fumiyau/python_no_comments_test100000 · Datasets at Hugging Face

f71b39d53d82554ce904392600c340709e0534bb

2,018

py

Python

pandas/tests/indexing/multiindex/test_chaining_and_caching.py

oricou/pandas

9405e58d9268041f5416711c051cf5429a19bf49

[ "PSF-2.0", "Apache-2.0", "BSD-3-Clause-No-Nuclear-License-2014", "MIT", "ECL-2.0", "BSD-3-Clause" ]

2

2021-05-07T04:58:36.000Z

2021-05-07T04:58:59.000Z

pandas/tests/indexing/multiindex/test_chaining_and_caching.py

oricou/pandas

9405e58d9268041f5416711c051cf5429a19bf49

[ "PSF-2.0", "Apache-2.0", "BSD-3-Clause-No-Nuclear-License-2014", "MIT", "ECL-2.0", "BSD-3-Clause" ]

null

pandas/tests/indexing/multiindex/test_chaining_and_caching.py

oricou/pandas

9405e58d9268041f5416711c051cf5429a19bf49

[ "PSF-2.0", "Apache-2.0", "BSD-3-Clause-No-Nuclear-License-2014", "MIT", "ECL-2.0", "BSD-3-Clause" ]

2

2021-06-16T07:19:12.000Z

2021-12-16T10:24:44.000Z

import numpy as np import pytest from pandas import ( DataFrame, MultiIndex, Series, ) import pandas._testing as tm import pandas.core.common as com def test_detect_chained_assignment(): # Inplace ops, originally from: # https://stackoverflow.com/questions/20508968/series-fillna-in-a-multiindex-dataframe-does-not-fill-is-this-a-bug a = [12, 23] b = [123, None] c = [1234, 2345] d = [12345, 23456] tuples = [("eyes", "left"), ("eyes", "right"), ("ears", "left"), ("ears", "right")] events = { ("eyes", "left"): a, ("eyes", "right"): b, ("ears", "left"): c, ("ears", "right"): d, } multiind = MultiIndex.from_tuples(tuples, names=["part", "side"]) zed = DataFrame(events, index=["a", "b"], columns=multiind) msg = "A value is trying to be set on a copy of a slice from a DataFrame" with pytest.raises(com.SettingWithCopyError, match=msg): zed["eyes"]["right"].fillna(value=555, inplace=True) def test_cache_updating(): # 5216 # make sure that we don't try to set a dead cache a = np.random.rand(10, 3) df = DataFrame(a, columns=["x", "y", "z"]) tuples = [(i, j) for i in range(5) for j in range(2)] index = MultiIndex.from_tuples(tuples) df.index = index # setting via chained assignment # but actually works, since everything is a view df.loc[0]["z"].iloc[0] = 1.0 result = df.loc[(0, 0), "z"] assert result == 1 # correct setting df.loc[(0, 0), "z"] = 2 result = df.loc[(0, 0), "z"] assert result == 2 @pytest.mark.arm_slow def test_indexer_caching(): # GH5727 # make sure that indexers are in the _internal_names_set n = 1000001 arrays = (range(n), range(n)) index = MultiIndex.from_tuples(zip(*arrays)) s = Series(np.zeros(n), index=index) str(s) # setitem expected = Series(np.ones(n), index=index) s = Series(np.zeros(n), index=index) s[s == 0] = 1 tm.assert_series_equal(s, expected)

28.422535

118

0.606541

import numpy as np import pytest from pandas import ( DataFrame, MultiIndex, Series, ) import pandas._testing as tm import pandas.core.common as com def test_detect_chained_assignment(): a = [12, 23] b = [123, None] c = [1234, 2345] d = [12345, 23456] tuples = [("eyes", "left"), ("eyes", "right"), ("ears", "left"), ("ears", "right")] events = { ("eyes", "left"): a, ("eyes", "right"): b, ("ears", "left"): c, ("ears", "right"): d, } multiind = MultiIndex.from_tuples(tuples, names=["part", "side"]) zed = DataFrame(events, index=["a", "b"], columns=multiind) msg = "A value is trying to be set on a copy of a slice from a DataFrame" with pytest.raises(com.SettingWithCopyError, match=msg): zed["eyes"]["right"].fillna(value=555, inplace=True) def test_cache_updating(): a = np.random.rand(10, 3) df = DataFrame(a, columns=["x", "y", "z"]) tuples = [(i, j) for i in range(5) for j in range(2)] index = MultiIndex.from_tuples(tuples) df.index = index # setting via chained assignment # but actually works, since everything is a view df.loc[0]["z"].iloc[0] = 1.0 result = df.loc[(0, 0), "z"] assert result == 1 # correct setting df.loc[(0, 0), "z"] = 2 result = df.loc[(0, 0), "z"] assert result == 2 @pytest.mark.arm_slow def test_indexer_caching(): # GH5727 # make sure that indexers are in the _internal_names_set n = 1000001 arrays = (range(n), range(n)) index = MultiIndex.from_tuples(zip(*arrays)) s = Series(np.zeros(n), index=index) str(s) # setitem expected = Series(np.ones(n), index=index) s = Series(np.zeros(n), index=index) s[s == 0] = 1 tm.assert_series_equal(s, expected)

true

f71b3ac0d45395a6a7e2ff0955877634c8665bca

3,872

py

Python

gym-kinova-gripper/plotting_code/other_plots.py

OSUrobotics/KinovaGrasping

f22af60d3683fdc4ffecf49ccff179fbc6750748

[ "Linux-OpenIB" ]

16

2020-05-16T00:40:31.000Z

2022-02-22T11:59:03.000Z

gym-kinova-gripper/plotting_code/other_plots.py

OSUrobotics/KinovaGrasping

f22af60d3683fdc4ffecf49ccff179fbc6750748

[ "Linux-OpenIB" ]

9

2020-08-10T08:33:55.000Z

2021-08-17T02:10:50.000Z

gym-kinova-gripper/plotting_code/other_plots.py

OSUrobotics/KinovaGrasping

f22af60d3683fdc4ffecf49ccff179fbc6750748

[ "Linux-OpenIB" ]

7

2020-07-27T09:45:05.000Z

2021-06-21T21:42:50.000Z

import matplotlib.pyplot as plt import numpy as np ## Extra plotting functions that can be called for quick analysis def plot_timestep_distribution(success_timesteps=None, fail_timesteps=None, all_timesteps=None, expert_saving_dir=None): """ Plot the distribution of time steps over successful and failed episodes """ if all_timesteps is None: success_timesteps = np.load(expert_saving_dir + "/success_timesteps.npy") fail_timesteps = np.load(expert_saving_dir + "/fail_timesteps.npy") all_timesteps = np.load(expert_saving_dir + "/all_timesteps.npy") n_bins = 40 # We can set the number of bins with the `bins` kwarg plt.hist(all_timesteps, bins=n_bins, color="g") plt.title("Total time steps distribution for all episodes (3x speed)", weight='bold') plt.xlabel('# of time steps per episode') plt.ylabel('# of episodes with the time step count') plt.xlim(0, 800) plt.savefig(expert_saving_dir + "/total_timestep_distribution") plt.clf() plt.hist(success_timesteps, bins=n_bins, color="b") plt.title("Time steps distribution for Successful episodes (3x speed)", weight='bold') plt.xlabel('# of time steps per episode') plt.ylabel('# of episodes with the time step count') plt.savefig(expert_saving_dir + "/success_timestep_distribution") plt.clf() plt.hist(fail_timesteps, bins=n_bins, color="r") plt.title("Time steps distribution for Failed episodes (3x speed)", weight='bold') plt.xlabel('# of time steps per episode') plt.ylabel('# of episodes with the time step count') plt.savefig(expert_saving_dir + "/fail_timestep_distribution") plt.clf() ''' # Plot the average velocity over an episode def plot_average_velocity(replay_buffer,num_timesteps): """ Plot the average velocity over a certain number of episodes """ velocity_dir = "./expert_average_velocity" if not os.path.isdir(velocity_dir): os.mkdir(velocity_dir) #num_episodes = len(f1_vels) #plt.plot(np.arrange(len(f1_vels)), f1_vels) max_timesteps = 30 timestep_vel_count = np.zeros(max_timesteps) wrist_avg_vels = np.zeros(max_timesteps) f1_avg_vels = np.zeros(max_timesteps) f2_avg_vels = np.zeros(max_timesteps) f3_avg_vels = np.zeros(max_timesteps) for episode_actions in replay_buffer.action: for timestep_idx in range(len(episode_actions)): timestep_vel_count[timestep_idx] += 1 wrist_avg_vels[timestep_idx] = (wrist_avg_vels[timestep_idx] + episode_actions[timestep_idx][0]) / timestep_vel_count[timestep_idx] f1_avg_vels[timestep_idx] = (f1_avg_vels[timestep_idx] + episode_actions[timestep_idx][1]) / \ timestep_vel_count[timestep_idx] f2_avg_vels[timestep_idx] = (f2_avg_vels[timestep_idx] + episode_actions[timestep_idx][2]) / \ timestep_vel_count[timestep_idx] f3_avg_vels[timestep_idx] = (f3_avg_vels[timestep_idx] + episode_actions[timestep_idx][3]) / \ timestep_vel_count[timestep_idx] num_episodes = len(replay_buffer.action) print("replay_buffer.action: ",replay_buffer.action) print("f1_avg_vels: ",f1_avg_vels) plt.plot(np.arange(num_timesteps), f1_avg_vels, color="r", label="Finger1") plt.plot(np.arange(num_timesteps), f2_avg_vels, color="b", label="Finger2") plt.plot(np.arange(num_timesteps), f3_avg_vels, color="g", label="Finger3") plt.plot(np.arange(num_timesteps), wrist_avg_vels, color="y", label="Wrist") plt.legend() plt.title("Average velocity over "+str(num_episodes)+" episodes", weight='bold') plt.xlabel('Timestep within an episode') plt.ylabel('Average Velocity at Timestep') #plt.savefig(velocity_dir + "/velocity_plot") #plt.clf() plt.show() '''

46.650602

143

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import matplotlib.pyplot as plt import numpy as np mesteps=None, all_timesteps=None, expert_saving_dir=None): if all_timesteps is None: success_timesteps = np.load(expert_saving_dir + "/success_timesteps.npy") fail_timesteps = np.load(expert_saving_dir + "/fail_timesteps.npy") all_timesteps = np.load(expert_saving_dir + "/all_timesteps.npy") n_bins = 40 plt.hist(all_timesteps, bins=n_bins, color="g") plt.title("Total time steps distribution for all episodes (3x speed)", weight='bold') plt.xlabel('# of time steps per episode') plt.ylabel('# of episodes with the time step count') plt.xlim(0, 800) plt.savefig(expert_saving_dir + "/total_timestep_distribution") plt.clf() plt.hist(success_timesteps, bins=n_bins, color="b") plt.title("Time steps distribution for Successful episodes (3x speed)", weight='bold') plt.xlabel('# of time steps per episode') plt.ylabel('# of episodes with the time step count') plt.savefig(expert_saving_dir + "/success_timestep_distribution") plt.clf() plt.hist(fail_timesteps, bins=n_bins, color="r") plt.title("Time steps distribution for Failed episodes (3x speed)", weight='bold') plt.xlabel('# of time steps per episode') plt.ylabel('# of episodes with the time step count') plt.savefig(expert_saving_dir + "/fail_timestep_distribution") plt.clf()

true

f71b3b475439ea9ed08d69fbc7b9ab409bb33d5a

2,223

py

Python

src/utils.py

wenyuC94/LogConcComp

b17d6ba6a102ba83a8415774b0e6da27a362bd5d

[ "MIT" ]

null

src/utils.py

wenyuC94/LogConcComp

b17d6ba6a102ba83a8415774b0e6da27a362bd5d

[ "MIT" ]

null

src/utils.py

wenyuC94/LogConcComp

b17d6ba6a102ba83a8415774b0e6da27a362bd5d

[ "MIT" ]

null

import os import numpy as np import numba as nb def create_folder(storage_path): if not os.path.isdir(storage_path): os.makedirs(storage_path,exist_ok=True) lsdir = os.listdir(storage_path) for item in ["info","hist","soln","figs"]: if item not in lsdir: os.makedirs(storage_path+item+"/",exist_ok=True) if item == "figs": lsdir_figs = os.listdir(storage_path+item+"/") for item1 in ["crop","raw"]: if item1 not in lsdir_figs: os.makedirs(storage_path+item+"/"+item1+"/",exist_ok=True) def time_to_string(runtime): seconds = runtime%60 runmins = (runtime-seconds)/60 mins = int(runmins%60) runhrs = (runmins-mins)/60 hrs = int(runhrs) return "%.2d:%.2d:%05.2f"%(hrs,mins,seconds) def multivariate_laplace(n,d,rng=None, random_state=None): rng = rng if rng is not None else np.random.RandomState(random_state) X = rng.randn(n,d) Z = rng.exponential(size=(n,1)) return X*np.sqrt(Z) @nb.njit(cache=True) def np_apply_along_axis(func1d, axis, arr): assert arr.ndim == 2 assert axis in [0, 1] if axis == 0: result = np.empty(arr.shape[1]) for i in range(len(result)): result[i] = func1d(arr[:, i]) else: result = np.empty(arr.shape[0]) for i in range(len(result)): result[i] = func1d(arr[i, :]) return result @nb.njit(cache=True) def np_apply_along_axis_kd(funckd, axis, arr, k = -1): assert arr.ndim == 2 assert axis in [0, 1] if axis == 0: k = k if k > 0 else arr.shape[0] result = np.empty((k,arr.shape[1])) for i in range(arr.shape[1]): result[:, i] = funckd(arr[:, i]) else: k = k if k > 0 else arr.shape[1] result = np.empty((arr.shape[0],k)) for i in range(arr.shape[0]): result[i, :] = funckd(arr[i, :]) return result @nb.njit(cache=True) def split(n, B): sep = n//B rem = n%B indices = [] last = 0 cur = 0 for i in range(B): cur = last + sep + (i < rem) indices.append(cur) last = cur return indices

28.5

78

0.559154

import os import numpy as np import numba as nb def create_folder(storage_path): if not os.path.isdir(storage_path): os.makedirs(storage_path,exist_ok=True) lsdir = os.listdir(storage_path) for item in ["info","hist","soln","figs"]: if item not in lsdir: os.makedirs(storage_path+item+"/",exist_ok=True) if item == "figs": lsdir_figs = os.listdir(storage_path+item+"/") for item1 in ["crop","raw"]: if item1 not in lsdir_figs: os.makedirs(storage_path+item+"/"+item1+"/",exist_ok=True) def time_to_string(runtime): seconds = runtime%60 runmins = (runtime-seconds)/60 mins = int(runmins%60) runhrs = (runmins-mins)/60 hrs = int(runhrs) return "%.2d:%.2d:%05.2f"%(hrs,mins,seconds) def multivariate_laplace(n,d,rng=None, random_state=None): rng = rng if rng is not None else np.random.RandomState(random_state) X = rng.randn(n,d) Z = rng.exponential(size=(n,1)) return X*np.sqrt(Z) @nb.njit(cache=True) def np_apply_along_axis(func1d, axis, arr): assert arr.ndim == 2 assert axis in [0, 1] if axis == 0: result = np.empty(arr.shape[1]) for i in range(len(result)): result[i] = func1d(arr[:, i]) else: result = np.empty(arr.shape[0]) for i in range(len(result)): result[i] = func1d(arr[i, :]) return result @nb.njit(cache=True) def np_apply_along_axis_kd(funckd, axis, arr, k = -1): assert arr.ndim == 2 assert axis in [0, 1] if axis == 0: k = k if k > 0 else arr.shape[0] result = np.empty((k,arr.shape[1])) for i in range(arr.shape[1]): result[:, i] = funckd(arr[:, i]) else: k = k if k > 0 else arr.shape[1] result = np.empty((arr.shape[0],k)) for i in range(arr.shape[0]): result[i, :] = funckd(arr[i, :]) return result @nb.njit(cache=True) def split(n, B): sep = n//B rem = n%B indices = [] last = 0 cur = 0 for i in range(B): cur = last + sep + (i < rem) indices.append(cur) last = cur return indices

true

f71b3b5ffd9a0d39ef3e2a5f01e2965d34e8b74e

1,448

py

Python

make_string_alphabetic.py

Maffey/FunPythonScripts

7682ab1f8dc7924eb4b3fb19d58ebbabe3901a22

[ "Apache-2.0" ]

1

2022-01-29T21:07:38.000Z

make_string_alphabetic.py

Maffey/fun-python-scripts

7682ab1f8dc7924eb4b3fb19d58ebbabe3901a22

[ "Apache-2.0" ]

2

2019-05-15T13:48:58.000Z

2019-05-29T18:33:34.000Z

make_string_alphabetic.py

Maffey/FunPythonScripts

7682ab1f8dc7924eb4b3fb19d58ebbabe3901a22

[ "Apache-2.0" ]

1

2020-08-04T11:09:17.000Z

#! python # Removes letter from word to make characters go alphabetically. # It doesn't work all the time, but is efficient. import unittest class TestRemoveLettersAlphabet(unittest.TestCase): def test_object1(self): self.assertEqual(letters_to_remove('mateusz'), 3) def test_object2(self): self.assertEqual(letters_to_remove('cba'), 2) def test_object3(self): self.assertEqual(letters_to_remove('dirt'), 0) def test_object4(self): self.assertEqual(letters_to_remove('jablko'), 2) def test_repeating_letters1(self): self.assertEqual(letters_to_remove('gabriela'), 5) def test_repeating_letters2(self): self.assertEqual(letters_to_remove('banana'), 3) def test_repeating_letters3(self): self.assertEqual(letters_to_remove('apple'), 2) def letters_to_remove(string: str) -> int: string = list(string) sorted_string = sorted(string) letters_removed = 0 remaining_string = "" for character in sorted_string: index = string.index(character) to_remove = string[:index] letters_removed += len(to_remove) for letter in to_remove: string.remove(letter) sorted_string.remove(letter) remaining_string += character string.remove(character) print(f"[+] Remaining string: {remaining_string}") return letters_removed if __name__ == "__main__": unittest.main()

27.320755

64

0.685083

import unittest class TestRemoveLettersAlphabet(unittest.TestCase): def test_object1(self): self.assertEqual(letters_to_remove('mateusz'), 3) def test_object2(self): self.assertEqual(letters_to_remove('cba'), 2) def test_object3(self): self.assertEqual(letters_to_remove('dirt'), 0) def test_object4(self): self.assertEqual(letters_to_remove('jablko'), 2) def test_repeating_letters1(self): self.assertEqual(letters_to_remove('gabriela'), 5) def test_repeating_letters2(self): self.assertEqual(letters_to_remove('banana'), 3) def test_repeating_letters3(self): self.assertEqual(letters_to_remove('apple'), 2) def letters_to_remove(string: str) -> int: string = list(string) sorted_string = sorted(string) letters_removed = 0 remaining_string = "" for character in sorted_string: index = string.index(character) to_remove = string[:index] letters_removed += len(to_remove) for letter in to_remove: string.remove(letter) sorted_string.remove(letter) remaining_string += character string.remove(character) print(f"[+] Remaining string: {remaining_string}") return letters_removed if __name__ == "__main__": unittest.main()

true

f71b3ba93636d762ed00bb7c81089b7edadd08c2

1,407

py

Python

verifiers/statusCode.py

CalConnect/caldavtester

a17683554df8a9b80fceab91085de99945fefe48

[ "Apache-2.0" ]

2

2019-04-23T04:06:08.000Z

2020-08-26T17:30:45.000Z

verifiers/statusCode.py

CalConnect/caldavtester

a17683554df8a9b80fceab91085de99945fefe48

[ "Apache-2.0" ]

3

2017-09-28T13:23:39.000Z

2017-10-03T15:59:36.000Z

verifiers/statusCode.py

CalConnect/caldavtester

a17683554df8a9b80fceab91085de99945fefe48

[ "Apache-2.0" ]

2

2017-02-08T04:47:31.000Z

2021-08-05T20:09:10.000Z

## # Copyright (c) 2006-2016 Apple Inc. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ## """ Verifier that chec ks the response status code for a specific value. """ class Verifier(object): def verify(self, manager, uri, response, respdata, args): # @UnusedVariable # If no status verification requested, then assume all 2xx codes are OK teststatus = args.get("status", ["2xx"]) for test in teststatus: if test[1:3] == "xx": test = int(test[0]) else: test = int(test) if test < 100: result = ((response.status / 100) == test) else: result = (response.status == test) if result: return True, "" return False, " HTTP Status Code Wrong: %d expected one of %s" % (response.status, ", ".join(teststatus))

34.317073

120

0.628998

class Verifier(object): def verify(self, manager, uri, response, respdata, args): teststatus = args.get("status", ["2xx"]) for test in teststatus: if test[1:3] == "xx": test = int(test[0]) else: test = int(test) if test < 100: result = ((response.status / 100) == test) else: result = (response.status == test) if result: return True, "" return False, " HTTP Status Code Wrong: %d expected one of %s" % (response.status, ", ".join(teststatus))

true

f71b3d10ad093122f20e0962b3f6645057d8279b

613

py

Python

classifier_stgcn_real_only/utils/temp.py

1suancaiyu/STEP

54195112990feaee137f5137775c736d07c2d26f

[ "MIT" ]

32

2020-02-21T16:12:13.000Z

2022-03-11T09:00:47.000Z

classifier_stgcn_real_only/utils/temp.py

1suancaiyu/STEP

54195112990feaee137f5137775c736d07c2d26f

[ "MIT" ]

12

2020-06-23T08:11:25.000Z

2022-03-26T11:34:42.000Z

classifier_stgcn_real_only/utils/temp.py

1suancaiyu/STEP

54195112990feaee137f5137775c736d07c2d26f

[ "MIT" ]

13

2020-04-01T16:51:50.000Z

2022-03-03T10:15:10.000Z

import h5py import os import numpy as np base_path = os.path.dirname(os.path.realpath(__file__)) feature_file = '/media/uttaran/FCE1-7BF3/Gamma/Gait/classifier_stgcn/model_classifier_stgcn/featuresCombineddeep_features.txt' f = np.loadtxt(feature_file) fCombined = h5py.File('/media/uttaran/FCE1-7BF3/Gamma/Gait/data/featuresCombined.h5', 'r') fkeys = fCombined.keys() dfCombined = h5py.File('/media/uttaran/FCE1-7BF3/Gamma/Gait/data/deepFeaturesCombined.h5', 'w') for i, fkey in enumerate(fkeys): fname = [fkey][0] feature = f[i, :] dfCombined.create_dataset(fname, data=feature) dfCombined.close()

38.3125

126

0.76509

import h5py import os import numpy as np base_path = os.path.dirname(os.path.realpath(__file__)) feature_file = '/media/uttaran/FCE1-7BF3/Gamma/Gait/classifier_stgcn/model_classifier_stgcn/featuresCombineddeep_features.txt' f = np.loadtxt(feature_file) fCombined = h5py.File('/media/uttaran/FCE1-7BF3/Gamma/Gait/data/featuresCombined.h5', 'r') fkeys = fCombined.keys() dfCombined = h5py.File('/media/uttaran/FCE1-7BF3/Gamma/Gait/data/deepFeaturesCombined.h5', 'w') for i, fkey in enumerate(fkeys): fname = [fkey][0] feature = f[i, :] dfCombined.create_dataset(fname, data=feature) dfCombined.close()

true

f71b3da82b76452958e6ff8037b2e5c369373cfd

2,253

py

Python

python/hostconfig/machines/alexf.py

stu-l/Chaste

8efa8b440660553af66804067639f237c855f557

[ "Apache-2.0", "BSD-3-Clause" ]

null

python/hostconfig/machines/alexf.py

stu-l/Chaste

8efa8b440660553af66804067639f237c855f557

[ "Apache-2.0", "BSD-3-Clause" ]

null

python/hostconfig/machines/alexf.py

stu-l/Chaste

8efa8b440660553af66804067639f237c855f557

[ "Apache-2.0", "BSD-3-Clause" ]

null

# Configuration for Finarfin """Copyright (c) 2005-2022, University of Oxford. All rights reserved. University of Oxford means the Chancellor, Masters and Scholars of the University of Oxford, having an administrative office at Wellington Square, Oxford OX1 2JD, UK. This file is part of Chaste. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the University of Oxford nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. """ petsc_2_2_path = None petsc_2_3_path = '/home/alex/petsc-2.3.2-p4/' petsc_build_name = 'linux-gnu' petsc_build_name_optimized = 'linux-gnu-opt' dealii_path = None metis_path = None intel_path = None #'/opt/intel_cc_80' #icpc = 'icpc' other_includepaths = [] other_libpaths = ['/home/alex/hdf5/lib'] blas_lapack = ['lapack', 'blas'] other_libraries = ['boost_serialization', 'boost_filesystem', 'xerces-c'] tools = {'mpirun': '/home/alex/mpi/bin/mpirun', 'mpicxx': '/home/alex/mpi/bin/mpicxx'}

44.176471

79

0.782956

petsc_2_2_path = None petsc_2_3_path = '/home/alex/petsc-2.3.2-p4/' petsc_build_name = 'linux-gnu' petsc_build_name_optimized = 'linux-gnu-opt' dealii_path = None metis_path = None intel_path = None other_includepaths = [] other_libpaths = ['/home/alex/hdf5/lib'] blas_lapack = ['lapack', 'blas'] other_libraries = ['boost_serialization', 'boost_filesystem', 'xerces-c'] tools = {'mpirun': '/home/alex/mpi/bin/mpirun', 'mpicxx': '/home/alex/mpi/bin/mpicxx'}

true

f71b400d9193c88784e05204664ee4a910ee628d

1,831

py

Python

awss3upload-capi/s3upload.py

krlex/aws-python-examples

1310e82c748e3d0898809bbe909870fe4c7cb37b

[ "MIT" ]

21

2021-02-13T04:11:01.000Z

2022-03-28T09:13:53.000Z

awss3upload-capi/s3upload.py

krlex/aws-python-examples

1310e82c748e3d0898809bbe909870fe4c7cb37b

[ "MIT" ]

null

awss3upload-capi/s3upload.py

krlex/aws-python-examples

1310e82c748e3d0898809bbe909870fe4c7cb37b

[ "MIT" ]

17

2019-07-26T06:02:27.000Z

2022-03-23T00:06:12.000Z

#!/usr/bin/python # -*- coding: utf-8 -*- # s3upload.py # It is an example that handles S3 buckets on AWS. # It uses Client API (low-level) of Boto3. # Upload a local file to a S3 bucket. # You must provide 1 parameter: # BUCKET_NAME = Name of the bucket # OBJECT_NAME = Object file name in the bucket # LOCAL_FILE_NAME = Local file name import sys import os import boto3 import botocore def main(): # Make a list of command line arguments, omitting the [0] element # which is the script itself. args = sys.argv[1:] if len(args) < 3: print('Not enough parameters.\n'\ 'Proper Usage is: python s3upload.py '\ '<BUCKET_NAME> <OBJECT_NAME> <LOCAL_FILE_NAME>') sys.exit(1) bucket_name = args[0] key_name = args[1] local_file_name = args[2] print('Bucket: ' + bucket_name) print('Object/Key: ' + key_name) print('Local file: ' + local_file_name) # Create an S3 Client s3_client = boto3.client('s3') if not os.path.isfile(local_file_name): print("Error: File Not Found!!") sys.exit(1) # Upload object try: print('Uploading object ...') s3_client.upload_file(local_file_name, bucket_name, key_name) print('Uploaded') except botocore.exceptions.ClientError as e: if e.response['Error']['Code'] == "NoSuchBucket": print("Error: Bucket does not exist!!") elif e.response['Error']['Code'] == "InvalidBucketName": print("Error: Invalid Bucket name!!") elif e.response['Error']['Code'] == "AllAccessDisabled": print("Error: You do not have access to the Bucket!!") else: raise return # This is the standard boilerplate that calls the main() function. if __name__ == '__main__': main()

28.609375

69

0.621518

import sys import os import boto3 import botocore def main(): args = sys.argv[1:] if len(args) < 3: print('Not enough parameters.\n'\ 'Proper Usage is: python s3upload.py '\ '<BUCKET_NAME> <OBJECT_NAME> <LOCAL_FILE_NAME>') sys.exit(1) bucket_name = args[0] key_name = args[1] local_file_name = args[2] print('Bucket: ' + bucket_name) print('Object/Key: ' + key_name) print('Local file: ' + local_file_name) s3_client = boto3.client('s3') if not os.path.isfile(local_file_name): print("Error: File Not Found!!") sys.exit(1) try: print('Uploading object ...') s3_client.upload_file(local_file_name, bucket_name, key_name) print('Uploaded') except botocore.exceptions.ClientError as e: if e.response['Error']['Code'] == "NoSuchBucket": print("Error: Bucket does not exist!!") elif e.response['Error']['Code'] == "InvalidBucketName": print("Error: Invalid Bucket name!!") elif e.response['Error']['Code'] == "AllAccessDisabled": print("Error: You do not have access to the Bucket!!") else: raise return if __name__ == '__main__': main()

true

f71b4058fcf0bfe5202371ab731ffe619ab85852

827

py

Python

src/lambda/face-detector-function/main/image_ops/resizer.py

gai6948/video-analytics-for-ppe-compliance

bb41ac010f5917bd7e85adfed689489f24830617

[ "Apache-2.0" ]

null

src/lambda/face-detector-function/main/image_ops/resizer.py

gai6948/video-analytics-for-ppe-compliance

bb41ac010f5917bd7e85adfed689489f24830617

[ "Apache-2.0" ]

null

src/lambda/face-detector-function/main/image_ops/resizer.py

gai6948/video-analytics-for-ppe-compliance

bb41ac010f5917bd7e85adfed689489f24830617

[ "Apache-2.0" ]

null

import numpy as np import timeit import cv2 from PIL import Image from aws_lambda_powertools.logging import Logger from aws_lambda_powertools.tracing import Tracer logger = Logger(service='face-detector', child=True) tracer = Tracer(service='face-detector') @tracer.capture_method(capture_response=False) def resize_image(frame: np.ndarray, target_image_width: int, target_image_height: int) -> np.ndarray: """ Resize the drawn image and save to /tmp directory before uploading to S3 :param `frame`: frame data in numpy array """ start_time = timeit.default_timer() new_frame: np.ndarray = cv2.resize(frame, dsize=( target_image_width, target_image_height), interpolation=cv2.INTER_LINEAR) logger.info(f'Resized frame after: {timeit.default_timer() - start_time}') return new_frame

33.08

101

0.759371

import numpy as np import timeit import cv2 from PIL import Image from aws_lambda_powertools.logging import Logger from aws_lambda_powertools.tracing import Tracer logger = Logger(service='face-detector', child=True) tracer = Tracer(service='face-detector') @tracer.capture_method(capture_response=False) def resize_image(frame: np.ndarray, target_image_width: int, target_image_height: int) -> np.ndarray: start_time = timeit.default_timer() new_frame: np.ndarray = cv2.resize(frame, dsize=( target_image_width, target_image_height), interpolation=cv2.INTER_LINEAR) logger.info(f'Resized frame after: {timeit.default_timer() - start_time}') return new_frame

true

f71b4158a84075698aa6f4b4d391c6b10747b9c5

2,190

py

Python

config/settings/local.py

suyash143/Base_todo

3284f24f8b5c611088af6189a2f264a280fbbbd6

[ "MIT" ]

1

2022-03-16T10:22:34.000Z

config/settings/local.py

suyash143/Base_todo

3284f24f8b5c611088af6189a2f264a280fbbbd6

[ "MIT" ]

1

2022-03-30T21:29:43.000Z

config/settings/local.py

suyash143/Base_todo

3284f24f8b5c611088af6189a2f264a280fbbbd6

[ "MIT" ]

null

from .base import * # noqa from .base import env # GENERAL # ------------------------------------------------------------------------------ # https://docs.djangoproject.com/en/dev/ref/settings/#debug DEBUG = True # https://docs.djangoproject.com/en/dev/ref/settings/#secret-key SECRET_KEY = env( "DJANGO_SECRET_KEY", default="00nep4XZom6FM9dVyJO6Y7kqt5JV8TN5GTNmcDnDhH0jTq3cDYEGLsyOsUYnOAsM", ) # https://docs.djangoproject.com/en/dev/ref/settings/#allowed-hosts ALLOWED_HOSTS = ["localhost", "0.0.0.0", "127.0.0.1"] # CACHES # ------------------------------------------------------------------------------ # https://docs.djangoproject.com/en/dev/ref/settings/#caches CACHES = { "default": { "BACKEND": "django.core.cache.backends.locmem.LocMemCache", "LOCATION": "", } } # EMAIL # ------------------------------------------------------------------------------ # https://docs.djangoproject.com/en/dev/ref/settings/#email-backend EMAIL_BACKEND = env( "DJANGO_EMAIL_BACKEND", default="django.core.mail.backends.console.EmailBackend" ) # django-debug-toolbar # ------------------------------------------------------------------------------ # https://django-debug-toolbar.readthedocs.io/en/latest/installation.html#prerequisites INSTALLED_APPS += ["debug_toolbar"] # noqa F405 # https://django-debug-toolbar.readthedocs.io/en/latest/installation.html#middleware MIDDLEWARE += ["debug_toolbar.middleware.DebugToolbarMiddleware"] # noqa F405 # https://django-debug-toolbar.readthedocs.io/en/latest/configuration.html#debug-toolbar-config DEBUG_TOOLBAR_CONFIG = { "DISABLE_PANELS": ["debug_toolbar.panels.redirects.RedirectsPanel"], "SHOW_TEMPLATE_CONTEXT": True, } # https://django-debug-toolbar.readthedocs.io/en/latest/installation.html#internal-ips INTERNAL_IPS = ["127.0.0.1", "10.0.2.2"] # django-extensions # ------------------------------------------------------------------------------ # https://django-extensions.readthedocs.io/en/latest/installation_instructions.html#configuration INSTALLED_APPS += ["django_extensions"] # noqa F405 # Your stuff... # ------------------------------------------------------------------------------

39.818182

97

0.583105

from .base import * from .base import env = True = env( "DJANGO_SECRET_KEY", default="00nep4XZom6FM9dVyJO6Y7kqt5JV8TN5GTNmcDnDhH0jTq3cDYEGLsyOsUYnOAsM", ) = ["localhost", "0.0.0.0", "127.0.0.1"] = { "default": { "BACKEND": "django.core.cache.backends.locmem.LocMemCache", "LOCATION": "", } } = env( "DJANGO_EMAIL_BACKEND", default="django.core.mail.backends.console.EmailBackend" ) S += ["debug_toolbar"] += ["debug_toolbar.middleware.DebugToolbarMiddleware"] = { "DISABLE_PANELS": ["debug_toolbar.panels.redirects.RedirectsPanel"], "SHOW_TEMPLATE_CONTEXT": True, } = ["127.0.0.1", "10.0.2.2"] S += ["django_extensions"]

true

f71b41b1f6968010d18f796949243c59c3f77265

2,847

py

Python

wagtail/hooks.py

stevedya/wagtail

52e5abfe62547cdfd90ea7dfeb8bf5a52f16324c

[ "BSD-3-Clause" ]

1

2022-02-09T05:25:30.000Z

wagtail/hooks.py

stevedya/wagtail

52e5abfe62547cdfd90ea7dfeb8bf5a52f16324c

[ "BSD-3-Clause" ]

null

wagtail/hooks.py

stevedya/wagtail

52e5abfe62547cdfd90ea7dfeb8bf5a52f16324c

[ "BSD-3-Clause" ]

null

from contextlib import ContextDecorator from operator import itemgetter from wagtail.utils.apps import get_app_submodules _hooks = {} def register(hook_name, fn=None, order=0): """ Register hook for ``hook_name``. Can be used as a decorator:: @register('hook_name') def my_hook(...): pass or as a function call:: def my_hook(...): pass register('hook_name', my_hook) """ # Pretend to be a decorator if fn is not supplied if fn is None: def decorator(fn): register(hook_name, fn, order=order) return fn return decorator if hook_name not in _hooks: _hooks[hook_name] = [] _hooks[hook_name].append((fn, order)) class TemporaryHook(ContextDecorator): def __init__(self, hooks, order): self.hooks = hooks self.order = order def __enter__(self): for hook_name, fn in self.hooks: if hook_name not in _hooks: _hooks[hook_name] = [] _hooks[hook_name].append((fn, self.order)) def __exit__(self, exc_type, exc_value, traceback): for hook_name, fn in self.hooks: _hooks[hook_name].remove((fn, self.order)) def register_temporarily(hook_name_or_hooks, fn=None, *, order=0): """ Register hook for ``hook_name`` temporarily. This is useful for testing hooks. Can be used as a decorator:: def my_hook(...): pass class TestMyHook(Testcase): @hooks.register_temporarily('hook_name', my_hook) def test_my_hook(self): pass or as a context manager:: def my_hook(...): pass with hooks.register_temporarily('hook_name', my_hook): # Hook is registered here # Hook is unregistered here To register multiple hooks at the same time, pass in a list of 2-tuples: def my_hook(...): pass def my_other_hook(...): pass with hooks.register_temporarily([ ('hook_name', my_hook), ('hook_name', my_other_hook), ]): # Hooks are registered here """ if not isinstance(hook_name_or_hooks, list) and fn is not None: hooks = [(hook_name_or_hooks, fn)] else: hooks = hook_name_or_hooks return TemporaryHook(hooks, order) _searched_for_hooks = False def search_for_hooks(): global _searched_for_hooks if not _searched_for_hooks: list(get_app_submodules("wagtail_hooks")) _searched_for_hooks = True def get_hooks(hook_name): """Return the hooks function sorted by their order.""" search_for_hooks() hooks = _hooks.get(hook_name, []) hooks = sorted(hooks, key=itemgetter(1)) return [hook[0] for hook in hooks]

24.543103

82

0.60555

from contextlib import ContextDecorator from operator import itemgetter from wagtail.utils.apps import get_app_submodules _hooks = {} def register(hook_name, fn=None, order=0): if fn is None: def decorator(fn): register(hook_name, fn, order=order) return fn return decorator if hook_name not in _hooks: _hooks[hook_name] = [] _hooks[hook_name].append((fn, order)) class TemporaryHook(ContextDecorator): def __init__(self, hooks, order): self.hooks = hooks self.order = order def __enter__(self): for hook_name, fn in self.hooks: if hook_name not in _hooks: _hooks[hook_name] = [] _hooks[hook_name].append((fn, self.order)) def __exit__(self, exc_type, exc_value, traceback): for hook_name, fn in self.hooks: _hooks[hook_name].remove((fn, self.order)) def register_temporarily(hook_name_or_hooks, fn=None, *, order=0): if not isinstance(hook_name_or_hooks, list) and fn is not None: hooks = [(hook_name_or_hooks, fn)] else: hooks = hook_name_or_hooks return TemporaryHook(hooks, order) _searched_for_hooks = False def search_for_hooks(): global _searched_for_hooks if not _searched_for_hooks: list(get_app_submodules("wagtail_hooks")) _searched_for_hooks = True def get_hooks(hook_name): search_for_hooks() hooks = _hooks.get(hook_name, []) hooks = sorted(hooks, key=itemgetter(1)) return [hook[0] for hook in hooks]

true

f71b41c7e4894d5f578583e52382342d197a0a53

373

py

Python

tests/schema/mutation/snapshots/snap_test_maps_delete.py

TaiSakuma/acondbs

990ab44ce4081cc0e04148a8375f7ce7081c2dee

[ "MIT" ]

null

tests/schema/mutation/snapshots/snap_test_maps_delete.py

TaiSakuma/acondbs

990ab44ce4081cc0e04148a8375f7ce7081c2dee

[ "MIT" ]

null

tests/schema/mutation/snapshots/snap_test_maps_delete.py

TaiSakuma/acondbs

990ab44ce4081cc0e04148a8375f7ce7081c2dee

[ "MIT" ]

null

# -*- coding: utf-8 -*- # snapshottest: v1 - https://goo.gl/zC4yUc from __future__ import unicode_literals from snapshottest import Snapshot snapshots = Snapshot() snapshots['test_schema[deleteMap] 1'] = { 'data': { 'deleteMap': { 'ok': True } } } snapshots['test_schema[deleteMap] 2'] = { 'data': { 'map': None } }

16.217391

42

0.571046

from __future__ import unicode_literals from snapshottest import Snapshot snapshots = Snapshot() snapshots['test_schema[deleteMap] 1'] = { 'data': { 'deleteMap': { 'ok': True } } } snapshots['test_schema[deleteMap] 2'] = { 'data': { 'map': None } }

true

f71b4212c0a7a5b644b48292c8eaeacc6cbdda01

1,035

py

Python

src/products/mixins.py

bopopescu/django-estore

c092ffa965b8ef68e71d27d34a17fde1beacd90e

[ "MIT" ]

null

src/products/mixins.py

bopopescu/django-estore

c092ffa965b8ef68e71d27d34a17fde1beacd90e

[ "MIT" ]

null

src/products/mixins.py

bopopescu/django-estore

c092ffa965b8ef68e71d27d34a17fde1beacd90e

[ "MIT" ]

2

2019-04-29T14:16:10.000Z

2020-07-23T12:04:17.000Z

from django.contrib.admin.views.decorators import staff_member_required from django.contrib.auth.decorators import login_required from django.utils.decorators import method_decorator from django.http import Http404 class StaffRequiredMixin(object): @classmethod def as_view(self, *args, **kwargs): view = super(StaffRequiredMixin, self).as_view(*args, **kwargs) return login_required(view) @method_decorator(login_required) def dispatch(self, request, *args, **kwargs): if request.user.is_staff: return super(StaffRequiredMixin, self).dispatch(request, *args, **kwargs) else: raise Http404 class LoginRequiredMixin(object): @classmethod def as_view(self, *args, **kwargs): view = super(LoginRequiredMixin, self).as_view(*args, **kwargs) return login_required(view) @method_decorator(login_required) def dispatch(self, request, *args, **kwargs): return super(LoginRequiredMixin, self).dispatch(request, *args, **kwargs)

36.964286

85

0.713043

from django.contrib.admin.views.decorators import staff_member_required from django.contrib.auth.decorators import login_required from django.utils.decorators import method_decorator from django.http import Http404 class StaffRequiredMixin(object): @classmethod def as_view(self, *args, **kwargs): view = super(StaffRequiredMixin, self).as_view(*args, **kwargs) return login_required(view) @method_decorator(login_required) def dispatch(self, request, *args, **kwargs): if request.user.is_staff: return super(StaffRequiredMixin, self).dispatch(request, *args, **kwargs) else: raise Http404 class LoginRequiredMixin(object): @classmethod def as_view(self, *args, **kwargs): view = super(LoginRequiredMixin, self).as_view(*args, **kwargs) return login_required(view) @method_decorator(login_required) def dispatch(self, request, *args, **kwargs): return super(LoginRequiredMixin, self).dispatch(request, *args, **kwargs)

true

f71b4286b66f22ca7786f177364f1945a65ac3fc

3,123

py

Python

app/app/settings.py

M0narc/recipe-api

3b6c204ca76d98310d26fcbeaa4537646a93e023

[ "MIT" ]

1

2022-03-11T20:38:42.000Z

app/app/settings.py

M0narc/recipe-api

3b6c204ca76d98310d26fcbeaa4537646a93e023

[ "MIT" ]

null

app/app/settings.py

M0narc/recipe-api

3b6c204ca76d98310d26fcbeaa4537646a93e023

[ "MIT" ]

null

""" Django settings for app project. Generated by 'django-admin startproject' using Django 2.1.15. For more information on this file, see https://docs.djangoproject.com/en/2.1/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/2.1/ref/settings/ """ import os # Build paths inside the project like this: os.path.join(BASE_DIR, ...) BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/2.1/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = 'p2-y-!@#t0dny#e+nx-txbsphwp(yt(9t939=o_*sf%&3z2_p%' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True ALLOWED_HOSTS = [] # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'core', ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'app.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'app.wsgi.application' # Database # https://docs.djangoproject.com/en/2.1/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'), } } # Password validation # https://docs.djangoproject.com/en/2.1/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/2.1/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/2.1/howto/static-files/ STATIC_URL = '/static/' AUTH_USER_MODEL = 'core.User'

25.185484

91

0.693244

import os BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) SECRET_KEY = 'p2-y-!@#t0dny#e+nx-txbsphwp(yt(9t939=o_*sf%&3z2_p%' DEBUG = True ALLOWED_HOSTS = [] # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'core', ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'app.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'app.wsgi.application' # Database # https://docs.djangoproject.com/en/2.1/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'), } } # Password validation # https://docs.djangoproject.com/en/2.1/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/2.1/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/2.1/howto/static-files/ STATIC_URL = '/static/' AUTH_USER_MODEL = 'core.User'

true

f71b43813e699367c1eaeee665cc3b3fd3d5c5d0

4,573

py

Python

hgext/fsmonitor/state.py

EnjoyLifeFund/macHighSierra-py36-pkgs

5668b5785296b314ea1321057420bcd077dba9ea

[ "BSD-3-Clause", "BSD-2-Clause", "MIT" ]

null

hgext/fsmonitor/state.py

EnjoyLifeFund/macHighSierra-py36-pkgs

5668b5785296b314ea1321057420bcd077dba9ea

[ "BSD-3-Clause", "BSD-2-Clause", "MIT" ]

null

hgext/fsmonitor/state.py

EnjoyLifeFund/macHighSierra-py36-pkgs

5668b5785296b314ea1321057420bcd077dba9ea

[ "BSD-3-Clause", "BSD-2-Clause", "MIT" ]

null

# state.py - fsmonitor persistent state # # Copyright 2013-2016 Facebook, Inc. # # This software may be used and distributed according to the terms of the # GNU General Public License version 2 or any later version. from __future__ import absolute_import import errno import os import socket import struct from mercurial.i18n import _ from mercurial import ( pathutil, util, ) _version = 4 _versionformat = ">I" class state(object): def __init__(self, repo): self._vfs = repo.vfs self._ui = repo.ui self._rootdir = pathutil.normasprefix(repo.root) self._lastclock = None self._identity = util.filestat(None) self.mode = self._ui.config('fsmonitor', 'mode', default='on') self.walk_on_invalidate = self._ui.configbool( 'fsmonitor', 'walk_on_invalidate', False) self.timeout = float(self._ui.config( 'fsmonitor', 'timeout', default='2')) def get(self): try: file = self._vfs('fsmonitor.state', 'rb') except IOError as inst: self._identity = util.filestat(None) if inst.errno != errno.ENOENT: raise return None, None, None self._identity = util.filestat.fromfp(file) versionbytes = file.read(4) if len(versionbytes) < 4: self._ui.log( 'fsmonitor', 'fsmonitor: state file only has %d bytes, ' 'nuking state\n' % len(versionbytes)) self.invalidate() return None, None, None try: diskversion = struct.unpack(_versionformat, versionbytes)[0] if diskversion != _version: # different version, nuke state and start over self._ui.log( 'fsmonitor', 'fsmonitor: version switch from %d to ' '%d, nuking state\n' % (diskversion, _version)) self.invalidate() return None, None, None state = file.read().split('\0') # state = hostname\0clock\0ignorehash\0 + list of files, each # followed by a \0 if len(state) < 3: self._ui.log( 'fsmonitor', 'fsmonitor: state file truncated (expected ' '3 chunks, found %d), nuking state\n', len(state)) self.invalidate() return None, None, None diskhostname = state[0] hostname = socket.gethostname() if diskhostname != hostname: # file got moved to a different host self._ui.log('fsmonitor', 'fsmonitor: stored hostname "%s" ' 'different from current "%s", nuking state\n' % (diskhostname, hostname)) self.invalidate() return None, None, None clock = state[1] ignorehash = state[2] # discard the value after the last \0 notefiles = state[3:-1] finally: file.close() return clock, ignorehash, notefiles def set(self, clock, ignorehash, notefiles): if clock is None: self.invalidate() return # Read the identity from the file on disk rather than from the open file # pointer below, because the latter is actually a brand new file. identity = util.filestat.frompath(self._vfs.join('fsmonitor.state')) if identity != self._identity: self._ui.debug('skip updating fsmonitor.state: identity mismatch\n') return try: file = self._vfs('fsmonitor.state', 'wb', atomictemp=True, checkambig=True) except (IOError, OSError): self._ui.warn(_("warning: unable to write out fsmonitor state\n")) return with file: file.write(struct.pack(_versionformat, _version)) file.write(socket.gethostname() + '\0') file.write(clock + '\0') file.write(ignorehash + '\0') if notefiles: file.write('\0'.join(notefiles)) file.write('\0') def invalidate(self): try: os.unlink(os.path.join(self._rootdir, '.hg', 'fsmonitor.state')) except OSError as inst: if inst.errno != errno.ENOENT: raise self._identity = util.filestat(None) def setlastclock(self, clock): self._lastclock = clock def getlastclock(self): return self._lastclock

33.625

80

0.557839

from __future__ import absolute_import import errno import os import socket import struct from mercurial.i18n import _ from mercurial import ( pathutil, util, ) _version = 4 _versionformat = ">I" class state(object): def __init__(self, repo): self._vfs = repo.vfs self._ui = repo.ui self._rootdir = pathutil.normasprefix(repo.root) self._lastclock = None self._identity = util.filestat(None) self.mode = self._ui.config('fsmonitor', 'mode', default='on') self.walk_on_invalidate = self._ui.configbool( 'fsmonitor', 'walk_on_invalidate', False) self.timeout = float(self._ui.config( 'fsmonitor', 'timeout', default='2')) def get(self): try: file = self._vfs('fsmonitor.state', 'rb') except IOError as inst: self._identity = util.filestat(None) if inst.errno != errno.ENOENT: raise return None, None, None self._identity = util.filestat.fromfp(file) versionbytes = file.read(4) if len(versionbytes) < 4: self._ui.log( 'fsmonitor', 'fsmonitor: state file only has %d bytes, ' 'nuking state\n' % len(versionbytes)) self.invalidate() return None, None, None try: diskversion = struct.unpack(_versionformat, versionbytes)[0] if diskversion != _version: self._ui.log( 'fsmonitor', 'fsmonitor: version switch from %d to ' '%d, nuking state\n' % (diskversion, _version)) self.invalidate() return None, None, None state = file.read().split('\0') if len(state) < 3: self._ui.log( 'fsmonitor', 'fsmonitor: state file truncated (expected ' '3 chunks, found %d), nuking state\n', len(state)) self.invalidate() return None, None, None diskhostname = state[0] hostname = socket.gethostname() if diskhostname != hostname: self._ui.log('fsmonitor', 'fsmonitor: stored hostname "%s" ' 'different from current "%s", nuking state\n' % (diskhostname, hostname)) self.invalidate() return None, None, None clock = state[1] ignorehash = state[2] notefiles = state[3:-1] finally: file.close() return clock, ignorehash, notefiles def set(self, clock, ignorehash, notefiles): if clock is None: self.invalidate() return identity = util.filestat.frompath(self._vfs.join('fsmonitor.state')) if identity != self._identity: self._ui.debug('skip updating fsmonitor.state: identity mismatch\n') return try: file = self._vfs('fsmonitor.state', 'wb', atomictemp=True, checkambig=True) except (IOError, OSError): self._ui.warn(_("warning: unable to write out fsmonitor state\n")) return with file: file.write(struct.pack(_versionformat, _version)) file.write(socket.gethostname() + '\0') file.write(clock + '\0') file.write(ignorehash + '\0') if notefiles: file.write('\0'.join(notefiles)) file.write('\0') def invalidate(self): try: os.unlink(os.path.join(self._rootdir, '.hg', 'fsmonitor.state')) except OSError as inst: if inst.errno != errno.ENOENT: raise self._identity = util.filestat(None) def setlastclock(self, clock): self._lastclock = clock def getlastclock(self): return self._lastclock

true

f71b440f7ca22eaf9b320a66190d76f7e190c156

39,690

py

Python

galaxy_milkyway_files/tools/wohl-proteomics/ssl_converter/bak_ssl_converter.py

wohllab/milkyway_proteomics

622969f7f4a5955ae2bff299ae7b08572d422814

[ "MIT" ]

null

galaxy_milkyway_files/tools/wohl-proteomics/ssl_converter/bak_ssl_converter.py

wohllab/milkyway_proteomics

622969f7f4a5955ae2bff299ae7b08572d422814

[ "MIT" ]

null

galaxy_milkyway_files/tools/wohl-proteomics/ssl_converter/bak_ssl_converter.py

wohllab/milkyway_proteomics

622969f7f4a5955ae2bff299ae7b08572d422814

[ "MIT" ]

null

import os, sys, re import shutil import optparse import shutil import pandas import numpy import subprocess import fnmatch from joblib import Parallel, delayed import multiprocessing from Bio import SeqIO import glob ##################################### #This is the script to produce SSL file outputs for skyline spectral library construction # #Fraction parsing is taken from from after the final "-" in the file name. For example, "2015-10-05-wb-HEK293-BioRep1-F1.mzML" #would belong to fraction "F1" # #VERSION 1.7.5 version="1.7.5" #DATE: 3/03/2016 date="3/03/2016" ##################################### print "-----------------------------------------------------------------------" print "Welcome to the SSL file converter for Galaxy, Wohlschlegel Lab UCLA" print "Written by William Barshop" print "Version: ",version print "Date: ",date def applyParallel(dfGrouped, func): retLst = Parallel(n_jobs=multiprocessing.cpu_count())(delayed(func)(group) for name, group in dfGrouped) return pandas.concat(retLst) def applyParallelQuarter(dfGrouped, func): retLst = Parallel(n_jobs=multiprocessing.cpu_count()/4)(delayed(func)(group) for name, group in dfGrouped) return pandas.concat(retLst) def applyParallelHalf(dfGrouped, func): retLst = Parallel(n_jobs=multiprocessing.cpu_count()/2)(delayed(func)(group) for name, group in dfGrouped) return pandas.concat(retLst) def fido_filter(unfiltered):#dataframe,float arguments... #filtered=[] #print unfiltered,"This is it GOING IN...." #unfiltered.apply(fido_filter_row,axis=1) for index,eachrow in unfiltered.iterrows(): prot_q_vals = eachrow['protein q-values'].split(',') protein_ids = eachrow['protein id'].split(',') indicies = eachrow['peptide prot-indicies'].split(',') new_q_vals=[] new_prot_ids=[] new_indicies=[] for each_tuple in zip(protein_ids,prot_q_vals,indicies): if float(each_tuple[1])<=gfido_q_threshold: #KEEP IT new_q_vals.append(each_tuple[1]) new_prot_ids.append(each_tuple[0]) new_indicies.append(each_tuple[2]) else: pass # THROW IT OUT if len(new_prot_ids) >= 1: unfiltered.loc[index,'protein q-values']=",".join(new_q_vals) unfiltered.loc[index,'protein id']=",".join(new_prot_ids) unfiltered.loc[index,'indicies']=",".join(new_indicies) unfiltered.loc[index,'fido_filter']=1 #This means we'll keep it when we do our df filter... We'll end up dropping this column later. else: unfiltered.loc[index,'fido_filter']=0 #print changed,type(changed),"this is changed..." return unfiltered def fido_filter_row(eachrow): #print eachrow,"this is my row!",type(eachrow) #print str(gfido_q_threshold),"this is the threshold..." prot_q_vals = eachrow['protein q-values'].split(',') protein_ids = eachrow['protein id'].split(',') indicies = eachrow['peptide prot-indicies'].split(',') new_q_vals=[] new_prot_ids=[] new_indicies=[] for each_tuple in zip(protein_ids,prot_q_vals,indicies): if float(each_tuple[1])<=gfido_q_threshold: #KEEP IT new_q_vals.append(each_tuple[1]) new_prot_ids.append(each_tuple[0]) new_indicies.append(each_tuple[2]) else: pass # THROW IT OUT if len(new_prot_ids) >= 1: eachrow['protein q-values']=",".join(new_q_vals) eachrow['protein id']=",".join(new_prot_ids) eachrow['indicies']=",".join(new_indicies) eachrow['fido_filter']=1 #This means we'll keep it when we do our df filter... We'll end up dropping this column later. else: eachrow['fido_filter']=0 #################################### #Argument parsing! So much fun! #We'll use OptParse even though some #people really rave about argparse... # # # NB: With Optparse, if an option is # not specified, it will take a # value of None #################################### fractions=False #print sys.argv,"THESE ARE THE ARGS" parser = optparse.OptionParser() parser.add_option("--pout",action="store",type="string",dest="operation_folder") parser.add_option("--qthresh",action="store", type="float", dest="q_threshold") parser.add_option("--fidoq",action="store", type="float", dest="fido_q_threshold") parser.add_option("--fido_q_raw",action="store", type="string", dest="fido_q_raw") parser.add_option("--pRS_prob_thresh",action="store", type="float", dest="prs_prob_threshold") parser.add_option("--ptmRS_prob_thresh",action="store", type="float", dest="ptmrs_prob_threshold") parser.add_option("--FLRthresh",action="store", type="float", dest="flr_threshold") parser.add_option("--LFLR",action="store_true", dest="local_flr") parser.add_option("--mzml",action="store",type="string",dest="mzml_files") parser.add_option("--mass_corrected_mzml",action="store",type="string",dest="mc_mzml_files") parser.add_option("--blib",action="store_true",dest="blib") parser.add_option("--fasta",action="store",type="string",dest="fasta") parser.add_option("--ffasta",action="store",type="string",dest="ffasta") # THIS IS THE FILTERED FIDO OUTPUT parser.add_option("--expgroups",action="store",type="string",dest="exp_group_file") parser.add_option("--ssl",action="store",type="string",dest="ssl_output_folder") parser.add_option("--fractions",action="store_true",dest="fractions") parser.add_option("--OnePPS",action="store_true",dest="one_pps") parser.add_option("--only_mod",action="store_true",dest="only_mod") parser.add_option("--diaumpire",action="store_true",dest="diaumpire") parser.add_option("--no_mzml",action="store_true",dest="no_mzml") #parser.add_option("--saint",action="store",type="string",dest="saint_outputs") (options,args) = parser.parse_args() if options.fractions is True: fractions=True else: print "We'll give outputs by acquisition (runs), not by experimental set." #saint=False #if options.saint_outputs is not None: # saint=True # saint_interact,saint_bait,saint_prey=options.saint_outputs.split(",") #### Check for FIDO q-filter fido_q=False if options.fido_q_threshold is not None: fido_q=True global gfido_q_threshold #For paralell access via JobLib/Multiprocessing... gfido_q_threshold = options.fido_q_threshold print "We're going to filter by Fido Q-Value of ",options.fido_q_threshold #### Check for LuciPHOr luciphor=False if options.flr_threshold is not None: luciphor=True print "We will filter by LuciPHOr FLR of ",options.flr_threshold #### Check for PhosphoRS phosphoRS=False if options.prs_prob_threshold is not None: phosphoRS=True print "We will filter by a PhosphoRS probability of ",options.prs_prob_threshold #### Check for ptmRS ptmRS=False if options.ptmrs_prob_threshold is not None: ptmRS=True print "We will filter by a ptmRS probability of ",options.prs_prob_threshold psms_files=[] for root, subFolders, files in os.walk(options.operation_folder): for eachfile in files: if 'target.psms.txt' in eachfile and 'uncorrected' not in eachfile: psms_files.append(str(os.path.join(root,eachfile))) dataframe_vector=[] for eachfile in psms_files: newdf=pandas.DataFrame.from_csv(eachfile,sep='\t',index_col=False) dataframe_vector.append(newdf) combined_results=pandas.concat(dataframe_vector) del dataframe_vector group_information = pandas.read_csv(options.exp_group_file,sep='\t',dtype={'Fractionation Group ID String': object,'Fractionation Group Name':object,'Biological Condition':object}) run_dict={} # Key is file_idx, value is file_name.mzML rev_run_dict={} #Key is file_nzme.mzML, value is file_idx group_to_run_dict={} # Key is group, value is [1, 2, 3, 4] list of file_idx belonging to runs in the group... run_to_group_dict={} # Key is file_idx, value is group... group_to_file_name={} # key is group, value is ["xxxx.mzML", "xxxx.mzML"] if fractions: fractions_to_run_dict={} for index,row in group_information.iterrows(): print row run_dict[str(row['Crux File Integer'])]=row['Original File Name']+".mzML" rev_run_dict[row['Original File Name']+".mzML"]=str(row['Crux File Integer']) if row['Fractionation Group ID String'] in group_to_run_dict: group_to_run_dict[row['Fractionation Group ID String']].append(str(row['Crux File Integer'])) else: group_to_run_dict[row['Fractionation Group ID String']] = [str(row['Crux File Integer'])] if str(row['Crux File Integer']) in run_to_group_dict: #run_to_group_dict[str(row['Crux File Integer'])].append(row['Fractionation Group ID String']) print "YOU HAVE MULTIPLE COPIES OF A RUN IN THE EXPERIMENTAL INFORMATION FILE... WARNING!" else: run_to_group_dict[str(row['Crux File Integer'])]=row['Fractionation Group ID String'] if row['Fractionation Group ID String'] in group_to_file_name: group_to_file_name[row['Fractionation Group ID String']].append(str(row['Original File Name'])+".mzML") else: group_to_file_name[row['Fractionation Group ID String']] = [str(row['Original File Name'])+".mzML"] if fractions: fraction_tag=str(row['Original File Name'].rsplit("-",1)[1]) if fraction_tag in fractions_to_run_dict: fractions_to_run_dict[fraction_tag].append(str(row['Crux File Integer']))#str(row['Original File Name'])+".mzML") else: fractions_to_run_dict[fraction_tag]=[str(row['Crux File Integer'])] combined_results['file_idx']=combined_results['file_idx'].astype(str) combined_results['file']=combined_results['file'].astype(str) ####################### We'll handle putting in the file names just to be sure this has been handled! fix_combined=[] print run_dict,"This is run dict" print rev_run_dict,"this is rev dict" for each_idx in run_dict: mask = combined_results[(combined_results.file.str.contains(run_dict[each_idx]))] # MODIFIED.... mask['file']=run_dict[each_idx] fix_combined.append(mask) combined_results=pandas.concat(fix_combined) if options.diaumpire: print "DIAUmpire inputs: Decrementing scans to match mzML file index." combined_results['scan']=combined_results['scan']-1 if luciphor: #print combined_results combined_results['luci_numPPS']=combined_results['luci_numPPS'].fillna(0) combined_results['luci_numPPS']=combined_results['luci_numPPS'].astype(int).fillna(0) combined_results['luci_globalFLR']=combined_results['luci_globalFLR'].astype(float).fillna(0.0) combined_results['luci_localFLR']=combined_results['luci_localFLR'].astype(float).fillna(0.0) if phosphoRS: combined_results['pRS_peptideLocalizationProbability']=combined_results['pRS_peptideLocalizationProbability'].astype(float).fillna(1.00) combined_results['pRS_numPPS']=combined_results['pRS_numPPS'].astype(int).fillna(0) if ptmRS: combined_results['ptmRS_peptideLocalizationProbability']=combined_results['ptmRS_peptideLocalizationProbability'].astype(float).fillna(1.00) combined_results['ptmRS_totalNumPPS']=combined_results['ptmRS_totalNumPPS'].fillna(0).astype(int) #for i,each in combined_results.iterrows(): new_results={} # KEY IS GROUP NAME results_per_run={} # KEY IS FILE_IDX #print "tehse are items in group to run dict",group_to_run_dict ################################################## THIS BLOCK OF CODE BELOW DOES NOT WORK WELL... FILTERING BY "IN" and for FILE IDX IS A BAD IDEA SINCE "1" IS ALSO IN "10" and "11"... STUPID. #for each_group in group_to_run_dict: # bool_filter=combined_results.copy(deep=True) # bool_filter['file_idx']=bool_filter['file_idx'].astype(str) # #mask8=combined_results[(bool_filter.file_idx == "8" )] # mask = combined_results[(combined_results.file_idx.str.contains('|'.join(group_to_run_dict[each_group])))] #############Was this inappropriate set to file instead of file_idx? # new_results[each_group]=mask # results by group # for each_file in set(mask['file_idx']): # each_file_mask=mask[(mask.file_idx == each_file)] # results_per_run[str(each_file)]=each_file_mask # results by run for each_group in group_to_file_name: bool_filter=combined_results.copy(deep=True) #bool_filter['file_idx']=bool_filter['file_idx'].astype(str) #mask8=combined_results[(bool_filter.file_idx == "8" )] mask = combined_results[(combined_results.file.str.contains('|'.join(group_to_file_name[each_group])))] new_results[each_group]=mask # results by group for each_file in set(mask['file']): each_file_mask=mask[(mask.file.str.contains(each_file))] results_per_run[str(rev_run_dict[each_file])]=each_file_mask # results by run #################################################### if options.one_pps is True: print "Allowing Unambiguous Localization Peptides Through..." basedir=os.getcwd() if not fractions: #This is for all times when we have 1-D runs to compare. for eachgroup in set(group_information['Fractionation Group ID String']): os.chdir(basedir) os.chdir(options.operation_folder+eachgroup+".pin_out/crux-output/") #os.mkdir(basedir+"/"+options.ssl_output_folder+"/"+eachgroup) #if not options.no_mzml: for eachfile in group_to_file_name[eachgroup]: #shutil.copy(os.path.join(basedir,eachfile),basedir+"/"+options.ssl_output_folder+"/"+eachgroup+"/"+eachfile) shutil.copy(os.path.join(basedir,eachfile),basedir+"/"+options.ssl_output_folder+"/"+eachfile) if not fractions: for eachrun in group_to_run_dict[eachgroup]: this_run_results=results_per_run[eachrun] this_run_results['protein q-values']=this_run_results['protein q-values'].astype(str) if fido_q: this_run_results['fido_filter']=1 this_run_grouped=this_run_results.groupby(numpy.arange(len(this_run_results))//multiprocessing.cpu_count()) this_run_results=applyParallelHalf(this_run_grouped,fido_filter) #print type(this_run_results),"and type" this_run_results=this_run_results[this_run_results['fido_filter']==1] this_run_results=this_run_results.drop('fido_filter',axis=1) del this_run_grouped if luciphor: if options.local_flr is True: if options.one_pps is True: mask=this_run_results[numpy.logical_or(( this_run_results.luci_localFLR <= options.flr_threshold ) , (this_run_results.luci_numPPS - this_run_results.luci_numRPS == 0))] type2_mask=this_run_results[numpy.logical_and(( this_run_results.luci_localFLR > options.flr_threshold ), (this_run_results.luci_numPPS - this_run_results.luci_numRPS > 0))] #Unambiguous and poorly localized. else: mask=this_run_results[( this_run_results.luci_localFLR <= options.flr_threshold)] type2_mask=this_run_results[( this_run_results.luci_localFLR > options.flr_threshold )] #Unambiguous and poorly localized. else: if options.one_pps is True: mask=this_run_results[numpy.logical_or(( this_run_results.luci_globalFLR <= options.flr_threshold ) , (this_run_results.luci_numPPS - this_run_results.luci_numRPS == 0))] type2_mask=this_run_results[numpy.logical_and(( this_run_results.luci_globalFLR > options.flr_threshold ), (this_run_results.luci_numPPS - this_run_results.luci_numRPS > 0))] #Unambiguous and poorly localized. else: mask=this_run_results[( this_run_results.luci_globalFLR <= options.flr_threshold)] type2_mask=this_run_results[( this_run_results.luci_globalFLR > options.flr_threshold )] #Unambiguous and poorly localized. elif phosphoRS: if options.one_pps is True: mask=this_run_results[numpy.logical_or(( this_run_results.pRS_peptideLocalizationProbability >= options.prs_prob_threshold ) , (this_run_results.pRS_numPPS - this_run_results.numModSites == 0))] type2_mask=this_run_results[numpy.logical_and(( this_run_results.pRS_peptideLocalizationProbability >= options.prs_prob_threshold ), (this_run_results.pRS_numPPS - this_run_results.numModSites > 0))] #Unambiguous and poorly localized. else: mask=this_run_results[( this_run_results.pRS_peptideLocalizationProbability >= options.prs_prob_threshold)] type2_mask=this_run_results[( this_run_results.pRS_peptideLocalizationProbability < options.prs_prob_threshold )] #Unambiguous and poorly localized. elif ptmRS: #if options.one_pps is True: # mask=this_run_results[numpy.logical_or(( this_run_results.ptmRS_peptideLocalizationProbability >= options.ptmrs_prob_threshold ) , (this_run_results.ptmRS_numPPS - this_run_results.numModSites == 0))] # type2_mask=this_run_results[numpy.logical_and(( this_run_results.ptmRS_peptideLocalizationProbability >= options.ptmrs_prob_threshold ), (this_run_results.ptmRS_numPPS - this_run_results.numModSites > 0))] #Unambiguous and poorly localized. #else: mask=this_run_results[( this_run_results.ptmRS_peptideLocalizationProbability >= options.ptmrs_prob_threshold)] type2_mask=this_run_results[( this_run_results.ptmRS_peptideLocalizationProbability < options.ptmrs_prob_threshold )] #Unambiguous and poorly localized. else: mask=this_run_results if options.only_mod is True: if luciphor: mask=mask[mask['luci_numRPS'] >= 1].copy(deep=True) elif phosphoRS: mask=mask[mask['numModSites'] >= 1].copy(deep=True) elif ptmRS: mask=mask[mask['ptmRS_numMods'] >= 1].copy(deep=True) if luciphor: ssl_df=mask[['file','scan','charge','luci_sequence','percolator q-value']] ssl_df.rename(columns={'luci_sequence':'sequence'}, inplace=True) elif phosphoRS: ssl_df=mask[['file','scan','charge','pRS_sequence','percolator q-value']] ssl_df.rename(columns={'pRS_sequence':'sequence'}, inplace=True) elif ptmRS: ssl_df=mask[['file','scan','charge','ptmRS_sequence','percolator q-value']] ssl_df.rename(columns={'ptmRS_sequence':'sequence'}, inplace=True) else: ssl_df=mask[['file','scan','charge','sequence','percolator q-value']] ssl_df.rename(columns={'percolator q-value':'percolator qvalue'}, inplace=True) ssl_df_filtered=ssl_df[(ssl_df['percolator qvalue']<=options.q_threshold)] #with open(basedir+"/"+options.ssl_output_folder+"/"+eachgroup+"/"+run_dict[eachrun].replace(".mzML","")+".ssl",'wb') as ssl_writer: with open(basedir+"/"+options.ssl_output_folder+"/"+run_dict[eachrun].replace(".mzML","")+".ssl",'wb') as ssl_writer: ssl_df_filtered.rename(columns={'percolator qvalue':'score'}, inplace=True) ssl_df_filtered['score-type']="PERCOLATOR QVALUE" ssl_df_filtered.to_csv(path_or_buf=ssl_writer,sep="\t",index=False,header=True) if luciphor: with open(basedir+"/"+options.ssl_output_folder+"/"+run_dict[eachrun].replace(".mzML","")+"_type2.ssl",'wb') as type2_ssl_writer: ssl_df_type2=type2_mask[['file','scan','charge','luci_sequence','percolator q-value']] ssl_df_type2.rename(columns={'percolator q-value':'percolator qvalue'}, inplace=True) ssl_df_type2.rename(columns={'luci_sequence':'sequence'}, inplace=True) ssl_df_type2_filtered=ssl_df_type2[(ssl_df_type2['percolator qvalue']<=options.q_threshold)] ssl_df_type2_filtered.rename(columns={'percolator qvalue':'score'}, inplace=True) ssl_df_type2_filtered['score-type']="PERCOLATOR QVALUE" ssl_df_type2_filtered.to_csv(path_or_buf=type2_ssl_writer,sep="\t",index=False,header=True) elif phosphoRS: with open(basedir+"/"+options.ssl_output_folder+"/"+run_dict[eachrun].replace(".mzML","")+"_type2.ssl",'wb') as type2_ssl_writer: ssl_df_type2=type2_mask[['file','scan','charge','pRS_sequence','percolator q-value']] ssl_df_type2.rename(columns={'percolator q-value':'percolator qvalue'}, inplace=True) ssl_df_type2.rename(columns={'pRS_sequence':'sequence'}, inplace=True) ssl_df_type2_filtered=ssl_df_type2[(ssl_df_type2['percolator qvalue']<=options.q_threshold)] ssl_df_type2_filtered.rename(columns={'percolator qvalue':'score'}, inplace=True) ssl_df_type2_filtered['score-type']="PERCOLATOR QVALUE" ssl_df_type2_filtered.to_csv(path_or_buf=type2_ssl_writer,sep="\t",index=False,header=True) elif ptmRS: with open(basedir+"/"+options.ssl_output_folder+"/"+run_dict[eachrun].replace(".mzML","")+"_type2.ssl",'wb') as type2_ssl_writer: ssl_df_type2=type2_mask[['file','scan','charge','ptmRS_sequence','percolator q-value']] ssl_df_type2.rename(columns={'percolator q-value':'percolator qvalue'}, inplace=True) ssl_df_type2.rename(columns={'ptmRS_sequence':'sequence'}, inplace=True) ssl_df_type2_filtered=ssl_df_type2[(ssl_df_type2['percolator qvalue']<=options.q_threshold)] ssl_df_type2_filtered.rename(columns={'percolator qvalue':'score'}, inplace=True) ssl_df_type2_filtered['score-type']="PERCOLATOR QVALUE" ssl_df_type2_filtered.to_csv(path_or_buf=type2_ssl_writer,sep="\t",index=False,header=True) if options.blib: print "Let's build some blib files..." os.chdir(basedir) os.chdir(basedir+"/"+options.ssl_output_folder) #cmd = '/galaxy-central/tools/wohl-proteomics/ssl_converter/blibbuild/bin/BlibBuild -a milkyway-galaxy *.ssl combined_spectral_lib.blib' #cmd = 'ls *.ssl | /galaxy-central/tools/wohl-proteomics/ssl_converter/blibbuild/bin/BlibBuild -a milkyway-galaxy -m 1000M combined_spectral_lib.blib' #cmd = 'wine /galaxy-central/tools/wohl-proteomics/ssl_converter/skylineblib/BlibBuild.exe -a milkyway-galaxy -m 1000M *.ssl redundant_spectral_lib.blib' #print "running command ",cmd #subprocess.call(cmd,shell=True) #filtercmd='wine /galaxy-central/tools/wohl-proteomics/ssl_converter/skylineblib/BlibFilter.exe redundant_spectral_lib.blib filtered_spectral_lib.blib' #subprocess.call(filtercmd,shell=True) for file in glob.glob("*type2.ssl"): os.rename(file,file.split(".")[0]+".not_ssl") print "protected",file,"from inclusion in spectral lib..." cmd = 'wine /galaxy-central/tools/wohl-proteomics/ssl_converter/skylineblib/BlibBuild.exe -a milkyway-galaxy -m 1000M *.ssl combined_spectral_lib.blib' print "running command ",cmd subprocess.call(cmd,shell=True) for file in glob.glob("*.not_ssl"): os.rename(file,file.split(".")[0]+".ssl") filtercmd='wine /galaxy-central/tools/wohl-proteomics/ssl_converter/skylineblib/BlibFilter.exe combined_spectral_lib.blib filtered.blib' subprocess.call(filtercmd,shell=True) if options.mc_mzml_files is not None: #and not options.no_mzml: for file in glob.glob("*.mzML"): os.remove(file) print "removing ",file os.chdir(basedir) for file in glob.glob("mc_*.mzML"): print "replacing ",file shutil.copy(file,basedir+"/"+options.ssl_output_folder+"/"+file.split("_",1)[1]) else: #This is for when fractions is true... so we'll organize the output into fractions #fractions_to_run_dict[fraction_tag].append(str(row['Original File Name'])+".mzML") blib_cmds=[] filter_cmds=[] for eachfraction in fractions_to_run_dict: fraction_set=fractions_to_run_dict[eachfraction] for each_chrom_fraction in fraction_set: if not os.path.isdir(basedir+"/"+options.ssl_output_folder+"/"+eachfraction.replace("-","")+"/"): os.mkdir(basedir+"/"+options.ssl_output_folder+"/"+eachfraction.replace("-","")+"/") #if not options.no_mzml: shutil.copy(os.path.join(basedir,run_dict[each_chrom_fraction]),basedir+"/"+options.ssl_output_folder+"/"+eachfraction.replace("-","")+"/"+run_dict[each_chrom_fraction]) for eachfraction in fractions_to_run_dict: os.chdir(basedir) fraction_set=fractions_to_run_dict[eachfraction] for eachrun in fraction_set: this_run_results=results_per_run[eachrun] if fido_q: this_run_results['fido_filter']=0 this_run_grouped=this_run_results.groupby(numpy.arange(len(this_run_results))//multiprocessing.cpu_count()) this_run_results=applyParallelHalf(this_run_grouped,fido_filter) print this_run_results.columns,"this is columns..." this_run_results=this_run_results[this_run_results['fido_filter']==1] this_run_results=this_run_results.drop('fido_filter',axis=1) del this_run_grouped #print "------------------------------" #print "each run is ",eachrun #print set(this_run_results['file_idx']),"idx set..." #print set(this_run_results['file']),"file set..." #print "------------------------------" os.chdir(basedir) os.chdir(options.operation_folder+str(run_to_group_dict[eachrun])+".pin_out/crux-output/") if luciphor: if options.local_flr is True: if options.one_pps is True: mask=this_run_results[numpy.logical_or(( this_run_results.luci_localFLR <= options.flr_threshold ) , (this_run_results.luci_numPPS - this_run_results.luci_numRPS == 0))] type2_mask=this_run_results[numpy.logical_and(( this_run_results.luci_localFLR >= options.flr_threshold ), (this_run_results.luci_numPPS - this_run_results.luci_numRPS > 0))] #Unambiguous and poorly localized. else: mask=this_run_results[( this_run_results.luci_localFLR <= options.flr_threshold)] type2_mask=this_run_results[( this_run_results.luci_localFLR >= options.flr_threshold )] #Unambiguous and poorly localized. else: if options.one_pps is True: mask=this_run_results[numpy.logical_or(( this_run_results.luci_globalFLR <= options.flr_threshold ) , (this_run_results.luci_numPPS - this_run_results.luci_numRPS == 0))] type2_mask=this_run_results[numpy.logical_and(( this_run_results.luci_globalFLR >= options.flr_threshold ), (this_run_results.luci_numPPS - this_run_results.luci_numRPS > 0))] #Unambiguous and poorly localized. else: mask=this_run_results[( this_run_results.luci_globalFLR <= options.flr_threshold)] type2_mask=this_run_results[( this_run_results.luci_globalFLR >= options.flr_threshold )] #Unambiguous and poorly localized. elif phosphoRS: if options.one_pps is True: mask=this_run_results[numpy.logical_or(( this_run_results.pRS_peptideLocalizationProbability >= options.prs_prob_threshold ) , (this_run_results.pRS_numPPS - this_run_results.numModSites == 0))] type2_mask=this_run_results[numpy.logical_and(( this_run_results.pRS_peptideLocalizationProbability >= options.prs_prob_threshold ), (this_run_results.pRS_numPPS - this_run_results.numModSites > 0))] #Unambiguous and poorly localized. else: mask=this_run_results[( this_run_results.pRS_peptideLocalizationProbability >= options.prs_prob_threshold)] type2_mask=this_run_results[( this_run_results.pRS_peptideLocalizationProbability < options.prs_prob_threshold )] #Unambiguous and poorly localized. elif ptmRS: #if options.one_pps is True: # mask=this_run_results[numpy.logical_or(( this_run_results.ptmRS_peptideLocalizationProbability >= options.ptmrs_prob_threshold ) , (this_run_results.ptmRS_numPPS - this_run_results.ptmRS_numMods == 0))] # type2_mask=this_run_results[numpy.logical_and(( this_run_results.ptmRS_peptideLocalizationProbability >= options.ptmrs_prob_threshold ), (this_run_results.ptmRS_numPPS - this_run_results.ptmRS_numMods > 0))] #Unambiguous and poorly localized. mask=this_run_results[( this_run_results.ptmRS_peptideLocalizationProbability >= options.ptmrs_prob_threshold)] type2_mask=this_run_results[( this_run_results.ptmRS_peptideLocalizationProbability < options.ptmrs_prob_threshold )] #Unambiguous and poorly localized. else: mask=this_run_results if options.only_mod is True: if luciphor: mask=mask[mask['luci_numRPS'] >= 1].copy(deep=True) elif phosphoRS: mask=mask[mask['numModSites'] >= 1].copy(deep=True) elif ptmRS: mask=mask[mask['ptmRS_numMods'] >= 1].copy(deep=True) if luciphor: ssl_df=mask[['file','scan','charge','luci_sequence','percolator q-value']] ssl_df.rename(columns={'luci_sequence':'sequence'}, inplace=True) elif phosphoRS: ssl_df=mask[['file','scan','charge','pRS_sequence','percolator q-value']] ssl_df.rename(columns={'pRS_sequence':'sequence'}, inplace=True) elif ptmRS: ssl_df=mask[['file','scan','charge','ptmRS_sequence','percolator q-value']] ssl_df.rename(columns={'ptmRS_sequence':'sequence'}, inplace=True) else: ssl_df=mask[['file','scan','charge','sequence','percolator q-value']] ssl_df.rename(columns={'percolator q-value':'percolator qvalue'}, inplace=True) ssl_df_filtered=ssl_df[(ssl_df['percolator qvalue']<=options.q_threshold)] with open(basedir+"/"+options.ssl_output_folder+"/"+eachfraction.replace("-","")+"/"+run_dict[eachrun].replace(".mzML","")+".ssl",'wb') as ssl_writer: ssl_df_filtered.rename(columns={'percolator qvalue':'score'}, inplace=True) ssl_df_filtered['score-type']="PERCOLATOR QVALUE" ssl_df_filtered.to_csv(path_or_buf=ssl_writer,sep="\t",index=False,header=True) if luciphor: with open(basedir+"/"+options.ssl_output_folder+"/"+eachfraction.replace("-","")+"/"+run_dict[eachrun].replace(".mzML","")+"_type2.ssl",'wb') as type2_ssl_writer: ssl_df_type2=type2_mask[['file','scan','charge','luci_sequence','percolator q-value']] ssl_df_type2.rename(columns={'percolator q-value':'percolator qvalue'}, inplace=True) ssl_df_type2.rename(columns={'luci_sequence':'sequence'}, inplace=True) ssl_df_type2_filtered=ssl_df_type2[(ssl_df_type2['percolator qvalue']<=options.q_threshold)] ssl_df_type2_filtered.rename(columns={'percolator qvalue':'score'}, inplace=True) ssl_df_type2_filtered['score-type']="PERCOLATOR QVALUE" ssl_df_type2_filtered.to_csv(path_or_buf=type2_ssl_writer,sep="\t",index=False,header=True) elif phosphoRS: with open(basedir+"/"+options.ssl_output_folder+"/"+eachfraction.replace("-","")+"/"+run_dict[eachrun].replace(".mzML","")+"_type2.ssl",'wb') as type2_ssl_writer: ssl_df_type2=type2_mask[['file','scan','charge','pRS_sequence','percolator q-value']] ssl_df_type2.rename(columns={'percolator q-value':'percolator qvalue'}, inplace=True) ssl_df_type2.rename(columns={'pRS_sequence':'sequence'}, inplace=True) ssl_df_type2_filtered=ssl_df_type2[(ssl_df_type2['percolator qvalue']<=options.q_threshold)] ssl_df_type2_filtered.rename(columns={'percolator qvalue':'score'}, inplace=True) ssl_df_type2_filtered['score-type']="PERCOLATOR QVALUE" ssl_df_type2_filtered.to_csv(path_or_buf=type2_ssl_writer,sep="\t",index=False,header=True) elif ptmRS: with open(basedir+"/"+options.ssl_output_folder+"/"+eachfraction.replace("-","")+"/"+run_dict[eachrun].replace(".mzML","")+"_type2.ssl",'wb') as type2_ssl_writer: ssl_df_type2=type2_mask[['file','scan','charge','ptmRS_sequence','percolator q-value']] ssl_df_type2.rename(columns={'percolator q-value':'percolator qvalue'}, inplace=True) ssl_df_type2.rename(columns={'ptmRS_sequence':'sequence'}, inplace=True) ssl_df_type2_filtered=ssl_df_type2[(ssl_df_type2['percolator qvalue']<=options.q_threshold)] ssl_df_type2_filtered.rename(columns={'percolator qvalue':'score'}, inplace=True) ssl_df_type2_filtered['score-type']="PERCOLATOR QVALUE" ssl_df_type2_filtered.to_csv(path_or_buf=type2_ssl_writer,sep="\t",index=False,header=True) if options.blib: print "We're going to build blib files!" os.chdir(basedir) #os.chdir(basedir+"/"+options.ssl_output_folder) os.chdir(basedir+"/"+options.ssl_output_folder+"/"+eachfraction.replace("-","")+"/") #cmd = '/galaxy-central/tools/wohl-proteomics/ssl_converter/blibbuild/bin/BlibBuild -a milkyway-galaxy *.ssl '+eachfraction.replace("-","")+".ssl" #combined_spectral_lib.blib for file in glob.glob("*type2.ssl"): os.rename(file,file.split(".")[0]+".not_ssl") print "protected",file,"from inclusion in spectral lib..." command_folder=basedir+"/"+options.ssl_output_folder+"/"+eachfraction.replace("-","")+"/" cmd = 'wine /galaxy-central/tools/wohl-proteomics/ssl_converter/skylineblib/BlibBuild.exe -a milkyway-galaxy -m 1000M {0}*.ssl {0}combined_spectral_lib.blib'.format(command_folder) blib_cmds.append(cmd) print "storing command to run later",cmd #subprocess.call(cmd,shell=True) filtercmd='wine /galaxy-central/tools/wohl-proteomics/ssl_converter/skylineblib/BlibFilter.exe {0} {1}'.format(command_folder+"combined_spectral_lib.blib",command_folder+"filtered_spectral_lib.blib") filter_cmds.append(filtercmd) print "storing command for filter later",filtercmd #subprocess.call(filtercmd,shell=True) #cmd = 'ls *.ssl | /galaxy-central/tools/wohl-proteomics/ssl_converter/blibbuild/bin/BlibBuild -a milkyway-galaxy -m 1000M combined_spectral_lib.blib' #print "Running command ...",cmd #subprocess.call(cmd,shell=True) if fractions and options.blib: chunk_size=8 # Max concurrency... job_list=[blib_cmds[i:i + chunk_size] for i in range(0, len(blib_cmds), chunk_size)] for each_jobset in job_list: processes=[] for each_job in each_jobset: print "Running ...",each_job processes.append(subprocess.Popen(each_job,shell=True)) for each_proc in processes: each_proc.wait() job_list=[filter_cmds[i:i + chunk_size] for i in range(0, len(filter_cmds), chunk_size)] for each_jobset in job_list: processes=[] for each_job in each_jobset: print "Running Filter...",each_job processes.append(subprocess.Popen(each_job,shell=True)) for each_proc in processes: each_proc.wait() for eachfraction in fractions_to_run_dict: os.chdir(basedir) fraction_set=fractions_to_run_dict[eachfraction] for eachrun in fraction_set: os.chdir(basedir) os.chdir(basedir+"/"+options.ssl_output_folder+"/"+eachfraction.replace("-","")+"/") if options.mc_mzml_files is not None: file_list=[file for file in glob.glob("*.mzML")] for file in file_list: os.remove(file) os.chdir(basedir) for file in glob.glob("mc_*.mzML"): if file.split("_",1)[1] in file_list: shutil.copy(file,basedir+"/"+options.ssl_output_folder+"/"+eachfraction.replace("-","")+"/"+file.split("_",1)[1]) for file in glob.glob("*.not_ssl"): os.rename(file,file.split(".")[0]+".ssl") os.chdir(basedir) if options.fido_q_threshold is not None: os.chdir(basedir) #While we're here, let's go ahead and handle database filtering! fido_q_df = pandas.read_csv(options.fido_q_raw,sep="\t") fido_q_df=fido_q_df[fido_q_df['q-value']<=options.fido_q_threshold] #filter down... proteins_to_keep=fido_q_df['protein group'].unique().tolist()#this is the list of proteins we want to keep. with open(options.fasta,'rb') as fasta_file: fasta_dict=SeqIO.to_dict(SeqIO.parse(fasta_file,"fasta")) new_fasta=[] for eachprotein in proteins_to_keep: if eachprotein in fasta_dict: new_fasta.append(fasta_dict[eachprotein]) #print new_fasta,len(new_fasta),len(proteins_to_keep) shutil.copy(options.fasta,basedir+"/"+options.ssl_output_folder+"/"+"UNFILTERED_"+options.fasta) with open(basedir+"/"+options.ssl_output_folder+"/"+"FIDO_FILTERED_"+options.fasta,'wb') as fasta_writer: SeqIO.write(new_fasta,fasta_writer,"fasta") print "FIDO filtered FASTA written!" if options.ffasta is not None: shutil.copy(basedir+"/"+options.ssl_output_folder+"/"+"FIDO_FILTERED_"+options.fasta,options.ffasta) print "Copied the filtered fasta to the output location specified..." else: os.chdir(basedir) shutil.copy(options.fasta,basedir+"/"+options.ssl_output_folder+"/"+"UNFILTERED_"+options.fasta) os.chdir(basedir) if options.no_mzml: mzml_files=[] for root, dirs, files in os.walk("."): for file_name in fnmatch.filter(files, '*.mzML'): mzml_files.append(os.path.join(root,file_name)) #print "will have to remove ",file_name for each_file in mzml_files: os.remove(each_file) print "All done!" sys.exit(0)

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0.657798

import os, sys, re import shutil import optparse import shutil import pandas import numpy import subprocess import fnmatch from joblib import Parallel, delayed import multiprocessing from Bio import SeqIO import glob pend(each_tuple[1]) new_prot_ids.append(each_tuple[0]) new_indicies.append(each_tuple[2]) else: pass if len(new_prot_ids) >= 1: unfiltered.loc[index,'protein q-values']=",".join(new_q_vals) unfiltered.loc[index,'protein id']=",".join(new_prot_ids) unfiltered.loc[index,'indicies']=",".join(new_indicies) unfiltered.loc[index,'fido_filter']=1 else: unfiltered.loc[index,'fido_filter']=0 return unfiltered def fido_filter_row(eachrow): prot_q_vals = eachrow['protein q-values'].split(',') protein_ids = eachrow['protein id'].split(',') indicies = eachrow['peptide prot-indicies'].split(',') new_q_vals=[] new_prot_ids=[] new_indicies=[] for each_tuple in zip(protein_ids,prot_q_vals,indicies): if float(each_tuple[1])<=gfido_q_threshold: new_q_vals.append(each_tuple[1]) new_prot_ids.append(each_tuple[0]) new_indicies.append(each_tuple[2]) else: pass if len(new_prot_ids) >= 1: eachrow['protein q-values']=",".join(new_q_vals) eachrow['protein id']=",".join(new_prot_ids) eachrow['indicies']=",".join(new_indicies) eachrow['fido_filter']=1 else: eachrow['fido_filter']=0 ction="store", type="float", dest="prs_prob_threshold") parser.add_option("--ptmRS_prob_thresh",action="store", type="float", dest="ptmrs_prob_threshold") parser.add_option("--FLRthresh",action="store", type="float", dest="flr_threshold") parser.add_option("--LFLR",action="store_true", dest="local_flr") parser.add_option("--mzml",action="store",type="string",dest="mzml_files") parser.add_option("--mass_corrected_mzml",action="store",type="string",dest="mc_mzml_files") parser.add_option("--blib",action="store_true",dest="blib") parser.add_option("--fasta",action="store",type="string",dest="fasta") parser.add_option("--ffasta",action="store",type="string",dest="ffasta") # THIS IS THE FILTERED FIDO OUTPUT parser.add_option("--expgroups",action="store",type="string",dest="exp_group_file") parser.add_option("--ssl",action="store",type="string",dest="ssl_output_folder") parser.add_option("--fractions",action="store_true",dest="fractions") parser.add_option("--OnePPS",action="store_true",dest="one_pps") parser.add_option("--only_mod",action="store_true",dest="only_mod") parser.add_option("--diaumpire",action="store_true",dest="diaumpire") parser.add_option("--no_mzml",action="store_true",dest="no_mzml") #parser.add_option("--saint",action="store",type="string",dest="saint_outputs") (options,args) = parser.parse_args() if options.fractions is True: fractions=True else: print "We'll give outputs by acquisition (runs), not by experimental set." bal gfido_q_threshold gfido_q_threshold = options.fido_q_threshold print "We're going to filter by Fido Q-Value of ",options.fido_q_threshold #### Check for LuciPHOr luciphor=False if options.flr_threshold is not None: luciphor=True print "We will filter by LuciPHOr FLR of ",options.flr_threshold #### Check for PhosphoRS phosphoRS=False if options.prs_prob_threshold is not None: phosphoRS=True print "We will filter by a PhosphoRS probability of ",options.prs_prob_threshold #### Check for ptmRS ptmRS=False if options.ptmrs_prob_threshold is not None: ptmRS=True print "We will filter by a ptmRS probability of ",options.prs_prob_threshold psms_files=[] for root, subFolders, files in os.walk(options.operation_folder): for eachfile in files: if 'target.psms.txt' in eachfile and 'uncorrected' not in eachfile: psms_files.append(str(os.path.join(root,eachfile))) dataframe_vector=[] for eachfile in psms_files: newdf=pandas.DataFrame.from_csv(eachfile,sep='\t',index_col=False) dataframe_vector.append(newdf) combined_results=pandas.concat(dataframe_vector) del dataframe_vector group_information = pandas.read_csv(options.exp_group_file,sep='\t',dtype={'Fractionation Group ID String': object,'Fractionation Group Name':object,'Biological Condition':object}) run_dict={} # Key is file_idx, value is file_name.mzML rev_run_dict={} #Key is file_nzme.mzML, value is file_idx group_to_run_dict={} # Key is group, value is [1, 2, 3, 4] list of file_idx belonging to runs in the group... run_to_group_dict={} # Key is file_idx, value is group... group_to_file_name={} # key is group, value is ["xxxx.mzML", "xxxx.mzML"] if fractions: fractions_to_run_dict={} for index,row in group_information.iterrows(): print row run_dict[str(row['Crux File Integer'])]=row['Original File Name']+".mzML" rev_run_dict[row['Original File Name']+".mzML"]=str(row['Crux File Integer']) if row['Fractionation Group ID String'] in group_to_run_dict: group_to_run_dict[row['Fractionation Group ID String']].append(str(row['Crux File Integer'])) else: group_to_run_dict[row['Fractionation Group ID String']] = [str(row['Crux File Integer'])] if str(row['Crux File Integer']) in run_to_group_dict: #run_to_group_dict[str(row['Crux File Integer'])].append(row['Fractionation Group ID String']) print "YOU HAVE MULTIPLE COPIES OF A RUN IN THE EXPERIMENTAL INFORMATION FILE... WARNING!" else: run_to_group_dict[str(row['Crux File Integer'])]=row['Fractionation Group ID String'] if row['Fractionation Group ID String'] in group_to_file_name: group_to_file_name[row['Fractionation Group ID String']].append(str(row['Original File Name'])+".mzML") else: group_to_file_name[row['Fractionation Group ID String']] = [str(row['Original File Name'])+".mzML"] if fractions: fraction_tag=str(row['Original File Name'].rsplit("-",1)[1]) if fraction_tag in fractions_to_run_dict: fractions_to_run_dict[fraction_tag].append(str(row['Crux File Integer']))#str(row['Original File Name'])+".mzML") else: fractions_to_run_dict[fraction_tag]=[str(row['Crux File Integer'])] combined_results['file_idx']=combined_results['file_idx'].astype(str) combined_results['file']=combined_results['file'].astype(str) ####################### We'll handle putting in the file names just to be sure this has been handled! fix_combined=[] print run_dict,"This is run dict" print rev_run_dict,"this is rev dict" for each_idx in run_dict: mask = combined_results[(combined_results.file.str.contains(run_dict[each_idx]))] mask['file']=run_dict[each_idx] fix_combined.append(mask) combined_results=pandas.concat(fix_combined) if options.diaumpire: print "DIAUmpire inputs: Decrementing scans to match mzML file index." combined_results['scan']=combined_results['scan']-1 if luciphor: combined_results['luci_numPPS']=combined_results['luci_numPPS'].fillna(0) combined_results['luci_numPPS']=combined_results['luci_numPPS'].astype(int).fillna(0) combined_results['luci_globalFLR']=combined_results['luci_globalFLR'].astype(float).fillna(0.0) combined_results['luci_localFLR']=combined_results['luci_localFLR'].astype(float).fillna(0.0) if phosphoRS: combined_results['pRS_peptideLocalizationProbability']=combined_results['pRS_peptideLocalizationProbability'].astype(float).fillna(1.00) combined_results['pRS_numPPS']=combined_results['pRS_numPPS'].astype(int).fillna(0) if ptmRS: combined_results['ptmRS_peptideLocalizationProbability']=combined_results['ptmRS_peptideLocalizationProbability'].astype(float).fillna(1.00) combined_results['ptmRS_totalNumPPS']=combined_results['ptmRS_totalNumPPS'].fillna(0).astype(int) new_results={} results_per_run={} ): os.rename(file,file.split(".")[0]+".ssl") filtercmd='wine /galaxy-central/tools/wohl-proteomics/ssl_converter/skylineblib/BlibFilter.exe combined_spectral_lib.blib filtered.blib' subprocess.call(filtercmd,shell=True) if options.mc_mzml_files is not None: #and not options.no_mzml: for file in glob.glob("*.mzML"): os.remove(file) print "removing ",file os.chdir(basedir) for file in glob.glob("mc_*.mzML"): print "replacing ",file shutil.copy(file,basedir+"/"+options.ssl_output_folder+"/"+file.split("_",1)[1]) else: #This is for when fractions is true... so we'll organize the output into fractions blib_cmds=[] filter_cmds=[] for eachfraction in fractions_to_run_dict: fraction_set=fractions_to_run_dict[eachfraction] for each_chrom_fraction in fraction_set: if not os.path.isdir(basedir+"/"+options.ssl_output_folder+"/"+eachfraction.replace("-","")+"/"): os.mkdir(basedir+"/"+options.ssl_output_folder+"/"+eachfraction.replace("-","")+"/") shutil.copy(os.path.join(basedir,run_dict[each_chrom_fraction]),basedir+"/"+options.ssl_output_folder+"/"+eachfraction.replace("-","")+"/"+run_dict[each_chrom_fraction]) for eachfraction in fractions_to_run_dict: os.chdir(basedir) fraction_set=fractions_to_run_dict[eachfraction] for eachrun in fraction_set: this_run_results=results_per_run[eachrun] if fido_q: this_run_results['fido_filter']=0 this_run_grouped=this_run_results.groupby(numpy.arange(len(this_run_results))//multiprocessing.cpu_count()) this_run_results=applyParallelHalf(this_run_grouped,fido_filter) print this_run_results.columns,"this is columns..." this_run_results=this_run_results[this_run_results['fido_filter']==1] this_run_results=this_run_results.drop('fido_filter',axis=1) del this_run_grouped os.chdir(basedir) os.chdir(options.operation_folder+str(run_to_group_dict[eachrun])+".pin_out/crux-output/") if luciphor: if options.local_flr is True: if options.one_pps is True: mask=this_run_results[numpy.logical_or(( this_run_results.luci_localFLR <= options.flr_threshold ) , (this_run_results.luci_numPPS - this_run_results.luci_numRPS == 0))] type2_mask=this_run_results[numpy.logical_and(( this_run_results.luci_localFLR >= options.flr_threshold ), (this_run_results.luci_numPPS - this_run_results.luci_numRPS > 0))] else: mask=this_run_results[( this_run_results.luci_localFLR <= options.flr_threshold)] type2_mask=this_run_results[( this_run_results.luci_localFLR >= options.flr_threshold )] else: if options.one_pps is True: mask=this_run_results[numpy.logical_or(( this_run_results.luci_globalFLR <= options.flr_threshold ) , (this_run_results.luci_numPPS - this_run_results.luci_numRPS == 0))] type2_mask=this_run_results[numpy.logical_and(( this_run_results.luci_globalFLR >= options.flr_threshold ), (this_run_results.luci_numPPS - this_run_results.luci_numRPS > 0))] else: mask=this_run_results[( this_run_results.luci_globalFLR <= options.flr_threshold)] type2_mask=this_run_results[( this_run_results.luci_globalFLR >= options.flr_threshold )] elif phosphoRS: if options.one_pps is True: mask=this_run_results[numpy.logical_or(( this_run_results.pRS_peptideLocalizationProbability >= options.prs_prob_threshold ) , (this_run_results.pRS_numPPS - this_run_results.numModSites == 0))] type2_mask=this_run_results[numpy.logical_and(( this_run_results.pRS_peptideLocalizationProbability >= options.prs_prob_threshold ), (this_run_results.pRS_numPPS - this_run_results.numModSites > 0))] else: mask=this_run_results[( this_run_results.pRS_peptideLocalizationProbability >= options.prs_prob_threshold)] type2_mask=this_run_results[( this_run_results.pRS_peptideLocalizationProbability < options.prs_prob_threshold )] elif ptmRS: mask=this_run_results[( this_run_results.ptmRS_peptideLocalizationProbability >= options.ptmrs_prob_threshold)] type2_mask=this_run_results[( this_run_results.ptmRS_peptideLocalizationProbability < options.ptmrs_prob_threshold )] else: mask=this_run_results if options.only_mod is True: if luciphor: mask=mask[mask['luci_numRPS'] >= 1].copy(deep=True) elif phosphoRS: mask=mask[mask['numModSites'] >= 1].copy(deep=True) elif ptmRS: mask=mask[mask['ptmRS_numMods'] >= 1].copy(deep=True) if luciphor: ssl_df=mask[['file','scan','charge','luci_sequence','percolator q-value']] ssl_df.rename(columns={'luci_sequence':'sequence'}, inplace=True) elif phosphoRS: ssl_df=mask[['file','scan','charge','pRS_sequence','percolator q-value']] ssl_df.rename(columns={'pRS_sequence':'sequence'}, inplace=True) elif ptmRS: ssl_df=mask[['file','scan','charge','ptmRS_sequence','percolator q-value']] ssl_df.rename(columns={'ptmRS_sequence':'sequence'}, inplace=True) else: ssl_df=mask[['file','scan','charge','sequence','percolator q-value']] ssl_df.rename(columns={'percolator q-value':'percolator qvalue'}, inplace=True) ssl_df_filtered=ssl_df[(ssl_df['percolator qvalue']<=options.q_threshold)] with open(basedir+"/"+options.ssl_output_folder+"/"+eachfraction.replace("-","")+"/"+run_dict[eachrun].replace(".mzML","")+".ssl",'wb') as ssl_writer: ssl_df_filtered.rename(columns={'percolator qvalue':'score'}, inplace=True) ssl_df_filtered['score-type']="PERCOLATOR QVALUE" ssl_df_filtered.to_csv(path_or_buf=ssl_writer,sep="\t",index=False,header=True) if luciphor: with open(basedir+"/"+options.ssl_output_folder+"/"+eachfraction.replace("-","")+"/"+run_dict[eachrun].replace(".mzML","")+"_type2.ssl",'wb') as type2_ssl_writer: ssl_df_type2=type2_mask[['file','scan','charge','luci_sequence','percolator q-value']] ssl_df_type2.rename(columns={'percolator q-value':'percolator qvalue'}, inplace=True) ssl_df_type2.rename(columns={'luci_sequence':'sequence'}, inplace=True) ssl_df_type2_filtered=ssl_df_type2[(ssl_df_type2['percolator qvalue']<=options.q_threshold)] ssl_df_type2_filtered.rename(columns={'percolator qvalue':'score'}, inplace=True) ssl_df_type2_filtered['score-type']="PERCOLATOR QVALUE" ssl_df_type2_filtered.to_csv(path_or_buf=type2_ssl_writer,sep="\t",index=False,header=True) elif phosphoRS: with open(basedir+"/"+options.ssl_output_folder+"/"+eachfraction.replace("-","")+"/"+run_dict[eachrun].replace(".mzML","")+"_type2.ssl",'wb') as type2_ssl_writer: ssl_df_type2=type2_mask[['file','scan','charge','pRS_sequence','percolator q-value']] ssl_df_type2.rename(columns={'percolator q-value':'percolator qvalue'}, inplace=True) ssl_df_type2.rename(columns={'pRS_sequence':'sequence'}, inplace=True) ssl_df_type2_filtered=ssl_df_type2[(ssl_df_type2['percolator qvalue']<=options.q_threshold)] ssl_df_type2_filtered.rename(columns={'percolator qvalue':'score'}, inplace=True) ssl_df_type2_filtered['score-type']="PERCOLATOR QVALUE" ssl_df_type2_filtered.to_csv(path_or_buf=type2_ssl_writer,sep="\t",index=False,header=True) elif ptmRS: with open(basedir+"/"+options.ssl_output_folder+"/"+eachfraction.replace("-","")+"/"+run_dict[eachrun].replace(".mzML","")+"_type2.ssl",'wb') as type2_ssl_writer: ssl_df_type2=type2_mask[['file','scan','charge','ptmRS_sequence','percolator q-value']] ssl_df_type2.rename(columns={'percolator q-value':'percolator qvalue'}, inplace=True) ssl_df_type2.rename(columns={'ptmRS_sequence':'sequence'}, inplace=True) ssl_df_type2_filtered=ssl_df_type2[(ssl_df_type2['percolator qvalue']<=options.q_threshold)] ssl_df_type2_filtered.rename(columns={'percolator qvalue':'score'}, inplace=True) ssl_df_type2_filtered['score-type']="PERCOLATOR QVALUE" ssl_df_type2_filtered.to_csv(path_or_buf=type2_ssl_writer,sep="\t",index=False,header=True) if options.blib: print "We're going to build blib files!" os.chdir(basedir) #os.chdir(basedir+"/"+options.ssl_output_folder) os.chdir(basedir+"/"+options.ssl_output_folder+"/"+eachfraction.replace("-","")+"/") #cmd = '/galaxy-central/tools/wohl-proteomics/ssl_converter/blibbuild/bin/BlibBuild -a milkyway-galaxy *.ssl '+eachfraction.replace("-","")+".ssl" #combined_spectral_lib.blib for file in glob.glob("*type2.ssl"): os.rename(file,file.split(".")[0]+".not_ssl") print "protected",file,"from inclusion in spectral lib..." command_folder=basedir+"/"+options.ssl_output_folder+"/"+eachfraction.replace("-","")+"/" cmd = 'wine /galaxy-central/tools/wohl-proteomics/ssl_converter/skylineblib/BlibBuild.exe -a milkyway-galaxy -m 1000M {0}*.ssl {0}combined_spectral_lib.blib'.format(command_folder) blib_cmds.append(cmd) print "storing command to run later",cmd #subprocess.call(cmd,shell=True) filtercmd='wine /galaxy-central/tools/wohl-proteomics/ssl_converter/skylineblib/BlibFilter.exe {0} {1}'.format(command_folder+"combined_spectral_lib.blib",command_folder+"filtered_spectral_lib.blib") filter_cmds.append(filtercmd) print "storing command for filter later",filtercmd #subprocess.call(filtercmd,shell=True) #cmd = 'ls *.ssl | /galaxy-central/tools/wohl-proteomics/ssl_converter/blibbuild/bin/BlibBuild -a milkyway-galaxy -m 1000M combined_spectral_lib.blib' #print "Running command ...",cmd #subprocess.call(cmd,shell=True) if fractions and options.blib: chunk_size=8 # Max concurrency... job_list=[blib_cmds[i:i + chunk_size] for i in range(0, len(blib_cmds), chunk_size)] for each_jobset in job_list: processes=[] for each_job in each_jobset: print "Running ...",each_job processes.append(subprocess.Popen(each_job,shell=True)) for each_proc in processes: each_proc.wait() job_list=[filter_cmds[i:i + chunk_size] for i in range(0, len(filter_cmds), chunk_size)] for each_jobset in job_list: processes=[] for each_job in each_jobset: print "Running Filter...",each_job processes.append(subprocess.Popen(each_job,shell=True)) for each_proc in processes: each_proc.wait() for eachfraction in fractions_to_run_dict: os.chdir(basedir) fraction_set=fractions_to_run_dict[eachfraction] for eachrun in fraction_set: os.chdir(basedir) os.chdir(basedir+"/"+options.ssl_output_folder+"/"+eachfraction.replace("-","")+"/") if options.mc_mzml_files is not None: file_list=[file for file in glob.glob("*.mzML")] for file in file_list: os.remove(file) os.chdir(basedir) for file in glob.glob("mc_*.mzML"): if file.split("_",1)[1] in file_list: shutil.copy(file,basedir+"/"+options.ssl_output_folder+"/"+eachfraction.replace("-","")+"/"+file.split("_",1)[1]) for file in glob.glob("*.not_ssl"): os.rename(file,file.split(".")[0]+".ssl") os.chdir(basedir) if options.fido_q_threshold is not None: os.chdir(basedir) #While we're here, let's go ahead and handle database filtering! fido_q_df = pandas.read_csv(options.fido_q_raw,sep="\t") fido_q_df=fido_q_df[fido_q_df['q-value']<=options.fido_q_threshold] #filter down... proteins_to_keep=fido_q_df['protein group'].unique().tolist()#this is the list of proteins we want to keep. with open(options.fasta,'rb') as fasta_file: fasta_dict=SeqIO.to_dict(SeqIO.parse(fasta_file,"fasta")) new_fasta=[] for eachprotein in proteins_to_keep: if eachprotein in fasta_dict: new_fasta.append(fasta_dict[eachprotein]) #print new_fasta,len(new_fasta),len(proteins_to_keep) shutil.copy(options.fasta,basedir+"/"+options.ssl_output_folder+"/"+"UNFILTERED_"+options.fasta) with open(basedir+"/"+options.ssl_output_folder+"/"+"FIDO_FILTERED_"+options.fasta,'wb') as fasta_writer: SeqIO.write(new_fasta,fasta_writer,"fasta") print "FIDO filtered FASTA written!" if options.ffasta is not None: shutil.copy(basedir+"/"+options.ssl_output_folder+"/"+"FIDO_FILTERED_"+options.fasta,options.ffasta) print "Copied the filtered fasta to the output location specified..." else: os.chdir(basedir) shutil.copy(options.fasta,basedir+"/"+options.ssl_output_folder+"/"+"UNFILTERED_"+options.fasta) os.chdir(basedir) if options.no_mzml: mzml_files=[] for root, dirs, files in os.walk("."): for file_name in fnmatch.filter(files, '*.mzML'): mzml_files.append(os.path.join(root,file_name)) #print "will have to remove ",file_name for each_file in mzml_files: os.remove(each_file) print "All done!" sys.exit(0)

false

true

f71b45087b55f236efe7a8b8b2c2a962a1bf0ef8

25,938

py

Python

Src/StdLib/Lib/modulefinder.py

cwensley/ironpython2

f854444e1e08afc8850cb7c1a739a7dd2d10d32a

[ "Apache-2.0" ]

1,078

2016-07-19T02:48:30.000Z

2022-03-30T21:22:34.000Z

Src/StdLib/Lib/modulefinder.py

cwensley/ironpython2

f854444e1e08afc8850cb7c1a739a7dd2d10d32a

[ "Apache-2.0" ]

576

2017-05-21T12:36:48.000Z

2022-03-30T13:47:03.000Z

Src/StdLib/Lib/modulefinder.py

cwensley/ironpython2

f854444e1e08afc8850cb7c1a739a7dd2d10d32a

[ "Apache-2.0" ]

269

2017-05-21T04:44:47.000Z

2022-03-31T16:18:13.000Z

"""Find modules used by a script, using introspection.""" from __future__ import generators import dis import imp import marshal import os import sys import types import struct if hasattr(sys.__stdout__, "newlines"): READ_MODE = "U" # universal line endings else: # Python < 2.3 compatibility, no longer strictly required READ_MODE = "r" LOAD_CONST = dis.opmap['LOAD_CONST'] IMPORT_NAME = dis.opmap['IMPORT_NAME'] STORE_NAME = dis.opmap['STORE_NAME'] STORE_GLOBAL = dis.opmap['STORE_GLOBAL'] STORE_OPS = STORE_NAME, STORE_GLOBAL HAVE_ARGUMENT = dis.HAVE_ARGUMENT EXTENDED_ARG = dis.EXTENDED_ARG def _unpack_opargs(code): # enumerate() is not an option, since we sometimes process # multiple elements on a single pass through the loop extended_arg = 0 n = len(code) i = 0 while i < n: op = ord(code[i]) offset = i i = i+1 arg = None if op >= HAVE_ARGUMENT: arg = ord(code[i]) + ord(code[i+1])*256 + extended_arg extended_arg = 0 i = i+2 if op == EXTENDED_ARG: extended_arg = arg*65536 yield (offset, op, arg) # Modulefinder does a good job at simulating Python's, but it can not # handle __path__ modifications packages make at runtime. Therefore there # is a mechanism whereby you can register extra paths in this map for a # package, and it will be honored. # Note this is a mapping is lists of paths. packagePathMap = {} # A Public interface def AddPackagePath(packagename, path): paths = packagePathMap.get(packagename, []) paths.append(path) packagePathMap[packagename] = paths replacePackageMap = {} # This ReplacePackage mechanism allows modulefinder to work around the # way the _xmlplus package injects itself under the name "xml" into # sys.modules at runtime by calling ReplacePackage("_xmlplus", "xml") # before running ModuleFinder. def ReplacePackage(oldname, newname): replacePackageMap[oldname] = newname class Module: def __init__(self, name, file=None, path=None): self.__name__ = name self.__file__ = file self.__path__ = path self.__code__ = None # The set of global names that are assigned to in the module. # This includes those names imported through starimports of # Python modules. self.globalnames = {} # The set of starimports this module did that could not be # resolved, ie. a starimport from a non-Python module. self.starimports = {} def __repr__(self): s = "Module(%r" % (self.__name__,) if self.__file__ is not None: s = s + ", %r" % (self.__file__,) if self.__path__ is not None: s = s + ", %r" % (self.__path__,) s = s + ")" return s class ModuleFinder: def __init__(self, path=None, debug=0, excludes=[], replace_paths=[]): if path is None: path = sys.path self.path = path self.modules = {} self.badmodules = {} self.debug = debug self.indent = 0 self.excludes = excludes self.replace_paths = replace_paths self.processed_paths = [] # Used in debugging only def msg(self, level, str, *args): if level <= self.debug: for i in range(self.indent): print " ", print str, for arg in args: print repr(arg), print def msgin(self, *args): level = args[0] if level <= self.debug: self.indent = self.indent + 1 self.msg(*args) def msgout(self, *args): level = args[0] if level <= self.debug: self.indent = self.indent - 1 self.msg(*args) def run_script(self, pathname): self.msg(2, "run_script", pathname) with open(pathname, READ_MODE) as fp: stuff = ("", "r", imp.PY_SOURCE) self.load_module('__main__', fp, pathname, stuff) def load_file(self, pathname): dir, name = os.path.split(pathname) name, ext = os.path.splitext(name) with open(pathname, READ_MODE) as fp: stuff = (ext, "r", imp.PY_SOURCE) self.load_module(name, fp, pathname, stuff) def import_hook(self, name, caller=None, fromlist=None, level=-1): self.msg(3, "import_hook", name, caller, fromlist, level) parent = self.determine_parent(caller, level=level) q, tail = self.find_head_package(parent, name) m = self.load_tail(q, tail) if not fromlist: return q if m.__path__: self.ensure_fromlist(m, fromlist) return None def determine_parent(self, caller, level=-1): self.msgin(4, "determine_parent", caller, level) if not caller or level == 0: self.msgout(4, "determine_parent -> None") return None pname = caller.__name__ if level >= 1: # relative import if caller.__path__: level -= 1 if level == 0: parent = self.modules[pname] assert parent is caller self.msgout(4, "determine_parent ->", parent) return parent if pname.count(".") < level: raise ImportError, "relative importpath too deep" pname = ".".join(pname.split(".")[:-level]) parent = self.modules[pname] self.msgout(4, "determine_parent ->", parent) return parent if caller.__path__: parent = self.modules[pname] assert caller is parent self.msgout(4, "determine_parent ->", parent) return parent if '.' in pname: i = pname.rfind('.') pname = pname[:i] parent = self.modules[pname] assert parent.__name__ == pname self.msgout(4, "determine_parent ->", parent) return parent self.msgout(4, "determine_parent -> None") return None def find_head_package(self, parent, name): self.msgin(4, "find_head_package", parent, name) if '.' in name: i = name.find('.') head = name[:i] tail = name[i+1:] else: head = name tail = "" if parent: qname = "%s.%s" % (parent.__name__, head) else: qname = head q = self.import_module(head, qname, parent) if q: self.msgout(4, "find_head_package ->", (q, tail)) return q, tail if parent: qname = head parent = None q = self.import_module(head, qname, parent) if q: self.msgout(4, "find_head_package ->", (q, tail)) return q, tail self.msgout(4, "raise ImportError: No module named", qname) raise ImportError, "No module named " + qname def load_tail(self, q, tail): self.msgin(4, "load_tail", q, tail) m = q while tail: i = tail.find('.') if i < 0: i = len(tail) head, tail = tail[:i], tail[i+1:] mname = "%s.%s" % (m.__name__, head) m = self.import_module(head, mname, m) if not m: self.msgout(4, "raise ImportError: No module named", mname) raise ImportError, "No module named " + mname self.msgout(4, "load_tail ->", m) return m def ensure_fromlist(self, m, fromlist, recursive=0): self.msg(4, "ensure_fromlist", m, fromlist, recursive) for sub in fromlist: if sub == "*": if not recursive: all = self.find_all_submodules(m) if all: self.ensure_fromlist(m, all, 1) elif not hasattr(m, sub): subname = "%s.%s" % (m.__name__, sub) submod = self.import_module(sub, subname, m) if not submod: raise ImportError, "No module named " + subname def find_all_submodules(self, m): if not m.__path__: return modules = {} # 'suffixes' used to be a list hardcoded to [".py", ".pyc", ".pyo"]. # But we must also collect Python extension modules - although # we cannot separate normal dlls from Python extensions. suffixes = [] for triple in imp.get_suffixes(): suffixes.append(triple[0]) for dir in m.__path__: try: names = os.listdir(dir) except os.error: self.msg(2, "can't list directory", dir) continue for name in names: mod = None for suff in suffixes: n = len(suff) if name[-n:] == suff: mod = name[:-n] break if mod and mod != "__init__": modules[mod] = mod return modules.keys() def import_module(self, partname, fqname, parent): self.msgin(3, "import_module", partname, fqname, parent) try: m = self.modules[fqname] except KeyError: pass else: self.msgout(3, "import_module ->", m) return m if fqname in self.badmodules: self.msgout(3, "import_module -> None") return None if parent and parent.__path__ is None: self.msgout(3, "import_module -> None") return None try: fp, pathname, stuff = self.find_module(partname, parent and parent.__path__, parent) except ImportError: self.msgout(3, "import_module ->", None) return None try: m = self.load_module(fqname, fp, pathname, stuff) finally: if fp: fp.close() if parent: setattr(parent, partname, m) self.msgout(3, "import_module ->", m) return m def load_module(self, fqname, fp, pathname, file_info): suffix, mode, type = file_info self.msgin(2, "load_module", fqname, fp and "fp", pathname) if type == imp.PKG_DIRECTORY: m = self.load_package(fqname, pathname) self.msgout(2, "load_module ->", m) return m if type == imp.PY_SOURCE: co = compile(fp.read()+'\n', pathname, 'exec') elif type == imp.PY_COMPILED: if fp.read(4) != imp.get_magic(): self.msgout(2, "raise ImportError: Bad magic number", pathname) raise ImportError, "Bad magic number in %s" % pathname fp.read(4) co = marshal.load(fp) else: co = None m = self.add_module(fqname) m.__file__ = pathname if co: if self.replace_paths: co = self.replace_paths_in_code(co) m.__code__ = co self.scan_code(co, m) self.msgout(2, "load_module ->", m) return m def _add_badmodule(self, name, caller): if name not in self.badmodules: self.badmodules[name] = {} if caller: self.badmodules[name][caller.__name__] = 1 else: self.badmodules[name]["-"] = 1 def _safe_import_hook(self, name, caller, fromlist, level=-1): # wrapper for self.import_hook() that won't raise ImportError if name in self.badmodules: self._add_badmodule(name, caller) return try: self.import_hook(name, caller, level=level) except ImportError, msg: self.msg(2, "ImportError:", str(msg)) self._add_badmodule(name, caller) else: if fromlist: for sub in fromlist: if sub in self.badmodules: self._add_badmodule(sub, caller) continue try: self.import_hook(name, caller, [sub], level=level) except ImportError, msg: self.msg(2, "ImportError:", str(msg)) fullname = name + "." + sub self._add_badmodule(fullname, caller) def scan_opcodes_cli(self, co): import ast with open(co.co_filename, 'rU') as f: nodes = ast.parse(f.read(), co.co_filename) items = [] class ModuleFinderVisitor(ast.NodeVisitor): def visit_Assign(self, node): for x in node.targets: if isinstance(x, ast.Subscript): if isinstance(x.value, ast.Name): items.append(("store", (x.value.id, ))) elif isinstance(x.value, ast.Attribute): items.append(("store", (x.value.attr, ))) else: print 'Unknown in store: %s' % type(x.value).__name__ elif isinstance(x, ast.Name): items.append(("store", (x.id, ))) def visit_Import(self, node): items.extend([("import", (None, x.name)) for x in node.names]) def visit_ImportFrom(self, node): if node.level == 1: items.append(("relative_import", (node.level, [x.name for x in node.names], node.module))) else: items.extend([("import", ([x.name for x in node.names], node.module))]) v = ModuleFinderVisitor() v.visit(nodes) for what, args in items: yield what, args def scan_opcodes(self, co, unpack = struct.unpack): # Scan the code, and yield 'interesting' opcode combinations # Version for Python 2.4 and older code = co.co_code names = co.co_names consts = co.co_consts opargs = [(op, arg) for _, op, arg in _unpack_opargs(code) if op != EXTENDED_ARG] for i, (op, oparg) in enumerate(opargs): if c in STORE_OPS: yield "store", (names[oparg],) continue if (op == IMPORT_NAME and i >= 1 and opargs[i-1][0] == LOAD_CONST): fromlist = consts[opargs[i-1][1]] yield "import", (fromlist, names[oparg]) continue def scan_opcodes_25(self, co): # Scan the code, and yield 'interesting' opcode combinations code = co.co_code names = co.co_names consts = co.co_consts opargs = [(op, arg) for _, op, arg in _unpack_opargs(code) if op != EXTENDED_ARG] for i, (op, oparg) in enumerate(opargs): if op in STORE_OPS: yield "store", (names[oparg],) continue if (op == IMPORT_NAME and i >= 2 and opargs[i-1][0] == opargs[i-2][0] == LOAD_CONST): level = consts[opargs[i-2][1]] fromlist = consts[opargs[i-1][1]] if level == -1: # normal import yield "import", (fromlist, names[oparg]) elif level == 0: # absolute import yield "absolute_import", (fromlist, names[oparg]) else: # relative import yield "relative_import", (level, fromlist, names[oparg]) continue def scan_code(self, co, m): code = co.co_code if sys.platform == 'cli': scanner = self.scan_opcodes_cli elif sys.version_info >= (2, 5): scanner = self.scan_opcodes_25 else: scanner = self.scan_opcodes for what, args in scanner(co): if what == "store": name, = args m.globalnames[name] = 1 elif what in ("import", "absolute_import"): fromlist, name = args have_star = 0 if fromlist is not None: if "*" in fromlist: have_star = 1 fromlist = [f for f in fromlist if f != "*"] if what == "absolute_import": level = 0 else: level = -1 self._safe_import_hook(name, m, fromlist, level=level) if have_star: # We've encountered an "import *". If it is a Python module, # the code has already been parsed and we can suck out the # global names. mm = None if m.__path__: # At this point we don't know whether 'name' is a # submodule of 'm' or a global module. Let's just try # the full name first. mm = self.modules.get(m.__name__ + "." + name) if mm is None: mm = self.modules.get(name) if mm is not None: m.globalnames.update(mm.globalnames) m.starimports.update(mm.starimports) if mm.__code__ is None: m.starimports[name] = 1 else: m.starimports[name] = 1 elif what == "relative_import": level, fromlist, name = args if name: self._safe_import_hook(name, m, fromlist, level=level) else: parent = self.determine_parent(m, level=level) self._safe_import_hook(parent.__name__, None, fromlist, level=0) else: # We don't expect anything else from the generator. raise RuntimeError(what) for c in co.co_consts: if isinstance(c, type(co)): self.scan_code(c, m) def load_package(self, fqname, pathname): self.msgin(2, "load_package", fqname, pathname) newname = replacePackageMap.get(fqname) if newname: fqname = newname m = self.add_module(fqname) m.__file__ = pathname m.__path__ = [pathname] # As per comment at top of file, simulate runtime __path__ additions. m.__path__ = m.__path__ + packagePathMap.get(fqname, []) fp, buf, stuff = self.find_module("__init__", m.__path__) self.load_module(fqname, fp, buf, stuff) self.msgout(2, "load_package ->", m) if fp: fp.close() return m def add_module(self, fqname): if fqname in self.modules: return self.modules[fqname] self.modules[fqname] = m = Module(fqname) return m def find_module(self, name, path, parent=None): if parent is not None: # assert path is not None fullname = parent.__name__+'.'+name else: fullname = name if fullname in self.excludes: self.msgout(3, "find_module -> Excluded", fullname) raise ImportError, name if path is None: if name in sys.builtin_module_names: return (None, None, ("", "", imp.C_BUILTIN)) path = self.path return imp.find_module(name, path) def report(self): """Print a report to stdout, listing the found modules with their paths, as well as modules that are missing, or seem to be missing. """ print print " %-25s %s" % ("Name", "File") print " %-25s %s" % ("----", "----") # Print modules found keys = self.modules.keys() keys.sort() for key in keys: m = self.modules[key] if m.__path__: print "P", else: print "m", print "%-25s" % key, m.__file__ or "" # Print missing modules missing, maybe = self.any_missing_maybe() if missing: print print "Missing modules:" for name in missing: mods = self.badmodules[name].keys() mods.sort() print "?", name, "imported from", ', '.join(mods) # Print modules that may be missing, but then again, maybe not... if maybe: print print "Submodules that appear to be missing, but could also be", print "global names in the parent package:" for name in maybe: mods = self.badmodules[name].keys() mods.sort() print "?", name, "imported from", ', '.join(mods) def any_missing(self): """Return a list of modules that appear to be missing. Use any_missing_maybe() if you want to know which modules are certain to be missing, and which *may* be missing. """ missing, maybe = self.any_missing_maybe() return missing + maybe def any_missing_maybe(self): """Return two lists, one with modules that are certainly missing and one with modules that *may* be missing. The latter names could either be submodules *or* just global names in the package. The reason it can't always be determined is that it's impossible to tell which names are imported when "from module import *" is done with an extension module, short of actually importing it. """ missing = [] maybe = [] for name in self.badmodules: if name in self.excludes: continue i = name.rfind(".") if i < 0: missing.append(name) continue subname = name[i+1:] pkgname = name[:i] pkg = self.modules.get(pkgname) if pkg is not None: if pkgname in self.badmodules[name]: # The package tried to import this module itself and # failed. It's definitely missing. missing.append(name) elif subname in pkg.globalnames: # It's a global in the package: definitely not missing. pass elif pkg.starimports: # It could be missing, but the package did an "import *" # from a non-Python module, so we simply can't be sure. maybe.append(name) else: # It's not a global in the package, the package didn't # do funny star imports, it's very likely to be missing. # The symbol could be inserted into the package from the # outside, but since that's not good style we simply list # it missing. missing.append(name) else: missing.append(name) missing.sort() maybe.sort() return missing, maybe def replace_paths_in_code(self, co): new_filename = original_filename = os.path.normpath(co.co_filename) for f, r in self.replace_paths: if original_filename.startswith(f): new_filename = r + original_filename[len(f):] break if self.debug and original_filename not in self.processed_paths: if new_filename != original_filename: self.msgout(2, "co_filename %r changed to %r" \ % (original_filename,new_filename,)) else: self.msgout(2, "co_filename %r remains unchanged" \ % (original_filename,)) self.processed_paths.append(original_filename) consts = list(co.co_consts) for i in range(len(consts)): if isinstance(consts[i], type(co)): consts[i] = self.replace_paths_in_code(consts[i]) return types.CodeType(co.co_argcount, co.co_nlocals, co.co_stacksize, co.co_flags, co.co_code, tuple(consts), co.co_names, co.co_varnames, new_filename, co.co_name, co.co_firstlineno, co.co_lnotab, co.co_freevars, co.co_cellvars) def test(): # Parse command line import getopt try: opts, args = getopt.getopt(sys.argv[1:], "dmp:qx:") except getopt.error, msg: print msg return # Process options debug = 1 domods = 0 addpath = [] exclude = [] for o, a in opts: if o == '-d': debug = debug + 1 if o == '-m': domods = 1 if o == '-p': addpath = addpath + a.split(os.pathsep) if o == '-q': debug = 0 if o == '-x': exclude.append(a) # Provide default arguments if not args: script = "hello.py" else: script = args[0] # Set the path based on sys.path and the script directory path = sys.path[:] path[0] = os.path.dirname(script) path = addpath + path if debug > 1: print "path:" for item in path: print " ", repr(item) # Create the module finder and turn its crank mf = ModuleFinder(path, debug, exclude) for arg in args[1:]: if arg == '-m': domods = 1 continue if domods: if arg[-2:] == '.*': mf.import_hook(arg[:-2], None, ["*"]) else: mf.import_hook(arg) else: mf.load_file(arg) mf.run_script(script) mf.report() return mf # for -i debugging if __name__ == '__main__': try: mf = test() except KeyboardInterrupt: print "\n[interrupt]"

36.175732

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0.523441

"""Find modules used by a script, using introspection.""" from __future__ import generators import dis import imp import marshal import os import sys import types import struct if hasattr(sys.__stdout__, "newlines"): READ_MODE = "U" else: READ_MODE = "r" LOAD_CONST = dis.opmap['LOAD_CONST'] IMPORT_NAME = dis.opmap['IMPORT_NAME'] STORE_NAME = dis.opmap['STORE_NAME'] STORE_GLOBAL = dis.opmap['STORE_GLOBAL'] STORE_OPS = STORE_NAME, STORE_GLOBAL HAVE_ARGUMENT = dis.HAVE_ARGUMENT EXTENDED_ARG = dis.EXTENDED_ARG def _unpack_opargs(code): extended_arg = 0 n = len(code) i = 0 while i < n: op = ord(code[i]) offset = i i = i+1 arg = None if op >= HAVE_ARGUMENT: arg = ord(code[i]) + ord(code[i+1])*256 + extended_arg extended_arg = 0 i = i+2 if op == EXTENDED_ARG: extended_arg = arg*65536 yield (offset, op, arg) # handle __path__ modifications packages make at runtime. Therefore there # is a mechanism whereby you can register extra paths in this map for a # package, and it will be honored. # Note this is a mapping is lists of paths. packagePathMap = {} # A Public interface def AddPackagePath(packagename, path): paths = packagePathMap.get(packagename, []) paths.append(path) packagePathMap[packagename] = paths replacePackageMap = {} # This ReplacePackage mechanism allows modulefinder to work around the # way the _xmlplus package injects itself under the name "xml" into # sys.modules at runtime by calling ReplacePackage("_xmlplus", "xml") # before running ModuleFinder. def ReplacePackage(oldname, newname): replacePackageMap[oldname] = newname class Module: def __init__(self, name, file=None, path=None): self.__name__ = name self.__file__ = file self.__path__ = path self.__code__ = None # The set of global names that are assigned to in the module. # This includes those names imported through starimports of # Python modules. self.globalnames = {} # The set of starimports this module did that could not be # resolved, ie. a starimport from a non-Python module. self.starimports = {} def __repr__(self): s = "Module(%r" % (self.__name__,) if self.__file__ is not None: s = s + ", %r" % (self.__file__,) if self.__path__ is not None: s = s + ", %r" % (self.__path__,) s = s + ")" return s class ModuleFinder: def __init__(self, path=None, debug=0, excludes=[], replace_paths=[]): if path is None: path = sys.path self.path = path self.modules = {} self.badmodules = {} self.debug = debug self.indent = 0 self.excludes = excludes self.replace_paths = replace_paths self.processed_paths = [] # Used in debugging only def msg(self, level, str, *args): if level <= self.debug: for i in range(self.indent): print " ", print str, for arg in args: print repr(arg), print def msgin(self, *args): level = args[0] if level <= self.debug: self.indent = self.indent + 1 self.msg(*args) def msgout(self, *args): level = args[0] if level <= self.debug: self.indent = self.indent - 1 self.msg(*args) def run_script(self, pathname): self.msg(2, "run_script", pathname) with open(pathname, READ_MODE) as fp: stuff = ("", "r", imp.PY_SOURCE) self.load_module('__main__', fp, pathname, stuff) def load_file(self, pathname): dir, name = os.path.split(pathname) name, ext = os.path.splitext(name) with open(pathname, READ_MODE) as fp: stuff = (ext, "r", imp.PY_SOURCE) self.load_module(name, fp, pathname, stuff) def import_hook(self, name, caller=None, fromlist=None, level=-1): self.msg(3, "import_hook", name, caller, fromlist, level) parent = self.determine_parent(caller, level=level) q, tail = self.find_head_package(parent, name) m = self.load_tail(q, tail) if not fromlist: return q if m.__path__: self.ensure_fromlist(m, fromlist) return None def determine_parent(self, caller, level=-1): self.msgin(4, "determine_parent", caller, level) if not caller or level == 0: self.msgout(4, "determine_parent -> None") return None pname = caller.__name__ if level >= 1: # relative import if caller.__path__: level -= 1 if level == 0: parent = self.modules[pname] assert parent is caller self.msgout(4, "determine_parent ->", parent) return parent if pname.count(".") < level: raise ImportError, "relative importpath too deep" pname = ".".join(pname.split(".")[:-level]) parent = self.modules[pname] self.msgout(4, "determine_parent ->", parent) return parent if caller.__path__: parent = self.modules[pname] assert caller is parent self.msgout(4, "determine_parent ->", parent) return parent if '.' in pname: i = pname.rfind('.') pname = pname[:i] parent = self.modules[pname] assert parent.__name__ == pname self.msgout(4, "determine_parent ->", parent) return parent self.msgout(4, "determine_parent -> None") return None def find_head_package(self, parent, name): self.msgin(4, "find_head_package", parent, name) if '.' in name: i = name.find('.') head = name[:i] tail = name[i+1:] else: head = name tail = "" if parent: qname = "%s.%s" % (parent.__name__, head) else: qname = head q = self.import_module(head, qname, parent) if q: self.msgout(4, "find_head_package ->", (q, tail)) return q, tail if parent: qname = head parent = None q = self.import_module(head, qname, parent) if q: self.msgout(4, "find_head_package ->", (q, tail)) return q, tail self.msgout(4, "raise ImportError: No module named", qname) raise ImportError, "No module named " + qname def load_tail(self, q, tail): self.msgin(4, "load_tail", q, tail) m = q while tail: i = tail.find('.') if i < 0: i = len(tail) head, tail = tail[:i], tail[i+1:] mname = "%s.%s" % (m.__name__, head) m = self.import_module(head, mname, m) if not m: self.msgout(4, "raise ImportError: No module named", mname) raise ImportError, "No module named " + mname self.msgout(4, "load_tail ->", m) return m def ensure_fromlist(self, m, fromlist, recursive=0): self.msg(4, "ensure_fromlist", m, fromlist, recursive) for sub in fromlist: if sub == "*": if not recursive: all = self.find_all_submodules(m) if all: self.ensure_fromlist(m, all, 1) elif not hasattr(m, sub): subname = "%s.%s" % (m.__name__, sub) submod = self.import_module(sub, subname, m) if not submod: raise ImportError, "No module named " + subname def find_all_submodules(self, m): if not m.__path__: return modules = {} # 'suffixes' used to be a list hardcoded to [".py", ".pyc", ".pyo"]. # But we must also collect Python extension modules - although # we cannot separate normal dlls from Python extensions. suffixes = [] for triple in imp.get_suffixes(): suffixes.append(triple[0]) for dir in m.__path__: try: names = os.listdir(dir) except os.error: self.msg(2, "can't list directory", dir) continue for name in names: mod = None for suff in suffixes: n = len(suff) if name[-n:] == suff: mod = name[:-n] break if mod and mod != "__init__": modules[mod] = mod return modules.keys() def import_module(self, partname, fqname, parent): self.msgin(3, "import_module", partname, fqname, parent) try: m = self.modules[fqname] except KeyError: pass else: self.msgout(3, "import_module ->", m) return m if fqname in self.badmodules: self.msgout(3, "import_module -> None") return None if parent and parent.__path__ is None: self.msgout(3, "import_module -> None") return None try: fp, pathname, stuff = self.find_module(partname, parent and parent.__path__, parent) except ImportError: self.msgout(3, "import_module ->", None) return None try: m = self.load_module(fqname, fp, pathname, stuff) finally: if fp: fp.close() if parent: setattr(parent, partname, m) self.msgout(3, "import_module ->", m) return m def load_module(self, fqname, fp, pathname, file_info): suffix, mode, type = file_info self.msgin(2, "load_module", fqname, fp and "fp", pathname) if type == imp.PKG_DIRECTORY: m = self.load_package(fqname, pathname) self.msgout(2, "load_module ->", m) return m if type == imp.PY_SOURCE: co = compile(fp.read()+'\n', pathname, 'exec') elif type == imp.PY_COMPILED: if fp.read(4) != imp.get_magic(): self.msgout(2, "raise ImportError: Bad magic number", pathname) raise ImportError, "Bad magic number in %s" % pathname fp.read(4) co = marshal.load(fp) else: co = None m = self.add_module(fqname) m.__file__ = pathname if co: if self.replace_paths: co = self.replace_paths_in_code(co) m.__code__ = co self.scan_code(co, m) self.msgout(2, "load_module ->", m) return m def _add_badmodule(self, name, caller): if name not in self.badmodules: self.badmodules[name] = {} if caller: self.badmodules[name][caller.__name__] = 1 else: self.badmodules[name]["-"] = 1 def _safe_import_hook(self, name, caller, fromlist, level=-1): if name in self.badmodules: self._add_badmodule(name, caller) return try: self.import_hook(name, caller, level=level) except ImportError, msg: self.msg(2, "ImportError:", str(msg)) self._add_badmodule(name, caller) else: if fromlist: for sub in fromlist: if sub in self.badmodules: self._add_badmodule(sub, caller) continue try: self.import_hook(name, caller, [sub], level=level) except ImportError, msg: self.msg(2, "ImportError:", str(msg)) fullname = name + "." + sub self._add_badmodule(fullname, caller) def scan_opcodes_cli(self, co): import ast with open(co.co_filename, 'rU') as f: nodes = ast.parse(f.read(), co.co_filename) items = [] class ModuleFinderVisitor(ast.NodeVisitor): def visit_Assign(self, node): for x in node.targets: if isinstance(x, ast.Subscript): if isinstance(x.value, ast.Name): items.append(("store", (x.value.id, ))) elif isinstance(x.value, ast.Attribute): items.append(("store", (x.value.attr, ))) else: print 'Unknown in store: %s' % type(x.value).__name__ elif isinstance(x, ast.Name): items.append(("store", (x.id, ))) def visit_Import(self, node): items.extend([("import", (None, x.name)) for x in node.names]) def visit_ImportFrom(self, node): if node.level == 1: items.append(("relative_import", (node.level, [x.name for x in node.names], node.module))) else: items.extend([("import", ([x.name for x in node.names], node.module))]) v = ModuleFinderVisitor() v.visit(nodes) for what, args in items: yield what, args def scan_opcodes(self, co, unpack = struct.unpack): # Scan the code, and yield 'interesting' opcode combinations # Version for Python 2.4 and older code = co.co_code names = co.co_names consts = co.co_consts opargs = [(op, arg) for _, op, arg in _unpack_opargs(code) if op != EXTENDED_ARG] for i, (op, oparg) in enumerate(opargs): if c in STORE_OPS: yield "store", (names[oparg],) continue if (op == IMPORT_NAME and i >= 1 and opargs[i-1][0] == LOAD_CONST): fromlist = consts[opargs[i-1][1]] yield "import", (fromlist, names[oparg]) continue def scan_opcodes_25(self, co): # Scan the code, and yield 'interesting' opcode combinations code = co.co_code names = co.co_names consts = co.co_consts opargs = [(op, arg) for _, op, arg in _unpack_opargs(code) if op != EXTENDED_ARG] for i, (op, oparg) in enumerate(opargs): if op in STORE_OPS: yield "store", (names[oparg],) continue if (op == IMPORT_NAME and i >= 2 and opargs[i-1][0] == opargs[i-2][0] == LOAD_CONST): level = consts[opargs[i-2][1]] fromlist = consts[opargs[i-1][1]] if level == -1: # normal import yield "import", (fromlist, names[oparg]) elif level == 0: # absolute import yield "absolute_import", (fromlist, names[oparg]) else: # relative import yield "relative_import", (level, fromlist, names[oparg]) continue def scan_code(self, co, m): code = co.co_code if sys.platform == 'cli': scanner = self.scan_opcodes_cli elif sys.version_info >= (2, 5): scanner = self.scan_opcodes_25 else: scanner = self.scan_opcodes for what, args in scanner(co): if what == "store": name, = args m.globalnames[name] = 1 elif what in ("import", "absolute_import"): fromlist, name = args have_star = 0 if fromlist is not None: if "*" in fromlist: have_star = 1 fromlist = [f for f in fromlist if f != "*"] if what == "absolute_import": level = 0 else: level = -1 self._safe_import_hook(name, m, fromlist, level=level) if have_star: # We've encountered an "import *". If it is a Python module, mm = None if m.__path__: # submodule of 'm' or a global module. Let's just try mm = self.modules.get(m.__name__ + "." + name) if mm is None: mm = self.modules.get(name) if mm is not None: m.globalnames.update(mm.globalnames) m.starimports.update(mm.starimports) if mm.__code__ is None: m.starimports[name] = 1 else: m.starimports[name] = 1 elif what == "relative_import": level, fromlist, name = args if name: self._safe_import_hook(name, m, fromlist, level=level) else: parent = self.determine_parent(m, level=level) self._safe_import_hook(parent.__name__, None, fromlist, level=0) else: raise RuntimeError(what) for c in co.co_consts: if isinstance(c, type(co)): self.scan_code(c, m) def load_package(self, fqname, pathname): self.msgin(2, "load_package", fqname, pathname) newname = replacePackageMap.get(fqname) if newname: fqname = newname m = self.add_module(fqname) m.__file__ = pathname m.__path__ = [pathname] # As per comment at top of file, simulate runtime __path__ additions. m.__path__ = m.__path__ + packagePathMap.get(fqname, []) fp, buf, stuff = self.find_module("__init__", m.__path__) self.load_module(fqname, fp, buf, stuff) self.msgout(2, "load_package ->", m) if fp: fp.close() return m def add_module(self, fqname): if fqname in self.modules: return self.modules[fqname] self.modules[fqname] = m = Module(fqname) return m def find_module(self, name, path, parent=None): if parent is not None: # assert path is not None fullname = parent.__name__+'.'+name else: fullname = name if fullname in self.excludes: self.msgout(3, "find_module -> Excluded", fullname) raise ImportError, name if path is None: if name in sys.builtin_module_names: return (None, None, ("", "", imp.C_BUILTIN)) path = self.path return imp.find_module(name, path) def report(self): """Print a report to stdout, listing the found modules with their paths, as well as modules that are missing, or seem to be missing. """ print print " %-25s %s" % ("Name", "File") print " %-25s %s" % ("----", "----") # Print modules found keys = self.modules.keys() keys.sort() for key in keys: m = self.modules[key] if m.__path__: print "P", else: print "m", print "%-25s" % key, m.__file__ or "" # Print missing modules missing, maybe = self.any_missing_maybe() if missing: print print "Missing modules:" for name in missing: mods = self.badmodules[name].keys() mods.sort() print "?", name, "imported from", ', '.join(mods) # Print modules that may be missing, but then again, maybe not... if maybe: print print "Submodules that appear to be missing, but could also be", print "global names in the parent package:" for name in maybe: mods = self.badmodules[name].keys() mods.sort() print "?", name, "imported from", ', '.join(mods) def any_missing(self): """Return a list of modules that appear to be missing. Use any_missing_maybe() if you want to know which modules are certain to be missing, and which *may* be missing. """ missing, maybe = self.any_missing_maybe() return missing + maybe def any_missing_maybe(self): """Return two lists, one with modules that are certainly missing and one with modules that *may* be missing. The latter names could either be submodules *or* just global names in the package. The reason it can't always be determined is that it's impossible to tell which names are imported when "from module import *" is done with an extension module, short of actually importing it. """ missing = [] maybe = [] for name in self.badmodules: if name in self.excludes: continue i = name.rfind(".") if i < 0: missing.append(name) continue subname = name[i+1:] pkgname = name[:i] pkg = self.modules.get(pkgname) if pkg is not None: if pkgname in self.badmodules[name]: # The package tried to import this module itself and # failed. It's definitely missing. missing.append(name) elif subname in pkg.globalnames: pass elif pkg.starimports: # It could be missing, but the package did an "import *" # from a non-Python module, so we simply can't be sure. maybe.append(name) else: # The symbol could be inserted into the package from the # outside, but since that's not good style we simply list missing.append(name) else: missing.append(name) missing.sort() maybe.sort() return missing, maybe def replace_paths_in_code(self, co): new_filename = original_filename = os.path.normpath(co.co_filename) for f, r in self.replace_paths: if original_filename.startswith(f): new_filename = r + original_filename[len(f):] break if self.debug and original_filename not in self.processed_paths: if new_filename != original_filename: self.msgout(2, "co_filename %r changed to %r" \ % (original_filename,new_filename,)) else: self.msgout(2, "co_filename %r remains unchanged" \ % (original_filename,)) self.processed_paths.append(original_filename) consts = list(co.co_consts) for i in range(len(consts)): if isinstance(consts[i], type(co)): consts[i] = self.replace_paths_in_code(consts[i]) return types.CodeType(co.co_argcount, co.co_nlocals, co.co_stacksize, co.co_flags, co.co_code, tuple(consts), co.co_names, co.co_varnames, new_filename, co.co_name, co.co_firstlineno, co.co_lnotab, co.co_freevars, co.co_cellvars) def test(): import getopt try: opts, args = getopt.getopt(sys.argv[1:], "dmp:qx:") except getopt.error, msg: print msg return debug = 1 domods = 0 addpath = [] exclude = [] for o, a in opts: if o == '-d': debug = debug + 1 if o == '-m': domods = 1 if o == '-p': addpath = addpath + a.split(os.pathsep) if o == '-q': debug = 0 if o == '-x': exclude.append(a) if not args: script = "hello.py" else: script = args[0] path = sys.path[:] path[0] = os.path.dirname(script) path = addpath + path if debug > 1: print "path:" for item in path: print " ", repr(item) mf = ModuleFinder(path, debug, exclude) for arg in args[1:]: if arg == '-m': domods = 1 continue if domods: if arg[-2:] == '.*': mf.import_hook(arg[:-2], None, ["*"]) else: mf.import_hook(arg) else: mf.load_file(arg) mf.run_script(script) mf.report() return mf if __name__ == '__main__': try: mf = test() except KeyboardInterrupt: print "\n[interrupt]"

false

true

f71b45acabf22bbb1840898ea73829472d8e7060

731

py

Python

tests/test_python3_dbb.py

bopopescu/NovalIDE

590c2adb69d54fa4a6c9dad5459198be057b1329

[ "MulanPSL-1.0" ]

null

tests/test_python3_dbb.py

bopopescu/NovalIDE

590c2adb69d54fa4a6c9dad5459198be057b1329

[ "MulanPSL-1.0" ]

null

tests/test_python3_dbb.py

bopopescu/NovalIDE

590c2adb69d54fa4a6c9dad5459198be057b1329

[ "MulanPSL-1.0" ]

null

import inspect class _D: def _m(self): pass class _C: def _m(self): pass _x = _C() _x2 = _D() a=121111 r = input('hahah') print(r) raise AttributeError('Provider test already registered') print (type(_C),_x.__class__,dir(_x),"------------") import types ###print (dir(types)) print (type(inspect)) ###print type(inspect) is types.InstanceType,"=============" print (type(_x),type(type)) print (inspect.isclass(_x)) print (inspect.isclass(_x2)) print (inspect.isclass(_D)) print (inspect.isclass(_C)) print (inspect.ismodule(_C)) print (isinstance(inspect,object),"------------") print (1) ###print (g) print (2) print (3) print (4) print (6) print (7) print (8) print (9) print (10) print ("11111111111111")

17.404762

60

0.642955

import inspect class _D: def _m(self): pass class _C: def _m(self): pass _x = _C() _x2 = _D() a=121111 r = input('hahah') print(r) raise AttributeError('Provider test already registered') print (type(_C),_x.__class__,dir(_x),"------------") import types lass(_C)) print (inspect.ismodule(_C)) print (isinstance(inspect,object),"------------") print (1) print (4) print (6) print (7) print (8) print (9) print (10) print ("11111111111111")

true

f71b45baa1e78f59775296f091528acf3ccf2008

718

py

Python

meus_projetos/projetos_python/lista_de_tarefas/minhas_funcoes.py

SabinoEduardo/Python

f46e47f166150afdf0f4c4358b5848d52667a764

[ "MIT" ]

null

meus_projetos/projetos_python/lista_de_tarefas/minhas_funcoes.py

SabinoEduardo/Python

f46e47f166150afdf0f4c4358b5848d52667a764

[ "MIT" ]

null

meus_projetos/projetos_python/lista_de_tarefas/minhas_funcoes.py

SabinoEduardo/Python

f46e47f166150afdf0f4c4358b5848d52667a764

[ "MIT" ]

null

def adicionar_tarefa(lista_tarefas, tarefas): lista_tarefas.append(tarefas) def deletar_tarefa(lista_tarefas, tarefas_deletadas): """ Esta função serve para deletar a ultima tarefa da lista e guarda esta tarefa em outra lista. """ if not lista_tarefas: print("Nada a deletar") return tarefas_deletadas.append(lista_tarefas[-1]) lista_tarefas.pop() def repor_tarefa(lista_tarefas, tarefas_deletadas): """ Esta função serve para repor ultima tarefa deletada da lista de tarefas. """ if not tarefas_deletadas: print("Nada a repor") return else: lista_tarefas.append(tarefas_deletadas[-1]) tarefas_deletadas.pop()

27.615385

100

0.682451

def adicionar_tarefa(lista_tarefas, tarefas): lista_tarefas.append(tarefas) def deletar_tarefa(lista_tarefas, tarefas_deletadas): if not lista_tarefas: print("Nada a deletar") return tarefas_deletadas.append(lista_tarefas[-1]) lista_tarefas.pop() def repor_tarefa(lista_tarefas, tarefas_deletadas): if not tarefas_deletadas: print("Nada a repor") return else: lista_tarefas.append(tarefas_deletadas[-1]) tarefas_deletadas.pop()

true

f71b47bfd1af85c0318ff27db55a1a089a9d0ee9

4,549

py

Python

pyuvm/s09_phasing.py

ktbarrett/pyuvm

725e6e4b8088aa085a5ce16861b46db49ce46672

[ "Apache-2.0" ]

4

2021-11-07T13:22:27.000Z

2022-02-17T08:51:24.000Z

pyuvm/s09_phasing.py

ktbarrett/pyuvm

725e6e4b8088aa085a5ce16861b46db49ce46672

[ "Apache-2.0" ]

null

pyuvm/s09_phasing.py

ktbarrett/pyuvm

725e6e4b8088aa085a5ce16861b46db49ce46672

[ "Apache-2.0" ]

null

from pyuvm.s05_base_classes import uvm_object import pyuvm.error_classes as error_classes import cocotb # 9.1 # # This is a dramatically simplified version of UVM phasing. We don't have # to deal with simulation time and we are not going to deal with a generalized # phasing system. # # So this system simply traverses the common phases, calling the appropriate # method in each component. # # Much of the work in the SV phasing code has to do with handling the passage # of time. There is no timewheel in Python, so all of that code can go # away. # # Also, the generalized phasing system is rarely used and so that # is left as an exercise for future developers. Instead we have a simple # topdown and bottom up traversal of calling methods in component # classes based on the phase name. # # We're not doing schedules or domains. We're just creating a list of classes # and traversing them in order. The order it dependent upon whether they # are topdown or bottom up phases. # 9.3.1.2 Class declaration class uvm_phase(uvm_object): # Strips the "uvm_" from this class's name and uses the remainder # to get a function call out of the component and execute it. # 'uvm_run_phase' becomes 'run_phase' and is called as 'run_phase()' @classmethod def execute(cls, comp): """ :param comp: The component whose turn it is to execute """ method_name = cls.__name__[4:] try: method = getattr(comp, method_name) except AttributeError: raise error_classes.UVMBadPhase( f"{comp.get_name()} is missing {method_name} function") method() def __str__(self): return self.__name__[4:] class uvm_topdown_phase(uvm_phase): """ Runs phases from the top down. """ @classmethod def traverse(cls, comp): """ Given a component, we traverse the component tree top to bottom calling the phase functions as we go :param comp: The component whose hierarchy will be traversed """ cls.execute(comp) # first we execute this node then its children for child in comp.get_children(): cls.traverse(child) class uvm_bottomup_phase(uvm_phase): """ Runs the phases from bottom up. """ @classmethod def traverse(cls, comp): for child in comp.get_children(): cls.traverse(child) cls.execute(comp) class uvm_threaded_execute_phase(uvm_phase): """ This phase launches the phase function in a thread and returns the thread to the caller. The caller can then join all the threads. """ @classmethod def execute(cls, comp): phase_name = cls.__name__ assert phase_name.startswith("uvm_"), \ "We only support phases whose names start with uvm_" method_name = cls.__name__[4:] try: method = getattr(comp, method_name) except AttributeError: raise error_classes.UVMBadPhase( f"{comp.get_name()} is missing {method_name} function") cocotb.start_soon(method()) # 9.8 Predefined Phases # 9.8.1 Common Phases # The common phases are described in the order of their execution. # 9.8.1.1 class uvm_build_phase(uvm_topdown_phase): ... # 9.8.1.2 class uvm_connect_phase(uvm_bottomup_phase): ... # 9.8.1.3 class uvm_end_of_elaboration_phase(uvm_topdown_phase): ... # 9.8.1.4 class uvm_start_of_simulation_phase(uvm_topdown_phase): ... # 9.8.1.5 class uvm_run_phase(uvm_threaded_execute_phase, uvm_bottomup_phase): ... # 9.8.1.6 class uvm_extract_phase(uvm_topdown_phase): ... # 9.8.1.7 class uvm_check_phase(uvm_topdown_phase): ... # 9.8.1.8 class uvm_report_phase(uvm_topdown_phase): ... # 9.8.1.9 class uvm_final_phase(uvm_topdown_phase): ... # 9.8.2 # UVM run-time phases are left as an exercise for an enterprising soul # I cannot imagine why anyone would implement this. # One could add phases by simply extending uvm_topdown_phase # or uvm_bottom_up phase with a new phase named 'uvm_my_phase' and adding # the my_phase() method to a uvm component with setattr. uvm_common_phases = [uvm_build_phase, uvm_connect_phase, uvm_end_of_elaboration_phase, uvm_start_of_simulation_phase, uvm_run_phase, uvm_extract_phase, uvm_check_phase, uvm_report_phase, uvm_final_phase]

27.737805

78

0.667619

from pyuvm.s05_base_classes import uvm_object import pyuvm.error_classes as error_classes import cocotb # to deal with simulation time and we are not going to deal with a generalized # phasing system. # # So this system simply traverses the common phases, calling the appropriate # method in each component. # # Much of the work in the SV phasing code has to do with handling the passage # of time. There is no timewheel in Python, so all of that code can go # away. # # Also, the generalized phasing system is rarely used and so that # is left as an exercise for future developers. Instead we have a simple # topdown and bottom up traversal of calling methods in component # classes based on the phase name. # # We're not doing schedules or domains. We're just creating a list of classes # and traversing them in order. The order it dependent upon whether they # are topdown or bottom up phases. # 9.3.1.2 Class declaration class uvm_phase(uvm_object): # Strips the "uvm_" from this class's name and uses the remainder @classmethod def execute(cls, comp): method_name = cls.__name__[4:] try: method = getattr(comp, method_name) except AttributeError: raise error_classes.UVMBadPhase( f"{comp.get_name()} is missing {method_name} function") method() def __str__(self): return self.__name__[4:] class uvm_topdown_phase(uvm_phase): @classmethod def traverse(cls, comp): cls.execute(comp) for child in comp.get_children(): cls.traverse(child) class uvm_bottomup_phase(uvm_phase): @classmethod def traverse(cls, comp): for child in comp.get_children(): cls.traverse(child) cls.execute(comp) class uvm_threaded_execute_phase(uvm_phase): @classmethod def execute(cls, comp): phase_name = cls.__name__ assert phase_name.startswith("uvm_"), \ "We only support phases whose names start with uvm_" method_name = cls.__name__[4:] try: method = getattr(comp, method_name) except AttributeError: raise error_classes.UVMBadPhase( f"{comp.get_name()} is missing {method_name} function") cocotb.start_soon(method()) class uvm_build_phase(uvm_topdown_phase): ... class uvm_connect_phase(uvm_bottomup_phase): ... class uvm_end_of_elaboration_phase(uvm_topdown_phase): ... class uvm_start_of_simulation_phase(uvm_topdown_phase): ... class uvm_run_phase(uvm_threaded_execute_phase, uvm_bottomup_phase): ... class uvm_extract_phase(uvm_topdown_phase): ... class uvm_check_phase(uvm_topdown_phase): ... class uvm_report_phase(uvm_topdown_phase): ... class uvm_final_phase(uvm_topdown_phase): ... uvm_common_phases = [uvm_build_phase, uvm_connect_phase, uvm_end_of_elaboration_phase, uvm_start_of_simulation_phase, uvm_run_phase, uvm_extract_phase, uvm_check_phase, uvm_report_phase, uvm_final_phase]

true

f71b486fd1687af447da51c84625b2c67f3d5401

3,199

py

Python

homeassistant/components/harmony/subscriber.py

PiotrMachowski/core

b9d7d0cae2ccd2d88e90e49cc09e154a27ed809b

[ "Apache-2.0" ]

3

2020-11-27T06:26:27.000Z

2020-12-09T14:55:16.000Z

homeassistant/components/harmony/subscriber.py

PiotrMachowski/core

b9d7d0cae2ccd2d88e90e49cc09e154a27ed809b

[ "Apache-2.0" ]

277

2021-10-04T06:39:33.000Z

2021-12-28T22:04:17.000Z

homeassistant/components/harmony/subscriber.py

PiotrMachowski/core

b9d7d0cae2ccd2d88e90e49cc09e154a27ed809b

[ "Apache-2.0" ]

3

2022-01-02T18:49:54.000Z

2022-01-25T02:03:54.000Z

"""Mixin class for handling harmony callback subscriptions.""" import asyncio import logging # pylint: disable-next=deprecated-typing-alias # Issue with Python 3.9.0 and 3.9.1 with collections.abc.Callable # https://bugs.python.org/issue42965 from typing import Any, Callable, NamedTuple, Optional from homeassistant.core import callback _LOGGER = logging.getLogger(__name__) NoParamCallback = Optional[Callable[[object], Any]] ActivityCallback = Optional[Callable[[object, tuple], Any]] class HarmonyCallback(NamedTuple): """Callback type for Harmony Hub notifications.""" connected: NoParamCallback disconnected: NoParamCallback config_updated: NoParamCallback activity_starting: ActivityCallback activity_started: ActivityCallback class HarmonySubscriberMixin: """Base implementation for a subscriber.""" def __init__(self, hass): """Initialize an subscriber.""" super().__init__() self._hass = hass self._subscriptions = [] self._activity_lock = asyncio.Lock() async def async_lock_start_activity(self): """Acquire the lock.""" await self._activity_lock.acquire() @callback def async_unlock_start_activity(self): """Release the lock.""" if self._activity_lock.locked(): self._activity_lock.release() @callback def async_subscribe(self, update_callbacks: HarmonyCallback) -> Callable: """Add a callback subscriber.""" self._subscriptions.append(update_callbacks) def _unsubscribe(): self.async_unsubscribe(update_callbacks) return _unsubscribe @callback def async_unsubscribe(self, update_callback: HarmonyCallback): """Remove a callback subscriber.""" self._subscriptions.remove(update_callback) def _config_updated(self, _=None) -> None: _LOGGER.debug("config_updated") self._call_callbacks("config_updated") def _connected(self, _=None) -> None: _LOGGER.debug("connected") self.async_unlock_start_activity() self._available = True self._call_callbacks("connected") def _disconnected(self, _=None) -> None: _LOGGER.debug("disconnected") self.async_unlock_start_activity() self._available = False self._call_callbacks("disconnected") def _activity_starting(self, activity_info: tuple) -> None: _LOGGER.debug("activity %s starting", activity_info) self._call_callbacks("activity_starting", activity_info) def _activity_started(self, activity_info: tuple) -> None: _LOGGER.debug("activity %s started", activity_info) self.async_unlock_start_activity() self._call_callbacks("activity_started", activity_info) def _call_callbacks(self, callback_func_name: str, argument: tuple = None): for subscription in self._subscriptions: current_callback = getattr(subscription, callback_func_name) if current_callback: if argument: self._hass.async_run_job(current_callback, argument) else: self._hass.async_run_job(current_callback)

32.979381

79

0.689278

import asyncio import logging from typing import Any, Callable, NamedTuple, Optional from homeassistant.core import callback _LOGGER = logging.getLogger(__name__) NoParamCallback = Optional[Callable[[object], Any]] ActivityCallback = Optional[Callable[[object, tuple], Any]] class HarmonyCallback(NamedTuple): connected: NoParamCallback disconnected: NoParamCallback config_updated: NoParamCallback activity_starting: ActivityCallback activity_started: ActivityCallback class HarmonySubscriberMixin: def __init__(self, hass): super().__init__() self._hass = hass self._subscriptions = [] self._activity_lock = asyncio.Lock() async def async_lock_start_activity(self): await self._activity_lock.acquire() @callback def async_unlock_start_activity(self): if self._activity_lock.locked(): self._activity_lock.release() @callback def async_subscribe(self, update_callbacks: HarmonyCallback) -> Callable: self._subscriptions.append(update_callbacks) def _unsubscribe(): self.async_unsubscribe(update_callbacks) return _unsubscribe @callback def async_unsubscribe(self, update_callback: HarmonyCallback): self._subscriptions.remove(update_callback) def _config_updated(self, _=None) -> None: _LOGGER.debug("config_updated") self._call_callbacks("config_updated") def _connected(self, _=None) -> None: _LOGGER.debug("connected") self.async_unlock_start_activity() self._available = True self._call_callbacks("connected") def _disconnected(self, _=None) -> None: _LOGGER.debug("disconnected") self.async_unlock_start_activity() self._available = False self._call_callbacks("disconnected") def _activity_starting(self, activity_info: tuple) -> None: _LOGGER.debug("activity %s starting", activity_info) self._call_callbacks("activity_starting", activity_info) def _activity_started(self, activity_info: tuple) -> None: _LOGGER.debug("activity %s started", activity_info) self.async_unlock_start_activity() self._call_callbacks("activity_started", activity_info) def _call_callbacks(self, callback_func_name: str, argument: tuple = None): for subscription in self._subscriptions: current_callback = getattr(subscription, callback_func_name) if current_callback: if argument: self._hass.async_run_job(current_callback, argument) else: self._hass.async_run_job(current_callback)

true

f71b492ed7fe05c2dd8f787b6d743c15e42b6651

23,428

py

Python

test/test_framework/util.py

vpubchain/Phore

7819f046e629ccb5a00fa4f89a7399a7732b4113

[ "MIT" ]

null

test/test_framework/util.py

vpubchain/Phore

7819f046e629ccb5a00fa4f89a7399a7732b4113

[ "MIT" ]

null

test/test_framework/util.py

vpubchain/Phore

7819f046e629ccb5a00fa4f89a7399a7732b4113

[ "MIT" ]

1

2019-08-10T08:20:56.000Z

#!/usr/bin/env python3 # Copyright (c) 2014-2016 The Bitcoin Core developers # Distributed under the MIT software license, see the accompanying # file COPYING or http://www.opensource.org/licenses/mit-license.php. # # Helpful routines for regression testing # import os import sys from binascii import hexlify, unhexlify from base64 import b64encode from decimal import Decimal, ROUND_DOWN import json import http.client import random import shutil import subprocess import time import re import errno from . import coverage from .authproxy import AuthServiceProxy, JSONRPCException COVERAGE_DIR = None # The maximum number of nodes a single test can spawn MAX_NODES = 8 # Don't assign rpc or p2p ports lower than this PORT_MIN = 11000 # The number of ports to "reserve" for p2p and rpc, each PORT_RANGE = 5000 VPUBD_PROC_WAIT_TIMEOUT = 60 class PortSeed: # Must be initialized with a unique integer for each process n = None #Set Mocktime default to OFF. #MOCKTIME is only needed for scripts that use the #cached version of the blockchain. If the cached #version of the blockchain is used without MOCKTIME #then the mempools will not sync due to IBD. MOCKTIME = 0 def enable_mocktime(): #For backwared compatibility of the python scripts #with previous versions of the cache, set MOCKTIME #to Jan 1, 2014 + (201 * 10 * 60) global MOCKTIME MOCKTIME = 1388534400 + (201 * 10 * 60) def disable_mocktime(): global MOCKTIME MOCKTIME = 0 def get_mocktime(): return MOCKTIME def enable_coverage(dirname): """Maintain a log of which RPC calls are made during testing.""" global COVERAGE_DIR COVERAGE_DIR = dirname def get_rpc_proxy(url, node_number, timeout=None): """ Args: url (str): URL of the RPC server to call node_number (int): the node number (or id) that this calls to Kwargs: timeout (int): HTTP timeout in seconds Returns: AuthServiceProxy. convenience object for making RPC calls. """ proxy_kwargs = {} if timeout is not None: proxy_kwargs['timeout'] = timeout proxy = AuthServiceProxy(url, **proxy_kwargs) proxy.url = url # store URL on proxy for info coverage_logfile = coverage.get_filename( COVERAGE_DIR, node_number) if COVERAGE_DIR else None return coverage.AuthServiceProxyWrapper(proxy, coverage_logfile) def p2p_port(n): assert(n <= MAX_NODES) return PORT_MIN + n + (MAX_NODES * PortSeed.n) % (PORT_RANGE - 1 - MAX_NODES) def rpc_port(n): return PORT_MIN + PORT_RANGE + n + (MAX_NODES * PortSeed.n) % (PORT_RANGE - 1 - MAX_NODES) def check_json_precision(): """Make sure json library being used does not lose precision converting BTC values""" n = Decimal("20000000.00000003") satoshis = int(json.loads(json.dumps(float(n)))*1.0e8) if satoshis != 2000000000000003: raise RuntimeError("JSON encode/decode loses precision") def count_bytes(hex_string): return len(bytearray.fromhex(hex_string)) def bytes_to_hex_str(byte_str): return hexlify(byte_str).decode('ascii') def hex_str_to_bytes(hex_str): return unhexlify(hex_str.encode('ascii')) def str_to_b64str(string): return b64encode(string.encode('utf-8')).decode('ascii') def sync_blocks(rpc_connections, wait=1, timeout=60): """ Wait until everybody has the same tip """ while timeout > 0: tips = [ x.getbestblockhash() for x in rpc_connections ] if tips == [ tips[0] ]*len(tips): return True time.sleep(wait) timeout -= wait raise AssertionError("Block sync failed") def sync_mempools(rpc_connections, wait=1, timeout=60): """ Wait until everybody has the same transactions in their memory pools """ while timeout > 0: pool = set(rpc_connections[0].getrawmempool()) num_match = 1 for i in range(1, len(rpc_connections)): if set(rpc_connections[i].getrawmempool()) == pool: num_match = num_match+1 if num_match == len(rpc_connections): return True time.sleep(wait) timeout -= wait raise AssertionError("Mempool sync failed") vpubd_processes = {} def initialize_datadir(dirname, n): datadir = os.path.join(dirname, "node"+str(n)) if not os.path.isdir(datadir): os.makedirs(datadir) rpc_u, rpc_p = rpc_auth_pair(n) with open(os.path.join(datadir, "vpub.conf"), 'w', encoding='utf8') as f: f.write("regtest=1\n") f.write("server=1\n") f.write("rpcuser=" + rpc_u + "\n") f.write("rpcpassword=" + rpc_p + "\n") f.write("port="+str(p2p_port(n))+"\n") f.write("rpcport="+str(rpc_port(n))+"\n") f.write("listenonion=0\n") return datadir def rpc_auth_pair(n): return 'rpcuser' + str(n), 'rpcpass' + str(n) def rpc_url(i, rpchost=None): rpc_u, rpc_p = rpc_auth_pair(i) host = '127.0.0.1' port = rpc_port(i) if rpchost: parts = rpchost.split(':') if len(parts) == 2: host, port = parts else: host = rpchost return "http://%s:%s@%s:%d" % (rpc_u, rpc_p, host, int(port)) def wait_for_vpubd_start(process, url, i): ''' Wait for vpubd to start. This means that RPC is accessible and fully initialized. Raise an exception if vpubd exits during initialization. ''' while True: if process.poll() is not None: raise Exception('vpubd exited with status %i during initialization' % process.returncode) try: rpc = get_rpc_proxy(url, i) blocks = rpc.getblockcount() break # break out of loop on success except IOError as e: if e.errno != errno.ECONNREFUSED: # Port not yet open? raise # unknown IO error except JSONRPCException as e: # Initialization phase if e.error['code'] != -28: # RPC in warmup? raise # unkown JSON RPC exception time.sleep(0.25) def initialize_chain(test_dir, num_nodes): """ Create a cache of a 200-block-long chain (with wallet) for MAX_NODES Afterward, create num_nodes copies from the cache """ assert num_nodes <= MAX_NODES create_cache = False for i in range(MAX_NODES): if not os.path.isdir(os.path.join('cache', 'node'+str(i))): create_cache = True break if create_cache: #find and delete old cache directories if any exist for i in range(MAX_NODES): if os.path.isdir(os.path.join("cache","node"+str(i))): shutil.rmtree(os.path.join("cache","node"+str(i))) # Create cache directories, run vpubds: for i in range(MAX_NODES): datadir=initialize_datadir("cache", i) args = [ os.getenv("VPUBD", "vpubd"), "-server", "-keypool=1", "-datadir="+datadir, "-discover=0" ] if i > 0: args.append("-connect=127.0.0.1:"+str(p2p_port(0))) vpubd_processes[i] = subprocess.Popen(args) if os.getenv("PYTHON_DEBUG", ""): print("initialize_chain: vpubd started, waiting for RPC to come up") wait_for_vpubd_start(vpubd_processes[i], rpc_url(i), i) if os.getenv("PYTHON_DEBUG", ""): print("initialize_chain: RPC succesfully started") rpcs = [] for i in range(MAX_NODES): try: rpcs.append(get_rpc_proxy(rpc_url(i), i)) except: sys.stderr.write("Error connecting to "+url+"\n") sys.exit(1) # Create a 200-block-long chain; each of the 4 first nodes # gets 25 mature blocks and 25 immature. # Note: To preserve compatibility with older versions of # initialize_chain, only 4 nodes will generate coins. # # blocks are created with timestamps 10 minutes apart # starting from 2010 minutes in the past enable_mocktime() block_time = get_mocktime() - (201 * 10 * 60) for i in range(2): for peer in range(4): for j in range(25): set_node_times(rpcs, block_time) rpcs[peer].setgenerate(True, 1) block_time += 10*60 # Must sync before next peer starts generating blocks sync_blocks(rpcs) # Shut them down, and clean up cache directories: stop_nodes(rpcs) disable_mocktime() for i in range(MAX_NODES): os.remove(log_filename("cache", i, "debug.log")) os.remove(log_filename("cache", i, "db.log")) os.remove(log_filename("cache", i, "peers.dat")) os.remove(log_filename("cache", i, "fee_estimates.dat")) for i in range(num_nodes): from_dir = os.path.join("cache", "node"+str(i)) to_dir = os.path.join(test_dir, "node"+str(i)) shutil.copytree(from_dir, to_dir) initialize_datadir(test_dir, i) # Overwrite port/rpcport in vpub.conf def initialize_chain_clean(test_dir, num_nodes): """ Create an empty blockchain and num_nodes wallets. Useful if a test case wants complete control over initialization. """ for i in range(num_nodes): datadir=initialize_datadir(test_dir, i) def _rpchost_to_args(rpchost): '''Convert optional IP:port spec to rpcconnect/rpcport args''' if rpchost is None: return [] match = re.match('(\[[0-9a-fA-f:]+\]|[^:]+)(?::([0-9]+))?$', rpchost) if not match: raise ValueError('Invalid RPC host spec ' + rpchost) rpcconnect = match.group(1) rpcport = match.group(2) if rpcconnect.startswith('['): # remove IPv6 [...] wrapping rpcconnect = rpcconnect[1:-1] rv = ['-rpcconnect=' + rpcconnect] if rpcport: rv += ['-rpcport=' + rpcport] return rv def start_node(i, dirname, extra_args=None, rpchost=None, timewait=None, binary=None): """ Start a vpubd and return RPC connection to it """ datadir = os.path.join(dirname, "node"+str(i)) if binary is None: binary = os.getenv("VPUBD", "vpubd") args = [ binary, "-datadir="+datadir, "-server", "-keypool=1", "-discover=0", "-rest", "-mocktime="+str(get_mocktime()), "-regtest", "-sporkkey=923EhWh2bJHynX6d4Tqt2Q75bhTDCT1b4kff3qzDKDZHZ6pkQs7"] if extra_args is not None: args.extend(extra_args) vpubd_processes[i] = subprocess.Popen(args) if os.getenv("PYTHON_DEBUG", ""): print("start_node: phroed started, waiting for RPC to come up") url = rpc_url(i, rpchost) wait_for_vpubd_start(vpubd_processes[i], url, i) if os.getenv("PYTHON_DEBUG", ""): print("start_node: RPC succesfully started") proxy = get_rpc_proxy(url, i, timeout=timewait) if COVERAGE_DIR: coverage.write_all_rpc_commands(COVERAGE_DIR, proxy) return proxy def start_nodes(num_nodes, dirname, extra_args=None, rpchost=None, timewait=None, binary=None): """ Start multiple vpubds, return RPC connections to them """ if extra_args is None: extra_args = [ None for _ in range(num_nodes) ] if binary is None: binary = [ None for _ in range(num_nodes) ] rpcs = [] try: for i in range(num_nodes): rpcs.append(start_node(i, dirname, extra_args[i], rpchost, timewait=timewait, binary=binary[i])) except: # If one node failed to start, stop the others stop_nodes(rpcs) raise return rpcs def log_filename(dirname, n_node, logname): return os.path.join(dirname, "node"+str(n_node), "regtest", logname) def stop_node(node, i): try: node.stop() except http.client.CannotSendRequest as e: print("WARN: Unable to stop node: " + repr(e)) vpubd_processes[i].wait(timeout=VPUBD_PROC_WAIT_TIMEOUT) del vpubd_processes[i] def stop_nodes(nodes): for node in nodes: try: node.stop() except http.client.CannotSendRequest as e: print("WARN: Unable to stop node: " + repr(e)) del nodes[:] # Emptying array closes connections as a side effect wait_vpubds() def set_node_times(nodes, t): for node in nodes: node.setmocktime(t) def wait_vpubds(): # Wait for all vpubds to cleanly exit for vpubd in vpubd_processes.values(): vpubd.wait(timeout=VPUBD_PROC_WAIT_TIMEOUT) vpubd_processes.clear() def connect_nodes(from_connection, node_num): ip_port = "127.0.0.1:"+str(p2p_port(node_num)) from_connection.addnode(ip_port, "onetry") # poll until version handshake complete to avoid race conditions # with transaction relaying while any(peer['version'] == 0 for peer in from_connection.getpeerinfo()): time.sleep(0.1) def connect_nodes_bi(nodes, a, b): connect_nodes(nodes[a], b) connect_nodes(nodes[b], a) def find_output(node, txid, amount): """ Return index to output of txid with value amount Raises exception if there is none. """ txdata = node.getrawtransaction(txid, 1) for i in range(len(txdata["vout"])): if txdata["vout"][i]["value"] == amount: return i raise RuntimeError("find_output txid %s : %s not found"%(txid,str(amount))) def gather_inputs(from_node, amount_needed, confirmations_required=1): """ Return a random set of unspent txouts that are enough to pay amount_needed """ assert(confirmations_required >=0) utxo = from_node.listunspent(confirmations_required) random.shuffle(utxo) inputs = [] total_in = Decimal("0.00000000") while total_in < amount_needed and len(utxo) > 0: t = utxo.pop() total_in += t["amount"] inputs.append({ "txid" : t["txid"], "vout" : t["vout"], "address" : t["address"] } ) if total_in < amount_needed: raise RuntimeError("Insufficient funds: need %d, have %d"%(amount_needed, total_in)) return (total_in, inputs) def make_change(from_node, amount_in, amount_out, fee): """ Create change output(s), return them """ outputs = {} amount = amount_out+fee change = amount_in - amount if change > amount*2: # Create an extra change output to break up big inputs change_address = from_node.getnewaddress() # Split change in two, being careful of rounding: outputs[change_address] = Decimal(change/2).quantize(Decimal('0.00000001'), rounding=ROUND_DOWN) change = amount_in - amount - outputs[change_address] if change > 0: outputs[from_node.getnewaddress()] = change return outputs def send_zeropri_transaction(from_node, to_node, amount, fee): """ Create&broadcast a zero-priority transaction. Returns (txid, hex-encoded-txdata) Ensures transaction is zero-priority by first creating a send-to-self, then using its output """ # Create a send-to-self with confirmed inputs: self_address = from_node.getnewaddress() (total_in, inputs) = gather_inputs(from_node, amount+fee*2) outputs = make_change(from_node, total_in, amount+fee, fee) outputs[self_address] = float(amount+fee) self_rawtx = from_node.createrawtransaction(inputs, outputs) self_signresult = from_node.signrawtransaction(self_rawtx) self_txid = from_node.sendrawtransaction(self_signresult["hex"], True) vout = find_output(from_node, self_txid, amount+fee) # Now immediately spend the output to create a 1-input, 1-output # zero-priority transaction: inputs = [ { "txid" : self_txid, "vout" : vout } ] outputs = { to_node.getnewaddress() : float(amount) } rawtx = from_node.createrawtransaction(inputs, outputs) signresult = from_node.signrawtransaction(rawtx) txid = from_node.sendrawtransaction(signresult["hex"], True) return (txid, signresult["hex"]) def random_zeropri_transaction(nodes, amount, min_fee, fee_increment, fee_variants): """ Create a random zero-priority transaction. Returns (txid, hex-encoded-transaction-data, fee) """ from_node = random.choice(nodes) to_node = random.choice(nodes) fee = min_fee + fee_increment*random.randint(0,fee_variants) (txid, txhex) = send_zeropri_transaction(from_node, to_node, amount, fee) return (txid, txhex, fee) def random_transaction(nodes, amount, min_fee, fee_increment, fee_variants): """ Create a random transaction. Returns (txid, hex-encoded-transaction-data, fee) """ from_node = random.choice(nodes) to_node = random.choice(nodes) fee = min_fee + fee_increment*random.randint(0,fee_variants) (total_in, inputs) = gather_inputs(from_node, amount+fee) outputs = make_change(from_node, total_in, amount, fee) outputs[to_node.getnewaddress()] = float(amount) rawtx = from_node.createrawtransaction(inputs, outputs) signresult = from_node.signrawtransaction(rawtx) txid = from_node.sendrawtransaction(signresult["hex"], True) return (txid, signresult["hex"], fee) def assert_fee_amount(fee, tx_size, fee_per_kB): """Assert the fee was in range""" target_fee = tx_size * fee_per_kB / 1000 if fee < target_fee: raise AssertionError("Fee of %s BTC too low! (Should be %s BTC)"%(str(fee), str(target_fee))) # allow the wallet's estimation to be at most 2 bytes off if fee > (tx_size + 2) * fee_per_kB / 1000: raise AssertionError("Fee of %s BTC too high! (Should be %s BTC)"%(str(fee), str(target_fee))) def assert_equal(thing1, thing2): if thing1 != thing2: raise AssertionError("%s != %s"%(str(thing1),str(thing2))) def assert_greater_than(thing1, thing2): if thing1 <= thing2: raise AssertionError("%s <= %s"%(str(thing1),str(thing2))) def assert_raises(exc, fun, *args, **kwds): assert_raises_message(exc, None, fun, *args, **kwds) def assert_raises_message(exc, message, fun, *args, **kwds): try: fun(*args, **kwds) except exc as e: if message is not None and message not in e.error['message']: raise AssertionError("Expected substring not found:"+e.error['message']) except Exception as e: raise AssertionError("Unexpected exception raised: "+type(e).__name__) else: raise AssertionError("No exception raised") def assert_is_hex_string(string): try: int(string, 16) except Exception as e: raise AssertionError( "Couldn't interpret %r as hexadecimal; raised: %s" % (string, e)) def assert_is_hash_string(string, length=64): if not isinstance(string, str): raise AssertionError("Expected a string, got type %r" % type(string)) elif length and len(string) != length: raise AssertionError( "String of length %d expected; got %d" % (length, len(string))) elif not re.match('[abcdef0-9]+$', string): raise AssertionError( "String %r contains invalid characters for a hash." % string) def assert_array_result(object_array, to_match, expected, should_not_find = False): """ Pass in array of JSON objects, a dictionary with key/value pairs to match against, and another dictionary with expected key/value pairs. If the should_not_find flag is true, to_match should not be found in object_array """ if should_not_find == True: assert_equal(expected, { }) num_matched = 0 for item in object_array: all_match = True for key,value in to_match.items(): if item[key] != value: all_match = False if not all_match: continue elif should_not_find == True: num_matched = num_matched+1 for key,value in expected.items(): if item[key] != value: raise AssertionError("%s : expected %s=%s"%(str(item), str(key), str(value))) num_matched = num_matched+1 if num_matched == 0 and should_not_find != True: raise AssertionError("No objects matched %s"%(str(to_match))) if num_matched > 0 and should_not_find == True: raise AssertionError("Objects were found %s"%(str(to_match))) def satoshi_round(amount): return Decimal(amount).quantize(Decimal('0.00000001'), rounding=ROUND_DOWN) # Helper to create at least "count" utxos # Pass in a fee that is sufficient for relay and mining new transactions. def create_confirmed_utxos(fee, node, count): node.setgenerate(True, int(0.5*count)+101) utxos = node.listunspent() iterations = count - len(utxos) addr1 = node.getnewaddress() addr2 = node.getnewaddress() if iterations <= 0: return utxos for i in range(iterations): t = utxos.pop() inputs = [] inputs.append({ "txid" : t["txid"], "vout" : t["vout"]}) outputs = {} send_value = t['amount'] - fee outputs[addr1] = satoshi_round(send_value/2) outputs[addr2] = satoshi_round(send_value/2) raw_tx = node.createrawtransaction(inputs, outputs) signed_tx = node.signrawtransaction(raw_tx)["hex"] txid = node.sendrawtransaction(signed_tx) while (node.getmempoolinfo()['size'] > 0): node.setgenerate(True, 1) utxos = node.listunspent() assert(len(utxos) >= count) return utxos # Create large OP_RETURN txouts that can be appended to a transaction # to make it large (helper for constructing large transactions). def gen_return_txouts(): # Some pre-processing to create a bunch of OP_RETURN txouts to insert into transactions we create # So we have big transactions (and therefore can't fit very many into each block) # create one script_pubkey script_pubkey = "6a4d0200" #OP_RETURN OP_PUSH2 512 bytes for i in range (512): script_pubkey = script_pubkey + "01" # concatenate 128 txouts of above script_pubkey which we'll insert before the txout for change txouts = "81" for k in range(128): # add txout value txouts = txouts + "0000000000000000" # add length of script_pubkey txouts = txouts + "fd0402" # add script_pubkey txouts = txouts + script_pubkey return txouts def create_tx(node, coinbase, to_address, amount): inputs = [{ "txid" : coinbase, "vout" : 0}] outputs = { to_address : amount } rawtx = node.createrawtransaction(inputs, outputs) signresult = node.signrawtransaction(rawtx) assert_equal(signresult["complete"], True) return signresult["hex"] # Create a spend of each passed-in utxo, splicing in "txouts" to each raw # transaction to make it large. See gen_return_txouts() above. def create_lots_of_big_transactions(node, txouts, utxos, fee): addr = node.getnewaddress() txids = [] for i in range(len(utxos)): t = utxos.pop() inputs = [] inputs.append({ "txid" : t["txid"], "vout" : t["vout"]}) outputs = {} send_value = t['amount'] - fee outputs[addr] = satoshi_round(send_value) rawtx = node.createrawtransaction(inputs, outputs) newtx = rawtx[0:92] newtx = newtx + txouts newtx = newtx + rawtx[94:] signresult = node.signrawtransaction(newtx, None, None, "NONE") txid = node.sendrawtransaction(signresult["hex"], True) txids.append(txid) return txids def get_bip9_status(node, key): info = node.getblockchaininfo() return info['bip9_softforks'][key]

35.713415

201

0.651742

import os import sys from binascii import hexlify, unhexlify from base64 import b64encode from decimal import Decimal, ROUND_DOWN import json import http.client import random import shutil import subprocess import time import re import errno from . import coverage from .authproxy import AuthServiceProxy, JSONRPCException COVERAGE_DIR = None MAX_NODES = 8 PORT_MIN = 11000 # The number of ports to "reserve" for p2p and rpc, each PORT_RANGE = 5000 VPUBD_PROC_WAIT_TIMEOUT = 60 class PortSeed: # Must be initialized with a unique integer for each process n = None #Set Mocktime default to OFF. #MOCKTIME is only needed for scripts that use the #cached version of the blockchain. If the cached #version of the blockchain is used without MOCKTIME #then the mempools will not sync due to IBD. MOCKTIME = 0 def enable_mocktime(): #For backwared compatibility of the python scripts #with previous versions of the cache, set MOCKTIME #to Jan 1, 2014 + (201 * 10 * 60) global MOCKTIME MOCKTIME = 1388534400 + (201 * 10 * 60) def disable_mocktime(): global MOCKTIME MOCKTIME = 0 def get_mocktime(): return MOCKTIME def enable_coverage(dirname): global COVERAGE_DIR COVERAGE_DIR = dirname def get_rpc_proxy(url, node_number, timeout=None): proxy_kwargs = {} if timeout is not None: proxy_kwargs['timeout'] = timeout proxy = AuthServiceProxy(url, **proxy_kwargs) proxy.url = url # store URL on proxy for info coverage_logfile = coverage.get_filename( COVERAGE_DIR, node_number) if COVERAGE_DIR else None return coverage.AuthServiceProxyWrapper(proxy, coverage_logfile) def p2p_port(n): assert(n <= MAX_NODES) return PORT_MIN + n + (MAX_NODES * PortSeed.n) % (PORT_RANGE - 1 - MAX_NODES) def rpc_port(n): return PORT_MIN + PORT_RANGE + n + (MAX_NODES * PortSeed.n) % (PORT_RANGE - 1 - MAX_NODES) def check_json_precision(): n = Decimal("20000000.00000003") satoshis = int(json.loads(json.dumps(float(n)))*1.0e8) if satoshis != 2000000000000003: raise RuntimeError("JSON encode/decode loses precision") def count_bytes(hex_string): return len(bytearray.fromhex(hex_string)) def bytes_to_hex_str(byte_str): return hexlify(byte_str).decode('ascii') def hex_str_to_bytes(hex_str): return unhexlify(hex_str.encode('ascii')) def str_to_b64str(string): return b64encode(string.encode('utf-8')).decode('ascii') def sync_blocks(rpc_connections, wait=1, timeout=60): while timeout > 0: tips = [ x.getbestblockhash() for x in rpc_connections ] if tips == [ tips[0] ]*len(tips): return True time.sleep(wait) timeout -= wait raise AssertionError("Block sync failed") def sync_mempools(rpc_connections, wait=1, timeout=60): while timeout > 0: pool = set(rpc_connections[0].getrawmempool()) num_match = 1 for i in range(1, len(rpc_connections)): if set(rpc_connections[i].getrawmempool()) == pool: num_match = num_match+1 if num_match == len(rpc_connections): return True time.sleep(wait) timeout -= wait raise AssertionError("Mempool sync failed") vpubd_processes = {} def initialize_datadir(dirname, n): datadir = os.path.join(dirname, "node"+str(n)) if not os.path.isdir(datadir): os.makedirs(datadir) rpc_u, rpc_p = rpc_auth_pair(n) with open(os.path.join(datadir, "vpub.conf"), 'w', encoding='utf8') as f: f.write("regtest=1\n") f.write("server=1\n") f.write("rpcuser=" + rpc_u + "\n") f.write("rpcpassword=" + rpc_p + "\n") f.write("port="+str(p2p_port(n))+"\n") f.write("rpcport="+str(rpc_port(n))+"\n") f.write("listenonion=0\n") return datadir def rpc_auth_pair(n): return 'rpcuser' + str(n), 'rpcpass' + str(n) def rpc_url(i, rpchost=None): rpc_u, rpc_p = rpc_auth_pair(i) host = '127.0.0.1' port = rpc_port(i) if rpchost: parts = rpchost.split(':') if len(parts) == 2: host, port = parts else: host = rpchost return "http://%s:%s@%s:%d" % (rpc_u, rpc_p, host, int(port)) def wait_for_vpubd_start(process, url, i): while True: if process.poll() is not None: raise Exception('vpubd exited with status %i during initialization' % process.returncode) try: rpc = get_rpc_proxy(url, i) blocks = rpc.getblockcount() break # break out of loop on success except IOError as e: if e.errno != errno.ECONNREFUSED: # Port not yet open? raise # unknown IO error except JSONRPCException as e: # Initialization phase if e.error['code'] != -28: # RPC in warmup? raise # unkown JSON RPC exception time.sleep(0.25) def initialize_chain(test_dir, num_nodes): assert num_nodes <= MAX_NODES create_cache = False for i in range(MAX_NODES): if not os.path.isdir(os.path.join('cache', 'node'+str(i))): create_cache = True break if create_cache: #find and delete old cache directories if any exist for i in range(MAX_NODES): if os.path.isdir(os.path.join("cache","node"+str(i))): shutil.rmtree(os.path.join("cache","node"+str(i))) # Create cache directories, run vpubds: for i in range(MAX_NODES): datadir=initialize_datadir("cache", i) args = [ os.getenv("VPUBD", "vpubd"), "-server", "-keypool=1", "-datadir="+datadir, "-discover=0" ] if i > 0: args.append("-connect=127.0.0.1:"+str(p2p_port(0))) vpubd_processes[i] = subprocess.Popen(args) if os.getenv("PYTHON_DEBUG", ""): print("initialize_chain: vpubd started, waiting for RPC to come up") wait_for_vpubd_start(vpubd_processes[i], rpc_url(i), i) if os.getenv("PYTHON_DEBUG", ""): print("initialize_chain: RPC succesfully started") rpcs = [] for i in range(MAX_NODES): try: rpcs.append(get_rpc_proxy(rpc_url(i), i)) except: sys.stderr.write("Error connecting to "+url+"\n") sys.exit(1) # Create a 200-block-long chain; each of the 4 first nodes # gets 25 mature blocks and 25 immature. # Note: To preserve compatibility with older versions of # initialize_chain, only 4 nodes will generate coins. # # blocks are created with timestamps 10 minutes apart # starting from 2010 minutes in the past enable_mocktime() block_time = get_mocktime() - (201 * 10 * 60) for i in range(2): for peer in range(4): for j in range(25): set_node_times(rpcs, block_time) rpcs[peer].setgenerate(True, 1) block_time += 10*60 # Must sync before next peer starts generating blocks sync_blocks(rpcs) # Shut them down, and clean up cache directories: stop_nodes(rpcs) disable_mocktime() for i in range(MAX_NODES): os.remove(log_filename("cache", i, "debug.log")) os.remove(log_filename("cache", i, "db.log")) os.remove(log_filename("cache", i, "peers.dat")) os.remove(log_filename("cache", i, "fee_estimates.dat")) for i in range(num_nodes): from_dir = os.path.join("cache", "node"+str(i)) to_dir = os.path.join(test_dir, "node"+str(i)) shutil.copytree(from_dir, to_dir) initialize_datadir(test_dir, i) # Overwrite port/rpcport in vpub.conf def initialize_chain_clean(test_dir, num_nodes): for i in range(num_nodes): datadir=initialize_datadir(test_dir, i) def _rpchost_to_args(rpchost): if rpchost is None: return [] match = re.match('(\[[0-9a-fA-f:]+\]|[^:]+)(?::([0-9]+))?$', rpchost) if not match: raise ValueError('Invalid RPC host spec ' + rpchost) rpcconnect = match.group(1) rpcport = match.group(2) if rpcconnect.startswith('['): # remove IPv6 [...] wrapping rpcconnect = rpcconnect[1:-1] rv = ['-rpcconnect=' + rpcconnect] if rpcport: rv += ['-rpcport=' + rpcport] return rv def start_node(i, dirname, extra_args=None, rpchost=None, timewait=None, binary=None): datadir = os.path.join(dirname, "node"+str(i)) if binary is None: binary = os.getenv("VPUBD", "vpubd") args = [ binary, "-datadir="+datadir, "-server", "-keypool=1", "-discover=0", "-rest", "-mocktime="+str(get_mocktime()), "-regtest", "-sporkkey=923EhWh2bJHynX6d4Tqt2Q75bhTDCT1b4kff3qzDKDZHZ6pkQs7"] if extra_args is not None: args.extend(extra_args) vpubd_processes[i] = subprocess.Popen(args) if os.getenv("PYTHON_DEBUG", ""): print("start_node: phroed started, waiting for RPC to come up") url = rpc_url(i, rpchost) wait_for_vpubd_start(vpubd_processes[i], url, i) if os.getenv("PYTHON_DEBUG", ""): print("start_node: RPC succesfully started") proxy = get_rpc_proxy(url, i, timeout=timewait) if COVERAGE_DIR: coverage.write_all_rpc_commands(COVERAGE_DIR, proxy) return proxy def start_nodes(num_nodes, dirname, extra_args=None, rpchost=None, timewait=None, binary=None): if extra_args is None: extra_args = [ None for _ in range(num_nodes) ] if binary is None: binary = [ None for _ in range(num_nodes) ] rpcs = [] try: for i in range(num_nodes): rpcs.append(start_node(i, dirname, extra_args[i], rpchost, timewait=timewait, binary=binary[i])) except: # If one node failed to start, stop the others stop_nodes(rpcs) raise return rpcs def log_filename(dirname, n_node, logname): return os.path.join(dirname, "node"+str(n_node), "regtest", logname) def stop_node(node, i): try: node.stop() except http.client.CannotSendRequest as e: print("WARN: Unable to stop node: " + repr(e)) vpubd_processes[i].wait(timeout=VPUBD_PROC_WAIT_TIMEOUT) del vpubd_processes[i] def stop_nodes(nodes): for node in nodes: try: node.stop() except http.client.CannotSendRequest as e: print("WARN: Unable to stop node: " + repr(e)) del nodes[:] # Emptying array closes connections as a side effect wait_vpubds() def set_node_times(nodes, t): for node in nodes: node.setmocktime(t) def wait_vpubds(): # Wait for all vpubds to cleanly exit for vpubd in vpubd_processes.values(): vpubd.wait(timeout=VPUBD_PROC_WAIT_TIMEOUT) vpubd_processes.clear() def connect_nodes(from_connection, node_num): ip_port = "127.0.0.1:"+str(p2p_port(node_num)) from_connection.addnode(ip_port, "onetry") # poll until version handshake complete to avoid race conditions # with transaction relaying while any(peer['version'] == 0 for peer in from_connection.getpeerinfo()): time.sleep(0.1) def connect_nodes_bi(nodes, a, b): connect_nodes(nodes[a], b) connect_nodes(nodes[b], a) def find_output(node, txid, amount): txdata = node.getrawtransaction(txid, 1) for i in range(len(txdata["vout"])): if txdata["vout"][i]["value"] == amount: return i raise RuntimeError("find_output txid %s : %s not found"%(txid,str(amount))) def gather_inputs(from_node, amount_needed, confirmations_required=1): assert(confirmations_required >=0) utxo = from_node.listunspent(confirmations_required) random.shuffle(utxo) inputs = [] total_in = Decimal("0.00000000") while total_in < amount_needed and len(utxo) > 0: t = utxo.pop() total_in += t["amount"] inputs.append({ "txid" : t["txid"], "vout" : t["vout"], "address" : t["address"] } ) if total_in < amount_needed: raise RuntimeError("Insufficient funds: need %d, have %d"%(amount_needed, total_in)) return (total_in, inputs) def make_change(from_node, amount_in, amount_out, fee): outputs = {} amount = amount_out+fee change = amount_in - amount if change > amount*2: # Create an extra change output to break up big inputs change_address = from_node.getnewaddress() # Split change in two, being careful of rounding: outputs[change_address] = Decimal(change/2).quantize(Decimal('0.00000001'), rounding=ROUND_DOWN) change = amount_in - amount - outputs[change_address] if change > 0: outputs[from_node.getnewaddress()] = change return outputs def send_zeropri_transaction(from_node, to_node, amount, fee): # Create a send-to-self with confirmed inputs: self_address = from_node.getnewaddress() (total_in, inputs) = gather_inputs(from_node, amount+fee*2) outputs = make_change(from_node, total_in, amount+fee, fee) outputs[self_address] = float(amount+fee) self_rawtx = from_node.createrawtransaction(inputs, outputs) self_signresult = from_node.signrawtransaction(self_rawtx) self_txid = from_node.sendrawtransaction(self_signresult["hex"], True) vout = find_output(from_node, self_txid, amount+fee) # Now immediately spend the output to create a 1-input, 1-output # zero-priority transaction: inputs = [ { "txid" : self_txid, "vout" : vout } ] outputs = { to_node.getnewaddress() : float(amount) } rawtx = from_node.createrawtransaction(inputs, outputs) signresult = from_node.signrawtransaction(rawtx) txid = from_node.sendrawtransaction(signresult["hex"], True) return (txid, signresult["hex"]) def random_zeropri_transaction(nodes, amount, min_fee, fee_increment, fee_variants): from_node = random.choice(nodes) to_node = random.choice(nodes) fee = min_fee + fee_increment*random.randint(0,fee_variants) (txid, txhex) = send_zeropri_transaction(from_node, to_node, amount, fee) return (txid, txhex, fee) def random_transaction(nodes, amount, min_fee, fee_increment, fee_variants): from_node = random.choice(nodes) to_node = random.choice(nodes) fee = min_fee + fee_increment*random.randint(0,fee_variants) (total_in, inputs) = gather_inputs(from_node, amount+fee) outputs = make_change(from_node, total_in, amount, fee) outputs[to_node.getnewaddress()] = float(amount) rawtx = from_node.createrawtransaction(inputs, outputs) signresult = from_node.signrawtransaction(rawtx) txid = from_node.sendrawtransaction(signresult["hex"], True) return (txid, signresult["hex"], fee) def assert_fee_amount(fee, tx_size, fee_per_kB): target_fee = tx_size * fee_per_kB / 1000 if fee < target_fee: raise AssertionError("Fee of %s BTC too low! (Should be %s BTC)"%(str(fee), str(target_fee))) # allow the wallet's estimation to be at most 2 bytes off if fee > (tx_size + 2) * fee_per_kB / 1000: raise AssertionError("Fee of %s BTC too high! (Should be %s BTC)"%(str(fee), str(target_fee))) def assert_equal(thing1, thing2): if thing1 != thing2: raise AssertionError("%s != %s"%(str(thing1),str(thing2))) def assert_greater_than(thing1, thing2): if thing1 <= thing2: raise AssertionError("%s <= %s"%(str(thing1),str(thing2))) def assert_raises(exc, fun, *args, **kwds): assert_raises_message(exc, None, fun, *args, **kwds) def assert_raises_message(exc, message, fun, *args, **kwds): try: fun(*args, **kwds) except exc as e: if message is not None and message not in e.error['message']: raise AssertionError("Expected substring not found:"+e.error['message']) except Exception as e: raise AssertionError("Unexpected exception raised: "+type(e).__name__) else: raise AssertionError("No exception raised") def assert_is_hex_string(string): try: int(string, 16) except Exception as e: raise AssertionError( "Couldn't interpret %r as hexadecimal; raised: %s" % (string, e)) def assert_is_hash_string(string, length=64): if not isinstance(string, str): raise AssertionError("Expected a string, got type %r" % type(string)) elif length and len(string) != length: raise AssertionError( "String of length %d expected; got %d" % (length, len(string))) elif not re.match('[abcdef0-9]+$', string): raise AssertionError( "String %r contains invalid characters for a hash." % string) def assert_array_result(object_array, to_match, expected, should_not_find = False): if should_not_find == True: assert_equal(expected, { }) num_matched = 0 for item in object_array: all_match = True for key,value in to_match.items(): if item[key] != value: all_match = False if not all_match: continue elif should_not_find == True: num_matched = num_matched+1 for key,value in expected.items(): if item[key] != value: raise AssertionError("%s : expected %s=%s"%(str(item), str(key), str(value))) num_matched = num_matched+1 if num_matched == 0 and should_not_find != True: raise AssertionError("No objects matched %s"%(str(to_match))) if num_matched > 0 and should_not_find == True: raise AssertionError("Objects were found %s"%(str(to_match))) def satoshi_round(amount): return Decimal(amount).quantize(Decimal('0.00000001'), rounding=ROUND_DOWN) # Helper to create at least "count" utxos # Pass in a fee that is sufficient for relay and mining new transactions. def create_confirmed_utxos(fee, node, count): node.setgenerate(True, int(0.5*count)+101) utxos = node.listunspent() iterations = count - len(utxos) addr1 = node.getnewaddress() addr2 = node.getnewaddress() if iterations <= 0: return utxos for i in range(iterations): t = utxos.pop() inputs = [] inputs.append({ "txid" : t["txid"], "vout" : t["vout"]}) outputs = {} send_value = t['amount'] - fee outputs[addr1] = satoshi_round(send_value/2) outputs[addr2] = satoshi_round(send_value/2) raw_tx = node.createrawtransaction(inputs, outputs) signed_tx = node.signrawtransaction(raw_tx)["hex"] txid = node.sendrawtransaction(signed_tx) while (node.getmempoolinfo()['size'] > 0): node.setgenerate(True, 1) utxos = node.listunspent() assert(len(utxos) >= count) return utxos # Create large OP_RETURN txouts that can be appended to a transaction # to make it large (helper for constructing large transactions). def gen_return_txouts(): # Some pre-processing to create a bunch of OP_RETURN txouts to insert into transactions we create # So we have big transactions (and therefore can't fit very many into each block) script_pubkey = "6a4d0200" for i in range (512): script_pubkey = script_pubkey + "01" txouts = "81" for k in range(128): # add txout value txouts = txouts + "0000000000000000" # add length of script_pubkey txouts = txouts + "fd0402" # add script_pubkey txouts = txouts + script_pubkey return txouts def create_tx(node, coinbase, to_address, amount): inputs = [{ "txid" : coinbase, "vout" : 0}] outputs = { to_address : amount } rawtx = node.createrawtransaction(inputs, outputs) signresult = node.signrawtransaction(rawtx) assert_equal(signresult["complete"], True) return signresult["hex"] # Create a spend of each passed-in utxo, splicing in "txouts" to each raw # transaction to make it large. See gen_return_txouts() above. def create_lots_of_big_transactions(node, txouts, utxos, fee): addr = node.getnewaddress() txids = [] for i in range(len(utxos)): t = utxos.pop() inputs = [] inputs.append({ "txid" : t["txid"], "vout" : t["vout"]}) outputs = {} send_value = t['amount'] - fee outputs[addr] = satoshi_round(send_value) rawtx = node.createrawtransaction(inputs, outputs) newtx = rawtx[0:92] newtx = newtx + txouts newtx = newtx + rawtx[94:] signresult = node.signrawtransaction(newtx, None, None, "NONE") txid = node.sendrawtransaction(signresult["hex"], True) txids.append(txid) return txids def get_bip9_status(node, key): info = node.getblockchaininfo() return info['bip9_softforks'][key]

true

f71b49b3ed0e3ba6b9a8f90ca5e35100450fb249

13,385

py

Python

skimage/segmentation/tests/test_random_walker.py

thewtex/scikit-image

22bb6b94698b8889cbdf26b25d9e4fdb8b968d97

[ "BSD-3-Clause" ]

1

2019-01-12T13:17:32.000Z

skimage/segmentation/tests/test_random_walker.py

thewtex/scikit-image

22bb6b94698b8889cbdf26b25d9e4fdb8b968d97

[ "BSD-3-Clause" ]

null

skimage/segmentation/tests/test_random_walker.py

thewtex/scikit-image

22bb6b94698b8889cbdf26b25d9e4fdb8b968d97

[ "BSD-3-Clause" ]

null

import numpy as np from skimage.segmentation import random_walker from skimage.transform import resize from skimage._shared._warnings import expected_warnings from skimage._shared import testing # older versions of scipy raise a warning with new NumPy because they use # numpy.rank() instead of arr.ndim or numpy.linalg.matrix_rank. SCIPY_EXPECTED = 'numpy.linalg.matrix_rank|\A\Z' PYAMG_EXPECTED_WARNING = 'pyamg|\A\Z' PYAMG_SCIPY_EXPECTED = SCIPY_EXPECTED + '|' + PYAMG_EXPECTED_WARNING def make_2d_syntheticdata(lx, ly=None): if ly is None: ly = lx np.random.seed(1234) data = np.zeros((lx, ly)) + 0.1 * np.random.randn(lx, ly) small_l = int(lx // 5) data[lx // 2 - small_l:lx // 2 + small_l, ly // 2 - small_l:ly // 2 + small_l] = 1 data[lx // 2 - small_l + 1:lx // 2 + small_l - 1, ly // 2 - small_l + 1:ly // 2 + small_l - 1] = ( 0.1 * np.random.randn(2 * small_l - 2, 2 * small_l - 2)) data[lx // 2 - small_l, ly // 2 - small_l // 8:ly // 2 + small_l // 8] = 0 seeds = np.zeros_like(data) seeds[lx // 5, ly // 5] = 1 seeds[lx // 2 + small_l // 4, ly // 2 - small_l // 4] = 2 return data, seeds def make_3d_syntheticdata(lx, ly=None, lz=None): if ly is None: ly = lx if lz is None: lz = lx np.random.seed(1234) data = np.zeros((lx, ly, lz)) + 0.1 * np.random.randn(lx, ly, lz) small_l = int(lx // 5) data[lx // 2 - small_l:lx // 2 + small_l, ly // 2 - small_l:ly // 2 + small_l, lz // 2 - small_l:lz // 2 + small_l] = 1 data[lx // 2 - small_l + 1:lx // 2 + small_l - 1, ly // 2 - small_l + 1:ly // 2 + small_l - 1, lz // 2 - small_l + 1:lz // 2 + small_l - 1] = 0 # make a hole hole_size = np.max([1, small_l // 8]) data[lx // 2 - small_l, ly // 2 - hole_size:ly // 2 + hole_size, lz // 2 - hole_size:lz // 2 + hole_size] = 0 seeds = np.zeros_like(data) seeds[lx // 5, ly // 5, lz // 5] = 1 seeds[lx // 2 + small_l // 4, ly // 2 - small_l // 4, lz // 2 - small_l // 4] = 2 return data, seeds def test_2d_bf(): lx = 70 ly = 100 data, labels = make_2d_syntheticdata(lx, ly) labels_bf = random_walker(data, labels, beta=90, mode='bf') assert (labels_bf[25:45, 40:60] == 2).all() assert data.shape == labels.shape full_prob_bf = random_walker(data, labels, beta=90, mode='bf', return_full_prob=True) assert (full_prob_bf[1, 25:45, 40:60] >= full_prob_bf[0, 25:45, 40:60]).all() assert data.shape == labels.shape # Now test with more than two labels labels[55, 80] = 3 full_prob_bf = random_walker(data, labels, beta=90, mode='bf', return_full_prob=True) assert (full_prob_bf[1, 25:45, 40:60] >= full_prob_bf[0, 25:45, 40:60]).all() assert len(full_prob_bf) == 3 assert data.shape == labels.shape def test_2d_cg(): lx = 70 ly = 100 data, labels = make_2d_syntheticdata(lx, ly) with expected_warnings(['"cg" mode' + '|' + SCIPY_EXPECTED]): labels_cg = random_walker(data, labels, beta=90, mode='cg') assert (labels_cg[25:45, 40:60] == 2).all() assert data.shape == labels.shape with expected_warnings(['"cg" mode' + '|' + SCIPY_EXPECTED]): full_prob = random_walker(data, labels, beta=90, mode='cg', return_full_prob=True) assert (full_prob[1, 25:45, 40:60] >= full_prob[0, 25:45, 40:60]).all() assert data.shape == labels.shape return data, labels_cg def test_2d_cg_mg(): lx = 70 ly = 100 data, labels = make_2d_syntheticdata(lx, ly) expected = 'scipy.sparse.sparsetools|%s' % PYAMG_SCIPY_EXPECTED with expected_warnings([expected]): labels_cg_mg = random_walker(data, labels, beta=90, mode='cg_mg') assert (labels_cg_mg[25:45, 40:60] == 2).all() assert data.shape == labels.shape with expected_warnings([expected]): full_prob = random_walker(data, labels, beta=90, mode='cg_mg', return_full_prob=True) assert (full_prob[1, 25:45, 40:60] >= full_prob[0, 25:45, 40:60]).all() assert data.shape == labels.shape return data, labels_cg_mg def test_types(): lx = 70 ly = 100 data, labels = make_2d_syntheticdata(lx, ly) data = 255 * (data - data.min()) // (data.max() - data.min()) data = data.astype(np.uint8) with expected_warnings([PYAMG_SCIPY_EXPECTED]): labels_cg_mg = random_walker(data, labels, beta=90, mode='cg_mg') assert (labels_cg_mg[25:45, 40:60] == 2).all() assert data.shape == labels.shape return data, labels_cg_mg def test_reorder_labels(): lx = 70 ly = 100 data, labels = make_2d_syntheticdata(lx, ly) labels[labels == 2] = 4 labels_bf = random_walker(data, labels, beta=90, mode='bf') assert (labels_bf[25:45, 40:60] == 2).all() assert data.shape == labels.shape return data, labels_bf def test_2d_inactive(): lx = 70 ly = 100 data, labels = make_2d_syntheticdata(lx, ly) labels[10:20, 10:20] = -1 labels[46:50, 33:38] = -2 labels = random_walker(data, labels, beta=90) assert (labels.reshape((lx, ly))[25:45, 40:60] == 2).all() assert data.shape == labels.shape return data, labels def test_3d(): n = 30 lx, ly, lz = n, n, n data, labels = make_3d_syntheticdata(lx, ly, lz) with expected_warnings(['"cg" mode' + '|' + SCIPY_EXPECTED]): labels = random_walker(data, labels, mode='cg') assert (labels.reshape(data.shape)[13:17, 13:17, 13:17] == 2).all() assert data.shape == labels.shape return data, labels def test_3d_inactive(): n = 30 lx, ly, lz = n, n, n data, labels = make_3d_syntheticdata(lx, ly, lz) old_labels = np.copy(labels) labels[5:25, 26:29, 26:29] = -1 after_labels = np.copy(labels) with expected_warnings(['"cg" mode|CObject type' + '|' + SCIPY_EXPECTED]): labels = random_walker(data, labels, mode='cg') assert (labels.reshape(data.shape)[13:17, 13:17, 13:17] == 2).all() assert data.shape == labels.shape return data, labels, old_labels, after_labels def test_multispectral_2d(): lx, ly = 70, 100 data, labels = make_2d_syntheticdata(lx, ly) data = data[..., np.newaxis].repeat(2, axis=-1) # Expect identical output with expected_warnings(['"cg" mode' + '|' + SCIPY_EXPECTED]): multi_labels = random_walker(data, labels, mode='cg', multichannel=True) assert data[..., 0].shape == labels.shape with expected_warnings(['"cg" mode' + '|' + SCIPY_EXPECTED]): single_labels = random_walker(data[..., 0], labels, mode='cg') assert (multi_labels.reshape(labels.shape)[25:45, 40:60] == 2).all() assert data[..., 0].shape == labels.shape return data, multi_labels, single_labels, labels def test_multispectral_3d(): n = 30 lx, ly, lz = n, n, n data, labels = make_3d_syntheticdata(lx, ly, lz) data = data[..., np.newaxis].repeat(2, axis=-1) # Expect identical output with expected_warnings(['"cg" mode' + '|' + SCIPY_EXPECTED]): multi_labels = random_walker(data, labels, mode='cg', multichannel=True) assert data[..., 0].shape == labels.shape with expected_warnings(['"cg" mode' + '|' + SCIPY_EXPECTED]): single_labels = random_walker(data[..., 0], labels, mode='cg') assert (multi_labels.reshape(labels.shape)[13:17, 13:17, 13:17] == 2).all() assert (single_labels.reshape(labels.shape)[13:17, 13:17, 13:17] == 2).all() assert data[..., 0].shape == labels.shape return data, multi_labels, single_labels, labels def test_spacing_0(): n = 30 lx, ly, lz = n, n, n data, _ = make_3d_syntheticdata(lx, ly, lz) # Rescale `data` along Z axis data_aniso = np.zeros((n, n, n // 2)) for i, yz in enumerate(data): data_aniso[i, :, :] = resize(yz, (n, n // 2), mode='constant', anti_aliasing=False) # Generate new labels small_l = int(lx // 5) labels_aniso = np.zeros_like(data_aniso) labels_aniso[lx // 5, ly // 5, lz // 5] = 1 labels_aniso[lx // 2 + small_l // 4, ly // 2 - small_l // 4, lz // 4 - small_l // 8] = 2 # Test with `spacing` kwarg with expected_warnings(['"cg" mode' + '|' + SCIPY_EXPECTED]): labels_aniso = random_walker(data_aniso, labels_aniso, mode='cg', spacing=(1., 1., 0.5)) assert (labels_aniso[13:17, 13:17, 7:9] == 2).all() def test_spacing_1(): n = 30 lx, ly, lz = n, n, n data, _ = make_3d_syntheticdata(lx, ly, lz) # Rescale `data` along Y axis # `resize` is not yet 3D capable, so this must be done by looping in 2D. data_aniso = np.zeros((n, n * 2, n)) for i, yz in enumerate(data): data_aniso[i, :, :] = resize(yz, (n * 2, n), mode='constant', anti_aliasing=False) # Generate new labels small_l = int(lx // 5) labels_aniso = np.zeros_like(data_aniso) labels_aniso[lx // 5, ly // 5, lz // 5] = 1 labels_aniso[lx // 2 + small_l // 4, ly - small_l // 2, lz // 2 - small_l // 4] = 2 # Test with `spacing` kwarg # First, anisotropic along Y with expected_warnings(['"cg" mode' + '|' + SCIPY_EXPECTED]): labels_aniso = random_walker(data_aniso, labels_aniso, mode='cg', spacing=(1., 2., 1.)) assert (labels_aniso[13:17, 26:34, 13:17] == 2).all() # Rescale `data` along X axis # `resize` is not yet 3D capable, so this must be done by looping in 2D. data_aniso = np.zeros((n, n * 2, n)) for i in range(data.shape[1]): data_aniso[i, :, :] = resize(data[:, 1, :], (n * 2, n), mode='constant', anti_aliasing=False) # Generate new labels small_l = int(lx // 5) labels_aniso2 = np.zeros_like(data_aniso) labels_aniso2[lx // 5, ly // 5, lz // 5] = 1 labels_aniso2[lx - small_l // 2, ly // 2 + small_l // 4, lz // 2 - small_l // 4] = 2 # Anisotropic along X with expected_warnings(['"cg" mode' + '|' + SCIPY_EXPECTED]): labels_aniso2 = random_walker(data_aniso, labels_aniso2, mode='cg', spacing=(2., 1., 1.)) assert (labels_aniso2[26:34, 13:17, 13:17] == 2).all() def test_trivial_cases(): # When all voxels are labeled img = np.ones((10, 10)) labels = np.ones((10, 10)) with expected_warnings(["Returning provided labels"]): pass_through = random_walker(img, labels) np.testing.assert_array_equal(pass_through, labels) # When all voxels are labeled AND return_full_prob is True labels[:, :5] = 3 expected = np.concatenate(((labels == 1)[..., np.newaxis], (labels == 3)[..., np.newaxis]), axis=2) with expected_warnings(["Returning provided labels"]): test = random_walker(img, labels, return_full_prob=True) np.testing.assert_array_equal(test, expected) def test_length2_spacing(): # If this passes without raising an exception (warnings OK), the new # spacing code is working properly. np.random.seed(42) img = np.ones((10, 10)) + 0.2 * np.random.normal(size=(10, 10)) labels = np.zeros((10, 10), dtype=np.uint8) labels[2, 4] = 1 labels[6, 8] = 4 random_walker(img, labels, spacing=(1., 2.)) def test_bad_inputs(): # Too few dimensions img = np.ones(10) labels = np.arange(10) with testing.raises(ValueError): random_walker(img, labels) with testing.raises(ValueError): random_walker(img, labels, multichannel=True) # Too many dimensions np.random.seed(42) img = np.random.normal(size=(3, 3, 3, 3, 3)) labels = np.arange(3 ** 5).reshape(img.shape) with testing.raises(ValueError): random_walker(img, labels) with testing.raises(ValueError): random_walker(img, labels, multichannel=True) # Spacing incorrect length img = np.random.normal(size=(10, 10)) labels = np.zeros((10, 10)) labels[2, 4] = 2 labels[6, 8] = 5 with testing.raises(ValueError): random_walker(img, labels, spacing=(1,)) # Invalid mode img = np.random.normal(size=(10, 10)) labels = np.zeros((10, 10)) with testing.raises(ValueError): random_walker(img, labels, mode='bad') def test_isolated_seeds(): np.random.seed(0) a = np.random.random((7, 7)) mask = - np.ones(a.shape) # This pixel is an isolated seed mask[1, 1] = 1 # Unlabeled pixels mask[3:, 3:] = 0 # Seeds connected to unlabeled pixels mask[4, 4] = 2 mask[6, 6] = 1 # Test that no error is raised, and that labels of isolated seeds are OK res = random_walker(a, mask) assert res[1, 1] == 1 res = random_walker(a, mask, return_full_prob=True) assert res[0, 1, 1] == 1 assert res[1, 1, 1] == 0

36.175676

80

0.582219

import numpy as np from skimage.segmentation import random_walker from skimage.transform import resize from skimage._shared._warnings import expected_warnings from skimage._shared import testing SCIPY_EXPECTED = 'numpy.linalg.matrix_rank|\A\Z' PYAMG_EXPECTED_WARNING = 'pyamg|\A\Z' PYAMG_SCIPY_EXPECTED = SCIPY_EXPECTED + '|' + PYAMG_EXPECTED_WARNING def make_2d_syntheticdata(lx, ly=None): if ly is None: ly = lx np.random.seed(1234) data = np.zeros((lx, ly)) + 0.1 * np.random.randn(lx, ly) small_l = int(lx // 5) data[lx // 2 - small_l:lx // 2 + small_l, ly // 2 - small_l:ly // 2 + small_l] = 1 data[lx // 2 - small_l + 1:lx // 2 + small_l - 1, ly // 2 - small_l + 1:ly // 2 + small_l - 1] = ( 0.1 * np.random.randn(2 * small_l - 2, 2 * small_l - 2)) data[lx // 2 - small_l, ly // 2 - small_l // 8:ly // 2 + small_l // 8] = 0 seeds = np.zeros_like(data) seeds[lx // 5, ly // 5] = 1 seeds[lx // 2 + small_l // 4, ly // 2 - small_l // 4] = 2 return data, seeds def make_3d_syntheticdata(lx, ly=None, lz=None): if ly is None: ly = lx if lz is None: lz = lx np.random.seed(1234) data = np.zeros((lx, ly, lz)) + 0.1 * np.random.randn(lx, ly, lz) small_l = int(lx // 5) data[lx // 2 - small_l:lx // 2 + small_l, ly // 2 - small_l:ly // 2 + small_l, lz // 2 - small_l:lz // 2 + small_l] = 1 data[lx // 2 - small_l + 1:lx // 2 + small_l - 1, ly // 2 - small_l + 1:ly // 2 + small_l - 1, lz // 2 - small_l + 1:lz // 2 + small_l - 1] = 0 hole_size = np.max([1, small_l // 8]) data[lx // 2 - small_l, ly // 2 - hole_size:ly // 2 + hole_size, lz // 2 - hole_size:lz // 2 + hole_size] = 0 seeds = np.zeros_like(data) seeds[lx // 5, ly // 5, lz // 5] = 1 seeds[lx // 2 + small_l // 4, ly // 2 - small_l // 4, lz // 2 - small_l // 4] = 2 return data, seeds def test_2d_bf(): lx = 70 ly = 100 data, labels = make_2d_syntheticdata(lx, ly) labels_bf = random_walker(data, labels, beta=90, mode='bf') assert (labels_bf[25:45, 40:60] == 2).all() assert data.shape == labels.shape full_prob_bf = random_walker(data, labels, beta=90, mode='bf', return_full_prob=True) assert (full_prob_bf[1, 25:45, 40:60] >= full_prob_bf[0, 25:45, 40:60]).all() assert data.shape == labels.shape labels[55, 80] = 3 full_prob_bf = random_walker(data, labels, beta=90, mode='bf', return_full_prob=True) assert (full_prob_bf[1, 25:45, 40:60] >= full_prob_bf[0, 25:45, 40:60]).all() assert len(full_prob_bf) == 3 assert data.shape == labels.shape def test_2d_cg(): lx = 70 ly = 100 data, labels = make_2d_syntheticdata(lx, ly) with expected_warnings(['"cg" mode' + '|' + SCIPY_EXPECTED]): labels_cg = random_walker(data, labels, beta=90, mode='cg') assert (labels_cg[25:45, 40:60] == 2).all() assert data.shape == labels.shape with expected_warnings(['"cg" mode' + '|' + SCIPY_EXPECTED]): full_prob = random_walker(data, labels, beta=90, mode='cg', return_full_prob=True) assert (full_prob[1, 25:45, 40:60] >= full_prob[0, 25:45, 40:60]).all() assert data.shape == labels.shape return data, labels_cg def test_2d_cg_mg(): lx = 70 ly = 100 data, labels = make_2d_syntheticdata(lx, ly) expected = 'scipy.sparse.sparsetools|%s' % PYAMG_SCIPY_EXPECTED with expected_warnings([expected]): labels_cg_mg = random_walker(data, labels, beta=90, mode='cg_mg') assert (labels_cg_mg[25:45, 40:60] == 2).all() assert data.shape == labels.shape with expected_warnings([expected]): full_prob = random_walker(data, labels, beta=90, mode='cg_mg', return_full_prob=True) assert (full_prob[1, 25:45, 40:60] >= full_prob[0, 25:45, 40:60]).all() assert data.shape == labels.shape return data, labels_cg_mg def test_types(): lx = 70 ly = 100 data, labels = make_2d_syntheticdata(lx, ly) data = 255 * (data - data.min()) // (data.max() - data.min()) data = data.astype(np.uint8) with expected_warnings([PYAMG_SCIPY_EXPECTED]): labels_cg_mg = random_walker(data, labels, beta=90, mode='cg_mg') assert (labels_cg_mg[25:45, 40:60] == 2).all() assert data.shape == labels.shape return data, labels_cg_mg def test_reorder_labels(): lx = 70 ly = 100 data, labels = make_2d_syntheticdata(lx, ly) labels[labels == 2] = 4 labels_bf = random_walker(data, labels, beta=90, mode='bf') assert (labels_bf[25:45, 40:60] == 2).all() assert data.shape == labels.shape return data, labels_bf def test_2d_inactive(): lx = 70 ly = 100 data, labels = make_2d_syntheticdata(lx, ly) labels[10:20, 10:20] = -1 labels[46:50, 33:38] = -2 labels = random_walker(data, labels, beta=90) assert (labels.reshape((lx, ly))[25:45, 40:60] == 2).all() assert data.shape == labels.shape return data, labels def test_3d(): n = 30 lx, ly, lz = n, n, n data, labels = make_3d_syntheticdata(lx, ly, lz) with expected_warnings(['"cg" mode' + '|' + SCIPY_EXPECTED]): labels = random_walker(data, labels, mode='cg') assert (labels.reshape(data.shape)[13:17, 13:17, 13:17] == 2).all() assert data.shape == labels.shape return data, labels def test_3d_inactive(): n = 30 lx, ly, lz = n, n, n data, labels = make_3d_syntheticdata(lx, ly, lz) old_labels = np.copy(labels) labels[5:25, 26:29, 26:29] = -1 after_labels = np.copy(labels) with expected_warnings(['"cg" mode|CObject type' + '|' + SCIPY_EXPECTED]): labels = random_walker(data, labels, mode='cg') assert (labels.reshape(data.shape)[13:17, 13:17, 13:17] == 2).all() assert data.shape == labels.shape return data, labels, old_labels, after_labels def test_multispectral_2d(): lx, ly = 70, 100 data, labels = make_2d_syntheticdata(lx, ly) data = data[..., np.newaxis].repeat(2, axis=-1) with expected_warnings(['"cg" mode' + '|' + SCIPY_EXPECTED]): multi_labels = random_walker(data, labels, mode='cg', multichannel=True) assert data[..., 0].shape == labels.shape with expected_warnings(['"cg" mode' + '|' + SCIPY_EXPECTED]): single_labels = random_walker(data[..., 0], labels, mode='cg') assert (multi_labels.reshape(labels.shape)[25:45, 40:60] == 2).all() assert data[..., 0].shape == labels.shape return data, multi_labels, single_labels, labels def test_multispectral_3d(): n = 30 lx, ly, lz = n, n, n data, labels = make_3d_syntheticdata(lx, ly, lz) data = data[..., np.newaxis].repeat(2, axis=-1) with expected_warnings(['"cg" mode' + '|' + SCIPY_EXPECTED]): multi_labels = random_walker(data, labels, mode='cg', multichannel=True) assert data[..., 0].shape == labels.shape with expected_warnings(['"cg" mode' + '|' + SCIPY_EXPECTED]): single_labels = random_walker(data[..., 0], labels, mode='cg') assert (multi_labels.reshape(labels.shape)[13:17, 13:17, 13:17] == 2).all() assert (single_labels.reshape(labels.shape)[13:17, 13:17, 13:17] == 2).all() assert data[..., 0].shape == labels.shape return data, multi_labels, single_labels, labels def test_spacing_0(): n = 30 lx, ly, lz = n, n, n data, _ = make_3d_syntheticdata(lx, ly, lz) data_aniso = np.zeros((n, n, n // 2)) for i, yz in enumerate(data): data_aniso[i, :, :] = resize(yz, (n, n // 2), mode='constant', anti_aliasing=False) small_l = int(lx // 5) labels_aniso = np.zeros_like(data_aniso) labels_aniso[lx // 5, ly // 5, lz // 5] = 1 labels_aniso[lx // 2 + small_l // 4, ly // 2 - small_l // 4, lz // 4 - small_l // 8] = 2 with expected_warnings(['"cg" mode' + '|' + SCIPY_EXPECTED]): labels_aniso = random_walker(data_aniso, labels_aniso, mode='cg', spacing=(1., 1., 0.5)) assert (labels_aniso[13:17, 13:17, 7:9] == 2).all() def test_spacing_1(): n = 30 lx, ly, lz = n, n, n data, _ = make_3d_syntheticdata(lx, ly, lz) data_aniso = np.zeros((n, n * 2, n)) for i, yz in enumerate(data): data_aniso[i, :, :] = resize(yz, (n * 2, n), mode='constant', anti_aliasing=False) small_l = int(lx // 5) labels_aniso = np.zeros_like(data_aniso) labels_aniso[lx // 5, ly // 5, lz // 5] = 1 labels_aniso[lx // 2 + small_l // 4, ly - small_l // 2, lz // 2 - small_l // 4] = 2 with expected_warnings(['"cg" mode' + '|' + SCIPY_EXPECTED]): labels_aniso = random_walker(data_aniso, labels_aniso, mode='cg', spacing=(1., 2., 1.)) assert (labels_aniso[13:17, 26:34, 13:17] == 2).all() data_aniso = np.zeros((n, n * 2, n)) for i in range(data.shape[1]): data_aniso[i, :, :] = resize(data[:, 1, :], (n * 2, n), mode='constant', anti_aliasing=False) small_l = int(lx // 5) labels_aniso2 = np.zeros_like(data_aniso) labels_aniso2[lx // 5, ly // 5, lz // 5] = 1 labels_aniso2[lx - small_l // 2, ly // 2 + small_l // 4, lz // 2 - small_l // 4] = 2 with expected_warnings(['"cg" mode' + '|' + SCIPY_EXPECTED]): labels_aniso2 = random_walker(data_aniso, labels_aniso2, mode='cg', spacing=(2., 1., 1.)) assert (labels_aniso2[26:34, 13:17, 13:17] == 2).all() def test_trivial_cases(): img = np.ones((10, 10)) labels = np.ones((10, 10)) with expected_warnings(["Returning provided labels"]): pass_through = random_walker(img, labels) np.testing.assert_array_equal(pass_through, labels) labels[:, :5] = 3 expected = np.concatenate(((labels == 1)[..., np.newaxis], (labels == 3)[..., np.newaxis]), axis=2) with expected_warnings(["Returning provided labels"]): test = random_walker(img, labels, return_full_prob=True) np.testing.assert_array_equal(test, expected) def test_length2_spacing(): np.random.seed(42) img = np.ones((10, 10)) + 0.2 * np.random.normal(size=(10, 10)) labels = np.zeros((10, 10), dtype=np.uint8) labels[2, 4] = 1 labels[6, 8] = 4 random_walker(img, labels, spacing=(1., 2.)) def test_bad_inputs(): img = np.ones(10) labels = np.arange(10) with testing.raises(ValueError): random_walker(img, labels) with testing.raises(ValueError): random_walker(img, labels, multichannel=True) np.random.seed(42) img = np.random.normal(size=(3, 3, 3, 3, 3)) labels = np.arange(3 ** 5).reshape(img.shape) with testing.raises(ValueError): random_walker(img, labels) with testing.raises(ValueError): random_walker(img, labels, multichannel=True) img = np.random.normal(size=(10, 10)) labels = np.zeros((10, 10)) labels[2, 4] = 2 labels[6, 8] = 5 with testing.raises(ValueError): random_walker(img, labels, spacing=(1,)) img = np.random.normal(size=(10, 10)) labels = np.zeros((10, 10)) with testing.raises(ValueError): random_walker(img, labels, mode='bad') def test_isolated_seeds(): np.random.seed(0) a = np.random.random((7, 7)) mask = - np.ones(a.shape) mask[1, 1] = 1 mask[3:, 3:] = 0 mask[4, 4] = 2 mask[6, 6] = 1 res = random_walker(a, mask) assert res[1, 1] == 1 res = random_walker(a, mask, return_full_prob=True) assert res[0, 1, 1] == 1 assert res[1, 1, 1] == 0

true

f71b4a1187468b74d940dc87ca1f6757b192c07f

412

py

Python

docs/core/howto/listings/pb/pb3server.py

ioggstream/twisted

34f9b1e3f097685839000c656332c66ee85be5d8

[ "Unlicense", "MIT" ]

3

2020-04-02T06:23:44.000Z

2020-08-13T20:32:31.000Z

docs/core/howto/listings/pb/pb3server.py

ioggstream/twisted

34f9b1e3f097685839000c656332c66ee85be5d8

[ "Unlicense", "MIT" ]

1

2022-03-04T17:40:22.000Z

docs/core/howto/listings/pb/pb3server.py

ioggstream/twisted

34f9b1e3f097685839000c656332c66ee85be5d8

[ "Unlicense", "MIT" ]

1

2020-04-02T06:26:10.000Z

#!/usr/bin/env python # Copyright (c) Twisted Matrix Laboratories. # See LICENSE for details. from twisted.spread import pb from twisted.internet import reactor class One(pb.Root): def remote_takeTwo(self, two): print "received a Two called", two print "telling it to print(12)" two.callRemote("print", 12) reactor.listenTCP(8800, pb.PBServerFactory(One())) reactor.run()

24.235294

50

0.68932

from twisted.spread import pb from twisted.internet import reactor class One(pb.Root): def remote_takeTwo(self, two): print "received a Two called", two print "telling it to print(12)" two.callRemote("print", 12) reactor.listenTCP(8800, pb.PBServerFactory(One())) reactor.run()

false

true

f71b4ba03c952835c47e87d25a7a9beba942d977

8,309

py

Python

mmdet/models/necks/mscatfpn.py

JHuang-CV/OD

290bf90a5f210199b6a3750c88152f7dd2fbc276

[ "Apache-2.0" ]

null

mmdet/models/necks/mscatfpn.py

JHuang-CV/OD

290bf90a5f210199b6a3750c88152f7dd2fbc276

[ "Apache-2.0" ]

null

mmdet/models/necks/mscatfpn.py

JHuang-CV/OD

290bf90a5f210199b6a3750c88152f7dd2fbc276

[ "Apache-2.0" ]

null

import torch import torch.nn as nn import torch.nn.functional as F from mmcv.cnn import xavier_init from mmdet.core import auto_fp16 from ..registry import NECKS from ..utils import ConvModule from mmdet.ops.context_block import ContextBlock from mmdet.models.plugins.squeeze_excitation import ChannelSELayer @NECKS.register_module class MSCATFPN(nn.Module): def __init__(self, in_channels, out_channels, num_outs, start_level=0, end_level=-1, add_extra_convs=False, extra_convs_on_inputs=True, relu_before_extra_convs=False, no_norm_on_lateral=False, conv_cfg=None, norm_cfg=None, activation=None): super(MSCATFPN, self).__init__() assert isinstance(in_channels, list) self.in_channels = in_channels self.out_channels = out_channels self.num_ins = len(in_channels) self.num_outs = num_outs self.activation = activation self.relu_before_extra_convs = relu_before_extra_convs self.no_norm_on_lateral = no_norm_on_lateral self.norm_cfg = norm_cfg self.fp16_enabled = False self.epsilon = 1e-4 self.se = ChannelSELayer(768) if end_level == -1: self.backbone_end_level = self.num_ins assert num_outs >= self.num_ins - start_level else: # if end_level < inputs, no extra level is allowed self.backbone_end_level = end_level assert end_level <= len(in_channels) assert num_outs == end_level - start_level self.start_level = start_level self.end_level = end_level self.add_extra_convs = add_extra_convs self.extra_convs_on_inputs = extra_convs_on_inputs self.lateral_convs = nn.ModuleList() self.fpn_convs = nn.ModuleList() self.cat_convs = nn.ModuleList() self.add_convs = nn.ModuleList() #self.gc_block = nn.ModuleList() self.relu = nn.ReLU() self.gc_block1 = ContextBlock(inplanes=256, ratio=1./4.) self.gc_block2 = ContextBlock(inplanes=256, ratio=1. / 4.) self.scat_conv = ConvModule( out_channels * (self.backbone_end_level-self.start_level), out_channels, 3, padding=1, conv_cfg=conv_cfg, norm_cfg=norm_cfg, activation=self.activation, inplace=False) self.c3_w = nn.Parameter(torch.ones(3, dtype=torch.float32), requires_grad=True) self.c4_w = nn.Parameter(torch.ones(3, dtype=torch.float32), requires_grad=True) self.c5_w = nn.Parameter(torch.ones(3, dtype=torch.float32), requires_grad=True) for i in range(self.start_level, self.backbone_end_level): l_conv = ConvModule( in_channels[i], out_channels, 1, conv_cfg=conv_cfg, norm_cfg=norm_cfg if not self.no_norm_on_lateral else None, activation=self.activation, inplace=False) cat_conv = ConvModule( out_channels * (self.backbone_end_level-self.start_level), out_channels, 3, padding=1, conv_cfg=conv_cfg, norm_cfg=norm_cfg, activation=self.activation, inplace=False) add_conv = ConvModule( out_channels, out_channels, 3, padding=1, conv_cfg=conv_cfg, norm_cfg=norm_cfg, activation=self.activation, inplace=False) self.cat_convs.append(cat_conv) self.lateral_convs.append(l_conv) self.add_convs.append(add_conv) #self.gc_block.append(ContextBlock(inplanes=256, ratio=1./4.)) # add extra conv layers (e.g., RetinaNet) extra_levels = num_outs - self.backbone_end_level + self.start_level if add_extra_convs and extra_levels >= 1: for i in range(extra_levels): if i == 0 and self.extra_convs_on_inputs: in_channels = self.in_channels[self.backbone_end_level - 1] else: in_channels = out_channels extra_fpn_conv = ConvModule( in_channels, out_channels, 3, stride=2, padding=1, conv_cfg=conv_cfg, norm_cfg=norm_cfg, activation=self.activation, inplace=False) self.fpn_convs.append(extra_fpn_conv) # default init_weights for conv(msra) and norm in ConvModule def init_weights(self): for m in self.modules(): if isinstance(m, nn.Conv2d): xavier_init(m, distribution='uniform') @auto_fp16() def forward(self, inputs): assert len(inputs) == len(self.in_channels) # build laterals laterals = [ lateral_conv(inputs[i + self.start_level]) for i, lateral_conv in enumerate(self.lateral_convs) ] used_backbone_levels = len(laterals) mulscale_per_level = [] for i in range(used_backbone_levels): level = [] m = i - 0 n = used_backbone_levels - 1 - i level.append(laterals[i]) for x in range(m): level.insert(0, F.interpolate(level[0], scale_factor=2, mode='nearest')) for y in range(n): level.append(F.max_pool2d(level[-1], 2, stride=2)) mulscale_per_level.append(level) sglscale_per_level = list(zip(*mulscale_per_level)) feat_cat = [torch.cat(scale, 1)for scale in sglscale_per_level] #channel_se = [self.se(cat_ft) for cat_ft in feat_cat] mcat = [cat_conv(feat_cat[i]) for i, cat_conv in enumerate(self.cat_convs)] #outs = [gc(outs[i]) for i, gc in enumerate(self.gc_block)] mcat = [self.gc_block1(ft) for ft in mcat] single_list = [] level = used_backbone_levels // 2 for i in range(used_backbone_levels): if i < level: single_list.append(F.max_pool2d(laterals[i], 2, stride=2)) elif i == level: single_list.append(laterals[i]) else: single_list.append(F.interpolate(laterals[i], scale_factor=2, mode='nearest')) single_cat = torch.cat(single_list, 1) single_cat = self.scat_conv(single_cat) single_cat = self.gc_block2(single_cat) m = level - 0 n = used_backbone_levels - 1 - level scat = [single_cat] for x in range(m): scat.insert(0, F.interpolate(scat[0], scale_factor=2, mode='nearest')) for y in range(n): scat.append(F.max_pool2d(scat[-1], 2, stride=2)) # outs = [scat[i]+lateral for i, lateral in enumerate(laterals)] # outs = [add_conv(outs[i]) for i, add_conv in enumerate(self.add_convs)] outs = [] for i, (m, s, l) in enumerate(zip(mcat, scat, laterals)): outs.append( self.add_convs[i](m.sigmoid()*s/2 + l / 2) ) if self.num_outs > used_backbone_levels: if not self.add_extra_convs: for i in range(self.num_outs - used_backbone_levels): outs.append(F.max_pool2d(outs[-1], 1, stride=2)) else: if self.extra_convs_on_inputs: orig = inputs[self.backbone_end_level - 1] outs.append(self.fpn_convs[0](orig)) else: outs.append(self.fpn_convs[0](outs[-1])) for i in range(1, self.num_outs-used_backbone_levels): if self.relu_before_extra_convs: outs.append(self.fpn_convs[i](F.relu(outs[-1]))) else: outs.append(self.fpn_convs[i](outs[-1])) return tuple(outs)

37.768182

94

0.564087

import torch import torch.nn as nn import torch.nn.functional as F from mmcv.cnn import xavier_init from mmdet.core import auto_fp16 from ..registry import NECKS from ..utils import ConvModule from mmdet.ops.context_block import ContextBlock from mmdet.models.plugins.squeeze_excitation import ChannelSELayer @NECKS.register_module class MSCATFPN(nn.Module): def __init__(self, in_channels, out_channels, num_outs, start_level=0, end_level=-1, add_extra_convs=False, extra_convs_on_inputs=True, relu_before_extra_convs=False, no_norm_on_lateral=False, conv_cfg=None, norm_cfg=None, activation=None): super(MSCATFPN, self).__init__() assert isinstance(in_channels, list) self.in_channels = in_channels self.out_channels = out_channels self.num_ins = len(in_channels) self.num_outs = num_outs self.activation = activation self.relu_before_extra_convs = relu_before_extra_convs self.no_norm_on_lateral = no_norm_on_lateral self.norm_cfg = norm_cfg self.fp16_enabled = False self.epsilon = 1e-4 self.se = ChannelSELayer(768) if end_level == -1: self.backbone_end_level = self.num_ins assert num_outs >= self.num_ins - start_level else: self.backbone_end_level = end_level assert end_level <= len(in_channels) assert num_outs == end_level - start_level self.start_level = start_level self.end_level = end_level self.add_extra_convs = add_extra_convs self.extra_convs_on_inputs = extra_convs_on_inputs self.lateral_convs = nn.ModuleList() self.fpn_convs = nn.ModuleList() self.cat_convs = nn.ModuleList() self.add_convs = nn.ModuleList() self.relu = nn.ReLU() self.gc_block1 = ContextBlock(inplanes=256, ratio=1./4.) self.gc_block2 = ContextBlock(inplanes=256, ratio=1. / 4.) self.scat_conv = ConvModule( out_channels * (self.backbone_end_level-self.start_level), out_channels, 3, padding=1, conv_cfg=conv_cfg, norm_cfg=norm_cfg, activation=self.activation, inplace=False) self.c3_w = nn.Parameter(torch.ones(3, dtype=torch.float32), requires_grad=True) self.c4_w = nn.Parameter(torch.ones(3, dtype=torch.float32), requires_grad=True) self.c5_w = nn.Parameter(torch.ones(3, dtype=torch.float32), requires_grad=True) for i in range(self.start_level, self.backbone_end_level): l_conv = ConvModule( in_channels[i], out_channels, 1, conv_cfg=conv_cfg, norm_cfg=norm_cfg if not self.no_norm_on_lateral else None, activation=self.activation, inplace=False) cat_conv = ConvModule( out_channels * (self.backbone_end_level-self.start_level), out_channels, 3, padding=1, conv_cfg=conv_cfg, norm_cfg=norm_cfg, activation=self.activation, inplace=False) add_conv = ConvModule( out_channels, out_channels, 3, padding=1, conv_cfg=conv_cfg, norm_cfg=norm_cfg, activation=self.activation, inplace=False) self.cat_convs.append(cat_conv) self.lateral_convs.append(l_conv) self.add_convs.append(add_conv) extra_levels = num_outs - self.backbone_end_level + self.start_level if add_extra_convs and extra_levels >= 1: for i in range(extra_levels): if i == 0 and self.extra_convs_on_inputs: in_channels = self.in_channels[self.backbone_end_level - 1] else: in_channels = out_channels extra_fpn_conv = ConvModule( in_channels, out_channels, 3, stride=2, padding=1, conv_cfg=conv_cfg, norm_cfg=norm_cfg, activation=self.activation, inplace=False) self.fpn_convs.append(extra_fpn_conv) def init_weights(self): for m in self.modules(): if isinstance(m, nn.Conv2d): xavier_init(m, distribution='uniform') @auto_fp16() def forward(self, inputs): assert len(inputs) == len(self.in_channels) laterals = [ lateral_conv(inputs[i + self.start_level]) for i, lateral_conv in enumerate(self.lateral_convs) ] used_backbone_levels = len(laterals) mulscale_per_level = [] for i in range(used_backbone_levels): level = [] m = i - 0 n = used_backbone_levels - 1 - i level.append(laterals[i]) for x in range(m): level.insert(0, F.interpolate(level[0], scale_factor=2, mode='nearest')) for y in range(n): level.append(F.max_pool2d(level[-1], 2, stride=2)) mulscale_per_level.append(level) sglscale_per_level = list(zip(*mulscale_per_level)) feat_cat = [torch.cat(scale, 1)for scale in sglscale_per_level] mcat = [cat_conv(feat_cat[i]) for i, cat_conv in enumerate(self.cat_convs)] mcat = [self.gc_block1(ft) for ft in mcat] single_list = [] level = used_backbone_levels // 2 for i in range(used_backbone_levels): if i < level: single_list.append(F.max_pool2d(laterals[i], 2, stride=2)) elif i == level: single_list.append(laterals[i]) else: single_list.append(F.interpolate(laterals[i], scale_factor=2, mode='nearest')) single_cat = torch.cat(single_list, 1) single_cat = self.scat_conv(single_cat) single_cat = self.gc_block2(single_cat) m = level - 0 n = used_backbone_levels - 1 - level scat = [single_cat] for x in range(m): scat.insert(0, F.interpolate(scat[0], scale_factor=2, mode='nearest')) for y in range(n): scat.append(F.max_pool2d(scat[-1], 2, stride=2)) outs = [] for i, (m, s, l) in enumerate(zip(mcat, scat, laterals)): outs.append( self.add_convs[i](m.sigmoid()*s/2 + l / 2) ) if self.num_outs > used_backbone_levels: if not self.add_extra_convs: for i in range(self.num_outs - used_backbone_levels): outs.append(F.max_pool2d(outs[-1], 1, stride=2)) else: if self.extra_convs_on_inputs: orig = inputs[self.backbone_end_level - 1] outs.append(self.fpn_convs[0](orig)) else: outs.append(self.fpn_convs[0](outs[-1])) for i in range(1, self.num_outs-used_backbone_levels): if self.relu_before_extra_convs: outs.append(self.fpn_convs[i](F.relu(outs[-1]))) else: outs.append(self.fpn_convs[i](outs[-1])) return tuple(outs)

true

f71b4bb600bb418ed1ef7e86a5615b6ad8bfabf3

2,753

py

Python

python/dgl/nn/mxnet/conv/agnnconv.py

jinghuix/dgl

fae26dd15caac92458a08ad34889086e1e333ddd

[ "Apache-2.0" ]

2

2020-07-24T19:26:51.000Z

2021-08-21T21:04:11.000Z

python/dgl/nn/mxnet/conv/agnnconv.py

jinghuix/dgl

fae26dd15caac92458a08ad34889086e1e333ddd

[ "Apache-2.0" ]

null

python/dgl/nn/mxnet/conv/agnnconv.py

jinghuix/dgl

fae26dd15caac92458a08ad34889086e1e333ddd

[ "Apache-2.0" ]

1

2021-03-09T12:42:46.000Z

"""MXNet Module for Attention-based Graph Neural Network layer""" # pylint: disable= no-member, arguments-differ, invalid-name import mxnet as mx from mxnet.gluon import nn from .... import function as fn from ..softmax import edge_softmax from ..utils import normalize from ....utils import expand_as_pair class AGNNConv(nn.Block): r"""Attention-based Graph Neural Network layer from paper `Attention-based Graph Neural Network for Semi-Supervised Learning <https://arxiv.org/abs/1803.03735>`__. .. math:: H^{l+1} = P H^{l} where :math:`P` is computed as: .. math:: P_{ij} = \mathrm{softmax}_i ( \beta \cdot \cos(h_i^l, h_j^l)) Parameters ---------- init_beta : float, optional The :math:`\beta` in the formula. learn_beta : bool, optional If True, :math:`\beta` will be learnable parameter. """ def __init__(self, init_beta=1., learn_beta=True): super(AGNNConv, self).__init__() with self.name_scope(): self.beta = self.params.get('beta', shape=(1,), grad_req='write' if learn_beta else 'null', init=mx.init.Constant(init_beta)) def forward(self, graph, feat): r"""Compute AGNN Layer. Parameters ---------- graph : DGLGraph The graph. feat : mxnet.NDArray The input feature of shape :math:`(N, *)` :math:`N` is the number of nodes, and :math:`*` could be of any shape. If a pair of mxnet.NDArray is given, the pair must contain two tensors of shape :math:`(N_{in}, *)` and :math:`(N_{out}, *})`, the the :math:`*` in the later tensor must equal the previous one. Returns ------- mxnet.NDArray The output feature of shape :math:`(N, *)` where :math:`*` should be the same as input shape. """ with graph.local_scope(): feat_src, feat_dst = expand_as_pair(feat, graph) graph.srcdata['h'] = feat_src graph.srcdata['norm_h'] = normalize(feat_src, p=2, axis=-1) if isinstance(feat, tuple) or graph.is_block: graph.dstdata['norm_h'] = normalize(feat_dst, p=2, axis=-1) # compute cosine distance graph.apply_edges(fn.u_dot_v('norm_h', 'norm_h', 'cos')) cos = graph.edata.pop('cos') e = self.beta.data(feat_src.context) * cos graph.edata['p'] = edge_softmax(graph, e) graph.update_all(fn.u_mul_e('h', 'p', 'm'), fn.sum('m', 'h')) return graph.dstdata.pop('h')

36.706667

91

0.553578

import mxnet as mx from mxnet.gluon import nn from .... import function as fn from ..softmax import edge_softmax from ..utils import normalize from ....utils import expand_as_pair class AGNNConv(nn.Block): def __init__(self, init_beta=1., learn_beta=True): super(AGNNConv, self).__init__() with self.name_scope(): self.beta = self.params.get('beta', shape=(1,), grad_req='write' if learn_beta else 'null', init=mx.init.Constant(init_beta)) def forward(self, graph, feat): with graph.local_scope(): feat_src, feat_dst = expand_as_pair(feat, graph) graph.srcdata['h'] = feat_src graph.srcdata['norm_h'] = normalize(feat_src, p=2, axis=-1) if isinstance(feat, tuple) or graph.is_block: graph.dstdata['norm_h'] = normalize(feat_dst, p=2, axis=-1) graph.apply_edges(fn.u_dot_v('norm_h', 'norm_h', 'cos')) cos = graph.edata.pop('cos') e = self.beta.data(feat_src.context) * cos graph.edata['p'] = edge_softmax(graph, e) graph.update_all(fn.u_mul_e('h', 'p', 'm'), fn.sum('m', 'h')) return graph.dstdata.pop('h')

true

f71b4c870143ba858ce8cb1244e06a703ac0343d

2,539

py

Python

Algorithms/MotorModule/MotorModule.py

TechMatt1337/Bastion

1e89143905babc06ace4f0ae7ab59750427b0f03

[ "BSD-3-Clause" ]

1

2018-12-06T03:57:21.000Z

Algorithms/MotorModule/MotorModule.py

TechMatt1337/Bastion

1e89143905babc06ace4f0ae7ab59750427b0f03

[ "BSD-3-Clause" ]

null

Algorithms/MotorModule/MotorModule.py

TechMatt1337/Bastion

1e89143905babc06ace4f0ae7ab59750427b0f03

[ "BSD-3-Clause" ]

null

from Adafruit_MotorHAT import Adafruit_MotorHAT, Adafruit_DCMotor, Adafruit_StepperMotor import time import atexit MH = Adafruit_MotorHAT(0x60) STEPRES = 1.8 # Step resulition in units of degree/step DIRECTIONS = { "ccw":Adafruit_MotorHAT.FORWARD, "cw":Adafruit_MotorHAT.BACKWARD} STEPTYPES = { "single":Adafruit_MotorHAT.SINGLE, "double":Adafruit_MotorHAT.DOUBLE, "interleave":Adafruit_MotorHAT.INTERLEAVE, "micro":Adafruit_MotorHAT.MICROSTEP} MOTORS = {"horizontal":1,"vertical":2} class MotorController: def __init__(self,motor,steps = 200,addr = 0x60): motorPort = MOTORS[motor] self.motorPort = motorPort self.steps = steps self.hatAddress = addr global MH MH = Adafruit_MotorHAT(addr) self.stepperMotor = MH.getStepper(steps, motorPort) self.stepperMotor.setSpeed(180) def rotateMotor(self,degree,dir = "cw",step = "single"): """ Rotate motor for a certain degree from where it is located at in a specified direction. Inputs: degree - Degrees to rotate dir - cw or ccw rotation step - Type of step motor should make for rotation. By default it is set to 'double'; which provides the highest torque that the motor is able to provide. Other types types of steps include 'single', 'interleave', and 'microstep'. """ # print("ROTATING MOTOR") x = 0 if step == "interleave": x = int(degree/STEPRES)*2 else: x = int(degree/STEPRES) self.stepperMotor.step(x,DIRECTIONS[dir],STEPTYPES[step]) def turnOffMotors(): """ Turn off all motors """ MH.getMotor(1).run(Adafruit_MotorHAT.RELEASE) MH.getMotor(2).run(Adafruit_MotorHAT.RELEASE) MH.getMotor(3).run(Adafruit_MotorHAT.RELEASE) MH.getMotor(4).run(Adafruit_MotorHAT.RELEASE) # recommended for auto-disabling motors on shutdown! atexit.register(turnOffMotors) if __name__ == '__main__': m = MotorController(motor="vertical") m.rotateMotor(degree=1000,step="double") # m.rotateMotor(degree=360,step="double",dir="ccw")

38.469697

89

0.569909

from Adafruit_MotorHAT import Adafruit_MotorHAT, Adafruit_DCMotor, Adafruit_StepperMotor import time import atexit MH = Adafruit_MotorHAT(0x60) STEPRES = 1.8 DIRECTIONS = { "ccw":Adafruit_MotorHAT.FORWARD, "cw":Adafruit_MotorHAT.BACKWARD} STEPTYPES = { "single":Adafruit_MotorHAT.SINGLE, "double":Adafruit_MotorHAT.DOUBLE, "interleave":Adafruit_MotorHAT.INTERLEAVE, "micro":Adafruit_MotorHAT.MICROSTEP} MOTORS = {"horizontal":1,"vertical":2} class MotorController: def __init__(self,motor,steps = 200,addr = 0x60): motorPort = MOTORS[motor] self.motorPort = motorPort self.steps = steps self.hatAddress = addr global MH MH = Adafruit_MotorHAT(addr) self.stepperMotor = MH.getStepper(steps, motorPort) self.stepperMotor.setSpeed(180) def rotateMotor(self,degree,dir = "cw",step = "single"): """ Rotate motor for a certain degree from where it is located at in a specified direction. Inputs: degree - Degrees to rotate dir - cw or ccw rotation step - Type of step motor should make for rotation. By default it is set to 'double'; which provides the highest torque that the motor is able to provide. Other types types of steps include 'single', 'interleave', and 'microstep'. """ x = 0 if step == "interleave": x = int(degree/STEPRES)*2 else: x = int(degree/STEPRES) self.stepperMotor.step(x,DIRECTIONS[dir],STEPTYPES[step]) def turnOffMotors(): """ Turn off all motors """ MH.getMotor(1).run(Adafruit_MotorHAT.RELEASE) MH.getMotor(2).run(Adafruit_MotorHAT.RELEASE) MH.getMotor(3).run(Adafruit_MotorHAT.RELEASE) MH.getMotor(4).run(Adafruit_MotorHAT.RELEASE) atexit.register(turnOffMotors) if __name__ == '__main__': m = MotorController(motor="vertical") m.rotateMotor(degree=1000,step="double")

false

true

f71b4d5f7826768bee64b7feec106bd5368db512

1,099

py

Python

test/test_arrow_result.py

mariusvniekerk/snowflake-connector-python

4c6b728f9ca7ac9c8a318741924a963a5574e216

[ "Apache-2.0" ]

null

test/test_arrow_result.py

mariusvniekerk/snowflake-connector-python

4c6b728f9ca7ac9c8a318741924a963a5574e216

[ "Apache-2.0" ]

null

test/test_arrow_result.py

mariusvniekerk/snowflake-connector-python

4c6b728f9ca7ac9c8a318741924a963a5574e216

[ "Apache-2.0" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- # # Copyright (c) 2012-2019 Snowflake Computing Inc. All right reserved. # import pytest @pytest.mark.skip( reason="Cython is not enabled in build env") def test_select_with_num(conn_cnx): with conn_cnx() as json_cnx: with conn_cnx() as arrow_cnx: row_count = 50000 sql_text = ("select seq4() as c1, uniform(1, 10, random(12)) as c2 from " + "table(generator(rowcount=>50000)) order by c1") cursor_json = json_cnx.cursor() cursor_json.execute("alter session set query_result_format='JSON'") cursor_json.execute(sql_text) cursor_arrow = arrow_cnx.cursor() cursor_arrow.execute("alter session set query_result_format='ARROW_FORCE'") cursor_arrow.execute(sql_text) for i in range(0, row_count): (json_c1, json_c2) = cursor_json.fetchone() (arrow_c1, arrow_c2) = cursor_arrow.fetchone() assert json_c1 == arrow_c1 assert json_c2 == arrow_c2

36.633333

87

0.610555

import pytest @pytest.mark.skip( reason="Cython is not enabled in build env") def test_select_with_num(conn_cnx): with conn_cnx() as json_cnx: with conn_cnx() as arrow_cnx: row_count = 50000 sql_text = ("select seq4() as c1, uniform(1, 10, random(12)) as c2 from " + "table(generator(rowcount=>50000)) order by c1") cursor_json = json_cnx.cursor() cursor_json.execute("alter session set query_result_format='JSON'") cursor_json.execute(sql_text) cursor_arrow = arrow_cnx.cursor() cursor_arrow.execute("alter session set query_result_format='ARROW_FORCE'") cursor_arrow.execute(sql_text) for i in range(0, row_count): (json_c1, json_c2) = cursor_json.fetchone() (arrow_c1, arrow_c2) = cursor_arrow.fetchone() assert json_c1 == arrow_c1 assert json_c2 == arrow_c2

true

f71b4eb0cc83cbb94a84bbec221dd9f3a3147026

25,712

py

Python

draco/core/io.py

sjforeman/draco

b0ab40b6984637642b28a5485af1c09c9cf183f2

[ "MIT" ]

null

draco/core/io.py

sjforeman/draco

b0ab40b6984637642b28a5485af1c09c9cf183f2

[ "MIT" ]

null

draco/core/io.py

sjforeman/draco

b0ab40b6984637642b28a5485af1c09c9cf183f2

[ "MIT" ]

null

"""Tasks for reading and writing data. Tasks ===== .. autosummary:: :toctree: LoadFiles LoadMaps LoadFilesFromParams Save Print LoadBeamTransfer File Groups =========== Several tasks accept groups of files as arguments. These are specified in the YAML file as a dictionary like below. .. code-block:: yaml list_of_file_groups: - tag: first_group # An optional tag naming the group files: - 'file1.h5' - 'file[3-4].h5' # Globs are processed - 'file7.h5' - files: # No tag specified, implicitly gets the tag 'group_2' - 'another_file1.h5' - 'another_file2.h5' single_group: files: ['file1.h5', 'file2.h5'] """ import os.path import h5py import numpy as np from yaml import dump as yamldump from caput import pipeline from caput import config from cora.util import units from . import task from ..util.truncate import bit_truncate_weights, bit_truncate_fixed from .containers import SiderealStream, TimeStream, TrackBeam TRUNC_SPEC = { SiderealStream: { "dataset": ["vis", "vis_weight"], "weight_dataset": ["vis_weight", None], "fixed_precision": 1e-4, "variance_increase": 1e-3, }, TimeStream: { "dataset": ["vis", "vis_weight"], "weight_dataset": ["vis_weight", None], "fixed_precision": 1e-4, "variance_increase": 1e-3, }, TrackBeam: { "dataset": ["beam", "weight"], "weight_dataset": ["weight", None], "fixed_precision": 1e-4, "variance_increase": 1e-3, }, } def _list_of_filelists(files): # Take in a list of lists/glob patterns of filenames import glob f2 = [] for filelist in files: if isinstance(filelist, str): filelist = glob.glob(filelist) elif isinstance(filelist, list): pass else: raise Exception("Must be list or glob pattern.") f2.append(filelist) return f2 def _list_or_glob(files): # Take in a list of lists/glob patterns of filenames import glob if isinstance(files, str): files = sorted(glob.glob(files)) elif isinstance(files, list): pass else: raise ValueError("Argument must be list or glob pattern, got %s" % repr(files)) return files def _list_of_filegroups(groups): # Process a file group/groups import glob # Convert to list if the group was not included in a list if not isinstance(groups, list): groups = [groups] # Iterate over groups, set the tag if needed, and process the file list # through glob for gi, group in enumerate(groups): files = group["files"] if "tag" not in group: group["tag"] = "group_%i" % gi flist = [] for fname in files: flist += glob.glob(fname) if not len(flist): raise RuntimeError("No files in group exist (%s)." % files) group["files"] = flist return groups class LoadMaps(task.MPILoggedTask): """Load a series of maps from files given in the tasks parameters. Maps are given as one, or a list of `File Groups` (see :mod:`draco.core.io`). Maps within the same group are added together before being passed on. Attributes ---------- maps : list or dict A dictionary specifying a file group, or a list of them. """ maps = config.Property(proptype=_list_of_filegroups) def next(self): """Load the groups of maps from disk and pass them on. Returns ------- map : :class:`containers.Map` """ from . import containers # Exit this task if we have eaten all the file groups if len(self.maps) == 0: raise pipeline.PipelineStopIteration group = self.maps.pop(0) map_stack = None # Iterate over all the files in the group, load them into a Map # container and add them all together for mfile in group["files"]: self.log.debug("Loading file %s", mfile) current_map = containers.Map.from_file(mfile, distributed=True) current_map.redistribute("freq") # Start the stack if needed if map_stack is None: map_stack = current_map # Otherwise, check that the new map has consistent frequencies, # nside and pol and stack up. else: if (current_map.freq != map_stack.freq).all(): raise RuntimeError("Maps do not have consistent frequencies.") if (current_map.index_map["pol"] != map_stack.index_map["pol"]).all(): raise RuntimeError("Maps do not have the same polarisations.") if ( current_map.index_map["pixel"] != map_stack.index_map["pixel"] ).all(): raise RuntimeError("Maps do not have the same pixelisation.") map_stack.map[:] += current_map.map[:] # Assign a tag to the stack of maps map_stack.attrs["tag"] = group["tag"] return map_stack class LoadFITSCatalog(task.SingleTask): """Load an SDSS-style FITS source catalog. Catalogs are given as one, or a list of `File Groups` (see :mod:`draco.core.io`). Catalogs within the same group are combined together before being passed on. Attributes ---------- catalogs : list or dict A dictionary specifying a file group, or a list of them. z_range : list, optional Select only sources with a redshift within the given range. freq_range : list, optional Select only sources with a 21cm line freq within the given range. Overrides `z_range`. """ catalogs = config.Property(proptype=_list_of_filegroups) z_range = config.list_type(type_=float, length=2, default=None) freq_range = config.list_type(type_=float, length=2, default=None) def process(self): """Load the groups of catalogs from disk, concatenate them and pass them on. Returns ------- catalog : :class:`containers.SpectroscopicCatalog` """ from astropy.io import fits from . import containers # Exit this task if we have eaten all the file groups if len(self.catalogs) == 0: raise pipeline.PipelineStopIteration group = self.catalogs.pop(0) # Set the redshift selection if self.freq_range: zl = units.nu21 / self.freq_range[1] - 1 zh = units.nu21 / self.freq_range[0] - 1 self.z_range = (zl, zh) if self.z_range: zl, zh = self.z_range self.log.info(f"Applying redshift selection {zl:.2f} <= z <= {zh:.2f}") # Load the data only on rank=0 and then broadcast if self.comm.rank == 0: # Iterate over all the files in the group, load them into a Map # container and add them all together catalog_stack = [] for cfile in group["files"]: self.log.debug("Loading file %s", cfile) # TODO: read out the weights from the catalogs with fits.open(cfile, mode="readonly") as cat: pos = np.array([cat[1].data[col] for col in ["RA", "DEC", "Z"]]) # Apply any redshift selection to the objects if self.z_range: zsel = (pos[2] >= self.z_range[0]) & (pos[2] <= self.z_range[1]) pos = pos[:, zsel] catalog_stack.append(pos) # NOTE: this one is tricky, for some reason the concatenate in here # produces a non C contiguous array, so we need to ensure that otherwise # the broadcasting will get very confused catalog_array = np.concatenate(catalog_stack, axis=-1).astype(np.float64) catalog_array = np.ascontiguousarray(catalog_array) num_objects = catalog_array.shape[-1] else: num_objects = None catalog_array = None # Broadcast the size of the catalog to all ranks, create the target array and # broadcast into it num_objects = self.comm.bcast(num_objects, root=0) self.log.debug(f"Constructing catalog with {num_objects} objects.") if self.comm.rank != 0: catalog_array = np.zeros((3, num_objects), dtype=np.float64) self.comm.Bcast(catalog_array, root=0) catalog = containers.SpectroscopicCatalog(object_id=num_objects) catalog["position"]["ra"] = catalog_array[0] catalog["position"]["dec"] = catalog_array[1] catalog["redshift"]["z"] = catalog_array[2] catalog["redshift"]["z_error"] = 0 # Assign a tag to the stack of maps catalog.attrs["tag"] = group["tag"] return catalog class LoadFilesFromParams(task.SingleTask): """Load data from files given in the tasks parameters. Attributes ---------- files : glob pattern, or list Can either be a glob pattern, or lists of actual files. distributed : bool, optional Whether the file should be loaded distributed across ranks. convert_strings : bool, optional Convert strings to unicode when loading. selections : dict, optional A dictionary of axis selections. See the section below for details. Selections ---------- Selections can be given to limit the data read to specified subsets. They can be given for any named axis in the container. Selections can be given as a slice with an `<axis name>_range` key with either `[start, stop]` or `[start, stop, step]` as the value. Alternatively a list of explicit indices to extract can be given with the `<axis name>_index` key, and the value is a list of the indices. If both `<axis name>_range` and `<axis name>_index` keys are given the former will take precedence, but you should clearly avoid doing this. Additionally index based selections currently don't work for distributed reads. Here's an example in the YAML format that the pipeline uses: .. code-block:: yaml selections: freq_range: [256, 512, 4] # A strided slice stack_index: [1, 2, 4, 9, 16, 25, 36, 49, 64] # A sparse selection stack_range: [1, 14] # Will override the selection above """ files = config.Property(proptype=_list_or_glob) distributed = config.Property(proptype=bool, default=True) convert_strings = config.Property(proptype=bool, default=True) selections = config.Property(proptype=dict, default=None) def setup(self): """Resolve the selections.""" self._sel = self._resolve_sel() def process(self): """Load the given files in turn and pass on. Returns ------- cont : subclass of `memh5.BasicCont` """ from caput import memh5 # Garbage collect to workaround leaking memory from containers. # TODO: find actual source of leak import gc gc.collect() if len(self.files) == 0: raise pipeline.PipelineStopIteration # Fetch and remove the first item in the list file_ = self.files.pop(0) self.log.info(f"Loading file {file_}") self.log.debug(f"Reading with selections: {self._sel}") # If we are applying selections we need to dispatch the `from_file` via the # correct subclass, rather than relying on the internal detection of the # subclass. To minimise the number of files being opened this is only done on # rank=0 and is then broadcast if self._sel: if self.comm.rank == 0: with h5py.File(file_, "r") as fh: clspath = memh5.MemDiskGroup._detect_subclass_path(fh) else: clspath = None clspath = self.comm.bcast(clspath, root=0) new_cls = memh5.MemDiskGroup._resolve_subclass(clspath) else: new_cls = memh5.BasicCont cont = new_cls.from_file( file_, distributed=self.distributed, comm=self.comm, convert_attribute_strings=self.convert_strings, convert_dataset_strings=self.convert_strings, **self._sel, ) if "tag" not in cont.attrs: # Get the first part of the actual filename and use it as the tag tag = os.path.splitext(os.path.basename(file_))[0] cont.attrs["tag"] = tag return cont def _resolve_sel(self): # Turn the selection parameters into actual selectable types sel = {} sel_parsers = {"range": self._parse_range, "index": self._parse_index} # To enforce the precedence of range vs index selections, we rely on the fact # that a sort will place the axis_range keys after axis_index keys for k in sorted(self.selections or []): # Parse the key to get the axis name and type, accounting for the fact the # axis name may contain an underscore *axis, type_ = k.split("_") axis_name = "_".join(axis) if type_ not in sel_parsers: raise ValueError( f'Unsupported selection type "{type_}", or invalid key "{k}"' ) sel[f"{axis_name}_sel"] = sel_parsers[type_](self.selections[k]) return sel def _parse_range(self, x): # Parse and validate a range type selection if not isinstance(x, (list, tuple)) or len(x) > 3 or len(x) < 2: raise ValueError( f"Range spec must be a length 2 or 3 list or tuple. Got {x}." ) for v in x: if not isinstance(v, int): raise ValueError(f"All elements of range spec must be ints. Got {x}") return slice(*x) def _parse_index(self, x): # Parse and validate an index type selection if not isinstance(x, (list, tuple)) or len(x) == 0: raise ValueError(f"Index spec must be a non-empty list or tuple. Got {x}.") for v in x: if not isinstance(v, int): raise ValueError(f"All elements of index spec must be ints. Got {x}") return list(x) # Define alias for old code LoadBasicCont = LoadFilesFromParams class FindFiles(pipeline.TaskBase): """Take a glob or list of files specified as a parameter in the configuration file and pass on to other tasks. Parameters ---------- files : list or glob """ files = config.Property(proptype=_list_or_glob) def setup(self): """Return list of files specified in the parameters.""" if not isinstance(self.files, (list, tuple)): raise RuntimeError("Argument must be list of files.") return self.files class LoadFiles(LoadFilesFromParams): """Load data from files passed into the setup routine. File must be a serialised subclass of :class:`memh5.BasicCont`. """ files = None def setup(self, files): """Set the list of files to load. Parameters ---------- files : list """ # Call the baseclass setup to resolve any selections super().setup() if not isinstance(files, (list, tuple)): raise RuntimeError(f'Argument must be list of files. Got "{files}"') self.files = files class Save(pipeline.TaskBase): """Save out the input, and pass it on. Assumes that the input has a `to_hdf5` method. Appends a *tag* if there is a `tag` entry in the attributes, otherwise just uses a count. Attributes ---------- root : str Root of the file name to output to. """ root = config.Property(proptype=str) count = 0 def next(self, data): """Write out the data file. Assumes it has an MPIDataset interface. Parameters ---------- data : mpidataset.MPIDataset Data to write out. """ if "tag" not in data.attrs: tag = self.count self.count += 1 else: tag = data.attrs["tag"] fname = "%s_%s.h5" % (self.root, str(tag)) data.to_hdf5(fname) return data class Print(pipeline.TaskBase): """Stupid module which just prints whatever it gets. Good for debugging.""" def next(self, input_): print(input_) return input_ class LoadBeamTransfer(pipeline.TaskBase): """Loads a beam transfer manager from disk. Attributes ---------- product_directory : str Path to the saved Beam Transfer products. """ product_directory = config.Property(proptype=str) def setup(self): """Load the beam transfer matrices. Returns ------- tel : TransitTelescope Object describing the telescope. bt : BeamTransfer BeamTransfer manager. feed_info : list, optional Optional list providing additional information about each feed. """ import os from drift.core import beamtransfer if not os.path.exists(self.product_directory): raise RuntimeError("BeamTransfers do not exist.") bt = beamtransfer.BeamTransfer(self.product_directory) tel = bt.telescope try: return tel, bt, tel.feeds except AttributeError: return tel, bt class LoadProductManager(pipeline.TaskBase): """Loads a driftscan product manager from disk. Attributes ---------- product_directory : str Path to the root of the products. This is the same as the output directory used by ``drift-makeproducts``. """ product_directory = config.Property(proptype=str) def setup(self): """Load the beam transfer matrices. Returns ------- manager : ProductManager Object describing the telescope. """ import os from drift.core import manager if not os.path.exists(self.product_directory): raise RuntimeError("Products do not exist.") # Load ProductManager and Timestream pm = manager.ProductManager.from_config(self.product_directory) return pm class Truncate(task.SingleTask): """Precision truncate data prior to saving with bitshuffle compression. If no configuration is provided, will look for preset values for the input container. Any properties defined in the config will override the presets. If available, each specified dataset will be truncated relative to a (specified) weight dataset with the truncation increasing the variance up to the specified maximum in `variance_increase`. If there is no specified weight dataset then the truncation falls back to using the `fixed_precision`. Attributes ---------- dataset : list of str Datasets to truncate. weight_dataset : list of str Datasets to use as inverse variance for truncation precision. fixed_precision : float Relative precision to truncate to (default 1e-4). variance_increase : float Maximum fractional increase in variance from numerical truncation. """ dataset = config.Property(proptype=list, default=None) weight_dataset = config.Property(proptype=list, default=None) fixed_precision = config.Property(proptype=float, default=None) variance_increase = config.Property(proptype=float, default=None) def _get_params(self, container): """Load truncation parameters from config or container defaults.""" if container in TRUNC_SPEC: self.log.info("Truncating from preset for container {}".format(container)) for key in [ "dataset", "weight_dataset", "fixed_precision", "variance_increase", ]: attr = getattr(self, key) if attr is None: setattr(self, key, TRUNC_SPEC[container][key]) else: self.log.info("Overriding container default for '{}'.".format(key)) else: if ( self.dataset is None or self.fixed_precision is None or self.variance_increase is None ): raise pipeline.PipelineConfigError( "Container {} has no preset values. You must define all of 'dataset', " "'fixed_precision', and 'variance_increase' properties.".format( container ) ) # Factor of 3 for variance over uniform distribution of truncation errors self.variance_increase *= 3 def process(self, data): """Truncate the incoming data. The truncation is done *in place*. Parameters ---------- data : containers.ContainerBase Data to truncate. Returns ------- truncated_data : containers.ContainerBase Truncated data. """ # get truncation parameters from config or container defaults self._get_params(type(data)) if self.weight_dataset is None: self.weight_dataset = [None] * len(self.dataset) for dset, wgt in zip(self.dataset, self.weight_dataset): old_shape = data[dset].local_shape val = np.ndarray.reshape(data[dset][:], data[dset][:].size) if wgt is None: if np.iscomplexobj(data[dset]): data[dset][:].real = bit_truncate_fixed( val.real, self.fixed_precision ).reshape(old_shape) data[dset][:].imag = bit_truncate_fixed( val.imag, self.fixed_precision ).reshape(old_shape) else: data[dset][:] = bit_truncate_fixed( val, self.fixed_precision ).reshape(old_shape) else: if data[dset][:].shape != data[wgt][:].shape: raise pipeline.PipelineRuntimeError( "Dataset and weight arrays must have same shape ({} != {})".format( data[dset].shape, data[wgt].shape ) ) invvar = np.ndarray.reshape(data[wgt][:], data[dset][:].size) if np.iscomplexobj(data[dset]): data[dset][:].real = bit_truncate_weights( val.real, invvar * 2.0 / self.variance_increase, self.fixed_precision, ).reshape(old_shape) data[dset][:].imag = bit_truncate_weights( val.imag, invvar * 2.0 / self.variance_increase, self.fixed_precision, ).reshape(old_shape) else: data[dset][:] = bit_truncate_weights( val, invvar / self.variance_increase, self.fixed_precision ).reshape(old_shape) return data class SaveModuleVersions(task.SingleTask): """Write module versions to a YAML file. The list of modules should be added to the configuration under key 'save_versions'. The version strings are written to a YAML file. Attributes ---------- root : str Root of the file name to output to. """ root = config.Property(proptype=str) done = True def setup(self): """Save module versions.""" fname = "{}_versions.yml".format(self.root) f = open(fname, "w") f.write(yamldump(self.versions)) f.close() self.done = True def process(self): """Do nothing.""" self.done = True return class SaveConfig(task.SingleTask): """Write pipeline config to a text file. Yaml configuration document is written to a text file. Attributes ---------- root : str Root of the file name to output to. """ root = config.Property(proptype=str) done = True def setup(self): """Save module versions.""" fname = "{}_config.yml".format(self.root) f = open(fname, "w") f.write(yamldump(self.pipeline_config)) f.close() self.done = True def process(self): """Do nothing.""" self.done = True return def get_telescope(obj): """Return a telescope object out of the input (either `ProductManager`, `BeamTransfer` or `TransitTelescope`). """ from drift.core import telescope try: return get_beamtransfer(obj).telescope except RuntimeError: if isinstance(obj, telescope.TransitTelescope): return obj raise RuntimeError("Could not get telescope instance out of %s" % repr(obj)) def get_beamtransfer(obj): """Return a BeamTransfer object out of the input (either `ProductManager`, `BeamTransfer`). """ from drift.core import manager, beamtransfer if isinstance(obj, beamtransfer.BeamTransfer): return obj if isinstance(obj, manager.ProductManager): return obj.beamtransfer raise RuntimeError("Could not get BeamTransfer instance out of %s" % repr(obj))

30.285041

115

0.592253

import os.path import h5py import numpy as np from yaml import dump as yamldump from caput import pipeline from caput import config from cora.util import units from . import task from ..util.truncate import bit_truncate_weights, bit_truncate_fixed from .containers import SiderealStream, TimeStream, TrackBeam TRUNC_SPEC = { SiderealStream: { "dataset": ["vis", "vis_weight"], "weight_dataset": ["vis_weight", None], "fixed_precision": 1e-4, "variance_increase": 1e-3, }, TimeStream: { "dataset": ["vis", "vis_weight"], "weight_dataset": ["vis_weight", None], "fixed_precision": 1e-4, "variance_increase": 1e-3, }, TrackBeam: { "dataset": ["beam", "weight"], "weight_dataset": ["weight", None], "fixed_precision": 1e-4, "variance_increase": 1e-3, }, } def _list_of_filelists(files): import glob f2 = [] for filelist in files: if isinstance(filelist, str): filelist = glob.glob(filelist) elif isinstance(filelist, list): pass else: raise Exception("Must be list or glob pattern.") f2.append(filelist) return f2 def _list_or_glob(files): import glob if isinstance(files, str): files = sorted(glob.glob(files)) elif isinstance(files, list): pass else: raise ValueError("Argument must be list or glob pattern, got %s" % repr(files)) return files def _list_of_filegroups(groups): import glob if not isinstance(groups, list): groups = [groups] for gi, group in enumerate(groups): files = group["files"] if "tag" not in group: group["tag"] = "group_%i" % gi flist = [] for fname in files: flist += glob.glob(fname) if not len(flist): raise RuntimeError("No files in group exist (%s)." % files) group["files"] = flist return groups class LoadMaps(task.MPILoggedTask): maps = config.Property(proptype=_list_of_filegroups) def next(self): from . import containers if len(self.maps) == 0: raise pipeline.PipelineStopIteration group = self.maps.pop(0) map_stack = None for mfile in group["files"]: self.log.debug("Loading file %s", mfile) current_map = containers.Map.from_file(mfile, distributed=True) current_map.redistribute("freq") if map_stack is None: map_stack = current_map else: if (current_map.freq != map_stack.freq).all(): raise RuntimeError("Maps do not have consistent frequencies.") if (current_map.index_map["pol"] != map_stack.index_map["pol"]).all(): raise RuntimeError("Maps do not have the same polarisations.") if ( current_map.index_map["pixel"] != map_stack.index_map["pixel"] ).all(): raise RuntimeError("Maps do not have the same pixelisation.") map_stack.map[:] += current_map.map[:] map_stack.attrs["tag"] = group["tag"] return map_stack class LoadFITSCatalog(task.SingleTask): catalogs = config.Property(proptype=_list_of_filegroups) z_range = config.list_type(type_=float, length=2, default=None) freq_range = config.list_type(type_=float, length=2, default=None) def process(self): from astropy.io import fits from . import containers if len(self.catalogs) == 0: raise pipeline.PipelineStopIteration group = self.catalogs.pop(0) if self.freq_range: zl = units.nu21 / self.freq_range[1] - 1 zh = units.nu21 / self.freq_range[0] - 1 self.z_range = (zl, zh) if self.z_range: zl, zh = self.z_range self.log.info(f"Applying redshift selection {zl:.2f} <= z <= {zh:.2f}") if self.comm.rank == 0: catalog_stack = [] for cfile in group["files"]: self.log.debug("Loading file %s", cfile) with fits.open(cfile, mode="readonly") as cat: pos = np.array([cat[1].data[col] for col in ["RA", "DEC", "Z"]]) if self.z_range: zsel = (pos[2] >= self.z_range[0]) & (pos[2] <= self.z_range[1]) pos = pos[:, zsel] catalog_stack.append(pos) catalog_array = np.concatenate(catalog_stack, axis=-1).astype(np.float64) catalog_array = np.ascontiguousarray(catalog_array) num_objects = catalog_array.shape[-1] else: num_objects = None catalog_array = None num_objects = self.comm.bcast(num_objects, root=0) self.log.debug(f"Constructing catalog with {num_objects} objects.") if self.comm.rank != 0: catalog_array = np.zeros((3, num_objects), dtype=np.float64) self.comm.Bcast(catalog_array, root=0) catalog = containers.SpectroscopicCatalog(object_id=num_objects) catalog["position"]["ra"] = catalog_array[0] catalog["position"]["dec"] = catalog_array[1] catalog["redshift"]["z"] = catalog_array[2] catalog["redshift"]["z_error"] = 0 catalog.attrs["tag"] = group["tag"] return catalog class LoadFilesFromParams(task.SingleTask): files = config.Property(proptype=_list_or_glob) distributed = config.Property(proptype=bool, default=True) convert_strings = config.Property(proptype=bool, default=True) selections = config.Property(proptype=dict, default=None) def setup(self): self._sel = self._resolve_sel() def process(self): from caput import memh5 import gc gc.collect() if len(self.files) == 0: raise pipeline.PipelineStopIteration file_ = self.files.pop(0) self.log.info(f"Loading file {file_}") self.log.debug(f"Reading with selections: {self._sel}") if self._sel: if self.comm.rank == 0: with h5py.File(file_, "r") as fh: clspath = memh5.MemDiskGroup._detect_subclass_path(fh) else: clspath = None clspath = self.comm.bcast(clspath, root=0) new_cls = memh5.MemDiskGroup._resolve_subclass(clspath) else: new_cls = memh5.BasicCont cont = new_cls.from_file( file_, distributed=self.distributed, comm=self.comm, convert_attribute_strings=self.convert_strings, convert_dataset_strings=self.convert_strings, **self._sel, ) if "tag" not in cont.attrs: tag = os.path.splitext(os.path.basename(file_))[0] cont.attrs["tag"] = tag return cont def _resolve_sel(self): sel = {} sel_parsers = {"range": self._parse_range, "index": self._parse_index} for k in sorted(self.selections or []): *axis, type_ = k.split("_") axis_name = "_".join(axis) if type_ not in sel_parsers: raise ValueError( f'Unsupported selection type "{type_}", or invalid key "{k}"' ) sel[f"{axis_name}_sel"] = sel_parsers[type_](self.selections[k]) return sel def _parse_range(self, x): if not isinstance(x, (list, tuple)) or len(x) > 3 or len(x) < 2: raise ValueError( f"Range spec must be a length 2 or 3 list or tuple. Got {x}." ) for v in x: if not isinstance(v, int): raise ValueError(f"All elements of range spec must be ints. Got {x}") return slice(*x) def _parse_index(self, x): if not isinstance(x, (list, tuple)) or len(x) == 0: raise ValueError(f"Index spec must be a non-empty list or tuple. Got {x}.") for v in x: if not isinstance(v, int): raise ValueError(f"All elements of index spec must be ints. Got {x}") return list(x) LoadBasicCont = LoadFilesFromParams class FindFiles(pipeline.TaskBase): files = config.Property(proptype=_list_or_glob) def setup(self): if not isinstance(self.files, (list, tuple)): raise RuntimeError("Argument must be list of files.") return self.files class LoadFiles(LoadFilesFromParams): files = None def setup(self, files): super().setup() if not isinstance(files, (list, tuple)): raise RuntimeError(f'Argument must be list of files. Got "{files}"') self.files = files class Save(pipeline.TaskBase): root = config.Property(proptype=str) count = 0 def next(self, data): if "tag" not in data.attrs: tag = self.count self.count += 1 else: tag = data.attrs["tag"] fname = "%s_%s.h5" % (self.root, str(tag)) data.to_hdf5(fname) return data class Print(pipeline.TaskBase): def next(self, input_): print(input_) return input_ class LoadBeamTransfer(pipeline.TaskBase): product_directory = config.Property(proptype=str) def setup(self): import os from drift.core import beamtransfer if not os.path.exists(self.product_directory): raise RuntimeError("BeamTransfers do not exist.") bt = beamtransfer.BeamTransfer(self.product_directory) tel = bt.telescope try: return tel, bt, tel.feeds except AttributeError: return tel, bt class LoadProductManager(pipeline.TaskBase): product_directory = config.Property(proptype=str) def setup(self): import os from drift.core import manager if not os.path.exists(self.product_directory): raise RuntimeError("Products do not exist.") pm = manager.ProductManager.from_config(self.product_directory) return pm class Truncate(task.SingleTask): dataset = config.Property(proptype=list, default=None) weight_dataset = config.Property(proptype=list, default=None) fixed_precision = config.Property(proptype=float, default=None) variance_increase = config.Property(proptype=float, default=None) def _get_params(self, container): if container in TRUNC_SPEC: self.log.info("Truncating from preset for container {}".format(container)) for key in [ "dataset", "weight_dataset", "fixed_precision", "variance_increase", ]: attr = getattr(self, key) if attr is None: setattr(self, key, TRUNC_SPEC[container][key]) else: self.log.info("Overriding container default for '{}'.".format(key)) else: if ( self.dataset is None or self.fixed_precision is None or self.variance_increase is None ): raise pipeline.PipelineConfigError( "Container {} has no preset values. You must define all of 'dataset', " "'fixed_precision', and 'variance_increase' properties.".format( container ) ) self.variance_increase *= 3 def process(self, data): self._get_params(type(data)) if self.weight_dataset is None: self.weight_dataset = [None] * len(self.dataset) for dset, wgt in zip(self.dataset, self.weight_dataset): old_shape = data[dset].local_shape val = np.ndarray.reshape(data[dset][:], data[dset][:].size) if wgt is None: if np.iscomplexobj(data[dset]): data[dset][:].real = bit_truncate_fixed( val.real, self.fixed_precision ).reshape(old_shape) data[dset][:].imag = bit_truncate_fixed( val.imag, self.fixed_precision ).reshape(old_shape) else: data[dset][:] = bit_truncate_fixed( val, self.fixed_precision ).reshape(old_shape) else: if data[dset][:].shape != data[wgt][:].shape: raise pipeline.PipelineRuntimeError( "Dataset and weight arrays must have same shape ({} != {})".format( data[dset].shape, data[wgt].shape ) ) invvar = np.ndarray.reshape(data[wgt][:], data[dset][:].size) if np.iscomplexobj(data[dset]): data[dset][:].real = bit_truncate_weights( val.real, invvar * 2.0 / self.variance_increase, self.fixed_precision, ).reshape(old_shape) data[dset][:].imag = bit_truncate_weights( val.imag, invvar * 2.0 / self.variance_increase, self.fixed_precision, ).reshape(old_shape) else: data[dset][:] = bit_truncate_weights( val, invvar / self.variance_increase, self.fixed_precision ).reshape(old_shape) return data class SaveModuleVersions(task.SingleTask): root = config.Property(proptype=str) done = True def setup(self): fname = "{}_versions.yml".format(self.root) f = open(fname, "w") f.write(yamldump(self.versions)) f.close() self.done = True def process(self): self.done = True return class SaveConfig(task.SingleTask): root = config.Property(proptype=str) done = True def setup(self): fname = "{}_config.yml".format(self.root) f = open(fname, "w") f.write(yamldump(self.pipeline_config)) f.close() self.done = True def process(self): self.done = True return def get_telescope(obj): from drift.core import telescope try: return get_beamtransfer(obj).telescope except RuntimeError: if isinstance(obj, telescope.TransitTelescope): return obj raise RuntimeError("Could not get telescope instance out of %s" % repr(obj)) def get_beamtransfer(obj): from drift.core import manager, beamtransfer if isinstance(obj, beamtransfer.BeamTransfer): return obj if isinstance(obj, manager.ProductManager): return obj.beamtransfer raise RuntimeError("Could not get BeamTransfer instance out of %s" % repr(obj))

true

f71b4f651dc252f16edc83bd218126c89ab19ffc

2,507

py

Python

scripts/remove_orphans.py

dbaio/portsfallout

2512036a9983b833f4ece2a0801541dca4d8d58c

[ "BSD-2-Clause" ]

6

2020-10-11T07:54:50.000Z

2022-01-25T22:03:18.000Z

scripts/remove_orphans.py

dbaio/portsfallout

2512036a9983b833f4ece2a0801541dca4d8d58c

[ "BSD-2-Clause" ]

null

scripts/remove_orphans.py

dbaio/portsfallout

2512036a9983b833f4ece2a0801541dca4d8d58c

[ "BSD-2-Clause" ]

null

# Copyright (c) 2020-2021 Danilo G. Baio <dbaio@bsd.com.br> # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # 1. Redistributions of source code must retain the above copyright notice, this # list of conditions and the following disclaimer. # # 2. Redistributions in binary form must reproduce the above copyright notice, # this list of conditions and the following disclaimer in the documentation # and/or other materials provided with the distribution. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND # ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED # WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE # DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE # FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL # DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR # SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER # CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, # OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. import os import sys import requests import bz2 sys.path.insert(1, r'../') os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'portsfallout.settings') import django django.setup() from ports.models import Port def fetch_index(): url = "https://www.FreeBSD.org/ports/INDEX-13.bz2" r = requests.get(url, allow_redirects=True) open('INDEX-13.bz2', 'wb').write(r.content) def populate_set(): Ports = Port.objects.all().values('origin').order_by('origin') sPorts = set() for port in Ports: sPorts.add(port['origin']) return sPorts def read_index(sPorts): with bz2.open('INDEX-13.bz2', mode='rt') as index_file: for row in index_file: row_list = row.split("|") p_origin = row_list[1].replace("/usr/ports/", "") if p_origin in sPorts: sPorts.remove(p_origin) return sPorts def remove_orphans(sPortsOrp): for port in sPortsOrp: print('Removing {}'.format(port)) Port.objects.filter(origin=port).delete() if __name__ == "__main__": fetch_index() sPorts = populate_set() sPortsOrp = read_index(sPorts) remove_orphans(sPortsOrp)

32.986842

81

0.723574

import os import sys import requests import bz2 sys.path.insert(1, r'../') os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'portsfallout.settings') import django django.setup() from ports.models import Port def fetch_index(): url = "https://www.FreeBSD.org/ports/INDEX-13.bz2" r = requests.get(url, allow_redirects=True) open('INDEX-13.bz2', 'wb').write(r.content) def populate_set(): Ports = Port.objects.all().values('origin').order_by('origin') sPorts = set() for port in Ports: sPorts.add(port['origin']) return sPorts def read_index(sPorts): with bz2.open('INDEX-13.bz2', mode='rt') as index_file: for row in index_file: row_list = row.split("|") p_origin = row_list[1].replace("/usr/ports/", "") if p_origin in sPorts: sPorts.remove(p_origin) return sPorts def remove_orphans(sPortsOrp): for port in sPortsOrp: print('Removing {}'.format(port)) Port.objects.filter(origin=port).delete() if __name__ == "__main__": fetch_index() sPorts = populate_set() sPortsOrp = read_index(sPorts) remove_orphans(sPortsOrp)

true

f71b510ba2e775050928e7d131af51fdd10e9af6

4,481

py

Python

scripts/mergemessages.py

burakozdemir32/django-internationalflavor

4c5d29519050c929a608d2054c14faa44ee273c9

[ "BSD-3-Clause" ]

null

scripts/mergemessages.py

burakozdemir32/django-internationalflavor

4c5d29519050c929a608d2054c14faa44ee273c9

[ "BSD-3-Clause" ]

null

scripts/mergemessages.py

burakozdemir32/django-internationalflavor

4c5d29519050c929a608d2054c14faa44ee273c9

[ "BSD-3-Clause" ]

null

import os import sys import polib import django from django.conf import settings from django.core.management.base import BaseCommand from django.utils import translation # This is almost a management command, but we do not want it to be added to the django-admin namespace for the simple # reason that it is not expected to be executed by package users, only by the package maintainers. # We use a thin __main__ wrapper to make it work (ish) like a management command. MODULE_PATH = os.path.join(os.path.dirname(os.path.realpath(__file__)), '..', 'internationalflavor') LOCALE_PATH = os.path.join(MODULE_PATH, 'locale') def mark_entry(entry): if 'fuzzy' in entry.flags: entry.flags.remove('fuzzy') entry.comment = "auto-generated from CLDR -- see docs before updating" class Command(BaseCommand): help = 'Updates messages in the PO file with messages from the CLDR' def handle(self, *args, **options): translation.deactivate_all() if options['l']: languages = (options['l'], dict(settings.LANGUAGES)[options['l']]), else: languages = settings.LANGUAGES for lc, language in languages: try: self.stdout.write("Parsing language %s [%s]" % (language, lc)) # Get some files ready # The pofile is our combined file pofile = polib.pofile(os.path.join(LOCALE_PATH, lc, 'LC_MESSAGES', 'django.po')) # The cldrfile contain only messages from CLDR cldrfile = polib.pofile(os.path.join(LOCALE_PATH, lc, 'LC_MESSAGES', 'cldr.po')) # The djangofile will only contain messages not from CLDR try: djangofile = polib.pofile(os.path.join(LOCALE_PATH, lc, 'LC_MESSAGES', 'django_only.po')) except IOError: djangofile = polib.POFile() djangofile.metadata = pofile.metadata djangofile.header = pofile.header # Merge all non-django messages to the djangofile django_only_messages = polib.POFile() for entry in pofile: if cldrfile.find(entry.msgid) is None and not entry.obsolete and not 'fuzzy' in entry.flags: django_only_messages.append(entry) djangofile.merge(django_only_messages) djangofile.save(os.path.join(LOCALE_PATH, lc, 'LC_MESSAGES', 'django_only.po')) # Add all entries from the CLDR file to the combined file for entry in cldrfile: e = pofile.find(entry.msgid) if e is None: e = polib.POEntry() e.msgid = entry.msgid pofile.append(e) elif 'manual' in e.tcomment.lower(): self.stdout.write("-- Skipping %s of %s" % (e.msgid, language)) continue e.obsolete = False e.msgstr = entry.msgstr e.comment = entry.comment if 'fuzzy' in e.flags: e.flags.remove('fuzzy') # Add entries from the Django file to the combined file for entry in djangofile: e = pofile.find(entry.msgid) # If not in main file, then skip if e is None: continue e.obsolete = entry.obsolete e.msgstr = entry.msgstr e.comment = entry.comment e.flags = entry.flags # We copy over the header and metadata from the djangofile. pofile.metadata = djangofile.metadata pofile.header = djangofile.header pofile.save() pofile.save_as_mofile(os.path.join(LOCALE_PATH, lc, 'LC_MESSAGES', 'django.mo')) except IOError as e: self.stderr.write("Error while handling %s: %s (possibly no valid .po file)" % (language, e)) except Exception as e: self.stderr.write("Error while handling %s: %s" % (language, e)) def add_arguments(self, parser): parser.add_argument('-l') if __name__ == '__main__': settings.configure() django.setup() Command().run_from_argv(["django-admin.py", "mergemessages"] + sys.argv[1:])

41.110092

117

0.567061

import os import sys import polib import django from django.conf import settings from django.core.management.base import BaseCommand from django.utils import translation MODULE_PATH = os.path.join(os.path.dirname(os.path.realpath(__file__)), '..', 'internationalflavor') LOCALE_PATH = os.path.join(MODULE_PATH, 'locale') def mark_entry(entry): if 'fuzzy' in entry.flags: entry.flags.remove('fuzzy') entry.comment = "auto-generated from CLDR -- see docs before updating" class Command(BaseCommand): help = 'Updates messages in the PO file with messages from the CLDR' def handle(self, *args, **options): translation.deactivate_all() if options['l']: languages = (options['l'], dict(settings.LANGUAGES)[options['l']]), else: languages = settings.LANGUAGES for lc, language in languages: try: self.stdout.write("Parsing language %s [%s]" % (language, lc)) pofile = polib.pofile(os.path.join(LOCALE_PATH, lc, 'LC_MESSAGES', 'django.po')) cldrfile = polib.pofile(os.path.join(LOCALE_PATH, lc, 'LC_MESSAGES', 'cldr.po')) try: djangofile = polib.pofile(os.path.join(LOCALE_PATH, lc, 'LC_MESSAGES', 'django_only.po')) except IOError: djangofile = polib.POFile() djangofile.metadata = pofile.metadata djangofile.header = pofile.header django_only_messages = polib.POFile() for entry in pofile: if cldrfile.find(entry.msgid) is None and not entry.obsolete and not 'fuzzy' in entry.flags: django_only_messages.append(entry) djangofile.merge(django_only_messages) djangofile.save(os.path.join(LOCALE_PATH, lc, 'LC_MESSAGES', 'django_only.po')) for entry in cldrfile: e = pofile.find(entry.msgid) if e is None: e = polib.POEntry() e.msgid = entry.msgid pofile.append(e) elif 'manual' in e.tcomment.lower(): self.stdout.write("-- Skipping %s of %s" % (e.msgid, language)) continue e.obsolete = False e.msgstr = entry.msgstr e.comment = entry.comment if 'fuzzy' in e.flags: e.flags.remove('fuzzy') for entry in djangofile: e = pofile.find(entry.msgid) if e is None: continue e.obsolete = entry.obsolete e.msgstr = entry.msgstr e.comment = entry.comment e.flags = entry.flags pofile.metadata = djangofile.metadata pofile.header = djangofile.header pofile.save() pofile.save_as_mofile(os.path.join(LOCALE_PATH, lc, 'LC_MESSAGES', 'django.mo')) except IOError as e: self.stderr.write("Error while handling %s: %s (possibly no valid .po file)" % (language, e)) except Exception as e: self.stderr.write("Error while handling %s: %s" % (language, e)) def add_arguments(self, parser): parser.add_argument('-l') if __name__ == '__main__': settings.configure() django.setup() Command().run_from_argv(["django-admin.py", "mergemessages"] + sys.argv[1:])

true

f71b518086dd6b504569820b3ca2a1c860242389

213

py

Python

wadeem/wadeem/doctype/coordinators/test_coordinators.py

siddhantsinha-oodles/Wadeem-app

eee05dead7ccee8878cf3630d3cdf32adb155c7f

[ "MIT" ]

null

wadeem/wadeem/doctype/coordinators/test_coordinators.py

siddhantsinha-oodles/Wadeem-app

eee05dead7ccee8878cf3630d3cdf32adb155c7f

[ "MIT" ]

null

wadeem/wadeem/doctype/coordinators/test_coordinators.py

siddhantsinha-oodles/Wadeem-app

eee05dead7ccee8878cf3630d3cdf32adb155c7f

[ "MIT" ]

null

# -*- coding: utf-8 -*- # Copyright (c) 2020, Siddhant and Contributors # See license.txt from __future__ import unicode_literals # import frappe import unittest class TestCoordinators(unittest.TestCase): pass

19.363636

47

0.765258

from __future__ import unicode_literals import unittest class TestCoordinators(unittest.TestCase): pass

true

f71b52d065d31336559f6dfb1eeccf44823e1409

2,392

py

Python

auto_nag/tests/test_agents.py

Mozilla-GitHub-Standards/f9c78643f5862cda82001d4471255ac29ef0c6b2c6171e2c1cbecab3d2fef4dd

28d999fcba9ad47d1dd0b2222880b71726ddd47c

[ "BSD-3-Clause" ]

null

auto_nag/tests/test_agents.py

Mozilla-GitHub-Standards/f9c78643f5862cda82001d4471255ac29ef0c6b2c6171e2c1cbecab3d2fef4dd

28d999fcba9ad47d1dd0b2222880b71726ddd47c

[ "BSD-3-Clause" ]

null

auto_nag/tests/test_agents.py

Mozilla-GitHub-Standards/f9c78643f5862cda82001d4471255ac29ef0c6b2c6171e2c1cbecab3d2fef4dd

28d999fcba9ad47d1dd0b2222880b71726ddd47c

[ "BSD-3-Clause" ]

null

try: # If relman-auto-nag is installed from bugzilla.agents import BMOAgent from bugzilla.utils import os from bugzilla.utils import get_config_path except: # If relman-auto-nag not installed, add project root directory into # PYTHONPATH import os import sys import inspect currentdir = os.path.dirname(os.path.abspath( inspect.getfile(inspect.currentframe()))) parentdir = os.path.dirname(currentdir) sys.path.insert(0, parentdir) from bugzilla.agents import BMOAgent from bugzilla.utils import os from bugzilla.utils import get_config_path import json from nose.tools import * class TestAgent: # Test bugzilla agent methods def test_get_bug_list(self): # Set whatever REST API options we want options = { 'chfieldfrom': ['2012-12-24'], 'chfieldto': ['2012-12-27'], 'chfield': ['bug_status'], 'chfieldvalue': ['RESOLVED'], 'product': ['Firefox'], 'resolution': ['FIXED'], 'include_fields': ['attachments'], } # Load our agent for BMO bmo = BMOAgent() # Get the bugs from the api buglist = bmo.get_bug_list(options) assert buglist != [] def test_get_bug(self): # Load our agent for BMO bmo = BMOAgent() # Get the bugs from the api bug = bmo.get_bug(656222) assert bug != [] @raises(Exception) def test_get_bug_list_wrng_api_k(self): """ Wrong API Key, it should raise an Error""" # Set whatever REST API options we want options = { 'chfieldfrom': ['2012-12-24'], 'chfieldto': ['2012-12-27'], 'chfield': ['bug_status'], 'chfieldvalue': ['RESOLVED'], 'product': ['Firefox'], 'resolution': ['FIXED'], 'include_fields': ['attachments'], } # Load our agent for BMO bmo = BMOAgent('wrong_api_key_test') # Get the bugs from the api bmo.get_bug_list(options) @raises(Exception) def test_get_bug_wrng_api_k(self): """ Wrong API Key, it should raise an Error""" # Load our agent for BMO bmo = BMOAgent('wrong_api_key_test') # Get the bugs from the api bug = bmo.get_bug(656222) print bug

30.278481

74

0.581522

try: from bugzilla.agents import BMOAgent from bugzilla.utils import os from bugzilla.utils import get_config_path except: import os import sys import inspect currentdir = os.path.dirname(os.path.abspath( inspect.getfile(inspect.currentframe()))) parentdir = os.path.dirname(currentdir) sys.path.insert(0, parentdir) from bugzilla.agents import BMOAgent from bugzilla.utils import os from bugzilla.utils import get_config_path import json from nose.tools import * class TestAgent: def test_get_bug_list(self): options = { 'chfieldfrom': ['2012-12-24'], 'chfieldto': ['2012-12-27'], 'chfield': ['bug_status'], 'chfieldvalue': ['RESOLVED'], 'product': ['Firefox'], 'resolution': ['FIXED'], 'include_fields': ['attachments'], } bmo = BMOAgent() buglist = bmo.get_bug_list(options) assert buglist != [] def test_get_bug(self): bmo = BMOAgent() bug = bmo.get_bug(656222) assert bug != [] @raises(Exception) def test_get_bug_list_wrng_api_k(self): """ Wrong API Key, it should raise an Error""" options = { 'chfieldfrom': ['2012-12-24'], 'chfieldto': ['2012-12-27'], 'chfield': ['bug_status'], 'chfieldvalue': ['RESOLVED'], 'product': ['Firefox'], 'resolution': ['FIXED'], 'include_fields': ['attachments'], } bmo = BMOAgent('wrong_api_key_test') bmo.get_bug_list(options) @raises(Exception) def test_get_bug_wrng_api_k(self): """ Wrong API Key, it should raise an Error""" bmo = BMOAgent('wrong_api_key_test') bug = bmo.get_bug(656222) print bug

false

true

f71b5347247e075de1cdb3afefef4dff57d2b7aa

802

py

Python

multiqc_npm/modules/npm_calculate_callability.py

c-BIG/MultiQC_NPM

c8dbf15c2b2ce03f2932db0bb50d49dcab865d75

[ "MIT" ]

null

multiqc_npm/modules/npm_calculate_callability.py

c-BIG/MultiQC_NPM

c8dbf15c2b2ce03f2932db0bb50d49dcab865d75

[ "MIT" ]

null

multiqc_npm/modules/npm_calculate_callability.py

c-BIG/MultiQC_NPM

c8dbf15c2b2ce03f2932db0bb50d49dcab865d75

[ "MIT" ]

null

#!/usr/bin/env python3 """ Parser for calculate_callability.py """ import logging import json from multiqc.utils import report log = logging.getLogger(__name__) def parse_reports(self): # Set up vars self.calculate_callability = dict() # Collect metrics for f in self.find_log_files('multiqc_npm/calculate_callability'): parsed_data = json.loads(f["f"]) # Save results s_name = f["s_name"] self.calculate_callability[s_name] = parsed_data # Write results if len(self.calculate_callability) > 0: # Write parsed data to a file self.write_data_file(self.calculate_callability, 'multiqc_npm_calculate_callability') # Return the number of detected samples to the parent module return len(self.calculate_callability)

22.914286

93

0.706983

import logging import json from multiqc.utils import report log = logging.getLogger(__name__) def parse_reports(self): self.calculate_callability = dict() for f in self.find_log_files('multiqc_npm/calculate_callability'): parsed_data = json.loads(f["f"]) s_name = f["s_name"] self.calculate_callability[s_name] = parsed_data if len(self.calculate_callability) > 0: self.write_data_file(self.calculate_callability, 'multiqc_npm_calculate_callability') return len(self.calculate_callability)

true

f71b54183262fd12198dc0255f4b1059568df90a

14,252

py

Python

airflow/models/connection.py

dorranh/airflow

1a9a2cadcf8606cfcb729d1323dd33dfacc64633

[ "Apache-2.0" ]

null

airflow/models/connection.py

dorranh/airflow

1a9a2cadcf8606cfcb729d1323dd33dfacc64633

[ "Apache-2.0" ]

1

2019-05-14T14:32:40.000Z

airflow/models/connection.py

dorranh/airflow

1a9a2cadcf8606cfcb729d1323dd33dfacc64633

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- # # Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you 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 json from urllib.parse import parse_qsl, quote, unquote, urlencode, urlparse from sqlalchemy import Boolean, Column, Integer, String from sqlalchemy.ext.declarative import declared_attr from sqlalchemy.orm import synonym from airflow.exceptions import AirflowException from airflow.models.base import ID_LEN, Base from airflow.models.crypto import get_fernet from airflow.utils.log.logging_mixin import LoggingMixin # Python automatically converts all letters to lowercase in hostname # See: https://issues.apache.org/jira/browse/AIRFLOW-3615 def parse_netloc_to_hostname(uri_parts): hostname = unquote(uri_parts.hostname or '') if '/' in hostname: hostname = uri_parts.netloc if "@" in hostname: hostname = hostname.rsplit("@", 1)[1] if ":" in hostname: hostname = hostname.split(":", 1)[0] hostname = unquote(hostname) return hostname class Connection(Base, LoggingMixin): """ Placeholder to store information about different database instances connection information. The idea here is that scripts use references to database instances (conn_id) instead of hard coding hostname, logins and passwords when using operators or hooks. """ __tablename__ = "connection" id = Column(Integer(), primary_key=True) conn_id = Column(String(ID_LEN)) conn_type = Column(String(500)) host = Column(String(500)) schema = Column(String(500)) login = Column(String(500)) _password = Column('password', String(5000)) port = Column(Integer()) is_encrypted = Column(Boolean, unique=False, default=False) is_extra_encrypted = Column(Boolean, unique=False, default=False) _extra = Column('extra', String(5000)) _types = [ ('docker', 'Docker Registry'), ('fs', 'File (path)'), ('ftp', 'FTP'), ('google_cloud_platform', 'Google Cloud Platform'), ('hdfs', 'HDFS'), ('http', 'HTTP'), ('pig_cli', 'Pig Client Wrapper'), ('hive_cli', 'Hive Client Wrapper'), ('hive_metastore', 'Hive Metastore Thrift'), ('hiveserver2', 'Hive Server 2 Thrift'), ('jdbc', 'JDBC Connection'), ('odbc', 'ODBC Connection'), ('jenkins', 'Jenkins'), ('mysql', 'MySQL'), ('postgres', 'Postgres'), ('oracle', 'Oracle'), ('vertica', 'Vertica'), ('presto', 'Presto'), ('s3', 'S3'), ('samba', 'Samba'), ('sqlite', 'Sqlite'), ('ssh', 'SSH'), ('cloudant', 'IBM Cloudant'), ('mssql', 'Microsoft SQL Server'), ('mesos_framework-id', 'Mesos Framework ID'), ('jira', 'JIRA'), ('redis', 'Redis'), ('wasb', 'Azure Blob Storage'), ('databricks', 'Databricks'), ('aws', 'Amazon Web Services'), ('emr', 'Elastic MapReduce'), ('snowflake', 'Snowflake'), ('segment', 'Segment'), ('sqoop', 'Sqoop'), ('azure_data_lake', 'Azure Data Lake'), ('azure_container_instances', 'Azure Container Instances'), ('azure_cosmos', 'Azure CosmosDB'), ('cassandra', 'Cassandra'), ('qubole', 'Qubole'), ('mongo', 'MongoDB'), ('gcpcloudsql', 'Google Cloud SQL'), ('grpc', 'GRPC Connection'), ] def __init__( self, conn_id=None, conn_type=None, host=None, login=None, password=None, schema=None, port=None, extra=None, uri=None): self.conn_id = conn_id if uri: self.parse_from_uri(uri) else: self.conn_type = conn_type self.host = host self.login = login self.password = password self.schema = schema self.port = port self.extra = extra def parse_from_uri(self, uri): uri_parts = urlparse(uri) conn_type = uri_parts.scheme if conn_type == 'postgresql': conn_type = 'postgres' elif '-' in conn_type: conn_type = conn_type.replace('-', '_') self.conn_type = conn_type self.host = parse_netloc_to_hostname(uri_parts) quoted_schema = uri_parts.path[1:] self.schema = unquote(quoted_schema) if quoted_schema else quoted_schema self.login = unquote(uri_parts.username) \ if uri_parts.username else uri_parts.username self.password = unquote(uri_parts.password) \ if uri_parts.password else uri_parts.password self.port = uri_parts.port if uri_parts.query: self.extra = json.dumps(dict(parse_qsl(uri_parts.query, keep_blank_values=True))) def get_uri(self) -> str: uri = '{}://'.format(str(self.conn_type).lower().replace('_', '-')) authority_block = '' if self.login is not None: authority_block += quote(self.login, safe='') if self.password is not None: authority_block += ':' + quote(self.password, safe='') if authority_block > '': authority_block += '@' uri += authority_block host_block = '' if self.host: host_block += quote(self.host, safe='') if self.port: if host_block > '': host_block += ':{}'.format(self.port) else: host_block += '@:{}'.format(self.port) if self.schema: host_block += '/{}'.format(quote(self.schema, safe='')) uri += host_block if self.extra_dejson: uri += '?{}'.format(urlencode(self.extra_dejson)) return uri def get_password(self): if self._password and self.is_encrypted: fernet = get_fernet() if not fernet.is_encrypted: raise AirflowException( "Can't decrypt encrypted password for login={}, \ FERNET_KEY configuration is missing".format(self.login)) return fernet.decrypt(bytes(self._password, 'utf-8')).decode() else: return self._password def set_password(self, value): if value: fernet = get_fernet() self._password = fernet.encrypt(bytes(value, 'utf-8')).decode() self.is_encrypted = fernet.is_encrypted @declared_attr def password(cls): return synonym('_password', descriptor=property(cls.get_password, cls.set_password)) def get_extra(self): if self._extra and self.is_extra_encrypted: fernet = get_fernet() if not fernet.is_encrypted: raise AirflowException( "Can't decrypt `extra` params for login={},\ FERNET_KEY configuration is missing".format(self.login)) return fernet.decrypt(bytes(self._extra, 'utf-8')).decode() else: return self._extra def set_extra(self, value): if value: fernet = get_fernet() self._extra = fernet.encrypt(bytes(value, 'utf-8')).decode() self.is_extra_encrypted = fernet.is_encrypted else: self._extra = value self.is_extra_encrypted = False @declared_attr def extra(cls): return synonym('_extra', descriptor=property(cls.get_extra, cls.set_extra)) def rotate_fernet_key(self): fernet = get_fernet() if self._password and self.is_encrypted: self._password = fernet.rotate(self._password.encode('utf-8')).decode() if self._extra and self.is_extra_encrypted: self._extra = fernet.rotate(self._extra.encode('utf-8')).decode() def get_hook(self): if self.conn_type == 'mysql': from airflow.providers.mysql.hooks.mysql import MySqlHook return MySqlHook(mysql_conn_id=self.conn_id) elif self.conn_type == 'google_cloud_platform': from airflow.providers.google.cloud.hooks.bigquery import BigQueryHook return BigQueryHook(bigquery_conn_id=self.conn_id) elif self.conn_type == 'postgres': from airflow.providers.postgres.hooks.postgres import PostgresHook return PostgresHook(postgres_conn_id=self.conn_id) elif self.conn_type == 'pig_cli': from airflow.providers.apache.pig.hooks.pig import PigCliHook return PigCliHook(pig_cli_conn_id=self.conn_id) elif self.conn_type == 'hive_cli': from airflow.providers.apache.hive.hooks.hive import HiveCliHook return HiveCliHook(hive_cli_conn_id=self.conn_id) elif self.conn_type == 'presto': from airflow.providers.presto.hooks.presto import PrestoHook return PrestoHook(presto_conn_id=self.conn_id) elif self.conn_type == 'hiveserver2': from airflow.providers.apache.hive.hooks.hive import HiveServer2Hook return HiveServer2Hook(hiveserver2_conn_id=self.conn_id) elif self.conn_type == 'sqlite': from airflow.providers.sqlite.hooks.sqlite import SqliteHook return SqliteHook(sqlite_conn_id=self.conn_id) elif self.conn_type == 'jdbc': from airflow.providers.jdbc.hooks.jdbc import JdbcHook return JdbcHook(jdbc_conn_id=self.conn_id) elif self.conn_type == 'mssql': from airflow.providers.microsoft.mssql.hooks.mssql import MsSqlHook return MsSqlHook(mssql_conn_id=self.conn_id) elif self.conn_type == 'odbc': from airflow.providers.odbc.hooks.odbc import OdbcHook return OdbcHook(odbc_conn_id=self.conn_id) elif self.conn_type == 'oracle': from airflow.providers.oracle.hooks.oracle import OracleHook return OracleHook(oracle_conn_id=self.conn_id) elif self.conn_type == 'vertica': from airflow.providers.vertica.hooks.vertica import VerticaHook return VerticaHook(vertica_conn_id=self.conn_id) elif self.conn_type == 'cloudant': from airflow.providers.cloudant.hooks.cloudant import CloudantHook return CloudantHook(cloudant_conn_id=self.conn_id) elif self.conn_type == 'jira': from airflow.providers.jira.hooks.jira import JiraHook return JiraHook(jira_conn_id=self.conn_id) elif self.conn_type == 'redis': from airflow.providers.redis.hooks.redis import RedisHook return RedisHook(redis_conn_id=self.conn_id) elif self.conn_type == 'wasb': from airflow.providers.microsoft.azure.hooks.wasb import WasbHook return WasbHook(wasb_conn_id=self.conn_id) elif self.conn_type == 'docker': from airflow.providers.docker.hooks.docker import DockerHook return DockerHook(docker_conn_id=self.conn_id) elif self.conn_type == 'azure_data_lake': from airflow.providers.microsoft.azure.hooks.azure_data_lake import AzureDataLakeHook return AzureDataLakeHook(azure_data_lake_conn_id=self.conn_id) elif self.conn_type == 'azure_cosmos': from airflow.providers.microsoft.azure.hooks.azure_cosmos import AzureCosmosDBHook return AzureCosmosDBHook(azure_cosmos_conn_id=self.conn_id) elif self.conn_type == 'cassandra': from airflow.providers.apache.cassandra.hooks.cassandra import CassandraHook return CassandraHook(cassandra_conn_id=self.conn_id) elif self.conn_type == 'mongo': from airflow.providers.mongo.hooks.mongo import MongoHook return MongoHook(conn_id=self.conn_id) elif self.conn_type == 'gcpcloudsql': from airflow.providers.google.cloud.hooks.cloud_sql import CloudSQLDatabaseHook return CloudSQLDatabaseHook(gcp_cloudsql_conn_id=self.conn_id) elif self.conn_type == 'grpc': from airflow.providers.grpc.hooks.grpc import GrpcHook return GrpcHook(grpc_conn_id=self.conn_id) raise AirflowException("Unknown hook type {}".format(self.conn_type)) def __repr__(self): return self.conn_id def log_info(self): return ("id: {}. Host: {}, Port: {}, Schema: {}, " "Login: {}, Password: {}, extra: {}". format(self.conn_id, self.host, self.port, self.schema, self.login, "XXXXXXXX" if self.password else None, "XXXXXXXX" if self.extra_dejson else None)) def debug_info(self): return ("id: {}. Host: {}, Port: {}, Schema: {}, " "Login: {}, Password: {}, extra: {}". format(self.conn_id, self.host, self.port, self.schema, self.login, "XXXXXXXX" if self.password else None, self.extra_dejson)) @property def extra_dejson(self): """Returns the extra property by deserializing json.""" obj = {} if self.extra: try: obj = json.loads(self.extra) except Exception as e: self.log.exception(e) self.log.error("Failed parsing the json for conn_id %s", self.conn_id) return obj

40.72

97

0.613388

import json from urllib.parse import parse_qsl, quote, unquote, urlencode, urlparse from sqlalchemy import Boolean, Column, Integer, String from sqlalchemy.ext.declarative import declared_attr from sqlalchemy.orm import synonym from airflow.exceptions import AirflowException from airflow.models.base import ID_LEN, Base from airflow.models.crypto import get_fernet from airflow.utils.log.logging_mixin import LoggingMixin def parse_netloc_to_hostname(uri_parts): hostname = unquote(uri_parts.hostname or '') if '/' in hostname: hostname = uri_parts.netloc if "@" in hostname: hostname = hostname.rsplit("@", 1)[1] if ":" in hostname: hostname = hostname.split(":", 1)[0] hostname = unquote(hostname) return hostname class Connection(Base, LoggingMixin): __tablename__ = "connection" id = Column(Integer(), primary_key=True) conn_id = Column(String(ID_LEN)) conn_type = Column(String(500)) host = Column(String(500)) schema = Column(String(500)) login = Column(String(500)) _password = Column('password', String(5000)) port = Column(Integer()) is_encrypted = Column(Boolean, unique=False, default=False) is_extra_encrypted = Column(Boolean, unique=False, default=False) _extra = Column('extra', String(5000)) _types = [ ('docker', 'Docker Registry'), ('fs', 'File (path)'), ('ftp', 'FTP'), ('google_cloud_platform', 'Google Cloud Platform'), ('hdfs', 'HDFS'), ('http', 'HTTP'), ('pig_cli', 'Pig Client Wrapper'), ('hive_cli', 'Hive Client Wrapper'), ('hive_metastore', 'Hive Metastore Thrift'), ('hiveserver2', 'Hive Server 2 Thrift'), ('jdbc', 'JDBC Connection'), ('odbc', 'ODBC Connection'), ('jenkins', 'Jenkins'), ('mysql', 'MySQL'), ('postgres', 'Postgres'), ('oracle', 'Oracle'), ('vertica', 'Vertica'), ('presto', 'Presto'), ('s3', 'S3'), ('samba', 'Samba'), ('sqlite', 'Sqlite'), ('ssh', 'SSH'), ('cloudant', 'IBM Cloudant'), ('mssql', 'Microsoft SQL Server'), ('mesos_framework-id', 'Mesos Framework ID'), ('jira', 'JIRA'), ('redis', 'Redis'), ('wasb', 'Azure Blob Storage'), ('databricks', 'Databricks'), ('aws', 'Amazon Web Services'), ('emr', 'Elastic MapReduce'), ('snowflake', 'Snowflake'), ('segment', 'Segment'), ('sqoop', 'Sqoop'), ('azure_data_lake', 'Azure Data Lake'), ('azure_container_instances', 'Azure Container Instances'), ('azure_cosmos', 'Azure CosmosDB'), ('cassandra', 'Cassandra'), ('qubole', 'Qubole'), ('mongo', 'MongoDB'), ('gcpcloudsql', 'Google Cloud SQL'), ('grpc', 'GRPC Connection'), ] def __init__( self, conn_id=None, conn_type=None, host=None, login=None, password=None, schema=None, port=None, extra=None, uri=None): self.conn_id = conn_id if uri: self.parse_from_uri(uri) else: self.conn_type = conn_type self.host = host self.login = login self.password = password self.schema = schema self.port = port self.extra = extra def parse_from_uri(self, uri): uri_parts = urlparse(uri) conn_type = uri_parts.scheme if conn_type == 'postgresql': conn_type = 'postgres' elif '-' in conn_type: conn_type = conn_type.replace('-', '_') self.conn_type = conn_type self.host = parse_netloc_to_hostname(uri_parts) quoted_schema = uri_parts.path[1:] self.schema = unquote(quoted_schema) if quoted_schema else quoted_schema self.login = unquote(uri_parts.username) \ if uri_parts.username else uri_parts.username self.password = unquote(uri_parts.password) \ if uri_parts.password else uri_parts.password self.port = uri_parts.port if uri_parts.query: self.extra = json.dumps(dict(parse_qsl(uri_parts.query, keep_blank_values=True))) def get_uri(self) -> str: uri = '{}://'.format(str(self.conn_type).lower().replace('_', '-')) authority_block = '' if self.login is not None: authority_block += quote(self.login, safe='') if self.password is not None: authority_block += ':' + quote(self.password, safe='') if authority_block > '': authority_block += '@' uri += authority_block host_block = '' if self.host: host_block += quote(self.host, safe='') if self.port: if host_block > '': host_block += ':{}'.format(self.port) else: host_block += '@:{}'.format(self.port) if self.schema: host_block += '/{}'.format(quote(self.schema, safe='')) uri += host_block if self.extra_dejson: uri += '?{}'.format(urlencode(self.extra_dejson)) return uri def get_password(self): if self._password and self.is_encrypted: fernet = get_fernet() if not fernet.is_encrypted: raise AirflowException( "Can't decrypt encrypted password for login={}, \ FERNET_KEY configuration is missing".format(self.login)) return fernet.decrypt(bytes(self._password, 'utf-8')).decode() else: return self._password def set_password(self, value): if value: fernet = get_fernet() self._password = fernet.encrypt(bytes(value, 'utf-8')).decode() self.is_encrypted = fernet.is_encrypted @declared_attr def password(cls): return synonym('_password', descriptor=property(cls.get_password, cls.set_password)) def get_extra(self): if self._extra and self.is_extra_encrypted: fernet = get_fernet() if not fernet.is_encrypted: raise AirflowException( "Can't decrypt `extra` params for login={},\ FERNET_KEY configuration is missing".format(self.login)) return fernet.decrypt(bytes(self._extra, 'utf-8')).decode() else: return self._extra def set_extra(self, value): if value: fernet = get_fernet() self._extra = fernet.encrypt(bytes(value, 'utf-8')).decode() self.is_extra_encrypted = fernet.is_encrypted else: self._extra = value self.is_extra_encrypted = False @declared_attr def extra(cls): return synonym('_extra', descriptor=property(cls.get_extra, cls.set_extra)) def rotate_fernet_key(self): fernet = get_fernet() if self._password and self.is_encrypted: self._password = fernet.rotate(self._password.encode('utf-8')).decode() if self._extra and self.is_extra_encrypted: self._extra = fernet.rotate(self._extra.encode('utf-8')).decode() def get_hook(self): if self.conn_type == 'mysql': from airflow.providers.mysql.hooks.mysql import MySqlHook return MySqlHook(mysql_conn_id=self.conn_id) elif self.conn_type == 'google_cloud_platform': from airflow.providers.google.cloud.hooks.bigquery import BigQueryHook return BigQueryHook(bigquery_conn_id=self.conn_id) elif self.conn_type == 'postgres': from airflow.providers.postgres.hooks.postgres import PostgresHook return PostgresHook(postgres_conn_id=self.conn_id) elif self.conn_type == 'pig_cli': from airflow.providers.apache.pig.hooks.pig import PigCliHook return PigCliHook(pig_cli_conn_id=self.conn_id) elif self.conn_type == 'hive_cli': from airflow.providers.apache.hive.hooks.hive import HiveCliHook return HiveCliHook(hive_cli_conn_id=self.conn_id) elif self.conn_type == 'presto': from airflow.providers.presto.hooks.presto import PrestoHook return PrestoHook(presto_conn_id=self.conn_id) elif self.conn_type == 'hiveserver2': from airflow.providers.apache.hive.hooks.hive import HiveServer2Hook return HiveServer2Hook(hiveserver2_conn_id=self.conn_id) elif self.conn_type == 'sqlite': from airflow.providers.sqlite.hooks.sqlite import SqliteHook return SqliteHook(sqlite_conn_id=self.conn_id) elif self.conn_type == 'jdbc': from airflow.providers.jdbc.hooks.jdbc import JdbcHook return JdbcHook(jdbc_conn_id=self.conn_id) elif self.conn_type == 'mssql': from airflow.providers.microsoft.mssql.hooks.mssql import MsSqlHook return MsSqlHook(mssql_conn_id=self.conn_id) elif self.conn_type == 'odbc': from airflow.providers.odbc.hooks.odbc import OdbcHook return OdbcHook(odbc_conn_id=self.conn_id) elif self.conn_type == 'oracle': from airflow.providers.oracle.hooks.oracle import OracleHook return OracleHook(oracle_conn_id=self.conn_id) elif self.conn_type == 'vertica': from airflow.providers.vertica.hooks.vertica import VerticaHook return VerticaHook(vertica_conn_id=self.conn_id) elif self.conn_type == 'cloudant': from airflow.providers.cloudant.hooks.cloudant import CloudantHook return CloudantHook(cloudant_conn_id=self.conn_id) elif self.conn_type == 'jira': from airflow.providers.jira.hooks.jira import JiraHook return JiraHook(jira_conn_id=self.conn_id) elif self.conn_type == 'redis': from airflow.providers.redis.hooks.redis import RedisHook return RedisHook(redis_conn_id=self.conn_id) elif self.conn_type == 'wasb': from airflow.providers.microsoft.azure.hooks.wasb import WasbHook return WasbHook(wasb_conn_id=self.conn_id) elif self.conn_type == 'docker': from airflow.providers.docker.hooks.docker import DockerHook return DockerHook(docker_conn_id=self.conn_id) elif self.conn_type == 'azure_data_lake': from airflow.providers.microsoft.azure.hooks.azure_data_lake import AzureDataLakeHook return AzureDataLakeHook(azure_data_lake_conn_id=self.conn_id) elif self.conn_type == 'azure_cosmos': from airflow.providers.microsoft.azure.hooks.azure_cosmos import AzureCosmosDBHook return AzureCosmosDBHook(azure_cosmos_conn_id=self.conn_id) elif self.conn_type == 'cassandra': from airflow.providers.apache.cassandra.hooks.cassandra import CassandraHook return CassandraHook(cassandra_conn_id=self.conn_id) elif self.conn_type == 'mongo': from airflow.providers.mongo.hooks.mongo import MongoHook return MongoHook(conn_id=self.conn_id) elif self.conn_type == 'gcpcloudsql': from airflow.providers.google.cloud.hooks.cloud_sql import CloudSQLDatabaseHook return CloudSQLDatabaseHook(gcp_cloudsql_conn_id=self.conn_id) elif self.conn_type == 'grpc': from airflow.providers.grpc.hooks.grpc import GrpcHook return GrpcHook(grpc_conn_id=self.conn_id) raise AirflowException("Unknown hook type {}".format(self.conn_type)) def __repr__(self): return self.conn_id def log_info(self): return ("id: {}. Host: {}, Port: {}, Schema: {}, " "Login: {}, Password: {}, extra: {}". format(self.conn_id, self.host, self.port, self.schema, self.login, "XXXXXXXX" if self.password else None, "XXXXXXXX" if self.extra_dejson else None)) def debug_info(self): return ("id: {}. Host: {}, Port: {}, Schema: {}, " "Login: {}, Password: {}, extra: {}". format(self.conn_id, self.host, self.port, self.schema, self.login, "XXXXXXXX" if self.password else None, self.extra_dejson)) @property def extra_dejson(self): obj = {} if self.extra: try: obj = json.loads(self.extra) except Exception as e: self.log.exception(e) self.log.error("Failed parsing the json for conn_id %s", self.conn_id) return obj

true

f71b5426abe6a21a2aafe3e9e5c4721319783d05

5,751

py

Python

original_author_notes/yolo_video.py

adam-blinzler/simple-lane-detection

8814e0aaf7ac56b7e5be59634e363ca17839effb

[ "MIT" ]

null

original_author_notes/yolo_video.py

adam-blinzler/simple-lane-detection

8814e0aaf7ac56b7e5be59634e363ca17839effb

[ "MIT" ]

null

original_author_notes/yolo_video.py

adam-blinzler/simple-lane-detection

8814e0aaf7ac56b7e5be59634e363ca17839effb

[ "MIT" ]

null

# USAGE # python yolo_video.py --input videos/airport.mp4 --output output/airport_output.avi --object_detection object_detection-coco # import the necessary packages import numpy as np import argparse import imutils import time import cv2 import os # construct the argument parse and parse the arguments ap = argparse.ArgumentParser() ap.add_argument("-i", "--input", required=True, help="path to input video") ap.add_argument("-o", "--output", required=True, help="path to output video") ap.add_argument("-y", "--object_detection", required=True, help="base path to YOLO directory") ap.add_argument("-c", "--confidence", type=float, default=0.5, help="minimum probability to filter weak detections") ap.add_argument("-t", "--threshold", type=float, default=0.3, help="threshold when applyong non-maxima suppression") args = vars(ap.parse_args()) # load the COCO class labels our YOLO model was trained on labelsPath = os.path.sep.join([args["object_detection"], "coco.names"]) LABELS = open(labelsPath).read().strip().split("\n") # initialize a list of colors to represent each possible class label np.random.seed(42) COLORS = np.random.randint(0, 255, size=(len(LABELS), 3), dtype="uint8") # derive the paths to the YOLO weights and model configuration weightsPath = os.path.sep.join([args["object_detection"], "yolov3.weights"]) configPath = os.path.sep.join([args["object_detection"], "yolov3.cfg"]) # load our YOLO object detector trained on COCO dataset (80 classes) # and determine only the *output* layer names that we need from YOLO print("[INFO] loading YOLO from disk...") net = cv2.dnn.readNetFromDarknet(configPath, weightsPath) ln = net.getLayerNames() ln = [ln[i[0] - 1] for i in net.getUnconnectedOutLayers()] # initialize the video stream, pointer to output video file, and # frame dimensions vs = cv2.VideoCapture(args["input"]) writer = None (W, H) = (None, None) # try to determine the total number of frames in the video file try: prop = cv2.cv.CV_CAP_PROP_FRAME_COUNT if imutils.is_cv2() \ else cv2.CAP_PROP_FRAME_COUNT total = int(vs.get(prop)) print("[INFO] {} total frames in video".format(total)) # an error occurred while trying to determine the total # number of frames in the video file except: print("[INFO] could not determine # of frames in video") print("[INFO] no approx. completion time can be provided") total = -1 # loop over frames from the video file stream while True: # read the next frame from the file (grabbed, frame) = vs.read() # if the frame was not grabbed, then we have reached the end # of the stream if not grabbed: break # if the frame dimensions are empty, grab them if W is None or H is None: (H, W) = frame.shape[:2] # construct a blob from the input frame and then perform a forward # pass of the YOLO object detector, giving us our bounding boxes # and associated probabilities blob = cv2.dnn.blobFromImage(frame, 1 / 255.0, (416, 416), swapRB=True, crop=False) net.setInput(blob) start = time.time() layerOutputs = net.forward(ln) end = time.time() # initialize our lists of detected bounding boxes, confidences, # and class IDs, respectively boxes = [] confidences = [] classIDs = [] # loop over each of the layer outputs for output in layerOutputs: # loop over each of the detections for detection in output: # extract the class ID and confidence (i.e., probability) # of the current object detection scores = detection[5:] classID = np.argmax(scores) confidence = scores[classID] # filter out weak predictions by ensuring the detected # probability is greater than the minimum probability if confidence > args["confidence"]: # scale the bounding box coordinates back relative to # the size of the image, keeping in mind that YOLO # actually returns the center (x, y)-coordinates of # the bounding box followed by the boxes' width and # height box = detection[0:4] * np.array([W, H, W, H]) (centerX, centerY, width, height) = box.astype("int") # use the center (x, y)-coordinates to derive the top # and and left corner of the bounding box x = int(centerX - (width / 2)) y = int(centerY - (height / 2)) # update our list of bounding box coordinates, # confidences, and class IDs boxes.append([x, y, int(width), int(height)]) confidences.append(float(confidence)) classIDs.append(classID) # apply non-maxima suppression to suppress weak, overlapping # bounding boxes idxs = cv2.dnn.NMSBoxes(boxes, confidences, args["confidence"], args["threshold"]) # ensure at least one detection exists if len(idxs) > 0: # loop over the indexes we are keeping for i in idxs.flatten(): # extract the bounding box coordinates (x, y) = (boxes[i][0], boxes[i][1]) (w, h) = (boxes[i][2], boxes[i][3]) # draw a bounding box rectangle and label on the frame color = [int(c) for c in COLORS[classIDs[i]]] cv2.rectangle(frame, (x, y), (x + w, y + h), color, 2) text = "{}: {:.4f}".format(LABELS[classIDs[i]], confidences[i]) cv2.putText(frame, text, (x, y - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2) # check if the video writer is None if writer is None: # initialize our video writer fourcc = cv2.VideoWriter_fourcc(*"MJPG") writer = cv2.VideoWriter(args["output"], fourcc, 30, (frame.shape[1], frame.shape[0]), True) # some information on processing single frame if total > 0: elap = (end - start) print("[INFO] single frame took {:.4f} seconds".format(elap)) print("[INFO] estimated total time to finish: {:.4f}".format( elap * total)) # write the output frame to disk writer.write(frame) # release the file pointers print("[INFO] cleaning up...") writer.release() vs.release()

34.029586

125

0.704051

import numpy as np import argparse import imutils import time import cv2 import os ap = argparse.ArgumentParser() ap.add_argument("-i", "--input", required=True, help="path to input video") ap.add_argument("-o", "--output", required=True, help="path to output video") ap.add_argument("-y", "--object_detection", required=True, help="base path to YOLO directory") ap.add_argument("-c", "--confidence", type=float, default=0.5, help="minimum probability to filter weak detections") ap.add_argument("-t", "--threshold", type=float, default=0.3, help="threshold when applyong non-maxima suppression") args = vars(ap.parse_args()) labelsPath = os.path.sep.join([args["object_detection"], "coco.names"]) LABELS = open(labelsPath).read().strip().split("\n") np.random.seed(42) COLORS = np.random.randint(0, 255, size=(len(LABELS), 3), dtype="uint8") weightsPath = os.path.sep.join([args["object_detection"], "yolov3.weights"]) configPath = os.path.sep.join([args["object_detection"], "yolov3.cfg"]) print("[INFO] loading YOLO from disk...") net = cv2.dnn.readNetFromDarknet(configPath, weightsPath) ln = net.getLayerNames() ln = [ln[i[0] - 1] for i in net.getUnconnectedOutLayers()] vs = cv2.VideoCapture(args["input"]) writer = None (W, H) = (None, None) try: prop = cv2.cv.CV_CAP_PROP_FRAME_COUNT if imutils.is_cv2() \ else cv2.CAP_PROP_FRAME_COUNT total = int(vs.get(prop)) print("[INFO] {} total frames in video".format(total)) except: print("[INFO] could not determine # of frames in video") print("[INFO] no approx. completion time can be provided") total = -1 while True: (grabbed, frame) = vs.read() if not grabbed: break if W is None or H is None: (H, W) = frame.shape[:2] blob = cv2.dnn.blobFromImage(frame, 1 / 255.0, (416, 416), swapRB=True, crop=False) net.setInput(blob) start = time.time() layerOutputs = net.forward(ln) end = time.time() boxes = [] confidences = [] classIDs = [] for output in layerOutputs: for detection in output: scores = detection[5:] classID = np.argmax(scores) confidence = scores[classID] if confidence > args["confidence"]: # height box = detection[0:4] * np.array([W, H, W, H]) (centerX, centerY, width, height) = box.astype("int") # use the center (x, y)-coordinates to derive the top # and and left corner of the bounding box x = int(centerX - (width / 2)) y = int(centerY - (height / 2)) # update our list of bounding box coordinates, # confidences, and class IDs boxes.append([x, y, int(width), int(height)]) confidences.append(float(confidence)) classIDs.append(classID) # apply non-maxima suppression to suppress weak, overlapping # bounding boxes idxs = cv2.dnn.NMSBoxes(boxes, confidences, args["confidence"], args["threshold"]) # ensure at least one detection exists if len(idxs) > 0: # loop over the indexes we are keeping for i in idxs.flatten(): # extract the bounding box coordinates (x, y) = (boxes[i][0], boxes[i][1]) (w, h) = (boxes[i][2], boxes[i][3]) # draw a bounding box rectangle and label on the frame color = [int(c) for c in COLORS[classIDs[i]]] cv2.rectangle(frame, (x, y), (x + w, y + h), color, 2) text = "{}: {:.4f}".format(LABELS[classIDs[i]], confidences[i]) cv2.putText(frame, text, (x, y - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2) # check if the video writer is None if writer is None: # initialize our video writer fourcc = cv2.VideoWriter_fourcc(*"MJPG") writer = cv2.VideoWriter(args["output"], fourcc, 30, (frame.shape[1], frame.shape[0]), True) # some information on processing single frame if total > 0: elap = (end - start) print("[INFO] single frame took {:.4f} seconds".format(elap)) print("[INFO] estimated total time to finish: {:.4f}".format( elap * total)) # write the output frame to disk writer.write(frame) # release the file pointers print("[INFO] cleaning up...") writer.release() vs.release()

true

f71b5468bb09f935a4b8dd8609a936248498eb63

7,498

py

Python

tessia/server/api/views/auth.py

tessia-project/tessia

b9ded8dc7f0b9a7a0ea00d95b5ccc4af4d2e7540

[ "Apache-2.0" ]

5

2020-06-04T10:20:33.000Z

2020-10-26T15:09:19.000Z

tessia/server/api/views/auth.py

tessia-project/tessia

b9ded8dc7f0b9a7a0ea00d95b5ccc4af4d2e7540

[ "Apache-2.0" ]

null

tessia/server/api/views/auth.py

tessia-project/tessia

b9ded8dc7f0b9a7a0ea00d95b5ccc4af4d2e7540

[ "Apache-2.0" ]

null

# Copyright 2016, 2017 IBM Corp. # # 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. """ Authentication routines """ # # IMPORTS # from base64 import b64decode from flask import g as flask_global from flask import request as flask_request from sqlalchemy.sql import func from tessia.server import auth from tessia.server.api.db import API_DB from tessia.server.api.exceptions import UnauthorizedError from tessia.server.config import CONF from tessia.server.db.models import User from tessia.server.db.models import UserKey # use the exception directly so that potion custom error handler can catch # it and convert to a valid json response from werkzeug.exceptions import BadRequest # # CONSTANTS AND DEFINITIONS # # # CODE # class _LoginManager: # holds the login manager object _manager = None @classmethod def get_login_manager(cls): """ Return the login manager object, as defined by the auth module. """ if cls._manager is None: cls._manager = auth.get_manager() return cls._manager # get_login_manager() @classmethod def authenticate_basic(cls, auth_value): """ Basic authentication with username and password, validate against the login manager defined in configured file (usually LDAP) Args: auth_value (str): the value part of the Authorization header form username:password base64 encoded Raises: BadRequest: if value is malformed UnauthorizedError: if credentials are invalid Returns: User: instance of User's sqlalchemy model """ try: # http headers are always ascii user, passwd = b64decode(auth_value).decode( 'ascii').split(':', 1) except Exception: raise BadRequest() case_sensitive = CONF.get_config().get( 'auth', {}).get('case_sensitive', False) if not case_sensitive: # logins should be case-insensitive user = user.lower() # user authentication with login provider failed: return unauthorized result = cls.get_login_manager().authenticate(user, passwd) if result is None: raise UnauthorizedError() # find user entry in database user_entry = User.query.filter_by(login=user).first() if user_entry is not None: # update db in case user information has changed changed = False if user_entry.name != result['fullname']: changed = True user_entry.name = result['fullname'] if user_entry.title != result.get('title', None): changed = True user_entry.title = result.get('title', None) if changed: API_DB.db.session.add(user_entry) API_DB.db.session.commit() return user_entry allow_auto_create = CONF.get_config().get( 'auth', {}).get('allow_user_auto_create', False) # auto creation of users not allowed: report unauthorized if not allow_auto_create: raise UnauthorizedError( msg='User authenticated but not registered in database') # create user in database new_user = User() if case_sensitive: new_user.login = result['login'] else: # save login as lowercase to avoid duplicates or user having to # worry about entering the right case new_user.login = result['login'].lower() new_user.name = result['fullname'] # job title is optional new_user.title = result.get('title', None) new_user.restricted = False new_user.admin = False API_DB.db.session.add(new_user) API_DB.db.session.commit() return new_user # authenticate_basic() @classmethod def authenticate_key(cls, auth_value): """ API key-based authentication Args: auth_value (str): the value part of the Authorization header in the form key_id:key_value Raises: BadRequest: if value is malformed UnauthorizedError: if credentials are invalid Returns: User: instance of User's sqlalchemy model """ try: # http headers are always ascii key_id, key_secret = auth_value.split(':', 1) except Exception: raise BadRequest() key_entry = UserKey.query.filter_by( key_id=key_id, key_secret=key_secret).first() if key_entry is None: raise UnauthorizedError() key_entry.last_used = func.now() API_DB.db.session.add(key_entry) API_DB.db.session.commit() return key_entry.user_rel # authenticate_key() # _LoginManager def authorize(decorated_view): """ A decorator view which implements authorization routine. Args: decorated_view (method): the view function to be decorated Returns: method: the authenticate wrapper containing the original view Raises: None """ def authenticate(*args, **kwargs): """ The wrapper that takes the authorization related actions before executing the actual view Args: args: packed args for the decorated view kwargs: packed keyargs for the decorated view Returns: any: the return value of the decorated view Raises: UnauthorizedError: if auth header is missing or invalid """ # no credentials provided: reply that authorization is needed. # The exception takes care of providing the scheme allowed # via WWW-Authenticate response header auth_header = flask_request.headers.get('Authorization', None) if not auth_header: raise UnauthorizedError(auth_provided=False) try: auth_scheme, auth_value = auth_header.split(None, 1) except ValueError: raise UnauthorizedError() auth_scheme = auth_scheme.lower() if auth_scheme == 'basic': user_entry = _LoginManager.authenticate_basic(auth_value) elif auth_scheme == 'x-key': user_entry = _LoginManager.authenticate_key(auth_value) else: # scheme not supported raise UnauthorizedError() # set model as session variable flask_global.auth_user = user_entry # pylint: disable=assigning-non-slot # this might be relevant depending on the nature of the operation. # i.e. api key operations are only allowed after entering password # (basic scheme) flask_global.auth_method = auth_scheme # pylint: disable=assigning-non-slot return decorated_view(*args, **kwargs) # authenticate() return authenticate # authorize()

31.771186

83

0.634169

from base64 import b64decode from flask import g as flask_global from flask import request as flask_request from sqlalchemy.sql import func from tessia.server import auth from tessia.server.api.db import API_DB from tessia.server.api.exceptions import UnauthorizedError from tessia.server.config import CONF from tessia.server.db.models import User from tessia.server.db.models import UserKey from werkzeug.exceptions import BadRequest class _LoginManager: _manager = None @classmethod def get_login_manager(cls): if cls._manager is None: cls._manager = auth.get_manager() return cls._manager @classmethod def authenticate_basic(cls, auth_value): try: user, passwd = b64decode(auth_value).decode( 'ascii').split(':', 1) except Exception: raise BadRequest() case_sensitive = CONF.get_config().get( 'auth', {}).get('case_sensitive', False) if not case_sensitive: user = user.lower() result = cls.get_login_manager().authenticate(user, passwd) if result is None: raise UnauthorizedError() user_entry = User.query.filter_by(login=user).first() if user_entry is not None: changed = False if user_entry.name != result['fullname']: changed = True user_entry.name = result['fullname'] if user_entry.title != result.get('title', None): changed = True user_entry.title = result.get('title', None) if changed: API_DB.db.session.add(user_entry) API_DB.db.session.commit() return user_entry allow_auto_create = CONF.get_config().get( 'auth', {}).get('allow_user_auto_create', False) if not allow_auto_create: raise UnauthorizedError( msg='User authenticated but not registered in database') new_user = User() if case_sensitive: new_user.login = result['login'] else: new_user.login = result['login'].lower() new_user.name = result['fullname'] new_user.title = result.get('title', None) new_user.restricted = False new_user.admin = False API_DB.db.session.add(new_user) API_DB.db.session.commit() return new_user @classmethod def authenticate_key(cls, auth_value): try: key_id, key_secret = auth_value.split(':', 1) except Exception: raise BadRequest() key_entry = UserKey.query.filter_by( key_id=key_id, key_secret=key_secret).first() if key_entry is None: raise UnauthorizedError() key_entry.last_used = func.now() API_DB.db.session.add(key_entry) API_DB.db.session.commit() return key_entry.user_rel def authorize(decorated_view): def authenticate(*args, **kwargs): auth_header = flask_request.headers.get('Authorization', None) if not auth_header: raise UnauthorizedError(auth_provided=False) try: auth_scheme, auth_value = auth_header.split(None, 1) except ValueError: raise UnauthorizedError() auth_scheme = auth_scheme.lower() if auth_scheme == 'basic': user_entry = _LoginManager.authenticate_basic(auth_value) elif auth_scheme == 'x-key': user_entry = _LoginManager.authenticate_key(auth_value) else: raise UnauthorizedError() flask_global.auth_user = user_entry flask_global.auth_method = auth_scheme return decorated_view(*args, **kwargs) return authenticate

true

f71b54b467c00b5f3318a9985c87b77ec8bf71f6

853

py

Python

med2img/tests/test_med2img.py

mohitchandarana/pl-med2img

c8c9df64a5e244d57e2fdb9ffadeea3c455eab23

[ "MIT" ]

4

2017-07-06T20:32:08.000Z

2021-09-18T02:46:55.000Z

med2img/tests/test_med2img.py

mohitchandarana/pl-med2img

c8c9df64a5e244d57e2fdb9ffadeea3c455eab23

[ "MIT" ]

1

2021-11-07T20:17:16.000Z

2021-11-08T20:11:48.000Z

med2img/tests/test_med2img.py

mohitchandarana/pl-med2img

c8c9df64a5e244d57e2fdb9ffadeea3c455eab23

[ "MIT" ]

3

2020-09-01T07:13:19.000Z

2021-03-25T16:06:39.000Z

from unittest import TestCase from unittest import mock from med2img.med2img import Med2img class Med2imgTests(TestCase): """ Test Med2img. """ def setUp(self): self.app = Med2img() def test_run(self): """ Test the run code. """ args = [] if self.app.TYPE == 'ds': args.append('inputdir') # you may want to change this inputdir mock args.append('outputdir') # you may want to change this outputdir mock # you may want to add more of your custom defined optional arguments to test # your app with # eg. # args.append('--custom-int') # args.append(10) options = self.app.parse_args(args) self.app.run(options) # write your own assertions self.assertEqual(options.outputdir, 'outputdir')

25.088235

84

0.594373

from unittest import TestCase from unittest import mock from med2img.med2img import Med2img class Med2imgTests(TestCase): def setUp(self): self.app = Med2img() def test_run(self): args = [] if self.app.TYPE == 'ds': args.append('inputdir') args.append('outputdir') options = self.app.parse_args(args) self.app.run(options) self.assertEqual(options.outputdir, 'outputdir')

true

f71b555a4703e5eb655f1c62d6c59060c0f772cf

6,488

py

Python

MPL/MPL_envs/reach/reach_v0.py

vikashplus/MPL

4a784fd94dc7a5988a1eca85851ee546ca1992f9

[ "Apache-2.0" ]

null

MPL/MPL_envs/reach/reach_v0.py

vikashplus/MPL

4a784fd94dc7a5988a1eca85851ee546ca1992f9

[ "Apache-2.0" ]

null

MPL/MPL_envs/reach/reach_v0.py

vikashplus/MPL

4a784fd94dc7a5988a1eca85851ee546ca1992f9

[ "Apache-2.0" ]

null

import numpy as np from gym import utils from mjrl.envs import mujoco_env from mujoco_py import MjViewer from MPL.MPL_robot.robot import Robot import os # TODO: Action normalization is missing class sallyReachEnv(mujoco_env.MujocoEnv, utils.EzPickle): def __init__(self, noise_scale=0.0): # prep utils.EzPickle.__init__(self) self._noise_scale = noise_scale self.initializing = True curr_dir = os.path.dirname(os.path.abspath(__file__)) self.Rtarget = 0 self.Ltarget = 0 self.Rgrasp = 0 self.Lgrasp = 0 # acquire robot self.mpl = Robot(name='sallyReach', model_file=curr_dir+'/reach_v0.xml', config_file=curr_dir+'/reach_v0.config') # acquire env mujoco_env.MujocoEnv.__init__(self, curr_dir+'/reach_v0.xml', 20) self.Rtarget = self.sim.model.site_name2id('Rtarget') self.Ltarget = self.sim.model.site_name2id('Ltarget') self.Rgrasp = self.sim.model.site_name2id('Rgrasp') self.Lgrasp = self.sim.model.site_name2id('Lgrasp') # env ready self.initializing = False def step(self, a): self.mpl.step(self, a, self.frame_skip*self.sim.model.opt.timestep) obs = self.get_obs() score, reward_dict, solved, done = self._get_score_reward_solved_done(self.obs_dict) # finalize step env_info = { 'time': self.obs_dict['t'], 'obs_dict': self.obs_dict, 'rewards': reward_dict, 'score': score, 'solved': solved } return obs, reward_dict['total'], done, env_info # query robot and populate observations def get_obs(self): # ask robot for sensor data sen = self.mpl.get_sensors(self, noise_scale=self._noise_scale) # parse sensor data into obs dict self.obs_dict = {} self.obs_dict['t'] = sen['time'] self.obs_dict['Tmpl_pos'] = sen['Tmpl_pos'] self.obs_dict['Rmpl_pos'] = sen['Rmpl_pos'] self.obs_dict['Lmpl_pos'] = sen['Lmpl_pos'] self.obs_dict['Tmpl_vel'] = sen['Tmpl_vel'] self.obs_dict['Rmpl_vel'] = sen['Rmpl_vel'] self.obs_dict['Lmpl_vel'] = sen['Lmpl_vel'] self.obs_dict['Rerr'] = self.sim.data.site_xpos[self.Rtarget]-self.sim.data.site_xpos[self.Rgrasp] self.obs_dict['Lerr'] = self.sim.data.site_xpos[self.Ltarget]-self.sim.data.site_xpos[self.Lgrasp] # vectorize observations return np.concatenate([ self.obs_dict['Tmpl_pos'], self.obs_dict['Rmpl_pos'], self.obs_dict['Lmpl_pos'], self.obs_dict['Tmpl_vel'], self.obs_dict['Rmpl_vel'], self.obs_dict['Lmpl_vel'], self.obs_dict['Lerr'], self.obs_dict['Rerr']]) # evaluate observations def _get_score_reward_solved_done(self, obs, act=None): Ldist = np.linalg.norm(obs['Lerr']) Rdist = np.linalg.norm(obs['Rerr']) # print(Rdist, Ldist) done = (bool( Ldist > 1.0) or bool(Rdist>1.0)) \ if not self.initializing else False reward_dict = {} avg_dist = (Ldist+Rdist)/2.0 score = -1.* avg_dist reward_dict["avg_dist"] = score reward_dict["small_bonus"] = 2.0*(Ldist<.1) + 2.0*(Rdist<.1) reward_dict["big_bonus"] = 2.0*(Ldist<.1) * 2.0*(Rdist<.1) reward_dict["total"] = reward_dict["avg_dist"] + reward_dict["small_bonus"] + reward_dict["big_bonus"] - 50.0 * int(done) solved = bool(avg_dist<0.100) return score, reward_dict, solved, done # reset model def reset_model(self): raise NotImplementedError # for child class to define # evaluate a path def compute_path_rewards(self, paths): # path has two keys: observations and actions # path["observations"] : (num_traj, horizon, obs_dim) # path["rewards"] should have shape (num_traj, horizon) obs = paths["observations"] score, rewards, done = self._get_score_reward_solved_done(obs) paths["rewards"] = rewards if rewards.shape[0] > 1 else rewards.ravel() # evaluate policy's success from a collection of paths def evaluate_success(self, paths, logger=None): success = 0.0 for p in paths: if np.mean(p['env_infos']['solved'][-4:]) > 0.0: success += 1.0 success_rate = 100.0*success/len(paths) if logger is None: # nowhere to log so return the value return success_rate else: # log the success # can log multiple statistics here if needed logger.log_kv('success_rate', success_rate) return None # -------------------------------- # get and set states # -------------------------------- def get_env_state(self): return dict(qp=self.data.qpos.copy(), qv=self.data.qvel.copy()) def set_env_state(self, state): self.sim.reset() qp = state['qp'].copy() qv = state['qv'].copy() self.set_state(qp, qv) self.sim.forward() # -------------------------------- # utility functions # -------------------------------- def get_env_infos(self): return dict(state=self.get_env_state()) def mj_viewer_setup(self): self.viewer = MjViewer(self.sim) self.viewer.cam.azimuth = -90 self.viewer.cam.distance = 2.5 self.viewer.cam.elevation = -30 self.sim.forward() def close_env(self): pass # Reach at fixed targets class sallyReachEnvFixed(sallyReachEnv): def __init__(self): super().__init__() def reset_model(self): self.sim.model.site_pos[self.Rtarget] = np.array([0.15, 0.2, .6]) self.sim.model.site_pos[self.Ltarget] = np.array([-0.15, 0.2, .3]) self.set_state(self.init_qpos, self.init_qvel) self.sim.forward() return self.get_obs() # Reach at random targets class sallyReachEnvRandom(sallyReachEnv): def __init__(self): super().__init__() def reset_model(self): self.sim.model.site_pos[self.Rtarget] = self.np_random.uniform(high=[0.5, .5, .6], low=[0, .1, .3]) self.sim.model.site_pos[self.Ltarget] = self.np_random.uniform(high=[0, .5, .6], low=[-.5, .1, .3]) self.set_state(self.init_qpos, self.init_qvel) self.sim.forward() return self.get_obs()

33.968586

130

0.594945

import numpy as np from gym import utils from mjrl.envs import mujoco_env from mujoco_py import MjViewer from MPL.MPL_robot.robot import Robot import os class sallyReachEnv(mujoco_env.MujocoEnv, utils.EzPickle): def __init__(self, noise_scale=0.0): utils.EzPickle.__init__(self) self._noise_scale = noise_scale self.initializing = True curr_dir = os.path.dirname(os.path.abspath(__file__)) self.Rtarget = 0 self.Ltarget = 0 self.Rgrasp = 0 self.Lgrasp = 0 self.mpl = Robot(name='sallyReach', model_file=curr_dir+'/reach_v0.xml', config_file=curr_dir+'/reach_v0.config') mujoco_env.MujocoEnv.__init__(self, curr_dir+'/reach_v0.xml', 20) self.Rtarget = self.sim.model.site_name2id('Rtarget') self.Ltarget = self.sim.model.site_name2id('Ltarget') self.Rgrasp = self.sim.model.site_name2id('Rgrasp') self.Lgrasp = self.sim.model.site_name2id('Lgrasp') self.initializing = False def step(self, a): self.mpl.step(self, a, self.frame_skip*self.sim.model.opt.timestep) obs = self.get_obs() score, reward_dict, solved, done = self._get_score_reward_solved_done(self.obs_dict) env_info = { 'time': self.obs_dict['t'], 'obs_dict': self.obs_dict, 'rewards': reward_dict, 'score': score, 'solved': solved } return obs, reward_dict['total'], done, env_info def get_obs(self): sen = self.mpl.get_sensors(self, noise_scale=self._noise_scale) self.obs_dict = {} self.obs_dict['t'] = sen['time'] self.obs_dict['Tmpl_pos'] = sen['Tmpl_pos'] self.obs_dict['Rmpl_pos'] = sen['Rmpl_pos'] self.obs_dict['Lmpl_pos'] = sen['Lmpl_pos'] self.obs_dict['Tmpl_vel'] = sen['Tmpl_vel'] self.obs_dict['Rmpl_vel'] = sen['Rmpl_vel'] self.obs_dict['Lmpl_vel'] = sen['Lmpl_vel'] self.obs_dict['Rerr'] = self.sim.data.site_xpos[self.Rtarget]-self.sim.data.site_xpos[self.Rgrasp] self.obs_dict['Lerr'] = self.sim.data.site_xpos[self.Ltarget]-self.sim.data.site_xpos[self.Lgrasp] return np.concatenate([ self.obs_dict['Tmpl_pos'], self.obs_dict['Rmpl_pos'], self.obs_dict['Lmpl_pos'], self.obs_dict['Tmpl_vel'], self.obs_dict['Rmpl_vel'], self.obs_dict['Lmpl_vel'], self.obs_dict['Lerr'], self.obs_dict['Rerr']]) def _get_score_reward_solved_done(self, obs, act=None): Ldist = np.linalg.norm(obs['Lerr']) Rdist = np.linalg.norm(obs['Rerr']) done = (bool( Ldist > 1.0) or bool(Rdist>1.0)) \ if not self.initializing else False reward_dict = {} avg_dist = (Ldist+Rdist)/2.0 score = -1.* avg_dist reward_dict["avg_dist"] = score reward_dict["small_bonus"] = 2.0*(Ldist<.1) + 2.0*(Rdist<.1) reward_dict["big_bonus"] = 2.0*(Ldist<.1) * 2.0*(Rdist<.1) reward_dict["total"] = reward_dict["avg_dist"] + reward_dict["small_bonus"] + reward_dict["big_bonus"] - 50.0 * int(done) solved = bool(avg_dist<0.100) return score, reward_dict, solved, done def reset_model(self): raise NotImplementedError def compute_path_rewards(self, paths): obs = paths["observations"] score, rewards, done = self._get_score_reward_solved_done(obs) paths["rewards"] = rewards if rewards.shape[0] > 1 else rewards.ravel() def evaluate_success(self, paths, logger=None): success = 0.0 for p in paths: if np.mean(p['env_infos']['solved'][-4:]) > 0.0: success += 1.0 success_rate = 100.0*success/len(paths) if logger is None: # nowhere to log so return the value return success_rate else: # log the success # can log multiple statistics here if needed logger.log_kv('success_rate', success_rate) return None # -------------------------------- # get and set states # -------------------------------- def get_env_state(self): return dict(qp=self.data.qpos.copy(), qv=self.data.qvel.copy()) def set_env_state(self, state): self.sim.reset() qp = state['qp'].copy() qv = state['qv'].copy() self.set_state(qp, qv) self.sim.forward() # -------------------------------- # utility functions # -------------------------------- def get_env_infos(self): return dict(state=self.get_env_state()) def mj_viewer_setup(self): self.viewer = MjViewer(self.sim) self.viewer.cam.azimuth = -90 self.viewer.cam.distance = 2.5 self.viewer.cam.elevation = -30 self.sim.forward() def close_env(self): pass # Reach at fixed targets class sallyReachEnvFixed(sallyReachEnv): def __init__(self): super().__init__() def reset_model(self): self.sim.model.site_pos[self.Rtarget] = np.array([0.15, 0.2, .6]) self.sim.model.site_pos[self.Ltarget] = np.array([-0.15, 0.2, .3]) self.set_state(self.init_qpos, self.init_qvel) self.sim.forward() return self.get_obs() # Reach at random targets class sallyReachEnvRandom(sallyReachEnv): def __init__(self): super().__init__() def reset_model(self): self.sim.model.site_pos[self.Rtarget] = self.np_random.uniform(high=[0.5, .5, .6], low=[0, .1, .3]) self.sim.model.site_pos[self.Ltarget] = self.np_random.uniform(high=[0, .5, .6], low=[-.5, .1, .3]) self.set_state(self.init_qpos, self.init_qvel) self.sim.forward() return self.get_obs()

true

f71b560fdbc2811c07e38208065e693e4befa940

40,786

py

Python

metaflow/plugins/aws/step_functions/step_functions.py

cclauss/metaflow

5186c6c5bba36d9e77077413ee2495dc79da3dca

[ "Apache-2.0" ]

1

2021-11-29T22:37:54.000Z

metaflow/plugins/aws/step_functions/step_functions.py

sappier/metaflow

dfe1a216e342555d5fb127607b35491a4ef11627

[ "Apache-2.0" ]

null

metaflow/plugins/aws/step_functions/step_functions.py

sappier/metaflow

dfe1a216e342555d5fb127607b35491a4ef11627

[ "Apache-2.0" ]

1

2021-11-29T22:37:50.000Z

import os from collections import defaultdict import sys import hashlib import json import time import string import random import uuid from metaflow.exception import MetaflowException, MetaflowInternalError from metaflow.plugins import ResourcesDecorator, BatchDecorator, RetryDecorator from metaflow.parameters import deploy_time_eval from metaflow.decorators import flow_decorators from metaflow.util import compress_list, dict_to_cli_options, to_pascalcase from metaflow.metaflow_config import ( SFN_IAM_ROLE, EVENTS_SFN_ACCESS_IAM_ROLE, SFN_DYNAMO_DB_TABLE, SFN_EXECUTION_LOG_GROUP_ARN, ) from metaflow import R from .step_functions_client import StepFunctionsClient from .event_bridge_client import EventBridgeClient from ..batch.batch import Batch class StepFunctionsException(MetaflowException): headline = "AWS Step Functions error" class StepFunctionsSchedulingException(MetaflowException): headline = "AWS Step Functions scheduling error" class StepFunctions(object): def __init__( self, name, graph, flow, code_package_sha, code_package_url, production_token, metadata, flow_datastore, environment, event_logger, monitor, tags=None, namespace=None, username=None, max_workers=None, workflow_timeout=None, is_project=False, ): self.name = name self.graph = graph self.flow = flow self.code_package_sha = code_package_sha self.code_package_url = code_package_url self.production_token = production_token self.metadata = metadata self.flow_datastore = flow_datastore self.environment = environment self.event_logger = event_logger self.monitor = monitor self.tags = tags self.namespace = namespace self.username = username self.max_workers = max_workers self.workflow_timeout = workflow_timeout self._client = StepFunctionsClient() self._workflow = self._compile() self._cron = self._cron() self._state_machine_arn = None def to_json(self): return self._workflow.to_json(pretty=True) def trigger_explanation(self): if self._cron: # Sometime in the future, we should vendor (or write) a utility # that can translate cron specifications into a human readable # format and push to the user for a better UX, someday. return ( "This workflow triggers automatically " "via a cron schedule *%s* defined in AWS EventBridge." % self.event_bridge_rule ) else: return "No triggers defined. " "You need to launch this workflow manually." def deploy(self, log_execution_history): if SFN_IAM_ROLE is None: raise StepFunctionsException( "No IAM role found for AWS Step " "Functions. You can create one " "following the instructions listed at " "*https://admin-docs.metaflow.org/meta" "flow-on-aws/deployment-guide/manual-d" "eployment#scheduling* and " "re-configure Metaflow using " "*metaflow configure aws* on your " "terminal." ) if log_execution_history: if SFN_EXECUTION_LOG_GROUP_ARN is None: raise StepFunctionsException( "No AWS CloudWatch Logs log " "group ARN found for emitting " "state machine execution logs for " "your workflow. You can set it in " "your environment by using the " "METAFLOW_SFN_EXECUTION_LOG_GROUP_ARN " "environment variable." ) try: self._state_machine_arn = self._client.push( name=self.name, definition=self.to_json(), role_arn=SFN_IAM_ROLE, log_execution_history=log_execution_history, ) except Exception as e: raise StepFunctionsException(repr(e)) def schedule(self): # Scheduling is currently enabled via AWS Event Bridge. if EVENTS_SFN_ACCESS_IAM_ROLE is None: raise StepFunctionsSchedulingException( "No IAM role found for AWS " "Events Bridge. You can " "create one following the " "instructions listed at " "*https://admin-docs.metaflo" "w.org/metaflow-on-aws/deplo" "yment-guide/manual-deployme" "nt#scheduling* and " "re-configure Metaflow " "using *metaflow configure " "aws* on your terminal." ) try: self.event_bridge_rule = ( EventBridgeClient(self.name) .cron(self._cron) .role_arn(EVENTS_SFN_ACCESS_IAM_ROLE) .state_machine_arn(self._state_machine_arn) .schedule() ) except Exception as e: raise StepFunctionsSchedulingException(repr(e)) @classmethod def trigger(cls, name, parameters): try: state_machine = StepFunctionsClient().get(name) except Exception as e: raise StepFunctionsException(repr(e)) if state_machine is None: raise StepFunctionsException( "The workflow *%s* doesn't exist " "on AWS Step Functions. Please " "deploy your flow first." % name ) # Dump parameters into `Parameters` input field. input = json.dumps({"Parameters": json.dumps(parameters)}) # AWS Step Functions limits input to be 32KiB, but AWS Batch # has it's own limitation of 30KiB for job specification length. # Reserving 10KiB for rest of the job sprecification leaves 20KiB # for us, which should be enough for most use cases for now. if len(input) > 20480: raise StepFunctionsException( "Length of parameter names and " "values shouldn't exceed 20480 as " "imposed by AWS Step Functions." ) try: state_machine_arn = state_machine.get("stateMachineArn") return StepFunctionsClient().trigger(state_machine_arn, input) except Exception as e: raise StepFunctionsException(repr(e)) @classmethod def list(cls, name, states): try: state_machine = StepFunctionsClient().get(name) except Exception as e: raise StepFunctionsException(repr(e)) if state_machine is None: raise StepFunctionsException( "The workflow *%s* doesn't exist " "on AWS Step Functions." % name ) try: state_machine_arn = state_machine.get("stateMachineArn") return StepFunctionsClient().list_executions(state_machine_arn, states) except Exception as e: raise StepFunctionsException(repr(e)) @classmethod def get_existing_deployment(cls, name): workflow = StepFunctionsClient().get(name) if workflow is not None: try: start = json.loads(workflow["definition"])["States"]["start"] parameters = start["Parameters"]["Parameters"] return parameters.get("metaflow.owner"), parameters.get( "metaflow.production_token" ) except KeyError as e: raise StepFunctionsException( "An existing non-metaflow " "workflow with the same name as " "*%s* already exists in AWS Step " "Functions. Please modify the " "name of this flow or delete your " "existing workflow on AWS Step " "Functions." % name ) return None def _compile(self): # Visit every node of the flow and recursively build the state machine. def _visit(node, workflow, exit_node=None): # Assign an AWS Batch job to the AWS Step Functions state # and pass the intermediate state by exposing `JobId` and # `Parameters` to the child job(s) as outputs. `Index` and # `SplitParentTaskId` are populated optionally, when available. # We can't modify the names of keys in AWS Step Functions aside # from a blessed few which are set as `Parameters` for the Map # state. That's why even though `JobId` refers to the parent task # id, we can't call it as such. Similar situation for `Parameters`. state = ( State(node.name) .batch(self._batch(node)) .output_path( "$.['JobId', " "'Parameters', " "'Index', " "'SplitParentTaskId']" ) ) # End the (sub)workflow if we have reached the end of the flow or # the parent step of matching_join of the sub workflow. if node.type == "end" or exit_node in node.out_funcs: workflow.add_state(state.end()) # Continue linear assignment within the (sub)workflow if the node # doesn't branch or fork. elif node.type in ("linear", "join"): workflow.add_state(state.next(node.out_funcs[0])) _visit(self.graph[node.out_funcs[0]], workflow, exit_node) # Create a `Parallel` state and assign sub workflows if the node # branches out. elif node.type == "split-and": branch_name = hashlib.sha224( "&".join(node.out_funcs).encode("utf-8") ).hexdigest() workflow.add_state(state.next(branch_name)) branch = Parallel(branch_name).next(node.matching_join) # Generate as many sub workflows as branches and recurse. for n in node.out_funcs: branch.branch( _visit( self.graph[n], Workflow(n).start_at(n), node.matching_join ) ) workflow.add_state(branch) # Continue the traversal from the matching_join. _visit(self.graph[node.matching_join], workflow, exit_node) # Create a `Map` state and assign sub workflow if the node forks. elif node.type == "foreach": # Fetch runtime cardinality via an AWS DynamoDb Get call before # configuring the node cardinality_state_name = "#%s" % node.out_funcs[0] workflow.add_state(state.next(cardinality_state_name)) cardinality_state = ( State(cardinality_state_name) .dynamo_db(SFN_DYNAMO_DB_TABLE, "$.JobId", "for_each_cardinality") .result_path("$.Result") ) iterator_name = "*%s" % node.out_funcs[0] workflow.add_state(cardinality_state.next(iterator_name)) workflow.add_state( Map(iterator_name) .items_path("$.Result.Item.for_each_cardinality.NS") .parameter("JobId.$", "$.JobId") .parameter("SplitParentTaskId.$", "$.JobId") .parameter("Parameters.$", "$.Parameters") .parameter("Index.$", "$$.Map.Item.Value") .next(node.matching_join) .iterator( _visit( self.graph[node.out_funcs[0]], Workflow(node.out_funcs[0]).start_at(node.out_funcs[0]), node.matching_join, ) ) .max_concurrency(self.max_workers) .output_path("$.[0]") ) # Continue the traversal from the matching_join. _visit(self.graph[node.matching_join], workflow, exit_node) # We shouldn't ideally ever get here. else: raise StepFunctionsException( "Node type *%s* for step *%s* " "is not currently supported by " "AWS Step Functions." % (node.type, node.name) ) return workflow workflow = Workflow(self.name).start_at("start") if self.workflow_timeout: workflow.timeout_seconds(self.workflow_timeout) return _visit(self.graph["start"], workflow) def _cron(self): schedule = self.flow._flow_decorators.get("schedule") if schedule: return schedule.schedule return None def _process_parameters(self): parameters = [] has_schedule = self._cron() is not None seen = set() for var, param in self.flow._get_parameters(): # Throw an exception if the parameter is specified twice. norm = param.name.lower() if norm in seen: raise MetaflowException( "Parameter *%s* is specified twice. " "Note that parameter names are " "case-insensitive." % param.name ) seen.add(norm) is_required = param.kwargs.get("required", False) # Throw an exception if a schedule is set for a flow with required # parameters with no defaults. We currently don't have any notion # of data triggers in AWS Event Bridge. if "default" not in param.kwargs and is_required and has_schedule: raise MetaflowException( "The parameter *%s* does not have a " "default and is required. Scheduling " "such parameters via AWS Event Bridge " "is not currently supported." % param.name ) value = deploy_time_eval(param.kwargs.get("default")) parameters.append(dict(name=param.name, value=value)) return parameters def _batch(self, node): attrs = { # metaflow.user is only used for setting the AWS Job Name. # Since job executions are no longer tied to a specific user # identity, we will just set their user to `SFN`. We still do need # access to the owner of the workflow for production tokens, which # we can stash in metaflow.owner. "metaflow.user": "SFN", "metaflow.owner": self.username, "metaflow.flow_name": self.flow.name, "metaflow.step_name": node.name, "metaflow.run_id.$": "$$.Execution.Name", # Unfortunately we can't set the task id here since AWS Step # Functions lacks any notion of run-scoped task identifiers. We # instead co-opt the AWS Batch job id as the task id. This also # means that the AWS Batch job name will have missing fields since # the job id is determined at job execution, but since the job id is # part of the job description payload, we don't lose much except for # a few ugly looking black fields in the AWS Batch UI. # Also, unfortunately we can't set the retry count since # `$$.State.RetryCount` resolves to an int dynamically and # AWS Batch job specification only accepts strings. We handle # retries/catch within AWS Batch to get around this limitation. "metaflow.version": self.environment.get_environment_info()[ "metaflow_version" ], # We rely on step names and task ids of parent steps to construct # input paths for a task. Since the only information we can pass # between states (via `InputPath` and `ResultPath`) in AWS Step # Functions is the job description, we run the risk of exceeding # 32K state size limit rather quickly if we don't filter the job # description to a minimal set of fields. Unfortunately, the partial # `JsonPath` implementation within AWS Step Functions makes this # work a little non-trivial; it doesn't like dots in keys, so we # have to add the field again. # This pattern is repeated in a lot of other places, where we use # AWS Batch parameters to store AWS Step Functions state # information, since this field is the only field in the AWS Batch # specification that allows us to set key-values. "step_name": node.name, } # Store production token within the `start` step, so that subsequent # `step-functions create` calls can perform a rudimentary authorization # check. if node.name == "start": attrs["metaflow.production_token"] = self.production_token # Add env vars from the optional @environment decorator. env_deco = [deco for deco in node.decorators if deco.name == "environment"] env = {} if env_deco: env = env_deco[0].attributes["vars"] if node.name == "start": # Initialize parameters for the flow in the `start` step. parameters = self._process_parameters() if parameters: # Get user-defined parameters from State Machine Input. # Since AWS Step Functions doesn't allow for optional inputs # currently, we have to unfortunately place an artificial # constraint that every parameterized workflow needs to include # `Parameters` as a key in the input to the workflow. # `step-functions trigger` already takes care of this # requirement, but within the UI, the users will be required to # specify an input with key as `Parameters` and value as a # stringified json of the actual parameters - # {"Parameters": "{\"alpha\": \"beta\"}"} env["METAFLOW_PARAMETERS"] = "$.Parameters" default_parameters = {} for parameter in parameters: if parameter["value"] is not None: default_parameters[parameter["name"]] = parameter["value"] # Dump the default values specified in the flow. env["METAFLOW_DEFAULT_PARAMETERS"] = json.dumps(default_parameters) # `start` step has no upstream input dependencies aside from # parameters. input_paths = None else: # We need to rely on the `InputPath` of the AWS Step Functions # specification to grab task ids and the step names of the parent # to properly construct input_paths at runtime. Thanks to the # JsonPath-foo embedded in the parent states, we have this # information easily available. # Handle foreach join. if ( node.type == "join" and self.graph[node.split_parents[-1]].type == "foreach" ): input_paths = ( "sfn-${METAFLOW_RUN_ID}/%s/:" "${METAFLOW_PARENT_TASK_IDS}" % node.in_funcs[0] ) # Unfortunately, AWS Batch only allows strings as value types # in it's specification and we don't have any way to concatenate # the task ids array from the parent steps within AWS Step # Functions and pass it down to AWS Batch. We instead have to # rely on publishing the state to DynamoDb and fetching it back # in within the AWS Batch entry point to set # `METAFLOW_PARENT_TASK_IDS`. The state is scoped to the parent # foreach task `METAFLOW_SPLIT_PARENT_TASK_ID`. We decided on # AWS DynamoDb and not AWS Lambdas, because deploying and # debugging Lambdas would be a nightmare as far as OSS support # is concerned. env["METAFLOW_SPLIT_PARENT_TASK_ID"] = ( "$.Parameters.split_parent_task_id_%s" % node.split_parents[-1] ) else: # Set appropriate environment variables for runtime replacement. if len(node.in_funcs) == 1: input_paths = ( "sfn-${METAFLOW_RUN_ID}/%s/${METAFLOW_PARENT_TASK_ID}" % node.in_funcs[0] ) env["METAFLOW_PARENT_TASK_ID"] = "$.JobId" else: # Generate the input paths in a quasi-compressed format. # See util.decompress_list for why this is written the way # it is. input_paths = "sfn-${METAFLOW_RUN_ID}:" + ",".join( "/${METAFLOW_PARENT_%s_STEP}/" "${METAFLOW_PARENT_%s_TASK_ID}" % (idx, idx) for idx, _ in enumerate(node.in_funcs) ) for idx, _ in enumerate(node.in_funcs): env["METAFLOW_PARENT_%s_TASK_ID" % idx] = "$.[%s].JobId" % idx env["METAFLOW_PARENT_%s_STEP" % idx] = ( "$.[%s].Parameters.step_name" % idx ) env["METAFLOW_INPUT_PATHS"] = input_paths if node.is_inside_foreach: # Set the task id of the parent job of the foreach split in # our favorite dumping ground, the AWS Batch attrs. For # subsequent descendent tasks, this attrs blob becomes the # input to those descendent tasks. We set and propagate the # task ids pointing to split_parents through every state. if any(self.graph[n].type == "foreach" for n in node.in_funcs): attrs[ "split_parent_task_id_%s.$" % node.split_parents[-1] ] = "$.SplitParentTaskId" for parent in node.split_parents[:-1]: if self.graph[parent].type == "foreach": attrs["split_parent_task_id_%s.$" % parent] = ( "$.Parameters.split_parent_task_id_%s" % parent ) elif node.type == "join": if self.graph[node.split_parents[-1]].type == "foreach": # A foreach join only gets one set of input from the # parent tasks. We filter the Map state to only output # `$.[0]`, since we don't need any of the other outputs, # that information is available to us from AWS DynamoDB. # This has a nice side-effect of making our foreach # splits infinitely scalable because otherwise we would # be bounded by the 32K state limit for the outputs. So, # instead of referencing `Parameters` fields by index # (like in `split-and`), we can just reference them # directly. attrs["split_parent_task_id_%s.$" % node.split_parents[-1]] = ( "$.Parameters.split_parent_task_id_%s" % node.split_parents[-1] ) for parent in node.split_parents[:-1]: if self.graph[parent].type == "foreach": attrs["split_parent_task_id_%s.$" % parent] = ( "$.Parameters.split_parent_task_id_%s" % parent ) else: for parent in node.split_parents: if self.graph[parent].type == "foreach": attrs["split_parent_task_id_%s.$" % parent] = ( "$.[0].Parameters.split_parent_task_id_%s" % parent ) else: for parent in node.split_parents: if self.graph[parent].type == "foreach": attrs["split_parent_task_id_%s.$" % parent] = ( "$.Parameters.split_parent_task_id_%s" % parent ) # Set `METAFLOW_SPLIT_PARENT_TASK_ID_FOR_FOREACH_JOIN` if the # next transition is to a foreach join, so that the # stepfunctions decorator can write the mapping for input path # to DynamoDb. if any( self.graph[n].type == "join" and self.graph[self.graph[n].split_parents[-1]].type == "foreach" for n in node.out_funcs ): env["METAFLOW_SPLIT_PARENT_TASK_ID_FOR_FOREACH_JOIN"] = attrs[ "split_parent_task_id_%s.$" % self.graph[node.out_funcs[0]].split_parents[-1] ] # Set ttl for the values we set in AWS DynamoDB. if node.type == "foreach": if self.workflow_timeout: env["METAFLOW_SFN_WORKFLOW_TIMEOUT"] = self.workflow_timeout # Handle split index for for-each. if any(self.graph[n].type == "foreach" for n in node.in_funcs): env["METAFLOW_SPLIT_INDEX"] = "$.Index" env["METAFLOW_CODE_URL"] = self.code_package_url env["METAFLOW_FLOW_NAME"] = attrs["metaflow.flow_name"] env["METAFLOW_STEP_NAME"] = attrs["metaflow.step_name"] env["METAFLOW_RUN_ID"] = attrs["metaflow.run_id.$"] env["METAFLOW_PRODUCTION_TOKEN"] = self.production_token env["SFN_STATE_MACHINE"] = self.name env["METAFLOW_OWNER"] = attrs["metaflow.owner"] # Can't set `METAFLOW_TASK_ID` due to lack of run-scoped identifiers. # We will instead rely on `AWS_BATCH_JOB_ID` as the task identifier. # Can't set `METAFLOW_RETRY_COUNT` either due to integer casting issue. metadata_env = self.metadata.get_runtime_environment("step-functions") env.update(metadata_env) metaflow_version = self.environment.get_environment_info() metaflow_version["flow_name"] = self.graph.name metaflow_version["production_token"] = self.production_token env["METAFLOW_VERSION"] = json.dumps(metaflow_version) # Set AWS DynamoDb Table Name for state tracking for for-eaches. # There are three instances when metaflow runtime directly interacts # with AWS DynamoDB. # 1. To set the cardinality of foreaches (which are subsequently) # read prior to the instantiation of the Map state by AWS Step # Functions. # 2. To set the input paths from the parent steps of a foreach join. # 3. To read the input paths in a foreach join. if ( node.type == "foreach" or ( node.is_inside_foreach and any( self.graph[n].type == "join" and self.graph[self.graph[n].split_parents[-1]].type == "foreach" for n in node.out_funcs ) ) or ( node.type == "join" and self.graph[node.split_parents[-1]].type == "foreach" ) ): if SFN_DYNAMO_DB_TABLE is None: raise StepFunctionsException( "An AWS DynamoDB table is needed " "to support foreach in your flow. " "You can create one following the " "instructions listed at *https://a" "dmin-docs.metaflow.org/metaflow-o" "n-aws/deployment-guide/manual-dep" "loyment#scheduling* and " "re-configure Metaflow using " "*metaflow configure aws* on your " "terminal." ) env["METAFLOW_SFN_DYNAMO_DB_TABLE"] = SFN_DYNAMO_DB_TABLE # Resolve AWS Batch resource requirements. batch_deco = [deco for deco in node.decorators if deco.name == "batch"][0] resources = batch_deco.attributes # Resolve retry strategy. user_code_retries, total_retries = self._get_retries(node) task_spec = { "flow_name": attrs["metaflow.flow_name"], "step_name": attrs["metaflow.step_name"], "run_id": "sfn-$METAFLOW_RUN_ID", # Use AWS Batch job identifier as the globally unique # task identifier. "task_id": "$AWS_BATCH_JOB_ID", # Since retries are handled by AWS Batch, we can rely on # AWS_BATCH_JOB_ATTEMPT as the job counter. "retry_count": "$((AWS_BATCH_JOB_ATTEMPT-1))", } return ( Batch(self.metadata, self.environment) .create_job( step_name=node.name, step_cli=self._step_cli( node, input_paths, self.code_package_url, user_code_retries ), task_spec=task_spec, code_package_sha=self.code_package_sha, code_package_url=self.code_package_url, code_package_ds=self.flow_datastore.TYPE, image=resources["image"], queue=resources["queue"], iam_role=resources["iam_role"], execution_role=resources["execution_role"], cpu=resources["cpu"], gpu=resources["gpu"], memory=resources["memory"], run_time_limit=batch_deco.run_time_limit, shared_memory=resources["shared_memory"], max_swap=resources["max_swap"], swappiness=resources["swappiness"], env=env, attrs=attrs, host_volumes=resources["host_volumes"], ) .attempts(total_retries + 1) ) def _get_retries(self, node): max_user_code_retries = 0 max_error_retries = 0 # Different decorators may have different retrying strategies, so take # the max of them. for deco in node.decorators: user_code_retries, error_retries = deco.step_task_retry_count() max_user_code_retries = max(max_user_code_retries, user_code_retries) max_error_retries = max(max_error_retries, error_retries) return max_user_code_retries, max_user_code_retries + max_error_retries def _step_cli(self, node, paths, code_package_url, user_code_retries): cmds = [] script_name = os.path.basename(sys.argv[0]) executable = self.environment.executable(node.name) if R.use_r(): entrypoint = [R.entrypoint()] else: entrypoint = [executable, script_name] # Use AWS Batch job identifier as the globally unique task identifier. task_id = "${AWS_BATCH_JOB_ID}" # FlowDecorators can define their own top-level options. They are # responsible for adding their own top-level options and values through # the get_top_level_options() hook. See similar logic in runtime.py. top_opts_dict = {} for deco in flow_decorators(): top_opts_dict.update(deco.get_top_level_options()) top_opts = list(dict_to_cli_options(top_opts_dict)) if node.name == "start": # We need a separate unique ID for the special _parameters task task_id_params = "%s-params" % task_id # Export user-defined parameters into runtime environment param_file = "".join( random.choice(string.ascii_lowercase) for _ in range(10) ) export_params = ( "python -m " "metaflow.plugins.aws.step_functions.set_batch_environment " "parameters %s && . `pwd`/%s" % (param_file, param_file) ) params = ( entrypoint + top_opts + [ "--quiet", "--metadata=%s" % self.metadata.TYPE, "--environment=%s" % self.environment.TYPE, "--datastore=s3", "--event-logger=%s" % self.event_logger.logger_type, "--monitor=%s" % self.monitor.monitor_type, "--no-pylint", "init", "--run-id sfn-$METAFLOW_RUN_ID", "--task-id %s" % task_id_params, ] ) # Assign tags to run objects. if self.tags: params.extend("--tag %s" % tag for tag in self.tags) # If the start step gets retried, we must be careful not to # regenerate multiple parameters tasks. Hence we check first if # _parameters exists already. exists = entrypoint + [ "dump", "--max-value-size=0", "sfn-${METAFLOW_RUN_ID}/_parameters/%s" % (task_id_params), ] cmd = "if ! %s >/dev/null 2>/dev/null; then %s && %s; fi" % ( " ".join(exists), export_params, " ".join(params), ) cmds.append(cmd) paths = "sfn-${METAFLOW_RUN_ID}/_parameters/%s" % (task_id_params) if node.type == "join" and self.graph[node.split_parents[-1]].type == "foreach": parent_tasks_file = "".join( random.choice(string.ascii_lowercase) for _ in range(10) ) export_parent_tasks = ( "python -m " "metaflow.plugins.aws.step_functions.set_batch_environment " "parent_tasks %s && . `pwd`/%s" % (parent_tasks_file, parent_tasks_file) ) cmds.append(export_parent_tasks) top_level = top_opts + [ "--quiet", "--metadata=%s" % self.metadata.TYPE, "--environment=%s" % self.environment.TYPE, "--datastore=%s" % self.flow_datastore.TYPE, "--datastore-root=%s" % self.flow_datastore.datastore_root, "--event-logger=%s" % self.event_logger.logger_type, "--monitor=%s" % self.monitor.monitor_type, "--no-pylint", "--with=step_functions_internal", ] step = [ "step", node.name, "--run-id sfn-$METAFLOW_RUN_ID", "--task-id %s" % task_id, # Since retries are handled by AWS Batch, we can rely on # AWS_BATCH_JOB_ATTEMPT as the job counter. "--retry-count $((AWS_BATCH_JOB_ATTEMPT-1))", "--max-user-code-retries %d" % user_code_retries, "--input-paths %s" % paths, # Set decorator to batch to execute `task_*` hooks for batch # decorator. "--with=batch", ] if any(self.graph[n].type == "foreach" for n in node.in_funcs): # We set the `METAFLOW_SPLIT_INDEX` through JSONPath-foo # to pass the state from the parent DynamoDb state for for-each. step.append("--split-index $METAFLOW_SPLIT_INDEX") if self.tags: step.extend("--tag %s" % tag for tag in self.tags) if self.namespace is not None: step.append("--namespace=%s" % self.namespace) cmds.append(" ".join(entrypoint + top_level + step)) return " && ".join(cmds) class Workflow(object): def __init__(self, name): self.name = name tree = lambda: defaultdict(tree) self.payload = tree() def start_at(self, start_at): self.payload["StartAt"] = start_at return self def add_state(self, state): self.payload["States"][state.name] = state.payload return self def timeout_seconds(self, timeout_seconds): self.payload["TimeoutSeconds"] = timeout_seconds return self def to_json(self, pretty=False): return json.dumps(self.payload, indent=4 if pretty else None) class State(object): def __init__(self, name): self.name = name tree = lambda: defaultdict(tree) self.payload = tree() self.payload["Type"] = "Task" def resource(self, resource): self.payload["Resource"] = resource return self def next(self, state): self.payload["Next"] = state return self def end(self): self.payload["End"] = True return self def parameter(self, name, value): self.payload["Parameters"][name] = value return self def output_path(self, output_path): self.payload["OutputPath"] = output_path return self def result_path(self, result_path): self.payload["ResultPath"] = result_path return self def _partition(self): # This is needed to support AWS Gov Cloud and AWS CN regions return SFN_IAM_ROLE.split(":")[1] def batch(self, job): self.resource( "arn:%s:states:::batch:submitJob.sync" % self._partition() ).parameter("JobDefinition", job.payload["jobDefinition"]).parameter( "JobName", job.payload["jobName"] ).parameter( "JobQueue", job.payload["jobQueue"] ).parameter( "Parameters", job.payload["parameters"] ).parameter( "ContainerOverrides", to_pascalcase(job.payload["containerOverrides"]) ).parameter( "RetryStrategy", to_pascalcase(job.payload["retryStrategy"]) ).parameter( "Timeout", to_pascalcase(job.payload["timeout"]) ) # tags may not be present in all scenarios if "tags" in job.payload: self.parameter("Tags", job.payload["tags"]) return self def dynamo_db(self, table_name, primary_key, values): self.resource("arn:%s:states:::dynamodb:getItem" % self._partition()).parameter( "TableName", table_name ).parameter("Key", {"pathspec": {"S.$": primary_key}}).parameter( "ConsistentRead", True ).parameter( "ProjectionExpression", values ) return self class Parallel(object): def __init__(self, name): self.name = name tree = lambda: defaultdict(tree) self.payload = tree() self.payload["Type"] = "Parallel" def branch(self, workflow): if "Branches" not in self.payload: self.payload["Branches"] = [] self.payload["Branches"].append(workflow.payload) return self def next(self, state): self.payload["Next"] = state return self def output_path(self, output_path): self.payload["OutputPath"] = output_path return self def result_path(self, result_path): self.payload["ResultPath"] = result_path return self class Map(object): def __init__(self, name): self.name = name tree = lambda: defaultdict(tree) self.payload = tree() self.payload["Type"] = "Map" self.payload["MaxConcurrency"] = 0 def iterator(self, workflow): self.payload["Iterator"] = workflow.payload return self def next(self, state): self.payload["Next"] = state return self def items_path(self, items_path): self.payload["ItemsPath"] = items_path return self def parameter(self, name, value): self.payload["Parameters"][name] = value return self def max_concurrency(self, max_concurrency): self.payload["MaxConcurrency"] = max_concurrency return self def output_path(self, output_path): self.payload["OutputPath"] = output_path return self def result_path(self, result_path): self.payload["ResultPath"] = result_path return self

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import os from collections import defaultdict import sys import hashlib import json import time import string import random import uuid from metaflow.exception import MetaflowException, MetaflowInternalError from metaflow.plugins import ResourcesDecorator, BatchDecorator, RetryDecorator from metaflow.parameters import deploy_time_eval from metaflow.decorators import flow_decorators from metaflow.util import compress_list, dict_to_cli_options, to_pascalcase from metaflow.metaflow_config import ( SFN_IAM_ROLE, EVENTS_SFN_ACCESS_IAM_ROLE, SFN_DYNAMO_DB_TABLE, SFN_EXECUTION_LOG_GROUP_ARN, ) from metaflow import R from .step_functions_client import StepFunctionsClient from .event_bridge_client import EventBridgeClient from ..batch.batch import Batch class StepFunctionsException(MetaflowException): headline = "AWS Step Functions error" class StepFunctionsSchedulingException(MetaflowException): headline = "AWS Step Functions scheduling error" class StepFunctions(object): def __init__( self, name, graph, flow, code_package_sha, code_package_url, production_token, metadata, flow_datastore, environment, event_logger, monitor, tags=None, namespace=None, username=None, max_workers=None, workflow_timeout=None, is_project=False, ): self.name = name self.graph = graph self.flow = flow self.code_package_sha = code_package_sha self.code_package_url = code_package_url self.production_token = production_token self.metadata = metadata self.flow_datastore = flow_datastore self.environment = environment self.event_logger = event_logger self.monitor = monitor self.tags = tags self.namespace = namespace self.username = username self.max_workers = max_workers self.workflow_timeout = workflow_timeout self._client = StepFunctionsClient() self._workflow = self._compile() self._cron = self._cron() self._state_machine_arn = None def to_json(self): return self._workflow.to_json(pretty=True) def trigger_explanation(self): if self._cron: return ( "This workflow triggers automatically " "via a cron schedule *%s* defined in AWS EventBridge." % self.event_bridge_rule ) else: return "No triggers defined. " "You need to launch this workflow manually." def deploy(self, log_execution_history): if SFN_IAM_ROLE is None: raise StepFunctionsException( "No IAM role found for AWS Step " "Functions. You can create one " "following the instructions listed at " "*https://admin-docs.metaflow.org/meta" "flow-on-aws/deployment-guide/manual-d" "eployment#scheduling* and " "re-configure Metaflow using " "*metaflow configure aws* on your " "terminal." ) if log_execution_history: if SFN_EXECUTION_LOG_GROUP_ARN is None: raise StepFunctionsException( "No AWS CloudWatch Logs log " "group ARN found for emitting " "state machine execution logs for " "your workflow. You can set it in " "your environment by using the " "METAFLOW_SFN_EXECUTION_LOG_GROUP_ARN " "environment variable." ) try: self._state_machine_arn = self._client.push( name=self.name, definition=self.to_json(), role_arn=SFN_IAM_ROLE, log_execution_history=log_execution_history, ) except Exception as e: raise StepFunctionsException(repr(e)) def schedule(self): if EVENTS_SFN_ACCESS_IAM_ROLE is None: raise StepFunctionsSchedulingException( "No IAM role found for AWS " "Events Bridge. You can " "create one following the " "instructions listed at " "*https://admin-docs.metaflo" "w.org/metaflow-on-aws/deplo" "yment-guide/manual-deployme" "nt#scheduling* and " "re-configure Metaflow " "using *metaflow configure " "aws* on your terminal." ) try: self.event_bridge_rule = ( EventBridgeClient(self.name) .cron(self._cron) .role_arn(EVENTS_SFN_ACCESS_IAM_ROLE) .state_machine_arn(self._state_machine_arn) .schedule() ) except Exception as e: raise StepFunctionsSchedulingException(repr(e)) @classmethod def trigger(cls, name, parameters): try: state_machine = StepFunctionsClient().get(name) except Exception as e: raise StepFunctionsException(repr(e)) if state_machine is None: raise StepFunctionsException( "The workflow *%s* doesn't exist " "on AWS Step Functions. Please " "deploy your flow first." % name ) # Dump parameters into `Parameters` input field. input = json.dumps({"Parameters": json.dumps(parameters)}) # AWS Step Functions limits input to be 32KiB, but AWS Batch # has it's own limitation of 30KiB for job specification length. if len(input) > 20480: raise StepFunctionsException( "Length of parameter names and " "values shouldn't exceed 20480 as " "imposed by AWS Step Functions." ) try: state_machine_arn = state_machine.get("stateMachineArn") return StepFunctionsClient().trigger(state_machine_arn, input) except Exception as e: raise StepFunctionsException(repr(e)) @classmethod def list(cls, name, states): try: state_machine = StepFunctionsClient().get(name) except Exception as e: raise StepFunctionsException(repr(e)) if state_machine is None: raise StepFunctionsException( "The workflow *%s* doesn't exist " "on AWS Step Functions." % name ) try: state_machine_arn = state_machine.get("stateMachineArn") return StepFunctionsClient().list_executions(state_machine_arn, states) except Exception as e: raise StepFunctionsException(repr(e)) @classmethod def get_existing_deployment(cls, name): workflow = StepFunctionsClient().get(name) if workflow is not None: try: start = json.loads(workflow["definition"])["States"]["start"] parameters = start["Parameters"]["Parameters"] return parameters.get("metaflow.owner"), parameters.get( "metaflow.production_token" ) except KeyError as e: raise StepFunctionsException( "An existing non-metaflow " "workflow with the same name as " "*%s* already exists in AWS Step " "Functions. Please modify the " "name of this flow or delete your " "existing workflow on AWS Step " "Functions." % name ) return None def _compile(self): def _visit(node, workflow, exit_node=None): # from a blessed few which are set as `Parameters` for the Map # state. That's why even though `JobId` refers to the parent task state = ( State(node.name) .batch(self._batch(node)) .output_path( "$.['JobId', " "'Parameters', " "'Index', " "'SplitParentTaskId']" ) ) # End the (sub)workflow if we have reached the end of the flow or # the parent step of matching_join of the sub workflow. if node.type == "end" or exit_node in node.out_funcs: workflow.add_state(state.end()) # Continue linear assignment within the (sub)workflow if the node # doesn't branch or fork. elif node.type in ("linear", "join"): workflow.add_state(state.next(node.out_funcs[0])) _visit(self.graph[node.out_funcs[0]], workflow, exit_node) elif node.type == "split-and": branch_name = hashlib.sha224( "&".join(node.out_funcs).encode("utf-8") ).hexdigest() workflow.add_state(state.next(branch_name)) branch = Parallel(branch_name).next(node.matching_join) for n in node.out_funcs: branch.branch( _visit( self.graph[n], Workflow(n).start_at(n), node.matching_join ) ) workflow.add_state(branch) _visit(self.graph[node.matching_join], workflow, exit_node) elif node.type == "foreach": cardinality_state_name = "#%s" % node.out_funcs[0] workflow.add_state(state.next(cardinality_state_name)) cardinality_state = ( State(cardinality_state_name) .dynamo_db(SFN_DYNAMO_DB_TABLE, "$.JobId", "for_each_cardinality") .result_path("$.Result") ) iterator_name = "*%s" % node.out_funcs[0] workflow.add_state(cardinality_state.next(iterator_name)) workflow.add_state( Map(iterator_name) .items_path("$.Result.Item.for_each_cardinality.NS") .parameter("JobId.$", "$.JobId") .parameter("SplitParentTaskId.$", "$.JobId") .parameter("Parameters.$", "$.Parameters") .parameter("Index.$", "$$.Map.Item.Value") .next(node.matching_join) .iterator( _visit( self.graph[node.out_funcs[0]], Workflow(node.out_funcs[0]).start_at(node.out_funcs[0]), node.matching_join, ) ) .max_concurrency(self.max_workers) .output_path("$.[0]") ) _visit(self.graph[node.matching_join], workflow, exit_node) else: raise StepFunctionsException( "Node type *%s* for step *%s* " "is not currently supported by " "AWS Step Functions." % (node.type, node.name) ) return workflow workflow = Workflow(self.name).start_at("start") if self.workflow_timeout: workflow.timeout_seconds(self.workflow_timeout) return _visit(self.graph["start"], workflow) def _cron(self): schedule = self.flow._flow_decorators.get("schedule") if schedule: return schedule.schedule return None def _process_parameters(self): parameters = [] has_schedule = self._cron() is not None seen = set() for var, param in self.flow._get_parameters(): # Throw an exception if the parameter is specified twice. norm = param.name.lower() if norm in seen: raise MetaflowException( "Parameter *%s* is specified twice. " "Note that parameter names are " "case-insensitive." % param.name ) seen.add(norm) is_required = param.kwargs.get("required", False) # Throw an exception if a schedule is set for a flow with required # parameters with no defaults. We currently don't have any notion if "default" not in param.kwargs and is_required and has_schedule: raise MetaflowException( "The parameter *%s* does not have a " "default and is required. Scheduling " "such parameters via AWS Event Bridge " "is not currently supported." % param.name ) value = deploy_time_eval(param.kwargs.get("default")) parameters.append(dict(name=param.name, value=value)) return parameters def _batch(self, node): attrs = { "metaflow.user": "SFN", "metaflow.owner": self.username, "metaflow.flow_name": self.flow.name, "metaflow.step_name": node.name, "metaflow.run_id.$": "$$.Execution.Name", # Functions lacks any notion of run-scoped task identifiers. We # instead co-opt the AWS Batch job id as the task id. This also # means that the AWS Batch job name will have missing fields since # the job id is determined at job execution, but since the job id is # part of the job description payload, we don't lose much except for # `$$.State.RetryCount` resolves to an int dynamically and # AWS Batch job specification only accepts strings. We handle # retries/catch within AWS Batch to get around this limitation. "metaflow.version": self.environment.get_environment_info()[ "metaflow_version" ], # We rely on step names and task ids of parent steps to construct # input paths for a task. Since the only information we can pass # between states (via `InputPath` and `ResultPath`) in AWS Step # Functions is the job description, we run the risk of exceeding # 32K state size limit rather quickly if we don't filter the job # have to add the field again. # This pattern is repeated in a lot of other places, where we use # AWS Batch parameters to store AWS Step Functions state # information, since this field is the only field in the AWS Batch # specification that allows us to set key-values. "step_name": node.name, } # Store production token within the `start` step, so that subsequent # `step-functions create` calls can perform a rudimentary authorization # check. if node.name == "start": attrs["metaflow.production_token"] = self.production_token # Add env vars from the optional @environment decorator. env_deco = [deco for deco in node.decorators if deco.name == "environment"] env = {} if env_deco: env = env_deco[0].attributes["vars"] if node.name == "start": # Initialize parameters for the flow in the `start` step. parameters = self._process_parameters() if parameters: # Get user-defined parameters from State Machine Input. # Since AWS Step Functions doesn't allow for optional inputs env["METAFLOW_PARAMETERS"] = "$.Parameters" default_parameters = {} for parameter in parameters: if parameter["value"] is not None: default_parameters[parameter["name"]] = parameter["value"] env["METAFLOW_DEFAULT_PARAMETERS"] = json.dumps(default_parameters) input_paths = None else: if ( node.type == "join" and self.graph[node.split_parents[-1]].type == "foreach" ): input_paths = ( "sfn-${METAFLOW_RUN_ID}/%s/:" "${METAFLOW_PARENT_TASK_IDS}" % node.in_funcs[0] ) env["METAFLOW_SPLIT_PARENT_TASK_ID"] = ( "$.Parameters.split_parent_task_id_%s" % node.split_parents[-1] ) else: if len(node.in_funcs) == 1: input_paths = ( "sfn-${METAFLOW_RUN_ID}/%s/${METAFLOW_PARENT_TASK_ID}" % node.in_funcs[0] ) env["METAFLOW_PARENT_TASK_ID"] = "$.JobId" else: input_paths = "sfn-${METAFLOW_RUN_ID}:" + ",".join( "/${METAFLOW_PARENT_%s_STEP}/" "${METAFLOW_PARENT_%s_TASK_ID}" % (idx, idx) for idx, _ in enumerate(node.in_funcs) ) for idx, _ in enumerate(node.in_funcs): env["METAFLOW_PARENT_%s_TASK_ID" % idx] = "$.[%s].JobId" % idx env["METAFLOW_PARENT_%s_STEP" % idx] = ( "$.[%s].Parameters.step_name" % idx ) env["METAFLOW_INPUT_PATHS"] = input_paths if node.is_inside_foreach: if any(self.graph[n].type == "foreach" for n in node.in_funcs): attrs[ "split_parent_task_id_%s.$" % node.split_parents[-1] ] = "$.SplitParentTaskId" for parent in node.split_parents[:-1]: if self.graph[parent].type == "foreach": attrs["split_parent_task_id_%s.$" % parent] = ( "$.Parameters.split_parent_task_id_%s" % parent ) elif node.type == "join": if self.graph[node.split_parents[-1]].type == "foreach": # that information is available to us from AWS DynamoDB. # This has a nice side-effect of making our foreach # splits infinitely scalable because otherwise we would # be bounded by the 32K state limit for the outputs. So, # instead of referencing `Parameters` fields by index # (like in `split-and`), we can just reference them # directly. attrs["split_parent_task_id_%s.$" % node.split_parents[-1]] = ( "$.Parameters.split_parent_task_id_%s" % node.split_parents[-1] ) for parent in node.split_parents[:-1]: if self.graph[parent].type == "foreach": attrs["split_parent_task_id_%s.$" % parent] = ( "$.Parameters.split_parent_task_id_%s" % parent ) else: for parent in node.split_parents: if self.graph[parent].type == "foreach": attrs["split_parent_task_id_%s.$" % parent] = ( "$.[0].Parameters.split_parent_task_id_%s" % parent ) else: for parent in node.split_parents: if self.graph[parent].type == "foreach": attrs["split_parent_task_id_%s.$" % parent] = ( "$.Parameters.split_parent_task_id_%s" % parent ) # Set `METAFLOW_SPLIT_PARENT_TASK_ID_FOR_FOREACH_JOIN` if the # next transition is to a foreach join, so that the # stepfunctions decorator can write the mapping for input path # to DynamoDb. if any( self.graph[n].type == "join" and self.graph[self.graph[n].split_parents[-1]].type == "foreach" for n in node.out_funcs ): env["METAFLOW_SPLIT_PARENT_TASK_ID_FOR_FOREACH_JOIN"] = attrs[ "split_parent_task_id_%s.$" % self.graph[node.out_funcs[0]].split_parents[-1] ] # Set ttl for the values we set in AWS DynamoDB. if node.type == "foreach": if self.workflow_timeout: env["METAFLOW_SFN_WORKFLOW_TIMEOUT"] = self.workflow_timeout # Handle split index for for-each. if any(self.graph[n].type == "foreach" for n in node.in_funcs): env["METAFLOW_SPLIT_INDEX"] = "$.Index" env["METAFLOW_CODE_URL"] = self.code_package_url env["METAFLOW_FLOW_NAME"] = attrs["metaflow.flow_name"] env["METAFLOW_STEP_NAME"] = attrs["metaflow.step_name"] env["METAFLOW_RUN_ID"] = attrs["metaflow.run_id.$"] env["METAFLOW_PRODUCTION_TOKEN"] = self.production_token env["SFN_STATE_MACHINE"] = self.name env["METAFLOW_OWNER"] = attrs["metaflow.owner"] # Can't set `METAFLOW_TASK_ID` due to lack of run-scoped identifiers. metadata_env = self.metadata.get_runtime_environment("step-functions") env.update(metadata_env) metaflow_version = self.environment.get_environment_info() metaflow_version["flow_name"] = self.graph.name metaflow_version["production_token"] = self.production_token env["METAFLOW_VERSION"] = json.dumps(metaflow_version) # Set AWS DynamoDb Table Name for state tracking for for-eaches. # There are three instances when metaflow runtime directly interacts # with AWS DynamoDB. # 1. To set the cardinality of foreaches (which are subsequently) # read prior to the instantiation of the Map state by AWS Step # Functions. # 2. To set the input paths from the parent steps of a foreach join. # 3. To read the input paths in a foreach join. if ( node.type == "foreach" or ( node.is_inside_foreach and any( self.graph[n].type == "join" and self.graph[self.graph[n].split_parents[-1]].type == "foreach" for n in node.out_funcs ) ) or ( node.type == "join" and self.graph[node.split_parents[-1]].type == "foreach" ) ): if SFN_DYNAMO_DB_TABLE is None: raise StepFunctionsException( "An AWS DynamoDB table is needed " "to support foreach in your flow. " "You can create one following the " "instructions listed at *https://a" "dmin-docs.metaflow.org/metaflow-o" "n-aws/deployment-guide/manual-dep" "loyment#scheduling* and " "re-configure Metaflow using " "*metaflow configure aws* on your " "terminal." ) env["METAFLOW_SFN_DYNAMO_DB_TABLE"] = SFN_DYNAMO_DB_TABLE # Resolve AWS Batch resource requirements. batch_deco = [deco for deco in node.decorators if deco.name == "batch"][0] resources = batch_deco.attributes # Resolve retry strategy. user_code_retries, total_retries = self._get_retries(node) task_spec = { "flow_name": attrs["metaflow.flow_name"], "step_name": attrs["metaflow.step_name"], "run_id": "sfn-$METAFLOW_RUN_ID", # Use AWS Batch job identifier as the globally unique # task identifier. "task_id": "$AWS_BATCH_JOB_ID", # Since retries are handled by AWS Batch, we can rely on # AWS_BATCH_JOB_ATTEMPT as the job counter. "retry_count": "$((AWS_BATCH_JOB_ATTEMPT-1))", } return ( Batch(self.metadata, self.environment) .create_job( step_name=node.name, step_cli=self._step_cli( node, input_paths, self.code_package_url, user_code_retries ), task_spec=task_spec, code_package_sha=self.code_package_sha, code_package_url=self.code_package_url, code_package_ds=self.flow_datastore.TYPE, image=resources["image"], queue=resources["queue"], iam_role=resources["iam_role"], execution_role=resources["execution_role"], cpu=resources["cpu"], gpu=resources["gpu"], memory=resources["memory"], run_time_limit=batch_deco.run_time_limit, shared_memory=resources["shared_memory"], max_swap=resources["max_swap"], swappiness=resources["swappiness"], env=env, attrs=attrs, host_volumes=resources["host_volumes"], ) .attempts(total_retries + 1) ) def _get_retries(self, node): max_user_code_retries = 0 max_error_retries = 0 # Different decorators may have different retrying strategies, so take # the max of them. for deco in node.decorators: user_code_retries, error_retries = deco.step_task_retry_count() max_user_code_retries = max(max_user_code_retries, user_code_retries) max_error_retries = max(max_error_retries, error_retries) return max_user_code_retries, max_user_code_retries + max_error_retries def _step_cli(self, node, paths, code_package_url, user_code_retries): cmds = [] script_name = os.path.basename(sys.argv[0]) executable = self.environment.executable(node.name) if R.use_r(): entrypoint = [R.entrypoint()] else: entrypoint = [executable, script_name] # Use AWS Batch job identifier as the globally unique task identifier. task_id = "${AWS_BATCH_JOB_ID}" # FlowDecorators can define their own top-level options. They are # responsible for adding their own top-level options and values through # the get_top_level_options() hook. See similar logic in runtime.py. top_opts_dict = {} for deco in flow_decorators(): top_opts_dict.update(deco.get_top_level_options()) top_opts = list(dict_to_cli_options(top_opts_dict)) if node.name == "start": # We need a separate unique ID for the special _parameters task task_id_params = "%s-params" % task_id # Export user-defined parameters into runtime environment param_file = "".join( random.choice(string.ascii_lowercase) for _ in range(10) ) export_params = ( "python -m " "metaflow.plugins.aws.step_functions.set_batch_environment " "parameters %s && . `pwd`/%s" % (param_file, param_file) ) params = ( entrypoint + top_opts + [ "--quiet", "--metadata=%s" % self.metadata.TYPE, "--environment=%s" % self.environment.TYPE, "--datastore=s3", "--event-logger=%s" % self.event_logger.logger_type, "--monitor=%s" % self.monitor.monitor_type, "--no-pylint", "init", "--run-id sfn-$METAFLOW_RUN_ID", "--task-id %s" % task_id_params, ] ) # Assign tags to run objects. if self.tags: params.extend("--tag %s" % tag for tag in self.tags) # If the start step gets retried, we must be careful not to # regenerate multiple parameters tasks. Hence we check first if # _parameters exists already. exists = entrypoint + [ "dump", "--max-value-size=0", "sfn-${METAFLOW_RUN_ID}/_parameters/%s" % (task_id_params), ] cmd = "if ! %s >/dev/null 2>/dev/null; then %s && %s; fi" % ( " ".join(exists), export_params, " ".join(params), ) cmds.append(cmd) paths = "sfn-${METAFLOW_RUN_ID}/_parameters/%s" % (task_id_params) if node.type == "join" and self.graph[node.split_parents[-1]].type == "foreach": parent_tasks_file = "".join( random.choice(string.ascii_lowercase) for _ in range(10) ) export_parent_tasks = ( "python -m " "metaflow.plugins.aws.step_functions.set_batch_environment " "parent_tasks %s && . `pwd`/%s" % (parent_tasks_file, parent_tasks_file) ) cmds.append(export_parent_tasks) top_level = top_opts + [ "--quiet", "--metadata=%s" % self.metadata.TYPE, "--environment=%s" % self.environment.TYPE, "--datastore=%s" % self.flow_datastore.TYPE, "--datastore-root=%s" % self.flow_datastore.datastore_root, "--event-logger=%s" % self.event_logger.logger_type, "--monitor=%s" % self.monitor.monitor_type, "--no-pylint", "--with=step_functions_internal", ] step = [ "step", node.name, "--run-id sfn-$METAFLOW_RUN_ID", "--task-id %s" % task_id, # Since retries are handled by AWS Batch, we can rely on # AWS_BATCH_JOB_ATTEMPT as the job counter. "--retry-count $((AWS_BATCH_JOB_ATTEMPT-1))", "--max-user-code-retries %d" % user_code_retries, "--input-paths %s" % paths, # Set decorator to batch to execute `task_*` hooks for batch # decorator. "--with=batch", ] if any(self.graph[n].type == "foreach" for n in node.in_funcs): # We set the `METAFLOW_SPLIT_INDEX` through JSONPath-foo # to pass the state from the parent DynamoDb state for for-each. step.append("--split-index $METAFLOW_SPLIT_INDEX") if self.tags: step.extend("--tag %s" % tag for tag in self.tags) if self.namespace is not None: step.append("--namespace=%s" % self.namespace) cmds.append(" ".join(entrypoint + top_level + step)) return " && ".join(cmds) class Workflow(object): def __init__(self, name): self.name = name tree = lambda: defaultdict(tree) self.payload = tree() def start_at(self, start_at): self.payload["StartAt"] = start_at return self def add_state(self, state): self.payload["States"][state.name] = state.payload return self def timeout_seconds(self, timeout_seconds): self.payload["TimeoutSeconds"] = timeout_seconds return self def to_json(self, pretty=False): return json.dumps(self.payload, indent=4 if pretty else None) class State(object): def __init__(self, name): self.name = name tree = lambda: defaultdict(tree) self.payload = tree() self.payload["Type"] = "Task" def resource(self, resource): self.payload["Resource"] = resource return self def next(self, state): self.payload["Next"] = state return self def end(self): self.payload["End"] = True return self def parameter(self, name, value): self.payload["Parameters"][name] = value return self def output_path(self, output_path): self.payload["OutputPath"] = output_path return self def result_path(self, result_path): self.payload["ResultPath"] = result_path return self def _partition(self): # This is needed to support AWS Gov Cloud and AWS CN regions return SFN_IAM_ROLE.split(":")[1] def batch(self, job): self.resource( "arn:%s:states:::batch:submitJob.sync" % self._partition() ).parameter("JobDefinition", job.payload["jobDefinition"]).parameter( "JobName", job.payload["jobName"] ).parameter( "JobQueue", job.payload["jobQueue"] ).parameter( "Parameters", job.payload["parameters"] ).parameter( "ContainerOverrides", to_pascalcase(job.payload["containerOverrides"]) ).parameter( "RetryStrategy", to_pascalcase(job.payload["retryStrategy"]) ).parameter( "Timeout", to_pascalcase(job.payload["timeout"]) ) # tags may not be present in all scenarios if "tags" in job.payload: self.parameter("Tags", job.payload["tags"]) return self def dynamo_db(self, table_name, primary_key, values): self.resource("arn:%s:states:::dynamodb:getItem" % self._partition()).parameter( "TableName", table_name ).parameter("Key", {"pathspec": {"S.$": primary_key}}).parameter( "ConsistentRead", True ).parameter( "ProjectionExpression", values ) return self class Parallel(object): def __init__(self, name): self.name = name tree = lambda: defaultdict(tree) self.payload = tree() self.payload["Type"] = "Parallel" def branch(self, workflow): if "Branches" not in self.payload: self.payload["Branches"] = [] self.payload["Branches"].append(workflow.payload) return self def next(self, state): self.payload["Next"] = state return self def output_path(self, output_path): self.payload["OutputPath"] = output_path return self def result_path(self, result_path): self.payload["ResultPath"] = result_path return self class Map(object): def __init__(self, name): self.name = name tree = lambda: defaultdict(tree) self.payload = tree() self.payload["Type"] = "Map" self.payload["MaxConcurrency"] = 0 def iterator(self, workflow): self.payload["Iterator"] = workflow.payload return self def next(self, state): self.payload["Next"] = state return self def items_path(self, items_path): self.payload["ItemsPath"] = items_path return self def parameter(self, name, value): self.payload["Parameters"][name] = value return self def max_concurrency(self, max_concurrency): self.payload["MaxConcurrency"] = max_concurrency return self def output_path(self, output_path): self.payload["OutputPath"] = output_path return self def result_path(self, result_path): self.payload["ResultPath"] = result_path return self

true

f71b5640c381e4a1a513cc6857ecd00c92aa7029

279

py

Python

circle_core/writer/base.py

glucoseinc/CircleCore

577f814ce2944efb6e5997f3d7838c71ce9aea6a

[ "MIT" ]

3

2019-01-11T04:30:18.000Z

2019-01-11T04:31:18.000Z

circle_core/writer/base.py

glucoseinc/CircleCore

577f814ce2944efb6e5997f3d7838c71ce9aea6a

[ "MIT" ]

16

2018-11-21T11:47:18.000Z

2021-09-01T03:52:35.000Z

circle_core/writer/base.py

glucoseinc/CircleCore

577f814ce2944efb6e5997f3d7838c71ce9aea6a

[ "MIT" ]

null

import abc class DBWriter(metaclass=abc.ABCMeta): @abc.abstractmethod async def store(self, message_box, message) -> bool: raise NotImplementedError @abc.abstractmethod async def flush(self, flush_all=False) -> None: raise NotImplementedError

21.461538

56

0.706093

import abc class DBWriter(metaclass=abc.ABCMeta): @abc.abstractmethod async def store(self, message_box, message) -> bool: raise NotImplementedError @abc.abstractmethod async def flush(self, flush_all=False) -> None: raise NotImplementedError

true

f71b56535865b78456cbc8ac2192f63ee5287cfd

588

py

Python

src/UI_Code_Q2/Test_code_smaller_parts/PythonWriteUSB.py

KevinEwoudLee/HU3-UI

16d63e0be8c515540daf4f9cfcff2d0a85c1cbab

[ "MIT" ]

1

2019-12-11T15:27:53.000Z

src/UI_Code_Q2/Test_code_smaller_parts/PythonWriteUSB.py

KevinEwoudLee/HU3-UI

16d63e0be8c515540daf4f9cfcff2d0a85c1cbab

[ "MIT" ]

null

src/UI_Code_Q2/Test_code_smaller_parts/PythonWriteUSB.py

KevinEwoudLee/HU3-UI

16d63e0be8c515540daf4f9cfcff2d0a85c1cbab

[ "MIT" ]

1

2019-12-11T15:23:56.000Z

# -*- coding: utf-8 -*- """ Created on Tue Nov 26 09:36:21 2019 @author: kevin """ import os import time from time import sleep from datetime import datetime file = open("E:/test2.csv", "a") i=0 if os.stat("E:/test2.csv").st_size == 0: file.write("Time,Sensor1,Sensor2,Sensor3,Sensor4,Sensor5\n") while True: i=i+1 now = datetime.now() file.write(str(now)+","+str(i)+","+str(-i)+","+str(i-10)+","+str(i+5)+","+str(i*i)+"\n") file.flush() time.sleep(1) if (i>=10): break file.close()

22.615385

97

0.52551

import os import time from time import sleep from datetime import datetime file = open("E:/test2.csv", "a") i=0 if os.stat("E:/test2.csv").st_size == 0: file.write("Time,Sensor1,Sensor2,Sensor3,Sensor4,Sensor5\n") while True: i=i+1 now = datetime.now() file.write(str(now)+","+str(i)+","+str(-i)+","+str(i-10)+","+str(i+5)+","+str(i*i)+"\n") file.flush() time.sleep(1) if (i>=10): break file.close()

true

f71b56ae1d0e79e35e8bd9e7c4c05e6ff33f45bf

3,286

py

Python

src/util/pos_util.py

tiefenauer/ip7-python

512105ba39110ec77d2ea0961dd7c2a42d4ec26d

[ "MIT" ]

null

src/util/pos_util.py

tiefenauer/ip7-python

512105ba39110ec77d2ea0961dd7c2a42d4ec26d

[ "MIT" ]

null

src/util/pos_util.py

tiefenauer/ip7-python

512105ba39110ec77d2ea0961dd7c2a42d4ec26d

[ "MIT" ]

null

import collections from src.importer.known_jobs import KnownJobs from src.preprocessing import preproc from src.util import loe_util, jobtitle_util mw_tokens = ['m/w', 'w/m', 'm/f', 'f/m', 'M/W', 'W/M', 'M/F', 'F/M'] def find_jobs(sentence): jobs = [] # find known jobs for hit in find_job_by_keyword(sentence, KnownJobs()): jobs.append((hit, 'known-job')) # find by m/w patterns sentence_without_percentage = loe_util.remove_percentage(sentence) for hit in find_job_by_keyword(sentence_without_percentage, mw_tokens): jobs.append((hit, 'mw')) # find by percentages sentence_without_mw = jobtitle_util.remove_mw(sentence) for hit in find_job_by_keyword(sentence_without_mw, loe_util.find_all_loe(sentence_without_mw)): jobs.append((hit, 'loe')) # find by gender forms # sentence_without_mw_and_percentage = loe_util.remove_percentage(sentence_without_mw) # jobs += find_job_by_keyword(sentence_without_mw_and_percentage, ['/in', '/-in']) # search by keyword: gender # for match in jobtitle_util.find_all_genderized(sentence): # gender_job = expand_left_right(sentence.split(match[0])[0], sentence) # if gender_job: # yield gender_job return jobs def find_job_by_keyword(sentence, keywords): # job_names = [] for keyword in keywords: if keyword in sentence: job_name = expand_left_right(keyword, sentence) if job_name: yield job_name # job_names.append(job_name) # return job_names def expand_left_right(token, sentence): if token not in sentence: return None job_name_tokens = preproc.to_words(token) sentence_tokens = [word for word in preproc.to_words(sentence) if word not in ['(', ')']] ix_from, ix_to = calculate_positions(job_name_tokens, sentence_tokens) sentence_pos = preproc.pos_tag(sentence_tokens) left = sentence_pos[:ix_from] right = sentence_pos[ix_to:] initial_content = [token] if token not in mw_tokens and not loe_util.is_percentate(token) else [] tokens = collections.deque(initial_content) search_left(left, tokens) search_right(right, tokens) return ' '.join(tokens) def search_left(pos_tagged_words, tokens=collections.deque()): i = len(pos_tagged_words) - 1 while 0 <= i: word, pos_tag = pos_tagged_words[i] if is_part_of_name(word, pos_tag): tokens.appendleft(word) else: break i -= 1 return tokens def search_right(pos_tagged_words, tokens=collections.deque()): i = 0 while 0 <= i < len(pos_tagged_words): word, pos_tag = pos_tagged_words[i] if is_part_of_name(word, pos_tag): tokens.append(word) else: break i += 1 return tokens def is_part_of_name(word, pos_tag): return is_noun(pos_tag) or word in ['/'] def is_noun(pos_tag): return pos_tag[0] in ['N', 'F'] def is_punctuation(pos_tag): return pos_tag.startswith('$') def calculate_positions(job_name_tokens, sentence_tokens): ix_from = [i for i, word in enumerate(sentence_tokens) if job_name_tokens[0] in word][0] ix_to = ix_from + len(job_name_tokens) return ix_from, ix_to

30.71028

101

0.676811

import collections from src.importer.known_jobs import KnownJobs from src.preprocessing import preproc from src.util import loe_util, jobtitle_util mw_tokens = ['m/w', 'w/m', 'm/f', 'f/m', 'M/W', 'W/M', 'M/F', 'F/M'] def find_jobs(sentence): jobs = [] for hit in find_job_by_keyword(sentence, KnownJobs()): jobs.append((hit, 'known-job')) sentence_without_percentage = loe_util.remove_percentage(sentence) for hit in find_job_by_keyword(sentence_without_percentage, mw_tokens): jobs.append((hit, 'mw')) sentence_without_mw = jobtitle_util.remove_mw(sentence) for hit in find_job_by_keyword(sentence_without_mw, loe_util.find_all_loe(sentence_without_mw)): jobs.append((hit, 'loe')) return jobs def find_job_by_keyword(sentence, keywords): for keyword in keywords: if keyword in sentence: job_name = expand_left_right(keyword, sentence) if job_name: yield job_name def expand_left_right(token, sentence): if token not in sentence: return None job_name_tokens = preproc.to_words(token) sentence_tokens = [word for word in preproc.to_words(sentence) if word not in ['(', ')']] ix_from, ix_to = calculate_positions(job_name_tokens, sentence_tokens) sentence_pos = preproc.pos_tag(sentence_tokens) left = sentence_pos[:ix_from] right = sentence_pos[ix_to:] initial_content = [token] if token not in mw_tokens and not loe_util.is_percentate(token) else [] tokens = collections.deque(initial_content) search_left(left, tokens) search_right(right, tokens) return ' '.join(tokens) def search_left(pos_tagged_words, tokens=collections.deque()): i = len(pos_tagged_words) - 1 while 0 <= i: word, pos_tag = pos_tagged_words[i] if is_part_of_name(word, pos_tag): tokens.appendleft(word) else: break i -= 1 return tokens def search_right(pos_tagged_words, tokens=collections.deque()): i = 0 while 0 <= i < len(pos_tagged_words): word, pos_tag = pos_tagged_words[i] if is_part_of_name(word, pos_tag): tokens.append(word) else: break i += 1 return tokens def is_part_of_name(word, pos_tag): return is_noun(pos_tag) or word in ['/'] def is_noun(pos_tag): return pos_tag[0] in ['N', 'F'] def is_punctuation(pos_tag): return pos_tag.startswith('$') def calculate_positions(job_name_tokens, sentence_tokens): ix_from = [i for i, word in enumerate(sentence_tokens) if job_name_tokens[0] in word][0] ix_to = ix_from + len(job_name_tokens) return ix_from, ix_to

true

f71b56c4085612ca2aacc209126330286fb3b4f9

2,980

py

Python

webvep/main/settings.py

IanVermes/vep_api

9d9d31eddd969aad1c462278ea1f1fb09153e054

[ "MIT" ]

null

webvep/main/settings.py

IanVermes/vep_api

9d9d31eddd969aad1c462278ea1f1fb09153e054

[ "MIT" ]

1

2020-03-30T10:52:58.000Z

2020-03-30T16:46:31.000Z

webvep/main/settings.py

IanVermes/vep_api

9d9d31eddd969aad1c462278ea1f1fb09153e054

[ "MIT" ]

null

""" Django settings for webvep project. Generated by 'django-admin startproject' using Django 3.0.4. For more information on this file, see https://docs.djangoproject.com/en/3.0/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/3.0/ref/settings/ """ import os # Build paths inside the project like this: os.path.join(BASE_DIR, ...) BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/3.0/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = "ec)mzu6ls4qaj!8)txrke(uxxtb1gmz^2a_^1$lqe9&ys17^!$" # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True ALLOWED_HOSTS = [] # Application definition INSTALLED_APPS = [ "django.contrib.admin", "django.contrib.auth", "django.contrib.contenttypes", "django.contrib.sessions", "django.contrib.messages", "django.contrib.staticfiles", "rest_framework", "webvep_frontend", "webvep_api", ] MIDDLEWARE = [ "django.middleware.security.SecurityMiddleware", "django.contrib.sessions.middleware.SessionMiddleware", "django.middleware.common.CommonMiddleware", "django.middleware.csrf.CsrfViewMiddleware", "django.contrib.auth.middleware.AuthenticationMiddleware", "django.contrib.messages.middleware.MessageMiddleware", "django.middleware.clickjacking.XFrameOptionsMiddleware", ] ROOT_URLCONF = "main.urls" TEMPLATES = [ { "BACKEND": "django.template.backends.django.DjangoTemplates", "DIRS": [], "APP_DIRS": True, "OPTIONS": { "context_processors": [ "django.template.context_processors.debug", "django.template.context_processors.request", "django.contrib.auth.context_processors.auth", "django.contrib.messages.context_processors.messages", ], }, }, ] WSGI_APPLICATION = "main.wsgi.application" # Database # https://docs.djangoproject.com/en/3.0/ref/settings/#databases ## DONT NEED ONE # Password validation # https://docs.djangoproject.com/en/3.0/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { "NAME": "django.contrib.auth.password_validation.UserAttributeSimilarityValidator", }, {"NAME": "django.contrib.auth.password_validation.MinimumLengthValidator",}, {"NAME": "django.contrib.auth.password_validation.CommonPasswordValidator",}, {"NAME": "django.contrib.auth.password_validation.NumericPasswordValidator",}, ] # Internationalization # https://docs.djangoproject.com/en/3.0/topics/i18n/ LANGUAGE_CODE = "en-us" TIME_ZONE = "UTC" USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/3.0/howto/static-files/ STATIC_URL = "/static/"

26.607143

91

0.711409

import os BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) SECRET_KEY = "ec)mzu6ls4qaj!8)txrke(uxxtb1gmz^2a_^1$lqe9&ys17^!$" DEBUG = True ALLOWED_HOSTS = [] # Application definition INSTALLED_APPS = [ "django.contrib.admin", "django.contrib.auth", "django.contrib.contenttypes", "django.contrib.sessions", "django.contrib.messages", "django.contrib.staticfiles", "rest_framework", "webvep_frontend", "webvep_api", ] MIDDLEWARE = [ "django.middleware.security.SecurityMiddleware", "django.contrib.sessions.middleware.SessionMiddleware", "django.middleware.common.CommonMiddleware", "django.middleware.csrf.CsrfViewMiddleware", "django.contrib.auth.middleware.AuthenticationMiddleware", "django.contrib.messages.middleware.MessageMiddleware", "django.middleware.clickjacking.XFrameOptionsMiddleware", ] ROOT_URLCONF = "main.urls" TEMPLATES = [ { "BACKEND": "django.template.backends.django.DjangoTemplates", "DIRS": [], "APP_DIRS": True, "OPTIONS": { "context_processors": [ "django.template.context_processors.debug", "django.template.context_processors.request", "django.contrib.auth.context_processors.auth", "django.contrib.messages.context_processors.messages", ], }, }, ] WSGI_APPLICATION = "main.wsgi.application" # Database # https://docs.djangoproject.com/en/3.0/ref/settings/#databases ## DONT NEED ONE # Password validation # https://docs.djangoproject.com/en/3.0/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { "NAME": "django.contrib.auth.password_validation.UserAttributeSimilarityValidator", }, {"NAME": "django.contrib.auth.password_validation.MinimumLengthValidator",}, {"NAME": "django.contrib.auth.password_validation.CommonPasswordValidator",}, {"NAME": "django.contrib.auth.password_validation.NumericPasswordValidator",}, ] # Internationalization # https://docs.djangoproject.com/en/3.0/topics/i18n/ LANGUAGE_CODE = "en-us" TIME_ZONE = "UTC" USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/3.0/howto/static-files/ STATIC_URL = "/static/"

true

f71b57614025c4aecf90a7ea7cf4fe3d2b9c8499

77

py

Python

zmon_aws_agent/__main__.py

jrake-revelant/zmon-aws-agent

67ed3d0230f3bec9b3a3950c3eefa48404ee9d6b

[ "Apache-2.0" ]

9

2016-07-28T09:28:20.000Z

2022-02-27T23:40:09.000Z

zmon_aws_agent/__main__.py

jrake-revelant/zmon-aws-agent

67ed3d0230f3bec9b3a3950c3eefa48404ee9d6b

[ "Apache-2.0" ]

146

2016-05-23T13:43:42.000Z

2020-03-10T09:47:07.000Z

zmon_aws_agent/__main__.py

jrake-revelant/zmon-aws-agent

67ed3d0230f3bec9b3a3950c3eefa48404ee9d6b

[ "Apache-2.0" ]

11

2017-02-18T12:46:27.000Z

2020-01-22T13:13:36.000Z

from zmon_aws_agent.main import main if __name__ == '__main__': main()

12.833333

36

0.701299

from zmon_aws_agent.main import main if __name__ == '__main__': main()

true

f71b584d40b3c272f7646b95950ee740aeb0fc1c

5,834

py

Python

tests/model_connectors/test_spawn_ets.py

macintoshpie/geojson-modelica-translator

5ed02d53f06961b8d0f3705343368e4c920e7d7d

[ "BSD-3-Clause" ]

null

tests/model_connectors/test_spawn_ets.py

macintoshpie/geojson-modelica-translator

5ed02d53f06961b8d0f3705343368e4c920e7d7d

[ "BSD-3-Clause" ]

null

tests/model_connectors/test_spawn_ets.py

macintoshpie/geojson-modelica-translator

5ed02d53f06961b8d0f3705343368e4c920e7d7d

[ "BSD-3-Clause" ]

null

""" **************************************************************************************************** :copyright (c) 2019-2020 URBANopt, Alliance for Sustainable Energy, LLC, and other contributors. All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. **************************************************************************************************** """ import os import shutil import unittest from pathlib import Path from geojson_modelica_translator.geojson_modelica_translator import ( GeoJsonModelicaTranslator ) from geojson_modelica_translator.model_connectors.spawnBui_ETS_Coupling import ( SpawnConnectorETS ) from geojson_modelica_translator.modelica.modelica_runner import ModelicaRunner from geojson_modelica_translator.system_parameters.system_parameters import ( SystemParameters ) class SpawnModelConnectorSingleBuildingTest(unittest.TestCase): def setUp(self): self.data_dir = os.path.join(os.path.dirname(__file__), 'data') self.output_dir = os.path.join(os.path.dirname(__file__), 'output') project_name = "spawn_single" if os.path.exists(os.path.join(self.output_dir, project_name)): shutil.rmtree(os.path.join(self.output_dir, project_name)) # load in the example geojson with a single offie building filename = os.path.join(self.data_dir, "spawn_geojson_ex1.json") self.gj = GeoJsonModelicaTranslator.from_geojson(filename) # use the GeoJson translator to scaffold out the directory self.gj.scaffold_directory(self.output_dir, project_name) # load system parameter data filename = os.path.join(self.data_dir, "spawn_system_params_ex1.json") sys_params = SystemParameters(filename) # now test the spawn connector (independent of the larger geojson translator self.spawn = SpawnConnectorETS(sys_params) for b in self.gj.buildings: self.spawn.add_building(b) def test_spawn_init(self): self.assertIsNotNone(self.spawn) self.assertEqual(self.spawn.system_parameters.get_param("buildings.custom")[0]["load_model"], "Spawn") def test_spawn_to_modelica_and_run(self): self.spawn.to_modelica(self.gj.scaffold) # make sure the model can run using the ModelicaRunner class mr = ModelicaRunner() file_to_run = os.path.abspath( os.path.join( self.gj.scaffold.loads_path.files_dir, 'B5a6b99ec37f4de7f94020090', 'CouplingETS_SpawnBuilding.mo' ) ) run_path = Path(os.path.abspath(self.gj.scaffold.project_path)).parent exitcode = mr.run_in_docker(file_to_run, run_path=run_path) self.assertEqual(0, exitcode) class SpawnModelConnectorTwoBuildingTest(unittest.TestCase): def setUp(self): self.data_dir = os.path.join(os.path.dirname(__file__), 'data') self.output_dir = os.path.join(os.path.dirname(__file__), 'output') project_name = "spawn_two_building" if os.path.exists(os.path.join(self.output_dir, project_name)): shutil.rmtree(os.path.join(self.output_dir, project_name)) # load in the example geojson with a single offie building filename = os.path.join(self.data_dir, "spawn_geojson_ex2.json") self.gj = GeoJsonModelicaTranslator.from_geojson(filename) # use the GeoJson translator to scaffold out the directory self.gj.scaffold_directory(self.output_dir, project_name) # load system parameter data filename = os.path.join(self.data_dir, "spawn_system_params_ex2.json") sys_params = SystemParameters(filename) # now test the spawn connector (independent of the larger geojson translator self.spawn = SpawnConnectorETS(sys_params) for b in self.gj.buildings: self.spawn.add_building(b) def test_spawn_to_modelica_and_run(self): self.spawn.to_modelica(self.gj.scaffold) # make sure the model can run using the ModelicaRunner class mr = ModelicaRunner() file_to_run = os.path.abspath( os.path.join( self.gj.scaffold.loads_path.files_dir, 'B5a6b99ec37f4de7f94021950', 'CouplingETS_SpawnBuilding.mo' ) ) run_path = Path(os.path.abspath(self.gj.scaffold.project_path)).parent exitcode = mr.run_in_docker(file_to_run, run_path=run_path) self.assertEqual(0, exitcode)

44.876923

114

0.711176

import os import shutil import unittest from pathlib import Path from geojson_modelica_translator.geojson_modelica_translator import ( GeoJsonModelicaTranslator ) from geojson_modelica_translator.model_connectors.spawnBui_ETS_Coupling import ( SpawnConnectorETS ) from geojson_modelica_translator.modelica.modelica_runner import ModelicaRunner from geojson_modelica_translator.system_parameters.system_parameters import ( SystemParameters ) class SpawnModelConnectorSingleBuildingTest(unittest.TestCase): def setUp(self): self.data_dir = os.path.join(os.path.dirname(__file__), 'data') self.output_dir = os.path.join(os.path.dirname(__file__), 'output') project_name = "spawn_single" if os.path.exists(os.path.join(self.output_dir, project_name)): shutil.rmtree(os.path.join(self.output_dir, project_name)) filename = os.path.join(self.data_dir, "spawn_geojson_ex1.json") self.gj = GeoJsonModelicaTranslator.from_geojson(filename) self.gj.scaffold_directory(self.output_dir, project_name) filename = os.path.join(self.data_dir, "spawn_system_params_ex1.json") sys_params = SystemParameters(filename) self.spawn = SpawnConnectorETS(sys_params) for b in self.gj.buildings: self.spawn.add_building(b) def test_spawn_init(self): self.assertIsNotNone(self.spawn) self.assertEqual(self.spawn.system_parameters.get_param("buildings.custom")[0]["load_model"], "Spawn") def test_spawn_to_modelica_and_run(self): self.spawn.to_modelica(self.gj.scaffold) mr = ModelicaRunner() file_to_run = os.path.abspath( os.path.join( self.gj.scaffold.loads_path.files_dir, 'B5a6b99ec37f4de7f94020090', 'CouplingETS_SpawnBuilding.mo' ) ) run_path = Path(os.path.abspath(self.gj.scaffold.project_path)).parent exitcode = mr.run_in_docker(file_to_run, run_path=run_path) self.assertEqual(0, exitcode) class SpawnModelConnectorTwoBuildingTest(unittest.TestCase): def setUp(self): self.data_dir = os.path.join(os.path.dirname(__file__), 'data') self.output_dir = os.path.join(os.path.dirname(__file__), 'output') project_name = "spawn_two_building" if os.path.exists(os.path.join(self.output_dir, project_name)): shutil.rmtree(os.path.join(self.output_dir, project_name)) filename = os.path.join(self.data_dir, "spawn_geojson_ex2.json") self.gj = GeoJsonModelicaTranslator.from_geojson(filename) self.gj.scaffold_directory(self.output_dir, project_name) filename = os.path.join(self.data_dir, "spawn_system_params_ex2.json") sys_params = SystemParameters(filename) self.spawn = SpawnConnectorETS(sys_params) for b in self.gj.buildings: self.spawn.add_building(b) def test_spawn_to_modelica_and_run(self): self.spawn.to_modelica(self.gj.scaffold) mr = ModelicaRunner() file_to_run = os.path.abspath( os.path.join( self.gj.scaffold.loads_path.files_dir, 'B5a6b99ec37f4de7f94021950', 'CouplingETS_SpawnBuilding.mo' ) ) run_path = Path(os.path.abspath(self.gj.scaffold.project_path)).parent exitcode = mr.run_in_docker(file_to_run, run_path=run_path) self.assertEqual(0, exitcode)

true

f71b58bc6969e1f4e15e0b32876c55ba66d9757e

18,715

py

Python

OLD/losses.py

ivankreso/semseg

fcd2889a1e9e03c3d1a71d19d68d15ce25a5dc79

[ "MIT" ]

2

2017-11-17T06:55:44.000Z

2019-06-11T13:07:05.000Z

OLD/losses.py

ivankreso/semseg

fcd2889a1e9e03c3d1a71d19d68d15ce25a5dc79

[ "MIT" ]

null

OLD/losses.py

ivankreso/semseg

fcd2889a1e9e03c3d1a71d19d68d15ce25a5dc79

[ "MIT" ]

null

import tensorflow as tf import slim FLAGS = tf.app.flags.FLAGS def add_loss_summaries(total_loss): """Add summaries for losses in model. Generates moving average for all losses and associated summaries for visualizing the performance of the network. Args: total_loss: Total loss from loss(). Returns: loss_averages_op: op for generating moving averages of losses. """ # Compute the moving average of all individual losses and the total loss. loss_averages = tf.train.ExponentialMovingAverage(0.9, name='avg') losses = tf.get_collection('losses') loss_averages_op = loss_averages.apply(losses + [total_loss]) # Attach a scalar summary to all individual losses and the total loss; do the # same for the averaged version of the losses. for l in losses + [total_loss]: #print(l.op.name) # Name each loss as '(raw)' and name the moving average version of the loss # as the original loss name. #tf.scalar_summary(l.op.name + ' (raw)', l) #tf.scalar_summary(l.op.name, loss_averages.average(l)) tf.summary.scalar(l.op.name + ' (raw)', l) tf.summary.scalar(l.op.name, loss_averages.average(l)) #tf.scalar_summary([l.op.name + ' (raw)'], l) #tf.scalar_summary([l.op.name], loss_averages.average(l)) return loss_averages_op def total_loss_sum(losses): # Assemble all of the losses for the current tower only. #losses = tf.get_collection(slim.losses.LOSSES_COLLECTION) #print(losses) # Calculate the total loss for the current tower. #regularization_losses = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES) #regularization_losses = tf.contrib.losses.get_regularization_losses() regularization_losses = tf.losses.get_regularization_losses() #total_loss = tf.add_n(losses + regularization_losses, name='total_loss') total_loss = tf.add_n(losses + regularization_losses, name='total_loss') return total_loss def cross_entropy_loss(logits, labels): print('loss: cross-entropy') num_pixels = -1 with tf.name_scope(None, 'CrossEntropyLoss', [logits, labels]): labels = tf.reshape(labels, shape=[num_pixels]) logits = tf.reshape(logits, [num_pixels, FLAGS.num_classes]) mask = labels < FLAGS.num_classes idx = tf.where(mask) # # labels = tf.reshape(labels, shape=[num_pixels]) # print(idx) labels = tf.to_float(labels) labels = tf.gather_nd(labels, idx) # labels = tf.boolean_mask(labels, mask) labels = tf.to_int32(labels) logits = tf.gather_nd(logits, idx) # logits = tf.boolean_mask(logits, mask) onehot_labels = tf.one_hot(labels, FLAGS.num_classes) xent = tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=onehot_labels) # range_idx = tf.range(tf.shape(labels)[0], dtype=tf.int32) # print(range_idx, labels) # labels = tf.reshape(labels, shape=[-1,1]) # range_idx = tf.reshape(range_idx, shape=[-1,1]) # idx = tf.concat([range_idx, labels], axis=1) # print(idx) # probs = tf.nn.softmax(logits) # probs = tf.gather_nd(probs, idx) # print(probs) # xent = tf.square(1 - probs) * xent # # xent = tf.pow(1 - probs, 3) * xent # # xent = (1 - probs) * xent #num_labels = tf.to_float(tf.reduce_sum(num_labels)) #num_labels = tf.reduce_sum(tf.to_float(num_labels)) #class_hist = tf.Print(class_hist, [class_hist], 'hist = ', summarize=30) #num_labels = tf.reduce_sum(onehot_labels) #class_hist = tf.to_float(tf.reduce_sum(class_hist, axis=0)) ##num_labels = tf.Print(num_labels, [num_labels, tf.reduce_sum(onehot_labels)], 'lab = ', summarize=30) #class_weights = num_labels / (class_hist + 1) ##class_weights = tf.Print(class_weights, [class_weights], 'wgt hist = ', summarize=30) ## we need to append 0 here for ignore pixels #class_weights = tf.concat([class_weights, [0]], axis=0) ##class_weights = tf.Print(class_weights, [class_weights], 'wgt hist = ', summarize=30) #class_weights = tf.minimum(tf.to_float(max_weight), class_weights) # class_weights = tf.ones([FLAGS.num_classes]) # class_weights = tf.concat([class_weights, [0]], axis=0) # #class_weights = tf.Print(class_weights, [class_weights], 'wgt hist = ', summarize=30) # weights = tf.gather(class_weights, labels) xent = tf.reduce_mean(xent) return xent def weighted_cross_entropy_loss(logits, labels, class_hist=None, max_weight=1): print('loss: cross-entropy') print('Using balanced loss with max weight = ', max_weight) num_pixels = -1 with tf.name_scope(None, 'CrossEntropyLoss', [logits, labels]): labels = tf.reshape(labels, shape=[num_pixels]) onehot_labels = tf.one_hot(labels, FLAGS.num_classes) logits = tf.reshape(logits, [num_pixels, FLAGS.num_classes]) xent = tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=onehot_labels) #num_labels = tf.to_float(tf.reduce_sum(num_labels)) #num_labels = tf.reduce_sum(tf.to_float(num_labels)) #class_hist = tf.Print(class_hist, [class_hist], 'hist = ', summarize=30) num_labels = tf.reduce_sum(onehot_labels) #class_hist = tf.to_float(tf.reduce_sum(class_hist, axis=0)) ##num_labels = tf.Print(num_labels, [num_labels, tf.reduce_sum(onehot_labels)], 'lab = ', summarize=30) #class_weights = num_labels / (class_hist + 1) ##class_weights = tf.Print(class_weights, [class_weights], 'wgt hist = ', summarize=30) ## we need to append 0 here for ignore pixels #class_weights = tf.concat([class_weights, [0]], axis=0) ##class_weights = tf.Print(class_weights, [class_weights], 'wgt hist = ', summarize=30) #class_weights = tf.minimum(tf.to_float(max_weight), class_weights) class_weights = tf.ones([FLAGS.num_classes]) class_weights = tf.concat([class_weights, [0]], axis=0) #class_weights = tf.Print(class_weights, [class_weights], 'wgt hist = ', summarize=30) weights = tf.gather(class_weights, labels) if max_weight > 1: raise ValueError() wgt_sum = tf.reduce_sum(weights) norm_factor = num_labels / wgt_sum # weights need to sum to 1 weights = tf.multiply(weights, norm_factor) xent = tf.multiply(weights, xent) #num_labels = tf.Print(num_labels, [num_labels, wgt_sum], 'num_labels = ') #xent = tf.Print(xent, [xent], 'num_labels = ') xent = tf.reduce_sum(xent) / num_labels return xent def weighted_cross_entropy_loss_dense(logits, labels, weights=None, num_labels=None, max_weight=100): print('loss: cross-entropy') num_pixels = -1 with tf.name_scope(None, 'CrossEntropyLoss', [logits, labels]): labels = tf.reshape(labels, shape=[num_pixels]) onehot_labels = tf.one_hot(labels, FLAGS.num_classes) logits = tf.reshape(logits, [num_pixels, FLAGS.num_classes]) xent = tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=onehot_labels) if num_labels is None: num_labels = tf.reduce_sum(onehot_labels) else: num_labels = tf.reduce_sum(num_labels) print('Using balanced loss with max weight = ', max_weight) weights = tf.reshape(weights, shape=[num_pixels]) weights = tf.minimum(tf.to_float(max_weight), weights) wgt_sum = tf.reduce_sum(weights) norm_factor = num_labels / wgt_sum # weights need to sum to 1 weights = tf.multiply(weights, norm_factor) xent = tf.multiply(weights, xent) #num_labels = tf.Print(num_labels, [num_labels, wgt_sum], 'num_labels = ') #xent = tf.Print(xent, [xent], 'num_labels = ') xent = tf.reduce_sum(xent) / num_labels print(xent) return xent def cross_entropy_loss_old(logits, labels, weights, num_labels): print('loss: cross-entropy') num_pixels = -1 with tf.name_scope(None, 'CrossEntropyLoss', [logits, labels, num_labels]): labels = tf.reshape(labels, shape=[num_pixels]) onehot_labels = tf.one_hot(labels, FLAGS.num_classes) logits = tf.reshape(logits, [num_pixels, FLAGS.num_classes]) xent = tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=onehot_labels) weights = tf.reshape(weights, shape=[num_pixels]) xent = tf.multiply(weights, xent) xent = tf.reduce_sum(xent) / tf.reduce_sum(num_labels) print(xent) return xent def mse(yp, yt): num_examples = FLAGS.batch_size * FLAGS.img_height * FLAGS.img_width with tf.name_scope('MeanSquareError'): yt = tf.reshape(yt, shape=[num_examples]) yp = tf.reshape(yp, shape=[num_examples]) return tf.reduce_mean(tf.square(yt - yp)) def weighted_cross_entropy_loss_deprecated(logits, labels, weights=None, max_weight=100): #def weighted_cross_entropy_loss(logits, labels, weights=None, max_weight=1e2): #def weighted_cross_entropy_loss(logits, labels, weights=None, max_weight=1e3): print('loss: Weighted Cross Entropy Loss') shape = labels.get_shape().as_list() print(shape) #num_examples = shape[0] * shape[1] num_examples = -1 #num_examples = FLAGS.batch_size * FLAGS.img_height * FLAGS.img_width with tf.name_scope(None, 'WeightedCrossEntropyLoss', [logits, labels]): labels = tf.reshape(labels, shape=[num_examples]) #num_labels = tf.to_float(tf.reduce_sum(num_labels)) one_hot_labels = tf.one_hot(tf.to_int64(labels), FLAGS.num_classes, 1, 0) one_hot_labels = tf.reshape(one_hot_labels, [num_examples, FLAGS.num_classes]) num_labels = tf.to_float(tf.reduce_sum(one_hot_labels)) logits_1d = tf.reshape(logits, [num_examples, FLAGS.num_classes]) # todo #log_softmax = tf.log(tf.nn.softmax(logits_1d)) - never do this! log_softmax = tf.nn.log_softmax(logits_1d) xent = tf.reduce_sum(-tf.multiply(tf.to_float(one_hot_labels), log_softmax), 1) #weighted_xent = tf.mul(weights, xent) if weights != None: weights = tf.reshape(weights, shape=[num_examples]) xent = tf.mul(tf.minimum(tf.to_float(max_weight), weights), xent) #weighted_xent = xent total_loss = tf.div(tf.reduce_sum(xent), tf.to_float(num_labels), name='value') print(total_loss) return total_loss def flip_xent_loss(logits, labels, weights, max_weight=10): print('Loss: Weighted Cross Entropy Loss') assert(FLAGS.batch_size == 2) num_examples = FLAGS.batch_size * FLAGS.img_height * FLAGS.img_width labels = tf.reshape(labels, shape=[num_examples]) weights = tf.reshape(weights, shape=[num_examples]) #num_labels = tf.to_float(tf.reduce_sum(num_labels)) with tf.name_scope('FlipXentLoss', [logits, labels]): one_hot_labels = tf.one_hot(tf.to_int64(labels), FLAGS.num_classes, 1, 0) one_hot_labels = tf.reshape(one_hot_labels, [num_examples, FLAGS.num_classes]) num_labels = tf.to_float(tf.reduce_sum(one_hot_labels)) #print(logits[].get_shape()) logits_1d = tf.reshape(logits, [num_examples, FLAGS.num_classes]) # TODO #log_softmax = tf.log(tf.nn.softmax(logits_1d)) log_softmax = tf.nn.log_softmax(logits_1d) xent = tf.reduce_sum(tf.mul(tf.to_float(one_hot_labels), log_softmax), 1) #weighted_xent = tf.mul(weights, xent) weighted_xent = tf.mul(tf.minimum(tf.to_float(max_weight), weights), xent) #weighted_xent = xent total_loss = - tf.div(tf.reduce_sum(weighted_xent), num_labels, name='value') return total_loss def slim_cross_entropy_loss(logits, labels, num_labels): print('Loss: Cross Entropy Loss') num_examples = FLAGS.batch_size * FLAGS.img_height * FLAGS.img_width one_hot_labels = tf.one_hot(tf.to_int64(labels), FLAGS.num_classes, 1, 0) logits_1d = tf.reshape(logits, [num_examples, FLAGS.num_classes]) xent_loss = slim.losses.cross_entropy_loss(logits_1d, one_hot_labels) return xent_loss def softmax(logits): num_examples = FLAGS.batch_size * FLAGS.img_height * FLAGS.img_width with tf.op_scope([logits], None, 'Softmax'): logits_1d = tf.reshape(logits, [num_examples, FLAGS.num_classes]) softmax_1d = tf.nn.softmax(logits_1d) softmax_2d = tf.reshape(softmax_1d, [FLAGS.img_height, FLAGS.img_width, FLAGS.num_classes]) return softmax_2d def multiclass_hinge_loss(logits, labels, weights): print('loss: Hinge loss') num_examples = FLAGS.batch_size * FLAGS.img_height * FLAGS.img_width num_classes = FLAGS.num_classes with tf.op_scope([logits, labels], None, 'MulticlassHingeLoss'): #logits = tf.reshape(logits, [num_examples, num_classes]) #labels = tf.reshape(labels, [num_examples]) #weights = tf.reshape(weights, [num_examples]) logits = tf.reshape(logits, [-1, num_classes]) labels = tf.reshape(labels, [-1]) weights = tf.reshape(weights, [-1]) select_mask = tf.greater_equal(labels, 0) logits = tf.boolean_mask(logits, select_mask) labels = tf.boolean_mask(labels, select_mask) weights = tf.boolean_mask(weights, select_mask) num_examples = tf.reduce_sum(tf.to_int32(select_mask)) #num_examples = tf.Print(num_examples, [num_examples, num_labels_old], 'num_examples = ') #print(labels) #print(logits) #print(weights) #print(select_mask) partitions = tf.one_hot(tf.to_int64(labels), FLAGS.num_classes, 1, 0, dtype=tf.int32) #print(partitions) #one_hot_labels = tf.one_hot(tf.to_int64(labels), FLAGS.num_classes, 1, 0) #one_hot_labels = tf.reshape(one_hot_labels, [num_examples, FLAGS.num_classes]) #partitions = tf.to_int32(one_hot_labels) num_partitions = 2 scores, score_yt = tf.dynamic_partition(logits, partitions, num_partitions) #scores = tf.reshape(scores, [num_examples, num_classes - 1]) #score_yt = tf.reshape(score_yt, [num_examples, 1]) scores = tf.reshape(scores, [-1, num_classes - 1]) score_yt = tf.reshape(score_yt, [-1, 1]) #print(scores) #print(score_yt) #hinge_loss = tf.maximum(0.0, scores - score_yt + margin) hinge_loss = tf.square(tf.maximum(0.0, scores - score_yt + 1.0)) hinge_loss = tf.reduce_sum(hinge_loss, 1) #total_loss = tf.reduce_sum(tf.mul(weights, hinge_loss)) #total_loss = tf.div(total_loss, tf.to_float(num_examples), name='value') total_loss = tf.reduce_mean(tf.mul(tf.minimum(100.0, weights), hinge_loss)) #tf.nn.l2_normalize(x, dim, epsilon=1e-12, name=None) #tf.nn.l2_loss(t, name=None) return total_loss def metric_hinge_loss(logits, labels, weights, num_labels): print('loss: Hinge loss') num_examples = FLAGS.batch_size * FLAGS.img_height * FLAGS.img_width with tf.op_scope([logits, labels], None, 'weightedhingeloss'): one_hot_labels = tf.one_hot(tf.to_int64(labels), FLAGS.num_classes, 1, 0) logits_1d = tf.reshape(logits, [num_examples, FLAGS.num_classes]) #codes = tf.nn.softmax(logits_1d) codes = tf.nn.l2_normalize(logits_1d, 1) # works worse # l2 loss -> bad! # todo - this is not true svm loss, try it from cs231n l2_dist = tf.sqrt(tf.reduce_sum(tf.square(tf.to_float(one_hot_labels) - codes), 1)) m = 0.2 #l2_dist = tf.reduce_sum(tf.square(tf.to_float(one_hot_labels) - codes), 1) #m = 0.2 ** 2 #m = 0.1 ** 2 #m = 0.3 ** 2 for i in range(num_classes): for j in range(num_classes): raise valueerror(1) hinge_loss = tf.maximum(tf.to_float(0), l2_dist - m) total_loss = tf.reduce_sum(tf.mul(weights, hinge_loss)) total_loss = tf.div(total_loss, tf.to_float(num_labels), name='value') tf.add_to_collection(slim.losses.LOSSES_COLLECTION, total_loss) #tf.nn.l2_normalize(x, dim, epsilon=1e-12, name=None) #tf.nn.l2_loss(t, name=None) return total_loss #def weighted_hinge_loss(logits, labels, weights, num_labels): # print('Loss: Hinge Loss') # num_examples = FLAGS.batch_size * FLAGS.img_height * FLAGS.img_width # with tf.op_scope([logits, labels], None, 'WeightedHingeLoss'): # weights = tf.reshape(weights, shape=[num_examples]) # labels = tf.reshape(labels, shape=[num_examples]) # num_labels = tf.to_float(tf.reduce_sum(num_labels)) # one_hot_labels = tf.one_hot(tf.to_int64(labels), FLAGS.num_classes, 1, 0) # one_hot_labels = tf.reshape(one_hot_labels, [num_examples, FLAGS.num_classes]) # logits_1d = tf.reshape(logits, [num_examples, FLAGS.num_classes]) # #codes = tf.nn.softmax(logits_1d) # codes = tf.nn.l2_normalize(logits_1d, 1) # # works worse # #l2_dist = tf.sqrt(tf.reduce_sum(tf.square(tf.to_float(one_hot_labels) - codes), 1)) # #m = 0.2 # l2_dist = tf.reduce_sum(tf.square(tf.to_float(one_hot_labels) - codes), 1) # m = 0.2 ** 2 # #m = 0.1 ** 2 # #m = 0.3 ** 2 # hinge_loss = tf.maximum(tf.to_float(0), l2_dist - m) # total_loss = tf.reduce_sum(tf.mul(weights, hinge_loss)) # # total_loss = tf.div(total_loss, tf.to_float(num_labels), name='value') # tf.add_to_collection(slim.losses.LOSSES_COLLECTION, total_loss) # # #tf.nn.l2_normalize(x, dim, epsilon=1e-12, name=None) # #tf.nn.l2_loss(t, name=None) # return total_loss def flip_xent_loss_symmetric(logits, labels, weights, num_labels): print('Loss: Weighted Cross Entropy Loss') num_examples = FLAGS.img_height * FLAGS.img_width with tf.op_scope([logits, labels], None, 'WeightedCrossEntropyLoss'): labels = tf.reshape(labels, shape=[2, num_examples]) weights = tf.reshape(weights, shape=[2, num_examples]) num_labels = tf.to_float(tf.reduce_sum(num_labels)) #num_labels = tf.to_float(num_labels[0]) logits_flip = logits[1,:,:,:] #weights_flip = weights[1,:] logits = logits[0,:,:,:] weights = weights[0,:] labels = labels[0,:] one_hot_labels = tf.one_hot(tf.to_int64(labels), FLAGS.num_classes, 1, 0) one_hot_labels = tf.reshape(one_hot_labels, [num_examples, FLAGS.num_classes]) #logits_orig, logits_flip = tf.split(0, 2, logits) logits_flip = tf.image.flip_left_right(logits_flip) #print(logits[].get_shape()) logits_1d = tf.reshape(logits, [num_examples, FLAGS.num_classes]) logits_1d_flip = tf.reshape(logits_flip, [num_examples, FLAGS.num_classes]) # TODO log_softmax = tf.nn.log_softmax(logits_1d) #log_softmax_flip = tf.nn.log_softmax(logits_1d_flip) softmax_flip = tf.nn.softmax(logits_1d_flip) xent = tf.reduce_sum(tf.mul(tf.to_float(one_hot_labels), log_softmax), 1) weighted_xent = tf.mul(tf.minimum(tf.to_float(100), weights), xent) xent_flip = tf.reduce_sum(tf.mul(softmax_flip, log_softmax), 1) xent_flip = tf.mul(tf.minimum(tf.to_float(100), weights), xent_flip) #weighted_xent = tf.mul(weights, xent) #weighted_xent = xent #total_loss = tf.div(- tf.reduce_sum(weighted_xent_flip), # num_labels, name='value') total_loss = - tf.div(tf.reduce_sum(weighted_xent) + tf.reduce_sum(xent_flip), num_labels, name='value') tf.add_to_collection(slim.losses.LOSSES_COLLECTION, total_loss) return total_loss

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import tensorflow as tf import slim FLAGS = tf.app.flags.FLAGS def add_loss_summaries(total_loss): loss_averages = tf.train.ExponentialMovingAverage(0.9, name='avg') losses = tf.get_collection('losses') loss_averages_op = loss_averages.apply(losses + [total_loss]) for l in losses + [total_loss]: tf.summary.scalar(l.op.name + ' (raw)', l) tf.summary.scalar(l.op.name, loss_averages.average(l)) return loss_averages_op def total_loss_sum(losses): regularization_losses = tf.losses.get_regularization_losses() total_loss = tf.add_n(losses + regularization_losses, name='total_loss') return total_loss def cross_entropy_loss(logits, labels): print('loss: cross-entropy') num_pixels = -1 with tf.name_scope(None, 'CrossEntropyLoss', [logits, labels]): labels = tf.reshape(labels, shape=[num_pixels]) logits = tf.reshape(logits, [num_pixels, FLAGS.num_classes]) mask = labels < FLAGS.num_classes idx = tf.where(mask) = tf.gather_nd(labels, idx) labels = tf.to_int32(labels) logits = tf.gather_nd(logits, idx) onehot_labels = tf.one_hot(labels, FLAGS.num_classes) xent = tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=onehot_labels) = tf.one_hot(labels, FLAGS.num_classes) logits = tf.reshape(logits, [num_pixels, FLAGS.num_classes]) xent = tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=onehot_labels) num_labels = tf.reduce_sum(onehot_labels) sum weights = tf.multiply(weights, norm_factor) xent = tf.multiply(weights, xent) xent = tf.reduce_sum(xent) / num_labels return xent def weighted_cross_entropy_loss_dense(logits, labels, weights=None, num_labels=None, max_weight=100): print('loss: cross-entropy') num_pixels = -1 with tf.name_scope(None, 'CrossEntropyLoss', [logits, labels]): labels = tf.reshape(labels, shape=[num_pixels]) onehot_labels = tf.one_hot(labels, FLAGS.num_classes) logits = tf.reshape(logits, [num_pixels, FLAGS.num_classes]) xent = tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=onehot_labels) if num_labels is None: num_labels = tf.reduce_sum(onehot_labels) else: num_labels = tf.reduce_sum(num_labels) print('Using balanced loss with max weight = ', max_weight) weights = tf.reshape(weights, shape=[num_pixels]) weights = tf.minimum(tf.to_float(max_weight), weights) wgt_sum = tf.reduce_sum(weights) norm_factor = num_labels / wgt_sum weights = tf.multiply(weights, norm_factor) xent = tf.multiply(weights, xent) xent = tf.reduce_sum(xent) / num_labels print(xent) return xent def cross_entropy_loss_old(logits, labels, weights, num_labels): print('loss: cross-entropy') num_pixels = -1 with tf.name_scope(None, 'CrossEntropyLoss', [logits, labels, num_labels]): labels = tf.reshape(labels, shape=[num_pixels]) onehot_labels = tf.one_hot(labels, FLAGS.num_classes) logits = tf.reshape(logits, [num_pixels, FLAGS.num_classes]) xent = tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=onehot_labels) weights = tf.reshape(weights, shape=[num_pixels]) xent = tf.multiply(weights, xent) xent = tf.reduce_sum(xent) / tf.reduce_sum(num_labels) print(xent) return xent def mse(yp, yt): num_examples = FLAGS.batch_size * FLAGS.img_height * FLAGS.img_width with tf.name_scope('MeanSquareError'): yt = tf.reshape(yt, shape=[num_examples]) yp = tf.reshape(yp, shape=[num_examples]) return tf.reduce_mean(tf.square(yt - yp)) def weighted_cross_entropy_loss_deprecated(logits, labels, weights=None, max_weight=100): print('loss: Weighted Cross Entropy Loss') shape = labels.get_shape().as_list() print(shape) num_examples = -1 with tf.name_scope(None, 'WeightedCrossEntropyLoss', [logits, labels]): labels = tf.reshape(labels, shape=[num_examples]) one_hot_labels = tf.one_hot(tf.to_int64(labels), FLAGS.num_classes, 1, 0) one_hot_labels = tf.reshape(one_hot_labels, [num_examples, FLAGS.num_classes]) num_labels = tf.to_float(tf.reduce_sum(one_hot_labels)) logits_1d = tf.reshape(logits, [num_examples, FLAGS.num_classes]) log_softmax = tf.nn.log_softmax(logits_1d) xent = tf.reduce_sum(-tf.multiply(tf.to_float(one_hot_labels), log_softmax), 1) if weights != None: weights = tf.reshape(weights, shape=[num_examples]) xent = tf.mul(tf.minimum(tf.to_float(max_weight), weights), xent) total_loss = tf.div(tf.reduce_sum(xent), tf.to_float(num_labels), name='value') print(total_loss) return total_loss def flip_xent_loss(logits, labels, weights, max_weight=10): print('Loss: Weighted Cross Entropy Loss') assert(FLAGS.batch_size == 2) num_examples = FLAGS.batch_size * FLAGS.img_height * FLAGS.img_width labels = tf.reshape(labels, shape=[num_examples]) weights = tf.reshape(weights, shape=[num_examples]) with tf.name_scope('FlipXentLoss', [logits, labels]): one_hot_labels = tf.one_hot(tf.to_int64(labels), FLAGS.num_classes, 1, 0) one_hot_labels = tf.reshape(one_hot_labels, [num_examples, FLAGS.num_classes]) num_labels = tf.to_float(tf.reduce_sum(one_hot_labels)) logits_1d = tf.reshape(logits, [num_examples, FLAGS.num_classes]) log_softmax = tf.nn.log_softmax(logits_1d) xent = tf.reduce_sum(tf.mul(tf.to_float(one_hot_labels), log_softmax), 1) weighted_xent = tf.mul(tf.minimum(tf.to_float(max_weight), weights), xent) total_loss = - tf.div(tf.reduce_sum(weighted_xent), num_labels, name='value') return total_loss def slim_cross_entropy_loss(logits, labels, num_labels): print('Loss: Cross Entropy Loss') num_examples = FLAGS.batch_size * FLAGS.img_height * FLAGS.img_width one_hot_labels = tf.one_hot(tf.to_int64(labels), FLAGS.num_classes, 1, 0) logits_1d = tf.reshape(logits, [num_examples, FLAGS.num_classes]) xent_loss = slim.losses.cross_entropy_loss(logits_1d, one_hot_labels) return xent_loss def softmax(logits): num_examples = FLAGS.batch_size * FLAGS.img_height * FLAGS.img_width with tf.op_scope([logits], None, 'Softmax'): logits_1d = tf.reshape(logits, [num_examples, FLAGS.num_classes]) softmax_1d = tf.nn.softmax(logits_1d) softmax_2d = tf.reshape(softmax_1d, [FLAGS.img_height, FLAGS.img_width, FLAGS.num_classes]) return softmax_2d def multiclass_hinge_loss(logits, labels, weights): print('loss: Hinge loss') num_examples = FLAGS.batch_size * FLAGS.img_height * FLAGS.img_width num_classes = FLAGS.num_classes with tf.op_scope([logits, labels], None, 'MulticlassHingeLoss'): logits = tf.reshape(logits, [-1, num_classes]) labels = tf.reshape(labels, [-1]) weights = tf.reshape(weights, [-1]) select_mask = tf.greater_equal(labels, 0) logits = tf.boolean_mask(logits, select_mask) labels = tf.boolean_mask(labels, select_mask) weights = tf.boolean_mask(weights, select_mask) num_examples = tf.reduce_sum(tf.to_int32(select_mask)) partitions = tf.one_hot(tf.to_int64(labels), FLAGS.num_classes, 1, 0, dtype=tf.int32) num_partitions = 2 scores, score_yt = tf.dynamic_partition(logits, partitions, num_partitions) scores = tf.reshape(scores, [-1, num_classes - 1]) score_yt = tf.reshape(score_yt, [-1, 1]) hinge_loss = tf.square(tf.maximum(0.0, scores - score_yt + 1.0)) hinge_loss = tf.reduce_sum(hinge_loss, 1) total_loss = tf.reduce_mean(tf.mul(tf.minimum(100.0, weights), hinge_loss)) return total_loss def metric_hinge_loss(logits, labels, weights, num_labels): print('loss: Hinge loss') num_examples = FLAGS.batch_size * FLAGS.img_height * FLAGS.img_width with tf.op_scope([logits, labels], None, 'weightedhingeloss'): one_hot_labels = tf.one_hot(tf.to_int64(labels), FLAGS.num_classes, 1, 0) logits_1d = tf.reshape(logits, [num_examples, FLAGS.num_classes]) codes = tf.nn.l2_normalize(logits_1d, 1) l2_dist = tf.sqrt(tf.reduce_sum(tf.square(tf.to_float(one_hot_labels) - codes), 1)) m = 0.2 for i in range(num_classes): for j in range(num_classes): raise valueerror(1) hinge_loss = tf.maximum(tf.to_float(0), l2_dist - m) total_loss = tf.reduce_sum(tf.mul(weights, hinge_loss)) total_loss = tf.div(total_loss, tf.to_float(num_labels), name='value') tf.add_to_collection(slim.losses.LOSSES_COLLECTION, total_loss) return total_loss pyLoss'): labels = tf.reshape(labels, shape=[2, num_examples]) weights = tf.reshape(weights, shape=[2, num_examples]) num_labels = tf.to_float(tf.reduce_sum(num_labels)) logits_flip = logits[1,:,:,:] logits = logits[0,:,:,:] weights = weights[0,:] labels = labels[0,:] one_hot_labels = tf.one_hot(tf.to_int64(labels), FLAGS.num_classes, 1, 0) one_hot_labels = tf.reshape(one_hot_labels, [num_examples, FLAGS.num_classes]) logits_flip = tf.image.flip_left_right(logits_flip) logits_1d = tf.reshape(logits, [num_examples, FLAGS.num_classes]) logits_1d_flip = tf.reshape(logits_flip, [num_examples, FLAGS.num_classes]) log_softmax = tf.nn.log_softmax(logits_1d) softmax_flip = tf.nn.softmax(logits_1d_flip) xent = tf.reduce_sum(tf.mul(tf.to_float(one_hot_labels), log_softmax), 1) weighted_xent = tf.mul(tf.minimum(tf.to_float(100), weights), xent) xent_flip = tf.reduce_sum(tf.mul(softmax_flip, log_softmax), 1) xent_flip = tf.mul(tf.minimum(tf.to_float(100), weights), xent_flip) total_loss = - tf.div(tf.reduce_sum(weighted_xent) + tf.reduce_sum(xent_flip), num_labels, name='value') tf.add_to_collection(slim.losses.LOSSES_COLLECTION, total_loss) return total_loss

true

f71b599c49ef3382050c2d01eff0c192906c1d7b

1,580

py

Python

aiLogic/tankAI.py

JoelEager/pyTanks.Player

a35a653e9df2416c63204aba87a95f33e6815b63

[ "MIT" ]

2

2017-03-09T15:32:55.000Z

2017-09-04T11:25:41.000Z

aiLogic/tankAI.py

JoelEager/pyTanks.Player

a35a653e9df2416c63204aba87a95f33e6815b63

[ "MIT" ]

null

aiLogic/tankAI.py

JoelEager/pyTanks.Player

a35a653e9df2416c63204aba87a95f33e6815b63

[ "MIT" ]

4

2017-05-16T15:10:09.000Z

2017-07-06T15:24:50.000Z

""" The player's AI code Functions here are called by clock.py to run the AI code """ import random import math from clientLogic.logging import logPrint from clientLogic import clientData, commands def onConnect(): """ Called when the player initially connects to the server but before the tank first spawns """ commands.setInfo("Python player instance running the example AI.\n" + "Fork me at https://github.com/JoelEager/pyTanks.Player") def onSpawn(): """ Called when the tank spawns in a new game """ pass def onTick(elapsedTime): """ Called once every frame while the tank is alive :param elapsedTime: The time elapsed, in seconds, since the last frame """ gs = clientData.gameState # Collided so try to get moving again if not gs.myTank.moving: commands.turn((math.pi / 4) * random.randint(0, 7)) commands.go() logPrint("Turned and starting moving", 2) # Shooting logic if gs.myTank.canShoot and random.randint(0, 4) == 0: # Select a target random.shuffle(gs.tanks) for target in gs.tanks: if target.alive: # Do the math deltaX = abs(gs.myTank.x - target.x) if deltaX == 0: return deltaY = gs.myTank.y - target.y angle = math.atan(deltaY / deltaX) if target.x < gs.myTank.x: angle = math.pi - angle commands.fire(angle) logPrint("Fired", 2) break

27.719298

92

0.588608

import random import math from clientLogic.logging import logPrint from clientLogic import clientData, commands def onConnect(): commands.setInfo("Python player instance running the example AI.\n" + "Fork me at https://github.com/JoelEager/pyTanks.Player") def onSpawn(): pass def onTick(elapsedTime): gs = clientData.gameState if not gs.myTank.moving: commands.turn((math.pi / 4) * random.randint(0, 7)) commands.go() logPrint("Turned and starting moving", 2) if gs.myTank.canShoot and random.randint(0, 4) == 0: random.shuffle(gs.tanks) for target in gs.tanks: if target.alive: deltaX = abs(gs.myTank.x - target.x) if deltaX == 0: return deltaY = gs.myTank.y - target.y angle = math.atan(deltaY / deltaX) if target.x < gs.myTank.x: angle = math.pi - angle commands.fire(angle) logPrint("Fired", 2) break

true

f71b5a856153c3564ce7371764011afc06ba93ae

12,294

py

Python

tests/unit/common_utils.py

dannielarriola/uai-coursebuilder

fbd440a8bfe1a928ac52985aea2949d5e91ad203

[ "Apache-2.0" ]

null

tests/unit/common_utils.py

dannielarriola/uai-coursebuilder

fbd440a8bfe1a928ac52985aea2949d5e91ad203

[ "Apache-2.0" ]

27

2016-08-31T19:04:46.000Z

2016-09-29T00:22:32.000Z

tests/unit/common_utils.py

dannielarriola/uai-coursebuilder

fbd440a8bfe1a928ac52985aea2949d5e91ad203

[ "Apache-2.0" ]

null

# Copyright 2014 Google Inc. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS-IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Unit tests for common.tags.""" __author__ = 'Mike Gainer (mgainer@google.com)' import datetime import os import unittest import appengine_config from common import utils class CommonUnitTests(unittest.TestCase): # --------------------------- String-to-list. def test_list_parsing(self): self.assertListEqual(['foo'], utils.text_to_list('foo')) self.assertListEqual(['foo'], utils.text_to_list(' foo')) self.assertListEqual(['foo'], utils.text_to_list('foo ')) self.assertListEqual(['foo'], utils.text_to_list(' foo ')) self.assertListEqual(['foo'], utils.text_to_list('foo\t')) self.assertListEqual(['foo'], utils.text_to_list('\tfoo')) self.assertListEqual(['foo'], utils.text_to_list('\tfoo\t')) self.assertListEqual(['foo'], utils.text_to_list('foo ')) self.assertListEqual(['foo'], utils.text_to_list(' foo')) self.assertListEqual(['foo'], utils.text_to_list(' foo ')) self.assertListEqual(['foo'], utils.text_to_list('foo\n')) self.assertListEqual(['foo'], utils.text_to_list('\nfoo')) self.assertListEqual(['foo'], utils.text_to_list('\nfoo\n')) self.assertListEqual(['foo'], utils.text_to_list('foo,')) self.assertListEqual(['foo'], utils.text_to_list(',foo')) self.assertListEqual(['foo'], utils.text_to_list(',foo,')) self.assertListEqual(['foo'], utils.text_to_list(' foo ,\n')) self.assertListEqual(['foo'], utils.text_to_list('\tfoo,\t\n')) self.assertListEqual(['foo'], utils.text_to_list(',foo,\n')) self.assertListEqual(['foo'], utils.text_to_list( '[foo]', utils.BACKWARD_COMPATIBLE_SPLITTER)) self.assertListEqual(['foo'], utils.text_to_list( '[foo],', utils.BACKWARD_COMPATIBLE_SPLITTER)) self.assertListEqual(['foo'], utils.text_to_list( '[foo], ', utils.BACKWARD_COMPATIBLE_SPLITTER)) self.assertListEqual(['foo'], utils.text_to_list( '[foo],\n', utils.BACKWARD_COMPATIBLE_SPLITTER)) self.assertListEqual(['foo'], utils.text_to_list( '[foo], \n', utils.BACKWARD_COMPATIBLE_SPLITTER)) self.assertListEqual(['foo', 'bar'], utils.text_to_list('foo bar')) self.assertListEqual(['foo', 'bar'], utils.text_to_list(' foo bar')) self.assertListEqual(['foo', 'bar'], utils.text_to_list('foo bar ')) self.assertListEqual(['foo', 'bar'], utils.text_to_list('foo\tbar')) self.assertListEqual(['foo', 'bar'], utils.text_to_list('\tfoo\tbar')) self.assertListEqual(['foo', 'bar'], utils.text_to_list('foo\tbar\t')) self.assertListEqual(['foo', 'bar'], utils.text_to_list('foo\nbar\n')) self.assertListEqual(['foo', 'bar'], utils.text_to_list('\nfoo\nbar\n')) self.assertListEqual(['foo', 'bar'], utils.text_to_list('\n foo\n bar\n')) self.assertListEqual(['foo', 'bar'], utils.text_to_list(' \n foo \n bar \n')) self.assertListEqual(['foo', 'bar'], utils.text_to_list( '[foo][bar]', utils.BACKWARD_COMPATIBLE_SPLITTER)) self.assertListEqual(['foo', 'bar'], utils.text_to_list( ' [foo] [bar] ', utils.BACKWARD_COMPATIBLE_SPLITTER)) self.assertListEqual(['foo', 'bar'], utils.text_to_list( '\n[foo]\n[bar]\n', utils.BACKWARD_COMPATIBLE_SPLITTER)) self.assertListEqual(['foo', 'bar'], utils.text_to_list( '\n,[foo],\n[bar],\n', utils.BACKWARD_COMPATIBLE_SPLITTER)) def test_none_split(self): self.assertListEqual([], utils.text_to_list(None)) def test_empty_split(self): self.assertListEqual([], utils.text_to_list('')) def test_all_separators_split(self): self.assertListEqual([], utils.text_to_list(' ,,, \t\t\n\t ')) def test_one_item_split(self): self.assertListEqual(['x'], utils.text_to_list('x')) def test_join_none(self): self.assertEquals('', utils.list_to_text(None)) def test_join_empty(self): self.assertEquals('', utils.list_to_text([])) def test_join_one(self): self.assertEquals('x', utils.list_to_text(['x'])) def test_join_two(self): self.assertEquals('x y', utils.list_to_text(['x', 'y'])) def test_join_split(self): l = ['a', 'b', 'c'] self.assertListEqual(l, utils.text_to_list(utils.list_to_text(l))) def test_split_join(self): text = 'a b c' self.assertEquals(text, utils.list_to_text(utils.text_to_list(text))) class ZipAwareOpenTests(unittest.TestCase): def test_find_in_lib_without_relative_path(self): path = os.path.join( appengine_config.BUNDLE_ROOT, 'lib', 'babel-0.9.6.zip', 'babel', 'localedata', 'root.dat') with self.assertRaises(IOError): open(path) # This fails. with utils.ZipAwareOpen(): data = open(path).read() self.assertEquals(17490, len(data)) data = open(path, 'r').read() self.assertEquals(17490, len(data)) data = open(path, mode='r').read() self.assertEquals(17490, len(data)) data = open(name=path, mode='r').read() self.assertEquals(17490, len(data)) data = open(name=path).read() self.assertEquals(17490, len(data)) with self.assertRaises(IOError): open(path) # This fails again; open has been reset to normal. def test_find_in_lib_with_relative_path(self): path = os.path.join( appengine_config.BUNDLE_ROOT, 'lib', 'markdown-2.5.zip', 'setup.cfg') with self.assertRaises(IOError): open(path) # This fails. with utils.ZipAwareOpen(): data = open(path).read() self.assertEquals(12, len(data)) class ParseTimedeltaTests(unittest.TestCase): def test_parse_empty_string(self): self.assertEquals( utils.parse_timedelta_string(''), datetime.timedelta()) def test_parse_zero(self): self.assertEquals( utils.parse_timedelta_string('0'), datetime.timedelta()) def test_parse_gibberish(self): self.assertEquals( utils.parse_timedelta_string('Amidst the mists and coldest frosts'), datetime.timedelta()) def test_parse_leading_valid_partial_gibberish(self): self.assertEquals( utils.parse_timedelta_string( '5 days and a partridge in a pear tree'), datetime.timedelta(days=5)) def test_parse_trailing_valid_partial_gibberish(self): self.assertEquals( utils.parse_timedelta_string('we will leave in 5 days'), datetime.timedelta(days=5)) def test_parse_units(self): for unit in ('week', 'day', 'hour', 'minute', 'second'): self._test_parse_units(unit) def _test_parse_units(self, unit): expected1 = datetime.timedelta(**{unit + 's': 1}) expected2 = datetime.timedelta(**{unit + 's': 2}) self.assertEquals( utils.parse_timedelta_string('1%s' % unit[0]), expected1) self.assertEquals( utils.parse_timedelta_string('1%s' % unit), expected1) self.assertEquals( utils.parse_timedelta_string('2%ss' % unit), expected2) self.assertEquals( utils.parse_timedelta_string('2 %s' % unit[0]), expected2) self.assertEquals( utils.parse_timedelta_string('1 %s' % unit), expected1) self.assertEquals( utils.parse_timedelta_string('2 %s' % unit), expected2) self.assertEquals( utils.parse_timedelta_string('2 \t\t\n %ss' % unit), expected2) def test_parse_out_of_bounds_handled_successfully(self): self.assertEquals( utils.parse_timedelta_string('86400s'), datetime.timedelta(days=1)) self.assertEquals( utils.parse_timedelta_string('19d, 86400s'), datetime.timedelta(weeks=2, days=6)) def test_parse_combinations(self): self.assertEquals( utils.parse_timedelta_string('3w1d3m'), datetime.timedelta(weeks=3, days=1, minutes=3)) self.assertEquals( utils.parse_timedelta_string('3w, 1d, 3m'), datetime.timedelta(weeks=3, days=1, minutes=3)) self.assertEquals( utils.parse_timedelta_string('3 w 1 d 3 m'), datetime.timedelta(weeks=3, days=1, minutes=3)) self.assertEquals( utils.parse_timedelta_string('3 weeks 1 day 3 minutes'), datetime.timedelta(weeks=3, days=1, minutes=3)) self.assertEquals( utils.parse_timedelta_string('3 weeks, 1 day, 3 minutes'), datetime.timedelta(weeks=3, days=1, minutes=3)) class ValidateTimedeltaTests(unittest.TestCase): def test_blank_is_allowed(self): errors = [] utils.ValidateTimedelta.validate('', errors) self.assertEquals(0, len(errors)) def test_none_is_allowed(self): errors = [] utils.ValidateTimedelta.validate(None, errors) self.assertEquals(0, len(errors)) def test_bare_numbers_not_allowed(self): errors = [] utils.ValidateTimedelta.validate('0', errors) self.assertEquals(1, len(errors)) errors = [] utils.ValidateTimedelta.validate('1', errors) self.assertEquals(1, len(errors)) errors = [] utils.ValidateTimedelta.validate('-1', errors) self.assertEquals(1, len(errors)) errors = [] utils.ValidateTimedelta.validate('100', errors) self.assertEquals(1, len(errors)) def test_valid_items_allowed(self): errors = [] utils.ValidateTimedelta.validate('1s', errors) utils.ValidateTimedelta.validate('2m', errors) utils.ValidateTimedelta.validate('3h', errors) utils.ValidateTimedelta.validate('4d', errors) utils.ValidateTimedelta.validate('5w', errors) utils.ValidateTimedelta.validate('5 Weeks, 1D,2HOURS 3 seconds', errors) self.assertEquals(0, len(errors)) def test_invalid_items_disallowed(self): errors = [] utils.ValidateTimedelta.validate('1t', errors) self.assertEquals(1, len(errors)) errors = [] utils.ValidateTimedelta.validate('1 year', errors) self.assertEquals(1, len(errors)) def test_parse_months_gives_error(self): errors = [] utils.ValidateTimedelta.validate('3 months', errors) self.assertEquals(1, len(errors))

38.299065

80

0.577843

__author__ = 'Mike Gainer (mgainer@google.com)' import datetime import os import unittest import appengine_config from common import utils class CommonUnitTests(unittest.TestCase): def test_list_parsing(self): self.assertListEqual(['foo'], utils.text_to_list('foo')) self.assertListEqual(['foo'], utils.text_to_list(' foo')) self.assertListEqual(['foo'], utils.text_to_list('foo ')) self.assertListEqual(['foo'], utils.text_to_list(' foo ')) self.assertListEqual(['foo'], utils.text_to_list('foo\t')) self.assertListEqual(['foo'], utils.text_to_list('\tfoo')) self.assertListEqual(['foo'], utils.text_to_list('\tfoo\t')) self.assertListEqual(['foo'], utils.text_to_list('foo ')) self.assertListEqual(['foo'], utils.text_to_list(' foo')) self.assertListEqual(['foo'], utils.text_to_list(' foo ')) self.assertListEqual(['foo'], utils.text_to_list('foo\n')) self.assertListEqual(['foo'], utils.text_to_list('\nfoo')) self.assertListEqual(['foo'], utils.text_to_list('\nfoo\n')) self.assertListEqual(['foo'], utils.text_to_list('foo,')) self.assertListEqual(['foo'], utils.text_to_list(',foo')) self.assertListEqual(['foo'], utils.text_to_list(',foo,')) self.assertListEqual(['foo'], utils.text_to_list(' foo ,\n')) self.assertListEqual(['foo'], utils.text_to_list('\tfoo,\t\n')) self.assertListEqual(['foo'], utils.text_to_list(',foo,\n')) self.assertListEqual(['foo'], utils.text_to_list( '[foo]', utils.BACKWARD_COMPATIBLE_SPLITTER)) self.assertListEqual(['foo'], utils.text_to_list( '[foo],', utils.BACKWARD_COMPATIBLE_SPLITTER)) self.assertListEqual(['foo'], utils.text_to_list( '[foo], ', utils.BACKWARD_COMPATIBLE_SPLITTER)) self.assertListEqual(['foo'], utils.text_to_list( '[foo],\n', utils.BACKWARD_COMPATIBLE_SPLITTER)) self.assertListEqual(['foo'], utils.text_to_list( '[foo], \n', utils.BACKWARD_COMPATIBLE_SPLITTER)) self.assertListEqual(['foo', 'bar'], utils.text_to_list('foo bar')) self.assertListEqual(['foo', 'bar'], utils.text_to_list(' foo bar')) self.assertListEqual(['foo', 'bar'], utils.text_to_list('foo bar ')) self.assertListEqual(['foo', 'bar'], utils.text_to_list('foo\tbar')) self.assertListEqual(['foo', 'bar'], utils.text_to_list('\tfoo\tbar')) self.assertListEqual(['foo', 'bar'], utils.text_to_list('foo\tbar\t')) self.assertListEqual(['foo', 'bar'], utils.text_to_list('foo\nbar\n')) self.assertListEqual(['foo', 'bar'], utils.text_to_list('\nfoo\nbar\n')) self.assertListEqual(['foo', 'bar'], utils.text_to_list('\n foo\n bar\n')) self.assertListEqual(['foo', 'bar'], utils.text_to_list(' \n foo \n bar \n')) self.assertListEqual(['foo', 'bar'], utils.text_to_list( '[foo][bar]', utils.BACKWARD_COMPATIBLE_SPLITTER)) self.assertListEqual(['foo', 'bar'], utils.text_to_list( ' [foo] [bar] ', utils.BACKWARD_COMPATIBLE_SPLITTER)) self.assertListEqual(['foo', 'bar'], utils.text_to_list( '\n[foo]\n[bar]\n', utils.BACKWARD_COMPATIBLE_SPLITTER)) self.assertListEqual(['foo', 'bar'], utils.text_to_list( '\n,[foo],\n[bar],\n', utils.BACKWARD_COMPATIBLE_SPLITTER)) def test_none_split(self): self.assertListEqual([], utils.text_to_list(None)) def test_empty_split(self): self.assertListEqual([], utils.text_to_list('')) def test_all_separators_split(self): self.assertListEqual([], utils.text_to_list(' ,,, \t\t\n\t ')) def test_one_item_split(self): self.assertListEqual(['x'], utils.text_to_list('x')) def test_join_none(self): self.assertEquals('', utils.list_to_text(None)) def test_join_empty(self): self.assertEquals('', utils.list_to_text([])) def test_join_one(self): self.assertEquals('x', utils.list_to_text(['x'])) def test_join_two(self): self.assertEquals('x y', utils.list_to_text(['x', 'y'])) def test_join_split(self): l = ['a', 'b', 'c'] self.assertListEqual(l, utils.text_to_list(utils.list_to_text(l))) def test_split_join(self): text = 'a b c' self.assertEquals(text, utils.list_to_text(utils.text_to_list(text))) class ZipAwareOpenTests(unittest.TestCase): def test_find_in_lib_without_relative_path(self): path = os.path.join( appengine_config.BUNDLE_ROOT, 'lib', 'babel-0.9.6.zip', 'babel', 'localedata', 'root.dat') with self.assertRaises(IOError): open(path) with utils.ZipAwareOpen(): data = open(path).read() self.assertEquals(17490, len(data)) data = open(path, 'r').read() self.assertEquals(17490, len(data)) data = open(path, mode='r').read() self.assertEquals(17490, len(data)) data = open(name=path, mode='r').read() self.assertEquals(17490, len(data)) data = open(name=path).read() self.assertEquals(17490, len(data)) with self.assertRaises(IOError): open(path) def test_find_in_lib_with_relative_path(self): path = os.path.join( appengine_config.BUNDLE_ROOT, 'lib', 'markdown-2.5.zip', 'setup.cfg') with self.assertRaises(IOError): open(path) with utils.ZipAwareOpen(): data = open(path).read() self.assertEquals(12, len(data)) class ParseTimedeltaTests(unittest.TestCase): def test_parse_empty_string(self): self.assertEquals( utils.parse_timedelta_string(''), datetime.timedelta()) def test_parse_zero(self): self.assertEquals( utils.parse_timedelta_string('0'), datetime.timedelta()) def test_parse_gibberish(self): self.assertEquals( utils.parse_timedelta_string('Amidst the mists and coldest frosts'), datetime.timedelta()) def test_parse_leading_valid_partial_gibberish(self): self.assertEquals( utils.parse_timedelta_string( '5 days and a partridge in a pear tree'), datetime.timedelta(days=5)) def test_parse_trailing_valid_partial_gibberish(self): self.assertEquals( utils.parse_timedelta_string('we will leave in 5 days'), datetime.timedelta(days=5)) def test_parse_units(self): for unit in ('week', 'day', 'hour', 'minute', 'second'): self._test_parse_units(unit) def _test_parse_units(self, unit): expected1 = datetime.timedelta(**{unit + 's': 1}) expected2 = datetime.timedelta(**{unit + 's': 2}) self.assertEquals( utils.parse_timedelta_string('1%s' % unit[0]), expected1) self.assertEquals( utils.parse_timedelta_string('1%s' % unit), expected1) self.assertEquals( utils.parse_timedelta_string('2%ss' % unit), expected2) self.assertEquals( utils.parse_timedelta_string('2 %s' % unit[0]), expected2) self.assertEquals( utils.parse_timedelta_string('1 %s' % unit), expected1) self.assertEquals( utils.parse_timedelta_string('2 %s' % unit), expected2) self.assertEquals( utils.parse_timedelta_string('2 \t\t\n %ss' % unit), expected2) def test_parse_out_of_bounds_handled_successfully(self): self.assertEquals( utils.parse_timedelta_string('86400s'), datetime.timedelta(days=1)) self.assertEquals( utils.parse_timedelta_string('19d, 86400s'), datetime.timedelta(weeks=2, days=6)) def test_parse_combinations(self): self.assertEquals( utils.parse_timedelta_string('3w1d3m'), datetime.timedelta(weeks=3, days=1, minutes=3)) self.assertEquals( utils.parse_timedelta_string('3w, 1d, 3m'), datetime.timedelta(weeks=3, days=1, minutes=3)) self.assertEquals( utils.parse_timedelta_string('3 w 1 d 3 m'), datetime.timedelta(weeks=3, days=1, minutes=3)) self.assertEquals( utils.parse_timedelta_string('3 weeks 1 day 3 minutes'), datetime.timedelta(weeks=3, days=1, minutes=3)) self.assertEquals( utils.parse_timedelta_string('3 weeks, 1 day, 3 minutes'), datetime.timedelta(weeks=3, days=1, minutes=3)) class ValidateTimedeltaTests(unittest.TestCase): def test_blank_is_allowed(self): errors = [] utils.ValidateTimedelta.validate('', errors) self.assertEquals(0, len(errors)) def test_none_is_allowed(self): errors = [] utils.ValidateTimedelta.validate(None, errors) self.assertEquals(0, len(errors)) def test_bare_numbers_not_allowed(self): errors = [] utils.ValidateTimedelta.validate('0', errors) self.assertEquals(1, len(errors)) errors = [] utils.ValidateTimedelta.validate('1', errors) self.assertEquals(1, len(errors)) errors = [] utils.ValidateTimedelta.validate('-1', errors) self.assertEquals(1, len(errors)) errors = [] utils.ValidateTimedelta.validate('100', errors) self.assertEquals(1, len(errors)) def test_valid_items_allowed(self): errors = [] utils.ValidateTimedelta.validate('1s', errors) utils.ValidateTimedelta.validate('2m', errors) utils.ValidateTimedelta.validate('3h', errors) utils.ValidateTimedelta.validate('4d', errors) utils.ValidateTimedelta.validate('5w', errors) utils.ValidateTimedelta.validate('5 Weeks, 1D,2HOURS 3 seconds', errors) self.assertEquals(0, len(errors)) def test_invalid_items_disallowed(self): errors = [] utils.ValidateTimedelta.validate('1t', errors) self.assertEquals(1, len(errors)) errors = [] utils.ValidateTimedelta.validate('1 year', errors) self.assertEquals(1, len(errors)) def test_parse_months_gives_error(self): errors = [] utils.ValidateTimedelta.validate('3 months', errors) self.assertEquals(1, len(errors))

true

f71b5b1995d1ecc2a1ba880156e9343a02f0c212

578

py

Python

bark_ml/commons/tracer.py

bark-simulator/rl

84f9c74b60becbc4bc758e19b201d85a21880717

[ "MIT" ]

null

bark_ml/commons/tracer.py

bark-simulator/rl

84f9c74b60becbc4bc758e19b201d85a21880717

[ "MIT" ]

null

bark_ml/commons/tracer.py

bark-simulator/rl

84f9c74b60becbc4bc758e19b201d85a21880717

[ "MIT" ]

null

# Copyright (c) 2020 fortiss GmbH # # Authors: Patrick Hart # # This work is licensed under the terms of the MIT license. # For a copy, see <https://opensource.org/licenses/MIT>. import pickle class Tracer: """The tracer can be used to log certain values during episodes.""" def __init__(self, states=None, trace_history=True): self._trace_history = trace_history self._states = [] def Trace(self, eval_dict): """Traces and stores a state.""" if self._trace_history: self._states.append(eval_dict) def Reset(self): self._trace_history = []

25.130435

69

0.698962

import pickle class Tracer: def __init__(self, states=None, trace_history=True): self._trace_history = trace_history self._states = [] def Trace(self, eval_dict): if self._trace_history: self._states.append(eval_dict) def Reset(self): self._trace_history = []

true

f71b5b6b59934a264b9a46047b70741641a2db51

10,504

py

Python

tests/test_clone.py

mohammadroghani/django-clone

603037194ae43f5e2eb96bd0aa159c1fbcf8c51c

[ "MIT" ]

null

tests/test_clone.py

mohammadroghani/django-clone

603037194ae43f5e2eb96bd0aa159c1fbcf8c51c

[ "MIT" ]

null

tests/test_clone.py

mohammadroghani/django-clone

603037194ae43f5e2eb96bd0aa159c1fbcf8c51c

[ "MIT" ]

null

# -*- coding: utf-8 -*- # Django Clone - https://github.com/mohammadroghani/django-clone # Copyright © 2016 Mohammad Roghani <mohammadroghani43@gmail.com> # Copyright © 2016 Amir Keivan Mohtashami <akmohtashami97@gmail.com> # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in all # copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. from django.test import TestCase from django.utils import timezone from django_clone.clone import Cloner from tests.models import * def get_information_list(object_list): information_list = [] for object in object_list: information_list.append((object.pk, object.__module__ + "." + object.__class__.__name__)) information_list.sort() return information_list class VersionControlTests(TestCase): def test_get_all_neighbor_objects(self): question = Question(question_text='question1', pub_date=timezone.now()) question.save() choice = question.choice_set.create(choice_text='a', votes=0) c = Choice(question=question, choice_text='b', votes=0) c.save() choice.save() person = Person() person.save() person.questions.add(question) test_list = [(question.pk, question.__module__ + "." + question.__class__.__name__)] cloner = Cloner() self.assertEqual(get_information_list(cloner.get_all_neighbor_objects(person)), test_list) self.assertEqual(get_information_list(cloner.get_all_neighbor_objects(person)), test_list) test_list.clear() test_list = [(choice.pk, choice.__module__ + "." + choice.__class__.__name__), (c.pk, c.__module__ + "." + c.__class__.__name__), (person.pk, person.__module__ + "." + person.__class__.__name__)] test_list.sort() self.assertEqual(get_information_list(cloner.get_all_neighbor_objects(question)), test_list) def test_get_all_related_objects(self): question = Question(question_text='question1', pub_date=timezone.now()) q1 = Question(question_text='q1', pub_date=timezone.now()) q1.save() question.save() choice = question.choice_set.create(choice_text='a', votes=0) c = Choice(question=question, choice_text='b', votes=0) c.save() choice.save() c1 = q1.choice_set.create(choice_text='a', votes=0) c1.save() person = Person() person.save() person.questions.add(question) cloner = Cloner() test_list = [(q1.pk, q1.__module__+ "." + q1.__class__.__name__), (c1.pk, c1.__module__ + "." + c1.__class__.__name__)] test_list.sort() self.assertEqual(get_information_list(cloner.get_all_related_object(q1)), test_list) self.assertEqual(get_information_list(cloner.get_all_related_object(c1)), test_list) test_list.clear() test_list = [(question.pk, question.__module__ + "." + question.__class__.__name__), (c.pk, c.__module__ + "." + c.__class__.__name__), (choice.pk, choice.__module__ + "." + choice.__class__.__name__), (person.pk, person.__module__ + "." + person.__class__.__name__)] test_list.sort() self.assertEqual(get_information_list(cloner.get_all_related_object(question)), test_list) self.assertEqual(get_information_list(cloner.get_all_related_object(c)), test_list) self.assertEqual(get_information_list(cloner.get_all_related_object(choice)), test_list) self.assertEqual(get_information_list(cloner.get_all_related_object(person)), test_list) self.assertNotEqual(get_information_list(Cloner().get_all_related_object(q1)), test_list) def test_get_all_related_objects_with_circular_relation(self): a_object = A() b_object = B() c_object = C() a_object.save() b_object.save() c_object.save() a_object.b.add(b_object) b_object.c.add(c_object) c_object.a.add(a_object) test_list = [(b_object.pk, b_object.__module__ + "." + b_object.__class__.__name__), (c_object.pk, c_object.__module__ + "." + c_object.__class__.__name__), (a_object.pk, a_object.__module__ + "." + a_object.__class__.__name__)] test_list.sort() cloner = Cloner() self.assertEqual(get_information_list(cloner.get_all_related_object(a_object)), test_list) self.assertEqual(get_information_list(cloner.get_all_related_object(b_object)), test_list) self.assertEqual(get_information_list(cloner.get_all_related_object(c_object)), test_list) def test_clone_with_one_object(self): question = Question(question_text='a', pub_date=timezone.now()) question.save() q = Cloner().clone(question) self.assertNotEqual(q.pk, question.pk) self.assertEqual(q.question_text, question.question_text) self.assertEqual(q.pub_date, question.pub_date) def test_clone_with_foreign_key(self): question = Question(question_text='a', pub_date=timezone.now()) question.save() choice = Choice(question=question, choice_text='c', votes=0) choice.save() cloner = Cloner() c = cloner.clone(choice) self.assertNotEqual(choice.id, c.id) self.assertNotEqual(choice.question.id, c.question.id) self.assertEqual(choice.question.question_text, c.question.question_text) q = cloner.clone(question) self.assertNotEqual(q.id, question.id) self.assertNotEqual(question.choice_set.get(choice_text='c').pk, q.choice_set.get(choice_text='c').pk) def test_clone_with_ignore_list(self): question = Question(question_text='a', pub_date=timezone.now()) question.save() choice = Choice(question=question, choice_text='c', votes=0) choice.save() c = Cloner(ignored_models=["tests.Question"]).clone(choice) self.assertNotEqual(choice.id, c.id) self.assertEqual(choice.question.id, c.question.id) def test_clone_with_many_to_many_field(self): question = Question(question_text='question1', pub_date=timezone.now()) question.save() person = Person() person.save() person.questions.add(question) p = Cloner().clone(person) self.assertNotEqual(person.id, p.id) self.assertNotEqual(person.questions.get(question_text='question1').id, p.questions.get(question_text='question1').id) def test_clone_many_to_many_field_with_repeated_instance(self): question = Question(question_text='question1', pub_date=timezone.now()) question.save() person = Person() person.save() person.questions.add(question) person.questions.add(question) p = Cloner().clone(person) self.assertEqual(person.questions.all().count(), p.questions.all().count()) def test_clone_with_through_field(self): student = Student(name='Ali') group = Group(name='ACM') student.save() group.save() membership = Membership(student=student, group=group) membership.save() g = Cloner().clone(group) self.assertNotEqual(g.id, group.id) self.assertNotEqual(group.members.get(name='Ali').id, g.members.get(name='Ali').id) s = Cloner().clone(student) self.assertNotEqual(s.id, student.id) self.assertNotEqual(student.group_set.get(name='ACM').id, s.group_set.get(name='ACM').id) def test_clone_many_to_many_field_with_through_field_and_repeated_instance(self): student = Student(name='Ali') group = Group(name='ACM') student.save() group.save() membership1 = Membership(student=student, group=group) membership1.save() membership2 = Membership(student=student, group=group) membership2.save() g = Cloner().clone(group) self.assertEqual(g.members.all().count(), group.members.all().count()) s = Cloner().clone(student) self.assertEqual(s.group_set.all().count(), student.group_set.all().count()) def test_clone_subclass(self): question = Question(question_text='a', pub_date=timezone.now()) question.save() choice = BigChoice(question=question, choice_text='c', votes=0) choice.save() Cloner().clone(question) self.assertEqual(Question.objects.count(), 2) self.assertEqual(Choice.objects.count(), 2) Cloner().clone(choice) self.assertEqual(Question.objects.count(), 3) self.assertEqual(Choice.objects.count(), 3) def test_clone_subclass_explicit_relation(self): question = Question(question_text='a', pub_date=timezone.now()) question.save() choice = BigChoice2(question=question, choice_text='c', votes=0) choice.save() Cloner().clone(question) self.assertEqual(Question.objects.count(), 2) self.assertEqual(Choice.objects.count(), 2) Cloner().clone(choice) self.assertEqual(Question.objects.count(), 3) self.assertEqual(Choice.objects.count(), 3) def test_clone_unique(self): def unique_editor(obj): if isinstance(obj, BigChoice): obj.unique_value += "S" return obj question = Question(question_text='a', pub_date=timezone.now()) question.save() choice = BigChoice(question=question, choice_text='c', votes=0, unique_value="S") choice.save() new_choice = Cloner().clone(choice, unique_editor) self.assertNotEqual(new_choice.pk, choice.pk)

47.315315

236

0.680217

from django.test import TestCase from django.utils import timezone from django_clone.clone import Cloner from tests.models import * def get_information_list(object_list): information_list = [] for object in object_list: information_list.append((object.pk, object.__module__ + "." + object.__class__.__name__)) information_list.sort() return information_list class VersionControlTests(TestCase): def test_get_all_neighbor_objects(self): question = Question(question_text='question1', pub_date=timezone.now()) question.save() choice = question.choice_set.create(choice_text='a', votes=0) c = Choice(question=question, choice_text='b', votes=0) c.save() choice.save() person = Person() person.save() person.questions.add(question) test_list = [(question.pk, question.__module__ + "." + question.__class__.__name__)] cloner = Cloner() self.assertEqual(get_information_list(cloner.get_all_neighbor_objects(person)), test_list) self.assertEqual(get_information_list(cloner.get_all_neighbor_objects(person)), test_list) test_list.clear() test_list = [(choice.pk, choice.__module__ + "." + choice.__class__.__name__), (c.pk, c.__module__ + "." + c.__class__.__name__), (person.pk, person.__module__ + "." + person.__class__.__name__)] test_list.sort() self.assertEqual(get_information_list(cloner.get_all_neighbor_objects(question)), test_list) def test_get_all_related_objects(self): question = Question(question_text='question1', pub_date=timezone.now()) q1 = Question(question_text='q1', pub_date=timezone.now()) q1.save() question.save() choice = question.choice_set.create(choice_text='a', votes=0) c = Choice(question=question, choice_text='b', votes=0) c.save() choice.save() c1 = q1.choice_set.create(choice_text='a', votes=0) c1.save() person = Person() person.save() person.questions.add(question) cloner = Cloner() test_list = [(q1.pk, q1.__module__+ "." + q1.__class__.__name__), (c1.pk, c1.__module__ + "." + c1.__class__.__name__)] test_list.sort() self.assertEqual(get_information_list(cloner.get_all_related_object(q1)), test_list) self.assertEqual(get_information_list(cloner.get_all_related_object(c1)), test_list) test_list.clear() test_list = [(question.pk, question.__module__ + "." + question.__class__.__name__), (c.pk, c.__module__ + "." + c.__class__.__name__), (choice.pk, choice.__module__ + "." + choice.__class__.__name__), (person.pk, person.__module__ + "." + person.__class__.__name__)] test_list.sort() self.assertEqual(get_information_list(cloner.get_all_related_object(question)), test_list) self.assertEqual(get_information_list(cloner.get_all_related_object(c)), test_list) self.assertEqual(get_information_list(cloner.get_all_related_object(choice)), test_list) self.assertEqual(get_information_list(cloner.get_all_related_object(person)), test_list) self.assertNotEqual(get_information_list(Cloner().get_all_related_object(q1)), test_list) def test_get_all_related_objects_with_circular_relation(self): a_object = A() b_object = B() c_object = C() a_object.save() b_object.save() c_object.save() a_object.b.add(b_object) b_object.c.add(c_object) c_object.a.add(a_object) test_list = [(b_object.pk, b_object.__module__ + "." + b_object.__class__.__name__), (c_object.pk, c_object.__module__ + "." + c_object.__class__.__name__), (a_object.pk, a_object.__module__ + "." + a_object.__class__.__name__)] test_list.sort() cloner = Cloner() self.assertEqual(get_information_list(cloner.get_all_related_object(a_object)), test_list) self.assertEqual(get_information_list(cloner.get_all_related_object(b_object)), test_list) self.assertEqual(get_information_list(cloner.get_all_related_object(c_object)), test_list) def test_clone_with_one_object(self): question = Question(question_text='a', pub_date=timezone.now()) question.save() q = Cloner().clone(question) self.assertNotEqual(q.pk, question.pk) self.assertEqual(q.question_text, question.question_text) self.assertEqual(q.pub_date, question.pub_date) def test_clone_with_foreign_key(self): question = Question(question_text='a', pub_date=timezone.now()) question.save() choice = Choice(question=question, choice_text='c', votes=0) choice.save() cloner = Cloner() c = cloner.clone(choice) self.assertNotEqual(choice.id, c.id) self.assertNotEqual(choice.question.id, c.question.id) self.assertEqual(choice.question.question_text, c.question.question_text) q = cloner.clone(question) self.assertNotEqual(q.id, question.id) self.assertNotEqual(question.choice_set.get(choice_text='c').pk, q.choice_set.get(choice_text='c').pk) def test_clone_with_ignore_list(self): question = Question(question_text='a', pub_date=timezone.now()) question.save() choice = Choice(question=question, choice_text='c', votes=0) choice.save() c = Cloner(ignored_models=["tests.Question"]).clone(choice) self.assertNotEqual(choice.id, c.id) self.assertEqual(choice.question.id, c.question.id) def test_clone_with_many_to_many_field(self): question = Question(question_text='question1', pub_date=timezone.now()) question.save() person = Person() person.save() person.questions.add(question) p = Cloner().clone(person) self.assertNotEqual(person.id, p.id) self.assertNotEqual(person.questions.get(question_text='question1').id, p.questions.get(question_text='question1').id) def test_clone_many_to_many_field_with_repeated_instance(self): question = Question(question_text='question1', pub_date=timezone.now()) question.save() person = Person() person.save() person.questions.add(question) person.questions.add(question) p = Cloner().clone(person) self.assertEqual(person.questions.all().count(), p.questions.all().count()) def test_clone_with_through_field(self): student = Student(name='Ali') group = Group(name='ACM') student.save() group.save() membership = Membership(student=student, group=group) membership.save() g = Cloner().clone(group) self.assertNotEqual(g.id, group.id) self.assertNotEqual(group.members.get(name='Ali').id, g.members.get(name='Ali').id) s = Cloner().clone(student) self.assertNotEqual(s.id, student.id) self.assertNotEqual(student.group_set.get(name='ACM').id, s.group_set.get(name='ACM').id) def test_clone_many_to_many_field_with_through_field_and_repeated_instance(self): student = Student(name='Ali') group = Group(name='ACM') student.save() group.save() membership1 = Membership(student=student, group=group) membership1.save() membership2 = Membership(student=student, group=group) membership2.save() g = Cloner().clone(group) self.assertEqual(g.members.all().count(), group.members.all().count()) s = Cloner().clone(student) self.assertEqual(s.group_set.all().count(), student.group_set.all().count()) def test_clone_subclass(self): question = Question(question_text='a', pub_date=timezone.now()) question.save() choice = BigChoice(question=question, choice_text='c', votes=0) choice.save() Cloner().clone(question) self.assertEqual(Question.objects.count(), 2) self.assertEqual(Choice.objects.count(), 2) Cloner().clone(choice) self.assertEqual(Question.objects.count(), 3) self.assertEqual(Choice.objects.count(), 3) def test_clone_subclass_explicit_relation(self): question = Question(question_text='a', pub_date=timezone.now()) question.save() choice = BigChoice2(question=question, choice_text='c', votes=0) choice.save() Cloner().clone(question) self.assertEqual(Question.objects.count(), 2) self.assertEqual(Choice.objects.count(), 2) Cloner().clone(choice) self.assertEqual(Question.objects.count(), 3) self.assertEqual(Choice.objects.count(), 3) def test_clone_unique(self): def unique_editor(obj): if isinstance(obj, BigChoice): obj.unique_value += "S" return obj question = Question(question_text='a', pub_date=timezone.now()) question.save() choice = BigChoice(question=question, choice_text='c', votes=0, unique_value="S") choice.save() new_choice = Cloner().clone(choice, unique_editor) self.assertNotEqual(new_choice.pk, choice.pk)

true

f71b5cd7fa3f30ff2ff0a5a2c5acbd05b042c711

497

py

Python

examples/example_proj/dependency_app_o2o/migrations/0001_initial.py

philsupertramp/dj-migration-test

97ec4513b9848d96436907de7940841866895e3c

[ "MIT" ]

4

2019-07-05T19:32:07.000Z

2020-02-07T00:47:15.000Z

examples/example_proj/dependency_app_o2o/migrations/0001_initial.py

philsupertramp/dj-migration-test

97ec4513b9848d96436907de7940841866895e3c

[ "MIT" ]

17

2019-08-23T07:21:23.000Z

2021-09-22T18:44:26.000Z

examples/example_proj/dependency_app_o2o/migrations/0001_initial.py

philsupertramp/dj-migration-test

97ec4513b9848d96436907de7940841866895e3c

[ "MIT" ]

null

# Generated by Django 2.2.3 on 2019-07-27 12:22 from django.db import migrations, models class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='DepModO2O', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('placeholder', models.BooleanField(default=True)), ], ), ]

22.590909

114

0.583501

from django.db import migrations, models class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='DepModO2O', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('placeholder', models.BooleanField(default=True)), ], ), ]

true

f71b5d0be6cfd82d1e2beb6b6ec45e9a21282a6a

1,163

py

Python

src/bot.py

Shignum/ShiggyBot

292d99300dea55848d1aa458c8b8893a8dd78fc2

[ "MIT" ]

null

src/bot.py

Shignum/ShiggyBot

292d99300dea55848d1aa458c8b8893a8dd78fc2

[ "MIT" ]

null

src/bot.py

Shignum/ShiggyBot

292d99300dea55848d1aa458c8b8893a8dd78fc2

[ "MIT" ]

null

import os from discord import Embed from discord import Intents from discord.ext import commands from dotenv import load_dotenv load_dotenv() intents = Intents.default() bot = commands.Bot(command_prefix=os.getenv('PREFIX')) TOKEN = os.getenv('BOT_TOKEN') @bot.event async def on_ready(): print(f'{bot.user} has logged in.') initial_extensions = ['cogs.event','cogs.music','cogs.other','cogs.playlist'] for extension in initial_extensions: bot.load_extension(extension) @bot.event async def on_command_error(ctx, error): if isinstance(error, commands.CommandNotFound): await ctx.send(embed=Embed(title='Command not found.')) return elif isinstance(error, commands.MissingRequiredArgument): await ctx.send(embed=Embed(title='Command needs an Argument.')) return elif isinstance(error, commands.CommandInvokeError): await ctx.send(embed=Embed(title=f'{error.original}')) return elif isinstance(error, commands.MissingPermissions): await ctx.send(embed=Embed(title="You don't have the permission to use this command.")) return raise error bot.run(TOKEN)

29.075

95

0.715391

import os from discord import Embed from discord import Intents from discord.ext import commands from dotenv import load_dotenv load_dotenv() intents = Intents.default() bot = commands.Bot(command_prefix=os.getenv('PREFIX')) TOKEN = os.getenv('BOT_TOKEN') @bot.event async def on_ready(): print(f'{bot.user} has logged in.') initial_extensions = ['cogs.event','cogs.music','cogs.other','cogs.playlist'] for extension in initial_extensions: bot.load_extension(extension) @bot.event async def on_command_error(ctx, error): if isinstance(error, commands.CommandNotFound): await ctx.send(embed=Embed(title='Command not found.')) return elif isinstance(error, commands.MissingRequiredArgument): await ctx.send(embed=Embed(title='Command needs an Argument.')) return elif isinstance(error, commands.CommandInvokeError): await ctx.send(embed=Embed(title=f'{error.original}')) return elif isinstance(error, commands.MissingPermissions): await ctx.send(embed=Embed(title="You don't have the permission to use this command.")) return raise error bot.run(TOKEN)

true

f71b5d97598ff53100bfb2598cdb30dd30469fd8

10,221

py

Python

client-py/iotdb/utils/IoTDBRpcDataSet.py

slawr/iotdb

96b5269f0fc6e02927563d4481da3bfb310fc7b1

[ "Apache-2.0" ]

null

client-py/iotdb/utils/IoTDBRpcDataSet.py

slawr/iotdb

96b5269f0fc6e02927563d4481da3bfb310fc7b1

[ "Apache-2.0" ]

27

2021-10-19T09:41:40.000Z

2022-03-30T16:22:17.000Z

client-py/iotdb/utils/IoTDBRpcDataSet.py

slawr/iotdb

96b5269f0fc6e02927563d4481da3bfb310fc7b1

[ "Apache-2.0" ]

null

# Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you 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. # # for package import logging from thrift.transport import TTransport from iotdb.thrift.rpc.TSIService import TSFetchResultsReq, TSCloseOperationReq from iotdb.utils.IoTDBConstants import TSDataType logger = logging.getLogger("IoTDB") class IoTDBRpcDataSet(object): TIMESTAMP_STR = "Time" # VALUE_IS_NULL = "The value got by %s (column name) is NULL." START_INDEX = 2 FLAG = 0x80 def __init__( self, sql, column_name_list, column_type_list, column_name_index, ignore_timestamp, query_id, client, session_id, query_data_set, fetch_size, ): self.__session_id = session_id self.__ignore_timestamp = ignore_timestamp self.__sql = sql self.__query_id = query_id self.__client = client self.__fetch_size = fetch_size self.__column_size = len(column_name_list) self.__default_time_out = 1000 self.__column_name_list = [] self.__column_type_list = [] self.__column_ordinal_dict = {} if not ignore_timestamp: self.__column_name_list.append(IoTDBRpcDataSet.TIMESTAMP_STR) self.__column_type_list.append(TSDataType.INT64) self.__column_ordinal_dict[IoTDBRpcDataSet.TIMESTAMP_STR] = 1 if column_name_index is not None: self.__column_type_deduplicated_list = [ None for _ in range(len(column_name_index)) ] for i in range(len(column_name_list)): name = column_name_list[i] self.__column_name_list.append(name) self.__column_type_list.append(TSDataType[column_type_list[i]]) if name not in self.__column_ordinal_dict: index = column_name_index[name] self.__column_ordinal_dict[name] = ( index + IoTDBRpcDataSet.START_INDEX ) self.__column_type_deduplicated_list[index] = TSDataType[ column_type_list[i] ] else: index = IoTDBRpcDataSet.START_INDEX self.__column_type_deduplicated_list = [] for i in range(len(column_name_list)): name = column_name_list[i] self.__column_name_list.append(name) self.__column_type_list.append(TSDataType[column_type_list[i]]) if name not in self.__column_ordinal_dict: self.__column_ordinal_dict[name] = index index += 1 self.__column_type_deduplicated_list.append( TSDataType[column_type_list[i]] ) self.__time_bytes = bytes(0) self.__current_bitmap = [ bytes(0) for _ in range(len(self.__column_type_deduplicated_list)) ] self.__value = [None for _ in range(len(self.__column_type_deduplicated_list))] self.__query_data_set = query_data_set self.__is_closed = False self.__empty_resultSet = False self.__has_cached_record = False self.__rows_index = 0 def close(self): if self.__is_closed: return if self.__client is not None: try: status = self.__client.closeOperation( TSCloseOperationReq(self.__session_id, self.__query_id) ) logger.debug( "close session {}, message: {}".format( self.__session_id, status.message ) ) except TTransport.TException as e: raise RuntimeError( "close session {} failed because: ".format(self.__session_id), e ) self.__is_closed = True self.__client = None def next(self): if self.has_cached_result(): self.construct_one_row() return True if self.__empty_resultSet: return False if self.fetch_results(): self.construct_one_row() return True return False def has_cached_result(self): return (self.__query_data_set is not None) and ( len(self.__query_data_set.time) != 0 ) def construct_one_row(self): # simulating buffer, read 8 bytes from data set and discard first 8 bytes which have been read. self.__time_bytes = self.__query_data_set.time[:8] self.__query_data_set.time = self.__query_data_set.time[8:] for i in range(len(self.__query_data_set.bitmapList)): bitmap_buffer = self.__query_data_set.bitmapList[i] # another 8 new rows, should move the bitmap buffer position to next byte if self.__rows_index % 8 == 0: self.__current_bitmap[i] = bitmap_buffer[0] self.__query_data_set.bitmapList[i] = bitmap_buffer[1:] if not self.is_null(i, self.__rows_index): value_buffer = self.__query_data_set.valueList[i] data_type = self.__column_type_deduplicated_list[i] # simulating buffer if data_type == TSDataType.BOOLEAN: self.__value[i] = value_buffer[:1] self.__query_data_set.valueList[i] = value_buffer[1:] elif data_type == TSDataType.INT32: self.__value[i] = value_buffer[:4] self.__query_data_set.valueList[i] = value_buffer[4:] elif data_type == TSDataType.INT64: self.__value[i] = value_buffer[:8] self.__query_data_set.valueList[i] = value_buffer[8:] elif data_type == TSDataType.FLOAT: self.__value[i] = value_buffer[:4] self.__query_data_set.valueList[i] = value_buffer[4:] elif data_type == TSDataType.DOUBLE: self.__value[i] = value_buffer[:8] self.__query_data_set.valueList[i] = value_buffer[8:] elif data_type == TSDataType.TEXT: length = int.from_bytes( value_buffer[:4], byteorder="big", signed=False ) self.__value[i] = value_buffer[4 : 4 + length] self.__query_data_set.valueList[i] = value_buffer[4 + length :] else: raise RuntimeError("unsupported data type {}.".format(data_type)) self.__rows_index += 1 self.__has_cached_record = True def fetch_results(self): self.__rows_index = 0 request = TSFetchResultsReq( self.__session_id, self.__sql, self.__fetch_size, self.__query_id, True, self.__default_time_out, ) try: resp = self.__client.fetchResults(request) if not resp.hasResultSet: self.__empty_resultSet = True else: self.__query_data_set = resp.queryDataSet return resp.hasResultSet except TTransport.TException as e: raise RuntimeError( "Cannot fetch result from server, because of network connection: ", e ) def is_null(self, index, row_num): bitmap = self.__current_bitmap[index] shift = row_num % 8 return ((IoTDBRpcDataSet.FLAG >> shift) & (bitmap & 0xFF)) == 0 def is_null_by_index(self, column_index): index = ( self.__column_ordinal_dict[self.find_column_name_by_index(column_index)] - IoTDBRpcDataSet.START_INDEX ) # time column will never be None if index < 0: return True return self.is_null(index, self.__rows_index - 1) def is_null_by_name(self, column_name): index = self.__column_ordinal_dict[column_name] - IoTDBRpcDataSet.START_INDEX # time column will never be None if index < 0: return True return self.is_null(index, self.__rows_index - 1) def find_column_name_by_index(self, column_index): if column_index <= 0: raise Exception("Column index should start from 1") if column_index > len(self.__column_name_list): raise Exception( "column index {} out of range {}".format( column_index, self.__column_size ) ) return self.__column_name_list[column_index - 1] def get_fetch_size(self): return self.__fetch_size def set_fetch_size(self, fetch_size): self.__fetch_size = fetch_size def get_column_names(self): return self.__column_name_list def get_column_types(self): return self.__column_type_list def get_column_size(self): return self.__column_size def get_ignore_timestamp(self): return self.__ignore_timestamp def get_column_ordinal_dict(self): return self.__column_ordinal_dict def get_column_type_deduplicated_list(self): return self.__column_type_deduplicated_list def get_values(self): return self.__value def get_time_bytes(self): return self.__time_bytes def get_has_cached_record(self): return self.__has_cached_record

37.577206

103

0.604246

import logging from thrift.transport import TTransport from iotdb.thrift.rpc.TSIService import TSFetchResultsReq, TSCloseOperationReq from iotdb.utils.IoTDBConstants import TSDataType logger = logging.getLogger("IoTDB") class IoTDBRpcDataSet(object): TIMESTAMP_STR = "Time" START_INDEX = 2 FLAG = 0x80 def __init__( self, sql, column_name_list, column_type_list, column_name_index, ignore_timestamp, query_id, client, session_id, query_data_set, fetch_size, ): self.__session_id = session_id self.__ignore_timestamp = ignore_timestamp self.__sql = sql self.__query_id = query_id self.__client = client self.__fetch_size = fetch_size self.__column_size = len(column_name_list) self.__default_time_out = 1000 self.__column_name_list = [] self.__column_type_list = [] self.__column_ordinal_dict = {} if not ignore_timestamp: self.__column_name_list.append(IoTDBRpcDataSet.TIMESTAMP_STR) self.__column_type_list.append(TSDataType.INT64) self.__column_ordinal_dict[IoTDBRpcDataSet.TIMESTAMP_STR] = 1 if column_name_index is not None: self.__column_type_deduplicated_list = [ None for _ in range(len(column_name_index)) ] for i in range(len(column_name_list)): name = column_name_list[i] self.__column_name_list.append(name) self.__column_type_list.append(TSDataType[column_type_list[i]]) if name not in self.__column_ordinal_dict: index = column_name_index[name] self.__column_ordinal_dict[name] = ( index + IoTDBRpcDataSet.START_INDEX ) self.__column_type_deduplicated_list[index] = TSDataType[ column_type_list[i] ] else: index = IoTDBRpcDataSet.START_INDEX self.__column_type_deduplicated_list = [] for i in range(len(column_name_list)): name = column_name_list[i] self.__column_name_list.append(name) self.__column_type_list.append(TSDataType[column_type_list[i]]) if name not in self.__column_ordinal_dict: self.__column_ordinal_dict[name] = index index += 1 self.__column_type_deduplicated_list.append( TSDataType[column_type_list[i]] ) self.__time_bytes = bytes(0) self.__current_bitmap = [ bytes(0) for _ in range(len(self.__column_type_deduplicated_list)) ] self.__value = [None for _ in range(len(self.__column_type_deduplicated_list))] self.__query_data_set = query_data_set self.__is_closed = False self.__empty_resultSet = False self.__has_cached_record = False self.__rows_index = 0 def close(self): if self.__is_closed: return if self.__client is not None: try: status = self.__client.closeOperation( TSCloseOperationReq(self.__session_id, self.__query_id) ) logger.debug( "close session {}, message: {}".format( self.__session_id, status.message ) ) except TTransport.TException as e: raise RuntimeError( "close session {} failed because: ".format(self.__session_id), e ) self.__is_closed = True self.__client = None def next(self): if self.has_cached_result(): self.construct_one_row() return True if self.__empty_resultSet: return False if self.fetch_results(): self.construct_one_row() return True return False def has_cached_result(self): return (self.__query_data_set is not None) and ( len(self.__query_data_set.time) != 0 ) def construct_one_row(self): self.__time_bytes = self.__query_data_set.time[:8] self.__query_data_set.time = self.__query_data_set.time[8:] for i in range(len(self.__query_data_set.bitmapList)): bitmap_buffer = self.__query_data_set.bitmapList[i] if self.__rows_index % 8 == 0: self.__current_bitmap[i] = bitmap_buffer[0] self.__query_data_set.bitmapList[i] = bitmap_buffer[1:] if not self.is_null(i, self.__rows_index): value_buffer = self.__query_data_set.valueList[i] data_type = self.__column_type_deduplicated_list[i] if data_type == TSDataType.BOOLEAN: self.__value[i] = value_buffer[:1] self.__query_data_set.valueList[i] = value_buffer[1:] elif data_type == TSDataType.INT32: self.__value[i] = value_buffer[:4] self.__query_data_set.valueList[i] = value_buffer[4:] elif data_type == TSDataType.INT64: self.__value[i] = value_buffer[:8] self.__query_data_set.valueList[i] = value_buffer[8:] elif data_type == TSDataType.FLOAT: self.__value[i] = value_buffer[:4] self.__query_data_set.valueList[i] = value_buffer[4:] elif data_type == TSDataType.DOUBLE: self.__value[i] = value_buffer[:8] self.__query_data_set.valueList[i] = value_buffer[8:] elif data_type == TSDataType.TEXT: length = int.from_bytes( value_buffer[:4], byteorder="big", signed=False ) self.__value[i] = value_buffer[4 : 4 + length] self.__query_data_set.valueList[i] = value_buffer[4 + length :] else: raise RuntimeError("unsupported data type {}.".format(data_type)) self.__rows_index += 1 self.__has_cached_record = True def fetch_results(self): self.__rows_index = 0 request = TSFetchResultsReq( self.__session_id, self.__sql, self.__fetch_size, self.__query_id, True, self.__default_time_out, ) try: resp = self.__client.fetchResults(request) if not resp.hasResultSet: self.__empty_resultSet = True else: self.__query_data_set = resp.queryDataSet return resp.hasResultSet except TTransport.TException as e: raise RuntimeError( "Cannot fetch result from server, because of network connection: ", e ) def is_null(self, index, row_num): bitmap = self.__current_bitmap[index] shift = row_num % 8 return ((IoTDBRpcDataSet.FLAG >> shift) & (bitmap & 0xFF)) == 0 def is_null_by_index(self, column_index): index = ( self.__column_ordinal_dict[self.find_column_name_by_index(column_index)] - IoTDBRpcDataSet.START_INDEX ) if index < 0: return True return self.is_null(index, self.__rows_index - 1) def is_null_by_name(self, column_name): index = self.__column_ordinal_dict[column_name] - IoTDBRpcDataSet.START_INDEX if index < 0: return True return self.is_null(index, self.__rows_index - 1) def find_column_name_by_index(self, column_index): if column_index <= 0: raise Exception("Column index should start from 1") if column_index > len(self.__column_name_list): raise Exception( "column index {} out of range {}".format( column_index, self.__column_size ) ) return self.__column_name_list[column_index - 1] def get_fetch_size(self): return self.__fetch_size def set_fetch_size(self, fetch_size): self.__fetch_size = fetch_size def get_column_names(self): return self.__column_name_list def get_column_types(self): return self.__column_type_list def get_column_size(self): return self.__column_size def get_ignore_timestamp(self): return self.__ignore_timestamp def get_column_ordinal_dict(self): return self.__column_ordinal_dict def get_column_type_deduplicated_list(self): return self.__column_type_deduplicated_list def get_values(self): return self.__value def get_time_bytes(self): return self.__time_bytes def get_has_cached_record(self): return self.__has_cached_record

true

f71b5dbf84e94f967043c63798744db773956c70

2,822

py

Python

awesome-bot.py

ksmirenko/awesome-irc-bot

2d39da7efc3621d737bcec458fc0f50ee7189e05

[ "MIT" ]

null

awesome-bot.py

ksmirenko/awesome-irc-bot

2d39da7efc3621d737bcec458fc0f50ee7189e05

[ "MIT" ]

null

awesome-bot.py

ksmirenko/awesome-irc-bot

2d39da7efc3621d737bcec458fc0f50ee7189e05

[ "MIT" ]

null

import re import socket import sys import threading from random import randint host = 'irc.freenode.org' port = 6667 nick = 'gabe_the_dog' real_name = 'Gabe the dog' channel = '#spbnet' size = 2048 youtube_prefix = 'https://www.youtube.com/watch?v=' gabe_the_dog_sources = [ 'i1H0leZhXcY', 'i11RMG_U3R4', 'xK6cUQQ9cJY', 'b2p8Zxmuq4g', 'iY4Ci0wg258', 'd6ysCgOu8N8', 'dvZGs9QRNIw', 'TsIZG5QbS1g', 'gwkRRED5WxY', 'oFRSLqpq9xk', 'h4-pHUVthf0', 'gIx6_Srsrog', 'eWu5eB62dT8', 'vwGnXKNGjT0', 'AeEH5ugJrUU', 'WCFnvj4Lztg', 'Gl1uq4tg7YU', 'rcIpIw4YtZk', '9u9vlj8CgS0', 'gvOWADwCDNg', 'JtA_WnBP_Co', 'R78ZxZW_N-o', 'd1lth7uX02g', 'onZcB3y2RTM', 'j20cTvQYe6s', 'tVznLG3PAdM', 'muLAN-kP5pE', 'VJxNv2m7qns', 'y3PcelCeraw' ] def send_cmd(sock, cmd): sock.send(bytes(cmd)) def connect(): sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) sock.connect((host, port)) return sock def login(sock): send_cmd(sock, "USER {0} * * :{1}\r\n".format(nick, real_name)) send_cmd(sock, "NICK {0}\r\n".format(nick)) send_cmd(sock, "JOIN {0}\r\n".format(channel)) def send_msg(sock, msg): send_cmd(sock, "PRIVMSG {} :{}\r\n".format(channel, msg)) # magic def magic(sock): index = randint(0, len(gabe_the_dog_sources) - 1) msg = "Check this out: {}{}".format(youtube_prefix, gabe_the_dog_sources[index]) send_msg(sock, msg) # thread routines def send_routine(sock): while True: msg = raw_input() if msg.startswith("/q"): send_cmd(sock, "QUIT") sock.close() return send_msg(sock, msg) def receive_routine(sock): try: while True: text = str(sock.recv(size)) if text.startswith("PING "): send_cmd(sock, "PONG {}".format(text[5:])) continue if len(text) > 1: print_message(text, "PRIVMSG" in text and channel in text) if "show some magic" in text and nick in text: magic(sock) except: print("Disconnected!") def print_message(msg, is_private): if is_private: sender_nick = re.sub(r":(.*)!.*PRIVMSG " + channel + r" :(.*)", r"\1", msg) msg_text = re.sub(r":(.*)!.*PRIVMSG " + channel + r" :(.*)", r"\2", msg) print("<{}>: {}".format(sender_nick[:-1], msg_text[:-1])) else: print(msg) def main(): sock = connect() login(sock) print("Connected!") sender_thread = threading.Thread(target=send_routine, args=(sock,)) receiver_thread = threading.Thread(target=receive_routine, args=(sock,)) sender_thread.start() receiver_thread.start() sender_thread.join() receiver_thread.join() main()

22.576

84

0.592488

import re import socket import sys import threading from random import randint host = 'irc.freenode.org' port = 6667 nick = 'gabe_the_dog' real_name = 'Gabe the dog' channel = '#spbnet' size = 2048 youtube_prefix = 'https://www.youtube.com/watch?v=' gabe_the_dog_sources = [ 'i1H0leZhXcY', 'i11RMG_U3R4', 'xK6cUQQ9cJY', 'b2p8Zxmuq4g', 'iY4Ci0wg258', 'd6ysCgOu8N8', 'dvZGs9QRNIw', 'TsIZG5QbS1g', 'gwkRRED5WxY', 'oFRSLqpq9xk', 'h4-pHUVthf0', 'gIx6_Srsrog', 'eWu5eB62dT8', 'vwGnXKNGjT0', 'AeEH5ugJrUU', 'WCFnvj4Lztg', 'Gl1uq4tg7YU', 'rcIpIw4YtZk', '9u9vlj8CgS0', 'gvOWADwCDNg', 'JtA_WnBP_Co', 'R78ZxZW_N-o', 'd1lth7uX02g', 'onZcB3y2RTM', 'j20cTvQYe6s', 'tVznLG3PAdM', 'muLAN-kP5pE', 'VJxNv2m7qns', 'y3PcelCeraw' ] def send_cmd(sock, cmd): sock.send(bytes(cmd)) def connect(): sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) sock.connect((host, port)) return sock def login(sock): send_cmd(sock, "USER {0} * * :{1}\r\n".format(nick, real_name)) send_cmd(sock, "NICK {0}\r\n".format(nick)) send_cmd(sock, "JOIN {0}\r\n".format(channel)) def send_msg(sock, msg): send_cmd(sock, "PRIVMSG {} :{}\r\n".format(channel, msg)) def magic(sock): index = randint(0, len(gabe_the_dog_sources) - 1) msg = "Check this out: {}{}".format(youtube_prefix, gabe_the_dog_sources[index]) send_msg(sock, msg) def send_routine(sock): while True: msg = raw_input() if msg.startswith("/q"): send_cmd(sock, "QUIT") sock.close() return send_msg(sock, msg) def receive_routine(sock): try: while True: text = str(sock.recv(size)) if text.startswith("PING "): send_cmd(sock, "PONG {}".format(text[5:])) continue if len(text) > 1: print_message(text, "PRIVMSG" in text and channel in text) if "show some magic" in text and nick in text: magic(sock) except: print("Disconnected!") def print_message(msg, is_private): if is_private: sender_nick = re.sub(r":(.*)!.*PRIVMSG " + channel + r" :(.*)", r"\1", msg) msg_text = re.sub(r":(.*)!.*PRIVMSG " + channel + r" :(.*)", r"\2", msg) print("<{}>: {}".format(sender_nick[:-1], msg_text[:-1])) else: print(msg) def main(): sock = connect() login(sock) print("Connected!") sender_thread = threading.Thread(target=send_routine, args=(sock,)) receiver_thread = threading.Thread(target=receive_routine, args=(sock,)) sender_thread.start() receiver_thread.start() sender_thread.join() receiver_thread.join() main()

true

f71b5dcfe6e6dcab397ded91c0b2aed0f4eaaa39

3,380

py

Python

drawSS.py

banroku/analySS

15ba9e9216f86a1bf74062eae479a3ce1c9c5a11

[ "MIT" ]

null

drawSS.py

banroku/analySS

15ba9e9216f86a1bf74062eae479a3ce1c9c5a11

[ "MIT" ]

null

drawSS.py

banroku/analySS

15ba9e9216f86a1bf74062eae479a3ce1c9c5a11

[ "MIT" ]

null

# coding=utf-8 def thinningSS(file, max_strain=10, interval=0.1): '''a function to conduct data thinning of SS curve at range (0, MAX_STRAIN), with INTERVAL This returns np.series of stress with strain in the index. FILE should be passed as dictionary containing following: 'name': name of sample like 'RL7785' 'crv': path(relative) of xxx_crv.csv file 'rlt': path(relative) of xxx_rlt.csv file 'set': path(relative) of xxx_set.csv file ''' import pandas as pd import numpy as np # read files and parameters data = pd.read_csv(file['crv'], sep=',', encoding='shift_jis', skiprows=1, index_col=0) data_rlt = pd.read_csv(file['rlt'], sep=',', encoding='shift_jis') L = 64 # span b = float(data_rlt.iloc[2, 3]) # width of first specimen h = float(data_rlt.iloc[2, 4]) # height of first specimen #print('span, width, height of first specimen:', L, ',', b, ',', h)#cut out curve of first specimen col = ['mm', 'N'] data = data.reindex(columns=col) data.dropna(subset=['mm'], inplace=True) #%% convert (mm, N) to (%, MPa) # sigma = 3*F*L / (2*b*h^2) # epsilon = 6*100*s*h / (L^2) # F: load, L:span = 64 mm, b:width, h:height, s=strain/mm data['strain'] = data['mm'] * 6 * 100 * h / L / L data['stress'] = data['N'] * 3 * L / (2 * b * h * h) #%% data thinnings interval_steps = int(max_strain/interval) marker = pd.DataFrame({'strain': np.round(np.linspace(0, max_strain, interval_steps, endpoint=False), 2), 'marker': True}) data_marked = pd.merge(data, marker, on='strain', how='outer') data_marked.rename(data_marked['strain'], inplace=True) data_marked.sort_values(by=['strain'], inplace=True) data_marked.interpolate(method='slinear', limit=1, inplace=True) data_marked['marker'].fillna('False', inplace=True) data_skipped = data_marked[data_marked['marker']==True] thinnedSS = data_skipped['stress'] thinnedSS.name = file['name'] return thinnedSS #%% def parameters(file): '''a function to pick following parameters as pd.Series: parameters = ['width', 'height', 'FM', 'FS_max', 'FS_break', 'FE_max', 'FE_break', 'd_width', 'd_height', 'd_FM', 'd_FS_max', 'd_FS_break', 'd_FE_max', 'd_FE_break'] FILE should be passed as dictionary containing following: 'name': name of sample like 'RL7785' 'crv': path(relative) of xxx_crv.csv file 'rlt': path(relative) of xxx_rlt.csv file 'set': path(relative) of xxx_set.csv file ''' file_rlt = file['rlt'] data_rlt = pd.read_csv(file_rlt, sep=',', skiprows=[1,2], index_col=0, encoding='shift_jis') parameters = ['幅', '厚さ', '弾性率', '最大点', '破壊点', '最大点.1', '破壊点.1'] data_rlt = data_rlt.loc[['単純平均', '標準偏差'], parameters] data_rlt.index = ['average', 'stdev'] data_rlt.columns = ['width', 'height', 'FM', 'FS_max', 'FS_break', 'FE_max', 'FE_break'] data_rlt = data_rlt.values data_flattened = [item for sublist in data_rlt for item in sublist] #see below parameters = ['width', 'height', 'FM', 'FS_max', 'FS_break', 'FE_max', 'FE_break', 'd_width', 'd_height', 'd_FM', 'd_FS_max', 'd_FS_break', 'd_FE_max', 'd_FE_break'] data_rlt = pd.Series(data_flattened, index=parameters) data_rlt.name = file['name'] return data_rlt

47.605634

126

0.628107

def thinningSS(file, max_strain=10, interval=0.1): import pandas as pd import numpy as np data = pd.read_csv(file['crv'], sep=',', encoding='shift_jis', skiprows=1, index_col=0) data_rlt = pd.read_csv(file['rlt'], sep=',', encoding='shift_jis') L = 64 b = float(data_rlt.iloc[2, 3]) h = float(data_rlt.iloc[2, 4]) = data.reindex(columns=col) data.dropna(subset=['mm'], inplace=True) data['strain'] = data['mm'] * 6 * 100 * h / L / L data['stress'] = data['N'] * 3 * L / (2 * b * h * h) interval_steps = int(max_strain/interval) marker = pd.DataFrame({'strain': np.round(np.linspace(0, max_strain, interval_steps, endpoint=False), 2), 'marker': True}) data_marked = pd.merge(data, marker, on='strain', how='outer') data_marked.rename(data_marked['strain'], inplace=True) data_marked.sort_values(by=['strain'], inplace=True) data_marked.interpolate(method='slinear', limit=1, inplace=True) data_marked['marker'].fillna('False', inplace=True) data_skipped = data_marked[data_marked['marker']==True] thinnedSS = data_skipped['stress'] thinnedSS.name = file['name'] return thinnedSS def parameters(file): file_rlt = file['rlt'] data_rlt = pd.read_csv(file_rlt, sep=',', skiprows=[1,2], index_col=0, encoding='shift_jis') parameters = ['幅', '厚さ', '弾性率', '最大点', '破壊点', '最大点.1', '破壊点.1'] data_rlt = data_rlt.loc[['単純平均', '標準偏差'], parameters] data_rlt.index = ['average', 'stdev'] data_rlt.columns = ['width', 'height', 'FM', 'FS_max', 'FS_break', 'FE_max', 'FE_break'] data_rlt = data_rlt.values data_flattened = [item for sublist in data_rlt for item in sublist] parameters = ['width', 'height', 'FM', 'FS_max', 'FS_break', 'FE_max', 'FE_break', 'd_width', 'd_height', 'd_FM', 'd_FS_max', 'd_FS_break', 'd_FE_max', 'd_FE_break'] data_rlt = pd.Series(data_flattened, index=parameters) data_rlt.name = file['name'] return data_rlt

true

f71b5dd3b2f1f6ba21eafc9f59670a50d9efc222

207

py

Python

sciencer/expanders/__init__.py

SciencerIO/sciencer-toolkit

f17c4a5dfb6cc5dbabefe03b13eb1e5345f7b1b9

[ "MIT" ]

2

2022-03-28T17:27:21.000Z

2022-03-29T22:27:15.000Z

sciencer/expanders/__init__.py

SciencerIO/sciencer-toolkit

f17c4a5dfb6cc5dbabefe03b13eb1e5345f7b1b9

[ "MIT" ]

null

sciencer/expanders/__init__.py

SciencerIO/sciencer-toolkit

f17c4a5dfb6cc5dbabefe03b13eb1e5345f7b1b9

[ "MIT" ]

1

2022-03-28T14:47:53.000Z

"""Sciencer Expanders""" from .expander import Expander from .expand_by_authors import ExpandByAuthors from .expand_by_references import ExpandByReferences from .expand_by_citations import ExpandByCitations

34.5

52

0.864734

from .expander import Expander from .expand_by_authors import ExpandByAuthors from .expand_by_references import ExpandByReferences from .expand_by_citations import ExpandByCitations

true

f71b5e233cb62b6fa8ba747a25edcddd0d4c142f

1,068

py

Python

get-git-lfs.py

rcmurphy/pre-commit-hooks

17fcaab5769b7628e872601d852d3dcf13c0930e

[ "MIT" ]

null

get-git-lfs.py

rcmurphy/pre-commit-hooks

17fcaab5769b7628e872601d852d3dcf13c0930e

[ "MIT" ]

null

get-git-lfs.py

rcmurphy/pre-commit-hooks

17fcaab5769b7628e872601d852d3dcf13c0930e

[ "MIT" ]

1

2016-05-06T15:27:07.000Z

#!/usr/bin/env python3.4 """This is a script to install git-lfs to a tempdir for use in tests""" import io import os.path import shutil import tarfile from urllib.request import urlopen DOWNLOAD_PATH = ( 'https://github.com/github/git-lfs/releases/download/' 'v1.1.0/git-lfs-linux-amd64-1.1.0.tar.gz' ) PATH_IN_TAR = 'git-lfs-1.1.0/git-lfs' DEST_PATH = '/tmp/git-lfs/git-lfs' DEST_DIR = os.path.dirname(DEST_PATH) def main(): if ( os.path.exists(DEST_PATH) and os.path.isfile(DEST_PATH) and os.access(DEST_PATH, os.X_OK) ): print('Already installed!') return 0 shutil.rmtree(DEST_DIR, ignore_errors=True) os.makedirs(DEST_DIR, exist_ok=True) contents = io.BytesIO(urlopen(DOWNLOAD_PATH).read()) with tarfile.open(fileobj=contents) as tar: with tar.extractfile(PATH_IN_TAR) as src_file: with open(DEST_PATH, 'wb') as dest_file: shutil.copyfileobj(src_file, dest_file) os.chmod(DEST_PATH, 0o755) if __name__ == '__main__': exit(main())

27.384615

71

0.661985

import io import os.path import shutil import tarfile from urllib.request import urlopen DOWNLOAD_PATH = ( 'https://github.com/github/git-lfs/releases/download/' 'v1.1.0/git-lfs-linux-amd64-1.1.0.tar.gz' ) PATH_IN_TAR = 'git-lfs-1.1.0/git-lfs' DEST_PATH = '/tmp/git-lfs/git-lfs' DEST_DIR = os.path.dirname(DEST_PATH) def main(): if ( os.path.exists(DEST_PATH) and os.path.isfile(DEST_PATH) and os.access(DEST_PATH, os.X_OK) ): print('Already installed!') return 0 shutil.rmtree(DEST_DIR, ignore_errors=True) os.makedirs(DEST_DIR, exist_ok=True) contents = io.BytesIO(urlopen(DOWNLOAD_PATH).read()) with tarfile.open(fileobj=contents) as tar: with tar.extractfile(PATH_IN_TAR) as src_file: with open(DEST_PATH, 'wb') as dest_file: shutil.copyfileobj(src_file, dest_file) os.chmod(DEST_PATH, 0o755) if __name__ == '__main__': exit(main())

true

f71b5e5ba3ad4fa2190d7a089a3fbcdfd842d9d6

4,150

py

Python

ptvs_virtualenv_proxy.py

SpaceTheArcher/test

469ba40a6e3a5719e90f521d851252b1d5499dab

[ "Apache-2.0" ]

null

ptvs_virtualenv_proxy.py

SpaceTheArcher/test

469ba40a6e3a5719e90f521d851252b1d5499dab

[ "Apache-2.0" ]

2

2020-06-05T18:25:57.000Z

2021-06-01T22:22:13.000Z

ptvs_virtualenv_proxy.py

bruno-zaccariello/test

469ba40a6e3a5719e90f521d851252b1d5499dab

[ "Apache-2.0" ]

null

# ############################################################################ # # Copyright (c) Microsoft Corporation. # # This source code is subject to terms and conditions of the Apache License, Version 2.0. A # copy of the license can be found in the License.html file at the root of this distribution. If # you cannot locate the Apache License, Version 2.0, please send an email to # vspython@microsoft.com. By using this source code in any fashion, you are agreeing to be bound # by the terms of the Apache License, Version 2.0. # # You must not remove this notice, or any other, from this software. # # ########################################################################### import datetime import os import sys if sys.version_info[0] == 3: def to_str(value): return value.decode(sys.getfilesystemencoding()) def execfile(path, global_dict): """Execute a file""" with open(path, 'r') as f: code = f.read() code = code.replace('\r\n', '\n') + '\n' exec(code, global_dict) else: def to_str(value): return value.encode(sys.getfilesystemencoding()) def log(txt): """Logs fatal errors to a log file if WSGI_LOG env var is defined""" log_file = os.environ.get('WSGI_LOG') if log_file: f = open(log_file, 'a+') try: f.write('%s: %s' % (datetime.datetime.now(), txt)) finally: f.close() ptvsd_secret = os.getenv('WSGI_PTVSD_SECRET') if ptvsd_secret: log('Enabling ptvsd ...\n') try: import ptvsd try: ptvsd.enable_attach(ptvsd_secret) log('ptvsd enabled.\n') except: log('ptvsd.enable_attach failed\n') except ImportError: log('error importing ptvsd.\n'); def get_wsgi_handler(handler_name): if not handler_name: raise Exception('WSGI_HANDLER env var must be set') if not isinstance(handler_name, str): handler_name = to_str(handler_name) module_name, _, callable_name = handler_name.rpartition('.') should_call = callable_name.endswith('()') callable_name = callable_name[:-2] if should_call else callable_name name_list = [(callable_name, should_call)] handler = None while module_name: try: handler = __import__(module_name, fromlist=[name_list[0][0]]) for name, should_call in name_list: handler = getattr(handler, name) if should_call: handler = handler() break except ImportError: module_name, _, callable_name = module_name.rpartition('.') should_call = callable_name.endswith('()') callable_name = callable_name[:-2] if should_call else callable_name name_list.insert(0, (callable_name, should_call)) handler = None if handler is None: raise ValueError('"%s" could not be imported' % handler_name) return handler activate_this = os.getenv('WSGI_ALT_VIRTUALENV_ACTIVATE_THIS') if not activate_this: raise Exception('WSGI_ALT_VIRTUALENV_ACTIVATE_THIS is not set') def get_virtualenv_handler(): log('Activating virtualenv with %s\n' % activate_this) execfile(activate_this, dict(__file__=activate_this)) log('Getting handler %s\n' % os.getenv('WSGI_ALT_VIRTUALENV_HANDLER')) handler = get_wsgi_handler(os.getenv('WSGI_ALT_VIRTUALENV_HANDLER')) log('Got handler: %r\n' % handler) return handler def get_venv_handler(): log('Activating venv with executable at %s\n' % activate_this) import site sys.executable = activate_this old_sys_path, sys.path = sys.path, [] site.main() sys.path.insert(0, '') for item in old_sys_path: if item not in sys.path: sys.path.append(item) log('Getting handler %s\n' % os.getenv('WSGI_ALT_VIRTUALENV_HANDLER')) handler = get_wsgi_handler(os.getenv('WSGI_ALT_VIRTUALENV_HANDLER')) log('Got handler: %r\n' % handler) return handler

34.87395

98

0.608675

true

f71b5f38bc0959d120c19af81b07d70402e40457

2,779

py

Python

bifurcation-diagram/run.py

ExplosiveJam/fickettmodel-reproducibility

e47af1d3e2513d35dad65c16d4fd68c23e505f87

[ "MIT" ]

1

2019-06-08T20:06:33.000Z

bifurcation-diagram/run.py

ExplosiveJam/fickettmodel-reproducibility

e47af1d3e2513d35dad65c16d4fd68c23e505f87

[ "MIT" ]

null

bifurcation-diagram/run.py

ExplosiveJam/fickettmodel-reproducibility

e47af1d3e2513d35dad65c16d4fd68c23e505f87

[ "MIT" ]

1

2019-06-24T13:00:02.000Z

#!/usr/bin/env python r""" Run many simulations with varying :math:`\theta`. The simulations are run. Separate script should plot bifurcation diagram. """ import argparse import os import sys import shutil import numpy as np from mpi4py import MPI from saf.fm.nonlinear import Config from saf.action import solve from saf.util import reset_logging TOTAL_THETAS = 251 FINAL_TIME = 1000 Q = 4 IO_FORMAT = 'numpy' # Format for floating-point numbers. FMT = '.3f' def _worker(tasks, rank): for t in tasks: _worker_single_task(t, rank) def _worker_single_task(task, rank): theta = task worker_name = rank try: outdir = 'theta={:{fmt}}'.format(theta, fmt=FMT) outdir = os.path.join(OUTPUT_DIR, outdir) if os.path.exists(outdir): shutil.rmtree(outdir) os.mkdir(outdir) outname = os.path.join(outdir, 'stdout.log') errname = os.path.join(outdir, 'stderr.log') sys.stdout = open(outname, 'w') sys.stderr = open(errname, 'w') msg = 'Worker {} | theta={:{fmt}}'.format(worker_name, theta, fmt=FMT) print(msg) except Exception as e: print('theta={:{fmt}} | {}'.format(theta, str(e), fmt=FMT)) return try: c = _get_config(theta) solve('nonlinear', c, outdir, log_to_file=False) reset_logging() except Exception as e: print('theta={:{fmt}} | {}'.format(theta, str(e), fmt=FMT)) sys.stdout = sys.__stdout__ print('theta={:{fmt}} | {}'.format(theta, str(e), fmt=FMT)) def _get_config(theta): c = Config() c.n12 = N12 c.final_time = FINAL_TIME c.dt = 0.005 c.approximator = 'godunov-minmod' c.time_integrator = 'dopri5' c.plot_time_step = 0 c.io_format = IO_FORMAT c.play_animation = False c.lambda_tol = 1e-6 c.q = Q c.theta = theta c.reaction_rate_version = 'v2' # Expression exactly as in FariaEtAl2015. c.f = 1 c.ic_amplitude = 0.0 c.ic_type = 'gaussian' c.truncation_coef = 1e6 return c p = argparse.ArgumentParser() p.add_argument('N12', help='Resolution', type=int) args = p.parse_args() N12 = args.N12 OUTPUT_DIR = os.path.join('_output', 'N12={:04d}'.format(N12)) comm = MPI.COMM_WORLD rank = comm.Get_rank() size = comm.Get_size() all_tasks = [] # Build `all_tasks` in master process to distribute it to all processes. if rank == 0: # Uniformly spaced values of :math:`\theta`. theta_values = np.linspace(0.90, 1.15, num=TOTAL_THETAS) for i in range(size): all_tasks.append([]) for i in range(len(theta_values)): all_tasks[i % size].append(theta_values[i]) # Now distribute the tasks to each process. tasks = comm.scatter(all_tasks, root=0) _worker(tasks, rank)

23.956897

78

0.640158

import argparse import os import sys import shutil import numpy as np from mpi4py import MPI from saf.fm.nonlinear import Config from saf.action import solve from saf.util import reset_logging TOTAL_THETAS = 251 FINAL_TIME = 1000 Q = 4 IO_FORMAT = 'numpy' FMT = '.3f' def _worker(tasks, rank): for t in tasks: _worker_single_task(t, rank) def _worker_single_task(task, rank): theta = task worker_name = rank try: outdir = 'theta={:{fmt}}'.format(theta, fmt=FMT) outdir = os.path.join(OUTPUT_DIR, outdir) if os.path.exists(outdir): shutil.rmtree(outdir) os.mkdir(outdir) outname = os.path.join(outdir, 'stdout.log') errname = os.path.join(outdir, 'stderr.log') sys.stdout = open(outname, 'w') sys.stderr = open(errname, 'w') msg = 'Worker {} | theta={:{fmt}}'.format(worker_name, theta, fmt=FMT) print(msg) except Exception as e: print('theta={:{fmt}} | {}'.format(theta, str(e), fmt=FMT)) return try: c = _get_config(theta) solve('nonlinear', c, outdir, log_to_file=False) reset_logging() except Exception as e: print('theta={:{fmt}} | {}'.format(theta, str(e), fmt=FMT)) sys.stdout = sys.__stdout__ print('theta={:{fmt}} | {}'.format(theta, str(e), fmt=FMT)) def _get_config(theta): c = Config() c.n12 = N12 c.final_time = FINAL_TIME c.dt = 0.005 c.approximator = 'godunov-minmod' c.time_integrator = 'dopri5' c.plot_time_step = 0 c.io_format = IO_FORMAT c.play_animation = False c.lambda_tol = 1e-6 c.q = Q c.theta = theta c.reaction_rate_version = 'v2' c.f = 1 c.ic_amplitude = 0.0 c.ic_type = 'gaussian' c.truncation_coef = 1e6 return c p = argparse.ArgumentParser() p.add_argument('N12', help='Resolution', type=int) args = p.parse_args() N12 = args.N12 OUTPUT_DIR = os.path.join('_output', 'N12={:04d}'.format(N12)) comm = MPI.COMM_WORLD rank = comm.Get_rank() size = comm.Get_size() all_tasks = [] if rank == 0: theta_values = np.linspace(0.90, 1.15, num=TOTAL_THETAS) for i in range(size): all_tasks.append([]) for i in range(len(theta_values)): all_tasks[i % size].append(theta_values[i]) tasks = comm.scatter(all_tasks, root=0) _worker(tasks, rank)

true

f71b5f65fde60a4fce5bcdd06e514fa54d419c62

2,762

py

Python

mwparserfromhell/nodes/wikilink.py

hperala/kontuwikibot

f409e6fb45adf4e553dc326d9fb3c0d29eda6373

[ "MIT" ]

null

mwparserfromhell/nodes/wikilink.py

hperala/kontuwikibot

f409e6fb45adf4e553dc326d9fb3c0d29eda6373

[ "MIT" ]

null

mwparserfromhell/nodes/wikilink.py

hperala/kontuwikibot

f409e6fb45adf4e553dc326d9fb3c0d29eda6373

[ "MIT" ]

null

# -*- coding: utf-8 -*- # # Copyright (C) 2012-2016 Ben Kurtovic <ben.kurtovic@gmail.com> # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. from __future__ import unicode_literals from . import Node from ..compat import str from ..utils import parse_anything __all__ = ["Wikilink"] class Wikilink(Node): """Represents an internal wikilink, like ``[[Foo|Bar]]``.""" def __init__(self, title, text=None): super(Wikilink, self).__init__() self._title = title self._text = text def __unicode__(self): if self.text is not None: return "[[" + str(self.title) + "|" + str(self.text) + "]]" return "[[" + str(self.title) + "]]" def __children__(self): yield self.title if self.text is not None: yield self.text def __strip__(self, normalize, collapse): if self.text is not None: return self.text.strip_code(normalize, collapse) return self.title.strip_code(normalize, collapse) def __showtree__(self, write, get, mark): write("[[") get(self.title) if self.text is not None: write(" | ") mark() get(self.text) write("]]") @property def title(self): """The title of the linked page, as a :class:`.Wikicode` object.""" return self._title @property def text(self): """The text to display (if any), as a :class:`.Wikicode` object.""" return self._text @title.setter def title(self, value): self._title = parse_anything(value) @text.setter def text(self, value): if value is None: self._text = None else: self._text = parse_anything(value)

33.277108

79

0.654598

from __future__ import unicode_literals from . import Node from ..compat import str from ..utils import parse_anything __all__ = ["Wikilink"] class Wikilink(Node): def __init__(self, title, text=None): super(Wikilink, self).__init__() self._title = title self._text = text def __unicode__(self): if self.text is not None: return "[[" + str(self.title) + "|" + str(self.text) + "]]" return "[[" + str(self.title) + "]]" def __children__(self): yield self.title if self.text is not None: yield self.text def __strip__(self, normalize, collapse): if self.text is not None: return self.text.strip_code(normalize, collapse) return self.title.strip_code(normalize, collapse) def __showtree__(self, write, get, mark): write("[[") get(self.title) if self.text is not None: write(" | ") mark() get(self.text) write("]]") @property def title(self): return self._title @property def text(self): return self._text @title.setter def title(self, value): self._title = parse_anything(value) @text.setter def text(self, value): if value is None: self._text = None else: self._text = parse_anything(value)

true

f71b5f8ccdadb4be20d3cb2813522c3537586cb1

2,254

py

Python

bcs-ui/backend/tests/container_service/observability/log_stream/test_log_stream.py

laodiu/bk-bcs

2a956a42101ff6487ff521fb3ef429805bfa7e26

[ "Apache-2.0" ]

599

2019-06-25T03:20:46.000Z

2022-03-31T12:14:33.000Z

bcs-ui/backend/tests/container_service/observability/log_stream/test_log_stream.py

laodiu/bk-bcs

2a956a42101ff6487ff521fb3ef429805bfa7e26

[ "Apache-2.0" ]

537

2019-06-27T06:03:44.000Z

2022-03-31T12:10:01.000Z

bcs-ui/backend/tests/container_service/observability/log_stream/test_log_stream.py

laodiu/bk-bcs

2a956a42101ff6487ff521fb3ef429805bfa7e26

[ "Apache-2.0" ]

214

2019-06-25T03:26:05.000Z

2022-03-31T07:52:03.000Z

# -*- coding: utf-8 -*- """ Tencent is pleased to support the open source community by making 蓝鲸智云PaaS平台社区版 (BlueKing PaaS Community Edition) available. Copyright (C) 2017-2021 THL A29 Limited, a Tencent company. All rights reserved. Licensed under the MIT License (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://opensource.org/licenses/MIT 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 pytest from channels.testing import WebsocketCommunicator from backend.accounts.middlewares import BCSChannelAuthMiddlewareStack from backend.container_service.observability.log_stream.views import LogStreamHandler @pytest.fixture def session_id(api_client, project_id, cluster_id, namespace, pod_name, container_name): response = api_client.post( f'/api/logs/projects/{project_id}/clusters/{cluster_id}/namespaces/{namespace}/pods/{pod_name}/stdlogs/sessions/', # noqa {"container_name": container_name}, ) result = response.json() return result['data']['session_id'] @pytest.mark.skip(reason='暂时跳过标准日志部分单元测试') @pytest.mark.django_db @pytest.mark.asyncio async def test_log_stream(project_id, cluster_id, namespace, pod_name, session_id): app = BCSChannelAuthMiddlewareStack(LogStreamHandler.as_asgi()) # Test a normal connection communicator = WebsocketCommunicator( app, f'/ws/logs/projects/{project_id}/clusters/{cluster_id}/namespaces/{namespace}/pods/{pod_name}/stdlogs/stream/?session_id={session_id}', # noqa ) communicator.scope['url_route'] = { 'kwargs': { 'project_id': project_id, 'cluster_id': cluster_id, 'namespace': namespace, 'pod': pod_name, } } connected, _ = await communicator.connect() assert connected # Test sending text await communicator.send_to(text_data="hello") # Close out await communicator.disconnect()

35.777778

151

0.733807

import pytest from channels.testing import WebsocketCommunicator from backend.accounts.middlewares import BCSChannelAuthMiddlewareStack from backend.container_service.observability.log_stream.views import LogStreamHandler @pytest.fixture def session_id(api_client, project_id, cluster_id, namespace, pod_name, container_name): response = api_client.post( f'/api/logs/projects/{project_id}/clusters/{cluster_id}/namespaces/{namespace}/pods/{pod_name}/stdlogs/sessions/', {"container_name": container_name}, ) result = response.json() return result['data']['session_id'] @pytest.mark.skip(reason='暂时跳过标准日志部分单元测试') @pytest.mark.django_db @pytest.mark.asyncio async def test_log_stream(project_id, cluster_id, namespace, pod_name, session_id): app = BCSChannelAuthMiddlewareStack(LogStreamHandler.as_asgi()) communicator = WebsocketCommunicator( app, f'/ws/logs/projects/{project_id}/clusters/{cluster_id}/namespaces/{namespace}/pods/{pod_name}/stdlogs/stream/?session_id={session_id}', ) communicator.scope['url_route'] = { 'kwargs': { 'project_id': project_id, 'cluster_id': cluster_id, 'namespace': namespace, 'pod': pod_name, } } connected, _ = await communicator.connect() assert connected await communicator.send_to(text_data="hello") await communicator.disconnect()

true

f71b5fa3d07b50277b17d00725bcbd1f7fff771e

6,977

py

Python

tensorflow/contrib/cmake/tools/create_def_file.py

uve/tensorflow

e08079463bf43e5963acc41da1f57e95603f8080

[ "Apache-2.0" ]

6

2022-02-04T18:12:24.000Z

2022-03-21T23:57:12.000Z

Lib/site-packages/tensorflow/contrib/cmake/tools/create_def_file.py

shfkdroal/Robot-Learning-in-Mixed-Adversarial-and-Collaborative-Settings

1fa4cd6a566c8745f455fc3d2273208f21f88ced

[ "bzip2-1.0.6" ]

null

Lib/site-packages/tensorflow/contrib/cmake/tools/create_def_file.py

shfkdroal/Robot-Learning-in-Mixed-Adversarial-and-Collaborative-Settings

1fa4cd6a566c8745f455fc3d2273208f21f88ced

[ "bzip2-1.0.6" ]

1

2022-02-08T03:53:23.000Z

# Copyright 2017 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """create_def_file.py - tool to create a windows def file. The def file can be used to export symbols from the tensorflow dll to enable tf.load_library(). Because the linker allows only 64K symbols to be exported per dll we filter the symbols down to the essentials. The regular expressions we use for this are specific to tensorflow. TODO: this works fine but there is an issue with exporting 'const char * const' and importing it from a user_ops. The problem is on the importing end and using __declspec(dllimport) works around it. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import argparse import codecs import os import re import subprocess import sys import tempfile # External tools we use that come with visual studio sdk and # we assume that the caller has the correct PATH to the sdk UNDNAME = "undname.exe" DUMPBIN = "dumpbin.exe" # Exclude if matched EXCLUDE_RE = re.compile(r"RTTI|deleting destructor|::internal::|Internal|" r"python_op_gen_internal|grappler") # Include if matched before exclude INCLUDEPRE_RE = re.compile(r"google::protobuf::internal::ExplicitlyConstructed|" r"tensorflow::internal::LogMessage|" r"tensorflow::internal::LogString|" r"tensorflow::internal::CheckOpMessageBuilder|" r"tensorflow::internal::PickUnusedPortOrDie|" r"tensorflow::internal::ValidateDevice|" r"tensorflow::ops::internal::Enter|" r"tensorflow::strings::internal::AppendPieces|" r"tensorflow::strings::internal::CatPieces|" r"tensorflow::errors::Internal|" r"tensorflow::Tensor::CopyFromInternal|" r"tensorflow::kernel_factory::" r"OpKernelRegistrar::InitInternal|" r"tensorflow::io::internal::JoinPathImpl") # Include if matched after exclude INCLUDE_RE = re.compile(r"^(TF_\w*)$|" r"^(TFE_\w*)$|" r"tensorflow::|" r"functor::|" r"\?nsync_|" r"stream_executor::") # We want to identify data members explicitly in the DEF file, so that no one # can implicitly link against the DLL if they use one of the variables exported # from the DLL and the header they use does not decorate the symbol with # __declspec(dllimport). It is easier to detect what a data symbol does # NOT look like, so doing it with the below regex. DATA_EXCLUDE_RE = re.compile(r"[)(]|" r"vftable|" r"vbtable|" r"vcall|" r"RTTI|" r"protobuf::internal::ExplicitlyConstructed") def get_args(): """Parse command line.""" filename_list = lambda x: x.split(";") parser = argparse.ArgumentParser() parser.add_argument("--input", type=filename_list, help="paths to input libraries separated by semicolons", required=True) parser.add_argument("--output", help="output deffile", required=True) parser.add_argument("--target", help="name of the target", required=True) parser.add_argument("--bitness", help="build target bitness", required=True) args = parser.parse_args() return args def main(): """main.""" args = get_args() # Pipe dumpbin to extract all linkable symbols from libs. # Good symbols are collected in candidates and also written to # a temp file. candidates = [] tmpfile = tempfile.NamedTemporaryFile(mode="w", delete=False) for lib_path in args.input: proc = subprocess.Popen([DUMPBIN, "/nologo", "/linkermember:1", lib_path], stdout=subprocess.PIPE) for line in codecs.getreader("utf-8")(proc.stdout): cols = line.split() if len(cols) < 2: continue sym = cols[1] tmpfile.file.write(sym + "\n") candidates.append(sym) exit_code = proc.wait() if exit_code != 0: print("{} failed, exit={}".format(DUMPBIN, exit_code)) return exit_code tmpfile.file.close() # Run the symbols through undname to get their undecorated name # so we can filter on something readable. with open(args.output, "w") as def_fp: # track dupes taken = set() # Header for the def file. def_fp.write("LIBRARY " + args.target + "\n") def_fp.write("EXPORTS\n") if args.bitness == "64": def_fp.write("\t??1OpDef@tensorflow@@UEAA@XZ\n") else: def_fp.write("\t??1OpDef@tensorflow@@UAE@XZ\n") # Each symbols returned by undname matches the same position in candidates. # We compare on undname but use the decorated name from candidates. dupes = 0 proc = subprocess.Popen([UNDNAME, tmpfile.name], stdout=subprocess.PIPE) for idx, line in enumerate(codecs.getreader("utf-8")(proc.stdout)): decorated = candidates[idx] if decorated in taken: # Symbol is already in output, done. dupes += 1 continue if not INCLUDEPRE_RE.search(line): if EXCLUDE_RE.search(line): continue if not INCLUDE_RE.search(line): continue if "deleting destructor" in line: # Some of the symbols convered by INCLUDEPRE_RE export deleting # destructor symbols, which is a bad idea. # So we filter out such symbols here. continue if DATA_EXCLUDE_RE.search(line): def_fp.write("\t" + decorated + "\n") else: def_fp.write("\t" + decorated + " DATA\n") taken.add(decorated) exit_code = proc.wait() if exit_code != 0: print("{} failed, exit={}".format(UNDNAME, exit_code)) return exit_code os.unlink(tmpfile.name) print("symbols={}, taken={}, dupes={}" .format(len(candidates), len(taken), dupes)) return 0 if __name__ == "__main__": sys.exit(main())

38.546961

81

0.611151

from __future__ import absolute_import from __future__ import division from __future__ import print_function import argparse import codecs import os import re import subprocess import sys import tempfile UNDNAME = "undname.exe" DUMPBIN = "dumpbin.exe" EXCLUDE_RE = re.compile(r"RTTI|deleting destructor|::internal::|Internal|" r"python_op_gen_internal|grappler") INCLUDEPRE_RE = re.compile(r"google::protobuf::internal::ExplicitlyConstructed|" r"tensorflow::internal::LogMessage|" r"tensorflow::internal::LogString|" r"tensorflow::internal::CheckOpMessageBuilder|" r"tensorflow::internal::PickUnusedPortOrDie|" r"tensorflow::internal::ValidateDevice|" r"tensorflow::ops::internal::Enter|" r"tensorflow::strings::internal::AppendPieces|" r"tensorflow::strings::internal::CatPieces|" r"tensorflow::errors::Internal|" r"tensorflow::Tensor::CopyFromInternal|" r"tensorflow::kernel_factory::" r"OpKernelRegistrar::InitInternal|" r"tensorflow::io::internal::JoinPathImpl") INCLUDE_RE = re.compile(r"^(TF_\w*)$|" r"^(TFE_\w*)$|" r"tensorflow::|" r"functor::|" r"\?nsync_|" r"stream_executor::") DATA_EXCLUDE_RE = re.compile(r"[)(]|" r"vftable|" r"vbtable|" r"vcall|" r"RTTI|" r"protobuf::internal::ExplicitlyConstructed") def get_args(): filename_list = lambda x: x.split(";") parser = argparse.ArgumentParser() parser.add_argument("--input", type=filename_list, help="paths to input libraries separated by semicolons", required=True) parser.add_argument("--output", help="output deffile", required=True) parser.add_argument("--target", help="name of the target", required=True) parser.add_argument("--bitness", help="build target bitness", required=True) args = parser.parse_args() return args def main(): args = get_args() candidates = [] tmpfile = tempfile.NamedTemporaryFile(mode="w", delete=False) for lib_path in args.input: proc = subprocess.Popen([DUMPBIN, "/nologo", "/linkermember:1", lib_path], stdout=subprocess.PIPE) for line in codecs.getreader("utf-8")(proc.stdout): cols = line.split() if len(cols) < 2: continue sym = cols[1] tmpfile.file.write(sym + "\n") candidates.append(sym) exit_code = proc.wait() if exit_code != 0: print("{} failed, exit={}".format(DUMPBIN, exit_code)) return exit_code tmpfile.file.close() with open(args.output, "w") as def_fp: taken = set() def_fp.write("LIBRARY " + args.target + "\n") def_fp.write("EXPORTS\n") if args.bitness == "64": def_fp.write("\t??1OpDef@tensorflow@@UEAA@XZ\n") else: def_fp.write("\t??1OpDef@tensorflow@@UAE@XZ\n") dupes = 0 proc = subprocess.Popen([UNDNAME, tmpfile.name], stdout=subprocess.PIPE) for idx, line in enumerate(codecs.getreader("utf-8")(proc.stdout)): decorated = candidates[idx] if decorated in taken: dupes += 1 continue if not INCLUDEPRE_RE.search(line): if EXCLUDE_RE.search(line): continue if not INCLUDE_RE.search(line): continue if "deleting destructor" in line: continue if DATA_EXCLUDE_RE.search(line): def_fp.write("\t" + decorated + "\n") else: def_fp.write("\t" + decorated + " DATA\n") taken.add(decorated) exit_code = proc.wait() if exit_code != 0: print("{} failed, exit={}".format(UNDNAME, exit_code)) return exit_code os.unlink(tmpfile.name) print("symbols={}, taken={}, dupes={}" .format(len(candidates), len(taken), dupes)) return 0 if __name__ == "__main__": sys.exit(main())

true

f71b5fa9abf8cdc0cf3fabe615159d23770b9aaa

4,790

py

Python

flanker/mime/message/headers/encodedword.py

skshetry/flanker

63d1cdf927777f49f97e8d7f01e105a3b0d25cd2

[ "Apache-2.0" ]

929

2015-01-01T11:14:21.000Z

2022-03-28T23:47:40.000Z

flanker/mime/message/headers/encodedword.py

skshetry/flanker

63d1cdf927777f49f97e8d7f01e105a3b0d25cd2

[ "Apache-2.0" ]

141

2015-01-10T19:02:03.000Z

2021-07-26T18:04:14.000Z

flanker/mime/message/headers/encodedword.py

skshetry/flanker

63d1cdf927777f49f97e8d7f01e105a3b0d25cd2

[ "Apache-2.0" ]

179

2015-01-01T18:42:46.000Z

2022-02-16T21:57:14.000Z

# coding:utf-8 import logging from base64 import b64encode import regex as re import six from flanker import _email from flanker.mime.message import charsets, errors _log = logging.getLogger(__name__) _RE_FOLDING_WHITE_SPACES = re.compile(r"(?:\n\r?|\r\n?)") # This spec refers to http://tools.ietf.org/html/rfc2047 _RE_ENCODED_WORD = re.compile(r'''(?P<encodedWord> =\? # literal =? (?P<charset>[^?]*?) # non-greedy up to the next ? is the charset \? # literal ? (?P<encoding>[qb]) # either a "q" or a "b", case insensitive \? # literal ? (?P<encoded>.*?) # non-greedy up to the next ?= is the encoded string \?= # literal ?= )''', re.VERBOSE | re.IGNORECASE | re.MULTILINE) def unfold(value): """ Unfolding is accomplished by simply removing any CRLF that is immediately followed by WSP. Each header field should be treated in its unfolded form for further syntactic and semantic evaluation. """ return _RE_FOLDING_WHITE_SPACES.sub('', value) def decode(header): return mime_to_unicode(header) def mime_to_unicode(header): """ Takes a header value and returns a fully decoded unicode string. It differs from standard Python's mail.header.decode_header() because: - it is higher level, i.e. returns a unicode string instead of an array of tuples - it accepts Unicode and non-ASCII strings as well >>> header_to_unicode("=?UTF-8?B?UmVbMl06INCX0LXQvNC70Y/QutC4?=") u"Земляки" >>> header_to_unicode("hello") u"Hello" """ # Only string header values need to be converted. if not isinstance(header, six.string_types): return header try: header = unfold(header) decoded = [] # decoded parts while header: match = _RE_ENCODED_WORD.search(header) if not match: # Append the remainder of the string to the list of chunks. decoded.append((header, 'ascii')) break start = match.start() if start != 0: # decodes unencoded ascii part to unicode value = header[0:start] if value.strip(): decoded.append((value, 'ascii')) # decode a header =?...?= of encoding charset, value = _decode_part(match.group('charset').lower(), match.group('encoding').lower(), match.group('encoded')) if decoded and decoded[-1][1] == charset: decoded[-1] = (decoded[-1][0]+value, charset) else: decoded.append((value, charset)) header = header[match.end():] return u"".join(charsets.convert_to_unicode(c, v) for v, c in decoded) except Exception: try: logged_header = header if isinstance(logged_header, six.text_type): logged_header = logged_header.encode('utf-8') # encode header as utf-8 so all characters can be base64 encoded logged_header = b64encode(logged_header) _log.warning( u"HEADER-DECODE-FAIL: ({0}) - b64encoded".format( logged_header)) except Exception: _log.exception("Failed to log exception") return header def _decode_part(charset, encoding, value): """ Attempts to decode part, understands 'q' - quoted encoding 'b' - base64 mime encoding Returns (charset, decoded-string) """ if encoding == 'q': return charset, _decode_quoted_printable(value) if encoding == 'b': # Postel's law: add missing padding paderr = len(value) % 4 if paderr: value += '==='[:4 - paderr] return charset, _email.decode_base64(value) if not encoding: return charset, value raise errors.DecodingError('Unknown encoding: %s' % encoding) def _decode_quoted_printable(qp): if six.PY2: return _email.decode_quoted_printable(str(qp)) buf = bytearray() size = len(qp) i = 0 while i < size: ch = qp[i] i += 1 if ch == '_': buf.append(ord(' ')) continue if ch != '=': buf.append(ord(ch)) continue # If there is no enough characters left, then treat them as is. if size - i < 2: buf.append(ord(ch)) continue try: codepoint = int(qp[i:i + 2], 16) except ValueError: buf.append(ord(ch)) continue buf.append(codepoint) i += 2 return six.binary_type(buf)

30.125786

80

0.56618

import logging from base64 import b64encode import regex as re import six from flanker import _email from flanker.mime.message import charsets, errors _log = logging.getLogger(__name__) _RE_FOLDING_WHITE_SPACES = re.compile(r"(?:\n\r?|\r\n?)") _RE_ENCODED_WORD = re.compile(r'''(?P<encodedWord> =\? # literal =? (?P<charset>[^?]*?) # non-greedy up to the next ? is the charset \? # literal ? (?P<encoding>[qb]) # either a "q" or a "b", case insensitive \? # literal ? (?P<encoded>.*?) # non-greedy up to the next ?= is the encoded string \?= # literal ?= )''', re.VERBOSE | re.IGNORECASE | re.MULTILINE) def unfold(value): return _RE_FOLDING_WHITE_SPACES.sub('', value) def decode(header): return mime_to_unicode(header) def mime_to_unicode(header): if not isinstance(header, six.string_types): return header try: header = unfold(header) decoded = [] while header: match = _RE_ENCODED_WORD.search(header) if not match: decoded.append((header, 'ascii')) break start = match.start() if start != 0: value = header[0:start] if value.strip(): decoded.append((value, 'ascii')) charset, value = _decode_part(match.group('charset').lower(), match.group('encoding').lower(), match.group('encoded')) if decoded and decoded[-1][1] == charset: decoded[-1] = (decoded[-1][0]+value, charset) else: decoded.append((value, charset)) header = header[match.end():] return u"".join(charsets.convert_to_unicode(c, v) for v, c in decoded) except Exception: try: logged_header = header if isinstance(logged_header, six.text_type): logged_header = logged_header.encode('utf-8') logged_header = b64encode(logged_header) _log.warning( u"HEADER-DECODE-FAIL: ({0}) - b64encoded".format( logged_header)) except Exception: _log.exception("Failed to log exception") return header def _decode_part(charset, encoding, value): if encoding == 'q': return charset, _decode_quoted_printable(value) if encoding == 'b': paderr = len(value) % 4 if paderr: value += '==='[:4 - paderr] return charset, _email.decode_base64(value) if not encoding: return charset, value raise errors.DecodingError('Unknown encoding: %s' % encoding) def _decode_quoted_printable(qp): if six.PY2: return _email.decode_quoted_printable(str(qp)) buf = bytearray() size = len(qp) i = 0 while i < size: ch = qp[i] i += 1 if ch == '_': buf.append(ord(' ')) continue if ch != '=': buf.append(ord(ch)) continue # If there is no enough characters left, then treat them as is. if size - i < 2: buf.append(ord(ch)) continue try: codepoint = int(qp[i:i + 2], 16) except ValueError: buf.append(ord(ch)) continue buf.append(codepoint) i += 2 return six.binary_type(buf)

true

f71b5fdd3e686df0976041498cd2acf2ea0dd77c

352

py

Python

Exercicios/PythonExercicios/ex001 - 010/ex005.py

sggrilo/Curso-em-Video-Python

a0e6f3d80d89eb8709345a38e207d81a77891192

[ "MIT" ]

null

Exercicios/PythonExercicios/ex001 - 010/ex005.py

sggrilo/Curso-em-Video-Python

a0e6f3d80d89eb8709345a38e207d81a77891192

[ "MIT" ]

null

Exercicios/PythonExercicios/ex001 - 010/ex005.py

sggrilo/Curso-em-Video-Python

a0e6f3d80d89eb8709345a38e207d81a77891192

[ "MIT" ]

null

# ANTECESSOR E SUCESSOR — Faça um programa que leia um número # inteiro e mostre na tela o seu antecessor e o seu sucessor. n = int(input('Digite um número inteiro: ')) a = n - 1 s = n + 1 print('O antecessor de \033[4;33m{}\033[m equivale a \033[4;31m{}\033[m. '.format(n, a), end='') print('Seu sucessor equivale a \033[4;32m{}\033[m.'.format(s))

32

96

0.664773

n = int(input('Digite um número inteiro: ')) a = n - 1 s = n + 1 print('O antecessor de \033[4;33m{}\033[m equivale a \033[4;31m{}\033[m. '.format(n, a), end='') print('Seu sucessor equivale a \033[4;32m{}\033[m.'.format(s))

true

f71b5fe3a6cd69858a329449b4f2842d872d3cb0

27,953

py

Python

canvasapi/user.py

onomou/canvasapi

94d269e8e771bcf03fd57e235190aced3b5af87a

[ "MIT" ]

null

canvasapi/user.py

onomou/canvasapi

94d269e8e771bcf03fd57e235190aced3b5af87a

[ "MIT" ]

null

canvasapi/user.py

onomou/canvasapi

94d269e8e771bcf03fd57e235190aced3b5af87a

[ "MIT" ]

null

from __future__ import absolute_import, division, print_function, unicode_literals from six import python_2_unicode_compatible, string_types import warnings from canvasapi.calendar_event import CalendarEvent from canvasapi.canvas_object import CanvasObject from canvasapi.communication_channel import CommunicationChannel from canvasapi.folder import Folder from canvasapi.paginated_list import PaginatedList from canvasapi.upload import Uploader from canvasapi.util import combine_kwargs, obj_or_id @python_2_unicode_compatible class User(CanvasObject): def __str__(self): return "{} ({})".format(self.name, self.id) def get_profile(self, **kwargs): """ Retrieve this user's profile. :calls: `GET /api/v1/users/:user_id/profile \ <https://canvas.instructure.com/doc/api/users.html#method.profile.settings>`_ :rtype: dict """ response = self._requester.request( 'GET', 'users/{}/profile'.format(self.id) ) return response.json() def get_page_views(self, **kwargs): """ Retrieve this user's page views. :calls: `GET /api/v1/users/:user_id/page_views \ <https://canvas.instructure.com/doc/api/users.html#method.page_views.index>`_ :rtype: :class:`canvasapi.paginated_list.PaginatedList` of :class:`canvasapi.course.PageView` """ from canvasapi.page_view import PageView return PaginatedList( PageView, self._requester, 'GET', 'users/{}/page_views'.format(self.id), _kwargs=combine_kwargs(**kwargs) ) def get_courses(self, **kwargs): """ Retrieve all courses this user is enrolled in. :calls: `GET /api/v1/users/:user_id/courses \ <https://canvas.instructure.com/doc/api/courses.html#method.courses.user_index>`_ :rtype: :class:`canvasapi.paginated_list.PaginatedList` of :class:`canvasapi.course.Course` """ from canvasapi.course import Course return PaginatedList( Course, self._requester, 'GET', 'users/{}/courses'.format(self.id), _kwargs=combine_kwargs(**kwargs) ) def get_missing_submissions(self): """ Retrieve all past-due assignments for which the student does not have a submission. :calls: `GET /api/v1/users/:user_id/missing_submissions \ <https://canvas.instructure.com/doc/api/users.html#method.users.missing_submissions>`_ :rtype: :class:`canvasapi.paginated_list.PaginatedList` of :class:`canvasapi.assignment.Assignment` """ from canvasapi.assignment import Assignment return PaginatedList( Assignment, self._requester, 'GET', 'users/{}/missing_submissions'.format(self.id) ) def update_settings(self, **kwargs): """ Update this user's settings. :calls: `PUT /api/v1/users/:id/settings \ <https://canvas.instructure.com/doc/api/users.html#method.users.settings>`_ :rtype: dict """ response = self._requester.request( 'PUT', 'users/{}/settings'.format(self.id), _kwargs=combine_kwargs(**kwargs) ) return response.json() def get_color(self, asset_string): """ Return the custom colors that have been saved by this user for a given context. The `asset_string` parameter should be in the format 'context_id', for example 'course_42'. :calls: `GET /api/v1/users/:id/colors/:asset_string \ <https://canvas.instructure.com/doc/api/users.html#method.users.get_custom_color>`_ :param asset_string: The asset to retrieve the color from. :type asset_string: str :rtype: dict """ response = self._requester.request( 'GET', 'users/{}/colors/{}'.format(self.id, asset_string) ) return response.json() def get_colors(self): """ Return all custom colors that have been saved by this user. :calls: `GET /api/v1/users/:id/colors \ <https://canvas.instructure.com/doc/api/users.html#method.users.get_custom_colors>`_ :rtype: dict """ response = self._requester.request( 'GET', 'users/{}/colors'.format(self.id) ) return response.json() def update_color(self, asset_string, hexcode): """ Update a custom color for this user for a given context. This allows colors for the calendar and elsewhere to be customized on a user basis. The `asset_string` parameter should be in the format 'context_id', for example 'course_42'. The `hexcode` parameter need not include the '#'. :calls: `PUT /api/v1/users/:id/colors/:asset_string \ <https://canvas.instructure.com/doc/api/users.html#method.users.set_custom_color>`_ :param asset_string: The asset to modify the color for. :type asset_string: str :param hexcode: The hexcode of the color to use. :type hexcode: str :rtype: dict """ response = self._requester.request( 'PUT', 'users/{}/colors/{}'.format(self.id, asset_string), hexcode=hexcode ) return response.json() def edit(self, **kwargs): """ Modify this user's information. :calls: `PUT /api/v1/users/:id \ <https://canvas.instructure.com/doc/api/users.html#method.users.update>`_ :rtype: :class:`canvasapi.user.User` """ response = self._requester.request( 'PUT', 'users/{}'.format(self.id), _kwargs=combine_kwargs(**kwargs) ) super(User, self).set_attributes(response.json()) return self def merge_into(self, destination_user): """ Merge this user into another user. :calls: `PUT /api/v1/users/:id/merge_into/:destination_user_id \ <https://canvas.instructure.com/doc/api/users.html#method.users.merge_into>`_ :param destination_user: The object or ID of the user to merge into. :type destination_user: :class:`canvasapi.user.User` or int :rtype: :class:`canvasapi.user.User` """ dest_user_id = obj_or_id(destination_user, 'destination_user', (User, )) response = self._requester.request( 'PUT', 'users/{}/merge_into/{}'.format(self.id, dest_user_id), ) super(User, self).set_attributes(response.json()) return self def get_avatars(self): """ Retrieve the possible user avatar options that can be set with the user update endpoint. :calls: `GET /api/v1/users/:user_id/avatars \ <https://canvas.instructure.com/doc/api/users.html#method.profile.profile_pics>`_ :rtype: :class:`canvasapi.paginated_list.PaginatedList` of :class:`canvasapi.avatar.Avatar` """ from canvasapi.avatar import Avatar return PaginatedList( Avatar, self._requester, 'GET', 'users/{}/avatars'.format(self.id) ) def get_assignments(self, course, **kwargs): """ Return the list of assignments for this user if the current user (the API key owner) has rights to view. See List assignments for valid arguments. :calls: `GET /api/v1/users/:user_id/courses/:course_id/assignments \ <https://canvas.instructure.com/doc/api/assignments.html#method.assignments_api.user_index>`_ :param course: The object or ID of the course to retrieve. :type course: :class:`canvasapi.course.Course` or int :rtype: :class:`canvasapi.paginated_list.PaginatedList` of :class:`canvasapi.assignment.Assignment` """ from canvasapi.assignment import Assignment from canvasapi.course import Course course_id = obj_or_id(course, "course", (Course,)) return PaginatedList( Assignment, self._requester, 'GET', 'users/{}/courses/{}/assignments'.format(self.id, course_id), _kwargs=combine_kwargs(**kwargs) ) def get_enrollments(self, **kwargs): """ List all of the enrollments for this user. :calls: `GET /api/v1/users/:user_id/enrollments \ <https://canvas.instructure.com/doc/api/enrollments.html#method.enrollments_api.index>`_ :rtype: :class:`canvasapi.paginated_list.PaginatedList` of :class:`canvasapi.enrollment.Enrollment` """ from canvasapi.enrollment import Enrollment return PaginatedList( Enrollment, self._requester, 'GET', 'users/{}/enrollments'.format(self.id), _kwargs=combine_kwargs(**kwargs) ) def upload(self, file, **kwargs): """ Upload a file for a user. NOTE: You *must* have authenticated with this user's API key to upload on their behalf no matter what permissions the issuer of the request has. :calls: `POST /api/v1/users/:user_id/files \ <https://canvas.instructure.com/doc/api/users.html#method.users.create_file>`_ :param file: The file or path of the file to upload. :type file: file or str :returns: True if the file uploaded successfully, False otherwise, \ and the JSON response from the API. :rtype: tuple """ return Uploader( self._requester, 'users/{}/files'.format(self.id), file, **kwargs ).start() def list_calendar_events_for_user(self, **kwargs): """ List calendar events that the current user can view or manage. .. warning:: .. deprecated:: 0.10.0 Use :func:`canvasapi.user.User.get_calendar_events_for_user` instead. :calls: `GET /api/v1/users/:user_id/calendar_events \ <https://canvas.instructure.com/doc/api/calendar_events.html#method.calendar_events_api.user_index>`_ :rtype: :class:`canvasapi.paginated_list.PaginatedList` of :class:`canvasapi.calendar_event.CalendarEvent` """ warnings.warn( "`list_calendar_events_for_user`" " is being deprecated and will be removed in a future version." " Use `get_calendar_events_for_user` instead", DeprecationWarning ) return self.get_calendar_events_for_user(**kwargs) def get_calendar_events_for_user(self, **kwargs): """ List calendar events that the current user can view or manage. :calls: `GET /api/v1/users/:user_id/calendar_events \ <https://canvas.instructure.com/doc/api/calendar_events.html#method.calendar_events_api.user_index>`_ :rtype: :class:`canvasapi.paginated_list.PaginatedList` of :class:`canvasapi.calendar_event.CalendarEvent` """ return PaginatedList( CalendarEvent, self._requester, 'GET', 'users/{}/calendar_events'.format(self.id), _kwargs=combine_kwargs(**kwargs) ) def list_communication_channels(self, **kwargs): """ List communication channels for the specified user, sorted by position. .. warning:: .. deprecated:: 0.10.0 Use :func:`canvasapi.user.User.get_communication_channels` instead. :calls: `GET /api/v1/users/:user_id/communication_channels \ <https://canvas.instructure.com/doc/api/communication_channels.html#method.communication_channels.index>`_ :rtype: :class:`canvasapi.paginated_list.PaginatedList` of :class:`canvasapi.communication_channel.CommunicationChannel` """ warnings.warn( "`list_communication_channels`" " is being deprecated and will be removed in a future version." " Use `get_communication_channels` instead", DeprecationWarning ) return self.get_communication_channels(**kwargs) def get_communication_channels(self, **kwargs): """ List communication channels for the specified user, sorted by position. :calls: `GET /api/v1/users/:user_id/communication_channels \ <https://canvas.instructure.com/doc/api/communication_channels.html#method.communication_channels.index>`_ :rtype: :class:`canvasapi.paginated_list.PaginatedList` of :class:`canvasapi.communication_channel.CommunicationChannel` """ return PaginatedList( CommunicationChannel, self._requester, 'GET', 'users/{}/communication_channels'.format(self.id), _kwargs=combine_kwargs(**kwargs) ) def create_communication_channel(self, **kwargs): """ Create a communication channel for this user :calls: `POST /api/v1/users/:user_id/communication_channels \ <https://canvas.instructure.com/doc/api/communication_channels.html#method.communication_channels.create>`_ :rtype: :class:`canvasapi.communication_channel.CommunicationChannel` """ response = self._requester.request( 'POST', 'users/{}/communication_channels'.format(self.id), _kwargs=combine_kwargs(**kwargs) ) return CommunicationChannel(self._requester, response.json()) def list_files(self, **kwargs): """ Returns the paginated list of files for the user. .. warning:: .. deprecated:: 0.10.0 Use :func:`canvasapi.user.User.get_files` instead. :calls: `GET /api/v1/users/:user_id/files \ <https://canvas.instructure.com/doc/api/files.html#method.files.api_index>`_ :rtype: :class:`canvasapi.paginated_list.PaginatedList` of :class:`canvasapi.file.File` """ warnings.warn( "`list_files` is being deprecated and will be removed in a future " "version. Use `get_files` instead", DeprecationWarning ) return self.get_files(**kwargs) def get_files(self, **kwargs): """ Returns the paginated list of files for the user. :calls: `GET /api/v1/users/:user_id/files \ <https://canvas.instructure.com/doc/api/files.html#method.files.api_index>`_ :rtype: :class:`canvasapi.paginated_list.PaginatedList` of :class:`canvasapi.file.File` """ from canvasapi.file import File return PaginatedList( File, self._requester, 'GET', 'users/{}/files'.format(self.id), _kwargs=combine_kwargs(**kwargs) ) def get_file(self, file, **kwargs): """ Return the standard attachment json object for a file. :calls: `GET /api/v1/users/:user_id/files/:id \ <https://canvas.instructure.com/doc/api/files.html#method.files.api_show>`_ :param file: The object or ID of the file to retrieve. :type file: :class:`canvasapi.file.File` or int :rtype: :class:`canvasapi.file.File` """ from canvasapi.file import File file_id = obj_or_id(file, "file", (File,)) response = self._requester.request( 'GET', 'users/{}/files/{}'.format(self.id, file_id), _kwargs=combine_kwargs(**kwargs) ) return File(self._requester, response.json()) def get_folder(self, folder): """ Returns the details for a user's folder :calls: `GET /api/v1/users/:user_id/folders/:id \ <https://canvas.instructure.com/doc/api/files.html#method.folders.show>`_ :param folder: The object or ID of the folder to retrieve. :type folder: :class:`canvasapi.folder.Folder` or int :rtype: :class:`canvasapi.folder.Folder` """ from canvasapi.folder import Folder folder_id = obj_or_id(folder, "folder", (Folder,)) response = self._requester.request( 'GET', 'users/{}/folders/{}'.format(self.id, folder_id) ) return Folder(self._requester, response.json()) def list_folders(self, **kwargs): """ Returns the paginated list of all folders for the given user. This will be returned as a flat list containing all subfolders as well. .. warning:: .. deprecated:: 0.10.0 Use :func:`canvasapi.user.User.get_folders` instead. :calls: `GET /api/v1/users/:user_id/folders \ <https://canvas.instructure.com/doc/api/files.html#method.folders.list_all_folders>`_ :rtype: :class:`canvasapi.paginated_list.PaginatedList` of :class:`canvasapi.folder.Folder` """ warnings.warn( "`list_folders` is being deprecated and will be removed in a " "future version. Use `get_folders` instead.", DeprecationWarning ) return self.get_folders(**kwargs) def get_folders(self, **kwargs): """ Returns the paginated list of all folders for the given user. This will be returned as a flat list containing all subfolders as well. :calls: `GET /api/v1/users/:user_id/folders \ <https://canvas.instructure.com/doc/api/files.html#method.folders.list_all_folders>`_ :rtype: :class:`canvasapi.paginated_list.PaginatedList` of :class:`canvasapi.folder.Folder` """ return PaginatedList( Folder, self._requester, 'GET', 'users/{}/folders'.format(self.id), _kwargs=combine_kwargs(**kwargs) ) def create_folder(self, name, **kwargs): """ Creates a folder in this user. :calls: `POST /api/v1/users/:user_id/folders \ <https://canvas.instructure.com/doc/api/files.html#method.folders.create>`_ :param name: The name of the folder. :type name: str :rtype: :class:`canvasapi.folder.Folder` """ response = self._requester.request( 'POST', 'users/{}/folders'.format(self.id), name=name, _kwargs=combine_kwargs(**kwargs) ) return Folder(self._requester, response.json()) def list_user_logins(self, **kwargs): """ Given a user ID, return that user's logins for the given account. .. warning:: .. deprecated:: 0.10.0 Use :func:`canvasapi.user.User.get_user_logins` instead. :calls: `GET /api/v1/users/:user_id/logins \ <https://canvas.instructure.com/doc/api/logins.html#method.pseudonyms.index>`_ :rtype: :class:`canvasapi.paginated_list.PaginatedList` of :class:`canvasapi.login.Login` """ warnings.warn( "`list_user_logins` is being deprecated and will be removed in a future version." " Use `get_user_logins` instead", DeprecationWarning ) return self. get_user_logins(**kwargs) def get_user_logins(self, **kwargs): """ Given a user ID, return that user's logins for the given account. :calls: `GET /api/v1/users/:user_id/logins \ <https://canvas.instructure.com/doc/api/logins.html#method.pseudonyms.index>`_ :rtype: :class:`canvasapi.paginated_list.PaginatedList` of :class:`canvasapi.login.Login` """ from canvasapi.login import Login return PaginatedList( Login, self._requester, 'GET', 'users/{}/logins'.format(self.id), _kwargs=combine_kwargs(**kwargs) ) def list_observees(self, **kwargs): """ List the users that the given user is observing .. warning:: .. deprecated:: 0.10.0 Use :func:`canvasapi.user.User.get_observees` instead. :calls: `GET /api/v1/users/:user_id/observees \ <https://canvas.instructure.com/doc/api/user_observees.html#method.user_observees.index>`_ :rtype: :class:`canvasapi.paginated_list.PaginatedList` of :class:`canvasapi.user.User` """ warnings.warn( "`list_observees` is being deprecated and will be removed in a " "future version. Use `get_observees` instead", DeprecationWarning ) return self.get_observees(**kwargs) def get_observees(self, **kwargs): """ List the users that the given user is observing :calls: `GET /api/v1/users/:user_id/observees \ <https://canvas.instructure.com/doc/api/user_observees.html#method.user_observees.index>`_ :rtype: :class:`canvasapi.paginated_list.PaginatedList` of :class:`canvasapi.user.User` """ return PaginatedList( User, self._requester, 'GET', 'users/{}/observees'.format(self.id), _kwargs=combine_kwargs(**kwargs) ) def add_observee_with_credentials(self, **kwargs): """ Register the given user to observe another user, given the observee's credentials. :calls: `POST /api/v1/users/:user_id/observees \ <https://canvas.instructure.com/doc/api/user_observees.html#method.user_observees.create>`_ :rtype: :class:`canvasapi.user.User` """ response = self._requester.request( 'POST', 'users/{}/observees'.format(self.id), _kwargs=combine_kwargs(**kwargs) ) return User(self._requester, response.json()) def show_observee(self, observee_id): """ Gets information about an observed user. :calls: `GET /api/v1/users/:user_id/observees/:observee_id \ <https://canvas.instructure.com/doc/api/user_observees.html#method.user_observees.show>`_ :param observee_id: The login id for the user to observe. :type observee_id: int :rtype: :class: `canvasapi.user.User` """ response = self._requester.request( 'GET', 'users/{}/observees/{}'.format(self.id, observee_id) ) return User(self._requester, response.json()) def add_observee(self, observee_id): """ Registers a user as being observed by the given user. :calls: `PUT /api/v1/users/:user_id/observees/:observee_id \ <https://canvas.instructure.com/doc/api/user_observees.html#method.user_observees.update>`_ :param observee_id: The login id for the user to observe. :type observee_id: int :rtype: :class: `canvasapi.user.User` """ response = self._requester.request( 'PUT', 'users/{}/observees/{}'.format(self.id, observee_id) ) return User(self._requester, response.json()) def remove_observee(self, observee_id): """ Unregisters a user as being observed by the given user. :calls: `DELETE /api/v1/users/:user_id/observees/:observee_id \ <https://canvas.instructure.com/doc/api/user_observees.html#method.user_observees.destroy>`_ :param observee_id: The login id for the user to observe. :type observee_id: int :rtype: :class: `canvasapi.user.User` """ response = self._requester.request( 'DELETE', 'users/{}/observees/{}'.format(self.id, observee_id) ) return User(self._requester, response.json()) def create_content_migration(self, migration_type, **kwargs): """ Create a content migration. :calls: `POST /api/v1/users/:user_id/content_migrations \ <https://canvas.instructure.com/doc/api/content_migrations.html#method.content_migrations.create>`_ :param migration_type: The migrator type to use in this migration :type migration_type: str or :class:`canvasapi.content_migration.Migrator` :rtype: :class:`canvasapi.content_migration.ContentMigration` """ from canvasapi.content_migration import ContentMigration, Migrator if isinstance(migration_type, Migrator): kwargs['migration_type'] = migration_type.type elif isinstance(migration_type, string_types): kwargs['migration_type'] = migration_type else: raise TypeError('Parameter migration_type must be of type Migrator or str') response = self._requester.request( 'POST', 'users/{}/content_migrations'.format(self.id), _kwargs=combine_kwargs(**kwargs) ) response_json = response.json() response_json.update({'user_id': self.id}) return ContentMigration(self._requester, response_json) def get_content_migration(self, content_migration, **kwargs): """ Retrive a content migration by its ID :calls: `GET /api/v1/users/:user_id/content_migrations/:id \ <https://canvas.instructure.com/doc/api/content_migrations.html#method.content_migrations.show>`_ :param content_migration: The object or ID of the content migration to retrieve. :type content_migration: int, str or :class:`canvasapi.content_migration.ContentMigration` :rtype: :class:`canvasapi.content_migration.ContentMigration` """ from canvasapi.content_migration import ContentMigration migration_id = obj_or_id(content_migration, "content_migration", (ContentMigration,)) response = self._requester.request( 'GET', 'users/{}/content_migrations/{}'.format(self.id, migration_id), _kwargs=combine_kwargs(**kwargs) ) response_json = response.json() response_json.update({'user_id': self.id}) return ContentMigration(self._requester, response_json) def get_content_migrations(self, **kwargs): """ List content migrations that the current account can view or manage. :calls: `GET /api/v1/users/:user_id/content_migrations/ \ <https://canvas.instructure.com/doc/api/content_migrations.html#method.content_migrations.index>`_ :rtype: :class:`canvasapi.paginated_list.PaginatedList` of :class:`canvasapi.content_migration.ContentMigration` """ from canvasapi.content_migration import ContentMigration return PaginatedList( ContentMigration, self._requester, 'GET', 'users/{}/content_migrations'.format(self.id), {'user_id': self.id}, _kwargs=combine_kwargs(**kwargs) ) def get_migration_systems(self, **kwargs): """ Return a list of migration systems. :calls: `GET /api/v1/users/:user_id/content_migrations/migrators \ <https://canvas.instructure.com/doc/api/content_migrations.html#method.content_migrations.available_migrators>`_ :rtype: :class:`canvasapi.paginated_list.PaginatedList` of :class:`canvasapi.content_migration.Migrator` """ from canvasapi.content_migration import Migrator return PaginatedList( Migrator, self._requester, 'GET', 'users/{}/content_migrations/migrators'.format(self.id), _kwargs=combine_kwargs(**kwargs) ) @python_2_unicode_compatible class UserDisplay(CanvasObject): def __str__(self): return "{}".format(self.display_name)

34.595297

120

0.615676

from __future__ import absolute_import, division, print_function, unicode_literals from six import python_2_unicode_compatible, string_types import warnings from canvasapi.calendar_event import CalendarEvent from canvasapi.canvas_object import CanvasObject from canvasapi.communication_channel import CommunicationChannel from canvasapi.folder import Folder from canvasapi.paginated_list import PaginatedList from canvasapi.upload import Uploader from canvasapi.util import combine_kwargs, obj_or_id @python_2_unicode_compatible class User(CanvasObject): def __str__(self): return "{} ({})".format(self.name, self.id) def get_profile(self, **kwargs): response = self._requester.request( 'GET', 'users/{}/profile'.format(self.id) ) return response.json() def get_page_views(self, **kwargs): from canvasapi.page_view import PageView return PaginatedList( PageView, self._requester, 'GET', 'users/{}/page_views'.format(self.id), _kwargs=combine_kwargs(**kwargs) ) def get_courses(self, **kwargs): from canvasapi.course import Course return PaginatedList( Course, self._requester, 'GET', 'users/{}/courses'.format(self.id), _kwargs=combine_kwargs(**kwargs) ) def get_missing_submissions(self): from canvasapi.assignment import Assignment return PaginatedList( Assignment, self._requester, 'GET', 'users/{}/missing_submissions'.format(self.id) ) def update_settings(self, **kwargs): response = self._requester.request( 'PUT', 'users/{}/settings'.format(self.id), _kwargs=combine_kwargs(**kwargs) ) return response.json() def get_color(self, asset_string): response = self._requester.request( 'GET', 'users/{}/colors/{}'.format(self.id, asset_string) ) return response.json() def get_colors(self): response = self._requester.request( 'GET', 'users/{}/colors'.format(self.id) ) return response.json() def update_color(self, asset_string, hexcode): response = self._requester.request( 'PUT', 'users/{}/colors/{}'.format(self.id, asset_string), hexcode=hexcode ) return response.json() def edit(self, **kwargs): response = self._requester.request( 'PUT', 'users/{}'.format(self.id), _kwargs=combine_kwargs(**kwargs) ) super(User, self).set_attributes(response.json()) return self def merge_into(self, destination_user): dest_user_id = obj_or_id(destination_user, 'destination_user', (User, )) response = self._requester.request( 'PUT', 'users/{}/merge_into/{}'.format(self.id, dest_user_id), ) super(User, self).set_attributes(response.json()) return self def get_avatars(self): from canvasapi.avatar import Avatar return PaginatedList( Avatar, self._requester, 'GET', 'users/{}/avatars'.format(self.id) ) def get_assignments(self, course, **kwargs): from canvasapi.assignment import Assignment from canvasapi.course import Course course_id = obj_or_id(course, "course", (Course,)) return PaginatedList( Assignment, self._requester, 'GET', 'users/{}/courses/{}/assignments'.format(self.id, course_id), _kwargs=combine_kwargs(**kwargs) ) def get_enrollments(self, **kwargs): from canvasapi.enrollment import Enrollment return PaginatedList( Enrollment, self._requester, 'GET', 'users/{}/enrollments'.format(self.id), _kwargs=combine_kwargs(**kwargs) ) def upload(self, file, **kwargs): return Uploader( self._requester, 'users/{}/files'.format(self.id), file, **kwargs ).start() def list_calendar_events_for_user(self, **kwargs): warnings.warn( "`list_calendar_events_for_user`" " is being deprecated and will be removed in a future version." " Use `get_calendar_events_for_user` instead", DeprecationWarning ) return self.get_calendar_events_for_user(**kwargs) def get_calendar_events_for_user(self, **kwargs): return PaginatedList( CalendarEvent, self._requester, 'GET', 'users/{}/calendar_events'.format(self.id), _kwargs=combine_kwargs(**kwargs) ) def list_communication_channels(self, **kwargs): warnings.warn( "`list_communication_channels`" " is being deprecated and will be removed in a future version." " Use `get_communication_channels` instead", DeprecationWarning ) return self.get_communication_channels(**kwargs) def get_communication_channels(self, **kwargs): return PaginatedList( CommunicationChannel, self._requester, 'GET', 'users/{}/communication_channels'.format(self.id), _kwargs=combine_kwargs(**kwargs) ) def create_communication_channel(self, **kwargs): response = self._requester.request( 'POST', 'users/{}/communication_channels'.format(self.id), _kwargs=combine_kwargs(**kwargs) ) return CommunicationChannel(self._requester, response.json()) def list_files(self, **kwargs): warnings.warn( "`list_files` is being deprecated and will be removed in a future " "version. Use `get_files` instead", DeprecationWarning ) return self.get_files(**kwargs) def get_files(self, **kwargs): from canvasapi.file import File return PaginatedList( File, self._requester, 'GET', 'users/{}/files'.format(self.id), _kwargs=combine_kwargs(**kwargs) ) def get_file(self, file, **kwargs): from canvasapi.file import File file_id = obj_or_id(file, "file", (File,)) response = self._requester.request( 'GET', 'users/{}/files/{}'.format(self.id, file_id), _kwargs=combine_kwargs(**kwargs) ) return File(self._requester, response.json()) def get_folder(self, folder): from canvasapi.folder import Folder folder_id = obj_or_id(folder, "folder", (Folder,)) response = self._requester.request( 'GET', 'users/{}/folders/{}'.format(self.id, folder_id) ) return Folder(self._requester, response.json()) def list_folders(self, **kwargs): warnings.warn( "`list_folders` is being deprecated and will be removed in a " "future version. Use `get_folders` instead.", DeprecationWarning ) return self.get_folders(**kwargs) def get_folders(self, **kwargs): return PaginatedList( Folder, self._requester, 'GET', 'users/{}/folders'.format(self.id), _kwargs=combine_kwargs(**kwargs) ) def create_folder(self, name, **kwargs): response = self._requester.request( 'POST', 'users/{}/folders'.format(self.id), name=name, _kwargs=combine_kwargs(**kwargs) ) return Folder(self._requester, response.json()) def list_user_logins(self, **kwargs): warnings.warn( "`list_user_logins` is being deprecated and will be removed in a future version." " Use `get_user_logins` instead", DeprecationWarning ) return self. get_user_logins(**kwargs) def get_user_logins(self, **kwargs): from canvasapi.login import Login return PaginatedList( Login, self._requester, 'GET', 'users/{}/logins'.format(self.id), _kwargs=combine_kwargs(**kwargs) ) def list_observees(self, **kwargs): warnings.warn( "`list_observees` is being deprecated and will be removed in a " "future version. Use `get_observees` instead", DeprecationWarning ) return self.get_observees(**kwargs) def get_observees(self, **kwargs): return PaginatedList( User, self._requester, 'GET', 'users/{}/observees'.format(self.id), _kwargs=combine_kwargs(**kwargs) ) def add_observee_with_credentials(self, **kwargs): response = self._requester.request( 'POST', 'users/{}/observees'.format(self.id), _kwargs=combine_kwargs(**kwargs) ) return User(self._requester, response.json()) def show_observee(self, observee_id): response = self._requester.request( 'GET', 'users/{}/observees/{}'.format(self.id, observee_id) ) return User(self._requester, response.json()) def add_observee(self, observee_id): response = self._requester.request( 'PUT', 'users/{}/observees/{}'.format(self.id, observee_id) ) return User(self._requester, response.json()) def remove_observee(self, observee_id): response = self._requester.request( 'DELETE', 'users/{}/observees/{}'.format(self.id, observee_id) ) return User(self._requester, response.json()) def create_content_migration(self, migration_type, **kwargs): from canvasapi.content_migration import ContentMigration, Migrator if isinstance(migration_type, Migrator): kwargs['migration_type'] = migration_type.type elif isinstance(migration_type, string_types): kwargs['migration_type'] = migration_type else: raise TypeError('Parameter migration_type must be of type Migrator or str') response = self._requester.request( 'POST', 'users/{}/content_migrations'.format(self.id), _kwargs=combine_kwargs(**kwargs) ) response_json = response.json() response_json.update({'user_id': self.id}) return ContentMigration(self._requester, response_json) def get_content_migration(self, content_migration, **kwargs): from canvasapi.content_migration import ContentMigration migration_id = obj_or_id(content_migration, "content_migration", (ContentMigration,)) response = self._requester.request( 'GET', 'users/{}/content_migrations/{}'.format(self.id, migration_id), _kwargs=combine_kwargs(**kwargs) ) response_json = response.json() response_json.update({'user_id': self.id}) return ContentMigration(self._requester, response_json) def get_content_migrations(self, **kwargs): from canvasapi.content_migration import ContentMigration return PaginatedList( ContentMigration, self._requester, 'GET', 'users/{}/content_migrations'.format(self.id), {'user_id': self.id}, _kwargs=combine_kwargs(**kwargs) ) def get_migration_systems(self, **kwargs): from canvasapi.content_migration import Migrator return PaginatedList( Migrator, self._requester, 'GET', 'users/{}/content_migrations/migrators'.format(self.id), _kwargs=combine_kwargs(**kwargs) ) @python_2_unicode_compatible class UserDisplay(CanvasObject): def __str__(self): return "{}".format(self.display_name)

true

f71b604290c4284cdb29c7ba708ed37267f359af

3,054

py

Python

copyright_updater/logger_factory.py

swasun/copyright-updater

750ced32ee9738e4d65189bc0e917e0581a59668

[ "MIT" ]

null

copyright_updater/logger_factory.py

swasun/copyright-updater

750ced32ee9738e4d65189bc0e917e0581a59668

[ "MIT" ]

null

copyright_updater/logger_factory.py

swasun/copyright-updater

750ced32ee9738e4d65189bc0e917e0581a59668

[ "MIT" ]

null

##################################################################################### # MIT License # # # # Copyright (C) 2018 Charly Lamothe # # # # This file is part of copyright-updater. # # # # Permission is hereby granted, free of charge, to any person obtaining a copy # # of this software and associated documentation files (the "Software"), to deal # # in the Software without restriction, including without limitation the rights # # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # # copies of the Software, and to permit persons to whom the Software is # # furnished to do so, subject to the following conditions: # # # # The above copyright notice and this permission notice shall be included in all # # copies or substantial portions of the Software. # # # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # # SOFTWARE. # ##################################################################################### import logging from logging.handlers import RotatingFileHandler import os import errno class LoggerFactory: @staticmethod def create(path, module_name): # Create logger logger = logging.getLogger(module_name) logger.setLevel(logging.DEBUG) try: os.makedirs(path) except OSError as e: if e.errno != errno.EEXIST: raise # Create file handler fh = RotatingFileHandler(path + os.sep + module_name + '.log', maxBytes=1000000, backupCount=5) fh.setLevel(logging.DEBUG) # Create formatter formatter = logging.Formatter('%(asctime)s - %(filename)s:%(lineno)s - %(name)s - %(levelname)s - %(message)s') # Add formatter to handler fh.setFormatter(formatter) # Add fh to logger logger.addHandler(fh) return logger

51.762712

119

0.47053

true

f71b60c66400900beffb67846939db70bfb9249f

4,381

py

Python

avax/webdav/tests/benchmarks.py

eavatar/avax.webdav

e4d4915fd5af8878ba88e3641e624e64033ece96

[ "MIT" ]

null

avax/webdav/tests/benchmarks.py

eavatar/avax.webdav

e4d4915fd5af8878ba88e3641e624e64033ece96

[ "MIT" ]

null

avax/webdav/tests/benchmarks.py

eavatar/avax.webdav

e4d4915fd5af8878ba88e3641e624e64033ece96

[ "MIT" ]

null

# -*- coding: iso-8859-1 -*- # (c) 2009-2014 Martin Wendt and contributors; see WsgiDAV https://github.com/mar10/wsgidav # Licensed under the MIT license: http://www.opensource.org/licenses/mit-license.php """ Benchmark suite for WsgiDAV. This test suite uses davclient to generate WebDAV requests. A first collection of ideas =========================== - The result is printable HTML, copy/pastable - It also contains date, environment info (Hardware, package versions, ...) - The suite can be run stand-alone against a running WsgiDAV server, just like litmus. - It uses `davclient` and generates an HTML file. - There should be detailed results as well as a few summarizing numbers: ('Total time', 'Byte reads per second', 'Byte write per second', or something like this), so one can compare benchmarks at a glance. - Optional parameters allow to run only a single test - Parameter allows to pass configuration infos that are dumped with the result: benchEnviron = { "comment": "Test with caching enabled", "server_os": "Ubuntu 9.01", "server_cpu": "Intel 3GHz", "server_ram": "2GB", "wsgidav_version": "0.4.b1" "network_bandwidth": "100MBit", >> these can be automatically set?: "client_os": "Windows XP", "client_cpu": "AMD 5000", "date": now() } - Allow to print profiling info (from WsgiDAV server and from becnhmark client!) - The result file could also contain the results of test suites ('PASSED'), so we could use it as documentation for tests on different platforms/setups. Questions ========= - is lxml really faster? - compare this to mod_dav's performance Test cases ========== - PUT 1 x 10 MB - PUT 100 x 1 kB - GET 1 x 10 MB - GET 100 x 1 kB - 100 x PROPFIND depth 0 - 1 x PROPFIND depth infinity - COPY: big file, many small files, big tree - MOVE: big file, many small files, big tree - DELETE: big file, many small files, big tree - LOCK - UNLOCK - Check if locked - PROPPATCH - PROPFIND: depth 0, many small files depth infinity - run litmus in a timed script - Simulate typical Windows Client request sequences: - dir browsing - file reading - file editing - http://groups.google.com/group/paste-users/t/b2afc88a86caade1?hl=en use httperf http://www.hpl.hp.com/research/linux/httperf/httperf-man-0.9.txt and openwebload http://openwebload.sourceforge.net/index.html - makeTree(roofolderName="/bench", folderCount=10, subfolderCount=10, fileCount=10, fileSize=1024) Big tree with 100 folders and 1000 files bench/ folder1/ .. folder10/ subfolder10-1/ .. subfolder10-10/ file10-10-1.txt -> 1k """ import logging _benchmarks = [#"proppatch_many", #"proppatch_big", #"proppatch_deep", "test_scripted", ] def _real_run_bench(bench, opts): if bench == "*": for bench in _benchmarks: run_bench(bench, opts) return assert bench in _benchmarks if bench == "test_scripted": from avax.webdav.tests import test_scripted test_scripted.main() else: raise ValueError() def run_bench(bench, opts): profile_benchmarks = opts["profile_benchmarks"] if bench in profile_benchmarks: # http://docs.python.org/library/profile.html#module-cProfile import cProfile, pstats, StringIO prof = cProfile.Profile() prof = prof.runctx("_real_run_bench(bench, opts)", globals(), locals()) stream = StringIO.StringIO() stats = pstats.Stats(prof, stream=stream) # stats.sort_stats("time") # Or cumulative stats.sort_stats("cumulative") # Or time stats.print_stats(80) # 80 = how many to print # The rest is optional. # stats.print_callees() # stats.print_callers() logging.warning("Profile data for '%s':\n%s" % (bench, stream.getvalue())) else: _real_run_bench(bench, opts) def bench_all(opts): run_bench("*", opts) def main(): opts = {"num": 10, "profile_benchmarks": ["*"], } bench_all(opts) if __name__ == "__main__": main()

31.070922

99

0.625428

import logging _benchmarks = [ "test_scripted", ] def _real_run_bench(bench, opts): if bench == "*": for bench in _benchmarks: run_bench(bench, opts) return assert bench in _benchmarks if bench == "test_scripted": from avax.webdav.tests import test_scripted test_scripted.main() else: raise ValueError() def run_bench(bench, opts): profile_benchmarks = opts["profile_benchmarks"] if bench in profile_benchmarks: Profile, pstats, StringIO prof = cProfile.Profile() prof = prof.runctx("_real_run_bench(bench, opts)", globals(), locals()) stream = StringIO.StringIO() stats = pstats.Stats(prof, stream=stream) ort_stats("cumulative") stats.print_stats(80) logging.warning("Profile data for '%s':\n%s" % (bench, stream.getvalue())) else: _real_run_bench(bench, opts) def bench_all(opts): run_bench("*", opts) def main(): opts = {"num": 10, "profile_benchmarks": ["*"], } bench_all(opts) if __name__ == "__main__": main()

true

f71b6170ec1ea5471b4314a0e09ef42e3e38daff

1,001

py

Python

project/urls.py

tgavankar/PlaydohSlideSync

5718d661e78d361a0dcda908b63c736bab886bb4

[ "BSD-3-Clause" ]

null

project/urls.py

tgavankar/PlaydohSlideSync

5718d661e78d361a0dcda908b63c736bab886bb4

[ "BSD-3-Clause" ]

null

project/urls.py

tgavankar/PlaydohSlideSync

5718d661e78d361a0dcda908b63c736bab886bb4

[ "BSD-3-Clause" ]

null

from django.conf import settings from django.conf.urls.defaults import patterns, include from django.contrib.staticfiles.urls import staticfiles_urlpatterns from .examples import urls from funfactory.monkeypatches import patch patch() # Uncomment the next two lines to enable the admin: # from django.contrib import admin # admin.autodiscover() urlpatterns = patterns('', # Example: (r'', include(urls)), # Generate a robots.txt (r'^robots\.txt$', lambda r: HttpResponse( "User-agent: *\n%s: /" % 'Allow' if settings.ENGAGE_ROBOTS else 'Disallow' , mimetype="text/plain" ) ) # Uncomment the admin/doc line below to enable admin documentation: # (r'^admin/doc/', include('django.contrib.admindocs.urls')), # Uncomment the next line to enable the admin: # (r'^admin/', include(admin.site.urls)), ) ## In DEBUG mode, serve media files through Django. if settings.DEBUG: urlpatterns += staticfiles_urlpatterns()

27.805556

88

0.685315

from django.conf import settings from django.conf.urls.defaults import patterns, include from django.contrib.staticfiles.urls import staticfiles_urlpatterns from .examples import urls from funfactory.monkeypatches import patch patch() urlpatterns = patterns('', (r'', include(urls)), (r'^robots\.txt$', lambda r: HttpResponse( "User-agent: *\n%s: /" % 'Allow' if settings.ENGAGE_ROBOTS else 'Disallow' , mimetype="text/plain" ) ) ) _urlpatterns()

true

f71b63178ebdc11ae83bec5b2f2f47ff8b336dd6

1,011

py

Python

src/sellers/migrations/0004_alter_seller_logo_url.py

evis-market/web-interface-backend

f8930ff1c009ad18e522ab29680b4bcd50a6020e

[ "MIT" ]

2

2021-08-30T22:58:32.000Z

2021-12-12T10:47:52.000Z

src/sellers/migrations/0004_alter_seller_logo_url.py

evis-market/web-interface-backend

f8930ff1c009ad18e522ab29680b4bcd50a6020e

[ "MIT" ]

null

src/sellers/migrations/0004_alter_seller_logo_url.py

evis-market/web-interface-backend

f8930ff1c009ad18e522ab29680b4bcd50a6020e

[ "MIT" ]

1

2021-08-22T19:12:44.000Z

# Generated by Django 3.2.7 on 2021-10-27 07:05 import django.core.validators from django.db import migrations, models from django.db.transaction import atomic from sellers.models import Seller """ Note: Migrations includes data migration that set to null logo_url field for currently existing records. Logo_url field would be change to FileField type in this migration """ def clear_seller_logo_url(apps, schema_editor): with atomic(): for seller in Seller.objects.all(): seller.logo_url = None seller.save() class Migration(migrations.Migration): dependencies = [ ('sellers', '0003_auto_20211012_1929'), ] operations = [ migrations.RunPython(clear_seller_logo_url, migrations.RunPython.noop), migrations.AlterField( model_name='seller', name='logo_url', field=models.FileField(blank=True, help_text='Logo', max_length=1000, null=True, upload_to='', verbose_name='Logo'), ), ]

26.605263

128

0.68546

import django.core.validators from django.db import migrations, models from django.db.transaction import atomic from sellers.models import Seller def clear_seller_logo_url(apps, schema_editor): with atomic(): for seller in Seller.objects.all(): seller.logo_url = None seller.save() class Migration(migrations.Migration): dependencies = [ ('sellers', '0003_auto_20211012_1929'), ] operations = [ migrations.RunPython(clear_seller_logo_url, migrations.RunPython.noop), migrations.AlterField( model_name='seller', name='logo_url', field=models.FileField(blank=True, help_text='Logo', max_length=1000, null=True, upload_to='', verbose_name='Logo'), ), ]

true

f71b632bb314545ed7732ce47684f88d027b19e7

78

py

Python

xpring/proto/__init__.py

mvadari/xpring-py

b837420127d1c1e5051ed305ed4f19fe9910a4f6

[ "0BSD" ]

6

2019-12-11T00:54:56.000Z

2021-03-11T19:44:44.000Z

xpring/proto/__init__.py

mvadari/xpring-py

b837420127d1c1e5051ed305ed4f19fe9910a4f6

[ "0BSD" ]

null

xpring/proto/__init__.py

mvadari/xpring-py

b837420127d1c1e5051ed305ed4f19fe9910a4f6

[ "0BSD" ]

9

2020-02-28T18:40:46.000Z

2022-02-28T23:01:09.000Z

# The rest of this package, but not this __init__.py, is generated by protoc.

39

77

0.75641

true

f71b6354cd7ddb3ba58cc906feac3f6233ca894c

537

py

Python

ImgVidProcessing/Exercise1/solution1.py

SystemNinja/MyPythonPrograms

6bdebb5017994c3431aea769319f702075fff9b9

[ "MIT" ]

null

ImgVidProcessing/Exercise1/solution1.py

SystemNinja/MyPythonPrograms

6bdebb5017994c3431aea769319f702075fff9b9

[ "MIT" ]

null

ImgVidProcessing/Exercise1/solution1.py

SystemNinja/MyPythonPrograms

6bdebb5017994c3431aea769319f702075fff9b9

[ "MIT" ]

null

""" This solution implements glob library in order to 'automatize' task. Instead of manually processing each image Reference: https://pymotw.com/2/glob/ """ import cv2 import glob2 images=glob2.glob("*.jpg") #images=glob2.glob("Exercise1\*.jpg") for image in images: img=cv2.imread(image, 0) re=cv2.resize(img,(100,100)) cv2.imshow("Resized image", re) cv2.waitKey(500) cv2.destroyAllWindows() cv2.imwrite(image+"_resized.jpg", re) #cv2.imwrite("Exercise1\\"+image+"_resized.jpg", re)

25.571429

69

0.670391

import cv2 import glob2 images=glob2.glob("*.jpg") for image in images: img=cv2.imread(image, 0) re=cv2.resize(img,(100,100)) cv2.imshow("Resized image", re) cv2.waitKey(500) cv2.destroyAllWindows() cv2.imwrite(image+"_resized.jpg", re)

true

f71b63892ebbad403e4916b665b53156a244c0fa

87

py

Python

Python/100Excersises/.history/51 to 75/69/69_20201119121845.py

magusikrak/NAMI-TERM-I-GroupWork

f0a9a5f219ccbec024eb5316361db3fca46e171c

[ "MIT" ]

null

Python/100Excersises/.history/51 to 75/69/69_20201119121845.py

magusikrak/NAMI-TERM-I-GroupWork

f0a9a5f219ccbec024eb5316361db3fca46e171c

[ "MIT" ]

1

2021-07-24T03:18:30.000Z

2021-07-24T12:45:07.000Z

Python/100Excersises/.history/51 to 75/69/69_20201119121845.py

magusikrak/NAMI-TERM-I-GroupWork

f0a9a5f219ccbec024eb5316361db3fca46e171c

[ "MIT" ]

null

import requests sugam = requests.get("http://www.pythonhow.com") print(rp.text[:100])

17.4

48

0.724138

import requests sugam = requests.get("http://www.pythonhow.com") print(rp.text[:100])

true

f71b64529d9237153dd6f12a58b0280dfcb69bfe

454

py

Python

.history/ClassFiles/WorkingWithExternalFiles/FileHandling_20210107190119.py

minefarmer/Comprehensive-Python

f97b9b83ec328fc4e4815607e6a65de90bb8de66

[ "Unlicense" ]

null

.history/ClassFiles/WorkingWithExternalFiles/FileHandling_20210107190119.py

minefarmer/Comprehensive-Python

f97b9b83ec328fc4e4815607e6a65de90bb8de66

[ "Unlicense" ]

null

.history/ClassFiles/WorkingWithExternalFiles/FileHandling_20210107190119.py

minefarmer/Comprehensive-Python

f97b9b83ec328fc4e4815607e6a65de90bb8de66

[ "Unlicense" ]

null

""" Opening and Reading Files Syntax to open file. f = open("Myfile.txt) # assigned to the variable f. f = open("Myfile.txt","rt") # if in the same directory. f = open("c:\\MyFolders\Myfile.txt") # if hot in the same directory. """ f = open("Quotes.txt") # print(f.readable()) # print(f.read()) # f.close() # print(f.readable()) print(f.read(11)) print(f.readlines()) for quote in f: print(quote) print("HI")

15.655172

69

0.594714

f = open("Quotes.txt") print(f.read(11)) print(f.readlines()) for quote in f: print(quote) print("HI")

true

f71b64ca7afadced29875edb91d99ac16e7d6ba0

11,148

py

Python

src/main/app-resources/notebook/libexec/helpers.py

ec-better/ewf-ethz-03-01-01

5ca616e5c25bbba29013a7de248af4b69757921b

[ "Apache-2.0" ]

1

2021-09-23T02:20:11.000Z

src/main/app-resources/notebook/libexec/helpers.py

ec-better/ewf-ethz-03-01-01

5ca616e5c25bbba29013a7de248af4b69757921b

[ "Apache-2.0" ]

null

src/main/app-resources/notebook/libexec/helpers.py

ec-better/ewf-ethz-03-01-01

5ca616e5c25bbba29013a7de248af4b69757921b

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- import os import numpy as np import cv2 import re from shapely import wkt from shapely.geometry import box, Polygon import pandas as pd import geopandas as gpd from osgeo import gdal, gdalnumeric, osr, ogr def ensure_dir(file_path): directory = os.path.dirname(file_path) if not os.path.exists(directory): os.makedirs(directory) def getResolution(demFolder, return_full_paths = False): rasterFilePaths = [f for f in os.listdir(demFolder) if os.path.isfile(os.path.join(demFolder, f))] if return_full_paths: rasterFilePaths = [demFolder + '/' + f for f in rasterFilePaths if f[:4] == 'DEM_' and f[-4:] == '.tif'] rasterFilePaths.sort(reverse=True) else: rasterFilePaths = [int(f[4:-4]) for f in rasterFilePaths if f[:4] == 'DEM_' and f[-4:] == '.tif'] return rasterFilePaths def readGDAL2numpy(rasterPath, return_geoInformation = False): try: ds = gdal.Open(rasterPath) except RuntimeError: print('Unable to open input file') sys.exit(1) data = gdalnumeric.LoadFile(rasterPath, False) noDataVal = ds.GetRasterBand(1).GetNoDataValue() try: if data.dtype in ['float16', 'float32', 'float64'] and noDataVal is not None: data[data == noDataVal] = np.NaN except: print("Issue in no data value") if return_geoInformation == False: return data else: geoTransform = ds.GetGeoTransform() projection = ds.GetProjection() return data, geoTransform, projection def writeNumpyArr2Geotiff(outputPath, data, geoTransform = None, projection = None, GDAL_dtype = gdal.GDT_Byte, noDataValue = None): nscn, npix = data.shape if np.isnan(data).any() and noDataValue is not None: data[np.isnan(data)] = noDataValue ds_new = gdal.GetDriverByName('GTiff').Create(outputPath, npix, nscn, 1, GDAL_dtype) if geoTransform != None: ds_new.SetGeoTransform(geoTransform) if projection != None: ds_new.SetProjection(projection) outBand = ds_new.GetRasterBand(1) outBand.WriteArray(data) if noDataValue != None: ds_new.GetRasterBand(1).SetNoDataValue(noDataValue) # Close dataset ds_new.FlushCache() ds_new = None outBand = None def writeNumpyArr2Saga(outputPath, data, geoTransform = None, projection = None, GDAL_dtype = gdal.GDT_Byte, noDataValue = None): nscn, npix = data.shape if np.isnan(data).any() and noDataValue is not None: data[np.isnan(data)] = noDataValue ds_new = gdal.GetDriverByName('SAGA').Create(outputPath, npix, nscn, 1, GDAL_dtype) outBand = ds_new.GetRasterBand(1) outBand.WriteArray(data) if noDataValue != None: ds_new.GetRasterBand(1).SetNoDataValue(noDataValue) if projection != None: ds_new.SetProjection(projection) # Close dataset ds_new.FlushCache() ds_new = None outBand = None def wkt2bbox(wkt_input): wkt_geometry = wkt.loads(wkt_input) minx, miny, maxx, maxy = wkt_geometry.bounds b = box(minx, miny, maxx, maxy) bbox_tuple = list(b.exterior.coords) bbox = [] for point in bbox_tuple: bbox.append([point[0],point[1]]) return bbox def wkt2shp(wkt_input, target_epsg, dst_file, bbox=False): ensure_dir(dst_file) if bbox: polygon = Polygon(wkt2bbox(wkt_input)) else: polygon = wkt.loads(wkt_input) gpd.GeoDataFrame(pd.DataFrame(['p1'], columns = ['geom']), crs = {'init':'epsg:' + str(target_epsg)}, geometry = [polygon]).to_file(dst_file) def rescaleDEM(image, noData = None, maxVal = 255): if noData: image = np.float32(image) image[image == noData] = np.nan minElev = np.nanmin(image) maxElev = np.nanmax(image) rescaled = ( ((image - minElev)/(maxElev- minElev)) * (maxVal - 1) ) + 1 return np.uint8(rescaled) def joinStrArg(str1, str2, str3 = None): if str3 is not None: return str(str1) + ' ' + str(str2) + ' ' + str(str3) else: return str(str1) + ' ' + str(str2) def wkt2EPSG(wkt, epsg='/usr/local/share/proj/epsg', forceProj4=False): ''' Transform a WKT string to an EPSG code Arguments --------- wkt: WKT definition epsg: the proj.4 epsg file (defaults to '/usr/local/share/proj/epsg') forceProj4: whether to perform brute force proj4 epsg file check (last resort) Returns: EPSG code ''' code = None p_in = osr.SpatialReference() s = p_in.ImportFromWkt(wkt) if s == 5: # invalid WKT return None if p_in.IsLocal() == 1: # this is a local definition return p_in.ExportToWkt() if p_in.IsGeographic() == 1: # this is a geographic srs cstype = 'GEOGCS' else: # this is a projected srs cstype = 'PROJCS' an = p_in.GetAuthorityName(cstype) ac = p_in.GetAuthorityCode(cstype) if an is not None and ac is not None: # return the EPSG code return '%s:%s' % \ (p_in.GetAuthorityName(cstype), p_in.GetAuthorityCode(cstype)) else: # try brute force approach by grokking proj epsg definition file p_out = p_in.ExportToProj4() if p_out: if forceProj4 is True: return p_out f = open(epsg) for line in f: if line.find(p_out) != -1: m = re.search('<(\\d+)>', line) if m: code = m.group(1) break if code: # match return 'EPSG:%s' % code else: # no match return None else: return None def getCornerCoordinates(gdal_dataSet, target_srs = False): """ :param gdal_dataSet: /path/to/file OR gdal dataset :param target_srs: False for output coordinates in same coordinate system OR 'wgs84' for lat long values OR custom osr.SpatialReference() object :return: list of corner coordinates --0--------3-- | | | | <--- Index of coordinates returned in list | | --1--------2-- """ if type(gdal_dataSet) is str: gdal_dataSet = gdal.Open(gdal_dataSet) gt=gdal_dataSet.GetGeoTransform() # gt = [ulx, xres, xskew, uly, yskew, yres] cols = gdal_dataSet.RasterXSize rows = gdal_dataSet.RasterYSize def GetExtent(gt,cols,rows): ''' Return list of corner coordinates from a geotransform @type gt: C{tuple/list} @param gt: geotransform @type cols: C{int} @param cols: number of columns in the dataset @type rows: C{int} @param rows: number of rows in the dataset @rtype: C{[float,...,float]} @return: coordinates of each corner ''' ext=[] xarr=[0,cols] yarr=[0,rows] for px in xarr: for py in yarr: x=gt[0]+(px*gt[1])+(py*gt[2]) y=gt[3]+(px*gt[4])+(py*gt[5]) ext.append([x,y]) #print(x,y) yarr.reverse() return ext def ReprojectCoords(coords,src_srs,tgt_srs): ''' Reproject a list of x,y coordinates. @type geom: C{tuple/list} @param geom: List of [[x,y],...[x,y]] coordinates @type src_srs: C{osr.SpatialReference} @param src_srs: OSR SpatialReference object @type tgt_srs: C{osr.SpatialReference} @param tgt_srs: OSR SpatialReference object @rtype: C{tuple/list} @return: List of transformed [[x,y],...[x,y]] coordinates ''' trans_coords=[] transform = osr.CoordinateTransformation( src_srs, tgt_srs) for x,y in coords: x,y,z = transform.TransformPoint(x,y) trans_coords.append([x,y]) return trans_coords ext = GetExtent(gt,cols,rows) src_srs=osr.SpatialReference() src_srs.ImportFromWkt(gdal_dataSet.GetProjection()) if target_srs == False: return ext elif target_srs == 'wgs84': #target_srs = src_srs.CloneGeogCS() # target_srs=osr.SpatialReference() target_srs.ImportFromEPSG(4326) return ReprojectCoords(ext,src_srs,target_srs) def resizeToDEM(imPath, sizeDEM = None, geoTransform = None, projection = None, noData = None): imDS = gdal.Open(imPath, gdal.GA_ReadOnly) imPix = imDS.RasterXSize imScn = imDS.RasterYSize nscn, npix = sizeDEM if sizeDEM is not None: if nscn != imScn or npix != imPix: print("Size Mismatch") image = imDS.ReadAsArray() if noData is not None: image = np.float32(image) image[image == noData] = np.nan imNew = cv2.resize(image, (npix, nscn), interpolation=cv2.INTER_CUBIC) writeNumpyArr2Geotiff(imPath, imNew, geoTransform = geoTransform, projection = projection, GDAL_dtype = gdal.GDT_UInt16, noDataValue = noData) def map_uint16_to_uint8(img, lower_bound=None, upper_bound=None): ''' Map a 16-bit image trough a lookup table to convert it to 8-bit. ''' if not(0 <= lower_bound < 2**16) and lower_bound is not None: raise ValueError( '"lower_bound" must be in the range [0, 65535]') if not(0 <= upper_bound < 2**16) and upper_bound is not None: raise ValueError( '"upper_bound" must be in the range [0, 65535]') if lower_bound is None: lower_bound = np.min(img) if upper_bound is None: upper_bound = np.max(img) if lower_bound >= upper_bound: raise ValueError( '"lower_bound" must be smaller than "upper_bound"') lut = np.concatenate([ np.zeros(lower_bound, dtype=np.uint16), np.linspace(0, 255, upper_bound - lower_bound).astype(np.uint16), np.ones(2**16 - upper_bound, dtype=np.uint16) * 255 ]) return lut[img].astype(np.uint8) def closeCV(mask, kernelSize = 11): kernel = np.ones((kernelSize, kernelSize),np.uint8) return cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel) def newGeoTransform(geoTransform, maskBounds): newGeoTransform = (geoTransform[0]+ maskBounds['xMin'] * geoTransform[1], geoTransform[1], geoTransform[2], geoTransform[3] + maskBounds['yMin'] * geoTransform[5], geoTransform[4], geoTransform[5]) return newGeoTransform def shrinkGeoTransform(geoTransform, factor): newGeoTransform = (geoTransform[0], geoTransform[1] / factor, geoTransform[2], geoTransform[3], geoTransform[4], geoTransform[5] / factor) return newGeoTransform

33.884498

157

0.591137

import os import numpy as np import cv2 import re from shapely import wkt from shapely.geometry import box, Polygon import pandas as pd import geopandas as gpd from osgeo import gdal, gdalnumeric, osr, ogr def ensure_dir(file_path): directory = os.path.dirname(file_path) if not os.path.exists(directory): os.makedirs(directory) def getResolution(demFolder, return_full_paths = False): rasterFilePaths = [f for f in os.listdir(demFolder) if os.path.isfile(os.path.join(demFolder, f))] if return_full_paths: rasterFilePaths = [demFolder + '/' + f for f in rasterFilePaths if f[:4] == 'DEM_' and f[-4:] == '.tif'] rasterFilePaths.sort(reverse=True) else: rasterFilePaths = [int(f[4:-4]) for f in rasterFilePaths if f[:4] == 'DEM_' and f[-4:] == '.tif'] return rasterFilePaths def readGDAL2numpy(rasterPath, return_geoInformation = False): try: ds = gdal.Open(rasterPath) except RuntimeError: print('Unable to open input file') sys.exit(1) data = gdalnumeric.LoadFile(rasterPath, False) noDataVal = ds.GetRasterBand(1).GetNoDataValue() try: if data.dtype in ['float16', 'float32', 'float64'] and noDataVal is not None: data[data == noDataVal] = np.NaN except: print("Issue in no data value") if return_geoInformation == False: return data else: geoTransform = ds.GetGeoTransform() projection = ds.GetProjection() return data, geoTransform, projection def writeNumpyArr2Geotiff(outputPath, data, geoTransform = None, projection = None, GDAL_dtype = gdal.GDT_Byte, noDataValue = None): nscn, npix = data.shape if np.isnan(data).any() and noDataValue is not None: data[np.isnan(data)] = noDataValue ds_new = gdal.GetDriverByName('GTiff').Create(outputPath, npix, nscn, 1, GDAL_dtype) if geoTransform != None: ds_new.SetGeoTransform(geoTransform) if projection != None: ds_new.SetProjection(projection) outBand = ds_new.GetRasterBand(1) outBand.WriteArray(data) if noDataValue != None: ds_new.GetRasterBand(1).SetNoDataValue(noDataValue) ds_new.FlushCache() ds_new = None outBand = None def writeNumpyArr2Saga(outputPath, data, geoTransform = None, projection = None, GDAL_dtype = gdal.GDT_Byte, noDataValue = None): nscn, npix = data.shape if np.isnan(data).any() and noDataValue is not None: data[np.isnan(data)] = noDataValue ds_new = gdal.GetDriverByName('SAGA').Create(outputPath, npix, nscn, 1, GDAL_dtype) outBand = ds_new.GetRasterBand(1) outBand.WriteArray(data) if noDataValue != None: ds_new.GetRasterBand(1).SetNoDataValue(noDataValue) if projection != None: ds_new.SetProjection(projection) ds_new.FlushCache() ds_new = None outBand = None def wkt2bbox(wkt_input): wkt_geometry = wkt.loads(wkt_input) minx, miny, maxx, maxy = wkt_geometry.bounds b = box(minx, miny, maxx, maxy) bbox_tuple = list(b.exterior.coords) bbox = [] for point in bbox_tuple: bbox.append([point[0],point[1]]) return bbox def wkt2shp(wkt_input, target_epsg, dst_file, bbox=False): ensure_dir(dst_file) if bbox: polygon = Polygon(wkt2bbox(wkt_input)) else: polygon = wkt.loads(wkt_input) gpd.GeoDataFrame(pd.DataFrame(['p1'], columns = ['geom']), crs = {'init':'epsg:' + str(target_epsg)}, geometry = [polygon]).to_file(dst_file) def rescaleDEM(image, noData = None, maxVal = 255): if noData: image = np.float32(image) image[image == noData] = np.nan minElev = np.nanmin(image) maxElev = np.nanmax(image) rescaled = ( ((image - minElev)/(maxElev- minElev)) * (maxVal - 1) ) + 1 return np.uint8(rescaled) def joinStrArg(str1, str2, str3 = None): if str3 is not None: return str(str1) + ' ' + str(str2) + ' ' + str(str3) else: return str(str1) + ' ' + str(str2) def wkt2EPSG(wkt, epsg='/usr/local/share/proj/epsg', forceProj4=False): code = None p_in = osr.SpatialReference() s = p_in.ImportFromWkt(wkt) if s == 5: return None if p_in.IsLocal() == 1: return p_in.ExportToWkt() if p_in.IsGeographic() == 1: cstype = 'GEOGCS' else: cstype = 'PROJCS' an = p_in.GetAuthorityName(cstype) ac = p_in.GetAuthorityCode(cstype) if an is not None and ac is not None: return '%s:%s' % \ (p_in.GetAuthorityName(cstype), p_in.GetAuthorityCode(cstype)) else: p_out = p_in.ExportToProj4() if p_out: if forceProj4 is True: return p_out f = open(epsg) for line in f: if line.find(p_out) != -1: m = re.search('<(\\d+)>', line) if m: code = m.group(1) break if code: return 'EPSG:%s' % code else: return None else: return None def getCornerCoordinates(gdal_dataSet, target_srs = False): if type(gdal_dataSet) is str: gdal_dataSet = gdal.Open(gdal_dataSet) gt=gdal_dataSet.GetGeoTransform() cols = gdal_dataSet.RasterXSize rows = gdal_dataSet.RasterYSize def GetExtent(gt,cols,rows): ext=[] xarr=[0,cols] yarr=[0,rows] for px in xarr: for py in yarr: x=gt[0]+(px*gt[1])+(py*gt[2]) y=gt[3]+(px*gt[4])+(py*gt[5]) ext.append([x,y]) yarr.reverse() return ext def ReprojectCoords(coords,src_srs,tgt_srs): trans_coords=[] transform = osr.CoordinateTransformation( src_srs, tgt_srs) for x,y in coords: x,y,z = transform.TransformPoint(x,y) trans_coords.append([x,y]) return trans_coords ext = GetExtent(gt,cols,rows) src_srs=osr.SpatialReference() src_srs.ImportFromWkt(gdal_dataSet.GetProjection()) if target_srs == False: return ext elif target_srs == 'wgs84': target_srs=osr.SpatialReference() target_srs.ImportFromEPSG(4326) return ReprojectCoords(ext,src_srs,target_srs) def resizeToDEM(imPath, sizeDEM = None, geoTransform = None, projection = None, noData = None): imDS = gdal.Open(imPath, gdal.GA_ReadOnly) imPix = imDS.RasterXSize imScn = imDS.RasterYSize nscn, npix = sizeDEM if sizeDEM is not None: if nscn != imScn or npix != imPix: print("Size Mismatch") image = imDS.ReadAsArray() if noData is not None: image = np.float32(image) image[image == noData] = np.nan imNew = cv2.resize(image, (npix, nscn), interpolation=cv2.INTER_CUBIC) writeNumpyArr2Geotiff(imPath, imNew, geoTransform = geoTransform, projection = projection, GDAL_dtype = gdal.GDT_UInt16, noDataValue = noData) def map_uint16_to_uint8(img, lower_bound=None, upper_bound=None): if not(0 <= lower_bound < 2**16) and lower_bound is not None: raise ValueError( '"lower_bound" must be in the range [0, 65535]') if not(0 <= upper_bound < 2**16) and upper_bound is not None: raise ValueError( '"upper_bound" must be in the range [0, 65535]') if lower_bound is None: lower_bound = np.min(img) if upper_bound is None: upper_bound = np.max(img) if lower_bound >= upper_bound: raise ValueError( '"lower_bound" must be smaller than "upper_bound"') lut = np.concatenate([ np.zeros(lower_bound, dtype=np.uint16), np.linspace(0, 255, upper_bound - lower_bound).astype(np.uint16), np.ones(2**16 - upper_bound, dtype=np.uint16) * 255 ]) return lut[img].astype(np.uint8) def closeCV(mask, kernelSize = 11): kernel = np.ones((kernelSize, kernelSize),np.uint8) return cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel) def newGeoTransform(geoTransform, maskBounds): newGeoTransform = (geoTransform[0]+ maskBounds['xMin'] * geoTransform[1], geoTransform[1], geoTransform[2], geoTransform[3] + maskBounds['yMin'] * geoTransform[5], geoTransform[4], geoTransform[5]) return newGeoTransform def shrinkGeoTransform(geoTransform, factor): newGeoTransform = (geoTransform[0], geoTransform[1] / factor, geoTransform[2], geoTransform[3], geoTransform[4], geoTransform[5] / factor) return newGeoTransform

true

f71b651c18866a5ae438540b4c87f225edab7b8a

21,388

py

Python

gym_let_mpc/let_mpc.py

eivindeb/gym-letMPC

7041aa56a25aa9a1c749088f2b370c910d21fe75

[ "MIT" ]

6

2020-12-04T18:15:29.000Z

2022-02-26T11:01:31.000Z

gym_let_mpc/let_mpc.py

eivindeb/gym-letMPC

7041aa56a25aa9a1c749088f2b370c910d21fe75

[ "MIT" ]

null

gym_let_mpc/let_mpc.py

eivindeb/gym-letMPC

7041aa56a25aa9a1c749088f2b370c910d21fe75

[ "MIT" ]

5

2021-03-08T06:00:27.000Z

2021-11-22T08:14:12.000Z

import gym from gym.utils import seeding import numpy as np import json from gym_let_mpc.simulator import ControlSystem from gym_let_mpc.controllers import ETMPC, AHMPC import collections.abc import matplotlib.pyplot as plt from gym_let_mpc.utils import str_replace_whole_words import copy class LetMPCEnv(gym.Env): def __init__(self, config_path): with open(config_path) as file_object: config = json.load(file_object) if config["mpc"]["model"] == "plant": config["mpc"]["model"] = copy.deepcopy(config["plant"]["model"]) elif config["mpc"]["model"].get("parameters", None) == "plant": config["mpc"]["model"]["parameters"] = copy.deepcopy(config["plant"]["model"]["parameters"]) if config["lqr"]["model"] == "plant": config["lqr"]["model"] = copy.deepcopy(config["plant"]["model"]) elif config["lqr"]["model"] == "mpc": config["lqr"]["model"] = copy.deepcopy(config["mpc"]["model"]) elif config["lqr"]["model"].get("parameters", None) == "plant": config["lqr"]["model"]["parameters"] = copy.deepcopy(config["plant"]["model"]["parameters"]) elif config["lqr"]["model"].get("parameters", None) == "mpc": config["lqr"]["model"]["parameters"] = copy.deepcopy(config["mpc"]["model"]["parameters"]) self.config = config assert "max_steps" in self.config["environment"] self.max_steps = self.config["environment"]["max_steps"] assert "randomize" in self.config["environment"] assert "state" in self.config["environment"]["randomize"] and "reference" in self.config["environment"]["randomize"] assert "render" in self.config["environment"] if config["mpc"]["type"] == "ETMPC": assert len(config["environment"]["action"]["variables"]) == 1 and \ config["environment"]["action"]["variables"][0]["name"] == "mpc_compute" controller = ETMPC(config["mpc"], config["lqr"]) self.action_space = gym.spaces.Discrete(2) elif config["mpc"]["type"] == "AHMPC": assert len(config["environment"]["action"]["variables"]) == 1 and \ config["environment"]["action"]["variables"][0]["name"] == "mpc_horizon" controller = AHMPC(config["mpc"]) self.action_space = gym.spaces.Box(low=np.array([1]), high=np.array([50]), dtype=np.float32) else: raise ValueError self.control_system = ControlSystem(config["plant"], controller=controller) self.history = None self.steps_count = None self.np_random = None self.min_constraint_delta = 0.25 # TODO: how and where to set obs_high = [] obs_low = [] for obs_var in self.config["environment"]["observation"]["variables"]: for var_transform in obs_var.get("transform", ["none"]): for lim_i, lim in enumerate(obs_var.get("limits", [None, None])): if lim is None: if lim_i == 0: obs_low.append(-np.finfo(np.float32).max) else: obs_high.append(np.finfo(np.float32).max) else: if var_transform == "none": if lim_i == 0: obs_low.append(lim) else: obs_high.append(lim) elif var_transform == "absolute": if lim_i == 0: obs_low.append(0) else: obs_high.append(lim) elif var_transform == "square": if lim_i == 0: obs_low.append(0) else: obs_high.append(lim ** 2) else: raise NotImplementedError self.observation_space = gym.spaces.Box(low=np.array(obs_low, dtype=np.float32), high=np.array(obs_high, dtype=np.float32), dtype=np.float32) self.value_function_is_set = False self.viewer = None def seed(self, seed=None): """ Seed the random number generator of the control system. :param seed: (int) seed for random state """ self.np_random, seed = gym.utils.seeding.np_random(seed) self.control_system.seed(seed) return [seed] def reset(self, state=None, reference=None, constraint=None, model=None, process_noise=None, tvp=None): """ Reset state of environment. Note that the simulator is reset, the MPC solution is computed and the first MPC action is applied to the plant. :param state: (dict) initial conditions (value) for state name (key). :param reference: (dict) reference value (value) for reference name (key). :param constraint: (dict) constraint values (value) for constraint names (key). :param model: (dict) dictionary of dictionary where first key is model that it applies to ["plant", "mpc", "lqr"], first value is dictionary of model parameters where second value is the specified model parameter value. :param process_noise: (dict) process noise values (value) as ndarray for state name (key). The process noise at each time step loops through the provided array. :param tvp: (dict) values of time-varying parameters. New values are generated if values arent specified for all time steps elapsed. :return: ([float]) observation vector """ def update_dict_recursively(d, u): for k, v in u.items(): if isinstance(v, collections.abc.Mapping): d[k] = update_dict_recursively(d.get(k, {}), v) else: d[k] = v return d sampled_state = self.sample_state() sampled_reference = self.sample_reference() sampled_constraint = self.sample_constraints() sampled_model = self.sample_model() if state is not None: sampled_state.update(state) elif len(sampled_state) == 0: sampled_state = None if reference is not None: sampled_reference.update(reference) elif len(sampled_reference) == 0: sampled_reference = None if constraint is not None: sampled_constraint.update(constraint) elif len(sampled_constraint) == 0: sampled_constraint = None if model is not None: sampled_model = update_dict_recursively(sampled_model, model) elif len(sampled_model) == 0: sampled_model = None self.control_system.reset(state=sampled_state, reference=sampled_reference, constraint=sampled_constraint, model=sampled_model, process_noise=process_noise, tvp=tvp) if self.config["mpc"]["type"] == "ETMPC": self.control_system.step(action=np.array([1])) obs = self.get_observation() self.history = {"obs": [obs], "actions": [], "rewards": []} self.steps_count = 0 return obs def step(self, action): a_dict = {a_props["name"]: action[a_i] for a_i, a_props in enumerate(self.config["environment"]["action"]["variables"])} self.control_system.step(np.round(a_dict["mpc_horizon"]).astype(np.int32))#np.atleast_1d(int(a_dict["mpc_compute"]))) self.history["actions"].append(a_dict) self.steps_count += 1 info = {} obs = self.get_observation() done = False if self.steps_count >= self.max_steps: done = True info["termination"] = "steps" elif len(self.config["environment"].get("end_on_constraint_violation", [])) > 0: for c_name, c_d in self.control_system.get_constraint_distances().items(): if c_name.split("-")[1] in self.config["environment"]["end_on_constraint_violation"] and c_d > 0: done = True info["termination"] = "constraint" break rew = self.get_reward(done=done) for category, v in self.config["environment"].get("info", {}).items(): if category == "reward": for rew_name, rew_expr in v.items(): info["reward/{}".format(rew_name)] = self.get_reward(rew_expr, done=done) else: raise NotImplementedError if self.value_function_is_set: step_vf_data = {"mpc_state": self.control_system.get_state_vector(self.control_system.history["state"][-2]), "mpc_next_state": self.control_system.controller.mpc_state_preds[:, -1, -1]} step_vf_data["mpc_n_horizon"] = self.control_system.controller.history["mpc_horizon"][-1] info["mpc_value_fn"] = (self.control_system.controller.value_function.eval([step_vf_data["mpc_next_state"].reshape(1, -1)])[0][0, 0]).astype(np.float64) step_vf_data["mpc_rewards"] = self.control_system.controller.mpc.opt_f_num.toarray()[0, 0] - \ self.config["mpc"]["objective"].get("discount_factor") ** (step_vf_data["mpc_n_horizon"] + 1) * info["mpc_value_fn"] info["mpc_computation_time"] = sum([v for k, v in self.control_system.controller.mpc.solver_stats.items() if k.startswith("t_proc")]) info["data"] = step_vf_data info["mpc_avg_stage_cost"] = step_vf_data["mpc_rewards"] / step_vf_data["mpc_n_horizon"] info.update({k: v.astype(np.float64) if hasattr(v, "dtype") else v for k, v in a_dict.items()}) self.history["obs"].append(obs) self.history["rewards"].append(rew) return obs, rew, done, info def render(self, mode='human', save_path=None): # TODO: add env renders figure, axes = None, None if self.viewer is None: env_plots = [plot_name for plot_name, make_plot in self.config["environment"]["render"].items() if make_plot] if len(env_plots) > 0: figure, axes = plt.subplots(self.control_system.render_n_axes + len(env_plots), sharex=True, figsize=(9, 16)) self.viewer = self.control_system.render(figure=figure, axes=axes, return_viewer=True) for i, plot in enumerate(env_plots): self.viewer["axes"][plot] = axes[-(i + 1)] else: self.viewer = self.control_system.render(figure=figure, axes=axes, return_viewer=True) for plot_name, make_plot in self.config["environment"]["render"].items(): if make_plot: self.viewer["axes"][plot_name].set_ylabel("-".join(plot_name.split("_")[1:])) x_data = np.array(range(self.steps_count)) * self.control_system.config["params"]["t_step"] self.viewer["axes"][plot_name].clear() if plot_name == "plot_action": for a_var in self.config["environment"]["action"]["variables"]: y_data = [step_a[a_var["name"]] for step_a in self.history["actions"]] self.viewer["axes"][plot_name].plot(x_data, y_data, label=a_var["name"], drawstyle="steps") elif plot_name == "plot_reward": self.viewer["axes"][plot_name].plot(x_data, self.history["rewards"], label="reward") self.viewer["axes"][plot_name].text(max(x_data) + self.control_system.config["params"]["t_step"], self.history["rewards"][-1], "{:.3f}".format(np.sum(self.history["rewards"]))) else: raise ValueError for axis in self.viewer["axes"].values(): axis.legend() if save_path is not None: self.viewer["figure"].savefig(save_path, bbox_inches="tight", format="png") plt.close(self.viewer["figure"]) else: self.viewer["figure"].show() def get_observation(self): obs = [] for var in self.config["environment"]["observation"]["variables"]: var_val = self._get_variable_value(var) for transform in var.get("transform", ["none"]): if transform == "none": obs.append(var_val) elif transform == "absolute": obs.append(abs(var_val)) elif transform == "square": obs.append(var_val ** 2) else: raise ValueError return np.array(obs) def get_reward(self, rew_expr=None, done=False): if rew_expr is None: rew_expr = self.config["environment"]["reward"]["expression"] rew_expr = str_replace_whole_words(rew_expr, "done", int(done)) for var in sorted(self.config["environment"]["reward"]["variables"], key=lambda x: len(x), reverse=True): var_val = self._get_variable_value(var) if isinstance(var_val, list) or isinstance(var_val, np.ndarray): # TODO: needs to be better way to do this var_val = var_val[0] rew_expr = str_replace_whole_words(rew_expr, var["name"], var_val) return eval(rew_expr) def _get_variable_value(self, var): if var["type"] == "state": val = self.control_system.current_state[var["name"]] elif var["type"] == "input": if var.get("value_type", "absolute") == "absolute": val = self.control_system.controller.current_input[var["name"]] elif var.get("value_type") == "delta": val = self.control_system.controller.history["inputs"][-2][var["name"]] - \ self.control_system.controller.current_input[var["name"]] else: raise ValueError elif var["type"] == "reference": val = self.control_system.controller.current_reference[var["name"]] elif var["type"] == "tvp": val = self.control_system.tvps[var["name"]].get_values(self.steps_count) elif var["type"] == "error": val = self.control_system.controller.history["errors"][-1][var["name"]] if np.isnan(val): val = 0 elif var["type"] == "epsilon": val = self.control_system.controller.history["epsilons"][-1][var["name"]] if np.isnan(val): val = 0 elif var["type"] == "constraint": if var.get("value_type") == "distance": val = self.control_system.get_constraint_distances((var["name"],))[var["name"]] else: raise ValueError elif var["type"] == "action": if var.get("value_type", "agent") == "agent": val = self.history["actions"][-1][var["name"]] elif var.get("value_type") == "controller": val = self.control_system.controller.history[var["name"]][-1] else: raise ValueError elif var["type"] == "time": if var.get("value_type") == "fraction": val = self.control_system.controller.steps_since_mpc_computation / self.control_system.controller.mpc.n_horizon elif var.get("value_type") == "absolute": val = self.control_system.controller.steps_since_mpc_computation else: raise ValueError elif var["type"] == "parameter": if var["value_type"] in ["plant", "mpc", "lqr"]: val = self.config[var["value_type"]]["model"]["parameters"][var["name"]] else: raise ValueError else: raise ValueError if isinstance(val, np.ndarray): val = val[0] if "limits" in var: val = np.clip(val, var["limits"][0], var["limits"][1]) return val def sample_constraints(self): constraints = {} for c_name, c_props in self.config["environment"].get("randomize", {}).get("constraints", {}).items(): constraint_val = getattr(self.np_random, c_props["type"])(**c_props["kw"]) if c_name.split("-")[1] in [k.split("-")[1] for k in constraints.keys()]: other_bound_type = "u" if c_name.split("-")[2] == "l" else "l" other_bound_val = constraints[c_name[:-1] + other_bound_type] if other_bound_type == "u": constraint_val = min(other_bound_val - self.min_constraint_delta, constraint_val) else: constraint_val = max(other_bound_val + self.min_constraint_delta, constraint_val) constraints[c_name] = constraint_val return constraints def sample_state(self): state = {} for s_name, s_props in self.config["environment"].get("randomize", {}).get("state", {}).items(): state[s_name] = getattr(self.np_random, s_props["type"])(**s_props["kw"]) return state def sample_reference(self): reference = {} for r_name, r_props in self.config["environment"].get("randomize", {}).get("reference", {}).items(): reference[r_name] = getattr(self.np_random, r_props["type"])(**r_props["kw"]) return reference def sample_model(self): model = {} for s_name, s_props in self.config["environment"].get("randomize", {}).get("model", {}).get("states", {}).items(): model["states"] = {s_name: {}} for component_name, component_props in s_props.items(): model["states"][s_name][component_name] = \ {comp_v_name: getattr(self.np_random, v_prop["type"])(**v_prop["kw"]) for comp_v_name, v_prop in component_props.items()} model = {dest: model for dest in self.config["environment"].get("randomize", {}).get("model", {}).get("apply", [])} return model def stop(self): pass def create_dataset(self, n_scenarios): dataset = [] self.reset() for i in range(n_scenarios): process_noise = np.array([self.control_system._get_process_noise() for i in range(self.max_steps)]) ep_dict = {"state": self.sample_state(), "reference": self.sample_reference(), "constraint": self.sample_constraints(), "model": self.sample_model(), "process_noise": {}, "tvp": {}} s_i = 0 for s_name, s_props in self.config["plant"]["model"]["states"].items(): if "W" in s_props: ep_dict["process_noise"][s_name] = process_noise[:, s_i] s_i += 1 for tvp_name, tvp_obj in self.control_system.tvps.items(): tvp_obj.generate_values(self.max_steps) ep_dict["tvp"][tvp_name] = tvp_obj.values dataset.append(ep_dict) self.reset() return dataset def set_value_function(self, input_ph, output_ph, tf_session): self.control_system.controller.set_value_function(input_ph, output_ph, tf_session) self.value_function_is_set = True def set_learning_status(self, status): if self.value_function_is_set: self.control_system.controller.value_function.set_enabled(status) if __name__ == "__main__": # TODO: constraints on pendulum and end episode if constraints violated env = LetMPCEnv("configs/cart_pendulum_horizon.json") env.seed(0) """ from tensorflow_casadi import TensorFlowEvaluator, MLP import tensorflow as tf a = tf.placeholder(shape=(None, 4), dtype=tf.float32) mlp = MLP(a) sess = tf.Session() val_fun = TensorFlowEvaluator([mlp.input_ph], [mlp.output], sess) env.set_value_function(mlp.input_ph, mlp.output, sess) """ import pickle with open("../../lmpc-horizon/datasets/cart_pendulum_10.pkl", "rb") as f: test_set = pickle.load(f) rews = {} for i in range(1): import time obs = env.reset(**test_set[5]) done = False t_before = time.process_time() horizon = 10 while not done: t_step = time.process_time() if env.steps_count % 1 == 0 and False: horizon = 25 if horizon == 50 else 50 obs, rew, done, info = env.step([horizon])#[np.random.randint(1, 10)]) for rew_comp, v in info.items(): if rew_comp.startswith("reward/"): if rew_comp not in rews: rews[rew_comp] = [] rews[rew_comp].append(v) if time.process_time() - t_step > 1: print(env.control_system.controller.mpc.solver_stats) print(env.steps_count) for k, v in rews.items(): print("{}: {}".format(k, sum(v))) print("Elapsed time {}".format(time.process_time() - t_before)) env.render()

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import gym from gym.utils import seeding import numpy as np import json from gym_let_mpc.simulator import ControlSystem from gym_let_mpc.controllers import ETMPC, AHMPC import collections.abc import matplotlib.pyplot as plt from gym_let_mpc.utils import str_replace_whole_words import copy class LetMPCEnv(gym.Env): def __init__(self, config_path): with open(config_path) as file_object: config = json.load(file_object) if config["mpc"]["model"] == "plant": config["mpc"]["model"] = copy.deepcopy(config["plant"]["model"]) elif config["mpc"]["model"].get("parameters", None) == "plant": config["mpc"]["model"]["parameters"] = copy.deepcopy(config["plant"]["model"]["parameters"]) if config["lqr"]["model"] == "plant": config["lqr"]["model"] = copy.deepcopy(config["plant"]["model"]) elif config["lqr"]["model"] == "mpc": config["lqr"]["model"] = copy.deepcopy(config["mpc"]["model"]) elif config["lqr"]["model"].get("parameters", None) == "plant": config["lqr"]["model"]["parameters"] = copy.deepcopy(config["plant"]["model"]["parameters"]) elif config["lqr"]["model"].get("parameters", None) == "mpc": config["lqr"]["model"]["parameters"] = copy.deepcopy(config["mpc"]["model"]["parameters"]) self.config = config assert "max_steps" in self.config["environment"] self.max_steps = self.config["environment"]["max_steps"] assert "randomize" in self.config["environment"] assert "state" in self.config["environment"]["randomize"] and "reference" in self.config["environment"]["randomize"] assert "render" in self.config["environment"] if config["mpc"]["type"] == "ETMPC": assert len(config["environment"]["action"]["variables"]) == 1 and \ config["environment"]["action"]["variables"][0]["name"] == "mpc_compute" controller = ETMPC(config["mpc"], config["lqr"]) self.action_space = gym.spaces.Discrete(2) elif config["mpc"]["type"] == "AHMPC": assert len(config["environment"]["action"]["variables"]) == 1 and \ config["environment"]["action"]["variables"][0]["name"] == "mpc_horizon" controller = AHMPC(config["mpc"]) self.action_space = gym.spaces.Box(low=np.array([1]), high=np.array([50]), dtype=np.float32) else: raise ValueError self.control_system = ControlSystem(config["plant"], controller=controller) self.history = None self.steps_count = None self.np_random = None self.min_constraint_delta = 0.25 obs_high = [] obs_low = [] for obs_var in self.config["environment"]["observation"]["variables"]: for var_transform in obs_var.get("transform", ["none"]): for lim_i, lim in enumerate(obs_var.get("limits", [None, None])): if lim is None: if lim_i == 0: obs_low.append(-np.finfo(np.float32).max) else: obs_high.append(np.finfo(np.float32).max) else: if var_transform == "none": if lim_i == 0: obs_low.append(lim) else: obs_high.append(lim) elif var_transform == "absolute": if lim_i == 0: obs_low.append(0) else: obs_high.append(lim) elif var_transform == "square": if lim_i == 0: obs_low.append(0) else: obs_high.append(lim ** 2) else: raise NotImplementedError self.observation_space = gym.spaces.Box(low=np.array(obs_low, dtype=np.float32), high=np.array(obs_high, dtype=np.float32), dtype=np.float32) self.value_function_is_set = False self.viewer = None def seed(self, seed=None): self.np_random, seed = gym.utils.seeding.np_random(seed) self.control_system.seed(seed) return [seed] def reset(self, state=None, reference=None, constraint=None, model=None, process_noise=None, tvp=None): def update_dict_recursively(d, u): for k, v in u.items(): if isinstance(v, collections.abc.Mapping): d[k] = update_dict_recursively(d.get(k, {}), v) else: d[k] = v return d sampled_state = self.sample_state() sampled_reference = self.sample_reference() sampled_constraint = self.sample_constraints() sampled_model = self.sample_model() if state is not None: sampled_state.update(state) elif len(sampled_state) == 0: sampled_state = None if reference is not None: sampled_reference.update(reference) elif len(sampled_reference) == 0: sampled_reference = None if constraint is not None: sampled_constraint.update(constraint) elif len(sampled_constraint) == 0: sampled_constraint = None if model is not None: sampled_model = update_dict_recursively(sampled_model, model) elif len(sampled_model) == 0: sampled_model = None self.control_system.reset(state=sampled_state, reference=sampled_reference, constraint=sampled_constraint, model=sampled_model, process_noise=process_noise, tvp=tvp) if self.config["mpc"]["type"] == "ETMPC": self.control_system.step(action=np.array([1])) obs = self.get_observation() self.history = {"obs": [obs], "actions": [], "rewards": []} self.steps_count = 0 return obs def step(self, action): a_dict = {a_props["name"]: action[a_i] for a_i, a_props in enumerate(self.config["environment"]["action"]["variables"])} self.control_system.step(np.round(a_dict["mpc_horizon"]).astype(np.int32)) self.history["actions"].append(a_dict) self.steps_count += 1 info = {} obs = self.get_observation() done = False if self.steps_count >= self.max_steps: done = True info["termination"] = "steps" elif len(self.config["environment"].get("end_on_constraint_violation", [])) > 0: for c_name, c_d in self.control_system.get_constraint_distances().items(): if c_name.split("-")[1] in self.config["environment"]["end_on_constraint_violation"] and c_d > 0: done = True info["termination"] = "constraint" break rew = self.get_reward(done=done) for category, v in self.config["environment"].get("info", {}).items(): if category == "reward": for rew_name, rew_expr in v.items(): info["reward/{}".format(rew_name)] = self.get_reward(rew_expr, done=done) else: raise NotImplementedError if self.value_function_is_set: step_vf_data = {"mpc_state": self.control_system.get_state_vector(self.control_system.history["state"][-2]), "mpc_next_state": self.control_system.controller.mpc_state_preds[:, -1, -1]} step_vf_data["mpc_n_horizon"] = self.control_system.controller.history["mpc_horizon"][-1] info["mpc_value_fn"] = (self.control_system.controller.value_function.eval([step_vf_data["mpc_next_state"].reshape(1, -1)])[0][0, 0]).astype(np.float64) step_vf_data["mpc_rewards"] = self.control_system.controller.mpc.opt_f_num.toarray()[0, 0] - \ self.config["mpc"]["objective"].get("discount_factor") ** (step_vf_data["mpc_n_horizon"] + 1) * info["mpc_value_fn"] info["mpc_computation_time"] = sum([v for k, v in self.control_system.controller.mpc.solver_stats.items() if k.startswith("t_proc")]) info["data"] = step_vf_data info["mpc_avg_stage_cost"] = step_vf_data["mpc_rewards"] / step_vf_data["mpc_n_horizon"] info.update({k: v.astype(np.float64) if hasattr(v, "dtype") else v for k, v in a_dict.items()}) self.history["obs"].append(obs) self.history["rewards"].append(rew) return obs, rew, done, info def render(self, mode='human', save_path=None): figure, axes = None, None if self.viewer is None: env_plots = [plot_name for plot_name, make_plot in self.config["environment"]["render"].items() if make_plot] if len(env_plots) > 0: figure, axes = plt.subplots(self.control_system.render_n_axes + len(env_plots), sharex=True, figsize=(9, 16)) self.viewer = self.control_system.render(figure=figure, axes=axes, return_viewer=True) for i, plot in enumerate(env_plots): self.viewer["axes"][plot] = axes[-(i + 1)] else: self.viewer = self.control_system.render(figure=figure, axes=axes, return_viewer=True) for plot_name, make_plot in self.config["environment"]["render"].items(): if make_plot: self.viewer["axes"][plot_name].set_ylabel("-".join(plot_name.split("_")[1:])) x_data = np.array(range(self.steps_count)) * self.control_system.config["params"]["t_step"] self.viewer["axes"][plot_name].clear() if plot_name == "plot_action": for a_var in self.config["environment"]["action"]["variables"]: y_data = [step_a[a_var["name"]] for step_a in self.history["actions"]] self.viewer["axes"][plot_name].plot(x_data, y_data, label=a_var["name"], drawstyle="steps") elif plot_name == "plot_reward": self.viewer["axes"][plot_name].plot(x_data, self.history["rewards"], label="reward") self.viewer["axes"][plot_name].text(max(x_data) + self.control_system.config["params"]["t_step"], self.history["rewards"][-1], "{:.3f}".format(np.sum(self.history["rewards"]))) else: raise ValueError for axis in self.viewer["axes"].values(): axis.legend() if save_path is not None: self.viewer["figure"].savefig(save_path, bbox_inches="tight", format="png") plt.close(self.viewer["figure"]) else: self.viewer["figure"].show() def get_observation(self): obs = [] for var in self.config["environment"]["observation"]["variables"]: var_val = self._get_variable_value(var) for transform in var.get("transform", ["none"]): if transform == "none": obs.append(var_val) elif transform == "absolute": obs.append(abs(var_val)) elif transform == "square": obs.append(var_val ** 2) else: raise ValueError return np.array(obs) def get_reward(self, rew_expr=None, done=False): if rew_expr is None: rew_expr = self.config["environment"]["reward"]["expression"] rew_expr = str_replace_whole_words(rew_expr, "done", int(done)) for var in sorted(self.config["environment"]["reward"]["variables"], key=lambda x: len(x), reverse=True): var_val = self._get_variable_value(var) if isinstance(var_val, list) or isinstance(var_val, np.ndarray): var_val = var_val[0] rew_expr = str_replace_whole_words(rew_expr, var["name"], var_val) return eval(rew_expr) def _get_variable_value(self, var): if var["type"] == "state": val = self.control_system.current_state[var["name"]] elif var["type"] == "input": if var.get("value_type", "absolute") == "absolute": val = self.control_system.controller.current_input[var["name"]] elif var.get("value_type") == "delta": val = self.control_system.controller.history["inputs"][-2][var["name"]] - \ self.control_system.controller.current_input[var["name"]] else: raise ValueError elif var["type"] == "reference": val = self.control_system.controller.current_reference[var["name"]] elif var["type"] == "tvp": val = self.control_system.tvps[var["name"]].get_values(self.steps_count) elif var["type"] == "error": val = self.control_system.controller.history["errors"][-1][var["name"]] if np.isnan(val): val = 0 elif var["type"] == "epsilon": val = self.control_system.controller.history["epsilons"][-1][var["name"]] if np.isnan(val): val = 0 elif var["type"] == "constraint": if var.get("value_type") == "distance": val = self.control_system.get_constraint_distances((var["name"],))[var["name"]] else: raise ValueError elif var["type"] == "action": if var.get("value_type", "agent") == "agent": val = self.history["actions"][-1][var["name"]] elif var.get("value_type") == "controller": val = self.control_system.controller.history[var["name"]][-1] else: raise ValueError elif var["type"] == "time": if var.get("value_type") == "fraction": val = self.control_system.controller.steps_since_mpc_computation / self.control_system.controller.mpc.n_horizon elif var.get("value_type") == "absolute": val = self.control_system.controller.steps_since_mpc_computation else: raise ValueError elif var["type"] == "parameter": if var["value_type"] in ["plant", "mpc", "lqr"]: val = self.config[var["value_type"]]["model"]["parameters"][var["name"]] else: raise ValueError else: raise ValueError if isinstance(val, np.ndarray): val = val[0] if "limits" in var: val = np.clip(val, var["limits"][0], var["limits"][1]) return val def sample_constraints(self): constraints = {} for c_name, c_props in self.config["environment"].get("randomize", {}).get("constraints", {}).items(): constraint_val = getattr(self.np_random, c_props["type"])(**c_props["kw"]) if c_name.split("-")[1] in [k.split("-")[1] for k in constraints.keys()]: other_bound_type = "u" if c_name.split("-")[2] == "l" else "l" other_bound_val = constraints[c_name[:-1] + other_bound_type] if other_bound_type == "u": constraint_val = min(other_bound_val - self.min_constraint_delta, constraint_val) else: constraint_val = max(other_bound_val + self.min_constraint_delta, constraint_val) constraints[c_name] = constraint_val return constraints def sample_state(self): state = {} for s_name, s_props in self.config["environment"].get("randomize", {}).get("state", {}).items(): state[s_name] = getattr(self.np_random, s_props["type"])(**s_props["kw"]) return state def sample_reference(self): reference = {} for r_name, r_props in self.config["environment"].get("randomize", {}).get("reference", {}).items(): reference[r_name] = getattr(self.np_random, r_props["type"])(**r_props["kw"]) return reference def sample_model(self): model = {} for s_name, s_props in self.config["environment"].get("randomize", {}).get("model", {}).get("states", {}).items(): model["states"] = {s_name: {}} for component_name, component_props in s_props.items(): model["states"][s_name][component_name] = \ {comp_v_name: getattr(self.np_random, v_prop["type"])(**v_prop["kw"]) for comp_v_name, v_prop in component_props.items()} model = {dest: model for dest in self.config["environment"].get("randomize", {}).get("model", {}).get("apply", [])} return model def stop(self): pass def create_dataset(self, n_scenarios): dataset = [] self.reset() for i in range(n_scenarios): process_noise = np.array([self.control_system._get_process_noise() for i in range(self.max_steps)]) ep_dict = {"state": self.sample_state(), "reference": self.sample_reference(), "constraint": self.sample_constraints(), "model": self.sample_model(), "process_noise": {}, "tvp": {}} s_i = 0 for s_name, s_props in self.config["plant"]["model"]["states"].items(): if "W" in s_props: ep_dict["process_noise"][s_name] = process_noise[:, s_i] s_i += 1 for tvp_name, tvp_obj in self.control_system.tvps.items(): tvp_obj.generate_values(self.max_steps) ep_dict["tvp"][tvp_name] = tvp_obj.values dataset.append(ep_dict) self.reset() return dataset def set_value_function(self, input_ph, output_ph, tf_session): self.control_system.controller.set_value_function(input_ph, output_ph, tf_session) self.value_function_is_set = True def set_learning_status(self, status): if self.value_function_is_set: self.control_system.controller.value_function.set_enabled(status) if __name__ == "__main__": env = LetMPCEnv("configs/cart_pendulum_horizon.json") env.seed(0) import pickle with open("../../lmpc-horizon/datasets/cart_pendulum_10.pkl", "rb") as f: test_set = pickle.load(f) rews = {} for i in range(1): import time obs = env.reset(**test_set[5]) done = False t_before = time.process_time() horizon = 10 while not done: t_step = time.process_time() if env.steps_count % 1 == 0 and False: horizon = 25 if horizon == 50 else 50 obs, rew, done, info = env.step([horizon]) for rew_comp, v in info.items(): if rew_comp.startswith("reward/"): if rew_comp not in rews: rews[rew_comp] = [] rews[rew_comp].append(v) if time.process_time() - t_step > 1: print(env.control_system.controller.mpc.solver_stats) print(env.steps_count) for k, v in rews.items(): print("{}: {}".format(k, sum(v))) print("Elapsed time {}".format(time.process_time() - t_before)) env.render()

true

f71b65b3b003148f57d2ed310d5f76f0d067c474

933

py

Python

violas_client/canoser/bool_t.py

violas-core/violas-client

e8798f7d081ac218b78b81fd7eb2f8da92631a16

[ "MIT" ]

null

violas_client/canoser/bool_t.py

violas-core/violas-client

e8798f7d081ac218b78b81fd7eb2f8da92631a16

[ "MIT" ]

null

violas_client/canoser/bool_t.py

violas-core/violas-client

e8798f7d081ac218b78b81fd7eb2f8da92631a16

[ "MIT" ]

1

2022-01-05T06:49:42.000Z

from violas_client.canoser.base import Base class BoolT(Base): @classmethod def encode(self, value): if value: return b'\1' else: return b'\0' @classmethod def decode_bytes(self, value): if value == b'\0': return False elif value == b'\1': return True else: raise TypeError("bool should be 0 or 1.") @classmethod def decode(self, cursor): value = cursor.read_bytes(1) return self.decode_bytes(value) @classmethod def from_value(cls, value): if value: return True return False @classmethod def check_value(self, value): if not isinstance(value, bool): raise TypeError('value {} is not bool'.format(value)) @classmethod def to_json_serializable(cls, value): return value

23.923077

66

0.543408

from violas_client.canoser.base import Base class BoolT(Base): @classmethod def encode(self, value): if value: return b'\1' else: return b'\0' @classmethod def decode_bytes(self, value): if value == b'\0': return False elif value == b'\1': return True else: raise TypeError("bool should be 0 or 1.") @classmethod def decode(self, cursor): value = cursor.read_bytes(1) return self.decode_bytes(value) @classmethod def from_value(cls, value): if value: return True return False @classmethod def check_value(self, value): if not isinstance(value, bool): raise TypeError('value {} is not bool'.format(value)) @classmethod def to_json_serializable(cls, value): return value

true

f71b6618acab7a74ff8f4e811e451717d08dc511

1,097

py

Python

4.conditionals/challenge3_rouillonh.py

rouillonh/ChallengePython

7e7d9b69f60394fd1f00a6a4aa32f97de95b1b92

[ "MIT" ]

null

4.conditionals/challenge3_rouillonh.py

rouillonh/ChallengePython

7e7d9b69f60394fd1f00a6a4aa32f97de95b1b92

[ "MIT" ]

null

4.conditionals/challenge3_rouillonh.py

rouillonh/ChallengePython

7e7d9b69f60394fd1f00a6a4aa32f97de95b1b92

[ "MIT" ]

null

print("\tWelcome to the Voter Registration App") #Pedimos nombre y edad para asi registrar su voto name = input("\nPlease enter your name: ").title() age = int(input("Please enter your age: ")) partidos = ['Republican','Democratic','Independent','Libertarian','Green'] #Si es mayor de edad, podrá votar if age >= 18: #Dependiendo del partido que escoja, se imprimirá un mensaje print("\nCongratulations ",name,"! You are old enough to register to vote.") print("\nHere is a list of political parties to join.") for i in partidos: print("-",i) p = input("\nWhat party would you like to join: ").capitalize() if p in 'Republican,Democratic': print("Congratulations ",name,"! You have joined the ",p," party!") print("That is a major party!") elif p == 'Independent': print("Congratulations ",name,"! You have joined the ",p," party!") print("You are an independent person!") else: print("That is not a major party") #Si no lo es, no podrá hacerlo elif age < 18: print("You are not old enough to register to vote.")

43.88

80

0.65907

print("\tWelcome to the Voter Registration App") name = input("\nPlease enter your name: ").title() age = int(input("Please enter your age: ")) partidos = ['Republican','Democratic','Independent','Libertarian','Green'] if age >= 18: print("\nCongratulations ",name,"! You are old enough to register to vote.") print("\nHere is a list of political parties to join.") for i in partidos: print("-",i) p = input("\nWhat party would you like to join: ").capitalize() if p in 'Republican,Democratic': print("Congratulations ",name,"! You have joined the ",p," party!") print("That is a major party!") elif p == 'Independent': print("Congratulations ",name,"! You have joined the ",p," party!") print("You are an independent person!") else: print("That is not a major party") elif age < 18: print("You are not old enough to register to vote.")

true

f71b6745cb39d3ccd6a45e1c0ecd693cdffb6acf

2,559

py

Python

etc/mtrace/parse_mtrace.py

diamantopoulos/memluv

f3a283d65f07b19d48589e02ac484563e12e22e8

[ "Apache-2.0" ]

9

2015-12-16T08:05:06.000Z

2022-02-25T08:29:30.000Z

etc/mtrace/parse_mtrace.py

diamantopoulos/memluv

f3a283d65f07b19d48589e02ac484563e12e22e8

[ "Apache-2.0" ]

1

2022-02-26T07:40:23.000Z

2022-03-15T03:27:59.000Z

etc/mtrace/parse_mtrace.py

diamantopoulos/memluv

f3a283d65f07b19d48589e02ac484563e12e22e8

[ "Apache-2.0" ]

null

""" Parsing a mtrace log file and append to timeline-footprint format """ from numpy import * import numpy as np import glob import os import linecache import csv # 1038 bytes is the size of a heap log file with no heap activity (only heap info) def ValidHeapFile(fpath): header_lines=1 with open(fpath) as f: lines = len(list(f)) return True if os.path.isfile(fpath) and lines > header_lines else False print ("INFO: --------------------- \nINFO: Parsing mtrace logs \nINFO: ---------------------") mtrace_files = glob.glob("/tmp/mtrace*.txt") mtraces=len(mtrace_files) print ("INFO: Total mtrace logs found:", mtraces) colours=['b','g','r','c','m','y','k'] elapsed_time=208000 #with plt.xkcd(): total_bytes_allocated=0 index=0 fout = open("/tmp/mtrace.out",'w') lines_parsed=0 event_time=0 #Heaps log parsing for cur_mtrace in sorted(mtrace_files): if ValidHeapFile(cur_mtrace): fin = open(cur_mtrace,'r') total_lines = len(fin.readlines()) tic=elapsed_time/(total_lines-3) print ("total_lines = ", total_lines, "tic = ", tic) fin.close() fin = open(cur_mtrace,'r') for line in fin: line = line.rstrip().split(' ') #print ("length(line) = ", len(line), "index=", index) if lines_parsed>=2 and lines_parsed<total_lines-1: sign = line[2] if sign == '+': cur_bytes = line[4] cur_bytes_dec = int(cur_bytes, 16) total_bytes_allocated = total_bytes_allocated + cur_bytes_dec #print ("INFO: Adding ", cur_bytes_dec, "bytes", "total_bytes_allocated=", total_bytes_allocated) elif sign == '-': total_bytes_allocated = total_bytes_allocated - cur_bytes_dec #print ("INFO: Subtracting ", cur_bytes_dec, "bytes", "total_bytes_allocated=", total_bytes_allocated) else: print ("ERROR: Unknown sign", sign, "Aborting...") __exit__ event_time=event_time+tic fout.write(str(index)+" "+str(event_time)+" "+str(total_bytes_allocated)+"\n") index=index+1 else: print ("WARNING: Ignoring this line", line) lines_parsed=lines_parsed+1 else: print ("INFO: Current mtrace path :", cur_mtrace, "-> Skipping empty file") fin.close() fout.close()

34.581081

126

0.569754

from numpy import * import numpy as np import glob import os import linecache import csv def ValidHeapFile(fpath): header_lines=1 with open(fpath) as f: lines = len(list(f)) return True if os.path.isfile(fpath) and lines > header_lines else False print ("INFO: --------------------- \nINFO: Parsing mtrace logs \nINFO: ---------------------") mtrace_files = glob.glob("/tmp/mtrace*.txt") mtraces=len(mtrace_files) print ("INFO: Total mtrace logs found:", mtraces) colours=['b','g','r','c','m','y','k'] elapsed_time=208000 total_bytes_allocated=0 index=0 fout = open("/tmp/mtrace.out",'w') lines_parsed=0 event_time=0 for cur_mtrace in sorted(mtrace_files): if ValidHeapFile(cur_mtrace): fin = open(cur_mtrace,'r') total_lines = len(fin.readlines()) tic=elapsed_time/(total_lines-3) print ("total_lines = ", total_lines, "tic = ", tic) fin.close() fin = open(cur_mtrace,'r') for line in fin: line = line.rstrip().split(' ') if lines_parsed>=2 and lines_parsed<total_lines-1: sign = line[2] if sign == '+': cur_bytes = line[4] cur_bytes_dec = int(cur_bytes, 16) total_bytes_allocated = total_bytes_allocated + cur_bytes_dec elif sign == '-': total_bytes_allocated = total_bytes_allocated - cur_bytes_dec else: print ("ERROR: Unknown sign", sign, "Aborting...") __exit__ event_time=event_time+tic fout.write(str(index)+" "+str(event_time)+" "+str(total_bytes_allocated)+"\n") index=index+1 else: print ("WARNING: Ignoring this line", line) lines_parsed=lines_parsed+1 else: print ("INFO: Current mtrace path :", cur_mtrace, "-> Skipping empty file") fin.close() fout.close()

true

f71b675d58f0489d8b6561c581bfe700396f87fb

965

py

Python

python/day12-2.py

Aerdan/adventcode-2020

83120aa8c7fc9d1f2d34780610401e3c6d4f583b

[ "BSD-1-Clause" ]

null

python/day12-2.py

Aerdan/adventcode-2020

83120aa8c7fc9d1f2d34780610401e3c6d4f583b

[ "BSD-1-Clause" ]

null

python/day12-2.py

Aerdan/adventcode-2020

83120aa8c7fc9d1f2d34780610401e3c6d4f583b

[ "BSD-1-Clause" ]

null

#!/usr/bin/env python3 from math import sin, cos, radians data = [] with open('input12.txt') as f: for line in f: data.append(line.strip()) x, y = 10, 1 sx, sy = 0, 0 d = 'E' c = 'NESW' for line in data: insn = line[0] dist = int(line[1:]) if insn == 'F': # move to waypoint dist times for i in range(dist): sx += x sy += y elif insn == 'N': y += dist elif insn == 'E': x += dist elif insn == 'S': y -= dist elif insn == 'W': x -= dist elif insn == 'L': dist = radians(dist) nx = x * cos(dist) - y * sin(dist) ny = y * cos(dist) + x * sin(dist) x = round(nx) y = round(ny) elif insn == 'R': dist = radians(360 - dist) nx = x * cos(dist) - y * sin(dist) ny = y * cos(dist) + x * sin(dist) x = round(nx) y = round(ny) md = abs(sx) + abs(sy) print(sx, sy, md)

19.693878

42

0.449741

from math import sin, cos, radians data = [] with open('input12.txt') as f: for line in f: data.append(line.strip()) x, y = 10, 1 sx, sy = 0, 0 d = 'E' c = 'NESW' for line in data: insn = line[0] dist = int(line[1:]) if insn == 'F': for i in range(dist): sx += x sy += y elif insn == 'N': y += dist elif insn == 'E': x += dist elif insn == 'S': y -= dist elif insn == 'W': x -= dist elif insn == 'L': dist = radians(dist) nx = x * cos(dist) - y * sin(dist) ny = y * cos(dist) + x * sin(dist) x = round(nx) y = round(ny) elif insn == 'R': dist = radians(360 - dist) nx = x * cos(dist) - y * sin(dist) ny = y * cos(dist) + x * sin(dist) x = round(nx) y = round(ny) md = abs(sx) + abs(sy) print(sx, sy, md)

true

f71b693c8f73a9ec5102fb39ced2b8f6a4ea8b4b

511

py

Python

tcfcli/cmds/local/libs/local/debug_context.py

tencentyun/scfcli

ef15508ad34a851cf0d2750dfaa5202f6a600887

[ "Apache-2.0" ]

103

2019-06-11T06:09:56.000Z

2021-12-18T22:48:59.000Z

tcfcli/cmds/local/libs/local/debug_context.py

TencentCloud/Serverless-cli

57f98b24cfd10712770a4806212cfb69d981a11a

[ "Apache-2.0" ]

8

2019-07-12T12:08:40.000Z

2020-10-20T07:18:17.000Z

tcfcli/cmds/local/libs/local/debug_context.py

TencentCloud/Serverless-cli

57f98b24cfd10712770a4806212cfb69d981a11a

[ "Apache-2.0" ]

49

2019-06-11T06:26:05.000Z

2020-02-19T08:13:36.000Z

# -*- coding: utf-8 -*- import os class DebugContext(object): def __init__(self, debug_port=None, debugger_path=None, debug_args=None): self.debug_port = debug_port self.debugger_path = debugger_path self.debug_args = debug_args if self.debug_port: os.environ["PYTHONUNBUFFERED"] = "1" def __bool__(self): return bool(self.debug_port) def __nonzero__(self): return self.__bool__()

21.291667

48

0.579256

import os class DebugContext(object): def __init__(self, debug_port=None, debugger_path=None, debug_args=None): self.debug_port = debug_port self.debugger_path = debugger_path self.debug_args = debug_args if self.debug_port: os.environ["PYTHONUNBUFFERED"] = "1" def __bool__(self): return bool(self.debug_port) def __nonzero__(self): return self.__bool__()

true

f71b6aed9afed4cf56533fb2127e350f2b0dc11b

289

py

Python

tests/integration/test_notes.py

mhk001/python-alerta-client

6e02f8a2245cef223df3048d445921e1ba90ad1c

[ "Apache-2.0" ]

20

2017-04-14T08:05:48.000Z

2022-01-11T06:26:17.000Z

tests/integration/test_notes.py

mhk001/python-alerta-client

6e02f8a2245cef223df3048d445921e1ba90ad1c

[ "Apache-2.0" ]

99

2016-09-30T20:53:05.000Z

2022-03-14T10:00:59.000Z

tests/integration/test_notes.py

mhk001/python-alerta-client

6e02f8a2245cef223df3048d445921e1ba90ad1c

[ "Apache-2.0" ]

33

2016-10-04T20:44:58.000Z

2022-03-04T21:35:49.000Z

import unittest from alertaclient.api import Client class AlertTestCase(unittest.TestCase): def setUp(self): self.client = Client(endpoint='http://api:8080', key='demo-key') def test_notes(self): # add tests here when /notes endpoints are created pass

20.642857

72

0.681661

import unittest from alertaclient.api import Client class AlertTestCase(unittest.TestCase): def setUp(self): self.client = Client(endpoint='http://api:8080', key='demo-key') def test_notes(self): pass

true

f71b6b5b67e80a03f5062113889382389fc8dc72

29,281

py

Python

resource/pypi/cryptography-1.7.1/tests/hazmat/primitives/fixtures_rsa.py

hipnusleo/Laserjet

f53e0b740f48f2feb0c0bb285ec6728b313b4ccc

[ "Apache-2.0" ]

null

resource/pypi/cryptography-1.7.1/tests/hazmat/primitives/fixtures_rsa.py

hipnusleo/Laserjet

f53e0b740f48f2feb0c0bb285ec6728b313b4ccc

[ "Apache-2.0" ]

null

resource/pypi/cryptography-1.7.1/tests/hazmat/primitives/fixtures_rsa.py

hipnusleo/Laserjet

f53e0b740f48f2feb0c0bb285ec6728b313b4ccc

[ "Apache-2.0" ]

null

# This file is dual licensed under the terms of the Apache License, Version # 2.0, and the BSD License. See the LICENSE file in the root of this repository # for complete details. from __future__ import absolute_import, division, print_function from cryptography.hazmat.primitives.asymmetric.rsa import ( RSAPrivateNumbers, RSAPublicNumbers ) RSA_KEY_512 = RSAPrivateNumbers( p=int( "d57846898d5c0de249c08467586cb458fa9bc417cdf297f73cfc52281b787cd9", 16 ), q=int( "d10f71229e87e010eb363db6a85fd07df72d985b73c42786191f2ce9134afb2d", 16 ), d=int( "272869352cacf9c866c4e107acc95d4c608ca91460a93d28588d51cfccc07f449" "18bbe7660f9f16adc2b4ed36ca310ef3d63b79bd447456e3505736a45a6ed21", 16 ), dmp1=int( "addff2ec7564c6b64bc670d250b6f24b0b8db6b2810099813b7e7658cecf5c39", 16 ), dmq1=int( "463ae9c6b77aedcac1397781e50e4afc060d4b216dc2778494ebe42a6850c81", 16 ), iqmp=int( "54deef8548f65cad1d411527a32dcb8e712d3e128e4e0ff118663fae82a758f4", 16 ), public_numbers=RSAPublicNumbers( e=65537, n=int( "ae5411f963c50e3267fafcf76381c8b1e5f7b741fdb2a544bcf48bd607b10c991" "90caeb8011dc22cf83d921da55ec32bd05cac3ee02ca5e1dbef93952850b525", 16 ), ) ) RSA_KEY_512_ALT = RSAPrivateNumbers( p=int( "febe19c29a0b50fefa4f7b1832f84df1caf9be8242da25c9d689e18226e67ce5", 16), q=int( "eb616c639dd999feda26517e1c77b6878f363fe828c4e6670ec1787f28b1e731", 16), d=int( "80edecfde704a806445a4cc782b85d3f36f17558f385654ea767f006470fdfcbda5e2" "206839289d3f419b4e4fb8e1acee1b4fb9c591f69b64ec83937f5829241", 16), dmp1=int( "7f4fa06e2a3077a54691cc5216bf13ad40a4b9fa3dd0ea4bca259487484baea5", 16), dmq1=int( "35eaa70d5a8711c352ed1c15ab27b0e3f46614d575214535ae279b166597fac1", 16), iqmp=int( "cc1f272de6846851ec80cb89a02dbac78f44b47bc08f53b67b4651a3acde8b19", 16), public_numbers=RSAPublicNumbers( e=65537, n=int( "ea397388b999ef0f7e7416fa000367efd9a0ba0deddd3f8160d1c36d62267f210" "fbd9c97abeb6654450ff03e7601b8caa6c6f4cba18f0b52c179d17e8f258ad5", 16), ) ) RSA_KEY_522 = RSAPrivateNumbers( p=int( "1a8aab9a069f92b52fdf05824f2846223dc27adfc806716a247a77d4c36885e4bf", 16), q=int( "19e8d620d177ec54cdb733bb1915e72ef644b1202b889ceb524613efa49c07eb4f", 16), d=int( "10b8a7c0a92c1ae2d678097d69db3bfa966b541fb857468291d48d1b52397ea2bac0d" "4370c159015c7219e3806a01bbafaffdd46f86e3da1e2d1fe80a0369ccd745", 16), dmp1=int( "3eb6277f66e6e2dcf89f1b8529431f730839dbd9a3e49555159bc8470eee886e5", 16), dmq1=int( "184b4d74aa54c361e51eb23fee4eae5e4786b37b11b6e0447af9c0b9c4e4953c5b", 16), iqmp=int( "f80e9ab4fa7b35d0d232ef51c4736d1f2dcf2c7b1dd8716211b1bf1337e74f8ae", 16), public_numbers=RSAPublicNumbers( e=65537, n=int( "2afaea0e0bb6fca037da7d190b5270a6c665bc18e7a456f7e69beaac4433db748" "ba99acdd14697e453bca596eb35b47f2d48f1f85ef08ce5109dad557a9cf85ebf" "1", 16), ), ) RSA_KEY_599 = RSAPrivateNumbers( p=int( "cf95d20be0c7af69f4b3d909f65d858c26d1a7ef34da8e3977f4fa230580e58814b54" "24be99", 16), q=int( "6052be4b28debd4265fe12ace5aa4a0c4eb8d63ff8853c66824b35622161eb48a3bc8" "c3ada5", 16), d=int( "69d9adc465e61585d3142d7cc8dd30605e8d1cbbf31009bc2cd5538dc40528d5d68ee" "fe6a42d23674b6ec76e192351bf368c8968f0392110bf1c2825dbcff071270b80adcc" "fa1d19d00a1", 16), dmp1=int( "a86d10edde456687fba968b1f298d2e07226adb1221b2a466a93f3d83280f0bb46c20" "2b6811", 16), dmq1=int( "40d570e08611e6b1da94b95d46f8e7fe80be48f7a5ff8838375b08039514a399b11c2" "80735", 16), iqmp=int( "cd051cb0ea68b88765c041262ace2ec4db11dab14afd192742e34d5da3328637fabdf" "bae26e", 16), public_numbers=RSAPublicNumbers( e=65537, n=int( "4e1b470fe00642426f3808e74c959632dd67855a4c503c5b7876ccf4dc7f6a1a4" "9107b90d26daf0a7879a6858218345fbc6e59f01cd095ca5647c27c25265e6c47" "4fea89537191c7073d9d", 16), ) ) RSA_KEY_745 = RSAPrivateNumbers( p=int( "1c5a0cfe9a86debd19eca33ba961f15bc598aa7983a545ce775b933afc89eb51bcf90" "836257fdd060d4b383240241d", 16 ), q=int( "fb2634f657f82ee6b70553382c4e2ed26b947c97ce2f0016f1b282cf2998184ad0527" "a9eead826dd95fe06b57a025", 16 ), d=int( "402f30f976bc07d15ff0779abff127b20a8b6b1d0024cc2ad8b6762d38f174f81e792" "3b49d80bdbdd80d9675cbc7b2793ec199a0430eb5c84604dacfdb29259ae6a1a44676" "22f0b23d4cb0f5cb1db4b8173c8d9d3e57a74dbd200d2141", 16), dmp1=int( "e5e95b7751a6649f199be21bef7a51c9e49821d945b6fc5f538b4a670d8762c375b00" "8e70f31d52b3ea2bd14c3101", 16), dmq1=int( "12b85d5843645f72990fcf8d2f58408b34b3a3b9d9078dd527fceb5d2fb7839008092" "dd4aca2a1fb00542801dcef5", 16), iqmp=int( "5672740d947f621fc7969e3a44ec26736f3f819863d330e63e9409e139d20753551ac" "c16544dd2bdadb9dee917440", 16), public_numbers=RSAPublicNumbers( e=65537, n=int( "1bd085f92237774d34013b477ceebbb2f2feca71118db9b7429341477947e7b1d" "04e8c43ede3c52bb25781af58d4ff81289f301eac62dc3bcd7dafd7a4d5304e9f" "308e766952fbf2b62373e66611fa53189987dbef9f7243dcbbeb25831", 16), ) ) RSA_KEY_768 = RSAPrivateNumbers( p=int( "f80c0061b607f93206b68e208906498d68c6e396faf457150cf975c8f849848465869" "7ecd402313397088044c4c2071b", 16), q=int( "e5b5dbecc93c6d306fc14e6aa9737f9be2728bc1a326a8713d2849b34c1cb54c63468" "3a68abb1d345dbf15a3c492cf55", 16), d=int( "d44601442255ffa331212c60385b5e898555c75c0272632ff42d57c4b16ca97dbca9f" "d6d99cd2c9fd298df155ed5141b4be06c651934076133331d4564d73faed7ce98e283" "2f7ce3949bc183be7e7ca34f6dd04a9098b6c73649394b0a76c541", 16), dmp1=int( "a5763406fa0b65929661ce7b2b8c73220e43a5ebbfe99ff15ddf464fd238105ad4f2a" "c83818518d70627d8908703bb03", 16), dmq1=int( "cb467a9ef899a39a685aecd4d0ad27b0bfdc53b68075363c373d8eb2bed8eccaf3533" "42f4db735a9e087b7539c21ba9d", 16), iqmp=int( "5fe86bd3aee0c4d09ef11e0530a78a4534c9b833422813b5c934a450c8e564d8097a0" "6fd74f1ebe2d5573782093f587a", 16), public_numbers=RSAPublicNumbers( e=65537, n=int( "de92f1eb5f4abf426b6cac9dd1e9bf57132a4988b4ed3f8aecc15e251028bd6df" "46eb97c711624af7db15e6430894d1b640c13929329241ee094f5a4fe1a20bc9b" "75232320a72bc567207ec54d6b48dccb19737cf63acc1021abb337f19130f7", 16), ) ) RSA_KEY_1024 = RSAPrivateNumbers( p=int( "ea4d9d9a1a068be44b9a5f8f6de0512b2c5ba1fb804a4655babba688e6e890b347c1a" "7426685a929337f513ae4256f0b7e5022d642237f960c5b24b96bee8e51", 16), q=int( "cffb33e400d6f08b410d69deb18a85cf0ed88fcca9f32d6f2f66c62143d49aff92c11" "4de937d4f1f62d4635ee89af99ce86d38a2b05310f3857c7b5d586ac8f9", 16), d=int( "3d12d46d04ce942fb99be7bf30587b8cd3e21d75a2720e7bda1b867f1d418d91d8b9f" "e1c00181fdde94f2faf33b4e6f800a1b3ae3b972ccb6d5079dcb6c794070ac8306d59" "c00b58b7a9a81122a6b055832de7c72334a07494d8e7c9fbeed2cc37e011d9e6bfc6e" "9bcddbef7f0f5771d9cf82cd4b268c97ec684575c24b6c881", 16), dmp1=int( "470f2b11257b7ec9ca34136f487f939e6861920ad8a9ae132a02e74af5dceaa5b4c98" "2949ccb44b67e2bcad2f58674db237fe250e0d62b47b28fa1dfaa603b41", 16), dmq1=int( "c616e8317d6b3ae8272973709b80e8397256697ff14ea03389de454f619f99915a617" "45319fefbe154ec1d49441a772c2f63f7d15c478199afc60469bfd0d561", 16), iqmp=int( "d15e7c9ad357dfcd5dbdc8427680daf1006761bcfba93a7f86589ad88832a8d564b1c" "d4291a658c96fbaea7ca588795820902d85caebd49c2d731e3fe0243130", 16), public_numbers=RSAPublicNumbers( e=65537, n=int( "be5aac07456d990133ebce69c06b48845b972ab1ad9f134bc5683c6b5489b5119" "ede07be3bed0e355d48e0dfab1e4fb5187adf42d7d3fb0401c082acb8481bf17f" "0e871f8877be04c3a1197d40aa260e2e0c48ed3fd2b93dc3fc0867591f67f3cd6" "0a77adee1d68a8c3730a5702485f6ac9ede7f0fd2918e037ee4cc1fc1b4c9", 16), ) ) RSA_KEY_1025 = RSAPrivateNumbers( p=int( "18e9bfb7071725da04d31c103fa3563648c69def43a204989214eb57b0c8b299f9ef3" "5dda79a62d8d67fd2a9b69fbd8d0490aa2edc1e111a2b8eb7c737bb691a5", 16), q=int( "d8eccaeeb95815f3079d13685f3f72ca2bf2550b349518049421375df88ca9bbb4ba8" "cb0e3502203c9eeae174112509153445d251313e4711a102818c66fcbb7", 16), d=int( "fe9ac54910b8b1bc948a03511c54cab206a1d36d50d591124109a48abb7480977ccb0" "47b4d4f1ce7b0805df2d4fa3fe425f49b78535a11f4b87a4eba0638b3340c23d4e6b2" "1ecebe9d5364ea6ead2d47b27836019e6ecb407000a50dc95a8614c9d0031a6e3a524" "d2345cfb76e15c1f69d5ba35bdfb6ec63bcb115a757ef79d9", 16), dmp1=int( "18537e81006a68ea76d590cc88e73bd26bc38d09c977959748e5265c0ce21c0b5fd26" "53d975f97ef759b809f791487a8fff1264bf561627fb4527a3f0bbb72c85", 16), dmq1=int( "c807eac5a1f1e1239f04b04dd16eff9a00565127a91046fa89e1eb5d6301cace85447" "4d1f47b0332bd35b4214b66e9166953241538f761f30d969272ee214f17", 16), iqmp=int( "133aa74dd41fe70fa244f07d0c4091a22f8c8f0134fe6aea9ec8b55383b758fefe358" "2beec36eca91715eee7d21931f24fa9e97e8e3a50f9cd0f731574a5eafcc", 16), public_numbers=RSAPublicNumbers( e=65537, n=int( "151c44fed756370fb2d4a0e6ec7dcac84068ca459b6aaf22daf902dca72c77563" "bf276fe3523f38f5ddaf3ea9aa88486a9d8760ff732489075862bee0e599de5c5" "f509b4519f4f446521bad15cd279a498fe1e89107ce0d237e3103d7c5eb801666" "42e2924b152aebff97b71fdd2d68ebb45034cc784e2e822ff6d1edf98af3f3", 16), ) ) RSA_KEY_1026 = RSAPrivateNumbers( p=int( "1fcbfb8719c5bdb5fe3eb0937c76bb096e750b9442dfe31d6a877a13aed2a6a4e9f79" "40f815f1c307dd6bc2b4b207bb6fe5be3a15bd2875a957492ce197cdedb1", 16), q=int( "1f704a0f6b8966dd52582fdc08227dd3dbaeaa781918b41144b692711091b4ca4eb62" "985c3513853828ce8739001dfba9a9a7f1a23cbcaf74280be925e2e7b50d", 16), d=int( "c67975e35a1d0d0b3ebfca736262cf91990cb31cf4ac473c0c816f3bc2720bcba2475" "e8d0de8535d257816c0fc53afc1b597eada8b229069d6ef2792fc23f59ffb4dc6c3d9" "0a3c462082025a4cba7561296dd3d8870c4440d779406f00879afe2c681e7f5ee055e" "ff829e6e55883ec20830c72300762e6e3a333d94b4dbe4501", 16), dmp1=int( "314730ca7066c55d086a9fbdf3670ef7cef816b9efea8b514b882ae9d647217cf41d7" "e9989269dc9893d02e315cb81f058c49043c2cac47adea58bdf5e20e841", 16), dmq1=int( "1da28a9d687ff7cfeebc2439240de7505a8796376968c8ec723a2b669af8ce53d9c88" "af18540bd78b2da429014923fa435f22697ac60812d7ca9c17a557f394cd", 16), iqmp=int( "727947b57b8a36acd85180522f1b381bce5fdbd962743b3b14af98a36771a80f58ddd" "62675d72a5935190da9ddc6fd6d6d5e9e9f805a2e92ab8d56b820493cdf", 16), public_numbers=RSAPublicNumbers( e=65537, n=int( "3e7a5e6483e55eb8b723f9c46732d21b0af9e06a4a1099962d67a35ee3f62e312" "9cfae6ab0446da18e26f33e1d753bc1cc03585c100cf0ab5ef056695706fc8b0c" "9c710cd73fe6e5beda70f515a96fabd3cc5ac49efcb2594b220ff3b603fcd927f" "6a0838ef04bf52f3ed9eab801f09e5aed1613ddeb946ed0fbb02060b3a36fd", 16), ) ) RSA_KEY_1027 = RSAPrivateNumbers( p=int( "30135e54cfb072c3d3eaf2000f3ed92ceafc85efc867b9d4bf5612f2978c432040093" "4829f741c0f002b54af2a4433ff872b6321ef00ff1e72cba4e0ced937c7d", 16), q=int( "1d01a8aead6f86b78c875f18edd74214e06535d65da054aeb8e1851d6f3319b4fb6d8" "6b01e07d19f8261a1ded7dc08116345509ab9790e3f13e65c037e5bb7e27", 16), d=int( "21cf4477df79561c7818731da9b9c88cd793f1b4b8e175bd0bfb9c0941a4dc648ecf1" "6d96b35166c9ea116f4c2eb33ce1c231e641a37c25e54c17027bdec08ddafcb83642e" "795a0dd133155ccc5eed03b6e745930d9ac7cfe91f9045149f33295af03a2198c660f" "08d8150d13ce0e2eb02f21ac75d63b55822f77bd5be8d07619", 16), dmp1=int( "173fb695931e845179511c18b546b265cb79b517c135902377281bdf9f34205e1f399" "4603ad63e9f6e7885ea73a929f03fa0d6bed943051ce76cddde2d89d434d", 16), dmq1=int( "10956b387b2621327da0c3c8ffea2af8be967ee25163222746c28115a406e632a7f12" "5a9397224f1fa5c116cd3a313e5c508d31db2deb83b6e082d213e33f7fcf", 16), iqmp=int( "234f833949f2c0d797bc6a0e906331e17394fa8fbc8449395766d3a8d222cf6167c48" "8e7fe1fe9721d3e3b699a595c8e6f063d92bd840dbc84d763b2b37002109", 16), public_numbers=RSAPublicNumbers( e=65537, n=int( "57281707d7f9b1369c117911758980e32c05b133ac52c225bcf68b79157ff47ea" "0a5ae9f579ef1fd7e42937f921eb3123c4a045cc47a2159fbbf904783e654954c" "42294c30a95c15db7c7b91f136244e548f62474b137087346c5522e54f226f49d" "6c93bc58cb39972e41bde452bb3ae9d60eb93e5e1ce91d222138d9890c7d0b", 16), ) ) RSA_KEY_1028 = RSAPrivateNumbers( p=int( "359d17378fae8e9160097daee78a206bd52efe1b757c12a6da8026cc4fc4bb2620f12" "b8254f4db6aed8228be8ee3e5a27ec7d31048602f01edb00befd209e8c75", 16), q=int( "33a2e70b93d397c46e63b273dcd3dcfa64291342a6ce896e1ec8f1c0edc44106550f3" "c06e7d3ca6ea29eccf3f6ab5ac6235c265313d6ea8e8767e6a343f616581", 16), d=int( "880640088d331aa5c0f4cf2887809a420a2bc086e671e6ffe4e47a8c80792c038a314" "9a8e45ef9a72816ab45b36e3af6800351067a6b2751843d4232413146bb575491463a" "8addd06ce3d1bcf7028ec6c5d938c545a20f0a40214b5c574ca7e840062b2b5f8ed49" "4b144bb2113677c4b10519177fee1d4f5fb8a1c159b0b47c01", 16), dmp1=int( "75f8c52dad2c1cea26b8bba63236ee4059489e3d2db766136098bcc6b67fde8f77cd3" "640035107bfb1ffc6480983cfb84fe0c3be008424ebc968a7db7e01f005", 16), dmq1=int( "3893c59469e4ede5cd0e6ff9837ca023ba9b46ff40c60ccf1bec10f7d38db5b1ba817" "6c41a3f750ec4203b711455aca06d1e0adffc5cffa42bb92c7cb77a6c01", 16), iqmp=int( "ad32aafae3c962ac25459856dc8ef1f733c3df697eced29773677f435d186cf759d1a" "5563dd421ec47b4d7e7f12f29647c615166d9c43fc49001b29089344f65", 16), public_numbers=RSAPublicNumbers( e=65537, n=int( "ad0696bef71597eb3a88e135d83c596930cac73868fbd7e6b2d64f34eea5c28cc" "e3510c68073954d3ba4deb38643e7a820a4cf06e75f7f82eca545d412bd637819" "45c28d406e95a6cced5ae924a8bfa4f3def3e0250d91246c269ec40c89c93a85a" "cd3770ba4d2e774732f43abe94394de43fb57f93ca25f7a59d75d400a3eff5", 16), ) ) RSA_KEY_1029 = RSAPrivateNumbers( p=int( "66f33e513c0b6b6adbf041d037d9b1f0ebf8de52812a3ac397a963d3f71ba64b3ad04" "e4d4b5e377e6fa22febcac292c907dc8dcfe64c807fd9a7e3a698850d983", 16), q=int( "3b47a89a19022461dcc2d3c05b501ee76955e8ce3cf821beb4afa85a21a26fd7203db" "deb8941f1c60ada39fd6799f6c07eb8554113f1020460ec40e93cd5f6b21", 16), d=int( "280c42af8b1c719821f2f6e2bf5f3dd53c81b1f3e1e7cc4fce6e2f830132da0665bde" "bc1e307106b112b52ad5754867dddd028116cf4471bc14a58696b99524b1ad8f05b31" "cf47256e54ab4399b6a073b2c0452441438dfddf47f3334c13c5ec86ece4d33409056" "139328fafa992fb5f5156f25f9b21d3e1c37f156d963d97e41", 16), dmp1=int( "198c7402a4ec10944c50ab8488d7b5991c767e75eb2817bd427dff10335ae141fa2e8" "7c016dc22d975cac229b9ffdf7d943ddfd3a04b8bf82e83c3b32c5698b11", 16), dmq1=int( "15fd30c7687b68ef7c2a30cdeb913ec56c4757c218cf9a04d995470797ee5f3a17558" "fbb6d00af245d2631d893b382da48a72bc8a613024289895952ab245b0c1", 16), iqmp=int( "4f8fde17e84557a3f4e242d889e898545ab55a1a8e075c9bb0220173ccffe84659abe" "a235104f82e32750309389d4a52af57dbb6e48d831917b6efeb190176570", 16), public_numbers=RSAPublicNumbers( e=65537, n=int( "17d6e0a09aa5b2d003e51f43b9c37ffde74688f5e3b709fd02ef375cb6b8d15e2" "99a9f74981c3eeaaf947d5c2d64a1a80f5c5108a49a715c3f7be95a016b8d3300" "965ead4a4df76e642d761526803e9434d4ec61b10cb50526d4dcaef02593085de" "d8c331c1b27b200a45628403065efcb2c0a0ca1f75d648d40a007fbfbf2cae3", 16), ) ) RSA_KEY_1030 = RSAPrivateNumbers( p=int( "6f4ac8a8172ef1154cf7f80b5e91de723c35a4c512860bfdbafcc3b994a2384bf7796" "3a2dd0480c7e04d5d418629651a0de8979add6f47b23da14c27a682b69c9", 16), q=int( "65a9f83e07dea5b633e036a9dccfb32c46bf53c81040a19c574c3680838fc6d28bde9" "55c0ff18b30481d4ab52a9f5e9f835459b1348bbb563ad90b15a682fadb3", 16), d=int( "290db707b3e1a96445ae8ea93af55a9f211a54ebe52995c2eb28085d1e3f09c986e73" "a00010c8e4785786eaaa5c85b98444bd93b585d0c24363ccc22c482e150a3fd900176" "86968e4fa20423ae72823b0049defceccb39bb34aa4ef64e6b14463b76d6a871c859e" "37285455b94b8e1527d1525b1682ac6f7c8fd79d576c55318c1", 16), dmp1=int( "23f7fa84010225dea98297032dac5d45745a2e07976605681acfe87e0920a8ab3caf5" "9d9602f3d63dc0584f75161fd8fff20c626c21c5e02a85282276a74628a9", 16), dmq1=int( "18ebb657765464a8aa44bf019a882b72a2110a77934c54915f70e6375088b10331982" "962bce1c7edd8ef9d3d95aa2566d2a99da6ebab890b95375919408d00f33", 16), iqmp=int( "3d59d208743c74054151002d77dcdfc55af3d41357e89af88d7eef2767be54c290255" "9258d85cf2a1083c035a33e65a1ca46dc8b706847c1c6434cef7b71a9dae", 16), public_numbers=RSAPublicNumbers( e=65537, n=int( "2c326574320818a6a8cb6b3328e2d6c1ba2a3f09b6eb2bc543c03ab18eb5efdaa" "8fcdbb6b4e12168304f587999f9d96a421fc80cb933a490df85d25883e6a88750" "d6bd8b3d4117251eee8f45e70e6daac7dbbd92a9103c623a09355cf00e3f16168" "e38b9c4cb5b368deabbed8df466bc6835eaba959bc1c2f4ec32a09840becc8b", 16), ) ) RSA_KEY_1031 = RSAPrivateNumbers( p=int( "c0958c08e50137db989fb7cc93abf1984543e2f955d4f43fb2967f40105e79274c852" "293fa06ce63ca8436155e475ed6d1f73fea4c8e2516cc79153e3dc83e897", 16), q=int( "78cae354ea5d6862e5d71d20273b7cddb8cdfab25478fe865180676b04250685c4d03" "30c216574f7876a7b12dfe69f1661d3b0cea6c2c0dcfb84050f817afc28d", 16), d=int( "1d55cc02b17a5d25bfb39f2bc58389004d0d7255051507f75ef347cdf5519d1a00f4b" "d235ce4171bfab7bdb7a6dcfae1cf41433fb7da5923cc84f15a675c0b83492c95dd99" "a9fc157aea352ffdcbb5d59dbc3662171d5838d69f130678ee27841a79ef64f679ce9" "3821fa69c03f502244c04b737edad8967def8022a144feaab29", 16), dmp1=int( "5b1c2504ec3a984f86b4414342b5bcf59a0754f13adf25b2a0edbc43f5ba8c3cc061d" "80b03e5866d059968f0d10a98deaeb4f7830436d76b22cf41f2914e13eff", 16), dmq1=int( "6c361e1819691ab5d67fb2a8f65c958d301cdf24d90617c68ec7005edfb4a7b638cde" "79d4b61cfba5c86e8c0ccf296bc7f611cb8d4ae0e072a0f68552ec2d5995", 16), iqmp=int( "b7d61945fdc8b92e075b15554bab507fa8a18edd0a18da373ec6c766c71eece61136a" "84b90b6d01741d40458bfad17a9bee9d4a8ed2f6e270782dc3bf5d58b56e", 16), public_numbers=RSAPublicNumbers( e=65537, n=int( "5adebaa926ea11fb635879487fdd53dcfbb391a11ac7279bb3b4877c9b811370a" "9f73da0690581691626d8a7cf5d972cced9c2091ccf999024b23b4e6dc6d99f80" "a454737dec0caffaebe4a3fac250ed02079267c8f39620b5ae3e125ca35338522" "dc9353ecac19cb2fe3b9e3a9291619dbb1ea3a7c388e9ee6469fbf5fb22892b", 16), ) ) RSA_KEY_1536 = RSAPrivateNumbers( p=int( "f1a65fa4e2aa6e7e2b560251e8a4cd65b625ad9f04f6571785782d1c213d91c961637" "0c572f2783caf2899f7fb690cf99a0184257fbd4b071b212c88fb348279a5387e61f1" "17e9c62980c45ea863fa9292087c0f66ecdcde6443d5a37268bf71", 16), q=int( "e54c2cbc3839b1da6ae6fea45038d986d6f523a3ae76051ba20583aab711ea5965cf5" "3cf54128cc9573f7460bba0fd6758a57aaf240c391790fb38ab473d83ef735510c53d" "1d10c31782e8fd7da42615e33565745c30a5e6ceb2a3ae0666cc35", 16), d=int( "7bcad87e23da2cb2a8c328883fabce06e1f8e9b776c8bf253ad9884e6200e3bd9bd3b" "a2cbe87d3854527bf005ba5d878c5b0fa20cfb0a2a42884ae95ca12bf7304285e9214" "5e992f7006c7c0ae839ad550da495b143bec0f4806c7f44caed45f3ccc6dc44cfaf30" "7abdb757e3d28e41c2d21366835c0a41e50a95af490ac03af061d2feb36ac0afb87be" "a13fb0f0c5a410727ebedb286c77f9469473fae27ef2c836da6071ef7efc1647f1233" "4009a89eecb09a8287abc8c2afd1ddd9a1b0641", 16), dmp1=int( "a845366cd6f9df1f34861bef7594ed025aa83a12759e245f58adaa9bdff9c3befb760" "75d3701e90038e888eec9bf092df63400152cb25fc07effc6c74c45f0654ccbde15cd" "90dd5504298a946fa5cf22a956072da27a6602e6c6e5c97f2db9c1", 16), dmq1=int( "28b0c1e78cdac03310717992d321a3888830ec6829978c048156152d805b4f8919c61" "70b5dd204e5ddf3c6c53bc6aff15d0bd09faff7f351b94abb9db980b31f150a6d7573" "08eb66938f89a5225cb4dd817a824c89e7a0293b58fc2eefb7e259", 16), iqmp=int( "6c1536c0e16e42a094b6caaf50231ba81916871497d73dcbbbd4bdeb9e60cae0413b3" "8143b5d680275b29ed7769fe5577e4f9b3647ddb064941120914526d64d80016d2eb7" "dc362da7c569623157f3d7cff8347f11494bf5c048d77e28d3f515", 16), public_numbers=RSAPublicNumbers( e=65537, n=int( "d871bb2d27672e54fc62c4680148cbdf848438da804e2c48b5a9c9f9daf6cc6e8" "ea7d2296f25064537a9a542aef3dd449ea75774238d4da02c353d1bee70013dcc" "c248ceef4050160705c188043c8559bf6dbfb6c4bb382eda4e9547575a8227d5b" "3c0a7088391364cf9f018d8bea053b226ec65e8cdbeaf48a071d0074860a734b1" "cb7d2146d43014b20776dea42f7853a54690e6cbbf3331a9f43763cfe2a51c329" "3bea3b2eebec0d8e43eb317a443afe541107d886e5243c096091543ae65", 16), ) ) RSA_KEY_2048 = RSAPrivateNumbers( p=int( "e14202e58c5f7446648d75e5dc465781f661f6b73000c080368afcfb21377f4ef19da" "845d4ef9bc6b151f6d9f34629103f2e57615f9ba0a3a2fbb035069e1d63b4bb0e78ad" "dad1ec3c6f87e25c877a1c4c1972098e09158ef7b9bc163852a18d44a70b7b31a03dc" "2614fd9ab7bf002cba79054544af3bfbdb6aed06c7b24e6ab", 16), q=int( "dbe2bea1ff92599bd19f9d045d6ce62250c05cfeac5117f3cf3e626cb696e3d886379" "557d5a57b7476f9cf886accfd40508a805fe3b45a78e1a8a125e516cda91640ee6398" "ec5a39d3e6b177ef12ab00d07907a17640e4ca454fd8487da3c4ffa0d5c2a5edb1221" "1c8e33c7ee9fa6753771fd111ec04b8317f86693eb2928c89", 16), d=int( "aef17f80f2653bc30539f26dd4c82ed6abc1d1b53bc0abcdbee47e9a8ab433abde865" "9fcfae1244d22de6ad333c95aee7d47f30b6815065ac3322744d3ea75058002cd1b29" "3141ee2a6dc682342432707080071bd2131d6262cab07871c28aa5238b87173fb78c3" "7f9c7bcd18c12e8971bb77fd9fa3e0792fec18d8d9bed0b03ba02b263606f24dbace1" "c8263ce2802a769a090e993fd49abc50c3d3c78c29bee2de0c98055d2f102f1c5684b" "8dddee611d5205392d8e8dd61a15bf44680972a87f040a611a149271eeb2573f8bf6f" "627dfa70e77def2ee6584914fa0290e041349ea0999cdff3e493365885b906cbcf195" "843345809a85098cca90fea014a21", 16), dmp1=int( "9ba56522ffcfa5244eae805c87cc0303461f82be29691b9a7c15a5a050df6c143c575" "7c288d3d7ab7f32c782e9d9fcddc10a604e6425c0e5d0e46069035d95a923646d276d" "d9d95b8696fa29ab0de18e53f6f119310f8dd9efca62f0679291166fed8cbd5f18fe1" "3a5f1ead1d71d8c90f40382818c18c8d069be793dbc094f69", 16), dmq1=int( "a8d4a0aaa2212ccc875796a81353da1fdf00d46676c88d2b96a4bfcdd924622d8e607" "f3ac1c01dda7ebfb0a97dd7875c2a7b2db6728fb827b89c519f5716fb3228f4121647" "04b30253c17de2289e9cce3343baa82eb404f789e094a094577a9b0c5314f1725fdf5" "8e87611ad20da331bd30b8aebc7dc97d0e9a9ba8579772c9", 16), iqmp=int( "17bd5ef638c49440d1853acb3fa63a5aca28cb7f94ed350db7001c8445da8943866a7" "0936e1ee2716c98b484e357cc054d82fbbd98d42f880695d38a1dd4eb096f629b9417" "aca47e6de5da9f34e60e8a0ffd7e35be74deeef67298d94b3e0db73fc4b7a4cb360c8" "9d2117a0bfd9434d37dc7c027d6b01e5295c875015510917d", 16), public_numbers=RSAPublicNumbers( e=65537, n=int( "c17afc7e77474caa5aa83036158a3ffbf7b5216851ba2230e5d6abfcc1c6cfef5" "9e923ea1330bc593b73802ab608a6e4a3306523a3116ba5aa3966145174e13b6c" "49e9b78062e449d72efb10fd49e91fa08b96d051e782e9f5abc5b5a6f7984827a" "db8e73da00f22b2efdcdb76eab46edad98ed65662743fdc6c0e336a5d0cdbaa7d" "c29e53635e24c87a5b2c4215968063cdeb68a972babbc1e3cff00fb9a80e372a4" "d0c2c920d1e8cee333ce470dc2e8145adb05bf29aee1d24f141e8cc784989c587" "fc6fbacd979f3f2163c1d7299b365bc72ffe2848e967aed1e48dcc515b3a50ed4" "de04fd053846ca10a223b10cc841cc80fdebee44f3114c13e886af583", 16), ) ) RSA_KEY_2048_ALT = RSAPrivateNumbers( d=int( "7522768467449591813737881904131688860626637897199391200040629" "8641018746450502628484395471408986929218353894683769457466923" "3079369551423094451013669595729568593462009746342148367797495" "5529909313614750246672441810743580455199636293179539903480635" "3091286716112931976896334411287175213124504134181121011488550" "5290054443979198998564749640800633368957384058700741073997703" "8877364695937023906368630297588990131009278072614118207348356" "4640244134189285070202534488517371577359510236833464698189075" "5160693085297816063285814039518178249628112908466649245545732" "5791532385553960363601827996980725025898649392004494256400884" "092073" ), dmp1=int( "5847872614112935747739644055317429405973942336206460017493394" "9737607778799766591021036792892472774720417920838206576785118" "8889624058962939702950175807073343659386156232294197300491647" "1029508414050591959344812347424476498076532682798598325230069" "0925827594762920534235575029199380552228825468180187156871965" "973" ), dmq1=int( "2949536259161239302081155875068405238857801001054083407704879" "8210876832264504685327766351157044892283801611558399025326793" "4131638001934454489864437565651739832511702151461257267169691" "6611992398459006200708626815153304591390855807749769768978152" "9854112656599931724820610358669306523835327459478374630794532" "167" ), iqmp=int( "7331180989818931535458916053540252830484856703208982675535284" "4613815808798190559315018094080936347757336989616401164752221" "8101156529898067044923499386460167055405998646366011838018441" "3678947694258190172377716154009305082091341215866326061721180" "3836418654472188816187630316821692982783286322262994892003058" "782" ), p=int( "1460007723851883695617573533155574746587863843382715314919865" "2434108956187429726002840717317310431378483921058946835896252" "7109559207437158778332364464259678946305487699031865937075508" "8616612925453842458055546540240601585731206561647892336916583" "0023641764106581040198845259766246869529221084602380669333021" "0819" ), q=int( "1433897765867889178402883410610177836503402597775250087462018" "4617952933433119527945447840336616357136736935069377619782227" "2822380830300262175671282877680573202309319960687756231128996" "9764855320953993690199846269451095044922353809602378616938811" "7513900906279873343591486841303392490561500301994171338761080" "4439" ), public_numbers=RSAPublicNumbers( e=65537, n=int( "209350181338107812610165420955871971489973659392253291327" "839812910252466502190690572476688311285621239204212139711" "207388949164851984253143698667018532039612470954223918242" "145976986600705122576087630525229796950722166468064721258" "490916138706756006902066136471049807637157890128560592039" "941717275079733754782848729566190631725183735944031456237" "089928120178187552521649483240599003240074352860189285952" "078970127554801074176375499583703254849309993132931268013" "715070507278514207864914944621214574162116786377990456375" "964817771730371110612100247262908550409785456157505694419" "00451152778245269283276012328748538414051025541" ) ) )

48.478477

80

0.765787

from __future__ import absolute_import, division, print_function from cryptography.hazmat.primitives.asymmetric.rsa import ( RSAPrivateNumbers, RSAPublicNumbers ) RSA_KEY_512 = RSAPrivateNumbers( p=int( "d57846898d5c0de249c08467586cb458fa9bc417cdf297f73cfc52281b787cd9", 16 ), q=int( "d10f71229e87e010eb363db6a85fd07df72d985b73c42786191f2ce9134afb2d", 16 ), d=int( "272869352cacf9c866c4e107acc95d4c608ca91460a93d28588d51cfccc07f449" "18bbe7660f9f16adc2b4ed36ca310ef3d63b79bd447456e3505736a45a6ed21", 16 ), dmp1=int( "addff2ec7564c6b64bc670d250b6f24b0b8db6b2810099813b7e7658cecf5c39", 16 ), dmq1=int( "463ae9c6b77aedcac1397781e50e4afc060d4b216dc2778494ebe42a6850c81", 16 ), iqmp=int( "54deef8548f65cad1d411527a32dcb8e712d3e128e4e0ff118663fae82a758f4", 16 ), public_numbers=RSAPublicNumbers( e=65537, n=int( "ae5411f963c50e3267fafcf76381c8b1e5f7b741fdb2a544bcf48bd607b10c991" "90caeb8011dc22cf83d921da55ec32bd05cac3ee02ca5e1dbef93952850b525", 16 ), ) ) RSA_KEY_512_ALT = RSAPrivateNumbers( p=int( "febe19c29a0b50fefa4f7b1832f84df1caf9be8242da25c9d689e18226e67ce5", 16), q=int( "eb616c639dd999feda26517e1c77b6878f363fe828c4e6670ec1787f28b1e731", 16), d=int( "80edecfde704a806445a4cc782b85d3f36f17558f385654ea767f006470fdfcbda5e2" "206839289d3f419b4e4fb8e1acee1b4fb9c591f69b64ec83937f5829241", 16), dmp1=int( "7f4fa06e2a3077a54691cc5216bf13ad40a4b9fa3dd0ea4bca259487484baea5", 16), dmq1=int( "35eaa70d5a8711c352ed1c15ab27b0e3f46614d575214535ae279b166597fac1", 16), iqmp=int( "cc1f272de6846851ec80cb89a02dbac78f44b47bc08f53b67b4651a3acde8b19", 16), public_numbers=RSAPublicNumbers( e=65537, n=int( "ea397388b999ef0f7e7416fa000367efd9a0ba0deddd3f8160d1c36d62267f210" "fbd9c97abeb6654450ff03e7601b8caa6c6f4cba18f0b52c179d17e8f258ad5", 16), ) ) RSA_KEY_522 = RSAPrivateNumbers( p=int( "1a8aab9a069f92b52fdf05824f2846223dc27adfc806716a247a77d4c36885e4bf", 16), q=int( "19e8d620d177ec54cdb733bb1915e72ef644b1202b889ceb524613efa49c07eb4f", 16), d=int( "10b8a7c0a92c1ae2d678097d69db3bfa966b541fb857468291d48d1b52397ea2bac0d" "4370c159015c7219e3806a01bbafaffdd46f86e3da1e2d1fe80a0369ccd745", 16), dmp1=int( "3eb6277f66e6e2dcf89f1b8529431f730839dbd9a3e49555159bc8470eee886e5", 16), dmq1=int( "184b4d74aa54c361e51eb23fee4eae5e4786b37b11b6e0447af9c0b9c4e4953c5b", 16), iqmp=int( "f80e9ab4fa7b35d0d232ef51c4736d1f2dcf2c7b1dd8716211b1bf1337e74f8ae", 16), public_numbers=RSAPublicNumbers( e=65537, n=int( "2afaea0e0bb6fca037da7d190b5270a6c665bc18e7a456f7e69beaac4433db748" "ba99acdd14697e453bca596eb35b47f2d48f1f85ef08ce5109dad557a9cf85ebf" "1", 16), ), ) RSA_KEY_599 = RSAPrivateNumbers( p=int( "cf95d20be0c7af69f4b3d909f65d858c26d1a7ef34da8e3977f4fa230580e58814b54" "24be99", 16), q=int( "6052be4b28debd4265fe12ace5aa4a0c4eb8d63ff8853c66824b35622161eb48a3bc8" "c3ada5", 16), d=int( "69d9adc465e61585d3142d7cc8dd30605e8d1cbbf31009bc2cd5538dc40528d5d68ee" "fe6a42d23674b6ec76e192351bf368c8968f0392110bf1c2825dbcff071270b80adcc" "fa1d19d00a1", 16), dmp1=int( "a86d10edde456687fba968b1f298d2e07226adb1221b2a466a93f3d83280f0bb46c20" "2b6811", 16), dmq1=int( "40d570e08611e6b1da94b95d46f8e7fe80be48f7a5ff8838375b08039514a399b11c2" "80735", 16), iqmp=int( "cd051cb0ea68b88765c041262ace2ec4db11dab14afd192742e34d5da3328637fabdf" "bae26e", 16), public_numbers=RSAPublicNumbers( e=65537, n=int( "4e1b470fe00642426f3808e74c959632dd67855a4c503c5b7876ccf4dc7f6a1a4" "9107b90d26daf0a7879a6858218345fbc6e59f01cd095ca5647c27c25265e6c47" "4fea89537191c7073d9d", 16), ) ) RSA_KEY_745 = RSAPrivateNumbers( p=int( "1c5a0cfe9a86debd19eca33ba961f15bc598aa7983a545ce775b933afc89eb51bcf90" "836257fdd060d4b383240241d", 16 ), q=int( "fb2634f657f82ee6b70553382c4e2ed26b947c97ce2f0016f1b282cf2998184ad0527" "a9eead826dd95fe06b57a025", 16 ), d=int( "402f30f976bc07d15ff0779abff127b20a8b6b1d0024cc2ad8b6762d38f174f81e792" "3b49d80bdbdd80d9675cbc7b2793ec199a0430eb5c84604dacfdb29259ae6a1a44676" "22f0b23d4cb0f5cb1db4b8173c8d9d3e57a74dbd200d2141", 16), dmp1=int( "e5e95b7751a6649f199be21bef7a51c9e49821d945b6fc5f538b4a670d8762c375b00" "8e70f31d52b3ea2bd14c3101", 16), dmq1=int( "12b85d5843645f72990fcf8d2f58408b34b3a3b9d9078dd527fceb5d2fb7839008092" "dd4aca2a1fb00542801dcef5", 16), iqmp=int( "5672740d947f621fc7969e3a44ec26736f3f819863d330e63e9409e139d20753551ac" "c16544dd2bdadb9dee917440", 16), public_numbers=RSAPublicNumbers( e=65537, n=int( "1bd085f92237774d34013b477ceebbb2f2feca71118db9b7429341477947e7b1d" "04e8c43ede3c52bb25781af58d4ff81289f301eac62dc3bcd7dafd7a4d5304e9f" "308e766952fbf2b62373e66611fa53189987dbef9f7243dcbbeb25831", 16), ) ) RSA_KEY_768 = RSAPrivateNumbers( p=int( "f80c0061b607f93206b68e208906498d68c6e396faf457150cf975c8f849848465869" "7ecd402313397088044c4c2071b", 16), q=int( "e5b5dbecc93c6d306fc14e6aa9737f9be2728bc1a326a8713d2849b34c1cb54c63468" "3a68abb1d345dbf15a3c492cf55", 16), d=int( "d44601442255ffa331212c60385b5e898555c75c0272632ff42d57c4b16ca97dbca9f" "d6d99cd2c9fd298df155ed5141b4be06c651934076133331d4564d73faed7ce98e283" "2f7ce3949bc183be7e7ca34f6dd04a9098b6c73649394b0a76c541", 16), dmp1=int( "a5763406fa0b65929661ce7b2b8c73220e43a5ebbfe99ff15ddf464fd238105ad4f2a" "c83818518d70627d8908703bb03", 16), dmq1=int( "cb467a9ef899a39a685aecd4d0ad27b0bfdc53b68075363c373d8eb2bed8eccaf3533" "42f4db735a9e087b7539c21ba9d", 16), iqmp=int( "5fe86bd3aee0c4d09ef11e0530a78a4534c9b833422813b5c934a450c8e564d8097a0" "6fd74f1ebe2d5573782093f587a", 16), public_numbers=RSAPublicNumbers( e=65537, n=int( "de92f1eb5f4abf426b6cac9dd1e9bf57132a4988b4ed3f8aecc15e251028bd6df" "46eb97c711624af7db15e6430894d1b640c13929329241ee094f5a4fe1a20bc9b" "75232320a72bc567207ec54d6b48dccb19737cf63acc1021abb337f19130f7", 16), ) ) RSA_KEY_1024 = RSAPrivateNumbers( p=int( "ea4d9d9a1a068be44b9a5f8f6de0512b2c5ba1fb804a4655babba688e6e890b347c1a" "7426685a929337f513ae4256f0b7e5022d642237f960c5b24b96bee8e51", 16), q=int( "cffb33e400d6f08b410d69deb18a85cf0ed88fcca9f32d6f2f66c62143d49aff92c11" "4de937d4f1f62d4635ee89af99ce86d38a2b05310f3857c7b5d586ac8f9", 16), d=int( "3d12d46d04ce942fb99be7bf30587b8cd3e21d75a2720e7bda1b867f1d418d91d8b9f" "e1c00181fdde94f2faf33b4e6f800a1b3ae3b972ccb6d5079dcb6c794070ac8306d59" "c00b58b7a9a81122a6b055832de7c72334a07494d8e7c9fbeed2cc37e011d9e6bfc6e" "9bcddbef7f0f5771d9cf82cd4b268c97ec684575c24b6c881", 16), dmp1=int( "470f2b11257b7ec9ca34136f487f939e6861920ad8a9ae132a02e74af5dceaa5b4c98" "2949ccb44b67e2bcad2f58674db237fe250e0d62b47b28fa1dfaa603b41", 16), dmq1=int( "c616e8317d6b3ae8272973709b80e8397256697ff14ea03389de454f619f99915a617" "45319fefbe154ec1d49441a772c2f63f7d15c478199afc60469bfd0d561", 16), iqmp=int( "d15e7c9ad357dfcd5dbdc8427680daf1006761bcfba93a7f86589ad88832a8d564b1c" "d4291a658c96fbaea7ca588795820902d85caebd49c2d731e3fe0243130", 16), public_numbers=RSAPublicNumbers( e=65537, n=int( "be5aac07456d990133ebce69c06b48845b972ab1ad9f134bc5683c6b5489b5119" "ede07be3bed0e355d48e0dfab1e4fb5187adf42d7d3fb0401c082acb8481bf17f" "0e871f8877be04c3a1197d40aa260e2e0c48ed3fd2b93dc3fc0867591f67f3cd6" "0a77adee1d68a8c3730a5702485f6ac9ede7f0fd2918e037ee4cc1fc1b4c9", 16), ) ) RSA_KEY_1025 = RSAPrivateNumbers( p=int( "18e9bfb7071725da04d31c103fa3563648c69def43a204989214eb57b0c8b299f9ef3" "5dda79a62d8d67fd2a9b69fbd8d0490aa2edc1e111a2b8eb7c737bb691a5", 16), q=int( "d8eccaeeb95815f3079d13685f3f72ca2bf2550b349518049421375df88ca9bbb4ba8" "cb0e3502203c9eeae174112509153445d251313e4711a102818c66fcbb7", 16), d=int( "fe9ac54910b8b1bc948a03511c54cab206a1d36d50d591124109a48abb7480977ccb0" "47b4d4f1ce7b0805df2d4fa3fe425f49b78535a11f4b87a4eba0638b3340c23d4e6b2" "1ecebe9d5364ea6ead2d47b27836019e6ecb407000a50dc95a8614c9d0031a6e3a524" "d2345cfb76e15c1f69d5ba35bdfb6ec63bcb115a757ef79d9", 16), dmp1=int( "18537e81006a68ea76d590cc88e73bd26bc38d09c977959748e5265c0ce21c0b5fd26" "53d975f97ef759b809f791487a8fff1264bf561627fb4527a3f0bbb72c85", 16), dmq1=int( "c807eac5a1f1e1239f04b04dd16eff9a00565127a91046fa89e1eb5d6301cace85447" "4d1f47b0332bd35b4214b66e9166953241538f761f30d969272ee214f17", 16), iqmp=int( "133aa74dd41fe70fa244f07d0c4091a22f8c8f0134fe6aea9ec8b55383b758fefe358" "2beec36eca91715eee7d21931f24fa9e97e8e3a50f9cd0f731574a5eafcc", 16), public_numbers=RSAPublicNumbers( e=65537, n=int( "151c44fed756370fb2d4a0e6ec7dcac84068ca459b6aaf22daf902dca72c77563" "bf276fe3523f38f5ddaf3ea9aa88486a9d8760ff732489075862bee0e599de5c5" "f509b4519f4f446521bad15cd279a498fe1e89107ce0d237e3103d7c5eb801666" "42e2924b152aebff97b71fdd2d68ebb45034cc784e2e822ff6d1edf98af3f3", 16), ) ) RSA_KEY_1026 = RSAPrivateNumbers( p=int( "1fcbfb8719c5bdb5fe3eb0937c76bb096e750b9442dfe31d6a877a13aed2a6a4e9f79" "40f815f1c307dd6bc2b4b207bb6fe5be3a15bd2875a957492ce197cdedb1", 16), q=int( "1f704a0f6b8966dd52582fdc08227dd3dbaeaa781918b41144b692711091b4ca4eb62" "985c3513853828ce8739001dfba9a9a7f1a23cbcaf74280be925e2e7b50d", 16), d=int( "c67975e35a1d0d0b3ebfca736262cf91990cb31cf4ac473c0c816f3bc2720bcba2475" "e8d0de8535d257816c0fc53afc1b597eada8b229069d6ef2792fc23f59ffb4dc6c3d9" "0a3c462082025a4cba7561296dd3d8870c4440d779406f00879afe2c681e7f5ee055e" "ff829e6e55883ec20830c72300762e6e3a333d94b4dbe4501", 16), dmp1=int( "314730ca7066c55d086a9fbdf3670ef7cef816b9efea8b514b882ae9d647217cf41d7" "e9989269dc9893d02e315cb81f058c49043c2cac47adea58bdf5e20e841", 16), dmq1=int( "1da28a9d687ff7cfeebc2439240de7505a8796376968c8ec723a2b669af8ce53d9c88" "af18540bd78b2da429014923fa435f22697ac60812d7ca9c17a557f394cd", 16), iqmp=int( "727947b57b8a36acd85180522f1b381bce5fdbd962743b3b14af98a36771a80f58ddd" "62675d72a5935190da9ddc6fd6d6d5e9e9f805a2e92ab8d56b820493cdf", 16), public_numbers=RSAPublicNumbers( e=65537, n=int( "3e7a5e6483e55eb8b723f9c46732d21b0af9e06a4a1099962d67a35ee3f62e312" "9cfae6ab0446da18e26f33e1d753bc1cc03585c100cf0ab5ef056695706fc8b0c" "9c710cd73fe6e5beda70f515a96fabd3cc5ac49efcb2594b220ff3b603fcd927f" "6a0838ef04bf52f3ed9eab801f09e5aed1613ddeb946ed0fbb02060b3a36fd", 16), ) ) RSA_KEY_1027 = RSAPrivateNumbers( p=int( "30135e54cfb072c3d3eaf2000f3ed92ceafc85efc867b9d4bf5612f2978c432040093" "4829f741c0f002b54af2a4433ff872b6321ef00ff1e72cba4e0ced937c7d", 16), q=int( "1d01a8aead6f86b78c875f18edd74214e06535d65da054aeb8e1851d6f3319b4fb6d8" "6b01e07d19f8261a1ded7dc08116345509ab9790e3f13e65c037e5bb7e27", 16), d=int( "21cf4477df79561c7818731da9b9c88cd793f1b4b8e175bd0bfb9c0941a4dc648ecf1" "6d96b35166c9ea116f4c2eb33ce1c231e641a37c25e54c17027bdec08ddafcb83642e" "795a0dd133155ccc5eed03b6e745930d9ac7cfe91f9045149f33295af03a2198c660f" "08d8150d13ce0e2eb02f21ac75d63b55822f77bd5be8d07619", 16), dmp1=int( "173fb695931e845179511c18b546b265cb79b517c135902377281bdf9f34205e1f399" "4603ad63e9f6e7885ea73a929f03fa0d6bed943051ce76cddde2d89d434d", 16), dmq1=int( "10956b387b2621327da0c3c8ffea2af8be967ee25163222746c28115a406e632a7f12" "5a9397224f1fa5c116cd3a313e5c508d31db2deb83b6e082d213e33f7fcf", 16), iqmp=int( "234f833949f2c0d797bc6a0e906331e17394fa8fbc8449395766d3a8d222cf6167c48" "8e7fe1fe9721d3e3b699a595c8e6f063d92bd840dbc84d763b2b37002109", 16), public_numbers=RSAPublicNumbers( e=65537, n=int( "57281707d7f9b1369c117911758980e32c05b133ac52c225bcf68b79157ff47ea" "0a5ae9f579ef1fd7e42937f921eb3123c4a045cc47a2159fbbf904783e654954c" "42294c30a95c15db7c7b91f136244e548f62474b137087346c5522e54f226f49d" "6c93bc58cb39972e41bde452bb3ae9d60eb93e5e1ce91d222138d9890c7d0b", 16), ) ) RSA_KEY_1028 = RSAPrivateNumbers( p=int( "359d17378fae8e9160097daee78a206bd52efe1b757c12a6da8026cc4fc4bb2620f12" "b8254f4db6aed8228be8ee3e5a27ec7d31048602f01edb00befd209e8c75", 16), q=int( "33a2e70b93d397c46e63b273dcd3dcfa64291342a6ce896e1ec8f1c0edc44106550f3" "c06e7d3ca6ea29eccf3f6ab5ac6235c265313d6ea8e8767e6a343f616581", 16), d=int( "880640088d331aa5c0f4cf2887809a420a2bc086e671e6ffe4e47a8c80792c038a314" "9a8e45ef9a72816ab45b36e3af6800351067a6b2751843d4232413146bb575491463a" "8addd06ce3d1bcf7028ec6c5d938c545a20f0a40214b5c574ca7e840062b2b5f8ed49" "4b144bb2113677c4b10519177fee1d4f5fb8a1c159b0b47c01", 16), dmp1=int( "75f8c52dad2c1cea26b8bba63236ee4059489e3d2db766136098bcc6b67fde8f77cd3" "640035107bfb1ffc6480983cfb84fe0c3be008424ebc968a7db7e01f005", 16), dmq1=int( "3893c59469e4ede5cd0e6ff9837ca023ba9b46ff40c60ccf1bec10f7d38db5b1ba817" "6c41a3f750ec4203b711455aca06d1e0adffc5cffa42bb92c7cb77a6c01", 16), iqmp=int( "ad32aafae3c962ac25459856dc8ef1f733c3df697eced29773677f435d186cf759d1a" "5563dd421ec47b4d7e7f12f29647c615166d9c43fc49001b29089344f65", 16), public_numbers=RSAPublicNumbers( e=65537, n=int( "ad0696bef71597eb3a88e135d83c596930cac73868fbd7e6b2d64f34eea5c28cc" "e3510c68073954d3ba4deb38643e7a820a4cf06e75f7f82eca545d412bd637819" "45c28d406e95a6cced5ae924a8bfa4f3def3e0250d91246c269ec40c89c93a85a" "cd3770ba4d2e774732f43abe94394de43fb57f93ca25f7a59d75d400a3eff5", 16), ) ) RSA_KEY_1029 = RSAPrivateNumbers( p=int( "66f33e513c0b6b6adbf041d037d9b1f0ebf8de52812a3ac397a963d3f71ba64b3ad04" "e4d4b5e377e6fa22febcac292c907dc8dcfe64c807fd9a7e3a698850d983", 16), q=int( "3b47a89a19022461dcc2d3c05b501ee76955e8ce3cf821beb4afa85a21a26fd7203db" "deb8941f1c60ada39fd6799f6c07eb8554113f1020460ec40e93cd5f6b21", 16), d=int( "280c42af8b1c719821f2f6e2bf5f3dd53c81b1f3e1e7cc4fce6e2f830132da0665bde" "bc1e307106b112b52ad5754867dddd028116cf4471bc14a58696b99524b1ad8f05b31" "cf47256e54ab4399b6a073b2c0452441438dfddf47f3334c13c5ec86ece4d33409056" "139328fafa992fb5f5156f25f9b21d3e1c37f156d963d97e41", 16), dmp1=int( "198c7402a4ec10944c50ab8488d7b5991c767e75eb2817bd427dff10335ae141fa2e8" "7c016dc22d975cac229b9ffdf7d943ddfd3a04b8bf82e83c3b32c5698b11", 16), dmq1=int( "15fd30c7687b68ef7c2a30cdeb913ec56c4757c218cf9a04d995470797ee5f3a17558" "fbb6d00af245d2631d893b382da48a72bc8a613024289895952ab245b0c1", 16), iqmp=int( "4f8fde17e84557a3f4e242d889e898545ab55a1a8e075c9bb0220173ccffe84659abe" "a235104f82e32750309389d4a52af57dbb6e48d831917b6efeb190176570", 16), public_numbers=RSAPublicNumbers( e=65537, n=int( "17d6e0a09aa5b2d003e51f43b9c37ffde74688f5e3b709fd02ef375cb6b8d15e2" "99a9f74981c3eeaaf947d5c2d64a1a80f5c5108a49a715c3f7be95a016b8d3300" "965ead4a4df76e642d761526803e9434d4ec61b10cb50526d4dcaef02593085de" "d8c331c1b27b200a45628403065efcb2c0a0ca1f75d648d40a007fbfbf2cae3", 16), ) ) RSA_KEY_1030 = RSAPrivateNumbers( p=int( "6f4ac8a8172ef1154cf7f80b5e91de723c35a4c512860bfdbafcc3b994a2384bf7796" "3a2dd0480c7e04d5d418629651a0de8979add6f47b23da14c27a682b69c9", 16), q=int( "65a9f83e07dea5b633e036a9dccfb32c46bf53c81040a19c574c3680838fc6d28bde9" "55c0ff18b30481d4ab52a9f5e9f835459b1348bbb563ad90b15a682fadb3", 16), d=int( "290db707b3e1a96445ae8ea93af55a9f211a54ebe52995c2eb28085d1e3f09c986e73" "a00010c8e4785786eaaa5c85b98444bd93b585d0c24363ccc22c482e150a3fd900176" "86968e4fa20423ae72823b0049defceccb39bb34aa4ef64e6b14463b76d6a871c859e" "37285455b94b8e1527d1525b1682ac6f7c8fd79d576c55318c1", 16), dmp1=int( "23f7fa84010225dea98297032dac5d45745a2e07976605681acfe87e0920a8ab3caf5" "9d9602f3d63dc0584f75161fd8fff20c626c21c5e02a85282276a74628a9", 16), dmq1=int( "18ebb657765464a8aa44bf019a882b72a2110a77934c54915f70e6375088b10331982" "962bce1c7edd8ef9d3d95aa2566d2a99da6ebab890b95375919408d00f33", 16), iqmp=int( "3d59d208743c74054151002d77dcdfc55af3d41357e89af88d7eef2767be54c290255" "9258d85cf2a1083c035a33e65a1ca46dc8b706847c1c6434cef7b71a9dae", 16), public_numbers=RSAPublicNumbers( e=65537, n=int( "2c326574320818a6a8cb6b3328e2d6c1ba2a3f09b6eb2bc543c03ab18eb5efdaa" "8fcdbb6b4e12168304f587999f9d96a421fc80cb933a490df85d25883e6a88750" "d6bd8b3d4117251eee8f45e70e6daac7dbbd92a9103c623a09355cf00e3f16168" "e38b9c4cb5b368deabbed8df466bc6835eaba959bc1c2f4ec32a09840becc8b", 16), ) ) RSA_KEY_1031 = RSAPrivateNumbers( p=int( "c0958c08e50137db989fb7cc93abf1984543e2f955d4f43fb2967f40105e79274c852" "293fa06ce63ca8436155e475ed6d1f73fea4c8e2516cc79153e3dc83e897", 16), q=int( "78cae354ea5d6862e5d71d20273b7cddb8cdfab25478fe865180676b04250685c4d03" "30c216574f7876a7b12dfe69f1661d3b0cea6c2c0dcfb84050f817afc28d", 16), d=int( "1d55cc02b17a5d25bfb39f2bc58389004d0d7255051507f75ef347cdf5519d1a00f4b" "d235ce4171bfab7bdb7a6dcfae1cf41433fb7da5923cc84f15a675c0b83492c95dd99" "a9fc157aea352ffdcbb5d59dbc3662171d5838d69f130678ee27841a79ef64f679ce9" "3821fa69c03f502244c04b737edad8967def8022a144feaab29", 16), dmp1=int( "5b1c2504ec3a984f86b4414342b5bcf59a0754f13adf25b2a0edbc43f5ba8c3cc061d" "80b03e5866d059968f0d10a98deaeb4f7830436d76b22cf41f2914e13eff", 16), dmq1=int( "6c361e1819691ab5d67fb2a8f65c958d301cdf24d90617c68ec7005edfb4a7b638cde" "79d4b61cfba5c86e8c0ccf296bc7f611cb8d4ae0e072a0f68552ec2d5995", 16), iqmp=int( "b7d61945fdc8b92e075b15554bab507fa8a18edd0a18da373ec6c766c71eece61136a" "84b90b6d01741d40458bfad17a9bee9d4a8ed2f6e270782dc3bf5d58b56e", 16), public_numbers=RSAPublicNumbers( e=65537, n=int( "5adebaa926ea11fb635879487fdd53dcfbb391a11ac7279bb3b4877c9b811370a" "9f73da0690581691626d8a7cf5d972cced9c2091ccf999024b23b4e6dc6d99f80" "a454737dec0caffaebe4a3fac250ed02079267c8f39620b5ae3e125ca35338522" "dc9353ecac19cb2fe3b9e3a9291619dbb1ea3a7c388e9ee6469fbf5fb22892b", 16), ) ) RSA_KEY_1536 = RSAPrivateNumbers( p=int( "f1a65fa4e2aa6e7e2b560251e8a4cd65b625ad9f04f6571785782d1c213d91c961637" "0c572f2783caf2899f7fb690cf99a0184257fbd4b071b212c88fb348279a5387e61f1" "17e9c62980c45ea863fa9292087c0f66ecdcde6443d5a37268bf71", 16), q=int( "e54c2cbc3839b1da6ae6fea45038d986d6f523a3ae76051ba20583aab711ea5965cf5" "3cf54128cc9573f7460bba0fd6758a57aaf240c391790fb38ab473d83ef735510c53d" "1d10c31782e8fd7da42615e33565745c30a5e6ceb2a3ae0666cc35", 16), d=int( "7bcad87e23da2cb2a8c328883fabce06e1f8e9b776c8bf253ad9884e6200e3bd9bd3b" "a2cbe87d3854527bf005ba5d878c5b0fa20cfb0a2a42884ae95ca12bf7304285e9214" "5e992f7006c7c0ae839ad550da495b143bec0f4806c7f44caed45f3ccc6dc44cfaf30" "7abdb757e3d28e41c2d21366835c0a41e50a95af490ac03af061d2feb36ac0afb87be" "a13fb0f0c5a410727ebedb286c77f9469473fae27ef2c836da6071ef7efc1647f1233" "4009a89eecb09a8287abc8c2afd1ddd9a1b0641", 16), dmp1=int( "a845366cd6f9df1f34861bef7594ed025aa83a12759e245f58adaa9bdff9c3befb760" "75d3701e90038e888eec9bf092df63400152cb25fc07effc6c74c45f0654ccbde15cd" "90dd5504298a946fa5cf22a956072da27a6602e6c6e5c97f2db9c1", 16), dmq1=int( "28b0c1e78cdac03310717992d321a3888830ec6829978c048156152d805b4f8919c61" "70b5dd204e5ddf3c6c53bc6aff15d0bd09faff7f351b94abb9db980b31f150a6d7573" "08eb66938f89a5225cb4dd817a824c89e7a0293b58fc2eefb7e259", 16), iqmp=int( "6c1536c0e16e42a094b6caaf50231ba81916871497d73dcbbbd4bdeb9e60cae0413b3" "8143b5d680275b29ed7769fe5577e4f9b3647ddb064941120914526d64d80016d2eb7" "dc362da7c569623157f3d7cff8347f11494bf5c048d77e28d3f515", 16), public_numbers=RSAPublicNumbers( e=65537, n=int( "d871bb2d27672e54fc62c4680148cbdf848438da804e2c48b5a9c9f9daf6cc6e8" "ea7d2296f25064537a9a542aef3dd449ea75774238d4da02c353d1bee70013dcc" "c248ceef4050160705c188043c8559bf6dbfb6c4bb382eda4e9547575a8227d5b" "3c0a7088391364cf9f018d8bea053b226ec65e8cdbeaf48a071d0074860a734b1" "cb7d2146d43014b20776dea42f7853a54690e6cbbf3331a9f43763cfe2a51c329" "3bea3b2eebec0d8e43eb317a443afe541107d886e5243c096091543ae65", 16), ) ) RSA_KEY_2048 = RSAPrivateNumbers( p=int( "e14202e58c5f7446648d75e5dc465781f661f6b73000c080368afcfb21377f4ef19da" "845d4ef9bc6b151f6d9f34629103f2e57615f9ba0a3a2fbb035069e1d63b4bb0e78ad" "dad1ec3c6f87e25c877a1c4c1972098e09158ef7b9bc163852a18d44a70b7b31a03dc" "2614fd9ab7bf002cba79054544af3bfbdb6aed06c7b24e6ab", 16), q=int( "dbe2bea1ff92599bd19f9d045d6ce62250c05cfeac5117f3cf3e626cb696e3d886379" "557d5a57b7476f9cf886accfd40508a805fe3b45a78e1a8a125e516cda91640ee6398" "ec5a39d3e6b177ef12ab00d07907a17640e4ca454fd8487da3c4ffa0d5c2a5edb1221" "1c8e33c7ee9fa6753771fd111ec04b8317f86693eb2928c89", 16), d=int( "aef17f80f2653bc30539f26dd4c82ed6abc1d1b53bc0abcdbee47e9a8ab433abde865" "9fcfae1244d22de6ad333c95aee7d47f30b6815065ac3322744d3ea75058002cd1b29" "3141ee2a6dc682342432707080071bd2131d6262cab07871c28aa5238b87173fb78c3" "7f9c7bcd18c12e8971bb77fd9fa3e0792fec18d8d9bed0b03ba02b263606f24dbace1" "c8263ce2802a769a090e993fd49abc50c3d3c78c29bee2de0c98055d2f102f1c5684b" "8dddee611d5205392d8e8dd61a15bf44680972a87f040a611a149271eeb2573f8bf6f" "627dfa70e77def2ee6584914fa0290e041349ea0999cdff3e493365885b906cbcf195" "843345809a85098cca90fea014a21", 16), dmp1=int( "9ba56522ffcfa5244eae805c87cc0303461f82be29691b9a7c15a5a050df6c143c575" "7c288d3d7ab7f32c782e9d9fcddc10a604e6425c0e5d0e46069035d95a923646d276d" "d9d95b8696fa29ab0de18e53f6f119310f8dd9efca62f0679291166fed8cbd5f18fe1" "3a5f1ead1d71d8c90f40382818c18c8d069be793dbc094f69", 16), dmq1=int( "a8d4a0aaa2212ccc875796a81353da1fdf00d46676c88d2b96a4bfcdd924622d8e607" "f3ac1c01dda7ebfb0a97dd7875c2a7b2db6728fb827b89c519f5716fb3228f4121647" "04b30253c17de2289e9cce3343baa82eb404f789e094a094577a9b0c5314f1725fdf5" "8e87611ad20da331bd30b8aebc7dc97d0e9a9ba8579772c9", 16), iqmp=int( "17bd5ef638c49440d1853acb3fa63a5aca28cb7f94ed350db7001c8445da8943866a7" "0936e1ee2716c98b484e357cc054d82fbbd98d42f880695d38a1dd4eb096f629b9417" "aca47e6de5da9f34e60e8a0ffd7e35be74deeef67298d94b3e0db73fc4b7a4cb360c8" "9d2117a0bfd9434d37dc7c027d6b01e5295c875015510917d", 16), public_numbers=RSAPublicNumbers( e=65537, n=int( "c17afc7e77474caa5aa83036158a3ffbf7b5216851ba2230e5d6abfcc1c6cfef5" "9e923ea1330bc593b73802ab608a6e4a3306523a3116ba5aa3966145174e13b6c" "49e9b78062e449d72efb10fd49e91fa08b96d051e782e9f5abc5b5a6f7984827a" "db8e73da00f22b2efdcdb76eab46edad98ed65662743fdc6c0e336a5d0cdbaa7d" "c29e53635e24c87a5b2c4215968063cdeb68a972babbc1e3cff00fb9a80e372a4" "d0c2c920d1e8cee333ce470dc2e8145adb05bf29aee1d24f141e8cc784989c587" "fc6fbacd979f3f2163c1d7299b365bc72ffe2848e967aed1e48dcc515b3a50ed4" "de04fd053846ca10a223b10cc841cc80fdebee44f3114c13e886af583", 16), ) ) RSA_KEY_2048_ALT = RSAPrivateNumbers( d=int( "7522768467449591813737881904131688860626637897199391200040629" "8641018746450502628484395471408986929218353894683769457466923" "3079369551423094451013669595729568593462009746342148367797495" "5529909313614750246672441810743580455199636293179539903480635" "3091286716112931976896334411287175213124504134181121011488550" "5290054443979198998564749640800633368957384058700741073997703" "8877364695937023906368630297588990131009278072614118207348356" "4640244134189285070202534488517371577359510236833464698189075" "5160693085297816063285814039518178249628112908466649245545732" "5791532385553960363601827996980725025898649392004494256400884" "092073" ), dmp1=int( "5847872614112935747739644055317429405973942336206460017493394" "9737607778799766591021036792892472774720417920838206576785118" "8889624058962939702950175807073343659386156232294197300491647" "1029508414050591959344812347424476498076532682798598325230069" "0925827594762920534235575029199380552228825468180187156871965" "973" ), dmq1=int( "2949536259161239302081155875068405238857801001054083407704879" "8210876832264504685327766351157044892283801611558399025326793" "4131638001934454489864437565651739832511702151461257267169691" "6611992398459006200708626815153304591390855807749769768978152" "9854112656599931724820610358669306523835327459478374630794532" "167" ), iqmp=int( "7331180989818931535458916053540252830484856703208982675535284" "4613815808798190559315018094080936347757336989616401164752221" "8101156529898067044923499386460167055405998646366011838018441" "3678947694258190172377716154009305082091341215866326061721180" "3836418654472188816187630316821692982783286322262994892003058" "782" ), p=int( "1460007723851883695617573533155574746587863843382715314919865" "2434108956187429726002840717317310431378483921058946835896252" "7109559207437158778332364464259678946305487699031865937075508" "8616612925453842458055546540240601585731206561647892336916583" "0023641764106581040198845259766246869529221084602380669333021" "0819" ), q=int( "1433897765867889178402883410610177836503402597775250087462018" "4617952933433119527945447840336616357136736935069377619782227" "2822380830300262175671282877680573202309319960687756231128996" "9764855320953993690199846269451095044922353809602378616938811" "7513900906279873343591486841303392490561500301994171338761080" "4439" ), public_numbers=RSAPublicNumbers( e=65537, n=int( "209350181338107812610165420955871971489973659392253291327" "839812910252466502190690572476688311285621239204212139711" "207388949164851984253143698667018532039612470954223918242" "145976986600705122576087630525229796950722166468064721258" "490916138706756006902066136471049807637157890128560592039" "941717275079733754782848729566190631725183735944031456237" "089928120178187552521649483240599003240074352860189285952" "078970127554801074176375499583703254849309993132931268013" "715070507278514207864914944621214574162116786377990456375" "964817771730371110612100247262908550409785456157505694419" "00451152778245269283276012328748538414051025541" ) ) )

true

f71b6b65aa6aa47c57fda3ac6483ee6b1a2be140

239

py

Python

more-python-for-beginners/03 - Classes/basic_class.py

CloudBreadPaPa/c9-python-getting-started

c49580be5e7e88a480d05596a7a53c89d0be7dd3

[ "MIT" ]

null

more-python-for-beginners/03 - Classes/basic_class.py

CloudBreadPaPa/c9-python-getting-started

c49580be5e7e88a480d05596a7a53c89d0be7dd3

[ "MIT" ]

null

more-python-for-beginners/03 - Classes/basic_class.py

CloudBreadPaPa/c9-python-getting-started

c49580be5e7e88a480d05596a7a53c89d0be7dd3

[ "MIT" ]

1

2021-09-12T15:34:13.000Z

class Presenter(): def __init__(self, name): # 생성자(Constructor) self.name = name def say_hello(self): # 메서드(method) print('Hello, ' + self.name) presenter = Presenter('Chris') presenter.name = 'Christopher' presenter.say_hello()

21.727273

30

0.698745

class Presenter(): def __init__(self, name): self.name = name def say_hello(self): print('Hello, ' + self.name) presenter = Presenter('Chris') presenter.name = 'Christopher' presenter.say_hello()

true

f71b6c3e8f25504c53f9b02239b585cd06f3f509

1,080

py

Python

posts/views.py

hamzabell/hackernews_mvp

54beff25f6d23f42b39a13dfe0c289768faa4c3d

[ "MIT" ]

null

posts/views.py

hamzabell/hackernews_mvp

54beff25f6d23f42b39a13dfe0c289768faa4c3d

[ "MIT" ]

null

posts/views.py

hamzabell/hackernews_mvp

54beff25f6d23f42b39a13dfe0c289768faa4c3d

[ "MIT" ]

null

from django.core.checks import messages from rest_framework import generics from rest_framework.response import Response from posts.models import Post from .serializers import PostSerializer, UpVoteSerializer class PostList(generics.ListCreateAPIView): queryset = Post.objects.all() serializer_class = PostSerializer class PostDetail(generics.RetrieveUpdateDestroyAPIView): queryset = Post.objects.all() serializer_class = PostSerializer class UpVoteAPIView(generics.GenericAPIView): serializer_class = UpVoteSerializer def post(self, request, format=None): serializer = self.get_serializer(data=request.data) serializer.is_valid(raise_exception=True) post_id = serializer.data['post_id'] post= Post.objects.filter(pk=post_id).first() if post: post.upvotes_count += 1 post.save() return Response({ 'message': 'Post has been sucessfully upvoted' }) return Response({ "message": "Post does not exist" })

26.341463

62

0.682407

from django.core.checks import messages from rest_framework import generics from rest_framework.response import Response from posts.models import Post from .serializers import PostSerializer, UpVoteSerializer class PostList(generics.ListCreateAPIView): queryset = Post.objects.all() serializer_class = PostSerializer class PostDetail(generics.RetrieveUpdateDestroyAPIView): queryset = Post.objects.all() serializer_class = PostSerializer class UpVoteAPIView(generics.GenericAPIView): serializer_class = UpVoteSerializer def post(self, request, format=None): serializer = self.get_serializer(data=request.data) serializer.is_valid(raise_exception=True) post_id = serializer.data['post_id'] post= Post.objects.filter(pk=post_id).first() if post: post.upvotes_count += 1 post.save() return Response({ 'message': 'Post has been sucessfully upvoted' }) return Response({ "message": "Post does not exist" })

true

f71b6d9c04fe09d52e0af50a82d2a1e90ad0f9f1

8,352

py

Python

estimagic/tests/differentiation/test_derivatives.py

vishalbelsare/estimagic

afae1be3a1566056d11962c495b67e64bc4a0822

[ "BSD-3-Clause" ]

null

estimagic/tests/differentiation/test_derivatives.py

vishalbelsare/estimagic

afae1be3a1566056d11962c495b67e64bc4a0822

[ "BSD-3-Clause" ]

null

estimagic/tests/differentiation/test_derivatives.py

vishalbelsare/estimagic

afae1be3a1566056d11962c495b67e64bc4a0822

[ "BSD-3-Clause" ]

null

from functools import partial from pathlib import Path import numpy as np import pandas as pd import pytest from numpy.testing import assert_array_almost_equal as aaae from pandas.testing import assert_frame_equal from scipy.optimize._numdiff import approx_derivative from estimagic.differentiation.derivatives import _consolidate_one_step_derivatives from estimagic.differentiation.derivatives import _convert_evaluation_data_to_frame from estimagic.differentiation.derivatives import ( _convert_richardson_candidates_to_frame, ) from estimagic.differentiation.derivatives import _nan_skipping_batch_evaluator from estimagic.differentiation.derivatives import _select_minimizer_along_axis from estimagic.differentiation.derivatives import first_derivative from estimagic.examples.numdiff_functions import logit_loglike from estimagic.examples.numdiff_functions import logit_loglike_gradient from estimagic.examples.numdiff_functions import logit_loglikeobs from estimagic.examples.numdiff_functions import logit_loglikeobs_jacobian from estimagic.utilities import namedtuple_from_kwargs @pytest.fixture def binary_choice_inputs(): fix_path = Path(__file__).resolve().parent / "binary_choice_inputs.pickle" inputs = pd.read_pickle(fix_path) return inputs methods = ["forward", "backward", "central"] @pytest.mark.parametrize("method", methods) def test_first_derivative_jacobian(binary_choice_inputs, method): fix = binary_choice_inputs func = partial(logit_loglikeobs, y=fix["y"], x=fix["x"]) calculated = first_derivative( func=func, method=method, params=fix["params_np"], n_steps=1, base_steps=None, lower_bounds=np.full(fix["params_np"].shape, -np.inf), upper_bounds=np.full(fix["params_np"].shape, np.inf), min_steps=1e-8, step_ratio=2.0, f0=func(fix["params_np"]), n_cores=1, ) expected = logit_loglikeobs_jacobian(fix["params_np"], fix["y"], fix["x"]) aaae(calculated["derivative"], expected, decimal=6) def test_first_derivative_jacobian_works_at_defaults(binary_choice_inputs): fix = binary_choice_inputs func = partial(logit_loglikeobs, y=fix["y"], x=fix["x"]) calculated = first_derivative(func=func, params=fix["params_np"], n_cores=1) expected = logit_loglikeobs_jacobian(fix["params_np"], fix["y"], fix["x"]) aaae(calculated["derivative"], expected, decimal=6) @pytest.mark.parametrize("method", methods) def test_first_derivative_gradient(binary_choice_inputs, method): fix = binary_choice_inputs func = partial(logit_loglike, y=fix["y"], x=fix["x"]) calculated = first_derivative( func=func, method=method, params=fix["params_np"], n_steps=1, f0=func(fix["params_np"]), n_cores=1, ) expected = logit_loglike_gradient(fix["params_np"], fix["y"], fix["x"]) aaae(calculated["derivative"], expected, decimal=4) @pytest.mark.parametrize("method", methods) def test_first_derivative_scalar(method): def f(x): return x ** 2 calculated = first_derivative(f, 3.0, n_cores=1) expected = 6.0 assert calculated["derivative"] == expected @pytest.mark.parametrize("method", methods) def test_first_derivative_scalar_with_return_func_value(method): def f(x): return x ** 2 calculated = first_derivative( f, 3.0, return_func_value=True, return_info=False, n_cores=1 ) expected = {"derivative": 6.0, "func_value": 9.0} assert calculated == expected def test_nan_skipping_batch_evaluator(): arglist = [np.nan, np.ones(2), np.array([3, 4]), np.nan, np.array([1, 2])] expected = [ np.full(2, np.nan), np.ones(2), np.array([9, 16]), np.full(2, np.nan), np.array([1, 4]), ] calculated = _nan_skipping_batch_evaluator( func=lambda x: x ** 2, arguments=arglist, n_cores=1, error_handling="continue", batch_evaluator="joblib", ) for arr_calc, arr_exp in zip(calculated, expected): if np.isnan(arr_exp).all(): assert np.isnan(arr_calc).all() else: aaae(arr_calc, arr_exp) def test_consolidate_one_step_derivatives(): forward = np.ones((1, 4, 3)) forward[:, :, 0] = np.nan backward = np.zeros_like(forward) calculated = _consolidate_one_step_derivatives( {"forward": forward, "backward": backward}, ["forward", "backward"] ) expected = np.array([[0, 1, 1]] * 4) aaae(calculated, expected) @pytest.fixture() def example_function_gradient_fixtures(): def f(x): """f:R^3 -> R""" x1, x2, x3 = x[0], x[1], x[2] y1 = np.sin(x1) + np.cos(x2) + x3 - x3 return y1 def fprime(x): """Gradient(f)(x):R^3 -> R^3""" x1, x2, x3 = x[0], x[1], x[2] grad = np.array([np.cos(x1), -np.sin(x2), x3 - x3]) return grad return {"func": f, "func_prime": fprime} @pytest.fixture() def example_function_jacobian_fixtures(): def f(x): """f:R^3 -> R^2""" x1, x2, x3 = x[0], x[1], x[2] y1, y2 = np.sin(x1) + np.cos(x2), np.exp(x3) return np.array([y1, y2]) def fprime(x): """Jacobian(f)(x):R^3 -> R^(2x3)""" x1, x2, x3 = x[0], x[1], x[2] jac = np.array([[np.cos(x1), -np.sin(x2), 0], [0, 0, np.exp(x3)]]) return jac return {"func": f, "func_prime": fprime} def test_first_derivative_gradient_richardson(example_function_gradient_fixtures): f = example_function_gradient_fixtures["func"] fprime = example_function_gradient_fixtures["func_prime"] true_fprime = fprime(np.ones(3)) scipy_fprime = approx_derivative(f, np.ones(3)) our_fprime = first_derivative(f, np.ones(3), n_steps=3, method="central", n_cores=1) aaae(scipy_fprime, our_fprime["derivative"]) aaae(true_fprime, our_fprime["derivative"]) def test_first_derivative_jacobian_richardson(example_function_jacobian_fixtures): f = example_function_jacobian_fixtures["func"] fprime = example_function_jacobian_fixtures["func_prime"] true_fprime = fprime(np.ones(3)) scipy_fprime = approx_derivative(f, np.ones(3)) our_fprime = first_derivative(f, np.ones(3), n_steps=3, method="central", n_cores=1) aaae(scipy_fprime, our_fprime["derivative"]) aaae(true_fprime, our_fprime["derivative"]) def test_convert_evaluation_data_to_frame(): arr = np.arange(4).reshape(2, 2) arr2 = arr.reshape(2, 1, 2) steps = namedtuple_from_kwargs(pos=arr, neg=-arr) evals = namedtuple_from_kwargs(pos=arr2, neg=-arr2) expected = [ [1, 0, 0, 0, 0, 0], [1, 0, 1, 0, 1, 1], [1, 1, 0, 0, 2, 2], [1, 1, 1, 0, 3, 3], [-1, 0, 0, 0, 0, 0], [-1, 0, 1, 0, 1, -1], [-1, 1, 0, 0, 2, -2], [-1, 1, 1, 0, 3, -3], ] expected = pd.DataFrame( expected, columns=["sign", "step_number", "dim_x", "dim_f", "step", "eval"] ) got = _convert_evaluation_data_to_frame(steps, evals) assert_frame_equal(expected, got.reset_index(), check_dtype=False) def test__convert_richardson_candidates_to_frame(): jac = { "forward1": np.array([[0, 1], [2, 3]]), "forward2": np.array([[0.5, 1], [2, 3]]), } err = { "forward1": np.array([[0, 0], [0, 1]]), "forward2": np.array([[1, 0], [0, 0]]), } expected = [ ["forward", 1, 0, 0, 0, 0], ["forward", 1, 1, 0, 1, 0], ["forward", 1, 0, 1, 2, 0], ["forward", 1, 1, 1, 3, 1], ["forward", 2, 0, 0, 0.5, 1], ["forward", 2, 1, 0, 1, 0], ["forward", 2, 0, 1, 2, 0], ["forward", 2, 1, 1, 3, 0], ] expected = pd.DataFrame( expected, columns=["method", "num_term", "dim_x", "dim_f", "der", "err"] ) expected = expected.set_index(["method", "num_term", "dim_x", "dim_f"]) got = _convert_richardson_candidates_to_frame(jac, err) assert_frame_equal(got, expected, check_dtype=False) def test__select_minimizer_along_axis(): der = np.array([[[0, 1], [2, 3]], [[4, 5], [6, 7]]]) err = np.array([[[0, 1], [1, 0]], [[1, 0], [0, 1]]]) expected = (np.array([[0, 5], [6, 3]]), np.array([[0, 0], [0, 0]])) got = _select_minimizer_along_axis(der, err) aaae(expected, got)

32.498054

88

0.639727

from functools import partial from pathlib import Path import numpy as np import pandas as pd import pytest from numpy.testing import assert_array_almost_equal as aaae from pandas.testing import assert_frame_equal from scipy.optimize._numdiff import approx_derivative from estimagic.differentiation.derivatives import _consolidate_one_step_derivatives from estimagic.differentiation.derivatives import _convert_evaluation_data_to_frame from estimagic.differentiation.derivatives import ( _convert_richardson_candidates_to_frame, ) from estimagic.differentiation.derivatives import _nan_skipping_batch_evaluator from estimagic.differentiation.derivatives import _select_minimizer_along_axis from estimagic.differentiation.derivatives import first_derivative from estimagic.examples.numdiff_functions import logit_loglike from estimagic.examples.numdiff_functions import logit_loglike_gradient from estimagic.examples.numdiff_functions import logit_loglikeobs from estimagic.examples.numdiff_functions import logit_loglikeobs_jacobian from estimagic.utilities import namedtuple_from_kwargs @pytest.fixture def binary_choice_inputs(): fix_path = Path(__file__).resolve().parent / "binary_choice_inputs.pickle" inputs = pd.read_pickle(fix_path) return inputs methods = ["forward", "backward", "central"] @pytest.mark.parametrize("method", methods) def test_first_derivative_jacobian(binary_choice_inputs, method): fix = binary_choice_inputs func = partial(logit_loglikeobs, y=fix["y"], x=fix["x"]) calculated = first_derivative( func=func, method=method, params=fix["params_np"], n_steps=1, base_steps=None, lower_bounds=np.full(fix["params_np"].shape, -np.inf), upper_bounds=np.full(fix["params_np"].shape, np.inf), min_steps=1e-8, step_ratio=2.0, f0=func(fix["params_np"]), n_cores=1, ) expected = logit_loglikeobs_jacobian(fix["params_np"], fix["y"], fix["x"]) aaae(calculated["derivative"], expected, decimal=6) def test_first_derivative_jacobian_works_at_defaults(binary_choice_inputs): fix = binary_choice_inputs func = partial(logit_loglikeobs, y=fix["y"], x=fix["x"]) calculated = first_derivative(func=func, params=fix["params_np"], n_cores=1) expected = logit_loglikeobs_jacobian(fix["params_np"], fix["y"], fix["x"]) aaae(calculated["derivative"], expected, decimal=6) @pytest.mark.parametrize("method", methods) def test_first_derivative_gradient(binary_choice_inputs, method): fix = binary_choice_inputs func = partial(logit_loglike, y=fix["y"], x=fix["x"]) calculated = first_derivative( func=func, method=method, params=fix["params_np"], n_steps=1, f0=func(fix["params_np"]), n_cores=1, ) expected = logit_loglike_gradient(fix["params_np"], fix["y"], fix["x"]) aaae(calculated["derivative"], expected, decimal=4) @pytest.mark.parametrize("method", methods) def test_first_derivative_scalar(method): def f(x): return x ** 2 calculated = first_derivative(f, 3.0, n_cores=1) expected = 6.0 assert calculated["derivative"] == expected @pytest.mark.parametrize("method", methods) def test_first_derivative_scalar_with_return_func_value(method): def f(x): return x ** 2 calculated = first_derivative( f, 3.0, return_func_value=True, return_info=False, n_cores=1 ) expected = {"derivative": 6.0, "func_value": 9.0} assert calculated == expected def test_nan_skipping_batch_evaluator(): arglist = [np.nan, np.ones(2), np.array([3, 4]), np.nan, np.array([1, 2])] expected = [ np.full(2, np.nan), np.ones(2), np.array([9, 16]), np.full(2, np.nan), np.array([1, 4]), ] calculated = _nan_skipping_batch_evaluator( func=lambda x: x ** 2, arguments=arglist, n_cores=1, error_handling="continue", batch_evaluator="joblib", ) for arr_calc, arr_exp in zip(calculated, expected): if np.isnan(arr_exp).all(): assert np.isnan(arr_calc).all() else: aaae(arr_calc, arr_exp) def test_consolidate_one_step_derivatives(): forward = np.ones((1, 4, 3)) forward[:, :, 0] = np.nan backward = np.zeros_like(forward) calculated = _consolidate_one_step_derivatives( {"forward": forward, "backward": backward}, ["forward", "backward"] ) expected = np.array([[0, 1, 1]] * 4) aaae(calculated, expected) @pytest.fixture() def example_function_gradient_fixtures(): def f(x): x1, x2, x3 = x[0], x[1], x[2] y1 = np.sin(x1) + np.cos(x2) + x3 - x3 return y1 def fprime(x): x1, x2, x3 = x[0], x[1], x[2] grad = np.array([np.cos(x1), -np.sin(x2), x3 - x3]) return grad return {"func": f, "func_prime": fprime} @pytest.fixture() def example_function_jacobian_fixtures(): def f(x): x1, x2, x3 = x[0], x[1], x[2] y1, y2 = np.sin(x1) + np.cos(x2), np.exp(x3) return np.array([y1, y2]) def fprime(x): x1, x2, x3 = x[0], x[1], x[2] jac = np.array([[np.cos(x1), -np.sin(x2), 0], [0, 0, np.exp(x3)]]) return jac return {"func": f, "func_prime": fprime} def test_first_derivative_gradient_richardson(example_function_gradient_fixtures): f = example_function_gradient_fixtures["func"] fprime = example_function_gradient_fixtures["func_prime"] true_fprime = fprime(np.ones(3)) scipy_fprime = approx_derivative(f, np.ones(3)) our_fprime = first_derivative(f, np.ones(3), n_steps=3, method="central", n_cores=1) aaae(scipy_fprime, our_fprime["derivative"]) aaae(true_fprime, our_fprime["derivative"]) def test_first_derivative_jacobian_richardson(example_function_jacobian_fixtures): f = example_function_jacobian_fixtures["func"] fprime = example_function_jacobian_fixtures["func_prime"] true_fprime = fprime(np.ones(3)) scipy_fprime = approx_derivative(f, np.ones(3)) our_fprime = first_derivative(f, np.ones(3), n_steps=3, method="central", n_cores=1) aaae(scipy_fprime, our_fprime["derivative"]) aaae(true_fprime, our_fprime["derivative"]) def test_convert_evaluation_data_to_frame(): arr = np.arange(4).reshape(2, 2) arr2 = arr.reshape(2, 1, 2) steps = namedtuple_from_kwargs(pos=arr, neg=-arr) evals = namedtuple_from_kwargs(pos=arr2, neg=-arr2) expected = [ [1, 0, 0, 0, 0, 0], [1, 0, 1, 0, 1, 1], [1, 1, 0, 0, 2, 2], [1, 1, 1, 0, 3, 3], [-1, 0, 0, 0, 0, 0], [-1, 0, 1, 0, 1, -1], [-1, 1, 0, 0, 2, -2], [-1, 1, 1, 0, 3, -3], ] expected = pd.DataFrame( expected, columns=["sign", "step_number", "dim_x", "dim_f", "step", "eval"] ) got = _convert_evaluation_data_to_frame(steps, evals) assert_frame_equal(expected, got.reset_index(), check_dtype=False) def test__convert_richardson_candidates_to_frame(): jac = { "forward1": np.array([[0, 1], [2, 3]]), "forward2": np.array([[0.5, 1], [2, 3]]), } err = { "forward1": np.array([[0, 0], [0, 1]]), "forward2": np.array([[1, 0], [0, 0]]), } expected = [ ["forward", 1, 0, 0, 0, 0], ["forward", 1, 1, 0, 1, 0], ["forward", 1, 0, 1, 2, 0], ["forward", 1, 1, 1, 3, 1], ["forward", 2, 0, 0, 0.5, 1], ["forward", 2, 1, 0, 1, 0], ["forward", 2, 0, 1, 2, 0], ["forward", 2, 1, 1, 3, 0], ] expected = pd.DataFrame( expected, columns=["method", "num_term", "dim_x", "dim_f", "der", "err"] ) expected = expected.set_index(["method", "num_term", "dim_x", "dim_f"]) got = _convert_richardson_candidates_to_frame(jac, err) assert_frame_equal(got, expected, check_dtype=False) def test__select_minimizer_along_axis(): der = np.array([[[0, 1], [2, 3]], [[4, 5], [6, 7]]]) err = np.array([[[0, 1], [1, 0]], [[1, 0], [0, 1]]]) expected = (np.array([[0, 5], [6, 3]]), np.array([[0, 0], [0, 0]])) got = _select_minimizer_along_axis(der, err) aaae(expected, got)

true

f71b6dab14627d4699e80c6acbce3ef420b0a543

35

py

Python

ciphey/basemods/Searchers/__init__.py

paramint/ciphey

26195dfe1f216c3d43d07b50279b64eb026f0c13

[ "MIT" ]

1

2021-05-30T19:55:00.000Z

ciphey/basemods/Searchers/__init__.py

usama7628674/Ciphey

e18801c506e93e7e9377d0bbc6870ecd84ae2f61

[ "MIT" ]

4

2020-11-13T19:01:56.000Z

2022-02-10T02:14:00.000Z

ciphey/basemods/Searchers/__init__.py

usama7628674/Ciphey

e18801c506e93e7e9377d0bbc6870ecd84ae2f61

[ "MIT" ]

null

from . import ausearch, perfection

17.5

34

0.8

from . import ausearch, perfection

true

f71b6e4295ed13a2ac4d43cdf95ee46cabd50a60

18,334

py

Python

python/helpers/pycharm/teamcity/pytest_plugin.py

janchochol/intellij-community

fce543ac6018b411e519fe01ddc71a8c1bbd138b

[ "Apache-2.0" ]

null

python/helpers/pycharm/teamcity/pytest_plugin.py

janchochol/intellij-community

fce543ac6018b411e519fe01ddc71a8c1bbd138b

[ "Apache-2.0" ]

null

python/helpers/pycharm/teamcity/pytest_plugin.py

janchochol/intellij-community

fce543ac6018b411e519fe01ddc71a8c1bbd138b

[ "Apache-2.0" ]

null

# coding=utf-8 """ Aaron Buchanan Nov. 2012 Plug-in for py.test for reporting to TeamCity server Report results to TeamCity during test execution for immediate reporting when using TeamCity. This should be installed as a py.test plugin and will be automatically enabled by running tests under TeamCity build. """ import os import pprint import sys import re import traceback from datetime import timedelta from teamcity.messages import TeamcityServiceMessages from teamcity.common import convert_error_to_string, dump_test_stderr, dump_test_stdout from teamcity import is_running_under_teamcity from teamcity import diff_tools diff_tools.patch_unittest_diff() def unformat_pytest_explanation(s): """ Undo _pytest.assertion.util.format_explanation """ return s.replace("\\n", "\n") def fetch_diff_error_from_message(err_message, swap_diff): line_with_diff = None diff_error_message = None lines = err_message.split("\n") if err_message.startswith("AssertionError: assert"): # Everything in one line line_with_diff = lines[0][len("AssertionError: assert "):] elif len(err_message.split("\n")) > 1: err_line = lines[1] line_with_diff = err_line[len("assert "):] diff_error_message = lines[0] if line_with_diff and line_with_diff.count("==") == 1: parts = [x.strip() for x in line_with_diff.split("==")] parts = [s[1:-1] if s.startswith("'") or s.startswith('"') else s for s in parts] # Pytest cuts too long lines, no need to check is_too_big expected, actual = parts[1], parts[0] if swap_diff: expected, actual = actual, expected expected = unformat_pytest_explanation(expected) actual = unformat_pytest_explanation(actual) return diff_tools.EqualsAssertionError(expected, actual, diff_error_message) else: return None def _is_bool_supported(): """ Type "bool" is not supported before 2.9 """ try: from pytest import __version__ from distutils import version return version.LooseVersion(str(__version__)) >= version.LooseVersion("2.9") except ImportError: return False def pytest_addoption(parser): group = parser.getgroup("terminal reporting", "reporting", after="general") group._addoption('--teamcity', action="count", dest="teamcity", default=0, help="force output of JetBrains TeamCity service messages") group._addoption('--no-teamcity', action="count", dest="no_teamcity", default=0, help="disable output of JetBrains TeamCity service messages") kwargs = {"help": "skip output of passed tests for JetBrains TeamCity service messages"} if _is_bool_supported(): kwargs.update({"type": "bool"}) parser.addini("skippassedoutput", **kwargs) parser.addini("swapdiff", **kwargs) def pytest_configure(config): if config.option.no_teamcity >= 1: enabled = False elif config.option.teamcity >= 1: enabled = True else: enabled = is_running_under_teamcity() if enabled: output_capture_enabled = getattr(config.option, 'capture', 'fd') != 'no' coverage_controller = _get_coverage_controller(config) skip_passed_output = bool(config.getini('skippassedoutput')) config.option.verbose = 2 # don't truncate assert explanations config._teamcityReporting = EchoTeamCityMessages( output_capture_enabled, coverage_controller, skip_passed_output, bool(config.getini('swapdiff')) ) config.pluginmanager.register(config._teamcityReporting) def pytest_unconfigure(config): teamcity_reporting = getattr(config, '_teamcityReporting', None) if teamcity_reporting: del config._teamcityReporting config.pluginmanager.unregister(teamcity_reporting) def _get_coverage_controller(config): cov_plugin = config.pluginmanager.getplugin('_cov') if not cov_plugin: return None return cov_plugin.cov_controller class EchoTeamCityMessages(object): def __init__(self, output_capture_enabled, coverage_controller, skip_passed_output, swap_diff): self.coverage_controller = coverage_controller self.output_capture_enabled = output_capture_enabled self.skip_passed_output = skip_passed_output self.teamcity = TeamcityServiceMessages() self.test_start_reported_mark = set() self.max_reported_output_size = 1 * 1024 * 1024 self.reported_output_chunk_size = 50000 self.swap_diff = swap_diff def get_id_from_location(self, location): if type(location) is not tuple or len(location) != 3 or not hasattr(location[2], "startswith"): return None def convert_file_to_id(filename): filename = re.sub(r"\.pyc?$", "", filename) return filename.replace(os.sep, ".").replace("/", ".") def add_prefix_to_filename_id(filename_id, prefix): dot_location = filename_id.rfind('.') if dot_location <= 0 or dot_location >= len(filename_id) - 1: return None return filename_id[:dot_location + 1] + prefix + filename_id[dot_location + 1:] pylint_prefix = '[pylint] ' if location[2].startswith(pylint_prefix): id_from_file = convert_file_to_id(location[2][len(pylint_prefix):]) return id_from_file + ".Pylint" if location[2] == "PEP8-check": id_from_file = convert_file_to_id(location[0]) return id_from_file + ".PEP8" return None def format_test_id(self, nodeid, location): id_from_location = self.get_id_from_location(location) if id_from_location is not None: return id_from_location test_id = nodeid if test_id: if test_id.find("::") < 0: test_id += "::top_level" else: test_id = "top_level" first_bracket = test_id.find("[") if first_bracket > 0: # [] -> (), make it look like nose parameterized tests params = "(" + test_id[first_bracket + 1:] if params.endswith("]"): params = params[:-1] + ")" test_id = test_id[:first_bracket] if test_id.endswith("::"): test_id = test_id[:-2] else: params = "" test_id = test_id.replace("::()::", "::") test_id = re.sub(r"\.pyc?::", r"::", test_id) test_id = test_id.replace(".", "_").replace(os.sep, ".").replace("/", ".").replace('::', '.') if params: params = params.replace(".", "_") test_id += params return test_id def format_location(self, location): if type(location) is tuple and len(location) == 3: return "%s:%s (%s)" % (str(location[0]), str(location[1]), str(location[2])) return str(location) def pytest_collection_finish(self, session): self.teamcity.testCount(len(session.items)) def pytest_runtest_logstart(self, nodeid, location): # test name fetched from location passed as metainfo to PyCharm # it will be used to run specific test # See IDEA-176950, PY-31836 test_name = location[2] if test_name: test_name = str(test_name).split(".")[-1] self.ensure_test_start_reported(self.format_test_id(nodeid, location), test_name) def ensure_test_start_reported(self, test_id, metainfo=None): if test_id not in self.test_start_reported_mark: if self.output_capture_enabled: capture_standard_output = "false" else: capture_standard_output = "true" self.teamcity.testStarted(test_id, flowId=test_id, captureStandardOutput=capture_standard_output, metainfo=metainfo) self.test_start_reported_mark.add(test_id) def report_has_output(self, report): for (secname, data) in report.sections: if report.when in secname and ('stdout' in secname or 'stderr' in secname): return True return False def report_test_output(self, report, test_id): for (secname, data) in report.sections: # https://github.com/JetBrains/teamcity-messages/issues/112 # CollectReport didn't have 'when' property, but now it has. # But we still need output on 'collect' state if hasattr(report, "when") and report.when not in secname and report.when != 'collect': continue if not data: continue if 'stdout' in secname: dump_test_stdout(self.teamcity, test_id, test_id, data) elif 'stderr' in secname: dump_test_stderr(self.teamcity, test_id, test_id, data) def report_test_finished(self, test_id, duration=None): self.teamcity.testFinished(test_id, testDuration=duration, flowId=test_id) self.test_start_reported_mark.remove(test_id) def report_test_failure(self, test_id, report, message=None, report_output=True): if hasattr(report, 'duration'): duration = timedelta(seconds=report.duration) else: duration = None if message is None: message = self.format_location(report.location) self.ensure_test_start_reported(test_id) if report_output: self.report_test_output(report, test_id) diff_error = None try: err_message = str(report.longrepr.reprcrash.message) diff_name = diff_tools.EqualsAssertionError.__name__ # There is a string like "foo.bar.DiffError: [serialized_data]" if diff_name in err_message: serialized_data = err_message[err_message.index(diff_name) + len(diff_name) + 1:] diff_error = diff_tools.deserialize_error(serialized_data) # AssertionError is patched in py.test, we can try to fetch diff from it # In general case message starts with "AssertionError: ", but can also starts with "assert" for top-level # function. To support both cases we unify them if err_message.startswith("assert"): err_message = "AssertionError: " + err_message if err_message.startswith("AssertionError:"): diff_error = fetch_diff_error_from_message(err_message, self.swap_diff) except Exception: pass if not diff_error: from .jb_local_exc_store import get_exception diff_error = get_exception() if diff_error: # Cut everything after postfix: it is internal view of DiffError strace = str(report.longrepr) data_postfix = "_ _ _ _ _" if data_postfix in strace: strace = strace[0:strace.index(data_postfix)] self.teamcity.testFailed(test_id, diff_error.msg if diff_error.msg else message, strace, flowId=test_id, comparison_failure=diff_error ) else: self.teamcity.testFailed(test_id, message, str(report.longrepr), flowId=test_id) self.report_test_finished(test_id, duration) def report_test_skip(self, test_id, report): if type(report.longrepr) is tuple and len(report.longrepr) == 3: reason = report.longrepr[2] else: reason = str(report.longrepr) if hasattr(report, 'duration'): duration = timedelta(seconds=report.duration) else: duration = None self.ensure_test_start_reported(test_id) self.report_test_output(report, test_id) self.teamcity.testIgnored(test_id, reason, flowId=test_id) self.report_test_finished(test_id, duration) def pytest_assertrepr_compare(self, config, op, left, right): if op in ('==', '!='): return ['{0} {1} {2}'.format(pprint.pformat(left), op, pprint.pformat(right))] def pytest_runtest_logreport(self, report): """ :type report: _pytest.runner.TestReport """ test_id = self.format_test_id(report.nodeid, report.location) duration = timedelta(seconds=report.duration) if report.passed: # Do not report passed setup/teardown if no output if report.when == 'call': self.ensure_test_start_reported(test_id) if not self.skip_passed_output: self.report_test_output(report, test_id) self.report_test_finished(test_id, duration) else: if self.report_has_output(report) and not self.skip_passed_output: block_name = "test " + report.when self.teamcity.blockOpened(block_name, flowId=test_id) self.report_test_output(report, test_id) self.teamcity.blockClosed(block_name, flowId=test_id) elif report.failed: if report.when == 'call': self.report_test_failure(test_id, report) elif report.when == 'setup': if self.report_has_output(report): self.teamcity.blockOpened("test setup", flowId=test_id) self.report_test_output(report, test_id) self.teamcity.blockClosed("test setup", flowId=test_id) self.report_test_failure(test_id, report, message="test setup failed", report_output=False) elif report.when == 'teardown': # Report failed teardown as a separate test as original test is already finished self.report_test_failure(test_id + "_teardown", report) elif report.skipped: self.report_test_skip(test_id, report) def pytest_collectreport(self, report): test_id = self.format_test_id(report.nodeid, report.location) + "_collect" if report.failed: self.report_test_failure(test_id, report) elif report.skipped: self.report_test_skip(test_id, report) def pytest_terminal_summary(self): if self.coverage_controller is not None: try: self._report_coverage() except Exception: tb = traceback.format_exc() self.teamcity.customMessage("Coverage statistics reporting failed", "ERROR", errorDetails=tb) def _report_coverage(self): from coverage.misc import NotPython from coverage.results import Numbers class _Reporter(object): def __init__(self, coverage, config): try: from coverage.report import Reporter except ImportError: # Support for coverage >= 5.0.1. from coverage.report import get_analysis_to_report class Reporter(object): def __init__(self, coverage, config): self.coverage = coverage self.config = config self._file_reporters = [] def find_file_reporters(self, morfs): return [fr for fr, _ in get_analysis_to_report(self.coverage, morfs)] self._reporter = Reporter(coverage, config) def find_file_reporters(self, morfs): self.file_reporters = self._reporter.find_file_reporters(morfs) def __getattr__(self, name): return getattr(self._reporter, name) class _CoverageReporter(_Reporter): def __init__(self, coverage, config, messages): super(_CoverageReporter, self).__init__(coverage, config) if hasattr(coverage, 'data'): self.branches = coverage.data.has_arcs() else: self.branches = coverage.get_data().has_arcs() self.messages = messages def report(self, morfs, outfile=None): if hasattr(self, 'find_code_units'): self.find_code_units(morfs) else: self.find_file_reporters(morfs) total = Numbers() if hasattr(self, 'code_units'): units = self.code_units else: units = self.file_reporters for cu in units: try: analysis = self.coverage._analyze(cu) nums = analysis.numbers total += nums except KeyboardInterrupt: raise except Exception: if self.config.ignore_errors: continue err = sys.exc_info() typ, msg = err[:2] if typ is NotPython and not cu.should_be_python(): continue test_id = cu.name details = convert_error_to_string(err) self.messages.testStarted(test_id, flowId=test_id) self.messages.testFailed(test_id, message="Coverage analysis failed", details=details, flowId=test_id) self.messages.testFinished(test_id, flowId=test_id) if total.n_files > 0: covered = total.n_executed total_statements = total.n_statements if self.branches: covered += total.n_executed_branches total_statements += total.n_branches self.messages.buildStatisticLinesCovered(covered) self.messages.buildStatisticTotalLines(total_statements) self.messages.buildStatisticLinesUncovered(total_statements - covered) reporter = _CoverageReporter( self.coverage_controller.cov, self.coverage_controller.cov.config, self.teamcity, ) reporter.report(None)

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import os import pprint import sys import re import traceback from datetime import timedelta from teamcity.messages import TeamcityServiceMessages from teamcity.common import convert_error_to_string, dump_test_stderr, dump_test_stdout from teamcity import is_running_under_teamcity from teamcity import diff_tools diff_tools.patch_unittest_diff() def unformat_pytest_explanation(s): return s.replace("\\n", "\n") def fetch_diff_error_from_message(err_message, swap_diff): line_with_diff = None diff_error_message = None lines = err_message.split("\n") if err_message.startswith("AssertionError: assert"): line_with_diff = lines[0][len("AssertionError: assert "):] elif len(err_message.split("\n")) > 1: err_line = lines[1] line_with_diff = err_line[len("assert "):] diff_error_message = lines[0] if line_with_diff and line_with_diff.count("==") == 1: parts = [x.strip() for x in line_with_diff.split("==")] parts = [s[1:-1] if s.startswith("'") or s.startswith('"') else s for s in parts] # Pytest cuts too long lines, no need to check is_too_big expected, actual = parts[1], parts[0] if swap_diff: expected, actual = actual, expected expected = unformat_pytest_explanation(expected) actual = unformat_pytest_explanation(actual) return diff_tools.EqualsAssertionError(expected, actual, diff_error_message) else: return None def _is_bool_supported(): try: from pytest import __version__ from distutils import version return version.LooseVersion(str(__version__)) >= version.LooseVersion("2.9") except ImportError: return False def pytest_addoption(parser): group = parser.getgroup("terminal reporting", "reporting", after="general") group._addoption('--teamcity', action="count", dest="teamcity", default=0, help="force output of JetBrains TeamCity service messages") group._addoption('--no-teamcity', action="count", dest="no_teamcity", default=0, help="disable output of JetBrains TeamCity service messages") kwargs = {"help": "skip output of passed tests for JetBrains TeamCity service messages"} if _is_bool_supported(): kwargs.update({"type": "bool"}) parser.addini("skippassedoutput", **kwargs) parser.addini("swapdiff", **kwargs) def pytest_configure(config): if config.option.no_teamcity >= 1: enabled = False elif config.option.teamcity >= 1: enabled = True else: enabled = is_running_under_teamcity() if enabled: output_capture_enabled = getattr(config.option, 'capture', 'fd') != 'no' coverage_controller = _get_coverage_controller(config) skip_passed_output = bool(config.getini('skippassedoutput')) config.option.verbose = 2 # don't truncate assert explanations config._teamcityReporting = EchoTeamCityMessages( output_capture_enabled, coverage_controller, skip_passed_output, bool(config.getini('swapdiff')) ) config.pluginmanager.register(config._teamcityReporting) def pytest_unconfigure(config): teamcity_reporting = getattr(config, '_teamcityReporting', None) if teamcity_reporting: del config._teamcityReporting config.pluginmanager.unregister(teamcity_reporting) def _get_coverage_controller(config): cov_plugin = config.pluginmanager.getplugin('_cov') if not cov_plugin: return None return cov_plugin.cov_controller class EchoTeamCityMessages(object): def __init__(self, output_capture_enabled, coverage_controller, skip_passed_output, swap_diff): self.coverage_controller = coverage_controller self.output_capture_enabled = output_capture_enabled self.skip_passed_output = skip_passed_output self.teamcity = TeamcityServiceMessages() self.test_start_reported_mark = set() self.max_reported_output_size = 1 * 1024 * 1024 self.reported_output_chunk_size = 50000 self.swap_diff = swap_diff def get_id_from_location(self, location): if type(location) is not tuple or len(location) != 3 or not hasattr(location[2], "startswith"): return None def convert_file_to_id(filename): filename = re.sub(r"\.pyc?$", "", filename) return filename.replace(os.sep, ".").replace("/", ".") def add_prefix_to_filename_id(filename_id, prefix): dot_location = filename_id.rfind('.') if dot_location <= 0 or dot_location >= len(filename_id) - 1: return None return filename_id[:dot_location + 1] + prefix + filename_id[dot_location + 1:] pylint_prefix = '[pylint] ' if location[2].startswith(pylint_prefix): id_from_file = convert_file_to_id(location[2][len(pylint_prefix):]) return id_from_file + ".Pylint" if location[2] == "PEP8-check": id_from_file = convert_file_to_id(location[0]) return id_from_file + ".PEP8" return None def format_test_id(self, nodeid, location): id_from_location = self.get_id_from_location(location) if id_from_location is not None: return id_from_location test_id = nodeid if test_id: if test_id.find("::") < 0: test_id += "::top_level" else: test_id = "top_level" first_bracket = test_id.find("[") if first_bracket > 0: # [] -> (), make it look like nose parameterized tests params = "(" + test_id[first_bracket + 1:] if params.endswith("]"): params = params[:-1] + ")" test_id = test_id[:first_bracket] if test_id.endswith("::"): test_id = test_id[:-2] else: params = "" test_id = test_id.replace("::()::", "::") test_id = re.sub(r"\.pyc?::", r"::", test_id) test_id = test_id.replace(".", "_").replace(os.sep, ".").replace("/", ".").replace('::', '.') if params: params = params.replace(".", "_") test_id += params return test_id def format_location(self, location): if type(location) is tuple and len(location) == 3: return "%s:%s (%s)" % (str(location[0]), str(location[1]), str(location[2])) return str(location) def pytest_collection_finish(self, session): self.teamcity.testCount(len(session.items)) def pytest_runtest_logstart(self, nodeid, location): # test name fetched from location passed as metainfo to PyCharm # it will be used to run specific test # See IDEA-176950, PY-31836 test_name = location[2] if test_name: test_name = str(test_name).split(".")[-1] self.ensure_test_start_reported(self.format_test_id(nodeid, location), test_name) def ensure_test_start_reported(self, test_id, metainfo=None): if test_id not in self.test_start_reported_mark: if self.output_capture_enabled: capture_standard_output = "false" else: capture_standard_output = "true" self.teamcity.testStarted(test_id, flowId=test_id, captureStandardOutput=capture_standard_output, metainfo=metainfo) self.test_start_reported_mark.add(test_id) def report_has_output(self, report): for (secname, data) in report.sections: if report.when in secname and ('stdout' in secname or 'stderr' in secname): return True return False def report_test_output(self, report, test_id): for (secname, data) in report.sections: # https://github.com/JetBrains/teamcity-messages/issues/112 # CollectReport didn't have 'when' property, but now it has. # But we still need output on 'collect' state if hasattr(report, "when") and report.when not in secname and report.when != 'collect': continue if not data: continue if 'stdout' in secname: dump_test_stdout(self.teamcity, test_id, test_id, data) elif 'stderr' in secname: dump_test_stderr(self.teamcity, test_id, test_id, data) def report_test_finished(self, test_id, duration=None): self.teamcity.testFinished(test_id, testDuration=duration, flowId=test_id) self.test_start_reported_mark.remove(test_id) def report_test_failure(self, test_id, report, message=None, report_output=True): if hasattr(report, 'duration'): duration = timedelta(seconds=report.duration) else: duration = None if message is None: message = self.format_location(report.location) self.ensure_test_start_reported(test_id) if report_output: self.report_test_output(report, test_id) diff_error = None try: err_message = str(report.longrepr.reprcrash.message) diff_name = diff_tools.EqualsAssertionError.__name__ # There is a string like "foo.bar.DiffError: [serialized_data]" if diff_name in err_message: serialized_data = err_message[err_message.index(diff_name) + len(diff_name) + 1:] diff_error = diff_tools.deserialize_error(serialized_data) # AssertionError is patched in py.test, we can try to fetch diff from it # In general case message starts with "AssertionError: ", but can also starts with "assert" for top-level # function. To support both cases we unify them if err_message.startswith("assert"): err_message = "AssertionError: " + err_message if err_message.startswith("AssertionError:"): diff_error = fetch_diff_error_from_message(err_message, self.swap_diff) except Exception: pass if not diff_error: from .jb_local_exc_store import get_exception diff_error = get_exception() if diff_error: # Cut everything after postfix: it is internal view of DiffError strace = str(report.longrepr) data_postfix = "_ _ _ _ _" if data_postfix in strace: strace = strace[0:strace.index(data_postfix)] self.teamcity.testFailed(test_id, diff_error.msg if diff_error.msg else message, strace, flowId=test_id, comparison_failure=diff_error ) else: self.teamcity.testFailed(test_id, message, str(report.longrepr), flowId=test_id) self.report_test_finished(test_id, duration) def report_test_skip(self, test_id, report): if type(report.longrepr) is tuple and len(report.longrepr) == 3: reason = report.longrepr[2] else: reason = str(report.longrepr) if hasattr(report, 'duration'): duration = timedelta(seconds=report.duration) else: duration = None self.ensure_test_start_reported(test_id) self.report_test_output(report, test_id) self.teamcity.testIgnored(test_id, reason, flowId=test_id) self.report_test_finished(test_id, duration) def pytest_assertrepr_compare(self, config, op, left, right): if op in ('==', '!='): return ['{0} {1} {2}'.format(pprint.pformat(left), op, pprint.pformat(right))] def pytest_runtest_logreport(self, report): test_id = self.format_test_id(report.nodeid, report.location) duration = timedelta(seconds=report.duration) if report.passed: # Do not report passed setup/teardown if no output if report.when == 'call': self.ensure_test_start_reported(test_id) if not self.skip_passed_output: self.report_test_output(report, test_id) self.report_test_finished(test_id, duration) else: if self.report_has_output(report) and not self.skip_passed_output: block_name = "test " + report.when self.teamcity.blockOpened(block_name, flowId=test_id) self.report_test_output(report, test_id) self.teamcity.blockClosed(block_name, flowId=test_id) elif report.failed: if report.when == 'call': self.report_test_failure(test_id, report) elif report.when == 'setup': if self.report_has_output(report): self.teamcity.blockOpened("test setup", flowId=test_id) self.report_test_output(report, test_id) self.teamcity.blockClosed("test setup", flowId=test_id) self.report_test_failure(test_id, report, message="test setup failed", report_output=False) elif report.when == 'teardown': # Report failed teardown as a separate test as original test is already finished self.report_test_failure(test_id + "_teardown", report) elif report.skipped: self.report_test_skip(test_id, report) def pytest_collectreport(self, report): test_id = self.format_test_id(report.nodeid, report.location) + "_collect" if report.failed: self.report_test_failure(test_id, report) elif report.skipped: self.report_test_skip(test_id, report) def pytest_terminal_summary(self): if self.coverage_controller is not None: try: self._report_coverage() except Exception: tb = traceback.format_exc() self.teamcity.customMessage("Coverage statistics reporting failed", "ERROR", errorDetails=tb) def _report_coverage(self): from coverage.misc import NotPython from coverage.results import Numbers class _Reporter(object): def __init__(self, coverage, config): try: from coverage.report import Reporter except ImportError: # Support for coverage >= 5.0.1. from coverage.report import get_analysis_to_report class Reporter(object): def __init__(self, coverage, config): self.coverage = coverage self.config = config self._file_reporters = [] def find_file_reporters(self, morfs): return [fr for fr, _ in get_analysis_to_report(self.coverage, morfs)] self._reporter = Reporter(coverage, config) def find_file_reporters(self, morfs): self.file_reporters = self._reporter.find_file_reporters(morfs) def __getattr__(self, name): return getattr(self._reporter, name) class _CoverageReporter(_Reporter): def __init__(self, coverage, config, messages): super(_CoverageReporter, self).__init__(coverage, config) if hasattr(coverage, 'data'): self.branches = coverage.data.has_arcs() else: self.branches = coverage.get_data().has_arcs() self.messages = messages def report(self, morfs, outfile=None): if hasattr(self, 'find_code_units'): self.find_code_units(morfs) else: self.find_file_reporters(morfs) total = Numbers() if hasattr(self, 'code_units'): units = self.code_units else: units = self.file_reporters for cu in units: try: analysis = self.coverage._analyze(cu) nums = analysis.numbers total += nums except KeyboardInterrupt: raise except Exception: if self.config.ignore_errors: continue err = sys.exc_info() typ, msg = err[:2] if typ is NotPython and not cu.should_be_python(): continue test_id = cu.name details = convert_error_to_string(err) self.messages.testStarted(test_id, flowId=test_id) self.messages.testFailed(test_id, message="Coverage analysis failed", details=details, flowId=test_id) self.messages.testFinished(test_id, flowId=test_id) if total.n_files > 0: covered = total.n_executed total_statements = total.n_statements if self.branches: covered += total.n_executed_branches total_statements += total.n_branches self.messages.buildStatisticLinesCovered(covered) self.messages.buildStatisticTotalLines(total_statements) self.messages.buildStatisticLinesUncovered(total_statements - covered) reporter = _CoverageReporter( self.coverage_controller.cov, self.coverage_controller.cov.config, self.teamcity, ) reporter.report(None)

true

f71b6ec1ac6a3e138fec3e28c7e2f2eda3b7aa07

2,948

py

Python

mayan/apps/mayan_statistics/views.py

sophiawa/Mayan-EDMS

42f20576d0c690b645a60bf53c5169cda4264231

[ "Apache-2.0" ]

null

mayan/apps/mayan_statistics/views.py

sophiawa/Mayan-EDMS

42f20576d0c690b645a60bf53c5169cda4264231

[ "Apache-2.0" ]

10

2021-03-19T23:48:12.000Z

2022-03-12T00:41:49.000Z

mayan/apps/mayan_statistics/views.py

sophiawa/Mayan-EDMS

42f20576d0c690b645a60bf53c5169cda4264231

[ "Apache-2.0" ]

1

2020-12-17T02:35:09.000Z

from django.contrib import messages from django.http import Http404 from django.utils.translation import ugettext_lazy as _ from mayan.apps.common.generics import ConfirmView, SimpleView, SingleObjectListView from .classes import Statistic, StatisticNamespace from .permissions import permission_statistics_view from .tasks import task_execute_statistic class NamespaceListView(SingleObjectListView): extra_context = { 'hide_link': True, 'title': _('Statistics namespaces'), } template_name = 'appearance/generic_list.html' view_permission = permission_statistics_view def get_source_queryset(self): return StatisticNamespace.get_all() class NamespaceDetailView(SingleObjectListView): view_permission = permission_statistics_view def get_extra_context(self): return { 'hide_link': True, 'object': self.get_namespace(), 'title': _('Namespace details for: %s') % self.get_namespace(), } def get_namespace(self): return StatisticNamespace.get(slug=self.kwargs['slug']) def get_source_queryset(self): return self.get_namespace().statistics class StatisticDetailView(SimpleView): view_permission = permission_statistics_view def get_extra_context(self): obj = self.get_object() return { 'chart_data': obj.get_chart_data(), 'namespace': obj.namespace, 'navigation_object_list': ('namespace', 'object'), 'no_data': not obj.get_results_data()['series'], 'object': obj, 'title': _('Results for: %s') % obj, } def get_object(self): try: return Statistic.get(self.kwargs['slug']) except KeyError: raise Http404(_('Statistic "%s" not found.') % self.kwargs['slug']) def get_template_names(self): return (self.get_object().renderer.template_name,) class StatisticQueueView(ConfirmView): view_permission = permission_statistics_view def get_extra_context(self): obj = self.get_object() return { 'namespace': obj.namespace, 'object': obj, # Translators: This text is asking users if they want to queue # (to send to the queue) a statistic for it to be update ahead # of schedule 'title': _( 'Queue statistic "%s" to be updated?' ) % obj, } def get_object(self): try: return Statistic.get(slug=self.kwargs['slug']) except KeyError: raise Http404(_('Statistic "%s" not found.') % self.kwargs['slug']) def view_action(self): task_execute_statistic.delay(slug=self.get_object().slug) messages.success( message=_( 'Statistic "%s" queued successfully for update.' ) % self.get_object().label, request=self.request )

31.031579

84

0.636364

from django.contrib import messages from django.http import Http404 from django.utils.translation import ugettext_lazy as _ from mayan.apps.common.generics import ConfirmView, SimpleView, SingleObjectListView from .classes import Statistic, StatisticNamespace from .permissions import permission_statistics_view from .tasks import task_execute_statistic class NamespaceListView(SingleObjectListView): extra_context = { 'hide_link': True, 'title': _('Statistics namespaces'), } template_name = 'appearance/generic_list.html' view_permission = permission_statistics_view def get_source_queryset(self): return StatisticNamespace.get_all() class NamespaceDetailView(SingleObjectListView): view_permission = permission_statistics_view def get_extra_context(self): return { 'hide_link': True, 'object': self.get_namespace(), 'title': _('Namespace details for: %s') % self.get_namespace(), } def get_namespace(self): return StatisticNamespace.get(slug=self.kwargs['slug']) def get_source_queryset(self): return self.get_namespace().statistics class StatisticDetailView(SimpleView): view_permission = permission_statistics_view def get_extra_context(self): obj = self.get_object() return { 'chart_data': obj.get_chart_data(), 'namespace': obj.namespace, 'navigation_object_list': ('namespace', 'object'), 'no_data': not obj.get_results_data()['series'], 'object': obj, 'title': _('Results for: %s') % obj, } def get_object(self): try: return Statistic.get(self.kwargs['slug']) except KeyError: raise Http404(_('Statistic "%s" not found.') % self.kwargs['slug']) def get_template_names(self): return (self.get_object().renderer.template_name,) class StatisticQueueView(ConfirmView): view_permission = permission_statistics_view def get_extra_context(self): obj = self.get_object() return { 'namespace': obj.namespace, 'object': obj, 'title': _( 'Queue statistic "%s" to be updated?' ) % obj, } def get_object(self): try: return Statistic.get(slug=self.kwargs['slug']) except KeyError: raise Http404(_('Statistic "%s" not found.') % self.kwargs['slug']) def view_action(self): task_execute_statistic.delay(slug=self.get_object().slug) messages.success( message=_( 'Statistic "%s" queued successfully for update.' ) % self.get_object().label, request=self.request )

true

f71b6ef7a59d7d39b2bcee735e05d0bb4fe7d665

2,447

py

Python

display_recognized_faces.py

theTechie/face-recognition

4236405914971fa971eb8dab7f31022f154ac10b

[ "MIT" ]

null

display_recognized_faces.py

theTechie/face-recognition

4236405914971fa971eb8dab7f31022f154ac10b

[ "MIT" ]

null

display_recognized_faces.py

theTechie/face-recognition

4236405914971fa971eb8dab7f31022f154ac10b

[ "MIT" ]

null

import face_recognition from PIL import Image, ImageDraw from pathlib import Path import recognize_face known_path = Path("data/sample-2/jpeg/picked/known") known_images = list(known_path.glob('*.jpeg')) known_face_encodings = [] known_face_names = [] known_faces = [recognize_face.image_to_known_face(str(image_path), image_path.stem) for image_path in known_images] print('I just learned to recognize %d persons... \n' % len(known_images)) unknown_path = Path("data/sample-4/unknown") unknown_images = list(unknown_path.glob('**/*.jpeg')) print('I am starting to identify %d unknown persons; lets see how many i know !! \n' % len(unknown_images)) output_path = Path("data/sample-4/output") for image_to_identify in unknown_images: unknown_image = face_recognition.load_image_file(str(image_to_identify)) # face_locations = face_recognition.face_locations(unknown_image) # face_encodings = face_recognition.face_encodings(unknown_image, face_locations) detected_faces = recognize_face.recognize(known_faces, unknown_image) # Convert the image to a PIL-format image so that we can draw on top of it with the Pillow library # See http://pillow.readthedocs.io/ for more about PIL/Pillow pil_image = Image.fromarray(unknown_image) # Create a Pillow ImageDraw Draw instance to draw with draw = ImageDraw.Draw(pil_image) known_color = (0, 255, 0) unknown_color = (255, 0, 0) # Loop through each face found in the unknown image for name, (top, right, bottom, left), distance in detected_faces: # Draw a box around the face using the Pillow module if name == 'Unknown': color = unknown_color else: color = known_color draw.rectangle(((left, top), (right, bottom)), outline=color) # Draw a label with a name below the face label = name + ' - ' + str("{0:.2f}".format(distance)) text_width, text_height = draw.textsize(label) draw.rectangle(((left, bottom - text_height - 10), (right, bottom)), fill=color, outline=color) draw.text((left + 6, bottom - text_height - 5), label, fill=(255, 0, 0, 255)) # Display the resulting image # pil_image.show() # Remove the drawing library from memory as per the Pillow docs del draw # You can also save a copy of the new image to disk if you want by uncommenting this line pil_image.save(output_path/image_to_identify.name)

38.84127

115

0.707805

import face_recognition from PIL import Image, ImageDraw from pathlib import Path import recognize_face known_path = Path("data/sample-2/jpeg/picked/known") known_images = list(known_path.glob('*.jpeg')) known_face_encodings = [] known_face_names = [] known_faces = [recognize_face.image_to_known_face(str(image_path), image_path.stem) for image_path in known_images] print('I just learned to recognize %d persons... \n' % len(known_images)) unknown_path = Path("data/sample-4/unknown") unknown_images = list(unknown_path.glob('**/*.jpeg')) print('I am starting to identify %d unknown persons; lets see how many i know !! \n' % len(unknown_images)) output_path = Path("data/sample-4/output") for image_to_identify in unknown_images: unknown_image = face_recognition.load_image_file(str(image_to_identify)) detected_faces = recognize_face.recognize(known_faces, unknown_image) pil_image = Image.fromarray(unknown_image) draw = ImageDraw.Draw(pil_image) known_color = (0, 255, 0) unknown_color = (255, 0, 0) for name, (top, right, bottom, left), distance in detected_faces: if name == 'Unknown': color = unknown_color else: color = known_color draw.rectangle(((left, top), (right, bottom)), outline=color) label = name + ' - ' + str("{0:.2f}".format(distance)) text_width, text_height = draw.textsize(label) draw.rectangle(((left, bottom - text_height - 10), (right, bottom)), fill=color, outline=color) draw.text((left + 6, bottom - text_height - 5), label, fill=(255, 0, 0, 255)) del draw pil_image.save(output_path/image_to_identify.name)

true

f71b6fb24f25ebf3dab99241ef1f473abaf2eb72

4,448

py

Python

releaseNewVersion.py

r-owen/TUI

8f130368254161a2748167b7c8260cc24170c28c

[ "BSD-3-Clause" ]

1

2015-04-29T20:28:20.000Z

releaseNewVersion.py

ApachePointObservatory/TUI

8f130368254161a2748167b7c8260cc24170c28c

[ "BSD-3-Clause" ]

1

2017-06-05T22:53:58.000Z

releaseNewVersion.py

r-owen/TUI

8f130368254161a2748167b7c8260cc24170c28c

[ "BSD-3-Clause" ]

1

2020-01-28T06:28:02.000Z

#!/usr/bin/env python """A script to release a new version of TUI from a git working copy If run on a unix box it exports the current version of TUI and then zips that. If run on MacOS X it also tries to build the Mac binary. Not intended for use on Windows. To use: ./releaseNewVersion.py """ from __future__ import with_statement import os import re import shutil import sys import subprocess roPath = os.environ.get("RO_DIR") if not roPath: print "RO not setup" sys.exit(1) else: print "RO found at", roPath PkgName = "TUI" import TUI.Version versionName = TUI.Version.VersionName fullVersStr = "%s %s" % (versionName, TUI.Version.VersionDate) queryStr = "Version in Version.py = %s; is this OK? (y/[n]) " % (fullVersStr,) getOK = raw_input(queryStr) if not getOK.lower().startswith("y"): sys.exit(0) versRegEx = re.compile(r"<h3>(\d.*\s\d\d\d\d-\d\d-\d\d)</h3>") with file(os.path.join("TUI", "Help", "VersionHistory.html")) as vhist: for line in vhist: versMatch = versRegEx.match(line) if versMatch: histVersStr = versMatch.groups()[0] if histVersStr == fullVersStr: print "Version in VersionHistory.html matches" break else: print "Error: version in VersionHistory.html = %s != %s" % (histVersStr, fullVersStr) sys.exit(0) print "Status of git repository:" subprocess.call(["git", "status"]) getOK = raw_input("Is the git repository up to date? (y/[n]) ") if not getOK.lower().startswith("y"): sys.exit(0) exportRoot = os.environ["HOME"] exportFileName = "%s_%s_Source" % (PkgName, versionName) exportPath = os.path.abspath(os.path.join(exportRoot, exportFileName)) zipFileName = "%s.zip" % (exportFileName,) zipFilePath = os.path.abspath(os.path.join(exportRoot, zipFileName)) if os.path.exists(exportPath): print "Export directory %r already exists" % (exportPath,) getOK = raw_input("Should I delete the old %r? (yes/[n]) " % (exportPath,)) if not getOK.lower() == "yes": sys.exit(0) print "Deleting %r" % (exportPath,) shutil.rmtree(exportPath) if os.path.exists(zipFilePath): getOK = raw_input("File %r already exists! Should I delete it? (yes/[n]) " % (zipFilePath,)) if not getOK.lower() == "yes": sys.exit(0) print "Deleting %r" % (zipFilePath,) os.remove(zipFilePath) print "Copying %s repository to %r" % (PkgName, exportPath) # to write directly to a .zip file (but it won't include the RO package): # git archive --prefix=<exportFileName>/ -o <zipFilePath> HEAD #status = subprocess.call(["git", "archive", "--prefix=%s/" % (exportFileName,), "-o", zipFilePath, "HEAD"]) # to write to a directory, tar and untar in one command: # git archive --format=tar --prefix=<exportFileName>/ HEAD | (cd <exportRoot> && tar xf -) cmdStr = "git archive --format=tar --prefix=%s/ HEAD | (cd %s && tar xf -)" % \ (exportFileName, exportRoot) status = subprocess.call(cmdStr, shell=True) if status != 0: print "git archive failed!" sys.exit(1) print "Copying RO repository" roTempName = "ROTemp" roTempDir = os.path.join(exportRoot, roTempName) cmdStr = "git archive --format=tar --prefix=%s/ HEAD python/RO | (cd %s && tar xf -)" % \ (roTempName, exportRoot,) status = subprocess.call(cmdStr, shell=True, cwd=roPath) if status != 0: print "git archive failed!" sys.exit(1) # copy RO source into the output repo and delete the empty extra crap shutil.move(os.path.join(roTempDir, "python", "RO"), exportPath) shutil.rmtree(os.path.join(roTempDir)) print "Zipping %r" % (exportPath,) status = subprocess.call(["zip", "-r", "-q", zipFileName, exportFileName], cwd=exportRoot) if status != 0: print "Zip failed!" else: print "Source zipped" if sys.platform == "darwin": # open the directory in Finder, as a convenience for the user status = subprocess.call(["open", exportRoot]) print "Building Mac version" macBuildDir = os.path.join(exportPath, "BuildForMac") status = subprocess.call(["python", "setup.py", "-q", "py2app"], cwd=macBuildDir) if status != 0: print "Mac build failed!" else: print "Mac build finished!" status = subprocess.call(["open", os.path.join(macBuildDir, "dist")]) print "TUI releases: <http://www.apo.nmsu.edu/35m_operations/TUI-images/>" print "TUI betas: <http://www.apo.nmsu.edu/35m_operations/TUI-images/files/>"

35.870968

108

0.665243

"""A script to release a new version of TUI from a git working copy If run on a unix box it exports the current version of TUI and then zips that. If run on MacOS X it also tries to build the Mac binary. Not intended for use on Windows. To use: ./releaseNewVersion.py """ from __future__ import with_statement import os import re import shutil import sys import subprocess roPath = os.environ.get("RO_DIR") if not roPath: print "RO not setup" sys.exit(1) else: print "RO found at", roPath PkgName = "TUI" import TUI.Version versionName = TUI.Version.VersionName fullVersStr = "%s %s" % (versionName, TUI.Version.VersionDate) queryStr = "Version in Version.py = %s; is this OK? (y/[n]) " % (fullVersStr,) getOK = raw_input(queryStr) if not getOK.lower().startswith("y"): sys.exit(0) versRegEx = re.compile(r"<h3>(\d.*\s\d\d\d\d-\d\d-\d\d)</h3>") with file(os.path.join("TUI", "Help", "VersionHistory.html")) as vhist: for line in vhist: versMatch = versRegEx.match(line) if versMatch: histVersStr = versMatch.groups()[0] if histVersStr == fullVersStr: print "Version in VersionHistory.html matches" break else: print "Error: version in VersionHistory.html = %s != %s" % (histVersStr, fullVersStr) sys.exit(0) print "Status of git repository:" subprocess.call(["git", "status"]) getOK = raw_input("Is the git repository up to date? (y/[n]) ") if not getOK.lower().startswith("y"): sys.exit(0) exportRoot = os.environ["HOME"] exportFileName = "%s_%s_Source" % (PkgName, versionName) exportPath = os.path.abspath(os.path.join(exportRoot, exportFileName)) zipFileName = "%s.zip" % (exportFileName,) zipFilePath = os.path.abspath(os.path.join(exportRoot, zipFileName)) if os.path.exists(exportPath): print "Export directory %r already exists" % (exportPath,) getOK = raw_input("Should I delete the old %r? (yes/[n]) " % (exportPath,)) if not getOK.lower() == "yes": sys.exit(0) print "Deleting %r" % (exportPath,) shutil.rmtree(exportPath) if os.path.exists(zipFilePath): getOK = raw_input("File %r already exists! Should I delete it? (yes/[n]) " % (zipFilePath,)) if not getOK.lower() == "yes": sys.exit(0) print "Deleting %r" % (zipFilePath,) os.remove(zipFilePath) print "Copying %s repository to %r" % (PkgName, exportPath) # git archive --prefix=<exportFileName>/ -o <zipFilePath> HEAD #status = subprocess.call(["git", "archive", "--prefix=%s/" % (exportFileName,), "-o", zipFilePath, "HEAD"]) # to write to a directory, tar and untar in one command: # git archive --format=tar --prefix=<exportFileName>/ HEAD | (cd <exportRoot> && tar xf -) cmdStr = "git archive --format=tar --prefix=%s/ HEAD | (cd %s && tar xf -)" % \ (exportFileName, exportRoot) status = subprocess.call(cmdStr, shell=True) if status != 0: print "git archive failed!" sys.exit(1) print "Copying RO repository" roTempName = "ROTemp" roTempDir = os.path.join(exportRoot, roTempName) cmdStr = "git archive --format=tar --prefix=%s/ HEAD python/RO | (cd %s && tar xf -)" % \ (roTempName, exportRoot,) status = subprocess.call(cmdStr, shell=True, cwd=roPath) if status != 0: print "git archive failed!" sys.exit(1) # copy RO source into the output repo and delete the empty extra crap shutil.move(os.path.join(roTempDir, "python", "RO"), exportPath) shutil.rmtree(os.path.join(roTempDir)) print "Zipping %r" % (exportPath,) status = subprocess.call(["zip", "-r", "-q", zipFileName, exportFileName], cwd=exportRoot) if status != 0: print "Zip failed!" else: print "Source zipped" if sys.platform == "darwin": # open the directory in Finder, as a convenience for the user status = subprocess.call(["open", exportRoot]) print "Building Mac version" macBuildDir = os.path.join(exportPath, "BuildForMac") status = subprocess.call(["python", "setup.py", "-q", "py2app"], cwd=macBuildDir) if status != 0: print "Mac build failed!" else: print "Mac build finished!" status = subprocess.call(["open", os.path.join(macBuildDir, "dist")]) print "TUI releases: <http://www.apo.nmsu.edu/35m_operations/TUI-images/>" print "TUI betas: <http://www.apo.nmsu.edu/35m_operations/TUI-images/files/>"

false

true

f71b6fe68084b084c7f741b11c1012ffaf12dd0a

3,230

py

Python

srv/fluffi/data/fluffiweb/app/utils/ftp.py

sears-s/fluffi

5f2f6d019041a6268199b69bf2f34487b18b84fe

[ "MIT" ]

96

2019-09-19T10:28:05.000Z

2022-02-28T11:53:06.000Z

srv/fluffi/data/fluffiweb/app/utils/ftp.py

sears-s/fluffi

5f2f6d019041a6268199b69bf2f34487b18b84fe

[ "MIT" ]

123

2019-11-19T09:47:14.000Z

2021-10-19T03:10:51.000Z

srv/fluffi/data/fluffiweb/app/utils/ftp.py

sears-s/fluffi

5f2f6d019041a6268199b69bf2f34487b18b84fe

[ "MIT" ]

23

2019-11-11T06:04:56.000Z

2022-02-11T15:35:26.000Z

# Copyright 2017-2020 Siemens AG # # Permission is hereby granted, free of charge, to any person obtaining # a copy of this software and associated documentation files (the # "Software"), to deal in the Software without restriction, including without # limitation the rights to use, copy, modify, merge, publish, distribute, # sublicense, and/or sell copies of the Software, and to permit persons to whom the # Software is furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be # included in all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, # EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF # MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT # SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR # OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, # ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER # DEALINGS IN THE SOFTWARE. # # Author(s): Michael Kraus, Junes Najah, Fabian Russwurm, Thomas Riedmaier from ftplib import FTP class FTPConnector: def __init__(self, ftpURL): self.ftpURL = ftpURL self.ftpClient = FTP() def getListOfFilesOnFTPServer(self, path): self.ftpClient.connect(self.ftpURL) self.ftpClient.login() self.ftpClient.cwd(path) ls = [] ls = self.ftpClient.nlst() tupelsOfLS = zip(ls, ls) self.ftpClient.quit() return tupelsOfLS def getListOfArchitecturesOnFTPServer(self, path, group): self.ftpClient.connect(self.ftpURL) self.ftpClient.login() self.ftpClient.cwd(path) ls = [] ls = self.ftpClient.nlst() for i, w in enumerate(ls): ls[i] = group + "-" + w tupelsOfLS = zip(ls, ls) self.ftpClient.quit() return tupelsOfLS def saveTargetFileOnFTPServer(self, targetFileData, name): # ftplib storbinary is programmed to read file from disk before sending to ftp server # solution is to extend the lib and rewrite storbinary... # https://stackoverflow.com/questions/2671118/can-i-upload-an-object-in-memory-to-ftp-using-python # workaround: write file to disk # ..... path = 'tmp.zip' target = open(path, 'wb') target.write(targetFileData) target.close() self.ftpClient.connect(self.ftpURL) self.ftpClient.login() f = open('tmp.zip', 'rb') self.ftpClient.storbinary("STOR /SUT/" + name.split('.', 1)[0] + ".zip", f) self.ftpClient.quit() def saveArchivedProjectOnFTPServer(self, fileName): self.ftpClient.connect(self.ftpURL) self.ftpClient.login() myFile = open(fileName, 'rb') if myFile: self.ftpClient.storbinary("STOR /archive/" + fileName, myFile) self.ftpClient.quit() myFile.close() return True print("Error: File not found") self.ftpClient.quit() myFile.close() return False

35.108696

107

0.653251

from ftplib import FTP class FTPConnector: def __init__(self, ftpURL): self.ftpURL = ftpURL self.ftpClient = FTP() def getListOfFilesOnFTPServer(self, path): self.ftpClient.connect(self.ftpURL) self.ftpClient.login() self.ftpClient.cwd(path) ls = [] ls = self.ftpClient.nlst() tupelsOfLS = zip(ls, ls) self.ftpClient.quit() return tupelsOfLS def getListOfArchitecturesOnFTPServer(self, path, group): self.ftpClient.connect(self.ftpURL) self.ftpClient.login() self.ftpClient.cwd(path) ls = [] ls = self.ftpClient.nlst() for i, w in enumerate(ls): ls[i] = group + "-" + w tupelsOfLS = zip(ls, ls) self.ftpClient.quit() return tupelsOfLS def saveTargetFileOnFTPServer(self, targetFileData, name): path = 'tmp.zip' target = open(path, 'wb') target.write(targetFileData) target.close() self.ftpClient.connect(self.ftpURL) self.ftpClient.login() f = open('tmp.zip', 'rb') self.ftpClient.storbinary("STOR /SUT/" + name.split('.', 1)[0] + ".zip", f) self.ftpClient.quit() def saveArchivedProjectOnFTPServer(self, fileName): self.ftpClient.connect(self.ftpURL) self.ftpClient.login() myFile = open(fileName, 'rb') if myFile: self.ftpClient.storbinary("STOR /archive/" + fileName, myFile) self.ftpClient.quit() myFile.close() return True print("Error: File not found") self.ftpClient.quit() myFile.close() return False

true

f71b701cb0a9f7edf9be18a1b9115d0dbedac0c4

17,383

py

Python

examples/language_model/bert/run_glue.py

weiwei1115/PaddleNLP

dd98f7f8b25b41d39228ba8a958b11a6212709a3

[ "Apache-2.0" ]

1

2021-02-24T14:03:55.000Z

examples/language_model/bert/run_glue.py

weiwei1115/PaddleNLP

dd98f7f8b25b41d39228ba8a958b11a6212709a3

[ "Apache-2.0" ]

null

examples/language_model/bert/run_glue.py

weiwei1115/PaddleNLP

dd98f7f8b25b41d39228ba8a958b11a6212709a3

[ "Apache-2.0" ]

null

# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import argparse import logging import os import sys import random import time import math import distutils.util from functools import partial import numpy as np import paddle from paddle.io import DataLoader from paddle.metric import Metric, Accuracy, Precision, Recall from paddlenlp.datasets import GlueCoLA, GlueSST2, GlueMRPC, GlueSTSB, GlueQQP, GlueMNLI, GlueQNLI, GlueRTE from paddlenlp.data import Stack, Tuple, Pad from paddlenlp.transformers import BertForSequenceClassification, BertTokenizer from paddlenlp.transformers import ElectraForSequenceClassification, ElectraTokenizer from paddlenlp.transformers import ErnieForSequenceClassification, ErnieTokenizer from paddlenlp.transformers import LinearDecayWithWarmup from paddlenlp.metrics import AccuracyAndF1, Mcc, PearsonAndSpearman FORMAT = '%(asctime)s-%(levelname)s: %(message)s' logging.basicConfig(level=logging.INFO, format=FORMAT) logger = logging.getLogger(__name__) TASK_CLASSES = { "cola": (GlueCoLA, Mcc), "sst-2": (GlueSST2, Accuracy), "mrpc": (GlueMRPC, AccuracyAndF1), "sts-b": (GlueSTSB, PearsonAndSpearman), "qqp": (GlueQQP, AccuracyAndF1), "mnli": (GlueMNLI, Accuracy), "qnli": (GlueQNLI, Accuracy), "rte": (GlueRTE, Accuracy), } MODEL_CLASSES = { "bert": (BertForSequenceClassification, BertTokenizer), "ernie": (ErnieForSequenceClassification, ErnieTokenizer) } def parse_args(): parser = argparse.ArgumentParser() # Required parameters parser.add_argument( "--task_name", default=None, type=str, required=True, help="The name of the task to train selected in the list: " + ", ".join(TASK_CLASSES.keys()), ) parser.add_argument( "--model_type", default=None, type=str, required=True, help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys()), ) parser.add_argument( "--model_name_or_path", default=None, type=str, required=True, help="Path to pre-trained model or shortcut name selected in the list: " + ", ".join( sum([ list(classes[-1].pretrained_init_configuration.keys()) for classes in MODEL_CLASSES.values() ], [])), ) parser.add_argument( "--output_dir", default=None, type=str, required=True, help="The output directory where the model predictions and checkpoints will be written.", ) parser.add_argument( "--max_seq_length", default=128, type=int, help="The maximum total input sequence length after tokenization. Sequences longer " "than this will be truncated, sequences shorter will be padded.", ) parser.add_argument( "--learning_rate", default=1e-4, type=float, help="The initial learning rate for Adam.") parser.add_argument( "--num_train_epochs", default=3, type=int, help="Total number of training epochs to perform.", ) parser.add_argument( "--logging_steps", type=int, default=100, help="Log every X updates steps.") parser.add_argument( "--save_steps", type=int, default=100, help="Save checkpoint every X updates steps.") parser.add_argument( "--batch_size", default=32, type=int, help="Batch size per GPU/CPU for training.", ) parser.add_argument( "--weight_decay", default=0.0, type=float, help="Weight decay if we apply some.") parser.add_argument( "--warmup_steps", default=0, type=int, help="Linear warmup over warmup_steps. If > 0: Override warmup_proportion" ) parser.add_argument( "--warmup_proportion", default=0., type=float, help="Linear warmup proportion over total steps.") parser.add_argument( "--adam_epsilon", default=1e-6, type=float, help="Epsilon for Adam optimizer.") parser.add_argument( "--max_steps", default=-1, type=int, help="If > 0: set total number of training steps to perform. Override num_train_epochs.", ) parser.add_argument( "--seed", default=42, type=int, help="random seed for initialization") parser.add_argument( "--n_cards", default=1, type=int, help="Number cards for the training, only support multi cards in the gpu." ) parser.add_argument( "--select_device", type=str, default="gpu", help="Device for selecting for the training.") parser.add_argument( "--use_amp", type=distutils.util.strtobool, default=False, help="Enable mixed precision training.") parser.add_argument( "--scale_loss", type=float, default=2**15, help="The value of scale_loss for fp16.") args = parser.parse_args() return args def set_seed(args): # Use the same data seed(for data shuffle) for all procs to guarantee data # consistency after sharding. random.seed(args.seed) np.random.seed(args.seed) # Maybe different op seeds(for dropout) for different procs is better. By: # `paddle.seed(args.seed + paddle.distributed.get_rank())` paddle.seed(args.seed) def evaluate(model, loss_fct, metric, data_loader): model.eval() metric.reset() for batch in data_loader: input_ids, segment_ids, labels = batch logits = model(input_ids, segment_ids) loss = loss_fct(logits, labels) correct = metric.compute(logits, labels) metric.update(correct) res = metric.accumulate() if isinstance(metric, AccuracyAndF1): logger.info( "eval loss: %f, acc: %s, precision: %s, recall: %s, f1: %s, acc and f1: %s." % (loss.numpy(), res[0], res[1], res[2], res[3], res[4])) elif isinstance(metric, Mcc): logger.info("eval loss: %f, mcc: %s." % (loss.numpy(), res[0])) elif isinstance(metric, PearsonAndSpearman): logger.info( "eval loss: %f, pearson: %s, spearman: %s, pearson and spearman: %s." % (loss.numpy(), res[0], res[1], res[2])) else: logger.info("eval loss: %f, acc: %s." % (loss.numpy(), res)) model.train() def convert_example(example, tokenizer, label_list, max_seq_length=512, is_test=False): """convert a glue example into necessary features""" def _truncate_seqs(seqs, max_seq_length): if len(seqs) == 1: # single sentence # Account for [CLS] and [SEP] with "- 2" seqs[0] = seqs[0][0:(max_seq_length - 2)] else: # Sentence pair # Account for [CLS], [SEP], [SEP] with "- 3" tokens_a, tokens_b = seqs max_seq_length -= 3 while True: # Truncate with longest_first strategy total_length = len(tokens_a) + len(tokens_b) if total_length <= max_seq_length: break if len(tokens_a) > len(tokens_b): tokens_a.pop() else: tokens_b.pop() return seqs def _concat_seqs(seqs, separators, seq_mask=0, separator_mask=1): concat = sum((seq + sep for sep, seq in zip(separators, seqs)), []) segment_ids = sum( ([i] * (len(seq) + len(sep)) for i, (sep, seq) in enumerate(zip(separators, seqs))), []) if isinstance(seq_mask, int): seq_mask = [[seq_mask] * len(seq) for seq in seqs] if isinstance(separator_mask, int): separator_mask = [[separator_mask] * len(sep) for sep in separators] p_mask = sum((s_mask + mask for sep, seq, s_mask, mask in zip( separators, seqs, seq_mask, separator_mask)), []) return concat, segment_ids, p_mask if not is_test: # `label_list == None` is for regression task label_dtype = "int64" if label_list else "float32" # Get the label label = example[-1] example = example[:-1] # Create label maps if classification task if label_list: label_map = {} for (i, l) in enumerate(label_list): label_map[l] = i label = label_map[label] label = np.array([label], dtype=label_dtype) # Tokenize raw text tokens_raw = [tokenizer(l) for l in example] # Truncate to the truncate_length, tokens_trun = _truncate_seqs(tokens_raw, max_seq_length) # Concate the sequences with special tokens tokens_trun[0] = [tokenizer.cls_token] + tokens_trun[0] tokens, segment_ids, _ = _concat_seqs(tokens_trun, [[tokenizer.sep_token]] * len(tokens_trun)) # Convert the token to ids input_ids = tokenizer.convert_tokens_to_ids(tokens) valid_length = len(input_ids) # The mask has 1 for real tokens and 0 for padding tokens. Only real # tokens are attended to. # input_mask = [1] * len(input_ids) if not is_test: return input_ids, segment_ids, valid_length, label else: return input_ids, segment_ids, valid_length def do_train(args): paddle.set_device(args.select_device) if paddle.distributed.get_world_size() > 1: paddle.distributed.init_parallel_env() set_seed(args) args.task_name = args.task_name.lower() dataset_class, metric_class = TASK_CLASSES[args.task_name] args.model_type = args.model_type.lower() model_class, tokenizer_class = MODEL_CLASSES[args.model_type] train_dataset = dataset_class.get_datasets(["train"]) tokenizer = tokenizer_class.from_pretrained(args.model_name_or_path) trans_func = partial( convert_example, tokenizer=tokenizer, label_list=train_dataset.get_labels(), max_seq_length=args.max_seq_length) train_dataset = train_dataset.apply(trans_func, lazy=True) train_batch_sampler = paddle.io.DistributedBatchSampler( train_dataset, batch_size=args.batch_size, shuffle=True) batchify_fn = lambda samples, fn=Tuple( Pad(axis=0, pad_val=tokenizer.pad_token_id), # input Pad(axis=0, pad_val=tokenizer.pad_token_id), # segment Stack(), # length Stack(dtype="int64" if train_dataset.get_labels() else "float32") # label ): [data for i, data in enumerate(fn(samples)) if i != 2] train_data_loader = DataLoader( dataset=train_dataset, batch_sampler=train_batch_sampler, collate_fn=batchify_fn, num_workers=0, return_list=True) if args.task_name == "mnli": dev_dataset_matched, dev_dataset_mismatched = dataset_class.get_datasets( ["dev_matched", "dev_mismatched"]) dev_dataset_matched = dev_dataset_matched.apply(trans_func, lazy=True) dev_dataset_mismatched = dev_dataset_mismatched.apply( trans_func, lazy=True) dev_batch_sampler_matched = paddle.io.BatchSampler( dev_dataset_matched, batch_size=args.batch_size, shuffle=False) dev_data_loader_matched = DataLoader( dataset=dev_dataset_matched, batch_sampler=dev_batch_sampler_matched, collate_fn=batchify_fn, num_workers=0, return_list=True) dev_batch_sampler_mismatched = paddle.io.BatchSampler( dev_dataset_mismatched, batch_size=args.batch_size, shuffle=False) dev_data_loader_mismatched = DataLoader( dataset=dev_dataset_mismatched, batch_sampler=dev_batch_sampler_mismatched, collate_fn=batchify_fn, num_workers=0, return_list=True) else: dev_dataset = dataset_class.get_datasets(["dev"]) dev_dataset = dev_dataset.apply(trans_func, lazy=True) dev_batch_sampler = paddle.io.BatchSampler( dev_dataset, batch_size=args.batch_size, shuffle=False) dev_data_loader = DataLoader( dataset=dev_dataset, batch_sampler=dev_batch_sampler, collate_fn=batchify_fn, num_workers=0, return_list=True) num_classes = 1 if train_dataset.get_labels() == None else len( train_dataset.get_labels()) model = model_class.from_pretrained( args.model_name_or_path, num_classes=num_classes) if paddle.distributed.get_world_size() > 1: model = paddle.DataParallel(model) num_training_steps = args.max_steps if args.max_steps > 0 else ( len(train_data_loader) * args.num_train_epochs) warmup = args.warmup_steps if args.warmup_steps > 0 else args.warmup_proportion lr_scheduler = LinearDecayWithWarmup(args.learning_rate, num_training_steps, warmup) optimizer = paddle.optimizer.AdamW( learning_rate=lr_scheduler, beta1=0.9, beta2=0.999, epsilon=args.adam_epsilon, parameters=model.parameters(), weight_decay=args.weight_decay, apply_decay_param_fun=lambda x: x in [ p.name for n, p in model.named_parameters() if not any(nd in n for nd in ["bias", "norm"]) ]) loss_fct = paddle.nn.loss.CrossEntropyLoss() if train_dataset.get_labels( ) else paddle.nn.loss.MSELoss() metric = metric_class() if args.use_amp: scaler = paddle.amp.GradScaler(init_loss_scaling=args.scale_loss) global_step = 0 tic_train = time.time() for epoch in range(args.num_train_epochs): for step, batch in enumerate(train_data_loader): global_step += 1 input_ids, segment_ids, labels = batch with paddle.amp.auto_cast( args.use_amp, custom_white_list=["layer_norm", "softmax", "gelu"]): logits = model(input_ids, segment_ids) loss = loss_fct(logits, labels) if args.use_amp: scaler.scale(loss).backward() scaler.minimize(optimizer, loss) else: loss.backward() optimizer.step() lr_scheduler.step() optimizer.clear_gradients() if global_step % args.logging_steps == 0: logger.info( "global step %d/%d, epoch: %d, batch: %d, rank_id: %s, loss: %f, lr: %.10f, speed: %.4f step/s" % (global_step, num_training_steps, epoch, step, paddle.distributed.get_rank(), loss, optimizer.get_lr(), args.logging_steps / (time.time() - tic_train))) tic_train = time.time() if global_step % args.save_steps == 0: tic_eval = time.time() if args.task_name == "mnli": evaluate(model, loss_fct, metric, dev_data_loader_matched) evaluate(model, loss_fct, metric, dev_data_loader_mismatched) logger.info("eval done total : %s s" % (time.time() - tic_eval)) else: evaluate(model, loss_fct, metric, dev_data_loader) logger.info("eval done total : %s s" % (time.time() - tic_eval)) if (not args.n_cards > 1) or paddle.distributed.get_rank() == 0: output_dir = os.path.join(args.output_dir, "%s_ft_model_%d.pdparams" % (args.task_name, global_step)) if not os.path.exists(output_dir): os.makedirs(output_dir) # Need better way to get inner model of DataParallel model_to_save = model._layers if isinstance( model, paddle.DataParallel) else model model_to_save.save_pretrained(output_dir) tokenizer.save_pretrained(output_dir) def print_arguments(args): """print arguments""" print('----------- Configuration Arguments -----------') for arg, value in sorted(vars(args).items()): print('%s: %s' % (arg, value)) print('------------------------------------------------') if __name__ == "__main__": args = parse_args() print_arguments(args) if args.n_cards > 1 and args.select_device == "gpu": paddle.distributed.spawn(do_train, args=(args, ), nprocs=args.n_cards) else: do_train(args)

38.037199

115

0.611805

import argparse import logging import os import sys import random import time import math import distutils.util from functools import partial import numpy as np import paddle from paddle.io import DataLoader from paddle.metric import Metric, Accuracy, Precision, Recall from paddlenlp.datasets import GlueCoLA, GlueSST2, GlueMRPC, GlueSTSB, GlueQQP, GlueMNLI, GlueQNLI, GlueRTE from paddlenlp.data import Stack, Tuple, Pad from paddlenlp.transformers import BertForSequenceClassification, BertTokenizer from paddlenlp.transformers import ElectraForSequenceClassification, ElectraTokenizer from paddlenlp.transformers import ErnieForSequenceClassification, ErnieTokenizer from paddlenlp.transformers import LinearDecayWithWarmup from paddlenlp.metrics import AccuracyAndF1, Mcc, PearsonAndSpearman FORMAT = '%(asctime)s-%(levelname)s: %(message)s' logging.basicConfig(level=logging.INFO, format=FORMAT) logger = logging.getLogger(__name__) TASK_CLASSES = { "cola": (GlueCoLA, Mcc), "sst-2": (GlueSST2, Accuracy), "mrpc": (GlueMRPC, AccuracyAndF1), "sts-b": (GlueSTSB, PearsonAndSpearman), "qqp": (GlueQQP, AccuracyAndF1), "mnli": (GlueMNLI, Accuracy), "qnli": (GlueQNLI, Accuracy), "rte": (GlueRTE, Accuracy), } MODEL_CLASSES = { "bert": (BertForSequenceClassification, BertTokenizer), "ernie": (ErnieForSequenceClassification, ErnieTokenizer) } def parse_args(): parser = argparse.ArgumentParser() parser.add_argument( "--task_name", default=None, type=str, required=True, help="The name of the task to train selected in the list: " + ", ".join(TASK_CLASSES.keys()), ) parser.add_argument( "--model_type", default=None, type=str, required=True, help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys()), ) parser.add_argument( "--model_name_or_path", default=None, type=str, required=True, help="Path to pre-trained model or shortcut name selected in the list: " + ", ".join( sum([ list(classes[-1].pretrained_init_configuration.keys()) for classes in MODEL_CLASSES.values() ], [])), ) parser.add_argument( "--output_dir", default=None, type=str, required=True, help="The output directory where the model predictions and checkpoints will be written.", ) parser.add_argument( "--max_seq_length", default=128, type=int, help="The maximum total input sequence length after tokenization. Sequences longer " "than this will be truncated, sequences shorter will be padded.", ) parser.add_argument( "--learning_rate", default=1e-4, type=float, help="The initial learning rate for Adam.") parser.add_argument( "--num_train_epochs", default=3, type=int, help="Total number of training epochs to perform.", ) parser.add_argument( "--logging_steps", type=int, default=100, help="Log every X updates steps.") parser.add_argument( "--save_steps", type=int, default=100, help="Save checkpoint every X updates steps.") parser.add_argument( "--batch_size", default=32, type=int, help="Batch size per GPU/CPU for training.", ) parser.add_argument( "--weight_decay", default=0.0, type=float, help="Weight decay if we apply some.") parser.add_argument( "--warmup_steps", default=0, type=int, help="Linear warmup over warmup_steps. If > 0: Override warmup_proportion" ) parser.add_argument( "--warmup_proportion", default=0., type=float, help="Linear warmup proportion over total steps.") parser.add_argument( "--adam_epsilon", default=1e-6, type=float, help="Epsilon for Adam optimizer.") parser.add_argument( "--max_steps", default=-1, type=int, help="If > 0: set total number of training steps to perform. Override num_train_epochs.", ) parser.add_argument( "--seed", default=42, type=int, help="random seed for initialization") parser.add_argument( "--n_cards", default=1, type=int, help="Number cards for the training, only support multi cards in the gpu." ) parser.add_argument( "--select_device", type=str, default="gpu", help="Device for selecting for the training.") parser.add_argument( "--use_amp", type=distutils.util.strtobool, default=False, help="Enable mixed precision training.") parser.add_argument( "--scale_loss", type=float, default=2**15, help="The value of scale_loss for fp16.") args = parser.parse_args() return args def set_seed(args): random.seed(args.seed) np.random.seed(args.seed) paddle.seed(args.seed) def evaluate(model, loss_fct, metric, data_loader): model.eval() metric.reset() for batch in data_loader: input_ids, segment_ids, labels = batch logits = model(input_ids, segment_ids) loss = loss_fct(logits, labels) correct = metric.compute(logits, labels) metric.update(correct) res = metric.accumulate() if isinstance(metric, AccuracyAndF1): logger.info( "eval loss: %f, acc: %s, precision: %s, recall: %s, f1: %s, acc and f1: %s." % (loss.numpy(), res[0], res[1], res[2], res[3], res[4])) elif isinstance(metric, Mcc): logger.info("eval loss: %f, mcc: %s." % (loss.numpy(), res[0])) elif isinstance(metric, PearsonAndSpearman): logger.info( "eval loss: %f, pearson: %s, spearman: %s, pearson and spearman: %s." % (loss.numpy(), res[0], res[1], res[2])) else: logger.info("eval loss: %f, acc: %s." % (loss.numpy(), res)) model.train() def convert_example(example, tokenizer, label_list, max_seq_length=512, is_test=False): def _truncate_seqs(seqs, max_seq_length): if len(seqs) == 1: seqs[0] = seqs[0][0:(max_seq_length - 2)] else: tokens_a, tokens_b = seqs max_seq_length -= 3 while True: total_length = len(tokens_a) + len(tokens_b) if total_length <= max_seq_length: break if len(tokens_a) > len(tokens_b): tokens_a.pop() else: tokens_b.pop() return seqs def _concat_seqs(seqs, separators, seq_mask=0, separator_mask=1): concat = sum((seq + sep for sep, seq in zip(separators, seqs)), []) segment_ids = sum( ([i] * (len(seq) + len(sep)) for i, (sep, seq) in enumerate(zip(separators, seqs))), []) if isinstance(seq_mask, int): seq_mask = [[seq_mask] * len(seq) for seq in seqs] if isinstance(separator_mask, int): separator_mask = [[separator_mask] * len(sep) for sep in separators] p_mask = sum((s_mask + mask for sep, seq, s_mask, mask in zip( separators, seqs, seq_mask, separator_mask)), []) return concat, segment_ids, p_mask if not is_test: label_dtype = "int64" if label_list else "float32" label = example[-1] example = example[:-1] if label_list: label_map = {} for (i, l) in enumerate(label_list): label_map[l] = i label = label_map[label] label = np.array([label], dtype=label_dtype) tokens_raw = [tokenizer(l) for l in example] tokens_trun = _truncate_seqs(tokens_raw, max_seq_length) tokens_trun[0] = [tokenizer.cls_token] + tokens_trun[0] tokens, segment_ids, _ = _concat_seqs(tokens_trun, [[tokenizer.sep_token]] * len(tokens_trun)) input_ids = tokenizer.convert_tokens_to_ids(tokens) valid_length = len(input_ids) if not is_test: return input_ids, segment_ids, valid_length, label else: return input_ids, segment_ids, valid_length def do_train(args): paddle.set_device(args.select_device) if paddle.distributed.get_world_size() > 1: paddle.distributed.init_parallel_env() set_seed(args) args.task_name = args.task_name.lower() dataset_class, metric_class = TASK_CLASSES[args.task_name] args.model_type = args.model_type.lower() model_class, tokenizer_class = MODEL_CLASSES[args.model_type] train_dataset = dataset_class.get_datasets(["train"]) tokenizer = tokenizer_class.from_pretrained(args.model_name_or_path) trans_func = partial( convert_example, tokenizer=tokenizer, label_list=train_dataset.get_labels(), max_seq_length=args.max_seq_length) train_dataset = train_dataset.apply(trans_func, lazy=True) train_batch_sampler = paddle.io.DistributedBatchSampler( train_dataset, batch_size=args.batch_size, shuffle=True) batchify_fn = lambda samples, fn=Tuple( Pad(axis=0, pad_val=tokenizer.pad_token_id), Pad(axis=0, pad_val=tokenizer.pad_token_id), Stack(), Stack(dtype="int64" if train_dataset.get_labels() else "float32") ): [data for i, data in enumerate(fn(samples)) if i != 2] train_data_loader = DataLoader( dataset=train_dataset, batch_sampler=train_batch_sampler, collate_fn=batchify_fn, num_workers=0, return_list=True) if args.task_name == "mnli": dev_dataset_matched, dev_dataset_mismatched = dataset_class.get_datasets( ["dev_matched", "dev_mismatched"]) dev_dataset_matched = dev_dataset_matched.apply(trans_func, lazy=True) dev_dataset_mismatched = dev_dataset_mismatched.apply( trans_func, lazy=True) dev_batch_sampler_matched = paddle.io.BatchSampler( dev_dataset_matched, batch_size=args.batch_size, shuffle=False) dev_data_loader_matched = DataLoader( dataset=dev_dataset_matched, batch_sampler=dev_batch_sampler_matched, collate_fn=batchify_fn, num_workers=0, return_list=True) dev_batch_sampler_mismatched = paddle.io.BatchSampler( dev_dataset_mismatched, batch_size=args.batch_size, shuffle=False) dev_data_loader_mismatched = DataLoader( dataset=dev_dataset_mismatched, batch_sampler=dev_batch_sampler_mismatched, collate_fn=batchify_fn, num_workers=0, return_list=True) else: dev_dataset = dataset_class.get_datasets(["dev"]) dev_dataset = dev_dataset.apply(trans_func, lazy=True) dev_batch_sampler = paddle.io.BatchSampler( dev_dataset, batch_size=args.batch_size, shuffle=False) dev_data_loader = DataLoader( dataset=dev_dataset, batch_sampler=dev_batch_sampler, collate_fn=batchify_fn, num_workers=0, return_list=True) num_classes = 1 if train_dataset.get_labels() == None else len( train_dataset.get_labels()) model = model_class.from_pretrained( args.model_name_or_path, num_classes=num_classes) if paddle.distributed.get_world_size() > 1: model = paddle.DataParallel(model) num_training_steps = args.max_steps if args.max_steps > 0 else ( len(train_data_loader) * args.num_train_epochs) warmup = args.warmup_steps if args.warmup_steps > 0 else args.warmup_proportion lr_scheduler = LinearDecayWithWarmup(args.learning_rate, num_training_steps, warmup) optimizer = paddle.optimizer.AdamW( learning_rate=lr_scheduler, beta1=0.9, beta2=0.999, epsilon=args.adam_epsilon, parameters=model.parameters(), weight_decay=args.weight_decay, apply_decay_param_fun=lambda x: x in [ p.name for n, p in model.named_parameters() if not any(nd in n for nd in ["bias", "norm"]) ]) loss_fct = paddle.nn.loss.CrossEntropyLoss() if train_dataset.get_labels( ) else paddle.nn.loss.MSELoss() metric = metric_class() if args.use_amp: scaler = paddle.amp.GradScaler(init_loss_scaling=args.scale_loss) global_step = 0 tic_train = time.time() for epoch in range(args.num_train_epochs): for step, batch in enumerate(train_data_loader): global_step += 1 input_ids, segment_ids, labels = batch with paddle.amp.auto_cast( args.use_amp, custom_white_list=["layer_norm", "softmax", "gelu"]): logits = model(input_ids, segment_ids) loss = loss_fct(logits, labels) if args.use_amp: scaler.scale(loss).backward() scaler.minimize(optimizer, loss) else: loss.backward() optimizer.step() lr_scheduler.step() optimizer.clear_gradients() if global_step % args.logging_steps == 0: logger.info( "global step %d/%d, epoch: %d, batch: %d, rank_id: %s, loss: %f, lr: %.10f, speed: %.4f step/s" % (global_step, num_training_steps, epoch, step, paddle.distributed.get_rank(), loss, optimizer.get_lr(), args.logging_steps / (time.time() - tic_train))) tic_train = time.time() if global_step % args.save_steps == 0: tic_eval = time.time() if args.task_name == "mnli": evaluate(model, loss_fct, metric, dev_data_loader_matched) evaluate(model, loss_fct, metric, dev_data_loader_mismatched) logger.info("eval done total : %s s" % (time.time() - tic_eval)) else: evaluate(model, loss_fct, metric, dev_data_loader) logger.info("eval done total : %s s" % (time.time() - tic_eval)) if (not args.n_cards > 1) or paddle.distributed.get_rank() == 0: output_dir = os.path.join(args.output_dir, "%s_ft_model_%d.pdparams" % (args.task_name, global_step)) if not os.path.exists(output_dir): os.makedirs(output_dir) model_to_save = model._layers if isinstance( model, paddle.DataParallel) else model model_to_save.save_pretrained(output_dir) tokenizer.save_pretrained(output_dir) def print_arguments(args): print('----------- Configuration Arguments -----------') for arg, value in sorted(vars(args).items()): print('%s: %s' % (arg, value)) print('------------------------------------------------') if __name__ == "__main__": args = parse_args() print_arguments(args) if args.n_cards > 1 and args.select_device == "gpu": paddle.distributed.spawn(do_train, args=(args, ), nprocs=args.n_cards) else: do_train(args)

true

f71b70742d77f2a612297f4412d6829e00b6cebd

21,406

py

Python

pypureclient/flasharray/FA_2_13/api/file_systems_api.py

ashahid-ps/py-pure-client

2e3565d37b2a41db69308769f6f485d08a7c46c3

[ "BSD-2-Clause" ]

null

pypureclient/flasharray/FA_2_13/api/file_systems_api.py

ashahid-ps/py-pure-client

2e3565d37b2a41db69308769f6f485d08a7c46c3

[ "BSD-2-Clause" ]

null

pypureclient/flasharray/FA_2_13/api/file_systems_api.py

ashahid-ps/py-pure-client

2e3565d37b2a41db69308769f6f485d08a7c46c3

[ "BSD-2-Clause" ]

null

# coding: utf-8 """ FlashArray REST API No description provided (generated by Swagger Codegen https://github.com/swagger-api/swagger-codegen) OpenAPI spec version: 2.13 Generated by: https://github.com/swagger-api/swagger-codegen.git """ from __future__ import absolute_import import re # python 2 and python 3 compatibility library import six from typing import List, Optional from .. import models class FileSystemsApi(object): def __init__(self, api_client): self.api_client = api_client def api213_file_systems_delete_with_http_info( self, authorization=None, # type: str x_request_id=None, # type: str ids=None, # type: List[str] names=None, # type: List[str] async_req=False, # type: bool _return_http_data_only=False, # type: bool _preload_content=True, # type: bool _request_timeout=None, # type: Optional[int] ): # type: (...) -> None """Delete file system Deletes a file system that has been destroyed and is pending eradication. Eradicated file systems cannot be recovered. File systems are destroyed using the PATCH method. This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.api213_file_systems_delete_with_http_info(async_req=True) >>> result = thread.get() :param str authorization: Access token (in JWT format) required to use any API endpoint (except `/oauth2`, `/login`, and `/logout`) :param str x_request_id: Supplied by client during request or generated by server. :param list[str] ids: Performs the operation on the unique resource IDs specified. Enter multiple resource IDs in comma-separated format. The `ids` and `names` parameters cannot be provided together. :param list[str] names: Performs the operation on the unique name specified. Enter multiple names in comma-separated format. For example, `name01,name02`. :param bool async_req: Request runs in separate thread and method returns multiprocessing.pool.ApplyResult. :param bool _return_http_data_only: Returns only data field. :param bool _preload_content: Response is converted into objects. :param int _request_timeout: Total request timeout in seconds. It can also be a tuple of (connection time, read time) timeouts. :return: None If the method is called asynchronously, returns the request thread. """ if ids is not None: if not isinstance(ids, list): ids = [ids] if names is not None: if not isinstance(names, list): names = [names] params = {k: v for k, v in six.iteritems(locals()) if v is not None} # Convert the filter into a string if params.get('filter'): params['filter'] = str(params['filter']) if params.get('sort'): params['sort'] = [str(_x) for _x in params['sort']] collection_formats = {} path_params = {} query_params = [] if 'ids' in params: query_params.append(('ids', params['ids'])) collection_formats['ids'] = 'csv' if 'names' in params: query_params.append(('names', params['names'])) collection_formats['names'] = 'csv' header_params = {} if 'authorization' in params: header_params['Authorization'] = params['authorization'] if 'x_request_id' in params: header_params['X-Request-ID'] = params['x_request_id'] form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( ['application/json']) # Authentication setting auth_settings = [] return self.api_client.call_api( '/api/2.13/file-systems', 'DELETE', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, auth_settings=auth_settings, async_req=async_req, _return_http_data_only=_return_http_data_only, _preload_content=_preload_content, _request_timeout=_request_timeout, collection_formats=collection_formats, ) def api213_file_systems_get_with_http_info( self, authorization=None, # type: str x_request_id=None, # type: str continuation_token=None, # type: str destroyed=None, # type: bool filter=None, # type: str ids=None, # type: List[str] limit=None, # type: int names=None, # type: List[str] offset=None, # type: int sort=None, # type: List[str] total_item_count=None, # type: bool async_req=False, # type: bool _return_http_data_only=False, # type: bool _preload_content=True, # type: bool _request_timeout=None, # type: Optional[int] ): # type: (...) -> models.FileSystemGetResponse """List file systems Displays a list of file systems, including those pending eradication. This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.api213_file_systems_get_with_http_info(async_req=True) >>> result = thread.get() :param str authorization: Access token (in JWT format) required to use any API endpoint (except `/oauth2`, `/login`, and `/logout`) :param str x_request_id: Supplied by client during request or generated by server. :param str continuation_token: A token used to retrieve the next page of data with some consistency guaranteed. The token is a Base64 encoded value. Set `continuation_token` to the system-generated token taken from the `x-next-token` header field of the response. A query has reached its last page when the response does not include a token. Pagination requires the `limit` and `continuation_token` query parameters. :param bool destroyed: If set to `true`, lists only destroyed objects that are in the eradication pending state. If set to `false`, lists only objects that are not destroyed. For destroyed objects, the time remaining is displayed in milliseconds. :param str filter: Narrows down the results to only the response objects that satisfy the filter criteria. :param list[str] ids: Performs the operation on the unique resource IDs specified. Enter multiple resource IDs in comma-separated format. The `ids` and `names` parameters cannot be provided together. :param int limit: Limits the size of the response to the specified number of objects on each page. To return the total number of resources, set `limit=0`. The total number of resources is returned as a `total_item_count` value. If the page size requested is larger than the system maximum limit, the server returns the maximum limit, disregarding the requested page size. :param list[str] names: Performs the operation on the unique name specified. Enter multiple names in comma-separated format. For example, `name01,name02`. :param int offset: The starting position based on the results of the query in relation to the full set of response objects returned. :param list[str] sort: Returns the response objects in the order specified. Set `sort` to the name in the response by which to sort. Sorting can be performed on any of the names in the response, and the objects can be sorted in ascending or descending order. By default, the response objects are sorted in ascending order. To sort in descending order, append the minus sign (`-`) to the name. A single request can be sorted on multiple objects. For example, you can sort all volumes from largest to smallest volume size, and then sort volumes of the same size in ascending order by volume name. To sort on multiple names, list the names as comma-separated values. :param bool total_item_count: If set to `true`, the `total_item_count` matching the specified query parameters is calculated and returned in the response. If set to `false`, the `total_item_count` is `null` in the response. This may speed up queries where the `total_item_count` is large. If not specified, defaults to `false`. :param bool async_req: Request runs in separate thread and method returns multiprocessing.pool.ApplyResult. :param bool _return_http_data_only: Returns only data field. :param bool _preload_content: Response is converted into objects. :param int _request_timeout: Total request timeout in seconds. It can also be a tuple of (connection time, read time) timeouts. :return: FileSystemGetResponse If the method is called asynchronously, returns the request thread. """ if ids is not None: if not isinstance(ids, list): ids = [ids] if names is not None: if not isinstance(names, list): names = [names] if sort is not None: if not isinstance(sort, list): sort = [sort] params = {k: v for k, v in six.iteritems(locals()) if v is not None} # Convert the filter into a string if params.get('filter'): params['filter'] = str(params['filter']) if params.get('sort'): params['sort'] = [str(_x) for _x in params['sort']] if 'limit' in params and params['limit'] < 1: raise ValueError("Invalid value for parameter `limit` when calling `api213_file_systems_get`, must be a value greater than or equal to `1`") if 'offset' in params and params['offset'] < 0: raise ValueError("Invalid value for parameter `offset` when calling `api213_file_systems_get`, must be a value greater than or equal to `0`") collection_formats = {} path_params = {} query_params = [] if 'continuation_token' in params: query_params.append(('continuation_token', params['continuation_token'])) if 'destroyed' in params: query_params.append(('destroyed', params['destroyed'])) if 'filter' in params: query_params.append(('filter', params['filter'])) if 'ids' in params: query_params.append(('ids', params['ids'])) collection_formats['ids'] = 'csv' if 'limit' in params: query_params.append(('limit', params['limit'])) if 'names' in params: query_params.append(('names', params['names'])) collection_formats['names'] = 'csv' if 'offset' in params: query_params.append(('offset', params['offset'])) if 'sort' in params: query_params.append(('sort', params['sort'])) collection_formats['sort'] = 'csv' if 'total_item_count' in params: query_params.append(('total_item_count', params['total_item_count'])) header_params = {} if 'authorization' in params: header_params['Authorization'] = params['authorization'] if 'x_request_id' in params: header_params['X-Request-ID'] = params['x_request_id'] form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( ['application/json']) # Authentication setting auth_settings = [] return self.api_client.call_api( '/api/2.13/file-systems', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FileSystemGetResponse', auth_settings=auth_settings, async_req=async_req, _return_http_data_only=_return_http_data_only, _preload_content=_preload_content, _request_timeout=_request_timeout, collection_formats=collection_formats, ) def api213_file_systems_patch_with_http_info( self, file_system=None, # type: models.FileSystemPatch authorization=None, # type: str x_request_id=None, # type: str ids=None, # type: List[str] names=None, # type: List[str] async_req=False, # type: bool _return_http_data_only=False, # type: bool _preload_content=True, # type: bool _request_timeout=None, # type: Optional[int] ): # type: (...) -> models.FileSystemResponse """Modify a file system Modifies a file system. You can rename, destroy, move, or recover a file system. To rename a file system, set `name` to the new name. To destroy a file system, set `destroyed=true`. To move a file system, set 'pod' to the destination pod reference. To recover a file system that has been destroyed and is pending eradication, set `destroyed=false`. This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.api213_file_systems_patch_with_http_info(file_system, async_req=True) >>> result = thread.get() :param FileSystemPatch file_system: (required) :param str authorization: Access token (in JWT format) required to use any API endpoint (except `/oauth2`, `/login`, and `/logout`) :param str x_request_id: Supplied by client during request or generated by server. :param list[str] ids: Performs the operation on the unique resource IDs specified. Enter multiple resource IDs in comma-separated format. The `ids` and `names` parameters cannot be provided together. :param list[str] names: Performs the operation on the unique name specified. Enter multiple names in comma-separated format. For example, `name01,name02`. :param bool async_req: Request runs in separate thread and method returns multiprocessing.pool.ApplyResult. :param bool _return_http_data_only: Returns only data field. :param bool _preload_content: Response is converted into objects. :param int _request_timeout: Total request timeout in seconds. It can also be a tuple of (connection time, read time) timeouts. :return: FileSystemResponse If the method is called asynchronously, returns the request thread. """ if ids is not None: if not isinstance(ids, list): ids = [ids] if names is not None: if not isinstance(names, list): names = [names] params = {k: v for k, v in six.iteritems(locals()) if v is not None} # Convert the filter into a string if params.get('filter'): params['filter'] = str(params['filter']) if params.get('sort'): params['sort'] = [str(_x) for _x in params['sort']] # verify the required parameter 'file_system' is set if file_system is None: raise TypeError("Missing the required parameter `file_system` when calling `api213_file_systems_patch`") collection_formats = {} path_params = {} query_params = [] if 'ids' in params: query_params.append(('ids', params['ids'])) collection_formats['ids'] = 'csv' if 'names' in params: query_params.append(('names', params['names'])) collection_formats['names'] = 'csv' header_params = {} if 'authorization' in params: header_params['Authorization'] = params['authorization'] if 'x_request_id' in params: header_params['X-Request-ID'] = params['x_request_id'] form_params = [] local_var_files = {} body_params = None if 'file_system' in params: body_params = params['file_system'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( ['application/json']) # Authentication setting auth_settings = [] return self.api_client.call_api( '/api/2.13/file-systems', 'PATCH', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FileSystemResponse', auth_settings=auth_settings, async_req=async_req, _return_http_data_only=_return_http_data_only, _preload_content=_preload_content, _request_timeout=_request_timeout, collection_formats=collection_formats, ) def api213_file_systems_post_with_http_info( self, names=None, # type: List[str] authorization=None, # type: str x_request_id=None, # type: str async_req=False, # type: bool _return_http_data_only=False, # type: bool _preload_content=True, # type: bool _request_timeout=None, # type: Optional[int] ): # type: (...) -> models.FileSystemResponse """Create file system Creates one or more file systems. This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.api213_file_systems_post_with_http_info(names, async_req=True) >>> result = thread.get() :param list[str] names: Performs the operation on the unique name specified. For example, `name01`. Enter multiple names in comma-separated format. (required) :param str authorization: Access token (in JWT format) required to use any API endpoint (except `/oauth2`, `/login`, and `/logout`) :param str x_request_id: Supplied by client during request or generated by server. :param bool async_req: Request runs in separate thread and method returns multiprocessing.pool.ApplyResult. :param bool _return_http_data_only: Returns only data field. :param bool _preload_content: Response is converted into objects. :param int _request_timeout: Total request timeout in seconds. It can also be a tuple of (connection time, read time) timeouts. :return: FileSystemResponse If the method is called asynchronously, returns the request thread. """ if names is not None: if not isinstance(names, list): names = [names] params = {k: v for k, v in six.iteritems(locals()) if v is not None} # Convert the filter into a string if params.get('filter'): params['filter'] = str(params['filter']) if params.get('sort'): params['sort'] = [str(_x) for _x in params['sort']] # verify the required parameter 'names' is set if names is None: raise TypeError("Missing the required parameter `names` when calling `api213_file_systems_post`") collection_formats = {} path_params = {} query_params = [] if 'names' in params: query_params.append(('names', params['names'])) collection_formats['names'] = 'csv' header_params = {} if 'authorization' in params: header_params['Authorization'] = params['authorization'] if 'x_request_id' in params: header_params['X-Request-ID'] = params['x_request_id'] form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( ['application/json']) # Authentication setting auth_settings = [] return self.api_client.call_api( '/api/2.13/file-systems', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FileSystemResponse', auth_settings=auth_settings, async_req=async_req, _return_http_data_only=_return_http_data_only, _preload_content=_preload_content, _request_timeout=_request_timeout, collection_formats=collection_formats, )

47.568889

671

0.640989

from __future__ import absolute_import import re import six from typing import List, Optional from .. import models class FileSystemsApi(object): def __init__(self, api_client): self.api_client = api_client def api213_file_systems_delete_with_http_info( self, authorization=None, x_request_id=None, ids=None, names=None, async_req=False, _return_http_data_only=False, _preload_content=True, _request_timeout=None, ): if ids is not None: if not isinstance(ids, list): ids = [ids] if names is not None: if not isinstance(names, list): names = [names] params = {k: v for k, v in six.iteritems(locals()) if v is not None} if params.get('filter'): params['filter'] = str(params['filter']) if params.get('sort'): params['sort'] = [str(_x) for _x in params['sort']] collection_formats = {} path_params = {} query_params = [] if 'ids' in params: query_params.append(('ids', params['ids'])) collection_formats['ids'] = 'csv' if 'names' in params: query_params.append(('names', params['names'])) collection_formats['names'] = 'csv' header_params = {} if 'authorization' in params: header_params['Authorization'] = params['authorization'] if 'x_request_id' in params: header_params['X-Request-ID'] = params['x_request_id'] form_params = [] local_var_files = {} body_params = None header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) header_params['Content-Type'] = self.api_client.select_header_content_type( ['application/json']) auth_settings = [] return self.api_client.call_api( '/api/2.13/file-systems', 'DELETE', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, auth_settings=auth_settings, async_req=async_req, _return_http_data_only=_return_http_data_only, _preload_content=_preload_content, _request_timeout=_request_timeout, collection_formats=collection_formats, ) def api213_file_systems_get_with_http_info( self, authorization=None, x_request_id=None, continuation_token=None, destroyed=None, filter=None, ids=None, limit=None, names=None, offset=None, sort=None, total_item_count=None, async_req=False, _return_http_data_only=False, _preload_content=True, _request_timeout=None, ): if ids is not None: if not isinstance(ids, list): ids = [ids] if names is not None: if not isinstance(names, list): names = [names] if sort is not None: if not isinstance(sort, list): sort = [sort] params = {k: v for k, v in six.iteritems(locals()) if v is not None} if params.get('filter'): params['filter'] = str(params['filter']) if params.get('sort'): params['sort'] = [str(_x) for _x in params['sort']] if 'limit' in params and params['limit'] < 1: raise ValueError("Invalid value for parameter `limit` when calling `api213_file_systems_get`, must be a value greater than or equal to `1`") if 'offset' in params and params['offset'] < 0: raise ValueError("Invalid value for parameter `offset` when calling `api213_file_systems_get`, must be a value greater than or equal to `0`") collection_formats = {} path_params = {} query_params = [] if 'continuation_token' in params: query_params.append(('continuation_token', params['continuation_token'])) if 'destroyed' in params: query_params.append(('destroyed', params['destroyed'])) if 'filter' in params: query_params.append(('filter', params['filter'])) if 'ids' in params: query_params.append(('ids', params['ids'])) collection_formats['ids'] = 'csv' if 'limit' in params: query_params.append(('limit', params['limit'])) if 'names' in params: query_params.append(('names', params['names'])) collection_formats['names'] = 'csv' if 'offset' in params: query_params.append(('offset', params['offset'])) if 'sort' in params: query_params.append(('sort', params['sort'])) collection_formats['sort'] = 'csv' if 'total_item_count' in params: query_params.append(('total_item_count', params['total_item_count'])) header_params = {} if 'authorization' in params: header_params['Authorization'] = params['authorization'] if 'x_request_id' in params: header_params['X-Request-ID'] = params['x_request_id'] form_params = [] local_var_files = {} body_params = None header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) header_params['Content-Type'] = self.api_client.select_header_content_type( ['application/json']) auth_settings = [] return self.api_client.call_api( '/api/2.13/file-systems', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FileSystemGetResponse', auth_settings=auth_settings, async_req=async_req, _return_http_data_only=_return_http_data_only, _preload_content=_preload_content, _request_timeout=_request_timeout, collection_formats=collection_formats, ) def api213_file_systems_patch_with_http_info( self, file_system=None, authorization=None, x_request_id=None, ids=None, names=None, async_req=False, _return_http_data_only=False, _preload_content=True, _request_timeout=None, ): if ids is not None: if not isinstance(ids, list): ids = [ids] if names is not None: if not isinstance(names, list): names = [names] params = {k: v for k, v in six.iteritems(locals()) if v is not None} if params.get('filter'): params['filter'] = str(params['filter']) if params.get('sort'): params['sort'] = [str(_x) for _x in params['sort']] if file_system is None: raise TypeError("Missing the required parameter `file_system` when calling `api213_file_systems_patch`") collection_formats = {} path_params = {} query_params = [] if 'ids' in params: query_params.append(('ids', params['ids'])) collection_formats['ids'] = 'csv' if 'names' in params: query_params.append(('names', params['names'])) collection_formats['names'] = 'csv' header_params = {} if 'authorization' in params: header_params['Authorization'] = params['authorization'] if 'x_request_id' in params: header_params['X-Request-ID'] = params['x_request_id'] form_params = [] local_var_files = {} body_params = None if 'file_system' in params: body_params = params['file_system'] header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) header_params['Content-Type'] = self.api_client.select_header_content_type( ['application/json']) auth_settings = [] return self.api_client.call_api( '/api/2.13/file-systems', 'PATCH', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FileSystemResponse', auth_settings=auth_settings, async_req=async_req, _return_http_data_only=_return_http_data_only, _preload_content=_preload_content, _request_timeout=_request_timeout, collection_formats=collection_formats, ) def api213_file_systems_post_with_http_info( self, names=None, authorization=None, x_request_id=None, async_req=False, _return_http_data_only=False, _preload_content=True, _request_timeout=None, ): if names is not None: if not isinstance(names, list): names = [names] params = {k: v for k, v in six.iteritems(locals()) if v is not None} if params.get('filter'): params['filter'] = str(params['filter']) if params.get('sort'): params['sort'] = [str(_x) for _x in params['sort']] if names is None: raise TypeError("Missing the required parameter `names` when calling `api213_file_systems_post`") collection_formats = {} path_params = {} query_params = [] if 'names' in params: query_params.append(('names', params['names'])) collection_formats['names'] = 'csv' header_params = {} if 'authorization' in params: header_params['Authorization'] = params['authorization'] if 'x_request_id' in params: header_params['X-Request-ID'] = params['x_request_id'] form_params = [] local_var_files = {} body_params = None header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) header_params['Content-Type'] = self.api_client.select_header_content_type( ['application/json']) auth_settings = [] return self.api_client.call_api( '/api/2.13/file-systems', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='FileSystemResponse', auth_settings=auth_settings, async_req=async_req, _return_http_data_only=_return_http_data_only, _preload_content=_preload_content, _request_timeout=_request_timeout, collection_formats=collection_formats, )

true

f71b714d70924c77c72ecc7b8ec3e29d445e7a15

1,829

py

Python

tests/test_servicer.py

yangtt0509/sea

f62bcdff00ef71e8c5b92bd5fc5f63d41b753ce2

[ "MIT" ]

null

tests/test_servicer.py

yangtt0509/sea

f62bcdff00ef71e8c5b92bd5fc5f63d41b753ce2

[ "MIT" ]

null

tests/test_servicer.py

yangtt0509/sea

f62bcdff00ef71e8c5b92bd5fc5f63d41b753ce2

[ "MIT" ]

null

import grpc from sea.servicer import ServicerMeta, msg2dict, stream2dict from sea import exceptions from sea.pb2 import default_pb2 from tests.wd.protos import helloworld_pb2 def test_meta_servicer(app, logstream): class HelloContext(): def __init__(self): self.code = None self.details = None def set_code(self, code): self.code = code def set_details(self, details): self.details = details class HelloServicer(metaclass=ServicerMeta): def return_error(self, request, context): raise exceptions.BadRequestException('error') def return_normal(self, request, context): return 'Got it!' logstream.truncate(0) logstream.seek(0) servicer = HelloServicer() context = HelloContext() ret = servicer.return_error(None, context) assert isinstance(ret, default_pb2.Empty) assert context.code is grpc.StatusCode.INVALID_ARGUMENT assert context.details == 'error' p = logstream.tell() assert p > 0 content = logstream.getvalue() assert 'HelloServicer.return_error' in content ret = servicer.return_normal(None, context) assert ret == 'Got it!' assert logstream.tell() > p def test_msg2dict(app): app.name = 'v-name' app.msg = 'v-msg' ret = msg2dict(app, ['name', 'msg', 'tz']) assert ret == {'name': 'v-name', 'msg': 'v-msg', 'tz': 'Asia/Shanghai'} request = helloworld_pb2.HelloRequest(name="value") ret = msg2dict(request) assert ret == {"name": "value"} def test_stream2dict(): def stream_generator(): for i in range(5): yield helloworld_pb2.HelloRequest(name=str(i)) ret = stream2dict(stream_generator()) for i, part in enumerate(ret): assert part == {"name": str(i)}

25.760563

75

0.645708

import grpc from sea.servicer import ServicerMeta, msg2dict, stream2dict from sea import exceptions from sea.pb2 import default_pb2 from tests.wd.protos import helloworld_pb2 def test_meta_servicer(app, logstream): class HelloContext(): def __init__(self): self.code = None self.details = None def set_code(self, code): self.code = code def set_details(self, details): self.details = details class HelloServicer(metaclass=ServicerMeta): def return_error(self, request, context): raise exceptions.BadRequestException('error') def return_normal(self, request, context): return 'Got it!' logstream.truncate(0) logstream.seek(0) servicer = HelloServicer() context = HelloContext() ret = servicer.return_error(None, context) assert isinstance(ret, default_pb2.Empty) assert context.code is grpc.StatusCode.INVALID_ARGUMENT assert context.details == 'error' p = logstream.tell() assert p > 0 content = logstream.getvalue() assert 'HelloServicer.return_error' in content ret = servicer.return_normal(None, context) assert ret == 'Got it!' assert logstream.tell() > p def test_msg2dict(app): app.name = 'v-name' app.msg = 'v-msg' ret = msg2dict(app, ['name', 'msg', 'tz']) assert ret == {'name': 'v-name', 'msg': 'v-msg', 'tz': 'Asia/Shanghai'} request = helloworld_pb2.HelloRequest(name="value") ret = msg2dict(request) assert ret == {"name": "value"} def test_stream2dict(): def stream_generator(): for i in range(5): yield helloworld_pb2.HelloRequest(name=str(i)) ret = stream2dict(stream_generator()) for i, part in enumerate(ret): assert part == {"name": str(i)}

true

f71b721716046fe128e3f99bbf0b9f20f56d1f2c

22,880

py

Python

venv/Lib/site-packages/sklearn/linear_model/_base.py

star10919/drf

77c005794087484d72ffc0d76612a6ac9845821e

[ "BSD-3-Clause" ]

7

2021-01-30T17:42:00.000Z

2022-01-09T08:08:48.000Z

venv/Lib/site-packages/sklearn/linear_model/_base.py

star10919/drf

77c005794087484d72ffc0d76612a6ac9845821e

[ "BSD-3-Clause" ]

25

2020-11-16T15:36:41.000Z

2021-06-01T05:15:31.000Z

venv/Lib/site-packages/sklearn/linear_model/_base.py

star10919/drf

77c005794087484d72ffc0d76612a6ac9845821e

[ "BSD-3-Clause" ]

2

2021-09-13T17:20:56.000Z

2021-11-21T16:05:16.000Z

""" Generalized Linear Models. """ # Author: Alexandre Gramfort <alexandre.gramfort@inria.fr> # Fabian Pedregosa <fabian.pedregosa@inria.fr> # Olivier Grisel <olivier.grisel@ensta.org> # Vincent Michel <vincent.michel@inria.fr> # Peter Prettenhofer <peter.prettenhofer@gmail.com> # Mathieu Blondel <mathieu@mblondel.org> # Lars Buitinck # Maryan Morel <maryan.morel@polytechnique.edu> # Giorgio Patrini <giorgio.patrini@anu.edu.au> # License: BSD 3 clause from abc import ABCMeta, abstractmethod import numbers import warnings import numpy as np import scipy.sparse as sp from scipy import linalg from scipy import optimize from scipy import sparse from scipy.special import expit from joblib import Parallel from ..base import (BaseEstimator, ClassifierMixin, RegressorMixin, MultiOutputMixin) from ..utils import check_array from ..utils.validation import FLOAT_DTYPES from ..utils.validation import _deprecate_positional_args from ..utils import check_random_state from ..utils.extmath import safe_sparse_dot from ..utils.sparsefuncs import mean_variance_axis, inplace_column_scale from ..utils.fixes import sparse_lsqr from ..utils._seq_dataset import ArrayDataset32, CSRDataset32 from ..utils._seq_dataset import ArrayDataset64, CSRDataset64 from ..utils.validation import check_is_fitted, _check_sample_weight from ..utils.fixes import delayed from ..preprocessing import normalize as f_normalize # TODO: bayesian_ridge_regression and bayesian_regression_ard # should be squashed into its respective objects. SPARSE_INTERCEPT_DECAY = 0.01 # For sparse data intercept updates are scaled by this decay factor to avoid # intercept oscillation. def make_dataset(X, y, sample_weight, random_state=None): """Create ``Dataset`` abstraction for sparse and dense inputs. This also returns the ``intercept_decay`` which is different for sparse datasets. Parameters ---------- X : array-like, shape (n_samples, n_features) Training data y : array-like, shape (n_samples, ) Target values. sample_weight : numpy array of shape (n_samples,) The weight of each sample random_state : int, RandomState instance or None (default) Determines random number generation for dataset shuffling and noise. Pass an int for reproducible output across multiple function calls. See :term:`Glossary <random_state>`. Returns ------- dataset The ``Dataset`` abstraction intercept_decay The intercept decay """ rng = check_random_state(random_state) # seed should never be 0 in SequentialDataset64 seed = rng.randint(1, np.iinfo(np.int32).max) if X.dtype == np.float32: CSRData = CSRDataset32 ArrayData = ArrayDataset32 else: CSRData = CSRDataset64 ArrayData = ArrayDataset64 if sp.issparse(X): dataset = CSRData(X.data, X.indptr, X.indices, y, sample_weight, seed=seed) intercept_decay = SPARSE_INTERCEPT_DECAY else: X = np.ascontiguousarray(X) dataset = ArrayData(X, y, sample_weight, seed=seed) intercept_decay = 1.0 return dataset, intercept_decay def _preprocess_data(X, y, fit_intercept, normalize=False, copy=True, sample_weight=None, return_mean=False, check_input=True): """Center and scale data. Centers data to have mean zero along axis 0. If fit_intercept=False or if the X is a sparse matrix, no centering is done, but normalization can still be applied. The function returns the statistics necessary to reconstruct the input data, which are X_offset, y_offset, X_scale, such that the output X = (X - X_offset) / X_scale X_scale is the L2 norm of X - X_offset. If sample_weight is not None, then the weighted mean of X and y is zero, and not the mean itself. If return_mean=True, the mean, eventually weighted, is returned, independently of whether X was centered (option used for optimization with sparse data in coordinate_descend). This is here because nearly all linear models will want their data to be centered. This function also systematically makes y consistent with X.dtype """ if isinstance(sample_weight, numbers.Number): sample_weight = None if sample_weight is not None: sample_weight = np.asarray(sample_weight) if check_input: X = check_array(X, copy=copy, accept_sparse=['csr', 'csc'], dtype=FLOAT_DTYPES) elif copy: if sp.issparse(X): X = X.copy() else: X = X.copy(order='K') y = np.asarray(y, dtype=X.dtype) if fit_intercept: if sp.issparse(X): X_offset, X_var = mean_variance_axis(X, axis=0) if not return_mean: X_offset[:] = X.dtype.type(0) if normalize: # TODO: f_normalize could be used here as well but the function # inplace_csr_row_normalize_l2 must be changed such that it # can return also the norms computed internally # transform variance to norm in-place X_var *= X.shape[0] X_scale = np.sqrt(X_var, X_var) del X_var X_scale[X_scale == 0] = 1 inplace_column_scale(X, 1. / X_scale) else: X_scale = np.ones(X.shape[1], dtype=X.dtype) else: X_offset = np.average(X, axis=0, weights=sample_weight) X -= X_offset if normalize: X, X_scale = f_normalize(X, axis=0, copy=False, return_norm=True) else: X_scale = np.ones(X.shape[1], dtype=X.dtype) y_offset = np.average(y, axis=0, weights=sample_weight) y = y - y_offset else: X_offset = np.zeros(X.shape[1], dtype=X.dtype) X_scale = np.ones(X.shape[1], dtype=X.dtype) if y.ndim == 1: y_offset = X.dtype.type(0) else: y_offset = np.zeros(y.shape[1], dtype=X.dtype) return X, y, X_offset, y_offset, X_scale # TODO: _rescale_data should be factored into _preprocess_data. # Currently, the fact that sag implements its own way to deal with # sample_weight makes the refactoring tricky. def _rescale_data(X, y, sample_weight): """Rescale data sample-wise by square root of sample_weight. For many linear models, this enables easy support for sample_weight. Returns ------- X_rescaled : {array-like, sparse matrix} y_rescaled : {array-like, sparse matrix} """ n_samples = X.shape[0] sample_weight = np.asarray(sample_weight) if sample_weight.ndim == 0: sample_weight = np.full(n_samples, sample_weight, dtype=sample_weight.dtype) sample_weight = np.sqrt(sample_weight) sw_matrix = sparse.dia_matrix((sample_weight, 0), shape=(n_samples, n_samples)) X = safe_sparse_dot(sw_matrix, X) y = safe_sparse_dot(sw_matrix, y) return X, y class LinearModel(BaseEstimator, metaclass=ABCMeta): """Base class for Linear Models""" @abstractmethod def fit(self, X, y): """Fit model.""" def _decision_function(self, X): check_is_fitted(self) X = check_array(X, accept_sparse=['csr', 'csc', 'coo']) return safe_sparse_dot(X, self.coef_.T, dense_output=True) + self.intercept_ def predict(self, X): """ Predict using the linear model. Parameters ---------- X : array-like or sparse matrix, shape (n_samples, n_features) Samples. Returns ------- C : array, shape (n_samples,) Returns predicted values. """ return self._decision_function(X) _preprocess_data = staticmethod(_preprocess_data) def _set_intercept(self, X_offset, y_offset, X_scale): """Set the intercept_ """ if self.fit_intercept: self.coef_ = self.coef_ / X_scale self.intercept_ = y_offset - np.dot(X_offset, self.coef_.T) else: self.intercept_ = 0. def _more_tags(self): return {'requires_y': True} # XXX Should this derive from LinearModel? It should be a mixin, not an ABC. # Maybe the n_features checking can be moved to LinearModel. class LinearClassifierMixin(ClassifierMixin): """Mixin for linear classifiers. Handles prediction for sparse and dense X. """ def decision_function(self, X): """ Predict confidence scores for samples. The confidence score for a sample is proportional to the signed distance of that sample to the hyperplane. Parameters ---------- X : array-like or sparse matrix, shape (n_samples, n_features) Samples. Returns ------- array, shape=(n_samples,) if n_classes == 2 else (n_samples, n_classes) Confidence scores per (sample, class) combination. In the binary case, confidence score for self.classes_[1] where >0 means this class would be predicted. """ check_is_fitted(self) X = check_array(X, accept_sparse='csr') n_features = self.coef_.shape[1] if X.shape[1] != n_features: raise ValueError("X has %d features per sample; expecting %d" % (X.shape[1], n_features)) scores = safe_sparse_dot(X, self.coef_.T, dense_output=True) + self.intercept_ return scores.ravel() if scores.shape[1] == 1 else scores def predict(self, X): """ Predict class labels for samples in X. Parameters ---------- X : array-like or sparse matrix, shape (n_samples, n_features) Samples. Returns ------- C : array, shape [n_samples] Predicted class label per sample. """ scores = self.decision_function(X) if len(scores.shape) == 1: indices = (scores > 0).astype(int) else: indices = scores.argmax(axis=1) return self.classes_[indices] def _predict_proba_lr(self, X): """Probability estimation for OvR logistic regression. Positive class probabilities are computed as 1. / (1. + np.exp(-self.decision_function(X))); multiclass is handled by normalizing that over all classes. """ prob = self.decision_function(X) expit(prob, out=prob) if prob.ndim == 1: return np.vstack([1 - prob, prob]).T else: # OvR normalization, like LibLinear's predict_probability prob /= prob.sum(axis=1).reshape((prob.shape[0], -1)) return prob class SparseCoefMixin: """Mixin for converting coef_ to and from CSR format. L1-regularizing estimators should inherit this. """ def densify(self): """ Convert coefficient matrix to dense array format. Converts the ``coef_`` member (back) to a numpy.ndarray. This is the default format of ``coef_`` and is required for fitting, so calling this method is only required on models that have previously been sparsified; otherwise, it is a no-op. Returns ------- self Fitted estimator. """ msg = "Estimator, %(name)s, must be fitted before densifying." check_is_fitted(self, msg=msg) if sp.issparse(self.coef_): self.coef_ = self.coef_.toarray() return self def sparsify(self): """ Convert coefficient matrix to sparse format. Converts the ``coef_`` member to a scipy.sparse matrix, which for L1-regularized models can be much more memory- and storage-efficient than the usual numpy.ndarray representation. The ``intercept_`` member is not converted. Returns ------- self Fitted estimator. Notes ----- For non-sparse models, i.e. when there are not many zeros in ``coef_``, this may actually *increase* memory usage, so use this method with care. A rule of thumb is that the number of zero elements, which can be computed with ``(coef_ == 0).sum()``, must be more than 50% for this to provide significant benefits. After calling this method, further fitting with the partial_fit method (if any) will not work until you call densify. """ msg = "Estimator, %(name)s, must be fitted before sparsifying." check_is_fitted(self, msg=msg) self.coef_ = sp.csr_matrix(self.coef_) return self class LinearRegression(MultiOutputMixin, RegressorMixin, LinearModel): """ Ordinary least squares Linear Regression. LinearRegression fits a linear model with coefficients w = (w1, ..., wp) to minimize the residual sum of squares between the observed targets in the dataset, and the targets predicted by the linear approximation. Parameters ---------- fit_intercept : bool, default=True Whether to calculate the intercept for this model. If set to False, no intercept will be used in calculations (i.e. data is expected to be centered). normalize : bool, default=False This parameter is ignored when ``fit_intercept`` is set to False. If True, the regressors X will be normalized before regression by subtracting the mean and dividing by the l2-norm. If you wish to standardize, please use :class:`~sklearn.preprocessing.StandardScaler` before calling ``fit`` on an estimator with ``normalize=False``. copy_X : bool, default=True If True, X will be copied; else, it may be overwritten. n_jobs : int, default=None The number of jobs to use for the computation. This will only provide speedup for n_targets > 1 and sufficient large problems. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary <n_jobs>` for more details. positive : bool, default=False When set to ``True``, forces the coefficients to be positive. This option is only supported for dense arrays. .. versionadded:: 0.24 Attributes ---------- coef_ : array of shape (n_features, ) or (n_targets, n_features) Estimated coefficients for the linear regression problem. If multiple targets are passed during the fit (y 2D), this is a 2D array of shape (n_targets, n_features), while if only one target is passed, this is a 1D array of length n_features. rank_ : int Rank of matrix `X`. Only available when `X` is dense. singular_ : array of shape (min(X, y),) Singular values of `X`. Only available when `X` is dense. intercept_ : float or array of shape (n_targets,) Independent term in the linear model. Set to 0.0 if `fit_intercept = False`. See Also -------- Ridge : Ridge regression addresses some of the problems of Ordinary Least Squares by imposing a penalty on the size of the coefficients with l2 regularization. Lasso : The Lasso is a linear model that estimates sparse coefficients with l1 regularization. ElasticNet : Elastic-Net is a linear regression model trained with both l1 and l2 -norm regularization of the coefficients. Notes ----- From the implementation point of view, this is just plain Ordinary Least Squares (scipy.linalg.lstsq) or Non Negative Least Squares (scipy.optimize.nnls) wrapped as a predictor object. Examples -------- >>> import numpy as np >>> from sklearn.linear_model import LinearRegression >>> X = np.array([[1, 1], [1, 2], [2, 2], [2, 3]]) >>> # y = 1 * x_0 + 2 * x_1 + 3 >>> y = np.dot(X, np.array([1, 2])) + 3 >>> reg = LinearRegression().fit(X, y) >>> reg.score(X, y) 1.0 >>> reg.coef_ array([1., 2.]) >>> reg.intercept_ 3.0... >>> reg.predict(np.array([[3, 5]])) array([16.]) """ @_deprecate_positional_args def __init__(self, *, fit_intercept=True, normalize=False, copy_X=True, n_jobs=None, positive=False): self.fit_intercept = fit_intercept self.normalize = normalize self.copy_X = copy_X self.n_jobs = n_jobs self.positive = positive def fit(self, X, y, sample_weight=None): """ Fit linear model. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) Training data y : array-like of shape (n_samples,) or (n_samples, n_targets) Target values. Will be cast to X's dtype if necessary sample_weight : array-like of shape (n_samples,), default=None Individual weights for each sample .. versionadded:: 0.17 parameter *sample_weight* support to LinearRegression. Returns ------- self : returns an instance of self. """ n_jobs_ = self.n_jobs accept_sparse = False if self.positive else ['csr', 'csc', 'coo'] X, y = self._validate_data(X, y, accept_sparse=accept_sparse, y_numeric=True, multi_output=True) if sample_weight is not None: sample_weight = _check_sample_weight(sample_weight, X, dtype=X.dtype) X, y, X_offset, y_offset, X_scale = self._preprocess_data( X, y, fit_intercept=self.fit_intercept, normalize=self.normalize, copy=self.copy_X, sample_weight=sample_weight, return_mean=True) if sample_weight is not None: # Sample weight can be implemented via a simple rescaling. X, y = _rescale_data(X, y, sample_weight) if self.positive: if y.ndim < 2: self.coef_, self._residues = optimize.nnls(X, y) else: # scipy.optimize.nnls cannot handle y with shape (M, K) outs = Parallel(n_jobs=n_jobs_)( delayed(optimize.nnls)(X, y[:, j]) for j in range(y.shape[1])) self.coef_, self._residues = map(np.vstack, zip(*outs)) elif sp.issparse(X): X_offset_scale = X_offset / X_scale def matvec(b): return X.dot(b) - b.dot(X_offset_scale) def rmatvec(b): return X.T.dot(b) - X_offset_scale * np.sum(b) X_centered = sparse.linalg.LinearOperator(shape=X.shape, matvec=matvec, rmatvec=rmatvec) if y.ndim < 2: out = sparse_lsqr(X_centered, y) self.coef_ = out[0] self._residues = out[3] else: # sparse_lstsq cannot handle y with shape (M, K) outs = Parallel(n_jobs=n_jobs_)( delayed(sparse_lsqr)(X_centered, y[:, j].ravel()) for j in range(y.shape[1])) self.coef_ = np.vstack([out[0] for out in outs]) self._residues = np.vstack([out[3] for out in outs]) else: self.coef_, self._residues, self.rank_, self.singular_ = \ linalg.lstsq(X, y) self.coef_ = self.coef_.T if y.ndim == 1: self.coef_ = np.ravel(self.coef_) self._set_intercept(X_offset, y_offset, X_scale) return self def _pre_fit(X, y, Xy, precompute, normalize, fit_intercept, copy, check_input=True, sample_weight=None): """Aux function used at beginning of fit in linear models Parameters ---------- order : 'F', 'C' or None, default=None Whether X and y will be forced to be fortran or c-style. Only relevant if sample_weight is not None. """ n_samples, n_features = X.shape if sparse.isspmatrix(X): # copy is not needed here as X is not modified inplace when X is sparse precompute = False X, y, X_offset, y_offset, X_scale = _preprocess_data( X, y, fit_intercept=fit_intercept, normalize=normalize, copy=False, return_mean=True, check_input=check_input) else: # copy was done in fit if necessary X, y, X_offset, y_offset, X_scale = _preprocess_data( X, y, fit_intercept=fit_intercept, normalize=normalize, copy=copy, check_input=check_input, sample_weight=sample_weight) if sample_weight is not None: X, y = _rescale_data(X, y, sample_weight=sample_weight) if hasattr(precompute, '__array__') and ( fit_intercept and not np.allclose(X_offset, np.zeros(n_features)) or normalize and not np.allclose(X_scale, np.ones(n_features))): warnings.warn("Gram matrix was provided but X was centered" " to fit intercept, " "or X was normalized : recomputing Gram matrix.", UserWarning) # recompute Gram precompute = 'auto' Xy = None # precompute if n_samples > n_features if isinstance(precompute, str) and precompute == 'auto': precompute = (n_samples > n_features) if precompute is True: # make sure that the 'precompute' array is contiguous. precompute = np.empty(shape=(n_features, n_features), dtype=X.dtype, order='C') np.dot(X.T, X, out=precompute) if not hasattr(precompute, '__array__'): Xy = None # cannot use Xy if precompute is not Gram if hasattr(precompute, '__array__') and Xy is None: common_dtype = np.find_common_type([X.dtype, y.dtype], []) if y.ndim == 1: # Xy is 1d, make sure it is contiguous. Xy = np.empty(shape=n_features, dtype=common_dtype, order='C') np.dot(X.T, y, out=Xy) else: # Make sure that Xy is always F contiguous even if X or y are not # contiguous: the goal is to make it fast to extract the data for a # specific target. n_targets = y.shape[1] Xy = np.empty(shape=(n_features, n_targets), dtype=common_dtype, order='F') np.dot(y.T, X, out=Xy.T) return X, y, X_offset, y_offset, X_scale, precompute, Xy

35.583204

79

0.612981

from abc import ABCMeta, abstractmethod import numbers import warnings import numpy as np import scipy.sparse as sp from scipy import linalg from scipy import optimize from scipy import sparse from scipy.special import expit from joblib import Parallel from ..base import (BaseEstimator, ClassifierMixin, RegressorMixin, MultiOutputMixin) from ..utils import check_array from ..utils.validation import FLOAT_DTYPES from ..utils.validation import _deprecate_positional_args from ..utils import check_random_state from ..utils.extmath import safe_sparse_dot from ..utils.sparsefuncs import mean_variance_axis, inplace_column_scale from ..utils.fixes import sparse_lsqr from ..utils._seq_dataset import ArrayDataset32, CSRDataset32 from ..utils._seq_dataset import ArrayDataset64, CSRDataset64 from ..utils.validation import check_is_fitted, _check_sample_weight from ..utils.fixes import delayed from ..preprocessing import normalize as f_normalize SPARSE_INTERCEPT_DECAY = 0.01 def make_dataset(X, y, sample_weight, random_state=None): rng = check_random_state(random_state) seed = rng.randint(1, np.iinfo(np.int32).max) if X.dtype == np.float32: CSRData = CSRDataset32 ArrayData = ArrayDataset32 else: CSRData = CSRDataset64 ArrayData = ArrayDataset64 if sp.issparse(X): dataset = CSRData(X.data, X.indptr, X.indices, y, sample_weight, seed=seed) intercept_decay = SPARSE_INTERCEPT_DECAY else: X = np.ascontiguousarray(X) dataset = ArrayData(X, y, sample_weight, seed=seed) intercept_decay = 1.0 return dataset, intercept_decay def _preprocess_data(X, y, fit_intercept, normalize=False, copy=True, sample_weight=None, return_mean=False, check_input=True): if isinstance(sample_weight, numbers.Number): sample_weight = None if sample_weight is not None: sample_weight = np.asarray(sample_weight) if check_input: X = check_array(X, copy=copy, accept_sparse=['csr', 'csc'], dtype=FLOAT_DTYPES) elif copy: if sp.issparse(X): X = X.copy() else: X = X.copy(order='K') y = np.asarray(y, dtype=X.dtype) if fit_intercept: if sp.issparse(X): X_offset, X_var = mean_variance_axis(X, axis=0) if not return_mean: X_offset[:] = X.dtype.type(0) if normalize: X_var *= X.shape[0] X_scale = np.sqrt(X_var, X_var) del X_var X_scale[X_scale == 0] = 1 inplace_column_scale(X, 1. / X_scale) else: X_scale = np.ones(X.shape[1], dtype=X.dtype) else: X_offset = np.average(X, axis=0, weights=sample_weight) X -= X_offset if normalize: X, X_scale = f_normalize(X, axis=0, copy=False, return_norm=True) else: X_scale = np.ones(X.shape[1], dtype=X.dtype) y_offset = np.average(y, axis=0, weights=sample_weight) y = y - y_offset else: X_offset = np.zeros(X.shape[1], dtype=X.dtype) X_scale = np.ones(X.shape[1], dtype=X.dtype) if y.ndim == 1: y_offset = X.dtype.type(0) else: y_offset = np.zeros(y.shape[1], dtype=X.dtype) return X, y, X_offset, y_offset, X_scale def _rescale_data(X, y, sample_weight): n_samples = X.shape[0] sample_weight = np.asarray(sample_weight) if sample_weight.ndim == 0: sample_weight = np.full(n_samples, sample_weight, dtype=sample_weight.dtype) sample_weight = np.sqrt(sample_weight) sw_matrix = sparse.dia_matrix((sample_weight, 0), shape=(n_samples, n_samples)) X = safe_sparse_dot(sw_matrix, X) y = safe_sparse_dot(sw_matrix, y) return X, y class LinearModel(BaseEstimator, metaclass=ABCMeta): @abstractmethod def fit(self, X, y): def _decision_function(self, X): check_is_fitted(self) X = check_array(X, accept_sparse=['csr', 'csc', 'coo']) return safe_sparse_dot(X, self.coef_.T, dense_output=True) + self.intercept_ def predict(self, X): return self._decision_function(X) _preprocess_data = staticmethod(_preprocess_data) def _set_intercept(self, X_offset, y_offset, X_scale): if self.fit_intercept: self.coef_ = self.coef_ / X_scale self.intercept_ = y_offset - np.dot(X_offset, self.coef_.T) else: self.intercept_ = 0. def _more_tags(self): return {'requires_y': True} class LinearClassifierMixin(ClassifierMixin): def decision_function(self, X): check_is_fitted(self) X = check_array(X, accept_sparse='csr') n_features = self.coef_.shape[1] if X.shape[1] != n_features: raise ValueError("X has %d features per sample; expecting %d" % (X.shape[1], n_features)) scores = safe_sparse_dot(X, self.coef_.T, dense_output=True) + self.intercept_ return scores.ravel() if scores.shape[1] == 1 else scores def predict(self, X): scores = self.decision_function(X) if len(scores.shape) == 1: indices = (scores > 0).astype(int) else: indices = scores.argmax(axis=1) return self.classes_[indices] def _predict_proba_lr(self, X): prob = self.decision_function(X) expit(prob, out=prob) if prob.ndim == 1: return np.vstack([1 - prob, prob]).T else: prob /= prob.sum(axis=1).reshape((prob.shape[0], -1)) return prob class SparseCoefMixin: def densify(self): msg = "Estimator, %(name)s, must be fitted before densifying." check_is_fitted(self, msg=msg) if sp.issparse(self.coef_): self.coef_ = self.coef_.toarray() return self def sparsify(self): msg = "Estimator, %(name)s, must be fitted before sparsifying." check_is_fitted(self, msg=msg) self.coef_ = sp.csr_matrix(self.coef_) return self class LinearRegression(MultiOutputMixin, RegressorMixin, LinearModel): @_deprecate_positional_args def __init__(self, *, fit_intercept=True, normalize=False, copy_X=True, n_jobs=None, positive=False): self.fit_intercept = fit_intercept self.normalize = normalize self.copy_X = copy_X self.n_jobs = n_jobs self.positive = positive def fit(self, X, y, sample_weight=None): n_jobs_ = self.n_jobs accept_sparse = False if self.positive else ['csr', 'csc', 'coo'] X, y = self._validate_data(X, y, accept_sparse=accept_sparse, y_numeric=True, multi_output=True) if sample_weight is not None: sample_weight = _check_sample_weight(sample_weight, X, dtype=X.dtype) X, y, X_offset, y_offset, X_scale = self._preprocess_data( X, y, fit_intercept=self.fit_intercept, normalize=self.normalize, copy=self.copy_X, sample_weight=sample_weight, return_mean=True) if sample_weight is not None: # Sample weight can be implemented via a simple rescaling. X, y = _rescale_data(X, y, sample_weight) if self.positive: if y.ndim < 2: self.coef_, self._residues = optimize.nnls(X, y) else: # scipy.optimize.nnls cannot handle y with shape (M, K) outs = Parallel(n_jobs=n_jobs_)( delayed(optimize.nnls)(X, y[:, j]) for j in range(y.shape[1])) self.coef_, self._residues = map(np.vstack, zip(*outs)) elif sp.issparse(X): X_offset_scale = X_offset / X_scale def matvec(b): return X.dot(b) - b.dot(X_offset_scale) def rmatvec(b): return X.T.dot(b) - X_offset_scale * np.sum(b) X_centered = sparse.linalg.LinearOperator(shape=X.shape, matvec=matvec, rmatvec=rmatvec) if y.ndim < 2: out = sparse_lsqr(X_centered, y) self.coef_ = out[0] self._residues = out[3] else: # sparse_lstsq cannot handle y with shape (M, K) outs = Parallel(n_jobs=n_jobs_)( delayed(sparse_lsqr)(X_centered, y[:, j].ravel()) for j in range(y.shape[1])) self.coef_ = np.vstack([out[0] for out in outs]) self._residues = np.vstack([out[3] for out in outs]) else: self.coef_, self._residues, self.rank_, self.singular_ = \ linalg.lstsq(X, y) self.coef_ = self.coef_.T if y.ndim == 1: self.coef_ = np.ravel(self.coef_) self._set_intercept(X_offset, y_offset, X_scale) return self def _pre_fit(X, y, Xy, precompute, normalize, fit_intercept, copy, check_input=True, sample_weight=None): n_samples, n_features = X.shape if sparse.isspmatrix(X): # copy is not needed here as X is not modified inplace when X is sparse precompute = False X, y, X_offset, y_offset, X_scale = _preprocess_data( X, y, fit_intercept=fit_intercept, normalize=normalize, copy=False, return_mean=True, check_input=check_input) else: # copy was done in fit if necessary X, y, X_offset, y_offset, X_scale = _preprocess_data( X, y, fit_intercept=fit_intercept, normalize=normalize, copy=copy, check_input=check_input, sample_weight=sample_weight) if sample_weight is not None: X, y = _rescale_data(X, y, sample_weight=sample_weight) if hasattr(precompute, '__array__') and ( fit_intercept and not np.allclose(X_offset, np.zeros(n_features)) or normalize and not np.allclose(X_scale, np.ones(n_features))): warnings.warn("Gram matrix was provided but X was centered" " to fit intercept, " "or X was normalized : recomputing Gram matrix.", UserWarning) # recompute Gram precompute = 'auto' Xy = None # precompute if n_samples > n_features if isinstance(precompute, str) and precompute == 'auto': precompute = (n_samples > n_features) if precompute is True: # make sure that the 'precompute' array is contiguous. precompute = np.empty(shape=(n_features, n_features), dtype=X.dtype, order='C') np.dot(X.T, X, out=precompute) if not hasattr(precompute, '__array__'): Xy = None # cannot use Xy if precompute is not Gram if hasattr(precompute, '__array__') and Xy is None: common_dtype = np.find_common_type([X.dtype, y.dtype], []) if y.ndim == 1: # Xy is 1d, make sure it is contiguous. Xy = np.empty(shape=n_features, dtype=common_dtype, order='C') np.dot(X.T, y, out=Xy) else: # Make sure that Xy is always F contiguous even if X or y are not # contiguous: the goal is to make it fast to extract the data for a # specific target. n_targets = y.shape[1] Xy = np.empty(shape=(n_features, n_targets), dtype=common_dtype, order='F') np.dot(y.T, X, out=Xy.T) return X, y, X_offset, y_offset, X_scale, precompute, Xy

true

f71b728f6b7ee0bb3e520d6e3e1bb4a53edb161c

595

py

Python

website/system.py

timlyo/timlyo.github.io

fb3e3b65822351e49e3ba4ee17ba4ed5151c969a

[ "Apache-2.0" ]

1

2016-01-14T13:52:25.000Z

website/system.py

timlyo/personalWebsite

fb3e3b65822351e49e3ba4ee17ba4ed5151c969a

[ "Apache-2.0" ]

null

website/system.py

timlyo/personalWebsite

fb3e3b65822351e49e3ba4ee17ba4ed5151c969a

[ "Apache-2.0" ]

null

import os import psutil COEFFICIENT = 2 ** 20 def get_other_ram() -> int: """Ram used by other processes""" return get_ram_used() - get_process_ram() def get_total_ram() -> int: mem = psutil.virtual_memory() return mem[0] / COEFFICIENT def get_process_ram() -> int: process = psutil.Process(os.getpid()) return process.memory_info()[0] / COEFFICIENT def get_ram_used() -> int: """ram used by all processes""" mem = psutil.virtual_memory() return mem[4] / COEFFICIENT def get_cpu() -> list: """get all cpu core usage""" percentage = psutil.cpu_percent() return percentage

18.030303

46

0.697479

import os import psutil COEFFICIENT = 2 ** 20 def get_other_ram() -> int: return get_ram_used() - get_process_ram() def get_total_ram() -> int: mem = psutil.virtual_memory() return mem[0] / COEFFICIENT def get_process_ram() -> int: process = psutil.Process(os.getpid()) return process.memory_info()[0] / COEFFICIENT def get_ram_used() -> int: mem = psutil.virtual_memory() return mem[4] / COEFFICIENT def get_cpu() -> list: percentage = psutil.cpu_percent() return percentage

true

f71b72b888e77e3334994d29892f03d292b9f189

1,820

py

Python

libweasyl/libweasyl/configuration.py

hyena/weasyl

a43ad885eb07ae89d6639f289a5b95f3a177439c

[ "Apache-2.0" ]

1

2019-02-15T04:21:48.000Z

libweasyl/libweasyl/configuration.py

hyena/weasyl

a43ad885eb07ae89d6639f289a5b95f3a177439c

[ "Apache-2.0" ]

254

2017-12-23T19:36:43.000Z

2020-04-14T21:46:13.000Z

libweasyl/libweasyl/configuration.py

hyena/weasyl

a43ad885eb07ae89d6639f289a5b95f3a177439c

[ "Apache-2.0" ]

1

2017-12-23T18:42:16.000Z

""" Configuration of libweasyl. libweasyl depends on some global state to be set up in order for e.g. database access to work correctly. This might be nicer if python had a way of parameterizing modules, but we can't, so this is what we have. It does mean that only one libweasyl configuration can exist in a running python process. """ from libweasyl.models.media import DiskMediaItem, MediaItem from libweasyl.models.meta import BaseQuery, _configure_dbsession from libweasyl.staff import _init_staff def configure_libweasyl( dbsession, not_found_exception, base_file_path, staff_config_dict, media_link_formatter_callback): """ Configure libweasyl for the current application. This sets up some global state around libweasyl. This function can be called multiple times without issues; each call will replace the values set by the previous call. Parameters: dbsession: A SQLAlchemy ``scoped_session`` instance configured for the application's database usage. not_found_exception: An exception to be raised on the ``*_or_404`` methods of queries. base_file_path: The path to where static content lives on disk. staff_config_dict: A dictionary of staff levels and user IDs. media_link_formatter_callback: A callback to format the URL for a media link. The callback will be called as ``callback(media_item, link)`` and is expected to return a URL or ``None`` to use the default. """ _configure_dbsession(dbsession) BaseQuery._not_found_exception = staticmethod(not_found_exception) DiskMediaItem._base_file_path = staticmethod(base_file_path) _init_staff(**staff_config_dict) MediaItem._media_link_formatter_callback = staticmethod(media_link_formatter_callback)

44.390244

90

0.752198

from libweasyl.models.media import DiskMediaItem, MediaItem from libweasyl.models.meta import BaseQuery, _configure_dbsession from libweasyl.staff import _init_staff def configure_libweasyl( dbsession, not_found_exception, base_file_path, staff_config_dict, media_link_formatter_callback): _configure_dbsession(dbsession) BaseQuery._not_found_exception = staticmethod(not_found_exception) DiskMediaItem._base_file_path = staticmethod(base_file_path) _init_staff(**staff_config_dict) MediaItem._media_link_formatter_callback = staticmethod(media_link_formatter_callback)

true

f71b731df78a211a9b978d951f533de530a3905f

3,840

py

Python

tensorforce/core/memories/latest.py

zysilence/tensorforce

7539e5dde66f3a93b881006f9b7f38c926ced21b

[ "Apache-2.0" ]

2

2021-11-14T12:28:24.000Z

2022-02-14T19:23:51.000Z

tensorforce/core/memories/latest.py

zysilence/tensorforce

7539e5dde66f3a93b881006f9b7f38c926ced21b

[ "Apache-2.0" ]

null

tensorforce/core/memories/latest.py

zysilence/tensorforce

7539e5dde66f3a93b881006f9b7f38c926ced21b

[ "Apache-2.0" ]

3

2021-03-04T17:26:43.000Z

2021-03-04T17:27:10.000Z

# Copyright 2017 reinforce.io. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== from __future__ import absolute_import from __future__ import print_function from __future__ import division import tensorflow as tf from tensorforce.core.memories import Queue class Latest(Queue): """ Memory which always retrieves most recent experiences. """ def __init__(self, states, internals, actions, include_next_states, capacity, scope='latest', summary_labels=None): """ Latest memory. Args: states: States specifiction. internals: Internal states specification. actions: Actions specification. include_next_states: Include subsequent state if true. capacity: Memory capacity. """ super(Latest, self).__init__( states=states, internals=internals, actions=actions, include_next_states=include_next_states, capacity=capacity, scope=scope, summary_labels=summary_labels ) def tf_retrieve_timesteps(self, n): num_timesteps = (self.memory_index - self.episode_indices[-1] - 2) % self.capacity + 1 n = tf.minimum(x=n, y=num_timesteps) indices = tf.range( start=(self.memory_index - n), limit=self.memory_index ) % self.capacity return self.retrieve_indices(indices=indices) def tf_retrieve_episodes(self, n): n = tf.minimum(x=n, y=self.episode_count) start = self.episode_indices[self.episode_count - n - 1] + 1 limit = self.episode_indices[self.episode_count - 1] + 1 limit += tf.where(condition=(start < limit), x=0, y=self.capacity) indices = tf.range(start=start, limit=limit) % self.capacity return self.retrieve_indices(indices=indices) def tf_retrieve_sequences(self, n, sequence_length): # Remove once #128 is resolved tf.logging.warn("Sampling sequences is not validated yet. Use timesteps or episodes instead.") num_sequences = (self.memory_index - self.episode_indices[-1] - 2 - sequence_length + 1) % self.capacity + 1 n = tf.minimum(x=n, y=num_sequences) indices = tf.range( start=(self.memory_index - n - sequence_length), # or '- 1' implied in sequence length? limit=self.memory_index ) % self.capacity # sequence_indices = [tf.range(start=indices[n], limit=(indices[n] + sequence_length)) for k in range(n)] # sequence_indices = [indices[k: k + sequence_length] for k in tf.unstack(value=tf.range(start=0, limit=n), num=n)] sequence_indices = tf.expand_dims(input=tf.range(start=0, limit=n), axis=1) + tf.expand_dims(input=tf.constant(value=list(range(sequence_length))), axis=0) sequence_indices = tf.reshape(tensor=sequence_indices, shape=(n * sequence_length,)) # sequence_indices = tf.concat(values=sequence_indices, axis=0) # tf.stack !!!!! terminal = tf.gather(params=self.terminal_memory, indices=indices) sequence_indices = tf.boolean_mask(tensor=sequence_indices, mask=tf.logical_not(x=terminal)) return self.retrieve_indices(indices=sequence_indices)

45.176471

163

0.663542

from __future__ import absolute_import from __future__ import print_function from __future__ import division import tensorflow as tf from tensorforce.core.memories import Queue class Latest(Queue): def __init__(self, states, internals, actions, include_next_states, capacity, scope='latest', summary_labels=None): super(Latest, self).__init__( states=states, internals=internals, actions=actions, include_next_states=include_next_states, capacity=capacity, scope=scope, summary_labels=summary_labels ) def tf_retrieve_timesteps(self, n): num_timesteps = (self.memory_index - self.episode_indices[-1] - 2) % self.capacity + 1 n = tf.minimum(x=n, y=num_timesteps) indices = tf.range( start=(self.memory_index - n), limit=self.memory_index ) % self.capacity return self.retrieve_indices(indices=indices) def tf_retrieve_episodes(self, n): n = tf.minimum(x=n, y=self.episode_count) start = self.episode_indices[self.episode_count - n - 1] + 1 limit = self.episode_indices[self.episode_count - 1] + 1 limit += tf.where(condition=(start < limit), x=0, y=self.capacity) indices = tf.range(start=start, limit=limit) % self.capacity return self.retrieve_indices(indices=indices) def tf_retrieve_sequences(self, n, sequence_length): ing.warn("Sampling sequences is not validated yet. Use timesteps or episodes instead.") num_sequences = (self.memory_index - self.episode_indices[-1] - 2 - sequence_length + 1) % self.capacity + 1 n = tf.minimum(x=n, y=num_sequences) indices = tf.range( start=(self.memory_index - n - sequence_length), limit=self.memory_index ) % self.capacity sequence_indices = tf.expand_dims(input=tf.range(start=0, limit=n), axis=1) + tf.expand_dims(input=tf.constant(value=list(range(sequence_length))), axis=0) sequence_indices = tf.reshape(tensor=sequence_indices, shape=(n * sequence_length,)) l = tf.gather(params=self.terminal_memory, indices=indices) sequence_indices = tf.boolean_mask(tensor=sequence_indices, mask=tf.logical_not(x=terminal)) return self.retrieve_indices(indices=sequence_indices)

true

f71b73477d2f539f36cc389b2a439621a3f79453

18,562

py

Python

osa/scripts/provprocess.py

gae-ucm/LSTOSA

d44df4dc1daa87f57d95272014f05908d2c9a211

[ "BSD-3-Clause" ]

2

2022-02-21T17:45:38.000Z

2022-03-25T11:48:52.000Z

osa/scripts/provprocess.py

gae-ucm/LSTOSA

d44df4dc1daa87f57d95272014f05908d2c9a211

[ "BSD-3-Clause" ]

79

2021-12-02T10:37:42.000Z

2022-03-29T23:56:44.000Z

osa/scripts/provprocess.py

cta-observatory/lstosa

dd7a3a4967f265217929a1271c3f9be559a122ac

[ "BSD-3-Clause" ]

1

2021-11-25T09:56:12.000Z

#!/usr/bin/env python """Provenance post processing script for OSA pipeline.""" import copy import logging import shutil import sys from pathlib import Path, PurePath import yaml from osa.configs import options from osa.configs.config import cfg from osa.provenance.capture import get_activity_id, get_file_hash from osa.provenance.io import provdoc2graph, provdoc2json, provlist2provdoc, read_prov from osa.provenance.utils import get_log_config from osa.utils.cliopts import provprocessparsing from osa.utils.logging import myLogger __all__ = ["copy_used_file", "parse_lines_log", "parse_lines_run", "produce_provenance"] log = myLogger(logging.getLogger()) provconfig = yaml.safe_load(get_log_config()) LOG_FILENAME = provconfig["handlers"]["provHandler"]["filename"] PROV_PREFIX = provconfig["PREFIX"] PATH_DL1 = cfg.get("LST1", "DL1_DIR") PATH_DL2 = cfg.get("LST1", "DL2_DIR") def copy_used_file(src, outdir): """ Copy file used in process. Parameters ---------- src outdir """ # check src file exists if not Path(src).is_file(): log.warning(f"{src} file cannot be accessed") hash_src = get_file_hash(src, buffer="content") filename = PurePath(src).name destpath = Path(outdir) / filename hash_out = "" # get hash and new name if destpath.exists(): hash_out = get_file_hash(str(destpath), buffer="content") filename = filename + "_" destpath = Path(outdir) / filename # try copy file if hash_src != hash_out: try: shutil.copyfile(src, str(destpath)) log.info(f"copying {destpath}") except Exception as ex: log.warning(f"could not copy {src} file into {destpath}: {ex}") def parse_lines_log(filter_cut, calib_runs, run_number): """ Filter content in log file to produce a run/process wise session log. Parameters ---------- filter_cut calib_runs run_number Returns ------- filtered """ filtered = [] if not filter_cut: filter_cut = "all" cuts = { "calibration": ["drs4_pedestal", "calibrate_charge"], "r0_to_dl1": ["r0_to_dl1", "dl1ab"], "dl1_to_dl2": ["dl1_datacheck", "dl1_to_dl2"], } cuts["all"] = cuts["calibration"] + cuts["r0_to_dl1"] + cuts["dl1_to_dl2"] with open(LOG_FILENAME, "r") as f: for line in f.readlines(): ll = line.split(PROV_PREFIX) if len(ll) != 3: log.warning( f"format {PROV_PREFIX} mismatch in log file {LOG_FILENAME}\n{line}" ) continue prov_str = ll.pop() prov_dict = yaml.safe_load(prov_str) keep = False session_tag = prov_dict.get("session_tag", "0:0") session_id = prov_dict.get("session_id", False) tag_activity, tag_run = session_tag.split(":") # filter by run and calib runs if tag_run in [run_number, calib_runs]: keep = True # filter by activity if tag_activity not in cuts[filter_cut]: keep = False # only keep first session start if session_id and (tag_run in [run_number, calib_runs]): keep = True # make session starts with calibration if session_id and filter_cut == "all" and not filtered: prov_dict["session_id"] = f"{options.date}{run_number}" prov_dict["name"] = run_number prov_dict["observation_run"] = run_number line = f"{ll[0]}{PROV_PREFIX}{ll[1]}{PROV_PREFIX}{prov_dict}\n" # remove parallel sessions if session_id and filtered: keep = False if keep: filtered.append(line) return filtered def parse_lines_run(filter_step, prov_lines, out): """ Process provenance info to reduce session at run/process wise scope. Parameters ---------- filter_step prov_lines out Returns ------- working_lines """ size = 0 container = {} working_lines = [] r0filepath_str = "" dl1filepath_str = "" dl2filepath_str = "" mufilepath_str = "" ckfilepath_str = "" id_activity_run = "" end_time_line = "" osa_config_copied = False for line in prov_lines: # get info remove = False endTime = line.get("endTime", "") session_id = line.get("session_id", "") activity_id = line.get("activity_id", "") filepath = line.get("filepath", "") used_role = line.get("used_role", "") generated_role = line.get("generated_role", "") parameters = line.get("parameters", "") name = line.get("name", "") content_type = line.get("contentType", "") used_id = line.get("used_id", "") osa_cfg = line.get("config_file", "") # filter grain session_tag = line.get("session_tag", "0:0") tag_activity, _ = session_tag.split(":") if tag_activity != filter_step and not session_id: continue # remove subruns info if name == "DL1CheckSubrunDataset": ckfilepath_str = filepath elif name == "DL1SubrunDataset": dl1filepath_str = filepath elif name == "DL2SubrunDataset": dl2filepath_str = filepath elif name == "MuonsSubrunDataset": mufilepath_str = filepath elif name == "R0SubrunDataset": r0filepath_str = filepath if "Subrun" in name or "subrun" in used_role or "subrun" in generated_role: remove = True if parameters and "ObservationSubRun" in parameters: del line["parameters"]["ObservationSubRun"] # remove sub-runs activities and info if name == filter_step and not id_activity_run: id_activity_run = get_activity_id() if name in container or used_id in container: remove = True if parameters and "parameters" in container: remove = True if name: container[name] = True if used_id: container[used_id] = True if parameters: container["parameters"] = True if endTime: remove = True end_time_line = line size += 1 # remove duplicated produced files if generated_role in container: remove = True if name == "DL2MergedFile": container[name] = True if "merged" in generated_role: container[generated_role] = True if name == "DL1CheckHDF5File": container[name] = True if "DL1Check HDF5 file" in generated_role: container[generated_role] = True if name == "DL1CheckPDFFile": container[name] = True if "DL1Check PDF file" in generated_role: container[generated_role] = True # replace with new run-wise activity_id if activity_id: line["activity_id"] = id_activity_run # copy used files not subruns not RFs not mergedDL2 if ( filepath and content_type != "application/x-spss-sav" and name != "DL2MergedFile" and not name.startswith("DL1Check") and not remove ): copy_used_file(filepath, out) if session_id and osa_cfg and not osa_config_copied: copy_used_file(osa_cfg, out) osa_config_copied = True if not remove: working_lines.append(line) # append collections used and generated at endtime line of last activity if end_time_line: working_lines.append(end_time_line) if r0filepath_str and filter_step == "r0_to_dl1": r0_entity_id = get_file_hash(r0filepath_str + "r0", buffer="path") r0filepath_str = r0filepath_str.replace(PurePath(r0filepath_str).name, "") used = {"entity_id": r0_entity_id} used.update({"name": "R0Collection"}) used.update({"type": "SetCollection"}) used.update({"size": size}) used.update({"filepath": r0filepath_str}) working_lines.append(used) used = {"activity_id": id_activity_run} used.update({"used_id": r0_entity_id}) used.update({"used_role": "R0 Collection"}) working_lines.append(used) if dl1filepath_str: dl1filepath_str = dl1filepath_str.replace(PurePath(dl1filepath_str).name, "") dl1_entity_id = get_file_hash(dl1filepath_str + "dl1", buffer="path") dl1 = {"entity_id": dl1_entity_id} dl1.update({"name": "DL1Collection"}) dl1.update({"type": "SetCollection"}) dl1.update({"size": size}) dl1.update({"filepath": dl1filepath_str}) working_lines.append(dl1) if mufilepath_str: mufilepath_str = mufilepath_str.replace(PurePath(mufilepath_str).name, "") mu_entity_id = get_file_hash(mufilepath_str + "muons", buffer="path") muons = {"entity_id": mu_entity_id} muons.update({"name": "MuonsCollection"}) muons.update({"type": "SetCollection"}) muons.update({"size": size}) muons.update({"filepath": mufilepath_str}) working_lines.append(muons) if mufilepath_str and filter_step == "r0_to_dl1": generated = {"activity_id": id_activity_run} generated.update({"generated_id": mu_entity_id}) generated.update({"generated_role": "Muons Collection"}) working_lines.append(generated) if dl1filepath_str and filter_step in ["r0_to_dl1", "dl1ab"]: generated = {"activity_id": id_activity_run} generated.update({"generated_id": dl1_entity_id}) generated.update({"generated_role": "DL1 Collection"}) working_lines.append(generated) if dl1filepath_str and filter_step in ["dl1_to_dl2", "dl1ab"]: used = {"activity_id": id_activity_run} used.update({"used_id": dl1_entity_id}) used.update({"used_role": "DL1 Collection"}) working_lines.append(used) if dl1filepath_str and filter_step == "dl1_datacheck": used = {"activity_id": id_activity_run} used.update({"used_id": dl1_entity_id}) used.update({"used_role": "DL1 Collection"}) working_lines.append(used) if mufilepath_str and filter_step == "dl1_datacheck": used = {"activity_id": id_activity_run} used.update({"used_id": mu_entity_id}) used.update({"used_role": "Muons Collection"}) working_lines.append(used) if ckfilepath_str and filter_step == "dl1_datacheck": ckfilepath_str = ckfilepath_str.replace(PurePath(ckfilepath_str).name, "") chk_entity_id = get_file_hash(ckfilepath_str + "check", buffer="path") dl1check = {"entity_id": chk_entity_id} dl1check.update({"name": "DL1CheckCollection"}) dl1check.update({"type": "SetCollection"}) dl1check.update({"size": size}) dl1check.update({"filepath": ckfilepath_str}) working_lines.append(dl1check) generated = {"activity_id": id_activity_run} generated.update({"generated_id": chk_entity_id}) generated.update({"generated_role": "DL1Checks Collection"}) working_lines.append(generated) if dl2filepath_str and filter_step == "dl1_to_dl2": dl2_entity_id = get_file_hash(dl2filepath_str + "dl2", buffer="path") dl2filepath_str = dl2filepath_str.replace(PurePath(dl2filepath_str).name, "") used = {"entity_id": dl2_entity_id} used.update({"name": "DL2Collection"}) used.update({"type": "SetCollection"}) used.update({"size": size}) used.update({"filepath": dl2filepath_str}) working_lines.append(used) used = {"activity_id": id_activity_run} used.update({"generated_id": dl2_entity_id}) used.update({"generated_role": "DL2 Collection"}) working_lines.append(used) else: working_lines = [] return working_lines def define_paths(grain, start_path, end_path, base_filename): """Define target folders according to granularity.""" paths = {} # check destination folder exists step_path = Path(start_path) / options.date / options.prod_id / end_path if not step_path.exists(): log.error(f"Path {step_path} does not exist") # make folder log/ if does not exist paths["out_path"] = step_path / "log" paths["out_path"].mkdir(parents=True, exist_ok=True) # define paths for prov products paths["log_path"] = paths["out_path"] / f"{grain}_{base_filename}.log" paths["json_filepath"] = paths["out_path"] / f"{grain}_{base_filename}.json" paths["graph_filepath"] = paths["out_path"] / f"{grain}_{base_filename}.pdf" return paths def produce_provenance_files(processed_lines, paths): """Create provenance products as JSON logs and graphs.""" with open(paths["log_path"], "w") as f: for line in processed_lines: f.write(f"{line}\n") log.info(f"creating {paths['log_path']}") provdoc = provlist2provdoc(processed_lines) # make json try: provdoc2json(provdoc, str(paths["json_filepath"])) log.info(f"creating {paths['json_filepath']}") except Exception as ex: log.exception(f"problem while creating json: {ex}") # make graph try: provdoc2graph(provdoc, str(paths["graph_filepath"]), "pdf") log.info(f"creating {paths['graph_filepath']}") except Exception as ex: log.exception(f"problem while creating graph: {ex}") def produce_provenance(session_log_filename, base_filename): """ Create run-wise provenance products as JSON logs and graphs according to granularity. """ if options.filter == "calibration" or not options.filter: paths_calibration = define_paths( "calibration_to_dl1", PATH_DL1, options.dl1_prod_id, base_filename ) plines_drs4 = parse_lines_run( "drs4_pedestal", read_prov(filename=session_log_filename), str(paths_calibration["out_path"]), ) plines_calib = parse_lines_run( "calibrate_charge", read_prov(filename=session_log_filename), str(paths_calibration["out_path"]), ) calibration_lines = plines_drs4 + plines_calib[1:] # TODO # create calibration prov files only if filtering if options.filter == "calibration": pass if options.filter == "r0_to_dl1" or not options.filter: paths_r0_dl1 = define_paths( "r0_to_dl1", PATH_DL1, options.dl1_prod_id, base_filename ) plines_r0 = parse_lines_run( "r0_to_dl1", read_prov(filename=session_log_filename), str(paths_r0_dl1["out_path"]), ) plines_ab = parse_lines_run( "dl1ab", read_prov(filename=session_log_filename), str(paths_r0_dl1["out_path"]), ) dl1_lines = plines_r0 + plines_ab[1:] # create r0_to_dl1 prov files only if filtering if options.filter == "r0_to_dl1": produce_provenance_files(plines_r0 + plines_ab[1:], paths_r0_dl1) if options.filter == "dl1_to_dl2" or not options.filter: paths_dl1_dl2 = define_paths( "dl1_to_dl2", PATH_DL2, options.dl2_prod_id, base_filename ) plines_check = parse_lines_run( "dl1_datacheck", read_prov(filename=session_log_filename), str(paths_dl1_dl2["out_path"]), ) plines_dl2 = parse_lines_run( "dl1_to_dl2", read_prov(filename=session_log_filename), str(paths_dl1_dl2["out_path"]), ) dl1_dl2_lines = plines_check + plines_dl2[1:] # create dl1_to_dl2 prov files only if filtering if options.filter == "dl1_to_dl2": produce_provenance_files(plines_check + plines_dl2[1:], paths_dl1_dl2) # create calibration_to_dl1 and calibration_to_dl2 prov files if not options.filter: calibration_to_dl1 = define_paths( "calibration_to_dl1", PATH_DL1, options.dl1_prod_id, base_filename ) calibration_to_dl2 = define_paths( "calibration_to_dl2", PATH_DL2, options.dl2_prod_id, base_filename ) calibration_to_dl1_lines = calibration_lines + dl1_lines[1:] lines_dl1 = copy.deepcopy(calibration_to_dl1_lines) calibration_to_dl2_lines = calibration_to_dl1_lines + dl1_dl2_lines[1:] lines_dl2 = copy.deepcopy(calibration_to_dl2_lines) produce_provenance_files(lines_dl1, calibration_to_dl1) produce_provenance_files(lines_dl2, calibration_to_dl2) def main(): """Extract the provenance information.""" provprocessparsing() # Logging if options.verbose: log.setLevel(logging.DEBUG) else: log.setLevel(logging.INFO) # check LOG_FILENAME exists if not Path(LOG_FILENAME).exists(): log.error(f"file {LOG_FILENAME} does not exist") # check LOG_FILENAME is not empty if not Path(LOG_FILENAME).stat().st_size: log.warning(f"file {LOG_FILENAME} is empty") sys.exit(1) # build base_filename base_filename = f"{options.run}_prov" session_log_filename = f"{base_filename}.log" # parse LOG_FILENAME content for a specific run / process calib_runs = f"{options.drs4_pedestal_run_id}-{options.pedcal_run_id}" parsed_content = parse_lines_log(options.filter, calib_runs, options.run) # create temporal session log file with open(session_log_filename, "w") as f: for line in parsed_content: f.write(line) try: # create run-wise JSON logs and graphs for each produce_provenance(session_log_filename, base_filename) finally: # remove temporal session log file remove_session_log_file = Path(session_log_filename) remove_session_log_file.unlink() # remove LOG_FILENAME if options.quit: remove_log_file = Path(LOG_FILENAME) remove_log_file.unlink() if __name__ == "__main__": main()

36.396078

89

0.619815

import copy import logging import shutil import sys from pathlib import Path, PurePath import yaml from osa.configs import options from osa.configs.config import cfg from osa.provenance.capture import get_activity_id, get_file_hash from osa.provenance.io import provdoc2graph, provdoc2json, provlist2provdoc, read_prov from osa.provenance.utils import get_log_config from osa.utils.cliopts import provprocessparsing from osa.utils.logging import myLogger __all__ = ["copy_used_file", "parse_lines_log", "parse_lines_run", "produce_provenance"] log = myLogger(logging.getLogger()) provconfig = yaml.safe_load(get_log_config()) LOG_FILENAME = provconfig["handlers"]["provHandler"]["filename"] PROV_PREFIX = provconfig["PREFIX"] PATH_DL1 = cfg.get("LST1", "DL1_DIR") PATH_DL2 = cfg.get("LST1", "DL2_DIR") def copy_used_file(src, outdir): if not Path(src).is_file(): log.warning(f"{src} file cannot be accessed") hash_src = get_file_hash(src, buffer="content") filename = PurePath(src).name destpath = Path(outdir) / filename hash_out = "" if destpath.exists(): hash_out = get_file_hash(str(destpath), buffer="content") filename = filename + "_" destpath = Path(outdir) / filename if hash_src != hash_out: try: shutil.copyfile(src, str(destpath)) log.info(f"copying {destpath}") except Exception as ex: log.warning(f"could not copy {src} file into {destpath}: {ex}") def parse_lines_log(filter_cut, calib_runs, run_number): filtered = [] if not filter_cut: filter_cut = "all" cuts = { "calibration": ["drs4_pedestal", "calibrate_charge"], "r0_to_dl1": ["r0_to_dl1", "dl1ab"], "dl1_to_dl2": ["dl1_datacheck", "dl1_to_dl2"], } cuts["all"] = cuts["calibration"] + cuts["r0_to_dl1"] + cuts["dl1_to_dl2"] with open(LOG_FILENAME, "r") as f: for line in f.readlines(): ll = line.split(PROV_PREFIX) if len(ll) != 3: log.warning( f"format {PROV_PREFIX} mismatch in log file {LOG_FILENAME}\n{line}" ) continue prov_str = ll.pop() prov_dict = yaml.safe_load(prov_str) keep = False session_tag = prov_dict.get("session_tag", "0:0") session_id = prov_dict.get("session_id", False) tag_activity, tag_run = session_tag.split(":") if tag_run in [run_number, calib_runs]: keep = True if tag_activity not in cuts[filter_cut]: keep = False if session_id and (tag_run in [run_number, calib_runs]): keep = True if session_id and filter_cut == "all" and not filtered: prov_dict["session_id"] = f"{options.date}{run_number}" prov_dict["name"] = run_number prov_dict["observation_run"] = run_number line = f"{ll[0]}{PROV_PREFIX}{ll[1]}{PROV_PREFIX}{prov_dict}\n" if session_id and filtered: keep = False if keep: filtered.append(line) return filtered def parse_lines_run(filter_step, prov_lines, out): size = 0 container = {} working_lines = [] r0filepath_str = "" dl1filepath_str = "" dl2filepath_str = "" mufilepath_str = "" ckfilepath_str = "" id_activity_run = "" end_time_line = "" osa_config_copied = False for line in prov_lines: remove = False endTime = line.get("endTime", "") session_id = line.get("session_id", "") activity_id = line.get("activity_id", "") filepath = line.get("filepath", "") used_role = line.get("used_role", "") generated_role = line.get("generated_role", "") parameters = line.get("parameters", "") name = line.get("name", "") content_type = line.get("contentType", "") used_id = line.get("used_id", "") osa_cfg = line.get("config_file", "") session_tag = line.get("session_tag", "0:0") tag_activity, _ = session_tag.split(":") if tag_activity != filter_step and not session_id: continue if name == "DL1CheckSubrunDataset": ckfilepath_str = filepath elif name == "DL1SubrunDataset": dl1filepath_str = filepath elif name == "DL2SubrunDataset": dl2filepath_str = filepath elif name == "MuonsSubrunDataset": mufilepath_str = filepath elif name == "R0SubrunDataset": r0filepath_str = filepath if "Subrun" in name or "subrun" in used_role or "subrun" in generated_role: remove = True if parameters and "ObservationSubRun" in parameters: del line["parameters"]["ObservationSubRun"] if name == filter_step and not id_activity_run: id_activity_run = get_activity_id() if name in container or used_id in container: remove = True if parameters and "parameters" in container: remove = True if name: container[name] = True if used_id: container[used_id] = True if parameters: container["parameters"] = True if endTime: remove = True end_time_line = line size += 1 if generated_role in container: remove = True if name == "DL2MergedFile": container[name] = True if "merged" in generated_role: container[generated_role] = True if name == "DL1CheckHDF5File": container[name] = True if "DL1Check HDF5 file" in generated_role: container[generated_role] = True if name == "DL1CheckPDFFile": container[name] = True if "DL1Check PDF file" in generated_role: container[generated_role] = True if activity_id: line["activity_id"] = id_activity_run if ( filepath and content_type != "application/x-spss-sav" and name != "DL2MergedFile" and not name.startswith("DL1Check") and not remove ): copy_used_file(filepath, out) if session_id and osa_cfg and not osa_config_copied: copy_used_file(osa_cfg, out) osa_config_copied = True if not remove: working_lines.append(line) if end_time_line: working_lines.append(end_time_line) if r0filepath_str and filter_step == "r0_to_dl1": r0_entity_id = get_file_hash(r0filepath_str + "r0", buffer="path") r0filepath_str = r0filepath_str.replace(PurePath(r0filepath_str).name, "") used = {"entity_id": r0_entity_id} used.update({"name": "R0Collection"}) used.update({"type": "SetCollection"}) used.update({"size": size}) used.update({"filepath": r0filepath_str}) working_lines.append(used) used = {"activity_id": id_activity_run} used.update({"used_id": r0_entity_id}) used.update({"used_role": "R0 Collection"}) working_lines.append(used) if dl1filepath_str: dl1filepath_str = dl1filepath_str.replace(PurePath(dl1filepath_str).name, "") dl1_entity_id = get_file_hash(dl1filepath_str + "dl1", buffer="path") dl1 = {"entity_id": dl1_entity_id} dl1.update({"name": "DL1Collection"}) dl1.update({"type": "SetCollection"}) dl1.update({"size": size}) dl1.update({"filepath": dl1filepath_str}) working_lines.append(dl1) if mufilepath_str: mufilepath_str = mufilepath_str.replace(PurePath(mufilepath_str).name, "") mu_entity_id = get_file_hash(mufilepath_str + "muons", buffer="path") muons = {"entity_id": mu_entity_id} muons.update({"name": "MuonsCollection"}) muons.update({"type": "SetCollection"}) muons.update({"size": size}) muons.update({"filepath": mufilepath_str}) working_lines.append(muons) if mufilepath_str and filter_step == "r0_to_dl1": generated = {"activity_id": id_activity_run} generated.update({"generated_id": mu_entity_id}) generated.update({"generated_role": "Muons Collection"}) working_lines.append(generated) if dl1filepath_str and filter_step in ["r0_to_dl1", "dl1ab"]: generated = {"activity_id": id_activity_run} generated.update({"generated_id": dl1_entity_id}) generated.update({"generated_role": "DL1 Collection"}) working_lines.append(generated) if dl1filepath_str and filter_step in ["dl1_to_dl2", "dl1ab"]: used = {"activity_id": id_activity_run} used.update({"used_id": dl1_entity_id}) used.update({"used_role": "DL1 Collection"}) working_lines.append(used) if dl1filepath_str and filter_step == "dl1_datacheck": used = {"activity_id": id_activity_run} used.update({"used_id": dl1_entity_id}) used.update({"used_role": "DL1 Collection"}) working_lines.append(used) if mufilepath_str and filter_step == "dl1_datacheck": used = {"activity_id": id_activity_run} used.update({"used_id": mu_entity_id}) used.update({"used_role": "Muons Collection"}) working_lines.append(used) if ckfilepath_str and filter_step == "dl1_datacheck": ckfilepath_str = ckfilepath_str.replace(PurePath(ckfilepath_str).name, "") chk_entity_id = get_file_hash(ckfilepath_str + "check", buffer="path") dl1check = {"entity_id": chk_entity_id} dl1check.update({"name": "DL1CheckCollection"}) dl1check.update({"type": "SetCollection"}) dl1check.update({"size": size}) dl1check.update({"filepath": ckfilepath_str}) working_lines.append(dl1check) generated = {"activity_id": id_activity_run} generated.update({"generated_id": chk_entity_id}) generated.update({"generated_role": "DL1Checks Collection"}) working_lines.append(generated) if dl2filepath_str and filter_step == "dl1_to_dl2": dl2_entity_id = get_file_hash(dl2filepath_str + "dl2", buffer="path") dl2filepath_str = dl2filepath_str.replace(PurePath(dl2filepath_str).name, "") used = {"entity_id": dl2_entity_id} used.update({"name": "DL2Collection"}) used.update({"type": "SetCollection"}) used.update({"size": size}) used.update({"filepath": dl2filepath_str}) working_lines.append(used) used = {"activity_id": id_activity_run} used.update({"generated_id": dl2_entity_id}) used.update({"generated_role": "DL2 Collection"}) working_lines.append(used) else: working_lines = [] return working_lines def define_paths(grain, start_path, end_path, base_filename): paths = {} step_path = Path(start_path) / options.date / options.prod_id / end_path if not step_path.exists(): log.error(f"Path {step_path} does not exist") paths["out_path"] = step_path / "log" paths["out_path"].mkdir(parents=True, exist_ok=True) paths["log_path"] = paths["out_path"] / f"{grain}_{base_filename}.log" paths["json_filepath"] = paths["out_path"] / f"{grain}_{base_filename}.json" paths["graph_filepath"] = paths["out_path"] / f"{grain}_{base_filename}.pdf" return paths def produce_provenance_files(processed_lines, paths): with open(paths["log_path"], "w") as f: for line in processed_lines: f.write(f"{line}\n") log.info(f"creating {paths['log_path']}") provdoc = provlist2provdoc(processed_lines) try: provdoc2json(provdoc, str(paths["json_filepath"])) log.info(f"creating {paths['json_filepath']}") except Exception as ex: log.exception(f"problem while creating json: {ex}") try: provdoc2graph(provdoc, str(paths["graph_filepath"]), "pdf") log.info(f"creating {paths['graph_filepath']}") except Exception as ex: log.exception(f"problem while creating graph: {ex}") def produce_provenance(session_log_filename, base_filename): if options.filter == "calibration" or not options.filter: paths_calibration = define_paths( "calibration_to_dl1", PATH_DL1, options.dl1_prod_id, base_filename ) plines_drs4 = parse_lines_run( "drs4_pedestal", read_prov(filename=session_log_filename), str(paths_calibration["out_path"]), ) plines_calib = parse_lines_run( "calibrate_charge", read_prov(filename=session_log_filename), str(paths_calibration["out_path"]), ) calibration_lines = plines_drs4 + plines_calib[1:] if options.filter == "calibration": pass if options.filter == "r0_to_dl1" or not options.filter: paths_r0_dl1 = define_paths( "r0_to_dl1", PATH_DL1, options.dl1_prod_id, base_filename ) plines_r0 = parse_lines_run( "r0_to_dl1", read_prov(filename=session_log_filename), str(paths_r0_dl1["out_path"]), ) plines_ab = parse_lines_run( "dl1ab", read_prov(filename=session_log_filename), str(paths_r0_dl1["out_path"]), ) dl1_lines = plines_r0 + plines_ab[1:] if options.filter == "r0_to_dl1": produce_provenance_files(plines_r0 + plines_ab[1:], paths_r0_dl1) if options.filter == "dl1_to_dl2" or not options.filter: paths_dl1_dl2 = define_paths( "dl1_to_dl2", PATH_DL2, options.dl2_prod_id, base_filename ) plines_check = parse_lines_run( "dl1_datacheck", read_prov(filename=session_log_filename), str(paths_dl1_dl2["out_path"]), ) plines_dl2 = parse_lines_run( "dl1_to_dl2", read_prov(filename=session_log_filename), str(paths_dl1_dl2["out_path"]), ) dl1_dl2_lines = plines_check + plines_dl2[1:] if options.filter == "dl1_to_dl2": produce_provenance_files(plines_check + plines_dl2[1:], paths_dl1_dl2) if not options.filter: calibration_to_dl1 = define_paths( "calibration_to_dl1", PATH_DL1, options.dl1_prod_id, base_filename ) calibration_to_dl2 = define_paths( "calibration_to_dl2", PATH_DL2, options.dl2_prod_id, base_filename ) calibration_to_dl1_lines = calibration_lines + dl1_lines[1:] lines_dl1 = copy.deepcopy(calibration_to_dl1_lines) calibration_to_dl2_lines = calibration_to_dl1_lines + dl1_dl2_lines[1:] lines_dl2 = copy.deepcopy(calibration_to_dl2_lines) produce_provenance_files(lines_dl1, calibration_to_dl1) produce_provenance_files(lines_dl2, calibration_to_dl2) def main(): provprocessparsing() if options.verbose: log.setLevel(logging.DEBUG) else: log.setLevel(logging.INFO) if not Path(LOG_FILENAME).exists(): log.error(f"file {LOG_FILENAME} does not exist") if not Path(LOG_FILENAME).stat().st_size: log.warning(f"file {LOG_FILENAME} is empty") sys.exit(1) base_filename = f"{options.run}_prov" session_log_filename = f"{base_filename}.log" calib_runs = f"{options.drs4_pedestal_run_id}-{options.pedcal_run_id}" parsed_content = parse_lines_log(options.filter, calib_runs, options.run) with open(session_log_filename, "w") as f: for line in parsed_content: f.write(line) try: produce_provenance(session_log_filename, base_filename) finally: remove_session_log_file = Path(session_log_filename) remove_session_log_file.unlink() if options.quit: remove_log_file = Path(LOG_FILENAME) remove_log_file.unlink() if __name__ == "__main__": main()

true