xszheng2020/the_stack_dedup_python_hits_7 · Datasets at Hugging Face

497293531805fa38f1eb473a0ce11f6a9d156fbf

10,181

py

Python

codes/dgmpm_stability/comparison.py

adRenaud/research

2f0062a1800d7a17577bbfc2393b084253d567f4

[ "MIT" ]

1

2021-06-18T14:52:03.000Z

codes/dgmpm_stability/comparison.py

adRenaud/research

2f0062a1800d7a17577bbfc2393b084253d567f4

[ "MIT" ]

1

2019-01-07T13:11:11.000Z

codes/dgmpm_stability/comparison.py

adRenaud/research

2f0062a1800d7a17577bbfc2393b084253d567f4

[ "MIT" ]

null

#!/usr/bin/python import numpy as np from scipy import optimize from sympy import * import matplotlib.pyplot as plt import pdb def residualRK2(point,S,Sp): CFL = symbols('CFL') Res=0. if S.shape[0]==1: S1=[S[0,0]] S2=[S[0,1]] Sum1=np.sum(S1) ; Sum2=np.sum(S2) Nmp=1 else: S1=np.asarray(S[0,:])[0] S2=np.asarray(S[1,:])[0] Sum1=np.sum(S1) ; Sum2=np.sum(S2) Nmp=len(S1) if Sp.shape[0]==1: Sp1=[Sp[0,0]] Sp2=[Sp[0,0]] Sump1=np.sum(Sp1) ; Sump2=np.sum(Sp2) Nmpp=1 else: Sp1=np.asarray(Sp[0,:])[0] Sp2=np.asarray(Sp[1,:])[0] Sump1=np.sum(Sp1) ; Sump2=np.sum(Sp2) Nmpp=len(Sp1) # Sum over material points in curent cell for p in range(Nmp): ## First order contributions D_mu = S1[p]*S1[point]/Sum1 + S2[p]*S2[point]/Sum2 + CFL*( S2[point]/Sum2 - S1[point]/Sum1 -Nmp*S2[p]*S2[point]/(Sum2**2) ) ## Second order contributions D_mu += 0.5*Nmp*(CFL**2)*((S2[p]/Sum2)*(S1[point]/Sum1-S2[point]/Sum2) + (S2[point]/Sum2)*(Nmp*S2[p]/Sum2-1.)/Sum2) # D_mu += 0.5*Nmp*(CFL**2)*(S2[p]/Sum2)*( S1[point]/Sum1-S2[point]/Sum2 + (Nmp*S2[p]/Sum2-1.)/Sum2) Res = Res +np.abs(D_mu) # Sum over material points in previous cell for p in range(Nmpp): ## First order contributions D_mu = CFL*Nmp*Sp2[p]*S1[point]/(Sum1*Sump2) ## Second order contributions D_mu +=0.5*Nmp*(CFL**2)*( S1[point]/(Sum1*Sump2)*(1.-Nmpp*Sp2[p]/Sump2) -(Sp2[p]/Sump2)*(S1[point]/Sum1-S2[point]/Sum2) ) Res=Res + np.abs(D_mu) Residual = lambdify((CFL),Res-1.) return Residual def residualEuler(point,S,Sp): CFL = symbols('CFL') Res=0. if S.shape[0]==1: S1=[S[0,0]] S2=[S[0,1]] Sum1=np.sum(S1) ; Sum2=np.sum(S2) Nmp=1 else: S1=np.asarray(S[0,:])[0] S2=np.asarray(S[1,:])[0] Sum1=np.sum(S1) ; Sum2=np.sum(S2) Nmp=len(S1) if Sp.shape[0]==1: Sp1=[Sp[0,0]] Sp2=[Sp[0,0]] Sump1=np.sum(Sp1) ; Sump2=np.sum(Sp2) Nmpp=1 else: Sp1=np.asarray(Sp[0,:])[0] Sp2=np.asarray(Sp[1,:])[0] Sump1=np.sum(Sp1) ; Sump2=np.sum(Sp2) Nmpp=len(Sp1) # Sum over material points in curent cell for p in range(Nmp): D_ma = S1[point]*S1[p]/Sum1 + S2[point]*S2[p]/Sum2 + CFL*( S2[point]/Sum2 - S1[point]/Sum1 -Nmp*S2[point]*S2[p]/(Sum2**2) ) Res = Res +np.abs(D_ma) for p in range(Nmpp): D_ma = CFL*Nmp*S1[point]*Sp2[p]/(Sum1*Sump2) Res=Res + np.abs(D_ma) Residual = lambdify((CFL),Res-1.) return Residual # Symbolic function to evaluate shape functions shape_functions=lambda x: np.matrix([(1-x)/DX,x/DX]) xn = np.array([0.,1.]) DX = 1. ## required for plotting residual CFL=np.linspace(0.,1.,100.) shift=0.1 # 1PPC print "**************************************************************" print "****************** 1PPC discretization **********************" print "**************************************************************" print " " shapes=shape_functions(0.25) eulerSolution=optimize.root(residualEuler(0,shapes,shapes),1.,method='hybr',options={'xtol':1.e-4}).x rk2Solution=optimize.root(residualRK2(0,shapes,shapes),1.,method='hybr',options={'xtol':1.e-4}).x print "Euler solution, CFL= ",eulerSolution print "RK2 solution, CFL= ",rk2Solution # 2PPC print "**************************************************************" print "****************** 2PPC discretization **********************" print "**************************************************************" print " " shapes=shape_functions(np.array([0.25,0.75])) ## Gauss-Legendre integration #shapes=shape_functions(0.5*np.array([1.-1./np.sqrt(3.),1.+1./np.sqrt(3.)])) eulerSolution=[] rk2Solution=[] for i in range(np.shape(shapes)[0]): eulerSolution.append(optimize.root(residualEuler(i,shapes,shapes),1.,method='hybr',options={'xtol':1.e-4}).x[0]) rk2Solution.append(optimize.root(residualRK2(i,shapes,shapes),1.,method='hybr',options={'xtol':1.e-4}).x[0]) print "Euler solution, CFL= ",min(eulerSolution) print "RK2 solution, CFL= ",min(rk2Solution) print " " shift=0.1 print "Shifted ++",shift X=np.array([0.25+shift,0.75+shift]) shapes=shape_functions(X) eulerSolution=[] rk2Solution=[] for i in range(np.shape(shapes)[0]): eulerSolution.append(optimize.root(residualEuler(i,shapes,shapes),1.,method='hybr',options={'xtol':1.e-4}).x[0]) rk2Solution.append(optimize.root(residualRK2(i,shapes,shapes),1.,method='hybr',options={'xtol':1.e-4}).x[0]) print "Euler solution, CFL= ",min(eulerSolution) print "RK2 solution, CFL= ",min(rk2Solution) shift=0.25 print " " print "Shifted --",shift X=np.array([0.25-shift,0.75-shift]) shapes=shape_functions(X) eulerSolution=[] rk2Solution=[] for i in range(np.shape(shapes)[0]): eulerSolution.append(optimize.root(residualEuler(i,shapes,shapes),1.,method='hybr',options={'xtol':1.e-4}).x[0]) rk2Solution.append(optimize.root(residualRK2(i,shapes,shapes),1.,method='hybr',options={'xtol':1.e-4}).x[0]) print "Euler solution, CFL= ",min(eulerSolution) print "RK2 solution, CFL= ",min(rk2Solution) print " " print "Shifted ++",shift X=np.array([0.25+shift,0.75+shift]) shapes=shape_functions(X) eulerSolution=[] rk2Solution=[] for i in range(np.shape(shapes)[0]): eulerSolution.append(optimize.root(residualEuler(i,shapes,shapes),1.,method='hybr',options={'xtol':1.e-4}).x[0]) rk2Solution.append(optimize.root(residualRK2(i,shapes,shapes),1.,method='hybr',options={'xtol':1.e-4}).x[0]) print "Euler solution, CFL= ",min(eulerSolution) print "RK2 solution, CFL= ",min(rk2Solution) pdb.set_trace() # 3PPC print "**************************************************************" print "****************** 3PPC discretization **********************" print "**************************************************************" print " " shapes=shape_functions(np.array([1./3.,0.5,2./3.])) eulerSolution=[] rk2Solution=[] for i in range(np.shape(shapes)[0]): eulerSolution.append(optimize.root(residualEuler(i,shapes,shapes),1.,method='hybr',options={'xtol':1.e-4}).x[0]) rk2Solution.append(optimize.root(residualRK2(i,shapes,shapes),1.,method='hybr',options={'xtol':1.e-4}).x[0]) print "Euler solution, CFL= ",min(eulerSolution) print "RK2 solution, CFL= ",min(rk2Solution) print " " shift=0.1 print "Shifted ++",shift shapes=shape_functions(np.array([1./3.+shift,0.5+shift,2./3.+shift])) eulerSolution=[] rk2Solution=[] for i in range(np.shape(shapes)[0]): eulerSolution.append(optimize.root(residualEuler(i,shapes,shapes),1.,method='hybr',options={'xtol':1.e-4}).x[0]) rk2Solution.append(optimize.root(residualRK2(i,shapes,shapes),1.,method='hybr',options={'xtol':1.e-4}).x[0]) print "Euler solution, CFL= ",min(eulerSolution) print "RK2 solution, CFL= ",min(rk2Solution) shift=1./3. print " " print "Shifted --",shift shapes=shape_functions(np.array([1./3.-shift,0.5-shift,2./3.-shift])) eulerSolution=[] rk2Solution=[] for i in range(np.shape(shapes)[0]): eulerSolution.append(optimize.root(residualEuler(i,shapes,shapes),1.,method='hybr',options={'xtol':1.e-4}).x[0]) rk2Solution.append(optimize.root(residualRK2(i,shapes,shapes),1.,method='hybr',options={'xtol':1.e-4}).x[0]) print "Euler solution, CFL= ",min(eulerSolution) print "RK2 solution, CFL= ",min(rk2Solution) print " " print "Shifted ++",shift shapes=shape_functions(np.array([1./3.+shift,0.5+shift,2./3.+shift])) eulerSolution=[] rk2Solution=[] for i in range(np.shape(shapes)[0]): eulerSolution.append(optimize.root(residualEuler(i,shapes,shapes),1.,method='hybr',options={'xtol':1.e-4}).x[0]) rk2Solution.append(optimize.root(residualRK2(i,shapes,shapes),1.,method='hybr',options={'xtol':1.e-4}).x[0]) print "Euler solution, CFL= ",min(eulerSolution) print "RK2 solution, CFL= ",min(rk2Solution) pdb.set_trace() # 4PPC print "**************************************************************" print "****************** 4PPC discretization **********************" print "**************************************************************" print " " shapes=shape_functions(np.array([1./8.,3./8.,5./8.,7./8.])) eulerSolution=[] rk2Solution=[] for i in range(np.shape(shapes)[0]): eulerSolution.append(optimize.root(residualEuler(i,shapes,shapes),1.,method='hybr',options={'xtol':1.e-4}).x[0]) rk2Solution.append(optimize.root(residualRK2(i,shapes,shapes),1.,method='hybr',options={'xtol':1.e-4}).x[0]) print "Euler solution, CFL= ",min(eulerSolution) print "RK2 solution, CFL= ",min(rk2Solution) print " " shift=0.1 print "Shifted ++",shift shapes=shape_functions(np.array([1./8.,3./8.,5./8.,7./8.])+shift) eulerSolution=[] rk2Solution=[] for i in range(np.shape(shapes)[0]): eulerSolution.append(optimize.root(residualEuler(i,shapes,shapes),1.,method='hybr',options={'xtol':1.e-4}).x[0]) rk2Solution.append(optimize.root(residualRK2(i,shapes,shapes),1.,method='hybr',options={'xtol':1.e-4}).x[0]) print "Euler solution, CFL= ",min(eulerSolution) print "RK2 solution, CFL= ",min(rk2Solution) shift=1/8. print " " print "Shifted --",shift shapes=shape_functions(np.array([1./8.,3./8.,5./8.,7./8.])-shift) eulerSolution=[] rk2Solution=[] for i in range(np.shape(shapes)[0]): eulerSolution.append(optimize.root(residualEuler(i,shapes,shapes),1.,method='hybr',options={'xtol':1.e-4}).x[0]) rk2Solution.append(optimize.root(residualRK2(i,shapes,shapes),1.,method='hybr',options={'xtol':1.e-4}).x[0]) print "Euler solution, CFL= ",min(eulerSolution) print "RK2 solution, CFL= ",min(rk2Solution) print " " print "Shifted ++",shift shapes=shape_functions(np.array([1./8.,3./8.,5./8.,7./8.])+shift) eulerSolution=[] rk2Solution=[] for i in range(np.shape(shapes)[0]): eulerSolution.append(optimize.root(residualEuler(i,shapes,shapes),1.,method='hybr',options={'xtol':1.e-4}).x[0]) rk2Solution.append(optimize.root(residualRK2(i,shapes,shapes),1.,method='hybr',options={'xtol':1.e-4}).x[0]) print "Euler solution, CFL= ",min(eulerSolution) print "RK2 solution, CFL= ",min(rk2Solution)

38.418868

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1

0

null

0

1

0

8

b8cdb067713bd0f456b050c11bcfaea00d2e9480

12,143

py

Python

api/test/api/responders_test/resource/test_cliques.py

korenlev/calipso-cvim

39278a5cf09c40b26a8a143ccc0c8d437961abc2

[ "Apache-2.0" ]

null

api/test/api/responders_test/resource/test_cliques.py

korenlev/calipso-cvim

39278a5cf09c40b26a8a143ccc0c8d437961abc2

[ "Apache-2.0" ]

null

api/test/api/responders_test/resource/test_cliques.py

korenlev/calipso-cvim

39278a5cf09c40b26a8a143ccc0c8d437961abc2

[ "Apache-2.0" ]

null

############################################################################### # Copyright (c) 2017-2020 Koren Lev (Cisco Systems), # # Yaron Yogev (Cisco Systems), Ilia Abashin (Cisco Systems) and others # # # # All rights reserved. This program and the accompanying materials # # are made available under the terms of the Apache License, Version 2.0 # # which accompanies this distribution, and is available at # # http://www.apache.org/licenses/LICENSE-2.0 # ############################################################################### from unittest.mock import patch from api.test.api.responders_test.test_data import base from api.test.api.responders_test.test_data import cliques from api.test.api.test_base import TestBase class TestCliques(TestBase): def test_get_cliques_list_without_env_name(self): self.validate_get_request(cliques.URL, params={}, expected_code=base.BAD_REQUEST_CODE) def test_get_cliques_list_with_invalid_filter(self): self.validate_get_request(cliques.URL, params={ "env_name": base.ENV_NAME, "invalid": "invalid" }, expected_code=base.BAD_REQUEST_CODE) def test_get_cliques_list_with_non_int_page(self): self.validate_get_request(cliques.URL, params={ "env_name": base.ENV_NAME, "page": base.NON_INT_PAGE }, expected_code=base.BAD_REQUEST_CODE) @patch(base.RESPONDER_BASE_READ) def test_get_cliques_list_with_int_page(self, read): self.validate_get_request(cliques.URL, params={ "env_name": base.ENV_NAME, "page": base.INT_PAGE }, mocks={ read: cliques.CLIQUES }, expected_code=base.SUCCESSFUL_CODE, expected_response=cliques.CLIQUES_RESPONSE) def test_get_cliques_list_with_non_int_pagesize(self): self.validate_get_request(cliques.URL, params={ "env_name": base.ENV_NAME, "page_size": base.NON_INT_PAGESIZE }, expected_code=base.BAD_REQUEST_CODE) @patch(base.RESPONDER_BASE_READ) def test_get_cliques_list_with_int_pagesize(self, read): self.validate_get_request(cliques.URL, params={ "env_name": base.ENV_NAME, "page_size": base.INT_PAGESIZE }, mocks={ read: cliques.CLIQUES }, expected_code=base.SUCCESSFUL_CODE, expected_response=cliques.CLIQUES_RESPONSE) def test_get_clique_with_wrong_clique_id(self): self.validate_get_request(cliques.URL, params={ 'env_name': base.ENV_NAME, 'id': cliques.WRONG_CLIQUE_ID }, expected_code=base.BAD_REQUEST_CODE) @patch(base.RESPONDER_BASE_READ) def test_get_clique_with_clique_id(self, read): self.validate_get_request(cliques.URL, params={ "env_name": base.ENV_NAME, "id": cliques.CORRECT_CLIQUE_ID }, mocks={ read: cliques.CLIQUES_WITH_SPECIFIC_ID }, expected_code=base.SUCCESSFUL_CODE, expected_response=cliques.CLIQUES_WITH_SPECIFIC_ID[0] ) def test_get_cliques_list_with_wrong_focal_point(self): self.validate_get_request(cliques.URL, params={ "env_name": base.ENV_NAME, "focal_point": cliques.WRONG_FOCAL_POINT }, expected_code=base.BAD_REQUEST_CODE) @patch(base.RESPONDER_BASE_READ) def test_get_cliques_list_with_focal_point(self, read): self.validate_get_request(cliques.URL, params={ "env_name": base.ENV_NAME, "focal_point": cliques.CORRECT_FOCAL_POINT }, mocks={ read: cliques.CLIQUES_WITH_SPECIFIC_FOCAL_POINT }, expected_code=base.SUCCESSFUL_CODE, expected_response=cliques. CLIQUES_WITH_SPECIFIC_FOCAL_POINT_RESPONSE ) def test_get_cliques_list_with_wrong_focal_point_type(self): self.validate_get_request(cliques.URL, params={ "env_name": base.ENV_NAME, "focal_point_type": cliques.WRONG_FOCAL_POINT_TYPE }, expected_code=base.BAD_REQUEST_CODE) @patch(base.RESPONDER_BASE_READ) def test_get_cliques_list_with_focal_point_type(self, read): self.validate_get_request(cliques.URL, params={ "env_name": base.ENV_NAME, "focal_point_type": cliques.CORRECT_FOCAL_POINT_TYPE }, mocks={ read: cliques.CLIQUES_WITH_SPECIFIC_FOCAL_POINT_TYPE }, expected_code=base.SUCCESSFUL_CODE, expected_response=cliques. CLIQUES_WITH_SPECIFIC_FOCAL_POINT_TYPE_RESPONSE ) def test_get_cliques_list_with_wrong_link_type(self): self.validate_get_request(cliques.URL, params={ "env_name": base.ENV_NAME, "link_type": base.WRONG_LINK_TYPE }, expected_code=base.BAD_REQUEST_CODE) @patch(base.RESPONDER_BASE_READ) def test_get_cliques_list_with_link_type(self, read): self.validate_get_request(cliques.URL, params={ "env_name": base.ENV_NAME, "link_type": cliques.CORRECT_LINK_TYPE }, mocks={ read: cliques.CLIQUES_WITH_SPECIFIC_LINK_TYPE }, expected_code=base.SUCCESSFUL_CODE, expected_response=cliques. CLIQUES_WITH_SPECIFIC_LINK_TYPE_RESPONSE ) def test_get_cliques_list_with_wrong_link_id(self): self.validate_get_request(cliques.URL, { "env_name": base.ENV_NAME, "link_id": cliques.WRONG_LINK_ID }, expected_code=base.BAD_REQUEST_CODE) @patch(base.RESPONDER_BASE_READ) def test_get_clique_ids_with_correct_link_id(self, read): self.validate_get_request(cliques.URL, params={ "env_name": base.ENV_NAME, "link_id": cliques.CORRECT_LINK_ID }, mocks={ read: cliques.CLIQUES_WITH_SPECIFIC_LINK_ID }, expected_code=base.SUCCESSFUL_CODE, expected_response=cliques. CLIQUES_WITH_SPECIFIC_LINK_ID_RESPONSE ) @patch(base.RESPONDER_BASE_CHECK_ENVIRONMENT_NAME) @patch(base.RESPONDER_BASE_READ) def test_get_cliques_list_with_env_name_and_nonexistent_link_id(self, read, check_env_name): self.validate_get_request(cliques.URL, params={ "env_name": base.ENV_NAME, "link_id": cliques.NONEXISTENT_LINK_ID }, mocks={ read: [], check_env_name: True }, expected_code=base.NOT_FOUND_CODE) @patch(base.RESPONDER_BASE_CHECK_ENVIRONMENT_NAME) @patch(base.RESPONDER_BASE_READ) def test_get_cliques_list_with_unknown_env_name(self, read, check_env_name): self.validate_get_request(cliques.URL, params={ "env_name": base.UNKNOWN_ENV }, mocks={ read: [], check_env_name: False }, expected_code=base.BAD_REQUEST_CODE) @patch(base.RESPONDER_BASE_CHECK_ENVIRONMENT_NAME) @patch(base.RESPONDER_BASE_READ) def test_get_clique_with_env_name_and_nonexistent_clique_id(self, read, check_env_name): self.validate_get_request(cliques.URL, params={ "env_name": base.ENV_NAME, "id": cliques.NONEXISTENT_CLIQUE_ID }, mocks={ read: [], check_env_name: True }, expected_code=base.NOT_FOUND_CODE) @patch(base.RESPONDER_BASE_CHECK_ENVIRONMENT_NAME) @patch(base.RESPONDER_BASE_READ) def test_get_clique_with_unknown_env_name_and_clique_id(self, read, check_env_name): self.validate_get_request(cliques.URL, params={ "env_name": base.UNKNOWN_ENV, "id": cliques.NONEXISTENT_CLIQUE_ID }, mocks={ read: [], check_env_name: False }, expected_code=base.BAD_REQUEST_CODE)

50.177686

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12,143

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1

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null

0

7

772ec7f9ee17158933e8d6ff5b2f2e72afb0cb60

255

py

Python

testplan/testing/multitest/entries/schemas/__init__.py

ymn1k/testplan

b1bde8495c449d75a74a7fe4e7c6501b0476f833

[ "Apache-2.0" ]

null

testplan/testing/multitest/entries/schemas/__init__.py

ymn1k/testplan

b1bde8495c449d75a74a7fe4e7c6501b0476f833

[ "Apache-2.0" ]

null

testplan/testing/multitest/entries/schemas/__init__.py

ymn1k/testplan

b1bde8495c449d75a74a7fe4e7c6501b0476f833

[ "Apache-2.0" ]

1

2019-09-11T09:13:18.000Z

""" Entry point for schema serialization bindings. """ from .. import assertions as asr # from .. import logs # from .. import graphs from . import assertions as asr_schemas # from . import log_schemas # from . import graph_schemas from . import base

19.615385

48

0.729412

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255

5.382353

0.5

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0.278689

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255

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true

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0

1

0

1

0

1

0

1

0

7

77447a1f3701cf6486ccf68c2c40e404fb318d93

74

py

Python

richtiles/__init__.py

jsosa/richtiles

d5dd1335ea6dc3f717996e0e0eeba40ef4d50b36

[ "BSD-3-Clause" ]

null

richtiles/__init__.py

jsosa/richtiles

d5dd1335ea6dc3f717996e0e0eeba40ef4d50b36

[ "BSD-3-Clause" ]

null

richtiles/__init__.py

jsosa/richtiles

d5dd1335ea6dc3f717996e0e0eeba40ef4d50b36

[ "BSD-3-Clause" ]

null

from .core import get_tiles_by_extent from .core import write_tiles_layout

37

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13

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4.615385

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null

0

1

0

1

0

1

0

7

624bfffa1a9be774b606a35caef1093021aba466

4,882

py

Python

ores/tests/test_util.py

ureesoriano/ores

dda9db6c8737d12acbae5b0d43938d93c9e7ea8e

[ "MIT" ]

null

ores/tests/test_util.py

ureesoriano/ores

dda9db6c8737d12acbae5b0d43938d93c9e7ea8e

[ "MIT" ]

null

ores/tests/test_util.py

ureesoriano/ores

dda9db6c8737d12acbae5b0d43938d93c9e7ea8e

[ "MIT" ]

null

import time import re from pytest import raises from ..errors import TimeoutError from ..util import timeout def test_timeout(): timeout(int, 5, seconds=0.5) def test_timeout_error(): with raises(TimeoutError): timeout(time.sleep, 2, seconds=1) def test_timeout_error_badfunc(): # This regex causes a near-infinite loop, should timeout # See https://wikitech.wikimedia.org/wiki/Incident_documentation/20170623-ORES with raises(TimeoutError): bad_re = re.compile('(j+[aeiou]*)*(\\b)', re.I) edit = "JAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJputoAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJgayAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJcasamelaAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJJAJAJJAJAJAJAJAJAJlentos, y se hacen visibles con el paso de los años. El medio físico condiciona desigualmente los grupos humanos." # noqa : E501 timeout(bad_re.match, edit, seconds=2)

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8

65665a6af453d95b24ae9f6fbb3619b3c2a23a54

3,747

py

Python

project/api/migrations/0085_auto_20210919_1855.py

hlystovea/BBBS

7164ef67615e45d750e965bf958af229b56d49e3

[ "BSD-3-Clause" ]

null

project/api/migrations/0085_auto_20210919_1855.py

hlystovea/BBBS

7164ef67615e45d750e965bf958af229b56d49e3

[ "BSD-3-Clause" ]

2

2021-06-07T14:06:05.000Z

2021-06-18T16:27:29.000Z

project/api/migrations/0085_auto_20210919_1855.py

hlystovea/BBBS

7164ef67615e45d750e965bf958af229b56d49e3

[ "BSD-3-Clause" ]

2

2021-07-27T20:40:18.000Z

2021-09-12T16:48:19.000Z

# Generated by Django 3.2.3 on 2021-09-19 11:55 import api.validators from django.db import migrations import django_resized.forms class Migration(migrations.Migration): dependencies = [ ('api', '0084_auto_20210919_1845'), ] operations = [ migrations.AlterField( model_name='article', name='image', field=django_resized.forms.ResizedImageField(blank=True, crop=['middle', 'center'], force_format='JPEG', help_text='Поддерживаемые форматы jpg, jpeg, gif, png, bmp. Размер до 10 Мб.', keep_meta=True, null=True, quality=100, size=[1280, 720], upload_to='articles/', validators=[api.validators.file_size_validator, api.validators.image_extension_validator], verbose_name='Изображение'), ), migrations.AlterField( model_name='catalog', name='image', field=django_resized.forms.ResizedImageField(blank=True, crop=['middle', 'center'], force_format='JPEG', help_text='Поддерживаемые форматы jpg, jpeg, gif, png, bmp. Размер до 10 Мб.', keep_meta=True, null=True, quality=100, size=[1280, 720], upload_to='catalog/', validators=[api.validators.file_size_validator, api.validators.image_extension_validator], verbose_name='Изображение'), ), migrations.AlterField( model_name='diary', name='image', field=django_resized.forms.ResizedImageField(blank=True, crop=['middle', 'center'], force_format='JPEG', help_text='Поддерживаемые форматы jpg, jpeg, gif, png, bmp. Размер до 10 Мб.', keep_meta=True, null=True, quality=100, size=[1280, 720], upload_to='diaries/', validators=[api.validators.file_size_validator, api.validators.image_extension_validator], verbose_name='Изображение'), ), migrations.AlterField( model_name='history', name='image', field=django_resized.forms.ResizedImageField(blank=True, crop=['middle', 'center'], force_format='JPEG', help_text='Поддерживаемые форматы jpg, jpeg, gif, png, bmp. Размер до 10 Мб.', keep_meta=True, null=True, quality=100, size=[1280, 720], upload_to='history/', validators=[api.validators.file_size_validator, api.validators.image_extension_validator], verbose_name='Изображение'), ), migrations.AlterField( model_name='movie', name='image', field=django_resized.forms.ResizedImageField(blank=True, crop=['middle', 'center'], force_format='JPEG', help_text='Поддерживаемые форматы jpg, jpeg, gif, png, bmp. Размер до 10 Мб.', keep_meta=True, null=True, quality=100, size=[1280, 720], upload_to='movies/', validators=[api.validators.file_size_validator, api.validators.image_extension_validator], verbose_name='Изображение'), ), migrations.AlterField( model_name='place', name='image', field=django_resized.forms.ResizedImageField(blank=True, crop=['middle', 'center'], force_format='JPEG', help_text='Поддерживаемые форматы jpg, jpeg, gif, png, bmp. Размер до 10 Мб.', keep_meta=True, null=True, quality=100, size=[1280, 720], upload_to='places/', validators=[api.validators.file_size_validator, api.validators.image_extension_validator], verbose_name='Изображение'), ), migrations.AlterField( model_name='video', name='image', field=django_resized.forms.ResizedImageField(blank=True, crop=['middle', 'center'], force_format='JPEG', help_text='Поддерживаемые форматы jpg, jpeg, gif, png, bmp. Размер до 10 Мб.', keep_meta=True, null=True, quality=100, size=[1280, 720], upload_to='videos/', validators=[api.validators.file_size_validator, api.validators.image_extension_validator], verbose_name='Изображение'), ), ]

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null

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7

02dd0cad75f7259484731d8201f9e88de682c86b

43

py

Python

src/utility/__init__.py

tws0002/footage-importer

a797b79efa184167ca472369b07d1a029dd86cbd

[ "MIT" ]

null

src/utility/__init__.py

tws0002/footage-importer

a797b79efa184167ca472369b07d1a029dd86cbd

[ "MIT" ]

null

src/utility/__init__.py

tws0002/footage-importer

a797b79efa184167ca472369b07d1a029dd86cbd

[ "MIT" ]

null

from .to_time_string import to_time_string

21.5

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null

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1

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1

0

8

02e3832a965896015e9be55a9422656931c82a96

612

py

Python

flask_rebar/swagger_generation/__init__.py

l-vincent-l/flask-rebar

b44488e4ddab52883aa3b10e28d24136f36866ca

[ "MIT" ]

null

flask_rebar/swagger_generation/__init__.py

l-vincent-l/flask-rebar

b44488e4ddab52883aa3b10e28d24136f36866ca

[ "MIT" ]

1

2020-12-15T14:48:26.000Z

2020-12-15T15:22:48.000Z

flask_rebar/swagger_generation/__init__.py

l-vincent-l/flask-rebar

b44488e4ddab52883aa3b10e28d24136f36866ca

[ "MIT" ]

1

2020-12-15T14:43:46.000Z

from flask_rebar.swagger_generation.swagger_objects import ExternalDocumentation from flask_rebar.swagger_generation.swagger_generator import SwaggerV2Generator from flask_rebar.swagger_generation.swagger_generator import SwaggerV3Generator from flask_rebar.swagger_generation.swagger_objects import Tag from flask_rebar.swagger_generation.swagger_objects import Server from flask_rebar.swagger_generation.swagger_objects import ServerVariable from flask_rebar.swagger_generation.marshmallow_to_swagger import sets_swagger_attr from flask_rebar.swagger_generation.marshmallow_to_swagger import ConverterRegistry

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null

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0

1

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8

02e70e4eae5874b501e2be9be037c3620d7dd6ea

16,570

py

Python

proteinham/lattice/turn_ancilla.py

couteiral/proteinham

c7300fd7df4daad7f341e1217bfc11819963a978

[ "CC-BY-4.0" ]

null

proteinham/lattice/turn_ancilla.py

couteiral/proteinham

c7300fd7df4daad7f341e1217bfc11819963a978

[ "CC-BY-4.0" ]

null

proteinham/lattice/turn_ancilla.py

couteiral/proteinham

c7300fd7df4daad7f341e1217bfc11819963a978

[ "CC-BY-4.0" ]

null

import math import numpy as np import sympy as sp #import symengine as se from abc import * from tqdm import tqdm from copy import deepcopy from functools import reduce from .qlogic import * from proteinham.core.hamiltonian import Hamiltonian class CommonTurnAncillaHamiltonian(Hamiltonian): def __init__(self, pepstring): """Encapsulates the expression and methods of a protein hamiltonian of the "turn ancilla encoding" form, described by Babbush et al., 2012.""" self._proc_input(pepstring) self.start_bit = None self.n_bits = self.dim * (self.naas-1) self.n_bits += sum([ sum([ self.mu(i, j) for j in range(i+4, self.naas)]) for i in range(self.naas-4)]) self.n_bits += sum([ sum([ 1 if self.int_mat[i, j] != 0 else 0 for j in range(i+3, self.naas)]) for i in range(self.naas-3)]) self._create_bitreg() #self.build_exp() def build_exp(self): self.expr = (self.naas+1) * self.back_term() if self.dim == 3: self.expr += (self.naas+1)**2 * self.redun_term() self.expr += (self.naas+1) * self.steric_term() self.expr += self.interaction_term() #self.expr = se.expand(self.expr) self.expr = sp.expand(self.expr) self.n_terms = len(self.expr.args) def get(self, k): """Access the kth bit of the hamiltonian.""" return self.bit_list[k] @property @abstractmethod def dim(self): pass class TurnAncillaHamiltonian2D(CommonTurnAncillaHamiltonian): is_2D = True @property def dim(self): return 2 def r_pointer(self, i): """Points to the start of the string describing the ith turn.""" if i > self.naas: raise ValueError('There are only %s residues' % self.naas) return 2*i-2 if i > 0 else 0 def o_pointer(self, i, j): """Points to the start of the string containing ancillary bits.""" return 2*self.naas-2 + sum([ sum([ self.mu(m, n) for n in range(m+4, self.naas)]) for m in range(i)]) + \ sum([ self.mu(i, n) for n in range(i+4, j)]) def i_pointer(self, i, j): """Points to the ancilla bit encoding the interaction between the ith and jth residues.""" if not self.start_bit: self.start_bit = 2*self.naas-2 + sum([ sum([ self.mu(i, j) for j in range(i+4, self.naas)]) for i in range(self.naas-4)]) return self.start_bit + \ sum([ sum([ 1 if self.int_mat[m, n] != 0 else 0 for n in range(m+3, self.naas)]) for m in range(i-1)]) + \ sum([ 1 if self.int_mat[i, n] != 0 else 0 for n in range(i+3, j)]) def circuit_xp(self, q_i, q_j): """Implements a circuit that returns 1 if the chain moves in the direction x+.""" return (1-q_i)*q_j def circuit_xn(self, q_i, q_j): """Implements a circuit that returns 1 if the chain moves in the direction x-.""" return q_i*(1-q_j) def circuit_yp(self, q_i, q_j): """Implements a circuit that returns 1 if the chain moves in the direction y+.""" return q_i*q_j def circuit_yn(self, q_i, q_j): """Implements a circuit that returns 1 if the chain moves in the direction y-.""" return (1-q_i)*(1-q_j) def x_position(self, n): """Computes the x coordinate of the nth residue.""" if n > self.naas: raise ValueError('n greater than number of residues') return sum([ self.circuit_xp(self.get(self.r_pointer(i+1)), self.get(self.r_pointer(i+1)+1)) - \ self.circuit_xn(self.get(self.r_pointer(i+1)), self.get(self.r_pointer(i+1)+1)) for i in range(n)]) def y_position(self, n): """Computes the y coordinate of the nth residue.""" if n > self.naas: raise ValueError('n greater than number of residues') return sum([ self.circuit_yp(self.get(self.r_pointer(i+1)), self.get(self.r_pointer(i+1)+1)) - \ self.circuit_yn(self.get(self.r_pointer(i+1)), self.get(self.r_pointer(i+1)+1)) for i in range(n)]) def g(self, i, j): """Computes the distance between residues i and j.""" return (self.x_position(i) - self.x_position(j))**2 \ + (self.y_position(i) - self.y_position(j))**2 def mu(self, i, j): """Computes \mu_{ij}.""" if i == j: return 0 elif abs(i-j) < 3: return 0 else: return 2 * int(math.ceil(math.log2(abs(i-j)))) \ * ((1+i-j) % 2) def alpha(self, i, j): """Computes \alpha_{ij}.""" return sum([ 2**k * self.get(self.o_pointer(i, j) + k) for k in range(self.mu(i,j))]) def back_term(self): """Ensures that the chain does not go back on itself.""" return sum([ self.circuit_xp(self.get(self.r_pointer(i)), self.get(self.r_pointer(i)+1)) * self.circuit_xn(self.get(self.r_pointer(i+1)), self.get(self.r_pointer(i+1)+1)) + \ self.circuit_xn(self.get(self.r_pointer(i)), self.get(self.r_pointer(i)+1)) * self.circuit_xp(self.get(self.r_pointer(i+1)), self.get(self.r_pointer(i+1)+1)) + \ self.circuit_yp(self.get(self.r_pointer(i)), self.get(self.r_pointer(i)+1)) * self.circuit_yn(self.get(self.r_pointer(i+1)), self.get(self.r_pointer(i+1)+1)) + \ self.circuit_yn(self.get(self.r_pointer(i)), self.get(self.r_pointer(i)+1)) * self.circuit_yp(self.get(self.r_pointer(i+1)), self.get(self.r_pointer(i+1)+1)) for i in range(self.naas-1)]) def steric_term(self): """Ensures that the chain does not overlap.""" term = sp.numbers.Integer(0) for i in range(self.naas-4): for j in range(i+4, self.naas): if (1+i-j) % 2: term += (2**self.mu(i, j) -self.g(i, j) \ - self.alpha(i, j))**2 return term def interaction_term_ij(self, i, j): return -1 * self.get(self.i_pointer(i, j)) \ * self.int_mat[i, j] \ * ( 2 - self.g(i, j) ) def interaction_term(self): """Computes contacts between residues.""" term = sp.numbers.Integer(0) for i in range(self.naas-3): for j in range(i+3, self.naas): if self.int_mat[i, j] == 0: continue term -= self.get(self.i_pointer(i, j)) \ * self.int_mat[i, j] \ * ( 2 - self.g(i, j) ) return term class TurnAncillaHamiltonian3D(CommonTurnAncillaHamiltonian): is_3D = True @property def dim(self): return 3 def r_pointer(self, i): """Points to the start of the string describing the ith turn.""" return 3*i-3 if i > 0 else 0 def o_pointer(self, i, j): """Points to the start of the string containing ancillary bits.""" return 3*self.naas-3 + sum([ sum([ self.mu(m, n) for n in range(m+4, self.naas)]) for m in range(i)]) + \ sum([ self.mu(i, n) for n in range(i+4, j)]) def i_pointer(self, i, j): """Points to the ancilla bit encoding the interaction between the ith and jth residues.""" if not self.start_bit: self.start_bit = 3*self.naas-3 + sum([ sum([ self.mu(i, j) for j in range(i+4, self.naas)]) for i in range(self.naas-4)]) return self.start_bit + \ sum([ sum([ 1 if self.int_mat[m, n] != 0 else 0 for n in range(m+3, self.naas)]) for m in range(i-1)]) + \ sum([ 1 if self.int_mat[i, n] != 0 else 0 for n in range(i+3, j)]) def circuit_xp(self, q_i, q_j, q_k): """Implements a circuit that returns 1 if the chain moves in the direction x+.""" return q_i * q_j * q_k def circuit_xn(self, q_i, q_j, q_k): """Implements a circuit that returns 1 if the chain moves in the direction x-.""" return q_i * (1-q_j) * (1-q_k) def circuit_yp(self, q_i, q_j, q_k): """Implements a circuit that returns 1 if the chain moves in the direction y+.""" return q_i * (1-q_j) * q_k def circuit_yn(self, q_i, q_j, q_k): """Implements a circuit that returns 1 if the chain moves in the direction y-.""" return q_i * q_j * (1-q_k) def circuit_zp(self, q_i, q_j, q_k): """Implements a circuit that returns 1 if the chain moves in the direction z+.""" return (1-q_i) * (1-q_j) * q_k def circuit_zn(self, q_i, q_j, q_k): """Implements a circuit that returns 1 if the chain moves in the direction z-.""" return (1-q_i) * q_j * (1-q_k) def circuit_000(self, q_i, q_j, q_k): """Implements a circuit that checks the nonsensical string 000.""" return (1-q_i) * (1-q_j) * (1-q_k) def circuit_011(self, q_i, q_j, q_k): """Implements a circuit that checks the nonsensical string 000.""" return (1-q_i) * q_j * q_k def x_position(self, n): """Computes the x coordinate of the nth residue.""" if n > self.naas: raise ValueError('n greater than number of residues') return sum([ self.circuit_xp(self.get(self.r_pointer(i+1)), self.get(self.r_pointer(i+1)+1), self.get(self.r_pointer(i+1)+2)) - \ self.circuit_xn(self.get(self.r_pointer(i+1)), self.get(self.r_pointer(i+1)+1), self.get(self.r_pointer(i+1)+2)) for i in range(n)]) def y_position(self, n): """Computes the x coordinate of the nth residue.""" if n > self.naas: raise ValueError('n greater than number of residues') return sum([ self.circuit_yp(self.get(self.r_pointer(i+1)), self.get(self.r_pointer(i+1)+1), self.get(self.r_pointer(i+1)+2)) - \ self.circuit_yn(self.get(self.r_pointer(i+1)), self.get(self.r_pointer(i+1)+1), self.get(self.r_pointer(i+1)+2)) for i in range(n)]) def z_position(self, n): """Computes the x coordinate of the nth residue.""" if n > self.naas: raise ValueError('n greater than number of residues') return sum([ self.circuit_zp(self.get(self.r_pointer(i+1)), self.get(self.r_pointer(i+1)+1), self.get(self.r_pointer(i+1)+2)) - \ self.circuit_zn(self.get(self.r_pointer(i+1)), self.get(self.r_pointer(i+1)+1), self.get(self.r_pointer(i+1)+2)) for i in range(n)]) def g(self, i, j): """Computes the distance between residues i and j.""" return (self.x_position(i) - self.x_position(j))**2 \ + (self.y_position(i) - self.y_position(j))**2 \ + (self.z_position(i) - self.z_position(j))**2 def mu(self, i, j): """Computes \mu_{ij}.""" if i == j: return 0 elif abs(i-j) < 3: return 0 else: return 2 * int(math.ceil(math.log2(abs(i-j)))) \ * ((1+i-j) % 2) def alpha(self, i, j): """Computes \alpha_{ij}.""" return sum([ 2**k * self.get(self.o_pointer(i, j) + k) for k in range(self.mu(i,j))]) def redun_term(self): """Implements the term that penalises meaningless residue bitstrings 000 and 011.""" return sum([ self.circuit_000(self.get(self.r_pointer(k)), self.get(self.r_pointer(k)+1), self.get(self.r_pointer(k)+2)) + \ self.circuit_011(self.get(self.r_pointer(k)), self.get(self.r_pointer(k)+1), self.get(self.r_pointer(k)+2)) for k in range(self.naas)]) def back_term(self): """Ensures that the chain does not go back on itself.""" return sum([ self.circuit_xp(self.get(self.r_pointer(i)), self.get(self.r_pointer(i)+1), self.get(self.r_pointer(i)+2)) * self.circuit_xn(self.get(self.r_pointer(i+1)), self.get(self.r_pointer(i+1)+1), self.get(self.r_pointer(i+1)+2)) + \ self.circuit_xn(self.get(self.r_pointer(i)), self.get(self.r_pointer(i)+1), self.get(self.r_pointer(i)+2)) * self.circuit_xp(self.get(self.r_pointer(i+1)), self.get(self.r_pointer(i+1)+1), self.get(self.r_pointer(i+1)+2)) + \ self.circuit_yp(self.get(self.r_pointer(i)), self.get(self.r_pointer(i)+1), self.get(self.r_pointer(i)+2)) * self.circuit_yn(self.get(self.r_pointer(i+1)), self.get(self.r_pointer(i+1)+1), self.get(self.r_pointer(i+1)+2)) + \ self.circuit_yn(self.get(self.r_pointer(i)), self.get(self.r_pointer(i)+1), self.get(self.r_pointer(i)+2)) * self.circuit_yp(self.get(self.r_pointer(i+1)), self.get(self.r_pointer(i+1)+1), self.get(self.r_pointer(i+1)+2)) + \ self.circuit_zp(self.get(self.r_pointer(i)), self.get(self.r_pointer(i)+1), self.get(self.r_pointer(i)+2)) * self.circuit_zn(self.get(self.r_pointer(i+1)), self.get(self.r_pointer(i+1)+1), self.get(self.r_pointer(i+1)+2)) + \ self.circuit_zn(self.get(self.r_pointer(i)), self.get(self.r_pointer(i)+1), self.get(self.r_pointer(i)+2)) * self.circuit_zp(self.get(self.r_pointer(i+1)), self.get(self.r_pointer(i+1)+1), self.get(self.r_pointer(i+1)+2)) for i in range(self.naas-1)]) def steric_term(self): """Ensures that the chain does not overlap.""" term = sp.numbers.Integer(0) for i in range(self.naas-4): for j in range(i+4, self.naas): if (1+i-j) % 2: term += (2**self.mu(i, j) -self.g(i, j) \ - self.alpha(i, j))**2 return term def interaction_term_ij(self, i, j): return -1 * self.get(self.i_pointer(i, j)) \ * self.int_mat[i, j] \ * ( 2 - self.g(i, j) ) def interaction_term(self): """Computes contacts between residues.""" term = sp.numbers.Integer(0) for i in range(self.naas-3): for j in range(i+3, self.naas): if self.int_mat[i, j] == 0: continue term -= self.get(self.i_pointer(i, j)) \ * self.int_mat[i, j] \ * ( 2 - self.g(i, j) ) return term

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Python

orange3/Orange/tests/test_continuize.py

rgschmitz1/BioDepot-workflow-builder

f74d904eeaf91ec52ec9b703d9fb38e9064e5a66

[ "MIT" ]

54

2017-01-08T17:21:49.000Z

2021-11-02T08:46:07.000Z

orange3/Orange/tests/test_continuize.py

Synthia-3/BioDepot-workflow-builder

4ee93abe2d79465755e82a145af3b6a6e1e79fd4

[ "MIT" ]

22

2017-03-28T06:03:14.000Z

2021-07-28T05:43:55.000Z

orange3/Orange/tests/test_continuize.py

Synthia-3/BioDepot-workflow-builder

4ee93abe2d79465755e82a145af3b6a6e1e79fd4

[ "MIT" ]

21

2017-01-26T21:12:09.000Z

2022-01-31T21:34:59.000Z

# Test methods with long descriptive names can omit docstrings # pylint: disable=missing-docstring import unittest from Orange.data import Table, Variable from Orange.preprocess.continuize import DomainContinuizer from Orange.preprocess import Continuize from Orange.preprocess import transformation from Orange.tests import test_filename class TestDomainContinuizer(unittest.TestCase): def setUp(self): Variable._clear_all_caches() self.data = Table(test_filename("test4")) def test_default(self): for inp in (self.data, self.data.domain): dom = DomainContinuizer() dom = dom(inp) self.assertTrue(all(attr.is_continuous for attr in dom.attributes)) self.assertIs(dom.class_var, self.data.domain.class_var) self.assertIs(dom[0], self.data.domain[0]) self.assertIs(dom[1], self.data.domain[1]) self.assertEqual( [attr.name for attr in dom.attributes], ["c1", "c2", "d2=a", "d2=b", "d3=a", "d3=b", "d3=c"], ) self.assertIsInstance(dom[2].compute_value, transformation.Indicator) dat2 = Table(dom, self.data) # c1 c2 d2 d3 cl1 self.assertEqual(dat2[0], [1, -2, 1, 0, 1, 0, 0, "a"]) self.assertEqual(dat2[1], [0, 0, 0, 1, 0, 1, 0, "b"]) self.assertEqual(dat2[2], [2, 2, 0, 1, 0, 0, 1, "c"]) def test_continuous_transform_class(self): for inp in (self.data, self.data.domain): dom = DomainContinuizer(transform_class=True) dom = dom(inp) self.assertTrue(all(attr.is_continuous for attr in dom.variables)) self.assertIsNot(dom.class_var, self.data.domain.class_var) self.assertIs(dom[0], self.data.domain[0]) self.assertIs(dom[1], self.data.domain[1]) self.assertEqual( [attr.name for attr in dom.attributes], ["c1", "c2", "d2=a", "d2=b", "d3=a", "d3=b", "d3=c"], ) self.assertIsInstance(dom[2].compute_value, transformation.Indicator) dat2 = Table(dom, self.data) # c1 c2 d2 d3 cl1 self.assertEqual(dat2[0], [1, -2, 1, 0, 1, 0, 0, 1, 0, 0]) self.assertEqual(dat2[1], [0, 0, 0, 1, 0, 1, 0, 0, 1, 0]) self.assertEqual(dat2[2], [2, 2, 0, 1, 0, 0, 1, 0, 0, 1]) def test_multi_indicators(self): for inp in (self.data, self.data.domain): dom = DomainContinuizer(multinomial_treatment=Continuize.Indicators) dom = dom(inp) self.assertTrue(all(attr.is_continuous for attr in dom.attributes)) self.assertIs(dom.class_var, self.data.domain.class_var) self.assertIs(dom[0], self.data.domain[0]) self.assertIs(dom[1], self.data.domain[1]) self.assertEqual( [attr.name for attr in dom.attributes], ["c1", "c2", "d2=a", "d2=b", "d3=a", "d3=b", "d3=c"], ) self.assertIsInstance(dom[2].compute_value, transformation.Indicator) dat2 = Table(dom, self.data) # c1 c2 d2 d3 cl1 self.assertEqual(dat2[0], [1, -2, 1, 0, 1, 0, 0, "a"]) self.assertEqual(dat2[1], [0, 0, 0, 1, 0, 1, 0, "b"]) self.assertEqual(dat2[2], [2, 2, 0, 1, 0, 0, 1, "c"]) def test_multi_lowest_base(self): for inp in (self.data, self.data.domain): dom = DomainContinuizer(multinomial_treatment=Continuize.FirstAsBase) dom = dom(inp) self.assertTrue(all(attr.is_continuous for attr in dom.attributes)) self.assertIs(dom.class_var, self.data.domain.class_var) self.assertIs(dom[0], self.data.domain[0]) self.assertIs(dom[1], self.data.domain[1]) self.assertEqual( [attr.name for attr in dom.attributes], ["c1", "c2", "d2=b", "d3=b", "d3=c"], ) self.assertIsInstance(dom[2].compute_value, transformation.Indicator) dat2 = Table(dom, self.data) # c1 c2 d2 d3 cl1 self.assertEqual(dat2[0], [1, -2, 0, 0, 0, "a"]) self.assertEqual(dat2[1], [0, 0, 1, 1, 0, "b"]) self.assertEqual(dat2[2], [2, 2, 1, 0, 1, "c"]) def test_multi_lowest_base_base(self): self.data.domain[4].base_value = 1 for inp in (self.data, self.data.domain): dom = DomainContinuizer(multinomial_treatment=Continuize.FirstAsBase) dom = dom(inp) self.assertTrue(all(attr.is_continuous for attr in dom.attributes)) self.assertIs(dom.class_var, self.data.domain.class_var) self.assertIs(dom[0], self.data.domain[0]) self.assertIs(dom[1], self.data.domain[1]) self.assertEqual( [attr.name for attr in dom.attributes], ["c1", "c2", "d2=b", "d3=a", "d3=c"], ) self.assertIsInstance(dom[2].compute_value, transformation.Indicator) dat2 = Table(dom, self.data) # c1 c2 d2 d3 cl1 self.assertEqual(dat2[0], [1, -2, 0, 1, 0, "a"]) self.assertEqual(dat2[1], [0, 0, 1, 0, 0, "b"]) self.assertEqual(dat2[2], [2, 2, 1, 0, 1, "c"]) def test_multi_ignore(self): dom = DomainContinuizer(multinomial_treatment=Continuize.Remove) dom = dom(self.data.domain) self.assertTrue(all(attr.is_continuous for attr in dom.attributes)) self.assertEqual([attr.name for attr in dom.attributes], ["c1", "c2"]) def test_multi_ignore_class(self): dom = DomainContinuizer( multinomial_treatment=Continuize.Remove, transform_class=True ) dom = dom(self.data.domain) self.assertTrue(all(attr.is_continuous for attr in dom.attributes)) self.assertEqual([attr.name for attr in dom.attributes], ["c1", "c2"]) self.assertEqual(len(dom.class_vars), 0) self.assertIsNone(dom.class_var) def test_multi_ignore_multi(self): dom = DomainContinuizer(multinomial_treatment=Continuize.RemoveMultinomial) dom = dom(self.data.domain) self.assertTrue(all(attr.is_continuous for attr in dom.attributes)) self.assertEqual( [attr.name for attr in dom.variables], ["c1", "c2", "d2=b", "cl1"] ) def test_multi_ignore_class(self): dom = DomainContinuizer( multinomial_treatment=Continuize.RemoveMultinomial, transform_class=True ) dom = dom(self.data.domain) self.assertTrue(all(attr.is_continuous for attr in dom.attributes)) self.assertEqual([attr.name for attr in dom.attributes], ["c1", "c2", "d2=b"]) self.assertEqual(len(dom.class_vars), 0) self.assertIsNone(dom.class_var) def test_multi_error(self): self.assertRaises( ValueError, DomainContinuizer(multinomial_treatment=Continuize.ReportError), self.data.domain, ) def test_as_ordinal(self): for inp in (self.data, self.data.domain): dom = DomainContinuizer(multinomial_treatment=Continuize.AsOrdinal) dom = dom(inp) self.assertTrue(all(attr.is_continuous for attr in dom.attributes)) self.assertIs(dom.class_var, self.data.domain.class_var) self.assertIs(dom[0], self.data.domain[0]) self.assertIs(dom[1], self.data.domain[1]) self.assertEqual( [attr.name for attr in dom.variables], ["c1", "c2", "d2", "d3", "cl1"] ) dat2 = Table(dom, self.data) # c1 c2 d2 d3 cl1 self.assertEqual(dat2[0], [1, -2, 0, 0, "a"]) self.assertEqual(dat2[1], [0, 0, 1, 1, "b"]) self.assertEqual(dat2[2], [2, 2, 1, 2, "c"]) def test_as_ordinal_class(self): for inp in (self.data, self.data.domain): dom = DomainContinuizer( multinomial_treatment=Continuize.AsOrdinal, transform_class=True ) dom = dom(inp) self.assertTrue(all(attr.is_continuous for attr in dom.attributes)) self.assertTrue(dom.has_continuous_class) self.assertIs(dom[0], self.data.domain[0]) self.assertIs(dom[1], self.data.domain[1]) self.assertEqual( [attr.name for attr in dom.variables], ["c1", "c2", "d2", "d3", "cl1"] ) dat2 = Table(dom, self.data) # c1 c2 d2 d3 cl1 self.assertEqual(dat2[0], [1, -2, 0, 0, 0]) self.assertEqual(dat2[1], [0, 0, 1, 1, 1]) self.assertEqual(dat2[2], [2, 2, 1, 2, 2]) def test_as_normalized_ordinal(self): for inp in (self.data, self.data.domain): dom = DomainContinuizer( multinomial_treatment=Continuize.AsNormalizedOrdinal ) dom = dom(inp) self.assertTrue(all(attr.is_continuous for attr in dom.attributes)) self.assertIs(dom.class_var, self.data.domain.class_var) self.assertIs(dom[0], self.data.domain[0]) self.assertIs(dom[1], self.data.domain[1]) self.assertEqual( [attr.name for attr in dom.variables], ["c1", "c2", "d2", "d3", "cl1"] ) dat2 = Table(dom, self.data) # c1 c2 d2 d3 cl1 self.assertEqual(dat2[0], [1, -2, 0, 0, "a"]) self.assertEqual(dat2[1], [0, 0, 1, 0.5, "b"]) self.assertEqual(dat2[2], [2, 2, 1, 1, "c"])

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b84ed7728e1a97d05657822548a14a67ac91acfa

4,792

py

Python

stackoverflow-solutions/submission-scripts/SO_Q59513077_stacked_bar_plotting_dataframe_groups.py

MarkMoretto/python-examples-main

37b8c41d2f175029f4536ca970f037ff19b4e951

[ "MIT" ]

1

2020-07-21T23:24:25.000Z

stackoverflow-solutions/submission-scripts/SO_Q59513077_stacked_bar_plotting_dataframe_groups.py

MarkMoretto/python-examples-main

37b8c41d2f175029f4536ca970f037ff19b4e951

[ "MIT" ]

4

2021-06-29T00:38:57.000Z

2022-01-15T00:22:15.000Z

stackoverflow-solutions/submission-scripts/SO_Q59513077_stacked_bar_plotting_dataframe_groups.py

MarkMoretto/python-examples-main

37b8c41d2f175029f4536ca970f037ff19b4e951

[ "MIT" ]

null

""" Purpose: Stackoverflow: Plotting stacked bar chart Date created: 2019-12-28 URI: https://stackoverflow.com/questions/59513077/stacked-bar-plotting-dataframe-groups/59513363#59513363 Contributor(s): Mark M. """ import pandas as pd dates = ['2019-12-23', '2019-12-23', '2019-12-23', '2019-12-23', '2019-12-23', '2019-12-23', '2019-12-23', '2019-12-23', '2019-12-23', '2019-12-23', '2019-12-23', '2019-12-23', '2019-12-23', '2019-12-23', '2019-12-23', '2019-12-23', '2019-12-23', '2019-12-23', '2019-12-23', '2019-12-23', '2019-12-23', '2019-12-23', '2019-12-23', '2019-12-23', '2019-12-23', '2019-12-23', '2019-12-23', '2019-12-23', '2019-12-16', '2019-12-16', '2019-12-16', '2019-12-16', '2019-12-16', '2019-12-16', '2019-12-16', '2019-12-16', '2019-12-16', '2019-12-16', '2019-12-16', '2019-12-16', '2019-12-16', '2019-12-16', '2019-12-16', '2019-12-16', '2019-12-16', '2019-12-16', '2019-12-16', '2019-12-16', '2019-12-16', '2019-12-16', '2019-12-16', '2019-12-16', '2019-12-16', '2019-12-16', '2019-12-16', '2019-12-16', '2019-12-16', '2019-12-16', '2019-12-16', '2019-12-16', '2019-12-16', '2019-12-16', '2019-12-16', '2019-12-16', '2019-12-16', '2019-12-16', '2019-12-16', '2019-12-16', '2019-12-16', '2019-12-16', '2019-12-16', '2019-12-16', '2019-12-16', '2019-12-16', '2019-12-16', '2019-12-16', '2019-12-16', '2019-12-16', '2019-12-16', '2019-12-16', '2019-12-16', '2019-12-16', '2019-12-16', '2019-12-16', '2019-12-16', '2019-12-16', '2019-12-16', '2019-12-16', '2019-12-16'] sources = [ 'airport-suppliers.com', 'airport-suppliers.com', 'airport-suppliers.com', 'airport-suppliers.com', 'airport-suppliers.com', 'airport-suppliers.com', 'airport-suppliers.com', 'airport-suppliers.com', 'airport-suppliers.com', 'airport-suppliers.com', 'airport-suppliers.com', 'airport-suppliers.com', 'airport-suppliers.com', 'airport-suppliers.com', 'airport-suppliers.com', 'airport-suppliers.com', 'airport-suppliers.com', 'airport-suppliers.com', 'airport-suppliers.com', 'airport-suppliers.com', 'airport-suppliers.com', 'airportsinternational.keypublishing.com', 'airportsinternational.keypublishing.com', 'internationalairportreview.com', 'internationalairportreview.com', 'internationalairportreview.com', 'internationalairportreview.com', 'internationalairportreview.com', 'passengerterminaltoday.com', 'airportsinternational.keypublishing.com', 'airportsinternational.keypublishing.com', 'airportsinternational.keypublishing.com', 'airportsinternational.keypublishing.com', 'airportsinternational.keypublishing.com', 'airportsinternational.keypublishing.com', 'airportsinternational.keypublishing.com', 'internationalairportreview.com', 'internationalairportreview.com', 'internationalairportreview.com', 'airport-suppliers.com', 'passengerterminaltoday.com', 'internationalairportreview.com', 'internationalairportreview.com', 'internationalairportreview.com', 'internationalairportreview.com', 'passengerterminaltoday.com', 'passengerterminaltoday.com', 'internationalairportreview.com', 'internationalairportreview.com', 'internationalairportreview.com', 'airport-suppliers.com', 'passengerterminaltoday.com', 'airport-suppliers.com', 'airport-suppliers.com', 'passengerterminaltoday.com', 'passengerterminaltoday.com', 'passengerterminaltoday.com', 'passengerterminaltoday.com', 'passengerterminaltoday.com', 'airport-suppliers.com', 'airport-suppliers.com', 'airport-suppliers.com', 'airportsinternational.keypublishing.com', 'airportsinternational.keypublishing.com', 'airportsinternational.keypublishing.com', 'airportsinternational.keypublishing.com', 'airportsinternational.keypublishing.com', 'internationalairportreview.com', 'internationalairportreview.com', 'internationalairportreview.com', 'internationalairportreview.com', 'passengerterminaltoday.com', 'passengerterminaltoday.com', 'passengerterminaltoday.com', 'passengerterminaltoday.com', 'passengerterminaltoday.com', 'passengerterminaltoday.com', 'passengerterminaltoday.com', 'passengerterminaltoday.com', 'passengerterminaltoday.com', 'passengerterminaltoday.com', 'passengerterminaltoday.com', 'airport-suppliers.com', 'airport-suppliers.com', 'internationalairportreview.com', 'internationalairportreview.com', 'internationalairportreview.com', 'internationalairportreview.com', 'internationalairportreview.com' ] df = pd.DataFrame({"date": dates, "news_source": sources}) # df1['_id'] = [rand_string(35) for i in range(len(df1.index))] df1 = df.groupby(['date', 'news_source']).size().reset_index().rename(columns={0:'count'}) # Plot results pd.crosstab(index=df2['date'], columns=df2['news_source'], values=df2['count'], aggfunc=sum)

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1

0

11

b8ac664b556c756c7319853d9c74c079675b7bfd

130

py

Python

src/sprites/backgrounds/__init__.py

LeonGeorgi/ballsurf

4ae5fdb21e67d84ff7a0168481720dcd10155705

[ "MIT" ]

null

src/sprites/backgrounds/__init__.py

LeonGeorgi/ballsurf

4ae5fdb21e67d84ff7a0168481720dcd10155705

[ "MIT" ]

null

src/sprites/backgrounds/__init__.py

LeonGeorgi/ballsurf

4ae5fdb21e67d84ff7a0168481720dcd10155705

[ "MIT" ]

null

from sprites.backgrounds.cloud import Cloud from sprites.backgrounds.grass import Grass from sprites.backgrounds.tree import Tree

32.5

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7

b8b022095274f671b8f348d15bcb94ba9ea9208b

653

py

Python

tests/test_jsontools.py

dragid10/instascrape

116b58559613673812686fea57e962e93591689d

[ "MIT" ]

null

tests/test_jsontools.py

dragid10/instascrape

116b58559613673812686fea57e962e93591689d

[ "MIT" ]

null

tests/test_jsontools.py

dragid10/instascrape

116b58559613673812686fea57e962e93591689d

[ "MIT" ]

null

import pytest class TestJsonScraper: @pytest.mark.skip("Nothing to do here yet") def test_parse_json(self): assert False @pytest.mark.skip("Nothing to do here yet") def test_scraped_attr(self): assert False @pytest.mark.skip("Nothing to do here yet") def test_to_dict(self): assert False @pytest.mark.skip("Nothing to do here yet") def test_load_value(self): assert False @pytest.mark.skip("Nothing to do here yet") def test_from_json_string(self): assert False @pytest.mark.skip("Nothing to do here yet") def test_from_json_file(self): assert False

23.321429

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8

b8c125a3022634fcfa5cef1eec4ed3473af1eb67

1,716

py

Python

tests/test_provider_brightbox_brightbox.py

mjuenema/python-terrascript

6d8bb0273a14bfeb8ff8e950fe36f97f7c6e7b1d

[ "BSD-2-Clause" ]

507

2017-07-26T02:58:38.000Z

2022-01-21T12:35:13.000Z

tests/test_provider_brightbox_brightbox.py

mjuenema/python-terrascript

6d8bb0273a14bfeb8ff8e950fe36f97f7c6e7b1d

[ "BSD-2-Clause" ]

135

2017-07-20T12:01:59.000Z

2021-10-04T22:25:40.000Z

tests/test_provider_brightbox_brightbox.py

mjuenema/python-terrascript

6d8bb0273a14bfeb8ff8e950fe36f97f7c6e7b1d

[ "BSD-2-Clause" ]

81

2018-02-20T17:55:28.000Z

2022-01-31T07:08:40.000Z

# tests/test_provider_brightbox_brightbox.py # Automatically generated by tools/makecode.py (24-Sep-2021 15:13:43 UTC) def test_provider_import(): import terrascript.provider.brightbox.brightbox def test_resource_import(): from terrascript.resource.brightbox.brightbox import brightbox_api_client from terrascript.resource.brightbox.brightbox import brightbox_cloudip from terrascript.resource.brightbox.brightbox import brightbox_config_map from terrascript.resource.brightbox.brightbox import brightbox_database_server from terrascript.resource.brightbox.brightbox import brightbox_firewall_policy from terrascript.resource.brightbox.brightbox import brightbox_firewall_rule from terrascript.resource.brightbox.brightbox import brightbox_load_balancer from terrascript.resource.brightbox.brightbox import brightbox_orbit_container from terrascript.resource.brightbox.brightbox import brightbox_server from terrascript.resource.brightbox.brightbox import brightbox_server_group def test_datasource_import(): from terrascript.data.brightbox.brightbox import brightbox_database_type from terrascript.data.brightbox.brightbox import brightbox_image from terrascript.data.brightbox.brightbox import brightbox_server_group # TODO: Shortcut imports without namespace for official and supported providers. # TODO: This has to be moved into a required_providers block. # def test_version_source(): # # import terrascript.provider.brightbox.brightbox # # t = terrascript.provider.brightbox.brightbox.brightbox() # s = str(t) # # assert 'https://github.com/brightbox/terraform-provider-brightbox' in s # assert '2.0.6' in s

33.647059

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1,716

6.6

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0.239468

0.230599

0.317073

0.641537

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0.554324

0.190687

0

0.010027

0.128205

1,716

50

83

34.32

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true

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null

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0

1

0

1

0

1

0

8

b261ea67eb4507a7f7993560b4b699fe8f1f8458

114

py

Python

test/example/python/defaultParameters.py

Hirse/brackets-outline-list

a42568bb92d2b9a98f1d33d89f251c8b97b662b1

[ "MIT" ]

88

2015-01-03T11:20:13.000Z

2021-08-19T19:15:40.000Z

test/example/python/defaultParameters.py

Hirse/brackets-outline-list

a42568bb92d2b9a98f1d33d89f251c8b97b662b1

[ "MIT" ]

101

2015-01-08T12:28:47.000Z

2022-03-02T03:34:12.000Z

test/example/python/defaultParameters.py

Hirse/brackets-outline-list

a42568bb92d2b9a98f1d33d89f251c8b97b662b1

[ "MIT" ]

51

2015-01-03T11:20:14.000Z

2021-02-23T07:09:59.000Z

def default(dummy=0): pass def defaultWithSpaces(dummy = 0): pass def defaultFloat(dummy=0.1): pass

12.666667

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114

4.75

0.5

0.236842

0.263158

0.342105

0

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114

8

34

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null

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7

b26c90622f180726ef9602ceb0bbf2d334477587

16,756

py

Python

src/model.py

andrewmumblebee/AuthorArtistAnimator

05826c6f278665f733b41257f7f5394db18f19b6

[ "MIT" ]

1

2018-04-23T12:43:05.000Z

src/model.py

andrewmumblebee/AuthorArtistAnimator

05826c6f278665f733b41257f7f5394db18f19b6

[ "MIT" ]

4

2021-03-18T20:30:21.000Z

2022-03-11T23:19:52.000Z

src/model.py

andrewmumblebee/AuthorArtistAnimator

05826c6f278665f733b41257f7f5394db18f19b6

[ "MIT" ]

null

""" Models Module. - Builds the models that are then fed into a training loop. - This will save the output graphs after every epoch, so they can be used in a different environment. """ import os import tensorflow as tf import numpy as np import argparse import math, time import scipy from utility import BatchGenerator, tileImage from operations import * from architecture import discriminator, artist_generator, animator_generator class GAN(object): """ Base class of GAN. Sets attributes that are shared across both GAN models. Args: - sess: Tensorflow session to attach to. - isTraining: toggles the updating of models, when feeding in examples. - imageSize: dimensions of the images used in training. - args: extra arguments fed in through the training script. """ def __init__(self, sess, isTraining, imageSize, labelSize, args): self.bs = args.batch_size self.learning_rate = args.learning_rate self.zdim = args.zdim self.isTraining = isTraining self.imageSize = imageSize self.save_folder = args.save_folder self.reload = args.reload self.epoch = args.epoch self.cdim = args.cdim self.labelSize = labelSize self.sess = sess self.gf_dim = args.gf_dim self.df_dim = args.df_dim def loadModel(self, model_path=None): """ Restores an existing checkpoint to use in training. """ if model_path: self.saver.restore(self.sess, model_path) class Animator(GAN): """ Animator model setup which learns to generate animations, creates models and variables and then runs the training cycle. """ def __init__(self, sess, isTraining, imageSize, labelSize, args): GAN.__init__(self, sess, isTraining, imageSize, labelSize, args) self.buildModel() return def buildModel(self): """ Build models networks, and set up loss and variables. """ self.batch_size = tf.placeholder(tf.int32, [None, 1], name="batch_size") # Enable dynamic batch size. self.l = tf.placeholder(tf.float32, [self.batch_size.get_shape()[0], self.labelSize], name="label") img_dimensions = [self.imageSize[0], self.imageSize[1], self.cdim] self.z = tf.placeholder(tf.float32, [self.batch_size.get_shape()[0]] + img_dimensions, name="base") self.g_real = tf.placeholder(tf.float32, [self.batch_size.get_shape()[0]] + img_dimensions, name="images") ### GENERATORS ### self.g_fake = animator_generator(self.z, self.l, img_dimensions, self.gf_dim, self.cdim, self.batch_size, self.labelSize) self.g_sample = animator_generator(self.z, self.l, img_dimensions, self.gf_dim, self.cdim, self.batch_size, self.labelSize, reuse=True, isTraining=False) ### DISCRIMINATORS ### self.d_real = discriminator(self.z, self.l, self.df_dim, self.cdim, self.batch_size, self.labelSize, isTraining=self.isTraining) self.d_fake = discriminator(self.z, self.l, self.df_dim, self.cdim, self.batch_size, self.labelSize, reuse=True, isTraining=self.isTraining) print("BUILT MODELS") # Define loss self.d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=self.d_real, labels=tf.ones_like (self.d_real))) self.d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=self.d_fake, labels=tf.zeros_like(self.d_fake))) self.g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=self.d_fake, labels=tf.ones_like (self.d_fake))) \ + 100 * tf.reduce_mean(tf.abs(self.g_real - self.g_fake)) self.d_loss = self.d_loss_real + self.d_loss_fake print("DEFINED LOSS FUNCTIONS") self.g_optimizer = tf.train.AdamOptimizer(self.learning_rate, beta1=0.5).minimize(self.g_loss, var_list=[x for x in tf.trainable_variables() if "Generator" in x.name]) self.d_optimizer = tf.train.AdamOptimizer(self.learning_rate, beta1=0.5).minimize(self.d_loss, var_list=[x for x in tf.trainable_variables() if "Discriminator" in x.name]) print("DEFINED OPTIMIZERS") ############################# ### saver self.saver = tf.train.Saver() self.summary = tf.summary.merge_all() if self.save_folder: self.writer = tf.summary.FileWriter(self.save_folder, self.sess.graph) def train(self, batch_generator): """ Runs training loop of model. Args: - batch_generator: object to use to retrieve batches of images from. """" if self.save_folder and not os.path.exists(os.path.join(self.save_folder,"images")): os.makedirs(os.path.join(self.save_folder,"images")) init = tf.global_variables_initializer() self.sess.run(init) self.loadModel(self.reload) start = time.time() self.batch_s = np.zeros((self.bs, 1)) for epoch in range(self.epoch): batch_steps = batch_generator.get_file_count() // self.bs for step in range(batch_steps): batch_z = np.random.uniform(-1., +1., [self.bs, self.zdim]) batch_images, batch_labels, batch_bases = batch_generator.get_batch(self.bs) if step % 5 == 1: feed_dict = {self.z : batch_bases, self.l : batch_labels, self.g_real : batch_images, self.batch_size: self.batch_s} _, d_loss, g_real, summary = self.sess.run([self.d_optimizer, self.d_loss, self.g_real, self.summary], feed_dict = feed_dict) else: # Update generators twice. _, g_loss = self.sess.run([self.g_optimizer, self.g_loss], feed_dict={self.z: batch_bases, self.l: batch_labels, self.g_real: batch_images, self.batch_size: self.batch_s}) _, g_loss = self.sess.run([self.g_optimizer, self.g_loss], feed_dict={self.z: batch_bases, self.l: batch_labels, self.g_real: batch_images, self.batch_size: self.batch_s}) feed_dict = {self.z : batch_bases, self.l : batch_labels, self.g_real : batch_images, self.batch_size: self.batch_s} _, d_loss, g_fake, g_real, summary = self.sess.run([self.d_optimizer, self.d_loss, self.g_fake, self.g_real, self.summary], feed_dict = feed_dict) if step % 10 == 0: print ("Epoch {}: [{}/{}] loss(D)={:.4f}, loss(G)={:.4f}; time/step = {:.2f} sec".format(epoch, step, batch_steps, d_loss, g_loss, time.time() - start)) start = time.time() if step % 100 == 0: # Run models outputting images as training is run. self.writer.add_summary(summary, step) scipy.misc.imsave(os.path.join(self.save_folder,"images","img_{}_{}_bases.png".format(epoch, step)), tileImage(batch_bases)) self.generate_sample(g_real, batch_z, batch_labels, epoch, step, batch_bases) batch_generator.reset_buffer() freeze_graph('Generator_1/sprite', 'Animator', self.save_folder) def generate_sample(self, real_image, batch_z, batch_labels, epoch, step, bases): """ Generate sample images during training of the networks. One image is matched to the real_image that is fed into this function. In order to show how close the output is to the target output. Args: - real_image: example of a real image from the batch. - batch_z: noise vector used to generate a match to the real image. - batch_labels: labels that match the real images labels. - epoch: current epoch number.all - step: current step of epoch. - bases: base frames of the animations to reproduce. """ l0 = np.random.uniform(-1, +1, [self.bs, self.labelSize]) l1 = np.array([np.random.binomial(1, 0.5, self.labelSize) for x in range(self.bs)]) binomial_image = self.sess.run(self.g_sample, feed_dict={self.z:bases, self.l:l1, self.batch_size: self.batch_s}) noise_image = self.sess.run(self.g_sample, feed_dict={self.z:bases, self.l:l0, self.batch_size: self.batch_s}) matched_image = self.sess.run(self.g_sample, feed_dict={self.z:bases, self.l:batch_labels, self.batch_size: self.batch_s}) scipy.misc.imsave(os.path.join(self.save_folder,"images","anim_img_{}_{}_real.png".format(epoch, step)), tileImage(real_image)) scipy.misc.imsave(os.path.join(self.save_folder,"images","anim_img_{}_{}_matched.png".format(epoch, step)), tileImage(matched_image)) scipy.misc.imsave(os.path.join(self.save_folder,"images","anim_img_{}_{}_noise.png".format(epoch, step)), tileImage(noise_image)) scipy.misc.imsave(os.path.join(self.save_folder,"images","anim_img_{}_{}_binomial.png".format(epoch, step)), tileImage(binomial_image)) self.saver.save(self.sess, os.path.join(self.save_folder, "model.ckpt"), step) class Artist(GAN): """ Model for artist network, which learns how to draw sprites. Creates models and variables and then runs the training cycle. """ def __init__(self, sess, isTraining, imageSize, labelSize, args): GAN.__init__(self, sess, isTraining, imageSize, labelSize, args) self.buildModel() return def buildModel(self): """ Build models networks, and set up loss and variables. """ # define variables self.batch_size = tf.placeholder(tf.int32, [None, 1], name="batch_size") self.z = tf.placeholder(tf.float32, [self.batch_size.get_shape()[0], self.zdim], name="z") self.l = tf.placeholder(tf.float32, [self.batch_size.get_shape()[0], self.labelSize], name="label") img_dimensions = [self.imageSize[0], self.imageSize[1], self.cdim] self.g_real = tf.placeholder(tf.float32, [self.batch_size.get_shape()[0]] + img_dimensions, name="images") ### GENERATORS ### self.g_fake = artist_generator(self.z, self.l, img_dimensions, self.gf_dim, self.cdim, self.batch_size, self.labelSize) self.g_sample = artist_generator(self.z, self.l, img_dimensions, self.gf_dim, self.cdim, self.batch_size, self.labelSize, reuse=True, isTraining=False) ### DISCRIMINATORS ### self.d_real = discriminator(self.g_real, self.l, self.df_dim, self.cdim, self.batch_size, self.labelSize, isTraining=self.isTraining) self.d_fake = discriminator(self.g_fake, self.l, self.df_dim, self.cdim, self.batch_size, self.labelSize, reuse=True, isTraining=self.isTraining) print("BUILT MODELS") # define loss self.d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=self.d_real, labels=tf.ones_like (self.d_real))) self.d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=self.d_fake, labels=tf.zeros_like(self.d_fake))) self.g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=self.d_fake, labels=tf.ones_like (self.d_fake))) self.d_loss = self.d_loss_real + self.d_loss_fake print("DEFINED LOSS FUNCTIONS") # define optimizer self.g_optimizer = tf.train.AdamOptimizer(self.learning_rate, beta1=0.5).minimize(self.g_loss, var_list=[x for x in tf.trainable_variables() if "Generator" in x.name]) self.d_optimizer = tf.train.AdamOptimizer(self.learning_rate, beta1=0.5).minimize(self.d_loss, var_list=[x for x in tf.trainable_variables() if "Discriminator" in x.name]) print("DEFINED OPTIMIZERS") tf.summary.scalar("d_loss_real" ,self.d_loss_real) tf.summary.scalar("d_loss_fake" ,self.d_loss_fake) tf.summary.scalar("d_loss" ,self.d_loss) tf.summary.scalar("g_loss" ,self.g_loss) ############################# ### saver self.saver = tf.train.Saver() self.summary = tf.summary.merge_all() if self.save_folder: self.writer = tf.summary.FileWriter(self.save_folder, self.sess.graph) return def train(self, batch_generator): """ Runs training loop of model. Args: - batch_generator: object to use to retrieve batches of images from. """" if self.save_folder and not os.path.exists(os.path.join(self.save_folder,"images")): os.makedirs(os.path.join(self.save_folder,"images")) init = tf.global_variables_initializer() self.sess.run(init) self.loadModel(self.reload) start = time.time() self.batch_s = np.zeros((self.bs, 1)) for epoch in range(self.epoch): batch_steps = batch_generator.get_file_count() // self.bs for step in range(batch_steps): batch_z = np.random.uniform(-1., +1., [self.bs, self.zdim]) batch_images, batch_labels = batch_generator.get_batch(self.bs) # Add some random noise to the labels every 5 steps, to train GAN to generalize. if step % 5 == 0: batch_labels = batch_labels * np.random.uniform(0, 1, [self.bs, self.labelSize]) feed_dict = {self.z : batch_z, self.l : batch_labels, self.g_real : batch_images, self.batch_size : self.batch_s} # Every now and again train discriminator model more. if step % 5 == 1: _, d_loss, g_real, summary = self.sess.run([self.d_optimizer, self.d_loss, self.g_real, self.summary], feed_dict = feed_dict) else: # Update generator _, g_loss = self.sess.run([self.g_optimizer, self.g_loss],feed_dict={self.z: batch_z, self.l: batch_labels, self.batch_size : self.batch_s}) _, g_loss = self.sess.run([self.g_optimizer, self.g_loss],feed_dict={self.z: batch_z, self.l: batch_labels, self.batch_size : self.batch_s}) _, d_loss, g_fake, g_real, summary = self.sess.run([self.d_optimizer, self.d_loss, self.g_fake, self.g_real, self.summary], feed_dict = feed_dict) if step % 10 == 0: print ("Epoch {}: [{}/{}] loss(D)={:.4f}, loss(G)={:.4f}; time/step = {:.2f} sec".format(epoch, step, batch_steps, d_loss, g_loss, time.time() - start)) start = time.time() if step % 100 == 0: # Run models outputting images as training is run. self.writer.add_summary(summary, step) self.generate_sample(g_real, batch_z, batch_labels, epoch, step) freeze_graph('Generator_1/sprite', 'Artist', self.save_folder) batch_generator.reset_buffer() def generate_sample(self, real_image, batch_z, batch_labels, epoch, step): """ Generate sample images during training of the networks. One image is matched to the real_image that is fed into this function. In order to show how close the output is to the target output. Args: - real_image: example of a real image from the batch. - batch_z: noise vector used to generate a match to the real image. - batch_labels: labels that match the real images labels. - epoch: current epoch number.all - step: current step of epoch. """ l0 = np.random.uniform(-1, +1, [self.bs, self.labelSize]) l1 = np.array([np.random.binomial(1, 0.5, self.labelSize) for x in range(self.bs)]) z1 = np.random.uniform(-1, +1, [self.bs, self.zdim]) binomial_image = self.sess.run(self.g_sample, feed_dict={self.z:z1, self.l:l1, self.batch_size : self.batch_s}) noise_image = self.sess.run(self.g_sample, feed_dict={self.z:z1, self.l:l0, self.batch_size : self.batch_s}) matched_image = self.sess.run(self.g_sample, feed_dict={self.z:batch_z, self.l:batch_labels, self.batch_size : self.batch_s}) scipy.misc.imsave(os.path.join(self.save_folder,"images","img_{}_{}_real.png".format(epoch, step)), tileImage(real_image)) scipy.misc.imsave(os.path.join(self.save_folder,"images","img_{}_{}_matched.png".format(epoch, step)), tileImage(matched_image)) scipy.misc.imsave(os.path.join(self.save_folder,"images","img_{}_{}_noise.png".format(epoch, step)), tileImage(noise_image)) scipy.misc.imsave(os.path.join(self.save_folder,"images","img_{}_{}_binomial.png".format(epoch, step)), tileImage(binomial_image)) self.saver.save(self.sess, os.path.join(self.save_folder, "model.ckpt"), step)

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0.815495

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0

0.007971

0.228814

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0.052632

null

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0

null

0

1

0

null

0

1

0

7

b28812f98ba0906094b1c23f49ad23e776e65206

3,120

py

Python

test/imputation/ts/test_moving_window.py

tahmidmehdi/impyute

232497d53c68f47c3ed600b3de4a386cb6d4f2f3

[ "MIT" ]

null

test/imputation/ts/test_moving_window.py

tahmidmehdi/impyute

232497d53c68f47c3ed600b3de4a386cb6d4f2f3

[ "MIT" ]

null

test/imputation/ts/test_moving_window.py

tahmidmehdi/impyute

232497d53c68f47c3ed600b3de4a386cb6d4f2f3

[ "MIT" ]

null

""" test/imputation/ts/test_moving_window.py """ import unittest import numpy as np import impyute as impy class TestMovingWindowDefaults(unittest.TestCase): """ Tests for moving_window default parameters """ def setUp(self): self.data = np.arange(0, 25).reshape(5, 5).astype(float) def test_impute_leftmost_index(self): self.data[2][0] = np.nan imputed = impy.moving_window(self.data) self.assertFalse(np.isnan(imputed).any()) self.assertEqual(imputed[2][0], 11.5) def test_impute_middle_index(self): self.data[2][2] = np.nan imputed = impy.moving_window(self.data) self.assertFalse(np.isnan(imputed).any()) self.assertEqual(imputed[2][2], 12) def test_impute_rightmost_index(self): self.data[2][-1] = np.nan imputed = impy.moving_window(self.data) self.assertFalse(np.isnan(imputed).any()) self.assertEqual(imputed[2][-1], 12.5) class TestMovingWindowCustomFunction(unittest.TestCase): """ Tests for passing a custom function """ def setUp(self): self.data = np.arange(0, 25).reshape(5, 5).astype(float) def test_impute_leftmost_index(self): self.data[2][0] = np.nan imputed = impy.moving_window(self.data, func=lambda l: max(l) * 2) self.assertFalse(np.isnan(imputed).any()) self.assertEqual(imputed[2][0], 24) def test_impute_middle_index(self): self.data[2][2] = np.nan imputed = impy.moving_window(self.data, func=lambda l: max(l) * 2) self.assertFalse(np.isnan(imputed).any()) self.assertEqual(imputed[2][2], 28) def test_impute_rightmost_index(self): self.data[2][-1] = np.nan imputed = impy.moving_window(self.data, func=lambda l: max(l) * 2) self.assertFalse(np.isnan(imputed).any()) self.assertEqual(imputed[2][-1], 26) class TestMovingWindowCustomNindex(unittest.TestCase): """ Test for edge cases of nindex when the window completely falls off the array """ def setUp(self): self.data = np.arange(0, 25).reshape(5, 5).astype(float) def test_impute_leftmost_index_falls_off(self): self.data[2][0] = np.nan imputed = impy.moving_window(self.data, nindex=-1) self.assertFalse(np.isnan(imputed).any()) self.assertEqual(imputed[2][0], 11.5) def test_impute_rightmost_valid(self): self.data[2][0] = np.nan imputed = impy.moving_window(self.data, nindex=0) self.assertFalse(np.isnan(imputed).any()) self.assertEqual(imputed[2][0], 12.5) def test_impute_leftmost_falls_off(self): self.data[2][-1] = np.nan imputed = impy.moving_window(self.data, nindex=0) self.assertFalse(np.isnan(imputed).any()) self.assertEqual(imputed[2][-1], 12.5) def test_impute_rightmost_index_valid(self): self.data[2][-1] = np.nan imputed = impy.moving_window(self.data, nindex=-1) self.assertFalse(np.isnan(imputed).any()) self.assertEqual(imputed[2][-1], 11.5) if __name__ == "__main__": unittest.main()

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4.497717

0.166667

0.093401

0.079188

0.06599

0.79797

0.766497

0.758376

0

0.035441

0.204167

3,120

83

89

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0.757954

0.063782

0

0.71875

0

0.002763

0

0.3125

1

0.203125

false

0

0.046875

0

0.296875

0

null

0

1

0

1

0

null

0

1

0

7

b29acd5daec3d160fa09f31ab958cb15a9d04ac2

23

py

Python

FaBoLCD_PCF8574/__init__.py

FaBoPlatform/FaBoLCD-PCF8574-Python

d8f0d9887f7eb3f1c8882c930c03a2d00f6139c0

[ "Apache-2.0" ]

null

FaBoLCD_PCF8574/__init__.py

FaBoPlatform/FaBoLCD-PCF8574-Python

d8f0d9887f7eb3f1c8882c930c03a2d00f6139c0

[ "Apache-2.0" ]

null

FaBoLCD_PCF8574/__init__.py

FaBoPlatform/FaBoLCD-PCF8574-Python

d8f0d9887f7eb3f1c8882c930c03a2d00f6139c0

[ "Apache-2.0" ]

1

2017-05-21T13:28:55.000Z

from .PCF8574 import *

11.5

22

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3

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0.173913

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1

23

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null

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null

0

1

0

1

0

1

0

7

b2aa4cb3cfd1a2fdb7816e194fc004b3f3fa020d

16,857

py

Python

ast_version/src/parser_temp.py

lucassa3/CCompiler

ad788f692dc2863da9111b4a42f54277ac29d5ae

[ "MIT" ]

1

2020-04-29T21:30:11.000Z

ast_version/src/parser_temp.py

lucassa3/CCompiler

ad788f692dc2863da9111b4a42f54277ac29d5ae

[ "MIT" ]

10

2018-08-20T18:10:56.000Z

2019-04-05T14:45:11.000Z

ast_version/src/parser_temp.py

lucassa3/CCompiler

ad788f692dc2863da9111b4a42f54277ac29d5ae

[ "MIT" ]

null

from tokenizer import Tokenizer from binop import BinOp from noop import NoOp from unop import UnOp from intval import IntVal from assignernode import AssignerNode from identifiernode import IdentifierNode from printnode import PrintNode from commandsnode import CommandsNode from condnode import CondNode from loopnode import LoopNode from scannode import ScanNode from vardecnode import VarDecNode from multinode import MultiNode from funcdecnode import FuncDecNode from symboltable import SymbolTable from funccallnode import FuncCallNode from returnnode import ReturnNode class Parser(): tokens = Tokenizer() def init_parse(): Parser.tokens.next() result = Parser.parse_program() if Parser.tokens.current != None: raise ValueError("oops something wr0ng happ3n3d") return result def parse_funcdec(): result = 0 if Parser.tokens.current.type == "INT" \ or Parser.tokens.current.type == "VOID"\ or Parser.tokens.current.type == "CHAR": func_type = Parser.tokens.current.type Parser.tokens.next() if Parser.tokens.current.type == "IDENTIFIER": result = FuncDecNode(Parser.tokens.current.value, func_type) result.self_reference = result print(f"Criando funcdec da funcao {Parser.tokens.current.value}") Parser.tokens.next() if Parser.tokens.current.type == "OPEN_PAR": Parser.tokens.next() vardec = [] if Parser.tokens.current.type == "INT" \ or Parser.tokens.current.type == "CHAR": var_type = Parser.tokens.current.type Parser.tokens.next() if Parser.tokens.current.type == "IDENTIFIER": vardec.append((var_type, Parser.tokens.current.value)) Parser.tokens.next() while Parser.tokens.current.type == "COMMA": Parser.tokens.next() if Parser.tokens.current.type == "INT" \ or Parser.tokens.current.type == "CHAR": var_type = Parser.tokens.current.type Parser.tokens.next() if Parser.tokens.current.type == "IDENTIFIER": vardec.append((var_type, Parser.tokens.current.value)) Parser.tokens.next() else: raise ValueError(f"Expecting IDENTIFIER token. Got: {Parser.tokens.current.type}") result.vardec = vardec print(f"Parametros: {result.vardec}") if Parser.tokens.current.type != "CLOSE_PAR": raise ValueError(f"Expecting CLOSE_PAR token. Got: {Parser.tokens.current.type}") Parser.tokens.next() if Parser.tokens.current.type == "OPEN_BLOCK": result.set_child(Parser.parse_commands()) else: raise ValueError(f"Expecting OPEN_BLOCK token. Got: {Parser.tokens.current.type}") else: raise ValueError(f"Expecting OPEN_PAR token. Got: {Parser.tokens.current.type}") else: raise ValueError(f"Expecting IDENTIFIER token. Got: {Parser.tokens.current.type}") else: raise ValueError(f"Expecting some TYPE token. Got: {Parser.tokens.current.type}") return result def parse_program(): result = MultiNode() result.set_child(Parser.parse_funcdec()) while Parser.tokens.current: result.set_child(Parser.parse_funcdec()) result.set_child(FuncCallNode("main")) return result def parse_commands(): if Parser.tokens.current.type == "OPEN_BLOCK": result = CommandsNode(Parser.tokens.current.type, SymbolTable()) has_op = True Parser.tokens.next() while has_op: child = Parser.parse_command() result.set_child(child) if isinstance(child, NoOp): has_op = False if Parser.tokens.current.type != "CLOSE_BLOCK": raise ValueError(f"Expecting closing block. Got: {Parser.tokens.current.type}") else: raise ValueError(f"Expecting closing block. Got: {Parser.tokens.current.type}") Parser.tokens.next() return result def parse_command(): if Parser.tokens.current.type == "PRINT": return Parser.parse_print() if Parser.tokens.current.type == "INT" \ or Parser.tokens.current.type == "CHAR": return Parser.parse_vardec() if Parser.tokens.current.type == "IDENTIFIER": preview = Parser.tokens.peek() if preview.type == "OPEN_PAR": result = Parser.parse_funccall() Parser.tokens.next() if Parser.tokens.current.type != "CMD_END": raise ValueError(f"Expecting CMD_END token. Got: {Parser.tokens.current.type}") Parser.tokens.next() return result else: return Parser.parse_assignment() if Parser.tokens.current.type == "OPEN_BLOCK": return Parser.parse_commands() if Parser.tokens.current.type == "IF": return Parser.parse_if_else() if Parser.tokens.current.type == "WHILE": return Parser.parse_while() if Parser.tokens.current.type == "RETURN": return Parser.parse_return() else: return NoOp() def parse_return(): result = ReturnNode() Parser.tokens.next() if Parser.tokens.current.type == "OPEN_PAR": Parser.tokens.next() result.set_child(Parser.parse_expression()) if Parser.tokens.current.type != "CLOSE_PAR": raise ValueError(f"Expecting CLOSE_PAR token. Got: {Parser.tokens.current.type}") Parser.tokens.next() if Parser.tokens.current.type != "CMD_END": raise ValueError(f"Expecting CMD_END token. Got: {Parser.tokens.current.type}") Parser.tokens.next() return result def parse_funccall(): result = FuncCallNode(Parser.tokens.current.value) Parser.tokens.next() if Parser.tokens.current.type == "OPEN_PAR": Parser.tokens.next() if Parser.tokens.current.type == "NUMBER" \ or Parser.tokens.current.type == "IDENTIFIER": result.set_child(Parser.parse_expression()) while Parser.tokens.current.type == "COMMA": Parser.tokens.next() if Parser.tokens.current.type == "NUMBER" \ or Parser.tokens.current.type == "IDENTIFIER": result.set_child(Parser.parse_expression()) if Parser.tokens.current.type != "CLOSE_PAR": raise ValueError(f"Parse error") return result def parse_vardec(): result = MultiNode() var_type = Parser.tokens.current.type Parser.tokens.next() if Parser.tokens.current.type == "IDENTIFIER": result.set_child(VarDecNode(Parser.tokens.current.value, var_type)) else: raise ValueError(f"Expecting IDENTIFIER token. Got: {Parser.tokens.current.type}") Parser.tokens.next() while Parser.tokens.current.type == "COMMA": Parser.tokens.next() if Parser.tokens.current.type == "IDENTIFIER": result.set_child(VarDecNode(Parser.tokens.current.value, var_type)) else: raise ValueError(f"Expecting IDENTIFIER token. Got: {Parser.tokens.current.type}") Parser.tokens.next() if Parser.tokens.current.type != "CMD_END": raise ValueError(f"Expecting CMD_END token. Got: {Parser.tokens.current.type}") Parser.tokens.next() return result def parse_if_else(): result = CondNode() Parser.tokens.next() if Parser.tokens.current.type == "OPEN_PAR": result.set_child(Parser.parse_bool_expression()) if Parser.tokens.current.type == "CLOSE_PAR": Parser.tokens.next() if Parser.tokens.current.type == "OPEN_BLOCK": result.set_child(Parser.parse_commands()) if Parser.tokens.current.type == "ELSE": Parser.tokens.next() result.set_child(Parser.parse_commands()) else: raise ValueError(f"Expecting opening block. Got: {Parser.tokens.current.type}") else: raise ValueError(f"Expecting closing parenthesis. Got: {Parser.tokens.current.type}") else: raise ValueError(f"Expecting opening parenthesis. Got: {Parser.tokens.current.type}") return result def parse_while(): result = LoopNode() Parser.tokens.next() if Parser.tokens.current.type == "OPEN_PAR": result.set_child(Parser.parse_bool_expression()) if Parser.tokens.current.type == "CLOSE_PAR": Parser.tokens.next() if Parser.tokens.current.type == "OPEN_BLOCK": result.set_child(Parser.parse_commands()) else: raise ValueError(f"Expecting opening block. Got: {Parser.tokens.current.type}") else: raise ValueError(f"Expecting closing parenthesis. Got: {Parser.tokens.current.type}") else: raise ValueError(f"Expecting opening parenthesis. Got: {Parser.tokens.current.type}") return result def parse_bool_expression(): result = Parser.parse_bool_term() while Parser.tokens.current != None and Parser.tokens.current.type == "OR": result_cp = result result = BinOp(Parser.tokens.current.type) result.set_child(result_cp) result.set_child(Parser.parse_bool_term()) return result def parse_bool_term(): result = Parser.parse_bool_factor() while Parser.tokens.current != None and Parser.tokens.current.type == "AND": result_cp = result result = BinOp(Parser.tokens.current.type) result.set_child(result_cp) result.set_child(Parser.parse_bool_factor()) return result def parse_bool_factor(): result = 0 if Parser.tokens.current.type == "NOT": result = UnOp(Parser.tokens.current.type) Parser.tokens.next() result.set_child(Parser.parse_bool_factor()) else: result = Parser.parse_rel_expression() return result def parse_rel_expression(): Parser.tokens.next() result = Parser.parse_expression() if Parser.tokens.current.type == "LESS" or Parser.tokens.current.type == "GREATER" \ or Parser.tokens.current.type == "EQUALS" or Parser.tokens.current.type == "LE" or \ Parser.tokens.current.type == "GE": result_cp = result result = BinOp(Parser.tokens.current.type) Parser.tokens.next() result.set_child(result_cp) result.set_child(Parser.parse_expression()) return result def parse_print(): result = PrintNode() Parser.tokens.next() if Parser.tokens.current.type == "OPEN_PAR": Parser.tokens.next() result.set_child(Parser.parse_expression()) if Parser.tokens.current.type != "CLOSE_PAR": raise ValueError(f"Parse error") else: raise ValueError(f"Parse error") Parser.tokens.next() if Parser.tokens.current.type != "CMD_END": raise ValueError(f"Expecting CMD_END token. Got: {Parser.tokens.current.type}") Parser.tokens.next() return result def parse_assignment(): result = AssignerNode(Parser.tokens.current.value) Parser.tokens.next() if Parser.tokens.current.type =="EQUAL": Parser.tokens.next() print(Parser.tokens.current.type) if Parser.tokens.current.type =="SCANF": result.set_child(Parser.parse_scan()) elif Parser.tokens.current.type == "NUMBER" or Parser.tokens.current.type == "IDENTIFIER": result.set_child(Parser.parse_expression()) elif Parser.tokens.current.type == "DIGIT": result.set_child(IntVal(Parser.tokens.current.value)) Parser.tokens.next() else: raise ValueError(f"Parse error") if Parser.tokens.current.type != "CMD_END": raise ValueError(f"Expecting CMD_END token. Got: {Parser.tokens.current.type}") Parser.tokens.next() return result def parse_scan(): result = 0 Parser.tokens.next() if Parser.tokens.current.type == "OPEN_PAR": Parser.tokens.next() if Parser.tokens.current.type == "CLOSE_PAR": result = ScanNode() Parser.tokens.next() else: raise ValueError(f"Expecting closing parenthesis. Got: {Parser.tokens.current.type}") else: raise ValueError(f"Expecting opening parenthesis. Got: {Parser.tokens.current.type}") return result def parse_expression(): result = Parser.parse_term() while Parser.tokens.current != None and (Parser.tokens.current.type == "PLUS" or Parser.tokens.current.type == "MINUS"): result_cp = result result = BinOp(Parser.tokens.current.type) Parser.tokens.next() result.set_child(result_cp) result.set_child(Parser.parse_term()) return result def parse_term(): result = Parser.parse_factor() while Parser.tokens.current != None and (Parser.tokens.current.type == "MULT" or Parser.tokens.current.type == "DIV"): result_cp = result result = BinOp(Parser.tokens.current.type) Parser.tokens.next() result.set_child(result_cp) result.set_child(Parser.parse_factor()) return result def parse_factor(): result = 0 if Parser.tokens.current.type == "NUMBER": result = IntVal(Parser.tokens.current.value) Parser.tokens.next() elif Parser.tokens.current.type == "IDENTIFIER": preview = Parser.tokens.peek() if preview.type == "OPEN_PAR": result = Parser.parse_funccall() else: result = IdentifierNode(Parser.tokens.current.value) Parser.tokens.next() elif Parser.tokens.current.type == "MINUS": result = UnOp(Parser.tokens.current.type) Parser.tokens.next() result.set_child(Parser.parse_factor()) elif Parser.tokens.current.type == "PLUS": result = UnOp(Parser.tokens.current.type) Parser.tokens.next() result.set_child(Parser.parse_factor()) elif Parser.tokens.current.type == "OPEN_PAR": Parser.tokens.next() result = Parser.parse_expression() if Parser.tokens.current != None: if Parser.tokens.current.type == "CLOSE_PAR": Parser.tokens.next() else: raise ValueError(f"Expecting Closing Parenthesis, got: {Parser.tokens.current.type}") else: raise ValueError(f"Expecting Closing Parenthesis, got: {Parser.tokens.current.type}") else: raise ValueError(f"Parse error, got: {Parser.tokens.current.type}") return result

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a282b8605b7b9105554ec1adfd170873b323e555

27,332

py

Python

dfd/timm/data/loader.py

TARTRL/Deepfake_Dection

3fde260419ad709217ce7c7e3810a6681f7365d2

[ "Apache-2.0" ]

5

2021-08-10T15:16:28.000Z

2022-03-31T07:42:04.000Z

dfd/timm/data/loader.py

TARTRL/Deepfake_Dection

3fde260419ad709217ce7c7e3810a6681f7365d2

[ "Apache-2.0" ]

1

2022-03-24T05:32:50.000Z

2022-03-24T07:42:26.000Z

dfd/timm/data/loader.py

TARTRL/Deepfake_Dection

3fde260419ad709217ce7c7e3810a6681f7365d2

[ "Apache-2.0" ]

2

2021-12-13T03:46:31.000Z

2022-02-24T08:21:55.000Z

import torch.utils.data import numpy as np from .transforms_factory import create_transform, create_deepfake_transform, create_deepfake_transform_v1, \ create_deepfake_transform_v3, transforms_deepfake_train_v3, transforms_deepfake_eval_v3 from .constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD from .distributed_sampler import OrderedDistributedSampler from .random_erasing import RandomErasing from .mixup import FastCollateMixup def fast_collate(batch): """ A fast collation function optimized for uint8 images (np array or torch) and int64 targets (labels)""" assert isinstance(batch[0], tuple) batch_size = len(batch) if isinstance(batch[0][0], tuple): # This branch 'deinterleaves' and flattens tuples of input tensors into one tensor ordered by position # such that all tuple of position n will end up in a torch.split(tensor, batch_size) in nth position inner_tuple_size = len(batch[0][0]) flattened_batch_size = batch_size * inner_tuple_size targets = torch.zeros(flattened_batch_size, dtype=torch.int64) tensor = torch.zeros((flattened_batch_size, *batch[0][0][0].shape), dtype=torch.uint8) for i in range(batch_size): assert len(batch[i][0]) == inner_tuple_size # all input tensor tuples must be same length for j in range(inner_tuple_size): targets[i + j * batch_size] = batch[i][1] tensor[i + j * batch_size] += torch.from_numpy(batch[i][0][j]) return tensor, targets elif isinstance(batch[0][0], np.ndarray): targets = torch.tensor([b[1] for b in batch], dtype=torch.int64) assert len(targets) == batch_size tensor = torch.zeros((batch_size, *batch[0][0].shape), dtype=torch.uint8) # print('tshape:',tensor.shape) for i in range(batch_size): # print('bshape:', batch[i][0].shape) tensor[i] += torch.from_numpy(batch[i][0]) return tensor, targets elif isinstance(batch[0][0], torch.Tensor): targets = torch.tensor([b[1] for b in batch], dtype=torch.int64) assert len(targets) == batch_size tensor = torch.zeros((batch_size, *batch[0][0].shape), dtype=torch.uint8) for i in range(batch_size): tensor[i].copy_(batch[i][0]) return tensor, targets else: assert False, type(batch[0][0]) def fast_collate_v1(batch): """ A fast collation function optimized for uint8 images (np array or torch) and int64 targets (labels)""" assert isinstance(batch[0], tuple) batch_size = len(batch) if isinstance(batch[0][0], tuple): # This branch 'deinterleaves' and flattens tuples of input tensors into one tensor ordered by position # such that all tuple of position n will end up in a torch.split(tensor, batch_size) in nth position inner_tuple_size = len(batch[0][0]) flattened_batch_size = batch_size * inner_tuple_size targets = torch.zeros(flattened_batch_size, dtype=torch.int64) tensor = torch.zeros((flattened_batch_size, *batch[0][0][0].shape), dtype=torch.uint8) for i in range(batch_size): assert len(batch[i][0]) == inner_tuple_size # all input tensor tuples must be same length for j in range(inner_tuple_size): targets[i + j * batch_size] = batch[i][1] tensor[i + j * batch_size] += torch.from_numpy(batch[i][0][j]) return tensor, targets elif isinstance(batch[0][0], np.ndarray): # rotateds = torch.tensor([b[2] for b in batch], dtype=torch.int64) # assert len(rotateds) == batch_size targets = torch.tensor([i % 2 for i in range(2 * batch_size)], dtype=torch.int64) # targets = torch.zeros( batch_size*2, dtype=torch.int64) # fake_tensor = torch.zeros((batch_size, *batch[0][0].shape), dtype=torch.uint8) # real_tensor = torch.zeros((batch_size, *batch[0][1].shape), dtype=torch.uint8) # print('tshape:',tensor.shape) tensor = torch.zeros((batch_size * 2, *batch[0][0].shape), dtype=torch.uint8) for i in range(batch_size): tensor[2 * i] += torch.from_numpy(batch[i][0]) # targets[2*i] = torch.tensor(0) tensor[2 * i + 1] += torch.from_numpy(batch[i][1]) # targets[2 * i + 1] = torch.tensor(1) random_indexes = torch.randperm(tensor.size()[0]) targets = targets[random_indexes] tensor = tensor[random_indexes] return tensor, targets elif isinstance(batch[0][0], torch.Tensor): targets = torch.tensor([b[1] for b in batch], dtype=torch.int64) assert len(targets) == batch_size tensor = torch.zeros((batch_size, *batch[0][0].shape), dtype=torch.uint8) for i in range(batch_size): tensor[i].copy_(batch[i][0]) return tensor, targets else: assert False class PrefetchLoader_v1: def __init__(self, loader, mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, fp16=False, re_prob=0., re_mode='const', re_count=1, re_num_splits=0, re_max=0.1, has_gpu=True): self.loader = loader self.has_gpu = has_gpu if has_gpu: self.mean = torch.tensor([x * 255 for x in mean]).cuda().view(1, 3, 1, 1) self.std = torch.tensor([x * 255 for x in std]).cuda().view(1, 3, 1, 1) else: self.mean = torch.tensor([x * 255 for x in mean]).view(1, 3, 1, 1) self.std = torch.tensor([x * 255 for x in std]).view(1, 3, 1, 1) self.fp16 = fp16 if fp16: self.mean = self.mean.half() self.std = self.std.half() if re_prob > 0.: self.random_erasing = RandomErasing( probability=re_prob, max_area=re_max, mode=re_mode, max_count=re_count, num_splits=re_num_splits) else: self.random_erasing = None def __iter__(self): stream = torch.cuda.Stream() first = True for next_input, next_target in self.loader: with torch.cuda.stream(stream): next_input = next_input.cuda(non_blocking=True) next_target = next_target.cuda(non_blocking=True) if self.fp16: next_input = next_input.half().sub_(self.mean).div_(self.std) else: next_input = next_input.float().sub_(self.mean).div_(self.std) if self.random_erasing is not None: next_input = self.random_erasing(next_input) if not first: yield input, target else: first = False torch.cuda.current_stream().wait_stream(stream) input = next_input target = next_target yield input, target def iter_bak(self): stream = torch.cuda.Stream() first = True for next_fake_input, next_real_input, next_rotated in self.loader: with torch.cuda.stream(stream): next_fake_input = next_fake_input.cuda(non_blocking=True) next_real_input = next_real_input.cuda(non_blocking=True) next_rotated = next_rotated.cuda(non_blocking=True) if self.fp16: next_fake_input = next_fake_input.half().sub_(self.mean).div_(self.std) next_real_input = next_real_input.half().sub_(self.mean).div_(self.std) else: next_fake_input = next_fake_input.float().sub_(self.mean).div_(self.std) next_real_input = next_real_input.float().sub_(self.mean).div_(self.std) if self.random_erasing is not None: next_fake_input = self.random_erasing(next_fake_input) next_real_input = self.random_erasing(next_real_input) if not first: yield next_fake_input, next_real_input, next_rotated else: first = False torch.cuda.current_stream().wait_stream(stream) fake_input = next_fake_input real_input = next_real_input rotated = next_rotated yield fake_input, real_input, rotated def __len__(self): return len(self.loader) @property def sampler(self): return self.loader.sampler @property def dataset(self): return self.loader.dataset @property def mixup_enabled(self): if isinstance(self.loader.collate_fn, FastCollateMixup): return self.loader.collate_fn.mixup_enabled else: return False @mixup_enabled.setter def mixup_enabled(self, x): if isinstance(self.loader.collate_fn, FastCollateMixup): self.loader.collate_fn.mixup_enabled = x class PrefetchLoader_v3: def __init__(self, loader, mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, fp16=False, re_prob=0., re_mode='const', re_count=1, re_num_splits=0, re_max=0.1, img_num=4 ): self.loader = loader self.mean = torch.tensor([[x * 255 for x in mean] for _ in range(img_num)]).cuda().view(1, 3 * img_num, 1, 1) self.std = torch.tensor([[x * 255 for x in std] for _ in range(img_num)]).cuda().view(1, 3 * img_num, 1, 1) self.fp16 = fp16 if fp16: self.mean = self.mean.half() self.std = self.std.half() if re_prob > 0.: self.random_erasing = RandomErasing( probability=re_prob, max_area=re_max, mode=re_mode, max_count=re_count, num_splits=re_num_splits, img_num=img_num) else: self.random_erasing = None def __iter__(self): stream = torch.cuda.Stream() first = True for next_input, next_target in self.loader: with torch.cuda.stream(stream): next_input = next_input.cuda(non_blocking=True) next_target = next_target.cuda(non_blocking=True) if self.fp16: next_input = next_input.half().sub_(self.mean).div_(self.std) else: next_input = next_input.float().sub_(self.mean).div_(self.std) if self.random_erasing is not None: next_input = self.random_erasing(next_input) if not first: yield input, target else: first = False torch.cuda.current_stream().wait_stream(stream) input = next_input target = next_target yield input, target def __len__(self): return len(self.loader) @property def sampler(self): return self.loader.sampler @property def dataset(self): return self.loader.dataset @property def mixup_enabled(self): if isinstance(self.loader.collate_fn, FastCollateMixup): return self.loader.collate_fn.mixup_enabled else: return False @mixup_enabled.setter def mixup_enabled(self, x): if isinstance(self.loader.collate_fn, FastCollateMixup): self.loader.collate_fn.mixup_enabled = x class PrefetchLoader: def __init__(self, loader, mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, fp16=False, re_prob=0., re_mode='const', re_count=1, re_max=0.02, re_num_splits=0, has_gpu=True): self.loader = loader self.has_gpu = has_gpu if has_gpu: self.mean = torch.tensor([x * 255 for x in mean]).cuda().view(1, 3, 1, 1) self.std = torch.tensor([x * 255 for x in std]).cuda().view(1, 3, 1, 1) else: self.mean = torch.tensor([x * 255 for x in mean]).view(1, 3, 1, 1) self.std = torch.tensor([x * 255 for x in std]).view(1, 3, 1, 1) self.fp16 = fp16 if fp16: self.mean = self.mean.half() self.std = self.std.half() if re_prob > 0.: self.random_erasing = RandomErasing( probability=re_prob, mode=re_mode, max_count=re_count, num_splits=re_num_splits, max_area=re_max) else: self.random_erasing = None def __iter__(self): stream = torch.cuda.Stream() first = True for next_input, next_target in self.loader: with torch.cuda.stream(stream): next_input = next_input.cuda(non_blocking=True) next_target = next_target.cuda(non_blocking=True) if self.fp16: next_input = next_input.half().sub_(self.mean).div_(self.std) else: next_input = next_input.float().sub_(self.mean).div_(self.std) if self.random_erasing is not None: next_input = self.random_erasing(next_input) if not first: yield input, target else: first = False torch.cuda.current_stream().wait_stream(stream) input = next_input target = next_target yield input, target def __len__(self): return len(self.loader) @property def sampler(self): return self.loader.sampler @property def dataset(self): return self.loader.dataset @property def mixup_enabled(self): if isinstance(self.loader.collate_fn, FastCollateMixup): return self.loader.collate_fn.mixup_enabled else: return False @mixup_enabled.setter def mixup_enabled(self, x): if isinstance(self.loader.collate_fn, FastCollateMixup): self.loader.collate_fn.mixup_enabled = x def create_loader( dataset, input_size, batch_size, is_training=False, use_prefetcher=True, re_prob=0., re_mode='const', re_count=1, re_split=False, color_jitter=0.4, auto_augment=None, num_aug_splits=0, interpolation='bilinear', mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, num_workers=1, distributed=False, crop_pct=None, collate_fn=None, pin_memory=False, fp16=False, tf_preprocessing=False, has_gpu=True ): re_num_splits = 0 if re_split: # apply RE to second half of batch if no aug split otherwise line up with aug split re_num_splits = num_aug_splits or 2 dataset.transform = create_transform( input_size, is_training=is_training, use_prefetcher=use_prefetcher, color_jitter=color_jitter, auto_augment=auto_augment, interpolation=interpolation, mean=mean, std=std, crop_pct=crop_pct, tf_preprocessing=tf_preprocessing, re_prob=re_prob, re_mode=re_mode, re_count=re_count, re_num_splits=re_num_splits, separate=num_aug_splits > 0, ) sampler = None if distributed: if is_training: sampler = torch.utils.data.distributed.DistributedSampler(dataset) else: # This will add extra duplicate entries to result in equal num # of samples per-process, will slightly alter validation results sampler = OrderedDistributedSampler(dataset) if collate_fn is None: collate_fn = fast_collate if use_prefetcher else torch.utils.data.dataloader.default_collate loader = torch.utils.data.DataLoader( dataset, batch_size=batch_size, shuffle=sampler is None and is_training, num_workers=num_workers, sampler=sampler, collate_fn=collate_fn, pin_memory=pin_memory, drop_last=is_training, ) if use_prefetcher: loader = PrefetchLoader( loader, mean=mean, std=std, fp16=fp16, re_prob=re_prob if is_training else 0., re_mode=re_mode, re_count=re_count, re_num_splits=re_num_splits, has_gpu=has_gpu ) return loader def create_deepfake_loader( dataset, input_size, batch_size, is_training=False, use_prefetcher=True, re_prob=0., re_mode='const', re_count=1, re_split=False, re_max=0.1, color_jitter=0.4, auto_augment=None, num_aug_splits=0, interpolation='bilinear', mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, num_workers=1, distributed=False, crop_pct=None, collate_fn=None, pin_memory=False, fp16=False, tf_preprocessing=False, ): re_num_splits = 0 if re_split: # apply RE to second half of batch if no aug split otherwise line up with aug split re_num_splits = num_aug_splits or 2 dataset.transform = create_deepfake_transform( input_size, is_training=is_training, use_prefetcher=use_prefetcher, color_jitter=color_jitter, auto_augment=auto_augment, interpolation=interpolation, mean=mean, std=std, crop_pct=crop_pct, tf_preprocessing=tf_preprocessing, re_prob=re_prob, re_mode=re_mode, re_count=re_count, re_max=re_max, re_num_splits=re_num_splits, separate=num_aug_splits > 0, ) sampler = None if distributed: if is_training: sampler = torch.utils.data.distributed.DistributedSampler(dataset) else: # This will add extra duplicate entries to result in equal num # of samples per-process, will slightly alter validation results sampler = OrderedDistributedSampler(dataset) if collate_fn is None: collate_fn = fast_collate if use_prefetcher else torch.utils.data.dataloader.default_collate loader = torch.utils.data.DataLoader( dataset, batch_size=batch_size, shuffle=sampler is None, num_workers=num_workers, sampler=sampler, collate_fn=collate_fn, pin_memory=pin_memory, drop_last=is_training, ) if use_prefetcher: loader = PrefetchLoader( loader, mean=mean, std=std, fp16=fp16, re_prob=re_prob if is_training else 0., re_mode=re_mode, re_count=re_count, re_max=re_max, re_num_splits=re_num_splits ) return loader def create_deepfake_loader_v1( dataset, input_size, batch_size, is_training=False, use_prefetcher=True, re_prob=0., re_mode='const', re_count=1, re_split=False, re_max=0.02, color_jitter=0.4, auto_augment=None, num_aug_splits=0, interpolation='bilinear', mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, num_workers=1, distributed=False, crop_pct=None, collate_fn=None, pin_memory=True, fp16=True, tf_preprocessing=False, has_gpu=True ): re_num_splits = 0 if re_split: # apply RE to second half of batch if no aug split otherwise line up with aug split re_num_splits = num_aug_splits or 2 dataset.transform, dataset.transform_rotateds = create_deepfake_transform_v1( input_size, is_training=is_training, use_prefetcher=use_prefetcher, color_jitter=color_jitter, auto_augment=auto_augment, interpolation=interpolation, mean=mean, std=std, crop_pct=crop_pct, tf_preprocessing=tf_preprocessing, re_prob=re_prob, re_mode=re_mode, re_count=re_count, re_num_splits=re_num_splits, separate=num_aug_splits > 0, ) sampler = None if distributed: if is_training: sampler = torch.utils.data.distributed.DistributedSampler(dataset) else: # This will add extra duplicate entries to result in equal num # of samples per-process, will slightly alter validation results sampler = OrderedDistributedSampler(dataset) if collate_fn is None: collate_fn = fast_collate if use_prefetcher else torch.utils.data.dataloader.default_collate # batch_size = max(1, int(batch_size / 2)) loader = torch.utils.data.DataLoader( dataset, batch_size=batch_size, shuffle=sampler is None, num_workers=num_workers, sampler=sampler, collate_fn=collate_fn, pin_memory=pin_memory, drop_last=is_training, ) if use_prefetcher: loader = PrefetchLoader_v1( loader, mean=mean, std=std, fp16=fp16, re_prob=re_prob if is_training else 0., re_mode=re_mode, re_count=re_count, re_num_splits=re_num_splits, re_max=re_max, has_gpu=has_gpu ) return loader def create_deepfake_loader_v2( dataset, input_size, batch_size, is_training=False, use_prefetcher=True, re_prob=0., re_mode='const', re_count=1, re_split=False, re_max=0.02, color_jitter=0.4, auto_augment=None, num_aug_splits=0, interpolation='bilinear', mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, num_workers=1, distributed=False, crop_pct=None, collate_fn=None, pin_memory=True, fp16=True, tf_preprocessing=False, has_gpu=True ): re_num_splits = 0 if re_split: # apply RE to second half of batch if no aug split otherwise line up with aug split re_num_splits = num_aug_splits or 2 transform, transform_rotateds = create_deepfake_transform_v1( input_size, is_training=is_training, use_prefetcher=use_prefetcher, color_jitter=color_jitter, auto_augment=auto_augment, interpolation=interpolation, mean=mean, std=std, crop_pct=crop_pct, tf_preprocessing=tf_preprocessing, re_prob=re_prob, re_mode=re_mode, re_count=re_count, re_num_splits=re_num_splits, separate=num_aug_splits > 0, ) dataset.set_transform(transform, transform_rotateds) sampler = None if distributed: if is_training: sampler = torch.utils.data.distributed.DistributedSampler(dataset) else: # This will add extra duplicate entries to result in equal num # of samples per-process, will slightly alter validation results sampler = OrderedDistributedSampler(dataset) if collate_fn is None: collate_fn = fast_collate if use_prefetcher else torch.utils.data.dataloader.default_collate # batch_size = max(1, int(batch_size / 2)) loader = torch.utils.data.DataLoader( dataset, batch_size=batch_size, shuffle=sampler is None, num_workers=num_workers, sampler=sampler, collate_fn=collate_fn, pin_memory=pin_memory, drop_last=is_training, ) if use_prefetcher: loader = PrefetchLoader_v1( loader, mean=mean, std=std, fp16=fp16, re_prob=re_prob if is_training else 0., re_mode=re_mode, re_count=re_count, re_num_splits=re_num_splits, re_max=re_max, has_gpu=has_gpu ) return loader def create_deepfake_loader_v3( dataset, input_size, batch_size, is_training=False, use_prefetcher=True, re_prob=0., re_mode='const', re_count=1, re_split=False, re_max=0.02, color_jitter=0.4, auto_augment=None, num_aug_splits=0, interpolation='bilinear', mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, num_workers=1, distributed=False, crop_pct=None, collate_fn=None, pin_memory=True, fp16=True, tf_preprocessing=False, has_gpu=True, flicker=0., rotate_range=0, noise_std=0, noise_prob=0, blur_radiu=0, blur_prob=0 ): re_num_splits = 0 if re_split: # apply RE to second half of batch if no aug split otherwise line up with aug split re_num_splits = num_aug_splits or 2 separate = num_aug_splits > 0 if isinstance(input_size, tuple): img_size = input_size[-2:] else: img_size = input_size if is_training: transform = transforms_deepfake_train_v3( img_size, color_jitter=color_jitter, use_prefetcher=use_prefetcher, flicker=flicker, rotate_range=rotate_range, re_prob=re_prob, re_mode=re_mode, re_count=re_count, re_num_splits=re_num_splits, noise_std=noise_std, noise_prob=noise_prob, blur_radiu=blur_radiu, blur_prob=blur_prob ) else: assert not separate, "Separate transforms not supported for validation preprocessing" transform = transforms_deepfake_eval_v3( img_size, use_prefetcher=use_prefetcher) dataset.set_transform(transform) sampler = None if distributed: if is_training: sampler = torch.utils.data.distributed.DistributedSampler(dataset) else: # This will add extra duplicate entries to result in equal num # of samples per-process, will slightly alter validation results sampler = OrderedDistributedSampler(dataset) if collate_fn is None: collate_fn = fast_collate if use_prefetcher else torch.utils.data.dataloader.default_collate # batch_size = max(1, int(batch_size / 2)) loader = torch.utils.data.DataLoader( dataset, batch_size=batch_size, shuffle=sampler is None, num_workers=num_workers, sampler=sampler, collate_fn=collate_fn, pin_memory=pin_memory, drop_last=is_training, ) if use_prefetcher: loader = PrefetchLoader_v3( loader, mean=mean, std=std, fp16=fp16, re_prob=re_prob if is_training else 0., re_mode=re_mode, re_count=re_count, re_num_splits=re_num_splits, re_max=re_max, img_num=int(input_size[0] / 3) ) return loader

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Python

pkgs/sdk-pkg/src/genie/libs/sdk/triggers/unconfigconfig/bgp/nxos/unconfigconfig.py

miott/genielibs

6464642cdd67aa2367bdbb12561af4bb060e5e62

[ "Apache-2.0" ]

null

pkgs/sdk-pkg/src/genie/libs/sdk/triggers/unconfigconfig/bgp/nxos/unconfigconfig.py

miott/genielibs

6464642cdd67aa2367bdbb12561af4bb060e5e62

[ "Apache-2.0" ]

null

pkgs/sdk-pkg/src/genie/libs/sdk/triggers/unconfigconfig/bgp/nxos/unconfigconfig.py

miott/genielibs

6464642cdd67aa2367bdbb12561af4bb060e5e62

[ "Apache-2.0" ]

null

'''NXOS Implementation for BGP unconfigconfig triggers''' # python import logging import time from functools import partial log = logging.getLogger(__name__) # ATS from ats import aetest from ats.utils.objects import Not, NotExists # Genie Libs from genie.libs.sdk.libs.utils.mapping import Mapping from genie.libs.sdk.libs.utils.triggeractions import Configure, verify_ops_or_logic from genie.libs.sdk.libs.utils.mapping import Mapping, Different from genie.libs.sdk.triggers.unconfigconfig.unconfigconfig import TriggerUnconfigConfig # ipaddress from ipaddress import IPv4Address, IPv6Address # Which keys to exclude for BGP Ops comparison bgp_exclude = ['maker', 'bgp_session_transport', 'route_refresh', 'bgp_negotiated_capabilities', 'notifications', 'last_reset', 'keepalives', 'total', 'total_bytes', 'up_time', 'bgp_negotiated_keepalive_timers', 'updates', 'opens', 'bgp_table_version', 'holdtime', 'keepalive_interval', 'route_reflector_client', 'capability', 'distance_internal_as', 'bgp_neighbor_counters', 'memory_usage', 'total_entries', 'routing_table_version', 'total_memory', 'path', 'prefixes', 'cluster_id', 'distance_extern_as'] trm_exclude = ['maker', 'keepalives', 'total', 'up_time', 'total_bytes',] class TriggerUnconfigConfigBgpNeighborSendCommunity(TriggerUnconfigConfig): """Unconfigure send-community under BGP and reapply the whole configurations for learned BGP.""" __description__ = """Unconfigure send-community under BGP and reapply the whole configurations for learned BGP trigger_datafile: Mandatory: timeout: max_time (`int`): Maximum wait time for the trigger, in second. Default: 180 interval (`int`): Wait time between iteration when looping is needed, in second. Default: 15 method (`str`): Method to recover the device configuration, Support methods: 'checkpoint': Rollback the configuration by checkpoint (nxos), archive file (iosxe), load the saved running-config file on disk (iosxr) Optional: tgn_timeout (`int`): Maximum wait time for all traffic threads to be restored to the reference rate, in second. Default: 60 tgn_delay (`int`): Wait time between each poll to verify if traffic is resumed, in second. Default: 10 timeout_recovery: Buffer recovery timeout make sure devices are recovered at the end of the trigger execution. Used when previous timeouts have been exhausted. max_time (`int`): Maximum wait time for the last step of the trigger, in second. Default: 180 interval (`int`): Wait time between iteration when looping is needed, in second. Default: 15 static: The keys below are dynamically learnt by default. However, they can also be set to a custom value when provided in the trigger datafile. instance: `str` vrf: `str` neighbor: `str` address_family: `str` send_community: `str` bgp_id: `int` (e.g) interface: '(?P<interface>Ethernet1*)' (Regex supported) OR interface: 'Ethernet1/1/1' (Specific value) steps: 1. Learn BGP Ops object and store the BGP instance(s) if has any, otherwise, SKIP the trigger 2. Save the current device configurations through "method" which user uses 3. Unconfigure send-community under bgp pid from step 1 with BGP Conf object 4. Verify the send-comunity from step 3 are no longer existed 5. Recover the device configurations to the one in step 2 6. Learn BGP Ops again and verify it is the same as the Ops in step 1 """ mapping = Mapping(requirements={'ops.bgp.bgp.Bgp':{ 'requirements':[['info', 'instance', '(?P<instance>.*)','vrf', '(?P<vrf>.*)', 'neighbor','(?P<neighbor>.*)','address_family','(?P<address_family>.*)', 'send_community','(?P<send_community>.*)'], ['info', 'instance', '(?P<instance>.*)', 'bgp_id', '(?P<bgp_id>.*)']], 'all_keys':True, 'kwargs':{'attributes': ['info']}, 'exclude': bgp_exclude}}, config_info={'conf.bgp.Bgp':{ 'requirements':[['device_attr', '{uut}', 'vrf_attr', '(?P<vrf>.*)', 'neighbor_attr','(?P<neighbor>.*)', 'address_family_attr','(?P<address_family>.*)', 'nbr_af_send_community','(?P<send_community>.*)']], 'verify_conf':False, 'kwargs':{'mandatory':{'bgp_id': '(?P<bgp_id>.*)'}}}}, verify_ops={'ops.bgp.bgp.Bgp':{ 'requirements':[['info', 'instance', '(?P<instance>.*)','vrf', '(?P<vrf>.*)', 'neighbor','(?P<neighbor>.*)','address_family','(?P<address_family>.*)', NotExists('send_community')]], 'kwargs':{'attributes':['info']}, 'exclude': bgp_exclude}}, num_values={'bgp_id':1, 'instance':1 , 'vrf':1, 'address_family':1, 'neighbor':1 }) class TriggerUnconfigConfigBgpNeighborSendCommunityExtended(TriggerUnconfigConfig): """Unconfigure send-community extended under a BGP neighbor and reapply the whole configurations of dynamically learned BGP pid""" __description__ = """Unconfigure send-community extended for a BGP neighbor and reapply the whole configurations of dynamically learned BGP pid trigger_datafile: Mandatory: timeout: max_time (`int`): Maximum wait time for the trigger, in second. Default: 180 interval (`int`): Wait time between iteration when looping is needed, in second. Default: 15 method (`str`): Method to recover the device configuration, Support methods: 'checkpoint': Rollback the configuration by checkpoint (nxos), archive file (iosxe), load the saved running-config file on disk (iosxr) Optional: tgn_timeout (`int`): Maximum wait time for all traffic threads to be restored to the reference rate, in second. Default: 60 tgn_delay (`int`): Wait time between each poll to verify if traffic is resumed, in second. Default: 10 timeout_recovery: Buffer recovery timeout make sure devices are recovered at the end of the trigger execution. Used when previous timeouts have been exhausted. max_time (`int`): Maximum wait time for the last step of the trigger, in second. Default: 180 interval (`int`): Wait time between iteration when looping is needed, in second. Default: 15 static: The keys below are dynamically learnt by default. However, they can also be set to a custom value when provided in the trigger datafile. instance: `str` vrf: `str` neighbor: `str` address_family: `str` send_community: `str` bgp_id: `int` (e.g) interface: '(?P<interface>Ethernet1*)' (Regex supported) OR interface: 'Ethernet1/1/1' (Specific value) steps: 1. Learn BGP Ops object and store the BGP instance(s) if has any, otherwise, SKIP the trigger 2. Save the current device configurations through "method" which user uses 3. Unconfigure send-community extended for a BGP neighbor of learned BGP pid from step 1 4. Verify the send-community extended for BGP neighbor from step 3 are no longer existed 5. Recover the device configurations to the one in step 2 6. Learn BGP Ops again and verify it is the same as the Ops in step 1 """ mapping = Mapping(requirements={'ops.bgp.bgp.Bgp':{ 'requirements':[['info', 'instance', '(?P<instance>.*)','vrf', '(?P<vrf>.*)', 'neighbor','(?P<neighbor>.*)','address_family','(?P<address_family>.*)', 'send_community','(?P<send_community>(both|extended)+)$'], ['info', 'instance', '(?P<instance>.*)', 'bgp_id', '(?P<bgp_id>.*)']], 'all_keys':True, 'kwargs':{'attributes': ['info']}, 'exclude': bgp_exclude}}, config_info={'conf.bgp.Bgp':{ 'requirements':[['device_attr', '{uut}', 'vrf_attr', '(?P<vrf>.*)', 'neighbor_attr','(?P<neighbor>.*)', 'address_family_attr','(?P<address_family>.*)', 'nbr_af_send_community','(?P<send_community>.*)']], 'verify_conf':False, 'kwargs':{'mandatory':{'bgp_id': '(?P<bgp_id>.*)'}}}}, verify_ops={'ops.bgp.bgp.Bgp':{ 'requirements':[['info', 'instance', '(?P<instance>.*)','vrf', '(?P<vrf>.*)', 'neighbor','(?P<neighbor>.*)','address_family','(?P<address_family>.*)', NotExists('send_community')]], 'kwargs':{'attributes':['info']}, 'exclude': bgp_exclude}}, num_values={'bgp_id':1, 'instance':1 , 'vrf':1, 'address_family':1, 'neighbor':1 }) class TriggerUnconfigConfigBgpNeighborSoftReconfiguration(TriggerUnconfigConfig): """Unconfigure soft-reconfiguration inbound for a BGP neighbor and reapply the whole configurations for learned BGP pid""" __description__ = """Unconfigure soft-reconfiguration inbound for a BGP neighbor and reapply the whole configurations for learned BGP pid trigger_datafile: Mandatory: timeout: max_time (`int`): Maximum wait time for the trigger, in second. Default: 180 interval (`int`): Wait time between iteration when looping is needed, in second. Default: 15 method (`str`): Method to recover the device configuration, Support methods: 'checkpoint': Rollback the configuration by checkpoint (nxos), archive file (iosxe), load the saved running-config file on disk (iosxr) Optional: tgn_timeout (`int`): Maximum wait time for all traffic threads to be restored to the reference rate, in second. Default: 60 tgn_delay (`int`): Wait time between each poll to verify if traffic is resumed, in second. Default: 10 timeout_recovery: Buffer recovery timeout make sure devices are recovered at the end of the trigger execution. Used when previous timeouts have been exhausted. max_time (`int`): Maximum wait time for the last step of the trigger, in second. Default: 180 interval (`int`): Wait time between iteration when looping is needed, in second. Default: 15 static: The keys below are dynamically learnt by default. However, they can also be set to a custom value when provided in the trigger datafile. instance: `str` vrf: `str` neighbor: `str` address_family: `str` bgp_id: `int` (e.g) interface: '(?P<interface>Ethernet1*)' (Regex supported) OR interface: 'Ethernet1/1/1' (Specific value) steps: 1. Learn BGP Ops object and store the BGP instance(s) if has any, otherwise, SKIP the trigger 2. Save the current device configurations through "method" which user uses 3. Unconfigure soft-reconfiguration inbound for a BGP neighbor of learned BGP pid from step 1 4. Verify the soft-reconfiguration for BGP neighbor from step 3 are no longer existed 5. Recover the device configurations to the one in step 2 6. Learn BGP Ops again and verify it is the same as the Ops in step 1 """ mapping = Mapping(requirements={'ops.bgp.bgp.Bgp':{ 'requirements':[['info', 'instance', '(?P<instance>.*)','vrf', '(?P<vrf>.*)', 'neighbor','(?P<neighbor>.*)','address_family','(?P<address_family>.*)', 'soft_configuration',True ], ['info', 'instance', '(?P<instance>.*)', 'bgp_id', '(?P<bgp_id>.*)']], 'all_keys':True, 'kwargs':{'attributes': ['info']}, 'exclude': bgp_exclude}}, config_info={'conf.bgp.Bgp':{ 'requirements': [['device_attr', '{uut}', 'vrf_attr', '(?P<vrf>.*)', 'neighbor_attr','(?P<neighbor>.*)', 'address_family_attr','(?P<address_family>.*)', 'nbr_af_soft_reconfiguration',True]], 'verify_conf':False, 'kwargs':{'mandatory':{'bgp_id': '(?P<bgp_id>.*)'}}}}, verify_ops={'ops.bgp.bgp.Bgp':{ 'requirements':[['info', 'instance', '(?P<instance>.*)','vrf', '(?P<vrf>.*)', 'neighbor','(?P<neighbor>.*)','address_family','(?P<address_family>.*)', NotExists('soft_configuration')]], 'kwargs':{'attributes':['info']}, 'exclude': bgp_exclude}}, num_values={'bgp_id':1, 'instance':1 , 'vrf':1, 'neighbor':1 }) class TriggerUnconfigConfigBgpKeepaliveHoldtime(TriggerUnconfigConfig): """Unconfigure keepalive interval and holdtime and reapply the whole configurations for learned BGP pid""" __description__ = """Unconfigure keepalive interval and holdtime and reapply the whole configurations for learned BGP pid trigger_datafile: Mandatory: timeout: max_time (`int`): Maximum wait time for the trigger, in second. Default: 180 interval (`int`): Wait time between iteration when looping is needed, in second. Default: 15 method (`str`): Method to recover the device configuration, Support methods: 'checkpoint': Rollback the configuration by checkpoint (nxos), archive file (iosxe), load the saved running-config file on disk (iosxr) Optional: tgn_timeout (`int`): Maximum wait time for all traffic threads to be restored to the reference rate, in second. Default: 60 tgn_delay (`int`): Wait time between each poll to verify if traffic is resumed, in second. Default: 10 timeout_recovery: Buffer recovery timeout make sure devices are recovered at the end of the trigger execution. Used when previous timeouts have been exhausted. max_time (`int`): Maximum wait time for the last step of the trigger, in second. Default: 180 interval (`int`): Wait time between iteration when looping is needed, in second. Default: 15 static: The keys below are dynamically learnt by default. However, they can also be set to a custom value when provided in the trigger datafile. instance: `str` vrf: `str` neighbor: `str` keepalive_interval: `int` holdtime: `int` bgp_id: `int` (e.g) interface: '(?P<interface>Ethernet1*)' (Regex supported) OR interface: 'Ethernet1/1/1' (Specific value) steps: 1. Learn BGP Ops object and store the BGP instance(s) if has any, otherwise, SKIP the trigger 2. Save the current device configurations through "method" which user uses 3. Unconfigure keepalive interval and holdtime for learned BGP pid from step 1 4. Verify the keepalive interval and holdtime for BGP pid from step 3 are no longer existed 5. Recover the device configurations to the one in step 2 6. Learn BGP Ops again and verify it is the same as the Ops in step 1 """ mapping = Mapping(\ requirements={\ 'conf.bgp.Bgp': { 'requirements': [\ [['device_attr', '{uut}', '_vrf_attr', '(?P<vrf>.*)', 'keepalive_interval', '(?P<keepalive_interval>.*)']], [['device_attr', '{uut}', '_vrf_attr', '(?P<vrf>.*)', 'holdtime', '(?P<holdtime>.*)']]], 'all_keys':True, 'exclude': bgp_exclude}, 'ops.bgp.bgp.Bgp': { 'requirements': [\ ['info', 'instance', '(?P<instance>.*)', 'vrf', '(?P<vrf>.*)', 'neighbor', '(?P<neighbor>.*)', 'session_state', 'established'], ['info', 'instance', '(?P<instance>.*)', 'bgp_id', '(?P<bgp_id>.*)']], 'all_keys':True, 'kwargs': {'attributes': ['info']}, 'exclude': bgp_exclude}}, config_info={\ 'conf.bgp.Bgp': { 'requirements': [\ ['device_attr', '{uut}', 'vrf_attr', '(?P<vrf>.*)', 'keepalive_interval', '(?P<keepalive_interval>.*)'], ['device_attr', '{uut}', 'vrf_attr', '(?P<vrf>.*)', 'holdtime', '(?P<holdtime>.*)']], 'verify_conf': False, 'kwargs':{'mandatory':{'bgp_id': '(?P<bgp_id>.*)'}}}}, verify_ops={\ 'conf.bgp.Bgp': { 'requirements': [\ ['device_attr', '{uut}', '_vrf_attr', '(?P<vrf>.*)', NotExists('keepalive_interval')], ['device_attr', '{uut}', '_vrf_attr', '(?P<vrf>.*)', NotExists('holdtime')]], 'exclude': bgp_exclude}}, num_values={'device': 1, 'bgp_id': 1, 'vrf': 1, 'instance': 1, 'neighbor': 1}) class TriggerUnconfigConfigBgpFastExternalFallover(TriggerUnconfigConfig): """Unconfigure fast-external-fallover and reapply the whole configurations for learned BGP pid""" __description__ = """Unconfigure fast-external-fallover under a BGP and reapply the whole configurations for learned BGP pid trigger_datafile: Mandatory: timeout: max_time (`int`): Maximum wait time for the trigger, in second. Default: 180 interval (`int`): Wait time between iteration when looping is needed, in second. Default: 15 method (`str`): Method to recover the device configuration, Support methods: 'checkpoint': Rollback the configuration by checkpoint (nxos), archive file (iosxe), load the saved running-config file on disk (iosxr) Optional: tgn_timeout (`int`): Maximum wait time for all traffic threads to be restored to the reference rate, in second. Default: 60 tgn_delay (`int`): Wait time between each poll to verify if traffic is resumed, in second. Default: 10 timeout_recovery: Buffer recovery timeout make sure devices are recovered at the end of the trigger execution. Used when previous timeouts have been exhausted. max_time (`int`): Maximum wait time for the last step of the trigger, in second. Default: 180 interval (`int`): Wait time between iteration when looping is needed, in second. Default: 15 static: The keys below are dynamically learnt by default. However, they can also be set to a custom value when provided in the trigger datafile. instance: `str` vrf: `str` neighbor: `str` bgp_id: `int` (e.g) interface: '(?P<interface>Ethernet1*)' (Regex supported) OR interface: 'Ethernet1/1/1' (Specific value) steps: 1. Learn BGP Ops object and store the BGP instance(s) if has any, otherwise, SKIP the trigger 2. Save the current device configurations through "method" which user uses 3. Unconfigure fast-external-fallover under learned BGP pid from step 1 4. Verify the fast-external-fallover under BGP pid from step 3 are no longer existed 5. Recover the device configurations to the one in step 2 6. Learn BGP Ops again and verify it is the same as the Ops in step 1 """ mapping = Mapping(\ requirements={\ 'conf.bgp.Bgp': { 'requirements': [\ ['device_attr', '{uut}', '_vrf_attr', '(?P<vrf>.*)', 'fast_external_fallover', True]], 'exclude': bgp_exclude}, 'ops.bgp.bgp.Bgp': { 'requirements': [\ ['info', 'instance', '(?P<instance>.*)', 'vrf', '(?P<vrf>.*)', 'neighbor', '(?P<neighbor>.*)', 'session_state', 'established'], ['info', 'instance', '(?P<instance>.*)', 'bgp_id', '(?P<bgp_id>.*)']], 'all_keys':True, 'kwargs': {'attributes': ['info']}, 'exclude': bgp_exclude}}, config_info={\ 'conf.bgp.Bgp': { 'requirements': [\ ['device_attr', '{uut}', 'vrf_attr', '(?P<vrf>.*)', 'fast_external_fallover', True]], 'verify_conf': False, 'kwargs': {'mandatory': {'bgp_id': '(?P<bgp_id>.*)'}}}}, verify_ops={\ 'conf.bgp.Bgp': { 'requirements': [\ ['device_attr', '{uut}', '_vrf_attr', '(?P<vrf>.*)', NotExists('fast_external_fallover')]], 'exclude': bgp_exclude}}, num_values={'device': 1, 'bgp_id': 1, 'vrf': 1, 'instance': 1, 'neighbor': 1}) class TriggerUnconfigConfigBgpGracefulRestart(TriggerUnconfigConfig): """Unconfigure graceful restart configured under BGP and then reapply the whole configuration of dynamically learned BGP instance(s).""" __description__ = """Unconfigure graceful restart configured under BGP and then reapply the whole configuration of dynamically learned BGP instance(s). trigger_datafile: Mandatory: timeout: max_time (`int`): Maximum wait time for the trigger, in second. Default: 180 interval (`int`): Wait time between iteration when looping is needed, in second. Default: 15 method (`str`): Method to recover the device configuration, Support methods: 'checkpoint': Rollback the configuration by checkpoint (nxos), archive file (iosxe), load the saved running-config file on disk (iosxr) Optional: tgn_timeout (`int`): Maximum wait time for all traffic threads to be restored to the reference rate, in second. Default: 60 tgn_delay (`int`): Wait time between each poll to verify if traffic is resumed, in second. Default: 10 timeout_recovery: Buffer recovery timeout make sure devices are recovered at the end of the trigger execution. Used when previous timeouts have been exhausted. max_time (`int`): Maximum wait time for the last step of the trigger, in second. Default: 180 interval (`int`): Wait time between iteration when looping is needed, in second. Default: 15 static: The keys below are dynamically learnt by default. However, they can also be set to a custom value when provided in the trigger datafile. instance: `str` vrf: `str` bgp_id: `int` (e.g) interface: '(?P<interface>Ethernet1*)' (Regex supported) OR interface: 'Ethernet1/1/1' (Specific value) Steps: 1. Learn BGP Ops object and store the BGP instance(s) if any, else SKIP the trigger 2. Save the current device configurations using the "method" specified by user in Trigger YAML. 3. Unconfigure the learned BGP instance(s) from step 1 with BGP Conf object 4. Verify the BGP instance(s) from step 3 no longer exists 5. Recover the device configurations to the one in step 2 6. Learn BGP Ops again and verify it is the same as the Ops in step 1 """ mapping = Mapping(\ requirements={\ 'ops.bgp.bgp.Bgp':{ 'requirements':[\ ['info', 'instance', '(?P<instance>.*)', 'bgp_id', '(?P<bgp_id>.*)']], 'kwargs':{'attributes':['info']}, 'exclude': bgp_exclude}, 'conf.bgp.Bgp':{ 'requirements':[\ ['device_attr', '{uut}', '_vrf_attr', '(?P<vrf>.*)', 'graceful_restart', True]], 'exclude': bgp_exclude}}, config_info={\ 'conf.bgp.Bgp':{ 'requirements':[\ ['device_attr', '{uut}', 'vrf_attr', '(?P<vrf>.*)', 'graceful_restart', True]], 'kwargs':{'mandatory':{'bgp_id': '(?P<bgp_id>.*)'}}}}, verify_ops={\ 'conf.bgp.Bgp':{ 'requirements':[\ ['device_attr', '{uut}', '_vrf_attr', '(?P<vrf>.*)', 'graceful_restart', False]], 'exclude': bgp_exclude}}, num_values={'instance':1, 'vrf':1, 'neighbor':1}) class TriggerUnconfigConfigBgpNeighborDefaultOriginate(TriggerUnconfigConfig): """Unconfigure default originate configured under BGP neighbor and then reapply the whole configuration of dynamically learned BGP instance(s).""" __description__ = """Unconfigure default originate configured under BGP neighbor and then reapply the whole configuration of dynamically learned BGP instance(s). trigger_datafile: Mandatory: timeout: max_time (`int`): Maximum wait time for the trigger, in second. Default: 180 interval (`int`): Wait time between iteration when looping is needed, in second. Default: 15 method (`str`): Method to recover the device configuration, Support methods: 'checkpoint': Rollback the configuration by checkpoint (nxos), archive file (iosxe), load the saved running-config file on disk (iosxr) Optional: tgn_timeout (`int`): Maximum wait time for all traffic threads to be restored to the reference rate, in second. Default: 60 tgn_delay (`int`): Wait time between each poll to verify if traffic is resumed, in second. Default: 10 timeout_recovery: Buffer recovery timeout make sure devices are recovered at the end of the trigger execution. Used when previous timeouts have been exhausted. max_time (`int`): Maximum wait time for the last step of the trigger, in second. Default: 180 interval (`int`): Wait time between iteration when looping is needed, in second. Default: 15 static: The keys below are dynamically learnt by default. However, they can also be set to a custom value when provided in the trigger datafile. instance: `str` vrf: `str` neighbor: `str` address_family: `str` bgp_id: `int` (e.g) interface: '(?P<interface>Ethernet1*)' (Regex supported) OR interface: 'Ethernet1/1/1' (Specific value) Steps: 1. Learn BGP Ops object and store the BGP instance(s) if any, else SKIP the trigger 2. Save the current device configurations using the "method" specified by user in Trigger YAML. 3. Unconfigure the learned BGP instance(s) from step 1 with BGP Conf object 4. Verify the BGP instance(s) from step 3 no longer exists 5. Recover the device configurations to the one in step 2 6. Learn BGP Ops again and verify it is the same as the Ops in step 1 """ mapping = Mapping(\ requirements={\ 'ops.bgp.bgp.Bgp':{ 'requirements':[\ ['info', 'instance', '(?P<instance>.*)', 'vrf', '(?P<vrf>.*)', 'neighbor', '(?P<neighbor>.*)', 'address_family', '(?P<address_family>.*)', 'default_originate', True], ['info', 'instance', '(?P<instance>.*)', 'vrf', '(?P<vrf>.*)', 'neighbor', '(?P<neighbor>.*)', 'session_state', 'established'], ['info', 'instance', '(?P<instance>.*)', 'bgp_id', '(?P<bgp_id>.*)']], 'all_keys': True, 'kwargs':{'attributes':['info']}, 'exclude': bgp_exclude}}, config_info={\ 'conf.bgp.Bgp':{ 'requirements':[\ ['device_attr', '{uut}', 'vrf_attr', '(?P<vrf>.*)', 'neighbor_attr', '(?P<neighbor>.*)', 'address_family_attr', '(?P<address_family>.*)', 'nbr_af_default_originate', True]], 'verify_conf':False, 'kwargs':{'mandatory':{'bgp_id': '(?P<bgp_id>.*)'}}}}, verify_ops={\ 'ops.bgp.bgp.Bgp':{ 'requirements':[\ ['info', 'instance', '(?P<instance>.*)', 'vrf', '(?P<vrf>.*)', 'neighbor', '(?P<neighbor>.*)', 'address_family', '(?P<address_family>.*)', NotExists('default_originate')]], 'kwargs':{'attributes':['info']}, 'exclude': bgp_exclude}}, num_values={'instance':1, 'vrf':1, 'neighbor':1}) class TriggerUnconfigConfigBgpNeighborNextHopSelf(TriggerUnconfigConfig): """Unconfigure next hop self configured under BGP neighbor and then reapply the whole configuration of dynamically learned BGP instance(s).""" __description__ = """Unconfigure next hop self configured under BGP neighbor and then reapply the whole configuration of dynamically learned BGP instance(s). trigger_datafile: Mandatory: timeout: max_time (`int`): Maximum wait time for the trigger, in second. Default: 180 interval (`int`): Wait time between iteration when looping is needed, in second. Default: 15 method (`str`): Method to recover the device configuration, Support methods: 'checkpoint': Rollback the configuration by checkpoint (nxos), archive file (iosxe), load the saved running-config file on disk (iosxr) Optional: tgn_timeout (`int`): Maximum wait time for all traffic threads to be restored to the reference rate, in second. Default: 60 tgn_delay (`int`): Wait time between each poll to verify if traffic is resumed, in second. Default: 10 timeout_recovery: Buffer recovery timeout make sure devices are recovered at the end of the trigger execution. Used when previous timeouts have been exhausted. max_time (`int`): Maximum wait time for the last step of the trigger, in second. Default: 180 interval (`int`): Wait time between iteration when looping is needed, in second. Default: 15 static: The keys below are dynamically learnt by default. However, they can also be set to a custom value when provided in the trigger datafile. instance: `str` vrf: `str` neighbor: `str` address_family: `str` bgp_id: `int` (e.g) interface: '(?P<interface>Ethernet1*)' (Regex supported) OR interface: 'Ethernet1/1/1' (Specific value) Steps: 1. Learn BGP Ops object and store the BGP instance(s) if any, else SKIP the trigger 2. Save the current device configurations using the "method" specified by user in Trigger YAML. 3. Unconfigure the learned BGP instance(s) from step 1 with BGP Conf object 4. Verify the BGP instance(s) from step 3 no longer exists 5. Recover the device configurations to the one in step 2 6. Learn BGP Ops again and verify it is the same as the Ops in step 1 """ mapping = Mapping(\ requirements={\ 'ops.bgp.bgp.Bgp':{ 'requirements':[\ ['info', 'instance', '(?P<instance>.*)', 'vrf', '(?P<vrf>.*)', 'neighbor', '(?P<neighbor>.*)', 'address_family', '(?P<address_family>.*)', 'next_hop_self', True], ['info', 'instance', '(?P<instance>.*)', 'vrf', '(?P<vrf>.*)', 'neighbor', '(?P<neighbor>.*)', 'session_state', 'established'], ['info', 'instance', '(?P<instance>.*)', 'bgp_id', '(?P<bgp_id>.*)']], 'all_keys': True, 'kwargs':{'attributes':['info']}, 'exclude': bgp_exclude}}, config_info={\ 'conf.bgp.Bgp':{ 'requirements':[\ ['device_attr', '{uut}', 'vrf_attr', '(?P<vrf>.*)', 'neighbor_attr', '(?P<neighbor>.*)', 'address_family_attr', '(?P<address_family>.*)', 'nbr_af_next_hop_self', True]], 'verify_conf':False, 'kwargs':{'mandatory':{'bgp_id': '(?P<bgp_id>.*)'}}}}, verify_ops={\ 'ops.bgp.bgp.Bgp':{ 'requirements':[\ ['info', 'instance', '(?P<instance>.*)', 'vrf', '(?P<vrf>.*)', 'neighbor', '(?P<neighbor>.*)', 'address_family', '(?P<address_family>.*)', NotExists('next_hop_self')]], 'kwargs':{'attributes':['info']}, 'exclude': bgp_exclude}}, num_values={'instance':1, 'vrf':1, 'neighbor':1}) class TriggerUnconfigConfigBgpNeighborTransportConnectionModePassive(TriggerUnconfigConfig): """Unconfigure transportation connection mode (if passive) configured under BGP neighbor and then reapply the whole configuration of dynamically learned BGP instance(s).""" __description__ = """Unconfigure transportation connection mode (if passive) configured under BGP neighbor and then reapply the whole configuration of dynamically learned BGP instance(s). trigger_datafile: Mandatory: timeout: max_time (`int`): Maximum wait time for the trigger, in second. Default: 180 interval (`int`): Wait time between iteration when looping is needed, in second. Default: 15 method (`str`): Method to recover the device configuration, Support methods: 'checkpoint': Rollback the configuration by checkpoint (nxos), archive file (iosxe), load the saved running-config file on disk (iosxr) Optional: tgn_timeout (`int`): Maximum wait time for all traffic threads to be restored to the reference rate, in second. Default: 60 tgn_delay (`int`): Wait time between each poll to verify if traffic is resumed, in second. Default: 10 timeout_recovery: Buffer recovery timeout make sure devices are recovered at the end of the trigger execution. Used when previous timeouts have been exhausted. max_time (`int`): Maximum wait time for the last step of the trigger, in second. Default: 180 interval (`int`): Wait time between iteration when looping is needed, in second. Default: 15 static: The keys below are dynamically learnt by default. However, they can also be set to a custom value when provided in the trigger datafile. instance: `str` vrf: `str` neighbor: `str` bgp_id: `int` (e.g) interface: '(?P<interface>Ethernet1*)' (Regex supported) OR interface: 'Ethernet1/1/1' (Specific value) Steps: 1. Learn BGP Ops object and store the BGP instance(s) if any, else SKIP the trigger 2. Save the current device configurations using the "method" specified by user in Trigger YAML. 3. Unconfigure the learned BGP instance(s) from step 1 with BGP Conf object 4. Verify the BGP instance(s) from step 3 no longer exists 5. Recover the device configurations to the one in step 2 6. Learn BGP Ops again and verify it is the same as the Ops in step 1 """ mapping = Mapping(\ requirements={\ 'ops.bgp.bgp.Bgp':{ 'requirements':[\ ['info', 'instance', '(?P<instance>.*)', 'vrf', '(?P<vrf>.*)', 'neighbor', '(?P<neighbor>.*)', 'bgp_session_transport', 'connection', 'mode', 'passive'], ['info', 'instance', '(?P<instance>.*)', 'vrf', '(?P<vrf>.*)', 'neighbor', '(?P<neighbor>.*)', 'session_state', 'established'], ['info', 'instance', '(?P<instance>.*)', 'bgp_id', '(?P<bgp_id>.*)']], 'all_keys': True, 'kwargs':{'attributes':['info']}, 'exclude': bgp_exclude}}, config_info={\ 'conf.bgp.Bgp':{ 'requirements':[\ ['device_attr', '{uut}', 'vrf_attr', '(?P<vrf>.*)', 'neighbor_attr', '(?P<neighbor>.*)', 'nbr_transport_connection_mode', 'passive']], 'verify_conf':False, 'kwargs':{'mandatory':{'bgp_id': '(?P<bgp_id>.*)'}}}}, verify_ops={\ 'ops.bgp.bgp.Bgp':{ 'requirements':[\ ['info', 'instance', '(?P<instance>.*)', 'vrf', '(?P<vrf>.*)', 'neighbor', '(?P<neighbor>.*)', 'bgp_session_transport', 'connection', NotExists('mode')]], 'kwargs':{'attributes':['info']}, 'exclude': bgp_exclude}}, num_values={'instance':1, 'vrf':1, 'neighbor':1}) class TriggerUnconfigConfigBgpNeighborPassword(TriggerUnconfigConfig): """Unconfigure the password configured under BGP neighbor and then reapply the whole configuration of dynamically learned BGP instance(s).""" __description__ = """Unconfigure the password configured under BGP neighbor and then reapply the whole configuration of dynamically learned BGP instance(s). trigger_datafile: Mandatory: timeout: max_time (`int`): Maximum wait time for the trigger, in second. Default: 180 interval (`int`): Wait time between iteration when looping is needed, in second. Default: 15 method (`str`): Method to recover the device configuration, Support methods: 'checkpoint': Rollback the configuration by checkpoint (nxos), archive file (iosxe), load the saved running-config file on disk (iosxr) Optional: tgn_timeout (`int`): Maximum wait time for all traffic threads to be restored to the reference rate, in second. Default: 60 tgn_delay (`int`): Wait time between each poll to verify if traffic is resumed, in second. Default: 10 timeout_recovery: Buffer recovery timeout make sure devices are recovered at the end of the trigger execution. Used when previous timeouts have been exhausted. max_time (`int`): Maximum wait time for the last step of the trigger, in second. Default: 180 interval (`int`): Wait time between iteration when looping is needed, in second. Default: 15 static: The keys below are dynamically learnt by default. However, they can also be set to a custom value when provided in the trigger datafile. instance: `str` vrf: `str` neighbor: `str` password_text: `str` bgp_id: `int` (e.g) interface: '(?P<interface>Ethernet1*)' (Regex supported) OR interface: 'Ethernet1/1/1' (Specific value) Steps: 1. Learn BGP Ops object and store the BGP instance(s) if any, else SKIP the trigger 2. Save the current device configurations using the "method" specified by user in Trigger YAML. 3. Unconfigure the learned BGP instance(s) from step 1 with BGP Conf object 4. Verify the BGP instance(s) from step 3 no longer exists 5. Recover the device configurations to the one in step 2 6. Learn BGP Ops again and verify it is the same as the Ops in step 1 """ mapping = Mapping(\ requirements={\ 'ops.bgp.bgp.Bgp':{ 'requirements':[\ ['info', 'instance', '(?P<instance>.*)', 'vrf', '(?P<vrf>.*)', 'neighbor', '(?P<neighbor>.*)', 'password_text', '(?P<password_text>.*)'], ['info', 'instance', '(?P<instance>.*)', 'vrf', '(?P<vrf>.*)', 'neighbor', '(?P<neighbor>.*)', 'session_state', 'established'], ['info', 'instance', '(?P<instance>.*)', 'bgp_id', '(?P<bgp_id>.*)']], 'all_keys': True, 'kwargs':{'attributes':['info']}, 'exclude': bgp_exclude}}, config_info={\ 'conf.bgp.Bgp':{ 'requirements':[\ ['device_attr', '{uut}', 'vrf_attr', '(?P<vrf>.*)', 'neighbor_attr', '(?P<neighbor>.*)', 'nbr_password_text', '(?P<password_text>.*)']], 'verify_conf':False, 'kwargs':{'mandatory':{'bgp_id': '(?P<bgp_id>.*)'}}}}, verify_ops={\ 'ops.bgp.bgp.Bgp':{ 'requirements':[\ ['info', 'instance', '(?P<instance>.*)', 'vrf', '(?P<vrf>.*)', 'neighbor', '(?P<neighbor>.*)', NotExists('nbr_password_text')]], 'kwargs':{'attributes':['info']}, 'exclude': bgp_exclude}}, num_values={'instance':1, 'vrf':1, 'neighbor':1}) class TriggerUnconfigConfigBgpNeighborBfd(TriggerUnconfigConfig): """Unconfigure bfd configured under BGP neighbor and then reapply the whole configuration of dynamically learned BGP instance(s).""" __description__ = """Unconfigure bfd configured under BGP neighbor and then reapply the whole configuration of dynamically learned BGP instance(s). trigger_datafile: Mandatory: timeout: max_time (`int`): Maximum wait time for the trigger, in second. Default: 180 interval (`int`): Wait time between iteration when looping is needed, in second. Default: 15 method (`str`): Method to recover the device configuration, Support methods: 'checkpoint': Rollback the configuration by checkpoint (nxos), archive file (iosxe), load the saved running-config file on disk (iosxr) Optional: tgn_timeout (`int`): Maximum wait time for all traffic threads to be restored to the reference rate, in second. Default: 60 tgn_delay (`int`): Wait time between each poll to verify if traffic is resumed, in second. Default: 10 timeout_recovery: Buffer recovery timeout make sure devices are recovered at the end of the trigger execution. Used when previous timeouts have been exhausted. max_time (`int`): Maximum wait time for the last step of the trigger, in second. Default: 180 interval (`int`): Wait time between iteration when looping is needed, in second. Default: 15 static: The keys below are dynamically learnt by default. However, they can also be set to a custom value when provided in the trigger datafile. instance: `str` vrf: `str` neighbor: `str` bgp_id: `int` (e.g) interface: '(?P<interface>Ethernet1*)' (Regex supported) OR interface: 'Ethernet1/1/1' (Specific value) Steps: 1. Learn BGP Ops object and store the BGP instance(s) if any, else SKIP the trigger 2. Save the current device configurations using the "method" specified by user in Trigger YAML. 3. Unconfigure the learned BGP instance(s) from step 1 with BGP Conf object 4. Verify the BGP instance(s) from step 3 no longer exists 5. Recover the device configurations to the one in step 2 6. Learn BGP Ops again and verify it is the same as the Ops in step 1 """ mapping = Mapping(\ requirements={\ 'ops.bgp.bgp.Bgp':{ 'requirements':[\ ['info', 'instance', '(?P<instance>.*)', 'vrf', '(?P<vrf>.*)', 'neighbor', '(?P<neighbor>.*)', 'fall_over_bfd', True], ['info', 'instance', '(?P<instance>.*)', 'vrf', '(?P<vrf>.*)', 'neighbor', '(?P<neighbor>.*)', 'session_state', 'established'], ['info', 'instance', '(?P<instance>.*)', 'bgp_id', '(?P<bgp_id>.*)']], 'all_keys': True, 'kwargs':{'attributes':['info']}, 'exclude': bgp_exclude}}, config_info={\ 'conf.bgp.Bgp':{ 'requirements':[\ ['device_attr', '{uut}', 'vrf_attr', '(?P<vrf>.*)', 'neighbor_attr', '(?P<neighbor>.*)', 'nbr_fall_over_bfd', True]], 'verify_conf':False, 'kwargs':{'mandatory':{'bgp_id': '(?P<bgp_id>.*)'}}}}, verify_ops={\ 'ops.bgp.bgp.Bgp':{ 'requirements':[\ ['info', 'instance', '(?P<instance>.*)', 'vrf', '(?P<vrf>.*)', 'neighbor', '(?P<neighbor>.*)', NotExists('fall_over_bfd')]], 'kwargs':{'attributes':['info']}, 'exclude': bgp_exclude}}, num_values={'instance':1, 'vrf':1, 'neighbor':1}) class TriggerUnconfigConfigBgpNeighborRouteReflectorClient(TriggerUnconfigConfig): """Unconfigure and reapply the whole configurations of dynamically learned BGP neighbor(s) route-reflector-client.""" __description__ = """Unconfigure and reapply the whole configurations of dynamically learned BGP neighbor(s) route-reflector-client. trigger_datafile: Mandatory: timeout: max_time (`int`): Maximum wait time for the trigger, in second. Default: 180 interval (`int`): Wait time between iteration when looping is needed, in second. Default: 15 method (`str`): Method to recover the device configuration, Support methods: 'checkpoint': Rollback the configuration by checkpoint (nxos), archive file (iosxe), load the saved running-config file on disk (iosxr) Optional: tgn_timeout (`int`): Maximum wait time for all traffic threads to be restored to the reference rate, in second. Default: 60 tgn_delay (`int`): Wait time between each poll to verify if traffic is resumed, in second. Default: 10 timeout_recovery: Buffer recovery timeout when the previous timeout has been exhausted, to make sure the devices are recovered before ending the trigger max_time (`int`): Maximum wait time for the last step of the trigger, in second. Default: 180 interval (`int`): Wait time between iteration when looping is needed, in second. Default: 15 static: The keys below are dynamically learnt by default. However, they can also be set to a custom value when provided in the trigger datafile. instance: `str` vrf: `str` neighbor: `str` address_family: `str` bgp_id: `int` (e.g) interface: '(?P<interface>Ethernet1*)' (Regex supported) OR interface: 'Ethernet1/1/1' (Specific value) steps: 1. Learn BGP Ops object and store the BGP neighbor(s) with route-reflector-client if has any, otherwise, SKIP the trigger 2. Save the current device configurations through "method" which user uses 3. Unconfigure the learned BGP neighbor(s) route-reflector-client from step 1 with BGP Conf object 4. Verify the BGP vrf(s) route_distinguisher from step 3 are no longer existed 5. Recover the device configurations to the one in step 2 6. Learn BGP Ops again and verify it is the same as the Ops in step 1 """ # configuration steps callable def unconfigure_route_ref(self, conf_obj, path, **kwargs): paths = self._path_population([path], kwargs['device']) # find position that neighbor (ip) sit # replace ip string to IPv4Address object for path in paths: ipv4_index_list = [path.index(val) for val in path if '.' in str(val)] ipv6_index_list = [path.index(val) for val in path if ':' in str(val)] for index in ipv4_index_list: path[index] = IPv4Address(path[index]) for index in ipv6_index_list: path[index] = IPv6Address(path[index]) config = '\n'.join([str(conf_path) for conf_path in paths]) log.info('With following configuration:\n{c}' .format(c=config)) Configure.conf_configure(device=kwargs['device'], conf=conf_obj, conf_structure=paths, unconfig=True) mapping = Mapping(requirements={'ops.bgp.bgp.Bgp':{ 'requirements':[['info', 'instance', '(?P<instance>.*)', 'vrf', '(?P<vrf>.*)', 'neighbor', '(?P<neighbor>.*)', 'address_family', '(?P<address_family>.*)', 'route_reflector_client', True], ['info', 'instance', '(?P<instance>.*)', 'vrf', '(?P<vrf>.*)', 'neighbor', '(?P<neighbor>.*)', 'session_state', 'established'], ['info', 'instance', '(?P<instance>.*)', 'bgp_id', '(?P<bgp_id>.*)']], 'all_keys':True, 'kwargs':{'attributes':['info']}, 'exclude': bgp_exclude}}, config_info={'conf.bgp.Bgp':{ 'requirements':[[partial(unconfigure_route_ref, path = ['device_attr', '{uut}', 'vrf_attr', '(?P<vrf>.*)', 'neighbor_attr', '(?P<neighbor>.*)', 'address_family_attr', '(?P<address_family>.*)', 'nbr_af_route_reflector_client', True]) ]], 'verify_conf':False, 'kwargs':{'mandatory':{'bgp_id': '(?P<bgp_id>.*)'}}}}, verify_ops={'ops.bgp.bgp.Bgp':{ 'requirements': [['info', 'instance', '(?P<instance>.*)', 'vrf', '(?P<vrf>.*)', 'neighbor', '(?P<neighbor>.*)', 'address_family', '(?P<address_family>.*)', NotExists('route_reflector_client')]], 'kwargs':{'attributes':['info']}, 'exclude': bgp_exclude}}, num_values={'vrf':'all', 'instance':'all', 'neighbor': 'all', 'address_family':'all', 'rd': 'all'}) class TriggerUnconfigConfigBgpNeighborIpv4(TriggerUnconfigConfig): """Unconfigure and reapply the whole configurations of dynamically learned BGP IPv4 neighbor(s).""" __description__ = """Unconfigure and reapply the whole configurations of dynamically learned BGP IPv4 neighbor(s). trigger_datafile: Mandatory: timeout: max_time (`int`): Maximum wait time for the trigger, in second. Default: 180 interval (`int`): Wait time between iteration when looping is needed, in second. Default: 15 method (`str`): Method to recover the device configuration, Support methods: 'checkpoint': Rollback the configuration by checkpoint (nxos), archive file (iosxe), load the saved running-config file on disk (iosxr) Optional: tgn_timeout (`int`): Maximum wait time for all traffic threads to be restored to the reference rate, in second. Default: 60 tgn_delay (`int`): Wait time between each poll to verify if traffic is resumed, in second. Default: 10 timeout_recovery: Buffer recovery timeout when the previous timeout has been exhausted, to make sure the devices are recovered before ending the trigger max_time (`int`): Maximum wait time for the last step of the trigger, in second. Default: 180 interval (`int`): Wait time between iteration when looping is needed, in second. Default: 15 static: The keys below are dynamically learnt by default. However, they can also be set to a custom value when provided in the trigger datafile. instance: `str` vrf: `str` neighbor: `str` bgp_id: `int` (e.g) interface: '(?P<interface>Ethernet1*)' (Regex supported) OR interface: 'Ethernet1/1/1' (Specific value) steps: 1. Learn BGP Ops object and store the BGP IPv4 neighbor(s) if has any, otherwise, SKIP the trigger 2. Save the current device configurations through "method" which user uses 3. Unconfigure the learned BGP IPv4 neighbor(s) from step 1 with BGP Conf object 4. Verify the BGP IPv4 neighbor(s) from step 3 are no longer existed 5. Recover the device configurations to the one in step 2 6. Learn BGP Ops again and verify it is the same as the Ops in step 1 """ mapping = Mapping(requirements={'ops.bgp.bgp.Bgp':{ 'requirements':[['info', 'instance', '(?P<instance>.*)', 'vrf', '(?P<vrf>.*)', 'neighbor', '(?P<neighbor>^[\d\.]+$)', 'session_state', 'established'], ['info', 'instance', '(?P<instance>.*)', 'bgp_id', '(?P<bgp_id>.*)']], 'all_keys':True, 'kwargs':{'attributes':['info']}, 'exclude': bgp_exclude}}, config_info={'conf.bgp.Bgp':{ 'requirements':[['device_attr', '{uut}', 'vrf_attr', '(?P<vrf>.*)', 'neighbor_attr','(?P<neighbor>^[\d\.]+$)']], 'verify_conf':False, 'kwargs':{'mandatory':{'bgp_id': '(?P<bgp_id>.*)'}}}}, verify_ops={'ops.bgp.bgp.Bgp':{ 'requirements': [[partial(verify_ops_or_logic, requires=[['info', 'instance', '(?P<instance>.*)', 'vrf', '(?P<vrf>.*)', 'neighbor', NotExists('(?P<neighbor>.*)')], ['info', 'instance', '(?P<instance>.*)', 'vrf', '(?P<vrf>.*)', NotExists('neighbor')], ]) ]], 'kwargs':{'attributes':['info']}, 'exclude': bgp_exclude + ['vpnv4 unicast', 'distance_local']}}, num_values={'vrf':'all', 'instance':'all', 'neighbor':'all'}) class TriggerUnconfigConfigBgpNeighborIpv6(TriggerUnconfigConfig): """Unconfigure and reapply the whole configurations of dynamically learned BGP IPv6 neighbor(s).""" __description__ = """Unconfigure and reapply the whole configurations of dynamically learned BGP IPv6 neighbor(s). trigger_datafile: Mandatory: timeout: max_time (`int`): Maximum wait time for the trigger, in second. Default: 180 interval (`int`): Wait time between iteration when looping is needed, in second. Default: 15 method (`str`): Method to recover the device configuration, Support methods: 'checkpoint': Rollback the configuration by checkpoint (nxos), archive file (iosxe), load the saved running-config file on disk (iosxr) Optional: tgn_timeout (`int`): Maximum wait time for all traffic threads to be restored to the reference rate, in second. Default: 60 tgn_delay (`int`): Wait time between each poll to verify if traffic is resumed, in second. Default: 10 timeout_recovery: Buffer recovery timeout when the previous timeout has been exhausted, to make sure the devices are recovered before ending the trigger max_time (`int`): Maximum wait time for the last step of the trigger, in second. Default: 180 interval (`int`): Wait time between iteration when looping is needed, in second. Default: 15 static: The keys below are dynamically learnt by default. However, they can also be set to a custom value when provided in the trigger datafile. instance: `str` vrf: `str` neighbor: `str` bgp_id: `int` (e.g) interface: '(?P<interface>Ethernet1*)' (Regex supported) OR interface: 'Ethernet1/1/1' (Specific value) steps: 1. Learn BGP Ops object and store the BGP IPv6 neighbor(s) if has any, otherwise, SKIP the trigger 2. Save the current device configurations through "method" which user uses 3. Unconfigure the learned BGP IPv6 neighbor(s) from step 1 with BGP Conf object 4. Verify the BGP IPv6 neighbor(s) from step 3 are no longer existed 5. Recover the device configurations to the one in step 2 6. Learn BGP Ops again and verify it is the same as the Ops in step 1 """ mapping = Mapping(requirements={'ops.bgp.bgp.Bgp':{ 'requirements':[['info', 'instance', '(?P<instance>.*)', 'vrf', '(?P<vrf>.*)', 'neighbor', '(?P<neighbor>^[\w\:]+$)', 'session_state', 'established'], ['info', 'instance', '(?P<instance>.*)', 'bgp_id', '(?P<bgp_id>.*)']], 'all_keys':True, 'kwargs':{'attributes':['info']}, 'exclude': bgp_exclude}}, config_info={'conf.bgp.Bgp':{ 'requirements':[['device_attr', '{uut}', 'vrf_attr', '(?P<vrf>.*)', 'neighbor_attr','(?P<neighbor>^[\w\:]+$)']], 'verify_conf':False, 'kwargs':{'mandatory':{'bgp_id': '(?P<bgp_id>.*)'}}}}, verify_ops={'ops.bgp.bgp.Bgp':{ 'requirements': [['info', 'instance', '(?P<instance>.*)', 'vrf', '(?P<vrf>.*)', 'neighbor', NotExists('(?P<neighbor>.*)')]], 'kwargs':{'attributes':['info']}, 'exclude': bgp_exclude}}, num_values={'vrf':'all', 'instance':'all', 'neighbor':'all'}) class TriggerUnconfigConfigBgpNeighborIbgp(TriggerUnconfigConfig): """Unconfigure and reapply the whole configurations of dynamically learned iBGP neighbor(s).""" __description__ = """Unconfigure and reapply the whole configurations of dynamically learned iBGP neighbor(s). trigger_datafile: Mandatory: timeout: max_time (`int`): Maximum wait time for the trigger, in second. Default: 180 interval (`int`): Wait time between iteration when looping is needed, in second. Default: 15 method (`str`): Method to recover the device configuration, Support methods: 'checkpoint': Rollback the configuration by checkpoint (nxos), archive file (iosxe), load the saved running-config file on disk (iosxr) Optional: tgn_timeout (`int`): Maximum wait time for all traffic threads to be restored to the reference rate, in second. Default: 60 tgn_delay (`int`): Wait time between each poll to verify if traffic is resumed, in second. Default: 10 timeout_recovery: Buffer recovery timeout when the previous timeout has been exhausted, to make sure the devices are recovered before ending the trigger max_time (`int`): Maximum wait time for the last step of the trigger, in second. Default: 180 interval (`int`): Wait time between iteration when looping is needed, in second. Default: 15 static: The keys below are dynamically learnt by default. However, they can also be set to a custom value when provided in the trigger datafile. instance: `str` vrf: `str` neighbor: `str` bgp_id: `int` (e.g) interface: '(?P<interface>Ethernet1*)' (Regex supported) OR interface: 'Ethernet1/1/1' (Specific value) steps: 1. Learn BGP Ops object and store the iBGP neighbor(s) if has any, otherwise, SKIP the trigger 2. Save the current device configurations through "method" which user uses 3. Unconfigure the learned iBGP neighbor(s) from step 1 with BGP Conf object 4. Verify the iBGP neighbor(s) from step 3 are no longer existed 5. Recover the device configurations to the one in step 2 6. Learn BGP Ops again and verify it is the same as the Ops in step 1 """ mapping = Mapping(requirements={'ops.bgp.bgp.Bgp':{ 'requirements':[['info', 'instance', '(?P<instance>.*)', 'bgp_id', '(?P<bgp_id>.*)'], ['info', 'instance', '(?P<instance>.*)', 'vrf', '(?P<vrf>.*)', 'neighbor', '(?P<neighbor>.*)', 'remote_as', '(?P<bgp_id>.*)'], ['info', 'instance', '(?P<instance>.*)', 'vrf', '(?P<vrf>.*)', 'neighbor', '(?P<neighbor>.*)', 'session_state', 'established']], 'all_keys':True, 'kwargs':{'attributes':['info']}, 'exclude': bgp_exclude}}, config_info={'conf.bgp.Bgp':{ 'requirements':[['device_attr', '{uut}', 'vrf_attr', '(?P<vrf>.*)', 'neighbor_attr','(?P<neighbor>.*)'] ], 'verify_conf':False, 'kwargs':{'mandatory':{'bgp_id': '(?P<bgp_id>.*)'}}}}, verify_ops={'ops.bgp.bgp.Bgp':{ 'requirements': [[partial(verify_ops_or_logic, requires=[['info', 'instance', '(?P<instance>.*)', 'vrf', '(?P<vrf>.*)', 'neighbor', NotExists('(?P<neighbor>.*)')], ['info', 'instance', '(?P<instance>.*)', 'vrf', '(?P<vrf>.*)', NotExists('neighbor')], ]) ]], 'kwargs':{'attributes':['info']}, 'exclude': bgp_exclude}}, num_values={'vrf':1, 'instance':1, 'neighbor':1, 'bgp_id': 1}) class TriggerUnconfigConfigBgpRouterId(TriggerUnconfigConfig): """Unconfigure and reapply the bgp-id of dynamically learned BGP instance(s).""" __description__ = """Unconfigure and reapply the bgp-id of dynamically learned BGP instance(s). trigger_datafile: Mandatory: timeout: max_time (`int`): Maximum wait time for the trigger, in second. Default: 180 interval (`int`): Wait time between iteration when looping is needed, in second. Default: 15 method (`str`): Method to recover the device configuration, Support methods: 'checkpoint': Rollback the configuration by checkpoint (nxos), archive file (iosxe), load the saved running-config file on disk (iosxr) Optional: tgn_timeout (`int`): Maximum wait time for all traffic threads to be restored to the reference rate, in second. Default: 60 tgn_delay (`int`): Wait time between each poll to verify if traffic is resumed, in second. Default: 10 timeout_recovery: Buffer recovery timeout when the previous timeout has been exhausted, to make sure the devices are recovered before ending the trigger max_time (`int`): Maximum wait time for the last step of the trigger, in second. Default: 180 interval (`int`): Wait time between iteration when looping is needed, in second. Default: 15 static: The keys below are dynamically learnt by default. However, they can also be set to a custom value when provided in the trigger datafile. instance: `str` vrf: `str` neighbor: `str` bgp_id: `int` (e.g) interface: '(?P<interface>Ethernet1*)' (Regex supported) OR interface: 'Ethernet1/1/1' (Specific value) steps: 1. Learn BGP Conf object and store the BGP instance(s) if has bgp_id configured, otherwise, SKIP the trigger. And learn BGP ops object for verifying in step 4 and 6 2. Save the current device configurations through "method" which user uses 3. Unconfigure the learned BGP instance(s) bgp-id from step 1 with BGP Conf object 4. Verify the BGP instance(s) bgp-id from step 3 are no longer existed 5. Recover the device configurations to the one in step 2 6. Learn BGP Ops again and verify it is the same as the Ops in step 1 """ mapping = Mapping(requirements={'ops.bgp.bgp.Bgp':{ 'requirements':[['info', 'instance', '(?P<instance>.*)', 'vrf', '(?P<vrf>.*)','router_id', '(?P<routerId>.*)'], ['info', 'instance', '(?P<instance>.*)', 'bgp_id', '(?P<bgp_id>.*)']], 'all_keys':True, 'kwargs':{'attributes':['info']}, 'exclude': bgp_exclude}, 'conf.bgp.Bgp':{ 'requirements':[['device_attr', '{uut}', '_vrf_attr', '(?P<vrf>.*)', 'router_id', '(?P<router_id>.*)']]}}, config_info={'conf.bgp.Bgp':{ 'requirements':[['device_attr', '{uut}', 'vrf_attr', '(?P<vrf>.*)', 'router_id', '(?P<router_id>.*)']], 'verify_conf':False, 'kwargs':{'mandatory':{'bgp_id': '(?P<bgp_id>.*)'}}}}, verify_ops={'ops.bgp.bgp.Bgp':{ 'requirements': [['info', 'instance', '(?P<instance>.*)', 'vrf', '(?P<vrf>.*)', 'router_id', '(.*)']], # will still pick up some loopback interace ip 'kwargs':{'attributes':['info']}, 'exclude': bgp_exclude}, 'conf.bgp.Bgp':{ 'requirements': [['device_attr', '{uut}', '_vrf_attr', '(?P<vrf>.*)', NotExists('router_id')]], # no router_id should exists in conf 'kwargs':{'attributes':['info']}, 'exclude': bgp_exclude}}, num_values={'vrf':'all', 'instance':'all', 'router_id':'all'}) class TriggerUnconfigConfigBgpNeighborVrf(TriggerUnconfigConfig): """Unconfigure and reapply the whole configurations of dynamically learned BGP IPv6 neighbor(s).""" __description__ = """Unconfigure and reapply the whole configurations of dynamically learned BGP IPv6 neighbor(s). trigger_datafile: Mandatory: timeout: max_time (`int`): Maximum wait time for the trigger, in second. Default: 180 interval (`int`): Wait time between iteration when looping is needed, in second. Default: 15 method (`str`): Method to recover the device configuration, Support methods: 'checkpoint': Rollback the configuration by checkpoint (nxos), archive file (iosxe), load the saved running-config file on disk (iosxr) Optional: tgn_timeout (`int`): Maximum wait time for all traffic threads to be restored to the reference rate, in second. Default: 60 tgn_delay (`int`): Wait time between each poll to verify if traffic is resumed, in second. Default: 10 timeout_recovery: Buffer recovery timeout when the previous timeout has been exhausted, to make sure the devices are recovered before ending the trigger max_time (`int`): Maximum wait time for the last step of the trigger, in second. Default: 180 interval (`int`): Wait time between iteration when looping is needed, in second. Default: 15 static: The keys below are dynamically learnt by default. However, they can also be set to a custom value when provided in the trigger datafile. instance: `str` vrf: `str` neighbor: `str` bgp_id: `int` (e.g) interface: '(?P<interface>Ethernet1*)' (Regex supported) OR interface: 'Ethernet1/1/1' (Specific value) steps: 1. Learn BGP Ops object and store the BGP IPv6 neighbor(s) if has any, otherwise, SKIP the trigger 2. Save the current device configurations through "method" which user uses 3. Unconfigure the learned BGP IPv6 neighbor(s) from step 1 with BGP Conf object 4. Verify the BGP IPv6 neighbor(s) from step 3 are no longer existed 5. Recover the device configurations to the one in step 2 6. Learn BGP Ops again and verify it is the same as the Ops in step 1 """ mapping = Mapping(requirements={'ops.bgp.bgp.Bgp':{ 'requirements':[['info', 'instance', '(?P<instance>.*)', 'vrf', '(?P<vrf>^(?!default).*)', 'neighbor', '(?P<neighbor>.*)', 'session_state', 'established'], ['info', 'instance', '(?P<instance>.*)', 'bgp_id', '(?P<bgp_id>.*)']], 'all_keys':True, 'kwargs':{'attributes':['info']}, 'exclude': bgp_exclude}}, config_info={'conf.bgp.Bgp':{ 'requirements':[['device_attr', '{uut}', 'vrf_attr', '(?P<vrf>^(?!default).*)']], 'verify_conf':False, 'kwargs':{'mandatory':{'bgp_id': '(?P<bgp_id>.*)'}}}}, verify_ops={'ops.bgp.bgp.Bgp':{ 'requirements': [[partial(verify_ops_or_logic, requires=[['info', 'instance', '(?P<instance>.*)', NotExists('vrf')], ['info', 'instance', '(?P<instance>.*)', 'vrf', NotExists('(?P<vrf>.*)')], ['info', 'instance', '(?P<instance>.*)', 'vrf', '(?P<vrf>.*)', NotExists('neighbor')] ]) ]], 'kwargs':{'attributes':['info']}, 'exclude': bgp_exclude}}, num_values={'vrf':'all', 'instance':'all', 'neighbor':'all'}) class TriggerUnconfigConfigBgpNeighborAsOverride(TriggerUnconfigConfig): """Unconfigure and reapply the whole configurations of dynamically learned BGP neighbors(s) as_override.""" __description__ = """Unconfigure and reapply the whole configurations of dynamically learned BGP neighbors(s) as_override. trigger_datafile: Mandatory: timeout: max_time (`int`): Maximum wait time for the trigger, in second. Default: 180 interval (`int`): Wait time between iteration when looping is needed, in second. Default: 15 method (`str`): Method to recover the device configuration, Support methods: 'checkpoint': Rollback the configuration by checkpoint (nxos), archive file (iosxe), load the saved running-config file on disk (iosxr) Optional: tgn_timeout (`int`): Maximum wait time for all traffic threads to be restored to the reference rate, in second. Default: 60 tgn_delay (`int`): Wait time between each poll to verify if traffic is resumed, in second. Default: 10 timeout_recovery: Buffer recovery timeout when the previous timeout has been exhausted, to make sure the devices are recovered before ending the trigger max_time (`int`): Maximum wait time for the last step of the trigger, in second. Default: 180 interval (`int`): Wait time between iteration when looping is needed, in second. Default: 15 static: The keys below are dynamically learnt by default. However, they can also be set to a custom value when provided in the trigger datafile. instance: `str` vrf: `str` neighbor: `str` address_family: `str` bgp_id: `int` (e.g) interface: '(?P<interface>Ethernet1*)' (Regex supported) OR interface: 'Ethernet1/1/1' (Specific value) steps: 1. Learn BGP Ops object and store the BGP neighbors(s) if has as_override enabled, otherwise, SKIP the trigger 2. Save the current device configurations through "method" which user uses 3. Unconfigure the learned BGP neighbors(s) as_override from step 1 with BGP Conf object 4. Verify the BGP neighbors(s) as_override from step 3 are no longer existed 5. Recover the device configurations to the one in step 2 6. Learn BGP Ops again and verify it is the same as the Ops in step 1 """ # configuration steps callable def unconfigure_route_ref(self, conf_obj, path, **kwargs): paths = self._path_population([path], kwargs['device']) # find position that neighbor (ip) sit # replace ip string to IPv4Address object for path in paths: ipv4_index_list = [path.index(val) for val in path if '.' in str(val)] ipv6_index_list = [path.index(val) for val in path if ':' in str(val)] for index in ipv4_index_list: path[index] = IPv4Address(path[index]) for index in ipv6_index_list: path[index] = IPv6Address(path[index]) config = '\n'.join([str(conf_path) for conf_path in paths]) log.info('With following configuration:\n{c}' .format(c=config)) Configure.conf_configure(device=kwargs['device'], conf=conf_obj, conf_structure=paths, unconfig=True) mapping = Mapping(requirements={'ops.bgp.bgp.Bgp':{ 'requirements':[['info', 'instance', '(?P<instance>.*)', 'vrf', '(?P<vrf>.*)', 'neighbor', '(?P<neighbor>.*)', 'address_family', '(?P<address_family>.*)', 'as_override', True], ['info', 'instance', '(?P<instance>.*)', 'vrf', '(?P<vrf>.*)', 'neighbor', '(?P<neighbor>.*)', 'session_state', 'established'], ['info', 'instance', '(?P<instance>.*)', 'bgp_id', '(?P<bgp_id>.*)']], 'all_keys':True, 'kwargs':{'attributes':['info']}, 'exclude': bgp_exclude}}, config_info={'conf.bgp.Bgp':{ 'requirements':[[partial(unconfigure_route_ref, path = [ 'device_attr', '{uut}', 'vrf_attr', '(?P<vrf>.*)', 'neighbor_attr', '(?P<neighbor>.*)', 'address_family_attr', '(?P<address_family>.*)', 'nbr_af_as_override', True]), ]], 'verify_conf':False, 'kwargs':{'mandatory':{'bgp_id': '(?P<bgp_id>.*)'}}}}, verify_ops={'ops.bgp.bgp.Bgp':{ 'requirements': [['info', 'instance', '(?P<instance>.*)', 'vrf', '(?P<vrf>.*)', 'neighbor', '(?P<neighbor>.*)', 'address_family', '(?P<address_family>.*)', NotExists('as_override')]], 'kwargs':{'attributes':['info']}, 'exclude': bgp_exclude}}, num_values={'vrf':'all', 'instance':'all', 'address_family':'all', 'neighbor': 'all'}) class TriggerUnconfigConfigBgpNeighborEbgp(TriggerUnconfigConfig): """Unconfigure and reapply the whole configurations of dynamically learned EBGP neighbor(s).""" __description__ = """Unconfigure and reapply the whole configurations of dynamically learned EBGP neighbor(s). trigger_datafile: Mandatory: timeout: max_time (`int`): Maximum wait time for the trigger, in second. Default: 180 interval (`int`): Wait time between iteration when looping is needed, in second. Default: 15 method (`str`): Method to recover the device configuration, Support methods: 'checkpoint': Rollback the configuration by checkpoint (nxos), archive file (iosxe), load the saved running-config file on disk (iosxr) Optional: tgn_timeout (`int`): Maximum wait time for all traffic threads to be restored to the reference rate, in second. Default: 60 tgn_delay (`int`): Wait time between each poll to verify if traffic is resumed, in second. Default: 10 timeout_recovery: Buffer recovery timeout when the previous timeout has been exhausted, to make sure the devices are recovered before ending the trigger max_time (`int`): Maximum wait time for the last step of the trigger, in second. Default: 180 interval (`int`): Wait time between iteration when looping is needed, in second. Default: 15 static: The keys below are dynamically learnt by default. However, they can also be set to a custom value when provided in the trigger datafile. instance: `str` vrf: `str` neighbor: `str` bgp_id: `int` (e.g) interface: '(?P<interface>Ethernet1*)' (Regex supported) OR interface: 'Ethernet1/1/1' (Specific value) steps: 1. Learn BGP Ops object and store the EBGP neighbor(s) if has any, otherwise, SKIP the trigger 2. Save the current device configurations through "method" which user uses 3. Unconfigure the learned EBGP neighbor(s) from step 1 with BGP Conf object 4. Verify the BGP IPv6 neighbor(s) from step 3 are no longer existed 5. Recover the device configurations to the one in step 2 6. Learn BGP Ops again and verify it is the same as the Ops in step 1 """ mapping = Mapping(requirements={'ops.bgp.bgp.Bgp':{ 'requirements':[['info', 'instance', '(?P<instance>.*)', 'vrf', '(?P<vrf>^(?!default).*)', 'neighbor', '(?P<neighbor>.*)', 'session_state', 'established'], ['info', 'instance', '(?P<instance>.*)', 'bgp_id', '(?P<bgp_id>.*)'], ['info', 'instance', '(?P<instance>.*)', 'vrf', '(?P<vrf>^(?!default).*)', 'neighbor', '(?P<neighbor>.*)', 'remote_as', Different('(?P<bgp_id>.*)')]], 'all_keys':True, 'kwargs':{'attributes':['info']}, 'exclude': bgp_exclude}}, config_info={'conf.bgp.Bgp':{ 'requirements':[['device_attr', '{uut}', 'vrf_attr', '(?P<vrf>^(?!default).*)', 'neighbor_attr','(?P<neighbor>.*)']], 'verify_conf':False, 'kwargs':{'mandatory':{'bgp_id': '(?P<bgp_id>.*)'}}}}, verify_ops={'ops.bgp.bgp.Bgp':{ 'requirements': [[partial(verify_ops_or_logic, requires=[['info', 'instance', '(?P<instance>.*)', 'vrf', '(?P<vrf>.*)', 'neighbor', NotExists('(?P<neighbor>.*)')], ['info', 'instance', '(?P<instance>.*)', 'vrf', '(?P<vrf>.*)', NotExists('neighbor')] ]) ]], 'kwargs':{'attributes':['info']}, 'exclude': bgp_exclude + ['vpnv4 unicast']}}, num_values={'vrf':'all', 'instance':'all', 'neighbor':'all'}) class TriggerUnconfigConfigBgpVpnRd(TriggerUnconfigConfig): """Unconfigure and reapply the whole configurations of dynamically learned BGP vrf(s) route-distinguisher.""" __description__ = """Unconfigure and reapply the whole configurations of dynamically learned BGP vrf(s) route-distinguisher. trigger_datafile: Mandatory: timeout: max_time (`int`): Maximum wait time for the trigger, in second. Default: 180 interval (`int`): Wait time between iteration when looping is needed, in second. Default: 15 method (`str`): Method to recover the device configuration, Support methods: 'checkpoint': Rollback the configuration by checkpoint (nxos), archive file (iosxe), load the saved running-config file on disk (iosxr) Optional: tgn_timeout (`int`): Maximum wait time for all traffic threads to be restored to the reference rate, in second. Default: 60 tgn_delay (`int`): Wait time between each poll to verify if traffic is resumed, in second. Default: 10 timeout_recovery: Buffer recovery timeout when the previous timeout has been exhausted, to make sure the devices are recovered before ending the trigger max_time (`int`): Maximum wait time for the last step of the trigger, in second. Default: 180 interval (`int`): Wait time between iteration when looping is needed, in second. Default: 15 static: The keys below are dynamically learnt by default. However, they can also be set to a custom value when provided in the trigger datafile. instance: `str` vrf: `str` neighbor: `str` address_family: `str` rd: `str` default_vrf: `str` (e.g) interface: '(?P<interface>Ethernet1*)' (Regex supported) OR interface: 'Ethernet1/1/1' (Specific value) steps: 1. Learn BGP Ops object and store the BGP vrf(s) with route_distinguisher if has any, otherwise, SKIP the trigger 2. Save the current device configurations through "method" which user uses 3. Unconfigure the learned BGP vrf(s) route-distinguisher from step 1 with BGP Conf object 4. Verify the BGP vrf(s) route_distinguisher from step 3 are no longer existed 5. Recover the device configurations to the one in step 2 6. Learn BGP Ops again and verify it is the same as the Ops in step 1 """ @aetest.test def verify_unconfigure(self, uut, abstract, steps): time.sleep(120) super().verify_unconfigure(uut, abstract, steps) mapping = Mapping(requirements={'ops.bgp.bgp.Bgp':{ 'requirements':[['table', 'instance', '(?P<instance>.*)', 'vrf', '(?P<vrf>.*)', 'address_family', '(?P<address_family>.*)', 'route_distinguisher', '(?P<rd>.*)'], ['table', 'instance', '(?P<instance>.*)', 'vrf', '(?P<vrf>.*)', 'address_family', '(?P<address_family>.*)', 'default_vrf', '(?P<default_vrf>.*)']], 'kwargs':{'attributes':['table', 'info']}, 'exclude': bgp_exclude}, 'ops.vrf.vrf.Vrf':{ 'requirements':[['info', 'vrfs', '(?P<default_vrf>^(?!default).*)', 'route_distinguisher', '(?P<rd>.*)']], 'kwargs':{'attributes':['info']}, 'exclude': ['maker']}}, config_info={'conf.vrf.Vrf':{ 'requirements':[['device_attr', '{uut}', 'rd', '(?P<rd>.*)']], 'verify_conf':False, 'kwargs':{'mandatory':{'name': '(?P<default_vrf>.*)'}}}}, verify_ops={'ops.bgp.bgp.Bgp':{ 'requirements': [['table', 'instance', '(?P<instance>.*)', 'vrf', '(?P<vrf>.*)', 'address_family', '(?P<address_family>.*)', NotExists('default_vrf')]], 'kwargs':{'attributes':['table', 'info']}, 'exclude': bgp_exclude + ['label_allocation_mode', 'vpnv4 unicast', 'vpnv6 unicast']}, 'ops.vrf.vrf.Vrf':{ 'requirements':[['info', 'vrfs', '(?P<default_vrf>.*)', 'route_distinguisher', '0:0']], 'kwargs':{'attributes':['info']}, 'exclude': ['maker']}}, num_values={'vrf': 'all', 'instance':1, 'address_family': 'all', 'rd': 1, 'default_vrf': 1}) class TriggerUnconfigConfigBgpL2vpnCapability(TriggerUnconfigConfig): """Unconfigure and reapply the whole configurations of dynamically learned BGP l2vpn evpn address-family.""" __description__ = """Unconfigure and reapply the whole configurations of dynamically learned BGP l2vpn evpn address-family. trigger_datafile: Mandatory: timeout: max_time (`int`): Maximum wait time for the trigger, in second. Default: 180 interval (`int`): Wait time between iteration when looping is needed, in second. Default: 15 method (`str`): Method to recover the device configuration, Support methods: 'checkpoint': Rollback the configuration by checkpoint (nxos), archive file (iosxe), load the saved running-config file on disk (iosxr) Optional: tgn_timeout (`int`): Maximum wait time for all traffic threads to be restored to the reference rate, in second. Default: 60 tgn_delay (`int`): Wait time between each poll to verify if traffic is resumed, in second. Default: 10 timeout_recovery: Buffer recovery timeout when the previous timeout has been exhausted, to make sure the devices are recovered before ending the trigger max_time (`int`): Maximum wait time for the last step of the trigger, in second. Default: 180 interval (`int`): Wait time between iteration when looping is needed, in second. Default: 15 static: The keys below are dynamically learnt by default. However, they can also be set to a custom value when provided in the trigger datafile. instance: `str` vrf: `str` neighbor: `str` bgp_id: `int` (e.g) interface: '(?P<interface>Ethernet1*)' (Regex supported) OR interface: 'Ethernet1/1/1' (Specific value) steps: 1. Learn BGP Ops object and store the BGP l2vpn evpn address-family if has any, otherwise, SKIP the trigger 2. Save the current device configurations through "method" which user uses 3. Unconfigure the learned BGP l2vpn evpn address-family from step 1 with BGP Conf object 4. Verify the BGP l2vpn evpn address-family from step 3 are no longer existed 5. Recover the device configurations to the one in step 2 6. Learn BGP Ops again and verify it is the same as the Ops in step 1 """ mapping = Mapping(\ requirements={\ 'ops.bgp.bgp.Bgp':{ 'requirements':[\ ['info', 'instance', '(?P<instance>.*)', 'vrf', '(?P<vrf>.*)', 'neighbor', '(?P<neighbor>.*)', 'address_family', '(?P<address_family>^l2vpn +evpn$)', 'session_state', 'established'], ['info', 'instance', '(?P<instance>.*)', 'bgp_id', '(?P<bgp_id>.*)']], 'all_keys': True, 'kwargs':{'attributes':['info[instance][(.*)][bgp_id]', 'info[list_of_vrfs]', 'info[instance][(.*)][vrf][(.*)][neighbor]' '[(.*)][address_family][(.*)][session_state]']}, 'exclude': bgp_exclude}}, config_info={\ 'conf.bgp.Bgp':{ 'requirements':[\ ['device_attr', '{uut}', 'vrf_attr', '(?P<vrf>.*)', 'neighbor_attr', '(?P<neighbor>.*)', 'address_family_attr', '(?P<address_family>.*)']], 'verify_conf':False, 'kwargs':{'mandatory':{'bgp_id': '(?P<bgp_id>.*)'}}}}, verify_ops={\ 'ops.bgp.bgp.Bgp':{ 'requirements':[[partial(verify_ops_or_logic, requires=[['info', 'instance', '(?P<instance>.*)', 'vrf', '(?P<vrf>.*)', 'neighbor', '(?P<neighbor>.*)', 'address_family', NotExists('(?P<address_family>.*)')], ['info', 'instance', '(?P<instance>.*)', 'vrf', '(?P<vrf>.*)', 'neighbor', NotExists('(?P<neighbor>.*)')], ['info', 'instance', '(?P<instance>.*)', 'vrf', '(?P<vrf>.*)', 'neighbor', '(?P<neighbor>.*)', 'address_family', NotExists('(?P<address_family>^l2vpn +evpn$)')] ]) ]], 'kwargs':{'attributes':['info[instance][(.*)][bgp_id]', 'info[list_of_vrfs]', 'info[instance][(.*)][vrf][(.*)][neighbor]' '[(.*)][address_family][(.*)][session_state]']}, 'exclude': bgp_exclude}}, num_values={'instance':1, 'vrf':1, 'neighbor':1 , 'address_family': 1}) class TriggerUnconfigConfigBgpAfL2vpnEvpnRewriteEvpnRtAsn(TriggerUnconfigConfig): """Unconfigure and reapply the whole configurations of dynamically learned BGP l2vpn evpn address-family evpn rewrite-evpn-rt-asn.""" __description__ = """Unconfigure and reapply the whole configurations of dynamically learned BGP l2vpn evpn address-family evpn rewrite-evpn-rt-asn. trigger_datafile: Mandatory: timeout: max_time (`int`): Maximum wait time for the trigger, in second. Default: 180 interval (`int`): Wait time between iteration when looping is needed, in second. Default: 15 method (`str`): Method to recover the device configuration, Support methods: 'checkpoint': Rollback the configuration by checkpoint (nxos), archive file (iosxe), load the saved running-config file on disk (iosxr) Optional: tgn_timeout (`int`): Maximum wait time for all traffic threads to be restored to the reference rate, in second. Default: 60 tgn_delay (`int`): Wait time between each poll to verify if traffic is resumed, in second. Default: 10 timeout_recovery: Buffer recovery timeout when the previous timeout has been exhausted, to make sure the devices are recovered before ending the trigger max_time (`int`): Maximum wait time for the last step of the trigger, in second. Default: 180 interval (`int`): Wait time between iteration when looping is needed, in second. Default: 15 static: The keys below are dynamically learnt by default. However, they can also be set to a custom value when provided in the trigger datafile. instance: `str` vrf: `str` neighbor: `str` address_family: `str` bgp_id: `int` (e.g) interface: '(?P<interface>Ethernet1*)' (Regex supported) OR interface: 'Ethernet1/1/1' (Specific value) steps: 1. Learn BGP Ops object and store the BGP l2vpn evpn address-family evpn rewrite-evpn-rt-asn if has any, otherwise, SKIP the trigger 2. Save the current device configurations through "method" which user uses 3. Unconfigure the learned rewrite-evpn-rt-asn from step 1 with BGP Conf object 4. Verify the evpn rewrite-evpn-rt-asn under bgp l2vpn evpn address-family from step 3 no longer existed 5. Recover the device configurations to the one in step 2 6. Learn BGP Ops again and verify it is the same as the Ops in step 1 """ mapping = Mapping(\ requirements={ \ 'conf.bgp.Bgp': { 'requirements': [ \ ['device_attr', '{uut}', '_vrf_attr', '(?P<vrf>.*)', '_neighbor_attr', '(?P<neighbor>.*)', '_address_family_attr', '(?P<af>.*)', 'nbr_af_rewrite_evpn_rt_asn', True]], 'exclude': bgp_exclude}, 'ops.bgp.bgp.Bgp':{ 'requirements':[\ [['info', 'instance', '(?P<instance>.*)', 'vrf', '(?P<vrf>.*)', 'neighbor', '(?P<neighbor>.*)', 'address_family', '(?P<address_family>^l2vpn +evpn$)', 'session_state', 'established']], [['info', 'instance', '(?P<instance>.*)', 'bgp_id', '(?P<bgp_id>.*)']], [['info', 'instance', '(?P<instance>.*)', 'vrf', \ '(?P<vrf>.*)', 'neighbor', '(?P<neighbor>.*)', 'remote_as', Different('(?P<bgp_id>.*)')]]], 'all_keys': True, 'kwargs':{'attributes':['info[instance][(.*)][bgp_id]', 'info[list_of_vrfs]', 'info[instance][(.*)][vrf][(.*)][neighbor]' '[(.*)][address_family][(.*)][session_state]', 'info[instance][(.*)][vrf][(.*)][neighbor]' '[(.*)][remote_as]']}, 'exclude': bgp_exclude}}, config_info={\ 'conf.bgp.Bgp':{ 'requirements':[\ ['device_attr', '{uut}', 'vrf_attr', '(?P<vrf>.*)', 'neighbor_attr', '(?P<neighbor>.*)', 'address_family_attr', '(?P<address_family>.*)',\ 'nbr_af_rewrite_evpn_rt_asn', True]], 'verify_conf':False, 'kwargs':{'mandatory':{'bgp_id': '(?P<bgp_id>.*)'}}}}, verify_ops={ \ 'conf.bgp.Bgp': { 'requirements': [ \ ['device_attr', '{uut}', '_vrf_attr', '(?P<vrf>.*)', '_neighbor_attr', '(?P<neighbor>.*)', '_address_family_attr', '(?P<af>.*)', NotExists('nbr_af_rewrite_evpn_rt_asn')]], 'exclude': bgp_exclude}, 'ops.bgp.bgp.Bgp':{ 'requirements':[\ ['info', 'instance', '(?P<instance>.*)', 'vrf', '(?P<vrf>.*)', 'neighbor', '(?P<neighbor>.*)', 'address_family', '(?P<address_family>^l2vpn +evpn$)', 'session_state', 'established']], 'kwargs':{'attributes':['info[instance][(.*)][bgp_id]', 'info[list_of_vrfs]', 'info[instance][(.*)][vrf][(.*)][neighbor][(.*)][address_family][(.*)][session_state]', 'info[instance][(.*)][vrf][(.*)][neighbor][(.*)][remote_as]']}, 'exclude': bgp_exclude}}, num_values={'instance':1, 'vrf':1, 'neighbor':1 , 'address_family': 1}) class TriggerUnconfigConfigBgpAddressFamilyIpv4Mvpn(TriggerUnconfigConfig): """Unconfigure and reapply the whole configurations of dynamically learned BGP ipv4 mvpn address-family.""" __description__ = """Unconfigure and reapply the whole configurations of dynamically learned BGP ipv4 mvpn address-family. trigger_datafile: Mandatory: timeout: max_time (`int`): Maximum wait time for the trigger, in second. Default: 180 interval (`int`): Wait time between iteration when looping is needed, in second. Default: 15 method (`str`): Method to recover the device configuration, Support methods: 'checkpoint': Rollback the configuration by checkpoint (nxos), archive file (iosxe), load the saved running-config file on disk (iosxr) Optional: tgn_timeout (`int`): Maximum wait time for all traffic threads to be restored to the reference rate, in second. Default: 60 tgn_delay (`int`): Wait time between each poll to verify if traffic is resumed, in second. Default: 10 timeout_recovery: Buffer recovery timeout when the previous timeout has been exhausted, to make sure the devices are recovered before ending the trigger max_time (`int`): Maximum wait time for the last step of the trigger, in second. Default: 180 interval (`int`): Wait time between iteration when looping is needed, in second. Default: 15 static: The keys below are dynamically learnt by default. However, they can also be set to a custom value when provided in the trigger datafile. instance: `str` vrf: `str` neighbor: `str` address_family: `str` bgp_id: `int` (e.g) interface: '(?P<interface>Ethernet1*)' (Regex supported) OR interface: 'Ethernet1/1/1' (Specific value) steps: 1. Learn BGP Ops object and store the BGP ipv4 mvpn address-family if has any, otherwise, SKIP the trigger 2. Save the current device configurations through "method" which user uses 3. Unconfigure the learned ipv4 mvpn addrres-family from step 1 with BGP Conf object 4. Verify the ipv4 mvpn address-family under router bgp from step 3 no longer existed 5. Recover the device configurations to the one in step 2 6. Learn BGP Ops again and verify it is the same as the Ops in step 1 """ mapping = Mapping( \ requirements={ \ 'ops.bgp.bgp.Bgp': { 'requirements': [ \ ['info', 'instance', '(?P<instance>.*)', 'vrf', '(?P<vrf>.*)', 'address_family', '(?P<af>(ipv4 mvpn))', '(?P<af_info>.*)'], ['info', 'instance', '(?P<instance>.*)', 'vrf', '(?P<vrf>.*)', 'neighbor', '(?P<neighbor>.*)', 'session_state', 'established'], ['info', 'instance', '(?P<instance>.*)', 'bgp_id', '(?P<bgp_id>.*)']], 'all_keys': True, 'kwargs': {'attributes': ['info']}, 'exclude': trm_exclude +['bgp_table_version','updates']}}, config_info={ \ 'conf.bgp.Bgp': { 'requirements': [ \ ['device_attr', '{uut}', 'vrf_attr', '(?P<vrf>.*)', 'address_family_attr', '(?P<af>.*)']], 'verify_conf': False, 'kwargs': {'mandatory': {'bgp_id': '(?P<bgp_id>.*)'}}}}, verify_ops={ \ 'conf.bgp.Bgp': { 'requirements': [ \ ['device_attr', '{uut}', '_vrf_attr', '(?P<vrf>.*)', '_address_family_attr', '(?P<af>(?!ipv4 mvpn).*)']], 'exclude': trm_exclude }}, num_values={'instance': 1, 'vrf': 1, 'neighbor': 'all', 'af':1}) class TriggerUnconfigConfigBgpNeighborAddressFamilyIpv4Mvpn(TriggerUnconfigConfig): """Unconfigure and reapply the whole configurations of dynamically learned ipv4 mvpn address-family under BGP neighbors.""" __description__ = """Unconfigure and reapply the whole configurations of dynamically learned ipv4 mvpn address-family under BGP neighbors. trigger_datafile: Mandatory: timeout: max_time (`int`): Maximum wait time for the trigger, in second. Default: 180 interval (`int`): Wait time between iteration when looping is needed, in second. Default: 15 method (`str`): Method to recover the device configuration, Support methods: 'checkpoint': Rollback the configuration by checkpoint (nxos), archive file (iosxe), load the saved running-config file on disk (iosxr) Optional: tgn_timeout (`int`): Maximum wait time for all traffic threads to be restored to the reference rate, in second. Default: 60 tgn_delay (`int`): Wait time between each poll to verify if traffic is resumed, in second. Default: 10 timeout_recovery: Buffer recovery timeout when the previous timeout has been exhausted, to make sure the devices are recovered before ending the trigger max_time (`int`): Maximum wait time for the last step of the trigger, in second. Default: 180 interval (`int`): Wait time between iteration when looping is needed, in second. Default: 15 static: The keys below are dynamically learnt by default. However, they can also be set to a custom value when provided in the trigger datafile. instance: `str` vrf: `str` neighbor: `str` bgp_id: `int` (e.g) interface: '(?P<interface>Ethernet1*)' (Regex supported) OR interface: 'Ethernet1/1/1' (Specific value) steps: 1. Learn BGP Ops object and store the ipv4 mvpn address-family under BGP neighbors if has any, otherwise, SKIP the trigger 2. Save the current device configurations through "method" which user uses 3. Unconfigure the learned ipv4 mvpn addrres-family under BGP neighbors from step 1 with BGP Conf object 4. Verify the ipv4 mvpn address-family under BGP neighbors from step 3 no longer existed 5. Recover the device configurations to the one in step 2 6. Learn BGP Ops again and verify it is the same as the Ops in step 1 """ mapping = Mapping( \ requirements={ \ 'ops.bgp.bgp.Bgp': { 'requirements': [\ ['info', 'instance', '(?P<instance>.*)', 'vrf', '(?P<vrf>.*)', 'neighbor', '(?P<neighbor>.*)', 'address_family', '(?P<af>(ipv4 mvpn))', '(?P<af_info>.*)'], ['info', 'instance', '(?P<instance>.*)', 'vrf', '(?P<vrf>.*)', 'neighbor', '(?P<neighbor>.*)', 'bgp_negotiated_capabilities', 'ipv4_mvpn', '(?P<negotiated_cap>^(advertised).*)'], ['info', 'instance', '(?P<instance>.*)', 'vrf', '(?P<vrf>.*)', 'neighbor', '(?P<neighbor>.*)', 'session_state', 'established'], ['info', 'instance', '(?P<instance>.*)', 'bgp_id', '(?P<bgp_id>.*)']], 'all_keys': True, 'kwargs': {'attributes': ['info']}, 'exclude': trm_exclude + ['bgp_table_version','updates','capability']}}, config_info={ \ 'conf.bgp.Bgp': { 'requirements': [ \ ['device_attr', '{uut}', 'vrf_attr', '(?P<vrf>.*)', 'neighbor_attr', '(?P<neighbor>.*)', 'address_family_attr', '(?P<af>.*)']], 'verify_conf': False, 'kwargs': {'mandatory': {'bgp_id': '(?P<bgp_id>.*)'}}}}, verify_ops={ \ 'conf.bgp.Bgp': { 'requirements': [ \ ['device_attr', '{uut}', '_vrf_attr', '(?P<vrf>.*)', '_neighbor_attr', '(?P<neighbor>.*)', '_address_family_attr', Not('ipv4 mvpn')]], 'exclude': trm_exclude}, 'ops.bgp.bgp.Bgp': { 'requirements': [ \ ['info', 'instance', '(?P<instance>.*)', 'vrf', '(?P<vrf>.*)', 'neighbor', '(?P<neighbor>.*)', 'address_family', NotExists('ipv4 mvpn'), NotExists('(.*)') ], ['info', 'instance', '(?P<instance>.*)', 'vrf', '(?P<vrf>.*)', 'neighbor', '(?P<neighbor>.*)', 'bgp_negotiated_capabilities', 'ipv4_mvpn', Not('advertised')]], 'kwargs': {'attributes': ['info']}, 'exclude': trm_exclude + ['updates','bgp_table_version','capability']}}, num_values={'instance': 1, 'vrf': 1, 'neighbor': 1, 'af': 1, 'negotiated_cap':1})

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null

0

8

a2b7ab83a9fb6c1d336d89119e3d0686513cef4b

6,544

py

Python

test/python/test_sparse.py

plandes/dltools

c0824547755b305e3c3cfc8464d2ae78ba30c4a4

[ "MIT" ]

2

2021-04-30T17:19:14.000Z

2021-05-04T03:48:59.000Z

test/python/test_sparse.py

plandes/deeplearn

925f02200c62a7dc798e474ed94a86e009fd1ebf

[ "MIT" ]

null

test/python/test_sparse.py

plandes/deeplearn

925f02200c62a7dc798e474ed94a86e009fd1ebf

[ "MIT" ]

null

import logging import torch from scipy.sparse.csr import csr_matrix from zensols.deeplearn import TorchConfig from zensols.deeplearn.vectorize import SparseTensorFeatureContext from util import TargetTestCase logger = logging.getLogger(__name__) class TestSparseMatrixContext(TargetTestCase): CONF = None def setUp(self): super().setUp() self.conf = TorchConfig(False, data_type=torch.float64) def test_sparse(self): conf = self.conf should = [ [ 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00], [ 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00], [ 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00], [ 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00], [ 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00], [ 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00], [ 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00], [ 0.00, 0.00, 0.00, 1.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00], [ 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00], [ 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00], [ 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00], [ 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00], [ 0.00, 0.00, 1.50, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00], [ 0.00, 0.00, 0.00, 0.00, 0.00, 10.50, 0.00, 0.00, 0.00, 0.00], [ 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00], [ 2.50, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00], [ 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00], [ 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00], [ 0.00, 0.00, 0.00, 0.00, 1.00, 0.00, 0.00, 0.00, 0.00, 0.00], [ 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00], [ 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 13.20, 0.00, 0.00, 0.00]] tarr = torch.tensor(should) ctx = SparseTensorFeatureContext.instance('afeattype', tarr, conf) should = conf.singleton(should, dtype=tarr.dtype) dense = ctx.to_tensor(conf) self.assertTensorEquals(should, dense) def rand_assert(self, iters, size, conf): for i in range(iters): should = torch.rand(size, dtype=conf.data_type) should = conf.to(should) ctx = SparseTensorFeatureContext.instance( 'some_feature_id', should, conf) self.assertTensorEquals(should, conf.to(ctx.to_tensor(conf))) def test_rand(self): conf = self.conf size = (10, 20) self.rand_assert(50, size, conf) conf = TorchConfig(True, data_type=torch.float64) self.rand_assert(50, size, conf) def test_1d_int_mat(self): should = torch.randint(0, 5, (11,)) ctx = SparseTensorFeatureContext.instance('afeattype', should, self.conf) for m in ctx.sparse_arr: self.assertTrue(isinstance(m, csr_matrix)) dense = ctx.to_tensor(self.conf) self.assertTensorEquals(should, dense) self.assertEqual(should.shape, dense.shape) def test_2d_int_mat(self): should = torch.randint(0, 5, (7, 11)) ctx = SparseTensorFeatureContext.instance('afeattype', should, self.conf) for m in ctx.sparse_arr: self.assertTrue(isinstance(m, csr_matrix)) dense = ctx.to_tensor(self.conf) self.assertTensorEquals(should, dense) self.assertEqual(should.shape, dense.shape) def test_2d_1_int_mat(self): should = torch.randint(0, 5, (1, 11)) ctx = SparseTensorFeatureContext.instance('afeattype', should, self.conf) for m in ctx.sparse_arr: self.assertTrue(isinstance(m, csr_matrix)) dense = ctx.to_tensor(self.conf) self.assertTensorEquals(should, dense) self.assertEqual(should.shape, dense.shape) def test_3d_int_mat(self): should = torch.randint(0, 5, (2, 7, 11)) ctx = SparseTensorFeatureContext.instance('afeattype', should, self.conf) for m in ctx.sparse_arr: self.assertTrue(isinstance(m, csr_matrix)) dense = ctx.to_tensor(self.conf) self.assertTensorEquals(should, dense) self.assertEqual(should.shape, dense.shape) def test_3d_1_int_mat(self): should = torch.randint(0, 5, (1, 7, 11)) ctx = SparseTensorFeatureContext.instance('afeattype', should, self.conf) for m in ctx.sparse_arr: self.assertTrue(isinstance(m, csr_matrix)) dense = ctx.to_tensor(self.conf) self.assertTensorEquals(should, dense) self.assertEqual(should.shape, dense.shape) def test_3d_1_1_int_mat(self): should = torch.randint(0, 5, (1, 1, 11)) ctx = SparseTensorFeatureContext.instance('afeattype', should, self.conf) for m in ctx.sparse_arr: self.assertTrue(isinstance(m, csr_matrix)) dense = ctx.to_tensor(self.conf) self.assertTensorEquals(should, dense) self.assertEqual(should.shape, dense.shape) def test_1d_float_mat(self): should = torch.rand((11,)) ctx = SparseTensorFeatureContext.instance('afeattype', should, self.conf) for m in ctx.sparse_arr: self.assertTrue(isinstance(m, csr_matrix)) dense = ctx.to_tensor(self.conf) self.assertTensorEquals(should, dense) self.assertEqual(should.shape, dense.shape) def test_2d_float_mat(self): should = torch.rand((7, 11)) ctx = SparseTensorFeatureContext.instance('afeattype', should, self.conf) for m in ctx.sparse_arr: self.assertTrue(isinstance(m, csr_matrix)) dense = ctx.to_tensor(self.conf) self.assertTensorEquals(should, dense) self.assertEqual(should.shape, dense.shape) def test_3d_float_mat(self): should = torch.rand((2, 7, 11)) ctx = SparseTensorFeatureContext.instance('afeattype', should, self.conf) for m in ctx.sparse_arr: self.assertTrue(isinstance(m, csr_matrix)) dense = ctx.to_tensor(self.conf) self.assertTensorEquals(should, dense) self.assertEqual(should.shape, dense.shape)

45.762238

82

0.578392

1,004

6,544

3.695219

0.077689

0.16496

0.268194

0.318598

0.786792

0.776819

0.739623

0.715364

0

0.146454

0.271699

6,544

142

83

46.084507

0.631977

0

0.587302

0

0.016045

0

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1

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false

0

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0.166667

0

null

0

1

0

1

0

null

0

7

0c158fd786a37625e60421363849720190ef95f3

11

py

Python

login.py

guan93910/hello_world

bb9315f150e860c7296ddce9eea3ccf89268e73c

[ "MIT" ]

null

login.py

guan93910/hello_world

bb9315f150e860c7296ddce9eea3ccf89268e73c

[ "MIT" ]

null

login.py

guan93910/hello_world

bb9315f150e860c7296ddce9eea3ccf89268e73c

[ "MIT" ]

null

a=1 b=1 c=3

3.666667

3

0.545455

6

11

1

0.833333

0

0.333333

0.181818

11

3

4

3.666667

0.333333

0

1

0

false

0

1

null

0

1

0

1

0

1

0

null

0

7

0c47a3ebbab3a95eebb3247e4d86bbea0e022370

1,117

py

Python

openhgnn/utils/logger.py

zsy0828/OpenHGNN

7fe0917008c9f50269bbd308e411a1d8199d667d

[ "Apache-2.0" ]

null

openhgnn/utils/logger.py

zsy0828/OpenHGNN

7fe0917008c9f50269bbd308e411a1d8199d667d

[ "Apache-2.0" ]

null

openhgnn/utils/logger.py

zsy0828/OpenHGNN

7fe0917008c9f50269bbd308e411a1d8199d667d

[ "Apache-2.0" ]

null

def printInfo(metric, epoch, train_score, train_loss, val_score, val_loss): if metric == 'f1_lr': print(( f"Epoch: {epoch:03d}, Train_loss: {train_loss:.4f}, Train_macro_f1: {train_score[0]:.4f}, Train_micro_f1: {train_score[1]:.4f}, " f"Val_macro_f1: {val_score[0]:.4f}, Val_micro_f1: {val_score[1]:.4f}, ValLoss:{val_loss: .4f}" )) # use acc elif metric == 'acc': print(( f"Epoch: {epoch:03d}, Train_loss: {train_loss:.4f}, Train_acc: {train_score:.4f}, " f"Val_acc: {val_score:.4f}, ValLoss:{val_loss: .4f}" )) elif metric == 'acc-ogbn-mag': print(( f"Epoch: {epoch:03d}, Train_loss: {train_loss:.4f}, Train_acc: {train_score:.4f}, " f"Val_acc: {val_score:.4f}, ValLoss:{val_loss: .4f}" )) else: print(( f"Epoch: {epoch:03d}, Train_loss: {train_loss:.4f}, Train_macro_f1: {train_score[0]:.4f}, Train_micro_f1: {train_score[1]:.4f}, " f"Val_macro_f1: {val_score[0]:.4f}, Val_micro_f1: {val_score[1]:.4f}, ValLoss:{val_loss: .4f}" ))

48.565217

141

0.573859

163

1,117

3.631902

0.159509

0.136824

0.074324

0.108108

0.810811

0

0.052817

0.237243

1,117

23

142

48.565217

0.642019

0.006267

0

0.761905

0

0.285714

0.644404

0.075812

0

1

0.047619

false

0

0.047619

0.238095

0

null

0

1

0

1

0

1

0

null

0

9

a7496dd9fa6fc06211182ea9ba68446829b87d60

49

py

Python

test_modules/file_module.py

ryanking13/pyodide-importer

fb9f83e54eb307fcdb2590588f0b75db1c87ca97

[ "MIT" ]

1

2021-11-16T11:55:54.000Z

test_modules/file_module.py

ryanking13/pyodide-importer

fb9f83e54eb307fcdb2590588f0b75db1c87ca97

[ "MIT" ]

null

test_modules/file_module.py

ryanking13/pyodide-importer

fb9f83e54eb307fcdb2590588f0b75db1c87ca97

[ "MIT" ]

null

def hello(): return "hello from file_module"

16.333333

35

0.693878

7

49

4.714286

0.857143

0

0.204082

49

2

36

24.5

0.846154

0

0.44898

0

1

0.5

true

0

0.5

1

0

1

0

null

0

1

0

null

0

1

0

1

0

7

a751d1f3281283baa6038b5009314f463366c3dc

133

py

Python

schoolNet/AutoCheckIn/encode_srun_md5.py

qhlai/hitsz_srun_autoconnect

b76489f825f7197b8d72cafcf0581e047478c7ed

[ "MIT" ]

2

2021-07-30T09:12:57.000Z

2021-11-19T03:18:49.000Z

schoolNet/AutoCheckIn/encode_srun_md5.py

qhlai/hitsz_srun_autoconnect

b76489f825f7197b8d72cafcf0581e047478c7ed

[ "MIT" ]

null

schoolNet/AutoCheckIn/encode_srun_md5.py

qhlai/hitsz_srun_autoconnect

b76489f825f7197b8d72cafcf0581e047478c7ed

[ "MIT" ]

null

import hmac import hashlib def _encode(password,token): return hmac.new(token.encode(), password.encode(), hashlib.md5).hexdigest()

26.6

76

0.774436

18

133

5.666667

0.611111

0.27451

0

0.008197

0.082707

133

4

77

33.25

0.827869

0

1

0.25

false

0.5

0.25

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

8

a761632b33d278cfd426da09c4440a792f9529e1

10,193

py

Python

ambiente_virtual/Lib/site-packages/alembic/testing/suite/test_environment.py

PI-UNIVESP-Penapolis/PRODEA

1ced58f52bace8b6de0de3c6516b9fb7231da09c

[ "MIT" ]

358

2015-01-06T00:09:20.000Z

2022-01-24T20:42:36.000Z

ambiente_virtual/Lib/site-packages/alembic/testing/suite/test_environment.py

PI-UNIVESP-Penapolis/PRODEA

1ced58f52bace8b6de0de3c6516b9fb7231da09c

[ "MIT" ]

30

2015-01-09T16:27:39.000Z

2019-06-28T17:01:25.000Z

ambiente_virtual/Lib/site-packages/alembic/testing/suite/test_environment.py

PI-UNIVESP-Penapolis/PRODEA

1ced58f52bace8b6de0de3c6516b9fb7231da09c

[ "MIT" ]

101

2015-01-09T16:06:30.000Z

2022-01-28T02:46:13.000Z

import io from ...migration import MigrationContext from ...testing import assert_raises from ...testing import config from ...testing import eq_ from ...testing import is_false from ...testing import is_true from ...testing.fixtures import TestBase class MigrationTransactionTest(TestBase): __backend__ = True conn = None def _fixture(self, opts): self.conn = conn = config.db.connect() if opts.get("as_sql", False): self.context = MigrationContext.configure( dialect=conn.dialect, opts=opts ) self.context.output_buffer = ( self.context.impl.output_buffer ) = io.StringIO() else: self.context = MigrationContext.configure( connection=conn, opts=opts ) return self.context def teardown(self): if self.conn: self.conn.close() def test_proxy_transaction_rollback(self): context = self._fixture( {"transaction_per_migration": True, "transactional_ddl": True} ) is_false(self.conn.in_transaction()) proxy = context.begin_transaction(_per_migration=True) is_true(self.conn.in_transaction()) proxy.rollback() is_false(self.conn.in_transaction()) def test_proxy_transaction_commit(self): context = self._fixture( {"transaction_per_migration": True, "transactional_ddl": True} ) proxy = context.begin_transaction(_per_migration=True) is_true(self.conn.in_transaction()) proxy.commit() is_false(self.conn.in_transaction()) def test_proxy_transaction_contextmanager_commit(self): context = self._fixture( {"transaction_per_migration": True, "transactional_ddl": True} ) proxy = context.begin_transaction(_per_migration=True) is_true(self.conn.in_transaction()) with proxy: pass is_false(self.conn.in_transaction()) def test_proxy_transaction_contextmanager_rollback(self): context = self._fixture( {"transaction_per_migration": True, "transactional_ddl": True} ) proxy = context.begin_transaction(_per_migration=True) is_true(self.conn.in_transaction()) def go(): with proxy: raise Exception("hi") assert_raises(Exception, go) is_false(self.conn.in_transaction()) def test_proxy_transaction_contextmanager_explicit_rollback(self): context = self._fixture( {"transaction_per_migration": True, "transactional_ddl": True} ) proxy = context.begin_transaction(_per_migration=True) is_true(self.conn.in_transaction()) with proxy: is_true(self.conn.in_transaction()) proxy.rollback() is_false(self.conn.in_transaction()) is_false(self.conn.in_transaction()) def test_proxy_transaction_contextmanager_explicit_commit(self): context = self._fixture( {"transaction_per_migration": True, "transactional_ddl": True} ) proxy = context.begin_transaction(_per_migration=True) is_true(self.conn.in_transaction()) with proxy: is_true(self.conn.in_transaction()) proxy.commit() is_false(self.conn.in_transaction()) is_false(self.conn.in_transaction()) def test_transaction_per_migration_transactional_ddl(self): context = self._fixture( {"transaction_per_migration": True, "transactional_ddl": True} ) is_false(self.conn.in_transaction()) with context.begin_transaction(): is_false(self.conn.in_transaction()) with context.begin_transaction(_per_migration=True): is_true(self.conn.in_transaction()) is_false(self.conn.in_transaction()) is_false(self.conn.in_transaction()) def test_transaction_per_migration_non_transactional_ddl(self): context = self._fixture( {"transaction_per_migration": True, "transactional_ddl": False} ) is_false(self.conn.in_transaction()) with context.begin_transaction(): is_false(self.conn.in_transaction()) with context.begin_transaction(_per_migration=True): is_true(self.conn.in_transaction()) is_false(self.conn.in_transaction()) is_false(self.conn.in_transaction()) def test_transaction_per_all_transactional_ddl(self): context = self._fixture({"transactional_ddl": True}) is_false(self.conn.in_transaction()) with context.begin_transaction(): is_true(self.conn.in_transaction()) with context.begin_transaction(_per_migration=True): is_true(self.conn.in_transaction()) is_true(self.conn.in_transaction()) is_false(self.conn.in_transaction()) def test_transaction_per_all_non_transactional_ddl(self): context = self._fixture({"transactional_ddl": False}) is_false(self.conn.in_transaction()) with context.begin_transaction(): is_false(self.conn.in_transaction()) with context.begin_transaction(_per_migration=True): is_true(self.conn.in_transaction()) is_false(self.conn.in_transaction()) is_false(self.conn.in_transaction()) def test_transaction_per_all_sqlmode(self): context = self._fixture({"as_sql": True}) context.execute("step 1") with context.begin_transaction(): context.execute("step 2") with context.begin_transaction(_per_migration=True): context.execute("step 3") context.execute("step 4") context.execute("step 5") if context.impl.transactional_ddl: self._assert_impl_steps( "step 1", "BEGIN", "step 2", "step 3", "step 4", "COMMIT", "step 5", ) else: self._assert_impl_steps( "step 1", "step 2", "step 3", "step 4", "step 5" ) def test_transaction_per_migration_sqlmode(self): context = self._fixture( {"as_sql": True, "transaction_per_migration": True} ) context.execute("step 1") with context.begin_transaction(): context.execute("step 2") with context.begin_transaction(_per_migration=True): context.execute("step 3") context.execute("step 4") context.execute("step 5") if context.impl.transactional_ddl: self._assert_impl_steps( "step 1", "step 2", "BEGIN", "step 3", "COMMIT", "step 4", "step 5", ) else: self._assert_impl_steps( "step 1", "step 2", "step 3", "step 4", "step 5" ) @config.requirements.autocommit_isolation def test_autocommit_block(self): context = self._fixture({"transaction_per_migration": True}) is_false(self.conn.in_transaction()) with context.begin_transaction(): is_false(self.conn.in_transaction()) with context.begin_transaction(_per_migration=True): is_true(self.conn.in_transaction()) with context.autocommit_block(): is_false(self.conn.in_transaction()) is_true(self.conn.in_transaction()) is_false(self.conn.in_transaction()) is_false(self.conn.in_transaction()) @config.requirements.autocommit_isolation def test_autocommit_block_no_transaction(self): context = self._fixture({"transaction_per_migration": True}) is_false(self.conn.in_transaction()) with context.autocommit_block(): is_false(self.conn.in_transaction()) is_false(self.conn.in_transaction()) def test_autocommit_block_transactional_ddl_sqlmode(self): context = self._fixture( { "transaction_per_migration": True, "transactional_ddl": True, "as_sql": True, } ) with context.begin_transaction(): context.execute("step 1") with context.begin_transaction(_per_migration=True): context.execute("step 2") with context.autocommit_block(): context.execute("step 3") context.execute("step 4") context.execute("step 5") self._assert_impl_steps( "step 1", "BEGIN", "step 2", "COMMIT", "step 3", "BEGIN", "step 4", "COMMIT", "step 5", ) def test_autocommit_block_nontransactional_ddl_sqlmode(self): context = self._fixture( { "transaction_per_migration": True, "transactional_ddl": False, "as_sql": True, } ) with context.begin_transaction(): context.execute("step 1") with context.begin_transaction(_per_migration=True): context.execute("step 2") with context.autocommit_block(): context.execute("step 3") context.execute("step 4") context.execute("step 5") self._assert_impl_steps( "step 1", "step 2", "step 3", "step 4", "step 5" ) def _assert_impl_steps(self, *steps): to_check = self.context.output_buffer.getvalue() self.context.impl.output_buffer = buf = io.StringIO() for step in steps: if step == "BEGIN": self.context.impl.emit_begin() elif step == "COMMIT": self.context.impl.emit_commit() else: self.context.impl._exec(step) eq_(to_check, buf.getvalue())

31.753894

75

0.589326

1,073

10,193

5.294501

0.079217

0.07041

0.082732

0.173737

0.836472

0.817814

0.816054

0.810949

0.762542

0.759021

0

0.007074

0.306583

10,193

320

76

31.853125

0.796689

0

0.670635

0

0.089866

0.031885

0

0.035714

1

0.079365

false

0.003968

0.031746

0

0.126984

0

null

0

1

0

null

0

7

a76688f3104fec7b0dcd905698fd60d1b3177c1c

13,277

py

Python

tests/test_countTable.py

zztin/SingleCellMultiOmics

d3035c33eb1375f0703cc49537417b755ad8a693

[ "MIT" ]

17

2019-05-21T09:12:16.000Z

2022-02-14T19:26:58.000Z

tests/test_countTable.py

zztin/SingleCellMultiOmics

d3035c33eb1375f0703cc49537417b755ad8a693

[ "MIT" ]

70

2019-05-20T08:08:45.000Z

2021-06-22T15:58:01.000Z

tests/test_countTable.py

zztin/SingleCellMultiOmics

d3035c33eb1375f0703cc49537417b755ad8a693

[ "MIT" ]

7

2020-04-09T15:11:12.000Z

2022-02-14T15:23:31.000Z

#!/usr/bin/env python3 # -*- coding: utf-8 -*- import unittest from types import SimpleNamespace import singlecellmultiomics.bamProcessing.bamToCountTable from singlecellmultiomics.bamProcessing.bamBinCounts import range_contains_overlap,blacklisted_binning class TestIterables(unittest.TestCase): def test_blacklisted_binning(self): bin_size = 250 blacklist = [(450,1001),(1007,1019),(1550,1600),(2300,2510)] blacklist = sorted(blacklist) self.assertFalse( range_contains_overlap( list( blacklisted_binning(0,2000,bin_size,blacklist) ) + blacklist) ) class TestCountTable(unittest.TestCase): def test_total_read_counting(self): """ Test if the amount of raw reads in a bam file is counted properly """ df = singlecellmultiomics.bamProcessing.bamToCountTable.create_count_table( SimpleNamespace( alignmentfiles=['./data/mini_nla_test.bam'], head=None, o=None, bin=None, binTag='DS', sliding=None, bedfile=None, showtags=False, featureTags=None, joinedFeatureTags='reference_name', byValue=None, sampleTags='SM', proper_pairs_only=False, no_indels=False, max_base_edits=None, no_softclips=False, minMQ=0, filterXA=False, dedup=False, divideMultimapping=False, doNotDivideFragments=True, contig=None, blacklist=None, r1only=False, r2only=False, filterMP=False, splitFeatures=False, feature_delimiter=',', noNames=False) , return_df=True) # !samtools idxstats ./data/mini_nla_test.bam | head -n 1 | cut -f 3 self.assertEqual(df.loc['chr1'].sum(),563) def test_total_read1_counting(self): """ Test if the amount of valid deduped R1 reads in a bam file is counted properly samtools view ./data/mini_nla_test.bam -f 64 -F 3840 | grep DS | wc -l : 210 """ df = singlecellmultiomics.bamProcessing.bamToCountTable.create_count_table( SimpleNamespace( alignmentfiles=['./data/mini_nla_test.bam'], head=None, o=None, bin=None, binTag='DS', sliding=None, bedfile=None, showtags=False, featureTags=None, joinedFeatureTags='reference_name', byValue=None, sampleTags='SM', proper_pairs_only=False, no_indels=False, max_base_edits=None, no_softclips=False, minMQ=0, filterXA=False, dedup=True, divideMultimapping=False, doNotDivideFragments=True, contig=None, blacklist=None, r1only=True, r2only=False, filterMP=False, splitFeatures=False, feature_delimiter=',', noNames=False) , return_df=True) # !samtools idxstats ./data/mini_nla_test.bam | head -n 1 | cut -f 3 self.assertEqual(df.loc['chr1'].sum(),210) def test_contig_selection(self): """ Test if a contig is selected properly""" df = singlecellmultiomics.bamProcessing.bamToCountTable.create_count_table( SimpleNamespace( alignmentfiles=['./data/mini_nla_test.bam'], head=None, o=None, bin=None, binTag='DS', sliding=None, bedfile=None, showtags=False, featureTags=None, joinedFeatureTags='reference_name', byValue=None, sampleTags='SM', proper_pairs_only=False, no_indels=False, max_base_edits=None, no_softclips=False, contig='chr5', minMQ=0, filterXA=False, dedup=False, r1only=False, r2only=False, divideMultimapping=False, doNotDivideFragments=True, splitFeatures=False, blacklist=None, filterMP=False, feature_delimiter=',', noNames=False) , return_df=True) # !samtools idxstats ./data/mini_nla_test.bam | head -n 1 | cut -f 3 self.assertEqual(df.sum().sum(),0) def test_total_molecule_counting(self): """ Test if the amount of molecules in a bam file is counted properly """ df = singlecellmultiomics.bamProcessing.bamToCountTable.create_count_table( SimpleNamespace( alignmentfiles=['./data/mini_nla_test.bam'], o=None, head=None, bin=None, binTag='DS', byValue=None, sliding=None, bedfile=None, showtags=False, featureTags=None, joinedFeatureTags='reference_name', sampleTags='SM', proper_pairs_only=False, no_indels=False, max_base_edits=None, no_softclips=False, minMQ=0, filterXA=False, dedup=True, divideMultimapping=False, doNotDivideFragments=True, contig=None, r1only=False, r2only=False, blacklist=None, filterMP=False, splitFeatures=False, feature_delimiter=',', noNames=False) , return_df=True) # !samtools view ./singlecellmultiomics/data/mini_nla_test.bam | grep 'RC:i:1' | wc -l self.assertEqual(df.loc['chr1'].sum(),383) def test_singleFeatureTags_molecule_counting(self): """ Test if the single feature counting feature works """ df = singlecellmultiomics.bamProcessing.bamToCountTable.create_count_table( SimpleNamespace( alignmentfiles=['./data/mini_nla_test.bam'], o=None, head=None, bin=None, sliding=None, binTag=None, byValue=None, bedfile=None, showtags=False, featureTags='reference_name,RC', joinedFeatureTags=None, sampleTags='SM', proper_pairs_only=False, no_indels=False, max_base_edits=None, no_softclips=False, minMQ=0, filterXA=False, dedup=False, divideMultimapping=False, contig=None, r1only=False, r2only=False, keepOverBounds=False, doNotDivideFragments=True, blacklist=None, filterMP=False, splitFeatures=False, feature_delimiter=',', noNames=False) , return_df=True) # !samtools view ./singlecellmultiomics/data/mini_nla_test.bam | grep 'RC:i:1' | wc -l self.assertEqual(df.loc['chr1'].sum(),563) self.assertEqual(df.loc['1'].sum(),383) # Amount of RC:2 obs: self.assertEqual(df.loc['2'].sum(),97) def test_singleFeatureTags_molecule_counting_contig(self): """ Test if the single feature counting feature works with -contig """ df = singlecellmultiomics.bamProcessing.bamToCountTable.create_count_table( SimpleNamespace( alignmentfiles=['./data/mini_nla_test.bam'], o=None, head=None, bin=None, sliding=None, binTag=None, byValue=None, bedfile=None, showtags=False, featureTags='reference_name,RC', joinedFeatureTags=None, sampleTags='SM', proper_pairs_only=False, no_indels=False, max_base_edits=None, no_softclips=False, minMQ=0, filterXA=False, dedup=False, divideMultimapping=False, contig='chr1', r1only=False, r2only=False, keepOverBounds=False, doNotDivideFragments=True, blacklist=None, filterMP=False, splitFeatures=False, feature_delimiter=',', noNames=False) , return_df=True) # !samtools view ./singlecellmultiomics/data/mini_nla_test.bam | grep 'RC:i:1' | wc -l self.assertEqual(df.loc['chr1'].sum(),563) self.assertEqual(df.loc['1'].sum(),383) # Amount of RC:2 obs: self.assertEqual(df.loc['2'].sum(),97) def test_bed_counting(self): """ Test if the bed feature counting feature works """ df = singlecellmultiomics.bamProcessing.bamToCountTable.create_count_table( SimpleNamespace( alignmentfiles=['./data/mini_nla_test.bam'], o=None, head=None, bin=None, binTag='DS', byValue=None, sliding=None, bedfile='./data/mini_test.bed', showtags=False, featureTags=None, joinedFeatureTags='reference_name', sampleTags='SM', proper_pairs_only=False, no_indels=False, max_base_edits=None, no_softclips=False, minMQ=0, filterXA=False, dedup=True, divideMultimapping=False, doNotDivideFragments=True, contig=None, r1only=False, r2only=False, blacklist=None, filterMP=False, splitFeatures=False, feature_delimiter=',', noNames=False) , return_df=True) # !samtools view ./singlecellmultiomics/data/mini_nla_test.bam | grep 'RC:i:1' | wc -l self.assertEqual( df.xs( 'test4',level='bname', drop_level=False).iloc[0].sum() , 1) self.assertEqual( df.xs( 'test3',level='bname', drop_level=False).iloc[0].sum() , 383) def test_byValue(self): """ Test if the by value counting feature works, this counts the value of a feature instead of its presence""" df = singlecellmultiomics.bamProcessing.bamToCountTable.create_count_table( SimpleNamespace( alignmentfiles=['./data/mini_nla_test.bam'], o=None, head=None, bin=30, sliding=None, binTag='DS', byValue='RC', bedfile=None, showtags=False, featureTags=None, joinedFeatureTags='reference_name,RC', sampleTags='SM', proper_pairs_only=False, no_indels=False, max_base_edits=None, no_softclips=False, minMQ=0, filterXA=False, dedup=False, divideMultimapping=False, contig=None, blacklist=None, r1only=False, r2only=False, filterMP=False, keepOverBounds=False, doNotDivideFragments=True, splitFeatures=False, feature_delimiter=',', noNames=False) , return_df=True) self.assertEqual( df.sum(1).sum(), 765 ) self.assertEqual( df.loc[:,['A3-P15-1-1_25']].sum(skipna=True).sum(skipna=True), 12.0 ) def test_byValue_binned_autofill_joined(self): """ Test if the by value counting feature works, this counts the value of a feature instead of its presence""" df = singlecellmultiomics.bamProcessing.bamToCountTable.create_count_table( SimpleNamespace( alignmentfiles=['./data/mini_nla_test.bam'], o=None, head=None, bin=30, sliding=None, binTag='DS', byValue='RC', bedfile=None, showtags=False, featureTags=None, joinedFeatureTags='reference_name,RC', sampleTags='SM', proper_pairs_only=False, no_indels=False, max_base_edits=None, no_softclips=False, minMQ=0, filterXA=False, dedup=False, divideMultimapping=False, contig=None, blacklist=None, r1only=False, r2only=False, filterMP=False, keepOverBounds=False, doNotDivideFragments=True, splitFeatures=False, feature_delimiter=',', noNames=False) , return_df=True) self.assertEqual( df.sum(1).sum(), 765 ) self.assertEqual( df.loc[:,['A3-P15-1-1_25']].sum(skipna=True).sum(skipna=True), 12.0 ) if __name__ == '__main__': unittest.main()

38.821637

118

0.53363

1,241

13,277

5.563255

0.139404

0.020857

0.027086

0.036935

0.881518

0.854432

0.846756

0.83372

0.81909

0.803447

0

0.020115

0.370942

13,277

341

119

38.935484

0.806513

0.099646

0

0.885017

0

0.042551

0.0182

0

0.059233

1

0.034843

false

0

0.013937

0

0.055749

0

null

0

1

0

1

0

null

0

7

a7c68566618d32dd674f6b5302ace4c5d66be403

4,680

py

Python

tests/integration/test_lambda_function.py

sandeepchugh/aws-apigw-cognito

257a89800a2d860fc43bc8db788fd054d2558400

[ "MIT" ]

2

2020-09-24T07:59:08.000Z

2021-03-06T01:32:05.000Z

tests/integration/test_lambda_function.py

sandeepchugh/aws-apigw-cognito

257a89800a2d860fc43bc8db788fd054d2558400

[ "MIT" ]

null

tests/integration/test_lambda_function.py

sandeepchugh/aws-apigw-cognito

257a89800a2d860fc43bc8db788fd054d2558400

[ "MIT" ]

null

import os from src.lambda_function import function_handler def test_function_handler(): os.environ["LogLevel"] = "DEBUG" os.environ["Region"] = "us-east-1" os.environ["TableName"] = "profile-table-dev" function_handler(event,None) def test_function_handler_post(): os.environ["LogLevel"] = "DEBUG" os.environ["Region"] = "us-east-1" os.environ["TableName"] = "profile-table-dev" function_handler(post_event, None) event = { "body": "eyJ0ZXN0IjoiYm9keSJ9", "resource": "/{proxy+}", "path": "/profile/1/students/1", "httpMethod": "GET", "isBase64Encoded": True, "queryStringParameters": { "org_id": "1", "user_id": "1" }, "multiValueQueryStringParameters": { "foo": [ "bar" ] }, "pathParameters": { "user_id": "1", "org_id":1 }, "stageVariables": { "baz": "qux" }, "headers": { "Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8", "Accept-Encoding": "gzip, deflate, sdch", "Accept-Language": "en-US,en;q=0.8", "Cache-Control": "max-age=0", "CloudFront-Forwarded-Proto": "https", "CloudFront-Is-Desktop-Viewer": "true", "CloudFront-Is-Mobile-Viewer": "false", "CloudFront-Is-SmartTV-Viewer": "false", "CloudFront-Is-Tablet-Viewer": "false", "CloudFront-Viewer-Country": "US", "Host": "1234567890.execute-api.us-east-1.amazonaws.com", "Upgrade-Insecure-Requests": "1", "User-Agent": "Custom User Agent String", "Via": "1.1 08f323deadbeefa7af34d5feb414ce27.cloudfront.net (CloudFront)", "X-Amz-Cf-Id": "cDehVQoZnx43VYQb9j2-nvCh-9z396Uhbp027Y2JvkCPNLmGJHqlaA==", "X-Forwarded-For": "127.0.0.1, 127.0.0.2", "X-Forwarded-Port": "443", "X-Forwarded-Proto": "https" }, "requestContext": { "accountId": "123456789012", "resourceId": "123456", "stage": "prod", "requestId": "c6af9ac6-7b61-11e6-9a41-93e8deadbeef", "requestTime": "09/Apr/2015:12:34:56 +0000", "requestTimeEpoch": 1428582896000, "identity": { "cognitoIdentityPoolId": None, "accountId": None, "cognitoIdentityId": None, "caller": None, "accessKey": None, "sourceIp": "127.0.0.1", "cognitoAuthenticationType": None, "cognitoAuthenticationProvider": None, "userArn": None, "userAgent": "Custom User Agent String", "user": None }, "path": "/profile/1/students/1", "resourcePath": "/{proxy+}", "httpMethod": "GET", "apiId": "1234567890", "protocol": "HTTP/1.1" } } post_event = { "body": "{\"org_id\": \"1\",\"user_id\": \"2\",\"grade\": \"7\",\"last_name\": \"D\",\"first_name\": \"John\"}", "resource": "/{proxy+}", "path": "/path/to/resource", "httpMethod": "POST", "isBase64Encoded": True, "queryStringParameters": { }, "multiValueQueryStringParameters": { "foo": [ "bar" ] }, "pathParameters": { "proxy": "/path/to/resource" }, "stageVariables": { "baz": "qux" }, "headers": { "Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8", "Accept-Encoding": "gzip, deflate, sdch", "Accept-Language": "en-US,en;q=0.8", "Cache-Control": "max-age=0", "CloudFront-Forwarded-Proto": "https", "CloudFront-Is-Desktop-Viewer": "true", "CloudFront-Is-Mobile-Viewer": "false", "CloudFront-Is-SmartTV-Viewer": "false", "CloudFront-Is-Tablet-Viewer": "false", "CloudFront-Viewer-Country": "US", "Host": "1234567890.execute-api.us-east-1.amazonaws.com", "Upgrade-Insecure-Requests": "1", "User-Agent": "Custom User Agent String", "Via": "1.1 08f323deadbeefa7af34d5feb414ce27.cloudfront.net (CloudFront)", "X-Amz-Cf-Id": "cDehVQoZnx43VYQb9j2-nvCh-9z396Uhbp027Y2JvkCPNLmGJHqlaA==", "X-Forwarded-For": "127.0.0.1, 127.0.0.2", "X-Forwarded-Port": "443", "X-Forwarded-Proto": "https" }, "requestContext": { "accountId": "123456789012", "resourceId": "123456", "stage": "prod", "requestId": "c6af9ac6-7b61-11e6-9a41-93e8deadbeef", "requestTime": "09/Apr/2015:12:34:56 +0000", "requestTimeEpoch": 1428582896000, "identity": { "cognitoIdentityPoolId": None, "accountId": None, "cognitoIdentityId": None, "caller": None, "accessKey": None, "sourceIp": "127.0.0.1", "cognitoAuthenticationType": None, "cognitoAuthenticationProvider": None, "userArn": None, "userAgent": "Custom User Agent String", "user": None }, "path": "/prod/path/to/resource", "resourcePath": "/{proxy+}", "httpMethod": "POST", "apiId": "1234567890", "protocol": "HTTP/1.1" } }

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38f5f804dd3d2ff0d25ab8bcc8bdc2301308f488

6,040

py

Python

backend/benefit/applications/migrations/0002_field_changes_for_application_api.py

iivoraitahila/yjdh

4a9b46e0458529548af818534600eadd4f96a048

[ "MIT" ]

2

2021-05-10T09:28:35.000Z

2021-05-17T12:15:34.000Z

backend/benefit/applications/migrations/0002_field_changes_for_application_api.py

iivoraitahila/yjdh

4a9b46e0458529548af818534600eadd4f96a048

[ "MIT" ]

931

2021-05-21T15:24:35.000Z

2022-03-31T20:07:40.000Z

backend/benefit/applications/migrations/0002_field_changes_for_application_api.py

iivoraitahila/yjdh

4a9b46e0458529548af818534600eadd4f96a048

[ "MIT" ]

6

2021-07-06T11:07:02.000Z

2022-02-07T12:42:21.000Z

# Generated by Django 3.2.4 on 2021-06-16 12:01 from django.db import migrations, models import localflavor.generic.models class Migration(migrations.Migration): dependencies = [ ("applications", "0001_initial"), ] operations = [ migrations.AlterModelOptions( name="deminimisaid", options={ "ordering": ["application__created_at", "ordering"], "verbose_name": "de minimis aid", "verbose_name_plural": "de minimis aids", }, ), migrations.AddField( model_name="applicationbasis", name="is_active", field=models.BooleanField(default=True), ), migrations.AddField( model_name="deminimisaid", name="ordering", field=models.IntegerField(default=0), ), migrations.AddField( model_name="historicalapplicationbasis", name="is_active", field=models.BooleanField(default=True), ), migrations.AddField( model_name="historicaldeminimisaid", name="ordering", field=models.IntegerField(default=0), ), migrations.AlterField( model_name="application", name="alternative_company_city", field=models.CharField( blank=True, max_length=256, verbose_name="company city" ), ), migrations.AlterField( model_name="application", name="alternative_company_postcode", field=models.CharField( blank=True, max_length=256, verbose_name="company post code" ), ), migrations.AlterField( model_name="application", name="alternative_company_street_address", field=models.CharField( blank=True, max_length=256, verbose_name="company street address" ), ), migrations.AlterField( model_name="application", name="benefit_type", field=models.CharField( blank=True, choices=[ ("employment_benefit", "Employment Benefit"), ("salary_benefit", "Salary Benefit"), ("commission_benefit", "Commission Benefit"), ], max_length=64, ), ), migrations.AlterField( model_name="application", name="co_operation_negotiations_description", field=models.CharField( blank=True, max_length=256, verbose_name="additional information about the ongoing co-operation negotiations", ), ), migrations.AlterField( model_name="application", name="company_bank_account_number", field=localflavor.generic.models.IBANField( blank=True, include_countries=("FI",), max_length=34, use_nordea_extensions=False, verbose_name="company bank account number", ), ), migrations.AlterField( model_name="application", name="company_contact_person_phone_number", field=models.CharField( blank=True, max_length=64, verbose_name="company contact person's phone number", ), ), migrations.AlterField( model_name="historicalapplication", name="alternative_company_city", field=models.CharField( blank=True, max_length=256, verbose_name="company city" ), ), migrations.AlterField( model_name="historicalapplication", name="alternative_company_postcode", field=models.CharField( blank=True, max_length=256, verbose_name="company post code" ), ), migrations.AlterField( model_name="historicalapplication", name="alternative_company_street_address", field=models.CharField( blank=True, max_length=256, verbose_name="company street address" ), ), migrations.AlterField( model_name="historicalapplication", name="benefit_type", field=models.CharField( blank=True, choices=[ ("employment_benefit", "Employment Benefit"), ("salary_benefit", "Salary Benefit"), ("commission_benefit", "Commission Benefit"), ], max_length=64, ), ), migrations.AlterField( model_name="historicalapplication", name="co_operation_negotiations_description", field=models.CharField( blank=True, max_length=256, verbose_name="additional information about the ongoing co-operation negotiations", ), ), migrations.AlterField( model_name="historicalapplication", name="company_bank_account_number", field=localflavor.generic.models.IBANField( blank=True, include_countries=("FI",), max_length=34, use_nordea_extensions=False, verbose_name="company bank account number", ), ), migrations.AlterField( model_name="historicalapplication", name="company_contact_person_phone_number", field=models.CharField( blank=True, max_length=64, verbose_name="company contact person's phone number", ), ), migrations.AlterUniqueTogether( name="deminimisaid", unique_together={("application", "ordering")}, ), ]

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null

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ac2285c72441ace662ec39147cb4145792a986e0

13,572

py

Python

Environments/SimGlucose/simglucose/envs/simglucose_gym_env.py

yashchandak/UnO

caa709679236ec70d37c249e0cebace27fb30c51

[ "Apache-2.0" ]

null

Environments/SimGlucose/simglucose/envs/simglucose_gym_env.py

yashchandak/UnO

caa709679236ec70d37c249e0cebace27fb30c51

[ "Apache-2.0" ]

null

Environments/SimGlucose/simglucose/envs/simglucose_gym_env.py

yashchandak/UnO

caa709679236ec70d37c249e0cebace27fb30c51

[ "Apache-2.0" ]

null

from Environments.SimGlucose.simglucose.simulation.env import T1DSimEnv as _T1DSimEnv from Environments.SimGlucose.simglucose.patient.t1dpatient import T1DPatient from Environments.SimGlucose.simglucose.sensor.cgm import CGMSensor from Environments.SimGlucose.simglucose.actuator.pump import InsulinPump from Environments.SimGlucose.simglucose.simulation.scenario_gen import RandomScenario, WeightScenario from Environments.SimGlucose.simglucose.simulation.env import risk_diff, neg_risk from Environments.SimGlucose.simglucose.controller.base import Action import pandas as pd import numpy as np import gym from gym import error, spaces, utils from gym.utils import seeding from datetime import datetime from Src.Utils.utils import Space import matplotlib.pyplot as plt from os import path curr_path = path.abspath(path.join(path.dirname(__file__))) PATIENT_PARA_FILE = path.join(curr_path, '..', 'params', 'vpatient_params.csv') class T1DSimEnv_original(gym.Env): ''' A wrapper of simglucose.simulation.env.T1DSimEnv to support gym API ''' metadata = {'render.modes': ['human']} def __init__(self, patient_name=None, reward_fun=None): ''' patient_name must be 'adolescent#001' to 'adolescent#010', or 'adult#001' to 'adult#010', or 'child#001' to 'child#010' ''' seeds = [0, 0, 0, 0, 0] #self._seed() # have to hard code the patient_name, gym has some interesting # error when choosing the patient if patient_name is None: patient_name = 'adolescent#001' print(patient_name) patient = T1DPatient.withName(patient_name) sensor = CGMSensor.withName('Dexcom', seed=seeds[1]) # sensor = CGMSensor.withName('Navigator', seed=seeds[1]) # sensor = CGMSensor.withName('GuardianRT', seed=seeds[1]) hour = 0 #self.np_random.randint(low=0.0, high=24.0) start_time = datetime(2018, 1, 1, hour, 0, 0) scenario = RandomScenario(start_time=start_time, seed=seeds[2]) pump = InsulinPump.withName('Insulet') self.env = _T1DSimEnv(patient, sensor, pump, scenario) self.reward_fun = reward_fun @staticmethod def pick_patient(): # TODO: cannot be used to pick patient at the env constructing space # for now patient_params = pd.read_csv(PATIENT_PARA_FILE) while True: print('Select patient:') for j in range(len(patient_params)): print('[{0}] {1}'.format(j + 1, patient_params['Name'][j])) try: select = int(input('>>> ')) except ValueError: print('Please input a number.') continue if select < 1 or select > len(patient_params): print('Please input 1 to {}'.format(len(patient_params))) continue return select def _step(self, action): # This gym only controls basal insulin act = Action(basal=action, bolus=0) if self.reward_fun is None: return self.env.step(act) else: return self.env.step(act, reward_fun=self.reward_fun) def _reset(self): obs, _, _, _ = self.env.reset() return obs def _seed(self, seed=None): self.np_random, seed1 = seeding.np_random(seed=seed) # Derive a random seed. This gets passed as a uint, but gets # checked as an int elsewhere, so we need to keep it below # 2**31. seed2 = seeding.hash_seed(seed1 + 1) % 2**31 seed3 = seeding.hash_seed(seed2 + 1) % 2**31 return [seed1, seed2, seed3] def _render(self, mode='human', close=False): self.env.render(close=close) @property def action_space(self): ub = self.env.pump._params['max_basal'] return spaces.Box(low=0, high=ub, shape=(1,)) @property def observation_space(self): return spaces.Box(low=0, high=np.inf, shape=(1,)) class T1DSimEnv(gym.Env): ''' A wrapper of simglucose.simulation.env.T1DSimEnv to support gym API ''' metadata = {'render.modes': ['human']} def __init__(self, patient_name=None, reward_fun=neg_risk, seed=0): ''' patient_name must be 'adolescent#001' to 'adolescent#010', or 'adult#001' to 'adult#010', or 'child#001' to 'child#010' ''' self._gym_disable_underscore_compat = True seeds = [0, 0, 0, 0, 0] #self._seed() patient_name_a = 'adolescent#003' patient_a = T1DPatient.withName(patient_name_a) # sensor = CGMSensor.withName('Navigator', seed=seeds[1]) # Sample frequency = 1 min sensor = CGMSensor.withName('Dexcom', seed=seed)# seed=seeds[1]) # Sample frequency = 3 min # sensor = CGMSensor.withName('GuardianRT', seed=seeds[1]) # Sample frequency = 5 min pump = InsulinPump.withName('Insulet') hour = 0 #self.np_random.randint(low=0.0, high=24.0) start_time = datetime(2018, 1, 1, hour, 0, 0) scenario = RandomScenario(start_time=start_time, seed=seed)#, seed=seeds[2]) # scenario = WeightScenario(weight=patient._params.BW, start_time=start_time, seed=seeds[2]) self.env = _T1DSimEnv(patient_a, sensor, pump, scenario) self.reward_fun = reward_fun self.target = 140 # CR and CF lower and upper bound self.lb = np.array([3, 5]) self.ub = np.array([30, 50]) self.max_horizon = 1 self.min_reward = -15 self.max_reward = +15 @staticmethod def pick_patient(): # TODO: cannot be used to pick patient at the env constructing space for now patient_params = pd.read_csv(PATIENT_PARA_FILE) while True: print('Select patient:') for j in range(len(patient_params)): print('[{0}] {1}'.format(j + 1, patient_params['Name'][j])) try: select = int(input('>>> ')) except ValueError: print('Please input a number.') continue if select < 1 or select > len(patient_params): print('Please input 1 to {}'.format(len(patient_params))) continue return select def step(self, action): # Goal is to estimate the correct CR and CF value for the patient CR, CF = action # Clip them to be within the range CR = np.clip(CR, self.lb[0], self.ub[0]) CF = np.clip(CF, self.lb[1], self.ub[1]) basal = 0 obs, r, done, info = self.all_vars total_r = 0 ctr = 0 # temp = [] while not done: meal = info['meal'] glucose = obs[0] bolus = 0 # Basal-Bolus controller # Note: Value of Bolus gets clipped to the desired range in the simulator if meal > 0: bolus = meal / CR + (glucose > 150) * (glucose - self.target) / CF # Clip bolus to be positive always if bolus < 0: bolus = 0 # This gym only controls bolus insulin # Divide bolus by sample time because this action will be repeated 'sample time' times in the simulator bolus = bolus / info['sample_time'] act = Action(basal=basal, bolus=bolus) obs, r, done, info = self.env.step(act, reward_fun=self.reward_fun) total_r += r ctr += 1 reward = (total_r/ctr + 26.5) * 2 # makes the return _roughly_ normalized to [-10, 10] reward = np.clip(reward, self.min_reward, self.max_reward) return [1], reward, done, info def reset(self): self.all_vars = self.env.reset() obs, _, _, _ = self.all_vars return [1] def seed(self, seed=None): self.np_random, seed1 = seeding.np_random(seed=seed) # Derive a random seed. This gets passed as a uint, but gets # checked as an int elsewhere, so we need to keep it below # 2**31. seed2 = seeding.hash_seed(seed1 + 1) % 2**31 seed3 = seeding.hash_seed(seed2 + 1) % 2**31 return [seed1, seed2, seed3] def _render(self, mode='human', close=False): self.env.render(close=close) @property def action_space(self): return spaces.Box(low=self.lb, high=self.ub) @property def observation_space(self): return spaces.Box(low=0, high=1, shape=(1,)) class T1DSimEnv_discrete(gym.Env): ''' A wrapper of simglucose.simulation.env.T1DSimEnv to support gym API ''' metadata = {'render.modes': ['human']} def __init__(self, patient_name=None, reward_fun=neg_risk, seed=0): ''' patient_name must be 'adolescent#001' to 'adolescent#010', or 'adult#001' to 'adult#010', or 'child#001' to 'child#010' ''' self._gym_disable_underscore_compat = True seeds = [0, 0, 0, 0, 0] #self._seed() patient_name_a = 'adolescent#003' patient_a = T1DPatient.withName(patient_name_a) # sensor = CGMSensor.withName('Navigator', seed=seeds[1]) # Sample frequency = 1 min sensor = CGMSensor.withName('Dexcom', seed=seed)# seed=seeds[1]) # Sample frequency = 3 min # sensor = CGMSensor.withName('GuardianRT', seed=seeds[1]) # Sample frequency = 5 min pump = InsulinPump.withName('Insulet') hour = 0 #self.np_random.randint(low=0.0, high=24.0) start_time = datetime(2018, 1, 1, hour, 0, 0) scenario = RandomScenario(start_time=start_time, seed=seed)#, seed=seeds[2]) # scenario = WeightScenario(weight=patient._params.BW, start_time=start_time, seed=seeds[2]) self.env = _T1DSimEnv(patient_a, sensor, pump, scenario) self.reward_fun = reward_fun self.target = 140 # CR and CF lower and upper bound self.lb = np.array([3, 5]) self.ub = np.array([30, 50]) self.max_horizon = 1 self.min_reward = -15 self.max_reward = +15 # Discretizer self.bins_per_dim = 4 self.n_max_actions = self.bins_per_dim ** 2 def mapper(self, val): # val = val - 1 x1 = val // self.bins_per_dim y1 = val % self.bins_per_dim x2 = (x1 / (self.bins_per_dim - 1)) * (self.ub[0] - self.lb[0]) + self.lb[0] y2 = (y1 / (self.bins_per_dim - 1)) * (self.ub[1] - self.lb[1]) + self.lb[1] return x2, y2 @staticmethod def pick_patient(): # TODO: cannot be used to pick patient at the env constructing space for now patient_params = pd.read_csv(PATIENT_PARA_FILE) while True: print('Select patient:') for j in range(len(patient_params)): print('[{0}] {1}'.format(j + 1, patient_params['Name'][j])) try: select = int(input('>>> ')) except ValueError: print('Please input a number.') continue if select < 1 or select > len(patient_params): print('Please input 1 to {}'.format(len(patient_params))) continue return select def step(self, action): # Goal is to estimate the correct CR and CF value for the patient assert 0 <= action < self.n_max_actions CR, CF = self.mapper(action) # Clip them to be within the range # CR = np.clip(CR, self.lb[0], self.ub[0]) # CF = np.clip(CF, self.lb[1], self.ub[1]) # basal = 0 obs, r, done, info = self.all_vars total_r = 0 ctr = 0 # temp = [] while not done: meal = info['meal'] glucose = obs[0] bolus = 0 # Basal-Bolus controller # Note: Value of Bolus gets clipped to the desired range in the simulator if meal > 0: bolus = meal / CR + (glucose > 150) * (glucose - self.target) / CF # Clip bolus to be positive always if bolus < 0: bolus = 0 # This gym only controls bolus insulin # Divide bolus by sample time because this action will be repeated 'sample time' times in the simulator bolus = bolus / info['sample_time'] act = Action(basal=basal, bolus=bolus) obs, r, done, info = self.env.step(act, reward_fun=self.reward_fun) total_r += r ctr += 1 reward = (total_r/ctr + 26.5) * 2 # makes the return _roughly_ normalized to [-10, 10] reward = np.clip(reward, self.min_reward, self.max_reward) return [1], reward, done, info def reset(self): self.all_vars = self.env.reset() obs, _, _, _ = self.all_vars return [1] def seed(self, seed=None): self.np_random, seed1 = seeding.np_random(seed=seed) # Derive a random seed. This gets passed as a uint, but gets # checked as an int elsewhere, so we need to keep it below # 2**31. seed2 = seeding.hash_seed(seed1 + 1) % 2**31 seed3 = seeding.hash_seed(seed2 + 1) % 2**31 return [seed1, seed2, seed3] def _render(self, mode='human', close=False): self.env.render(close=close) @property def action_space(self): return Space(size=self.n_max_actions) # return spaces.Box(low=self.lb, high=self.ub) @property def observation_space(self): return spaces.Box(low=0, high=1, shape=(1,))

34.8

115

0.594091

1,798

13,572

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0.003437

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0.778103

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0

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13,572

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116

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null

0

1

0

1

0

null

0

7

ac47fd228dad7e52e53554a57fec0e8436684448

107

py

Python

anaadementia/extractors/__init__.py

lbsantos/ANAA-Dementia

fb1b013f0de45526283d06291e99b70bf858a19d

[ "MIT" ]

null

anaadementia/extractors/__init__.py

lbsantos/ANAA-Dementia

fb1b013f0de45526283d06291e99b70bf858a19d

[ "MIT" ]

null

anaadementia/extractors/__init__.py

lbsantos/ANAA-Dementia

fb1b013f0de45526283d06291e99b70bf858a19d

[ "MIT" ]

null

from extractor.extractors.exact_match import ExactMatch from extractor.extractors.ranker import ChunkRanker

53.5

55

0.897196

13

107

7.307692

0.692308

0.273684

0.484211

0

0.065421

107

2

56

53.5

0.95

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true

0

1

0

1

0

1

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null

1

0

1

0

null

0

1

0

1

0

1

0

7

ac7851f9d0db1d1944a31c46b67f8076ab9b0d1a

115

py

Python

khlbot/__init__.py

RMTT/khlbot

3bfc4478b3237bbc8aab08566d2aae04aff691ad

[ "MIT" ]

3

2021-05-10T07:36:42.000Z

2021-06-07T21:06:24.000Z

khlbot/__init__.py

RMTT/khlbot

3bfc4478b3237bbc8aab08566d2aae04aff691ad

[ "MIT" ]

null

khlbot/__init__.py

RMTT/khlbot

3bfc4478b3237bbc8aab08566d2aae04aff691ad

[ "MIT" ]

null

from khlbot.core.Bot import Bot from khlbot.core.Logger import Logger from khlbot.core.Commander import Commander

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ac7d3a902c4f17a1eb344081eea0a7dd07ad9a85

45

py

Python

rlberry/_version.py

riccardodv/rlberry

8bb03772cda1e13c57de0e1da7bc7356a3014cfb

[ "MIT" ]

null

rlberry/_version.py

riccardodv/rlberry

8bb03772cda1e13c57de0e1da7bc7356a3014cfb

[ "MIT" ]

null

rlberry/_version.py

riccardodv/rlberry

8bb03772cda1e13c57de0e1da7bc7356a3014cfb

[ "MIT" ]

null

__version__ = "v0.2.1.post110.dev0+fda411b "

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3ba9b9fd7dc8a42ef8fcf86b31ccb277638a57ac

210

py

Python

train/siamese/extractors/__init__.py

openem-team/openem

45222c9c77084eacab278da25a8734ae7d43f677

[ "MIT" ]

10

2019-01-23T23:58:01.000Z

2021-08-30T19:42:35.000Z

train/siamese/extractors/__init__.py

openem-team/openem

45222c9c77084eacab278da25a8734ae7d43f677

[ "MIT" ]

3

2020-03-20T15:21:41.000Z

2020-09-18T18:49:38.000Z

train/siamese/extractors/__init__.py

openem-team/openem

45222c9c77084eacab278da25a8734ae7d43f677

[ "MIT" ]

2

2020-05-08T17:39:12.000Z

2020-10-09T01:27:17.000Z

from extractors.appearance_feature_extractor import * from extractors.motion_feature_extractor import * from extractors.init_feature_extractor import * from extractors.classification_feature_extractor import *

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3bb58568e13dd7aa06dccb4a8d55de2feb4398d2

152

py

Python

src/compas/hpc/geometry/__init__.py

gonzalocasas/compas

2fabc7e5c966a02d823fa453564151e1a1e7e3c6

[ "MIT" ]

null

src/compas/hpc/geometry/__init__.py

gonzalocasas/compas

2fabc7e5c966a02d823fa453564151e1a1e7e3c6

[ "MIT" ]

null

src/compas/hpc/geometry/__init__.py

gonzalocasas/compas

2fabc7e5c966a02d823fa453564151e1a1e7e3c6

[ "MIT" ]

null

from .basic_numba import * from .average_numba import * from .basic_numba import __all__ as a from .average_numba import __all__ as b __all__ = a + b

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py

Python

test/test_read_vcf.py

troycomi/bed_vcf_match

66ca12b3d41cfa9bea060f1a97afae9095ee8db5

[ "MIT" ]

null

test/test_read_vcf.py

troycomi/bed_vcf_match

66ca12b3d41cfa9bea060f1a97afae9095ee8db5

[ "MIT" ]

null

test/test_read_vcf.py

troycomi/bed_vcf_match

66ca12b3d41cfa9bea060f1a97afae9095ee8db5

[ "MIT" ]

null

from bed_vcf_match import read_vcf from io import StringIO import numpy as np import pytest def test_import_vcf(): # add individual to target vcf = StringIO( '##comment\n' '##comment\n' '#chrom\tpos\tid\tref\talt\tqual\tfilter\tinfor\tformat\tUV1\tUV2\n' '8\t10346\t.\tA\tG\t.\tPASS\t.\tGT\t0|1\t0|0\n' '3\t1036\t.\tC\tG\t.\tPASS\t.\tGT\t0|0\t1/0\n' '1\t1336\t.\tG\tT\t.\tPASS\t.\tGT\t1/0\t1|0\n' ) with pytest.raises(ValueError) as e: df = read_vcf.import_vcf(vcf, individuals=['UV3']) assert 'UV3 not in file!' in str(e) vcf = StringIO( '##comment\n' '##comment\n' '#chrom\tpos\tid\tref\talt\tqual\tfilter\tinfor\tformat\tUV1\tUV2\n' '8\t10346\t.\tA\tG\t.\tPASS\t.\tGT\t0|1\t0|0\n' '3\t1036\t.\tC\tG\t.\tPASS\t.\tGT\t0|0\t1/0\n' '1\t1336\t.\tG\tT\t.\tPASS\t.\tGT\t1/0\t1|0\n' ) df = read_vcf.import_vcf(vcf, individuals=['UV2']) assert list(df.columns.values) ==\ ['chrom', 'pos', 'ref', 'alt', 'UV2'] assert list(df['chrom']) ==\ [8, 3, 1] assert list(df['pos']) ==\ [10346, 1036, 1336] assert list(df['ref']) ==\ 'A C G'.split() assert list(df['alt']) ==\ 'G G T'.split() assert list(df['UV2']) ==\ ['0|0', np.nan, '1|0'] vcf = StringIO( '##comment\n' '##comment\n' '#chrom\tpos\tid\tref\talt\tqual\tfilter\tinfor\tformat\tUV1\tUV2\n' '8\t10346\t.\tA\tG\t.\tPASS\t.\tGT\t0|1\t0|0\n' '3\t1036\t.\tC\tG\t.\tPASS\t.\tGT\t0|0\t1/0\n' '1\t1336\t.\tG\tT\t.\tPASS\t.\tGT\t1/0\t1|0\n' ) df = read_vcf.import_vcf(vcf, individuals=['UV2', 'UV1']) assert list(df.columns.values) ==\ ['chrom', 'pos', 'ref', 'alt', 'UV1', 'UV2'] assert list(df['chrom']) ==\ [8, 3, 1] assert list(df['pos']) ==\ [10346, 1036, 1336] assert list(df['ref']) ==\ 'A C G'.split() assert list(df['alt']) ==\ 'G G T'.split() assert list(df['UV1']) ==\ ['0|1', '0|0', np.nan] assert list(df['UV2']) ==\ ['0|0', np.nan, '1|0'] # with new dataframe vcf = StringIO( '##comment\n' '##comment\n' '#chrom\tpos\tid\tref\talt\tqual\tfilter\tinfor\tformat\tUV1\tUV2\n' '8\t10346\t.\tA\tG\t.\tPASS\t.\tGT\t0|1\t0|0\n' '3\t1036\t.\tC\tG\t.\tPASS\t.\tGT\t0|0\t1/0\n' '1\t1336\t.\tG\tT\t.\tPASS\t.\tGT\t1/0\t1|0\n' ) df = read_vcf.import_vcf(vcf) assert list(df.columns.values) ==\ ['chrom', 'pos', 'ref', 'alt', 'UV1', 'UV2'] assert list(df['chrom']) ==\ [8, 3, 1] assert list(df['pos']) ==\ [10346, 1036, 1336] assert list(df['ref']) ==\ 'A C G'.split() assert list(df['alt']) ==\ 'G G T'.split() assert list(df['UV1']) ==\ ['0|1', '0|0', np.nan] assert list(df['UV2']) ==\ ['0|0', np.nan, '1|0'] # with existing dataframe vcf = StringIO( '##comment\n' '##comment\n' '#chrom\tpos\tid\tref\talt\tqual\tfilter\tinfor\tformat\tUV3\tUV2\n' '10\t10346\t.\tA\tG\t.\tPASS\t.\tGT\t0|1\t0|0\n' '1\t1036\t.\tC\tG\t.\tPASS\t.\tGT\t0|0\t1/0\n' ) df = read_vcf.import_vcf(vcf, df) assert list(df.columns.values) ==\ ['chrom', 'pos', 'ref', 'alt', 'UV1', 'UV2', 'UV3'] assert list(df['chrom']) ==\ [8, 3, 1, 10, 1] assert list(df['pos']) ==\ [10346, 1036, 1336, 10346, 1036] assert list(df['ref']) ==\ 'A C G A C'.split() assert list(df['alt']) ==\ 'G G T G G'.split() assert list(df['UV1']) ==\ ['0|1', '0|0', np.nan, np.nan, np.nan] assert list(df['UV2']) ==\ ['0|0', np.nan, '1|0', '0|0', np.nan] assert list(df['UV3']) ==\ [np.nan, np.nan, np.nan, '0|1', '0|0'] # skip multi allelic sites vcf = StringIO( '##comment\n' '##comment\n' '#chrom\tpos\tid\tref\talt\tqual\tfilter\tinfor\tformat\tUV3\tUV2\n' '10\t10346\t.\tAA\tG\t.\tPASS\t.\tGT\t0|1\t0|0\n' '1\t1036\t.\tC\tGG\t.\tPASS\t.\tGT\t0|0\t1/0\n' '1\t1036\t.\tC\tG\t.\tPASS\t.\tGT\t0|0\t1/0\n' ) df = read_vcf.import_vcf(vcf) assert list(df.columns.values) ==\ ['chrom', 'pos', 'ref', 'alt', 'UV3', 'UV2'] assert list(df['chrom']) ==\ [1] assert list(df['pos']) ==\ [1036] assert list(df['ref']) ==\ ['C'] assert list(df['alt']) ==\ ['G'] assert list(df['UV3']) ==\ ['0|0'] assert np.isnan(df['UV2']).all() # all multi-allelic vcf = StringIO( '##comment\n' '##comment\n' '#chrom\tpos\tid\tref\talt\tqual\tfilter\tinfor\tformat\tUV3\tUV2\n' '10\t10346\t.\tAA\tG\t.\tPASS\t.\tGT\t0|1\t0|0\n' '1\t1036\t.\tC\tGG\t.\tPASS\t.\tGT\t0|0\t1/0\n' ) df = read_vcf.import_vcf(vcf) assert df is None # check phasing vcf = StringIO( '##comment\n' '##comment\n' '#chrom\tpos\tid\tref\talt\tqual\tfilter\tinfor\tformat\tUV3\tUV2\n' '10\t10346\t.\tA\tG\t.\tPASS\t.\tGT\t0|1\t0|0\n' '1\t1036\t.\tC\tG\t.\tPASS\t.\tGT\t0|0\t1/0\n' ) with pytest.raises(ValueError) as e: df = read_vcf.import_vcf(vcf, df, check_phasing=True) assert 'Unexpected unphased haplotype for UV2 on position 1036' in str(e) vcf = StringIO( '#chrom\tpos\tid\tref\talt\tqual\tfilter\tinfor\tformat\tUV3\tUV2\n' '10\t10346\t.\tA\tG\t.\tPASS\t.\tGT\t0/1\t0|0\n' '1\t1036\t.\tC\tG\t.\tPASS\t.\tGT\t0|0\t1/0\n' ) with pytest.raises(ValueError) as e: df = read_vcf.import_vcf(vcf, df, check_phasing=True) assert 'Unexpected unphased haplotype for UV3 on position 10346' in str(e) def test_import_archaic_vcf(): # actual line vcf = StringIO('14\t19073582\t.\tG\tA\t3576.58\t.\t' 'AC=2;AF=1.00;AN=2;BaseQRankSum=-1.395;DP=121;Dels=0.00;' 'FS=0.000;HRun=0;HaplotypeScore=1.9955;MQ=30.12;MQ0=8;' 'MQRankSum=-0.322;QD=29.56;ReadPosRankSum=0.904\t' 'GT:DP:GQ:PL:A:C:G:T:IR\t' '1/1:121:99:3577,296,0:79,40:0,0:1,0:0,0:0') df = read_vcf.import_archaic_vcf(vcf) assert list(df['variant']) ==\ [2] assert list(df['chrom']) ==\ [14] assert list(df['pos']) ==\ [19073582] assert list(df['ref']) ==\ ['G'] assert list(df['alt']) ==\ ['A'] # more lines, simplify extra stuff vcf = StringIO( '14\t19073582\t.\tG\tA\t.\t.\t.\t.\t1/1:\n' '1\t1073582\t.\tA\tT\t.\t.\t.\t.\t0|1:\n' '4\t1973582\t.\tT\tC\t.\t.\t.\t.\t1/0:\n' '2\t1903582\t.\tC\tG\t.\t.\t.\t.\t0/0:\n' '3\t1907582\t.\tG\t.\t.\t.\t.\t.\t./.:\n' '3\t1907582\t.\tGG\tA\t.\t.\t.\t.\t./.:\n' ) df = read_vcf.import_archaic_vcf(vcf) assert list(df['variant']) ==\ [2, 1, 1, 0, 0] assert list(df['chrom']) ==\ [14, 1, 4, 2, 3] assert list(df['pos']) ==\ [19073582, 1073582, 1973582, 1903582, 1907582] assert list(df['ref']) ==\ 'G A T C G'.split() assert list(df['alt']) ==\ 'A T C G .'.split() def test_import_archaic_vcf_with_canc(): # actual line vcf = StringIO( '14\t19073582\t.\tG\tA\t3576.58\t.\t' 'AC=2;AF=1.00;AN=2;BaseQRankSum=-1.395;DP=121;Dels=0.00;' 'FS=0.000;HRun=0;HaplotypeScore=1.9955;MQ=30.12;MQ0=8;' 'MQRankSum=-0.322;QD=29.56;ReadPosRankSum=0.904\t' 'GT:DP:GQ:PL:A:C:G:T:IR\t' '1/1:121:99:3577,296,0:79,40:0,0:1,0:0,0:0\n' '19\t362108\trs113739665\tT\tC\t748.77\t.\t' 'AC=2;AF=1.00;AN=2;DP=20;Dels=0.00;FS=0.000;HRun=1;' 'HaplotypeScore=0.9578;MQ=37.00;MQ0=0;QD=37.44;1000gALT=C;' 'AF1000g=0.01;AMR_AF=0.01;ASN_AF=0.01;ASN_AF=0.01;TS=HPGMC;' 'TSseq=T,N,C,T,C;CAnc=T;GAnc=T;bSC=982;mSC=0.000;pSC=0.060;' 'Map20=1\tGT:DP:GQ:PL:A:C:G:T:IR\t' '1/0:20:60.17:782,60,0:0,0:8,12:0,0:0,0:0' ) df = read_vcf.import_archaic_vcf(vcf, include_canc=True) assert list(df['variant']) ==\ [1] assert list(df['chrom']) ==\ [19] assert list(df['pos']) ==\ [362108] assert list(df['ref']) ==\ ['T'] assert list(df['alt']) ==\ ['C'] assert list(df['CAnc']) ==\ ['T'] # more lines, simplify extra stuff vcf = StringIO( '4\t1907358\t.\tG\tA\t.\t.\tCAnc=C\t.\t1/1:\n' '1\t1073582\t.\tA\tT\t.\t.\tCAnc=T\t.\t0|1:\n' '4\t1973582\t.\tT\tC\t.\t.\t.\t.\t1/0:\n' '2\t1903582\t.\tC\tG\t.\t.\tCAnc=C\t.\t0/0:\n' '3\t1907582\t.\tG\t.\t.\t.\tCAnc=G\t.\t./.:\n' '3\t1907582\t.\tGG\tA\t.\t.\t.\t.\t./.:\n' ) df = read_vcf.import_archaic_vcf(vcf, include_canc=True) assert list(df['variant']) ==\ [2, 1, 0] assert list(df['chrom']) ==\ [4, 1, 2] assert list(df['pos']) ==\ [1907358, 1073582, 1903582] assert list(df['ref']) ==\ 'G A C'.split() assert list(df['alt']) ==\ 'A T G'.split() assert list(df['CAnc']) ==\ 'C T C'.split() def test_import_archaic_vcf_header(): # actual line vcf = StringIO( '##contig=<ID=Y,length=59373566>\n' '##reference=file:///mnt/scratch/Genomes/hg19_1000g/whole_genome.fa\n' '#CHROM\tPOS\tID\tREF\tALT\tQUAL\tFILTER\tINFO\tFORMAT\tDenisova\n' '14\t19073582\t.\tG\tA\t3576.58\t.\t' 'AC=2;AF=1.00;AN=2;BaseQRankSum=-1.395;DP=121;Dels=0.00;' 'FS=0.000;HRun=0;HaplotypeScore=1.9955;MQ=30.12;MQ0=8;' 'MQRankSum=-0.322;QD=29.56;ReadPosRankSum=0.904\t' 'GT:DP:GQ:PL:A:C:G:T:IR\t' '1/1:121:99:3577,296,0:79,40:0,0:1,0:0,0:0') df = read_vcf.import_archaic_vcf(vcf) assert list(df['variant']) ==\ [2] assert list(df['chrom']) ==\ [14] assert list(df['pos']) ==\ [19073582] assert list(df['ref']) ==\ ['G'] assert list(df['alt']) ==\ ['A']

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ce2a7a8444b596eebe4f8d56c2b6af40ef53f903

14,400

py

Python

Volume 1 - Supervised Deep Learning/Part 1 - Artificial Neural Networks (ANN)/Section 4 - Building an ANN/my_bank_ann.py

Menosse/Deep-learning

cfac44588bb532367cbcf4d2117cb260c557d919

[ "MIT" ]

null

Volume 1 - Supervised Deep Learning/Part 1 - Artificial Neural Networks (ANN)/Section 4 - Building an ANN/my_bank_ann.py

Menosse/Deep-learning

cfac44588bb532367cbcf4d2117cb260c557d919

[ "MIT" ]

null

Volume 1 - Supervised Deep Learning/Part 1 - Artificial Neural Networks (ANN)/Section 4 - Building an ANN/my_bank_ann.py

Menosse/Deep-learning

cfac44588bb532367cbcf4d2117cb260c557d919

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Created on Wed Apr 29 11:08:29 2020 @author: Fernando !! Note that Datapreprocessing consists in 6 Steps 1 - Import main libraries numpy, pandas and matplotlib.pyplot 2 - Import the dataset 3 - Take care of missing data. (It is possible to assume the mean value among the column for the missing data) 4 - Encode Categorical variables, like country, gender, etc 5 - Split the dataset into training and test sets 6 - Apply feature scailing for accurate prediction NOTES: # Check if using GPU # from tensorflow.python.client import device_lib # print(device_lib.list_local_devices()) # # ### ================================= ================================= ================================= ### # # ### ================================= Data Pre processing ================================= ### # # ### ================================= ================================= ================================= ### # # ### ================================= ### # # ### 1 - Import the main libraries ### # import numpy as np # import pandas as pd # import matplotlib.pyplot as plt # # ### ================================= ### # # ### 2 - Import the dataset ### # dataset = pd.read_csv("Churn_Modelling.csv") # # Create Independent variable (IV) and dependent variable (DV) # # IV Age and estimated Salary # x = dataset.iloc[:, 3:13].values # # DV Buy or not # y = dataset.iloc[:, 13].values # ### ================================= ### # ### 4 - Encode Categorical variables ### # from sklearn.preprocessing import LabelEncoder, OneHotEncoder # from sklearn.compose import ColumnTransformer # # Encode boolean feature (yes/no, male/female) - This example encodes x and y # labelencoder_x1 = LabelEncoder() # x[:, 1] = labelencoder_x1.fit_transform(x[:, 1]) # labelencoder_x2 = LabelEncoder() # x[:, 2] = labelencoder_x2.fit_transform(x[:, 2]) # # Encode categorical variable with multiple values - categorical_features = which column to be encoded # onehotencoder = ColumnTransformer([('one_hot_encoder', OneHotEncoder(), [1])], remainder='passthrough') # x = onehotencoder.fit_transform(x) # x = x[:, 1:] # # ### ================================= ### # # ### 5 - Split a training and test set ### # from sklearn.model_selection import train_test_split # x_train, x_test, y_train, y_test = train_test_split(x,y, test_size = 0.2, random_state = 0) # # ### ================================= ### # # ### 6 - Feature scaling ### # from sklearn.preprocessing import StandardScaler # scaler_x = StandardScaler() # x_train = scaler_x.fit_transform(x_train) # x_test = scaler_x.fit_transform(x_test) # # ### ================================= ================================= ================================= ### # # ### ================================= Apply Deep Learning Model ================================= ### # # ### ================================= ================================= ================================= ### # # ### ================================= ### # # ### 1 - Import Keras libraries and models ### # import keras # from keras.models import Sequential # from keras.layers import Dense # # ### ================================= ### # # ### 2 - Initialize the ANN ### # classifier = Sequential() # # ### ================================= ### # # ### 3 - Create input layer and hidden layers ### # # Create the input layer and the first hidden layer # classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'relu', input_dim = 11)) # # Create the second hidden layer # classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'relu')) # # Add the output layer # #!!! If the dependent variable has more than 2 categories use ==> activation = 'softmax' <== !!!# # classifier.add(Dense(units = 1, kernel_initializer = 'uniform', activation = 'sigmoid')) # # ### ================================= ### # # ### 4 - Compile the ANN### # classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy']) # # ### ================================= ### # # ### 5 - Fit the classifier to the training set### # classifier.fit(x_train, y_train, batch_size = 10, epochs = 100) # # ### ================================= ### # # ### 6 - Make the predictions on the test set ### # y_pred = classifier.predict(x_test) # # Create the threshold and convert the predicted values to categorical data # y_pred = (y_pred > 0.5) # # ### ================================= ### # # ### 7 - Evaluate the logistic regression classifier with CONFUSION MATRIX computation ### # from sklearn.metrics import confusion_matrix # cm = confusion_matrix(y_test, y_pred) # # ### ================================= ### # # ### Predict a single new observation (customer) ### # # new_pred = classifier.predict(scaler_x.transform(np.array([0.0, 0, 600, 1, 40, 3, 60000, 2, 1, 1, 50000]))) # # new_pred = (new_pred > 0.5) # # new_pred # # ### ========================= ================================================= ========================= ### # # ### ========================= Evaluating, improving and Tunning the ANN ========================= ### # # ### ========================= Evaluate using K-FOLD Cross validation ========================= ### # # ### ========================= ================================================= ========================= ### # # ### ================================= ### # # ### 1 - Import the main libraries ### # import numpy as np # import pandas as pd # import matplotlib.pyplot as plt # # ### ================================= ### # # ### 2 - Import the dataset ### # dataset = pd.read_csv("Churn_Modelling.csv") # # Create Independent variable (IV) and dependent variable (DV) # # IV Age and estimated Salary # x = dataset.iloc[:, 3:13].values # # DV Buy or not # y = dataset.iloc[:, 13].values # ### ================================= ### # ### 4 - Encode Categorical variables ### # from sklearn.preprocessing import LabelEncoder, OneHotEncoder # from sklearn.compose import ColumnTransformer # # Encode boolean feature (yes/no, male/female) - This example encodes x and y # labelencoder_x1 = LabelEncoder() # x[:, 1] = labelencoder_x1.fit_transform(x[:, 1]) # labelencoder_x2 = LabelEncoder() # x[:, 2] = labelencoder_x2.fit_transform(x[:, 2]) # # Encode categorical variable with multiple values - categorical_features = which column to be encoded # onehotencoder = ColumnTransformer([('one_hot_encoder', OneHotEncoder(), [1])], remainder='passthrough') # x = onehotencoder.fit_transform(x) # x = x[:, 1:] # # ### ================================= ### # # ### 5 - Split a training and test set ### # from sklearn.model_selection import train_test_split # x_train, x_test, y_train, y_test = train_test_split(x,y, test_size = 0.2, random_state = 0) # # ### ================================= ### # # ### 6 - Feature scaling ### # from sklearn.preprocessing import StandardScaler # scaler_x = StandardScaler() # x_train = scaler_x.fit_transform(x_train) # x_test = scaler_x.fit_transform(x_test) # # Evaluating the ANN # from keras.wrappers.scikit_learn import KerasClassifier # from sklearn.model_selection import cross_val_score # from keras.models import Sequential # from keras.layers import Dense # from keras.layers import Dropout # def build_classifier(): # classifier = Sequential() # # Create the input layer and the first hidden layer # classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'relu', input_dim = 11)) # # #add dropout to input and 1st hidden layer # classifier.add(Dropout(p = 0.1)) # # Create the second hidden layer # classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'relu')) # # #add dropout to 2nd hiden layer # # classifier.add(Dropout(p = 0.1)) # # Add the output layer # classifier.add(Dense(units = 1, kernel_initializer = 'uniform', activation = 'sigmoid')) # classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy']) # return classifier # # K-FOLD # global_classifier = KerasClassifier(build_fn = build_classifier, batch_size = 10, epochs = 100) # accuracies = cross_val_score(estimator = global_classifier, X = x_train, y = y_train, cv = 10, n_jobs = -1) # mean = accuracies.mean() # variance = accuracies.std() # # ### ========================= ======================== ========================= ### # # ### ========================= Improving the ANN ========================= ### # # ### ========================= ======================== ========================= ### # # ### ========================= Tunning the ANN ========================= ### # # Using Dropout regularization to reduce overfiting if needed # from keras.wrappers.scikit_learn import KerasClassifier # from sklearn.model_selection import GridSearchCV # from keras.models import Sequential # from keras.layers import Dense # def build_classifier1(optimizer): # classifier = Sequential() # # Create the input layer and the first hidden layer # classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'relu', input_dim = 11)) # # Create the second hidden layer # classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'relu')) # # Add the output layer # classifier.add(Dense(units = 1, kernel_initializer = 'uniform', activation = 'sigmoid')) # classifier.compile(optimizer = optimizer, loss = 'binary_crossentropy', metrics = ['accuracy']) # return classifier # classifier = KerasClassifier(build_fn = build_classifier1) # param = {'batch_size' : [25, 32], # 'epochs': [100, 500], # 'optimizer': ['adam','rmsprop']} # grid_search = GridSearchCV(estimator = classifier, # param_grid = param, # scoring = 'accuracy', # cv = 10) # grid_search = grid_search.fit(x_train, y_train) # best_parameter = grid_search.best_params_ # best_accuracy = grid_search.best_score_ # """ # ### ================================= After Tunning ================================= ### # ### ================================= Data Pre processing ================================= ### # ### ================================= ### # ### 1 - Import the main libraries ### import numpy as np import pandas as pd # import matplotlib.pyplot as plt # ### ================================= ### # ### 2 - Import the dataset ### dataset = pd.read_csv("Churn_Modelling.csv") # Create Independent variable (IV) and dependent variable (DV) # IV Age and estimated Salary x = dataset.iloc[:, 3:13].values y = dataset.iloc[:, 13].values ### ================================= ### ### 4 - Encode Categorical variables ### from sklearn.preprocessing import LabelEncoder, OneHotEncoder from sklearn.compose import ColumnTransformer # Encode boolean feature (yes/no, male/female) - This example encodes x and y labelencoder_x1 = LabelEncoder() x[:, 1] = labelencoder_x1.fit_transform(x[:, 1]) labelencoder_x2 = LabelEncoder() x[:, 2] = labelencoder_x2.fit_transform(x[:, 2]) # Encode categorical variable with multiple values - categorical_features = which column to be encoded onehotencoder = ColumnTransformer([('one_hot_encoder', OneHotEncoder(), [1])], remainder='passthrough') x = onehotencoder.fit_transform(x) # Remove first encoded variable to avoid dummy variable trap x = x[:, 1:] # ### ================================= ### # ### 5 - Split a training and test set ### from sklearn.model_selection import train_test_split x_train, x_test, y_train, y_test = train_test_split(x,y, test_size = 0.2, random_state = 0) # ### ================================= ### # ### 6 - Feature scaling ### from sklearn.preprocessing import StandardScaler scaler_x = StandardScaler() x_train = scaler_x.fit_transform(x_train) x_test = scaler_x.fit_transform(x_test) # ### =================== =========================================== =================== ### # ### =================== Apply Deep Learning on tunned Model =================== ### # ### =================== =========================================== =================== ### # ### ================================= ### # ### 1 - Import Keras libraries and models ### # import keras from keras.models import Sequential from keras.layers import Dense # ### ================================= ### # ### 2 - Initialize the ANN ### classifier = Sequential() # ### ================================= ### # ### 3 - Create input layer and hidden layers ### # Create the input layer and the first hidden layer classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'relu', input_dim = 11)) # Create the second hidden layer classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'relu')) # Add the output layer #!!! If the dependent variable has more than 2 categories use ==> activation = 'softmax' <== !!!# classifier.add(Dense(units = 1, kernel_initializer = 'uniform', activation = 'sigmoid')) # ### ================================= ### # ### 4 - Compile the ANN### classifier.compile(optimizer = 'rmsprop', loss = 'binary_crossentropy', metrics = ['accuracy']) # ### ================================= ### # ### 5 - Fit the classifier to the training set### classifier.fit(x_train, y_train, batch_size = 32, epochs = 500) # ### ================================= ### # ### 6 - Make the predictions on the test set ### y_pred = classifier.predict(x_test) # Create the threshold and convert the predicted values to categorical data y_pred = (y_pred > 0.5) # ### ================================= ### # ### 7 - Evaluate the logistic regression classifier with CONFUSION MATRIX computation ### from sklearn.metrics import confusion_matrix cm = confusion_matrix(y_test, y_pred) # a = np.array([[0.0, 0, 200, 1, 40, 3, 60000, 2, 1, 1, 50000]]) a = np.array([[1, 0, 716, 1, 41, 8, 120000, 2, 1, 1, 138000]]) a = scaler_x.fit_transform(a) a # a = np.array([1, 1, 600, 1, 40, 3, 60000, 2, 1, 1, 50000]) # a = np.array([1, 0, 716, 1, 41, 8, 120000, 2, 1, 1, 138000]) # a = scaler_x.fit_transform(a[:, np.newaxis]) # a = a.reshape(1, -1) # a new_pred = classifier.predict(a) new_pred = (new_pred > 0.5) new_pred

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Python

test/mlprogram/encoders/test_action_sequence_encoder.py

HiroakiMikami/mlprogram

573e94c567064705fa65267dd83946bf183197de

[ "MIT" ]

9

2020-05-24T11:25:01.000Z

2022-03-28T15:32:10.000Z

test/mlprogram/encoders/test_action_sequence_encoder.py

HiroakiMikami/mlprogram

573e94c567064705fa65267dd83946bf183197de

[ "MIT" ]

87

2020-05-09T08:56:55.000Z

2022-03-31T14:46:45.000Z

test/mlprogram/encoders/test_action_sequence_encoder.py

HiroakiMikami/NL2Prog

573e94c567064705fa65267dd83946bf183197de

[ "MIT" ]

3

2021-02-22T20:38:29.000Z

2021-11-11T18:48:44.000Z

import numpy as np import torch from mlprogram.actions import ( ActionSequence, ApplyRule, CloseVariadicFieldRule, ExpandTreeRule, GenerateToken, NodeConstraint, NodeType, ) from mlprogram.encoders import ActionSequenceEncoder, Samples from mlprogram.languages import Token class TestEncoder(object): def test_reserved_labels(self): encoder = ActionSequenceEncoder(Samples([], [], []), 0) assert 2 == len(encoder._rule_encoder.vocab) assert 1 == len(encoder._token_encoder.vocab) def test_encode_raw_value(self): encoder = ActionSequenceEncoder( Samples([], [], [("", "foo"), ("x", "foo")]), 0) assert [1, 2] == encoder.encode_raw_value("foo") assert [0] == encoder.encode_raw_value("bar") def test_encode_action(self): funcdef = ExpandTreeRule( NodeType("def", NodeConstraint.Node, False), [("name", NodeType("value", NodeConstraint.Token, True)), ("body", NodeType("expr", NodeConstraint.Node, True))]) expr = ExpandTreeRule( NodeType("expr", NodeConstraint.Node, False), [("op", NodeType("value", NodeConstraint.Token, True)), ("arg0", NodeType("value", NodeConstraint.Token, True)), ("arg1", NodeType("value", NodeConstraint.Token, True))]) encoder = ActionSequenceEncoder( Samples([funcdef, expr], [NodeType("def", NodeConstraint.Node, False), NodeType("value", NodeConstraint.Token, True), NodeType("expr", NodeConstraint.Node, True)], [("", "f"), ("", "2")]), 0) action_sequence = ActionSequence() action_sequence.eval(ApplyRule(funcdef)) action_sequence.eval(GenerateToken("", "f")) action_sequence.eval(GenerateToken("", "1")) action_sequence.eval(GenerateToken("", "2")) action_sequence.eval(ApplyRule(CloseVariadicFieldRule())) action = encoder.encode_action(action_sequence, [Token("", "1", "1"), Token("", "2", "2")]) assert np.array_equal( [ [-1, 2, -1, -1], [2, -1, 1, -1], [2, -1, -1, 0], [2, -1, 2, 1], [2, 1, -1, -1], [3, -1, -1, -1] ], action.numpy() ) def test_encode_parent(self): funcdef = ExpandTreeRule( NodeType("def", NodeConstraint.Node, False), [("name", NodeType("value", NodeConstraint.Token, True)), ("body", NodeType("expr", NodeConstraint.Node, True))]) expr = ExpandTreeRule( NodeType("expr", NodeConstraint.Node, False), [("op", NodeType("value", NodeConstraint.Token, True)), ("arg0", NodeType("value", NodeConstraint.Token, True)), ("arg1", NodeType("value", NodeConstraint.Token, True))]) encoder = ActionSequenceEncoder( Samples([funcdef, expr], [NodeType("def", NodeConstraint.Node, False), NodeType("value", NodeConstraint.Token, True), NodeType("expr", NodeConstraint.Node, False)], [("", "f"), ("", "2")]), 0) action_sequence = ActionSequence() action_sequence.eval(ApplyRule(funcdef)) action_sequence.eval(GenerateToken("", "f")) action_sequence.eval(GenerateToken("", "1")) action_sequence.eval(GenerateToken("", "2")) action_sequence.eval(ApplyRule(CloseVariadicFieldRule())) parent = encoder.encode_parent(action_sequence) assert np.array_equal( [ [-1, -1, -1, -1], [1, 2, 0, 0], [1, 2, 0, 0], [1, 2, 0, 0], [1, 2, 0, 0], [1, 2, 0, 1] ], parent.numpy() ) def test_encode_tree(self): funcdef = ExpandTreeRule( NodeType("def", NodeConstraint.Node, False), [("name", NodeType("value", NodeConstraint.Token, True)), ("body", NodeType("expr", NodeConstraint.Node, True))]) expr = ExpandTreeRule( NodeType("expr", NodeConstraint.Node, False), [("op", NodeType("value", NodeConstraint.Token, True)), ("arg0", NodeType("value", NodeConstraint.Token, True)), ("arg1", NodeType("value", NodeConstraint.Token, True))]) encoder = ActionSequenceEncoder( Samples([funcdef, expr], [NodeType("def", NodeConstraint.Node, False), NodeType("value", NodeConstraint.Token, True), NodeType("expr", NodeConstraint.Node, False)], [("", "f"), ("", "2")]), 0) action_sequence = ActionSequence() action_sequence.eval(ApplyRule(funcdef)) action_sequence.eval(GenerateToken("", "f")) action_sequence.eval(GenerateToken("", "1")) d, m = encoder.encode_tree(action_sequence) assert np.array_equal( [0, 1, 1], d.numpy() ) assert np.array_equal( [[0, 1, 1], [0, 0, 0], [0, 0, 0]], m.numpy() ) def test_encode_empty_sequence(self): funcdef = ExpandTreeRule( NodeType("def", NodeConstraint.Node, False), [("name", NodeType("value", NodeConstraint.Token, False)), ("body", NodeType("expr", NodeConstraint.Node, True))]) expr = ExpandTreeRule( NodeType("expr", NodeConstraint.Node, False), [("op", NodeType("value", NodeConstraint.Token, False)), ("arg0", NodeType("value", NodeConstraint.Token, False)), ("arg1", NodeType("value", NodeConstraint.Token, False))]) encoder = ActionSequenceEncoder( Samples([funcdef, expr], [NodeType("def", NodeConstraint.Node, False), NodeType("value", NodeConstraint.Token, False), NodeType("expr", NodeConstraint.Node, False)], [("", "f")]), 0) action_sequence = ActionSequence() action = encoder.encode_action(action_sequence, [Token("", "1", "1")]) parent = encoder.encode_parent(action_sequence) d, m = encoder.encode_tree(action_sequence) assert np.array_equal( [ [-1, -1, -1, -1] ], action.numpy() ) assert np.array_equal( [ [-1, -1, -1, -1] ], parent.numpy() ) assert np.array_equal(np.zeros((0,)), d.numpy()) assert np.array_equal(np.zeros((0, 0)), m.numpy()) def test_encode_invalid_sequence(self): funcdef = ExpandTreeRule( NodeType("def", NodeConstraint.Node, False), [("name", NodeType("value", NodeConstraint.Token, True)), ("body", NodeType("expr", NodeConstraint.Node, True))]) expr = ExpandTreeRule( NodeType("expr", NodeConstraint.Node, False), [("op", NodeType("value", NodeConstraint.Token, False)), ("arg0", NodeType("value", NodeConstraint.Token, True)), ("arg1", NodeType("value", NodeConstraint.Token, True))]) encoder = ActionSequenceEncoder( Samples([funcdef, expr], [NodeType("def", NodeConstraint.Node, False), NodeType("value", NodeConstraint.Token, True), NodeType("expr", NodeConstraint.Node, True)], [("", "f")]), 0) action_sequence = ActionSequence() action_sequence.eval(ApplyRule(funcdef)) action_sequence.eval(GenerateToken("", "f")) action_sequence.eval(GenerateToken("", "1")) action_sequence.eval(ApplyRule(CloseVariadicFieldRule())) assert encoder.encode_action(action_sequence, [Token("", "2", "2")]) is None def test_encode_completed_sequence(self): none = ExpandTreeRule(NodeType("value", NodeConstraint.Node, False), []) encoder = ActionSequenceEncoder( Samples([none], [NodeType("value", NodeConstraint.Node, False)], [("", "f")]), 0) action_sequence = ActionSequence() action_sequence.eval(ApplyRule(none)) action = encoder.encode_action(action_sequence, [Token("", "1", "1")]) parent = encoder.encode_parent(action_sequence) assert np.array_equal( [ [-1, 2, -1, -1], [-1, -1, -1, -1] ], action.numpy() ) assert np.array_equal( [ [-1, -1, -1, -1], [-1, -1, -1, -1] ], parent.numpy() ) def test_decode(self): funcdef = ExpandTreeRule( NodeType("def", NodeConstraint.Node, False), [("name", NodeType("value", NodeConstraint.Token, True)), ("body", NodeType("expr", NodeConstraint.Node, True))]) expr = ExpandTreeRule( NodeType("expr", NodeConstraint.Node, False), [("op", NodeType("value", NodeConstraint.Token, True)), ("arg0", NodeType("value", NodeConstraint.Token, True)), ("arg1", NodeType("value", NodeConstraint.Token, True))]) encoder = ActionSequenceEncoder( Samples([funcdef, expr], [NodeType("def", NodeConstraint.Node, False), NodeType("value", NodeConstraint.Token, True), NodeType("expr", NodeConstraint.Node, False)], [("", "f")]), 0) action_sequence = ActionSequence() action_sequence.eval(ApplyRule(funcdef)) action_sequence.eval(GenerateToken("", "f")) action_sequence.eval(GenerateToken("", "1")) action_sequence.eval(ApplyRule(CloseVariadicFieldRule())) expected_action_sequence = ActionSequence() expected_action_sequence.eval(ApplyRule(funcdef)) expected_action_sequence.eval(GenerateToken("", "f")) expected_action_sequence.eval(GenerateToken("", "1")) expected_action_sequence.eval(ApplyRule(CloseVariadicFieldRule())) result = encoder.decode(encoder.encode_action( action_sequence, [Token(None, "1", "1")])[: -1, 1:], [Token(None, "1", "1")]) assert \ expected_action_sequence.action_sequence == result.action_sequence def test_decode_invalid_tensor(self): funcdef = ExpandTreeRule( NodeType("def", NodeConstraint.Node, False), [("name", NodeType("value", NodeConstraint.Token, False)), ("body", NodeType("expr", NodeConstraint.Node, True))]) expr = ExpandTreeRule( NodeType("expr", NodeConstraint.Node, False), [("op", NodeType("value", NodeConstraint.Token, False)), ("arg0", NodeType("value", NodeConstraint.Token, False)), ("arg1", NodeType("value", NodeConstraint.Token, False))]) encoder = ActionSequenceEncoder( Samples([funcdef, expr], [NodeType("def", NodeConstraint.Node, False), NodeType("value", NodeConstraint.Token, False), NodeType("expr", NodeConstraint.Node, False)], [("", "f")]), 0) assert encoder.decode(torch.LongTensor([[-1, -1, -1]]), []) is None assert encoder.decode(torch.LongTensor([[-1, -1, 1]]), []) is None def test_encode_each_action(self): funcdef = ExpandTreeRule( NodeType("def", NodeConstraint.Node, False), [("name", NodeType("value", NodeConstraint.Token, True)), ("body", NodeType("expr", NodeConstraint.Node, True))]) expr = ExpandTreeRule( NodeType("expr", NodeConstraint.Node, False), [("constant", NodeType("value", NodeConstraint.Token, True))]) encoder = ActionSequenceEncoder( Samples([funcdef, expr], [NodeType("def", NodeConstraint.Node, False), NodeType("value", NodeConstraint.Token, True), NodeType("expr", NodeConstraint.Node, False), NodeType("expr", NodeConstraint.Node, True)], [("", "f"), ("", "2")]), 0) action_sequence = ActionSequence() action_sequence.eval(ApplyRule(funcdef)) action_sequence.eval(GenerateToken("", "f")) action_sequence.eval(GenerateToken("", "1")) action_sequence.eval(GenerateToken("", "2")) action_sequence.eval(ApplyRule(CloseVariadicFieldRule())) action_sequence.eval(ApplyRule(expr)) action_sequence.eval(GenerateToken("", "f")) action_sequence.eval(ApplyRule(CloseVariadicFieldRule())) action_sequence.eval(ApplyRule(CloseVariadicFieldRule())) action = encoder.encode_each_action( action_sequence, [Token("", "1", "1"), Token("", "2", "2")], 1) assert np.array_equal( np.array([ [[1, -1, -1], [2, -1, -1]], # funcdef [[-1, -1, -1], [-1, 1, -1]], # f [[-1, -1, -1], [-1, -1, 0]], # 1 [[-1, -1, -1], [-1, 2, 1]], # 2 [[-1, -1, -1], [-1, -1, -1]], # CloseVariadicField [[3, -1, -1], [2, -1, -1]], # expr [[-1, -1, -1], [-1, 1, -1]], # f [[-1, -1, -1], [-1, -1, -1]], # CloseVariadicField [[-1, -1, -1], [-1, -1, -1]] # CloseVariadicField ], dtype=np.long), action.numpy() ) def test_encode_path(self): funcdef = ExpandTreeRule( NodeType("def", NodeConstraint.Node, False), [("name", NodeType("value", NodeConstraint.Token, True)), ("body", NodeType("expr", NodeConstraint.Node, True))]) expr = ExpandTreeRule( NodeType("expr", NodeConstraint.Node, False), [("constant", NodeType("value", NodeConstraint.Token, True))]) encoder = ActionSequenceEncoder( Samples([funcdef, expr], [NodeType("def", NodeConstraint.Node, False), NodeType("value", NodeConstraint.Token, True), NodeType("expr", NodeConstraint.Node, True)], [("", "f"), ("", "2")]), 0) action_sequence = ActionSequence() action_sequence.eval(ApplyRule(funcdef)) action_sequence.eval(GenerateToken("", "f")) action_sequence.eval(GenerateToken("", "1")) action_sequence.eval(GenerateToken("", "2")) action_sequence.eval(ApplyRule(CloseVariadicFieldRule())) action_sequence.eval(ApplyRule(expr)) action_sequence.eval(GenerateToken("", "f")) action_sequence.eval(ApplyRule(CloseVariadicFieldRule())) action_sequence.eval(ApplyRule(CloseVariadicFieldRule())) path = encoder.encode_path(action_sequence, 2) assert np.array_equal( np.array([ [-1, -1], # funcdef [2, -1], # f [2, -1], # 1 [2, -1], # 2 [2, -1], # CloseVariadicField [2, -1], # expr [3, 2], # f [3, 2], # CloseVariadicField [2, -1], # CloseVariadicField ], dtype=np.long), path.numpy() ) path = encoder.encode_path(action_sequence, 1) assert np.array_equal( np.array([ [-1], # funcdef [2], # f [2], # 1 [2], # 2 [2], # CloseVariadicField [2], # expr [3], # f [3], # CloseVariadicField [2], # CloseVariadicField ], dtype=np.long), path.numpy() )

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py

Python

napalm_yang/models/openconfig/network_instances/network_instance/protocols/protocol/isis/interfaces/interface/__init__.py

ckishimo/napalm-yang

8f2bd907bd3afcde3c2f8e985192de74748baf6c

[ "Apache-2.0" ]

64

2016-10-20T15:47:18.000Z

2021-11-11T11:57:32.000Z

napalm_yang/models/openconfig/network_instances/network_instance/protocols/protocol/isis/interfaces/interface/__init__.py

ckishimo/napalm-yang

8f2bd907bd3afcde3c2f8e985192de74748baf6c

[ "Apache-2.0" ]

126

2016-10-05T10:36:14.000Z

2019-05-15T08:43:23.000Z

napalm_yang/models/openconfig/network_instances/network_instance/protocols/protocol/isis/interfaces/interface/__init__.py

ckishimo/napalm-yang

8f2bd907bd3afcde3c2f8e985192de74748baf6c

[ "Apache-2.0" ]

63

2016-11-07T15:23:08.000Z

2021-09-22T14:41:16.000Z

# -*- coding: utf-8 -*- from operator import attrgetter from pyangbind.lib.yangtypes import RestrictedPrecisionDecimalType from pyangbind.lib.yangtypes import RestrictedClassType from pyangbind.lib.yangtypes import TypedListType from pyangbind.lib.yangtypes import YANGBool from pyangbind.lib.yangtypes import YANGListType from pyangbind.lib.yangtypes import YANGDynClass from pyangbind.lib.yangtypes import ReferenceType from pyangbind.lib.base import PybindBase from collections import OrderedDict from decimal import Decimal from bitarray import bitarray import six # PY3 support of some PY2 keywords (needs improved) if six.PY3: import builtins as __builtin__ long = int elif six.PY2: import __builtin__ from . import config from . import state from . import circuit_counters from . import authentication from . import afi_safi from . import levels from . import timers from . import bfd from . import interface_ref class interface(PybindBase): """ This class was auto-generated by the PythonClass plugin for PYANG from YANG module openconfig-network-instance - based on the path /network-instances/network-instance/protocols/protocol/isis/interfaces/interface. Each member element of the container is represented as a class variable - with a specific YANG type. YANG Description: This list contains ISIS interfaces. """ __slots__ = ( "_path_helper", "_extmethods", "__interface_id", "__config", "__state", "__circuit_counters", "__authentication", "__afi_safi", "__levels", "__timers", "__bfd", "__interface_ref", ) _yang_name = "interface" _pybind_generated_by = "container" def __init__(self, *args, **kwargs): self._path_helper = False self._extmethods = False self.__interface_id = YANGDynClass( base=six.text_type, is_leaf=True, yang_name="interface-id", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, is_keyval=True, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="leafref", is_config=True, ) self.__config = YANGDynClass( base=config.config, is_container="container", yang_name="config", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="container", is_config=True, ) self.__state = YANGDynClass( base=state.state, is_container="container", yang_name="state", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="container", is_config=True, ) self.__circuit_counters = YANGDynClass( base=circuit_counters.circuit_counters, is_container="container", yang_name="circuit-counters", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="container", is_config=True, ) self.__authentication = YANGDynClass( base=authentication.authentication, is_container="container", yang_name="authentication", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="container", is_config=True, ) self.__afi_safi = YANGDynClass( base=afi_safi.afi_safi, is_container="container", yang_name="afi-safi", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="container", is_config=True, ) self.__levels = YANGDynClass( base=levels.levels, is_container="container", yang_name="levels", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="container", is_config=True, ) self.__timers = YANGDynClass( base=timers.timers, is_container="container", yang_name="timers", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="container", is_config=True, ) self.__bfd = YANGDynClass( base=bfd.bfd, is_container="container", yang_name="bfd", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="container", is_config=True, ) self.__interface_ref = YANGDynClass( base=interface_ref.interface_ref, is_container="container", yang_name="interface-ref", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="container", is_config=True, ) load = kwargs.pop("load", None) if args: if len(args) > 1: raise TypeError("cannot create a YANG container with >1 argument") all_attr = True for e in self._pyangbind_elements: if not hasattr(args[0], e): all_attr = False break if not all_attr: raise ValueError("Supplied object did not have the correct attributes") for e in self._pyangbind_elements: nobj = getattr(args[0], e) if nobj._changed() is False: continue setmethod = getattr(self, "_set_%s" % e) if load is None: setmethod(getattr(args[0], e)) else: setmethod(getattr(args[0], e), load=load) def _path(self): if hasattr(self, "_parent"): return self._parent._path() + [self._yang_name] else: return [ "network-instances", "network-instance", "protocols", "protocol", "isis", "interfaces", "interface", ] def _get_interface_id(self): """ Getter method for interface_id, mapped from YANG variable /network_instances/network_instance/protocols/protocol/isis/interfaces/interface/interface_id (leafref) YANG Description: Reference to interface-id """ return self.__interface_id def _set_interface_id(self, v, load=False): """ Setter method for interface_id, mapped from YANG variable /network_instances/network_instance/protocols/protocol/isis/interfaces/interface/interface_id (leafref) If this variable is read-only (config: false) in the source YANG file, then _set_interface_id is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_interface_id() directly. YANG Description: Reference to interface-id """ parent = getattr(self, "_parent", None) if parent is not None and load is False: raise AttributeError( "Cannot set keys directly when" + " within an instantiated list" ) if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass( v, base=six.text_type, is_leaf=True, yang_name="interface-id", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, is_keyval=True, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="leafref", is_config=True, ) except (TypeError, ValueError): raise ValueError( { "error-string": """interface_id must be of a type compatible with leafref""", "defined-type": "leafref", "generated-type": """YANGDynClass(base=six.text_type, is_leaf=True, yang_name="interface-id", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, is_keyval=True, namespace='http://openconfig.net/yang/network-instance', defining_module='openconfig-network-instance', yang_type='leafref', is_config=True)""", } ) self.__interface_id = t if hasattr(self, "_set"): self._set() def _unset_interface_id(self): self.__interface_id = YANGDynClass( base=six.text_type, is_leaf=True, yang_name="interface-id", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, is_keyval=True, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="leafref", is_config=True, ) def _get_config(self): """ Getter method for config, mapped from YANG variable /network_instances/network_instance/protocols/protocol/isis/interfaces/interface/config (container) YANG Description: This container defines ISIS interface configuration. """ return self.__config def _set_config(self, v, load=False): """ Setter method for config, mapped from YANG variable /network_instances/network_instance/protocols/protocol/isis/interfaces/interface/config (container) If this variable is read-only (config: false) in the source YANG file, then _set_config is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_config() directly. YANG Description: This container defines ISIS interface configuration. """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass( v, base=config.config, is_container="container", yang_name="config", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="container", is_config=True, ) except (TypeError, ValueError): raise ValueError( { "error-string": """config must be of a type compatible with container""", "defined-type": "container", "generated-type": """YANGDynClass(base=config.config, is_container='container', yang_name="config", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/network-instance', defining_module='openconfig-network-instance', yang_type='container', is_config=True)""", } ) self.__config = t if hasattr(self, "_set"): self._set() def _unset_config(self): self.__config = YANGDynClass( base=config.config, is_container="container", yang_name="config", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="container", is_config=True, ) def _get_state(self): """ Getter method for state, mapped from YANG variable /network_instances/network_instance/protocols/protocol/isis/interfaces/interface/state (container) YANG Description: This container defines state information for ISIS interfaces. """ return self.__state def _set_state(self, v, load=False): """ Setter method for state, mapped from YANG variable /network_instances/network_instance/protocols/protocol/isis/interfaces/interface/state (container) If this variable is read-only (config: false) in the source YANG file, then _set_state is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_state() directly. YANG Description: This container defines state information for ISIS interfaces. """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass( v, base=state.state, is_container="container", yang_name="state", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="container", is_config=True, ) except (TypeError, ValueError): raise ValueError( { "error-string": """state must be of a type compatible with container""", "defined-type": "container", "generated-type": """YANGDynClass(base=state.state, is_container='container', yang_name="state", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/network-instance', defining_module='openconfig-network-instance', yang_type='container', is_config=True)""", } ) self.__state = t if hasattr(self, "_set"): self._set() def _unset_state(self): self.__state = YANGDynClass( base=state.state, is_container="container", yang_name="state", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="container", is_config=True, ) def _get_circuit_counters(self): """ Getter method for circuit_counters, mapped from YANG variable /network_instances/network_instance/protocols/protocol/isis/interfaces/interface/circuit_counters (container) YANG Description: This container defines state information for ISIS circuit counters. """ return self.__circuit_counters def _set_circuit_counters(self, v, load=False): """ Setter method for circuit_counters, mapped from YANG variable /network_instances/network_instance/protocols/protocol/isis/interfaces/interface/circuit_counters (container) If this variable is read-only (config: false) in the source YANG file, then _set_circuit_counters is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_circuit_counters() directly. YANG Description: This container defines state information for ISIS circuit counters. """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass( v, base=circuit_counters.circuit_counters, is_container="container", yang_name="circuit-counters", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="container", is_config=True, ) except (TypeError, ValueError): raise ValueError( { "error-string": """circuit_counters must be of a type compatible with container""", "defined-type": "container", "generated-type": """YANGDynClass(base=circuit_counters.circuit_counters, is_container='container', yang_name="circuit-counters", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/network-instance', defining_module='openconfig-network-instance', yang_type='container', is_config=True)""", } ) self.__circuit_counters = t if hasattr(self, "_set"): self._set() def _unset_circuit_counters(self): self.__circuit_counters = YANGDynClass( base=circuit_counters.circuit_counters, is_container="container", yang_name="circuit-counters", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="container", is_config=True, ) def _get_authentication(self): """ Getter method for authentication, mapped from YANG variable /network_instances/network_instance/protocols/protocol/isis/interfaces/interface/authentication (container) YANG Description: This container defines ISIS authentication. """ return self.__authentication def _set_authentication(self, v, load=False): """ Setter method for authentication, mapped from YANG variable /network_instances/network_instance/protocols/protocol/isis/interfaces/interface/authentication (container) If this variable is read-only (config: false) in the source YANG file, then _set_authentication is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_authentication() directly. YANG Description: This container defines ISIS authentication. """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass( v, base=authentication.authentication, is_container="container", yang_name="authentication", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="container", is_config=True, ) except (TypeError, ValueError): raise ValueError( { "error-string": """authentication must be of a type compatible with container""", "defined-type": "container", "generated-type": """YANGDynClass(base=authentication.authentication, is_container='container', yang_name="authentication", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/network-instance', defining_module='openconfig-network-instance', yang_type='container', is_config=True)""", } ) self.__authentication = t if hasattr(self, "_set"): self._set() def _unset_authentication(self): self.__authentication = YANGDynClass( base=authentication.authentication, is_container="container", yang_name="authentication", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="container", is_config=True, ) def _get_afi_safi(self): """ Getter method for afi_safi, mapped from YANG variable /network_instances/network_instance/protocols/protocol/isis/interfaces/interface/afi_safi (container) YANG Description: This container defines address-family specific configuration and state information. """ return self.__afi_safi def _set_afi_safi(self, v, load=False): """ Setter method for afi_safi, mapped from YANG variable /network_instances/network_instance/protocols/protocol/isis/interfaces/interface/afi_safi (container) If this variable is read-only (config: false) in the source YANG file, then _set_afi_safi is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_afi_safi() directly. YANG Description: This container defines address-family specific configuration and state information. """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass( v, base=afi_safi.afi_safi, is_container="container", yang_name="afi-safi", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="container", is_config=True, ) except (TypeError, ValueError): raise ValueError( { "error-string": """afi_safi must be of a type compatible with container""", "defined-type": "container", "generated-type": """YANGDynClass(base=afi_safi.afi_safi, is_container='container', yang_name="afi-safi", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/network-instance', defining_module='openconfig-network-instance', yang_type='container', is_config=True)""", } ) self.__afi_safi = t if hasattr(self, "_set"): self._set() def _unset_afi_safi(self): self.__afi_safi = YANGDynClass( base=afi_safi.afi_safi, is_container="container", yang_name="afi-safi", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="container", is_config=True, ) def _get_levels(self): """ Getter method for levels, mapped from YANG variable /network_instances/network_instance/protocols/protocol/isis/interfaces/interface/levels (container) YANG Description: This container defines ISIS level specific configuration and state information. """ return self.__levels def _set_levels(self, v, load=False): """ Setter method for levels, mapped from YANG variable /network_instances/network_instance/protocols/protocol/isis/interfaces/interface/levels (container) If this variable is read-only (config: false) in the source YANG file, then _set_levels is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_levels() directly. YANG Description: This container defines ISIS level specific configuration and state information. """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass( v, base=levels.levels, is_container="container", yang_name="levels", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="container", is_config=True, ) except (TypeError, ValueError): raise ValueError( { "error-string": """levels must be of a type compatible with container""", "defined-type": "container", "generated-type": """YANGDynClass(base=levels.levels, is_container='container', yang_name="levels", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/network-instance', defining_module='openconfig-network-instance', yang_type='container', is_config=True)""", } ) self.__levels = t if hasattr(self, "_set"): self._set() def _unset_levels(self): self.__levels = YANGDynClass( base=levels.levels, is_container="container", yang_name="levels", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="container", is_config=True, ) def _get_timers(self): """ Getter method for timers, mapped from YANG variable /network_instances/network_instance/protocols/protocol/isis/interfaces/interface/timers (container) YANG Description: This container describes ISIS interface timers configuration """ return self.__timers def _set_timers(self, v, load=False): """ Setter method for timers, mapped from YANG variable /network_instances/network_instance/protocols/protocol/isis/interfaces/interface/timers (container) If this variable is read-only (config: false) in the source YANG file, then _set_timers is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_timers() directly. YANG Description: This container describes ISIS interface timers configuration """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass( v, base=timers.timers, is_container="container", yang_name="timers", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="container", is_config=True, ) except (TypeError, ValueError): raise ValueError( { "error-string": """timers must be of a type compatible with container""", "defined-type": "container", "generated-type": """YANGDynClass(base=timers.timers, is_container='container', yang_name="timers", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/network-instance', defining_module='openconfig-network-instance', yang_type='container', is_config=True)""", } ) self.__timers = t if hasattr(self, "_set"): self._set() def _unset_timers(self): self.__timers = YANGDynClass( base=timers.timers, is_container="container", yang_name="timers", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="container", is_config=True, ) def _get_bfd(self): """ Getter method for bfd, mapped from YANG variable /network_instances/network_instance/protocols/protocol/isis/interfaces/interface/bfd (container) YANG Description: This container defines BFD. """ return self.__bfd def _set_bfd(self, v, load=False): """ Setter method for bfd, mapped from YANG variable /network_instances/network_instance/protocols/protocol/isis/interfaces/interface/bfd (container) If this variable is read-only (config: false) in the source YANG file, then _set_bfd is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_bfd() directly. YANG Description: This container defines BFD. """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass( v, base=bfd.bfd, is_container="container", yang_name="bfd", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="container", is_config=True, ) except (TypeError, ValueError): raise ValueError( { "error-string": """bfd must be of a type compatible with container""", "defined-type": "container", "generated-type": """YANGDynClass(base=bfd.bfd, is_container='container', yang_name="bfd", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/network-instance', defining_module='openconfig-network-instance', yang_type='container', is_config=True)""", } ) self.__bfd = t if hasattr(self, "_set"): self._set() def _unset_bfd(self): self.__bfd = YANGDynClass( base=bfd.bfd, is_container="container", yang_name="bfd", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="container", is_config=True, ) def _get_interface_ref(self): """ Getter method for interface_ref, mapped from YANG variable /network_instances/network_instance/protocols/protocol/isis/interfaces/interface/interface_ref (container) YANG Description: Reference to an interface or subinterface """ return self.__interface_ref def _set_interface_ref(self, v, load=False): """ Setter method for interface_ref, mapped from YANG variable /network_instances/network_instance/protocols/protocol/isis/interfaces/interface/interface_ref (container) If this variable is read-only (config: false) in the source YANG file, then _set_interface_ref is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_interface_ref() directly. YANG Description: Reference to an interface or subinterface """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass( v, base=interface_ref.interface_ref, is_container="container", yang_name="interface-ref", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="container", is_config=True, ) except (TypeError, ValueError): raise ValueError( { "error-string": """interface_ref must be of a type compatible with container""", "defined-type": "container", "generated-type": """YANGDynClass(base=interface_ref.interface_ref, is_container='container', yang_name="interface-ref", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/network-instance', defining_module='openconfig-network-instance', yang_type='container', is_config=True)""", } ) self.__interface_ref = t if hasattr(self, "_set"): self._set() def _unset_interface_ref(self): self.__interface_ref = YANGDynClass( base=interface_ref.interface_ref, is_container="container", yang_name="interface-ref", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="container", is_config=True, ) interface_id = __builtin__.property(_get_interface_id, _set_interface_id) config = __builtin__.property(_get_config, _set_config) state = __builtin__.property(_get_state, _set_state) circuit_counters = __builtin__.property( _get_circuit_counters, _set_circuit_counters ) authentication = __builtin__.property(_get_authentication, _set_authentication) afi_safi = __builtin__.property(_get_afi_safi, _set_afi_safi) levels = __builtin__.property(_get_levels, _set_levels) timers = __builtin__.property(_get_timers, _set_timers) bfd = __builtin__.property(_get_bfd, _set_bfd) interface_ref = __builtin__.property(_get_interface_ref, _set_interface_ref) _pyangbind_elements = OrderedDict( [ ("interface_id", interface_id), ("config", config), ("state", state), ("circuit_counters", circuit_counters), ("authentication", authentication), ("afi_safi", afi_safi), ("levels", levels), ("timers", timers), ("bfd", bfd), ("interface_ref", interface_ref), ] ) from . import config from . import state from . import circuit_counters from . import authentication from . import afi_safi from . import levels from . import timers from . import bfd from . import interface_ref class interface(PybindBase): """ This class was auto-generated by the PythonClass plugin for PYANG from YANG module openconfig-network-instance-l2 - based on the path /network-instances/network-instance/protocols/protocol/isis/interfaces/interface. Each member element of the container is represented as a class variable - with a specific YANG type. YANG Description: This list contains ISIS interfaces. """ __slots__ = ( "_path_helper", "_extmethods", "__interface_id", "__config", "__state", "__circuit_counters", "__authentication", "__afi_safi", "__levels", "__timers", "__bfd", "__interface_ref", ) _yang_name = "interface" _pybind_generated_by = "container" def __init__(self, *args, **kwargs): self._path_helper = False self._extmethods = False self.__interface_id = YANGDynClass( base=six.text_type, is_leaf=True, yang_name="interface-id", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, is_keyval=True, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="leafref", is_config=True, ) self.__config = YANGDynClass( base=config.config, is_container="container", yang_name="config", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="container", is_config=True, ) self.__state = YANGDynClass( base=state.state, is_container="container", yang_name="state", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="container", is_config=True, ) self.__circuit_counters = YANGDynClass( base=circuit_counters.circuit_counters, is_container="container", yang_name="circuit-counters", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="container", is_config=True, ) self.__authentication = YANGDynClass( base=authentication.authentication, is_container="container", yang_name="authentication", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="container", is_config=True, ) self.__afi_safi = YANGDynClass( base=afi_safi.afi_safi, is_container="container", yang_name="afi-safi", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="container", is_config=True, ) self.__levels = YANGDynClass( base=levels.levels, is_container="container", yang_name="levels", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="container", is_config=True, ) self.__timers = YANGDynClass( base=timers.timers, is_container="container", yang_name="timers", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="container", is_config=True, ) self.__bfd = YANGDynClass( base=bfd.bfd, is_container="container", yang_name="bfd", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="container", is_config=True, ) self.__interface_ref = YANGDynClass( base=interface_ref.interface_ref, is_container="container", yang_name="interface-ref", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="container", is_config=True, ) load = kwargs.pop("load", None) if args: if len(args) > 1: raise TypeError("cannot create a YANG container with >1 argument") all_attr = True for e in self._pyangbind_elements: if not hasattr(args[0], e): all_attr = False break if not all_attr: raise ValueError("Supplied object did not have the correct attributes") for e in self._pyangbind_elements: nobj = getattr(args[0], e) if nobj._changed() is False: continue setmethod = getattr(self, "_set_%s" % e) if load is None: setmethod(getattr(args[0], e)) else: setmethod(getattr(args[0], e), load=load) def _path(self): if hasattr(self, "_parent"): return self._parent._path() + [self._yang_name] else: return [ "network-instances", "network-instance", "protocols", "protocol", "isis", "interfaces", "interface", ] def _get_interface_id(self): """ Getter method for interface_id, mapped from YANG variable /network_instances/network_instance/protocols/protocol/isis/interfaces/interface/interface_id (leafref) YANG Description: Reference to interface-id """ return self.__interface_id def _set_interface_id(self, v, load=False): """ Setter method for interface_id, mapped from YANG variable /network_instances/network_instance/protocols/protocol/isis/interfaces/interface/interface_id (leafref) If this variable is read-only (config: false) in the source YANG file, then _set_interface_id is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_interface_id() directly. YANG Description: Reference to interface-id """ parent = getattr(self, "_parent", None) if parent is not None and load is False: raise AttributeError( "Cannot set keys directly when" + " within an instantiated list" ) if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass( v, base=six.text_type, is_leaf=True, yang_name="interface-id", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, is_keyval=True, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="leafref", is_config=True, ) except (TypeError, ValueError): raise ValueError( { "error-string": """interface_id must be of a type compatible with leafref""", "defined-type": "leafref", "generated-type": """YANGDynClass(base=six.text_type, is_leaf=True, yang_name="interface-id", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, is_keyval=True, namespace='http://openconfig.net/yang/network-instance', defining_module='openconfig-network-instance', yang_type='leafref', is_config=True)""", } ) self.__interface_id = t if hasattr(self, "_set"): self._set() def _unset_interface_id(self): self.__interface_id = YANGDynClass( base=six.text_type, is_leaf=True, yang_name="interface-id", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, is_keyval=True, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="leafref", is_config=True, ) def _get_config(self): """ Getter method for config, mapped from YANG variable /network_instances/network_instance/protocols/protocol/isis/interfaces/interface/config (container) YANG Description: This container defines ISIS interface configuration. """ return self.__config def _set_config(self, v, load=False): """ Setter method for config, mapped from YANG variable /network_instances/network_instance/protocols/protocol/isis/interfaces/interface/config (container) If this variable is read-only (config: false) in the source YANG file, then _set_config is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_config() directly. YANG Description: This container defines ISIS interface configuration. """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass( v, base=config.config, is_container="container", yang_name="config", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="container", is_config=True, ) except (TypeError, ValueError): raise ValueError( { "error-string": """config must be of a type compatible with container""", "defined-type": "container", "generated-type": """YANGDynClass(base=config.config, is_container='container', yang_name="config", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/network-instance', defining_module='openconfig-network-instance', yang_type='container', is_config=True)""", } ) self.__config = t if hasattr(self, "_set"): self._set() def _unset_config(self): self.__config = YANGDynClass( base=config.config, is_container="container", yang_name="config", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="container", is_config=True, ) def _get_state(self): """ Getter method for state, mapped from YANG variable /network_instances/network_instance/protocols/protocol/isis/interfaces/interface/state (container) YANG Description: This container defines state information for ISIS interfaces. """ return self.__state def _set_state(self, v, load=False): """ Setter method for state, mapped from YANG variable /network_instances/network_instance/protocols/protocol/isis/interfaces/interface/state (container) If this variable is read-only (config: false) in the source YANG file, then _set_state is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_state() directly. YANG Description: This container defines state information for ISIS interfaces. """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass( v, base=state.state, is_container="container", yang_name="state", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="container", is_config=True, ) except (TypeError, ValueError): raise ValueError( { "error-string": """state must be of a type compatible with container""", "defined-type": "container", "generated-type": """YANGDynClass(base=state.state, is_container='container', yang_name="state", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/network-instance', defining_module='openconfig-network-instance', yang_type='container', is_config=True)""", } ) self.__state = t if hasattr(self, "_set"): self._set() def _unset_state(self): self.__state = YANGDynClass( base=state.state, is_container="container", yang_name="state", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="container", is_config=True, ) def _get_circuit_counters(self): """ Getter method for circuit_counters, mapped from YANG variable /network_instances/network_instance/protocols/protocol/isis/interfaces/interface/circuit_counters (container) YANG Description: This container defines state information for ISIS circuit counters. """ return self.__circuit_counters def _set_circuit_counters(self, v, load=False): """ Setter method for circuit_counters, mapped from YANG variable /network_instances/network_instance/protocols/protocol/isis/interfaces/interface/circuit_counters (container) If this variable is read-only (config: false) in the source YANG file, then _set_circuit_counters is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_circuit_counters() directly. YANG Description: This container defines state information for ISIS circuit counters. """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass( v, base=circuit_counters.circuit_counters, is_container="container", yang_name="circuit-counters", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="container", is_config=True, ) except (TypeError, ValueError): raise ValueError( { "error-string": """circuit_counters must be of a type compatible with container""", "defined-type": "container", "generated-type": """YANGDynClass(base=circuit_counters.circuit_counters, is_container='container', yang_name="circuit-counters", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/network-instance', defining_module='openconfig-network-instance', yang_type='container', is_config=True)""", } ) self.__circuit_counters = t if hasattr(self, "_set"): self._set() def _unset_circuit_counters(self): self.__circuit_counters = YANGDynClass( base=circuit_counters.circuit_counters, is_container="container", yang_name="circuit-counters", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="container", is_config=True, ) def _get_authentication(self): """ Getter method for authentication, mapped from YANG variable /network_instances/network_instance/protocols/protocol/isis/interfaces/interface/authentication (container) YANG Description: This container defines ISIS authentication. """ return self.__authentication def _set_authentication(self, v, load=False): """ Setter method for authentication, mapped from YANG variable /network_instances/network_instance/protocols/protocol/isis/interfaces/interface/authentication (container) If this variable is read-only (config: false) in the source YANG file, then _set_authentication is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_authentication() directly. YANG Description: This container defines ISIS authentication. """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass( v, base=authentication.authentication, is_container="container", yang_name="authentication", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="container", is_config=True, ) except (TypeError, ValueError): raise ValueError( { "error-string": """authentication must be of a type compatible with container""", "defined-type": "container", "generated-type": """YANGDynClass(base=authentication.authentication, is_container='container', yang_name="authentication", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/network-instance', defining_module='openconfig-network-instance', yang_type='container', is_config=True)""", } ) self.__authentication = t if hasattr(self, "_set"): self._set() def _unset_authentication(self): self.__authentication = YANGDynClass( base=authentication.authentication, is_container="container", yang_name="authentication", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="container", is_config=True, ) def _get_afi_safi(self): """ Getter method for afi_safi, mapped from YANG variable /network_instances/network_instance/protocols/protocol/isis/interfaces/interface/afi_safi (container) YANG Description: This container defines address-family specific configuration and state information. """ return self.__afi_safi def _set_afi_safi(self, v, load=False): """ Setter method for afi_safi, mapped from YANG variable /network_instances/network_instance/protocols/protocol/isis/interfaces/interface/afi_safi (container) If this variable is read-only (config: false) in the source YANG file, then _set_afi_safi is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_afi_safi() directly. YANG Description: This container defines address-family specific configuration and state information. """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass( v, base=afi_safi.afi_safi, is_container="container", yang_name="afi-safi", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="container", is_config=True, ) except (TypeError, ValueError): raise ValueError( { "error-string": """afi_safi must be of a type compatible with container""", "defined-type": "container", "generated-type": """YANGDynClass(base=afi_safi.afi_safi, is_container='container', yang_name="afi-safi", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/network-instance', defining_module='openconfig-network-instance', yang_type='container', is_config=True)""", } ) self.__afi_safi = t if hasattr(self, "_set"): self._set() def _unset_afi_safi(self): self.__afi_safi = YANGDynClass( base=afi_safi.afi_safi, is_container="container", yang_name="afi-safi", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="container", is_config=True, ) def _get_levels(self): """ Getter method for levels, mapped from YANG variable /network_instances/network_instance/protocols/protocol/isis/interfaces/interface/levels (container) YANG Description: This container defines ISIS level specific configuration and state information. """ return self.__levels def _set_levels(self, v, load=False): """ Setter method for levels, mapped from YANG variable /network_instances/network_instance/protocols/protocol/isis/interfaces/interface/levels (container) If this variable is read-only (config: false) in the source YANG file, then _set_levels is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_levels() directly. YANG Description: This container defines ISIS level specific configuration and state information. """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass( v, base=levels.levels, is_container="container", yang_name="levels", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="container", is_config=True, ) except (TypeError, ValueError): raise ValueError( { "error-string": """levels must be of a type compatible with container""", "defined-type": "container", "generated-type": """YANGDynClass(base=levels.levels, is_container='container', yang_name="levels", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/network-instance', defining_module='openconfig-network-instance', yang_type='container', is_config=True)""", } ) self.__levels = t if hasattr(self, "_set"): self._set() def _unset_levels(self): self.__levels = YANGDynClass( base=levels.levels, is_container="container", yang_name="levels", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="container", is_config=True, ) def _get_timers(self): """ Getter method for timers, mapped from YANG variable /network_instances/network_instance/protocols/protocol/isis/interfaces/interface/timers (container) YANG Description: This container describes ISIS interface timers configuration """ return self.__timers def _set_timers(self, v, load=False): """ Setter method for timers, mapped from YANG variable /network_instances/network_instance/protocols/protocol/isis/interfaces/interface/timers (container) If this variable is read-only (config: false) in the source YANG file, then _set_timers is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_timers() directly. YANG Description: This container describes ISIS interface timers configuration """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass( v, base=timers.timers, is_container="container", yang_name="timers", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="container", is_config=True, ) except (TypeError, ValueError): raise ValueError( { "error-string": """timers must be of a type compatible with container""", "defined-type": "container", "generated-type": """YANGDynClass(base=timers.timers, is_container='container', yang_name="timers", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/network-instance', defining_module='openconfig-network-instance', yang_type='container', is_config=True)""", } ) self.__timers = t if hasattr(self, "_set"): self._set() def _unset_timers(self): self.__timers = YANGDynClass( base=timers.timers, is_container="container", yang_name="timers", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="container", is_config=True, ) def _get_bfd(self): """ Getter method for bfd, mapped from YANG variable /network_instances/network_instance/protocols/protocol/isis/interfaces/interface/bfd (container) YANG Description: This container defines BFD. """ return self.__bfd def _set_bfd(self, v, load=False): """ Setter method for bfd, mapped from YANG variable /network_instances/network_instance/protocols/protocol/isis/interfaces/interface/bfd (container) If this variable is read-only (config: false) in the source YANG file, then _set_bfd is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_bfd() directly. YANG Description: This container defines BFD. """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass( v, base=bfd.bfd, is_container="container", yang_name="bfd", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="container", is_config=True, ) except (TypeError, ValueError): raise ValueError( { "error-string": """bfd must be of a type compatible with container""", "defined-type": "container", "generated-type": """YANGDynClass(base=bfd.bfd, is_container='container', yang_name="bfd", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/network-instance', defining_module='openconfig-network-instance', yang_type='container', is_config=True)""", } ) self.__bfd = t if hasattr(self, "_set"): self._set() def _unset_bfd(self): self.__bfd = YANGDynClass( base=bfd.bfd, is_container="container", yang_name="bfd", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="container", is_config=True, ) def _get_interface_ref(self): """ Getter method for interface_ref, mapped from YANG variable /network_instances/network_instance/protocols/protocol/isis/interfaces/interface/interface_ref (container) YANG Description: Reference to an interface or subinterface """ return self.__interface_ref def _set_interface_ref(self, v, load=False): """ Setter method for interface_ref, mapped from YANG variable /network_instances/network_instance/protocols/protocol/isis/interfaces/interface/interface_ref (container) If this variable is read-only (config: false) in the source YANG file, then _set_interface_ref is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_interface_ref() directly. YANG Description: Reference to an interface or subinterface """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass( v, base=interface_ref.interface_ref, is_container="container", yang_name="interface-ref", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="container", is_config=True, ) except (TypeError, ValueError): raise ValueError( { "error-string": """interface_ref must be of a type compatible with container""", "defined-type": "container", "generated-type": """YANGDynClass(base=interface_ref.interface_ref, is_container='container', yang_name="interface-ref", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/network-instance', defining_module='openconfig-network-instance', yang_type='container', is_config=True)""", } ) self.__interface_ref = t if hasattr(self, "_set"): self._set() def _unset_interface_ref(self): self.__interface_ref = YANGDynClass( base=interface_ref.interface_ref, is_container="container", yang_name="interface-ref", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="container", is_config=True, ) interface_id = __builtin__.property(_get_interface_id, _set_interface_id) config = __builtin__.property(_get_config, _set_config) state = __builtin__.property(_get_state, _set_state) circuit_counters = __builtin__.property( _get_circuit_counters, _set_circuit_counters ) authentication = __builtin__.property(_get_authentication, _set_authentication) afi_safi = __builtin__.property(_get_afi_safi, _set_afi_safi) levels = __builtin__.property(_get_levels, _set_levels) timers = __builtin__.property(_get_timers, _set_timers) bfd = __builtin__.property(_get_bfd, _set_bfd) interface_ref = __builtin__.property(_get_interface_ref, _set_interface_ref) _pyangbind_elements = OrderedDict( [ ("interface_id", interface_id), ("config", config), ("state", state), ("circuit_counters", circuit_counters), ("authentication", authentication), ("afi_safi", afi_safi), ("levels", levels), ("timers", timers), ("bfd", bfd), ("interface_ref", interface_ref), ] )

40.747826

407

0.607528

7,618

74,976

5.738514

0.026647

0.070684

0.05188

0.058148

0.991422

0.986458

0

0.000342

0.298335

74,976

1,839

408

40.769984

0.830634

0.19757

0

0.893258

0

0.014045

0.264849

0.093462

0

1

0.044944

false

0

0.023174

0

0.106039

0

null

0

1

0

1

0

null

0

7

0247173f46447abc5f7806a99b6be4e3f0665541

12,918

py

Python

losses/cluster_loss.py

mondrasovic/reid_baseline_syncbn

3d21a786fb1a0519caaa0572c649f750036689b5

[ "MIT" ]

1

2022-01-05T15:42:44.000Z

losses/cluster_loss.py

mondrasovic/reid_baseline_syncbn

3d21a786fb1a0519caaa0572c649f750036689b5

[ "MIT" ]

null

losses/cluster_loss.py

mondrasovic/reid_baseline_syncbn

3d21a786fb1a0519caaa0572c649f750036689b5

[ "MIT" ]

null

from __future__ import absolute_import import torch from torch import nn import torch.nn.functional as F class ClusterLoss(nn.Module): def __init__( self, margin=10, use_gpu=True, ordered=True, ids_per_batch=16, imgs_per_id=4 ): super(ClusterLoss, self).__init__() self.use_gpu = use_gpu self.margin = margin self.ordered = ordered self.ids_per_batch = ids_per_batch self.imgs_per_id = imgs_per_id def _euclidean_dist(self, x, y): """ Args: x: pytorch Variable, with shape [m, d] y: pytorch Variable, with shape [n, d] Returns: dist: pytorch Variable, with shape [m, n] """ m, n = x.size(0), y.size(0) xx = torch.pow(x, 2).sum(1, keepdim=True).expand(m, n) yy = torch.pow(y, 2).sum(1, keepdim=True).expand(n, m).t() dist = xx + yy dist.addmm_(1, -2, x, y.t()) dist = dist.clamp(min=1e-12).sqrt() # for numerical stability return dist def _cluster_loss( self, features, targets, ordered=True, ids_per_batch=16, imgs_per_id=4 ): """ Args: features: prediction matrix (before softmax) with shape (batch_size, feature_dim) targets: ground truth labels with shape (batch_size) ordered: bool type. If the train data per batch are formed as p*k, where p is the num of ids per batch and k is the num of images per id. ids_per_batch: num of different ids per batch imgs_per_id: num of images per id Return: cluster_loss """ if self.use_gpu: if ordered: if targets.size(0) == ids_per_batch * imgs_per_id: unique_labels = targets[0:targets.size(0):imgs_per_id] else: unique_labels = targets.cpu().unique().cuda() else: unique_labels = targets.cpu().unique().cuda() else: if ordered: if targets.size(0) == ids_per_batch * imgs_per_id: unique_labels = targets[0:targets.size(0):imgs_per_id] else: unique_labels = targets.unique() else: unique_labels = targets.unique() inter_min_distance = torch.zeros(unique_labels.size(0)) intra_max_distance = torch.zeros(unique_labels.size(0)) center_features = torch.zeros(unique_labels.size(0), features.size(1)) if self.use_gpu: inter_min_distance = inter_min_distance.cuda() intra_max_distance = intra_max_distance.cuda() center_features = center_features.cuda() index = torch.range(0, unique_labels.size(0) - 1) for i in range(unique_labels.size(0)): label = unique_labels[i] same_class_features = features[targets == label] center_features[i] = same_class_features.mean(dim=0) intra_class_distance = self._euclidean_dist( center_features[index == i], same_class_features ) # print('intra_class_distance', intra_class_distance) intra_max_distance[i] = intra_class_distance.max() # print('intra_max_distance:', intra_max_distance) for i in range(unique_labels.size(0)): inter_class_distance = self._euclidean_dist( center_features[index == i], center_features[index != i] ) # print('inter_class_distance', inter_class_distance) inter_min_distance[i] = inter_class_distance.min() # print('inter_min_distance:', inter_min_distance) cluster_loss = torch.mean( torch.relu(intra_max_distance - inter_min_distance + self.margin) ) return cluster_loss, intra_max_distance, inter_min_distance def forward(self, features, targets): """ Args: features: prediction matrix (before softmax) with shape (batch_size, feature_dim) targets: ground truth labels with shape (batch_size) ordered: bool type. If the train data per batch are formed as p*k, where p is the num of ids per batch and k is the num of images per id. ids_per_batch: num of different ids per batch imgs_per_id: num of images per id Return: cluster_loss """ assert features.size(0) == targets.size( 0 ), "features.size(0) is not equal to targets.size(0)" cluster_loss, cluster_dist_ap, cluster_dist_an = self._cluster_loss( features, targets, self.ordered, self.ids_per_batch, self.imgs_per_id ) return cluster_loss, cluster_dist_ap, cluster_dist_an class ClusterLoss_local(nn.Module): def __init__( self, margin=10, use_gpu=True, ordered=True, ids_per_batch=32, imgs_per_id=4 ): super(ClusterLoss_local, self).__init__() self.use_gpu = use_gpu self.margin = margin self.ordered = ordered self.ids_per_batch = ids_per_batch self.imgs_per_id = imgs_per_id def _euclidean_dist(self, x, y): """ Args: x: pytorch Variable, with shape [m, d] y: pytorch Variable, with shape [n, d] Returns: dist: pytorch Variable, with shape [m, n] """ m, n = x.size(0), y.size(0) xx = torch.pow(x, 2).sum(1, keepdim=True).expand(m, n) yy = torch.pow(y, 2).sum(1, keepdim=True).expand(n, m).t() dist = xx + yy dist.addmm_(1, -2, x, y.t()) dist = dist.clamp(min=1e-12).sqrt() # for numerical stability return dist def _shortest_dist(self, dist_mat): """Parallel version. Args: dist_mat: pytorch Variable, available shape: 1) [m, n] 2) [m, n, N], N is batch size 3) [m, n, *], * can be arbitrary additional dimensions Returns: dist: three cases corresponding to `dist_mat`: 1) scalar 2) pytorch Variable, with shape [N] 3) pytorch Variable, with shape [*] """ m, n = dist_mat.size()[:2] # Just offering some reference for accessing intermediate distance. dist = [[0 for _ in range(n)] for _ in range(m)] for i in range(m): for j in range(n): if (i == 0) and (j == 0): dist[i][j] = dist_mat[i, j] elif (i == 0) and (j > 0): dist[i][j] = dist[i][j - 1] + dist_mat[i, j] elif (i > 0) and (j == 0): dist[i][j] = dist[i - 1][j] + dist_mat[i, j] else: dist[i][j] = torch.min(dist[i - 1][j], dist[i][j - 1]) + dist_mat[i, j] dist = dist[-1][-1] return dist def _local_dist(self, x, y): """ Args: x: pytorch Variable, with shape [M, m, d] y: pytorch Variable, with shape [N, n, d] Returns: dist: pytorch Variable, with shape [M, N] """ M, m, d = x.size() N, n, d = y.size() x = x.contiguous().view(M * m, d) y = y.contiguous().view(N * n, d) # shape [M * m, N * n] dist_mat = self._euclidean_dist(x, y) dist_mat = (torch.exp(dist_mat) - 1.) / (torch.exp(dist_mat) + 1.) # shape [M * m, N * n] -> [M, m, N, n] -> [m, n, M, N] dist_mat = dist_mat.contiguous().view(M, m, N, n).permute(1, 3, 0, 2) # shape [M, N] dist_mat = self._shortest_dist(dist_mat) return dist_mat def _cluster_loss( self, features, targets, ordered=True, ids_per_batch=32, imgs_per_id=4 ): """ Args: features: prediction matrix (before softmax) with shape (batch_size, H, feature_dim) targets: ground truth labels with shape (batch_size) ordered: bool type. If the train data per batch are formed as p*k, where p is the num of ids per batch and k is the num of images per id. ids_per_batch: num of different ids per batch imgs_per_id: num of images per id Return: cluster_loss """ if self.use_gpu: if ordered: if targets.size(0) == ids_per_batch * imgs_per_id: unique_labels = targets[0:targets.size(0):imgs_per_id] else: unique_labels = targets.cpu().unique().cuda() else: unique_labels = targets.cpu().unique().cuda() else: if ordered: if targets.size(0) == ids_per_batch * imgs_per_id: unique_labels = targets[0:targets.size(0):imgs_per_id] else: unique_labels = targets.unique() else: unique_labels = targets.unique() inter_min_distance = torch.zeros(unique_labels.size(0)) intra_max_distance = torch.zeros(unique_labels.size(0)) center_features = torch.zeros( unique_labels.size(0), features.size(1), features.size(2) ) if self.use_gpu: inter_min_distance = inter_min_distance.cuda() intra_max_distance = intra_max_distance.cuda() center_features = center_features.cuda() index = torch.range(0, unique_labels.size(0) - 1) for i in range(unique_labels.size(0)): label = unique_labels[i] same_class_features = features[targets == label] center_features[i] = same_class_features.mean(dim=0) intra_class_distance = self._local_dist( center_features[index == i], same_class_features ) # print('intra_class_distance', intra_class_distance) intra_max_distance[i] = intra_class_distance.max() # print('intra_max_distance:', intra_max_distance) for i in range(unique_labels.size(0)): inter_class_distance = self._local_dist( center_features[index == i], center_features[index != i] ) # print('inter_class_distance', inter_class_distance) inter_min_distance[i] = inter_class_distance.min() # print('inter_min_distance:', inter_min_distance) cluster_loss = torch.mean( torch.relu(intra_max_distance - inter_min_distance + self.margin) ) return cluster_loss, intra_max_distance, inter_min_distance def forward(self, features, targets): """ Args: features: prediction matrix (before softmax) with shape (batch_size, H, feature_dim) targets: ground truth labels with shape (batch_size) ordered: bool type. If the train data per batch are formed as p*k, where p is the num of ids per batch and k is the num of images per id. ids_per_batch: num of different ids per batch imgs_per_id: num of images per id Return: cluster_loss """ assert features.size(0) == targets.size( 0 ), "features.size(0) is not equal to targets.size(0)" cluster_loss, cluster_dist_ap, cluster_dist_an = self._cluster_loss( features, targets, self.ordered, self.ids_per_batch, self.imgs_per_id ) return cluster_loss, cluster_dist_ap, cluster_dist_an if __name__ == '__main__': use_gpu = True cluster_loss = ClusterLoss(use_gpu=use_gpu, ids_per_batch=4, imgs_per_id=4) features = torch.rand(16, 2048) targets = torch.Tensor([0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3]) if use_gpu: features = torch.rand(16, 2048).cuda() targets = torch.Tensor( [0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3] ).cuda() loss = cluster_loss(features, targets) print(loss) cluster_loss_local = ClusterLoss_local( use_gpu=use_gpu, ids_per_batch=4, imgs_per_id=4 ) features = torch.rand(16, 8, 2048) targets = torch.Tensor([0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3]) if use_gpu: features = torch.rand(16, 8, 2048).cuda() targets = torch.Tensor( [0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3] ).cuda() loss = cluster_loss_local(features, targets) print(loss)

39.264438

150

0.550085

1,689

12,918

3.982238

0.09177

0.038061

0.045792

0.03033

0.883883

0.86634

0.857419

0.853256

0.844335

0

0.023606

0.347422

12,918

328

151

39.384146

0.774259

0.231537

0

0.715596

0

0.011543

0

0.009174

1

0.045872

false

0

0.018349

0

0.110092

0.009174

0

null

0

1

0

1

0

null

0

7

025d0ddb2de976e32266b4f4506bbee6a32c81eb

10,279

py

Python

02a_deep_fc_network.py

metataro/DirectFeedbackAlignment

7e2cbc3f001ac2290a15440628bb2b97d4ec52ab

[ "MIT" ]

5

2020-04-30T11:36:46.000Z

2021-09-09T06:08:34.000Z

02a_deep_fc_network.py

metataro/DirectFeedbackAlignment

7e2cbc3f001ac2290a15440628bb2b97d4ec52ab

[ "MIT" ]

null

02a_deep_fc_network.py

metataro/DirectFeedbackAlignment

7e2cbc3f001ac2290a15440628bb2b97d4ec52ab

[ "MIT" ]

1

2021-01-07T03:10:32.000Z

from multiprocessing import freeze_support import matplotlib.pyplot as plt import numpy as np import dataset.cifar10_dataset from network import activation, weight_initializer from network.layers.conv_to_fully_connected import ConvToFullyConnected from network.layers.convolution_im2col import Convolution from network.layers.dropout import Dropout from network.layers.fully_connected import FullyConnected from network.layers.max_pool import MaxPool from network.model import Model from network.optimizer import GDMomentumOptimizer if __name__ == '__main__': freeze_support() num_iteration = 20 data = dataset.cifar10_dataset.load() layers = [ ConvToFullyConnected(), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=240, activation=activation.tanh), FullyConnected(size=10, activation=None, last_layer=True) ] # ------------------------------------------------------- # Train with DFA # ------------------------------------------------------- model = Model( layers=layers, num_classes=10, optimizer=GDMomentumOptimizer(lr=1e-4, mu=0.9), regularization=0.001, # lr_decay=0.5, # lr_decay_interval=100 ) print("\nRun training:\n------------------------------------") stats_dfa = model.train(data_set=data, method='dfa', num_passes=num_iteration, batch_size=64) loss, accuracy = model.cost(*data.test_set()) print("\nResult:\n------------------------------------") print('loss on test set: {}'.format(loss)) print('accuracy on test set: {}'.format(accuracy)) print("\nTrain statisistics:\n------------------------------------") print("time spend during forward pass: {}".format(stats_dfa['forward_time'])) print("time spend during backward pass: {}".format(stats_dfa['backward_time'])) print("time spend during update pass: {}".format(stats_dfa['update_time'])) print("time spend in total: {}".format(stats_dfa['total_time'])) # ------------------------------------------------------- # Train with BP # ------------------------------------------------------- model = Model( layers=layers, num_classes=10, optimizer=GDMomentumOptimizer(lr=1e-4, mu=0.9), regularization=0.001, # lr_decay=0.5, # lr_decay_interval=100 ) print("\nRun training:\n------------------------------------") stats_bp = model.train(data_set=data, method='bp', num_passes=num_iteration, batch_size=64) loss, accuracy = model.cost(*data.test_set()) print("\nResult:\n------------------------------------") print('loss on test set: {}'.format(loss)) print('accuracy on test set: {}'.format(accuracy)) print("\nTrain statisistics:\n------------------------------------") print("time spend during forward pass: {}".format(stats_bp['forward_time'])) print("time spend during backward pass: {}".format(stats_bp['backward_time'])) print("time spend during update pass: {}".format(stats_bp['update_time'])) print("time spend in total: {}".format(stats_bp['total_time'])) plt.title('Loss function') plt.xlabel('epoch') plt.ylabel('loss') plt.plot(np.arange(len(stats_dfa['train_loss'])), stats_dfa['train_loss']) plt.plot(stats_dfa['valid_step'], stats_dfa['valid_loss']) plt.plot(np.arange(len(stats_bp['train_loss'])), stats_bp['train_loss']) plt.plot(stats_bp['valid_step'], stats_bp['valid_loss']) plt.legend(['train loss dfa', 'validation loss dfa', 'train loss bp', 'validation loss bp'], loc='upper right') plt.grid(True) plt.show() plt.title('Accuracy') plt.xlabel('epoch') plt.ylabel('accuracy') plt.plot(np.arange(len(stats_dfa['train_accuracy'])), stats_dfa['train_accuracy']) plt.plot(stats_dfa['valid_step'], stats_dfa['valid_accuracy']) plt.plot(np.arange(len(stats_bp['train_accuracy'])), stats_bp['train_accuracy']) plt.plot(stats_bp['valid_step'], stats_bp['valid_accuracy']) plt.legend(['train accuracy dfa', 'validation accuracy dfa', 'train loss dfa', 'validation loss dfa'], loc='lower right') plt.grid(True) plt.show()

49.657005

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0

null

1

0

1

0

null

0

10

02679543d3eec4cab27e37186466e4812e33c6c3

8,997

py

Python

examples/simple/src/schema/versions/4489bf015a81_20203594956_add_assoc_edge_config_table_add_event_.py

lazytype/ent

d9729f3bb5c2410021e58dfdac0ef03efb439edb

[ "MIT" ]

35

2021-05-28T00:16:56.000Z

2022-03-14T10:02:09.000Z

examples/simple/src/schema/versions/4489bf015a81_20203594956_add_assoc_edge_config_table_add_event_.py

lazytype/ent

d9729f3bb5c2410021e58dfdac0ef03efb439edb

[ "MIT" ]

187

2021-05-26T19:23:59.000Z

2022-03-30T17:53:49.000Z

examples/simple/src/schema/versions/4489bf015a81_20203594956_add_assoc_edge_config_table_add_event_.py

lazytype/ent

d9729f3bb5c2410021e58dfdac0ef03efb439edb

[ "MIT" ]

6

2021-06-11T23:09:22.000Z

2022-02-01T23:45:35.000Z

# Code generated by github.com/lolopinto/ent/ent, DO NOT edit. """add assoc_edge_config table add event_hosts_edges table add event_rsvps_edges table add user_created_events_edges table add user_friends_edges table add edges EventToAttendingEdge, EventToDeclinedEdge, EventToHostsEdge, EventToInvitedEdge, EventToMaybeEdge, UserToCreatedEventsEdge, UserToDeclinedEventsEdge, UserToEventsAttendingEdge, UserToFriendsEdge, UserToInvitedEventsEdge, UserToMaybeEventsEdge Revision ID: 4489bf015a81 Revises: 3c6b810ea389 Create Date: 2020-03-05 09:49:56.645382+00:00 """ from alembic import op import sqlalchemy as sa from sqlalchemy.dialects import postgresql # revision identifiers, used by Alembic. revision = '4489bf015a81' down_revision = '3c6b810ea389' branch_labels = None depends_on = None def upgrade(): # ### commands auto generated by Alembic - please adjust! ### op.create_table('assoc_edge_config', sa.Column('edge_type', postgresql.UUID(), nullable=False), sa.Column('edge_name', sa.Text(), nullable=False), sa.Column('symmetric_edge', sa.Boolean(), server_default='false', nullable=False), sa.Column('inverse_edge_type', postgresql.UUID(), nullable=True), sa.Column('edge_table', sa.Text(), nullable=False), sa.Column('created_at', sa.TIMESTAMP(), nullable=False), sa.Column('updated_at', sa.TIMESTAMP(), nullable=False), sa.ForeignKeyConstraint(['inverse_edge_type'], ['assoc_edge_config.edge_type'], name='assoc_edge_config_inverse_edge_type_fkey', ondelete='RESTRICT'), sa.PrimaryKeyConstraint('edge_type', name='assoc_edge_config_edge_type_pkey'), sa.UniqueConstraint('edge_name', name='assoc_edge_config_unique_edge_name') ) op.create_table('event_hosts_edges', sa.Column('id1', postgresql.UUID(), nullable=False), sa.Column('id1_type', sa.Text(), nullable=False), sa.Column('edge_type', postgresql.UUID(), nullable=False), sa.Column('id2', postgresql.UUID(), nullable=False), sa.Column('id2_type', sa.Text(), nullable=False), sa.Column('time', sa.TIMESTAMP(), nullable=False), sa.Column('data', sa.Text(), nullable=True), sa.PrimaryKeyConstraint('id1', 'edge_type', 'id2', name='event_hosts_edges_id1_edge_type_id2_pkey') ) op.create_table('event_rsvps_edges', sa.Column('id1', postgresql.UUID(), nullable=False), sa.Column('id1_type', sa.Text(), nullable=False), sa.Column('edge_type', postgresql.UUID(), nullable=False), sa.Column('id2', postgresql.UUID(), nullable=False), sa.Column('id2_type', sa.Text(), nullable=False), sa.Column('time', sa.TIMESTAMP(), nullable=False), sa.Column('data', sa.Text(), nullable=True), sa.PrimaryKeyConstraint('id1', 'edge_type', 'id2', name='event_rsvps_edges_id1_edge_type_id2_pkey') ) op.create_table('user_created_events_edges', sa.Column('id1', postgresql.UUID(), nullable=False), sa.Column('id1_type', sa.Text(), nullable=False), sa.Column('edge_type', postgresql.UUID(), nullable=False), sa.Column('id2', postgresql.UUID(), nullable=False), sa.Column('id2_type', sa.Text(), nullable=False), sa.Column('time', sa.TIMESTAMP(), nullable=False), sa.Column('data', sa.Text(), nullable=True), sa.PrimaryKeyConstraint('id1', 'edge_type', 'id2', name='user_created_events_edges_id1_edge_type_id2_pkey') ) op.create_table('user_friends_edges', sa.Column('id1', postgresql.UUID(), nullable=False), sa.Column('id1_type', sa.Text(), nullable=False), sa.Column('edge_type', postgresql.UUID(), nullable=False), sa.Column('id2', postgresql.UUID(), nullable=False), sa.Column('id2_type', sa.Text(), nullable=False), sa.Column('time', sa.TIMESTAMP(), nullable=False), sa.Column('data', sa.Text(), nullable=True), sa.PrimaryKeyConstraint('id1', 'edge_type', 'id2', name='user_friends_edges_id1_edge_type_id2_pkey') ) op.add_edges( [ {'edge_name': 'EventToAttendingEdge', 'edge_type': '6ebc0c47-ea29-4635-b991-95e44162174d', 'edge_table': 'event_rsvps_edges', 'symmetric_edge': False, 'inverse_edge_type': '2a98ba02-e342-4bb4-93f6-5d7ed02f5c48'}, {'edge_name': 'EventToDeclinedEdge', 'edge_type': 'db8d2454-f7b2-4147-aae1-e666daf3f3c3', 'edge_table': 'event_rsvps_edges', 'symmetric_edge': False, 'inverse_edge_type': '1c7c173b-63ce-4002-b121-4a87f82047dd'}, {'edge_name': 'EventToHostsEdge', 'edge_type': 'ebe3e709-845c-4723-ac9c-29f983f2b8ea', 'edge_table': 'event_hosts_edges', 'symmetric_edge': False, 'inverse_edge_type': None}, {'edge_name': 'EventToInvitedEdge', 'edge_type': 'a72f5f64-3580-44fd-9bd0-d1335b803a46', 'edge_table': 'event_rsvps_edges', 'symmetric_edge': False, 'inverse_edge_type': 'e439f2b2-d93a-4d1a-83f0-865bda5c8337'}, {'edge_name': 'EventToMaybeEdge', 'edge_type': 'b0f6311b-fdab-4c26-b6bf-b751e0997735', 'edge_table': 'event_rsvps_edges', 'symmetric_edge': False, 'inverse_edge_type': '8d5b1dee-ce65-452e-9f8d-78eca1993800'}, {'edge_name': 'UserToCreatedEventsEdge', 'edge_type': 'daa3b2a3-8245-40ca-ae77-25bfb82578a7', 'edge_table': 'user_created_events_edges', 'symmetric_edge': False, 'inverse_edge_type': None}, {'edge_name': 'UserToDeclinedEventsEdge', 'edge_type': '1c7c173b-63ce-4002-b121-4a87f82047dd', 'edge_table': 'event_rsvps_edges', 'symmetric_edge': False, 'inverse_edge_type': 'db8d2454-f7b2-4147-aae1-e666daf3f3c3'}, {'edge_name': 'UserToEventsAttendingEdge', 'edge_type': '2a98ba02-e342-4bb4-93f6-5d7ed02f5c48', 'edge_table': 'event_rsvps_edges', 'symmetric_edge': False, 'inverse_edge_type': '6ebc0c47-ea29-4635-b991-95e44162174d'}, {'edge_name': 'UserToFriendsEdge', 'edge_type': 'd1a9316d-090f-4b02-b393-fd9372e2c905', 'edge_table': 'user_friends_edges', 'symmetric_edge': True, 'inverse_edge_type': None}, {'edge_name': 'UserToInvitedEventsEdge', 'edge_type': 'e439f2b2-d93a-4d1a-83f0-865bda5c8337', 'edge_table': 'event_rsvps_edges', 'symmetric_edge': False, 'inverse_edge_type': 'a72f5f64-3580-44fd-9bd0-d1335b803a46'}, {'edge_name': 'UserToMaybeEventsEdge', 'edge_type': '8d5b1dee-ce65-452e-9f8d-78eca1993800', 'edge_table': 'event_rsvps_edges', 'symmetric_edge': False, 'inverse_edge_type': 'b0f6311b-fdab-4c26-b6bf-b751e0997735'}, ] ) # ### end Alembic commands ### def downgrade(): # ### commands auto generated by Alembic - please adjust! ### op.remove_edges( [ {'edge_name': 'EventToAttendingEdge', 'edge_type': '6ebc0c47-ea29-4635-b991-95e44162174d', 'edge_table': 'event_rsvps_edges', 'symmetric_edge': False, 'inverse_edge_type': '2a98ba02-e342-4bb4-93f6-5d7ed02f5c48'}, {'edge_name': 'EventToDeclinedEdge', 'edge_type': 'db8d2454-f7b2-4147-aae1-e666daf3f3c3', 'edge_table': 'event_rsvps_edges', 'symmetric_edge': False, 'inverse_edge_type': '1c7c173b-63ce-4002-b121-4a87f82047dd'}, {'edge_name': 'EventToHostsEdge', 'edge_type': 'ebe3e709-845c-4723-ac9c-29f983f2b8ea', 'edge_table': 'event_hosts_edges', 'symmetric_edge': False, 'inverse_edge_type': None}, {'edge_name': 'EventToInvitedEdge', 'edge_type': 'a72f5f64-3580-44fd-9bd0-d1335b803a46', 'edge_table': 'event_rsvps_edges', 'symmetric_edge': False, 'inverse_edge_type': 'e439f2b2-d93a-4d1a-83f0-865bda5c8337'}, {'edge_name': 'EventToMaybeEdge', 'edge_type': 'b0f6311b-fdab-4c26-b6bf-b751e0997735', 'edge_table': 'event_rsvps_edges', 'symmetric_edge': False, 'inverse_edge_type': '8d5b1dee-ce65-452e-9f8d-78eca1993800'}, {'edge_name': 'UserToCreatedEventsEdge', 'edge_type': 'daa3b2a3-8245-40ca-ae77-25bfb82578a7', 'edge_table': 'user_created_events_edges', 'symmetric_edge': False, 'inverse_edge_type': None}, {'edge_name': 'UserToDeclinedEventsEdge', 'edge_type': '1c7c173b-63ce-4002-b121-4a87f82047dd', 'edge_table': 'event_rsvps_edges', 'symmetric_edge': False, 'inverse_edge_type': 'db8d2454-f7b2-4147-aae1-e666daf3f3c3'}, {'edge_name': 'UserToEventsAttendingEdge', 'edge_type': '2a98ba02-e342-4bb4-93f6-5d7ed02f5c48', 'edge_table': 'event_rsvps_edges', 'symmetric_edge': False, 'inverse_edge_type': '6ebc0c47-ea29-4635-b991-95e44162174d'}, {'edge_name': 'UserToFriendsEdge', 'edge_type': 'd1a9316d-090f-4b02-b393-fd9372e2c905', 'edge_table': 'user_friends_edges', 'symmetric_edge': True, 'inverse_edge_type': None}, {'edge_name': 'UserToInvitedEventsEdge', 'edge_type': 'e439f2b2-d93a-4d1a-83f0-865bda5c8337', 'edge_table': 'event_rsvps_edges', 'symmetric_edge': False, 'inverse_edge_type': 'a72f5f64-3580-44fd-9bd0-d1335b803a46'}, {'edge_name': 'UserToMaybeEventsEdge', 'edge_type': '8d5b1dee-ce65-452e-9f8d-78eca1993800', 'edge_table': 'event_rsvps_edges', 'symmetric_edge': False, 'inverse_edge_type': 'b0f6311b-fdab-4c26-b6bf-b751e0997735'}, ] ) op.drop_table('user_friends_edges') op.drop_table('user_created_events_edges') op.drop_table('event_rsvps_edges') op.drop_table('event_hosts_edges') op.drop_table('assoc_edge_config') # ### end Alembic commands ###

74.355372

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7

0289ac022137ec151e9d5b53192b273514bbd1da

6,683

py

Python

loldib/getratings/models/NA/na_trundle/na_trundle_jng.py

koliupy/loldib

c9ab94deb07213cdc42b5a7c26467cdafaf81b7f

[ "Apache-2.0" ]

null

loldib/getratings/models/NA/na_trundle/na_trundle_jng.py

koliupy/loldib

c9ab94deb07213cdc42b5a7c26467cdafaf81b7f

[ "Apache-2.0" ]

null

loldib/getratings/models/NA/na_trundle/na_trundle_jng.py

koliupy/loldib

c9ab94deb07213cdc42b5a7c26467cdafaf81b7f

[ "Apache-2.0" ]

null

from getratings.models.ratings import Ratings class NA_Trundle_Jng_Aatrox(Ratings): pass class NA_Trundle_Jng_Ahri(Ratings): pass class NA_Trundle_Jng_Akali(Ratings): pass class NA_Trundle_Jng_Alistar(Ratings): pass class NA_Trundle_Jng_Amumu(Ratings): pass class NA_Trundle_Jng_Anivia(Ratings): pass class NA_Trundle_Jng_Annie(Ratings): pass class NA_Trundle_Jng_Ashe(Ratings): pass class NA_Trundle_Jng_AurelionSol(Ratings): pass class NA_Trundle_Jng_Azir(Ratings): pass class NA_Trundle_Jng_Bard(Ratings): pass class NA_Trundle_Jng_Blitzcrank(Ratings): pass class NA_Trundle_Jng_Brand(Ratings): pass class NA_Trundle_Jng_Braum(Ratings): pass class NA_Trundle_Jng_Caitlyn(Ratings): pass class NA_Trundle_Jng_Camille(Ratings): pass class NA_Trundle_Jng_Cassiopeia(Ratings): pass class NA_Trundle_Jng_Chogath(Ratings): pass class NA_Trundle_Jng_Corki(Ratings): pass class NA_Trundle_Jng_Darius(Ratings): pass class NA_Trundle_Jng_Diana(Ratings): pass class NA_Trundle_Jng_Draven(Ratings): pass class NA_Trundle_Jng_DrMundo(Ratings): pass class NA_Trundle_Jng_Ekko(Ratings): pass class NA_Trundle_Jng_Elise(Ratings): pass class NA_Trundle_Jng_Evelynn(Ratings): pass class NA_Trundle_Jng_Ezreal(Ratings): pass class NA_Trundle_Jng_Fiddlesticks(Ratings): pass class NA_Trundle_Jng_Fiora(Ratings): pass class NA_Trundle_Jng_Fizz(Ratings): pass class NA_Trundle_Jng_Galio(Ratings): pass class NA_Trundle_Jng_Gangplank(Ratings): pass class NA_Trundle_Jng_Garen(Ratings): pass class NA_Trundle_Jng_Gnar(Ratings): pass class NA_Trundle_Jng_Gragas(Ratings): pass class NA_Trundle_Jng_Graves(Ratings): pass class NA_Trundle_Jng_Hecarim(Ratings): pass class NA_Trundle_Jng_Heimerdinger(Ratings): pass class NA_Trundle_Jng_Illaoi(Ratings): pass class NA_Trundle_Jng_Irelia(Ratings): pass class NA_Trundle_Jng_Ivern(Ratings): pass class NA_Trundle_Jng_Janna(Ratings): pass class NA_Trundle_Jng_JarvanIV(Ratings): pass class NA_Trundle_Jng_Jax(Ratings): pass class NA_Trundle_Jng_Jayce(Ratings): pass class NA_Trundle_Jng_Jhin(Ratings): pass class NA_Trundle_Jng_Jinx(Ratings): pass class NA_Trundle_Jng_Kalista(Ratings): pass class NA_Trundle_Jng_Karma(Ratings): pass class NA_Trundle_Jng_Karthus(Ratings): pass class NA_Trundle_Jng_Kassadin(Ratings): pass class NA_Trundle_Jng_Katarina(Ratings): pass class NA_Trundle_Jng_Kayle(Ratings): pass class NA_Trundle_Jng_Kayn(Ratings): pass class NA_Trundle_Jng_Kennen(Ratings): pass class NA_Trundle_Jng_Khazix(Ratings): pass class NA_Trundle_Jng_Kindred(Ratings): pass class NA_Trundle_Jng_Kled(Ratings): pass class NA_Trundle_Jng_KogMaw(Ratings): pass class NA_Trundle_Jng_Leblanc(Ratings): pass class NA_Trundle_Jng_LeeSin(Ratings): pass class NA_Trundle_Jng_Leona(Ratings): pass class NA_Trundle_Jng_Lissandra(Ratings): pass class NA_Trundle_Jng_Lucian(Ratings): pass class NA_Trundle_Jng_Lulu(Ratings): pass class NA_Trundle_Jng_Lux(Ratings): pass class NA_Trundle_Jng_Malphite(Ratings): pass class NA_Trundle_Jng_Malzahar(Ratings): pass class NA_Trundle_Jng_Maokai(Ratings): pass class NA_Trundle_Jng_MasterYi(Ratings): pass class NA_Trundle_Jng_MissFortune(Ratings): pass class NA_Trundle_Jng_MonkeyKing(Ratings): pass class NA_Trundle_Jng_Mordekaiser(Ratings): pass class NA_Trundle_Jng_Morgana(Ratings): pass class NA_Trundle_Jng_Nami(Ratings): pass class NA_Trundle_Jng_Nasus(Ratings): pass class NA_Trundle_Jng_Nautilus(Ratings): pass class NA_Trundle_Jng_Nidalee(Ratings): pass class NA_Trundle_Jng_Nocturne(Ratings): pass class NA_Trundle_Jng_Nunu(Ratings): pass class NA_Trundle_Jng_Olaf(Ratings): pass class NA_Trundle_Jng_Orianna(Ratings): pass class NA_Trundle_Jng_Ornn(Ratings): pass class NA_Trundle_Jng_Pantheon(Ratings): pass class NA_Trundle_Jng_Poppy(Ratings): pass class NA_Trundle_Jng_Quinn(Ratings): pass class NA_Trundle_Jng_Rakan(Ratings): pass class NA_Trundle_Jng_Rammus(Ratings): pass class NA_Trundle_Jng_RekSai(Ratings): pass class NA_Trundle_Jng_Renekton(Ratings): pass class NA_Trundle_Jng_Rengar(Ratings): pass class NA_Trundle_Jng_Riven(Ratings): pass class NA_Trundle_Jng_Rumble(Ratings): pass class NA_Trundle_Jng_Ryze(Ratings): pass class NA_Trundle_Jng_Sejuani(Ratings): pass class NA_Trundle_Jng_Shaco(Ratings): pass class NA_Trundle_Jng_Shen(Ratings): pass class NA_Trundle_Jng_Shyvana(Ratings): pass class NA_Trundle_Jng_Singed(Ratings): pass class NA_Trundle_Jng_Sion(Ratings): pass class NA_Trundle_Jng_Sivir(Ratings): pass class NA_Trundle_Jng_Skarner(Ratings): pass class NA_Trundle_Jng_Sona(Ratings): pass class NA_Trundle_Jng_Soraka(Ratings): pass class NA_Trundle_Jng_Swain(Ratings): pass class NA_Trundle_Jng_Syndra(Ratings): pass class NA_Trundle_Jng_TahmKench(Ratings): pass class NA_Trundle_Jng_Taliyah(Ratings): pass class NA_Trundle_Jng_Talon(Ratings): pass class NA_Trundle_Jng_Taric(Ratings): pass class NA_Trundle_Jng_Teemo(Ratings): pass class NA_Trundle_Jng_Thresh(Ratings): pass class NA_Trundle_Jng_Tristana(Ratings): pass class NA_Trundle_Jng_Trundle(Ratings): pass class NA_Trundle_Jng_Tryndamere(Ratings): pass class NA_Trundle_Jng_TwistedFate(Ratings): pass class NA_Trundle_Jng_Twitch(Ratings): pass class NA_Trundle_Jng_Udyr(Ratings): pass class NA_Trundle_Jng_Urgot(Ratings): pass class NA_Trundle_Jng_Varus(Ratings): pass class NA_Trundle_Jng_Vayne(Ratings): pass class NA_Trundle_Jng_Veigar(Ratings): pass class NA_Trundle_Jng_Velkoz(Ratings): pass class NA_Trundle_Jng_Vi(Ratings): pass class NA_Trundle_Jng_Viktor(Ratings): pass class NA_Trundle_Jng_Vladimir(Ratings): pass class NA_Trundle_Jng_Volibear(Ratings): pass class NA_Trundle_Jng_Warwick(Ratings): pass class NA_Trundle_Jng_Xayah(Ratings): pass class NA_Trundle_Jng_Xerath(Ratings): pass class NA_Trundle_Jng_XinZhao(Ratings): pass class NA_Trundle_Jng_Yasuo(Ratings): pass class NA_Trundle_Jng_Yorick(Ratings): pass class NA_Trundle_Jng_Zac(Ratings): pass class NA_Trundle_Jng_Zed(Ratings): pass class NA_Trundle_Jng_Ziggs(Ratings): pass class NA_Trundle_Jng_Zilean(Ratings): pass class NA_Trundle_Jng_Zyra(Ratings): pass

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8

65f044cab1a45760ec4b033180be0b3cf797780b

78

py

Python

Python/Tests/TestData/Grammar/FromFuture26.py

techkey/PTVS

8355e67eedd8e915ca49bd38a2f36172696fd903

[ "Apache-2.0" ]

695

2019-05-06T23:49:37.000Z

2022-03-30T01:56:00.000Z

Python/Tests/TestData/Grammar/FromFuture26.py

techkey/PTVS

8355e67eedd8e915ca49bd38a2f36172696fd903

[ "Apache-2.0" ]

1,672

2019-05-06T21:09:38.000Z

2022-03-31T23:16:04.000Z

Python/Tests/TestData/Grammar/FromFuture26.py

techkey/PTVS

8355e67eedd8e915ca49bd38a2f36172696fd903

[ "Apache-2.0" ]

186

2019-05-13T03:17:37.000Z

2022-03-31T16:24:05.000Z

from __future__ import print_function from __future__ import unicode_literals

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5a5afc24bc9587b85669f5022f6301ccb09e3a83

16,020

py

Python

tests/notWorking/test_IDFgeneration_MultipleZone/test_accim_IDFgeneration_MultipleZone.py

dsanchez-garcia/accim

0f64df81a9ecd8424317f9213d90d802e8282c9f

[ "MIT" ]

null

tests/notWorking/test_IDFgeneration_MultipleZone/test_accim_IDFgeneration_MultipleZone.py

dsanchez-garcia/accim

0f64df81a9ecd8424317f9213d90d802e8282c9f

[ "MIT" ]

null

tests/notWorking/test_IDFgeneration_MultipleZone/test_accim_IDFgeneration_MultipleZone.py

dsanchez-garcia/accim

0f64df81a9ecd8424317f9213d90d802e8282c9f

[ "MIT" ]

null

import pytest from accim.sim import accim_Main def test_genIDFMultipleZone(): pass from eppy.modeleditor import IDF from os import listdir import numpy iddfile = 'C:/EnergyPlusV9-5-0/Energy+.idd' IDF.setiddname(iddfile) z = accim_Main.accimJob( filename_temp='TestModel_MultipleZone', ScriptType='mz', EnergyPlus_version='ep95', verboseMode=False) z.addEMSProgramsMultipleZone(verboseMode=False) z.saveaccim(verboseMode=False) idf1 = IDF('TestModel_MultipleZone_pymod.idf') SetInputData = ( [program for program in idf1.idfobjects['EnergyManagementSystem:Program'] if program.Name == 'SetInputData']) z.genIDFMultipleZone( AdapStand=[1, 2], CAT=[1, 80], ComfMod=[1, 2], HVACmode=[0, 2], VentCtrl=[0, 1], VSToffset=[0, 1], MinOToffset=[5, 10], MaxWindSpeed=[5, 10], ASTtol_start=0.1, ASTtol_end_input=0.2, ASTtol_steps=0.1, NameSuffix='whatever', verboseMode=False, confirmGen=True) filelist_pymod = ([file for file in listdir() if file.endswith('_pymod.idf')]) filelist_pymod = ([file.split('.idf')[0] for file in filelist_pymod]) print(filelist_pymod) AdapStand_List = [1, 2] CAT_List = [1, 80] ComfMod_List = [1, 80] HVACmode_List = [0, 2] VentCtrl_List = [0, 1] VSToffset_List = [0, 1] MinOToffset_List = [5, 10] MaxWindSpeed_List = [5, 10] ASTtol_value_from = 0.1 ASTtol_value_to = 0.2 ASTtol_value_steps = 0.1 suffix = 'whatever' for file in filelist_pymod: filename = file fname1 = filename + '.idf' idf1 = IDF(fname1) # print(filename) SetInputData = ( [program for program in idf1.idfobjects['EnergyManagementSystem:Program'] if program.Name == 'SetInputData']) for AdapStand_value in AdapStand_List: assert SetInputData[0].Program_Line_1 == 'set AdapStand = ' + repr(AdapStand_value) if AdapStand_value == 0: assert SetInputData[0].Program_Line_2 == 'set CAT = 1' assert SetInputData[0].Program_Line_3 == 'set ComfMod = 0' for HVACmode_value in HVACmode_List: assert SetInputData[0].Program_Line_4 == 'set HVACmode = ' + repr(HVACmode_value) if HVACmode_value == 0: for ASTtol_value in numpy.arange(ASTtol_value_from, ASTtol_value_to, ASTtol_value_steps): assert SetInputData[0].Program_Line_5 == 'set ACSTtol = ' + repr(-ASTtol_value) assert SetInputData[0].Program_Line_6 == 'set AHSTtol = ' + repr(ASTtol_value) outputname = ( filename + '[AS_CTE' + '[CA_X' + '[CM_X' + '[HM_' + repr(HVACmode_value) + '[VC_X' + '[VO_X' + '[MT_X' + '[MW_X' + '[AT_' + repr(ASTtol_value) + suffix + '.idf' ) assert outputname == filename else: for VentCtrl_value in VentCtrl_List: assert SetInputData[0].Program_Line_5 == 'set VentCtrl = ' + repr(VentCtrl_value) for VSToffset_value in VSToffset_List: assert SetInputData[0].Program_Line_6 == 'set VSToffset = ' + repr(VSToffset_value) for MinOToffset_value in MinOToffset_List: assert SetInputData[0].Program_Line_7 == 'set MinOToffset = ' + repr(MinOToffset_value) for MaxWindSpeed_value in MaxWindSpeed_List: assert SetInputData[0].Program_Line_8 == 'set MaxWindSpeed = ' + repr( MaxWindSpeed_value) for ASTtol_value in numpy.arange(ASTtol_value_from, ASTtol_value_to, ASTtol_value_steps): assert SetInputData[0].Program_Line_9 == 'set ACSTtol = ' + repr(-ASTtol_value) assert SetInputData[0].Program_Line_10 == 'set AHSTtol = ' + repr(ASTtol_value) outputname = ( filename + '[AS_CTE' + '[CA_X' + '[CM_X' + '[HM_' + repr(HVACmode_value) + '[VC_' + repr(VentCtrl_value) + '[VO_' + repr(VSToffset_value) + '[MT_' + repr(MinOToffset_value) + '[MW_' + repr(MaxWindSpeed_value) + '[AT_' + repr(ASTtol_value) + suffix + '.idf' ) assert outputname == filename elif AdapStand_value == 1: for CAT_value in CAT_List: if CAT_value not in range(0, 4): continue else: assert SetInputData[0].Program_Line_2 == 'set CAT = ' + repr(CAT_value) for ComfMod_value in ComfMod_List: assert SetInputData[0].Program_Line_3 == 'set ComfMod = ' + repr(ComfMod_value) for HVACmode_value in HVACmode_List: assert SetInputData[0].Program_Line_4 == 'set HVACmode = ' + repr(HVACmode_value) if HVACmode_value == 0: for ASTtol_value in numpy.arange(ASTtol_value_from, ASTtol_value_to, ASTtol_value_steps): assert SetInputData[0].Program_Line_9 == 'set ACSTtol = ' + repr(-ASTtol_value) assert SetInputData[0].Program_Line_10 == 'set AHSTtol = ' + repr(ASTtol_value) outputname = ( filename + '[AS_EN16798' + '[CA_' + repr(CAT_value) + '[CM_' + repr(ComfMod_value) + '[HM_' + repr(HVACmode_value) + '[VC_X' + '[VO_X' + '[MT_X' + '[MW_X' + '[AT_' + repr(ASTtol_value) + suffix + '.idf' ) assert outputname == filename else: for VentCtrl_value in VentCtrl_List: assert SetInputData[0].Program_Line_5 == 'set VentCtrl = ' + repr(VentCtrl_value) for VSToffset_value in VSToffset_List: assert SetInputData[0].Program_Line_6 == 'set VSToffset = ' + repr(VSToffset_value) for MinOToffset_value in MinOToffset_List: assert SetInputData[0].Program_Line_7 == 'set MinOToffset = ' + repr( MinOToffset_value) for MaxWindSpeed_value in MaxWindSpeed_List: assert SetInputData[0].Program_Line_8 == 'set MaxWindSpeed = ' + repr( MaxWindSpeed_value) for ASTtol_value in numpy.arange(ASTtol_value_from, ASTtol_value_to, ASTtol_value_steps): assert SetInputData[0].Program_Line_9 == 'set ACSTtol = ' + repr( -ASTtol_value) assert SetInputData[0].Program_Line_10 == 'set AHSTtol = ' + repr( ASTtol_value) outputname = ( filename + '[AS_EN16798' + '[CA_' + repr(CAT_value) + '[CM_' + repr(ComfMod_value) + '[HM_' + repr(HVACmode_value) + '[VC_' + repr(VentCtrl_value) + '[VO_' + repr(VSToffset_value) + '[MT_' + repr(MinOToffset_value) + '[MW_' + repr(MaxWindSpeed_value) + '[AT_' + repr(ASTtol_value) + suffix + '.idf' ) assert outputname == filename elif AdapStand_value == 2: for CAT_value in CAT_List: if CAT_value not in range(80, 91, 10): continue else: assert SetInputData[0].Program_Line_2 == 'set CAT = ' + repr(CAT_value) for ComfMod_value in ComfMod_List: assert SetInputData[0].Program_Line_3 == 'set ComfMod = ' + repr(ComfMod_value) for HVACmode_value in HVACmode_List: assert SetInputData[0].Program_Line_4 == 'set HVACmode = ' + repr(HVACmode_value) if HVACmode_value == 0: for ASTtol_value in numpy.arange(ASTtol_value_from, ASTtol_value_to, ASTtol_value_steps): assert SetInputData[0].Program_Line_9 == 'set ACSTtol = ' + repr(-ASTtol_value) assert SetInputData[0].Program_Line_10 == 'set AHSTtol = ' + repr(ASTtol_value) outputname = ( filename + '[AS_EN16798' + '[CA_' + repr(CAT_value) + '[CM_' + repr(ComfMod_value) + '[HM_' + repr(HVACmode_value) + '[VC_X' + '[VO_X' + '[MT_X' + '[MW_X' + '[AT_' + repr(ASTtol_value) + suffix + '.idf' ) assert outputname == filename else: for VentCtrl_value in VentCtrl_List: assert SetInputData[0].Program_Line_5 == 'set VentCtrl = ' + repr(VentCtrl_value) for VSToffset_value in VSToffset_List: assert SetInputData[0].Program_Line_6 == 'set VSToffset = ' + repr(VSToffset_value) for MinOToffset_value in MinOToffset_List: assert SetInputData[0].Program_Line_7 == 'set MinOToffset = ' + repr( MinOToffset_value) for MaxWindSpeed_value in MaxWindSpeed_List: assert SetInputData[0].Program_Line_8 == 'set MaxWindSpeed = ' + repr( MaxWindSpeed_value) for ASTtol_value in numpy.arange(ASTtol_value_from, ASTtol_value_to, ASTtol_value_steps): assert SetInputData[0].Program_Line_9 == 'set ACSTtol = ' + repr( -ASTtol_value) assert SetInputData[0].Program_Line_10 == 'set AHSTtol = ' + repr( ASTtol_value) outputname = ( filename + '[AS_ASHRAE55' + '[CA_' + repr(CAT_value) + '[CM_' + repr(ComfMod_value) + '[HM_' + repr(HVACmode_value) + '[VC_' + repr(VentCtrl_value) + '[VO_' + repr(VSToffset_value) + '[MT_' + repr(MinOToffset_value) + '[MW_' + repr(MaxWindSpeed_value) + '[AT_' + repr(ASTtol_value) + suffix + '.idf' ) assert outputname == filename

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9

5a7605a38f2b19e0e3a6e5405961dac6082d6a40

27,031

py

Python

Wind_SMS_software/AFM_ERPA_processor/energy_resolved_pitch_angle/erpa_data.py

lynnbwilsoniii/Wind_Decom_Code

ef596644fe0ed3df5ff3b462602e7550a04323e2

[ "Apache-2.0" ]

null

Wind_SMS_software/AFM_ERPA_processor/energy_resolved_pitch_angle/erpa_data.py

lynnbwilsoniii/Wind_Decom_Code

ef596644fe0ed3df5ff3b462602e7550a04323e2

[ "Apache-2.0" ]

null

Wind_SMS_software/AFM_ERPA_processor/energy_resolved_pitch_angle/erpa_data.py

lynnbwilsoniii/Wind_Decom_Code

ef596644fe0ed3df5ff3b462602e7550a04323e2

[ "Apache-2.0" ]

null

#This module contains tools for getting the energy resolved pitch angle #distribution from Wind/STICS #define libraries to import import numpy as np import matplotlib.pyplot as plt import math import os import matplotlib.mlab as mlab import matplotlib import calendar import constants_pat as cnst from ion_mq_stats import ion_mq_stats #weird way to access function in .py file with same name as .py file import cPickle as pickle def get_erpa_data(STICS_data): ''' Function to get the data required to create an energy resolved pitch angle distribution from Wind/STICS data Arguments: STICS_data: an nd array of the STICS data for the current time period ''' ion_m, ion_q = ion_mq_stats(STICS_data['ion'][0]) #return mass [amu], charge [e] #determine how many unique time steps exist in current data set #(may need to change this later to be a subset of the input data?) leap_add=np.array([int(calendar.isleap(a)) for a in STICS_data['year']]) #isleap only works on scalar years, need to use list comprehension yearfrac=STICS_data['year']+(STICS_data['doy']-1.0)/(365.0+leap_add) unique_times=np.unique(yearfrac) n_time_steps=unique_times.size #prepare total time range string: for use in plotting later delta_t=STICS_data['delT'][0] #seconds start_yearfrac=yearfrac[0] - delta_t/(60.0*60.0*24.0*(365+calendar.isleap(np.floor(yearfrac[0])))) #yearfrac stop_yearfrac=yearfrac[-1] #yearfrac #Define STICS measurement parameters n_epq=32 #number of E/q steps in data n_telescope=3 #number of telescopes n_sector=16 #number of sectors epq_table=np.unique(STICS_data['eoq']) #keV/e #Define edges for pitch angle bins PA_resolution=15 #deg PA_low_bin_edges=np.arange(0, 180, PA_resolution) #deg total_ERPA=np.zeros( (len(PA_low_bin_edges), n_epq) ) #sum contribution to ERPA over time (not properly weighted) viewtime_tot_arr=total_ERPA.copy() #preallocate view time array to same size as "total_ERPA", store total observation time #for each PA-E/q bin #Compute velocity direction/ solid angle for each telescope and sector #combination for use in the loop bin_v_dir_data_type=np.dtype([ ('vx', np.float64) ,( 'vy', np.float64), ('vz', np.float64)]) bin_v_dir_arr=np.zeros( (n_telescope, n_sector), bin_v_dir_data_type) #this is a 2D array of unit vectors #pretty sweet that we can make it so easily in python (can reference both with component names and 2D indexing!) bin_SA_arr=np.zeros( (n_telescope,n_sector) ) #Define central theta/phi angle for each STICS bin telescope_num_arr=np.array([0,1,2]) #store index number of telescopes theta_bin_width=53.0 * np.pi/180.0 #rad, bin width in polar direction theta_lower_bin_edges=np.array([90-79.5, 90-26.5, 90 + 26.5]) * np.pi/180.0 #rad, polar angle theta_upper_bin_edges=theta_lower_bin_edges.copy() + theta_bin_width #rad theta_mid_bin=(theta_upper_bin_edges + theta_lower_bin_edges) / (2.0) #Azimuth direction bins #Define zero degrees in azimuth as the sunward facing sector center (sector 9) sector_num_arr = np.arange(0,16,1) #store index number of sectors phi_bin_width=22.5*np.pi/180.0 #rad, azimuthal sector width phi_mid_bin=np.arange(202.5, 202.5-360, -22.5)*np.pi/180.0 ind1=np.where(phi_mid_bin < 0.0) phi_mid_bin[ind1]=phi_mid_bin[ind1]+2.0*np.pi #set azimuth range to [0,360) deg #Loop over all bins and compute unit vector for i in xrange(len(theta_mid_bin)): #loop over telescope for j in xrange(len(phi_mid_bin)): #loop over sector #make sure sector numbers line up with indices! #mid bin angle correspond to look direction, we need to #take (-) of that to get observed velocity/flow direction bin_v_dir_arr[telescope_num_arr[i], sector_num_arr[j]]['vx']=-np.sin(theta_mid_bin[i])*np.cos(phi_mid_bin[j]) bin_v_dir_arr[telescope_num_arr[i], sector_num_arr[j]]['vy']=-np.sin(theta_mid_bin[i])*np.sin(phi_mid_bin[j]) bin_v_dir_arr[telescope_num_arr[i], sector_num_arr[j]]['vz']=-np.cos(theta_mid_bin[i]) bin_SA_arr[telescope_num_arr[i], sector_num_arr[j]]=phi_bin_width*(np.cos(theta_lower_bin_edges[i]) - np.cos(theta_upper_bin_edges[i]) ) #solid angle, steradians #End of loop over j #End of loop over i #loop over number of unique time steps for i in xrange(n_time_steps): current_stop_yearfrac= unique_times[i] #this should be the stop time for the current time step #find indices of STICS data that are in current time step small_num=1.0E-5 STICS_time_ind=np.where( (yearfrac > current_stop_yearfrac -small_num) & (yearfrac < current_stop_yearfrac + small_num) )[0] current_start_yearfrac= current_stop_yearfrac - STICS_data['delT'][STICS_time_ind[0]]/(60.0*60.0*24.0*(365+calendar.isleap(np.floor(yearfrac[0])))) #Load in MFIcurr data for the relevant year #mag_data_dir= 'C:/Users/ptracy/Box Sync/00_postdoc_projects/Wind-STICS/wind_mfi/pickled/' mag_data_dir= '/Users/ptracy/Box Sync/00_postdoc_projects/Wind-STICS/wind_mfi/pickled/' #mac compatible #load in mag data takes a while if int(current_stop_yearfrac) == int(current_start_yearfrac): with open(mag_data_dir+'wind_mfi_'+str(int(current_stop_yearfrac))+'.pkl') as fp: mfi_data=pickle.load(fp) print 'after load mag erpa' #find all indices within current time range mag_ind=np.where( (mfi_data['yearfrac'] > current_start_yearfrac) & (mfi_data['yearfrac'] < current_stop_yearfrac) ) #returns tuple! #compute average component of B vector (GSE coords) ave_bx=np.mean(mfi_data['bx_gse (nT)'][mag_ind]) #nT -> may need to check for fill values and NaNs here... ave_by=np.mean(mfi_data['by_gse (nT)'][mag_ind]) #nT ave_bz=np.mean(mfi_data['bz_gse (nT)'][mag_ind]) #nT ave_b_vec=np.array([ave_bx, ave_by, ave_bz]) #nT elif int(current_stop_yearfrac)-1 == int(current_start_yearfrac): #spanning a year with open(mag_data_dir+'wind_mfi_'+str(int(current_start_yearfrac))+'.pkl') as fp: mfi_data_1=pickle.load(fp) with open(mag_data_dir+'wind_mfi_'+str(int(current_stop_yearfrac))+'.pkl') as fp: mfi_data_2=pickle.load(fp) #find indices form year 1 that are within time range mag_ind1=np.where(mfi_data_1['yearfrac'] > current_start_yearfrac) mag_ind2=np.where(mfi_data_2['yearfrac'] < current_stop_yearfrac) bx_combined_arr=np.concatenate( (mfi_data_1['bx_gse (nT)'][mag_ind1], mfi_data_2['bx_gse (nT)'][mag_ind2]), axis=0) by_combined_arr=np.concatenate( (mfi_data_1['by_gse (nT)'][mag_ind1], mfi_data_2['by_gse (nT)'][mag_ind2]), axis=0) bz_combined_arr=np.concatenate( (mfi_data_1['bz_gse (nT)'][mag_ind1], mfi_data_2['bz_gse (nT)'][mag_ind2]), axis=0) #compute average component of B vector (GSE coords) ave_bx=np.mean(bx_combined_arr) #nT -> may need to check for fill values and NaNs here... ave_by=np.mean(by_combined_arr) #nT ave_bz=np.mean(bz_combined_arr) #nT ave_b_vec=np.array([ave_bx, ave_by, ave_bz]) #nT else: raise NameError, 'strange yearfractions detected for mag data loading' #preallocate arrays used overwritten each time step ERPA_array_SA_wtd=np.zeros( (len(PA_low_bin_edges), n_epq) ) #keep track of SA weighted PSD in each PA-E/q bin SA_arr=np.zeros( (len(PA_low_bin_edges), n_epq) ) #keep track of total solid angle that observed each PA-Eq bin #Compute the pitch angle of each telescope and sector combo of Wind STICS for this time step #(and average b vector direction), need to loop over each telescope and sector combo of STICS, #but don't need to to it for every E/q step as look direction don't change between E/q steps. PA_for_telescope_sector=np.zeros((n_telescope,n_sector)) #preallocate to store PA value of each angular bin of STICS PA_bin_ind_for_telescope_sector=np.zeros((n_telescope,n_sector)) #preallocate to store PA value of each angular bin of STICS for tele_ind in xrange(n_telescope): #loop over telescope (tele_ind = telescope index) for sec_ind in xrange(n_sector): #loop over sector (sec_ind = sector index) v_unit_vec=np.array([bin_v_dir_arr[tele_ind,sec_ind]['vx'], bin_v_dir_arr[tele_ind,sec_ind]['vy'], bin_v_dir_arr[tele_ind,sec_ind]['vz']]) #call unit vec from array PA_for_telescope_sector[tele_ind,sec_ind]=np.arccos(np.dot(ave_b_vec, v_unit_vec)/ (np.linalg.norm(ave_b_vec) * np.linalg.norm(v_unit_vec)))*180.0/np.pi #deg PA_bin_ind=np.where(PA_low_bin_edges == np.floor(PA_for_telescope_sector[tele_ind,sec_ind]/PA_resolution)*PA_resolution) PA_bin_ind_for_telescope_sector[tele_ind,sec_ind]=PA_bin_ind[0] #store pitch angle bin indices of each angular bin of STICS #record total solid angle in each PA- E/q bin SA_arr[PA_bin_ind, :]=SA_arr[PA_bin_ind, :]+bin_SA_arr[tele_ind,sec_ind] #now we can use this PA_bin_ind for each E/q step in this angular bin #find all entries at current sector/telescope (for current time ind), this covers E/q steps epq_subind=np.where( (STICS_data[STICS_time_ind]['telescope']==tele_ind) & (STICS_data[STICS_time_ind]['sector']==sec_ind) )[0] #take first element of returned tuple for kk in xrange(len(epq_subind)): small_val=0.01 #small number, smaller than % difference of adjacent E/q steps (for np.where search) eoq_step_ind=np.where( (STICS_data['eoq'][STICS_time_ind[epq_subind[kk]]] > epq_table*(1.0-small_val)) & (STICS_data['eoq'][STICS_time_ind[epq_subind[kk]]] < epq_table*(1.0+small_val)) )[0] #"[0]" at end of where statement extracts 1D indices from tuple PSD_temp= ( ion_m**2/(2.0*epq_table[eoq_step_ind]*ion_q) ) * STICS_data['dJ'][STICS_time_ind[epq_subind[kk]]] #units of (amu^2/keV) * (1/(cm^2*sec*sr*keV) ) PSD_temp=PSD_temp*( (1/cnst.keV2eV)*(1/cnst.e2C)*(cnst.amu2kg**2) )*( (1/cnst.cm2m**2)*(1/cnst.keV2eV)*(1/cnst.e2C) ) #s^3/m^6 PSD_temp=PSD_temp*(cnst.km2m**6) #s^3/km^6 ERPA_array_SA_wtd[PA_bin_ind,eoq_step_ind]=ERPA_array_SA_wtd[PA_bin_ind, eoq_step_ind] + bin_SA_arr[tele_ind,sec_ind]*PSD_temp #sr* s^3/km^6 #End of loop over kk #End of loop over j #End of loop over i #Back to loop over time. #normalize PSD by solid angle (accounts for the weighting by solid angle done previously) ERPA_array=ERPA_array_SA_wtd/SA_arr #divide element by element #set NaN values to zero zero_SA_ind=np.where(SA_arr < 1.0E-10) if len(zero_SA_ind[0]) > 0: ERPA_array[zero_SA_ind]=0.0 #set NaN values to zero (works out #to be same as not including them in weighted average) #Need to weight each scan time by the accumulation time total_ERPA= total_ERPA + ERPA_array*STICS_data['delT'][STICS_time_ind[0]] # (s^3/km^6) * s #assume "delT" is same for all telescope/sector/epq bins in current time step nonzero_SA_ind=np.where(SA_arr > 0.0) if len(nonzero_SA_ind[0]) > 0: viewtime_tot_arr[nonzero_SA_ind]=viewtime_tot_arr[nonzero_SA_ind] + STICS_data['delT'][STICS_time_ind[0]] #s #End of loop over time steps final_ERPA=total_ERPA/viewtime_tot_arr #element by element division #ERPA bins that were never observed over the whole time period need to be seperately identified in the array. #We will set them to -1. zero_viewtime_ind=np.where(viewtime_tot_arr < 1.0E-5) #1.0E-5 is arbitrary low bound, just lower than single accum time if len(zero_viewtime_ind[0]) > 0: final_ERPA[zero_viewtime_ind]=-1.0 #should overwrite all remaining NaN values return final_ERPA, start_yearfrac, stop_yearfrac, delta_t # (s^3/km^6), at the moment def get_erpa_data_mag_input(STICS_data, mfi_data): ''' Function to get the data required to create an energy resolved pitch angle distribution from Wind/STICS data. Differs from "get_erpa_data" in that is takes a year of mfi data as an input so that the mfi data doesn't have to be loaded repeatedly with each call to get_erpa_data (speed improvement) Arguments: STICS_data: an nd array of the STICS data for the current time period mfi_data : mag data for the year of the current STICS data ''' ion_m, ion_q = ion_mq_stats(STICS_data['ion'][0]) #return mass [amu], charge [e] #determine how many unique time steps exist in current data set #(may need to change this later to be a subset of the input data?) leap_add=np.array([int(calendar.isleap(a)) for a in STICS_data['year']]) #isleap only works on scalar years, need to use list comprehension yearfrac=STICS_data['year']+(STICS_data['doy']-1.0)/(365.0+leap_add) unique_times=np.unique(yearfrac) n_time_steps=unique_times.size #prepare total time range string: for use in plotting later delta_t=STICS_data['delT'][0] #seconds start_yearfrac=yearfrac[0] - delta_t/(60.0*60.0*24.0*(365+calendar.isleap(np.floor(yearfrac[0])))) #yearfrac stop_yearfrac=yearfrac[-1] #yearfrac #Define STICS measurement parameters n_epq=32 #number of E/q steps in data n_telescope=3 #number of telescopes n_sector=16 #number of sectors epq_table=np.unique(STICS_data['eoq']) #keV/e #Define edges for pitch angle bins PA_resolution=15 #deg PA_low_bin_edges=np.arange(0, 180, PA_resolution) #deg total_ERPA=np.zeros( (len(PA_low_bin_edges), n_epq) ) #sum contribution to ERPA over time (not properly weighted) viewtime_tot_arr=total_ERPA.copy() #preallocate view time array to same size as "total_ERPA", store total observation time #for each PA-E/q bin #Compute velocity direction/ solid angle for each telescope and sector #combination for use in the loop bin_v_dir_data_type=np.dtype([ ('vx', np.float64) ,( 'vy', np.float64), ('vz', np.float64)]) bin_v_dir_arr=np.zeros( (n_telescope, n_sector), bin_v_dir_data_type) #this is a 2D array of unit vectors #pretty sweet that we can make it so easily in python (can reference both with component names and 2D indexing!) bin_SA_arr=np.zeros( (n_telescope,n_sector) ) #Define central theta/phi angle for each STICS bin telescope_num_arr=np.array([0,1,2]) #store index number of telescopes theta_bin_width=53.0 * np.pi/180.0 #rad, bin width in polar direction theta_lower_bin_edges=np.array([90-79.5, 90-26.5, 90 + 26.5]) * np.pi/180.0 #rad, polar angle theta_upper_bin_edges=theta_lower_bin_edges.copy() + theta_bin_width #rad theta_mid_bin=(theta_upper_bin_edges + theta_lower_bin_edges) / (2.0) #Azimuth direction bins #Define zero degrees in azimuth as the sunward facing sector center (sector 9) sector_num_arr = np.arange(0,16,1) #store index number of sectors phi_bin_width=22.5*np.pi/180.0 #rad, azimuthal sector width phi_mid_bin=np.arange(202.5, 202.5-360, -22.5)*np.pi/180.0 ind1=np.where(phi_mid_bin < 0.0) phi_mid_bin[ind1]=phi_mid_bin[ind1]+2.0*np.pi #set azimuth range to [0,360) deg #Loop over all bins and compute unit vector for i in xrange(len(theta_mid_bin)): #loop over telescope for j in xrange(len(phi_mid_bin)): #loop over sector #make sure sector numbers line up with indices! #mid bin angle correspond to look direction, we need to #take (-) of that to get observed velocity/flow direction bin_v_dir_arr[telescope_num_arr[i], sector_num_arr[j]]['vx']=-np.sin(theta_mid_bin[i])*np.cos(phi_mid_bin[j]) bin_v_dir_arr[telescope_num_arr[i], sector_num_arr[j]]['vy']=-np.sin(theta_mid_bin[i])*np.sin(phi_mid_bin[j]) bin_v_dir_arr[telescope_num_arr[i], sector_num_arr[j]]['vz']=-np.cos(theta_mid_bin[i]) bin_SA_arr[telescope_num_arr[i], sector_num_arr[j]]=phi_bin_width*(np.cos(theta_lower_bin_edges[i]) - np.cos(theta_upper_bin_edges[i]) ) #solid angle, steradians #End of loop over j #End of loop over i #loop over number of unique time steps for i in xrange(n_time_steps): current_stop_yearfrac= unique_times[i] #this should be the stop time for the current time step #find indices of STICS data that are in current time step small_num=1.0E-5 STICS_time_ind=np.where( (yearfrac > current_stop_yearfrac -small_num) & (yearfrac < current_stop_yearfrac + small_num) )[0] current_start_yearfrac= current_stop_yearfrac - STICS_data['delT'][STICS_time_ind[0]]/(60.0*60.0*24.0*(365+calendar.isleap(np.floor(yearfrac[0])))) #Load in MFIcurr data for the relevant year ##mag_data_dir= 'C:/Users/ptracy/Box Sync/00_postdoc_projects/Wind-STICS/wind_mfi/pickled/' #mag_data_dir= '/Users/ptracy/Box Sync/00_postdoc_projects/Wind-STICS/wind_mfi/pickled/' #mac compatible ##load in mag data takes a while #if int(current_stop_yearfrac) == int(current_start_yearfrac): # with open(mag_data_dir+'wind_mfi_'+str(int(current_stop_yearfrac))+'.pkl') as fp: # mfi_data=pickle.load(fp) # print 'after load mag erpa' #find all indices within current time range mag_ind=np.where( (mfi_data['yearfrac'] > current_start_yearfrac) & (mfi_data['yearfrac'] < current_stop_yearfrac) ) #returns tuple! #compute average component of B vector (GSE coords) ave_bx=np.mean(mfi_data['bx_gse (nT)'][mag_ind]) #nT -> may need to check for fill values and NaNs here... ave_by=np.mean(mfi_data['by_gse (nT)'][mag_ind]) #nT ave_bz=np.mean(mfi_data['bz_gse (nT)'][mag_ind]) #nT ave_b_vec=np.array([ave_bx, ave_by, ave_bz]) #nT #Don't have this functionality yet... ''' elif int(current_stop_yearfrac)-1 == int(current_start_yearfrac): #spanning a year with open(mag_data_dir+'wind_mfi_'+str(int(current_start_yearfrac))+'.pkl') as fp: mfi_data_1=pickle.load(fp) with open(mag_data_dir+'wind_mfi_'+str(int(current_stop_yearfrac))+'.pkl') as fp: mfi_data_2=pickle.load(fp) #find indices form year 1 that are within time range mag_ind1=np.where(mfi_data_1['yearfrac'] > current_start_yearfrac) mag_ind2=np.where(mfi_data_2['yearfrac'] < current_stop_yearfrac) bx_combined_arr=np.concatenate( (mfi_data_1['bx_gse (nT)'][mag_ind1], mfi_data_2['bx_gse (nT)'][mag_ind2]), axis=0) by_combined_arr=np.concatenate( (mfi_data_1['by_gse (nT)'][mag_ind1], mfi_data_2['by_gse (nT)'][mag_ind2]), axis=0) bz_combined_arr=np.concatenate( (mfi_data_1['bz_gse (nT)'][mag_ind1], mfi_data_2['bz_gse (nT)'][mag_ind2]), axis=0) #compute average component of B vector (GSE coords) ave_bx=np.mean(bx_combined_arr) #nT -> may need to check for fill values and NaNs here... ave_by=np.mean(by_combined_arr) #nT ave_bz=np.mean(bz_combined_arr) #nT ave_b_vec=np.array([ave_bx, ave_by, ave_bz]) #nT else: raise NameError, 'strange yearfractions detected for mag data loading' ''' #preallocate arrays used overwritten each time step ERPA_array_SA_wtd=np.zeros( (len(PA_low_bin_edges), n_epq) ) #keep track of SA weighted PSD in each PA-E/q bin SA_arr=np.zeros( (len(PA_low_bin_edges), n_epq) ) #keep track of total solid angle that observed each PA-Eq bin #Compute the pitch angle of each telescope and sector combo of Wind STICS for this time step #(and average b vector direction), need to loop over each telescope and sector combo of STICS, #but don't need to to it for every E/q step as look direction don't change between E/q steps. PA_for_telescope_sector=np.zeros((n_telescope,n_sector)) #preallocate to store PA value of each angular bin of STICS PA_bin_ind_for_telescope_sector=np.zeros((n_telescope,n_sector)) #preallocate to store PA value of each angular bin of STICS for tele_ind in xrange(n_telescope): #loop over telescope (tele_ind = telescope index) for sec_ind in xrange(n_sector): #loop over sector (sec_ind = sector index) v_unit_vec=np.array([bin_v_dir_arr[tele_ind,sec_ind]['vx'], bin_v_dir_arr[tele_ind,sec_ind]['vy'], bin_v_dir_arr[tele_ind,sec_ind]['vz']]) #call unit vec from array PA_for_telescope_sector[tele_ind,sec_ind]=np.arccos(np.dot(ave_b_vec, v_unit_vec)/ (np.linalg.norm(ave_b_vec) * np.linalg.norm(v_unit_vec)))*180.0/np.pi #deg PA_bin_ind=np.where(PA_low_bin_edges == np.floor(PA_for_telescope_sector[tele_ind,sec_ind]/PA_resolution)*PA_resolution) PA_bin_ind_for_telescope_sector[tele_ind,sec_ind]=PA_bin_ind[0] #store pitch angle bin indices of each angular bin of STICS #record total solid angle in each PA- E/q bin SA_arr[PA_bin_ind, :]=SA_arr[PA_bin_ind, :]+bin_SA_arr[tele_ind,sec_ind] #now we can use this PA_bin_ind for each E/q step in this angular bin #find all entries at current sector/telescope (for current time ind), this covers E/q steps epq_subind=np.where( (STICS_data[STICS_time_ind]['telescope']==tele_ind) & (STICS_data[STICS_time_ind]['sector']==sec_ind) )[0] #take first element of returned tuple for kk in xrange(len(epq_subind)): small_val=0.01 #small number, smaller than % difference of adjacent E/q steps (for np.where search) eoq_step_ind=np.where( (STICS_data['eoq'][STICS_time_ind[epq_subind[kk]]] > epq_table*(1.0-small_val)) & (STICS_data['eoq'][STICS_time_ind[epq_subind[kk]]] < epq_table*(1.0+small_val)) )[0] #"[0]" at end of where statement extracts 1D indices from tuple PSD_temp= ( ion_m**2/(2.0*epq_table[eoq_step_ind]*ion_q) ) * STICS_data['dJ'][STICS_time_ind[epq_subind[kk]]] #units of (amu^2/keV) * (1/(cm^2*sec*sr*keV) ) PSD_temp=PSD_temp*( (1/cnst.keV2eV)*(1/cnst.e2C)*(cnst.amu2kg**2) )*( (1/cnst.cm2m**2)*(1/cnst.keV2eV)*(1/cnst.e2C) ) #s^3/m^6 PSD_temp=PSD_temp*(cnst.km2m**6) #s^3/km^6 ERPA_array_SA_wtd[PA_bin_ind,eoq_step_ind]=ERPA_array_SA_wtd[PA_bin_ind, eoq_step_ind] + bin_SA_arr[tele_ind,sec_ind]*PSD_temp #sr* s^3/km^6 #End of loop over kk #End of loop over j #End of loop over i #Back to loop over time. #normalize PSD by solid angle (accounts for the weighting by solid angle done previously) ERPA_array=ERPA_array_SA_wtd/SA_arr #divide element by element #set NaN values to zero zero_SA_ind=np.where(SA_arr < 1.0E-10) if len(zero_SA_ind[0]) > 0: ERPA_array[zero_SA_ind]=0.0 #set NaN values to zero (works out #to be same as not including them in weighted average) #Need to weight each scan time by the accumulation time total_ERPA= total_ERPA + ERPA_array*STICS_data['delT'][STICS_time_ind[0]] # (s^3/km^6) * s #assume "delT" is same for all telescope/sector/epq bins in current time step nonzero_SA_ind=np.where(SA_arr > 0.0) if len(nonzero_SA_ind[0]) > 0: viewtime_tot_arr[nonzero_SA_ind]=viewtime_tot_arr[nonzero_SA_ind] + STICS_data['delT'][STICS_time_ind[0]] #s #End of loop over time steps final_ERPA=total_ERPA/viewtime_tot_arr #element by element division #ERPA bins that were never observed over the whole time period need to be seperately identified in the array. #We will set them to -1. zero_viewtime_ind=np.where(viewtime_tot_arr < 1.0E-5) #1.0E-5 is arbitrary low bound, just lower than single accum time if len(zero_viewtime_ind[0]) > 0: final_ERPA[zero_viewtime_ind]=-1.0 #should overwrite all remaining NaN values return final_ERPA, start_yearfrac, stop_yearfrac, delta_t # (s^3/km^6), at the moment def load_mag_data(start_year, stop_year, mag_data_dir): ''' Load in mag data for a given year range. This can be used in conjunction with get_erpa_data_mag_input.py INPUTS: start_year - start year of mag data stop_year - stop year of mag data ''' #Load in MFI data for the relevant year #mag_data_dir= 'C:/Users/ptracy/Box Sync/00_postdoc_projects/Wind-STICS/wind_mfi/pickled/' #mag_data_dir= '/Users/ptracy/Box Sync/00_postdoc_projects/Wind-STICS/wind_mfi/pickled/' #mac compatible #load in mag data takes a while if int(stop_year) == int(start_year): with open(mag_data_dir+'wind_mfi_'+str(int(stop_year))+'.pkl') as fp: mfi_data=pickle.load(fp) return mfi_data elif int(stop_year)-1 == int(start_year): #spanning a year with open(mag_data_dir+'wind_mfi_'+str(int(start_year))+'.pkl') as fp: mfi_data_1=pickle.load(fp) with open(mag_data_dir+'wind_mfi_'+str(int(stop_year))+'.pkl') as fp: mfi_data_2=pickle.load(fp) return mfi_data_1, mfi_data_2 else: raise NameError, 'strange yearfractions detected for mag data loading' return -999

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ce5a3834cc1c37d8a57ab4803e83d519bfffa431

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py

Python

tests/test_transformer/test_pipeline/test_pipeline_transformer.py

neil-tan/utensor_cgen

ffaf692bf6d1f8572039ad7e82e695f97b050cd2

[ "Apache-2.0" ]

null

tests/test_transformer/test_pipeline/test_pipeline_transformer.py

neil-tan/utensor_cgen

ffaf692bf6d1f8572039ad7e82e695f97b050cd2

[ "Apache-2.0" ]

null

tests/test_transformer/test_pipeline/test_pipeline_transformer.py

neil-tan/utensor_cgen

ffaf692bf6d1f8572039ad7e82e695f97b050cd2

[ "Apache-2.0" ]

null

from utensor_cgen.transformer import TransformerPipeline # three random tests def test_pipeline_1(methods): pipeline = TransformerPipeline(methods, {}) assert len(pipeline.pipeline) == len(methods) for transformer, method_name in zip(pipeline.pipeline, methods): assert isinstance(transformer, pipeline._TRANSFORMER_MAP[method_name]) def test_pipeline_2(methods): pipeline = TransformerPipeline(methods, {}) assert len(pipeline.pipeline) == len(methods) for transformer, method_name in zip(pipeline.pipeline, methods): assert isinstance(transformer, pipeline._TRANSFORMER_MAP[method_name]) def test_pipeline_3(methods): pipeline = TransformerPipeline(methods, {}) assert len(pipeline.pipeline) == len(methods) for transformer, method_name in zip(pipeline.pipeline, methods): assert isinstance(transformer, pipeline._TRANSFORMER_MAP[method_name])

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false

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null

0

1

0

1

0

null

0

8

ce6f234ea0153675aefda83abad7ba92492cbab4

5,463

py

Python

scripts/python/clock/arabic.py

jeremiahmarks/dangerzone

fe2946b8463ed018d2136ca0eb178161ad370565

[ "MIT" ]

1

2015-08-15T05:25:35.000Z

scripts/python/clock/arabic.py

jeremiahmarks/dangerzone

fe2946b8463ed018d2136ca0eb178161ad370565

[ "MIT" ]

null

scripts/python/clock/arabic.py

jeremiahmarks/dangerzone

fe2946b8463ed018d2136ca0eb178161ad370565

[ "MIT" ]

null

from fvh import MyTurtle def one(starth=0, startpos=(0,0), lm=None, height=40): if not lm: lm=MyTurtle() lm.pu() lm.goto(startpos) lm.seth(starth) lm.pd() unit=height/8.0 lm.fd(unit) lm.right(90) lm.fd(8*unit) lm.right(90) lm.fd(unit) lm.right(90) lm.fd(8*unit) def two(starth=0, startpos=(0,0), lm=None, height=40): if not lm: lm=MyTurtle() lm.pu() lm.goto(startpos) lm.seth(starth) lm.pd() unit=height/8.0 lm.fd(4*unit) #right 4 lm.right(90) lm.fd(4.5*unit) # d 4.5 lm.right(90) lm.fd(3*unit) #in 3 lm.left(90) lm.fd(2.5*unit) # d 2.5 lm.left(90) lm.fd(3*unit) # out 3 lm.right(90) lm.fd(unit) #down 1 lm.right(90) lm.fd(4*unit) lm.right(90) lm.fd(4.5*unit) lm.right(90) lm.fd(3*unit) lm.left(90) lm.fd(2.5*unit) lm.left(90) lm.fd(3*unit) lm.right(90) lm.fd(unit) def three(starth=0, startpos=(0,0), lm=None, height=40): if not lm: lm=MyTurtle() lm.pu() lm.goto(startpos) lm.seth(starth) lm.pd() unit=height/8.0 lm.fd(unit*4) lm.right(90) lm.fd(8*unit) lm.right(90) lm.fd(4*unit) lm.right(90) lm.fd(unit) lm.right(90) lm.fd(3*unit) lm.left(90) lm.fd(2.5*unit) lm.left(90) lm.fd(3*unit) lm.right(90) lm.fd(unit) lm.right(90) lm.fd(3*unit) lm.left(90) lm.fd(2.5*unit) lm.left(90) lm.fd(3*unit) lm.right(90) lm.fd(unit) def four(starth=0, startpos=(0,0), lm=None, height=40): if not lm: lm=MyTurtle() lm.pu() lm.goto(startpos) lm.seth(starth) lm.pd() unit=height/8.0 lm.fd(unit) lm.right(90) lm.fd(3*unit) lm.left(90) lm.fd(2*unit) lm.left(90) lm.fd(3*unit) lm.right(90) lm.fd(unit) lm.right(90) lm.fd(8*unit) lm.right(90) lm.fd(unit) lm.right(90) lm.fd(4*unit) lm.left(90) lm.fd(3*unit) lm.right(90) lm.fd(4*unit) def five(starth=0, startpos=(0,0), lm=None, height=40): if not lm: lm=MyTurtle() lm.pu() lm.goto(startpos) lm.seth(starth) lm.pd() unit=height/8.0 lm.fd(4*unit) lm.right(90) lm.fd(unit) lm.right(90) lm.fd(3*unit) lm.left(90) lm.fd(2.5*unit) lm.left(90) lm.fd(3*unit) lm.right(90) lm.fd(4.5*unit) lm.right(90) lm.fd(4*unit) lm.right(90) lm.fd(unit) lm.right(90) lm.fd(3*unit) lm.left(90) lm.fd(2.5*unit) lm.left(90) lm.fd(3*unit) lm.right(90) lm.fd(4.5*unit) def six(starth=0, startpos=(0,0), lm=None, height=40): if not lm: lm=MyTurtle() lm.pu() lm.goto(startpos) lm.seth(starth) lm.pd() unit=height/8.0 lm.fd(4*unit) lm.right(90) lm.fd(unit) lm.right(90) lm.fd(3*unit) lm.left(90) lm.fd(6*unit) lm.left(90) lm.fd(2*unit) lm.left(90) lm.fd(2*unit) lm.left(90) lm.fd(2*unit) lm.right(90) lm.fd(unit) lm.right(90) lm.fd(3*unit) lm.right(90) lm.fd(4*unit) lm.right(90) lm.fd(4*unit) lm.right(90) lm.fd(8*unit) def seven(starth=0, startpos=(0,0), lm=None, height=40): if not lm: lm=MyTurtle() lm.pu() lm.goto(startpos) lm.seth(starth) lm.pd() unit=height/8.0 lm.fd(4*unit) lm.right(90) lm.fd(8*unit) lm.right(90) lm.fd(unit) lm.right(90) lm.fd(7*unit) lm.left(90) lm.fd(3*unit) lm.right(90) lm.fd(unit) def eight(starth=0, startpos=(0,0), lm=None, height=40): if not lm: lm=MyTurtle() lm.pu() lm.goto(startpos) lm.seth(starth) lm.pd() unit=height/8.0 lm.fd(4*unit) lm.right(90) lm.fd(8*unit) lm.right(90) lm.fd(unit) lm.pu() lm.right(90) lm.fd(unit) lm.pd() for x in range(2): lm.fd(2.5*unit) lm.left(90) lm.fd(2*unit) lm.left(90) lm.pu() lm.fd(3.5*unit) lm.pd() for x in range(2): lm.fd(2.5*unit) lm.left(90) lm.fd(2*unit) lm.left(90) lm.pu() lm.right(180) lm.fd(4.5*unit) lm.pd() lm.right(90) lm.fd(3*unit) lm.right(90) lm.fd(8*unit) def nine(starth=0, startpos=(0,0), lm=None, height=40): if not lm: lm=MyTurtle() lm.pu() lm.goto(startpos) lm.seth(starth) lm.pd() unit=height/8.0 lm.fd(4*unit) lm.right(90) lm.fd(8*unit) lm.right(90) lm.fd(4*unit) lm.right(90) lm.fd(unit) lm.right(90) lm.fd(3*unit) lm.left(90) lm.fd(6*unit) lm.left(90) lm.fd(2*unit) lm.left(90) lm.fd(2*unit) lm.left(90) lm.fd(2*unit) lm.right(90) lm.fd(unit) lm.right(90) lm.fd(3*unit) lm.right(90) lm.fd(4*unit) def zero(starth=0, startpos=(0,0), lm=None, height=40): if not lm: lm=MyTurtle() lm.pu() lm.goto(startpos) lm.seth(starth) lm.pd() unit=height/8.0 lm.fd(4*unit) lm.right(90) lm.fd(8*unit) lm.right(90) lm.fd(unit) lm.pu() lm.right(90) lm.fd(unit) lm.pd() lm.fd(6*unit) lm.left(90) lm.fd(2*unit) lm.left(90) lm.fd(6*unit) lm.left(90) lm.fd(2*unit) lm.pu() lm.right(90) lm.fd(unit) lm.pd() lm.right(90) lm.fd(3*unit) lm.right(90) lm.fd(8*unit)

18.332215

56

0.517298

1,024

5,463

2.759766

0.047852

0.145789

0.186837

0.229653

0.963553

0.954706

0.942321

0.929229

0.92569

0

0.092344

0.29233

5,463

297

57

18.393939

0.638645

0.006407

0

0.947735

0

1

0.034843

false

0

0.003484

0

0.038328

0

null

0

1

0

1

0

null

0

9

0c91b13d020701e2ad32c9d24372753782fc76f7

3,669

py

Python

all_sanitize.py

app-git-hub/pyCSV

172f796defaa008b6be0202bf47ead760da49fe0

[ "Unlicense" ]

null

all_sanitize.py

app-git-hub/pyCSV

172f796defaa008b6be0202bf47ead760da49fe0

[ "Unlicense" ]

null

all_sanitize.py

app-git-hub/pyCSV

172f796defaa008b6be0202bf47ead760da49fe0

[ "Unlicense" ]

null

masterPBdict = dict() def writeDict(dictName, fileHandle): for key, value in dictName.items(): final = "" try: value.sort() except AttributeError: final+= "%(value)s: %(key)s" % locals() else: # print only the longest name, cz it is most descriptive # final+= "\n" fileHandle.write(final) del final return def clean(aLine): global masterPBdict, longestName name, sep, num = aLine.partition("\t") longestName = max(len(name), longestName) try: masterPBdict[num] # wait if number exists in DB except KeyError: masterPBdict[num] = name # if number is uniques, create key: value pair else: try: masterPBdict[num].append(name) # num is duplicae, so appended name to old name list except AttributeError: old = masterPBdict.pop(num) # replace str by list and masterPBdict[num] = [old] masterPBdict[num].append(name) return with open("all.tsv", mode="rt", encoding="UTF-8") as h: for _ in range(950): x = h.readline() if len(x) > 0: clean(x) with open("clean.txt", mode="wt", encoding="UTF-8") as o: writeDict(masterPBdict, o) """ masterPBdict = dict() def writeDict(dictName, fileHandle): for key, value in dictName.items(): final = "" try: value.sort() except AttributeError: final+= "%(value)s: %(key)s" % locals() else: for index, aName in enumerate( list(set(value)) ): if len(value)//2-1 == index: final+= "%(aName)s: %(key)s" % locals() else: final+= "%(aName)s" % locals() # final+= "\n" fileHandle.write(final) del final return def clean(aLine): global masterPBdict name, sep, num = aLine.partition("\t") try: masterPBdict[num] # wait if number exists in DB except KeyError: masterPBdict[num] = name # if number is uniques, create key: value pair else: try: masterPBdict[num].append(name) # num is duplicae, so appended name to old name list except AttributeError: old = masterPBdict.pop(num) # replace str by list and masterPBdict[num] = [old] masterPBdict[num].append(name) return with open("all.tsv", mode="rt", encoding="UTF-8") as h: for _ in range(950): x = h.readline() if len(x) > 0: clean(x) with open("clean.txt", mode="wt", encoding="UTF-8") as o: writeDict(masterPBdict, o) """ """ masterPBdict = dict() longestName = 0 def pretty(num, names, mode='s'): midpt = len(names)//2-1 for index, aName in enumerate(names): if mode is 's': if midpt == index: s+= "%(aName)s: %(num)s" % locals() else: s+= "%(aName)s" % locals() else: global longestName longestName+= 5 if midpt == index: s = "%(aName)s: " + "-"*longestName-len(aName) + " %(num)s" print(s % locals()) elif index > midpt: lower() else: upper() print(s) return def writeDict(d): for key, value in d.items(): try: value.sort() except AttributeError: print("%(value)s: %(key)s" % locals()) else: pretty(key, value, mode="s") return def clean(aLine): global masterPBdict, longestName name, sep, num = aLine.partition("\t") longestName = max(len(name), longestName) try: masterPBdict[num] # wait if number exists in DB except KeyError: masterPBdict[num] = name # if number is uniques, create key: value pair else: try: masterPBdict[num].append(name) # num is duplicae, so appended name to old name list except AttributeError: old = masterPBdict.pop(num) # replace str by list and masterPBdict[num] = [old] masterPBdict[num].append(name) return with open("all.tsv", mode="rt", encoding="UTF-8") as h: for _ in range(950): x = h.readline() if len(x) > 0: clean(x) writeDict(masterPBdict) """

23.221519

86

0.641319

513

3,669

4.580897

0.189084

0.095745

0.028085

0.06383

0.840426

0.8

0.764681

0

0.007917

0.208231

3,669

158

87

23.221519

0.801033

0.058599

0

0.314286

0

0.059887

0

null

0

null

0

null

0

1

0

null

0

1

0

7

0cd2101069b08ad8894195383dce6c5b3fe8b64e

11,122

py

Python

tests/integration/offer/absolute_benefit_tests.py

endgame/django-oscar

e5d78436e20b55902537a6cc82edf4e22568f9d6

[ "BSD-3-Clause" ]

null

tests/integration/offer/absolute_benefit_tests.py

endgame/django-oscar

e5d78436e20b55902537a6cc82edf4e22568f9d6

[ "BSD-3-Clause" ]

null

tests/integration/offer/absolute_benefit_tests.py

endgame/django-oscar

e5d78436e20b55902537a6cc82edf4e22568f9d6

[ "BSD-3-Clause" ]

1

2019-07-10T06:32:14.000Z

from decimal import Decimal as D from django.test import TestCase from django_dynamic_fixture import G from oscar.apps.offer import models from oscar.apps.basket.models import Basket from oscar_testsupport.factories import create_product class TestAnAbsoluteDiscountAppliedWithCountCondition(TestCase): def setUp(self): range = models.Range.objects.create( name="All products", includes_all_products=True) self.condition = models.CountCondition.objects.create( range=range, type=models.Condition.COUNT, value=2) self.benefit = models.AbsoluteDiscountBenefit.objects.create( range=range, type=models.Benefit.FIXED, value=D('3.00')) self.basket = G(Basket) def test_applies_correctly_to_empty_basket(self): discount = self.benefit.apply(self.basket, self.condition) self.assertEqual(D('0.00'), discount) self.assertEqual(0, self.basket.num_items_with_discount) self.assertEqual(0, self.basket.num_items_without_discount) def test_applies_correctly_to_basket_which_matches_condition(self): for product in [create_product(price=D('12.00'))]: self.basket.add_product(product, 2) discount = self.benefit.apply(self.basket, self.condition) self.assertEqual(D('3.00'), discount) self.assertEqual(2, self.basket.num_items_with_discount) self.assertEqual(0, self.basket.num_items_without_discount) def test_applies_correctly_to_basket_which_exceeds_condition(self): for product in [create_product(price=D('12.00')), create_product(price=D('10.00'))]: self.basket.add_product(product, 2) discount = self.benefit.apply(self.basket, self.condition) self.assertEqual(D('3.00'), discount) self.assertEqual(2, self.basket.num_items_with_discount) self.assertEqual(2, self.basket.num_items_without_discount) def test_applies_correctly_to_basket_which_exceeds_condition_with_smaller_prices_than_discount(self): for product in [create_product(price=D('2.00')), create_product(price=D('4.00'))]: self.basket.add_product(product, 2) discount = self.benefit.apply(self.basket, self.condition) self.assertEqual(D('3.00'), discount) self.assertEqual(2, self.basket.num_items_with_discount) self.assertEqual(2, self.basket.num_items_without_discount) def test_applies_correctly_to_basket_which_exceeds_condition_with_smaller_prices_than_discount_and_higher_prices_first(self): for product in [create_product(price=D('4.00')), create_product(price=D('2.00'))]: self.basket.add_product(product, 2) discount = self.benefit.apply(self.basket, self.condition) self.assertEqual(D('3.00'), discount) self.assertEqual(2, self.basket.num_items_with_discount) self.assertEqual(2, self.basket.num_items_without_discount) class TestAnAbsoluteDiscountWithMaxItemsSetAppliedWithCountCondition(TestCase): def setUp(self): range = models.Range.objects.create( name="All products", includes_all_products=True) self.condition = models.CountCondition.objects.create( range=range, type=models.Condition.COUNT, value=2) self.benefit = models.AbsoluteDiscountBenefit.objects.create( range=range, type=models.Benefit.FIXED, value=D('3.00'), max_affected_items=1) self.basket = G(Basket) def test_applies_correctly_to_empty_basket(self): discount = self.benefit.apply(self.basket, self.condition) self.assertEqual(D('0.00'), discount) self.assertEqual(0, self.basket.num_items_with_discount) self.assertEqual(0, self.basket.num_items_without_discount) def test_applies_correctly_to_basket_which_matches_condition(self): for product in [create_product(price=D('12.00'))]: self.basket.add_product(product, 2) discount = self.benefit.apply(self.basket, self.condition) self.assertEqual(D('3.00'), discount) self.assertEqual(2, self.basket.num_items_with_discount) self.assertEqual(0, self.basket.num_items_without_discount) def test_applies_correctly_to_basket_which_exceeds_condition(self): for product in [create_product(price=D('12.00')), create_product(price=D('10.00'))]: self.basket.add_product(product, 2) discount = self.benefit.apply(self.basket, self.condition) self.assertEqual(D('3.00'), discount) self.assertEqual(2, self.basket.num_items_with_discount) self.assertEqual(2, self.basket.num_items_without_discount) def test_applies_correctly_to_basket_which_exceeds_condition_but_with_smaller_prices_than_discount(self): for product in [create_product(price=D('2.00')), create_product(price=D('1.00'))]: self.basket.add_product(product, 2) discount = self.benefit.apply(self.basket, self.condition) self.assertEqual(D('1.00'), discount) self.assertEqual(2, self.basket.num_items_with_discount) self.assertEqual(2, self.basket.num_items_without_discount) class TestAnAbsoluteDiscountAppliedWithValueCondition(TestCase): def setUp(self): range = models.Range.objects.create( name="All products", includes_all_products=True) self.condition = models.ValueCondition.objects.create( range=range, type=models.Condition.VALUE, value=D('10.00')) self.benefit = models.AbsoluteDiscountBenefit.objects.create( range=range, type=models.Benefit.FIXED, value=D('3.00')) self.basket = G(Basket) def test_applies_correctly_to_empty_basket(self): discount = self.benefit.apply(self.basket, self.condition) self.assertEqual(D('0.00'), discount) self.assertEqual(0, self.basket.num_items_with_discount) self.assertEqual(0, self.basket.num_items_without_discount) def test_applies_correctly_to_single_item_basket_which_matches_condition(self): for product in [create_product(price=D('10.00'))]: self.basket.add_product(product, 1) discount = self.benefit.apply(self.basket, self.condition) self.assertEqual(D('3.00'), discount) self.assertEqual(1, self.basket.num_items_with_discount) self.assertEqual(0, self.basket.num_items_without_discount) def test_applies_correctly_to_multi_item_basket_which_matches_condition(self): for product in [create_product(price=D('5.00'))]: self.basket.add_product(product, 2) discount = self.benefit.apply(self.basket, self.condition) self.assertEqual(D('3.00'), discount) self.assertEqual(2, self.basket.num_items_with_discount) self.assertEqual(0, self.basket.num_items_without_discount) def test_applies_correctly_to_multi_item_basket_which_exceeds_condition(self): for product in [create_product(price=D('4.00'))]: self.basket.add_product(product, 3) discount = self.benefit.apply(self.basket, self.condition) self.assertEqual(D('3.00'), discount) self.assertEqual(3, self.basket.num_items_with_discount) self.assertEqual(0, self.basket.num_items_without_discount) def test_applies_correctly_to_multi_item_basket_which_exceeds_condition_but_matches_boundary(self): for product in [create_product(price=D('5.00'))]: self.basket.add_product(product, 3) discount = self.benefit.apply(self.basket, self.condition) self.assertEqual(D('3.00'), discount) self.assertEqual(2, self.basket.num_items_with_discount) self.assertEqual(1, self.basket.num_items_without_discount) class TestAnAbsoluteDiscountWithMaxItemsSetAppliedWithValueCondition(TestCase): def setUp(self): range = models.Range.objects.create( name="All products", includes_all_products=True) self.condition = models.ValueCondition.objects.create( range=range, type=models.Condition.VALUE, value=D('10.00')) self.benefit = models.AbsoluteDiscountBenefit.objects.create( range=range, type=models.Benefit.FIXED, value=D('3.00'), max_affected_items=1) self.basket = G(Basket) def test_applies_correctly_to_empty_basket(self): discount = self.benefit.apply(self.basket, self.condition) self.assertEqual(D('0.00'), discount) self.assertEqual(0, self.basket.num_items_with_discount) self.assertEqual(0, self.basket.num_items_without_discount) def test_applies_correctly_to_single_item_basket_which_matches_condition(self): for product in [create_product(price=D('10.00'))]: self.basket.add_product(product, 1) discount = self.benefit.apply(self.basket, self.condition) self.assertEqual(D('3.00'), discount) self.assertEqual(1, self.basket.num_items_with_discount) self.assertEqual(0, self.basket.num_items_without_discount) def test_applies_correctly_to_multi_item_basket_which_matches_condition(self): for product in [create_product(price=D('5.00'))]: self.basket.add_product(product, 2) discount = self.benefit.apply(self.basket, self.condition) self.assertEqual(D('3.00'), discount) self.assertEqual(2, self.basket.num_items_with_discount) self.assertEqual(0, self.basket.num_items_without_discount) def test_applies_correctly_to_multi_item_basket_which_exceeds_condition(self): for product in [create_product(price=D('4.00'))]: self.basket.add_product(product, 3) discount = self.benefit.apply(self.basket, self.condition) self.assertEqual(D('3.00'), discount) self.assertEqual(3, self.basket.num_items_with_discount) self.assertEqual(0, self.basket.num_items_without_discount) def test_applies_correctly_to_multi_item_basket_which_exceeds_condition_but_matches_boundary(self): for product in [create_product(price=D('5.00'))]: self.basket.add_product(product, 3) discount = self.benefit.apply(self.basket, self.condition) self.assertEqual(D('3.00'), discount) self.assertEqual(2, self.basket.num_items_with_discount) self.assertEqual(1, self.basket.num_items_without_discount) def test_applies_correctly_to_multi_item_basket_which_matches_condition_but_with_lower_prices_than_discount(self): for product in [create_product(price=D('2.00'))]: self.basket.add_product(product, 6) discount = self.benefit.apply(self.basket, self.condition) self.assertEqual(D('2.00'), discount) self.assertEqual(5, self.basket.num_items_with_discount) self.assertEqual(1, self.basket.num_items_without_discount)

47.939655

129

0.699245

1,409

11,122

5.267566

0.061036

0.107788

0.123956

0.097009

0.934519

0.934115

0.933441

0

0.023562

0.194839

11,122

231

130

48.147186

0.805248

0

0.883249

0

0.022118

0

0.304569

1

0.121827

false

0

0.030457

0

0.172589

0

null

0

1

0

1

0

null

0

7

4902a99c5f4c3b3b59441b735359ba4589c37cb1

16

py

Python

CodeHS/Basic Python and Console Interaction/AddParentheses.py

Kev-in123/ICS2O7

425c59975d4ce6aa0937fd8715b51d04487e4fa9

[ "MIT" ]

2

2021-08-10T18:16:08.000Z

2021-09-26T19:49:26.000Z

CodeHS/Basic Python and Console Interaction/AddParentheses.py

Kev-in123/ICS2O7

425c59975d4ce6aa0937fd8715b51d04487e4fa9

[ "MIT" ]

null

CodeHS/Basic Python and Console Interaction/AddParentheses.py

Kev-in123/ICS2O7

425c59975d4ce6aa0937fd8715b51d04487e4fa9

[ "MIT" ]

null

print(2+3*(4+8))

16

0.5625

5

16

1.8

1

0

0.25

0

16

1

16

0.3125

0

1

0

true

0

1

null

0

1

0

1

0

1

0

null

0

1

0

1

0

8

4907d67b50b24a140633f3c62b77b26b62f2f9c3

495,261

py

Python

examples/LaTiO3_tilted_t2g_only/lto_rham.py

hungdt/scf_dmft

845a2e144268350af0340927bba0044d538c34db

[ "MIT" ]

8

2015-06-05T17:44:10.000Z

2021-06-10T11:55:13.000Z

examples/LaTiO3_tilted_t2g_only/lto_rham.py

hungdt/scf_dmft

845a2e144268350af0340927bba0044d538c34db

[ "MIT" ]

null

examples/LaTiO3_tilted_t2g_only/lto_rham.py

hungdt/scf_dmft

845a2e144268350af0340927bba0044d538c34db

[ "MIT" ]

null

Hopping={} Hopping[( -2, -2, -2)]=[ [ -0.00000609+ -0.00000000j, -0.00000432+ -0.00000009j, -0.00001043+ -0.00000042j, -0.00001043+ 0.00000002j, -0.00000320+ -0.00000005j, 0.00000304+ 0.00000024j, 0.00000577+ 0.00000065j, 0.00000106+ 0.00000012j, -0.00000067+ -0.00000011j, 0.00000437+ 0.00000017j, 0.00000516+ 0.00000016j, -0.00001377+ -0.00000047j] , [ -0.00000432+ 0.00000009j, 0.00000236+ -0.00000000j, 0.00000252+ -0.00000002j, 0.00001168+ -0.00000024j, 0.00000558+ 0.00000001j, -0.00000040+ 0.00000025j, -0.00000110+ 0.00000038j, -0.00000382+ -0.00000000j, -0.00000550+ -0.00000008j, 0.00000034+ -0.00000001j, 0.00000028+ 0.00000001j, 0.00000218+ 0.00000000j] , [ -0.00001043+ 0.00000042j, 0.00000252+ 0.00000002j, -0.00001257+ -0.00000000j, -0.00000683+ 0.00000001j, -0.00000267+ 0.00000008j, 0.00000959+ 0.00000004j, 0.00000072+ 0.00000013j, -0.00000550+ -0.00000003j, 0.00000181+ 0.00000067j, 0.00000894+ 0.00000033j, 0.00000335+ -0.00000003j, -0.00001317+ 0.00000014j] , [ -0.00001043+ 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0.00000007j, -0.00000073+ -0.00000001j, -0.00001046+ -0.00000143j, -0.00001169+ 0.00000041j, 0.00000682+ -0.00000004j, -0.00000653+ 0.00000000j, 0.00000434+ 0.00000000j, -0.00001042+ 0.00000057j] , [ 0.00000516+ -0.00000016j, 0.00000028+ -0.00000001j, 0.00000335+ 0.00000003j, 0.00000107+ -0.00000012j, -0.00000382+ -0.00000000j, 0.00000550+ -0.00000003j, 0.00000321+ 0.00000050j, 0.00000557+ -0.00000002j, -0.00000267+ -0.00000008j, 0.00000434+ -0.00000000j, 0.00000237+ 0.00000000j, -0.00000255+ -0.00000001j] , [ -0.00001377+ 0.00000047j, 0.00000218+ -0.00000000j, -0.00001317+ -0.00000014j, 0.00000063+ -0.00000008j, 0.00000550+ -0.00000001j, 0.00000183+ -0.00000063j, -0.00000308+ -0.00000049j, -0.00000032+ 0.00000019j, 0.00000954+ -0.00000003j, -0.00001042+ -0.00000057j, -0.00000255+ 0.00000001j, -0.00001237+ 0.00000000j] ] Hopping[( -2, -2, -1)]=[ [ -0.00002631+ 0.00000001j, 0.00001186+ 0.00000041j, 0.00000094+ 0.00000003j, -0.00002086+ -0.00000007j, 0.00002339+ 0.00000042j, 0.00001368+ 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0.00000260j, -0.00002322+ 0.00000104j, -0.00001360+ 0.00000150j] , [ 0.00002725+ -0.00000088j, 0.00002605+ 0.00000001j, -0.00003576+ 0.00000010j, 0.00000611+ -0.00000007j, -0.00002805+ -0.00000002j, -0.00003002+ 0.00000000j, -0.00001191+ -0.00000095j, -0.00002519+ 0.00000000j, 0.00001030+ 0.00000016j, 0.00000644+ 0.00000020j, 0.00001114+ -0.00000001j, 0.00000531+ 0.00000009j] , [ -0.00004718+ 0.00000167j, -0.00006233+ 0.00000032j, -0.00002786+ -0.00000048j, -0.00002662+ 0.00000110j, 0.00004227+ -0.00000050j, 0.00000683+ -0.00000078j, -0.00000125+ -0.00000088j, 0.00001030+ -0.00000016j, 0.00003226+ -0.00000001j, 0.00000610+ 0.00000051j, 0.00000067+ -0.00000053j, 0.00001910+ -0.00000023j] , [ -0.00000752+ 0.00000127j, 0.00001404+ -0.00000107j, -0.00002066+ -0.00000079j, 0.00001201+ -0.00000076j, -0.00002697+ 0.00000082j, -0.00004739+ 0.00000264j, -0.00004273+ -0.00000087j, -0.00001221+ 0.00000045j, 0.00002172+ 0.00000071j, -0.00002665+ 0.00000001j, -0.00001187+ 0.00000068j, 0.00000133+ -0.00000063j] , [ 0.00004261+ -0.00000166j, -0.00007137+ -0.00000000j, -0.00004311+ 0.00000011j, -0.00000643+ 0.00000000j, 0.00002604+ -0.00000003j, 0.00006237+ -0.00000002j, -0.00001012+ -0.00000075j, 0.00003724+ 0.00000000j, 0.00001462+ 0.00000018j, -0.00001186+ -0.00000070j, -0.00002520+ -0.00000000j, -0.00001031+ -0.00000013j] , [ -0.00006199+ 0.00000283j, 0.00004050+ -0.00000035j, 0.00016662+ -0.00000056j, -0.00000536+ 0.00000015j, 0.00003591+ 0.00000015j, -0.00002752+ 0.00000034j, 0.00000063+ 0.00000197j, 0.00000024+ 0.00000016j, -0.00011701+ -0.00000025j, 0.00000143+ 0.00000058j, -0.00001032+ 0.00000012j, 0.00003227+ -0.00000001j] ] Hopping[( -2, -2, 0)]=[ [ -0.00004200+ -0.00000000j, 0.00002342+ 0.00000072j, -0.00000190+ -0.00000026j, -0.00004268+ -0.00000008j, 0.00001226+ 0.00000030j, 0.00002273+ 0.00000114j, 0.00001663+ 0.00000175j, 0.00002136+ 0.00000070j, -0.00003615+ -0.00000154j, 0.00003735+ 0.00000288j, 0.00005357+ 0.00000159j, 0.00003844+ 0.00000056j] , [ 0.00002342+ 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0.00004227+ 0.00000050j, -0.00002697+ -0.00000082j, 0.00002604+ 0.00000003j, 0.00003591+ -0.00000015j] , [ 0.00001368+ 0.00000000j, 0.00000535+ -0.00000015j, 0.00001919+ 0.00000009j, -0.00000097+ -0.00000003j, -0.00001038+ 0.00000007j, 0.00003227+ 0.00000003j, -0.00005921+ -0.00000329j, -0.00003002+ -0.00000000j, 0.00000683+ 0.00000078j, -0.00004739+ -0.00000264j, 0.00006237+ 0.00000002j, -0.00002752+ -0.00000034j] , [ 0.00001197+ -0.00000100j, -0.00002705+ 0.00000091j, 0.00004739+ -0.00000325j, -0.00000721+ 0.00000040j, -0.00004272+ 0.00000166j, 0.00006100+ -0.00000123j, -0.00002601+ -0.00000001j, -0.00001191+ 0.00000095j, -0.00000125+ 0.00000088j, -0.00004273+ 0.00000087j, -0.00001012+ 0.00000075j, 0.00000063+ -0.00000197j] , [ -0.00000644+ 0.00000001j, 0.00002603+ -0.00000003j, -0.00006236+ 0.00000001j, -0.00001367+ 0.00000015j, -0.00007140+ 0.00000000j, 0.00004061+ 0.00000020j, -0.00001192+ -0.00000093j, -0.00002519+ -0.00000000j, 0.00001030+ 0.00000016j, -0.00001221+ -0.00000045j, 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, [ 0.00005909+ -0.00000218j, -0.00002996+ 0.00000025j, 0.00000699+ -0.00000017j, 0.00004718+ -0.00000167j, 0.00006233+ -0.00000026j, -0.00002787+ -0.00000043j, -0.00001360+ -0.00000150j, 0.00000531+ -0.00000009j, 0.00001910+ 0.00000023j, 0.00000133+ 0.00000063j, -0.00001031+ 0.00000013j, 0.00003227+ 0.00000001j] ] Hopping[( -2, -2, 2)]=[ [ -0.00000609+ -0.00000000j, -0.00000432+ -0.00000009j, -0.00001043+ -0.00000042j, -0.00001043+ 0.00000002j, -0.00000320+ -0.00000005j, 0.00000304+ 0.00000024j, 0.00000577+ 0.00000065j, 0.00000106+ 0.00000012j, -0.00000067+ -0.00000011j, 0.00000437+ 0.00000017j, 0.00000516+ 0.00000016j, -0.00001377+ -0.00000047j] , [ -0.00000432+ 0.00000009j, 0.00000236+ -0.00000000j, 0.00000252+ -0.00000002j, 0.00001168+ -0.00000024j, 0.00000558+ 0.00000001j, -0.00000040+ 0.00000025j, -0.00000110+ 0.00000038j, -0.00000382+ -0.00000000j, -0.00000550+ -0.00000008j, 0.00000034+ -0.00000001j, 0.00000028+ 0.00000001j, 0.00000218+ 0.00000000j] , [ -0.00001043+ 0.00000042j, 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, [ 0.00002476+ -0.00000187j, -0.00002909+ 0.00000193j, 0.00005944+ -0.00000269j, 0.00001164+ -0.00000083j, -0.00000207+ 0.00000012j, -0.00000142+ 0.00000003j, 0.00012317+ 0.00000352j, 0.00005700+ -0.00000206j, -0.00016028+ 0.00000561j, 0.00017114+ 0.00000008j, -0.00001378+ 0.00000045j, 0.00012169+ -0.00000363j] , [ 0.00000602+ -0.00000012j, -0.00002805+ -0.00000003j, 0.00003001+ -0.00000002j, 0.00000215+ -0.00000025j, -0.00000764+ -0.00000000j, 0.00001095+ -0.00000003j, -0.00002331+ -0.00000123j, 0.00006107+ 0.00000003j, -0.00001711+ 0.00000016j, -0.00001396+ -0.00000056j, 0.00001063+ 0.00000001j, 0.00001925+ -0.00000010j] , [ 0.00002654+ -0.00000103j, -0.00004229+ 0.00000044j, 0.00000676+ -0.00000071j, 0.00000125+ -0.00000019j, 0.00001105+ -0.00000005j, 0.00000363+ -0.00000128j, -0.00003145+ -0.00000283j, -0.00005353+ -0.00000043j, -0.00000379+ 0.00000208j, 0.00012136+ 0.00000343j, 0.00001923+ 0.00000013j, 0.00002476+ 0.00000019j] ] Hopping[( -2, -1, -1)]=[ [ 0.00051784+ 0.00000002j, 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0.00000003j] ] Hopping[( -2, 0, 0)]=[ [ -0.00244004+ 0.00000000j, -0.00139089+ -0.00004254j, -0.00010714+ -0.00000949j, 0.00258160+ 0.00000545j, 0.00079493+ 0.00001382j, -0.00317490+ -0.00010870j, -0.00092180+ -0.00008012j, 0.00044293+ 0.00001283j, -0.00005274+ 0.00000606j, -0.00002211+ 0.00000833j, 0.00017382+ -0.00000120j, 0.00013248+ 0.00001021j] , [ -0.00139089+ 0.00004254j, -0.00113326+ 0.00000000j, 0.00008631+ -0.00000417j, 0.00087591+ -0.00002669j, 0.00091259+ -0.00000021j, -0.00022054+ -0.00000095j, -0.00044354+ -0.00002688j, 0.00010783+ -0.00000006j, 0.00012438+ 0.00000524j, 0.00035517+ 0.00000700j, 0.00032329+ -0.00000032j, -0.00095981+ 0.00000055j] , [ -0.00010714+ 0.00000949j, 0.00008631+ 0.00000417j, -0.00014792+ 0.00000000j, 0.00109512+ -0.00004427j, 0.00186307+ -0.00000296j, 0.00138284+ 0.00000522j, 0.00004141+ -0.00000669j, 0.00012685+ -0.00000105j, 0.00095836+ 0.00000299j, -0.00248526+ -0.00013047j, 0.00136079+ -0.00000017j, -0.00157441+ -0.00003079j] , [ 0.00258160+ 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-0.00002972j, 0.00118470+ -0.00005031j] , [ 0.00053476+ -0.00001861j, -0.00058166+ 0.00000029j, -0.00049874+ 0.00000192j, -0.00004082+ 0.00000047j, 0.00020010+ -0.00000011j, 0.00023021+ -0.00000007j, 0.00229831+ 0.00011978j, 0.00066203+ -0.00000095j, 0.00091162+ -0.00002694j, 0.00071647+ 0.00002967j, -0.00041956+ 0.00000003j, -0.00030181+ 0.00000642j] , [ 0.00012857+ -0.00000074j, -0.00015483+ -0.00000146j, -0.00012926+ -0.00000170j, 0.00005795+ -0.00000218j, 0.00037477+ 0.00000053j, 0.00063471+ 0.00000148j, 0.00116515+ 0.00008438j, 0.00002617+ 0.00001108j, -0.00046646+ 0.00000754j, 0.00118466+ 0.00005021j, -0.00030171+ -0.00000640j, -0.00026940+ -0.00000003j] ] Hopping[( -2, 0, 2)]=[ [ -0.00076624+ -0.00000000j, -0.00036885+ -0.00001096j, 0.00084716+ 0.00002328j, -0.00083931+ 0.00000263j, 0.00054997+ 0.00001634j, -0.00108428+ -0.00002872j, -0.00000805+ 0.00000099j, -0.00000843+ -0.00000014j, -0.00017540+ -0.00000605j, -0.00006814+ -0.00000519j, -0.00018165+ -0.00000596j, 0.00002701+ 0.00000029j] , [ -0.00036885+ 0.00001096j, -0.00052476+ 0.00000000j, -0.00010885+ -0.00000153j, -0.00115326+ 0.00003701j, 0.00033073+ -0.00000061j, -0.00044918+ 0.00000476j, 0.00000848+ 0.00000175j, 0.00022979+ 0.00000002j, -0.00005257+ -0.00000023j, -0.00001235+ -0.00000032j, -0.00027339+ 0.00000008j, 0.00000649+ -0.00000000j] , [ 0.00084716+ -0.00002328j, -0.00010885+ 0.00000153j, -0.00000251+ -0.00000000j, 0.00057653+ -0.00001334j, -0.00000896+ -0.00000196j, -0.00024441+ 0.00000209j, 0.00017469+ 0.00000973j, -0.00005251+ 0.00000000j, 0.00004674+ -0.00000143j, 0.00001227+ 0.00000054j, 0.00015313+ 0.00000067j, -0.00001815+ -0.00000010j] , [ -0.00083931+ -0.00000263j, -0.00115326+ -0.00003701j, 0.00057653+ 0.00001334j, -0.00076618+ 0.00000000j, -0.00036886+ -0.00001105j, -0.00084718+ -0.00002322j, -0.00006784+ -0.00000568j, 0.00001230+ 0.00000058j, -0.00001300+ 0.00000037j, -0.00000785+ 0.00000064j, -0.00000841+ -0.00000013j, 0.00017535+ 0.00000595j] , [ 0.00054997+ -0.00001634j, 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0.00000006j] ] Hopping[( -2, 1, 1)]=[ [ 0.00051784+ -0.00000002j, -0.00014121+ -0.00000368j, 0.00027576+ 0.00001000j, 0.00022347+ 0.00000157j, 0.00006800+ 0.00000088j, -0.00039149+ -0.00000714j, 0.00002387+ 0.00000169j, -0.00001286+ 0.00000005j, 0.00001090+ -0.00000005j, 0.00007489+ 0.00000627j, 0.00001026+ 0.00000020j, 0.00007130+ 0.00000179j] , [ -0.00014116+ 0.00000384j, -0.00020601+ 0.00000000j, -0.00010066+ -0.00000061j, -0.00000662+ -0.00000223j, -0.00030564+ -0.00000007j, 0.00064027+ 0.00000017j, -0.00005418+ -0.00000177j, 0.00005209+ 0.00000005j, -0.00007186+ 0.00000040j, -0.00010729+ -0.00000574j, -0.00018033+ 0.00000001j, -0.00011235+ -0.00000164j] , [ 0.00027573+ -0.00000999j, -0.00010061+ 0.00000070j, 0.00008230+ 0.00000000j, -0.00003691+ 0.00000634j, 0.00042404+ 0.00000002j, -0.00167504+ 0.00000151j, 0.00009467+ 0.00000602j, -0.00012468+ 0.00000002j, -0.00005524+ -0.00000105j, 0.00008010+ 0.00000735j, 0.00001028+ -0.00000009j, 0.00042864+ 0.00000220j] , [ 0.00024460+ 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-0.00000013j] , [ -0.00006199+ 0.00000283j, 0.00004050+ -0.00000035j, 0.00016662+ -0.00000056j, -0.00000536+ 0.00000015j, 0.00003591+ 0.00000015j, -0.00002752+ 0.00000034j, 0.00000063+ 0.00000197j, 0.00000024+ 0.00000016j, -0.00011701+ -0.00000025j, 0.00000143+ 0.00000058j, -0.00001032+ 0.00000012j, 0.00003227+ -0.00000001j] ] Hopping[( -2, 2, 0)]=[ [ -0.00004200+ -0.00000000j, 0.00002342+ 0.00000072j, -0.00000190+ -0.00000026j, -0.00004268+ -0.00000008j, 0.00001226+ 0.00000030j, 0.00002273+ 0.00000114j, 0.00001663+ 0.00000175j, 0.00002136+ 0.00000070j, -0.00003615+ -0.00000154j, 0.00003735+ 0.00000288j, 0.00005357+ 0.00000159j, 0.00003844+ 0.00000056j] , [ 0.00002342+ -0.00000072j, -0.00006755+ 0.00000000j, 0.00001252+ 0.00000010j, 0.00001001+ -0.00000016j, 0.00003725+ -0.00000000j, -0.00001469+ -0.00000005j, -0.00002097+ -0.00000076j, 0.00004420+ 0.00000000j, -0.00004215+ 0.00000010j, -0.00000519+ -0.00000022j, -0.00009017+ -0.00000003j, -0.00000523+ -0.00000016j] , [ -0.00000190+ 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0.00000001j] ] Hopping[( -2, 2, 2)]=[ [ -0.00000609+ -0.00000000j, -0.00000432+ -0.00000009j, -0.00001043+ -0.00000042j, -0.00001043+ 0.00000002j, -0.00000320+ -0.00000005j, 0.00000304+ 0.00000024j, 0.00000577+ 0.00000065j, 0.00000106+ 0.00000012j, -0.00000067+ -0.00000011j, 0.00000437+ 0.00000017j, 0.00000516+ 0.00000016j, -0.00001377+ -0.00000047j] , [ -0.00000432+ 0.00000009j, 0.00000236+ -0.00000000j, 0.00000252+ -0.00000002j, 0.00001168+ -0.00000024j, 0.00000558+ 0.00000001j, -0.00000040+ 0.00000025j, -0.00000110+ 0.00000038j, -0.00000382+ -0.00000000j, -0.00000550+ -0.00000008j, 0.00000034+ -0.00000001j, 0.00000028+ 0.00000001j, 0.00000218+ 0.00000000j] , [ -0.00001043+ 0.00000042j, 0.00000252+ 0.00000002j, -0.00001257+ -0.00000000j, -0.00000683+ 0.00000001j, -0.00000267+ 0.00000008j, 0.00000959+ 0.00000004j, 0.00000072+ 0.00000013j, -0.00000550+ -0.00000003j, 0.00000181+ 0.00000067j, 0.00000894+ 0.00000033j, 0.00000335+ -0.00000003j, -0.00001317+ 0.00000014j] , [ -0.00001043+ 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, [ -0.00001027+ -0.00000025j, -0.00013518+ -0.00000001j, -0.00000693+ -0.00000012j, -0.00002229+ 0.00000129j, 0.00003273+ -0.00000002j, -0.00009639+ -0.00000001j, 0.00014889+ 0.00001014j, 0.00011962+ -0.00000004j, -0.00014255+ -0.00000245j, 0.00004905+ 0.00000331j, 0.00002390+ -0.00000003j, 0.00010412+ 0.00000069j] , [ -0.00007527+ 0.00000303j, -0.00000695+ 0.00000004j, 0.00014578+ 0.00000004j, -0.00005989+ 0.00000149j, 0.00005900+ 0.00000026j, 0.00017342+ 0.00000002j, 0.00005570+ 0.00000744j, -0.00006364+ -0.00000018j, -0.00034693+ -0.00000197j, -0.00004605+ -0.00000383j, 0.00003131+ 0.00000052j, -0.00015922+ -0.00000106j] , [ -0.00008508+ 0.00000023j, 0.00002023+ 0.00000018j, -0.00000056+ -0.00000108j, -0.00006043+ 0.00000005j, 0.00002814+ 0.00000078j, 0.00008050+ 0.00000220j, -0.00001456+ -0.00000212j, 0.00008517+ 0.00000318j, 0.00012398+ 0.00000570j, 0.00001426+ 0.00000152j, -0.00001988+ -0.00000090j, 0.00001906+ 0.00000101j] , [ 0.00002433+ -0.00000065j, 0.00007449+ -0.00000001j, 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0.00000390j, 0.00079291+ 0.00000002j] ] Hopping[( -1, 0, -1)]=[ [ -0.00382148+ 0.00000000j, 0.01185439+ 0.00034013j, 0.01695476+ 0.00048306j, -0.00005011+ 0.00000229j, -0.00542260+ -0.00016816j, -0.00226912+ -0.00006864j, 0.00158804+ 0.00016740j, -0.00423450+ -0.00010960j, -0.00369937+ -0.00012876j, 0.00009040+ -0.00001083j, -0.00257851+ -0.00007494j, -0.00054323+ -0.00002158j] , [ 0.01185425+ -0.00034027j, 0.00281571+ -0.00000004j, 0.00512648+ 0.00003939j, -0.00287212+ 0.00008230j, -0.00334663+ 0.00000099j, 0.00080346+ 0.00001376j, 0.00489163+ 0.00027721j, -0.00054355+ 0.00000056j, 0.00011277+ -0.00006153j, -0.00060660+ -0.00002921j, -0.00043786+ 0.00000121j, -0.00011431+ -0.00000602j] , [ 0.01695424+ -0.00048363j, 0.00512633+ -0.00003970j, 0.00587631+ -0.00000010j, 0.00056831+ -0.00001654j, -0.00037798+ 0.00001958j, 0.00094559+ 0.00000286j, 0.00530660+ 0.00035873j, -0.00526590+ 0.00001499j, -0.00473423+ -0.00006879j, 0.00333823+ 0.00016464j, -0.00151737+ 0.00000830j, 0.00106040+ 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0.00069904+ 0.00001759j, -0.00219418+ -0.00008183j, -0.00086555+ 0.00001078j, -0.00101032+ 0.00000011j] ] Hopping[( -1, 1, 0)]=[ [ -0.00050527+ -0.00000006j, 0.00200499+ 0.00006232j, 0.00331955+ 0.00008310j, 0.00033403+ -0.00002873j, -0.00383756+ -0.00008554j, 0.00210003+ 0.00008833j, -0.00002933+ -0.00000058j, 0.00036201+ 0.00001295j, -0.00003678+ 0.00000320j, -0.00033019+ -0.00002455j, 0.00083232+ 0.00002676j, -0.00018249+ -0.00001222j] , [ 0.00200415+ -0.00006363j, -0.00357135+ -0.00000000j, -0.00024931+ 0.00000583j, 0.00114344+ -0.00001873j, 0.00243622+ 0.00000242j, -0.00445788+ -0.00000486j, -0.00044828+ -0.00001942j, 0.00006827+ -0.00000004j, -0.00048802+ 0.00000452j, 0.00024147+ 0.00000718j, -0.00029065+ -0.00000050j, -0.00048265+ 0.00000074j] , [ 0.00332011+ -0.00008496j, -0.00024949+ -0.00000605j, 0.00441080+ -0.00000132j, 0.00508687+ -0.00013539j, -0.00854399+ 0.00001694j, -0.00661571+ -0.00002558j, -0.00029809+ -0.00000530j, -0.00043200+ -0.00000130j, -0.00083569+ 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, [ -0.00005386+ 0.00000069j, -0.00006386+ -0.00000012j, -0.00034769+ -0.00000014j, 0.00004481+ -0.00000113j, 0.00003169+ 0.00000099j, -0.00015984+ -0.00000002j, 0.00007430+ 0.00000133j, -0.00000682+ 0.00000040j, 0.00014663+ -0.00000004j, 0.00006023+ 0.00000314j, 0.00005911+ 0.00000004j, 0.00017269+ 0.00000026j] , [ -0.00001466+ 0.00000152j, -0.00008535+ 0.00000282j, -0.00012178+ 0.00000225j, 0.00001367+ -0.00000005j, 0.00002000+ -0.00000030j, -0.00001898+ 0.00000054j, -0.00008553+ -0.00000075j, -0.00002030+ 0.00000119j, -0.00000148+ 0.00000339j, -0.00006181+ 0.00000001j, -0.00002838+ 0.00000166j, -0.00007977+ 0.00000243j] , [ -0.00002734+ 0.00000027j, -0.00014280+ 0.00000001j, -0.00008121+ -0.00000042j, 0.00003283+ -0.00000030j, 0.00010218+ 0.00000006j, -0.00013879+ -0.00000016j, -0.00002437+ -0.00000122j, 0.00007448+ -0.00000001j, -0.00000043+ 0.00000044j, -0.00002825+ -0.00000174j, -0.00007468+ 0.00000001j, -0.00001425+ -0.00000058j] , [ -0.00004286+ 0.00000348j, 0.00008661+ 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0.00000023j, -0.00023416+ -0.00000046j, -0.00005224+ 0.00000013j, -0.00004488+ -0.00000011j, 0.00015644+ 0.00000006j] ] Hopping[( -1, 2, 1)]=[ [ -0.00006041+ 0.00000005j, 0.00002817+ 0.00000080j, -0.00008049+ -0.00000216j, -0.00009003+ 0.00000011j, -0.00001118+ -0.00000022j, 0.00009094+ 0.00000431j, 0.00001416+ 0.00000179j, -0.00001994+ -0.00000096j, -0.00001905+ -0.00000111j, -0.00016697+ -0.00001680j, -0.00012154+ -0.00000398j, -0.00035623+ -0.00001646j] , [ 0.00002829+ -0.00000075j, -0.00007467+ -0.00000000j, 0.00001438+ 0.00000042j, -0.00011653+ 0.00000368j, 0.00038530+ -0.00000007j, 0.00006055+ 0.00000007j, -0.00003326+ -0.00000373j, 0.00010216+ 0.00000002j, 0.00013853+ 0.00000091j, -0.00041459+ -0.00001647j, -0.00014554+ 0.00000009j, 0.00023331+ -0.00000353j] , [ -0.00008040+ 0.00000236j, 0.00001438+ -0.00000040j, 0.00001256+ 0.00000004j, -0.00011469+ 0.00000530j, -0.00004485+ -0.00000003j, -0.00042415+ 0.00000005j, -0.00008346+ -0.00000492j, 0.00003029+ -0.00000004j, 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0.00000720j, 0.00038534+ -0.00000001j, -0.00005984+ -0.00000148j, -0.00011940+ -0.00000649j, -0.00000778+ -0.00000002j, -0.00006904+ -0.00000150j] , [ -0.00009189+ 0.00000616j, -0.00024126+ -0.00000085j, 0.00048285+ 0.00000154j, 0.00014342+ -0.00000258j, -0.00004668+ -0.00000199j, -0.00018838+ -0.00000032j, -0.00011152+ 0.00000003j, 0.00004482+ -0.00000020j, -0.00042555+ 0.00000058j, -0.00006394+ -0.00000045j, -0.00006903+ 0.00000152j, 0.00006806+ -0.00000000j] ] Hopping[( 0, -2, 0)]=[ [ -0.00030711+ 0.00000000j, 0.00067720+ 0.00002161j, -0.00004036+ -0.00000457j, -0.00008997+ 0.00000012j, -0.00001110+ -0.00000030j, 0.00009089+ 0.00000433j, -0.00059914+ -0.00005102j, -0.00120632+ -0.00004097j, -0.00019738+ 0.00000352j, -0.00009186+ -0.00000879j, -0.00016106+ -0.00000445j, 0.00001130+ -0.00000561j] , [ 0.00067720+ -0.00002161j, -0.00221280+ 0.00000000j, -0.00012268+ 0.00000271j, -0.00011654+ 0.00000363j, 0.00038530+ -0.00000005j, 0.00006055+ 0.00000011j, 0.00120001+ 0.00005890j, 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-0.00007145+ 0.00000000j] ] Hopping[( 0, 0, -1)]=[ [ -0.03514906+ 0.00000032j, -0.01110354+ -0.00036037j, -0.00254427+ -0.00024013j, -0.00005014+ -0.00000238j, -0.00287207+ -0.00008245j, -0.00056833+ -0.00001659j, -0.00226924+ -0.00015506j, 0.00198745+ 0.00006303j, -0.00228966+ -0.00003356j, 0.00011253+ 0.00004354j, 0.00060673+ 0.00001857j, 0.00333155+ 0.00010860j] , [ -0.01110360+ 0.00036066j, -0.02369161+ -0.00000003j, 0.00478500+ -0.00004069j, -0.00542258+ 0.00016798j, -0.00334663+ -0.00000101j, 0.00037771+ 0.00001950j, -0.01410764+ -0.00085518j, 0.00469078+ 0.00000270j, 0.00359112+ 0.00019163j, 0.00258604+ 0.00013271j, -0.00043694+ -0.00000106j, 0.00150015+ 0.00002992j] , [ -0.00254427+ 0.00024058j, 0.00478520+ 0.00004061j, 0.00782145+ -0.00000018j, 0.00226909+ -0.00006854j, -0.00080353+ 0.00001383j, 0.00094567+ -0.00000289j, -0.00266714+ -0.00006143j, -0.00090862+ -0.00000414j, -0.00702582+ 0.00001996j, -0.00053527+ -0.00001461j, 0.00011826+ -0.00000133j, 0.00108787+ -0.00001482j] 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, [ -0.00287207+ 0.00008245j, -0.00334663+ 0.00000101j, -0.00080353+ -0.00001383j, -0.01110340+ 0.00036041j, -0.02369161+ -0.00000002j, -0.00478543+ 0.00004049j, -0.00060621+ -0.00003652j, -0.00043791+ 0.00000118j, 0.00011480+ 0.00000766j, -0.01411998+ -0.00068756j, 0.00469195+ 0.00000367j, -0.00357868+ -0.00015513j] , [ -0.00056833+ 0.00001659j, 0.00037771+ -0.00001950j, 0.00094567+ 0.00000289j, 0.00254262+ -0.00023968j, -0.00478563+ -0.00004042j, 0.00782164+ -0.00000020j, -0.00333504+ -0.00020467j, 0.00151696+ -0.00000840j, 0.00106355+ 0.00004849j, 0.00266073+ 0.00005090j, 0.00090849+ 0.00000385j, -0.00702811+ 0.00001256j] , [ -0.00226924+ 0.00015506j, -0.01410764+ 0.00085518j, -0.00266714+ 0.00006143j, 0.00661213+ -0.00033393j, -0.01005638+ 0.00022603j, -0.02557302+ 0.00122590j, -0.03517902+ -0.00000030j, 0.01106063+ -0.00056804j, 0.00231389+ 0.00042288j, 0.17349161+ -0.00098453j, -0.00380182+ -0.00173679j, 0.08033382+ -0.00378233j] , [ 0.00198745+ -0.00006303j, 0.00469078+ 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-0.00249923+ -0.00014167j, -0.00027030+ 0.00000680j, -0.00388583+ 0.00000083j] ] Hopping[( 0, 1, 0)]=[ [ 0.00298700+ -0.00000041j, 0.00261317+ 0.00010126j, 0.00510871+ 0.00009560j, 0.00207947+ 0.00000691j, -0.00099081+ -0.00004707j, -0.00234020+ -0.00007087j, -0.00119817+ -0.00010161j, -0.00241345+ -0.00008056j, -0.00039418+ 0.00000856j, -0.00033301+ -0.00003151j, -0.00024431+ -0.00000885j, -0.00071342+ -0.00002828j] , [ 0.00261302+ -0.00010283j, -0.01657417+ -0.00000000j, -0.00399336+ 0.00000589j, -0.00120263+ 0.00003757j, 0.00092828+ -0.00000036j, -0.00045652+ 0.00000477j, 0.00239919+ 0.00011876j, 0.00818148+ 0.00000026j, 0.00136374+ -0.00002592j, -0.00082777+ -0.00003420j, -0.00029097+ 0.00000044j, 0.00046752+ -0.00000608j] , [ 0.00510839+ -0.00009499j, -0.00399389+ -0.00000669j, 0.01959234+ 0.00000075j, 0.00062784+ -0.00001851j, 0.00460229+ 0.00000353j, -0.00204580+ 0.00000717j, 0.00039353+ 0.00004728j, 0.00134954+ 0.00000883j, -0.00166835+ -0.00000933j, -0.00017907+ 0.00000321j, 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0.00000004j, -0.00003522+ 0.00000796j, 0.00012719+ 0.00000651j, 0.00052873+ -0.00000000j] ] Hopping[( 0, 2, 1)]=[ [ -0.00017788+ -0.00000004j, 0.00011883+ 0.00000332j, -0.00006525+ -0.00000283j, -0.00009003+ -0.00000011j, -0.00011653+ -0.00000368j, 0.00011470+ 0.00000522j, -0.00016256+ -0.00001563j, -0.00021963+ -0.00000681j, 0.00005071+ 0.00000428j, -0.00016503+ -0.00001308j, 0.00041513+ 0.00001403j, -0.00009189+ -0.00000616j] , [ 0.00011876+ -0.00000336j, -0.00000769+ -0.00000003j, 0.00006966+ 0.00000053j, -0.00001118+ 0.00000024j, 0.00038530+ 0.00000007j, 0.00004485+ -0.00000004j, 0.00013442+ 0.00000651j, -0.00025462+ 0.00000003j, 0.00008838+ -0.00000144j, 0.00012049+ 0.00000317j, -0.00014534+ -0.00000013j, -0.00024126+ 0.00000085j] , [ -0.00006526+ 0.00000259j, 0.00006968+ -0.00000065j, 0.00006718+ -0.00000007j, -0.00009092+ 0.00000429j, -0.00006056+ 0.00000008j, -0.00042418+ -0.00000006j, 0.00014324+ 0.00001068j, 0.00004523+ 0.00000070j, -0.00018710+ -0.00000267j, -0.00035506+ 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0.00000004j, -0.00004484+ -0.00000022j, 0.00001017+ 0.00000020j, -0.00013516+ 0.00000001j, 0.00000681+ 0.00000036j] , [ -0.00035623+ 0.00001646j, 0.00023331+ 0.00000353j, 0.00048144+ -0.00000207j, -0.00006931+ 0.00000352j, 0.00014242+ -0.00000211j, -0.00034689+ 0.00000186j, -0.00008792+ 0.00000150j, 0.00005972+ -0.00000119j, -0.00042556+ 0.00000002j, -0.00007383+ -0.00000097j, 0.00000672+ -0.00000027j, 0.00014676+ 0.00000004j] ] Hopping[( 1, -2, 0)]=[ [ -0.00008317+ -0.00000001j, -0.00002939+ -0.00000117j, -0.00005348+ -0.00000246j, -0.00008535+ 0.00000015j, 0.00002004+ 0.00000034j, 0.00000058+ 0.00000102j, -0.00001473+ -0.00000063j, 0.00018112+ 0.00000633j, -0.00001782+ 0.00000106j, 0.00007500+ 0.00000588j, 0.00000962+ 0.00000061j, 0.00007108+ 0.00000160j] , [ -0.00002944+ 0.00000106j, -0.00012703+ -0.00000002j, 0.00004444+ 0.00000001j, 0.00002451+ -0.00000061j, 0.00007450+ 0.00000001j, 0.00000032+ 0.00000008j, -0.00022390+ -0.00000996j, 0.00003413+ -0.00000000j, -0.00024412+ 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0.00017269+ -0.00000026j, -0.00007977+ -0.00000243j, -0.00001425+ 0.00000058j, 0.00001365+ 0.00000005j] ] Hopping[( 1, -2, 2)]=[ [ -0.00003043+ 0.00000002j, 0.00001787+ 0.00000063j, -0.00000213+ 0.00000001j, -0.00002087+ -0.00000007j, 0.00002335+ 0.00000046j, 0.00001363+ 0.00000001j, -0.00000210+ -0.00000013j, -0.00003156+ -0.00000105j, 0.00000445+ 0.00000021j, 0.00000896+ 0.00000098j, -0.00000054+ 0.00000008j, 0.00001810+ 0.00000066j] , [ 0.00001790+ -0.00000065j, -0.00000756+ -0.00000000j, -0.00001094+ 0.00000011j, -0.00000643+ 0.00000009j, 0.00001116+ -0.00000002j, 0.00000535+ 0.00000016j, -0.00000361+ 0.00000016j, 0.00000953+ 0.00000001j, 0.00000644+ 0.00000005j, -0.00001020+ -0.00000068j, 0.00000055+ -0.00000002j, -0.00000658+ -0.00000016j] , [ -0.00000215+ -0.00000000j, -0.00001096+ -0.00000012j, -0.00001666+ -0.00000001j, -0.00000612+ 0.00000047j, 0.00000080+ 0.00000051j, 0.00001917+ -0.00000007j, 0.00002193+ 0.00000142j, 0.00000040+ 0.00000008j, 0.00000044+ 0.00000003j, 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1, 1, 0)]=[ [ -0.00076322+ -0.00000001j, 0.00335805+ 0.00010623j, 0.00163409+ 0.00003521j, -0.00137927+ 0.00000667j, 0.00112679+ 0.00003538j, -0.00157487+ -0.00004608j, -0.00003099+ -0.00000637j, 0.00045102+ 0.00001581j, 0.00030232+ 0.00001438j, -0.00002987+ -0.00000437j, 0.00017173+ 0.00000572j, -0.00024751+ -0.00001095j] , [ 0.00335692+ -0.00010733j, -0.00021054+ -0.00000007j, -0.00145755+ 0.00001161j, 0.00052487+ -0.00001973j, -0.00063828+ -0.00000025j, 0.00106157+ -0.00000160j, -0.00035952+ -0.00001479j, 0.00006829+ -0.00000003j, -0.00043411+ 0.00000327j, 0.00005653+ 0.00000502j, -0.00028547+ -0.00000016j, 0.00016202+ 0.00000108j] , [ 0.00163482+ -0.00003726j, -0.00145757+ -0.00001184j, 0.00339662+ -0.00000139j, -0.00010465+ 0.00000550j, -0.00032953+ -0.00000403j, 0.00068885+ 0.00000630j, 0.00004043+ 0.00001446j, -0.00048543+ -0.00000204j, -0.00083360+ -0.00000397j, -0.00008171+ 0.00000278j, -0.00017253+ -0.00000068j, 0.00066687+ 0.00000253j] , [ 0.00207935+ 0.00000689j, 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-0.00000006j] ] Hopping[( 1, 2, 1)]=[ [ -0.00005774+ -0.00000006j, -0.00001027+ 0.00000025j, -0.00007527+ -0.00000303j, -0.00008508+ -0.00000023j, 0.00002433+ 0.00000065j, 0.00004519+ 0.00000272j, 0.00000006+ 0.00000086j, -0.00014800+ -0.00000419j, -0.00005386+ -0.00000069j, -0.00001466+ -0.00000152j, -0.00002734+ -0.00000027j, -0.00004286+ -0.00000348j] , [ -0.00001016+ -0.00000003j, -0.00013518+ 0.00000001j, -0.00000695+ -0.00000004j, 0.00002023+ -0.00000018j, 0.00007449+ 0.00000001j, -0.00002941+ -0.00000013j, -0.00002626+ -0.00000079j, 0.00011956+ 0.00000008j, -0.00006386+ 0.00000012j, -0.00008535+ -0.00000282j, -0.00014280+ -0.00000001j, 0.00008661+ -0.00000074j] , [ -0.00007543+ 0.00000279j, -0.00000693+ 0.00000012j, 0.00014578+ -0.00000004j, -0.00000056+ 0.00000108j, -0.00000032+ 0.00000006j, -0.00023371+ -0.00000010j, -0.00006748+ 0.00000019j, -0.00014345+ 0.00000059j, -0.00034769+ 0.00000014j, -0.00012178+ -0.00000225j, -0.00008121+ 0.00000042j, 0.00033401+ 0.00000004j] , [ 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-0.00000006j, -0.00001778+ 0.00000075j, -0.00000203+ -0.00000004j] , [ 0.00004912+ -0.00000176j, -0.00001482+ -0.00000001j, -0.00004262+ 0.00000021j, 0.00000357+ -0.00000008j, 0.00000953+ -0.00000000j, -0.00000644+ 0.00000001j, 0.00001083+ 0.00000016j, 0.00001637+ 0.00000001j, 0.00004824+ 0.00000003j, -0.00001783+ -0.00000073j, -0.00000758+ 0.00000000j, 0.00001105+ -0.00000011j] , [ -0.00005123+ 0.00000100j, 0.00005089+ -0.00000009j, -0.00014186+ 0.00000044j, 0.00002196+ -0.00000079j, -0.00000065+ -0.00000033j, 0.00000031+ -0.00000044j, -0.00003003+ -0.00000216j, -0.00002958+ 0.00000005j, 0.00008642+ 0.00000016j, -0.00000191+ -0.00000000j, 0.00001103+ 0.00000012j, -0.00001658+ -0.00000002j] ] Hopping[( 2, -2, -2)]=[ [ -0.00000609+ -0.00000000j, -0.00000432+ -0.00000009j, -0.00001043+ -0.00000042j, -0.00001043+ 0.00000002j, -0.00000320+ -0.00000005j, 0.00000304+ 0.00000024j, 0.00000577+ 0.00000065j, 0.00000106+ 0.00000012j, -0.00000067+ -0.00000011j, 0.00000437+ 0.00000017j, 0.00000516+ 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-0.00000000j, 0.00000550+ -0.00000003j, 0.00000321+ 0.00000050j, 0.00000557+ -0.00000002j, -0.00000267+ -0.00000008j, 0.00000434+ -0.00000000j, 0.00000237+ 0.00000000j, -0.00000255+ -0.00000001j] , [ -0.00001377+ 0.00000047j, 0.00000218+ -0.00000000j, -0.00001317+ -0.00000014j, 0.00000063+ -0.00000008j, 0.00000550+ -0.00000001j, 0.00000183+ -0.00000063j, -0.00000308+ -0.00000049j, -0.00000032+ 0.00000019j, 0.00000954+ -0.00000003j, -0.00001042+ -0.00000057j, -0.00000255+ 0.00000001j, -0.00001237+ 0.00000000j] ] Hopping[( 2, -2, -1)]=[ [ -0.00002631+ 0.00000001j, 0.00001186+ 0.00000041j, 0.00000094+ 0.00000003j, -0.00002086+ -0.00000007j, 0.00002339+ 0.00000042j, 0.00001368+ -0.00000000j, 0.00001197+ 0.00000100j, -0.00000644+ -0.00000001j, 0.00000537+ 0.00000014j, 0.00002464+ 0.00000247j, 0.00002888+ 0.00000085j, 0.00005909+ 0.00000218j] , [ 0.00001183+ -0.00000040j, -0.00002518+ 0.00000000j, 0.00001037+ 0.00000007j, -0.00000641+ 0.00000009j, 0.00001115+ -0.00000002j, 0.00000535+ 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0.00000020j, 0.00001114+ -0.00000001j, 0.00000531+ 0.00000009j] , [ -0.00004718+ 0.00000167j, -0.00006233+ 0.00000032j, -0.00002786+ -0.00000048j, -0.00002662+ 0.00000110j, 0.00004227+ -0.00000050j, 0.00000683+ -0.00000078j, -0.00000125+ -0.00000088j, 0.00001030+ -0.00000016j, 0.00003226+ -0.00000001j, 0.00000610+ 0.00000051j, 0.00000067+ -0.00000053j, 0.00001910+ -0.00000023j] , [ -0.00000752+ 0.00000127j, 0.00001404+ -0.00000107j, -0.00002066+ -0.00000079j, 0.00001201+ -0.00000076j, -0.00002697+ 0.00000082j, -0.00004739+ 0.00000264j, -0.00004273+ -0.00000087j, -0.00001221+ 0.00000045j, 0.00002172+ 0.00000071j, -0.00002665+ 0.00000001j, -0.00001187+ 0.00000068j, 0.00000133+ -0.00000063j] , [ 0.00004261+ -0.00000166j, -0.00007137+ -0.00000000j, -0.00004311+ 0.00000011j, -0.00000643+ 0.00000000j, 0.00002604+ -0.00000003j, 0.00006237+ -0.00000002j, -0.00001012+ -0.00000075j, 0.00003724+ 0.00000000j, 0.00001462+ 0.00000018j, -0.00001186+ -0.00000070j, -0.00002520+ -0.00000000j, -0.00001031+ -0.00000013j] , [ -0.00006199+ 0.00000283j, 0.00004050+ -0.00000035j, 0.00016662+ -0.00000056j, -0.00000536+ 0.00000015j, 0.00003591+ 0.00000015j, -0.00002752+ 0.00000034j, 0.00000063+ 0.00000197j, 0.00000024+ 0.00000016j, -0.00011701+ -0.00000025j, 0.00000143+ 0.00000058j, -0.00001032+ 0.00000012j, 0.00003227+ -0.00000001j] ] Hopping[( 2, -2, 0)]=[ [ -0.00004200+ -0.00000000j, 0.00002342+ 0.00000072j, -0.00000190+ -0.00000026j, -0.00004268+ -0.00000008j, 0.00001226+ 0.00000030j, 0.00002273+ 0.00000114j, 0.00001663+ 0.00000175j, 0.00002136+ 0.00000070j, -0.00003615+ -0.00000154j, 0.00003735+ 0.00000288j, 0.00005357+ 0.00000159j, 0.00003844+ 0.00000056j] , [ 0.00002342+ -0.00000072j, -0.00006755+ 0.00000000j, 0.00001252+ 0.00000010j, 0.00001001+ -0.00000016j, 0.00003725+ -0.00000000j, -0.00001469+ -0.00000005j, -0.00002097+ -0.00000076j, 0.00004420+ 0.00000000j, -0.00004215+ 0.00000010j, -0.00000519+ -0.00000022j, -0.00009017+ -0.00000003j, -0.00000523+ -0.00000016j] , [ 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-0.00011686+ 0.00000014j, 0.00000189+ -0.00000025j, -0.00001251+ 0.00000011j, 0.00002883+ 0.00000000j, -0.00003959+ -0.00000503j, -0.00000514+ 0.00000003j, 0.00021426+ 0.00000113j, -0.00003632+ -0.00000149j, 0.00004197+ 0.00000010j, -0.00000204+ -0.00000019j] , [ 0.00001663+ -0.00000175j, -0.00002097+ 0.00000076j, 0.00003625+ -0.00000194j, 0.00003750+ -0.00000389j, -0.00005361+ 0.00000378j, -0.00003959+ 0.00000503j, -0.00004188+ -0.00000000j, -0.00002354+ 0.00000152j, 0.00000153+ 0.00000081j, -0.00004270+ 0.00000037j, -0.00001212+ 0.00000072j, -0.00002166+ -0.00000041j] , [ 0.00002136+ -0.00000070j, 0.00004420+ -0.00000000j, -0.00004197+ 0.00000012j, 0.00000480+ -0.00000024j, -0.00009019+ -0.00000003j, -0.00000514+ -0.00000003j, -0.00002354+ -0.00000152j, -0.00006755+ -0.00000000j, 0.00001243+ 0.00000032j, -0.00001026+ -0.00000051j, 0.00003724+ -0.00000000j, -0.00001465+ -0.00000018j] , [ -0.00003615+ 0.00000154j, -0.00004215+ -0.00000010j, -0.00000208+ 0.00000029j, -0.00003558+ 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-0.00000204+ 0.00000019j, -0.00002166+ 0.00000041j, -0.00001465+ 0.00000018j, -0.00011705+ 0.00000026j, -0.00000150+ 0.00000053j, -0.00001240+ 0.00000027j, 0.00002881+ 0.00000000j] ] Hopping[( 2, -2, 1)]=[ [ -0.00002631+ -0.00000001j, 0.00001183+ 0.00000040j, 0.00000097+ -0.00000003j, -0.00004254+ 0.00000008j, 0.00001011+ 0.00000006j, 0.00000029+ 0.00000054j, 0.00001218+ 0.00000183j, 0.00002725+ 0.00000088j, -0.00004718+ -0.00000167j, -0.00000752+ -0.00000127j, 0.00004261+ 0.00000166j, -0.00006199+ -0.00000283j] , [ 0.00001186+ -0.00000041j, -0.00002518+ -0.00000000j, 0.00001038+ 0.00000007j, 0.00001216+ -0.00000032j, 0.00003724+ -0.00000001j, 0.00000016+ 0.00000002j, 0.00000675+ 0.00000146j, 0.00002605+ -0.00000001j, -0.00006233+ -0.00000032j, 0.00001404+ 0.00000107j, -0.00007137+ 0.00000000j, 0.00004050+ 0.00000035j] , [ 0.00000094+ -0.00000003j, 0.00001037+ -0.00000007j, 0.00003227+ -0.00000003j, -0.00002259+ 0.00000132j, 0.00001470+ -0.00000007j, -0.00011686+ 0.00000008j, 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-0.00000000j, 0.00000683+ 0.00000078j, -0.00004739+ -0.00000264j, 0.00006237+ 0.00000002j, -0.00002752+ -0.00000034j] , [ 0.00001197+ -0.00000100j, -0.00002705+ 0.00000091j, 0.00004739+ -0.00000325j, -0.00000721+ 0.00000040j, -0.00004272+ 0.00000166j, 0.00006100+ -0.00000123j, -0.00002601+ -0.00000001j, -0.00001191+ 0.00000095j, -0.00000125+ 0.00000088j, -0.00004273+ 0.00000087j, -0.00001012+ 0.00000075j, 0.00000063+ -0.00000197j] , [ -0.00000644+ 0.00000001j, 0.00002603+ -0.00000003j, -0.00006236+ 0.00000001j, -0.00001367+ 0.00000015j, -0.00007140+ 0.00000000j, 0.00004061+ 0.00000020j, -0.00001192+ -0.00000093j, -0.00002519+ -0.00000000j, 0.00001030+ 0.00000016j, -0.00001221+ -0.00000045j, 0.00003724+ -0.00000000j, 0.00000024+ -0.00000016j] , [ 0.00000537+ -0.00000014j, -0.00003587+ -0.00000020j, -0.00002754+ 0.00000043j, 0.00002141+ -0.00000165j, -0.00004327+ -0.00000048j, 0.00016696+ 0.00000009j, -0.00000132+ -0.00000085j, 0.00001030+ -0.00000016j, 0.00003226+ 0.00000001j, 0.00002172+ -0.00000071j, 0.00001462+ -0.00000018j, -0.00011701+ 0.00000025j] , [ 0.00002464+ -0.00000247j, -0.00000621+ 0.00000059j, 0.00002672+ -0.00000111j, 0.00001229+ -0.00000147j, 0.00000692+ -0.00000094j, 0.00000516+ 0.00000011j, -0.00002096+ -0.00000260j, 0.00000644+ -0.00000020j, 0.00000610+ -0.00000051j, -0.00002665+ -0.00000001j, -0.00001186+ 0.00000070j, 0.00000143+ -0.00000058j] , [ 0.00002888+ -0.00000085j, -0.00002804+ 0.00000004j, 0.00004249+ 0.00000003j, 0.00002727+ -0.00000088j, 0.00002605+ 0.00000001j, 0.00003575+ -0.00000010j, -0.00002322+ -0.00000104j, 0.00001114+ 0.00000001j, 0.00000067+ 0.00000053j, -0.00001187+ -0.00000068j, -0.00002520+ 0.00000000j, -0.00001032+ -0.00000012j] , [ 0.00005909+ -0.00000218j, -0.00002996+ 0.00000025j, 0.00000699+ -0.00000017j, 0.00004718+ -0.00000167j, 0.00006233+ -0.00000026j, -0.00002787+ -0.00000043j, -0.00001360+ -0.00000150j, 0.00000531+ -0.00000009j, 0.00001910+ 0.00000023j, 0.00000133+ 0.00000063j, -0.00001031+ 0.00000013j, 0.00003227+ 0.00000001j] ] Hopping[( 2, -2, 2)]=[ [ -0.00000609+ -0.00000000j, -0.00000432+ -0.00000009j, -0.00001043+ -0.00000042j, -0.00001043+ 0.00000002j, -0.00000320+ -0.00000005j, 0.00000304+ 0.00000024j, 0.00000577+ 0.00000065j, 0.00000106+ 0.00000012j, -0.00000067+ -0.00000011j, 0.00000437+ 0.00000017j, 0.00000516+ 0.00000016j, -0.00001377+ -0.00000047j] , [ -0.00000432+ 0.00000009j, 0.00000236+ -0.00000000j, 0.00000252+ -0.00000002j, 0.00001168+ -0.00000024j, 0.00000558+ 0.00000001j, -0.00000040+ 0.00000025j, -0.00000110+ 0.00000038j, -0.00000382+ -0.00000000j, -0.00000550+ -0.00000008j, 0.00000034+ -0.00000001j, 0.00000028+ 0.00000001j, 0.00000218+ 0.00000000j] , [ -0.00001043+ 0.00000042j, 0.00000252+ 0.00000002j, -0.00001257+ -0.00000000j, -0.00000683+ 0.00000001j, -0.00000267+ 0.00000008j, 0.00000959+ 0.00000004j, 0.00000072+ 0.00000013j, -0.00000550+ -0.00000003j, 0.00000181+ 0.00000067j, 0.00000894+ 0.00000033j, 0.00000335+ -0.00000003j, -0.00001317+ 0.00000014j] , [ 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-0.00000104+ 0.00000007j, -0.00000073+ -0.00000001j, -0.00001046+ -0.00000143j, -0.00001169+ 0.00000041j, 0.00000682+ -0.00000004j, -0.00000653+ 0.00000000j, 0.00000434+ 0.00000000j, -0.00001042+ 0.00000057j] , [ 0.00000516+ -0.00000016j, 0.00000028+ -0.00000001j, 0.00000335+ 0.00000003j, 0.00000107+ -0.00000012j, -0.00000382+ -0.00000000j, 0.00000550+ -0.00000003j, 0.00000321+ 0.00000050j, 0.00000557+ -0.00000002j, -0.00000267+ -0.00000008j, 0.00000434+ -0.00000000j, 0.00000237+ 0.00000000j, -0.00000255+ -0.00000001j] , [ -0.00001377+ 0.00000047j, 0.00000218+ -0.00000000j, -0.00001317+ -0.00000014j, 0.00000063+ -0.00000008j, 0.00000550+ -0.00000001j, 0.00000183+ -0.00000063j, -0.00000308+ -0.00000049j, -0.00000032+ 0.00000019j, 0.00000954+ -0.00000003j, -0.00001042+ -0.00000057j, -0.00000255+ 0.00000001j, -0.00001237+ 0.00000000j] ] Hopping[( 2, -1, -2)]=[ [ 0.00016945+ -0.00000008j, 0.00001393+ 0.00000028j, 0.00012237+ 0.00000443j, 0.00012229+ -0.00000072j, 0.00002357+ 0.00000081j, 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0.00000019j] ] Hopping[( 2, 0, -2)]=[ [ -0.00076624+ -0.00000000j, -0.00036885+ -0.00001096j, 0.00084716+ 0.00002328j, -0.00083931+ 0.00000263j, 0.00054997+ 0.00001634j, -0.00108428+ -0.00002872j, -0.00000805+ 0.00000099j, -0.00000843+ -0.00000014j, -0.00017540+ -0.00000605j, -0.00006814+ -0.00000519j, -0.00018165+ -0.00000596j, 0.00002701+ 0.00000029j] , [ -0.00036885+ 0.00001096j, -0.00052476+ 0.00000000j, -0.00010885+ -0.00000153j, -0.00115326+ 0.00003701j, 0.00033073+ -0.00000061j, -0.00044918+ 0.00000476j, 0.00000848+ 0.00000175j, 0.00022979+ 0.00000002j, -0.00005257+ -0.00000023j, -0.00001235+ -0.00000032j, -0.00027339+ 0.00000008j, 0.00000649+ -0.00000000j] , [ 0.00084716+ -0.00002328j, -0.00010885+ 0.00000153j, -0.00000251+ -0.00000000j, 0.00057653+ -0.00001334j, -0.00000896+ -0.00000196j, -0.00024441+ 0.00000209j, 0.00017469+ 0.00000973j, -0.00005251+ 0.00000000j, 0.00004674+ -0.00000143j, 0.00001227+ 0.00000054j, 0.00015313+ 0.00000067j, -0.00001815+ -0.00000010j] , [ 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, [ 0.00079493+ -0.00001382j, 0.00091259+ 0.00000021j, 0.00186307+ 0.00000296j, -0.00139089+ 0.00004255j, -0.00113327+ -0.00000000j, -0.00008637+ 0.00000413j, -0.00018138+ -0.00002916j, 0.00032314+ 0.00000019j, 0.00136027+ 0.00000620j, -0.00044407+ -0.00002164j, 0.00010787+ -0.00000003j, -0.00012391+ -0.00000421j] , [ -0.00317490+ 0.00010870j, -0.00022054+ 0.00000095j, 0.00138284+ -0.00000522j, 0.00010705+ -0.00000945j, -0.00008637+ -0.00000413j, -0.00014791+ -0.00000000j, -0.00012383+ 0.00001289j, -0.00095738+ -0.00000142j, -0.00159234+ 0.00000538j, -0.00004022+ 0.00000511j, -0.00012683+ 0.00000098j, 0.00095843+ 0.00000316j] , [ -0.00092180+ 0.00008012j, -0.00044354+ 0.00002688j, 0.00004141+ 0.00000669j, -0.00003390+ 0.00003439j, -0.00018138+ 0.00002916j, -0.00012383+ -0.00001289j, -0.00244144+ -0.00000000j, 0.00138840+ -0.00007830j, 0.00009565+ 0.00002172j, 0.00260987+ -0.00001326j, -0.00080481+ 0.00007397j, 0.00314877+ -0.00012674j] , [ 0.00044293+ -0.00001283j, 0.00010783+ 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-0.00041956+ 0.00000003j, -0.00030181+ 0.00000642j] , [ 0.00012857+ -0.00000074j, -0.00015483+ -0.00000146j, -0.00012926+ -0.00000170j, 0.00005795+ -0.00000218j, 0.00037477+ 0.00000053j, 0.00063471+ 0.00000148j, 0.00116515+ 0.00008438j, 0.00002617+ 0.00001108j, -0.00046646+ 0.00000754j, 0.00118466+ 0.00005021j, -0.00030171+ -0.00000640j, -0.00026940+ -0.00000003j] ] Hopping[( 2, 0, 2)]=[ [ -0.00076624+ -0.00000000j, -0.00036885+ -0.00001096j, 0.00084716+ 0.00002328j, -0.00083931+ 0.00000263j, 0.00054997+ 0.00001634j, -0.00108428+ -0.00002872j, -0.00000805+ 0.00000099j, -0.00000843+ -0.00000014j, -0.00017540+ -0.00000605j, -0.00006814+ -0.00000519j, -0.00018165+ -0.00000596j, 0.00002701+ 0.00000029j] , [ -0.00036885+ 0.00001096j, -0.00052476+ 0.00000000j, -0.00010885+ -0.00000153j, -0.00115326+ 0.00003701j, 0.00033073+ -0.00000061j, -0.00044918+ 0.00000476j, 0.00000848+ 0.00000175j, 0.00022979+ 0.00000002j, -0.00005257+ -0.00000023j, -0.00001235+ -0.00000032j, -0.00027339+ 0.00000008j, 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0.00000010j, 0.00002476+ -0.00000019j] ] Hopping[( 2, 1, -1)]=[ [ -0.00009756+ 0.00000004j, -0.00005901+ -0.00000298j, 0.00024966+ 0.00000685j, 0.00021974+ -0.00000007j, -0.00011034+ -0.00000211j, -0.00016659+ -0.00000537j, 0.00002436+ 0.00000345j, 0.00005455+ 0.00000184j, -0.00009428+ -0.00000371j, 0.00001848+ 0.00000165j, -0.00000114+ 0.00000010j, 0.00003616+ 0.00000157j] , [ -0.00005886+ 0.00000307j, 0.00006152+ 0.00000000j, 0.00029428+ -0.00000027j, 0.00007679+ -0.00000214j, 0.00000404+ 0.00000005j, -0.00009240+ -0.00000023j, 0.00001335+ 0.00000299j, 0.00005209+ -0.00000002j, -0.00012471+ -0.00000063j, -0.00002052+ -0.00000126j, 0.00000110+ -0.00000004j, -0.00001328+ -0.00000032j] , [ 0.00024972+ -0.00000683j, 0.00029427+ 0.00000033j, 0.00006047+ 0.00000001j, 0.00017973+ -0.00000586j, -0.00029942+ 0.00000075j, -0.00015878+ 0.00000010j, -0.00001041+ -0.00000004j, -0.00007141+ -0.00000018j, -0.00005588+ 0.00000072j, -0.00005531+ -0.00000343j, -0.00000889+ 0.00000049j, -0.00005196+ 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-0.00000698+ -0.00000004j, 0.00001728+ 0.00000093j, 0.00035665+ -0.00001351j] , [ -0.00155429+ 0.00004349j, -0.00174340+ -0.00000030j, -0.00101084+ -0.00000576j, 0.00044286+ -0.00001305j, 0.00010790+ 0.00000004j, -0.00012681+ -0.00000092j, 0.00000993+ 0.00000870j, -0.00030570+ -0.00000003j, -0.00064025+ -0.00000225j, 0.00001772+ -0.00000094j, -0.00000050+ -0.00000001j, -0.00017317+ -0.00000015j] , [ -0.00228954+ 0.00008673j, -0.00102131+ -0.00000603j, 0.00091298+ -0.00002010j, 0.00005362+ 0.00000388j, -0.00012388+ 0.00000417j, 0.00095873+ -0.00000307j, -0.00002934+ 0.00001739j, -0.00042373+ 0.00000220j, -0.00167237+ 0.00000446j, 0.00035658+ 0.00001353j, -0.00017323+ 0.00000023j, -0.00013657+ 0.00000006j] ] Hopping[( 2, 1, 1)]=[ [ 0.00051784+ -0.00000002j, -0.00014121+ -0.00000368j, 0.00027576+ 0.00001000j, 0.00022347+ 0.00000157j, 0.00006800+ 0.00000088j, -0.00039149+ -0.00000714j, 0.00002387+ 0.00000169j, -0.00001286+ 0.00000005j, 0.00001090+ -0.00000005j, 0.00007489+ 0.00000627j, 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0.00020016+ 0.00000008j, 0.00037348+ -0.00000062j, 0.00011160+ 0.00001010j, 0.00000405+ -0.00000003j, -0.00029883+ -0.00000262j, 0.00014211+ 0.00000800j, -0.00020593+ -0.00000001j, 0.00009970+ 0.00000190j] , [ 0.00002647+ -0.00000007j, -0.00030868+ -0.00000263j, -0.00003648+ -0.00000076j, 0.00036879+ -0.00001266j, 0.00023003+ -0.00000105j, 0.00063251+ -0.00000321j, 0.00016590+ 0.00000852j, -0.00009184+ 0.00000109j, -0.00015649+ -0.00000033j, 0.00027265+ 0.00000707j, 0.00009974+ -0.00000193j, 0.00007856+ 0.00000003j] ] Hopping[( 2, 1, 2)]=[ [ 0.00016945+ 0.00000008j, 0.00001402+ 0.00000057j, 0.00012240+ 0.00000460j, 0.00010980+ 0.00000004j, 0.00003832+ 0.00000101j, -0.00008983+ -0.00000295j, 0.00001165+ 0.00000124j, 0.00000216+ 0.00000025j, -0.00000123+ -0.00000028j, 0.00002476+ 0.00000187j, 0.00000602+ 0.00000012j, 0.00002654+ 0.00000103j] , [ 0.00001393+ -0.00000028j, 0.00001063+ 0.00000000j, -0.00001910+ 0.00000015j, -0.00005510+ 0.00000104j, 0.00000202+ -0.00000003j, 0.00014966+ 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0.00001923+ -0.00000013j] , [ 0.00013224+ -0.00000266j, 0.00025123+ 0.00000193j, -0.00006407+ 0.00000019j, 0.00017514+ -0.00000585j, 0.00005251+ -0.00000018j, 0.00004700+ 0.00000086j, 0.00008943+ 0.00000448j, 0.00014927+ -0.00000108j, -0.00007822+ -0.00000024j, 0.00012169+ 0.00000363j, 0.00001925+ 0.00000010j, 0.00002476+ -0.00000019j] ] Hopping[( 2, 2, -2)]=[ [ -0.00000609+ -0.00000000j, -0.00000432+ -0.00000009j, -0.00001043+ -0.00000042j, -0.00001043+ 0.00000002j, -0.00000320+ -0.00000005j, 0.00000304+ 0.00000024j, 0.00000577+ 0.00000065j, 0.00000106+ 0.00000012j, -0.00000067+ -0.00000011j, 0.00000437+ 0.00000017j, 0.00000516+ 0.00000016j, -0.00001377+ -0.00000047j] , [ -0.00000432+ 0.00000009j, 0.00000236+ -0.00000000j, 0.00000252+ -0.00000002j, 0.00001168+ -0.00000024j, 0.00000558+ 0.00000001j, -0.00000040+ 0.00000025j, -0.00000110+ 0.00000038j, -0.00000382+ -0.00000000j, -0.00000550+ -0.00000008j, 0.00000034+ -0.00000001j, 0.00000028+ 0.00000001j, 0.00000218+ 0.00000000j] , [ 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0b9c04e884f5b96b42dda961be90b917962a93f0

17,087

py

Python

mozurestsdk/commerce/orders/orderitem.py

Mozu/mozu-python-sdk

9cc918aca7db3639264184e5266e8e508a08a7dd

[ "Apache-2.0" ]

1

2021-03-22T12:38:42.000Z

mozurestsdk/commerce/orders/orderitem.py

Mozu/mozu-python-sdk

9cc918aca7db3639264184e5266e8e508a08a7dd

[ "Apache-2.0" ]

null

mozurestsdk/commerce/orders/orderitem.py

Mozu/mozu-python-sdk

9cc918aca7db3639264184e5266e8e508a08a7dd

[ "Apache-2.0" ]

2

2015-09-30T19:49:00.000Z

2015-09-30T19:51:03.000Z

""" This code was generated by Codezu. Changes to this file may cause incorrect behavior and will be lost if the code is regenerated. """ from mozurestsdk.mozuclient import default as default_client from mozurestsdk.mozuurl import MozuUrl; from mozurestsdk.urllocation import UrlLocation from mozurestsdk.apicontext import ApiContext; class OrderItem(object): def __init__(self, apiContext: ApiContext = None, mozuClient = None): self.client = mozuClient or default_client(); if (apiContext is not None): self.client.withApiContext(apiContext); else: self.client.withApiContext(ApiContext()); def getOrderItemViaLineId(self,orderId, lineId, draft = False, responseFields = None): """ Retrieves an order item with the order line ID. Args: | orderId (string) - Unique identifier of the order. | lineId (int) - The specific line id that's associated with the order item. | draft (bool) - If true, retrieve the draft version of the order, which might include uncommitted changes to the order or its components. | responseFields (string) - Filtering syntax appended to an API call to increase or decrease the amount of data returned inside a JSON object. This parameter should only be used to retrieve data. Attempting to update data using this parameter may cause data loss. Returns: | OrderItem Raises: | ApiException """ url = MozuUrl("/api/commerce/orders/{orderId}/items/{lineId}?draft={draft}&responseFields={responseFields}", "GET", UrlLocation.TenantPod, False); url.formatUrl("draft", draft); url.formatUrl("lineId", lineId); url.formatUrl("orderId", orderId); url.formatUrl("responseFields", responseFields); self.client.withResourceUrl(url).execute(); return self.client.result(); def getOrderItem(self,orderId, orderItemId, draft = False, responseFields = None): """ Retrieves the details of a single order item. Args: | orderId (string) - Unique identifier of the order. | orderItemId (string) - Unique identifier of the item to remove from the order. | draft (bool) - If true, retrieve the draft version of the order, which might include uncommitted changes to the order or its components. | responseFields (string) - Use this field to include those fields which are not included by default. Returns: | OrderItem Raises: | ApiException """ url = MozuUrl("/api/commerce/orders/{orderId}/items/{orderItemId}?draft={draft}&responseFields={responseFields}", "GET", UrlLocation.TenantPod, False); url.formatUrl("draft", draft); url.formatUrl("orderId", orderId); url.formatUrl("orderItemId", orderItemId); url.formatUrl("responseFields", responseFields); self.client.withResourceUrl(url).execute(); return self.client.result(); def getOrderItems(self,orderId, draft = False, responseFields = None): """ Retrieves the details of all items in an order. Args: | orderId (string) - Unique identifier of the order. | draft (bool) - If true, retrieve the draft version of the order, which might include uncommitted changes to the order or its components. | responseFields (string) - Use this field to include those fields which are not included by default. Returns: | OrderItemCollection Raises: | ApiException """ url = MozuUrl("/api/commerce/orders/{orderId}/items?draft={draft}&responseFields={responseFields}", "GET", UrlLocation.TenantPod, False); url.formatUrl("draft", draft); url.formatUrl("orderId", orderId); url.formatUrl("responseFields", responseFields); self.client.withResourceUrl(url).execute(); return self.client.result(); def createOrderItem(self,orderItem, orderId, updateMode = None, version = None, skipInventoryCheck = False, responseFields = None): """ Adds a new item to a defined order. Args: | orderItem(orderItem) - The details associated with a specific item in an order. | orderId (string) - Unique identifier of the order. | updateMode (string) - Specifies whether to update the original order, update the order in draft mode, or update the order in draft mode and then commit the changes to the original. Draft mode enables users to make incremental order changes before committing the changes to the original order. Valid values are "ApplyToOriginal," "ApplyToDraft," or "ApplyAndCommit." | version (string) - System-supplied integer that represents the current version of the order, which prevents users from unintentionally overriding changes to the order. When a user performs an operation for a defined order, the system validates that the version of the updated order matches the version of the order on the server. After the operation completes successfully, the system increments the version number by one. | skipInventoryCheck (bool) - If true, skip the process to validate inventory when creating this product reservation. | responseFields (string) - Use this field to include those fields which are not included by default. Returns: | Order Raises: | ApiException """ url = MozuUrl("/api/commerce/orders/{orderId}/items?updatemode={updateMode}&version={version}&skipInventoryCheck={skipInventoryCheck}&responseFields={responseFields}", "POST", UrlLocation.TenantPod, False); url.formatUrl("orderId", orderId); url.formatUrl("responseFields", responseFields); url.formatUrl("skipInventoryCheck", skipInventoryCheck); url.formatUrl("updateMode", updateMode); url.formatUrl("version", version); self.client.withResourceUrl(url).withBody(orderItem).execute(); return self.client.result(); def updateOrderItemDiscount(self,discount, orderId, orderItemId, discountId, updateMode = None, version = None, responseFields = None): """ Update the discount applied to an item in an order. Args: | discount(discount) - Properties of all applied discounts for an associated cart, order, or product. | orderId (string) - Unique identifier of the order. | orderItemId (string) - Unique identifier of the item to remove from the order. | discountId (int) - discountId parameter description DOCUMENT_HERE | updateMode (string) - Specifies whether to update the original order, update the order in draft mode, or update the order in draft mode and then commit the changes to the original. Draft mode enables users to make incremental order changes before committing the changes to the original order. Valid values are "ApplyToOriginal," "ApplyToDraft," or "ApplyAndCommit." | version (string) - System-supplied integer that represents the current version of the order, which prevents users from unintentionally overriding changes to the order. When a user performs an operation for a defined order, the system validates that the version of the updated order matches the version of the order on the server. After the operation completes successfully, the system increments the version number by one. | responseFields (string) - Use this field to include those fields which are not included by default. Returns: | Order Raises: | ApiException """ url = MozuUrl("/api/commerce/orders/{orderId}/items/{orderItemId}/discounts/{discountId}?updatemode={updateMode}&version={version}&responseFields={responseFields}", "PUT", UrlLocation.TenantPod, False); url.formatUrl("discountId", discountId); url.formatUrl("orderId", orderId); url.formatUrl("orderItemId", orderItemId); url.formatUrl("responseFields", responseFields); url.formatUrl("updateMode", updateMode); url.formatUrl("version", version); self.client.withResourceUrl(url).withBody(discount).execute(); return self.client.result(); def updateItemDuty(self,orderId, orderItemId, dutyAmount, updateMode = None, version = None, responseFields = None): """ Update the duty fee information for an order item. Args: | orderId (string) - Unique identifier of the order. | orderItemId (string) - Unique identifier of the item to remove from the order. | dutyAmount (decimal) - The amount added to the order item for duty fees. | updateMode (string) - Specifies whether to update the original order, update the order in draft mode, or update the order in draft mode and then commit the changes to the original. Draft mode enables users to make incremental order changes before committing the changes to the original order. Valid values are "ApplyToOriginal," "ApplyToDraft," or "ApplyAndCommit." | version (string) - Determines whether or not to check versioning of items for concurrency purposes. | responseFields (string) - Filtering syntax appended to an API call to increase or decrease the amount of data returned inside a JSON object. This parameter should only be used to retrieve data. Attempting to update data using this parameter may cause data loss. Returns: | Order Raises: | ApiException """ url = MozuUrl("/api/commerce/orders/{orderId}/items/{orderItemId}/dutyAmount/{dutyAmount}?updatemode={updateMode}&version={version}&responseFields={responseFields}", "PUT", UrlLocation.TenantPod, False); url.formatUrl("dutyAmount", dutyAmount); url.formatUrl("orderId", orderId); url.formatUrl("orderItemId", orderItemId); url.formatUrl("responseFields", responseFields); url.formatUrl("updateMode", updateMode); url.formatUrl("version", version); self.client.withResourceUrl(url).execute(); return self.client.result(); def updateItemFulfillment(self,orderItem, orderId, orderItemId, updateMode = None, version = None, responseFields = None): """ Updates the item fulfillment information for the order specified in the request. Args: | orderItem(orderItem) - The details associated with a specific item in an order. | orderId (string) - Unique identifier of the order. | orderItemId (string) - Unique identifier of the item to remove from the order. | updateMode (string) - Specifies whether to update the original order, update the order in draft mode, or update the order in draft mode and then commit the changes to the original. Draft mode enables users to make incremental order changes before committing the changes to the original order. Valid values are "ApplyToOriginal," "ApplyToDraft," or "ApplyAndCommit." | version (string) - System-supplied integer that represents the current version of the order, which prevents users from unintentionally overriding changes to the order. When a user performs an operation for a defined order, the system validates that the version of the updated order matches the version of the order on the server. After the operation completes successfully, the system increments the version number by one. | responseFields (string) - Use this field to include those fields which are not included by default. Returns: | Order Raises: | ApiException """ url = MozuUrl("/api/commerce/orders/{orderId}/items/{orderItemId}/fulfillment?updatemode={updateMode}&version={version}&responseFields={responseFields}", "PUT", UrlLocation.TenantPod, False); url.formatUrl("orderId", orderId); url.formatUrl("orderItemId", orderItemId); url.formatUrl("responseFields", responseFields); url.formatUrl("updateMode", updateMode); url.formatUrl("version", version); self.client.withResourceUrl(url).withBody(orderItem).execute(); return self.client.result(); def updateItemProductPrice(self,orderId, orderItemId, price, updateMode = None, version = None, responseFields = None): """ Override the price of an individual product on a line item in the specified order. Args: | orderId (string) - Unique identifier of the order. | orderItemId (string) - Unique identifier of the item to remove from the order. | price (decimal) - The override price to specify for this item in the specified order. | updateMode (string) - Specifies whether to update the original order, update the order in draft mode, or update the order in draft mode and then commit the changes to the original. Draft mode enables users to make incremental order changes before committing the changes to the original order. Valid values are "ApplyToOriginal," "ApplyToDraft," or "ApplyAndCommit." | version (string) - System-supplied integer that represents the current version of the order, which prevents users from unintentionally overriding changes to the order. When a user performs an operation for a defined order, the system validates that the version of the updated order matches the version of the order on the server. After the operation completes successfully, the system increments the version number by one. | responseFields (string) - Use this field to include those fields which are not included by default. Returns: | Order Raises: | ApiException """ url = MozuUrl("/api/commerce/orders/{orderId}/items/{orderItemId}/price/{price}?updatemode={updateMode}&version={version}&responseFields={responseFields}", "PUT", UrlLocation.TenantPod, False); url.formatUrl("orderId", orderId); url.formatUrl("orderItemId", orderItemId); url.formatUrl("price", price); url.formatUrl("responseFields", responseFields); url.formatUrl("updateMode", updateMode); url.formatUrl("version", version); self.client.withResourceUrl(url).execute(); return self.client.result(); def updateItemQuantity(self,orderId, orderItemId, quantity, updateMode = None, version = None, responseFields = None): """ Update the quantity of an item in an order. Args: | orderId (string) - Unique identifier of the order. | orderItemId (string) - Unique identifier of the item to remove from the order. | quantity (int) - The number of cart items in the shopper's active cart. | updateMode (string) - Specifies whether to update the original order, update the order in draft mode, or update the order in draft mode and then commit the changes to the original. Draft mode enables users to make incremental order changes before committing the changes to the original order. Valid values are "ApplyToOriginal," "ApplyToDraft," or "ApplyAndCommit." | version (string) - System-supplied integer that represents the current version of the order, which prevents users from unintentionally overriding changes to the order. When a user performs an operation for a defined order, the system validates that the version of the updated order matches the version of the order on the server. After the operation completes successfully, the system increments the version number by one. | responseFields (string) - Use this field to include those fields which are not included by default. Returns: | Order Raises: | ApiException """ url = MozuUrl("/api/commerce/orders/{orderId}/items/{orderItemId}/quantity/{quantity}?updatemode={updateMode}&version={version}&responseFields={responseFields}", "PUT", UrlLocation.TenantPod, False); url.formatUrl("orderId", orderId); url.formatUrl("orderItemId", orderItemId); url.formatUrl("quantity", quantity); url.formatUrl("responseFields", responseFields); url.formatUrl("updateMode", updateMode); url.formatUrl("version", version); self.client.withResourceUrl(url).execute(); return self.client.result(); def deleteOrderItem(self,orderId, orderItemId, updateMode = None, version = None): """ Removes a previously added item from a defined order. Args: | orderId (string) - Unique identifier of the order. | orderItemId (string) - Unique identifier of the item to remove from the order. | updateMode (string) - Specifies whether to update the original order, update the order in draft mode, or update the order in draft mode and then commit the changes to the original. Draft mode enables users to make incremental order changes before committing the changes to the original order. Valid values are "ApplyToOriginal," "ApplyToDraft," or "ApplyAndCommit." | version (string) - System-supplied integer that represents the current version of the order, which prevents users from unintentionally overriding changes to the order. When a user performs an operation for a defined order, the system validates that the version of the updated order matches the version of the order on the server. After the operation completes successfully, the system increments the version number by one. Returns: | Order Raises: | ApiException """ url = MozuUrl("/api/commerce/orders/{orderId}/items/{orderItemId}?updatemode={updateMode}&version={version}", "DELETE", UrlLocation.TenantPod, False); url.formatUrl("orderId", orderId); url.formatUrl("orderItemId", orderItemId); url.formatUrl("updateMode", updateMode); url.formatUrl("version", version); self.client.withResourceUrl(url).execute(); return self.client.result();

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Python

search_imagent/imagenet_network/__init__.py

gbup-group/EAN-efficient-attention-network

ac9c049158873836e1c239fc35f65d4b79274b12

[ "MIT" ]

20

2020-11-28T02:38:59.000Z

2021-07-22T17:48:17.000Z

search_imagent/imagenet_network/__init__.py

gbup-group/EAN-efficient-attention-network

ac9c049158873836e1c239fc35f65d4b79274b12

[ "MIT" ]

null

search_imagent/imagenet_network/__init__.py

gbup-group/EAN-efficient-attention-network

ac9c049158873836e1c239fc35f65d4b79274b12

[ "MIT" ]

6

2020-11-29T15:37:04.000Z

2021-01-16T00:57:54.000Z

from __future__ import absolute_import from .forward_config_share_sge_fbresnet import * from .forward_config_dia_fbresnet import *

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py

Python

src/metrics/__init__.py

norikinishida/discourse-parsing

7377a78cc32ad6430d256694e31ed9426e7c6340

[ "Apache-2.0" ]

2

2022-02-16T20:41:22.000Z

2022-03-11T18:28:24.000Z

src/metrics/__init__.py

norikinishida/discourse-parsing

7377a78cc32ad6430d256694e31ed9426e7c6340

[ "Apache-2.0" ]

null

src/metrics/__init__.py

norikinishida/discourse-parsing

7377a78cc32ad6430d256694e31ed9426e7c6340

[ "Apache-2.0" ]

null

from .attachment_scores import attachment_scores

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f06158f159db2984f866b05ef85ba2bb6a99e9d3

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py

Python

plugins/carbon_black_defense/komand_carbon_black_defense/actions/__init__.py

lukaszlaszuk/insightconnect-plugins

8c6ce323bfbb12c55f8b5a9c08975d25eb9f8892

[ "MIT" ]

46

2019-06-05T20:47:58.000Z

2022-03-29T10:18:01.000Z

plugins/carbon_black_defense/komand_carbon_black_defense/actions/__init__.py

lukaszlaszuk/insightconnect-plugins

8c6ce323bfbb12c55f8b5a9c08975d25eb9f8892

[ "MIT" ]

386

2019-06-07T20:20:39.000Z

2022-03-30T17:35:01.000Z

plugins/carbon_black_defense/komand_carbon_black_defense/actions/__init__.py

lukaszlaszuk/insightconnect-plugins

8c6ce323bfbb12c55f8b5a9c08975d25eb9f8892

[ "MIT" ]

43

2019-07-09T14:13:58.000Z

2022-03-28T12:04:46.000Z

# GENERATED BY KOMAND SDK - DO NOT EDIT from .find_event.action import FindEvent from .get_details_for_specific_event.action import GetDetailsForSpecificEvent

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7

6511057f9443bf80647e8c196ba3f5ebc2e33f53

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py

Python

scrapyproject/utils/__init__.py

gas1121/JapanCinemaStatusSpider

67c7b963914565589f64dd1bcf18839a4160ea34

[ "MIT" ]

2

2018-06-07T13:28:03.000Z

2018-12-10T14:04:53.000Z

scrapyproject/utils/__init__.py

gas1121/JapanCinemaStatusSpider

67c7b963914565589f64dd1bcf18839a4160ea34

[ "MIT" ]

null

scrapyproject/utils/__init__.py

gas1121/JapanCinemaStatusSpider

67c7b963914565589f64dd1bcf18839a4160ea34

[ "MIT" ]

null

""" Util classes """ from scrapyproject.utils.screen_utils import ScreenUtils from scrapyproject.utils.site_utils import * from scrapyproject.utils.spider_helper import * from scrapyproject.utils.test_utils import TestUtil

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8

e8e99d14fc12d734d9ef6aedf69e33451a3e1cfa

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py

Python

lrz_sync_share/__init__.py

instance01/lrz-sync-share-lib

6952019c2aadf84d8d1cddfb8d656175ab4fbba1

[ "MIT" ]

null

lrz_sync_share/__init__.py

instance01/lrz-sync-share-lib

6952019c2aadf84d8d1cddfb8d656175ab4fbba1

[ "MIT" ]

null

lrz_sync_share/__init__.py

instance01/lrz-sync-share-lib

6952019c2aadf84d8d1cddfb8d656175ab4fbba1

[ "MIT" ]

null

from .lrz_session import lrz_session

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330eb841c3bd0cc68cfb170a6aedea6c2a6baafc

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py

Python

api/cueSearch/elasticSearch/elastic_search_querying.py

cuebook/CueSearch

8bf047de273b27bba41b8bf4e266aac1eee7f81a

[ "Apache-2.0" ]

3

2022-02-10T17:00:19.000Z

2022-03-29T14:31:25.000Z

api/cueSearch/elasticSearch/elastic_search_querying.py

cuebook/CueSearch

8bf047de273b27bba41b8bf4e266aac1eee7f81a

[ "Apache-2.0" ]

null

api/cueSearch/elasticSearch/elastic_search_querying.py

cuebook/CueSearch

8bf047de273b27bba41b8bf4e266aac1eee7f81a

[ "Apache-2.0" ]

null

import logging import os from typing import List from elasticsearch import Elasticsearch from elasticsearch_dsl import Search, Q # from config import ELASTICSEARCH_URL ELASTICSEARCH_URL = os.environ.get("ELASTICSEARCH_URL", "http://localhost:9200/") class ESQueryingUtils: GLOBAL_DIMENSIONS_INDEX_SEARCH_SUGGESTION_DATA = ( "cuesearch_global_dimensions_search_suggestion_data_index" ) GLOBAL_DIMENSIONS_NAMES_INDEX_NAME = ( "cuesearch_global_dimensions_names_for_search_index" ) GLOBAL_DIMENSIONS_INDEX_DATA = "cuesearch_global_dimensions_data_index" AUTO_GLOBAL_DIMENSIONS_INDEX_DATA_SEARCH_SUGGESTION = ( "cuesearch_auto_global_dimensions_search_suggestion_data_index" ) AUTO_GLOBAL_DIMENSIONS_INDEX_DATA = "cuesearch_auto_global_dimensions_data_index" DATASET_MEASURES_INDEX_NAME = "dataset_measures_index_cuesearch" @staticmethod def _getESClient() -> Elasticsearch: """ Method to get the ES Client """ esHost = ELASTICSEARCH_URL esClient = Elasticsearch(hosts=[esHost], timeout=30) return esClient @staticmethod def findGlobalDimensionResults( query: str, datasource=None, globalDimension: int = None, offset: int = 0, limit: int = 5, ): """ Method to run search queries on GlobalDimensions :param query: User search query :param dataset: name of cube, will match values associated to global dimension associated with this cube :param offset: Offset for the query :param limit: Number of results required :return List[ESQueryResponse] """ globalDimensionNameQuery = None if len(query.split(":")) == 2: globalDimensionNameQuery = query.split(":")[0] query = query.split(":")[1] logging.info("Querying global dimensions for: %s", query) query = "" if query is None else query client = ESQueryingUtils._getESClient() searchQuery = Search(index=ESQueryingUtils.GLOBAL_DIMENSIONS_INDEX_DATA).using( client ) if globalDimension: searchQuery = searchQuery.filter("match", globalDimensionId=globalDimension) elif globalDimensionNameQuery: searchQuery = searchQuery.filter( "match", globalDimensionName=globalDimensionNameQuery ) if query: searchQuery = searchQuery.query("term", globalDimensionDisplayValue=query) else: searchQuery = searchQuery.query("match_all") if datasource: searchQuery = searchQuery.filter("match", cubes=datasource) searchQuery = searchQuery[offset : offset + limit] logging.info("Calling Elasticsearch with the query") response = searchQuery.execute() output = [] for hit in response: obj = { "value": hit.globalDimensionDisplayValue, "dimension": hit.dimension, "globalDimensionName": hit.globalDimensionName, "user_entity_identifier": hit.globalDimensionName, "id": hit.globalDimensionId, "dataset": hit.dataset, "datasetId": hit.datasetId, "type": "GLOBALDIMENSION", } output.append(obj) logging.debug("User queries: %s", output) return output @staticmethod def findNonGlobalDimensionResults( query: str, datasource=None, globalDimension: str = None, offset: int = 0, limit: int = 5, ): """ Method to run search queries on GlobalDimensions :param query: User search query :param dataset: name of cube, will match values associated to global dimension associated with this cube :param offset: Offset for the query :param limit: Number of results required :return List[ESQueryResponse] """ globalDimensionNameQuery = None if len(query.split(":")) == 2: globalDimensionNameQuery = query.split(":")[0] query = query.split(":")[1] logging.info("Querying global dimensions for: %s", query) query = "" if query is None else query client = ESQueryingUtils._getESClient() searchQuery = Search( index=ESQueryingUtils.AUTO_GLOBAL_DIMENSIONS_INDEX_DATA ).using(client) if globalDimension: searchQuery = searchQuery.filter("match", globalDimensionId=globalDimension) elif globalDimensionNameQuery: searchQuery = searchQuery.filter( "match", globalDimensionName=globalDimensionNameQuery ) if query: searchQuery = searchQuery.query("term", globalDimensionDisplayValue=query) else: searchQuery = searchQuery.query("match_all") if datasource: searchQuery = searchQuery.filter("match", datasetId=datasource) searchQuery = searchQuery[offset : offset + limit] logging.info("Calling Elasticsearch with the query") response = searchQuery.execute() output = [] for hit in response: obj = { "value": hit.globalDimensionDisplayValue, "dimension": hit.dimension, "globalDimensionName": hit.globalDimensionName, "user_entity_identifier": hit.globalDimensionName, "id": hit.globalDimensionId, "dataset": hit.dataset, "datasetId": hit.datasetId, "type": "DATASETDIMENSION", } output.append(obj) logging.debug("User queries: %s", output) return output @staticmethod def findGlobalDimensionResultsForSearchSuggestion( query: str, datasource=None, globalDimension: int = None, offset: int = 0, limit: int = 5, ): """ Method to run search queries on GlobalDimensions :param query: User search query :param dataset: name of cube, will match values associated to global dimension associated with this cube :param offset: Offset for the query :param limit: Number of results required :return List[ESQueryResponse] """ globalDimensionNameQuery = None if len(query.split(":")) == 2: globalDimensionNameQuery = query.split(":")[0] query = query.split(":")[1] logging.info("Querying global dimensions for: %s", query) query = "" if query is None else query.lower() client = ESQueryingUtils._getESClient() searchQuery = Search( index=ESQueryingUtils.GLOBAL_DIMENSIONS_INDEX_SEARCH_SUGGESTION_DATA ).using(client) if globalDimension: searchQuery = searchQuery.filter("match", globalDimensionId=globalDimension) elif globalDimensionNameQuery: searchQuery = searchQuery.filter( "match", globalDimensionName=globalDimensionNameQuery ) if query: searchQuery = searchQuery.query("match", globalDimensionValue=query) else: searchQuery = searchQuery.query("match_all") if datasource: searchQuery = searchQuery.filter("match", cubes=datasource) searchQuery = searchQuery[offset : offset + limit] logging.info("Calling Elasticsearch with the query") response = searchQuery.execute() output = [] for hit in response: obj = { "value": hit.globalDimensionDisplayValue, "user_entity_identifier": hit.globalDimensionName, "id": hit.globalDimensionId, "type": "GLOBALDIMENSION", } output.append(obj) logging.debug("User queries: %s", output) return output @staticmethod def findNonGlobalDimensionResultsForSearchSuggestion( query: str, datasource=None, globalDimension: int = None, offset: int = 0, limit: int = 5, ): """ Method to run search queries on NonGlobalDimensions :param query: User search query :param dataset: name of cube, will match values associated to global dimension associated with this cube :param offset: Offset for the query :param limit: Number of results required :return List[ESQueryResponse] """ globalDimensionNameQuery = None if len(query.split(":")) == 2: globalDimensionNameQuery = query.split(":")[0] query = query.split(":")[1] logging.info("Querying global dimensions for: %s", query) query = "" if query is None else query.lower() client = ESQueryingUtils._getESClient() searchQuery = Search( index=ESQueryingUtils.AUTO_GLOBAL_DIMENSIONS_INDEX_DATA_SEARCH_SUGGESTION ).using(client) if globalDimension: searchQuery = searchQuery.filter("match", globalDimensionId=globalDimension) elif globalDimensionNameQuery: searchQuery = searchQuery.filter( "match", globalDimensionName=globalDimensionNameQuery ) if query: searchQuery = searchQuery.query("match", globalDimensionValue=query) else: searchQuery = searchQuery.query("match_all") if datasource: searchQuery = searchQuery.filter("match", cubes=datasource) searchQuery = searchQuery[offset : offset + limit] logging.info("Calling Elasticsearch with the query") response = searchQuery.execute() output = [] for hit in response: obj = { "value": hit.globalDimensionDisplayValue, "user_entity_identifier": hit.globalDimensionName, "id": hit.globalDimensionId, "datasetId": hit.datasetId, "globalDimensionId": hit.globalDimensionId, "type": "DATASETDIMENSION", } output.append(obj) logging.debug("User queries: %s", output) return output

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332200694213d02411af62ed23dd97c0f059b5e5

34,422

py

Python

codes/tmodules/Prediction/__init__.py

nguyenanhtuan1008/ImageAI

1585ad02f978c08580b6e734a3c6f1d131bf7dbd

[ "MIT" ]

null

codes/tmodules/Prediction/__init__.py

nguyenanhtuan1008/ImageAI

1585ad02f978c08580b6e734a3c6f1d131bf7dbd

[ "MIT" ]

null

codes/tmodules/Prediction/__init__.py

nguyenanhtuan1008/ImageAI

1585ad02f978c08580b6e734a3c6f1d131bf7dbd

[ "MIT" ]

null

import numpy as np from tensorflow.python.keras.preprocessing import image from PIL import Image from tensorflow.python.keras.layers import Input, Conv2D, MaxPool2D, Activation, concatenate, Dropout from tensorflow.python.keras.layers import GlobalAvgPool2D, GlobalMaxPool2D from tensorflow.python.keras.models import Model from tensorflow.python.keras.models import Sequential class ImagePrediction: """ This is the image prediction class in the tmodules library. It provides support for 4 different models which are: ResNet, SqueezeNet, DenseNet and Inception V3. After instantiating this class, you can set it's properties and make image predictions using it's pre-defined functions. The following functions are required to be called before a prediction can be made * setModelPath() * At least of of the following and it must correspond to the model set in the setModelPath() [setModelTypeAsSqueezeNet(), setModelTypeAsResNet(), setModelTypeAsDenseNet, setModelTypeAsInceptionV3] * loadModel() [This must be called once only before making a prediction] Once the above functions have been called, you can call the predictImage() function of the prediction instance object at anytime to predict an image. """ def __init__(self): self.__modelType = "" self.modelPath = "" self.__modelLoaded = False self.__model_collection = [] self.__input_image_size = 224 def setModelPath(self, model_path): """ 'setModelPath()' function is required and is used to set the file path to the model adopted from the list of the available 4 model types. The model path must correspond to the model type set for the prediction instance object. :param model_path: :return: """ self.modelPath = model_path def setModelTypeAsSqueezeNet(self): """ 'setModelTypeAsSqueezeNet()' is used to set the model type to the SqueezeNet model for the prediction instance object . :return: """ self.__modelType = "squeezenet" def setModelTypeAsResNet(self): """ 'setModelTypeAsResNet()' is used to set the model type to the ResNet model for the prediction instance object . :return: """ self.__modelType = "resnet" def setModelTypeAsDenseNet(self): """ 'setModelTypeAsDenseNet()' is used to set the model type to the DenseNet model for the prediction instance object . :return: """ self.__modelType = "densenet" def setModelTypeAsInceptionV3(self): """ 'setModelTypeAsInceptionV3()' is used to set the model type to the InceptionV3 model for the prediction instance object . :return: """ self.__modelType = "inceptionv3" def loadModel(self, prediction_speed="normal"): """ 'loadModel()' function is used to load the model structure into the program from the file path defined in the setModelPath() function. This function receives an optional value which is "prediction_speed". The value is used to reduce the time it takes to predict an image, down to about 50% of the normal time, with just slight changes or drop in prediction accuracy, depending on the nature of the image. * prediction_speed (optional); Acceptable values are "normal", "fast", "faster" and "fastest" :param prediction_speed : :return: """ if(prediction_speed=="normal"): self.__input_image_size = 224 elif(prediction_speed=="fast"): self.__input_image_size = 160 elif(prediction_speed=="faster"): self.__input_image_size = 120 elif (prediction_speed == "fastest"): self.__input_image_size = 100 if (self.__modelLoaded == False): image_input = Input(shape=(self.__input_image_size, self.__input_image_size, 3)) if(self.__modelType == "" ): raise ValueError("You must set a valid model type before loading the model.") elif(self.__modelType == "squeezenet"): import numpy as np from tensorflow.python.keras.preprocessing import image from .SqueezeNet.squeezenet import SqueezeNet from .imagenet_utils import preprocess_input, decode_predictions try: model = SqueezeNet(model_path=self.modelPath, model_input=image_input) self.__model_collection.append(model) self.__modelLoaded = True except: raise ("You have specified an incorrect path to the SqueezeNet model file.") elif(self.__modelType == "resnet"): import numpy as np from tensorflow.python.keras.preprocessing import image from .ResNet.resnet50 import ResNet50 from .imagenet_utils import preprocess_input, decode_predictions try: model = ResNet50(model_path=self.modelPath, model_input=image_input) self.__model_collection.append(model) self.__modelLoaded = True except: raise ValueError("You have specified an incorrect path to the ResNet model file.") elif (self.__modelType == "densenet"): from tensorflow.python.keras.preprocessing import image from .DenseNet.densenet import DenseNetImageNet121, preprocess_input, decode_predictions import numpy as np try: model = DenseNetImageNet121(model_path=self.modelPath, model_input=image_input) self.__model_collection.append(model) self.__modelLoaded = True except: raise ValueError("You have specified an incorrect path to the DenseNet model file.") elif (self.__modelType == "inceptionv3"): import numpy as np from tensorflow.python.keras.preprocessing import image from tmodules.Prediction.InceptionV3.inceptionv3 import InceptionV3 from tmodules.Prediction.InceptionV3.inceptionv3 import preprocess_input, decode_predictions try: model = InceptionV3(include_top=True, weights="imagenet", model_path=self.modelPath, model_input=image_input) self.__model_collection.append(model) self.__modelLoaded = True except: raise ValueError("You have specified an incorrect path to the InceptionV3 model file.") def predictImage(self, image_input, result_count=5, input_type="file" ): """ 'predictImage()' function is used to predict a given image by receiving the following arguments: * input_type (optional) , the type of input to be parsed. Acceptable values are "file", "array" and "stream" * image_input , file path/numpy array/image file stream of the image. * result_count (optional) , the number of predictions to be sent which must be whole numbers between 1 and 1000. The default is 5. This function returns 2 arrays namely 'prediction_results' and 'prediction_probabilities'. The 'prediction_results' contains possible objects classes arranged in descending of their percentage probabilities. The 'prediction_probabilities' contains the percentage probability of each object class. The position of each object class in the 'prediction_results' array corresponds with the positions of the percentage possibilities in the 'prediction_probabilities' array. :param input_type: :param image_input: :param result_count: :return prediction_results, prediction_probabilities: """ prediction_results = [] prediction_probabilities = [] if (self.__modelLoaded == False): raise ValueError("You must call the loadModel() function before making predictions.") else: if (self.__modelType == "squeezenet"): from .imagenet_utils import preprocess_input, decode_predictions if (input_type == "file"): try: image_to_predict = image.load_img(image_input, target_size=(self.__input_image_size, self.__input_image_size)) image_to_predict = image.img_to_array(image_to_predict, data_format="channels_last") image_to_predict = np.expand_dims(image_to_predict, axis=0) image_to_predict = preprocess_input(image_to_predict) except: raise ValueError("You have set a path to an invalid image file.") elif (input_type == "array"): try: image_input = Image.fromarray(np.uint8(image_input)) image_input = image_input.resize((self.__input_image_size, self.__input_image_size)) image_input = np.expand_dims(image_input, axis=0) image_to_predict = image_input.copy() image_to_predict = np.asarray(image_to_predict, dtype=np.float64) image_to_predict = preprocess_input(image_to_predict) except: raise ValueError("You have parsed in a wrong numpy array for the image") elif (input_type == "stream"): try: image_input = Image.open(image_input) image_input = image_input.resize((self.__input_image_size, self.__input_image_size)) image_input = np.expand_dims(image_input, axis=0) image_to_predict = image_input.copy() image_to_predict = np.asarray(image_to_predict, dtype=np.float64) image_to_predict = preprocess_input(image_to_predict) except: raise ValueError("You have parsed in a wrong stream for the image") model = self.__model_collection[0] prediction = model.predict(image_to_predict, steps=1) try: predictiondata = decode_predictions(prediction, top=int(result_count)) for results in predictiondata: countdown = 0 for result in results: countdown += 1 prediction_results.append(str(result[1])) prediction_probabilities.append(result[2] * 100) except: raise ValueError("An error occured! Try again.") return prediction_results, prediction_probabilities elif (self.__modelType == "resnet"): model = self.__model_collection[0] from .imagenet_utils import preprocess_input, decode_predictions if (input_type == "file"): try: image_to_predict = image.load_img(image_input, target_size=(self.__input_image_size, self.__input_image_size)) image_to_predict = image.img_to_array(image_to_predict, data_format="channels_last") image_to_predict = np.expand_dims(image_to_predict, axis=0) image_to_predict = preprocess_input(image_to_predict) except: raise ValueError("You have set a path to an invalid image file.") elif (input_type == "array"): try: image_input = Image.fromarray(np.uint8(image_input)) image_input = image_input.resize((self.__input_image_size, self.__input_image_size)) image_input = np.expand_dims(image_input, axis=0) image_to_predict = image_input.copy() image_to_predict = np.asarray(image_to_predict, dtype=np.float64) image_to_predict = preprocess_input(image_to_predict) except: raise ValueError("You have parsed in a wrong numpy array for the image") elif (input_type == "stream"): try: image_input = Image.open(image_input) image_input = image_input.resize((self.__input_image_size, self.__input_image_size)) image_input = np.expand_dims(image_input, axis=0) image_to_predict = image_input.copy() image_to_predict = np.asarray(image_to_predict, dtype=np.float64) image_to_predict = preprocess_input(image_to_predict) except: raise ValueError("You have parsed in a wrong stream for the image") prediction = model.predict(x=image_to_predict, steps=1) try: predictiondata = decode_predictions(prediction, top=int(result_count)) for results in predictiondata: countdown = 0 for result in results: countdown += 1 prediction_results.append(str(result[1])) prediction_probabilities.append(result[2] * 100) except: raise ValueError("An error occured! Try again.") return prediction_results, prediction_probabilities elif (self.__modelType == "densenet"): model = self.__model_collection[0] from .DenseNet.densenet import preprocess_input, decode_predictions from .DenseNet.densenet import DenseNetImageNet121 if (input_type == "file"): try: image_to_predict = image.load_img(image_input, target_size=(self.__input_image_size, self.__input_image_size)) image_to_predict = image.img_to_array(image_to_predict, data_format="channels_last") image_to_predict = np.expand_dims(image_to_predict, axis=0) image_to_predict = preprocess_input(image_to_predict) except: raise ValueError("You have set a path to an invalid image file.") elif (input_type == "array"): try: image_input = Image.fromarray(np.uint8(image_input)) image_input = image_input.resize((self.__input_image_size, self.__input_image_size)) image_input = np.expand_dims(image_input, axis=0) image_to_predict = image_input.copy() image_to_predict = np.asarray(image_to_predict, dtype=np.float64) image_to_predict = preprocess_input(image_to_predict) except: raise ValueError("You have parsed in a wrong numpy array for the image") elif (input_type == "stream"): try: image_input = Image.open(image_input) image_input = image_input.resize((self.__input_image_size, self.__input_image_size)) image_input = np.expand_dims(image_input, axis=0) image_to_predict = image_input.copy() image_to_predict = np.asarray(image_to_predict, dtype=np.float64) image_to_predict = preprocess_input(image_to_predict) except: raise ValueError("You have parsed in a wrong stream for the image") prediction = model.predict(x=image_to_predict, steps=1) try: predictiondata = decode_predictions(prediction, top=int(result_count)) for results in predictiondata: countdown = 0 for result in results: countdown += 1 prediction_results.append(str(result[1])) prediction_probabilities.append(result[2] * 100) except: raise ValueError("An error occured! Try again.") return prediction_results, prediction_probabilities elif (self.__modelType == "inceptionv3"): model = self.__model_collection[0] from tmodules.Prediction.InceptionV3.inceptionv3 import InceptionV3, preprocess_input, decode_predictions if (input_type == "file"): try: image_to_predict = image.load_img(image_input, target_size=(self.__input_image_size, self.__input_image_size)) image_to_predict = image.img_to_array(image_to_predict, data_format="channels_last") image_to_predict = np.expand_dims(image_to_predict, axis=0) image_to_predict = preprocess_input(image_to_predict) except: raise ValueError("You have set a path to an invalid image file.") elif (input_type == "array"): try: image_input = Image.fromarray(np.uint8(image_input)) image_input = image_input.resize((self.__input_image_size, self.__input_image_size)) image_input = np.expand_dims(image_input, axis=0) image_to_predict = image_input.copy() image_to_predict = np.asarray(image_to_predict, dtype=np.float64) image_to_predict = preprocess_input(image_to_predict) except: raise ValueError("You have parsed in a wrong numpy array for the image") elif (input_type == "stream"): try: image_input = Image.open(image_input) image_input = image_input.resize((self.__input_image_size, self.__input_image_size)) image_input = np.expand_dims(image_input, axis=0) image_to_predict = image_input.copy() image_to_predict = np.asarray(image_to_predict, dtype=np.float64) image_to_predict = preprocess_input(image_to_predict) except: raise ValueError("You have parsed in a wrong stream for the image") prediction = model.predict(x=image_to_predict, steps=1) try: predictiondata = decode_predictions(prediction, top=int(result_count)) for results in predictiondata: countdown = 0 for result in results: countdown += 1 prediction_results.append(str(result[1])) prediction_probabilities.append(result[2] * 100) except: raise ValueError("An error occured! Try again.") return prediction_results, prediction_probabilities def predictMultipleImages(self, sent_images_array, result_count_per_image=2, input_type="file"): """ 'predictMultipleImages()' function is used to predict more than one image by receiving the following arguments: * input_type , the type of inputs contained in the parsed array. Acceptable values are "file", "array" and "stream" * sent_images_array , an array of image file paths, image numpy array or image file stream * result_count_per_image (optionally) , the number of predictions to be sent per image, which must be whole numbers between 1 and 1000. The default is 2. This function returns an array of dictionaries, with each dictionary containing 2 arrays namely 'prediction_results' and 'prediction_probabilities'. The 'prediction_results' contains possible objects classes arranged in descending of their percentage probabilities. The 'prediction_probabilities' contains the percentage probability of each object class. The position of each object class in the 'prediction_results' array corresponds with the positions of the percentage possibilities in the 'prediction_probabilities' array. :param input_type: :param sent_images_array: :param result_count_per_image: :return output_array: """ output_array = [] for image_input in sent_images_array: prediction_results = [] prediction_probabilities = [] if (self.__modelLoaded == False): raise ValueError("You must call the loadModel() function before making predictions.") else: if (self.__modelType == "squeezenet"): from .imagenet_utils import preprocess_input, decode_predictions if (input_type == "file"): try: image_to_predict = image.load_img(image_input, target_size=(self.__input_image_size, self.__input_image_size)) image_to_predict = image.img_to_array(image_to_predict, data_format="channels_last") image_to_predict = np.expand_dims(image_to_predict, axis=0) image_to_predict = preprocess_input(image_to_predict) except: raise ValueError("You have set a path to an invalid image file.") elif (input_type == "array"): try: image_input = Image.fromarray(np.uint8(image_input)) image_input = image_input.resize((self.__input_image_size, self.__input_image_size)) image_input = np.expand_dims(image_input, axis=0) image_to_predict = image_input.copy() image_to_predict = np.asarray(image_to_predict, dtype=np.float64) image_to_predict = preprocess_input(image_to_predict) except: raise ValueError("You have parsed in a wrong numpy array for the image") elif (input_type == "stream"): try: image_input = Image.open(image_input) image_input = image_input.resize((self.__input_image_size, self.__input_image_size)) image_input = np.expand_dims(image_input, axis=0) image_to_predict = image_input.copy() image_to_predict = np.asarray(image_to_predict, dtype=np.float64) image_to_predict = preprocess_input(image_to_predict) except: raise ValueError("You have parsed in a wrong stream for the image") model = self.__model_collection[0] prediction = model.predict(image_to_predict, steps=1) try: predictiondata = decode_predictions(prediction, top=int(result_count_per_image)) for results in predictiondata: countdown = 0 for result in results: countdown += 1 prediction_results.append(str(result[1])) prediction_probabilities.append(result[2] * 100) except: raise ValueError("An error occured! Try again.") each_image_details = {} each_image_details["predictions"] = prediction_results each_image_details["percentage_probabilities"] = prediction_probabilities output_array.append(each_image_details) elif (self.__modelType == "resnet"): model = self.__model_collection[0] from .imagenet_utils import preprocess_input, decode_predictions if (input_type == "file"): try: image_to_predict = image.load_img(image_input, target_size=(self.__input_image_size, self.__input_image_size)) image_to_predict = image.img_to_array(image_to_predict, data_format="channels_last") image_to_predict = np.expand_dims(image_to_predict, axis=0) image_to_predict = preprocess_input(image_to_predict) except: raise ValueError("You have set a path to an invalid image file.") elif (input_type == "array"): try: image_input = Image.fromarray(np.uint8(image_input)) image_input = image_input.resize((self.__input_image_size, self.__input_image_size)) image_input = np.expand_dims(image_input, axis=0) image_to_predict = image_input.copy() image_to_predict = np.asarray(image_to_predict, dtype=np.float64) image_to_predict = preprocess_input(image_to_predict) except: raise ValueError("You have parsed in a wrong numpy array for the image") elif (input_type == "stream"): try: image_input = Image.open(image_input) image_input = image_input.resize((self.__input_image_size, self.__input_image_size)) image_input = np.expand_dims(image_input, axis=0) image_to_predict = image_input.copy() image_to_predict = np.asarray(image_to_predict, dtype=np.float64) image_to_predict = preprocess_input(image_to_predict) except: raise ValueError("You have parsed in a wrong stream for the image") prediction = model.predict(x=image_to_predict, steps=1) try: predictiondata = decode_predictions(prediction, top=int(result_count_per_image)) for results in predictiondata: countdown = 0 for result in results: countdown += 1 prediction_results.append(str(result[1])) prediction_probabilities.append(result[2] * 100) except: raise ValueError("An error occured! Try again.") each_image_details = {} each_image_details["predictions"] = prediction_results each_image_details["percentage_probabilities"] = prediction_probabilities output_array.append(each_image_details) elif (self.__modelType == "densenet"): model = self.__model_collection[0] from .DenseNet.densenet import preprocess_input, decode_predictions from .DenseNet.densenet import DenseNetImageNet121 if (input_type == "file"): try: image_to_predict = image.load_img(image_input, target_size=(self.__input_image_size, self.__input_image_size)) image_to_predict = image.img_to_array(image_to_predict, data_format="channels_last") image_to_predict = np.expand_dims(image_to_predict, axis=0) image_to_predict = preprocess_input(image_to_predict) except: raise ValueError("You have set a path to an invalid image file.") elif (input_type == "array"): try: image_input = Image.fromarray(np.uint8(image_input)) image_input = image_input.resize((self.__input_image_size, self.__input_image_size)) image_input = np.expand_dims(image_input, axis=0) image_to_predict = image_input.copy() image_to_predict = np.asarray(image_to_predict, dtype=np.float64) image_to_predict = preprocess_input(image_to_predict) except: raise ValueError("You have parsed in a wrong numpy array for the image") elif (input_type == "stream"): try: image_input = Image.open(image_input) image_input = image_input.resize((self.__input_image_size, self.__input_image_size)) image_input = np.expand_dims(image_input, axis=0) image_to_predict = image_input.copy() image_to_predict = np.asarray(image_to_predict, dtype=np.float64) image_to_predict = preprocess_input(image_to_predict) except: raise ValueError("You have parsed in a wrong stream for the image") prediction = model.predict(x=image_to_predict, steps=1) try: predictiondata = decode_predictions(prediction, top=int(result_count_per_image)) for results in predictiondata: countdown = 0 for result in results: countdown += 1 prediction_results.append(str(result[1])) prediction_probabilities.append(result[2] * 100) except: raise ValueError("An error occured! Try again.") each_image_details = {} each_image_details["predictions"] = prediction_results each_image_details["percentage_probabilities"] = prediction_probabilities output_array.append(each_image_details) elif (self.__modelType == "inceptionv3"): model = self.__model_collection[0] from tmodules.Prediction.InceptionV3.inceptionv3 import InceptionV3, preprocess_input, \ decode_predictions if (input_type == "file"): try: image_to_predict = image.load_img(image_input, target_size=(self.__input_image_size, self.__input_image_size)) image_to_predict = image.img_to_array(image_to_predict, data_format="channels_last") image_to_predict = np.expand_dims(image_to_predict, axis=0) image_to_predict = preprocess_input(image_to_predict) except: raise ValueError("You have set a path to an invalid image file.") elif (input_type == "array"): try: image_input = Image.fromarray(np.uint8(image_input)) image_input = image_input.resize((self.__input_image_size, self.__input_image_size)) image_input = np.expand_dims(image_input, axis=0) image_to_predict = image_input.copy() image_to_predict = np.asarray(image_to_predict, dtype=np.float64) image_to_predict = preprocess_input(image_to_predict) except: raise ValueError("You have parsed in a wrong numpy array for the image") elif (input_type == "stream"): try: image_input = Image.open(image_input) image_input = image_input.resize((self.__input_image_size, self.__input_image_size)) image_input = np.expand_dims(image_input, axis=0) image_to_predict = image_input.copy() image_to_predict = np.asarray(image_to_predict, dtype=np.float64) image_to_predict = preprocess_input(image_to_predict) except: raise ValueError("You have parsed in a wrong stream for the image") prediction = model.predict(x=image_to_predict, steps=1) try: predictiondata = decode_predictions(prediction, top=int(result_count_per_image)) for results in predictiondata: countdown = 0 for result in results: countdown += 1 prediction_results.append(str(result[1])) prediction_probabilities.append(result[2] * 100) except: raise ValueError("An error occured! Try again.") each_image_details = {} each_image_details["predictions"] = prediction_results each_image_details["percentage_probabilities"] = prediction_probabilities output_array.append(each_image_details) return output_array

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68695830f02ce77b87b1a9961fcd214b34676937

14,059

py

Python

features/steps/managers/keys.py

lordkyzr/launchkey-python

4a6c13c2e60c5f38c4cb749d6a887eb1ac813c0c

[ "MIT" ]

9

2017-10-12T02:45:23.000Z

2021-01-11T05:44:13.000Z

features/steps/managers/keys.py

lordkyzr/launchkey-python

4a6c13c2e60c5f38c4cb749d6a887eb1ac813c0c

[ "MIT" ]

31

2018-09-12T00:17:10.000Z

2022-01-31T21:35:04.000Z

features/steps/managers/keys.py

lordkyzr/launchkey-python

4a6c13c2e60c5f38c4cb749d6a887eb1ac813c0c

[ "MIT" ]

11

2017-01-31T21:45:29.000Z

2022-01-28T00:56:48.000Z

try: from base64 import decodebytes as decodestring except ImportError: # pragma: no cover from base64 import decodestring class KeysManager: alpha_p12 = decodestring(b"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") beta_p12 = decodestring(b"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") alpha_certificate_fingerprint = "7A:31:98:AD:89:68:19:63:39:75:34:A2:E6:9A:00:4E:42:DB:E0:33:7C:14:57:5D:C0:E5:90:B9:36:DD:BB:FA" beta_certificate_fingerprint = "BA:F6:DC:B0:0D:35:48:F9:EB:61:DC:78:B7:BE:4F:5C:83:63:A9:C2:B8:1F:A8:B0:F0:A7:47:D1:78:9B:FB:C7" alpha_public_key = decodestring(b'LS0tLS1CRUdJTiBQVUJMSUMgS0VZLS0tLS0KTUlJQ0lqQU5CZ2txaGtpRzl3MEJBUUVGQUFPQ0Fn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\nLQo=').decode("utf-8") beta_public_key = decodestring(b'LS0tLS1CRUdJTiBQVUJMSUMgS0VZLS0tLS0KTUlJQ0lqQU5CZ2txaGtpRzl3MEJBUUVGQUFPQ0Fn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\nLQo=').decode("utf-8") alpha_md5_fingerprint = "e6:60:3f:95:ea:c8:4d:2b:98:18:c0:0c:28:e8:9f:bb" beta_md5_fingerprint = "ee:6d:27:3f:6f:a2:42:94:33:d6:2a:12:a0:4d:1f:56"

878.6875

5,589

0.956256

524

14,059

25.629771

0.90458

0.003872

0.002383

0.007297

0.022487

0

0.147499

0.006117

14,059

15

5,590

937.266667

0.813641

0.001138

0

0.615385

0.96845

0.967737

0

1

0

1

0

false

0

0.230769

0

0.923077

0.307692

0

1

null

0

1

0

1

null

1

0

7

d7d59462c89edb7705badd660bbc6f0f5db8e187

18

py

Python

AprendaPython/Numeros/ex002.py

arthxvr/coding--python

1e91707be6cb8fef816dad0c1a65f2cc3327357e

[ "MIT" ]

null

AprendaPython/Numeros/ex002.py

arthxvr/coding--python

1e91707be6cb8fef816dad0c1a65f2cc3327357e

[ "MIT" ]

null

AprendaPython/Numeros/ex002.py

arthxvr/coding--python

1e91707be6cb8fef816dad0c1a65f2cc3327357e

[ "MIT" ]

null

print(21424 / 89)

9

17

0.666667

3

18

4

1

0

0.466667

0.166667

18

1

18

0.333333

0

1

0

true

0

1

0

null

0

1

0

1

0

1

0

null

0

1

0

1

0

7

cc1e81e5c553285a311e31ed8c1f179d803b35f2

89

py

Python

test/fixtures/rules/custom/S000.py

pvonglehn/sqlfluff

61eb251ee96b1b70aa477f3a2f2b9c351a04c1e8

[ "MIT" ]

3,024

2020-10-01T11:03:51.000Z

2022-03-31T16:42:00.000Z

test/fixtures/rules/custom/S000.py

pvonglehn/sqlfluff

61eb251ee96b1b70aa477f3a2f2b9c351a04c1e8

[ "MIT" ]

2,395

2020-09-30T12:59:21.000Z

2022-03-31T22:05:29.000Z

test/fixtures/rules/custom/S000.py

pvonglehn/sqlfluff

61eb251ee96b1b70aa477f3a2f2b9c351a04c1e8

[ "MIT" ]

246

2020-10-02T17:08:03.000Z

2022-03-30T17:43:51.000Z

"""Test std rule import.""" class Rule_S000: """Test std rule import.""" pass

11.125

31

0.58427

12

89

4.25

0.583333

0.27451

0.431373

0.666667

0

0.044776

0.247191

89

7

32

12.714286

0.716418

0.483146

0

1

0

true

0.5

0

0.5

0

1

0

null

1

0

1

0

null

0

1

0

7

04005a4f07ca793b89610ff23740dbadaa061deb

36,886

py

Python

tests/test_worker.py

cloudblue/connect-extension-runner

e7db788112af6c1d04a176a9849fa4ad9b8e7bfe

[ "Apache-2.0" ]

5

2021-05-13T10:07:04.000Z

2021-11-18T10:11:05.000Z

tests/test_worker.py

cloudblue/connect-extension-runner

e7db788112af6c1d04a176a9849fa4ad9b8e7bfe

[ "Apache-2.0" ]

4

2021-06-08T21:14:23.000Z

2022-03-31T08:29:52.000Z

tests/test_worker.py

cloudblue/connect-extension-runner

e7db788112af6c1d04a176a9849fa4ad9b8e7bfe

[ "Apache-2.0" ]

4

2021-07-01T10:50:29.000Z

2022-03-25T13:13:47.000Z

import asyncio import dataclasses import logging import pytest from websockets.exceptions import ConnectionClosedError, InvalidStatusCode, WebSocketException from connect.eaas.constants import RESULT_SENDER_MAX_RETRIES from connect.eaas.dataclasses import ( CapabilitiesPayload, ConfigurationPayload, Message, MessageType, ResultType, TaskCategory, TaskPayload, TaskType, ) from connect.eaas.extension import Extension, ProcessingResponse, ScheduledExecutionResponse from connect.eaas.worker import _on_communication_backoff, Worker from tests.utils import WSHandler @pytest.mark.asyncio async def test_capabilities_configuration(mocker, ws_server, unused_port, config_payload): mocker.patch( 'connect.eaas.config.get_environment', return_value={ 'ws_address': f'127.0.0.1:{unused_port}', 'api_address': f'127.0.0.1:{unused_port}', 'api_key': 'SU-000:XXXX', 'environment_id': 'ENV-000-0001', 'instance_id': 'INS-000-0002', 'background_task_max_execution_time': 300, 'interactive_task_max_execution_time': 120, 'scheduled_task_max_execution_time': 43200, }, ) capabilities = { TaskType.ASSET_PURCHASE_REQUEST_PROCESSING: ['pending', 'inquiring'], TaskType.ASSET_PURCHASE_REQUEST_VALIDATION: ['draft'], } class MyExtension(Extension): @classmethod def get_descriptor(cls): return { 'capabilities': capabilities, 'variables': [], 'schedulables': [], 'readme_url': 'https://example.com/README.md', 'changelog_url': 'https://example.com/CHANGELOG.md', } mocker.patch('connect.eaas.handler.get_extension_class', return_value=MyExtension) mocker.patch('connect.eaas.handler.get_extension_type', return_value='sync') data_to_send = dataclasses.asdict( Message( MessageType.CONFIGURATION, ConfigurationPayload(**config_payload), ), ) handler = WSHandler( '/public/v1/devops/ws/ENV-000-0001/INS-000-0002?running_tasks=0&running_scheduled_tasks=0', data_to_send, ['receive', 'send'], ) worker = None async with ws_server(handler): worker = Worker(secure=False) task = asyncio.create_task(worker.start()) await asyncio.sleep(.5) worker.stop() await task handler.assert_received( dataclasses.asdict( Message( MessageType.CAPABILITIES, CapabilitiesPayload( capabilities, [], [], 'https://example.com/README.md', 'https://example.com/CHANGELOG.md', ), ), ), ) assert worker.config.variables == config_payload['configuration'] assert worker.config.logging_api_key == config_payload['logging_api_key'] assert worker.config.environment_type == config_payload['environment_type'] assert worker.config.account_id == config_payload['account_id'] assert worker.config.account_name == config_payload['account_name'] assert worker.config.service_id == config_payload['service_id'] assert worker.config.product_id == config_payload['product_id'] assert worker.config.hub_id == config_payload['hub_id'] @pytest.mark.asyncio async def test_pr_task(mocker, ws_server, unused_port, httpx_mock, config_payload): pr_data = {'id': 'PR-000', 'status': 'pending'} httpx_mock.add_response( method='GET', url=f'https://127.0.0.1:{unused_port}/public/v1/requests/PR-000', json=pr_data, ) mocker.patch( 'connect.eaas.config.get_environment', return_value={ 'ws_address': f'127.0.0.1:{unused_port}', 'api_address': f'127.0.0.1:{unused_port}', 'api_key': 'SU-000:XXXX', 'environment_id': 'ENV-000-0001', 'instance_id': 'INS-000-0002', 'background_task_max_execution_time': 300, 'interactive_task_max_execution_time': 120, 'scheduled_task_max_execution_time': 43200, }, ) capabilities = { TaskType.ASSET_PURCHASE_REQUEST_PROCESSING: ['pending', 'inquiring'], TaskType.ASSET_PURCHASE_REQUEST_VALIDATION: ['draft'], } class MyExtension(Extension): @classmethod def get_descriptor(cls): return { 'capabilities': capabilities, 'variables': [], 'schedulables': [], 'readme_url': 'https://example.com/README.md', 'changelog_url': 'https://example.com/CHANGELOG.md', } def process_asset_purchase_request(self, request): self.logger.info('test log message') assert request == pr_data return ProcessingResponse.done() mocker.patch('connect.eaas.handler.get_extension_class', return_value=MyExtension) mocker.patch('connect.eaas.handler.get_extension_type', return_value='sync') dyn_config = ConfigurationPayload(**config_payload) data_to_send = [ dataclasses.asdict(Message(MessageType.CONFIGURATION, dyn_config)), dataclasses.asdict(Message(MessageType.TASK, TaskPayload( 'TQ-000', TaskCategory.BACKGROUND, TaskType.ASSET_PURCHASE_REQUEST_PROCESSING, 'PR-000', ))), ] handler = WSHandler( '/public/v1/devops/ws/ENV-000-0001/INS-000-0002?running_tasks=0&running_scheduled_tasks=0', data_to_send, ['receive', 'send', 'send', 'receive'], ) async with ws_server(handler): worker = Worker(secure=False) task = asyncio.create_task(worker.start()) await asyncio.sleep(.5) worker.stop() await task handler.assert_received( dataclasses.asdict( Message( MessageType.CAPABILITIES, CapabilitiesPayload( capabilities, [], [], 'https://example.com/README.md', 'https://example.com/CHANGELOG.md', ), ), ), ) handler.assert_received( dataclasses.asdict( Message(MessageType.TASK, TaskPayload( 'TQ-000', TaskCategory.BACKGROUND, TaskType.ASSET_PURCHASE_REQUEST_PROCESSING, 'PR-000', result=ResultType.SUCCESS, )), ), ) @pytest.mark.asyncio async def test_tcr_task(mocker, ws_server, unused_port, httpx_mock, config_payload): tcr_data = {'id': 'TCR-000', 'status': 'pending'} httpx_mock.add_response( method='GET', url=f'https://127.0.0.1:{unused_port}/public/v1/tier/config-requests/TCR-000', json=tcr_data, ) mocker.patch( 'connect.eaas.config.get_environment', return_value={ 'ws_address': f'127.0.0.1:{unused_port}', 'api_address': f'127.0.0.1:{unused_port}', 'api_key': 'SU-000:XXXX', 'environment_id': 'ENV-000-0001', 'instance_id': 'INS-000-0002', 'background_task_max_execution_time': 300, 'interactive_task_max_execution_time': 120, 'scheduled_task_max_execution_time': 43200, }, ) capabilities = { TaskType.TIER_CONFIG_SETUP_REQUEST_PROCESSING: ['pending'], TaskType.ASSET_PURCHASE_REQUEST_VALIDATION: ['draft'], } class MyExtension(Extension): @classmethod def get_descriptor(cls): return { 'capabilities': capabilities, 'variables': [], 'schedulables': [], 'readme_url': 'https://example.com/README.md', 'changelog_url': 'https://example.com/CHANGELOG.md', } def process_tier_config_setup_request(self, request): assert request == tcr_data return ProcessingResponse.done() mocker.patch('connect.eaas.handler.get_extension_class', return_value=MyExtension) mocker.patch('connect.eaas.handler.get_extension_type', return_value='sync') data_to_send = [ dataclasses.asdict(Message(MessageType.CONFIGURATION, ConfigurationPayload( **config_payload, ))), dataclasses.asdict( Message(MessageType.TASK, TaskPayload( 'TQ-000', TaskCategory.BACKGROUND, TaskType.TIER_CONFIG_SETUP_REQUEST_PROCESSING, 'TCR-000', )), ), ] handler = WSHandler( '/public/v1/devops/ws/ENV-000-0001/INS-000-0002?running_tasks=0&running_scheduled_tasks=0', data_to_send, ['receive', 'send', 'send', 'receive'], ) async with ws_server(handler): worker = Worker(secure=False) task = asyncio.create_task(worker.start()) await asyncio.sleep(.5) worker.stop() await task handler.assert_received( dataclasses.asdict( Message( MessageType.CAPABILITIES, CapabilitiesPayload( capabilities, [], [], 'https://example.com/README.md', 'https://example.com/CHANGELOG.md', ), ), ), ) handler.assert_received( dataclasses.asdict( Message(MessageType.TASK, TaskPayload( 'TQ-000', TaskCategory.BACKGROUND, TaskType.TIER_CONFIG_SETUP_REQUEST_PROCESSING, 'TCR-000', result=ResultType.SUCCESS, )), ), ) @pytest.mark.asyncio async def test_scheduled_task(mocker, ws_server, unused_port, httpx_mock, config_payload): schedule_data = { 'id': 'EFS-000', 'method': 'run_scheduled_task', 'parameter': {'param': 'data'}, } schedule_url = f'https://127.0.0.1:{unused_port}/public/v1/devops' service_id = config_payload['service_id'] schedule_url = f'{schedule_url}/services/{service_id}/environments/ENV-000-0001' schedule_url = f'{schedule_url}/schedules/{schedule_data["id"]}' httpx_mock.add_response( method='GET', url=schedule_url, json=schedule_data, ) mocker.patch( 'connect.eaas.config.get_environment', return_value={ 'ws_address': f'127.0.0.1:{unused_port}', 'api_address': f'127.0.0.1:{unused_port}', 'api_key': 'SU-000:XXXX', 'environment_id': 'ENV-000-0001', 'instance_id': 'INS-000-0002', 'background_task_max_execution_time': 300, 'interactive_task_max_execution_time': 120, 'scheduled_task_max_execution_time': 43200, }, ) capabilities = { TaskType.TIER_CONFIG_SETUP_REQUEST_PROCESSING: ['pending'], TaskType.ASSET_PURCHASE_REQUEST_VALIDATION: ['draft'], } class MyExtension(Extension): @classmethod def get_descriptor(cls): return { 'capabilities': capabilities, 'variables': [], 'schedulables': [ { 'method': 'run_scheduled_task', 'name': 'Run scheduled task', 'description': 'Description', }, ], 'readme_url': 'https://example.com/README.md', 'changelog_url': 'https://example.com/CHANGELOG.md', } def run_scheduled_task(self, schedule): assert schedule == schedule_data return ScheduledExecutionResponse.done() mocker.patch('connect.eaas.handler.get_extension_class', return_value=MyExtension) mocker.patch('connect.eaas.handler.get_extension_type', return_value='sync') data_to_send = [ dataclasses.asdict(Message(MessageType.CONFIGURATION, ConfigurationPayload( **config_payload, ))), dataclasses.asdict( Message(MessageType.TASK, TaskPayload( 'TQ-000', TaskCategory.SCHEDULED, TaskType.SCHEDULED_EXECUTION, schedule_data['id'], )), ), ] handler = WSHandler( '/public/v1/devops/ws/ENV-000-0001/INS-000-0002?running_tasks=0&running_scheduled_tasks=0', data_to_send, ['receive', 'send', 'send', 'receive'], ) async with ws_server(handler): worker = Worker(secure=False) task = asyncio.create_task(worker.start()) await asyncio.sleep(.5) worker.stop() await task handler.assert_received( dataclasses.asdict( Message( MessageType.CAPABILITIES, CapabilitiesPayload( capabilities, [], [ { 'method': 'run_scheduled_task', 'name': 'Run scheduled task', 'description': 'Description', }, ], 'https://example.com/README.md', 'https://example.com/CHANGELOG.md', ), ), ), ) handler.assert_received( dataclasses.asdict( Message(MessageType.TASK, TaskPayload( 'TQ-000', TaskCategory.SCHEDULED, TaskType.SCHEDULED_EXECUTION, schedule_data['id'], result=ResultType.SUCCESS, )), ), ) @pytest.mark.asyncio async def test_pause(mocker, ws_server, unused_port, config_payload): mocker.patch( 'connect.eaas.config.get_environment', return_value={ 'ws_address': f'127.0.0.1:{unused_port}', 'api_address': f'127.0.0.1:{unused_port}', 'api_key': 'SU-000:XXXX', 'environment_id': 'ENV-000-0001', 'instance_id': 'INS-000-0002', 'background_task_max_execution_time': 300, 'interactive_task_max_execution_time': 120, 'scheduled_task_max_execution_time': 43200, }, ) capabilities = { TaskType.ASSET_PURCHASE_REQUEST_PROCESSING: ['pending', 'inquiring'], TaskType.ASSET_PURCHASE_REQUEST_VALIDATION: ['draft'], } class MyExtension(Extension): @classmethod def get_descriptor(cls): return { 'capabilities': capabilities, 'variables': [], 'schedulables': [], 'readme_url': 'https://example.com/README.md', 'changelog_url': 'https://example.com/CHANGELOG.md', } mocker.patch('connect.eaas.handler.get_extension_class', return_value=MyExtension) mocker.patch('connect.eaas.handler.get_extension_type', return_value='sync') data_to_send = [ dataclasses.asdict(Message(MessageType.CONFIGURATION, ConfigurationPayload( **config_payload, ))), dataclasses.asdict(Message(MessageType.PAUSE)), ] handler = WSHandler( '/public/v1/devops/ws/ENV-000-0001/INS-000-0002?running_tasks=0&running_scheduled_tasks=0', data_to_send, ['receive', 'send', 'send'], ) async with ws_server(handler): worker = Worker(secure=False) task = asyncio.create_task(worker.start()) await asyncio.sleep(.5) assert worker.paused is True worker.stop() await task @pytest.mark.asyncio async def test_resume(mocker, ws_server, unused_port, config_payload): mocker.patch( 'connect.eaas.config.get_environment', return_value={ 'ws_address': f'127.0.0.1:{unused_port}', 'api_address': f'127.0.0.1:{unused_port}', 'api_key': 'SU-000:XXXX', 'environment_id': 'ENV-000-0001', 'instance_id': 'INS-000-0002', 'background_task_max_execution_time': 300, 'interactive_task_max_execution_time': 120, 'scheduled_task_max_execution_time': 43200, }, ) capabilities = { TaskType.ASSET_PURCHASE_REQUEST_PROCESSING: ['pending', 'inquiring'], TaskType.ASSET_PURCHASE_REQUEST_VALIDATION: ['draft'], } class MyExtension(Extension): @classmethod def get_descriptor(cls): return { 'capabilities': capabilities, 'variables': [], 'schedulables': [], 'readme_url': 'https://example.com/README.md', 'changelog_url': 'https://example.com/CHANGELOG.md', } mocker.patch('connect.eaas.handler.get_extension_class', return_value=MyExtension) mocker.patch('connect.eaas.handler.get_extension_type', return_value='sync') data_to_send = [ dataclasses.asdict(Message(MessageType.CONFIGURATION, ConfigurationPayload( **config_payload, ))), dataclasses.asdict(Message(MessageType.PAUSE)), dataclasses.asdict(Message(MessageType.RESUME)), ] handler = WSHandler( '/public/v1/devops/ws/ENV-000-0001/INS-000-0002?running_tasks=0&running_scheduled_tasks=0', data_to_send, ['receive', 'send', 'send', 'send'], ) async with ws_server(handler): worker = Worker(secure=False) worker.paused = True task = asyncio.create_task(worker.start()) await asyncio.sleep(.5) assert worker.paused is False worker.stop() await task @pytest.mark.asyncio async def test_shutdown(mocker, ws_server, unused_port, config_payload): mocker.patch( 'connect.eaas.config.get_environment', return_value={ 'ws_address': f'127.0.0.1:{unused_port}', 'api_address': f'127.0.0.1:{unused_port}', 'api_key': 'SU-000:XXXX', 'environment_id': 'ENV-000-0001', 'instance_id': 'INS-000-0002', 'background_task_max_execution_time': 300, 'interactive_task_max_execution_time': 120, 'scheduled_task_max_execution_time': 43200, }, ) capabilities = { TaskType.ASSET_PURCHASE_REQUEST_PROCESSING: ['pending', 'inquiring'], TaskType.ASSET_PURCHASE_REQUEST_VALIDATION: ['draft'], } class MyExtension(Extension): @classmethod def get_descriptor(cls): return { 'capabilities': capabilities, 'variables': [], 'schedulables': [], 'readme_url': 'https://example.com/README.md', 'changelog_url': 'https://example.com/CHANGELOG.md', } mocker.patch('connect.eaas.handler.get_extension_class', return_value=MyExtension) mocker.patch('connect.eaas.handler.get_extension_type', return_value='sync') data_to_send = [ dataclasses.asdict(Message(MessageType.CONFIGURATION, ConfigurationPayload( **config_payload, ))), dataclasses.asdict(Message(MessageType.SHUTDOWN)), ] handler = WSHandler( '/public/v1/devops/ws/ENV-000-0001/INS-000-0002?running_tasks=0&running_scheduled_tasks=0', data_to_send, ['receive', 'send', 'send'], ) async with ws_server(handler): worker = Worker(secure=False) asyncio.create_task(worker.start()) await asyncio.sleep(.5) assert worker.run_event.is_set() is False @pytest.mark.asyncio async def test_connection_closed_error(mocker, ws_server, unused_port, caplog): mocker.patch('connect.eaas.worker.MAX_RETRY_TIME_GENERIC_SECONDS', 1) mocker.patch('connect.eaas.worker.MAX_RETRY_DELAY_TIME_SECONDS', 1) mocker.patch('connect.eaas.handler.get_extension_class') mocker.patch('connect.eaas.handler.get_extension_type') mocker.patch( 'connect.eaas.config.get_environment', return_value={ 'ws_address': f'127.0.0.1:{unused_port}', 'api_address': f'127.0.0.1:{unused_port}', 'api_key': 'SU-000:XXXX', 'environment_id': 'ENV-000-0001', 'instance_id': 'INS-000-0002', 'background_task_max_execution_time': 300, 'interactive_task_max_execution_time': 120, 'scheduled_task_max_execution_time': 43200, }, ) handler = WSHandler( '/public/v1/devops/ws/ENV-000-0001/INS-000-0002?running_tasks=0&running_scheduled_tasks=0', None, [], ) async with ws_server(handler): worker = Worker(secure=False) worker.send = mocker.AsyncMock(side_effect=ConnectionClosedError(1006, 'disconnected')) with caplog.at_level(logging.INFO): task = asyncio.create_task(worker.start()) await asyncio.sleep(.5) worker.stop() await task assert ( f'Connection closed with code 1006 from: ws://127.0.0.1:{unused_port}' '/public/v1/devops/ws/ENV-000-0001/INS-000-0002' '?running_tasks=0&running_scheduled_tasks=0' ) in caplog.text @pytest.mark.asyncio async def test_connection_websocket_exception(mocker, ws_server, unused_port, caplog): mocker.patch('connect.eaas.handler.get_extension_class') mocker.patch('connect.eaas.handler.get_extension_type') mocker.patch( 'connect.eaas.config.get_environment', return_value={ 'ws_address': f'127.0.0.1:{unused_port}', 'api_address': f'127.0.0.1:{unused_port}', 'api_key': 'SU-000:XXXX', 'environment_id': 'ENV-000-0001', 'instance_id': 'INS-000-0002', 'background_task_max_execution_time': 300, 'interactive_task_max_execution_time': 120, 'scheduled_task_max_execution_time': 43200, }, ) handler = WSHandler( '/public/v1/devops/ws/ENV-000-0001/INS-000-0002?running_tasks=0&running_scheduled_tasks=0', None, [], ) async with ws_server(handler): worker = Worker(secure=False) worker.send = mocker.AsyncMock(side_effect=WebSocketException()) with caplog.at_level(logging.INFO): task = asyncio.create_task(worker.start()) await asyncio.sleep(.5) worker.stop() await task assert 'Unexpected websocket exception' in caplog.text @pytest.mark.asyncio async def test_connection_maintenance(mocker, ws_server, unused_port, caplog): mocker.patch('connect.eaas.worker.MAX_RETRY_TIME_MAINTENANCE_SECONDS', 1) mocker.patch('connect.eaas.worker.MAX_RETRY_DELAY_TIME_SECONDS', 1) mocker.patch('connect.eaas.handler.get_extension_class') mocker.patch('connect.eaas.handler.get_extension_type') mocker.patch( 'connect.eaas.config.get_environment', return_value={ 'ws_address': f'127.0.0.1:{unused_port}', 'api_address': f'127.0.0.1:{unused_port}', 'api_key': 'SU-000:XXXX', 'environment_id': 'ENV-000-0001', 'instance_id': 'INS-000-0002', 'background_task_max_execution_time': 300, 'interactive_task_max_execution_time': 120, 'scheduled_task_max_execution_time': 43200, }, ) handler = WSHandler( '/public/v1/devops/ws/ENV-000-0001/INS-000-0002?running_tasks=0&running_scheduled_tasks=0', None, [], ) async with ws_server(handler): worker = Worker(secure=False) worker.send = mocker.AsyncMock(side_effect=InvalidStatusCode(502, None)) with caplog.at_level(logging.INFO): task = asyncio.create_task(worker.start()) await asyncio.sleep(.5) worker.stop() await task assert 'InvalidStatusCode 502 raised. Maintenance in progress.' in caplog.text assert 'Backing off ' in caplog.text @pytest.mark.asyncio async def test_connection_internal_server_error(mocker, ws_server, unused_port, caplog): mocker.patch('connect.eaas.worker.MAX_RETRY_TIME_GENERIC_SECONDS', 1) mocker.patch('connect.eaas.worker.MAX_RETRY_DELAY_TIME_SECONDS', 1) mocker.patch('connect.eaas.handler.get_extension_class') mocker.patch('connect.eaas.handler.get_extension_type') mocker.patch( 'connect.eaas.config.get_environment', return_value={ 'ws_address': f'127.0.0.1:{unused_port}', 'api_address': f'127.0.0.1:{unused_port}', 'api_key': 'SU-000:XXXX', 'environment_id': 'ENV-000-0001', 'instance_id': 'INS-000-0002', 'background_task_max_execution_time': 300, 'interactive_task_max_execution_time': 120, 'scheduled_task_max_execution_time': 43200, }, ) handler = WSHandler( '/public/v1/devops/ws/ENV-000-0001/INS-000-0002?running_tasks=0&running_scheduled_tasks=0', None, [], ) async with ws_server(handler): worker = Worker(secure=False) worker.send = mocker.AsyncMock(side_effect=InvalidStatusCode(500, None)) with caplog.at_level(logging.INFO): task = asyncio.create_task(worker.start()) await asyncio.sleep(0.5) worker.stop() await task assert 'InvalidStatusCode 500 raised.' in caplog.text assert 'Backing off ' in caplog.text @pytest.mark.asyncio async def test_start_stop(mocker, ws_server, unused_port, caplog): mocker.patch( 'connect.eaas.config.get_environment', return_value={ 'ws_address': f'127.0.0.1:{unused_port}', 'api_address': f'127.0.0.1:{unused_port}', 'api_key': 'SU-000:XXXX', 'environment_id': 'ENV-000-0001', 'instance_id': 'INS-000-0002', 'background_task_max_execution_time': 300, 'interactive_task_max_execution_time': 120, 'scheduled_task_max_execution_time': 43200, }, ) capabilities = { TaskType.ASSET_PURCHASE_REQUEST_PROCESSING: ['pending', 'inquiring'], TaskType.ASSET_PURCHASE_REQUEST_VALIDATION: ['draft'], } class MyExtension(Extension): @classmethod def get_descriptor(cls): return { 'capabilities': capabilities, 'variables': [], 'schedulables': [], 'readme_url': 'https://example.com/README.md', 'changelog_url': 'https://example.com/CHANGELOG.md', } mocker.patch('connect.eaas.handler.get_extension_class', return_value=MyExtension) mocker.patch('connect.eaas.handler.get_extension_type', return_value='sync') handler = WSHandler( '/public/v1/devops/ws/ENV-000-0001/INS-000-0002?running_tasks=0&running_scheduled_tasks=0', None, ['receive', 'send'], ) async with ws_server(handler): worker = Worker(secure=False) with caplog.at_level(logging.INFO): task = asyncio.create_task(worker.start()) await asyncio.sleep(.5) assert 'Control worker started' in caplog.text worker.stop() await task assert 'Control worker stopped' in caplog.text @pytest.mark.asyncio async def test_capabilities_configuration_with_vars(mocker, ws_server, unused_port): mocker.patch( 'connect.eaas.config.get_environment', return_value={ 'ws_address': f'127.0.0.1:{unused_port}', 'api_address': f'127.0.0.1:{unused_port}', 'api_key': 'SU-000:XXXX', 'environment_id': 'ENV-000-0001', 'instance_id': 'INS-000-0002', 'background_task_max_execution_time': 300, 'interactive_task_max_execution_time': 120, 'scheduled_task_max_execution_time': 43200, }, ) capabilities = { TaskType.ASSET_PURCHASE_REQUEST_PROCESSING: ['pending', 'inquiring'], TaskType.ASSET_PURCHASE_REQUEST_VALIDATION: ['draft'], } variables = [ {'name': 'foo_var', 'initial_value': 'foo_value'}, {'name': 'bar_var', 'initial_value': 'bar_value'}, ] class MyExtension(Extension): @classmethod def get_descriptor(cls): return { 'capabilities': capabilities, 'variables': variables, 'schedulables': [], 'readme_url': 'https://example.com/README.md', 'changelog_url': 'https://example.com/CHANGELOG.md', } mocker.patch('connect.eaas.handler.get_extension_class', return_value=MyExtension) mocker.patch('connect.eaas.handler.get_extension_type', return_value='sync') data_to_send = dataclasses.asdict( Message( MessageType.CONFIGURATION, ConfigurationPayload( { 'var1': 'value1', 'var2': 'value2', }, 'token', 'development', ), ), ) handler = WSHandler( '/public/v1/devops/ws/ENV-000-0001/INS-000-0002?running_tasks=0&running_scheduled_tasks=0', data_to_send, ['receive', 'send'], ) async with ws_server(handler): worker = Worker(secure=False) task = asyncio.create_task(worker.start()) await asyncio.sleep(.5) worker.stop() await task handler.assert_received( dataclasses.asdict( Message( MessageType.CAPABILITIES, CapabilitiesPayload( capabilities, variables, [], 'https://example.com/README.md', 'https://example.com/CHANGELOG.md', ), ), ), ) @pytest.mark.asyncio async def test_capabilities_configuration_without_vars(mocker, ws_server, unused_port): mocker.patch( 'connect.eaas.config.get_environment', return_value={ 'ws_address': f'127.0.0.1:{unused_port}', 'api_address': f'127.0.0.1:{unused_port}', 'api_key': 'SU-000:XXXX', 'environment_id': 'ENV-000-0001', 'instance_id': 'INS-000-0002', 'background_task_max_execution_time': 300, 'interactive_task_max_execution_time': 120, 'scheduled_task_max_execution_time': 43200, }, ) capabilities = { TaskType.ASSET_PURCHASE_REQUEST_PROCESSING: ['pending', 'inquiring'], TaskType.ASSET_PURCHASE_REQUEST_VALIDATION: ['draft'], } class MyExtension(Extension): @classmethod def get_descriptor(cls): return { 'capabilities': capabilities, 'readme_url': 'https://example.com/README.md', 'changelog_url': 'https://example.com/CHANGELOG.md', } mocker.patch('connect.eaas.handler.get_extension_class', return_value=MyExtension) mocker.patch('connect.eaas.handler.get_extension_type', return_value='sync') data_to_send = dataclasses.asdict( Message( MessageType.CONFIGURATION, ConfigurationPayload( { 'var1': 'value1', 'var2': 'value2', }, 'token', 'development', ), ), ) handler = WSHandler( '/public/v1/devops/ws/ENV-000-0001/INS-000-0002?running_tasks=0&running_scheduled_tasks=0', data_to_send, ['receive', 'send'], ) async with ws_server(handler): worker = Worker(secure=False) task = asyncio.create_task(worker.start()) await asyncio.sleep(.5) worker.stop() await task handler.assert_received( dataclasses.asdict( Message( MessageType.CAPABILITIES, CapabilitiesPayload( capabilities, None, None, 'https://example.com/README.md', 'https://example.com/CHANGELOG.md', ), ), ), ) @pytest.mark.asyncio async def test_sender_retries(mocker, config_payload, task_payload, caplog): mocker.patch('connect.eaas.handler.get_extension_class') mocker.patch('connect.eaas.handler.get_extension_type') with caplog.at_level(logging.WARNING): worker = Worker(secure=True) worker.config.update_dynamic_config(ConfigurationPayload(**config_payload)) worker.run = mocker.AsyncMock() worker.send = mocker.AsyncMock(side_effect=[Exception('retry'), None]) worker.ws = mocker.MagicMock(closed=False) await worker.results_queue.put( TaskPayload(**task_payload(TaskCategory.BACKGROUND, 'test', 'TQ-000')), ) assert worker.results_queue.empty() is False task = asyncio.create_task(worker.start()) await asyncio.sleep(.01) worker.stop() await task assert ( 'Attemp 0 to send results for task TQ-000 has failed.' in [r.message for r in caplog.records] ) @pytest.mark.asyncio async def test_sender_max_retries_exceeded(mocker, config_payload, task_payload, caplog): mocker.patch('connect.eaas.handler.get_extension_class') mocker.patch('connect.eaas.handler.get_extension_type') with caplog.at_level(logging.WARNING): worker = Worker(secure=True) worker.config.update_dynamic_config(ConfigurationPayload(**config_payload)) worker.run = mocker.AsyncMock() worker.send = mocker.AsyncMock( side_effect=[Exception('retry') for _ in range(RESULT_SENDER_MAX_RETRIES)], ) worker.ws = mocker.MagicMock(closed=False) await worker.results_queue.put( TaskPayload(**task_payload(TaskCategory.BACKGROUND, 'test', 'TQ-000')), ) assert worker.results_queue.empty() is False task = asyncio.create_task(worker.start()) await asyncio.sleep(.01) worker.stop() await task assert ( ( f'Max retries exceeded ({RESULT_SENDER_MAX_RETRIES})' ' for sending results of task TQ-000' ) in [r.message for r in caplog.records] ) @pytest.mark.asyncio async def test_sender_paused(mocker, config_payload, task_payload): mocker.patch('connect.eaas.handler.get_extension_class') mocker.patch('connect.eaas.handler.get_extension_type') worker = Worker(secure=True) worker.config.update_dynamic_config(ConfigurationPayload(**config_payload)) worker.run = mocker.AsyncMock() worker.send = mocker.AsyncMock() worker.ws = mocker.MagicMock(closed=False) worker.paused = True await worker.results_queue.put( TaskPayload(**task_payload(TaskCategory.BACKGROUND, 'test', 'TQ-000')), ) assert worker.results_queue.empty() is False task = asyncio.create_task(worker.start()) await asyncio.sleep(.1) worker.stop() await task worker.send.assert_not_awaited() @pytest.mark.asyncio async def test_sender_ws_closed(mocker, config_payload, task_payload): mocker.patch('connect.eaas.handler.get_extension_class') mocker.patch('connect.eaas.handler.get_extension_type') worker = Worker(secure=True) worker.config.update_dynamic_config(ConfigurationPayload(**config_payload)) worker.run = mocker.AsyncMock() worker.send = mocker.AsyncMock() worker.ws = mocker.MagicMock(closed=True) await worker.results_queue.put( TaskPayload(**task_payload(TaskCategory.BACKGROUND, 'test', 'TQ-000')), ) assert worker.results_queue.empty() is False task = asyncio.create_task(worker.start()) await asyncio.sleep(.1) worker.stop() await task worker.send.assert_not_awaited() @pytest.mark.parametrize( ('tries', 'ordinal'), ( (14, 'th'), (21, 'st'), (22, 'nd'), (23, 'rd'), ), ) def test__on_communication_backoff(caplog, tries, ordinal): details = {'tries': tries, 'elapsed': 2.2, 'wait': 1.1} expected = ( f'{tries}{ordinal} communication attempt failed, backing off waiting ' f'{details["wait"]:.2f} seconds after next retry. Elapsed time: {details["elapsed"]:.2f}' ' seconds.' ) with caplog.at_level(logging.INFO): _on_communication_backoff(details) assert expected in caplog.records[0].message

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0.59936

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36,886

5.499482

0.066062

0.031091

0.047484

0.058037

0.888025

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0.863482

0.858065

0

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0.279212

36,886

1,085

100

33.996313

0.761462

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false

0

0.010549

0.049578

0

null

0

1

0

1

0

null

0

7

041e41e00df91ceee7e30d51dd07e0ae93c58488

2,198

py

Python

backend/apps/project/admin.py

renmcc/SA2

a524124c140ae0b291b10dafc11d38744dd93bd9

[ "MIT" ]

4

2020-06-25T05:57:39.000Z

2021-06-26T04:58:16.000Z

backend/apps/project/admin.py

renmcc/SA2

a524124c140ae0b291b10dafc11d38744dd93bd9

[ "MIT" ]

null

backend/apps/project/admin.py

renmcc/SA2

a524124c140ae0b291b10dafc11d38744dd93bd9

[ "MIT" ]

1

2020-12-10T15:12:11.000Z

from django.contrib import admin from .models import Project, ProjectRole, ProjectArea, ProjectRegion # Register your models here. @admin.register(Project) class ProjectAdmin(admin.ModelAdmin): list_display = ['id', 'name', 'remark', 'created', 'update_time'] # 排序方式 -为倒序 ordering = ['id'] # 为数据列表页的字段id和job设置路由地址，该路由地址可进入数据修改页 list_display_links = ['id', 'name'] # 设置过滤器，若有外键，则应该使用双下划线连接两个模型的字段 list_filter = ['name',] # 在数据列表页设置每一页显示的数据 list_per_page = 50 # 设置可搜索的字段 # search_fields = ['name', 'role'] # 在数据列表页设置日期选择器 date_hierarchy = 'created' # 在数据修改页添加'另存为'功能 save_as = True @admin.register(ProjectArea) class ProjectAreaAdmin(admin.ModelAdmin): list_display = ['id', 'name', 'remark', 'created', 'update_time'] # 排序方式 -为倒序 ordering = ['id'] # 为数据列表页的字段id和job设置路由地址，该路由地址可进入数据修改页 list_display_links = ['id', 'name'] # 设置过滤器，若有外键，则应该使用双下划线连接两个模型的字段 list_filter = ['name'] # 在数据列表页设置每一页显示的数据 list_per_page = 50 # 设置可搜索的字段 # search_fields = ['name', 'role'] # 在数据列表页设置日期选择器 date_hierarchy = 'created' # 在数据修改页添加'另存为'功能 save_as = True @admin.register(ProjectRole) class ProjectRoleAdmin(admin.ModelAdmin): list_display = ['id', 'name', 'remark', 'created', 'update_time'] # 排序方式 -为倒序 ordering = ['id'] # 为数据列表页的字段id和job设置路由地址，该路由地址可进入数据修改页 list_display_links = ['id', 'name'] # 设置过滤器，若有外键，则应该使用双下划线连接两个模型的字段 list_filter = ['name'] # 在数据列表页设置每一页显示的数据 list_per_page = 50 # 设置可搜索的字段 # search_fields = ['name', 'role'] # 在数据列表页设置日期选择器 date_hierarchy = 'created' # 在数据修改页添加'另存为'功能 save_as = True @admin.register(ProjectRegion) class ProjectRoleAdmin(admin.ModelAdmin): list_display = ['id', 'name', 'remark', 'created', 'update_time'] # 排序方式 -为倒序 ordering = ['id'] # 为数据列表页的字段id和job设置路由地址，该路由地址可进入数据修改页 list_display_links = ['id', 'name'] # 设置过滤器，若有外键，则应该使用双下划线连接两个模型的字段 list_filter = ['name'] # 在数据列表页设置每一页显示的数据 list_per_page = 50 # 设置可搜索的字段 # search_fields = ['name', 'role'] # 在数据列表页设置日期选择器 date_hierarchy = 'created' # 在数据修改页添加'另存为'功能 save_as = True

27.475

69

0.659691

224

2,198

6.294643

0.236607

0.062411

0.053901

0.073759

0.857447

0

0.004603

0.209281

2,198

80

70

27.475

0.806674

0.312102

0

0.763158

0

0.132522

0

1

0

false

0

0.052632

0

0.894737

0

null

0

1

0

1

0

null

0

1

0

8

f0905c64c033160cb783429b5e5dc43e98ddff41

49

py

Python

tutorial.py

Diegores14/the-race-of-theft

fa883bf4f7ba07ed7903f41c488ba2c1a525a3e2

[ "MIT" ]

null

tutorial.py

Diegores14/the-race-of-theft

fa883bf4f7ba07ed7903f41c488ba2c1a525a3e2

[ "MIT" ]

null

tutorial.py

Diegores14/the-race-of-theft

fa883bf4f7ba07ed7903f41c488ba2c1a525a3e2

[ "MIT" ]

null

import mapas def tutorial(): mapas.inicio(3)

12.25

19

0.693878

7

49

4.857143

0.857143

0

0.025

0.183673

49

4

19

12.25

0.825

0

1

0.333333

true

0

0.333333

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

7

f0ccc270181d2c8b168487f2e78fbfdb81678e36

20,741

py

Python

autoesda/autoesda.py

NicholasDeKock/autoesda

5ca60d5d72161dc7c551e48b845efe10efccbfe7

[ "BSD-3-Clause" ]

null

autoesda/autoesda.py

NicholasDeKock/autoesda

5ca60d5d72161dc7c551e48b845efe10efccbfe7

[ "BSD-3-Clause" ]

9

2022-02-13T09:55:37.000Z

2022-02-16T12:16:06.000Z

autoesda/autoesda.py

NicholasDeKock/autoESDA

fc1c759cd3c6d3f05e8279c0dd634cf7a841c4fb

[ "BSD-3-Clause" ]

null

"""Main module.""" import geopandas as gpd import pysal as ps import matplotlib.pyplot as plt import libpysal as lps from esda.moran import (Moran, Moran_Local) from splot.esda import (moran_scatterplot, lisa_cluster, plot_moran_simulation) from matplotlib.offsetbox import AnchoredText import seaborn as sns from seaborn import heatmap import io import base64 def generate_report(gdf): numeric_columns = list(gdf.select_dtypes(include=["int64","float64"]).columns) excluded_columns = set(list(gdf.columns))-set(numeric_columns) ##########----------SUMMARY PAGE----------########## #Create snapshot plot of Study Area study_area = gdf.plot(facecolor="none") study_area.figure.set_figheight(7) my_stringIObytes = io.BytesIO() study_area.figure.savefig(my_stringIObytes, format='jpg') my_stringIObytes.seek(0) study_area_image = base64.b64encode(my_stringIObytes.read()).decode('ascii') #Create Dataset Overview Table overview_string = ''' <table class="table table-striped table-hover"> </tr> <tr> <td>Coordinate System</td> <td>''' + str(gdf.crs) + '''</td> </tr> <tr> <td>Columns</td> <td>''' + str(gdf.shape[1]) + '''</td> </tr> <tr> <td>Rows</td> <td>''' + str(gdf.shape[0]) + '''</td> </tr> <tr> <td>Excluded Columns</td> <td>''' + str(excluded_columns) + '''</td> </tr> <tr> <td>Included Columns</td> <td>''' + str(numeric_columns) + '''</td> </table>''' #Create Descriptive Statistics Table descriptive_statistics = gdf.describe().round(2).to_html(classes="table table-striped table-hover", border = 0) #Create Sample Tables without_geom = gdf.drop(['geometry'], axis=1) head = without_geom.head(n=5).round(2).to_html(classes="table table-striped table-hover", border = 0) tail = without_geom.tail(n=5).round(2).to_html(classes="table table-striped table-hover", border = 0) #Create Summary Page HTML summary_page = ''' <div class="table-responsive"> <table> <tr> <td> <div> <h2>Study Area</h2> <img src="data:image/png;base64,''' + study_area_image +'''"> </div> </td> <td> <div> <h2>Dataset Overview</h2> ''' + overview_string + ''' </div> <div> <h2>Descriptive Statistics</h2> ''' + descriptive_statistics + ''' </div> </td> </tr> <tr> <td> <h2>Sample Rows</h2> <div> <h3>First 5 rows</h3> ''' + head + ''' </div> </td> </tr <tr> <td> <div> <h3>Last 5 rows</h3> ''' + tail + ''' </div> </td> </tr> </table> </div>''' ##########----------NUMERIC COLUMN SUMMARIES----------########## #Create Spatial weights matrix weight_matrix = lps.weights.Queen.from_dataframe(gdf) weight_matrix.transform = 'r' image_array = [] for cols in numeric_columns: #Create plot and grid plt.figure(figsize = (20, 12)) grid = plt.GridSpec(3, 4, height_ratios=[1,4,7]) #Populate grid with subplots g1 = plt.subplot(grid[0, 0]) g2 = plt.subplot(grid[0:2,1]) g3 = plt.subplot(grid[0:2,2]) g4 = plt.subplot(grid[0:2,3]) g5 = plt.subplot(grid[1,0]) g6 = plt.subplot(grid[2,0]) g7 = plt.subplot(grid[2,1]) g8 = plt.subplot(grid[2,2]) g9 = plt.subplot(grid[2,3]) #Boxplot g1.boxplot(gdf[cols], vert=False) g1.set_title('Boxplot of ' + cols) #Moran's Calculations column_values = gdf[cols].values moransI_queen = Moran(gdf[cols], weight_matrix) moran_local = Moran_Local(column_values, weight_matrix) #Reference Distribution plot_moran_simulation(moransI_queen,aspect_equal=False, ax=g2) g2.set_title("Reference Distribution of " + cols) anchorText = "Moran's I: " + str(round(moransI_queen.I, 5)) + "\nn: " + str(moransI_queen.n) + "\np-value: " + str(moransI_queen.p_sim) + "\nz-score: " + str(round(moransI_queen.z_sim,5)) + "\nPermutations: " + str(moransI_queen.permutations) at = AnchoredText(anchorText, prop=dict(size=10), frameon=True, loc='upper right') g2.add_artist(at) #LISA Scatterplot moran_scatterplot(moran_local, p=0.05, ax=g3, aspect_equal=False) g3.set_title("Morans Local Scatterplot of " + cols) g3.set_xlabel(cols) g3.set_ylabel('Spatial Lag of ' + cols) #LISA Cluster Map lisa_cluster(moran_local, gdf, ax=g4, legend_kwds={'loc': 'best'}) g4.set_title("LISA Cluster Map of " + cols) #Histogram g5.hist(gdf[cols], color='teal',edgecolor='black') g5.set_title('Histogram of ' + cols) g5.set_xlabel(cols) g5.set_ylabel('Count') #Quantiles g6.set_title('Quantiles') gdf.plot(ax = g6, column=cols, scheme='quantiles', legend=True, legend_kwds={'loc': 'best', 'title': cols, "fmt": "{:.0f}"}) #Equal Intervals g7.set_title('Equal Intervals') gdf.plot(ax = g7, column=cols, scheme='equal_interval', legend=True, legend_kwds={'loc': 'best', 'title': cols, "fmt": "{:.0f}"}) #Natural breaks g8.set_title('Natural Breaks') gdf.plot(ax = g8, column=cols, scheme='natural_breaks', legend=True, legend_kwds={'loc': 'best', 'title': cols, "fmt": "{:.0f}"}) #Percentiles g9.set_title('Percentiles') gdf.plot(ax = g9, column=cols, scheme='Percentiles', legend=True, legend_kwds={'loc': 'best', 'title': cols, "fmt": "{:.0f}"}) plt.tight_layout() my_stringIObytes = io.BytesIO() plt.savefig(my_stringIObytes, format='jpg') my_stringIObytes.seek(0) image_array.append(base64.b64encode(my_stringIObytes.read()).decode('ascii')) plt.close() #Correlation correlation_matrix = gdf.corr() correlation_heatmap = sns.heatmap(correlation_matrix, cmap='RdBu_r', annot=True, cbar=False, square=True) my_stringIObytes = io.BytesIO() plt.savefig(my_stringIObytes, format='jpg', bbox_inches='tight',pad_inches=0.5) my_stringIObytes.seek(0) correlation_heatmap_image = base64.b64encode(my_stringIObytes.read()).decode('ascii') plt.close() #Pairplot pairplot = sns.pairplot(gdf, height=1.5,plot_kws=dict(marker=".")) my_stringIObytes = io.BytesIO() plt.savefig(my_stringIObytes, format='jpg') my_stringIObytes.seek(0) pairplot = base64.b64encode(my_stringIObytes.read()).decode('ascii') ##########----------HTML REPORT SETUP----------########## #Create String for all Tabs and all div/figures tab_string = "" div_string = "" count = 0 for cols in numeric_columns: tab_string+=str('<button class="tablinks" onclick="openTab(event, \'' + cols + '\')">' + cols + '</button>\n') div_string+=str('<div id="' + cols + '" class="tabcontent table-responsive"><img src="data:image/png;base64,' + image_array[count] +'"></div>\n') count=count+1 #Create String for HTML report html_string = str(''' <!DOCTYPE html> <html> <head> <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.1/css/bootstrap.min.css"> <link rel="stylesheet" href="https://www.w3schools.com/w3css/4/w3.css"> <meta name="viewport" content="width=device-width, initial-scale=1"> <style> body {font-family: Arial;} /* Style the tab */ .tab { overflow: hidden; border: 1px solid #ccc; background-color: #f1f1f1; } /* Style the buttons inside the tab */ .tab button { background-color: inherit; float: left; border: none; outline: none; cursor: pointer; padding: 14px 16px; transition: 0.3s; font-size: 17px; } /* Change background color of buttons on hover */ .tab button:hover { background-color: #ddd; } /* Create an active/current tablink class */ .tab button.active { background-color: #ccc; } /* Style the tab content */ .tabcontent { display: none; padding: 6px 12px; border: 1px solid #ccc; border-top: none; } div { padding:0px; } .table-hover tbody tr:hover td, .table-hover tbody tr:hover th { background-color: #d0e8f7; } .table{ white-space: nowrap; width: 1%; } img {max-width:100%; height:auto} </style> </head> <body onload="openTab(event, 'Summary')"> <h1>autoESDA report</h1> <p>Click on the buttons inside the tabbed menu:</p> <div class="tab"> <button class="tablinks" onclick="openTab(event, 'Summary')">Summary</button>''' + tab_string + ''' <button class="tablinks" onclick="openTab(event, 'Correlation')">Correlation</button> </div> <div id="Summary" class="tabcontent"> ''' + summary_page + ''' </div>''' + div_string + ''' <div id="Correlation" class="tabcontent"> <table> <tr> <td> <div class="table-responsive"> <h2>Correlation Heatmap</h2> <img src="data:image/png;base64,''' + correlation_heatmap_image +'''"> </div> </td> <td> <div class="table-responsive"> <h2>Pairplot</h2> <img src="data:image/png;base64,''' + pairplot +'''"> </div> </td> </tr> </table> <script> function openTab(evt, tabName) { var i, tabcontent, tablinks; tabcontent = document.getElementsByClassName("tabcontent"); for (i = 0; i < tabcontent.length; i++) { tabcontent[i].style.display = "none"; } tablinks = document.getElementsByClassName("tablinks"); for (i = 0; i < tablinks.length; i++) { tablinks[i].className = tablinks[i].className.replace(" active", ""); } document.getElementById(tabName).style.display = "block"; evt.currentTarget.className += " active"; } </script> </body> </html> ''') file = open('autoESDAreport.html', 'w') file.write(html_string) file.close() print('Success! Report has been saved to your working folder directory.') def generate_html_string(gdf): numeric_columns = list(gdf.select_dtypes(include=["int64","float64"]).columns) excluded_columns = set(list(gdf.columns))-set(numeric_columns) ##########----------SUMMARY PAGE----------########## #Create snapshot plot of Study Area study_area = gdf.plot(facecolor="none") study_area.figure.set_figheight(7) my_stringIObytes = io.BytesIO() study_area.figure.savefig(my_stringIObytes, format='jpg') my_stringIObytes.seek(0) study_area_image = base64.b64encode(my_stringIObytes.read()).decode('ascii') #Create Dataset Overview Table overview_string = ''' <table class="table table-striped table-hover"> </tr> <tr> <td>Coordinate System</td> <td>''' + str(gdf.crs) + '''</td> </tr> <tr> <td>Columns</td> <td>''' + str(gdf.shape[1]) + '''</td> </tr> <tr> <td>Rows</td> <td>''' + str(gdf.shape[0]) + '''</td> </tr> <tr> <td>Excluded Columns</td> <td>''' + str(excluded_columns) + '''</td> </tr> <tr> <td>Included Columns</td> <td>''' + str(numeric_columns) + '''</td> </table>''' #Create Descriptive Statistics Table descriptive_statistics = gdf.describe().round(2).to_html(classes="table table-striped table-hover", border = 0) #Create Sample Tables without_geom = gdf.drop(['geometry'], axis=1) head = without_geom.head(n=5).round(2).to_html(classes="table table-striped table-hover", border = 0) tail = without_geom.tail(n=5).round(2).to_html(classes="table table-striped table-hover", border = 0) #Create Summary Page HTML summary_page = ''' <div class="table-responsive"> <table> <tr> <td> <div> <h2>Study Area</h2> <img src="data:image/png;base64,''' + study_area_image +'''"> </div> </td> <td> <div> <h2>Dataset Overview</h2> ''' + overview_string + ''' </div> <div> <h2>Descriptive Statistics</h2> ''' + descriptive_statistics + ''' </div> </td> </tr> <tr> <td> <h2>Sample Rows</h2> <div> <h3>First 5 rows</h3> ''' + head + ''' </div> </td> </tr <tr> <td> <div> <h3>Last 5 rows</h3> ''' + tail + ''' </div> </td> </tr> </table> </div>''' ##########----------NUMERIC COLUMN SUMMARIES----------########## #Create Spatial weights matrix weight_matrix = lps.weights.Queen.from_dataframe(gdf) weight_matrix.transform = 'r' image_array = [] for cols in numeric_columns: #Create plot and grid plt.figure(figsize = (20, 12)) grid = plt.GridSpec(3, 4, height_ratios=[1,4,7]) #Populate grid with subplots g1 = plt.subplot(grid[0, 0]) g2 = plt.subplot(grid[0:2,1]) g3 = plt.subplot(grid[0:2,2]) g4 = plt.subplot(grid[0:2,3]) g5 = plt.subplot(grid[1,0]) g6 = plt.subplot(grid[2,0]) g7 = plt.subplot(grid[2,1]) g8 = plt.subplot(grid[2,2]) g9 = plt.subplot(grid[2,3]) #Boxplot g1.boxplot(gdf[cols], vert=False) g1.set_title('Boxplot of ' + cols) #Moran's Calculations column_values = gdf[cols].values moransI_queen = Moran(gdf[cols], weight_matrix) moran_local = Moran_Local(column_values, weight_matrix) #Reference Distribution plot_moran_simulation(moransI_queen,aspect_equal=False, ax=g2) g2.set_title("Reference Distribution of " + cols) anchorText = "Moran's I: " + str(round(moransI_queen.I, 5)) + "\nn: " + str(moransI_queen.n) + "\np-value: " + str(moransI_queen.p_sim) + "\nz-score: " + str(round(moransI_queen.z_sim,5)) + "\nPermutations: " + str(moransI_queen.permutations) at = AnchoredText(anchorText, prop=dict(size=10), frameon=True, loc='upper right') g2.add_artist(at) #LISA Scatterplot moran_scatterplot(moran_local, p=0.05, ax=g3, aspect_equal=False) g3.set_title("Morans Local Scatterplot of " + cols) g3.set_xlabel(cols) g3.set_ylabel('Spatial Lag of ' + cols) #LISA Cluster Map lisa_cluster(moran_local, gdf, ax=g4, legend_kwds={'loc': 'best'}) g4.set_title("LISA Cluster Map of " + cols) #Histogram g5.hist(gdf[cols], color='teal',edgecolor='black') g5.set_title('Histogram of ' + cols) g5.set_xlabel(cols) g5.set_ylabel('Count') #Quantiles g6.set_title('Quantiles') gdf.plot(ax = g6, column=cols, scheme='quantiles', legend=True, legend_kwds={'loc': 'best', 'title': cols, "fmt": "{:.0f}"}) #Equal Intervals g7.set_title('Equal Intervals') gdf.plot(ax = g7, column=cols, scheme='equal_interval', legend=True, legend_kwds={'loc': 'best', 'title': cols, "fmt": "{:.0f}"}) #Natural breaks g8.set_title('Natural Breaks') gdf.plot(ax = g8, column=cols, scheme='natural_breaks', legend=True, legend_kwds={'loc': 'best', 'title': cols, "fmt": "{:.0f}"}) #Percentiles g9.set_title('Percentiles') gdf.plot(ax = g9, column=cols, scheme='Percentiles', legend=True, legend_kwds={'loc': 'best', 'title': cols, "fmt": "{:.0f}"}) plt.tight_layout() my_stringIObytes = io.BytesIO() plt.savefig(my_stringIObytes, format='jpg') my_stringIObytes.seek(0) image_array.append(base64.b64encode(my_stringIObytes.read()).decode('ascii')) plt.close() #Correlation correlation_matrix = gdf.corr() correlation_heatmap = sns.heatmap(correlation_matrix, cmap='RdBu_r', annot=True, cbar=False, square=True) my_stringIObytes = io.BytesIO() plt.savefig(my_stringIObytes, format='jpg', bbox_inches='tight',pad_inches=0.5) my_stringIObytes.seek(0) correlation_heatmap_image = base64.b64encode(my_stringIObytes.read()).decode('ascii') plt.close() #Pairplot pairplot = sns.pairplot(gdf, height=1.5,plot_kws=dict(marker=".")) my_stringIObytes = io.BytesIO() plt.savefig(my_stringIObytes, format='jpg') my_stringIObytes.seek(0) pairplot = base64.b64encode(my_stringIObytes.read()).decode('ascii') ##########----------HTML REPORT SETUP----------########## #Create String for all Tabs and all div/figures tab_string = "" div_string = "" count = 0 for cols in numeric_columns: tab_string+=str('<button class="tablinks" onclick="openTab(event, \'' + cols + '\')">' + cols + '</button>\n') div_string+=str('<div id="' + cols + '" class="tabcontent table-responsive"><img src="data:image/png;base64,' + image_array[count] +'"></div>\n') count=count+1 #Create String for HTML report html_string = str(''' <!DOCTYPE html> <html> <head> <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.1/css/bootstrap.min.css"> <link rel="stylesheet" href="https://www.w3schools.com/w3css/4/w3.css"> <meta name="viewport" content="width=device-width, initial-scale=1"> <style> body {font-family: Arial;} /* Style the tab */ .tab { overflow: hidden; border: 1px solid #ccc; background-color: #f1f1f1; } /* Style the buttons inside the tab */ .tab button { background-color: inherit; float: left; border: none; outline: none; cursor: pointer; padding: 14px 16px; transition: 0.3s; font-size: 17px; } /* Change background color of buttons on hover */ .tab button:hover { background-color: #ddd; } /* Create an active/current tablink class */ .tab button.active { background-color: #ccc; } /* Style the tab content */ .tabcontent { display: none; padding: 6px 12px; border: 1px solid #ccc; border-top: none; } div { padding:0px; } .table-hover tbody tr:hover td, .table-hover tbody tr:hover th { background-color: #d0e8f7; } .table{ white-space: nowrap; width: 1%; } img {max-width:100%; height:auto} </style> </head> <body onload="openTab(event, 'Summary')"> <h1>autoESDA report</h1> <p>Click on the buttons inside the tabbed menu:</p> <div class="tab"> <button class="tablinks" onclick="openTab(event, 'Summary')">Summary</button>''' + tab_string + ''' <button class="tablinks" onclick="openTab(event, 'Correlation')">Correlation</button> </div> <div id="Summary" class="tabcontent"> ''' + summary_page + ''' </div>''' + div_string + ''' <div id="Correlation" class="tabcontent"> <table> <tr> <td> <div class="table-responsive"> <h2>Correlation Heatmap</h2> <img src="data:image/png;base64,''' + correlation_heatmap_image +'''"> </div> </td> <td> <div class="table-responsive"> <h2>Pairplot</h2> <img src="data:image/png;base64,''' + pairplot +'''"> </div> </td> </tr> </table> <script> function openTab(evt, tabName) { var i, tabcontent, tablinks; tabcontent = document.getElementsByClassName("tabcontent"); for (i = 0; i < tabcontent.length; i++) { tabcontent[i].style.display = "none"; } tablinks = document.getElementsByClassName("tablinks"); for (i = 0; i < tablinks.length; i++) { tablinks[i].className = tablinks[i].className.replace(" active", ""); } document.getElementById(tabName).style.display = "block"; evt.currentTarget.className += " active"; } </script> </body> </html> ''') return html_string

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f0d227b7ab63be72b757de0c51004a405ca4e88f

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py

Python

api-flask/api/v1/views/__init__.py

jmg7173/boiler-plates-and-examples

6df2b79f8c56422b6612210e51edaf26e423e5b0

[ "MIT" ]

8

2021-12-07T13:18:37.000Z

2022-01-11T13:27:20.000Z

api-flask/api/v1/views/__init__.py

jmg7173/boiler-plates-and-examples

6df2b79f8c56422b6612210e51edaf26e423e5b0

[ "MIT" ]

22

2021-12-26T08:56:58.000Z

2022-03-31T19:57:48.000Z

api-flask/api/v1/views/__init__.py

jmg7173/boiler-plates-and-examples

6df2b79f8c56422b6612210e51edaf26e423e5b0

[ "MIT" ]

null

from v1.views.auth import auth_api from v1.views.users import users_api

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Python

src/pybind/ctc/ctc_pybind_test_gpu.py

aadps/kaldi

cd351bb31c98f9d540c409478cbf2c5fef1853ca

[ "Apache-2.0" ]

null

src/pybind/ctc/ctc_pybind_test_gpu.py

aadps/kaldi

cd351bb31c98f9d540c409478cbf2c5fef1853ca

[ "Apache-2.0" ]

null

src/pybind/ctc/ctc_pybind_test_gpu.py

aadps/kaldi

cd351bb31c98f9d540c409478cbf2c5fef1853ca

[ "Apache-2.0" ]

null

#!/usr/bin/env python3 # Copyright 2020 Mobvoi AI Lab, Beijing, China (author: Fangjun Kuang) # Apache 2.0 import math import os import sys sys.path.insert(0, os.path.join(os.path.dirname(__file__), os.pardir)) import unittest import kaldi from kaldi import ctc try: import torch except ImportError: print('This test needs PyTorch.') print('Please install PyTorch first.') print('PyTorch 1.3.0dev20191006 has been tested and is known to work.') sys.exit(0) from torch.utils.dlpack import to_dlpack from torch.nn.utils.rnn import pad_sequence if torch.cuda.is_available() == False: print('No GPU detected! Skip it') sys.exit(0) if kaldi.CudaCompiled() == False: print('Kaldi is not compiled with CUDA! Skip it') sys.exit(0) device_id = 0 kaldi.SelectGpuDevice(device_id=device_id) class TestCtcGpu(unittest.TestCase): def test_case1(self): device = torch.device('cuda', device_id) # refer to https://github.com/baidu-research/warp-ctc/blob/master/torch_binding/TUTORIAL.md # this is the simplest case # we have one sequence with probability: [0.2, 0.2, 0.2, 0.2, 0.2] label_lengths_tensor = torch.tensor([1], dtype=torch.int32) input_lengths_tensor = torch.tensor([1], dtype=torch.int32) alphabet_size = 5 minibatch = 1 info = ctc.CtcOptions() info.loc = ctc.CtcComputeLocation.CTC_GPU info.blank_label = 0 label_lengths = kaldi.IntSubVectorFromDLPack( to_dlpack(label_lengths_tensor)) input_lengths = kaldi.IntSubVectorFromDLPack( to_dlpack(input_lengths_tensor)) status, size_in_bytes = ctc.GetWorkspaceSize( label_lengths=label_lengths, input_lengths=input_lengths, alphabet_size=alphabet_size, minibatch=minibatch, info=info) self.assertEqual(status, ctc.CtcStatus.CTC_STATUS_SUCCESS) num_floats = size_in_bytes // 4 + 1 workspace_tensor = torch.empty( num_floats, dtype=torch.float32).contiguous().to(device) activations_tensor = torch.tensor( [0.2, 0.2, 0.2, 0.2, 0.2], dtype=torch.float32).contiguous().to(device) gradients_tensor = torch.empty_like(activations_tensor) flat_labels_tensor = torch.tensor([1], dtype=torch.int32) costs_tensor = torch.empty(minibatch, dtype=torch.float32) activations = kaldi.CuSubVectorFromDLPack(to_dlpack(activations_tensor)) gradients = kaldi.CuSubVectorFromDLPack(to_dlpack(gradients_tensor)) flat_labels = kaldi.IntSubVectorFromDLPack( to_dlpack(flat_labels_tensor)) costs = kaldi.FloatSubVectorFromDLPack(to_dlpack(costs_tensor)) workspace = kaldi.CuSubVectorFromDLPack(to_dlpack(workspace_tensor)) stream = torch.cuda.default_stream(device) with torch.cuda.stream(stream): status = ctc.ComputeCtcLossGpu(activations=activations, gradients=gradients, flat_labels=flat_labels, label_lengths=label_lengths, input_lengths=input_lengths, alphabet_size=alphabet_size, minibatch=minibatch, costs=costs, workspace=workspace, options=info) # 1.6094379425049 is copied from # https://github.com/baidu-research/warp-ctc/blob/master/torch_binding/TUTORIAL.md self.assertAlmostEqual(costs[0], 1.6094379425049) def test_case2(self): device = torch.device('cuda', device_id) # this is the second case # we have 3 sequences with probability: # [1, 2, 3, 4, 5] # [6, 7, 8, 9, 10] # [11, 12, 13, 14, 15] label_lengths_tensor = torch.tensor([2], dtype=torch.int32) input_lengths_tensor = torch.tensor([3], dtype=torch.int32) alphabet_size = 5 minibatch = 1 info = ctc.CtcOptions() info.loc = ctc.CtcComputeLocation.CTC_GPU info.blank_label = 0 label_lengths = kaldi.IntSubVectorFromDLPack( to_dlpack(label_lengths_tensor)) input_lengths = kaldi.IntSubVectorFromDLPack( to_dlpack(input_lengths_tensor)) status, size_in_bytes = ctc.GetWorkspaceSize( label_lengths=label_lengths, input_lengths=input_lengths, alphabet_size=alphabet_size, minibatch=minibatch, info=info) self.assertEqual(status, ctc.CtcStatus.CTC_STATUS_SUCCESS) num_floats = size_in_bytes // 4 + 1 workspace_tensor = torch.empty( num_floats, dtype=torch.float32).contiguous().to(device) activations_tensor = torch.tensor( [[1, 2, 3, 4, 5], [6, 7, 8, 9, 10], [11, 12, 13, 14, 15]], dtype=torch.float32).contiguous().view(-1).to(device) gradients_tensor = torch.empty_like(activations_tensor) # the target sequence is cc, which is 3 3 flat_labels_tensor = torch.tensor([3, 3], dtype=torch.int32) costs_tensor = torch.empty(minibatch, dtype=torch.float32) activations = kaldi.CuSubVectorFromDLPack(to_dlpack(activations_tensor)) gradients = kaldi.CuSubVectorFromDLPack(to_dlpack(gradients_tensor)) flat_labels = kaldi.IntSubVectorFromDLPack( to_dlpack(flat_labels_tensor)) costs = kaldi.FloatSubVectorFromDLPack(to_dlpack(costs_tensor)) workspace = kaldi.CuSubVectorFromDLPack(to_dlpack(workspace_tensor)) status = ctc.ComputeCtcLossGpu(activations=activations, gradients=gradients, flat_labels=flat_labels, label_lengths=label_lengths, input_lengths=input_lengths, alphabet_size=alphabet_size, minibatch=minibatch, costs=costs, workspace=workspace, options=info) # 7.355742931366 is copied from # https://github.com/baidu-research/warp-ctc/blob/master/torch_binding/TUTORIAL.md self.assertAlmostEqual(costs[0], 7.355742931366) def test_case3(self): device = torch.device('cuda', device_id) # this is the third case # we have 3 sequences with probability: # [-5, -4, -3, -2, -1] # [-10, -9, -8, -7, -6] # [-15, -14, -13, -12, -11] label_lengths_tensor = torch.tensor([2], dtype=torch.int32) input_lengths_tensor = torch.tensor([3], dtype=torch.int32) alphabet_size = 5 minibatch = 1 info = ctc.CtcOptions() info.loc = ctc.CtcComputeLocation.CTC_GPU info.blank_label = 0 label_lengths = kaldi.IntSubVectorFromDLPack( to_dlpack(label_lengths_tensor)) input_lengths = kaldi.IntSubVectorFromDLPack( to_dlpack(input_lengths_tensor)) status, size_in_bytes = ctc.GetWorkspaceSize( label_lengths=label_lengths, input_lengths=input_lengths, alphabet_size=alphabet_size, minibatch=minibatch, info=info) self.assertEqual(status, ctc.CtcStatus.CTC_STATUS_SUCCESS) num_floats = size_in_bytes // 4 + 1 workspace_tensor = torch.empty( num_floats, dtype=torch.float32).contiguous().to(device) activations_tensor = torch.tensor( [[-5, -4, -3, -2, -1], [-10, -9, -8, -7, -6], [-15, -14, -13, -12, -11]], dtype=torch.float32).contiguous().view(-1).to(device) gradients_tensor = torch.empty_like(activations_tensor) # the target sequence is b c, whichis 2 3 flat_labels_tensor = torch.tensor([2, 3], dtype=torch.int32) costs_tensor = torch.empty(minibatch, dtype=torch.float32) activations = kaldi.CuSubVectorFromDLPack(to_dlpack(activations_tensor)) gradients = kaldi.CuSubVectorFromDLPack(to_dlpack(gradients_tensor)) flat_labels = kaldi.IntSubVectorFromDLPack( to_dlpack(flat_labels_tensor)) costs = kaldi.FloatSubVectorFromDLPack(to_dlpack(costs_tensor)) workspace = kaldi.CuSubVectorFromDLPack(to_dlpack(workspace_tensor)) status = ctc.ComputeCtcLossGpu(activations=activations, gradients=gradients, flat_labels=flat_labels, label_lengths=label_lengths, input_lengths=input_lengths, alphabet_size=alphabet_size, minibatch=minibatch, costs=costs, workspace=workspace, options=info) # 4.938850402832 is copied from # https://github.com/baidu-research/warp-ctc/blob/master/torch_binding/TUTORIAL.md self.assertAlmostEqual(costs[0], 4.938850402832, places=6) def test_case4(self): device = torch.device('cuda', device_id) # combine case1 to case3 to a minibatch # the first example (a): input_length: 1, label_length: 1 # the second example (c, c): input_length: 3, label_length: 2 # the third example (b, c): input_length: 3, label_length: 2 label_lengths_tensor = torch.tensor([1, 2, 2], dtype=torch.int32) input_lengths_tensor = torch.tensor([1, 3, 3], dtype=torch.int32) alphabet_size = 5 minibatch = 3 info = ctc.CtcOptions() info.loc = ctc.CtcComputeLocation.CTC_GPU info.blank_label = 0 label_lengths = kaldi.IntSubVectorFromDLPack( to_dlpack(label_lengths_tensor)) input_lengths = kaldi.IntSubVectorFromDLPack( to_dlpack(input_lengths_tensor)) status, size_in_bytes = ctc.GetWorkspaceSize( label_lengths=label_lengths, input_lengths=input_lengths, alphabet_size=alphabet_size, minibatch=minibatch, info=info) self.assertEqual(status, ctc.CtcStatus.CTC_STATUS_SUCCESS) num_floats = size_in_bytes // 4 + 1 workspace_tensor = torch.empty( num_floats, dtype=torch.float32).contiguous().to(device) ex1 = torch.tensor([[0.2, 0.2, 0.2, 0.2, 0.2]], dtype=torch.float32) ex2 = torch.tensor( [[1, 2, 3, 4, 5], [6, 7, 8, 9, 10], [11, 12, 13, 14, 15]], dtype=torch.float32) ex3 = torch.tensor([[-5, -4, -3, -2, -1], [-10, -9, -8, -7, -6], [-15, -14, -13, -12, -11]], dtype=torch.float32) activations_tensor = pad_sequence([ex1, ex2, ex3], batch_first=False) activations_tensor = activations_tensor.contiguous().view(-1).to(device) gradients_tensor = torch.empty_like(activations_tensor) # labels are: (a), (c, c) (b, c) # which are: (1), (3, 3), (2, 3) flat_labels_tensor = torch.tensor([1, 3, 3, 2, 3], dtype=torch.int32) costs_tensor = torch.empty(minibatch, dtype=torch.float32) activations = kaldi.CuSubVectorFromDLPack(to_dlpack(activations_tensor)) gradients = kaldi.CuSubVectorFromDLPack(to_dlpack(gradients_tensor)) flat_labels = kaldi.IntSubVectorFromDLPack( to_dlpack(flat_labels_tensor)) costs = kaldi.FloatSubVectorFromDLPack(to_dlpack(costs_tensor)) workspace = kaldi.CuSubVectorFromDLPack(to_dlpack(workspace_tensor)) status = ctc.ComputeCtcLossGpu(activations=activations, gradients=gradients, flat_labels=flat_labels, label_lengths=label_lengths, input_lengths=input_lengths, alphabet_size=alphabet_size, minibatch=minibatch, costs=costs, workspace=workspace, options=info) self.assertAlmostEqual(costs[0], 1.6094379425049) self.assertAlmostEqual(costs[1], 7.355742931366) self.assertAlmostEqual(costs[2], 4.938850402832, places=6) if __name__ == '__main__': unittest.main()

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9bdfa1daba306c1e542cd9e41906580dc5ab60cc

9,734

py

Python

pepdb/core/migrations/0066_auto_20151227_0205.py

dchaplinsky/pep.org.ua

8633a65fb657d7f04dbdb12eb8ae705fa6be67e3

[ "MIT" ]

7

2015-12-21T03:52:46.000Z

2020-07-24T19:17:23.000Z

pepdb/core/migrations/0066_auto_20151227_0205.py

dchaplinsky/pep.org.ua

8633a65fb657d7f04dbdb12eb8ae705fa6be67e3

[ "MIT" ]

12

2016-03-05T18:11:05.000Z

2021-06-17T20:20:03.000Z

pepdb/core/migrations/0066_auto_20151227_0205.py

dchaplinsky/pep.org.ua

8633a65fb657d7f04dbdb12eb8ae705fa6be67e3

[ "MIT" ]

4

2016-07-17T20:19:38.000Z

2021-03-23T12:47:20.000Z

# -*- coding: utf-8 -*- from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('core', '0065_auto_20151226_2247'), ] operations = [ migrations.AlterField( model_name='person2person', name='from_relationship_type', field=models.CharField(blank=True, max_length=100, verbose_name='\u041f\u0435\u0440\u0441\u043e\u043d\u0430 1 \u0454', choices=[('\u0431\u0430\u0442\u044c\u043a\u043e/\u043c\u0430\u0442\u0438', '\u0431\u0430\u0442\u044c\u043a\u043e/\u043c\u0430\u0442\u0438'), ('\u0447\u043e\u043b\u043e\u0432\u0456\u043a', '\u0447\u043e\u043b\u043e\u0432\u0456\u043a'), ('\u043f\u0430\u0434\u0447\u0435\u0440\u043a\u0430', '\u043f\u0430\u0434\u0447\u0435\u0440\u043a\u0430'), ('\u043f\u0440\u0430\u0431\u0430\u0431\u0430', '\u043f\u0440\u0430\u0431\u0430\u0431\u0430'), ('\u043e\u0441\u043e\u0431\u0430, \u044f\u043a\u0430 \u043f\u0435\u0440\u0435\u0431\u0443\u0432\u0430\u0454 \u043f\u0456\u0434 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migrations.AlterField( model_name='person2person', name='to_relationship_type', field=models.CharField(blank=True, max_length=100, verbose_name='\u041f\u0435\u0440\u0441\u043e\u043d\u0430 2 \u0454', choices=[('\u0431\u0430\u0442\u044c\u043a\u043e/\u043c\u0430\u0442\u0438', '\u0431\u0430\u0442\u044c\u043a\u043e/\u043c\u0430\u0442\u0438'), ('\u0447\u043e\u043b\u043e\u0432\u0456\u043a', '\u0447\u043e\u043b\u043e\u0432\u0456\u043a'), ('\u043f\u0430\u0434\u0447\u0435\u0440\u043a\u0430', '\u043f\u0430\u0434\u0447\u0435\u0440\u043a\u0430'), ('\u043f\u0440\u0430\u0431\u0430\u0431\u0430', '\u043f\u0440\u0430\u0431\u0430\u0431\u0430'), ('\u043e\u0441\u043e\u0431\u0430, \u044f\u043a\u0430 \u043f\u0435\u0440\u0435\u0431\u0443\u0432\u0430\u0454 \u043f\u0456\u0434 \u043e\u043f\u0456\u043a\u043e\u044e \u0430\u0431\u043e \u043f\u0456\u043a\u043b\u0443\u0432\u0430\u043d\u043d\u044f\u043c', '\u043e\u0441\u043e\u0431\u0430, \u044f\u043a\u0430 \u043f\u0435\u0440\u0435\u0431\u0443\u0432\u0430\u0454 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389.36

4,619

0.754263

1,451

9,734

5.047553

0.045486

0.03823

0.040961

0.032769

0.977881

0.96532

0

0.540092

0.040374

9,734

24

4,620

405.583333

0.243978

0.002157

0

0.333333

0

2.222222

0.878694

0.805375

0

1

0

false

0

0.111111

0

0.277778

0

null

0

1

0

1

0

1

0

1

null

0

13

9bf1b8fd00e0f2473b389bfc992cb40f4ea639b1

75

py

Python

training/__init__.py

doublechenching/xray14-keras

399bb4904e941a1d9183bac1a77399bbecc72b43

[ "MIT" ]

15

2018-11-14T08:20:15.000Z

2021-12-23T11:11:42.000Z

hpi_keras/training/__init__.py

doublechenching/hpi

68675bbd06497e41593526d7d4f58001d758c29d

[ "MIT" ]

null

hpi_keras/training/__init__.py

doublechenching/hpi

68675bbd06497e41593526d7d4f58001d758c29d

[ "MIT" ]

2

2019-04-22T08:51:46.000Z

2021-08-25T15:23:43.000Z

from ._training import init_env from ._training import get_number_of_steps

25

42

0.866667

12

75

4.916667

0.75

0.40678

0.610169

0

0.106667

75

2

43

37.5

0.880597

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

7

5023eb68c61144f8d042a6095929cf3f624e527b

17,308

py

Python

cities_light/south_migrations/0001_initial.py

affan2/django-cities-light

5b65d47adc63e203879dc55bff5a360380adc5e4

[ "MIT" ]

null

cities_light/south_migrations/0001_initial.py

affan2/django-cities-light

5b65d47adc63e203879dc55bff5a360380adc5e4

[ "MIT" ]

null

cities_light/south_migrations/0001_initial.py

affan2/django-cities-light

5b65d47adc63e203879dc55bff5a360380adc5e4

[ "MIT" ]

2

2019-11-29T15:55:46.000Z

2020-01-08T09:06:33.000Z

# -*- coding: utf-8 -*- from south.utils import datetime_utils as datetime from south.db import db from south.v2 import SchemaMigration from django.db import models class Migration(SchemaMigration): def forwards(self, orm): # Adding model 'CountryTranslation' db.create_table(u'cities_light_country_translation', ( (u'id', self.gf('django.db.models.fields.AutoField')(primary_key=True)), ('name', self.gf('django.db.models.fields.CharField')(max_length=200)), ('language_code', self.gf('django.db.models.fields.CharField')(max_length=15, db_index=True)), ('master', self.gf('django.db.models.fields.related.ForeignKey')(related_name='translations', null=True, to=orm['cities_light.Country'])), )) db.send_create_signal(u'cities_light', ['CountryTranslation']) # Adding unique constraint on 'CountryTranslation', fields ['name', 'language_code', 'master'] db.create_unique(u'cities_light_country_translation', ['name', 'language_code', 'master_id']) # Adding unique constraint on 'CountryTranslation', fields ['language_code', 'master'] db.create_unique(u'cities_light_country_translation', ['language_code', 'master_id']) # Adding model 'Country' db.create_table(u'cities_light_country', ( (u'id', self.gf('django.db.models.fields.AutoField')(primary_key=True)), ('name_ascii', self.gf('django.db.models.fields.CharField')(db_index=True, max_length=200, blank=True)), ('slug', self.gf('autoslug.fields.AutoSlugField')(unique_with=(), max_length=50, populate_from='name_ascii')), ('geoname_id', self.gf('django.db.models.fields.IntegerField')(unique=True, null=True, blank=True)), ('alternate_names', self.gf('django.db.models.fields.TextField')(default='', null=True, blank=True)), ('state', self.gf('django.db.models.fields.SmallIntegerField')(default=1)), ('default_language', self.gf('django.db.models.fields.CharField')(default='en', max_length=2)), ('code2', self.gf('django.db.models.fields.CharField')(max_length=2, unique=True, null=True, blank=True)), ('code3', self.gf('django.db.models.fields.CharField')(max_length=3, unique=True, null=True, blank=True)), ('continent', self.gf('django.db.models.fields.CharField')(max_length=2, db_index=True)), ('tld', self.gf('django.db.models.fields.CharField')(db_index=True, max_length=5, blank=True)), ('phone', self.gf('django.db.models.fields.CharField')(max_length=20, null=True)), )) db.send_create_signal(u'cities_light', ['Country']) # Adding model 'RegionTranslation' db.create_table(u'cities_light_region_translation', ( (u'id', self.gf('django.db.models.fields.AutoField')(primary_key=True)), ('name', self.gf('django.db.models.fields.CharField')(max_length=200)), ('display_name', self.gf('django.db.models.fields.CharField')(max_length=200)), ('language_code', self.gf('django.db.models.fields.CharField')(max_length=15, db_index=True)), ('master', self.gf('django.db.models.fields.related.ForeignKey')(related_name='translations', null=True, to=orm['cities_light.Region'])), )) db.send_create_signal(u'cities_light', ['RegionTranslation']) # Adding unique constraint on 'RegionTranslation', fields ['name', 'language_code', 'master'] db.create_unique(u'cities_light_region_translation', ['name', 'language_code', 'master_id']) # Adding unique constraint on 'RegionTranslation', fields ['language_code', 'master'] db.create_unique(u'cities_light_region_translation', ['language_code', 'master_id']) # Adding model 'Region' db.create_table(u'cities_light_region', ( (u'id', self.gf('django.db.models.fields.AutoField')(primary_key=True)), ('name_ascii', self.gf('django.db.models.fields.CharField')(db_index=True, max_length=200, blank=True)), ('slug', self.gf('autoslug.fields.AutoSlugField')(unique_with=(), max_length=50, populate_from='name_ascii')), ('geoname_id', self.gf('django.db.models.fields.IntegerField')(unique=True, null=True, blank=True)), ('alternate_names', self.gf('django.db.models.fields.TextField')(default='', null=True, blank=True)), ('state', self.gf('django.db.models.fields.SmallIntegerField')(default=1)), ('default_language', self.gf('django.db.models.fields.CharField')(default='en', max_length=2)), ('geoname_code', self.gf('django.db.models.fields.CharField')(db_index=True, max_length=50, null=True, blank=True)), ('country', self.gf('django.db.models.fields.related.ForeignKey')(to=orm['cities_light.Country'])), )) db.send_create_signal(u'cities_light', ['Region']) # Adding unique constraint on 'Region', fields ['country', 'slug'] db.create_unique(u'cities_light_region', ['country_id', 'slug']) # Adding model 'CityTranslation' db.create_table(u'cities_light_city_translation', ( (u'id', self.gf('django.db.models.fields.AutoField')(primary_key=True)), ('name', self.gf('django.db.models.fields.CharField')(max_length=200, db_index=True)), ('display_name', self.gf('django.db.models.fields.CharField')(max_length=200)), ('language_code', self.gf('django.db.models.fields.CharField')(max_length=15, db_index=True)), ('master', self.gf('django.db.models.fields.related.ForeignKey')(related_name='translations', null=True, to=orm['cities_light.City'])), )) db.send_create_signal(u'cities_light', ['CityTranslation']) # Adding unique constraint on 'CityTranslation', fields ['name', 'language_code', 'master'] db.create_unique(u'cities_light_city_translation', ['name', 'language_code', 'master_id']) # Adding unique constraint on 'CityTranslation', fields ['language_code', 'master'] db.create_unique(u'cities_light_city_translation', ['language_code', 'master_id']) # Adding model 'City' db.create_table(u'cities_light_city', ( (u'id', self.gf('django.db.models.fields.AutoField')(primary_key=True)), ('name_ascii', self.gf('django.db.models.fields.CharField')(db_index=True, max_length=200, blank=True)), ('slug', self.gf('autoslug.fields.AutoSlugField')(unique_with=(), max_length=50, populate_from='name_ascii')), ('geoname_id', self.gf('django.db.models.fields.IntegerField')(unique=True, null=True, blank=True)), ('alternate_names', self.gf('django.db.models.fields.TextField')(default='', null=True, blank=True)), ('state', self.gf('django.db.models.fields.SmallIntegerField')(default=1)), ('default_language', self.gf('django.db.models.fields.CharField')(default='en', max_length=2)), ('search_names', self.gf('cities_light.models.ToSearchTextField')(default='', max_length=4000, blank=True)), ('latitude', self.gf('django.db.models.fields.DecimalField')(null=True, max_digits=8, decimal_places=5, blank=True)), ('longitude', self.gf('django.db.models.fields.DecimalField')(null=True, max_digits=8, decimal_places=5, blank=True)), ('region', self.gf('django.db.models.fields.related.ForeignKey')(to=orm['cities_light.Region'], null=True, blank=True)), ('country', self.gf('django.db.models.fields.related.ForeignKey')(to=orm['cities_light.Country'])), ('population', self.gf('django.db.models.fields.BigIntegerField')(db_index=True, null=True, blank=True)), ('feature_code', self.gf('django.db.models.fields.CharField')(db_index=True, max_length=10, null=True, blank=True)), )) db.send_create_signal(u'cities_light', ['City']) # Adding unique constraint on 'City', fields ['region', 'slug'] db.create_unique(u'cities_light_city', ['region_id', 'slug']) def backwards(self, orm): # Removing unique constraint on 'City', fields ['region', 'slug'] db.delete_unique(u'cities_light_city', ['region_id', 'slug']) # Removing unique constraint on 'CityTranslation', fields ['language_code', 'master'] db.delete_unique(u'cities_light_city_translation', ['language_code', 'master_id']) # Removing unique constraint on 'CityTranslation', fields ['name', 'language_code', 'master'] db.delete_unique(u'cities_light_city_translation', ['name', 'language_code', 'master_id']) # Removing unique constraint on 'Region', fields ['country', 'slug'] db.delete_unique(u'cities_light_region', ['country_id', 'slug']) # Removing unique constraint on 'RegionTranslation', fields ['language_code', 'master'] db.delete_unique(u'cities_light_region_translation', ['language_code', 'master_id']) # Removing unique constraint on 'RegionTranslation', fields ['name', 'language_code', 'master'] db.delete_unique(u'cities_light_region_translation', ['name', 'language_code', 'master_id']) # Removing unique constraint on 'CountryTranslation', fields ['language_code', 'master'] db.delete_unique(u'cities_light_country_translation', ['language_code', 'master_id']) # Removing unique constraint on 'CountryTranslation', fields ['name', 'language_code', 'master'] db.delete_unique(u'cities_light_country_translation', ['name', 'language_code', 'master_id']) # Deleting model 'CountryTranslation' db.delete_table(u'cities_light_country_translation') # Deleting model 'Country' db.delete_table(u'cities_light_country') # Deleting model 'RegionTranslation' db.delete_table(u'cities_light_region_translation') # Deleting model 'Region' db.delete_table(u'cities_light_region') # Deleting model 'CityTranslation' db.delete_table(u'cities_light_city_translation') # Deleting model 'City' db.delete_table(u'cities_light_city') models = { u'cities_light.city': { 'Meta': {'unique_together': "(('region', 'slug'),)", 'object_name': 'City', 'index_together': '()'}, 'alternate_names': ('django.db.models.fields.TextField', [], {'default': "''", 'null': 'True', 'blank': 'True'}), 'country': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['cities_light.Country']"}), 'default_language': ('django.db.models.fields.CharField', [], {'default': "'en'", 'max_length': '2'}), 'feature_code': ('django.db.models.fields.CharField', [], {'db_index': 'True', 'max_length': '10', 'null': 'True', 'blank': 'True'}), 'geoname_id': ('django.db.models.fields.IntegerField', [], {'unique': 'True', 'null': 'True', 'blank': 'True'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'latitude': ('django.db.models.fields.DecimalField', [], {'null': 'True', 'max_digits': '8', 'decimal_places': '5', 'blank': 'True'}), 'longitude': ('django.db.models.fields.DecimalField', [], {'null': 'True', 'max_digits': '8', 'decimal_places': '5', 'blank': 'True'}), 'name_ascii': ('django.db.models.fields.CharField', [], {'db_index': 'True', 'max_length': '200', 'blank': 'True'}), 'population': ('django.db.models.fields.BigIntegerField', [], {'db_index': 'True', 'null': 'True', 'blank': 'True'}), 'region': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['cities_light.Region']", 'null': 'True', 'blank': 'True'}), 'search_names': ('cities_light.models.ToSearchTextField', [], {'default': "''", 'max_length': '4000', 'blank': 'True'}), 'slug': ('autoslug.fields.AutoSlugField', [], {'unique_with': '()', 'max_length': '50', 'populate_from': "'name_ascii'"}), 'state': ('django.db.models.fields.SmallIntegerField', [], {'default': '1'}) }, u'cities_light.citytranslation': { 'Meta': {'unique_together': "(('name', 'language_code', 'master'), ('language_code', 'master'))", 'object_name': 'CityTranslation', 'db_table': "u'cities_light_city_translation'", 'index_together': '()'}, 'display_name': ('django.db.models.fields.CharField', [], {'max_length': '200'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'language_code': ('django.db.models.fields.CharField', [], {'max_length': '15', 'db_index': 'True'}), 'master': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'translations'", 'null': 'True', 'to': u"orm['cities_light.City']"}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '200', 'db_index': 'True'}) }, u'cities_light.country': { 'Meta': {'unique_together': '()', 'object_name': 'Country', 'index_together': '()'}, 'alternate_names': ('django.db.models.fields.TextField', [], {'default': "''", 'null': 'True', 'blank': 'True'}), 'code2': ('django.db.models.fields.CharField', [], {'max_length': '2', 'unique': 'True', 'null': 'True', 'blank': 'True'}), 'code3': ('django.db.models.fields.CharField', [], {'max_length': '3', 'unique': 'True', 'null': 'True', 'blank': 'True'}), 'continent': ('django.db.models.fields.CharField', [], {'max_length': '2', 'db_index': 'True'}), 'default_language': ('django.db.models.fields.CharField', [], {'default': "'en'", 'max_length': '2'}), 'geoname_id': ('django.db.models.fields.IntegerField', [], {'unique': 'True', 'null': 'True', 'blank': 'True'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name_ascii': ('django.db.models.fields.CharField', [], {'db_index': 'True', 'max_length': '200', 'blank': 'True'}), 'phone': ('django.db.models.fields.CharField', [], {'max_length': '20', 'null': 'True'}), 'slug': ('autoslug.fields.AutoSlugField', [], {'unique_with': '()', 'max_length': '50', 'populate_from': "'name_ascii'"}), 'state': ('django.db.models.fields.SmallIntegerField', [], {'default': '1'}), 'tld': ('django.db.models.fields.CharField', [], {'db_index': 'True', 'max_length': '5', 'blank': 'True'}) }, u'cities_light.countrytranslation': { 'Meta': {'unique_together': "(('name', 'language_code', 'master'), ('language_code', 'master'))", 'object_name': 'CountryTranslation', 'db_table': "u'cities_light_country_translation'", 'index_together': '()'}, u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'language_code': ('django.db.models.fields.CharField', [], {'max_length': '15', 'db_index': 'True'}), 'master': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'translations'", 'null': 'True', 'to': u"orm['cities_light.Country']"}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '200'}) }, u'cities_light.region': { 'Meta': {'unique_together': "(('country', 'slug'),)", 'object_name': 'Region', 'index_together': '()'}, 'alternate_names': ('django.db.models.fields.TextField', [], {'default': "''", 'null': 'True', 'blank': 'True'}), 'country': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['cities_light.Country']"}), 'default_language': ('django.db.models.fields.CharField', [], {'default': "'en'", 'max_length': '2'}), 'geoname_code': ('django.db.models.fields.CharField', [], {'db_index': 'True', 'max_length': '50', 'null': 'True', 'blank': 'True'}), 'geoname_id': ('django.db.models.fields.IntegerField', [], {'unique': 'True', 'null': 'True', 'blank': 'True'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name_ascii': ('django.db.models.fields.CharField', [], {'db_index': 'True', 'max_length': '200', 'blank': 'True'}), 'slug': ('autoslug.fields.AutoSlugField', [], {'unique_with': '()', 'max_length': '50', 'populate_from': "'name_ascii'"}), 'state': ('django.db.models.fields.SmallIntegerField', [], {'default': '1'}) }, u'cities_light.regiontranslation': { 'Meta': {'unique_together': "(('name', 'language_code', 'master'), ('language_code', 'master'))", 'object_name': 'RegionTranslation', 'db_table': "u'cities_light_region_translation'", 'index_together': '()'}, 'display_name': ('django.db.models.fields.CharField', [], {'max_length': '200'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'language_code': ('django.db.models.fields.CharField', [], {'max_length': '15', 'db_index': 'True'}), 'master': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'translations'", 'null': 'True', 'to': u"orm['cities_light.Region']"}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '200'}) } } complete_apps = ['cities_light']

75.580786

222

0.627282

2,008

17,308

5.219124

0.055279

0.069466

0.120229

0.171756

0.906679

0.905534

0.88979

0.843511

0.807347

0.78874

0

0.008736

0.166686

17,308

229

223

75.580786

0.717881

0.097123

0

0.427711

0

0.505578

0.276638

0

1

0.012048

false

0

0.024096

0

0.054217

0

null

0

1

0

null

0

7

50263a6acb0c15fce04530bff708c9a3cdf182e1

66

py

Python

test/run/t394.py

timmartin/skulpt

2e3a3fbbaccc12baa29094a717ceec491a8a6750

[ "MIT" ]

2,671

2015-01-03T08:23:25.000Z

2022-03-31T06:15:48.000Z

test/run/t394.py

timmartin/skulpt

2e3a3fbbaccc12baa29094a717ceec491a8a6750

[ "MIT" ]

972

2015-01-05T08:11:00.000Z

2022-03-29T13:47:15.000Z

test/run/t394.py

timmartin/skulpt

2e3a3fbbaccc12baa29094a717ceec491a8a6750

[ "MIT" ]

845

2015-01-03T19:53:36.000Z

2022-03-29T18:34:22.000Z

s = "01234" print s[-6:0] print s["hi":[0,4]] print s[-3000:4.5]

11

19

0.545455

16

66

2.25

0.5625

0.5

0

0.267857

0.151515

66

5

20

13.2

0.375

0

0.106061

0

null

0

null

0.75

1

0

null

1

0

1

0

1

0

1

0

null

0

1

0

1

0

7

acd5fd5cd47617cf9ea2ade9081ac438cac6c2c7

13,349

py

Python

data_loader/TorchFrameDataLoader.py

bzhai/Ubi-SleepNet

27837827dec608d06659421d073872fb1f68453e

[ "MIT" ]

3

2022-01-22T15:55:31.000Z

2022-01-28T16:09:02.000Z

data_loader/TorchFrameDataLoader.py

bzhai/Ubi-SleepNet

27837827dec608d06659421d073872fb1f68453e

[ "MIT" ]

null

data_loader/TorchFrameDataLoader.py

bzhai/Ubi-SleepNet

27837827dec608d06659421d073872fb1f68453e

[ "MIT" ]

null

""" This script should only contain the frame to label data loaders """ import torch from torch.utils.data import Dataset, DataLoader from utilities.utils import * from sklearn.metrics import f1_score, classification_report, confusion_matrix from sleep_stage_config import Config import numpy as np import pandas as pd class WindowedFrameDataLoader2D(torch.utils.data.Dataset): def __init__(self, data, target, idx, transform=None): self.data = torch.from_numpy(data).float() self.data = self.data.permute(0, 2, 1) # set it to batch_num, channel, time_dim self.data = self.data.unsqueeze(1) self.idx = torch.from_numpy(idx) self.target = torch.from_numpy(target).long() self.transform = transform def __getitem__(self, index): x = self.data[index] y = self.target[index] i = self.idx[index] if self.transform: x = self.transform(x) return x, y, i def __len__(self): return len(self.data) class WindowedFrameDataLoader(torch.utils.data.Dataset): def __init__(self, data, target, idx, transform=None): self.data = torch.from_numpy(data).float() self.data = self.data.permute(0, 2, 1) # set it to batch_num, channel, time_dim self.idx = torch.from_numpy(idx) self.target = torch.from_numpy(target).long() self.transform = transform def __getitem__(self, index): x = self.data[index] y = self.target[index] i = self.idx[index] if self.transform: x = self.transform(x) return x, y, i def __len__(self): return len(self.data) def get_test_df(cfg:Config, dataset, num_classes, seq_len): if dataset == "apple": import h5py as h5py with h5py.File(cfg.APPLE_LOOCV_ALL_WINDOWED % seq_len, 'r') as data: df_value = data["df_values"][:] df_columns = data['columns'][:].astype(str).tolist() data.close() df_test = pd.DataFrame(df_value, columns=df_columns) # read fold split and sort them by apple id # fold_num_df = pd.read_csv(cfg.APPLE_CV_PID_PATH) # pid_ordered_list = fold_num_df[fold_num_df['set_type'] == "test"]["pid"].values.tolist() # df_test = df_test.rename(columns={"appleid": "pid", "linetime": "line"}) # new_df = [] # for pid in pid_ordered_list: # new_df.append(df_test[df_test['pid']==pid]) # new_df = pd.concat(new_df, axis=0, ignore_index=True) elif dataset == "mesa": df_train, df_test, feature_name = load_h5_df_train_test_dataset(cfg.HRV30_ACC_STD_PATH) # load_h5_df_dataset(cfg.NN_ACC_HRV % seq_len) df_test = df_test.rename(columns={"mesaid": "pid"}) del df_train elif dataset == "mesa_hr_statistic": df_train, df_test, feature_name = load_h5_df_train_test_dataset(cfg.MESA_ACC_HR_STATISTICS_STD_DATA_PATH) # load_h5_df_dataset(cfg.NN_ACC_HRV % seq_len) df_test = df_test.rename(columns={"mesaid": "pid"}) del df_train if len(df_test['stages'].unique()) != num_classes: df_test['stages'] = df_test['stages'].apply(lambda x: cast_sleep_stages(x, classes=num_classes)) return df_test def get_windowed_train_test_val_loader(cfg, batch_size, seq_len, num_classes, dataset, fold): """ The method will read pre-windows acc and hrv data from H5PY """ import h5py as h5py if dataset == "mesa": assert fold == 0, print("mesa dataset only has 1 fold") cache_path = cfg.NN_ACC_HRV_STD % seq_len with h5py.File(cache_path, 'r') as data: x_train = data["x_train"][:] y_train = data["y_train"][:] x_val = data["x_val"][:] y_val = data["y_val"][:] x_test = data["x_test"][:] y_test = data["y_test"][:] data.close() train_idx = np.arange(y_train.shape[0]) val_idx = np.arange(x_val.shape[0]) test_idx = np.arange(x_test.shape[0]) elif dataset == "mesa_hr_statistic": assert fold == 0, print("mesa hr statistic dataset only has 1 fold") cache_path = cfg.MESA_NN_ACC_HR_STATISTIC % seq_len with h5py.File(cache_path, 'r') as data: x_train = data["x_train"][:] y_train = data["y_train"][:] x_val = data["x_val"][:] y_val = data["y_val"][:] x_test = data["x_test"][:] y_test = data["y_test"][:] data.close() train_idx = np.arange(y_train.shape[0]) val_idx = np.arange(x_val.shape[0]) test_idx = np.arange(x_test.shape[0]) elif dataset == "apple": cache_path = cfg.APPLE_LOOCV_ALL_WINDOWED % seq_len with h5py.File(cache_path, 'r') as data: df_data = data["df_values"][:] x = data["x"][:] y = data["y"][:] columns = data["columns"][:].astype(str).tolist() data.close() df = pd.DataFrame(df_data, columns=columns) split_df = pd.read_csv(cfg.APPLE_LOOCV_PID_PATH) train_pid = split_df[(split_df['set_type']=="train") & (split_df['fold_num']==fold)]['pid'].values.tolist() val_pid = split_df[(split_df['set_type']=="val") & (split_df['fold_num']==fold)]['pid'].values.tolist() test_pid = split_df[(split_df['set_type']=="test") & (split_df['fold_num']==fold)]['pid'].values.tolist() train_idx = df[df.pid.isin(train_pid)]['window_idx'].values.astype(int) val_idx = df[df.pid.isin(val_pid)]['window_idx'].values.astype(int) test_idx = df[df.pid.isin(test_pid)]['window_idx'].values.astype(int) x_train = x[train_idx, :, :] x_val = x[val_idx, :, :] x_test = x[test_idx, :, :] y_train = y[train_idx] y_val = y[val_idx] y_test = y[test_idx] else: raise ValueError('%s dataset is not found' % dataset) print("...Loading windowed cache dataset from %s" % cache_path) # make sure the sleep classes are casted if the not 5 stages if (len(y_train.shape) < 2) and (len(set(y_train))) != num_classes: y_train = cast_sleep_stages(y_train.astype(int), num_classes) if (len(y_test.shape) < 2) and (len(set(y_test))) != num_classes: y_test = cast_sleep_stages(y_test.astype(int), num_classes) if (len(y_val.shape) < 2) and (len(set(y_val))) != num_classes: y_val = cast_sleep_stages(y_val, num_classes) train_ds = WindowedFrameDataLoader(x_train, y_train, train_idx) train_loader = DataLoader( train_ds, batch_size=batch_size, shuffle=True, num_workers=0, pin_memory=torch.cuda.is_available() ) test_ds = WindowedFrameDataLoader(x_test, y_test, test_idx) test_loader = DataLoader( test_ds, batch_size=batch_size, shuffle=False, num_workers=0, pin_memory=torch.cuda.is_available() ) val_ds = WindowedFrameDataLoader(x_val, y_val, val_idx) val_loader = DataLoader( val_ds, batch_size=batch_size, shuffle=True, num_workers=0, pin_memory=torch.cuda.is_available() ) return train_loader, test_loader, val_loader def get_windowed_apple_loader(cfg, batch_size, num_classes): """ The method will read pre-windows acc and hrv data from H5PY """ cache_path = cfg.APPLE_LOOCV_ALL_WINDOWED import h5py as h5py with h5py.File(cache_path, 'r') as data: df_data = data["df_values"][:] x = data["x"][:] y = data["y"][:] columns = data["columns"][:].astype(str).tolist() data.close() df = pd.DataFrame(df_data, columns=columns) split_df = pd.read_csv(cfg.APPLE_LOOCV_PID_PATH) all_pid = split_df['pid'].values.tolist() all_idx = df[df.pid.isin(all_pid)]['window_idx'].values.astype(int) x = x[all_idx, :, :] y = y[all_idx] print("...Loading windowed cache dataset from %s" % cache_path) # make sure the sleep classes are casted if the not 5 stages if (len(y.shape) < 2) and (len(set(y))) != num_classes: y = cast_sleep_stages(y.astype(int), num_classes) ds = WindowedFrameDataLoader(x, y, all_idx) data_loader = DataLoader( ds, batch_size=batch_size, shuffle=False, num_workers=0, pin_memory=torch.cuda.is_available() ) return data_loader def get_windowed_train_test_val_loader_2D(cfg, batch_size, seq_len, num_classes, dataset, fold): """ The method will read pre-windows acc and hrv data from H5PY """ import h5py as h5py if dataset == "mesa": assert fold == 0, print("mesa dataset only has 1 fold") cache_path = cfg.NN_ACC_HRV_STD % seq_len with h5py.File(cache_path, 'r') as data: x_train = data["x_train"][:] y_train = data["y_train"][:] x_val = data["x_val"][:] y_val = data["y_val"][:] x_test = data["x_test"][:] y_test = data["y_test"][:] data.close() train_idx = np.arange(y_train.shape[0]) val_idx = np.arange(x_val.shape[0]) test_idx = np.arange(x_test.shape[0]) elif dataset == "mesa_hr_statistic": assert fold == 0, print("mesa hr statistic dataset only has 1 fold") cache_path = cfg.MESA_NN_ACC_HR_STATISTIC % seq_len with h5py.File(cache_path, 'r') as data: x_train = data["x_train"][:] y_train = data["y_train"][:] x_val = data["x_val"][:] y_val = data["y_val"][:] x_test = data["x_test"][:] y_test = data["y_test"][:] data.close() train_idx = np.arange(y_train.shape[0]) val_idx = np.arange(x_val.shape[0]) test_idx = np.arange(x_test.shape[0]) elif dataset == "apple": cache_path = cfg.APPLE_LOOCV_ALL_WINDOWED % seq_len with h5py.File(cache_path, 'r') as data: df_data = data["df_values"][:] x = data["x"][:] y = data["y"][:] columns = data["columns"][:].astype(str).tolist() data.close() df = pd.DataFrame(df_data, columns=columns) split_df = pd.read_csv(cfg.APPLE_LOOCV_PID_PATH) train_pid = split_df[(split_df['set_type']=="train") & (split_df['fold_num']==fold)]['pid'].values.tolist() val_pid = split_df[(split_df['set_type']=="val") & (split_df['fold_num']==fold)]['pid'].values.tolist() test_pid = split_df[(split_df['set_type']=="test") & (split_df['fold_num']==fold)]['pid'].values.tolist() train_idx = df[df.pid.isin(train_pid)]['window_idx'].values.astype(int) val_idx = df[df.pid.isin(val_pid)]['window_idx'].values.astype(int) test_idx = df[df.pid.isin(test_pid)]['window_idx'].values.astype(int) x_train = x[train_idx, :, :] x_val = x[val_idx, :, :] x_test = x[test_idx, :, :] y_train = y[train_idx] y_val = y[val_idx] y_test = y[test_idx] else: raise ValueError('%s dataset is not found' % dataset) print("...Loading windowed cache dataset from %s" % cache_path) # make sure the sleep classes are casted if the not 5 stages if (len(y_train.shape) < 2) and (len(set(y_train))) != num_classes: y_train = cast_sleep_stages(y_train.astype(int), num_classes) if (len(y_test.shape) < 2) and (len(set(y_test))) != num_classes: y_test = cast_sleep_stages(y_test.astype(int), num_classes) if (len(y_val.shape) < 2) and (len(set(y_val))) != num_classes: y_val = cast_sleep_stages(y_val, num_classes) train_ds = WindowedFrameDataLoader2D(x_train, y_train, train_idx) train_loader = DataLoader( train_ds, batch_size=batch_size, shuffle=True, num_workers=0, pin_memory=torch.cuda.is_available() ) test_ds = WindowedFrameDataLoader2D(x_test, y_test, test_idx) test_loader = DataLoader( test_ds, batch_size=batch_size, shuffle=False, num_workers=0, pin_memory=torch.cuda.is_available() ) val_ds = WindowedFrameDataLoader2D(x_val, y_val, val_idx) val_loader = DataLoader( val_ds, batch_size=batch_size, shuffle=True, num_workers=0, pin_memory=torch.cuda.is_available() ) return train_loader, test_loader, val_loader def get_apple_loocv_ids(cfg, fold): split_df = pd.read_csv(cfg.APPLE_LOOCV_PID_PATH) train_pid = split_df[(split_df['set_type']=="train") & (split_df['fold_num']==fold)]['pid'].values.tolist() val_pid = split_df[(split_df['set_type']=="val") & (split_df['fold_num']==fold)]['pid'].values.tolist() test_pid = split_df[(split_df['set_type']=="test") & (split_df['fold_num']==fold)]['pid'].values.tolist() return train_pid, val_pid, test_pid def get_mesa_loocv_ids(cfg:Config, fold): split_df = pd.read_csv(cfg.MESA_LOOCV_PID_PATH) train_pid = split_df[(split_df['set_type']=="train") & (split_df['fold_num']==fold)]['pid'].values.tolist() val_pid = split_df[(split_df['set_type']=="val") & (split_df['fold_num']==fold)]['pid'].values.tolist() test_pid = split_df[(split_df['set_type']=="test") & (split_df['fold_num']==fold)]['pid'].values.tolist() return train_pid, val_pid, test_pid

39.847761

160

0.619972

1,953

13,349

3.944188

0.090118

0.038167

0.027262

0.023368

0.838634

0.833442

0.825652

0.796313

0.790601

0

0.007467

0.237546

13,349

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null

0

1

0

1

0

null

0

7

acd6194e8bfcc4a2a1a94152f5da14877425e2d0

148

py

Python

botoless/core.py

beaucronin/botoless

29fc63c3aac799c4f392b90b352d9e50f18748ba

[ "MIT" ]

null

botoless/core.py

beaucronin/botoless

29fc63c3aac799c4f392b90b352d9e50f18748ba

[ "MIT" ]

null

botoless/core.py

beaucronin/botoless

29fc63c3aac799c4f392b90b352d9e50f18748ba

[ "MIT" ]

null

import boto3 def resource(service_name): return boto3.resource(service_name) def client(service_name): return boto3.client(service_name)

18.5

39

0.777027

20

148

5.55

0.4

0.396396

0.342342

0.396396

0

0.023622

0.141892

148

7

40

21.142857

0.850394

0

1

0.4

false

0

0.2

0.4

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

8

4a056762beacdb642be484a0bd9db9cc29b95913

104

py

Python

deepspeed/ops/transformer/__init__.py

bratao/DeepSpeed

c50d8955e942e5e26cf81835d59ec3f20ef8540d

[ "MIT" ]

1

2020-09-25T13:54:15.000Z

deepspeed/ops/transformer/__init__.py

bratao/DeepSpeed

c50d8955e942e5e26cf81835d59ec3f20ef8540d

[ "MIT" ]

null

deepspeed/ops/transformer/__init__.py

bratao/DeepSpeed

c50d8955e942e5e26cf81835d59ec3f20ef8540d

[ "MIT" ]

1

2020-09-13T08:06:51.000Z

from deepspeed.ops.transformer.transformer import DeepSpeedTransformerLayer, DeepSpeedTransformerConfig

52

103

0.913462

8

104

11.875

0.875

0

0.048077

104

1

104

0.959596

0

1

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true

0

1

0

1

0

1

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null

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1

0

null

0

1

0

1

0

1

0

7

4a22f46de4268f659661cfe1031cdad6caf3ee63

604

py

Python

tests/data/python2.py

StarryInternet/black

f90f50a7436ca13517933c290ef007e7cb2e7258

[ "MIT" ]

16,110

2019-07-22T21:54:54.000Z

2022-03-31T22:52:39.000Z

tests/data/python2.py

StarryInternet/black

f90f50a7436ca13517933c290ef007e7cb2e7258

[ "MIT" ]

1,981

2019-07-22T21:26:16.000Z

2022-03-31T23:14:35.000Z

tests/data/python2.py

StarryInternet/black

f90f50a7436ca13517933c290ef007e7cb2e7258

[ "MIT" ]

1,762

2019-07-22T21:23:00.000Z

2022-03-31T06:10:22.000Z

#!/usr/bin/env python2 import sys print >> sys.stderr , "Warning:" , print >> sys.stderr , "this is a blast from the past." print >> sys.stderr , "Look, a repr:", `sys` def function((_globals, _locals)): exec ur"print 'hi from exec!'" in _globals, _locals function((globals(), locals())) # output #!/usr/bin/env python2 import sys print >>sys.stderr, "Warning:", print >>sys.stderr, "this is a blast from the past." print >>sys.stderr, "Look, a repr:", ` sys ` def function((_globals, _locals)): exec ur"print 'hi from exec!'" in _globals, _locals function((globals(), locals()))

17.764706

55

0.652318

87

604

4.436782

0.310345

0.124352

0.217617

0.082902

0.984456

0

0.004008

0.173841

604

33

56

18.30303

0.769539

0.081126

0

1

0

0.26087

0

null

0

0.142857

null

0.571429

0

null

0

1

0

1

0

1

0

null

0

1

0

1

0

10

a886a2590df210fa46903f711eedade91105be84

58

py

Python

examples/duplicated_constants.py

doboy/Underscore

d98273db3144cda79191d2c90f45d81b6d700b1f

[ "MIT" ]

7

2016-09-23T00:44:05.000Z

2021-10-04T21:19:12.000Z

examples/duplicated_constants.py

jameswu1991/Underscore

d98273db3144cda79191d2c90f45d81b6d700b1f

[ "MIT" ]

1

2016-09-23T00:45:05.000Z

2019-02-16T19:05:37.000Z

examples/duplicated_constants.py

jameswu1991/Underscore

d98273db3144cda79191d2c90f45d81b6d700b1f

[ "MIT" ]

3

2016-09-23T01:13:15.000Z

2018-07-20T21:22:17.000Z

y = 1, 1, 2, 'a', 'a','b' print(y, 1, 1, 2, 'a', 'a','b')

19.333333

31

0.327586

15

58

1.266667

0.4

0.210526

0.315789

0.421053

0.736842

0

0.133333

0.224138

58

2

32

29

0.288889

0

0.103448

0

1

0

false

0

0.5

1

0

1

null

1

0

1

0

1

0

1

0

null

0

1

0

10

a8aba35b8ca96c18a2301dbb59d544be83108840

9,080

py

Python

src/config.py

yasui-salmon/RepBM

df0f9e6bcb092484eb4437307ddcbec1eb24aaea

[ "MIT" ]

null

src/config.py

yasui-salmon/RepBM

df0f9e6bcb092484eb4437307ddcbec1eb24aaea

[ "MIT" ]

null

src/config.py

yasui-salmon/RepBM

df0f9e6bcb092484eb4437307ddcbec1eb24aaea

[ "MIT" ]

null

class cartpole_config(): # domain parameters state_dim = 4 noise_dim = 4 action_size = 2 gamma = 1.0 n_win_ticks = 195 max_length = 200 #shorter than MRDR experiment oracle_reward = 1 rescale = [[1, 1, 1, 1, 1, 1, 1, 1]] # q training parameters dqn_batch_size = 64 dqn_hidden_dims = [24,48] dqn_num_episodes = 2000 #2000 buffer_capacity = 10000 dqn_alpha = 0.01 dqn_alpha_decay = 0.01 dqn_epsilon = 1.0 dqn_epsilon_min = 0.01 dqn_epsilon_decay = 0.995 sample_capacity = 200000 # model parameters fold_num = 4 sample_num_traj_eval = 1024 sample_num_traj = 1024 #1024 train_num_traj = 768 #768 dev_num_traj = 256 #124 transition_input_dims = 4 rep_hidden_dims = [16] # The last dim is the representation dim transition_hidden_dims = [] reward_hidden_dims = [] terminal_hidden_dims = [32,32] behavior_epsilon = 0.2#0.2 eval_epsilon = 0.0 # model training parameter print_per_epi = 10 train_num_episodes = 500 #100 train_num_batches = 100 #100 train_batch_size = 64 #64 test_batch_size = 16 tc_num_episode = 100 #100 tc_num_batches = 100 #100 tc_batch_size = 64 tc_test_batch_size = 16 lr = 0.08#0.00001 lr_decay = 0.9 alpha_rep = 0.01 weight_decay = 0 # policy net parameter policy_train_num_episodes = 100 #100 or 1024 policy_train_num_batches = 100 #100 policy_lr = 0.05 policy_train_batch_size = 256 # MRDR parameter soften_epsilon = 0.02 mrdr_lr = 0.01 mrdr_num_episodes = 100 #100 mrdr_num_batches = 100 #100 mrdr_batch_size = 1000 mrdr_test_batch_size = 100 #100 mrdr_hidden_dims = [32] eval_num_rollout = 1 N = 90 #should be 100 MAX_SEED = 1000000 class cartpole_test_config(): # domain parameters state_dim = 4 action_size = 2 gamma = 1.0 n_win_ticks = 195 max_length = 200 oracle_reward = 1 rescale = [[1, 1, 1, 1]] # q training parameters dqn_batch_size = 64 dqn_hidden_dims = [24,48] dqn_num_episodes = 2000 buffer_capacity = 10000 dqn_alpha = 0.01 dqn_alpha_decay = 0.01 dqn_epsilon = 1.0 dqn_epsilon_min = 0.01 dqn_epsilon_decay = 0.995 sample_capacity = 200000 # model parameters sample_num_traj = 1024 #1024 train_num_traj = 900 #900 dev_num_traj = 124 #124 transition_input_dims = 4 rep_hidden_dims = [16] # The last dim is the representation dim transition_hidden_dims = [] reward_hidden_dims = [] terminal_hidden_dims = [32,32] behavior_epsilon = 0.2 eval_epsilon = 0.0 # model training parameter print_per_epi = 10 train_num_episodes = 100 train_num_batches = 100 train_batch_size = 64 #64 test_batch_size = 16 #16 tc_num_episode = 100 tc_num_batches = 100 tc_batch_size = 64 #64 tc_test_batch_size = 16 #16 lr = 0.01 lr_decay = 0.9 alpha_rep = 0.01 weight_decay = 0 #or 0.00005 # MRDR parameter soften_epsilon = 0.02 mrdr_lr = 0.01 mrdr_num_episodes = 20 mrdr_num_batches = 50 mrdr_batch_size = 1000 #1000 mrdr_test_batch_size = 100 #100 mrdr_hidden_dims = [32] eval_num_traj = 1000 eval_num_rollout = 1 N = 2 MAX_SEED = 1000000 class mountaincar_config(): # domain parameters state_dim = 2 action_size = 3 gamma = 0.99 #not in used max_length = 200 oracle_reward = -1 rescale = [[1, 10]] # q training parameters dqn_batch_size = 256 dqn_hidden_dims = [100] dqn_num_episodes = 10000 buffer_capacity = 300000 dqn_alpha = 0.005 dqn_epsilon = 1 dqn_epsilon_min = 0.01 dqn_epsilon_decay = 0.9995 sample_capacity = 200000 target_update = 10 # model parameters fold_num = 4 sample_num_traj = 1024 sample_num_traj_eval = sample_num_traj train_num_traj = 900 dev_num_traj = 124 transition_input_dims = 4 rep_hidden_dims = [16] # The last dim is the representation dim transition_hidden_dims = [] reward_hidden_dims = [] terminal_hidden_dims = [32,32] behavior_epsilon = 0.2 eval_epsilon = 0.0 # model training parameter print_per_epi = 10 train_num_episodes = 100 train_num_batches = 50 train_batch_size = 16 test_batch_size = 16 tc_num_episode = 100 tc_num_batches = 50 tc_batch_size = 16 tc_test_batch_size = 16 lr = 0.01 lr_decay = 0.9 alpha_rep = 0.1 weight_decay = 0.00005 # policy net parameter policy_train_num_episodes = 100 #100 or 1024 policy_train_num_batches = 100 #100 policy_lr = 0.05 # MRDR parameter soften_epsilon = 0.02 mrdr_lr = 0.01 mrdr_num_episodes = 100 mrdr_num_batches = 50 mrdr_batch_size = 1000 mrdr_test_batch_size = 100 mrdr_hidden_dims = [32] eval_num_traj = 1000 eval_num_rollout = 1 N = 200 MAX_SEED = 1000000 class mountaincar_test_config(): # domain parameters state_dim = 2 action_size = 3 gamma = 0.99 max_length = 200 oracle_reward = -1 rescale = [[1, 10]] # q training parameters dqn_batch_size = 256 dqn_hidden_dims = [100] dqn_num_episodes = 10000 buffer_capacity = 20000 dqn_alpha = 0.01 #dqn_alpha_decay = 0.01 dqn_epsilon = 0.5 dqn_epsilon_min = 0.05 dqn_epsilon_decay = 0.9995 sample_capacity = 200000 target_update = 10 # model parameters sample_num_traj = 10 #1024 train_num_traj = 8 #900 dev_num_traj = 2 #124 transition_input_dims = 4 rep_hidden_dims = [16] # The last dim is the representation dim transition_hidden_dims = [] reward_hidden_dims = [] terminal_hidden_dims = [32,32] behavior_epsilon = 0.2 eval_epsilon = 0.0 # model training parameter print_per_epi = 10 train_num_episodes = 100 train_num_batches = 50 train_batch_size = 4 #16 test_batch_size = 2 #16 tc_num_episode = 100 tc_num_batches = 50 tc_batch_size = 2 #16 tc_test_batch_size = 2 #16 lr = 0.01 lr_decay = 0.9 alpha_rep = 0.1 weight_decay = 0.00005 policy_lr = 0.05 # MRDR parameter soften_epsilon = 0.02 mrdr_lr = 0.01 mrdr_num_episodes = 20 mrdr_num_batches = 50 mrdr_batch_size = 1000 #1000 mrdr_test_batch_size = 100 #100 mrdr_hidden_dims = [32] eval_num_traj = 1000 eval_num_rollout = 1 N = 2 MAX_SEED = 1000000 class hiv_config(): # domain parameters state_dim = 6 action_size = 4 gamma = 0.98 max_length = 200 # model parameters sample_num_traj = 40 train_num_traj = 45 dev_num_traj = 5 rep_hidden_dims = [64, 64] # The last layer is the representation dim transition_hidden_dims = [] reward_hidden_dims = [] print_per_epi = 10 train_num_episodes = 100 train_num_batches = 100 train_batch_size = 40 test_batch_size = 5 train_traj_batch_size = 4 lr = 0.01 lr_decay = 0.9 alpha_rep = 0.1 # eval_num_traj = 1000 eval_num_rollout = 1 eval_pib_num_rollout = 100 N = 10 fix_data = False behavior_eps = 0.05 standardize_rewards = True ins = 20 class gpu_config(): gpu_false_enforce = True # if false try to use gpu class acrobot_config(): # domain parameters state_dim = 6 noise_dim = 4 action_size = 3 gamma = 0.99 max_length = 500 oracle_reward = -1 rescale = [[1,1,1,1,1,1,1,1,1,1]] # q training parameters dqn_batch_size = 256 dqn_hidden_dims = [100]#[100] dqn_num_episodes = 10000 buffer_capacity = 300000 dqn_alpha = 0.0001 dqn_epsilon = 1 dqn_epsilon_min = 0.05 dqn_epsilon_decay = 0.99 # 0.9995 sample_capacity = 200000 target_update = 10 # model parameters fold_num = 2 sample_num_traj = 1024 sample_num_traj_eval = sample_num_traj train_num_traj = 512 dev_num_traj = 512 transition_input_dims = 4 rep_hidden_dims = [16] # The last dim is the representation dim transition_hidden_dims = [] reward_hidden_dims = [] terminal_hidden_dims = [32,32] behavior_epsilon = 0.05 eval_epsilon = 0.0 # model training parameter print_per_epi = 10 train_num_episodes = 100 train_num_batches = 100 train_batch_size = 64 test_batch_size = 16 tc_num_episode = 100 tc_num_batches = 100 tc_batch_size = 64 tc_test_batch_size = 16 lr = 0.05 lr_decay = 0.9 alpha_rep = 0.1 weight_decay = 0.00005 # policy net parameter policy_train_num_episodes = 100 #100 or 1024 policy_train_num_batches = 100 #100 policy_lr = 0.001 policy_train_batch_size = 256 # MRDR parameter soften_epsilon = 0.02 mrdr_lr = 0.01 mrdr_num_episodes = 100 mrdr_num_batches = 50 mrdr_batch_size = 1000 mrdr_test_batch_size = 100 mrdr_hidden_dims = [32] eval_num_traj = 1000 eval_num_rollout = 1 N = 180 MAX_SEED = 1000000

23.282051

73

0.656938

1,375

9,080

3.983273

0.106182

0.065729

0.008764

0.009494

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390

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23.282051

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1

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1

0

null

0

1

0

8

a8ce50059b418c232199144097cf5d0d9b65b82f

7,888

py

Python

day11_dumbo-octopus/day11.py

notromanramirez/advent-of-code_2021

067c2f0597b0123ed1f4406b1c336b6982afc563

[ "MIT" ]

null

day11_dumbo-octopus/day11.py

notromanramirez/advent-of-code_2021

067c2f0597b0123ed1f4406b1c336b6982afc563

[ "MIT" ]

null

day11_dumbo-octopus/day11.py

notromanramirez/advent-of-code_2021

067c2f0597b0123ed1f4406b1c336b6982afc563

[ "MIT" ]

null

# Roman Ramirez, rr8rk@virginia.edu # Advent of Code 2021, Day 11: Dumbo Octopus #%% LONG INPUT my_input = [] with open('input.txt', 'r') as f: for line in f: my_input.append(line.strip('\n')) #%% EXAMPLE INPUT my_input = [ '5483143223', '2745854711', '5264556173', '6141336146', '6357385478', '4167524645', '2176841721', '6882881134', '4846848554', '5283751526' ] #%% EXAMPLE INPUT 2 my_input = [ '11111', '19991', '19191', '19991', '11111', ] #%% PART 1 CODE class Octopus: def __init__(self, i): self.value = int(i) self.has_flashed = False def __str__(self): return str(self.value) def __repr__(self): return str(self.value) + ',' + str(int(self.has_flashed)) octopi = [[Octopus(s) for s in line] for line in my_input] def oprint(i=0): print(i) for line in octopi: print([str(o) for o in line]) total_flashes = 0 steps = 100 oprint() for i in range(1, steps+1): # First, the energy level of each octoups increases by 1 for line in octopi: for octopus in line: octopus.has_flashed = False octopus.value += 1 # The, any octopus with an energy level greater than 9 flashes. # This increases the energy level of all adjacent octopuses by 1, including octopuses that are diagonally adjacent. # If this causes an octopus to have an energy level great than 9, it also flashes. # This process continues as long as new octopuses keep having their energy level increased beyond 9. # An octopus can only flash at most once per step. process = True while process: process = False octopi_temp = [[Octopus(0) for i in line] for line in octopi] for y in range(len(octopi)): for x in range(len(octopi[y])): is_left = x != 0 is_right = x != len(octopi[y]) - 1 is_up = y != 0 is_down = y != len(octopi) - 1 if (octopi[y][x].value > 9) and (not octopi[y][x].has_flashed): process = True # check left if is_left: octopi_temp[y][x-1].value += 1 # check right if is_right: octopi_temp[y][x+1].value += 1 # check up if is_up: octopi_temp[y-1][x].value += 1 # check down if is_down: octopi_temp[y+1][x].value += 1 # check left up if is_left and is_up: octopi_temp[y-1][x-1].value += 1 # check left down if is_left and is_down: octopi_temp[y+1][x-1].value += 1 # check right up if is_right and is_up: octopi_temp[y-1][x+1].value += 1 # check right down if is_right and is_down: octopi_temp[y+1][x+1].value += 1 octopi[y][x].has_flashed = True for y in range(len(octopi)): for x in range(len(octopi[y])): octopi[y][x].value += octopi_temp[y][x].value # Finally, any octopus that flashed during this step has its energy level set to 0, as it used all of its energy to flash for y in range(len(octopi)): for x in range(len(octopi[y])): if octopi[y][x].value > 9: octopi[y][x].value = 0 total_flashes += 1 oprint(i) print(total_flashes) #%% PART 2 CODE class Octopus: def __init__(self, i): self.value = int(i) self.has_flashed = False def __str__(self): return str(self.value) def __repr__(self): return str(self.value) + ',' + str(int(self.has_flashed)) octopi = [[Octopus(s) for s in line] for line in my_input] def oprint(i=0): print(i) for line in octopi: print([str(o) for o in line]) sync_flash_bool = False sync_flash_step = 0 steps = 100 # oprint() step = 0 while(not sync_flash_bool): step += 1 # First, the energy level of each octoups increases by 1 for line in octopi: for octopus in line: octopus.has_flashed = False octopus.value += 1 # The, any octopus with an energy level greater than 9 flashes. # This increases the energy level of all adjacent octopuses by 1, including octopuses that are diagonally adjacent. # If this causes an octopus to have an energy level great than 9, it also flashes. # This process continues as long as new octopuses keep having their energy level increased beyond 9. # An octopus can only flash at most once per step. process = True while process: process = False octopi_temp = [[Octopus(0) for i in line] for line in octopi] for y in range(len(octopi)): for x in range(len(octopi[y])): is_left = x != 0 is_right = x != len(octopi[y]) - 1 is_up = y != 0 is_down = y != len(octopi) - 1 if (octopi[y][x].value > 9) and (not octopi[y][x].has_flashed): process = True # check left if is_left: octopi_temp[y][x-1].value += 1 # check right if is_right: octopi_temp[y][x+1].value += 1 # check up if is_up: octopi_temp[y-1][x].value += 1 # check down if is_down: octopi_temp[y+1][x].value += 1 # check left up if is_left and is_up: octopi_temp[y-1][x-1].value += 1 # check left down if is_left and is_down: octopi_temp[y+1][x-1].value += 1 # check right up if is_right and is_up: octopi_temp[y-1][x+1].value += 1 # check right down if is_right and is_down: octopi_temp[y+1][x+1].value += 1 octopi[y][x].has_flashed = True for y in range(len(octopi)): for x in range(len(octopi[y])): octopi[y][x].value += octopi_temp[y][x].value # Finally, any octopus that flashed during this step has its energy level set to 0, as it used all of its energy to flash for y in range(len(octopi)): for x in range(len(octopi[y])): if octopi[y][x].value > 9: octopi[y][x].value = 0 # oprint(step) sync_flash_bool = True for line in octopi: for octopus in line: if not octopus.has_flashed: sync_flash_bool = False if sync_flash_bool: sync_flash_step = step print(sync_flash_step)

30.933333

125

0.467165

972

7,888

3.675926

0.13786

0.055975

0.055416

0.053736

0.838231

0.829555

0

0.050332

0.445867

7,888

254

126

31.055118

0.767101

0.195614

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0.066667

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null

0

1

0

1

0

null

0

7

765530e136292bb1b35e61a4be480bda807285eb

154

py

Python

omoide/storage/repositories/__init__.py

IgorZyktin/Omoide

42eeafce05e0efcfeb62a12bf508971680e6b17d

[ "MIT" ]

null

omoide/storage/repositories/__init__.py

IgorZyktin/Omoide

42eeafce05e0efcfeb62a12bf508971680e6b17d

[ "MIT" ]

32

2021-09-02T06:38:59.000Z

2021-10-17T07:44:10.000Z

omoide/storage/repositories/__init__.py

IgorZyktin/Omoide

42eeafce05e0efcfeb62a12bf508971680e6b17d

[ "MIT" ]

1

2021-08-28T11:17:55.000Z

# -*- coding: utf-8 -*- from omoide.storage.repositories.preview import PreviewRepository from omoide.storage.repositories.search import SearchRepository

38.5

65

0.818182

17

154

7.411765

0.705882

0.15873

0.269841

0.460317

0

0.007092

0.084416

154

3

66

51.333333

0.886525

0.136364

0

1

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true

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null

0

1

0

1

0

1

0

7

769d2c984bf4acfc2e34c47d400b0156ed08b1bb

87

py

Python

tests/wasp1/AllAnswerSets/disjunction_4.test.py

bernardocuteri/wasp

05c8f961776dbdbf7afbf905ee00fc262eba51ad

[ "Apache-2.0" ]

19

2015-12-03T08:53:45.000Z

2022-03-31T02:09:43.000Z

tests/wasp1/AllAnswerSets/disjunction_4.test.py

bernardocuteri/wasp

05c8f961776dbdbf7afbf905ee00fc262eba51ad

[ "Apache-2.0" ]

80

2017-11-25T07:57:32.000Z

2018-06-10T19:03:30.000Z

tests/wasp1/AllAnswerSets/disjunction_4.test.py

bernardocuteri/wasp

05c8f961776dbdbf7afbf905ee00fc262eba51ad

[ "Apache-2.0" ]

6

2015-01-15T07:51:48.000Z

2020-06-18T14:47:48.000Z

input = """ v ; a. a; v. a ;v. a;v. v;a ; w. :- v. :- w. """ output = """ {a} """

5.117647

12

0.287356

16

87

1.5625

0.3125

0.32

0.24

0.32

0

0.333333

87

16

13

5.4375

0.431034

0

0.416667

0

0.632184

0

1

0

false

0

1

0

1

null

1

0

1

0

1

0

null

0

7

8c1bc77dec314f2990517f1d936ada81df80a73b

6,807

py

Python

usercodex/plugins/trolls.py

ipindanger/Codex-z

1cedebd4352e4adb914b40a219bbda752c9b39d7

[ "BSD-3-Clause" ]

2

2021-08-30T05:44:14.000Z

2021-09-13T06:04:21.000Z

usercodex/plugins/trolls.py

ipindanger/Codex-z

1cedebd4352e4adb914b40a219bbda752c9b39d7

[ "BSD-3-Clause" ]

null

usercodex/plugins/trolls.py

ipindanger/Codex-z

1cedebd4352e4adb914b40a219bbda752c9b39d7

[ "BSD-3-Clause" ]

1

2021-09-26T13:17:29.000Z

# credits to @mrconfused and @sandy1709 # Copyright (C) 2020 sandeep.n(π.$) import os from telegraph import exceptions, upload_file from usercodex import codex from ..core.managers import edit_or_reply from ..helpers.utils import _codtools, reply_id from . import convert_toimage, deEmojify, phcomment, threats, trap, trash plugin_category = "fun" @codex.cod_cmd( pattern="trash$", command=("trash", plugin_category), info={ "header": "Reply to image/sticker to get meme on that image.", "usage": "{tr}trash", }, ) async def codbot(event): "image meme creator." replied = await event.get_reply_message() codid = await reply_id(event) if not replied: return await edit_or_reply(event, "reply to a supported media file") output = await _codtools.media_to_pic(event, replied) if output[1] is None: return await edit_delete( output[0], "__Unable to extract image from the replied message.__" ) download_location = convert_toimage(output[1]) size = os.stat(download_location).st_size if size > 5242880: os.remove(download_location) return await output[0].edit( "the replied file size is not supported it must me below 5 mb" ) await event.reply(file=download_location) await output[0].edit("generating image..") try: response = upload_file(download_location) except exceptions.TelegraphException as exc: os.remove(download_location) return await output[0].edit(f"**Error: **\n`{str(exc)}`") cod = f"https://telegra.ph{response[0]}" cod = await trash(cod) os.remove(download_location) await output[0].delete() await event.client.send_file(event.chat_id, cod, reply_to=codid) @codex.cod_cmd( pattern="threats$", command=("threats", plugin_category), info={ "header": "Reply to image/sticker to get meme on that image.", "usage": "{tr}threats", }, ) async def codbot(event): "image meme creator." replied = await event.get_reply_message() codid = await reply_id(event) if not replied: return await edit_or_reply(event, "reply to a supported media file") output = await _codtools.media_to_pic(event, replied) if output[1] is None: return await edit_delete( output[0], "__Unable to extract image from the replied message.__" ) download_location = convert_toimage(output[1]) size = os.stat(download_location).st_size if size > 5242880: os.remove(download_location) return await output[0].edit( "the replied file size is not supported it must me below 5 mb" ) await output[0].edit("generating image..") try: response = upload_file(download_location) except exceptions.TelegraphException as exc: os.remove(download_location) return await output[0].edit(f"**Error: **\n`{str(exc)}`") cod = f"https://telegra.ph{response[0]}" cod = await threats(cod) await output[0].delete() os.remove(download_location) await event.client.send_file(event.chat_id, cod, reply_to=codid) @codex.cod_cmd( pattern="trap(?:\s|$)([\s\S]*)", command=("trap", plugin_category), info={ "header": "Reply to image/sticker to get meme on that image.", "Description": "creates a trap card", "usage": "{tr}trap (name of the person to trap) ; (trapper name)", }, ) async def codbot(event): "image meme creator." input_str = event.pattern_match.group(1) input_str = deEmojify(input_str) if ";" in input_str: text1, text2 = input_str.split(";") else: return await edit_or_reply( event, "**Syntax :** reply to image or sticker with `.trap (name of the person to trap);(trapper name)`", ) replied = await event.get_reply_message() codid = await reply_id(event) if not replied: return await edit_or_reply(event, "reply to a supported media file") output = await _codtools.media_to_pic(event, replied) if output[1] is None: return await edit_delete( output[0], "__Unable to extract image from the replied message.__" ) download_location = convert_toimage(output[1]) size = os.stat(download_location).st_size if size > 5242880: os.remove(download_location) return await output[0].edit( "the replied file size is not supported it must me below 5 mb" ) await output[0].edit("generating image..") try: response = upload_file(download_location) except exceptions.TelegraphException as exc: os.remove(download_location) return await output[0].edit(f"**Error: **\n`{str(exc)}`") cod = f"https://telegra.ph{response[0]}" cod = await trap(text1, text2, cod) await output[0].delete() os.remove(download_location) await event.client.send_file(event.chat_id, cod, reply_to=codid) @codex.cod_cmd( pattern="phub(?:\s|$)([\s\S]*)", command=("phub", plugin_category), info={ "header": "Reply to image/sticker to get meme on that image.", "description": "pornhub comment creator", "usage": "{tr}phub (username);(text in comment)", }, ) async def codbot(event): "image meme creator." input_str = event.pattern_match.group(1) input_str = deEmojify(input_str) if ";" in input_str: username, text = input_str.split(";") else: return await edit_or_reply( event, "**Syntax :** reply to image or sticker with `.phub (username);(text in comment)`", ) replied = await event.get_reply_message() codid = await reply_id(event) if not replied: return await edit_or_reply(event, "reply to a supported media file") output = await _codtools.media_to_pic(event, replied) if output[1] is None: return await edit_delete( output[0], "__Unable to extract image from the replied message.__" ) download_location = convert_toimage(output[1]) size = os.stat(download_location).st_size if size > 5242880: os.remove(download_location) return await output[0].edit( "the replied file size is not supported it must me below 5 mb" ) await output[0].edit("generating image..") try: response = upload_file(download_location) except exceptions.TelegraphException as exc: os.remove(download_location) return await output[0].edit(f"**Error: **\n`{str(exc)}`") cod = f"https://telegra.ph{response[0]}" cod = await phcomment(cod, text, username) await output[0].delete() os.remove(download_location) await event.client.send_file(event.chat_id, cod, reply_to=codid)

35.26943

110

0.648597

906

6,807

4.727373

0.143488

0.093392

0.044828

0.067243

0.87859

0.854775

0.837964

0

0.014945

0.233289

6,807

192

111

35.453125

0.80571

0.011018

0

0.727273

0

0.005682

0.240898

0.006242

0

1

0

false

0

0.034091

0

0.136364

0

null

0

1

0

1

0

null

0

7

8c29dc2eb74dc2b450dcdbf2b19958fa43e871cd

88

py

Python

HelloHactoberfest.py

AntishK/Hacktoberfest2021

b147bae1856a14c6991bff14921c9ee0aa8f9d2d

[ "MIT" ]

2

2022-01-05T13:01:09.000Z

2022-01-05T15:02:45.000Z

HelloHactoberfest.py

Shubhanshu156/Hacktoberfest2021-4

ce2ed6e72b5cb03d945a533ed499f0e8428d4746

[ "MIT" ]

null

HelloHactoberfest.py

Shubhanshu156/Hacktoberfest2021-4

ce2ed6e72b5cb03d945a533ed499f0e8428d4746

[ "MIT" ]

null

# Hactoberfest 2021 print("Welcome to Hactoberfest 2021") print("Have a great journey")

22

37

0.772727

12

88

5.666667

0.75

0.470588

0.617647

0

0.103896

0.125

88

3

38

29.333333

0.779221

0.193182

0

0.695652

0

1

0

true

0

1

0

null

1

0

1

0

1

0

null

0

1

0

1

0

7

8c571b1d23e0bbd3a43493c9961b6dd53b132b3f

208

py

Python

thingsvision/custom_models/__init__.py

LukasMut/THINGSvision

2bee03f34f9a338d986b9ae65e111f606660df17

[ "MIT" ]

null

thingsvision/custom_models/__init__.py

LukasMut/THINGSvision

2bee03f34f9a338d986b9ae65e111f606660df17

[ "MIT" ]

null

thingsvision/custom_models/__init__.py

LukasMut/THINGSvision

2bee03f34f9a338d986b9ae65e111f606660df17

[ "MIT" ]

null

from thingsvision.custom_models.resnet50_ecoset import Resnet50_ecoset from thingsvision.custom_models.vgg16bn_ecoset import VGG16bn_ecoset from thingsvision.custom_models.alexnet_ecoset import Alexnet_ecoset

69.333333

70

0.918269

27

208

6.740741

0.333333

0.263736

0.362637

0.461538

0.373626

0

0.040609

0.052885

208

3

71

69.333333

0.883249

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

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1

0

8

4fb3c2ae67a536ee4a17a1cd4a9958592d9a5c51

22,900

py

Python

site-packages/freezer/tests/integration/test_agent.py

hariza17/freezer_libraries

e0bd890eba5e7438976fb3b4d66c41c128bab790

[ "PSF-2.0" ]

null

site-packages/freezer/tests/integration/test_agent.py

hariza17/freezer_libraries

e0bd890eba5e7438976fb3b4d66c41c128bab790

[ "PSF-2.0" ]

null

site-packages/freezer/tests/integration/test_agent.py

hariza17/freezer_libraries

e0bd890eba5e7438976fb3b4d66c41c128bab790

[ "PSF-2.0" ]

1

2019-12-03T15:38:27.000Z

# Copyright 2015 Hewlett-Packard # # 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 copy import copy import json import os import unittest from oslo_utils import uuidutils from freezer.tests.integration import common class TestSimpleExecution(common.TestFS): def test_freezerc_executes(self): result = common.execute_freezerc({'version': ''}) self.assertIsNotNone(result) def test_freezerc_fails_with_wrong_params(self): result = common.execute_freezerc({'blabla': ''}, must_fail=True, merge_stderr=True) self.assertIn('unrecognized arguments', result) class TestBackupFSLocalstorage(common.TestFS): def test_trees(self): self.assertTreesMatch() self.source_tree.add_random_data() self.assertTreesMatchNot() def test_backup_single_level(self): """How it works? - use the default source and destination trees in /tmp (see common.TestFS) - use temporary directory for backup storage - add some random data - check that trees don't match anymore - execute backup of source tree - execute restore into destination tree - check that source and destination trees match :return: non on success """ self.source_tree.add_random_data() self.assertTreesMatchNot() with common.Temp_Tree() as storage_dir: backup_args = { 'action': 'backup', 'mode': 'fs', 'path_to_backup': self.source_tree.path, 'container': storage_dir.path, 'storage': 'local', 'max_level': '6', 'max_segment_size': '67108864', 'backup_name': uuidutils.generate_uuid(dashed=False) } restore_args = { 'action': 'restore', 'restore_abs_path': self.dest_tree.path, 'backup_name': copy(backup_args['backup_name']), 'storage': 'local', 'container': storage_dir.path } result = common.execute_freezerc(backup_args) self.assertIsNotNone(result) result = common.execute_freezerc(restore_args) self.assertIsNotNone(result) self.assertTreesMatch() def test_backup_preexisting_dir(self): """ Use external pre-defined directory for tests. If directory does not exist, then skip Restore to temporary folder (removed on exit) :return: """ workdir = os.path.expanduser('~/test_dir') if not os.path.isdir(workdir): return self.source_tree = common.Temp_Tree(dir='/work', create=False) with common.Temp_Tree() as storage_dir: backup_args = { 'action': 'backup', 'mode': 'fs', 'path_to_backup': self.source_tree.path, 'container': storage_dir.path, 'storage': 'local', 'max_level': '6', 'max_segment_size': '67108864', 'backup_name': uuidutils.generate_uuid(dashed=False) } restore_args = { 'action': 'restore', 'restore_abs_path': self.dest_tree.path, 'backup_name': copy(backup_args['backup_name']), 'storage': 'local', 'container': storage_dir.path } result = common.execute_freezerc(backup_args) self.assertIsNotNone(result) result = common.execute_freezerc(restore_args) self.assertIsNotNone(result) self.assertTreesMatch() @unittest.skipIf(not common.TestFS.use_lvm, "No LVM support") def test_backup_local_storage_use_lvm_snapshot_and_path_to_backup(self): self.source_tree.add_random_data() self.assertTreesMatchNot() backup_name = uuidutils.generate_uuid(dashed=False) path_to_backup = self.source_tree.path lvm_snapsize = '50M' lvm_snapname = 'freezer-snap_{0}'.format(backup_name) lvm_dirmount = '/var/freezer/freezer-{0}'.format(backup_name) with common.Temp_Tree() as storage_dir: backup_args = { 'action': 'backup', 'mode': 'fs', 'path_to_backup': path_to_backup, 'snapshot': '', 'lvm_dirmount': lvm_dirmount, 'lvm_snapsize': lvm_snapsize, 'lvm_snapname': lvm_snapname, 'container': storage_dir.path, 'storage': 'local', 'max_level': '6', 'max_segment_size': '67108864', 'backup_name': backup_name } restore_args = { 'action': 'restore', 'restore_abs_path': self.dest_tree.path, 'backup_name': copy(backup_args['backup_name']), 'storage': 'local', 'container': storage_dir.path } result = common.execute_freezerc(backup_args) self.assertIsNotNone(result) result = common.execute_freezerc(restore_args) self.assertIsNotNone(result) self.assertTreesMatch() class TestBackupSSH(common.TestFS): """ Tests are executed if the following env vars are defined: - FREEZER_TEST_SSH_KEY - FREEZER_TEST_SSH_USERNAME - FREEZER_TEST_SSH_HOST - FREEZER_TEST_CONTAINER (directory on the remote machine used to store backups) """ @unittest.skipIf(not common.TestFS.use_ssh, "Cannot test with ssh, please provide" "'FREEZER_TEST_SSH_KEY,'" "'FREEZER_TEST_SSH_USERNAME'," "'FREEZER_TEST_SSH_HOST'," "'FREEZER_TEST_CONTAINER'") def test_backup_ssh(self): self.source_tree.add_random_data() self.assertTreesMatchNot() backup_args = { 'action': 'backup', 'mode': 'fs', 'path_to_backup': self.source_tree.path, 'max_level': '6', 'max_segment_size': '67108864', 'backup_name': uuidutils.generate_uuid(dashed=False), 'storage': 'ssh', 'container': self.container, 'ssh_key': self.ssh_key, 'ssh_username': self.ssh_username, 'ssh_host': self.ssh_host, 'metadata_out': '-' } restore_args = { 'action': 'restore', 'restore_abs_path': self.dest_tree.path, 'backup_name': copy(backup_args['backup_name']), 'storage': 'ssh', 'container': self.container, 'ssh_key': self.ssh_key, 'ssh_username': self.ssh_username, 'ssh_host': self.ssh_host } result = common.execute_freezerc(backup_args) self.assertIsNotNone(result) result = json.loads(result) sub_path = '_'.join([result['hostname'], result['backup_name']]) # It may be reasonable to insert a check of the files in the # storage directory # file_list = self.get_file_list_ssh(sub_path) self.assertIn('backup_name', result) self.assertEqual(result['backup_name'], backup_args['backup_name']) self.assertIn('container', result) self.assertEqual(result['container'], self.container) result = common.execute_freezerc(restore_args) self.assertIsNotNone(result) self.assertTreesMatch() self.remove_ssh_directory(sub_path) @unittest.skipIf(not common.TestFS.use_ssh, "Cannot test with ssh, please provide" "'FREEZER_TEST_SSH_KEY,'" "'FREEZER_TEST_SSH_USERNAME'," "'FREEZER_TEST_SSH_HOST'," "'FREEZER_TEST_CONTAINER'") def test_backup_ssh_incremental(self): self.source_tree.add_random_data() self.assertTreesMatchNot() backup_args = { 'action': 'backup', 'mode': 'fs', 'path_to_backup': self.source_tree.path, 'max_level': '6', 'max_segment_size': '67108864', 'backup_name': uuidutils.generate_uuid(dashed=False), 'storage': 'ssh', 'container': self.container, 'ssh_key': self.ssh_key, 'ssh_username': self.ssh_username, 'ssh_host': self.ssh_host, 'metadata_out': '-' } restore_args = { 'action': 'restore', 'restore_abs_path': self.dest_tree.path, 'backup_name': copy(backup_args['backup_name']), 'storage': 'ssh', 'container': self.container, 'ssh_key': self.ssh_key, 'ssh_username': self.ssh_username, 'ssh_host': self.ssh_host } result = common.execute_freezerc(backup_args) self.assertIsNotNone(result) result = json.loads(result) sub_path = '_'.join([result['hostname'], result['backup_name']]) # It may be reasonable to insert a check of the files in the # storage directory # file_list = self.get_file_list_ssh(sub_path) result = common.execute_freezerc(restore_args) self.assertIsNotNone(result) self.assertTreesMatch() # -- many levels self.do_backup_and_restore_with_check(backup_args, restore_args) self.do_backup_and_restore_with_check(backup_args, restore_args) self.do_backup_and_restore_with_check(backup_args, restore_args) self.do_backup_and_restore_with_check(backup_args, restore_args) self.do_backup_and_restore_with_check(backup_args, restore_args) self.do_backup_and_restore_with_check(backup_args, restore_args) self.do_backup_and_restore_with_check(backup_args, restore_args) self.remove_ssh_directory(sub_path) @unittest.skipIf(not common.TestFS.use_ssh, "Cannot test with ssh, please provide" "'FREEZER_TEST_SSH_KEY,'" "'FREEZER_TEST_SSH_USERNAME'," "'FREEZER_TEST_SSH_HOST'," "'FREEZER_TEST_CONTAINER'") @unittest.skipIf(not common.TestFS.use_lvm, "No LVM support") def test_backup_ssh_incremental_with_lvm(self): self.source_tree.add_random_data() self.assertTreesMatchNot() backup_name = uuidutils.generate_uuid(dashed=False) path_to_backup = self.source_tree.path lvm_snapsize = '1G' lvm_snapname = 'freezer-snap_{0}'.format(backup_name) lvm_dirmount = '/var/freezer/freezer-{0}'.format(backup_name) backup_args = { 'action': 'backup', 'mode': 'fs', 'path_to_backup': path_to_backup, 'lvm_dirmount': lvm_dirmount, 'lvm_snapsize': lvm_snapsize, 'lvm_snapname': lvm_snapname, 'backup_name': backup_name, 'max_level': '6', 'max_segment_size': '67108864', 'storage': 'ssh', 'container': self.container, 'ssh_key': self.ssh_key, 'ssh_username': self.ssh_username, 'ssh_host': self.ssh_host } restore_args = { 'action': 'restore', 'restore_abs_path': self.dest_tree.path, 'backup_name': copy(backup_args['backup_name']), 'storage': 'ssh', 'container': self.container, 'ssh_key': self.ssh_key, 'ssh_username': self.ssh_username, 'ssh_host': self.ssh_host } result = common.execute_freezerc(backup_args) self.assertIsNotNone(result) result = common.execute_freezerc(restore_args) self.assertIsNotNone(result) self.assertTreesMatch() # -- level 1 self.source_tree.add_random_data() self.assertTreesMatchNot() result = common.execute_freezerc(backup_args) self.assertIsNotNone(result) result = common.execute_freezerc(restore_args) self.assertIsNotNone(result) self.assertTreesMatch() # -- level 2 self.source_tree.add_random_data() self.assertTreesMatchNot() result = common.execute_freezerc(backup_args) self.assertIsNotNone(result) result = common.execute_freezerc(restore_args) self.assertIsNotNone(result) self.assertTreesMatch() class TestBackupUsingSwiftStorage(common.TestFS): """ Tests are executed if the following env vars are defined: - FREEZER_TEST_OS_TENANT_NAME - FREEZER_TEST_OS_USERNAME - FREEZER_TEST_OS_REGION_NAME - FREEZER_TEST_OS_PASSWORD - FREEZER_TEST_OS_AUTH_URL """ @unittest.skipIf(not common.TestFS.use_os, "Cannot test with swift, please provide" "'FREEZER_TEST_OS_TENANT_NAME'," "'FREEZER_TEST_OS_USERNAME'," "'FREEZER_TEST_OS_REGION_NAME'," "'FREEZER_TEST_OS_PASSWORD'," "'FREEZER_TEST_OS_AUTH_URL'") def test_backup_os_simple(self): self.source_tree.add_random_data() self.assertTreesMatchNot() backup_args = { 'action': 'backup', 'mode': 'fs', 'path_to_backup': self.source_tree.path, 'max_level': '6', 'max_segment_size': '67108864', 'backup_name': uuidutils.generate_uuid(dashed=False), 'storage': 'swift', 'container': 'freezer_test_backups_{0}'.format( uuidutils.generate_uuid(dashed=False)), 'metadata_out': '-' } restore_args = { 'action': 'restore', 'restore_abs_path': self.dest_tree.path, 'backup_name': copy(backup_args['backup_name']), 'storage': 'swift', 'container': copy(backup_args['container']), } remove_args = { 'action': 'admin', 'remove_older_than': 0, 'backup_name': copy(backup_args['backup_name']), 'storage': 'swift', 'container': copy(backup_args['container']), } # --- backup result = common.execute_freezerc(backup_args) self.assertIsNotNone(result) result = json.loads(result) self.assertIn('backup_name', result) self.assertEqual(result['backup_name'], backup_args['backup_name']) self.assertIn('container', result) self.assertEqual(result['container'], backup_args['container']) # It may be reasonable to insert a check of the files in the # swift container # file_list = self.get_file_list_openstack(result['container']) # --- restore result = common.execute_freezerc(restore_args) self.assertIsNotNone(result) self.assertTreesMatch() # --- remove backups and container result = common.execute_freezerc(remove_args) self.assertIsNotNone(result) result = self.remove_swift_container(backup_args['container']) self.assertIsNotNone(result) @unittest.skipIf(not common.TestFS.use_os, "Cannot test with swift, please provide" "'FREEZER_TEST_OS_TENANT_NAME'," "'FREEZER_TEST_OS_USERNAME'," "'FREEZER_TEST_OS_REGION_NAME'," "'FREEZER_TEST_OS_PASSWORD'," "'FREEZER_TEST_OS_AUTH_URL'") def test_backup_os_simple_with_bzip2(self): self.source_tree.add_random_data() self.assertTreesMatchNot() backup_args = { 'action': 'backup', 'mode': 'fs', 'compression': 'bzip2', 'path_to_backup': self.source_tree.path, 'max_level': '6', 'max_segment_size': '67108864', 'backup_name': uuidutils.generate_uuid(dashed=False), 'storage': 'swift', 'container': 'freezer_test_backups_{0}'.format( uuidutils.generate_uuid(dashed=False)), 'metadata_out': '-' } restore_args = { 'action': 'restore', 'compression': 'bzip2', 'restore_abs_path': self.dest_tree.path, 'backup_name': copy(backup_args['backup_name']), 'storage': 'swift', 'container': copy(backup_args['container']), } remove_args = { 'action': 'admin', 'remove_older_than': 0, 'backup_name': copy(backup_args['backup_name']), 'storage': 'swift', 'container': copy(backup_args['container']), } # --- backup result = common.execute_freezerc(backup_args) self.assertIsNotNone(result) result = json.loads(result) self.assertIn('backup_name', result) self.assertEqual(result['backup_name'], backup_args['backup_name']) self.assertIn('container', result) self.assertEqual(result['container'], backup_args['container']) # It may be reasonable to insert a check of the files in the # swift container # file_list = self.get_file_list_openstack(result['container']) # --- restore result = common.execute_freezerc(restore_args) self.assertIsNotNone(result) self.assertTreesMatch() # --- remove backups and container result = common.execute_freezerc(remove_args) self.assertIsNotNone(result) result = self.remove_swift_container(backup_args['container']) self.assertIsNotNone(result) @unittest.skipIf(not common.TestFS.use_os, "Cannot test with swift, please provide" "'FREEZER_TEST_OS_TENANT_NAME'," "'FREEZER_TEST_OS_USERNAME'," "'FREEZER_TEST_OS_REGION_NAME'," "'FREEZER_TEST_OS_PASSWORD'," "'FREEZER_TEST_OS_AUTH_URL'") @unittest.skipIf(not common.TestFS.use_lvm, "No LVM support") @unittest.skipIf(not os.path.isdir('/var/lib/mysql'), "No path /var/lib/mysql") def test_backup_swift_mysql(self): self.source_tree = common.Temp_Tree(dir='/var/lib/mysql', create=False) backup_name = uuidutils.generate_uuid(dashed=False) lvm_snapsize = '1G' lvm_snapname = 'freezer-snap_{0}'.format(backup_name) lvm_dirmount = '/var/freezer/freezer-{0}'.format(backup_name) backup_args = { 'action': 'backup', 'mode': 'mysql', 'mysql_conf': '/etc/mysql/debian.cnf', 'path_to_backup': self.source_tree.path, 'snapshot': '', 'lvm_dirmount': lvm_dirmount, 'lvm_snapsize': lvm_snapsize, 'lvm_snapname': lvm_snapname, 'container': 'freezer_test_container_{0}'.format(backup_name), 'storage': 'swift', 'max_level': '6', 'max_segment_size': '67108864', 'backup_name': backup_name } restore_args = { 'action': 'restore', 'restore_abs_path': self.dest_tree.path, 'backup_name': copy(backup_args['backup_name']), 'storage': 'swift', 'container': copy(backup_args['container']) } result = common.execute_freezerc(backup_args) self.assertIsNotNone(result) result = common.execute_freezerc(restore_args) self.assertIsNotNone(result) # we cannot test if trees as a running mysql instance # will modify the files @unittest.skipIf(not common.TestFS.use_os, "Cannot test with swift, please provide" "'FREEZER_TEST_OS_TENANT_NAME'," "'FREEZER_TEST_OS_USERNAME'," "'FREEZER_TEST_OS_REGION_NAME'," "'FREEZER_TEST_OS_PASSWORD'," "'FREEZER_TEST_OS_AUTH_URL'") def test_backup_os_simple_with_bandwidth_limit(self): self.source_tree.add_random_data() self.assertTreesMatchNot() backup_args = { 'action': 'backup', 'mode': 'fs', 'path_to_backup': self.source_tree.path, 'max_level': '6', 'upload_limit': '1M', 'download_limit': '1M', 'max_segment_size': '67108864', 'backup_name': uuidutils.generate_uuid(dashed=False), 'storage': 'swift', 'container': 'freezer_test_backups_{0}'.format( uuidutils.generate_uuid(dashed=False)), 'metadata_out': '-' } restore_args = { 'action': 'restore', 'restore_abs_path': self.dest_tree.path, 'backup_name': copy(backup_args['backup_name']), 'storage': 'swift', 'container': copy(backup_args['container']), } remove_args = { 'action': 'admin', 'remove_older_than': 0, 'backup_name': copy(backup_args['backup_name']), 'storage': 'swift', 'container': copy(backup_args['container']), } # --- backup result = common.execute_freezerc(backup_args) self.assertIsNotNone(result) result = json.loads(result) self.assertIn('backup_name', result) self.assertEqual(result['backup_name'], backup_args['backup_name']) self.assertIn('container', result) self.assertEqual(result['container'], backup_args['container']) # It may be reasonable to insert a check of the files in the # swift container # file_list = self.get_file_list_openstack(result['container']) # --- restore result = common.execute_freezerc(restore_args) self.assertIsNotNone(result) self.assertTreesMatch() # --- remove backups and container result = common.execute_freezerc(remove_args) self.assertIsNotNone(result) result = self.remove_swift_container(backup_args['container']) self.assertIsNotNone(result)

37.976783

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0.585895

2,378

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5.343566

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0.049579

0.06099

0.06162

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0

null

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1

0

1

0

null

0

7

4fdb375028612636a2460d6db98321c22dc75fc6

458

py

Python

tunepy2/interfaces/stubs/__init__.py

efortner/tunepy

28ab7aa0b851d42cf2a81a5573fb24b261daba89

[ "MIT" ]

null

tunepy2/interfaces/stubs/__init__.py

efortner/tunepy

28ab7aa0b851d42cf2a81a5573fb24b261daba89

[ "MIT" ]

null

tunepy2/interfaces/stubs/__init__.py

efortner/tunepy

28ab7aa0b851d42cf2a81a5573fb24b261daba89

[ "MIT" ]

null

from tunepy2.interfaces.stubs.stub_rngs import * from tunepy2.interfaces.stubs.stub_learners import * from tunepy2.interfaces.stubs.stub_model_comparers import * from tunepy2.interfaces.stubs.stub_optimizers import * from tunepy2.interfaces.stubs.stub_model_factories import * from tunepy2.interfaces.stubs.stub_validators import * from tunepy2.interfaces.stubs.stub_convergence_criteria import * from tunepy2.interfaces.stubs.stub_genome_factories import *

50.888889

64

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458

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0.230366

0.439791

0.544503

0.764398

0.685864

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0

0.018779

0.069869

458

8

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57.25

0.877934

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true

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null

0

1

0

1

0

1

0

7

4fdeb1bc3399cf50b00ba26e3c8c96b25c78493a

10,413

py

Python

goutdotcom/lab/migrations/0001_initial.py

Spiewart/goutdotcom

0916155732a72fcb8c8a2fb0f4dd81efef618af8

[ "MIT" ]

null

goutdotcom/lab/migrations/0001_initial.py

Spiewart/goutdotcom

0916155732a72fcb8c8a2fb0f4dd81efef618af8

[ "MIT" ]

null

goutdotcom/lab/migrations/0001_initial.py

Spiewart/goutdotcom

0916155732a72fcb8c8a2fb0f4dd81efef618af8

[ "MIT" ]

null

# Generated by Django 3.1.7 on 2022-01-08 23:46 import datetime from django.db import migrations, models import django_extensions.db.fields class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='ALT', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('created', django_extensions.db.fields.CreationDateTimeField(auto_now_add=True, verbose_name='created')), ('modified', django_extensions.db.fields.ModificationDateTimeField(auto_now=True, verbose_name='modified')), ('date_drawn', models.DateField(blank=True, default=None, help_text='What day was this lab drawn?', null=True)), ('value', models.IntegerField(help_text='ALT (SGPT) is typically reported in units per liter (U/L)')), ('units', models.CharField(blank=True, choices=[('mg/dL (milligrams per deciliter)', 'mg/dL (milligrams per deciliter)'), ('g/dL (grams per decliter)', 'g/dL (grams per decliter)'), ('cells/mm^3 (cells per cubmic millimeter)', 'cells/mm^3 (cells per cubmic millimeter)'), ('PLTS/μL (platelets per microliter)', 'PLTS/μL (platelets per microliter)'), ('U/L (units per liter)', 'U/L (units per liter)')], default='U/L (units per liter)', max_length=100, null=True)), ], options={ 'abstract': False, }, ), migrations.CreateModel( name='AST', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('created', django_extensions.db.fields.CreationDateTimeField(auto_now_add=True, verbose_name='created')), ('modified', django_extensions.db.fields.ModificationDateTimeField(auto_now=True, verbose_name='modified')), ('date_drawn', models.DateField(blank=True, default=None, help_text='What day was this lab drawn?', null=True)), ('value', models.IntegerField(help_text='AST (SGOT) is typically reported in units per liter (U/L)')), ('units', models.CharField(blank=True, choices=[('mg/dL (milligrams per deciliter)', 'mg/dL (milligrams per deciliter)'), ('g/dL (grams per decliter)', 'g/dL (grams per decliter)'), ('cells/mm^3 (cells per cubmic millimeter)', 'cells/mm^3 (cells per cubmic millimeter)'), ('PLTS/μL (platelets per microliter)', 'PLTS/μL (platelets per microliter)'), ('U/L (units per liter)', 'U/L (units per liter)')], default='U/L (units per liter)', max_length=100, null=True)), ], options={ 'abstract': False, }, ), migrations.CreateModel( name='Creatinine', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('created', django_extensions.db.fields.CreationDateTimeField(auto_now_add=True, verbose_name='created')), ('modified', django_extensions.db.fields.ModificationDateTimeField(auto_now=True, verbose_name='modified')), ('date_drawn', models.DateField(blank=True, default=None, help_text='What day was this lab drawn?', null=True)), ('value', models.DecimalField(decimal_places=2, help_text='Creatinine is typically reported as milligrams per deciliter (mg/dL)', max_digits=4)), ('units', models.CharField(blank=True, choices=[('mg/dL (milligrams per deciliter)', 'mg/dL (milligrams per deciliter)'), ('g/dL (grams per decliter)', 'g/dL (grams per decliter)'), ('cells/mm^3 (cells per cubmic millimeter)', 'cells/mm^3 (cells per cubmic millimeter)'), ('PLTS/μL (platelets per microliter)', 'PLTS/μL (platelets per microliter)'), ('U/L (units per liter)', 'U/L (units per liter)')], default='mg/dL (milligrams per deciliter)', max_length=100, null=True)), ], options={ 'abstract': False, }, ), migrations.CreateModel( name='Hemoglobin', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('created', django_extensions.db.fields.CreationDateTimeField(auto_now_add=True, verbose_name='created')), ('modified', django_extensions.db.fields.ModificationDateTimeField(auto_now=True, verbose_name='modified')), ('date_drawn', models.DateField(blank=True, default=None, help_text='What day was this lab drawn?', null=True)), ('value', models.DecimalField(decimal_places=1, help_text='HGB (hemoglobin) is typically reporeted in grams per deciliter (g/dL)', max_digits=3)), ('units', models.CharField(blank=True, choices=[('mg/dL (milligrams per deciliter)', 'mg/dL (milligrams per deciliter)'), ('g/dL (grams per decliter)', 'g/dL (grams per decliter)'), ('cells/mm^3 (cells per cubmic millimeter)', 'cells/mm^3 (cells per cubmic millimeter)'), ('PLTS/μL (platelets per microliter)', 'PLTS/μL (platelets per microliter)'), ('U/L (units per liter)', 'U/L (units per liter)')], default='g/dL (grams per decliter)', max_length=100, null=True)), ], options={ 'abstract': False, }, ), migrations.CreateModel( name='LabCheck', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('created', django_extensions.db.fields.CreationDateTimeField(auto_now_add=True, verbose_name='created')), ('modified', django_extensions.db.fields.ModificationDateTimeField(auto_now=True, verbose_name='modified')), ('due', models.DateField(default=datetime.date(2022, 2, 19), help_text='When is this lab check due?')), ('completed', models.BooleanField(choices=[(True, 'Yes'), (False, 'No')], default=False, help_text='Is this lab check completed?')), ('completed_date', models.DateField(blank=True, default=None, help_text='When was this lab check completed?', null=True)), ], options={ 'get_latest_by': 'modified', 'abstract': False, }, ), migrations.CreateModel( name='Platelet', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('created', django_extensions.db.fields.CreationDateTimeField(auto_now_add=True, verbose_name='created')), ('modified', django_extensions.db.fields.ModificationDateTimeField(auto_now=True, verbose_name='modified')), ('date_drawn', models.DateField(blank=True, default=None, help_text='What day was this lab drawn?', null=True)), ('value', models.IntegerField(help_text='PLT (platelets) is typically reported in platelets per microliter (PLT/microL)')), ('units', models.CharField(blank=True, choices=[('mg/dL (milligrams per deciliter)', 'mg/dL (milligrams per deciliter)'), ('g/dL (grams per decliter)', 'g/dL (grams per decliter)'), ('cells/mm^3 (cells per cubmic millimeter)', 'cells/mm^3 (cells per cubmic millimeter)'), ('PLTS/μL (platelets per microliter)', 'PLTS/μL (platelets per microliter)'), ('U/L (units per liter)', 'U/L (units per liter)')], default='PLTS/μL (platelets per microliter)', max_length=100, null=True)), ], options={ 'abstract': False, }, ), migrations.CreateModel( name='Urate', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('created', django_extensions.db.fields.CreationDateTimeField(auto_now_add=True, verbose_name='created')), ('modified', django_extensions.db.fields.ModificationDateTimeField(auto_now=True, verbose_name='modified')), ('date_drawn', models.DateField(blank=True, default=None, help_text='What day was this lab drawn?', null=True)), ('value', models.DecimalField(decimal_places=1, help_text='Uric acid is typically reported in micrograms per deciliter (mg/dL)', max_digits=3)), ('units', models.CharField(blank=True, choices=[('mg/dL (milligrams per deciliter)', 'mg/dL (milligrams per deciliter)'), ('g/dL (grams per decliter)', 'g/dL (grams per decliter)'), ('cells/mm^3 (cells per cubmic millimeter)', 'cells/mm^3 (cells per cubmic millimeter)'), ('PLTS/μL (platelets per microliter)', 'PLTS/μL (platelets per microliter)'), ('U/L (units per liter)', 'U/L (units per liter)')], default='mg/dL (milligrams per deciliter)', max_length=100, null=True)), ], options={ 'abstract': False, }, ), migrations.CreateModel( name='WBC', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('created', django_extensions.db.fields.CreationDateTimeField(auto_now_add=True, verbose_name='created')), ('modified', django_extensions.db.fields.ModificationDateTimeField(auto_now=True, verbose_name='modified')), ('date_drawn', models.DateField(blank=True, default=None, help_text='What day was this lab drawn?', null=True)), ('value', models.DecimalField(decimal_places=1, help_text='WBC (white blood cells) is typically reported as cells per cubic millimeter (cells/mm^3)', max_digits=3)), ('units', models.CharField(blank=True, choices=[('mg/dL (milligrams per deciliter)', 'mg/dL (milligrams per deciliter)'), ('g/dL (grams per decliter)', 'g/dL (grams per decliter)'), ('cells/mm^3 (cells per cubmic millimeter)', 'cells/mm^3 (cells per cubmic millimeter)'), ('PLTS/μL (platelets per microliter)', 'PLTS/μL (platelets per microliter)'), ('U/L (units per liter)', 'U/L (units per liter)')], default='cells/mm^3 (cells per cubmic millimeter)', max_length=100, null=True)), ], options={ 'abstract': False, }, ), ]

80.1

499

0.628253

1,229

10,413

5.222945

0.102522

0.041128

0.036454

0.063561

0.879576

0.864776

0.858701

0.853716

0.840785

0

0.008235

0.218669

10,413

129

500

80.72093

0.780728

0.004322

0

0.680328

1

0.008197

0.355007

0

1

0

false

0

0.02459

0

0.057377

0

null

0

1

0

1

0

null

0

7

4ff32f215bbb82f26fe396af53d6fd06cddefe2a

1,978

py

Python

ansible/roles/apply-cis-benchmarks-to-ubuntu-18-04/molecule/default/tests/section_1_1_1.py

jdrowne/monorepo

0b16ceb6194ec4a51419933219d22df0815ace98

[ "MIT" ]

null

ansible/roles/apply-cis-benchmarks-to-ubuntu-18-04/molecule/default/tests/section_1_1_1.py

jdrowne/monorepo

0b16ceb6194ec4a51419933219d22df0815ace98

[ "MIT" ]

null

ansible/roles/apply-cis-benchmarks-to-ubuntu-18-04/molecule/default/tests/section_1_1_1.py

jdrowne/monorepo

0b16ceb6194ec4a51419933219d22df0815ace98

[ "MIT" ]

null

import pytest # 1.1.1.1 Ensure mounting of cramfs filesystems is disabled def test_cis_benchmark_1_1_1_1a(host): assert 'install /bin/true' in host.check_output('modprobe -n -v cramfs') # 1.1.1.1 Ensure mounting of cramfs filesystems is disabled def test_cis_benchmark_1_1_1_1b(host): assert host.run_expect([1], 'lsmod | grep cramfs') # 1.1.1.2 Ensure mounting of freevxfs filesystems is disabled def test_cis_benchmark_1_1_1_2a(host): assert 'install /bin/true' in host.check_output('modprobe -n -v freevxfs') # 1.1.1.2 Ensure mounting of freevxfs filesystems is disabled def test_cis_benchmark_1_1_1_2b(host): assert host.run_expect([1], 'lsmod | grep freevxfs') # 1.1.1.3 Ensure mounting of jffs2 filesystems is disabled def test_cis_benchmark_1_1_1_3a(host): assert 'install /bin/true' in host.check_output('modprobe -n -v jffs2') # 1.1.1.3 Ensure mounting of jffs2 filesystems is disabled def test_cis_benchmark_1_1_1_3b(host): assert host.run_expect([1], 'lsmod | grep jffs2') # 1.1.1.4 Ensure mounting of hfs filesystems is disabled def test_cis_benchmark_1_1_1_4a(host): assert 'install /bin/true' in host.check_output('modprobe -n -v hfs') # 1.1.1.4 Ensure mounting of hfs filesystems is disabled def test_cis_benchmark_1_1_1_4b(host): assert host.run_expect([1], 'lsmod | grep hfs') # 1.1.1.5 Ensure mounting of hfsplus filesystems is disabled def test_cis_benchmark_1_1_1_5a(host): assert 'install /bin/true' in host.check_output('modprobe -n -v hfsplus') # 1.1.1.5 Ensure mounting of hfsplus filesystems is disabled def test_cis_benchmark_1_1_1_5b(host): assert host.run_expect([1], 'lsmod | grep hfsplus') # 1.1.1.6 Ensure mounting of udf filesystems is disabled def test_cis_benchmark_1_1_1_7a(host): assert 'install /bin/true' in host.check_output('modprobe -n -v udf') # 1.1.1.6 Ensure mounting of udf filesystems is disabled def test_cis_benchmark_1_1_1_7b(host): assert host.run_expect([1], 'lsmod | grep udf')

31.903226

76

0.762386

362

1,978

3.933702

0.140884

0.070225

0.054775

0.202247

0.929775

0.79073

0

0.061916

0.134479

1,978

61

77

32.42623

0.76986

0.347321

0

0.261346

0

0.48

1

0.48

false

0

0.04

0

0.52

0

null

0

1

0

null

0

1

0

1

0

1

0

10

8c7c024bb0d15926fb2f90479d7d18e9e03b6f5e

182

py

Python

loss/__init__.py

navervision/MemVir

ea7aef66331bdc8d65137a90c7d9f80e29f23da3

[ "Apache-2.0" ]

8

2021-10-08T05:52:29.000Z

2022-03-04T03:30:00.000Z

loss/__init__.py

navervision/MemVir

ea7aef66331bdc8d65137a90c7d9f80e29f23da3

[ "Apache-2.0" ]

null

loss/__init__.py

navervision/MemVir

ea7aef66331bdc8d65137a90c7d9f80e29f23da3

[ "Apache-2.0" ]

3

2021-10-08T09:26:00.000Z

2022-02-15T06:40:19.000Z

20.222222

41

0.796703

25

182

5.72

0.68

0.153846

0.265734

0.34965

0

0.037736

0.126374

182

8

42

22.75

0.861635

0.357143

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

7

8c86602e0ec1521331ffc89d2113c89f87f59b51

37,152

py

Python

openregistry/assets/core/tests/blanks/asset.py

EBRD-ProzorroSale/openregistry.assets.core

e4ce9a0de9bcd9ae19ea3350c5c7f1f2bd5340e7

[ "Apache-2.0" ]

null

openregistry/assets/core/tests/blanks/asset.py

EBRD-ProzorroSale/openregistry.assets.core

e4ce9a0de9bcd9ae19ea3350c5c7f1f2bd5340e7

[ "Apache-2.0" ]

52

2017-08-01T16:12:27.000Z

2019-02-22T14:22:57.000Z

openregistry/assets/core/tests/blanks/asset.py

EBRD-ProzorroSale/openregistry.assets.core

e4ce9a0de9bcd9ae19ea3350c5c7f1f2bd5340e7

[ "Apache-2.0" ]

12

2017-07-31T09:15:51.000Z

2018-09-14T11:02:27.000Z

# -*- coding: utf-8 -*- from uuid import uuid4 import unittest from copy import deepcopy from openprocurement.api.tests.base import create_blacklist from openregistry.assets.core.constants import ( STATUS_CHANGES, ASSET_STATUSES, SANDBOX_MODE, ) from openregistry.assets.core.tests.base import DEFAULT_ACCELERATION from openregistry.assets.core.utils import calculate_business_date # AssetResourceTest @unittest.skipIf(not SANDBOX_MODE, 'If sandbox mode is disabled assetParameters has not procurementMethodDetails field') def sandbox_parameter(self): response = self.app.post_json('/', {'data': self.initial_data}) response_sandbox_parameters = response.json['data']['sandboxParameters'] default_sandbox_parameters = "quick, accelerator={}".format(DEFAULT_ACCELERATION) self.assertEqual(response_sandbox_parameters, default_sandbox_parameters) def patch_asset(self): response = self.app.get('/') self.assertEqual(response.status, '200 OK') self.assertEqual(len(response.json['data']), 0) asset = self.create_resource() dateModified = asset.pop('dateModified') response = self.app.patch_json('/{}'.format(asset['id']), headers=self.access_header, params={'data': {'title': ' PATCHED'}}) self.assertEqual(response.status, '200 OK') self.assertEqual(response.content_type, 'application/json') self.assertNotEqual(response.json['data']['dateModified'], dateModified) asset = self.create_resource() self.set_status('draft') # Move status from Draft to Active response = self.app.patch_json('/{}'.format(asset['id']), headers=self.access_header, params={'data': {'status': 'active'}}, status=403) self.assertEqual(response.status, '403 Forbidden') self.assertEqual(response.content_type, 'application/json') self.assertEqual(response.json['errors'][0]['name'], u'data') self.assertEqual(response.json['errors'][0]['location'], u'body') self.assertEqual(response.json['errors'][0]['description'], u"Can't update asset in current (draft) status") # Move status from Draft to Deleted response = self.app.patch_json('/{}'.format(asset['id']), headers=self.access_header, params={'data': {'status': 'deleted'}}, status=403) self.assertEqual(response.status, '403 Forbidden') self.assertEqual(response.content_type, 'application/json') self.assertEqual(response.json['errors'][0]['name'], u'data') self.assertEqual(response.json['errors'][0]['location'], u'body') self.assertEqual(response.json['errors'][0]['description'], u"Can't update asset in current (draft) status") # Move status from Draft to Complete response = self.app.patch_json('/{}'.format(asset['id']), headers=self.access_header, params={'data': {'status': 'complete'}}, status=403) self.assertEqual(response.status, '403 Forbidden') self.assertEqual(response.content_type, 'application/json') self.assertEqual(response.json['errors'][0]['name'], u'data') self.assertEqual(response.json['errors'][0]['location'], u'body') self.assertEqual(response.json['errors'][0]['description'], u"Can't update asset in current (draft) status") # Move status from Draft to Pending response = self.app.patch_json('/{}'.format(asset['id']), headers=self.access_header, params={'data': {'status': 'pending'}}) self.assertEqual(response.status, '200 OK') self.assertEqual(response.content_type, 'application/json') self.assertEqual(response.json['data']['status'], 'pending') # Move status from Pending to Draft response = self.app.patch_json('/{}'.format(asset['id']), headers=self.access_header, params={'data': {'status': 'draft'}}, status=403) self.assertEqual(response.status, '403 Forbidden') self.assertEqual(response.content_type, 'application/json') self.assertEqual(response.json['errors'][0]['name'], u'data') self.assertEqual(response.json['errors'][0]['location'], u'body') self.assertEqual(response.json['errors'][0]['description'], u"Can't switch asset to draft status") # Move status from Pending to Active response = self.app.patch_json('/{}'.format(asset['id']), headers=self.access_header, params={'data': {'status': 'active'}}, status=403) self.assertEqual(response.status, '403 Forbidden') self.assertEqual(response.content_type, 'application/json') self.assertEqual(response.json['errors'][0]['name'], u'data') self.assertEqual(response.json['errors'][0]['location'], u'body') self.assertEqual(response.json['errors'][0]['description'], u"Can't update asset in current (pending) status") # Move status from Pending to Complete response = self.app.patch_json('/{}'.format(asset['id']), headers=self.access_header, params={'data': {'status': 'complete'}}, status=403) self.assertEqual(response.status, '403 Forbidden') self.assertEqual(response.content_type, 'application/json') self.assertEqual(response.json['errors'][0]['name'], u'data') self.assertEqual(response.json['errors'][0]['location'], u'body') self.assertEqual(response.json['errors'][0]['description'], u"Can't update asset in current (pending) status") # Move status from Pending to Deleted response = self.app.patch_json('/{}'.format(asset['id']), headers=self.access_header, params={'data': {'status': 'deleted'}}) self.assertEqual(response.status, '200 OK') self.assertEqual(response.content_type, 'application/json') self.assertEqual(response.json['data']['status'], 'deleted') # Move status from Deleted to Draft response = self.app.patch_json('/{}'.format(asset['id']), headers=self.access_header, params={'data': {'status': 'draft'}}, status=403) self.assertEqual(response.status, '403 Forbidden') self.assertEqual(response.content_type, 'application/json') self.assertEqual(response.json['errors'][0]['name'], u'data') self.assertEqual(response.json['errors'][0]['location'], u'body') self.assertEqual(response.json['errors'][0]['description'], u"Can't update asset in current (deleted) status") # Move status from Deleted to Pending response = self.app.patch_json('/{}'.format(asset['id']), headers=self.access_header, params={'data': {'status': 'pending'}}, status=403) self.assertEqual(response.status, '403 Forbidden') self.assertEqual(response.content_type, 'application/json') self.assertEqual(response.json['errors'][0]['name'], u'data') self.assertEqual(response.json['errors'][0]['location'], u'body') self.assertEqual(response.json['errors'][0]['description'], u"Can't update asset in current (deleted) status") # Move status from Deleted to Active response = self.app.patch_json('/{}'.format(asset['id']), headers=self.access_header, params={'data': {'status': 'active'}}, status=403) self.assertEqual(response.status, '403 Forbidden') self.assertEqual(response.content_type, 'application/json') self.assertEqual(response.json['errors'][0]['name'], u'data') self.assertEqual(response.json['errors'][0]['location'], u'body') self.assertEqual(response.json['errors'][0]['description'], u"Can't update asset in current (deleted) status") # Move status from Deleted to Complete response = self.app.patch_json('/{}'.format(asset['id']), headers=self.access_header, params={'data': {'status': 'complete'}}, status=403) self.assertEqual(response.status, '403 Forbidden') self.assertEqual(response.content_type, 'application/json') self.assertEqual(response.json['errors'][0]['name'], u'data') self.assertEqual(response.json['errors'][0]['location'], u'body') self.assertEqual(response.json['errors'][0]['description'], u"Can't update asset in current (deleted) status") def asset_concierge_patch(self): asset = self.create_resource() response = self.app.get('/{}'.format(asset['id'])) self.assertEqual(response.status, '200 OK') self.assertEqual(response.content_type, 'application/json') self.assertEqual(response.json['data'], asset) # Move status from Draft to Pending response = self.app.patch_json('/{}'.format(asset['id']), headers=self.access_header, params={'data': {'status': 'pending'}}) self.assertEqual(response.status, '200 OK') self.assertEqual(response.content_type, 'application/json') self.assertEqual(response.json['data']['status'], 'pending') self.app.authorization = ('Basic', ('concierge', '')) # Move status from pending to verification response = self.app.patch_json('/{}'.format( asset['id']), {'data': {'status': 'verification'}}) self.assertEqual(response.status, '200 OK') self.assertEqual(response.content_type, 'application/json') self.assertEqual(response.json['data']['status'], 'verification') # Move status from verification to Pending response = self.app.patch_json('/{}'.format( asset['id']), {'data': {'status': 'pending'}}) self.assertEqual(response.status, '200 OK') self.assertEqual(response.content_type, 'application/json') self.assertEqual(response.json['data']['status'], 'pending') # Move status from pending to verification response = self.app.patch_json('/{}'.format( asset['id']), {'data': {'status': 'verification'}}) self.assertEqual(response.status, '200 OK') self.assertEqual(response.content_type, 'application/json') self.assertEqual(response.json['data']['status'], 'verification') # Move status from verification to Active withour relatedLot response = self.app.patch_json('/{}'.format( asset['id']), {'data': {'status': 'active'}}, status=422) self.assertEqual(response.status, '422 Unprocessable Entity') self.assertEqual(response.content_type, 'application/json') self.assertEqual(response.json['errors'][0]['description'][0], 'This field is required.') # Move status from verification to Active relatedLot = uuid4().hex response = self.app.patch_json('/{}'.format( asset['id']), {'data': {'status': 'active', 'relatedLot': relatedLot}}) self.assertEqual(response.status, '200 OK') self.assertEqual(response.content_type, 'application/json') self.assertEqual(response.json['data']['status'], 'active') self.assertEqual(response.json['data']['relatedLot'], relatedLot) # Move status from Active to Draft response = self.app.patch_json('/{}'.format( asset['id']), {'data': {'status': 'draft'}}, status=403) self.assertEqual(response.status, '403 Forbidden') self.assertEqual(response.content_type, 'application/json') self.assertEqual(response.json['errors'][0]['name'], u'data') self.assertEqual(response.json['errors'][0]['location'], u'body') self.assertEqual(response.json['errors'][0]['description'], u"Can't switch asset to draft status") # Move status from Active to Deleted response = self.app.patch_json('/{}'.format( asset['id']), {'data': {'status': 'deleted'}}, status=403) self.assertEqual(response.status, '403 Forbidden') self.assertEqual(response.content_type, 'application/json') self.assertEqual(response.json['errors'][0]['name'], u'data') self.assertEqual(response.json['errors'][0]['location'], u'body') self.assertEqual(response.json['errors'][0]['description'], u"Can't update asset in current (active) status") # Move status from Active to Pending response = self.app.patch_json('/{}'.format( asset['id']), {'data': {'status': 'pending'}}) self.assertEqual(response.status, '200 OK') self.assertEqual(response.content_type, 'application/json') self.assertEqual(response.json['data']['status'], 'pending') self.assertNotIn('relatedLot', response.json['data']) # Move status from Pending to Deleted response = self.app.patch_json('/{}'.format( asset['id']), {'data': {'status': 'deleted'}}, status=403) self.assertEqual(response.status, '403 Forbidden') self.assertEqual(response.content_type, 'application/json') self.assertEqual(response.json['errors'][0]['name'], u'data') self.assertEqual(response.json['errors'][0]['location'], u'body') self.assertEqual(response.json['errors'][0]['description'], u"Can't update asset in current (pending) status") # Move status from Pending to Draft response = self.app.patch_json('/{}'.format( asset['id']), {'data': {'status': 'draft'}}, status=403) self.assertEqual(response.status, '403 Forbidden') self.assertEqual(response.content_type, 'application/json') self.assertEqual(response.json['errors'][0]['name'], u'data') self.assertEqual(response.json['errors'][0]['location'], u'body') self.assertEqual(response.json['errors'][0]['description'], u"Can't switch asset to draft status") # Move status from Pending to Complete response = self.app.patch_json('/{}'.format( asset['id']), {'data': {'status': 'complete'}}, status=403) self.assertEqual(response.status, '403 Forbidden') self.assertEqual(response.content_type, 'application/json') self.assertEqual(response.json['errors'][0]['name'], u'data') self.assertEqual(response.json['errors'][0]['location'], u'body') self.assertEqual(response.json['errors'][0]['description'], u"Can't update asset in current (pending) status") # Move status from pending to verification response = self.app.patch_json('/{}'.format(asset['id']), headers=self.access_header, params={'data': {'status': 'verification'}}) self.assertEqual(response.status, '200 OK') self.assertEqual(response.content_type, 'application/json') self.assertEqual(response.json['data']['status'], 'verification') # Move status from verification to active relatedLot = uuid4().hex response = self.app.patch_json('/{}'.format( asset['id']), {'data': {'status': 'active', 'relatedLot': relatedLot}}) self.assertEqual(response.status, '200 OK') self.assertEqual(response.content_type, 'application/json') self.assertEqual(response.json['data']['status'], 'active') self.assertEqual(response.json['data']['relatedLot'], relatedLot) # Move status from Active to Complete response = self.app.patch_json('/{}'.format( asset['id']), {'data': {'status': 'complete'}}) self.assertEqual(response.status, '200 OK') self.assertEqual(response.content_type, 'application/json') self.assertEqual(response.json['data']['status'], 'complete') # Move status from Complete to Draft response = self.app.patch_json('/{}'.format( asset['id']), {'data': {'status': 'deleted'}}, status=403) self.assertEqual(response.status, '403 Forbidden') self.assertEqual(response.content_type, 'application/json') self.assertEqual(response.json['errors'][0]['name'], u'data') self.assertEqual(response.json['errors'][0]['location'], u'body') self.assertEqual(response.json['errors'][0]['description'], u"Can't update asset in current (complete) status") # Move status from Complete to Pending response = self.app.patch_json('/{}'.format( asset['id']), {'data': {'status': 'deleted'}}, status=403) self.assertEqual(response.status, '403 Forbidden') self.assertEqual(response.content_type, 'application/json') self.assertEqual(response.json['errors'][0]['name'], u'data') self.assertEqual(response.json['errors'][0]['location'], u'body') self.assertEqual(response.json['errors'][0]['description'], u"Can't update asset in current (complete) status") # Move status from Complete to Active response = self.app.patch_json('/{}'.format( asset['id']), {'data': {'status': 'deleted'}}, status=403) self.assertEqual(response.status, '403 Forbidden') self.assertEqual(response.content_type, 'application/json') self.assertEqual(response.json['errors'][0]['name'], u'data') self.assertEqual(response.json['errors'][0]['location'], u'body') self.assertEqual(response.json['errors'][0]['description'], u"Can't update asset in current (complete) status") # Move status from Complete to Deleted response = self.app.patch_json('/{}'.format( asset['id']), {'data': {'status': 'deleted'}}, status=403) self.assertEqual(response.status, '403 Forbidden') self.assertEqual(response.content_type, 'application/json') self.assertEqual(response.json['errors'][0]['name'], u'data') self.assertEqual(response.json['errors'][0]['location'], u'body') self.assertEqual(response.json['errors'][0]['description'], u"Can't update asset in current (complete) status") def administrator_change_delete_status(self): response = self.app.get('/') self.assertEqual(response.status, '200 OK') self.assertEqual(len(response.json['data']), 0) self.app.authorization = ('Basic', ('broker', '')) asset = self.create_resource() response = self.app.get('/{}'.format(asset['id'])) self.assertEqual(response.status, '200 OK') self.assertEqual(response.content_type, 'application/json') self.assertEqual(response.json['data'], asset) self.app.authorization = ('Basic', ('administrator', '')) response = self.app.patch_json( '/{}'.format(asset['id']), {'data': {'status': 'pending'}} ) self.assertEqual(response.status, '200 OK') response = self.app.patch_json( '/{}'.format(asset['id']), {'data': {'status': 'deleted'}} ) self.assertEqual(response.status, '200 OK') response = self.app.patch_json('/{}'.format( asset['id']), {'data': {'status': 'deleted'}}, status=403) self.assertEqual(response.status, '403 Forbidden') self.assertEqual(response.content_type, 'application/json') self.assertEqual(response.json['errors'][0]['name'], u'data') self.assertEqual(response.json['errors'][0]['location'], u'body') self.assertEqual(response.json['errors'][0]['description'], u"Can't update asset in current (deleted) status") def administrator_change_complete_status(self): response = self.app.get('/') self.assertEqual(response.status, '200 OK') self.assertEqual(len(response.json['data']), 0) self.app.authorization = ('Basic', ('broker', '')) asset = self.create_resource() response = self.app.get('/{}'.format(asset['id'])) self.assertEqual(response.status, '200 OK') self.assertEqual(response.content_type, 'application/json') self.assertEqual(response.json['data'], asset) self.app.authorization = ('Basic', ('administrator', '')) response = self.app.patch_json( '/{}'.format(asset['id']), {'data': {'status': 'pending'}} ) self.assertEqual(response.status, '200 OK') response = self.app.patch_json( '/{}'.format(asset['id']), {'data': {'status': 'verification'}} ) self.assertEqual(response.status, '200 OK') # XXX TODO Describe actives response = self.app.patch_json( '/{}'.format(asset['id']), {'data': {'status': 'pending'}} ) self.assertEqual(response.status, '200 OK') response = self.app.patch_json( '/{}'.format(asset['id']), {'data': {'status': 'verification'}} ) self.assertEqual(response.status, '200 OK') response = self.app.patch_json( '/{}'.format(asset['id']), {'data': {'status': 'active', 'relatedLot': uuid4().hex}} ) self.assertEqual(response.status, '200 OK') response = self.app.patch_json( '/{}'.format(asset['id']), {'data': {'status': 'pending'}} ) self.assertEqual(response.status, '200 OK') response = self.app.patch_json( '/{}'.format(asset['id']), {'data': {'status': 'verification'}} ) self.assertEqual(response.status, '200 OK') response = self.app.patch_json( '/{}'.format(asset['id']), {'data': {'status': 'active', 'relatedLot': uuid4().hex}} ) self.assertEqual(response.status, '200 OK') response = self.app.patch_json( '/{}'.format(asset['id']), {'data': {'status': 'complete'}} ) self.assertEqual(response.status, '200 OK') response = self.app.patch_json('/{}'.format( asset['id']), {'data': {'status': 'deleted'}}, status=403) self.assertEqual(response.status, '403 Forbidden') self.assertEqual(response.content_type, 'application/json') self.assertEqual(response.json['errors'][0]['name'], u'data') self.assertEqual(response.json['errors'][0]['location'], u'body') self.assertEqual(response.json['errors'][0]['description'], u"Can't update asset in current (complete) status") # AssetTest def simple_add_asset(self): u = self.asset_model(self.initial_data) u.assetID = "UA-X" assert u.id is None assert u.rev is None u.store(self.db) assert u.id is not None assert u.rev is not None fromdb = self.db.get(u.id) assert u.assetID == fromdb['assetID'] assert u.doc_type == "Asset" u.delete_instance(self.db) # Asset workflow test ROLES = ['asset_owner', 'Administrator', 'concierge', 'convoy'] STATUS_BLACKLIST = create_blacklist(STATUS_CHANGES, ASSET_STATUSES, ROLES) def check_patch_status_200(self, asset_id, asset_status, headers=None, extra_data={}): patch_data = {'status': asset_status} patch_data = patch_data.update(extra_data) or patch_data response = self.app.patch_json( '/{}'.format(asset_id), params={'data': patch_data}, headers=headers ) self.assertEqual(response.status, '200 OK') self.assertEqual(response.content_type, 'application/json') self.assertEqual(response.json['data']['status'], patch_data['status']) for field, value in extra_data.items(): self.assertEqual(response.json['data'][field], value) return response def check_patch_status_403(self, asset_id, asset_status, headers=None): response = self.app.patch_json( '/{}'.format(asset_id), params={'data': {'status': asset_status}}, headers=headers, status=403 ) self.assertEqual(response.status, '403 Forbidden') self.assertEqual(response.content_type, 'application/json') return response def change_draft_asset(self): self.initial_status = 'draft' response = self.app.get('/') self.assertEqual(response.status, '200 OK') self.assertEqual(len(response.json['data']), 0) asset = self.create_resource() self.app.authorization = ('Basic', ('concierge', '')) # Move from 'draft' to one of blacklist status for status in STATUS_BLACKLIST['draft']['concierge']: check_patch_status_403(self, asset['id'], status) self.app.authorization = ('Basic', ('convoy', '')) # Move from 'draft' to one of blacklist status for status in STATUS_BLACKLIST['draft']['convoy']: check_patch_status_403(self, asset['id'], status) self.app.authorization = ('Basic', ('broker', '')) # Move from 'draft' to one of blacklist status for status in STATUS_BLACKLIST['draft']['asset_owner']: check_patch_status_403(self, asset['id'], status, self.access_header) # Move from 'draft' to 'draft' status check_patch_status_200(self, asset['id'], 'draft', self.access_header) # Move from 'draft' to 'pending' status check_patch_status_200(self, asset['id'], 'pending', self.access_header) asset = self.create_resource() self.app.authorization = ('Basic', ('administrator', '')) # Move from 'draft' to one of blacklist status for status in STATUS_BLACKLIST['draft']['Administrator']: check_patch_status_403(self, asset['id'], status) # Move from 'draft' to 'draft' status check_patch_status_200(self, asset['id'], 'draft', self.access_header) # Move from 'draft' to 'pending' status check_patch_status_200(self, asset['id'], 'pending', self.access_header) def change_pending_asset(self): response = self.app.get('/') self.assertEqual(response.status, '200 OK') self.assertEqual(len(response.json['data']), 0) asset = self.create_resource() self.app.authorization = ('Basic', ('convoy', '')) # Move from 'pending' to one of blacklist status for status in STATUS_BLACKLIST['pending']['convoy']: check_patch_status_403(self, asset['id'], status) self.app.authorization = ('Basic', ('broker', '')) # Move from 'pending' to one of blacklist status for status in STATUS_BLACKLIST['pending']['asset_owner']: check_patch_status_403(self, asset['id'], status, self.access_header) # Move from 'pending' to 'pending' status check_patch_status_200(self, asset['id'], 'pending', self.access_header) # Move from 'pending' to 'deleted' status check_patch_status_200(self, asset['id'], 'deleted', self.access_header) asset = self.create_resource() self.app.authorization = ('Basic', ('administrator', '')) # Move from 'pending' to one of blacklist status for status in STATUS_BLACKLIST['pending']['Administrator']: check_patch_status_403(self, asset['id'], status) # Move from 'pending' to 'pending' status check_patch_status_200(self, asset['id'], 'pending') # Move from 'pending' to 'verification' status check_patch_status_200(self, asset['id'], 'verification') # Move from 'verification' to 'pending' status check_patch_status_200(self, asset['id'], 'pending') # Move from 'pending' to 'deleted' status check_patch_status_200(self, asset['id'], 'deleted') self.app.authorization = ('Basic', ('broker', '')) asset = self.create_resource() self.app.authorization = ('Basic', ('concierge', '')) # Move from 'pending' to one of blacklist status for status in STATUS_BLACKLIST['pending']['concierge']: check_patch_status_403(self, asset['id'], status) # Move from 'pending' to 'pending' status check_patch_status_200(self, asset['id'], 'pending') # Move from 'pending' to 'verification' status check_patch_status_200(self, asset['id'], 'verification') def change_verification_asset(self): self.initial_status = 'verification' response = self.app.get('/') self.assertEqual(response.status, '200 OK') self.assertEqual(len(response.json['data']), 0) asset = self.create_resource() # Move from 'verification' to one of blacklist status for status in STATUS_BLACKLIST['verification']['asset_owner']: check_patch_status_403(self, asset['id'], status, self.access_header) self.app.authorization = ('Basic', ('convoy', '')) # Move from 'verification' to one of blacklist status for status in STATUS_BLACKLIST['verification']['convoy']: check_patch_status_403(self, asset['id'], status) self.app.authorization = ('Basic', ('concierge', '')) # Move from 'verification' to one of blacklist status for status in STATUS_BLACKLIST['verification']['concierge']: check_patch_status_403(self, asset['id'], status) # Move from 'verification' to 'verification status check_patch_status_200(self, asset['id'], 'verification') # Move from 'verification to 'pending' status check_patch_status_200(self, asset['id'], 'pending') # Move from 'pending' to 'verification' status check_patch_status_200(self, asset['id'], 'verification') # Move from 'verification' to 'active' status check_patch_status_200(self, asset['id'], 'active', extra_data={'relatedLot': uuid4().hex}) self.app.authorization = ('Basic', ('broker', '')) asset = self.create_resource() self.app.authorization = ('Basic', ('administrator', '')) # Move from 'verification' to one of blacklist status for status in STATUS_BLACKLIST['verification']['Administrator']: check_patch_status_403(self, asset['id'], status) # Move from 'verification' to 'verification' status check_patch_status_200(self, asset['id'], 'verification') # Move from 'verification to 'pending' status check_patch_status_200(self, asset['id'], 'pending') # Move from 'pending' to 'verification' status check_patch_status_200(self, asset['id'], 'verification') # Move from 'verification' to 'active' status check_patch_status_200(self, asset['id'], 'active', extra_data={'relatedLot': uuid4().hex}) def change_active_asset(self): self.initial_status = 'active' response = self.app.get('/') self.assertEqual(response.status, '200 OK') self.assertEqual(len(response.json['data']), 0) asset = self.create_resource() # Move from 'active' to one of blacklist status for status in STATUS_BLACKLIST['active']['asset_owner']: check_patch_status_403(self, asset['id'], status, self.access_header) self.app.authorization = ('Basic', ('convoy', '')) # Move from 'active' to one of blacklist status for status in STATUS_BLACKLIST['active']['convoy']: check_patch_status_403(self, asset['id'], status) self.app.authorization = ('Basic', ('concierge', '')) # Move from 'active' to one of blacklist status for status in STATUS_BLACKLIST['active']['concierge']: check_patch_status_403(self, asset['id'], status) # Move from 'active' to 'active status check_patch_status_200(self, asset['id'], 'active', extra_data={'relatedLot': uuid4().hex}) # Move from 'active' to 'pending' status check_patch_status_200(self, asset['id'], 'pending') # Move from 'pending' to 'verification' status check_patch_status_200(self, asset['id'], 'verification') # Move from 'verification' to 'active' status check_patch_status_200(self, asset['id'], 'active', extra_data={'relatedLot': uuid4().hex}) # Move from 'active' to 'complete' status check_patch_status_200(self, asset['id'], 'complete') self.app.authorization = ('Basic', ('broker', '')) asset = self.create_resource() self.app.authorization = ('Basic', ('administrator', '')) # Move from 'active' to one of blacklist status for status in STATUS_BLACKLIST['active']['Administrator']: check_patch_status_403(self, asset['id'], status) # Move from 'active' to 'active status check_patch_status_200(self, asset['id'], 'active', extra_data={'relatedLot': uuid4().hex}) # Move from 'active' to 'pending' status check_patch_status_200(self, asset['id'], 'pending') # Move from 'pending' to 'verification' status check_patch_status_200(self, asset['id'], 'verification') # Move from 'verification' to 'active' status check_patch_status_200(self, asset['id'], 'active', extra_data={'relatedLot': uuid4().hex}) # Move from 'active' to 'complete' status check_patch_status_200(self, asset['id'], 'complete') def change_deleted_asset(self): self.initial_status = 'deleted' response = self.app.get('/') self.assertEqual(response.status, '200 OK') self.assertEqual(len(response.json['data']), 0) asset = self.create_resource() # Move from 'deleted' to one of blacklist status for status in STATUS_BLACKLIST['deleted']['asset_owner']: check_patch_status_403(self, asset['id'], status, self.access_header) self.app.authorization = ('Basic', ('convoy', '')) # Move from 'deleted' to one of blacklist status for status in STATUS_BLACKLIST['deleted']['convoy']: check_patch_status_403(self, asset['id'], status) self.app.authorization = ('Basic', ('concierge', '')) # Move from 'deleted' to one of blacklist status for status in STATUS_BLACKLIST['deleted']['concierge']: check_patch_status_403(self, asset['id'], status) self.app.authorization = ('Basic', ('administrator', '')) # Move from 'deleted' to one of blacklist status for status in STATUS_BLACKLIST['deleted']['Administrator']: check_patch_status_403(self, asset['id'], status) def change_complete_asset(self): self.initial_status = 'complete' response = self.app.get('/') self.assertEqual(response.status, '200 OK') self.assertEqual(len(response.json['data']), 0) asset = self.create_resource() # Move from 'complete' to one of blacklist status for status in STATUS_BLACKLIST['complete']['asset_owner']: check_patch_status_403(self, asset['id'], status, self.access_header) self.app.authorization = ('Basic', ('convoy', '')) # Move from 'complete' to one of blacklist status for status in STATUS_BLACKLIST['complete']['convoy']: check_patch_status_403(self, asset['id'], status) self.app.authorization = ('Basic', ('concierge', '')) # Move from 'complete' to one of blacklist status for status in STATUS_BLACKLIST['complete']['concierge']: check_patch_status_403(self, asset['id'], status) self.app.authorization = ('Basic', ('administrator', '')) # Move from 'complete' to one of blacklist status for status in STATUS_BLACKLIST['complete']['Administrator']: check_patch_status_403(self, asset['id'], status) def patch_decimal_quantity(self): """Testing different decimal quantity (decimal_numbers) at the root of assets.""" asset = self.create_resource() for quantity in [3, '3', 7.658, '7.658', 2.3355, '2.3355']: response = self.app.patch_json('/{}'.format(asset['id']), headers=self.access_header, params={'data': {'quantity': quantity}}) self.assertEqual(response.status, '200 OK') self.assertEqual(response.content_type, 'application/json') self.assertNotIsInstance(response.json['data']['quantity'], basestring) rounded_quantity = round(float(quantity), 3) self.assertEqual(response.json['data']['quantity'], rounded_quantity) def patch_decimal_item_quantity(self): """ Testing different decimal quantity (decimal_numbers) at the root and items of assets.""" precision = self.precision if hasattr(self, 'precision') else 3 asset = self.create_resource() for quantity in [3, '3', 7.658, '7.658', 2.3355, '2.3355']: response = self.app.patch_json('/{}'.format(asset['id']), headers=self.access_header, params={'data': {'items': [{'quantity': quantity} for _ in asset['items']]}}) self.assertEqual(response.status, '200 OK') self.assertEqual(response.content_type, 'application/json') for item in response.json['data']['items']: self.assertNotIsInstance(item['quantity'], basestring) rounded_quantity = round(float(quantity), precision) for item in response.json['data']['items']: self.assertEqual(item['quantity'], rounded_quantity) def koatuu_additional_classification(self): input_classification = [{"scheme": "koatuu", "id": "0110136600", "description": "test"}] initial_data = deepcopy(self.initial_data) initial_data['additionalClassifications'] = input_classification response = self.app.post_json('/', {'data': initial_data}) output_classification = response.json['data']['additionalClassifications'] self.assertEqual(input_classification, output_classification) initial_data['additionalClassifications'][0]['id'] = '1421580802' self.app.post_json('/', {'data': initial_data}, status=201) initial_data['additionalClassifications'][0]['id'] = '1110136600' response = self.app.post_json('/', {'data': initial_data}, status=422) self.assertEqual(response.status, '422 Unprocessable Entity') initial_data['additionalClassifications'][0]['id'] = '7510136600' response = self.app.post_json('/', {'data': initial_data}, status=422) self.assertEqual(response.status, '422 Unprocessable Entity')

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50874dab738449759dabee5668c3f4eabff04d9e

2,297

py

Python

tests/aat/common/matcher/__init__.py

abelov-spirent/openperf

303b6b5534973ea145a8223a55ee7b65e7464e25

[ "Apache-2.0" ]

null

tests/aat/common/matcher/__init__.py

abelov-spirent/openperf

303b6b5534973ea145a8223a55ee7b65e7464e25

[ "Apache-2.0" ]

null

tests/aat/common/matcher/__init__.py

abelov-spirent/openperf

303b6b5534973ea145a8223a55ee7b65e7464e25

[ "Apache-2.0" ]

null

from __future__ import absolute_import # api_exception.py from common.matcher.api_exception import raise_api_exception # interface.py from common.matcher.interface import be_valid_interface # module.py from common.matcher.module import be_valid_module # packet_analyzer.py from common.matcher.packet_analyzer import be_valid_packet_analyzer from common.matcher.packet_analyzer import be_valid_packet_analyzer_result from common.matcher.packet_analyzer import be_valid_receive_flow # packet_capture.py from common.matcher.packet_capture import be_valid_packet_capture from common.matcher.packet_capture import be_valid_packet_capture_result # packet_generator.py from common.matcher.packet_generator import be_valid_packet_generator from common.matcher.packet_generator import be_valid_packet_generator_config from common.matcher.packet_generator import be_valid_packet_generator_flow_counters from common.matcher.packet_generator import be_valid_packet_generator_result from common.matcher.packet_generator import be_valid_transmit_flow from common.matcher.packet_generator import be_valid_traffic_definition from common.matcher.packet_generator import be_valid_traffic_duration from common.matcher.packet_generator import be_valid_traffic_length from common.matcher.packet_generator import be_valid_traffic_load from common.matcher.packet_generator import be_valid_traffic_packet_template # port.py from common.matcher.port import be_valid_port # stack.py from common.matcher.stack import be_valid_stack # timesync.py from common.matcher.timesync import be_valid_counter from common.matcher.timesync import be_valid_keeper from common.matcher.timesync import be_valid_source # block.py from common.matcher.block import be_valid_block_generator from common.matcher.block import be_valid_block_generator_result from common.matcher.block import be_valid_block_device from common.matcher.block import be_valid_block_file # memory.py from common.matcher.memory import be_valid_memory_generator from common.matcher.memory import be_valid_memory_generator_result from common.matcher.memory import be_valid_memory_info # cpu.py from common.matcher.cpu import be_valid_cpu_info from common.matcher.cpu import be_valid_cpu_generator from common.matcher.cpu import be_valid_cpu_generator_result

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null

0

1

0

1

0

1

0

7

5089a2ede0bc6199d249cb9a112877653839f32d

40,865

py

Python

opnsense_cli/commands/plugin/haproxy/acl.py

jan-win1993/opn-cli

83c4792571dacbe6483722a95276954c7a2d0b3c

[ "BSD-2-Clause" ]

13

2021-05-17T10:42:25.000Z

2022-02-21T02:10:41.000Z

opnsense_cli/commands/plugin/haproxy/acl.py

jan-win1993/opn-cli

83c4792571dacbe6483722a95276954c7a2d0b3c

[ "BSD-2-Clause" ]

14

2021-05-17T13:53:27.000Z

2021-12-16T12:45:44.000Z

opnsense_cli/commands/plugin/haproxy/acl.py

jan-win1993/opn-cli

83c4792571dacbe6483722a95276954c7a2d0b3c

[ "BSD-2-Clause" ]

2

2021-04-28T08:41:07.000Z

2022-03-28T10:20:51.000Z

import click from opnsense_cli.formatters.cli_output import CliOutputFormatter from opnsense_cli.callbacks.click import \ formatter_from_formatter_name, bool_as_string, available_formats, int_as_string, tuple_to_csv, \ resolve_linked_names_to_uuids from opnsense_cli.types.click_param_type.int_or_empty import INT_OR_EMPTY from opnsense_cli.commands.plugin.haproxy import haproxy from opnsense_cli.api.client import ApiClient from opnsense_cli.api.plugin.haproxy import Settings, Service from opnsense_cli.facades.commands.plugin.haproxy.acl import HaproxyAclFacade pass_api_client = click.make_pass_decorator(ApiClient) pass_haproxy_acl_svc = click.make_pass_decorator(HaproxyAclFacade) @haproxy.group() @pass_api_client @click.pass_context def acl(ctx, api_client: ApiClient, **kwargs): """ Specify various conditions. Define custom rules for blocking malicious requests, choosing backends, redirecting to HTTPS and using cached objects. """ settings_api = Settings(api_client) service_api = Service(api_client) ctx.obj = HaproxyAclFacade(settings_api, service_api) @acl.command() @click.option( '--output', '-o', help='Specifies the Output format.', default="table", type=click.Choice(available_formats()), callback=formatter_from_formatter_name, show_default=True, ) @click.option( '--cols', '-c', help='Which columns should be printed? Pass empty string (-c '') to show all columns', default=( "uuid,name,description,expression,negate" ), show_default=True, ) @pass_haproxy_acl_svc def list(haproxy_acl_svc: HaproxyAclFacade, **kwargs): """ Show all acl """ result = haproxy_acl_svc.list_acls() CliOutputFormatter(result, kwargs['output'], kwargs['cols'].split(",")).echo() @acl.command() @click.argument('uuid') @click.option( '--output', '-o', help='Specifies the Output format.', default="table", type=click.Choice(available_formats()), callback=formatter_from_formatter_name, show_default=True, ) @click.option( '--cols', '-c', help='Which columns should be printed? Pass empty string (-c '') to show all columns', default=( "name,description,expression,negate,hdr_beg,hdr_end,hdr,hdr_reg,hdr_sub,path_beg,path_end,path,path_reg," "path_dir,path_sub,cust_hdr_beg_name,cust_hdr_beg,cust_hdr_end_name,cust_hdr_end,cust_hdr_name,cust_hdr," "cust_hdr_reg_name,cust_hdr_reg,cust_hdr_sub_name,cust_hdr_sub,url_param,url_param_value,ssl_c_verify_code," "ssl_c_ca_commonname,src,src_bytes_in_rate_comparison,src_bytes_in_rate,src_bytes_out_rate_comparison," "src_bytes_out_rate,src_conn_cnt_comparison,src_conn_cnt,src_conn_cur_comparison,src_conn_cur," "src_conn_rate_comparison,src_conn_rate,src_http_err_cnt_comparison,src_http_err_cnt," "src_http_err_rate_comparison,src_http_err_rate,src_http_req_cnt_comparison,src_http_req_cnt," "src_http_req_rate_comparison,src_http_req_rate,src_kbytes_in_comparison,src_kbytes_in," "src_kbytes_out_comparison,src_kbytes_out,src_port_comparison,src_port,src_sess_cnt_comparison," "src_sess_cnt,src_sess_rate_comparison,src_sess_rate,nbsrv,nbsrv_backend,BackendNrSrv,ssl_fc_sni,ssl_sni," "ssl_sni_sub,ssl_sni_beg,ssl_sni_end,ssl_sni_reg,custom_acl,value,urlparam," "queryBackend,BackendQuery,allowedUsers,Users,allowedGroups,Groups" ), show_default=True, ) @pass_haproxy_acl_svc def show(haproxy_acl_svc: HaproxyAclFacade, **kwargs): """ Show details for acl """ result = haproxy_acl_svc.show_acl(kwargs['uuid']) CliOutputFormatter(result, kwargs['output'], kwargs['cols'].split(",")).echo() @acl.command() @click.argument('name') @click.option( '--description', help=('Description for this condition.'), show_default=True, default=None, required=False, ) @click.option( '--expression', help=('Type of condition'), type=click.Choice( [ 'http_auth', 'hdr_beg', 'hdr_end', 'hdr', 'hdr_reg', 'hdr_sub', 'path_beg', 'path_end', 'path', 'path_reg', 'path_dir', 'path_sub', 'cust_hdr_beg', 'cust_hdr_end', 'cust_hdr', 'cust_hdr_reg', 'cust_hdr_sub', 'url_param', 'ssl_c_verify', 'ssl_c_verify_code', 'ssl_c_ca_commonname', 'src', 'src_is_local', 'src_port', 'src_bytes_in_rate', 'src_bytes_out_rate', 'src_kbytes_in', 'src_kbytes_out', 'src_conn_cnt', 'src_conn_cur', 'src_conn_rate', 'src_http_err_cnt', 'src_http_err_rate', 'src_http_req_cnt', 'src_http_req_rate', 'src_sess_cnt', 'src_sess_rate', 'nbsrv', 'traffic_is_http', 'traffic_is_ssl', 'ssl_fc', 'ssl_fc_sni', 'ssl_sni', 'ssl_sni_sub', 'ssl_sni_beg', 'ssl_sni_end', 'ssl_sni_reg', 'custom_acl' ] ), multiple=False, callback=tuple_to_csv, show_default=True, default=None, required=True, ) @click.option( '--negate/--no-negate', help=('Use this to invert the meaning of the expression.'), show_default=True, is_flag=True, callback=bool_as_string, default=True, required=True, ) @click.option( '--hdr_beg', help=('HTTP host header starts with string (prefix match)'), show_default=True, default=None, required=False, ) @click.option( '--hdr_end', help=('HTTP host header ends with string (suffix match)'), show_default=True, default=None, required=False, ) @click.option( '--hdr', help=('HTTP host header matches exact string'), show_default=True, default=None, required=False, ) @click.option( '--hdr_reg', help=('HTTP host header matches regular expression'), show_default=True, default=None, required=False, ) @click.option( '--hdr_sub', help=('HTTP host header contains string (substring match)'), show_default=True, default=None, required=False, ) @click.option( '--path_beg', help=('HTTP request URL path starts with string (prefix match)'), show_default=True, default=None, required=False, ) @click.option( '--path_end', help=('HTTP request URL path ends with string (suffix match)'), show_default=True, default=None, required=False, ) @click.option( '--path', help=('HTTP request URL path matches exact string'), show_default=True, default=None, required=False, ) @click.option( '--path_reg', help=('HTTP request URL path matches regular expression'), show_default=True, default=None, required=False, ) @click.option( '--path_dir', help=('HTTP request URL path contains directory (subdir match)'), show_default=True, default=None, required=False, ) @click.option( '--path_sub', help=('HTTP request URL path contains string (substring match)'), show_default=True, default=None, required=False, ) @click.option( '--cust_hdr_beg_name', help=('The name of the HTTP Header.'), show_default=True, default=None, required=False, ) @click.option( '--cust_hdr_beg', help=('HTTP Header starts with string (prefix match)'), show_default=True, default=None, required=False, ) @click.option( '--cust_hdr_end_name', help=('The name of the HTTP Header.'), show_default=True, default=None, required=False, ) @click.option( '--cust_hdr_end', help=('HTTP Header ends with string (suffix match)'), show_default=True, default=None, required=False, ) @click.option( '--cust_hdr_name', help=('The name of the HTTP Header.'), show_default=True, default=None, required=False, ) @click.option( '--cust_hdr', help=('HTTP Header matches exact string'), show_default=True, default=None, required=False, ) @click.option( '--cust_hdr_reg_name', help=('The name of the HTTP Header.'), show_default=True, default=None, required=False, ) @click.option( '--cust_hdr_reg', help=('HTTP Header matches regular expression'), show_default=True, default=None, required=False, ) @click.option( '--cust_hdr_sub_name', help=('The name of the HTTP Header.'), show_default=True, default=None, required=False, ) @click.option( '--cust_hdr_sub', help=('HTTP Header contains string (substring match)'), show_default=True, default=None, required=False, ) @click.option( '--url_param', help=('Specify the URL parameter to be checked for the value specified below.'), show_default=True, default=None, required=False, ) @click.option( '--url_param_value', help=('Specify the value for the URL parameter.'), show_default=True, default=None, required=False, ) @click.option( '--ssl_c_verify_code', help=( 'Specify the SSL/TLS error ID that should be checked for the incoming connection. ' 'Please refer to your SSL library\'s documentation for an exhaustive list of error codes.' ), show_default=True, type=INT_OR_EMPTY, callback=int_as_string, default=None, required=False, ) @click.option( '--ssl_c_ca_commonname', help=('Verify the CA Common-Name of the certificate presented by the client against the specified string.'), show_default=True, default=None, required=False, ) @click.option( '--src', help=('Verify the source IPv4 address of the client of the session matches the specified IPv4 or IPv6 address.'), show_default=True, default=None, required=False, ) @click.option( '--src_bytes_in_rate_comparison', help=('None'), type=click.Choice(['', 'gt', 'ge', 'eq', 'lt', 'le']), multiple=False, callback=tuple_to_csv, show_default=True, default='gt', required=False, ) @click.option( '--src_bytes_in_rate', help=('The average bytes rate from the incoming connection\'s source address.'), show_default=True, type=INT_OR_EMPTY, callback=int_as_string, default=None, required=False, ) @click.option( '--src_bytes_out_rate_comparison', help=('None'), type=click.Choice(['', 'gt', 'ge', 'eq', 'lt', 'le']), multiple=False, callback=tuple_to_csv, show_default=True, default='gt', required=False, ) @click.option( '--src_bytes_out_rate', help=('The average bytes rate to the incoming connection\'s source address.'), show_default=True, type=INT_OR_EMPTY, callback=int_as_string, default=None, required=False, ) @click.option( '--src_conn_cnt_comparison', help=('None'), type=click.Choice(['', 'gt', 'ge', 'eq', 'lt', 'le']), multiple=False, callback=tuple_to_csv, show_default=True, default='gt', required=False, ) @click.option( '--src_conn_cnt', help=('The cumulative number of connections initiated from the current incoming connection\'s source address.'), show_default=True, type=INT_OR_EMPTY, callback=int_as_string, default=None, required=False, ) @click.option( '--src_conn_cur_comparison', help=('None'), type=click.Choice(['', 'gt', 'ge', 'eq', 'lt', 'le']), multiple=False, callback=tuple_to_csv, show_default=True, default='gt', required=False, ) @click.option( '--src_conn_cur', help=( 'The current amount of concurrent connections initiated from the current incoming connection\'s source address.' ), show_default=True, type=INT_OR_EMPTY, callback=int_as_string, default=None, required=False, ) @click.option( '--src_conn_rate_comparison', help=('None'), type=click.Choice(['', 'gt', 'ge', 'eq', 'lt', 'le']), multiple=False, callback=tuple_to_csv, show_default=True, default='gt', required=False, ) @click.option( '--src_conn_rate', help=('The average connection rate from the incoming connection\'s source address.'), show_default=True, type=INT_OR_EMPTY, callback=int_as_string, default=None, required=False, ) @click.option( '--src_http_err_cnt_comparison', help=('None'), type=click.Choice(['', 'gt', 'ge', 'eq', 'lt', 'le']), multiple=False, callback=tuple_to_csv, show_default=True, default='gt', required=False, ) @click.option( '--src_http_err_cnt', help=('The cumulative number of HTTP errors from the incoming connection\'s source address.'), show_default=True, type=INT_OR_EMPTY, callback=int_as_string, default=None, required=False, ) @click.option( '--src_http_err_rate_comparison', help=('None'), type=click.Choice(['', 'gt', 'ge', 'eq', 'lt', 'le']), multiple=False, callback=tuple_to_csv, show_default=True, default='gt', required=False, ) @click.option( '--src_http_err_rate', help=('The average rate of HTTP errors from the incoming connection\'s source address.'), show_default=True, type=INT_OR_EMPTY, callback=int_as_string, default=None, required=False, ) @click.option( '--src_http_req_cnt_comparison', help=('None'), type=click.Choice(['', 'gt', 'ge', 'eq', 'lt', 'le']), multiple=False, callback=tuple_to_csv, show_default=True, default='gt', required=False, ) @click.option( '--src_http_req_cnt', help=('The cumulative number of HTTP requests from the incoming connection\'s source address.'), show_default=True, type=INT_OR_EMPTY, callback=int_as_string, default=None, required=False, ) @click.option( '--src_http_req_rate_comparison', help=('None'), type=click.Choice(['', 'gt', 'ge', 'eq', 'lt', 'le']), multiple=False, callback=tuple_to_csv, show_default=True, default='gt', required=False, ) @click.option( '--src_http_req_rate', help=('The average rate of HTTP requests from the incoming connection\'s source address.'), show_default=True, type=INT_OR_EMPTY, callback=int_as_string, default=None, required=False, ) @click.option( '--src_kbytes_in_comparison', help=('None'), type=click.Choice(['', 'gt', 'ge', 'eq', 'lt', 'le']), multiple=False, callback=tuple_to_csv, show_default=True, default='gt', required=False, ) @click.option( '--src_kbytes_in', help=('The total amount of data received from the incoming connection\'s source address (in kilobytes).'), show_default=True, type=INT_OR_EMPTY, callback=int_as_string, default=None, required=False, ) @click.option( '--src_kbytes_out_comparison', help=('None'), type=click.Choice(['', 'gt', 'ge', 'eq', 'lt', 'le']), multiple=False, callback=tuple_to_csv, show_default=True, default='gt', required=False, ) @click.option( '--src_kbytes_out', help=('The total amount of data sent to the incoming connection\'s source address (in kilobytes).'), show_default=True, type=INT_OR_EMPTY, callback=int_as_string, default=None, required=False, ) @click.option( '--src_port_comparison', help=('None'), type=click.Choice(['', 'gt', 'ge', 'eq', 'lt', 'le']), multiple=False, callback=tuple_to_csv, show_default=True, default='gt', required=False, ) @click.option( '--src_port', help=( 'An integer value corresponding to the TCP source port of the connection on the client side, ' 'which is the port the client connected from.' ), show_default=True, type=INT_OR_EMPTY, callback=int_as_string, default=None, required=False, ) @click.option( '--src_sess_cnt_comparison', help=('None'), type=click.Choice(['', 'gt', 'ge', 'eq', 'lt', 'le']), multiple=False, callback=tuple_to_csv, show_default=True, default='gt', required=False, ) @click.option( '--src_sess_cnt', help=('The cumulative number of connections initiated from the incoming connection\'s source address.'), show_default=True, type=INT_OR_EMPTY, callback=int_as_string, default=None, required=False, ) @click.option( '--src_sess_rate_comparison', help=('None'), type=click.Choice(['', 'gt', 'ge', 'eq', 'lt', 'le']), multiple=False, callback=tuple_to_csv, show_default=True, default='gt', required=False, ) @click.option( '--src_sess_rate', help=('None'), show_default=True, type=INT_OR_EMPTY, callback=int_as_string, default=None, required=False, ) @click.option( '--nbsrv', help=('Verify the minimum number of usable servers in the named backend matches the specified value.'), show_default=True, type=INT_OR_EMPTY, callback=int_as_string, default=None, required=False, ) @click.option( '--nbsrv_backend', help=('Use the specified backend to count usable servers. Leave empty to use the current backend.'), callback=resolve_linked_names_to_uuids, show_default=True, default=None, required=False, ) @click.option( '--ssl_fc_sni', help=('The value of the Server Name TLS extension sent by a client matches the exact string.'), show_default=True, default=None, required=False, ) @click.option( '--ssl_sni', help=('The value of the Server Name TLS extension sent by a client matches the exact string.'), show_default=True, default=None, required=False, ) @click.option( '--ssl_sni_sub', help=( 'The value of the Server Name TLS extension sent by a client contains the specified string (substring match).' ), show_default=True, default=None, required=False, ) @click.option( '--ssl_sni_beg', help=( 'The value of the Server Name TLS extension sent by a client starts with the specified string (prefix match).' ), show_default=True, default=None, required=False, ) @click.option( '--ssl_sni_end', help=('The value of the Server Name TLS extension sent by a client ends with the specified string (suffix match).'), show_default=True, default=None, required=False, ) @click.option( '--ssl_sni_reg', help=('The value of the Server Name TLS extension sent by a client matches with the specified regular expression.'), show_default=True, default=None, required=False, ) @click.option( '--custom_acl', help=('Specify a HAProxy condition/ACL that is currently not supported by the GUI.'), show_default=True, default=None, required=False, ) @click.option( '--value', help=('None'), show_default=True, default=None, required=False, ) @click.option( '--urlparam', help=('None'), show_default=True, default=None, required=False, ) @click.option( '--queryBackend', help=('None'), callback=resolve_linked_names_to_uuids, show_default=True, default=None, required=False, ) @click.option( '--allowedUsers', help=('None'), callback=resolve_linked_names_to_uuids, show_default=True, default=None, required=False, ) @click.option( '--allowedGroups', help=('None'), callback=resolve_linked_names_to_uuids, show_default=True, default=None, required=False, ) @click.option( '--output', '-o', help='Specifies the Output format.', default="plain", type=click.Choice(available_formats()), callback=formatter_from_formatter_name, show_default=True, ) @click.option( '--cols', '-c', help='Which columns should be printed? Pass empty string (-c '') to show all columns', default="result,validations", show_default=True, ) @pass_haproxy_acl_svc def create(haproxy_acl_svc: HaproxyAclFacade, **kwargs): """ Create a new acl """ json_payload = { 'acl': { "name": kwargs['name'], "description": kwargs['description'], "expression": kwargs['expression'], "negate": kwargs['negate'], "hdr_beg": kwargs['hdr_beg'], "hdr_end": kwargs['hdr_end'], "hdr": kwargs['hdr'], "hdr_reg": kwargs['hdr_reg'], "hdr_sub": kwargs['hdr_sub'], "path_beg": kwargs['path_beg'], "path_end": kwargs['path_end'], "path": kwargs['path'], "path_reg": kwargs['path_reg'], "path_dir": kwargs['path_dir'], "path_sub": kwargs['path_sub'], "cust_hdr_beg_name": kwargs['cust_hdr_beg_name'], "cust_hdr_beg": kwargs['cust_hdr_beg'], "cust_hdr_end_name": kwargs['cust_hdr_end_name'], "cust_hdr_end": kwargs['cust_hdr_end'], "cust_hdr_name": kwargs['cust_hdr_name'], "cust_hdr": kwargs['cust_hdr'], "cust_hdr_reg_name": kwargs['cust_hdr_reg_name'], "cust_hdr_reg": kwargs['cust_hdr_reg'], "cust_hdr_sub_name": kwargs['cust_hdr_sub_name'], "cust_hdr_sub": kwargs['cust_hdr_sub'], "url_param": kwargs['url_param'], "url_param_value": kwargs['url_param_value'], "ssl_c_verify_code": kwargs['ssl_c_verify_code'], "ssl_c_ca_commonname": kwargs['ssl_c_ca_commonname'], "src": kwargs['src'], "src_bytes_in_rate_comparison": kwargs['src_bytes_in_rate_comparison'], "src_bytes_in_rate": kwargs['src_bytes_in_rate'], "src_bytes_out_rate_comparison": kwargs['src_bytes_out_rate_comparison'], "src_bytes_out_rate": kwargs['src_bytes_out_rate'], "src_conn_cnt_comparison": kwargs['src_conn_cnt_comparison'], "src_conn_cnt": kwargs['src_conn_cnt'], "src_conn_cur_comparison": kwargs['src_conn_cur_comparison'], "src_conn_cur": kwargs['src_conn_cur'], "src_conn_rate_comparison": kwargs['src_conn_rate_comparison'], "src_conn_rate": kwargs['src_conn_rate'], "src_http_err_cnt_comparison": kwargs['src_http_err_cnt_comparison'], "src_http_err_cnt": kwargs['src_http_err_cnt'], "src_http_err_rate_comparison": kwargs['src_http_err_rate_comparison'], "src_http_err_rate": kwargs['src_http_err_rate'], "src_http_req_cnt_comparison": kwargs['src_http_req_cnt_comparison'], "src_http_req_cnt": kwargs['src_http_req_cnt'], "src_http_req_rate_comparison": kwargs['src_http_req_rate_comparison'], "src_http_req_rate": kwargs['src_http_req_rate'], "src_kbytes_in_comparison": kwargs['src_kbytes_in_comparison'], "src_kbytes_in": kwargs['src_kbytes_in'], "src_kbytes_out_comparison": kwargs['src_kbytes_out_comparison'], "src_kbytes_out": kwargs['src_kbytes_out'], "src_port_comparison": kwargs['src_port_comparison'], "src_port": kwargs['src_port'], "src_sess_cnt_comparison": kwargs['src_sess_cnt_comparison'], "src_sess_cnt": kwargs['src_sess_cnt'], "src_sess_rate_comparison": kwargs['src_sess_rate_comparison'], "src_sess_rate": kwargs['src_sess_rate'], "nbsrv": kwargs['nbsrv'], "nbsrv_backend": kwargs['nbsrv_backend'], "ssl_fc_sni": kwargs['ssl_fc_sni'], "ssl_sni": kwargs['ssl_sni'], "ssl_sni_sub": kwargs['ssl_sni_sub'], "ssl_sni_beg": kwargs['ssl_sni_beg'], "ssl_sni_end": kwargs['ssl_sni_end'], "ssl_sni_reg": kwargs['ssl_sni_reg'], "custom_acl": kwargs['custom_acl'], "value": kwargs['value'], "urlparam": kwargs['urlparam'], "queryBackend": kwargs['querybackend'], "allowedUsers": kwargs['allowedusers'], "allowedGroups": kwargs['allowedgroups'], } } result = haproxy_acl_svc.create_acl(json_payload) CliOutputFormatter(result, kwargs['output'], kwargs['cols'].split(",")).echo() @acl.command() @click.argument('uuid') @click.option( '--name', help=('Name to identify this condition.'), show_default=True, default=None ) @click.option( '--description', help=('Description for this condition.'), show_default=True, default=None ) @click.option( '--expression', help=('None'), type=click.Choice( [ 'http_auth', 'hdr_beg', 'hdr_end', 'hdr', 'hdr_reg', 'hdr_sub', 'path_beg', 'path_end', 'path', 'path_reg', 'path_dir', 'path_sub', 'cust_hdr_beg', 'cust_hdr_end', 'cust_hdr', 'cust_hdr_reg', 'cust_hdr_sub', 'url_param', 'ssl_c_verify', 'ssl_c_verify_code', 'ssl_c_ca_commonname', 'src', 'src_is_local', 'src_port', 'src_bytes_in_rate', 'src_bytes_out_rate', 'src_kbytes_in', 'src_kbytes_out', 'src_conn_cnt', 'src_conn_cur', 'src_conn_rate', 'src_http_err_cnt', 'src_http_err_rate', 'src_http_req_cnt', 'src_http_req_rate', 'src_sess_cnt', 'src_sess_rate', 'nbsrv', 'traffic_is_http', 'traffic_is_ssl', 'ssl_fc', 'ssl_fc_sni', 'ssl_sni', 'ssl_sni_sub', 'ssl_sni_beg', 'ssl_sni_end', 'ssl_sni_reg', 'custom_acl' ] ), multiple=False, callback=tuple_to_csv, show_default=True, default=None ) @click.option( '--negate/--no-negate', help=('Use this to invert the meaning of the expression.'), show_default=True, is_flag=True, callback=bool_as_string, default=None ) @click.option( '--hdr_beg', help=('HTTP host header starts with string (prefix match)'), show_default=True, default=None ) @click.option( '--hdr_end', help=('HTTP host header ends with string (suffix match)'), show_default=True, default=None ) @click.option( '--hdr', help=('HTTP host header matches exact string'), show_default=True, default=None ) @click.option( '--hdr_reg', help=('HTTP host header matches regular expression'), show_default=True, default=None ) @click.option( '--hdr_sub', help=('HTTP host header contains string (substring match)'), show_default=True, default=None ) @click.option( '--path_beg', help=('HTTP request URL path starts with string (prefix match)'), show_default=True, default=None ) @click.option( '--path_end', help=('HTTP request URL path ends with string (suffix match)'), show_default=True, default=None ) @click.option( '--path', help=('HTTP request URL path matches exact string'), show_default=True, default=None ) @click.option( '--path_reg', help=('HTTP request URL path matches regular expression'), show_default=True, default=None ) @click.option( '--path_dir', help=('HTTP request URL path contains directory (subdir match)'), show_default=True, default=None ) @click.option( '--path_sub', help=('HTTP request URL path contains string (substring match)'), show_default=True, default=None ) @click.option( '--cust_hdr_beg_name', help=('The name of the HTTP Header.'), show_default=True, default=None ) @click.option( '--cust_hdr_beg', help=('HTTP Header starts with string (prefix match)'), show_default=True, default=None ) @click.option( '--cust_hdr_end_name', help=('The name of the HTTP Header.'), show_default=True, default=None ) @click.option( '--cust_hdr_end', help=('HTTP Header ends with string (suffix match)'), show_default=True, default=None ) @click.option( '--cust_hdr_name', help=('The name of the HTTP Header.'), show_default=True, default=None ) @click.option( '--cust_hdr', help=('HTTP Header matches exact string'), show_default=True, default=None ) @click.option( '--cust_hdr_reg_name', help=('The name of the HTTP Header.'), show_default=True, default=None ) @click.option( '--cust_hdr_reg', help=('HTTP Header matches regular expression'), show_default=True, default=None ) @click.option( '--cust_hdr_sub_name', help=('The name of the HTTP Header.'), show_default=True, default=None ) @click.option( '--cust_hdr_sub', help=('HTTP Header contains string (substring match)'), show_default=True, default=None ) @click.option( '--url_param', help=('Specify the URL parameter to be checked for the value specified below.'), show_default=True, default=None ) @click.option( '--url_param_value', help=('Specify the value for the URL parameter.'), show_default=True, default=None ) @click.option( '--ssl_c_verify_code', help=( 'Specify the SSL/TLS error ID that should be checked for the incoming connection. ' 'Please refer to your SSL library\'s documentation for an exhaustive list of error codes.' ), show_default=True, type=INT_OR_EMPTY, callback=int_as_string, default=None ) @click.option( '--ssl_c_ca_commonname', help=('Verify the CA Common-Name of the certificate presented by the client against the specified string.'), show_default=True, default=None ) @click.option( '--src', help=('Verify the source IPv4 address of the client of the session matches the specified IPv4 or IPv6 address.'), show_default=True, default=None ) @click.option( '--src_bytes_in_rate_comparison', help=('None'), type=click.Choice(['', 'gt', 'ge', 'eq', 'lt', 'le']), multiple=False, callback=tuple_to_csv, show_default=True, default=None ) @click.option( '--src_bytes_in_rate', help=('The average bytes rate from the incoming connection\'s source address.'), show_default=True, type=INT_OR_EMPTY, callback=int_as_string, default=None ) @click.option( '--src_bytes_out_rate_comparison', help=('None'), type=click.Choice(['', 'gt', 'ge', 'eq', 'lt', 'le']), multiple=False, callback=tuple_to_csv, show_default=True, default=None ) @click.option( '--src_bytes_out_rate', help=('The average bytes rate to the incoming connection\'s source address.'), show_default=True, type=INT_OR_EMPTY, callback=int_as_string, default=None ) @click.option( '--src_conn_cnt_comparison', help=('None'), type=click.Choice(['', 'gt', 'ge', 'eq', 'lt', 'le']), multiple=False, callback=tuple_to_csv, show_default=True, default=None ) @click.option( '--src_conn_cnt', help=('The cumulative number of connections initiated from the current incoming connection\'s source address.'), show_default=True, type=INT_OR_EMPTY, callback=int_as_string, default=None ) @click.option( '--src_conn_cur_comparison', help=('None'), type=click.Choice(['', 'gt', 'ge', 'eq', 'lt', 'le']), multiple=False, callback=tuple_to_csv, show_default=True, default=None ) @click.option( '--src_conn_cur', help=( 'The current amount of concurrent connections initiated from the current incoming connection\'s source address.' ), show_default=True, type=INT_OR_EMPTY, callback=int_as_string, default=None ) @click.option( '--src_conn_rate_comparison', help=('None'), type=click.Choice(['', 'gt', 'ge', 'eq', 'lt', 'le']), multiple=False, callback=tuple_to_csv, show_default=True, default=None ) @click.option( '--src_conn_rate', help=('The average connection rate from the incoming connection\'s source address.'), show_default=True, type=INT_OR_EMPTY, callback=int_as_string, default=None ) @click.option( '--src_http_err_cnt_comparison', help=('None'), type=click.Choice(['', 'gt', 'ge', 'eq', 'lt', 'le']), multiple=False, callback=tuple_to_csv, show_default=True, default=None ) @click.option( '--src_http_err_cnt', help=('The cumulative number of HTTP errors from the incoming connection\'s source address.'), show_default=True, type=INT_OR_EMPTY, callback=int_as_string, default=None ) @click.option( '--src_http_err_rate_comparison', help=('None'), type=click.Choice(['', 'gt', 'ge', 'eq', 'lt', 'le']), multiple=False, callback=tuple_to_csv, show_default=True, default=None ) @click.option( '--src_http_err_rate', help=('The average rate of HTTP errors from the incoming connection\'s source address.'), show_default=True, type=INT_OR_EMPTY, callback=int_as_string, default=None ) @click.option( '--src_http_req_cnt_comparison', help=('None'), type=click.Choice(['', 'gt', 'ge', 'eq', 'lt', 'le']), multiple=False, callback=tuple_to_csv, show_default=True, default=None ) @click.option( '--src_http_req_cnt', help=('The cumulative number of HTTP requests from the incoming connection\'s source address.'), show_default=True, type=INT_OR_EMPTY, callback=int_as_string, default=None ) @click.option( '--src_http_req_rate_comparison', help=('None'), type=click.Choice(['', 'gt', 'ge', 'eq', 'lt', 'le']), multiple=False, callback=tuple_to_csv, show_default=True, default=None ) @click.option( '--src_http_req_rate', help=('The average rate of HTTP requests from the incoming connection\'s source address.'), show_default=True, type=INT_OR_EMPTY, callback=int_as_string, default=None ) @click.option( '--src_kbytes_in_comparison', help=('None'), type=click.Choice(['', 'gt', 'ge', 'eq', 'lt', 'le']), multiple=False, callback=tuple_to_csv, show_default=True, default=None ) @click.option( '--src_kbytes_in', help=('The total amount of data received from the incoming connection\'s source address (in kilobytes).'), show_default=True, type=INT_OR_EMPTY, callback=int_as_string, default=None ) @click.option( '--src_kbytes_out_comparison', help=('None'), type=click.Choice(['', 'gt', 'ge', 'eq', 'lt', 'le']), multiple=False, callback=tuple_to_csv, show_default=True, default=None ) @click.option( '--src_kbytes_out', help=('The total amount of data sent to the incoming connection\'s source address (in kilobytes).'), show_default=True, type=INT_OR_EMPTY, callback=int_as_string, default=None ) @click.option( '--src_port_comparison', help=('None'), type=click.Choice(['', 'gt', 'ge', 'eq', 'lt', 'le']), multiple=False, callback=tuple_to_csv, show_default=True, default=None ) @click.option( '--src_port', help=( 'An integer value corresponding to the TCP source port of the connection on the client side, ' 'which is the port the client connected from.' ), show_default=True, type=INT_OR_EMPTY, callback=int_as_string, default=None ) @click.option( '--src_sess_cnt_comparison', help=('None'), type=click.Choice(['', 'gt', 'ge', 'eq', 'lt', 'le']), multiple=False, callback=tuple_to_csv, show_default=True, default=None ) @click.option( '--src_sess_cnt', help=('The cumulative number of connections initiated from the incoming connection\'s source address.'), show_default=True, type=INT_OR_EMPTY, callback=int_as_string, default=None ) @click.option( '--src_sess_rate_comparison', help=('None'), type=click.Choice(['', 'gt', 'ge', 'eq', 'lt', 'le']), multiple=False, callback=tuple_to_csv, show_default=True, default=None ) @click.option( '--src_sess_rate', help=('None'), show_default=True, type=INT_OR_EMPTY, callback=int_as_string, default=None ) @click.option( '--nbsrv', help=('Verify the minimum number of usable servers in the named backend matches the specified value.'), show_default=True, type=INT_OR_EMPTY, callback=int_as_string, default=None ) @click.option( '--nbsrv_backend', help=('Use the specified backend to count usable servers. Leave empty to use the current backend.'), callback=resolve_linked_names_to_uuids, show_default=True, default=None ) @click.option( '--ssl_fc_sni', help=('The value of the Server Name TLS extension sent by a client matches the exact string.'), show_default=True, default=None ) @click.option( '--ssl_sni', help=('The value of the Server Name TLS extension sent by a client matches the exact string.'), show_default=True, default=None ) @click.option( '--ssl_sni_sub', help=( 'The value of the Server Name TLS extension sent by a client contains the specified string (substring match).' ), show_default=True, default=None ) @click.option( '--ssl_sni_beg', help=( 'The value of the Server Name TLS extension sent by a client starts with the specified string (prefix match).' ), show_default=True, default=None ) @click.option( '--ssl_sni_end', help=('The value of the Server Name TLS extension sent by a client ends with the specified string (suffix match).'), show_default=True, default=None ) @click.option( '--ssl_sni_reg', help=('The value of the Server Name TLS extension sent by a client matches with the specified regular expression.'), show_default=True, default=None ) @click.option( '--custom_acl', help=('Specify a HAProxy condition/ACL that is currently not supported by the GUI.'), show_default=True, default=None ) @click.option( '--value', help=('None'), show_default=True, default=None ) @click.option( '--urlparam', help=('None'), show_default=True, default=None ) @click.option( '--queryBackend', help=('None'), callback=resolve_linked_names_to_uuids, show_default=True, default=None ) @click.option( '--allowedUsers', help=('None'), callback=resolve_linked_names_to_uuids, show_default=True, default=None ) @click.option( '--allowedGroups', help=('None'), callback=resolve_linked_names_to_uuids, show_default=True, default=None ) @click.option( '--output', '-o', help='Specifies the Output format.', default="plain", type=click.Choice(available_formats()), callback=formatter_from_formatter_name, show_default=True, ) @click.option( '--cols', '-c', help='Which columns should be printed? Pass empty string (-c '') to show all columns', default="result,validations", show_default=True, ) @pass_haproxy_acl_svc def update(haproxy_acl_svc: HaproxyAclFacade, **kwargs): """ Update a acl. """ json_payload = { 'acl': {} } options = [ 'name', 'description', 'expression', 'negate', 'hdr_beg', 'hdr_end', 'hdr', 'hdr_reg', 'hdr_sub', 'path_beg', 'path_end', 'path', 'path_reg', 'path_dir', 'path_sub', 'cust_hdr_beg_name', 'cust_hdr_beg', 'cust_hdr_end_name', 'cust_hdr_end', 'cust_hdr_name', 'cust_hdr', 'cust_hdr_reg_name', 'cust_hdr_reg', 'cust_hdr_sub_name', 'cust_hdr_sub', 'url_param', 'url_param_value', 'ssl_c_verify_code', 'ssl_c_ca_commonname', 'src', 'src_bytes_in_rate_comparison', 'src_bytes_in_rate', 'src_bytes_out_rate_comparison', 'src_bytes_out_rate', 'src_conn_cnt_comparison', 'src_conn_cnt', 'src_conn_cur_comparison', 'src_conn_cur', 'src_conn_rate_comparison', 'src_conn_rate', 'src_http_err_cnt_comparison', 'src_http_err_cnt', 'src_http_err_rate_comparison', 'src_http_err_rate', 'src_http_req_cnt_comparison', 'src_http_req_cnt', 'src_http_req_rate_comparison', 'src_http_req_rate', 'src_kbytes_in_comparison', 'src_kbytes_in', 'src_kbytes_out_comparison', 'src_kbytes_out', 'src_port_comparison', 'src_port', 'src_sess_cnt_comparison', 'src_sess_cnt', 'src_sess_rate_comparison', 'src_sess_rate', 'nbsrv', 'nbsrv_backend', 'ssl_fc_sni', 'ssl_sni', 'ssl_sni_sub', 'ssl_sni_beg', 'ssl_sni_end', 'ssl_sni_reg', 'custom_acl', 'value', 'urlparam', 'queryBackend', 'allowedUsers', 'allowedGroups' ] for option in options: if kwargs[option.lower()] is not None: json_payload['acl'][option] = kwargs[option.lower()] result = haproxy_acl_svc.update_acl(kwargs['uuid'], json_payload) CliOutputFormatter(result, kwargs['output'], kwargs['cols'].split(",")).echo() @acl.command() @click.argument('uuid') @click.option( '--output', '-o', help='Specifies the Output format.', default="plain", type=click.Choice(available_formats()), callback=formatter_from_formatter_name, show_default=True, ) @click.option( '--cols', '-c', help='Which columns should be printed? Pass empty string (-c '') to show all columns', default="result,validations", show_default=True, ) @pass_haproxy_acl_svc def delete(haproxy_acl_svc: HaproxyAclFacade, **kwargs): """ Delete acl """ result = haproxy_acl_svc.delete_acl(kwargs['uuid']) CliOutputFormatter(result, kwargs['output'], kwargs['cols'].split(",")).echo()

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0.658926

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4.751352

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0.892117

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null

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7

50e9f6b88a9f74901398eddd8f4920716a123c31

1,826

py

Python

python3/lib/python3.6/site-packages/tensorflow/_api/v1/random/__init__.py

TruongThuyLiem/keras2tensorflow

726f2370160701081cb43fbd8b56154c10d7ad63

[ "MIT" ]

3

2020-10-12T15:47:01.000Z

2022-01-14T19:51:26.000Z

python3/lib/python3.6/site-packages/tensorflow/_api/v1/random/__init__.py

TruongThuyLiem/keras2tensorflow

726f2370160701081cb43fbd8b56154c10d7ad63

[ "MIT" ]

null

python3/lib/python3.6/site-packages/tensorflow/_api/v1/random/__init__.py

TruongThuyLiem/keras2tensorflow

726f2370160701081cb43fbd8b56154c10d7ad63

[ "MIT" ]

2

2020-08-03T13:02:06.000Z

2020-11-04T03:15:44.000Z

# This file is MACHINE GENERATED! Do not edit. # Generated by: tensorflow/python/tools/api/generator/create_python_api.py script. """Public API for tf.random namespace. """ from __future__ import print_function as _print_function from tensorflow._api.v1.random import experimental from tensorflow.python import categorical from tensorflow.python import get_seed from tensorflow.python import multinomial from tensorflow.python import random_gamma as gamma from tensorflow.python import random_normal as normal from tensorflow.python import random_poisson as poisson from tensorflow.python import random_shuffle as shuffle from tensorflow.python import random_uniform as uniform from tensorflow.python import set_random_seed from tensorflow.python import stateless_categorical from tensorflow.python import stateless_multinomial from tensorflow.python import stateless_random_normal as stateless_normal from tensorflow.python import stateless_random_uniform as stateless_uniform from tensorflow.python import stateless_truncated_normal from tensorflow.python import truncated_normal from tensorflow.python.ops.candidate_sampling_ops import all_candidate_sampler from tensorflow.python.ops.candidate_sampling_ops import fixed_unigram_candidate_sampler from tensorflow.python.ops.candidate_sampling_ops import learned_unigram_candidate_sampler from tensorflow.python.ops.candidate_sampling_ops import log_uniform_candidate_sampler from tensorflow.python.ops.candidate_sampling_ops import uniform_candidate_sampler del _print_function import sys as _sys from tensorflow.python.util import deprecation_wrapper as _deprecation_wrapper if not isinstance(_sys.modules[__name__], _deprecation_wrapper.DeprecationWrapper): _sys.modules[__name__] = _deprecation_wrapper.DeprecationWrapper( _sys.modules[__name__], "random")

48.052632

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6.234568

0.251029

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0.657426

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0.288449

0.256106

0.180858

0

0.0006

0.086528

1,826

37

91

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0.907674

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1

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0.003623

0

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0

true

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0.857143

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0.857143

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0

null

1

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1

0

null

0

1

0

1

0

1

0

7

0ff815e5c8ca7247fb51aa428617e607e29eec67

87

py

Python

trikit/estimators/__init__.py

pzh1989/trikit

77dce1d1d5ce901d2679fbe403e694085933c466

[ "MIT" ]

null

trikit/estimators/__init__.py

pzh1989/trikit

77dce1d1d5ce901d2679fbe403e694085933c466

[ "MIT" ]

null

trikit/estimators/__init__.py

pzh1989/trikit

77dce1d1d5ce901d2679fbe403e694085933c466

[ "MIT" ]

null

from .chainladder import BaseChainLadder from .chainladder import BaseChainLadderResult

43.5

46

0.896552

8

87

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0.538462

0

0.08046

87

2

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43.5

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true

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1

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null

1

0

1

0

null

0

1

0

1

0

1

0

7

0fff08421967e3ed89421861d3a342b4c66f4de3

219,218

py

Python

internos/activityinfo/views.py

UNICEFLebanonInnovation/Staging-Neuro

aac1e4f335ff4ec32041f989a9c22f8581a4961a

[ "MIT" ]

null

internos/activityinfo/views.py

UNICEFLebanonInnovation/Staging-Neuro

aac1e4f335ff4ec32041f989a9c22f8581a4961a

[ "MIT" ]

null

internos/activityinfo/views.py

UNICEFLebanonInnovation/Staging-Neuro

aac1e4f335ff4ec32041f989a9c22f8581a4961a

[ "MIT" ]

null

from __future__ import absolute_import, unicode_literals import os import json import datetime import re from django.db.models import Q, Sum from dal import autocomplete from django.views.generic import ListView,TemplateView, FormView from django.http import HttpResponse, JsonResponse from .models import ActivityReport, LiveActivityReport, Database, Indicator, Partner, IndicatorTag, ReportingYear, Activity from django.shortcuts import render from datetime import date from django.http import HttpResponseRedirect from .templatetags.util_tags import * from .utils import * from .utils import calculate_internal_indicators_values, calculate_internal_cumulative_results,link_etools_partnerships from internos.etools.utils import get_interventions_details from django.contrib.auth.mixins import LoginRequiredMixin from internos.etools.models import PCA from django.core import serializers class IndexView(LoginRequiredMixin,TemplateView): template_name = 'activityinfo/index.html' def get_context_data(self, **kwargs): year = date.today().year instance = ReportingYear.objects.get(current=True) reporting_year = self.request.GET.get('rep_year', instance.year) databases = Database.objects.filter(reporting_year__name=reporting_year, display=True).exclude(ai_id=10240).order_by('label') return { 'ai_databases': databases, 'reporting_year': reporting_year } class DashboardView(LoginRequiredMixin,TemplateView): template_name = 'activityinfo/dashboard.html' def get_context_data(self, **kwargs): month = int(self.request.GET.get('month', int(datetime.now().strftime("%m")) - 1)) month_name = self.request.GET.get('month', datetime.now().strftime("%B")) ai_id = int(self.request.GET.get('ai_id', 0)) year = date.today().year reporting_year = self.request.GET.get('rep_year', year) if ai_id: database = Database.objects.get(ai_id=ai_id) else: try: section = self.request.user.section database = Database.objects.get(section=section, reporting_year__name=reporting_year) except Exception: database = Database.objects.filter(reporting_year__name=reporting_year).first() report = ActivityReport.objects.filter( database=database, start_date__month=month, funded_by__contains='UNICEF') months = ActivityReport.objects.values('month_name').distinct() partners = report.values('partner_id').distinct().count() activity_categories = report.values('form_category').distinct().count() activities = report.values('form').distinct().count() indicators = report.values('indicator_name').distinct().count() unicef_funds = report.filter(funded_by__contains='UNICEF').values('funded_by').count() not_reported = report.filter(Q(indicator_value__isnull=True) | Q(indicator_value=0)).count() return { 'month': month, 'month_name': month_name, 'months': months, 'months_nbr': months.count(), 'database': database, 'partners': partners, 'activity_categories': activity_categories, 'activities': activities, 'not_reported': not_reported, 'indicators': indicators, 'unicef_funds': unicef_funds } class ReportView(LoginRequiredMixin,TemplateView): template_name = 'activityinfo/report.html' def get_context_data(self, **kwargs): selected_filter = False display_live = True selected_partners = self.request.GET.getlist('partners', []) selected_months = self.request.GET.getlist('months', []) selected_governorates = self.request.GET.getlist('governorates', []) support_covid = self.request.GET.get('support_covid', -1) tag_filter = self.request.GET.get('tag_filter', None) current_year = date.today().year current_month = date.today().month partner_info = {} today = datetime.date.today() first = today.replace(day=1) last_month = first - datetime.timedelta(days=1) month_number = last_month.strftime("%m") month = int(last_month.strftime("%m")) month_name = last_month.strftime("%B") # month_number = '12' # month = 12 # month_name = 'December' ai_id = int(self.request.GET.get('ai_id', 0)) database = Database.objects.get(ai_id=ai_id) report = ActivityReport.objects.filter(database_id=database.ai_id) reporting_year = database.reporting_year.name months = [] if selected_partners or selected_governorates or selected_months: selected_filter = True partners = get_partners_list(database, govs=selected_governorates,months=selected_months) governorates = get_governorates_list(database,partners=selected_partners,months=selected_months) if selected_months is not None and len(selected_months) > 0: for mon in selected_months: months.append((mon, calendar.month_name[int(mon)])) else: months = get_months_list(database, selected_partners, selected_governorates) else: partners = get_partners_list(database) governorates = get_governorates_list(database) if int(reporting_year) == current_year: for i in range(1, current_month): months.append((i, datetime.date(2008, i, 1).strftime('%B'))) else: for i in range(1, 13): months.append((i, datetime.date(2008, i, 1).strftime('%B'))) all_indicators = Indicator.objects.filter(activity__database=database).order_by('sequence') master_indicators = all_indicators.exclude(is_sector=True) if tag_filter == 'support_covid': master_indicators = master_indicators.filter(support_COVID=True) if tag_filter == 'hpm_indicator': master_indicators = all_indicators.filter(hpm_indicator=True) if tag_filter == 'is_lcrp': master_indicators = all_indicators.filter(is_lcrp=True) if tag_filter == 'is_standalone_HAC_2': master_indicators = all_indicators.filter(is_standalone_HAC_2=True) if tag_filter == 'is_additional_indicators': master_indicators = all_indicators.filter(is_additional_indicators=True) if database.mapped_db: master_indicators1 = master_indicators.filter(master_indicator=True) master_indicators2 = master_indicators.filter(sub_indicators__isnull=True, individual_indicator=True) master_indicators = master_indicators1 | master_indicators2 # none_ai_indicators = Indicator.objects.filter(activity__none_ai_database=database).exclude(is_sector=True) master_indicators = master_indicators.values( 'id', 'ai_id', 'name', 'master_indicator', 'master_indicator_sub', 'individual_indicator', 'awp_code', 'measurement_type', 'units', 'target', 'status_color', 'status', 'cumulative_values', 'values_partners_gov', 'values_partners', 'values_gov', 'values', 'is_cumulative', 'support_COVID', 'highest_values', 'ram_result', ).distinct() for master_ind in master_indicators: if master_ind['is_cumulative']: master_ind['cumulative'] = get_indicator_cumulative_months(master_ind, selected_months, selected_partners, selected_governorates, ) else: master_ind['cumulative'] = get_indicator_highest_value(master_ind, selected_months, selected_partners, selected_governorates, ) try: if master_ind['measurement_type'] == 'percentage': cumulative_total = get_indicator_cumulative_months(master_ind, selected_months, selected_partners, selected_governorates, ) cumulative_count = get_indicator_cumulative_count(master_ind, selected_months, selected_partners, selected_governorates, ) if cumulative_total == '0': cumulative_total = '0 %' cum_size = len(cumulative_total) updated_cumulative = cumulative_total[:cum_size - 2] if cumulative_count != 0: master_ind['cumulative'] = str(int(float(updated_cumulative)/float(cumulative_count)))+ '%' else: master_ind['cumulative'] = '0%' except Exception as ex: logger.error('get_indicator_cumulative_months error ' + ex.message) master_ind['cumulative'] = "-" + get_indicator_cumulative_months(master_ind, selected_months, selected_partners, selected_governorates, ) + ex.message #master_ind['achieved'] = master_ind['is_cumulative'] master_ind['achieved'] = str(calculate_achievement_new(master_ind['target'], master_ind['cumulative']))+ '%' # cum_size = len(master_ind['cumulative']) # updated_cumulative = master_ind['cumulative'][:cum_size - 2] # try: # if not type(updated_cumulative) == float: # updated_cumulative = updated_cumulative.replace(",", "") # updated_cumulative = float(updated_cumulative) # master_ind['achieved'] = str(updated_cumulative / len(master_ind['cumulative_values'])) # except Exception as ex: # master_ind['achieved'] = cum_size sub_indicators = get_sub_indicators_data_new(master_ind['id'], all_indicators) master_ind['sub_list'] = sub_indicators master_ind['sub_list_filtered'] = sub_indicators for sub_ind in sub_indicators: if sub_ind['master_indicator']: sub_ind['cumulative'] = 0 continue else: if master_ind['is_cumulative']: sub_ind['cumulative'] = get_indicator_cumulative_months(sub_ind, selected_months, selected_partners, selected_governorates, ) else: sub_ind['cumulative'] = get_indicator_highest_value(sub_ind, selected_months, selected_partners, selected_governorates, ) if sub_ind['master_indicator_sub']: sub_ind['achieved'] = str(calculate_achievement_new(sub_ind['target'], sub_ind['cumulative']) )+ '%' sub_sub_indicators = get_sub_indicators_data_new(sub_ind['id'], all_indicators) sub_ind['sub_list'] = sub_sub_indicators sub_ind['sub_list_filtered'] = sub_sub_indicators for ind in sub_sub_indicators: if ind['master_indicator']: ind['cumulative'] = 0 continue else: if master_ind['is_cumulative']: ind['cumulative'] = get_indicator_cumulative_months(ind, selected_months, selected_partners, selected_governorates, ) else: ind['cumulative'] = get_indicator_highest_value(ind, selected_months, selected_partners, selected_governorates, ) filtered_list = [] if selected_filter: for master_ind in master_indicators: if not (master_ind['cumulative'] == '0' or master_ind['cumulative'] == 0 or master_ind['cumulative'] == "0%"): filtered_list.append(master_ind) master_ind['sub_list_filtered'] = [] for sub_indicator in master_ind['sub_list']: sub_indicator['sub_list_filtered'] = [] if not (sub_indicator['cumulative'] == '0' or sub_indicator['cumulative'] == 0): master_ind['sub_list_filtered'].append(sub_indicator) for sub_sub_ind in sub_indicator['sub_list']: if sub_sub_ind['cumulative'] == '0' or sub_sub_ind['cumulative'] == 0: continue else: sub_indicator['sub_list_filtered'].append(sub_sub_ind) else: for sub_indicator in master_ind['sub_list']: if not (sub_indicator['cumulative'] == '0' or sub_indicator['cumulative'] == 0): filtered_list.append(sub_indicator) sub_indicator['sub_list_filtered'] = [] for sub_sub_ind in sub_indicator['sub_list']: if sub_sub_ind['cumulative'] == '0' or sub_sub_ind['cumulative'] == 0: continue else: sub_indicator['sub_list_filtered'].append(sub_sub_ind) else: for sub_sub_ind in sub_indicator['sub_list']: if sub_sub_ind['cumulative'] == '0' or sub_sub_ind['cumulative'] == 0: continue else: filtered_list.append(sub_sub_ind) else: filtered_list = master_indicators t_months = [] if selected_months is not None and len(selected_months) > 0: for mon in selected_months: t_months.append((mon, datetime.date(2008, int(mon), 1).strftime('%B'))) else: if int(reporting_year) == current_year: display_live = True if current_month == 1: t_months.append((1, datetime.date(2008, 1, 1).strftime('%B'))) if current_month >= 2 : for i in range(1, current_month): t_months.append((i, datetime.date(2008, i, 1).strftime('%B'))) # if current_month > 4 : # for i in range(current_month - 3, current_month): # months.append((i, datetime.date(2008, i, 1).strftime('%B'))) else: display_live = False for i in range(1, 13): t_months.append((i, datetime.date(2008, i, 1).strftime('%B'))) if self.request.user.is_authenticated: template = "base2.html" else: template = "base_empty.html" return { 'reports': report.order_by('id'), 'selected_partners': selected_partners, 'selected_governorates': selected_governorates, 'selected_months': selected_months, 'selected_tag': tag_filter, 'support_covid':int(support_covid), 'month': month, 'year': today.year, 'month_name': month_name, 'month_number': month_number, 't_months': t_months, 'database': database, 'partners': partners, 'governorates': governorates, 'months': months, 'master_indicators': filtered_list, 'partner_info': partner_info, 'selected_filter': selected_filter, 'reporting_year': str(reporting_year), 'display_live': display_live, 'current_month': current_month, 'current_month_name': datetime.datetime.now().strftime("%B"), 'template':template } class ReportCrisisViewOld(LoginRequiredMixin,TemplateView): template_name = 'activityinfo/report_crisis_old.html' def get_context_data(self, **kwargs): ai_id = int(self.request.GET.get('ai_id', 0)) database = Database.objects.get(ai_id=ai_id) selected_partners = self.request.GET.getlist('partners', []) selected_months = self.request.GET.getlist('months', []) selected_partner_name = self.request.GET.get('partner_name', 'All Partners') selected_governorates = self.request.GET.getlist('governorates', []) selected_governorate_name = self.request.GET.get('governorate_name', 'All Governorates') selected_sections = self.request.GET.getlist('sections',[]) selected_type = self.request.GET.get('filter_type', '') current_month = date.today().month selected_filter = False reporting_year = database.reporting_year.year if selected_partners or selected_governorates or selected_months or selected_sections: selected_filter = True partners = get_partners_list(database) governorates = get_governorates_list(database) sections = get_reporting_sections_list(database) master_indicators = Indicator.objects.filter(activity__database=database).exclude(type='quality')\ .order_by('sequence') if len(selected_type) > 0: master_indicators = master_indicators.filter(tag_focus__label=selected_type) master_indicators = master_indicators.filter(Q(master_indicator=True) | Q(sub_indicators__isnull=True, individual_indicator=True))\ .values( 'id', 'ai_id', 'name', 'master_indicator', 'master_indicator_sub', 'master_indicator_sub_sub', 'individual_indicator', 'awp_code', 'measurement_type', 'units', 'cumulative_values', 'values_partners_gov', 'values_partners', 'values_gov', 'values', 'is_cumulative', 'activity', 'tag_focus__label', 'hpm_global_indicator', 'category', 'values_sections', 'values_sections_partners', 'values_sections_gov', 'values_sections_partners_gov', 'values_weekly', 'values_gov_weekly', 'values_partners_weekly', 'values_partners_gov_weekly', 'values_cumulative_weekly', ).distinct() covid_indicators = Indicator.objects.filter(support_COVID=True).exclude(is_imported=True).values( 'id', 'ai_id', 'name', 'master_indicator', 'master_indicator_sub', 'master_indicator_sub_sub', 'individual_indicator', 'awp_code', 'measurement_type', 'units', 'target', 'status_color', 'status', 'cumulative_values', 'values_partners_gov', 'values_partners', 'values_gov', 'values', 'values_live', 'values_gov_live', 'values_partners_live', 'values_partners_gov_live', 'cumulative_values_live', 'is_cumulative', 'activity', 'tag_focus', 'tag_focus__label', 'hpm_global_indicator', ).distinct() start_month = 4 # used to get cumulative values starting this month for covid reporting months = [] if selected_months is not None and len(selected_months) > 0: for mon in selected_months: months.append((mon, calendar.month_abbr[int(mon)])) else: for i in range(1, current_month+1): months.append((i, calendar.month_abbr[i])) sliced_months = months[3:] return { # 'reports': report.order_by('id'), 'database': database, 'reporting_year': str(reporting_year), 'current_month_name': datetime.datetime.now().strftime("%B"), 'months': months, 'sliced_months': sliced_months, 'partners': partners, 'governorates': governorates, 'indicators': master_indicators, 'covid_indicators': covid_indicators, 'selected_filter': selected_filter, 'selected_partners': selected_partners, 'selected_partner_name': selected_partner_name, 'selected_governorates': selected_governorates, 'selected_governorate_name': selected_governorate_name, 'selected_months': selected_months, 'sections': sections, 'selected_sections': selected_sections, 'selected_type': selected_type, 'start_month':start_month } class ReportCrisisView(LoginRequiredMixin,TemplateView): template_name = 'activityinfo/report_crisis.html' def get_context_data(self, **kwargs): ai_id = int(self.request.GET.get('ai_id', 0)) database = Database.objects.get(ai_id=ai_id) selected_partners = self.request.GET.getlist('partners', []) selected_months = self.request.GET.getlist('months', []) selected_governorates = self.request.GET.getlist('governorates', []) selected_sections = self.request.GET.getlist('sections',[]) selected_type = self.request.GET.get('filter_type', '') current_year = date.today().year report = ActivityReport.objects.filter(database_id=database.ai_id) current_month = date.today().month selected_filter = False reporting_year = database.reporting_year.year months = [] if selected_partners or selected_governorates or selected_months or selected_sections: selected_filter = True partners = get_partners_list(database,selected_sections,selected_governorates,selected_months) governorates = get_governorates_list(database,selected_sections,selected_partners,selected_months) sections = get_reporting_sections_list(database,selected_partners,selected_governorates,selected_months) if selected_months is not None and len(selected_months) > 0: for mon in selected_months: months.append((mon, calendar.month_name[int(mon)])) else: months = get_months_list(database, selected_partners, selected_governorates, selected_sections) else: partners = get_partners_list(database) governorates = get_governorates_list(database) sections = get_reporting_sections_list(database) # if selected_months is not None and len(selected_months) > 0: # for mon in selected_months: # months.append((mon, calendar.month_abbr[int(mon)])) # else: for i in range(1, current_month + 1): months.append((i, calendar.month_name[i])) # sliced_months = months[3:] # if current_year - 1 == int(reporting_year) and current_month == 1 and not selected_filter: # months = [] # for i in range(1, 13): # months.append((i, datetime.date(2008, i, 1).strftime('%B'))) if current_year - 1 == int(reporting_year) and not selected_filter: months = [] for i in range(1, 13): months.append((i, datetime.date(2008, i, 1).strftime('%B'))) all_indicators = Indicator.objects.filter(activity__database=database).exclude(type='quality')\ .order_by('sequence') imported_indicators = Indicator.objects.filter(activity__database__support_covid=True).exclude(type='quality') mixed_indicators = all_indicators | imported_indicators master_indicators = all_indicators.filter(Q(master_indicator=True) | Q(sub_indicators__isnull=True, individual_indicator=True))\ .values( 'id', 'ai_id', 'name', 'master_indicator', 'master_indicator_sub', 'individual_indicator', 'awp_code', 'measurement_type', 'units', 'activity', 'tag_focus__label', 'hpm_global_indicator', 'category', 'values_sections', 'values_sections_partners', 'values_sections_gov', 'values_sections_partners_gov', 'values_weekly', 'values_gov_weekly', 'values_partners_weekly', 'values_partners_gov_weekly', 'values_cumulative_weekly', 'sub_indicators' ).order_by('id').distinct('id') if len(selected_type) > 0: master_indicators = master_indicators.filter(tag_focus__label=selected_type) for master_ind in master_indicators: master_ind['cumulative'] = get_indicator_cumulative_months_sections(master_ind, selected_months, selected_partners, selected_governorates, selected_sections) sub_indicators = get_sub_indicators_crisis_data(master_ind['id'],mixed_indicators) master_ind['sub_list'] = sub_indicators master_ind['sub_list_filtered'] = sub_indicators for ind in sub_indicators: if ind['master_indicator'] and not ind['is_imported']: ind['cumulative'] = 0 continue if ind['is_imported']: if len(selected_sections) > 0: for section in selected_sections: if '/' in section: var1 = section.split('/')[0] var2 = section.split('/')[1] if var1 in ind['activity__database__label'] or var2 in ind[ 'activity__database__label']: ind['cumulative'] = get_indicator_cumulative_months_sections(ind, selected_months, selected_partners, selected_governorates) elif ind['activity__database__label'] == section: ind['cumulative'] = get_indicator_cumulative_months_sections(ind, selected_months, selected_partners, selected_governorates) else: ind['cumulative'] = 0 else: ind['cumulative'] = get_indicator_cumulative_months_sections(ind, selected_months, selected_partners, selected_governorates, ) else: ind['cumulative'] = get_indicator_cumulative_months_sections(ind, selected_months, selected_partners, selected_governorates, selected_sections) sub_sub_indicators = get_sub_indicators_crisis_data(ind['id'],mixed_indicators) ind['sub_list'] = sub_sub_indicators ind['sub_list_filtered'] = sub_sub_indicators for ind_sub in sub_sub_indicators: if ind_sub['master_indicator']: ind_sub['cumulative'] = 0 continue if ind['is_imported']: if len(selected_sections) > 0: for section in selected_sections: if '/' in section: var1 = section.split('/')[0] var2 = section.split('/')[1] if var1 in ind_sub['activity__database__label'] or var2 in ind_sub[ 'activity__database__label']: ind_sub['cumulative'] = get_indicator_cumulative_months_sections(ind_sub, selected_months, selected_partners, selected_governorates) elif ind_sub['activity__database__label'] == section: ind_sub['cumulative'] = get_indicator_cumulative_months_sections(ind_sub, selected_months, selected_partners, selected_governorates ) else: ind_sub['cumulative'] = 0 else: ind_sub['cumulative'] = get_indicator_cumulative_months_sections(ind_sub, selected_months, selected_partners, selected_governorates, ) else: ind_sub['cumulative'] = get_indicator_cumulative_months_sections(ind_sub, selected_months, selected_partners, selected_governorates, selected_sections) indicators = get_sub_indicators_crisis_data(ind_sub['id'],mixed_indicators) ind_sub['sub_list'] = indicators ind_sub['sub_list_filtered'] = indicators for item in indicators: if item['master_indicator']: item['cumulative'] = 0 continue if ind['is_imported']: if len(selected_sections) > 0: for section in selected_sections: if '/' in section: var1 = section.split('/')[0] var2 = section.split('/')[1] if var1 in item['activity__database__label'] or var2 in item[ 'activity__database__label']: item['cumulative'] = get_indicator_cumulative_months_sections(item, selected_months, selected_partners, selected_governorates) elif item['activity__database__label'] == section: item['cumulative'] = get_indicator_cumulative_months_sections(item, selected_months, selected_partners, selected_governorates) else: item['cumulative'] = 0 else: item['cumulative'] = get_indicator_cumulative_months_sections(item, selected_months, selected_partners, selected_governorates, ) else: item['cumulative'] = get_indicator_cumulative_months_sections(item, selected_months, selected_partners, selected_governorates, selected_sections) filtered_list=[] if selected_filter: for master_ind in master_indicators: if not (master_ind['cumulative'] == '0' or master_ind['cumulative'] == 0): filtered_list.append(master_ind) master_ind['sub_list_filtered'] =[] for sub_indicator in master_ind['sub_list']: sub_indicator['sub_list_filtered'] = [] if not (sub_indicator['cumulative'] == '0' or sub_indicator['cumulative'] == 0): master_ind['sub_list_filtered'].append(sub_indicator) for sub_sub_ind in sub_indicator['sub_list']: if sub_sub_ind['cumulative'] == '0' or sub_sub_ind['cumulative'] == 0 : continue else: sub_indicator['sub_list_filtered'].append(sub_sub_ind) else: for sub_indicator in master_ind['sub_list']: if not (sub_indicator['cumulative'] == '0' or sub_indicator['cumulative'] == 0): filtered_list.append(sub_indicator) sub_indicator['sub_list_filtered']=[] for sub_sub_ind in sub_indicator['sub_list']: if sub_sub_ind['cumulative'] == '0' or sub_sub_ind['cumulative'] == 0 : continue else: sub_indicator['sub_list_filtered'].append(sub_sub_ind) else: for sub_sub_ind in sub_indicator['sub_list']: if sub_sub_ind['cumulative'] == '0' or sub_sub_ind['cumulative'] == 0: continue else: filtered_list.append(sub_sub_ind) else: filtered_list = master_indicators covid_indicators = imported_indicators.filter(support_COVID=True).filter(master_indicator=True).exclude(is_imported=True).values( 'id', 'ai_id', 'name', 'master_indicator', 'master_indicator_sub', 'master_indicator_sub_sub', 'individual_indicator', 'awp_code', 'measurement_type', 'units', 'category', 'cumulative_values', 'values_partners_gov', 'values_partners', 'values_gov', 'values', 'activity', ).distinct() start_month = 4 # used to get cumulative values starting this month for covid reporting filtered_covid_indicators =[] if len(selected_sections) > 0: for section in selected_sections: if '/' in section: section = section.split('/') filtered =list(covid_indicators.filter(Q(activity__database__label__contains=section[0]) | Q(activity__database__label__contains=section[1]))) for item in filtered: filtered_covid_indicators.append(item) else: filtered= list(covid_indicators.filter(activity__database__label__contains=section)) for item in filtered: filtered_covid_indicators.append(item) else: filtered_covid_indicators = covid_indicators for master_ind in filtered_covid_indicators: master_ind['cumulative'] = get_indicator_cumulative_months(master_ind, selected_months, selected_partners, selected_governorates, start_month) sub_indicators = get_sub_indicators_data_new(master_ind['id'], imported_indicators) master_ind['sub_list'] = sub_indicators master_ind['sub_list_filtered'] = sub_indicators for ind in sub_indicators: ind['cumulative'] = get_indicator_cumulative_months(ind, selected_months, selected_partners, selected_governorates, start_month ) sub_sub_indicators = get_sub_indicators_data_new(ind['id'], imported_indicators) ind['sub_list'] = sub_sub_indicators ind['sub_list_filtered'] = sub_sub_indicators for sub_ind in sub_sub_indicators: sub_ind['cumulative'] = get_indicator_cumulative_months(sub_ind, selected_months, selected_partners, selected_governorates, start_month) indicators = get_sub_indicators_data_new(sub_ind['id'],imported_indicators) sub_ind['sub_list'] = indicators sub_ind['sub_list_filtered'] = indicators covid_filtered_list=[] if selected_filter: for master_ind in filtered_covid_indicators: if not (master_ind['cumulative'] == '0' or master_ind['cumulative'] == 0): covid_filtered_list.append(master_ind) master_ind['sub_list_filtered'] = [] for sub_indicator in master_ind['sub_list']: sub_indicator['sub_list_filtered'] = [] if not (sub_indicator['cumulative'] == '0' or sub_indicator['cumulative'] == 0): master_ind['sub_list_filtered'].append(sub_indicator) for sub_sub_ind in sub_indicator['sub_list']: if sub_sub_ind['cumulative'] == '0' or sub_sub_ind['cumulative'] == 0: continue else: sub_indicator['sub_list_filtered'].append(sub_sub_ind) else: for sub_indicator in master_ind['sub_list']: if not (sub_indicator['cumulative'] == '0' or sub_indicator['cumulative'] == 0): covid_filtered_list.append(sub_indicator) sub_indicator['sub_list_filtered'] = [] for sub_sub_ind in sub_indicator['sub_list']: if sub_sub_ind['cumulative'] == '0' or sub_sub_ind['cumulative'] == 0: continue else: sub_indicator['sub_list_filtered'].append(sub_sub_ind) else: for sub_sub_ind in sub_indicator['sub_list']: if sub_sub_ind['cumulative'] == '0' or sub_sub_ind['cumulative'] == 0: continue else: covid_filtered_list.append(sub_sub_ind) else: covid_filtered_list = filtered_covid_indicators if self.request.user.is_authenticated: template = "base2.html" else: template = "base_empty.html" return { 'reports': report.order_by('id'), 'database': database, 'reporting_year': str(reporting_year), 'current_month_name': datetime.datetime.now().strftime("%B"), 'months': months, # 'sliced_months': sliced_months, 'partners': partners, 'governorates': governorates, 'sections': sections, 'indicators': filtered_list, 'covid_indicators': covid_filtered_list, 'selected_filter':selected_filter , 'selected_partners': selected_partners, 'selected_governorates': selected_governorates, 'selected_months': selected_months, 'selected_sections': selected_sections, 'selected_type': selected_type, 'start_month':start_month, 'template':template } class ReportSocioEconomicView(LoginRequiredMixin,TemplateView): template_name = 'activityinfo/report_socio_economic.html' def get_context_data(self, **kwargs): ai_id = int(self.request.GET.get('ai_id', 0)) database = Database.objects.get(ai_id=ai_id) selected_partners = self.request.GET.getlist('partners', []) selected_months = self.request.GET.getlist('months', []) selected_governorates = self.request.GET.getlist('governorates', []) selected_sections = self.request.GET.getlist('sections',[]) selected_type = self.request.GET.get('filter_type', '') current_year = date.today().year report = ActivityReport.objects.filter(database_id=database.ai_id) current_month = date.today().month selected_filter = False reporting_year = database.reporting_year.year months = [] if selected_partners or selected_governorates or selected_months or selected_sections: selected_filter = True partners = get_partners_list(database,selected_sections,selected_governorates,selected_months) governorates = get_governorates_list(database,selected_sections,selected_partners,selected_months) sections = get_reporting_sections_list(database,selected_partners,selected_governorates,selected_months) if selected_months is not None and len(selected_months) > 0: for mon in selected_months: months.append((mon, calendar.month_name[int(mon)])) else: months = get_months_list(database, selected_partners, selected_governorates, selected_sections) else: partners = get_partners_list(database) governorates = get_governorates_list(database) sections = get_reporting_sections_list(database) # if selected_months is not None and len(selected_months) > 0: # for mon in selected_months: # months.append((mon, calendar.month_abbr[int(mon)])) # else: for i in range(1, current_month + 1): months.append((i, calendar.month_name[i])) # sliced_months = months[3:] # if current_year - 1 == int(reporting_year) and current_month == 1 and not selected_filter: # months = [] # for i in range(1, 13): # months.append((i, datetime.date(2008, i, 1).strftime('%B'))) if current_year - 1 == int(reporting_year) and not selected_filter: months = [] for i in range(1, 13): months.append((i, datetime.date(2008, i, 1).strftime('%B'))) all_indicators = Indicator.objects.filter(activity__database=database).exclude(type='quality')\ .order_by('sequence') imported_indicators = Indicator.objects.filter(activity__database__support_covid=True).exclude(type='quality') mixed_indicators = all_indicators | imported_indicators master_indicators = all_indicators.filter(Q(master_indicator=True) | Q(sub_indicators__isnull=True, individual_indicator=True))\ .values( 'id', 'ai_id', 'name', 'master_indicator', 'master_indicator_sub', 'individual_indicator', 'awp_code', 'measurement_type', 'units', 'activity', 'tag_focus__label', 'hpm_global_indicator', 'category', 'values_sections', 'values_sections_partners', 'values_sections_gov', 'values_sections_partners_gov', 'values_weekly', 'values_gov_weekly', 'values_partners_weekly', 'values_partners_gov_weekly', 'values_cumulative_weekly', 'sub_indicators' ).order_by('id').distinct('id') if len(selected_type) > 0: master_indicators = master_indicators.filter(tag_focus__label=selected_type) for master_ind in master_indicators: master_ind['cumulative'] = get_indicator_cumulative_months_sections(master_ind, selected_months, selected_partners, selected_governorates, selected_sections) sub_indicators = get_sub_indicators_crisis_data(master_ind['id'],mixed_indicators) master_ind['sub_list'] = sub_indicators master_ind['sub_list_filtered'] = sub_indicators for ind in sub_indicators: if ind['master_indicator'] and not ind['is_imported']: ind['cumulative'] = 0 continue if ind['is_imported']: if len(selected_sections) > 0: for section in selected_sections: if '/' in section: var1 = section.split('/')[0] var2 = section.split('/')[1] if var1 in ind['activity__database__label'] or var2 in ind[ 'activity__database__label']: ind['cumulative'] = get_indicator_cumulative_months_sections(ind, selected_months, selected_partners, selected_governorates) elif ind['activity__database__label'] == section: ind['cumulative'] = get_indicator_cumulative_months_sections(ind, selected_months, selected_partners, selected_governorates) else: ind['cumulative'] = 0 else: ind['cumulative'] = get_indicator_cumulative_months_sections(ind, selected_months, selected_partners, selected_governorates, ) else: ind['cumulative'] = get_indicator_cumulative_months_sections(ind, selected_months, selected_partners, selected_governorates, selected_sections) sub_sub_indicators = get_sub_indicators_crisis_data(ind['id'],mixed_indicators) ind['sub_list'] = sub_sub_indicators ind['sub_list_filtered'] = sub_sub_indicators for ind_sub in sub_sub_indicators: if ind_sub['master_indicator']: ind_sub['cumulative'] = 0 continue if ind['is_imported']: if len(selected_sections) > 0: for section in selected_sections: if '/' in section: var1 = section.split('/')[0] var2 = section.split('/')[1] if var1 in ind_sub['activity__database__label'] or var2 in ind_sub[ 'activity__database__label']: ind_sub['cumulative'] = get_indicator_cumulative_months_sections(ind_sub, selected_months, selected_partners, selected_governorates) elif ind_sub['activity__database__label'] == section: ind_sub['cumulative'] = get_indicator_cumulative_months_sections(ind_sub, selected_months, selected_partners, selected_governorates ) else: ind_sub['cumulative'] = 0 else: ind_sub['cumulative'] = get_indicator_cumulative_months_sections(ind_sub, selected_months, selected_partners, selected_governorates, ) else: ind_sub['cumulative'] = get_indicator_cumulative_months_sections(ind_sub, selected_months, selected_partners, selected_governorates, selected_sections) indicators = get_sub_indicators_crisis_data(ind_sub['id'],mixed_indicators) ind_sub['sub_list'] = indicators ind_sub['sub_list_filtered'] = indicators for item in indicators: if item['master_indicator']: item['cumulative'] = 0 continue if ind['is_imported']: if len(selected_sections) > 0: for section in selected_sections: if '/' in section: var1 = section.split('/')[0] var2 = section.split('/')[1] if var1 in item['activity__database__label'] or var2 in item[ 'activity__database__label']: item['cumulative'] = get_indicator_cumulative_months_sections(item, selected_months, selected_partners, selected_governorates) elif item['activity__database__label'] == section: item['cumulative'] = get_indicator_cumulative_months_sections(item, selected_months, selected_partners, selected_governorates) else: item['cumulative'] = 0 else: item['cumulative'] = get_indicator_cumulative_months_sections(item, selected_months, selected_partners, selected_governorates, ) else: item['cumulative'] = get_indicator_cumulative_months_sections(item, selected_months, selected_partners, selected_governorates, selected_sections) filtered_list=[] if selected_filter: for master_ind in master_indicators: if not (master_ind['cumulative'] == '0' or master_ind['cumulative'] == 0): filtered_list.append(master_ind) master_ind['sub_list_filtered'] =[] for sub_indicator in master_ind['sub_list']: sub_indicator['sub_list_filtered'] = [] if not (sub_indicator['cumulative'] == '0' or sub_indicator['cumulative'] == 0): master_ind['sub_list_filtered'].append(sub_indicator) for sub_sub_ind in sub_indicator['sub_list']: if sub_sub_ind['cumulative'] == '0' or sub_sub_ind['cumulative'] == 0 : continue else: sub_indicator['sub_list_filtered'].append(sub_sub_ind) else: for sub_indicator in master_ind['sub_list']: if not (sub_indicator['cumulative'] == '0' or sub_indicator['cumulative'] == 0): filtered_list.append(sub_indicator) sub_indicator['sub_list_filtered']=[] for sub_sub_ind in sub_indicator['sub_list']: if sub_sub_ind['cumulative'] == '0' or sub_sub_ind['cumulative'] == 0 : continue else: sub_indicator['sub_list_filtered'].append(sub_sub_ind) else: for sub_sub_ind in sub_indicator['sub_list']: if sub_sub_ind['cumulative'] == '0' or sub_sub_ind['cumulative'] == 0: continue else: filtered_list.append(sub_sub_ind) else: filtered_list = master_indicators covid_indicators = imported_indicators.filter(support_COVID=True).filter(master_indicator=True).exclude(is_imported=True).values( 'id', 'ai_id', 'name', 'master_indicator', 'master_indicator_sub', 'master_indicator_sub_sub', 'individual_indicator', 'awp_code', 'measurement_type', 'units', 'category', 'cumulative_values', 'values_partners_gov', 'values_partners', 'values_gov', 'values', 'activity', ).distinct() start_month = 4 # used to get cumulative values starting this month for covid reporting filtered_covid_indicators =[] if len(selected_sections) > 0: for section in selected_sections: if '/' in section: section = section.split('/') filtered =list(covid_indicators.filter(Q(activity__database__label__contains=section[0]) | Q(activity__database__label__contains=section[1]))) for item in filtered: filtered_covid_indicators.append(item) else: filtered= list(covid_indicators.filter(activity__database__label__contains=section)) for item in filtered: filtered_covid_indicators.append(item) else: filtered_covid_indicators = covid_indicators for master_ind in filtered_covid_indicators: master_ind['cumulative'] = get_indicator_cumulative_months(master_ind, selected_months, selected_partners, selected_governorates, start_month) sub_indicators = get_sub_indicators_data_new(master_ind['id'], imported_indicators) master_ind['sub_list'] = sub_indicators master_ind['sub_list_filtered'] = sub_indicators for ind in sub_indicators: ind['cumulative'] = get_indicator_cumulative_months(ind, selected_months, selected_partners, selected_governorates, start_month ) sub_sub_indicators = get_sub_indicators_data_new(ind['id'], imported_indicators) ind['sub_list'] = sub_sub_indicators ind['sub_list_filtered'] = sub_sub_indicators for sub_ind in sub_sub_indicators: sub_ind['cumulative'] = get_indicator_cumulative_months(sub_ind, selected_months, selected_partners, selected_governorates, start_month) indicators = get_sub_indicators_data_new(sub_ind['id'],imported_indicators) sub_ind['sub_list'] = indicators sub_ind['sub_list_filtered'] = indicators if self.request.user.is_authenticated: template = "base2.html" else: template = "base_empty.html" return { 'reports': report.order_by('id'), 'database': database, 'reporting_year': str(reporting_year), 'current_month_name': datetime.datetime.now().strftime("%B"), 'months': months, # 'sliced_months': sliced_months, 'partners': partners, 'governorates': governorates, 'sections': sections, 'indicators': filtered_list, # 'covid_indicators': covid_filtered_list, 'selected_filter':selected_filter , 'selected_partners': selected_partners, 'selected_governorates': selected_governorates, 'selected_months': selected_months, 'selected_sections': selected_sections, 'selected_type': selected_type, 'start_month':start_month, 'template':template } class ReportLiveCrisis(LoginRequiredMixin,TemplateView): template_name = 'activityinfo/report_crisis_live.html' def get_context_data(self,**kwargs): ai_id = int(self.request.GET.get('ai_id', 0)) database = Database.objects.get(ai_id=ai_id) selected_partners = self.request.GET.getlist('partners', []) selected_governorates = self.request.GET.getlist('governorates', []) selected_sections = self.request.GET.getlist('sections', []) selected_type = self.request.GET.get('filter_type', '') selected_filter = False today = datetime.date.today() day_number = today.strftime("%d") month_number = today.strftime("%m") month = int(today.strftime("%m")) month_name = calendar.month_name[month] reporting_year = database.reporting_year.year report = LiveActivityReport.objects.filter(database_id=database.ai_id) if selected_partners or selected_governorates or selected_sections: selected_filter = True partners = get_partners_list(database,sections=selected_sections,govs=selected_governorates,report_type='live') governorates = get_governorates_list(database,sections=selected_sections,partners=selected_partners,report_type='live') sections = get_reporting_sections_list(database,partners=selected_partners,govs=selected_governorates,report_type='live') else: partners = get_partners_list(database,report_type='live') governorates = get_governorates_list(database, report_type='live') sections = get_reporting_sections_list(database, report_type='live') all_indicators = Indicator.objects.filter(activity__database=database).exclude(type='quality') \ .order_by('sequence') imported_indicators = Indicator.objects.filter(activity__database__support_covid=True).exclude(type='quality') mixed_indicators = all_indicators | imported_indicators master_indicators = all_indicators.filter(Q(master_indicator=True) | Q(sub_indicators__isnull=True, individual_indicator=True)) \ .values( 'id', 'ai_id', 'name', 'master_indicator', 'master_indicator_sub', 'individual_indicator', 'awp_code', 'measurement_type', 'units', 'activity', 'tag_focus__label', 'hpm_global_indicator', 'category', 'values_sections_live', 'values_sections_partners_live', 'values_sections_gov_live', 'values_sections_partners_gov_live', 'values_crisis_live', 'values_crisis_gov_live', 'values_crisis_partners_live', 'values_crisis_partners_gov_live', 'values_crisis_cumulative_live', 'sub_indicators' ).order_by('id').distinct('id') if len(selected_type) > 0: master_indicators = master_indicators.filter(tag_focus__label=selected_type) for master_ind in master_indicators: master_ind['cumulative'] = get_indicator_live_cumulative_section(master_ind, month, selected_partners, selected_governorates, selected_sections) sub_indicators = get_sub_indicators_live_crisis_data(master_ind['id'], mixed_indicators) master_ind['sub_list'] = sub_indicators master_ind['sub_list_filtered'] = sub_indicators for ind in sub_indicators: if ind['master_indicator'] and not ind['is_imported']: ind['cumulative'] = 0 continue if ind['is_imported']: if len(selected_sections) > 0: for section in selected_sections: if '/' in section: var1 = section.split('/')[0] var2 = section.split('/')[1] if var1 in ind['activity__database__label'] or var2 in ind[ 'activity__database__label']: ind['cumulative'] = get_indicator_live_cumulative_section(ind, month, selected_partners, selected_governorates) elif ind['activity__database__label'] == section: ind['cumulative'] = get_indicator_live_cumulative_section(ind, month, selected_partners, selected_governorates) else: ind['cumulative'] = 0 else: ind['cumulative'] = get_indicator_live_cumulative_section(ind, month, selected_partners, selected_governorates, ) else: ind['cumulative'] = get_indicator_live_cumulative_section(ind, month, selected_partners, selected_governorates, selected_sections) sub_sub_indicators = get_sub_indicators_live_crisis_data(ind['id'], mixed_indicators) ind['sub_list'] = sub_sub_indicators ind['sub_list_filtered'] = sub_sub_indicators for ind_sub in sub_sub_indicators: if ind_sub['master_indicator']: ind_sub['cumulative'] = 0 continue if ind['is_imported']: if len(selected_sections) > 0: for section in selected_sections: if '/' in section: var1 = section.split('/')[0] var2 = section.split('/')[1] if var1 in ind_sub['activity__database__label'] or var2 in ind_sub[ 'activity__database__label']: ind_sub['cumulative'] = get_indicator_live_cumulative_section(ind_sub, month, selected_partners, selected_governorates) elif ind_sub['activity__database__label'] == section: ind_sub['cumulative'] = get_indicator_live_cumulative_section(ind_sub, month, selected_partners, selected_governorates) else: ind_sub['cumulative'] = 0 else: ind_sub['cumulative'] = get_indicator_live_cumulative_section(ind_sub, month, selected_partners, selected_governorates, ) else: ind_sub['cumulative'] = get_indicator_live_cumulative_section(ind_sub, month, selected_partners, selected_governorates, selected_sections) indicators = get_sub_indicators_live_crisis_data(ind_sub['id'], mixed_indicators) ind_sub['sub_list'] = indicators ind_sub['sub_list_filtered'] = indicators for item in indicators: if item['master_indicator']: item['cumulative'] = 0 continue if ind['is_imported']: if len(selected_sections) > 0: for section in selected_sections: if '/' in section: var1 = section.split('/')[0] var2 = section.split('/')[1] if var1 in item['activity__database__label'] or var2 in item[ 'activity__database__label']: item['cumulative'] = get_indicator_live_cumulative_section(item, month, selected_partners, selected_governorates) elif item['activity__database__label'] == section: item['cumulative'] = get_indicator_live_cumulative_section(item, month, selected_partners, selected_governorates) else: item['cumulative'] = 0 else: item['cumulative'] = get_indicator_live_cumulative_section(item, month, selected_partners, selected_governorates, ) else: item['cumulative'] = get_indicator_live_cumulative_section(item, month, selected_partners, selected_governorates, selected_sections) filtered_list = [] if selected_filter: for master_ind in master_indicators: if not (master_ind['cumulative'] == '0' or master_ind['cumulative'] == 0): filtered_list.append(master_ind) master_ind['sub_list_filtered'] = [] for sub_indicator in master_ind['sub_list']: sub_indicator['sub_list_filtered'] = [] if not (sub_indicator['cumulative'] == '0' or sub_indicator['cumulative'] == 0): master_ind['sub_list_filtered'].append(sub_indicator) for sub_sub_ind in sub_indicator['sub_list']: if sub_sub_ind['cumulative'] == '0' or sub_sub_ind['cumulative'] == 0: continue else: sub_indicator['sub_list_filtered'].append(sub_sub_ind) else: for sub_indicator in master_ind['sub_list']: if not (sub_indicator['cumulative'] == '0' or sub_indicator['cumulative'] == 0): filtered_list.append(sub_indicator) sub_indicator['sub_list_filtered'] = [] for sub_sub_ind in sub_indicator['sub_list']: if sub_sub_ind['cumulative'] == '0' or sub_sub_ind['cumulative'] == 0: continue else: sub_indicator['sub_list_filtered'].append(sub_sub_ind) else: for sub_sub_ind in sub_indicator['sub_list']: if sub_sub_ind['cumulative'] == '0' or sub_sub_ind['cumulative'] == 0: continue else: filtered_list.append(sub_sub_ind) else: filtered_list = master_indicators covid_indicators = imported_indicators.filter(support_COVID=True).filter(master_indicator=True).exclude( is_imported=True).values( 'id', 'ai_id', 'name', 'master_indicator', 'master_indicator_sub', 'individual_indicator', 'awp_code', 'measurement_type', 'units', 'category', 'values_live', 'values_gov_live', 'values_partners_live', 'values_partners_gov_live', 'cumulative_values_live', 'activity', ).distinct() start_month = 4 # used to get cumulative values starting this month for covid reporting filtered_covid_indicators = [] if len(selected_sections) > 0: for section in selected_sections: if '/' in section: section = section.split('/') filtered = list(covid_indicators.filter(Q(activity__database__label__contains=section[0]) | Q( activity__database__label__contains=section[1]))) for item in filtered: filtered_covid_indicators.append(item) else: filtered = list(covid_indicators.filter(activity__database__label__contains=section)) for item in filtered: filtered_covid_indicators.append(item) else: filtered_covid_indicators = covid_indicators for master_ind in filtered_covid_indicators: master_ind['cumulative'] = get_indicator_live_cumulative(master_ind, month, selected_partners, selected_governorates, start_month) sub_indicators = get_sub_indicators_live_data(master_ind['id'], imported_indicators) master_ind['sub_list'] = sub_indicators master_ind['sub_list_filtered'] = sub_indicators for ind in sub_indicators: ind['cumulative'] = get_indicator_live_cumulative(ind, month, selected_partners, selected_governorates, start_month ) sub_sub_indicators = get_sub_indicators_live_data(ind['id'], imported_indicators) ind['sub_list'] = sub_sub_indicators ind['sub_list_filtered'] = sub_sub_indicators for sub_ind in sub_sub_indicators: sub_ind['cumulative'] = get_indicator_live_cumulative(sub_ind, month, selected_partners, selected_governorates, start_month) indicators = get_sub_indicators_live_data(sub_ind['id'], imported_indicators) sub_ind['sub_list'] = indicators sub_ind['sub_list_filtered'] = indicators covid_filtered_list = [] if selected_filter: for master_ind in filtered_covid_indicators: if not (master_ind['cumulative'] == '0' or master_ind['cumulative'] == 0): covid_filtered_list.append(master_ind) master_ind['sub_list_filtered'] = [] for sub_indicator in master_ind['sub_list']: sub_indicator['sub_list_filtered'] = [] if not (sub_indicator['cumulative'] == '0' or sub_indicator['cumulative'] == 0): master_ind['sub_list_filtered'].append(sub_indicator) for sub_sub_ind in sub_indicator['sub_list']: if sub_sub_ind['cumulative'] == '0' or sub_sub_ind['cumulative'] == 0: continue else: sub_indicator['sub_list_filtered'].append(sub_sub_ind) else: for sub_indicator in master_ind['sub_list']: if not (sub_indicator['cumulative'] == '0' or sub_indicator['cumulative'] == 0): covid_filtered_list.append(sub_indicator) sub_indicator['sub_list_filtered'] = [] for sub_sub_ind in sub_indicator['sub_list']: if sub_sub_ind['cumulative'] == '0' or sub_sub_ind['cumulative'] == 0: continue else: sub_indicator['sub_list_filtered'].append(sub_sub_ind) else: for sub_sub_ind in sub_indicator['sub_list']: if sub_sub_ind['cumulative'] == '0' or sub_sub_ind['cumulative'] == 0: continue else: covid_filtered_list.append(sub_sub_ind) else: covid_filtered_list = filtered_covid_indicators if self.request.user.is_authenticated: template = "base2.html" else: template = "base_empty.html" return { 'reports': report.order_by('id'), 'database': database, 'reporting_year': str(reporting_year), 'current_month_name': datetime.datetime.now().strftime("%B"), 'partners': partners, 'governorates': governorates, 'indicators': filtered_list, 'covid_indicators': covid_filtered_list, 'selected_filter': selected_filter, 'selected_partners': selected_partners, 'selected_governorates': selected_governorates, 'month': month, 'month_name': month_name, 'month_number': month_number, 'day_number':day_number, 'sections':sections, 'selected_sections':selected_sections, 'selected_type': selected_type, 'start_month':start_month, 'template':template } class ReportInternalView(LoginRequiredMixin,TemplateView): template_name = 'activityinfo/report_internal.html' def get_context_data(self, **kwargs): ai_id = int(self.request.GET.get('ai_id', 0)) database = Database.objects.get(ai_id=ai_id) reporting_year = database.reporting_year.year report = ActivityReport.objects.filter(database_id=database.ai_id) none_ai_indicators = Indicator.objects.filter(none_ai_indicator=True,activity__database=database).values( 'id', 'ai_id', 'name', 'master_indicator', 'master_indicator_sub', 'awp_code', 'measurement_type', 'units', 'target', 'status_color', 'status', 'cumulative_values', 'values_partners_gov', 'values_partners', 'values_gov', 'values', 'is_cumulative', 'type' ).distinct() db_url ="" if database.reporting_year.name == database.reporting_year.year+' Crisis': db_url = '/activityinfo/report-crisis' elif database.reporting_year.name == database.reporting_year.year+' Socio Enonomic': db_url = '/activityinfo/report-socio-economic' else: db_url= '/activityinfo/report' if self.request.user.is_authenticated: template = "base2.html" else: template = "base_empty.html" months = [] for i in range(1, 13): months.append((i, calendar.month_abbr[i])) return { 'reports': report.order_by('id'), 'database': database, 'reporting_year': str(reporting_year), 'current_month_name': datetime.datetime.now().strftime("%B"), 'list_indicators':none_ai_indicators, 'months':months, 'db_url': str(db_url), 'template':template } class ReportInternalFormView(LoginRequiredMixin,TemplateView): template_name = 'activityinfo/report_internal_form.html' def get_context_data(self, **kwargs): indicator_id = self.request.GET.get('id', 0) ai_id = self.request.GET.get('ai_id', 0) step = int(self.request.GET.get('step', 0)) database = Database.objects.get(ai_id=ai_id) reporting_year = database.reporting_year.year activities = Activity.objects.filter(database=database.id) report = ActivityReport.objects.filter(database_id=database.ai_id,indicator_id=indicator_id).values( 'indicator_id', 'indicator_name', 'indicator_units', 'indicator_value', 'location_adminlevel_governorate_code', 'location_adminlevel_governorate', 'database_id', 'start_date', 'month' ) governorates=[] governorates.append((2,'Akkar')) governorates.append((3,' Baalbek_Hermel')) governorates.append((4,'North')) governorates.append((5,'Mount Lebanon')) governorates.append((6,'Bekaa')) governorates.append((7,'Beirut')) governorates.append((8,'South')) governorates.append((9,'Nabatiye')) governorates.append((10, 'National')) if indicator_id != 0: indicator = Indicator.objects.get(id=indicator_id) else: step=1 indicator = None months =[] for i in range(1,13): months.append((i,calendar.month_name[i])) db_url ="" if database.reporting_year.name == database.reporting_year.year+' Crisis': db_url = '/activityinfo/report-crisis' if database.reporting_year.name == database.reporting_year.year+' Socio Enonomic': db_url = '/activityinfo/report-socio-economic' else: db_url= '/activityinfo/report' if self.request.user.is_authenticated: template = "base2.html" else: template = "base_empty.html" return { 'reports': report.order_by('id'), 'database': database, 'reporting_year': str(reporting_year), 'current_month_name': datetime.datetime.now().strftime("%B"), 'activities':activities, 'governorates':governorates, 'indicator':indicator, 'step':step, 'months':months, 'db_url':db_url, 'template':template } def post(self, request, *args, **kwargs): form_name = self.request.POST.get('form_name', 0) indicator_id = self.request.POST.get('id', 0) ai_id = self.request.POST.get('ai_id', 0) database = Database.objects.get(ai_id=ai_id) step = 0 if indicator_id: indicator = Indicator.objects.get(id=indicator_id) else: step = 2 indicator = Indicator(ai_indicator=None) gov = "" value = 0 month = 0 governorates = [] governorates.append((2, 'Akkar')) governorates.append((3, ' Baalbek_Hermel')) governorates.append((4, 'North')) governorates.append((5, 'Mount Lebanon')) governorates.append((6, 'Bekaa')) governorates.append((7, 'Beirut')) governorates.append((8, 'South')) governorates.append((9, 'Nabatiye')) governorates.append((10, 'National')) if form_name == 'valuesform': row_values = self.request.POST.get('row_values', "") json_string = json.loads(row_values) if 'myrows' in json_string: ActivityReport.objects.filter(database_id=ai_id, indicator_id=indicator_id).delete() indicator.values = {} indicator.values_gov = {} indicator.values_partners = {} indicator.values_partners_gov = {} indicator.cumulative_values = {} indicator.save() for row in json_string['myrows']: if 'Governorate' in row: gov = row['Governorate'] if 'Month' in row: month = row['Month'] if "Value" in row: value = row['Value'] gov_name="" for num , name in governorates: if num == gov: gov_name=name date = datetime.datetime.strptime(month, "%Y-%m") date = date.replace(day=01) report = ActivityReport() report.indicator_name = indicator.name report.indicator_id = indicator.id report.database_id = ai_id report.master_indicator = indicator.master_indicator report.master_indicator_sub = indicator.master_indicator_sub report.month = month report.start_date = date report.location_adminlevel_governorate_code = gov report.location_adminlevel_governorate = gov_name report.indicator_value = value report.indicator_units = indicator.units report.partner_label = 'UNICEF' report.partner_id = 'UNICEF' report.funded_by = 'UNICEF' report.save() calculate_internal_indicators_values(ai_id,indicator_id) if database: calculate_internal_cumulative_results(database.id,indicator_id) return HttpResponseRedirect('/activityinfo/report-internal/?rep_year=2020&ai_id=' + str(ai_id)) if form_name == 'resultsform': row_results = self.request.POST.get('row_results', "") json_string = json.loads(row_results) result="" gov="" indicator.results = {} indicator.save() results_list = {} if 'myrows' in json_string: for row in json_string['myrows']: if 'Result' in row: result = row['Result'] if 'Month' in row: month = row['Month'] if "Governorate" in row: gov = row['Governorate'] key = '{}-{}'.format(month,gov) results_list[key] = result indicator.results = results_list indicator.save() return HttpResponseRedirect('/activityinfo/report-internal/?rep_year=2020&ai_id=' + str(ai_id)) if form_name == 'indicatorform': name = self.request.POST.get('name', "") activity_id = self.request.POST.get('activity', "") awp_code = self.request.POST.get('awp_code',"") description = self.request.POST.get('description',"") qualitative_target = self.request.POST.get('qualitative_target',"") unit = self.request.POST.get('unit',"") level = self.request.POST.get('level',"") type = self.request.POST.get('type',"") measurement = self.request.POST.get('measurement',"") activity = Activity.objects.get(id=activity_id) qualitative_result = self.request.POST.get('qualitative_result',"") status = self.request.POST.get('status',"") if self.request.POST.get('target'): target = self.request.POST.get('target', default=0) else: target=0 if level == 'master_indicator': master_indicator = True else: master_indicator = False if level == 'sub_master_indicator': sub_master_indicator = True else: sub_master_indicator = False indicator.label = name indicator.name = name indicator.type = type indicator.activity = activity indicator.units = unit indicator.master_indicator = master_indicator indicator.awp_code = awp_code indicator.description = description indicator.master_indicator_sub = sub_master_indicator indicator.none_ai_indicator = True indicator.target = target indicator.qualitative_target = qualitative_target indicator.qualitative_result = qualitative_result indicator.status = status indicator.measurement_type = measurement indicator.funded_by = 'UNICEF' indicator.save() return HttpResponseRedirect('/activityinfo/report-internal-form/?rep_year=2020&ai_id='+str(ai_id)+'&id='+str(indicator.id)+'&step='+str(step)) class ReportPartnerView(LoginRequiredMixin,TemplateView): template_name = 'activityinfo/report_partner.html' def get_context_data(self, **kwargs): selected_filters = False today = datetime.date.today() first = today.replace(day=1) last_month = first - datetime.timedelta(days=1) month_number = last_month.strftime("%m") month = int(last_month.strftime("%m")) month_name = last_month.strftime("%B") selected_indicator = int(self.request.GET.get('indicator_id', 0)) selected_sub_indicator = self.request.GET.getlist('sub_indicator_id', []) selected_governorate = self.request.GET.get('governorate', 0) selected_governorate_name = self.request.GET.get('governorate_name', 'All Governorates') selected_partner = self.request.GET.get('partner', "") selected_partner_name = self.request.GET.get('partners_name', 'All Partners') if selected_indicator or selected_governorate: selected_filters = True ai_id = int(self.request.GET.get('ai_id', 0)) database = Database.objects.get(ai_id=ai_id) reporting_year = database.reporting_year.year if selected_indicator: indicator = Indicator.objects.get(id=selected_indicator) indicator = { 'id': indicator.id, 'ai_id': indicator.ai_id, 'name': indicator.name, 'ai_indicator':indicator.ai_indicator, 'explication': indicator.explication, 'awp_code': indicator.awp_code, 'measurement_type': indicator.measurement_type, 'units': indicator.units, 'target': indicator.target, 'status_color': indicator.status_color, 'status': indicator.status, 'cumulative_values': indicator.cumulative_values, 'values_partners_gov': indicator.values_partners_gov, 'values_partners': indicator.values_partners, 'values_gov': indicator.values_gov, 'values': indicator.values, } selected_indicator_name = indicator['name'] partners_values = indicator['values_partners'] partners_list=[] if partners_values: for key , value in partners_values.items(): p = key.split('-')[1] if (p,p) not in partners_list: partners_list.append((p,p)) else: indicator = [] selected_indicator_name = "" partners=[] governorates=[] partners_list=[] # report = ActivityReport.objects.filter(database_id=database.ai_id) # # if database.is_funded_by_unicef: # report = report.filter(funded_by__contains='UNICEF') partners = get_partners_list(database) governorates = get_governorates_list(database) # partners = report.values('partner_label', 'partner_id').distinct() # governorates = report.values('location_adminlevel_governorate_code', # 'location_adminlevel_governorate').distinct() master_indicators = Indicator.objects.filter(activity__database=database, master_indicator=True).exclude( is_sector=True).order_by('sequence') individual_indicators = Indicator.objects.filter(activity__database=database, individual_indicator=True).exclude(is_sector=True).order_by('sequence') indicators = master_indicators | individual_indicators indicators = indicators.values( 'id', 'ai_id', 'name', 'master_indicator', 'master_indicator_sub', 'master_indicator_sub_sub', 'individual_indicator', 'explication', 'awp_code', 'measurement_type', 'units', 'target', 'status_color', 'status', 'cumulative_values', 'values_partners_gov', 'values_partners', 'values_gov', 'values', 'values_live', 'values_gov_live', 'values_partners_live', 'values_partners_gov_live', 'cumulative_values_live', ).distinct() selected_sub_indicator = [int(x) for x in selected_sub_indicator] list_selected_sub = Indicator.objects.filter(id__in=selected_sub_indicator) list_selected_sub = list_selected_sub.values( 'id', 'ai_id', 'name', 'units', 'target', 'measurement_type', 'cumulative_values', 'values_partners_gov', 'values_partners', 'values_gov', 'values', ) months = [] for i in range(1, 13): months.append((i, datetime.date(2008, i, 1).strftime('%B'))) # if selected_governorate is not None: # for x in governorates: # if x["location_adminlevel_governorate_code"] == selected_governorate: # selected_governorate_name = x["location_adminlevel_governorate"] if selected_partner is not None and len(selected_partner) > 0: for x in partners: if x["partner_id"] == selected_partner: selected_partner_name = x["partner_label"] if self.request.user.is_authenticated: template = "base2.html" else: template = "base_empty.html" return { # 'reports': report.order_by('id'), 'month': month, 'year': today.year, 'month_name': month_name, 'month_number': month_number, 'months': months, 'database': database, 'partners': partners, 'governorates': governorates, 'indicators': indicators, 'indicator': indicator, 'selected_governorate': selected_governorate, 'selected_governorate_name': selected_governorate_name, 'selected_indicator': selected_indicator, 'selected_sub_indicator': selected_sub_indicator, 'selected_indicator_name': selected_indicator_name, 'list_selected_sub': list_selected_sub, # 'locations': locations, 'selected_filters': selected_filters, 'current_month': datetime.datetime.now().strftime("%B"), 'reporting_year': str(reporting_year), 'selected_partner':selected_partner, 'selected_partner_name':selected_partner_name, 'partners_list':partners_list, 'template':template } class ReportInterventionMapView(LoginRequiredMixin,TemplateView): template_name = 'activityinfo/report_intervention_map.html' def get_context_data(self, **kwargs): ai_id = int(self.request.GET.get('ai_id', 0)) template = "base2.html" database = Database.objects.get(ai_id=ai_id) reporting_year = database.reporting_year.year """ load all related filters from one database, the using page should be designed to load these filters upon initial load and then to get its data through ajax without loading these filters again. """ from django.db import connection cursor = connection.cursor() params = [str(ai_id) ] queryset = """ SELECT rep.id, rep.site_id, rep.location_name, rep.location_longitude, rep.location_latitude, rep.indicator_units, rep.location_adminlevel_governorate, rep.location_adminlevel_governorate_code, rep.location_adminlevel_caza, rep.location_adminlevel_caza_code, rep.location_adminlevel_cadastral_area, rep.location_adminlevel_cadastral_area_code, rep.partner_label, rep.partner_id, rep.indicator_value, rep.location_adminlevel_cadastral_area_code, rep.reporting_section, SUBSTRING(month_name,6,2) as month_number, rep.month_name, ind.id, ind.tag_gender_id, ind.name AS indicator_name, tag_nationality_id FROM public.activityinfo_activityreport rep, public.activityinfo_indicator ind WHERE rep.database_id = %s AND rep.ai_indicator_id = ind.id """ database = Database.objects.get(ai_id=ai_id) if database.is_funded_by_unicef: queryset += " AND rep.funded_by = 'UNICEF' " cursor.execute(queryset, params) desc = cursor.description data = [ dict(zip([col[0] for col in desc], row)) for row in cursor.fetchall() ] filters = {} filters['partners'] = sorted(list(set([d['partner_label'] for d in data if 'partner_label' in d]))) filters['months'] = sorted(list(set([d['month_name'] for d in data if 'month_name' in d]))) filters['governorates'] = sorted(list(set([d['location_adminlevel_governorate'] for d in data if 'location_adminlevel_governorate' in d and d['location_adminlevel_governorate'] != '']))) filters['cazas'] = sorted(list(set([d['location_adminlevel_caza'] for d in data if 'location_adminlevel_caza' in d and d['location_adminlevel_caza'] != '']))) filters['nationalities'] = IndicatorTag.objects.filter(id__in= sorted(list(set([d['tag_nationality_id'] for d in data if 'tag_nationality_id' in d])))).values('id','label') return { 'filters': filters, 'database': database, 'current_month': datetime.datetime.now().strftime("%B"), 'reporting_year': str(reporting_year), 'template':template } def send_notify_me(request): from mailjet_rest import Client fullname = request.GET.get('fullname') email = request.GET.get('email') api_key = 'aca0b7c59149cb33d24aa3f4c8d84243' api_secret = '4397fcd9f1e3c9bdb14546b079b6dd92' mailjet = Client(auth=(api_key, api_secret), version='v3') id = '52688' data = { 'Name': fullname, 'Properties': "object", 'Action': "addnoforce", 'Email': email } result = mailjet.contactslist_managecontact.create(id=id, data=data) return JsonResponse({'result':result.json()}) def load_intervention_locations(request): from django.db import connection ai_id = int(request.GET.get('ai_id', 0)) partners = request.GET.getlist('partners[]', None) months = request.GET.getlist('months[]', None) governorates = request.GET.getlist('governorates[]', None) cazas = request.GET.getlist('cazas[]', None) nationalities = request.GET.getlist('nationalities[]', None) today = datetime.date.today() first = today.replace(day=1) last_month = first - datetime.timedelta(days=1) month_number = last_month.strftime("%m") month = int(last_month.strftime("%m")) month_name = last_month.strftime("%B") now = datetime.datetime.now() params = [str(ai_id) ] cursor = connection.cursor() main_queryset = """ SELECT rep.id, rep.site_id, rep.location_name, rep.location_longitude, rep.location_latitude, rep.indicator_units, rep.location_adminlevel_governorate, rep.location_adminlevel_caza, rep.location_adminlevel_caza_code, rep.location_adminlevel_cadastral_area, rep.location_adminlevel_cadastral_area_code, rep.partner_label, rep.indicator_value, ind.id, ind.tag_gender_id, ind.name AS indicator_name, ind.tag_nationality_id FROM public.activityinfo_activityreport rep, public.activityinfo_indicator ind """ activity_report_filter = """ WHERE rep.database_id = %s AND rep.ai_indicator_id = ind.id AND rep.ai_indicator_id in (select to_indicator_id from public.activityinfo_indicator_sub_indicators) """ database = Database.objects.get(ai_id=ai_id) if database.is_funded_by_unicef: activity_report_filter += " AND rep.funded_by = 'UNICEF' " if partners: params.append(partners) activity_report_filter += " AND TRIM(rep.partner_label) = ANY(%s) " if months: params.append(months) activity_report_filter += " AND TRIM(rep.month_name) = ANY(%s) " if governorates: params.append(governorates) activity_report_filter += " AND TRIM(rep.location_adminlevel_governorate) = ANY(%s) " if cazas: params.append(cazas) activity_report_filter += " AND TRIM(rep.location_adminlevel_caza) = ANY(%s) " if nationalities: nationalities = [int(x) for x in nationalities] params.append(nationalities) activity_report_filter += " AND ind.tag_nationality_id = ANY(%s) " queryset = main_queryset + activity_report_filter cursor.execute(queryset, params) desc = cursor.description data = [dict(zip([col[0] for col in desc], row)) for row in cursor.fetchall() ] summary = {} years = (now.year, now.year - 1) total_beneficiaries = sum([d['indicator_value'] for d in data if 'indicator_value' in d]) total_femmale = sum([d['indicator_value'] for d in data if 'indicator_value' in d and d['tag_gender_id'] == 2]) total_male = sum([d['indicator_value'] for d in data if 'indicator_value' in d and d['tag_gender_id'] == 1]) total_interventions = len(data) gender_tagged_beneficiaries = int(total_femmale) + int(total_male) if gender_tagged_beneficiaries > 0: male_perc = round(float(total_male/gender_tagged_beneficiaries * 100),2) female_perc = round(float(total_femmale/gender_tagged_beneficiaries * 100),2) else: male_perc = '' female_perc = '' summary['total_beneficiaries'] = total_beneficiaries summary['gender_tagged_beneficiaries'] = gender_tagged_beneficiaries summary['female_perc'] = female_perc summary['male_perc'] = male_perc summary['total_interventions'] = total_interventions # get indicators summary master_indicators = Indicator.objects.filter(activity__database__ai_id=ai_id)\ .filter(Q(master_indicator=True) | Q(individual_indicator=True))\ .exclude(is_sector=True).values('id') master_indicators = [d['id'] for d in master_indicators] main_queryset = """ select a.total_indicator_value, b.* from( select si.to_indicator_id, sum(rep.indicator_value) as total_indicator_value from public.activityinfo_activityreport rep , public.activityinfo_indicator ind, public.activityinfo_indicator_sub_indicators si [where] GROUP by si.to_indicator_id) A, public.activityinfo_indicator B where A.to_indicator_id = B.id AND B.id = ANY(%s) """ activity_report_filter += " AND ind.id = si.from_indicator_id " if master_indicators: params.append(master_indicators) queryset = main_queryset.replace('[where]', activity_report_filter) cursor.execute(queryset, params) desc = cursor.description indicators_data = [dict(zip([col[0] for col in desc], row)) for row in cursor.fetchall() ] return JsonResponse({'locations':data,'summary':summary, 'indicators_data':indicators_data}) def load_intervention_locations_old(request): # selected_donor = request.GET.get('donor', 'G45301') from internos.activityinfo.utils import load_reporting_map from internos.activityinfo.templatetags.util_tags import get_hpm_indicator_data_new , get_hpm_sub_indicators import math ai_id = int(request.GET.get('ai_id', 0)) partners = request.GET.getlist('partner_id[]') months = request.GET.getlist('month_id[]') today = datetime.date.today() first = today.replace(day=1) last_month = first - datetime.timedelta(days=1) month_number = last_month.strftime("%m") month = int(last_month.strftime("%m")) month_name = last_month.strftime("%B") now = datetime.datetime.now() summary = [] years = (now.year, now.year - 1) ai_reports = ActivityReport.objects.filter(project_label__isnull=False).filter(database_id=ai_id) if partners: ai_reports = ai_reports.filter(partner_id__in=partners) if months: ai_reports = ai_reports.filter(month__in=months) ai_reports = ai_reports.values() last_data = 0 old_data = {} ai_reports_arr = [] key = 0 locations = list(ai_reports) return JsonResponse({'locations':locations,'summary':summary}) class ReportPartnerCrisisView(LoginRequiredMixin,TemplateView): template_name = 'activityinfo/report_partner_crisis.html' def get_context_data(self, **kwargs): selected_filters = False today = datetime.date.today() first = today.replace(day=1) last_month = first - datetime.timedelta(days=1) month_number = last_month.strftime("%m") month = int(last_month.strftime("%m")) month_name = last_month.strftime("%B") selected_indicator = int(self.request.GET.get('indicator_id', 0)) selected_sub_indicator = self.request.GET.getlist('sub_indicator_id', []) selected_governorate = self.request.GET.get('governorate', 0) selected_governorate_name = self.request.GET.get('governorate_name', 'All Governorates') selected_partner = self.request.GET.get('partner', "") selected_partner_name = self.request.GET.get('partners_name', 'All Partners') selected_section = self.request.GET.get('section', "") selected_section_name = self.request.GET.get('section_name', 'All Sections') if selected_indicator or selected_governorate or selected_section: selected_filters = True ai_id = int(self.request.GET.get('ai_id', 0)) database = Database.objects.get(ai_id=ai_id) reporting_year = database.reporting_year.year if selected_indicator: indicator = Indicator.objects.get(id=selected_indicator) indicator = { 'id': indicator.id, 'ai_id': indicator.ai_id, 'name': indicator.name, 'ai_indicator':indicator.ai_indicator, 'measurement_type': indicator.measurement_type, 'units': indicator.units, 'cumulative_values': indicator.cumulative_values, 'values_cumulative_weekly': indicator.values_cumulative_weekly, 'values_partners_gov_weekly': indicator.values_partners_gov_weekly, 'values_partners_weekly': indicator.values_partners_weekly, 'values_gov_weekly': indicator.values_gov_weekly, 'values_weekly': indicator.values_weekly, 'values_sections':indicator.values_sections, 'values_sections_partners':indicator.values_sections_partners, 'values_sections_partners_gov':indicator.values_sections_partners_gov, 'values_sections_gov':indicator.values_sections_gov } selected_indicator_name = indicator['name'] partners_list=[] if len(selected_section) > 0: partners_values = indicator['values_sections_partners'] if partners_values: for key, value in partners_values.items(): keys = key.split('-') if keys[1] == selected_section: p = keys[2] if (p, p) not in partners_list: partners_list.append((p, p)) else: partners_values = indicator['values_partners_weekly'] if partners_values: for key, value in partners_values.items(): keys = key.split('-') p = keys[1] if (p, p) not in partners_list: partners_list.append((p, p)) else: indicator = [] selected_indicator_name = "" partners=[] governorates=[] partners_list=[] # report = ActivityReport.objects.filter(database_id=database.ai_id) # # if database.is_funded_by_unicef: # report = report.filter(funded_by__contains='UNICEF') partners = get_partners_list(database) governorates = get_governorates_list(database) sections = get_reporting_sections_list(database) # partners = report.values('partner_label', 'partner_id').distinct() # governorates = report.values('location_adminlevel_governorate_code', # 'location_adminlevel_governorate').distinct() master_indicators = Indicator.objects.filter(activity__database=database, master_indicator=True).exclude( is_sector=True).order_by('sequence') individual_indicators = Indicator.objects.filter(activity__database=database, individual_indicator=True).exclude(is_sector=True).order_by('sequence') indicators = master_indicators | individual_indicators indicators = indicators.values( 'id', 'ai_id', 'name', 'measurement_type', 'units', 'values_cumulative_weekly', 'values_partners_gov_weekly', 'values_partners_weekly', 'values_gov_weekly', 'values_weekly', 'values_sections', 'values_sections_partners', 'values_sections_partners_gov', 'values_sections_gov', ).distinct() selected_sub_indicator = [int(x) for x in selected_sub_indicator] list_selected_sub = Indicator.objects.filter(id__in=selected_sub_indicator) list_selected_sub = list_selected_sub.values( 'id', 'ai_id', 'name', 'units', 'target', 'measurement_type', 'cumulative_values', 'values_partners_gov', 'values_partners', 'values_gov', 'values', ) months = [] for i in range(1, 13): months.append((i, datetime.date(2008, i, 1).strftime('%B'))) # if selected_governorate is not None: # for x in governorates: # if x["location_adminlevel_governorate_code"] == selected_governorate: # selected_governorate_name = x["location_adminlevel_governorate"] if selected_partner is not None and len(selected_partner) > 0: for x in partners: if x["partner_id"] == selected_partner: selected_partner_name = x["partner_label"] if selected_section is not None and len(selected_section) > 0: for x in sections: if x["reporting_section"] == selected_section: selected_section_name = x["reporting_section"] report_type = 'weekly' if self.request.user.is_authenticated: template = "base2.html" else: template = "base_empty.html" return { 'month': month, 'year': today.year, 'month_name': month_name, 'month_number': month_number, 'months': months, 'database': database, 'partners': partners, 'governorates': governorates, 'sections':sections, 'indicators': indicators, 'indicator': indicator, 'selected_governorate': selected_governorate, 'selected_governorate_name': selected_governorate_name, 'selected_section_name': selected_section_name, 'selected_section': selected_section, 'selected_indicator': selected_indicator, 'selected_sub_indicator': selected_sub_indicator, 'selected_indicator_name': selected_indicator_name, 'list_selected_sub': list_selected_sub, 'selected_filters': selected_filters, 'current_month': datetime.datetime.now().strftime("%B"), 'reporting_year': str(reporting_year), 'selected_partner':selected_partner, 'selected_partner_name':selected_partner_name, 'partners_list':partners_list, 'report_type':report_type, 'template':template } class ReportMapView(LoginRequiredMixin,TemplateView): template_name = 'activityinfo/report_map.html' def get_context_data(self, **kwargs): from internos.etools.models import PCA from internos.activityinfo.utils import load_reporting_map now = datetime.datetime.now() selected_partner = self.request.GET.get('partner', 0) selected_governorate = self.request.GET.get('governorate', 0) selected_caza = self.request.GET.get('caza', 0) selected_donor = self.request.GET.get('donor', 0) partner_info = {} today = datetime.date.today() first = today.replace(day=1) last_month = first - datetime.timedelta(days=1) month_number = last_month.strftime("%m") month = int(last_month.strftime("%m")) month_name = last_month.strftime("%B") month_number = '12' month = 12 month_name = 'December' ai_id = int(self.request.GET.get('ai_id', 0)) database = Database.objects.get(ai_id=ai_id) report = ActivityReport.objects.filter(database=database) if database.is_funded_by_unicef: report = report.filter(funded_by__contains='UNICEF') rows = load_reporting_map(ai_id, partner=selected_partner, governorate=selected_governorate, caza=selected_caza, donor=selected_donor) rows = [] locations = {} ctr = 0 for item in rows: if not item[2] or not item[3]: continue if item[0] not in locations: ctr += 1 locations[item[0]] = { 'location_name': item[1], 'location_longitude': item[2], 'location_latitude': item[3], 'governorate': item[5], 'caza': '{}-{}'.format(item[6], item[7]), 'cadastral': '{}-{}'.format(item[8], item[9]), 'indicators': [] } try: cumulative_value = "{:,}".format(round(float(item[12]), 1)) except Exception: cumulative_value = 0 locations[item[0]]['indicators'].append({ 'indicator_units': item[4].upper(), 'partner_label': item[10], 'indicator_name': item[11], 'cumulative_value': cumulative_value, }) locations = json.dumps(locations.values()) partners = report.values('partner_label', 'partner_id').distinct() governorates = report.values('location_adminlevel_governorate_code', 'location_adminlevel_governorate').distinct() cazas = report.values('location_adminlevel_caza_code', 'location_adminlevel_caza').distinct() indicator_categories = report.values('indicator_category').distinct() form_categories = report.values('form_category').distinct() months = report.values('month', 'month_name').distinct() donors_set = PCA.objects.filter(end__year=now.year, donors__isnull=False, donors__len__gt=0).values('number', 'donors').distinct() donors = {} for item in donors_set: for donor in item['donors']: donors[donor] = donor return { 'selected_partner': selected_partner, 'selected_governorate': selected_governorate, 'selected_caza': selected_caza, 'selected_donor': selected_donor, 'reports': report.order_by('id'), 'month': month, 'year': today.year, 'month_name': month_name, 'month_number': month_number, 'database': database, 'partners': partners, 'governorates': governorates, 'cazas': cazas, 'donors': donors, 'locations': locations, 'indicator_categories': indicator_categories, 'form_categories': form_categories, 'months': months, 'locations_count': ctr } class ReportPartnerSectorView(LoginRequiredMixin,TemplateView): template_name = 'activityinfo/report_partner_sector.html' def get_context_data(self, **kwargs): partner_info = {} today = datetime.date.today() first = today.replace(day=1) last_month = first - datetime.timedelta(days=1) month_number = last_month.strftime("%m") month = int(last_month.strftime("%m")) month_name = last_month.strftime("%B") # month_number = '12' # month = 12 # month_name = 'December' selected_indicator = int(self.request.GET.get('indicator_id', 0)) selected_governorate = self.request.GET.get('governorate', 0) ai_id = int(self.request.GET.get('ai_id', 0)) database = Database.objects.get(ai_id=ai_id) indicator = Indicator.objects.get(id=selected_indicator) indicator = { 'id': indicator.id, 'ai_id': indicator.ai_id, 'name': indicator.name, 'explication': indicator.explication, 'awp_code': indicator.awp_code, 'measurement_type': indicator.measurement_type, 'units': indicator.units, 'target_sector': indicator.target_sector, 'status_color_sector': indicator.status_color_sector, 'status_sector': indicator.status_sector, 'cumulative_values_sector': indicator.cumulative_values_sector, 'values_partners_sites_sector': indicator.values_partners_sites_sector, 'values_partners_sector': indicator.values_partners_sector, 'values_sites_sector': indicator.values_sites_sector, 'values_sector': indicator.values_sector, } # report = ActivityReport.objects.filter(database=database) partners = get_partners_list(database) governorates = get_governorates_list(database) cadastrals = get_cadastrals_list(database) # partners = report.values('partner_label', 'partner_id').distinct() # governorates = report.values('location_adminlevel_governorate_code', # 'location_adminlevel_governorate').distinct() # cadastrals = report.values('location_adminlevel_cadastral_area_code', # 'location_adminlevel_cadastral_area').distinct() indicators = Indicator.objects.filter(activity__database=database).exclude(is_section=True).order_by('sequence') indicators = indicators.values( 'id', 'ai_id', 'name', 'master_indicator', 'master_indicator_sub', 'master_indicator_sub_sub', 'individual_indicator', 'explication', 'awp_code', 'measurement_type', 'units', 'target_sector', 'status_color_sector', 'status_sector', 'cumulative_values_sector', 'values_partners_sites_sector', 'values_partners_sector', 'values_sites_sector', 'values_sector', ).distinct() months = [] for i in range(1, 13): months.append((i, datetime.date(2008, i, 1).strftime('%B'))) return { # 'reports': report.order_by('id'), 'month': month, 'year': today.year, 'month_name': month_name, 'month_number': month_number, 'months': months, 'database': database, 'partners': partners, 'governorates': governorates, 'cadastrals': cadastrals, 'indicators': indicators, 'indicator': indicator, 'selected_governorate': selected_governorate, 'selected_indicator': selected_indicator, 'selected_partner': 0, } class ReportMapSectorView(LoginRequiredMixin,TemplateView): template_name = 'activityinfo/report_map_sector.html' def get_context_data(self, **kwargs): from django.db import connection from internos.activityinfo.utils import load_reporting_map now = datetime.now() cursor = connection.cursor() selected_filter = False partner = None rows = [] selected_partner = self.request.GET.get('partner', 0) selected_governorate = self.request.GET.get('governorate', 0) selected_caza = self.request.GET.get('caza', 0) partner_info = {} today = datetime.date.today() first = today.replace(day=1) last_month = first - datetime.timedelta(days=1) month_number = last_month.strftime("%m") month = int(last_month.strftime("%m")) month_name = last_month.strftime("%B") # month_number = '12' # month = 12 # month_name = 'December' ai_id = int(self.request.GET.get('ai_id', 0)) database = Database.objects.get(ai_id=ai_id) report = ActivityReport.objects.filter(database=database) rows = load_reporting_map(ai_id, partner=selected_partner, governorate=selected_governorate, caza=selected_caza) locations = {} ctr = 0 for item in rows: if not item[2] or not item[3]: continue if item[0] not in locations: ctr += 1 locations[item[0]] = { 'location_name': item[1], 'location_longitude': item[2], 'location_latitude': item[3], 'governorate': item[5], 'caza': '{}-{}'.format(item[6], item[7]), 'cadastral': '{}-{}'.format(item[8], item[9]), 'indicators': [] } try: cumulative_value = "{:,}".format(round(float(item[12]), 1)) except Exception: cumulative_value = 0 locations[item[0]]['indicators'].append({ 'indicator_units': item[4].upper(), 'partner_label': item[10], 'indicator_name': item[11], 'cumulative_value': cumulative_value, }) locations = json.dumps(locations.values()) if selected_partner: try: partner = Partner.objects.get(number=selected_partner) if partner.partner_etools: partner_info = partner.detailed_info except Exception as ex: print(ex) pass partners = report.values('partner_label', 'partner_id').distinct() governorates = report.values('location_adminlevel_governorate_code', 'location_adminlevel_governorate').distinct() cazas = report.values('location_adminlevel_caza_code', 'location_adminlevel_caza').distinct() return { 'selected_partner': selected_partner, 'selected_governorate': selected_governorate, 'selected_caza': selected_caza, 'reports': report.order_by('id'), 'month': month, 'year': today.year, 'month_name': month_name, 'month_number': month_number, 'database': database, 'partners': partners, 'governorates': governorates, 'cazas': cazas, 'partner_info': partner_info, 'partner': partner, 'selected_filter': selected_filter, 'locations': locations, 'locations_count': ctr } class ReportDisabilityView(LoginRequiredMixin,TemplateView): template_name = 'activityinfo/report_disability.html' def get_context_data(self, **kwargs): from internos.activityinfo.templatetags.util_tags import get_indicator_tag_value selected_filter = False selected_partners = self.request.GET.getlist('partners', []) selected_governorates = self.request.GET.getlist('governorates', []) selected_months = self.request.GET.getlist('months',[]) current_year = date.today().year current_month = date.today().month today = datetime.date.today() first = today.replace(day=1) last_month = first - datetime.timedelta(days=1) month_number = last_month.strftime("%m") month = int(last_month.strftime("%m")) month_name = last_month.strftime("%B") ai_id = int(self.request.GET.get('ai_id', 0)) database = Database.objects.get(ai_id=ai_id) reporting_year = database.reporting_year.year months = [] if self.request.user.is_authenticated: template = "base2.html" else: template = "base_empty.html" if selected_partners or selected_governorates or selected_months: selected_filter = True partners = get_partners_list(database, govs=selected_governorates,months=selected_months) governorates = get_governorates_list(database,partners=selected_partners,months=selected_months) if selected_months is not None and len(selected_months) > 0: for mon in selected_months: months.append((mon, calendar.month_name[int(mon)])) else: months = get_months_list(database, selected_partners, selected_governorates) else: partners = get_partners_list(database) governorates = get_governorates_list(database) if int(reporting_year) == current_year: for i in range(1, current_month): months.append((i, datetime.date(2008, i, 1).strftime('%B'))) else: for i in range(1, 13): months.append((i, datetime.date(2008, i, 1).strftime('%B'))) tags_disability = Indicator.objects.filter(activity__database__id__exact=database.id, tag_disability__isnull=False).exclude(is_sector=True) \ .values('tag_disability_id', 'tag_disability__name', 'tag_disability__label').distinct().order_by( 'tag_disability__sequence') master_indicators = Indicator.objects.filter(activity__database=database).exclude(is_sector=True).order_by( 'sequence') if database.mapped_db: master_indicators = master_indicators.filter(Q(master_indicator=True) | Q(individual_indicator=True)) support_disabilities = master_indicators.filter(support_disability=True) support_disabilities = support_disabilities.values( 'id', 'ai_id', 'name', 'measurement_type', 'units', 'values_tags', 'cumulative_values', 'values_partners_gov', 'values_partners', 'values_gov', 'values', ).distinct() for item in support_disabilities: item['cumulative'] = get_indicator_cumulative_months(item, selected_months, selected_partners, selected_governorates,) return { 'selected_partners': selected_partners, 'selected_governorates': selected_governorates, 'selected_months':selected_months, 'month': month, 'reporting_year':reporting_year, 'month_name': month_name, 'month_number': month_number, 'months': months, 'database': database, 'partners': partners, 'governorates': governorates, 'master_indicators': support_disabilities, 'selected_filter': selected_filter, 'tags_disability': tags_disability, 'template':template } class ReportDisabilityCrisisView(LoginRequiredMixin,TemplateView): template_name = 'activityinfo/report_crisis_disability.html' def get_context_data(self, **kwargs): from internos.activityinfo.templatetags.util_tags import get_indicator_tag_value selected_filter = False selected_partners = self.request.GET.getlist('partners', []) selected_governorates = self.request.GET.getlist('governorates', []) selected_sections = self.request.GET.getlist('sections', []) selected_months = self.request.GET.getlist('months', []) today = datetime.date.today() first = today.replace(day=1) last_month = first - datetime.timedelta(days=1) month_number = last_month.strftime("%m") month = int(last_month.strftime("%m")) month_name = last_month.strftime("%B") current_month = date.today().month ai_id = int(self.request.GET.get('ai_id', 0)) database = Database.objects.get(ai_id=ai_id) months = [] if selected_partners or selected_governorates or selected_months or selected_sections: selected_filter = True partners = get_partners_list(database,selected_sections,selected_governorates,selected_months) governorates = get_governorates_list(database,selected_sections,selected_partners,selected_months) sections = get_reporting_sections_list(database,selected_partners,selected_governorates,selected_months) if selected_months is not None and len(selected_months) > 0: for mon in selected_months: months.append((mon, calendar.month_name[int(mon)])) else: months = get_months_list(database, selected_partners, selected_governorates, selected_sections) else: partners = get_partners_list(database) governorates = get_governorates_list(database) sections = get_reporting_sections_list(database) for i in range(1, current_month + 1): months.append((i, calendar.month_name[i])) tags_disability = Indicator.objects.filter(activity__database__id__exact=database.id, tag_disability__isnull=False).exclude(is_sector=True) \ .values('tag_disability_id', 'tag_disability__name', 'tag_disability__label').distinct().order_by( 'tag_disability__sequence') master_indicators = Indicator.objects.filter(activity__database=database).exclude(is_sector=True).order_by( 'sequence') if database.mapped_db: master_indicators = master_indicators.filter(Q(master_indicator=True) | Q(individual_indicator=True)) support_disabilities = master_indicators.filter(support_disability=True) support_disabilities = support_disabilities.values( 'id', 'ai_id', 'name', 'measurement_type', 'units', 'values_weekly', 'values_tags_weekly', 'values_sections', 'values_sections_partners', 'values_sections_gov', 'values_sections_partners_gov', 'values_gov_weekly', 'values_partners_weekly', 'values_partners_gov_weekly', 'values_cumulative_weekly', ).distinct() for item in support_disabilities: item['cumulative'] = get_indicator_cumulative_months_sections(item, selected_months, selected_partners, selected_governorates, selected_sections) report_type = 'weekly' if self.request.user.is_authenticated: template = "base2.html" else: template = "base_empty.html" return { 'selected_partners': selected_partners, 'selected_governorates': selected_governorates, 'selected_sections': selected_sections, 'selected_months': selected_months, 'month': month, 'reporting_year': database.reporting_year.year, 'month_name': month_name, 'month_number': month_number, 'months': months, 'database': database, 'partners': partners, 'governorates': governorates, 'sections':sections, 'master_indicators': support_disabilities, 'selected_filter': selected_filter, 'tags_disability': tags_disability, 'report_type':report_type, 'template':template } class ReportSectorView(LoginRequiredMixin,TemplateView): template_name = 'activityinfo/report_sector.html' def get_context_data(self, **kwargs): selected_filter = False display_live = False selected_partner = self.request.GET.get('partner', 0) selected_partners = self.request.GET.getlist('partners', []) selected_cadastral = self.request.GET.getlist('cadastral', []) selected_months = self.request.GET.getlist('s_months', []) partner_info = {} current_year = date.today().year current_month = date.today().month today = datetime.date.today() first = today.replace(day=1) last_month = first - datetime.timedelta(days=1) month_number = last_month.strftime("%m") month = int(last_month.strftime("%m")) month_name = last_month.strftime("%B") # month_number = '12' # month = 12 # month_name = 'December' ai_id = int(self.request.GET.get('ai_id', 0)) database = Database.objects.get(ai_id=ai_id) reporting_year = database.reporting_year.name report = ActivityReport.objects.filter(database_id=database.ai_id) if selected_partner: try: partner = Partner.objects.get(number=selected_partner) if partner.partner_etools: partner_info = partner.detailed_info except Exception as ex: print(ex) pass if selected_partners or selected_cadastral or selected_months: selected_filter = True if selected_partners == [] and selected_cadastral == [] and selected_months == []: selected_filter = False partners = report.values('partner_label', 'partner_id').distinct() cadastrals = report.values('location_adminlevel_cadastral_area_code', 'location_adminlevel_cadastral_area').distinct() s_months = [] if int(reporting_year) == current_year: for i in range(1, current_month): s_months.append((i, datetime.date(2008, i, 1).strftime('%B'))) else: for i in range(1, 13): s_months.append((i, datetime.date(2008, i, 1).strftime('%B'))) master_indicators = Indicator.objects.filter(activity__database=database).exclude(is_section=True).order_by( 'sequence') if database.mapped_db: master_indicators = master_indicators.filter(Q(master_indicator=True) | Q(individual_indicator=True)) master_indicators = master_indicators.values( 'id', 'ai_id', 'name', 'master_indicator', 'master_indicator_sub', 'master_indicator_sub_sub', 'individual_indicator', 'explication', 'awp_sector_code', 'measurement_type', 'units', 'target', 'target_sector', 'status_color', 'status_color_sector', 'status', 'status_sector', 'cumulative_values_sector', 'values_partners_sites_sector', 'values_partners_sector', 'values_sites_sector', 'values_sector', ).distinct() months = [] if selected_months is not None and len(selected_months) > 0: for mon in selected_months: months.append((mon, datetime.date(2008, int(mon), 1).strftime('%B'))) else: if int(reporting_year) == current_year: display_live = True if current_month == 1: months.append((1, datetime.date(2008, 1, 1).strftime('%B'))) if current_month == 2: for i in range(1, 3): months.append((i, datetime.date(2008, i, 1).strftime('%B'))) if current_month > 2: for i in range(current_month - 2, current_month + 1): months.append((i, datetime.date(2008, i, 1).strftime('%B'))) else: display_live = False for i in range(1, 13): months.append((i, datetime.date(2008, i, 1).strftime('%B'))) if self.request.user.is_authenticated: template = "base2.html" else: template = "base_empty.html" return { 'selected_partners': selected_partners, 'selected_cadastral': selected_cadastral, 'selected_months': selected_months, 'reports': report.order_by('id'), 'month': month, 'year': today.year, 'month_name': month_name, 'month_number': month_number, 'months': months, 'database': database, 'partners': partners, 's_months': s_months, 'cadastrals': cadastrals, 'master_indicators': master_indicators, 'partner_info': partner_info, 'selected_filter': selected_filter, 'reporting_year': str(reporting_year), 'display_live': display_live, 'template':template } class ReportTagView(LoginRequiredMixin,TemplateView): template_name = 'activityinfo/report_tags.html' def get_context_data(self, **kwargs): from internos.activityinfo.templatetags.util_tags import get_indicator_tag_value selected_filter = False current_year = date.today().year current_month = date.today().month selected_partners = self.request.GET.getlist('partners', []) selected_months = self.request.GET.getlist('months', []) selected_governorates = self.request.GET.getlist('governorates', []) today = datetime.date.today() first = today.replace(day=1) last_month = first - datetime.timedelta(days=1) month_number = last_month.strftime("%m") month = int(last_month.strftime("%m")) month_name = last_month.strftime("%B") ai_id = int(self.request.GET.get('ai_id', 0)) database = Database.objects.get(ai_id=ai_id) reporting_year = database.reporting_year.year report = ActivityReport.objects.filter(database_id=database.ai_id) if database.is_funded_by_unicef: report = report.filter(funded_by__contains='UNICEF') months = [] if selected_partners or selected_governorates or selected_months: selected_filter = True partners = get_partners_list(database, govs=selected_governorates, months=selected_months) governorates = get_governorates_list(database, partners=selected_partners, months=selected_months) if selected_months is not None and len(selected_months) > 0: for mon in selected_months: months.append((mon, calendar.month_name[int(mon)])) else: months = get_months_list(database, selected_partners, selected_governorates) else: partners = get_partners_list(database) governorates = get_governorates_list(database) if int(reporting_year) == current_year: for i in range(1, current_month): months.append((i, datetime.date(2008, i, 1).strftime('%B'))) else: for i in range(1, 13): months.append((i, datetime.date(2008, i, 1).strftime('%B'))) # if selected_partners or selected_governorates or selected_months : # selected_filter = True # # partners = get_partners_list(database) # governorates = get_governorates_list(database) tags = IndicatorTag.objects.all().order_by('sequence') # if selected_partners or selected_governorates or selected_months: # selected_filter = True # if int(reporting_year) == current_year: # for i in range(1, current_month): # months.append((i, datetime.date(2008, i, 1).strftime('%B'))) # else: # for i in range(1, 13): # months.append((i, datetime.date(2008, i, 1).strftime('%B'))) all_indicators = Indicator.objects.filter(activity__database=database).exclude(is_sector=True).order_by( 'sequence') tags_gender = all_indicators.filter(tag_gender__isnull=False).exclude(is_sector=True).values( 'tag_gender__name', 'tag_gender__label').distinct().order_by('tag_gender__sequence') tags_gender_number = len(tags_gender) tags_nationality = all_indicators.filter(tag_nationality__isnull=False).values( 'tag_nationality__name', 'tag_nationality__label').distinct().order_by('tag_nationality__sequence') tags_nationality_number = len(tags_nationality) tags_age = all_indicators.filter(tag_age__isnull=False).values('tag_age__name', 'tag_age__label').distinct()\ .order_by('tag_age__sequence') tags_age_number = len(tags_age) tags_disability = all_indicators.filter(tag_disability__isnull=False).values( 'tag_disability__name', 'tag_disability__label').distinct().order_by('tag_disability__sequence') tags_disability_number = len(tags_disability) if database.mapped_db: master_indicators = all_indicators.filter(Q(master_indicator=True) | Q(individual_indicator=True)) master_indicators = master_indicators.values( 'id', 'ai_id', 'name', 'master_indicator', 'master_indicator_sub', 'individual_indicator', 'awp_code', 'measurement_type', 'units', 'target', 'status_color', 'status', 'cumulative_values', 'values_partners_gov', 'values_partners', 'values_gov', 'values', 'values_tags', 'is_cumulative', 'highest_values' ).distinct() for master_ind in master_indicators: if master_ind['is_cumulative']: master_ind['cumulative'] = get_indicator_cumulative_months(master_ind, selected_months, selected_partners, selected_governorates, ) else: master_ind['cumulative'] = get_indicator_highest_value(master_ind, selected_months, selected_partners, selected_governorates, ) master_ind['achieved'] = str( calculate_achievement_new(master_ind['target'], master_ind['cumulative'])) + '%' sub_indicators = get_sub_master_indicators_data(master_ind['id'], all_indicators) master_ind['sub_list'] = sub_indicators master_ind['sub_list_filtered'] = sub_indicators for sub_ind in sub_indicators: if sub_ind['master_indicator']: sub_ind['cumulative'] = 0 continue else: if master_ind['is_cumulative']: sub_ind['cumulative'] = get_indicator_cumulative_months(sub_ind, selected_months, selected_partners, selected_governorates, ) else: sub_ind['cumulative'] = get_indicator_highest_value(sub_ind, selected_months, selected_partners, selected_governorates, ) if sub_ind['master_indicator_sub']: sub_ind['achieved'] = str( calculate_achievement_new(sub_ind['target'], sub_ind['cumulative'])) + '%' filtered_list = [] if selected_filter: for master_ind in master_indicators: if not (master_ind['cumulative'] == '0' or master_ind['cumulative'] == 0): filtered_list.append(master_ind) master_ind['sub_list_filtered'] = [] for sub_indicator in master_ind['sub_list']: sub_indicator['sub_list_filtered'] = [] if not (sub_indicator['cumulative'] == '0' or sub_indicator['cumulative'] == 0): master_ind['sub_list_filtered'].append(sub_indicator) else: for sub_indicator in master_ind['sub_list']: if not (sub_indicator['cumulative'] == '0' or sub_indicator['cumulative'] == 0): filtered_list.append(sub_indicator) else: filtered_list = master_indicators # gender_calculation = {} # nationality_calculation = {} # age_calculation = {} # disability_calculation = {} # for item in filtered_list: # for tag in tags_gender: # if tag['tag_gender__label'] not in gender_calculation: # gender_calculation[tag['tag_gender__label']] = 0 # value = get_indicator_tag_value(item, tag['tag_gender__name']) # gender_calculation[tag['tag_gender__label']] += float(value) # # for tag in tags_nationality: # if tag['tag_nationality__label'] not in nationality_calculation: # nationality_calculation[tag['tag_nationality__label']] = 0 # value = get_indicator_tag_value(item, tag['tag_nationality__name']) # nationality_calculation[tag['tag_nationality__label']] += float(value) # # for tag in tags_disability: # if tag['tag_disability__label'] not in disability_calculation: # disability_calculation[tag['tag_disability__label']] = 0 # value = get_indicator_tag_value(item, tag['tag_disability__name']) # disability_calculation[tag['tag_disability__label']] += float(value) # # for tag in tags_age: # if tag['tag_age__name'] not in age_calculation: # age_calculation[tag['tag_age__name']] = 0 # value = get_indicator_tag_value(item, tag['tag_age__name']) # age_calculation[tag['tag_age__name']] += float(value) # # gender_values = [] # for key, value in gender_calculation.items(): # gender_values.append({"label": key, "value": value}) # # nationality_values = [] # for key, value in nationality_calculation.items(): # nationality_values.append({"label": key, "value": value}) # # disability_values = [] # for key, value in disability_calculation.items(): # disability_values.append({"label": key, "value": value}) # # age_values = [] # for key, value in age_calculation.items(): # age_values.append({"label": key, "value": value}) # months = [] # for i in range(1, 13): # months.append((i, datetime.date(2008, i, 1).strftime('%B'))) if self.request.user.is_authenticated: template = "base2.html" else: template = "base_empty.html" return { 'selected_partners': selected_partners, 'selected_governorates': selected_governorates, 'selected_months': selected_months, 'reports': report.order_by('id'), 'month': month, 'year': today.year, 'month_name': month_name, 'month_number': month_number, # 'months': months, 'database': database, 'partners': partners, 'governorates': governorates, 'months':months, 'master_indicators': filtered_list, 'selected_filter': selected_filter, 'tags': tags, 'tags_gender': tags_gender, 'tags_gender_number': tags_gender_number, 'tags_nationality': tags_nationality, 'tags_nationality_number': tags_nationality_number, 'tags_age_number': tags_age_number, 'tags_age': tags_age, 'tags_disability_number': tags_disability_number, 'tags_disability': tags_disability, 'template':template, # 'gender_values': json.dumps(gender_values), # 'nationality_values': json.dumps(nationality_values), # 'disability_values': json.dumps(disability_values), # 'age_values': json.dumps(age_values), # 'gender_keys': json.dumps(gender_calculation.keys()), # 'nationality_keys': json.dumps(nationality_calculation.keys()), # 'disability_keys': json.dumps(disability_calculation.keys()), # 'age_keys': json.dumps(age_calculation.keys()), 'reporting_year': str(reporting_year), 'current_month_name': datetime.datetime.now().strftime("%B") } class ReportCrisisTags(LoginRequiredMixin,TemplateView): template_name = 'activityinfo/report_crisis_tags.html' def get_context_data(self, **kwargs): from internos.activityinfo.templatetags.util_tags import get_indicator_tag_value selected_filter = False current_year = date.today().year current_month = date.today().month selected_partners = self.request.GET.getlist('partners', []) selected_months = self.request.GET.getlist('months', []) selected_governorates = self.request.GET.getlist('governorates', []) selected_sections = self.request.GET.getlist('sections', []) today = datetime.date.today() first = today.replace(day=1) last_month = first - datetime.timedelta(days=1) month_number = last_month.strftime("%m") month = int(last_month.strftime("%m")) month_name = last_month.strftime("%B") ai_id = int(self.request.GET.get('ai_id', 0)) database = Database.objects.get(ai_id=ai_id) reporting_year = database.reporting_year.year months = [] if selected_partners or selected_governorates or selected_months or selected_sections: selected_filter = True partners = get_partners_list(database,selected_sections,selected_governorates,selected_months) governorates = get_governorates_list(database,selected_sections,selected_partners,selected_months) sections = get_reporting_sections_list(database,selected_partners,selected_governorates,selected_months) if selected_months is not None and len(selected_months) > 0: for mon in selected_months: months.append((mon, calendar.month_name[int(mon)])) else: months = get_months_list(database, selected_partners, selected_governorates, selected_sections) else: partners = get_partners_list(database) governorates = get_governorates_list(database) sections = get_reporting_sections_list(database) for i in range(1, current_month + 1): months.append((i, calendar.month_name[i])) tags = IndicatorTag.objects.all().order_by('sequence') db_indicators = Indicator.objects.filter(activity__database=database).exclude(type='quality') \ .order_by('sequence') imported_indicators = Indicator.objects.filter(activity__database__support_covid=True).exclude(type='quality') mixed_indicators = db_indicators | imported_indicators tags_gender = mixed_indicators.filter(tag_gender__isnull=False).exclude(is_sector=True).values( 'tag_gender__name', 'tag_gender__label').distinct().order_by('tag_gender__sequence') tags_gender_number = len(tags_gender) tags_nationality = mixed_indicators.filter( tag_nationality__isnull=False).exclude(is_sector=True).values( 'tag_nationality__name', 'tag_nationality__label').distinct().order_by('tag_nationality__sequence') tags_nationality_number = len(tags_nationality) tags_age = mixed_indicators.filter(tag_age__isnull=False).exclude(is_sector=True).values( 'tag_age__name', 'tag_age__label').distinct().order_by('tag_age__sequence') tags_age_number = len(tags_age) tags_disability = mixed_indicators.filter(tag_disability__isnull=False).exclude(is_sector=True).values( 'tag_disability__name', 'tag_disability__label').distinct().order_by('tag_disability__sequence') tags_disability_number = len(tags_disability) master_indicators = db_indicators.filter(activity__database=database).exclude(is_sector=True).order_by( 'sequence') if database.mapped_db: master_indicators = master_indicators.filter(Q(master_indicator=True) | Q(individual_indicator=True)) master_indicators = master_indicators.values( 'id', 'ai_id', 'name', 'master_indicator', 'master_indicator_sub', 'master_indicator_sub_sub', 'individual_indicator', 'awp_code', 'measurement_type', 'units', 'values_tags', 'values_tags_weekly', 'values_sections', 'values_sections_partners', 'values_sections_gov', 'values_sections_partners_gov', 'values_weekly', 'values_gov_weekly', 'values_partners_weekly', 'values_partners_gov_weekly', 'values_cumulative_weekly', 'activity', 'activity__database__label' ).distinct() for master_ind in master_indicators: master_ind['cumulative'] = get_indicator_cumulative_months_sections(master_ind, selected_months, selected_partners, selected_governorates, selected_sections) sub_indicators = get_sub_master_indicators_crisis_data(master_ind['id'],mixed_indicators) master_ind['sub_list'] = sub_indicators master_ind['sub_list_filtered'] = sub_indicators for ind in sub_indicators: if ind['master_indicator'] and not ind['is_imported']: ind['cumulative'] = 0 continue if ind['is_imported']: if len(selected_sections) > 0: for section in selected_sections: if '/' in section: var1 = section.split('/')[0] var2 = section.split('/')[1] if var1 in ind['activity__database__label'] or var2 in ind['activity__database__label']: ind['cumulative'] = get_indicator_cumulative_months_sections(ind, selected_months, selected_partners, selected_governorates) elif ind['activity__database__label'] == section: ind['cumulative'] = get_indicator_cumulative_months_sections(ind, selected_months, selected_partners, selected_governorates) else: ind['cumulative'] = 0 else: ind['cumulative'] = get_indicator_cumulative_months_sections(ind, selected_months, selected_partners, selected_governorates, ) else: ind['cumulative'] = get_indicator_cumulative_months_sections(ind, selected_months, selected_partners, selected_governorates, selected_sections) filtered_list=[] if selected_filter: for master_ind in master_indicators: if not (master_ind['cumulative'] == '0' or master_ind['cumulative'] == 0): filtered_list.append(master_ind) master_ind['sub_list_filtered']=[] for sub_indicator in master_ind['sub_list']: sub_indicator['sub_list_filtered'] = [] if not (sub_indicator['cumulative'] == '0' or sub_indicator['cumulative'] == 0): master_ind['sub_list_filtered'].append(sub_indicator) else: for sub_indicator in master_ind['sub_list']: if not (sub_indicator['cumulative'] == '0' or sub_indicator['cumulative'] == 0): filtered_list.append(sub_indicator) else: continue else: filtered_list = master_indicators covid_indicators = imported_indicators.filter(support_COVID=True).filter(master_indicator=True).exclude(is_imported=True) covid_indicators = covid_indicators.values( 'id', 'ai_id', 'name', 'master_indicator', 'master_indicator_sub', 'master_indicator_sub_sub', 'individual_indicator', 'awp_code', 'measurement_type', 'units', 'values_tags', 'values_tags_weekly', 'values', 'values_gov', 'values_partners', 'values_partners_gov', 'cumulative_values', 'activity' ).distinct() start_month = 4 # used to get cumulative values starting this month for covid reporting filtered_covid_indicators = [] if len(selected_sections) > 0: for section in selected_sections: if '/' in section: section = section.split('/') filtered =list(covid_indicators.filter(Q(activity__database__label__contains=section[0]) | Q(activity__database__label__contains=section[1]))) for item in filtered: filtered_covid_indicators.append(item) else: filtered = list(covid_indicators.filter(activity__database__label__contains=section)) for item in filtered: filtered_covid_indicators.append(item) else: filtered_covid_indicators = covid_indicators for master_ind in filtered_covid_indicators: master_ind['cumulative'] = get_indicator_cumulative_months(master_ind, selected_months, selected_partners, selected_governorates, start_month) sub_indicators = get_sub_master_indicators_data(master_ind['id'], imported_indicators) master_ind['sub_list'] = sub_indicators master_ind['sub_list_filtered'] = sub_indicators for ind in sub_indicators: ind['cumulative'] = get_indicator_cumulative_months(ind, selected_months, selected_partners, selected_governorates, start_month ) covid_filtered_list = [] if selected_filter: for master_ind in filtered_covid_indicators: if not (master_ind['cumulative'] == '0' or master_ind['cumulative'] == 0): covid_filtered_list.append(master_ind) master_ind['sub_list_filtered'] = [] for sub_indicator in master_ind['sub_list']: sub_indicator['sub_list_filtered'] = [] if not (sub_indicator['cumulative'] == '0' or sub_indicator['cumulative'] == 0): master_ind['sub_list_filtered'].append(sub_indicator) else: for sub_indicator in master_ind['sub_list']: if not (sub_indicator['cumulative'] == '0' or sub_indicator['cumulative'] == 0): covid_filtered_list.append(sub_indicator) else: continue else: covid_filtered_list = filtered_covid_indicators start_month = 4 report_type='weekly' # months = [] # for i in range(1, 13): # months.append((i, datetime.date(2008, i, 1).strftime('%B'))) if self.request.user.is_authenticated: template = "base2.html" else: template = "base_empty.html" return { 'selected_partners': selected_partners, 'selected_governorates': selected_governorates, 'selected_months': selected_months, 'selected_sections':selected_sections, 'month': month, 'year': today.year, 'month_name': month_name, 'month_number': month_number, 'months': months, 'database': database, 'partners': partners, 'governorates': governorates, 'master_indicators': filtered_list, 'selected_filter': selected_filter, 'tags': tags, 'tags_gender': tags_gender, 'tags_gender_number': tags_gender_number, 'tags_nationality': tags_nationality, 'tags_nationality_number': tags_nationality_number, 'tags_age_number': tags_age_number, 'tags_age': tags_age, 'tags_disability_number': tags_disability_number, 'tags_disability': tags_disability, # 'gender_values': json.dumps(gender_values), # 'nationality_values': json.dumps(nationality_values), # 'disability_values': json.dumps(disability_values), # 'age_values': json.dumps(age_values), # 'gender_keys': json.dumps(gender_calculation.keys()), # 'nationality_keys': json.dumps(nationality_calculation.keys()), # 'disability_keys': json.dumps(disability_calculation.keys()), # 'age_keys': json.dumps(age_calculation.keys()), 'reporting_year': str(reporting_year), 'current_month_name': datetime.datetime.now().strftime("%B"), 'sections':sections, 'covid_indicators':covid_filtered_list, 'start_month':start_month, 'report_type':report_type, 'template':template } class ReportCrisisVisualView(LoginRequiredMixin,TemplateView): template_name = 'activityinfo/report_crisis_visual.html' def get_context_data(self, **kwargs): if self.request.user.is_authenticated: template = "base2.html" else: template = "base_empty.html" return { 'template':template } class LiveReportView(LoginRequiredMixin,TemplateView): template_name = 'activityinfo/live.html' def get_context_data(self, **kwargs): selected_filter = False selected_partners = self.request.GET.getlist('partners', []) selected_governorates = self.request.GET.getlist('governorates', []) partner_info = {} today = datetime.date.today() day_number = today.strftime("%d") month_number = today.strftime("%m") month = int(today.strftime("%m"))-1 month_name = calendar.month_name[month] ai_id = int(self.request.GET.get('ai_id', 0)) database = Database.objects.get(ai_id=ai_id) reporting_year = database.reporting_year report = LiveActivityReport.objects.filter(database_id=database.ai_id) if database.is_funded_by_unicef: report = report.filter(funded_by__contains='UNICEF') if selected_partners or selected_governorates : selected_filter = True partners = get_partners_list(database, govs= selected_governorates, report_type= 'live') governorates = get_governorates_list(database, partners=selected_partners, report_type= 'live') else: partners = get_partners_list(database, report_type='live') governorates = get_governorates_list(database, report_type='live') all_indicators = Indicator.objects.filter(activity__database=database).order_by('sequence') master_indicators = all_indicators.filter(activity__database=database).exclude(is_sector=True).order_by( 'sequence') if database.mapped_db: master_indicators = master_indicators.filter(Q(master_indicator=True) | Q(individual_indicator=True)) master_indicators = master_indicators.values( 'id', 'ai_id', 'name', 'master_indicator', 'master_indicator_sub', 'individual_indicator', 'awp_code', 'measurement_type', 'units', 'target', 'status_color', 'status', 'cumulative_values_live', 'values_partners_gov_live', 'values_partners_live', 'values_gov_live', 'values_live', 'is_cumulative', 'highest_values_live' ).distinct() for master_ind in master_indicators: if master_ind['is_cumulative']: master_ind['cumulative'] = get_indicator_live_cumulative(master_ind, month, selected_partners, selected_governorates, ) else: master_ind['cumulative'] = get_indicator_highest_value_live(master_ind, month, selected_partners, selected_governorates, ) master_ind['achieved'] = str( calculate_achievement_new(master_ind['target'], master_ind['cumulative'])) + '%' sub_indicators = get_sub_indicators_live_data(master_ind['id'], all_indicators) master_ind['sub_list'] = sub_indicators master_ind['sub_list_filtered'] = sub_indicators for sub_ind in sub_indicators: if sub_ind['master_indicator']: sub_ind['cumulative'] = 0 continue else: if master_ind['is_cumulative']: sub_ind['cumulative'] = get_indicator_live_cumulative(sub_ind, month, selected_partners, selected_governorates, ) else: sub_ind['cumulative'] = get_indicator_highest_value_live(sub_ind, month, selected_partners, selected_governorates, ) if sub_ind['master_indicator_sub']: sub_ind['achieved'] = str( calculate_achievement_new(sub_ind['target'], sub_ind['cumulative'])) + '%' sub_sub_indicators = get_sub_indicators_live_data(sub_ind['id'], all_indicators) sub_ind['sub_list'] = sub_sub_indicators sub_ind['sub_list_filtered'] = sub_sub_indicators for ind in sub_sub_indicators: if ind['master_indicator']: ind['cumulative'] = 0 continue else: if master_ind['is_cumulative']: ind['cumulative'] = get_indicator_live_cumulative(ind, month, selected_partners, selected_governorates, ) else: ind['cumulative'] = get_indicator_highest_value_live(ind, month, selected_partners, selected_governorates, ) filtered_list = [] if selected_filter: for master_ind in master_indicators: if not (master_ind['cumulative'] == '0' or master_ind['cumulative'] == 0): filtered_list.append(master_ind) master_ind['sub_list_filtered'] = [] for sub_indicator in master_ind['sub_list']: sub_indicator['sub_list_filtered'] = [] if not (sub_indicator['cumulative'] == '0' or sub_indicator['cumulative'] == 0): master_ind['sub_list_filtered'].append(sub_indicator) for sub_sub_ind in sub_indicator['sub_list']: if sub_sub_ind['cumulative'] == '0' or sub_sub_ind['cumulative'] == 0: continue else: sub_indicator['sub_list_filtered'].append(sub_sub_ind) else: for sub_indicator in master_ind['sub_list']: if not (sub_indicator['cumulative'] == '0' or sub_indicator['cumulative'] == 0): filtered_list.append(sub_indicator) sub_indicator['sub_list_filtered'] = [] for sub_sub_ind in sub_indicator['sub_list']: if sub_sub_ind['cumulative'] == '0' or sub_sub_ind['cumulative'] == 0: continue else: sub_indicator['sub_list_filtered'].append(sub_sub_ind) else: for sub_sub_ind in sub_indicator['sub_list']: if sub_sub_ind['cumulative'] == '0' or sub_sub_ind['cumulative'] == 0: continue else: filtered_list.append(sub_sub_ind) else: filtered_list = master_indicators if self.request.user.is_authenticated: template = "base2.html" else: template = "base_empty.html" return { 'selected_partners': selected_partners, 'selected_governorates': selected_governorates, 'reports': report.order_by('id'), 'month': month, 'month_name': month_name, 'month_number': month_number, 'database': database, 'partners': partners, 'governorates': governorates, 'master_indicators': filtered_list, 'selected_filter': selected_filter, 'partner_info': partner_info, 'day_number': day_number, 'reporting_year': str(reporting_year), 'template':template } class HPMView(LoginRequiredMixin,TemplateView): template_name = 'activityinfo/hpm.html' def get_context_data(self, **kwargs): current_month = date.today().month current_year = date.today().year is_current_year = True title = "" table_title="" month = int(self.request.GET.get('month', 0)) type = self.request.GET.get('quarter', "") today = datetime.date.today() day_number = int(today.strftime("%d")) if month == 0: if day_number >= 15: month = current_month - 1 else: month = current_month - 2 # year = date.today().year # reporting_year = self.request.GET.get('rep_year', year) instance = ReportingYear.objects.get(current=True) reporting_year = self.request.GET.get('rep_year', instance.year) # if not reporting_year: # reporting_year = year if type == '1' or type == '2' or type == '3' or type == '4': selected_month_name='Quarter ' + type table_title = "" else: selected_month_name = calendar.month_name[month] if type == '1': month = 3 elif type == '2': month = 6 elif type == '3': month = 9 elif type == '4': month = 12 month_name = calendar.month_name[month] if int(reporting_year) != current_year: is_current_year = False databases = Database.objects.filter(reporting_year__name=reporting_year).exclude(ai_id=10240).order_by('hpm_sequence') SGBV_db = [x for x in databases if x.label == 'SGBV'] if len(SGBV_db) == 0: SGBV_db_id=0 else: SGBV_db_id = SGBV_db[0].ai_id if month == 1 and type == "": title = '{} {}'.format('HPM Table | Data of January |', str(reporting_year)) table_title='{} {} {}'.format('SUMMARY OF PROGRAMME RESULTS | January | ',str(reporting_year),'SITREP-LEBANON') else: title = '{} {} {} {}'.format('HPM Table | Data of January to ', str(month_name),'|', str(reporting_year)) table_title='{} {} {} {} {}'.format('SUMMARY OF PROGRAMME RESULTS | January to', month_name , '|', reporting_year,'SITREP-LEBANON') months = [] if int(reporting_year) == current_year: if current_month == 1: months.append((1, datetime.date(2008, 1, 1).strftime('%B'))) if current_month > 2: if day_number >= 15: for i in range(1, current_month): months.append((i, datetime.date(2008, i, 1).strftime('%B'))) else: for i in range(1, current_month-1): months.append((i, datetime.date(2008, i, 1).strftime('%B'))) else: for i in range(1, 13): months.append((i, datetime.date(2008, i, 1).strftime('%B'))) if current_year - 1 == int(reporting_year) and current_month == 1: months = [] is_current_year = True for i in range(1, 13): months.append((i, datetime.date(2008, i, 1).strftime('%B'))) return { 'ai_databases': databases, 'month_name': month_name, 'month': month, 'months': months, 'reporting_year': reporting_year, 'is_current_year': is_current_year, 'title': title, 'SGBV_db': SGBV_db_id, 'table_title':table_title, 'selected_month':selected_month_name, 'current_month':current_month, # 'periodic_months':periodic_list, 'type':type } def post(self, request, *args, **kwargs): indicator_id = self.request.POST.get('indicator', 0) comment = self.request.POST.get('comment',"") indicator = Indicator.objects.get(id=indicator_id) month = self.request.POST.get('month',0) if indicator: comments_list = indicator.comment if comments_list is None: comments_list = {} if month > 0: comments_list[month] = comment indicator.comment = comments_list indicator.save() return HttpResponseRedirect('/activityinfo/HPM/?rep_year=2021&month='+month) class HPMExportViewSet(ListView): model = Indicator queryset = Indicator.objects.filter(hpm_indicator=True) def get(self, request, *args, **kwargs): from .utils import update_hpm_table_docx from django.core.files import File from .templatetags.convertor import StreamingConvertedPdf year = date.today().year reporting_year = self.request.GET.get('rep_year', year) type = self.request.GET.get('type', "") if reporting_year is None: reporting_year = year today = datetime.date.today() first = today.replace(day=1) currnet_month = first - datetime.timedelta(days=1) day_number = int(today.strftime("%d")) month = int(self.request.GET.get('month', currnet_month.strftime("%m"))) # month = int(self.request.GET.get('month', int(today.strftime("%m")) - 1)) # month = 12 # if day_number < 15: # month = month - 1 months = [] for i in range(1, 13): months.append((datetime.date(2008, i, 1).strftime('%B'))) filename = "HPM Table {} {}.docx".format(months[month-1], reporting_year) new_file = update_hpm_table_docx(self.queryset, month, months[month-1], filename,reporting_year,type) with open(new_file, 'rb') as fh: response = HttpResponse( fh.read(), content_type='application/vnd.openxmlformats-officedocument.wordprocessingml.document' ) response['Content-Disposition'] = 'attachment; filename=' + filename # print(new_file) if type == "docx": return response else: r_file = open(new_file, 'rb') inst = StreamingConvertedPdf(r_file,filename) return inst.stream_content() class ExportViewSet1(ListView): model = ActivityReport queryset = ActivityReport.objects.all() def get(self, request, *args, **kwargs): ai_id = self.request.GET.get('ai_id', 0) month = int(self.request.GET.get('month', int(datetime.now().strftime("%m")) - 1)) report_format = self.request.GET.get('format', 0) instance = Database.objects.get(ai_id=ai_id) report_mapping = getattr(instance, report_format) qs = ActivityReport.objects.filter( database_id=ai_id, start_date__month=month) if instance.is_funded_by_unicef: qs = qs.filter(funded_by__contains='UNICEF') filename = "extraction.csv" fields = report_mapping.keys() header = report_mapping.values() if report_format == 'mapping_extraction3': header = fields meta = { 'file': filename, # 'file': '/{}/{}'.format('tmp', filename), 'queryset': qs, 'fields': fields, 'header': header } from internos.backends.gistfile import get_model_as_csv_file_response return get_model_as_csv_file_response(meta, content_type='text/csv', filename=filename) class ExportDataSet(ListView): model = ActivityReport queryset = ActivityReport.objects.all() def get(self, request, *args, **kwargs): qs = ActivityReport.objects.filter( start_date__year='2020') path = os.path.dirname(os.path.abspath(__file__)) filename = "AI full raw data.csv" filename = path + '/AIReports/' + filename fields = [] model_fields = ActivityReport._meta.fields for field in model_fields: fields.append(field.name) meta = { 'file': filename, # 'file': '/{}/{}'.format('tmp', filename), 'queryset': qs, 'fields': fields, 'header': fields } # from internos.backends.djqscsv import render_to_csv_response # return render_to_csv_response(qs, field_header_map=fields, field_order=fields) from internos.backends.gistfile import get_model_as_csv_file_response return get_model_as_csv_file_response(meta, content_type='text/csv', filename=filename) class ExportViewSet(ListView): model = ActivityReport queryset = ActivityReport.objects.none() def get(self, request, *args, **kwargs): ai_id = self.request.GET.get('ai_id', 0) instance = Database.objects.get(ai_id=ai_id) today = datetime.date.today() first = today.replace(day=1) last_month = first - datetime.timedelta(days=1) month_name = last_month.strftime("%B") path = os.path.dirname(os.path.abspath(__file__)) if instance.reporting_year.name == '2021': path2file = path + '/AIReports/' + str(instance.ai_id) + '_ai_data.xlsx' filename = '{}_{}_{}_Raw Data.xlsx'.format(instance.label, month_name, instance.reporting_year.name) elif instance.reporting_year.name == '2020': path2file = path + '/AIReports/' + str(instance.db_id) + '_ai_data.csv' filename = '{}_{}_{}_Raw Data.csv'.format(instance.label, month_name, instance.reporting_year.name) else: path2file = path + '/AIReports/' + str(instance.ai_id) + '_ai_data.xlsx' filename = '{}_{}_{}_Raw Data.xlsx'.format(instance.label, month_name, instance.reporting_year.name) with open(path2file, 'r') as f: response = HttpResponse(f.read(), content_type='text/csv') response['Content-Disposition'] = 'attachment; filename=%s;' % filename return response class ReportBBlastView(LoginRequiredMixin,TemplateView): template_name = 'activityinfo/blast.html' def get_context_data(self, **kwargs): if self.request.user.is_authenticated: template = "base2.html" else: template = "base_empty.html" return { 'template':template } class ReportBlastView(LoginRequiredMixin,TemplateView): template_name = 'activityinfo/report_blast.html' def get_context_data(self, **kwargs): ai_id = int(self.request.GET.get('ai_id', 0)) database = Database.objects.get(ai_id=ai_id) reporting_year = database.reporting_year.year current_month = date.today().month all_indicators = Indicator.objects.filter(activity__database=database).exclude(type='quality') \ .order_by('sequence') months = [] for i in range(1, current_month + 1): months.append((i, calendar.month_name[i])) return { 'database': database, 'reporting_year': str(reporting_year), 'current_month_name': datetime.datetime.now().strftime("%B"), 'months' : months, 'indicators': all_indicators } def load_sections(request): from django.db import connection partners = request.GET.getlist('partner_id[]') govs = request.GET.getlist('gov_id[]') ai_id = request.GET.get('ai_id') months = request.GET.getlist('month_id[]') report_type= request.GET.get('type') database = Database.objects.get(ai_id=ai_id) result = {} cursor = connection.cursor() where_condition = "" funded_condition = "" if database.is_funded_by_unicef: funded_condition = " AND funded_by = 'UNICEF' " if partners: partners_list = ", ".join("'" + str(n) + "'" for n in partners) where_condition += " AND partner_id in (" + partners_list + ")" if govs: govs_list = ", ".join("'" + str(n) + "'" for n in govs) where_condition = " and location_adminlevel_governorate_code in (" + govs_list + ")" if months: month_list = ", ".join("'" + str(n).zfill(2) + "'" for n in months) where_condition += " AND SUBSTRING(month_name,6,2) in ("+ str(month_list) + ")" query_condition = "{}'{}'".format(" WHERE database_id =", str(ai_id)) + funded_condition + where_condition if report_type == 'live': cursor.execute( "SELECT DISTINCT reporting_section " "FROM activityinfo_liveactivityreport " + query_condition) else: cursor.execute( "SELECT DISTINCT reporting_section " "FROM activityinfo_activityreport " + query_condition ) rows = cursor.fetchall() for row in rows: result[row[0]] = { 'reporting_section': row[0], } # if partnerId and govId and monthId: # # if type =='live': # report = LiveActivityReport.objects.filter(database_id=ai_id, location_adminlevel_governorate_code__in=govId, # partner_id__in=partnerId, start_date__month__in=monthId) \ # .order_by('location_adminlevel_governorate_code').distinct('location_adminlevel_governorate_code') # else: # report = ActivityReport.objects.filter(database_id=ai_id, location_adminlevel_governorate_code__in=govId, # partner_id__in=partnerId, start_date__month__in=monthId) \ # .order_by('location_adminlevel_governorate_code','partner_id').distinct('location_adminlevel_governorate_code','partner_id') # # elif govId and partnerId and len(monthId) == 0: # # if type == 'live': # report = LiveActivityReport.objects.filter(database_id=ai_id, location_adminlevel_governorate_code__in=govId, # partner_id__in=partnerId) \ # .order_by('location_adminlevel_governorate_code').distinct('location_adminlevel_governorate_code',) # else : # report = ActivityReport.objects.filter(database_id=ai_id, location_adminlevel_governorate_code__in=govId, # partner_id__in=partnerId) # # elif partnerId and monthId and (govId is None and len(govId)) == 0: # # if type == 'live': # report = LiveActivityReport.objects.filter(database_id=ai_id, start_date__month__in=monthId, partner_id__in=partnerId) \ # .order_by('partner_id').distinct('partner_id') # else: # report = ActivityReport.objects.filter(database_id=ai_id, start_date__month__in=monthId, # partner_id__in=partnerId) # # elif govId and monthId and (partnerId is None and len(partnerId)) == 0: # # if type == 'live': # report = LiveActivityReport.objects.filter(database_id=ai_id, start_date__month__in=monthId, # location_adminlevel_governorate_code__in=govId)\ # .order_by('location_adminlevel_governorate_code').distinct('location_adminlevel_governorate_code') # else: # report = ActivityReport.objects.filter(database_id=ai_id, start_date__month__in=monthId, # location_adminlevel_governorate_code__in=govId) # # elif monthId and (govId is None and len(govId)) == 0 and (partnerId is None and len(partnerId)) == 0: # # if type == 'live': # report = LiveActivityReport.objects.filter(database_id=ai_id, start_date__month__in=monthId) # else : # report = ActivityReport.objects.filter(database_id=ai_id, start_date__month__in=monthId) # # elif partnerId and (govId is None and len(govId)) == 0 and (monthId is None and len(monthId)) == 0: # # if type == 'live': # report = LiveActivityReport.objects.filter(database_id=ai_id, partner_id__in=partnerId) \ # .order_by('partner_id').distinct('partner_id') # else: # report = ActivityReport.objects.filter(database_id=ai_id, partner_id__in=partnerId) # # elif govId and (partnerId is None and len(partnerId)) == 0 and (monthId is None and len(monthId)) == 0: # # if type == 'live': # report = LiveActivityReport.objects.filter(database_id=ai_id, location_adminlevel_governorate_code__in=govId) \ # .order_by('location_adminlevel_governorate_code').distinct('location_adminlevel_governorate_code') # else: # report = ActivityReport.objects.filter(database_id=ai_id, location_adminlevel_governorate_code__in=govId) # else: # # if type == 'live': # report = LiveActivityReport.objects.filter(database_id=ai_id) # else : # report = ActivityReport.objects.filter(database_id=ai_id) # # if database.is_funded_by_unicef: # report = report.filter(funded_by__contains='UNICEF') # # sections = report.values('reporting_section').order_by('reporting_section').distinct('reporting_section') return render(request, 'activityinfo/section_dropdown_list_options.html', {'sections': result.values()}) def load_partners(request): from django.db import connection govId = request.GET.getlist('gov_id[]') ai_id = request.GET.get('ai_id') monthId = request.GET.getlist('month_id[]') sectionId = request.GET.getlist('section_id[]') database = Database.objects.get(ai_id=ai_id) report_type = request.GET.get('type') result = {} cursor = connection.cursor() where_condition = "" funded_condition = "" query_condition = "" if database.is_funded_by_unicef: funded_condition = " AND funded_by = 'UNICEF' " if govId: govs_list = ", ".join("'" + str(n) + "'" for n in govId) where_condition = " and location_adminlevel_governorate_code in (" + govs_list + ")" if sectionId: section_list = ", ".join("'" + str(n) + "'" for n in sectionId) where_condition += " AND reporting_section in (" + section_list + ")" if monthId: month_list = ", ".join("'" + str(n).zfill(2) + "'" for n in monthId) where_condition += " AND SUBSTRING(month_name,6,2) in ("+ str(month_list) + ")" query_condition = "{}'{}'".format(" WHERE database_id =", str(ai_id)) + funded_condition + where_condition if report_type == 'live': cursor.execute( "SELECT DISTINCT partner_id, partner_label " "FROM activityinfo_liveactivityreport " + query_condition) else: cursor.execute( "SELECT DISTINCT partner_id, partner_label " "FROM activityinfo_activityreport " + query_condition) rows = cursor.fetchall() for row in rows: result[row[0]] = { 'partner_id': row[0], 'partner_label': row[1] } # return result.values() return render(request, 'activityinfo/partner_dropdown_list_options.html', {'partners': result.values()}) def load_governorates(request): from django.db import connection partners = request.GET.getlist('partner_id[]') sections = request.GET.getlist('section_id[]') months= request.GET.getlist('month_id[]') ai_id = request.GET.get('ai_id') database = Database.objects.get(ai_id=ai_id) report_type = request.GET.get('type') result = {} cursor = connection.cursor() #where_condition = "" //added by souheil below where_condition = "AND location_adminlevel_governorate_code <> '' " query_condition = "" funded_condition = "" if database.is_funded_by_unicef: funded_condition = " AND funded_by = 'UNICEF' " if sections: section_list = ", ".join("'" + str(n) + "'" for n in sections) where_condition += " AND reporting_section in (" + section_list + ")" if partners: partners_list = ", ".join("'" + str(n) + "'" for n in partners) where_condition += " AND partner_id in (" + partners_list + ")" if months: month_list = ", ".join("'" + str(n).zfill(2) + "'" for n in months) where_condition += " AND SUBSTRING(month_name,6,2) in ("+ str(month_list) + ")" query_condition = "{}'{}'".format(" WHERE database_id =", str(ai_id)) + funded_condition + where_condition if report_type == 'live': cursor.execute( "SELECT DISTINCT location_adminlevel_governorate_code, location_adminlevel_governorate " "FROM activityinfo_liveactivityreport " + query_condition) else: cursor.execute( "SELECT DISTINCT location_adminlevel_governorate_code, location_adminlevel_governorate " "FROM activityinfo_activityreport " + query_condition) rows = cursor.fetchall() for row in rows: result[row[0]] = { 'location_adminlevel_governorate_code': row[0], 'location_adminlevel_governorate': row[1] } # if partnerId and sectionId and monthId: # if type == 'live': # report = ActivityReport.objects.filter(database_id=ai_id, partner_id__in=partnerId, # reporting_section__in=sectionId , start_date__month__in=monthId) \ # .order_by('reporting_section').distinct('reporting_section') # # else : # report = ActivityReport.objects.filter(database_id=ai_id, partner_id__in=partnerId, # reporting_section__in=sectionId, start_date__month__in=monthId) # elif partnerId and (sectionId is None or len(sectionId) == 0) and monthId: # if type == 'live': # report = LiveActivityReport.objects.filter(database_id=ai_id, partner_id__in=partnerId, start_date__month__in=monthId)\ # .order_by('partner_id').distinct('partner_id') # # else: # report = ActivityReport.objects.filter(database_id=ai_id, partner_id__in=partnerId, # start_date__month__in=monthId) # # elif partnerId and sectionId and len(monthId) == 0 : # if type == 'live': # report = LiveActivityReport.objects.filter(database_id=ai_id , partner_id__in=partnerId,reporting_section__in=sectionId) \ # .order_by('partner_id').distinct('partner_id') # else: # report = ActivityReport.objects.filter(database_id=ai_id, partner_id__in=partnerId, # reporting_section__in=sectionId) # # elif monthId and sectionId and len(partnerId) == 0: # if type == 'live': # report = LiveActivityReport.objects.filter(database_id=ai_id, start_date__month__in=monthId, # reporting_section__in=sectionId) \ # .order_by('reporting_section').distinct('reporting_section') # else : # report = ActivityReport.objects.filter(database_id=ai_id, start_date__month__in=monthId, # reporting_section__in=sectionId) # # elif partnerId and (sectionId is None or len(sectionId) == 0) and len(monthId) ==0 : # if type == 'live': # report = LiveActivityReport.objects.filter(database_id=ai_id, partner_id__in=partnerId) \ # .order_by('partner_id').distinct('partner_id') # else : # report = ActivityReport.objects.filter(database_id=ai_id, partner_id__in=partnerId) # # elif sectionId and (partnerId is None or len(partnerId) == 0) and len(monthId) == 0: # # if type == 'live': # report = LiveActivityReport.objects.filter(database_id=ai_id, reporting_section__in=sectionId) \ # .order_by('reporting_section').distinct('reporting_section') # else : # report = ActivityReport.objects.filter(database_id=ai_id, reporting_section__in=sectionId) # # elif monthId and (sectionId is None or len(sectionId) == 0) and len(partnerId) == 0: # if type == 'live': # report = LiveActivityReport.objects.filter(database_id=ai_id, start_date__month__in=monthId) # else : # report = ActivityReport.objects.filter(database_id=ai_id, start_date__month__in=monthId) # else: # if type == 'live': # # report = LiveActivityReport.objects.filter(database_id=ai_id) # else : # report = ActivityReport.objects.filter(database_id=ai_id) # # if database.is_funded_by_unicef: # report = report.filter(funded_by__contains='UNICEF') # governorates = report.values('location_adminlevel_governorate_code', # 'location_adminlevel_governorate').distinct() return render(request, 'activityinfo/gov_dropdown_list_options.html', {'governorates': result.values()}) def load_months(request): from django.db import connection partners = request.GET.getlist('partner_id[]') sections = request.GET.getlist('section_id[]') govs = request.GET.getlist('gov_id[]') ai_id = request.GET.get('ai_id') database = Database.objects.get(ai_id=ai_id) report_type = request.GET.get('type') result = {} months = [] cursor = connection.cursor() #where_condition = "" //condition added by souheil below where_condition = "AND location_adminlevel_governorate_code <> '' " funded_condition = "" query_condition = "" if database.is_funded_by_unicef: funded_condition = " AND funded_by = 'UNICEF' " if partners: partners_list = ", ".join("'" + str(n) + "'" for n in partners) where_condition += " AND partner_id in (" + partners_list + ")" if govs: govs_list = ", ".join("'" + str(n) + "'" for n in govs) where_condition += " and location_adminlevel_governorate_code in (" + govs_list + ")" if sections: section_list = ", ".join("'" + str(n) + "'" for n in sections) where_condition += " AND reporting_section in (" + section_list + ")" query_condition = "{}'{}'".format(" WHERE database_id =", str(ai_id)) + funded_condition + where_condition if report_type == 'live': cursor.execute( "SELECT DISTINCT SUBSTRING(month_name,6,2) " "FROM activityinfo_liveactivityreport " + query_condition ) else: cursor.execute( "SELECT DISTINCT SUBSTRING(month_name,6,2) " "FROM activityinfo_activityreport " + query_condition ) #fullquery = "SELECT DISTINCT SUBSTRING(month_name,6,2) FROM activityinfo_activityreport " + query_condition #print(fullquery) rows = cursor.fetchall() for row in rows: if row[0] is not None: result[row[0]] = { 'start_date': row[0], } sorted_list = sorted(result) for record in sorted_list: if record is not None: m = int(record) if (m, calendar.month_name[m]) not in months: months.append((m, calendar.month_name[m])) return render(request, 'activityinfo/month_dropdown_list_options.html', {'months': months}) # return months # if partnerId and sectionId and govId: # report = ActivityReport.objects.filter(database_id=ai_id, partner_id__in=partnerId, # reporting_section__in=sectionId,location_adminlevel_governorate_code__in=govId) # result_list = report.values('start_date').distinct() # # for record in result_list: # if 'start_date' in record and record['start_date'] is not None: # m = record['start_date'].month # if (m, calendar.month_name[m]) not in months: # months.append((m,calendar.month_name[m])) # # elif partnerId and sectionId and (govId is None or len(govId) == 0): # # report = ActivityReport.objects.filter(database_id=ai_id , partner_id__in=partnerId,reporting_section__in=sectionId) # result_list = report.values('start_date').distinct() # # for record in result_list: # if 'start_date' in record and record['start_date'] is not None: # m = record['start_date'].month # if (m, calendar.month_name[m]) not in months: # months.append((m, calendar.month_name[m])) # # elif partnerId and govId and (sectionId is None or len(sectionId) == 0): # # report = ActivityReport.objects.filter(database_id=ai_id, partner_id__in=partnerId,location_adminlevel_governorate_code__in=govId) # result_list = report.values('start_date').distinct() # for record in result_list: # if 'start_date' in record and record['start_date'] is not None: # m = record['start_date'].month # if (m, calendar.month_name[m]) not in months: # months.append((m, calendar.month_name[m])) # # elif sectionId and govId and (partnerId is None or len(partnerId) == 0): # # report = ActivityReport.objects.filter(database_id=ai_id, reporting_section__in=sectionId, # location_adminlevel_governorate_code__in=govId) # result_list = report.values('start_date').distinct() # for record in result_list: # if 'start_date' in record and record['start_date'] is not None: # m = record['start_date'].month # if (m, calendar.month_name[m]) not in months: # months.append((m, calendar.month_name[m])) # # elif partnerId and (sectionId is None or len(sectionId) == 0) and (govId is None or len(govId) == 0): # report = ActivityReport.objects.filter(database_id=ai_id, partner_id__in=partnerId) # result_list = report.values('start_date').distinct() # for record in result_list: # if 'start_date' in record and record['start_date'] is not None: # m = record['start_date'].month # if (m, calendar.month_name[m]) not in months: # months.append((m, calendar.month_name[m])) # # elif govId and (sectionId is None or len(sectionId) == 0) and (partnerId is None or len(partnerId) == 0): # # report = ActivityReport.objects.filter(database_id=ai_id,location_adminlevel_governorate_code__in=govId) # result_list = report.values('start_date').distinct() # # for record in result_list: # if 'start_date' in record and record['start_date'] is not None: # m = record['start_date'].month # if (m, calendar.month_name[m]) not in months: # months.append((m, calendar.month_name[m])) # elif sectionId and (govId is None or len(govId) == 0) and (partnerId is None or len(partnerId) == 0): # # report = ActivityReport.objects.filter(database_id=ai_id, reporting_section__in=sectionId) # result_list = report.values('start_date').distinct() # # for record in result_list: # if 'start_date' in record and record['start_date'] is not None: # m = record['start_date'].month # if (m, calendar.month_name[m]) not in months: # months.append((m, calendar.month_name[m])) # else: # # for i in range(1, 13): # months.append((i, calendar.month_abbr[i])) class ActivityAutocomplete(autocomplete.Select2QuerySetView): def get_queryset(self): if not self.request.user.is_authenticated(): return Activity.objects.none() qs = Activity.objects.filter(database__reporting_year__year=datetime.datetime.now().year) if self.q: qs = Activity.objects.filter(name__istartswith=self.q) return qs class IndicatorsListVisualView(LoginRequiredMixin,TemplateView): template_name = 'activityinfo/indicators_list_visual.html' def get_context_data(self, **kwargs): pillar = self.request.GET.get('pillar', 0) reporting_level = self.request.GET.get('reporting_level', 0) focus_name = self.request.GET.get('focus_name', 0) color = self.request.GET.get('color', 0) indicators = Indicator.objects.filter(activity__database__ai_id='202020', master_indicator=True).order_by('sequence') if pillar: indicators = indicators.filter(category=pillar) if reporting_level: indicators = indicators.filter(reporting_level__contains=reporting_level) if focus_name: indicators = indicators.filter(tag_focus__name=focus_name) indicators = indicators.values( 'id', 'ai_id', 'name', 'master_indicator', 'master_indicator_sub', 'master_indicator_sub_sub', 'individual_indicator', 'measurement_type', 'units', 'target', 'status_color', 'status', 'cumulative_values', 'values_partners_gov', 'values_partners', 'values_gov', 'values', 'values_live', 'values_gov_live', 'values_partners_live', 'values_partners_gov_live', 'cumulative_values_live', 'is_cumulative', 'support_COVID', 'category', 'tag_focus__name', 'values_tags', 'reporting_level', ).distinct().order_by('sequence') return { 'count': indicators.count(), 'indicators': indicators, 'color': color, 'filter': 'level3-filter', 'display_tags': True } class IndicatorsSubListVisualView(LoginRequiredMixin,TemplateView): template_name = 'activityinfo/indicators_list_visual.html' def get_context_data(self, **kwargs): parent_id = self.request.GET.get('parent_id', 0) color = self.request.GET.get('color', 0) indicators = Indicator.objects.filter(activity__database__ai_id='202020', master_indicator=False) if parent_id: indicators = indicators.filter(sub_indicators=int(parent_id)) indicators = indicators.values( 'id', 'ai_id', 'name', 'master_indicator', 'master_indicator_sub', 'master_indicator_sub_sub', 'individual_indicator', 'measurement_type', 'units', 'target', 'status_color', 'status', 'cumulative_values', 'values_partners_gov', 'values_partners', 'values_gov', 'values', 'values_live', 'values_gov_live', 'values_partners_live', 'values_partners_gov_live', 'cumulative_values_live', 'is_cumulative', 'support_COVID', 'category', 'tag_focus__name', 'values_tags', 'reporting_level', ).distinct().order_by('sequence') return { 'count': indicators.count(), 'indicators': indicators, 'color': color, 'filter': 'level4-filter', 'display_tags': False }

46.602466

194

0.535645

20,215

219,218

5.49112

0.025723

0.008252

0.014378

0.031134

0.865941

0.837266

0.808708

0.779141

0.765961

0.753763

0

0.007036

0.372378

219,218

4,703

195

46.612375

0.799754

0.089016

0

0.791892

0

0.000541

0.143119

0.03771

0

null

0.000541

0.021892

null

0.000541

0

null

0

1

0

1

0

null

0

1

0

8

d717e356c286614a75497b1246ce113ab30b4ffd

134,987

py

Python

lbry/lbry/schema/types/v2/claim_pb2.py

Nykseli/lbry-sdk

07afc0aa0a1e6c0ef6aa284fb47513af940440c1

[ "MIT" ]

null

lbry/lbry/schema/types/v2/claim_pb2.py

Nykseli/lbry-sdk

07afc0aa0a1e6c0ef6aa284fb47513af940440c1

[ "MIT" ]

4

2020-10-27T21:53:05.000Z

2022-02-11T03:10:54.000Z

lbry/lbry/schema/types/v2/claim_pb2.py

braveheart12/lbry-sdk

dc709b468f9dce60d206161785def5c7ace2b763

[ "MIT" ]

null

# Generated by the protocol buffer compiler. DO NOT EDIT! # source: claim.proto import sys _b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1')) from google.protobuf import descriptor as _descriptor from google.protobuf import message as _message from google.protobuf import reflection as _reflection from google.protobuf import symbol_database as _symbol_database # @@protoc_insertion_point(imports) _sym_db = _symbol_database.Default() DESCRIPTOR = _descriptor.FileDescriptor( name='claim.proto', package='pb', syntax='proto3', serialized_options=None, serialized_pb=_b('\n\x0b\x63laim.proto\x12\x02pb\"\xab\x02\n\x05\x43laim\x12\x1c\n\x06stream\x18\x01 \x01(\x0b\x32\n.pb.StreamH\x00\x12\x1e\n\x07\x63hannel\x18\x02 \x01(\x0b\x32\x0b.pb.ChannelH\x00\x12#\n\ncollection\x18\x03 \x01(\x0b\x32\r.pb.ClaimListH\x00\x12$\n\x06repost\x18\x04 \x01(\x0b\x32\x12.pb.ClaimReferenceH\x00\x12\r\n\x05title\x18\x08 \x01(\t\x12\x13\n\x0b\x64\x65scription\x18\t \x01(\t\x12\x1d\n\tthumbnail\x18\n 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) _CLAIMLIST_LISTTYPE = _descriptor.EnumDescriptor( name='ListType', full_name='pb.ClaimList.ListType', filename=None, file=DESCRIPTOR, values=[ _descriptor.EnumValueDescriptor( name='COLLECTION', index=0, number=0, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='DERIVATION', index=1, number=2, serialized_options=None, type=None), ], containing_type=None, serialized_options=None, serialized_start=852, serialized_end=894, ) _sym_db.RegisterEnumDescriptor(_CLAIMLIST_LISTTYPE) _FEE_CURRENCY = _descriptor.EnumDescriptor( name='Currency', full_name='pb.Fee.Currency', filename=None, file=DESCRIPTOR, values=[ _descriptor.EnumValueDescriptor( name='UNKNOWN_CURRENCY', index=0, number=0, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='LBC', index=1, number=1, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='BTC', index=2, number=2, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='USD', index=3, number=3, serialized_options=None, type=None), ], containing_type=None, serialized_options=None, serialized_start=1075, serialized_end=1134, ) _sym_db.RegisterEnumDescriptor(_FEE_CURRENCY) _SOFTWARE_OS = _descriptor.EnumDescriptor( name='OS', full_name='pb.Software.OS', filename=None, file=DESCRIPTOR, values=[ _descriptor.EnumValueDescriptor( name='UNKNOWN_OS', index=0, number=0, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='ANY', index=1, number=1, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='LINUX', index=2, number=2, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='WINDOWS', index=3, number=3, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='MAC', index=4, number=4, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='ANDROID', index=5, number=5, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='IOS', index=6, 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_descriptor.EnumValueDescriptor( name='kn', index=84, number=84, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='ko', index=85, number=85, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='kr', index=86, number=86, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='ks', index=87, number=87, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='ku', index=88, number=88, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='kv', index=89, number=89, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='kw', index=90, number=90, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='ky', index=91, number=91, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='la', index=92, number=92, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='lb', index=93, number=93, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='lg', index=94, number=94, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='li', index=95, number=95, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='ln', index=96, number=96, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='lo', index=97, number=97, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='lt', index=98, number=98, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='lu', index=99, number=99, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='lv', index=100, number=100, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='mg', index=101, number=101, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='mh', index=102, number=102, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='mi', index=103, number=103, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='mk', index=104, number=104, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='ml', index=105, number=105, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='mn', index=106, number=106, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='mr', index=107, number=107, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='ms', index=108, number=108, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='mt', index=109, number=109, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='my', index=110, number=110, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='na', index=111, number=111, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='nb', index=112, number=112, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='nd', index=113, number=113, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='ne', index=114, number=114, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='ng', index=115, number=115, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='nl', index=116, number=116, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='nn', index=117, number=117, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='no', index=118, number=118, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='nr', index=119, number=119, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='nv', index=120, number=120, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='ny', index=121, number=121, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='oc', index=122, number=122, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='oj', index=123, number=123, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='om', index=124, number=124, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='or', index=125, number=125, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='os', index=126, number=126, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='pa', index=127, number=127, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='pi', index=128, number=128, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='pl', index=129, number=129, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='ps', index=130, number=130, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='pt', index=131, number=131, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='qu', index=132, number=132, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='rm', index=133, number=133, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='rn', index=134, number=134, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='ro', index=135, number=135, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='ru', index=136, number=136, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='rw', index=137, number=137, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='sa', index=138, number=138, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='sc', index=139, number=139, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='sd', index=140, number=140, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='se', index=141, number=141, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='sg', index=142, number=142, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='si', index=143, number=143, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='sk', index=144, number=144, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='sl', index=145, number=145, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='sm', index=146, number=146, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='sn', index=147, number=147, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='so', index=148, number=148, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='sq', index=149, number=149, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='sr', index=150, number=150, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='ss', index=151, number=151, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='st', index=152, number=152, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='su', index=153, number=153, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='sv', index=154, number=154, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='sw', index=155, number=155, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='ta', index=156, number=156, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='te', index=157, number=157, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='tg', index=158, number=158, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='th', index=159, number=159, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='ti', index=160, number=160, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='tk', index=161, number=161, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='tl', index=162, number=162, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='tn', index=163, number=163, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='to', index=164, number=164, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='tr', index=165, number=165, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='ts', index=166, number=166, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='tt', index=167, number=167, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='tw', index=168, number=168, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='ty', index=169, number=169, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='ug', index=170, number=170, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='uk', index=171, number=171, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='ur', index=172, number=172, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='uz', index=173, number=173, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='ve', index=174, number=174, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='vi', index=175, number=175, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='vo', index=176, number=176, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='wa', index=177, number=177, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='wo', index=178, number=178, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='xh', index=179, number=179, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='yi', index=180, number=180, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='yo', index=181, number=181, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='za', index=182, number=182, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='zh', index=183, number=183, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='zu', index=184, number=184, serialized_options=None, type=None), ], containing_type=None, serialized_options=None, serialized_start=1527, serialized_end=3088, ) _sym_db.RegisterEnumDescriptor(_LANGUAGE_LANGUAGE) _LANGUAGE_SCRIPT = _descriptor.EnumDescriptor( name='Script', full_name='pb.Language.Script', filename=None, file=DESCRIPTOR, values=[ _descriptor.EnumValueDescriptor( name='UNKNOWN_SCRIPT', index=0, number=0, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Adlm', index=1, number=1, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Afak', index=2, number=2, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Aghb', index=3, number=3, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Ahom', index=4, number=4, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Arab', index=5, number=5, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Aran', index=6, number=6, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Armi', index=7, number=7, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Armn', index=8, number=8, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Avst', index=9, number=9, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Bali', index=10, number=10, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Bamu', index=11, number=11, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Bass', index=12, number=12, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Batk', index=13, number=13, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Beng', index=14, number=14, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Bhks', index=15, number=15, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Blis', index=16, number=16, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Bopo', index=17, number=17, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Brah', index=18, number=18, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Brai', index=19, number=19, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Bugi', index=20, number=20, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Buhd', index=21, number=21, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Cakm', index=22, number=22, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Cans', index=23, number=23, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Cari', index=24, number=24, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Cham', index=25, number=25, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Cher', index=26, number=26, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Cirt', index=27, number=27, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Copt', index=28, number=28, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Cpmn', index=29, number=29, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Cprt', index=30, number=30, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Cyrl', index=31, number=31, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Cyrs', index=32, number=32, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Deva', index=33, number=33, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Dogr', index=34, number=34, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Dsrt', index=35, number=35, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Dupl', index=36, number=36, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Egyd', index=37, number=37, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Egyh', index=38, number=38, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Egyp', index=39, number=39, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Elba', index=40, number=40, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Elym', index=41, number=41, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Ethi', index=42, number=42, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Geok', index=43, number=43, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Geor', index=44, number=44, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Glag', index=45, number=45, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Gong', index=46, number=46, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Gonm', index=47, number=47, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Goth', index=48, number=48, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Gran', index=49, number=49, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Grek', index=50, number=50, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Gujr', index=51, number=51, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Guru', index=52, number=52, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Hanb', index=53, number=53, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Hang', index=54, number=54, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Hani', index=55, number=55, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Hano', index=56, number=56, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Hans', index=57, number=57, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Hant', index=58, number=58, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Hatr', index=59, number=59, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Hebr', index=60, number=60, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Hira', index=61, number=61, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Hluw', index=62, number=62, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Hmng', index=63, number=63, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Hmnp', index=64, number=64, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Hrkt', index=65, number=65, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Hung', index=66, number=66, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Inds', index=67, number=67, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Ital', index=68, number=68, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Jamo', index=69, number=69, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Java', index=70, number=70, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Jpan', index=71, number=71, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Jurc', index=72, number=72, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Kali', index=73, number=73, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Kana', index=74, number=74, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Khar', index=75, number=75, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Khmr', index=76, number=76, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Khoj', index=77, number=77, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Kitl', index=78, number=78, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Kits', index=79, number=79, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Knda', index=80, number=80, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Kore', index=81, number=81, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Kpel', index=82, number=82, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Kthi', index=83, number=83, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Lana', index=84, number=84, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Laoo', index=85, number=85, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Latf', index=86, number=86, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Latg', index=87, number=87, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Latn', index=88, number=88, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Leke', index=89, number=89, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Lepc', index=90, number=90, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Limb', index=91, number=91, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Lina', index=92, number=92, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Linb', index=93, number=93, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Lisu', index=94, number=94, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Loma', index=95, number=95, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Lyci', index=96, number=96, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Lydi', index=97, number=97, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Mahj', index=98, number=98, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Maka', index=99, number=99, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Mand', index=100, number=100, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Mani', index=101, number=101, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Marc', index=102, number=102, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Maya', index=103, number=103, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Medf', index=104, number=104, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Mend', index=105, number=105, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Merc', index=106, number=106, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Mero', index=107, number=107, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Mlym', index=108, number=108, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Modi', index=109, number=109, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Mong', index=110, number=110, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Moon', index=111, number=111, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Mroo', index=112, number=112, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Mtei', index=113, number=113, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Mult', index=114, number=114, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Mymr', index=115, number=115, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Nand', index=116, number=116, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Narb', index=117, number=117, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Nbat', index=118, number=118, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Newa', index=119, number=119, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Nkdb', index=120, number=120, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Nkgb', index=121, number=121, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Nkoo', index=122, number=122, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Nshu', index=123, number=123, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Ogam', index=124, number=124, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Olck', index=125, number=125, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Orkh', index=126, number=126, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Orya', index=127, number=127, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Osge', index=128, number=128, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Osma', index=129, number=129, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Palm', index=130, number=130, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Pauc', index=131, number=131, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Perm', index=132, number=132, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Phag', index=133, number=133, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Phli', index=134, number=134, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Phlp', index=135, number=135, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Phlv', index=136, number=136, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Phnx', index=137, number=137, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Plrd', index=138, number=138, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Piqd', index=139, number=139, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Prti', index=140, number=140, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Qaaa', index=141, number=141, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Qabx', index=142, number=142, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Rjng', index=143, number=143, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Rohg', index=144, number=144, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Roro', index=145, number=145, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Runr', index=146, number=146, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Samr', index=147, number=147, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Sara', index=148, number=148, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Sarb', index=149, number=149, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Saur', index=150, number=150, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Sgnw', index=151, number=151, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Shaw', index=152, number=152, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Shrd', index=153, number=153, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Shui', index=154, number=154, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Sidd', index=155, number=155, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Sind', index=156, number=156, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Sinh', index=157, number=157, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Sogd', index=158, number=158, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Sogo', index=159, number=159, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Sora', index=160, number=160, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Soyo', index=161, number=161, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Sund', index=162, number=162, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Sylo', index=163, number=163, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Syrc', index=164, number=164, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Syre', index=165, number=165, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Syrj', index=166, number=166, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Syrn', index=167, number=167, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Tagb', index=168, number=168, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Takr', index=169, number=169, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Tale', index=170, number=170, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Talu', index=171, number=171, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Taml', index=172, number=172, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Tang', index=173, number=173, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Tavt', index=174, number=174, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Telu', index=175, number=175, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Teng', index=176, number=176, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Tfng', index=177, number=177, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Tglg', index=178, number=178, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Thaa', index=179, number=179, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Thai', index=180, number=180, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Tibt', index=181, number=181, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Tirh', index=182, number=182, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Ugar', index=183, number=183, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Vaii', index=184, number=184, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Visp', index=185, number=185, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Wara', index=186, number=186, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Wcho', index=187, number=187, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Wole', index=188, number=188, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Xpeo', index=189, number=189, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Xsux', index=190, number=190, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Yiii', index=191, number=191, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Zanb', index=192, number=192, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Zinh', index=193, number=193, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Zmth', index=194, number=194, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Zsye', index=195, number=195, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Zsym', index=196, number=196, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Zxxx', index=197, number=197, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Zyyy', index=198, number=198, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='Zzzz', index=199, number=199, serialized_options=None, type=None), ], containing_type=None, serialized_options=None, serialized_start=3091, serialized_end=5181, ) _sym_db.RegisterEnumDescriptor(_LANGUAGE_SCRIPT) _LOCATION_COUNTRY = _descriptor.EnumDescriptor( name='Country', full_name='pb.Location.Country', filename=None, file=DESCRIPTOR, values=[ _descriptor.EnumValueDescriptor( name='UNKNOWN_COUNTRY', index=0, number=0, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='AF', index=1, number=1, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='AX', index=2, number=2, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='AL', index=3, number=3, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='DZ', index=4, number=4, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='AS', index=5, number=5, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='AD', index=6, number=6, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='AO', index=7, number=7, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='AI', index=8, number=8, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='AQ', index=9, number=9, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='AG', index=10, number=10, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='AR', index=11, number=11, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='AM', index=12, number=12, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='AW', index=13, number=13, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='AU', index=14, number=14, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='AT', index=15, number=15, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='AZ', index=16, number=16, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='BS', index=17, number=17, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='BH', index=18, number=18, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='BD', index=19, number=19, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='BB', index=20, number=20, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='BY', index=21, number=21, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='BE', index=22, number=22, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='BZ', index=23, number=23, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='BJ', index=24, number=24, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='BM', index=25, number=25, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='BT', index=26, number=26, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='BO', index=27, number=27, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='BQ', index=28, number=28, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='BA', index=29, number=29, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='BW', index=30, number=30, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='BV', index=31, number=31, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='BR', index=32, number=32, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='IO', index=33, number=33, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='BN', index=34, number=34, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='BG', index=35, number=35, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='BF', index=36, number=36, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='BI', index=37, number=37, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='KH', index=38, number=38, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='CM', index=39, number=39, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='CA', index=40, number=40, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='CV', index=41, number=41, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='KY', index=42, number=42, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='CF', index=43, number=43, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='TD', index=44, number=44, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='CL', index=45, number=45, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='CN', index=46, number=46, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='CX', index=47, number=47, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='CC', index=48, number=48, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='CO', index=49, number=49, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='KM', index=50, number=50, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='CG', index=51, number=51, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='CD', index=52, number=52, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='CK', index=53, number=53, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='CR', index=54, number=54, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='CI', index=55, number=55, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='HR', index=56, number=56, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='CU', index=57, number=57, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='CW', index=58, number=58, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='CY', index=59, number=59, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='CZ', index=60, number=60, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='DK', index=61, number=61, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='DJ', index=62, number=62, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='DM', index=63, number=63, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='DO', index=64, number=64, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='EC', index=65, number=65, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='EG', index=66, number=66, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='SV', index=67, number=67, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='GQ', index=68, number=68, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='ER', index=69, number=69, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='EE', index=70, number=70, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='ET', index=71, number=71, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='FK', index=72, number=72, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='FO', index=73, number=73, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='FJ', index=74, number=74, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='FI', index=75, number=75, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='FR', index=76, number=76, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='GF', index=77, number=77, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='PF', index=78, number=78, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='TF', index=79, number=79, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='GA', index=80, number=80, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='GM', index=81, number=81, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='GE', index=82, number=82, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='DE', index=83, number=83, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='GH', index=84, number=84, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='GI', index=85, number=85, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='GR', index=86, number=86, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='GL', index=87, number=87, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='GD', index=88, number=88, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='GP', index=89, number=89, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='GU', index=90, number=90, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='GT', index=91, number=91, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='GG', index=92, number=92, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='GN', index=93, number=93, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='GW', index=94, number=94, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='GY', index=95, number=95, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='HT', index=96, number=96, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='HM', index=97, number=97, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='VA', index=98, number=98, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='HN', index=99, number=99, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='HK', index=100, number=100, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='HU', index=101, number=101, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='IS', index=102, number=102, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='IN', index=103, number=103, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='ID', index=104, number=104, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='IR', index=105, number=105, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='IQ', index=106, number=106, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='IE', index=107, number=107, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='IM', index=108, number=108, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='IL', index=109, number=109, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='IT', index=110, number=110, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='JM', index=111, number=111, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='JP', index=112, number=112, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='JE', index=113, number=113, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='JO', index=114, number=114, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='KZ', index=115, number=115, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='KE', index=116, number=116, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='KI', index=117, number=117, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='KP', index=118, number=118, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='KR', index=119, number=119, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='KW', index=120, number=120, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='KG', index=121, number=121, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='LA', index=122, number=122, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='LV', index=123, number=123, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='LB', index=124, number=124, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='LS', index=125, number=125, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='LR', index=126, number=126, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='LY', index=127, number=127, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='LI', index=128, number=128, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='LT', index=129, number=129, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='LU', index=130, number=130, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='MO', index=131, number=131, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='MK', index=132, number=132, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='MG', index=133, number=133, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='MW', index=134, number=134, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='MY', index=135, number=135, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='MV', index=136, number=136, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='ML', index=137, number=137, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='MT', index=138, number=138, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='MH', index=139, number=139, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='MQ', index=140, number=140, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='MR', index=141, number=141, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='MU', index=142, number=142, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='YT', index=143, number=143, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='MX', index=144, number=144, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='FM', index=145, number=145, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='MD', index=146, number=146, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='MC', index=147, number=147, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='MN', index=148, number=148, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='ME', index=149, number=149, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='MS', index=150, number=150, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='MA', index=151, number=151, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='MZ', index=152, number=152, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='MM', index=153, number=153, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='NA', index=154, number=154, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='NR', index=155, number=155, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='NP', index=156, number=156, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='NL', index=157, number=157, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='NC', index=158, number=158, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='NZ', index=159, number=159, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='NI', index=160, number=160, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='NE', index=161, number=161, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='NG', index=162, number=162, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='NU', index=163, number=163, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='NF', index=164, number=164, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='MP', index=165, number=165, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='NO', index=166, number=166, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='OM', index=167, number=167, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='PK', index=168, number=168, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='PW', index=169, number=169, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='PS', index=170, number=170, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='PA', index=171, number=171, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='PG', index=172, number=172, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='PY', index=173, number=173, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='PE', index=174, number=174, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='PH', index=175, number=175, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='PN', index=176, number=176, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='PL', index=177, number=177, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='PT', index=178, number=178, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='PR', index=179, number=179, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='QA', index=180, number=180, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='RE', index=181, number=181, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='RO', index=182, number=182, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='RU', index=183, number=183, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='RW', index=184, number=184, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='BL', index=185, number=185, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='SH', index=186, number=186, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='KN', index=187, number=187, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='LC', index=188, number=188, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='MF', index=189, number=189, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='PM', index=190, number=190, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='VC', index=191, number=191, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='WS', index=192, number=192, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='SM', index=193, number=193, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='ST', index=194, number=194, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='SA', index=195, number=195, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='SN', index=196, number=196, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='RS', index=197, number=197, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='SC', index=198, number=198, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='SL', index=199, number=199, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='SG', index=200, number=200, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='SX', index=201, number=201, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='SK', index=202, number=202, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='SI', index=203, number=203, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='SB', index=204, number=204, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='SO', index=205, number=205, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='ZA', index=206, number=206, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='GS', index=207, number=207, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='SS', index=208, number=208, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='ES', index=209, number=209, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='LK', index=210, number=210, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='SD', index=211, number=211, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='SR', index=212, number=212, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='SJ', index=213, number=213, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='SZ', index=214, number=214, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='SE', index=215, number=215, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='CH', index=216, number=216, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='SY', index=217, number=217, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='TW', index=218, number=218, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='TJ', index=219, number=219, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='TZ', index=220, number=220, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='TH', index=221, number=221, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='TL', index=222, number=222, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='TG', index=223, number=223, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='TK', index=224, number=224, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='TO', index=225, number=225, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='TT', index=226, number=226, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='TN', index=227, number=227, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='TR', index=228, number=228, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='TM', index=229, number=229, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='TC', index=230, number=230, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='TV', index=231, number=231, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='UG', index=232, number=232, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='UA', index=233, number=233, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='AE', index=234, number=234, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='GB', index=235, number=235, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='US', index=236, number=236, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='UM', index=237, number=237, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='UY', index=238, number=238, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='UZ', index=239, number=239, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='VU', index=240, number=240, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='VE', index=241, number=241, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='VN', index=242, number=242, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='VG', index=243, number=243, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='VI', index=244, number=244, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='WF', index=245, number=245, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='EH', index=246, number=246, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='YE', index=247, number=247, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='ZM', index=248, number=248, serialized_options=None, type=None), _descriptor.EnumValueDescriptor( name='ZW', index=249, number=249, serialized_options=None, type=None), ], containing_type=None, serialized_options=None, serialized_start=5316, serialized_end=7460, ) _sym_db.RegisterEnumDescriptor(_LOCATION_COUNTRY) _CLAIM = _descriptor.Descriptor( name='Claim', full_name='pb.Claim', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='stream', full_name='pb.Claim.stream', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='channel', full_name='pb.Claim.channel', index=1, number=2, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='collection', full_name='pb.Claim.collection', index=2, number=3, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='repost', full_name='pb.Claim.repost', index=3, number=4, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='title', full_name='pb.Claim.title', index=4, number=8, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='description', full_name='pb.Claim.description', index=5, number=9, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='thumbnail', full_name='pb.Claim.thumbnail', index=6, number=10, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='tags', full_name='pb.Claim.tags', index=7, number=11, type=9, cpp_type=9, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='languages', full_name='pb.Claim.languages', index=8, number=12, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='locations', full_name='pb.Claim.locations', index=9, number=13, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ _descriptor.OneofDescriptor( name='type', full_name='pb.Claim.type', index=0, containing_type=None, fields=[]), ], serialized_start=20, serialized_end=319, ) _STREAM = _descriptor.Descriptor( name='Stream', full_name='pb.Stream', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='source', full_name='pb.Stream.source', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='author', full_name='pb.Stream.author', index=1, number=2, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='license', full_name='pb.Stream.license', index=2, number=3, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='license_url', full_name='pb.Stream.license_url', index=3, number=4, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='release_time', full_name='pb.Stream.release_time', index=4, number=5, type=3, cpp_type=2, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='fee', full_name='pb.Stream.fee', index=5, number=6, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='image', full_name='pb.Stream.image', index=6, number=10, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='video', full_name='pb.Stream.video', index=7, number=11, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='audio', full_name='pb.Stream.audio', index=8, number=12, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='software', full_name='pb.Stream.software', index=9, number=13, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ _descriptor.OneofDescriptor( name='type', full_name='pb.Stream.type', index=0, containing_type=None, fields=[]), ], serialized_start=322, serialized_end=582, ) _CHANNEL = _descriptor.Descriptor( name='Channel', full_name='pb.Channel', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='public_key', full_name='pb.Channel.public_key', index=0, number=1, type=12, cpp_type=9, label=1, has_default_value=False, default_value=_b(""), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='email', full_name='pb.Channel.email', index=1, number=2, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='website_url', full_name='pb.Channel.website_url', index=2, number=3, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='cover', full_name='pb.Channel.cover', index=3, number=4, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='featured', full_name='pb.Channel.featured', index=4, number=5, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=584, serialized_end=709, ) _CLAIMREFERENCE = _descriptor.Descriptor( name='ClaimReference', full_name='pb.ClaimReference', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='claim_hash', full_name='pb.ClaimReference.claim_hash', index=0, number=1, type=12, cpp_type=9, label=1, has_default_value=False, default_value=_b(""), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=711, serialized_end=747, ) _CLAIMLIST = _descriptor.Descriptor( name='ClaimList', full_name='pb.ClaimList', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='list_type', full_name='pb.ClaimList.list_type', index=0, number=1, type=14, cpp_type=8, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='claim_references', full_name='pb.ClaimList.claim_references', index=1, number=2, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ _CLAIMLIST_LISTTYPE, ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=750, serialized_end=894, ) _SOURCE = _descriptor.Descriptor( name='Source', full_name='pb.Source', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='hash', full_name='pb.Source.hash', index=0, number=1, type=12, cpp_type=9, label=1, has_default_value=False, default_value=_b(""), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='name', full_name='pb.Source.name', index=1, number=2, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='size', full_name='pb.Source.size', index=2, number=3, type=4, cpp_type=4, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='media_type', full_name='pb.Source.media_type', index=3, number=4, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='url', full_name='pb.Source.url', index=4, number=5, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='sd_hash', full_name='pb.Source.sd_hash', index=5, number=6, type=12, cpp_type=9, label=1, has_default_value=False, default_value=_b(""), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=896, serialized_end=996, ) _FEE = _descriptor.Descriptor( name='Fee', full_name='pb.Fee', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='currency', full_name='pb.Fee.currency', index=0, number=1, type=14, cpp_type=8, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='address', full_name='pb.Fee.address', index=1, number=2, type=12, cpp_type=9, label=1, has_default_value=False, default_value=_b(""), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='amount', full_name='pb.Fee.amount', index=2, number=3, type=4, cpp_type=4, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ _FEE_CURRENCY, ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=999, serialized_end=1134, ) _IMAGE = _descriptor.Descriptor( name='Image', full_name='pb.Image', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='width', full_name='pb.Image.width', index=0, number=1, type=13, cpp_type=3, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='height', full_name='pb.Image.height', index=1, number=2, type=13, cpp_type=3, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=1136, serialized_end=1174, ) _VIDEO = _descriptor.Descriptor( name='Video', full_name='pb.Video', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='width', full_name='pb.Video.width', index=0, number=1, type=13, cpp_type=3, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='height', full_name='pb.Video.height', index=1, number=2, type=13, cpp_type=3, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='duration', full_name='pb.Video.duration', index=2, number=3, type=13, cpp_type=3, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='audio', full_name='pb.Video.audio', index=3, number=15, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=1176, serialized_end=1258, ) _AUDIO = _descriptor.Descriptor( name='Audio', full_name='pb.Audio', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='duration', full_name='pb.Audio.duration', index=0, number=1, type=13, cpp_type=3, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=1260, serialized_end=1285, ) _SOFTWARE = _descriptor.Descriptor( name='Software', full_name='pb.Software', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='os', full_name='pb.Software.os', index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ _SOFTWARE_OS, ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=1287, serialized_end=1395, ) _LANGUAGE = _descriptor.Descriptor( name='Language', full_name='pb.Language', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='language', full_name='pb.Language.language', index=0, number=1, type=14, cpp_type=8, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='script', full_name='pb.Language.script', index=1, number=2, type=14, cpp_type=8, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='region', full_name='pb.Language.region', index=2, number=3, type=14, cpp_type=8, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ _LANGUAGE_LANGUAGE, _LANGUAGE_SCRIPT, ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=1398, serialized_end=5181, ) _LOCATION = _descriptor.Descriptor( name='Location', full_name='pb.Location', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='country', full_name='pb.Location.country', index=0, number=1, type=14, cpp_type=8, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='state', full_name='pb.Location.state', index=1, number=2, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='city', full_name='pb.Location.city', index=2, number=3, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='code', full_name='pb.Location.code', index=3, number=4, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='latitude', full_name='pb.Location.latitude', index=4, number=5, type=17, cpp_type=1, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='longitude', full_name='pb.Location.longitude', index=5, number=6, type=17, cpp_type=1, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ _LOCATION_COUNTRY, ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=5184, serialized_end=7460, ) _CLAIM.fields_by_name['stream'].message_type = _STREAM _CLAIM.fields_by_name['channel'].message_type = _CHANNEL _CLAIM.fields_by_name['collection'].message_type = _CLAIMLIST _CLAIM.fields_by_name['repost'].message_type = _CLAIMREFERENCE _CLAIM.fields_by_name['thumbnail'].message_type = _SOURCE _CLAIM.fields_by_name['languages'].message_type = _LANGUAGE _CLAIM.fields_by_name['locations'].message_type = _LOCATION _CLAIM.oneofs_by_name['type'].fields.append( _CLAIM.fields_by_name['stream']) _CLAIM.fields_by_name['stream'].containing_oneof = _CLAIM.oneofs_by_name['type'] _CLAIM.oneofs_by_name['type'].fields.append( _CLAIM.fields_by_name['channel']) _CLAIM.fields_by_name['channel'].containing_oneof = _CLAIM.oneofs_by_name['type'] _CLAIM.oneofs_by_name['type'].fields.append( _CLAIM.fields_by_name['collection']) _CLAIM.fields_by_name['collection'].containing_oneof = _CLAIM.oneofs_by_name['type'] _CLAIM.oneofs_by_name['type'].fields.append( _CLAIM.fields_by_name['repost']) _CLAIM.fields_by_name['repost'].containing_oneof = _CLAIM.oneofs_by_name['type'] _STREAM.fields_by_name['source'].message_type = _SOURCE _STREAM.fields_by_name['fee'].message_type = _FEE _STREAM.fields_by_name['image'].message_type = _IMAGE _STREAM.fields_by_name['video'].message_type = _VIDEO _STREAM.fields_by_name['audio'].message_type = _AUDIO _STREAM.fields_by_name['software'].message_type = _SOFTWARE _STREAM.oneofs_by_name['type'].fields.append( _STREAM.fields_by_name['image']) _STREAM.fields_by_name['image'].containing_oneof = _STREAM.oneofs_by_name['type'] _STREAM.oneofs_by_name['type'].fields.append( _STREAM.fields_by_name['video']) _STREAM.fields_by_name['video'].containing_oneof = _STREAM.oneofs_by_name['type'] _STREAM.oneofs_by_name['type'].fields.append( _STREAM.fields_by_name['audio']) _STREAM.fields_by_name['audio'].containing_oneof = _STREAM.oneofs_by_name['type'] _STREAM.oneofs_by_name['type'].fields.append( _STREAM.fields_by_name['software']) _STREAM.fields_by_name['software'].containing_oneof = _STREAM.oneofs_by_name['type'] _CHANNEL.fields_by_name['cover'].message_type = _SOURCE _CHANNEL.fields_by_name['featured'].message_type = _CLAIMLIST _CLAIMLIST.fields_by_name['list_type'].enum_type = _CLAIMLIST_LISTTYPE _CLAIMLIST.fields_by_name['claim_references'].message_type = _CLAIMREFERENCE _CLAIMLIST_LISTTYPE.containing_type = _CLAIMLIST _FEE.fields_by_name['currency'].enum_type = _FEE_CURRENCY _FEE_CURRENCY.containing_type = _FEE _VIDEO.fields_by_name['audio'].message_type = _AUDIO _SOFTWARE_OS.containing_type = _SOFTWARE _LANGUAGE.fields_by_name['language'].enum_type = _LANGUAGE_LANGUAGE _LANGUAGE.fields_by_name['script'].enum_type = _LANGUAGE_SCRIPT _LANGUAGE.fields_by_name['region'].enum_type = _LOCATION_COUNTRY _LANGUAGE_LANGUAGE.containing_type = _LANGUAGE _LANGUAGE_SCRIPT.containing_type = _LANGUAGE _LOCATION.fields_by_name['country'].enum_type = _LOCATION_COUNTRY _LOCATION_COUNTRY.containing_type = _LOCATION DESCRIPTOR.message_types_by_name['Claim'] = _CLAIM DESCRIPTOR.message_types_by_name['Stream'] = _STREAM DESCRIPTOR.message_types_by_name['Channel'] = _CHANNEL DESCRIPTOR.message_types_by_name['ClaimReference'] = _CLAIMREFERENCE DESCRIPTOR.message_types_by_name['ClaimList'] = _CLAIMLIST DESCRIPTOR.message_types_by_name['Source'] = _SOURCE DESCRIPTOR.message_types_by_name['Fee'] = _FEE DESCRIPTOR.message_types_by_name['Image'] = _IMAGE DESCRIPTOR.message_types_by_name['Video'] = _VIDEO DESCRIPTOR.message_types_by_name['Audio'] = _AUDIO DESCRIPTOR.message_types_by_name['Software'] = _SOFTWARE DESCRIPTOR.message_types_by_name['Language'] = _LANGUAGE DESCRIPTOR.message_types_by_name['Location'] = _LOCATION _sym_db.RegisterFileDescriptor(DESCRIPTOR) Claim = _reflection.GeneratedProtocolMessageType('Claim', (_message.Message,), dict( DESCRIPTOR = _CLAIM, __module__ = 'claim_pb2' # @@protoc_insertion_point(class_scope:pb.Claim) )) _sym_db.RegisterMessage(Claim) Stream = _reflection.GeneratedProtocolMessageType('Stream', (_message.Message,), dict( DESCRIPTOR = _STREAM, __module__ = 'claim_pb2' # @@protoc_insertion_point(class_scope:pb.Stream) )) _sym_db.RegisterMessage(Stream) Channel = _reflection.GeneratedProtocolMessageType('Channel', (_message.Message,), dict( DESCRIPTOR = _CHANNEL, __module__ = 'claim_pb2' # @@protoc_insertion_point(class_scope:pb.Channel) )) _sym_db.RegisterMessage(Channel) ClaimReference = _reflection.GeneratedProtocolMessageType('ClaimReference', (_message.Message,), dict( DESCRIPTOR = _CLAIMREFERENCE, __module__ = 'claim_pb2' # @@protoc_insertion_point(class_scope:pb.ClaimReference) )) _sym_db.RegisterMessage(ClaimReference) ClaimList = _reflection.GeneratedProtocolMessageType('ClaimList', (_message.Message,), dict( DESCRIPTOR = _CLAIMLIST, __module__ = 'claim_pb2' # @@protoc_insertion_point(class_scope:pb.ClaimList) )) _sym_db.RegisterMessage(ClaimList) Source = _reflection.GeneratedProtocolMessageType('Source', (_message.Message,), dict( DESCRIPTOR = _SOURCE, __module__ = 'claim_pb2' # @@protoc_insertion_point(class_scope:pb.Source) )) _sym_db.RegisterMessage(Source) Fee = _reflection.GeneratedProtocolMessageType('Fee', (_message.Message,), dict( DESCRIPTOR = _FEE, __module__ = 'claim_pb2' # @@protoc_insertion_point(class_scope:pb.Fee) )) _sym_db.RegisterMessage(Fee) Image = _reflection.GeneratedProtocolMessageType('Image', (_message.Message,), dict( DESCRIPTOR = _IMAGE, __module__ = 'claim_pb2' # @@protoc_insertion_point(class_scope:pb.Image) )) _sym_db.RegisterMessage(Image) Video = _reflection.GeneratedProtocolMessageType('Video', (_message.Message,), dict( DESCRIPTOR = _VIDEO, __module__ = 'claim_pb2' # @@protoc_insertion_point(class_scope:pb.Video) )) _sym_db.RegisterMessage(Video) Audio = _reflection.GeneratedProtocolMessageType('Audio', (_message.Message,), dict( DESCRIPTOR = _AUDIO, __module__ = 'claim_pb2' # @@protoc_insertion_point(class_scope:pb.Audio) )) _sym_db.RegisterMessage(Audio) Software = _reflection.GeneratedProtocolMessageType('Software', (_message.Message,), dict( DESCRIPTOR = _SOFTWARE, __module__ = 'claim_pb2' # @@protoc_insertion_point(class_scope:pb.Software) )) _sym_db.RegisterMessage(Software) Language = _reflection.GeneratedProtocolMessageType('Language', (_message.Message,), dict( DESCRIPTOR = _LANGUAGE, __module__ = 'claim_pb2' # @@protoc_insertion_point(class_scope:pb.Language) )) _sym_db.RegisterMessage(Language) Location = _reflection.GeneratedProtocolMessageType('Location', (_message.Message,), dict( DESCRIPTOR = _LOCATION, __module__ = 'claim_pb2' # @@protoc_insertion_point(class_scope:pb.Location) )) _sym_db.RegisterMessage(Location) # @@protoc_insertion_point(module_scope)

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1

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1

0

null

0

10

d71a849973f4ef8754a3b01444afab1d6e007be6

8,101

py

Python

loaders/depth/validation.py

VladimirYugay/SGDepth

ed68e05431ad0131658ee76b8c50142979f859a5

[ "MIT" ]

null

loaders/depth/validation.py

VladimirYugay/SGDepth

ed68e05431ad0131658ee76b8c50142979f859a5

[ "MIT" ]

null

loaders/depth/validation.py

VladimirYugay/SGDepth

ed68e05431ad0131658ee76b8c50142979f859a5

[ "MIT" ]

null

from torch.utils.data import DataLoader from dataloader.pt_data_loader.specialdatasets import StandardDataset import dataloader.pt_data_loader.mytransforms as tf def motsynth_validation(img_height, img_width, batch_size, num_workers): """A loader that loads images and depth ground truth for depth validation from the kitti validation set. """ transforms = [ tf.CreateScaledImage(True), tf.Resize( (img_height, img_width), image_types=('color', ) ), tf.ConvertDepth(), tf.CreateColoraug(), tf.ToTensor(), tf.NormalizeZeroMean(), tf.AddKeyValue('domain', 'kitti_zhou_val_depth'), tf.AddKeyValue('validation_mask', 'validation_mask_kitti_zhou'), tf.AddKeyValue('validation_clamp', 'validation_clamp_kitti'), tf.AddKeyValue('purposes', ('depth', )), ] dataset = StandardDataset( dataset='kek', trainvaltest_split='validation', video_mode='mono', stereo_mode='mono', keys_to_load=('color', 'depth'), data_transforms=transforms, video_frames=(0, ), simple_mode=True, labels_mode='fromid', seq_to_load=['001'] ) loader = DataLoader( dataset, batch_size, False, num_workers=num_workers, pin_memory=True, drop_last=False ) print(f" - Can use {len(dataset)} images from the motsynth validation set for depth validation", flush=True) return loader def motsynth_test(img_height, img_width, batch_size, num_workers): """A loader that loads images and depth ground truth for depth evaluation from the kitti test set. """ transforms = [ tf.CreateScaledImage(True), tf.Resize( (img_height, img_width), image_types=('color', ) ), tf.ConvertDepth(), tf.CreateColoraug(), tf.ToTensor(), tf.NormalizeZeroMean(), tf.AddKeyValue('domain', 'kitti_zhou_test_depth'), tf.AddKeyValue('validation_mask', 'validation_mask_kitti_zhou'), tf.AddKeyValue('validation_clamp', 'validation_clamp_kitti'), tf.AddKeyValue('purposes', ('depth', )), ] dataset = StandardDataset( dataset='kitti', split='zhou_split', trainvaltest_split='test', video_mode='mono', stereo_mode='mono', keys_to_load=('color', 'depth'), data_transforms=transforms, video_frames=(0, ), disable_const_items=True ) loader = DataLoader( dataset, batch_size, False, num_workers=num_workers, pin_memory=True, drop_last=False ) print(f" - Can use {len(dataset)} images from the kitti (zhou_split) test set for depth evaluation", flush=True) return loader def kitti_zhou_validation(img_height, img_width, batch_size, num_workers): """A loader that loads images and depth ground truth for depth validation from the kitti validation set. """ transforms = [ tf.CreateScaledImage(True), tf.Resize( (img_height, img_width), image_types=('color', ) ), tf.ConvertDepth(), tf.CreateColoraug(), tf.ToTensor(), tf.NormalizeZeroMean(), tf.AddKeyValue('domain', 'kitti_zhou_val_depth'), tf.AddKeyValue('validation_mask', 'validation_mask_kitti_zhou'), tf.AddKeyValue('validation_clamp', 'validation_clamp_kitti'), tf.AddKeyValue('purposes', ('depth', )), ] dataset = StandardDataset( dataset='kitti', split='zhou_split', trainvaltest_split='validation', video_mode='mono', stereo_mode='mono', keys_to_load=('color', 'depth'), data_transforms=transforms, video_frames=(0, ), disable_const_items=True ) loader = DataLoader( dataset, batch_size, False, num_workers=num_workers, pin_memory=True, drop_last=False ) print(f" - Can use {len(dataset)} images from the kitti (zhou_split) validation set for depth validation", flush=True) return loader def kitti_zhou_test(img_height, img_width, batch_size, num_workers): """A loader that loads images and depth ground truth for depth evaluation from the kitti test set. """ transforms = [ tf.CreateScaledImage(True), tf.Resize( (img_height, img_width), image_types=('color', ) ), tf.ConvertDepth(), tf.CreateColoraug(), tf.ToTensor(), tf.NormalizeZeroMean(), tf.AddKeyValue('domain', 'kitti_zhou_test_depth'), tf.AddKeyValue('validation_mask', 'validation_mask_kitti_zhou'), tf.AddKeyValue('validation_clamp', 'validation_clamp_kitti'), tf.AddKeyValue('purposes', ('depth', )), ] dataset = StandardDataset( dataset='kitti', split='zhou_split', trainvaltest_split='test', video_mode='mono', stereo_mode='mono', keys_to_load=('color', 'depth'), data_transforms=transforms, video_frames=(0, ), disable_const_items=True ) loader = DataLoader( dataset, batch_size, False, num_workers=num_workers, pin_memory=True, drop_last=False ) print(f" - Can use {len(dataset)} images from the kitti (zhou_split) test set for depth evaluation", flush=True) return loader def kitti_kitti_validation(img_height, img_width, batch_size, num_workers): """A loader that loads images and depth ground truth for depth validation from the kitti validation set. """ transforms = [ tf.CreateScaledImage(True), tf.Resize( (img_height, img_width), image_types=('color', ) ), tf.ConvertDepth(), tf.CreateColoraug(), tf.ToTensor(), tf.NormalizeZeroMean(), tf.AddKeyValue('domain', 'kitti_kitti_val_depth'), tf.AddKeyValue('validation_mask', 'validation_mask_kitti_kitti'), tf.AddKeyValue('validation_clamp', 'validation_clamp_kitti'), tf.AddKeyValue('purposes', ('depth', )), ] dataset = StandardDataset( dataset='kitti', split='kitti_split', trainvaltest_split='validation', video_mode='mono', stereo_mode='mono', keys_to_load=('color', 'depth'), data_transforms=transforms, video_frames=(0, ), disable_const_items=True ) loader = DataLoader( dataset, batch_size, False, num_workers=num_workers, pin_memory=True, drop_last=False ) print(f" - Can use {len(dataset)} images from the kitti (kitti_split) validation set for depth validation", flush=True) return loader def kitti_2015_train(img_height, img_width, batch_size, num_workers): """A loader that loads images and depth ground truth for depth evaluation from the kitti_2015 training set (but for evaluation). """ transforms = [ tf.CreateScaledImage(True), tf.Resize( (img_height, img_width), image_types=('color', ) ), tf.ConvertDepth(), tf.CreateColoraug(), tf.ToTensor(), tf.NormalizeZeroMean(), tf.AddKeyValue('domain', 'kitti_2015_train_depth'), tf.AddKeyValue('validation_mask', 'validation_mask_kitti_kitti'), tf.AddKeyValue('validation_clamp', 'validation_clamp_kitti'), tf.AddKeyValue('purposes', ('depth', )), ] dataset = StandardDataset( dataset='kitti_2015', trainvaltest_split='train', video_mode='mono', stereo_mode='mono', keys_to_load=('color', 'depth'), data_transforms=transforms, video_frames=(0, ), disable_const_items=True ) loader = DataLoader( dataset, batch_size, False, num_workers=num_workers, pin_memory=True, drop_last=False ) print(f" - Can use {len(dataset)} images from the kitti_2015 test set for depth evaluation", flush=True) return loader

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117

0.627207

886

8,101

5.494357

0.103837

0.064092

0.029581

0.041906

0.936113

0.93447

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0

0.004829

0.258733

8,101

265

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null

0

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0

1

0

null

0

7

d765053fe0c17cf3ff932f9d820b0e23292e902b

54,670

py

Python

tests/fast_tests/test_goal_conditioned.py

jiangsy/h-baselines

f745d7db323b82050360618110f907c3e43638d2

[ "MIT" ]

1

2021-01-15T08:51:01.000Z

tests/fast_tests/test_goal_conditioned.py

jiangsy/h-baselines

f745d7db323b82050360618110f907c3e43638d2

[ "MIT" ]

null

tests/fast_tests/test_goal_conditioned.py

jiangsy/h-baselines

f745d7db323b82050360618110f907c3e43638d2

[ "MIT" ]

null

"""Tests for the policies in the hbaselines/goal_conditioned subdirectory.""" import unittest import numpy as np import tensorflow as tf from gym.spaces import Box from hbaselines.utils.tf_util import get_trainable_vars from hbaselines.goal_conditioned.td3 import GoalConditionedPolicy as \ TD3GoalConditionedPolicy from hbaselines.goal_conditioned.sac import GoalConditionedPolicy as \ SACGoalConditionedPolicy from hbaselines.algorithms.off_policy import SAC_PARAMS, TD3_PARAMS from hbaselines.algorithms.off_policy import GOAL_CONDITIONED_PARAMS class TestBaseGoalConditionedPolicy(unittest.TestCase): """Test GoalConditionedPolicy in hbaselines/goal_conditioned/base.py.""" def setUp(self): self.policy_params = { 'sess': tf.compat.v1.Session(), 'ac_space': Box(low=-1, high=1, shape=(1,)), 'ob_space': Box(low=-2, high=2, shape=(2,)), 'co_space': Box(low=-3, high=3, shape=(2,)), 'verbose': 0, } self.policy_params.update(TD3_PARAMS.copy()) self.policy_params.update(GOAL_CONDITIONED_PARAMS.copy()) def tearDown(self): self.policy_params['sess'].close() del self.policy_params # Clear the graph. tf.compat.v1.reset_default_graph() def test_store_transition(self): """Check the functionality of the store_transition() method. This method is tested for the following cases: 1. hindsight = False, relative_goals = False 2. hindsight = False, relative_goals = True 3. hindsight = True, relative_goals = False 4. hindsight = True, relative_goals = True """ # =================================================================== # # test case 1 # # =================================================================== # policy_params = self.policy_params.copy() policy_params['relative_goals'] = False policy_params['hindsight'] = False policy_params['subgoal_testing_rate'] = 1 policy_params['meta_period'] = 4 policy_params['batch_size'] = 2 policy = TD3GoalConditionedPolicy(**policy_params) # Initialize the variables of the policy. policy.sess.run(tf.compat.v1.global_variables_initializer()) # Run the initialize method. policy.initialize() policy._meta_action = [np.array([5, 5])] for i in range(4): obs0 = np.array([i for _ in range(2)]) context0 = np.array([i for _ in range(3)]) action = np.array([i for _ in range(1)]) reward = i obs1 = np.array([i+1 for _ in range(2)]) context1 = np.array([i for _ in range(3)]) done, is_final_step, evaluate = False, False, False policy.store_transition( obs0=obs0, context0=context0, action=action, reward=reward, obs1=obs1, context1=context1, done=done, is_final_step=is_final_step, evaluate=evaluate, env_num=0, ) obs_t = policy.replay_buffer._obs_t[0] action_t = policy.replay_buffer._action_t[0] reward = policy.replay_buffer._reward_t[0] done = policy.replay_buffer._done_t[0] # check the various attributes self.assertTrue( all(all(obs_t[i] == [np.array([0, 0]), np.array([1, 1]), np.array([2, 2]), np.array([3, 3]), np.array([4, 4])][i]) for i in range(len(obs_t))) ) for i in range(len(action_t)): self.assertTrue( all(all(action_t[i][j] == [[np.array([5, 5]), np.array([5, 5]), np.array([5, 5]), np.array([5, 5]), np.array([5, 5])], [np.array([0]), np.array([1]), np.array([2]), np.array([3])]][i][j]) for j in range(len(action_t[i]))) ) self.assertEqual(reward, [[6], [-5.656854249501219, -4.24264068713107, -2.8284271247638677, -1.4142135624084504]]) self.assertEqual(done, [False, False, False, False]) def test_store_transition_2(self): policy_params = self.policy_params.copy() policy_params['relative_goals'] = True policy_params['hindsight'] = False policy_params['subgoal_testing_rate'] = 1 policy_params['meta_period'] = 4 policy_params['batch_size'] = 2 policy = TD3GoalConditionedPolicy(**policy_params) # Initialize the variables of the policy. policy.sess.run(tf.compat.v1.global_variables_initializer()) # Run the initialize method. policy.initialize() policy._meta_action = [np.array([5, 5])] for i in range(4): obs0 = np.array([i for _ in range(2)]) context0 = np.array([i for _ in range(3)]) action = np.array([i for _ in range(1)]) reward = i obs1 = np.array([i+1 for _ in range(2)]) context1 = np.array([i for _ in range(3)]) done, is_final_step, evaluate = False, False, False policy.store_transition( obs0=obs0, context0=context0, action=action, reward=reward, obs1=obs1, context1=context1, done=done, is_final_step=is_final_step, evaluate=evaluate, env_num=0, ) obs_t = policy.replay_buffer._obs_t[0] action_t = policy.replay_buffer._action_t[0] reward = policy.replay_buffer._reward_t[0] done = policy.replay_buffer._done_t[0] # check the various attributes self.assertTrue( all(all(obs_t[i] == [np.array([0, 0]), np.array([1, 1]), np.array([2, 2]), np.array([3, 3]), np.array([4, 4])][i]) for i in range(len(obs_t))) ) for i in range(len(action_t)): self.assertTrue( all(all(action_t[i][j] == [[np.array([5, 5]), np.array([5, 5]), np.array([5, 5]), np.array([5, 5]), np.array([4, 4])], [np.array([0]), np.array([1]), np.array([2]), np.array([3])]][i][j]) for j in range(len(action_t[i]))) ) self.assertEqual(reward, [[6], [-5.656854249501219, -5.656854249501219, -5.656854249501219, -5.656854249501219]]) self.assertEqual(done, [False, False, False, False]) def test_store_transition_3(self): policy_params = self.policy_params.copy() policy_params['relative_goals'] = False policy_params['hindsight'] = True policy_params['subgoal_testing_rate'] = 1 policy_params['meta_period'] = 4 policy_params['batch_size'] = 2 policy = TD3GoalConditionedPolicy(**policy_params) # Initialize the variables of the policy. policy.sess.run(tf.compat.v1.global_variables_initializer()) # Run the initialize method. policy.initialize() policy._meta_action = [np.array([5, 5])] for i in range(4): obs0 = np.array([i for _ in range(2)]) context0 = np.array([i for _ in range(3)]) action = np.array([i for _ in range(1)]) reward = i obs1 = np.array([i+1 for _ in range(2)]) context1 = np.array([i for _ in range(3)]) done, is_final_step, evaluate = False, False, False policy.store_transition( obs0=obs0, context0=context0, action=action, reward=reward, obs1=obs1, context1=context1, done=done, is_final_step=is_final_step, evaluate=evaluate, env_num=0, ) # unchanged sample obs_t = policy.replay_buffer._obs_t[0] action_t = policy.replay_buffer._action_t[0] reward_t = policy.replay_buffer._reward_t[0] done_t = policy.replay_buffer._done_t[0] # check the various attributes self.assertTrue( all(all(obs_t[i] == [np.array([0, 0]), np.array([1, 1]), np.array([2, 2]), np.array([3, 3]), np.array([4, 4])][i]) for i in range(len(obs_t))) ) for i in range(len(action_t)): self.assertTrue( all(all(action_t[i][j] == [[np.array([5, 5]), np.array([5, 5]), np.array([5, 5]), np.array([5, 5]), np.array([5, 5])], [np.array([0]), np.array([1]), np.array([2]), np.array([3])]][i][j]) for j in range(len(action_t[i]))) ) self.assertEqual(reward_t, [[6], [-5.656854249501219, -4.24264068713107, -2.8284271247638677, -1.4142135624084504]]) self.assertEqual(done_t, [False, False, False, False]) # hindsight sample obs_t = policy.replay_buffer._obs_t[1] action_t = policy.replay_buffer._action_t[1] reward_t = policy.replay_buffer._reward_t[1] done_t = policy.replay_buffer._done_t[1] # check the various attributes self.assertTrue( all(all(obs_t[i] == [np.array([0, 0]), np.array([1, 1]), np.array([2, 2]), np.array([3, 3]), np.array([4, 4])][i]) for i in range(len(obs_t))) ) for i in range(len(action_t)): self.assertTrue( all(all(action_t[i][j] == [[np.array([4, 4]), np.array([4, 4]), np.array([4, 4]), np.array([4, 4]), np.array([4, 4])], [np.array([0]), np.array([1]), np.array([2]), np.array([3])]][i][j]) for j in range(len(action_t[i]))) ) self.assertEqual(reward_t, [[6], [-4.24264068713107, -2.8284271247638677, -1.4142135624084504, -1e-05]]) self.assertEqual(done_t, [False, False, False, False]) def test_store_transition_4(self): policy_params = self.policy_params.copy() policy_params['relative_goals'] = True policy_params['hindsight'] = True policy_params['subgoal_testing_rate'] = 1 policy_params['meta_period'] = 4 policy_params['batch_size'] = 2 policy = TD3GoalConditionedPolicy(**policy_params) # Initialize the variables of the policy. policy.sess.run(tf.compat.v1.global_variables_initializer()) # Run the initialize method. policy.initialize() policy._meta_action = [np.array([5, 5])] for i in range(4): obs0 = np.array([i for _ in range(2)]) context0 = np.array([i for _ in range(3)]) action = np.array([i for _ in range(1)]) reward = i obs1 = np.array([i+1 for _ in range(2)]) context1 = np.array([i for _ in range(3)]) done, is_final_step, evaluate = False, False, False policy.store_transition( obs0=obs0, context0=context0, action=action, reward=reward, obs1=obs1, context1=context1, done=done, is_final_step=is_final_step, evaluate=evaluate, env_num=0, ) # unchanged sample obs_t = policy.replay_buffer._obs_t[0] action_t = policy.replay_buffer._action_t[0] reward = policy.replay_buffer._reward_t[0] done = policy.replay_buffer._done_t[0] # check the various attributes self.assertTrue( all(all(obs_t[i] == [np.array([0, 0]), np.array([1, 1]), np.array([2, 2]), np.array([3, 3]), np.array([4, 4])][i]) for i in range(len(obs_t))) ) for i in range(len(action_t)): self.assertTrue( all(all(action_t[i][j] == [[np.array([5, 5]), np.array([5, 5]), np.array([5, 5]), np.array([5, 5]), np.array([4, 4])], [np.array([0]), np.array([1]), np.array([2]), np.array([3])]][i][j]) for j in range(len(action_t[i]))) ) self.assertEqual(reward, [[6], [-5.656854249501219, -5.656854249501219, -5.656854249501219, -5.656854249501219]]) self.assertEqual(done, [False, False, False, False]) # hindsight sample obs_t = policy.replay_buffer._obs_t[1] action_t = policy.replay_buffer._action_t[1] reward_t = policy.replay_buffer._reward_t[1] done_t = policy.replay_buffer._done_t[1] # check the various attributes self.assertTrue( all(all(obs_t[i] == [np.array([0, 0]), np.array([1, 1]), np.array([2, 2]), np.array([3, 3]), np.array([4, 4])][i]) for i in range(len(obs_t))) ) for i in range(len(action_t)): self.assertTrue( all(all(action_t[i][j] == [[np.array([4, 4]), np.array([3, 3]), np.array([2, 2]), np.array([1, 1]), np.array([0, 0])], [np.array([0]), np.array([1]), np.array([2]), np.array([3])]][i][j]) for j in range(len(action_t[i]))) ) self.assertEqual(reward_t, [[6], [-4.24264068713107, -2.8284271247638677, -1.4142135624084504, -1e-05]]) self.assertEqual(done_t, [False, False, False, False]) def test_update_meta(self): """Validate the functionality of the _update_meta function. This is tested for two cases: 1. level = 0 after 0 steps --> True 2. level = 1 after 0 steps --> True 3. level = 0 after 2 steps --> False 4. level = 1 after 2 steps --> False 5. level = 0 after 5 steps --> False 6. level = 1 after 5 steps --> True 7. level = 0 after 10 steps --> False 8. level = 1 after 10 steps --> True """ policy_params = self.policy_params.copy() policy_params['meta_period'] = 5 policy_params['num_levels'] = 3 policy = TD3GoalConditionedPolicy(**policy_params) # test case 1 policy._observations = [[] for _ in range(1)] self.assertEqual(policy._update_meta(0, env_num=0), True) # test case 2 policy._observations = [[] for _ in range(1)] self.assertEqual(policy._update_meta(1, env_num=0), True) # test case 3 policy._observations = [[0 for _ in range(2)] for _ in range(1)] self.assertEqual(policy._update_meta(0, env_num=0), False) # test case 4 policy._observations = [[0 for _ in range(2)] for _ in range(1)] self.assertEqual(policy._update_meta(1, env_num=0), False) # test case 5 policy._observations = [[0 for _ in range(5)] for _ in range(1)] self.assertEqual(policy._update_meta(0, env_num=0), False) # test case 6 policy._observations = [[0 for _ in range(5)] for _ in range(1)] self.assertEqual(policy._update_meta(1, env_num=0), True) # test case 7 policy._observations = [[0 for _ in range(10)] for _ in range(1)] self.assertEqual(policy._update_meta(0, env_num=0), False) # test case 8 policy._observations = [[0 for _ in range(10)] for _ in range(1)] self.assertEqual(policy._update_meta(1, env_num=0), True) def test_intrinsic_rewards(self): """Validate the functionality of the intrinsic rewards. This is done for the following cases: 1. intrinsic_reward_type = "negative_distance" 2. intrinsic_reward_type = "scaled_negative_distance" 3. intrinsic_reward_type = "non_negative_distance" 4. intrinsic_reward_type = "scaled_non_negative_distance" 5. intrinsic_reward_type = "exp_negative_distance" 6. intrinsic_reward_type = "scaled_exp_negative_distance" 7. intrinsic_reward_type = "error" -> raises ValueError """ # test case 1 policy_params = self.policy_params.copy() policy_params["intrinsic_reward_type"] = "negative_distance" policy = TD3GoalConditionedPolicy(**policy_params) self.assertAlmostEqual( policy.intrinsic_reward_fn( states=np.array([1, 2]), goals=np.array([3, 2]), next_states=np.array([0, 0]) ), -3.6055512754778567 ) # Clear the graph. del policy tf.compat.v1.reset_default_graph() # test case 2 policy_params = self.policy_params.copy() policy_params["intrinsic_reward_type"] = "scaled_negative_distance" policy = TD3GoalConditionedPolicy(**policy_params) self.assertAlmostEqual( policy.intrinsic_reward_fn( states=np.array([1, 2]), goals=np.array([3, 2]), next_states=np.array([0, 0]) ), -1.8027756377597297 ) # Clear the graph. del policy tf.compat.v1.reset_default_graph() # test case 3 policy_params = self.policy_params.copy() policy_params["intrinsic_reward_type"] = "non_negative_distance" policy = TD3GoalConditionedPolicy(**policy_params) self.assertAlmostEqual( policy.intrinsic_reward_fn( states=np.array([1, 2]), goals=np.array([3, 2]), next_states=np.array([0, 0]) ), 2.0513028772015867 ) # Clear the graph. del policy tf.compat.v1.reset_default_graph() # test case 4 policy_params = self.policy_params.copy() policy_params["intrinsic_reward_type"] = "scaled_non_negative_distance" policy = TD3GoalConditionedPolicy(**policy_params) self.assertAlmostEqual( policy.intrinsic_reward_fn( states=np.array([1, 2]), goals=np.array([3, 2]), next_states=np.array([0, 0]) ), 3.8540785149197134 ) # Clear the graph. del policy tf.compat.v1.reset_default_graph() # test case 5 policy_params = self.policy_params.copy() policy_params["intrinsic_reward_type"] = "exp_negative_distance" policy = TD3GoalConditionedPolicy(**policy_params) self.assertAlmostEqual( policy.intrinsic_reward_fn( states=np.array([1, 2]), goals=np.array([3, 2]), next_states=np.array([0, 0]) ), 2.2603294067550214e-06 ) # Clear the graph. del policy tf.compat.v1.reset_default_graph() # test case 6 policy_params = self.policy_params.copy() policy_params["intrinsic_reward_type"] = "scaled_exp_negative_distance" policy = TD3GoalConditionedPolicy(**policy_params) self.assertAlmostEqual( policy.intrinsic_reward_fn( states=np.array([1, 2]), goals=np.array([3, 2]), next_states=np.array([0, 0]) ), 0.03877420782784459 ) # Clear the graph. del policy tf.compat.v1.reset_default_graph() # test case 7 policy_params = self.policy_params.copy() policy_params["intrinsic_reward_type"] = "error" self.assertRaises( ValueError, TD3GoalConditionedPolicy, **policy_params) def test_relative_goals(self): """Validate the functionality of relative goals. This should affect the intrinsic reward function as well as transformation from relative goals to absolute goals. """ policy_params = self.policy_params.copy() policy_params["relative_goals"] = True policy = TD3GoalConditionedPolicy(**policy_params) # Test the updated reward function. states = np.array([1, 2]) goals = np.array([4, 5]) next_states = np.array([7, 8]) self.assertAlmostEqual( policy.intrinsic_reward_fn(states, goals, next_states), -2.2360679775221506 ) def test_sample_best_meta_action(self): """Check the functionality of the _sample_best_meta_action() method.""" pass # TODO def test_sample(self): """Check the functionality of the _sample() method. This test checks for the following features: 1. that the shape of the output candidate goals is correct 2. that the last few elements are the deterministic components that they are expected to be (see method's docstring) """ policy = TD3GoalConditionedPolicy(**self.policy_params) # some variables to try on states = np.array( [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12], [13, 14], [15, 16], [17, 18], [19, 20]] ) next_states = -states num_samples = 10 orig_goals = np.array( [[1, 1], [1, 1], [0, 0], [1, 1], [1, 1], [0, 0], [1, 1], [1, 1], [0, 0], [1, 1]] ) samples = policy._sample(states, next_states, orig_goals, num_samples) # test case 1 self.assertTupleEqual( samples.shape, (states.shape[0], states.shape[1], num_samples)) # test case 2 np.testing.assert_array_almost_equal( samples[:, :, -2:].reshape(states.shape[0] * states.shape[1], 2).T, np.vstack( [np.array([-2] * states.shape[0] * states.shape[1]), orig_goals.flatten()] ) ) class TestTD3GoalConditionedPolicy(unittest.TestCase): """Test GoalConditionedPolicy in hbaselines/goal_conditioned/td3.py.""" def setUp(self): self.policy_params = { 'sess': tf.compat.v1.Session(), 'ac_space': Box(low=-1, high=1, shape=(1,)), 'ob_space': Box(low=-2, high=2, shape=(2,)), 'co_space': Box(low=-3, high=3, shape=(2,)), 'verbose': 0, } self.policy_params.update(TD3_PARAMS.copy()) self.policy_params.update(GOAL_CONDITIONED_PARAMS.copy()) def tearDown(self): self.policy_params['sess'].close() del self.policy_params # Clear the graph. tf.compat.v1.reset_default_graph() def test_init_2_levels(self): """Validate that the graph and variables are initialized properly.""" policy_params = self.policy_params.copy() policy_params['num_levels'] = 2 policy = TD3GoalConditionedPolicy(**policy_params) # Check that the abstract class has all the required attributes. self.assertEqual(policy.meta_period, self.policy_params['meta_period']) self.assertEqual(policy.relative_goals, self.policy_params['relative_goals']) self.assertEqual(policy.off_policy_corrections, self.policy_params['off_policy_corrections']) self.assertEqual(policy.cooperative_gradients, self.policy_params['cooperative_gradients']) self.assertEqual(policy.cg_weights, self.policy_params['cg_weights']) # Check that all trainable variables have been created in the # TensorFlow graph. self.assertListEqual( sorted([var.name for var in get_trainable_vars()]), ['level_0/model/pi/fc0/bias:0', 'level_0/model/pi/fc0/kernel:0', 'level_0/model/pi/fc1/bias:0', 'level_0/model/pi/fc1/kernel:0', 'level_0/model/pi/output/bias:0', 'level_0/model/pi/output/kernel:0', 'level_0/model/qf_0/fc0/bias:0', 'level_0/model/qf_0/fc0/kernel:0', 'level_0/model/qf_0/fc1/bias:0', 'level_0/model/qf_0/fc1/kernel:0', 'level_0/model/qf_0/qf_output/bias:0', 'level_0/model/qf_0/qf_output/kernel:0', 'level_0/model/qf_1/fc0/bias:0', 'level_0/model/qf_1/fc0/kernel:0', 'level_0/model/qf_1/fc1/bias:0', 'level_0/model/qf_1/fc1/kernel:0', 'level_0/model/qf_1/qf_output/bias:0', 'level_0/model/qf_1/qf_output/kernel:0', 'level_0/target/pi/fc0/bias:0', 'level_0/target/pi/fc0/kernel:0', 'level_0/target/pi/fc1/bias:0', 'level_0/target/pi/fc1/kernel:0', 'level_0/target/pi/output/bias:0', 'level_0/target/pi/output/kernel:0', 'level_0/target/qf_0/fc0/bias:0', 'level_0/target/qf_0/fc0/kernel:0', 'level_0/target/qf_0/fc1/bias:0', 'level_0/target/qf_0/fc1/kernel:0', 'level_0/target/qf_0/qf_output/bias:0', 'level_0/target/qf_0/qf_output/kernel:0', 'level_0/target/qf_1/fc0/bias:0', 'level_0/target/qf_1/fc0/kernel:0', 'level_0/target/qf_1/fc1/bias:0', 'level_0/target/qf_1/fc1/kernel:0', 'level_0/target/qf_1/qf_output/bias:0', 'level_0/target/qf_1/qf_output/kernel:0', 'level_1/model/pi/fc0/bias:0', 'level_1/model/pi/fc0/kernel:0', 'level_1/model/pi/fc1/bias:0', 'level_1/model/pi/fc1/kernel:0', 'level_1/model/pi/output/bias:0', 'level_1/model/pi/output/kernel:0', 'level_1/model/qf_0/fc0/bias:0', 'level_1/model/qf_0/fc0/kernel:0', 'level_1/model/qf_0/fc1/bias:0', 'level_1/model/qf_0/fc1/kernel:0', 'level_1/model/qf_0/qf_output/bias:0', 'level_1/model/qf_0/qf_output/kernel:0', 'level_1/model/qf_1/fc0/bias:0', 'level_1/model/qf_1/fc0/kernel:0', 'level_1/model/qf_1/fc1/bias:0', 'level_1/model/qf_1/fc1/kernel:0', 'level_1/model/qf_1/qf_output/bias:0', 'level_1/model/qf_1/qf_output/kernel:0', 'level_1/target/pi/fc0/bias:0', 'level_1/target/pi/fc0/kernel:0', 'level_1/target/pi/fc1/bias:0', 'level_1/target/pi/fc1/kernel:0', 'level_1/target/pi/output/bias:0', 'level_1/target/pi/output/kernel:0', 'level_1/target/qf_0/fc0/bias:0', 'level_1/target/qf_0/fc0/kernel:0', 'level_1/target/qf_0/fc1/bias:0', 'level_1/target/qf_0/fc1/kernel:0', 'level_1/target/qf_0/qf_output/bias:0', 'level_1/target/qf_0/qf_output/kernel:0', 'level_1/target/qf_1/fc0/bias:0', 'level_1/target/qf_1/fc0/kernel:0', 'level_1/target/qf_1/fc1/bias:0', 'level_1/target/qf_1/fc1/kernel:0', 'level_1/target/qf_1/qf_output/bias:0', 'level_1/target/qf_1/qf_output/kernel:0'] ) def test_init_3_levels(self): """Validate that the graph and variables are initialized properly.""" policy_params = self.policy_params.copy() policy_params['num_levels'] = 3 policy = TD3GoalConditionedPolicy(**policy_params) # Check that the abstract class has all the required attributes. self.assertEqual(policy.meta_period, self.policy_params['meta_period']) self.assertEqual(policy.relative_goals, self.policy_params['relative_goals']) self.assertEqual(policy.off_policy_corrections, self.policy_params['off_policy_corrections']) self.assertEqual(policy.cooperative_gradients, self.policy_params['cooperative_gradients']) self.assertEqual(policy.cg_weights, self.policy_params['cg_weights']) # Check that all trainable variables have been created in the # TensorFlow graph. self.assertListEqual( sorted([var.name for var in get_trainable_vars()]), ['level_0/model/pi/fc0/bias:0', 'level_0/model/pi/fc0/kernel:0', 'level_0/model/pi/fc1/bias:0', 'level_0/model/pi/fc1/kernel:0', 'level_0/model/pi/output/bias:0', 'level_0/model/pi/output/kernel:0', 'level_0/model/qf_0/fc0/bias:0', 'level_0/model/qf_0/fc0/kernel:0', 'level_0/model/qf_0/fc1/bias:0', 'level_0/model/qf_0/fc1/kernel:0', 'level_0/model/qf_0/qf_output/bias:0', 'level_0/model/qf_0/qf_output/kernel:0', 'level_0/model/qf_1/fc0/bias:0', 'level_0/model/qf_1/fc0/kernel:0', 'level_0/model/qf_1/fc1/bias:0', 'level_0/model/qf_1/fc1/kernel:0', 'level_0/model/qf_1/qf_output/bias:0', 'level_0/model/qf_1/qf_output/kernel:0', 'level_0/target/pi/fc0/bias:0', 'level_0/target/pi/fc0/kernel:0', 'level_0/target/pi/fc1/bias:0', 'level_0/target/pi/fc1/kernel:0', 'level_0/target/pi/output/bias:0', 'level_0/target/pi/output/kernel:0', 'level_0/target/qf_0/fc0/bias:0', 'level_0/target/qf_0/fc0/kernel:0', 'level_0/target/qf_0/fc1/bias:0', 'level_0/target/qf_0/fc1/kernel:0', 'level_0/target/qf_0/qf_output/bias:0', 'level_0/target/qf_0/qf_output/kernel:0', 'level_0/target/qf_1/fc0/bias:0', 'level_0/target/qf_1/fc0/kernel:0', 'level_0/target/qf_1/fc1/bias:0', 'level_0/target/qf_1/fc1/kernel:0', 'level_0/target/qf_1/qf_output/bias:0', 'level_0/target/qf_1/qf_output/kernel:0', 'level_1/model/pi/fc0/bias:0', 'level_1/model/pi/fc0/kernel:0', 'level_1/model/pi/fc1/bias:0', 'level_1/model/pi/fc1/kernel:0', 'level_1/model/pi/output/bias:0', 'level_1/model/pi/output/kernel:0', 'level_1/model/qf_0/fc0/bias:0', 'level_1/model/qf_0/fc0/kernel:0', 'level_1/model/qf_0/fc1/bias:0', 'level_1/model/qf_0/fc1/kernel:0', 'level_1/model/qf_0/qf_output/bias:0', 'level_1/model/qf_0/qf_output/kernel:0', 'level_1/model/qf_1/fc0/bias:0', 'level_1/model/qf_1/fc0/kernel:0', 'level_1/model/qf_1/fc1/bias:0', 'level_1/model/qf_1/fc1/kernel:0', 'level_1/model/qf_1/qf_output/bias:0', 'level_1/model/qf_1/qf_output/kernel:0', 'level_1/target/pi/fc0/bias:0', 'level_1/target/pi/fc0/kernel:0', 'level_1/target/pi/fc1/bias:0', 'level_1/target/pi/fc1/kernel:0', 'level_1/target/pi/output/bias:0', 'level_1/target/pi/output/kernel:0', 'level_1/target/qf_0/fc0/bias:0', 'level_1/target/qf_0/fc0/kernel:0', 'level_1/target/qf_0/fc1/bias:0', 'level_1/target/qf_0/fc1/kernel:0', 'level_1/target/qf_0/qf_output/bias:0', 'level_1/target/qf_0/qf_output/kernel:0', 'level_1/target/qf_1/fc0/bias:0', 'level_1/target/qf_1/fc0/kernel:0', 'level_1/target/qf_1/fc1/bias:0', 'level_1/target/qf_1/fc1/kernel:0', 'level_1/target/qf_1/qf_output/bias:0', 'level_1/target/qf_1/qf_output/kernel:0', 'level_2/model/pi/fc0/bias:0', 'level_2/model/pi/fc0/kernel:0', 'level_2/model/pi/fc1/bias:0', 'level_2/model/pi/fc1/kernel:0', 'level_2/model/pi/output/bias:0', 'level_2/model/pi/output/kernel:0', 'level_2/model/qf_0/fc0/bias:0', 'level_2/model/qf_0/fc0/kernel:0', 'level_2/model/qf_0/fc1/bias:0', 'level_2/model/qf_0/fc1/kernel:0', 'level_2/model/qf_0/qf_output/bias:0', 'level_2/model/qf_0/qf_output/kernel:0', 'level_2/model/qf_1/fc0/bias:0', 'level_2/model/qf_1/fc0/kernel:0', 'level_2/model/qf_1/fc1/bias:0', 'level_2/model/qf_1/fc1/kernel:0', 'level_2/model/qf_1/qf_output/bias:0', 'level_2/model/qf_1/qf_output/kernel:0', 'level_2/target/pi/fc0/bias:0', 'level_2/target/pi/fc0/kernel:0', 'level_2/target/pi/fc1/bias:0', 'level_2/target/pi/fc1/kernel:0', 'level_2/target/pi/output/bias:0', 'level_2/target/pi/output/kernel:0', 'level_2/target/qf_0/fc0/bias:0', 'level_2/target/qf_0/fc0/kernel:0', 'level_2/target/qf_0/fc1/bias:0', 'level_2/target/qf_0/fc1/kernel:0', 'level_2/target/qf_0/qf_output/bias:0', 'level_2/target/qf_0/qf_output/kernel:0', 'level_2/target/qf_1/fc0/bias:0', 'level_2/target/qf_1/fc0/kernel:0', 'level_2/target/qf_1/fc1/bias:0', 'level_2/target/qf_1/fc1/kernel:0', 'level_2/target/qf_1/qf_output/bias:0', 'level_2/target/qf_1/qf_output/kernel:0'] ) def test_initialize(self): """Check the functionality of the initialize() method. This test validates that the target variables are properly initialized when initialize is called. """ policy = TD3GoalConditionedPolicy(**self.policy_params) # Initialize the variables of the policy. policy.sess.run(tf.compat.v1.global_variables_initializer()) # Run the initialize method. policy.initialize() model_var_list = [ 'level_0/model/pi/fc0/bias:0', 'level_0/model/pi/fc0/kernel:0', 'level_0/model/pi/fc1/bias:0', 'level_0/model/pi/fc1/kernel:0', 'level_0/model/pi/output/bias:0', 'level_0/model/pi/output/kernel:0', 'level_0/model/qf_0/fc0/bias:0', 'level_0/model/qf_0/fc0/kernel:0', 'level_0/model/qf_0/fc1/bias:0', 'level_0/model/qf_0/fc1/kernel:0', 'level_0/model/qf_0/qf_output/bias:0', 'level_0/model/qf_0/qf_output/kernel:0', 'level_0/model/qf_1/fc0/bias:0', 'level_0/model/qf_1/fc0/kernel:0', 'level_0/model/qf_1/fc1/bias:0', 'level_0/model/qf_1/fc1/kernel:0', 'level_0/model/qf_1/qf_output/bias:0', 'level_0/model/qf_1/qf_output/kernel:0', 'level_1/model/pi/fc0/bias:0', 'level_1/model/pi/fc0/kernel:0', 'level_1/model/pi/fc1/bias:0', 'level_1/model/pi/fc1/kernel:0', 'level_1/model/pi/output/bias:0', 'level_1/model/pi/output/kernel:0', 'level_1/model/qf_0/fc0/bias:0', 'level_1/model/qf_0/fc0/kernel:0', 'level_1/model/qf_0/fc1/bias:0', 'level_1/model/qf_0/fc1/kernel:0', 'level_1/model/qf_0/qf_output/bias:0', 'level_1/model/qf_0/qf_output/kernel:0', 'level_1/model/qf_1/fc0/bias:0', 'level_1/model/qf_1/fc0/kernel:0', 'level_1/model/qf_1/fc1/bias:0', 'level_1/model/qf_1/fc1/kernel:0', 'level_1/model/qf_1/qf_output/bias:0', 'level_1/model/qf_1/qf_output/kernel:0', ] target_var_list = [ 'level_0/target/pi/fc0/bias:0', 'level_0/target/pi/fc0/kernel:0', 'level_0/target/pi/fc1/bias:0', 'level_0/target/pi/fc1/kernel:0', 'level_0/target/pi/output/bias:0', 'level_0/target/pi/output/kernel:0', 'level_0/target/qf_0/fc0/bias:0', 'level_0/target/qf_0/fc0/kernel:0', 'level_0/target/qf_0/fc1/bias:0', 'level_0/target/qf_0/fc1/kernel:0', 'level_0/target/qf_0/qf_output/bias:0', 'level_0/target/qf_0/qf_output/kernel:0', 'level_0/target/qf_1/fc0/bias:0', 'level_0/target/qf_1/fc0/kernel:0', 'level_0/target/qf_1/fc1/bias:0', 'level_0/target/qf_1/fc1/kernel:0', 'level_0/target/qf_1/qf_output/bias:0', 'level_0/target/qf_1/qf_output/kernel:0', 'level_1/target/pi/fc0/bias:0', 'level_1/target/pi/fc0/kernel:0', 'level_1/target/pi/fc1/bias:0', 'level_1/target/pi/fc1/kernel:0', 'level_1/target/pi/output/bias:0', 'level_1/target/pi/output/kernel:0', 'level_1/target/qf_0/fc0/bias:0', 'level_1/target/qf_0/fc0/kernel:0', 'level_1/target/qf_0/fc1/bias:0', 'level_1/target/qf_0/fc1/kernel:0', 'level_1/target/qf_0/qf_output/bias:0', 'level_1/target/qf_0/qf_output/kernel:0', 'level_1/target/qf_1/fc0/bias:0', 'level_1/target/qf_1/fc0/kernel:0', 'level_1/target/qf_1/fc1/bias:0', 'level_1/target/qf_1/fc1/kernel:0', 'level_1/target/qf_1/qf_output/bias:0', 'level_1/target/qf_1/qf_output/kernel:0' ] for model, target in zip(model_var_list, target_var_list): with tf.compat.v1.variable_scope( tf.compat.v1.get_variable_scope(), reuse=True): model_val = policy.sess.run(model) target_val = policy.sess.run(target) np.testing.assert_almost_equal(model_val, target_val) def test_log_probs(self): """Check the functionality of the log_probs() method.""" pass # TODO def test_cooperative_gradients(self): """Check the functionality of the cooperative-gradients feature.""" pass # TODO class TestSACGoalConditionedPolicy(unittest.TestCase): """Test GoalConditionedPolicy in hbaselines/goal_conditioned/sac.py.""" def setUp(self): self.policy_params = { 'sess': tf.compat.v1.Session(), 'ac_space': Box(low=-1, high=1, shape=(1,)), 'ob_space': Box(low=-2, high=2, shape=(2,)), 'co_space': Box(low=-3, high=3, shape=(2,)), 'verbose': 0, } self.policy_params.update(SAC_PARAMS.copy()) self.policy_params.update(GOAL_CONDITIONED_PARAMS.copy()) def tearDown(self): self.policy_params['sess'].close() del self.policy_params # Clear the graph. tf.compat.v1.reset_default_graph() def test_init_2_levels(self): """Validate that the graph and variables are initialized properly.""" policy_params = self.policy_params.copy() policy_params['num_levels'] = 2 policy = SACGoalConditionedPolicy(**policy_params) # Check that the abstract class has all the required attributes. self.assertEqual(policy.meta_period, self.policy_params['meta_period']) self.assertEqual(policy.relative_goals, self.policy_params['relative_goals']) self.assertEqual(policy.off_policy_corrections, self.policy_params['off_policy_corrections']) self.assertEqual(policy.cooperative_gradients, self.policy_params['cooperative_gradients']) self.assertEqual(policy.cg_weights, self.policy_params['cg_weights']) self.assertListEqual( sorted([var.name for var in get_trainable_vars()]), ['level_0/model/log_alpha:0', 'level_0/model/pi/fc0/bias:0', 'level_0/model/pi/fc0/kernel:0', 'level_0/model/pi/fc1/bias:0', 'level_0/model/pi/fc1/kernel:0', 'level_0/model/pi/log_std/bias:0', 'level_0/model/pi/log_std/kernel:0', 'level_0/model/pi/mean/bias:0', 'level_0/model/pi/mean/kernel:0', 'level_0/model/value_fns/qf1/fc0/bias:0', 'level_0/model/value_fns/qf1/fc0/kernel:0', 'level_0/model/value_fns/qf1/fc1/bias:0', 'level_0/model/value_fns/qf1/fc1/kernel:0', 'level_0/model/value_fns/qf1/qf_output/bias:0', 'level_0/model/value_fns/qf1/qf_output/kernel:0', 'level_0/model/value_fns/qf2/fc0/bias:0', 'level_0/model/value_fns/qf2/fc0/kernel:0', 'level_0/model/value_fns/qf2/fc1/bias:0', 'level_0/model/value_fns/qf2/fc1/kernel:0', 'level_0/model/value_fns/qf2/qf_output/bias:0', 'level_0/model/value_fns/qf2/qf_output/kernel:0', 'level_0/model/value_fns/vf/fc0/bias:0', 'level_0/model/value_fns/vf/fc0/kernel:0', 'level_0/model/value_fns/vf/fc1/bias:0', 'level_0/model/value_fns/vf/fc1/kernel:0', 'level_0/model/value_fns/vf/vf_output/bias:0', 'level_0/model/value_fns/vf/vf_output/kernel:0', 'level_0/target/value_fns/vf/fc0/bias:0', 'level_0/target/value_fns/vf/fc0/kernel:0', 'level_0/target/value_fns/vf/fc1/bias:0', 'level_0/target/value_fns/vf/fc1/kernel:0', 'level_0/target/value_fns/vf/vf_output/bias:0', 'level_0/target/value_fns/vf/vf_output/kernel:0', 'level_1/model/log_alpha:0', 'level_1/model/pi/fc0/bias:0', 'level_1/model/pi/fc0/kernel:0', 'level_1/model/pi/fc1/bias:0', 'level_1/model/pi/fc1/kernel:0', 'level_1/model/pi/log_std/bias:0', 'level_1/model/pi/log_std/kernel:0', 'level_1/model/pi/mean/bias:0', 'level_1/model/pi/mean/kernel:0', 'level_1/model/value_fns/qf1/fc0/bias:0', 'level_1/model/value_fns/qf1/fc0/kernel:0', 'level_1/model/value_fns/qf1/fc1/bias:0', 'level_1/model/value_fns/qf1/fc1/kernel:0', 'level_1/model/value_fns/qf1/qf_output/bias:0', 'level_1/model/value_fns/qf1/qf_output/kernel:0', 'level_1/model/value_fns/qf2/fc0/bias:0', 'level_1/model/value_fns/qf2/fc0/kernel:0', 'level_1/model/value_fns/qf2/fc1/bias:0', 'level_1/model/value_fns/qf2/fc1/kernel:0', 'level_1/model/value_fns/qf2/qf_output/bias:0', 'level_1/model/value_fns/qf2/qf_output/kernel:0', 'level_1/model/value_fns/vf/fc0/bias:0', 'level_1/model/value_fns/vf/fc0/kernel:0', 'level_1/model/value_fns/vf/fc1/bias:0', 'level_1/model/value_fns/vf/fc1/kernel:0', 'level_1/model/value_fns/vf/vf_output/bias:0', 'level_1/model/value_fns/vf/vf_output/kernel:0', 'level_1/target/value_fns/vf/fc0/bias:0', 'level_1/target/value_fns/vf/fc0/kernel:0', 'level_1/target/value_fns/vf/fc1/bias:0', 'level_1/target/value_fns/vf/fc1/kernel:0', 'level_1/target/value_fns/vf/vf_output/bias:0', 'level_1/target/value_fns/vf/vf_output/kernel:0', ] ) def test_init_3_levels(self): """Validate that the graph and variables are initialized properly.""" policy_params = self.policy_params.copy() policy_params['num_levels'] = 3 policy = SACGoalConditionedPolicy(**policy_params) # Check that the abstract class has all the required attributes. self.assertEqual(policy.meta_period, self.policy_params['meta_period']) self.assertEqual(policy.relative_goals, self.policy_params['relative_goals']) self.assertEqual(policy.off_policy_corrections, self.policy_params['off_policy_corrections']) self.assertEqual(policy.cooperative_gradients, self.policy_params['cooperative_gradients']) self.assertEqual(policy.cg_weights, self.policy_params['cg_weights']) self.assertListEqual( sorted([var.name for var in get_trainable_vars()]), ['level_0/model/log_alpha:0', 'level_0/model/pi/fc0/bias:0', 'level_0/model/pi/fc0/kernel:0', 'level_0/model/pi/fc1/bias:0', 'level_0/model/pi/fc1/kernel:0', 'level_0/model/pi/log_std/bias:0', 'level_0/model/pi/log_std/kernel:0', 'level_0/model/pi/mean/bias:0', 'level_0/model/pi/mean/kernel:0', 'level_0/model/value_fns/qf1/fc0/bias:0', 'level_0/model/value_fns/qf1/fc0/kernel:0', 'level_0/model/value_fns/qf1/fc1/bias:0', 'level_0/model/value_fns/qf1/fc1/kernel:0', 'level_0/model/value_fns/qf1/qf_output/bias:0', 'level_0/model/value_fns/qf1/qf_output/kernel:0', 'level_0/model/value_fns/qf2/fc0/bias:0', 'level_0/model/value_fns/qf2/fc0/kernel:0', 'level_0/model/value_fns/qf2/fc1/bias:0', 'level_0/model/value_fns/qf2/fc1/kernel:0', 'level_0/model/value_fns/qf2/qf_output/bias:0', 'level_0/model/value_fns/qf2/qf_output/kernel:0', 'level_0/model/value_fns/vf/fc0/bias:0', 'level_0/model/value_fns/vf/fc0/kernel:0', 'level_0/model/value_fns/vf/fc1/bias:0', 'level_0/model/value_fns/vf/fc1/kernel:0', 'level_0/model/value_fns/vf/vf_output/bias:0', 'level_0/model/value_fns/vf/vf_output/kernel:0', 'level_0/target/value_fns/vf/fc0/bias:0', 'level_0/target/value_fns/vf/fc0/kernel:0', 'level_0/target/value_fns/vf/fc1/bias:0', 'level_0/target/value_fns/vf/fc1/kernel:0', 'level_0/target/value_fns/vf/vf_output/bias:0', 'level_0/target/value_fns/vf/vf_output/kernel:0', 'level_1/model/log_alpha:0', 'level_1/model/pi/fc0/bias:0', 'level_1/model/pi/fc0/kernel:0', 'level_1/model/pi/fc1/bias:0', 'level_1/model/pi/fc1/kernel:0', 'level_1/model/pi/log_std/bias:0', 'level_1/model/pi/log_std/kernel:0', 'level_1/model/pi/mean/bias:0', 'level_1/model/pi/mean/kernel:0', 'level_1/model/value_fns/qf1/fc0/bias:0', 'level_1/model/value_fns/qf1/fc0/kernel:0', 'level_1/model/value_fns/qf1/fc1/bias:0', 'level_1/model/value_fns/qf1/fc1/kernel:0', 'level_1/model/value_fns/qf1/qf_output/bias:0', 'level_1/model/value_fns/qf1/qf_output/kernel:0', 'level_1/model/value_fns/qf2/fc0/bias:0', 'level_1/model/value_fns/qf2/fc0/kernel:0', 'level_1/model/value_fns/qf2/fc1/bias:0', 'level_1/model/value_fns/qf2/fc1/kernel:0', 'level_1/model/value_fns/qf2/qf_output/bias:0', 'level_1/model/value_fns/qf2/qf_output/kernel:0', 'level_1/model/value_fns/vf/fc0/bias:0', 'level_1/model/value_fns/vf/fc0/kernel:0', 'level_1/model/value_fns/vf/fc1/bias:0', 'level_1/model/value_fns/vf/fc1/kernel:0', 'level_1/model/value_fns/vf/vf_output/bias:0', 'level_1/model/value_fns/vf/vf_output/kernel:0', 'level_1/target/value_fns/vf/fc0/bias:0', 'level_1/target/value_fns/vf/fc0/kernel:0', 'level_1/target/value_fns/vf/fc1/bias:0', 'level_1/target/value_fns/vf/fc1/kernel:0', 'level_1/target/value_fns/vf/vf_output/bias:0', 'level_1/target/value_fns/vf/vf_output/kernel:0', 'level_2/model/log_alpha:0', 'level_2/model/pi/fc0/bias:0', 'level_2/model/pi/fc0/kernel:0', 'level_2/model/pi/fc1/bias:0', 'level_2/model/pi/fc1/kernel:0', 'level_2/model/pi/log_std/bias:0', 'level_2/model/pi/log_std/kernel:0', 'level_2/model/pi/mean/bias:0', 'level_2/model/pi/mean/kernel:0', 'level_2/model/value_fns/qf1/fc0/bias:0', 'level_2/model/value_fns/qf1/fc0/kernel:0', 'level_2/model/value_fns/qf1/fc1/bias:0', 'level_2/model/value_fns/qf1/fc1/kernel:0', 'level_2/model/value_fns/qf1/qf_output/bias:0', 'level_2/model/value_fns/qf1/qf_output/kernel:0', 'level_2/model/value_fns/qf2/fc0/bias:0', 'level_2/model/value_fns/qf2/fc0/kernel:0', 'level_2/model/value_fns/qf2/fc1/bias:0', 'level_2/model/value_fns/qf2/fc1/kernel:0', 'level_2/model/value_fns/qf2/qf_output/bias:0', 'level_2/model/value_fns/qf2/qf_output/kernel:0', 'level_2/model/value_fns/vf/fc0/bias:0', 'level_2/model/value_fns/vf/fc0/kernel:0', 'level_2/model/value_fns/vf/fc1/bias:0', 'level_2/model/value_fns/vf/fc1/kernel:0', 'level_2/model/value_fns/vf/vf_output/bias:0', 'level_2/model/value_fns/vf/vf_output/kernel:0', 'level_2/target/value_fns/vf/fc0/bias:0', 'level_2/target/value_fns/vf/fc0/kernel:0', 'level_2/target/value_fns/vf/fc1/bias:0', 'level_2/target/value_fns/vf/fc1/kernel:0', 'level_2/target/value_fns/vf/vf_output/bias:0', 'level_2/target/value_fns/vf/vf_output/kernel:0'] ) def test_initialize(self): """Check the functionality of the initialize() method. This test validates that the target variables are properly initialized when initialize is called. """ policy = SACGoalConditionedPolicy(**self.policy_params) # Initialize the variables of the policy. policy.sess.run(tf.compat.v1.global_variables_initializer()) # Run the initialize method. policy.initialize() model_var_list = [ 'level_0/model/value_fns/vf/fc0/kernel:0', 'level_0/model/value_fns/vf/fc0/bias:0', 'level_0/model/value_fns/vf/fc1/kernel:0', 'level_0/model/value_fns/vf/fc1/bias:0', 'level_0/model/value_fns/vf/vf_output/kernel:0', 'level_0/model/value_fns/vf/vf_output/bias:0', 'level_1/model/value_fns/vf/fc0/kernel:0', 'level_1/model/value_fns/vf/fc0/bias:0', 'level_1/model/value_fns/vf/fc1/kernel:0', 'level_1/model/value_fns/vf/fc1/bias:0', 'level_1/model/value_fns/vf/vf_output/kernel:0', 'level_1/model/value_fns/vf/vf_output/bias:0', ] target_var_list = [ 'level_0/target/value_fns/vf/fc0/kernel:0', 'level_0/target/value_fns/vf/fc0/bias:0', 'level_0/target/value_fns/vf/fc1/kernel:0', 'level_0/target/value_fns/vf/fc1/bias:0', 'level_0/target/value_fns/vf/vf_output/kernel:0', 'level_0/target/value_fns/vf/vf_output/bias:0', 'level_1/target/value_fns/vf/fc0/kernel:0', 'level_1/target/value_fns/vf/fc0/bias:0', 'level_1/target/value_fns/vf/fc1/kernel:0', 'level_1/target/value_fns/vf/fc1/bias:0', 'level_1/target/value_fns/vf/vf_output/kernel:0', 'level_1/target/value_fns/vf/vf_output/bias:0', ] for model, target in zip(model_var_list, target_var_list): with tf.compat.v1.variable_scope( tf.compat.v1.get_variable_scope(), reuse=True): model_val = policy.sess.run(model) target_val = policy.sess.run(target) np.testing.assert_almost_equal(model_val, target_val) def test_log_probs(self): """Check the functionality of the log_probs() method.""" pass # TODO def test_cooperative_gradients(self): """Check the functionality of the cooperative-gradients feature.""" pass # TODO if __name__ == '__main__': unittest.main()

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