_t1_t2a = ['permit tcp 10.231.69.128 0.0.0.127 10.0.0.0 0.0.0.63 eq 7080', 'permit tcp 10.231.69.128 0.0.0.127 10.0.0.128 0.0.0.127 eq 7080', 'permit tcp 10.231.69.128 0.0.0.127 10.0.0.64 0.0.0.63 eq 7080'] self.results_t2a_t2a = ['deny tcp 10.230.0.0 0.0.0.127 10.240.0.0 0.0.0.127 eq 22', 'permit tcp 10.231.69.128 0.0.0.127 10.0.0.0 0.0.0.255 eq 7080'] self.results_t3_t4 = ['permit tcp 10.231.69.128 0.0.0.127 10.0.0.0 0.0.0.255 eq 7080', 'permit tcp 10.231.69.128 0.0.0.127 10.0.0.0 0.0.0.255 eq 7081'] self.results_t5_t6 = ['permit tcp 10.231.69.128 0.0.0.127 10.0.0.0 0.0.0.63 eq 7080', 'permit tcp 10.231.69.128 0.0.0.127 10.0.0.128 0.0.0.127 eq 7080', 'permit tcp 10.231.69.128 0.0.0.127 10.0.0.64 0.0.0.63 eq 7080'] self.results_t7_t8 = ['permit tcp 10.231.69.128 0.0.0.127 10.0.0.0 0.0.0.63 eq 7080', 'permit tcp 10.231.69.128 0.0.0.127 10.0.0.64 0.0.0.63 eq 7080', 'permit tcp 10.231.69.128 0.0.0.127 10.0.0.128 0.0.0.63 eq 7080', 'permit tcp 10.231.69.128 0.0.0.127 10.0.0.192 0.0.0.63 eq 7080'] self.results_t8_t7 = ['permit tcp 10.231.69.128 0.0.0.127 10.0.0.0 0.0.0.254 eq 7080', 'permit tcp 10.231.69.128 0.0.0.127 10.0.0.1 0.0.0.254 eq 7080'] self.results_t7_t7 = ['permit tcp 10.231.69.128 0.0.0.127 10.0.0.0 0.0.0.63 eq 7080', 'permit tcp 10.231.69.128 0.0.0.127 10.0.0.64 0.0.0.63 eq 7080', 'permit tcp 10.231.69.128 0.0.0.127 10.0.0.128 0.0.0.63 eq 7080', 'permit tcp 10.231.69.128 0.0.0.127 10.0.0.192 0.0.0.63 eq 7080'] ''' The T9 - T9 comparison is an interesting case. T9 has two shadowed rules inside, so when we try to compare it with itself, the two shadowed rules are shown like NOT matched. That is completely TRUE. Although it could seem to be inconsistent, indeed these two lines will be never matched, so in this case, smartCompare is working fine. The same would apply to T10 - T10 and T9 - T10 ''' self.results_t9_t9 = ['term testt1', 'term testt2', 'term testt3', 'term testt4'] self.results_t10_t10 = ['term testt1', "term testt2{2{1{['10.0.0.192/255.255.255.192', '10.0.1.0/255.255.255.128']", "term testt2{2{2{['10.0.0.192/255.255.255.192', '10.0.1.128/255.255.255.192']", 'term testt3', 'term testt4'] self.results_t9_t10 = ['term testt3', 'term testt4'] self.results_t11_t12 = ['term testt2', 'term testt3', 'term testt5'] self.results_t11_t12_is = ['term testt3', 'term testt5'] self.results_t13_t13 = ['permit udp 0.0.0.0 0.0.0.0 eq 67 255.255.255.255 0.0.0.0 eq 68', 'permit udp any eq 68 255.255.255.255 0.0.0.0 eq 67', 'permit udp 192.168.1.0 0.0.0.63 eq 68 any eq 68', 'permit udp 192.168.1.192 0.0.0.63 eq 68 any eq 68'] self.results_t14_t15 = ['permit tcp 10.230.0.0 0.0.0.127 10.240.0.0 0.0.0.127'] self.results_t15_t14 = [] self.results_t16_t17 = [] self.results_t17_t16 = [] self.results_t18_t19 = ['term testt1', 'term testt2'] self.results_t19_t18 = ["term testt1{1{1{['10.0.0.0/255.255.255.0', '10.0.1.0/255.255.255.0']", "term testt1{1{2{['10.0.0.0/255.255.255.0', '10.0.1.0/255.255.255.0']"] null = open(os.devnull, 'w') self.stdout = sys.stdout sys.stdout = null self.longMessage = True def test_smartCompare_t1_t2(self): policy1 = linkdef.FWPolicy('', self.filet1, False) link_cisco.acl_parser(self.filet1, policy1, False) policy2 = linkdef.FWPolicy('', self.filet2, False) link_cisco.acl_parser(self.filet2, policy2, False) policy1.split_ips() policy2.split_ips() smartacl_result = smartACL.smartCompare2(policy1, policy2, verbose=False,only_different=False,outprint=False,ignore_lines='',ignoredeny=False, ignoreshadowed=False, DEBUG=False) self.assertEqual(smartacl_result, self.results_t1_t2, 'Normal Test') smartacl_result = smartACL.smartCompare2(policy1, policy2, verbose=False,only_different=False,outprint=False,ignore_lines='',ignoredeny=False, ignoreshadowed=True, DEBUG=False) self.assertEqual(smartacl_result, self.results_t1_t2, 'Ignoring Shadowed Rules') def test_smartCompare_t1_t2a(self): policy1 = linkdef.FWPolicy('', self.filet1, False) link_cisco.acl_parser(self.filet1, policy1, False) policy2 = linkdef.FWPolicy('', self.filet2a, False) link_cisco.acl_parser(self.filet2a, policy2, False) policy1.split_ips() policy2.split_ips() smartacl_result = smartACL.smartCompare2(policy1, policy2, verbose=False,only_different=False,outprint=False,ignore_lines='',ignoredeny=False, ignoreshadowed=False, DEBUG=False) self.assertEqual(smartacl_result, self.results_t1_t2a, 'Normal Test') smartacl_result = smartACL.smartCompare2(policy1, policy2, verbose=False,only_different=False,outprint=False,ignore_lines='',ignoredeny=False, ignoreshadowed=True, DEBUG=False) self.assertEqual(smartacl_result, self.results_t1_t2a, 'Ignoring Shadowed Rules') def test_smartCompare_t2a_t2a(self): policy1 = linkdef.FWPolicy('', self.filet2a, False) link_cisco.acl_parser(self.filet2a, policy1, False) policy2 = linkdef.FWPolicy('', self.filet2a, False) link_cisco.acl_parser(self.filet2a, policy2, False) policy1.split_ips() policy2.split_ips() smartacl_result = smartACL.smartCompare2(policy1, policy2, verbose=False,only_different=False,outprint=False,ignore_lines='',ignoredeny=False, ignoreshadowed=False, DEBUG=False) self.assertEqual(smartacl_result, self.results_t2a_t2a, 'Normal Test') smartacl_result = smartACL.smartCompare2(policy1, policy2, verbose=False,only_different=False,outprint=False,ignore_lines='',ignoredeny=False, ignoreshadowed=True, DEBUG=False) self.assertEqual(smartacl_result, self.results_t2a_t2a, 'Ignoring Shadowed Rules') def test_smartCompare_t3_t4(self): policy1 = linkdef.FWPolicy('', self.filet3, False) link_cisco.acl_parser(self.filet3, policy1, False) policy2 = linkdef.FWPolicy('', self.filet4, False) link_cisco.acl_parser(self.filet4, policy2, False) policy1.split_ips() policy2.split_ips() smartacl_result = smartACL.smartCompare2(policy1, policy2, verbose=False,only_different=False,outprint=False,ignore_lines='',ignoredeny=False, ignoreshadowed=False, DEBUG=False) self.assertEqual(smartacl_result, self.results_t3_t4, 'Normal Test') smartacl_result = smartACL.smartCompare2(policy1, policy2, verbose=False,only_different=False,outprint=False,ignore_lines='',ignoredeny=False, ignoreshadowed=True, DEBUG=False) self.assertEqual(smartacl_result, self.results_t3_t4, 'Ignoring Shadowed Rules') def test_smartCompare_t5_t6(self): policy1 = linkdef.FWPolicy('', self.filet5, False) link_cisco.acl_parser(self.filet5, policy1, False) policy2 = linkdef.FWPolicy('', self.filet6, False) link_cisco.acl_parser(self.filet6, policy2, False) policy1.split_ips() policy2.split_ips() smartacl_result = smartACL.smartCompare2(policy1, policy2, verbose=False,only_different=False,outprint=False,ignore_lines='',ignoredeny=False, ignoreshadowed=False, DEBUG=False) self.assertEqual(smartacl_result, self.results_t5_t6, 'Normal Test') smartacl_result = smartACL.smartCompare2(policy1, policy2, verbose=False,only_different=False,outprint=False,ignore_lines='',ignoredeny=False, ignoreshadowed=True, DEBUG=False) self.assertEqual(smartacl_result, self.results_t5_t6, 'Ignoring Shadowed Rules') def test_smartCompare_t7_t7(self): policy1 = linkdef.FWPolicy('', self.filet7, False) link_cisco.acl_parser(self.filet7, policy1, False) policy2 = linkdef.FWPolicy('', self.filet7, False) link_cisco.acl_parser(self.filet7, policy2, False) policy1.split_ips() policy2.split_ips() smartacl_result = smartACL.smartCompare2(policy1, policy2, verbose=False,only_different=False,outprint=False,ignore_lines='',ignoredeny=False, ignoreshadowed=False, DEBUG=False) self.assertEqual(smartacl_result, self.results_t7_t7, 'Normal Test') smartacl_result = smartACL.smartCompare2(policy1, policy2, verbose=False,only_different=False,outprint=False,ignore_lines='',ignoredeny=False, ignoreshadowed=True, DEBUG=False) self.assertEqual(smartacl_result, self.results_t7_t7, 'Ignoring Shadowed Rules') def test_smartCompare_t7_t8(self): policy1 = linkdef.FWPolicy('', self.filet7, False) link_cisco.acl_parser(self.filet7, policy1, False) policy2 = linkdef.FWPolicy('', self.filet8, False) link_cisco.acl_parser(self.filet8, policy2, False) policy1.split_ips() policy2.split_ips() smartacl_result = smartACL.smartCompare2(policy1, policy2, verbose=False,only_different=False,outprint=False,ignore_lines='',ignoredeny=False, ignoreshadowed=False, DEBUG=False) self.assertEqual(smartacl_result, self.results_t7_t8, 'Normal Test') smartacl_result = smartACL.smartCompare2(policy1, policy2, verbose=False,only_different=False,outprint=False,ignore_lines='',ignoredeny=False, ignoreshadowed=True, DEBUG=False) self.assertEqual(smartacl_result, self.results_t7_t8, 'Ignoring Shadowed Rules') def test_smartCompare_t8_t7(self): policy1 = linkdef.FWPolicy('', self.filet8, False) link_cisco.acl_parser(self.filet8, policy1, False) policy2 = linkdef.FWPolicy('', self.filet7, False) link_cisco.acl_parser(self.filet7, policy2, False) policy1.split_ips() policy2.split_ips() smartacl_result = smartACL.smartCompare2(policy1, policy2, verbose=False,only_different=False,outprint=False,ignore_lines='',ignoredeny=False, ignoreshadowed=False, DEBUG=False) self.assertEqual(smartacl_result, self.results_t8_t7, 'Normal Test') smartacl_result = smartACL.smartCompare2(policy1, policy2, verbose=False,only_different=False,outprint=False,ignore_lines='',ignoredeny=False, ignoreshadowed=True, DEBUG=False) self.assertEqual(smartacl_result, self.results_t8_t7, 'Ignoring Shadowed Rules') def test_smartCompare_t9_t9(self): policy1 = linkdef.FWPolicy('', self.filet9, False) link_juniper.jcl_parser(self.filet9, policy1, False) policy2 = linkdef.FWPolicy('', self.filet9, False) link_juniper.jcl_parser(self.filet9, policy2, False) policy1.split_ips() policy2.split_ips() smartacl_result = smartACL.smartCompare2(policy1, policy2, verbose=False,only_different=False,outprint=False,ignore_lines='',ignoredeny=False, ignoreshadowed=False, DEBUG=False) self.assertEqual(smartacl_result, self.results_t9_t9, 'Normal Test') smartacl_result = smartACL.smartCompare2(policy1, policy2, verbose=False,only_different=False,outprint=False,ignore_lines='',ignoredeny=False, ignoreshadowed=True, DEBUG=False) # Because the shadowed rule is removed, both list need to be sorted first. self.assertEqual(smartacl_result.sort(), self.results_t9_t9.sort(), 'Ignoring Shadowed Rules') def test_smartCompare_t10_t10(self): policy1 = linkdef.FWPolicy('', self.filet10, False) link_juniper.jcl_parser(self.filet10, policy1, False) policy2 = linkdef.FWPolicy('', self.filet10, False) link_juniper.jcl_parser(self.filet10, policy2, False) policy1.split_ips() policy2.split_ips() smartacl_result = smartACL.smartCompare2(policy1, policy2, verbose=False,only_different=False,outprint=False,ignore_lines='',ignoredeny=False, ignoreshadowed=False, DEBUG=False) self.assertEqual(smartacl_result, self.results_t10_t10, 'Normal Test') smartacl_result = smartACL.smartCompare2(policy1, policy2, verbose=False,only_different=False,outprint=False,ignore_lines='',ignoredeny=False, ignoreshadowed=True, DEBUG=False) self.assertEqual(smartacl_result, self.results_t10_t10, 'Ignoring Shadowed Rules') def test_smartCompare_t9_t10(self): policy1 = linkdef.FWPolicy('', self.filet9, False) link_juniper.jcl_parser(self.filet9, policy1, False) policy2 = linkdef.FWPolicy('', self.filet10, False) link_juniper.jcl_parser(self.filet10, policy2, False) policy1.split_ips() policy2.split_ips() smartacl_result = smartACL.smartCompare2(policy1, policy2, verbose=False,only_different=False,outprint=False,ignore_lines='',ignoredeny=False, ignoreshadowed=False, DEBUG=False) self.assertEqual(smartacl_result, self.results_t9_t10, 'Normal Test') smartacl_result = smartACL.smartCompare2(policy1, policy2, verbose=False,only_different=False,outprint=False,ignore_lines='',ignoredeny=False, ignoreshadowed=True, DEBUG=False) self.assertEqual(smartacl_result, self.results_t9_t10, 'Ignoring Shadowed Rules') def test_smartCompare_t11_t12(self): policy1 = linkdef.FWPolicy('', self.filet11, False) link_juniper.jcl_parser(self.filet11, policy1, False) policy2 = linkdef.FWPolicy('', self.filet12, False) link_juniper.jcl_parser(self.filet12, policy2, False) policy1.split_ips() policy2.split_ips() smartacl_result = smartACL.smartCompare2(policy1, policy2, verbose=False,only_different=False,outprint=False,ignore_lines='',ignoredeny=False, ignoreshadowed=False, DEBUG=False) self.assertEqual(smartacl_result, self.results_t11_t12, 'Normal Test') ''' This is a very special case that it's better to have it separated because the results are different with/without "ignoreshadow" option. Explanation (simplified): - ACL1: - Rule1 - Rule2 -> Shadowed by Rule1 - ACL2: - Rule2 With ignoreshadow FALSE: - The Rule1 is NOT in ACL2 - The Rule2 is in ACL2 - The output shows that Rule1 is missing With ignoreshadow TRUE: - The Rule2 is removed because it's shadowed by Rule1 - The Rule1 is NOT in ACL2 - The output shows Rule1 and Rule2 are missing (Rule1 logically, but also all shadowed rules like Rule2) ''' def test_smartCompare_t11_t12_ignoreshadowed(self): policy1 = linkdef.FWPolicy('', self.filet11, False) link_juniper.jcl_parser(self.filet11, policy1, False) policy2 = linkdef.FWPolicy('', self.filet12, False) link_juniper.jcl_parser(self.filet12, policy2, False) policy1.split_ips() policy2.split_ips() smartacl_result = smartACL.smartCompare2(policy1, policy2, verbose=False,only_different=False,outprint=False,ignore_lines='',ignoredeny=False, ignoreshadowed=True, DEBUG=False) self.assertEqual(smartacl_result, self.results_t11_t12_is, 'Ignoring Shadowed Rules') def test_smartCompare_t13_t13(self): policy1 = linkdef.FWPolicy('', self.filet13, False) link_cisco.acl_parser(self.filet13, policy1, False) policy2 = linkdef.FWPolicy('', self.filet13, False) link_cisco.acl_parser(self.filet13, policy2, False) smartacl_result = smartACL.smartCompare2(policy1, policy2, verbose=False,only_different=False,outprint=False,ignore_lines='',ignoredeny=False, ignoreshadowed=False, DEBUG=False) self.assertEqual(smartacl_result, self.results_t13_t13, 'Normal Test') smartacl_result = smartACL.smartCompare2(policy1, policy2, verbose=False,only_different=False,outprint=False,ignore_lines='',ignoredeny=False, ignoreshadowed=True, DEBUG=False) self.assertEqual(smartacl_result, self.results_t13_t13, 'Ignoring Shadowed Rules') def test_smartCompare_t14_t15(self): policy1 = linkdef.FWPolicy('', self.filet14, False) link_cisco.acl_parser(self.filet14, policy1, False) policy2 = linkdef.FWPolicy('', self.filet15, False) link_cisco.acl_parser(self.filet15, policy2, False) smartacl_result = smartACL.smartCompare2(policy1, policy2, verbose=False,only_different=False,outprint=False,ignore_lines='',ignoredeny=False, ignoreshadowed=False, DEBUG=False) self.assertEqual(smartacl_result, self.results_t14_t15, 'Normal Test') smartacl_result = smartACL.smartCompare2(policy1, policy2, verbose=False,only_different=False,outprint=False,ignore_lines='',ignoredeny=False, ignoreshadowed=True, DEBUG=False) self.assertEqual(smartacl_result, self.results_t14_t15, 'Ignoring Shadowed Rules') def test_smartCompare_t15_t14(self): policy1 = linkdef.FWPolicy('', self.filet15, False) link_cisco.acl_parser(self.filet15, policy1, False) policy2 = linkdef.FWPolicy('', self.filet14, False) link_cisco.acl_parser(self.filet14, policy2, False) smartacl_result = smartACL.smartCompare2(policy1, policy2, verbose=False,only_different=False,outprint=False,ignore_lines='',ignoredeny=False, ignoreshadowed=False, DEBUG=False) self.assertEqual(smartacl_result, self.results_t15_t14, 'Normal Test') smartacl_result = smartACL.smartCompare2(policy1, policy2, verbose=False,only_different=False,outprint=False,ignore_lines='',ignoredeny=False, ignoreshadowed=True, DEBUG=False) self.assertEqual(smartacl_result, self.results_t15_t14, 'Ignoring Shadowed Rules') def test_smartCompare_t16_t17(self): policy1 = linkdef.FWPolicy('', self.filet16, False) link_cisco.acl_parser(self.filet16, policy1, False) policy2 = linkdef.FWPolicy('', self.filet17, False) link_cisco.acl_parser(self.filet17, policy2, False) smartacl_result = smartACL.smartCompare2(policy1, policy2, verbose=False,only_different=False,outprint=False,ignore_lines='',ignoredeny=False, ignoreshadowed=False, DEBUG=False) self.assertEqual(smartacl_result, self.results_t16_t17, 'Normal Test') smartacl_result = smartACL.smartCompare2(policy1, policy2, verbose=False,only_different=False,outprint=False,ignore_lines='',ignoredeny=False, ignoreshadowed=True, DEBUG=False) self.assertEqual(smartacl_result, self.results_t16_t17, 'Ignoring Shadowed Rules') def test_smartCompare_t17_t16(self): policy1 = linkdef.FWPolicy('', self.filet17, False) link_cisco.acl_parser(self.filet17, policy1, False) policy2 = linkdef.FWPolicy('', self.filet16, False) link_cisco.acl_parser(self.filet16, policy2, False) smartacl_result = smartACL.smartCompare2(policy1, policy2, verbose=False,only_different=False,outprint=False,ignore_lines='',ignoredeny=False, ignoreshadowed=False, DEBUG=False) self.assertEqual(smartacl_result, self.results_t17_t16, 'Normal Test') smartacl_result = smartACL.smartCompare2(policy1, policy2, verbose=False,only_different=False,outprint=False,ignore_lines='',ignoredeny=False, ignoreshadowed=True, DEBUG=False) self.assertEqual(smartacl_result, self.results_t17_t16, 'Ignoring Shadowed Rules') def test_smartCompare_t18_t19(self): policy1 = linkdef.FWPolicy('', self.filet18, False) link_juniper.jcl_parser(self.filet18, policy1, False) policy2 = linkdef.FWPolicy('', self.filet19, False) link_juniper.jcl_parser(self.filet19, policy2, False) policy1.split_ips() policy2.split_ips() smartacl_result = smartACL.smartCompare2(policy1, policy2, verbose=False,only_different=False,outprint=False,ignore_lines='',ignoredeny=False, ignoreshadowed=False, DEBUG=False) self.assertEqual(smartacl_result, self.results_t18_t19, 'Normal Test') def test_smartCompare_t19_t18(self): policy1 = linkdef.FWPolicy('', self.filet19, False) link_juniper.jcl_parser(self.filet19, policy1, False) policy2 = linkdef.FWPolicy('', self.filet18, False) link_juniper.jcl_parser(self.filet18, policy2, False) policy1.split_ips() policy2.split_ips() smartacl_result = smartACL.smartCompare2(policy1, policy2, verbose=False,only_different=False,outprint=False,ignore_lines='',ignoredeny=False, ignoreshadowed=False, DEBUG=False) self.assertEqual(smartacl_result, self.results_t19_t18, 'Normal Test') def tearDown(self): sys.stdout = self.stdout if __name__ == '__main__': unittest.main()

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b37f659598aede38ce5c3895f879e9aef08c302a

75

py

Python

code_export/run.py

EPFLMachineLearningTeamYoor/Project02

1274e45b8fe6f43e959dbf862a50fdab7def7797

[ "MIT" ]

null

code_export/run.py

EPFLMachineLearningTeamYoor/Project02

1274e45b8fe6f43e959dbf862a50fdab7def7797

[ "MIT" ]

13

2017-11-15T18:08:15.000Z

2017-12-26T19:27:02.000Z

code_export/run.py

EPFLMachineLearningTeamYoor/Project02

1274e45b8fe6f43e959dbf862a50fdab7def7797

[ "MIT" ]

1

2018-05-25T19:39:20.000Z

import os os.system("python 00clean.py") os.system("python 01classify.py")

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py

Python

gmplot/__init__.py

Monti03/gmplot

888ed6e2845913a8623009757e03ec49a11da7db

[ "MIT" ]

606

2015-10-04T02:43:48.000Z

2020-04-17T16:57:36.000Z

gmplot/__init__.py

Monti03/gmplot

888ed6e2845913a8623009757e03ec49a11da7db

[ "MIT" ]

100

2020-04-20T04:46:16.000Z

2022-01-07T00:41:47.000Z

gmplot/__init__.py

Monti03/gmplot

888ed6e2845913a8623009757e03ec49a11da7db

[ "MIT" ]

266

2015-05-10T21:44:15.000Z

2020-04-12T15:11:03.000Z

from .google_map_plotter import GoogleMapPlotter

24.5

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b3acd2e000318018920640120ff7b09a5c66d8ea

10,588

py

Python

projects/vdk-control-cli/tests/vdk/internal/control/command_groups/job/test_execute.py

alod83/versatile-data-kit

9ca672d3929eb3dc6fe5c677e8c8a75e2a0d2be8

[ "Apache-2.0" ]

100

2021-10-04T09:32:04.000Z

2022-03-30T11:23:53.000Z

projects/vdk-control-cli/tests/vdk/internal/control/command_groups/job/test_execute.py

alod83/versatile-data-kit

9ca672d3929eb3dc6fe5c677e8c8a75e2a0d2be8

[ "Apache-2.0" ]

208

2021-10-04T16:56:40.000Z

2022-03-31T10:41:44.000Z

projects/vdk-control-cli/tests/vdk/internal/control/command_groups/job/test_execute.py

alod83/versatile-data-kit

9ca672d3929eb3dc6fe5c677e8c8a75e2a0d2be8

[ "Apache-2.0" ]

14

2021-10-11T14:15:13.000Z

2022-03-11T13:39:17.000Z

# Copyright 2021 VMware, Inc. # SPDX-License-Identifier: Apache-2.0 import json import os from unittest.mock import patch from click.testing import CliRunner from py._path.local import LocalPath from pytest_httpserver.pytest_plugin import PluginHTTPServer from taurus_datajob_api import DataJobDeployment from taurus_datajob_api import DataJobExecution from vdk.internal import test_utils from vdk.internal.control.command_groups.job.execute import execute from werkzeug import Response test_utils.disable_vdk_authentication() def test_execute(httpserver: PluginHTTPServer, tmpdir: LocalPath): rest_api_url = httpserver.url_for("") team_name = "test-team" job_name = "test-job" httpserver.expect_request( uri=f"/data-jobs/for-team/{team_name}/jobs/{job_name}/deployments/production/executions", method="POST", ).respond_with_response( Response( status=200, headers=dict( Location=f"/data-jobs/for-team/{team_name}/jobs/{job_name}/executions/foo" ), ) ) runner = CliRunner() result = runner.invoke( execute, ["-n", job_name, "-t", team_name, "--start", "-u", rest_api_url] ) assert result.exit_code == 0, ( f"result exit code is not 0, result output: {result.output}, " f"result.exception: {result.exception}" ) def test_cancel(httpserver: PluginHTTPServer, tmpdir: LocalPath): rest_api_url = httpserver.url_for("") team_name = "test-team" job_name = "test-job" execution_id = "test-execution" httpserver.expect_request( uri=f"/data-jobs/for-team/{team_name}/jobs/{job_name}/executions/{execution_id}", method="DELETE", ).respond_with_response(Response(status=200, headers={})) runner = CliRunner() result = runner.invoke( execute, [ "-n", job_name, "-t", team_name, "-i", execution_id, "--cancel", "-u", rest_api_url, ], ) assert result.exit_code == 0, ( f"result exit code is not 0, result output: {result.output}, " f"result.exception: {result.exception}" ) def test_execute_without_url(httpserver: PluginHTTPServer, tmpdir: LocalPath): runner = CliRunner() result = runner.invoke(execute, ["-n", "job_name", "-t", "team_name", "-u", ""]) assert ( result.exit_code == 2 ), f"result exit code is not 2, result output: {result.output}, exc: {result.exc_info}" assert "what" in result.output and "why" in result.output def test_execute_with_empty_url(httpserver: PluginHTTPServer, tmpdir: LocalPath): runner = CliRunner() result = runner.invoke(execute, ["-n", "job_name", "-t", "team_name", "-u", ""]) assert ( result.exit_code == 2 ), f"result exit code is not 2, result output: {result.output}, exc: {result.exc_info}" assert "what" in result.output and "why" in result.output def test_execute_start_output_text(httpserver: PluginHTTPServer, tmpdir: LocalPath): rest_api_url = httpserver.url_for("") team_name = "test-team" job_name = "test-job" httpserver.expect_request( uri=f"/data-jobs/for-team/{team_name}/jobs/{job_name}/deployments/production/executions", method="POST", ).respond_with_response( Response( status=200, headers=dict( Location=f"/data-jobs/for-team/{team_name}/jobs/{job_name}/executions/foo" ), ) ) runner = CliRunner() result = runner.invoke( execute, ["-n", job_name, "-t", team_name, "--start", "-u", rest_api_url] ) assert f"-n {job_name}" in result.output assert f"-t {team_name}" in result.output def test_execute_start_output_json(httpserver: PluginHTTPServer, tmpdir: LocalPath): rest_api_url = httpserver.url_for("") team_name = "test-team" job_name = "test-job" httpserver.expect_request( uri=f"/data-jobs/for-team/{team_name}/jobs/{job_name}/deployments/production/executions", method="POST", ).respond_with_response( Response( status=200, headers=dict( Location=f"/data-jobs/for-team/{team_name}/jobs/{job_name}/executions/foo" ), ) ) runner = CliRunner() result = runner.invoke( execute, ["-n", job_name, "-t", team_name, "--start", "-u", rest_api_url, "-o", "json"], ) json_output = json.loads(result.output) assert job_name == json_output.get("job_name") assert team_name == json_output.get("team") def test_execute_with_exception(httpserver: PluginHTTPServer, tmpdir: LocalPath): runner = CliRunner() result = runner.invoke( execute, ["--start", "-n", "job_name", "-t", "team_name", "-u", "localhost"] ) assert ( result.exit_code == 2 ), f"result exit code is not 2, result output: {result.output}, exc: {result.exc_info}" assert "what" in result.output and "why" in result.output def test_execute_no_execution_id(httpserver: PluginHTTPServer, tmpdir: LocalPath): rest_api_url = httpserver.url_for("") team_name = "test-team" job_name = "test-job" execution: DataJobExecution = DataJobExecution( id="1", job_name=job_name, logs_url="", deployment=DataJobDeployment(), start_time="2021-09-24T14:14:03.922Z", ) older_execution = DataJobExecution( id="2", job_name=job_name, logs_url="", deployment=DataJobDeployment(), start_time="2020-09-24T14:14:03.922Z", ) httpserver.expect_request( uri=f"/data-jobs/for-team/{team_name}/jobs/{job_name}/executions", method="GET", ).respond_with_json( [older_execution.to_dict(), execution.to_dict(), older_execution.to_dict()] ) httpserver.expect_request( uri=f"/data-jobs/for-team/{team_name}/jobs/{job_name}/executions/1/logs", method="GET", ).respond_with_json({"logs": "We are the logs! We are awesome!"}) runner = CliRunner() result = runner.invoke( execute, ["-n", job_name, "-t", team_name, "--logs", "-u", rest_api_url], ) test_utils.assert_click_status(result, 0) assert result.output.strip() == "We are the logs! We are awesome!".strip() def test_execute_logs_using_api(httpserver: PluginHTTPServer, tmpdir: LocalPath): rest_api_url = httpserver.url_for("") team_name = "test-team" job_name = "test-job" id = "1" execution: DataJobExecution = DataJobExecution( id=id, job_name=job_name, logs_url="", deployment=DataJobDeployment() ) httpserver.expect_request( uri=f"/data-jobs/for-team/{team_name}/jobs/{job_name}/executions/1", method="GET", ).respond_with_json(execution.to_dict()) httpserver.expect_request( uri=f"/data-jobs/for-team/{team_name}/jobs/{job_name}/executions/1/logs", method="GET", ).respond_with_json({"logs": "We are the logs! We are awesome!"}) runner = CliRunner() result = runner.invoke( execute, ["-n", job_name, "-t", team_name, "-i", id, "--logs", "-u", rest_api_url], ) test_utils.assert_click_status(result, 0) assert result.output.strip() == "We are the logs! We are awesome!".strip() def test_execute_logs_with_external_log_url( httpserver: PluginHTTPServer, tmpdir: LocalPath ): rest_api_url = httpserver.url_for("") team_name = "test-team" job_name = "test-job" id = "1" execution: DataJobExecution = DataJobExecution( id=id, job_name=job_name, logs_url="http://external-service-job-logs", deployment=DataJobDeployment(), ) httpserver.expect_request( uri=f"/data-jobs/for-team/{team_name}/jobs/{job_name}/executions/1", method="GET", ).respond_with_json(execution.to_dict()) with patch("webbrowser.open") as mock_browser_open: mock_browser_open.return_value = False runner = CliRunner() result = runner.invoke( execute, ["-n", job_name, "-t", team_name, "-i", id, "--logs", "-u", rest_api_url], ) test_utils.assert_click_status(result, 0) mock_browser_open.assert_called_once_with("http://external-service-job-logs") def test_execute_start_extra_arguments_invalid_json( httpserver: PluginHTTPServer, tmpdir: LocalPath ): rest_api_url = httpserver.url_for("") team_name = "test-team" job_name = "test-job" httpserver.expect_request( uri=f"/data-jobs/for-team/{team_name}/jobs/{job_name}/deployments/production/executions", method="POST", ) runner = CliRunner() result = runner.invoke( execute, [ "-n", job_name, "-t", team_name, "--start", "-u", rest_api_url, "--arguments", '{key1": "value1", "key2": "value2"}', ], ) assert ( result.exit_code == 2 ), f"Result exit code not 2. result output {result.output}, exc: {result.exc_info}" assert "Failed to validate job arguments" in result.output assert "what" and "why" in result.output assert "Make sure provided --arguments is a valid JSON string." in result.output def test_execute_start_extra_arguments(httpserver: PluginHTTPServer, tmpdir: LocalPath): rest_api_url = httpserver.url_for("") team_name = "test-team" job_name = "test-job" arguments = '{"key1": "value1", "key2": "value2"}' httpserver.expect_request( uri=f"/data-jobs/for-team/{team_name}/jobs/{job_name}/deployments/production/executions", method="POST", json=json.loads( '{"args": {"key1": "value1", "key2": "value2"}, "started_by": "vdk-control-cli"}' ), ).respond_with_response( Response( status=200, headers=dict( Location=f"/data-jobs/for-team/{team_name}/jobs/{job_name}/executions/foo" ), ) ) runner = CliRunner() result = runner.invoke( execute, [ "-n", job_name, "-t", team_name, "--start", "-u", rest_api_url, "--arguments", arguments, ], ) assert ( result.exit_code == 0 ), f"Result exit code not 0. result output {result.output}, exc: {result.exc_info}"

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null

0

1

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null

0

6

b61224b359b1b664d0d88a9fb2fe8fd54623ffd0

165

py

Python

main/admin.py

drhoet/photo-workflow

4d1e6be82a71fec34e37ddf4096c46d871b24b66

[ "MIT" ]

null

main/admin.py

drhoet/photo-workflow

4d1e6be82a71fec34e37ddf4096c46d871b24b66

[ "MIT" ]

null

main/admin.py

drhoet/photo-workflow

4d1e6be82a71fec34e37ddf4096c46d871b24b66

[ "MIT" ]

null

from django.contrib import admin from .models import Author, Directory, Image admin.site.register(Directory) admin.site.register(Image) admin.site.register(Author)

23.571429

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6

37a107ac9978b71007d044eaab66a8782c71ec7b

38

py

Python

maps/lotus_island/__init__.py

56kyle/bloons_auto

419d55b51d1cddc49099593970adf1c67985b389

[ "MIT" ]

null

maps/lotus_island/__init__.py

56kyle/bloons_auto

419d55b51d1cddc49099593970adf1c67985b389

[ "MIT" ]

null

maps/lotus_island/__init__.py

56kyle/bloons_auto

419d55b51d1cddc49099593970adf1c67985b389

[ "MIT" ]

null

from .lotus_island import LotusIsland

19

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6

809e95004d6bb84c2a15c2de65286c1bbf8a2c1c

10,372

py

Python

tests/test_model_decisions.py

djmhunt/TTpy

0f0997314bf0f54831494b2ef1a64f1bff95c097

[ "MIT" ]

null

tests/test_model_decisions.py

djmhunt/TTpy

0f0997314bf0f54831494b2ef1a64f1bff95c097

[ "MIT" ]

4

2020-04-19T11:43:41.000Z

2020-07-21T09:57:51.000Z

tests/test_model_decisions.py

djmhunt/TTpy

0f0997314bf0f54831494b2ef1a64f1bff95c097

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ :Author: Dominic """ import collections import model.decision.binary as binary import model.decision.discrete as discrete import numpy as np #%% For binary.single class TestClass_decSingle: def test_S_normal(self): np.random.seed(100) d = binary.single() result = d(0.23) correct_result = (0, collections.OrderedDict([(0, 0.77), (1, 0.23)])) assert result == correct_result def test_S_normal_2(self): last_action = 0 np.random.seed(100) d = binary.single() result = d(0.23, last_action) correct_result = (0, collections.OrderedDict([(0, 0.77), (1, 0.23)])) assert result == correct_result def test_S_normal_3(self): last_action = 0 np.random.seed(104) d = binary.single() result = d(0.23, last_action) correct_result = (1, collections.OrderedDict([(0, 0.77), (1, 0.23)])) assert result == correct_result def test_S_valid_1(self): np.random.seed(100) d = binary.single() result = d(0.23, trial_responses=[1]) correct_result = (1, collections.OrderedDict([(0, 0), (1, 1)])) assert result == correct_result def test_S_valid_2(self): np.random.seed(100) d = binary.single() result = d(0.23, trial_responses=[]) correct_result = (None, collections.OrderedDict([(0, 0.77), (1, 0.23)])) assert result == correct_result #%% For discrete.weightProb class TestClass_decWeightProb: def test_WP_normal(self): np.random.seed(100) d = discrete.weightProb(task_responses=[1, 2, 3]) result = d([0.8, 0.5, 0.7]) correct_result = (2, collections.OrderedDict([(1, 0.4), (2, 0.25), (3, 0.35)])) assert result == correct_result def test_WP_normal_2(self): np.random.seed(101) d = discrete.weightProb(task_responses=[1, 2, 3]) result = d([0.2, 0.3, 0.5]) correct_result = (3, collections.OrderedDict([(1, 0.2), (2, 0.3), (3, 0.5)])) assert result == correct_result def test_WP_valid(self): np.random.seed(100) d = discrete.weightProb(task_responses=[1, 2, 3]) result = d([0.2, 0.3, 0.5], trial_responses=[1, 2]) correct_result = (2, collections.OrderedDict([(1, 0.4), (2, 0.6), (3, 0)])) assert result == correct_result def test_WP_valid_2(self): np.random.seed(100) d = discrete.weightProb(task_responses=[1, 2, 3]) result = d([0.2, 0.3, 0.5], trial_responses=[1]) correct_result = (1, collections.OrderedDict([(1, 1), (2, 0), (3, 0)])) assert result == correct_result def test_WP_no_valid(self): np.random.seed(100) d = discrete.weightProb(task_responses=[1, 2, 3]) result = d([0.2, 0.3, 0.5], trial_responses=[]) correct_result = (None, collections.OrderedDict([(1, 0.2), (2, 0.3), (3, 0.5)])) assert result == correct_result def test_WP_string(self): np.random.seed(100) d = discrete.weightProb(["A", "B", "C"]) result = d([0.2, 0.3, 0.5], trial_responses=["A", "B"]) correct_result = ('B', collections.OrderedDict([('A', 0.4), ('B', 0.6), ('C', 0)])) assert result == correct_result def test_WP_err(self): np.random.seed(100) d = discrete.weightProb(task_responses=[1, 2, 3]) result = d([0.6, 0.3, 0.5], trial_responses=[0, 3]) correct_result = (3, collections.OrderedDict([(1, 0), (2, 0), (3, 1)])) assert result == correct_result def test_WP_err_2(self): np.random.seed(100) d = discrete.weightProb(task_responses=[1, 2, 3]) result = d([0.6, 0.3, 0.5], trial_responses=[1, 1]) correct_result = (1, collections.OrderedDict([(1, 1), (2, 0), (3, 0)])) assert result == correct_result #%% For discrete.maxProb class TestClass_decMaxProb: def test_MP_normal(self): np.random.seed(100) d = discrete.maxProb(task_responses=[1, 2, 3]) result = d([0.6, 0.3, 0.5]) correct_result = (1, collections.OrderedDict([(1, 0.6), (2, 0.3), (3, 0.5)])) assert result == correct_result def test_MP_normal_2(self): np.random.seed(101) d = discrete.maxProb(task_responses=[1, 2, 3]) result = d([0.5, 0.3, 0.5]) correct_result = (3, collections.OrderedDict([(1, 0.5), (2, 0.3), (3, 0.5)])) assert result == correct_result def test_MP_valid(self): np.random.seed(100) d = discrete.maxProb(task_responses=[1, 2, 3]) result = d([0.2, 0.3, 0.5], trial_responses=[1, 2]) correct_result = (2, collections.OrderedDict([(1, 0.2), (2, 0.3), (3, 0.5)])) assert result == correct_result def test_MP_valid_2(self): np.random.seed(100) d = discrete.maxProb(task_responses=[1, 2, 3]) result = d([0.2, 0.3, 0.5], trial_responses=[1]) correct_result = (1, collections.OrderedDict([(1, 0.2), (2, 0.3), (3, 0.5)])) assert result == correct_result def test_MP_no_valid(self): np.random.seed(100) d = discrete.maxProb(task_responses=[1, 2, 3]) result = d([0.2, 0.3, 0.5], trial_responses=[]) correct_result = (None, collections.OrderedDict([(1, 0.2), (2, 0.3), (3, 0.5)])) assert result == correct_result def test_MP_string(self): np.random.seed(100) d = discrete.maxProb(["A", "B", "C"]) result = d([0.2, 0.3, 0.5], trial_responses=["A", "B"]) correct_result = ('B', collections.OrderedDict([('A', 0.2), ('B', 0.3), ('C', 0.5)])) assert result == correct_result def test_MP_err(self): np.random.seed(100) d = discrete.maxProb(task_responses=[1, 2, 3]) result = d([0.6, 0.3, 0.5], trial_responses=[0, 3]) correct_result = (3, collections.OrderedDict([(1, 0.6), (2, 0.3), (3, 0.5)])) assert result == correct_result def test_MP_err_2(self): np.random.seed(100) d = discrete.maxProb(task_responses=[1, 2, 3]) result = d([0.6, 0.3, 0.5], trial_responses=[1, 1]) correct_result = (1, collections.OrderedDict([(1, 0.6), (2, 0.3), (3, 0.5)])) assert result == correct_result #%% For discrete.probThresh class TestClass_decProbThresh: def test_PT_normal(self): np.random.seed(100) d = discrete.probThresh(task_responses=[0, 1, 2, 3], eta=0.8) correct_result = (1, collections.OrderedDict([(0, 0.2), (1, 0.8), (2, 0.3), (3, 0.5)])) result = d([0.2, 0.8, 0.3, 0.5]) assert result == correct_result def test_PT_normal_2(self): np.random.seed(100) d = discrete.probThresh(task_responses=[0, 1, 2, 3], eta=0.8) correct_result = (0, collections.OrderedDict([(0, 0.2), (1, 0.5), (2, 0.3), (3, 0.5)])) result = d([0.2, 0.5, 0.3, 0.5]) assert result == correct_result def test_PT_normal_3(self): np.random.seed(101) d = discrete.probThresh(task_responses=[0, 1, 2, 3], eta=0.8) correct_result = (3, collections.OrderedDict([(0, 0.2), (1, 0.5), (2, 0.3), (3, 0.5)])) result = d([0.2, 0.5, 0.3, 0.5]) assert result == correct_result def test_PT_valid(self): np.random.seed(100) d = discrete.probThresh(task_responses=[0, 1, 2, 3], eta=0.8) correct_result = (0, collections.OrderedDict([(0, 0.2), (1, 0.8), (2, 0.3), (3, 0.5)])) result = d([0.2, 0.8, 0.3, 0.5], trial_responses=[0, 2]) assert result == correct_result def test_PT_no_valid(self): np.random.seed(100) d = discrete.probThresh(task_responses=[0, 1, 2, 3], eta=0.8) correct_result = (None, collections.OrderedDict([(0, 0.2), (1, 0.8), (2, 0.3), (3, 0.5)])) result = d([0.2, 0.8, 0.3, 0.5], trial_responses=[]) assert result == correct_result def test_PT_string(self): np.random.seed(100) d = discrete.probThresh(["A", "B", "C"]) correct_result = ('A', collections.OrderedDict([('A', 0.2), ('B', 0.3), ('C', 0.8)])) result = d([0.2, 0.3, 0.8], trial_responses=["A", "B"]) assert result == correct_result def test_PT_err(self): np.random.seed(100) d = discrete.probThresh(["A", "B", "C"]) correct_result = ('A', collections.OrderedDict([('A', 0.2), ('B', 0.3), ('C', 0.8)])) result = d([0.2, 0.3, 0.8], trial_responses=["A", "D"]) assert result == correct_result #%% For discrete._validProbabilities class TestClass_validProbabilities: def test_VP_reduced_int(self): correct_result = (np.array([0.1, 0.7]), np.array([2, 3])) result = discrete._validProbabilities([0.2, 0.1, 0.7], [1, 2, 3], [2, 3]) assert (result[0] == correct_result[0]).all() assert (result[1] == correct_result[1]).all() def test_VP_reduced_str(self): correct_result = (np.array([0.1, 0.7]), np.array(['B', 'C'])) result = discrete._validProbabilities([0.2, 0.1, 0.7], ["A", "B", "C"], ["B", "C"]) assert (result[0] == correct_result[0]).all() assert (result[1] == correct_result[1]).all() def test_VP_normal(self): correct_result = (np.array([0.2, 0.1, 0.7]), np.array(["A", "B", "C"])) result = discrete._validProbabilities([0.2, 0.1, 0.7], ["A", "B", "C"], ["A", "B", "C"]) assert (result[0] == correct_result[0]).all() assert (result[1] == correct_result[1]).all() def test_VP_err(self): correct_result = (np.array([0.2]), np.array(['A'])) result = discrete._validProbabilities([0.2, 0.1, 0.7], ["A", "B", "C"], ["A", "D"]) assert (result[0] == correct_result[0]).all() assert (result[1] == correct_result[1]).all() def test_VP_err_2(self): correct_result = (np.array([0.2]), np.array(['A'])) result = discrete._validProbabilities([0.2, 0.1, 0.7], ["A", "B", "C"], ["A", "A"]) assert (result[0] == correct_result[0]).all() assert (result[1] == correct_result[1]).all()

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0.922509

0.907911

0.861597

0.81348

0.786448

0

0.088647

0.256074

10,372

253

100

40.996047

0.630508

0.016294

0

0.593137

0

0.006439

0

0.186275

1

0.161765

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0

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0

0.205882

0

null

0

1

0

null

0

6

809eed726ebdd675fbefd9df1532efbcc6869cd5

1,289

py

Python

interactions/__init__.py

Jalancar/discord-interactions

d443d7de39780987ea8c8a580a2ca6f083542c21

[ "MIT" ]

null

interactions/__init__.py

Jalancar/discord-interactions

d443d7de39780987ea8c8a580a2ca6f083542c21

[ "MIT" ]

null

interactions/__init__.py

Jalancar/discord-interactions

d443d7de39780987ea8c8a580a2ca6f083542c21

[ "MIT" ]

null

""" (interactions) discord-interactions Easy, simple, scalable and modular: a Python API wrapper for interactions. To see the documentation, please head over to the link here: https://discord-interactions.rtfd.io/en/latest for ``stable`` builds. https://discord-interactions.rtfd.io/en/unstable for ``unstable`` builds. (c) 2021 goverfl0w. Co-authored by DeltaXW. """ from .api.models.channel import * # noqa: F401 F403 from .api.models.flags import * # noqa: F401 F403 from .api.models.guild import * # noqa: F401 F403 from .api.models.gw import * # noqa: F401 F403 from .api.models.member import * # noqa: F401 F403 from .api.models.message import * # noqa: F401 F403 from .api.models.misc import * # noqa: F401 F403 from .api.models.presence import * # noqa: F401 F403 from .api.models.role import * # noqa: F401 F403 from .api.models.team import * # noqa: F401 F403 from .api.models.user import * # noqa: F401 F403 from .base import * # noqa: F401 F403 from .client import * # noqa: F401 F403 from .context import * # noqa: F401 F403 from .decor import * # noqa: F401 F403 from .enums import * # noqa: F401 F403 from .models.command import * # noqa: F401 F403 from .models.component import * # noqa: F401 F403 from .models.misc import * # noqa: F401 F403

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6

80ec0b533e065032c62d77a15af81c098418bb7a

130

py

Python

tests/conftest.py

rhanka/addok

320d145e72964d54eb33742f0329e9f46f5c5ab5

[ "WTFPL" ]

215

2016-01-29T08:37:56.000Z

2022-03-28T06:28:41.000Z

tests/conftest.py

bendathierrycom/addok

07346046ed53993d8e2b66262f52d505f26f5ba9

[ "MIT" ]

487

2016-01-13T10:11:34.000Z

2022-03-31T10:56:24.000Z

tests/conftest.py

bendathierrycom/addok

07346046ed53993d8e2b66262f52d505f26f5ba9

[ "MIT" ]

52

2016-01-12T13:10:28.000Z

2022-03-24T15:45:39.000Z

def pytest_configure(): from addok.config import config as addok_config addok_config.SYNONYMS_PATH = 'tests/synonyms.txt'

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130

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null

0

1

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1

0

6

038e9fece67b1cf6dca75b65528af70db559ef7a

90

py

Python

camp/Core/__init__.py

blakezim/CAMP

a42a407dc62151ab8a7eb4be3aee1318b984502c

[ "MIT" ]

4

2021-03-02T05:18:06.000Z

2021-11-29T16:06:39.000Z

camp/Core/__init__.py

blakezim/CAMP

a42a407dc62151ab8a7eb4be3aee1318b984502c

[ "MIT" ]

null

camp/Core/__init__.py

blakezim/CAMP

a42a407dc62151ab8a7eb4be3aee1318b984502c

[ "MIT" ]

1

2021-03-26T20:38:11.000Z

from .StructuredGridClass import * from .TriangleMeshClass import * from .Display import *

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9

90

8.111111

0.555556

0.273973

0

0.122222

90

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30

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true

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1

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1

0

6

ff1ff5eb8a3d4b3cfe2f72163ab368db619cda37

195

py

Python

pasa/dict/__init__.py

sonoisa/pasa

90dbcd72890bfe390d2a58f2a4cdb79d42a9f9f8

[ "MIT" ]

5

2018-07-23T05:45:24.000Z

2021-04-04T14:59:15.000Z

pasa/dict/__init__.py

sonoisa/pasa

90dbcd72890bfe390d2a58f2a4cdb79d42a9f9f8

[ "MIT" ]

2

2019-01-28T04:33:12.000Z

2019-11-20T14:30:27.000Z

pasa/dict/__init__.py

sonoisa/pasa

90dbcd72890bfe390d2a58f2a4cdb79d42a9f9f8

[ "MIT" ]

1

2020-02-07T08:09:12.000Z

# -*- coding: utf-8 -*- from . import category from . import cchart from . import filter from . import frame from . import idiom from . import compound_predicate from .load_json import LoadJson

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6

209cc331543d15ad5bdae8f487f601e323119dd4

516

py

Python

cbpro/__init__.py

Cattes/coinbasepro-python

0a9c9ba2188f6bfa08a842a666ab12fe1cc02276

[ "MIT" ]

null

cbpro/__init__.py

Cattes/coinbasepro-python

0a9c9ba2188f6bfa08a842a666ab12fe1cc02276

[ "MIT" ]

null

cbpro/__init__.py

Cattes/coinbasepro-python

0a9c9ba2188f6bfa08a842a666ab12fe1cc02276

[ "MIT" ]

null

from cbpro.auth import Auth from cbpro.messenger import Messenger from cbpro.public import PublicClient from cbpro.public import public_client from cbpro.private import PrivateClient from cbpro.private import private_client from cbpro.models import PublicModel from cbpro.models import PrivateModel from cbpro.websocket import get_message from cbpro.websocket import WebsocketHeader from cbpro.websocket import WebsocketStream from cbpro.websocket import WebsocketEvent from cbpro.websocket import WebsocketClient

32.25

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516

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516

16

44

32.25

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null

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1

0

1

0

1

0

6

20b43209e8d703bc2cd7dc94334a85f61041e9d0

9,938

py

Python

tests/test_decompressor_fuzzing.py

thewtex/python-zstandard

51d4c71ab6ca6aa915e4caf3f51a73fa6fc3b43a

[ "BSD-3-Clause" ]

null

tests/test_decompressor_fuzzing.py

thewtex/python-zstandard

51d4c71ab6ca6aa915e4caf3f51a73fa6fc3b43a

[ "BSD-3-Clause" ]

null

tests/test_decompressor_fuzzing.py

thewtex/python-zstandard

51d4c71ab6ca6aa915e4caf3f51a73fa6fc3b43a

[ "BSD-3-Clause" ]

null

import io import os import unittest try: import hypothesis import hypothesis.strategies as strategies except ImportError: raise unittest.SkipTest('hypothesis not available') import zstandard as zstd from . common import ( make_cffi, random_input_data, ) @unittest.skipUnless('ZSTD_SLOW_TESTS' in os.environ, 'ZSTD_SLOW_TESTS not set') @make_cffi class TestDecompressor_stream_reader_fuzzing(unittest.TestCase): @hypothesis.settings( suppress_health_check=[hypothesis.HealthCheck.large_base_example]) @hypothesis.given(original=strategies.sampled_from(random_input_data()), level=strategies.integers(min_value=1, max_value=5), source_read_size=strategies.integers(1, 16384), read_sizes=strategies.data()) def test_stream_source_read_variance(self, original, level, source_read_size, read_sizes): cctx = zstd.ZstdCompressor(level=level) frame = cctx.compress(original) dctx = zstd.ZstdDecompressor() source = io.BytesIO(frame) chunks = [] with dctx.stream_reader(source, read_size=source_read_size) as reader: while True: read_size = read_sizes.draw(strategies.integers(1, 16384)) chunk = reader.read(read_size) if not chunk: break chunks.append(chunk) self.assertEqual(b''.join(chunks), original) @hypothesis.settings( suppress_health_check=[hypothesis.HealthCheck.large_base_example]) @hypothesis.given(original=strategies.sampled_from(random_input_data()), level=strategies.integers(min_value=1, max_value=5), source_read_size=strategies.integers(1, 16384), read_sizes=strategies.data()) def test_buffer_source_read_variance(self, original, level, source_read_size, read_sizes): cctx = zstd.ZstdCompressor(level=level) frame = cctx.compress(original) dctx = zstd.ZstdDecompressor() chunks = [] with dctx.stream_reader(frame, read_size=source_read_size) as reader: while True: read_size = read_sizes.draw(strategies.integers(1, 16384)) chunk = reader.read(read_size) if not chunk: break chunks.append(chunk) self.assertEqual(b''.join(chunks), original) @hypothesis.settings( suppress_health_check=[hypothesis.HealthCheck.large_base_example]) @hypothesis.given( original=strategies.sampled_from(random_input_data()), level=strategies.integers(min_value=1, max_value=5), source_read_size=strategies.integers(1, 16384), seek_amounts=strategies.data(), read_sizes=strategies.data()) def test_relative_seeks(self, original, level, source_read_size, seek_amounts, read_sizes): cctx = zstd.ZstdCompressor(level=level) frame = cctx.compress(original) dctx = zstd.ZstdDecompressor() with dctx.stream_reader(frame, read_size=source_read_size) as reader: while True: amount = seek_amounts.draw(strategies.integers(0, 16384)) reader.seek(amount, os.SEEK_CUR) offset = reader.tell() read_amount = read_sizes.draw(strategies.integers(1, 16384)) chunk = reader.read(read_amount) if not chunk: break self.assertEqual(original[offset:offset + len(chunk)], chunk) @unittest.skipUnless('ZSTD_SLOW_TESTS' in os.environ, 'ZSTD_SLOW_TESTS not set') @make_cffi class TestDecompressor_write_to_fuzzing(unittest.TestCase): @hypothesis.given(original=strategies.sampled_from(random_input_data()), level=strategies.integers(min_value=1, max_value=5), write_size=strategies.integers(min_value=1, max_value=8192), input_sizes=strategies.data()) def test_write_size_variance(self, original, level, write_size, input_sizes): cctx = zstd.ZstdCompressor(level=level) frame = cctx.compress(original) dctx = zstd.ZstdDecompressor() source = io.BytesIO(frame) dest = io.BytesIO() with dctx.write_to(dest, write_size=write_size) as decompressor: while True: input_size = input_sizes.draw(strategies.integers(1, 4096)) chunk = source.read(input_size) if not chunk: break decompressor.write(chunk) self.assertEqual(dest.getvalue(), original) @unittest.skipUnless('ZSTD_SLOW_TESTS' in os.environ, 'ZSTD_SLOW_TESTS not set') @make_cffi class TestDecompressor_copy_stream_fuzzing(unittest.TestCase): @hypothesis.given(original=strategies.sampled_from(random_input_data()), level=strategies.integers(min_value=1, max_value=5), read_size=strategies.integers(min_value=1, max_value=8192), write_size=strategies.integers(min_value=1, max_value=8192)) def test_read_write_size_variance(self, original, level, read_size, write_size): cctx = zstd.ZstdCompressor(level=level) frame = cctx.compress(original) source = io.BytesIO(frame) dest = io.BytesIO() dctx = zstd.ZstdDecompressor() dctx.copy_stream(source, dest, read_size=read_size, write_size=write_size) self.assertEqual(dest.getvalue(), original) @unittest.skipUnless('ZSTD_SLOW_TESTS' in os.environ, 'ZSTD_SLOW_TESTS not set') @make_cffi class TestDecompressor_decompressobj_fuzzing(unittest.TestCase): @hypothesis.given(original=strategies.sampled_from(random_input_data()), level=strategies.integers(min_value=1, max_value=5), chunk_sizes=strategies.data()) def test_random_input_sizes(self, original, level, chunk_sizes): cctx = zstd.ZstdCompressor(level=level) frame = cctx.compress(original) source = io.BytesIO(frame) dctx = zstd.ZstdDecompressor() dobj = dctx.decompressobj() chunks = [] while True: chunk_size = chunk_sizes.draw(strategies.integers(1, 4096)) chunk = source.read(chunk_size) if not chunk: break chunks.append(dobj.decompress(chunk)) self.assertEqual(b''.join(chunks), original) @hypothesis.given(original=strategies.sampled_from(random_input_data()), level=strategies.integers(min_value=1, max_value=5), write_size=strategies.integers(min_value=1, max_value=4 * zstd.DECOMPRESSION_RECOMMENDED_OUTPUT_SIZE), chunk_sizes=strategies.data()) def test_random_output_sizes(self, original, level, write_size, chunk_sizes): cctx = zstd.ZstdCompressor(level=level) frame = cctx.compress(original) source = io.BytesIO(frame) dctx = zstd.ZstdDecompressor() dobj = dctx.decompressobj(write_size=write_size) chunks = [] while True: chunk_size = chunk_sizes.draw(strategies.integers(1, 4096)) chunk = source.read(chunk_size) if not chunk: break chunks.append(dobj.decompress(chunk)) self.assertEqual(b''.join(chunks), original) @unittest.skipUnless('ZSTD_SLOW_TESTS' in os.environ, 'ZSTD_SLOW_TESTS not set') @make_cffi class TestDecompressor_read_to_iter_fuzzing(unittest.TestCase): @hypothesis.given(original=strategies.sampled_from(random_input_data()), level=strategies.integers(min_value=1, max_value=5), read_size=strategies.integers(min_value=1, max_value=4096), write_size=strategies.integers(min_value=1, max_value=4096)) def test_read_write_size_variance(self, original, level, read_size, write_size): cctx = zstd.ZstdCompressor(level=level) frame = cctx.compress(original) source = io.BytesIO(frame) dctx = zstd.ZstdDecompressor() chunks = list(dctx.read_to_iter(source, read_size=read_size, write_size=write_size)) self.assertEqual(b''.join(chunks), original) @unittest.skipUnless('ZSTD_SLOW_TESTS' in os.environ, 'ZSTD_SLOW_TESTS not set') class TestDecompressor_multi_decompress_to_buffer_fuzzing(unittest.TestCase): @hypothesis.given(original=strategies.lists(strategies.sampled_from(random_input_data()), min_size=1, max_size=1024), threads=strategies.integers(min_value=1, max_value=8), use_dict=strategies.booleans()) def test_data_equivalence(self, original, threads, use_dict): kwargs = {} if use_dict: kwargs['dict_data'] = zstd.ZstdCompressionDict(original[0]) cctx = zstd.ZstdCompressor(level=1, write_content_size=True, write_checksum=True, **kwargs) frames_buffer = cctx.multi_compress_to_buffer(original, threads=-1) dctx = zstd.ZstdDecompressor(**kwargs) result = dctx.multi_decompress_to_buffer(frames_buffer) self.assertEqual(len(result), len(original)) for i, frame in enumerate(result): self.assertEqual(frame.tobytes(), original[i]) frames_list = [f.tobytes() for f in frames_buffer] result = dctx.multi_decompress_to_buffer(frames_list) self.assertEqual(len(result), len(original)) for i, frame in enumerate(result): self.assertEqual(frame.tobytes(), original[i])

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0.717801

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0

null

0

1

0

null

0

6

20e678b65518d51c75c0bea4ebbfc07e9a58fa53

511

py

Python

relfs/relfs/error.py

matus-chochlik/various

2a9f5eddd964213f7d1e1ce8328e2e0b2a8e998b

[ "MIT" ]

1

2020-10-25T12:28:50.000Z

relfs/relfs/error.py

matus-chochlik/various

2a9f5eddd964213f7d1e1ce8328e2e0b2a8e998b

[ "MIT" ]

null

relfs/relfs/error.py

matus-chochlik/various

2a9f5eddd964213f7d1e1ce8328e2e0b2a8e998b

[ "MIT" ]

null

# coding=utf-8 #------------------------------------------------------------------------------# from __future__ import print_function import sys #------------------------------------------------------------------------------# class RelFsError(Exception): pass #------------------------------------------------------------------------------# def print_error(error): print("relfs error: %s" % (str(error)), file=sys.stderr) #------------------------------------------------------------------------------#

42.583333

80

0.268102

27

511

4.851852

0.740741

0

0.002083

0.060665

511

11

81

46.454545

0.270833

0.634051

0

0.084746

0

1

0.166667

false

0.166667

0.333333

0

0.666667

0.5

1

0

null

0

1

0

null

0

1

0

1

0

6

45ac294f52215a510ce54899c0cff4967a472747

30

py

Python

BHT_ARIMA/__init__.py

ridwanfathin/BHT-ARIMA

65df2af999cb13e15e39c4729638d31eb553c9c2

[ "MIT" ]

74

2020-02-25T13:28:47.000Z

2022-03-29T09:10:41.000Z

BHT_ARIMA/__init__.py

tongnie/BHT-ARIMA

d88c7cedaa9c60b317d501eb595ec6f6ee72dced

[ "MIT" ]

6

2020-02-27T20:04:58.000Z

2021-12-04T08:58:01.000Z

BHT_ARIMA/__init__.py

tongnie/BHT-ARIMA

d88c7cedaa9c60b317d501eb595ec6f6ee72dced

[ "MIT" ]

29

2020-03-09T03:14:14.000Z

2022-03-29T09:09:21.000Z

from .BHTARIMA import BHTARIMA

30

0.866667

4

30

6.5

0.75

0

0.1

30

1

30

0.962963

0

1

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true

0

1

0

1

0

1

0

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1

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null

0

1

0

1

0

1

0

6

affe65db0edb95b01441f0d328b87045db9c06de

95

py

Python

fcmeans/__init__.py

agrande-analog/fuzzy-c-means

f005223362a978ea183abc7cc4dd91f2e299f34a

[ "MIT" ]

null

fcmeans/__init__.py

agrande-analog/fuzzy-c-means

f005223362a978ea183abc7cc4dd91f2e299f34a

[ "MIT" ]

null

fcmeans/__init__.py

agrande-analog/fuzzy-c-means

f005223362a978ea183abc7cc4dd91f2e299f34a

[ "MIT" ]

null

"""fuzzy-c-means - A simple implementation of Fuzzy C-means algorithm.""" from .fcm import FCM

31.666667

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0.736842

15

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4.666667

0.733333

0.171429

0.314286

0

0.136842

95

2

74

47.5

0.853659

0.705263

0

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true

0

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1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

6

b33e6bdebf0f161a4e73a547d7ce5d9d2ae121f7

44

py

Python

scripts/pipeline/try.py

ahsanbarkati/DRQA

d03dbd3ee12e80594e47f3003e6576e86d037f81

[ "BSD-3-Clause" ]

null

scripts/pipeline/try.py

ahsanbarkati/DRQA

d03dbd3ee12e80594e47f3003e6576e86d037f81

[ "BSD-3-Clause" ]

null

scripts/pipeline/try.py

ahsanbarkati/DRQA

d03dbd3ee12e80594e47f3003e6576e86d037f81

[ "BSD-3-Clause" ]

null

import images print(images.images("apple"))

14.666667

29

0.772727

6

44

5.666667

0.666667

0

0.068182

44

2

30

22

0.829268

0

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0

1

0

true

0

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0

null

0

1

0

1

0

1

0

6

2fced5b34fc78bcda8f8146448f6f22365d26f60

2,889

py

Python

src/tests/services/metrics/test_console.py

cicadatesting/cicada-distributed

cb9caa4107fd5da30e508f34e6e11d0f8f58c142

[ "Apache-2.0" ]

6

2021-07-12T20:53:13.000Z

2022-01-14T19:34:25.000Z

src/tests/services/metrics/test_console.py

cicadatesting/cicada-distributed

cb9caa4107fd5da30e508f34e6e11d0f8f58c142

[ "Apache-2.0" ]

9

2021-04-24T04:20:12.000Z

2022-03-22T02:14:17.000Z

src/tests/services/metrics/test_console.py

cicadatesting/cicada-distributed

cb9caa4107fd5da30e508f34e6e11d0f8f58c142

[ "Apache-2.0" ]

null

from unittest.mock import patch from cicadad.metrics import console def sample_collector(latest_results): return [ float(result.output) for result in latest_results if result.exception is None ] @patch("cicadad.services.datastore.get_metric_statistics") def test_console_stats(metrics_mock): metrics_mock.return_value = { "min": 1.23456, "median": 1.23456, "max": 1.23456, "average": 1.23456, "len": 1, } console_stats = console.console_stats() metrics_string = console_stats("foo", "bar") assert ( metrics_string == "Min: 1.235, Median: 1.235, Average: 1.235, Max: 1.235, Len: 1" ), "Metrics string not equal to expected" @patch("cicadad.services.datastore.get_metric_statistics") def test_console_stats_none(metrics_mock): metrics_mock.return_value = None console_stats = console.console_stats() metrics_string = console_stats("foo", "bar") assert metrics_string is None, "Metrics string not equal to expected" @patch("cicadad.services.datastore.get_metric_total") def test_console_count(metrics_mock): metrics_mock.return_value = 60 console_count = console.console_count() metrics_string = console_count("foo", "bar") assert metrics_string == "60", "Metrics string not equal to expected" @patch("cicadad.services.datastore.get_metric_total") def test_console_count_none(metrics_mock): metrics_mock.return_value = None console_count = console.console_count() metrics_string = console_count("foo", "bar") assert metrics_string is None, "Metrics string not equal to expected" @patch("cicadad.services.datastore.get_last_metric") def test_console_latest(metrics_mock): metrics_mock.return_value = 1.2345 console_latest = console.console_latest() metrics_string = console_latest("foo", "bar") assert metrics_string == "1.234", "Metrics string not equal to expected" @patch("cicadad.services.datastore.get_last_metric") def test_console_latest_none(metrics_mock): metrics_mock.return_value = None console_latest = console.console_latest() metrics_string = console_latest("foo", "bar") assert metrics_string is None, "Metrics string not equal to expected" @patch("cicadad.services.datastore.get_metric_rate") def test_console_percent(metrics_mock): metrics_mock.return_value = 1.2345 console_percent = console.console_percent(1) metrics_string = console_percent("foo", "bar") assert metrics_string == "1.234", "Metrics string not equal to expected" @patch("cicadad.services.datastore.get_metric_rate") def test_console_percent_none(metrics_mock): metrics_mock.return_value = None console_percent = console.console_percent(1) metrics_string = console_percent("foo", "bar") assert metrics_string is None, "Metrics string not equal to expected"

27

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0.731741

381

2,889

5.278215

0.144357

0.155147

0.079562

0.115365

0.876181

0.843362

0.724018

0

0.027386

0.165801

2,889

106

86

27.254717

0.807054

0

0.571429

0

0.015873

0.270336

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0

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0.142857

false

0

0.031746

0.015873

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0

null

0

1

0

null

0

6

2fdfb4c931e0a5abf9c4fd2206cab5adb4db2618

68

py

Python

jwtornadodemo/account/cache.py

jaggerwang/jw-pyserver

80d621e5fe5474c3ee38b78395778c59543916cf

[ "MIT" ]

10

2019-03-07T02:11:17.000Z

2021-08-24T06:51:13.000Z

jwtornadodemo/account/cache.py

jaggerwang/jw-pyserver

80d621e5fe5474c3ee38b78395778c59543916cf

[ "MIT" ]

1

2021-06-01T21:50:48.000Z

jwtornadodemo/account/cache.py

jaggerwang/jw-pyserver

80d621e5fe5474c3ee38b78395778c59543916cf

[ "MIT" ]

3

2019-03-07T02:11:18.000Z

2020-06-22T07:13:02.000Z

from ..common import cache class UserCache(cache.Cache): pass

11.333333

29

0.720588

9

68

5.444444

0.777778

0

0.191176

68

5

30

13.6

0.890909

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1

0

true

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null

0

1

0

1

0

6

6417cdfcc4d25762f0bddd3d8e12b5d04ce98023

29

py

Python

libsaas/services/compete/__init__.py

MidtownFellowship/libsaas

541bb731b996b08ede1d91a235cb82895765c38a

[ "MIT" ]

155

2015-01-27T15:17:59.000Z

2022-02-20T00:14:08.000Z

libsaas/services/compete/__init__.py

MidtownFellowship/libsaas

541bb731b996b08ede1d91a235cb82895765c38a

[ "MIT" ]

14

2015-01-12T08:22:37.000Z

2021-06-16T19:49:31.000Z

libsaas/services/compete/__init__.py

MidtownFellowship/libsaas

541bb731b996b08ede1d91a235cb82895765c38a

[ "MIT" ]

43

2015-01-28T22:41:45.000Z

2021-09-21T04:44:26.000Z

from .service import Compete

14.5

28

0.827586

4

29

6

1

0

0.137931

29

1

29

0.96

0

1

0

true

0

1

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1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

6432ccabf89a81ad9e221e5310bc810e793b5fd5

20,674

py

Python

integration/sawtooth_integration/tests/test_tp_validator_registry.py

lcarranco/sawtooth-core

70cd65bfe4204545501d73f748d908e6695828f3

[ "Apache-2.0" ]

null

integration/sawtooth_integration/tests/test_tp_validator_registry.py

lcarranco/sawtooth-core

70cd65bfe4204545501d73f748d908e6695828f3

[ "Apache-2.0" ]

1

2021-12-09T23:11:26.000Z

integration/sawtooth_integration/tests/test_tp_validator_registry.py

lcarranco/sawtooth-core

70cd65bfe4204545501d73f748d908e6695828f3

[ "Apache-2.0" ]

null

# Copyright 2017 Intel Corporation # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ------------------------------------------------------------------------------ import unittest import json import base64 import hashlib from sawtooth_integration.message_factories.validator_reg_message_factory \ import ValidatorRegistryMessageFactory from sawtooth_poet_common import sgx_structs from sawtooth_poet_common.protobuf.validator_registry_pb2 import \ ValidatorRegistryPayload class TestValidatorRegistry(unittest.TestCase): """ Set of tests to run in a test suite with an existing TPTester and transaction processor. """ def __init__(self, test_name, tester): super().__init__(test_name) self.tester = tester self.private_key = '5HsjpyQzpeoGAAvNeG5PzQsn1Ght18GgSmDaEUCd1c1HpA2a'\ 'vzc' self.public_key = '02f3d385777ab35888fc47af6d123bba6f8b04817a4746e97'\ '446ce1562fc4307d7' self.factory = ValidatorRegistryMessageFactory( private=self.private_key, public=self.public_key) def _expect_invalid_transaction(self): self.tester.expect( self.factory.create_tp_response("INVALID_TRANSACTION")) def _expect_ok(self): self.tester.expect(self.factory.create_tp_response("OK")) def test_valid_signup_info(self): """ Testing valid validator_registry transaction. This includes sending new signup info for a validator that has already been registered. """ signup_info = self.factory.create_signup_info( self.factory.pubkey_hash, "000") payload = ValidatorRegistryPayload( verb="reg", name="val_1", id=self.factory.public_key, signup_info=signup_info) # Send validator registry payload self.tester.send( self.factory.create_tp_process_request(payload.id, payload)) # Expect Request for the ValidatorMap received = self.tester.expect( self.factory.create_get_request_validator_map()) # Respond with a empty validator Map self.tester.respond( self.factory.create_get_empty_response_validator_map(), received) # Expect a set the new validator to the ValidatorMap received = self.tester.expect( self.factory.create_set_request_validator_map()) # Respond with the ValidatorMap address self.tester.respond(self.factory.create_set_response_validator_map(), received) # Expect a request to set ValidatorInfo for val_1 received = self.tester.expect( self.factory.create_set_request_validator_info("val_1", "registered")) # Respond with address for val_1 # val_1 address is derived from the validators id # val id is the same as the pubkey for the factory self.tester.respond(self.factory.create_set_response_validator_info(), received) self._expect_ok() # -------------------------- signup_info = self.factory.create_signup_info( self.factory.pubkey_hash, "000") payload = ValidatorRegistryPayload( verb="reg", name="val_1", id=self.factory.public_key, signup_info=signup_info) # Send validator registry payload self.tester.send( self.factory.create_tp_process_request(payload.id, payload)) # Expect Request for the ValidatorMap received = self.tester.expect( self.factory.create_get_request_validator_map()) # Respond with a validator Map self.tester.respond(self.factory.create_get_response_validator_map(), received) # Expect to receive a validator_info request received = self.tester.expect( self.factory.create_get_request_validator_info()) # Respond with the ValidatorInfo self.tester.respond( self.factory.create_get_response_validator_info("val_1"), received) # Expect a request to set ValidatorInfo for val_1 received = self.tester.expect( self.factory.create_set_request_validator_info("val_1", "revoked")) # Respond with address for val_1 # val_1 address is derived from the validators id # val id is the same as the pubkey for the factory self.tester.respond( self.factory.create_set_response_validator_info(), received) # Expect a request to set ValidatorInfo for val_1 received = self.tester.expect( self.factory.create_set_request_validator_info("val_1", "registered")) # Respond with address for val_1 # val_1 address is derived from the validators id # val id is the same as the pubkey for the factory self.tester.respond(self.factory.create_set_response_validator_info(), received) self._expect_ok() def test_invalid_name(self): """ Test that a transaction with an invalid name returns an invalid transaction. """ signup_info = self.factory.create_signup_info( self.factory.pubkey_hash, "000") # The name is longer the 64 characters payload = ValidatorRegistryPayload( verb="reg", name="val_11111111111111111111111111111111111111111111111111111111" "11111", id=self.factory.public_key, signup_info=signup_info) # Send validator registry payload self.tester.send( self.factory.create_tp_process_request(payload.id, payload)) self._expect_invalid_transaction() def test_invalid_id(self): """ Test that a transaction with an id that does not match the signer_pubkey returns an invalid transaction. """ signup_info = self.factory.create_signup_info( self.factory.pubkey_hash, "000") # The idea should match the signer_pubkey in the transaction_header payload = ValidatorRegistryPayload( verb="reg", name="val_1", id="bad", signup_info=signup_info ) # Send validator registry payload self.tester.send( self.factory.create_tp_process_request(payload.id, payload)) self._expect_invalid_transaction() def test_invalid_poet_pubkey(self): """ Test that a transaction without a poet_public_key returns an invalid transaction. """ signup_info = self.factory.create_signup_info( self.factory.pubkey_hash, "000") signup_info.poet_public_key = "bad" payload = ValidatorRegistryPayload( verb="reg", name="val_1", id=self.factory.public_key, signup_info=signup_info) # Send validator registry payload self.tester.send( self.factory.create_tp_process_request(payload.id, payload)) self._expect_invalid_transaction() def _test_bad_signup_info(self, signup_info): payload = ValidatorRegistryPayload( verb="reg", name="val_1", id=self.factory.public_key, signup_info=signup_info) # Send validator registry payload self.tester.send( self.factory.create_tp_process_request(payload.id, payload)) self._expect_invalid_transaction() def test_invalid_verification_report(self): """ Test that a transaction whose verification report is invalid returns an invalid transaction. """ signup_info = self.factory.create_signup_info( self.factory.pubkey_hash, "000") # Verification Report is None proof_data = signup_info.proof_data signup_info.proof_data = json.dumps({}) self._test_bad_signup_info(signup_info) # ------------------------------------------------------ # No verification signature proof_data_dict = json.loads(proof_data) del proof_data_dict["signature"] signup_info.proof_data = json.dumps(proof_data_dict) self._test_bad_signup_info(signup_info) # ------------------------------------------------------ # Bad verification signature proof_data_dict["signature"] = "bads" signup_info.proof_data = json.dumps(proof_data_dict) self._test_bad_signup_info(signup_info) # ------------------------------------------------------ # No EPID pseudonym proof_data_dict = json.loads(proof_data) verification_report = \ json.loads(proof_data_dict["verification_report"]) del verification_report["epidPseudonym"] signup_info.proof_data = \ self.factory.create_proof_data( verification_report=verification_report, evidence_payload=proof_data_dict.get('evidence_payload')) self._test_bad_signup_info(signup_info) # ------------------------------------------------------ # Altered EPID pseudonym (does not match anti_sybil_id) proof_data_dict = json.loads(proof_data) verification_report = \ json.loads(proof_data_dict["verification_report"]) verification_report["epidPseudonym"] = "altered" signup_info.proof_data = \ self.factory.create_proof_data( verification_report=verification_report, evidence_payload=proof_data_dict.get('evidence_payload')) self._test_bad_signup_info(signup_info) # ------------------------------------------------------ # No Nonce proof_data_dict = json.loads(proof_data) verification_report = \ json.loads(proof_data_dict["verification_report"]) del verification_report["nonce"] signup_info.proof_data = \ self.factory.create_proof_data( verification_report=verification_report, evidence_payload=proof_data_dict.get('evidence_payload')) self._test_bad_signup_info(signup_info) def test_invalid_pse_manifest(self): """ Test that a transaction whose pse_manifast is invalid returns an invalid transaction. """ signup_info = self.factory.create_signup_info( self.factory.pubkey_hash, "000") proof_data = signup_info.proof_data proof_data_dict = json.loads(proof_data) # ------------------------------------------------------ # no pseManifestStatus verification_report = \ json.loads(proof_data_dict["verification_report"]) del verification_report['pseManifestStatus'] signup_info.proof_data = \ self.factory.create_proof_data( verification_report=verification_report, evidence_payload=proof_data_dict.get('evidence_payload')) self._test_bad_signup_info(signup_info) # ------------------------------------------------------ # Bad pseManifestStatus verification_report = \ json.loads(proof_data_dict["verification_report"]) verification_report['pseManifestStatus'] = "bad" signup_info.proof_data = \ self.factory.create_proof_data( verification_report=verification_report, evidence_payload=proof_data_dict.get('evidence_payload')) self._test_bad_signup_info(signup_info) # ------------------------------------------------------ # No pseManifestHash verification_report = \ json.loads(proof_data_dict["verification_report"]) del verification_report['pseManifestHash'] signup_info.proof_data = \ self.factory.create_proof_data( verification_report=verification_report, evidence_payload=proof_data_dict.get('evidence_payload')) self._test_bad_signup_info(signup_info) # ------------------------------------------------------ # Bad pseManifestHash verification_report = \ json.loads(proof_data_dict["verification_report"]) verification_report['pseManifestHash'] = "Bad" signup_info.proof_data = \ self.factory.create_proof_data( verification_report=verification_report, evidence_payload=proof_data_dict.get('evidence_payload')) self._test_bad_signup_info(signup_info) # ------------------------------------------------------ # Missing evidence payload evidence_payload = proof_data_dict["evidence_payload"] del proof_data_dict["evidence_payload"] signup_info.proof_data = json.dumps(proof_data_dict) self._test_bad_signup_info(signup_info) # ------------------------------------------------------ # Missing PSE manifest del evidence_payload["pse_manifest"] proof_data_dict["evidence_payload"] = evidence_payload signup_info.proof_data = json.dumps(proof_data_dict) self._test_bad_signup_info(signup_info) # ------------------------------------------------------ # Bad PSE manifest evidence_payload["pse_manifest"] = "bad" signup_info.proof_data = json.dumps(proof_data_dict) self._test_bad_signup_info(signup_info) def test_invalid_enclave_body(self): """ Test that a transaction whose enclave_body is invalid returns an invalid transaction. """ signup_info = self.factory.create_signup_info( self.factory.pubkey_hash, "000") proof_data = signup_info.proof_data proof_data_dict = json.loads(proof_data) # ------------------------------------------------------ # No isvEnclaveQuoteStatus verification_report = \ json.loads(proof_data_dict["verification_report"]) enclave_status = verification_report["isvEnclaveQuoteStatus"] verification_report["isvEnclaveQuoteStatus"] = None signup_info.proof_data = \ self.factory.create_proof_data( verification_report=verification_report, evidence_payload=proof_data_dict.get('evidence_payload')) self._test_bad_signup_info(signup_info) # ------------------------------------------------------ # Bad isvEnclaveQuoteStatus verification_report = \ json.loads(proof_data_dict["verification_report"]) verification_report["isvEnclaveQuoteStatus"] = "Bad" signup_info.proof_data = \ self.factory.create_proof_data( verification_report=verification_report, evidence_payload=proof_data_dict.get('evidence_payload')) self._test_bad_signup_info(signup_info) # ------------------------------------------------------ # No isvEnclaveQuoteBody verification_report = \ json.loads(proof_data_dict["verification_report"]) verification_report["isvEnclaveQuoteStatus"] = enclave_status verification_report['isvEnclaveQuoteBody'] = None signup_info.proof_data = \ self.factory.create_proof_data( verification_report=verification_report, evidence_payload=proof_data_dict.get('evidence_payload')) self._test_bad_signup_info(signup_info) # ------------------------------------------------------ # Malformed isvEnclaveQuoteBody (decode the enclave quote, chop off # the last byte, and re-encode) verification_report = \ json.loads(proof_data_dict["verification_report"]) verification_report['isvEnclaveQuoteBody'] = \ base64.b64encode( base64.b64decode( verification_report['isvEnclaveQuoteBody'].encode())[1:])\ .decode() signup_info.proof_data = \ self.factory.create_proof_data( verification_report=verification_report, evidence_payload=proof_data_dict.get('evidence_payload')) self._test_bad_signup_info(signup_info) # ------------------------------------------------------ # Invalid basename verification_report = \ json.loads(proof_data_dict["verification_report"]) sgx_quote = sgx_structs.SgxQuote() sgx_quote.parse_from_bytes( base64.b64decode( verification_report['isvEnclaveQuoteBody'].encode())) sgx_quote.basename.name = \ b'\xCC' * sgx_structs.SgxBasename.STRUCT_SIZE verification_report['isvEnclaveQuoteBody'] = \ base64.b64encode(sgx_quote.serialize_to_bytes()).decode() signup_info.proof_data = \ self.factory.create_proof_data( verification_report=verification_report, evidence_payload=proof_data_dict.get('evidence_payload')) self._test_bad_signup_info(signup_info) # ------------------------------------------------------ # Report data is not valid (bad OPK hash) verification_report = \ json.loads(proof_data_dict["verification_report"]) sgx_quote = sgx_structs.SgxQuote() sgx_quote.parse_from_bytes( base64.b64decode( verification_report['isvEnclaveQuoteBody'].encode())) hash_input = \ '{0}{1}'.format( 'Not a valid OPK Hash', self.factory.poet_public_key).upper().encode() sgx_quote.report_body.report_data.d = \ hashlib.sha256(hash_input).digest() verification_report['isvEnclaveQuoteBody'] = \ base64.b64encode(sgx_quote.serialize_to_bytes()).decode() signup_info.proof_data = \ self.factory.create_proof_data( verification_report=verification_report, evidence_payload=proof_data_dict.get('evidence_payload')) self._test_bad_signup_info(signup_info) # ------------------------------------------------------ # Report data is not valid (bad PPK) verification_report = \ json.loads(proof_data_dict["verification_report"]) sgx_quote = sgx_structs.SgxQuote() sgx_quote.parse_from_bytes( base64.b64decode( verification_report['isvEnclaveQuoteBody'].encode())) hash_input = \ '{0}{1}'.format( self.factory.pubkey_hash, "Not a valid PPK").encode() sgx_quote.report_body.report_data.d = \ hashlib.sha256(hash_input).digest() verification_report['isvEnclaveQuoteBody'] = \ base64.b64encode(sgx_quote.serialize_to_bytes()).decode() signup_info.proof_data = \ self.factory.create_proof_data( verification_report=verification_report, evidence_payload=proof_data_dict.get('evidence_payload')) self._test_bad_signup_info(signup_info) # ------------------------------------------------------ # Invalid enclave measurement verification_report = \ json.loads(proof_data_dict["verification_report"]) sgx_quote = sgx_structs.SgxQuote() sgx_quote.parse_from_bytes( base64.b64decode( verification_report['isvEnclaveQuoteBody'].encode())) sgx_quote.report_body.mr_enclave.m = \ b'\xCC' * sgx_structs.SgxMeasurement.STRUCT_SIZE verification_report['isvEnclaveQuoteBody'] = \ base64.b64encode(sgx_quote.serialize_to_bytes()).decode() signup_info.proof_data = \ self.factory.create_proof_data( verification_report=verification_report, evidence_payload=proof_data_dict.get('evidence_payload')) self._test_bad_signup_info(signup_info)

37.452899

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5.698466

0.110259

0.083284

0.050307

0.045428

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0

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0.060317

0

null

0

1

0

1

0

null

0

6

ff7a59fd8cce612a32e2603d1d2db5d3a9c23b13

204

py

Python

jupyterlab2pymolpysnips/Pymolrc/fetchPath.py

MooersLab/pymolpysnips

50a89c85adf8006d85c1d6cd3f8aad7e440a0b92

[ "MIT" ]

null

jupyterlab2pymolpysnips/Pymolrc/fetchPath.py

MooersLab/pymolpysnips

50a89c85adf8006d85c1d6cd3f8aad7e440a0b92

[ "MIT" ]

null

jupyterlab2pymolpysnips/Pymolrc/fetchPath.py

MooersLab/pymolpysnips

50a89c85adf8006d85c1d6cd3f8aad7e440a0b92

[ "MIT" ]

null

""" cmd.do('set fetch_path, ${1:/Users/blaine/pdbFiles};') """ cmd.do('set fetch_path, /Users/blaine/pdbFiles;') # Description: Set path for location to save fetched pdb files. # Source: placeHolder

22.666667

65

0.696078

29

204

4.827586

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0.071429

0.114286

0.185714

0.242857

0

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0.132353

204

8

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25.5

0.785311

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0.722222

0.425926

0

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0

true

0

1

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0

1

0

1

0

1

null

0

1

0

6

440b6a3eea96c535aa8895dd14241914f910d2a1

45

py

Python

server/parsing_lib/__init__.py

cnavrides/wireless-debugging

9c057d0127a5f8eebca4193af4bdb7e96c3ae6dd

[ "Apache-2.0" ]

3

2017-06-23T15:19:31.000Z

2018-03-07T01:31:37.000Z

server/parsing_lib/__init__.py

cnavrides/wireless-debugging

9c057d0127a5f8eebca4193af4bdb7e96c3ae6dd

[ "Apache-2.0" ]

75

2017-06-15T20:09:32.000Z

2018-01-17T01:30:26.000Z

server/parsing_lib/__init__.py

cnavrides/wireless-debugging

9c057d0127a5f8eebca4193af4bdb7e96c3ae6dd

[ "Apache-2.0" ]

3

2017-06-17T04:39:10.000Z

2017-08-16T15:25:00.000Z

from parsing_lib.log_parser import LogParser

22.5

44

0.888889

7

45

5.428571

1

0

0.088889

45

1

45

0.926829

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

4411dff36e164ce52bde15bdbb30214fe31d9aae

344

py

Python

tests/test_agents/test_bandit/__init__.py

matrig/genrl

25eb018f18a9a1d0865c16e5233a2a7ccddbfd78

[ "MIT" ]

390

2020-05-03T17:34:02.000Z

2022-03-05T11:29:07.000Z

tests/test_agents/test_bandit/__init__.py

matrig/genrl

25eb018f18a9a1d0865c16e5233a2a7ccddbfd78

[ "MIT" ]

306

2020-05-03T05:53:53.000Z

2022-03-12T00:27:28.000Z

tests/test_agents/test_bandit/__init__.py

matrig/genrl

25eb018f18a9a1d0865c16e5233a2a7ccddbfd78

[ "MIT" ]

64

2020-05-05T20:23:30.000Z

2022-03-30T08:43:10.000Z

from tests.test_agents.test_bandit.test_cb_agents import TestCBAgent # noqa from tests.test_agents.test_bandit.test_data_bandits import TestDataBandit # noqa from tests.test_agents.test_bandit.test_mab_agents import TestMABAgent # noqa from tests.test_agents.test_bandit.test_multi_armed_bandits import ( TestMultiArmedBandit, # noqa )

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6

4421cf48be1b7210e4fd2a9b6594eb5c4459f905

77

py

Python

modules/activation_functions/sigmoid.py

df424/ml

e12232ca4b90f983bfb14718afd314d3d6cc1bf9

[ "MIT" ]

null

modules/activation_functions/sigmoid.py

df424/ml

e12232ca4b90f983bfb14718afd314d3d6cc1bf9

[ "MIT" ]

null

modules/activation_functions/sigmoid.py

df424/ml

e12232ca4b90f983bfb14718afd314d3d6cc1bf9

[ "MIT" ]

null

import numpy as np def sigmoid(Y: np.ndarray): return 1/(1+np.exp(-Y))

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6

44939478cce1f268d37c5060a1ed2866f2780550

130

py

Python

app/app.py

Zuoxiaoxian/falcon_gq

58fd22e91864789bffca26d0f2e16797b7393d3a

[ "MIT" ]

null

app/app.py

Zuoxiaoxian/falcon_gq

58fd22e91864789bffca26d0f2e16797b7393d3a

[ "MIT" ]

null

app/app.py

Zuoxiaoxian/falcon_gq

58fd22e91864789bffca26d0f2e16797b7393d3a

[ "MIT" ]

null

# 这里是接口方法 from jsonrpc import dispatcher @dispatcher.add_method def foobar(**kwargs): return kwargs['name'] + kwargs['age']

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null

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6

9264ed44fbc67a6d209397197586c50ca1196e97

121

py

Python

mainRoadMap/views.py

h1gfun4/h1gfun4.github.io

e460467cb505b525ecd5b01b9eb3fd73de7ec6e1

[ "MIT" ]

null

mainRoadMap/views.py

h1gfun4/h1gfun4.github.io

e460467cb505b525ecd5b01b9eb3fd73de7ec6e1

[ "MIT" ]

null

mainRoadMap/views.py

h1gfun4/h1gfun4.github.io

e460467cb505b525ecd5b01b9eb3fd73de7ec6e1

[ "MIT" ]

null

from django.shortcuts import render def RoadMapView(request): return render(request, 'mainRoadMap/roadmapPage.html')

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null

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1

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6

928e11591af78268ba0af04278f08144a844085d

38

py

Python

src/gbt/__init__.py

firiceguo/Recommendation-NLP

526c0d50deb05331eebc1f4c82f76d12b6ba80c6

[ "MIT" ]

null

src/gbt/__init__.py

firiceguo/Recommendation-NLP

526c0d50deb05331eebc1f4c82f76d12b6ba80c6

[ "MIT" ]

null

src/gbt/__init__.py

firiceguo/Recommendation-NLP

526c0d50deb05331eebc1f4c82f76d12b6ba80c6

[ "MIT" ]

3

2017-03-14T17:27:29.000Z

2019-06-11T14:02:59.000Z

import traingbt import dataprocessing

12.666667

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6

2bab62cca2e407ccf7184884547dd4ec530f8380

1,990

py

Python

tatau_core/models/payments.py

makar21/core

e6a0c8d5456567dd3139ee3fd3cf6cd4acdd4a05

[ "Apache-2.0" ]

null

tatau_core/models/payments.py

makar21/core

e6a0c8d5456567dd3139ee3fd3cf6cd4acdd4a05

[ "Apache-2.0" ]

null

tatau_core/models/payments.py

makar21/core

e6a0c8d5456567dd3139ee3fd3cf6cd4acdd4a05

[ "Apache-2.0" ]

null

from logging import getLogger from tatau_core.db import models, fields from tatau_core.models.task import TaskDeclaration from tatau_core.models.nodes import ProducerNode, WorkerNode, VerifierNode from tatau_core.utils import cached_property logger = getLogger('tatau_core') class WorkerPayment(models.Model): producer_id = fields.CharField(immutable=True) worker_id = fields.CharField(immutable=True) task_declaration_id = fields.CharField(immutable=True) train_iteration = fields.IntegerField(immutable=True) train_iteration_retry = fields.IntegerField(immutable=True) tflops = fields.FloatField(immutable=True) tokens = fields.FloatField(immutable=True) @cached_property def producer(self) -> ProducerNode: return ProducerNode.get(self.producer_id, db=self.db, encryption=self.encryption) @cached_property def worker(self) -> WorkerNode: return WorkerNode.get(self.worker_id, db=self.db, encryption=self.encryption) @cached_property def task_declaration(self) -> TaskDeclaration: return TaskDeclaration.get(self.task_declaration_id, db=self.db, encryption=self.encryption) class VerifierPayment(models.Model): producer_id = fields.CharField(immutable=True) verifier_id = fields.CharField(immutable=True) task_declaration_id = fields.CharField(immutable=True) train_iteration = fields.IntegerField(immutable=True) tflops = fields.FloatField(immutable=True) tokens = fields.FloatField(immutable=True) @cached_property def producer(self) -> ProducerNode: return ProducerNode.get(self.producer_id, db=self.db, encryption=self.encryption) @cached_property def verifier(self) -> VerifierNode: return VerifierNode.get(self.verifier_id, db=self.db, encryption=self.encryption) @cached_property def task_declaration(self) -> TaskDeclaration: return TaskDeclaration.get(self.task_declaration_id, db=self.db, encryption=self.encryption)

37.54717

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null

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1

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null

0

1

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6

9200f83b61582c0c017bfc63101bb48b64375ecf

157

py

Python

tests/test_network.py

brunorijsman/quantum-path-computation-engine

021bca03f8555cd9cd0cdbd7d5c6a32050ab6271

[ "Apache-2.0" ]

null

tests/test_network.py

brunorijsman/quantum-path-computation-engine

021bca03f8555cd9cd0cdbd7d5c6a32050ab6271

[ "Apache-2.0" ]

1

2021-06-01T23:56:41.000Z

tests/test_network.py

brunorijsman/quantum-path-computation-element

021bca03f8555cd9cd0cdbd7d5c6a32050ab6271

[ "Apache-2.0" ]

null

"""Unit tests for module network.""" from network import Network def test_create_network(): """Test creation of a network.""" _network = Network()

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1

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6

a6007dc4ef5a842518819773606454e3a597339e

42

py

Python

gendist/gendist/__init__.py

probml/shift-happens

67bd65a7652e0cd148d94a2085d6e546ace584b2

[ "MIT" ]

5

2022-01-19T18:58:25.000Z

2022-03-08T16:08:54.000Z

gendist/gendist/__init__.py

probml/shift-happens

67bd65a7652e0cd148d94a2085d6e546ace584b2

[ "MIT" ]

null

gendist/gendist/__init__.py

probml/shift-happens

67bd65a7652e0cd148d94a2085d6e546ace584b2

[ "MIT" ]

1

2022-01-20T01:56:55.000Z

from . import training, processing, models

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6

a6250c01d95465ac7f8a79f65b0c4b979c6ea023

15,766

py

Python

Utils/util_opt.py

soshishimada/PhysCap_demo_release

542756ed9ecdca77eda8b6b44ba2348253b999c3

[ "Unlicense" ]

62

2021-09-05T19:36:06.000Z

2022-03-29T11:47:09.000Z

Utils/util_opt.py

soshishimada/PhysCap_demo_release

542756ed9ecdca77eda8b6b44ba2348253b999c3

[ "Unlicense" ]

4

2021-09-21T09:52:02.000Z

2022-03-27T09:08:30.000Z

Utils/util_opt.py

soshishimada/PhysCap_demo_release

542756ed9ecdca77eda8b6b44ba2348253b999c3

[ "Unlicense" ]

10

2021-09-05T00:27:17.000Z

2022-03-22T13:25:57.000Z

import numpy as np import sys import math sys.path.append("/HPS/Shimada/work/rbdl37/rbdl/build/python") import rbdl #import cvxopt from cvxopt import matrix, solvers solvers.options['show_progress'] = False class RbdlOpt(): def __init__(self, delta_t, l_kafth_ids, r_kafth_ids): self.delta_t = delta_t self.l_kafth_ids = l_kafth_ids self.r_kafth_ids = r_kafth_ids def c2d_func(self, v): vec = np.array([[0, -v[2], v[1]], [v[2], 0, -v[0]], [-v[1], v[0], 0], ]) return vec def mat_concatenate(self, mat): out = None for i in range(len(mat)): if i == 0: out = mat[i] else: out = np.concatenate((out, mat[i]), 1) return out def wrench_separator(self, wrench, contact_info, wrench_dim=6): extract_index = [np.arange(i * wrench_dim, (i + 1) * wrench_dim) for i in range(int(len(wrench) / wrench_dim)) if contact_info[i]] return wrench[np.array(extract_index).reshape(-1)] def cross2dot_convert(self, vectors): out = np.array(list(map(self.c2d_func, vectors))) out = self.mat_concatenate(out) return out def big_G_getter(self, Gtau): G = np.concatenate((Gtau, np.eye(3)), 0) G = np.concatenate((G, np.zeros(G.shape)), 1) print(G,G.shape) return G def big_G_getter2(self, Gtau): G = np.concatenate((Gtau, np.eye(3)), 0) #G = np.concatenate((G, np.zeros(G.shape)), 1) #print(G,G.shape) return G def get_wrench(self, model, com, q, body_id): contact = rbdl.CalcBodyToBaseCoordinates(model, q, body_id, np.zeros(3)) contact_vec = contact - com G_tau_converted = self.cross2dot_convert(np.array([contact_vec])) return G_tau_converted def jacobi_separator(self, jacobi, contact_info, jacobi_dim=6): extract_index = [np.arange(i * jacobi_dim, (i + 1) * jacobi_dim) for i in range(int(len(jacobi) / jacobi_dim)) if contact_info[i]] if len(extract_index) != 0: return jacobi[np.array(extract_index).reshape(-1)] else: return [] def jacobi_separator2(self, jacobi, contact_info, jacobi_dim=6): h,w=jacobi.shape jacobi=jacobi.reshape(4,int(h/4),w) jacobi=contact_info.reshape(-1,1,1)*jacobi #print(jacobi.shape) #print(contact_info,contact_info.shape) jacobi=jacobi.reshape(h,w) return jacobi # extract_index = [np.arange(i * jacobi_dim, (i + 1) * jacobi_dim) for i in range(int(len(jacobi) / jacobi_dim)) # if contact_info[i]] #if len(extract_index) != 0: # return jacobi[np.array(extract_index).reshape(-1)] #else: # return [] def qp_force_estimation_toe_heel(self, bullet_contacts_lth_rth, model, M, q, qdot, des_qddot, gcc, lr_J6D): M = M[:6] mass = np.zeros(q.shape) com = np.zeros(3) rbdl.CalcCenterOfMass(model, q, qdot, mass, com) l_toe_G_tau_converted = self.get_wrench(model, com, q, self.l_kafth_ids[3]) print(l_toe_G_tau_converted.shape) l_heel_G_tau_converted = self.get_wrench(model, com, q, self.l_kafth_ids[4]) r_toe_G_tau_converted = self.get_wrench(model, com, q, self.r_kafth_ids[3]) r_heel_G_tau_converted = self.get_wrench(model, com, q, self.r_kafth_ids[4]) R_l_toe = self.big_G_getter(l_toe_G_tau_converted) R_l_heel = self.big_G_getter(l_heel_G_tau_converted) R_r_toe = self.big_G_getter(r_toe_G_tau_converted) R_r_heel = self.big_G_getter(r_heel_G_tau_converted) R = np.concatenate((R_l_toe, np.concatenate((R_l_heel, np.concatenate((R_r_toe, R_r_heel), 0)), 0)), 0) jacobi = self.jacobi_separator(lr_J6D, bullet_contacts_lth_rth) if len(jacobi) == 0: return 0, 0 jacobi = jacobi[:, :6] R = self.wrench_separator(R, bullet_contacts_lth_rth) A = np.dot(jacobi.T, R) b = np.dot(M, des_qddot) + gcc[:6] W = np.dot(A.T, A) Q = -np.dot(b.T, A) mu = 1 / math.sqrt(2) G = np.array([[0, 0, -1, 0, 0, 0], [1, 0, -mu, 0, 0, 0], [-1, 0, -mu, 0, 0, 0], [0, 1, -mu, 0, 0, 0], [0, -1, -mu, 0, 0, 0], [0, 0, 0, 0, 0, -1], [0, 0, 0, 1, 0, -mu], [0, 0, 0, -1, 0, -mu], [0, 0, 0, 0, 1, -mu], [0, 0, 0, 0, -1, -mu] ]) h = np.array(np.zeros(10).tolist()) W = matrix(W.astype(np.double)) Q = matrix(Q.astype(np.double)) G = matrix(G.astype(np.double)) h = matrix(h.astype(np.double)) sol = solvers.qp(W, Q, G=G, h=h) GRF_opt = np.array(sol["x"]).reshape(-1) return GRF_opt, R def qp_force_estimation_toe_heel2(self, bullet_contacts_lth_rth, model, M, q, qdot, des_qddot, gcc, lr_J6D): M = M[:6] mass = np.zeros(q.shape) com = np.zeros(3) rbdl.CalcCenterOfMass(model, q, qdot, mass, com) l_toe_G_tau_converted = self.get_wrench(model, com, q, self.l_kafth_ids[3]) #print(l_toe_G_tau_converted.shape) l_heel_G_tau_converted = self.get_wrench(model, com, q, self.l_kafth_ids[4]) r_toe_G_tau_converted = self.get_wrench(model, com, q, self.r_kafth_ids[3]) r_heel_G_tau_converted = self.get_wrench(model, com, q, self.r_kafth_ids[4]) R_l_toe = self.big_G_getter2(l_toe_G_tau_converted) R_l_heel = self.big_G_getter2(l_heel_G_tau_converted) R_r_toe = self.big_G_getter2(r_toe_G_tau_converted) R_r_heel = self.big_G_getter2(r_heel_G_tau_converted) #"""""" #print("------------------------------------------------------------") #print(R_l_toe,R_l_heel,R_r_toe,R_r_heel,R_l_heel.shape) R_list=[R_l_toe,R_l_heel,R_r_toe,R_r_heel] R_h=24 R_w=12 R = np.zeros((R_h,R_w)) #print() for i in range(len(R_list)): R[i*6:(i+1)*6,i*3:(i+1)*3]=R_list[i] #R = np.concatenate((R_l_toe, np.concatenate((R_l_heel, np.concatenate((R_r_toe, R_r_heel), 0)), 0)), 0) #print(R) #print(lr_J6D.shape) jacobi = self.jacobi_separator2(lr_J6D, bullet_contacts_lth_rth) #print('jacobi',jacobi.shape) if len(jacobi) == 0: return 0, 0 jacobi = jacobi[:, :6] #print('ssssssssss',R.shape) #R = self.wrench_separator(R, bullet_contacts_lth_rth) #print('ssssssss222ss',R.shape,jacobi.shape) A = np.dot(jacobi.T, R) #print(A.shape,'A') b = np.dot(M, des_qddot) + gcc[:6] # print(b.shape,'b') W = np.dot(A.T, A) #print(W.shape,'W') Q = -np.dot(b.T, A) #print(Q.shape,'Q') mu = 1 / math.sqrt(2) """ G = np.array([[0, 0, -1, 0, 0, 0], [1, 0, -mu, 0, 0, 0], [-1, 0, -mu, 0, 0, 0], [0, 1, -mu, 0, 0, 0], [0, -1, -mu, 0, 0, 0], [0, 0, 0, 0, 0, -1], [0, 0, 0, 1, 0, -mu], [0, 0, 0, -1, 0, -mu], [0, 0, 0, 0, 1, -mu], [0, 0, 0, 0, -1, -mu] ])""" G = np.array([[0, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1,-mu,0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [-1,-mu,0 , 0, 0, 0, 0, 0, 0, 0, 0, 0], [0,-mu,1, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0,-mu,-1, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, -1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1,-mu,0, 0, 0, 0, 0, 0, 0], [0, 0, 0,-1,-mu,0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0,-mu,1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0,-mu,-1, 0, 0, 0, 0, 0, 0], [0, 0, 0,0, 0, 0, 0, -1, 0, 0, 0, 0, ], [0, 0, 0,0, 0, 0,1,-mu, 0, 0, 0, 0], [0, 0, 0,0, 0, 0,-1,-mu, 0 , 0, 0, 0], [0, 0, 0,0, 0, 0,0, -mu,1, 0, 0, 0], [0, 0, 0,0, 0, 0,0, -mu,-1, 0, 0, 0], [0, 0, 0,0, 0, 0,0, 0, 0,0, -1, 0], [0, 0, 0,0, 0, 0,0, 0, 0,1,-mu, 0], [0, 0, 0,0, 0, 0,0, 0, 0,-1,-mu, 0], [0, 0, 0,0, 0, 0,0, 0, 0,0, -mu,1], [0, 0, 0,0, 0, 0,0, 0, 0,0, -mu,-1], ]) #print(G.shape) h = np.array(np.zeros(20).tolist()) W = matrix(W.astype(np.double)) Q = matrix(Q.astype(np.double)) G = matrix(G.astype(np.double)) h = matrix(h.astype(np.double)) sol = solvers.qp(W, Q, G=G, h=h) GRF_opt = np.array(sol["x"]).reshape(-1) #print(GRF_opt) #print(GRF_opt.shape) return GRF_opt, R def qp_control_hc(self, bullet_contacts_lth_rth, M, qdot, des_qddot, gcc,lr_J6D, GRF_opt, R): lr_F_J6D = self.jacobi_separator2(lr_J6D, bullet_contacts_lth_rth) #print(lr_F_J6D.shape,'jacobi',R.shape,GRF_opt.shape) if len(lr_F_J6D) != 0: #print(lr_F_J6D.shape,'jacobi',R.shape,GRF_opt.shape) general_GRF = np.dot(lr_F_J6D.T, np.dot(R, GRF_opt)) else: general_GRF = 0 S = np.eye(M.shape[0]) A = np.concatenate((M, -S), 1) b = general_GRF - gcc # - gcc G_top = np.concatenate((-self.delta_t * lr_J6D, np.zeros((lr_J6D.shape[0], M.shape[1]))), 1) G_bottom = np.concatenate((self.delta_t * lr_J6D, np.zeros((lr_J6D.shape[0], M.shape[1]))), 1) G = np.concatenate((G_top, G_bottom), 0) max_vel = 0.01 max_vel_floor = 0 max_vel_no_contact = 10000 l_toe_xyz = [max_vel_no_contact, max_vel_no_contact, max_vel_no_contact, max_vel_no_contact, max_vel_no_contact, max_vel_no_contact] l_heel_xyz = [max_vel_no_contact, max_vel_no_contact, max_vel_no_contact, max_vel_no_contact, max_vel_no_contact, max_vel_no_contact] r_toe_xyz = [max_vel_no_contact, max_vel_no_contact, max_vel_no_contact, max_vel_no_contact, max_vel_no_contact, max_vel_no_contact] r_heel_xyz = [max_vel_no_contact, max_vel_no_contact, max_vel_no_contact, max_vel_no_contact, max_vel_no_contact, max_vel_no_contact] if bullet_contacts_lth_rth[0]: l_toe_xyz = [max_vel_no_contact, max_vel_no_contact, max_vel_no_contact, max_vel, max_vel_floor, max_vel] if bullet_contacts_lth_rth[1]: l_heel_xyz = [max_vel_no_contact, max_vel_no_contact, max_vel_no_contact, max_vel, max_vel_floor, max_vel] if bullet_contacts_lth_rth[2]: r_toe_xyz = [max_vel_no_contact, max_vel_no_contact, max_vel_no_contact, max_vel, max_vel_floor, max_vel] if bullet_contacts_lth_rth[3]: r_heel_xyz = [max_vel_no_contact, max_vel_no_contact, max_vel_no_contact, max_vel, max_vel_floor, max_vel] max_vel_no_contact2 = 10000 l_toe_xyz2 = [max_vel_no_contact2, max_vel_no_contact2, max_vel_no_contact2, max_vel_no_contact2, max_vel_no_contact2, max_vel_no_contact2] l_heel_xyz2 = [max_vel_no_contact2, max_vel_no_contact2, max_vel_no_contact2, max_vel_no_contact2, max_vel_no_contact2, max_vel_no_contact2] r_toe_xyz2 = [max_vel_no_contact2, max_vel_no_contact2, max_vel_no_contact2, max_vel_no_contact2, max_vel_no_contact2, max_vel_no_contact2] r_heel_xyz2 = [max_vel_no_contact2, max_vel_no_contact2, max_vel_no_contact2, max_vel_no_contact2, max_vel_no_contact2, max_vel_no_contact2] max_vel2 = 0.1 if bullet_contacts_lth_rth[0]: l_toe_xyz2 = [max_vel_no_contact2, max_vel_no_contact2, max_vel_no_contact2, max_vel2, max_vel_no_contact2, max_vel2] if bullet_contacts_lth_rth[1]: l_heel_xyz2 = [max_vel_no_contact2, max_vel_no_contact2, max_vel_no_contact2, max_vel2, max_vel_no_contact2, max_vel2] if bullet_contacts_lth_rth[2]: r_toe_xyz2 = [max_vel_no_contact2, max_vel_no_contact2, max_vel_no_contact2, max_vel2, max_vel_no_contact2, max_vel2] if bullet_contacts_lth_rth[3]: r_heel_xyz2 = [max_vel_no_contact2, max_vel_no_contact2, max_vel_no_contact2, max_vel2, max_vel_no_contact2, max_vel2] h_top = np.dot(lr_J6D, qdot) + np.array(l_toe_xyz + l_heel_xyz + r_toe_xyz + r_heel_xyz) h_bottom = np.array(l_toe_xyz2 + l_heel_xyz2 + r_toe_xyz2 + r_heel_xyz2) - np.dot(lr_J6D, qdot) h = np.concatenate((h_top, h_bottom), 0) a = np.eye(len(des_qddot)) bb = des_qddot W = np.dot(a.T, a) W = np.concatenate((W, np.zeros(M.shape)), 1) Q = -np.dot(bb.T, a) W_tau_bottom = 0.00045*np.eye(M.shape[0]) W_bottom = np.concatenate((np.zeros(M.shape), W_tau_bottom), 1) W = np.concatenate((W, W_bottom), 0) Q = np.concatenate((Q, np.zeros(des_qddot.shape[0])), 0) A = matrix(A.astype(np.double)) b = matrix(b.astype(np.double)) W = matrix(W.astype(np.double)) Q = matrix(Q.astype(np.double)) G = matrix(G.astype(np.double)) h = matrix(h.astype(np.double)) sol = solvers.qp(W, Q, A=A, b=b, G=G, h=h) x = np.array(sol['x']).reshape(-1) tau = x[int(len(x) / 2):].reshape(-1) acc = x[:int(len(x) / 2)].reshape(-1) return tau, acc, general_GRF def qp_control_fast(self, bullet_contacts_lth_rth, M, qdot, des_qddot, gcc,lr_J6D, GRF_opt, R): lr_F_J6D = self.jacobi_separator(lr_J6D, bullet_contacts_lth_rth) if len(lr_F_J6D) != 0: general_GRF = np.dot(lr_F_J6D.T, np.dot(R, GRF_opt)) else: general_GRF = 0 S = np.eye(M.shape[0]) A = np.concatenate((M, -S), 1) b = general_GRF - gcc # - gcc a = np.eye(len(des_qddot)) bb = des_qddot W = np.dot(a.T, a) W = np.concatenate((W, np.zeros(M.shape)), 1) Q = -np.dot(bb.T, a) W_tau_bottom = 0.00001 * np.eye(M.shape[0]) W_bottom = np.concatenate((np.zeros(M.shape), W_tau_bottom), 1) W = np.concatenate((W, W_bottom), 0) Q = np.concatenate((Q, np.zeros(des_qddot.shape[0])), 0) A = matrix(A.astype(np.double)) b = matrix(b.astype(np.double)) W = matrix(W.astype(np.double)) Q = matrix(Q.astype(np.double)) sol = solvers.qp(W, Q, A=A, b=b)#, G=G, h=h) x = np.array(sol['x']).reshape(-1) tau = x[int(len(x) / 2):].reshape(-1) acc = x[:int(len(x) / 2)].reshape(-1) return tau, acc, general_GRF

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a63afe42f4ad77faa3963963290020b0ea93e16c

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py

Python

sloth/uff/bin/__init__.py

frank26080115/jetbot

19354e5f8b2e3e4853f7b197b5e2714502822c3d

[ "MIT" ]

null

sloth/uff/bin/__init__.py

frank26080115/jetbot

19354e5f8b2e3e4853f7b197b5e2714502822c3d

[ "MIT" ]

null

sloth/uff/bin/__init__.py

frank26080115/jetbot

19354e5f8b2e3e4853f7b197b5e2714502822c3d

[ "MIT" ]

null

from uff.bin import convert_to_uff

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a663c8a30961d2064cef6deb84386ff7f1efea11

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py

Python

algorithms/map/__init__.py

upzone/algorithms

d56792abe70ddbb444fd7fd2662e087da0d225c8

[ "MIT" ]

4

2018-10-21T04:12:12.000Z

2019-07-24T08:38:20.000Z

algorithms/map/__init__.py

Tw1stFate/algorithms

a9ed2ce490c6ac1dd220530fe1afb765a51656f4

[ "MIT" ]

null

algorithms/map/__init__.py

Tw1stFate/algorithms

a9ed2ce490c6ac1dd220530fe1afb765a51656f4

[ "MIT" ]

7

2019-03-21T10:18:22.000Z

2021-09-22T07:34:10.000Z

from .hashtable import * from .separate_chaining_hashtable import *

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6

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167

py

Python

readpaircluster/svcf/__init__.py

RCollins13/Holmes

3eb0119638bb93c1cab914af60a1dfd472146e28

[ "MIT" ]

3

2017-03-14T17:49:16.000Z

2017-03-29T18:19:00.000Z

readpaircluster/svcf/__init__.py

talkowski-lab/holmes

3eb0119638bb93c1cab914af60a1dfd472146e28

[ "MIT" ]

null

readpaircluster/svcf/__init__.py

talkowski-lab/holmes

3eb0119638bb93c1cab914af60a1dfd472146e28

[ "MIT" ]

null

__all__ = [] from .rpc import RPC # from .svcall import SVCall, SVCallCluster, LumpyCall from .svcall import SVCall, SVCFRecord, LumpyCall from .svfile import SVFile

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6

4700ca84e05cf86a46c3eeac4da2eec28b570289

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py

Python

apps/entity/models/group/__init__.py

dy1zan/softwarecapstone

c121a2b2d43b72aac19b75c31519711c0ace9c02

[ "MIT" ]

null

apps/entity/models/group/__init__.py

dy1zan/softwarecapstone

c121a2b2d43b72aac19b75c31519711c0ace9c02

[ "MIT" ]

16

2018-11-10T21:46:40.000Z

2018-11-11T15:08:36.000Z

apps/entity/models/group/__init__.py

dy1zan/softwarecapstone

c121a2b2d43b72aac19b75c31519711c0ace9c02

[ "MIT" ]

null

from .group import Group

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1

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0

6

5b2b2429cbb37b823092275aa72379a1bd141465

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py

Python

vega/modules/necks/__init__.py

jie311/vega

1bba6100ead802697e691403b951e6652a99ccae

[ "MIT" ]

724

2020-06-22T12:05:30.000Z

2022-03-31T07:10:54.000Z

vega/modules/necks/__init__.py

jie311/vega

1bba6100ead802697e691403b951e6652a99ccae

[ "MIT" ]

147

2020-06-30T13:34:46.000Z

2022-03-29T11:30:17.000Z

vega/modules/necks/__init__.py

jie311/vega

1bba6100ead802697e691403b951e6652a99ccae

[ "MIT" ]

160

2020-06-29T18:27:58.000Z

2022-03-23T08:42:21.000Z

from .parallel_fpn import ParallelFPN

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6

5b35d0c948a0d8061ccfbc934d4df3b07235bd21

107

py

Python

udemy/01_walkthrough/script2.py

inderpal2406/python

7bd7d03a6b3cd09ff16a4447ff495a2393a87a33

[ "MIT" ]

null

udemy/01_walkthrough/script2.py

inderpal2406/python

7bd7d03a6b3cd09ff16a4447ff495a2393a87a33

[ "MIT" ]

null

udemy/01_walkthrough/script2.py

inderpal2406/python

7bd7d03a6b3cd09ff16a4447ff495a2393a87a33

[ "MIT" ]

null

#!/usr/bin/env python3 import script1 print(f"This is script2 and __name__ variable value is {__name__}")

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6

5b71d8f30fe552731a5f2190b3ffe243d27c1405

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py

Python

2015/day/13/solution.py

iangregson/advent-of-code

e2a2dde30dcaed027a5ba78f9270f8a1976577f1

[ "MIT" ]

null

2015/day/13/solution.py

iangregson/advent-of-code

e2a2dde30dcaed027a5ba78f9270f8a1976577f1

[ "MIT" ]

null

2015/day/13/solution.py

iangregson/advent-of-code

e2a2dde30dcaed027a5ba78f9270f8a1976577f1

[ "MIT" ]

null

#!/usr/bin/env python3 import os from collections import defaultdict from itertools import permutations dir_path = os.path.dirname(os.path.realpath(__file__)) file = open(dir_path + "/input.txt", "r") lines = file.readlines() lines = [line.strip().rstrip('.') for line in lines] # file = open(dir_path + "/test_input.txt", "r") # lines = file.readlines() # lines = [line.strip().rstrip('.') for line in lines] # print(lines) # Build graph G = defaultdict(dict) E = [] Ns = set() for line in lines: tokens = line.split(" ") n, N, = tokens[0], tokens[-1] cost = 0 if tokens[2] == 'gain': cost += int(tokens[3]) else: cost -= int(tokens[3]) edge = (n, N, cost) G[n][N] = cost E.append(edge) Ns.add(n) # print(G) # print(E) # print(Ns) happiness = [] for tour in permutations(Ns): tour_happiness = 0 for (idx, person) in enumerate(tour): next_idx = (idx + 1) % len(tour) next_person = tour[next_idx] cost1 = G[person][next_person] cost2 = G[next_person][person] tour_happiness += cost1 + cost2 happiness.append(tour_happiness) print("Part 1 answer:", max(happiness)) # Build graph G = defaultdict(dict) E = [] Ns = set() Ns.add('Me') for line in lines: tokens = line.split(" ") n, N, = tokens[0], tokens[-1] cost = 0 if tokens[2] == 'gain': cost += int(tokens[3]) else: cost -= int(tokens[3]) edge = (n, N, cost) G[n][N] = cost G['Me'][N] = 0 G[n]['Me'] = 0 E.append(edge) E.append(('Me', N, 0)) E.append((n, 'Me', 0)) Ns.add(n) # print(G) # print(E) # print(Ns) happiness = [] for tour in permutations(Ns): tour_happiness = 0 for (idx, person) in enumerate(tour): next_idx = (idx + 1) % len(tour) next_person = tour[next_idx] cost1 = G[person][next_person] cost2 = G[next_person][person] tour_happiness += cost1 + cost2 happiness.append(tour_happiness) print("Part 2 answer:", max(happiness))

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null

0

6

5ba03eea79e6526c17e85e553d782fae7b34e434

108

py

Python

tests/fixtures/__init__.py

andreroggeri/br-to-ynab

c5d0ef3804bb575badc05ac6dc771f6a9281f955

[ "MIT" ]

5

2021-09-20T13:15:37.000Z

2022-03-01T01:03:27.000Z

tests/fixtures/__init__.py

andreroggeri/br-to-ynab

c5d0ef3804bb575badc05ac6dc771f6a9281f955

[ "MIT" ]

4

2021-04-28T14:11:42.000Z

2021-10-09T16:18:15.000Z

tests/fixtures/__init__.py

andreroggeri/br-to-ynab

c5d0ef3804bb575badc05ac6dc771f6a9281f955

[ "MIT" ]

1

2021-09-27T15:13:30.000Z

from .config import config_for_nubank, config_for_bradesco, config_for_alelo from .ynab import ynab_account

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6

5ba169d04296c4c3800e341f00e9e47e8becca97

3,088

py

Python

tests/plugins/mockserver/test_tracing_enabled.py

okutane/yandex-taxi-testsuite

7e2e3dd5a65869ecbf37bf3f79cba7bb4e782b0c

[ "MIT" ]

128

2020-03-10T09:13:41.000Z

2022-02-11T20:16:16.000Z

tests/plugins/mockserver/test_tracing_enabled.py

okutane/yandex-taxi-testsuite

7e2e3dd5a65869ecbf37bf3f79cba7bb4e782b0c

[ "MIT" ]

3

2021-11-01T12:31:27.000Z

2022-02-11T13:08:38.000Z

tests/plugins/mockserver/test_tracing_enabled.py

okutane/yandex-taxi-testsuite

7e2e3dd5a65869ecbf37bf3f79cba7bb4e782b0c

[ "MIT" ]

22

2020-03-05T07:13:12.000Z

2022-03-15T10:30:58.000Z

# pylint: disable=protected-access import aiohttp.web import pytest from testsuite.plugins import mockserver as mockserver_module # pylint: disable=invalid-name async def test_mockserver_responds_with_handler_to_current_test( mockserver, create_service_client, ): @mockserver.handler('/arbitrary/path') def _handler(request): return aiohttp.web.Response(text='arbitrary text', status=200) client = create_service_client( mockserver.base_url, service_headers={mockserver.trace_id_header: mockserver.trace_id}, ) response = await client.post('arbitrary/path') assert response.status_code == 200 assert response.text == 'arbitrary text' async def test_mockserver_responds_with_json_handler_to_current_test( mockserver, create_service_client, ): @mockserver.json_handler('/arbitrary/path') def _json_handler(request): return {'arbitrary_key': True} client = create_service_client( mockserver.base_url, service_headers={mockserver.trace_id_header: mockserver.trace_id}, ) response = await client.post('arbitrary/path') assert response.status_code == 200 assert response.json() == {'arbitrary_key': True} async def test_mockserver_skips_handler_and_responds_500_to_other_test( mockserver, create_service_client, ): @mockserver.handler('/arbitrary/path') def _handler(request): return aiohttp.web.Response(text='arbitrary text', status=200) client = create_service_client( mockserver.base_url, service_headers={ mockserver.trace_id_header: mockserver_module._generate_trace_id(), }, ) response = await client.post('arbitrary/path') assert response.status_code == 500 assert response.text == mockserver_module.REQUEST_FROM_ANOTHER_TEST_ERROR async def test_mockserver_skips_json_handler_and_responds_500_to_other_test( mockserver, create_service_client, ): @mockserver.json_handler('/arbitrary/path') def _json_handler(request): return {'arbitrary_key': True} client = create_service_client( mockserver.base_url, service_headers={ mockserver.trace_id_header: mockserver_module._generate_trace_id(), }, ) response = await client.post('arbitrary/path') assert response.status_code == 500 assert response.text == mockserver_module.REQUEST_FROM_ANOTHER_TEST_ERROR @pytest.mark.parametrize( 'http_headers', [ {}, # no trace_id in http headers {mockserver_module._DEFAULT_TRACE_ID_HEADER: ''}, { mockserver_module._DEFAULT_TRACE_ID_HEADER: ( 'id_without_testsuite-_prefix' ), }, ], ) async def test_mockserver_responds_500_on_unhandled_request_from_other_sources( mockserver, http_headers, create_service_client, ): client = create_service_client( mockserver.base_url, service_headers=http_headers, ) response = await client.post('arbitrary/path') assert response.status_code == 500

29.692308

79

0.714054

351

3,088

5.897436

0.188034

0.037198

0.091787

0.126087

0.833333

0.792754

0.725121

0.682609

0

0.012097

0.196891

3,088

103

80

29.980583

0.822581

0.028821

0

0.607595

0

0.083806

0.009349

0

0.113924

1

0.050633

false

0

0.037975

0.050633

0.139241

0

null

0

1

0

null

0

6

75104617008938af895fcbe46077b0c8d76d36d0

129

py

Python

lambdapool/exceptions.py

rorodata/lambdapool

da7b514496f75484541ebcbcc596f1f72dab09bf

[ "Apache-2.0" ]

null

lambdapool/exceptions.py

rorodata/lambdapool

da7b514496f75484541ebcbcc596f1f72dab09bf

[ "Apache-2.0" ]

null

lambdapool/exceptions.py

rorodata/lambdapool

da7b514496f75484541ebcbcc596f1f72dab09bf

[ "Apache-2.0" ]

1

2019-12-30T12:46:24.000Z

class LambdaPoolError(Exception): pass class LambdaFunctionError(Exception): pass class AWSError(Exception): pass

12.9

37

0.744186

12

129

8

0.5

0.40625

0.375

0

0.186047

129

9

38

14.333333

0.914286

0

0.5

0

1

0

true

0.5

0

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0

1

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null

1

0

1

0

null

0

1

0

6

75306ab244d8390e38c5c1d7687d2acf47eaaf1c

3,591

py

Python

oop/property/class_book_property.py

levs72/pyneng-examples

d6288292dcf9d1ebc5a9db4a0d620bd11b4a2df9

[ "MIT" ]

11

2021-04-05T09:30:23.000Z

2022-03-09T13:27:56.000Z

oop/property/class_book_property.py

levs72/pyneng-examples

d6288292dcf9d1ebc5a9db4a0d620bd11b4a2df9

[ "MIT" ]

null

oop/property/class_book_property.py

levs72/pyneng-examples

d6288292dcf9d1ebc5a9db4a0d620bd11b4a2df9

[ "MIT" ]

11

2021-04-06T03:44:35.000Z

2022-03-04T21:20:40.000Z

## Стандартный вариант применения property без setter class Book: def __init__(self, title, price, quantity): self.title = title self.price = price self.quantity = quantity # метод, который декорирован property становится getter'ом @property def total(self): print("getter") return self.price * self.quantity ## Стандартный вариант применения property с setter class Book: def __init__(self, title, price, quantity): self.title = title self.price = price self.quantity = quantity # total остается атрибутом только для чтения @property def total(self): return round(self.price * self.quantity, 2) # а price доступен для чтения и записи @property # этот метод превращается в getter def price(self): print("price getter") return self._price # при записи делается проверка значения @price.setter def price(self, value): print("price setter") if not isinstance(value, (int, float)): raise TypeError("Значение должно быть числом") if not value >= 0: raise ValueError("Значение должно быть положительным") self._price = float(value) # Декораторы с явным getter class Book: def __init__(self, title, price, quantity): self.title = title self.price = price self.quantity = quantity # создаем пустую property для total total = property() @total.getter def total(self): return round(self.price * self.quantity, 2) # создаем пустую property для price price = property() # позже указываем getter @price.getter def price(self): print("price getter") return self._price @price.setter def price(self, value): print("price setter") if not isinstance(value, (int, float)): raise TypeError("Значение должно быть числом") if not value >= 0: raise ValueError("Значение должно быть положительным") self._price = float(value) # property без декораторов class Book: def __init__(self, title, price, quantity): self.title = title self.price = price self.quantity = quantity def _get_total(self): return round(self.price * self.quantity, 2) def _get_price(self): print("price getter") return self._price def _set_price(self, value): print("price setter") if not isinstance(value, (int, float)): raise TypeError("Значение должно быть числом") if not value >= 0: raise ValueError("Значение должно быть положительным") self._price = float(value) total = property(_get_total) price = property(_get_price, _set_price) # property без декораторов ver 2 class Book: def __init__(self, title, price, quantity): self.title = title self.price = price self.quantity = quantity def _get_total(self): return round(self.price * self.quantity, 2) def _get_price(self): print("price getter") return self._price def _set_price(self, value): print("price setter") if not isinstance(value, (int, float)): raise TypeError("Значение должно быть числом") if not value >= 0: raise ValueError("Значение должно быть положительным") self._price = float(value) total = property() total = total.getter(_get_total) price = property() price = price.getter(_get_price) price = price.setter(_set_price)

27.412214

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3,591

5.25

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0.073469

0.077098

0.036281

0.712925

0

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0.281816

3,591

130

67

27.623077

0.851493

0.133668

0

0.902174

0

0.111793

0

1

0.195652

false

0

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0.097826

0

null

0

1

0

1

0

null

0

6

753f6f871578fee0abddaadd84fe83762fc5fd23

48

py

Python

ppln/schedulers/__init__.py

amirassov/mmcv

aa517bbc62823d2c68753dd33ca1f840a75ceb2c

[ "MIT" ]

10

2020-02-10T10:43:59.000Z

2021-04-22T11:32:16.000Z

ppln/schedulers/__init__.py

amirassov/mmcv

aa517bbc62823d2c68753dd33ca1f840a75ceb2c

[ "MIT" ]

3

2020-07-16T14:06:28.000Z

2020-07-16T14:06:38.000Z

ppln/schedulers/__init__.py

amirassov/mmcv

aa517bbc62823d2c68753dd33ca1f840a75ceb2c

[ "MIT" ]

2

2019-12-23T07:52:56.000Z

2019-12-23T08:20:30.000Z

from .cosine import CosineAnnealingWithWarmupLR

24

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0.895833

4

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10.75

1

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0.977273

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null

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null

0

1

0

1

0

6

754e29d616b767924d014f4530e050d00950481e

46

py

Python

criss_cross/envs/__init__.py

mark-gluzman/RLinOR

2c54dfebdd8248353baf4c0e703ef29d642cc5a7

[ "MIT" ]

null

criss_cross/envs/__init__.py

mark-gluzman/RLinOR

2c54dfebdd8248353baf4c0e703ef29d642cc5a7

[ "MIT" ]

null

criss_cross/envs/__init__.py

mark-gluzman/RLinOR

2c54dfebdd8248353baf4c0e703ef29d642cc5a7

[ "MIT" ]

null

from criss_cross.envs.cc_env import CrissCross

46

0.891304

8

46

4.875

1

0

0.065217

46

1

46

0.906977

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true

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1

0

1

0

null

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null

0

1

0

1

0

1

0

6

755ebf006e7e23b978e12adf996be785ba00cf9d

12,720

py

Python

reclist/metrics/metadata_distribution.py

bbc/datalab-reclist

42ea321856e02e46cca8e3b032b3b088ff328f57

[ "MIT" ]

null

reclist/metrics/metadata_distribution.py

bbc/datalab-reclist

42ea321856e02e46cca8e3b032b3b088ff328f57

[ "MIT" ]

null

reclist/metrics/metadata_distribution.py

bbc/datalab-reclist

42ea321856e02e46cca8e3b032b3b088ff328f57

[ "MIT" ]

null

import os import matplotlib.pyplot as plt import numpy as np from collections import defaultdict, Counter from reclist.current import current from reclist.utils.vectorise_sounds_data import generate_genre_dict def round_up(number): return int(number) + (number % 1 > 0) def genre_distribution_by_gender(enriched_items, y_test, y_preds, k=10, top_genres=10, first_genre_only=False, debug=True): """ Calculates the distribution of genres by age in testing data """ genre_dict = generate_genre_dict(enriched_items) genres_per_gender = defaultdict(list) genres_count_per_gender = {} for target, pred in zip(y_test, y_preds): predicted_genres = [] target_gender = target[0]["gender"] if not target_gender: target_gender = 'unknown' for resource_obj in pred[:k]: try: predicted_genres.extend( [genre_dict.get(resource_obj["resourceId"])[0]]) if first_genre_only else predicted_genres.extend( genre_dict.get(resource_obj["resourceId"])) except IndexError: print(genre_dict.get(resource_obj["resourceId"])) genres_per_gender[target_gender].append(Counter(predicted_genres)) genres_counter = defaultdict(list) for gender in sorted(genres_per_gender.keys()): gender_len = len(genres_per_gender[gender]) for result_set in genres_per_gender[gender]: for genre, occurrences in result_set.items(): genres_counter[genre].append(occurrences) # padding to account for genres which appear in smaller number of recs for genre in genres_counter.keys(): padded_genre = genres_counter[genre] padded_genre += [0] * (gender_len - len(padded_genre)) genres_counter[genre] = padded_genre genres_count_per_gender[gender] = Counter( {genre: np.mean(genres_counter[genre]) for genre in genres_counter.keys()}).most_common(top_genres) if debug: nrows = round_up(len(genres_count_per_gender.keys()) / 2) ncols = 2 fig, ax = plt.subplots(nrows=nrows, ncols=ncols, figsize=(15, 12)) gen_counter = 0 for row in ax: for col in row: try: gender = genres_count_per_gender[list(genres_count_per_gender.keys())[gen_counter]] x_tick_names = [genre[0] for genre in gender] x_tick_idx = list(range(len(x_tick_names))) col.barh( # x_tick_names, x_tick_idx, [genre[1] for genre in gender], align='center', tick_label=x_tick_names ) col.set_title(list(genres_count_per_gender.keys())[gen_counter], y=1.0, pad=-14, fontsize=8) col.set_xlabel(f'Mean no. of items \n per genre (top {k} recs)', fontsize=8) gen_counter += 1 except IndexError: pass fig.tight_layout() plt.savefig(os.path.join(current.report_path, 'plots', f'genres_count_per_gender.png')) plt.clf() return genres_count_per_gender def genre_distribution_by_agerange(enriched_items, y_test, y_preds, k=10, top_genres=10, first_genre_only=False, debug=True): """ Calculates the distribution of genre by age range in testing data """ # extract genres genre_dict = generate_genre_dict(enriched_items) genres_per_age_range = defaultdict(list) genres_count_per_age_range = {} for target, pred in zip(y_test, y_preds): predicted_genres = [] target_age_range = target[0]["age_range"] if not target_age_range: target_age_range = 'unknown' for resource_obj in pred[:k]: try: predicted_genres.extend( [genre_dict.get(resource_obj["resourceId"])[0]]) if first_genre_only else predicted_genres.extend( genre_dict.get(resource_obj["resourceId"])) except IndexError: print(genre_dict.get(resource_obj["resourceId"])) genres_per_age_range[target_age_range].append(Counter(predicted_genres)) genres_counter = defaultdict(list) for age_range in sorted(genres_per_age_range.keys()): age_range_len = len(genres_per_age_range[age_range]) for result_set in genres_per_age_range[age_range]: for genre, occurrences in result_set.items(): genres_counter[genre].append(occurrences) # padding to account for genres which appear in smaller number of recs for genre in genres_counter.keys(): padded_genre = genres_counter[genre] padded_genre += [0] * (age_range_len - len(padded_genre)) genres_counter[genre] = padded_genre genres_count_per_age_range[age_range] = Counter( {genre: np.mean(genres_counter[genre]) for genre in genres_counter.keys()}).most_common(top_genres) # plots if debug: nrows = round_up(len(genres_count_per_age_range.keys()) / 2) ncols = 2 fig, ax = plt.subplots(nrows=nrows, ncols=ncols, figsize=(15, 12)) gen_counter = 0 for row in ax: for col in row: try: age_range = genres_count_per_age_range[list(genres_count_per_age_range.keys())[gen_counter]] x_tick_names = [genre[0] for genre in age_range] x_tick_idx = list(range(len(x_tick_names))) col.barh( x_tick_idx, [genre[1] for genre in age_range], align='center', tick_label=x_tick_names ) col.set_title(list(genres_count_per_age_range.keys())[gen_counter], y=1.0, pad=-14, fontsize=8) col.set_xlabel(f'Mean no. of items \n per genre (top {k} recs)', fontsize=8) gen_counter += 1 except IndexError: pass fig.tight_layout() plt.savefig(os.path.join(current.report_path, 'plots', f'genres_count_per_age_range.png')) plt.clf() return genres_count_per_age_range def masterbrand_distribution_by_gender(enriched_items, y_test, y_preds, k=10, top_masterbrands=10, debug=True): """ Calculates the distribution of genres by age in testing data """ masterbrand_dict = {item['resource_id']: item['master_brand'] for item in enriched_items} masterbrand_per_gender = defaultdict(list) masterbrand_count_per_gender = {} for target, pred in zip(y_test, y_preds): predicted_masterbrands = [] target_gender = target[0]["gender"] if not target_gender: target_gender = 'unknown' for resource_obj in pred[:k]: predicted_masterbrands.append(masterbrand_dict.get(resource_obj["resourceId"])) masterbrand_per_gender[target_gender].append(Counter(predicted_masterbrands)) masterbrands_counter = defaultdict(list) for gender in sorted(masterbrand_per_gender.keys()): gender_len = len(masterbrand_per_gender[gender]) for result_set in masterbrand_per_gender[gender]: for genre, occurrences in result_set.items(): masterbrands_counter[genre].append(occurrences) # padding to account for genres which appear in smaller number of recs for genre in masterbrands_counter.keys(): padded_genre = masterbrands_counter[genre] padded_genre += [0] * (gender_len - len(padded_genre)) masterbrands_counter[genre] = padded_genre masterbrand_count_per_gender[gender] = Counter( {masterbrand: np.mean(masterbrands_counter[masterbrand]) for masterbrand in masterbrands_counter.keys()}).most_common(top_masterbrands) if debug: nrows = round_up(len(masterbrand_count_per_gender.keys()) / 2) ncols = 2 fig, ax = plt.subplots(nrows=nrows, ncols=ncols) mb_counter = 0 for row in ax: for col in row: try: gender = masterbrand_count_per_gender[list(masterbrand_count_per_gender.keys())[mb_counter]] x_tick_names = [masterbrand[0] for masterbrand in gender] x_tick_idx = list(range(len(x_tick_names))) col.barh( # x_tick_names, x_tick_idx, [masterbrand[1] for masterbrand in gender], align='center', tick_label=x_tick_names ) col.set_title(list(masterbrand_count_per_gender.keys())[mb_counter], y=1.0, pad=-14, fontsize=8) col.set_xlabel(f'Avg. no. of items per \nmasterbrand (top {k} recs)', fontsize=8) mb_counter += 1 except IndexError: pass fig.tight_layout() plt.savefig(os.path.join(current.report_path, 'plots', f'masterbrand_count_per_gender.png')) plt.clf() return masterbrand_count_per_gender def masterbrand_distribution_by_agerange(enriched_items, y_test, y_preds, k=10, top_masterbrands=10, debug=True): """ Calculates the distribution of masterbrand by age range in testing data """ masterbrand_dict = {item['resource_id']: item['master_brand'] for item in enriched_items} # hits = defaultdict(int) masterbrands_per_age_range = defaultdict(list) masterbrand_count_per_age_range = {} for target, pred in zip(y_test, y_preds): predicted_masterbrands = [] target_age_range = target[0]["age_range"] if not target_age_range: target_age_range = 'unknown' for resource_obj in pred[:k]: predicted_masterbrands.append(masterbrand_dict.get(resource_obj["resourceId"])) masterbrands_per_age_range[target_age_range].append(Counter(predicted_masterbrands)) masterbrands_counter = defaultdict(list) for age_range in sorted(masterbrands_per_age_range.keys()): age_range_len = len(masterbrands_per_age_range[age_range]) for result_set in masterbrands_per_age_range[age_range]: for masterbrand, occurrences in result_set.items(): masterbrands_counter[masterbrand].append(occurrences) # padding to account for genres which appear in smaller number of recs for masterbrand in masterbrands_counter.keys(): padded_masterbrand = masterbrands_counter[masterbrand] padded_masterbrand += [0] * (age_range_len - len(padded_masterbrand)) masterbrands_counter[masterbrand] = padded_masterbrand masterbrand_count_per_age_range[age_range] = Counter( {masterbrand: np.mean(masterbrands_counter[masterbrand]) for masterbrand in masterbrands_counter.keys()}).most_common(top_masterbrands) # plots if debug: nrows = round_up(len(masterbrand_count_per_age_range.keys()) / 2) ncols = 2 fig, ax = plt.subplots(nrows=nrows, ncols=ncols, figsize=(15, 10)) mb_counter = 0 for row in ax: for col in row: try: age_range = masterbrand_count_per_age_range[ list(masterbrand_count_per_age_range.keys())[mb_counter]] x_tick_names = [masterbrand[0] for masterbrand in age_range] x_tick_idx = list(range(len(x_tick_names))) col.barh( x_tick_idx, [masterbrand[1] for masterbrand in age_range], align='center', tick_label=x_tick_names ) col.set_title(list(masterbrand_count_per_age_range.keys())[mb_counter], y=1.0, pad=-14, fontsize=8) col.set_xlabel(f'Avg. no. of items per \nmasterbrand (top {k} recs)', fontsize=8) mb_counter += 1 except IndexError: pass fig.tight_layout() plt.savefig(os.path.join(current.report_path, 'plots', f'masterbrands_count_per_age_range.png')) plt.clf() return masterbrand_count_per_age_range

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null

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null

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6

f351124db469647604e6a3f2c4659b97b636a995

211

py

Python

apps/secure_url/mixins.py

fryta/sercure-url

06029e8e3a95616f939f62f04c260d14d128f0b4

[ "MIT" ]

null

apps/secure_url/mixins.py

fryta/sercure-url

06029e8e3a95616f939f62f04c260d14d128f0b4

[ "MIT" ]

7

2020-02-11T23:49:48.000Z

2022-01-13T01:05:42.000Z

apps/secure_url/mixins.py

fryta/secure-url

06029e8e3a95616f939f62f04c260d14d128f0b4

[ "MIT" ]

null

from django.contrib.auth.mixins import UserPassesTestMixin class EditOnlyOwnSecuredEntitiesMixin(UserPassesTestMixin): def test_func(self): return self.get_object().user.pk == self.request.user.pk

30.142857

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1

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1

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6

f36803e44081834548ac1f3e13a66c28b269f627

115

py

Python

core/validators/cat.py

mzrwalzy/NJUPTCats

325dde6f48cac7dc85490935d78439d3b23a8395

[ "MIT" ]

null

core/validators/cat.py

mzrwalzy/NJUPTCats

325dde6f48cac7dc85490935d78439d3b23a8395

[ "MIT" ]

null

core/validators/cat.py

mzrwalzy/NJUPTCats

325dde6f48cac7dc85490935d78439d3b23a8395

[ "MIT" ]

null

import typing as tp from core.validators._base import BaseValidator class CatValidator(BaseValidator): pass

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1

0

6

f37990b546183d4d6faa45d1d602c02abaefaee9

10,427

py

Python

tests/test_get_scores.py

hsbc/pyratings

f71d7d2e9030f0e34eb6dd8f2e753611d049302c

[ "Apache-2.0" ]

9

2022-03-25T12:48:28.000Z

2022-03-28T15:17:49.000Z

tests/test_get_scores.py

rbirkby/pyratings

eb9dfa35dfec9676e4d16152bbcae278444869d4

[ "Apache-2.0" ]

null

tests/test_get_scores.py

rbirkby/pyratings

eb9dfa35dfec9676e4d16152bbcae278444869d4

[ "Apache-2.0" ]

3

2022-03-25T13:27:55.000Z

2022-03-28T10:06:28.000Z

""" Copyright 2022 HSBC Global Asset Management (Deutschland) GmbH Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ import numpy as np import pandas as pd import pytest from pandas.testing import assert_frame_equal, assert_series_equal import pyratings as rtg from tests import conftest # --- input: single rating/warf @pytest.mark.parametrize( ["rating_provider", "rating", "score"], list( pd.concat( [ conftest.rtg_df_long, conftest.scores_df_long["rtg_score"], ], axis=1, ).to_records(index=False) ), ) def test_get_scores_from_single_rating_longterm(rating_provider, rating, score): """Tests if function can handle single string objects.""" act = rtg.get_scores_from_ratings( ratings=rating, rating_provider=rating_provider, tenor="long-term" ) assert act == score @pytest.mark.parametrize( ["rating_provider", "rating", "score"], list( pd.concat( [ conftest.rtg_df_long_st, conftest.scores_df_long_st["rtg_score"], ], axis=1, ).to_records(index=False) ), ) def test_get_scores_from_single_rating_shortterm(rating_provider, rating, score): """Tests if function can handle single string objects.""" act = rtg.get_scores_from_ratings( ratings=rating, rating_provider=rating_provider, tenor="short-term" ) assert act == score @pytest.mark.parametrize("tenor", ["long-term", "short-term"]) def test_get_scores_from_single_rating_invalid_rating_provider(tenor): """Tests if correct error message will be raised.""" with pytest.raises(AssertionError) as err: rtg.get_scores_from_ratings(ratings="AA", rating_provider="foo", tenor=tenor) assert str(err.value) == conftest.ERR_MSG @pytest.mark.parametrize("tenor", ["long-term", "short-term"]) def test_get_scores_with_invalid_single_rating(tenor): """Tests if function returns NaN for invalid inputs.""" act = rtg.get_scores_from_ratings( ratings="foo", rating_provider="Fitch", tenor=tenor ) assert pd.isna(act) @pytest.mark.parametrize("tenor", ["long-term", "short-term"]) def test_get_scores_with_single_rating_and_no_rating_provider(tenor): """Tests if correct error message will be raised.""" with pytest.raises(ValueError) as err: rtg.get_scores_from_ratings(ratings="BBB", tenor=tenor) assert str(err.value) == "'rating_provider' must not be None." @pytest.mark.parametrize( "warf, score", [ (1, 1), (6, 2), (54.9999, 4), (55, 5), (55.00001, 5), (400, 9), (10_000, 22), ], ) def test_get_scores_from_single_warf(warf, score): """Tests if function can correctly handle individual warf (float).""" act = rtg.get_scores_from_warf(warf=warf) assert act == score @pytest.mark.parametrize("warf", [np.nan, -5, 20000.5]) def test_get_scores_from_invalid_single_warf(warf): """Tests if function returns NaN for invalid inputs.""" assert pd.isna(rtg.get_scores_from_warf(warf=warf)) # --- input: ratings series @pytest.mark.parametrize( ["rating_provider", "scores_series", "ratings_series"], conftest.params_provider_scores_ratings_lt, ) def test_get_scores_from_ratings_series_longterm( rating_provider, ratings_series, scores_series ): """Tests if function can correctly handle pd.Series objects.""" scores_series.name = f"rtg_score_{rating_provider}" act = rtg.get_scores_from_ratings( ratings=ratings_series, rating_provider=rating_provider ) assert_series_equal(act, scores_series) @pytest.mark.parametrize( ["rating_provider", "scores_series", "ratings_series"], conftest.params_provider_scores_ratings_st, ) def test_get_scores_from_ratings_series_shortterm( rating_provider, ratings_series, scores_series ): """Tests if function can correctly handle pd.Series objects.""" scores_series.name = f"rtg_score_{rating_provider}" act = rtg.get_scores_from_ratings( ratings=ratings_series, rating_provider=rating_provider, tenor="short-term" ) assert_series_equal(act, scores_series) @pytest.mark.parametrize("tenor", ["long-term", "short-term"]) def test_get_scores_from_ratings_series_invalid_rating_provider(tenor): """Tests if correct error message will be raised.""" with pytest.raises(AssertionError) as err: rtg.get_scores_from_ratings( ratings=pd.Series(data=["AAA", "AA", "D"], name="rating"), rating_provider="foo", tenor=tenor, ) assert str(err.value) == conftest.ERR_MSG @pytest.mark.parametrize("tenor", ["long-term", "short-term"]) def test_get_scores_from_invalid_ratings_series(tenor): """Tests if function can correctly handle pd.Series objects.""" ratings_series = pd.Series(data=[np.nan, "foo", -10], name="rating") scores_series = pd.Series(data=[np.nan, np.nan, np.nan], name="rtg_score_Fitch") act = rtg.get_scores_from_ratings( ratings=ratings_series, rating_provider="Fitch", tenor=tenor ) assert_series_equal(act, scores_series) def test_get_scores_from_warf_series(): """Tests if function can correctly handle pd.Series objects.""" warf_series = conftest.warf_df_wide.iloc[:, 0] scores_series = conftest.scores_df_wide.iloc[:, 0] scores_series.name = "rtg_score" act = rtg.get_scores_from_warf(warf=warf_series) assert_series_equal(act, scores_series) def test_get_scores_from_invalid_warf_series(): """Tests if function can correctly handle pd.Series objects.""" warf_series = pd.Series(data=[np.nan, "foo", -10], name="warf") scores_series = pd.Series(data=[np.nan, np.nan, np.nan], name="rtg_score") act = rtg.get_scores_from_warf(warf=warf_series) assert_series_equal(act, scores_series) # --- input: ratings dataframe exp_lt = conftest.scores_df_wide exp_lt = pd.concat( [ exp_lt, pd.DataFrame( data=[[np.nan, np.nan, np.nan, np.nan, np.nan, np.nan]], columns=exp_lt.columns, ), ], axis=0, ignore_index=True, ) exp_lt.columns = [ "rtg_score_Fitch", "rtg_score_Moody", "rtg_score_SP", "rtg_score_Bloomberg", "rtg_score_DBRS", "rtg_score_ICE", ] exp_st = conftest.scores_df_wide_st exp_st = pd.concat( [ exp_st, pd.DataFrame(data=[[np.nan, np.nan, np.nan, np.nan]], columns=exp_st.columns), ], axis=0, ignore_index=True, ) exp_st.columns = [ "rtg_score_Fitch", "rtg_score_Moody", "rtg_score_SP", "rtg_score_DBRS", ] def test_get_scores_from_ratings_dataframe_with_explicit_rating_provider_longterm(): """Tests if function can correctly handle pd.DataFrame objects.""" act = rtg.get_scores_from_ratings( ratings=conftest.rtg_df_wide_with_err_row, rating_provider=[ "rtg_Fitch", "Moody's rating", "Rating S&P", "Bloomberg Bloomberg RATING", "DBRS", "ICE", ], tenor="long-term", ) # noinspection PyTypeChecker assert_frame_equal(act, exp_lt) def test_get_scores_from_ratings_dataframe_with_explicit_rating_provider_shortterm(): """Tests if function can correctly handle pd.DataFrame objects.""" act = rtg.get_scores_from_ratings( ratings=conftest.rtg_df_wide_st_with_err_row, rating_provider=[ "rtg_Fitch", "Moody's rating", "Rating S&P", "DBRS", ], tenor="short-term", ) # noinspection PyTypeChecker assert_frame_equal(act, exp_st) def test_get_scores_from_ratings_dataframe_by_inferring_rating_provider_longterm(): """Tests if function can correctly handle pd.DataFrame objects.""" act = rtg.get_scores_from_ratings( ratings=conftest.rtg_df_wide_with_err_row, tenor="long-term" ) # noinspection PyTypeChecker assert_frame_equal(act, exp_lt) def test_get_scores_from_ratings_dataframe_by_inferring_rating_provider_shortterm(): """Tests if function can correctly handle pd.DataFrame objects.""" act = rtg.get_scores_from_ratings( ratings=conftest.rtg_df_wide_st_with_err_row, tenor="short-term" ) # noinspection PyTypeChecker assert_frame_equal(act, exp_st) @pytest.mark.parametrize("tenor", ["long-term", "short-term"]) def test_get_scores_from_ratings_dataframe_invalid_rating_provider(tenor): """Tests if correct error message will be raised.""" with pytest.raises(AssertionError) as err: rtg.get_scores_from_ratings( ratings=conftest.rtg_df_wide, rating_provider="foo", tenor=tenor ) assert str(err.value) == conftest.ERR_MSG @pytest.mark.parametrize("tenor", ["long-term", "short-term"]) def test_get_scores_from_invalid_ratings_dataframe(tenor): """Tests if function can correctly handle pd.DataFrame objects.""" act = rtg.get_scores_from_ratings(ratings=conftest.input_invalid_df, tenor=tenor) expectations = conftest.exp_invalid_df expectations.columns = ["rtg_score_Fitch", "rtg_score_DBRS"] # noinspection PyTypeChecker assert_frame_equal(act, expectations) def test_get_scores_from_warf_dataframe(): """Tests if function can correctly handle pd.DataFrame objects.""" act = rtg.get_scores_from_warf(warf=conftest.warf_df_wide_with_err_row) # noinspection PyTypeChecker assert_frame_equal(act, exp_lt) def test_get_scores_from_invalid_warf_dataframe(): """Tests if function can correctly handle pd.DataFrame objects.""" act = rtg.get_scores_from_warf(warf=conftest.input_invalid_df) expectations = conftest.exp_invalid_df expectations.columns = ["rtg_score_Fitch", "rtg_score_DBRS"] # noinspection PyTypeChecker assert_frame_equal(act, expectations)

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6

f3b671835453bcad9342db7a5d778bed4d3e924e

1,127

py

Python

torchexpo/vision/image_classification/shufflenet.py

torchexpo/torchexpo

88c875358e830065ee23f49f47d4995b5b2d3e3c

[ "Apache-2.0" ]

23

2020-09-08T05:08:46.000Z

2021-08-12T07:16:53.000Z

torchexpo/vision/image_classification/shufflenet.py

torchexpo/torchexpo

88c875358e830065ee23f49f47d4995b5b2d3e3c

[ "Apache-2.0" ]

1

2021-12-05T06:15:18.000Z

2021-12-20T08:10:19.000Z

torchexpo/vision/image_classification/shufflenet.py

torchexpo/torchexpo

88c875358e830065ee23f49f47d4995b5b2d3e3c

[ "Apache-2.0" ]

2

2021-01-12T06:10:53.000Z

2021-07-24T08:21:59.000Z

import torchvision from torchexpo.modules import ImageClassificationModule def shufflenet_v2_x0_5(): """ShuffleNet V2 0.5x Model pre-trained on ImageNet""" model = torchvision.models.shufflenet_v2_x0_5(pretrained=True) obj = ImageClassificationModule(model, "ShuffleNet_v2_x0_5", model_example="default") return obj def shufflenet_v2_x1_0(): """ShuffleNet V2 1.0x Model pre-trained on ImageNet""" model = torchvision.models.shufflenet_v2_x1_0(pretrained=True) obj = ImageClassificationModule(model, "ShuffleNet_v2_x1_0", model_example="default") return obj # def shufflenet_v2_x1_5(): # """ShuffleNet V2 1.5x Model pre-trained on ImageNet""" # model = torchvision.models.shufflenet_v2_x1_5(pretrained=True) # obj = ImageClassificationModule(model, "ShuffleNet_v2_x1_5", model_example="default") # return obj # def shufflenet_v2_x2_0(): # """ShuffleNet V2 2.0x Model pre-trained on ImageNet""" # model = torchvision.models.shufflenet_v2_x2_0(pretrained=True) # obj = ImageClassificationModule(model, "ShuffleNet_v2_x2_0", model_example="default") # return obj

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1

0

null

0

1

0

6

341415e48060a622d4be670188996ee2a77ade0f

53

py

Python

tests/core/test_import.py

vaporydev/bimini

7c26efec585742ef870bf58ea5d96e2deb242775

[ "MIT" ]

7

2019-02-28T01:42:27.000Z

2021-11-04T14:25:49.000Z

tests/core/test_import.py

vaporydev/bimini

7c26efec585742ef870bf58ea5d96e2deb242775

[ "MIT" ]

null

tests/core/test_import.py

vaporydev/bimini

7c26efec585742ef870bf58ea5d96e2deb242775

[ "MIT" ]

3

2020-10-01T03:02:26.000Z

2022-03-28T18:55:40.000Z

def test_import(): import bimini # noqa: F401

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53

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1

0

6

3417d30c8003a78f64f68acf07fb416e55d09463

32

py

Python

app.py

SRtsuki/SELab02

33a3728e2141838f3c797c1df40b1aa1758f23fd

[ "MIT" ]

null

app.py

SRtsuki/SELab02

33a3728e2141838f3c797c1df40b1aa1758f23fd

[ "MIT" ]

null

app.py

SRtsuki/SELab02

33a3728e2141838f3c797c1df40b1aa1758f23fd

[ "MIT" ]

null

#app.py print("this is app.py")

10.666667

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0.714286

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0.125

32

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null

0

1

0

1

0

6

343c54335bc955777387748ef0208b37c9bd5607

6,323

py

Python

tests-manual/manual_testing.py

rashidalyahyai/piecewise-regression

2cccd101ac6f44b5e2fe9e79d6782e3ce2a06ed7

[ "MIT" ]

24

2021-11-15T13:26:05.000Z

2022-03-19T16:42:23.000Z

tests-manual/manual_testing.py

rashidalyahyai/piecewise-regression

2cccd101ac6f44b5e2fe9e79d6782e3ce2a06ed7

[ "MIT" ]

5

2021-11-30T10:17:40.000Z

2022-03-30T23:22:24.000Z

tests-manual/manual_testing.py

rashidalyahyai/piecewise-regression

2cccd101ac6f44b5e2fe9e79d6782e3ce2a06ed7

[ "MIT" ]

4

2021-11-22T17:42:28.000Z

2022-03-20T18:36:15.000Z

from piecewise_regression import ModelSelection from piecewise_regression import Fit import numpy as np import matplotlib.pyplot as plt import os import sys sys.path.insert(1, os.path.join(sys.path[0], '..')) def on_data_1(): alpha = -4 beta_1 = -2 intercept = 100 breakpoint_1 = 7 n_points = 200 xx = np.linspace(0, 20, n_points) yy = intercept + alpha*xx + beta_1 * \ np.maximum(xx - breakpoint_1, 0) + np.random.normal(size=n_points) pw_fit = Fit(xx, yy, start_values=[5]) print("p-value is ", pw_fit.davies) pw_results = pw_fit.get_results() pw_estimates = pw_results["estimates"] print(pw_results) print(pw_estimates) pw_bootstrap_history = pw_fit.bootstrap_history print(pw_bootstrap_history) # print(bp_fit.breakpoint_history) # bp_fit.plot_data() # plt.show() def on_data_1b(): alpha = -4 beta_1 = -4 beta_2 = 4 intercept = 100 breakpoint_1 = 7 breakpoint_2 = 12 n_points = 200 xx = np.linspace(0, 20, n_points) yy = intercept + alpha*xx + beta_1 * np.maximum( xx - breakpoint_1, 0) + beta_2 * np.maximum( xx-breakpoint_2, 0) + np.random.normal(size=n_points) bp_fit = Fit(xx, yy, start_values=[5, 10]) bp_fit.summary() bp_fit.plot_best_muggeo_breakpoint_history() plt.show() bp_fit.plot_data() bp_fit.plot_fit(color="red", linewidth=4) bp_fit.plot_breakpoints() bp_fit.plot_breakpoint_confidence_intervals() plt.show() def on_data_1c(): alpha = -4 beta_1 = -2 beta_2 = 4 beta_3 = 1 intercept = 100 breakpoint_1 = 7 breakpoint_2 = 13 breakpoint_3 = 14 n_points = 200 xx = np.linspace(0, 20, n_points) yy = intercept + alpha*xx yy += beta_1 * np.maximum(xx - breakpoint_1, 0) yy += beta_2 * np.maximum(xx - breakpoint_2, 0) yy += beta_3 * np.maximum(xx - breakpoint_3, 0) yy += np.random.normal(size=n_points) bp_fit = Fit(xx, yy, start_values=[5, 10, 16]) bp_fit.summary() bp_fit.plot_data() bp_fit.plot_fit(color="red", linewidth=4) bp_fit.plot_breakpoints() bp_fit.plot_breakpoint_confidence_intervals() print("The fit data: ", bp_fit.__dict__) plt.show() plt.close() bp_fit.plot_best_muggeo_breakpoint_history() plt.legend() plt.show() plt.close() bp_fit.plot_bootstrap_restarting_history() plt.legend() plt.show() plt.close() def model_selection_1(): alpha = -4 beta_1 = -2 intercept = 100 breakpoint_1 = 17 n_points = 100 xx = np.linspace(10, 30, n_points) yy = intercept + alpha*xx + beta_1 * \ np.maximum(xx - breakpoint_1, 0) + np.random.normal(size=n_points) ModelSelection(xx, yy, max_breakpoints=6) def model_selection_2(): alpha = -4 beta_1 = -4 beta_2 = 4 intercept = 100 breakpoint_1 = 7 breakpoint_2 = 12 n_points = 200 xx = np.linspace(0, 20, n_points) yy = intercept + alpha*xx + beta_1 * np.maximum( xx - breakpoint_1, 0) + beta_2 * np.maximum( xx-breakpoint_2, 0) + np.random.normal(size=n_points) ModelSelection(xx, yy) def fit_3_check_this_makes_sense(): np.random.seed(0) alpha = 10 beta_1 = -8 beta_2 = -6 beta_3 = 10 intercept = 100 breakpoint_1 = 7 breakpoint_2 = 10 breakpoint_3 = 14 n_points = 200 xx = np.linspace(0, 20, n_points) yy = intercept + alpha*xx yy += beta_1 * np.maximum(xx - breakpoint_1, 0) yy += beta_2 * np.maximum(xx - breakpoint_2, 0) yy += beta_3 * np.maximum(xx - breakpoint_3, 0) yy += np.random.normal(size=n_points) pr = Fit(xx, yy, n_breakpoints=2) pr.plot() plt.show() pr3 = Fit(xx, yy, n_breakpoints=3) pr3.plot() plt.show() pr4 = Fit(xx, yy, n_breakpoints=4) pr4.plot() plt.show() ModelSelection(xx, yy, max_breakpoints=6) def fit_with_initally_diverging(): np.random.seed(2) alpha = 10 beta_1 = -8 beta_2 = 3 beta_3 = 10 intercept = 100 breakpoint_1 = 7 breakpoint_2 = 10 breakpoint_3 = 14 n_points = 200 xx = np.linspace(0, 20, n_points) yy = intercept + alpha*xx yy += beta_1 * np.maximum(xx - breakpoint_1, 0) yy += beta_2 * np.maximum(xx - breakpoint_2, 0) yy += beta_3 * np.maximum(xx - breakpoint_3, 0) yy += np.random.normal(size=n_points) pr = Fit(xx, yy, n_breakpoints=2) print(pr.summary) def fit_with_initially_diverging_start_values(): np.random.seed(0) alpha = 10 beta_1 = -8 beta_2 = 3 beta_3 = 10 intercept = 100 breakpoint_1 = 7 breakpoint_2 = 10 breakpoint_3 = 14 n_points = 200 xx = np.linspace(0, 20, n_points) yy = intercept + alpha*xx yy += beta_1 * np.maximum(xx - breakpoint_1, 0) yy += beta_2 * np.maximum(xx - breakpoint_2, 0) yy += beta_3 * np.maximum(xx - breakpoint_3, 0) yy += np.random.normal(size=n_points) pr = Fit(xx, yy, start_values=[2.15646833, 0.98300926], n_boot=20) pr.summary() def fit_with_initially_diverging_start_values_b(): np.random.seed(0) alpha = 10 beta_1 = -8 beta_2 = 3 beta_3 = 10 intercept = 100 breakpoint_1 = 7 breakpoint_2 = 10 breakpoint_3 = 14 n_points = 200 xx = np.linspace(0, 20, n_points) yy = intercept + alpha*xx yy += beta_1 * np.maximum(xx - breakpoint_1, 0) yy += beta_2 * np.maximum(xx - breakpoint_2, 0) yy += beta_3 * np.maximum(xx - breakpoint_3, 0) yy += np.random.normal(size=n_points) pr = Fit(xx, yy, start_values=[1.2, 0.53], n_boot=25) pr.summary() def fit_with_straight_line(): np.random.seed(0) alpha = 10 intercept = 100 n_points = 200 xx = np.linspace(0, 20, n_points) yy = intercept + alpha*xx yy += np.random.normal(size=n_points) pr = Fit(xx, yy, n_breakpoints=0, n_boot=25) pr.summary() def model_comparision_straight_line(): np.random.seed(0) alpha = 10 intercept = 100 n_points = 200 xx = np.linspace(0, 20, n_points) yy = intercept + alpha*xx yy += np.random.normal(size=n_points) ModelSelection(xx, yy, max_breakpoints=6) if __name__ == "__main__": model_comparision_straight_line()

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null

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1

0

1

0

null

0

6

34500cbc5d609d33d0365ed63be410b2cd714077

47

py

Python

src/chart/example.py

hasta13/chart

3292b81a15cdf361b0b85e120c58dc1f6a22ddb8

[ "MIT" ]

null

src/chart/example.py

hasta13/chart

3292b81a15cdf361b0b85e120c58dc1f6a22ddb8

[ "MIT" ]

null

src/chart/example.py

hasta13/chart

3292b81a15cdf361b0b85e120c58dc1f6a22ddb8

[ "MIT" ]

null

def test(): print('this is a placeholder')

15.666667

34

0.638298

7

47

4.285714

1

0

0.212766

47

2

35

23.5

0.810811

0

0.446809

0

1

0.5

true

0

0.5

1

0

null

0

1

0

null

0

1

0

1

0

6

346f7f3c8862ce237b48d855353cec42fdc02259

77

py

Python

src/python/stup/twistedutils/__init__.py

Wizmann/STUP-Protocol

e06a3442082e5061d2be32be3ffd681675e7ffb5

[ "MIT" ]

14

2017-05-06T10:14:32.000Z

2018-07-17T02:58:00.000Z

src/python/stup/twistedutils/__init__.py

Wizmann/STUP-Protocol

e06a3442082e5061d2be32be3ffd681675e7ffb5

[ "MIT" ]

2

2017-06-13T05:40:18.000Z

2017-06-13T16:23:01.000Z

src/python/stup/twistedutils/__init__.py

Wizmann/STUP-Protocol

e06a3442082e5061d2be32be3ffd681675e7ffb5

[ "MIT" ]

4

2017-06-09T20:20:54.000Z

2018-07-17T02:58:10.000Z

from .deferred_deque import * from .utils import * from .time_wheel import *

19.25

29

0.766234

11

77

5.181818

0.636364

0.350877

0

0.155844

77

3

30

25.666667

0.876923

0

1

0

true

0

1

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1

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0

null

0

1

0

1

0

1

0

6

3479c13138309154e6c90e770d2f2209b49c8376

252

py

Python

nndet/arch/heads/__init__.py

joeranbosma/nnDetection

2ebbf1cdc8a8794c73e325f06fea50632c78ae8c

[ "BSD-3-Clause" ]

242

2021-05-17T12:31:39.000Z

2022-03-31T11:51:29.000Z

nndet/arch/heads/__init__.py

joeranbosma/nnDetection

2ebbf1cdc8a8794c73e325f06fea50632c78ae8c

[ "BSD-3-Clause" ]

59

2021-06-02T07:32:10.000Z

2022-03-31T18:45:52.000Z

nndet/arch/heads/__init__.py

joeranbosma/nnDetection

2ebbf1cdc8a8794c73e325f06fea50632c78ae8c

[ "BSD-3-Clause" ]

38

2021-05-31T14:01:37.000Z

2022-03-21T08:24:40.000Z

from nndet.arch.heads.classifier import ClassifierType, Classifier from nndet.arch.heads.comb import HeadType, AbstractHead from nndet.arch.heads.regressor import RegressorType, Regressor from nndet.arch.heads.segmenter import SegmenterType, Segmenter

50.4

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true

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0

null

0

1

0

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6

347ed8336a84485625e89e82067bdc54d0863113

90,321

py

Python

cisco-ios-xr/ydk/models/cisco_ios_xr/Cisco_IOS_XR_asr9k_netflow_oper.py

tkamata-test/ydk-py

b637e7853a8edbbd31fbc05afa3aa4110b31c5f9

[ "ECL-2.0", "Apache-2.0" ]

null

cisco-ios-xr/ydk/models/cisco_ios_xr/Cisco_IOS_XR_asr9k_netflow_oper.py

tkamata-test/ydk-py

b637e7853a8edbbd31fbc05afa3aa4110b31c5f9

[ "ECL-2.0", "Apache-2.0" ]

null

cisco-ios-xr/ydk/models/cisco_ios_xr/Cisco_IOS_XR_asr9k_netflow_oper.py

tkamata-test/ydk-py

b637e7853a8edbbd31fbc05afa3aa4110b31c5f9

[ "ECL-2.0", "Apache-2.0" ]

null

""" Cisco_IOS_XR_asr9k_netflow_oper This module contains a collection of YANG definitions for Cisco IOS\-XR asr9k\-netflow package operational data. This module contains definitions for the following management objects\: net\-flow\: NetFlow operational data Copyright (c) 2013\-2016 by Cisco Systems, Inc. All rights reserved. """ import re import collections from enum import Enum from ydk.types import Empty, YList, YLeafList, DELETE, Decimal64, FixedBitsDict from ydk.errors import YPYError, YPYModelError class NfmgrFemEdmExpVerEnum(Enum): """ NfmgrFemEdmExpVerEnum Netflow export version .. data:: v9 = 0 Version 9 export format .. data:: ip_fix = 1 IPFIX export format """ v9 = 0 ip_fix = 1 @staticmethod def _meta_info(): from ydk.models.cisco_ios_xr._meta import _Cisco_IOS_XR_asr9k_netflow_oper as meta return meta._meta_table['NfmgrFemEdmExpVerEnum'] class NfmgrFemEdmTransProtoEnum(Enum): """ NfmgrFemEdmTransProtoEnum Netflow export transport protocol .. data:: unspecified = 0 Unspecified transport protocol .. data:: udp = 1 UDP transport protocol """ unspecified = 0 udp = 1 @staticmethod def _meta_info(): from ydk.models.cisco_ios_xr._meta import _Cisco_IOS_XR_asr9k_netflow_oper as meta return meta._meta_table['NfmgrFemEdmTransProtoEnum'] class NetFlow(object): """ NetFlow operational data .. attribute:: configuration NetFlow configuration information **type**\: :py:class:`Configuration <ydk.models.cisco_ios_xr.Cisco_IOS_XR_asr9k_netflow_oper.NetFlow.Configuration>` .. attribute:: statistics Node\-specific NetFlow statistics information **type**\: :py:class:`Statistics <ydk.models.cisco_ios_xr.Cisco_IOS_XR_asr9k_netflow_oper.NetFlow.Statistics>` """ _prefix = 'asr9k-netflow-oper' _revision = '2015-11-09' def __init__(self): self.configuration = NetFlow.Configuration() self.configuration.parent = self self.statistics = NetFlow.Statistics() self.statistics.parent = self class Configuration(object): """ NetFlow configuration information .. attribute:: flow_exporter_maps Flow exporter map configuration information **type**\: :py:class:`FlowExporterMaps <ydk.models.cisco_ios_xr.Cisco_IOS_XR_asr9k_netflow_oper.NetFlow.Configuration.FlowExporterMaps>` .. attribute:: flow_monitor_maps Flow monitor map configuration information **type**\: :py:class:`FlowMonitorMaps <ydk.models.cisco_ios_xr.Cisco_IOS_XR_asr9k_netflow_oper.NetFlow.Configuration.FlowMonitorMaps>` .. attribute:: flow_sampler_maps Flow sampler map configuration information **type**\: :py:class:`FlowSamplerMaps <ydk.models.cisco_ios_xr.Cisco_IOS_XR_asr9k_netflow_oper.NetFlow.Configuration.FlowSamplerMaps>` """ _prefix = 'asr9k-netflow-oper' _revision = '2015-11-09' def __init__(self): self.parent = None self.flow_exporter_maps = NetFlow.Configuration.FlowExporterMaps() self.flow_exporter_maps.parent = self self.flow_monitor_maps = NetFlow.Configuration.FlowMonitorMaps() self.flow_monitor_maps.parent = self self.flow_sampler_maps = NetFlow.Configuration.FlowSamplerMaps() self.flow_sampler_maps.parent = self class FlowExporterMaps(object): """ Flow exporter map configuration information .. attribute:: flow_exporter_map Flow exporter map information **type**\: list of :py:class:`FlowExporterMap <ydk.models.cisco_ios_xr.Cisco_IOS_XR_asr9k_netflow_oper.NetFlow.Configuration.FlowExporterMaps.FlowExporterMap>` """ _prefix = 'asr9k-netflow-oper' _revision = '2015-11-09' def __init__(self): self.parent = None self.flow_exporter_map = YList() self.flow_exporter_map.parent = self self.flow_exporter_map.name = 'flow_exporter_map' class FlowExporterMap(object): """ Flow exporter map information .. attribute:: exporter_name <key> Exporter name **type**\: str **pattern:** [\\w\\\-\\.\:,\_@#%$\\+=\\\|;]+ .. attribute:: collector Export collector array **type**\: list of :py:class:`Collector <ydk.models.cisco_ios_xr.Cisco_IOS_XR_asr9k_netflow_oper.NetFlow.Configuration.FlowExporterMaps.FlowExporterMap.Collector>` .. attribute:: id Unique ID in the global flow exporter ID space **type**\: int **range:** 0..4294967295 .. attribute:: name Name of the flow exporter map **type**\: str .. attribute:: version Export version data **type**\: :py:class:`Version <ydk.models.cisco_ios_xr.Cisco_IOS_XR_asr9k_netflow_oper.NetFlow.Configuration.FlowExporterMaps.FlowExporterMap.Version>` """ _prefix = 'asr9k-netflow-oper' _revision = '2015-11-09' def __init__(self): self.parent = None self.exporter_name = None self.collector = YList() self.collector.parent = self self.collector.name = 'collector' self.id = None self.name = None self.version = NetFlow.Configuration.FlowExporterMaps.FlowExporterMap.Version() self.version.parent = self class Version(object): """ Export version data .. attribute:: ipfix ipfix **type**\: :py:class:`Ipfix <ydk.models.cisco_ios_xr.Cisco_IOS_XR_asr9k_netflow_oper.NetFlow.Configuration.FlowExporterMaps.FlowExporterMap.Version.Ipfix>` .. attribute:: version version **type**\: :py:class:`NfmgrFemEdmExpVerEnum <ydk.models.cisco_ios_xr.Cisco_IOS_XR_asr9k_netflow_oper.NfmgrFemEdmExpVerEnum>` .. attribute:: version9 version9 **type**\: :py:class:`Version9 <ydk.models.cisco_ios_xr.Cisco_IOS_XR_asr9k_netflow_oper.NetFlow.Configuration.FlowExporterMaps.FlowExporterMap.Version.Version9>` """ _prefix = 'asr9k-netflow-oper' _revision = '2015-11-09' def __init__(self): self.parent = None self.ipfix = NetFlow.Configuration.FlowExporterMaps.FlowExporterMap.Version.Ipfix() self.ipfix.parent = self self.version = None self.version9 = NetFlow.Configuration.FlowExporterMaps.FlowExporterMap.Version.Version9() self.version9.parent = self class Version9(object): """ version9 .. attribute:: common_template_export_timeout Common template export timeout in seconds **type**\: int **range:** 0..4294967295 **units**\: second .. attribute:: data_template_export_timeout Data template export timeout in seconds **type**\: int **range:** 0..4294967295 **units**\: second .. attribute:: interface_table_export_timeout Interface table export timeout in seconds **type**\: int **range:** 0..4294967295 **units**\: second .. attribute:: options_template_export_timeout Options template export timeout in seconds **type**\: int **range:** 0..4294967295 **units**\: second .. attribute:: sampler_table_export_timeout Sampler table export timeout in seconds **type**\: int **range:** 0..4294967295 **units**\: second .. attribute:: vrf_table_export_timeout VRF table export timeout in seconds **type**\: int **range:** 0..4294967295 **units**\: second """ _prefix = 'asr9k-netflow-oper' _revision = '2015-11-09' def __init__(self): self.parent = None self.common_template_export_timeout = None self.data_template_export_timeout = None self.interface_table_export_timeout = None self.options_template_export_timeout = None self.sampler_table_export_timeout = None self.vrf_table_export_timeout = None @property def _common_path(self): if self.parent is None: raise YPYModelError('parent is not set . Cannot derive path.') return self.parent._common_path +'/Cisco-IOS-XR-asr9k-netflow-oper:version9' def is_config(self): ''' Returns True if this instance represents config data else returns False ''' return False def _has_data(self): if not self.is_config(): return False if self.common_template_export_timeout is not None: return True if self.data_template_export_timeout is not None: return True if self.interface_table_export_timeout is not None: return True if self.options_template_export_timeout is not None: return True if self.sampler_table_export_timeout is not None: return True if self.vrf_table_export_timeout is not None: return True return False @staticmethod def _meta_info(): from ydk.models.cisco_ios_xr._meta import _Cisco_IOS_XR_asr9k_netflow_oper as meta return meta._meta_table['NetFlow.Configuration.FlowExporterMaps.FlowExporterMap.Version.Version9']['meta_info'] class Ipfix(object): """ ipfix .. attribute:: common_template_export_timeout Common template export timeout in seconds **type**\: int **range:** 0..4294967295 **units**\: second .. attribute:: data_template_export_timeout Data template export timeout in seconds **type**\: int **range:** 0..4294967295 **units**\: second .. attribute:: interface_table_export_timeout Interface table export timeout in seconds **type**\: int **range:** 0..4294967295 **units**\: second .. attribute:: options_template_export_timeout Options template export timeout in seconds **type**\: int **range:** 0..4294967295 **units**\: second .. attribute:: sampler_table_export_timeout Sampler table export timeout in seconds **type**\: int **range:** 0..4294967295 **units**\: second .. attribute:: vrf_table_export_timeout VRF table export timeout in seconds **type**\: int **range:** 0..4294967295 **units**\: second """ _prefix = 'asr9k-netflow-oper' _revision = '2015-11-09' def __init__(self): self.parent = None self.common_template_export_timeout = None self.data_template_export_timeout = None self.interface_table_export_timeout = None self.options_template_export_timeout = None self.sampler_table_export_timeout = None self.vrf_table_export_timeout = None @property def _common_path(self): if self.parent is None: raise YPYModelError('parent is not set . Cannot derive path.') return self.parent._common_path +'/Cisco-IOS-XR-asr9k-netflow-oper:ipfix' def is_config(self): ''' Returns True if this instance represents config data else returns False ''' return False def _has_data(self): if not self.is_config(): return False if self.common_template_export_timeout is not None: return True if self.data_template_export_timeout is not None: return True if self.interface_table_export_timeout is not None: return True if self.options_template_export_timeout is not None: return True if self.sampler_table_export_timeout is not None: return True if self.vrf_table_export_timeout is not None: return True return False @staticmethod def _meta_info(): from ydk.models.cisco_ios_xr._meta import _Cisco_IOS_XR_asr9k_netflow_oper as meta return meta._meta_table['NetFlow.Configuration.FlowExporterMaps.FlowExporterMap.Version.Ipfix']['meta_info'] @property def _common_path(self): if self.parent is None: raise YPYModelError('parent is not set . Cannot derive path.') return self.parent._common_path +'/Cisco-IOS-XR-asr9k-netflow-oper:version' def is_config(self): ''' Returns True if this instance represents config data else returns False ''' return False def _has_data(self): if not self.is_config(): return False if self.ipfix is not None and self.ipfix._has_data(): return True if self.version is not None: return True if self.version9 is not None and self.version9._has_data(): return True return False @staticmethod def _meta_info(): from ydk.models.cisco_ios_xr._meta import _Cisco_IOS_XR_asr9k_netflow_oper as meta return meta._meta_table['NetFlow.Configuration.FlowExporterMaps.FlowExporterMap.Version']['meta_info'] class Collector(object): """ Export collector array .. attribute:: destination_address Destination IPv4 address in AAA.BBB.CCC.DDD format **type**\: str .. attribute:: destination_port Transport destination port number **type**\: int **range:** 0..65535 .. attribute:: dscp DSCP **type**\: int **range:** 0..255 .. attribute:: source_address Source IPv4 address in AAA.BBB.CCC.DDD format **type**\: str .. attribute:: source_interface Source interface name **type**\: str .. attribute:: transport_protocol Transport protocol **type**\: :py:class:`NfmgrFemEdmTransProtoEnum <ydk.models.cisco_ios_xr.Cisco_IOS_XR_asr9k_netflow_oper.NfmgrFemEdmTransProtoEnum>` .. attribute:: vrf_name VRF name **type**\: str """ _prefix = 'asr9k-netflow-oper' _revision = '2015-11-09' def __init__(self): self.parent = None self.destination_address = None self.destination_port = None self.dscp = None self.source_address = None self.source_interface = None self.transport_protocol = None self.vrf_name = None @property def _common_path(self): if self.parent is None: raise YPYModelError('parent is not set . Cannot derive path.') return self.parent._common_path +'/Cisco-IOS-XR-asr9k-netflow-oper:collector' def is_config(self): ''' Returns True if this instance represents config data else returns False ''' return False def _has_data(self): if not self.is_config(): return False if self.destination_address is not None: return True if self.destination_port is not None: return True if self.dscp is not None: return True if self.source_address is not None: return True if self.source_interface is not None: return True if self.transport_protocol is not None: return True if self.vrf_name is not None: return True return False @staticmethod def _meta_info(): from ydk.models.cisco_ios_xr._meta import _Cisco_IOS_XR_asr9k_netflow_oper as meta return meta._meta_table['NetFlow.Configuration.FlowExporterMaps.FlowExporterMap.Collector']['meta_info'] @property def _common_path(self): if self.exporter_name is None: raise YPYModelError('Key property exporter_name is None') return '/Cisco-IOS-XR-asr9k-netflow-oper:net-flow/Cisco-IOS-XR-asr9k-netflow-oper:configuration/Cisco-IOS-XR-asr9k-netflow-oper:flow-exporter-maps/Cisco-IOS-XR-asr9k-netflow-oper:flow-exporter-map[Cisco-IOS-XR-asr9k-netflow-oper:exporter-name = ' + str(self.exporter_name) + ']' def is_config(self): ''' Returns True if this instance represents config data else returns False ''' return False def _has_data(self): if not self.is_config(): return False if self.exporter_name is not None: return True if self.collector is not None: for child_ref in self.collector: if child_ref._has_data(): return True if self.id is not None: return True if self.name is not None: return True if self.version is not None and self.version._has_data(): return True return False @staticmethod def _meta_info(): from ydk.models.cisco_ios_xr._meta import _Cisco_IOS_XR_asr9k_netflow_oper as meta return meta._meta_table['NetFlow.Configuration.FlowExporterMaps.FlowExporterMap']['meta_info'] @property def _common_path(self): return '/Cisco-IOS-XR-asr9k-netflow-oper:net-flow/Cisco-IOS-XR-asr9k-netflow-oper:configuration/Cisco-IOS-XR-asr9k-netflow-oper:flow-exporter-maps' def is_config(self): ''' Returns True if this instance represents config data else returns False ''' return False def _has_data(self): if not self.is_config(): return False if self.flow_exporter_map is not None: for child_ref in self.flow_exporter_map: if child_ref._has_data(): return True return False @staticmethod def _meta_info(): from ydk.models.cisco_ios_xr._meta import _Cisco_IOS_XR_asr9k_netflow_oper as meta return meta._meta_table['NetFlow.Configuration.FlowExporterMaps']['meta_info'] class FlowMonitorMaps(object): """ Flow monitor map configuration information .. attribute:: flow_monitor_map Flow monitor map information **type**\: list of :py:class:`FlowMonitorMap <ydk.models.cisco_ios_xr.Cisco_IOS_XR_asr9k_netflow_oper.NetFlow.Configuration.FlowMonitorMaps.FlowMonitorMap>` """ _prefix = 'asr9k-netflow-oper' _revision = '2015-11-09' def __init__(self): self.parent = None self.flow_monitor_map = YList() self.flow_monitor_map.parent = self self.flow_monitor_map.name = 'flow_monitor_map' class FlowMonitorMap(object): """ Flow monitor map information .. attribute:: monitor_name <key> Monitor name **type**\: str **pattern:** [\\w\\\-\\.\:,\_@#%$\\+=\\\|;]+ .. attribute:: cache_active_timeout Cache active flow timeout in seconds **type**\: int **range:** 0..4294967295 **units**\: second .. attribute:: cache_aging_mode Aging mode for flow cache **type**\: str .. attribute:: cache_inactive_timeout Cache inactive flow timeout in seconds **type**\: int **range:** 0..4294967295 **units**\: second .. attribute:: cache_max_entry Max num of entries in flow cache **type**\: int **range:** 0..4294967295 .. attribute:: cache_timeout_rate_limit Maximum number of entries to age each second **type**\: int **range:** 0..4294967295 .. attribute:: cache_update_timeout Cache update timeout in seconds **type**\: int **range:** 0..4294967295 **units**\: second .. attribute:: exporter Name of the flow exporters used by the flow monitor **type**\: list of :py:class:`Exporter <ydk.models.cisco_ios_xr.Cisco_IOS_XR_asr9k_netflow_oper.NetFlow.Configuration.FlowMonitorMaps.FlowMonitorMap.Exporter>` .. attribute:: id Unique ID in the global flow monitor ID space **type**\: int **range:** 0..4294967295 .. attribute:: name Name of the flow monitor map **type**\: str .. attribute:: number_of_labels Number of MPLS labels in key **type**\: int **range:** 0..4294967295 .. attribute:: options Options applied to the flow monitor **type**\: int **range:** 0..4294967295 .. attribute:: record_map Name of the flow record map **type**\: str """ _prefix = 'asr9k-netflow-oper' _revision = '2015-11-09' def __init__(self): self.parent = None self.monitor_name = None self.cache_active_timeout = None self.cache_aging_mode = None self.cache_inactive_timeout = None self.cache_max_entry = None self.cache_timeout_rate_limit = None self.cache_update_timeout = None self.exporter = YList() self.exporter.parent = self self.exporter.name = 'exporter' self.id = None self.name = None self.number_of_labels = None self.options = None self.record_map = None class Exporter(object): """ Name of the flow exporters used by the flow monitor .. attribute:: name Exporter name **type**\: str """ _prefix = 'asr9k-netflow-oper' _revision = '2015-11-09' def __init__(self): self.parent = None self.name = None @property def _common_path(self): if self.parent is None: raise YPYModelError('parent is not set . Cannot derive path.') return self.parent._common_path +'/Cisco-IOS-XR-asr9k-netflow-oper:exporter' def is_config(self): ''' Returns True if this instance represents config data else returns False ''' return False def _has_data(self): if not self.is_config(): return False if self.name is not None: return True return False @staticmethod def _meta_info(): from ydk.models.cisco_ios_xr._meta import _Cisco_IOS_XR_asr9k_netflow_oper as meta return meta._meta_table['NetFlow.Configuration.FlowMonitorMaps.FlowMonitorMap.Exporter']['meta_info'] @property def _common_path(self): if self.monitor_name is None: raise YPYModelError('Key property monitor_name is None') return '/Cisco-IOS-XR-asr9k-netflow-oper:net-flow/Cisco-IOS-XR-asr9k-netflow-oper:configuration/Cisco-IOS-XR-asr9k-netflow-oper:flow-monitor-maps/Cisco-IOS-XR-asr9k-netflow-oper:flow-monitor-map[Cisco-IOS-XR-asr9k-netflow-oper:monitor-name = ' + str(self.monitor_name) + ']' def is_config(self): ''' Returns True if this instance represents config data else returns False ''' return False def _has_data(self): if not self.is_config(): return False if self.monitor_name is not None: return True if self.cache_active_timeout is not None: return True if self.cache_aging_mode is not None: return True if self.cache_inactive_timeout is not None: return True if self.cache_max_entry is not None: return True if self.cache_timeout_rate_limit is not None: return True if self.cache_update_timeout is not None: return True if self.exporter is not None: for child_ref in self.exporter: if child_ref._has_data(): return True if self.id is not None: return True if self.name is not None: return True if self.number_of_labels is not None: return True if self.options is not None: return True if self.record_map is not None: return True return False @staticmethod def _meta_info(): from ydk.models.cisco_ios_xr._meta import _Cisco_IOS_XR_asr9k_netflow_oper as meta return meta._meta_table['NetFlow.Configuration.FlowMonitorMaps.FlowMonitorMap']['meta_info'] @property def _common_path(self): return '/Cisco-IOS-XR-asr9k-netflow-oper:net-flow/Cisco-IOS-XR-asr9k-netflow-oper:configuration/Cisco-IOS-XR-asr9k-netflow-oper:flow-monitor-maps' def is_config(self): ''' Returns True if this instance represents config data else returns False ''' return False def _has_data(self): if not self.is_config(): return False if self.flow_monitor_map is not None: for child_ref in self.flow_monitor_map: if child_ref._has_data(): return True return False @staticmethod def _meta_info(): from ydk.models.cisco_ios_xr._meta import _Cisco_IOS_XR_asr9k_netflow_oper as meta return meta._meta_table['NetFlow.Configuration.FlowMonitorMaps']['meta_info'] class FlowSamplerMaps(object): """ Flow sampler map configuration information .. attribute:: flow_sampler_map Flow sampler map information **type**\: list of :py:class:`FlowSamplerMap <ydk.models.cisco_ios_xr.Cisco_IOS_XR_asr9k_netflow_oper.NetFlow.Configuration.FlowSamplerMaps.FlowSamplerMap>` """ _prefix = 'asr9k-netflow-oper' _revision = '2015-11-09' def __init__(self): self.parent = None self.flow_sampler_map = YList() self.flow_sampler_map.parent = self self.flow_sampler_map.name = 'flow_sampler_map' class FlowSamplerMap(object): """ Flow sampler map information .. attribute:: sampler_name <key> Sampler name **type**\: str **pattern:** [\\w\\\-\\.\:,\_@#%$\\+=\\\|;]+ .. attribute:: id Unique ID in the global flow sampler ID space **type**\: int **range:** 0..4294967295 .. attribute:: name Name of the flow sampler map **type**\: str .. attribute:: sampling_mode Sampling mode and parameters **type**\: str """ _prefix = 'asr9k-netflow-oper' _revision = '2015-11-09' def __init__(self): self.parent = None self.sampler_name = None self.id = None self.name = None self.sampling_mode = None @property def _common_path(self): if self.sampler_name is None: raise YPYModelError('Key property sampler_name is None') return '/Cisco-IOS-XR-asr9k-netflow-oper:net-flow/Cisco-IOS-XR-asr9k-netflow-oper:configuration/Cisco-IOS-XR-asr9k-netflow-oper:flow-sampler-maps/Cisco-IOS-XR-asr9k-netflow-oper:flow-sampler-map[Cisco-IOS-XR-asr9k-netflow-oper:sampler-name = ' + str(self.sampler_name) + ']' def is_config(self): ''' Returns True if this instance represents config data else returns False ''' return False def _has_data(self): if not self.is_config(): return False if self.sampler_name is not None: return True if self.id is not None: return True if self.name is not None: return True if self.sampling_mode is not None: return True return False @staticmethod def _meta_info(): from ydk.models.cisco_ios_xr._meta import _Cisco_IOS_XR_asr9k_netflow_oper as meta return meta._meta_table['NetFlow.Configuration.FlowSamplerMaps.FlowSamplerMap']['meta_info'] @property def _common_path(self): return '/Cisco-IOS-XR-asr9k-netflow-oper:net-flow/Cisco-IOS-XR-asr9k-netflow-oper:configuration/Cisco-IOS-XR-asr9k-netflow-oper:flow-sampler-maps' def is_config(self): ''' Returns True if this instance represents config data else returns False ''' return False def _has_data(self): if not self.is_config(): return False if self.flow_sampler_map is not None: for child_ref in self.flow_sampler_map: if child_ref._has_data(): return True return False @staticmethod def _meta_info(): from ydk.models.cisco_ios_xr._meta import _Cisco_IOS_XR_asr9k_netflow_oper as meta return meta._meta_table['NetFlow.Configuration.FlowSamplerMaps']['meta_info'] @property def _common_path(self): return '/Cisco-IOS-XR-asr9k-netflow-oper:net-flow/Cisco-IOS-XR-asr9k-netflow-oper:configuration' def is_config(self): ''' Returns True if this instance represents config data else returns False ''' return False def _has_data(self): if not self.is_config(): return False if self.flow_exporter_maps is not None and self.flow_exporter_maps._has_data(): return True if self.flow_monitor_maps is not None and self.flow_monitor_maps._has_data(): return True if self.flow_sampler_maps is not None and self.flow_sampler_maps._has_data(): return True return False @staticmethod def _meta_info(): from ydk.models.cisco_ios_xr._meta import _Cisco_IOS_XR_asr9k_netflow_oper as meta return meta._meta_table['NetFlow.Configuration']['meta_info'] class Statistics(object): """ Node\-specific NetFlow statistics information .. attribute:: statistic NetFlow statistics information for a particular node **type**\: list of :py:class:`Statistic <ydk.models.cisco_ios_xr.Cisco_IOS_XR_asr9k_netflow_oper.NetFlow.Statistics.Statistic>` """ _prefix = 'asr9k-netflow-oper' _revision = '2015-11-09' def __init__(self): self.parent = None self.statistic = YList() self.statistic.parent = self self.statistic.name = 'statistic' class Statistic(object): """ NetFlow statistics information for a particular node .. attribute:: node <key> Node location **type**\: str **pattern:** ([a\-zA\-Z0\-9\_]\*\\d+/){1,2}([a\-zA\-Z0\-9\_]\*\\d+) .. attribute:: producer NetFlow producer statistics **type**\: :py:class:`Producer <ydk.models.cisco_ios_xr.Cisco_IOS_XR_asr9k_netflow_oper.NetFlow.Statistics.Statistic.Producer>` .. attribute:: server NetFlow server statistics **type**\: :py:class:`Server <ydk.models.cisco_ios_xr.Cisco_IOS_XR_asr9k_netflow_oper.NetFlow.Statistics.Statistic.Server>` """ _prefix = 'asr9k-netflow-oper' _revision = '2015-11-09' def __init__(self): self.parent = None self.node = None self.producer = NetFlow.Statistics.Statistic.Producer() self.producer.parent = self self.server = NetFlow.Statistics.Statistic.Server() self.server.parent = self class Producer(object): """ NetFlow producer statistics .. attribute:: statistics Statistics information **type**\: :py:class:`Statistics_ <ydk.models.cisco_ios_xr.Cisco_IOS_XR_asr9k_netflow_oper.NetFlow.Statistics.Statistic.Producer.Statistics_>` """ _prefix = 'asr9k-netflow-oper' _revision = '2015-11-09' def __init__(self): self.parent = None self.statistics = NetFlow.Statistics.Statistic.Producer.Statistics_() self.statistics.parent = self class Statistics_(object): """ Statistics information .. attribute:: drops_no_space Drops (no space) **type**\: int **range:** 0..18446744073709551615 .. attribute:: drops_others Drops (others) **type**\: int **range:** 0..18446744073709551615 .. attribute:: flow_packet_counts Number of Rxed Flow Packets **type**\: int **range:** 0..18446744073709551615 .. attribute:: ipv4_egress_flows IPv4 egress flows **type**\: int **range:** 0..18446744073709551615 .. attribute:: ipv4_ingress_flows IPv4 ingress flows **type**\: int **range:** 0..18446744073709551615 .. attribute:: ipv6_egress_flows IPv6 egress flows **type**\: int **range:** 0..18446744073709551615 .. attribute:: ipv6_ingress_flows IPv6 ingress flows **type**\: int **range:** 0..18446744073709551615 .. attribute:: last_cleared Last time Statistics cleared in 'Mon Jan 1 12\:00 \:00 2xxx' format **type**\: str .. attribute:: mpls_egress_flows MPLS egress flows **type**\: int **range:** 0..18446744073709551615 .. attribute:: mpls_ingress_flows MPLS ingress flows **type**\: int **range:** 0..18446744073709551615 .. attribute:: spp_rx_counts Number of Rxed SPP Packets **type**\: int **range:** 0..18446744073709551615 .. attribute:: unknown_egress_flows Unknown egress flows **type**\: int **range:** 0..18446744073709551615 .. attribute:: unknown_ingress_flows Unknown ingress flows **type**\: int **range:** 0..18446744073709551615 .. attribute:: waiting_servers Number of waiting servers **type**\: int **range:** 0..18446744073709551615 """ _prefix = 'asr9k-netflow-oper' _revision = '2015-11-09' def __init__(self): self.parent = None self.drops_no_space = None self.drops_others = None self.flow_packet_counts = None self.ipv4_egress_flows = None self.ipv4_ingress_flows = None self.ipv6_egress_flows = None self.ipv6_ingress_flows = None self.last_cleared = None self.mpls_egress_flows = None self.mpls_ingress_flows = None self.spp_rx_counts = None self.unknown_egress_flows = None self.unknown_ingress_flows = None self.waiting_servers = None @property def _common_path(self): if self.parent is None: raise YPYModelError('parent is not set . Cannot derive path.') return self.parent._common_path +'/Cisco-IOS-XR-asr9k-netflow-oper:statistics' def is_config(self): ''' Returns True if this instance represents config data else returns False ''' return False def _has_data(self): if not self.is_config(): return False if self.drops_no_space is not None: return True if self.drops_others is not None: return True if self.flow_packet_counts is not None: return True if self.ipv4_egress_flows is not None: return True if self.ipv4_ingress_flows is not None: return True if self.ipv6_egress_flows is not None: return True if self.ipv6_ingress_flows is not None: return True if self.last_cleared is not None: return True if self.mpls_egress_flows is not None: return True if self.mpls_ingress_flows is not None: return True if self.spp_rx_counts is not None: return True if self.unknown_egress_flows is not None: return True if self.unknown_ingress_flows is not None: return True if self.waiting_servers is not None: return True return False @staticmethod def _meta_info(): from ydk.models.cisco_ios_xr._meta import _Cisco_IOS_XR_asr9k_netflow_oper as meta return meta._meta_table['NetFlow.Statistics.Statistic.Producer.Statistics_']['meta_info'] @property def _common_path(self): if self.parent is None: raise YPYModelError('parent is not set . Cannot derive path.') return self.parent._common_path +'/Cisco-IOS-XR-asr9k-netflow-oper:producer' def is_config(self): ''' Returns True if this instance represents config data else returns False ''' return False def _has_data(self): if not self.is_config(): return False if self.statistics is not None and self.statistics._has_data(): return True return False @staticmethod def _meta_info(): from ydk.models.cisco_ios_xr._meta import _Cisco_IOS_XR_asr9k_netflow_oper as meta return meta._meta_table['NetFlow.Statistics.Statistic.Producer']['meta_info'] class Server(object): """ NetFlow server statistics .. attribute:: flow_exporters Flow exporter information **type**\: :py:class:`FlowExporters <ydk.models.cisco_ios_xr.Cisco_IOS_XR_asr9k_netflow_oper.NetFlow.Statistics.Statistic.Server.FlowExporters>` """ _prefix = 'asr9k-netflow-oper' _revision = '2015-11-09' def __init__(self): self.parent = None self.flow_exporters = NetFlow.Statistics.Statistic.Server.FlowExporters() self.flow_exporters.parent = self class FlowExporters(object): """ Flow exporter information .. attribute:: flow_exporter Exporter information **type**\: list of :py:class:`FlowExporter <ydk.models.cisco_ios_xr.Cisco_IOS_XR_asr9k_netflow_oper.NetFlow.Statistics.Statistic.Server.FlowExporters.FlowExporter>` """ _prefix = 'asr9k-netflow-oper' _revision = '2015-11-09' def __init__(self): self.parent = None self.flow_exporter = YList() self.flow_exporter.parent = self self.flow_exporter.name = 'flow_exporter' class FlowExporter(object): """ Exporter information .. attribute:: exporter_name <key> Exporter name **type**\: str **pattern:** [\\w\\\-\\.\:,\_@#%$\\+=\\\|;]+ .. attribute:: exporter Statistics information for the exporter **type**\: :py:class:`Exporter <ydk.models.cisco_ios_xr.Cisco_IOS_XR_asr9k_netflow_oper.NetFlow.Statistics.Statistic.Server.FlowExporters.FlowExporter.Exporter>` """ _prefix = 'asr9k-netflow-oper' _revision = '2015-11-09' def __init__(self): self.parent = None self.exporter_name = None self.exporter = NetFlow.Statistics.Statistic.Server.FlowExporters.FlowExporter.Exporter() self.exporter.parent = self class Exporter(object): """ Statistics information for the exporter .. attribute:: statistic Array of flow exporters **type**\: list of :py:class:`Statistic_ <ydk.models.cisco_ios_xr.Cisco_IOS_XR_asr9k_netflow_oper.NetFlow.Statistics.Statistic.Server.FlowExporters.FlowExporter.Exporter.Statistic_>` """ _prefix = 'asr9k-netflow-oper' _revision = '2015-11-09' def __init__(self): self.parent = None self.statistic = YList() self.statistic.parent = self self.statistic.name = 'statistic' class Statistic_(object): """ Array of flow exporters .. attribute:: collector Statistics of all collectors **type**\: list of :py:class:`Collector <ydk.models.cisco_ios_xr.Cisco_IOS_XR_asr9k_netflow_oper.NetFlow.Statistics.Statistic.Server.FlowExporters.FlowExporter.Exporter.Statistic_.Collector>` .. attribute:: memory_usage Memory usage **type**\: int **range:** 0..4294967295 .. attribute:: name Exporter name **type**\: str .. attribute:: used_by_flow_monitor List of flow monitors that use the exporter **type**\: list of str """ _prefix = 'asr9k-netflow-oper' _revision = '2015-11-09' def __init__(self): self.parent = None self.collector = YList() self.collector.parent = self self.collector.name = 'collector' self.memory_usage = None self.name = None self.used_by_flow_monitor = YLeafList() self.used_by_flow_monitor.parent = self self.used_by_flow_monitor.name = 'used_by_flow_monitor' class Collector(object): """ Statistics of all collectors .. attribute:: bytes_dropped Bytes dropped **type**\: int **range:** 0..18446744073709551615 **units**\: byte .. attribute:: bytes_sent Bytes sent **type**\: int **range:** 0..18446744073709551615 **units**\: byte .. attribute:: destination_address Destination IPv4 address in AAA.BBB.CCC.DDD format **type**\: str .. attribute:: destination_port Destination port number **type**\: int **range:** 0..65535 .. attribute:: exporter_state Exporter state **type**\: str .. attribute:: flow_bytes_dropped Flow bytes dropped **type**\: int **range:** 0..18446744073709551615 **units**\: byte .. attribute:: flow_bytes_sent Flow bytes sent **type**\: int **range:** 0..18446744073709551615 **units**\: byte .. attribute:: flows_dropped Flows dropped **type**\: int **range:** 0..18446744073709551615 .. attribute:: flows_sent Flows sent **type**\: int **range:** 0..18446744073709551615 .. attribute:: last_hour_bytes_sent Total bytes exported over the last one hour **type**\: int **range:** 0..18446744073709551615 **units**\: byte .. attribute:: last_hour_flows_sent Total flows exported over the of last one hour **type**\: int **range:** 0..18446744073709551615 .. attribute:: last_hour_packest_sent Total packets exported over the last one hour **type**\: int **range:** 0..18446744073709551615 .. attribute:: last_minute_bytes_sent Total bytes exported over the last one minute **type**\: int **range:** 0..18446744073709551615 **units**\: byte .. attribute:: last_minute_flows_sent Total flows exported over the last one minute **type**\: int **range:** 0..18446744073709551615 .. attribute:: last_minute_packets Total packets exported over the last one minute **type**\: int **range:** 0..18446744073709551615 .. attribute:: last_second_bytes_sent Total bytes exported over the last one second **type**\: int **range:** 0..18446744073709551615 **units**\: byte .. attribute:: last_second_flows_sent Total flows exported over the last one second **type**\: int **range:** 0..18446744073709551615 .. attribute:: last_second_packets_sent Total packets exported over the last one second **type**\: int **range:** 0..18446744073709551615 .. attribute:: option_data_bytes_dropped Option data dropped **type**\: int **range:** 0..18446744073709551615 .. attribute:: option_data_bytes_sent Option data bytes sent **type**\: int **range:** 0..18446744073709551615 **units**\: byte .. attribute:: option_data_dropped Option data dropped **type**\: int **range:** 0..18446744073709551615 .. attribute:: option_data_sent Option data sent **type**\: int **range:** 0..18446744073709551615 .. attribute:: option_template_bytes_dropped Option template bytes dropped **type**\: int **range:** 0..18446744073709551615 **units**\: byte .. attribute:: option_template_bytes_sent Option template bytes sent **type**\: int **range:** 0..18446744073709551615 **units**\: byte .. attribute:: option_templates_dropped Option templates dropped **type**\: int **range:** 0..18446744073709551615 .. attribute:: option_templates_sent Option templates sent **type**\: int **range:** 0..18446744073709551615 .. attribute:: packets_dropped Packets dropped **type**\: int **range:** 0..18446744073709551615 .. attribute:: packets_sent Packets sent **type**\: int **range:** 0..18446744073709551615 .. attribute:: souce_port Source port number **type**\: int **range:** 0..65535 .. attribute:: source_address Source IPv4 address in AAA.BBB.CCC.DDD format **type**\: str .. attribute:: template_bytes_dropped Template bytes dropped **type**\: int **range:** 0..18446744073709551615 **units**\: byte .. attribute:: template_bytes_sent Template bytes sent **type**\: int **range:** 0..18446744073709551615 **units**\: byte .. attribute:: templates_dropped Templates dropped **type**\: int **range:** 0..18446744073709551615 .. attribute:: templates_sent Templates sent **type**\: int **range:** 0..18446744073709551615 .. attribute:: transport_protocol Transport protocol **type**\: str .. attribute:: vrf_name VRF Name **type**\: str """ _prefix = 'asr9k-netflow-oper' _revision = '2015-11-09' def __init__(self): self.parent = None self.bytes_dropped = None self.bytes_sent = None self.destination_address = None self.destination_port = None self.exporter_state = None self.flow_bytes_dropped = None self.flow_bytes_sent = None self.flows_dropped = None self.flows_sent = None self.last_hour_bytes_sent = None self.last_hour_flows_sent = None self.last_hour_packest_sent = None self.last_minute_bytes_sent = None self.last_minute_flows_sent = None self.last_minute_packets = None self.last_second_bytes_sent = None self.last_second_flows_sent = None self.last_second_packets_sent = None self.option_data_bytes_dropped = None self.option_data_bytes_sent = None self.option_data_dropped = None self.option_data_sent = None self.option_template_bytes_dropped = None self.option_template_bytes_sent = None self.option_templates_dropped = None self.option_templates_sent = None self.packets_dropped = None self.packets_sent = None self.souce_port = None self.source_address = None self.template_bytes_dropped = None self.template_bytes_sent = None self.templates_dropped = None self.templates_sent = None self.transport_protocol = None self.vrf_name = None @property def _common_path(self): if self.parent is None: raise YPYModelError('parent is not set . Cannot derive path.') return self.parent._common_path +'/Cisco-IOS-XR-asr9k-netflow-oper:collector' def is_config(self): ''' Returns True if this instance represents config data else returns False ''' return False def _has_data(self): if not self.is_config(): return False if self.bytes_dropped is not None: return True if self.bytes_sent is not None: return True if self.destination_address is not None: return True if self.destination_port is not None: return True if self.exporter_state is not None: return True if self.flow_bytes_dropped is not None: return True if self.flow_bytes_sent is not None: return True if self.flows_dropped is not None: return True if self.flows_sent is not None: return True if self.last_hour_bytes_sent is not None: return True if self.last_hour_flows_sent is not None: return True if self.last_hour_packest_sent is not None: return True if self.last_minute_bytes_sent is not None: return True if self.last_minute_flows_sent is not None: return True if self.last_minute_packets is not None: return True if self.last_second_bytes_sent is not None: return True if self.last_second_flows_sent is not None: return True if self.last_second_packets_sent is not None: return True if self.option_data_bytes_dropped is not None: return True if self.option_data_bytes_sent is not None: return True if self.option_data_dropped is not None: return True if self.option_data_sent is not None: return True if self.option_template_bytes_dropped is not None: return True if self.option_template_bytes_sent is not None: return True if self.option_templates_dropped is not None: return True if self.option_templates_sent is not None: return True if self.packets_dropped is not None: return True if self.packets_sent is not None: return True if self.souce_port is not None: return True if self.source_address is not None: return True if self.template_bytes_dropped is not None: return True if self.template_bytes_sent is not None: return True if self.templates_dropped is not None: return True if self.templates_sent is not None: return True if self.transport_protocol is not None: return True if self.vrf_name is not None: return True return False @staticmethod def _meta_info(): from ydk.models.cisco_ios_xr._meta import _Cisco_IOS_XR_asr9k_netflow_oper as meta return meta._meta_table['NetFlow.Statistics.Statistic.Server.FlowExporters.FlowExporter.Exporter.Statistic_.Collector']['meta_info'] @property def _common_path(self): if self.parent is None: raise YPYModelError('parent is not set . Cannot derive path.') return self.parent._common_path +'/Cisco-IOS-XR-asr9k-netflow-oper:statistic' def is_config(self): ''' Returns True if this instance represents config data else returns False ''' return False def _has_data(self): if not self.is_config(): return False if self.collector is not None: for child_ref in self.collector: if child_ref._has_data(): return True if self.memory_usage is not None: return True if self.name is not None: return True if self.used_by_flow_monitor is not None: for child in self.used_by_flow_monitor: if child is not None: return True return False @staticmethod def _meta_info(): from ydk.models.cisco_ios_xr._meta import _Cisco_IOS_XR_asr9k_netflow_oper as meta return meta._meta_table['NetFlow.Statistics.Statistic.Server.FlowExporters.FlowExporter.Exporter.Statistic_']['meta_info'] @property def _common_path(self): if self.parent is None: raise YPYModelError('parent is not set . Cannot derive path.') return self.parent._common_path +'/Cisco-IOS-XR-asr9k-netflow-oper:exporter' def is_config(self): ''' Returns True if this instance represents config data else returns False ''' return False def _has_data(self): if not self.is_config(): return False if self.statistic is not None: for child_ref in self.statistic: if child_ref._has_data(): return True return False @staticmethod def _meta_info(): from ydk.models.cisco_ios_xr._meta import _Cisco_IOS_XR_asr9k_netflow_oper as meta return meta._meta_table['NetFlow.Statistics.Statistic.Server.FlowExporters.FlowExporter.Exporter']['meta_info'] @property def _common_path(self): if self.parent is None: raise YPYModelError('parent is not set . Cannot derive path.') if self.exporter_name is None: raise YPYModelError('Key property exporter_name is None') return self.parent._common_path +'/Cisco-IOS-XR-asr9k-netflow-oper:flow-exporter[Cisco-IOS-XR-asr9k-netflow-oper:exporter-name = ' + str(self.exporter_name) + ']' def is_config(self): ''' Returns True if this instance represents config data else returns False ''' return False def _has_data(self): if not self.is_config(): return False if self.exporter_name is not None: return True if self.exporter is not None and self.exporter._has_data(): return True return False @staticmethod def _meta_info(): from ydk.models.cisco_ios_xr._meta import _Cisco_IOS_XR_asr9k_netflow_oper as meta return meta._meta_table['NetFlow.Statistics.Statistic.Server.FlowExporters.FlowExporter']['meta_info'] @property def _common_path(self): if self.parent is None: raise YPYModelError('parent is not set . Cannot derive path.') return self.parent._common_path +'/Cisco-IOS-XR-asr9k-netflow-oper:flow-exporters' def is_config(self): ''' Returns True if this instance represents config data else returns False ''' return False def _has_data(self): if not self.is_config(): return False if self.flow_exporter is not None: for child_ref in self.flow_exporter: if child_ref._has_data(): return True return False @staticmethod def _meta_info(): from ydk.models.cisco_ios_xr._meta import _Cisco_IOS_XR_asr9k_netflow_oper as meta return meta._meta_table['NetFlow.Statistics.Statistic.Server.FlowExporters']['meta_info'] @property def _common_path(self): if self.parent is None: raise YPYModelError('parent is not set . Cannot derive path.') return self.parent._common_path +'/Cisco-IOS-XR-asr9k-netflow-oper:server' def is_config(self): ''' Returns True if this instance represents config data else returns False ''' return False def _has_data(self): if not self.is_config(): return False if self.flow_exporters is not None and self.flow_exporters._has_data(): return True return False @staticmethod def _meta_info(): from ydk.models.cisco_ios_xr._meta import _Cisco_IOS_XR_asr9k_netflow_oper as meta return meta._meta_table['NetFlow.Statistics.Statistic.Server']['meta_info'] @property def _common_path(self): if self.node is None: raise YPYModelError('Key property node is None') return '/Cisco-IOS-XR-asr9k-netflow-oper:net-flow/Cisco-IOS-XR-asr9k-netflow-oper:statistics/Cisco-IOS-XR-asr9k-netflow-oper:statistic[Cisco-IOS-XR-asr9k-netflow-oper:node = ' + str(self.node) + ']' def is_config(self): ''' Returns True if this instance represents config data else returns False ''' return False def _has_data(self): if not self.is_config(): return False if self.node is not None: return True if self.producer is not None and self.producer._has_data(): return True if self.server is not None and self.server._has_data(): return True return False @staticmethod def _meta_info(): from ydk.models.cisco_ios_xr._meta import _Cisco_IOS_XR_asr9k_netflow_oper as meta return meta._meta_table['NetFlow.Statistics.Statistic']['meta_info'] @property def _common_path(self): return '/Cisco-IOS-XR-asr9k-netflow-oper:net-flow/Cisco-IOS-XR-asr9k-netflow-oper:statistics' def is_config(self): ''' Returns True if this instance represents config data else returns False ''' return False def _has_data(self): if not self.is_config(): return False if self.statistic is not None: for child_ref in self.statistic: if child_ref._has_data(): return True return False @staticmethod def _meta_info(): from ydk.models.cisco_ios_xr._meta import _Cisco_IOS_XR_asr9k_netflow_oper as meta return meta._meta_table['NetFlow.Statistics']['meta_info'] @property def _common_path(self): return '/Cisco-IOS-XR-asr9k-netflow-oper:net-flow' def is_config(self): ''' Returns True if this instance represents config data else returns False ''' return False def _has_data(self): if not self.is_config(): return False if self.configuration is not None and self.configuration._has_data(): return True if self.statistics is not None and self.statistics._has_data(): return True return False @staticmethod def _meta_info(): from ydk.models.cisco_ios_xr._meta import _Cisco_IOS_XR_asr9k_netflow_oper as meta return meta._meta_table['NetFlow']['meta_info']

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1

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null

0

6

1b20735c563570dc520739ec4ddd9cd22be22ca3

96

py

Python

venv/lib/python3.8/site-packages/debugpy/_vendored/pydevd/_pydev_imps/_pydev_xmlrpclib.py

Retraces/UkraineBot

3d5d7f8aaa58fa0cb8b98733b8808e5dfbdb8b71

[ "MIT" ]

2

2022-03-13T01:58:52.000Z

2022-03-31T06:07:54.000Z

venv/lib/python3.8/site-packages/debugpy/_vendored/pydevd/_pydev_imps/_pydev_xmlrpclib.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

19

2021-11-20T04:09:18.000Z

2022-03-23T15:05:55.000Z

venv/lib/python3.8/site-packages/debugpy/_vendored/pydevd/_pydev_imps/_pydev_xmlrpclib.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

null

/home/runner/.cache/pip/pool/be/43/ef/7355ddd663c90414eaec755b395245888202140781f7fcbdb1bdf2fea5

96

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1

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null

1

0

1

0

6

1b97da5065661dcc6d77181421005001b148607d

1,937

py

Python

processl.py

rustylocks79/Figgie

fa1af3224eba83c0f0f0e070acc160583f5a68e1

[ "MIT" ]

null

processl.py

rustylocks79/Figgie

fa1af3224eba83c0f0f0e070acc160583f5a68e1

[ "MIT" ]

null

processl.py

rustylocks79/Figgie

fa1af3224eba83c0f0f0e070acc160583f5a68e1

[ "MIT" ]

null

import pickle import matplotlib.pyplot as plt from sklearn import tree from sklearn.tree import DecisionTreeRegressor print('\nResults\n') prefix = 'faded_custom_' # ask pricer print(prefix + 'ask pricer: ') regression = DecisionTreeRegressor(max_depth=3) x = pickle.load(open('data/asking_x.pickle', 'rb')) y = pickle.load(open('data/asking_y.pickle', 'rb')) regression.fit(x, y) fig = plt.figure(figsize=(16, 10)) tree.plot_tree(regression, feature_names=['market price', 'model util', 'last_price'], filled=True, fontsize=10) fig.savefig('img/' + prefix + 'asking_tree.png') # asking empty pricing print(prefix + 'asking empty pricer: ') regression = DecisionTreeRegressor(max_depth=3) x = pickle.load(open('data/asking_x_empty.pickle', 'rb')) y = pickle.load(open('data/asking_y_empty.pickle', 'rb')) # x = x.reshape(-1, 1) regression.fit(x, y) fig = plt.figure(figsize=(16, 10)) tree.plot_tree(regression, feature_names=['model util', 'last_price'], filled=True, fontsize=10) fig.savefig('img/' + prefix + 'asking_tree_empty.png') # bidding pricer print(prefix + 'bidding pricer: ') regression = DecisionTreeRegressor(max_depth=3) x = pickle.load(open('data/bidding_x.pickle', 'rb')) y = pickle.load(open('data/bidding_y.pickle', 'rb')) regression.fit(x, y) fig = plt.figure(figsize=(16, 10)) tree.plot_tree(regression, feature_names=['market price', 'model util', 'last_price'], filled=True, fontsize=10) fig.savefig('img/' + prefix + 'bidding_tree.png') # empty bidding pricer print(prefix + 'bidding empty pricer: ') regression = DecisionTreeRegressor(max_depth=3) x = pickle.load(open('data/bidding_x_empty.pickle', 'rb')) y = pickle.load(open('data/bidding_y_empty.pickle', 'rb')) # x = x.reshape(-1, 1) regression.fit(x, y) fig = plt.figure(figsize=(16, 10)) tree.plot_tree(regression, feature_names=['model util', 'last_price'], filled=True, fontsize=10) fig.savefig('img/' + prefix + 'bidding_tree_empty.png')

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null

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null

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6

59ed25256496b74bf5ed5d0694727e1f77897e61

16,360

py

Python

platform/core/polyaxon/db/migrations/0021_auto_20190418_1600_v05.py

hackerwins/polyaxon

ff56a098283ca872abfbaae6ba8abba479ffa394

[ "Apache-2.0" ]

null

platform/core/polyaxon/db/migrations/0021_auto_20190418_1600_v05.py

hackerwins/polyaxon

ff56a098283ca872abfbaae6ba8abba479ffa394

[ "Apache-2.0" ]

null

platform/core/polyaxon/db/migrations/0021_auto_20190418_1600_v05.py

hackerwins/polyaxon

ff56a098283ca872abfbaae6ba8abba479ffa394

[ "Apache-2.0" ]

null

# Generated by Django 2.2 on 2019-04-18 14:00 import django.contrib.postgres.fields.jsonb from django.db import migrations, models from django.db.models import ExpressionWrapper, F import django.core.validators import libs.blacklist import re def create_cluster_owner(apps, schema_editor): Cluster = apps.get_model('db', 'Cluster') Owner = apps.get_model('db', 'Owner') ContentType = apps.get_model('contenttypes', 'ContentType') cluster_type_id = ContentType.objects.get_for_model(Cluster).id for cluster in Cluster.objects.all(): Owner.objects.create(object_id=cluster.id, content_type_id=cluster_type_id, name=cluster.uuid.hex) def migrate_experimentgroup_config(apps, schema_editor): ExperimentGroup = apps.get_model('db', 'ExperimentGroup') ExperimentGroup.objects.filter(backend__isnull=True).update(backend='native') def migrate_build_jobs_config(apps, schema_editor): BuildJob = apps.get_model('db', 'BuildJob') BuildJob.objects.update(content=ExpressionWrapper(F('config'), output_field=str)) def migrate_experiments_config(apps, schema_editor): Experiment = apps.get_model('db', 'Experiment') Experiment.objects.update(content=ExpressionWrapper(F('config'), output_field=str)) def migrate_jobs_config(apps, schema_editor): Job = apps.get_model('db', 'Job') Job.objects.update(content=ExpressionWrapper(F('config'), output_field=str)) def migrate_notebook_jobs_config(apps, schema_editor): NotebookJob = apps.get_model('db', 'NotebookJob') NotebookJob.objects.update(content=ExpressionWrapper(F('config'), output_field=str)) def migrate_tensorboard_jobs_config(apps, schema_editor): TensorboardJob = apps.get_model('db', 'TensorboardJob') TensorboardJob.objects.update(content=ExpressionWrapper(F('config'), output_field=str)) def migrate_experimentgroup_hptuning(apps, schema_editor): ExperimentGroup = apps.get_model('db', 'ExperimentGroup') groups = [] for group in ExperimentGroup.objects.exclude(hptuning__early_stopping=None): hptuning = group.hptuning [e.pop('policy', None) for e in hptuning['early_stopping']] group.hptuning = hptuning groups.append(group) ExperimentGroup.objects.bulk_update(groups, ['hptuning']) class Migration(migrations.Migration): dependencies = [ ('db', '0020_auto_20190307_1611'), ] operations = [ migrations.RenameField( model_name='buildjob', old_name='in_cluster', new_name='is_managed', ), migrations.RenameField( model_name='experiment', old_name='in_cluster', new_name='is_managed', ), migrations.RenameField( model_name='job', old_name='in_cluster', new_name='is_managed', ), migrations.RenameField( model_name='notebookjob', old_name='in_cluster', new_name='is_managed', ), migrations.RenameField( model_name='tensorboardjob', old_name='in_cluster', new_name='is_managed', ), migrations.AddField( model_name='job', name='backend', field=models.CharField(blank=True, help_text='The default backend use for running this entity.', max_length=16, null=True), ), migrations.AlterField( model_name='buildjob', name='backend', field=models.CharField(blank=True, help_text='The default backend use for running this entity.', max_length=16, null=True), ), migrations.AlterField( model_name='experiment', name='backend', field=models.CharField(blank=True, help_text='The default backend use for running this entity.', max_length=16, null=True), ), migrations.AddField( model_name='experimentgroup', name='backend', field=models.CharField(blank=True, help_text='The default backend use for running this entity.', max_length=16, null=True), ), migrations.AddField( model_name='pipeline', name='backend', field=models.CharField(blank=True, help_text='The default backend use for running this entity.', max_length=16, null=True), ), migrations.AlterField( model_name='buildjob', name='is_managed', field=models.BooleanField(default=True, help_text='If this entity is managed by the platform.'), ), migrations.AlterField( model_name='experiment', name='is_managed', field=models.BooleanField(default=True, help_text='If this entity is managed by the platform.'), ), migrations.AlterField( model_name='job', name='is_managed', field=models.BooleanField(default=True, help_text='If this entity is managed by the platform.'), ), migrations.AlterField( model_name='notebookjob', name='is_managed', field=models.BooleanField(default=True, help_text='If this entity is managed by the platform.'), ), migrations.AlterField( model_name='tensorboardjob', name='is_managed', field=models.BooleanField(default=True, help_text='If this entity is managed by the platform.'), ), migrations.AddField( model_name='experimentgroup', name='is_managed', field=models.BooleanField(default=True, help_text='If this entity is managed by the platform.'), ), migrations.AddField( model_name='pipeline', name='is_managed', field=models.BooleanField(default=True, help_text='If this entity is managed by the platform.'), ), migrations.AlterField( model_name='buildjob', name='config', field=django.contrib.postgres.fields.jsonb.JSONField( blank=True, help_text='The compiled polyaxonfile for the build job.', null=True), ), migrations.AlterField( model_name='job', name='config', field=django.contrib.postgres.fields.jsonb.JSONField( blank=True, help_text='The compiled polyaxonfile for the run job.', null=True), ), migrations.AddField( model_name='buildjob', name='content', field=models.TextField(blank=True, help_text='The yaml content of the polyaxonfile/specification.', null=True), ), migrations.AddField( model_name='experiment', name='content', field=models.TextField(blank=True, help_text='The yaml content of the polyaxonfile/specification.', null=True), ), migrations.AddField( model_name='job', name='content', field=models.TextField(blank=True, help_text='The yaml content of the polyaxonfile/specification.', null=True), ), migrations.AddField( model_name='notebookjob', name='content', field=models.TextField(blank=True, help_text='The yaml content of the polyaxonfile/specification.', null=True), ), migrations.AddField( model_name='tensorboardjob', name='content', field=models.TextField(blank=True, help_text='The yaml content of the polyaxonfile/specification.', null=True), ), migrations.AlterField( model_name='pipelinerunstatus', name='status', field=models.CharField(blank=True, choices=[('created', 'created'), ('warning', 'warning'), ('scheduled', 'scheduled'), ('running', 'running'), ('done', 'done'), ('failed', 'failed'), ('upstream_failed', 'upstream_failed'), ('stopped', 'stopped'), ('succeeded', 'succeeded'), ('stopping', 'stopping'), ('skipped', 'skipped'), ('unknown', 'unknown')], default='created', max_length=64, null=True), ), migrations.AlterField( model_name='buildjob', name='name', field=models.CharField(blank=True, default=None, max_length=128, null=True, validators=[ django.core.validators.RegexValidator(re.compile('^[-a-zA-Z0-9_]+\\Z'), "Enter a valid 'slug' consisting of letters, numbers, underscores or hyphens.", 'invalid'), libs.blacklist.validate_blacklist_name]), ), migrations.AlterField( model_name='experiment', name='name', field=models.CharField(blank=True, default=None, max_length=128, null=True, validators=[ django.core.validators.RegexValidator(re.compile('^[-a-zA-Z0-9_]+\\Z'), "Enter a valid 'slug' consisting of letters, numbers, underscores or hyphens.", 'invalid'), libs.blacklist.validate_blacklist_name]), ), migrations.AlterField( model_name='experimentchartview', name='name', field=models.CharField(blank=True, default=None, max_length=128, null=True, validators=[ django.core.validators.RegexValidator(re.compile('^[-a-zA-Z0-9_]+\\Z'), "Enter a valid 'slug' consisting of letters, numbers, underscores or hyphens.", 'invalid'), libs.blacklist.validate_blacklist_name]), ), migrations.AlterField( model_name='experimentgroup', name='name', field=models.CharField(blank=True, default=None, max_length=128, null=True, validators=[ django.core.validators.RegexValidator(re.compile('^[-a-zA-Z0-9_]+\\Z'), "Enter a valid 'slug' consisting of letters, numbers, underscores or hyphens.", 'invalid'), libs.blacklist.validate_blacklist_name]), ), migrations.AlterField( model_name='experimentgroupchartview', name='name', field=models.CharField(blank=True, default=None, max_length=128, null=True, validators=[ django.core.validators.RegexValidator(re.compile('^[-a-zA-Z0-9_]+\\Z'), "Enter a valid 'slug' consisting of letters, numbers, underscores or hyphens.", 'invalid'), libs.blacklist.validate_blacklist_name]), ), migrations.AlterField( model_name='job', name='name', field=models.CharField(blank=True, default=None, max_length=128, null=True, validators=[ django.core.validators.RegexValidator(re.compile('^[-a-zA-Z0-9_]+\\Z'), "Enter a valid 'slug' consisting of letters, numbers, underscores or hyphens.", 'invalid'), libs.blacklist.validate_blacklist_name]), ), migrations.AlterField( model_name='notebookjob', name='name', field=models.CharField(blank=True, default=None, max_length=128, null=True, validators=[ django.core.validators.RegexValidator(re.compile('^[-a-zA-Z0-9_]+\\Z'), "Enter a valid 'slug' consisting of letters, numbers, underscores or hyphens.", 'invalid'), libs.blacklist.validate_blacklist_name]), ), migrations.AlterField( model_name='operation', name='name', field=models.CharField(blank=True, default=None, max_length=128, null=True, validators=[ django.core.validators.RegexValidator(re.compile('^[-a-zA-Z0-9_]+\\Z'), "Enter a valid 'slug' consisting of letters, numbers, underscores or hyphens.", 'invalid'), libs.blacklist.validate_blacklist_name]), ), migrations.AlterField( model_name='pipeline', name='name', field=models.CharField(blank=True, default=None, max_length=128, null=True, validators=[ django.core.validators.RegexValidator(re.compile('^[-a-zA-Z0-9_]+\\Z'), "Enter a valid 'slug' consisting of letters, numbers, underscores or hyphens.", 'invalid'), libs.blacklist.validate_blacklist_name]), ), migrations.AlterField( model_name='search', name='name', field=models.CharField(blank=True, default=None, max_length=128, null=True, validators=[ django.core.validators.RegexValidator(re.compile('^[-a-zA-Z0-9_]+\\Z'), "Enter a valid 'slug' consisting of letters, numbers, underscores or hyphens.", 'invalid'), libs.blacklist.validate_blacklist_name]), ), migrations.AlterField( model_name='tensorboardjob', name='name', field=models.CharField(blank=True, default=None, max_length=128, null=True, validators=[ django.core.validators.RegexValidator(re.compile('^[-a-zA-Z0-9_]+\\Z'), "Enter a valid 'slug' consisting of letters, numbers, underscores or hyphens.", 'invalid'), libs.blacklist.validate_blacklist_name]), ), migrations.AddField( model_name='buildjob', name='valid', field=models.NullBooleanField(default=True), ), migrations.RenameField( model_name='experiment', old_name='declarations', new_name='params', ), migrations.RunPython(migrate_build_jobs_config), migrations.RunPython(migrate_experiments_config), migrations.RunPython(migrate_jobs_config), migrations.RunPython(migrate_notebook_jobs_config), migrations.RunPython(migrate_tensorboard_jobs_config), migrations.RunPython(migrate_experimentgroup_config), migrations.RunPython(migrate_experimentgroup_hptuning), migrations.RunPython(create_cluster_owner), ]

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null

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6

942abf8e6237eb68eec6a3cb18a4d59f6642d6e1

26

py

Python

packer/__init__.py

thekashifmalik/packer

736d052d2536ada7733f4b8459e32fb771af2e1c

[ "MIT" ]

null

packer/__init__.py

thekashifmalik/packer

736d052d2536ada7733f4b8459e32fb771af2e1c

[ "MIT" ]

null

packer/__init__.py

thekashifmalik/packer

736d052d2536ada7733f4b8459e32fb771af2e1c

[ "MIT" ]

null

from .packer import Packer

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0.846154

4

26

5.5

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0.956522

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null

0

1

0

1

0

1

0

6

945d6f6dbe61afc3aecec22d285574976adcecbc

37

py

Python

tf_image_classification/deploy_utils/__init__.py

ciandt-d1/tf_image_classification

76ff4cb9ec35418eb20ea3240221bbfb88970737

[ "MIT" ]

null

tf_image_classification/deploy_utils/__init__.py

ciandt-d1/tf_image_classification

76ff4cb9ec35418eb20ea3240221bbfb88970737

[ "MIT" ]

null

tf_image_classification/deploy_utils/__init__.py

ciandt-d1/tf_image_classification

76ff4cb9ec35418eb20ea3240221bbfb88970737

[ "MIT" ]

null

import pb_viewer import freeze_graph

18.5

19

0.891892

6

37

5.166667

0.833333

0

0.108108

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2

19

18.5

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1

0

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1

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0

1

0

null

0

1

0

1

0

1

0

6

94759511ab16e6c5bbc155be542bb1cd2225688a

106

py

Python

SigProfilerClusters/version.py

AlexandrovLab/SigProfilerClusters

804a6333bdde8df68241736ec1adba4faaa1adce

[ "BSD-2-Clause" ]

7

2022-02-20T09:12:38.000Z

2022-03-30T20:01:55.000Z

SigProfilerClusters/version.py

AlexandrovLab/SigProfilerClusters

804a6333bdde8df68241736ec1adba4faaa1adce

[ "BSD-2-Clause" ]

5

2022-02-21T09:34:45.000Z

2022-03-30T19:57:27.000Z

SigProfilerClusters/version.py

AlexandrovLab/SigProfilerClusters

804a6333bdde8df68241736ec1adba4faaa1adce

[ "BSD-2-Clause" ]

null

# THIS FILE IS GENERATED FROM SIGPROFILECLUSTERS SETUP.PY short_version = '1.0.11' version = '1.0.11'

17.666667

57

0.716981

17

106

4.411765

0.764706

0.213333

0.24

0.293333

0

0.090909

0.169811

106

6

58

17.666667

0.761364

0.518868

0

1

0

0.244898

0

1

0

false

0

1

0

null

1

0

1

0

1

0

null

0

6

84b021b3f02732e0ea7aad74bbe55301578a7f6c

40

py

Python

fcs_trade/gateway/idcm/__init__.py

fcscolorstone/fcs-trade

d76c5b8338ab55f49d78b218817326c2d1168151

[ "MIT" ]

2

2019-09-26T06:46:03.000Z

2020-01-29T23:28:07.000Z

fcs_trade/gateway/idcm/__init__.py

fcscolorstone/fcs-trade

d76c5b8338ab55f49d78b218817326c2d1168151

[ "MIT" ]

null

fcs_trade/gateway/idcm/__init__.py

fcscolorstone/fcs-trade

d76c5b8338ab55f49d78b218817326c2d1168151

[ "MIT" ]

2

2019-05-31T00:15:37.000Z

2022-02-11T08:32:27.000Z

from .idcm_gateway import IdcmGateway

10

37

0.825

5

40

6.4

1

0

0.15

40

3

38

13.333333

0.941176

0

1

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true

0

1

0

1

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1

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null

0

1

0

null

0

1

0

1

0

1

0

6

ca476e2a445d23aa004180ad3976155d29b184d7

77

py

Python

FastAPIMongoEngineGraphQL/app/util/get_json.py

scionoftech/FastAPI-Full-Stack-Samples

e7d42661ed59324ff20f419d05c6cd1e7dab7e97

[ "MIT" ]

29

2021-03-31T02:42:59.000Z

2022-03-12T16:20:05.000Z

FastAPIMongoEngine/app/util/get_json.py

scionoftech/FastAPI-Full-Stack-Samples

e7d42661ed59324ff20f419d05c6cd1e7dab7e97

[ "MIT" ]

null

FastAPIMongoEngine/app/util/get_json.py

scionoftech/FastAPI-Full-Stack-Samples

e7d42661ed59324ff20f419d05c6cd1e7dab7e97

[ "MIT" ]

4

2021-08-21T01:02:00.000Z

2022-01-09T15:33:51.000Z

import json def get_json(data): return json.loads(data.to_json())

12.833333

38

0.662338

12

77

4.083333

0.666667

0

0.220779

77

5

39

15.4

0.816667

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0.333333

false

0

0.333333

1

0

1

0

null

0

1

0

null

0

1

0

1

0

6

ca5e3cf48ab9ba2db1285063f80648945de13bb9

4,235

py

Python

tests/unittests/test_authentication.py

ZPascal/grafana_api_sdk

97c347790200e8e9a2aafd47e322297aa97b964c

[ "Apache-2.0" ]

2

2022-02-01T20:18:48.000Z

2022-02-02T01:22:14.000Z

tests/unittests/test_authentication.py

ZPascal/grafana_api_sdk

97c347790200e8e9a2aafd47e322297aa97b964c

[ "Apache-2.0" ]

5

2022-01-12T06:55:54.000Z

2022-03-26T13:35:50.000Z

tests/unittests/test_authentication.py

ZPascal/grafana_api_sdk

97c347790200e8e9a2aafd47e322297aa97b964c

[ "Apache-2.0" ]

null

from unittest import TestCase from unittest.mock import MagicMock, Mock, patch from src.grafana_api.model import APIModel from src.grafana_api.authentication import Authentication class AuthenticationTestCase(TestCase): @patch("src.grafana_api.api.Api.call_the_api") def test_get_api_tokens(self, call_the_api_mock): model: APIModel = APIModel(host=MagicMock(), token=MagicMock()) authentication: Authentication = Authentication(grafana_api_model=model) mock: Mock = Mock() mock.json = Mock(return_value=list([{"id": "test"}])) call_the_api_mock.return_value = mock self.assertEqual( list([{"id": "test"}]), authentication.get_api_tokens(), ) @patch("src.grafana_api.api.Api.call_the_api") def test_get_api_tokens_no_valid_result(self, call_the_api_mock): model: APIModel = APIModel(host=MagicMock(), token=MagicMock()) authentication: Authentication = Authentication(grafana_api_model=model) mock: Mock = Mock() mock.json = Mock(return_value=list()) call_the_api_mock.return_value = mock with self.assertRaises(Exception): authentication.get_api_tokens() @patch("src.grafana_api.api.Api.call_the_api") def test_create_api_token(self, call_the_api_mock): model: APIModel = APIModel(host=MagicMock(), token=MagicMock()) authentication: Authentication = Authentication(grafana_api_model=model) mock: Mock = Mock() mock.json = Mock(return_value=dict({"id": "test"})) call_the_api_mock.return_value = mock self.assertEqual( dict({"id": "test"}), authentication.create_api_token("name", "View"), ) @patch("src.grafana_api.api.Api.call_the_api") def test_create_api_token_no_name(self, call_the_api_mock): model: APIModel = APIModel(host=MagicMock(), token=MagicMock()) authentication: Authentication = Authentication(grafana_api_model=model) mock: Mock = Mock() mock.json = Mock(return_value=dict()) call_the_api_mock.return_value = mock with self.assertRaises(ValueError): authentication.create_api_token("", "") @patch("src.grafana_api.api.Api.call_the_api") def test_create_api_token_no_valid_result(self, call_the_api_mock): model: APIModel = APIModel(host=MagicMock(), token=MagicMock()) authentication: Authentication = Authentication(grafana_api_model=model) mock: Mock = Mock() mock.json = Mock(return_value=dict()) call_the_api_mock.return_value = mock with self.assertRaises(Exception): authentication.create_api_token("name", "View") @patch("src.grafana_api.api.Api.call_the_api") def test_delete_api_token(self, call_the_api_mock): model: APIModel = APIModel(host=MagicMock(), token=MagicMock()) authentication: Authentication = Authentication(grafana_api_model=model) mock: Mock = Mock() mock.json = Mock(return_value=dict({"message": "API key deleted"})) call_the_api_mock.return_value = mock self.assertEqual( None, authentication.delete_api_token(1), ) @patch("src.grafana_api.api.Api.call_the_api") def test_delete_api_token_no_token_id(self, call_the_api_mock): model: APIModel = APIModel(host=MagicMock(), token=MagicMock()) authentication: Authentication = Authentication(grafana_api_model=model) mock: Mock = Mock() mock.json = Mock(return_value=dict()) call_the_api_mock.return_value = mock with self.assertRaises(ValueError): authentication.delete_api_token(0) @patch("src.grafana_api.api.Api.call_the_api") def test_delete_api_token_no_valid_result(self, call_the_api_mock): model: APIModel = APIModel(host=MagicMock(), token=MagicMock()) authentication: Authentication = Authentication(grafana_api_model=model) mock: Mock = Mock() mock.json = Mock(return_value=dict()) call_the_api_mock.return_value = mock with self.assertRaises(Exception): authentication.delete_api_token(1)

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0.081961

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0.868277

0

0.000894

0.207556

4,235

117

81

36.196581

0.813468

0

0.626506

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0

0.096386

1

0.096386

false

0

0.048193

0

0.156627

0

null

0

1

0

null

0

6

04bcf98918f6433495dc5b34ff3810062c9864f9

39

py

Python

runtime/pubsub/__init__.py

akrantz01/backendless

27acada7ab5ee4e81f9e23e0079cfb15b9f6b09e

[ "MIT" ]

1

2020-10-17T04:39:29.000Z

runtime/pubsub/__init__.py

akrantz01/backendless

27acada7ab5ee4e81f9e23e0079cfb15b9f6b09e

[ "MIT" ]

null

runtime/pubsub/__init__.py

akrantz01/backendless

27acada7ab5ee4e81f9e23e0079cfb15b9f6b09e

[ "MIT" ]

null

from .setup import configure, shutdown

19.5

38

0.820513

5

39

6.4

1

0

0.128205

39

1

39

0.941176

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

b6f13a5419347d84a6288c6e719d119541a68613

110

py

Python

build/disco_f407vg/rpc_extra_script.py

swansk/avatar-fw

48bb98285ca1d5a102d17bc4df8bd77593c37dd4

[ "MIT" ]

null

build/disco_f407vg/rpc_extra_script.py

swansk/avatar-fw

48bb98285ca1d5a102d17bc4df8bd77593c37dd4

[ "MIT" ]

null

build/disco_f407vg/rpc_extra_script.py

swansk/avatar-fw

48bb98285ca1d5a102d17bc4df8bd77593c37dd4

[ "MIT" ]

null

Import('env') env.Prepend(CPPPATH=['/home/karl/.platformio/packages/framework-mbed/features/unsupported/rpc'])

55

96

0.790909

14

110

6.214286

0.928571

0

0.009091

110

2

96

55

0.798165

0

0.666667

0.63964

0

1

0

true

0

0.5

0

0.5

0

1

0

null

0

1

0

1

null

0

1

0

1

0

6

f3dc8e97a1de0f3177c93e2c134479dec218fad9

163

py

Python

Python/Polar Coordinates.py

MonwarAdeeb/HackerRank-Solutions

571327e9688061745000ae81c5fd74ff7a2976d4

[ "MIT" ]

null

Python/Polar Coordinates.py

MonwarAdeeb/HackerRank-Solutions

571327e9688061745000ae81c5fd74ff7a2976d4

[ "MIT" ]

null

Python/Polar Coordinates.py

MonwarAdeeb/HackerRank-Solutions

571327e9688061745000ae81c5fd74ff7a2976d4

[ "MIT" ]

null

# Enter your code here. Read input from STDIN. Print output to STDOUT import cmath r = complex(input().strip()) print(cmath.polar(r)[0]) print(cmath.polar(r)[1])

23.285714

69

0.717791

28

163

4.178571

0.714286

0.17094

0.25641

0.273504

0

0.014085

0.128834

163

7

70

23.285714

0.809859

0.411043

0

0.142857

0

1

0

false

0

0.25

0

0.25

0.5

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1

0

1

0

null

0

1

0

1

0

6

f3f2711cb5accf77b96ae587e85ce00bbc4ef71f

11,835

py

Python

tests/basic_tests.py

dhdsjy/Feature_auto_ml

d30aa3f884c51cc060e26d38a8c648f9744f43c1

[ "MIT" ]

null

tests/basic_tests.py

dhdsjy/Feature_auto_ml

d30aa3f884c51cc060e26d38a8c648f9744f43c1

[ "MIT" ]

null

tests/basic_tests.py

dhdsjy/Feature_auto_ml

d30aa3f884c51cc060e26d38a8c648f9744f43c1

[ "MIT" ]

null

""" To get standard out, run nosetests as follows: $ nosetests -s tests """ import datetime import os import random import sys sys.path = [os.path.abspath(os.path.dirname(__file__))] + sys.path from auto_ml import Predictor from nose.tools import assert_equal, assert_not_equal, with_setup from sklearn.metrics import accuracy_score import dill import numpy as np import utils_testing as utils def test_binary_classification(): np.random.seed(0) df_titanic_train, df_titanic_test = utils.get_titanic_binary_classification_dataset() ml_predictor = utils.train_basic_binary_classifier(df_titanic_train) test_score = ml_predictor.score(df_titanic_test, df_titanic_test.survived, verbose=0) # Right now we're getting a score of -.205 # Make sure our score is good, but not unreasonably good assert -0.215 < test_score < -0.17 def test_multilabel_classification(): np.random.seed(0) df_twitter_train, df_twitter_test = utils.get_twitter_sentiment_multilabel_classification_dataset() ml_predictor = utils.train_basic_multilabel_classifier(df_twitter_train) test_score = ml_predictor.score(df_twitter_test, df_twitter_test.airline_sentiment, verbose=0) # Right now we're getting a score of -.205 # Make sure our score is good, but not unreasonably good print('test_score') print(test_score) assert 0.67 < test_score < 0.79 def test_nlp_multilabel_classification(): np.random.seed(0) df_twitter_train, df_twitter_test = utils.get_twitter_sentiment_multilabel_classification_dataset() column_descriptions = { 'airline_sentiment': 'output' , 'airline': 'categorical' , 'text': 'nlp' , 'tweet_location': 'categorical' , 'user_timezone': 'categorical' , 'tweet_created': 'date' } ml_predictor = Predictor(type_of_estimator='classifier', column_descriptions=column_descriptions) ml_predictor.train(df_twitter_train) test_score = ml_predictor.score(df_twitter_test, df_twitter_test.airline_sentiment, verbose=0) # Make sure our score is good, but not unreasonably good print('test_score') print(test_score) assert 0.67 < test_score < 0.79 def test_regression(): np.random.seed(0) df_boston_train, df_boston_test = utils.get_boston_regression_dataset() ml_predictor = utils.train_basic_regressor(df_boston_train) test_score = ml_predictor.score(df_boston_test, df_boston_test.MEDV, verbose=0) # Currently, we expect to get a score of -3.09 # Make sure our score is good, but not unreasonably good assert -3.2 < test_score < -2.8 def test_saving_trained_pipeline_regression(): np.random.seed(0) df_boston_train, df_boston_test = utils.get_boston_regression_dataset() ml_predictor = utils.train_basic_regressor(df_boston_train) file_name = ml_predictor.save(str(random.random())) with open(file_name, 'rb') as read_file: saved_ml_pipeline = dill.load(read_file) os.remove(file_name) test_score = saved_ml_pipeline.score(df_boston_test, df_boston_test.MEDV) # Make sure our score is good, but not unreasonably good assert -3.2 < test_score < -2.8 def test_saving_trained_pipeline_binary_classification(): np.random.seed(0) df_titanic_train, df_titanic_test = utils.get_titanic_binary_classification_dataset() ml_predictor = utils.train_basic_binary_classifier(df_titanic_train) file_name = ml_predictor.save(str(random.random())) with open(file_name, 'rb') as read_file: saved_ml_pipeline = dill.load(read_file) os.remove(file_name) test_score = saved_ml_pipeline.score(df_titanic_test, df_titanic_test.survived) # Right now we're getting a score of -.205 assert -0.215 < test_score < -0.17 def test_saving_trained_pipeline_multilabel_classification(): np.random.seed(0) df_twitter_train, df_twitter_test = utils.get_twitter_sentiment_multilabel_classification_dataset() ml_predictor = utils.train_basic_multilabel_classifier(df_twitter_train) file_name = ml_predictor.save(str(random.random())) with open(file_name, 'rb') as read_file: saved_ml_pipeline = dill.load(read_file) os.remove(file_name) test_score = saved_ml_pipeline.score(df_twitter_test, df_twitter_test.airline_sentiment) # Right now we're getting a score of -.205 # Make sure our score is good, but not unreasonably good print('test_score') print(test_score) assert 0.67 < test_score < 0.79 def test_getting_single_predictions_regression(): np.random.seed(0) df_boston_train, df_boston_test = utils.get_boston_regression_dataset() ml_predictor = utils.train_basic_regressor(df_boston_train) file_name = ml_predictor.save(str(random.random())) with open(file_name, 'rb') as read_file: saved_ml_pipeline = dill.load(read_file) os.remove(file_name) df_boston_test_dictionaries = df_boston_test.to_dict('records') # 1. make sure the accuracy is the same predictions = [] for row in df_boston_test_dictionaries: predictions.append(saved_ml_pipeline.predict(row)) first_score = utils.calculate_rmse(df_boston_test.MEDV, predictions) print('first_score') print(first_score) # Make sure our score is good, but not unreasonably good assert -3.2 < first_score < -2.8 # 2. make sure the speed is reasonable (do it a few extra times) data_length = len(df_boston_test_dictionaries) start_time = datetime.datetime.now() for idx in range(1000): row_num = idx % data_length saved_ml_pipeline.predict(df_boston_test_dictionaries[row_num]) end_time = datetime.datetime.now() duration = end_time - start_time print('duration.total_seconds()') print(duration.total_seconds()) # It's very difficult to set a benchmark for speed that will work across all machines. # On my 2013 bottom of the line 15" MacBook Pro, this runs in about 0.8 seconds for 1000 predictions # That's about 1 millisecond per prediction # Assuming we might be running on a test box that's pretty weak, multiply by 3 # Also make sure we're not running unreasonably quickly assert 0.2 < duration.total_seconds() / 1.0 < 3 # 3. make sure we're not modifying the dictionaries (the score is the same after running a few experiments as it is the first time) predictions = [] for row in df_boston_test_dictionaries: predictions.append(saved_ml_pipeline.predict(row)) second_score = utils.calculate_rmse(df_boston_test.MEDV, predictions) print('second_score') print(second_score) # Make sure our score is good, but not unreasonably good assert -3.2 < second_score < -2.8 def test_getting_single_predictions_classification(): np.random.seed(0) df_titanic_train, df_titanic_test = utils.get_titanic_binary_classification_dataset() ml_predictor = utils.train_basic_binary_classifier(df_titanic_train) file_name = ml_predictor.save(str(random.random())) with open(file_name, 'rb') as read_file: saved_ml_pipeline = dill.load(read_file) os.remove(file_name) df_titanic_test_dictionaries = df_titanic_test.to_dict('records') # 1. make sure the accuracy is the same predictions = [] for row in df_titanic_test_dictionaries: predictions.append(saved_ml_pipeline.predict_proba(row)[1]) print('predictions') print(predictions) first_score = utils.calculate_brier_score_loss(df_titanic_test.survived, predictions) print('first_score') print(first_score) # Make sure our score is good, but not unreasonably good assert -0.215 < first_score < -0.17 # 2. make sure the speed is reasonable (do it a few extra times) data_length = len(df_titanic_test_dictionaries) start_time = datetime.datetime.now() for idx in range(1000): row_num = idx % data_length saved_ml_pipeline.predict(df_titanic_test_dictionaries[row_num]) end_time = datetime.datetime.now() duration = end_time - start_time print('duration.total_seconds()') print(duration.total_seconds()) # It's very difficult to set a benchmark for speed that will work across all machines. # On my 2013 bottom of the line 15" MacBook Pro, this runs in about 0.8 seconds for 1000 predictions # That's about 1 millisecond per prediction # Assuming we might be running on a test box that's pretty weak, multiply by 3 # Also make sure we're not running unreasonably quickly assert 0.2 < duration.total_seconds() < 3 # 3. make sure we're not modifying the dictionaries (the score is the same after running a few experiments as it is the first time) predictions = [] for row in df_titanic_test_dictionaries: predictions.append(saved_ml_pipeline.predict_proba(row)[1]) print('predictions') print(predictions) print('df_titanic_test_dictionaries') print(df_titanic_test_dictionaries) second_score = utils.calculate_brier_score_loss(df_titanic_test.survived, predictions) print('second_score') print(second_score) # Make sure our score is good, but not unreasonably good assert -0.215 < second_score < -0.17 # Note that while there is the raw data here to perform NLP, we are not actually performing any NLP for this test def test_getting_single_predictions_multilabel_classification_with_dates(): np.random.seed(0) df_twitter_train, df_twitter_test = utils.get_twitter_sentiment_multilabel_classification_dataset() ml_predictor = utils.train_basic_multilabel_classifier(df_twitter_train) file_name = ml_predictor.save(str(random.random())) with open(file_name, 'rb') as read_file: saved_ml_pipeline = dill.load(read_file) os.remove(file_name) df_twitter_test_dictionaries = df_twitter_test.to_dict('records') # 1. make sure the accuracy is the same predictions = [] for row in df_twitter_test_dictionaries: predictions.append(saved_ml_pipeline.predict(row)) print('predictions') print(predictions) first_score = accuracy_score(df_twitter_test.airline_sentiment, predictions) print('first_score') print(first_score) # Make sure our score is good, but not unreasonably good assert 0.67 < first_score < 0.79 # 2. make sure the speed is reasonable (do it a few extra times) data_length = len(df_twitter_test_dictionaries) start_time = datetime.datetime.now() for idx in range(1000): row_num = idx % data_length saved_ml_pipeline.predict(df_twitter_test_dictionaries[row_num]) end_time = datetime.datetime.now() duration = end_time - start_time print('duration.total_seconds()') print(duration.total_seconds()) # It's very difficult to set a benchmark for speed that will work across all machines. # On my 2013 bottom of the line 15" MacBook Pro, this runs in about 0.8 seconds for 1000 predictions # That's about 1 millisecond per prediction # Assuming we might be running on a test box that's pretty weak, multiply by 3 # Also make sure we're not running unreasonably quickly assert 0.2 < duration.total_seconds() < 3 # 3. make sure we're not modifying the dictionaries (the score is the same after running a few experiments as it is the first time) predictions = [] for row in df_twitter_test_dictionaries: predictions.append(saved_ml_pipeline.predict(row)) print('predictions') print(predictions) print('df_twitter_test_dictionaries') print(df_twitter_test_dictionaries) second_score = accuracy_score(df_twitter_test.airline_sentiment, predictions) print('second_score') print(second_score) # Make sure our score is good, but not unreasonably good assert 0.67 < second_score < 0.79

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6d0e1b1c0be4d8c3dacf7757e9a6671119c7bbf7

106

py

Python

train-or-finetune-model/tmp/test.py

ysh329/kaggle-invasive-species-monitoring-classification

782a15998900fcb60de6fc1cb9fd8a3eb525435c

[ "MIT" ]

null

train-or-finetune-model/tmp/test.py

ysh329/kaggle-invasive-species-monitoring-classification

782a15998900fcb60de6fc1cb9fd8a3eb525435c

[ "MIT" ]

null

train-or-finetune-model/tmp/test.py

ysh329/kaggle-invasive-species-monitoring-classification

782a15998900fcb60de6fc1cb9fd8a3eb525435c

[ "MIT" ]

null

import sys print sys.argv, len(sys.argv) for idx in xrange(len(sys.argv)): print idx, sys.argv[idx]

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0.688679

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106

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6d2b93cdf981eea9a5331fe0e85086a51a6e7e1b

132

py

Python

app/models/__init__.py

uk-gov-mirror/alphagov.digitalmarketplace-api

5a1db63691d0c4a435714837196ab6914badaf62

[ "MIT" ]

25

2015-01-14T10:45:13.000Z

2021-05-26T17:21:41.000Z

app/models/__init__.py

uk-gov-mirror/alphagov.digitalmarketplace-api

5a1db63691d0c4a435714837196ab6914badaf62

[ "MIT" ]

641

2015-01-15T11:10:50.000Z

2021-06-15T22:18:42.000Z

app/models/__init__.py

uk-gov-mirror/alphagov.digitalmarketplace-api

5a1db63691d0c4a435714837196ab6914badaf62

[ "MIT" ]

22

2015-06-13T15:37:45.000Z

2021-08-19T23:40:49.000Z

from .main import * # noqa from .direct_award import * # noqa from .buyer_domains import * # noqa from .outcomes import * # noqa

26.4

36

0.704545

18

132

5.055556

0.5

0.43956

0.461538

0

0.204545

132

4

37

33

0.866667

0.143939

0

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true

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null

0

1

0

1

0

6

6d2c9aa2eb55a138b2cac46ac0839af9f13f1fda

140

py

Python

pilog.humid_temp.py

marcheiligers/piscripts

453986872dd5dc784a4953607da6d70429417668

[ "MIT" ]

null

pilog.humid_temp.py

marcheiligers/piscripts

453986872dd5dc784a4953607da6d70429417668

[ "MIT" ]

null

pilog.humid_temp.py

marcheiligers/piscripts

453986872dd5dc784a4953607da6d70429417668

[ "MIT" ]

null

import socket from pilog import * from dht11 import * humidity, temperature = read_humidity_and_temp() post_weather(humidity, temperature)

20

48

0.814286

18

140

6.111111

0.666667

0.345455

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0.01626

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140

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23.333333

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null

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1

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6

ed957652349ce731a9f084d193e38af668bc087c

41

py

Python

python-renascence/module/__init__.py

jxt1234/Genetic-Program-Frame

c0a801e337a31de05f49047fd11920a3c2e32ed6

[ "Apache-2.0" ]

3

2016-01-04T09:23:31.000Z

2019-08-06T11:52:07.000Z

python-renascence/module/__init__.py

jxt1234/Renascence

c0a801e337a31de05f49047fd11920a3c2e32ed6

[ "Apache-2.0" ]

null

python-renascence/module/__init__.py

jxt1234/Renascence

c0a801e337a31de05f49047fd11920a3c2e32ed6

[ "Apache-2.0" ]

6

2016-05-10T16:05:12.000Z

2019-12-30T09:14:21.000Z

import RenascenceBasic import Renascence

13.666667

22

0.902439

4

41

9.25

0.75

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0.097561

41

2

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20.5

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6

eda63e60cb70e627e96422e4ec0d03f0c200810e

125

py

Python

net/model/decoder/__init__.py

lhq1/legal-predicetion

0919732d9aecba17630a3dcaedd3611ca990010c

[ "MIT" ]

87

2018-08-27T14:59:11.000Z

2022-03-01T07:29:27.000Z

net/model/decoder/__init__.py

lllybi/TopJudge

c9186b132e79830fd4e855777b06a601d76bf0a2

[ "MIT" ]

6

2018-10-11T09:29:05.000Z

2020-12-14T02:29:28.000Z

net/model/decoder/__init__.py

lllybi/TopJudge

c9186b132e79830fd4e855777b06a601d76bf0a2

[ "MIT" ]

31

2018-08-28T00:44:59.000Z

2022-02-18T18:17:01.000Z

from .fc_decoder import FCDecoder from .lstm_article_decoder import LSTMArticleDecoder from .lstm_decoder import LSTMDecoder

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edb5dc17eea618b45e20b4dc8709a561e933224e

22,434

py

Python

tests/utils.py

ruth-ann/deepsnap

35eeb5abdb304c53b2e0a68cbbeeaa55dca286a0

[ "MIT" ]

412

2020-06-20T01:37:29.000Z

2022-03-29T11:32:55.000Z

tests/utils.py

ruth-ann/deepsnap

35eeb5abdb304c53b2e0a68cbbeeaa55dca286a0

[ "MIT" ]

43

2020-06-21T09:16:10.000Z

2022-02-28T03:07:50.000Z

tests/utils.py

ruth-ann/deepsnap

35eeb5abdb304c53b2e0a68cbbeeaa55dca286a0

[ "MIT" ]

46

2020-06-20T02:00:48.000Z

2022-03-16T21:25:20.000Z

import numpy as np import networkx as nx import random import torch import itertools np.random.seed(0) def pyg_to_dicts(dataset, task="enzyme"): ds = [] for data in dataset: d = {} d["node_feature"] = data.x if task == "enzyme": d["grpah_label"] = data.y elif task == "cora": d["node_label"] = data.y d["directed"] = data.is_directed() edge_index = data.edge_index if not data.is_directed(): row, col = edge_index mask = row < col row, col = row[mask], col[mask] edge_index = torch.stack([row, col], dim=0) edge_index = torch.cat([edge_index, torch.flip(edge_index, [0])], dim=1) d["edge_index"] = edge_index ds.append(d) return ds def simple_networkx_small_graph(directed=True): if directed: G = nx.DiGraph() else: G = nx.Graph() G.add_node(0, node_label=0) G.add_node(1, node_label=1) G.add_node(2, node_label=2) G.add_node(3, node_label=0) G.add_node(4, node_label=1) G.add_edge(0, 1, edge_label=0) G.add_edge(0, 4, edge_label=1) G.add_edge(1, 2, edge_label=3) G.add_edge(1, 3, edge_label=3) G.add_edge(2, 4, edge_label=0) return G def simple_networkx_dense_multigraph(num_edges_removed=0): # TODO: restrict value of num_edges_removed G = nx.MultiDiGraph() for i in range(5): G.add_node(i, node_label=0) cnt = 0 for i in range(5): for j in range(5): if cnt >= num_edges_removed: for k in range(3): G.add_edge(i, j, edge_label=0) cnt += 1 return G def simple_networkx_dense_graph(num_edges_removed=0): # TODO: restrict value of num_edges_removed G = nx.DiGraph() for i in range(5): G.add_node(i, node_label=0) cnt = 0 for i in range(5): for j in range(5): if cnt >= num_edges_removed: G.add_edge(i, j, edge_label=0) cnt += 1 return G # TODO: update graph generator s.t. homogeneous & heterogeneous graph share the same format. def simple_networkx_graph(directed=True): num_nodes = 10 edge_index = ( torch.tensor( [ [0, 0, 1, 1, 2, 2, 3, 3, 3, 4, 4, 5, 6, 6, 7, 7, 9], [1, 2, 2, 3, 3, 8, 4, 5, 6, 5, 6, 7, 8, 9, 8, 9, 8] ] ).long() ) x = torch.zeros([num_nodes, 2]) y = torch.tensor([0, 0, 1, 1, 2, 2, 3, 3, 4, 4]).long() for i in range(num_nodes): x[i] = np.random.randint(1, num_nodes) edge_x = torch.zeros([edge_index.shape[1], 2]) edge_y = torch.tensor( [0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 3] ).long() for i in range(edge_index.shape[1]): edge_x[i] = np.random.randint(1, num_nodes) G = nx.DiGraph() G.add_nodes_from(range(num_nodes)) for i, (u, v) in enumerate(edge_index.T.tolist()): G.add_edge(u, v) # if it is undirected, modify the edge attributes if directed is False: G = G.to_undirected() H = G.to_directed() edge_index = np.zeros([2, edge_index.shape[1] * 2]).astype(np.int64) edge_x = np.zeros([edge_x.shape[0] * 2, edge_x.shape[1]]) edge_y = np.zeros(edge_y.shape[0] * 2).astype(np.int64) for i, nx_edge in enumerate(nx.to_edgelist(H)): edge_index[:, i] = ( np.array([nx_edge[0], nx_edge[1]]).astype(np.int64) ) edge_x[i] = nx_edge[2]['edge_attr'] edge_y[i] = nx_edge[2]['edge_y'] graph_x = torch.tensor([[0, 1]]) graph_y = torch.tensor([0]) return G, x, y, edge_x, edge_y, edge_index, graph_x, graph_y def simple_networkx_graph_alphabet(directed=True): num_nodes = 10 edge_index = ( torch.tensor( [ [0, 0, 1, 1, 2, 2, 3, 3, 3, 4, 4, 5, 6, 6, 7, 7, 9], [1, 2, 2, 3, 3, 8, 4, 5, 6, 5, 6, 7, 8, 9, 8, 9, 8] ] ).long() ) x = torch.zeros([num_nodes, 2]) y = torch.tensor([0, 0, 1, 1, 2, 2, 3, 3, 4, 4]).long() for i in range(num_nodes): x[i] = np.random.randint(1, num_nodes) edge_x = torch.zeros([edge_index.shape[1], 2]) edge_y = torch.tensor( [0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 3] ).long() for i in range(edge_index.shape[1]): edge_x[i] = np.random.randint(1, num_nodes) G = nx.DiGraph() G.add_nodes_from(range(num_nodes)) for i, (u, v) in enumerate(edge_index.T.tolist()): G.add_edge(u, v) # if it is undirected, modify the edge attributes if directed is False: G = G.to_undirected() H = G.to_directed() edge_index = np.zeros([2, edge_index.shape[1] * 2]).astype(np.int64) edge_x = np.zeros([edge_x.shape[0] * 2, edge_x.shape[1]]) edge_y = np.zeros(edge_y.shape[0] * 2).astype(np.int64) for i, nx_edge in enumerate(nx.to_edgelist(H)): edge_index[:, i] = ( np.array([nx_edge[0], nx_edge[1]]).astype(np.int64) ) edge_x[i] = nx_edge[2]['edge_attr'] edge_y[i] = nx_edge[2]['edge_y'] graph_x = torch.tensor([[0, 1]]) graph_y = torch.tensor([0]) # number -> alphabet transform keys = list(G.nodes) vals = [chr(x + 97) for x in list(range(len(keys)))] mapping = dict(zip(keys, vals)) G = nx.relabel_nodes(G, mapping, copy=True) return G, x, y, edge_x, edge_y, edge_index, graph_x, graph_y def simple_networkx_multigraph(): num_nodes = 10 edge_index = ( torch.tensor( [ [0, 0, 0, 0, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 5, 6, 6, 6, 7, 7, 9], [1, 1, 1, 2, 2, 3, 3, 8, 8, 4, 5, 6, 5, 6, 7, 8, 8, 9, 8, 9, 8] ] ).long() ) x = torch.zeros([num_nodes, 2]) y = torch.tensor([0, 0, 1, 1, 2, 2, 3, 3, 4, 4]).long() for i in range(num_nodes): x[i] = np.random.randint(1, num_nodes) edge_x = torch.zeros([edge_index.shape[1], 2]) edge_y = torch.tensor( [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 2, 3, 3, 3, 3, 3, 3] ).long() for i in range(edge_index.shape[1]): edge_x[i] = np.random.randint(1, num_nodes) G = nx.MultiDiGraph() G.add_nodes_from(range(num_nodes)) for i, (u, v) in enumerate(edge_index.T.tolist()): G.add_edge(u, v) graph_x = torch.tensor([[0, 1]]) graph_y = torch.tensor([0]) return G, x, y, edge_x, edge_y, edge_index, graph_x, graph_y def sample_neigh(graph, size): while True: start_node = np.random.choice(list(graph.nodes)) neigh = [start_node] frontier = list(set(graph.neighbors(start_node)) - set(neigh)) visited = set([start_node]) while len(neigh) < size and frontier: new_node = np.random.choice(list(frontier)) assert new_node not in neigh neigh.append(new_node) visited.add(new_node) frontier += list(graph.neighbors(new_node)) frontier = [x for x in frontier if x not in visited] if len(neigh) == size: return graph, neigh def gen_graph(size, graph): graph, neigh = sample_neigh(graph, size) return graph.subgraph(neigh) def generate_simple_dense_hete_graph(num_edges_removed=0): # TODO: restrict value of num_edges_removed G = nx.DiGraph() for i in range(3): G.add_node(i, node_label=0, node_type=0) for i in range(3, 5): G.add_node(i, node_label=0, node_type=1) # message_type (0, 0, 0) cnt = 0 for i in range(3): for j in range(3): if cnt >= num_edges_removed: G.add_edge(i, j, edge_label=1, edge_type=0) cnt += 1 # message_type (1, 1, 1) cnt = 0 for i in range(3, 5): for j in range(3, 5): if cnt >= num_edges_removed: G.add_edge(i, j, edge_label=1, edge_type=1) cnt += 1 return G def generate_simple_dense_hete_multigraph(num_edges_removed=0): # TODO: restrict value of num_edges_removed G = nx.MultiDiGraph() for i in range(3): G.add_node(i, node_label=0, node_type=0) for i in range(3, 5): G.add_node(i, node_label=0, node_type=1) # message_type (0, 0, 0) cnt = 0 for i in range(3): for j in range(3): if cnt >= num_edges_removed: for k in range(3): G.add_edge(i, j, edge_label=1, edge_type=0) cnt += 1 # message_type (1, 1, 1) cnt = 0 for i in range(3, 5): for j in range(3, 5): if cnt >= num_edges_removed: for k in range(3): G.add_edge(i, j, edge_label=1, edge_type=1) cnt += 1 return G def generate_simple_small_hete_graph(directed=True): if directed: G = nx.DiGraph() else: G = nx.Graph() G.add_node(0, node_label=0, node_type=0) G.add_node(1, node_label=1, node_type=0) G.add_node(2, node_label=2, node_type=0) G.add_node(3, node_label=0, node_type=0) G.add_node(4, node_label=1, node_type=1) G.add_node(5, node_label=1, node_type=1) G.add_node(6, node_label=1, node_type=1) # message_type (0, 0, 0) G.add_edge(0, 1, edge_label=0, edge_type=0) G.add_edge(0, 2, edge_label=0, edge_type=0) G.add_edge(0, 3, edge_label=0, edge_type=0) # message_type (0, 1, 1) G.add_edge(0, 4, edge_label=1, edge_type=1) G.add_edge(2, 4, edge_label=0, edge_type=1) G.add_edge(3, 5, edge_label=0, edge_type=1) G.add_edge(3, 6, edge_label=0, edge_type=1) # message_type (0, 1, 0) G.add_edge(1, 2, edge_label=3, edge_type=1) G.add_edge(1, 3, edge_label=3, edge_type=1) G.add_edge(2, 3, edge_label=3, edge_type=1) return G def generate_simple_hete_graph(add_edge_type=True): G = nx.DiGraph() for i in range(9): if i < 2: node_feature = torch.rand([10, ]) node_type = "n1" node_label = 0 G.add_node( i, node_type=node_type, node_label=node_label, node_feature=node_feature ) elif 2 <= i < 4: node_feature = torch.rand([12, ]) node_type = "n2" node_label = 0 G.add_node( i, node_type=node_type, node_label=node_label, node_feature=node_feature ) elif 4 <= i < 6: node_feature = torch.rand([10, ]) node_type = "n1" node_label = 1 G.add_node( i, node_type=node_type, node_label=node_label, node_feature=node_feature ) else: node_feature = torch.rand([12, ]) node_type = "n2" node_label = 1 G.add_node( i, node_type=node_type, node_label=node_label, node_feature=node_feature ) if add_edge_type: G.add_edge( 0, 1, edge_label=0, edge_feature=torch.rand([8, ]), edge_type="e1" ) G.add_edge( 0, 2, edge_label=1, edge_feature=torch.rand([12, ]), edge_type="e2" ) G.add_edge( 0, 5, edge_label=0, edge_feature=torch.rand([8, ]), edge_type="e1" ) G.add_edge( 1, 3, edge_label=0, edge_feature=torch.rand([12, ]), edge_type="e2" ) G.add_edge( 1, 5, edge_label=1, edge_feature=torch.rand([12, ]), edge_type="e2" ) G.add_edge( 2, 3, edge_label=1, edge_feature=torch.rand([8, ]), edge_type="e1" ) G.add_edge( 2, 4, edge_label=2, edge_feature=torch.rand([12, ]), edge_type="e2" ) G.add_edge( 3, 4, edge_label=2, edge_feature=torch.rand([12, ]), edge_type="e2" ) G.add_edge( 4, 0, edge_label=1, edge_feature=torch.rand([12, ]), edge_type="e2" ) G.add_edge( 4, 5, edge_label=1, edge_feature=torch.rand([8, ]), edge_type="e1" ) G.add_edge( 5, 7, edge_label=1, edge_feature=torch.rand([8, ]), edge_type="e1" ) G.add_edge( 6, 1, edge_label=1, edge_feature=torch.rand([8, ]), edge_type="e1" ) G.add_edge( 6, 2, edge_label=1, edge_feature=torch.rand([8, ]), edge_type="e1" ) G.add_edge( 7, 3, edge_label=2, edge_feature=torch.rand([8, ]), edge_type="e1" ) G.add_edge( 8, 0, edge_label=0, edge_feature=torch.rand([12, ]), edge_type="e2" ) G.add_edge( 8, 1, edge_label=0, edge_feature=torch.rand([12, ]), edge_type="e2" ) else: G.add_edge(0, 1, edge_label=0, edge_feature=torch.rand([8, ])) G.add_edge(0, 2, edge_label=1, edge_feature=torch.rand([8, ])) G.add_edge(0, 5, edge_label=0, edge_feature=torch.rand([8, ])) G.add_edge(1, 3, edge_label=0, edge_feature=torch.rand([8, ])) G.add_edge(1, 5, edge_label=1, edge_feature=torch.rand([8, ])) G.add_edge(2, 3, edge_label=1, edge_feature=torch.rand([8, ])) G.add_edge(2, 4, edge_label=2, edge_feature=torch.rand([8, ])) G.add_edge(3, 4, edge_label=2, edge_feature=torch.rand([8, ])) G.add_edge(4, 0, edge_label=1, edge_feature=torch.rand([8, ])) G.add_edge(4, 5, edge_label=1, edge_feature=torch.rand([8, ])) G.add_edge(5, 7, edge_label=1, edge_feature=torch.rand([8, ])) G.add_edge(6, 1, edge_label=1, edge_feature=torch.rand([8, ])) G.add_edge(6, 2, edge_label=1, edge_feature=torch.rand([8, ])) G.add_edge(7, 3, edge_label=2, edge_feature=torch.rand([8, ])) G.add_edge(8, 0, edge_label=0, edge_feature=torch.rand([8, ])) G.add_edge(8, 1, edge_label=0, edge_feature=torch.rand([8, ])) return G def generate_simple_hete_dataset(add_edge_type=True): G = nx.DiGraph() node_label_options = [0, 1, 2] for i in range(9): node_label = random.choice(node_label_options) if i < 2: node_feature = torch.rand([10, ]) node_type = "n1" elif 2 <= i < 4: node_feature = torch.rand([12, ]) node_type = "n2" elif 4 <= i < 6: node_feature = torch.rand([10, ]) node_type = "n1" else: node_feature = torch.rand([12, ]) node_type = "n2" G.add_node( i, node_type=node_type, node_label=node_label, node_feature=node_feature, ) if add_edge_type: G.add_edge(0, 1, edge_feature=torch.rand([8, ]), edge_type="e1") G.add_edge(0, 2, edge_feature=torch.rand([12, ]), edge_type="e2") G.add_edge(0, 5, edge_feature=torch.rand([8, ]), edge_type="e1") G.add_edge(1, 3, edge_feature=torch.rand([12, ]), edge_type="e2") G.add_edge(1, 5, edge_feature=torch.rand([12, ]), edge_type="e2") G.add_edge(2, 3, edge_feature=torch.rand([8, ]), edge_type="e1") G.add_edge(2, 4, edge_feature=torch.rand([12, ]), edge_type="e2") G.add_edge(3, 4, edge_feature=torch.rand([12, ]), edge_type="e2") G.add_edge(4, 0, edge_feature=torch.rand([12, ]), edge_type="e2") G.add_edge(4, 5, edge_feature=torch.rand([8, ]), edge_type="e1") G.add_edge(5, 7, edge_feature=torch.rand([8, ]), edge_type="e1") G.add_edge(6, 1, edge_feature=torch.rand([8, ]), edge_type="e1") G.add_edge(6, 2, edge_feature=torch.rand([8, ]), edge_type="e1") G.add_edge(7, 3, edge_feature=torch.rand([8, ]), edge_type="e1") G.add_edge(8, 0, edge_feature=torch.rand([12, ]), edge_type="e2") G.add_edge(8, 1, edge_feature=torch.rand([12, ]), edge_type="e2") else: G.add_edge(0, 1, edge_feature=torch.rand([8, ])) G.add_edge(0, 2, edge_feature=torch.rand([8, ])) G.add_edge(0, 5, edge_feature=torch.rand([8, ])) G.add_edge(1, 3, edge_feature=torch.rand([8, ])) G.add_edge(1, 5, edge_feature=torch.rand([8, ])) G.add_edge(2, 3, edge_feature=torch.rand([8, ])) G.add_edge(2, 4, edge_feature=torch.rand([8, ])) G.add_edge(3, 4, edge_feature=torch.rand([8, ])) G.add_edge(4, 0, edge_feature=torch.rand([8, ])) G.add_edge(4, 5, edge_feature=torch.rand([8, ])) G.add_edge(5, 7, edge_feature=torch.rand([8, ])) G.add_edge(6, 1, edge_feature=torch.rand([8, ])) G.add_edge(6, 2, edge_feature=torch.rand([8, ])) G.add_edge(7, 3, edge_feature=torch.rand([8, ])) G.add_edge(8, 0, edge_feature=torch.rand([8, ])) G.add_edge(8, 1, edge_feature=torch.rand([8, ])) return G def generate_dense_hete_graph(add_edge_type=True, directed=True): if directed: G = nx.DiGraph() else: G = nx.Graph() num_node = 20 for i in range(num_node): if i < 10: node_feature = torch.rand([10, ]) node_type = "n1" node_label = 0 G.add_node( i, node_type=node_type, node_label=node_label, node_feature=node_feature, ) else: node_feature = torch.rand([12, ]) node_type = "n2" node_label = 1 G.add_node( i, node_type=node_type, node_label=node_label, node_feature=node_feature, ) if add_edge_type: for i, j in itertools.permutations(range(num_node), 2): rand = np.random.random() if (rand > 0.8): continue elif rand > 0.4: G.add_edge( i, j, edge_label=0, edge_feature=torch.rand([8, ]), edge_type='e1', ) else: G.add_edge( i, j, edge_label=0, edge_feature=torch.rand([8, ]), edge_type='e2', ) else: for i, j in itertools.permutations(range(num_node), 2): rand = np.random.random() if (rand > 0.8): continue elif rand > 0.4: G.add_edge(i, j, edge_label=0, edge_feature=torch.rand([8, ])) else: G.add_edge(i, j, edge_label=0, edge_feature=torch.rand([8, ])) return G def generate_dense_hete_dataset(add_edge_type=True): G = nx.DiGraph() num_node = 20 node_label_options = [0, 1, 2, 3] edge_label_options = [0, 1, 2] for i in range(num_node): node_feature = torch.rand([1, ]) if i < 10: node_type = "n1" else: node_type = "n2" node_label = random.choice(node_label_options) G.add_node( i, node_type=node_type, node_label=node_label, node_feature=node_feature, ) if add_edge_type: for i, j in itertools.permutations(range(num_node), 2): rand = np.random.random() if rand > 0.8: continue elif rand > 0.4: edge_type = "e1" else: edge_type = "e2" edge_label = random.choice(edge_label_options) G.add_edge( i, j, edge_feature=torch.rand([1, ]), edge_label=edge_label, edge_type=edge_type, ) else: for i, j in itertools.permutations(range(num_node), 2): rand = np.random.random() if rand > 0.8: continue elif rand > 0.4: edge_label = 0 else: edge_label = 1 G.add_edge( i, j, edge_feature=torch.rand([1, ]), edge_label=edge_label ) return G def generate_dense_hete_multigraph(add_edge_type=True): G = nx.MultiDiGraph() num_node = 20 for i in range(num_node): if i < 10: node_feature = torch.rand([10, ]) node_type = "n1" node_label = 0 G.add_node( i, node_type=node_type, node_label=node_label, node_feature=node_feature, ) else: node_feature = torch.rand([12, ]) node_type = "n2" node_label = 1 G.add_node( i, node_type=node_type, node_label=node_label, node_feature=node_feature, ) if add_edge_type: for i, j in itertools.permutations(range(num_node), 2): rand = np.random.random() if (rand > 0.8): continue elif rand > 0.4: G.add_edge( i, j, edge_label=0, edge_feature=torch.rand([8, ]), edge_type='e1', ) G.add_edge( i, j, edge_label=0, edge_feature=torch.rand([8, ]), edge_type='e1', ) else: G.add_edge( i, j, edge_label=0, edge_feature=torch.rand([8, ]), edge_type='e2', ) G.add_edge( i, j, edge_label=0, edge_feature=torch.rand([8, ]), edge_type='e2', ) else: for i, j in itertools.permutations(range(num_node), 2): rand = np.random.random() if (rand > 0.8): continue elif rand > 0.4: G.add_edge(i, j, edge_label=0, edge_feature=torch.rand([8, ])) G.add_edge(i, j, edge_label=0, edge_feature=torch.rand([8, ])) else: G.add_edge(i, j, edge_label=0, edge_feature=torch.rand([8, ])) G.add_edge(i, j, edge_label=0, edge_feature=torch.rand([8, ])) return G

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610529a684e4e2d65e3385e34075668b0c7289f1

20,138

py

Python

MV3D_TF_release/lib/rpn_msr/proposal_layer_tf.py

ZiningWang/Sparse_Pooling

a160ddf9a03ef53bad630b4ac186a8437bd0475c

[ "Unlicense" ]

52

2018-08-28T03:44:51.000Z

2022-03-23T16:00:14.000Z

MV3D_TF_release/lib/rpn_msr/proposal_layer_tf.py

weidezhang/Sparse_Pooling

a160ddf9a03ef53bad630b4ac186a8437bd0475c

[ "Unlicense" ]

1

2019-06-25T01:32:35.000Z

2019-07-01T01:34:20.000Z

MV3D_TF_release/lib/rpn_msr/proposal_layer_tf.py

weidezhang/Sparse_Pooling

a160ddf9a03ef53bad630b4ac186a8437bd0475c

[ "Unlicense" ]

20

2018-07-31T18:17:35.000Z

2021-07-09T08:42:06.000Z

# -------------------------------------------------------- # Faster R-CNN # Copyright (c) 2015 Microsoft # Licensed under The MIT License [see LICENSE for details] # Written by Ross Girshick and Sean Bell # -------------------------------------------------------- import numpy as np import yaml from fast_rcnn.config import cfg from rpn_msr.generate_anchors import generate_anchors_bv, generate_anchors from rpn_msr.anchor_target_layer_tf import clip_anchors from fast_rcnn.bbox_transform import bbox_transform_inv, clip_boxes, bbox_transform_inv_3d from fast_rcnn.nms_wrapper import nms from utils.transform import bv_anchor_to_lidar, lidar_to_bv, lidar_3d_to_bv, lidar_3d_to_corners, lidar_cnr_to_img import pdb,time #DEBUG = False """ Outputs object detection proposals by applying estimated bounding-box transformations to a set of regular boxes (called "anchors"). """ def proposal_layer_3d_debug(rpn_cls_prob_reshape,rpn_bbox_pred,im_info,calib,cfg_in, _feat_stride = [8,], anchor_scales=[1.0, 1.0],debug_state=True): #copy part of the code from proposal_layer_3d for debug _anchors = generate_anchors_bv() # _anchors = generate_anchors(scales=np.array(anchor_scales)) _num_anchors = _anchors.shape[0] im_info = im_info[0] assert rpn_cls_prob_reshape.shape[0] == 1, \ 'Only single item batches are supported' # cfg_key = str(self.phase) # either 'TRAIN' or 'TEST' # the first set of _num_anchors channels are bg probs # the second set are the fg probs, which we want # print rpn_cls_prob_reshape.shape height, width = rpn_cls_prob_reshape.shape[1:3] # scores = rpn_cls_prob_reshape[:, _num_anchors:, :, :] scores = np.reshape(np.reshape(rpn_cls_prob_reshape, [1, height, width, _num_anchors, 2])[:,:,:,:,1],[1, height, width, _num_anchors]) bbox_deltas = rpn_bbox_pred if debug_state: print ('im_size: ({}, {})'.format(im_info[0], im_info[1])) print ('scale: {}'.format(im_info[2])) if debug_state: print ('score map size: {}'.format(scores.shape)) # Enumerate all shifts shift_x = np.arange(0, width) * _feat_stride shift_y = np.arange(0, height) * _feat_stride shift_x, shift_y = np.meshgrid(shift_x, shift_y) shifts = np.vstack((shift_x.ravel(), shift_y.ravel(), shift_x.ravel(), shift_y.ravel())).transpose() # Enumerate all shifted anchors: # # add A anchors (1, A, 4) to # cell K shifts (K, 1, 4) to get # shift anchors (K, A, 4) # reshape to (K*A, 4) shifted anchors A = _num_anchors K = shifts.shape[0] anchors = _anchors.reshape((1, A, 4)) + \ shifts.reshape((1, K, 4)).transpose((1, 0, 2)) anchors = anchors.reshape((K * A, 4)) bbox_deltas = bbox_deltas.reshape((-1, 6)) scores = scores.reshape((-1, 1)) # convert anchors bv to anchors_3d anchors_3d = bv_anchor_to_lidar(anchors) # Convert anchors into proposals via bbox transformations proposals_3d = bbox_transform_inv_3d(anchors_3d, bbox_deltas) # convert back to lidar_bv proposals_bv = lidar_3d_to_bv(proposals_3d) #[x1,y1,x2,y2] lidar_corners = lidar_3d_to_corners(proposals_3d) proposals_img = lidar_cnr_to_img(lidar_corners, calib[3], calib[2], calib[0]) if debug_state: # print "bbox_deltas: ", bbox_deltas[:10] # print "proposals number: ", proposals_3d[:10] print ("proposals_bv shape: ", proposals_bv.shape) print ("proposals_3d shape: ", proposals_3d.shape) print ("scores shape:", scores.shape) # 2. clip predicted boxes to image #WZN: delete those not in image ind_inside = clip_anchors(anchors, im_info[:2]) #ind_inside = np.logical_and(ind_inside,clip_anchors(proposals_bv, im_info[:2])) proposals_bv = proposals_bv[ind_inside,:] proposals_3d = proposals_3d[ind_inside,:] proposals_img = proposals_img[ind_inside,:] scores = scores[ind_inside,:] proposals_bv = clip_boxes(proposals_bv, im_info[:2]) # TODO: pass real image_info #keep = _filter_img_boxes(proposals_img, [375, 1242]) #proposals_bv = proposals_bv[keep, :] #proposals_3d = proposals_3d[keep, :] #proposals_img = proposals_img[keep, :] #scores = scores[keep] if debug_state: print ("proposals after clip") print ("proposals_bv shape: ", proposals_bv.shape) print ("proposals_3d shape: ", proposals_3d.shape) print ("proposals_img shape: ", proposals_img.shape) # 4. sort all (proposal, score) pairs by score from highest to lowest # 5. take top pre_nms_topN (e.g. 6000) order = scores.ravel().argsort()[::-1] if cfg_in['pre_keep_topN'] > 0: order = order[:cfg_in['pre_keep_topN']] #keep = keep[order] proposals_bv = proposals_bv[order, :] proposals_3d = proposals_3d[order, :] proposals_img = proposals_img[order, :] scores = scores[order] # 6. apply nms (e.g. threshold = 0.7) # 7. take after_nms_topN (e.g. 300) # 8. return the top proposals (-> RoIs top) if cfg_in['use_nms']: keep = nms(np.hstack((proposals_bv, scores)), cfg_in['nms_thresh']) if cfg_in['nms_topN'] > 0: keep = keep[:cfg_in['nms_topN']] proposals_bv = proposals_bv[keep, :] proposals_3d = proposals_3d[keep, :] proposals_img = proposals_img[keep, :] scores = scores[keep] if debug_state: print ("proposals after nms") print ("proposals_bv shape: ", proposals_bv.shape) print ("proposals_3d shape: ", proposals_3d.shape) # debug only: keep probabilities above a threshold if cfg_in['prob_thresh']: keep_ind = scores[:,0]>cfg_in['prob_thresh'] print ('scores: ',scores) print ('threshold: ', cfg_in['prob_thresh']) print ('score shape:', scores.shape) #print keep_ind.shape #print keep.shape #keep = keep[keep_ind] proposals_bv = proposals_bv[keep_ind, :] proposals_3d = proposals_3d[keep_ind, :] proposals_img = proposals_img[keep_ind, :] scores = scores[keep_ind] return proposals_bv,proposals_3d,proposals_img,scores def proposal_layer_3d(rpn_cls_prob_reshape,rpn_bbox_pred,im_info,calib,cfg_key, _feat_stride = [8,], anchor_scales=[1.0, 1.0],DEBUG = False): # Algorithm: # # for each (H, W) location i # generate A anchor boxes centered on cell i # apply predicted bbox deltas at cell i to each of the A anchors # clip predicted boxes to image # remove predicted boxes with either height or width < threshold # sort all (proposal, score) pairs by score from highest to lowest # take top pre_nms_topN proposals before NMS # apply NMS with threshold 0.7 to remaining proposals # take after_nms_topN proposals after NMS # return the top proposals (-> RoIs top, scores top) #layer_params = yaml.load(self.param_str_) #t0 = time.time() _anchors = generate_anchors_bv() # _anchors = generate_anchors(scales=np.array(anchor_scales)) _num_anchors = _anchors.shape[0] #print 'time for anchors: ', time.time()-t0 #t0 = time.time() im_info = im_info[0] assert rpn_cls_prob_reshape.shape[0] == 1, \ 'Only single item batches are supported' # cfg_key = str(self.phase) # either 'TRAIN' or 'TEST' if type(cfg_key) is bytes: cfg_key = cfg_key.decode('UTF-8','ignore') pre_score_filt = cfg[cfg_key].RPN_SCORE_FILT pre_nms_topN = cfg[cfg_key].RPN_PRE_NMS_TOP_N post_nms_topN = cfg[cfg_key].RPN_POST_NMS_TOP_N nms_thresh = cfg[cfg_key].RPN_NMS_THRESH min_size = cfg[cfg_key].RPN_MIN_SIZE # the first set of _num_anchors channels are bg probs # the second set are the fg probs, which we want # print rpn_cls_prob_reshape.shape height, width = rpn_cls_prob_reshape.shape[1:3] # scores = rpn_cls_prob_reshape[:, _num_anchors:, :, :] scores = np.reshape(np.reshape(rpn_cls_prob_reshape, [1, height, width, _num_anchors, 2])[:,:,:,:,1],[1, height, width, _num_anchors]) bbox_deltas = rpn_bbox_pred if DEBUG: print ('im_size: ({}, {})'.format(im_info[0], im_info[1])) print ('scale: {}'.format(im_info[2])) # 1. Generate proposals from bbox deltas and shifted anchors if DEBUG: print ('score map size: {}'.format(scores.shape)) # Enumerate all shifts shift_x = np.arange(0, width) * _feat_stride shift_y = np.arange(0, height) * _feat_stride shift_x, shift_y = np.meshgrid(shift_x, shift_y) shifts = np.vstack((shift_x.ravel(), shift_y.ravel(), shift_x.ravel(), shift_y.ravel())).transpose() # Enumerate all shifted anchors: # # add A anchors (1, A, 4) to # cell K shifts (K, 1, 4) to get # shift anchors (K, A, 4) # reshape to (K*A, 4) shifted anchors A = _num_anchors K = shifts.shape[0] anchors = _anchors.reshape((1, A, 4)) + \ shifts.reshape((1, K, 4)).transpose((1, 0, 2)) anchors = anchors.reshape((K * A, 4)) # Transpose and reshape predicted bbox transformations to get them # into the same order as the anchors: # # bbox deltas will be (1, 4 * A, H, W) format # transpose to (1, H, W, 4 * A) # reshape to (1 * H * W * A, 4) where rows are ordered by (h, w, a) # in slowest to fastest order # bbox_deltas = bbox_deltas.transpose((0, 2, 3, 1)).reshape((-1, 6)) bbox_deltas = bbox_deltas.reshape((-1, 6)) # Same story for the scores: # # scores are (1, A, H, W) format # transpose to (1, H, W, A) # reshape to (1 * H * W * A, 1) where rows are ordered by (h, w, a) # scores = scores.transpose((0, 2, 3, 1)).reshape((-1, 1)) scores = scores.reshape((-1, 1)) score_filter = scores[:,0] > pre_score_filt #WZN: pre score filt scores = scores[score_filter,:] anchors = anchors[score_filter,:] bbox_deltas = bbox_deltas[score_filter,:] #print 'time for score pre_filt: ', time.time()-t0, scores.shape #t0 = time.time() # 5. take top pre_nms_topN (e.g. 6000) order = scores.ravel().argsort()[::-1] if pre_nms_topN > 0 and pre_nms_topN<order.shape[0]: order = order[:pre_nms_topN] scores = scores[order,:] anchors = anchors[order,:] bbox_deltas = bbox_deltas[order,:] # print np.sort(scores.ravel())[-30:] # convert anchors bv to anchors_3d anchors_3d = bv_anchor_to_lidar(anchors) # Convert anchors into proposals via bbox transformations proposals_3d = bbox_transform_inv_3d(anchors_3d, bbox_deltas) # convert back to lidar_bv proposals_bv = lidar_3d_to_bv(proposals_3d) lidar_corners = lidar_3d_to_corners(proposals_3d) proposals_img = lidar_cnr_to_img(lidar_corners, calib[3], calib[2], calib[0]) #print 'time for generating proposal: ', time.time()-t0, scores.shape #t0 = time.time() #WZN: delete those not in image ind_inside = clip_anchors(anchors, im_info[:2]) #ind_inside = np.logical_and(ind_inside,clip_anchors(proposals_bv, im_info[:2])) proposals_bv = proposals_bv[ind_inside,:] proposals_3d = proposals_3d[ind_inside,:] proposals_img = proposals_img[ind_inside,:] scores = scores[ind_inside,:] #print 'time for score clip: ', time.time()-t0, scores.shape #t0 = time.time() if DEBUG: # print "bbox_deltas: ", bbox_deltas[:10] # print "proposals number: ", proposals_3d[:10] print ("proposals_bv shape: ", proposals_bv.shape) print ("proposals_3d shape: ", proposals_3d.shape) # 2. clip predicted boxes to image proposals_bv = clip_boxes(proposals_bv, im_info[:2]) # 3. remove predicted boxes with either height or width < threshold # (NOTE: convert min_size to input image scale stored in im_info[2]) keep = _filter_boxes(proposals_bv, min_size * im_info[2]) proposals_bv = proposals_bv[keep, :] proposals_3d = proposals_3d[keep, :] proposals_img = proposals_img[keep, :] scores = scores[keep] #WZN: discard ''' # TODO: pass real image_info keep = _filter_img_boxes(proposals_img, [375, 1242]) proposals_bv = proposals_bv[keep, :] proposals_3d = proposals_3d[keep, :] proposals_img = proposals_img[keep, :] scores = scores[keep] ''' if DEBUG: print ("proposals after clip") print ("proposals_bv shape: ", proposals_bv.shape) print ("proposals_3d shape: ", proposals_3d.shape) print ("proposals_img shape: ", proposals_img.shape) # 4. sort all (proposal, score) pairs by score from highest to lowest ''' WZN: moved to upper to save time # 5. take top pre_nms_topN (e.g. 6000) order = scores.ravel().argsort()[::-1] if pre_nms_topN > 0: order = order[:pre_nms_topN] proposals_bv = proposals_bv[order, :] proposals_3d = proposals_3d[order, :] proposals_img = proposals_img[order, :] scores = scores[order] ''' # 6. apply nms (e.g. threshold = 0.7) # 7. take after_nms_topN (e.g. 300) # 8. return the top proposals (-> RoIs top) keep = nms(np.hstack((proposals_bv, scores)), nms_thresh) if post_nms_topN > 0: keep = keep[:post_nms_topN] proposals_bv = proposals_bv[keep, :] proposals_3d = proposals_3d[keep, :] proposals_img = proposals_img[keep, :] scores = scores[keep] if DEBUG: print ("proposals after nms") print ("proposals_bv shape: ", proposals_bv.shape) print ("proposals_3d shape: ", proposals_3d.shape) # Output rois blob # Our RPN implementation only supports a single input image, so all # batch inds are 0 batch_inds = np.zeros((proposals_bv.shape[0], 1), dtype=np.float32) blob_bv = np.hstack((batch_inds, proposals_bv.astype(np.float32, copy=False))) blob_img = np.hstack((batch_inds, proposals_img.astype(np.float32, copy=False))) blob_3d = np.hstack((batch_inds, proposals_3d.astype(np.float32, copy=False))) if DEBUG: print ("blob shape ====================:") print (blob_bv.shape) print (blob_img.shape) return blob_bv, blob_img, blob_3d, scores def proposal_layer(rpn_cls_prob_reshape,rpn_bbox_pred,im_info,cfg_key,_feat_stride = [16,],anchor_scales = [8, 16, 32],DEBUG = False): # Algorithm: # # for each (H, W) location i # generate A anchor boxes centered on cell i # apply predicted bbox deltas at cell i to each of the A anchors # clip predicted boxes to image # remove predicted boxes with either height or width < threshold # sort all (proposal, score) pairs by score from highest to lowest # take top pre_nms_topN proposals before NMS # apply NMS with threshold 0.7 to remaining proposals # take after_nms_topN proposals after NMS # return the top proposals (-> RoIs top, scores top) #layer_params = yaml.load(self.param_str_) # _anchors = generate_anchors_bv() _anchors = generate_anchors(scales=np.array(anchor_scales)) _num_anchors = _anchors.shape[0] rpn_cls_prob_reshape = np.transpose(rpn_cls_prob_reshape,[0,3,1,2]) rpn_bbox_pred = np.transpose(rpn_bbox_pred,[0,3,1,2]) #rpn_cls_prob_reshape = np.transpose(np.reshape(rpn_cls_prob_reshape,[1,rpn_cls_prob_reshape.shape[0],rpn_cls_prob_reshape.shape[1],rpn_cls_prob_reshape.shape[2]]),[0,3,2,1]) #rpn_bbox_pred = np.transpose(rpn_bbox_pred,[0,3,2,1]) im_info = im_info[0] assert rpn_cls_prob_reshape.shape[0] == 1, \ 'Only single item batches are supported' # cfg_key = str(self.phase) # either 'TRAIN' or 'TEST' #cfg_key = 'TEST' pre_nms_topN = cfg[cfg_key].RPN_PRE_NMS_TOP_N post_nms_topN = cfg[cfg_key].RPN_POST_NMS_TOP_N nms_thresh = cfg[cfg_key].RPN_NMS_THRESH min_size = cfg[cfg_key].RPN_MIN_SIZE # the first set of _num_anchors channels are bg probs # the second set are the fg probs, which we want scores = rpn_cls_prob_reshape[:, _num_anchors:, :, :] bbox_deltas = rpn_bbox_pred #im_info = bottom[2].data[0, :] if DEBUG: print ('im_size: ({}, {})'.format(im_info[0], im_info[1])) print ('scale: {}'.format(im_info[2])) # 1. Generate proposals from bbox deltas and shifted anchors height, width = scores.shape[-2:] if DEBUG: print ('score map size: {}'.format(scores.shape)) # Enumerate all shifts shift_x = np.arange(0, width) * _feat_stride shift_y = np.arange(0, height) * _feat_stride shift_x, shift_y = np.meshgrid(shift_x, shift_y) shifts = np.vstack((shift_x.ravel(), shift_y.ravel(), shift_x.ravel(), shift_y.ravel())).transpose() # Enumerate all shifted anchors: # # add A anchors (1, A, 4) to # cell K shifts (K, 1, 4) to get # shift anchors (K, A, 4) # reshape to (K*A, 4) shifted anchors A = _num_anchors K = shifts.shape[0] anchors = _anchors.reshape((1, A, 4)) + \ shifts.reshape((1, K, 4)).transpose((1, 0, 2)) anchors = anchors.reshape((K * A, 4)) # Transpose and reshape predicted bbox transformations to get them # into the same order as the anchors: # # bbox deltas will be (1, 4 * A, H, W) format # transpose to (1, H, W, 4 * A) # reshape to (1 * H * W * A, 4) where rows are ordered by (h, w, a) # in slowest to fastest order bbox_deltas = bbox_deltas.transpose((0, 2, 3, 1)).reshape((-1, 4)) # Same story for the scores: # # scores are (1, A, H, W) format # transpose to (1, H, W, A) # reshape to (1 * H * W * A, 1) where rows are ordered by (h, w, a) scores = scores.transpose((0, 2, 3, 1)).reshape((-1, 1)) anchors_3d = bv_anchor_to_lidar(anchors) # Convert anchors into proposals via bbox transformations proposals = bbox_transform_inv_3d(anchors_3d, bbox_deltas) # 2. clip predicted boxes to image proposals = clip_boxes(proposals, im_info[:2]) # 3. remove predicted boxes with either height or width < threshold # (NOTE: convert min_size to input image scale stored in im_info[2]) keep = _filter_boxes(proposals, min_size * im_info[2]) proposals = proposals[keep, :] scores = scores[keep] # 4. sort all (proposal, score) pairs by score from highest to lowest # 5. take top pre_nms_topN (e.g. 6000) order = scores.ravel().argsort()[::-1] if pre_nms_topN > 0: order = order[:pre_nms_topN] proposals = proposals[order, :] scores = scores[order] # 6. apply nms (e.g. threshold = 0.7) # 7. take after_nms_topN (e.g. 300) # 8. return the top proposals (-> RoIs top) keep = nms(np.hstack((proposals, scores)), nms_thresh) if post_nms_topN > 0: keep = keep[:post_nms_topN] proposals = proposals[keep, :] scores = scores[keep] # Output rois blob # Our RPN implementation only supports a single input image, so all # batch inds are 0 batch_inds = np.zeros((proposals.shape[0], 1), dtype=np.float32) blob = np.hstack((batch_inds, proposals.astype(np.float32, copy=False))) return blob #top[0].reshape(*(blob.shape)) #top[0].data[...] = blob # [Optional] output scores blob #if len(top) > 1: # top[1].reshape(*(scores.shape)) # top[1].data[...] = scores def _filter_boxes(boxes, min_size): """Remove all boxes with any side smaller than min_size.""" ws = boxes[:, 2] - boxes[:, 0] + 1 hs = boxes[:, 3] - boxes[:, 1] + 1 #WZN: filter boxes too far away ds = (boxes[:, 3] + boxes[:, 1])/2 keep = np.where((ws >= min_size) & (hs >= min_size) )[0] #& (ds<460) return keep def _filter_img_boxes(boxes, im_info): """Remove all boxes with any side smaller than min_size.""" padding = 50 w_min = -padding w_max = im_info[1] + padding h_min = -padding h_max = im_info[0] + padding keep = np.where((w_min <= boxes[:,0]) & (boxes[:,2] <= w_max) & (h_min <= boxes[:,1]) & (boxes[:,3] <= h_max))[0] return keep

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py

Python

pycsw/core/test_spatialSimilarity.py

Anika2/aahll-pycsw

cacc662e4d252d3bb12ccd225d67e936a53b6e4a

[ "MIT" ]

null

pycsw/core/test_spatialSimilarity.py

Anika2/aahll-pycsw

cacc662e4d252d3bb12ccd225d67e936a53b6e4a

[ "MIT" ]

null

pycsw/core/test_spatialSimilarity.py

Anika2/aahll-pycsw

cacc662e4d252d3bb12ccd225d67e936a53b6e4a

[ "MIT" ]

null

# Author: Lia Kirsch import spatialSimilarity # Geometry def test_spatialdistance_Geometry(): total = spatialSimilarity.spatialDistance([13.0078125, 50.62507306341435, 5.44921875, 45.82879925192134], [ 17.7978515625, 52.09300763963822, 7.27294921875, 46.14939437647686]) assert total == 74.02 def test_spatialOverlap_Geometry(): total = spatialSimilarity.spatialOverlap([13.0078125, 50.62507306341435, 5.44921875, 45.82879925192134], [ 17.7978515625, 52.09300763963822, 7.27294921875, 46.14939437647686]) assert total == 41.26 def test_similarArea_Geometry(): total = spatialSimilarity.similarArea([13.0078125, 50.62507306341435, 5.44921875, 45.82879925192134], [ 17.7978515625, 52.09300763963822, 7.27294921875, 46.14939437647686]) assert total == 58.98 # Points def test_spatialdistance_Points(): total = spatialSimilarity.spatialDistance([13.0078125, 50.62507306341435, 13.0078125, 50.62507306341435], [ 13.0082125, 50.62513301341435, 13.0082125, 50.62513301341435]) assert total == 99.1 def test_spatialOverlap_Points(): total = spatialSimilarity.spatialOverlap([13.0078125, 50.62507306341435, 13.0078125, 50.62507306341435], [ 13.0082125, 50.62513301341435, 13.0082125, 50.62513301341435]) assert total == 81.45 def test_similarArea_Points(): total = spatialSimilarity.similarArea([13.0078125, 50.62507306341435, 13.0078125, 50.62507306341435], [ 13.0082125, 50.62513301341435, 13.0082125, 50.62513301341435]) assert total == 100.0 ##Line and Point def test_spatialdistance_LineAndPoint(): total = spatialSimilarity.spatialDistance([11.0078125, 50.62507306341435, 13.0078125, 50.62507306341435], [ 13.0082125, 50.62513301341435, 13.0082125, 50.62513301341435]) assert total == 49.97 def test_spatialOverlap_LineAndPoint(): total = spatialSimilarity.spatialOverlap([11.0078125, 50.62507306341435, 13.0078125, 50.62507306341435], [ 13.0082125, 50.62513301341435, 13.0082125, 50.62513301341435]) assert total == 0.09 def test_similarArea_LineAndPoint(): total = spatialSimilarity.similarArea([11.0078125, 50.62507306341435, 13.0078125, 50.62507306341435], [ 13.0082125, 50.62513301341435, 13.0082125, 50.62513301341435]) assert total == 100.0 ## Polygon and Point def test_spatialdistance_PolygonAndPoint(): total = spatialSimilarity.spatialDistance([13.0078125, 50.62507306341435, 5.44921875, 45.82879925192134], [ 13.0082125, 50.62513301341435, 13.0082125, 50.62513301341435]) assert total == 50.57 def test_spatialOverlap_PolygonAndPoint(): total = spatialSimilarity.spatialOverlap([13.0078125, 50.62507306341435, 5.44921875, 45.82879925192134], [ 13.0082125, 50.62513301341435, 13.0082125, 50.62513301341435]) assert total == 0.0 def test_similarArea_PolygonAndPoint(): total = spatialSimilarity.similarArea([13.0078125, 50.62507306341435, 5.44921875, 45.82879925192134], [ 13.0082125, 50.62513301341435, 13.0082125, 50.62513301341435]) assert total == 0.0 # SameBoundingBox def test_spatialdistance_SameBoundingBox(): total = spatialSimilarity.spatialDistance([0.439453, 29.688053, 3.911133, 31.765537], [ 0.439453, 29.688053, 3.911133, 31.765537]) assert total == 100.00 def test_spatialOverlap_SameBoundingBox(): total = spatialSimilarity.spatialOverlap([0.439453, 29.688053, 3.911133, 31.765537], [ 0.439453, 29.688053, 3.911133, 31.765537]) assert total == 100.0 def test_similarArea_SameBoundingBox(): total = spatialSimilarity.similarArea([0.439453, 29.688053, 3.911133, 31.765537], [ 0.439453, 29.688053, 3.911133, 31.765537]) assert total == 100.0 # similar boundingBoxes that are close together def test_spatialdistance_SBBTACT(): total = spatialSimilarity.spatialDistance([7.596703, 51.950402, 7.656441, 51.978536], [ 7.588205, 51.952412, 7.616014, 51.967644]) assert total == 66.08 def test_spatialOverlap_SSBBTACT(): total = spatialSimilarity.spatialOverlap([7.596703, 51.950402, 7.656441, 51.978536], [ 7.588205, 51.952412, 7.616014, 51.967644]) assert total == 17.5 def test_similarArea_SBBTACT(): total = spatialSimilarity.similarArea([7.596703, 51.950402, 7.656441, 51.978536], [ 7.588205, 51.952412, 7.616014, 51.967644]) assert total == 25.2 # Far away boundingboxes def test_spatialdistance_fABB(): total = spatialSimilarity.spatialDistance( [7.596703, 51.950402, 7.656441, 51.978536], [-96.800194, 32.760085, -96.796353, 32.761385]) assert total == 0 def test_spatialOverlap_fABB(): total = spatialSimilarity.spatialOverlap( [7.596703, 51.950402, 7.656441, 51.978536], [-96.800194, 32.760085, -96.796353, 32.761385]) assert total == 0.0 def test_similarArea_fABB(): total = spatialSimilarity.similarArea( [7.596703, 51.950402, 7.656441, 51.978536], [-96.800194, 32.760085, -96.796353, 32.761385]) assert total == 0.42

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0.040936

0.115288

0.125313

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b657c3743ca24bea90b32e037ba793c40689dd81

10,824

py

Python

stubs/micropython-v1_13-95-pyboard/pyb.py

mattytrentini/micropython-stubs

4d596273823b69e9e5bcf5fa67f249c374ee0bbc

[ "MIT" ]

null

stubs/micropython-v1_13-95-pyboard/pyb.py

mattytrentini/micropython-stubs

4d596273823b69e9e5bcf5fa67f249c374ee0bbc

[ "MIT" ]

null

stubs/micropython-v1_13-95-pyboard/pyb.py

mattytrentini/micropython-stubs

4d596273823b69e9e5bcf5fa67f249c374ee0bbc

[ "MIT" ]

null

""" Module: 'pyb' on pyboard 1.13.0-95 """ # MCU: (sysname='pyboard', nodename='pyboard', release='1.13.0', version='v1.13-95-g0fff2e03f on 2020-10-03', machine='PYBv1.1 with STM32F405RG') # Stubber: 1.3.4 - updated from typing import Any class ADC: """""" def read(self, *args) -> Any: pass def read_timed(self, *args) -> Any: pass def read_timed_multi(self, *args) -> Any: pass class ADCAll: """""" def read_channel(self, *args) -> Any: pass def read_core_temp(self, *args) -> Any: pass def read_core_vbat(self, *args) -> Any: pass def read_core_vref(self, *args) -> Any: pass def read_vref(self, *args) -> Any: pass class Accel: """""" def filtered_xyz(self, *args) -> Any: pass def read(self, *args) -> Any: pass def tilt(self, *args) -> Any: pass def write(self, *args) -> Any: pass def x(self, *args) -> Any: pass def y(self, *args) -> Any: pass def z(self, *args) -> Any: pass class CAN: """""" BUS_OFF = 4 ERROR_ACTIVE = 1 ERROR_PASSIVE = 3 ERROR_WARNING = 2 LIST16 = 1 LIST32 = 3 LOOPBACK = 67108864 MASK16 = 0 MASK32 = 2 NORMAL = 0 SILENT = 134217728 SILENT_LOOPBACK = 201326592 STOPPED = 0 def any(self, *args) -> Any: pass def clearfilter(self, *args) -> Any: pass def deinit(self, *args) -> Any: pass def info(self, *args) -> Any: pass def init(self, *args) -> Any: pass def initfilterbanks(self, *args) -> Any: pass def recv(self, *args) -> Any: pass def restart(self, *args) -> Any: pass def rxcallback(self, *args) -> Any: pass def send(self, *args) -> Any: pass def setfilter(self, *args) -> Any: pass def state(self, *args) -> Any: pass class DAC: """""" CIRCULAR = 256 NORMAL = 0 def deinit(self, *args) -> Any: pass def init(self, *args) -> Any: pass def noise(self, *args) -> Any: pass def triangle(self, *args) -> Any: pass def write(self, *args) -> Any: pass def write_timed(self, *args) -> Any: pass class ExtInt: """""" EVT_FALLING = 270663680 EVT_RISING = 269615104 EVT_RISING_FALLING = 271712256 IRQ_FALLING = 270598144 IRQ_RISING = 269549568 IRQ_RISING_FALLING = 271646720 def disable(self, *args) -> Any: pass def enable(self, *args) -> Any: pass def line(self, *args) -> Any: pass def regs(self, *args) -> Any: pass def swint(self, *args) -> Any: pass class Flash: """""" def ioctl(self, *args) -> Any: pass def readblocks(self, *args) -> Any: pass def writeblocks(self, *args) -> Any: pass class I2C: """""" MASTER = 0 SLAVE = 1 def deinit(self, *args) -> Any: pass def init(self, *args) -> Any: pass def is_ready(self, *args) -> Any: pass def mem_read(self, *args) -> Any: pass def mem_write(self, *args) -> Any: pass def recv(self, *args) -> Any: pass def scan(self, *args) -> Any: pass def send(self, *args) -> Any: pass class LCD: """""" def command(self, *args) -> Any: pass def contrast(self, *args) -> Any: pass def fill(self, *args) -> Any: pass def get(self, *args) -> Any: pass def light(self, *args) -> Any: pass def pixel(self, *args) -> Any: pass def show(self, *args) -> Any: pass def text(self, *args) -> Any: pass def write(self, *args) -> Any: pass class LED: """""" def intensity(self, *args) -> Any: pass def off(self, *args) -> Any: pass def on(self, *args) -> Any: pass def toggle(self, *args) -> Any: pass class Pin: """""" AF1_TIM1 = 1 AF1_TIM2 = 1 AF2_TIM3 = 2 AF2_TIM4 = 2 AF2_TIM5 = 2 AF3_TIM10 = 3 AF3_TIM11 = 3 AF3_TIM8 = 3 AF3_TIM9 = 3 AF4_I2C1 = 4 AF4_I2C2 = 4 AF5_SPI1 = 5 AF5_SPI2 = 5 AF7_USART1 = 7 AF7_USART2 = 7 AF7_USART3 = 7 AF8_UART4 = 8 AF8_USART6 = 8 AF9_CAN1 = 9 AF9_CAN2 = 9 AF9_TIM12 = 9 AF9_TIM13 = 9 AF9_TIM14 = 9 AF_OD = 18 AF_PP = 2 ALT = 2 ALT_OPEN_DRAIN = 18 ANALOG = 3 IN = 0 IRQ_FALLING = 270598144 IRQ_RISING = 269549568 OPEN_DRAIN = 17 OUT = 1 OUT_OD = 17 OUT_PP = 1 PULL_DOWN = 2 PULL_NONE = 0 PULL_UP = 1 def af(self, *args) -> Any: pass def af_list(self, *args) -> Any: pass board = None cpu = None def debug(self, *args) -> Any: pass def dict(self, *args) -> Any: pass def gpio(self, *args) -> Any: pass def high(self, *args) -> Any: pass def init(self, *args) -> Any: pass def irq(self, *args) -> Any: pass def low(self, *args) -> Any: pass def mapper(self, *args) -> Any: pass def mode(self, *args) -> Any: pass def name(self, *args) -> Any: pass def names(self, *args) -> Any: pass def off(self, *args) -> Any: pass def on(self, *args) -> Any: pass def pin(self, *args) -> Any: pass def port(self, *args) -> Any: pass def pull(self, *args) -> Any: pass def value(self, *args) -> Any: pass class RTC: """""" def calibration(self, *args) -> Any: pass def datetime(self, *args) -> Any: pass def info(self, *args) -> Any: pass def init(self, *args) -> Any: pass def wakeup(self, *args) -> Any: pass SD = None class SDCard: """""" def info(self, *args) -> Any: pass def ioctl(self, *args) -> Any: pass def power(self, *args) -> Any: pass def present(self, *args) -> Any: pass def read(self, *args) -> Any: pass def readblocks(self, *args) -> Any: pass def write(self, *args) -> Any: pass def writeblocks(self, *args) -> Any: pass class SPI: """""" LSB = 128 MASTER = 260 MSB = 0 SLAVE = 0 def deinit(self, *args) -> Any: pass def init(self, *args) -> Any: pass def read(self, *args) -> Any: pass def readinto(self, *args) -> Any: pass def recv(self, *args) -> Any: pass def send(self, *args) -> Any: pass def send_recv(self, *args) -> Any: pass def write(self, *args) -> Any: pass def write_readinto(self, *args) -> Any: pass class Servo: """""" def angle(self, *args) -> Any: pass def calibration(self, *args) -> Any: pass def pulse_width(self, *args) -> Any: pass def speed(self, *args) -> Any: pass class Switch: """""" def callback(self, *args) -> Any: pass def value(self, *args) -> Any: pass class Timer: """""" BOTH = 10 BRK_HIGH = 2 BRK_LOW = 1 BRK_OFF = 0 CENTER = 32 DOWN = 16 ENC_A = 9 ENC_AB = 11 ENC_B = 10 FALLING = 2 HIGH = 0 IC = 8 LOW = 2 OC_ACTIVE = 3 OC_FORCED_ACTIVE = 6 OC_FORCED_INACTIVE = 7 OC_INACTIVE = 4 OC_TIMING = 2 OC_TOGGLE = 5 PWM = 0 PWM_INVERTED = 1 RISING = 0 UP = 0 def callback(self, *args) -> Any: pass def channel(self, *args) -> Any: pass def counter(self, *args) -> Any: pass def deinit(self, *args) -> Any: pass def freq(self, *args) -> Any: pass def init(self, *args) -> Any: pass def period(self, *args) -> Any: pass def prescaler(self, *args) -> Any: pass def source_freq(self, *args) -> Any: pass class UART: """""" CTS = 512 IRQ_RXIDLE = 16 RTS = 256 def any(self, *args) -> Any: pass def deinit(self, *args) -> Any: pass def init(self, *args) -> Any: pass def irq(self, *args) -> Any: pass def read(self, *args) -> Any: pass def readchar(self, *args) -> Any: pass def readinto(self, *args) -> Any: pass def readline(self, *args) -> Any: pass def sendbreak(self, *args) -> Any: pass def write(self, *args) -> Any: pass def writechar(self, *args) -> Any: pass class USB_HID: """""" def recv(self, *args) -> Any: pass def send(self, *args) -> Any: pass class USB_VCP: """""" CTS = 2 RTS = 1 def any(self, *args) -> Any: pass def close(self, *args) -> Any: pass def init(self, *args) -> Any: pass def isconnected(self, *args) -> Any: pass def read(self, *args) -> Any: pass def readinto(self, *args) -> Any: pass def readline(self, *args) -> Any: pass def readlines(self, *args) -> Any: pass def recv(self, *args) -> Any: pass def send(self, *args) -> Any: pass def setinterrupt(self, *args) -> Any: pass def write(self, *args) -> Any: pass def bootloader(*args) -> Any: pass def country(*args) -> Any: pass def delay(*args) -> Any: pass def dht_readinto(*args) -> Any: pass def disable_irq(*args) -> Any: pass def elapsed_micros(*args) -> Any: pass def elapsed_millis(*args) -> Any: pass def enable_irq(*args) -> Any: pass def fault_debug(*args) -> Any: pass def freq(*args) -> Any: pass def hard_reset(*args) -> Any: pass def have_cdc(*args) -> Any: pass def hid(*args) -> Any: pass hid_keyboard = None hid_mouse = None def info(*args) -> Any: pass def main(*args) -> Any: pass def micros(*args) -> Any: pass def millis(*args) -> Any: pass def mount(*args) -> Any: pass def pwm(*args) -> Any: pass def repl_info(*args) -> Any: pass def repl_uart(*args) -> Any: pass def rng(*args) -> Any: pass def servo(*args) -> Any: pass def standby(*args) -> Any: pass def stop(*args) -> Any: pass def sync(*args) -> Any: pass def udelay(*args) -> Any: pass def unique_id(*args) -> Any: pass def usb_mode(*args) -> Any: pass def wfi(*args) -> Any: pass

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1

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6

b6628f7ce8905e934bd40723b480c40fa73204a6

59

py

Python

src/__init__.py

m1k1o/ext4-backup-pointers

b5a4ce1f7fce87d0feed8df8294f40c919ece578

[ "Apache-2.0" ]

1

2020-05-09T17:36:06.000Z

src/__init__.py

m1k1o/ext4-backup-pointers

b5a4ce1f7fce87d0feed8df8294f40c919ece578

[ "Apache-2.0" ]

1

2020-05-09T23:34:45.000Z

src/__init__.py

m1k1o/ext4-backup-pointers

b5a4ce1f7fce87d0feed8df8294f40c919ece578

[ "Apache-2.0" ]

null

from src.console import start def console(): start()

11.8

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true

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null

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6

b67462b8708f7acc5d93c5276a0508fddf2ff772

5,791

py

Python

mlxtend/mlxtend/regressor/tests/test_linear_regression.py

WhiteWolf21/fp-growth

01e1d853b09f244f14e66d7d0c87f139a0f67c81

[ "MIT" ]

null

mlxtend/mlxtend/regressor/tests/test_linear_regression.py

WhiteWolf21/fp-growth

01e1d853b09f244f14e66d7d0c87f139a0f67c81

[ "MIT" ]

null

mlxtend/mlxtend/regressor/tests/test_linear_regression.py

WhiteWolf21/fp-growth

01e1d853b09f244f14e66d7d0c87f139a0f67c81

[ "MIT" ]

null

# Sebastian Raschka 2014-2020 # mlxtend Machine Learning Library Extensions # Author: Sebastian Raschka <sebastianraschka.com> # # License: BSD 3 clause from mlxtend.regressor import LinearRegression from mlxtend.data import boston_housing_data import numpy as np from numpy.testing import assert_almost_equal from sklearn.base import clone X, y = boston_housing_data() X_rm = X[:, 5][:, np.newaxis] X_rm_lstat = X[:, [5, -1]] # standardized variables X_rm_std = (X_rm - X_rm.mean(axis=0)) / X_rm.std(axis=0) X_rm_lstat_std = ((X_rm_lstat - X_rm_lstat.mean(axis=0)) / X_rm_lstat.std(axis=0)) y_std = (y - y.mean()) / y.std() def test_univariate_normal_equation(): w_exp = np.array([[9.1]]) b_exp = np.array([-34.7]) ne_lr = LinearRegression() ne_lr.fit(X_rm, y) assert_almost_equal(ne_lr.w_, w_exp, decimal=1) assert_almost_equal(ne_lr.b_, b_exp, decimal=1) def test_univariate_normal_equation_std(): w_exp = np.array([[0.7]]) b_exp = np.array([0.0]) ne_lr = LinearRegression() ne_lr.fit(X_rm_std, y_std) assert_almost_equal(ne_lr.w_, w_exp, decimal=1) assert_almost_equal(ne_lr.b_, b_exp, decimal=1) def test_univariate_gradient_descent(): w_exp = np.array([[0.7]]) b_exp = np.array([0.0]) gd_lr = LinearRegression(method='sgd', minibatches=1, eta=0.001, epochs=500, random_seed=0) gd_lr.fit(X_rm_std, y_std) assert_almost_equal(gd_lr.w_, w_exp, decimal=1) assert_almost_equal(gd_lr.b_, b_exp, decimal=1) def test_univariate_qr(): w_exp = np.array([[9.1]]) b_exp = np.array([-34.7]) qr_lr = LinearRegression(method='qr') qr_lr.fit(X_rm, y) assert_almost_equal(qr_lr.w_, w_exp, decimal=1) assert_almost_equal(qr_lr.b_, b_exp, decimal=1) def test_univariate_svd(): w_exp = np.array([[9.1]]) b_exp = np.array([-34.7]) svd_lr = LinearRegression(method='svd') svd_lr.fit(X_rm, y) assert_almost_equal(svd_lr.w_, w_exp, decimal=1) assert_almost_equal(svd_lr.b_, b_exp, decimal=1) def test_progress_1(): gd_lr = LinearRegression(method='sgd', minibatches=1, eta=0.001, epochs=1, print_progress=1, random_seed=0) gd_lr.fit(X_rm_std, y_std) def test_progress_2(): gd_lr = LinearRegression(method='sgd', minibatches=1, eta=0.001, epochs=1, print_progress=2, random_seed=0) gd_lr.fit(X_rm_std, y_std) def test_progress_3(): gd_lr = LinearRegression(method='sgd', minibatches=1, eta=0.001, epochs=1, print_progress=2, random_seed=0) gd_lr.fit(X_rm_std, y_std) def test_univariate_stochastic_gradient_descent(): w_exp = np.array([[0.7]]) b_exp = np.array([0.0]) sgd_lr = LinearRegression(method='sgd', minibatches=len(y), eta=0.0001, epochs=150, random_seed=0) sgd_lr.fit(X_rm_std, y_std) assert_almost_equal(sgd_lr.w_, w_exp, decimal=1) assert_almost_equal(sgd_lr.b_, b_exp, decimal=1) def test_multivariate_normal_equation(): w_exp = np.array([[5.1], [-0.6]]) b_exp = np.array([-1.5]) ne_lr = LinearRegression() ne_lr.fit(X_rm_lstat, y) assert_almost_equal(ne_lr.w_, w_exp, decimal=1) assert_almost_equal(ne_lr.b_, b_exp, decimal=1) def test_multivariate_gradient_descent(): w_exp = np.array([[0.4], [-0.5]]) b_exp = np.array([0.0]) gd_lr = LinearRegression(method='sgd', eta=0.001, epochs=500, minibatches=1, random_seed=0) gd_lr.fit(X_rm_lstat_std, y_std) assert_almost_equal(gd_lr.w_, w_exp, decimal=1) assert_almost_equal(gd_lr.b_, b_exp, decimal=1) def test_multivariate_stochastic_gradient_descent(): w_exp = np.array([[0.4], [-0.5]]) b_exp = np.array([0.0]) sgd_lr = LinearRegression(method='sgd', eta=0.0001, epochs=500, minibatches=len(y), random_seed=0) sgd_lr.fit(X_rm_lstat_std, y_std) assert_almost_equal(sgd_lr.w_, w_exp, decimal=1) assert_almost_equal(sgd_lr.b_, b_exp, decimal=1) def test_ary_persistency_in_shuffling(): orig = X_rm_lstat_std.copy() sgd_lr = LinearRegression(method='sgd', eta=0.0001, epochs=500, minibatches=len(y), random_seed=0) sgd_lr.fit(X_rm_lstat_std, y_std) np.testing.assert_almost_equal(orig, X_rm_lstat_std, 6) def test_multivariate_qr(): w_exp = np.array([[5.1], [-0.6]]) b_exp = np.array([-1.5]) qr_lr = LinearRegression(method='qr') qr_lr.fit(X_rm_lstat, y) assert_almost_equal(qr_lr.w_, w_exp, decimal=1) assert_almost_equal(qr_lr.b_, b_exp, decimal=1) def test_multivariate_svd(): w_exp = np.array([[5.1], [-0.6]]) b_exp = np.array([-1.5]) svd_lr = LinearRegression(method='svd') svd_lr.fit(X_rm_lstat, y) assert_almost_equal(svd_lr.w_, w_exp, decimal=1) assert_almost_equal(svd_lr.b_, b_exp, decimal=1) def test_clone(): regr = LinearRegression() clone(regr)

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b6804856bec79c7d2d285078375638cc5601e190

145

py

Python

4_class/utils/__init__.py

Acrophase/Sleep_Staging_KD

e40bcef04fed669153fcb6192663bf0f1efaacb1

[ "MIT" ]

15

2022-01-16T01:22:32.000Z

2022-02-03T07:17:14.000Z

3_class/utils/__init__.py

Acrophase/Sleep_Staging_KD

e40bcef04fed669153fcb6192663bf0f1efaacb1

[ "MIT" ]

null

3_class/utils/__init__.py

Acrophase/Sleep_Staging_KD

e40bcef04fed669153fcb6192663bf0f1efaacb1

[ "MIT" ]

2

2022-01-17T03:51:40.000Z

2022-01-25T20:10:53.000Z

from .arg_utils import get_args from .dataset_utils import get_data from .callback_utils import get_callbacks from .model_utils import get_model

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6

b68f4fbccc758b2fc9e73a626b8b3eab5c219c65

6,111

py

Python

imcsdk/mometa/comm/CommMailAlert.py

vadimkuznetsov/imcsdk

ed038ce1dbc8031f99d2dfb3ccee3bf0b48309d8

[ "Apache-2.0" ]

null

imcsdk/mometa/comm/CommMailAlert.py

vadimkuznetsov/imcsdk

ed038ce1dbc8031f99d2dfb3ccee3bf0b48309d8

[ "Apache-2.0" ]

null

imcsdk/mometa/comm/CommMailAlert.py

vadimkuznetsov/imcsdk

ed038ce1dbc8031f99d2dfb3ccee3bf0b48309d8

[ "Apache-2.0" ]

1

2019-11-10T18:42:04.000Z

"""This module contains the general information for CommMailAlert ManagedObject.""" from ...imcmo import ManagedObject from ...imccoremeta import MoPropertyMeta, MoMeta from ...imcmeta import VersionMeta class CommMailAlertConsts: MIN_SEVERITY_LEVEL_CONDITION = "condition" MIN_SEVERITY_LEVEL_CRITICAL = "critical" MIN_SEVERITY_LEVEL_MAJOR = "major" MIN_SEVERITY_LEVEL_MINOR = "minor" MIN_SEVERITY_LEVEL_WARNING = "warning" class CommMailAlert(ManagedObject): """This is CommMailAlert class.""" consts = CommMailAlertConsts() naming_props = set([]) mo_meta = { "classic": MoMeta("CommMailAlert", "commMailAlert", "mail-alert-svc", VersionMeta.Version303a, "InputOutput", 0xff, [], ["admin", "read-only", "user"], [u'commSvcEp'], [u'mailRecipient'], ["Get", "Set"]), "modular": MoMeta("CommMailAlert", "commMailAlert", "mail-alert-svc", VersionMeta.Version303a, "InputOutput", 0xff, [], ["admin", "read-only", "user"], [u'commSvcEp'], [u'mailRecipient'], ["Get", "Set"]) } prop_meta = { "classic": { "admin_state": MoPropertyMeta("admin_state", "adminState", "string", VersionMeta.Version303a, MoPropertyMeta.READ_WRITE, 0x2, None, None, None, ["Disabled", "Enabled", "disabled", "enabled"], []), "child_action": MoPropertyMeta("child_action", "childAction", "string", VersionMeta.Version303a, MoPropertyMeta.INTERNAL, None, None, None, None, [], []), "dn": MoPropertyMeta("dn", "dn", "string", VersionMeta.Version303a, MoPropertyMeta.READ_WRITE, 0x4, 0, 255, None, [], []), "ip_address": MoPropertyMeta("ip_address", "ipAddress", "string", VersionMeta.Version303a, MoPropertyMeta.READ_WRITE, 0x8, 0, 255, r"""(([0-9A-Fa-f]{1,4}:([0-9A-Fa-f]{1,4}:([0-9A-Fa-f]{1,4}:([0-9A-Fa-f]{1,4}:([0-9A-Fa-f]{1,4}:[0-9A-Fa-f]{0,4}|:[0-9A-Fa-f]{1,4})?|(:[0-9A-Fa-f]{1,4}){0,2})|(:[0-9A-Fa-f]{1,4}){0,3})|(:[0-9A-Fa-f]{1,4}){0,4})|:(:[0-9A-Fa-f]{1,4}){0,5})((:[0-9A-Fa-f]{1,4}){2}|:(25[0-5]|(2[0-4]|1[0-9]|[1-9])?[0-9])(\.(25[0-5]|(2[0-4]|1[0-9]|[1-9])?[0-9])){3})|(([0-9A-Fa-f]{1,4}:){1,6}|:):[0-9A-Fa-f]{0,4}|([0-9A-Fa-f]{1,4}:){7}:) |((([a-zA-Z0-9]([a-zA-Z0-9\-]{0,61}[a-zA-Z0-9])?\.)+[a-zA-Z]{2,6})|(([a-zA-Z0-9]([a-zA-Z0-9\-]{0,61}[a-zA-Z0-9])?)+)|([1-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])\.([0-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])\.([0-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5])\.([1-9]|[1-9][0-9]|1[0-9][0-9]|2[0-4][0-9]|25[0-5]))""", [], []), "min_severity_level": MoPropertyMeta("min_severity_level", "minSeverityLevel", "string", VersionMeta.Version303a, MoPropertyMeta.READ_WRITE, 0x10, None, None, None, ["condition", "critical", "major", "minor", "warning"], []), "port": MoPropertyMeta("port", "port", "uint", VersionMeta.Version303a, MoPropertyMeta.READ_WRITE, 0x20, None, None, None, [], ["1-65535"]), "rn": MoPropertyMeta("rn", "rn", "string", VersionMeta.Version303a, MoPropertyMeta.READ_WRITE, 0x40, 0, 255, None, [], []), "status": MoPropertyMeta("status", "status", "string", VersionMeta.Version303a, MoPropertyMeta.READ_WRITE, 0x80, None, None, None, ["", "created", "deleted", "modified", "removed"], []), }, "modular": { "admin_state": MoPropertyMeta("admin_state", "adminState", "string", VersionMeta.Version303a, MoPropertyMeta.READ_WRITE, 0x2, None, None, None, ["Disabled", "Enabled", "disabled", "enabled"], []), "child_action": MoPropertyMeta("child_action", "childAction", "string", VersionMeta.Version303a, MoPropertyMeta.INTERNAL, None, None, None, None, [], []), "dn": MoPropertyMeta("dn", "dn", "string", VersionMeta.Version303a, MoPropertyMeta.READ_WRITE, 0x4, 0, 255, None, [], []), "ip_address": MoPropertyMeta("ip_address", "ipAddress", "string", VersionMeta.Version303a, MoPropertyMeta.READ_WRITE, 0x8, 0, 255, r"""([0-9A-Fa-f]{1,4}:([0-9A-Fa-f]{1,4}:([0-9A-Fa-f]{1,4}:([0-9A-Fa-f]{1,4}:([0-9A-Fa-f]{1,4}:[0-9A-Fa-f]{0,4}|:[0-9A-Fa-f]{1,4})?|(:[0-9A-Fa-f]{1,4}){0,2})|(:[0-9A-Fa-f]{1,4}){0,3})|(:[0-9A-Fa-f]{1,4}){0,4})|:(:[0-9A-Fa-f]{1,4}){0,5})((:[0-9A-Fa-f]{1,4}){2}|:(25[0-5]|(2[0-4]|1[0-9]|[1-9])?[0-9])(\.(25[0-5]|(2[0-4]|1[0-9]|[1-9])?[0-9])){3})|(([0-9A-Fa-f]{1,4}:){1,6}|:):[0-9A-Fa-f]{0,4}|([0-9A-Fa-f]{1,4}:){7}:""", [], []), "min_severity_level": MoPropertyMeta("min_severity_level", "minSeverityLevel", "string", VersionMeta.Version303a, MoPropertyMeta.READ_WRITE, 0x10, None, None, None, ["condition", "critical", "major", "minor", "warning"], []), "port": MoPropertyMeta("port", "port", "uint", VersionMeta.Version303a, MoPropertyMeta.READ_WRITE, 0x20, None, None, None, [], ["1-65535"]), "rn": MoPropertyMeta("rn", "rn", "string", VersionMeta.Version303a, MoPropertyMeta.READ_WRITE, 0x40, 0, 255, None, [], []), "status": MoPropertyMeta("status", "status", "string", VersionMeta.Version303a, MoPropertyMeta.READ_WRITE, 0x80, None, None, None, ["", "created", "deleted", "modified", "removed"], []), }, } prop_map = { "classic": { "adminState": "admin_state", "childAction": "child_action", "dn": "dn", "ipAddress": "ip_address", "minSeverityLevel": "min_severity_level", "port": "port", "rn": "rn", "status": "status", }, "modular": { "adminState": "admin_state", "childAction": "child_action", "dn": "dn", "ipAddress": "ip_address", "minSeverityLevel": "min_severity_level", "port": "port", "rn": "rn", "status": "status", }, } def __init__(self, parent_mo_or_dn, **kwargs): self._dirty_mask = 0 self.admin_state = None self.child_action = None self.ip_address = None self.min_severity_level = None self.port = None self.status = None ManagedObject.__init__(self, "CommMailAlert", parent_mo_or_dn, **kwargs)

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0.231884

0

null

0

1

0

null

0

6

fcd7a0e40264829f945d11a7a6fe887077226a24

28

py

Python

nblog/core/models/__init__.py

NestorMonroy/BlogTemplate

82dfc7eb26e8a8ff0d51f29176c3b4d537092be7

[ "MIT" ]

1

2019-09-16T13:23:44.000Z

nblog/core/models/__init__.py

NestorMonroy/BlogTemplate

82dfc7eb26e8a8ff0d51f29176c3b4d537092be7

[ "MIT" ]

8

2020-07-22T02:06:35.000Z

2021-09-22T19:22:27.000Z

nblog/core/models/__init__.py

NestorMonroy/BlogTemplate

82dfc7eb26e8a8ff0d51f29176c3b4d537092be7

[ "MIT" ]

1

2019-09-17T13:24:27.000Z

from .notifications import *

28

0.821429

3

28

7.666667

1

0

0.107143

28

1

28

0.92

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

fcd91f6d7ad6ea5ee46c94b95cd34dea3d40844d

9,558

py

Python

tests/modules/test_math.py

HelloMelanieC/batavia

1fc436e2cf7d14896bc485b6a25f2a396cfefaf3

[ "BSD-3-Clause" ]

1

2021-01-03T00:59:23.000Z

tests/modules/test_math.py

HelloMelanieC/batavia

1fc436e2cf7d14896bc485b6a25f2a396cfefaf3

[ "BSD-3-Clause" ]

null

tests/modules/test_math.py

HelloMelanieC/batavia

1fc436e2cf7d14896bc485b6a25f2a396cfefaf3

[ "BSD-3-Clause" ]

null

import sys from unittest import skipUnless from ..utils import ModuleFunctionTestCase, TranspileTestCase class MathTests(ModuleFunctionTestCase, TranspileTestCase): substitutions = { # A '7.32747...e-15': [ '7.35784...e-15' ], '1.53745...e-12': [ '1.53743...e-12' ], } @classmethod def add_math_tests(klass): klass.add_one_arg_tests('math', [ 'acos', 'acosh', 'asin', 'asinh', 'atan', 'atanh', 'ceil', 'cos', 'cosh', 'degrees', 'exp', 'expm1', 'erf', 'erfc', 'fabs', 'factorial', 'floor', 'frexp', 'fsum', 'gamma', 'isfinite', 'isinf', 'isnan', 'lgamma', 'log', 'log10', 'log1p', 'log2', 'modf', 'radians', 'sin', 'sinh', 'sqrt', 'tan', 'tanh', 'trunc', ], numerics_only=True) klass.add_two_arg_tests('math', [ 'atan2', 'copysign', 'fmod', 'hypot', 'ldexp', 'log', 'pow', ], numerics_only=True) if sys.version_info >= (3, 5): klass.add_two_arg_tests('math', [ 'gcd', 'isclose', ], numerics_only=True) not_implemented = [ 'test_math_acos_float', 'test_math_acos_int', 'test_math_asin_float', 'test_math_asin_int', 'test_math_fsum_NotImplemented', 'test_math_fsum_bytearray', 'test_math_fsum_bytes', 'test_math_fsum_complex', 'test_math_fsum_dict', 'test_math_fsum_range', ] def test_constants(self): self.assertCodeExecution(""" import math print(math.e) print(math.pi) """) @skipUnless(sys.version_info >= (3, 5), reason="Need CPython 3.5") def test_constants_35(self): self.assertCodeExecution(""" import math print(math.inf) print(math.nan) """) def test_erf(self): # test some of the edge cases of erf to 15 digits of precision self.assertCodeExecution(""" import math print(round(math.erf(0.75) * (10**15))) print(round(math.erf(1.40) * (10**15))) print(round(math.erf(1.60) * (10**15))) """) def test_frexp(self): # test some of the edge cases of for frexp self.assertCodeExecution(""" import math print(math.frexp(float('nan'))) print(math.frexp(float('inf'))) print(math.frexp(float('-inf'))) print(math.frexp(-0.0)) print(math.frexp(0.0)) print(math.frexp(2**-1026)) # denormal print(math.frexp(2**-1027)) # denormal print(math.frexp(1.9**-1150)) # denormal """) def test_docstrings(self): self.assertCodeExecution(""" import math print(math.acos.__doc__) print(math.acosh.__doc__) print(math.asin.__doc__) print(math.asinh.__doc__) print(math.atan.__doc__) print(math.atan2.__doc__) print(math.atanh.__doc__) print(math.ceil.__doc__) print(math.copysign.__doc__) print(math.cos.__doc__) print(math.cosh.__doc__) print(math.degrees.__doc__) print(math.erf.__doc__) print(math.erfc.__doc__) print(math.exp.__doc__) print(math.expm1.__doc__) print(math.fabs.__doc__) print(math.factorial.__doc__) print(math.floor.__doc__) print(math.fmod.__doc__) print(math.frexp.__doc__) print(math.fsum.__doc__) print(math.gamma.__doc__) print(math.hypot.__doc__) print(math.isfinite.__doc__) print(math.isinf.__doc__) print(math.isnan.__doc__) print(math.ldexp.__doc__) print(math.lgamma.__doc__) print(math.log.__doc__) print(math.log10.__doc__) print(math.log1p.__doc__) print(math.log2.__doc__) print(math.modf.__doc__) print(math.pow.__doc__) print(math.radians.__doc__) print(math.sin.__doc__) print(math.sinh.__doc__) print(math.sqrt.__doc__) print(math.tan.__doc__) print(math.tanh.__doc__) print(math.trunc.__doc__) """) @skipUnless(sys.version_info >= (3, 5), reason="Need CPython 3.5") def test_docstrings_35(self): self.assertCodeExecution(""" import math print(math.gcd.__doc__) print(math.isclose.__doc__) """) def test_big_log(self): self.assertCodeExecution(""" import math print(math.log(3**4000, 2**8100)) """) def test_big_log2(self): self.assertCodeExecution(""" import math print(math.log2(709874778209505449164547067054458951083931193926642747495017164186645751655744875421269098582104920390605124237633349342717862507319743615626304875347198674644024921355443346065756812077971384925385976688379587725754770781522846570196349704093046107733180534854434524219964358869238720766190004394476819714001258060050613741584644204075051799051905412773797764952606797949151269802416842454484878798473655073876851371491648288958091028337719596855793230188189772070425820554754458556422280418578218555923937387100230666995832561534819765559294519688376125270429521528901767210741847034724205354522740365483017692249820977706071766039350554571519522193325468892496901898817050295077767956445820266181178428873389385457683384690338027773216128156778372102965337341550587618703692943484656997212771985652065880479903542484558677381542438761681533659023452046408703963028815731967051176232784200533079872517065868927439508179887199747307382027271992367973398752203981370932249653618260869652706332493188390438513150963811297163798071038159044928635041637968052076196357089673578011991707879168341931769577280340919325350586520247371170996197238085208451603359009350099978722663674682758701714150597529361822020896347673618446802372853705086762735076753958743009317050891017544375235501024966086837113895250374683405794439114588602689057542397605899402997007278070746928906217565081884406372071588251849468961039107688076621346443316677903414760713997208752939061354773484040316969671376565833163179447504252238355930440353052925533204737199987806741014066788960204161921421661842362108279851816761342464770187337809510195703092924098813875328889150143200978204553812664656609886055094625058698584339535625840297952984122026248025706583198398576642795466682257872986978621024920781727232257799954982443614621424967054998813367293022309278882814078744897409296550039950294130952382751224274116423061468974653243010683217587256595866362285666690540129606522748087781211700305323617809257258869461082545385948387418641936602911595988585341866304680338433228594272988376062066145489663624587207289726229598577225356620186185104940607264294055665578166855271669760127452193136730694091219875987070929716353044606178328132861866481581176064425671499601899418528529810234656671652825363206294954548973847080477558394872610479878723418423615445410371409596278175917142530165486625362513678722139651643157453275449743997375003463533610556290097654020572842895402524185827121030107)) """) def test_isfinite(self): self.assertCodeExecution(""" import math print(math.isfinite(1)) print(math.isfinite(float('-inf'))) print(math.isfinite(float('inf'))) print(math.isfinite(float('nan'))) """) def test_isinf(self): self.assertCodeExecution(""" import math print(math.isinf(1)) print(math.isinf(float('-inf'))) print(math.isinf(float('inf'))) print(math.isinf(float('nan'))) """) def test_isnan(self): self.assertCodeExecution(""" import math print(math.isnan(1)) print(math.isnan(float('-inf'))) print(math.isnan(float('inf'))) print(math.isnan(float('nan'))) """) def test_ldexp_zero(self): self.assertCodeExecution(""" import math print(math.ldexp(0.0, 100000)) print(math.ldexp(-0.0, 100000)) """) def test_ldexp_int_exps_edge_cases(self): self.assertCodeExecution(""" import math for exp in range(-1100, -900): print(exp) print(math.ldexp(1.0, exp)) """) @skipUnless(sys.version_info >= (3, 5), reason="Need CPython 3.5") def test_isclose_kwargs(self): self.assertCodeExecution(""" import math print(math.isclose(1.0, 0.9)) print(math.isclose(1.0, 0.9, rel_tol=0.09)) print(math.isclose(1.0, 0.9, rel_tol=0.1)) print(math.isclose(1.0, 0.9, rel_tol=0.11)) print(math.isclose(1.0, 0.9, rel_tol=0.09, abs_tol=0.1)) print(math.isclose(1.0, 1.000000001, rel_tol=1.0, abs_tol=1.0)) """) MathTests.add_math_tests()

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0.263026

9,558

249

2,497

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0.714528

0.474553

0

1

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0.063063

1

0.067568

false

0

0.076577

0

0.157658

0.373874

0

null

0

1

0

1

0

1

0

1

null

1

0

6

fcf2ca34ce5d9ed7f03a3b101826e237fe90a9c9

23,227

py

Python

tests/tools/test_aux_methods_labels_descriptor_manager.py

nipy/nilabels

b065febc611eef638785651b4642d53bb61f1321

[ "MIT" ]

15

2019-04-09T21:47:47.000Z

2022-02-01T14:11:51.000Z

tests/tools/test_aux_methods_labels_descriptor_manager.py

SebastianoF/LabelsManager

b065febc611eef638785651b4642d53bb61f1321

[ "MIT" ]

4

2018-08-24T09:25:49.000Z

2018-08-29T10:47:50.000Z

tests/tools/test_aux_methods_labels_descriptor_manager.py

nipy/nilabels

b065febc611eef638785651b4642d53bb61f1321

[ "MIT" ]

1

2019-04-06T20:49:48.000Z

import collections from os.path import join as jph import pytest from nilabels.tools.aux_methods.label_descriptor_manager import LabelsDescriptorManager, \ generate_dummy_label_descriptor from tests.tools.decorators_tools import write_and_erase_temporary_folder, pfo_tmp_test, \ write_and_erase_temporary_folder_with_dummy_labels_descriptor, is_a_string_number, \ write_and_erase_temporary_folder_with_left_right_dummy_labels_descriptor # TESTING: # --- > Testing generate dummy descriptor @write_and_erase_temporary_folder def test_generate_dummy_labels_descriptor_wrong_input1(): with pytest.raises(IOError): generate_dummy_label_descriptor(jph(pfo_tmp_test, 'labels_descriptor.txt'), list_labels=range(5), list_roi_names=['1', '2']) @write_and_erase_temporary_folder def test_generate_dummy_labels_descriptor_wrong_input2(): with pytest.raises(IOError): generate_dummy_label_descriptor(jph(pfo_tmp_test, 'labels_descriptor.txt'), list_labels=range(5), list_roi_names=['1', '2', '3', '4', '5'], list_colors_triplets=[[0, 0, 0], [1, 1, 1]]) @write_and_erase_temporary_folder def test_generate_labels_descriptor_list_roi_names_None(): d = generate_dummy_label_descriptor(jph(pfo_tmp_test, 'dummy_labels_descriptor.txt'), list_labels=range(5), list_roi_names=None, list_colors_triplets=[[1, 1, 1], ] * 5) for k in d.keys(): assert d[k][-1] == 'label {}'.format(k) @write_and_erase_temporary_folder def test_generate_labels_descriptor_list_colors_triplets_None(): d = generate_dummy_label_descriptor(jph(pfo_tmp_test, 'dummy_labels_descriptor.txt'), list_labels=range(5), list_roi_names=None, list_colors_triplets=[[1, 1, 1], ] * 5) for k in d.keys(): assert len(d[k][1]) == 3 @write_and_erase_temporary_folder def test_generate_none_list_colour_triples(): generate_dummy_label_descriptor(jph(pfo_tmp_test, 'labels_descriptor.txt'), list_labels=range(5), list_roi_names=['1', '2', '3', '4', '5'], list_colors_triplets=None) loaded_dummy_ldm = LabelsDescriptorManager(jph(pfo_tmp_test, 'labels_descriptor.txt')) for k in loaded_dummy_ldm.dict_label_descriptor.keys(): assert len(loaded_dummy_ldm.dict_label_descriptor[k][0]) == 3 for k_rgb in loaded_dummy_ldm.dict_label_descriptor[k][0]: assert 0 <= k_rgb < 256 @write_and_erase_temporary_folder def test_generate_labels_descriptor_general(): list_labels = [1, 2, 3, 4, 5] list_color_triplets = [[1, 1, 1], [2, 2, 2], [3, 3, 3], [4, 4, 4], [5, 5, 5]] list_roi_names = ['one', 'two', 'three', 'four', 'five'] d = generate_dummy_label_descriptor(jph(pfo_tmp_test, 'dummy_label_descriptor.txt'), list_labels=list_labels, list_roi_names=list_roi_names, list_colors_triplets=list_color_triplets) for k_num, k in enumerate(d.keys()): assert int(k) == list_labels[k_num] assert d[k][0] == list_color_triplets[k_num] assert d[k][-1] == list_roi_names[k_num] # --- > Testing basics methods labels descriptor class manager @write_and_erase_temporary_folder def test_basics_methods_labels_descriptor_manager_wrong_input_path(): pfi_unexisting_label_descriptor_manager = 'zzz_path_to_spam' with pytest.raises(IOError): LabelsDescriptorManager(pfi_unexisting_label_descriptor_manager) @write_and_erase_temporary_folder def test_basics_methods_labels_descriptor_manager_wrong_input_convention(): not_allowed_convention_name = 'just_spam' with pytest.raises(IOError): LabelsDescriptorManager(jph(pfo_tmp_test, 'labels_descriptor.txt'), not_allowed_convention_name) @write_and_erase_temporary_folder_with_dummy_labels_descriptor def test_basic_dict_input(): dict_ld = collections.OrderedDict() # note that in the dictionary there are no double quotes " ", but by default the strings are '"label name"' dict_ld.update({0: [[0, 0, 0], [0, 0, 0], 'background']}) dict_ld.update({1: [[255, 0, 0], [1, 1, 1], 'label one (l1)']}) dict_ld.update({2: [[204, 0, 0], [1, 1, 1], 'label two (l2)']}) dict_ld.update({3: [[51, 51, 255], [1, 1, 1], 'label three']}) dict_ld.update({4: [[102, 102, 255], [1, 1, 1], 'label four']}) dict_ld.update({5: [[0, 204, 51], [1, 1, 1], 'label five (l5)']}) dict_ld.update({6: [[51, 255, 102], [1, 1, 1], 'label six']}) dict_ld.update({7: [[255, 255, 0], [1, 1, 1], 'label seven']}) dict_ld.update({8: [[255, 50, 50], [1, 1, 1], 'label eight']}) ldm = LabelsDescriptorManager(jph(pfo_tmp_test, 'labels_descriptor.txt')) for k in ldm.dict_label_descriptor.keys(): assert ldm.dict_label_descriptor[k] == dict_ld[k] @write_and_erase_temporary_folder_with_dummy_labels_descriptor def test_load_save_and_compare(): ldm = LabelsDescriptorManager(jph(pfo_tmp_test, 'labels_descriptor.txt')) ldm.save_label_descriptor(jph(pfo_tmp_test, 'labels_descriptor2.txt')) f1 = open(jph(pfo_tmp_test, 'labels_descriptor.txt'), 'r') f2 = open(jph(pfo_tmp_test, 'labels_descriptor2.txt'), 'r') for l1, l2 in zip(f1.readlines(), f2.readlines()): split_l1 = [float(a) if is_a_string_number(a) else a for a in [a.strip() for a in l1.split(' ') if a is not '']] split_l2 = [float(b) if is_a_string_number(b) else b for b in [b.strip() for b in l2.split(' ') if b is not '']] assert split_l1 == split_l2 @write_and_erase_temporary_folder_with_dummy_labels_descriptor def test_save_in_fsl_convention_reload_as_dict_and_compare(): ldm_itk = LabelsDescriptorManager(jph(pfo_tmp_test, 'labels_descriptor.txt')) # change convention ldm_itk.convention = 'fsl' ldm_itk.save_label_descriptor(jph(pfo_tmp_test, 'labels_descriptor_fsl.txt')) ldm_fsl = LabelsDescriptorManager(jph(pfo_tmp_test, 'labels_descriptor_fsl.txt'), labels_descriptor_convention='fsl') # NOTE: test works only with default 1.0 values - fsl convention is less informative than itk-snap.. for k in ldm_itk.dict_label_descriptor.keys(): ldm_itk.dict_label_descriptor[k] == ldm_fsl.dict_label_descriptor[k] @write_and_erase_temporary_folder_with_dummy_labels_descriptor def test_signature_for_variable_convention_wrong_input(): with pytest.raises(IOError): LabelsDescriptorManager(jph(pfo_tmp_test, 'labels_descriptor.txt'), labels_descriptor_convention='spam') @write_and_erase_temporary_folder_with_dummy_labels_descriptor def test_signature_for_variable_convention_wrong_input_after_initialisation(): my_ldm = LabelsDescriptorManager(jph(pfo_tmp_test, 'labels_descriptor.txt'), labels_descriptor_convention='itk-snap') with pytest.raises(IOError): my_ldm.convention = 'spam' my_ldm.save_label_descriptor(jph(pfo_tmp_test, 'labels_descriptor_again.txt')) # --> Testing labels permutations - permute_labels_in_descriptor @write_and_erase_temporary_folder_with_dummy_labels_descriptor def test_relabel_labels_descriptor(): dict_expected = collections.OrderedDict() dict_expected.update({0: [[0, 0, 0], [0, 0, 0], 'background']}) dict_expected.update({10: [[255, 0, 0], [1, 1, 1], 'label one (l1)']}) dict_expected.update({11: [[204, 0, 0], [1, 1, 1], 'label two (l2)']}) dict_expected.update({12: [[51, 51, 255], [1, 1, 1], 'label three']}) dict_expected.update({4: [[102, 102, 255], [1, 1, 1], 'label four']}) dict_expected.update({5: [[0, 204, 51], [1, 1, 1], 'label five (l5)']}) dict_expected.update({6: [[51, 255, 102], [1, 1, 1], 'label six']}) dict_expected.update({7: [[255, 255, 0], [1, 1, 1], 'label seven']}) dict_expected.update({8: [[255, 50, 50], [1, 1, 1], 'label eight']}) ldm_original = LabelsDescriptorManager(jph(pfo_tmp_test, 'labels_descriptor.txt')) old_labels = [1, 2, 3] new_labels = [10, 11, 12] ldm_relabelled = ldm_original.relabel(old_labels, new_labels, sort=True) for k in dict_expected.keys(): dict_expected[k] == ldm_relabelled.dict_label_descriptor[k] @write_and_erase_temporary_folder_with_dummy_labels_descriptor def test_relabel_labels_descriptor_with_merging(): dict_expected = collections.OrderedDict() dict_expected.update({0: [[0, 0, 0], [0, 0, 0], 'background']}) # dict_expected.update({1: [[255, 0, 0], [1, 1, 1], 'label one (l1)']}) # copied over label two dict_expected.update({1: [[204, 0, 0], [1, 1, 1], 'label two (l2)']}) dict_expected.update({5: [[51, 51, 255], [1, 1, 1], 'label three']}) dict_expected.update({4: [[102, 102, 255], [1, 1, 1], 'label four']}) dict_expected.update({5: [[0, 204, 51], [1, 1, 1], 'label five (l5)']}) dict_expected.update({6: [[51, 255, 102], [1, 1, 1], 'label six']}) dict_expected.update({7: [[255, 255, 0], [1, 1, 1], 'label seven']}) dict_expected.update({8: [[255, 50, 50], [1, 1, 1], 'label eight']}) ldm_original = LabelsDescriptorManager(jph(pfo_tmp_test, 'labels_descriptor.txt')) old_labels = [1, 2, 3] new_labels = [1, 1, 5] ldm_relabelled = ldm_original.relabel(old_labels, new_labels, sort=True) for k in dict_expected.keys(): dict_expected[k] == ldm_relabelled.dict_label_descriptor[k] @write_and_erase_temporary_folder_with_dummy_labels_descriptor def test_permute_labels_from_descriptor_wrong_input_permutation(): ldm = LabelsDescriptorManager(jph(pfo_tmp_test, 'labels_descriptor.txt')) perm = [[1, 2, 3], [1, 1]] with pytest.raises(IOError): ldm.permute_labels(perm) @write_and_erase_temporary_folder_with_dummy_labels_descriptor def test_permute_labels_from_descriptor_check(): dict_expected = collections.OrderedDict() dict_expected.update({0: [[0, 0, 0], [0, 0, 0], 'background']}) dict_expected.update({3: [[255, 0, 0], [1, 1, 1], 'label one (l1)']}) # copied over label two dict_expected.update({4: [[204, 0, 0], [1, 1, 1], 'label two (l2)']}) dict_expected.update({2: [[51, 51, 255], [1, 1, 1], 'label three']}) dict_expected.update({1: [[102, 102, 255], [1, 1, 1], 'label four']}) dict_expected.update({5: [[0, 204, 51], [1, 1, 1], 'label five (l5)']}) dict_expected.update({6: [[51, 255, 102], [1, 1, 1], 'label six']}) dict_expected.update({7: [[255, 255, 0], [1, 1, 1], 'label seven']}) dict_expected.update({8: [[255, 50, 50], [1, 1, 1], 'label eight']}) ldm_original = LabelsDescriptorManager(jph(pfo_tmp_test, 'labels_descriptor.txt')) perm = [[1, 2, 3, 4], [3, 4, 2, 1]] ldm_relabelled = ldm_original.permute_labels(perm) for k in dict_expected.keys(): dict_expected[k] == ldm_relabelled.dict_label_descriptor[k] @write_and_erase_temporary_folder_with_dummy_labels_descriptor def test_erase_labels(): dict_expected = collections.OrderedDict() dict_expected.update({0: [[0, 0, 0], [0, 0, 0], 'background']}) dict_expected.update({1: [[255, 0, 0], [1, 1, 1], 'label one (l1)']}) # copied over label two dict_expected.update({4: [[102, 102, 255], [1, 1, 1], 'label four']}) dict_expected.update({5: [[0, 204, 51], [1, 1, 1], 'label five (l5)']}) dict_expected.update({6: [[51, 255, 102], [1, 1, 1], 'label six']}) dict_expected.update({8: [[255, 50, 50], [1, 1, 1], 'label eight']}) ldm_original = LabelsDescriptorManager(jph(pfo_tmp_test, 'labels_descriptor.txt')) labels_to_erase = [2, 3, 7] ldm_relabelled = ldm_original.erase_labels(labels_to_erase) for k in dict_expected.keys(): assert dict_expected[k] == ldm_relabelled.dict_label_descriptor[k] # -> multi-labels dict @write_and_erase_temporary_folder_with_left_right_dummy_labels_descriptor def test_save_multi_labels_descriptor_custom(): # load it into a labels descriptor manager ldm_lr = LabelsDescriptorManager(jph(pfo_tmp_test, 'labels_descriptor_RL.txt')) # save it as labels descriptor text file pfi_multi_ld = jph(pfo_tmp_test, 'multi_labels_descriptor_LR.txt') ldm_lr.save_as_multi_label_descriptor(pfi_multi_ld) # expected lines: expected_lines = [['background', 0], ['label A Left', 1], ['label A Right', 2], ['label A', 1, 2], ['label B Left', 3], ['label B Right', 4], ['label B', 3, 4], ['label C', 5], ['label D', 6], ['label E Left', 7], ['label E Right', 8], ['label E', 7, 8]] # load saved labels descriptor with open(pfi_multi_ld, 'r') as g: multi_ld_lines = g.readlines() # modify as list of lists as the expected lines. multi_ld_lines_a_list_of_lists = [[int(a) if a.isdigit() else a for a in [n.strip() for n in m.split('&') if not n.startswith('#')]] for m in multi_ld_lines] # Compare: for li1, li2 in zip(expected_lines, multi_ld_lines_a_list_of_lists): assert li1 == li2 @write_and_erase_temporary_folder_with_left_right_dummy_labels_descriptor def test_get_multi_label_dict_standard_combine(): ldm_lr = LabelsDescriptorManager(jph(pfo_tmp_test, 'labels_descriptor_RL.txt')) multi_labels_dict_from_ldm = ldm_lr.get_multi_label_dict(combine_right_left=True) expected_multi_labels_dict = collections.OrderedDict() expected_multi_labels_dict.update({'background': [0]}) expected_multi_labels_dict.update({'label A Left': [1]}) expected_multi_labels_dict.update({'label A Right': [2]}) expected_multi_labels_dict.update({'label A': [1, 2]}) expected_multi_labels_dict.update({'label B Left': [3]}) expected_multi_labels_dict.update({'label B Right': [4]}) expected_multi_labels_dict.update({'label B': [3, 4]}) expected_multi_labels_dict.update({'label C': [5]}) expected_multi_labels_dict.update({'label D': [6]}) expected_multi_labels_dict.update({'label E Left': [7]}) expected_multi_labels_dict.update({'label E Right': [8]}) expected_multi_labels_dict.update({'label E': [7, 8]}) for k1, k2 in zip(multi_labels_dict_from_ldm.keys(), expected_multi_labels_dict.keys()): assert k1 == k2 assert multi_labels_dict_from_ldm[k1] == expected_multi_labels_dict[k2] @write_and_erase_temporary_folder_with_left_right_dummy_labels_descriptor def test_get_multi_label_dict_standard_not_combine(): ldm_lr = LabelsDescriptorManager(jph(pfo_tmp_test, 'labels_descriptor_RL.txt')) multi_labels_dict_from_ldm = ldm_lr.get_multi_label_dict(combine_right_left=False) expected_multi_labels_dict = collections.OrderedDict() expected_multi_labels_dict.update({'background': [0]}) expected_multi_labels_dict.update({'label A Left': [1]}) expected_multi_labels_dict.update({'label A Right': [2]}) expected_multi_labels_dict.update({'label B Left': [3]}) expected_multi_labels_dict.update({'label B Right': [4]}) expected_multi_labels_dict.update({'label C': [5]}) expected_multi_labels_dict.update({'label D': [6]}) expected_multi_labels_dict.update({'label E Left': [7]}) expected_multi_labels_dict.update({'label E Right': [8]}) for k1, k2 in zip(multi_labels_dict_from_ldm.keys(), expected_multi_labels_dict.keys()): assert k1 == k2 assert multi_labels_dict_from_ldm[k1] == expected_multi_labels_dict[k2] @write_and_erase_temporary_folder def test_save_multi_labels_descriptor_custom_test_robustness(): # save this as file multi labels descriptor then read and check that it went in order! d = collections.OrderedDict() d.update({0: [[0, 0, 0], [0, 0, 0], 'background']}) d.update({1: [[255, 0, 0], [1, 1, 1], 'label A Right']}) d.update({2: [[204, 0, 0], [1, 1, 1], 'label A Left']}) d.update({3: [[51, 51, 255], [1, 1, 1], 'label B left']}) d.update({4: [[102, 102, 255], [1, 1, 1], 'label B Right']}) d.update({5: [[0, 204, 51], [1, 1, 1], 'label C ']}) d.update({6: [[51, 255, 102], [1, 1, 1], 'label D Right']}) # unpaired label d.update({7: [[255, 255, 0], [1, 1, 1], 'label E right ']}) # small r and spaces d.update({8: [[255, 50, 50], [1, 1, 1], 'label E Left ']}) # ... paired with small l and spaces with open(jph(pfo_tmp_test, 'labels_descriptor_RL.txt'), 'w+') as f: for j in d.keys(): line = '{0: >5}{1: >6}{2: >6}{3: >6}{4: >9}{5: >6}{6: >6} "{7}"\n'.format( j, d[j][0][0], d[j][0][1], d[j][0][2], d[j][1][0], d[j][1][1], d[j][1][2], d[j][2]) f.write(line) # load it with an instance of LabelsDescriptorManager ldm_lr = LabelsDescriptorManager(jph(pfo_tmp_test, 'labels_descriptor_RL.txt')) multi_labels_dict_from_ldm = ldm_lr.get_multi_label_dict(combine_right_left=True) expected_multi_labels_dict = collections.OrderedDict() expected_multi_labels_dict.update({'background': [0]}) expected_multi_labels_dict.update({'label A Right': [1]}) expected_multi_labels_dict.update({'label A Left': [2]}) expected_multi_labels_dict.update({'label A': [1, 2]}) expected_multi_labels_dict.update({'label B left': [3]}) expected_multi_labels_dict.update({'label B Right': [4]}) expected_multi_labels_dict.update({'label C': [5]}) expected_multi_labels_dict.update({'label D Right': [6]}) expected_multi_labels_dict.update({'label E right': [7]}) expected_multi_labels_dict.update({'label E Left': [8]}) for k1, k2 in zip(multi_labels_dict_from_ldm.keys(), expected_multi_labels_dict.keys()): assert k1 == k2 assert multi_labels_dict_from_ldm[k1] == expected_multi_labels_dict[k2] # -> erase, assign and keep only one label relabeller. @write_and_erase_temporary_folder_with_dummy_labels_descriptor def test_relabel_standard(): dict_expected = collections.OrderedDict() dict_expected.update({0: [[0, 0, 0], [0, 0, 0], 'background']}) dict_expected.update({1: [[255, 0, 0], [1, 1, 1], 'label one (l1)']}) dict_expected.update({9: [[204, 0, 0], [1, 1, 1], 'label two (l2)']}) dict_expected.update({3: [[51, 51, 255], [1, 1, 1], 'label three']}) dict_expected.update({10: [[102, 102, 255], [1, 1, 1], 'label four']}) dict_expected.update({5: [[0, 204, 51], [1, 1, 1], 'label five (l5)']}) dict_expected.update({6: [[51, 255, 102], [1, 1, 1], 'label six']}) dict_expected.update({7: [[255, 255, 0], [1, 1, 1], 'label seven']}) dict_expected.update({8: [[255, 50, 50], [1, 1, 1], 'label eight']}) ldm_original = LabelsDescriptorManager(jph(pfo_tmp_test, 'labels_descriptor.txt')) old_labels = [2, 4] new_labels = [9, 10] ldm_relabelled = ldm_original.relabel(old_labels, new_labels) for k in dict_expected.keys(): assert dict_expected[k] == ldm_relabelled.dict_label_descriptor[k] @write_and_erase_temporary_folder_with_dummy_labels_descriptor def test_relabel_bad_input(): ldm_original = LabelsDescriptorManager(jph(pfo_tmp_test, 'labels_descriptor.txt')) old_labels = [2, 4, 180] new_labels = [9, 10, 12] with pytest.raises(IOError): ldm_original.relabel(old_labels, new_labels) @write_and_erase_temporary_folder_with_dummy_labels_descriptor def test_erase_labels_unexisting_labels(): dict_expected = collections.OrderedDict() dict_expected.update({0: [[0, 0, 0], [0, 0, 0], 'background']}) dict_expected.update({1: [[255, 0, 0], [1, 1, 1], 'label one (l1)']}) dict_expected.update({3: [[51, 51, 255], [1, 1, 1], 'label three']}) dict_expected.update({5: [[0, 204, 51], [1, 1, 1], 'label five (l5)']}) dict_expected.update({6: [[51, 255, 102], [1, 1, 1], 'label six']}) dict_expected.update({7: [[255, 255, 0], [1, 1, 1], 'label seven']}) dict_expected.update({8: [[255, 50, 50], [1, 1, 1], 'label eight']}) ldm_original = LabelsDescriptorManager(jph(pfo_tmp_test, 'labels_descriptor.txt')) labels_to_erase = [2, 4, 16, 32] ldm_relabelled = ldm_original.erase_labels(labels_to_erase) for k in dict_expected.keys(): assert dict_expected[k] == ldm_relabelled.dict_label_descriptor[k] @write_and_erase_temporary_folder_with_dummy_labels_descriptor def test_assign_all_other_labels_the_same_value(): dict_expected = collections.OrderedDict() dict_expected.update({0: [[0, 0, 0], [0, 0, 0], 'background']}) # Possible bug dict_expected.update({1: [[255, 0, 0], [1, 1, 1], 'label one (l1)']}) # copied over label two dict_expected.update({4: [[102, 102, 255], [1, 1, 1], 'label four']}) dict_expected.update({7: [[255, 255, 0], [1, 1, 1], 'label seven']}) ldm_original = LabelsDescriptorManager(jph(pfo_tmp_test, 'labels_descriptor.txt')) labels_to_keep = [0, 1, 4, 7] other_value = 12 ldm_relabelled = ldm_original.assign_all_other_labels_the_same_value(labels_to_keep, other_value) print(dict_expected) print(ldm_relabelled.dict_label_descriptor) for k in dict_expected.keys(): print() print(dict_expected[k]) print(ldm_relabelled.dict_label_descriptor[k]) assert dict_expected[k] == ldm_relabelled.dict_label_descriptor[k] @write_and_erase_temporary_folder_with_dummy_labels_descriptor def test_keep_one_label(): dict_expected = collections.OrderedDict() dict_expected.update({3: [[51, 51, 255], [1, 1, 1], 'label three']}) ldm_original = LabelsDescriptorManager(jph(pfo_tmp_test, 'labels_descriptor.txt')) label_to_keep = 3 ldm_relabelled = ldm_original.keep_one_label(label_to_keep) for k in dict_expected.keys(): assert dict_expected[k] == ldm_relabelled.dict_label_descriptor[k] if __name__ == '__main__': test_generate_dummy_labels_descriptor_wrong_input1() test_generate_dummy_labels_descriptor_wrong_input2() test_generate_labels_descriptor_list_roi_names_None() test_generate_labels_descriptor_list_colors_triplets_None() test_generate_none_list_colour_triples() test_generate_labels_descriptor_general() test_basics_methods_labels_descriptor_manager_wrong_input_path() test_basics_methods_labels_descriptor_manager_wrong_input_convention() test_basic_dict_input() test_load_save_and_compare() test_save_in_fsl_convention_reload_as_dict_and_compare() test_signature_for_variable_convention_wrong_input() test_signature_for_variable_convention_wrong_input_after_initialisation() test_relabel_labels_descriptor() test_relabel_labels_descriptor_with_merging() test_permute_labels_from_descriptor_wrong_input_permutation() test_permute_labels_from_descriptor_check() test_erase_labels() test_save_multi_labels_descriptor_custom() test_get_multi_label_dict_standard_combine() test_get_multi_label_dict_standard_not_combine() test_save_multi_labels_descriptor_custom_test_robustness() test_relabel_standard() test_relabel_bad_input() test_erase_labels_unexisting_labels() test_assign_all_other_labels_the_same_value() test_keep_one_label()

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Python

spikeforestwidgets/__init__.py

tjd2002/spikeforest2

2e393564b858b2995aa2ccccd9bd73065681b5de

[ "Apache-2.0" ]

null

spikeforestwidgets/__init__.py

tjd2002/spikeforest2

2e393564b858b2995aa2ccccd9bd73065681b5de

[ "Apache-2.0" ]

null

spikeforestwidgets/__init__.py

tjd2002/spikeforest2

2e393564b858b2995aa2ccccd9bd73065681b5de

[ "Apache-2.0" ]

null

from .timeserieswidget import TimeseriesWidget from .electrodegeometrywidget import ElectrodeGeometryWidget from .unitwaveformswidget import UnitWaveformWidget, UnitWaveformsWidget from .correlogramswidget import CorrelogramsWidget

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1

0

1

0

1

null

0

1

0

null

0

1

0

1

0

1

0

6